Energy Prediction NB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9da010b5-f7c2-47ba-b99c-39883f48a2f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unsupported operand type(s) for |: 'type' and 'NoneType'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ek\\miniconda3\\envs\\help\\lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated and will be removed in a future release\n",
      "  \"class\": algorithms.Blowfish,\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import syft as sy\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import TensorDataset as FederatedDataset\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, root_mean_squared_error, r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbc5c00b-5e71-4bab-a01e-73ee2f9567d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/samhithanair/Energy-Consumption-Using-LSTM-and-Federated-Learning/main/Household_electricity_dataset%202020-2024.csv'\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbe87699-e248-4430-91c8-9f61509b1325",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "      <th>Day</th>\n",
       "      <th>Temp_max</th>\n",
       "      <th>Temp_avg</th>\n",
       "      <th>Temp_min</th>\n",
       "      <th>Dew_max</th>\n",
       "      <th>Dew_avg</th>\n",
       "      <th>Dew_min</th>\n",
       "      <th>Hum_max</th>\n",
       "      <th>...</th>\n",
       "      <th>Hum_min</th>\n",
       "      <th>Wind_max</th>\n",
       "      <th>Wind_avg</th>\n",
       "      <th>Wind_min</th>\n",
       "      <th>Press_max</th>\n",
       "      <th>Press_avg</th>\n",
       "      <th>Press_min</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>Day.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07-08-2020</td>\n",
       "      <td>24.752</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>73.8</td>\n",
       "      <td>68</td>\n",
       "      <td>74</td>\n",
       "      <td>71.4</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07-09-2020</td>\n",
       "      <td>13.892</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>71.2</td>\n",
       "      <td>66</td>\n",
       "      <td>74</td>\n",
       "      <td>70.3</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>18</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07-10-2020</td>\n",
       "      <td>17.166</td>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>72.1</td>\n",
       "      <td>68</td>\n",
       "      <td>73</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>3.18</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07-11-2020</td>\n",
       "      <td>12.173</td>\n",
       "      <td>4</td>\n",
       "      <td>76</td>\n",
       "      <td>71.2</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>70.0</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07-12-2020</td>\n",
       "      <td>42.250</td>\n",
       "      <td>5</td>\n",
       "      <td>82</td>\n",
       "      <td>73.8</td>\n",
       "      <td>68</td>\n",
       "      <td>72</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>62</td>\n",
       "      <td>17</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.39</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>08-09-2024</td>\n",
       "      <td>28.431</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>84.7</td>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "      <td>72.8</td>\n",
       "      <td>67</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>08-10-2024</td>\n",
       "      <td>12.699</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>84.6</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>72.9</td>\n",
       "      <td>70</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>08-11-2024</td>\n",
       "      <td>24.830</td>\n",
       "      <td>5</td>\n",
       "      <td>95</td>\n",
       "      <td>85.4</td>\n",
       "      <td>79</td>\n",
       "      <td>75</td>\n",
       "      <td>73.5</td>\n",
       "      <td>68</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>08-12-2024</td>\n",
       "      <td>27.965</td>\n",
       "      <td>6</td>\n",
       "      <td>91</td>\n",
       "      <td>85.5</td>\n",
       "      <td>82</td>\n",
       "      <td>77</td>\n",
       "      <td>75.5</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>1497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>08-13-2024</td>\n",
       "      <td>27.641</td>\n",
       "      <td>7</td>\n",
       "      <td>93</td>\n",
       "      <td>84.5</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>76.0</td>\n",
       "      <td>72</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>29.8</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1498 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date   Value  Day  Temp_max  Temp_avg  Temp_min  Dew_max  Dew_avg  \\\n",
       "0     07-08-2020  24.752    1        85      73.8        68       74     71.4   \n",
       "1     07-09-2020  13.892    2        76      71.2        66       74     70.3   \n",
       "2     07-10-2020  17.166    3        79      72.1        68       73     70.0   \n",
       "3     07-11-2020  12.173    4        76      71.2        69       73     70.0   \n",
       "4     07-12-2020  42.250    5        82      73.8        68       72     70.0   \n",
       "...          ...     ...  ...       ...       ...       ...      ...      ...   \n",
       "1493  08-09-2024  28.431    3        96      84.7        74       76     72.8   \n",
       "1494  08-10-2024  12.699    4        97      84.6        73       76     72.9   \n",
       "1495  08-11-2024  24.830    5        95      85.4        79       75     73.5   \n",
       "1496  08-12-2024  27.965    6        91      85.5        82       77     75.5   \n",
       "1497  08-13-2024  27.641    7        93      84.5        80       80     76.0   \n",
       "\n",
       "      Dew_min  Hum_max  ...  Hum_min  Wind_max  Wind_avg  Wind_min  Press_max  \\\n",
       "0          66      100  ...       65        21       9.5         0       29.8   \n",
       "1          66      100  ...       89        18       7.8         0       29.8   \n",
       "2          68      100  ...       72        18       4.7         0       29.8   \n",
       "3          66      100  ...       85        20       7.0         0       29.8   \n",
       "4          68      100  ...       62        17       7.7         0       29.7   \n",
       "...       ...      ...  ...      ...       ...       ...       ...        ...   \n",
       "1493       67       91  ...       38        24       6.3         0       29.9   \n",
       "1494       70       94  ...       43        10       3.6         0       29.9   \n",
       "1495       68       88  ...       44        12       6.2         0       29.8   \n",
       "1496       72       82  ...       59        10       6.0         0       29.8   \n",
       "1497       72       91  ...       53        15       6.1         0       29.9   \n",
       "\n",
       "      Press_avg  Press_min  Precipitation  day_of_week  Day.1  \n",
       "0          29.8       29.7           0.00            2      1  \n",
       "1          29.8       29.7           4.33            5      2  \n",
       "2          29.8       29.7           3.18            6      3  \n",
       "3          29.7       29.7           2.37            2      4  \n",
       "4          29.7       29.7           0.39            4      5  \n",
       "...         ...        ...            ...          ...    ...  \n",
       "1493       29.9       29.8           0.00            5   1494  \n",
       "1494       29.8       29.7           0.00            1   1495  \n",
       "1495       29.7       29.7           0.00            3   1496  \n",
       "1496       29.8       29.7           0.00            6   1497  \n",
       "1497       29.8       29.8           0.00            1   1498  \n",
       "\n",
       "[1498 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "553d390b-83e1-4c21-9aea-521b02b5493d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_numeric = df.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3834aa14-0f42-4b96-bef1-e87d44a1da50",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_numeric.drop(columns=['Value'])\n",
    "y = df_numeric['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48170571-c0a1-4186-b3f4-1282f88d3311",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a55eb2c2-4baa-46ac-b7e7-73e335e9e8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_idx1 = len(X_scaled) // 3\n",
    "split_idx2 = 2 * len(X_scaled) // 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4add4ee2-72ef-4885-b3bd-2ee0c7a2dbf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c1 = X_scaled[:split_idx1]\n",
    "X_c2 = X_scaled[split_idx1:split_idx2]\n",
    "X_c3 = X_scaled[split_idx2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2c79d68-b193-474a-8a2d-63de03b3448c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_c1 = y[:split_idx1].values.reshape(-1, 1)\n",
    "y_c2 = y[split_idx1:split_idx2].values.reshape(-1, 1)\n",
    "y_c3 = y[split_idx2:].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "886f3fcc-0908-48d9-8fec-cf5bd0aa5408",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c1_tensor = torch.tensor(X_c1, dtype=torch.float32)\n",
    "X_c2_tensor = torch.tensor(X_c2, dtype=torch.float32)\n",
    "X_c3_tensor = torch.tensor(X_c3, dtype=torch.float32)\n",
    "\n",
    "y_c1_tensor = torch.tensor(y_c1, dtype=torch.float32)\n",
    "y_c2_tensor = torch.tensor(y_c2, dtype=torch.float32)\n",
    "y_c3_tensor = torch.tensor(y_c3, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd3680a9-cad5-466f-9019-3fec09b36e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_c1 = FederatedDataset(X_c1_tensor, y_c1_tensor)\n",
    "dataset_c2 = FederatedDataset(X_c2_tensor, y_c2_tensor)\n",
    "dataset_c3 = FederatedDataset(X_c3_tensor, y_c3_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5659b42b-352c-4254-9524-028202555f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d613ae82-f605-4919-bbb7-ab72f7555e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloader_c1 = DataLoader(dataset_c1, batch_size=batch_size, shuffle=True)\n",
    "dataloader_c2 = DataLoader(dataset_c2, batch_size=batch_size, shuffle=True)\n",
    "dataloader_c3 = DataLoader(dataset_c3, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b7e04b8-32be-4190-848b-dfb509352693",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)\n",
    "        c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)\n",
    "        out, _ = self.lstm(x, (h0, c0))\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7843c6b8-a0da-400c-ba92-b4c3a968b213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_size = X_c1_tensor.shape[1]\n",
    "hidden_size = 16\n",
    "output_size = 1\n",
    "input_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ac36699-b254-48c8-93b8-d07fa38568be",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTMModel(input_size, hidden_size, output_size)\n",
    "model_c1 = LSTMModel(input_size, hidden_size, output_size)\n",
    "model_c2 = LSTMModel(input_size, hidden_size, output_size)\n",
    "model_c3 = LSTMModel(input_size, hidden_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "96747ab5-9b84-4b72-b7f4-fb66575abb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a319a9b5-167e-4799-928c-a6bc46721a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def train(model,dataloader):\n",
    "    epochs = 1000\n",
    "    loss_values = []\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        for data in [dataloader_c1]:\n",
    "            for inputs, labels in data:\n",
    "                optimizer.zero_grad()\n",
    "                outputs = model(inputs.unsqueeze(1))\n",
    "                loss = criterion(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                running_loss += loss.item()\n",
    "        print(f\"Epoch {epoch+1}, Loss: {running_loss*0.0001}\")\n",
    "        loss_values.append(running_loss*0.0001)\n",
    "    \n",
    "    plt.plot(loss_values)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Training Loss')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "def5acd8-f96b-42ae-b2f9-d6df77c336a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 1.0163642333984375\n",
      "Epoch 2, Loss: 1.0164893432617188\n",
      "Epoch 3, Loss: 1.0220346801757814\n",
      "Epoch 4, Loss: 1.0183226196289064\n",
      "Epoch 5, Loss: 1.0204260864257813\n",
      "Epoch 6, Loss: 1.0197888427734376\n",
      "Epoch 7, Loss: 1.01746015625\n",
      "Epoch 8, Loss: 1.015642236328125\n",
      "Epoch 9, Loss: 1.0158916381835938\n",
      "Epoch 10, Loss: 1.017921697998047\n",
      "Epoch 11, Loss: 1.0182124267578125\n",
      "Epoch 12, Loss: 1.0162302612304688\n",
      "Epoch 13, Loss: 1.016308483886719\n",
      "Epoch 14, Loss: 1.02070595703125\n",
      "Epoch 15, Loss: 1.0208566284179688\n",
      "Epoch 16, Loss: 1.0210629638671875\n",
      "Epoch 17, Loss: 1.0202085327148438\n",
      "Epoch 18, Loss: 1.018873583984375\n",
      "Epoch 19, Loss: 1.0167262329101563\n",
      "Epoch 20, Loss: 1.01664052734375\n",
      "Epoch 21, Loss: 1.018963037109375\n",
      "Epoch 22, Loss: 1.0165720336914064\n",
      "Epoch 23, Loss: 1.017552685546875\n",
      "Epoch 24, Loss: 1.01979912109375\n",
      "Epoch 25, Loss: 1.0148715576171876\n",
      "Epoch 26, Loss: 1.0184446655273438\n",
      "Epoch 27, Loss: 1.0231848571777344\n",
      "Epoch 28, Loss: 1.017113134765625\n",
      "Epoch 29, Loss: 1.0179068603515626\n",
      "Epoch 30, Loss: 1.0165558349609376\n",
      "Epoch 31, Loss: 1.017185583496094\n",
      "Epoch 32, Loss: 1.023859033203125\n",
      "Epoch 33, Loss: 1.0204054565429688\n",
      "Epoch 34, Loss: 1.0194469970703126\n",
      "Epoch 35, Loss: 1.020382666015625\n",
      "Epoch 36, Loss: 1.0163249755859376\n",
      "Epoch 37, Loss: 1.0207417114257813\n",
      "Epoch 38, Loss: 1.0209182495117188\n",
      "Epoch 39, Loss: 1.0172173217773437\n",
      "Epoch 40, Loss: 1.0149537719726562\n",
      "Epoch 41, Loss: 1.015408642578125\n",
      "Epoch 42, Loss: 1.0146848754882813\n",
      "Epoch 43, Loss: 1.018065966796875\n",
      "Epoch 44, Loss: 1.0161388549804689\n",
      "Epoch 45, Loss: 1.0194341918945313\n",
      "Epoch 46, Loss: 1.0189655029296876\n",
      "Epoch 47, Loss: 1.0171866333007813\n",
      "Epoch 48, Loss: 1.0158422729492187\n",
      "Epoch 49, Loss: 1.0210942626953126\n",
      "Epoch 50, Loss: 1.0187702392578126\n",
      "Epoch 51, Loss: 1.0194084838867188\n",
      "Epoch 52, Loss: 1.017830810546875\n",
      "Epoch 53, Loss: 1.0208228637695314\n",
      "Epoch 54, Loss: 1.0182405395507812\n",
      "Epoch 55, Loss: 1.0247923583984375\n",
      "Epoch 56, Loss: 1.0189654296875001\n",
