DeepLearning V5 [Transformer].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48a4b739",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d9a99d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, multilabel_confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6206691b",
   "metadata": {},
   "source": [
    "# 2. Check the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61b22970",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bench = pd.read_csv('./labeling/benchpress_coords.csv')\n",
    "df_dead = pd.read_csv('./labeling/deadlift_coords.csv')\n",
    "df_squat = pd.read_csv('./labeling/squat_coords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "098732e0",
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ca142bf",
   "metadata": {},
   "outputs": [
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       "            class        x1        y1        z1        v1        x2        y2  \\\n",
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       "3    d_correct_up  0.704687  0.148774  0.440767  0.995209  0.700908  0.139350   \n",
       "4    d_correct_up  0.716225  0.146869  0.293682  0.998649  0.713821  0.136261   \n",
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       "\n",
       "[5 rows x 133 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_dead.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe058cdc",
   "metadata": {},
   "outputs": [
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       "      <th>v31</th>\n",
       "      <th>x32</th>\n",
       "      <th>y32</th>\n",
       "      <th>z32</th>\n",
       "      <th>v32</th>\n",
       "      <th>x33</th>\n",
       "      <th>y33</th>\n",
       "      <th>z33</th>\n",
       "      <th>v33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.578854</td>\n",
       "      <td>0.459936</td>\n",
       "      <td>-0.178820</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>0.594528</td>\n",
       "      <td>0.448642</td>\n",
       "      <td>-0.173299</td>\n",
       "      <td>0.999830</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.313403</td>\n",
       "      <td>0.976089</td>\n",
       "      <td>0.302474</td>\n",
       "      <td>0.597096</td>\n",
       "      <td>0.656582</td>\n",
       "      <td>0.806852</td>\n",
       "      <td>0.332788</td>\n",
       "      <td>0.694722</td>\n",
       "      <td>-0.373114</td>\n",
       "      <td>0.985341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.590740</td>\n",
       "      <td>0.410155</td>\n",
       "      <td>-0.341654</td>\n",
       "      <td>0.999727</td>\n",
       "      <td>0.595270</td>\n",
       "      <td>0.397050</td>\n",
       "      <td>-0.327615</td>\n",
       "      <td>0.999847</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.305247</td>\n",
       "      <td>0.975016</td>\n",
       "      <td>0.359256</td>\n",
       "      <td>0.611075</td>\n",
       "      <td>0.285869</td>\n",
       "      <td>0.773935</td>\n",
       "      <td>0.334483</td>\n",
       "      <td>0.695383</td>\n",
       "      <td>-0.373157</td>\n",
       "      <td>0.984979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.572588</td>\n",
       "      <td>0.146920</td>\n",
       "      <td>-0.468999</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.573966</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>-0.442385</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.573949</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.462458</td>\n",
       "      <td>0.976983</td>\n",
       "      <td>0.493459</td>\n",
       "      <td>0.641891</td>\n",
       "      <td>0.653005</td>\n",
       "      <td>0.840647</td>\n",
       "      <td>0.369003</td>\n",
       "      <td>0.695396</td>\n",
       "      <td>-0.618573</td>\n",
       "      <td>0.986593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.563577</td>\n",
       "      <td>0.157547</td>\n",
       "      <td>-0.483738</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>0.564866</td>\n",
       "      <td>0.136710</td>\n",
       "      <td>-0.457015</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.564586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401422</td>\n",
       "      <td>0.968094</td>\n",
       "      <td>0.488726</td>\n",
       "      <td>0.641720</td>\n",
       "      <td>0.591863</td>\n",
       "      <td>0.822999</td>\n",
       "      <td>0.332679</td>\n",
       "      <td>0.700715</td>\n",
       "      <td>-0.569745</td>\n",
       "      <td>0.981765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.579107</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.593653</td>\n",
       "      <td>0.400882</td>\n",
       "      <td>-0.152463</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.593684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.601359</td>\n",
       "      <td>0.977251</td>\n",
       "      <td>0.393079</td>\n",
       "      <td>0.617481</td>\n",
       "      <td>0.678957</td>\n",
       "      <td>0.802303</td>\n",
       "      <td>0.347247</td>\n",
       "      <td>0.690384</td>\n",
       "      <td>-0.761584</td>\n",
       "      <td>0.985078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  class        x1        y1        z1        v1        x2  \\\n",
       "0  s_spine_neutral_down  0.578854  0.459936 -0.178820  0.999752  0.594528   \n",
       "1  s_spine_neutral_down  0.590740  0.410155 -0.341654  0.999727  0.595270   \n",
       "2    s_spine_neutral_up  0.572588  0.146920 -0.468999  0.999881  0.573966   \n",
       "3    s_spine_neutral_up  0.563577  0.157547 -0.483738  0.999883  0.564866   \n",
       "4  s_spine_neutral_down  0.579107  0.414149 -0.166554  0.999601  0.593653   \n",
       "\n",
       "         y2        z2        v2        x3  ...       z31       v31       x32  \\\n",
       "0  0.448642 -0.173299  0.999830  0.594286  ... -0.313403  0.976089  0.302474   \n",
       "1  0.397050 -0.327615  0.999847  0.594844  ... -0.305247  0.975016  0.359256   \n",
       "2  0.125410 -0.442385  0.999909  0.573949  ... -0.462458  0.976983  0.493459   \n",
       "3  0.136710 -0.457015  0.999882  0.564586  ... -0.401422  0.968094  0.488726   \n",
       "4  0.400882 -0.152463  0.999702  0.593684  ... -0.601359  0.977251  0.393079   \n",
       "\n",
       "        y32       z32       v32       x33       y33       z33       v33  \n",
       "0  0.597096  0.656582  0.806852  0.332788  0.694722 -0.373114  0.985341  \n",
       "1  0.611075  0.285869  0.773935  0.334483  0.695383 -0.373157  0.984979  \n",
       "2  0.641891  0.653005  0.840647  0.369003  0.695396 -0.618573  0.986593  \n",
       "3  0.641720  0.591863  0.822999  0.332679  0.700715 -0.569745  0.981765  \n",
       "4  0.617481  0.678957  0.802303  0.347247  0.690384 -0.761584  0.985078  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_squat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03291a0",
   "metadata": {},
   "source": [
    "# 3. Data Preprocessing & Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4debbf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "bench_data = np.loadtxt(\"./labeling/benchpress_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels = bench_data[:, 0]\n",
    "bench_data = bench_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels = encoder.fit_transform(labels.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "bench_data = np.nan_to_num(bench_data)\n",
    "\n",
    "bench_data = bench_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "bench_X_train, bench_X_test, bench_y_train, bench_y_test = train_test_split(bench_data, labels, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "bench_X_train = torch.tensor(bench_X_train)\n",
    "bench_X_test = torch.tensor(bench_X_test)\n",
    "bench_y_train = torch.tensor(bench_y_train)\n",
    "bench_y_test = torch.tensor(bench_y_test)\n",
    "\n",
    "# float 변환\n",
    "bench_X_train = bench_X_train.float()\n",
    "bench_X_test = bench_X_test.float()\n",
    "bench_y_train = bench_y_train.float()\n",
    "bench_y_test = bench_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e36cffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "squat_data = np.loadtxt(\"./labeling/squat_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_squat = squat_data[:, 0]\n",
    "squat_data = squat_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_squat = encoder.fit_transform(labels_squat.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "squat_data = np.nan_to_num(squat_data)\n",
    "\n",
    "squat_data = squat_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "squat_X_train, squat_X_test, squat_y_train, squat_y_test = train_test_split(squat_data, labels_squat, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "squat_X_train = torch.tensor(squat_X_train)\n",
    "squat_X_test = torch.tensor(squat_X_test)\n",
    "squat_y_train = torch.tensor(squat_y_train)\n",
    "squat_y_test = torch.tensor(squat_y_test)\n",
    "\n",
    "# float 변환\n",
    "squat_X_train = squat_X_train.float()\n",
    "squat_X_test = squat_X_test.float()\n",
    "squat_y_train = squat_y_train.float()\n",
    "squat_y_test = squat_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "daf4f7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "deadlift_data = np.loadtxt(\"./labeling/deadlift_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_deadlift = deadlift_data[:, 0]\n",
    "deadlift_data = deadlift_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_deadlift = encoder.fit_transform(labels_deadlift.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "deadlift_data = np.nan_to_num(deadlift_data)\n",
    "\n",
    "deadlift_data = deadlift_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "deadlift_X_train, deadlift_X_test, deadlift_y_train, deadlift_y_test = train_test_split(deadlift_data, labels_deadlift, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "deadlift_X_train = torch.tensor(deadlift_X_train)\n",
    "deadlift_X_test = torch.tensor(deadlift_X_test)\n",
    "deadlift_y_train = torch.tensor(deadlift_y_train)\n",
    "deadlift_y_test = torch.tensor(deadlift_y_test)\n",
    "\n",
    "# float 변환\n",
    "deadlift_X_train = deadlift_X_train.float()\n",
    "deadlift_X_test = deadlift_X_test.float()\n",
    "deadlift_y_train = deadlift_y_train.float()\n",
    "deadlift_y_test = deadlift_y_test.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3122dfa3",
   "metadata": {},
   "source": [
    "# 4. Construct Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5c55c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 6])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53c59927",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([716, 6])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56f3fb",
   "metadata": {},
   "source": [
    "### Bench Press"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "85728780",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size, num_heads=4, num_layers=2):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.embedding = nn.Linear(input_size, 128)\n",
    "        self.transformer = nn.TransformerEncoder(\n",
    "            nn.TransformerEncoderLayer(d_model=128, nhead=num_heads),\n",
    "            num_layers=num_layers\n",
    "        )\n",
    "        self.fc1 = nn.Linear(128, 64)\n",
    "        self.fc2 = nn.Linear(64, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.embedding(x)  # Linear projection to embedding space\n",
    "        x = self.transformer(x.unsqueeze(1))  # Apply transformer\n",
    "        x = x.mean(dim=1)  # Global average pooling\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.fc2(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "30ab9554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 6\n",
    "bench_model = DeepClassifier(input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "afdac53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bench_criterion = nn.CrossEntropyLoss()\n",
    "bench_optimizer = optim.Adam(bench_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0a7e3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bench_train_losses = []\n",
    "bench_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    bench_model.train()\n",
    "    bench_optimizer.zero_grad()\n",
    "    bench_outputs = bench_model(bench_X_train)\n",
    "    bench_loss = bench_criterion(bench_outputs, bench_y_train)\n",
    "    bench_loss.backward()\n",
    "    bench_optimizer.step()\n",
    "    bench_train_losses.append(bench_loss.item())\n",
    "    \n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        bench_test_outputs = bench_model(bench_X_test)\n",
    "        bench_test_loss = bench_criterion(bench_test_outputs, bench_y_test)\n",
    "        bench_test_losses.append(bench_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), bench_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), bench_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fbca1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.060312271118164\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    test_outputs = bench_model(bench_X_test)\n",
    "    test_loss = bench_criterion(test_outputs, bench_y_test)\n",
    "    print(f'Test Loss: {test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8bcb0bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.3167,  0.3114,  0.5964,  1.0000,  0.2972,  0.3145,  0.6573,  1.0000,\n",
      "         0.2975,  0.3153,  0.6574,  1.0000,  0.2983,  0.3162,  0.6572,  1.0000,\n",
      "         0.2923,  0.3167,  0.6106,  1.0000,  0.2899,  0.3184,  0.6105,  1.0000,\n",
      "         0.2875,  0.3200,  0.6104,  1.0000,  0.3026,  0.3284,  0.8640,  1.0000,\n",
      "         0.2832,  0.3327,  0.6550,  1.0000,  0.3301,  0.3184,  0.6701,  1.0000,\n",
      "         0.3260,  0.3197,  0.6084,  1.0000,  0.4334,  0.3496,  0.8852,  1.0000,\n",
      "         0.3043,  0.3731,  0.4333,  1.0000,  0.5557,  0.3963,  0.9045,  0.3167,\n",
      "         0.2446,  0.4254,  0.0875,  0.9960,  0.6632,  0.4134,  0.8773,  0.3916,\n",
      "         0.2243,  0.3454,  0.0398,  0.9916,  0.6936,  0.4205,  0.8692,  0.3640,\n",
      "         0.2261,  0.3320, -0.0042,  0.9718,  0.6945,  0.4176,  0.8244,  0.3667,\n",
      "         0.2317,  0.3303,  0.0697,  0.9650,  0.6812,  0.4153,  0.8496,  0.3817,\n",
      "         0.2348,  0.3342,  0.0668,  0.9545,  0.6936,  0.4040,  0.1339,  1.0000,\n",
      "         0.5766,  0.4232, -0.1339,  1.0000,  0.9607,  0.4303, -0.1384,  0.9927,\n",
      "         0.6711,  0.5033, -0.5252,  0.9987,  0.8809,  0.5599, -0.0185,  0.9596,\n",
      "         0.6724,  0.6628, -0.7550,  0.9966,  0.8471,  0.5761, -0.0113,  0.9178,\n",
      "         0.6458,  0.6765, -0.7803,  0.9860,  0.9824,  0.6058, -0.1094,  0.9457,\n",
      "         0.7237,  0.7374, -0.9482,  0.9950])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9947', '0.0053', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.7427,  0.3081,  0.6680,  1.0000,  0.7626,  0.3070,  0.7456,  1.0000,\n",
      "         0.7695,  0.3098,  0.7456,  0.9999,  0.7763,  0.3128,  0.7450,  1.0000,\n",
      "         0.7458,  0.3017,  0.7532,  0.9999,  0.7413,  0.3011,  0.7532,  0.9998,\n",
      "         0.7369,  0.3007,  0.7534,  0.9999,  0.7869,  0.3289,  0.7378,  1.0000,\n",
      "         0.7415,  0.3135,  1.0374,  0.9999,  0.7467,  0.3235,  0.7008,  0.9999,\n",
      "         0.7302,  0.3169,  0.7490,  0.9998,  0.8146,  0.3741,  0.5046,  1.0000,\n",
      "         0.6359,  0.3450,  1.0039,  0.9998,  0.8509,  0.3026,  0.1545,  0.9787,\n",
      "         0.6006,  0.2772,  1.0878,  0.6228,  0.8344,  0.2156,  0.0248,  0.9474,\n",
      "         0.6121,  0.1994,  1.1902,  0.7842,  0.8241,  0.2009, -0.0325,  0.8982,\n",
      "         0.6106,  0.1786,  1.1745,  0.7743,  0.8213,  0.2001, -0.0049,  0.8697,\n",
      "         0.6195,  0.1798,  1.1977,  0.7663,  0.8207,  0.2053,  0.0282,  0.8274,\n",
      "         0.6211,  0.1878,  1.1871,  0.7444,  0.5995,  0.4343, -0.0951,  1.0000,\n",
      "         0.4807,  0.4135,  0.0945,  1.0000,  0.5264,  0.4917, -0.9415,  0.9922,\n",
      "         0.2143,  0.4378, -0.2644,  0.9704,  0.5262,  0.6262, -0.8798,  0.9798,\n",
      "         0.2351,  0.5640, -0.0342,  0.9567,  0.5400,  0.6422, -0.8674,  0.9720,\n",
      "         0.2684,  0.5898, -0.0209,  0.9444,  0.4783,  0.6984, -1.0564,  0.9944,\n",
      "         0.1205,  0.6031, -0.1728,  0.9718])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.3355,  0.2810,  0.4278,  1.0000,  0.3266,  0.2810,  0.4803,  1.0000,\n",
      "         0.3288,  0.2815,  0.4804,  1.0000,  0.3313,  0.2821,  0.4802,  1.0000,\n",
      "         0.3175,  0.2838,  0.4194,  1.0000,  0.3129,  0.2864,  0.4194,  1.0000,\n",
      "         0.3087,  0.2892,  0.4194,  1.0000,  0.3324,  0.2962,  0.6862,  1.0000,\n",
      "         0.3005,  0.3061,  0.4150,  1.0000,  0.3488,  0.2901,  0.5081,  1.0000,\n",
      "         0.3408,  0.2927,  0.4278,  1.0000,  0.4535,  0.3306,  0.8180,  1.0000,\n",
      "         0.3145,  0.3474,  0.1441,  1.0000,  0.5228,  0.2534,  0.9183,  0.7405,\n",
      "         0.3497,  0.2655, -0.1787,  0.9968,  0.5333,  0.1771,  0.9787,  0.8766,\n",
      "         0.4037,  0.1724, -0.1724,  0.9973,  0.5439,  0.1584,  1.0037,  0.8442,\n",
      "         0.4167,  0.1541, -0.2198,  0.9892,  0.5316,  0.1549,  0.9649,  0.8428,\n",
      "         0.4202,  0.1522, -0.1273,  0.9862,  0.5243,  0.1615,  0.9575,  0.8452,\n",
      "         0.4254,  0.1588, -0.1330,  0.9801,  0.6962,  0.4069,  0.1800,  1.0000,\n",
      "         0.5830,  0.4274, -0.1801,  1.0000,  0.9575,  0.4318, -0.1905,  0.9892,\n",
      "         0.6753,  0.4972, -0.6821,  0.9987,  0.8765,  0.5660,  0.0244,  0.9632,\n",
      "         0.6747,  0.6574, -0.8137,  0.9974,  0.8471,  0.5827,  0.0449,  0.9427,\n",
      "         0.6517,  0.6770, -0.8230,  0.9894,  0.9790,  0.6102, -0.0276,  0.9648,\n",
      "         0.7259,  0.7366, -0.9475,  0.9966])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.4849,  0.3303,  0.9432,  1.0000,  0.4931,  0.3301,  1.0020,  1.0000,\n",
      "         0.5006,  0.3316,  1.0024,  1.0000,  0.5081,  0.3333,  1.0020,  1.0000,\n",
      "         0.4736,  0.3290,  1.0102,  1.0000,  0.4671,  0.3295,  1.0102,  1.0000,\n",
      "         0.4602,  0.3303,  1.0106,  1.0000,  0.5181,  0.3434,  1.0459,  1.0000,\n",
      "         0.4466,  0.3408,  1.0863,  1.0000,  0.4997,  0.3389,  0.9349,  1.0000,\n",
      "         0.4741,  0.3374,  0.9444,  1.0000,  0.5901,  0.3816,  0.8601,  1.0000,\n",
      "         0.3719,  0.3806,  0.9188,  1.0000,  0.7402,  0.3706,  0.9107,  0.9865,\n",
      "         0.2440,  0.3588,  0.9349,  0.9954,  0.8040,  0.2943,  0.9861,  0.9946,\n",
      "         0.1724,  0.2918,  0.9371,  0.9993,  0.8107,  0.2745,  1.0107,  0.9862,\n",
      "         0.1592,  0.2704,  0.9547,  0.9980,  0.8015,  0.2734,  1.0146,  0.9842,\n",
      "         0.1704,  0.2697,  0.9647,  0.9968,  0.7964,  0.2800,  0.9815,  0.9876,\n",
      "         0.1768,  0.2773,  0.9350,  0.9974,  0.5768,  0.4731,  0.0021,  1.0000,\n",
      "         0.3803,  0.4821, -0.0029,  1.0000,  0.7693,  0.5689, -0.5795,  0.9982,\n",
      "         0.1493,  0.5541, -0.4919,  0.9986,  0.6924,  0.6562, -0.0441,  0.8516,\n",
      "         0.2435,  0.6465, -0.0541,  0.9797,  0.6559,  0.6706,  0.0060,  0.8817,\n",
      "         0.2850,  0.6609, -0.0125,  0.9493,  0.7781,  0.7171, -0.1152,  0.8935,\n",
      "         0.1598,  0.7071, -0.1609,  0.9626])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.3463,  0.2848,  0.6529,  1.0000,  0.3391,  0.2800,  0.7107,  1.0000,\n",
      "         0.3419,  0.2803,  0.7109,  1.0000,  0.3447,  0.2808,  0.7107,  1.0000,\n",
      "         0.3276,  0.2816,  0.6512,  1.0000,  0.3221,  0.2828,  0.6513,  1.0000,\n",
      "         0.3167,  0.2842,  0.6514,  1.0000,  0.3438,  0.2930,  0.8993,  1.0000,\n",
      "         0.3113,  0.2977,  0.6363,  1.0000,  0.3582,  0.2945,  0.7274,  1.0000,\n",
      "         0.3475,  0.2959,  0.6482,  1.0000,  0.4548,  0.3334,  0.9996,  1.0000,\n",
      "         0.3041,  0.3500,  0.4179,  1.0000,  0.5394,  0.2738,  1.1503,  0.6165,\n",
      "         0.2393,  0.2834,  0.2307,  0.9991,  0.5559,  0.1991,  1.1928,  0.8308,\n",
      "         0.2187,  0.1887,  0.4193,  0.9982,  0.5640,  0.1803,  1.2081,  0.7778,\n",
      "         0.2213,  0.1670,  0.3943,  0.9913,  0.5526,  0.1752,  1.1740,  0.7734,\n",
      "         0.2278,  0.1653,  0.4755,  0.9875,  0.5495,  0.1825,  1.1684,  0.7855,\n",
      "         0.2312,  0.1722,  0.4532,  0.9820,  0.6732,  0.3965,  0.1724,  1.0000,\n",
      "         0.5553,  0.4107, -0.1724,  1.0000,  0.9583,  0.4326, -0.1575,  0.9966,\n",
      "         0.6805,  0.4983, -0.6566,  0.9997,  0.9052,  0.5757, -0.0132,  0.9871,\n",
      "         0.6667,  0.6603, -0.9120,  0.9986,  0.8769,  0.5930,  0.0014,  0.9830,\n",
      "         0.6307,  0.6781, -0.9321,  0.9950,  1.0042,  0.6330, -0.0663,  0.9909,\n",
      "         0.7561,  0.7372, -1.0479,  0.9984])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.7027', '0.2973', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_40566/658835650.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = bench_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = bench_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = bench_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9cacd475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.983\n",
      "정밀도: 0.983\n",
      "재현율: 0.983\n",
      "F1 점수: 0.983\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = bench_model(bench_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(bench_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4414bf86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KMADy2c8//2z079/fqFChguHl5WX4+/sbTZo0MebNm2ekpaU5zstpucRRo0YZpUuXNnx8fIwmTZoYsbGx2Zb6e/PNN41mzZoZISEhht1uNypVqmSMGTPGSE5ONgzDMNLT040xY8YYdevWNfz9/Q1fX1+jbt26xhtvvJGr+q8ui5fTduTIkVxdY+/evcZjjz1mhIeHG0WLFjWKFy9utGzZ0liyZInTkpB//PGHMWLECMd5VapUMWbNmmVkZWU5XU9Sjss//v09dLVcomEYxnvvvWdEREQYXl5eRr169YzPP/8823KJa9asMVq3bm2EhoYaXl5eRrly5Yynn37aSExMzHaPvy8puGrVKqN+/fqG3W43goODjZ49exonTpxwOic6Otrw9fXNVtukSZOM3Py/gr8ul+hKTu/B77//bvTp08coUaKE4efnZ7Rp08Y4ePBgjsscvv3220ZERITh6enp9DrLly9vdOjQIcd7/vU6v/76qxEYGGh06tQp23kPP/yw4evraxw+fPi6rxUA3MVmGPzbHgAAAOBuzJgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABhfKbP33qD3Z3CQCgpP++5u4SAEA+Rd1dgTOz+rRL3956fweTmAMAAAAWUCgTcwAAAFiUjVzYFd4ZAAAA3NZiYmJ01113yd/fX6GhoXrooYcUHx/vdE5aWpoGDRqkkJAQ+fn5qUuXLjpz5ozTOcePH1eHDh1UrFgxhYaGasyYMbpy5Uqu66AxBwAAgHlsNnO2PNi2bZsGDRqkb775Rps2bVJGRoZat26t1NRUxzkjRozQunXrtHr1am3btk2nTp1S586dHcczMzPVoUMHXb58WTt37tSSJUu0ePFiTZw4MfdvjWEYRp4qvwXw4U8AVsCHPwFYgeU+/NlgmCn3ubT31Rt+7m+//abQ0FBt27ZNzZo1U3JyskqWLKkVK1bokUcekSQdPHhQNWrUUGxsrP7xj3/os88+U8eOHXXq1CmVKlVKkrRgwQKNGzdOv/32m7y8vK57XxJzAAAAmMfmYc52E5KTkyVJwcHBkqS9e/cqIyNDrVq1cpxTvXp1lStXTrGxsZKk2NhY1a5d29GUS1KbNm104cIF/fjjj7m6Lx/+BAAAQKGTnp6u9PR0p312u112u/2az8vKytLw4cPVpEkT1apVS5J0+vRpeXl5KSgoyOncUqVK6fTp045z/tqUXz1+9VhukJgDAADAPCbNmMfExCgwMNBpi4mJuW55gwYN0g8//KCVK1ea8GY4IzEHAABAoTN+/HiNHDnSad/10vLBgwdr/fr12r59u8qUKePYHxYWpsuXL+v8+fNOqfmZM2cUFhbmOOe///2v0/Wurtpy9ZzrITEHAACAeUyaMbfb7QoICHDaXDXmhmFo8ODB+uijj7R582ZVrFjR6XiDBg1UtGhRffnll4598fHxOn78uBo3bixJaty4sb7//nudPXvWcc6mTZsUEBCgyMjIXL01JOYAAAC4rQ0aNEgrVqzQxx9/LH9/f8dMeGBgoHx8fBQYGKi+fftq5MiRCg4OVkBAgIYMGaLGjRvrH//4hySpdevWioyM1OOPP66ZM2fq9OnT+te//qVBgwZdN6m/isYcAAAA5snjGuNmmD9/viSpRYsWTvsXLVqk3r17S5LmzJkjDw8PdenSRenp6WrTpo3eeOMNx7menp5av369nnnmGTVu3Fi+vr6Kjo7W1KlTc10H65gDQAFhHXMAVmC5dcwbjTHlPpd2zTLlPvmJxBwAAADmuck1xgsz3hkAAADAAkjMAQAAYB4LzphbBYk5AAAAYAE05gAAAIAFMMoCAAAA8/DhT5d4ZwAAAAALIDEHAACAefjwp0sk5gAAAIAFkJgDAADAPMyYu8Q7AwAAAFgAiTkAAADMw4y5SyTmAAAAgAWQmAMAAMA8zJi7xDsDAAAAWACJOQAAAMxDYu4S7wwAAABgASTmAAAAMI8Hq7K4QmIOAAAAWACJOQAAAMzDjLlLvDMAAACABZCYAwAAwDx886dLJOYAAACABZCYAwAAwDzMmLvEOwMAAABYAIk5AAAAzMOMuUsk5gAAAIAFkJgDAADAPMyYu8Q7AwAAAFgAiTkAAADMw4y5SyTmAAAAgAWQmAMAAMA8zJi7xDsDAAAAWACJOQAAAMzDjLlLJOYAAACABZCYAwAAwDzMmLvEOwMAAABYAIk5AAAAzMOMuUsk5gAAAIAFkJgDAADAPMyYu8Q7AwAAAFgAiTkAAADMQ2LuEu8MAAAAYAEk5gAAADAPq7K4RGIOAAAAWACJOQAAAMzDjLlLvDMAAACABZCYAwAAwDzMmLtEYg4AAABYAIk5AAAAzMOMuUu8MwAAAIAFkJgDAADAPMyYu0RiDgAAAFgAjTkAAABMY7PZTNnyYvv27erUqZPCw8Nls9m0du3aXNU8a9YsxzkVKlTIdnzGjBl5qoPGHAAAALe11NRU1a1bV6+//nqOxxMTE522d999VzabTV26dHE6b+rUqU7nDRkyJE91MGMOAAAA0+Q1zTZDu3bt1K5dO5fHw8LCnB5//PHHioqKUkREhNN+f3//bOfmBYk5AAAAkEtnzpzRhg0b1Ldv32zHZsyYoZCQENWvX1+zZs3SlStX8nRtEnMAAACYx6TAPD09Xenp6U777Ha77Hb7TV13yZIl8vf3V+fOnZ32Dx06VHfeeaeCg4O1c+dOjR8/XomJiXr55ZdzfW0ScwAAABQ6MTExCgwMdNpiYmJu+rrvvvuuevbsKW9vb6f9I0eOVIsWLVSnTh0NGDBAs2fP1rx587L9cnAtJOYAAAAwjVkz5uPHj9fIkSOd9t1sWv7VV18pPj5eq1atuu65jRo10pUrV3T06FFVq1YtV9enMQcAAEChkx9jK3+3cOFCNWjQQHXr1r3uuXFxcfLw8FBoaGiur09jDgAAANNYcVWWlJQUJSQkOB4fOXJEcXFxCg4OVrly5SRJFy5c0OrVqzV79uxsz4+NjdWuXbsUFRUlf39/xcbGasSIEerVq5eKFy+e6zpozAEAAHBb27Nnj6KiohyPr47AREdHa/HixZKklStXyjAM9ejRI9vz7Xa7Vq5cqcmTJys9PV0VK1bUiBEjso3SXI/NMAzjxl+GNfnUH+zuEgBASf99zd0lAIB8irq7AmcB3Zeacp8LK58w5T75iVVZAAAAAAtglAUAAACmseKMuVWQmAMAAAAWQGIOAAAA8xCYu0RiDgAAAFgAiTkAAABMw4y5ayTmAAAAgAWQmAMAAMA0JOaukZgDAAAAFkBiDgAAANOQmLtGYg4AAABYAIk5AAAATENi7hqJOQAAAGABJOYAAAAwD4G5SyTmAAAAgAWQmAMAAMA0zJi7RmIOAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA2JuWsk5gAAAIAFkJgDAADAPATmLpGYAwAAABZAYg4AAADTMGPuGok5AAAAYAEk5gAAADANiblrJOYAAACABZCYAwAAwDQk5q6RmAMAAAAWQGIOAAAA05CYu0ZiDgAAAFgAiTkAAADMQ2DuEok5AAAAYAEk5gAAADANM+aukZgDAAAAFkBiDgAAANOQmLtGYg4AAABYAIk5AAAATENi7polEvOvvvpKvXr1UuPGjXXy5ElJ0rJly7Rjxw43VwYAAACYw+2N+QcffKA2bdrIx8dH3377rdLT0yVJycnJmj59upurAwAAQL6ymbTdgtzemE+bNk0LFizQ22+/raJFizr2N2nSRPv27XNjZQAAAIB53D5jHh8fr2bNmmXbHxgYqPPnz5