DNN Covid-19 Malaysia Part B.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition and Model Training (Deep Neural Network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This Notebook is for Normalised Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "|Date |Date of incident\t|\n",
    "|Province/State |If there is a breakdown in states\t|\n",
    "|Lat |Latitude\t|\n",
    "|Lon |Longitude\t|\n",
    "|Confirmed |Confirmed cases\t|\n",
    "|Recovered |Recovered cases\t|\n",
    "|Deaths |Death cases\t|\n",
    "|Active |Active cases\t|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm      | MSE | RMSE | R2 |\n",
    "|----------------|-----|------|----|\n",
    "|Deep Neural Network | 2.051  | 1.432 | 0.315 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(60000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('darkgrid')\n",
    "sns.set(font_scale=1.2)\n",
    "#sets the default autosave frequency in seconds\n",
    "%autosave 60\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV,StratifiedKFold\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, OneHotEncoder\n",
    "from sklearn.metrics import confusion_matrix, classification_report, mean_absolute_error, mean_squared_error,r2_score\n",
    "from sklearn.metrics import plot_confusion_matrix, plot_precision_recall_curve, plot_roc_curve, accuracy_score\n",
    "from sklearn.metrics import auc, f1_score, precision_score, recall_score, roc_auc_score\n",
    "\n",
    "import tensorflow as tf\n",
    "# from tensorflow import keras\n",
    "# from tensorflow.keras.models import Sequential, save_model\n",
    "# from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Activation\n",
    "# from tensorflow.keras.wrappers.scikit_learn import KerasRegressor, KerasClassifier\n",
    "# from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "#For ANN and DNN\n",
    "import keras\n",
    "from keras.models import save_model, load_model, Sequential\n",
    "from keras.layers import Activation, BatchNormalization, Dense, Dropout, Flatten\n",
    "from keras.optimizers import Adam, Adagrad, RMSprop, SGD\n",
    "from keras.wrappers.scikit_learn import KerasRegressor, KerasClassifier\n",
    "from keras.regularizers import l1,l2,l1_l2\n",
    "\n",
    "#For Tuning\n",
    "from keras.callbacks import EarlyStopping, LearningRateScheduler, ModelCheckpoint, ReduceLROnPlateau, TensorBoard\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pickle\n",
    "from pickle import dump, load\n",
    "\n",
    "pd.set_option('display.max_columns',None)\n",
    "#pd.set_option('display.max_rows',None)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "np.random.seed(0)\n",
    "np.set_printoptions(suppress=True)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"traincovid.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>883.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>1673.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2348.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0        1   22          0.0          0.0         0.0\n",
       "1        1   23          0.0          0.0         0.0\n",
       "2        1   24          0.0          0.0         0.0\n",
       "3        1   25          3.0          0.0         0.0\n",
       "4        1   26          1.0          0.0         0.0\n",
       "..     ...  ...          ...          ...         ...\n",
       "304     11   21       1041.0       1405.0         3.0\n",
       "305     11   22       1096.0       1104.0         3.0\n",
       "306     11   23       1884.0        883.0         2.0\n",
       "307     11   24       2188.0       1673.0         4.0\n",
       "308     11   25        970.0       2348.0         4.0\n",
       "\n",
       "[309 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month          0\n",
       "Day            0\n",
       "ConfirmDiff    0\n",
       "RecoverDiff    0\n",
       "DeathsDiff     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Month        309 non-null    int64  \n",
      " 1   Day          309 non-null    int64  \n",
      " 2   ConfirmDiff  309 non-null    float64\n",
      " 3   RecoverDiff  309 non-null    float64\n",
      " 4   DeathsDiff   309 non-null    float64\n",
      "dtypes: float64(3), int64(2)\n",
      "memory usage: 12.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.265372</td>\n",
       "      <td>15.857605</td>\n",
       "      <td>193.582524</td>\n",
       "      <td>150.488673</td>\n",
       "      <td>1.116505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.943317</td>\n",
       "      <td>8.814048</td>\n",
       "      <td>359.375952</td>\n",
       "      <td>306.753450</td>\n",
       "      <td>1.988445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2188.000000</td>\n",
       "      <td>2348.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Month         Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "count  309.000000  309.000000   309.000000   309.000000  309.000000\n",
       "mean     6.265372   15.857605   193.582524   150.488673    1.116505\n",
       "std      2.943317    8.814048   359.375952   306.753450    1.988445\n",
       "min      1.000000    1.000000     0.000000     0.000000    0.000000\n",
       "25%      4.000000    8.000000     7.000000     6.000000    0.000000\n",
       "50%      6.000000   16.000000    33.000000    28.000000    0.000000\n",
       "75%      9.000000   23.000000   150.000000    96.000000    1.000000\n",
       "max     11.000000   31.000000  2188.000000  2348.000000   12.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Day', 'ConfirmDiff', 'RecoverDiff', 'DeathsDiff'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50, figsize=(20,10))\n",
    "plt.suptitle('Feature Distribution', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(20,5))\n",
    "plt.suptitle('BoxPlot', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hhBCiMtSRI4QQQgghhBCVoY4cIYQQQgghhKgMdeQIIYQQQgghRGWoI0cIIYQQQgghKkMdOUIIIYQQQghRGerIEUIIIYQQQojKUEeOEEIIIYQQQlSGOnKEEEIIIYQQojLUkSOEEEIIIYQQldHLnQElcDr7EQyG5M6GLEpKzDh6dEDubChOJsrF4bAI2s/p7IfNVkD3RQb5VB9SiUeu9jFXy4quSx4Uj6mh680sikdly7fyTRaP9I1cntPrdXJnQZHkLhe5z5+vqNyFy9WyoutSp1y/vnh0vcqmtvyqDZXvCOrIEUIIIYQQQojKUEeOEEIIIYQQQlSGxsgRkk80gGvAj95+H4otDKwFeiD+9X4h+whNGwLTEpsvIekLOc6sh8vtx9f7umFm9MKvWSixZUrURar7nGZd6vcMwTU4BI93CGNsJljNenzV1Y+vj7jF10MxeRo+pn/QD8aoh9vjR3FRnsT/KOVVajMhGAiyv9fpNDjm9rP3LP5nrQbocXnZ9qqzux/dvZ6Re5wPZSoVpbfHmWxHxKSjA7p6vehxeWC3meCwMUAgxTT0wOGekTTKShhgSERepKAFnC4fnMN5sVuMQFCmvEiAOnKE5AsNsPvgMaxqboXXHwBj0GFJfQ2mTrTFfFgbdR+BaS9bOBO+oeDoaaWRL6NeixXrt6d0HFe+muqq0bylDYecA8KvWcpyJ+on1X0WG+vDx7Z39qGjux8bWtrg9Qcwzm5G/dxKrN20S3w9hIg8Daf3zP/tRu2sE7BxOD95Ef+jtDuWQgMuO3tSzD268lsn47GXPmb3v2HeNLzwr71sm7Tw4il45d/tMOi0uPrbU9DVM8genxdlKhWlt8cZbkdSTkcHtO7twbrNI+1H4/xq1JxcKrwzpwda2zjSqCzNfmdOC+xs74lpD5vqqjGjolS1nTl6tZKQPOEa8LONOgB4/QGsam6Fa8Cf0j5C027vdAlKK518tXe6Uj6OK19rN+3CnJoJKV2zUGLLlKiLVPdZbKxHjm3vdLEf8AFgTs0E9kPLaMfy5V9MniLHzKmZwHbi0ikXNRmt3bng9IkJ9yjSiYvs/9hLH8e0Setf/QwXnD4Rc2om4MvD7pjj86FMpaL09jjT7Uiq6XT1etkOWCSddZt3oavXKziNwz3caRzuEZ6GVJwuX0J7uHbTLjhdvqznRSrUkSMkT/T2+9jGK8LrD6DX7UtpH6FpB0MhQWmlk69gKJTycXz5giZ5PsUSW6ZEXaS6z2JjPXJsQnxrkHY9FJMn9hiB588lo7Y78WXCU0bxbRI04X2Ftq0kkdLb40y3I6mm0+PycKbT4/KkncbRPuFpSMXJkxdnCtejNPRqJSFxrLYCAMLXkYnm9Q3BdWxQ6ixJotjCgDHoYhoxxqBDcaExpX2Epq3VaASllU6+tBpNTFpCjuPLV/TrJkKuWSixZUrURar7LDbWI8fyxXda9ZAnzWR5iqQn9Py5REi7I+QexbdJkVdWhbatJJHS2+NMtyOppmO3mTjTKbWa0k6jxCI8Danw5cWewvUoDX0jR0gcxqjHHavfxi8fejPlf4xRuX8bsRbosaS+JubD1ZL6GljNhpT2EZp2xXiroLTSyVfFeGvKx3Hlq6muGltbO1K6ZqHElilRF6nus9hYjxxbMd6KhtpKdt+tOzrQVFedVj0Uk6fIMVt3dGBBVH7yIf5Ha3de33Yw4R7dMG9azP43zJsW0yYtvHgKXv/wILbu6MDx5YUxx+dDmUpF6e1xptuRVNNx2Bg0zo9tPxrnV6OsmBGcRlkJdxrlpcLTkIrdYkxoD5vqqmG3KqMjL4YmFAopYXinrJzOfgSD+VkMDocF3d19cmdDURwOC+5Y/Tb8/tRH4T649Dze8hT6DZ/T2Q+7vSgz9yUyi5Xbh+JCY7hR55vlMdk+QtOGwLTE5ktI+kKOKzTA5fZjwB+A2aATfs1CiS3TDEolHrnax1xtO9K6Lqnuc5p1KWbWSisDa6EBvoAGXzv7xddDMXmKzFrp8YMxDM9ayXP+nIvHUcqr1MIgGAyxv9fptVGzVDLQ6WJ/1mo06Onzsu2VP6hBd+/gyD1WQJuSSZLeXwH1VNZ4zGQ7kuaslaVWU7gTJ3LWyqN9HpRYTOFOXBoTnaRVvtGzVlpN4U6cwic6SRaPyv36gBAivRBgLTDAWmBgfxa1TwppC0orjXxJclww/HPFxNLww0HqD0Riy5Soi1T3Oc26VMToUcREPd6DwHFlRTBqQqMey3sOMXmKO8ZeZEx+/lwipLyGf44oLNbFJBH/M3tPg8B4RxEM0QWZD2UqFaW3xxluR1IWAMosDMosDPtzyoaAciuDcivD/iybYLgtYtsjhXfiRkOvVhJCCCGEEEKIylBHjhBCCCGEEEJUhjpyhBBCCCGEEKIy1JEjhBBCCCGEEJWhjhwhhBBCCCGEqIzsHbmPPvoIs2fPZn/2+Xy48847ceaZZ+Kss87CunXreI8NhUJYuXIlZs+ejTPOOAO/+93vMDQk51Q4hBBCCCGEEJJ5snXkQqEQnnvuOVx77bXw+/3s9ocffhj79+9HS0sLnn/+eWzevBkvvvgiZxobN25ES0sLNm/ejH/+85/YtWsX1q5dm6UrIEThNIBr0I+D3W64PEOARuB+2gwfJ+X1CLnGFPbZta87/byLLXeh5xR7XK7gu/7h7Z09A3D2+3DwSArlE5XmV139yY/hiXvO80p1rzRAv3cInb0efH64X3n3XUn50wFdfV589pULX7s8cA8FEmIkpTYr3Xp6xA2n24dutxcdPYNoP9ynvPtHpDMcf1tbv0J3vxfQjX6I4rF16pj4a1LSc0sLOPt9aOt0wen2ie8JSVEuEpBtHblVq1bhzTffxI033og1a9aw2zdv3oz7778fNpsNNpsN1113HTZs2IArrrgiIY0XX3wRP/rRjzB27FgAwM0334zbbrsNP/3pT7N1GYQokwbYffAYVjW3wusPgDHosKS+BlMn2hIW7I3eb5zdjPq5lVi7aVdmjpP4eox6LVas385/TiHlILSs0sjnaOUn+JxS5lWN+K7/BBt2HziGZ/5vN2pnnYCNLW3CyyeVMuXYt6muGv/8zxf4xqnjYs67bOFM+IaC6d8rDdDe2YeO7n5sSOW6skVJ+dMBrft6sC6qHbph3jSMsZkwsbwQuw8k3rvmLW045ByQtm3gaB+v/NbJeOylj5V3/4h0dEDr3h6s2zwSf43zq1Fzcqm4tdeUQIprUtJzSwvsbO+J+azSVFeNGRWlqa0np6B7Lds3cg0NDdi0aROmTZvGbnO5XOju7sbkyZPZbZMmTUJbWxtnGvv27UNFRQX780knnYSuri709vZmLN+EqIFrwM82mgDg9QewqrkVrgF/0v3m1ExgG7hMHCf19bR3upKeU0g5CC2rdPI5WvkJPaeUeVUjvut3unxY1dyKOTUT2M5U9O+TlU8qZcq179pNu/CdORUJ523vdElyr1wDfrR3uthOUjppZYKS8tfV62U7cZF8PPbSxxjwBtgYib93c2om8OZZqno6p2YC24lLJR2iLl29XvaDPRC+z+s270JXr1fmnIknxTUp6bnldPkSPqus3bQLTpcvpXSUdK9l+0auvLw8YdvAwAAAwGQysdsKCgrg8Xg40xgYGEBBQQH7c+Q4vv352O1FKe2faxwOi9xZUCSDQVz1SLc8I/GYTjpf7+tmG5gIrz+AAX8AFRNL+ffTIKPHSX09wVAoYVv0OYWUg9CySiefo5afwHNKmVehkrWP2W47+K6/p98b3i4iDlMpU759Pb5AwvZgKCTJvfp6X7dkaWXifkmZPyGSxWOPy8uZj0Hf0EiMxP0u+hUvqdqGbLWP+fbsVuL1RsfjZ1+5OO/zUZcXp540