      "Epoch 57, Loss: 1.0195920532226563\n",
      "Epoch 58, Loss: 1.0145292968750002\n",
      "Epoch 59, Loss: 1.0182713623046875\n",
      "Epoch 60, Loss: 1.0121171630859376\n",
      "Epoch 61, Loss: 1.0210062866210938\n",
      "Epoch 62, Loss: 1.0172883056640625\n",
      "Epoch 63, Loss: 1.0166299926757814\n",
      "Epoch 64, Loss: 1.0148868774414064\n",
      "Epoch 65, Loss: 1.0200685791015625\n",
      "Epoch 66, Loss: 1.0198754272460937\n",
      "Epoch 67, Loss: 1.0183922241210939\n",
      "Epoch 68, Loss: 1.0191778076171876\n",
      "Epoch 69, Loss: 1.0236087768554687\n",
      "Epoch 70, Loss: 1.0164710815429687\n",
      "Epoch 71, Loss: 1.0197991943359375\n",
      "Epoch 72, Loss: 1.0213995971679688\n",
      "Epoch 73, Loss: 1.0186792114257812\n",
      "Epoch 74, Loss: 1.0170786132812502\n",
      "Epoch 75, Loss: 1.0178513793945314\n",
      "Epoch 76, Loss: 1.0227913452148438\n",
      "Epoch 77, Loss: 1.01783349609375\n",
      "Epoch 78, Loss: 1.0201804077148438\n",
      "Epoch 79, Loss: 1.019661767578125\n",
      "Epoch 80, Loss: 1.0218741821289063\n",
      "Epoch 81, Loss: 1.0149900024414062\n",
      "Epoch 82, Loss: 1.0184217041015626\n",
      "Epoch 83, Loss: 1.0180763671875\n",
      "Epoch 84, Loss: 1.017056591796875\n",
      "Epoch 85, Loss: 1.017718017578125\n",
      "Epoch 86, Loss: 1.0191087524414062\n",
      "Epoch 87, Loss: 1.0167163818359375\n",
      "Epoch 88, Loss: 1.0177588134765625\n",
      "Epoch 89, Loss: 1.0203692260742188\n",
      "Epoch 90, Loss: 1.0223685913085938\n",
      "Epoch 91, Loss: 1.017222998046875\n",
      "Epoch 92, Loss: 1.0172844482421874\n",
      "Epoch 93, Loss: 1.0205913696289064\n",
      "Epoch 94, Loss: 1.0206089111328125\n",
      "Epoch 95, Loss: 1.0187984008789064\n",
      "Epoch 96, Loss: 1.0159023071289064\n",
      "Epoch 97, Loss: 1.017635821533203\n",
      "Epoch 98, Loss: 1.0156207763671876\n",
      "Epoch 99, Loss: 1.0177908813476564\n",
      "Epoch 100, Loss: 1.0254269409179688\n",
      "Epoch 101, Loss: 1.0168438110351563\n",
      "Epoch 102, Loss: 1.0165080688476562\n",
      "Epoch 103, Loss: 1.022039990234375\n",
      "Epoch 104, Loss: 1.01771181640625\n",
      "Epoch 105, Loss: 1.0152951171875\n",
      "Epoch 106, Loss: 1.0230888183593752\n",
      "Epoch 107, Loss: 1.0161153198242188\n",
      "Epoch 108, Loss: 1.0179413696289064\n",
      "Epoch 109, Loss: 1.0162950439453124\n",
      "Epoch 110, Loss: 1.0163798583984376\n",
      "Epoch 111, Loss: 1.0155482788085939\n",
      "Epoch 112, Loss: 1.0190283447265625\n",
      "Epoch 113, Loss: 1.0149520751953125\n",
      "Epoch 114, Loss: 1.019895849609375\n",
      "Epoch 115, Loss: 1.0216339233398437\n",
      "Epoch 116, Loss: 1.0225284790039062\n",
      "Epoch 117, Loss: 1.0171457763671876\n",
      "Epoch 118, Loss: 1.0153021362304688\n",
      "Epoch 119, Loss: 1.011209539794922\n",
      "Epoch 120, Loss: 1.0189256958007813\n",
      "Epoch 121, Loss: 1.0188672851562501\n",
      "Epoch 122, Loss: 1.019082421875\n",
      "Epoch 123, Loss: 1.0191142456054687\n",
      "Epoch 124, Loss: 1.01664677734375\n",
      "Epoch 125, Loss: 1.0198108276367188\n",
      "Epoch 126, Loss: 1.017590692138672\n",
      "Epoch 127, Loss: 1.0161640258789062\n",
      "Epoch 128, Loss: 1.0204690551757813\n",
      "Epoch 129, Loss: 1.0177745239257814\n",
      "Epoch 130, Loss: 1.015889501953125\n",
      "Epoch 131, Loss: 1.0194701171875\n",
      "Epoch 132, Loss: 1.0207935424804688\n",
      "Epoch 133, Loss: 1.0181385375976564\n",
      "Epoch 134, Loss: 1.0193428100585937\n",
      "Epoch 135, Loss: 1.0166368286132812\n",
      "Epoch 136, Loss: 1.0227606323242189\n",
      "Epoch 137, Loss: 1.0206139770507814\n",
      "Epoch 138, Loss: 1.0209083374023438\n",
      "Epoch 139, Loss: 1.0211102661132814\n",
      "Epoch 140, Loss: 1.01575029296875\n",
      "Epoch 141, Loss: 1.0183404785156251\n",
      "Epoch 142, Loss: 1.0160829345703126\n",
      "Epoch 143, Loss: 1.0186943237304689\n",
      "Epoch 144, Loss: 1.018357177734375\n",
      "Epoch 145, Loss: 1.0180530883789063\n",
      "Epoch 146, Loss: 1.0173701904296875\n",
      "Epoch 147, Loss: 1.0213129028320314\n",
      "Epoch 148, Loss: 1.0186738403320312\n",
      "Epoch 149, Loss: 1.0192419311523437\n",
      "Epoch 150, Loss: 1.0145111206054689\n",
      "Epoch 151, Loss: 1.0192962280273439\n",
      "Epoch 152, Loss: 1.0188410766601563\n",
      "Epoch 153, Loss: 1.0158215942382813\n",
      "Epoch 154, Loss: 1.0193982177734375\n",
      "Epoch 155, Loss: 1.0188496704101564\n",
      "Epoch 156, Loss: 1.0159924438476562\n",
      "Epoch 157, Loss: 1.0186288940429689\n",
      "Epoch 158, Loss: 1.0197847534179687\n",
      "Epoch 159, Loss: 1.0207484497070314\n",
      "Epoch 160, Loss: 1.0192443481445312\n",
      "Epoch 161, Loss: 1.0161323852539064\n",
      "Epoch 162, Loss: 1.0187999877929688\n",
      "Epoch 163, Loss: 1.0170260498046875\n",
      "Epoch 164, Loss: 1.0167855102539063\n",
      "Epoch 165, Loss: 1.0184954467773437\n",
      "Epoch 166, Loss: 1.0185678466796875\n",
      "Epoch 167, Loss: 1.0195810180664062\n",
      "Epoch 168, Loss: 1.0147899536132814\n",
      "Epoch 169, Loss: 1.0157688720703126\n",
      "Epoch 170, Loss: 1.0168672851562501\n",
      "Epoch 171, Loss: 1.019712255859375\n",
      "Epoch 172, Loss: 1.018348876953125\n",
      "Epoch 173, Loss: 1.0230077758789062\n",
      "Epoch 174, Loss: 1.0162352416992189\n",
      "Epoch 175, Loss: 1.0180083862304687\n",
      "Epoch 176, Loss: 1.0223631225585939\n",
      "Epoch 177, Loss: 1.0162171997070313\n",
      "Epoch 178, Loss: 1.0151305541992188\n",
      "Epoch 179, Loss: 1.0220751464843751\n",
      "Epoch 180, Loss: 1.0189085815429688\n",
      "Epoch 181, Loss: 1.0218254150390627\n",
      "Epoch 182, Loss: 1.021076806640625\n",
      "Epoch 183, Loss: 1.0171881958007813\n",
      "Epoch 184, Loss: 1.0226576782226564\n",
      "Epoch 185, Loss: 1.0194575927734375\n",
      "Epoch 186, Loss: 1.0217543090820314\n",
      "Epoch 187, Loss: 1.0151788208007813\n",
      "Epoch 188, Loss: 1.0182652587890626\n",
      "Epoch 189, Loss: 1.0190369262695314\n",
      "Epoch 190, Loss: 1.0190367553710937\n",
      "Epoch 191, Loss: 1.0248530639648439\n",
      "Epoch 192, Loss: 1.0196777221679687\n",
      "Epoch 193, Loss: 1.0206907836914063\n",
      "Epoch 194, Loss: 1.0188208251953126\n",
      "Epoch 195, Loss: 1.0204496948242188\n",
      "Epoch 196, Loss: 1.018492431640625\n",
      "Epoch 197, Loss: 1.016760888671875\n",
      "Epoch 198, Loss: 1.0227068115234375\n",
      "Epoch 199, Loss: 1.013659307861328\n",
      "Epoch 200, Loss: 1.0160462524414062\n",
      "Epoch 201, Loss: 1.0226031494140626\n",
      "Epoch 202, Loss: 1.0218487792968751\n",
      "Epoch 203, Loss: 1.0178686950683595\n",
      "Epoch 204, Loss: 1.0148669311523437\n",
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      "Epoch 208, Loss: 1.0219138671875\n",
      "Epoch 209, Loss: 1.0148162170410158\n",
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      "Epoch 255, Loss: 1.0199053588867188\n",
      "Epoch 256, Loss: 1.0190447387695314\n",
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      "Epoch 268, Loss: 1.0237275512695312\n",
      "Epoch 269, Loss: 1.0197623657226562\n",
      "Epoch 270, Loss: 1.0201162963867187\n",
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      "Epoch 272, Loss: 1.0172993041992189\n",
      "Epoch 273, Loss: 1.0222798095703125\n",
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      "Epoch 277, Loss: 1.0180964721679688\n",
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      "Epoch 279, Loss: 1.0201729125976562\n",
      "Epoch 280, Loss: 1.016462744140625\n",
      "Epoch 281, Loss: 1.020482977294922\n",
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      "Epoch 283, Loss: 1.0213488159179689\n",
      "Epoch 284, Loss: 1.0197209106445313\n",
      "Epoch 285, Loss: 1.0235616943359376\n",
      "Epoch 286, Loss: 1.0161846923828126\n",
      "Epoch 287, Loss: 1.0183562255859375\n",
      "Epoch 288, Loss: 1.0217850830078126\n",
      "Epoch 289, Loss: 1.0180752197265626\n",
      "Epoch 290, Loss: 1.02101865234375\n",
      "Epoch 291, Loss: 1.0169142333984376\n",
      "Epoch 292, Loss: 1.0190892822265625\n",
      "Epoch 293, Loss: 1.0197965209960937\n",
      "Epoch 294, Loss: 1.0166028442382813\n",
      "Epoch 295, Loss: 1.02028623046875\n",
      "Epoch 296, Loss: 1.0173275512695312\n",
      "Epoch 297, Loss: 1.0181564697265626\n",
      "Epoch 298, Loss: 1.0184641357421875\n",
      "Epoch 299, Loss: 1.0196504760742189\n",
      "Epoch 300, Loss: 1.0180572143554687\n",
      "Epoch 301, Loss: 1.0192519409179688\n",
      "Epoch 302, Loss: 1.0203465454101563\n",
      "Epoch 303, Loss: 1.0222170166015625\n",
      "Epoch 304, Loss: 1.0181625732421875\n",
      "Epoch 305, Loss: 1.0173697631835938\n",
      "Epoch 306, Loss: 1.0194884643554687\n",
      "Epoch 307, Loss: 1.0192643310546876\n",
      "Epoch 308, Loss: 1.0176340576171876\n",
      "Epoch 309, Loss: 1.017580078125\n",
      "Epoch 310, Loss: 1.0223380004882814\n",
      "Epoch 311, Loss: 1.0189470092773438\n",
      "Epoch 312, Loss: 1.0172584350585938\n",
      "Epoch 313, Loss: 1.0187676879882812\n",
      "Epoch 314, Loss: 1.0191901245117188\n",
      "Epoch 315, Loss: 1.0201221801757814\n",
      "Epoch 316, Loss: 1.0196523315429689\n",
      "Epoch 317, Loss: 1.0204695068359375\n",
      "Epoch 318, Loss: 1.0177072875976563\n",
      "Epoch 319, Loss: 1.0182762573242188\n",
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     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 32.6 s\n",
      "Wall time: 30.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train(model_c1,dataloader_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cdb5747-f796-43b3-81dd-21eb142cb698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 0.9980648315429688\n",
      "Epoch 2, Loss: 0.9962964111328125\n",
      "Epoch 3, Loss: 0.9948444335937501\n",
      "Epoch 4, Loss: 0.9988217529296876\n",
      "Epoch 5, Loss: 0.9966094482421876\n",
      "Epoch 6, Loss: 0.995633203125\n",
      "Epoch 7, Loss: 0.9955751586914063\n",
      "Epoch 8, Loss: 0.9954673767089844\n",
      "Epoch 9, Loss: 0.9968672729492188\n",
      "Epoch 10, Loss: 0.998178955078125\n",
      "Epoch 11, Loss: 0.9940619506835938\n",
      "Epoch 12, Loss: 1.0018124877929688\n",
      "Epoch 13, Loss: 0.9915676330566406\n",
      "Epoch 14, Loss: 0.9951951049804688\n",
      "Epoch 15, Loss: 0.9988627380371095\n",
      "Epoch 16, Loss: 0.9966917358398438\n",
      "Epoch 17, Loss: 0.9990084594726563\n",
      "Epoch 18, Loss: 1.0010393188476563\n",
      "Epoch 19, Loss: 0.9974341430664063\n",
      "Epoch 20, Loss: 0.9944649536132812\n",
      "Epoch 21, Loss: 0.9943449462890626\n",
      "Epoch 22, Loss: 0.998478955078125\n",
      "Epoch 23, Loss: 1.001424792480469\n",
      "Epoch 24, Loss: 0.9986905517578125\n",
      "Epoch 25, Loss: 0.9962660766601563\n",
      "Epoch 26, Loss: 0.9962093139648438\n",
      "Epoch 27, Loss: 0.9966395751953125\n",
      "Epoch 28, Loss: 0.9969396850585938\n",
      "Epoch 29, Loss: 0.9967479980468751\n",
      "Epoch 30, Loss: 0.9960833862304688\n",
      "Epoch 31, Loss: 0.9999802978515625\n",
      "Epoch 32, Loss: 1.0018907165527344\n",
      "Epoch 33, Loss: 0.9953682312011719\n",
      "Epoch 34, Loss: 0.9987286376953125\n",
      "Epoch 35, Loss: 0.995871728515625\n",
      "Epoch 36, Loss: 0.9951448303222656\n",
      "Epoch 37, Loss: 1.0010193969726564\n",
      "Epoch 38, Loss: 0.9946330810546875\n",
      "Epoch 39, Loss: 0.9936373291015625\n",
      "Epoch 40, Loss: 0.9985047851562501\n",
      "Epoch 41, Loss: 0.9988567016601563\n",
      "Epoch 42, Loss: 0.9946650024414063\n",
      "Epoch 43, Loss: 0.9970060913085937\n",
      "Epoch 44, Loss: 0.9959540649414063\n",
      "Epoch 45, Loss: 0.996332421875\n",
      "Epoch 46, Loss: 0.9966096435546875\n",
      "Epoch 47, Loss: 0.9990703491210938\n",
      "Epoch 48, Loss: 1.0013844665527345\n",
      "Epoch 49, Loss: 0.995664697265625\n",
      "Epoch 50, Loss: 0.9975158203125001\n",
      "Epoch 51, Loss: 0.9910499145507813\n",
      "Epoch 52, Loss: 0.9965730224609376\n",
      "Epoch 53, Loss: 0.9966102294921876\n",
      "Epoch 54, Loss: 0.9958068908691406\n",
      "Epoch 55, Loss: 1.001690124511719\n",
      "Epoch 56, Loss: 0.9919390014648438\n",
      "Epoch 57, Loss: 0.9942866088867188\n",
      "Epoch 58, Loss: 1.000334912109375\n",
      "Epoch 59, Loss: 0.9957557861328126\n",
      "Epoch 60, Loss: 0.9983411987304688\n",
      "Epoch 61, Loss: 0.9969113525390626\n",
      "Epoch 62, Loss: 0.9984080688476563\n",
      "Epoch 63, Loss: 0.9952865966796876\n",
      "Epoch 64, Loss: 0.9949072875976563\n",
      "Epoch 65, Loss: 0.9996801025390626\n",
      "Epoch 66, Loss: 0.996752685546875\n",
      "Epoch 67, Loss: 0.9985830200195313\n",
      "Epoch 68, Loss: 0.9961723510742188\n",
      "Epoch 69, Loss: 0.9973471801757813\n",
      "Epoch 70, Loss: 0.998406884765625\n",
      "Epoch 71, Loss: 0.9934278808593751\n",
      "Epoch 72, Loss: 0.9964285278320313\n",
      "Epoch 73, Loss: 0.9940859619140625\n",
      "Epoch 74, Loss: 1.001269482421875\n",
      "Epoch 75, Loss: 0.9928820190429688\n",
      "Epoch 76, Loss: 0.996677392578125\n",
      "Epoch 77, Loss: 0.9965219604492188\n",
      "Epoch 78, Loss: 1.0015077514648438\n",
      "Epoch 79, Loss: 1.0015387451171875\n",
      "Epoch 80, Loss: 0.9946650512695313\n",
      "Epoch 81, Loss: 0.9952909301757813\n",
      "Epoch 82, Loss: 0.9943154174804688\n",
      "Epoch 83, Loss: 0.9959332519531251\n",
      "Epoch 84, Loss: 0.9945929809570313\n",
      "Epoch 85, Loss: 0.9968466186523438\n",
      "Epoch 86, Loss: 0.996544384765625\n",
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      "Epoch 851, Loss: 0.9985263671875001\n",
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      "Epoch 891, Loss: 0.9997307006835938\n",
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      "Epoch 893, Loss: 0.9993187622070313\n",
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      "Epoch 898, Loss: 0.9965166748046875\n",
      "Epoch 899, Loss: 0.9910606079101563\n",
      "Epoch 900, Loss: 0.9955761962890626\n",
      "Epoch 901, Loss: 0.99606142578125\n",
      "Epoch 902, Loss: 0.9973639404296876\n",
      "Epoch 903, Loss: 0.9955709106445313\n",
      "Epoch 904, Loss: 1.0013852172851563\n",
      "Epoch 905, Loss: 0.9947494995117188\n",
      "Epoch 906, Loss: 0.9983573608398438\n",
      "Epoch 907, Loss: 0.9995106689453126\n",
      "Epoch 908, Loss: 0.9976748901367188\n",
      "Epoch 909, Loss: 0.9997716674804688\n",
      "Epoch 910, Loss: 0.9954555664062501\n",
      "Epoch 911, Loss: 0.99623330078125\n",
      "Epoch 912, Loss: 0.9937468017578125\n",