tfEAAAAAoMM+auuT0xDwsLU0JCQrb9O3bsUEREhBsqAgAAAMzn9sa8f//+GjZsmHbt2iWbzaZTp05p+fLlGj16tJ555hl3lwcAAIB8ZLPZTNluRW4fZXn22WeVlZWlli1b6uLFi2rWrJnsdrtGjx6tIUOGuLs8AAAAwBRub8xtNpuee+45jRkzRgkJCUpJSVFkZKT8/PzcXRoAAADy2a2aZpvB7Y35VV5eXoqMjHR3GSikRj/ZWg/dV1dVK5TSpfQM7dp/WM+9+rF+OXbWcY7dq4hmjOysrm0ayO5VRF/EHtCw6at0NukPSVJwoK8WvRCt2lXvUHBgMf2WlKL1W7/TxNfW6Y/UNHe9NACFzN49u7Vk0UId+OkH/fbbb3r51dd1X8tW7i4LgAnc3phHRUVd8zenzZs3m1gNCqumd1bWglXbtffHYypSxFNTBnfS+vmDVb/zNF1MuyxJmjm6i9rdW1M9xy7UhZRLmvNsN62c3U/39ZkjScrKytL6bd9pyhvr9b/f/1BE2ZJ65dlumhfoq97/XOzGVwegMLl06aKqVqumhx7uopHDB7u7HAAmcntjXq9ePafHGRkZiouL0w8//KDo6Gj3FIVC58HBbzg9fmrSe/p18wzVjyyrr/cdUoCft3o/1Fi9/7lY23b/7Dhn/0cTdHftCvrv90d1/o9Lenv1/30b7fHE3/XW6q804gmSLAD5596mzXVv0+buLgMoMIyyuOb2xnzOnDk57p88ebJSUlJMrga3iwA/b0nS78kXJUn1a5STV9Ei2vxNvOOcn4+e0fHEJDWqU1H//f5otmuULhmoB++rp6/2/mJKzQAAoHBz+3KJrvTq1Uvvvvuuu8tAIWSz2TRr9CPa+e0h/XQoUZIUFhKg9MsZSk655HTu2XMXVCokwGnfkpjeOrfzZR3+zwu6kJqmZ6auMK12AABueTaTtluQZRvz2NhYeXt7X/e89PR0XbhwwWkzsjJNqBC3qlfGd1PNyqX1xLOLbuj5Y1/6QI0fe1GPDH9TEWVK6MVRnfO5QgAAcDty+yhL587OTY1hGEpMTNSePXs0YcKE6z4/JiZGU6ZMcdrnWeouFS19d77WicJhzriuat+0llr1fUUnz5537D997oLsXkUV6OfjlJqHhgTozLkLTtc4c+4PnTn3h34+eka/J6fqy0UjNePtjTr9P+fzAABAdsyYu+b2xDwwMNBpCw4OVosWLfTpp59q0qRJ133++PHjlZyc7LQVKdXAhMpxq5kzrqseuK+u2j49V8dOnXM69u2B47qccUVRjao59lUpH6pypYO167sjLq9p8/jzLxevom7/HRcAANyg7du3q1OnTgoPD5fNZtPatWudjvfu3TvbN4u2bdvW6ZykpCT17NlTAQEBCgoKUt++ffP8eUm3dhOZmZnq06ePateureLFi9/QNex2u+x2u9M+m4dnfpSHQuSV8d30aLuG6jriLaWkpqlUiL8kKTklTWnpGbqQkqbFa2P14qjOSkpO1R+paXp5XFd9s/+w44Ofbe6NVGhwgPb+eEwpF9MVWam0po94SDu/PaTjiUlufHUACpOLF1N1/Phxx+OTJ0/o4MEDCgwMVOnS4W6sDMgfVkzMU1NTVbduXT355JPZpjmuatu2rRYt+r8x2L/3nz179lRiYqI2bdqkjIwM9enTR0899ZRWrMj9Z9Hc2ph7enqqdevWOnDgwA035kBuPN2tmSRp0zvDnfb3n7hM763bJenP2fGsLEP/fqnfn18wtPOAhsWscpx7KS1DT3a+RzNHd5a9aBGdOHNeH2+O00vvbjLtdQAo/H784Qf1f/IJx+PZM2MkSZ0efFjPvzDDXWUBhVq7du3Url27a55jt9sVFhaW47EDBw5o48aN2r17txo2bChJmjdvntq3b6+XXnpJ4eG5+6Xa7f/+XqtWLR0+fFgVK1Z0dykoxHzqX/9LOtIvX9GIGe9rxIz3czy+fc8viur9cn6XBgBO7rq7keJ+iL/+icAtyqzAPD09Xenp6U77cpq0yK2tW7cqNDRUxYsX13333adp06YpJCRE0p+LlgQFBTmacklq1aqVPDw8tGvXLj388MO5uofbZ8ynTZum0aNHa/369UpMTMy2wgoAAACQVzExMdk+yxgTE3ND12rbtq2WLl2qL7/8Ui+++KK2bdumdu3aKTPzz5UAT58+rdDQUKfnFClSRMHBwTp9+nSu7+O2xHzq1KkaNWqU2rdvL0l64IEHnGaODMOQzWZzvGAAAADc+syaMR8/frxGjhzptO9G0/Lu3bs7/ly7dm3VqVNHlSpV0tatW9WyZcubqvOv3NaYT5kyRQMGDNCWLVvcVQIAAAAKqZsZW7meiIgIlShRQgkJCWrZsqXCwsJ09uxZp3OuXLmipKQkl3PpOXFbY24YhiSpefPm7ioBAAAAJrPgoix5duLECZ07d06lS5eWJDVu3Fjnz5/X3r171aDBn8t2b968WVlZWWrUqFGur+vWD39acbkcAAAA3F5SUlKUkJDgeHzkyBHFxcUpODhYwcHBmjJlirp06aKwsDAdOnRIY8eOVeXKldWmTRtJUo0aNdS2bVv1799fCxYsUEZGhgYPHqzu3bvnekUWyc2NedWqVa/bnCclsT40AABAYWHFYHbPnj2KiopyPL46mx4dHa358+fru+++05IlS3T+/HmFh4erdevWev75551GZZYvX67BgwerZcuW8vDwUJcuXTR37tw81eHWxnzKlCkKDAx0ZwkAAAC4zbVo0cIxZp2Tzz///LrXCA4OztOXCeXErY159+7dsy0tAwAAgMLLgoG5ZbhtHXMr/jMGAAAA4C5uX5UFAAAAtw8PD8JZV9zWmGdlZbnr1gAAAIDluHXGHAAAALcXppldc9uMOQAAAID/Q2IOAAAA07AAiGsk5gAAAIAFkJgDAADANATmrpGYAwAAABZAYg4AAADTMGPuGok5AAAAYAEk5gAAADANiblrJOYAAACABZCYAwAAwDQE5q6RmAMAAAAWQGIOAAAA0zBj7hqJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAWQmAMAAMA0zJi7RmIOAAAAWACJOQAAAExDYO4aiTkAAABgASTmAAAAMA0z5q6RmAMAAAAWQGIOAAAA0xCYu0ZiDgAAAFgAiTkAAABMw4y5ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANMwY+4aiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADANMyYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADANM+aukZgDAAAAFkBiDgAAANOQmLtGYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwDTPmrpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAEzDjLlrJOYAAACABdCYAwAAwDQ2mzlbXmzfvl2dOnVSeHi4bDab1q5d6ziWkZGhcePGqXbt2vL19VV4eLieeOIJnTp1yukaFSpUkM1mc9pmzJiRpzpozAEAAHBbS01NVd26dfX6669nO3bx4kXt27dPEyZM0L59+/Thhx8qPj5eDzzwQLZzp06dqsTERMc2ZMiQPNXBjDkAAABM42HBGfN27dqpXbt2OR4LDAzUpk2bnPa99tpruvvuu3X8+HGVK1fOsd/f319hYWE3XAeJOQAAAJAHycnJstlsCgoKcto/Y8YMhYSEqH79+po1a5auXLmSp+uSmAMAAMA0ZgXm6enpSk9Pd9pnt9tlt9tv6rppaWkaN26cevTooYCAAMf+oUOH6s4771RwcLB27typ8ePHKzExUS+//HKur01iDgAAgEInJiZGgYGBTltMTMxNXTMjI0PdunWTYRiaP3++07GRI0eqRYsWqlOnjgYMGKDZs2dr3rx52X45uBYScwAAAJjGrHXMx48fr5EjRzrtu5m0/GpTfuzYMW3evNkpLc9Jo0aNdOXKFR09elTVqlXL1T1ozAEAAFDo5MfYylVXm/JffvlFW7ZsUUhIyHWfExcXJw8PD4WGhub6PjTmAAAAMI2H9RZlUUpKihISEhyPjxw5ori4OAUHB6t06dJ65JFHtG/fPq1fv16ZmZk6ffq0JCk4OFheXl6KjY3Vrl27FBUVJX9/f8XGxmrEiBHq1auXihcvnus6aMwBAABwW9uzZ4+ioqIcj6+OwERHR2vy5Mn65JNPJEn16tVzet6WLVvUokUL2e12rVy5UpMnT1Z6eroqVqyoESNGZBuluR4acwAAAJjGrBnzvGjRooUMw3B5/FrHJOnOO+/UN998c9N1sCoLAAAAYAEk5gAAADCNBQNzyyAxBwAAACyAxBwAAACmsYnI3BUScwAAAMACSMwBAABgGiuuY24VJOYAAACABZCYAwAAwDRWXMfcKkjMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDEHAAAAKbxIDJ3icQcAAAAsAAScwAAAJiGwNw1EnMAAADAAkjMAQAAYBrWMXeNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGtYxd43EHAAAALAAEnMAAACYhrzctVw15p988kmuL/jAAw/ccDEAAADA7SpXjflDDz2Uq4vZbDZlZmbeTD0AAAAoxFjH3LVcNeZZWVkFXQcAAABwW7upD3+mpaXlVx0AAADAbS3PjXlmZqaef/553XHHHfLz89Phw4clSRMmTNDChQvzvUAAAAAUHh42c7ZbUZ4b8xdeeEGLFy/WzJkz5eXl5dhfq1YtvfPOO/laHAAAAHC7yHNjvnTpUr311lvq2bOnPD09Hfvr1q2rgwcP5mtxAAAAKFxsNpsp260oz435yZMnVbly5Wz7s7KylJGRkS9FAQAAALebPDfmkZGR+uqrr7LtX7NmjerXr58vRQEAAKBwstnM2W5Fef7mz4kTJyo6OlonT55UVlaWPvzwQ8XHx2vp0qVav359QdQIAAAAFHp5TswffPBBrVu3Tl988YV8fX01ceJEHThwQOvWrdP9999fEDUCAACgkGDG3LU8J+aS1LRpU23atCm/awEAAABuWzfUmEvSnj17dODAAUl/zp03aNAg34oCAABA4XSrrjFuhjw35idOnFCPHj309ddfKygoSJJ0/vx53XPPPVq5cqXKlCmT3zUCAAAAhV6eZ8z79eunjIwMHThwQElJSUpKStKBAweUlZWlfv36FUSNAAAAKCSYMXctz4n5tm3btHPnTlWrVs2xr1q1apo3b56aNm2ar8UBAAAAt4s8N+Zly5bN8YuEMjMzFR4eni9FAQAAoHC6NbNsc+R5lGXWrFkaMmSI9uzZ49i3Z88eDRs2TC+99FK+FgcAAADcLnKVmBcvXtxpVic1NVWNGjVSkSJ/Pv3KlSsqUqSInnzyST300EMFUigAAABufR636Py3GXLVmL/yyisFXAYAAABwe8tVYx4dHV3QdQAAAOA2QGDu2g1/wZAkpaWl6fLly077AgICbqogAAAA4HaU58Y8NTVV48aN0/vvv69z585lO56ZmZkvhQEAAKDwuVXXGDdDnldlGTt2rDZv3qz58+fLbrfrnXfe0ZQpUxQeHq6lS5cWRI0AAABAoZfnxHzdunVaunSpWrRooT59+qhp06aqXLmyypcvr+XLl6tnz54FUScAAAAKAQJz1/KcmCclJSkiIkLSn/PkSUlJkqR7771X27dvz9/qAAAAgNtEnhvziIgIHTlyRJJUvXp1vf/++5L+TNKDgoLytTgAAAAULh42mynbrSjPjXmfPn20f/9+SdKzzz6r119/Xd7e3hoxYoTGjBmT7wUCAAAAt4M8z5iPGDHC8edWrVrp4MGD2rt3rypXrqw6derka3EAAAAoXG7RMNsUN7WOuSSVL19e5cuXz49aAAAAgNtWrhrzuXPn5vqCQ4cOveFiAAAAULixjrlruWrM58yZk6uL2Ww2GnMAAADcUrZv365Zs2Zp7969SkxM1EcffaSHHnrIcdwwDE2aNElvv/22zp8/ryZNmmj+/PmqUqWK45ykpCQNGTJE69atk4eHh7p06aJXX31Vfn5+ua4jV4351VVYbhW/737N3SUAgA6fTXV3CQCgyHBfd5fgJM8rj5ggNTVVdevW1ZNPPqnOnTtnOz5z5kzNnTtXS5YsUcWKFTVhwgS1adNGP/30k7y9vSVJPXv2VGJiojZt2qSMjAz16dNHTz31lFasWJHrOmyGYRj59qosIu2KuysAABpzANZgtcZ8yEcHTLnPvIdr3NDzbDabU2JuGIbCw8M1atQojR49WpKUnJysUqVKafHixerevbsOHDigyMhI7d69Ww0bNpQkbdy4Ue3bt9eJEycUHh6eq3tb8ZcWAAAAFFI2m82ULb8cOXJEp0+fVqtWrRz7AgMD1ahRI8XGxkqSYmNjFRQU5GjKpT9XL/Tw8NCuXbtyfa+bXpUFAAAAsJr09HSlp6c77bPb7bLb7Xm6zunTpyVJpUqVctpfqlQpx7HTp08rNDTU6XiRIkUUHBzsOCc3SMwBAABgGg+bOVtMTIwCAwOdtpiYGHe//GsiMQcAAEChM378eI0cOdJpX17TckkKCwuTJJ05c0alS5d27D9z5ozq1avnOOfs2bNOz7ty5YqSkpIcz8+NG0rMv/rqK/Xq1UuNGzfWyZMnJUnLli3Tjh07buRyAAAAuE2YlZjb7XYFBAQ4bTfSmFesWFFhYWH68ssvHfsuXLigXbt2qXHjxpKkxo0b6/z589q7d6/jnM2bNysrK0uNGjXK/XuT1+I++OADtWnTRj4+Pvr2228dszvJycmaPn16Xi8HAAAAuFVKSori4uIUFxcn6c8PfMbFxen48eOy2WwaPny4pk2bpk8++UTff/+9nnjiCYWHhztWbqlRo4batm2r/v3767///a++/vprDR48WN27d8/1iizSDTTm06ZN04IFC/T222+raNGijv1NmjTRvn378no5AAAA3EasuCrLnj17VL9+fdWvX1+SNHLkSNWvX18TJ06UJI0dO1ZDhgzRU089pbvuukspKSnauHGjYw1zSVq+fLmqV6+uli1bqn379rr33nv11ltv5e29yes65sWKFdNPP/2kChUqyN/fX/v371dERIQOHz6syMhIpaWl5amAgsA65gCsgHXMAViB1dYxH7Uu3pT7zO5UzZT75Kc8J+ZhYWFKSEjItn/Hjh2KiIjIl6IAAABQOJk1Y34rynNj3r9/fw0bNky7du2SzWbTqVOntHz5co0ePVrPPPNMQdQIAAAAFHp5Xi7x2WefVVZWllq2bKmLFy+qWbNmstvtGj16tIYMGVIQNQIAAKCQyMcv5Sx08jxjftXly5eVkJCglJQURUZGys/PL79ru2HMmAOwAmbMAViB1WbMx24wZ8Z8Zodbb8b8hr9gyMvLS5GRkflZCwAAAAo5DyJzl/LcmEdFRV1zCZrNmzffVEEAAADA7SjPjfnVrx69KiMjQ3Fxcfrhhx8UHR2dX3UBAACgELqhr52/TeS5MZ8zZ06O+ydPnqyUlJSbLggAAAC4HeXbLy29evXSu+++m1+XAwAAQCFks5mz3YryrTGPjY11+lpSAAAAALmX51GWzp07Oz02DEOJiYnas2ePJkyYkG+FAQAAoPBhVRbX8tyYBwYGOj328PBQtWrVNHXqVLVu3TrfCgMAAABuJ3lqzDMzM9WnTx/Vrl1bxYsXL6iaAAAAUEgRmLuWpxlzT09PtW7dWufPny+gcgAAAIDbU54//FmrVi0dPny4IGoBAABAIedhM2e7FeW5MZ82bZpGjx6t9evXKzExURcuXHDaAAAAAORdrmfMp06dqlGjRql9+/aSpAceeEC2vwwJGYYhm82mzMzM/K8SAAAAhQKrsriW68Z8ypQpGjBggLZs2VKQ9QAAAAC3pVw35oZhSJKaN29eYMUAAACgcCMwdy1PM+Y23kkAAACgQORpHfOqVatetzlPSkq6qYIAAABQeN2qK6aYIU+N+ZQpU7J98ycAAACAm5enxrx79+4KDQ0tqFoAAABQyNlEZO5KrmfMmS8HAAAACk6eV2UBAAAAbhQz5q7lujHPysoqyDoAAACA21qeZswBAACAm0Fi7lqe1jEHAAAAUDBIzAEAAGAaFhRxjcQcAAAAsAAScwAAAJiGGXPXSMwBAAAACyAxBwAAgGkYMXeNxBwAAACwABJzAAAAmMaDyNwlEnMAAADAAkjMAQAAYBpWZXGNxBwAAACwABJzAAAAmIYRc9dIzAEAAAALIDEHAACAaTxEZO4KiTkAAABgASTmAAAAMA0z5q6RmAMAAAAWQGIOAAAA07COuWsk5gAAAIAFkJgDAADANB4MmbtEYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwDTPmrpGYAwAAABZAYg4AAADTEJi7RmIOAACA21qFChVks9mybYMGDZIktWjRItuxAQMG5HsdJOYAAAAwjRVT4d27dyszM9Px+IcfftD999+vrl27Ovb1799fU6dOdTwuVqxYvtdBYw4AAIDbWsmSJZ0ez5gxQ5UqVVLz5s0d+4oVK6awsLACrcOKv7QAAACgkMppZKQgtvT0dF24cMFpS09Pv259ly9f1nvvvacnn3xStr8MxC9fvlwlSpRQrVq1NH78eF28eDHf3xsacwAAABQ6MTExCgwMdNpiYmKu+7y1a9fq/Pnz6t27t2PfY489pvfee09btmzR+PHjtWzZMvXq1Svfa7YZhmHk+1XdLO2KuysAAOnw2VR3lwAAigz3dXcJTpbu+dWU+zxaOzRbQm6322W326/5vDZt2sjLy0vr1q1zec7mzZvVsmVLJSQkqFKlSvlSr8SMOQAAAAqh3DThf3fs2DF98cUX+vDDD695XqNGjSSJxhwAAAC3Lit/8+eiRYsUGhqqDh06XPO8uLg4SVLp0qXz9f405gAAALjtZWVladGiRYqOjlaRIv/XIh86dEgrVqxQ+/btFRISou+++04jRoxQs2bNVKdOnXytgcYcAAAAprFqXv7FF1/o+PHjevLJJ532e3l56YsvvtArr7yi1NRUlS1bVl26dNG//vWvfK+BxhwAAAC3vdatWyunNVHKli2rbdu2mVIDjTkAAABMY+ERc7djHXMAAADAAmjMAQAAAAtglAUAAACmsTHL4hKJOQAAAGABJOYAAAAwDamwa7w3AAAAgAWQmAMAAMA0zJi7RmIOAAAAWACJOQAAAExDXu4aiTkAAABgASTmAAAAMA0z5q6RmAMAAAAWQGIOAAAA05AKu8Z7AwAAAFgAiTkAAABMw4y5ayTmAAAAgAWQmAMAAMA05OWukZgDAAAAFkBiDgAAANMwYu4aiTkAAABgASTmAAAAMI0HU+YukZgDAAAAFkBiDgAAANMwY+4aiTkAAABgASTmAAAAMI2NGXOXSMwBAAAACyAxBwAAgGmYMXeNxBwAAACwABJzAAAAmIZ1zF0jMQcAAAAsgMQcAAAApmHG3DUScwAAAMACSMwBAABgGhJz10jMAQAAAAsgMQcAAIBp+OZP10jMAQAAAAsgMQcAAIBpPAjMXSIxBwAAACyAxBwAAACmYcbcNUsl5pcvX1Z8fLyuXLni7lIAAAAAU1miMb948aL69u2rYsWKqWbNmjp+/LgkaciQIZoxY4abqwMAAEB+sdnM2W5FlmjMx48fr/3792vr1q3y9vZ27G/VqpVWrVrlxsoAAAAAc1hixnzt2rVatWqV/vGPf8j2l19xatasqUOHDrmxMgAAAOQnZsxds0Ri/ttvvyk0NDTb/tTUVKdGHQAAACisLNGYN2zYUBs2bHA8vtqMv/POO2rcuLG7ygIAAEA+87CZs92KLDHKMn36dLVr104//fSTrly5oldffVU//fSTdu7cqW3btrm7PAAAAKDAWSIxv/feexUXF6crV66odu3a+s9//qPQ0FDFxsaqQYMG7i4PAAAA+cRm0v9uRTbDMAx3F5Hf0lgGHYAFHD6b6u4SAECR4b7uLsHJVz//bsp9mlYtbsp98pMlRln27dunokWLqnbt2pKkjz/+WIsWLVJkZKQmT54sLy8vN1eI28nKFcu1ZNFC/e9/v6lqtep69p8TVLtOHXeXBaCQ2vjxam38ZLXOnk6UJJWtEKFuTzylBo2aSJL+Nby/fty/1+k5rTt10TMjnzO9ViA/sK6Ha5YYZXn66af1888/S5IOHz6sRx99VMWKFdPq1as1duxYN1eH28nGzz7VSzNj9PTAQVq5+iNVq1ZdzzzdV+fOnXN3aQAKqZCSoXq8/1C99OZyzVrwnmrXv0sz/jVCx4/833LB93d4WO9+8B/HFv30MDdWDKCgWKIx//nnn1WvXj1J0urVq9W8eXOtWLFCixcv1gcffODe4nBbWbZkkTo/0k0PPdxFlSpX1r8mTZG3t7fWfsjPIYCCcdc9zdXgH/cqvEw53VG2vHr1Gyxvn2L6+afvHefYvb1VPLiEYyvm6+fGioGbYzNpuxVZojE3DENZWVmSpC+++ELt27eXJJUtW1b/+9//3FkabiMZly/rwE8/6h+N73Hs8/Dw0D/+cY++2/+tGysDcLvIzMzUV5s/V1raJVWr+X8jdNu/+ExPPHifhvbpqmVvz1N62iU3VgkUPpMnT5bNZnPaqlev7jielpamQYMGKSQkRH5+furSpYvOnDmT73VYYsa8YcOGmjZtmlq1aqVt27Zp/vz5kqQjR46oVKlSbq4Ot4vfz/+uzMxMhYSEOO0PCQnRkSOH3VQVgNvBscO/6NlBvXX58mV5+/jo2amzVbZChCSpWcu2KlmqtIJLlNTRQ79o2VtzdfLXo3p26mw3Vw3cGA+LDpnXrFlTX3zxheNxkSL/1yaPGDFCGzZs0OrVqxUYGKjBgwerc+fO+vrrr/O1Bks05q+88op69uyptWvX6rnnnlPlypUlSWvWrNE999xzzeemp6crPT3daZ/haZfdbi+wegEAyE/hZSvo5Xf+rYspKdq5/UvNnTFR0155R2UrRKh1py6O88pHVFHxkBKaNGqAEk/+qtJ3lHVj1UDhUqRIEYWFhWXbn5ycrIULF2rFihW67777JEmLFi1SjRo19M033+gf//hHvtVgiVGWOnXq6Pvvv1dycrImTZrk2D9r1iwtWbLkms+NiYlRYGCg0zbrxZiCLhmFUPGg4vL09Mz2Qc9z586pRIkSbqoKwO2gaNGiKn1HOVWqFqnH+w9RhUpVtf6DFTmeW7XGnyuYnT75q5klAvnGqjPmv/zyi8LDwxUREaGePXvq+PHjkqS9e/cqIyNDrVq1cpxbvXp1lStXTrGxsTdwJ9cs0Zi74u3traJFi17znPHjxys5OdlpGzNuvEkVojAp6uWlGpE1teub//s/sqysLO3aFas6deu7sTIAt5ssI0sZGRk5HjuSEC9JKh5CYABcS3p6ui5cuOC0/X3K4qpGjRpp8eLF2rhxo+bPn68jR46oadOm+uOPP3T69Gl5eXkpKCjI6TmlSpXS6dOn87Vmt42yFC9eXLZczhglJSW5PGa3Zx9b4QuGcKMej+6jCf8cp5o1a6lW7Tp6b9kSXbp0SQ893NndpQEopJa9PU933n2PSpYqrUsXU7X9y436MW6vJs58XYknf9VXX25Ug0ZN5B8YpKOHftG7b8xWZJ07VaFSVXeXDtwYk0bMY2JiNGXKFKd9kyZN0uTJk7Od265dO8ef69Spo0aNGql8+fJ6//335ePjU9ClOritMX/llVfcdWvApbbt2uv3pCS98dpc/e9/v6la9Rp64813FMIoC4ACkvx7kl6Nmajfk/6nYr5+qhBRRRNnvq56Df+h/509rf17d2ndByuUfumSSoSWUuOm96nr4/3cXTZgeePHj9fIkSOd9uX2M4hBQUGqWrWqEhISdP/99+vy5cs6f/68U2p+5syZHGfSb4bNMAwjX69oASTmAKzg8NlUd5cAAIoM93V3CU52HUo25T6NKgXe8HNTUlJUrlw5TZ48WdHR0SpZsqT+/e9/q0uXPz+MHR8fr+rVqys2NjZfP/xpiVVZ/iotLU2XL1922hcQEOCmagAAAFDYjR49Wp06dVL58uV16tQpTZo0SZ6enurRo4cCAwPVt29fjRw5UsHBwQoICNCQIUPUuHHjfG3KJYs05qmpqRo3bpzef//9HL/6PDMz0w1VAQAAIL9ZcRnzEydOqEePHjp37pxKliype++9V998841KliwpSZozZ448PDzUpUsXpaenq02bNnrjjTfyvQ5LjLIMGjRIW7Zs0fPPP6/HH39cr7/+uk6ePKk333xTM2bMUM+ePfN0PUZZAFgBoywArMBqoyz/PWzOKMvdETc+yuIulkjM161bp6VLl6pFixbq06ePmjZtqsqVK6t8+fJavnx5nhtzAAAAWJMFA3PLsMQ65klJSYqI+POrhwMCAhzLI957773avn27O0sDAAAATGGJxjwiIkJHjhyR9Oc3Kb3//vuS/kzS/76YOwAAAG5hVv3qTwtwa2N++PBhZWVlqU+fPtq/f78k6dlnn9Xrr78ub29vjRgxQmPGjHFniQAAAIAp3PrhT09PTyUmJio0NFSS9Oijj2ru3LlKS0vT3r17VblyZdWpUyfP1+XDnwCsgA9/ArACq334c8+RC6bcp2HFW2+5bbcm5n//neDTTz9Vamqqypcvr86dO99QUw4AAADciiyxKgsAAABuD1Zcx9wq3JqY22w22f72X+fvjwEAAIDbgVsTc8Mw1Lt3b9ntdklSWlqaBgwYIF9f51moDz/80B3lAQAAIJ8Rwbrm1sY8Ojra6XGvXr3cVAkAAADgXm5tzBctWuTO2wMAAMBsROYuWeILhgAAAIDbHauyAAAAwDQ2InOXSMwBAAAACyAxBwAAgGlYGds1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAeYjMXSIxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIBpWMfcNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgHmIzF0iMQcAAAAsgMQcAAAApmEdc9dIzAEAAAALIDEHAACAaVjH3DUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIB5iMxdIjEHAAAALIDEHAAAAKZhHXPXSMwBAAAACyAxBwAAgGlYx9w1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAeYjMXSIxBwAAACyAxBwAAACmYR1z10jMAQAAAAugMQcAAAAsgFEWAAAAmIYvGHKNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgHiJzl0jMAQAAAAugMQcAAIBpbCb9Ly9iYmJ01113yd/fX6GhoXrooYcUHx/vdE6LFi1ks9mctgEDBuTnW0NjDgAAgNvbtm3bNGjQIH3zzTfatGmTMjIy1Lp1a6Wmpjqd179/fyUmJjq2mTNn5msdzJgDAADANFZcx3zjxo1OjxcvXqzQ0FDt3btXzZo1c+wvVqyYwsLCCqwOEnMAAADgL5KTkyVJwcHBTvuXL1+uEiVKqFatWho/frwuXryYr/clMQcAAIBpzArM09PTlZ6e7rTPbrfLbrdf83lZWVkaPny4mjRpolq1ajn2P/bYYypfvrzCw8P13Xffady4cYqPj9eHH36YbzXTmAMAAKDQiYmJ0ZQpU5z2TZo0SZMnT77m8wYNGqQffvhBO3bscNr/1FNPOf5cu3ZtlS5dWi1bttShQ4dUqVKlfKnZZhiGkS9XspC0K+6uAACkw2dTr38SABSwyHBfd5fg5Oi5NFPuU9rPlufEfPDgwfr444+1fft2VaxY8ZrXT01NlZ+fnzZu3Kg2bdrkS80k5gAAACh0cjO2cpVhGBoyZIg++ugjbd269bpNuSTFxcVJkkqXLn0zZTqhMQcAAIBp8rrGuBkGDRqkFStW6OOPP5a/v79Onz4tSQoMDJSPj48OHTqkFStWqH379goJCdF3332nESNGqFmzZqpTp06+1cEoCwAUEEZZAFiB1UZZjp1Lv/5J+aB8SO7SckmyuVjDcdGiRerdu7d+/fVX9erVSz/88INSU1NVtmxZPfzww/rXv/6lgICA/CqZxhwACgqNOQArsFpjfjzJnMa8XHDuG3OrYB1zAAAAwAKYMQcAAIBprDdhbh0k5gAAAIAFkJgDAADANC4+ZwmRmAMAAACWQGIOAAAAExGZu0JiDgAAAFgAiTkAAABMw4y5ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANMwY+4aiTkAAABgASTmAAAAMI2NKXOXSMwBAAAACyAxBwAAgHkIzF0iMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIB5CMxdIjEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIZ1zF0jMQcAAAAsgMQcAAAApmEdc9dIzAEAAAALIDEHAACAaZgxd43EHAAAALAAGnMAAADAAmjMAQAAAAtgxhwAAACmYcbcNRJzAAAAwAJIzAEAAGAa1jF3jcQcAAAAsAAScwAAAJiGGXPXSMwBAAAACyAxBwAAgGkIzF0jMQcAAAAsgMQcAAAA5iEyd4nEHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDEHAAAAOYhMneJxBwAAACwABpzAAAAmMZm0v/y6vXXX1eFChXk7e2tRo0a6b///W8BvPprozEHAADAbW3VqlUaOXKkJk2apH379qlu3bpq06aNzp49a2odNsMwDFPvaIK0K+6uAACkw2dT3V0CACgy3NfdJTgxq0/zzsMnKRs1aqS77rpLr732miQpKytLZcuW1ZAhQ/Tss88WUIXZkZgDAACg0ElPT9eFCxectvT09GznXb58WXv37lWrVq0c+zw8PNSqVSvFxsaaWXLhXJUlL78hATlJT09XTEyMxo8fL7vd7u5ycIuyWkqFWwt/D6GwMqtPmzwtRlOmTHHaN2nSJE2ePNlp3//+9z9lZmaqVKlSTvtLlSqlgwcPFnSZTgrlKAtwsy5cuKDAwEAlJycrICDA3eUAuA3x9xBwc9LT07Ml5Ha7PdsvuqdOndIdd9yhnTt3qnHjxo79Y8eO1bZt27Rr1y5T6pUKaWIOAACA21tOTXhOSpQoIU9PT505c8Zp/5kzZxQWFlZQ5eWIGXMAAADctry8vNSgQQN9+eWXjn1ZWVn68ssvnRJ0M5CYAwAA4LY2cuRIRUdHq2HDhrr77rv1yiuvKDU1VX369DG1DhpzIAd2u12TJk3iA1cA3Ia/hwDzPProo/rtt980ceJEnT59WvXq1dPGjRuzfSC0oPHhTwAAAMACmDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQCwmMWLFysoKMjdZQAwGY05CjWbzXbN7e9fywsA+al37945/t2TkJDg7tIAWBDLJaJQS0xMdPx51apVmjhxouLj4x37/Pz8HH82DEOZmZkqUoT/swCQf9q2batFixY57StZsqSbqgFgZSTmKNTCwsIcW2BgoGw2m+PxwYMH5e/vr88++0wNGjSQ3W7Xjh071Lt3bz300ENO1xk+fLhatGjheJyVlaWYmBhVrFhRPj4+qlu3rtasWWPuiwNwS7Db7U5/F4WFhenVV19V7dq15evrq7Jly2rgwIFKSUlxeY39+/crKipK/v7+CggIUIMGDbRnzx7H8R07dqhp06by8fFR2bJlNXToUKWmpprx8gDkIxpz3PaeffZZzZgxQwcOHFCdOnVy9ZyYmBgtXbpUCxYs0I8//qgRI0aoV69e2rZtWwFXC6Aw8PDw0Ny5c/Xjjz9qyZIl2rx5s8aOHevy/J49e6pMmTLavXu39u7dq2effVZFixaVJB06dEht27ZVly5d9N1332nVqlXasWOHBg8ebNbLAZBP+Dd73PamTp2q+++/P9fnp6ena/r06friiy/UuHFjSVJERIR27NihN998U82bNy+oUgHcgtavX+80NteuXTutXr3a8bhChQqaNm2aBgwYoDfeeCPHaxw/flxjxoxR9erVJUlVqlRxHIuJiVHPnj01fPhwx7G5c+eqefPmmj9/vry9vQvgVQEoCDTmuO01bNgwT+cnJCTo4sWL2Zr5y5cvq379+vlZGoBCICoqSvPnz3c89vX11RdffKGYmBgdPHhQFy5c0JUrV5SWlqaLFy+qWLFi2a4xcuRI9evXT8uWLVOrVq3UtWtXVapUSdKfYy7fffedli9f7jjfMAxlZWXpyJEjqlGjRsG/SAD5gsYctz1fX1+nxx4eHjIMw2lfRkaG489X50A3bNigO+64w+k8u91eQFUCuFX5+vqqcuXKjsdHjx5Vx44d9cwzz+iFF15QcHCwduzYob59++ry5cs5NuaTJ0/WY489pg0bNuizzz7TpEmTtHLlSj388MNKSUnR008/raFDh2Z7Xrly5Qr0tQHIXzTmwN+ULFlSP/zwg9O+uLg4xzxnZGSk7Ha7jh8/ztgKgDzbu3evsrKyNHv2bHl4/PlRr/fff/+6z6tataqqVq2qESNGqEePHlq0aJEefvhh3Xnnnfrpp5+cmn8AtyY+/An8zX333ac9e/Zo6dKl+uWXXzRp0iSnRt3f31+jR4/WiBEjtGTJEh06dEj79u3TvHnztGTJEjdWDuBWULlyZWVkZGjevHk6fPiwli1bpgULFrg8/9KlSxo8eLC2bt2qY8eO6euvv9bu3bsdIyrjxo3Tzp07NXjwYMXFxemXX37Rxx9/zIc/gVsQjTnwN23atNGECRM0duxY3XXXXfrjjz/0xBNPOJ3z/PPPa8KECYqJiVGNGjXUtm1bbdiwQRUrVnRT1QBuFXXr1tXLL7+sF198UbVq1dLy5csVExPj8nxPT0+dO3dOTzzxhKpWrapu3bqpXbt2mjJliiSpTp062rZtm37++Wc1bdpU9evX18SJExUeHm7WSwKQT2zG34dpAQAAAJiOxBwAAACwABpzAAAAwAJozAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcwG2rd+/eeuihhxyPW7RooeHDh5tex9atW2Wz2XT+/HmX59hsNq1duzbX15w8ebLq1at3U3UdPXpUNptNcXFxN3UdAEDu0JgDsJTevXvLZrPJZrPJy8tLlStX1tSpU3XlypUCv/eHH36o559/Plfn5qaZBgAgL4q4uwAA+Lu2bdtq0aJFSk9P16effqpBgwapaNGiGj9+fLZzL1++LC8vr3y5b3BwcL5cBwCAG0FiDsBy7Ha7wsLCVL58eT3zzDNq1aqVPvnkE0n/N37ywgsvKDw8XNWqVZMk/frrr+rWrZuCgoIUHBysBx98UEePHnVcMzMzUyNHjlRQUJBCQkI0duxYGYbhdN+/j7Kkp6dr3LhxKlu2rOx2uypXrqyFCxfq6NGjioqKkiQVL15cNptNvXv3liRlZWUpJiZGFStWlI+Pj+rWras1a9Y43efTTz9V1apV5ePjo6ioKKc6c2vcuHGqWrWqihUrpoiICE2YMEEZGRnZznvzzTdVtmxZFStWTN26dVNycrLT8XfeeUc1atSQt7e3qlevrjfeeCPPtQAA8geNOQDL8/Hx0eXLlx2Pv/zyS8XHx2vTpk1av369MjIy1KZNG/n7++urr77S119/LT8/P7Vt29bxvNmzZ2vx4sV69913tWPHDiUlJemjjz665n2feOIJ/fvf/9bcuXN14MABvfnmm/Lz81PZsmX1wQcfSJLi4+OVmJioV199VZIUExOjpUuXasGCBfrxxx81YsQI9erVS9u2bZP05y8QnTt3VqdOnRQXF6d+/frp2WefzfN74u/vr8WLF+unn37Sq6++qrfffltz5sxxOichIUHvv/++1q1bp40bN+rbb7/VwIEDHceXL1+uiRMn6oUXXtCBAwc0ffp0TZgwQUuWLMlzPQCAfGAAgIVER0cbDz74oGEYhpGVlWVs2rTJsNvtxujRox3HS5UqZaSnpzues2zZMqNatWpGVlaWY196errh4+NjfP7554ZhGEbp0qWNmTNnOo5nZGQYZcqUcdzLMAyjefPmxrBhwwzDMIz4+HhDkrFp06Yc69yyZYshyfj9998d+9LS0oxixYoZO3fudDq3b9++Ro8ePQzDMIzx48cbkZGRTsfHjRuX7Vp/J8n46KOPXB6fNWuW0aBBA8fjSZMmGZ6ensaJEycc+z777DPDw8PDSExMNAzDMCpVqmSsWLHC6TrPP/+80bhxY8MwDOPIkSOGJOPbb791eV8AQP5hxhyA5axfv15+fn7KyMhQVlaWHnvsMU2ePNlxvHbt2k5z5fv371dCQoL8/f2drpOWlqZDhw4pOTlZiYmJatSokeNYkSJF1LBhw2zjLFfFxcXJ09NTzZs3z3XdCQkJunjxou6//36n/ZcvX1b9+vUlSQcOHHCqQ5IaN26c63tctWrVKs2dO1eHDh1SSkqKrly5ooCAAKdzypUrpzvuuMPpPllZWYqPj5e/v78OHTqkvn37qn///o5zrly5osDAwDzXAwC4eTTmACwnKipK8+fPl5eXl8LDw1WkiPNfVb6+vk6PU1JS1KBBAy1fvjzbtUqWLHlDNfj4+OT5OSkpKZKkDRs2ODXE0p9z8/klNjZWPXv21JQpU9SmTRsFBgZq5cqVmj17dp5rffvtt7P9ouDp6ZlvtQIAco/GHIDl+Pr6qnLlyrk+/84779SqVasUGhqaLTW+qnTp0tq1a5eaNWsm6c9keO/evbrzzjtzPL927drKysrStm3b1KpVq2zHryb2mZmZjn2RkZGy2+06fvy4y6S9Ro0ajg+yXvXNN99c/0X+xc6dO1W+fHk999xzjn3Hjh3Ldt7x48d16tQphYeHO+7j4eGhatWqqVSpUgoPD9fhw4fVs2fPPN0fAFAw+PAngFtez549VaJECT344IP66quvdOTIEW3dulVDhw7ViRMnJEnDhg3TjBkztHbtWh08eFADBw685hrkFSpUUHR0tJ588kmtXbvWcc33339fklS+fHnZbDatX79ev/32m1JSUuTv76/Ro0drxIgRWrJkiQ4dOqR9+/Zp3rx5jg9UDhgwQL/88ovGjBmj+Ph4rVixQosXL87T661SpYqOHz+ulStX6tChQ5o7d26OH2T19vZWdHS09u/fr6+++kpDhw5Vt27dFBYWJkmaMmWKYmJiNHfuXP3888/6/vvvtWjRIr388st5qgcAkD9ozAHc8ooVK6bt27erXLly6ty5s2rUqKG+ffsqLS3NkaCPGjVKjz/+uKKjo9W4cWP5+/vr4YcfvuZ158+fr0ceeUQDBw5U9erV1b9/f6WmpkqS7rjjDk2ZMkXPPvusSpUqpcGDB0uSnn/+eU2YMEExMTGqUaOG2rZtqw0bNqhixYqS/pz7/uCDD7R27VrVrVtXCxYs0PTp0/P0eh944AGNGDFCgwcPVr169bRz505NmDAh23mVK1dW586d1b59e7Vu3Vp16tRxWg6xX79+euedd7Ro0SLVrl1bzZs31+LFix21AgDMZTNcffIJAAAAgGlIzAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQduQxUqVFDv3r3dXUahs3v3bt1zzz3y9fWVzWZTXFxcvl5/69atstls2rp1a75e91bGzzKAwoTGHChEDh06pKeffloRERHy9vZWQECAmjRpoldffVWXLl1yd3nXtHv3bg0ePFg1a9aUr6+vypUrp27duunnn3/O03Xi4uLUq1cvlS1bVna7XcHBwWrVqpUWLVqkzMzMAqpeysjIUNeuXZWUlKQ5c+Zo2bJlKl++fIHdz2wtWrSQzWZTlSpVcjy+adMm2Ww22Ww2rVmzJs/X/+mnnzR58mQdPXr0JisFgFtXEXcXACB/bNiwQV27dpXdbtcTTzyhWrVq6fLly9qxY4fGjBmjH3/8UW+99Za7y3TpxRdf1Ndff62uXbuqTp06On36tF577TXdeeed+uabb1SrVq3rXuOdd97RgAEDVKpUKT3++OOqUqWK/vjjD3355Zfq27evEhMT9c9//rNA6j906JCOHTumt99+W/369SuQezRr1kyXLl2Sl5dXgVz/ery9vZWQkKD//ve/uvvuu52OLV++XN7e3kpLS7uha//000+aMmWKWrRooQoVKuT6efHx8fLwIGMCUDjQmAOFwJEjR9S9e3eVL19emzdvVunSpR3HBg0apISEBG3YsMGNFV7fyJEjtWLFCqem89FHH1Xt2rU1Y8YMvffee9d8/jfffKMBAwaocePG+vTTT+Xv7+84Nnz4cO3Zs0c//PBDgdV/9uxZSVJQUFCB3cPDw0Pe3t4Fdv3rqVSpkq5cuaJ///vfTo15WlqaPvroI3Xo0EEffPBBgddhGIbS0tLk4+Mju91e4PcDALMQMwCFwMyZM5WSkqKFCxc6NeVXVa5cWcOGDXP5/KSkJI0ePVq1a9eWn5+fAgIC1K5dO+3fvz/bufPmzVPNmjVVrFgxFS9eXA0bNtSKFSscx//44w8NHz5cFSpUkN1uV2hoqO6//37t27fvmq/hnnvuyZYEV6lSRTVr1tSBAweu9xZoypQpstlsWr58uVNTflXDhg2dZpFTU1M1atQox8hLtWrV9NJLL8kwDKfn2Ww2DR48WGvXrlWtWrVkt9tVs2ZNbdy40XFO79691bx5c0lS165dZbPZ1KJFC0l/joBc/fNf9e7dO1syvHLlSjVo0ED+/v4KCAhQ7dq19eqrrzqOu5oxX716tRo0aCAfHx+VKFFCvXr10smTJ7Pdz8/PTydPntRDDz0kPz8/lSxZUqNHj87TiE+PHj20atUqZWVlOfatW7dOFy9eVLdu3bKdf+zYMQ0cOFDVqlWTj4+PQkJC1LVrV6eRlcWLF6tr166SpKioKMdIzNXXWaFCBXXs2FGff/65GjZsKB8fH7355puOY1f/uxqGoaioKJUsWdLxi5IkXb58WbVr11alSpWUmpqa69cKAGajMQcKgXXr1ikiIkL33HPPDT3/8OHDWrt2rTp27KiXX35ZY8aM0ffff6/mzZvr1KlTjvPefvttDR06VJGRkXrllVc0ZcoU1atXT7t27XKcM2DAAM2fP19dunTRG2+8odGjR8vHxydXzfXfGYahM2fOqESJEtc87+LFi/ryyy/VrFkzlStXLlfXfeCBBzRnzhy1bdtWL7/8sqpVq6YxY8Zo5MiR2c7fsWOHBg4cqO7du2vmzJlKS0tTly5ddO7cOUnS008/7RiRGTp0qJYtW6bnnnsuT69106ZN6tGjh4oXL64XX3xRM2bMUIsWLfT1119f83mLFy9Wt27d5OnpqZiYGPXv318ffvih7r33Xp0/f97p3MzMTLVp00YhISF66aWX1Lx5c82ePTtPI06PPfaYEhMTnX45WLFihVq2bKnQ0NBs5+/evVs7d+5U9+7dNXfuXA0YMEBffvmlWrRooYsXL0r6c0Rn6NChkqR//vOfWrZsmZYtW6YaNWo4rhMfH68ePXro/vvv16uvvqp69eplu5fNZtO7776rtLQ0DRgwwLF/0qRJ+vHHH7Vo0SL5+vrm+rUCgOkMALe05ORkQ5Lx4IMP5vo55cuXN6Kjox2P09LSjMzMTKdzjhw5YtjtdmPq1KmOfQ8++KBRs2bNa147MDDQGDRoUK5ruZZly5YZkoyFCxde87z9+/cbkoxhw4bl6rpr1641JBnTpk1z2v/II48YNpvNSEhIcOyTZHh5eTntu3q/efPmOfZt2bLFkGSsXr3a6ZrNmzc3mjdvnq2G6Ohoo3z58o7Hw4YNMwICAowrV664rPvqPbZs2WIYhmFcvnzZCA0NNWrVqmVcunTJcd769esNScbEiROd7ifJ6b+nYRhG/fr1jQYNGri8519fx9X/9g0bNjT69u1rGIZh/P7774aXl5exZMmSHN+DixcvZrtWbGysIclYunSpY9/q1audXttflS9f3pBkbNy4Mcdjf/1ZNgzDePPNNw1JxnvvvWd88803hqenpzF8+PDrvkYAcDcSc+AWd+HCBUnKcXwjt+x2u+MDdJmZmTp37pz8/PxUrVo1pxGUoKAgnThxQrt373Z5raCgIO3atcspab8RBw8e1KBBg9S4cWNFR0df89y8vgeffvqpPD09HSntVaNGjZJhGPrss8+c9rdq1UqVKlVyPK5Tp44CAgJ0+PDhXN0vN4KCgpSamqpNmzbl+jl79uzR2bNnNXDgQKfZ8w4dOqh69eo5fq7gr0myJDVt2jTPr+Oxxx7Thx9+qMuXL2vNmjXy9PTUww8/nOO5Pj4+jj9nZGTo3Llzqly5soKCgq473vRXFStWVJs2bXJ17lNPPaU2bdpoyJAhevzxx1WpUiVNnz491/cCAHehMQducQEBAZL+nO2+UVlZWZozZ46qVKkiu92uEiVKqGTJkvruu++UnJzsOG/cuHHy8/PT3XffrSpVqmjQoEHZRi1mzpypH374QWXLltXdd9+tyZMn57nxO336tDp06KDAwEBH43cteX0Pjh07pvDw8GyN/NXRiWPHjjntz2k8pnjx4vr9999zdb/cGDhwoKpWrap27dqpTJkyevLJJ53m2HNytc5q1aplO1a9evVsr8Pb21slS5Z02ncjr6N79+5KTk7WZ599puXLl6tjx44ufym6dOmSJk6c6Jjlv/qzdf78eaefreupWLFinmpcuHChLl68qF9++UWLFy92+gUBAKyKxhy4xQUEBCg8PPymVhyZPn26Ro4cqWbNmum9997T559/rk2bNqlmzZpOH/KrUaOG4uPjtXLlSt1777364IMPdO+992rSpEmOc7p166bDhw9r3rx5Cg8P16xZs1SzZs1sKbQrycnJateunc6fP6+NGzcqPDz8us+pXLmyihQpou+//z7vLz4XXP1iYPztg6I5sdlsOe7/+wcuQ0NDFRcXp08++UQPPPCAtmzZonbt2l33Xwvy4nq/4ORW6dKl1aJFC82ePVvbt2/XY4895vLcIUOG6IUXXlC3bt30/vvv6z//+Y82bdqkkJAQp5+t68lrY71161alp6dLUoH9XABAfqMxBwqBjh076tChQ4qNjb2h569Zs0ZRUVFauHChunfvrtatW6tVq1bZPjwoSb6+vnr00Ue1aNEiHT9+XB06dNALL7zgtH516dKlNXDgQK1du1ZHjhxRSEiIXnjhhevWkZaWpk6dOunnn3/W+vXrFRkZmav6ixUrpvvuu0/bt2/Xr7/+et3zy5cvr1OnTmVL2A8ePOg4nl+KFy+e4/v49zRbkry8vNSpUye98cYbji+LWrp0qRISEnK89tU64+Pjsx2Lj48v0C84euyxx/TVV18pICBA7du3d3nemjVrFB0drdmzZ+uRRx7R/fffn+MHU139AnMjEhMTNWTIELVu3VodO3bU6NGjc3y/AcBqaMyBQmDs2LHy9fVVv379dObMmWzHDx065LTs3t95enpmS39Xr16dbcm9q6uQXOXl5aXIyEgZhqGMjAxlZmZmG08IDQ1VeHi4I710JTMzU48++qhiY2O1evVqNW7c+Jrn/92kSZNkGIYef/xxpaSkZDu+d+9eLVmyRJLUvn17ZWZm6rXXXnM6Z86cObLZbGrXrl2e7n0tlSpV0sGDB/Xbb7859u3fvz/bCNDf31sPDw/VqVNHkly+dw0bNlRoaKgWLFjgdM5nn32mAwcOqEOHDvn1MrJ55JFHNGnSJL3xxhvX/MKjnH625s2bl+1fDK6ulpLTLzF51b9/f2VlZWnhwoV66623VKRIEfXt2zdX/8IBAO7EFwwBhUClSpW0YsUKPfroo6pRo4bTN3/u3LlTq1evdlrD++86duyoqVOnqk+fPrrnnnv0/fffa/ny5YqIiHA6r3Xr1goLC1OTJk1UqlQpHThwQK+99po6dOggf39/nT9/XmXKlNEjjzyiunXrys/PT1988YV2796t2bNnX/M1jBo1Sp988ok6deqkpKSkbF8o1KtXr2s+/5577tHrr7+ugQMHqnr16k7f/Ll161Z98sknmjZtmiSpU6dOioqK0nPPPaejR4+qbt26+s9//qOPP/5Yw4cPd/qg58168skn9fLLL6tNmzbq27evzp49qwULFqhmzZqOD61KUr9+/ZSUlKT77rtPZcqU0bFjxzRv3jzVq1fPadnAvypatKhefPFF9enTR82bN1ePHj105swZvfrqq6pQoYJGjBiRb6/j7wIDAzV58uTrntexY0ctW7ZMgYGBioyMVGxsrL744guFhIQ4nVevXj15enrqxRdfVHJysux2u+67774cl2C8lkWLFmnDhg1avHixypQpI+nPXwR69eql+fPna+DAgXm6HgCYyo0rwgDIZz///LPRv39/o0KFCoaXl5fh7+9vNGnSxJg3b56RlpbmOC+n5RJHjRpllC5d2vDx8TGaNGlixMbGZlvq78033zSaNWtmhISEGHa73ahUqZIxZswYIzk52TAMw0hPTzfGjBlj1K1b1/D39zd8fX2NunXrGm+88cZ1a2/evLkhyeWWW3v37jUee+wxIzw83ChatKhRvHhxo2XLlsaSJUucloT8448/jBEjRjjOq1KlijFr1iwjKyvL6XqSclz+8e/voavlEg3DMN577z0jIiLC8PLyMurVq2d8/vnn2ZZLXLNmjdG6dWsjNDTU8PLyMsqVK2c8/fTTRmJiYrZ7/H1JwVWrVhn169c37Ha7ERwcbPTs2dM4ceKE0znR0dGGr69vttomTZqUq/f3r8slupLTe/D7778bffr0MUqUKGH4+fkZbdq0MQ4ePJjjModvv/22ERERYXh6ejq9zvLlyxsdOnTI8Z5/vc6vv/5qBAYGGp06dcp23sMPP2z4+voahw8fvu5rBQB3sRkG/7YHAAAAuBsz5gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWECh/OZPn/qD3V0CAOj33a+5uwQAkLfFuj2z+rRL3956fweTmAMAAAAWYLHfoQAAAFCo2ciFXeGdAQAAACyAxBwAAADmsdncXYFlkZgDAAAAFkBiDgAAAPMwY+4S7wwAAABgASTmAAAAMA8z5i6RmAMAAAAWQGIOAAAA8zBj7hLvDAAAAGABJOYAAAAwDzPmLpGYAwAAABZAYg4AAADzMGPuEu8MAAAAYAEk5gAAADAPM+YukZgDAAAAFkBiDgAAAPMwY+4S7wwAAABgASTmAAAAMA8z5i6RmAMAAAAWQGIOAAAA8zBj7hLvDAAAAGABJOYAAAAwDzPmLpGYAwAAABZAYg4AAADzMGPuEu8MAAAAYAEk5gAAADAPiblLvDMAAACABZCYAwAAwDwerMriCok5AAAAYAEk5gAAADAPM+Yu8c4AAAAAFkBiDgAAAPPwzZ8ukZgDAAAAFkBiDgAAAPMwY+4S7wwAAABgASTmAAAAMA8z5i6RmAMAAAAWQGIOAAAA8zBj7hLvDAAAAGABJOYAAAAwDzPmLpGYAwAAABZAYg4AAADzMGPuEu8MAAAAYAEk5gAAADAPM+YukZgDAAAAFkBiDgAAAPMwY+4S7wwAAABgASTmAAAAMA8z5i6RmAMAAAAWQGIOAAAA8zBj7hLvDAAAAGABJOYAAAAwD4m5S7wzAAAAgAXQmAMAAMA8Nps5Wx5s375dnTp1Unh4uGw2m9auXfu3km05brNmzXKcU6FChWzHZ8yYkac6aMwBAABwW0tNTVXdunX1+uuv53g8MTHRaXv33Xdls9nUpUsXp/OmTp3qdN6QIUPyVAcz5gAAADCPBWfM27Vrp3bt2rk8HhYW5vT4448/VlRUlCIiIpz2+/v7Zzs3L6z3zgAAAAA3KT09XRcuXHDa0tPTb/q6Z86c0YYNG9S3b99sx2bMmKGQkBDVr19fs2bN0pUrV/J0bRpzAAAAmMekGfOYmBgFBgY6bTExMTdd/pIlS+Tv76/OnTs77R86dKhWrlypLVu26Omnn9b06dM1duzYPF2bURYAAAAUOuPHj9fIkSOd9tnt9pu+7rvvvquePXvK29vbaf9f71WnTh15eXnp6aefVkxMTK7vS2MOAAAA85g0Y2632/OlEf+rr776SvHx8Vq1atV1z23UqJGuXLmio0ePqlq1arm6PqMsAAAAQC4sXLhQDRo0UN26da97blxcnDw8PBQaGprr65OYAwAAwDx5XGPcDCkpKUpISHA8PnLkiOLi4hQcHKxy5cpJki5cuKDVq1dr9uzZ2Z4fGxurXbt2KSoqSv7+/oqNjdWIESPUq1cvFS9ePNd10JgDAADgtrZnzx5FRUU5Hl+dF4+OjtbixYslSStXrpRhGOrRo0e259vtdq1cuVKTJ09Wenq6KlasqBEjRmSbcb8em2EYxo2/DGvyqT/Y3SUAgH7f/Zq7SwAAeVsshi3W5V1T7nPxgydNuU9+YsYcAAAAsACL/Q4FAACAwsxmwRlzqyAxBwAAACyAxhwAAACwAEZZAAAAYB4mWVwiMQcAAAAsgMQcAAAApuHDn66RmAMAAAAWQGIOAAAA05CYu0ZiDgAAAFgAiTkAAABMQ2LuGok5AAAAYAEk5gAAADANiblrJOYAAACABZCYAwAAwDwE5i6RmAMAAAAWQGIOAAAA0zBj7hqJOQAAAGABJOYAAAAwDYm5ayTmAAAAgAWQmAMAAMA0JOaukZgDAAAAFkBiDgAAANOQmLtGYg4AAABYAIk5AAAAzENg7hKJOQAAAGABJOYAAAAwDTPmrpGYAwAAABZAYg4AAADTkJi7RmIOAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA+BuUsk5gAAAIAFkJgDAADANMyYu0ZiDgAAAFgAiTkAAABMQ2LuGok5AAAAYAEk5gAAADANiblrJOYAAACABZCYAwAAwDQk5q6RmAMAAAAWQGIOAAAA8xCYu0RiDgAAAFgAiTkAAABMw4y5ayTmAAAAgAWQmAMAAMA0JOaukZgDAAAAFkBiDgAAANOQmLtmicT8q6++Uq9evdS4cWOdPHlSkrRs2TLt2LHDzZUBAAAA5nB7Y/7BBx+oTZs28vHx0bfffqv09HRJUnJysqZPn+7m6gAAAJCvbCZttyC3N+bTpk3TggUL9Pbbb6to0aKO/U2aNNG+ffvcWBkAAABgHrfPmMfHx6tZs2bZ9gcGBur8+fPmFwQAAIACw4y5a25PzMPCwpSQkJBt/44dOxQREeGGigAAAADzub0x79+/v4YNG6Zdu3bJZrPp1KlTWr58uUaPHq1nnnnG3eUBAAAgH9lsNlO2vNi+fbs6deqk8PBw2Ww2rV271ul47969s12/bdu2TuckJSWpZ8+eCggIUFBQkPr27auUlJQ81eH2UZZnn31WWVlZatmypS5evKhmzZrJbrdr9OjRGjJkiLvLAwAAQCGXmpqqunXr6sknn1Tnzp1zPKdt27ZatGiR47Hdbnc63rNnTyUmJmrTpk3KyMhQnz599NRTT2nFihW5rsPtjbnNZtNzzz2nMWPGKCEhQSkpKYqMjJSfn5+7S0Mh0+TOShrxRCvdGVlOpUsGqtuIt7Ru63eO46HB/po27EG1alxDgX4+2rEvQSNnrtah4785zikV4q/pwx/Wff+oLn9fu34+elYzF36utV/GueEVASiM9u7ZrcXvLtSBn37Qb7/9pjlzX9d9LVu5uywg31hxxrxdu3Zq167dNc+x2+0KCwvL8diBAwe0ceNG7d69Ww0bNpQkzZs3T+3bt9dLL72k8PDwXNXh9lGWq7y8vBQZGam7776bphwFwtfHru9/PqnhMatyPP7+nKdUsUwJdR3+pv7RY4aOJybp0wVDVMzby3HOO88/oaoVQtV1+Jtq2HW6Pt4cp/defFJ1q5Ux62UAKOQuXbqoatWqafy/Jrm7FAB/sXXrVoWGhqpatWp65plndO7cOcex2NhYBQUFOZpySWrVqpU8PDy0a9euXN/D7Yl5VFTUNX9z2rx5s4nVoDD7z9c/6T9f/5TjscrlQtWoTkXd2WWaDhw+LUkaOn2Vjn4xXd3aNdDij2IlSf+oG6Gh01dqz4/HJEkvvvO5hvS8T/Ujy2p//AlzXgiAQu3eps11b9Pm7i4DKDBmJebp6emO78e5ym63ZxtByY22bduqc+fOqlixog4dOqR//vOfateunWJjY+Xp6anTp08rNDTU6TlFihRRcHCwTp8+nev7uD0xr1evnurWrevYIiMjdfnyZe3bt0+1a9d2d3m4Tdi9/vwdNe3yFcc+wzB0+fIV3VOvkmPfN/sP65HWDVQ8oJhsNpu6tmkgb3sRbd/zi+k1AwAA12JiYhQYGOi0xcTE3NC1unfvrgceeEC1a9fWQw89pPXr12v37t3aunVrvtbs9sR8zpw5Oe6fPHlynj/JCtyo+KOndTwxSc8PeUCDp/1bqZcua2ivKJUJK66wEoGO83qNfVfLXnxSp7bNVEZGpi6mXdajI9/W4V//58bqAQC4hZg0Yj5+/HiNHDnSad+NpOU5iYiIUIkSJZSQkKCWLVsqLCxMZ8+edTrnypUrSkpKcjmXnhO3J+au9OrVS+++++51z0tPT9eFCxecNiMr04QKUZhcuZKl7qPeVuXyoUrcPktJsS+rWcOq2rjjR2UZWY7zJg3qqCB/H7V7eq6a9Jqpue9t1nszn1TNyrn7UAcAADCH3W5XQECA05ZfjfmJEyd07tw5lS5dWpLUuHFjnT9/Xnv37nWcs3nzZmVlZalRo0a5vq7bE3NXYmNj5e3tfd3zYmJiNGXKFKd9nqXuUtHSdxdUaSikvj3wq/7RfYYC/LzlVbSI/vd7irYvHa29Px2XJFUsU0LPdG/uNIf+/c8n1eTOSnr60WYa+sJKd5YPAMAtwYqrsqSkpDh94eWRI0cUFxen4OBgBQcHa8qUKerSpYvCwsJ06NAhjR07VpUrV1abNm0kSTVq1FDbtm3Vv39/LViwQBkZGRo8eLC6d++e6xVZJAs05n9fK9IwDCUmJmrPnj2aMGHCdZ+f0z9ThDYdl6814vZyISVNklSpXEndGVlOU95YL0mO1VmyDMPp/MxMQx4W/EsGAADkzp49exQVFeV4fLW3jI6O1vz58/Xdd99pyZIlOn/+vMLDw9W6dWs9//zzTgn88uXLNXjwYLVs2VIeHh7q0qWL5s6dm6c63N6YBwYGOj328PBQtWrVNHXqVLVu3fq6z8/p07U2D898rRGFg6+PlyqVLel4XOGOENWpeod+v3BRv57+XZ1b1ddvv6fo19NJqlUlXC+NeUTrtn6nL785KOnPOfSE42f12r96aPzLH+lccqoeiKqjlv+ops7DFrjrZQEoZC6mpur48eOOxydPnNDBAwcUGBio0nlI3gCrsmJi3qJFCxl/C97+6vPPP7/uNYKDg/P0ZUI5sRnXqqKAZWZm6uuvv1bt2rVVvHjxfLuuT/3B+XYtFB5NG1TRf94Zlm3/sk++0VOT3tPAHs014olWCg3x1+n/XdDy9bsU89ZGZVz5v88sVCpXUtOGPqjG9SLkV8yuQ7/+pleWfql/b9ht5kvBLeL33a+5uwTcgnb/d5f69Xki2/4HHnxYz0+f4YaKcKvzdnsM66zSqM9Muc+h2df+wiArcmtjLkne3t46cOCAKlasmG/XpDEHYAU05gCswGqNeeXR5jTmCS/deo2521dlqVWrlg4fPuzuMgAAAAC3cntjPm3aNI0ePVrr169XYmJitqUPAQAAUHjYbDZTtluR2/5xY+rUqRo1apTat28vSXrggQec3kTDMGSz2ZSZyZrkAAAAKPzc1phPmTJFAwYM0JYtW9xVAgAAAEx2i4bZpnBbY371M6fNmzd3VwkAAACAZbj1c7q36vwPAAAAbgz9n2tubcyrVq163f84SUlJJlUDAAAAuI9bG/MpU6Zk++ZPAAAAFF4E5q65tTHv3r27QkND3VkCAAAAYAlua8yZLwIAALj9eHjQA7riti8YuroqCwAAAAA3JuZZWVnuujUAAADchKEJ19yWmAMAAAD4P2798CcAAABuL3zO0DUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIBpmDF3jcQcAAAAsAAScwAAAJiGxNw1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaZgxd43EHAAAALAAEnMAAACYhsDcNRJzAAAAwAJIzAEAAGAaZsxdIzEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIYZc9dIzAEAAAALIDEHAACAaQjMXSMxBwAAACyAxBwAAACmYcbcNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgGmYMXeNxBwAAACwABJzAAAAmIbA3DUScwAAAMACaMwBAAAAC2CUBQAAAKbhw5+ukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATMOMuWsk5gAAAIAFkJgDAADANCTmrpGYAwAAABZAYg4AAADTEJi7RmIOAACA29r27dvVqVMnhYeHy2azae3atY5jGRkZGjdunGrXri1fX1+Fh4friSee0KlTp5yuUaFCBdlsNqdtxowZeaqDxhwAAACm+XvzWlBbXqSmpqpu3bp6/fXXsx27ePGi9u3bpwkTJmjfvn368MMPFR8frwceeCDbuVOnTlViYqJjGzJkSJ7qYJQFAAAAt7V27dqpXbt2OR4LDAzUpk2bnPa99tpruvvuu3X8+HGVK1fOsd/f319hYWE3XAeJOQAAAExjs5mzpaen68KFC05benp6vryG5ORk2Ww2BQUFOe2fMWOGQkJCVL9+fc2aNUtXrlzJ03VpzAEAAFDoxMTEKDAw0GmLiYm56eumpaVp3Lhx6tGjhwICAhz7hw4dqpUrV2rLli16+umnNX36dI0dOzZP12aUBQAAAKYxax3z8ePHa+TIkU777Hb7TV0zIyND3bp1k2EYmj9/vtOxv96rTp068vLy0tNPP62YmJhc35fGHAAAAIWO3W6/6Ub8r6425ceOHdPmzZud0vKcNGrUSFeuXNHRo0dVrVq1XN2DxhwAAACmuRXXMb/alP/yyy/asmWLQkJCrvucuLg4eXh4KDQ0NNf3oTEHAADAbS0lJUUJCQmOx0eOHFFcXJyCg4NVunRpPfLII9q3b5/Wr1+vzMxMnT59WpIUHBwsLy8vxcbGateuXYqKipK/v79iY2M1YsQI9erVS8WLF891HTTmAAAAMI2HBSPzPXv2KCoqyvH46rx4dHS0Jk+erE8++USSVK9ePafnbdmyRS1atJDdbtfKlSs1efJkpaenq2LFihoxYkS2GffroTEHAADAba1FixYyDMPl8Wsdk6Q777xT33zzzU3XQWMOAAAA01gwMLcM1jEHAAAALIDEHAAAAKYxax3zWxGJOQAAAGABJOYAAAAwjQeBuUsk5gAAAIAFkJgDAADANMyYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADCNTUTmrpCYAwAAABZAYg4AAADTsI65ayTmAAAAgAWQmAMAAMA0rGPuGok5AAAAYAEk5gAAADANgblrJOYAAACABZCYAwAAwDQeROYukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATMM65q6RmAMAAAAWQGIOAAAA0xCYu0ZiDgAAAFgAiTkAAABMwzrmrpGYAwAAABaQq8T8k08+yfUFH3jggRsuBgAAAIUbeblruWrMH3rooVxdzGazKTMz82bqAQAAAG5LuWrMs7KyCroOAAAA3AZYx9y1m5oxT0tLy686AAAAgNtanhvzzMxMPf/887rjjjvk5+enw4cPS5ImTJighQsX5nuBAAAAKDw8bOZst6I8N+YvvPCCFi9erJkzZ8rLy8uxv1atWnrnnXfytTgAAADgdpHnxnzp0qV666231LNnT3l6ejr2161bVwcPHszX4gAAAFC42Gw2U7ZbUZ4b85MnT6py5crZ9mdlZSkjIyNfigIAAABuN3luzCMjI/XVV19l279mzRrVr18/X4oCAABA4WSzmbPdinK1XOJfTZw4UdHR0Tp58qSysrL04YcfKj4+XkuXLtX69esLokYAAACg0MtzYv7ggw9q3bp1+uKLL+Tr66uJEyfqwIEDWrdune6///6CqBEAAACFBDPmruU5MZekpk2batOmTfldCwAAAHDbuqHGXJL27NmjAwcOSPpz7rxBgwb5VhQAAAAKp1t1jXEz5LkxP3HihHr06KGvv/5aQUFBkqTz58/rnnvu0cqVK1WmTJn8rhEAAAAo9PI8Y96vXz9lZGTowIEDSkpKUlJSkg4cOKCsrCz169evIGoEAABAIcGMuWt5Tsy3bdumnTt3qlq1ao591apV07x589S0adN8LQ4AAAC4XeS5MS9btmyOXySUmZmp8PDwfCkKAAAAhdOtmWWbI8+jLLNmzdKQIUO0Z88ex749e/Zo2LBheumll/K1OAAAAOB2kavEvHjx4k6zOqmpqWrUqJGKFPnz6VeuXFGRIkX05JNP6qGHHiqQQgEAAHDr87hF57/NkKvG/JVXXingMgAAAIDbW64a8+jo6IKuAwAAALcBAnPXbvgLhiQpLS1Nly9fdtoXEBBwUwUBAAAAt6M8N+apqakaN26c3n//fZ07dy7b8czMzHwpDAAAAIXPrbrGuBnyvCrL2LFjtXnzZs2fP192u13vvPOOpkyZovDwcC1durQgagQAAAAKvTwn5uvWrdPSpUvVokUL9enTR02bNlXlypVVvnx5LV++XD179iyIOgEAAFAIEJi7lufEPCkpSREREZL+nCdPSkqSJN17773avn17/lYHAAAA3Cby3JhHREToyJEjkqTq1avr/fffl/Rnkh4UFJSvxQEAAKBw8bDZTNnyYvv27erUqZPCw8Nls9m0du1ap+OGYWjixIkqXbq0fHx81KpVK/3yyy9O5yQlJalnz54KCAhQUFCQ+vbtq5SUlLy9N3k6W1KfPn20f/9+SdKzzz6r119/Xd7e3hoxYoTGjBmT18sBAAAAbpWamqq6devq9ddfz/H4zJkzNXfuXC1YsEC7du2Sr6+v2rRpo7S0NMc5PXv21I8//qhNmzZp/fr12r59u5566qk81WEzDMO4mRdy7Ngx7d27V5UrV1adOnVu5lL5xqf+YHeXAAD6ffdr7i4BAOR9U4tj57+BH/5kyn3e6Bx5Q8+z2Wz66KOPHN9mbxiGwsPDNWrUKI0ePVqSlJycrFKlSmnx4sXq3r27Dhw4oMjISO3evVsNGzaUJG3cuFHt27fXiRMnFB4enqt75zkx/7vy5curc+fOlmnKAQAAgPxy5MgRnT59Wq1atXLsCwwMVKNGjRQbGytJio2NVVBQkKMpl6RWrVrJw8NDu3btyvW9cvU71Ny5c3N9waFDh+b6XAAAANxezFrHPD39/7F33+FRVevbx+9JIJNACjWESAi9V0ERC0WQZqMoIqABEUWaBKlHKUEkqEcRUMFjSQBBiiJIEQ9FQKQcikFp0YSmElBBwARSSPb7hy/zcwgDCUz27CTfz7n2dTFrr9n7mdGDT+6sWZOmtLQ0pzG73S673Z6r65w8eVKSVK5cOafxcuXKOc6dPHlSwcHBTueLFCmiUqVKOebkRI4a82nTpuXoYjabjcYcAAAAHhcdHa2oqCinsQkTJmjixImeKSgHctSYX96FJb848z/WdQLwvJ9O5u7T+ACQF+pX8Pd0CU5ueh11Do0dO1bDhw93GsttWi5JISEhkqRTp06pfPnyjvFTp06pUaNGjjm//fab0/MuXbqkM2fOOJ6fE2a9NwAAAIBp7Ha7AgMDnY4bacwrV66skJAQrV+/3jF2/vx57dixQ82bN5ckNW/eXGfPntXu3bsdczZs2KCsrCw1a9Ysx/ey2Od0AQAAUJCZtcY8N5KTk5WQkOB4fOTIEcXFxalUqVKqWLGihg0bpsmTJ6t69eqqXLmyxo0bp9DQUMfOLbVr11aHDh3Uv39/zZ49WxkZGRo8eLB69OiR4x1ZJBpzAAAAFHK7du1S69atHY8vL4GJiIhQbGysRo0apZSUFD3zzDM6e/as7r77bq1Zs0a+vr6O58yfP1+DBw9WmzZt5OXlpW7duuVqAxXJDfuYW9HFDE9XAABSwinWmAPwPKutMR+2/JAp93nr4Vqm3MedWGMOAAAAWMANNebffPONevfurebNm+vXX3+VJM2bN09btmxxa3EAAAAoWLxs5hz5Ua4b888++0zt27eXn5+fvvvuO8fG7efOndOUKVPcXiAAAABQGOS6MZ88ebJmz56t999/X0WLFnWM33XXXdqzZ49biwMAAEDBYrPZTDnyo1w35vHx8WrRokW28aCgIJ09e9YdNQEAAACFTq4b85CQEKd9Hi/bsmWLqlSp4paiAAAAUDCxxty1XDfm/fv31/PPP68dO3bIZrPpxIkTmj9/vkaMGKHnnnsuL2oEAAAACrxcf8HQmDFjlJWVpTZt2ujChQtq0aKF7Ha7RowYoSFDhuRFjQAAACgg8unyb1Pc8BcMpaenKyEhQcnJyapTp478/a2zeT1fMATACviCIQBWYLUvGBq1Kt6U+7x2f01T7uNOuU7ML/Px8VGdOnXcWQsAAAAKOC8ic5dy3Zi3bt36mlvQbNiw4aYKAgAAAAqjXDfmjRo1cnqckZGhuLg47du3TxEREe6qCwAAAAXQDX3tfCGR68Z82rRpVx2fOHGikpNZTwkAAADcCLf90NK7d2999NFH7rocAAAACiCbzZwjP3JbY75t2zb5+vq663IAAABAoZLrpSxdu3Z1emwYhpKSkrRr1y6NGzfObYUBAACg4GFXFtdy3ZgHBQU5Pfby8lLNmjU1adIktWvXzm2FAQAAAIVJrhrzzMxM9e3bV/Xr11fJkiXzqiYAAAAUUATmruVqjbm3t7fatWuns2fP5lE5AAAAQOGU6w9/1qtXT4cPH86LWgAAAIBCK9eN+eTJkzVixAitXLlSSUlJOn/+vNMBAAAAuOJlM+fIj3K8xnzSpEl64YUX1KlTJ0nSQw89JNs/FgkZhiGbzabMzEz3VwkAAAAUcDluzKOiojRgwAB9/fXXeVkPAAAACjC2S3Qtx425YRiSpJYtW+ZZMQAAAEBhlavtEm38hAMAAICbQDvpWq4a8xo1aly3OT9z5sxNFQQAAAAURrlqzKOiorJ98ycAAACQU/l1xxQz5Kox79Gjh4KDg/OqFgAAAKDQynFjzvpyAAAA3Cyb6CldyfEXDF3elQUAAACA++U4Mc/KysrLOgAAAFAIsMbctRwn5gAAAADyTq4+/AkAAADcDBJz10jMAQAAAAsgMQcAAIBp2OnPNRJzAAAAwAJIzAEAAGAa1pi7RmIOAAAAWACJOQAAAEzDEnPXSMwBAAAACyAxBwAAgGm8iMxdIjEHAAAALIDEHAAAAKZhVxbXSMwBAAAACyAxBwAAgGlYYu4aiTkAAABgASTmAAAAMI2XiMxdITEHAAAALIDEHAAAAKZhjblrJOYAAACABZCYAwAAwDTsY+4aiTkAAABgASTmAAAAMI0Xi8xdIjEHAAAALIDEHAAAAKYhMHeNxBwAAACFWqVKlWSz2bIdgwYNkiS1atUq27kBAwa4vQ4ScwAAAJjGimvMd+7cqczMTMfjffv26b777tOjjz7qGOvfv78mTZrkeFysWDG310FjDgAAgEKtbNmyTo+nTp2qqlWrqmXLlo6xYsWKKSQkJE/rYCkLAAAATGOzmXPcqPT0dH388cd66qmnZPvHhebPn68yZcqoXr16Gjt2rC5cuOCGd8MZiTkAAAAKnLS0NKWlpTmN2e122e32az5v2bJlOnv2rPr06eMY69mzp8LDwxUaGqrvv/9eo0ePVnx8vJYuXerWmm2GYRhuvaIFXMzwdAUAICWcSvZ0CQCg+hX8PV2Ck9idx025z9FVHykqKsppbMKECZo4ceI1n9e+fXv5+PhoxYoVLuds2LBBbdq0UUJCgqpWreqOciWRmAMAAKAAGjt2rIYPH+40dr20/NixY1q3bt11k/BmzZpJEo05AAAA8i+bSbuy5GTZypViYmIUHBys+++//5rz4uLiJEnly5e/0fKuisYcAAAAhV5WVpZiYmIUERGhIkX+r0VOTEzUggUL1KlTJ5UuXVrff/+9IiMj1aJFCzVo0MCtNdCYAwAAwDTW28X8b+vWrdPx48f11FNPOY37+Pho3bp1euutt5SSkqKwsDB169ZNL730kttroDEHAABAodeuXTtdbU+UsLAwbdq0yZQaaMwBAABgGit+86dV8AVDAAAAgAWQmAMAAMA05OWukZgDAAAAFkBiDgAAANOwxNw1EnMAAADAAkjMAQAAYBqzvvkzPyIxBwAAACyAxBwAAACmIRV2jfcGAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANOQl7tGYg4AAABYAIk5AAAATMMac9dIzAEAAAALIDEHAACAaUiFXeO9AQAAACyAxBwAAACmYY25ayTmAAAAgAWQmAMAAMA05OWukZgDAAAAFkBiDgAAANOwxNw1EnMAAADAAkjMAQAAYBovVpm7RGIOAAAAWACJOQAAAEzDGnPXSMwBAAAACyAxBwAAgGlsrDF3icQcAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANOwj7lrJOYAAACABZCYAwAAwDSsMXeNxBwAAACwABJzAAAAmIbE3DUScwAAAMACSMwBAABgGr750zUScwAAAMACSMwBAABgGi8Cc5dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABlmrM09PTFR8fr0uXLnm6FAAAAOQBm82cIz+yRGN+4cIF9evXT8WKFVPdunV1/PhxSdKQIUM0depUD1cHAAAA5D1LNOZjx47V3r17tXHjRvn6+jrG27Ztq0WLFnmwMgAAALiTzaT/5UeW+PDnsmXLtGjRIt1xxx2y/eN3D3Xr1lViYqIHKwMAAADMYYnG/Pfff1dwcHC28ZSUFKdGHQAAAPkb+5i7ZomlLE2bNtWqVascjy834x988IGaN2/uqbIAAAAA01giMZ8yZYo6duyoAwcO6NKlS5o+fboOHDigrVu3atOmTZ4uDwAAAG6SX9d/m8ESifndd9+tuLg4Xbp0SfXr19d///tfBQcHa9u2bWrSpImnywMAAADynM0wDMPTRbjbxQxPVwAAUsKpZE+XAACqX8Hf0yU42fLTn6bc5+7qJU25jztZYinLnj17VLRoUdWvX1+StHz5csXExKhOnTqaOHGifHx8PFwhCoMP339P69f9V0ePHJbd11cNGzXWsMgRqlS5iqdLA1CAffXFEn31xaf6/VSSJCksvIoeeaK/bm12lyTpzzN/aN570/X97h26eDFFoRXC1a1XP93Roo0nywaQByyxlOXZZ5/Vjz/+KEk6fPiwHnvsMRUrVkxLlizRqFGjPFwdCovdu/6nxx7vpbkLFmv2f2J0KeOSnnumny5euODp0gAUYKXLlFPv/kP02qyP9eq781Sv8W16bfxw/Xz07+2CZ04drxM/H9PoyW/qzfcXqdk99+rNl8fo8E+HPFw5cGNsJh35kSUa8x9//FGNGjWSJC1ZskQtW7bUggULFBsbq88++8yzxaHQePe9D/Vw566qVq26ataqpUmvTFVS0gkdOLDf06UBKMCa3tlCtza7W+UrVFRoWLh69hskX79i+vHAD5KkH/d/r45dHlP1WvVULrSCHun9tIoVD9DhHw96uHKg4Jg4caJsNpvTUatWLcf51NRUDRo0SKVLl5a/v7+6deumU6dOub0OSzTmhmEoKytLkrRu3Tp16tRJkhQWFqY//vjDk6WhEEtO/kuSFBQU5OFKABQWmZmZ2rLhK6WmXlSNOg0kSTXqNtC3X/9Xf50/p6ysLG3Z8JUyMtJUt1FTD1cL3Bgvm82UI7fq1q2rpKQkx7FlyxbHucjISK1YsUJLlizRpk2bdOLECXXt2tWdb4ski6wxb9q0qSZPnqy2bdtq06ZNmjVrliTpyJEjKleunIerQ2GUlZWl16dOUaPGt6pa9RqeLgdAAXfs8E96cUhfpaeny9fPT6Oi/q2wSn9/vuWF8a/qzZfHqG+Xe+Xt7S27r69GRv1b5W8J83DVQMFSpEgRhYSEZBs/d+6cPvzwQy1YsED33nuvJCkmJka1a9fW9u3bdccdd7itBksk5m+99Zb27NmjwYMH68UXX1S1atUkSZ9++qnuvPPOaz43LS1N58+fdzrS0tLMKBsFWPTkKCUk/KRXX5/m6VIAFAKhYZX0+n8+UfQ7c9T+oUf09qsT9PPRw5KkhTGzlJL8l8a/PkuvzvpYDzzSW29OGqNjh3/ycNWAteW2R/zpp58UGhqqKlWqqFevXjp+/Lgkaffu3crIyFDbtm0dc2vVqqWKFStq27Ztbq3ZEo15gwYN9MMPP+jcuXOaMGGCY/z111/XnDlzrvnc6OhoBQUFOR2vvxqd1yWjAIt+ZZI2b9qoDz6ao3JX+ckZANytaNGiKn9LmKrWqK1eTw9ReNUaWr30E5088bO+XLZIg0ZOUINbb1elqjXU/clnVLVmHa1ZvsTTZQM3xKwPf16tR4yOvnqP2KxZM8XGxmrNmjWaNWuWjhw5onvuuUd//fWXTp48KR8fH5UoUcLpOeXKldPJkyfd9r5IFlnK4oqvr+9154wdO1bDhw93GsvysudVSSjADMPQ1Ckva8P6tfogZp5uqcCviQF4hpGVpYyMdKWlpkqSbDbnHM3Ly0uGkeWJ0oB842o9ot1+9R6xY8eOjj83aNBAzZo1U3h4uBYvXiw/P788rfOfPNaYlyxZUrYcLsw/c+aMy3N2uz3bm8wXDOFGTJkcpS9Xr9RbM95V8eLF9ccfv0uS/P0DcvRDIgDciPkfzFTj2+9SmeAQXbyQoi0b1mj/3t16aerbuqViJYXcEqb3pr2iJwcMU0BgkP63ZaO+371DY195y9OlAzfGpL0Mr9Yj5lSJEiVUo0YNJSQk6L777lN6errOnj3rlJqfOnXqqmvSb4bHGvO33nrLU7cGrmrJok8kSU/3fcJpPGpytB7u7P5PXgOAJJ3780/NnDpef575Q8WK+yu8SnW9NPVtNWz69wfKXpwyQx9/MFNTX4xUauoFhYSGafDoKN3a7G4PVw4UXMnJyUpMTNQTTzyhJk2aqGjRolq/fr26desmSYqPj9fx48fVvHlzt97XZhiG4dYrWgCJOQArSDiV7OkSAED1K/h7ugQnOxLPmXKfZlVzvt3xiBEj9OCDDyo8PFwnTpzQhAkTFBcXpwMHDqhs2bJ67rnntHr1asXGxiowMFBDhgyRJG3dutWtNVtujXlqaqrS09OdxgIDAz1UDQAAAAq6X375RY8//rhOnz6tsmXL6u6779b27dtVtmxZSdK0adPk5eWlbt26KS0tTe3bt9e7777r9joskZinpKRo9OjRWrx4sU6fPp3tfGZmZq6uR2IOwApIzAFYgdUS8/8dNicxv71K/vuCQEtslzhq1Cht2LBBs2bNkt1u1wcffKCoqCiFhoZq7ty5ni4PAAAAyHOWWMqyYsUKzZ07V61atVLfvn11zz33qFq1agoPD9f8+fPVq1cvT5cIAAAANzBpU5Z8yRKJ+ZkzZ1Slyt9fPRwYGOjYHvHuu+/W5s2bPVkaAAAAYApLNOZVqlTRkSNHJP39FaeLFy+W9HeSfuW3LAEAACAfM+urP/Mhjzbmhw8fVlZWlvr27au9e/dKksaMGaN33nlHvr6+ioyM1MiRIz1ZIgAAAGAKj+7K4u3traSkJAUHB0uSHnvsMc2YMUOpqanavXu3qlWrpgYNGuT6uuzKAsAK2JUFgBVYbVeWXUfOm3KfppXz33bbHk3Mr/yZYPXq1UpJSVF4eLi6du16Q005AAAAkB9ZYlcWAAAAFA62fLr+2wweTcxtNptsV/zTufIxAAAAUBh4NDE3DEN9+vSR3W6XJKWmpmrAgAEqXry407ylS5d6ojwAAAC4GRGsax5tzCMiIpwe9+7d20OVAAAAAJ7l0cY8JibGk7cHAACA2YjMXbLEFwwBAAAAhR27sgAAAMA0NiJzl0jMAQAAAAsgMQcAAIBp2BnbNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgHmIzF0iMQcAAAAsgMQcAAAApmEfc9dIzAEAAAALIDEHAACAadjH3DUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIB5iMxdIjEHAAAALIDEHAAAAKZhH3PXSMwBAAAACyAxBwAAgGnYx9w1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAeYjMXSIxBwAAACyAxBwAAACmYR9z10jMAQAAAAsgMQcAAIBp2MfcNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgHmIzF0iMQcAAAAsgMQcAAAApmEfc9dIzAEAAAALIDEHAACAadjH3DUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIB5iMxdIjEHAAAALIDEHAAAAKZhH3PXSMwBAABQqEVHR+u2225TQECAgoOD1blzZ8XHxzvNadWqlWw2m9MxYMAAt9ZBYw4AAADT2GzmHLmxadMmDRo0SNu3b9fatWuVkZGhdu3aKSUlxWle//79lZSU5Dhee+01N74zLGUBAABAIbdmzRqnx7GxsQoODtbu3bvVokULx3ixYsUUEhKSZ3WQmAMAAMA0NpOOm3Hu3DlJUqlSpZzG58+frzJlyqhevXoaO3asLly4cJN3ckZiDgAAgAInLS1NaWlpTmN2u112u/2az8vKytKwYcN01113qV69eo7xnj17Kjw8XKGhofr+++81evRoxcfHa+nSpW6rmcYcAAAA5jFpU5bo6GhFRUU5jU2YMEETJ0685vMGDRqkffv2acuWLU7jzzzzjOPP9evXV/ny5dWmTRslJiaqatWqbqnZZhiG4ZYrWcjFDE9XAABSwqlkT5cAAKpfwd/TJTg5ejrVlPuU97flOjEfPHiwli9frs2bN6ty5crXvH5KSor8/f21Zs0atW/f3i01k5gDAADANGbtY56TZSuXGYahIUOG6PPPP9fGjRuv25RLUlxcnCSpfPnyN1OmExpzAAAAFGqDBg3SggULtHz5cgUEBOjkyZOSpKCgIPn5+SkxMVELFixQp06dVLp0aX3//feKjIxUixYt1KBBA7fVwVIWAMgjLGUBYAVWW8py/Eza9Se5QcVSOUvLJcnmYuPzmJgY9enTRz///LN69+6tffv2KSUlRWFhYerSpYteeuklBQYGuqtkEnMAAAAUbtfLqcPCwrRp06Y8r4PGHAAAAKYxaVOWfIkvGAIAAAAsgMQcAAAApnGxnBsiMQcAAAAsgcQcAAAAJiIyd4XEHAAAALAAEnMAAACYhjXmrpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAEzDGnPXSMwBAAAACyAxBwAAgGlsrDJ3icQcAAAAsAAScwAAAJiHwNwlEnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAadjH3DUScwAAAMACSMwBAABgGvYxd43EHAAAALAAEnMAAACYh8DcJRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgGnYx9w1EnMAAADAAkjMAQAAYBr2MXeNxBwAAACwABJzAAAAmIY15q6RmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABfDhTwAAAJiGD3+6RmIOAAAAWACJOQAAAEzDFwy5RmIOAAAAWACJOQAAAEzDGnPXSMwBAAAACyAxBwAAgGkIzF0jMQcAAAAsgMQcAAAA5iEyd4nEHAAAALAAEnMAAACYhn3MXSMxBwAAACyAxBwAAACmYR9z10jMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDEHAAAAOYhMneJxBwAAACwABJzAAAAmIZ9zF0jMQcAAAAsgMQcAAAApmEfc9dIzAEAAAALsBmGYXi6CMBq0tLSFB0drbFjx8put3u6HACFEH8PAYUPjTlwFefPn1dQUJDOnTunwMBAT5cDoBDi7yGg8GEpCwAAAGABNOYAAACABdCYAwAAABZAYw5chd1u14QJE/jAFQCP4e8hoPDhw58AAACABZCYAwAAABZAYw4AAABYAI05AAAWExsbqxIlSni6DAAmozFHgWaz2a55TJw40dMlAijA+vTpc9W/exISEjxdGgALKuLpAoC8lJSU5PjzokWLNH78eMXHxzvG/P39HX82DEOZmZkqUoT/WwBwnw4dOigmJsZprGzZsh6qBoCVkZijQAsJCXEcQUFBstlsjseHDh1SQECAvvzySzVp0kR2u11btmxRnz591LlzZ6frDBs2TK1atXI8zsrKUnR0tCpXriw/Pz81bNhQn376qbkvDkC+YLfbnf4uCgkJ0fTp01W/fn0VL15cYWFhGjhwoJKTk11eY+/evWrdurUCAgIUGBioJk2aaNeuXY7zW7Zs0T333CM/Pz+FhYVp6NChSklJMePlAXAjGnMUemPGjNHUqVN18OBBNWjQIEfPiY6O1ty5czV79mzt379fkZGR6t27tzZt2pTH1QIoCLy8vDRjxgzt379fc+bM0YYNGzRq1CiX83v16qUKFSpo586d2r17t8aMGaOiRYtKkhITE9WhQwd169ZN33//vRYtWqQtW7Zo8ODBZr0cAG7C7+xR6E2aNEn33XdfjuenpaVpypQpWrdunZo3by5JqlKlirZs2aL33ntPLVu2zKtSAeRDK1eudFo217FjRy1ZssTxuFKlSpo8ebIGDBigd99996rXOH78uEaOHKlatWpJkqpXr+44Fx0drV69emnYsGGOczNmzFDLli01a9Ys+fr65sGrApAXaMxR6DVt2jRX8xMSEnThwoVszXx6eroaN27sztIAFACtW7fWrFmzHI+LFy+udevWKTo6WocOHdL58+d16dIlpaam6sKFCypWrFi2awwfPlxPP/205s2bp7Zt2+rRRx9V1apVJf29zOX777/X/PnzHfMNw1BWVpaOHDmi2rVr5/2LBOAWNOYo9IoXL+702MvLS1d+IW5GRobjz5fXga5atUq33HKL0zy+OhvAlYoXL65q1ao5Hh89elQPPPCAnnvuOb3yyisqVaqUtmzZon79+ik9Pf2qjfnEiRPVs2dPrVq1Sl9++aUmTJighQsXqkuXLkpOTtazzz6roUOHZntexYoV8/S1AXAvGnPgCmXLltW+ffucxuLi4hzrOevUqSO73a7jx4+zbAVAru3evVtZWVl644035OX190e9Fi9efN3n1ahRQzVq1FBkZKQef/xxxcTEqEuXLrr11lt14MABp+YfQP7Ehz+BK9x7773atWuX5s6dq59++kkTJkxwatQDAgI0YsQIRUZGas6cOUpMTNSePXs0c+ZMzZkzx4OVA8gPqlWrpoyMDM2cOVOHDx/WvHnzNHv2bJfzL168qMGDB2vjxo06duyYvv32W+3cudOxRGX06NHaunWrBg8erLi4OP30009avnw5H/4E8iEac+AK7du317hx4zRq1Cjddttt+uuvv/Tkk086zXn55Zc1btw4RUdHq3bt2urQoYNWrVqlypUre6hqAPlFw4YN9eabb+rVV19VvXr1NH/+fEVHR7uc7+3trdOnT+vJJ59UjRo11L17d3Xs2FFRUVGSpAYNGmjTpk368ccfdc8996hx48YaP368QkNDzXpJANzEZly5mBYAAACA6UjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozAEUWn369FHnzp0dj1u1aqVhw4aZXsfGjRtls9l09uxZl3NsNpuWLVuW42tOnDhRjRo1uqm6jh49KpvNpri4uJu6DgAgZ2jMAVhKnz59ZLPZZLPZ5OPjo2rVqmnSpEm6dOlSnt976dKlevnll3M0NyfNNAAAuVHE0wUAwJU6dOigmJgYpaWlafXq1Ro0aJCKFi2qsWPHZpubnp4uHx8ft9y3VKlSbrkOAAA3gsQcgOXY7XaFhIQoPDxczz33nNq2basvvvhC0v8tP3nllVcUGhqqmjVrSpJ+/vlnde/eXSVKlFCpUqX08MMP6+jRo45rZmZmavjw4SpRooRKly6tUaNGyTAMp/teuZQlLS1No0ePVlhYmOx2u6pVq6YPP/xQR48eVevWrSVJJUuWlM1mU58+fSRJWVlZio6OVuXKleXn56eGDRvq008/dbrP6tWrVaNGDfn5+al169ZOdebU6NGjVaNGDRUrVkxVqlTRuHHjlJGRkW3ee++9p7CwMBUrVkzdu3fXuXPnnM5/8MEHql27tnx9fVWrVi29++67ua4FAOAeNOYALM/Pz0/p6emOx+vXr1d8fLzWrl2rlStXKiMjQ+3bt1dAQIC++eYbffvtt/L391eHDh0cz3vjjTcUGxurjz76SFu2bNGZM2f0+eefX/O+Tz75pD755BPNmDFDBw8e1HvvvSd/f3+FhYXps88+kyTFx8crKSlJ06dPlyRFR0dr7ty5mj17tvbv36/IyEj17t1bmzZtkvT3DxBdu3bVgw8+qLi4OD399NMaM2ZMrt+TgIAAxcbG6sCBA5o+fbref/99TZs2zWlOQkKCFi9erBUrVmjNmjX67rvvNHDgQMf5+fPna/z48XrllVd08OBBTZkyRePGjdOcOXNyXQ8AwA0MALCQiIgI4+GHHzYMwzCysrKMtWvXGna73RgxYoTjfLly5Yy0tDTHc+bNm2fUrFnTyMrKcoylpaUZfn5+xldffWUYhmGUL1/eeO211xznMzIyjAoVKjjuZRiG0bJlS+P55583DMMw4uPjDUnG2rVrr1rn119/bUgy/vzzT8dYamqqUaxYMWPr1q1Oc/v162c8/vjjhmEYxtixY406deo4nR89enS2a11JkvH555+7PP/6668bTZo0cTyeMGGC4e3tbfzyyy+OsS+//NLw8vIykpKSDMMwjKpVqxoLFixwus7LL79sNG/e3DAMwzhy5Ighyfjuu+9c3hcA4D6sMQdgOStXrpS/v78yMjKUlZWlnj17auLEiY7z9evXd1pXvnfvXiUkJCggIMDpOqmpqUpMTNS5c+eUlJSkZs2aOc4VKVJETZs2zbac5bK4uDh5e3urZcuWOa47ISFBFy5c0H333ec0np6ersaNG0uSDh486FSHJDVv3jzH97hs0aJFmjFjhhITE5WcnKxLly4pMDDQaU7FihV1yy23ON0nKytL8fHxCggIUGJiovr166f+/fs75ly6dElBQUG5rgcAcPNozAFYTuvWrTVr1iz5+PgoNDRURYo4/1VVvHhxp8fJyclq0qSJ5s+fn+1aZcuWvaEa/Pz8cv2c5ORkSdKqVaucGmLp73Xz7rJt2zb16tVLUVFRat++vYKCgrRw4UK98cYbua71/fffz/aDgre3t9tqBQDkHI05AMspXry4qlWrluP5t956qxYtWqTg4OBsqfFl5cuX144dO9SiRQtJfyfDu3fv1q233nrV+fXr11dWVpY2bdqktm3bZjt/ObHPzMx0jNWpU0d2u13Hjx93mbTXrl3b8UHWy7Zv3379F/kPW7duVXh4uF588UXH2LFjx7LNO378uE6cOKHQ0FDHfby8vFSzZk2VK1dOoaGhOnz4sHr16pWr+wMA8gYf/gSQ7/Xq1UtlypTRww8/rG+++UZHjhzRxo0bNXToUP3yyy+SpOeff15Tp07VsmXLdOjQIQ0cOPCae5BXqlRJEREReuqpp7Rs2TLHNRcvXixJCg8Pl81m08qVK/X7778rOTlZAQEBGjFihCIjIzVnzhwlJiZqz549mjlzpuMDlQMGDNBPP/2kkSNHKj4+XgsWLFBsbGyuXm/16tV1/PhxLVy4UImJiZoxY8ZVP8jq6+uriIgI7d27V998842GDh2q7t27KyQkRJIUFRWl6OhozZgxQz/++KN++OEHxcTE6M0338xVPQAA96AxB5DvFStWTJs3b1bFihXVtWtX1a5dW/369VNqaqojQX/hhRf0xBNPKCIiQs2bN1dAQIC6dOlyzevOmjVLjzzyiAYOHKhatWqpf//+SklJkSTdcsstioqK0pgxY1SuXDkNHjxYkvTyyy9r3Lhxio6OVu3atdWhQwetWrVKlStXlvT3uu/PPvtMy5YtU8OGDTV79mxNmTIlV6/3oYceUmRkpAYPHqxGjRpp69atGjduXLZ51apVU9euXdWpUye1a9dODRo0cNoO8emnn9YHH3ygmJgY1a9fXy1btlRsbKyjVgCAuWyGq08+AQAAADANiTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOZAIVSpUiX16dPH02UUODt37tSdd96p4sWLy2azKS4uzq3X37hxo2w2mzZu3OjW6+Zn/LsMoCChMQcKkMTERD377LOqUqWKfH19FRgYqLvuukvTp0/XxYsXPV3eNe3fv1+PPvqoqlSpomLFiqlMmTJq0aKFVqxYkavrxMXFqXfv3goLC5PdblepUqXUtm1bxcTEKDMzM4+qlzIyMvToo4/qzJkzmjZtmubNm6fw8PA8u5/ZWrVqJZvNpurVq1/1/Nq1a2Wz2WSz2fTpp5/m+voHDhzQxIkTdfTo0ZusFADyryKeLgCAe6xatUqPPvqo7Ha7nnzySdWrV0/p6enasmWLRo4cqf379+s///mPp8t06dixY/rrr78UERGh0NBQXbhwQZ999pkeeughvffee3rmmWeue40PPvhAAwYMULly5fTEE0+oevXq+uuvv7R+/Xr169dPSUlJ+te//pUn9ScmJurYsWN6//339fTTT+fJPVq0aKGLFy/Kx8cnT65/Pb6+vkpISND//vc/3X777U7n5s+fL19fX6Wmpt7QtQ8cOKCoqCi1atVKlSpVyvHz4uPj5eVFxgSgYKAxBwqAI0eOqEePHgoPD9eGDRtUvnx5x7lBgwYpISFBq1at8mCF19epUyd16tTJaWzw4MFq0qSJ3nzzzes25tu3b9eAAQPUvHlzrV69WgEBAY5zw4YN065du7Rv3748qV2SfvvtN0lSiRIl8uweXl5e8vX1zbPrX0/VqlV16dIlffLJJ06NeWpqqj7//HPdf//9+uyzz/K8DsMwlJqaKj8/P9nt9jy/HwCYhZgBKABee+01JScn68MPP3Rqyi+rVq2ann/+eZfPP3PmjEaMGKH69evL399fgYGB6tixo/bu3Ztt7syZM1W3bl0VK1ZMJUuWVNOmTbVgwQLH+b/++kvDhg1TpUqVZLfbFRwcrPvuu0979uzJ9evy9vZWWFiYzp49e925UVFRstlsmj9/vlNTflnTpk2d1iKnpKTohRdecCx5qVmzpv7973/LMAyn59lsNg0ePFjLli1TvXr1ZLfbVbduXa1Zs8Yxp0+fPmrZsqUk6dFHH5XNZlOrVq0k/b0E5PKf/6lPnz7ZkuGFCxeqSZMmCggIUGBgoOrXr6/p06c7zrtaY75kyRI1adJEfn5+KlOmjHr37q1ff/012/38/f3166+/qnPnzvL391fZsmU1YsSIXC3xefzxx7Vo0SJlZWU5xlasWKELFy6oe/fu2eYfO3ZMAwcOVM2aNeXn56fSpUvr0UcfdVqyEhsbq0cffVSS1Lp1a8eSmMuvs1KlSnrggQf01VdfqWnTpvLz89N7773nOHf5n6thGGrdurXKli3r+EFJktLT01W/fn1VrVpVKSkpOX6tAGA2GnOgAFixYoWqVKmiO++884aef/jwYS1btkwPPPCA3nzzTY0cOVI//PCDWrZsqRMnTjjmvf/++xo6dKjq1Kmjt956S1FRUWrUqJF27NjhmDNgwADNmjVL3bp107vvvqsRI0bIz89PBw8ezFEtKSkp+uOPP5SYmKhp06bpyy+/VJs2ba75nAsXLmj9+vVq0aKFKlaseN17GIahhx56SNOmTVOHDh305ptvqmbNmho5cqSGDx+ebf6WLVs0cOBA9ejRQ6+99ppSU1PVrVs3nT59WpL07LPPOpbIDB06VPPmzdOLL76Yo9d72dq1a/X444+rZMmSevXVVzV16lS1atVK33777TWfFxsbq+7du8vb21vR0dHq37+/li5dqrvvvjvbDzSZmZlq3769SpcurX//+99q2bKl3njjjVwtcerZs6eSkpKcfjhYsGCB2rRpo+Dg4Gzzd+7cqa1bt6pHjx6aMWOGBgwYoPXr16tVq1a6cOGCpL+X6AwdOlSS9K9//Uvz5s3TvHnzVLt2bcd14uPj9fjjj+u+++7T9OnT1ahRo2z3stls+uijj5SamqoBAwY4xidMmKD9+/crJiZGxYsXz/FrBQDTGQDytXPnzhmSjIcffjjHzwkPDzciIiIcj1NTU43MzEynOUeOHDHsdrsxadIkx9jDDz9s1K1b95rXDgoKMgYNGpTjWq707LPPGpIMSYaXl5fxyCOPGGfOnLnmc/bu3WtIMp5//vkc3WPZsmWGJGPy5MlO44888ohhs9mMhIQEx5gkw8fHx2ns8v1mzpzpGPv6668NScaSJUucrtmyZUujZcuW2WqIiIgwwsPDHY+ff/55IzAw0Lh06ZLLui/f4+uvvzYMwzDS09ON4OBgo169esbFixcd81auXGlIMsaPH+90P0lO/zwNwzAaN25sNGnSxOU9//k6Lv+zb9q0qdGvXz/DMAzjzz//NHx8fIw5c+Zc9T24cOFCtmtt27bNkGTMnTvXMbZkyRKn1/ZP4eHhhiRjzZo1Vz33z3+XDcMw3nvvPUOS8fHHHxvbt283vL29jWHDhl33NQKAp5GYA/nc+fPnJemqyzdyym63Oz5Al5mZqdOnT8vf3181a9Z0WoJSokQJ/fLLL9q5c6fLa5UoUUI7duxwStpzY9iwYVq7dq3mzJmjjh07KjMzU+np6dd8Tm7fg9WrV8vb29uR0l72wgsvyDAMffnll07jbdu2VdWqVR2PGzRooMDAQB0+fDhH98uJEiVKKCUlRWvXrs3xc3bt2qXffvtNAwcOdFp7fv/996tWrVpX/VzBP5NkSbrnnnty/Tp69uyppUuXKj09XZ9++qm8vb3VpUuXq8718/Nz/DkjI0OnT59WtWrVVKJEiVwtb6pcubLat2+fo7nPPPOM2rdvryFDhuiJJ55Q1apVNWXKlBzfCwA8hcYcyOcCAwMl/b22+0ZlZWVp2rRpql69uux2u8qUKaOyZcvq+++/17lz5xzzRo8eLX9/f91+++2qXr26Bg0alG2pxWuvvaZ9+/YpLCxMt99+uyZOnJirxq9WrVpq27atnnzySa1cuVLJycl68MEHs639/qfcvgfHjh1TaGhotkb+8tKJY8eOOY1fbXlMyZIl9eeff+bofjkxcOBA1ahRQx07dlSFChX01FNPOa1jv5rLddasWTPbuVq1amV7Hb6+vipbtqzT2I28jh49eujcuXP68ssvNX/+fD3wwAMufyi6ePGixo8f71jLf/nfrbNnzzr9u3U9lStXzlWNH374oS5cuKCffvpJsbGxTj8gAIBV0ZgD+VxgYKBCQ0NvaseRKVOmaPjw4WrRooU+/vhjffXVV1q7dq3q1q3r9CG/2rVrKz4+XgsXLtTdd9+tzz77THfffbcmTJjgmNO9e3cdPnxYM2fOVGhoqF5//XXVrVs3WwqdU4888oh27typH3/80eWcatWqqUiRIvrhhx9u6B7X4+3tfdXxa/2wcJnNZrvq+JUfuAwODlZcXJy++OILPfTQQ/r666/VsWNHRURE5L5gF1y9jtwqX768WrVqpTfeeEObN29Wz549Xc4dMmSIXnnlFXXv3l2LFy/Wf//7X61du1alS5d2+nfrenLbWG/cuFFpaWmSlGf/XgCAu9GYAwXAAw88oMTERG3btu2Gnv/pp5+qdevW+vDDD9WjRw+1a9dObdu2vepuKMWLF9djjz2mmJgYHT9+XPfff79eeeUVp/2ry5cvr4EDB2rZsmU6cuSISpcurVdeeeWGarv8xUjXSleLFSume++9V5s3b9bPP/983WuGh4frxIkT2RL2Q4cOOc67S8mSJa/6Pl6ZZkuSj4+PHnzwQb377ruOL4uaO3euEhISrnrty3XGx8dnOxcfH5+nX3DUs2dPffPNNwoMDMy2zeU/ffrpp4qIiNAbb7yhRx55RPfdd99VP5jq6geYG5GUlKQhQ4aoXbt2euCBBzRixIirvt8AYDU05kABMGrUKBUvXlxPP/20Tp06le18YmKi07Z7V/L29s6W/i5ZsiTblnuXdyG5zMfHR3Xq1JFhGMrIyFBmZma2Bjo4OFihoaGO9NKVf25vd1lGRobmzp0rPz8/1alT55rPnzBhggzD0BNPPKHk5ORs53fv3q05c+ZI+nvP9MzMTL399ttOc6ZNmyabzaaOHTte8165UbVqVR06dEi///67Y2zv3r3ZlgBd+d56eXmpQYMGkuTyvWvatKmCg4M1e/ZspzlffvmlDh48qPvvv99dLyObRx55RBMmTNC77757zS88utq/WzNnzsz2G4PLu6XkZGvM6+nfv7+ysrL04Ycf6j//+Y+KFCmifv365eg3HADgSXzBEFAAVK1aVQsWLNBjjz2m2rVrO33z59atW7VkyRKnPbyv9MADD2jSpEnq27ev7rzzTv3www+aP3++qlSp4jSvXbt2CgkJ0V133aVy5crp4MGDevvtt3X//fcrICBAZ8+eVYUKFfTII4+oYcOG8vf317p167Rz50698cYb13wNzz77rM6fP68WLVrolltu0cmTJzV//nwdOnRIb7zxhvz9/a/5/DvvvFPvvPOOBg4cqFq1ajl98+fGjRv1xRdfaPLkyZKkBx98UK1bt9aLL76oo0ePqmHDhvrvf/+r5cuXa9iwYU4