JttZk4QU15Sp55aYeNj7dSf39fR7MWWSXXA6SrrXsnXkuEQ6ZV7vSI92cHAQZrOZd//oTlvk//n25+N09iMYzM/3GxwOC7q7++TOhqJEGge/X9zEOXzlKbTRcTr7YbcXpXVfzIwejEEX09AwBh3MBl1Munz7Zeo4qa9Hq4l90T7+nELKQWhZpZNPoeU32jmlzGsq8cjVPsrRdvBdf2kRA8agY39OpXxSKVO+fU1GXcJ2rUYjyb0yM3pJ0srU/ZIyf0Iki0e7jeHMR4FRz8ZI/O+iX+2Sqm3IRvuYb8/ubF+vmHjki78SK6PaeyXFNUn53IoQGw+lFp7rKUrtHmX7XieLR9lnrYxms9ngcDjw+eefs9v2798f86pltMmTJ2P//v3sz59//jkcDgesVmvG80qIklkL9FhSXxPz4XZJfQ2sZkPS/bbu6EBTXXXGjpP6eirGW5OeU0g5CC2rdPI5WvkJPaeUeVUjvuu3W41YUl+DrTs6sKC2MqXySaVMufZtqqvG37a2J5y3YrxVkntlLdCjYrwVDSleV7YoKX8OG4PGuHbohnnTYGZ0bIzE37utrR28eZaqnm7d0YEb5k2TvXxIZjlsDBrnx8Zf4/xqlBUzMudMPCmuSUnPLbvFmPBZpamuGnarMaV0lHSvNaFQSNavot577z3cdNNN2LZtGwDg97//PT766COsWrUKAwMDuP7667Fo0SIsXLgw4di//vWv+Otf/4rHHnsMBQUFuOmmmzBz5kz88pe/TCkP9I2cOv9SlCkOhwV3rH5b1DdyDy49TxHfyAEIzxI14Eev24fiQmO40eQK8/j9Cg1wuTN4nJTXAwHXKKQchvcZ8AdgNujSy7vYchd6TrHHxVHjN3IA+K9/eHu/xw/GoIfb4xdePlFpjrUXwagNJZ0chSvuOc8Lae4VNEC/ZwiuwSF4vEMYY2VSTiuj90ui/AkxajzqwuNXelxeFBcZYTEbUGjUxcRISm2WBPW0sMCAIELweoPw+gNw2BhYC9JrH/Pt2a2Gb+QAsPF31OVFiZUJf7BX60QnEWyd8qDUahJ3TRI9tyLSigdteKyc0+WB3WoKd+JSmegkQopyEShZPCrq1UoAWLp0Ke6//3585zvfQTAYxIIFC/D973+f/f1pp52Gu+++G5dffjm+//3vw+l0oqGhAR6PB5dccgmWLl0qY+4JUZAQYC0whD8wDP8saL9gho8Ti+d6Rj2nkHIY3qdiYmn44ZBO3sWWu9Bzij0uV/Bdf9x2e5Ex9vcC03Q4RvkjyihxH39eSe5VCChi9Chi9DHbFENJ+QsAZRYGZZaov4zzxIigNkuqegoAhbG/JzloOP5OPWlMuB1ReycOSKxTYq5JSc+tYLidZttqMZ04QJpykYDsHblZs2ax38YBAMMwWL58OZYvX865/44dO9j/12q1WLJkCZYsWZLxfBJCCCGEEEKIUsjekVMyq60AjFF8EXl9Q3AdG5QwR4QQQgghhBBCHbmkGKMev3zoTdHHP7j0PAlzQwghhBBCCCFhipq1khBCCCGEEELI6KgjRwghhBBCCCEqQx05QgghhBBCCFEZ6sgRQgghhBBCiMrQZCeEE83YqSCRhTT7fSi2MLAWhO/LqNvM+vBit9H7ZGKB7mTpi8272LwKyVMq+0l1TiHlwHW/4vcRmnam1+jRAm0HetB9zAMzo4PZpIfRqIOr3x9eENpmglYDdPV6YCk0QAMNelwe2G0m2G1GOHuHF2O1hRdjdfVnOf8kdxiAw04v9nS6UFzEwOMbQgGjh16nRf+AD8VFFFMki4bjcXfHMdhtJpSVMoA/xTSiFpq220xw2EQuNC3Vs0GOZwwRjDpyhBPN2KkQGmD3wWNY1dwKrz8AxqDDsoUz4RsKxmxbUl8Do16LFeu3w+sPYJzdjPq5lVi7aVfMPlMn2qRpgDnylZC+yLyLzquQPI22X5bKgeuam+qq0bylDYecA0nLSkjakt1nLlrgo8+PYs0LH7HnvOayqTAx+ph4a6itxDu7OnF29XhsaGljtzfOr8Zzr41cZ+P8arS89wV2H+jNTv5J7jAArXt6sG7zSNwtqK1Ey3sHcMnsSXjl3+3oc/sppkh2cMRj4/xq1FSVCu/M6YDWvRxpnFyaWmdOqmeDHM8YkhJ6tZIQBXMN+NkGFAC8/gDaO10J21Y1t6K908Vum1Mzgf1QHb2PayDVPw0Kz1d8+mLzLjavQvKUyn6ZLAeua167aRfm1Exgf+YrKyFpS3WfuThdPrYTFznnMbc/Id42tLThivMms524yPZ1m2Ovc93mXfjOnIqs5Z/kjsNOL/uBFwjHz8aWNsypmYD1r36GC06fSDFFsoYrHtdt3oXDTq/gNLp6udPo6hWeBiDds0GOZwxJDXXkCFGw3n4f24BGBEOhhG1efwDBUNSfxzTg3KfX7ctYvuLTF513kXkVkqdU9pPqnHz7cF0zNCM/85WVkLSlus9cnC6P4Pvq8QU4t0dfZ2S/6J8zmX+SO3o4YjESX9FxRjFFsoEvHo/2edJOo8clPA1AumeDHM8YkhrqyBGiYMUWBoxBF7NNq9EkbGMMOmg1moRt8T8XFxozlq/49NPNe6p5FZKnVPaT6px8+3Der6i+HV9ZCUlbqvvMxW4zCb6vJqOOc3v0dUb2i/45k/knuYMrFiPxFR1nFFMkG/jiscRiSjuNUqvwNADpng1yPGNIaqgjR4iCWQv0WFJfwzakjEGHivHWhG1L6mtQMd7Kbtu6owNNddUJ+1jNhozlKz59sXkXm1cheUplv0yWA9c1N9VVY2trB/szX1kJSVuq+8zFbjHixiunx5zTVmhIiLeG2kq8+OY+NNRWxmxvnB97nY3zq/G3re1Zyz/JHWWlDBrnx8bdgtpKbG3twMKLp+D1Dw9STJGs4YrHxvnVKLczgtNw2LjTKCsWngYg3bNBjmcMSY0mFArl/XBFp7MfwWBiMTgclrQn/Oju7ksnaxnncFg485gP187H4bDgjtVvw+8fSvnYZNftcFgEpeF09sNuLxpJJzJjlNuH4kIj24COuq3QEJ4FMXqfTMxamSx9sXkXm1cheUqyH199SPucQsqB637F7yM0bQFll0o8JrSPWuBovx9HjnlQwOhRwOjAGHVwuSOzVjLQajSJs1ZaTbAXR81aaQ3PYunqz2CcpkhUDKiA0q9LdDwOzxLY2++FrYiBzz8ExqiHITJrpQJiKhOUfj+llu3rTTcej/Z5UGIxhTtxacxaWWo1hTtx6cxama3nahblY/zzoVkrCVG6EGAtMMBaYGB/BgRsC3Lvk+l8SZJ3sXkVkqdU9pPqnELKged+iU07o4JA5QmlKIl7kBYVx76CU1RexP7/mKLhV3GGAHuREfbIzwEZ8k9yhx8otzKYVjFm+IPdyDcXpWaKKZJl8fEoZk6QAFBmYVBmYdifRZHq2SDHM4YIRq9WEkIIIYQQQojKUEeOEEIIIYQQQlSGOnKEEEIIIYQQojLUkSOEEEIIIYQQlaGOHCGEEEIIIYSoDHXkCCGEEEIIIURlqCNHSK7QAK5BPw52u+HyDAEaCdORKu10CMmDlHmX8jix+RJynDbL90YDDAQD2NV+BLs7jqGrz4vOYwNwun30RCHZZwAOu7x4a0cHDvd50R8I4MseGdspkt+i4rGrzwuIWTdbCc9bohq0jhwhuUAD7D54DKuaW+H1B8AYdFhSX4OpE22prfnCk45Rr8WK9dvTSzsdQq6PY59lC2fCNxRMvVzElmcKeRi1TAWm1VRXjeYtbTjkHMj8vdEAnT0D6DrqwbrNu9g8/GR+Nd7/uBNnVY/HjIpSIJiBcxMSzwC07umJicXG+dUosRjwwNPbcPUlU7PbTpH8xhOPNVWlwteTk+pZTvIG/f2UkBzgGvCzDT8AeP0BrGpuhWsgtdVI+dJp73SlnXY6hFwf1z7tnS5R5SK2PFPJw2hlKjSttZt2YU7NhJTyKZZrwI9AEOwHlcg5/7h5Fy495ySs3bQLTpcvI+cmJN5hpzchFtdt3gXGaMScmglZb6dIfuOLx8NOr+A0pHqWk/xBHTlCckBvv49t+CO8/gB63al9qOZLJxgKJWxLNe10CLk+rn2CoZCochFbnqnkYbQyTSWt6FdvMnlvevt9cLm5y8Y1EN7udHkycm5C4vW4PJyxeLTPA2iy306R/JY0HgWS6llO8gd15AjJAcUWBoxBF7ONMehQXGiUJB2tRpOwLdW00yHk+rj20Wo0ospFbHmmkofRyjSVtKJfucnkvSm2MLAWGTnzYDWHt9utpoycm5B4dpuJMxZLLCYglP12iuS3pPEokFTPcpI/qCNHSA6wFuixpL6GfQBE3qu3mlMbac2XTsV4a9ppp0PI9XHtUzHeKqpcxJZnKnkYrUyFptVUV42trR0p5VMsa4EeOg3QOL86Jg8/mV+Nf7z9OZrqqmG30gcOkh1lpUxCLDbOr4bX58PW1o6st1Mkv/HFY7mdEZyGVM9ykj80oVBIccMnX375ZSxfvjxmm8fjwezZs/Hkk0/GbH/33Xdx7bXXwmQa+YvH9ddfj5tuuknw+ZzOfgSDicXgcFjwy4feTDH3Ix5ceh66u/tEH58NDoeFM4/pXvvvbpoDg17c3wm8viG4jg2KPrfVVgDGmN48Pnesfht+/1DKxyW75w6HRVAaTmc/7Pai1GNHE36/vtftQ3GhMdzwi6ndXOlAorTTIeT60sx7TH0QW55C8yAkX0LSKjTA5U49n6nEY0z7qAEGAgG4+ofQ2+dBicWEYCgAo04f7sSpfKITvjZR7ZR+XaLj0RAem3R0OBbNZj16XR7YCmRqp7JE6fdTatm+XqnisdzOCJ/oJEKqZ3kOy8f456PIWSsvv/xyXH755ezPn376Ka699lrceuutCft++umnuOSSS7By5cpsZpGMwqDXiu4IPrj0vLTOzRj1aXfAVSkEWAsMsBYY2J+lTEeStNMh5PqkzLvY8kwhD5JcTzDL9yYEmLU6nFBRnPggVXknjqiQHyi3MphWMYaNR0tpYfh39OGXZFt8PIqZo0SqZznJC4p/tdLv92PZsmW4+eabMWXKlITff/LJJ5g6daoMOSOEEEIIIYQQeSjyG7lof/3rX2EymbBw4ULO33/yySfo6enBs88+i1AohG9/+9u45ZZbYDTSOA1CCCGEEEJIblJ0R87n8+GJJ57A3XffDY0mcWn7oaEhjB07FrW1tairq0NXVxeWLl0KjUbD+RomH7u9iPd3BkN6RST0PWs58eUx3WtP5/h0yy3dvKeTRrp5j8SjGmInF1G5x0rWPuZqWdF1KVc+xiMful75UTzKh8o3TNEdua1bt0Kr1eL888/n/L1er8ef//xn9ucTTjgBTU1NWLFiRUoduWSTnYiZ8CKa0gdjJpvsJN1rT+f4dMpNirwD4vMv22QnJG35NIBa9GD+qONzsazouuRB8Zgaut7Mn08Iikd55Fv5qm6yk4jXXnsN3/72t6HVcg/lO3z4MJ566qmYVyn9fj8YRvhUr5nkHwqm9ReDdGZvTGXmRvqrBiGEEEIIIeqi6I7czp07sXTpUt7fFxcX45VXXoHJZMJNN92Ejo4OrFmzBldeeWUWc8kvnZkbgfRmTxQ6c6PBoOf85km1MzcSQgghhBCSB9KetbKnpweHDh1CZ2dnzD8pfPXVVygrK4vZ9vLLL+O0004DADAMg8ceewzbtm3DrFmzcPXVV+OSSy7Bj3/8Y0nOT4giaADXoB8Hu91weYaAxOGi6R0Xv5+W4zixeRBKSPpc+2SybMSeT8qyEpKHbN8vHdA/FMDH7Uewu+MYDru8cPmG0DPgzUxsEJKMATjs8uKtHR043OdFfyAA0NrJRC5R8djV5xUXi1rA2e9DW6cLTrdPBfPLEzmJ/kbugw8+wLJly9DV1RWzPRQKQaPRYPfu3WlnrrW1NWFb/BpzU6dOxdNPP532uQhRJA2w++AxrGpuhdcfAGPQYUl9DaZOtCVfW0bocRz7NdVVo3lLGw45B8AYdFi2cCZ8Q8HU8yDlNfLsY9RrsWL9dunKJsk+gspB7P1KIZ/R1zzObkb93Eqs3bQr/XIRQgd0HnGj66gX6zaPnLNxfjVKrUb8u/ULnFY1VrrYICQZA9C6pychFstKTRjvMItbw4sQsXjisaaqVHgsaoGd7T0xbXpTXTVmVJTSOp2Ek+h+/m9+8xuccsopeOaZZ/Dyyy+z/1555RW8/PLLUuaRkLzlGvCzH+QBwOsPYFVzK1wDyZ8KQo/j2m/tpl2YUzOB/bm90yUqD1JeI98+7Z2ujJQN1z5CykHs/Uoln9HXPKdmAvvAT7dchOjq9UKj0bEfVCLpr9u8CxqNFt864wRJY4OQZA47vdyxCC0OO70y547kG754TCUWnS5fQpu+dtMuOF2+jOSZqJ/ob+QOHDiARx99FBMnTpQyP4SQKL39PrZBj/D6A+h1+2At4H9nQ+hxfPtFvx4XDIVE5UEoIXnl2ycYCiU9LtXzJdtHSDmIvV+p5DPmmjWQrFyE6HF5EApxn9M14INWo5E0NghJpsfl4a5v/V5oNEC5VRkTn5H8wBePR/s8gmPRyZOG0+WBvYjWRyaJRH8jV1VVhQMHDkiZF0JInGILA8agi9nGGHQoLkzeoAs9jm+/6NfitBqNqDwIJSSvfPto49aXlKpsuPYRUg5i71cq+eS6ZiH7SHG/7DYTb76sZiOKixhJY4OQZOw2E3d9K2JQYjHJlCuSr/jiMZVY5EvDbqV4JtxS6si9+eab7L/zzjsPt99+O/785z/j9ddfj/ndm2+Kn6mREDLCWqDHkvoatmGPjHeympN/2yH0OK79muqqsbW1g/25YrxVVB6kvEa+fSrGWzNSNlz7CCkHsfcrlXxGX/PWHR1oqquWpFyEcNgYhIIBNM6PPWfj/GqEQkH8a9sBSWODkGTKShnuWEQQ5Xb6No5kF188phKLdosxoU1vqquG3Up/HCPcNKFQSPCQ9ClTpghLVKLJTrIl2YKO6S4fkO7xYhc8FJr3ZMsPyHXt6Vw3IM19u2P126IWBE+Wd9ELgmvC46V63T4UFxrDH5KF1Fqhx8XvV2iAyx13HETmQSgheeXaR2y+eM4Xs8io2POJvV9C8xmfB5H3S/SCtzqg3xuAe2AIR/s8KLaYUMDoEAgGoIdW+tjIslxdaFbp1yU6Hg3hsUlH+zwosZhQaNajqECX8xOdKP1+Sk01C4LHxWO5nUk9FrXhsXJOlwd2qynciaOJTmLkY/zzSWmM3GeffZZ2ZgghKQoB1gLDyJgjoR+ShR4Xv1+Q+zhReRBKSF559slY2Yg9n9j7lUI+Zb1fAaBIr8OkiuK4B6le+nMRMhp/eCzctIoxI/GY4504omDx8SgmFoOAvcg4MiaOOnEkCdFj5BYtWgSXy5WwvaenB3V1dWllihBCCCGEEEIIv5S+kXvvvfewb98+AOF15DZs2IDCwsKYfT7//HMcPHhQuhwSQgghhBBCCImRUkeuuLgYTz75JEKhEEKhENavXw+tduRLPY1GA7PZjNtuu03yjBJCCCGEEEIICUupI1dVVYXXXnsNAPDDH/4QjzzyCGw2W0YyRgD/UFDwgNtckq/XTQghhBBCiFCiFwR/+umnAQB+vx+BQADxk18WFBSklzMCg16b1syPapXOdQPqvnZCCCGEEEKEEN2R27lzJ+68807s3bs3ZnsoFFLd8gOEEEIIIYQQoiaiO3L33nsvLBYLHn30URQVFUmZJ0LIaCLri/X7UGxhYC0IV+WYbWZ9eH2xZPsIPS7Z+nOj7Zep6+PbJiYPw+f7el83zIw+tWuOz4PY8sv0/ZKKDjg2MISv93Wz5zQZdQghgGBAA7tleM0jqeMjk/FG1EsL9HkC6P7CCZ8vgEHvEMyMHtACJoMOpRYjEBgljbjY0mqAHpeX4oykbngdud0dx2C3mVBWKmIdOR3Q1etFj8sDu80Eh40ZPYZJ3hLdkWtra8PGjRtRVVUlZX4IIaPRALsPHsOq5lZ4/QEwBh2W1NfAqNdixfrt8PoDGGc3o35uJdZu2sW7TyrHTZ1oi/0ww5OHhP0ydH2MQYdlC2fCNxRMPw9CryWNch8trYzfL6nogM87+9Db58NjL33MnvOGK6ahuMiI9o6jKCstwozJpdj9hYTxkcl4I+qlBQ529aN/cAjOY16sf/UzNj4aaithMupgKTLilBOK+T8Ic8RWQ20l/v7OfvS5/RRnRDgD0LqnB+s2j7THjfOrUVNVKrwzpwNa93KkcXIpdeYIJ9HryJ100kno6uqSMi+EEAFcA372QwcAeP0BrGpuRXuni902p2YC++Geb59UjnMN+AXlIX6/TF2f1x9Ae6dLkjwIvZZ0yn20tDJ9v6TS1euFzx9iO3GRcz724sfw+UOYfnI51m7aha5er6Txkcl4I+rldPmg0WjR0eVmO3FAOD42tLThmNuPjsNudPV6edPgiq0NLW244PSJFGckJYedXrYDBoRjad3mXTjs5I+/eF293Gkki2GS30R/I/fDH/4Qd955J66++mqceOKJMBgMMb8/7zyacIKQTOjt97GNfITXH0AwesIhDUbfJ4Xjet0+WAtG6jhfHuL3E0PQ9QEIhkKS5EHotaRT7qOmleH7JZUelweD3gDnOQd9Qwi5wvekx+WRNF+ZjDeiXk6XB/6hIG9bEKkrPS4PyiwMZxp8sQXNyP9TnBEh+Nq9o30elFu5409oGslimOQ30R2522+/HQCwYsWKhN/RZCeEZE6xhQFj0MU09oxBB61GE7Of0H2EHFdcaBSUh/j9xBB6fVqNRpI8CL2WdMpdaFqZul9SsdtMcMLLec4Cox4l1vB12a0mSfOVyXgj6mW3mTDgHYK2m7stiNSVUquJNw2+2Iq8SklxRoSy27jbvRILf/wJTSNZDJP8JvrVys8++4z3H3XiCMkca4EeS+prwh82AHa8UMV4K7tt644ONNVVJ90nleOsZoOgPMTvl6nrYww6VIy3SpIHodeSTrmPllam75dUHDYGRr0GN8ybFnPOG66YBqNBg4/2HkZTXTUcxYyk8ZHJeCPqZbcYEQoGMaGsEAsvnhITHw21lbAVGjChvBBlxfzfZHDFVkNtJV7/8CDFGUlJWSmDxvmx7XHj/GqU24V/k+awcaeRLIZJftOE4heAS9G7776LvXv3IhgMoqKiArNnz4ZeL/qLPlk4nf0IBhOLweGwpL2emVzHCz3WYNDD7x+S9NzpHi/nuSPH37H6bc5yEXJsd3cf5++ELnLudPbDbi/iTQfAyCxrbh+KC43sB42YbYWG8GyGyfYRelyyWRdH208MIdfHty2NWSsH/AGYDbrUrjk+D2LLL9P3K04q8RjTPg7PWjnoGQrP8lfEwMQMz1o5pIHdGjdrpVTxkcl4i+JwWJLXPZVS+nWJjsfhWSs9/gA7a2UBo4dGCzB6XTgehc5aORxbWq0GPX3ejMZZupR+P6WW7esVHY/Ds1Ye7fOgxGIKd+LSmLWy1GoKd+JoopMY+Rj/fET3uLq7u3HTTTfh008/xXHHHYdQKITOzk5MmjQJTz31FOx2u9ikCSGjCQHWAsPIuI3h50jMtqCAfVI4TmgeJCHk+pJsE3u+ioml4YdDitcsSfll+n5JJQDYGD0mTyiJe5Dq2fxE8iFpvjIZb0S9goDFqMNJxxVzf7AT8gE4PrYAFDF69neECOYHyq0MplWMCcejmHlyAkCZhRkZE0edOJKE6Fcr7733Xuh0Orz22mt49dVX8c9//hOvvfYaiouLcd9990mZR0IIIYQQQgghUUR35LZu3Ypf/epXKC8vZ7eVl5fjtttuw1tvvSVJ5gghhBBCCCGEJBLdkWMYBpq42dOA8IyVgQB9D0wIIYQQQgghmSK6I3fOOefgvvvuw5EjR9htR44cwf33349vfvObkmSOEEIIIYQQQkgi0ZOd3HrrrfjRj36Eb33rWxg/fjwA4KuvvsKUKVNwxx13SJZBQgghhBBCCCGxRHfkHA4HXn75Zfz73//G3r17UVBQwC4/IIXnn38ey5cvh9E4shDnr3/9a8yfPz9mv87OTvz3f/83WltbYbfbceedd+K8886TJA+EEEIIIYQQokQpdeRS6aS9++67KWcm2qeffoof//jHWLZsWdL9fv7zn6Ompgbr1q3Dhx9+iJtuugkvvfQSjj/++LTOT4iiRdY96veh2MLAWhCuyjHbzPrw+mLR+wiZSpsr7WTroCU7X3ye+LZJvf5cqnnn2i/baQk9n9hrlpjPF4DT7YN3KIBgEPB4h2ApNEKrAVxuH8yMARazAQiF4HL7wBj1cHv8KC6KyqNMeSe5x+cL4Ei/D0f7vLAUGmA06DDgGcKAx49iC4OiAj3Meh3FF8ms4XXkdnccg91mQllpeuvI2W0mOGwi15Gj9jUvpNSRc7lcCAaDOO200zB37lyUlJRkKl/45JNPsGjRoqT77N+/Hx9//DGefPJJGI1GzJ49GxdccAGef/553HLLLRnLGyGy0gC7Dx7DquZWeP0BMAYdli2cCd9QMGZbU101mre04ZBzAIxBhyX1NZg60Za8IedIm/O4uP3G2c2on1uJtZt2xRxn1GuxYv32UbeNmi+R5SIk7zH7SZlWSOJ90rlmqemAdz/uRJ/bB48vgA0tbbAUGvDdb1Zg/aufsXlpqK2E2WRAIBjEn/++OzaPJ9iw+4AMeSe5Rwe81dqBtZt2wVJowJXnT2bjMro9dNhMGG83U3yRzDAArXt6sG7zyHOwcX41aqpKhXfmdEDrXo40Ti5NrTMn17OBZF1Kk5288847+N3vfgebzYaHH34YL774IgYGBnDOOedg/vz5Mf/SEQgEsGfPHrz00kv45je/idraWvzxj39EKBQbfe3t7Rg3bhzMZjO77aSTTsKePXvSOj8hSuYa8LONMwB4/QG0d7oStq3dtAtzaiawP69qboVrIPnThCttruPi95tTM4HtxEUf197pErRttHyJLRcheefaL9tpCT2f2GuWWlevFx1dbhxz+9kPyxecPpHtxEXysqGlDUf7vHC5/Ql5dLp8suSd5J6uXi/b/lxw+sSYuARG2sNACBRfJGMOO71sBwwIx926zbtw2OkVnEZXL3caXb3C0wDkezaQ7EvpGzmbzcZ21AYGBvDGG2+gpaUFK1euxMknn4yLL74YtbW1OO6449LKVE9PD6ZNm4YrrrgCjzzyCNrb27F48WIUFhbiBz/4Abuf2+2GyWSKObagoAAejyel89ntRby/MxhEDyOU/Xihx/Ltp4a8Z+r4dNJwOCxpnTcSj3zpfL2vm22cI4KhUMI2rz8AaGJ/HvAHUDGxlPfcXGlzHZewnwacxwXj/vjCt220fAkhOu9R+wHhcpcirch+Uu2TzjWng6t9/OwrF3sf2fMLjIHI9qP93oznXax067BS5cJ18cVjdBzytYeuAR8MBq3s8SWVXLifqVDi9UbH4+6OY5xxd7TPg2kVYwSlFxPL0Wm4vDj1JGFpANl5NshNifEgB9Gfds1mMy699FJceuml8Pl8ePvtt/HPf/4TjzzyCE488UQ8//zzojPlcDjwzDPPsD9PnToVV199Nf75z3/GdOTMZjO83ti/UgwODsZ8QyeE09mPYDDxw4bDYYHfP5Ri7mPJebyQYw0GPe9+Ss97Jo9PJ43u7j7O7UIbHaezH3Z7EW86ZkYPxqCLaaS1Gk3CNsYQOx6EMehgNuh40+VLm+s4vv3if9bGrTXJt220fAmRbt7NBh2A8P2TIq3IflLtk841c0klHuPbR7uNwcGvNez5IucXEgOR7SVFjOi8Z5LDYZH1/Jmi9OtKNx6jY4mvPbSajbLHl1SUfj+llu3rFROPdpuJM+5KLCbBeY+PZTYNK5PS9afzbFCDfIx/PqLXkYv2+eef45NPPsHu3bsxODiIgoKCtNLbu3cvVq1aFbPN7/eDYZiYbRUVFejs7Iz5Bu7zzz/H5MmT0zo/IUpmLdBjSX1NuKOGcONcMd6asK2prhpbWzvYn5fU18BqNqScNtdx8ftt3dGBprrqhOMqxlsFbRstX2LLRUjeufbLdlpCzyf2mqXmsDGYUFYIW6EBDbWVYAw6vL7tIBZePCUmLw21lSixMLAWGhLyaLcaZck7yT0OG8O2P69vOxgTl8BIe6jTgOKLZExZKYPG+bHPwcb51Si3M6McOcJh406jrFh4GoB8zwaSfaK+kQsGg/jggw/w2muvYcuWLeju7sbs2bPxgx/8ABdeeCFKS9P72tZqteJPf/oTxo4di6uuugqffvopnn76adx5550x+5100kmYMmUKVq5ciV/84hfYvn07XnvtNWzcuDGt8xOiaCFg6kQbHlh8NnrdPhQXGtnGOWZboQGTxp4eu89og5z50o4/jmu/QsPoeeLbJsXg63TyHr9fttMSej6x1yy1ADB72nh83tkL71AAv7r2TAwOz1r5m8az4HL7UcDoo2Yp9eGextnhWSsjeQzKlHeSewLAuTUTcJyjaGTWSr0OFccXY8AzhOIiI4rMNGslyTA/UFNVirtuOAtH+zwosZjCnbhUhqUFgJqTS3H3T85Cj8uDUqsp3IlLddZKuZ4NJOtS6si1tLRgy5YteOONN+D3+3HuuefiF7/4Bc477zwUFfGPM0tVeXk5Vq9ejQcffBD33XcfSkpKsHjxYlxyySV4+eWXsXz5cuzYsQMA8PDDD+POO+/E7NmzUVJSgnvvvReVlZWS5YUQRQoB1gIDrAUG9mcgbluQex+xaY+6H8/5hG6ThNi8c+2X7bSEnk+q49JkNOpgLzRy/s5RFPvX4yIm/KixFw3vH8mjTHknucdo1GFMkRFjikZisjT+2weKL5JpfqDcymBaxZjwq39i5hYJAGUWBmUWhv1ZFGpf80JKHbmbb74ZBoMBZ555Jr75zW/CaDSit7cXL730UsK+0WPZxJg9ezY2bdqUsP3yyy/H5Zdfzv48btw4PP7442mdixBCCCGEEELUJKWO3Pjx4wGE12/bv38/734ajSbtjhwhhBBCCCGEEG4pdeRef/31TOWDEEIIIYQQQohAksxaSQghhBBCCCEke6gjRwghhBBCCCEqQx05QgghhBBCCFEZUevIEUJkpgFcA3709vtQbGHC63XFTy3MtQ8EbDPr4XKPkrbYPKWT90xOnTx8vq/3dcMcs/6ZRNeTQh5GvV9KmkJaA/R7hnBswI8BzxBKLAyAIDTQhteMKxq5jn7PEFyDQ/B4h2ArMmIoGABCWvT2eVFsYWAp1GPIF1TutRLlG47HoVAIQ4EQvL4ABr1DKGD0MJv0KCkMr6PlGvDj2IAPBr0eg14/7FYTxRuRBtezJNW40gOHe7zocXlgt5lQVsIAQ+LzknabqgO6ekfy47CJWNdOCzhdPjiH07BbjEBQRF6G09n7dSdKLYz4dHIIdeQIURsNsPvgMaxqboXXHwBj0GFJfQ2mTrSNNNI8+xj1WqxYv53dtmzhTPiGgjH7NdVVo3lLGw45B7jTFpunNPM+ah4kLs/4shJ9PZnOg0yCwRDaO/vQ0d2PDS1tbB4b51fjuddG4mfZwpkAMOp+8T8r6VqJ8kXi0R8IIBgCnMe8WP/qZ2y8NdRWYkJZIXz+EJ75v92onXUCNkbFI8UbSZsUzwM90NrWg3Wbd8W0lTWVpal15qR6NumA1r0c+Tm5VHhnTgvsbO/B2k27Yj5nzKgoTa0TJlU6OYZerSREZVwDfrZxBgCvP4BVza1wDfhH3ae90xWzrb3TlbDf2k27MKdmAm/aYvOUbt5Hy4NYQstK7PVkOg9yOXTEjfZOF9s5A8J5XLc5Nn7aO12C9ov/WUnXSpQvEo+FBUZ0dLnZThwQjqcNLW3wDwGrmlsxp2YC24mL/J7ijaRLiufB4R4v22mKpLFu8y4c7vFmPS9A+Js4rvx09QrPj9PlYztfkTTWbtoFp8uXUl6kSifXUEeOEJXp7fexDVmE1x9Ar9s36j7BUOyf4oKhEOd+0PCnLTZP6eZ9tDyIJbSsxF5PpvMglx7XoKD4CYZCguMs1bgjJCISjy63jzfeBn1DbJxls40h+UGK50GPy8OZxtE+T9bzkiw/PS7h+XHypOFMIQ0p08k11JEjRGWKLQwYgy5mG2PQobjQOOo+Wo0mZptWo+HcL/rVi/i0xeYp3byPlgexhJaV2OvJdB7kUmotEBQ/Wo1GcJylGneERETi0Vpk5I23AqOe3Z7NNobkBymeB3abiTONEosp63lJlp9Sq/D88KVhTyENKdPJNdSRI0RlrAV6LKmviflAsqS+BlazYdR9KsZbY7ZVjLcm7NdUV42trR28aYvNU7p5Hy0PYgktK7HXk+k8yGXcmEJUjLeiobYyJo+N82Pjp2K8VdB+8T8r6VqJ8kXi0T3gw4SyQiy8eEpMvDXUVsKgB5bU12Drjg4siItHijeSLimeB2UlDBrnVye0leWlTNbzAgAOG3d+yoqF58duMaKpLjaNprpq2K0pdiolSifXaEKhUN4P7XU6+xEMJhaDw2HBLx96U3S6Dy49T7bjhR5rMOjh9yeOoFVD3jN5/B2r3+YsFyHHdnf3cf7O4bAISsPp7IfdXsSbDoCR2ajcPhQXGsONM9/Mj9H7QMC2QkN41spkaYvNUzp5z8KslQP+AMwGHX9Zib2eFPIw6v2SqBxSiUe+9rH7SF/irJWaIDSh4Vkro66Dd9bKfi+Ki6JmrczWPefhcFiS1z2VUvp1SRmP8bNWmhg9Chk9Sop4Zq20MLLFm1hKv59Sy/b1io5HrmeJyFkrj/Z5UGIxhTtx6cxamW6bGjVrZanVFO7EpTNrpdUU7nylMWvl0X4vSooY8emoTLJ4pFkrCVGjEGAtMMBaYGB/FrrPqNuCAtIWm6c0854xw+ermFga/rCQpKwyltcU7pdihIAiRo8iJvFRYi8ysvsA/PuVW6P+slugU+61EuUbjkdWYdzvhz/wxdQpMOyxhKSN51mSkqFwu8i2jWI6cVF5SbtNDQBlFgZlFob9OWXB8DOBfS6I7XwNpzNlkj1cvnnQiRsNvVpJCCGEEEIIISpDHTlCCCGEEEIIURnqyBFCCCGEEEKIylBHjhBCCCGEEEJUhjpyhBBCCCGEEKIy1JEjhBBCCCGEEJWh5QcIyXeRtWb6fSi2MLCa9eF15CI/F4SbiZh9CvRZWddt1PNlMu9ceeBKS0j6QtISmnex1yMlLdB28Ci6jw7AUVKAUDCEI8c8sNtMsFvi1vXRAG5fAH0DQ3C5vSi1meAfCsI96IeZ0cNk1ME/FEABYxgpA7cPhSYDvL4hFJmN8lwjUQcD0NXjRVunC4UFBhQVGuD3B+Hs9WBMMUc8EpJJBuCw04vdHcdgt5lQVsoA/hTTiF5zjatNFUroczQb+ZHqmqQgVbkoJD/UkSMkn2mA3QePYVVzK7z+ABiDDk111Wje0oZDzgEwBh2WLZwJ31AwZp8l9TWYOtGWmcaPI0+c58tk3nnyYNRrsWL9dnaboPQ50uI6Tkjes34vuGiBne09WLtpFyyFBlx29iRsaGmLuY4ZFaXhh7QGONjlxpFjHjz20sec+y+8eApKLEa8vbMdZ1WPx9pNu9jfLaitRMt7B3D1JVOze41EHQzAzrYeNmbG2c248lsn47GXPuaOR0IyyQC07unBus0jbVjj/GrUVJUK78xFta9pxbDQ52g28iPVNUlBqnJRUH7o1UpC8phrwM82IADg9QewdtMuzKmZwP7c3ulK2GdVcytcA6n+mVF8nrjOl8m88+WhvdMVs01I+lxpcR0nJO/ZvhdcnC4f+0C+4PSJbKcs+jqcLh977QPeAPvBmmv/9a9+hs4jA7j0nJPYdCO/29jShjk1E7J+jUQdDju9MTEzp2YCG2tAYjwSkkmHnV62EweE42/d5l047PQKTiO6fY2kISaGhT5Hs5Efqa5JClKVi5LyQx05QvJYb7+PbUAivP4AoBn5ORgKce7T685MI8yXp/jzZTLvfGkHQ7F/IhOSPldafMeNlvds3wsuTpdnJA8acObH6fIACF/7oHdo1P2DoRBcA/z3M9vXSNShJzoWgVHjkZBMSohHhOPvaJ/w+HPypJFqDAt9jmYjP1JdkxSkKhcl5Yc6coTksWILA8agi9nGGHQxX+lrNRrOfYoLjVnNU/z5Mpl3vrS1Gk3MNiHpc6XFd9xoec/2veBit5li8sCVH7vVBCB87QUm/aj7azUaWM1G3jLJ9jUSdYiPRSB5PBKSSXzxWGIRHn98aaQaw0Kfo9nIj1TXJAWpykVJ+aGOHCF5zFqgx5L6GrYhiby7vrW1g/25Yrw1YZ8l9TWwmg1ZyxPX+TKZd748VIy3xmwTkj5XWlzHCcl7tu8FF7vFiKa6ajAGHV7fdhANtZUJ12G3GtlrNxt1uGHeNN79F148BePHmPGPtz9n0438bkFtJba2dmT9Gok6lJUyMTGzdUcHG2tAYjwSkkllpQwa58e2YY3zq1FuZwSnEd2+RtIQE8NCn6PZyI9U1yQFqcpFSfnRhEIhRQ4ff/vtt/E///M/+OKLL2C323HdddehoaEhYb93330X1157LUymkZ799ddfj5tuuknwuZzOfgSDicXgcFjwy4feFHcBAB5cep5sxws91mDQw+8fkvTc6R4v57kjx9+x+m3OchFybHd3H+fvHA6LoDSczn7Y7UW86UguMmOS24fiQiOshQZ25sDiQiPboMTsYzZkZ9bK0c4ncd4dDstIuXPlgSstIekLSUto3lO4nmRSiceE9lELHHUPhWetLC5AKDQ8a6XVFH44881aOeBFqXVk1sqC4VkrhwIBFBj0MWVQaDLA6x9CkcmQ+XiLEhMDOUTp1yU6Hodnrezt98Fs0sNiNsA/FITzmAdjuOIxRyj9fkot29ebTjwednpxtM+DEosp3IlLZ9bKdGJY6HM0G/mR6pqGpRUPUpWLVATkJ1k8KnLWykOHDuHmm2/GAw88gAsvvBAff/wxrr/+ehx33HGYM2dOzL6ffvopLrnkEqxcuVKm3BKiciHAWmCAtWD4L0DBuJ+HGxSubVnLE9/5Mpl3njyISl9IWinkPav3gksQqJxYgpKCkUdIaeRVkPiHcwgoNOhQaNNhrC3qL9M2jtdq4ssARjYNQjj5gTILg1NPGjPywY4BxvDFIyGZ5AfKrQymVQzHo5g5NIKAvcgIe1GaMSz0OZqN/Eh1TVKQqlykkmZ+FPlq5VdffYXvfOc7qK2thVarxfTp03HmmWdi+/btCft+8sknmDp1qgy5JIQQQgghhBB5KPIbuTPOOANnnHEG+3Nvby+2bduGefPmJez7ySefoKenB88++yxCoRC+/e1v45ZbboHRSO/EE0IIIYQQQnKTIjty0fr6+nDjjTdixowZuPDCC2N+NzQ0hLFjx6K2thZ1dXXo6urC0qVLodFocOutt8qUY0IIIYQQQgjJLEV35Pbv34/Fixdj8uTJWLFiBbTa2DdB9Xo9/vznP7M/n3DCCWhqasKKFStS6sjZ7UW8vzMY0isiOY8XeizffmrIe6aOTycNoYOk+UTiMd10iDhU7rGStY+5WlZ0XcqVj/HIh65XfhSP8qHyDVNsR+6DDz7A4sWL0dDQgJ///OfQxK3fBACHDx/GU089FfMqpd/vB8MIn+oVSD5rpZiZC6PJebyQY/lmrUz33OkeL3e5p5OG6matJKx8mgkurVkrkbtlRdclD4rH1ND1Zv58QlA8yiPfyld1s1YePHgQjY2NuOWWW/DDH/6Qd7/i4mK88sorMJlMuOmmm9DR0YE1a9bgyiuvzGJuCSGEEEIIISS7FDlr5V//+le43W784Q9/wGmnncb+e/DBB/Hyyy/jtNNOAwAwDIPHHnsM27Ztw6xZs3D11VfjkksuwY9//GOZr4AQQgghhBBCMkeR38jdfvvtuP3223l/f/nll7P/P3XqVDz99NPZyBYhyhZZVLLfh2ILA2uBXth6JPHHmfXhRZmj0wFH2lzb+BbCliItsWWQQj6/3tcNM6MXf36pr1nsPc2k4QVvd3ccg91mQlmpiAVvCZEKxSNREopHkmWK7MgRQlKkAXYfPIZVza3w+gNgDDosqa/B1Im25B/8444bZzejfm4l1m7aFZOOUa/FivXb2W3LFs6EbyiY/Hw8eYpPi2/bqHkXUAbp5DPl86dwzVnPl1QMQOueHqzbPBIfjfOrUVNVSh9WSPZRPBIloXgkMlDkq5WEkNS4BvzsB34A8PoDWNXcCtdA8qdH/HFzaiawnbjodNo7XTHb2jtdo56PL0/xafFtGy3vQsognXymev5Urjnb+ZLKYaeX/ZASydO6zbtw2OmVLU8kf1E8EiWheCRyoI4cITmgt9/HPjwivP4Aet2+1I7TgDOdYCj2K6BgKDTq+fjyFJ8W37bR8j7qtaSZz1TPnywtKctPTL6k0uPycObpaJ9HphyRfEbxSJSE4pHIgTpyhOSAYgsDxqCL2cYYdCguNIo6Lv5nbdzyH1qNZtTz8aUdnxbfttHyLuRa0slnqudPlpaU5ScmX1Kx20yceSqxmGTKEclnFI9ESSgeiRyoI0dIDrAW6LGkvoZ9iETGU1nNhpSO27qjA0111QnpVIy3xmyrGG8d9Xx8eYpPi2/baHkXUgbp5DPV86dyzdnOl1TKShk0zo+Nj8b51Si3p7Z2JyFSoHgkSkLxSORAk50QkgtCwNSJNjyw+Gz0un0oLjSGP/CPNikG13GFhsR0AGHbQqOkLTatdMoghXwO+AMwG3Tizi/1NYu9p5nkB2qqSnHXDWfhaJ8HJRZT+EMKDeQncqB4JEpC8UhkQB05QnJFCLAWGGAtMLA/izouyJ2O0G1C8iQqLTHXkmI+KyaWoru7L73OkpTXLPaeZpIfKLcymFYxJlxW9CGFyInikSgJxSPJMnq1khBCCCGEEEJUhjpyhBBCCCGEEKIy1JEjhBBCCCGEEJWhjhwhhBBCCCGEqAx15AghhBBCCCFEZagjRwghhBBCCCEqQx05QghJhwZwDfpxsNsNl2cI0Ig8TiswHbHHScUAHHZ58daODnT1eQH51icnhOKRKAvFI8kyWkeOEELE0gC7Dx7DquZWeP0BMAYdltTXYOpEW/I13ziOa6qrRvOWNhxyDvCnI/Y4qRiA1j09WLd5F3v+xvnVqKkqpfWSSPZRPBIloXgkMqBv5AghRCTXgJ/tVAGA1x/AquZWuAaSP7W5jlu7aRfm1ExImo7Y46Ry2OllP6REzrdu8y4cdnozcj5CkqF4JEpC8UjkQB05QggRqbffxz60I7z+AHrdPlHHRb8WyZWO2OOk0uPycJ7/aJ8nI+cjJBmKR6IkFI9EDtSRI4QQkYotDBiDLmYbY9ChuNAo6rjo1yG50hF7nFTsNhPn+Usspoycj5BkKB6JklA8EjlQR44QQkSyFuixpL6GfXhHxqhZzclHuHMd11RXja2tHUnTEXucVMpKGTTOr445f+P8apTbmYycj5BkKB6JklA8EjnQZCeEECJWCJg60YYHFp+NXrcPxYXGcCdqtIlGuI4rNGDS2NOTpyP2OKn4gZqqUtx1w1k42udBicUU/pBCA/mJHCgeiZJQPBIZUEeOEELSEQKsBQZYCwzsz6KOCwpMR+xxUvED5VYG0yrGoLu7jz6kEHlRPBIloXgkWUavVhJCCCGEEEKIytA3cgC02kyvoEvySbrxFDme4lIeVO6xkpVHrpYVXZdy5WM88qHrlR/Fo3yofMM0oVAo0y/iEEIIIYQQQgiREL1aSQghhBBCCCEqQx05QgghhBBCCFEZ6sgRQgghhBBCiMpQR44QQgghhBBCVIY6coQQQgghhBCiMtSRI4QQQgghhBCVoY4cIYQQQgghhKgMdeQIIYQQQgghRGWoI0cIIYQQQgghKkMdOUIIIYQQQghRGerIEUIIIYQQQojK6OXOgBI4nf0IBkNyZ0MWJSVmHD06IHc2FCcT5eJwWATt53T2w2YroPsig3yqD6nEI1f7mKtlRdclD4rH1ND1ZhbFo7LlW/kmi0f6Ri7P6fU6ubOgSHKXi9znz1dU7sLlalnRdalTrl9fPLpeZVNbftWGyncEdeQIIYQQQgghRGWoI0cIIYQQQgghKkMdOZIdGsA16MfBbjdcniFAI3eGCCGiaYCvuvqpPhNloHgkuYQ+L5EU0GQnJPM0wO6Dx7CquRVefwCMQYcl9TWYOtEG5OccM4SoF9VnoiQUjySXUDyTFNE3ciTjXAN+tlECAK8/gFXNrXAN+GXOGSEkVVSfiZJQPJJcQvFMUkUdOZJxvf0+tlGK8PoD6HX7ZMoRIUQsqs9ESSgeSS6heCapUs2rlf/617/whz/8AR0dHbDb7bj++uvR0NAAn8+H3/72t3j11Veh1Wrx4x//GI2NjXJnl0QptjBgDLqYxokx6FBcaJQxV4Qon9VWAMYovpn2+obgOjYoYY6oPhNloXgkuYTimaRKFR25rq4uLFmyBI888gjOO+88fPLJJ/j+97+P6upq/N///R/279+PlpYW9PX14frrr0d5eTmuuOIK8SfUhL/e7u33odjCwFqgz713k4ev8et93TAzeuHXKKJsrAV6LFs4E+2dLgRDIWg1GlSMt8JqNox+TrH3Ih/uIcl5jFGPXz70pujjH1x6noS5CbMW6nHntWfC5Q7/5dhsMkCrAaDRhAflR+oZ1UGSBdYiPf77x9/AkV4PTIwO3UcHMbbUDGuRAa5+ij+SZXrgcI8XuzuOwW4zoayEAYaEH24t0GNJfU3CGDlBn5fiURucF1TRkSsrK8O7776LoqIiBINB9Pb2QqfTobCwEJs3b8b9998Pm80Gm82G6667Dhs2bBDfkcuHgaZirzGNsvENBbH5jX0xxykxn4SQJLTA7gO96OoZxIaWNrZ+Lby4Ck++8gmuvmRquJ6B6iDJAh2wc28P1m7axcZZQ20l+j1+7D7Qi1Ubd1L8kezRA61tPVi3eSQeG+dXo6ayNKXOnFGvxfzzJ7N/+DbqRYyCos9BeUM1Y+SKioowODiI6upqXHvttfjBD36A0tJSdHd3Y/Lkyex+kyZNQltbm+jz5MNAU7HXmOvHEUKSc7p8+PKwm+3EAeH6tf7VPZhTM4GtZ1QHSTZ09XrZThwQjrMNLW3oPurBl4fdFH8kqw73eNlOHBCOu3Wbd+Fwj1dwGq4BP1as344NLXvQvKUNG1r2YMX67SnHLrXB+UMV38hFMAyDHTt2YM+ePfjJT34Ck8kEAOx/AaCgoAAejyeldO32Ivb/v97XzTnQdMAfQMXE0jRyrxxirzHXj4vncFgE7yulSDzKdf58p7RyNxjSa6bTvZ7o9nHv150IhkKc9QuakXqGEFTdjiotBqSSC9cVHY+ffeXijLNgKPErBzXFn1C5cD9TocTrjY7H3R3HOOPxaJ8H0yrGCEpPqs8v+fBZVonxIAdVdeS0Wi2MRiOqq6tRX1+Pjz/+GADg9Y78tWNwcBBmszmldJ3OfgSD4YbfzOg5B5qaDTp0d/dJcBXyE3uNuX5cNIfDIvn9FtroOJ39sNuLcibe1CQT9z0dDocFfn8K7+Rw4LueVOIx0j6WWhjs12g46xdCI/UMPPuooR1VWgxIRenXJSYe7TbuiSG0msQVlNUSf0Ip/X5KLdvXKy4eTZzxWGIxCc67VJ9Bc/2zbD7GPx9VvFr5/vvvo66uLmabz+eD1WqFw+HA559/zm7fv39/zKuWqYpMzNFQW4X6uZVoqK3CsoUzwwNNM0UDuAb9ONjthsszFJ4wIIMig2kZgw4AYgfTKuw4MfdC7PkAsPdi177urNwLQtTEbjFi4tgiNNRWxtSvhRdXYWtrB1vPktbBLLd3WZOr16VgDhuDG6+cHhNn11w2FScfb8Px5UVoqK3CmGJTas+ATKDYyAtlJQwa51fHxGPj/GqUlzKC00jr80sG0iFZkGb7oIpv5KZOnYrDhw/jT3/6ExYtWoSdO3fihRdewCOPPIIxY8bg0UcfRVVVFQYGBvDEE09g0aJFaZ1P1MQcYskxIDUETJ1owwOLz8aAPwCzQSd4RiSxg3DFHifqXkRdX6/bh+JCo+AZMmlwMImX7hIA/qEADHqdhDmSl1GvxcSxVixdUMPOWqnRALf98HRYC0bqGWcdRI7WMWo7ZKPXa7CgthK2Qga2QgPc3iH8/ukP2ftw45XTcdI4C4pMMs3YR7GRPzRAicWIO675BlwDPljNRgSDwdQ+mIv9/JKpdEhmSdA+aEIhjpfJFeiTTz7BPffcg7a2NowbNw5LlizBRRddBK/Xi/vvvx+vvvoqgsEgFixYgJ/97GfQcLxawSf6q3HXoB+3rX4n4evoBxafHf6QIrFsny9eKl9Pi81rto8TK9Pno1crlY+rPjgclrSXABB7fDrHRo6X8tVKZ78PH+7pZv+4EsEYdLincTbsRcnXOpK7vRNCzCs7uXpd2SQmHrv6vFj+x/+w5V5/YSVeeqtdUfchU7Gh9PspNTW8WnnY5cVdj/0n4V7fdcNZKLcK/1aOjC5X4l9o+5AsHlXxjRwAnHrqqXj22WcTtjMMg+XLl2P58uWSnKe338c5QLTX7cvIgyDb50uH2Lxm+zix1HQvCJGD0+XhnezE6fKM2pHL1TqWq9eldD0uT2y5a7gn2ZHzPlBs5I+EeMTIZCfUkSNcpGgfVDFGLpuKLQz7TnEEY9ChuDD5BxS1nC8dYvOa7ePEUtO9IEQOdpsJ2uGJTKIxBh3sVhPPUSNytY7l6nUpXWRyiWhKuw8UG/mDLx5LLKO3jSQ/SdE+UEcujhQTZaQyYDHtAalZHESdzuQjck1aktV7QUiOs1uMmDzBiuvnTYupJ4uvmg5ogH5fILGuRdVFrVaTUMd++r0ZsBaqu45R2yGP+MlOtu7oQGNdtaLuA8VG/igrTZx858Yrp6PcnuK3cdrwa+xtnS443T76pJ7DpGgfVPNqZdbIMFGG2IlA5BhELXYiGLHHiSobseWSxiQwhOSFEOD1BbHpX3sx79wKaLXAieNseOnNvTjz1HEwGXUYYytAxXhLuN5w1MVlC2fiVz8+E5/sdyIYBNa/+hmuvmSquid/oIkFZKPXa2KeEUUmPRZdOgXH3H5oNRpotAj/cUGue0GxkVf0uth41OtS/Ou6FtjZ3sMudM8YdGiqq8aMilIgmJk8ExlJ0D5QR45LCLAWGEbeTxVQoK4BP/thBQi/47qquXX0CT0G/FixfruogdBizylWOteYzbJJq1yG733FxNLwQFp62BLCiq5bza+1AQjXyXnnVmBDSxvmnz8Zx9x+OIpNsBYYOOviivXbMf/8ydjQ0samm8l2K2tEPDdIerp6vXh4486EZ8SSBTV47KVP2J+FTMSTURQbeeGw04uHmxPjMZXJTpwuH9uJA8Jt5tpNu+SPYZI5abYP9IWtRJINWMzEcekeK0a2r1GOMiWE8OOrW5FJJoKhEIKhEFvX+PYPxk2WTPWTiME3uYTHF4j52enyZDtrJA8lm+xEKCdPGhTDhA99I8dFE/7Lc2+/D8UWBtaC0defiQxYjP9LjNAJPVI9LnLsOLsZc2omsONStu7oEDZIcvgav97XDTOjF3yNYs6X7bJJp0xzWTrroXl9Q3AdG5Q4R0RtInXLUmjABadPBDRAoUmP8tJCNNRWYuJYK7qPDrB1ja8unjjWivq5lQCA17cdRJ/bn/f1k6QuMrlEdDxqNRqUlxbg5ONtOK2yHFotUGJh5H29kuQFu83E+RkplclO+NIQMplUxoj4TEyyhzpy8USOr4oMWIw/brR3Xa1mPZrqqhPeh7YWGkZ9H9pq1qN+bmXqx4q9RpHnE102MpRpLmOM+rTWMyPEatZjyYIZ6OoZxIaWNlgKDbjs7ElY+ez2mAWYI3WNqw7fcMU0PP3/PsUh5wAYgw4NtZWY4CiicUMkZY5iBjfXz0D30XA8RmJsjO1UXHLWifjjix/D6w9g8xvttAg3ybiyUgbfu7AS6zaPfPZonF8dnuzELywNu83ImYa92AgMZTb/nGhBe8WjVyvj8I2vcg2MUgujBizedf0sPLD4bEGB7nL70bylDfPOrUD93ErMO7cCzVva4HKPXutdbj/nu9SjHSv2GsWeT2zZyFGmhBB+Lrcfh3tGPjRfcPpE9v+BcJuw5oWPRuracB2+p3E2GmorsWRBDV54fS8OOQfY/Te0tMFRbKIPBSRlrj4/jvV7E2LwyDEv24mLbBP0HCckDV09XrYDBoTjbt3mXejq8QpOw9nr40zD2SvPq+eiPxOTrKFv5OKktTifiAGLvf0+HHIOsBMHsNsFnE9VC3SLHcyZ5TIlhPDr7ffB4wuMtANCFmAOAe5BPza0tKF+biXbiePdnxCBevt9OOb2J8Qg36L1FGckk/jGyPW4PCizCJ3shH+MnByTndCC9spH38jFUdMi1Lm+QLdYasknIWpTbGESFgQXUtei6yTVTSIVrngEwLtoPcUZySS+BcFLUxjfxpeGXGPk6POU8tE3cnEii1e3d7rYdUAqxlsFryWX6oDQdM6X9bFnIo9LS5bLVMwkMITkC2uBHtMmlaDy+DPQ9uVRMEYdFl48Betf/Sy2TSg0wOWOqrfmcNvxzP/txoLaSmyMGs/E2YbQ4Hr1keGeWQv0mDG5FJUTz0DbwaMIhiKTS4QXZl7zwkfZe1YlQ/GcFxw2BksbanDw6372s8fEsUUoK2aAwOjHA4DdYuQc42+3GmUZ45/W56lcp5B6TR05DqIWr05jQKjYxbJFLySYxsLXohcvFyPbZUqDeglJTgN0H/PEfMhYsqAGv75+Fvz+QLgNKjRg9wGOenSCDbddfTr6PX7c0zgbbo+fu82ieqg+ct0zLXDIORgzMcSNV07HmGIGjz73Ebto/dQTS3G8wyzPZFcUz/lDC3h9gZjPHk111eF33wR25BAEZlSU4p7G2eHXKa0m2TpxEaI/o+YyBdVrerUyjuiJQLJ8HGt4DNnEMYXh95VTHHtWXeEQfFxkge4NLXvQvKUNG1r2YMX67Rkb9Kq6e5FhVlsBHA6L6H+EpItrsdpVG1txrM+HQlN4LKvLzVOP3H5YCwwYX2KGvcjI22YpvR6SRHLds67exMkl1rzwEQIBsOOkN7S04XdPfSDbZFcUz/njsNPLOSHcYafwyU4AAEHAXmRE5XhreFycjJ04il9uSioX+kYujqomEMmybOeV7kWsdJYPAGgJAZI+voH4g74hdjB+uvVI6fWQJJLrnvFNLuEa8CVskyt+KJ7zR7IFwcutwiY7URqKX25KKhf6Ri4OTSDCTy0TweTDvSBEDnwD8QuMenYwfrr1iOqh+sh1z/ji0Wo2JmyTK34onvMHXzymsiC40lD8clNSudA3cnHyYgIRQNSkHtnOq9hBtukcJ/r6FDLolZBMigzEb97Shjk1E6DVAicfXwKdNsSO44ivR+PsZjTVTUev2wdoNLF1Qxt+XdPp8sBuM8FuMcrXJuYSrvYog+S6Zw5beEHwji4329YfX14Io0GLcXYzu+i8nPFD8axAGXpel5UyWLJgBr48HBuPqSwIrjSSxS9HWy/nK6PpUlK9po5cPLETiED8RCBZnUAEED9IM42yEUvsIFtRx4mdBEZBg14JyagQ4LCZUD+3Cms3xc4IyMZ6VD3q9/hxtM+H3z31QWLd0AA723sSZmebUVGa9XYmp/C0R/bSosydU4ZnQ+S8gWAopq1feHEVnvl/n2HBRVUYY2VQZDLIGz9ylQ3hlsnndQjw+YOJk52o+V5LEb9a/rZetZ05BdVrerWSi4gJRMROBJLtCUQi5xQ9SFPs5CpZzKcU15fqJDBKGfRKSCa5Bvz4eP9RthMH8MT7cD0qMhnwyHM7Offlmjhl7aZdcLp8WW1ncg1fe3ToiDuzJ5bhnjldPqx+PjYW17+6B3NqJmDN8x+BMeiVET8Uz4qRyef14R6eyU56UpzsRGnSjN+kbb2aKaReU0dOIskGPmbiuHTIcU4x1FKmailPQtLV2+9DMBQSHO/J6gbfxClOl0f6jOcRvjLv6RuUKUeZwxdD0FAsEW6ZfF4nm+wkn1Fbn1n0aqVEii0MxtnNmFMzAdCEt23d0SF4go3oIM/0gMm0zin23XIRx4nNpxTXl8rYQTnuISFyKLYwKGB0guM9Wd3wF4Q4fxeZNIWIw1fmpZYCZOxPxjKNEY5MLhF/rQgNx5JNAbFE46cVJZPPa754VPNkJ1LgKxfVt/UKqdv0jZxErGY96udW4qW32tG8pQ0vvdmO+rmVsBYmn4Y0MmAyMvtNzIDJTOVV7DmH3y2/bfU7uOuJ93Dbo29j98FjbMdV6uOsZj2a6qpj8tlUV525Mo3K5x1r3hGeTxnuISFysJr1mOAoxA1XTIuJ9xvmTUNvvy+hriSrG5GJU+Lrt91KfwBJB1+ZjxtTmJkTin0uSMBebETj/NgYWlBbia2tHWiorUSvy5uVfPCSsWwIt0w+r8tKmYR4bJxfHZ7sJI/lZFuvoLpN38hJxOX2c74D/MDis5OvKSHHgEmRk3rwvVs+2jWKPs7tR/OWNsw7tyJcOUJA85Y2TBp7ekbKVGw+lTTolZBMcrn9aP+qD29u/zKmXr7wr704b+bxKC4yxtaVZHUjBMyoKMU9jbPDM5lZTezMlyQNPGWu1WbmE4bodlMCXT1ePPda+BkxfkwhjAYdDh91Y07NBPz9nf3oc/uzkg8+cpYN4ZHB5/Vhpxct732BJQtq4PEFYDLq8Let7Rg3plC168hJIph7bb2S6jZ15CSS1uKAwwMm2f2y0QEYPmfFxFJ0d/cJOqccC3Qfcg6g+bW22O0ZKlPV3UNCsiwyRo6rXgZDIe66kqxuBAF7kRH2IiP7M5FAFtsjORfG7XF52Fisn1uJ5i1tCfvIuXCxkhYNJlEyVD96XB7sPtCL3Qc+jNmu5gXBJZNjbb2S6ja9WikRJS0OmCm5vkB3PtxDQtJRbGGg1Wg464lWo6G6kofkbDfjF2BWWvtNz5T8kosLghNuSqrb1JHjogFcg34c7HbD5RkS9M5rWu9dizhf2scOH7drX3fGrzHbx0VfX9buISF5wFqgx7RJJbjxyukx9WThxVMwraIUvW4fXJ4h9HuH+OtesrqZTltIZCFnu+mwMVj2g9Nw69VnYJy9ELcuOgPj7GY2H4uvmg6tTitbHNEzJb+UlTAJbeONV05HealM38ZJ1Z5KkU6Ote1Kqtv0amW8bC+Wnc7ilGKPTeOcohYvF1s22S5TsQuCE5IvNED3MQ87dlWrBapOKEEoGMJvn3ifrW8NtZXsGKWYupesbiKDC/WSzJF5jHDf4BDWxSw0PB0Dgz70DQ7B4x3CfX/+AFdfMlWeOKLx0/klBOj1mpjPSHq9Rp77LdXC51Kkk8lF2OWioLpN38jFyfZi2emcL9sLZqe1eLnYhROzXKZiFgQnJF9EFnaNjEva0NKG+/+8DW1fHoupbxta2nDB6RMT6l6yupnJhXpJhsm0MG5Xr5ftxAGRScY+QrHVhA0te/DkK59iTs0EeeNIIYsGk8zr6vXi4Y07Yz4jPbxxJ7p6s78guFTtqRTp5GzbrpC6Td/Ixcn2AMZ0zifH5CNKGdyZjFrymU+stgIwRvHNjX8oAINeN/qOHLy+IbiO5d5iyHLgW9g1GAolbIu8OhNd95IuxhsC1VuSEr4FmD2+kQ+MkcXBKY5IpvHFY4/LgzJLdl+vlOpzkBTp0GeyzKKOXJxsL+6czvmyvWC2Wha+Vks+8wlj1OOXD70p+vgHl54n+vgHl54n+rwkFt/CrlpN7ICHyKLMkf+P1L2kdXN4EhWqt0Qovng0GUfGrUQWB6c4IpnGF4+lMix8LdXnICnSoc9kmUWvVsbJ9gDGdM4nx+QjyxbORENtFernVqKhtgrLFs7M/IQuKVLSIFRCckn0wq5jik1oqK3CzfUzcMqkkphJJhpqK/H6hwdH6l6hAa5BP/oHfPjp92Zw1k2qtyRVDlviAsw3Xjkd/9p2cHgSnipsbe2gOCJZ4bAxuLl+RsxnpJvrZ6CsOPuTnUjVnkqRjuLa9hybeIW+kYsnwwBGUROIpJPXNCb18A0FsfmNfTEDVkeV7YGuChqESkhOGV7Y9Xc3no2Dh/uwNmqSiRuvmo4xVgZFpvDi05PGW8N1r9CA3QdG6v84uxl3XPMNhEKhhLpJ9ZakJAiMsxfgtkVnoO3gUQSDwMaWPVhQW4XvXzwFgx4/brv6dIojkh0hIBAMxXxGWnzVdHliT6rPQVKko6TPZDk48Qp9I8cliwMY05pAJJ28ipjUI9uTq6RFIYNQCck5QSAYDLGdOCBcp9c8/xEYgx7WAgOKGD1b91zu2Pp/yDmA3z31QfhhHl83qd6SFLgG/Ni5rwcP/GUbNrS0ofm1NhxyDmDNCx8hGAhhfImZ4ohkjdPlw+rnP4ppF1c//xGcLp88GZKqPZUiHYW07bk48Qp15GSWdPC/wojNq5qukRAyOr5JT5wuT8K+VP9JpvT2+xAMhQTHIiGZlEq7SOSRi88j6sjJTEmrw49GbF7VdI2EkNFFBvVHYww62DkG9VP9J5lSbGGgHZ4kJxpfLBKSSam0i0Qeufg8oo6czNKaQAQQP2hz+Lhd+7oFH5ftyVWi85krg1JJ9vmHgnA4LIL+AeDcRmLZrSOTngBgx8gN+vw41OtBv29k+QGu+r9s4UwAoHpN0mIt0KNivBUNtZUx8bX4qunQ6jQ4eITii2SP3WLEjVdOT5h8x25NsZOgtM89SstPGhQ38YoEaLITBRA1gQggftCm2OPSGLAqakKXHByUSrLPoNcKXrrAYNDD7x+K2UbLF8TRAO0dfQiFQli64DT4/AFYCo14/KXwQuGRWSsnOIpQMd6S0G6UWhh82eXGbavfoXpNJKHVarBkQQ28vgCKLQwGvX7cQfFFsk0DGPSamM86Br0mtY6P0j73KC0/6VLSxCsSoW/kZJbOwEtZJh8RMWBV7IQuuTgolRC1cw340d7pwrrNH+P3z2xD5xE3fv/0NhxyDgAI19MNLW1o73SN1NWodiMYDFG9JpKIPFv+9LdP8eAzH2JVcyvaDvZi1cadFF8k67p6vVi1cWfMZ51VG3eiq9crOA2lfe5RWn4koZCJV6Siio7c22+/jbq6OsycORO1tbXYsGEDAMDn8+HOO+/EmWeeibPOOgvr1q2TOaepS2fgpVomH1FLPgkho0uYYEIDznoaDIU46yrVayIVrljim/yE4otkWg/PZCc9KUx2orT2UWn5IYkU/2rloUOHcPPNN+OBBx7AhRdeiI8//hjXX389jjvuOLz//vvYv38/Wlpa0NfXh+uvvx7l5eW44oor5MmsJvzXi95+H4otDKwF+lF7+umseF9sYTDObsacmgnsV/dbd3QInnxEzDmzeY1p5VOs4ev7el83zIxe0PVFH5dKuRCiRqU2E04cGx6XFAwBBYyOs55qNRrOuipLvVa7VNqXPGqLuJ6BhSa9MuJLyvvAlRZRHLvNxPmZrDSFyU6