      "Epoch 913, Loss: 0.9976549682617188\n",
      "Epoch 914, Loss: 0.9974617797851563\n",
      "Epoch 915, Loss: 0.9975204833984376\n",
      "Epoch 916, Loss: 0.993304638671875\n",
      "Epoch 917, Loss: 0.9967301025390626\n",
      "Epoch 918, Loss: 0.9997821899414063\n",
      "Epoch 919, Loss: 0.9953623901367188\n",
      "Epoch 920, Loss: 0.9961148681640626\n",
      "Epoch 921, Loss: 0.9970705200195313\n",
      "Epoch 922, Loss: 0.9963607421875\n",
      "Epoch 923, Loss: 0.9977873046875001\n",
      "Epoch 924, Loss: 1.0009270019531251\n",
      "Epoch 925, Loss: 0.9925192260742188\n",
      "Epoch 926, Loss: 0.9988430175781251\n",
      "Epoch 927, Loss: 0.9970237060546876\n",
      "Epoch 928, Loss: 0.9957030639648438\n",
      "Epoch 929, Loss: 0.9977335693359376\n",
      "Epoch 930, Loss: 0.9962098266601563\n",
      "Epoch 931, Loss: 0.99546875\n",
      "Epoch 932, Loss: 0.9962437133789063\n",
      "Epoch 933, Loss: 0.9942640747070313\n",
      "Epoch 934, Loss: 0.9968548522949219\n",
      "Epoch 935, Loss: 0.999216162109375\n",
      "Epoch 936, Loss: 0.9967916381835938\n",
      "Epoch 937, Loss: 0.9948527709960938\n",
      "Epoch 938, Loss: 0.994322802734375\n",
      "Epoch 939, Loss: 0.9954602905273437\n",
      "Epoch 940, Loss: 0.9988545043945313\n",
      "Epoch 941, Loss: 1.0008211791992188\n",
      "Epoch 942, Loss: 0.9974483764648437\n",
      "Epoch 943, Loss: 0.9952143188476563\n",
      "Epoch 944, Loss: 0.9969707763671876\n",
      "Epoch 945, Loss: 0.997259423828125\n",
      "Epoch 946, Loss: 0.9965593139648438\n",
      "Epoch 947, Loss: 0.9975186767578126\n",
      "Epoch 948, Loss: 0.9912593688964845\n",
      "Epoch 949, Loss: 0.9930767700195313\n",
      "Epoch 950, Loss: 0.9949144409179688\n",
      "Epoch 951, Loss: 0.9968376342773438\n",
      "Epoch 952, Loss: 0.9954177734375\n",
      "Epoch 953, Loss: 0.9998818725585938\n",
      "Epoch 954, Loss: 0.9982576293945313\n",
      "Epoch 955, Loss: 0.9966765258789063\n",
      "Epoch 956, Loss: 0.9989629394531251\n",
      "Epoch 957, Loss: 0.9992774169921875\n",
      "Epoch 958, Loss: 0.99965283203125\n",
      "Epoch 959, Loss: 0.9960835083007813\n",
      "Epoch 960, Loss: 0.9980868164062501\n",
      "Epoch 961, Loss: 0.9997067504882813\n",
      "Epoch 962, Loss: 0.9953202758789063\n",
      "Epoch 963, Loss: 0.9939411987304688\n",
      "Epoch 964, Loss: 0.9979074829101563\n",
      "Epoch 965, Loss: 0.9942986328125001\n",
      "Epoch 966, Loss: 0.9981598388671875\n",
      "Epoch 967, Loss: 0.9945195556640626\n",
      "Epoch 968, Loss: 0.9992269897460938\n",
      "Epoch 969, Loss: 0.9982842407226563\n",
      "Epoch 970, Loss: 0.9955996948242188\n",
      "Epoch 971, Loss: 0.9942236328125\n",
      "Epoch 972, Loss: 0.9956601684570313\n",
      "Epoch 973, Loss: 0.9991522460937501\n",
      "Epoch 974, Loss: 0.997066326904297\n",
      "Epoch 975, Loss: 0.9995294067382813\n",
      "Epoch 976, Loss: 0.9936628540039063\n",
      "Epoch 977, Loss: 0.9967851196289063\n",
      "Epoch 978, Loss: 0.9941637084960938\n",
      "Epoch 979, Loss: 0.995721728515625\n",
      "Epoch 980, Loss: 0.9985130493164063\n",
      "Epoch 981, Loss: 0.9952110961914064\n",
      "Epoch 982, Loss: 0.9951919677734375\n",
      "Epoch 983, Loss: 1.0003758178710938\n",
      "Epoch 984, Loss: 0.99678056640625\n",
      "Epoch 985, Loss: 0.9955242065429688\n",
      "Epoch 986, Loss: 0.997949951171875\n",
      "Epoch 987, Loss: 0.9964962036132813\n",
      "Epoch 988, Loss: 0.9991216186523438\n",
      "Epoch 989, Loss: 0.9950615478515625\n",
      "Epoch 990, Loss: 0.9966001708984376\n",
      "Epoch 991, Loss: 0.9978912719726563\n",
      "Epoch 992, Loss: 0.9905031555175782\n",
      "Epoch 993, Loss: 0.9956959716796876\n",
      "Epoch 994, Loss: 0.9967556274414063\n",
      "Epoch 995, Loss: 0.994578515625\n",
      "Epoch 996, Loss: 0.9982961547851563\n",
      "Epoch 997, Loss: 0.9956855895996094\n",
      "Epoch 998, Loss: 0.9984631835937501\n",
      "Epoch 999, Loss: 0.9981308227539063\n",
      "Epoch 1000, Loss: 1.0004713989257814\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 30.1 s\n",
      "Wall time: 30.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train(model_c2,dataloader_c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd3cde66-45fe-47fa-8ecf-16e8706bb7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 1.0146484252929688\n",
      "Epoch 2, Loss: 1.0185496337890625\n",
      "Epoch 3, Loss: 1.0199917358398438\n",
      "Epoch 4, Loss: 1.0207253417968751\n",
      "Epoch 5, Loss: 1.0168466918945314\n",
      "Epoch 6, Loss: 1.0199429687500001\n",
      "Epoch 7, Loss: 1.018391357421875\n",
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     ]
    },
    {
     "data": {
      "image/png": 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oUGNdhbggppqkfv36QVX5D8oYTTt8zbp167jXWOUn8xqCIAhZ0AhEEMEkrhy3CYIg4hHSFBFBQ6FeKQQJSQRBEDGGpiuCqCVI7wMJSQRBxIxvdh3Bn977FsdOnIp1VQiCIEzEVQgAgiDqFr+YVrOlkAoVz97QI8a18Q/aSJsIGuS4LQZpkgiCiDm7Dp2IdRWEefGLbXh23uZYV4Mg6ixBEt9Ik0QQBCHIycpqPP/FVgDAyIvbIyczTeg60iMRRHxCmiSCIAgGoZD5e7Zac4y3Ye3Jymrf6kQQRHQhIYkgCMLArkNl6PlkPl5asM3RdRv2FaPzo/Pw2EcbfKoZQRDRhIQkgiAIA898thnF5ZX4R/5W3XE7/+sXvqgRqv67fLf+BNnbCEKYIL0uJCQRBEEY4K38oeD83vnv17swb0NhrKtBEEKQkEQQRFQ4VRXS+fTUJ+y+jOtLhIAthcfx2McbMfrNNbGuCuGAjfuL0e+5RZj77YFYVyXqkJBEEITvnKysxgVP5uPn/1wW66oI4VZj5FbYqS8aqkOlFbGuAuGCMW+txa7DJzDm7bWxrkrUISGJIAjfWb/3GEorqrDpQEmsqyKEbJmFgknWUF+EwbpGeT1esUlCEkEQvlNX5ka/7oNkKCLI1GfhloQkgiAIA/V5UvATEgaJeIOEJIIgfCf+hA65FbYTDuKvfQjCP4JkniYhiSAI36krm2mqGmkmQOM4QTgmyIK5GqDKkZBEBI5dh8owavo3WLP7SKyrQsgiOGNeTKAQADUEaO4jCCFISCICxx/eWIOFmw9ixNTlsa4KIQnt3Bikr0QevCoGv+YEIZ/63O9JSCICx96jJ2JdBUIyWqEjnuNJuo+fVE9URQRRxyAhiSAIAMCH637EB2t/9CVvrU9SKB40SY5P8DlZWY0jZae8VIcgiBhBQhIROOJgDmXy7LzNuP7lr3AyDgOvlVZU4b7/rcf9s9ajtKJKev7aZxoPz1emSfCGaV9Ly6suEQ9mV4IgIYkgJPHy4h+wds8xfLJ+f6yr4hitYFfhs5AXD5okHm5W6W3YFx9RxqNNHHeDeke0n1WQzNMkJBGBI96Xi5+qDsW6Cp7wY4DSO25Lz14Kc789gH8v3QGAb1XT1j1IA3k8Es/CMlF/SIp1BQiirhGKQ89kv+crrWklqEJwePPOizs29bU9DpacxMb9JejX6Yx6LWgFsxcQhB4SkghCMvEmI63edQQLNh+M/PZj2tY2SdDb53Ap38laRtUv+dtCVFarePHX5+Oa88+UkGN8QookIh4gIYkIHPE+eMabGeGGaVGIR6ULAeCtfUIhFYrir7mLb27z/mwrq2vy+HLbIY2QVD80SlotYlA1igShhXySCEIyQdeUxALd5OihfUIhFVe99CWue/nrmKyO0pboVaxJ0GVQ/zpNnH1L1HPq78MiIYkIHPH+OtLSZmu8tM+PR8uxufA4CvYeQ0VVfDvIJ9Qhf6R9x8rx6OwN2PFTaayrQnD4qGAf7pixGmU+hPioy5CQRBCSiTdzmxE/5u54i7jNE+R08Z48lpGgUyXFt8B0+39X440Vux2ZbuP8NYk77p1ZgM+/L8Irp1dwEmKQkEQEjxgOnodLK7Byx2FP2g4a/M1oBaN41rTJ9KOJb7FIz6YDNbGg7CKLK5q7Jp+k2HDsBEV/dwIJSQShod9zi/GrV1Yg//si13nEg6bECsWH6VurXQt6+4hWz6uwV5fMbaLot6eJYUUIQpCYCklLly7F8OHD0apVKyiKgtmzZ9tes3jxYlxwwQVITU1Fx44dMX36dN35iRMnok+fPmjUqBGaN2+Oa6+9Flu2bImcP3LkCO6++2506tQJ6enpaNu2Le655x4UFxdLvjsiHjl+2l6/ULMk3inxbm7zA12cJC9auihpH7hVlLi9SkL9k5F0xLNGMaicrKzGiVP+bitU34ipkFRWVoYePXpgypQpQul37tyJYcOGoX///igoKMC4ceNw++23Y/78+ZE0S5YswZgxY7BixQrk5+ejsrISgwYNQllZGQBg//792L9/P/7+979jw4YNmD59OubNm4ff/e53vtwj4Zx4V8PHYzBJv9EGIWe1DuuZsybRWA/WMouvz4EkgfhfoBE0QiEV3R//HF0nzEdFVfztHxlUYhonaejQoRg6dKhw+mnTpiE3NxeTJk0CAHTp0gXLli3D888/j8GDBwMA5s2bp7tm+vTpaN68OdasWYPLL78c3bp1w/vvvx8536FDBzz99NO4+eabUVVVhaQkCh1FeINkJDN6c5t9Az3xyff49LsD+PTey5DdIIWZxk+BKRqCepDNbRVV1Xhg1npccc4Z+EXvNr6UEWuBt65RURWKbIlUWHwS7Zo2YKZzI5zX50cVVz5Jy5cvx8CBA3XHBg8ejOXL+Ssqwma07OxsyzSZmZmWAlJFRQVKSkp0/wh/CMLgaRxHXl68HXe+uQbVAhJQvJvb/BAQQjpzm336/3y1E4UlJzH9613cNH4KMrw6yny0QTa3zfpmL+Z8ewAPvvetf4XE92sSOLTvg5VfYTyYOYP0asSVkFRYWIicnBzdsZycHJSUlKC8vNyUPhQKYdy4cbjkkkvQrVs3Zp6HDh3Ck08+iTvuuMOy7IkTJyIrKyvyr00bf76uiGDy7Lwt+GxDIRYJ+CoFfwiKPk41SWGqQ/pYSH61rejEISsoJmAMARAsjp6o9L2MeDerBw395sty8w5uT/WfuBKSnDJmzBhs2LABM2fOZJ4vKSnBsGHD0LVrVzz++OOWeY0fPx7FxcWRf3v37vWhxtHnSNmpwH1ZBKs2esor7W39QWtPp/hRfZ1PkoP8qy3iRcqsp6iJVGaZAba2RYU4f03iFjK3OSOuhKQWLVqgqEi/NLuoqAiZmZlIT0/XHR87dizmzJmDRYsWoXXr1qa8jh8/jiFDhqBRo0b48MMPkZycbFl2amoqMjMzdf/inaVbf8IFT+b7q1J3QZCFDJHxJd7NbX7g1NzGuq7mWu3eX/Iwrr4TqaNXTUiQfZKiUTNt623YV4xnPtuMUooG7RoadfwhroSkvLw8LFiwQHcsPz8feXl5kd+qqmLs2LH48MMPsXDhQuTm5pryKSkpwaBBg5CSkoKPP/4YaWlpvtc9iLy0YBsA4L01P8a4JvGDSAyheHfc9qP62hV/ToTIqmp+WpnCtHBsJGklxp8JQ/bHi7Yf/PyfyzBtyQ/4x+dbpZZRn9C2p5X8vf1gKe5+Zx22H6QtZESI6VKu0tJSbN++PfJ7586dKCgoQHZ2Ntq2bYvx48dj3759mDFjBgBg9OjRmDx5Mh566CGMGjUKCxcuxKxZszB37txIHmPGjMHbb7+Njz76CI0aNUJhYSEAICsrC+np6REB6cSJE3jzzTd1TthnnHEGEhMTo9gCsSU5MfoysqqqGP3mGiQmKHj5pl5RL18c91MYaZLMaAVHJ+1j0iTJqpABY5V4WiLVpUaMhR+apBU7DuOz7w7gT0M7IyNF3vBefqoaw176EhfmZuOZEd1d56Pb1oXRfluLjrvOu76jakzTVia1ZdsPRaE2dYeYCkmrV69G//79I7/vv/9+AMDIkSMxffp0HDhwAHv27Imcz83Nxdy5c3HffffhxRdfROvWrfHqq69Glv8DwNSpUwEA/fr105X1+uuv49Zbb8XatWuxcuVKAEDHjh11aXbu3In27dvLvMVAk5wUfSHpUOkpzN9YYzItPlGJrAxrM2c8Eu8ykh/mzmqXZrKqEN8pSaq5Dfr6ReMZ+uG3/etXVgAAGqQm4aEhnaXlO2/jAew4VIYdh8q8CUm6v82NHGRn9qCj0yTFsB51jZgKSf369bMckI3RtMPXrFu3jnuN3QBvV2ZdZ/wH3+JoWSWm3nwBUmKhSdJtS8D5Wo9WZVwg5JMU7/Y2H3Abcdsq5ILM11g0L5kb3Gq/9mUrlXYfOSE1PwtZ1Vk+Ng1IMpJ7RM1tbqjPcyZFTqxHqKqKd1bVrMr74acypCRFf0TSb3AZf4i0WLzLSH5Uv1rnk+TuOhO+BpMUSCNx7zbpc5Dk/KRlZyNkBtmZPejIGHcqqqpx4NhJtG/GDkRZH4krx23CG9qBOKSqMfFJ0o6BvEkm2h8tVdUh4f2OaHWbO7QDuJPmqTKM/HpFhETH7RiEAKiPWhO7OFMkJLlHhr/ciKlfo9/fF+Mr8luKQEJSPcL43iQlxEBI0vwdFFFi0PNL0XXCfJScrA2g52WsjnfVtB/Vd7u6zWy69KdtTXUSCgFgxkm30frfSJcNAmpusRNygyI4lp+qxlfbD6HSKlBXwNB9iLjMY8O+mkVM76/9EWt2H8G6PUe9VyzOISGpHmGcCGJibvPTxOCSHYdqNj9es1vOgBDv5jY/cBtx26hJ0iLVJ0k4nbxCfVWaGKpZWlGFfy/dgb2Cvkp+1c24um3noTKMm1nrYxoUTdKYt9fipldX4rn5W2JdFWH0sci89dPSk1UYMXU5rnv5a5yqCgXmgzYWkJBUjzC+N7Fw3A7GEOgF9h2oLoWAIOLHdhHVLk0Blo7bXipkzEvQI9tuCbsToikQPD33ezz96SZc9dKXrq6X1da6iRzAba+vwuyC/ZFjiQFRJS08vf3QG8t3x7gm4rgN2MrimGZbGqsVpvUBEpLqEcbJOyZxknR/x58wwZvXZKx6+qhgHxZsKrJPGIe4FS6sV7dJ9Eky/fa/b0ZTHvhq+2EAwPGTLiNaS2oObTahkIpdh/WarYAokuISudv01GYWFO1erCAhKeD8ff4WDHvpS5T5EK5fGycpWsvWtRPbwZIKXDPlK8xaHf/74OmEPxejVWHxSdw7swC/++9qeZVyiw9dQSvsOBFAjEKSX0o6k0sSpxzV4pdTRKK3B4FQSJUmNNo9Pz80Saqq4r9f73LlXxNPH3IytZxaze9Pxyu8ZWbBiVNVeOyjDVix47DueJDkMhKSAs7kRduxcX8J3pUgSBiDjWk1Saei5KCofXef+Wwz1u89hocCsnecwvmbl0aLtm0tl61zOFRaOxDFS5yl/cfKcctrK7Foy0HbtHqfJPEyqg2jvQznVCbCq9uclWqVPkgTgRXGZ+CN6K9um7+xCI99vBHXvfy19Lxls/fICVz5jyWYuWqPfWIDelOmt2emfc8ue3aRzvwmk8kLt+O/y3dHgqAGERKS4gQrB1ZRjCahlMTaAamiqkZI8nuC1tahuFz8xTt+shIHj5/0oUZy0IdXcH69VrCSOyk5R7T08R98hy+3HcJtr39jm9bt6jaTJslmknWLcVIRyVqkfKu+EE3/Gy+yhxuhn4fd6jY/BMftB+Nnq5Mn5nyPbQdL8fAH3zm+VqZPUrRW6G6Lg/3jSEiKE2R8YRknJ+0S5FNVIfzj8y3o8/QX2Hes3HNZPEQibrM47/HPceHTC3DsxClX5f50vALzNhSiyqPGjLcnktv7ClPlUoiIJUUl4kKr6zhJhg1uoxUniRvDy3G+/CtE3unv95dg6dafHJYql5CqShNI7fqBH5okq33MgsbJymrX18rUssoUjK3wOh5HAxKS4gQZH53abq9AP0hVVFXjpYXbcbjsFF7Id78Tt6qqOFJmIch41LhsLnT3VTj0xaUY/eYaTP96l6vrw/Aeg1d/AJ05Ksbjhh+BFfWr2xxokowb3Apemv99ES6euADf7DoilF5UMHXqoG/Vx0Xm7qte+hK//c8q7Pgpul/cWsGCN2F+8X0Rbpj6NTYXlgjna9wjz0hAFrfFJW63/mERLYs/z0ISpO9EEpLiBBlfQ1Yd71RV7czspX8+M28zLngyH7PX7WPXQVef6L0Jh0prBLcFm/j+M7y9tGT6ofAIkrlNFEdBIV36JBnNvzrtkUU+v5+xGvuLT+KW11aazrEmfV2/tNRROTNpWOUk4gMXZvdhuXuxOSEUYjf17TNWY/XuoxjygnhYAf3HhDnXoIQAiEdkapKiNTZr552gQkJSnCBFk2T6KmdPXF7ej38t2QGgxrbOrkPt327MSl6bwY2sqa2mSAgAzz5JMXbcFjVjuY2cHe53IgOxlSZJpHTjIDxl0Xb0+Ovn2Fqk10gaNYGy5girfFTO30wc9luZpshqieY2u3uOJ9OYW+55Zx3GvL1WuiAi0ycpaua2OFikQkJSvOCDJkkvGGnV4P51XL3vjm/FcHElJAmkcRtROkw1Q4gIOk5qqX3W4b9Fnr9VCAA3zfTc/C0orajCkwYhXtQh3KlPlGhedkQ7Vk3IILRr73X34TLcMcNdqAo7k1BdVyQVn6jEx+v3Y+63B/BTqdyl9fpxx6u5LTpjEG/blyDJyiQkxQkyBg9jx+dqPyS8HyK+O15exNKKKlfChJvYNNpyuJokXXrHReii2sZck+TCJ+n7/SV4dt5mHD/JXrHIEgJFnp/JcVuSAG/SWAhqqJyWbtXHnfRfzxpUi3OLNh80bclTbSH0j35zLT7/3l3QUzshN1HzXEpOVuKmV1dg1jfxH0ctjO4jUbKlSWacpGgNQZXVsR3rRCAhKU5IlKFJMvzN037I6LZ+CRMAsG7PUXR7bL6r+EqmudEQO4qFtpo8IcvrtiTawSIefZKueulLvLz4Bzw7j73XlcroXyIDsZVg70VgMn50aHMKqWI5i/kkiWH3dvulSfrx6AncNv0bjJiqjyFk0iRpbmTP4TLX5dk5bmuF16mLf8BX2w/jofe9xVHztlm1p6JNaMcP2Rp7mWN4rDVJQYKEpDhBxhhptFkbJ4Zo4FSYYMVtmrxwOwDg3TU/eq4Pb6LWDmYi9fTaltrBItar20Rh3efG/cXMtCzNhJtJQoaQDZiFEtGvcKdlWmqSOH+z8Mv8UFjMDuOgfV6+xUlSzfelddwucRBHLW7w0Ywky6+0Jq8o+SSRkETIQopDo9HUxXH0k+MTw9O4GOpgw4hpXxvMXYqnCcPYjiL3KqQx0KRxY9LTOhhHU5PEun/R0lnV5F3L8kkSuU2r5+WllUz5GkUWTuWcBrP0InBpPxCi7aIRcvieimLUBBrvS6vhi5U+9aCD+F+OkSjImLKW6Fdq9aH29so9+POH36GEY1p3ApnbCGnIGCSNXxq8gTBa3VZEY7JuzzGpg4lJg+A1A0ZGboQ4vSYpmkJSdK5lr24Tv/7LbT/hL7O/Q/mp2mB7c9bvt7jCGitNklXzO20vKyE8fObLbT9hW5E5/ldIL3l7wulHltlxuxYvvdOozTbWy5dgkg4bb8zba6XXIYzXoLNWyNQkVVkMzn/+8Du8vXIPvtp2yFshIHMb4QHj4Cpj8ND7A6jgrTTzU5HhxnFbZnXMPkn8c8w0nHy9frmdqvZv8LSCVZKoJpFVT96lrOXJTu7zltdW4c0VezB1yQ+RYxM/2yx8vZHjJ6uw90ht7CErUzQP76vbVGw/WIpbXluFXYw4SFqNotf33+nVWhObUevsBaO51KRJ0qiSYrXA6ZtdRz1dX1xeiSEvLMVLC7aZzvk5zqqMd8wtIrKLjP0+SUgiXFFVHcLP/7kMd765JnIsQcKTiqYmie+47dzXQbcxr9cvaou8eWjrzPsi96r82aKJWhzN1W1eTKvO9mAzX2ecMFkYW/vHo862zOHVcNWuI7js2UWRLXiMZiBefWT6JAHAdou9qyQqkhyj90kyLl7wgKGdjfgRAiDay8n/+/UubC48jn8wdi7wM7xHyNC2G/cXc1eb2uclxw3BDuPq1VqCEwOAhKQAsmb3UWzcX4LPNhRGjrnxczFi/NLg+SH5+SKLmjV413hF1OygTSZST69t9uaK2l2/o6lJYt2baPFOqsn6ynXTZrL75ro9Zq2BpbmN45MUCqmO/btYjsv6emg0SQ6lB8+Ouz4FNzW2n8lxO0gBclxiFUXaT02Str8s3XoIw15a5igauhYRh2oZq/Mq42CVSlKsK0CYYTmzSfHbNmiO/AzsKLKcXlRnJVNoMC39liQAyWy+aIZJ8jLQMQUsTlrW6jbefVrFpZLdNOFJ2cvChVBIxdVTliExIQHNG6Xqz9nkZfVaV8fQcdv4vPSaNveYVrfV7CIZOVbXI27rNOkOzNUiaPvanG9r/PXcblYu9mHoKusIFVXVOFnJE5KiOAjaQJqkAMKy00rxSTIMdMYvYe05r/D9e+QKZjOW78JRqw11TfBXNXFjIHGv1qTx8IVonJSja27zcq24Goq5rxSnbKvbdyswF+w9hmsmLzMdD0/KRmGRJzyyii86fhIb9pVg/d5j5mXrglopFtp2MO4l+MNPpb5qfLVDkMz+aDQJGV+oaEcW9wPLnf8MH6pSy9Vk57UdrRy3WeW5Yd2eY94yiBIkJAUQlkOcdMdtVeULLD7O0dqs3ey8XkNtW0z4aKOn1SiyvpiMTvFOMOYf64jbonjdu413vaUmyWXT/Opfy7H+R3MMp7Bm0Wry4gkj4cNaE5Exqd1KOZbmJNI+Wk2SJt1LC7ZjwKQleHruJn7mLuCZ3KtVtinRVRlGc5vhfKIPM1KQxC7eh2kYL8O8zjzr8aZFrGAyR6mMlESJucmFhKQAwtYkec/XGKuG77jt3yRtN0iw0DluM85//cNh4fLN5jZ2HXTJOF/04KRxrEky/I7q6jZxZZA5neAxwBjd3S6tRZkum6aC4ycSDl6o+0ZQnWkGtQKMScCy0iqA3Z/DWfD6/fNf1DgEv7psZ+TYgeJypqMwMwNefTRV1a1uM4QA8DIUGbXZxvcpaOY2GW/itqLjmPjpJhwtO8V8D3TleTK3aX44bMe53x7Q/RaJ1eZ1nNJe3qxhKj9hjCEhKYCwhCRtn3909gZc+Y8lupgxIugFApX79SxjjuY7mjs3t8kUGdz4uOgnK3uTnNP6xtTc5sUniVFPXt/R7912+nqehiaK/ggJEZ8kff14Pjj64+rpPGqPGScXe00S4zgjL7sW+e1rq3RLzt28w9pLjBG3pfkkaf9WVdP75IeMFG25y9j2w/65DP9augN//vA7XZ+X/Z7bfUxaYdTGC9XNIsmB4nLblXVetO/RhISkAFJZxVLD1nb7N1bsxraDpZj73QFTOivMIQDYAoufigw3NnmZIQCs6iOy3xwPL0KmcTzyW0Y6VRVC/vdFKDlZydYkCQ5YTu6TpbUUEdKNk6hsP5zwM7eKMG2nZU3QaZL056yDSVrfixNt1jaLUAKi6Mzv2tVtMtvcIPgZ37nKKhU/Ha+QV14MMLZWeLXbqp1HbMc/b/vM1ebnRygFU3mc/ltYfBJ5Exei+18/t8sggtm8FxyNIglJAYTlkySny/C1OLJNPOGX/VBphf4r3UWZqsRVokbfLrFtSUTS6H45qpNxsPHb3Pb3z7fg9zNWY9Tr33gqi3Ulb+BkbabM6xdWVZItQNbuFaavC1d7ZCO4GPuK1b1wNUmnL9J/zTu7ca8fE9oFtjJXaRs/xozVfP6Lrejz9Bc4WHJSmm5BRvgUGVRUhWyFJE/mNs1ziobZklfXtafDatjdS3B1R3pISAogbHObudM7fQ30A5TeGVPmvj9h3lvzI3o/9QWe4URGFp3wZH7JunEE1iYxXh+eyEQneWb+hvR+m9veO70x8OrdR9mCjuhzcWBuY04IuvPa43zNoVXfPHGqChv3FzvSNtWa2/QVE30fZn2zF+P+VxD5baWFMrJo80Hm8fAVXO2aQ/8iUXTPw6BJ8tK/dWUYGpo3ma/efdRV/hVV1YFd+FBRVa0PASC5nrpxSmrO9uVpES2b9eEUREhICiD8KKR6nEbh1n/FGEwMIXY6tygAnvhkIwDgX0t3sPP2MBm7r5cxBAAnncWKpTBLtv6Ebo/Nx0cF+7haERGM+fu9d5u2Bdw+67KKKpRXivvEmba5gGFgtHFoZSQzce2UrzDspWWYv7FIKD1QK3BYaTi5/kkAHnr/WyzZ+lPkmPH7xqr81buPYg1DGAhfwyvXr2XyVo7bWmRpH0MMTVKYtGTnU9PJymr0fvILDHlhqau6yYLXPJXVqmnxjEz8dEuwK0+LaNlOzMmxhISkAMIyt7G+jp2qkY2+FbzNFmX0V0VRbM0xRidGngZA6leGxeo2fgwk9hf9yP+sQnllNe6dWaA7fuzEKXyyfj8qqsSECLO5TegyObBMRgKXvaIRfHXXci7WCeghxjGDljOM8ZlY1W1rUY1fzkcF+yxS6WEGk7RIb6elMp6367vf7SvhnmM5uwPuolKLXMELdBhS+e+mU0wTI6diaUnOl4Rv3F+C4xVVUvyz/EJV2eOf7LyjYWLkVV/U1Gf1YRIkYiokLV26FMOHD0erVq2gKApmz55te83ixYtxwQUXIDU1FR07dsT06dN15ydOnIg+ffqgUaNGaN68Oa699lps2bJFl+bkyZMYM2YMmjZtioYNG2LEiBEoKipCUGCFtWd1IdGxMv/7Inz3Y7FJi8P7qpHVX9k+G9oya39c+Y8lGDH1a2Y+xhfIy1eS1aTLu219GvvGWbvnGO5+Z51wHBujUCTVUZbBYU3wTbem1SMWATwPlpzUbR4LGCb80//zHKJ1NTL5kNnXzckWHrUhANgCiVWZLMFBbyJz17rhq4x5hfFLS6AXYCzaw0sZuvL4OaUmy4ubE/XVbRYtpNfe1/44UFzuWRDlBR/1C64W3uKa6pCKZdsOobi80pP2PZrEVEgqKytDjx49MGXKFKH0O3fuxLBhw9C/f38UFBRg3LhxuP322zF//vxImiVLlmDMmDFYsWIF8vPzUVlZiUGDBqGsrCyS5r777sMnn3yCd999F0uWLMH+/ftx/fXXS78/t7CinbLeHxGJfWvRcfx+xmoMn7zMtJqNNyjK6rLMvaw4gtmOQ2VYu+cYZ+JhX+8GY5uJrEoTKZP1JTRj+W6hOpm0Dz6qkr7c9pPut4jzMYuUJP7QceH/LcBlzy5CsSb6NNtxu/YanbnX0lHYvm5OzFEKQ5NUFQphc+FxTYnig7lR++Nuf7qa/3na3cQoLF0yrvaTNTwYJ0bes0ryeI9+RiP3hllb968lPyBv4kJMXrjdW0iOKJvbeAOj1fs3Y/ku3PzaSvxi2tcW80+wiOnebUOHDsXQoUOF00+bNg25ubmYNGkSAKBLly5YtmwZnn/+eQwePBgAMG/ePN0106dPR/PmzbFmzRpcfvnlKC4uxmuvvYa3334bP/vZzwAAr7/+Orp06YIVK1bgoosuknR37mHt3cYamUTegx0/lTGPq4Y1PFELAeBg2XMYmVummNpMZ2Zh526lZbA7LoLxUj8dT41mMrcl8YQkbX57j5xA1plZANhaEb6vj9ZsoEekaRJdTBDauny9/TD3nJ1Zzrj9ipd+wXondx8uwwmH8dFE4Wl5jbfgZSI3tiVvPnVjftFv3RJ9DVJt4fxTrGc68fTClkn5W3FJx6a22S/Z+hPOatYAbbIzuHlHY3sX47tYVlGFd1btQbJF2PTZBTV7ym0tKjW4XujTBSmmaFz5JC1fvhwDBw7UHRs8eDCWL1/Ovaa4uGYrguzsbADAmjVrUFlZqcunc+fOaNu2rWU+0YRpbmO8eE5fhM83Fury0369v7VSTOvhBKaoZzP2sSceeyFFFKtgkiICEK94TyYIw+P209xmpUmL1Of0///7Zg+mLNrOzMdqIIzkw9EaRhyTdcInf8DU5ylXkxS+W21dEg1Slsr5xRToDcKgF3lXb6Ks+fs+zUo62fAmrRqzoZx3UDSAoNc3QGTLm1hgF0zSzpdoxY7DGPmfVbjs2UWmc9EwyfLKA4BnPtuMp+ZuwmMfb+Rew1s04iR0RrSJqSbJKYWFhcjJydEdy8nJQUlJCcrLy5Genq47FwqFMG7cOFxyySXo1q1bJI+UlBQ0btzYlE9hYSF4VFRUoKKiNshZSQnf4dIrrBAA4T7j9EXQpnlpYe2EFzIMfNqtPWT1TzdfvKxBTG/C8FY7k0+SJjv2rtxi3828epVWVKFhqvVrZtpc1U8hyVi2RVF/ev87AMCQbi3Q4YyGunOpPE0Sx89Iv7rNXLao6l2kZZz4JKnMuuhLcaLVMPpe2WtOWX3OXG74z59K/Qu0yGsDU4BMSWVAFVss4YZqVWVObjHVMIGvrYuc52mz1ZpwCazVkKz8ouK4bfj99Q+HTGmKT1QiKyM58jvBoO0LE9CoDQDiTJPklDFjxmDDhg2YOXOm57wmTpyIrKysyL82bdpIqCEbq/gz2o4ltGKFpx0Bv2PKmKT3HSt3VJ/IecYxmctmrX2S9JmfrKzGgElL8MCsAm6ayHFOebsOsc2dumsNFzNkZN9gDcrG+hw/WWVKk8LRJPG0fizhiRcnxeoRi3RNJ+4stR8ftceMLoE8YY5VU6NPkl0gRquJkhWWw21gR6fBBa20t17GB+2VPx4rx9ET7K0rVAsBigdPS2G1t54b9hw+gYWb+Qt9rEqwig3Ee6ce+2gD8iYuxLET/MUSgDGYpGVSKYg05aV/W6j7zXsWsdbwWRFXQlKLFi1Mq9CKioqQmZlp0iKNHTsWc+bMwaJFi9C6dWtdHqdOncKxY8dM+bRo0YJb9vjx41FcXBz5t3fvXu83xMFq4NRqO7xEVVWNzpi6svzDjU+S10CXokvKjRPQF5uKsONQGVbsOMJMry+DfXzXYXshyfi8/TW3GQ64LCqVE8fG6JMTRreknBWAU5cHX2CSbW5jmf5M7x/nnlgYTWR2/dXqg4i1+o+1qIOFq/dE87e2GJPjtge09/TQe99y0x3jCE9uytEi4zYuf24RRk1fjaVbf2Ket9SEak4ZP4Z4saH+u3w3CktO4g2bhSAhSXODKCJtebxC/4GlE2S1fwdXRoovISkvLw8LFizQHcvPz0deXl7kt6qqGDt2LD788EMsXLgQubm5uvS9evVCcnKyLp8tW7Zgz549unyMpKamIjMzU/fPL6w0CdpB1csCEFW10Ir42GHtNUmMr3ODU5CXSONQaiIdf3p63zte3BJFcdoO7MQs/zK7K/1c3SYmI+mPsvoJ76uX1546rYhNWqvRU+SZOJkgWE7kpmCSXBOI+ZhRA2NXXyuZh6XN8VPLyH12BpOzl94p+k7dPmM13lq5xyYvlRuKojqkoup0Y2l7g8x4POHtN5xg1c/SU6zDHlTYjCVOrQxecaP9MTrXR/6WUB+/iKmQVFpaioKCAhQUFACoWeJfUFCAPXtqXo7x48fjt7/9bST96NGjsWPHDjz00EPYvHkzXn75ZcyaNQv33XdfJM2YMWPw5ptv4u2330ajRo1QWFiIwsJClJfXmH+ysrLwu9/9Dvfffz8WLVqENWvW4LbbbkNeXl4gVrYB4n4KIpMBd9NW1UIrYpure+zyZk48jAnWCVrBUoGC26Z/g7veWns6NommHEPmzLbjVIBvurSvn5OtLLxi7DNui5q/ke2/xzNNsSZ8o39KbVrmYeZvFgI+5ab8dFovw8Pkm9vMWF3LwspxnqVR87NvWAlCTtpg3oYD2ClgZvbKs/O34IIn8yPBQ7V9e92eY+j06Dy8vFi/8EBm87nx+9FpLA19JSPF2nfxQPFJ4bz1wojYTTsVety0pcLRJVEwSQ6rV69Gz5490bNnTwDA/fffj549e2LChAkAgAMHDkQEJgDIzc3F3LlzkZ+fjx49emDSpEl49dVXI8v/AWDq1KkoLi5Gv3790LJly8i///3vf5E0zz//PH7+859jxIgRuPzyy9GiRQt88MEHUbpre6wGTlmaJJlRdJ3gpkyvq9t4L2DexIW2jpSi8C4VytOQxM8QAGZNElsgtwr0tmLHYSzawjY1hDiSD8v/wBiLh1UnN/4wrsxtOs2JIQ2nfFZNTD5JNvVlb3Jqbp/aYz4KSYzyAGD8B985Knf0m2vR/++LOWXIq//UxT8AAB5nrKb684ffoTqk4tl5+kDCFzyZj1nfyHGVsPoA5WHlX5luE0Dz/bU/6mKPWeWtrZroeOJ02HFj0uVqkkQ+UGNETFe39evXz/KlMUbTDl+zbt067jUiL2FaWhqmTJkiHMQy2rDCJEUGSUnOeSr8ddy2KtcK1mDMm0BFsRrgLX1RbNKLHbfHmMaPifBUVQi/mPY11v9YbCiLUyeLKmzRBFo0wou3xfS90S0HZ19nWvnHr1YER0LS6Ry1+Zr3bhN/HsaNeu2uZL7r4XOM+GBuBWihFuFM4CdOVevCh3ghWquYeCbrE6eq8dD73+KXffxbeGOFti8ZfQ/tzG0AsOkAf1W1tt9q3wHeSj+r60VwpUnSdETeR1LQiCufpPqClU+KrGWeluY2i/764bof8at/Lcdhl0uRbX2SGOd5e1iJojO3GZrM6co5Xvn84/aZmhy3ffA7Wbj5oElAAjimXVi6BVnCc7pm+R8YnYOZeZg0Scbf5so5ipMU0SSx66JJYv5boK/aDf5W/YPlpyVDy7hsm3mpdk0R/A+GYxYaDCdEay7UL3DxkJHk+vL2KATsNUkAkGEhSPG2JRFdEcnSyFlhbBoh9w/NnKXdYzG4IhIJSYHEalsS3deHJ8dt1WIFCL/L3ve/9Vi584hJjX3w+En8yWK1ijZ3p2etls2KYDVIVGkkEuMGkU6EUKfCk1UaP1a38YPryS2Hp0KvZgg+PLOalRpeRJPoLgSApo8JhwAwo7tPkfRWq9t0vngqN71Tbn5tJfNDTC8o8s95wUu0bn6eNWj7eFGJ/HhSZRVV+Gp7rYDJjfFkkYdOk6RxLgfENElWgpRe6NJrkkSwc5Q34jUq+uff165UD3IIgLgKJllfYGkSamOn6L8ui09UYuOBYlyU25QZRM/yRfYwsR81xOwY//53WLD5oO119pokcwIrR14RrF7mKl2QQ/vcucIQp2ZC2ilTGfIHDEcxqFVjHcTro98YlaMd0pQDxt+6/ACdEzBr8jZ+wDrZ26xWYKvFOKnwQlAw+6oh8Knds2T2D7UmRtcf312vyTecXk7fqFZVJBh6hV5Lpi9HVrl+zoW8tpbl3nLHG6vxlWbLGjc+SdpTIRU4UVkbcDcjJRFlFeaYZKJo+572FahmbnPlHZMfkU36A8XluqDFVnkFCdIkBZBqQU1SSFVx1Utf4jf/XokP1u0zXWOFMeI2qyy767Vs/6lUqFy7rJmaJIOU5FR9bvUlVaUZQETMXKycEizCBYh8ORu/6p1oC4pKTgoJVU4HdFUgDQueQKsPjKia8uUKVKqKAZMWszM1/wTgMAQArOvCK4N33KRJsimfqdGBite/2oXCkpOaY6fzl+TUY5eP2S9LSrG+mFUimjefJ9qvtrMneCdon3copKJcsytBUoL9dGx1jzxzm59x15xwxbOLuefIJ4lwRJXFm2D0eQhHtg7H/RFFVcVt1Sys6mhXrtPz+onHebnGgUmL1rTp9kVNTFAsVrc5z090Inx/zY/o+38L8MIX2wRSswUH5uo26IMH2tXmzMa1gVx5JjQ7h3yrDZaNztC6ujLydRQCIKxJ4tTFVB8H/bfmHXMmjISvO3j8pOGYyqybSD0Ali8eq1zte2Kf3g2ytKSsFWq8to5GYEUexjoZNZZazZEK1fYD0Oo58M75tVrW6bM8ZfEVStuSEI7gDZwAP2aQ4xgX4AscIoJI6ckqfLD2Rxw7cQplFVXYffiEWLm2UhLrGvbfolitorDaF441YPEchbltJlBhY51YDtYsHjhtjnlxgYiQxMaNBsza2dich6qqTOd7vezB0yRZ15et2XOiSTLnY4p1JLAKj5e5rSaJk0lSAt8U5gajfx3rI0en+fNLkyQpn4fer/V/DD/uaE+0p6pVLP/hsGmvTW1/Mbaz0b9St7+lKvARafgdCqnYfTqqP2+c9EtLIzvboPolkU9SAKli2JDDR9zsd8XCaodykb66evdRrN59FL3aNUGhTZAzXd425yuqzJviel7dpnWWNFxfWa1tz9rjTj4+vWqSjNeu3nWEndADvPthDaCffnsA3x/gL/O3ykM30KnAU3O+xyff7schzWpI9t5tustMaVnnasoz18drxG1ZO5Kr4C+OCMPqHyqARIPpxWkdbDUSrK1hLD5GZDlc++K4zdAGRoOXFmzDSwu24daL2+Pxq89lptFqqhUFugZfv/cYmmSkRH6L1N54jw9/8C1mrf4RT17bjfs++aVJkp0ty78wCJAmKYCwV7zUHGOtEjL+LYJq8dXiJKs1u49yN7PllWvFb/+zynTMizAIGMxthgo4NbexUiQqCt9vxUWelQ4dLRtrdtnm4WQlzksLt+OLTdqVJ9Z5679a9Xm/umwnikoqmOa0J+d8z8zDql+LbBnianWbTpPFTmOqm01vVFXYdljeu56c6G2TPbtnFi6Xdz9B1yQx8+Yc99vfZfrXu/T10BSn1SQlKoqub81a/SPuemut5joBc5vBYjVr9Y8AgBe/2MYNL+CXkPT8F1uxZre8D7pg6pFISAokLEe7iCaJYx5yrEmCxcobXwcy68w3MwIVelUdW+02rVvdJjCYsIpPSFC4bSkyPlkFLzxSdgrzNhwwqfS1ZDdI4Z4Lw9OuuJk/TFubCJrKatMDPx2vwLo9xzRpxfo1N0/NiV1OtsRQDf/D+nk40YSosH/+vOj6xhV6suf58Bhj1Pzx6iXNJ0lKLmzchrnwc69ErVUgMUGx8SkSqCsngaLotfBew6aIMmLqcml5BdV5m4SkAMKU/E8fMq6eifztsIOFQvwBa9WuIxE7t3RcvAfPza+NyeTK3KZpT2Pb6le36c+JKiQSEyw0SQLXG+9J+4xvfnUlRr+5Fmc/8pluJYxT+PciIhjyhYaa37V/ayecT9bv5+RnjgWm8v62eeDh09pHN7uAXS7z+ojpr/aY8P5rtho2+5jbvAk62eB9Lnv6CDe/sd1VVcWYt9bih5/0778bOYJ1bzImQl5/5LoPWOS1csdh9Hjic3yw9kfOtd7qazS3WX3s8Epq1jA18rfVcyg9qXUCr8XPbY5kElAZiYSkIML2Sao5xvPP4XUwrgYB1hPQ4BeW2lfUBW7eg+81ofjX/3gM8zcWWaQ2w7PVA/pgklYrrDRnTEcSFL4mSUx4NXy1ayqivfcZy3cJ5MXGTUwXUXj+RP9bzd4jizXxeP3ydTsRRPxZwH6veOmF8oaIJoldhlGTtHDzQaa/nltqNUn6cn86XoG5jJWybp4JcwWshP7G9/9z9g6WVVThV6+swPGTVbh/1npmGq9oiz5ZGcLv/rvaIq3zD5YwCQpQqlkpp2368kp5/cZPSJNECCO6us1LpwqpfMdtoOaF9gOv70F4U0sjQ15YigPFbN8onXO24aYrOf5KTmJIJSZYmYHYx7WIRjc+xNsKxkObOhfhrFO4NVlyfZIE83L7LtQ6/dYeszI1PaxZVSVSN/tgkubz6/ceM61ue2/Nj3hqziabEvmYQgBEHLf15fNi6rgxSbH36/OOMY/wb6fa9AGTlkiojTVOhWq7M7w0ChQc14YT0BQ8RuP3RDiHhKQAYhWDiLs3ltPhR42No5wfq1uAGl+mp+eyJ5FTVbUCn3ES0AbuFNHMsbBy3HYTxZt3TeMMtu8RK7Wqqnhl6Q9YsaMmAJ4MTdJHBfvw7LzNln5CotkZl6RzfZlsMvS6XYdq+B9gRdyu/Xu/o5WcIqvbzOdvn7HaJCQBwBsrdguXrW2PQ6UVJjNPZIzRPTuLadpF87K3V/L+/hvzOH6yCh8V7OPGfeMVqQ3W6RfcDxsWAh9avH6uKHpzm7Zf7RIMzxJr9PuSBgcSkgKI1ReYfnWbmImAhaXjto/4WeRJjlq5ooptUgMMPkmGyjHjJDHyT7AJAbCl8LjOrGfOU+xrnreKjfUc528swv99uhm/fmUFAP5myE6EuHtnFuDlxbWCFysPkefLXMXD0eTssHHCDid1G1W4NgQA/x54OdsWqbJN51p4sl2iYERM3qS5YPNB/GvJD9h1qAy9n/rC5GNktWccu56SNEkS3n/WLd87s4Bvbovy56B2tdvP/7lM+Dp+/e3TKDCY2wzDzTHDNlKy+XLbT9hzxJswFlBrGwlJQcR600v2YB5SVfx0vAKvfrlD6IUQWUlh5Nsfjzm7gEEs3gOtJslydZtJADULFmxzG98n6dUvd2DwC0t1we+MmKMbs9M1SuMISYxjJsd7iT5JRp8wkUHcrkzX5rLw9R40SaqqYtXO2qXMVqvb9Ndal6ky8jLCE3KSBeMYWOU/8bPNmLex0PI6UaWdGyGUpRGX4UPsdJ/EaE6+6/YcdX0t32RvfwOKohgct/XXXDvlK9f1smPN7iO45bVVuo9RNzj1KYsWJCQFELYmyWxWMDqb/mLa13hq7iahbSpU1dkX1qHSClw92fuL5meH12Z9uLQiIhzxlsYCesdtN2YjwNrcdrisRmD9YK1+bz1VVVF0Wt0vstWGFUwfH8NvfpwkAaHGThhQ2X/z8zPXx6mgZcSL4/bCzQfxssbXzRg93m2PDamq7fY9/K00xMuwgmW2AzhxkizykrW9jgytjlNhQtaII7JxspPAukb4OyDUYvW8eY7bgL8mt7W7j0nJJ6iL8EhICiBsW37N/7oNbg1blIRfBJGvGRWqo73bDhyTY7+Pxnuw5/AJ9HrqC1z10pcADD5Jpr3btO1pbzZiBi+0MLdpCYXUiJZv4meb0ff/FuCtlbsj14bHYG0Vu52ZqamTfHOCDJmVtzmtZZkWe4k5cnY9ndj9Jp4qFm05aFOGs+Pa8043khXNWzRdShJ7iI8ISYYPLS/aDCPMrU8k9LdHPtzAPM5ralmrpswBPuUi0s94Y7aiACdOsR23L8zNllE9LiICvZCmNwCaQBYkJAUQlvtKuJ/wtB1aQaB768a2ZYQcapKkhYuPQoeft7FmCfP2g6UADD5Jhrat4mxL4qRtEi1CAGi5+511OP+JfKzfewyvLN0BAHhqzqbIMw1/qWon1qYNamOkuJ2sAY+bfAoIA2HEoparpjzdavHCab1oklISE21L2XWoDEfLnPl1qLBvD/7ELlaGXf7GeEvG60QnIKdR4AFY+uF54X1OTCOvAqcdyQn+Tpc8FwiRjxBF0fcZ7d/pyXb9238qBb7I+YuSYgvt3RZAqlkdKvzFzIm4rT0uohaGah0CwIhQngL46UQZztl4X6d0jtv6k9qX17jlC3uDW/MxRRF7kcPxZ175coeh3jVXJyYokcmoxrlZb8bzMglwzW3uP/CY50X3qrMKe+Dkyz+iYfXgk5SabD357T92krldjm27qKqt4zZ3yb1gG9jdNs/cVhXRJNUi+4udrUny7/33I86ONsvkpATAZrGat49JnqbYLkXNwgyeMOV3/CGRDzC79wDg3xvPPzRakCYpgFjt0M3bu81q1QOLkApHIrqTndWt8PN9rY28qy/E2ieJLXRyX1jGsZpgkuL1PGVwcAw/7iTNl2pkhbZrM5T+NzcEgBQfEWd5qIxyRYM5GglH9XZiOtaVqwIpNivJNu4vsTxvlbfdBOUtCKm9cPjge+wFA5E4Sbo+L/cThlU3P/1OnJgK3QhrssZAHrwPM12YBgtNEk/gjbW5CrCONB6GFwIg1tUnISmAWNlvdX4zmu6jvUTkZRbZMkGL4IrkQGAcFCxDAHBXt1mXoRW8agYo8bY0CknhgU/70c8yh2jrp42/IrT6hRsCwPZS27ZwPPGpqilP3uacdvxl9gZ8vH6/+xAAULl+O2GyG4iHXjBiNzfwhBzRNnWrmalmaJIA96sEWdw7s8C0ItZPTbITTasbzaOYjORekFLBMbdp/ubGSYJRMGLPE34gcsci5lov7gR+EkdTX/3ByuGxWjep157XDm4iL7MKp5Nb8DVJtWXoCzlVzTe3aU2b+vbgf+E//vFGdPrLvMgxRXF2XyYh6fT/SRpJlOlYq7nmkmcW2tRUDz+YpARNksNBmKXE9LJZ8z3vrHMdnkJVgVQbIYknYIrkzTSda/DqbOxWpokIlQaNg8z3c9OBEtOKWD/ffyd7t7nxsfJ77BJZAs+rgqIYzW2afP1xDdOUZd8wrMVIRvxYmCIDEpICCDtOUs0xnrbD6Zd0jZOgk2vkdFQ/u3s4b2PzVVTyV7d9s+to5G9j2/KmRm2wOOC0P4CDehr34KrVJCmaY6frFDKnq8mDHbrAKSKX1qyE5Kd0Wr7K6Hti++bx2XeMvSWNbV1g71PBe7fsqqnCPgQA91rBy9z6m4TNzEYh3G//FT9zd6JJOiXZqby0osrzBwdXkyKQRjGki6ZPEi+IrxZvPkkOKyQZEpICCGtQZgkA2gEuvJIL0E/uVhoEJ+O3lCBwqmrSoviBsflOcTaxBaAPIigQAoBFzcoS8QuMA/RPx2tMZ1on29pgf/Z1Yn1pGY95cdwOqdb357Rr1ER7Z9XF/CEgSoMUd2tQ/rXkBzw553vLNG6dwv/79W789RPrvHmItoE2gKATfvufVdhadNxkovFdSPIxeyeaCNkr77o9Np/r/yWKiJDAfT6KUTBiX+8HIkEknfokBQla3RYwQiHeBFLzP8/cpkXUv9BJl5TRge96ay0+28COACwTk+O2RpNk6e9lEEBZKVnNoNRcIExlVW1iRQFGv1mzAaV2Lym2TxI7P5FH40XEUVXVUlMpa8VS2HHVTXZuV1+KOGVzhSSbev7nq50uanQ6a8E26Pf3xXj/zjxXZQx6fqm+TFe5OMNtXwmv9rTCyfvhxtxmx3trfsTALjmurxcR8qwW6MTKJ0lESBLRqJJPEiGEnWo/pH8TmGkVhtnGSEit3bvtsrOb2dZLhl07GgISwPJJ4q9u02I0tzmJGOzIcVvkqypUWw9jGSITjTGJlwHIzlfFnbmNcTxy3vmo6FdMHgB47OONzOOxcEJm8Xy+fYR9EVhmUNm4zV5M42k9dmoR0WyI5aTH6wI4uz7FM78bw4WIfFzJosLG3Pbj0RNC7X2k7BQ+XPejyXxHPkmEDt6L/uSc71FZHRLSJIVZs/sIbp+xmnlOO1G1bpLhul5BIuLHY6jqsROVtX+XV4KHzscrpOKut9aKFewxBAALK3ObUcgSKdrpfldabpv+DaZqtu0w5+0MFez+xNpPTJQ3V+5xcVVwic0WDc5M8O5KcKlJEkjD1ySZT7gTkvxFxKeKJwglKPr7jKZPkp0m6YrnFgtpm2789wrc97/1eHruJp2wGeuph8xtAcOqQyz/4TA3mCSLG19ZyT2njbhtt7rHrl5BIaKJMAyp6zUrn8L+Pyy0g8luzo7WPHObk/bRDhgnTrG/wljmtvDzPlFhdPy2L5OvSRKr+IsL+NoKN3vNWZky3fQ1rU9etPDznYjFR4mqRsFx27UmSYXdClt+zCnzMXcO9fZqIi+KJFVlr6TUC0Yq83hNMMna3263+XGDneN2dUjFSc44p+X4af+6N1bs1h2P9dRDmqQ4oryymhu6XUv4RbIy62j3brOLEwPEhyYpjHb8K62owt4jYiufnAigWkQjbocxrm4z5gXU3MPJymqs3n20tk6n/y87ZXTWFTC/OTzuBKfzTY3jNl+TFE99zS+c9j8pZSIay8XdXSekSeJkztJe8bS5Rg1TNHuiqrLrCo7w8/ZKvTDBE4z8NqGKaInKBVbABRUSkgKG1QRxsrKauz+PFmE/k9N/i2iS4mHiCg8G2qo68VXRO27zyrAuWwQrc1s4DEBIVXHf/wr09TtdQaP2Sex5sxOt0QhhUYOjSdKcjgt+91+2KVsGsdoR3W//D7f5i4w/zhy32e9geE9FTk62dfCCiL+j9h4f/ajWV84UcVvz90EL7bkMRFZ/Hna476EWv4U8O0hIChhW/eHEqWohbUe1quKdVdY+GqrGcdtuWwYgdoO2U/YcPoHSilq/Iyfvl8gWIKzDCpy1j5WGLyGiSVJNju4RTVKF82XfvOo9N3+L47y8ooLdvqRJqiVW5jbfi3VtbrNP48Rxm2duC29zEwtCPHMbR5OkRVH45rYDxScZV8hDZOzbdajMdf6xHg3IJylgWHWI8lPVSEq0163PXLXXVr2pHRCTOZqkUEhFwulZO9bSvAhfbjuEy59bpDvmpNZuJ6aa1YTi11oVE86LNfCE1Bot1HUvf63Pj1mG4WiAHl9N3cwVCldZJPBcXScWHyUqohAnyev1FvVzsoKzkqPNFdmsVRZtszOwR+v7qAKqwngvtH8LNmA0hWyRsl62WPhhx7PzNuOpa89zfb1XSJMUMCzNbVXVhtVt7LQi9t+aaarmep65TRuOIF40SUacCHdi5jbzGaeO21Ykhs1tnKjrS7f+JFQnU5oASUk8jUW4Px894V41X1eIxUdJjeO232W4K4C3clULX5NkPv6bV/mLWrzgRMhqk52u+801t+k03BxNkuG3qB+mDPwWyDLT2HsnRouYCklLly7F8OHD0apVKyiKgtmzZ9tes3jxYlxwwQVITU1Fx44dMX36dMd5lpaWYuzYsWjdujXS09PRtWtXTJs2Tc5NecSqv5WbzG3uywmptdoKnuO2iEAWdBxpkgTC1DLNbQ4dt63QmttMZaucff0E8g3S42PrkWqPHfHgv8Cin0G7GA/U1dVt7h237U2x3BVrDsr0qkdy5HBvKE3ExC/hFqXjt2DdtVWmvwXYEFMhqaysDD169MCUKVOE0u/cuRPDhg1D//79UVBQgHHjxuH222/H/PnzHeV5//33Y968eXjzzTexadMmjBs3DmPHjsXHH3/s+Z68YvWSlZ+qlhYkrOZr3tonqdJiY9h4wUm1RVYOsjAuv/VC2HGb5ViqQmWuZPISAiAW8CZj9fQtH5UsJO06zA7nEGScvNuyTES87WJk4jb7ayZ/ha+3H7Ich3gOxNHs+k5CCxgfG197Xft3EMdhv7WeF7bP9jV/O2LqkzR06FAMHTpUOP20adOQm5uLSZMmAQC6dOmCZcuW4fnnn8fgwYOF8/z6668xcuRI9OvXDwBwxx134F//+hdWrVqFq6++2t3NSMJSk1RZrTOBeTGhaPduS09JZKaRpbWKJU7aKCQyGDFVSfLMWeGBc+A/lprO/d+nmzHt5gvMVeJonXS/pdRODipUfL6xiHkc8LYSpq4Qi/dN++EkC+MHmNv8tx0sxW9eXYnNTw7hpuEJKFZbEcnGSZBKo3BbM+bYxYLiHY/dG+6n4PbY8K5onpnmW/4ixJVP0vLlyzFw4EDdscGDB2P58uWO8rn44ovx8ccfY9++fVBVFYsWLcLWrVsxaNAg7jUVFRUoKSnR/fMDq+4m09ymAqg6HRSFtzmodunofpe7rMccl5okfnbsNLKcjRNs9iD79Dvz1i5i5rbgiEmqCvwjf6vpeLhryza3xSPOzDbykC1PGBeaeM3e0tzGEVAcaYU9NqaTDbyNRbFuzdgPgqBJapiqny/8jK3VJ8ZaJCDOhKTCwkLk5Og3EMzJyUFJSQnKy8Un8X/+85/o2rUrWrdujZSUFAwZMgRTpkzB5Zdfzr1m4sSJyMrKivxr06aN6/uwwm5vMf1O9V40SbWdu0EqW0ga9PxSfLD2R6iq6nmH62h+zWlxUqpOre3gxVdgHSDSCYk2ozQzYriIuc1lffyglBPCINyf3e5sX5eoKz5Jpv7sMXurYYRrbvPkluDsYpF9GcMYv4eYfnpGjXAAXuRGaQYhKQiV8pG4EpJk8c9//hMrVqzAxx9/jDVr1mDSpEkYM2YMvvjiC+4148ePR3FxceTf3r17fambVX9ToV9x5tVxO6xJSk9mm9sA4K+ffC8ULMwOq13k/cRJsSKO6nO+PWA6pihiUWdFsPMvYWmyjEdUVcUkg6YmSOMYT1MUbv6KAO6rFW1itZrUf8dtb/lbXV/J0eY6MYVbvX4iVa+odG9uYxVgPBIEgSRBEXM4l0EUIzJwias4SS1atEBRkd6XoaioCJmZmUhPT+dcpae8vBx//vOf8eGHH2LYsGEAgO7du6OgoAB///vfTea8MKmpqUhNTfV2AwLY7wLtzrnYXA4QnousYi8pipwBW4ag5QZnPkm1aXn1/XLbIdMxBYqjwdEKG2sbJ9icvq4Fe4/pfv/1k42BUFuH4fkchZ+VE5NFXSUmmiS7UOgyyvCYv2rRNao56l8nZbLeLyc4+VgylsQact5f86NtmmiTYFCtxOoDOFrElSYpLy8PCxYs0B3Lz89HXl6ecB6VlZWorKxEguFJJyYmIuT3xkUCWGqSVEHnYgFCqhoZVBItZuYERZEyYMdMSDpdbEpiAh4c3MkyrZe2lWVuM36lGRH5sioxmKte/2oXPt9o9mWKFUfK2NskhJv8lKS2jGccTeyy9m5T5U/CJi2nRynMakJ2t2ltLXamNZF2djIOGDVJrNWFD72vd3MIgm+hcYzyU6D3KrTKIKaapNLSUmzfvj3ye+fOnSgoKEB2djbatm2L8ePHY9++fZgxYwYAYPTo0Zg8eTIeeughjBo1CgsXLsSsWbMwd+5c4TwzMzNxxRVX4MEHH0R6ejratWuHJUuWYMaMGfjHP/4RvZvnYNXfQqoqzdxWXa1GBJfEBAUpSQnML3gFcl6CmJnbwn8o9nvUae/T6Xgry9xmJbDyEFHJGwWnWHKklKNJCgtJMTS3ndsqExv3+7Mowwmx0STJL9c4qXvN3tpxm31u+te7hPKuDrFDbDjhuIP3TMDaZoIbMDOK3cUsJPlXVhDMbTHVJK1evRo9e/ZEz549AdTEL+rZsycmTJgAADhw4AD27Kndgyw3Nxdz585Ffn4+evTogUmTJuHVV1+NLP8XyRMAZs6ciT59+uCmm25C165d8cwzz+Dpp5/G6NGjo3Hbllh9ac359gC+2l5r7vHyVVEV0ghJioJUTqwkRVGkaIFi5rh9uo0U2AtJ2i/RnQ73GjopaZdru0Hh6x8Om46ZnTvNbS2yP5/fhAXAslPstgpPALEyt/XrdAYm/8YcYiHa9GjTOEaO28HflsSqfv9b7c1P1O5DTqRp5m0Q19iyVrfZvf+BMLeZhDsfNUkBEJJiqknq16+fZQMbo2mHr1m3bp3rPIEa36bXX39duJ7RxO4l+PbH4sjfXrrmqepQZFBITFCQmpwA1sKphHj3STpdrKLwI4uH4fk02JYBVZomyc7cxis/zObCEoyabt6d3u7eo0FigrXAHT4TKyGpdZN029WF0WB70XGsN/iVRQMV8jUSrEUFnvLzcRgJhbyHU9jnIFSKyQEa+vt79csdpmuKyytNx6KNUdtd181tsR85CR1OBhEvfbOyKhSZsJISFa6mQVHkaIFivbpNgWIrKLiNdaSqzla1WOHC2qbrByP/s4qZxk6LFg2SbG4u3M9iJSQlKEogvlx5mja/UVV5QVG1eVr9doqfE3JVFH1Sp950AcPcpr+3p+Zuilp9nGAyt/nYbEF4H12NnHv37sWPP9Z63a9atQrjxo3DK6+8Iq1i9RUnY4CXAcNobuMJEArkOG7Hyic+POgrir2Wxq22q+RkFY5XyPnCswsmyaKiKoTRb6wBABSVsJ2ig6BJshOSikpOYu+REzHzSUpQFFftH2vk1Vj19T09VRVy5LPDwk+FdLS03fcMOBtDz2vpyieJRzQ3sGZHCveHILyOrkbO3/zmN1i0aBGAmgCPV155JVatWoVHHnkETzzxhNQK1jec9DdP5raqUGTACTtus0hQ5GiBYq1JSlAUWyHJ7eqYTQdKmJGw3eDG3AYA82xWr7nNVyZJNn5RN0xbjsueXcSNdxMNgjAoxwo/gkmWV1Zj4mebcLTsFPo9twirdh3xlJ+fvo3VIbNTkB+lhfuYaYPbQIV85WN8R3w1twVg3HIlJG3YsAEXXnghAGDWrFno1q0bvv76a7z11ltMPyJCHLdxfZyidTS2EpIURZHydRnriNsK7FeOxcpvSouXSdrKVBuEIHRuVu5FExFBuq7jxyvwryU7MP6D77C/+KTnvPzsx9UhlelM/cNPpVLLCQtHxq62tajUkU+Tlrq6ui0I76MrIamysjISWPGLL76IbArbuXNnHDhgjkhMiOOow3nonCerDEKSlU9SXMdJijgl2QogTjan9Asvg4J1+AjX2UrDztwWLe64/Cxcc34r0/EEJTY+EF5NobIebY3jsD8dZf2Px6Tk42c/5mmS//LhBqnlRDRJjM7249Hg75GpNUknJshxx+CWFYAhw9Xbee6552LatGn48ssvkZ+fjyFDanZm3r9/P5o2bSq1gvUNR47bHso5qXE0TkxQkGQMo3oaRZEj4MTK3BaqlZF880mSiRchyaqNgxCELiiapASFvWYmISE2miSvTvWyHm2N47Y/yJpIfdckMR5/uaTwHmHCZQTjbXCO9jVOUPzVYsWtJulvf/sb/vWvf6Ffv3648cYb0aNHDwDAxx9/HDHDEe5w0t+8mLDKT+k1Sby+mKAoUl6CWJnbwoOqImBKCYSQ5GG+tFoVFgRzW3IAYjUBYY2RuS8YBeloyXSpSfy9E6OJn3GSZL1bfgr7PE1SuE1klRzuezL7VzTfbv074rMmKQAfVq7iJPXr1w+HDh1CSUkJmjRpEjl+xx13ICMjQ1rl6iNR0yRpzG1JCQlcASJBUeLacbtWSLLXZHjd1kAGXr6cznt8PvdcAG4tMJqkxAS2JqlGkNb/joazh2dNkqR6qPCvn8iKI+bv6rYQs1/I/niKaJIkaklipSkmcxuH8vJyVFRURASk3bt344UXXsCWLVvQvHlzqRWsbzha3eahb2rj+iQofA2GtG1JYqVJOn2bCuy/SrzWMdlio2BRvAycVtUPgiYpKD5JCYrCtHUYNUzRqm1aMn8YFukOsiZIVfVvspUV+8rfOEnsvKULSWHHbYl5yqyi3Xuq7SMnTlVj7xH//Kji1tx2zTXXRPZTO3bsGPr27YtJkybh2muvxdSpU6VWsL7hyNzmYcDQrqKwMkXJCiYZqzhJenObdVqvweRkvNAS5CwmsTJ3aklycHN+ClQ1Pknm/I3BJKM1PluZ26IZAVyFf+Y2aZokH8cRXjBZ2W1i5bjtFpmCXHqytfk3miNJAGQkd0LS2rVrcdlllwEA3nvvPeTk5GD37t2YMWMGXnrpJakVrG84eSFlBt3jvbCKosjZliTmEbftJxyvA40MIcmvL6cAyEhIdOBw5adpLjGBPfgmGAKORmtLhFQLTZKIT4asV2v7wVJp8b78wn/HbXN7+2duk5enzDqm2ghJ0RxLgqBJcuWTdOLECTRq1AgA8Pnnn+P6669HQkICLrroIuzevVtqBesbTsaAt1fusU8kCG8sVuD9BWyVlRY7c5vGJ8nuy82rT5KMid0/ISn2UpIT7ZCfQlICxycJRm1j1DRJfCFJRJO0TLPptRcmfLRRSj5+4re5jdXasoeu8Dsu19wmUUiy8ZGLpv9TEPZSdKVJ6tixI2bPno29e/di/vz5GDRoEADg4MGDyMzMlFrB+kasJjMrx223dfrzVZ0BAImJ/jr3WVGrwVLsHbc9RnqW8T77NSYEQEZyJiT5ODgmcvZoM2uSooOluS0gflxBIRbbkvj1gSfzgyiaH6GxDFwZC1wJSRMmTMAf//hHtG/fHhdeeCHy8vIA1GiVevbsKbWC9Y1YTWZcTZLiXnALd3BVjX0wyZq922qPs949rz5JMl5nvybFQGiSHPgk+bn0NyMlMWA+SRbmttjPEYHC7w1uWc9cvrnttCZJ4rON5vsd1X3iAhA1xJW57YYbbsCll16KAwcORGIkAcCAAQNw3XXXSascET20pqhGaUmRjSi9+CSFJ3xVjWWcpJr/javbWKENPPskSZjR/JqYAyEkufRJOrt5Q2w7KG9riLTkxID5JJEmSRSWqScrPRnF5d43mOa9/yFVRSikokqSD2it47aU7ADIE+Q6t2hkuwlxNBfhxK0mCQBatGiBnj17Yv/+/fjxxx8BABdeeCE6d+4srXL1kdiZ22r/7tqy1mTqxSdJO8DHKgZRWDhTBEwpXuso43X2a1DYsK/El3yd4MTcpntWkpskPYUtJBlXeQZBkxRrIema81vhD5efFdM6aGG9ol63dQlT45PEdty+ZspXKLERHkTxw6DrVUga0Lk5Rl/RATNG2QeDjm7gyigWxquDm4tCoRCeeOIJZGVloV27dmjXrh0aN26MJ598EqFYrfWuI8TO3FbbG7UDc0KCe8FNq52K/bYkis7PhSWMeK2ijCW9fu167XbjTJk4mfC1wblla3Rqljib8zSZZKWWysfa3Ca3Fjde2NZR+hd/3RP9Owcn9h1LIy0rXEQ1xyexKqTiu33FUsoAajXOMgUAr9+grZuk4+GhndE8M8324yCajttB0CS5Mrc98sgjeO211/DMM8/gkksuAQAsW7YMjz/+OE6ePImnn35aaiXrE7EyinCFJEVxbSoLCyXqaXV1LFB1q9tqj/vx7snIMvZDgn842ZYk0WdNEosanyRtudF5GlbtIluTdGXX5nhnlbNVsUGYqMKwhhFZ293wNMkVsvduC/8fQHObCOS4LcB///tfvPrqq7jzzjvRvXt3dO/eHXfddRf+/e9/Y/r06ZKrWL+IlblNL0DoJwq3WqDw2KUido7b4borMAt/PNpkp7sqS8b7HAT1sl84mfCdCChOI52nW/gk6ergKFf3WPVF2ZOEG8HP6SV+BgJlaTFkmduqQyrzocsKhBkm4rgtoYc9PLTGvUU7b3Ru0chzvlZE03E7COOhq9515MgRpu9R586dceTIEc+Vqs+E+3rb7Ays/stAXHHOGVEpV6dJMpgc3Mpt2tVtMgNfOiFiblP0IQCsBn73X6be3uiWWWmB+HLyCyer2/TPyvq6X/Vpg3//trdw3ukpicwnZWr7KD0Kqw8j2ZokN/3LaRX87MOsDzZ5miT2GCXbnzLcPDIebVggDfehpg1S8ObtfV3UiV2Zp67tZjpGmiQBevTogcmTJ5uOT548Gd27d/dcqfpNTQ9MUIBmDVOjJrNrX1ijMOFEC3Rh+2xTPipU3V5xftKsYYrut3YC0t6j1cuX7GAVlhav7/OsP+QFIgy/X7gNJml3lQIFV3bNwZ39OgjlnZGcJNTOMh7FxR2aYtWfB1imOWlhznEiWIrgJjfH2icf+7CfjtuV1exgkrKJ7N0mYxuj0+9JZbV2+yVv+Wovv6FXa9P5aFo7gjAeuvJJevbZZzFs2DB88cUXkRhJy5cvx969e/Hpp59KrWB9Q6v5ANw7ySmKM4lfv6rH4JPkIKN2TTOwaleNNlEbAiBamqQmGSk4VHoq8lsfJ0lMk+R2YvL6Pp/RKDVqfjBahvdohVBIxdzvDvhajpNtSbTyVJeWmfj+QM3qvGdv6I6H3vtWl9Zpk6WlJHDjJOl+S/jUb90kHQ1SrYfZcgshya3AzsOdJsnZNX72YNZYlCJJkPzju+ul5GOHTOVgkkGLpij+K0DdTEm/zWuHL74vwv7ik46ui8V4aMTVG3jFFVdg69atuO6663Ds2DEcO3YM119/PTZu3Ig33nhDdh3rFZG9xjz2Dad+AdrOqF8F5uzLIcEgYAE1ujHZzo880gwxZ8IadKOQZDXwGwceUXh53n/lOULXG1dXRYvmjVIx5aYL0KN1lq/lOPEd0mqSLju7GV741fmYe8+l+GXvNqa0iuF/O6ziJLHy9YKIgGG1oah0TZKL7KJtbrvs7Gbcc376JEWLWnOb92drHOcVyNW+MFcBO8zjtkva44lrumH4+a3kVCrKuNIkAUCrVq1Mq9jWr1+P1157Da+88ornitVXQhpHY8C9/TcxQYmoYEXgmtugOAoepv361mqSZDs/8kgzbBZa256KwXGbn0eyS0mFNzj179Qc/8jfan892LvT+020nOqd+NcYtX7XnH8mN63TCMZpSRyfJOOEI2G2SUhgb4ESZvzQzpY+L24Fdh6uzG0Or3Lz+vysc3Ms3HwQANC1VSa+3Mbej441FqVIbiO/kRlx2/hOGVdoeoWVlVPrRrj/nIrSHCCb+Opd9QDVaG5z6ZUkGt140i9qIqZznZoVZzGO2H4/ahSFJIMmiRMCwFqTJNlZVvAtUxTxtDKJmEJ9Vm07mczcPAORyXzyb3oiJSmBOZEYj8nRJFnX6w9XdEDrJvzVlG4Fdi4usnPaLdxoSLRXWO3bx9Jqy3LcjhZONZ9WmDRJivfXWNtfWc/S6TdVOIt22RleqhUzXGuSCH9QNY7bgHtNkugk075ZTcdlaYDC9XDy5cCKt6Sq0fuKME7E2m1JRFdMuR10eTmKTho1qvLoa5Iqo/Rs3MZJksnPu/NV/sYSpXzpC2Ty8+6t8OPRciQnKvi/Tzfrzlm12cUdmuLrHw47qo8bTaVjnyRXgpiYKdwoJD1w5TnYKnHLmmgQvj8ZPm9GTWOCIlcXzcrL6Yd7OI/f9G2Hw2WnkJaciOfmb/Fct2gRXyJ4PSCiSfLY1UV9ksKDk87cpjV1QMHkRduFy03UCVs1/6sAKqqi45NkFDJqNUniW064jfPCE3BEzUyK5AFOlMrTmqRqn6PlOxGS3EwgTiZntk+SSUxyXAdzOdbmNqCmf4zp3xF9NCtDw1h97LhZmu7KJ8nhLOH12Vldb3QhuHvA2Y7jZMWa8L3KqDVL0+hKk8e5hHXc6QdMOHlKUgIeGNQJF+aa+3mQcaRJuv766y3PHzt2zEtdCJgdt11rkgRHtshXDWd1W1Uo5GjfL+37o80nWuY24/ur2+BWJ/zxca1J4mTqRJMUi7gg4Ymn/JS/gmxykvi9JXL6oxVOWo69us2QRsKjcPI8Wfdp1Rdvvbg9Vu10FpcuGj5J7sqoxUrGYo0j2hWAXTUrIYNK5DH74JMkw9ymz8+c2Z39OuDPH24Q8mXMSEnEqEtzdceCECDSCY5mg6ysLMt/7dq1w29/+1u/6lovCKsyvfokiWovwsn0ZrLa85sLj+vS230FMFe3qdGLk2S8bbVWNaffl8uieeQLSeLXR2sAGXZey8jfYZ+kkz4/Iyc+Sa5MEY4EEkaZFtff1NfZnmdhrG65gWF7FFbpGZwtVICaVYnDurfknmfhxpwbjdVt2kusNBUsjbRW29bUECctiETiJEmQkoyaxhpzm4v2t7jG+DiaNUzFkgf72eaZ26wBvn1sEFpm6X3ugrCs3wmONEmvv/66X/UgTqPVfAD++ySFBzSdBkjzwhw37Hw9+Tc9ceHTCyzyq/07sncbomduM2rQwl87CvSCo1W7uo+TJMHcFqUBpGPzhpG/w/5ivGfUICURZRK0TK73bvNcshlWnsam1/50KzgnWJjbFj/Y37J8AGiUxh+iFQVIdVgvr/5CfpWh+7iyeF9YH1vaZyM7QrmfyKiqMfaYbE0SYB4ra8qwL0RVVebqzCBE0XYC+SQFjLDmw+sqJ1HhymnMjiYZ1l9q+sGuti7RCibZp30TdGmZGfn99sqazTydCCCipkojxux/3r0l3hud50jQjcX4EfZJ4mnPZQ1qznySnOfvyNzGSGzsH7xNn52QkMD+su+U0whnNErVl89I1ygt2SJ3xbHGLRrdy522SqytT7I0SZr0bt/daKJdcesVo9bNSiiXhddAJdrHe/+V56BP+yae6+Qnwe9R9Qyj47bbCDaiq8lqfZLE8rWbMFlBKWWY20QnqfSURHx6z6WReEmrdx8FAPzwU6lek2SRh1tHUONVd//sbPRun+3oGUbrK0tbTFhI4q5ilFQlnoZOVpwbZ47bznySXAtJnMvYQpr5WCNNtG5jv3RjnvWre43t3zHyt6umEjW3McYRrbbCz811ZRGStDinJi/9O6tIyNe2j3jUVmnHuGvPPzPw2r+YCklLly7F8OHD0apVKyiKgtmzZ9tes3jxYlxwwQVITU1Fx44dMX36dFd5btq0CVdffTWysrLQoEED9OnTB3v27PF+Ux6p9UmKHHCFqHnLqSbJLhUrKGWNuc2jkCRYv8SEhNOrxPTpVVWfhy/mNsU8iQE1tnnhPFyV7Bxt+5w67bjNbRJJsSZ5faxLS/Ou5drnIzogO5kcWCmN9dP+ci8kefuy15rbjMKk0YQshpvK2HeA/p2ba0pw4xOj+dvicqbjtuZ9TYyDlW5hwUaGbGBynnYhOAPOhB5RvyderxFdZRwUYioklZWVoUePHpgyZYpQ+p07d2LYsGHo378/CgoKMG7cONx+++2YP3++ozx/+OEHXHrppejcuTMWL16Mb7/9Fo8++ijS0tI835NXZAWTdKpJEl5B5ORlOv22VodU7DxUJn6hh3LDX5LGgSLJFPnYIsqxJJW9trjrevIjRmuJyeq2aK085J4wn9EJSX6IjgKaHO074VZD4SRuDevRN9SY24zbb7jZzNRN9xIxF2sFFXeTtKC5jbG9kfZ9lR580wdU4xJmDxjDQMiOuM1CgTcBLw4sojpiGkxy6NChGDp0qHD6adOmITc3F5MmTQIAdOnSBcuWLcPzzz+PwYMHC+f5yCOP4KqrrsKzzz4bOdahg9gO4n4j4ridmKDYLr8U9QGqXd0mVj8nL2BYc3PiVDVOnCoXvo6Zl2AFw+mM9UxJShDOw+1eUMbc9c7wYkRrAGGb26JXpu4445j240BYk+TE3MYo1apvuxVeHYUAYNSpYWrt6jZWv3SqSfJr+tQKKl5X0Fm1GUuTpNX8OtlEOVaohjHeC8Z5wKsAI4KiQKjyvPHEqEnye9zxSvB7lIbly5dj4MCBumODBw/G8uXLhfMIhUKYO3cuzjnnHAwePBjNmzdH3759hUx90UA1OPWx+o+Iz4zovm21wSRr8zxWfkroWnZ+tX/LtDWLmtvCX/zG1Kqqv0dLc5u0vdtcSElRMrgpqDXl9D3LOqyDrDGMKyQxjrsZOJ20HDsEAD+N276cmOBNS5uRUvsdm5pkDgfgXJPk/D5EHoVWUPHqdG+5uo3hRpASpz5JMrTGVYYAsKIrz4wonL/Zab25bhsF4oDLSPElJBUWFiInJ0d3LCcnByUlJSgvF9NUHDx4EKWlpXjmmWcwZMgQfP7557juuutw/fXXY8mSJdzrKioqUFJSovvnB+EOY/UCydzQkeW4venAcU5qd3lLycuhJon1DouOn243FeX5JNVUR6xwGWP8lV1z7BMB+PSey/CXYV3w8NAuAPiO26Lb0tgJErw2YGuSrM8z83GkSTJj8kmSICQ5iaLOel8aan2SDJokVVWdC0mOUoujFU7cxUnSaIMcO24rzL+Disnv1APGKO3RMNfXCGJi6djH9ZqkoEtJcSUkySB0WvK+5pprcN999+H888/Hww8/jJ///OeYNm0a97qJEyfqAme2adPGl/pFNEmG31rcmoNYRGQKTcd9YNA5UvK2mlicfvGJTlLhQdKkSYKqu0dtqxrz9sOvwUlASa/kndVUqJw22Rm4/bKz0PD0CiqvY5Wdtk9k0IwQA9Of1QQTDcdtpk+SZnUbq32dyvPOVgDW/C8iI2vNbV6DSVpH3GYFk9T4JMXBZrdGlwov5GSm4cHBnbjnz22VqQscKwNBaxu33xi3vXLrdxstgt+jNLRo0QJFRUW6Y0VFRcjMzER6On8nbS3NmjVDUlISunbtqjvepUsXy9Vt48ePR3FxceTf3r17nd+AAKZtSRhp/NEk1Xbca84/Ew8N4b94olhV0+mkI6wFOj1YGyde4wurXTprdOp2q0kymWu0fwvWX8aXoEhbORFMhIcwl1VnXaZ9PuJChngFhLYl0aRxa8Zx0pVYJTRrWBtLqSEjsKSohvX2S3Pxi16t0a1VluP6iExiWhObm5bS7TxvaW5jbUui0ULFgbktPBjJ2OAWAFo1rl1wZBw/LjqrKYb34G/q7AqXJr0w+l0Zgu+TFFPHbafk5eXh008/1R3Lz89HXl6ecB4pKSno06cPtmzR70K8detWtGvXjntdamoqUlNTuedlEe4vkdVtjA6ULKBJ+k3ftpFAilbUhgDQH89mBI086wzxpew1eVprkiok5WXMtya9/rixGY1LzFMSEyIDsPs4SUZzjXMThBQhSfJEITqI2RXrZDB0Mm66aTLWNax9sMJ4cdxmTSjZDczvF9snKRFz7r4Uqgr8I9+8c7povW69pD1aN8kQSmvMW+Q63T16fB5WGknm6rY49UmShVZ7xmo6p6YxOwGI5ZHUJCMZR09U2hdkrE/wH1dsNUmlpaUoKChAQUEBgJol/gUFBRGNzvjx43V7wY0ePRo7duzAQw89hM2bN+Pll1/GrFmzcN999wnnCQAPPvgg/ve//+Hf//43tm/fjsmTJ+OTTz7BXXfd5f9N2xAymNtYiKiUB3XNwdu/72ubLqJJspggwrzz+4ts82PlHUZrJnT6xed0dZvdDulGM6a2brL2bnOjSZKB2wndb3MbD7bjtr+fl6yaGt8BKRG3DTf3m75tcWFuNv42ortQrZISFHQ7Mwvntc4yaThVOFnQ4LxPh+veMDUJK/88AGdrtrIxoq2Fm/7Hiq/GgqVJ0r67vPdea7aMNTIjbgPGlYX6c368RkafpCeuORcf3HWJKR1PA6l9z2rMbcEmpkLS6tWr0bNnT/Ts2RMAcP/996Nnz56YMGECAODAgQM64SY3Nxdz585Ffn4+evTogUmTJuHVV1+NLP8XyRMArrvuOkybNg3PPvsszjvvPLz66qt4//33cemll0bjti0Jd+rI5rCMNCLmNkVRkJNpH/eJtXcbYNaKDO/RSig/LcbBLtWDIKI1O1gR9kky7jln9SYqUHRtKsuvQd+mUdQkCeThRDAR9RmwK5eXD8v0ZV7YbI+jpmMk9ieYpP73gM7NMesPeWiTbdbO2Gm3WL6IolpDJzJSWMvVq12TyLGczDSm9iuMqE8R93pNa+u1Gvp0LMdt7bt74lSV6TwAPDi4k+5+YonMiNsAkJLE11iLvruO9+fT1D05McGRBs/48ej3B5FXYipe9+vXz7KBjNG0w9esW7fOdZ5hRo0ahVGjRgnVM5oYvzJY9yJiblMgNlmG+7ZJCFHY6ezL5fsWpCUnRoQXJ5qhK845A7fktcNtr38jkJ7dNsbBwtiq2glIWsRtzsBvhQxrgds8vA5VdvfIfS2ZApuL8j1G3DZpZTQ/XQeTNFxndV+sErR96uEhnbFw00GUa0xOfmiS3r/zYsxctQe/uyzXUBex6706bmvHhuSEBF3MN9bebanJtfdWWsHeaaBZw1T8746L0PGRzxzVK0GRbx6L7M+puecL22dj1a4jrvLTaZI81UwMxVCQAmcfKHqfJNIkES6x6nQiO38rijMH3ut6nolf9GqN53/VA4D1V7UoxvK1miTRSWd495b4z619uBvr5mTqhTtevpaTk2I0t7kUklyeM9bFK0I+CA6eqLBPkttl8nbnRQV0B8WzV7cZ0mjPSTK3WdfJOm2b7Ays/os+TpxotZyYQnObNcD4q7qgeSO95tiqz8jcL8zKzMnySdKOh/07nQEAaNfUrKlzI7zJFpAA8+IcALiog3lFqlMXA1Omp8vyQ3Ayavuc9XPN3xLr5BfBMdQSABjmNk7EbTtEA36Fs0pOTMBzv+ihud6YzrnJw1i+VkgS3WMpPHGwhJ/f9G2L/cfKUVTyU22+PCHJ4oACY0A6ST5JnIHfChnmNhGhhm1uc15Wjbq85m+7iZi7fy6rLs6r4gjm6jaTX55msvbguC1eJ+f5CccPkxA/yOpWvDq58xY5GN97O5+kFplpKJhwJZITE3DuY7XbVRn9aGJJrd8p/z6BmrFZZAdO42oxvzHG/lLgfHscLQG3tpEmKWgYd3Vm2ZQVBXjx1+db5iM6KPA6t+mwi3fAmIc2YnCyQ0GEVc+LzmpqOi6qBTK2c3KiBE2S4CRinQc74aCuOZh60wVCeYh8/bJK4fkvWGUnY7NKpk+SNgSAcD4OymQkNq1uszgniqMQAEKaX2P+YvWSserLsn9r/nbzjcFz3Daavpk+SRohSVEUNM5IMV2nwLnfjV/UrmCuPcZ6juIfVrV/s67w476NqxmddC9tUhWBjyVJQlLQMG5wy0JRamIZ2eFFSDJ9sQprkjSTpuGc1ndAdHAPp2INvAqjDK5PkkEo0goSiqLofZLcapJMIQC05wTz4CR85be9MVQwKJxRABSFe5lFdnpNP/8uz27ekJuNLK2Wo7ABjGN+bEviNXaTHVYarrDpCZCjoRSPGu+tLL3ApH8XmT5Jmo+v2oD7/HdRlJ93lxuEMUyt36m1Jkm0ztq+6bbtFc7fIukBdj934oMYZEhIChi125Kc/s3oaCKDlQLBVU6cHmDhw2pbLu+iVDchAE4nY92LCvPLyfVJMvw2ChIyHLeN6MPvO/8qdIuIsOBkLLVaISNqknr/rovFCzSUKdp2TgQrtk+SUfugubeo+CQ5T2N1zegrajft9lOT1Dc3W/euey1Ja0I0anVZz1j77oavNdfVea1uvbi942tEYG1w60STZGwT3ZJ6wyV+rBwzWilq5hoH12vuXFXVwNvbSEgKGMY4SUwhSVDU96JJcpvO2iep9ovPqSDCKj8UUk0vJ9cnydCOxr3BtAKcrIjmuq8zwduV45NkP+jYL7sXQysYWQ2UmWnJ3Ho51STd1a8D/6QgLMHLau82t09F9i4ZxudmJbxptaUs36VLOzZzVjanb77z+4sMKzmdt5ZOINVqWCzGiUs61jg764QkzkdV+GfeWU1xVjOxoLh+Td1dWjYCoH9f2D5J7Hs3PnPjNh+ucHiZ8Xm7/RgIqWRuI5xiMLe57UCiznS8MdZqs1a7cnnX6DVJYl0vnB+rnlUh1VSG6BezccLW+iRpg/Z1OzNTKD8WbpxZZeiwQpKX5FgJLIkJ4pMjX/turao3BgJ8aEhnTv7e7ttK4HDr1+F28jizcTomXn+eZRrAus6ytQisktKSE4QC0drmrblG55PEGSfuGXA2XhvZB4Dxo0Zh1jX8++3f98UX91/hvIIS6d+pOQDD/pEOpGljm1j5BaoAerQW34pGBMVQDs9qwet+2ve5YWpS0BVJtLotaIQH+lpzG8tx234U8uq4bfbPcKNJ0pOarNEkifokcb4MgbAmSfzLWotqUCXpVrdpvl4HdsnBX68+F28s343ZBfuF8mYhOm/IcLIUctxmFeNydVsYl65c3NVtjw/viu0/leKis7LNCTwiYm6T4ZTuNgTAszd0xyUMTY9p8rfI364fOL0nK42fDK0bqxze+3xh+2yknR5PtL6O1SF2NOtwOymK+IbDfkzel53dLFKXas0DYvokcfJo2jAFpRW1QTN1++YxLmqemYYberXGe2t+jBxrmJqky0OoYE0ZuuetOHv305JrttoBgPSURJvUsYc0SQGj9r2p6YUPDKrZaFY7iYu84wo8qF4Z14qbi7TX6C9yEycpnIo12bA1SWJdutpqWxJNHg1Tk9CrXTaaCkb8bquJpOwumGRtwj7tm4hdZMDt2O51ddu4AecAAG6+qC3+NsKsBXFSMVVVcesluXjq2vN88Uli9Sdj15Ex8ZsjIPPRpuTdsrEtrPzA3Drw87AS+PR7IXoTk3TmNs44of2Q0Y6NVacDT5oDuzrHH3+e2pponw9LGDQ248a/DsaGvw5GerJesNBpcw13Gi6idRP9BvDnnelFu2QOL+PUTaDbmVnodroOXjXAfkNCUsCojZNU8/+VXXOw5i8Ddap3MQdPRciZTjQEgLCftUXl0pLdO0ezsq1WVbPjtmC+ukEdRnNbbR4NTquGRWv77A21e3Lpv7aca+JY21CI4HZw114mHKRQk7DbmVnY9MQQPHXtefhVn7bC5TpaGSPIf27tjbd/3xe/7N2aeT6N0bZGgcONudSII4dWnVDGeS8Nv62sNOGJMJXTj5y2sZXyUTvRefURtwoBECaZIyRVSjY1f3H/FXjuhu5okpEsJT9t22gCiXNWt+mPNUhNQsPUJFOb6OIkGR41f/80Q1kOxUiTJsmDYBx0cxsJSQGDtflh04apjr9qa1Si9im5Pkmm3xLMbbqlus7ya8DYoLK6OsSYNNy9rCkcLVeGQ3WwIvC3Fdrqux14RDQItjt9a85bCV36+lqrz/l7t4mnFSXvrGa4uAPfMTkjxdyfrNrE9eo2R3taaSY7ribJkL9FnZs0SMGavwzEuglXCtfBCqsuo//ocN5Wei1a7S+e76L2o0bbxpWMYJM1eTquElQAHZs3xC96t3F+MQdtNapDtXUd0q2FOS2nzpY+SaLjtEejqGl+8JAdCUmEIyKBxizMXbyBsVWWfhsBb5ok49eKfV6Afg84K8dtcUfmmnTZDVLw1LXddGacKoZPkpulzoqi6OumySPsZCimvTNMiq40SbXperVrgm5nZuLqHq2Erg0jFHGbdZ3mb+1tWJqIHPjtOIq47SZOkjYApWKdD0uYMweTdPhlwsCt4zZPuDK/l9b5N22YyhQI3cDsw6z2FbzlK845g3lc77jNzoxnVq/iaJKsmom3ca8fk7f2eWk1SRkpSRg/VL8ggVdl4+pb/eIJ9jV27hNOPsKNH+BeI26zSE5U8NrI3lLzdAsJSUEjvPmhhTqU1R83PzkED1/VRZNebGLm+z4Yf4u9BJd2bIYx/TvgpRt7ml5M1lJdJ/W7+aJ2OjNOtYMQAHZl8DRJEXObsFCn/VtcgGClS05MwCdjL8VLN/YUu/g0bi0O+ijXWk0S/xrd8mObm3QSW87NLTiZ1Ix+HYC1uc3tFOAo4jbnb+v85U5Of7j8LO45q5KM5ms7FjxwBV7mRJC3WxoP8KPit8lOZx7naU4GdG6OV27pZVHTGmTJS9o+ZfSLNAnpnPcpOUl/3OodDBeRZ9gbzqvfmF7z583Eymrb/9zaBwO65LjPVCK0ui1ghCc4a8nf3CPTkhNddVzey+JWnZqYoODBwTVfRMXllbpzxu0DvMLySXIXo0U/6CYoCppkJOPoiUqc28pZCABe8aKDiDm+i/P7ETO3mY/prhKur/glToQYr06zdl+2LDOq6cPAQX48HEXcdiBw1qZzXCXuta2bpGO85kNLpKywWTTE0OJZkZKYwPXZE/NJ0kufs8dcgh+PnsC5rTgOyZw6qeALQFptoyytks5x2/A1I+psbrx3K2E+XMKFudno2bYx1u05BsCbUKPA/Iy9+SSZG9ftXol+QEJSwIh0GAtNjlj/UTx9ZVpFHxa9zlhPrZqYV7VFf+yHI2UVGDF1OTMPLdXV5tVtbklJrB0QExMUfP3wAJyqDqFRWo3DpmgxvOck3n7avAQLNSAWTFK8Hpb5uJjYrfII40qTpMvTOp80AXMbHL9zZlyb2wQuU1XvHxt5ZzXF8h2HAdgHUbXqw06fl6IYtZW1OejHCXaZRuHp/DaNcX6bxvzyHNYvPTlRF19I1ko3bT2MmiSrbXG0GIUkbb+12nfywvbZESHJ67ApEkfPS5vJ1pB6gcxtASPcrcxCCvtvXRrDBOtFujd/KYhdZ6VZ0KvO2RnmNmsQiX9ilQ44rUny/LrXvPAphi1T0lMSkZXufEWLG5OJ/nqF+bcT3PqguHG+1e8bZZO/E8dtVz5J1nlqYWmSwu/LmY1rTDZDNc60bt8l4xex1cSh1wQLapLcVErDtJtrTU23X8Y3tfEI3472PkUmOGO8Im2rJAsISUZBQaQ8Ho3SzO/L09d10y9ecFQan3NyGkX+NmqSzPfKrvPALjXBKBuddgXQtrex7bXdTf8RwW8Pp4J3zbP0okkyHwuSkESapIARYiuSTAIQC/0EK1dIYmXVOCMZx07oTWpWEZgTBTRJdvXQwvJJcoMCo78UI1MX5Wjv3839On18f736XCzYfBC/6dsWT8z5XrhuLNwJxW41SeZjssxtXMdthk9S2A/w47GX4JtdRzGwS3M889lmbh3F6iGe1s1E49VhNisjGdueHoqdh8pwdvOGrvNpkZWGERe0RmpyAn46XmGb3lhrrblOJIyH0wUaVqk7t8jEvQPORkpSAp6bv8VRvk4YfUUHjOnfMfK72mRu06fn3eIverVBk4wU9DitOdP2Aas4cVqhzKS1sqi3EVGzoBfBiYQkgkt4cjD7R9hrGIw2fi8dzSpY2JIH++GHn0rxv2/2Yv