f9LxZTz31lN588021b99e/fr102+//abZs2erbt26jg+tStLTTz+tM2fO6N5771WFChV07NgxzZw5U40aNXLaNvCfihYtqldffVV9+/ZVy5Yt9fjjj+vUqVOaPn26KlWqpMjISLe9jisFBQVp4sSJ1533wAMPaN68eQoKClKdOnW0bds2rVu3TqVLl3aa16hRI3l7e+vVV1/VuXPnZLfbde+99151C8ZriYmJ0apVqxQbG6sKFSpI+vsHgd69e2vWrFkaOHBgrq4HAKby3IYwANztxx9/NPr3729UqlTJ8PHxMQICAoy77rrLmDlzppGamuqYd7XtEl944QWjfPnyhp+fn3HXXXcZ27Zty7bV33vvvWe0aNHCKF26tGG3242qVasaI0eONM6dO2cYhmGkpaUZI0eONBo2bGgEBAQYxYsXNxo2bGi8++671639k08+Mdq2bWuUK1fOKFKkiFGyZEmjbdu2xvLly3P1Huzevdvo2bOnERoaahQtWtQoWbKk0aZNG2POnDlOW0L+9ddfRmRkpGNe9erVjddff93Iyspyup6kq27/eOV76Gq7RMMwjI8//tioUqWK4ePjYzRq1Mj46quvsm2X+Omnnxrt2rUzgoODDR8fH6NixYrGs88+ayQlJWW7x5VbCi5atMho3LixYbfbjVKlShm9evUyfvnlF6c5ERERRvHixbPVNmHCBCMn/yn453aJrlztPfjzzz+Nvn37GmXKlDH8/f2N9u3bG4cOHbrqNofvv/++UaVKFcPb29vpdYaHhxv333//Ve/5z+v8/PPPRlBQkPHggw9mm9elSxejePHixuHDh6/7WgHAU2yGwe/2AAAAAE9jjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAAAo1KKjo3XbbbcpICBAwcHB6ty5c7ZvVU5NTdWgQYNUunRp+fv7q1u3btm+1O/yN2IXK1ZMwcHBGjlypC5dupTjOmjMAQAAUKht2rRJgwYN0vbt27V27VplZGSoXbt2SklJccyJjIzUihUrtGTJEm3atEknTpxQ165dHeczMzN1//33O77cb86cOYqNjdX48eNzXEeB3Mfcr/FgT5cAAPpz59ueLgEA5Gux73k3q0+7+N2N/x38+++/Kzg4WJs2bVKLFi107tw5lS1bVgsWLNAjjzwiSTp06JBq166tbdu26Y477tCXX36pBx54QCdOnFC5cuUkSbNnz9bo0aP1+++/y8fH57r3JTEHAAAA/uHcuXOSpFKlSkmSdu/erYyMDLVt29Yxp1atWqpYsaK2bdsmSdq2bZvq16/vaMolqX379jp//rz279+fo/ta7GcoAAAAFGg2c3LhtLQ0paWlOY3Z7XbZ7fZrPi8rK0vDhg3TXXfdpXr16kmSTp48KR8fH5UoUcJpbrly5XTy5EnHnH825ZfPXz6XEyTmAAAAKHCio6MVFBTkdERHR1/3eYMGDdK+ffu0cOFCE6p0RmIOAAAA89hsptxm7NixGj58uNPY9dLywYMHa+XKldq8ebMqVKjgGA8JCVF6errOnj3rlJqfOnVKISEhjjn/+9//nK53edeWy3Ouh8QcAAAABY7dbldgYKDT4aoxNwxDgwcP1ueff64NGzaocuXKTuebNGmiokWLav369Y6x+Ph4HT9+XM2bN5ckNW/eXD/88IN+++03x5y1a9cqMDBQderUyVHNJOYAAAAwj0lrzHNj0KBBWrBggZYvX66AgADHmvCgoCD5+fkpKChI/fr10/Dhw1WqVCkFBgZqyJAhat68ue644w5JUrt27VSnTh098cQTeu2113Ty5Em99NJLGjRo0HWT+stozAEAAFCozZo1S5LUqlUrp/GYmBj16dNHkjRt2jR5eXmpW7duSktLU/v27fXuu+865np7e2vlypV67rnn1Lx5cxUvXlwRERGaNGlSjutgH3MAyCPsYw7ACiy3j/ltw68/yQ0u7nzTlPu4k/V+lwAAAAAUQhb7GQoAAAAFmgXXmFsF7wwAAABgASTmAAAAMI9J+5jnRyTmAAAAgAWQmAMAAMA8rDF3iXcGAAAAsAAScwAAAJiHNeYukZgDAAAAFkBjDgAAAFgAS1kAAABgHj786RLvDAAAAGABJOYAAAAwDx/+dInEHAAAALAAEnMAAACYhzXmLvHOAAAAABZAYg4AAADzsMbcJRJzAAAAwAJIzAEAAGAe1pi7xDsDAAAAWACJOQAAAMxDYu4S7wwAAABgASTmAAAAMI8Xu7K4QmIOAAAAWACJOQAAAMzDGnOXeGcAAAAACyAxBwAAgHn45k+XSMwBAAAACyAxBwAAgHlYY+4S7wwAAABgASTmAAAAMA9rzF0iMQcAAAAsgMQcAAAA5mGNuUu8MwAAAIAFkJgDAADAPKwxd4nEHAAAALAAEnMAAACYhzXmLvHOAAAAABZAYg4AAADzsMbcJRJzAAAAwAJIzAEAAGAe1pi7xDsDAAAAWACJOQAAAMzDGnOXSMwBAAAACyAxBwAAgHlYY+4S7wwAAABgASTmAAAAMA+JuUu8MwAAAIAFkJgDAADAPOzK4hKJOQAAAGABJOYAAAAwD2vMXeKdAQAAACyAxBwAAADmYY25SyTmAAAAgAXQmAMAAMA8Ni9zjlzYvHmzHnzwQYWGhspms2nZsmXOJdtsVz1ef/11x5xKlSplOz916tRc1UFjDgAAgEItJSVFDRs21DvvvHPV80lJSU7HRx99JJvNpm7dujnNmzRpktO8IUOG5KoO1pgDAADAPBZcY96xY0d17NjR5fmQkBCnx8uXL1fr1q1VpUoVp/GAgIBsc3ODxBwAAAAFTlpams6fP+90pKWl3fR1T506pVWrVqlfv37Zzk2dOlWlS5dW48aN9frrr+vSpUu5ujaNOQAAAEzjar22u4/o6GgFBQU5HdHR0Tdd/5w5cxQQEKCuXbs6jQ8dOlQLFy7U119/rWeffVZTpkzRqFGjcnVtlrIAAACgwBk7dqyGDx/uNGa322/6uh999JF69eolX19fp/F/3qtBgwby8fHRs88+q+jo6Bzfl8YcAAAAprGZtMbcbre7pRH/p2+++Ubx8fFatGjRdec2a9ZMly5d0tGjR1WzZs0cXZ+lLAAAAEAOfPjhh2rSpIkaNmx43blxcXHy8vJScHBwjq9PYg4AAADzWG9TFiUnJyshIcHx+MiRI4qLi1OpUqVUsWJFSdL58+e1ZMkSvfHGG9mev23bNu3YsUOtW7dWQECAtm3bpsjISPXu3VslS5bMcR005gAAACjUdu3apdatWzseX14vHhERodjYWEnSwoULZRiGHn/88WzPt9vtWrhwoSZOnKi0tDRVrlxZkZGR2da4X4/NMAzjxl+GNfk1HuzpEgBAf+5829MlAIB8LRbD+nePNeU+yYv7mHIfd2KNOQAAAGABFvsZCgAAAAWZWbuy5Eck5gAAAIAFkJgDAADANCTmrpGYAwAAABZAYg4AAADTkJi7RmIOAAAAWACJOQAAAMxDYO4SiTkAAABgASTmAAAAMA1rzF0jMQcAAAAsgMQcAAAApiExd43EHAAAALAAEnMAAACYhsTcNRJzAAAAwAJIzAEAAGAaEnPXSMwBAAAACyAxBwAAgHkIzF0iMQcAAAAsgMQcAAAApmGNuWsk5gAAAIAFkJgDAADANCTmrpGYAwAAABZAYg4AAADTkJi7RmIOAAAAWACJOQAAAMxDYO4SiTkAAABgASTmAAAAMA1rzF0jMQcAAAAsgMQcAAAApiExd43EHAAAALAAEnMAAACYhsTcNRJzAAAAwAJIzAEAAGAaEnPXSMwBAAAACyAxBwAAgHkIzF0iMQcAAAAsgMQcAAAApmGNuWsk5gAAAIAFkJgDAADANCTmrpGYAwAAABZAYg4AAADTkJi7ZonE/JtvvlHv3r3VvHlz/frrr5KkefPmacuWLR6uDAAAADCHxxvzzz77TO3bt5efn5++++47paWlSZLOnTunKVOmeLg6AAAAuJXNpCMf8nhjPnnyZM2ePVvvv/++ihYt6hi/6667tGfPHg9WBgAAAJjH42vM4+Pj1aJFi2zjQUFBOnv2rPkFAQAAIM+wxtw1jyfmISEhSkhIyDa+ZcsWValSxQMVAQAAAObzeGPev39/Pf/889qxY4dsNptOnDih+fPna8SIEXruuec8XR4AAADcyGazmXLkRx5fyjJmzBhlZWWpTZs2unDhglq0aCG73a4RI0ZoyJAhni4PAAAAMIXHG3ObzaYXX3xRI0eOVEJCgpKTk1WnTh35+/t7ujQAAAC4WX5Ns83g8cb8Mh8fH9WpU8fTZaCAGvFUO3W+t6FqVCqni2kZ2rH3sF6cvlw/HfvNMcfuU0RTh3fVo+2byO5TROu2HdTzUxbptzN/Oea8MeoR3dGwiupWK69DR07pjh5TPfFyABQCCxfM15yYD/XHH7+rRs1aGvOvcarfoIGnywKQhzzemLdu3fqaPzlt2LDBxGpQUN1zazXNXrRZu/cfU5Ei3ooa/KBWzhqsxl0n60JquiTptRHd1PHuuuo16kOdT76oaWO6a+EbT+vevtOcrjV3+XbdVj9c9arf4omXAqAQWPPlav37tWi9NCFK9es31Px5c/Tcs/20fOUalS5d2tPlAcgjHm/MGzVq5PQ4IyNDcXFx2rdvnyIiIjxTFAqchwe/6/T4mQkf6+cNU9W4Tpi+3ZOoQH9f9encXH3+FatNO390zNn7+TjdXr+S/vfDUUnSC699KkkqU7ITjTmAPDNvToy6PtJdnbt0kyS9NCFKmzdv1LKln6lf/2c8XB1wc1jK4prHd2WZNm2a0/H2229ry5YtGjZsmNMXDgHuFOjvK0n689wFSVLj2hXlU7SINmyPd8z58egpHU86o2YNKnukRgCFU0Z6ug4e2K87mt/pGPPy8tIdd9yp7/d+58HKgIJr8+bNevDBBxUaGiqbzaZly5Y5ne/Tp0+2XV86dOjgNOfMmTPq1auXAgMDVaJECfXr10/Jycm5qsPjjbkrvXv31kcffeTpMlAA2Ww2vT7iEW39LlEHEpMkSSGlA5WWnqFzyRed5v52+rzKlQ70RJkACqk/z/6pzMzMbEtWSpcurT/++MNDVQFuZDPpyIWUlBQ1bNhQ77zzjss5HTp0UFJSkuP45JNPnM736tVL+/fv19q1a7Vy5Upt3rxZzzyTu99weXwpiyvbtm2Tr6/vdeelpaUpLS3NaczIypTNyzuvSkM+99bY7qpbrbzaXLF2HAAAFE4dO3ZUx44drznHbrcrJCTkqucOHjyoNWvWaOfOnWratKkkaebMmerUqZP+/e9/KzQ0NEd1eLwx79q1q9NjwzCUlJSkXbt2ady4cdd9fnR0tKKiopzGvMvdpqLlb3drnSgYpo1+VJ3uqae2/d7Sr7+ddYyfPH1edp+iCvL3c0rNg0sH6tTp8x6oFEBhVbJESXl7e+v06dNO46dPn1aZMmU8VBXgPmatMb9aeGu322W322/oehs3blRwcLBKliype++9V5MnT3b8Zmvbtm0qUaKEoymXpLZt28rLy0s7duxQly5dcnQPjy9lCQoKcjpKlSqlVq1aafXq1ZowYcJ1nz927FidO3fO6ShSrokJlSO/mTb6UT10b0N1eHaGjp1w/g/edwePKz3jklo3q+kYqx4erIrlS2nH90fMLhVAIVbUx0e169TVju3bHGNZWVnasWObGjRs7MHKgPwlOjo6W58ZHR19Q9fq0KGD5s6dq/Xr1+vVV1/Vpk2b1LFjR2VmZkqSTp48qeDgYKfnFClSRKVKldLJkydzfB+PJuaZmZnq27ev6tevr5IlS97QNa72kw/LWHClt8Z212Mdm+rRyP8oOSVV5UoHSJLOJacqNS1D55NTFbtsm159oavOnEvRXympenP0o9q+97BjRxZJqhJWRv5+dpUrEyg/e1E1qPH3ziwHD59UxqVMT7w0AAXQExF9Ne5fo1W3bj3Vq99AH8+bo4sXL6pzl67XfzJgcWYl5mPHjtXw4cOdxm40Le/Ro4fjz/Xr11eDBg1UtWpVbdy4UW3atLmpOv/Jo425t7e32rVrp4MHD95wYw7kxLPdW0iS1n4wzGm8//h5+njFDknSqH9/pqwsQ5/8++m/v2Bo60E9H73Iaf6s8b3Uoml1x+Mdi8ZKkmp2Gq/jSWfy8BUAKEw6dOykP8+c0btvz9Aff/yumrVq6933PlBplrIAOXYzy1aup0qVKipTpowSEhLUpk0bhYSE6LfffnOac+nSJZ05c8bluvSr8fga83r16unw4cOqXJkt6ZB3/BoPvu6ctPRLipy6WJFTF7uc077/dHeWBQAuPd6rtx7v1dvTZQBuVxC2Mf/ll190+vRplS9fXpLUvHlznT17Vrt371aTJn8vqd6wYYOysrLUrFmzHF/X42vMJ0+erBEjRmjlypVKSkrS+fPnnQ4AAAAgLyUnJysuLk5xcXGSpCNHjiguLk7Hjx9XcnKyRo4cqe3bt+vo0aNav369Hn74YVWrVk3t27eXJNWuXVsdOnRQ//799b///U/ffvutBg8erB49euR4RxZJshmGYeTFC7yeSZMm6YUXXlBAQMD/FfOPH6EMw5DNZnMsqs+NnKSjAJDX/tz5tqdLAAD5enx9hLPqI9eYcp+fXu9w/Un/38aNG9W6dets4xEREZo1a5Y6d+6s7777TmfPnlVoaKjatWunl19+WeXKlXPMPXPmjAYPHqwVK1bIy8tL3bp104wZM+Tv75/jOjzWmHt7eyspKUkHDx685ryWLVvm+to05gCsgMYcgBXQmOcfHvtHdfnngRtpvAEAAJA/FYQ15nnFo2vMzdouBwAAALA6j/5yo0aNGtdtzs+cYQs6AACAgoJg1jWPNuZRUVEKCgryZAkAAACAJXi0Me/Ro0e2ry8FAABAwUVg7prH1pjzawwAAADg/3h8VxYAAAAUHl5ehLOueKwxz8rK8tStAQAAAMux2JbzAAAAKMhYzeyaR/cxBwAAAPA3EnMAAACYhg1AXCMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANOQmLtGYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwDWvMXSMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATMMac9dIzAEAAAALIDEHAACAaQjMXSMxBwAAACyAxBwAAACmYY25ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANOwxtw1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAWQmAMAAMA0rDF3jcQcAAAAsAAScwAAAJiGxNw1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABJOYAAAAwDYG5ayTmAAAAKNQ2b96sBx98UKGhobLZbFq2bJnjXEZGhkaPHq369eurePHiCg0N1ZNPPqkTJ044XaNSpUqy2WxOx9SpU3NVB405AAAATHNl85pXR26kpKSoYcOGeuedd7Kdu3Dhgvbs2aNx48Zpz549Wrp0qeLj4/XQQw9lmztp0iQlJSU5jiFDhuSqDpayAAAAoFDr2LGjOnbseNVzQUFBWrt2rdPY22+/rdtvv13Hjx9XxYoVHeMBAQEKCQm54TpIzAEAAGAam82cIy+dO3dONptNJUqUcBqfOnWqSpcurcaNG+v111/XpUuXcnVdEnMAAAAUOGlpaUpLS3Mas9vtstvtN3Xd1NRUjR49Wo8//rgCAwMd40OHDtWtt96qUqVKaevWrRo7dqySkpL05ptv5vjaJOYAAAAwjZfNZsoRHR2toKAgpyM6Ovqmas/IyFD37t1lGIZmzZrldG748OFq1aqVGjRooAEDBuiNN97QzJkzs/1wcC0k5gAAAChwxo4dq+HDhzuN3UxafrkpP3bsmDZs2OCUll9Ns2bNdOnSJR09elQ1a9bM0T1ozAEAAGAas/Yxd8eylcsuN+U//fSTvv76a5UuXfq6z4mLi5OXl5eCg4NzfB8acwAAABRqycnJSkhIcDw+cuSI4uLiVKpUKZUvX16PPPKI9uzZo5UrVyozM1MnT56UJJUqVUo+Pj7atm2bduzYodatWysgIEDbtm1TZGSkevfurZIlS+a4DhpzAAAAmCa3e4ybYdeuXWrdurXj8eUlMBEREZo4caK++OILSVKjRo2cnvf111+rVatWstvtWrhwoSZOnKi0tDRVrlxZkZGR2ZbSXA+NOQAAAAq1Vq1ayTAMl+evdU6Sbr31Vm3fvv2m66AxBwAAgGm8rBeYWwbbJQIAAAAWQGIOAAAA01hxjblVkJgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATGMTkbkrJOYAAACABZCYAwAAwDTsY+4aiTkAAABgASTmAAAAMA37mLtGYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwjReRuUsk5gAAAIAFkJgDAADANATmrpGYAwAAABZAYg4AAADTsI+5ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANOwj7lrJOYAAACABZCYAwAAwDTk5a7lqDH/4osvcnzBhx566IaLAQAAAAqrHDXmnTt3ztHFbDabMjMzb6YeAAAAFGDsY+5ajhrzrKysvK4DAAAAKNRu6sOfqamp7qoDAAAAKNRy3ZhnZmbq5Zdf1i233CJ/f38dPnxYkjRu3Dh9+OGHbi8QAAAABYeXzZwjP8p1Y/7KK68oNjZWr732mnx8fBzj9erV0wcffODW4gAAAIDCIteN+dy5c/Wf//xHvXr1kre3t2O8YcOGOnTokFuLAwAAQMFis9lMOfKjXDfmv/76q6pVq5ZtPCsrSxkZGW4pCgAAAChsct2Y16lTR99880228U8//VSNGzd2S1EAAAAomGw2c478KNff/Dl+/HhFRETo119/VVZWlpYuXar4+HjNnTtXK1euzIsaAQAAgAIv14n5ww8/rBUrVmjdunUqXry4xo8fr4MHD2rFihW677778qJGAAAAFBCsMXct14m5JN1zzz1au3atu2sBAAAACq0baswladeuXTp48KCkv9edN2nSxG1FAQAAoGDKr3uMmyHXjfkvv/yixx9/XN9++61KlCghSTp79qzuvPNOLVy4UBUqVHB3jQAAAECBl+s15k8//bQyMjJ08OBBnTlzRmfOnNHBgweVlZWlp59+Oi9qBAAAQAHBGnPXcp2Yb9q0SVu3blXNmjUdYzVr1tTMmTN1zz33uLU4AAAAoLDIdWMeFhZ21S8SyszMVGhoqFuKAgAAQMGUP7Nsc+R6Kcvrr7+uIUOGaNeuXY6xXbt26fnnn9e///1vtxYHAAAAFBY5SsxLlizptFYnJSVFzZo1U5Eifz/90qVLKlKkiJ566il17tw5TwoFAABA/ueVT9d/myFHjflbb72Vx2UAAAAAhVuOGvOIiIi8rgMAAACFAIG5azf8BUOSlJqaqvT0dKexwMDAmyoIAAAAKIxy3ZinpKRo9OjRWrx4sU6fPp3tfGZmplsKAwAAQMGTX/cYN0Oud2UZNWqUNmzYoFmzZslut+uDDz5QVFSUQkNDNXfu3LyoEQAAACjwcp2Yr1ixQnPnzlWrVq3Ut29f3XPPPapWrZrCw8M1f/589erVKy/qBAAAQAFAYO5arhPzM2fOqEqVKpL+Xk9+5swZSdLdd9+tzZs3u7c6AAAAoJDIdWNepUoVHTlyRJJUq1YtLV68WNLfSXqJEiXcWhwAAAAKFi+bzZQjP8p1Y963b1/t3btXkjRmzBi988478vX1VWRkpEaOHOn2AgEAAIDCINdrzCMjIx1/btu2rQ4dOqTdu3erWrVqatCggVuLAwAAQMGST8NsU+Q6Mb9SeHi4unbtSlMOAACAfGnz5s168MEHFRoaKpvNpmXLljmdNwxD48ePV/ny5eXn56e2bdvqp59+cppz5swZ9erVS4GBgSpRooT69eun5OTkXNWRo8R8xowZOb7g0KFDc1UAAAAACg8r7mOekpKihg0b6qmnnlLXrl2znX/ttdc0Y8YMzZkzR5UrV9a4cePUvn17HThwQL6+vpKkXr16KSkpSWvXrlVGRob69u2rZ555RgsWLMhxHTbDMIzrTapcuXLOLmaz6fDhwzm+eV7xazzY0yUAgP7c+banSwAA+d7U97y736DPD5pyn3e61L6h59lsNn3++efq3LmzpL/T8tDQUL3wwgsaMWKEJOncuXMqV66cYmNj1aNHDx08eFB16tTRzp071bRpU0nSmjVr1KlTJ/3yyy8KDQ3N0b1z9I/q8i4s+cWZ//EfQwCel3Ayd7/CBIC8UK+Cv6dLcHLT66hzKC0tTWlpaU5jdrtddrs9V9c5cuSITp48qbZt2zrGgoKC1KxZM23btk09evTQtm3bVKJECUdTLv39WUwvLy/t2LFDXbp0ydG9zHpvAAAAANNER0crKCjI6YiOjs71dU6ePClJKleunNN4uXLlHOdOnjyp4OBgp/NFihRRqVKlHHNywmK/3AAAAEBBZtYa87Fjx2r48OFOY7lNy81GYw4AAIAC50aWrVxNSEiIJOnUqVMqX768Y/zUqVNq1KiRY85vv/3m9LxLly7pzJkzjufnBEtZAAAAYBovmzmHu1SuXFkhISFav369Y+z8+fPasWOHmjdvLklq3ry5zp49q927dzvmbNiwQVlZWWrWrFmO70ViDgAAgEItOTlZCQkJjsdHjhxRXFycSpUqpYoVK2rYsGGaPHmyqlev7tguMTQ01LFzS+3atdWhQwf1799fs2fPVkZGhgYPHqwePXrkeEcW6QYT82+++Ua9e/dW8+bN9euvv0qS5s2bpy1bttzI5QAAAFBIWDEx37Vrlxo3bqzGjRtLkoYPH67GjRtr/PjxkqRRo0ZpyJAheuaZZ3TbbbcpOTlZa9ascexhLknz589XrVq11KZNG3Xq1El33323/vOf/+SqjhztY/5Pn332mZ544gn16tVL8+bN04EDB1SlShW9/fbbWr16tVavXp2rAvLCxQxPVwAAUuIptksE4HlW2y5x+BeHTLnPmw/VMuU+7pTrxHzy5MmaPXu23n//fRUtWtQxftddd2nPnj1uLQ4AAAAFi81mM+XIj3LdmMfHx6tFixbZxoOCgnT27Fl31AQAAAAUOrluzENCQpwWx1+2ZcsWValSxS1FAQAAoGCy4hpzq8h1Y96/f389//zz2rFjh2w2m06cOKH58+drxIgReu655/KiRgAAAKDAy/V2iWPGjFFWVpbatGmjCxcuqEWLFrLb7RoxYoSGDBmSFzUCAACggMiny79NketdWS5LT09XQkKCkpOTVadOHfn7W+cTv+zKAsAK2JUFgBVYbVeWUaviTbnPa/fXNOU+7nTDXzDk4+OjOnXquLMWAAAAFHBeROYu5boxb9269TW3oNmwYcNNFQQAAAAURrluzBs1auT0OCMjQ3Fxcdq3b58iIiLcVRcAAAAKoBv62vlCIteN+bRp0646PnHiRCUns54SAAAAuBFu+6Gld+/e+uijj9x1OQAAABRANps5R37ktsZ827Zt8vX1ddflAAAAgEIl10tZunbt6vTYMAwlJSVp165dGjdunNsKAwAAQMHDriyu5boxDwoKcnrs5eWlmjVratKkSWrXrp3bCgMAAAAKk1w15pmZmerbt6/q16+vkiVL5lVNAAAAKKAIzF3L1Rpzb29vtWvXTmfPns2jcgAAAIDCKdcf/qxXr54OHz6cF7UAAACggPOymXPkR7luzCdPnqwRI0Zo5cqVSkpK0vnz550OAAAAALmX4zXmkyZN0gsvvKBOnTpJkh566CHZ/rFIyDAM2Ww2ZWZmur9KAAAAFAjsyuJajhvzqKgoDRgwQF9//XVe1gMAAAAUSjluzA3DkCS1bNkyz4oBAABAwUZg7lqu1pjbeCcBAACAPJGrfcxr1Khx3eb8zJkzN1UQAAAACq78umOKGXLVmEdFRWX75k8AAAAANy9XjXmPHj0UHBycV7UAAACggLOJyNyVHK8xZ305AAAAkHdyvSsLAAAAcKNYY+5ajhvzrKysvKwDAAAAKNRytcYcAAAAuBkk5q7lah9zAAAAAHmDxBwAAACmYUMR10jMAQAAAAsgMQcAAIBpWGPuGok5AAAAYAEk5gAAADANS8xdIzEHAAAALIDEHAAAAKbxIjJ3icQcAAAAsAAScwAAAJiGXVlcIzEHAAAALIDEHAAAAKZhiblrJOYAAACABZCYAwAAwDReIjJ3hcQcAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANOwj7lrJOYAAACABZCYAwAAwDReLDJ3icQcAAAAsAAScwAAAJiGwNw1EnMAAADAAkjMAQAAYBrWmLtGYg4AAIBCrVKlSrLZbNmOQYMGSZJatWqV7dyAAQPcXgeJOQAAAExjxcB8586dyszMdDzet2+f7rvvPj366KOOsf79+2vSpEmOx8WKFXN7HTTmAAAAKNTKli3r9Hjq1KmqWrWqWrZs6RgrVqyYQkJC8rQOlrIAAADANF4mHWlpaTp//rzTkZaWdt360tPT9fHHH+upp56S7R/x/vz581WmTBnVq1dPY8eO1YULF27+zbgCjTkAAAAKnOjoaAUFBTkd0dHR133esmXLdPbsWfXp08cx1rNnT3388cf6+uuvNXbsWM2bN0+9e/d2e802wzAMt1/Vwy5meLoCAJASTyV7ugQAUL0K/p4uwcmcXT+bcp8e9YOzJeR2u112u/2az2vfvr18fHy0YsUKl3M2bNigNm3aKCEhQVWrVnVLvRJrzAEAAFAA5aQJv9KxY8e0bt06LV269JrzmjVrJkk05gAAAMi/LLgpi0NMTIyCg4N1//33X3NeXFycJKl8+fJuvT+NOQAAAAq9rKwsxcTEKCIiQkWK/F+LnJiYqAULFqhTp04qXbq0vv/+e0VGRqpFixZq0KCBW2ugMQcAAIBprPrNn+vWrdPx48f11FNPOY37+Pho3bp1euutt5SSkqKwsDB169ZNL730kttroDEHAABAodeuXTtdbU+UsLAwbdq0yZQaaMwBAABgGmvm5dbAPuYAAACABZCYAwAAwDQWXWJuCSTmAAAAgAXQmAMAAAAWwFIWAAAAmMbGWhaXSMwBAAAACyAxBwAAgGlIhV3jvQEAAAAsgMQcAAAApmGNuWsk5gAAAIAFkJgDAADANOTlrpGYAwAAABZAYg4AAADTsMbcNRJzAAAAwAJIzAEAAGAaUmHXeG8AAAAACyAxBwAAgGlYY+4aiTkAAABgASTmAAAAMA15uWsk5gAAAIAFkJgDAADANCwxd43EHAAAALAAEnMAAACYxotV5i6RmAMAAAAWQGIOAAAA07DG3DUScwAAAMACSMwBAABgGhtrzF0iMQcAAAAsgMQcAAAApmGNuWsk5gAAAIAFkJgDAADANOxj7hqJOQAAAGABJOYAAAAwDWvMXSMxBwAAACyAxBwAAACmITF3jcQcAAAAsAAScwAAAJiGb/50jcQcAAAAsAAScwAAAJjGi8DcJRJzAAAAwAJIzAEAAGAa1pi7ZqnEPD09XfHx8bp06ZKnSwEAAABMZYnG/MKFC+rXr5+KFSumunXr6vjx45KkIUOGaOrUqR6uDgAAAO5is5lz5EeWaMzHjh2rvXv3auPGjfL19XWMt23bVosWLfJgZQAAAIA5LLHGfNmyZVq0aJHuuOMO2f7xI07dunWVmJjowcoAAADgTqwxd80Sifnvv/+u4ODgbOMpKSlOjToAAABQUFmiMW/atKlWrVrleHy5Gf/ggw/UvHlzT5UFAAAAN/OymXPkR5ZYyjJlyhR17NhRBw4c0KVLlzR9+nQdOHBAW7du1aZNmzxdHgAAAJDnLJGY33333YqLi9OlS5dUv359/fe//1VwcLC2bdumJk2aeLo8AAAAuInNpP/lRzbDMAxPF+FuFzM8XQEASImnkj1dAgCoXgV/T5fg5Jsf/zTlPvfUKGnKfdzJEktZ9uzZo6JFi6p+/fqSpOXLlysmJkZ16tTRxIkT5ePj4+EKUVjs3rVTc2I+1MED+/T777/rzenv6N42bT1dFoACbM0XS/TVF5/q91NJkqSw8Cp69In+urXZXfrt5Ak91+vBqz7vhfFTdWfL+8wsFXAL9vVwzRKN+bPPPqsxY8aofv36Onz4sB577DF17dpVS5Ys0YULF/TWW295ukQUEhcvXlCNmjXVuUs3DR822NPlACgESpcpp979h6j8LRUlw9DX/12pV8cP1+vvLdAtYZX0wZKvnOavXblUyxfPU+Pb7/JQxQDyiiXWmP/4449q1KiRJGnJkiVq2bKlFixYoNjYWH322WeeLQ6Fyt33tNTgoZG6ty0pFABz3HZnCzVpdrdCK1RUaFi4evUbJF+/YvrxwA/y9vZWyVJlnI7/fbtRd7a8T35+xTxdOnBDbCYduTFx4kTZbDano1atWo7zqampGjRokEqXLi1/f39169ZNp06duqHXfy2WaMwNw1BWVpYkad26derUqZMkKSwsTH/88YcnSwMAwDSZmZnasuErpaZeVM06DbKdT/zxoI4kxKtNp4c9UB1QsNWtW1dJSUmOY8uWLY5zkZGRWrFihZYsWaJNmzbpxIkT6tq1q9trsMRSlqZNm2ry5Mlq27atNm3apFmzZkmSjhw5onLlynm4OgAA8taxwz/pX0P6Kj09Xb5+fhoV9W+FVaqSbd76L5epQsXKqlW3oQeqBNzDy6KLzIsUKaKQkJBs4+fOndOHH36oBQsW6N5775UkxcTEqHbt2tq+fbvuuOMOt9VgicT8rbfe0p49ezR48GC9+OKLqlatmiTp008/1Z133nnN56alpen8+fNOR1pamhllAwDgFqFhlfTv/3yiqe/MUfuHHtHbr07Qz0cPO81JS0vVN+vXqE1H0nIgL/z0008KDQ1VlSpV1KtXLx0/flyStHv3bmVkZKht2//bDKJWrVqqWLGitm3b5tYaLJGYN2jQQD/88EO28ddff13e3t7XfG50dLSioqKcxv710gS9NH6iO0sEACDPFC1aVOVvCZMkVa1RWwnxB7Rq6ScaMPxFx5xtm9crPS1VLds94KkyAbcwKy9PS0vLFtba7XbZ7fZsc5s1a6bY2FjVrFlTSUlJioqK0j333KN9+/bp5MmT8vHxUYkSJZyeU65cOZ08edKtNVuiMXfF19f3unPGjh2r4cOHO41leWV/wwEAyC+MrCxlZKQ7jW34crmaNm+poBL5b29mwBOuFt5OmDBBEydOzDa3Y8eOjj83aNBAzZo1U3h4uBYvXiw/P7+8LtXBY415yZIlZcvhGqMzZ864PHe1n3z4giHcqAsXUhy/upKkX3/9RYcOHVRQUJDKlw/1YGUACqqPP5ipxrffpbLBIbp4IUXfbFij/Xt3a9zUtx1zkn79WQe+36MXp8zwYKWAm5gUmV8tvL1aWn41JUqUUI0aNZSQkKD77rtP6enpOnv2rFNqfurUqauuSb8ZHmvM2ZscVrR/3z71f+pJx+M3XouWJD34cBe9/MpUT5UFoAA79+efmjl1vP4884eKFfdXeJXqGjf1bTVs+n8fKNvw5XKVLhvsNAbg2lwtW8mJ5ORkJSYm6oknnlCTJk1UtGhRrV+/Xt26dZMkxcfH6/jx42revLk7S5bNMAzDrVe0ABJzAFaQeCrZ0yUAgOpV8Pd0CU52JJ4z5T7NqgbleO6IESP04IMPKjw8XCdOnNCECRMUFxenAwcOqGzZsnruuee0evVqxcbGKjAwUEOGDJEkbd261a01W26NeWpqqtLTndfVBQYGeqgaAAAAFHS//PKLHn/8cZ0+fVply5bV3Xffre3bt6ts2bKSpGnTpsnLy0vdunVTWlqa2rdvr3fffdftdVgiMU9JSdHo0aO1ePFinT59Otv5zMzMXF2PxByAFZCYA7ACqyXm/ztsTmJ+e5WcJ+ZWYYl9zEeNGqUNGzZo1qxZstvt+uCDDxQVFaXQ0FDNnTvX0+UBAAAAec4SS1lWrFihuXPnqlWrVurbt6/uueceVatWTeHh4Zo/f7569erl6RIBAADgBtb83k9rsERifubMGVWp8vdXDwcGBjq2R7z77ru1efNmT5YGAAAAmMISjXmVKlV05MgRSX9/xenixYsl/Z2kX/ktSwAAAMjHbCYd+ZBHG/PDhw8rKytLffv21d69eyVJY8aM0TvvvCNfX19FRkZq5MiRniwRAAAAMIVHd2Xx9vZWUlKSgoODJUmPPfaYZsyYodTUVO3evVvVqlVTgwYNcn1ddmUBYAXsygLACqy2K8uuI+dNuU/Tyvlvu22PJuZX/kywevVqpaSkKDw8XF27dr2hphwAAADIjyyxKwsAAAAKB1s+Xf9tBo8m5jabTbYr/ulc+RgAAAAoDDyamBuGoT59+shut0uSUlNTNWDAABUvXtxp3tKlSz1RHgAAANyMCNY1jzbmERERTo979+7toUoAAAAAz/JoYx4TE+PJ2wMAAMBsROYuWeILhgAAAIDCjl1ZAAAAYBobkblLJOYAAACABZCYAwAAwDTsjO0aiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADAPETmLpGYAwAAABZAYg4AAADTsI+5ayTmAAAAgAWQmAMAAMA07GPuGok5AAAAYAEk5gAAADANgblrJOYAAACABZCYAwAAwDxE5i6RmAMAAAAWQGIOAAAA07CPuWsk5gAAAIAFkJgDAADANOxj7hqJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAWQmAMAAMA8ROYukZgDAAAAFkBiDgAAANOwj7lrJOYAAACABZCYAwAAwDTsY+4aiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADAPETmLpGYAwAAABZAYg4AAADTsI+5ayTmAAAAgAXQmAMAAAAWwFIWAAAAmIYvGHKNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgHiJzl0jMAQAAUKhFR0frtttuU0BAgIKDg9W5c2fFx8c7zWnVqpVsNpvTMWDAALfWQWMOAAAA09hM+l9ubNq0SYMGDdL27du1du1aZWRkqF27dkpJSXGa179/fyUlJTmO1157zZ1vDUtZAAAAULitWbPG6XFsbKyCg4O1e/dutWjRwjFerFgxhYSE5FkdJOYAAAAwjc1mznEzzp07J0kqVaqU0/j8+fNVpkwZ1atXT2PHjtWFCxdu7kZXIDEHAABAgZOWlqa0tDSnMbvdLrvdfs3nZWVladiwYbrrrrtUr149x3jPnj0VHh6u0NBQff/99xo9erTi4+O1dOlSt9VMYw4AAADTmLUpS3R0tKKiopzGJkyYoIkTJ17zeYMGDdK+ffu0ZcsWp/FnnnnG8ef69eurfPnyatOmjRITE1W1alW31GwzDMNwy5Us5GKGpysAACnxVLKnSwAA1avg7+kSnBz9I9WU+5QPsOU6MR88eLCWL1+uzZs3q3Llyte8fkpKivz9/bVmzRq1b9/eLTWTmAMAAMA8JkXmOVm2cplhGBoyZIg+//xzbdy48bpNuSTFxcVJksqXL38zZTqhMQcAAEChNmjQIC1YsEDLly9XQECATp48KUkKCgqSn5+fEhMTtWDBAnXq1EmlS5fW999/r8jISLVo0UINGjRwWx0sZQGAPMJSFgBWYLWlLMdOp11/khuEl85ZWi5JNhfbuMTExKhPnz76+eef1bt3b+3bt08pKSkKCwtTly5d9NJLLykwMNBdJZOYAwAAoHC7Xk4dFhamTZs25XkdNOYAAAAwzc3uMV6Q8QVDAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANOwxtw1EnMAAADAAkjMAQAAYCIic1dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAWQmAMAAMA0rDF3jcQcAAAAsAAScwAAAJjGxipzl0jMAQAAAAsgMQcAAIB5CMxdIjEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIZ9zF0jMQcAAAAsgMQcAAAApmEfc9dIzAEAAAALIDEHAACAeQjMXSIxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiGfcxdIzEHAAAALIDEHAAAAKZhH3PXSMwBAAAACyAxBwAAgGlYY+4aiTkAAABgATTmAAAAgAXQmAMAAAAWwBpzAAAAmIY15q6RmAMAAAAWQGIOAAAA07CPuWsk5gAAAIAFkJgDAADANKwxd43EHAAAALAAEnMAAACYhsDcNRJzAAAAwAJIzAEAAGAeInOXSMwBAAAACyAxBwAAgGnYx9w1EnMAAADAAkjMAQAAYBr2MXeNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgHiJzl0jMAQAAAAugMQcAAIBpbCb9L7feeecdVapUSb6+vmrWrJn+97//5cGrvzYacwAAABRqixYt0vDhwzVhwgTt2bNHDRs2VPv27fXbb7+ZWofNMAzD1Dua4GKGpysAACnxVLKnSwAA1avg7+kSnKReMuc+vrn4JGWzZs1022236e2335YkZWVlKSwsTEOGDNGYMWPyqMLsSMwBAABQ4KSlpen8+fNOR1paWrZ56enp2r17t9q2besY8/LyUtu2bbVt2zYzSy6Yu7L4FfV0Bcjv0tLSFB0drbFjx8put3u6HORTVkupkL/w9xAKqtwk2Tdj4uRoRUVFOY1NmDBBEydOdBr7448/lJmZqXLlyjmNlytXTocOHcrrMp0UyKUswM06f/68goKCdO7cOQUGBnq6HACFEH8PATcnLS0tW0Jut9uz/aB74sQJ3XLLLdq6dauaN2/uGB81apQ2bdqkHTt2mFKvVEATcwAAABRuV2vCr6ZMmTLy9vbWqVOnnMZPnTqlkJCQvCrvqlhjDgAAgELLx8dHTZo00fr16x1jWVlZWr9+vVOCbgYScwAAABRqw4cPV0REhJo2barbb79db731llJSUtS3b19T66AxB67CbrdrwoQJfOAKgMfw9xBgnscee0y///67xo8fr5MnT6pRo0Zas2ZNtg+E5jU+/AkAAABYAGvMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAALCY2NlYlSpTwdBkATEZjjgLNZrNd87jya3kBwJ369Olz1b97EhISPF0aAAtiu0QUaElJSY4/L1q0SOPHj1d8fLxjzN/f3/FnwzCUmZmpIkX4vwUA9+nQoYNiYmKcxsqWLeuhagBYGYk5CrSQkBDHERQUJJvN5nh86NAhBQQE6Msvv1STJk1kt9u1ZcsW9enTR507d3a6zrBhw9SqVSvH46ysLEVHR6ty5cry8/NTw4YN9emnn5r74gDkC3a73envopCQEE2fPl3169dX8eLFFRYWpoEDByo5OdnlNfbu3avWrVsrICBAgYGBatKkiXbt2uU4v2XLFt1zzz3y8/NTWFiYhg4dqpSUFDNeHgA3ojFHoTdmzBhNnTpVBw8eVIMGDXL0nOjoaM2dO1ezZ8/W/v37FRkZqd69e2vTpk15XC2AgsDLy0szZszQ/v37NWfOHG3YsEGjRo1yOb9Xr16qUKGCdu7cqd27d2vMmDEqWrSoJCkxMVEdOnRQt27d9P3332vRokXasmWLBg8ebNbLAeAm/M4ehd6kSZN033335Xh+WlqapkyZonXr1ql58+aSpCpVqmjLli1677331LJly7wqFUA+tHLlSqdlcx07dtSSJUscjytVqqTJkydrwIABevfdd696jePHj2vkyJGqVauWJKl69eqOc9HR0erVq5eGDRvmODdjxgy1bNlSs2bNkq+vbx68KgB5gcYchV7Tpk1zNT8hIUEXLlzI1synp6ercePG7iwNQAHQunVrzZo1y/G4ePHiWrdunaKjo3Xo0CGdP39ely5dUmpqqi5cuKBixYplu8bw4cP19NNPa968eWrbtq0effRRVa1aVdLfy1y+//57zZ8/3zHfMAxlZWXpyJEjql27dt6/SABuQWOOQq948eJOj728vGQYhtNYRkaG48+X14GuWrVKt9xyi9M8u92eR1UCyK+KFy+uatWqOR4fPXpUDzzwgJ577jm98sorKlWqlLZs2aJ+/fopPT39qo35xIkT1bNnT61atUpffvmlJkyYoIULF6pLly5KTk7Ws88+q6FDh2Z7XsWKFfP0tQFwLxpz4Aply5bVvn37nMbi4uIc6znr1Kkju92u48ePs2wFQK7t3r1bWVlZeuONN+Tl9fdHvRYvXnzd59WoUUM1atRQZGSkHn/8ccXExKhLly669dZbdeDAAafmH0D+xIc/gSvce++92rVrl+bOnauffvpJEyZMcGrUAwICNGLECEVGRmrOnDlKTEzUnj17NHPmTM2ZM8eDlQPID6pVq6aMjAzNnDlThw8f1rx58zR79myX8y9evKjBgwdr48aNOnbsmL799lvt3LnTsURl9OjR2rp1qwYPHqy4uDj99NNPWr58OR/+BPIhGnPgCu3bt9e4ceM0atQo3Xbbbfrrr7/05JNPOs15+eWXNW7cOEVHR6t27drq0KGDVq1apcqVK3uoagD5RcOGDfXmm2/q1VdfVb169TR//nxFR0e7nO/t7a3Tp0/rySefVI0aNdS9e3d17NhRUVFRkqQGDRpo06ZN+vHHH3XPPfeocePGGj9+vEJDQ816SQDcxGZcuZgWAAAAgOlIzAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBFFp9+vRR586dHY9btWqlYcOGmV7Hxo0bZbPZdPbsWZdzbDabli1bluNrTpw4UY0aNbqpuo4ePSqbzaa4uLibug4AIGdozAFYSp8+fWSz2WSz2eTj46Nq1app0qRJunTpUp7fe+nSpXr55ZdzNDcnzTQAALlRxNMFAMCVOnTooJiYGKWlpWn16tUaNGiQihYtqrFjx2abm56eLh8fH7fct1SpUm65DgAAN4LEHIDl2O12hYSEKDw8XM8995zatm2rL774QtL/LT955ZVXFBoaqpo1a0qSfv75Z3Xv3l0lSpRQqVKl9PDDD+vo0aOOa2ZmZmr48OEqUaKESpcurVGjRskwDKf7XrmUJS0tTaNHj1ZYWJjsdruqVaumDz/8UEePHlXr1q0lSSVLlpTNZlOfPn0kSVlZWYqOjlblypXl5+enhg0b6tNPP3W6z+rVq1WjRg35+fmpdevWTnXm1OjRo1WjRg0VK1ZMVapU0bhx45SRkZFt3nvvvaewsDAVK1ZM3bt317lz55zOf/DBB6pdu7Z8fX1Vq1Ytvfvuu7muBQDgHjTmACzPz89P6enpjsfr169XfHy81q5dq5UrVyojI0Pt27dXQECAvvnmG3377bfy9/dXhw4dHM974403FBsbq48++khbtmzRmTNn9Pnnn1/zvk8++aQ++eQTzZgxQwcPHtR7770nf39/hYWF6bPPPpMkxcfHKykpSdOnT5ckRUdHa+7cuZo9e7b279+vyMhI9e7dW5s2bZL09w8QXbt21YMPPqi4uDg9/fTTGjNmTK7fk4CAAMXGxurAgQOaPn263n//fU2bNs1pTkJCghYvXqwVK1ZozZo1+u677zRw4EDH+fnz52v8+PF65ZVXdPDgQU2ZMkXjxo3TnDlzcl0PAMANDACwkIiICOPhhx82DMMwsrKyjLVr1xp2u90YMWKE43y5cuWMtLQ0x3PmzZtn1KxZ08jKynKMpaWlGX5+fsZXX31lGIZhlC9f3njttdcc5zMyMowKFSo47mUYhtGyZUvj+eefNwzDMOLj4w1Jxtq1a69a59dff21IMv7880/HWGpqqlGsWDFj69atTnP79etnPP7444ZhGMbYsWONOnXqOJ0fPXp0tmtdSZLx+eefuzz/+uuvG02aNHE8njBhguHt7W388ssvjrEvv/zS8PLyMpKSkgzDMIyqVasaCxYscLrOyy+/bDRv3twwDMM4cuSIIcn47rvvXN4XAOA+rDEHYDkrV66Uv7+/MjIylJWVpZ49e2rixImO8/Xr13daV753714lJCQoICDA6TqpqalKTEzUuXPnlJSUpGbNmjnOFSlSRE2bNs22nOWyuLg4eXt7q2XLljmuOyEhQRcuXNB9993nNJ6enq7GjRtLkg4ePOhUhyQ1b948x/e4bNGiRZoxY4YSExOVnJysS5cuKTAw0GlOxYoVdcsttzjdJysrS/Hx8QoICFBiYqL69eun/v37O+ZcunRJQUFBua4HAHDzaMwBWE7r1q01a9Ys+fj4KDQ0VEWKOP9VVbx4cafHycnJatKkiebPn5/tWmXLlr2hGvz8/HL9nOTkZEnSqlWrnBpi6e918+6ybds29erVS1FRUWrfvr2CgoK0cOFCvfHGG7mu9f3338/2g4K3t7fbagUA5ByNOQDLKV68uKpVq5bj+bfeeqsWLVqk4ODgbKnxZeXLl9eOHTvUokULSX8nw7t379att9561fn169dXVlaWNm3apLZt22Y7fzmxz8zMdIzVqVNHdrtdx48fd5m0165d2/FB1su2b99+/Rf5D1u3blV4eLhefPFFx9ixY8eyzTt+/LhOnDih0NBQx328vLxUs2ZNlStXTqGhoTp8+LB69eqVq/sDAPIGH/4EkO/16tVLZcqU0cMPP6xvvvlGR44c0caNGzV06FD98ssvkqTnn39eU6dO1bJly3To0CENHDjwmnuQV6pUSREREXrqqae0bNkyxzUXL14sSQoPD5fNZtPKlSv1+++/Kzk5WQEBARoxYoQiIyM1Z84cJSYmas+ePZo5c6bjA5UDBgzQTz/9pJEjRyo+Pl4LFixQbGxsrl5v9erVdfz4cS1cuFCJiYmaMWPGVT/I6uvrq4iICO3du1fffPONhg4dqu7duyskJESSFBUVpejoaM2YMUM//vijfvjhB8XExOjNN9/MVT0AAPegMQeQ7xUrVkybN29WxYoV1bVrV9WuXVv9+vVTamqqI0F/4YUX9MQTTygiIkLNmzdXQECAunTpcs3rzpo1S4888ogGDhyoWrVqqX///kpJSZEk3XLLLYqKitKYMWNUrlw5DR48WJL08ssva9y4cYqOjlbt2rXVoUMHrVq1SpUrV5b097rvzz77TMuWLVPDhg01e/ZsTZkyJVev96GHHlJkZKQGDx6sRo0aaevWrRo3bly2edWqVVPXrl3VqVMntWvXTg0aNHDaDvHpp5/WBx98oJiYGNWvX18tW7ZUbGyso1YAgLlshqtPPgEAAAAwDYk5AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmQCFVqVIl9enTx9NlFDhr1qxRo0aN5OvrK5vNprNnz7r1+rGxsbLZbDp69Khbr5uf2Ww2TZw40dNlAMBNozEHCpjExEQ9++yzqlKlinx9fRUYGKi77rpL06dP18WLFz1dXq688sorstlsqlevXq6et3HjRnXt2lUhISHy8fFRcHCwHnzwQS1dujSPKv3b6dOn1b17d/n5+emdd97RvHnzVLx48Ty9p5kqVaokm82mtm3bXvX8+++/L5vNJpvNpl27duX6+lu3btXEiRPd/sMMAOQXRTxdAAD3WbVqlR599FHZ7XY9+eSTqlevntLT07VlyxaNHDlS+/fv13/+8x9Pl5kjv/zyi6ZMmZLrxnbChAmaNGmSqlevrmeffVbh4eE6ffq0Vq9erW7dumn+/Pnq2bNnntS8c+dO/fXXX3r55ZddNq8364knnlCPHj1kt9vz5PrX4+vrq6+//lonT55USEiI07n58+fL19dXqampN3TtrVu3KioqSn369FGJEiVy/LyLFy+qSBH+cwYg/+NvMqCAOHLkiHr06KHw8HBt2LBB5cuXd5wbNGiQEhIStGrVKg9WmDsjRozQHXfcoczMTP3xxx85es6nn36qSZMm6ZFHHtGC/8fencfZXP///7+fwZwZzAyDMUZ22dcob9nfhFHIkoh3QyjKEhHzLctIxpuK0J61iFLxplK2LBnelibZJsvIW5bKWBqa/fX7o5/z6Rgv5jDzOi8zt+vn8rpcnOdre5yX3j6Puc/zPM+SJSpQoIBr3+jRo/X1118rNTU1p0rWr7/+KkkeNZWeypcvn/Lly5dj17+ZJk2aaOfOnVq2bJmGDx/uGj958qS2bNmiLl266NNPP83xOjIyMpSSkiI/Pz/5+fnl+P0AwApMZQFyiWnTpikxMVFz5851a8qvqly5slsjda2EhASNGjVKtWvXVuHChRUYGKjw8HD98MMPmY6dPXu2atasqYIFC6po0aJq2LChlixZ4tr/xx9/6Nlnn1X58uXldDoVEhKiBx54QHv27MnSe9m8ebOWL1+umTNnZun4q8aNG6fg4GDNmzfPrSm/ql27dnrooYdcr3/99Vf1799fJUuWlJ+fn+rWrauFCxe6nXP8+HE5HA698sorevfdd1WpUiU5nU7de++92rlzp+u4li1bKiIiQpJ07733yuFwuObwm83nb9mypVq2bOk2drNnazbH/M0331TNmjXldDoVFhamZ555JtOUkJYtW6pWrVo6cOCAWrVqpYIFC6p06dKaNm2a2SPNxM/PT127dnWrSZI++ugjFS1aVO3atct0zt69e9W3b1/X9KrQ0FA98cQTOnfunOuYiRMnavTo0ZKkChUquKbEXH2fDodDQ4YM0eLFi13vc82aNa59V+eY//nnn6pWrZqqVavmNnUrISFBpUqV0v3336/09PQsv18AsBKJOZBLrFq1ShUrVtT9999/S+cfO3ZMK1as0COPPKIKFSro7Nmzeuedd9SiRQsdOHBAYWFhkv6aRzxs2DB1795dw4cPV1JSkvbu3asdO3a4pogMGjRIy5cv15AhQ1SjRg2dO3dOW7du1cGDB3XPPffcsI709HQNHTpUAwYMUO3atbNc/+HDh3Xo0CE98cQTCggIuOnxf/75p1q2bKkjR45oyJAhqlChgj755BP17dtXFy5cyPRDzJIlS/THH3/oqaeeksPh0LRp09S1a1cdO3ZMBQoU0AsvvKCqVavq3Xff1aRJk1ShQgVVqlQpy/VLWXu21zNx4kRFRUWpTZs2Gjx4sOLi4vTWW29p586d+u6779x+SDl//rzat2+vrl27qkePHlq+fLnGjBmj2rVrKzw8PEt1PvbYY2rbtq2OHj3qeo9LlixR9+7dr/sD0dq1a3Xs2DH169dPoaGhrilV+/fv1/bt2+VwONS1a1f99NNP+uijjzRjxgwVL15cklSiRAnXdTZs2KCPP/5YQ4YMUfHixVW+fPlM9/L399fChQvVpEkTvfDCC3rttdck/fVbo4sXL2rBggVe/Y0DANyQAeCOd/HiRUOS0blz5yyfU65cOSMiIsL1OikpyUhPT3c7Jj4+3nA6ncakSZNcY507dzZq1qx5w2sHBQUZzzzzTJZr+bs5c+YYQUFBxq+//moYhmG0aNHipvczDMNYuXKlIcmYMWNGlu4zc+ZMQ5Lx4YcfusZSUlKMxo0bG4ULFzYuXbpkGMZfz0CSUaxYMSMhISHT/VatWuUamz9/viHJ2Llzp9u9rn3WV7Vo0cJo0aKF63VWnu3Ve8THxxuGYRi//vqr4evra7Rt29bt72/OnDmGJGPevHlu95NkLFq0yDWWnJxshIaGGt26dbvhfa++jwcffNBIS0szQkNDjZdeeskwDMM4cOCAIcnYtGnTdZ/BlStXMl3ro48+MiQZmzdvdo1Nnz7d7b39nSTDx8fH2L9//3X3TZgwwW0sMjLS8PHxMTZv3mx88sknhiRj5syZN32PAOBNTGUBcoFLly5JUpaSYjNOp1M+Pn/9k5Cenq5z586pcOHCqlq1qtsUlCJFiujkyZNu0ziuVaRIEe3YsUOnTp3yqIZz585p/PjxGjdunFtSmhWePoMvv/xSoaGh6tWrl2usQIECGjZsmBITE7Vp0ya34x999FEVLVrU9bpZs2aS/vpNQ3bJyrO91rp165SSkqJnn33W9fcnSQMHDlRgYGCmzxUULlxYffr0cb329fXVfffd59H7yJcvn3r06KGPPvpI0l8f+ixTpozrmVzL39/f9eekpCT9/vvv+sc//iFJWZ7eJEktWrRQjRo1snTsxIkTVbNmTUVEROjpp59WixYtNGzYsCzfCwC8gcYcyAUCAwMl/TW3+1ZlZGRoxowZuvvuu+V0OlW8eHGVKFFCe/fu1cWLF13HjRkzRoULF9Z9992nu+++W88884y+++47t2tNmzZN+/btU5kyZXTfffdp4sSJWWr8XnzxRQUHB2vo0KEe1+/pM/j555919913uzWzklS9enXX/r8rW7as2+urTfr58+c9rtVMVp7tta7WWbVqVbdxX19fVaxYMdP7uOuuu+RwONzGihYt6vH7eOyxx3TgwAH98MMPWrJkiXr27JnpulclJCRo+PDhKlmypPz9/VWiRAlVqFBBktz+27qZq+dkha+vr+bNm6f4+Hj98ccfmj9/vml9AGAXNOZALhAYGKiwsDDt27fvlq8xZcoUjRw5Us2bN9eHH36or7/+WmvXrlXNmjWVkZHhOq569eqKi4vT0qVL1bRpU3366adq2rSpJkyY4DqmR48eOnbsmGbPnq2wsDBNnz5dNWvW1FdffWV6/8OHD+vdd9/VsGHDdOrUKR0/flzHjx9XUlKSUlNTdfz4cSUkJJieX61aNUnSjz/+eMvP4EbM5iUbhnHTc80awms/hJiVZ3u7bud9/F2jRo1UqVIlPfvss4qPj7/hHPgePXrovffe06BBg/TZZ5/pm2++cX1w8+//bd3M35P3rPj6668l/ZXSHz582KNzAcAbaMyBXOKhhx7S0aNHFRMTc0vnL1++XK1atdLcuXPVs2dPtW3bVm3atLnul70UKlRIjz76qObPn68TJ07owQcf1Msvv+y2fnWpUqX09NNPa8WKFYqPj1exYsX08ssvm97/l19+UUZGhoYNG6YKFSq4th07duinn35ShQoVNGnSJNPzq1SpoqpVq2rlypVKTEy86fstV66cDh8+nKkxPHTokGt/dilatOh1n+O1abaUtWf7d1frjIuLcxtPSUlRfHx8tr6Pa/Xq1Uvffvutqlevrnr16l33mPPnz2v9+vUaO3asoqKi1KVLFz3wwAOqWLFipmOzM9Heu3evJk2apH79+ql+/foaMGCAR+k8LPHuIQAA5H1JREFUAHgDjTmQSzz//PMqVKiQBgwYoLNnz2baf/ToUb3++uum5+fLly9TavrJJ5/ol19+cRv7+xJ30l9TBmrUqCHDMJSamqr09PRMDVBISIjCwsKUnJxsev9atWrp888/z7TVrFlTZcuW1eeff67+/fubni9JUVFROnfunAYMGKC0tLRM+7/55hutXr1aktShQwedOXNGy5Ytc+1PS0vT7NmzVbhwYbVo0eKG9/JEpUqVtH37dqWkpLjGVq9erf/9739ux93s2V5PmzZt5Ovrq1mzZrn9/c2dO1cXL17Ugw8+mG3v41oDBgzQhAkT9Oqrr5oeczWhv/a/resthXn1y6Ru95s/U1NT1bdvX4WFhen111/XggULdPbsWY0YMeK2rgsAOY3lEoFcolKlSlqyZIkeffRRVa9e3e2bP7dt2+ZaCtDMQw895EoY77//fv34449avHhxpmSzbdu2Cg0NVZMmTVSyZEkdPHhQc+bM0YMPPqiAgABduHBBd911l7p37666deuqcOHCWrdunXbu3HnDBq548eJ6+OGHM41fbeCut+9ajz76qH788Ue9/PLL+v7779WrVy/XN3+uWbNG69evd62//eSTT+qdd95R3759tXv3bpUvX17Lly/Xd999p5kzZ97WB2mvNWDAAC1fvlzt27dXjx49dPToUX344YeZllO82bO9nhIlSigyMlJRUVFq3769OnXqpLi4OL355pu699573T7omd3KlSvnWj/cTGBgoJo3b65p06YpNTVVpUuX1jfffKP4+PhMxzZo0ECS9MILL6hnz54qUKCAOnbs6PG3v06ePFmxsbFav369AgICVKdOHY0fP14vvviiunfvrg4dOnh0PQCwjBdXhAGQA3766Sdj4MCBRvny5Q1fX18jICDAaNKkiTF79mwjKSnJddz1lkt87rnnjFKlShn+/v5GkyZNjJiYmExL+r3zzjtG8+bNjWLFihlOp9OoVKmSMXr0aOPixYuGYfy1/N7o0aONunXrGgEBAUahQoWMunXrGm+++eYtvZ+sLpf4d+vXrzc6d+5shISEGPnz5zdKlChhdOzY0Vi5cqXbcWfPnjX69etnFC9e3PD19TVq165tzJ8/3+2Yq8slTp8+PdN9dM0yfWbLJRqGYbz66qtG6dKlDafTaTRp0sTYtWuXx8/27/e4dknBOXPmGNWqVTMKFChglCxZ0hg8eLBx/vx5t2PMnmVERIRRrly5TOPXurpc4o1c7xmcPHnS6NKli1GkSBEjKCjIeOSRR4xTp05dd5nDl156yShdurTh4+Pj9j4lmS7B+ffr7N6928ifP78xdOhQt2PS0tKMe++91wgLC8v0XADALhyG4eEnfgAAAABkO+aYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgA7nymz/96w/xdgkAoPM753i7BACQn826Pav6tD+/v/P+DSYxBwAAAGzAZj9DAQAAIFdzkAub4ckAAAAANkBiDgAAAOs4HN6uwLZIzAEAAAAbIDEHAACAdZhjboonAwAAANgAiTkAAACswxxzUyTmAAAAgA2QmAMAAMA6zDE3xZMBAAAAbIDEHAAAANZhjrkpEnMAAADABkjMAQAAYB3mmJviyQAAAAA2QGIOAAAA6zDH3BSJOQAAAGADJOYAAACwDnPMTfFkAAAAABsgMQcAAIB1mGNuisQcAAAAsAEScwAAAFiHOeameDIAAACADZCYAwAAwDrMMTdFYg4AAADYAIk5AAAArMMcc1M8GQAAAMAGSMwBAABgHRJzUzwZAAAAwAZIzAEAAGAdH1ZlMUNiDgAAANgAiTkAAACswxxzUzwZAAAAwAZIzAEAAGAdvvnTFIk5AAAAYAMk5gAAALAOc8xN8WQAAAAAGyAxBwAAgHWYY26KxBwAAACwARJzAAAAWIc55qZ4MgAAAIANkJgDAADAOswxN0ViDgAAANgAiTkAAACswxxzUzwZAAAAwAZIzAEAAGAd5pibIjEHAAAAbIDEHAAAANZhjrkpngwAAABgAyTmAAAAsA5zzE2RmAMAAAA2QGIOAAAA6zDH3BRPBgAAAHna5s2b1bFjR4WFhcnhcGjFihVu+x0Ox3W36dOnu44pX758pv1Tp071qA4ScwAAAFjHhon55cuXVbduXT3xxBPq2rVrpv2nT592e/3VV1+pf//+6tatm9v4pEmTNHDgQNfrgIAAj+qgMQcAAECeFh4ervDwcNP9oaGhbq9XrlypVq1aqWLFim7jAQEBmY71hP1+ZAEAAEDu5XBYs+WQs2fP6osvvlD//v0z7Zs6daqKFSum+vXra/r06UpLS/Po2iTmAAAAyHWSk5OVnJzsNuZ0OuV0Om/rugsXLlRAQECmKS/Dhg3TPffco+DgYG3btk2RkZE6ffq0XnvttSxfm8QcAAAA1nH4WLJFR0crKCjIbYuOjr7t8ufNm6fevXvLz8/PbXzkyJFq2bKl6tSpo0GDBunVV1/V7NmzM/1wcCMk5gAAAMh1IiMjNXLkSLex203Lt2zZori4OC1btuymxzZq1EhpaWk6fvy4qlatmqXr05gDAADAOhZ982d2TFu51ty5c9WgQQPVrVv3psfGxsbKx8dHISEhWb4+jTkAAADytMTERB05csT1Oj4+XrGxsQoODlbZsmUlSZcuXdInn3yiV199NdP5MTEx2rFjh1q1aqWAgADFxMRoxIgR6tOnj4oWLZrlOmjMAQAAYB0brmO+a9cutWrVyvX66hSYiIgILViwQJK0dOlSGYahXr16ZTrf6XRq6dKlmjhxopKTk1WhQgWNGDEi01Sam3EYhmHc+tuwJ//6Q7xdAgDo/M453i4BAORnsxjWv8v7ltznz88HWHKf7GSzvyoAAADkahbNMb8T2e93CQAAAEAeRGIOAAAAyzhIzE2RmAMAAAA2QGIOAAAAy5CYmyMxBwAAAGyAxhwAAACwAaayAAAAwDrMZDFFYg4AAADYAIk5AAAALMOHP82RmAMAAAA2QGIOAAAAy5CYmyMxBwAAAGyAxBwAAACWITE3R2IOAAAA2ACJOQAAACxDYm6OxBwAAACwARJzAAAAWIfA3BSJOQAAAGADJOYAAACwDHPMzZGYAwAAADZAYg4AAADLkJibIzEHAAAAbIDEHAAAAJYhMTdHYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYh8DcFIk5AAAAYAMk5gAAALAMc8zNkZgDAAAANkBiDgAAAMuQmJsjMQcAAABsgMQcAAAAliExN0diDgAAANgAiTkAAACsQ2BuisQcAAAAsAEScwAAAFiGOebmSMwBAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAAy5CYmyMxBwAAAGyAxBwAAACWITE3R2IOAAAA2ACJOQAAAKxDYG6KxBwAAACwARJzAAAAWIY55uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADLkJibs0VivmXLFvXp00eNGzfWL7/8Ikn64IMPtHXrVi9XBgAAAFjD6435p59+qnbt2snf31/ff/+9kpOTJUkXL17UlClTvFwdAAAAspXDou0O5PXGfPLkyXr77bf13nvvqUCBAq7xJk2aaM+ePV6sDAAAALCO1xvzuLg4NW/ePNN4UFCQLly4YH1BAAAAyDEOh8OSzRObN29Wx44dFRYWJofDoRUrVrjt79u3b6brt2/f3u2YhIQE9e7dW4GBgSpSpIj69++vxMREj+rwemMeGhqqI0eOZBrfunWrKlas6IWKAAAAkJdcvnxZdevW1RtvvGF6TPv27XX69GnX9tFHH7nt7927t/bv36+1a9dq9erV2rx5s5588kmP6vD6qiwDBw7U8OHDNW/ePDkcDp06dUoxMTEaNWqUxo0b5+3yAAAAkI3suCpLeHi4wsPDb3iM0+lUaGjodfcdPHhQa9as0c6dO9WwYUNJ0uzZs9WhQwe98sorCgsLy1IdXm/Mx44dq4yMDLVu3VpXrlxR8+bN5XQ6NWrUKA0dOtTb5QEAAOAOlJyc7FpU5Cqn0ymn03lL1/v2228VEhKiokWL6p///KcmT56sYsWKSZJiYmJUpEgRV1MuSW3atJGPj4927NihLl26ZOkeXp/K4nA49MILLyghIUH79u3T9u3b9dtvv+mll17ydmnIZZrcU0nLZz6lY9+8rD+/n6OOLeu47Q8JDtC7UX107JuXdW7ba1o552lVKlvCtb9sqWD9+f2c625d29S3+u0AyOWWLlms8Af+qXvr11bvno/ox717vV0SkC2smmMeHR2toKAgty06OvqWam7fvr0WLVqk9evX69///rc2bdqk8PBwpaenS5LOnDmjkJAQt3Py58+v4OBgnTlzJsv38XpifpWvr69q1Kjh7TKQixXyd+rHn37RopUxWvZa5jlfH894Uqlp6Xrk2Xd06XKShvX5p758e6jqd52sK0kpOnn2vMq3iXQ754luTTTi8Tb6+rv9Vr0NAHnAmq++1CvTovXihCjVrl1Xiz9YqMFP9dfK1WtcCR2AG4uMjNTIkSPdxm41Le/Zs6frz7Vr11adOnVUqVIlffvtt2rduvVt1fl3Xm/MW7VqdcO5Rhs2bLCwGuRm33x3QN98d+C6+yqXDVGjOhV0T7fJOnjsr59sh01ZpuPrpqhHeAMt+DxGGRmGzp77w+28Tq3q6tO1e3T5z5Qcrx9A3vHBwvnq2r2HHu7STZL04oQobd78rVZ89qn6D/Tsw2SA3Vg1x/x2pq3cTMWKFVW8eHEdOXJErVu3VmhoqH799Ve3Y9LS0pSQkGA6L/16vD6VpV69eqpbt65rq1GjhlJSUrRnzx7Vrl3b2+Uhj3D6/vUzalJKmmvMMAylpKTp/nqVrntO/eplVK9aGS1cEWNJjQDyhtSUFB08sF//aHy/a8zHx0f/+Mf92vvD916sDMBVJ0+e1Llz51SqVClJUuPGjXXhwgXt3r3bdcyGDRuUkZGhRo0aZfm6Xk/MZ8yYcd3xiRMnerz2I3Cr4o6f0YnTCXppaCcNmfyRLv+ZomF9Wumu0KIKLR503XMiHm6sg8dOa/sP8RZXCyA3O3/hvNLT0zNNWSlWrJji4495qSogG9lvURYlJia6Ld8dHx+v2NhYBQcHKzg4WFFRUerWrZtCQ0N19OhRPf/886pcubLatWsnSapevbrat2+vgQMH6u2331ZqaqqGDBminj17ZnlFFskGibmZPn36aN68eTc9Ljk5WZcuXXLbjIx0CypEbpKWlqGez72nyuVCdHrzdCXEvKbmDatozdb9yjAyMh3v5yygR8MbkpYDAJAL7Nq1S/Xr11f9+n8t5jBy5EjVr19f48ePV758+bR371516tRJVapUUf/+/dWgQQNt2bLFbarM4sWLVa1aNbVu3VodOnRQ06ZN9e6773pUh9cTczMxMTHy8/O76XHR0dGKiopyG8tX8l4VKHVfTpWGXOr7g//TP3pOVWBhP/kWyK/fzydq86JR2n3gRKZju7Spp4J+vlq8+r9eqBRAbla0SFHly5dP586dcxs/d+6cihcv7qWqgOxjx3XMW7ZsKcMwTPd//fXXN71GcHCwlixZclt1eL0x79q1q9trwzB0+vRp7dq1K0tfMHS9T9yGNBuTrTUib7mUmCRJqlS2hO6pUVZRb67OdEzfh+/XF5t+1O/nmW4FIHsV8PVV9Ro1tWN7jP7Zuo0kKSMjQzt2xKhnrz5erg5ATvJ6Yx4U5D5/18fHR1WrVtWkSZPUtm3bm55/vU/cOnzyZWuNyB0K+fuqUpn/W5e8fOliqlOltM5fuqL/nTmvrm3q67fzifrfmQTVujtMr4zurlXf7tX67YfcrlOxTHE1vaeSHh76ltVvAUAe8a+Ifhr3/8aoZs1aqlW7jj78YKH+/PNPPdyl681PBmzOjom5XXi1MU9PT1e/fv1Uu3ZtFS1a1JulIA+4p0Y5ffP+cNfraaP+Wobsg/9s15MTPlRoiUD9+7muCikWoDO/X9Li1TsU/e6aTNeJ6NxYv5y9oHUxhzLtA4Ds0D68g84nJOjNObP0+++/qWq16nrznfdVjKksQK7mMG40ocYCfn5+OnjwoCpUqJBt1/SvPyTbrgUAt+r8zjneLgEA5Of1+RHuKo/6ypL7HHkl3JL7ZCevr8pSq1YtHTvG8k8AAADI27zemE+ePFmjRo3S6tWrdfr06UxLHwIAACD3cDgclmx3Iq/9cmPSpEl67rnn1KFDB0lSp06d3B6iYRhyOBxKT2dNcgAAAOR+XmvMo6KiNGjQIG3cuNFbJQAAAMBid2iYbQmvNeZXP3PaokULb5UAAAAA2IZXP6d7p87/AQAAwK2h/zPn1ca8SpUqN/3LSUhIsKgaAAAAwHu82phHRUVl+uZPAAAA5F4E5ua82pj37NlTISEh3iwBAAAAsAWvNebMLwIAAMh7fHzoAc147QuGrq7KAgAAAMCLiXlGRoa3bg0AAAAvYdKEOa8l5gAAAAD+j1c//AkAAIC8hc8ZmiMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAACzDHHNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAZQjMzZGYAwAAADZAYg4AAADLMMfcHIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYBnmmJsjMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswxxzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYY26OxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwDHPMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDGHAAAALABprIAAADAMnz40xyJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAZ5pibIzEHAAAAbIDGHAAAAJZxOByWbJ7YvHmzOnbsqLCwMDkcDq1YscK1LzU1VWPGjFHt2rVVqFAhhYWF6fHHH9epU6fcrlG+fPlMNUydOtWjOmjMAQAAkKddvnxZdevW1RtvvJFp35UrV7Rnzx6NGzdOe/bs0Weffaa4uDh16tQp07GTJk3S6dOnXdvQoUM9qoM55gAAALCMHaeYh4eHKzw8/Lr7goKCtHbtWrexOXPm6L777tOJEydUtmxZ13hAQIBCQ0NvuQ4ScwAAAMADFy9elMPhUJEiRdzGp06dqmLFiql+/fqaPn260tLSPLouiTkAAAAsY9WqLMnJyUpOTnYbczqdcjqdt3XdpKQkjRkzRr169VJgYKBrfNiwYbrnnnsUHBysbdu2KTIyUqdPn9Zrr72W5WuTmAMAACDXiY6OVlBQkNsWHR19W9dMTU1Vjx49ZBiG3nrrLbd9I0eOVMuWLVWnTh0NGjRIr776qmbPnp3ph4MbITEHAACAZayaYx4ZGamRI0e6jd1OWn61Kf/555+1YcMGt7T8eho1aqS0tDQdP35cVatWzdI9aMwBAACQ62THtJWrrjblhw8f1saNG1WsWLGbnhMbGysfHx+FhIRk+T405gAAALCMHb/5MzExUUeOHHG9jo+PV2xsrIKDg1WqVCl1795de/bs0erVq5Wenq4zZ85IkoKDg+Xr66uYmBjt2LFDrVq1UkBAgGJiYjRixAj16dNHRYsWzXIdNOYAAADI03bt2qVWrVq5Xl+dAhMREaGJEyfqP//5jySpXr16budt3LhRLVu2lNPp1NKlSzVx4kQlJyerQoUKGjFiRKapNDdDYw4AAADL2DAwV8uWLWUYhun+G+2TpHvuuUfbt2+/7TpYlQUAAACwARJzAAAAWMbHjpG5TZCYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJax4zrmdkFiDgAAANgAiTkAAAAs40NgborEHAAAALABEnMAAABYhjnm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMs4RGRuhsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMuwjrk5EnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMj5E5qZIzAEAAAAbIDEHAACAZQjMzZGYAwAAADZAYg4AAADLsI65ORJzAAAAwAZIzAEAAGAZAnNzJOYAAACADZCYAwAAwDKsY26OxBwAAACwgSwl5v/5z3+yfMFOnTrdcjEAAADI3cjLzWWpMX/44YezdDGHw6H09PTbqQcAAADIk7LUmGdkZOR0HQAAAMgDWMfc3G3NMU9KSsquOgAAAIA8zePGPD09XS+99JJKly6twoUL69ixY5KkcePGae7cudleIAAAAHIPH4c1253I48b85Zdf1oIFCzRt2jT5+vq6xmvVqqX3338/W4sDAAAA8gqPG/NFixbp3XffVe/evZUvXz7XeN26dXXo0KFsLQ4AAAC5i8PhsGS7E3ncmP/yyy+qXLlypvGMjAylpqZmS1EAAABAXuNxY16jRg1t2bIl0/jy5ctVv379bCkKAAAAuZPDYc12J8rScol/N378eEVEROiXX35RRkaGPvvsM8XFxWnRokVavXp1TtQIAAAA5HoeJ+adO3fWqlWrtG7dOhUqVEjjx4/XwYMHtWrVKj3wwAM5USMAAAByCeaYm/M4MZekZs2aae3atdldCwAAAJBn3VJjLkm7du3SwYMHJf0177xBgwbZVhQAAABypzt1jXEreNyYnzx5Ur169dJ3332nIkWKSJIuXLig+++/X0uXLtVdd92V3TUCAAAAuZ7Hc8wHDBig1NRUHTx4UAkJCUpISNDBgweVkZGhAQMG5ESNAAAAyCWYY27O48R806ZN2rZtm6pWreoaq1q1qmbPnq1mzZpla3EAAABAXuFxY16mTJnrfpFQenq6wsLCsqUoAAAA5E53ZpZtDY+nskyfPl1Dhw7Vrl27XGO7du3S8OHD9corr2RrcQAAAEBekaXEvGjRom5zdS5fvqxGjRopf/6/Tk9LS1P+/Pn1xBNP6OGHH86RQgEAAHDn87lD539bIUuN+cyZM3O4DAAAACBvy1JjHhERkdN1AAAAIA8gMDd3y18wJElJSUlKSUlxGwsMDLytggAAAIC8yOPG/PLlyxozZow+/vhjnTt3LtP+9PT0bCkMAAAAuc+dusa4FTxeleX555/Xhg0b9NZbb8npdOr9999XVFSUwsLCtGjRopyoEQAAAMj1PG7MV61apTfffFPdunVT/vz51axZM7344ouaMmWKFi9enBM1AgAAIJdwOKzZPLF582Z17NhRYWFhcjgcWrFihdt+wzA0fvx4lSpVSv7+/mrTpo0OHz7sdkxCQoJ69+6twMBAFSlSRP3791diYqJHdXjcmCckJKhixYqS/ppPnpCQIElq2rSpNm/e7OnlAAAAAK+6fPmy6tatqzfeeOO6+6dNm6ZZs2bp7bff1o4dO1SoUCG1a9dOSUlJrmN69+6t/fv3a+3atVq9erU2b96sJ5980qM6PJ5jXrFiRcXHx6ts2bKqVq2aPv74Y913331atWqVihQp4unlAAAAkIfYcR3z8PBwhYeHX3efYRiaOXOmXnzxRXXu3FmStGjRIpUsWVIrVqxQz549dfDgQa1Zs0Y7d+5Uw4YNJUmzZ89Whw4d9MorrygsLCxLdXicmPfr108//PCDJGns2LF644035OfnpxEjRmj06NGeXg4AAADIdsnJybp06ZLblpyc7PF14uPjdebMGbVp08Y1FhQUpEaNGikmJkaSFBMToyJFiriacklq06aNfHx8tGPHjizfy+PEfMSIEW43PHTokHbv3q3KlSurTp06nl4OAAAAeYhVgXl0dLSioqLcxiZMmKCJEyd6dJ0zZ85IkkqWLOk2XrJkSde+M2fOKCQkxG1//vz5FRwc7DomK25rHXNJKleunMqVK3e7lwEAAACyTWRkpEaOHOk25nQ6vVRN1mSpMZ81a1aWLzhs2LBbLgYAAAC5m1XrmDudzmxpxENDQyVJZ8+eValSpVzjZ8+eVb169VzH/Prrr27npaWlKSEhwXV+VmSpMZ8xY0aWLuZwOGjMAQAAkGtUqFBBoaGhWr9+vasRv3Tpknbs2KHBgwdLkho3bqwLFy5o9+7datCggSRpw4YNysjIUKNGjbJ8ryw15vHx8R6+Be9K+O8cb5cAADr4yx/eLgEAVL9cgLdLcOPxyiMWSExM1JEjR1yv4+PjFRsbq+DgYJUtW1bPPvusJk+erLvvvlsVKlTQuHHjFBYWpocffliSVL16dbVv314DBw7U22+/rdTUVA0ZMkQ9e/bM8oosUjbMMQcAAADuZLt27VKrVq1cr6/OTY+IiNCCBQv0/PPP6/Lly3ryySd14cIFNW3aVGvWrJGfn5/rnMWLF2vIkCFq3bq1fHx81K1bN4+mg0uSwzAMI3vekn38mertCgBAOnSKxByA99ktMR+24pAl95n1cDVL7pOd7PjbBAAAACDPYSoLAAAALONjvy/+tA0ScwAAAMAGbqkx37Jli/r06aPGjRvrl19+kSR98MEH2rp1a7YWBwAAgNzFx2HNdifyuDH/9NNP1a5dO/n7++v7779XcnKyJOnixYuaMmVKthcIAAAA5AUeN+aTJ0/W22+/rffee08FChRwjTdp0kR79uzJ1uIAAACQuzgcDku2O5HHjXlcXJyaN2+eaTwoKEgXLlzIjpoAAACAPMfjxjw0NNTtm5Gu2rp1qypWrJgtRQEAACB3Yo65OY8b84EDB2r48OHasWOHHA6HTp06pcWLF2vUqFEaPHhwTtQIAAAA5Hoer2M+duxYZWRkqHXr1rpy5YqaN28up9OpUaNGaejQoTlRIwAAAHKJO3T6tyUchmEYt3JiSkqKjhw5osTERNWoUUOFCxfO7tpu2Z+p3q4AAKRDp/7wdgkAoPrlArxdgpvnv4iz5D7THqxqyX2y0y1/86evr69q1KiRnbUAAAAgl/MhMjflcWPeqlWrGy5Bs2HDhtsqCAAAAMiLPG7M69Wr5/Y6NTVVsbGx2rdvnyIiIrKrLgAAAORCt/S183mEx435jBkzrjs+ceJEJSYm3nZBAAAAQF6UbT+09OnTR/PmzcuuywEAACAXcjis2e5E2daYx8TEyM/PL7suBwAAAOQpHk9l6dq1q9trwzB0+vRp7dq1S+PGjcu2wgAAAJD7sCqLOY8b86CgILfXPj4+qlq1qiZNmqS2bdtmW2EAAABAXuJRY56enq5+/fqpdu3aKlq0aE7VBAAAgFyKwNycR3PM8+XLp7Zt2+rChQs5VA4AAACQN3n84c9atWrp2LFjOVELAAAAkGd53JhPnjxZo0aN0urVq3X69GldunTJbQMAAADM+Dis2e5EWZ5jPmnSJD333HPq0KGDJKlTp05y/G2SkGEYcjgcSk9Pz/4qAQAAgFwuy415VFSUBg0apI0bN+ZkPQAAAMjFWC7RXJYbc8MwJEktWrTIsWIAAACAvMqj5RId/IQDAACA20A7ac6jxrxKlSo3bc4TEhJuqyAAAAAgL/KoMY+Kisr0zZ8AAABAVt2pK6ZYwaPGvGfPngoJCcmpWgAAAIA8K8uNOfPLAQAAcLscoqc0k+UvGLq6KgsAAACA7JflxDwjIyMn6wAAAEAewBxzc1lOzAEAAADkHI8+/AkAAADcDhJzcyTmAAAAgA2QmAMAAMAyrPRnjsQcAAAAsAEScwAAAFiGOebmSMwBAAAAGyAxBwAAgGWYYm6OxBwAAACwARJzAAAAWMaHyNwUiTkAAABgAyTmAAAAsAyrspgjMQcAAABsgMQcAAAAlmGKuTkScwAAAMAGSMwBAABgGR8RmZshMQcAAABsgMQcAAAAlmGOuTkScwAAAMAGSMwBAABgGdYxN0diDgAAANgAjTkAAAAs4+NwWLJ5onz58nI4HJm2Z555RpLUsmXLTPsGDRqU7c+GqSwAAADI03bu3Kn09HTX63379umBBx7QI4884hobOHCgJk2a5HpdsGDBbK+DxhwAAACWseOqLCVKlHB7PXXqVFWqVEktWrRwjRUsWFChoaE5WgdTWQAAAID/X0pKij788EM98cQTcvztp4jFixerePHiqlWrliIjI3XlypVsvzeJOQAAACzj6fzvW5WcnKzk5GS3MafTKafTecPzVqxYoQsXLqhv376usccee0zlypVTWFiY9u7dqzFjxiguLk6fffZZttbsMAzDyNYr2sCfqd6uAACkQ6f+8HYJAKD65QK8XYKbuf89Ycl9/vflPEVFRbmNTZgwQRMnTrzhee3atZOvr69WrVplesyGDRvUunVrHTlyRJUqVcqOciWRmAMAAMBCVs0xj4yM1MiRI93GbpaW//zzz1q3bt1Nk/BGjRpJEo05AAAAcDNZmbZyrfnz5yskJEQPPvjgDY+LjY2VJJUqVepWy7suGnMAAABYxq4rj2RkZGj+/PmKiIhQ/vz/1yIfPXpUS5YsUYcOHVSsWDHt3btXI0aMUPPmzVWnTp1srYHGHAAAAHneunXrdOLECT3xxBNu476+vlq3bp1mzpypy5cvq0yZMurWrZtefPHFbK+BxhwAAACWcdhxIXNJbdu21fXWRClTpow2bdpkSQ12/W0CAAAAkKeQmAMAAMAy9szL7YHEHAAAALABEnMAAABYxqpv/rwTkZgDAAAANkBiDgAAAMuQl5sjMQcAAABsgMQcAAAAlmGKuTkScwAAAMAGSMwBAABgGbt+86cdkJgDAAAANkBiDgAAAMuQCpvj2QAAAAA2QGIOAAAAyzDH3ByJOQAAAGADJOYAAACwDHm5ORJzAAAAwAZIzAEAAGAZ5pibIzEHAAAAbIDEHAAAAJYhFTbHswEAAABsgMQcAAAAlmGOuTkScwAAAMAGSMwBAABgGfJycyTmAAAAgA2QmAMAAMAyTDE3R2IOAAAA2ACJOQAAACzjwyxzUyTmAAAAgA2QmAMAAMAyzDE3R2IOAAAA2ACJOQAAACzjYI65KRJzAAAAwAZIzAEAAGAZ5pibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDLMMTdHYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYhm/+NEdiDgAAANgAiTkAAAAs40NgborEHAAAALABEnMAAABYhjnm5kjMAQAAABuwVWOekpKiuLg4paWlebsUAAAA5ACHw5rtTmSLxvzKlSvq37+/ChYsqJo1a+rEiROSpKFDh2rq1Klerg4AAADIebZozCMjI/XDDz/o22+/lZ+fn2u8TZs2WrZsmRcrAwAAQHZyWPR/dyJbfPhzxYoVWrZsmf7xj3/I8bffPdSsWVNHjx71YmUAAACANWzRmP/2228KCQnJNH758mW3Rh0AAAB3NtYxN2eLqSwNGzbUF1984Xp9tRl///331bhxY2+VBQAAAFjGFon5lClTFB4ergMHDigtLU2vv/66Dhw4oG3btmnTpk3eLg8AAADZ5E6d/20FWyTmTZs2VWxsrNLS0lS7dm198803CgkJUUxMjBo0aODt8gAAAIAcZ4vGXJIqVaqk9957T//973914MABffjhh6pdu7a3y0Ies3vXTg17ZpAeaNVU9WpV1Yb167xdEoA8ZOXSBerZtqEWvvWqa2zdF58patST6vdwC/Vs21CXE//wYoXA7bPjOuYTJ06Uw+Fw26pVq+ban5SUpGeeeUbFihVT4cKF1a1bN509ezabn4xNGvM9e/boxx9/dL1euXKlHn74Yf2///f/lJKS4sXKkNf8+ecVValaVZEvTPB2KQDymKNx+7Xui89UtuLdbuMpyUmq1/B+Pdyzn5cqA/KGmjVr6vTp065t69atrn0jRozQqlWr9Mknn2jTpk06deqUunbtmu012GKO+VNPPaWxY8eqdu3aOnbsmB599FF17dpVn3zyia5cuaKZM2d6u0TkEU2btVDTZi28XQaAPCbpzyuaPXWcnhzxgj5bMtdtX4euj0mS9v+wyxulAdnOrjPM8+fPr9DQ0EzjFy9e1Ny5c7VkyRL985//lCTNnz9f1atX1/bt2/WPf/wj22qwRWL+008/qV69epKkTz75RC1atNCSJUu0YMECffrpp94tDgCAHDZv9r9V/74mqn1PI2+XAuRZhw8fVlhYmCpWrKjevXu7vol+9+7dSk1NVZs2bVzHVqtWTWXLllVMTEy21mCLxNwwDGVkZEiS1q1bp4ceekiSVKZMGf3+++/eLA0AgBy1bePXij9ySC/PWeTtUgBL+Fj0HTXJyclKTk52G3M6nXI6nZmObdSokRYsWKCqVavq9OnTioqKUrNmzbRv3z6dOXNGvr6+KlKkiNs5JUuW1JkzZ7K1Zlsk5g0bNtTkyZP1wQcfaNOmTXrwwQclSfHx8SpZsqSXqwMAIGf8/usZLXzrVQ0ZO1m+vpmbBQC3Ljo6WkFBQW5bdHT0dY8NDw/XI488ojp16qhdu3b68ssvdeHCBX388ceW1myLxHzmzJnq3bu3VqxYoRdeeEGVK1eWJC1fvlz333//Dc+93k9DGT7X/2kIAAA7iT98SBcvJCjy6T6usYyMdB368Xt9vfJjffjFNvnky+fFCoE7V2RkpEaOHOk2ltX+sEiRIqpSpYqOHDmiBx54QCkpKbpw4YJban727Nnrzkm/HbZozOvUqeO2KstV06dPV76b/IMUHR2tqKgot7H/9+IEvTh+YnaWCABAtqtV/15Nf2ep29hbr05SWJly6twjgqYcuZJVH/40m7aSFYmJiTp69Kj+9a9/qUGDBipQoIDWr1+vbt26SZLi4uJ04sSJbP+Gels05mb8/Pxuesz1fhrK8CEtx625cuWy68MekvTLLyd16NBBBQUFqVSpMC9WBiA38i9YSGUqVHYbc/r5KSCwiGv8QsLvunD+nM6eOilJOhF/RP4FC6p4iVAVDgyyvGYgNxo1apQ6duyocuXK6dSpU5owYYLy5cunXr16KSgoSP3799fIkSMVHByswMBADR06VI0bN87WFVkkLzbmRYsWlSOLk/8TEhJM913vp6E/U2+rNORh+/ft08AnHne9fnXaX3PROnbuopdenuqtsgDkYWtXf6pPP3zP9TrquYGSpEGjJqhl247eKgu4dTZcL/HkyZPq1auXzp07pxIlSqhp06bavn27SpQoIUmaMWOGfHx81K1bNyUnJ6tdu3Z68803s70Oh2EYRrZfNQsWLlyY5WMjIiI8ujaNOQA7OHSKb2gE4H31ywV4uwQ3249esOQ+/6hUxJL7ZCevJeaeNtsAAAC48znsGJnbhO3mmCclJSklJcVtLDAw0EvVAAAAANawxTrmly9f1pAhQxQSEqJChQqpaNGibhsAAAByB4fDmu1OZIvG/Pnnn9eGDRv01ltvyel06v3331dUVJTCwsK0aBHfhAYAAIDczxZTWVatWqVFixapZcuW6tevn5o1a6bKlSurXLlyWrx4sXr37u3tEgEAAJAN7tAw2xK2SMwTEhJUsWJFSX/NJ7+6PGLTpk21efNmb5YGAAAAWMIWjXnFihUVHx8vSapWrZo+/vhjSX8l6X//6lMAAADc4RwWbXcgrzbmx44dU0ZGhvr166cffvhBkjR27Fi98cYb8vPz04gRIzR69GhvlggAAABYwmtfMCRJ+fLl0+nTpxUSEiJJevTRRzVr1iwlJSVp9+7dqly5surUqePxdfmCIQB2wBcMAbADu33B0K74S5bcp2GFO2+5ba8m5tf+TPDll1/q8uXLKleunLp27XpLTTkAAABwJ7LFqiwAAADIG+7UNcat4NXE3OFwyHHN3861rwEAAIC8wKuJuWEY6tu3r5xOpyQpKSlJgwYNUqFChdyO++yzz7xRHgAAALIZEaw5rzbmERERbq/79OnjpUoAAAAA7/JqYz5//nxv3h4AAABWIzI3ZYsvGAIAAADyOlZlAQAAgGUcROamSMwBAAAAGyAxBwAAgGVYGdsciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgHSJzUyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAA1iEyN0ViDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYB0ic1Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAANYhMjdFYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAdInNTJOYAAADI06Kjo3XvvfcqICBAISEhevjhhxUXF+d2TMuWLeVwONy2QYMGZWsdNOYAAACwjMOi//PEpk2b9Mwzz2j79u1au3atUlNT1bZtW12+fNntuIEDB+r06dOubdq0adn5aJjKAgAAgLxtzZo1bq8XLFigkJAQ7d69W82bN3eNFyxYUKGhoTlWB4k5AAAALONwWLMlJyfr0qVLbltycnKWarx48aIkKTg42G188eLFKl68uGrVqqXIyEhduXIlW58NjTkAAABynejoaAUFBblt0dHRNz0vIyNDzz77rJo0aaJatWq5xh977DF9+OGH2rhxoyIjI/XBBx+oT58+2VqzwzAMI1uvaAN/pnq7AgCQDp36w9slAIDqlwvwdglujv+eZMl9SgU4MiXkTqdTTqfzhucNHjxYX331lbZu3aq77rrL9LgNGzaodevWOnLkiCpVqpQtNTPHHAAAALlOVprwaw0ZMkSrV6/W5s2bb9iUS1KjRo0kicYcAAAAdygbrmNuGIaGDh2qzz//XN9++60qVKhw03NiY2MlSaVKlcq2OmjMAQAAkKc988wzWrJkiVauXKmAgACdOXNGkhQUFCR/f38dPXpUS5YsUYcOHVSsWDHt3btXI0aMUPPmzVWnTp1sq4M55gCQQ5hjDsAO7DbH/OdzWVsZ5XaVK5b1aSwOx/Vj/Pnz56tv37763//+pz59+mjfvn26fPmyypQpoy5duujFF19UYGBgdpVMYg4AAIC87WY5dZkyZbRp06Ycr4PGHAAAAJYxCach1jEHAAAAbIHEHAAAAJYhMDdHYg4AAADYAIk5AAAALMMcc3Mk5gAAAIANkJgDAADAQkTmZkjMAQAAABsgMQcAAIBlmGNujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAxzzM2RmAMAAAA2QGIOAAAAyziYZW6KxBwAAACwARJzAAAAWIfA3BSJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACzDOubmSMwBAAAAGyAxBwAAgHUIzE2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGOebmSMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAb48CcAAAAsw4c/zZGYAwAAADZAYg4AAADL8AVD5kjMAQAAABsgMQcAAIBlmGNujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsA6RuSkScwAAAMAGSMwBAABgGdYxN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAOsQmZsiMQcAAECe98Ybb6h8+fLy8/NTo0aN9N///tfyGmjMAQAAYBmHRf/niWXLlmnkyJGaMGGC9uzZo7p166pdu3b69ddfc+gpXJ/DMAzD0jta4M9Ub1cAANKhU394uwQAUP1yAd4uwY1VfZp/gawf26hRI917772aM2eOJCkjI0NlypTR0KFDNXbs2ByqMDMScwAAAFjG4bBmy6qUlBTt3r1bbdq0cY35+PioTZs2iomJyYEnYI4PfwIAACDXSU5OVnJystuY0+mU0+l0G/v999+Vnp6ukiVLuo2XLFlShw4dyvE6/y5XNuae/OoCuJ7k5GRFR0crMjIy0/+Agayy26+PcWfh3yHkVn4WdZ8TJ0crKirKbWzChAmaOHGiNQXcglw5xxy4XZcuXVJQUJAuXryowMBAb5cDIA/i3yHg9mQ1MU9JSVHBggW1fPlyPfzww67xiIgIXbhwQStXrrSiXEnMMQcAAEAu5HQ6FRgY6LZd77dPvr6+atCggdavX+8ay8jI0Pr169W4cWMrS86dU1kAAACArBo5cqQiIiLUsGFD3XfffZo5c6YuX76sfv36WVoHjTkAAADytEcffVS//fabxo8frzNnzqhevXpas2ZNpg+E5jQac+A6nE6nJkyYwAeuAHgN/w4B1hoyZIiGDBni1Rr48CcAAABgA3z4EwAAALABGnMAAADABmjMAQCwmQULFqhIkSLeLgOAxWjMkas5HI4bbnb+9i8Ad76+ffte99+eI0eOeLs0ADbEqizI1U6fPu3687JlyzR+/HjFxcW5xgoXLuz6s2EYSk9PV/78/M8CQPZp37695s+f7zZWokQJL1UDwM5IzJGrhYaGuragoCA5HA7X60OHDikgIEBfffWVGjRoIKfTqa1bt6pv375uX8krSc8++6xatmzpep2RkaHo6GhVqFBB/v7+qlu3rpYvX27tmwNwR3A6nW7/FoWGhur1119X7dq1VahQIZUpU0ZPP/20EhMTTa/xww8/qFWrVgoICFBgYKAaNGigXbt2ufZv3bpVzZo1k7+/v8qUKaNhw4bp8uXLVrw9ANmIxhx53tixYzV16lQdPHhQderUydI50dHRWrRokd5++23t379fI0aMUJ8+fbRp06YcrhZAbuDj46NZs2Zp//79WrhwoTZs2KDnn3/e9PjevXvrrrvu0s6dO7V7926NHTtWBQoUkCQdPXpU7du3V7du3bR3714tW7ZMW7du9fp6zAA8x+/skedNmjRJDzzwQJaPT05O1pQpU7Ru3To1btxYklSxYkVt3bpV77zzjlq0aJFTpQK4A61evdpt2lx4eLg++eQT1+vy5ctr8uTJGjRokN58883rXuPEiRMaPXq0qlWrJkm6++67Xfuio6PVu3dvPfvss659s2bNUosWLfTWW2/Jz88vB94VgJxAY448r2HDhh4df+TIEV25ciVTM5+SkqL69etnZ2kAcoFWrVrprbfecr0uVKiQ1q1bp+joaB06dEiXLl1SWlqakpKSdOXKFRUsWDDTNUaOHKkBAwbogw8+UJs2bfTII4+oUqVKkv6a5rJ3714tXrzYdbxhGMrIyFB8fLyqV6+e828SQLagMUeeV6hQIbfXPj4+uvYLcVNTU11/vjoP9IsvvlDp0qXdjuOrswFcq1ChQqpcubLr9fHjx/XQQw9p8ODBevnllxUcHKytW7eqf//+SklJuW5jPnHiRD322GP64osv9NVXX2nChAlaunSpunTposTERD311FMaNmxYpvPKli2bo+8NQPaiMQeuUaJECe3bt89tLDY21jWfs0aNGnI6nTpx4gTTVgB4bPfu3crIyNCrr74qH5+/Pur18ccf3/S8KlWqqEqVKhoxYoR69eql+fPnq0uXLrrnnnt04MABt+YfwJ2JD38C1/jnP/+pXbt2adGiRTp8+LAmTJjg1qgHBARo1KhRGjFihBYuXKijR49qz549mj17thYuXOjFygHcCSpXrqzU1FTNnj1bx44d0wcffKC3337b9Pg///xTQ4YM0bfffquff/5Z3333nXbu3OmaojJmzBht27ZNQ4YMUWxsrA4fPqyVK1fy4U/gDkRjDlyjXbt2GjdunJ5//nnde++9+uOPP/T444+7HfPSSy9p3Lhxio6OVvXq1dW+fXt98cUXqlChgpeqBnCnqFu3rl577TX9+9//Vq1atbR48WJFR0ebHp8vXz6dO3dOjz/+uKpUqaIePXooPDxcUVFRkqQ6depo06ZN+umnn9SsWTPVr19f48ePV1hYmFVvCUA2cRjXTqYFAAAAYDkScwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAeVbfvn318MMPu163bNlSzz77rOV1fPvtt3I4HLpw4YLpMQ6HQytWrMjyNSdOnKh69erdVl3Hjx+Xw+FQbGzsbV0HAJA1NOYAbKVv375yOBxyOBzy9fVV5cqVNWnSJKWlpeX4vT/77DO99NJLWTo2K800AACeyO/tAgDgWu3bt9f8+fOVnJysL7/8Us8884wKFCigyMjITMempKTI19c3W+4bHBycLdcBAOBWkJgDsB2n06nQ0FCVK1dOgwcPVps2bfSf//xH0v9NP3n55ZcVFhamqlWrSpL+97//qUePHipSpIiCg4PVuXNnHT9+3HXN9PR0jRw5UkWKFFGxYsX0/PPPyzAMt/teO5UlOTlZY8aMUZkyZeR0OlW5cmXNnTtXx48fV6tWrSRJRYsWlcPhUN++fSVJGRkZio6OVoUKFeTv76+6detq+fLlbvf58ssvVaVKFfn7+6tVq1ZudWbVmDFjVKVKFRUsWFAVK1bUuHHjlJqamum4d955R2XKlFHBggXVo0cPXbx40W3/+++/r+rVq8vPz0/VqlXTm2++6XEtAIDsQWMOwPb8/f2VkpLier1+/XrFxcVp7dq1Wr16tVJTU9WuXTsFBARoy5Yt+u6771S4cGG1b9/edd6rr76qBQsWaN68edq6dasSEhL0+eef3/C+jz/+uD766CPNmjVLBw8e1DvvvKPChQurTJky+vTTTyVJcXFxOn36tF5//XVJUnR0tBYtWqS3335b+/fv14gRI9SnTx9t2rRJ0l8/QHTt2lUdO3ZUbGysBgwYoLFjx3r8TAICArRgwQIdOHBAr7/+ut577z3NmDHD7ZgjR47o448/1qpVq7RmzRp9//33evrpp137Fy9erPHjx+vll1/WwYMHNWXKFI0bN04LFy70uB4AQDYwAMBGIiIijM6dOxuGYRgZGRnG2rVrDafTaYwaNcq1v2TJkkZycrLrnA8++MCoWrWqkZGR4RpLTk42/P39ja+//towDMMoVaqUMW3aNNf+1NRU46677nLdyzAMo0WLFsbw4cMNwzCMuLg4Q5Kxdu3a69a5ceNGQ5Jx/vx511hSUpJRsGBBY9u2bW7H9u/f3+jVq5dhGIYRGRlp1KhRw23/mDFjMl3rWpKMzz//3HT/9OnTjQYNGrheT5gwwciXL59x8uRJ19hXX31l+Pj4GKdPnzYMwzAqVapkLFmyxO06L730ktG4cWPDMAwjPj7ekGR8//33pvcFAGQf5pgDsJ3Vq1ercOHCSk1NVUZGhh577DFNnDjRtb927dpu88p/+OEHHTlyRAEBAW7XSUpK0tGjR3Xx4kWdPn1ajRo1cu3Lnz+/GjZsmGk6y1WxsbHKly+fWrRokeW6jxw5oitXruiBBx5wG09JSVH9+vUlSQcPHnSrQ5IaN26c5XtctWzZMs2aNUtHjx5VYmKi0tLSFBgY6HZM2bJlVbp0abf7ZGRkKC4uTgEBATp69Kj69++vgQMHuo5JS0tTUFCQx/UAAG4fjTkA22nVqpXeeust+fr6KiwsTPnzu/9TVahQIbfXiYmJatCggRYvXpzpWiVKlLilGvz9/T0+JzExUZL0xRdfuDXE0l/z5rNLTEyMevfuraioKLVr105BQUFaunSpXn31VY9rfe+99zL9oJAvX75sqxUAkHU05gBsp1ChQqpcuXKWj7/nnnu0bNkyhYSEZEqNrypVqpR27Nih5s2bS/orGd69e7fuueee6x5fu3ZtZWRkaNOmTWrTpk2m/VcT+/T0dNdYjRo15HQ6deLECdOkvXr16q4Psl61ffv2m7/Jv9m2bZvKlSunF154wTX2888/ZzruxIkTOnXqlMLCwlz38fHxUdWqVVWyZEmFhYXp2LFj6t27t0f3BwDkDD78CeCO17t3bxUvXlydO3fWli1bFB8fr2+//VbDhg3TyZMnJUnDhw/X1KlTtWLFCh06dEhPP/30DdcgL1++vCIiIvTEE09oxYoVrmt+/PHHkqRy5crJ4XBo9erV+u2335SYmKiAgACNGjVKI0aM0MKFC3X06FHt2bNHs2fPdn2gctCgQTp8+LBGjx6tuLg4LVmyRAsWLPDo/d599906ceKEli5dqqNHj2rWrFnX/SCrn5+fIiIi9MMPP2jLli0aNmyYevToodDQUElSVFSUoqOjNWvWLP3000/68ccfNX/+fL322mse1QMAyB405gDueAULFtTmzZtVtmxZde3aVdWrV1f//v2VlJTkStCfe+45/etf/1JERIQaN26sgIAAdenS5YbXfeutt9S9e3c9/fTTqlatmgYOHKjLly9LkkqXLq2oqCiNHTtWJUuW1JAhQyRJL730ksaNG6fo6GhVr15d7du31xdffKEKFSpI+mve96effqoVK1aobt26evvttzVlyhSP3m+nTp00YsQIDRkyRPXq1dO2bds0bty4TMdVrlxZXbt2VYcOHdS2bVvVqVPHbTnEAQMG6P3339f8+fNVu3ZttWjRQgsWLHDVCgCwlsMw++QTAAAAAMuQmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgAzTmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw7kUeXLl1ffvn29XUaus2bNGtWrV09+fn5yOBy6cOFCtl5/wYIFcjgcOn78eLZe907mcDg0ceJEb5cBALeNxhzIZY4ePaqnnnpKFStWlJ+fnwIDA9WkSRO9/vrr+vPPP71d3g19++23cjgc1922b9/u0XW6du2q0NBQ+fr6KiQkRB07dtRnn32Wg9VL586dU48ePeTv76833nhDH3zwgQoVKpSj97RS+fLl5XA41KZNm+vuf++991x/X7t27fL4+tu2bdPEiROz/YcZALhT5Pd2AQCyzxdffKFHHnlETqdTjz/+uGrVqqWUlBRt3bpVo0eP1v79+/Xuu+96u8ybGjZsmO699163scqVK2fp3AkTJmjSpEm6++679dRTT6lcuXI6d+6cvvzyS3Xr1k2LFy/WY489lhNla+fOnfrjjz/00ksvmTavt+tf//qXevbsKafTmSPXvxk/Pz9t3LhRZ86cUWhoqNu+xYsXy8/PT0lJSbd07W3btikqKkp9+/ZVkSJFsnzen3/+qfz5+X9nAO58/EsG5BLx8fHq2bOnypUrpw0bNqhUqVKufc8884yOHDmiL774wosVZl2zZs3UvXt3j89bvny5Jk2apO7du2vJkiUqUKCAa9/o0aP19ddfKzU1NTtLdfPrr79KkkdNpafy5cunfPny5dj1b6ZJkybauXOnli1bpuHDh7vGT548qS1btqhLly769NNPc7yOjIwMpaSkyM/PT35+fjl+PwCwAlNZgFxi2rRpSkxM1Ny5c92a8qsqV67s1khdKyEhQaNGjVLt2rVVuHBhBQYGKjw8XD/88EOmY2fPnq2aNWuqYMGCKlq0qBo2bKglS5a49v/xxx969tlnVb58eTmdToWEhOiBBx7Qnj17svx+/vjjD6WlpWX5eEkaN26cgoODNW/ePLem/Kp27drpoYcecr3+9ddf1b9/f5UsWVJ+fn6qW7euFi5c6HbO8ePH5XA49Morr+jdd99VpUqV5HQ6de+992rnzp2u41q2bKmIiAhJ0r333iuHw+Gaw282n79ly5Zq2bKl29jNnq3ZHPM333xTNWvWlNPpVFhYmJ555plMU0JatmypWrVq6cCBA2rVqpUKFiyo0qVLa9q0aWaPNBM/Pz917drVrSZJ+uijj1S0aFG1a9cu0zl79+5V3759