7GIkMd9IKalhTN4CfDTMYKJukGRdGrqZmB3YRDIHDuX7CiXia9kRe3x0jBTTntihHekFdgZU0YvtxjvtCduU3cL4a5uu30RU0bppqWZLfITDOlF8GRTxLnbyOJCQqqQyrOyWmIgyUnXdVLS3Jigk7Dwa0fS5jV/D3plz0AAL+fsVooL50mSZORVkvENTk5jCFm9xjuu/IcFJ+ojAhJpr7qUZXUrGEKHjasXjOa20S3gkpIUDDoXK2WU3+Oh6E0q+o6gldP0Q8dloaZhCSCS20wSf4LIzLu1miS3NfDSvPbrmkDtGvaAK9/tct0nZVPjTbmiZXQYdSI8agOyTG3Gf23vDgN6k0mtX+70iS5roV3RJvAydYd3NVtTE2SWPk87IQTpiaJcc0Hd12M0pNVaJElLiSFhZiaeghfJpz2u8cH4VRVjb9cgPxbHZGg6KM2ax+3iJYo2e0eOBYomiyjEbrH5LhtOC/H5F1bhlZbJ+r/xMJtPZ1AQhLBJSxVW8goFuY2w29P5ja+JikMa+8fqxD5+i8e0Xrwz1WHVNf7hRnL0O1mz8hTVDAVMYVaIWO/MBHsshbVaMkIfun1NsNtyxKseH5QbHObuSYXtG0CADhZWS1cH52QJHyVeB/JSElCxunQPrx3vG+u/P3uWLg1ixrfFW0+Wi0Rr0UcR+wXaFttCuN9+SE0mR23xTRJRnTaXMM5nU+SbusYsbwB4MuH+mPtnqO4d2aB+EUOYHUh0e2lokFwakIA0G4YaaFJEjK3eauHlZAWpowhJFlN8lYClK4sgXsFgN7tmnBq5gwF+oGG9YKKlGJ8193t3ca+3inhDSS52FTID00STz3ENuM4n5aYV3CySUpMwISfd9Uds/p4dfI+JelMkO4+0UUv4yV7+/cXiZcriFShXeHXXST2lmOfJIHk2r7sZrWeFayub/RJMt6S+IeKwvxb9Bo72mRnYPC55ojgYbjmNsH8Wekc+uX7SoCqQgAax22TOtSZHcarCtRqR/QwZRXmr2url4834L36297odmYm3h2dJ1S3Lx/qj2k3X8AM5e8GRdEPutI+YlyY22QsOwcQ2TySW4zN9W4GW5lq9xB7dwmpjLo0Fx3OaBD5baXidzKpWEVAtjLbuXnevHpFy1zhVsOiQL8iSr9voIDWRyDNM9r9LgXqpCvXcGN2GjM3JnpjH3f7vidazA3aajuNZcXD7C/rDWacJNIkETyEzG3cq+WZasxCmjkNy9xmNTZrX+amDWu3Aujdvgnm3H0Z+rTPNpXFKrdNdgaGdGtpWkbsFkXAJ0n8y579DIQdoW2S/XHQOWIV8Yhos2rHMvsQALyyzBda7QEnkwcGdYr8bSngO8gzkWH+mH5bHzx5bTd0b91YqAxh82wAR3ChbXEMmiTeBO7l9Q7HOBPNyOoVtbslO8GOdb2d47Yo2j5g7DfcsBtGS4WNH6iVdt/rGMzUJAXI2Y58kgJGZloy2jXNQFPNHmiAvlNzo2RL7FcmIU3QJ8nK/JKg1MRlKSwuN/gduJ+cZNxygqI3sXlZ3aa/xjl6Icucw9ifnY2RF7fH2LfXYcnWn1yUEM7b7rwLtb3Lh2EsKrdZA7z0657OM2LMznaTm6gGw4kmKYmhSerXqbntdc4DxsrV3rkpy62DfYLhA0dreZI1jomY7VWdMzO/YLv7TEgAIO62BoBlbjNo7wUFYJ0/pZWgp9Uqud27CD74SrLiJDn0OfOTAH6H1G9uvqgdljzY37RcVESTpEvjtY8JqFSNLzlgdCI0fnEoGNKtBW69JFc/6JjKEq+8FE2Souhs4KICgnHLEsVQH96kN2PUhdw8Rbb50H0hu8Sujd34JNl+TWue+ZPXnAsAuP/Kc3RlDTuvJRb9sR+62mwH8/R13Ux19Ro2wMpE5aSf6fNx0JfFi6i9JppzieSPMBFzmyezkPZvh5okpz5xMjZ4NVsPnH+oGKvBM7edqnJmz46mMA4ES5NEQlKc4NQpOxo+SbZ1MFzCm4SsHWbFy+0kEOuFh50NnFUN3gadkWt0f9f+OkvjB2NEdILwewyREWHdiPar/Za89ljzl4G4Z8DZ+r4qWO5NfduJJbQhpJuc+emc9MNEl5O8TJ8kP5BZkmky18a44pR6To6zYJdOlZwJHKHNWD8WdpM6y+/m/ivPQbOGKbj/yhoTumlTc9F3UHBBjLYGFQYhyV6zbHnW+mIb2I7bwRGSyNwWJ4i88G7U9dzyLMp3m4e234uGyLdDO7DNvMPdih5FsV9NIVpD3q2IO247L9MNtoOiYOn6qOLOahwxKWsu87ZRJuuY9eSm6nxh5LS41lTgJEc3qyED9MHtCFP/EtAkOd9uR5uPfUPxxifA3tzm5jm0yc7AN48MjNTNaisqUczCp+ZvQU2S03vhpRc1xbIdt4PTsUmTFCfY+arUpJFYnsCbcvk5Z1ieT0pMwOBzcyK/edFgrQKb2U/mtbi1YyuK872gasoWN1mJOnHrJwj/Bgq7nIWDX2r/trmG77jtvFx2/i7CBvgQAEfr3+Yo4nbANUkyUQyvm/6jSXNc84C6nWltgjXitC/xzH+AgOO2y44rcw811jW8OEmZDveltKqJ1x54Q6/WpmNBEpJIkxQnOFUdex07jX2UNZn888aeWLr1J6zedQT/Xb6bmc9V57WMbF2iX2KrVa/LeSHc5qJAQd/cpujaMpOv0mc0qBNtjJvgjLFEeINVXb/0PlHIvn1bx20fwgSyVrc5RViT5zJ/N8gU2o058TR6Wr/Hwee2QG6zhuje2jq8BSsfY9UbpSbheEWVkEN9TQWtT8sQVmVokkQdt++4/Cx8samIn9gC463y+oXouzX6ig44VHoK07/eFTkWJCGJNElxgkicJLsJK0EBnr2hu2B59mmy0pMxvEcrpDKiF9fmU5uR1m7P+3IE+L48dvlree6G7sg7q6nltdryU5ISMPeeS/GCm1VV2rxcmEyMdfFyvZtyPOWjvV+b0UTE4TzJQxQ5N1ohfzRJ7vqAm2fvp7bRT4wCAe8xVBs0Ib+7NDcSKsQOhfM3AHzxwBWYetMFuPkitm+bbMdtkdxMWXgcP2rKVZl/X5ibjdljLhEuyqqfud3XMExSYgKu6KS3SjgNFuonJCTFCSKCg90g27t9Nn7Zu42LEq0HDavlpPptKzT5CS75dTbJ1Cb+Re82eEfQRyl8laXqW6wCQnWzyitBMJ3XIcRO+MxukGJ5np2nNdec3wo/795StzLNeF1acnSHJA8robm4DbDpRhMXoLnEEUahgvcctBqmkEOJ1mo8zMlMw9DzWnI1Fo4dtyV0WymLZawctw23cH6bxo7z0J5//bY+ePKac3Heac3ezzo3dy3cyLh3vyAhKU4Q+cq0Cwg2Mq+9q/LssBo+RHxsLDfztSnbqRmSnYf9lawkdsd4dbMWCsXUCeE9xZxwZuP0yN92g/4Lvz4fPdo0xr9/2xsDu+TwE2oFX5vykxMTMPk3F5hWpmlvMzXJfRBJVvl286rbvces0O4rFmRNktNrxVPbt6lxJVezhmyhvDqk4sL22WiSkYy+uWKa4TD623N2r04dt52Ev+Dnof+t/dm5hdjKXUtzm9WFmvr/sk/Nh3SvdrVjDCvb/p2a4xbNnPLayN7Y+MRgoXqaijf8DpImiXyS4gQrAYgZ/NBwqHFGMoZ1b6k71sAiqrFJHW7xhlmd01ZNW0+dDwL/8qggU7jijZXNM2uDg1qt0rEyEWi544qzkJGahCfnfG9Ty1rm3H0pej6ZL5S2wxkN8dFpdXy/Tmdg/7Fy3DBtOX46XsGtr1t5Q5tHapJcc5uTYJKycOtP4cTMHCZAc4kjwuPLG7+7EEfKTuFI2SlmuuqQiv/94SJUhVTHiyu8mr61eI24LQb/Y/GjsZdABNOYxImTZMVtF7dH99ZZujhwIrenKIrrjxxzIM3gdOyYapKWLl2K4cOHo1WrVlAUBbNnz7a9ZvHixbjggguQmpqKjh07Yvr06Z7yHD16NBRFwQsvvOD6PqKBtg+ZVZPhRLordGnaNa2NzRO+vLeFbd9JF7V6+fRLxNlprPd7sy5bSuA5kQGAkch0RFUNwmzt30O7tcStF7fHP2/siaz0ZFxzfitmOaL3k5qUiN9dmmtfcW19OeZOO5ITE9CuaQPmNVb7XYnXqzYPL5okN/jhuH1281rnf7fmYj+vcYvMosL+iZedfQauOf9Mbn9U1Zp7dLX61IuW2eIFSWHUxS46tojG0qxJ8v5eqNwfNdx8UVtkpSfjFo1vVkKCgj7ts7kfc350uQBZ10zEVEgqKytDjx49MGXKFKH0O3fuxLBhw9C/f38UFBRg3LhxuP322zF//nxXeX744YdYsWIFWrViT1hBwspcEx4oLU06mpf0k7GX4qa+bTHplz345fE/SExYDQC6iMxaTZJVXR3EN3Hz9W2VBzeNi6y11yQmKHj86nMxvEdNXxvenSckaesld+TQtqU7XxzzRWc0SsXFHZrisrObITPdnWJap0ny4JPkRuCR7ZN0U9+2eOSqrujf6Qx0OzMTnVuIL1vXPXthc5vDCgYE0fHFqR+SvgzxccQO7Rh3ZVez+dmX1W0usrSqB6stn7r2PKx99Eqc0SiVcYW2Ls4r4+TRBbkfx9TcNnToUAwdOlQ4/bRp05Cbm4tJkyYBALp06YJly5bh+eefx+DBgx3luW/fPtx9992YP38+hg0b5u4GoojVclYRzaR2M8VuZ2bh6evOs0jt7KW3mmj0jtvsPD07TEfycZBYg9sBznhZ2JYfOe8xT9kDh1cNNmvQUxTgrdv7epuENJemeTC3sWZa+2CS7otjEX6v/nNrHwDOJhdFUfCzzs1xpOwUOp4hFl06Vg6unXIaYUvRcVzasZmr683xfNgPwrgJrKMyOH+LYPJJ0v3N2I7J7mPOpd+jU0zCp6b9eC3p1Dx8ZpN0+0QOifa2J06IK5+k5cuXY+DAgbpjgwcPxrhx4xzlEwqFcMstt+DBBx/EueeeK3RNRUUFKipq/TFKSkoclekVKyfEsOraagVVyNlWPY6w+oLXmwk111iMfW6dXUVIUMxCnZC9nXms9mhuswa4sU9bbP+pVJOvN/OJ7GFDFyzPTeBFgXzdoDMrWIST8AM/zG2A+zb5z619oKqq8PXRnFq0Zc343YX4YO0+/KqPebWsTMEzVhuxmla3cYIyhrGTM8TMbUZNkovxw7IOjrPTseqRAThVFUKmhL0jjQTIBclEXK1uKywsRE6OXtWZk5ODkpISlJeXC+fzt7/9DUlJSbjnnnuEr5k4cSKysrIi/9q0EV1KLwfn5jZ9Ii/LZ+2wFni0PklubFY2p52u0GE5WwtMNXYr2S5o2wQJCYrw1ysv1otTH6ux/TsCAO4dcLZtWu1A5Gbu8WMlmBFPjtsurmnawNrMEAuc9GkvmiQv81JOZhru7NfBVagIFryu5cUc6sVx26qvs06lWyyCEaWy2rCfmos8rMyYXt/e5o3S0LpJhnB6J+UFOd5XXAlJMlizZg1efPFFTJ8+3dGDGT9+PIqLiyP/9u7d62MtzVhpGMKnrBwVnc5vpr19LDKwNrc5F5Kc+BlZ+mEx68PIQ6KaWzSvrIxkbPjrYPQzBFFz6mP1wKBzsOTBfhg3UERI8uiX4elqPtpqeQoBwOijdnUe1DUHv78sF1N+Y71ZcVAJ4tzipk48jV61JE2SU3OOaKmTftEDbbLT8fdf8P07RfM7dqJS99vOGZxF+B2/uENNuARtuI1ofOS4JciapLgyt7Vo0QJFRfpQ6kVFRcjMzER6upid9Msvv8TBgwfRtm3byLHq6mo88MADeOGFF7Br1y7mdampqUhNjd1Xp5WvikgIAKdmhfLKagepxYJJJnIct2UhpBGC4qp0tgbK/HcrTSwiu8miYWqSbp8vwPnAqCiKbuWidVrNDxcDpl9jrLZaXjRJLPq0a4K53x7gnk9IUPDIsK5Sy4wmnkxKjsvybybja5IkCUmONUkW5zQtN6JXa4zo1VqKAHLkhD4Mghs/nfAzmjHqQhwuO4UcTTTsaMtIzvxJa1P/d9SF8ivjgbjSJOXl5WHBggW6Y/n5+cjLyxPO45ZbbsG3336LgoKCyL9WrVrhwQcf1K2SCxpWGobw1wNv+Tng/Ius/JReSLJe3cY/p9ckaa8Rq4/d4Obc3OY9j9oLzflmpCThm0cGYt2jV3pf0i15TtL2CTcf6P07WW9o7BbtLad58ElidSnethN1hSBFJg7jansYznFPmiSfPLZYVdK+t7/sbd6wVUQibd9Ub8rysqI2KTFBJyDVVCG6UpIzc1vt310EA2dGi5hqkkpLS7F9+/bI7507d6KgoADZ2dlo27Ytxo8fj3379mHGjBkAamIaTZ48GQ899BBGjRqFhQsXYtasWZg7d65wnk2bNkXTpvrIrcnJyWjRogU6derk8x27x+qrKCx8WKVxOnA5WcFgHSep9m/RAGFOnJedfi2yJhUZQ6l2QLZbTmuF6LYk7vL2dv2T13ZDSlICZq3+MXJMpF+l2GiH9I7bcr/bkhIT0DIrDQeKT0rNNygEUUhyQ9A0SV64oVcb3TsiyqCuLfDkNefi0Y82AnD3/muj6hsJsLVN348D1qVjqklavXo1evbsiZ49ewIA7r//fvTs2RMTJkwAABw4cAB79uyJpM/NzcXcuXORn5+PHj16YNKkSXj11Vcjy/9F8oxfrHyS7HuV08Gmc4tMTLu51k/D6nLZPklaHK10E0jj1ieJqYGSGNE3jHYTYNnmDW37uzEPNEpLxiiHASwB+2X9ep8kF0EDT/8f4DnAN7Rtd2H7bHRvnYXRV3Twpyxfcq2Bp+Xw5rit/VueT5JfW90kJCi6bT6cDCqv39YHt1+ay1xtGMaLwOk3Xp6V38RUk9SvXz/LDmWMph2+Zt26da7zZMHzQwoS+kBzNT/G9O+AKYt+wF+vrgljYNW13Aw2Q7q1tE8E65ePb27j5+fIlu0grbE+kTzcWtt8+FLV+m3JHiq0dXQ7XBoHMJH7tjOhafNwu6UHEOwvZb/QNldaSiJmjRZ3PQjWVMTGi/bTKracHdZzSHQ6mpMq9+/UHP07NbdME/Vo9i6DSQZNORpXPkn1GZZW4cHBnbF+wiBcdd5pYcaio/n6FeExmKQVtqvbGMKjTYaOywBEzHRy3mydkCTbJ0mTodsvdDdmXHsTWm2mXoQk+9zl0LtdE3Q7MxMdzhBzmPcX90L1WU7rL1iAMagqALzwq/MtrzH2o7//ogeaN0rFC7/qKVg5M24imItg1+dZ45Cb1+3a81vhzMbp+FVvOeFmHh7aGefkNMST13aTkp9M7DZnjyVxtbqtPqNTR2p+ZGUka9LwTTUxkpG4PkmiAShtHbcdvlxuNUmN060DqMnTJMnPUyZuqmT3BetlqXYs6HtWNh4c3BkPzFqPH34qi2ld3AgC7995MT5Zvx/3X3mOL3UafG4L5N93Of4yewNW7jwCAOjeOgvdW2fh2x+LmdcYNTc39GqNERec6cnk7Gnln+XqtujQMC0ZXz7UX9pmr60ap+Pz+66QkpcY7loqaDGTSEiKE5xOJMYUXjVJVkKN6Aa3ia40SQ7SCiRm+iQJ5J2TlWY65sfLnKiJAeCnwCArZopIEzjxM/IyH0Rr9U6QzHpuHP17tWuCXu2a+FOh05yd00hnZlUUxXKcYG9547X/a9vGWV4dmjvXErbJTsfeI+XodqZ5vz4375sC8cUu8U6A/bZJSIoXRJyErTQwfprb3IUAEMvbbqB0OoawNUn2mbTIZAhJzooWQu+47S2vfp3OwOItPzHPue0Obsxttj5JFvk7IdrCS5eWsV+q7Njc7KUshz0+wTDxRVs748bP5cO7Lsb3B0os/Xt4As+iB/qhKqR6CmOhJWAKFRe4u4Gg3Tf5JMUJIpK2brIxpPK627nbAU7nkyQaAgBawcrO3sY3MbK4Ja8mbs7l59TG/BF5KZlCkg8vs0yfnP+M7IOOzcU2ShXHef3sNEnRnOhlEK7ib7UrkWKEnyEjjDh9NEbH6VgGMxStes+2TXBT33aW/ZA3liYlJkgTkOoG4g88yCZ3EpLiEDdL1v0MSS+qpdItQRfMW/acObZ/R8y84yL86+ZetWUIXNc4Ixl5ZzXlnpdVTb3jtrdcExIUZKaxlcVuTVNuqmRn2gmy06aWLi1rzChX9zgTQE38p+6tsyLnHTtCSyZo8mWCYeJzam7zipfVbV55b3QeHtD4fLm5vaA9z/oKmdvihAQBjYlVEEbPmiSLc6LvsqiSROfsbeu47YykxARcZBB2RIQRRVHw9u/74o0VuzEhEuzND58kufnxnrvr1W0O0n5272VYuPkgfmcTW0n/vN23Ke9DQJZ26uOxl+DoiVNo3oi91cNbt/eVUo4TEiQK1XZ48Si0q5of/mT6sUNe24jUtHf7bPRun41J+VtdlxM0jYqf6O41YLdNQlKcIGRus+hcfvok/WlIZ6zceQS3XtzedE5brG51mySfJBnzgmgWiqL4um0IYHTc9o5sDaKTibhLy8yI9sUyT83fQY4gnZyYoBOQjLTMEo9SLwuj30+QMH7gWJrs/dAk+RDsFQj2RrFBQpbfY6whISlO0HUcnuM294e3PZAA6w7fJjsDq/48QMDJWmtuk7R3m4SpQUYwSbeM6d8BX2wqwogLavZ7kum4DQDVnAfnegDzUBdunpLuuT5OXX4JAsyyHPsk6f+2NLe5rJNw+T7k7zdBExb8JMjPioSkOMFq81q79ID/TpMiGgY3IQC8ahayG6TgSNkpyzROBK222RnM426r2bNtE3z3+CA0TK15FRO0cZIkDBehEOe4yw4RtIk41kR701AjRr+fIGF0EbBsKZ8HKJmmSFcb+Lq45pyc2K+ejAVBW7xBQlKcILKclZWmR5vGWL/3GK45v5V/lbNAOzZo6yc6aNj5JNmdf+v2vnh67ib8cbDF5sUO3snLz26GR3/eFV1aNpI2JTVKqw1UmaQ1t0koIMj7NbFwM0B2OL2CLxa3GuvmjaVzsh1OwpD4rUmSid+Ccf59l+NA8UkhU3VdgTRJhGf0fhvi6WfcdiGWbPsJg7rm+FEtW7T2e9Hl7U6Wg9oNhF1aZuJNG4daJy+loigRR+TSiioHV4qh1yR5R7aQ5Ie2womjvpb378zDih1H8MvT2zbEWqsTC9zEAnJdlsNnb1pIEkufJIn5utIkOeibZ+c0wtn1VIsEBE/YJyEpTnAccfv0BVkZybi6h3ctktsJSHuVq73bovDCuPZJklsNAO5MklZwV7e59FHz43k4iouloVe7bPRqly2/QnGEzs8vYDKizo1SsdYWpafIjy+U4NMWP0Fr57qAU3eSaEJCUpzgVK0erG5Wg3gIAPFJU4b92u1LqdWMVXN8f5yiNbd5DdsAxMdKHFmqdt6t+ilox7p5g/ieh9FrkqzjJI28uD2WbP0JQ85tIa98nyZeNx+Mse4nscDJLUdTI+oUCiYZJziNrCu9o0l4yRXdV69oAErv5drhtq1SNEGNKiVJSdqvX97KNCc8/6vzkZmWhKcMO38HaczWaxzcP/A7Lj8LAPBrxi70fhHrdoxmyASnRRk337Xqzg1TkzDrD3kYZRNTywlBnniJ+IE0SXFCksB230H8WnFTJyeTpozBz+1Eo437dKpKjpCUmOBckLSie+vGKJgwyLQljFtfpYqqas91MiJrMmvXtAG2PjUUKQ421I13lADfqmL4O9qLCPySi4I4zsY7RtNskAjwK0Zo0W1XIZBetl3X7biQwfE1kLW6TQYyXko/hCSvsa3CsPbMczvQn6yUZFfUoAs46vFhRFtAirU5U9taQZu8Ewx21Kjv3eaTJilgzRxY3L4bQfNJIiEpTkjSbT/ATiMScDLadG7RCLde3B7jh3Z2db2TAJWx5JQkc5vWcVuWkCSTri0zcdFZcp2ldWEipObsP38cVBNa4sYL28akfDcBWt3i+FVzuNhEPtoxk6SkIBNk0yiZ2+IE3R5NnAFHK7gHpaMpioLHrz7XdNxyLzgHL4yM25QxgPpjbpOSJRO3WSckKJh5Rx7aPzxXXl0kapKizcCuOVj76JVokpFsn9gHgtxe+mCS0Te3+bVlSyPOptFWkFxlR4D7cawrQIih1SSJOPTK6nJ3XH4WmjVMxe2XyXOoBIBbLmqH5o1Smfu9aYnGJCCjBFmaJK3A5uekEmszEQ8/fGz87kLZDVJiFiXYTYBWD6V5Sh19c5uzFcGi/PWac9GjdRZeurGnvEzrOQGW9UmTFC84deiVNWj/+aoueHhIZ6ZfixeaNEjBivEDmPnq4+ZY5yNlg9sA+SRpkbG6LR7QmokCPFYGEp2QFLtq2KIg+sE+/epLrZtk4KOxlzq7KMgPJwCc2bh2c+ikaDiiOoCEpDhBGz9HxFdFZjeTLSA5ydc24raMDW495+CPkOSnS1JQ5a8gm4+CiJMPimijN5srvvZn2/JJ/I46Th53WnIi1k8YhMREhfZuI9yRKGBu4+2TFm848kmSokmS4JMkK5qkBrdRsYXyliQlycgliL508YJWMEpK9Nd7wsuzURADc5vOcTu6ZRPOyYqRX58d5JMUJ2iFJD8nz6ARNz5JvmiS6s9zDkOaJGdo2ys5YKoko5ASbT84mZumPnDlOQCA+wae4zEnIt4gTVKcoB3/eDKSPgJAsAZMt9hrkiSY2wLqk+Sruc2/rIkookRRk+QFBUrU+5zMkChjf9YR1/dqjVZZaa6ur4+bL9eVbzwSkuIErTDA80mqM+Y2zd+2e7dJL9EdsrYl0RIP5jYZaDUMpElyhnZcSE70t+0ch0kyCCnR1yTZh01xkpfWuZioPwT304PgEqQJzm+iMWcGV5NUP56z9i79sBjVFa2qHdrFHX7g9D0x+hZG3XGbU5dYUE9eZR2xbnNZkJAUhwitbovnDmpYFWOZVMJ9epmYw75iHZo39F4RA75OKgEatPWO2/HccWNLku+aJPcht2sct2PX6ahXRZ+6IhiSkBSHiGgY6srXs22cJA95P3DlOWickYyHh3Zxncfcey7FDb1aY/Jv5AeW80OTdFazBgCAYd1bSs9bBgHzPY4rghZfxkhs926LbdvUEXmhXkI+SXFIiGPZqStLqfWxX+w0Se5v9O4BZ2NM/46e4kB1bpGJv/+ih+vrrfBj77ZP770MPx2vQJvsDOl5u0UXTDKeO26MSQ6Y47ZRSCFBgYhHgvVWEUJEc1uSWOP3ffgVKFMGfghJacmJgRKQgLqjlo81wY+TROa2+kRQtz5yCglJcUh9ipNkp1kIsIzjmtSkmtfy8nPOiHFNiHgiaOY2o+N09B23KZgk4Z2YCklLly7F8OHD0apVKyiKgtmzZ9tes3jxYlxwwQVITU1Fx44dMX36dEd5VlZW4k9/+hPOO+88NGjQAK1atcJvf/tb7N+/X96N+YyQJimORwVt1W3H/Ti+Tx4r/zwAc+6+FL3aNYl1VaKC33NnHewiTPx23HaK1vynQInpas264qMZTwQ5bpcTYnoXZWVl6NGjB6ZMmSKUfufOnRg2bBj69++PgoICjBs3Drfffjvmz58vnOeJEyewdu1aPProo1i7di0++OADbNmyBVdffbWUe4oGIl9kdWVIiGdhzy2NM1LQ7cysmJVf15q8jt0Ol2S/QwA4TJ+SpBGSFOD12/qgUWoSnv+VPz58QaaumJ5E+OeNPdG8USr+/dvesa6KFGLquD106FAMHTpUOP20adOQm5uLSZMmAQC6dOmCZcuW4fnnn8fgwYOF8szKykJ+fr7u2OTJk3HhhRdiz549aNu2rYs7iS5Cq9vieGbQB5MUT0vEJ/Vo/vCVoGmSUgyahIs7NMP6xwYF2g+Q8M7wHq3w8+4t68wHblzpw5YvX46BAwfqjg0ePBjLly/3lG9xcTEURUHjxo25aSoqKlBSUqL7FytEfJLqSgeNxga3hJ7oNylJSTIImnkj1aBJAoK9UMJP6lsPryvzDxBnQlJhYSFycnJ0x3JyclBSUoLy8nJXeZ48eRJ/+tOfcOONNyIzM5ObbuLEicjKyor8a9OmjavyZMDdlqSOfJI72U6AfA3iH7+7bdumDfwtICD4vcGt04lPZ26L8XtaH/dOI+QQV0KSbCorK/HLX/4Sqqpi6tSplmnHjx+P4uLiyL+9e/dGqZZm6st2FQDInhYDGqTUrfBpz47ojuE9WuG90XmxroqvBE2TpBWSYoFWpov1kBnr8gn3xNVo2KJFCxQVFemOFRUVITMzE+npzjYfDAtIu3fvxsKFCy21SACQmpqK1NRUx3X2Az/i5wQJJxvc1lPtvS/859beeHLOJkz6ZXQda/2eQFpkpeGfN8qPiB40mjVMiXUVdBgdt6MNDQ2EDOJKSMrLy8Onn36qO5afn4+8PGdfiGEBadu2bVi0aBGaNm0qs5q+U8dlJB31MAJAzPhZ5xz8rHOOfUIAuc0aYOehMlzdo5XncskU4o3nbuiOzYXHcUXA4mql6EIAEER8ElMhqbS0FNu3b4/83rlzJwoKCpCdnY22bdti/Pjx2LdvH2bMmAEAGD16NCZPnoyHHnoIo0aNwsKFCzFr1izMnTtXOM/KykrccMMNWLt2LebMmYPq6moUFhYCALKzs5GSEqyvMRb1ydxGQlAwmXP3pdh1uAxdW1prYAn/+UXv2PlHWqHXJEX/Rc5MT478nZGaGPXyibpBTIWk1atXo3///pHf999/PwBg5MiRmD59Og4cOIA9e/ZEzufm5mLu3Lm477778OKLL6J169Z49dVXI8v/RfLct28fPv74YwDA+eefr6vPokWL0K9fP9m3KZ06b27TBZMkx+0g0iA1Cee2khPLqR7J/HGNUzlHt7pNcl1ESEtORP59l0NRFKQmkZBEuCOmQlK/fv0sV2QZo2mHr1m3bp3rPNu3bx/3q8DqupCkxW5w7depxsTQrmmw9iMjxKk/vbl+EWufJAA4O6dRbAom6gxx5ZNE1MAzt9WVyUanHbIZXJtnpmH9hEGkTo9j4vybpd7QtIEzV4SUxNp3si7FzSHqFyQkxSF92mfHugpRQ8SclpWRbJuGIAhvjLo0Fxv2lWBItxZC6WMdAoAgZEBCUhyx+I/9sHDzQfymb/C3TvGEVpFEH6B1HlrdFh9kpCRh2i29hNOnkpBE1AFISIoj2jdrgFGX5sa6GlHFznGbqAOQjFQn6dSixh8oaPGbCMIJJCTVIcI7x+dkBiPopQxIRiKI+CQtORHfPzEYiRTxlYhjSEiqQzRMTcLGvw6Oe18ARdxvm6gDkCKp7pJRx7a4Ieof1IPrGA1S69YjJU0SQRAEESviW+VA1Em0chEtHa77xHvcMoIg6i4kJBGBhkSkug+JSARBBBUSkojAodUekSap7kOKJIIgggoJSUSgoYUxBEEQRKwgIYkINLSBbd2HFEkEQQQVEpKIwKF33I5ZNYgoQY7bBEEEFRKSCIIgCIIgGJCQRAQOrfaItiWp+5AeiSCIoEJCEhFoSEYiCIIgYgUJSUTg0Dprk5BUDyBVEkEQAYWEJCLQkLmt7qOSlEQQREAhIYkINCQi1X1ocRtBEEGFhCQicGiVR6RIqvuQkEQQRFAhIYkIOCQlEQRBELGBhCQi0JAmqe5DPkkEQQQVEpKIQEMyEkEQBBErSEgiAgcFk6xfkE8SQRBBhYQkInBoJ02Skeo+JCMRBBFUSEgiAo1CBrc6D2mSCIIIKiQkEYGDNEn1DZKSCIIIJiQkEYEjMaFWMspMS45hTQiCIIj6TFKsK0AQRlKSEjD9tj6orFaRlUFCUl2HzG0EQQQVEpKIQNKvU/NYV4GIEue2ysSCzQdjXQ2CIAgTJCQRBBFT7urfEUmJCRjQhQRjgiCCBQlJBEHElLTkRNwz4OxYV4MgCMJETB23ly5diuHDh6NVq1ZQFAWzZ8+2vWbx4sW44IILkJqaio4dO2L69OmO81RVFRMmTEDLli2Rnp6OgQMHYtu2bXJuiiAIgiCIOkFMhaSysjL06NEDU6ZMEUq/c+dODBs2DP3790dBQQHGjRuH22+/HfPnz3eU57PPPouXXnoJ06ZNw8qVK9GgQQMMHjwYJ0+e9HxPBEEQBEHUDWJqbhs6dCiGDh0qnH7atGnIzc3FpEmTAABdunTBsmXL8Pzzz2Pw4MFCeaqqihdeeAF/+ctfcM011wAAZsyYgZycHMyePRu//vWvPdwRQRAEQRB1hbiKk7R8+XIMHDhQd2zw4MFYvny5cB47d+5EYWGhLp+srCz07dvXUT4EQRAEQdRt4spxu7CwEDk5ObpjOTk5KCkpQXl5OdLT04XyCF9nzCd8jkVFRQUqKioiv0tKSpxUnSAIgiCIOCOuNEmxZOLEicjKyor8a9OmTayrRBAEQRCEj8SVkNSiRQsUFRXpjhUVFSEzM1NIixTOI3ydMZ/wORbjx49HcXFx5N/evXsd1p4gCIIgiHgiroSkvLw8LFiwQHcsPz8feXl5wnnk5uaiRYsWunxKSkqwcuVKy3xSU1ORmZmp+0cQBEEQRN0lpj5JpaWl2L59e+T3zp07UVBQgOzsbLRt2xbjx4/Hvn37MGPGDADA6NGjMXnyZDz00EMYNWoUFi5ciFmzZmHu3LnCeSqKgnHjxuGpp57C2WefjdzcXDz66KNo1aoVrr322qjdO0EQBEEQwSamQtLq1avRv3//yO/7778fADBy5EhMnz4dBw4cwJ49eyLnc3NzMXfuXNx333148cUX0bp1a7z66quR5f8ieQLAQw89hLKyMtxxxx04duwYLr30UsybNw9paWl+3i5BEARBEHGEoqq0B7cbSkpKkJWVheLiYjK9EQRBECbaP1xr5dj1zLAY1oTQ4mT+jiufJIIgCIIgiGhBQhJBEARBEAQDEpIIgiAIgiAYkJBEEARBEATBgIQkgiAIgiAIBiQkEQRBEARBMCAhiSAIgiAIggEJSQRBEARBEAxISCIIgiAIH3jzd33RNjsDb/++b6yrQriEIm67hCJuEwRBEET8QRG3CYIgCIIgPEJCEkEQBEEQBAMSkgiCIAiCIBiQkEQQBEEQBMGAhCSCIAiCIAgGJCQRBEEQBEEwICGJIAiCIAiCAQlJBEEQBEEQDEhIIgiCIAiCYEBCEkEQBEEQBAMSkgiCIAiCIBiQkEQQBEEQBMGAhCSCIAiCIAgGJCQRBEEQBEEwSIp1BeIVVVUBACUlJTGuCUEQBEEQooTn7fA8bgUJSS45fvw4AKBNmzYxrglBEARBEE45fvw4srKyLNMoqogoRZgIhULYv38/GjVqBEVRpOZdUlKCNm3aYO/evcjMzJSaN1ELtXN0oHaODtTO0YPaOjr41c6qquL48eNo1aoVEhKsvY5Ik+SShIQEtG7d2tcyMjMz6QWMAtTO0YHaOTpQO0cPauvo4Ec722mQwpDjNkEQBEEQBAMSkgiCIAiCIBiQkBRAUlNT8dhjjyE1NTXWVanTUDtHB2rn6EDtHD2oraNDENqZHLcJgiAIgiAYkCaJIAiCIAiCAQlJBEEQBEEQDEhIIgiCIAiCYEBCEkEQBEEQBAMSkgLGlClT0L59e6SlpaFv375YtWpVrKsUV0ycOBF9+vRBo0aN0Lx5c1x77bXYsmWLLs3JkycxZswYNG3aFA0bNsSIESNQVFSkS7Nnzx4MGzYMGRkZaN68OR588EFUVVVF81biimeeeQaKomDcuHGRY9TOcti3bx9uvvlmNG3aFOnp6TjvvPOwevXqyHlVVTFhwgS0bNkS6enpGDhwILZt26bL48iRI7jpppuQmZmJxo0b43e/+x1KS0ujfSuBpbq6Go8++ihyc3ORnp6ODh064Mknn9Tt7UXt7I6lS5di+PDhaNWqFRRFwezZs3XnZbXrt99+i8suuwxpaWlo06YNnn32WTk3oBKBYebMmWpKSor6n//8R924caP6+9//Xm3cuLFaVFQU66rFDYMHD1Zff/11dcOGDWpBQYF61VVXqW3btlVLS0sjaUaPHq22adNGXbBggbp69Wr1oosuUi+++OLI+aqqKrVbt27qwIED1XXr1qmffvqp2qxZM3X8+PGxuKXAs2rVKrV9+/Zq9+7d1XvvvTdynNrZO0eOHFHbtWun3nrrrerKlSvVHTt2qPPnz1e3b98eSfPMM8+oWVlZ6uzZs9X169erV199tZqbm6uWl5dH0gwZMkTt0aOHumLFCvXLL79UO3bsqN54442xuKVA8vTTT6tNmzZV58yZo+7cuVN999131YYNG6ovvvhiJA21szs+/fRT9ZFHHlE/+OADFYD64Ycf6s7LaNfi4mI1JydHvemmm9QNGzao77zzjpqenq7+61//8lx/EpICxIUXXqiOGTMm8ru6ulpt1aqVOnHixBjWKr45ePCgCkBdsmSJqqqqeuzYMTU5OVl99913I2k2bdqkAlCXL1+uqmrNS52QkKAWFhZG0kydOlXNzMxUKyoqonsDAef48ePq2Wefrebn56tXXHFFREiidpbDn/70J/XSSy/lng+FQmqLFi3U5557LnLs2LFjampqqvrOO++oqqqq33//vQpA/eabbyJpPvvsM1VRFHXfvn3+VT6OGDZsmDpq1Cjdseuvv1696aabVFWldpaFUUiS1a4vv/yy2qRJE9248ac//Unt1KmT5zqTuS0gnDp1CmvWrMHAgQMjxxISEjBw4EAsX748hjWLb4qLiwEA2dnZAIA1a9agsrJS186dO3dG27ZtI+28fPlynHfeecjJyYmkGTx4MEpKSrBx48Yo1j74jBkzBsOGDdO1J0DtLIuPP/4YvXv3xi9+8Qs0b94cPXv2xL///e/I+Z07d6KwsFDXzllZWejbt6+unRs3bozevXtH0gwcOBAJCQlYuXJl9G4mwFx88cVYsGABtm7dCgBYv349li1bhqFDhwKgdvYLWe26fPlyXH755UhJSYmkGTx4MLZs2YKjR496qiNtcBsQDh06hOrqat2EAQA5OTnYvHlzjGoV34RCIYwbNw6XXHIJunXrBgAoLCxESkoKGjdurEubk5ODwsLCSBrWcwifI2qYOXMm1q5di2+++cZ0jtpZDjt27MDUqVNx//33489//jO++eYb3HPPPUhJScHIkSMj7cRqR207N2/eXHc+KSkJ2dnZ1M6nefjhh1FSUoLOnTsjMTER1dXVePrpp3HTTTcBALWzT8hq18LCQuTm5pryCJ9r0qSJ6zqSkETUWcaMGYMNGzZg2bJlsa5KnWPv3r249957kZ+fj7S0tFhXp84SCoXQu3dv/N///R8AoGfPntiwYQOmTZuGkSNHxrh2dYdZs2bhrbfewttvv41zzz0XBQUFGDduHFq1akXtXM8hc1tAaNasGRITE02rf4qKitCiRYsY1Sp+GTt2LObMmYNFixahdevWkeMtWrTAqVOncOzYMV16bTu3aNGC+RzC54gac9rBgwdxwQUXICkpCUlJSViyZAleeuklJCUlIScnh9pZAi1btkTXrl11x7p06YI9e/YAqG0nq3GjRYsWOHjwoO58VVUVjhw5Qu18mgcffBAPP/wwfv3rX+O8887DLbfcgvvuuw8TJ04EQO3sF7La1c+xhISkgJCSkoJevXphwYIFkWOhUAgLFixAXl5eDGsWX6iqirFjx+LDDz/EwoULTSrYXr16ITk5WdfOW7ZswZ49eyLtnJeXh++++073Yubn5yMzM9M0YdVXBgwYgO+++w4FBQWRf71798ZNN90U+Zva2TuXXHKJKYTF1q1b0a5dOwBAbm4uWrRooWvnkpISrFy5UtfOx44dw5o1ayJpFi5ciFAohL59+0bhLoLPiRMnkJCgnw4TExMRCoUAUDv7hax2zcvLw9KlS1FZWRlJk5+fj06dOnkytQGgEABBYubMmWpqaqo6ffp09fvvv1fvuOMOtXHjxrrVP4Q1d955p5qVlaUuXrxYPXDgQOTfiRMnImlGjx6ttm3bVl24cKG6evVqNS8vT83Ly4ucDy9NHzRokFpQUKDOmzdPPeOMM2hpug3a1W2qSu0sg1WrVqlJSUnq008/rW7btk1966231IyMDPXNN9+MpHnmmWfUxo0bqx999JH67bffqtdccw1zCXXPnj3VlStXqsuWLVPPPvvser80XcvIkSPVM888MxIC4IMPPlCbNWumPvTQQ5E01M7uOH78uLpu3Tp13bp1KgD1H//4h7pu3Tp19+7dqqrKaddjx46pOTk56i233KJu2LBBnTlzppqRkUEhAOoi//znP9W2bduqKSkp6oUXXqiuWLEi1lWKKwAw/73++uuRNOXl5epdd92lNmnSRM3IyFCvu+469cCBA7p8du3apQ4dOlRNT09XmzVrpj7wwANqZWVllO8mvjAKSdTOcvjkk0/Ubt26qampqWrnzp3VV155RXc+FAqpjz76qJqTk6OmpqaqAwYMULds2aJLc/jwYfXGG29UGzZsqGZmZqq33Xabevz48WjeRqApKSlR7733XrVt27ZqWlqaetZZZ6mPPPKIbkk5tbM7Fi1axByTR44cqaqqvHZdv369eumll6qpqanqmWeeqT7zzDNS6q+oqiakKEEQBEEQBAGAfJIIgiAIgiCYkJBEEARBEATBgIQkgiAIgiAIBiQkEQRBEARBMCAhiSAIgiAIggEJSQRBEARBEAxISCIIgiAIgmBAQhJBEIQHFEXB7NmzY10NgiB8gIQkgiDilltvvRWKopj+DRkyJNZVIwiiDpAU6woQBEF4YciQIXj99dd1x1JTU2NUG4Ig6hKkSSIIIq5JTU1FixYtdP/CO38rioKpU6di6NChSE9Px1lnnYX33ntPd/13332Hn/3sZ0hPT0fTpk1xxx13oLS0VJfmP//5D84991ykpqaiZcuWGDt2rO78oUOHcN111yEjIwNnn302Pv7448i5o0eP4qabbsIZZ5yB9PR0nH322SahjiCIYEJCEkEQdZpHH30UI0aMwPr163HTTTfh17/+NTZt2gQAKCsrw+DBg9GkSRN88803ePfdd/HFF1/ohKCpU6dizJgxuOOOO/Ddd9/h448/RseOHXVl/PWvf8Uvf/lLfPvtt7jqqqtw00034ciRI5Hyv//+e3z22WfYtGkTpk6dimbNmkWvAQiCcI+UbXIJgiBiwMiRI9XExES1QYMGun9PP/20qqqqCkAdPXq07pq+ffuqd955p6qqqvrKK6+oTZo0UUtLSyPn586dqyYkJKiFhYWqqqpqq1at1EceeYRbBwDqX/7yl8jv0tJSFYD62WefqaqqqsOHD1dvu+02OTdMEERUIZ8kgiDimv79+2Pq1Km6Y9nZ2ZG/8/LydOfy8vJQUFAAANi0aRN69OiBBg0aRM5fcsklCIVC2LJlCxRFwf79+zFgwADLOnTv3j3yd4MGDZCZmYmDBw8CAO68806MGDECa9euxaBBg3Dttdfi4osvdnWvBEFEFxKSCIKIaxo0aGAyf8kiPT1dKF1ycrLut6IoCIVCAIChQ4di9+7d+PTTT5Gfn48BAwZgzJgx+Pvf/y69vgRByIV8kgiCqNOsWLHC9LtLly4AgC5dumD9+vUoKyuLnP/qq6+QkJCATp06oVGjRmjfvj0WLFjgqQ5nnHEGRo4ciTfffBMvvPACXnnlFU/5EQQRHUiTRBBEXFNRUYHCwkLdsaSkpIhz9LvvvovevXvj0ksvxVtvvYVVq1bhtddeAwDcdNNNeOyxxzBy5Eg8/vjj+Omnn3D33XfjlltuQU5ODgDg8ccfx+jRo9G8eXMMHToUx48fx1dffYW7775bqH4TJkxAr169cO6556KiogJz5syJCGkEQQQbEpIIgohr5s2bh5YtW+qOderUCZs3bwZQs/Js5syZuOuuu9CyZUu888476Nq1KwAgIyMD8+fPx7333os+ffogIyMDI0aMwD/+8Y9IXiNHjsTJkyfx/PPP449//COaNWuGG264Qbh+KSkpGD9+PHbt2oX09HRcdtllmDlzpoQ7JwjCbxRVVdVYV4IgCMIPFEXBhx9+iGuvvTbWVSEIIg4hnySCIAiCIAgGJCQRBEEQBEEwIJ8kgiDqLORNQBCEF0iTRBAEQRAEwYCEJIIgCIIgCAYkJBEEQRAEQTAgIYkgCIIgCIIBCUkEQRAEQRAMSEgiCIIgCIJgQEISQRAEQRAEAxKSCIIgCIIgGJCQRBAEQRAEweD/AbKRSzcvVrW7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 37.9 s\n",
      "Wall time: 31.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train(model_c3,dataloader_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f1281734-aa0a-4e12-9d89-4d9f275bf960",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, dataloader):\n",
    "    model.eval()\n",
    "    predictions = []\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in dataloader:\n",
    "            outputs = model(inputs.unsqueeze(1))\n",
    "            predictions.append(outputs.detach().numpy())\n",
    "    return np.vstack(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "18b1ee86-5918-455d-8c11-05dc7d22de78",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_c1 = predict(model_c1, dataloader_c1)\n",
    "predictions_c2 = predict(model_c2, dataloader_c2)\n",
    "predictions_c3 = predict(model_c3, dataloader_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "214fbcf9-7e66-4645-84da-b3b996cc2866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions for dataset_c1: [-0.1589306]\n",
      "Predictions for dataset_c2: [0.25666523]\n",
      "Predictions for dataset_c3: [-0.13775834]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predictions for dataset_c1:\", predictions_c1[-1])\n",
    "print(\"Predictions for dataset_c2:\", predictions_c2[-1])\n",
    "print(\"Predictions for dataset_c3:\", predictions_c3[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05f7daaf-c6d1-4490-894b-0a8654e82e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_numeric_scaled = scaler.fit_transform(df_numeric)\n",
    "y = df_numeric_scaled[:, -1]\n",
    "\n",
    "yc1 = y[:split_idx1]\n",
    "yc2 = y[split_idx1:split_idx2]\n",
    "yc3 = y[split_idx2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c3fc459-c948-4749-8af2-4cb162211bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_c1_numpy = np.array(predictions_c1)\n",
    "predictions_c2_numpy = np.array(predictions_c2)\n",
    "predictions_c3_numpy = np.array(predictions_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fa4bd54-b560-47d7-9fa9-1fcaf84cd879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error of Client 1: 0.11698619160817794\n",
      "Mean Squared Error of Client 2: 0.07351210017262581\n",
      "Mean Squared Error of Client 3: 0.9697799119233886\n"
     ]
    }
   ],
   "source": [
    "mse_c1 = mean_squared_error(predictions_c1_numpy, yc1)\n",
    "mse_c2 = mean_squared_error(predictions_c2_numpy, yc2)\n",
    "mse_c3 = mean_squared_error(predictions_c3_numpy, yc3)\n",
    "print(\"Mean Squared Error of Client 1:\", mse_c1)\n",
    "print(\"Mean Squared Error of Client 2:\", mse_c2)\n",
    "print(\"Mean Squared Error of Client 3:\", mse_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "755d35fe-cd9e-4ba1-b366-f9e29bcf1e92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error of Client 1: 0.34203244233285524\n",
      "Root Mean Squared Error of Client 2: 0.271131149395686\n",
      "Root Mean Squared Error of Client 3: 0.984774041048701\n"
     ]
    }
   ],
   "source": [
    "rmse_c1 = root_mean_squared_error(predictions_c1_numpy, yc1)\n",
    "rmse_c2 = root_mean_squared_error(predictions_c2_numpy, yc2)\n",
    "rmse_c3 = root_mean_squared_error(predictions_c3_numpy, yc3)\n",
    "print(\"Root Mean Squared Error of Client 1:\", rmse_c1)\n",
    "print(\"Root Mean Squared Error of Client 2:\", rmse_c2)\n",
    "print(\"Root Mean Squared Error of Client 3:\", rmse_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2e35be6e-1790-4f67-bb34-6875168fede3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of Client 1: 0.3278173508200951\n",
      "Mean Absolute Error of Client 2: 0.25268814766016817\n",
      "Mean Absolute Error of Client 3: 0.980045750161012\n"
     ]
    }
   ],
   "source": [
    "mae_c1 = mean_absolute_error(predictions_c1_numpy, yc1)\n",
    "mae_c2 = mean_absolute_error(predictions_c2_numpy, yc2)\n",
    "mae_c3 = mean_absolute_error(predictions_c3_numpy, yc3)\n",
    "print(\"Mean Absolute Error of Client 1:\", mae_c1)\n",
    "print(\"Mean Absolute Error of Client 2:\", mae_c2)\n",
    "print(\"Mean Absolute Error of Client 3:\", mae_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "83018c1f-4142-4d2e-8722-ab6861852b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score of Client 1: -470.0597230295848\n",
      "R2 Score of Client 2: -167.24961867726546\n",
      "R2 Score of Client 3: -5501.07410405605\n"
     ]
    }
   ],
   "source": [
    "r2_c1 = r2_score(predictions_c1_numpy, yc1)\n",
    "r2_c2 = r2_score(predictions_c2_numpy, yc2)\n",
    "r2_c3 = r2_score(predictions_c3_numpy, yc3)\n",
    "print(\"R2 Score of Client 1:\", r2_c1)\n",
    "print(\"R2 Score of Client 2:\", r2_c2)\n",
    "print(\"R2 Score of Client 3:\", r2_c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4fe597f0-38f1-4dcc-92f7-5b5587b5b0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_state_dict_c1 = model_c1.state_dict()\n",
    "model_state_dict_c2 = model_c2.state_dict()\n",
    "model_state_dict_c3 = model_c3.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "938c16d1-e672-4ffe-b687-56c17c44c2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_c1 = list(model_state_dict_c1.values())[0]\n",
    "tensor_c2 = list(model_state_dict_c2.values())[0]\n",
    "tensor_c3 = list(model_state_dict_c3.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5093153-8136-4669-846e-43adaca8ca34",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_2d = np.empty((0, tensor_c3.size(1)))\n",
    "\n",
    "for row in tensor_c3:\n",
    "    row_array = row.numpy().reshape(1, -1)\n",
    "    array_2d = np.vstack((array_2d, row_array))\n",
    "for row in tensor_c2:\n",
    "    row_array = row.numpy().reshape(1, -1)\n",
    "    array_2d = np.vstack((array_2d, row_array))\n",
    "for row in tensor_c1:\n",
    "    row_array = row.numpy().reshape(1, -1)\n",
    "    array_2d = np.vstack((array_2d, row_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "21cb2be8-3b2f-4691-8b03-249280905df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor_2d = torch.tensor(array_2d, dtype=torch.float32)\n",
    "input_size = tensor_2d.shape[1]\n",
    "input_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "65247afd-8849-44f0-9afb-40b4902dee64",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_dataset = FederatedDataset(tensor_2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7f4b97cf-cee0-427b-a07a-25550f1f3127",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_weights_dataloader = DataLoader(tensor_dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7b99dd81-be57-4a97-bfc9-45347aafe7ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 1.0154121704101562\n",
      "Epoch 2, Loss: 1.0114603881835937\n",
      "Epoch 3, Loss: 1.0018701293945314\n",
      "Epoch 4, Loss: 0.9998906005859376\n",
      "Epoch 5, Loss: 0.9968108764648438\n",
      "Epoch 6, Loss: 0.9965861206054688\n",
      "Epoch 7, Loss: 0.9876549194335938\n",
      "Epoch 8, Loss: 0.9832063598632813\n",
      "Epoch 9, Loss: 0.9759743652343751\n",
      "Epoch 10, Loss: 0.9703334838867188\n",
      "Epoch 11, Loss: 0.9570147094726563\n",
      "Epoch 12, Loss: 0.9490056518554688\n",
      "Epoch 13, Loss: 0.9428358764648438\n",
      "Epoch 14, Loss: 0.9316408142089844\n",
      "Epoch 15, Loss: 0.9200590209960938\n",
      "Epoch 16, Loss: 0.9151318115234376\n",
      "Epoch 17, Loss: 0.8975209350585938\n",
      "Epoch 18, Loss: 0.8921447265625\n",
      "Epoch 19, Loss: 0.8803645446777344\n",
      "Epoch 20, Loss: 0.8718156494140625\n",
      "Epoch 21, Loss: 0.8601293762207032\n",
      "Epoch 22, Loss: 0.8445458557128906\n",
      "Epoch 23, Loss: 0.8380911071777344\n",
      "Epoch 24, Loss: 0.8246904052734375\n",
      "Epoch 25, Loss: 0.8188372131347657\n",
      "Epoch 26, Loss: 0.8093684387207032\n",
      "Epoch 27, Loss: 0.8007926635742187\n",
      "Epoch 28, Loss: 0.7909558776855469\n",
      "Epoch 29, Loss: 0.7841293273925781\n",
      "Epoch 30, Loss: 0.7774484436035156\n",
      "Epoch 31, Loss: 0.7727052917480469\n",
      "Epoch 32, Loss: 0.76086767578125\n",
      "Epoch 33, Loss: 0.7513813720703125\n",
      "Epoch 34, Loss: 0.7466379150390625\n",
      "Epoch 35, Loss: 0.7361867980957032\n",
      "Epoch 36, Loss: 0.7340043823242188\n",
      "Epoch 37, Loss: 0.7319728881835937\n",
      "Epoch 38, Loss: 0.7217786437988282\n",
      "Epoch 39, Loss: 0.7202378173828126\n",
      "Epoch 40, Loss: 0.7097209228515625\n",
      "Epoch 41, Loss: 0.7041903747558594\n",
      "Epoch 42, Loss: 0.6976364318847657\n",
      "Epoch 43, Loss: 0.6945090576171875\n",
      "Epoch 44, Loss: 0.6838823181152344\n",
      "Epoch 45, Loss: 0.6826122802734376\n",
      "Epoch 46, Loss: 0.6739054321289063\n",
      "Epoch 47, Loss: 0.6714783691406251\n",
      "Epoch 48, Loss: 0.6648209533691407\n",
      "Epoch 49, Loss: 0.6596883361816407\n",
      "Epoch 50, Loss: 0.6552386291503907\n",
      "Epoch 51, Loss: 0.6517901000976563\n",
      "Epoch 52, Loss: 0.6427225769042969\n",
      "Epoch 53, Loss: 0.6411874877929687\n",
      "Epoch 54, Loss: 0.6344287658691407\n",
      "Epoch 55, Loss: 0.6288315185546876\n",
      "Epoch 56, Loss: 0.6252669738769532\n",
      "Epoch 57, Loss: 0.6250468994140626\n",
      "Epoch 58, Loss: 0.6172090393066406\n",
      "Epoch 59, Loss: 0.6116152099609375\n",
      "Epoch 60, Loss: 0.6072188537597657\n",
      "Epoch 61, Loss: 0.6044440368652344\n",
      "Epoch 62, Loss: 0.6004841430664063\n",
      "Epoch 63, Loss: 0.5955772216796875\n",
      "Epoch 64, Loss: 0.5904493713378907\n",
      "Epoch 65, Loss: 0.5878661376953125\n",
      "Epoch 66, Loss: 0.5808290222167969\n",
      "Epoch 67, Loss: 0.5818881164550782\n",
      "Epoch 68, Loss: 0.5728335144042969\n",
      "Epoch 69, Loss: 0.5664097839355469\n",
      "Epoch 70, Loss: 0.5655166931152344\n",
      "Epoch 71, Loss: 0.5617931274414063\n",
      "Epoch 72, Loss: 0.5563150512695313\n",
      "Epoch 73, Loss: 0.555455322265625\n",
      "Epoch 74, Loss: 0.5500370025634765\n",
      "Epoch 75, Loss: 0.5475328186035157\n",
      "Epoch 76, Loss: 0.5410434692382813\n",
      "Epoch 77, Loss: 0.5405772766113282\n",
      "Epoch 78, Loss: 0.5326242065429688\n",
      "Epoch 79, Loss: 0.5300959442138672\n",
      "Epoch 80, Loss: 0.5256677581787109\n",
      "Epoch 81, Loss: 0.5217480895996094\n",
      "Epoch 82, Loss: 0.5216074645996094\n",
      "Epoch 83, Loss: 0.5186552368164062\n",
      "Epoch 84, Loss: 0.5160937316894532\n",
      "Epoch 85, Loss: 0.5137891174316407\n",
      "Epoch 86, Loss: 0.5071034118652344\n",
      "Epoch 87, Loss: 0.5024845703125\n",
      "Epoch 88, Loss: 0.5004566162109375\n",
      "Epoch 89, Loss: 0.49613315429687505\n",
      "Epoch 90, Loss: 0.49409661254882814\n",
      "Epoch 91, Loss: 0.4904055389404297\n",
      "Epoch 92, Loss: 0.48389448852539063\n",
      "Epoch 93, Loss: 0.48269736938476565\n",
      "Epoch 94, Loss: 0.48219914855957036\n",
      "Epoch 95, Loss: 0.47693930969238285\n",
      "Epoch 96, Loss: 0.4713155334472656\n",
      "Epoch 97, Loss: 0.4714883697509766\n",
      "Epoch 98, Loss: 0.46631325988769534\n",
      "Epoch 99, Loss: 0.4644169189453125\n",
      "Epoch 100, Loss: 0.4623523803710938\n",
      "Epoch 101, Loss: 0.45746181945800785\n",
      "Epoch 102, Loss: 0.4541089050292969\n",
      "Epoch 103, Loss: 0.4529413360595703\n",
      "Epoch 104, Loss: 0.4498624267578125\n",
      "Epoch 105, Loss: 0.44249533081054687\n",
      "Epoch 106, Loss: 0.4414486938476563\n",
      "Epoch 107, Loss: 0.43749878540039067\n",
      "Epoch 108, Loss: 0.4354449920654297\n",
      "Epoch 109, Loss: 0.43592538757324223\n",
      "Epoch 110, Loss: 0.4299923950195313\n",
      "Epoch 111, Loss: 0.42957844848632815\n",
      "Epoch 112, Loss: 0.4248437194824219\n",
      "Epoch 113, Loss: 0.4213566131591797\n",
      "Epoch 114, Loss: 0.4168674896240235\n",
      "Epoch 115, Loss: 0.4175190155029297\n",
      "Epoch 116, Loss: 0.41345661926269534\n",
      "Epoch 117, Loss: 0.40931062622070313\n",
      "Epoch 118, Loss: 0.4080437561035156\n",
      "Epoch 119, Loss: 0.4073022094726563\n",
      "Epoch 120, Loss: 0.4033950866699219\n",
      "Epoch 121, Loss: 0.4033571990966797\n",
      "Epoch 122, Loss: 0.39720156860351563\n",
      "Epoch 123, Loss: 0.39476021423339847\n",
      "Epoch 124, Loss: 0.39344098510742187\n",
      "Epoch 125, Loss: 0.38959617614746095\n",
      "Epoch 126, Loss: 0.3851399169921875\n",
      "Epoch 127, Loss: 0.38587175598144535\n",
      "Epoch 128, Loss: 0.3828241394042969\n",
      "Epoch 129, Loss: 0.3793265838623047\n",
      "Epoch 130, Loss: 0.37870793457031254\n",
      "Epoch 131, Loss: 0.37462290649414065\n",
      "Epoch 132, Loss: 0.37237847290039067\n",
      "Epoch 133, Loss: 0.37088385620117187\n",
      "Epoch 134, Loss: 0.3673249755859375\n",
      "Epoch 135, Loss: 0.36706822509765624\n",
      "Epoch 136, Loss: 0.3650352111816406\n",
      "Epoch 137, Loss: 0.3610697540283203\n",
      "Epoch 138, Loss: 0.36015662841796875\n",
      "Epoch 139, Loss: 0.35655226745605473\n",
      "Epoch 140, Loss: 0.35571333923339843\n",
      "Epoch 141, Loss: 0.3521804718017578\n",
      "Epoch 142, Loss: 0.35001791992187503\n",
      "Epoch 143, Loss: 0.34701687622070315\n",
      "Epoch 144, Loss: 0.3470354156494141\n",
      "Epoch 145, Loss: 0.34421015625\n",
      "Epoch 146, Loss: 0.34200325622558597\n",
      "Epoch 147, Loss: 0.33897470703125\n",
      "Epoch 148, Loss: 0.33786213684082034\n",
      "Epoch 149, Loss: 0.33313812561035155\n",
      "Epoch 150, Loss: 0.3341850036621094\n",
      "Epoch 151, Loss: 0.331656674194336\n",
      "Epoch 152, Loss: 0.33164978942871093\n",
      "Epoch 153, Loss: 0.32712959899902344\n",
      "Epoch 154, Loss: 0.3249087951660156\n",
      "Epoch 155, Loss: 0.32471300354003907\n",
      "Epoch 156, Loss: 0.3209583282470703\n",
      "Epoch 157, Loss: 0.32184006042480473\n",
      "Epoch 158, Loss: 0.31763200988769535\n",
      "Epoch 159, Loss: 0.3145655334472656\n",
      "Epoch 160, Loss: 0.3115920349121094\n",
      "Epoch 161, Loss: 0.3122697418212891\n",
      "Epoch 162, Loss: 0.3100130096435547\n",
      "Epoch 163, Loss: 0.30942490234375003\n",
      "Epoch 164, Loss: 0.30595349731445315\n",
      "Epoch 165, Loss: 0.30490846252441406\n",
      "Epoch 166, Loss: 0.3015698577880859\n",
      "Epoch 167, Loss: 0.3020099578857422\n",
      "Epoch 168, Loss: 0.30056890563964844\n",
      "Epoch 169, Loss: 0.2968292236328125\n",
      "Epoch 170, Loss: 0.2959160552978516\n",
      "Epoch 171, Loss: 0.2953148559570313\n",
      "Epoch 172, Loss: 0.29148269653320313\n",
      "Epoch 173, Loss: 0.2896375\n",
      "Epoch 174, Loss: 0.2877772430419922\n",
      "Epoch 175, Loss: 0.2865710632324219\n",
      "Epoch 176, Loss: 0.2847891357421875\n",
      "Epoch 177, Loss: 0.2837958892822266\n",
      "Epoch 178, Loss: 0.28223623046875\n",
      "Epoch 179, Loss: 0.2798486785888672\n",
      "Epoch 180, Loss: 0.2783177947998047\n",
      "Epoch 181, Loss: 0.27549038696289063\n",
      "Epoch 182, Loss: 0.2748612854003906\n",
      "Epoch 183, Loss: 0.27626558532714846\n",
      "Epoch 184, Loss: 0.27314766540527347\n",
      "Epoch 185, Loss: 0.26933338317871097\n",
      "Epoch 186, Loss: 0.2694761474609375\n",
      "Epoch 187, Loss: 0.2673159942626953\n",
      "Epoch 188, Loss: 0.26606177368164063\n",
      "Epoch 189, Loss: 0.26397720947265624\n",
      "Epoch 190, Loss: 0.266276318359375\n",
      "Epoch 191, Loss: 0.26185065917968753\n",
      "Epoch 192, Loss: 0.25952159423828125\n",
      "Epoch 193, Loss: 0.25963972778320316\n",
      "Epoch 194, Loss: 0.25895933532714843\n",
      "Epoch 195, Loss: 0.2562067321777344\n",
      "Epoch 196, Loss: 0.25566698150634765\n",
      "Epoch 197, Loss: 0.25398786926269534\n",
      "Epoch 198, Loss: 0.2518255126953125\n",
      "Epoch 199, Loss: 0.24971781311035157\n",
      "Epoch 200, Loss: 0.2483975311279297\n",
      "Epoch 201, Loss: 0.2471279800415039\n",
      "Epoch 202, Loss: 0.2472022918701172\n",
      "Epoch 203, Loss: 0.246574169921875\n",
      "Epoch 204, Loss: 0.24354109497070314\n",
      "Epoch 205, Loss: 0.24165399017333986\n",
      "Epoch 206, Loss: 0.24111580200195315\n",
      "Epoch 207, Loss: 0.24023506774902345\n",
      "Epoch 208, Loss: 0.23995587158203127\n",
      "Epoch 209, Loss: 0.2390419097900391\n",
      "Epoch 210, Loss: 0.23749032440185547\n",
      "Epoch 211, Loss: 0.2346224334716797\n",
      "Epoch 212, Loss: 0.2334676513671875\n",
      "Epoch 213, Loss: 0.23358213806152345\n",
      "Epoch 214, Loss: 0.23183284606933593\n",
      "Epoch 215, Loss: 0.23105104370117188\n",
      "Epoch 216, Loss: 0.22896727600097658\n",
      "Epoch 217, Loss: 0.22752906646728516\n",
      "Epoch 218, Loss: 0.22626381988525393\n",
      "Epoch 219, Loss: 0.2260480728149414\n",
      "Epoch 220, Loss: 0.22555721588134767\n",
      "Epoch 221, Loss: 0.22529094390869142\n",
      "Epoch 222, Loss: 0.2232019775390625\n",
      "Epoch 223, Loss: 0.22278191375732423\n",
      "Epoch 224, Loss: 0.2216081008911133\n",
      "Epoch 225, Loss: 0.22000858154296876\n",
      "Epoch 226, Loss: 0.21894527587890625\n",
      "Epoch 227, Loss: 0.21921234283447266\n",
      "Epoch 228, Loss: 0.21570008850097658\n",
      "Epoch 229, Loss: 0.21629495849609376\n",
      "Epoch 230, Loss: 0.2145067367553711\n",
      "Epoch 231, Loss: 0.21424461822509766\n",
      "Epoch 232, Loss: 0.21118961181640625\n",
      "Epoch 233, Loss: 0.21127071380615237\n",
      "Epoch 234, Loss: 0.21020840454101564\n",
      "Epoch 235, Loss: 0.21034658508300783\n",
      "Epoch 236, Loss: 0.20853882293701173\n",
      "Epoch 237, Loss: 0.20576942901611328\n",
      "Epoch 238, Loss: 0.2059461883544922\n",
      "Epoch 239, Loss: 0.20625106964111328\n",
      "Epoch 240, Loss: 0.20512472381591798\n",
      "Epoch 241, Loss: 0.20471152343750001\n",
      "Epoch 242, Loss: 0.20187020568847658\n",
      "Epoch 243, Loss: 0.2031800750732422\n",
      "Epoch 244, Loss: 0.20175135650634768\n",
      "Epoch 245, Loss: 0.20047495727539064\n",
      "Epoch 246, Loss: 0.2008133514404297\n",
      "Epoch 247, Loss: 0.19852817840576173\n",
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      "Epoch 967, Loss: 0.1376590042114258\n",
      "Epoch 968, Loss: 0.13777999572753907\n",
      "Epoch 969, Loss: 0.13797986907958984\n",
      "Epoch 970, Loss: 0.1385177230834961\n",
      "Epoch 971, Loss: 0.13834330139160156\n",
      "Epoch 972, Loss: 0.13737489013671875\n",
      "Epoch 973, Loss: 0.13762020111083986\n",
      "Epoch 974, Loss: 0.13753879394531252\n",
      "Epoch 975, Loss: 0.13753884429931643\n",
      "Epoch 976, Loss: 0.1381027313232422\n",
      "Epoch 977, Loss: 0.13780447845458985\n",
      "Epoch 978, Loss: 0.13702588500976562\n",
      "Epoch 979, Loss: 0.13772372589111329\n",
      "Epoch 980, Loss: 0.13717791137695312\n",
      "Epoch 981, Loss: 0.13803863525390625\n",
      "Epoch 982, Loss: 0.13806995239257813\n",
      "Epoch 983, Loss: 0.13781800231933594\n",
      "Epoch 984, Loss: 0.1376233711242676\n",
      "Epoch 985, Loss: 0.13844802703857423\n",
      "Epoch 986, Loss: 0.1369575912475586\n",
      "Epoch 987, Loss: 0.13787936401367187\n",
      "Epoch 988, Loss: 0.1377635971069336\n",
      "Epoch 989, Loss: 0.13789532623291018\n",
      "Epoch 990, Loss: 0.13762506103515626\n",
      "Epoch 991, Loss: 0.13743621215820312\n",
      "Epoch 992, Loss: 0.13781995239257813\n",
      "Epoch 993, Loss: 0.13750125885009767\n",
      "Epoch 994, Loss: 0.1377609619140625\n",
      "Epoch 995, Loss: 0.13736709594726562\n",
      "Epoch 996, Loss: 0.13790443572998048\n",
      "Epoch 997, Loss: 0.13715518188476564\n",
      "Epoch 998, Loss: 0.13760001373291017\n",
      "Epoch 999, Loss: 0.13740299377441406\n",
      "Epoch 1000, Loss: 0.13752195281982424\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(model,model_weights_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "69267824-18a5-4cab-938d-75f329e970db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def global_predict(model, dataloader):\n",
    "    model.eval()\n",
    "    predictions = []\n",
    "    with torch.no_grad():\n",
    "        for data in dataloader:\n",
    "            inputs = data[0]  \n",
    "            outputs = model(inputs.unsqueeze(1))\n",
    "            predictions.append(outputs.detach().numpy())\n",
    "    return np.vstack(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fd558da5-7b4b-46c1-a30e-05685d254995",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_global = global_predict(model, model_weights_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4bfd0be2-dadd-4586-b0f1-cd1f97d4af7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.693756], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_global[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c51fce9b-70f3-48e1-9906-464d570eff7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_numeric['Value'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5903709d-7ee6-45c0-8564-fd0e613a271c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_tensor = torch.tensor(y, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8989f18-1144-464b-917b-9d02f20bebd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_y = FederatedDataset(y_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "de3dc5f9-3723-43d0-ada9-3e5267ec50af",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloader_y = DataLoader(dataset_y, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a56a5747-8b44-459f-ae7c-7a7460b376ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gradients(model):\n",
    "    gradients = {}\n",
    "    for name, param in model.named_parameters():\n",
    "        if param.requires_grad and param.grad is not None:\n",
    "            gradients[name] = param.grad.clone().detach()\n",
    "            print(f\"Gradient for {name} has shape {param.grad.shape}\")\n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e4aadad6-3122-464f-a743-deb6df45e770",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient for lstm.weight_ih_l0 has shape torch.Size([64, 19])\n",
      "Gradient for lstm.weight_hh_l0 has shape torch.Size([64, 16])\n",
      "Gradient for lstm.bias_ih_l0 has shape torch.Size([64])\n",
      "Gradient for lstm.bias_hh_l0 has shape torch.Size([64])\n",
      "Gradient for fc.weight has shape torch.Size([1, 16])\n",
      "Gradient for fc.bias has shape torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "gradients_y = get_gradients(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9ed90f04-516f-4f98-9965-8dc972f5d0a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Statistics: {'lstm.weight_ih_l0': {'mean': 1.0470615052327048e-05, 'std': 0.00021642450883518904, 'max': 0.0013092730659991503, 'min': -0.0008584724855609238}, 'lstm.weight_hh_l0': {'mean': 0.0, 'std': 0.0, 'max': 0.0, 'min': 0.0}, 'lstm.bias_ih_l0': {'mean': 3.8398684409912676e-05, 'std': 0.00013632992340717465, 'max': 0.0003779641992878169, 'min': -0.0002596652484498918}, 'lstm.bias_hh_l0': {'mean': 3.8398684409912676e-05, 'std': 0.00013632992340717465, 'max': 0.0003779641992878169, 'min': -0.0002596652484498918}, 'fc.weight': {'mean': -0.06264419853687286, 'std': 0.25061914324760437, 'max': 0.25071531534194946, 'min': -0.2507023811340332}, 'fc.bias': {'mean': -0.3290034532546997, 'std': nan, 'max': -0.3290034532546997, 'min': -0.3290034532546997}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ek\\AppData\\Local\\Temp\\ipykernel_12720\\1547819943.py:6: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\ReduceOps.cpp:1807.)\n",
      "  'std': grad.std().item(),\n"
     ]
    }
   ],
   "source": [
    "def gradient_statistics(gradients):\n",
    "    stats = {}\n",
    "    for name, grad in gradients.items():\n",
    "        stats[name] = {\n",
    "            'mean': grad.mean().item(),\n",
    "            'std': grad.std().item(),\n",
    "            'max': grad.max().item(),\n",
    "            'min': grad.min().item()\n",
    "        }\n",
    "    return stats\n",
    "\n",
    "grad_stats = gradient_statistics(gradients_y)\n",
    "print(\"Gradient Statistics:\", grad_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "379dd890-f952-4bae-a93c-1191d300596c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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