XdOrQkND9cQTT+jcuXOuYyZOnKjRo0dLkipUqOCaEnP1fTocDg0ZMkSLFy92vc81a9a49l2dY/7nn3+qWrVqqlatmtvUrYSEBJUqVUr333+/0tPTs/x+AcBKJOZALrFq1SpVrFhR999//y2df+zYMa1YsUKPPPKIKlSooLNnz+qdd95RixYtdODAAYWFhUn6ax7xsGHD1L17dw0fPlxJSUnau3evduzY4ZoiMmjQIC1fvlxDhgxRjRo1dO7cOW3dulUHDx7UPffcc9Na+vXrp8TEROXLl0/NmjXT9OnT1bBhwxuec/jwYR06dEhPPPGEAgICbnqPP//8Uy1bttSRI0c0ZMgQVahQQZ988on69u2rCxcuZPohZsmSJfrjjz/01FNPyeFwaNq0aeratauOHTumAgUK6IUXXlDVqlX17rvvatKkSapQoYIqVap00zr+LivP9nomTpyoqKgotWnTRoMHD1ZcXJzeeust7dy5U999953bDynnz59X+/bt1bVrV/Xo0UPLly/XmDFjVLt2bYWHh2epzscee0xt27bV0aNHXe9xyZIl6t69+3V/IFq7dq2OHTumfv36KTQ01DWlav/+/dq+fbscDoe6du2qn376SR999JFmzJih4sWLS5JKlCjhus6GDRv08ccfa8iQISpevLjKly+f6V7+/v5auHChmjRpohdeeEGvvfaapL9+a3Tx4kUtWLDAq79xAIAbMgDc8S5evGhIMjp37pzlc8qVK2dERES4XiclJRnp6elux8THxxtOp9OYNGmSa6xz585GzZo1b3jtoKAg45lnnslyLVd99913Rrdu3Yy5c+caK1euNKKjo41ixYoZfn5+xp49e2547sqVKw1JxowZM7J0r5kzZxqSjA8//NA1lpKSYjRu3NgoXLiwcenSJcMw/noGkoxixYoZCQkJme63atUq19j8+fMNScbOnTvd7nXts76qRYsWRosWLVyvs/Jsr94jPj7eMAzD+PXXXw1fX1+jbdu2bn9/c+bMMSQZ8+bNc7ufJGPRokWuseTkZCM0NNTo1q3bDe979X08+OCDRlpamhEaGmq89NJLhmEYxoEDBwxJxqZNm677DK5cuZLpWh999JEhydi8ebNrbPr06W7v7e8kGT4+Psb+/fuvu2/ChAluY5GRkYaPj4+xefNm45NPPjEkGTNnzrzpewQAb2IqC5ALXLp0SZKylBSbcTqd8vH565+E9PR0nTt3ToULF1bVqlXdpqAUKVJEJ0+edJvGca0iRYpox44dOnXqlEc13H///Vq+fLmeeOIJderUSWPHjnUlqpGRkTc819Nn8OWXXyo0NFS9evVyjRUoUEDDhg1TYmKiNm3a5Hb8o48+qqJFi7peN2vWTNJfv2nILll5ttdat26dUlJS9Oyzz7r+/iRp4MCBCgwMzPS5gsKFC6tPnz6u176+vrrvvvs8eh/58uVTjx499NFHH0n660OfZcqUcT2Ta/n7+7v+nJSUpN9//13/+Mc/JMmj6U0tWrRQjRo1snTsxIkTVbNmTUVEROjpp59WixYtNGzYsCzfCwC8gcYcyAUCAwMl/TUv+1ZlZGRoxowZuvvuu+V0OlW8eHGVKFFCe/fu1cWLF13HjRkzRoULF9Z9992nu+++W88884y+++47t2tNmzZN+/btU5kyZXTfffdp4sSJt9zAVq5cWZ07d9bGjRtvODfY02fw888/6+6773ZrZiWpevXqrv1/V7ZsWbfXV5v08+fPZ+l+WZGVZ3utq3VWrVrVbdzX11cVK1bM9D7uuusuORwOt7GiRYt6/D4ee+wxHThwQD/88IOWLFminj17ZrruVQkJCRo+fLhKliwpf39/lShRQhUqVJAkt/+2bubqOVnh6+urefPmKT4+Xn/88Yfmz59vWh8A2AWNOZALBAYGKiwsTPv27bvla0yZMkUjR45U8+bN9eGHH+rrr7/W2rVrVbNmTWVkZLiOq169uuLi4rR06VI1bdpUn376qZo2baoJEya4junRo4eOHTum2bNnKywsTNOnT1fNmjX11Vdf3VJtZcqUUUpKii5fvmx6TLVq1SRJP/744y3d42bM5iUbhnHTc80awmt/0MjKs71dt/M+/q5Ro0aqVKmSnn32WcXHx99wDnyPHj303nvvadCgQfrss8/0zTffuD64+ff/tm7m78l7Vnz99deS/krpDx8+7NG5AOANNOZALvHQQw/p6NGjiomJuaXzly9frlatWmnu3Lnq2bOn2rZtqzZt2lz3y14KFSqkRx99VPPnz9eJEyf04IMP6uWXX3Zbv7pUqVJ6+umntWLFCsXHx6tYsWJ6+eWXb6m2Y8eOyc/PT4ULFzY9pkqVKqpatapWrlypxMTEm16zXLlyOnz4cKbG8NChQ6792aVo0aLXfY7XptlS1p7t312tMy4uzm08JSVF8fHx2fo+rtWrVy99++23ql69uurVq3fdY86fP6/169dr7NixioqKUpcuXfTAAw+oYsWKmY7NzkR77969mjRpkvr166f69etrwIABHqXzAOANNOZALvH888+rUKFCGjBggM6ePZtp/9GjR/X666+bnp8vX75Mqeknn3yiX375xW3s70vcSX9NGahRo4YMw1BqaqrS09MzNUAhISEKCwtTcnLyDd/Db7/9lmnshx9+0H/+8x+1bds207STa0VFRencuXMaMGDAdZda/Oabb7R69WpJUocOHXTmzBktW7bMtT8tLU2zZ89W4cKF1aJFixveyxOVKlXS9u3blZKS4hpbvXq1/ve//7kdd7Nnez1t2rSRr6+vZs2a5fb3N3fuXF28eFEPPvhgtr2Paw0YMEATJkzQq6++anrM1YT+2v+2Zs6cmenYq9+Servf/Jmamqq+ffsqLCxMr7/+uhYsWKCzZ89qxIgRt3VdAMhpLJcI5BKVKlXSkiVL9Oijj6p69epu3/y5bds211KAZh566CFXwnj//ffrxx9/1OLFizMlm23btlVoaKiaNGmikiVL6uDBg5ozZ44efPBBBQQE6MKFC7rrrrvUvXt31a1bV4ULF9a6deu0c+fOGzZw0l8fsPT399f999+vkJAQHThwQO+++64KFiyoqVOn3vQZPProo/rxxx/18ssv6/vvv1evXr1c3/y5Zs0arV+/3rX+9pNPPql33nlHffv21e7du1W+fHktX75c3333nWbOnHlbH6S91oABA7R8+XK1b99ePXr00NGjR/Xhhx9mWk7xZs/2ekqUKKHIyEhFRUWpffv26tSpk+Li4vTmm2/q3nvvdfugZ3YrV66ca/1wM4GBgWrevLmmTZum1NRUlS5dWt98843i4+MzHdugQQNJ0gsvvKCePXuqQIEC6tixo6thz6rJkycrNjZW69evV0BAgOrUqaPx48frxRdfVPfu3dWhQwePrgcAlvHiijAAcsBPP/1kDBw40Chfvrzh6+trBAQEGE2aNDFmz55tJCUluY673nKJzz33nFGqVCnD39/faNKkiRETE5NpSb933nnHaN68uVGsWDHD6XQalSpVMkaPHm1cvHjRMIy/lt8bPXq0UbduXSMgIMAoVKiQUbduXePNN9+8ae2vv/66cd999xnBwcFG/vz5jVKlShl9+vQxDh8+7NEzWL9+vdG5c2cjJCTEyJ8/v1GiRAmjY8eOxsqVK92OO3v2rNGvXz+jePHihq+vr1G7dm1j/vz5bsdcXS5x+vTpme6ja5bpM1su0TAM49VXXzVKly5tOJ1Oo0mTJsauXbs8frZ/v8e1SwrOmTPHqFatmlGgQAGjZMmSxuDBg43z58+7HdOiRYvrLscYERFhlCtXLtP4ta4ul3gj13sGJ0+eNLp06WIUKVLECAoKMh555BHj1KlT113m8KWXXjJKly5t+Pj4uL1PSaZLcP79Ort37zby589vDB061O2YtLQ049577zXCwsIyPRcAsAuHYXj4iR8AAAAA2Y455gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgAzTmAAAAgA3QmAMAAAA2QGMOAAAA2ECu/OZP//pDvF0CACjhv3O8XQIAyL+AtytwZ1Wf9uf3d96/wSTmAAAAgA3kysQcAAAANuUgFzbDkwEAAABsgMQcAAAA1nE4vF2BbZGYAwAAADZAYg4AAADrMMfcFE8GAAAAsAEScwAAAFiHOeamSMwBAAAAGyAxBwAAgHWYY26KJwMAAADYAIk5AAAArMMcc1Mk5gAAAIANkJgDAADAOswxN8WTAQAAAGyAxBwAAADWYY65KRJzAAAAwAZozAEAAAAbYCoLAAAArMOHP03xZAAAAAAbIDEHAACAdfjwpykScwAAAMAGSMwBAABgHeaYm+LJAAAAADZAYg4AAADrMMfcFIk5AAAAYAMk5gAAALAOc8xN8WQAAAAAGyAxBwAAgHVIzE3xZAAAAAAbIDEHAACAdXxYlcUMiTkAAABgAyTmAAAAsA5zzE3xZAAAAAAbIDEHAACAdfjmT1Mk5gAAAIANkJgDAADAOswxN8WTAQAAAGyAxBwAAADWYY65KRJzAAAAwAZIzAEAAGAd5pib4skAAAAgT9u8ebM6duyosLAwORwOrVixwm2/w+G47jZ9+nTXMeXLl8+0f+rUqR7VQWIOAAAA69hwjvnly5dVt25dPfHEE+ratWum/adPn3Z7/dVXX6l///7q1q2b2/ikSZM0cOBA1+uAgACP6qAxBwAAQJ4WHh6u8PBw0/2hoaFur1euXKlWrVqpYsWKbuMBAQGZjvUEU1kAAABgHYePJVtycrIuXbrktiUnJ992+WfPntUXX3yh/v37Z9o3depUFStWTPXr19f06dOVlpbm0bVpzAEAAJDrREdHKygoyG2Ljo6+7esuXLhQAQEBmaa8DBs2TEuXLtXGjRv11FNPacqUKXr++ec9ujZTWQAAAGAdi+aYR0ZGauTIkW5jTqfztq87b9489e7dW35+fm7jf79XnTp15Ovrq6eeekrR0dFZvi+NOQAAAHIdp9OZLY34323ZskVxcXFatmzZTY9t1KiR0tLSdPz4cVWtWjVL16cxBwAAgHXu4HXM586dqwYNGqhu3bo3PTY2NlY+Pj4KCQnJ8vVpzAEAAJCnJSYm6siRI67X8fHxio2NVXBwsMqWLStJunTpkj755BO9+uqrmc6PiYnRjh071KpVKwUEBCgmJkYjRoxQnz59VLRo0SzXQWMOAAAA69hwHfNdu3apVatWrtdX54tHRERowYIFkqSlS5fKMAz16tUr0/lOp1NLly7VxIkTlZycrAoVKmjEiBGZ5rjfjMMwDOPW34Y9+dcf4u0SAEAJ/53j7RIAQP4FvF2BO/8HZ1lynz+/GGbJfbITiTkAAACscwfPMc9pPBkAAADABkjMAQAAYB0Sc1M8GQAAAMAGSMwBAABgHRuuymIXJOYAAACADZCYAwAAwDrMMTfFkwEAAABsgMQcAAAA1mGOuSkScwAAAMAGSMwBAABgHeaYm+LJAAAAADZAYg4AAADrMMfcFIk5AAAAYAMk5gAAALCMg8TcFIk5AAAAYAMk5gAAALAMibk5EnMAAADABkjMAQAAYB0Cc1Mk5gAAAIANkJgDAADAMswxN0diDgAAANgAiTkAAAAsQ2JujsQcAAAAsAEScwAAAFiGxNwciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgHQJzUyTmAAAAgA2QmAMAAMAyzDE3R2IOAAAA2ACJOQAAACxDYm6OxBwAAACwARJzAAAAWIbE3ByJOQAAAGADJOYAAACwDIm5ORJzAAAAwAZIzAEAAGAdAnNTJOYAAACADZCYAwAAwDLMMTdHYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYhsTcHIk5AAAAYAMk5gAAALAOgbkpEnMAAADABkjMAQAAYBnmmJsjMQcAAABsgMQcAAAAliExN0diDgAAANgAiTkAAAAsQ2JujsQcAAAAsAEScwAAAFiGxNwciTkAAABgAzTmAAAAsI7Dos0DmzdvVseOHRUWFiaHw6EVK1a47e/bt68cDofb1r59e7djEhIS1Lt3bwUGBqpIkSLq37+/EhMTPaqDxhwAAAB52uXLl1W3bl298cYbpse0b99ep0+fdm0fffSR2/7evXtr//79Wrt2rVavXq3NmzfrySef9KgO5pgDAADAMnacYx4eHq7w8PAbHuN0OhUaGnrdfQcPHtSaNWu0c+dONWzYUJI0e/ZsdejQQa+88orCwsKyVAeJOQAAAHKd5ORkXbp0yW1LTk6+5et9++23CgkJUdWqVTV48GCdO3fOtS8mJkZFihRxNeWS1KZNG/n4+GjHjh1ZvgeNOQAAACxz7VztnNqio6MVFBTktkVHR99Sze3bt9eiRYu0fv16/fvf/9amTZsUHh6u9PR0SdKZM2cUEhLidk7+/PkVHBysM2fOZPk+TGUBAABArhMZGamRI0e6jTmdzlu6Vs+ePV1/rl27turUqaNKlSrp22+/VevWrW+rzr+jMQcAAIBlrJpj7nQ6b7kRv5mKFSuqePHiOnLkiFq3bq3Q0FD9+uuvbsekpaUpISHBdF769dhiKsuWLVvUp08fNW7cWL/88osk6YMPPtDWrVu9XBkAAADg7uTJkzp37pxKlSolSWrcuLEuXLig3bt3u47ZsGGDMjIy1KhRoyxf1+uN+aeffqp27drJ399f33//vWtS/sWLFzVlyhQvVwcAAIBsZcN1zBMTExUbG6vY2FhJUnx8vGJjY3XixAklJiZq9OjR2r59u44fP67169erc+fOqly5stq1aydJql69utq3b6+BAwfqv//9r7777jsNGTJEPXv2zPKKLJINGvPJkyfr7bff1nvvvacCBQq4xps0aaI9e/Z4sTIAAADkBbt27VL9+vVVv359SdLIkSNVv359jR8/Xvny5dPevXvVqVMnValSRf3791eDBg20ZcsWt6kyixcvVrVq1dS6dWt16NBBTZs21bvvvutRHV6fYx4XF6fmzZtnGg8KCtKFCxesLwgAAAA5xo7rmLds2VKGYZju//rrr296jeDgYC1ZsuS26vB6Yh4aGqojR45kGt+6dasqVqzohYoAAAAA63m9MR84cKCGDx+uHTt2yOFw6NSpU1q8eLFGjRqlwYMHe7s8AAAAZCOr1jG/E3l9KsvYsWOVkZGh1q1b68qVK2revLmcTqdGjRqloUOHers8AAAAwBJeb8wdDodeeOEFjR49WkeOHFFiYqJq1KihwoULe7s0AAAAZLM7Nc22gtcb86t8fX1Vo0YNb5eBXKzJPZU04vE2uqdGWZUqEaQeI97Vqm/3uvaHBAdo8vDOatO4uoIK+2vrniMaOe0THT3xmySpaGBBjRv8oFr/o5rKhBbV7+cTterbvYp6c7UuJSZ5620ByGV279qphfPn6uCBffrtt9/02utv6J+t23i7LAAW8Hpj3qpVqxv+5LRhwwYLq0FuVsjfqR9/+kWLVsZo2WtPZtr/8YwnlZqWrkeefUeXLidpWJ9/6su3h6p+18m6kpSiUiWCVKpEkCJnfK6Dx86obKlgzX6hp0qVCNJjo+d64R0ByI3+/POKqlStqoe7dNPIZ4d4uxwAFvJ6Y16vXj2316mpqYqNjdW+ffsUERHhnaKQK33z3QF9892B6+6rXDZEjepU0D3dJuvgsTOSpGFTlun4uinqEd5ACz6P0YGjp9Vr1Puuc+JP/q6Jc1Zp3suPK18+H6WnZ1jyPgDkbk2btVDTZi28XQaQY5jKYs7rjfmMGTOuOz5x4kQlJiZaXA3yKqfvX/9TSEpJc40ZhqGUlDTdX6+SFnwec93zAgP8dOlyEk05AAC4bV5fLtFMnz59NG/ePG+XgTwi7vgZnTidoJeGdlKRAH8VyJ9Pz/Vto7tCiyq0eNB1zylWpJAiB4Zr3qfbLK4WAIA7mMOi7Q7k9cTcTExMjPz8/G56XHJyspKTk93GjIx0OXzy5VRpyIXS0jLU87n39NaE3jq9ebrS0tK1YUec1mzdr+v9xi2gkJ8+nzVYB4+d1uR3vrC+YAAAkOt4vTHv2rWr22vDMHT69Gnt2rVL48aNu+n50dHRioqKchvLV/JeFSh1X7bWidzv+4P/0z96TlVgYT/5Fsiv388navOiUdp94ITbcYULOvWfN57WH1eS9OjI95SWxjQWAACyijnm5rw+lSUoKMhtCw4OVsuWLfXll19qwoQJNz0/MjJSFy9edNvyl2xgQeXIrS4lJun384mqVLaE7qlRVqv/tqRiQCE/rX5riFJS09X92XeU/Lc56QAAALfDq4l5enq6+vXrp9q1a6to0aK3dA2n0ymn0+k2xjQWXE8hf19VKlPC9bp86WKqU6W0zl+6ov+dOa+uberrt/OJ+t+ZBNW6O0yvjO6uVd/u1frthyT9/035m8/I389X/V5YqMBCfgos9Nd0q9/OJyojw/DK+wKQu1y5clknTvzfb+p++eWkDh06qKCgIJUqFebFyoDsQWJuzquNeb58+dS2bVsdPHjwlhtzIKvuqVFO37w/3PV62qhukqQP/rNdT074UKElAvXv57oqpFiAzvx+SYtX71D0u2tcx9erVkb31akgSTqwaqLbtat2GK8TpxNy/k0AyPX279ungU887nr96rRoSVLHzl300stTvVUWAAs4DMPwaszXsGFD/fvf/1br1q2z7Zr+9flCBgDel/DfOd4uAQDkX8DbFbirPOorS+5z5JVwS+6Tnbw+x3zy5MkaNWqUVq9erdOnT+vSpUtuGwAAAJAXeG0qy6RJk/Tcc8+pQ4cOkqROnTq5zTkyDEMOh0Pp6eneKhEAAADZjDnm5rzWmEdFRWnQoEHauHGjt0oAAAAAbMNrjfnVqe0tWrTwVgkAAACwGIG5Oa/OMedXGQAAAMBfvLpcYpUqVW7anCcksAQdAABAbkEwa86rjXlUVJSCgoK8WQIAAABgC15tzHv27KmQkBBvlgAAAAALEZib89occ36NAQAAAPwfr6/KAgAAgLzDx4dw1ozXGvOMjAxv3RoAAACwHa/OMQcAAEDewmxmc15dxxwAAADAX0jMAQAAYBkWADFHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhjnm5kjMAQAAABsgMQcAAIBlSMzNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAAlmGOuTkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyzDE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIY55uZIzAEAAAAbIDEHAACAZQjMzZGYAwAAADZAYg4AAADLMMfcHIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYBnmmJsjMQcAAABsgMYcAAAAlnE4rNk8sXnzZnXs2FFhYWFyOBxasWKFa19qaqrGjBmj2rVrq1ChQgoLC9Pjjz+uU6dOuV2jfPnycjgcbtvUqVM9qoPGHAAAAHna5cuXVbduXb3xxhuZ9l25ckV79uzRuHHjtGfPHn322WeKi4tTp06dMh07adIknT592rUNHTrUozqYYw4AAADL2HGOeXh4uMLDw6+7LygoSGvXrnUbmzNnju677z6dOHFCZcuWdY0HBAQoNDT0lusgMQcAAAA8cPHiRTkcDhUpUsRtfOrUqSpWrJjq16+v6dOnKy0tzaPrkpgDAADAMlYF5snJyUpOTnYbczqdcjqdt3XdpKQkjRkzRr169VJgYKBrfNiwYbrnnnsUHBysbdu2KTIyUqdPn9Zrr72W5WuTmAMAACDXiY6OVlBQkNsWHR19W9dMTU1Vjx49ZBiG3nrrLbd9I0eOVMuWLVWnTh0NGjRIr776qmbPnp3ph4MbITEHAACAZayaYx4ZGamRI0e6jd1OWn61Kf/555+1YcMGt7T8eho1aqS0tDQdP35cVatWzdI9aMwBAACQ62THtJWrrjblhw8f1saNG1WsWLGbnhMbGysfHx+FhIRk+T405gAAALCMHVdlSUxM1JEjR1yv4+PjFRsbq+DgYJUqVUrdu3fXnj17tHr1aqWnp+vMmTOSpODgYPn6+iomJkY7duxQq1atFBAQoJiYGI0YMUJ9+vRR0aJFs1wHjTkAAADytF27dqlVq1au11enwERERGjixIn6z3/+I0mqV6+e23kbN25Uy5Yt5XQ6tXTpUk2cOFHJycmqUKGCRowYkWkqzc3QmAMAAMAyNgzM1bJlSxmGYbr/Rvsk6Z577tH27dtvuw5WZQEAAABsgMQcAAAAlrHjHHO7IDEHAAAAbIDEHAAAAJYhMDdHYg4AAADYAIk5AAAALMMcc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlfIjMTZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDI+BOamSMwBAAAAGyAxBwAAgGWYY26OxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwjENE5mZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAACzjQ2RuisQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlyMvNZakx/89//pPlC3bq1OmWiwEAAADyqiw15g8//HCWLuZwOJSenn479QAAACAXYx1zc1lqzDMyMnK6DgAAACBPu60PfyYlJWVXHQAAAECe5nFjnp6erpdeekmlS5dW4cKFdezYMUnSuHHjNHfu3GwvEAAAALmHj8Oa7U7kcWP+8ssva8GCBZo2bZp8fX1d47Vq1dL777+frcUBAAAAeYXHjfmiRYv07rvvqnfv3sqXL59rvG7dujp06FC2FgcAAIDcxeFwWLLdiTxuzH/55RdVrlw503hGRoZSU1OzpSgAAAAgr/G4Ma9Ro4a2bNmSaXz58uWqX79+thQFAACA3MnhsGa7E3n8zZ/jx49XRESEfvnlF2VkZOizzz5TXFycFi1apNWrV+dEjQAAAECu53Fi3rlzZ61atUrr1q1ToUKFNH78eB08eFCrVq3SAw88kBM1AgAAIJdgjrk5jxNzSWrWrJnWrl2b3bUAAAAAedYtNeaStGvXLh08eFDSX/POGzRokG1FAQAAIHe6U9cYt4LHjfnJkyfVq1cvfffddypSpIgk6cKFC7r//vu1dOlS3XXXXdldIwAAAJDreTzHfMCAAUpNTdXBgweVkJCghIQEHTx4UBkZGRowYEBO1AgAAIBcgjnm5jxOzDdt2qRt27apatWqrrGqVatq9uzZatasWbYWBwAAAOQVHjfmZcqUue4XCaWnpyssLCxbigIAAEDudGdm2dbweCrL9OnTNXToUO3atcs1tmvXLg0fPlyvvPJKthYHAAAA5LTNmzerY8eOCgsLk8Ph0IoVK9z2G4ah8ePHq1SpUvL391ebNm10+PBht2MSEhLUu3dvBQYGqkiRIurfv78SExM9qiNLjXnRokUVHBys4OBg9evXT7GxsWrUqJGcTqecTqcaNWqkPXv26IknnvDo5gAAAMhbfBwOSzZPXL58WXXr1tUbb7xx3f3Tpk3TrFmz9Pbbb2vHjh0qVKiQ2rVrp6SkJNcxvXv31v79+7V27VqtXr1amzdv1pNPPulRHVmayjJz5kyPLgoAAADcKcLDwxUeHn7dfYZhaObMmXrxxRfVuXNnSdKiRYtUsmRJrVixQj179tTBgwe1Zs0a7dy5Uw0bNpQkzZ49Wx06dNArr7yS5eneWWrMIyIisnQxAAAA4EasWjAlOTlZycnJbmNXZ3t4Ij4+XmfOnFGbNm1cY0FBQWrUqJFiYmLUs2dPxcTEqEiRIq6mXJLatGkjHx8f7dixQ126dMnSvTyeY/53SUlJunTpktsGAAAAeFt0dLSCgoLctujoaI+vc+bMGUlSyZIl3cZLlizp2nfmzBmFhIS47c+fP7+Cg4Ndx2SFx6uyXL58WWPGjNHHH3+sc+fOZdqfnp7u6SUBAACQR1i1xnhkZKRGjhzpNuZpWm41jxPz559/Xhs2bNBbb70lp9Op999/X1FRUQoLC9OiRYtyokYAAADAI06nU4GBgW7brTTmoaGhkqSzZ8+6jZ89e9a1LzQ0VL/++qvb/rS0NCUkJLiOyQqPG/NVq1bpzTffVLdu3ZQ/f341a9ZML774oqZMmaLFixd7ejkAAADkIQ6HNVt2qVChgkJDQ7V+/XrX2KVLl7Rjxw41btxYktS4cWNduHBBu3fvdh2zYcMGZWRkqFGjRlm+l8dTWRISElSxYkVJUmBgoBISEiRJTZs21eDBgz29HAAAAOBViYmJOnLkiOt1fHy8YmNjFRwcrLJly+rZZ5/V5MmTdffdd6tChQoaN26cwsLC9PDDD0uSqlevrvbt22vgwIF6++23lZqaqiFDhqhnz54efQGnx415xYoVFR8fr7Jly6patWr6+OOPdd9992nVqlUqUqSIp5cDAABAHuLpGuNW2LVrl1q1auV6fXVuekREhBYsWKDnn39ely9f1pNPPqkLFy6oadOmWrNmjfz8/FznLF68WEOGDFHr1q3l4+Ojbt26adasWR7V4TAMw/DkhBkzZihfvnwaNmyY1q1bp44dO8owDKWmpuq1117T8OHDPSogJ/jXH+LtEgBACf+d4+0SAED+BbxdgbvBnx6w5D5vdathyX2yk8eJ+YgRI1x/btOmjQ4dOqTdu3ercuXKqlOnTrYWBwAAgNzFhoG5bXjcmF+rXLlyKleuXHbUAgAAAORZWWrMPZkfM2zYsFsuBgAAALmbVeuY34my1JjPmDEjSxdzOBw05gAAAMAtyFJjHh8fn9N1ZKvzO/nAFQDv++Hni94uAQDUqFKQt0tw4/GX6OQhPBsAAADABm77w58AAABAVjHH3ByJOQAAAGADJOYAAACwjA+BuSkScwAAAMAGbqkx37Jli/r06aPGjRvrl19+kSR98MEH2rp1a7YWBwAAgNzFx2HNdifyuDH/9NNP1a5dO/n7++v7779XcnKyJOnixYuaMmVKthcIAAAA5AUeN+aTJ0/W22+/rffee08FChRwjTdp0kR79uzJ1uIAAACQuzgcDku2O5HHjXlcXJyaN2+eaTwoKEgXLlzIjpoAAACAPMfjxjw0NFRHjhzJNL5161ZVrFgxW4oCAABA7sQcc3MeN+YDBw7U8OHDtWPHDjkcDp06dUqLFy/WqFGjNHjw4JyoEQAAAMj1PF7HfOzYscrIyFDr1q115coVNW/eXE6nU6NGjdLQoUNzokYAAADkEnfo9G9LOAzDMG7lxJSUFB05ckSJiYmqUaOGChcunN213bKkNG9XAADSDz9f9HYJAKBGlYK8XYKb57+Is+Q+0x6sasl9stMtf/Onr6+vatSokZ21AAAAIJfzITI35XFj3qpVqxsuQbNhw4bbKggAAADIizxuzOvVq+f2OjU1VbGxsdq3b58iIiKyqy4AAADkQrf0tfN5hMeN+YwZM647PnHiRCUmJt52QQAAAEBelG0/tPTp00fz5s3LrssBAAAgF3I4rNnuRNnWmMfExMjPzy+7LgcAAADkKR5PZenatavba8MwdPr0ae3atUvjxo3LtsIAAACQ+7AqizmPG/OgIPe1MH18fFS1alVNmjRJbdu2zbbCAAAAgLzEo8Y8PT1d/fr1U+3atVW0aNGcqgkAAAC5FIG5OY/mmOfLl09t27bVhQsXcqgcAAAAIG/y+MOftWrV0rFjx3KiFgAAAORyPg5rtjuRx4355MmTNWrUKK1evVqnT5/WpUuX3DYAAAAAnsvyHPNJkybpueeeU4cOHSRJnTp1kuNvk4QMw5DD4VB6enr2VwkAAIBcgVVZzGW5MY+KitKgQYO0cePGnKwHAAAAyJOy3JgbhiFJatGiRY4VAwAAgNyNwNycR3PMHTxJAAAAIEd4tI55lSpVbtqcJyQk3FZBAAAAyL3u1BVTrOBRYx4VFZXpmz8BAAAA3D6PGvOePXsqJCQkp2oBAABALucQkbmZLM8xZ345AAAAkHM8XpUFAAAAuFXMMTeX5cY8IyMjJ+sAAAAA8jSP5pgDAAAAt4PE3JxH65gDAAAAyBkk5gAAALAMC4qYIzEHAAAAbIDEHAAAAJZhjrk5EnMAAADABkjMAQAAYBmmmJsjMQcAAABsgMQcAAAAlvEhMjdFYg4AAADYAIk5AAAALMOqLOZIzAEAAAAbIDEHAACAZZhibo7EHAAAALABEnMAAABYxkdE5mZIzAEAAAAboDEHAACAZRwOazZPlC9fXg6HI9P2zDPPSJJatmyZad+gQYOy/dkwlQUAAAB52s6dO5Wenu56vW/fPj3wwAN65JFHXGMDBw7UpEmTXK8LFiyY7XXQmAMAAMAydlzHvESJEm6vp06dqkqVKqlFixausYIFCyo0NDRH62AqCwAAAHKd5ORkXbp0yW1LTk6+6XkpKSn68MMP9cQTT8jxtzkxixcvVvHixVWrVi1FRkbqypUr2V4zjTkAAAAs4+NwWLJFR0crKCjIbYuOjr5pfStWrNCFCxfUt29f19hjjz2mDz/8UBs3blRkZKQ++OAD9enTJ9ufjcMwDCPbr+plSWnergAApB9+vujtEgBAjSoFebsEN+9u/9mS+0TUD82UkDudTjmdzhue165dO/n6+mrVqlWmx2zYsEGtW7fWkSNHVKlSpWypV2KOOQAAACxk1Td/ZqUJv9bPP/+sdevW6bPPPrvhcY0aNZKkbG/MmcoCAAAASJo/f75CQkL04IMP3vC42NhYSVKpUqWy9f4k5gAAALCMz//X3n2HR1Hu//9/bQLZ9FBDCIQuvQqKUaoizUZRBEEDAooSkCAtHwUSEMPBRlHBGoogTYUDKEeKBBHkUASUEiAEOSqggoAJps/vD37s1yUsJJLMDsnz4TXXxd4zO/Pe8ch555V77jUrMs+nnJwcxcfHKyIiQiVK/L8WOSkpSYsWLVLXrl1VtmxZ7du3T1FRUWrTpo0aN25coDXQmAMAAKDYW79+vU6cOKEnn3zSadzLy0vr16/X9OnTlZqaqrCwMPXs2VMvvvhigddAYw4AAADTWDQwV8eOHXW1NVHCwsKUkJBgSg3MMQcAAAAsgMQcAAAApiEVdo17AwAAAFgAiTkAAABMY7PqJHMLIDEHAAAALIDEHAAAAKYhL3eNxBwAAACwABJzAAAAmMaq3/xpBSTmAAAAgAWQmAMAAMA05OWukZgDAAAAFkBiDgAAANMwxdw1EnMAAADAAmjMAQAAAAtgKgsAAABMY2Mui0sk5gAAAIAFkJgDAADANKTCrnFvAAAAAAsgMQcAAIBpmGPuGok5AAAAYAEk5gAAADANeblrJOYAAACABZCYAwAAwDTMMXeNxBwAAACwABJzAAAAmIZU2DXuDQAAAGABJOYAAAAwDXPMXSMxBwAAACyAxBwAAACmIS93jcQcAAAAsAAScwAAAJiGKeaukZgDAAAAFkBiDgAAANN4MMvcJRJzAAAAwAJIzAEAAGAa5pi7RmIOAAAAWACJOQAAAExjY465SyTmAAAAgAWQmAMAAMA0zDF3jcQcAAAAsAAScwAAAJiGdcxdIzEHAAAALIDEHAAAAKZhjrlrJOYAAACABZCYAwAAwDQk5q6RmAMAAAAWQGIOAAAA0/DNn66RmAMAAAAWQGIOAAAA03gQmLtEYg4AAABYAIk5AAAATMMcc9cslZhnZGQoMTFRWVlZ7i4FAAAAMJUlGvOLFy9q4MCB8vX1VYMGDXTixAlJ0rBhwzR16lQ3VwcAAICCYrOZs92MLNGYR0dHa+/evdq0aZO8vb0d4x06dNCSJUvcWBkAAABgDkvMMV+xYoWWLFmiO+64Q7a//YjToEEDJSUlubEyAAAAFCTmmLtmicT8t99+U3BwcK7x1NRUp0YdAAAAKKos0Zi3aNFCa9ascby+3Iy///77Cg8Pd1dZAAAAKGAeNnO2/IiJiZHNZnPa6tat69iflpamoUOHqmzZsvL391fPnj11+vTpAr4zFpnK8vLLL6tLly46cOCAsrKyNGPGDB04cEBbt25VQkKCu8sDAABAEdegQQOtX7/e8bpEif/XJkdFRWnNmjVatmyZgoKCFBkZqR49euibb74p0BoskZi3atVKe/bsUVZWlho1aqQvv/xSwcHB2rZtm5o3b+7u8gAAAFBAbCb9k18lSpRQSEiIYytXrpwk6fz58/rggw/0+uuv6+6771bz5s0VHx+vrVu36ttvvy3Qe2OJxFySatasqffee8/dZQAAAKAYOnLkiEJDQ+Xt7a3w8HDFxcWpSpUq2rVrlzIzM9WhQwfHsXXr1lWVKlW0bds23XHHHQVWgyUS8927d+v77793vF65cqW6deum//u//1NGRoYbK0NxtHjRQnW5927d1qyR+vZ+RN/v2+fukgAUE6uWztMTXW/XR++87jR+5OA+xY17RoO6t9FTPdtryuinlJGe5qYqgRtj1jrm6enpunDhgtOWnp5+1ZpatmypuXPnau3atZo9e7aSk5PVunVr/fnnnzp16pS8vLxUqlQpp/dUqFBBp06dKtB7Y4nG/Omnn9bhw4clSceOHdOjjz4qX19fLVu2TGPGjHFzdShO1n7xuV6dFqennx2qxcs+U506dfXM0wN15swZd5cGoIg7dviAvvriU4VVr+U0fuTgPr06/jk1vPUOxUyPV+yMuerwwCOyeVji/8IBy4qLi1NQUJDTFhcXd9Vju3TpokceeUSNGzdWp06d9Pnnn+vcuXNaunSpqTVb4r/qw4cPq2nTppKkZcuWqW3btlq0aJHmzp2rTz75xL3FoVhZMC9ePR7upW7de6pmrVp6cWKsvL29teJT/ncIoPCk/XVRs6eN15PDX5Cff6DTvkXvTte9Dz6qB3pFqHLVmqpYuapatrlXJUt6uala4MbYTNqio6N1/vx5py06OjpPNZYqVUq1a9fW0aNHFRISooyMDJ07d87pmNOnTyskJOQf34ersURjbhiGcnJyJEnr169X165dJUlhYWH6/fff3VkaipHMjAwdPLBfd4Tf6Rjz8PDQHXfcqX17v3NjZQCKunlvT1PT2+9Sw2a3O41fOHdWSYk/KLBUaU16fqAiH+usKWOeVuL+Pe4pFLiJ2O12BQYGOm12uz1P701JSVFSUpIqVqyo5s2bq2TJktqwYYNjf2Jiok6cOFHgy3pbojFv0aKFXnrpJS1YsEAJCQm67777JEnJycmqUKGCm6tDcfHHuT+UnZ2tsmXLOo2XLVuWHxABFJpvE77Uj0cT9Uj/obn2/XrqZ0nSZwvfU7tO3TRq8gxVq1VH/4oeqlM/nzC7VKBAeNhspmz5MWrUKCUkJOj48ePaunWrunfvLk9PT/Xp00dBQUEaOHCgRo4cqa+++kq7du3SgAEDFB4eXqAPfkoWWZVl+vTp6tu3r1asWKEXXnhBtWpdml+3fPly3Xnnndd8b3p6eq6J/IanPc8/EQEA4C5nfjutj955XWOmzJKXV+7/3zJyDEnS3V16qE3HByRJ1WrW0YE9O7X5y1XqNSB3Mw8g/3766Sf16dNHZ86cUfny5dWqVSt9++23Kl++vCTpjTfekIeHh3r27Kn09HR16tRJb7/9doHXYYnGvHHjxk6rslz2yiuvyNPT85rvjYuLU2xsrNPYC+Mn6sUJMQVZIoqB0qVKy9PTM9eDnmfOnHGsZQoABen4kYO6cO6sJgx7wjGWk5OtxB++0/pVy/Sv95ZJkkKrVHd6X8WwajrzW8GuBgGYJf8rjBe+xYsXX3O/t7e33nrrLb311luFWoclGnNXvL29r3tMdHS0Ro4c6TRmeJKWI/9KenmpXv0G2v7tNt19z6W1SnNycrR9+zb17tPPzdUBKIrqN71NL7/9sdPYe29MUsXK1XT/I08oOKSSSpctr5M//eh0zKmfT6hJi2v/RhnAzcdtjXnp0qVly+P8n7Nnz7rcZ7fnnraSlnVDpaEYezxigMb/31g1aNBQDRs11kcL5umvv/5St+493F0agCLIx9dPlavVdBqze/vIPzDIMd6lZz999tG7qlLjFlWtUVtfr1+jkz/9qGEvTHVHycCNs2JkbhFua8ynT5/urksDLnXu0lV/nD2rt9+cqd9//0116tbT2++8r7JMZQHgJp279VFmRoYWvfuGUv68oCo1btGYKbNUoWJld5cGoIDZDMMw3F1EQSMxB2AFe3887+4SAEAtawa5uwQn25PM+bvRap87Lyw3xzwtLU0ZGRlOY4GBgS6OBgAAAIoGS6xjnpqaqsjISAUHB8vPz0+lS5d22gAAAFA02GzmbDcjSzTmY8aM0caNGzV79mzZ7Xa9//77io2NVWhoqObPn+/u8gAAAIBCZ4mpLKtWrdL8+fPVrl07DRgwQK1bt1atWrVUtWpVLVy4UH379nV3iQAAACgAN2mYbQpLJOZnz55VjRo1JF2aT355ecRWrVpp8+bN7iwNAAAAMIUlGvMaNWooOTlZklS3bl0tXbpU0qUkvVSpUm6sDAAAAAXKZtJ2E3JrY37s2DHl5ORowIAB2rt3ryRp3Lhxeuutt+Tt7a2oqCiNHj3anSUCAAAApnDrOuaenp46efKkgoODJUmPPvqoZs6cqbS0NO3atUu1atVS48aN831e1jEHYAWsYw7ACqy2nvfO5AumXKdF9ZtvuW23JuZX/kzw+eefKzU1VVWrVlWPHj3+UVMOAAAA3IwssSoLAAAAioebdY1xM7g1MbfZbLJd8W/nytcAAABAceDWxNwwDPXv3192u12SlJaWpiFDhsjPz8/puE8//dQd5QEAAKCAEcG65tbGPCIiwul1v3793FQJAAAA4F5ubczj4+PdeXkAAACYjcjcJUt8wRAAAABQ3LEqCwAAAExjIzJ3icQcAAAAsAAScwAAAJiGlbFdIzEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIfI3CUScwAAAMACSMwBAABgGtYxd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiHyNwlEnMAAADAAkjMAQAAYBrWMXeNxBwAAACwABJzAAAAmIZ1zF0jMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYh8jcJRJzAAAAwAJIzAEAAGAa1jF3jcQcAAAAsAAScwAAAJiGdcxdIzEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIfI3CUScwAAAMACSMwBAABgGtYxd43EHAAAALAAGnMAAADAApjKAgAAANPwBUOukZgDAAAAFkBjDgAAANPYTNryIy4uTrfddpsCAgIUHBysbt26KTEx0emYdu3ayWazOW1DhgzJ9+e/FhpzAAAAFGsJCQkaOnSovv32W61bt06ZmZnq2LGjUlNTnY4bPHiwTp486dimTZtWoHUwxxwAAADmseAc87Vr1zq9njt3roKDg7Vr1y61adPGMe7r66uQkJBCq4PEHAAAAPib8+fPS5LKlCnjNL5w4UKVK1dODRs2VHR0tC5evFig1yUxBwAAgGnM+oKh9PR0paenO43Z7XbZ7fZrvi8nJ0cjRozQXXfdpYYNGzrGH3vsMVWtWlWhoaHat2+fxo4dq8TERH366acFVjONOQAAAIqcuLg4xcbGOo1NnDhRMTEx13zf0KFD9cMPP2jLli1O40899ZTjz40aNVLFihV1zz33KCkpSTVr1iyQmm2GYRgFciYLSctydwUAIO398by7SwAAtawZ5O4SnCT/nmbKdUIDbPlOzCMjI7Vy5Upt3rxZ1atXv+b5U1NT5e/vr7Vr16pTp04FUjOJOQAAAIqcvExbucwwDA0bNkyfffaZNm3adN2mXJL27NkjSapYseKNlOmExhwAAACmseCiLBo6dKgWLVqklStXKiAgQKdOnZIkBQUFycfHR0lJSVq0aJG6du2qsmXLat++fYqKilKbNm3UuHHjAquDqSwAUEiYygLACqw2leW4SVNZqpXzzvOxNtvVf1yIj49X//799b///U/9+vXTDz/8oNTUVIWFhal79+568cUXFRgYWFAlk5gDAADARBaMzK+XU4eFhSkhIaHQ62AdcwAAAMACSMwBAABgGrPWMb8ZkZgDAAAAFkBiDgAAANO4eM4SIjEHAAAALIHEHAAAAKYhMHeNxBwAAACwABJzAAAAmIY55q6RmAMAAAAWQGIOAAAAExGZu0JiDgAAAFgAiTkAAABMwxxz10jMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDEHAAAAKZhjrlrJOYAAACABZCYAwAAwDQ2Zpm7RGIOAAAAWACJOQAAAMxDYO4SiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADANKxj7hqJOQAAAGABJOYAAAAwDeuYu0ZiDgAAAFgAiTkAAADMQ2DuEok5AAAAYAEk5gAAADANgblrJOYAAACABZCYAwAAwDSsY+4aiTkAAABgASTmAAAAMA3rmLtGYg4AAABYAIk5AAAATMMcc9dIzAEAAAALoDEHAAAALIDGHAAAALAA5pgDAADANMwxd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmYY65ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAAPMQmbtEYg4AAABYAIk5AAAATMM65q6RmAMAAAAWQGIOAAAA07COuWsk5gAAAIAFkJgDAADANATmrpGYAwAAABZAYg4AAADzEJm7RGIOAAAAWACNOQAAAExjM+mf/HrrrbdUrVo1eXt7q2XLlvrvf/9bCJ/+2mjMAQAAUKwtWbJEI0eO1MSJE7V79241adJEnTp10q+//mpqHTbDMAxTr2iCtCx3VwAA0t4fz7u7BABQy5pB7i7BiVl9mnc+nqRs2bKlbrvtNr355puSpJycHIWFhWnYsGEaN25cIVWYG4k5AAAAipz09HRduHDBaUtPT891XEZGhnbt2qUOHTo4xjw8PNShQwdt27bNzJKL5qos+fkJCbia9PR0xcXFKTo6Wna73d3l4CZltZQKNxf+HkJRZVafFvNSnGJjY53GJk6cqJiYGKex33//XdnZ2apQoYLTeIUKFXTo0KHCLtNJkZzKAtyoCxcuKCgoSOfPn1dgYKC7ywFQDPH3EHBj0tPTcyXkdrs91w+6v/zyiypVqqStW7cqPDzcMT5mzBglJCRo+/btptQrFdHEHAAAAMXb1ZrwqylXrpw8PT11+vRpp/HTp08rJCSksMq7KuaYAwAAoNjy8vJS8+bNtWHDBsdYTk6ONmzY4JSgm4HEHAAAAMXayJEjFRERoRYtWuj222/X9OnTlZqaqgEDBphaB405cBV2u10TJ07kgSsAbsPfQ4B5Hn30Uf3222+aMGGCTp06paZNm2rt2rW5HggtbDz8CQAAAFgAc8wBAAAAC6AxBwAAACyAxhwAAACwABpzAAAsZu7cuSpVqpS7ywBgMhpzFGk2m+2a25VfywsABal///5X/bvn6NGj7i4NgAWxXCKKtJMnTzr+vGTJEk2YMEGJiYmOMX9/f8efDcNQdna2SpTgPwsABadz586Kj493GitfvrybqgFgZSTmKNJCQkIcW1BQkGw2m+P1oUOHFBAQoC+++ELNmzeX3W7Xli1b1L9/f3Xr1s3pPCNGjFC7du0cr3NychQXF6fq1avLx8dHTZo00fLly839cABuCna73envopCQEM2YMUONGjWSn5+fwsLC9OyzzyolJcXlOfbu3av27dsrICBAgYGBat68uXbu3OnYv2XLFrVu3Vo+Pj4KCwvT8OHDlZqaasbHA1CAaMxR7I0bN05Tp07VwYMH1bhx4zy9Jy4uTvPnz9ecOXO0f/9+RUVFqV+/fkpISCjkagEUBR4eHpo5c6b279+vefPmaePGjRozZozL4/v27avKlStrx44d2rVrl8aNG6eSJUtKkpKSktS5c2f17NlT+/bt05IlS7RlyxZFRkaa9XEAFBB+Z49ib9KkSbr33nvzfHx6erpefvllrV+/XuHh4ZKkGjVqaMuWLXrnnXfUtm3bwioVwE1o9erVTtPmunTpomXLljleV6tWTS+99JKGDBmit99++6rnOHHihEaPHq26detKkm655RbHvri4OPXt21cjRoxw7Js5c6batm2r2bNny9vbuxA+FYDCQGOOYq9Fixb5Ov7o0aO6ePFirmY+IyNDzZo1K8jSABQB7du31+zZsx2v/fz8tH79esXFxenQoUO6cOGCsrKylJaWposXL8rX1zfXOUaOHKlBgwZpwYIF6tChgx555BHVrFlT0qVpLvv27dPChQsdxxuGoZycHCUnJ6tevXqF/yEBFAgacxR7fn5+Tq89PDxkGIbTWGZmpuPPl+eBrlmzRpUqVXI6zm63F1KVAG5Wfn5+qlWrluP18ePHdf/99+uZZ57RlClTVKZMGW3ZskUDBw5URkbGVRvzmJgYPfbYY1qzZo2++OILTZw4UYsXL1b37t2VkpKip59+WsOHD8/1vipVqhTqZwNQsGjMgSuUL19eP/zwg9PYnj17HPM569evL7vdrhMnTjBtBUC+7dq1Szk5OXrttdfk4XHpUa+lS5de9321a9dW7dq1FRUVpT59+ig+Pl7du3fXrbfeqgMHDjg1/wBuTjz8CVzh7rvv1s6dOzV//nwdOXJEEydOdGrUAwICNGrUKEVFRWnevHlKSkrS7t27NWvWLM2bN8+NlQO4GdSqVUuZmZmaNWuWjh07pgULFmjOnDkuj//rr78UGRmpTZs26ccff9Q333yjHTt2OKaojB07Vlu3blVkZKT27NmjI0eOaOXKlTz8CdyEaMyBK3Tq1Enjx4/XmDFjdNttt+nPP//UE0884XTM5MmTNX78eMXFxalevXrq3Lmz1qxZo+rVq7upagA3iyZNmuj111/Xv/71LzVs2FALFy5UXFycy+M9PT115swZPfHEE6pdu7Z69eqlLl26KDY2VpLUuHFjJSQk6PDhw2rdurWaNWumCRMmKDQ01KyPBKCA2IwrJ9MCAAAAMB2JOQAAAGABNOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFgAjTmAYqt///7q1q2b43W7du00YsQI0+vYtGmTbDabzp075/IYm82mFStW5PmcMTExatq06Q3Vdfz4cdlsNu3Zs+eGzgMAyBsacwCW0r9/f9lsNtlsNnl5ealWrVqaNGmSsrKyCv3an376qSZPnpynY/PSTAMAkB8l3F0AAFypc+fOio+PV3p6uj7//HMNHTpUJUuWVHR0dK5jMzIy5OXlVSDXLVOmTIGcBwCAf4LEHIDl2O12hYSEqGrVqnrmmWfUoUMH/fvf/5b0/6afTJkyRaGhoapTp44k6X//+5969eqlUqVKqUyZMnrooYd0/Phxxzmzs7M1cuRIlSpVSmXLltWYMWNkGIbTda+cypKenq6xY8cqLCxMdrtdtWrV0gcffKDjx4+rffv2kqTSpUvLZrOpf//+kqScnBzFxcWpevXq8vHxUZMmTbR8+XKn63z++eeqXbu2fHx81L59e6c682rs2LGqXbu2fH19VaNGDY0fP16ZmZm5jnvnnXcUFhYmX19f9erVS+fPn3fa//7776tevXry9vZW3bp19fbbb+e7FgBAwaAxB2B5Pj4+ysjIcLzesGGDEhMTtW7dOq1evVqZmZnq1KmTAgIC9PXXX+ubb76Rv7+/Onfu7Hjfa6+9prlz5+rDDz/Uli1bdPbsWX322WfXvO4TTzyhjz/+WDNnztTBgwf1zjvvyN/fX2FhYfrkk08kSYmJiTp58qRmzJghSYqLi9P8+fM1Z84c7d+/X1FRUerXr58SEhIkXfoBokePHnrggQe0Z88eDRo0SOPGjcv3PQkICNDcuXN14MABzZgxQ++9957eeOMNp2OOHj2qpUuXatWqVVq7dq2+++47Pfvss479Cxcu1IQJEzRlyhQdPHhQL7/8ssaPH6958+blux4AQAEwAMBCIiIijIceesgwDMPIyckx1q1bZ9jtdmPUqFGO/RUqVDDS09Md71mwYIFRp04dIycnxzGWnp5u+Pj4GP/5z38MwzCMihUrGtOmTXPsz8zMNCpXruy4lmEYRtu2bY3nnnvOMAzDSExMNCQZ69atu2qdX331lSHJ+OOPPxxjaWlphq+vr7F161anYwcOHGj06dPHMAzDiI6ONurXr++0f+zYsbnOdSVJxmeffeZy/yuvvGI0b97c8XrixImGp6en8dNPPznGvvjiC8PDw8M4efKkYRiGUbNmTWPRokVO55k8ebIRHh5uGIZhJCcnG5KM7777zuV1AQAFhznmACxn9erV8vf3V2ZmpnJycvTYY48pJibGsb9Ro0ZO88r37t2ro0ePKiAgwOk8aWlpSkpK0vnz53Xy5Em1bNnSsa9EiRJq0aJFruksl+3Zs0eenp5q27Ztnus+evSoLl68qHvvvddpPCMjQ82aNZMkHTx40KkOSQoPD8/zNS5bsmSJZs6cqaSkJKWkpCgrK0uBgYFOx1SpUkWVKlVyuk5OTo4SExMVEBCgpKQkDRw4UIMHD3Yck5WVpaCgoHzXAwC4cTTmACynffv2mj17try8vBQaGqoSJZz/qvLz83N6nZKSoubNm2vhwoW5zlW+fPl/VIOPj0++35OSkiJJWrNmjVNDLF2aN19Qtm3bpr59+yo2NladOnVSUFCQFi9erNdeey3ftb733nu5flDw9PQssFoBAHlHYw7Acvz8/FSrVq08H3/rrbdqyZIlCg4OzpUaX1axYkVt375dbdq0kXQpGd61a5duvfXWqx7fqFEj5eTkKCEhQR06dMi1/3Jin52d7RirX7++7Ha7Tpw44TJpr1evnuNB1su+/fbb63/Iv9m6dauqVq2qF154wTH2448/5jruxIkT+uWXXxQaGuq4joeHh+rUqaMKFSooNDRUx44dU9++ffN1fQBA4eDhTwA3vb59+6pcuXJ66KGH9PXXXys5OVmbNm3S8OHD9dNPP0mSnnvuOU2dOlUrVqzQoUOH9Oyzz15zDfJq1aopIiJCTz75pFasWOE459KlSyVJVatWlc1m0+rVq/Xbb78pJSVFAQEBGjVqlKKiojRv3jwlJSVp9+7dmjVrluOByiFDhujIkSMaPXq0EhMTtWjRIs2dOzdfn/eWW27RiRMntHjxYiUlJWnmzJlXfZDV29tbERER2rt3r77++msNHz5cvXr1UkhIiCQpNjZWcXFxmjlzpg4fPqzvv/9e8fHxev311/NVDwCgYNCYA7jp+fr6avPmzapSpYp69OihevXqaeDAgUpLS3Mk6M8//7wef/xxRUREKDw8XAEBAerevfs1zzt79mw9/PDDevbZZ1W3bl0NHjxYqampkqRKlSopNjZW48aNU4UKFRQZGSlJmjx5ssaPH6+4uDjVq1dPnTt31po1a1S9enVJl+Z9f/LJJ1qxYoWaNGmiOXPm6OWXX87X533wwQcVFRWlyMhINW3aVFu3btX48eNzHVerVi316NFDXbt2VceOHdW4cWOn5RAHDRqk999/X/Hx8WrUqJHatm2ruXPnOmoFAJjLZrh68gkAAACAaUjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC6AxB4qpatWqqX///u4uo8hZu3atmjZtKm9vb9lsNp07d65Azz937lzZbDYdP368QM97M7PZbIqJiXF3GQBww2jMgSImKSlJTz/9tGrUqCFvb28FBgbqrrvu0owZM/TXX3+5u7w82b17tx588EGVKVNGvr6+atiwoWbOnJnn92/atEk9evRQSEiIvLy8FBwcrAceeECffvppIVYtnTlzRr169ZKPj4/eeustLViwQH5+foV6TTNVq1ZNNptNHTp0uOr+9957TzabTTabTTt37sz3+bdu3aqYmJgC/2EGAG4WJdxdAICCs2bNGj3yyCOy2+164okn1LBhQ2VkZGjLli0aPXq09u/fr3fffdfdZV7Tl19+qQceeEDNmjXT+PHj5e/vr6SkJP300095ev/EiRM1adIk3XLLLXr66adVtWpVnTlzRp9//rl69uyphQsX6rHHHiuU2nfs2KE///xTkydPdtm83qjHH39cvXv3lt1uL5TzX4+3t7e++uornTp1SiEhIU77Fi5cKG9vb6Wlpf2jc2/dulWxsbHq37+/SpUqlef3/fXXXypRgv87A3Dz428yoIhITk5W7969VbVqVW3cuFEVK1Z07Bs6dKiOHj2qNWvWuLHC67tw4YKeeOIJ3XfffVq+fLk8PPL3S73ly5dr0qRJevjhh7Vo0SKVLFnSsW/06NH6z3/+o8zMzIIu2+HXX3+VpHw1lfnl6ekpT0/PQjv/9dx1113asWOHlixZoueee84x/tNPP+nrr79W9+7d9cknnxR6HTk5OcrIyJC3t7e8vb0L/XoAYAamsgBFxLRp05SSkqIPPvjAqSm/rFatWk6N1JXOnj2rUaNGqVGjRvL391dgYKC6dOmivXv35jp21qxZatCggXx9fVW6dGm1aNFCixYtcuz/888/NWLECFWrVk12u13BwcG69957tXv37mt+hkWLFun06dOaMmWKPDw8lJqaqpycnDzfg/Hjx6tMmTL68MMPnZryyzp16qT777/f8frXX3/VwIEDVaFCBXl7e6tJkyaaN2+e03uOHz8um82mV199Ve+++65q1qwpu92u2267TTt27HAc165dO0VEREiSbrvtNtlsNsccflfz+du1a6d27do5jV3v3rqaY/7222+rQYMGstvtCg0N1dChQ3NNCWnXrp0aNmyoAwcOqH379vL19VWlSpU0bdo0V7c0F29vb/Xo0cOpJkn6+OOPVbp0aXXq1CnXe/bt26f+/fs7pleFhIToySef1JkzZxzHxMTEaPTo0ZKk6tWrO6bEXP6cNptNkZGRWrhwoeNzrl271rHv8hzzv/76S3Xr1lXdunWdpm6dPXtWFStW1J133qns7Ow8f14AMBOJOVBErFq1SjVq1NCdd975j95/7NgxrVixQo888oiqV6+u06dP65133lHbtm114MABhYaGSro0j3j48OF6+OGH9dxzzyktLU379u3T9u3bHVNEhgwZouXLlysyMlL169fXmTNntGXLFh08eFC33nqryxrWr1+vwMBA/fzzz+rWrZsOHz4sPz8/Pf7443rjjTeumYweOXJEhw4d0pNPPqmAgIDrft6//vpL7dq109GjRxUZGanq1atr2bJl6t+/v86dO5frh5hFixbpzz//1NNPPy2bzaZp06apR48eOnbsmEqWLKkXXnhBderU0bvvvqtJkyapevXqqlmzZl5uvUNe7u3VxMTEKDY2Vh06dNAzzzyjxMREzZ49Wzt27NA333zj9EPKH3/8oc6dO6tHjx7q1auXli9frrFjx6pRo0bq0qVLnup87LHH1LFjRyUlJTk+46JFi/Twww9f9QeidevW6dixYxowYIBCQkIcU6r279+vb7/9VjabTT169NDhw4f18ccf64033lC5cuUkSeXLl3ecZ+PGjVq6dKkiIyNVrlw5VatWLde1fHx8NG/ePN1111164YUX9Prrr0u69Fuj8+fPa+7cuW79jQMAXJMB4KZ3/vx5Q5Lx0EMP5fk9VatWNSIiIhyv09LSjOzsbKdjkpOTDbvdbkyaNMkx9tBDDxkNGjS45rmDgoKMoUOH5rmWyxo3bmz4+voavr6+xrBhw4xPPvnEGDZsmCHJ6N279zXfu3LlSkOS8cYbb+TpWtOnTzckGR999JFjLCMjwwgPDzf8/f2NCxcuGIZx6R5IMsqWLWucPXs21/VWrVrlGIuPjzckGTt27HC61pX3+rK2bdsabdu2dbzOy729fI3k5GTDMAzj119/Nby8vIyOHTs6/ft78803DUnGhx9+6HQ9Scb8+fMdY+np6UZISIjRs2fPa1738ue47777jKysLCMkJMSYPHmyYRiGceDAAUOSkZCQcNV7cPHixVzn+vjjjw1JxubNmx1jr7zyitNn+ztJhoeHh7F///6r7ps4caLTWHR0tOHh4WFs3rzZWLZsmSHJmD59+nU/IwC4E1NZgCLgwoULkpSnpNgVu93umNOdnZ2tM2fOyN/fX3Xq1HGaglKqVCn99NNPTtM4rlSqVClt375dv/zyS75qSElJ0cWLF/XEE09o5syZ6tGjh2bOnKmnn35aixcv1pEjR1y+N7/34PPPP1dISIj69OnjGCtZsqSGDx+ulJQUJSQkOB3/6KOPqnTp0o7XrVu3lnTpNw0FJS/39krr169XRkaGRowY4TQnf/DgwQoMDMz1XIG/v7/69evneO3l5aXbb789X5/D09NTvXr10scffyzp0kOfYWFhjntyJR8fH8ef09LS9Pvvv+uOO+6QpOtOb/q7tm3bqn79+nk6NiYmRg0aNFBERISeffZZtW3bVsOHD8/ztQDAHWjMgSIgMDBQ0qW53f9UTk6O3njjDd1yyy2y2+0qV66cypcvr3379un8+fOO48aOHSt/f3/dfvvtuuWWWzR06FB98803TueaNm2afvjhB4WFhen2229XTExMnhq/yw3c35tlSY5pHNu2bXP53vzegx9//FG33HJLrgdM69Wr59j/d1WqVHF6fblJ/+OPP/J0vbzIy7290uU669Sp4zTu5eWlGjVq5PoclStXls1mcxorXbp0vj/HY489pgMHDmjv3r1atGiRevfuneu8l509e1bPPfecKlSoIB8fH5UvX17Vq1eXJKf/bV3P5ffkhZeXlz788EMlJyfrzz//VHx8vMv6AMAqaMyBIiAwMFChoaH64Ycf/vE5Xn75ZY0cOVJt2rTRRx99pP/85z9at26dGjRo4PQAZr169ZSYmKjFixerVatW+uSTT9SqVStNnDjRcUyvXr107NgxzZo1S6GhoXrllVfUoEEDffHFF9es4fI89goVKjiNBwcHS7p2E1y3bl1J0vfff5+/D55HruYlG4Zx3fe6agivfAgxL/f2Rt3I5/i7li1bqmbNmhoxYoSSk5OvOQe+V69eeu+99zRkyBB9+umn+vLLLx0Pbubn4d6/J+958Z///EfSpZT+Wr9tAQCroDEHioj7779fSUlJ10yVr2X58uVq3769PvjgA/Xu3VsdO3ZUhw4drvplL35+fnr00UcVHx+vEydO6L777tOUKVOc1q+uWLGinn32Wa1YsULJyckqW7aspkyZcs0amjdvLkn6+eefncYvT4n5+4OAV6pdu7bq1KmjlStXKiUl5bqft2rVqjpy5EiuxvDQoUOO/QWldOnSV72PV6bZUt7u7d9drjMxMdFpPCMjQ8nJyQX6Oa7Up08fbdq0SfXq1VPTpk2veswff/yhDRs2aNy4cYqNjVX37t117733qkaNGrmOLchEe9++fZo0aZIGDBigZs2aadCgQflK5wHAHWjMgSJizJgx8vPz06BBg3T69Olc+5OSkjRjxgyX7/f09MyVmi5btixXk/z3Je6kS1MG6tevL8MwlJmZqezs7FwNUHBwsEJDQ5Wenn7Nz9CrVy9J0gcffOA0/v7776tEiRK5lha8UmxsrM6cOaNBgwYpKysr1/4vv/xSq1evliR17dpVp06d0pIlSxz7s7KyNGvWLPn7+6tt27bXvFZ+1KxZU99++60yMjIcY6tXr9b//vc/p+Oud2+vpkOHDvLy8tLMmTOd/v198MEHOn/+vO67774C+xxXGjRokCZOnKjXXnvN5TGXE/or/7c1ffr0XMde/pbUG/3mz8zMTPXv31+hoaGaMWOG5s6dq9OnTysqKuqGzgsAhY3lEoEiombNmlq0aJEeffRR1atXz+mbP7du3epYCtCV+++/35Ew3nnnnfr++++1cOHCXMlmx44dFRISorvuuksVKlTQwYMH9eabb+q+++5TQECAzp07p8qVK+vhhx9WkyZN5O/vr/Xr12vHjh3XbOAkqVmzZnryySf14YcfKisrS23bttWmTZu0bNkyRUdHO6a6uPLoo4/q+++/15QpU/Tdd9+pT58+jm/+XLt2rTZs2OBYf/upp57SO++8o/79+2vXrl2qVq2ali9frm+++UbTp0+/oQdprzRo0CAtX75cnTt3Vq9evZSUlKSPPvoo13KK17u3V1O+fHlFR0crNjZWnTt31oMPPqjExES9/fbbuu2225we9CxoVatWdawf7kpgYKDatGmjadOmKTMzU5UqVdKXX36p5OTkXMde/o3JCy+8oN69e6tkyZJ64IEHHA17Xr300kvas2ePNmzYoICAADVu3FgTJkzQiy++qIcfflhdu3bN1/kAwDRuXBEGQCE4fPiwMXjwYKNatWqGl5eXERAQYNx1113GrFmzjLS0NMdxV1su8fnnnzcqVqxo+Pj4GHfddZexbdu2XEv6vfPOO0abNm2MsmXLGna73ahZs6YxevRo4/z584ZhXFp+b/To0UaTJk2MgIAAw8/Pz2jSpInx9ttv56n+jIwMIyYmxqhatapRsmRJo1atWnleAvGyDRs2GA899JARHBxslChRwihfvrzxwAMPGCtXrnQ67vTp08aAAQOMcuXKGV5eXkajRo2M+Ph4p2MuL5f4yiuv5LqOrlimz9VyiYZhGK+99ppRqVIlw263G3fddZexc+fOfN/bv1/jyiUF33zzTaNu3bpGyZIljQoVKhjPPPOM8ccffzgd07Zt26suxxgREWFUrVo11/iVLi+XeC1Xuwc//fST0b17d6NUqVJGUFCQ8cgjjxi//PLLVZc5nDx5slGpUiXDw8PD6XNKcrkE59/Ps2vXLqNEiRLGsGHDnI7JysoybrvtNiM0NDTXfQEAq7AZRj6f+AEAAABQ4JhjDgAAAFgAjTkAAABgATTmAAAAKNbi4uJ02223KSAgQMHBwerWrVuuZWjT0tI0dOhQlS1bVv7+/urZs2euVdAuL3Pr6+ur4OBgjR49+qqrhLlCYw4AAIBiLSEhQUOHDtW3336rdevWKTMzUx07dlRqaqrjmKioKK1atUrLli1TQkKCfvnlF/Xo0cOxPzs7W/fdd59jNbR58+Zp7ty5mjBhQp7r4OFPAAAA4G9+++03BQcHKyEhQW3atNH58+dVvnx5LVq0SA8//LCkS19IV69ePW3btk133HGHvvjiC91///365ZdfHN9gPWfOHI0dO1a//fabvLy8rntdEnMAAADgby5/UV6ZMmUkSbt27VJmZqY6dOjgOKZu3bqqUqWK4xu3t23bpkaNGjmacknq1KmTLly4oP379+fpunzBEAAAAIqc9PT0XN84bbfbZbfbr/m+nJwcjRgxQnfddZcaNmwoSTp16pS8vLxUqlQpp2MrVKigU6dOOY75e1N+ef/lfXlRJBtzn2aR7i4BAHT2v2+6uwQAkE9Jd1fgzKw+bexD5RQbG+s0NnHixOt+Y/HQoUP1ww8/aMuWLYVY3dUVycYcAAAAxVt0dLRGjhzpNHa9tDwyMlKrV6/W5s2bVblyZcd4SEiIMjIydO7cOafU/PTp0woJCXEc89///tfpfJdXbbl8zPUwxxwAAADmsXmYstntdgUGBjptrhpzwzAUGRmpzz77TBs3blT16tWd9jdv3lwlS5bUhg0bHGOJiYk6ceKEwsPDJUnh4eH6/vvv9euvvzqOWbdunQIDA1W/fv083RoScwAAABRrQ4cO1aJFi7Ry5UoFBAQ45oQHBQXJx8dHQUFBGjhwoEaOHKkyZcooMDBQw4YNU3h4uO644w5JUseOHVW/fn09/vjjmjZtmk6dOqUXX3xRQ4cOvW5SfxmNOQAAAMxjs7m7glxmz54tSWrXrp3TeHx8vPr37y9JeuONN+Th4aGePXsqPT1dnTp10ttvv+041tPTU6tXr9Yzzzyj8PBw+fn5KSIiQpMmTcpzHUVyHXMe/gRgBTz8CcAKLPfwZ/PnTLnOX7tmmHKdgkRiDgAAAPPYeMTRFe4MAAAAYAEk5gAAADCPBeeYWwWJOQAAAGABJOYAAAAwD3PMXeLOAAAAABZAYg4AAADzMMfcJRJzAAAAwAJIzAEAAGAe5pi7xJ0BAAAALIDEHAAAAOZhjrlLJOYAAACABZCYAwAAwDzMMXeJOwMAAABYAIk5AAAAzMMcc5dIzAEAAAALIDEHAACAeZhj7hJ3BgAAALAAEnMAAACYhznmLpGYAwAAABZAYg4AAADzMMfcJe4MAAAAYAEk5gAAADAPiblL3BkAAADAAkjMAQAAYB4PVmVxhcQcAAAAsAAScwAAAJiHOeYucWcAAAAACyAxBwAAgHn45k+XSMwBAAAACyAxBwAAgHmYY+4SdwYAAACwABJzAAAAmIc55i6RmAMAAAAWQGIOAAAA8zDH3CXuDAAAAGABJOYAAAAwD3PMXSIxBwAAACyAxBwAAADmYY65S9wZAAAAwAJIzAEAAGAe5pi7RGIOAAAAWACJOQAAAMzDHHOXuDMAAACABZCYAwAAwDzMMXeJxBwAAACwABJzAAAAmIc55i5xZwAAAAALIDEHAACAeUjMXeLOAAAAABZAYg4AAADzsCqLSyTmAAAAgAWQmAMAAMA8zDF3iTsDAAAAWACNOQAAAMxjs5mz5cPmzZv1wAMPKDQ0VDabTStWrLiiZNtVt1deecVxTLVq1XLtnzp1ar7qoDEHAABAsZaamqomTZrorbfeuur+kydPOm0ffvihbDabevbs6XTcpEmTnI4bNmxYvupgjjkAAADMY8E55l26dFGXLl1c7g8JCXF6vXLlSrVv3141atRwGg8ICMh1bH5Y784AAAAANyg9PV0XLlxw2tLT02/4vKdPn9aaNWs0cODAXPumTp2qsmXLqlmzZnrllVeUlZWVr3PTmAMAAMA8Js0xj4uLU1BQkNMWFxd3w+XPmzdPAQEB6tGjh9P48OHDtXjxYn311Vd6+umn9fLLL2vMmDH5OjdTWQAAAFDkREdHa+TIkU5jdrv9hs/74Ycfqm/fvvL29nYa//u1GjduLC8vLz399NOKi4vL83VpzAEAAGAam0nf/Gm32wukEf+7r7/+WomJiVqyZMl1j23ZsqWysrJ0/Phx1alTJ0/nZyoLAAAAkAcffPCBmjdvriZNmlz32D179sjDw0PBwcF5Pj+JOQAAAExjVmKeHykpKTp69KjjdXJysvbs2aMyZcqoSpUqkqQLFy5o2bJleu2113K9f9u2bdq+fbvat2+vgIAAbdu2TVFRUerXr59Kly6d5zpozAEAAFCs7dy5U+3bt3e8vjxfPCIiQnPnzpUkLV68WIZhqE+fPrneb7fbtXjxYsXExCg9PV3Vq1dXVFRUrjnu12MzDMP45x/DmnyaRbq7BADQ2f++6e4SAEA+Jd1dgTO/h+NNuU7q8gGmXKcgkZgDAADAPNabyWIZPPwJAAAAWACJOQAAAExjxYc/rYLEHAAAALAAEnMAAACYhsTcNRJzAAAAwAJIzAEAAGAaEnPXSMwBAAAACyAxBwAAgGlIzF0jMQcAAAAsgMQcAAAA5iEwd4nEHAAAALAAEnMAAACYhjnmrpGYAwAAABZAYg4AAADTkJi7RmIOAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA2JuWsk5gAAAIAFkJgDAADAPATmLpGYAwAAABZAYg4AAADTMMfcNRJzAAAAwAJIzAEAAGAaEnPXSMwBAAAACyAxBwAAgGlIzF0jMQcAAAAsgMQcAAAA5iEwd4nEHAAAALAAEnMAAACYhjnmrpGYAwAAABZAYg4AAADTkJi7RmIOAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA2JuWsk5gAAAIAFkJgDAADAPATmLpGYAwAAABZAYg4AAADTMMfcNRJzAAAAwAJIzAEAAGAaEnPXSMwBAAAACyAxBwAAgGlIzF2zRGL+9ddfq1+/fgoPD9fPP/8sSVqwYIG2bNni5soAAAAAc7i9Mf/kk0/UqVMn+fj46LvvvlN6erok6fz583r55ZfdXB0AAAAKlM2k7Sbk9sb8pZde0pw5c/Tee++pZMmSjvG77rpLu3fvdmNlAAAAgHncPsc8MTFRbdq0yTUeFBSkc+fOmV8QAAAACg1zzF1ze2IeEhKio0eP5hrfsmWLatSo4YaKAAAAAPO5vTEfPHiwnnvuOW3fvl02m02//PKLFi5cqFGjRumZZ55xd3kAAAAoQDabzZTtZuT2qSzjxo1TTk6O7rnnHl28eFFt2rSR3W7XqFGjNGzYMHeXBwAAAJjC7Y25zWbTCy+8oNGjR+vo0aNKSUlR/fr15e/v7+7SUISMerKjut3dRLWrVdBf6ZnavveYXpixUkd+/NVxjN2rhKaO7KFHOjWX3auE1m87qOdeXqJfz/7pOCYspLRm/N+jatuitlL+StfCVds1fta/lZ2d446PBaAI2rVzh+bFf6CDB37Qb7/9ptdnvKW77+ng7rKAAnOzptlmcPtUlsu8vLxUv3593X777TTlKHCtb62lOUs2q+0Tr+r+Z95UiRKeWj07Ur7eXo5jpo3qqfvaNFTfMR+o46Dpqlg+SItfG+TY7+Fh06czn5FXyRJq3/81DZ6wQP0ebKkJz9znjo8EoIj666+Lql2njqJfmOjuUgCYzO2Nefv27XX33Xe73ICC8FDk2/po1XYdPHZK3x/+WU9N/EhVKpZRs/phkqRAf2/17xausa9/qoQdh/Xdwf/pqYkfKbxpTd3eqJokqUN4PdWrEaInX5infYd/1pffHNCkt9fo6V5tVLKEpxs/HYCipFXrtoocHqW7O9zr7lKAQmHFOeabN2/WAw88oNDQUNlsNq1YscJpf//+/XOdv3Pnzk7HnD17Vn379lVgYKBKlSqlgQMHKiUlJV91uL0xb9q0qZo0aeLY6tevr4yMDO3evVuNGjVyd3koogL9vSVJf5y/KElqVq+KvEqW0MZvEx3HHD5+WidOnlXLxtUlSS0bV9cPR39xmtqybutBBQX4qH7NiiZWDwAAClJqaqqaNGmit956y+UxnTt31smTJx3bxx9/7LS/b9++2r9/v9atW6fVq1dr8+bNeuqpp/JVh9vnmL/xxhtXHY+Jicn3TxlAXthsNr0y6mFt/S5JB5JOSpJCygYqPSNT51P+cjr21zMXVKFsoCSpQtlA/XrmT+f9Zy9c2lcuUEoUAAC4HgtOMe/SpYu6dOlyzWPsdrtCQkKuuu/gwYNau3atduzYoRYtWkiSZs2apa5du+rVV19VaGhonupwe2LuSr9+/fThhx9e97j09HRduHDBaTNysk2oEDer6dG91KBWRT0xLt7dpQAAgEJytR4xPT39H59v06ZNCg4OVp06dfTMM8/ozJkzjn3btm1TqVKlHE25JHXo0EEeHh7avn17nq9h2cZ827Zt8vb2vu5xcXFxCgoKctqyTu8yoULcjN4Y+4i6tm6oToNn6udfzznGT525ILtXSQX5+zgdH1w2UKfPXErFT5+5oOCyAc77y1xK00//fqFwCwcAoIgwa4751XrEuLi4f1Rz586dNX/+fG3YsEH/+te/lJCQoC5duig7+1IYfOrUKQUHBzu9p0SJEipTpoxOnTqV5+u4fSpLjx49nF4bhqGTJ09q586dGj9+/HXfHx0drZEjRzqNBbceW6A1omh4Y+wjevDuJuo4eIZ+/OWM077vDp5QRmaW2resoxUb9kiSbqkarCoVy2j7vmRJ0vZ9yRo7sJPKl/bXb39cmmZ1zx11df7Pv3TwWN7/owMAAIXvaj2i3W7/R+fq3bu348+NGjVS48aNVbNmTW3atEn33HPPDdX5d25vzIOCgpxee3h4qE6dOpo0aZI6dux43ffb7fZcN9nmwQoZcDY9upce7dJCj0S9q5TUNFX4/5Pv8ylpSkvP1IWUNM1dsU3/er6Hzp5P1Z+paXp97CP6du8x/ff745Kk9dsO6uCxU/rgpQi9MGOFKpQN1MSh9+udpZuVkZnlxk8HoCi5eDFVJ06ccLz++eefdOjQQQUFBalixbzNUwWszKx1zK/WIxaUGjVqqFy5cjp69KjuuecehYSE6Ndff3U6JisrS2fPnnU5L/1q3NqYZ2dna8CAAWrUqJFKly7tzlJQxD3dq40kad37I5zGB09YoI9WXZr7NebVT5STY+jjVwdd+oKhrQf1XNwSx7E5OYZ6PjdbM/6vtzbNfV6paelauOq/mjR7jWmfA0DRt/+HHzT4ySccr1+bdulX7w881F2Tp0x1V1kA/uann37SmTNnVLHipVXZwsPDde7cOe3atUvNmzeXJG3cuFE5OTlq2bJlns9rMwzDKJSK88jb21sHDx5U9erVC+ycPs0iC+xcAPBPnf3vm+4uAQDkU9LdFTirNeoLU65z9NVrr7LydykpKTp69KgkqVmzZnr99dfVvn17lSlTRmXKlFFsbKx69uypkJAQJSUlacyYMfrzzz/1/fffO1L5Ll266PTp05ozZ44yMzM1YMAAtWjRQosWLcpzHW5/+LNhw4Y6duyYu8sAAABAMbVz5041a9ZMzZo1kySNHDlSzZo104QJE+Tp6al9+/bpwQcfVO3atTVw4EA1b95cX3/9tdNUmYULF6pu3bq655571LVrV7Vq1Urvvvtuvupwe2K+du1aRUdHa/LkyWrevLn8/Pyc9gcGBub7nCTmAKyAxByAFVgtMb9l9FpTrnPklc7XP8hi3DbHfNKkSXr++efVtWtXSdKDDz7o9DCAYRiy2WyOZWgAAACAosxtjXlsbKyGDBmir776yl0lAAAAwGQmLcpyU3JbY355Bk3btm3dVQIAAABgGW5dLtGsdSwBAABgDfR/rrm1Ma9du/Z1/+WcPXvWpGoAAAAA93FrYx4bG5vrmz8BAABQdBGYu+bWxrx3794KDg52ZwkAAACAJbitMWd+EQAAQPHj4UEP6IrbvvnTzd9rBAAAAFiK2xLznJwcd10aAAAAbsKkCdfclpgDAAAA+H/c+vAnAAAAiheeM3SNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGuaYu0ZiDgAAAFgAiTkAAABMQ2LuGok5AAAAYAEk5gAAADANgblrJOYAAACABZCYAwAAwDTMMXeNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGuaYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADANc8xdIzEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIY55q6RmAMAAAAWQGIOAAAA0xCYu0ZiDgAAAFgAiTkAAABMwxxz10jMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDGHAAAALAAprIAAADANDz86RqJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAWQmAMAAMA0zDF3jcQcAAAAsAAScwAAAJiGxNw1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaZhj7hqJOQAAAIq1zZs364EHHlBoaKhsNptWrFjh2JeZmamxY8eqUaNG8vPzU2hoqJ544gn98ssvTueoVq2abDab0zZ16tR81UFjDgAAANPYbOZs+ZGamqomTZrorbfeyrXv4sWL2r17t8aPH6/du3fr008/VWJioh588MFcx06aNEknT550bMOGDctXHUxlAQAAQLHWpUsXdenS5ar7goKCtG7dOqexN998U7fffrtOnDihKlWqOMYDAgIUEhLyj+sgMQcAAIBprpzuUVhbYTp//rxsNptKlSrlND516lSVLVtWzZo10yuvvKKsrKx8nZfEHAAAAEVOenq60tPTncbsdrvsdvsNnTctLU1jx45Vnz59FBgY6BgfPny4br31VpUpU0Zbt25VdHS0Tp48qddffz3P5yYxBwAAgGnMmmMeFxenoKAgpy0uLu6Gas/MzFSvXr1kGIZmz57ttG/kyJFq166dGjdurCFDhui1117TrFmzcv1wcC0k5gAAAChyoqOjNXLkSKexG0nLLzflP/74ozZu3OiUll9Ny5YtlZWVpePHj6tOnTp5ugaNOQAAAEzjYdI65gUxbeWyy035kSNH9NVXX6ls2bLXfc+ePXvk4eGh4ODgPF+HxhwAAADFWkpKio4ePep4nZycrD179qhMmTKqWLGiHn74Ye3evVurV69Wdna2Tp06JUkqU6aMvLy8tG3bNm3fvl3t27dXQECAtm3bpqioKPXr10+lS5fOcx005gAAADCNFb/4c+fOnWrfvr3j9eUpMBEREYqJidG///1vSVLTpk2d3vfVV1+pXbt2stvtWrx4sWJiYpSenq7q1asrKioq11Sa66ExBwAAQLHWrl07GYbhcv+19knSrbfeqm+//faG66AxBwAAgGkKe43xmxnLJQIAAAAWQGIOAAAA03gQmLtEYg4AAABYAIk5AAAATMMcc9dIzAEAAAALIDEHAACAaQjMXSMxBwAAACyAxBwAAACmsYnI3BUScwAAAMACSMwBAABgGtYxd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJjGg8jcJRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgGlYx9w1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaVjH3DUScwAAAMAC8pSY//vf/87zCR988MF/XAwAAACKNvJy1/LUmHfr1i1PJ7PZbMrOzr6RegAAAIBiKU+NeU5OTmHXAQAAgGKAdcxdu6E55mlpaQVVBwAAAFCs5bsxz87O1uTJk1WpUiX5+/vr2LFjkqTx48frgw8+KPACAQAAUHR42MzZbkb5bsynTJmiuXPnatq0afLy8nKMN2zYUO+//36BFgcAAAAUF/luzOfPn693331Xffv2laenp2O8SZMmOnToUIEWBwAAgKLFZrOZst2M8t2Y//zzz6pVq1au8ZycHGVmZhZIUQAAAEBxk+/GvH79+vr6669zjS9fvlzNmjUrkKIAAABQNNls5mw3ozwtl/h3EyZMUEREhH7++Wfl5OTo008/VWJioubPn6/Vq1cXRo0AAABAkZfvxPyhhx7SqlWrtH79evn5+WnChAk6ePCgVq1apXvvvbcwagQAAEARwRxz1/KdmEtS69attW7duoKuBQAAACi2/lFjLkk7d+7UwYMHJV2ad968efMCKwoAAABF0826xrgZ8t2Y//TTT+rTp4+++eYblSpVSpJ07tw53XnnnVq8eLEqV65c0DUCAAAARV6+55gPGjRImZmZOnjwoM6ePauzZ8/q4MGDysnJ0aBBgwqjRgAAABQRzDF3Ld+JeUJCgrZu3ao6deo4xurUqaNZs2apdevWBVocAAAAUFzkuzEPCwu76hcJZWdnKzQ0tECKAgAAQNF0c2bZ5sj3VJZXXnlFw4YN086dOx1jO3fu1HPPPadXX321QIsDAAAAios8JealS5d2mquTmpqqli1bqkSJS2/PyspSiRIl9OSTT6pbt26FUigAAABufh436fxvM+SpMZ8+fXohlwEAAAAUb3lqzCMiIgq7DgAAABQDBOau/eMvGJKktLQ0ZWRkOI0FBgbeUEEAAABAcZTvxjw1NVVjx47V0qVLdebMmVz7s7OzC6QwAAAAFD036xrjZsj3qixjxozRxo0bNXv2bNntdr3//vuKjY1VaGio5s+fXxg1AgAAAEVevhPzVatWaf78+WrXrp0GDBig1q1bq1atWqpataoWLlyovn37FkadAAAAKAIIzF3Ld2J+9uxZ1ahRQ9Kl+eRnz56VJLVq1UqbN28u2OoAAACAYiLfjXmNGjWUnJwsSapbt66WLl0q6VKSXqpUqQItDgAAAEWLh81mynYzyndjPmDAAO3du1eSNG7cOL311lvy9vZWVFSURo8eXeAFAgAAAMVBvhvzqKgoDR8+XJLUoUMHHTp0SIsWLdJ3332n5557rsALBAAAQNFhs5mz5cfmzZv1wAMPKDQ0VDabTStWrHDabxiGJkyYoIoVK8rHx0cdOnTQkSNHnI45e/as+vbtq8DAQJUqVUoDBw5USkpKvurId2N+papVq6pHjx5q3LjxjZ4KAAAAMF1qaqqaNGmit95666r7p02bppkzZ2rOnDnavn27/Pz81KlTJ6WlpTmO6du3r/bv369169Zp9erV2rx5s5566ql81ZGnVVlmzpyZ5xNeTtMBAACAK1lxHfMuXbqoS5cuV91nGIamT5+uF198UQ899JAkaf78+apQoYJWrFih3r176+DBg1q7dq127NihFi1aSJJmzZqlrl276tVXX1VoaGie6shTY/7GG2/k6WQ2m43GHAAAAG6Xnp6u9PR0pzG73S673Z6v8yQnJ+vUqVPq0KGDYywoKEgtW7bUtm3b1Lt3b23btk2lSpVyNOXSpSnfHh4e2r59u7p3756na+WpMb+8CsvN4o8db7q7BADQkVP5m1sIAIWhUWV/d5fg5IbnUedRXFycYmNjncYmTpyomJiYfJ3n1KlTkqQKFSo4jVeoUMGx79SpUwoODnbaX6JECZUpU8ZxTF7k+wuGAAAAAKuLjo7WyJEjncbym5abjcYcAAAApjFrjvk/mbZyNSEhIZKk06dPq2LFio7x06dPq2nTpo5jfv31V6f3ZWVl6ezZs47354VZv00AAAAAbjrVq1dXSEiINmzY4Bi7cOGCtm/frvDwcElSeHi4zp07p127djmO2bhxo3JyctSyZcs8X4vEHAAAAKbxsN6iLEpJSdHRo0cdr5OTk7Vnzx6VKVNGVapU0YgRI/TSSy/plltuUfXq1TV+/HiFhoaqW7dukqR69eqpc+fOGjx4sObMmaPMzExFRkaqd+/eeV6RRaIxBwAAQDG3c+dOtW/f3vH68tz0iIgIzZ07V2PGjFFqaqqeeuopnTt3Tq1atdLatWvl7e3teM/ChQsVGRmpe+65Rx4eHurZs2e+lhyXJJthGEZ+i//666/1zjvvKCkpScuXL1elSpW0YMECVa9eXa1atcrv6QpcWpa7KwAAVmUBYA1WW5Vl5L8PmXKd1x+sa8p1ClK+55h/8skn6tSpk3x8fPTdd9851oc8f/68Xn755QIvEAAAACgO8t2Yv/TSS5ozZ47ee+89lSxZ0jF+1113affu3QVaHAAAAIoWm81mynYzyndjnpiYqDZt2uQaDwoK0rlz5wqiJgAAAKDYyXdjHhIS4vTU6mVbtmxRjRo1CqQoAAAAFE0eNnO2m1G+G/PBgwfrueee0/bt22Wz2fTLL79o4cKFGjVqlJ555pnCqBEAAAAo8vK9XOK4ceOUk5Oje+65RxcvXlSbNm1kt9s1atQoDRs2rDBqBAAAQBFxk07/NsU/Wi5RkjIyMnT06FGlpKSofv368ve3zlI8LJcIwApYLhGAFVhtucQxaxJNuc60++qYcp2C9I+/YMjLy0v169cvyFoAAABQxHkQmbuU78a8ffv211yCZuPGjTdUEAAAAFAc5bsxb9q0qdPrzMxM7dmzRz/88IMiIiIKqi4AAAAUQfleeaQYyXdj/sYbb1x1PCYmRikpzKcEAAAA/okC+6GlX79++vDDDwvqdAAAACiCbDZztptRgTXm27Ztk7e3d0GdDgAAAChW8j2VpUePHk6vDcPQyZMntXPnTo0fP77ACgMAAEDRw6osruW7MQ8KCnJ67eHhoTp16mjSpEnq2LFjgRUGAAAAFCf5asyzs7M1YMAANWrUSKVLly6smgAAAFBEEZi7lq855p6enurYsaPOnTtXSOUAAAAAxVO+H/5s2LChjh07Vhi1AAAAAMVWvhvzl156SaNGjdLq1at18uRJXbhwwWkDAAAAXPGwmbPdjPI8x3zSpEl6/vnn1bVrV0nSgw8+KNvfJgkZhiGbzabs7OyCrxIAAAAo4vLcmMfGxmrIkCH66quvCrMeAAAAFGEsl+hanhtzwzAkSW3bti20YgAAAIDiKl/LJdr4CQcAAAA3gHbStXw15rVr175uc3727NkbKggAAAAojvLVmMfGxub65k8AAAAgr27WFVPMkK/GvHfv3goODi6sWgAAAIBiK8+NOfPLAQAAcKNsoqd0Jc9fMHR5VRYAAAAABS/PiXlOTk5h1gEAAIBigDnmruU5MQcAAABQePL18CcAAABwI0jMXSMxBwAAACyAxBwAAACmYaU/10jMAQAAAAsgMQcAAIBpmGPuGok5AAAAYAEk5gAAADANU8xdIzEHAAAALIDEHAAAAKbxIDJ3icQcAAAAsAAScwAAAJiGVVlcIzEHAAAALIDEHAAAAKZhirlrJOYAAACABZCYAwAAwDQeIjJ3hcQcAAAAsAAScwAAAJiGOeaukZgDAAAAFkBiDgAAANOwjrlrJOYAAACABZCYAwAAwDQeTDJ3icQcAAAAsAAScwAAAJiGwNw1EnMAAAAUa9WqVZPNZsu1DR06VJLUrl27XPuGDBlS4HWQmAMAAMA0VpxjvmPHDmVnZzte//DDD7r33nv1yCOPOMYGDx6sSZMmOV77+voWeB005gAAACjWypcv7/R66tSpqlmzptq2besY8/X1VUhISKHWwVQWAAAAmMZmM2f7pzIyMvTRRx/pySeflO1vJ1q4cKHKlSunhg0bKjo6WhcvXiyAu+GMxBwAAABFTnp6utLT053G7Ha77Hb7Nd+3YsUKnTt3Tv3793eMPfbYY6patapCQ0O1b98+jR07VomJifr0008LtGabYRhGgZ7RAtKy3F0BAEhHTqW4uwQAUKPK/u4uwcncHSdMuc7xNR8qNjbWaWzixImKiYm55vs6deokLy8vrVq1yuUxGzdu1D333KOjR4+qZs2aBVGuJBJzAAAAFEHR0dEaOXKk09j10vIff/xR69evv24S3rJlS0miMQcAAMDNy2bSqix5mbZypfj4eAUHB+u+++675nF79uyRJFWsWPGflndVNOYAAAAo9nJychQfH6+IiAiVKPH/WuSkpCQtWrRIXbt2VdmyZbVv3z5FRUWpTZs2aty4cYHWQGMOAAAA01hvFfNL1q9frxMnTujJJ590Gvfy8tL69es1ffp0paamKiwsTD179tSLL75Y4DXQmAMAAKDY69ixo662JkpYWJgSEhJMqYHGHAAAAKax4jd/WgVfMAQAAABYAIk5AAAATENe7hqJOQAAAGABJOYAAAAwDVPMXSMxBwAAACyAxBwAAACmMeubP29GJOYAAACABZCYAwAAwDSkwq5xbwAAAAALIDEHAACAaZhj7hqJOQAAAGABJOYAAAAwDXm5ayTmAAAAgAWQmAMAAMA0zDF3jcQcAAAAsAAScwAAAJiGVNg17g0AAABgASTmAAAAMA1zzF0jMQcAAAAsgMQcAAAApiEvd43EHAAAALAAEnMAAACYhinmrpGYAwAAABZAYg4AAADTeDDL3CUScwAAAMACSMwBAABgGuaYu0ZiDgAAAFgAiTkAAABMY2OOuUsk5gAAAIAFkJgDAADANMwxd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmYY65ayTmAAAAgAWQmAMAAMA0JOaukZgDAAAAFkBiDgAAANPwzZ+ukZgDAAAAFkBiDgAAANN4EJi7RGIOAAAAWACJOQAAAEzDHHPXSMwBAAAAC7BUY56RkaHExERlZWW5uxQAAAAUApvNnO1mZInG/OLFixo4cKB8fX3VoEEDnThxQpI0bNgwTZ061c3VAQAAAIXPEo15dHS09u7dq02bNsnb29sx3qFDBy1ZssSNlQEAAKAg2Uz652ZkiYc/V6xYoSVLluiOO+6Q7W+/e2jQoIGSkpLcWBkAAABgDks05r/99puCg4Nzjaempjo16gAAALi5sY65a5aYytKiRQutWbPG8fpyM/7+++8rPDzcXWUBAAAAprFEYv7yyy+rS5cuOnDggLKysjRjxgwdOHBAW7duVUJCgrvLAwAAQAG5Wed/m8ESiXmrVq20Z88eZWVlqVGjRvryyy8VHBysbdu2qXnz5u4uDwAAACh0NsMwDHcXUdDSWAYdgAUcOZXi7hIAQI0q+7u7BCdbjvxhynVa3VLalOsUJEtMZdm9e7dKliypRo0aSZJWrlyp+Ph41a9fXzExMfLy8nJzhShOFi9aqHnxH+j3339T7Tp1Ne7/xqtR48buLgtAEfWffy/Tf/69XL+dPilJCqtaQw8/Pli3trxLkvTH2d+14J0Z2rdru/76K1WhlauqZ9+BuqPNPe4sG0AhsMRUlqefflqHDx+WJB07dkyPPvqofH19tWzZMo0ZM8bN1aE4WfvF53p1WpyefnaoFi/7THXq1NUzTw/UmTNn3F0agCKqbLkK6jd4mKbN/kj/enuBGja7TdMmjNT/jl9aLnjW1An65X8/auxLr+v195aoZeu79frkcTp25JCbKwf+GZtJW37ExMTIZrM5bXXr1nXsT0tL09ChQ1W2bFn5+/urZ8+eOn369D/6/Ndiicb88OHDatq0qSRp2bJlatu2rRYtWqS5c+fqk08+cW9xKFYWzItXj4d7qVv3nqpZq5ZenBgrb29vrfiU/x0CKBwt7myjW1u2UsXKVRQaVlWPDRwqbx9fHT7wvSTp8P596tL9Ud1St6EqhFbWw/0GydcvQMcOH3Rz5UDR0qBBA508edKxbdmyxbEvKipKq1at0rJly5SQkKBffvlFPXr0KPAaLDGVxTAM5eTkSJLWr1+v+++/X5IUFham33//3Z2loRjJzMjQwQP7NXDw044xDw8P3XHHndq39zs3VgaguMjOzta2hPVKS/tLtetfmkJXu0FjffPVl7q1ZSv5+Qdo66Z1ysxMV4OmLdxcLfDPeFj0O2pKlCihkJCQXOPnz5/XBx98oEWLFunuu++WJMXHx6tevXr69ttvdccddxRcDQV2phvQokULvfTSS+rQoYMSEhI0e/ZsSVJycrIqVKjg5upQXPxx7g9lZ2erbNmyTuNly5ZVcvIxN1UFoDj48dgRvTBsgDIyMuTt46Mxsa8qrFoNSdLzE/6l1yeP04Dud8vT01N2b2+Njn1VFSuFublqoGg5cuSIQkND5e3trfDwcMXFxalKlSratWuXMjMz1aFDB8exdevWVZUqVbRt27ai15hPnz5dffv21YoVK/TCCy+oVq1akqTly5frzjvvvOZ709PTlZ6e7jRmeNplt9sLrV4AAApSaFg1vfLux7qYmqJvN6/Xm/+aqNjX31NYtRpaHD9bqSl/asIrsxUYVEr//WaTXp80TpOnv6+qNW5xd+mAZV2tR7Tbr94jtmzZUnPnzlWdOnV08uRJxcbGqnXr1vrhhx906tQpeXl5qVSpUk7vqVChgk6dOlWgNVuiMW/cuLG+//77XOOvvPKKPD09r/neuLg4xcbGOo29MH6iXpwQU5AlohgoXaq0PD09cz3oeebMGZUrV85NVQEoDkqWLOlIwGvWrqejiQf0+acf66HeT+iLFUv0xgdLFVatpiSpWs3aOvj9d1q7cpmejvo/d5YN/CNmTWS5Wo84ceJExcTE5Dq2S5cujj83btxYLVu2VNWqVbV06VL5+PgUdqkOlnj40xVvb2+VLFnymsdER0fr/PnzTtvosdEmVYiipKSXl+rVb6Dt325zjOXk5Gj79m1q3KSZGysDUNwYOTnKzMxQelqaJMlmc/6/aw8PDxlGjjtKA24aV+sRo6Pz1iOWKlVKtWvX1tGjRxUSEqKMjAydO3fO6ZjTp09fdU76jXBbYl66dGnZ8jj5/+zZsy73Xe1XEnzBEP6pxyMGaPz/jVWDBg3VsFFjfbRgnv766y91617wT14DgCQtfH+Wmt1+l8oFh+ivi6nasnGt9u/dpRenvqlKVaoppFKY3nljip4YMkIBgUH675ZN2rdru6KnTHd36cA/Y1Jk7mraSl6kpKQoKSlJjz/+uJo3b66SJUtqw4YN6tmzpyQpMTFRJ06cUHh4eEGW7L7GfPr06e66NOBS5y5d9cfZs3r7zZn6/fffVKduPb39zvsqy1QWAIXk/B9/aNbUCfrj7O/y9fNX1Rq36MWpb6pJi0sPlL3w8kx99P4sTX0hSmlpFxUSGqbIsbG6tWUrN1cOFB2jRo3SAw88oKpVq+qXX37RxIkT5enpqT59+igoKEgDBw7UyJEjVaZMGQUGBmrYsGEKDw8v0Ac/JclmGIZRoGe0ABJzAFZw5FSKu0sAADWq7O/uEpxsTzpvynVa1gzK87G9e/fW5s2bdebMGZUvX16tWrXSlClTVLPmpWc70tLS9Pzzz+vjjz9Wenq6OnXqpLfffrvAp7JYrjFPS0tTRkaG01hgYGD+zkFjDsACaMwBWAGN+c3DEg9/pqamKjIyUsHBwfLz81Pp0qWdNgAAABQNNps5283IEo35mDFjtHHjRs2ePVt2u13vv/++YmNjFRoaqvnz57u7PAAAAKDQWWId81WrVmn+/Plq166dBgwYoNatW6tWrVqqWrWqFi5cqL59+7q7RAAAABSAmzTMNoUlEvOzZ8+qRo1LXz0cGBjoWB6xVatW2rx5sztLAwAAAExhica8Ro0aSk5OliTVrVtXS5culXQpSb/y608BAABwE7OZtN2E3NqYHzt2TDk5ORowYID27t0rSRo3bpzeeusteXt7KyoqSqNHj3ZniQAAAIAp3Lpcoqenp06ePKng4GBJ0qOPPqqZM2cqLS1Nu3btUq1atdS4ceN8n5flEgFYAcslArACqy2XuDP5ginXaVE9f8ttW4FbE/Mrfyb4/PPPlZqaqqpVq6pHjx7/qCkHAAAAbkaWWJUFAAAAxcPNusa4GdyamNtsNtmu+Ldz5WsAAACgOHBrYm4Yhvr37y+73S5JSktL05AhQ+Tn5+d03KeffuqO8gAAAFDAiGBdc2tjHhER4fS6X79+bqoEAAAAcC+3Nubx8fHuvDwAAADMRmTukiW+YAgAAAAo7liVBQAAAKaxEZm7RGIOAAAAWACJOQAAAEzDytiukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAAzENk7hKJOQAAAGABJOYAAAAwDeuYu0ZiDgAAAFgAiTkAAABMwzrmrpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAMxDZO4SiTkAAABgASTmAAAAMA3rmLtGYg4AAABYAIk5AAAATMM65q6RmAMAAAAWQGIOAAAA0xCYu0ZiDgAAAFgAiTkAAADMQ2TuEok5AAAAYAEk5gAAADAN65i7RmIOAAAAWACJOQAAAEzDOuaukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAAzENk7hKJOQAAAGABJOYAAAAwDeuYu0ZiDgAAAFgAiTkAAABMwzrmrpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAMxDZO4SiTkAAABgATTmAAAAMI3NpH/yIy4uTrfddpsCAgIUHBysbt26KTEx0emYdu3ayWazOW1DhgwpyFtDYw4AAIDiLSEhQUOHDtW3336rdevWKTMzUx07dlRqaqrTcYMHD9bJkycd27Rp0wq0DuaYAwAAwDRWXMd87dq1Tq/nzp2r4OBg7dq1S23atHGM+/r6KiQkpNDqIDEHAABAkZOenq4LFy44benp6Xl67/nz5yVJZcqUcRpfuHChypUrp4YNGyo6OloXL14s0JppzAEAAGAam0lbXFycgoKCnLa4uLjr1peTk6MRI0borrvuUsOGDR3jjz32mD766CN99dVXio6O1oIFC9SvX78bvyF/YzMMwyjQM1pAWpa7KwAA6cipFHeXAABqVNnf3SU4Of57minXqRhgy5WQ2+122e32a77vmWee0RdffKEtW7aocuXKLo/buHGj7rnnHh09elQ1a9YskJqZYw4AAADzmDTHPC9N+JUiIyO1evVqbd68+ZpNuSS1bNlSkmjMAQAAgIJiGIaGDRumzz77TJs2bVL16tWv+549e/ZIkipWrFhgddCYAwAAwDT5XWPcDEOHDtWiRYu0cuVKBQQE6NSpU5KkoKAg+fj4KCkpSYsWLVLXrl1VtmxZ7du3T1FRUWrTpo0aN25cYHUwxxwACglzzAFYgdXmmP94Jm8ro9yoqmXzPo3F5mINx/j4ePXv31//+9//1K9fP/3www9KTU1VWFiYunfvrhdffFGBgYEFVTKJOQAAAMxjxXXMr5dTh4WFKSEhodDrYLlEAAAAwAJIzAEAAGAaCwbmlkFiDgAAAFgAiTkAAABMY8U55lZBYg4AAABYAIk5AAAATERk7gqJOQAAAGABJOYAAAAwDXPMXSMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiGOeaukZgDAAAAFkBiDgAAANPYmGXuEok5AAAAYAEk5gAAADAPgblLJOYAAACABZCYAwAAwDQE5q6RmAMAAAAWQGIOAAAA07COuWsk5gAAAIAFkJgDAADANKxj7hqJOQAAAGABJOYAAAAwD4G5SyTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANOwjrlrJOYAAACABZCYAwAAwDSsY+4aiTkAAABgASTmAAAAMA1zzF0jMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC+DhTwAAAJiGhz9dIzEHAAAALIDEHAAAAKbhC4ZcIzEHAAAALIDEHAAAAKZhjrlrJOYAAACABZCYAwAAwDQE5q6RmAMAAAAWQGIOAAAA8xCZu0RiDgAAAFgAiTkAAABMwzrmrpGYAwAAABZAYg4AAADTsI65ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAAPMQmbtEYg4AAIBi76233lK1atXk7e2tli1b6r///a/pNdCYAwAAwDQ2k/7JjyVLlmjkyJGaOHGidu/erSZNmqhTp0769ddfC+kuXJ3NMAzD1CuaIC3L3RUAgHTkVIq7SwAANars7+4SnPyVac51fErm/diWLVvqtttu05tvvilJysnJUVhYmIYNG6Zx48YVUoW5kZgDAADANDabOVteZWRkaNeuXerQoYNjzMPDQx06dNC2bdsK4Q64xsOfAAAAKHLS09OVnp7uNGa322W3253Gfv/9d2VnZ6tChQpO4xUqVNChQ4cKvc6/K5KNuXeR/FQwU3p6uuLi4hQdHZ3rP2Agr6z262PcXPh7CEWVWX1azEtxio2NdRqbOHGiYmJizCngHyiSc8yBG3XhwgUFBQXp/PnzCgwMdHc5AIoh/h4CbkxeE/OMjAz5+vpq+fLl6tatm2M8IiJC586d08qVK80oVxJzzAEAAFAE2e12BQYGOm1X++2Tl5eXmjdvrg0bNjjGcnJytGHDBoWHh5tZctGcygIAAADk1ciRIxUREaEWLVro9ttv1/Tp05WamqoBAwaYWgeNOQAAAIq1Rx99VL/99psmTJigU6dOqWnTplq7dm2uB0ILG405cBV2u10TJ07kgSsAbsPfQ4C5IiMjFRkZ6dYaePgTAAAAsAAe/gQAAAAsgMYcAAAAsAAacwAALGbu3LkqVaqUu8sAYDIacxRpNpvtmpuVv/0LwM2vf//+V/275+jRo+4uDYAFsSoLirSTJ086/rxkyRJNmDBBiYmJjjF////3lemGYSg7O1slSvCfBYCC07lzZ8XHxzuNlS9f3k3VALAyEnMUaSEhIY4tKChINpvN8frQoUMKCAjQF198oebNm8tut2vLli3q37+/01fyStKIESPUrl07x+ucnBzFxcWpevXq8vHxUZMmTbR8+XJzPxyAm4Ldbnf6uygkJEQzZsxQo0aN5Ofnp7CwMD377LNKSUlxeY69e/eqffv2CggIUGBgoJo3b66dO3c69m/ZskWtW7eWj4+PwsLCNHz4cKWmpprx8QAUIBpzFHvjxo3T1KlTdfDgQTVu3DhP74mLi9P8+fM1Z84c7d+/X1FRUerXr58SEhIKuVoARYGHh4dmzpyp/fv3a968edq4caPGjBnj8vi+ffuqcuXK2rFjh3bt2qVx48apZMmSkqSkpCR17txZPXv21L59+7RkyRJt2bLF7esxA8g/fmePYm/SpEm6995783x8enq6Xn75Za1fv17h4eGSpBo1amjLli1655131LZt28IqFcBNaPXq1U7T5rp06aJly5Y5XlerVk0vvfSShgwZorfffvuq5zhx4oRGjx6tunXrSpJuueUWx764uDj17dtXI0aMcOybOXOm2rZtq9mzZ8vb27sQPhWAwkBjjmKvRYsW+Tr+6NGjunjxYq5mPiMjQ82aNSvI0gAUAe3bt9fs2bMdr/38/LR+/XrFxcXp0KFDunDhgrKyspSWlqaLFy/K19c31zlGjhypQYMGacGCBerQoYMeeeQR1axZU9KlaS779u3TwoULHccbhqGcnBwlJyerXr16hf8hARQIGnMUe35+fk6vPTw8dOUX4mZmZjr+fHke6Jo1a1SpUiWn4/jqbABX8vPzU61atRyvjx8/rvvvv1/PPPOMpkyZojJlymjLli0aOHCgMjIyrtqYx8TE6LHHHtOaNWv0xRdfaOLEiVq8eLG6d++ulJQUPf300xo+fHiu91WpUqVQPxuAgkVjDlyhfPny+uGHH5zG9uzZ45jPWb9+fdntdp04cYJpKwDybdeuXcrJydFrr70mD49Lj3otXbr0uu+rXbu2ateuraioKPXp00fx8fHq3r27br31Vh04cMCp+Qdwc+LhT+AKd999t3bu3Kn58+fryJEjmjhxolOjHhAQoFGjRikqKkrz5s1TUlKSdu/erVmzZmnevHlurBzAzaBWrVrKzMzUrFmzdOzYMS1YsEBz5sxxefxff/2lyMhIbdq0ST/++KO++eYb7dixwzFFZezYsdq6dasiIyO1Z88eHTlyRCtXruThT+AmRGMOXKFTp04aP368xowZo9tuu01//vmnnnjiCadjJk+erPHjxysuLk716tVT586dtWbNGlWvXt1NVQO4WTRp0kSvv/66/vWvf6lhw4ZauHCh4uLiXB7v6empM2fO6IknnlDt2rXVq1cvdenSRbGxsZKkxo0bKyEhQYcPH1br1q3VrFkzTZgwQaGhoWZ9JAAFxGZcOZkWAAAAgOlIzAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBFFv9+/dXt27dHK/btWunESNGmF7Hpk2bZLPZdO7cOZfH2Gw2rVixIs/njImJUdOmTW+oruPHj8tms2nPnj03dB4AQN7QmAOwlP79+8tms8lms8nLy0u1atXSpEmTlJWVVejX/vTTTzV58uQ8HZuXZhoAgPwo4e4CAOBKnTt3Vnx8vNLT0/X5559r6NChKlmypKKjo3Mdm5GRIS8vrwK5bpkyZQrkPAAA/BMk5gAsx263KyQkRFWrVtUzzzyjDh066N///rek/zf9ZMqUKQoNDVWdOnUkSf/73//Uq1cvlSpVSmXKlNFDDz2k48ePO86ZnZ2tkSNHqlSpUipbtqzGjBkjwzCcrnvlVJb09HSNHTtWYWFhstvtqlWrlj744AMdP35c7du3lySVLl1aNptN/fv3lyTl5OQoLi5O1atXl4+Pj5o0aaLly5c7Xefzzz9X7dq15ePjo/bt2zvVmVdjx45V7dq15evrqxo1amj8+PHKzMzMddw777yjsLAw+fr6qlevXjp//rzT/vfff1/16tWTt7e36tatq7fffjvftQAACgaNOQDL8/HxUUZGhuP1hg0blJiYqHXr1mn16tXKzMxUp06dFBAQoK+//lrffPON/P391blzZ8f7XnvtNc2dO1cffvihtmzZorNnz+qzzz675nWfeOIJffzxx5o5c6YOHjyod955R/7+/goLC9Mnn3wiSUpMTNTJkyc1Y8YMSVJcXJzmz5+vOXPmaP/+/YqKilK/fv2UkJAg6dIPED169NADDzygPXv2aNCgQRo3bly+70lAQIDmzp2rAwcOaMaMGXrvvff0xhtvOB1z9OhRLV26VKtWrdLatWv13Xff6dlnn3XsX7hwoSZMmKApU6bo4MGDevnllzV+/HjNmzcv3/UAAAqAAQAWEhERYTz00EOGYRhGTk6OsW7dOsNutxujRo1y7K9QoYKRnp7ueM+CBQuMOnXqGDk5OY6x9PR0w8fHx/jPf/5jGIZhVKxY0Zg2bZpjf2ZmplG5cmXHtQzDMNq2bWs899xzhmEYRmJioiHJWLdu3VXr/OqrrwxJxh9//OEYS0tLM3x9fY2tW7c6HTtw4ECjT58+hmEYRnR0tFG/fn2n/WPHjs11ritJMj777DOX+1955RWjefPmjtcTJ040PD09jZ9++skx9sUXXxgeHh7GyZMnDcMwjJo1axqLFi1yOs/kyZON8PBwwzAMIzk52ZBkfPfddy6vCwAoOMwxB2A5q1evlr+/vzIzM5WTk6PHHntMMTExjv2NGjVymle+d+9eHT16VAEBAU7nSUtLU1JSks6fP6+TJ0+qZcuWjn0lSpRQixYtck1nuWzPnj3y9PRU27Zt81z30aNHdfHiRd17771O4xkZGWrWrJkk6eDBg051SFJ4eHier3HZkiVLNHPmTCUlJSklJUVZWVkKDAx0OqZKlSqqVKmS03VycnKUmJiogIAAJSUlaeDAgRo8eLDjmKysLAUFBeW7HgDAjaMxB2A57du31+zZs+Xl5aXQ0FCVKOH8V5Wfn5/T65SUFDVv3lwLFy7Mda7y5cv/oxp8fHzy/Z6UlBRJ0po1a5waYunSvPmCsm3bNvXt21exsbHq1KmTgoKCtHjxYr322mv5rvW9997L9YOCp6dngdUKAMg7GnMAluPn56datWrl+fhbb71VS5YsUXBwcK7U+LKKFStq+/btatOmjaRLyfCuXbt06623XvX4Ro0aKScnRwkJCerQoUOu/ZcT++zsbMdY/fr1ZbfbdeLECZdJe7169RwPsl727bffXv9D/s3WrVtVtWpVvfDCC46xH3/8MddxJ06c0C+//KLQ0FDHdTw8PFSnTh1VqFBBoaGhOnbsmPr27Zuv6wMACgcPfwK46fXt21flypXTQw89pK+//lrJycnatGmThg8frp9++kmS9Nxzz2nq1KlasWKFDh06pGefffaaa5BXq1ZNERERevLJJ7VixQrHOZcuXSpJqlq1qmw2m1avXq3ffvtNKSkpCggI0KhRoxQVFaV58+YpKSlJu3fv1qxZsxwPVA4ZMkRHjhzR6NGjlZiYqEWLFmnu3Ln5+ry33HKLTpw4ocWLFyspKUkzZ8686oOs3t7eioiI0N69e/X1119r+PDh6tWrl0JCQiRJsbGxiouL08yZM3X48GF9//33io+P1+uvv56vegAABYPGHMBNz9fXV5s3b1aVKlXUo0cP1atXTwMHDlRaWpojQX/++ef1+OOPKyIiQuHh4QoICFD37t2ved7Zs2fr4Ycf1rPPPqu6detq8ODBSk1NlSRVqlRJsbGxGjdunCpUqKDIyEhJ0uTJkzV+/HjFxcWpXr166ty5s9asWaPq1atLujTv+5NPPtGKFSvUpEkTzZkzRy+//HK+Pu+DDz6oqKgoRUZGqmnTptq6davGjx+f67hatWqpR48e6tq1qzp27KjGjRs7LYc4aNAgvf/++4qPj1ejRo3Utm1bzZ0711ErAMBcNsPVk08AAAAATENiDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFvD/AXSBIVrOW53lAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x3600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(bench_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5fffb7",
   "metadata": {},
   "source": [
    "### Squat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "352f5823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 8\n",
    "squat_model = DeepClassifier(input_size, output_size)\n",
    "squat_criterion = nn.CrossEntropyLoss()\n",
    "squat_optimizer = optim.Adam(squat_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8a9e4e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squat_train_losses = []\n",
    "squat_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    squat_model.train()\n",
    "    squat_optimizer.zero_grad()\n",
    "    squat_outputs = squat_model(squat_X_train)\n",
    "    squat_loss = squat_criterion(squat_outputs, squat_y_train)\n",
    "    squat_loss.backward()\n",
    "    squat_optimizer.step()\n",
    "    squat_train_losses.append(squat_loss.item())\n",
    "    \n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        squat_test_outputs = squat_model(squat_X_test)\n",
    "        squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "        squat_test_losses.append(squat_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), squat_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), squat_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a269f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.2885631322860718\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    squat_test_outputs = squat_model(squat_X_test)\n",
    "    squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "    print(f'Test Loss: {squat_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1ea2e0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5416,  0.2961, -1.0179,  1.0000,  0.5545,  0.2840, -1.0055,  1.0000,\n",
      "         0.5628,  0.2831, -1.0054,  1.0000,  0.5698,  0.2821, -1.0056,  1.0000,\n",
      "         0.5327,  0.2845, -0.9916,  1.0000,  0.5261,  0.2842, -0.9918,  1.0000,\n",
      "         0.5217,  0.2840, -0.9918,  1.0000,  0.5823,  0.2803, -0.7914,  1.0000,\n",
      "         0.5198,  0.2834, -0.7285,  1.0000,  0.5589,  0.3050, -0.9273,  1.0000,\n",
      "         0.5316,  0.3063, -0.9096,  1.0000,  0.6505,  0.3304, -0.5651,  0.9999,\n",
      "         0.4693,  0.3300, -0.5092,  1.0000,  0.7349,  0.3996, -0.9214,  0.9928,\n",
      "         0.3741,  0.3920, -0.8104,  0.9981,  0.6867,  0.3365, -1.3973,  0.9759,\n",
      "         0.4108,  0.3321, -1.3014,  0.9937,  0.6785,  0.3213, -1.4905,  0.9308,\n",
      "         0.4144,  0.3175, -1.3961,  0.9733,  0.6701,  0.3142, -1.4550,  0.9150,\n",
      "         0.4237,  0.3116, -1.3634,  0.9637,  0.6673,  0.3207, -1.3930,  0.8385,\n",
      "         0.4270,  0.3176, -1.2999,  0.9319,  0.6045,  0.4604, -0.0041,  0.9998,\n",
      "         0.5028,  0.4583,  0.0039,  0.9999,  0.6120,  0.5677, -0.2066,  0.9878,\n",
      "         0.4768,  0.5628, -0.2137,  0.9926,  0.5815,  0.6555,  0.2840,  0.9799,\n",
      "         0.4781,  0.6532,  0.2652,  0.9950,  0.5632,  0.6672,  0.3186,  0.9280,\n",
      "         0.4953,  0.6655,  0.2991,  0.9510,  0.6047,  0.6950,  0.0687,  0.9704,\n",
      "         0.4568,  0.6929,  0.0448,  0.9882])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(6)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0002', '0.0019', '0.0000', '0.0000', '0.9979', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.4864,  0.4385, -0.2912,  0.9999,  0.4946,  0.4200, -0.3375,  1.0000,\n",
      "         0.5016,  0.4180, -0.3373,  1.0000,  0.5089,  0.4159, -0.3376,  1.0000,\n",
      "         0.4857,  0.4203, -0.2668,  0.9999,  0.4859,  0.4185, -0.2668,  0.9999,\n",
      "         0.4864,  0.4163, -0.2669,  0.9999,  0.5572,  0.4085, -0.4257,  0.9999,\n",
      "         0.5330,  0.4071, -0.1219,  0.9999,  0.5235,  0.4481, -0.3196,  0.9999,\n",
      "         0.5105,  0.4485, -0.2296,  0.9998,  0.7027,  0.4846, -0.4959,  0.9998,\n",
      "         0.5972,  0.4573,  0.0330,  0.9978,  0.5930,  0.6068, -0.5439,  0.9644,\n",
      "         0.5012,  0.5182,  0.0752,  0.3342,  0.4920,  0.4843, -0.2926,  0.9505,\n",
      "         0.4690,  0.4774,  0.0029,  0.2474,  0.4697,  0.4533, -0.3108,  0.9196,\n",
      "         0.4619,  0.4511,  0.0069,  0.2169,  0.4948,  0.4426, -0.3436,  0.9195,\n",
      "         0.4905,  0.4476,  0.0308,  0.2067,  0.5138,  0.4512, -0.2988,  0.9006,\n",
      "         0.4985,  0.4604,  0.0053,  0.2099,  0.9343,  0.6433, -0.1535,  0.9999,\n",
      "         0.8124,  0.6201,  0.1537,  0.9998,  0.7529,  0.7172, -0.6263,  0.9972,\n",
      "         0.5695,  0.5980,  0.3296,  0.7181,  0.8541,  0.8530, -0.5566,  0.9904,\n",
      "         0.6501,  0.7168,  0.5067,  0.6720,  0.8949,  0.8729, -0.5502,  0.9654,\n",
      "         0.6859,  0.7365,  0.5188,  0.6581,  0.7591,  0.9123, -0.7346,  0.9831,\n",
      "         0.5244,  0.7340,  0.4854,  0.7645])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(6)\n",
      "예측 확률: ['0.0001', '0.0000', '0.0724', '0.0000', '0.0000', '0.0000', '0.9269', '0.0005']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.5381,  0.2727, -0.9690,  1.0000,  0.5516,  0.2611, -0.9532,  1.0000,\n",
      "         0.5597,  0.2606, -0.9531,  1.0000,  0.5669,  0.2602, -0.9534,  1.0000,\n",
      "         0.5301,  0.2610, -0.9392,  1.0000,  0.5237,  0.2606, -0.9393,  1.0000,\n",
      "         0.5189,  0.2602, -0.9393,  1.0000,  0.5791,  0.2616, -0.7464,  1.0000,\n",
      "         0.5168,  0.2627, -0.6799,  1.0000,  0.5539,  0.2824, -0.8822,  1.0000,\n",
      "         0.5273,  0.2823, -0.8643,  1.0000,  0.6458,  0.3174, -0.5333,  1.0000,\n",
      "         0.4655,  0.3176, -0.4735,  1.0000,  0.7305,  0.3887, -0.8799,  0.9966,\n",
      "         0.3825,  0.3832, -0.7527,  0.9978,  0.6861,  0.3216, -1.3671,  0.9903,\n",
      "         0.4006,  0.3216, -1.2567,  0.9955,  0.6783,  0.3056, -1.4667,  0.9704,\n",
      "         0.4055,  0.3056, -1.3567,  0.9832,  0.6717,  0.2972, -1.4341,  0.9642,\n",
      "         0.4122,  0.2993, -1.3313,  0.9781,  0.6686,  0.3042, -1.3646,  0.9290,\n",
      "         0.4152,  0.3055, -1.2598,  0.9586,  0.5959,  0.4540, -0.0039,  0.9998,\n",
      "         0.4947,  0.4521,  0.0036,  0.9999,  0.5946,  0.5600, -0.1227,  0.9938,\n",
      "         0.4864,  0.5602, -0.1025,  0.9947,  0.5758,  0.6567,  0.3443,  0.9886,\n",
      "         0.4793,  0.6526,  0.3549,  0.9939,  0.5590,  0.6685,  0.3767,  0.9402,\n",
      "         0.4949,  0.6644,  0.3871,  0.9272,  0.6023,  0.6953,  0.1244,  0.9821,\n",
      "         0.4588,  0.6934,  0.1272,  0.9880])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0002', '0.0007', '0.0013', '0.9956', '0.0000', '0.0000', '0.0020', '0.0002']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5023,  0.3713, -0.8101,  1.0000,  0.5124,  0.3603, -0.8115,  1.0000,\n",
      "         0.5196,  0.3595, -0.8114,  1.0000,  0.5281,  0.3583, -0.8116,  1.0000,\n",
      "         0.4990,  0.3598, -0.7902,  1.0000,  0.4956,  0.3587, -0.7903,  1.0000,\n",
      "         0.4925,  0.3575, -0.7904,  1.0000,  0.5582,  0.3538, -0.6683,  1.0000,\n",
      "         0.5044,  0.3554, -0.5688,  1.0000,  0.5187,  0.3784, -0.7448,  1.0000,\n",
      "         0.5015,  0.3781, -0.7166,  1.0000,  0.6392,  0.3914, -0.4516,  0.9999,\n",
      "         0.4658,  0.3925, -0.3828,  1.0000,  0.7045,  0.4702, -0.6801,  0.9926,\n",
      "         0.3887,  0.4656, -0.6056,  0.9961,  0.6554,  0.4128, -1.0255,  0.9790,\n",
      "         0.4115,  0.4165, -1.0164,  0.9935,  0.6538,  0.3951, -1.0938,  0.9304,\n",
      "         0.4123,  0.3978, -1.0918,  0.9758,  0.6468,  0.3864, -1.0679,  0.9224,\n",
      "         0.4207,  0.3925, -1.0670,  0.9707,  0.6412,  0.3907, -1.0221,  0.8650,\n",
      "         0.4247,  0.3982, -1.0157,  0.9501,  0.6020,  0.4867, -0.0065,  0.9997,\n",
      "         0.5029,  0.4838,  0.0063,  0.9998,  0.6058,  0.5763, -0.3499,  0.9893,\n",
      "         0.4774,  0.5708, -0.2919,  0.9920,  0.5830,  0.6570,  0.0034,  0.9858,\n",
      "         0.4749,  0.6548,  0.0557,  0.9925,  0.5684,  0.6691,  0.0268,  0.9297,\n",
      "         0.4907,  0.6661,  0.0788,  0.9192,  0.5995,  0.6935, -0.1964,  0.9813,\n",
      "         0.4588,  0.6918, -0.1390,  0.9863])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(6)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.4950,  0.0934, -0.4716,  0.9998,  0.4944,  0.0766, -0.4365,  0.9998,\n",
      "         0.4944,  0.0761, -0.4368,  0.9998,  0.4941,  0.0756, -0.4371,  0.9998,\n",
      "         0.4814,  0.0736, -0.4905,  0.9997,  0.4714,  0.0710, -0.4907,  0.9997,\n",
      "         0.4600,  0.0683, -0.4908,  0.9996,  0.4525,  0.0699, -0.2241,  0.9998,\n",
      "         0.4049,  0.0655, -0.4653,  0.9996,  0.4793,  0.1077, -0.3881,  0.9998,\n",
      "         0.4621,  0.1040, -0.4589,  0.9998,  0.4369,  0.1559, -0.0021,  0.9994,\n",
      "         0.2873,  0.1440, -0.4966,  0.9993,  0.5055,  0.2370, -0.0966,  0.8391,\n",
      "         0.2646,  0.2572, -0.8606,  0.9357,  0.5354,  0.1525, -0.3808,  0.8236,\n",
      "         0.3464,  0.1356, -1.0558,  0.9079,  0.5404,  0.1354, -0.4315,  0.7304,\n",
      "         0.3544,  0.1119, -1.1252,  0.8857,  0.5274,  0.1276, -0.3774,  0.7144,\n",
      "         0.3499,  0.1055, -1.1333,  0.8842,  0.5192,  0.1321, -0.3682,  0.6918,\n",
      "         0.3517,  0.1138, -1.0645,  0.8536,  0.2973,  0.3604,  0.1904,  0.9978,\n",
      "         0.1965,  0.3618, -0.1901,  0.9993,  0.4155,  0.4846,  0.4251,  0.8326,\n",
      "         0.3097,  0.5088, -0.2962,  0.9934,  0.3721,  0.6036,  0.7687,  0.8178,\n",
      "         0.2577,  0.6464, -0.2939,  0.9892,  0.3451,  0.6218,  0.7950,  0.7680,\n",
      "         0.2117,  0.6698, -0.3084,  0.9568,  0.5018,  0.6508,  0.7223,  0.8482,\n",
      "         0.3520,  0.7040, -0.5286,  0.9798])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_40566/3174289499.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = squat_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = squat_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = squat_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7d1e3234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.983\n",
      "정밀도: 0.987\n",
      "재현율: 0.983\n",
      "F1 점수: 0.985\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = squat_model(squat_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(squat_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e57f97ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(squat_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50889ab8",
   "metadata": {},
   "source": [
    "### Deadlift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d9c8d471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 8\n",
    "deadlift_model = DeepClassifier(input_size, output_size)\n",
    "deadlift_criterion = nn.CrossEntropyLoss()\n",
    "deadlift_optimizer = optim.Adam(deadlift_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a73ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deadlift_train_losses = []\n",
    "deadlift_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    deadlift_model.train()\n",
    "    deadlift_optimizer.zero_grad()\n",
    "    deadlift_outputs = deadlift_model(deadlift_X_train)\n",
    "    deadlift_loss = deadlift_criterion(deadlift_outputs, deadlift_y_train)\n",
    "    deadlift_loss.backward()\n",
    "    deadlift_optimizer.step()\n",
    "    deadlift_train_losses.append(deadlift_loss.item())\n",
    "    \n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "        deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "        deadlift_test_losses.append(deadlift_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), deadlift_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), deadlift_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f733762f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.3081626892089844\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "    deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "    print(f'Test Loss: {deadlift_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "262be563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.3497,  0.4478, -2.0146,  1.0000,  0.3649,  0.4336, -1.9934,  1.0000,\n",
      "         0.3760,  0.4317, -1.9934,  1.0000,  0.3856,  0.4294, -1.9939,  1.0000,\n",
      "         0.3359,  0.4325, -1.9863,  1.0000,  0.3273,  0.4301, -1.9864,  1.0000,\n",
      "         0.3188,  0.4273, -1.9863,  1.0000,  0.4022,  0.4156, -1.6764,  1.0000,\n",
      "         0.3030,  0.4133, -1.6469,  1.0000,  0.3670,  0.4542, -1.8805,  1.0000,\n",
      "         0.3337,  0.4530, -1.8713,  1.0000,  0.4891,  0.4507, -1.2663,  1.0000,\n",
      "         0.2299,  0.4467, -1.2126,  0.9999,  0.5943,  0.5443, -1.3515,  0.9933,\n",
      "         0.1347,  0.5330, -1.2668,  0.9927,  0.6122,  0.6401, -1.5851,  0.9802,\n",
      "         0.0800,  0.6227, -1.4880,  0.9707,  0.6267,  0.6647, -1.6974,  0.9454,\n",
      "         0.0569,  0.6460, -1.5835,  0.9225,  0.5926,  0.6643, -1.7487,  0.9504,\n",
      "         0.0818,  0.6469, -1.6532,  0.9280,  0.5828,  0.6583, -1.6126,  0.9343,\n",
      "         0.0938,  0.6405, -1.5229,  0.9072,  0.4401,  0.4979, -0.0025,  0.9999,\n",
      "         0.3032,  0.4951,  0.0030,  0.9999,  0.4444,  0.5957, -0.8757,  0.8827,\n",
      "         0.2630,  0.5908, -0.7590,  0.9110,  0.4057,  0.7388, -0.4113,  0.9048,\n",
      "         0.2822,  0.7371, -0.3062,  0.9532,  0.3822,  0.7569, -0.3888,  0.7220,\n",
      "         0.3097,  0.7557, -0.2835,  0.7907,  0.4338,  0.7822, -0.8110,  0.9175,\n",
      "         0.2213,  0.7770, -0.6805,  0.9437])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0001', '0.0000', '0.9999', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 6.7883e-01,  1.8852e-01,  6.3842e-01,  9.8407e-01,  6.7087e-01,\n",
      "         1.8134e-01,  5.8013e-01,  9.8553e-01,  6.6586e-01,  1.8175e-01,\n",
      "         5.8020e-01,  9.8500e-01,  6.6103e-01,  1.8211e-01,  5.8022e-01,\n",
      "         9.8544e-01,  6.8851e-01,  1.7929e-01,  6.0304e-01,  9.8807e-01,\n",
      "         6.9554e-01,  1.7838e-01,  6.0305e-01,  9.8778e-01,  7.0302e-01,\n",
      "         1.7744e-01,  6.0303e-01,  9.8797e-01,  6.5915e-01,  1.8616e-01,\n",
      "         3.3513e-01,  9.8973e-01,  7.1652e-01,  1.8056e-01,  4.3354e-01,\n",
      "         9.9210e-01,  6.7318e-01,  1.9678e-01,  5.5850e-01,  9.8955e-01,\n",
      "         6.9415e-01,  1.9615e-01,  5.8660e-01,  9.9054e-01,  6.2923e-01,\n",
      "         2.4041e-01,  1.1268e-01,  9.9995e-01,  7.9015e-01,  2.4078e-01,\n",
      "         4.2180e-01,  9.9875e-01,  6.2594e-01,  3.2972e-01,  5.3528e-02,\n",
      "         9.6597e-01,  8.2571e-01,  3.1164e-01,  5.4118e-01,  9.2029e-02,\n",
      "         6.1831e-01,  4.0935e-01,  1.4434e-01,  9.1449e-01,  8.0095e-01,\n",
      "         3.4562e-01,  6.6229e-01,  1.1207e-01,  6.1984e-01,  4.3057e-01,\n",
      "         1.1768e-01,  8.7036e-01,  8.0278e-01,  3.5447e-01,  6.9217e-01,\n",
      "         1.2558e-01,  6.2038e-01,  4.3029e-01,  1.4991e-01,  8.5389e-01,\n",
      "         7.8744e-01,  3.5273e-01,  6.6659e-01,  1.2059e-01,  6.2176e-01,\n",
      "         4.2246e-01,  1.6106e-01,  8.0423e-01,  7.7914e-01,  3.4933e-01,\n",
      "         6.5583e-01,  1.1841e-01,  7.4303e-01,  3.8716e-01, -1.0445e-01,\n",
      "         9.9991e-01,  8.4108e-01,  3.8076e-01,  1.0424e-01,  9.9981e-01,\n",
      "         6.9365e-01,  5.1250e-01, -6.2538e-02,  9.8831e-01,  8.0389e-01,\n",
      "         5.0414e-01,  2.2632e-01,  7.9954e-01,  6.9004e-01,  6.3671e-01,\n",
      "        -3.1283e-03,  9.6129e-01,  7.9146e-01,  6.2434e-01,  2.6232e-01,\n",
      "         8.2674e-01,  7.1279e-01,  6.6049e-01,  9.0742e-04,  9.4141e-01,\n",
      "         7.9500e-01,  6.5209e-01,  2.6587e-01,  8.7859e-01,  6.1206e-01,\n",
      "         6.4235e-01, -6.8488e-02,  8.5160e-01,  7.5252e-01,  6.2968e-01,\n",
      "         1.9925e-01,  7.3531e-01])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0387', '0.0000', '0.9595', '0.0000', '0.0017']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.6452,  0.2688,  0.8062,  0.9990,  0.6412,  0.2603,  0.7656,  0.9987,\n",
      "         0.6388,  0.2602,  0.7656,  0.9989,  0.6362,  0.2602,  0.7656,  0.9988,\n",
      "         0.6514,  0.2585,  0.7988,  0.9983,  0.6563,  0.2569,  0.7989,  0.9984,\n",
      "         0.6612,  0.2555,  0.7987,  0.9981,  0.6413,  0.2607,  0.5488,  0.9988,\n",
      "         0.6738,  0.2554,  0.6967,  0.9975,  0.6478,  0.2738,  0.7279,  0.9981,\n",
      "         0.6593,  0.2722,  0.7709,  0.9975,  0.6364,  0.2875,  0.2659,  1.0000,\n",
      "         0.7791,  0.2792,  0.6210,  0.9989,  0.6154,  0.3674,  0.1431,  0.9901,\n",
      "         0.8123,  0.3530,  0.5047,  0.2884,  0.5969,  0.4470,  0.2023,  0.9375,\n",
      "         0.7723,  0.4231,  0.3540,  0.0966,  0.5920,  0.4690,  0.1644,  0.8860,\n",
      "         0.7810,  0.4411,  0.3432,  0.0836,  0.5959,  0.4698,  0.1966,  0.8769,\n",
      "         0.7624,  0.4457,  0.3252,  0.0783,  0.6018,  0.4608,  0.2168,  0.8153,\n",
      "         0.7591,  0.4389,  0.3410,  0.0775,  0.7744,  0.4001, -0.0962,  0.9998,\n",
      "         0.8677,  0.3939,  0.0953,  0.9994,  0.7080,  0.5194, -0.0640,  0.9936,\n",
      "         0.8194,  0.5113,  0.1788,  0.9484,  0.6921,  0.6354, -0.1306,  0.9873,\n",
      "         0.8070,  0.6247,  0.1118,  0.9445,  0.7118,  0.6602, -0.1354,  0.9863,\n",
      "         0.8139,  0.6490,  0.1063,  0.9463,  0.6160,  0.6397, -0.1952,  0.8808,\n",
      "         0.7590,  0.6237,  0.0414,  0.6782])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0000', '0.0002', '0.0000', '0.8290', '0.0000', '0.1708', '0.0000', '0.0000']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5664,  0.4896, -0.5873,  0.9994,  0.5873,  0.4845, -0.5681,  0.9994,\n",
      "         0.5891,  0.4833, -0.5683,  0.9992,  0.5910,  0.4817, -0.5687,  0.9995,\n",
      "         0.5827,  0.4832, -0.6252,  0.9996,  0.5814,  0.4813, -0.6253,  0.9993,\n",
      "         0.5803,  0.4791, -0.6252,  0.9995,  0.5808,  0.4642, -0.4336,  0.9986,\n",
      "         0.5689,  0.4622, -0.6953,  0.9987,  0.5506,  0.4854, -0.5288,  0.9996,\n",
      "         0.5479,  0.4837, -0.6052,  0.9996,  0.5450,  0.4424, -0.2092,  0.9998,\n",
      "         0.4426,  0.4517, -0.6899,  0.9990,  0.5277,  0.5161, -0.0241,  0.8010,\n",
      "         0.3886,  0.5390, -0.7085,  0.9806,  0.5158,  0.5900, -0.0088,  0.8873,\n",
      "         0.3909,  0.6253, -0.6328,  0.9827,  0.5108,  0.6081, -0.0076,  0.8362,\n",
      "         0.3918,  0.6422, -0.7133,  0.9709,  0.5061,  0.6098, -0.0860,  0.8484,\n",
      "         0.4204,  0.6431, -0.7082,  0.9712,  0.5048,  0.6034, -0.0390,  0.8399,\n",
      "         0.4179,  0.6374, -0.6330,  0.9545,  0.3560,  0.4295,  0.1698,  0.9982,\n",
      "         0.2902,  0.4399, -0.1696,  0.9977,  0.4435,  0.5238,  0.0637,  0.4641,\n",
      "         0.3882,  0.5377, -0.2985,  0.9251,  0.4060,  0.6419,  0.2203,  0.4464,\n",
      "         0.3709,  0.6506, -0.1745,  0.9338,  0.3811,  0.6628,  0.2321,  0.4193,\n",
      "         0.3500,  0.6757, -0.1734,  0.8644,  0.4774,  0.6729,  0.1040,  0.5651,\n",
      "         0.4510,  0.6732, -0.3548,  0.9300])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(6)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.6725,  0.4456, -0.4471,  1.0000,  0.6753,  0.4350, -0.4155,  0.9999,\n",
      "         0.6747,  0.4344, -0.4156,  0.9999,  0.6740,  0.4339, -0.4158,  0.9999,\n",
      "         0.6707,  0.4344, -0.4876,  0.9999,  0.6669,  0.4333, -0.4878,  0.9999,\n",
      "         0.6626,  0.4321, -0.4878,  0.9999,  0.6433,  0.4286, -0.2485,  0.9999,\n",
      "         0.6277,  0.4253, -0.5722,  0.9999,  0.6558,  0.4518, -0.3789,  1.0000,\n",
      "         0.6504,  0.4502, -0.4732,  1.0000,  0.5689,  0.4589, -0.0458,  0.9999,\n",
      "         0.4899,  0.4540, -0.6372,  0.9998,  0.5579,  0.5328,  0.0719,  0.8088,\n",
      "         0.4225,  0.5448, -0.6794,  0.9857,  0.5578,  0.5982, -0.0096,  0.8570,\n",
      "         0.4100,  0.6262, -0.6676,  0.9773,  0.5635,  0.6142, -0.0092,  0.8010,\n",
      "         0.4059,  0.6488, -0.7531,  0.9610,  0.5642,  0.6138, -0.0841,  0.8182,\n",
      "         0.4184,  0.6513, -0.7480,  0.9602,  0.5601,  0.6087, -0.0418,  0.8107,\n",
      "         0.4260,  0.6455, -0.6679,  0.9333,  0.3667,  0.4952,  0.1540,  0.9988,\n",
      "         0.2928,  0.4934, -0.1542,  0.9986,  0.5322,  0.5455,  0.0701,  0.7923,\n",
      "         0.4446,  0.5346, -0.4360,  0.9725,  0.4503,  0.6633,  0.1207,  0.7833,\n",
      "         0.3860,  0.6280, -0.0793,  0.8410,  0.4150,  0.6807,  0.1247,  0.6342,\n",
      "         0.3658,  0.6365, -0.0387,  0.7538,  0.4995,  0.6960,  0.0440,  0.7816,\n",
      "         0.4356,  0.6651, -0.0584,  0.8218])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(6)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_40566/2186968047.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = deadlift_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = deadlift_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = deadlift_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "11299320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.961\n",
      "정밀도: 0.968\n",
      "재현율: 0.961\n",
      "F1 점수: 0.965\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = deadlift_model(deadlift_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(deadlift_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "41d1840a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(deadlift_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0eb5693",
   "metadata": {},
   "source": [
    "# Save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "38c44f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Bench Press\n",
    "with open('benchpress_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(bench_model, f)\n",
    "\n",
    "# Squat\n",
    "with open('squat_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(squat_model, f)\n",
    "\n",
    "# Deadlift\n",
    "with open('deadlift_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(deadlift_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "eca7f824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 132])"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "ffeba6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([232, 132])"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squat_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "923f2a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([285, 132])"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadlift_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc644d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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