U1j4WWxhMPaEY35lTAY83gAKTDq+81U7ttYIovjX46quv8J3vfAe1tbUAgOnTp+PMM8/E9u3bsXnzZtx///2w2Wyw2Wy47rrrsGHDBnk6ciLfI44MvIw/Tsg7u1azHvVzK2MW5W2qq4a10AAEM3DOLF9jOmUjSrbHHBKiNhrgy8P9MbG+8OIqXHPZVDz1991R26ZgvN3MWVezXq/VLpX2Jdm+OYjrGfiTK6ZhyYIZ7OuVkTGb3b2e7MWYlM8EnrTspUUZyToRz1HC4HsXVmLd5pF4bJxfjbJSBhD4JqLS2kdroR61s07Eqo2tMddkLTIAgdGPJ5mn+I7cGWecgTPOOIP9ube3F9u2bcO8efPQ3d2NyZMns7+bNGkS2tra5Mgm73vEDyw+O/wOLp80Bl663P6ERXnXbtqV0jkH/AGYDTpB58z6NWZ5UKrY6xNdLoSoDFesr391D37xg5mon3syvP4gtBoNpp1UipJCnrqag4PNMymV9iXZvo6s5zzzuJ6Bf3zxY9y26Ax2wgmEgL+/sx99bn/W2mQpnwl8aVVMKIZRxbMH5qKuHi/biQPC92rd5l24+ydnoczCCEtEYe2j85iP85ruaZwNexF9K6cEiu/IRevr68ONN96IGTNm4NRTTwUAmEwjX1kXFBTA40l94UW7Pf2/bH29r5vzPeIBfwAVE0tHPV7MQzbb55TjGtM5LlViry/dcokXiUeuqe8NhvSqbDrHpzsVv5x5T+VYrn3Vkncu6d636PaRL9b3dRxD85aRP6JNP9kOx5jksa/kjoWSlp1IpX1Jti+grOsSS0g8Hu3zYkPLnoRjxbbJqZLymcCXVk/fIKorlFyLpKfE+I2Ox8++cnHHo8uLU08ak1K6Srmze7/u5L6mfi+mTLLLlKswJcaDHFTTkdu/fz8WL16MyZMnY8WKFWyHzesdmQ1ocHAQZrM55bSdzn4Eg+n9ucPMcL+Xbzbo0N3dl1bamTynw2ERvK8c15hNYq9P6HFCGx2nsx92e1HCOR0OS8LU+KkSe7x/KAiDkCUjMnBuKY4XeizX8gPpnjvd49M9N1/sphKPkfaRL9aj/1qs9jYhlTYxG1Jpl5LtC/DHghJIGY8mI/e4zWzFpZTPSr60Si0Fir6fUst2vRQTj3Yb9/i2Eiuj2ntVyjNmr6RI3mtSWjudacniURUduQ8++ACLFy9GQ0MDfv7zn0Oj0YBhGDgcDnz++ecoLy8HEO7sRb9qmU1yvNec1jlFTOohxfmyNgBfxPnSGcu3bOFMtHe62LVjKsZbc+p1sVTWYuNCa7Hlhug6Yik0YO43TsCk8RaEoMHVl0zBUCCUc7Evt1TapaT7yiHD7X5028sYtSgvKURIEwovCn5lNVa/sCtrz+P4fKX1eSCu3JYtnIkV67fHpDVuTCGczv6MXwsRzmFj0Di/OnGMXDGT2ngyXXgpgx6XB3abCQ5bisdLyG4xoqmuOmEuBrvVmHQuhgR5NAlTtim+I3fw4EE0NjbilltuwQ9/+MOY311++eV49NFHUVVVhYGBATzxxBNYtGiRPBmV471msefM9oLg2Z4MRILrS2XsIJDGou6EqMlwHXnwp+egvbMPzVv2wGg4ARtb2ij2MyWVdldJ42uy1O77hoJ4c/uXqJ11Ah7auCNmwp07rz0TQ4Fg9sshnfvAU24P/vQc9PR52bS0WhogpzgBoObkUtz9k7Nw1OVFiZUR1Ylr3duT0BmsOblUns5cEJhRUYp7GmfD6fLAbjWJ6sTRhHCZo/h15P7617/C7XbjD3/4A0477TT234MPPoilS5fi5JNPxne+8x1cddVVuPjii/H9739fvszKscigyAW6s70geDYXlJTi+qorHIq9PkJkFQK8/iDWbvoIc2omsJ04gGI/Y1JpdxWy2G022sXIObjicP2rn+Hjz3vCHR85ykHkfeArt2AwJPs9JQIEgDILg2/WHBee4CTFzldXL/eEKaksKi65IGAvMqJyvDU8wUkqnTjQZ6RMU/w3crfffjtuv/123t8vX74cy5cvz2KO1C/ZAo+ZmNGLzkdIbnFGFr7lWQycYp9ko11kzzHKovRqikV6nuS3ZIuKC575UmEopjNL8d/IEelFFpyMlskFJ+l8hOQWu83ExjzFPuGSjXYx+hxc5+JblF7J6HmS36Lb1gjGoEtpUXGloZjOLOrISUkDuAb9ONjthsszBAh9hV3scSJFBmJHPwAzOSA+rfOJKBspzrdrX3d2zkeI2mgAxqBF05XTsXVHBxbUVo4e+1lu42STyetUWRlmo12MnGPX3i5cd/mpMedqqK0cmXhHDvH3Syvs/tHzROV0QFefF1tbv0J3vxfQjX5ItMiEKdH3n50wRaVyNqYV0iYr/tVK1RA7mFOOQaBpTOqR7vlydVIWo17LLkCr1WhgTHOqfkIUKaqORGatHFtqxq+vnwW/P8Bd1/JloHsmr1ONZZiliVcKTXpc8I0TsOlfezHv3ApotUDlxBKUlZpg1uvkKZ+4+zXObkb93MqYmf9475+SJqwhqZFiopKoCVN6XB6UWk2pT5iiQDn3GUlBbXJWSvL999/PxmlkJXYwp2yDQEVM6iHF+XJxUhbXgB8r1m/HhpY9aN7Shg0te7Bi/XYayEtyTnQdOdLrwYaWPXhoYyt27XPyTiqRLwPdM3mdqi3DDE+84hrw47ODvXj8pY9xyDmA5tfasKGlDQ/8ZRsGBwOKmehlTs0EthMHCLh/CpmwhqRGsolKhidMmXKcTdSEKUqTi5+RlNQmZ6Uj19TUhPPPPx8PPvgg9uzZk41TZl2ywZyZOC4fZLts6B4SkhxfrEcmlUjlmFyrH5m8znwpw1T19vsQDIU4y8bp8siUK477lWRSIJI7kk1Uks9ysf1S0jVlpSP37rvv4pe//CU+//xzXHXVVfjud7+LP/7xj+js7MzG6bNC7GBO2QaBihgLlm1plY2Id5dVdw8JyTK+WNdqNCgu4o73fKkfmbzOfCnDVBVbGGg1Gu6ysTCyPdf47lf8z/l+/3KN4iYqUcgYrlxsv5R0TVnpyDEMg8suuwxr1qzBv//9byxatAg7duzAZZddhh/84Ad47rnn4Ha7s5GVjLGa9Wiqix2g2lRXDWth8sGcYo9Ly/C7vbetfgd3rHkHtz36NnYfPKa4zpzosom6vrueeE/w9YkdkJuzA3kJiWM163HjldMTJpWwFRrQ6RxIfKJogO6jg2gQMiGKymWyHaA2hpu1UI+JY4sS4usnV0zDn//2iWzPtfj7tXVHR8KzjO5f7nGU8ExUUirDRCUiPwdlQi62X0q6pqxPdtLd3Y1Dhw7h0KFDGBoagtVqxcaNG7FixQrce++9mDt3brazJAmX24/mLW2Yd25FuKKEgOYtbZg09vSk62SIPS6tvPK82/vA4rMVtaaH6DIVe31iJ4GhwekkT7jcfhzr9+LnC2fCPegHY9Shq2cAL7yxD31uP+5pnB1eMDay//DYCEuhga3HWo0Gx5cV5l79yGQ7QG0MJ+cxH4w6HRijDndc8w24B/3QarV44V9t2PvlMXzeKdNzjet+FRro/uW4w04v3vu4E3dc8w24Bnywmo34x9ufY9yYQpRbs9uZU9TnvFxsvxR0TVnpyH355Zf4+9//jr///e/Yt28fampqsGDBAlx66aWw2WwAgP/93//FnXfeqdqOXG+/jx1sHbN9lAUPxR6Xbl7VsDhjOmUq+vqGB5lXTCxFd3ef8Eo5fBybvpobKEJ49Pb74PYMof2rY2je0pbwe6fLE9ORi9RFb28gph5POaEYRUwOTpqcyXaA2pgETpcH7sEh/PHFj1E/tzIhJmV9rsXfryDdv1zX4/LgvU+78N6nXTHbzzv9+Kx35BT3OS8X2y+FXFNWnqS1tbU48cQT8d3vfheXX345jj/++IR9Zs6ciU8//TQb2cmIyPuy0RUnlfFVqR4nR16zTU1lSkg+iIxJAsBZx+xxY0GoLpJMsttMCIY8Ma83UawRuUTGyMXHYIkl+2PkqO3NH1npyDU3N2P69OlJ9zn33HNx7rnnZiM7GRF5XzZ+TYnRvmq1FuixbOFMtHe62PU12EVMM9S7F5tXAOHBswN+9Pb7UGxhYC3QC8uniOPSKdN0r+/rfd0wM/qMXh8hamMt0GPKxGJotEDlojPQ2+dFb78HLe8dxILaKtitRiAYu390XRxnN6Opbnp4Zi+NJjfqSS7VfZVdi91ihHvQh9t/dAaO9vnwX4vOwMHDfXjl35+jz+0fvd3nul7IUAYqK3fCrayEwX8tOh1arRYutw/WIiOCgSDKSxlgKLt5SetzUCYoKcaVlBcJZKUjN336dHz66afYt28fgsHwUz4UCsHn8+GTTz7Bb37zm2xkI7PSeF/WNxTE5jf2xVS2bOU1pbFg2V70XGyZZntBcAUtDElIpvmHgujo7seGljY23m/63gxMm1ScuN5RVF3s9/hxtM+H3z31Qe7Uk1yq+2q8lhDgHgzE5LmhthLXfvdUTBhTiCJTkg9oHNe7bOFM+IaC2S0DNZY74RYCjvb5EhYEP2GsPHlRyhguRcW4kvIikazMWrlmzRrU1dXhrrvuwu23347f/e53uOOOO3DXXXehq6tr9ATUItuLXkuQ11QWBJdl0XOxC6NmeUFwpSwMSUgmuQb8aO90sZ04IBzvjz63E67+5IsbF5kMeOS5nTlVT3Kp7qvxWrjyvKGlDQe/7kcwGEra7nMd297pynoZqLHcCTfJFgSXikIWlldSjCspL1LJSkdu48aNuPXWW7F9+3Y4HA68/PLLeOuttzBjxgxMmzYtG1lQLCUtKjiaXF8wO9evj5B0JVuAOR/rSS5dkxqvRcwC9cmOFRvb6VBjuRNutCA4NyXFuJLyIpWsdOSOHDmCiy66CAAwdepU7NixAw6HA7/85S/x8ssvZyMLiqWkRQVHk+sLZuf69RGSrqQLMOdhPcmla1LjtSRdoF5EPIqN7XSosdwJN8UtCK4QSopxJeVFKlkZI1dSUoLe3l5MmDABJ554Ij777DN8+9vfRnl5eW69WimC4gakJiHLhC5ZHJQqNp9yTFhDiBysBXpUjLfimsum4pjbz8b7xHFF8AwFofUFUMToOONeTW1dUnFt0rKFM7Fi/XZ1XxNE3h+ZJw3gantthQbYLAy0Oi27/ijfsfHXWzHeylkGWq0GB7vdGblGseX+VVc/vj6SmTwRcRw2BjfXz0BHl5uNxwllhSgrZhLHD6uJFnC6fOHlZWwm2C2xk1qNRkltv5LyIpWsdOQuvPBC/PrXv8a9996LM888E7/97W9xzjnn4J///CfGjx+fjSwol5IGpApg1Gsx//zJbCNl1Av7UlfUhC4yDEoVO/FM1iesIUQOIaDiOAs8/gCe+vtuNt4XXlyFp/++GxfNOgETHEWoGG9JrKMqa+s48bRJD/70HPT0edV5TRGp3h8lTBqgAdzeoZi298Yrp+P1Dw5iV3tP8vzwXS8Qs62714NfPvJ25q5RjeVOeA0FQgnxqGpaYGd7D9ZuGpnApamuGjMqSoV35pTU9ispLxLJyquVt912G6ZNm4a2tjZceOGFOOuss3DNNddg8+bNuO2227KRBWVTyIDU0bgG/Fixfjs2tOxB85Y2bGjZgxXrt2dsMpBsD0pVSz4JkZPL7U+YtGT9q3swp2YCNrS0ob3TxR/7Kmnr+PDV9WAwpNpripHC/VFCu+d0+bDmhY9i8rDmhY/wrTMmCssP1/VGbQPAftsaST8j16iycifcunq9nPEo22QnEnC6fGwnDghf09pNu+B0pTimTEltv5LyIoGMfyPX09ODN954A4WFhfjwww+xZ88enH322Vi6dCkcDgf0+qx8KUgkkGyQKLuyvQKOE0st+SRETnzxDk3sRBO5GPtU10cooSycPJNLeHwjHzrTyY8SrlENeSJhySY7KbMwMuUqPXx1zOnywF6k3nFluSSjvag//elPeOihh6DRaDBhwgRYLBb09/fj6aefhl6vxy233IJFixZlMgtEQpFBotGVOpVJDrJ1nFhqySchcuKLd4SETzShVlTXRyihLCKTS8TnwWTUSZIfJVyjGvJEwvjiUc2TnfBdk13F15RrMvZq5QsvvICHHnoIt956K95991288sorWL9+PV5++WVs27YNt956K1auXIlXX301U1lQDw3gGvTjYLcbLs9QeIB2No5NUWRgeUNtFernVqKhtgrLFs5kxxUkO25JfQ07U1DM4NIMHCeWWvJJiJy44n1BbSW2tnbg+nnTMGVisbDYz2LbJRWq6yOUUBZ2ixFNddUxeWicXw2X24txdjN3flKIOyVcoxryRMIcNgaN8fFYVx2e7CQVCmobuepYU1017FaZ/nAwXDa79nXLXjZKkbFv5J5++mksW7YMCxcuTPgdwzD4/ve/j4GBAfzlL3/BxRdfnKlsKF86A5fVMhmI2MGl2R6UGnW+AX8AZoNOmfkkRGaRSY9MjBYnjS/G/s5ezKmZgE3/2ourL5k6egJqnbCB6voIJZRFCCgpYrB0wWkY9A2hwKjHoM+Pl9/6HAsvnoKpJ9hiJ2RINe6UcI08eXro5+fja2e/MvJEwoJAaVw8mhldSjM8Kq5tDAIzKkpxT+Ps8OuUVlO4E5fKNUlFaWWjEBn7Ru6LL77AN7/5zaT7XHDBBWhvb89UFlQhnYHLapkMBID4waXZHpQ6fL7qCoey80mITKInPRoYDODeP72PP/1tN5pfa8Mh50DuTxBEdX2EzGXhGvDjvr9sw++f2YaHm1vx+2e24Y+bP8acmgl45LmdcLn9CfunHHdKvN8h4LiyImXliXDG431/2ZZSu6bItjEI2IuMqBxvDY+Lk6MTB4WWjQJkrCPn8XhQVFSUdB+LxYJjx45lKguqkM4q89leoT7b5yOEKE9MOzA8wUk0IW0CtSVECqNNvBMfTxR3JJOkiC+KUX5UNtwyOtmJRpNnL6+KWBw1nYHLapkMBED2F47N9fMRIpNiC4NxdjPm1ExAeYkZDbVV2PLBARzp9QCgCYIIhwy1j9GxGBkrs3VHBzvxTnw8qSru6JmiOnzxmEp8SZFGrlJV/c2ijHbkNm3aBLPZzPt7t9udydNnl8h3d9NZZT7bK9SLPl+232vO9fMRIhcN0NvnxZXfOhmPvfQxG+8NtZX4+zv70ef2o6G2Et29nqTtQrbbLiKjDLaP1iI9vndhJdZtHlms+Cfzq/H+x52c8aSauKNniipxxWPj/GpYLQZgSGAaZj3q51YmLMBtLTTI9kqjUqim/mZZxjpy48ePx7PPPjvqfuPGjctUFrKK793dBxafnXxtl3QGU8s4GUgq5xNdNiLl+vkIkYtrwI/PDvayEx4B4Xjf0NKGJQtqcOBQH9uhSxr/SpxEgmREJtvHrqNe9kNzJO0/bt6Fu244C+U2JjGeVBJ39ExRJ654XLd5F+7+yVmC15Fzuf2cC3DTvYf4CelyXMY6cq+//nqmklaktBbpHB5Mze6XSlCmc6wYIs6nloW91XI+QuTS2+9DMBTijPcDX/eh+bW2kX1Hi/9st11EFplsH/kWYD7a50G5leeDswrijp4p6iTFguB070cxXH8rJpaiu7tPkfU32zI22Um+iby7G43e3Q3Ldtnk+vkIkUuxhYFWo+GM9+gHKsU/ichk+xhZrDg+bTUvwAzQM0WtpIhHuvckVRkdI5dPIotlt3e6EAyFoNVoUDHemvmvfcUOiB4+7ut93TAz+owOpJZjLF827wW9t03yhbVAjykTi3H9vGl4PGqM3I1XTce7O79C/YWV0GqBqSeWJh/TQRM55I1Mto8OG4MlC2bgy8Nutq0/vrwwvABzMBxjR1xeFDB6WMwGFDE6VcQZPVPUyWFjsLShBge/7mfjceLYonA8BkY/HlDovaf2WtGoIychUYtlp0PsgOhsD6SWYVxCVu+FSsZdECIFt2cIr39wALd8fya+OHQMwSDw6jv7cdFZJ8YM0OdtU2gih/ySyfYxBPiHQjFt/Y1XTgeQGGMNtZWY4ChCxXiL8uOMnimq5fUFYuKxqa46tQSUdu+pvVY8erVSInIsVCj2nLIsqpjFRVVz/foIkUukblVPLsPKZ7djQ0sbml9rQ/XksoQB+nx1jhZ1zUMZah+dLh/WvPBRTCyteeEjdPV6E2JsQ0sb2jtd6okzeqaoTlevl3Oikq5eb2oJKejeU3utfNSRk4gcCxWKPWeuL6qY69dHiFzYuhW/EHgKC4NT/SRScSaZXIJrezAUojgjGZNsshO1ovZa+agjJxE5BqiKPWeuD6bN9esjRC7RdYtzwpO4n7nqHNVPIpVkk0twbddqNBRnJGNycfIdaq+VjzpyEolMsNFQW4X6uZVoqK3CsoUzw+82Z/CcS+prYj5YsYNiFZbXbBJbLmnRAK5BPw52u+HyDAGazJ2KELlE2o5Ckx7XXX4qW8e27uhAU111TJ278crp0Oq0CXWBq37+9Hsz0D/op7oTQe2JIHaLETdeOT027uqmo6yUwR3XfAMNtZWon1uJcXYzGmorRya9UgK6xznHYWPQGNcONtZVhyc7SYVUsSFBOrJ8niIpoclOJJT1yU7SGBSb9bxmmVGvxfzzJ7MzRxn1GfybBQ0GJnnENxTE+lf3wFJowPzzJ+P48iIcZzfDWmjAA4vPxlfOAXx5uB/P/N9u9Ln9iXUhrt3SaDRYu+kjHHIOUN0BqD1JRQgoZPQxbT0Qwkd7j7Jj5xiDDouvmo6TxltRaFTIrJV0j3NTCLCaDVi64DQM+oZQYNTDaNCkdk+lig2p0lHa5CskAX0jJxHZBoSKGBSb64NXXQN+rFi/HRta9qB5Sxs2tOzBivXbM3Z9uV6ehEREx/qRXg82tOzBqo2t4V8OLzWwamMrNrTswZFeD39dGG63iguN+N1TH+CQcwAA1R2A2pNUcLX1X/cMJkyAsvr5jxAYCirmwyfd49zkdPnw4DPb8ftntuHh5lb8/pltePCZ7XC6hI8nkyo2JI0xBU2+QhKpqiP30UcfYfbs2ezPPp8Pd955J84880ycddZZWLdunWx5U9OAUDXlVYxsX1+ulychEaPFeqp1gepOIioT4bjKKhgKKb786B7nJr7Jd5wpTHYiVWxQjOUPVbxaGQqF8Pzzz+OBBx6I2f7www9j//79aGlpQV9fH66//nqUl5fjiiuuyHoeIwNCoytOVgaEiliosdjCYJzdjDk1E9h3prfu6MiZwatp3QsRC6XLdu8JybLRYr3UZkJDbRWCoXCleX3bQfS5/SguNMI1mNhOZaTuqHzx2mRlwlWG+YyrrAoYHRiDDpZCAy44fSKgAbQaDUotUeOU5IqR4fMaDDp6ZuQgu83E+dnKnsJkJ1K1iZK2rdrwt41Olwd2mwl2i5F9A0OVVP6MiKeKjtyqVavw5ptv4sYbb8SaNWvY7Zs3b8b9998Pm80Gm82G6667Dhs2bJClIxcZEBr/PnJG3yUW+Q601axH/dzKmMV7m+qqYS00qLtyDhN9L9Ioz6a66pwtT0IiktYtAF8e7o8ZextZhLm714MV67cn1CvJ280cGHvEVyZ8ZaiW68qE+LIaZzdjjNWEay6bCo8vvHZcpLyOLysKlxdkipGo2LQUGtBQWxmTv4x/XiAZZ7ca8b0LK7Fu88hngcb51bDbjEBg9OMB6T5LSta2aoGd7T0Jn29mVJSq8/NNDjwj4qmiI9fQ0IClS5fivffeY7e5XC50d3dj8uTJ7LZJkyahra1NjizKMiCU7x3oBxafHX6Pme84t59z0crRjlMNkfcinfJs3tKGeedWhP8KFwKat7Rh0tjTc6M8CYmIqlsD/gDMBh1bt1yDifVnQ0sb7mmajV+tfZe3XknZboqtw4rC0X5ptRr88pG31X1dmRBVVkf7fWCMetz9+H9QP/dkNG/Zy1leAGSJkejY9PYG8Pd39mP++ZMxeYINY6wMdeJygKvfz3bigHBsrduc4mcrqT5LSpSO0+Xj/Lx4T+Ns2IvU9w1yTjwj4qiiI1deXp6wbWAgPDjeZBr5yrqgoAAeT+oLL9rtReIzF8chWUqj+3pfN+c70AP+AComlkp+nNqkei/SKc9DzgE0vxb7RwSx5RmJR4fDkvA7gyG9KpvO8XKeO93jUzmWa1+15J0LVxylgqt95KpbfPXnaJ931HolVbspVduWbplJIbpMduXQdaUrWTxGysnrD/KWF0Lci9dn+vkXH5uRyYJ+t/hs0efNhfuZCiVeb3Q8SvnZSqo2Md109n7dyd2u93sxZZI9zdTTIyYecvHzryo6clwKCgoAAF6vl902ODgIs9mcclpOZz+CQfX9KczM6DnfgTYbdOju7pP8uFyX6fIU2ug4nf2w24sSzulwWOD3Dwm9HE7pHC/nudM9XuixBoOec1815J0PX+ymEo9c7aPDYYlJm68elBRxj9XIRHsjRdsWf11KkKvXFU2KeIyUEwDe8oJGI8vzT+rnrtLvp9Syfb1i4jEXP1uV8oy1KyliZL0msfGg1nuULB5VNWtlNJvNBofDgc8//5zdtn///phXLbMuywt8prMguOgFHtWyiKmIfMpSnoSojQbo9w7h4/Yj+PxwP5xuHzp7BqDTa/DT781IqAd2qzE79UMDaDVIWCA6F+oitTFJaAFnvw/v7OqEPxDEsoUzsXVHBxbUViaWV6GBM0aWLZwJABl9rtE9zH3WAj2WLZyJhtoq1M+tRENtFZYtnJn6PVbQ5yy7xYimuEXOm+qqYbeq77VKIDfroWq/kQOAyy+/HI8++iiqqqowMDCAJ554AosWLZInM3IMoEzjHWhRC2arZZBoGvkUVS60YCbJFxqgvbMPHd39MRM1XP3tKdB39ePlrZ9j3rkV0GqBqSeW4niHGQhmoX7ETSQRs1B5LtRFamO48UzEcMnsExEMhbB0wWnQaIBxpQWwFhqw+0BijBzvKMSXXW7ctvqdzD7X6B7mPi3Q7/HHTPjUVFcd/spE4GQnivucFQRmVJTinsbZ4VkrraZwJ06NE50ME/U5T8FU3ZFbunQp7r//fnznO99BMBjEggUL8P3vf1+WvMg2gHJ4oUb2HAIXBI/MfhbBGHSjT+qhkkGioictEVkuAETdB0LUxjXgR3uni/2gAoTr1zP/7zPMP39yzFjRmLqT4foRP5HEhpY97Plzpi5SG5OAbyKGeedWJMShy80dI/c0zs7ec43uYU7r6vVyxuPdPzkLZdHLXyShyM9ZQcBeZByZ3ETFnbi0PucplKo6crNmzcK2bdvYnxmGwfLly7F8+XIZcxWWbPFFpQWH2Lyq5Rpz/foIkUtvv493weXI2nHR27JVd6ju5ie+BZijX0WLxAHfJCd8aVDskFT18MRSj8sjuCNHbVlm5WL5qvv7RAWJLL4YTakLfIrNq1quMdevjxC5FFsYaIcni4jGGHTQajQJ27JVd6ju5ie7zcR536O/6YrEAV+M2K3caVDskFTxxWOpiAXB49OgeJRGLpYvdeQkoqYJRFQ1SQpNWkKIYlgL9KgYb0VD3EQSP/7OKbAVGmSrO1R385PdYsTiq2InLll81XQUFuhQP7cS4+xmNg74YiRrk/GQnOewMWiMmxiksa4aZcXCvo0DqC3LtFwsX1W9Wql0qplAJMmivkKPS2mwtthrFHuc2HyKLRdC8oUG8AeD0Go1WLKgBl5fAFazEUEEMd5RKN9EDjSRRH7SADqtJua5qwXw752dOPh1P376vRmYeoKNHdPDGSPZmIyH5AcNUGTSY+mC0zDoG0KBUQ+9Dqn9cZ7asszKwfKljpxEVDeByPCg64qJpeG1M4QGscjJVcROPiK6bMQOKhdbLoTkAafLh4c2tCa0c/PPn4ySIhPsRUb5JnKgiSTyTlevFw8370yIxyULavDgMx/iked2xj4v+GKEYodI4HCPF/+zfkdCPN51w1kotwr/Vo7iMcNyrHzp1UqJJBtAmYnj1ITKhpDcwDcxRDAUgtPlkSlXJF/xTS7h8Y388Y+eFyRb+OLxaB+1jSRzqCMnEZpggx+VDSG5gW8wv1ajgT2FAf2ESIEvHk3GkfEv9Lwg2cIXjyUWahtJ5tCrlRKxFuixbOFMtHe62Hf1K8ZbR333NjLwMn4cmOB3djXhVxB7+30otjCwFuhTOu7rfd0wM/qUj0vlfGKvUWyZpiWL5UKI2tgtRixtqMHBr/vZOmkrNMBmZTDg9cOg16qzzkTlxRfSwKgF1d94Srpfwxw2BjcvmIGOw242Hk8ab4HRoMMvrz4dFrMBWp02PEYpx+5nMBiCa1BZ9yPflZUw+MUPTsPQEDDoHUKBKTxGrryUAYZSSEhpdU1p+SExqCMnId9QkF0oN9JZEUL0KvPZnkQkjYlZxF6j2DIVRYZyIURVQkAohJg6eeOV0/HKm+3YfaBXnXVGSXlRKgWX0VAgFBOPjfOr8dzfPsUh5wAYgw4NtZWY4ChCxXiL7HmVjAZ4d9chrHx2u+LuR17TAP2DQ1g3vCh4ZNbKlCY7UVpdU1p+SAJ6tVIifBNzuAb8ox63Yv12bGjZg+YtbdjQsgcr1m8f9bh0z5nt48Rco9jziZXtciFEbVwDfqzaGBvra174CNWTy9if1VZnlJQXpVJqGXX1erHm+Y9i8rVu8y7MqZnA/ryhpQ3tnS7Z8yol14Cf7cQByrkf+e6w08t24oDheNy0C4edXsFpKK2uKS0/JBF15CQix4Qe2T6nWo4TSy35JEQufLEe/RdntdUZJeVFqZRaRnyTS8THYzAUkj2vUlLq/ch3Ukx2orR7q7T8kET0aqVEIhNzxE87K2RCj3F2c/gviMMPn607OgQN0BZ7bDp5zeY1ij2fWNkuF0LUhi/WTxpvQ/3cSgBJ6nbUOItCswHj7GYccg7EpCNHnclo/c2RsSVKbeMik0vE5yu6jCOT8STkVcX3Rqn3I9/xxWMqk52k85kwEySLNRXXN6Wjb+QkYjXr0VRXHbNafFNdNayFydc7s5r1qJ9biZfeakfzlja89GY76udWjnpcOseKXdk+29coNp9iib6+LOeTELlYzXrceOX0mFhvqK3EUdcgXt92EC+92Y7vza2EtSgu9ofHWdy2+h3c9cR7+NXad1E/txLj7GY2HbnqTMbqb9w13/bo29h98Fhq42UUQqltnKOESWizb5g3DVtbO9ifG2orMXFsUWw7rvJ7Yy3Q45bvz1Tc/ch3ZaUMGufHxmPj/GqU24WvIWct1ON7F8Z+XvrehRxtapZIUvdVXt+Ujr6Rk4jL7UfzljbMO7eCnSGreUsbJo09Pfmi124/1sa9U7120y5Bi16LPjZqZfsBfwBmg07QTJBZv8aofPa6fSguNGZ0xkqx15ftfBIiF5fbj2P9XnbiIoSAv7+zH31uP+adW4Hm19qwbtMu3NM4G/aikb/Yco2zWDu8n9vjl7fOxNXfsfYiGLWhtPPCN7ZESNuuOApt45xHfXC5fTHx+Op7X+C8mcfjxPFWGHVaHHL24+l/7MZtV4+046q/NyFgdvU4jC1R1v3Id109XrS89wWWLKiBxxeAyajD37a2Y7yjEGUWYZ055zEf1m2OG2e3ObFNzRoJ6r7q65vCUUdOIr39PhxyDqD5tbbY7W5f0kBN9v7xaAGezrGRle0rJpaiu7tPUKWU4xoj+WT3y+CDSuz1RfKVrXwSIpfefh/cniE0b2lL/OXwX1e9/gCcLk/Mhw6+NsDt8WPimMLwBjnrTFT9dTiKwm1imtJq95RIgW2c0+XhjMe9Xx5D/dzKmO3R5Z4L90ar1SjufuS7HpcHuw/0YveBDxO2C+7I8Yyzi29TsyrNup8L9U3J6NVKicix6HW2F8zO9YW91ZJPQuRSbGGg1Wg460nk4c4YdAmLg+dj3crHa842u800ajxGfo4ud7o3JBP4FgQvtQofI8eXRnybqiZU3zKLOnISET3uLI33j7M+hmx4ge6G2irUz61EQ20Vli2cmdFrzKa08qkBXIN+HOx2w+UZone/SU6yFuhRMd6KhtrKhHFyr394kB1XarcaE47jrFuFhpytN4pq9zTAV139OVfOdosRx5cXJsRjU111zDi5+HJX1L2JoGeI6jls3GPkyoqFj5GzW4wJ45BvvHJ6QpuqJoqrbzlW1+jVSqmIfY84zfePRS8mLpKoBboVOr4igcixg7RgJskbIaBivAVjx5hx4ngr9hw4CoNeC61Wg2suOxXlpQWwW4xAMPG4hDag0IDdB3K43iil3cvl9ikITJ1YjLGlhaiYUIwBjx+dR9z4xzv7cd7M41FeasbRPk/ic1Ep9yYil+9RPgkANSeX4u6fnIWjLi9KrEy4ExcY/VBWCChk9DGf6woZ9c/wmO3PqrxysK5RR05KYt8jFnlcZKHt+GlhMzWANK0BqwocX8FJxNhBGshL8koICAwFcf+ft3G2PQmduKjjotsAlzsP6o0C2r2cb5+CQInZgMICI5b+4Q32Ovd+eQyMQYd551ZgxfrtidergHsTkfP3KJ8EgDILg1NPGhP+DJFKJw7Z/1yXDUq6plysa/RqpYqpZcHsXEflQvKNFDFP9SY78qWce1yDvIuDK/168+UekdHlYiwo6ZqUlBepUEdOxdQy2Umuo3Ih+UaKmKd6kx35Us6l1gLeSU+Ufr35co/I6HIxFpR0TUrKi1To1UoVi0w+0t7pYt87rhhvFTyuyzXgx9f7umFm9LAWjP4OthTn6+33odjCCDpfWsSeT2S5LKmvSXjnWpHjAAmRgLVQj9uv+QY++6IHwRCwdUcHrv3OqQCAg93u5HUuUjfdPtxxzTewdtNHOOQcGL3eRB1XaDLA6xtCkdmY+bZE5XK+fRqOi/5BP+68bhbaO3ox1l4InQbwB0Poc/tGJuVS6PXm/D3KJwbgsNOL3R3HYLeZUFbKAH7hh+diLKT12TEDecm18qWOnMqJmnwkjcGe2T6fKGLPJ/Y4pQ2cJySTtMDOfT1Yu2kXW08WXzUdAHDb6neS1x2OOvbT781AicWIIpMhaScu/rgFtZVoee8Arr5kqqoHqmfccPv00M/Px9fO/txqn4bj4pn/243aWSdgY0sbGx8NtZXsYvWCnlNyomdIbjAArXt62AW9I7NW1lSVCu/M5WgsiPrsmAk5WL70aqWK8Q3adA0kbzHUcpxYsuRzeOD8xDGF4QGzKm4UCEnG6fKxnTggXE9WP/8R2jtdo9Ydrjr2yHM7w524JPWG67iNLW2YUzMho21JzggBx5UV5Vz7FImLOTUT2E4cEI6PDS1tuOD0iRl/3kiGniGqd9jpZTtxQDgO123ehcNOb2oJ5VgsZPsz4KhyrHypI6diYgdtquU4sdSST0LUyOnycNaTYCiUsC2+7khdN9UwkQXJHDYuhuMgWmR75P8pRkim9fC0jUf7PDLlSBnos1VmUUdOSlleZFDsoM1iC4NxdjPqL6xE/dzwv3F2s6DjxJ5P9OBSEWUqSz4JyRN2m4mznpiMidsKTQYc7Haj3xeAa9CPoWAIDbVVGFNsitlPbN1Uw0QWJHOi44IrPk4YaxH8fCMkXXxtY4nFxHNEhilk4Wv6bJVZ1JGTyvC7+retfgd3PfEebnv0bew+eCyjFScyaDP6QcYO2kx2nFmP+rmVeOmtdjRvacNLb7ajfm4lrIWjH9dUVx1zvqa66tGPE5lPsWWa9XwSki+0QHfvIBZePCWmnjTUVqK8tADj7GZ2W1NdNf7w7HY88sJOfPhZF25b/Q7u+dP72PzGPlx29iSMKTYJb7M46uaC2kpsbe2gOprHrEV6NM6vxtYdHVhQW5kQk3/62yeCn2+EpKuslEHj/NjPHo3zq1FuZ7KfGRk+k/Khz1aZRZOdSESWRQZFDtp0uf0JY1zWbto1al5dbj+at7Rh3rkV4cYgBDRvacOksacnv0ax+RRZplLkc8AfgNmgU/0gWEKk5HT58NCGVjTUVmL++ZPDr1OGwE4qcU/jbLg9fhSaDPjDs9txyDmA+gsrsYFj/NKd181CESOwjsW1IYUmA7z+Icw8+XSqo3ms66gXz70WHiup0QBLFtTAPxTE184B/P2d/TjSG36lTcjzjZB0HXZ60fLeF1iyoAYeXwAmow5/29qOcWMKUW7NbmdOUQtf5+AEI0pCHTmJJHsHOKOVZnjQJnsOARVDbF57+3045BxA82ttsduFXKPK8lkxsRTd3X3U0BASJTI+btAXQPOWtoTfuz1+TBxTiIPdbhxyDoQ38oxfGhoKwFpsEl7H4tsQGNntJD/1uDwJbX393MqE2MzKs5jkvR6XB7sP9GL3gQ9jth/t82S9IyfbZ1I+Ij4DEmHo1UqJqOkdYLWMIVNLPgnJF9FjQJLVsfg6SPWRZALXmCStRkPxRmShpDFy9Dkof9A3chKRbZFBEQtfi81rthd1VEs+CckXdosRTXXV+Od/vsDPF87E/s5j7ILgV18yla1jkbr7zP/tRoFRh+vnnYrHX/okeT0W0ZaR/OawMbi5fgY6utwjbf1xFvzkimn444sfw1JowNxvnIAJZUXwB0PhP10H5c41yVVlJQyWNtTg4Nf9bDxOHFuE8lIGGMpuXnJx4WsA7HPi633dMDN6ek6AOnLSkeMd4DQW2jbqtewYF61GA6Ne2JezWV3UMY0yVczik4TkkiAwY3IpdDot/rB+O1u/rp83DYWmqMdJCJh6gg0LL56CR57bCUuhAfPPn4zjy4twnN3M2YkT25aRPBYENNDEtPU3XjkdW94/gIbaShSZDTF/QGiqq8aMilLqzJHMCAFeXyAmHpvqquVpw3JxXBo9JzjRq5VSyvIig+ksfL1i/XZsaNmD5i1t2NCyByvWb1fcwt4ARJWp4hafJCSHuPr9ePS5nTH16/GXPsZnB3tj6pjL7ccjw/sd6fVgQ8serNrYGv5lXD2mOkvE4IqbNS98hOrJZRj0BthOXOR3azftgtNFa1eRzOjq9XJOJNfVm+KC4FLJsYWv6TnBjTpyKpbrC3uLpZZ8EqJGfPUrGArF1LFU6iHVWSJGsoXi+SbZcbrye3Fmkjl8C4L3UMxJgp4T3Kgjp2LZngxELYNn1ZJPQtSIr35pNZqYOpZKPaQ6S8RItlA8+/9xv7NbZVqcmeQ8vslOSinmJEHPCW7UkVMx0QuCZ/k4AOEBqoN+HOx2w+UZUuRC6YSQ0VnNevz0ezNi6tf186ZhysTimDqWSj2kOkvE4IqbG6+cjl37uvD6toNoiFskvKmuGnZrfn/oI5njsHEvCF5WnOLSA1n8vKQm9JzgRpOdqJnYwaxiF74Wez4ZBqiKncyFEJKEBth94Bj+8fbnI7NWBoFN/9qLqy+ZGrtvKu1FLg7MJ5kXAqaeaMNti85A28GjCAaBjS17sOCiKoyxMrCajZhWYYfzmAd2qynciaOJTkimBIGyYhPuuOYbcA34YDUbodMgtZijCT34if3smuOoI6d2YhdZFLvwtYjz8Q1QfWDx2RlZmDIymUv0u9TM/2fvzuOjqu7+gX9m3yfLEBYFIgQDKEtUXKB1qZKqFUVQAdfWgmWp4s/aPvbRWqy7tlbBDarWLlYBFdQ+WhWwWist1SICikZ2kC0Eksnsk5n5/ZFkksncmdxZ7zKf9+vlq2Uy986555577v3esxl0Bfs9olLReS1PPqsmPmtlJ8FrOpP6ggvGUhaaWkJ46E+fJJTFp1/ZiHtnj4fdpIfdBLg6u14xiKMCcvvCuOf5j3N69ij285LiZPvsqmKqaKb48ssvMX36dNTV1eHiiy/Gxo0bpU4SdVPsAaocEEtUGPFrK8VEErzGqNiaUkwwwUlNqNjy8ezB5xfKlOIDuVAohHnz5uHCCy/Exx9/jDlz5mDmzJnweDxSJ406FHuAKgfEEhVG92uL1xjJQaoJJjipCRVbPp49+PxCmVJ8IPef//wH4XAYP/jBD2AwGHDRRRdh2LBheOutt6ROGnUo9gBVDoglKozOa+vDT/dieo+JJHiNkRRcDiPmTB3NSU1Icvl49uDzC2VK8WPktm7dipqamoTPhg4dioaGBolSREmKPZEBJ04gKoyOa+u2a05BMBrFvUPHwxsI8xoj6USBsTWVuH/et9B41MdJTUg6+ZiMg88vlCHFB3I+nw9mc2IXCovFAr/fL1GKSFCxJzLgxAlEhdFxbVVVOdDY2AqX3Rj/nEgSUaB2cAUqLPr4v4kkkY/JOPj8QhlQfCBntVoRDAYTPvP7/bBaraL34XLZ850sRamqckidBFmSKl86y6PQ7xsMuV2yuWwv5W/nun0m2wp9VylpF5JrOU5XP6q17uBxyVcplsdUeLzSY3mUDvO3neIDuZqaGvzhD39I+Gz79u249NJLRe/j6FEvotHSfOXhctnR1MSJYXoqRL6IfWFw9KgXFRW2pN93uewIh9tySkMu20v527luL3Zbg0Ev+F0lpD2VVOU4k/IoVD+qte7gcUmD5TEzPN7C/54YLI/SKLX8TVceNbFYTNERTCgUQn19Pa6//npcffXVePfdd3HnnXdi9erVqKyslDp5REREREREeaf4QA4AGhoasGDBAnz55ZcYOHAgbr/9dowfP17qZBERERERERWEKgI5IiIiIiKiUqL4deSIiIiIiIhKDQM5IiIiIiIihWEgR0REREREpDAM5IiIiIiIiBSGgRwREREREZHCMJAjIiIiIiJSGAZyRERERERECsNAjoiIiIiISGEYyBERERERESmMXuoEyEFTkwfRaEzqZEiiosKKo0d9UidDdgqRL1VVDlHfa2ryoKzMwvMigVK6HjIpj0L1o1rzisclDZbHzPB4C4vlUd5KLX/TlUe2yJU4vV4ndRJkSep8kfr3SxXzXTy15hWPS5nUfnw98XjlTWnpVRrmbxcGckRERERERArDQI6IiIiIiEhhGMgR5YMGcPvD2N3ohTvQBmikTpDCMP+IiLqwTixdPPeUAU52QpQrDbBldwsWLd+AYDgCk0GH+dPqMHJwGVCac+hkhvlHRNSFdWLp4rmnDLFFjihHbl84XukCQDAcwaLlG+D2hSVOmTJIkn9840lEMsV7SulS7bnnPbdg2CJHlKNmbyhe6XYKhiNo9obgtBgkSpVyNHuKnH/p3ngSEUlJA3zT5OM9pUQV/X5YDGxlLCi2yBHlyGY2wGRInArXZNDBZlZopVtk5Q6TYP6V24wF+T3VvvEssiMtATR5Qkn/hUp0TU6ifHD7wthz0FPUOpHko9j3w2LgPbewGMgR5SgYasP0+tp45Wsy6DC9vhbBcJvEKVMGp0WP+dPqEvJv/rQ6OK2FCYTTvfEk8Zo9QTz3xuak/1p5cybKWrMnhNUf70q6p8y9bEzB6kSSj2LfD4uB99zCYtdKohzZrUasWrcLk8+qae/3HQNWrduFk48/ReqkKUMMGDm4DA/Nm4BmbwjlNmP7TatADTudbzy731iU/saTiNSh3GFCqzeMt9buiN9TtBoNhg5wsBtaKSjy/bAYeM8tLLbIEXXXMSB309ZG0QNynRY9rrlgJF7/xzYsX92A1/+xDddcMFLRb9CKLgY4LQYM7mNrHwdQwJuWbN54cvA3UWnI4FrvrJ9avWEsX9OA1z/YhkF97bCb+d69ZBTxflgMsrnn5ptM7uGsGYg6ZTsgV4Vv0FRNDueLg7+JSkOm17oc6ieifFJjmZbRPZwtckQdchqQq7I3aKpXyPMl4i0dB38TlYasrnXeT0htVFam5XQPZ4scUQcuI0A5E/mWTpVTTBNRIi4lQKRKcnpeZIscUYeclhGQSV9pVVNAHot9S6fGKaaJKFHBlhLQAN8c8si6LiQVUcC9t9jktOwUW+SIOnQuI7BsVUO8NaVrGYE0N10Z9ZVWLYXksdiWts7B3z2PR/HjBogorvtSAt3vK/GlBLK51hVSF5JKsLwJyvp5sQAYyBF1yHYZgVStMA/Nm8CuM3milDwWPc2yGgd/E1GCQiwloJS6kNSB5U2YnJadYtdKog7ZLiPAxS4LTyl5nNE0yyob/E1EiQqxlIBS6kJSB5Y3YXJadootckSdurWS+MIRWA06Ua0k5Q4TBrisOLNuYLzv+Ief7lXueCdN+1u4Zk8I5Q4TnBa95EGGYhYUZUsbEXUqQH3QWRc6bAace8rgeCtfpcOUv3QTdVDd802+yOhez0COqLuOVpKawZVobGwVdVE6rXpMm1iLxSs2xftKz5k6Gk6bAYgWPsl5JdP+8IoaU9ZRhuLdTuSWPiIqnjzXB06LHj+96mTsbfRgabfxOYP62iWvp0l9VPV8k28yudezayVRjtzecLySA9q7HSxesQlur/LWBJPT2igJur39umvW6Xho3gQ+tBBR6YkBVeXmeBAHyKieJtVR0/ONWjGQI8qRmvqQy/pYOKaMiEje9TSpCsua/DGQI8qRmtYEU9OxEBGpEetpKhaWNfljIEeUo4xmKpQ5NR0LEZEaOS163HLlyaynqeD4TCB/nOyEKFcymr0oZ2o6FiIiNYoB40cPQP8K1tNUYHwmkD0GckT5FAOg0UiditzIZCYmUWS4VAIRUUayqMe0Wo1y6mlStnw8E/BeXTAM5IhyJdMp+1WP+U5ESsd6jNSOZbygOEaOKEeynbJf5ZjvRKR0rMdI7VjGC4uBHFGOOD2vNJjvRKR0rMdI7VjGC4uBHFGOOD2vNJjvRKR0rMdI7VjGC4uBHFGOOD2vNJjvRKR0rMdI7VjGC4uTnRDlitPzSoP5TkRKx3qM1I5lvKAYyBHlg5Km7FcT5jsRKR3rMVI7lvGCYddKIiIiIiIihWEgR0REREREpDCSBHIbN27E+PHj4/8OhUK48847cdppp+GMM87AkiVLEr7/1ltvob6+HnV1dbjhhhvQ1NQU/9u+fftw/fXX46STTsLEiRPxwQcfFO04iIiIiIiIpFDUQC4Wi+Hll1/GD3/4Q4TDXQsBPv7449ixYwdWrVqFV155BStXrsRrr70GANi6dSvuuOMOPPDAA1i3bh2qq6txyy23xLf9yU9+guHDh2PdunW45557cMstt2DPnj3FPCwiQAO4/WHsbvTCHWgDNFInKAWlpJOIqJSwbiYqDpVda0Wd7GTRokX44IMPMHfuXDz99NPxz1euXIkHH3wQZWVlKCsrw8yZM7F06VJceumleOONN3Duuedi3LhxAIBbb70Vp556Knbu3IlYLIbNmzfj97//PYxGI8aPH49zzz0Xr7zySkKwR1RQGmDL7hYsWr4BwXAkPrXuyMFl8hrQq5R0EhGVEtbNRMWhwmutqC1yM2bMwIoVKzBq1Kj4Z263G42NjRg2bFj8syFDhqChoQFAe4tcTU1N/G8WiwUDBgxAQ0MDtm3bhgEDBsBqtcb/PnToUHz11VdFOBqidm5fOF4pAEAwHMGi5Rvg9oV72bK4lJJOIqJSwrqZqDjUeK0VtUWuX79+SZ/5fD4AgNlsjn9msVgQCATif7dYLAnbmM1m+P3+pO16biuWy2XP6PtqU1XlkDoJsiQ2Xw5sbYxXCp2C4Qh84QhqBldm/Lud5THf5yXf6VQrXg+JUtWPrd+0wGBIvoUYjXrF56HS05+KGo4r3f1aqceXbd2s1OPNlhyPV43lUSmyyV81PgdJvo5cZ5AWDAbjn/n9/ngrm1BgFggEYLPZEIvFErbrua1YTU0eRKMKbVPNUVWVA42NrVInQ3YyyRerSQ+TQZdQOZgMOlgNuoR9iK10mpo8cLnseT8vYtNZygp2PWja3wQ2e0Iod5jgtOgl78aRSXlMVT+Gw21Jn4VCbYouT2qtE+V+XLmWR7kfXzrZ1M1KPt5s5PV4RdTHpVweCyLP98Bs81epz0HpyqPkgVxZWRmqqqqwffv2eIvdjh074l0thw0bhh07dsS/7/f7sX//ftTU1CAWi2Hfvn0IBALxlrnt27cndNMkKjSnVY85U0dj8YpN8T7Xc6aOhtNmAKJSp66L06LH/Gl1SX3DnVaD5EGF5DpuMge2NsJq0uc30FJhn3wiyp9e62ahh2DKDuvjzOUahMkoz9X4HCSL2uCSSy7Bk08+ieHDh8Pn8+G5557DddddBwCYNGkSrrrqKqxbtw4nnXQSHnnkEYwcORJDhgwBAIwYMQKPPvoobr31Vqxfvx5r1qzBsmXLpDwcKjFubxjLVzdg8lk17bMfxYDlqxswpP8pcFoMUievSwwYObgMD82bgGZvCOU2o6Irr7wp8E0mVZ/8h+ZNkFf5ICJppKubU9RPrsrSHhKSLdbHGcrD/VFWea7C56C0k508+uijcLvdANrXa4vFCnOkN998M44//nhMmjQJl19+Oc4//3xceeWVAIDhw4fj/vvvx4IFC3D66adj69atWLhwYXzbxx9/HNu2bcP48ePxi1/8Avfddx9qa2sLkk4iIYfdQexv8mH5mgYsX92A5WsasL/Jh8PuYO8bF1sMcFoMGNzH1l6BKrjyypdCD35u9oQE++Q3e0N52T8RqUCKujlV/bT/sFfCxCoX6+PM5OP+KLs8V9lzUNoWueeffx5XXHEFnE4nzjvvPHz00UeorMx9MODpp5+OTz75JP5vk8mEBQsWYMGCBYLfP//883H++ecL/m3AgAF49tlnc04TUbYsKfpcm02yaPCmXqS7yeTjbWG5wyRYPsptxpz3TUTqlqp+OtLqR3+nOcVWlArr48zk4/7IPC+stE+a1dXVuOmmmzBixAjEYjHce++9MJlMgt994IEHCpJAIrlzWA2YUV+Lpasa4l0PZtTXchyDQhT6JqPGPvlEVByp6qdKhwWsQDLH+jgz+bg/Ms8LK+2T5mOPPYY//OEP8e6VPp8PbW3Js5QRlTK7SYeBVXZMOWcYorEYtBoNBlbZYTdLPzMh9a7gNxkV9sknouJIVT8N6GNDU5NH6uQpD+vjjOTl/sg8L6i0gdySJUvw85//HJWVlbj22mtx//3356VrJZGqxICaYxyoKjezklKibjcZXzgCq0GX//PX0Sc/3hWFZYOIxEjxEKzVaqROmXKxPhYvX0EY87xg0k528vbbb8db47qPaSOiHlQ2eLbkdJy/0TVVPH9EJC+8v5CUWP5kLW2L3AknnIDrrrsO1dXViMVi+PGPfwyDQXhw45/+9KeCJJCIiIiIiIgSpQ3knnjiCbzyyitwu934+OOPMWLEiPjC20SqVMiFoUkauS5mSkSUK9ZDROoik2s6bSDXp08fzJkzBwBw9OhR3HrrrbDbuQglqVQuC1/K5IKWlBzzQOw5ZQBPRPnUrT6sLDNjz0FPQj104xVjceJx5UBU6oQS9UKO9/Zc5ON48rBQer6kDeQ++OADTJgwAQaDARdccAH++9//pvzu2WefnffEERVTqoUvH5o3If16KTK6oCUj0zwQdU5lmnYiUqgedcqM+uFY+f7WhHroiZc/w+0/OBWD+lhZz5B8qe3+mKfjyfp5sQDSBnKzZ8/GRx99BJfLhdmzZ6f8nkajwZYtW/KeOKJiOuwOCi58edgdTHthyumClopc80DMYqZyTTsRKVPPOiUaiwnWQ1t2HkGZ1cB6hmRLbffHfB1PPhZKz5e0gdyXX34p+P+J1Mhi0gsufGk2pV/YW04XtFTkmgdiFjOVa9qJSJmE6hSheigaBesZkjW13R/zdTz5WCg9X9IuP9BdKBTC119/jU8//RTbtm3jwuCkOg6rATPqa2Ey6AC0X5Qz6mvb+0+n0XlBdyfVBS0VueZB52Km3c9pfDHTDnJNOxEpU8865b1PdifdW6bX1+LDDXtZz5Csqe3+mK/jEfNsUSzpn1AB7N69G7/5zW/w/vvvIxwOIxZr70RqNpsxceJE/L//9/8wcODAgieUqNDsJh0GVtkx5ZxhiMZi0Go0GFhlh92cfiCs06LHT686Gdv2uePb1RzjLKlFwTsrtZ79zp02A9xeCQdJi1jMNGXaS+j8EVH+9KxTWr1hDKyy4xfXn4bPdzQhGgU+/nw/5kwdg2ZvCNBolD+BBMlPHib1UNv9MW/Hk6+F0vMgbSC3detWXHXVVRg6dCjuvvtuDBs2DE6nEx6PB59//jleffVVXHbZZVi+fDmqq6uLlWaiwogBNcc4UFVuhi8cgdWgE31hhtqi8cHsnRVDSRGq1GwGbNklg0HSHYuZxrtN9PztbmnP9LwTESVJ9ZAHwGHpB08gjEH97Lj/Dx+rYwIJkp98TVIio4AlL/J5PL09WxRJ2q6VjzzyCCZMmICXXnoJl156KUaNGoXBgwfjhBNOwBVXXIGlS5fi1FNPxdNPP12s9BIVVseFObqmqv3iFHFhpho86/aFC5xYmenIu8F9bHBa2lviFJMvWZx3IqKUetSHiHV9Zjcb8MTLnymjbiRFyutziVBZVjKVHU/aQG79+vWYNWsWNBpNyu/ccMMN+M9//pP3hBEpRbrBs6WM+UJElIx1IxUay1jpSBvItba2oqqqKu0O+vfvj0OHDuU1UURKorbBwPnCfCEiSsa6kQqNZax0pA3kotEo9Pr086HodDpEIpG03yFSMznNXiQnzBciomSsG6nQWMZKR9ooTaPRIBAIwO/3p/xOur8RlQS1DQbOF+YLEVEy1o1UaCxjJSNtIBeLxTBx4sS0O4jFYmnH0BGVBJnMXiQ7zBciomSsG6nQWMZKQtpA7k9/+lOx0kFEREREREQipQ3kTjvttGKlg4iIiIiIiERKP5NJN263G3/84x/x9ddfIxRKnr508eLFeU0YERERERERCRMdyN166634/PPPcfbZZ6OioqKQaSIiIiIiIqI0RAdyn3zyCX7/+9/jpJNOKmR6iIiIiIiIqBdp15HrbuDAgYjFOOUNERERERGR1ES3yN1111245557cM0112DgwIHQahNjwFNPPTXviSMqOg3g9oVxYGsjrCY9nBa9+qfs7TjmZk8I5Q5TaRwzEakX6zQieeE1WTCiA7kvvvgCDQ0NuOOOO5L+ptFosGXLlrwmjKjoNMCW3S1YtHwDguEITAYd5k+rw8jBZeqtcErxmIlIvVinEckLr8mCEt218qmnnsLs2bOxdu1arF+/PuG///73v4VMI1FRuH3heEUDAMFwBIuWb4DbF5Y4ZYVTisdMROrFOo1IXnhNFpboFrlwOIwpU6agsrKykOkhkkyzJxSvaDoFwxE0e0NwWgzpN1Zot4GcjlkpFHpuiChzsqnTWO8QAZDRNalSogO5K6+8En/84x9x++23J42PI1KDcocJJoMuocIxGXQotxnTb6jgbgNZH7NSKPjcEFHmZFGnsd4hipPFNalioiOyXbt2YdmyZTj99NNx6aWX4vLLL0/4j0jpnBY95k+rg8mgA4D4zddpTf/GSMndBrI9ZqVQ8rlJSQO4/WHsbvTCHWgDNFIniEg+5FCn9ax3HDYD9hzyYPsBD69ZteuonzdtbeS57qDVADPqaxOuyRn1tdBqmTn5ILpFrra2FrW1tYVMC5HkjHotppwzDNFYDFqNBkZ97+86FN1tIAaMHFyGh+ZNQLM3hHKbsf2BRyVvjRV9boTwTT9RejKo07rXO33KzfjehCFYtqqB16zasX4WdMQdxJtrd2DyWTXtgW0MeHPtDgw5xgm7SXQYQimIzkG9Xo9LL70U/fv3L2R6iCTj9oXxmxfXJzX/PzRvQtqHfsV3G4gBTouh6xiVdMPpZRyK4s9ND6laGHsro0QlRWydVqBxbN3rnXNPGRwP4gBes2rG+llYucOEVm8Yy9c0xD+T9D6ssvGrortWPvvsswiHFdwdiagXzd7UrTfpyKErT0nqePt521Nrcddz63Dbkx9hy+6WhK4sajs36VoYiSgDIuqPbCXUOxrwmi0RrJ+Fyeo+XMDrXiqiW+S+853v4Pnnn8eNN97ImStJlWxmg2Drjc3cS2Ujg648pUjU20+VnRu1tTASSaWgrSfd6h1vMILXP9jGa7YEsH5OQUb3YTW2mopukdu2bRtefPFFfOtb38JJJ52E8ePHJ/xHpHTBUBum9xiQO72+FsFwW+8bd3TlGdzH1l4ZKDRQUBLRbz9VdG5k9WaTSMEK3nrSUe8MqDDzmi0RrJ/TkMl9WI2tpqJb5K699tpCpoNIcnarER9/vh/zp9chEIzAYtLhrx9uw8nH95E6aQQk9WuvLDOX3ttPGb3ZJFIy0a0nuY6n4TVbOrqda184AqtBp45zraIxZWpsNRUdyE2ZMqWQ6SCSnNOqx3fPOA6LlnXNODVn6mg4bQYgKnXqSlyK2cB+etXJ8QlqEt5+KvQmI4qSJ6chkonO1pOedUpC/ZGvWQh5zZaOjnNdM7gSjY2tyj/XKpuJU9R1rzBpA7nLL78czz33HMrKynDZZZdBo0k9GvCVV17Je+KIisntDWPxik0JfacXr9ik6L7TapGuXzvfdBNRxkS0lKlxPA1RJlR3DaiwhTxtIHfOOefAaGxvbvzOd75TlAQRSUV1a46pSLpzE+9zDyi6MiaiIuulpYz3BCp1qrwGVNZCnjaQ+/e//40ZM2bAYrFg4MCBuPDCC2EymYqVNqKiUmPfabXguSGiYmO9Q6WO14D8pZ21cuPGjWhsbAQA/O///i+8Xm9REkUkBc44JV88N0RUbKx3qNTxGpC/tC1y48ePx4wZM+ByuRCLxXDZZZdBqxWO/dasWVOQBBIVjVpnnFIDFfZrJyKZY71DpY7XgOylDeQee+wxrF69Gm63G/fccw+uvPJK2Gy2YqWNqPjUNuOUmqisXzsRKQDrHSp1vAZkLW0gZ7FYcPHFFwMAjh49imuvvRYWi6UgCXnllVewYMGC+OQqAPDLX/4SF110Ee655x6888470Gq1uP766zF79uz4d9566y08+uijaGxsxKmnnooHH3wQLperIGkkIiIiIiKSA9HryN1444344osv8PnnnyMcDiMWSwzJr7766pwS8sUXX+D666/HT3/604TPH3nkEezYsQOrVq1Ca2srZs2ahX79+uHSSy/F1q1bcccdd+CZZ57B6NGj8etf/xq33HIL/vSnP+WUFiIiIiIiIjkTHcg9/fTTWLhwIcrKypK6V2o0mpwDuc8//xzXXXdd0ucrV67Egw8+iLKyMpSVlWHmzJlYunQpLr30Urzxxhs499xzMW7cOADArbfeilNPPRU7d+7Ecccdl1N6iIiIiIiI5Ep0IPfyyy/j5ptvxty5c/OeiEgkgq+++gqvv/46HnjgAVgsFlxxxRWYPn06GhsbMWzYsPh3hwwZgoaGBgDA1q1bMWrUqPjfLBYLBgwYgIaGBgZylB1N+wKYB7Y2wmrSw2nRl15/8I48aPaEUO4wlWYeEJEysf4iKfEZgopMdCDX3NyMCy+8sCCJOHLkCEaNGoVLL70UTzzxBLZt24Z58+YhFAoBAMxmc/y7FosFgUAAAODz+ZLG7JnNZvj9/oKkk1ROA2zZ3YJFyzcgGI7Ep9kdObisdCpi5gERKRXrL5ISyx9JQHQgd/755+Ovf/0rbrrpprwnoqqqCi+88EL83yNHjsQ111yDf/zjHwCAYDAY/5vf74fVagWQGNR1CgQCGc+s6XLZs026KlRVOaROgix8c8gTr4ABIBiOYNHyDVj4k3NwbN/ilZHO8ijFeZFLHkiJ10OiVPVj6zctMBiSbyFGo17xeaj09KeihuNKd78ORTUlVX+p4XxmQo7H27088v5ZXHIsD1IQHciZzWYsWbIEb7/9Nqqrq2EwJC4GuHDhwqwT8fXXX+Nvf/sb5s+fH/8sHA7DZDKhqqoK27dvR79+/QAAO3bsiHe1HDZsGHbs2BHfxu/3Y//+/aipqcno95uaPIhGS/N1SVWVo32afcKBw954BdwpGI7gQJMHRk3u5UNspdPU5IHLZZfkvBQ6D+SulK6HTMpjqvoxHG5L+iwUalN0Hqq1DMj9uHItj1VVDhw47CmZ+kvu5zPfin282ZTHUr9/FlMplv9UhFf3FhAIBHDxxRdjzJgxKCsrg9VqTfgvF06nE88//zyWL1+OaDSKzZs3489//jOmTp2KSy65BE8++SSOHDmCvXv34rnnnsMll1wCAJg0aRLWrFmDdevWIRQK4ZFHHsHIkSMxZMiQnNJDpancYYLJoEv4zGTQodxmTLGF+jAPiEipWH+RlFj+SAqiW+QeeOCBgiWiX79+eOqpp/DrX/8aDzzwACoqKjBv3jxccMEF+M53voMHH3wQkyZNQjQaxfTp03HllVcCAIYPH477778fCxYswMGDBzF27NicWgaptDktesyfVpfUv91pNZRM/3bmAREpFesvkhLLH0lBE+u5IFwaX331FZ599lls3boV0WgUQ4cOxbXXXouTTz65kGksOHatLJ3m6V51zDjlC0dgNejyWgEroWslgK5Z37whlNuMJXUTKqXrIdeubK2hKJ5+ZUPS5zMvGQWXXblvoNVaBuR+XPnoWtnY2Foy9Zfcz2e+KaFrJYCCPkNQl1Is/6mI7lr5wQcfYOrUqWhubsb555+PCy64AF6vF9deey3++c9/5iWhRJKLAU6LAaNrquC0lGgF3JEHg/vYSjcPiEiZWH+RlPgMQUUmumvlY489hrlz5+LGG29M+Pypp57CwoUL8e1vfzvviSMiIiIiIqJkolvktm3bhosvvjjp84suuii+QDcREREREREVnuhAbsCAAfjiiy+SPv/888/hcrnymigiIiIiIiJKTXTXyquvvhp33XVXfHZIANiwYQMWL16MmTNnFiyBRERERERElEh0IHfdddfB6/ViyZIlOHr0KDQaDfr27YubbroJ11xzTSHTSFQ8HTNOHdjaCKtJD6dFz8HKYnXOFucJodxhYt4RUfGxHiLKL15TstZrIBcKhfDqq6/ie9/7HubOnYu5c+fi8OHDWLp0KcrLyzFt2rRipJOo8DTAlt0tSWvAjBxcxkqrN8w7IpIa6yGi/OI1JXtpx8i1trbi6quvxgMPPIAdO3bEP+/Tpw88Hg8eeeQRfP/734fH4yl4QokKze0LxysrAAiGI1i0fAPcvrDEKZM/5h0RSY31EFF+8ZqSv7SB3FNPPYVAIIB3330XdXV1CX/7+c9/jr/+9a84cuQIlixZUsg0EhVFsycUr6w6BcMRNHtDEqVI5jSA2x/G7kYvDruDzDsiklSvdXi3OssdaAM0EiSSSEFU+VyksnogbdfKd999F3fffTf69+8v+PeBAwfiZz/7GR5++GHceuutBUkgUbGUO0wwGXQJlZbJoEO5zShhqmSqR3eLGfXDmXdEJKm0dTi7iBFlTHXPRSqsB9K2yB0+fBjHHXdc2h2MGDEChw4dymeaiCSh1QAz6mthMugAtFdWM+prodUq/HVNAfTsbrH6411JeTd/Wh2cVoOUySSiEuK06DF/Wp1gPcQuYkSZS3dNKZEa64G0LXL9+/fHzp07ceyxx6b8zq5du9CnT5+8J4yo2I64g3hz7Q5MPqumvak9Bry5dgeGHOOE3SR6gteS0LO7xeHmAN5cuwN3zjwdbW0RlNuM7RW9Qt9wEZECxYCRg8vw0LwJaPaGEuqhdF3EnBZlPpQSFVyaa0qJ1FgPpH06vfDCC/H444/j1FNPhdGY3IwaCoXw+OOP45xzzilU+oiKptxhQqs3jOVrGuKfKboLQQEJdbdo9YZhN+ngLDe3f6DQip6IFCwGOC2GroeyjnpIdV3EiIolxTWlRGqsB9J2rZw9eza8Xi8uu+wyLF++HF988QX27NmDzZs346WXXsKUKVPQ3NyMefPmFSu9RAWjti4EhcS8IiIlYZ1FRGqsB9K2yFksFrz00kv4zW9+g4cffhherxcAEIvFUF5ejsmTJ2PevHkoKysrSmKJCioGjKwuw72zx+OoJ4gKuwkupxGISp2wLBVyEU+VdbcgIpVIVe+xzqJi6Ch/B7Y2wmrSc/FsuVFhPdDrwB+73Y677roLt99+O/bs2QO3242KigpUV1dDo+EkEKQiGmDLLpXMZlSMmZlU1N2CiFSgt3qPdRYVkgpnRFQlldUDabtWdmc0GlFTU4OTTjoJxx13HIO4UqWy9Te6U9NsRm5fGC+8vQWTz6rBtIm1mHx2DV54e4sij4WIqDfRaAxNrSHsOdSKyWfXoE+5WdF1OCkP77skBU7FR+Kp/G2TmmYz8vjDqD+9GstWNcTP1fT6WngCYcUdCxFRWhrgX5v249GX1ifUd2+t3YHDzQFF1uGkPLzvkhREt8gRqanFSkjnbEbdKXU2I5NRH7+ZAO3natmqBpgMfHdDROri9oXjQRzQVd+de8pgxdbhpDy875IUGMiRaOlarNRATbMZeQNhwXPlDagj6CYi6pTq3qTVQrF1OCkP77skBb4mINHUuP5Ggm6zGfnCEVgNOsXOZlRuV/m5IiLqkOredMrwvnA5jIqsw0l5eN8lKbBFTg4UMoGImlqsUuqYzWh0TVV7n3aFPgCUxLlSOoVc90Ry57TqcZNAfccgjopJtfdd3qtkjS1yUlPSBCIqXH9DtXiu5E1J1z2RnHUsG/OXjtkCtVpg5HGVGFRlVe4aoKRMKurVE8d7leyxRU5iOU9XW+w3JR0tVoP72BTdYqV4Ys47z1VxZHENqn3iIKJi8QTasOeQB2eeNBDQAKs/3o37//Ax3N4wWxKo+FTSq6dT3u5V+boWO/azaWsjr+kObJGTWE7T1fJNSWnieZePLM+Fmpa6IJKMBti+vxUr39+atOyAJxDGN4d9rCeJcpCXe1W+nln47COILXISy2W6Wr7VL0087/KR7blQ01IXRFJx+8J4+tWNSffPiadWw2TQs54kylE+7lX5embhs48wBnISy2W6WrUvB5ATFXepUdR5V/F5ALI/F6odFE9URKmuv0H97CnvrbKsJ0k9VNb1Lx/3qnw9syjq2aeI2LVSYrlMV5vTcgCa9rcbB7Y2wmrSw2nRq6dpWuXN7/k4782eEModpsKed5WfByCDc6EFmtwhNLkDcJWZ4XIYORkNUY5SXX8VDjMMeg2ngqfi0gDb9rVi2z43orEYtBoNao5xouYYh3Lr9jxMnJavpatUvwRWlhjIScxp1WPO1NFYvGJT/GF3ztTRcNoMvc645bTqcfOMOuw+4IlXGoP723vfVuUP2Kma3x+aN0EV448635D1PH+9Vq5iz3uegr2czkMxA84ciDoXWuCzbUeSrvGxNZVwWgxdeSHD48uXUDSGVoHuLw6rAUatwl9Zk2ScFj1uvGIsnnj5s4Qxcn9+63Ncft5wzJo8Cs++vjnx2rQZ4PbKv24h5fEE2rC30ZMwZnNGfS36VVpgN2XwuC23+1/HBC7Z3qucNj1mTxmNJSu77oGzp4yG024AIr1vH9+PRY+fXnVyUqBc6i9BGchJzO0N49+b9uH2H5wKty8Ep82It/65HUP6O3p92PUGI/AHIwmVxo+mjIY3GIGtR5/mhN9UeaCj+okksnxDJuq85zHIT3UePB3dhlPepOTyokHMzVTEuWhyh+JBHNCeB4tXbMK9s8fDZe/lTaLcbuhZavWF8dwbm5M+n3nJqN7zgCiVGOC0GXDLlSdh5343olHgX5v2YfzoY/Dwnz+Bw2bAlHOG4dgqG/pVWtGnzIgtO2VQt5Aquf1tWNpjzoOlqxpw/OAK8YGcXO5/PdKUy32oqSWEl9c0YPJZNe1dTWPAy2saMKjvyRnX/6G2aMIz7/xpdRltr0YcIycxf6gN3xp7LLbsPIo9Bz3YsuMovjX2WPjDbb1u6/G34XcrEx8Qf7dyEzz+9NuqvZ9xucOEkdXl+Nk1p+CmK+rwP9eegpHV5epqfheztECP8Wkef+9jRvI5mFhokPQAlxVHW0O47am1uOu5dbjtyY+wZXdLwjgCWQxo7riZpktnkhgATfIXmtwBwXxvcgeSfjNhPKE2RRq06h53SJQJk1EPfzCC1R/vBgBMO68WoXAUk8+uwbnjBmP1x7vwxMufYf1XjdhzyCd93UKqFQi2Cdb1gWDvz3Od3P4U9z+/RGW0o7vov784hC92HcW/Pz+IbftaM7rvNLkDCEe6dRPTAOFINPke2AtZPBsUQo5zCbBFTmJ6vQ5HW0MJbxiuOn8EBvSx9bptc2tQ+MHcE0Q/pynldmrvZ+y06VF/+nFYtKzrjVY2zfiKJvBW78YrxmKAy4r9Tb7413qe93y2Zgp1O5wzdQzu/8PHaVsFM0pDgVqsRLdai3h76iozC15vLqc57X5uvGIsXnzny4Q0vPD2Flx1/oiErmSSv60lkpBBr4XVrMfV54+E2xtEizckuByBXqfBlp1H1N1bgyTVp8yMAS4rzqwbGH8Y//DTveiT5nmsp8YW4Rd/jS1BOM3FL6P56C5aVWHBRROGxFsrO/dRVW7JKC2q7G2VhxZYtshJLBCKJD2svfjOlwiEeo84yp0mDHBZMe28Wkyb2P7fAJcV5fb0lYbaZ8xragnF+2ID7Xm6ZOUmNLWoo8VRDKFA5ImXP8OcqWOSz7vNEH8bZLMaMMBlTdhX1kF+t26Hd806HQ/Nm4BYLNZrq6Do6Y6zaTUTSWyrtZg3hC6HEXOmjk7I9zlTR8PlNKbdzxMvf9b+QNDNmXUD40Fcqt8jKhkaYM9BD3774nosXPYp/MEInnltc8L10bkcwYA+NkRjiF+HfcrNmHZeLWbU18JmNrBlm3LmtOoxbWItXv/HNixf3YDXP9iGaRNr2+ctEMlk0As+1/W8JxZLqu6i7l56fnWn00BwH7oMIxA1LtuTj1ZGtshJzJ+iKd4vqik+iivOq00aQApNL7OkxICR1WW4d/Z4HPEEUWk3tT9U9rKZUqTryqbq8TjdWqcMBh0cNgOCzV35EAxHEIvFEsdz2QzYsqulR6vZaCxf3YD9TT7xE6n0+P3urWMJg6Q1vc8kJ3Yyl0xazTJttUvXau32d+2r2SviDWEUGFtTiXtnj28vg05z0vWWaj/aHjc6rRbqeyNJlKXOdeQcNgMmn1KDqnKL4PXR32XFoaM+fPjp3nhLd/3p1fE1XFe+v40t25QztzeM5au7jQUDsHx1A4b0P0V0/Ww2aXHZd47HM90m6blh8iiYTFm0u+Shx0og2Ba/vjqP6b1PdmfUXbS5NcV90hPKqJUx64neepLR2PN8tDIykJOY3WIQfGC0iTiBeq1OsOXp3jnj02+oQdLDu5puYq4U3RsSurKpjUDz/Iz6Wry5dgcON7f3Q+8MRLoHVm5vcjDUORGHNxAWP9WwyO4BoipikZO5pKoAD7uDXRW0VZ9VWU+VzsbmAH7z4vr4Z7f/4FRx3ZRjgEGngdmgg0GvTfptm1m4Hhg11IUZ9cPjM3SdOKRS1d2iiTLR7AnBYTPgexOGYNW6Xbj2wpGC14fdbECLJ4hrLhiJkceV4cYr6nD3c+t6fwlElAGPP4yLzxwKtzccr7MvPnMoPIGw6HKlgSYexAHtZfOZ1zf3/lyXvKO8TJpS4TQJdousyKC7aN6G83R7NvCFI7AadFkFcXKaTCYfecOulRLT67WYUV+b0O1qRn1t+8NeL1pSPMi2eNJ3IVTtgNEOrjIjrjgvsXvDFefVwlWuoofdHoNjhc7p0o4uRUDq7rOpgiFvIJx+IpUeRJcpge6WghWoiMlcUnWz2Lq3Jd7Vck9jlpMbCKRzUF9bPIjr3NfiFRtx4xVj03dTFtEFNBhqw/Qe9cD3LxqJJncAK9/fiuWrG7Dy/a0IBNtU3S2aKBPlDhMmnlqNVet2of70avz5b1uSrqPp9bV47q+bMby6AiOry+D2hLFp22FRXaeJMmExGxAMRRLq7GAoAotJfP3sTTEpmTfDyU7y9ZwXDEUEu0UGRQz/6ZTX4Twdzwaja6pEP590J7fn33zkDVvkJHakJYA31+5ImJb1zbU7MKDKhj69ROQWk14wkjf3MgBVVHewVGTUJJ1KU7PwGDlR070rgdAbpel1gud02MAy3DXr9JStWvl6U5ZR94Ac16TpJNRq1tkK2fn7OU1u0COduxu9Sfva3+RDhcOYtvVQTBdQu9WIVet2JdQDgVAEf3xzS8J2v3lxPX5947eSusdyXSwqRVoNcGyVDWfWDYx3k3yr436q1QKD+jnw/P99jsPNAWxoaMSgvg7YLPr4WDm2bFM++YLC48mGDSpHhcgH83J7fu7Jh93Ck+EddgczanX2BYSH//gCbUCZyJ3kYVHxfJHdhCl5yBsGchIrs5vQ6g1j+ZqG+Gcmgw5Oa+/N1g6rATPqa5OavJ2W9Kc1VTcuW299lWXWJJ2K2sfICQUGew56BM9pH6cpbcCUrz7nksyE2qMC1Ot1eGzp+nhXUgB5fWBLdYx2syFtYNrbxCmdAdgPJ52Y0G0zVXB+pDXY1VKp8m7SROkccQdhNOgSxo4ebg7E76fTJtbicHMAJoMO0SiwaPkG3Dt7PD78dC+m19fGg7/OWWJLfWFhyo0vpzkP2uXrnmw1C7/ot5oze+zvk2LW5Uxm4gSQtxe4uZLlrO055g0DOYnZrXrcMHlU0sBWh7X3U2M36TCwyo4p5wyL98ceWGWH3Zz+jXww1IbvXzQyoR+302ZAMNwGIHVhVspC4pVO4YqnUiVj5IQCg9Uf78KsySfi2dc/TwjqtdpepmLL05uylDefQrcWdasA3YE2tHoTu0d0Tm7Qc7r+Yh5jqhuHRqPBbU+tTdjXr2/8Fo60BttvKiImhlHKNUlUCOUOE5as3IhpE4cnXCt9ys2YeGo1qsotmFE/HMf0seKv/9yOYDiCYLgN11wwEi+8vSXecjfyuEoMqrKqZsIvkobDKvyS3J5JXdxtMrpUk2OJYTK0L2XVOSt6578znf0ybxOMyITajgdgICc5m0GHPmVm3Dz9JPhDbbAY9bCadLAZdQUrVE67CbrDvoTPdFotnNb0byRk1ySdQgwxwZbKmFKv0h6EAoNWbxgeXzipi+6QY5y9r/WSjzdlQjefMiO27Cxea5FQBX3NBSMxsjpPXTqEgl6BWT97HqNQum68YiwWr9goGIAN7lxDUoNebzhyvCb1ei2aeozTjcbUce2RvDgtekz6dg2a3H7MmjwKz76+GQ6bARdNGIJ31+3CmXUDodW2Lxp+7imDsPuAB3azAcdUWnHbNack1gkM4ihHep0WP7z4RBxtDcZfklc4TNCLmPMgLk+9LPzBMAx6TcKLfoNeA38wLLqbJwBZdYvMC7UdDxjISS8GDO5rax93lmGhcvvCCZMvAO1vYXp9Gx+LwRcIJy3w2BtZNkkLaPWGsXbTPsyfXodAMAKLSYeVH2zFoH4OVPWyxp4SpAoMXnzny4TFvge4rLCZDdjd6C1Ma1i38ZKVZWbsOejpdUHrRcs34Nc3fgvRaCz/rXSpKuholoGqiOUUhGb9TGoRE0iXxx9OOFed2yYEYCJuOHK8Jn2BNvz5rS8SPrv2eydIlBpStY4XSIdagvh8exPuuP40aHXAk8s/S1heoHNZlZ9edXL8GpJDNy9Sl0AwDKGZuwLBMCCyTs5XLwuTQZ8wxrr9Mx3unZ3h7JfdxQBoVLDgosqufwZycpBlocp20pJUCzweP7gibeuNUpqk+1ZYMGH0MVi0LHESjL7l6uhamapl6JoLRsbPzQCXFdMm1uIXS/5VmNawHuMlZ9QPj78YALoWtJ58Vk3C+E+HzYDt+1vx9KsbE9NVXZafLpj5qqA1wLZ9rdi2zx1/m1lzjBM1xzgS9tk5/XnPNXaSrsEs1tMTczxKuSaJCkKg9WLuZWPw3dOrk+5xi1dswkPzJvC6oIKxmY3wBdxJL8ltZvEv1nKajK4bbyDF7JeBcGZzBShkboRSxkBODrRAkzvU3iWtzAyXQ1x/aJvZILheWm+TlviDwhd4rwNyY8DI48rwqx+dgSPuACqdZvQtNwFiZqEt4myX0WhUMFA9qbZPYX5QCj0f8KOJwZ3NbIgHcUAvb/V6lj+nEW5P+nPV861hNBYTLFM9F7SeeGp1PIjrnq7bf3Aq7v/Dx/EbxU+vOhlV5ebENCCPZahnebTq4faGcWBrI6wmPXQ6DfY2epJuyP0qLQkvOyrLzIJr7FQ60rf8CgVgP73qZABIbEHt7ZhV2E2ESCyh1ounX92Im6fXiXvBQpRHvlCKWSsHi5+1Mtvnup7yNfslx2HLHwM5qWmBz7YdweIVmxK6gIytqew1mIshiivOq41PtW8y6DB7ymjENOk3dNqMghe409bLRakFPtuaRVpzeaOTRZB7uCXFtLstQTgzrAwVpVtwJzRVvuBbPYHyN3vqaLy8ugH7m3wpz5XQ2CyhMjXyuK4FrE0GHQb1swumq/syAQ6bAXsbPYkzOE6rg82sx5e7m9O2kIkiUB7nTB2N5d2O+effHyeq1ToaSfHS4PheXhr0CMAqHSbsOeRNmvwko2NWYLcXofF0QPukAcbeJuqhktfUKlzXa7WarF6wEOXCn2Kqfn9A/KyVMU12z3U95au3hhzHYVMiBnISa3KH4g/RQFcXEHFrnmkF10u7p5c+0NEYBGczivZycTe5Qzh0xIO7buhokSszY2PDQTRV2dOm1e0L49OvDuCXM09HsyeIcocJf/94F47tY01fEWQZ5Ga7vp5sCbVmIv1nNmv7W73u47A638a5/V3bhSPRpPK3ZMWmeJfIVG/feo7Neu+T3UkTzMyfVodBVdaE1qJUXQqj3c7nuacMTgqOXnh7C6Z+5/heW8jEBP5CbxgX9zjmr3YdFbx5RaMRNHm69h8MCd+4D7uDvbccdp9x0y/81vNnV5+SsMnhFn/iMSu824vQeDoAmHnJKFUsFUKFZbUIzxKo1WoEX7CMrnEBjOWoQJx24ZfkjgxawaJRjeBz3a9+dEZmiclTbw05jsOmRAp9sk305ZdfYsGCBfjqq68waNAg3HfffRgzZozUyRIllzXPjqZ4G3m0NYg+abZt9Ybw139uS5jh8K//3IYf9RmNfmneWGr1MdQMrMBRdxCBYARH3UHUDKyAVp++ZmiLRfHtumPRFokiGo2hLdLx716a1prcIfx70z7c/oNT4faG4LQb8dY/t2NgL4Gjw27AnKmjkwLAXlsc5SjFg3rPlpoRg8vhDbT1WCR8LA4e8SMQisRbcxqbAwktXTdNE+6ChG6NIcFwBC3+EMKRWDyAsZh1+MFFI9HSbQmLMrsR984ZD68/nHqiES0Ez82/N+3DtPNqAQ1w3AAHHDYDgs1dZfvMuoFY8fevu8osgHfX7cKIIZUJE6fsa/Ji4dLE8TJjaioSuoqmGoNgNHT1AzUatEk3rwEuK460hvD0K5907f/yMUkB8wCXFaFwFA0H3F2taMc6kscAarqCTp1OI5gmj8CkRIG2KILh9u3KHab4NOqd+fLC21tw27WnADFwkXBStUgkgv93ZR127ffE6yFXmQnfNHoEr6cjrUFUCj2A9nhZptW0r1HHa4cyodVokspj9QA7dBn0LjiaYiHvo61B9M20RTkPY8Y5Dlv+FB/IhUIhzJs3D9dddx1eeOEFvPvuu5g5cyb+/ve/w263Z7dTA3CwKYgjHQ+tfStNQLj3zbLZrk95isUWy3qfmMOW5YKPFpNecBFySy8tVhro0OwJ4ZnXuq15d+koVJZZ0m6n0+lwxO1N6ipQ5kh/jMFIG04bdUzC2KkfTRmNUCT9enfBYASBYFvCtLuBYBuCoQjsGa6hIjWh1iOh1qlZk0dhxd+/TuieeOiIP2HWtnmXj8HSd79K2FdTiz+pC9IPLhqJCqcF0ya2z2S66etD8AbasGVHe+C4c38rhh7rgEajSUjDNReOQDDU1jV9fgxJD0iIxbB8dUPCS4R3/70T508Ygqdf2ZgQsLy5dkd8cW+bRZc0C91V5w/H0dYA7nppQ7fPRsSDwGA4gmWrvoLZeAJ27m+Nl4Uxw1yC183xg8rjx1xmNyW1MM6aPBoP//mTxPE4r2zEHdefii92HEU0FoPNrIfdYsDCZZ8m5GegLYInlieuZWez6OALROEPtqGqwiKYJq8/lBCk/efz/agsM+Opjry6ftJIXDD+OLz4zlcJ+dLYHMBv/pLYNVUprXREYpU5TDjcHEyoh+ZOHQ1HipY6wXucwMuyzvqn1RvmtUOiWSw6BBujieXxsjGwWMQ/d5Q7U7SASTXjNsdhy14Gi1vI03/+8x+Ew2H84Ac/gMFgwEUXXYRhw4bhrbfeym6HBmDDV0dw1zP/xq9f+C8W/O7f2PDVEaC3xpwst9NAgxn1tfFFGjtvIhoRY12cNqPgtmW9DKrVaiH8m72UhkAwEg/igPYH2Wde24xAMP1sJ/5gRLCrgL+X7XQaHX7XY7vfrdwErSZ9pegPtuEPb27B0lVfYfnqBixd9RX+8OaWXn9PjoRaj86sG4hnX088D8++vrl9cHQHoe6JT72yMeE7ANDWY4yXw2ZAIBTBoy+tx/LVDXj9g204f/wQtLjbH5aWr27Ayve3Qgstnv+/LxIDzL99CZOx24NSxwPSbU+txV3PrcNtT36Eb5p82N/kw/I1DVi+ugHL1zRg9LC+8SCuc19LVzVg4qnVANrL55BjyuNBXOd3XnznK+ze7+nx2Ze4cPxx8SR89/Rq7D/sTUj70dYgpvco/9Pra7Fzf0v8mLUaLdZu2ofJZ9Vg2sRaTD6rBu4ULXlubyi+f38wgt/1uEZavOF4ENf52aLlGxCJarBw2ad4/OUNePrVz3DD5FFJ12R/lx2v/2NbPF2Tzz4+HsQBQD+XPR7Edc+XaAxJv+f2iXkbRaQcXl9b0uRJT6/YhAFVdvxoyuik60loPS+hl2VLVzXg3FMG89qhjHi9AuXx1Y3wesWPkQuF2jCrx71g1uRRCIXF7yPvOlr2BvextbfuMYiTFcW3yG3duhU1NTUJnw0dOhQNDQ0ptkjvYFNQMOi464Yz0M+Z+o1Itts1Nvvx5todSQs5D+hj63WWI7tJh4FV9oSWp4FVdtjN6buCmAw6mI26hO3MRh1M+vQBUrNHuMm/2RNE/7LUx3g0RffR5tYA+qfJmyMptjvaGkibp/5gRHC7XmfllCGbOfnNslYLweNLmCFSI+I7AMJt0YTvCQWAT7+6EVPOGZbwmdsnHNS0eEJwdXRdEnpA2nPQI/p4qso7WgVjwPZvmgW/03Oh6WA4gnJ7V0tvv0orFi5LTsMH6/ckXHOrOhYP7vzO4hXtx7x01Vfxff3PteME35TuPdQ1sYzQ7J2pZvT8ek/XOLz9TT68+vevMX96HXYdaI3XAxNPHZyQ9p37WxL25U8xxbQvEE76jIPTSW1S3ZNafWGEQm2YfFYN+rmsaDzqh9mog82YfI9LNZlDZys4rx0SqznFcJdmT0h0t0iDQYf3Pt7Vvg5uKAKzUYf/+3AbruFanJSC4gM5n88Hszmxi57FYoHf7xe9D5erqwvmlr0tghfi0dYARtWknoku2+2O+tsEuzlWVVhRVeXoPe2Vdgzq78SRVj8qHRYM6GODtpf+2GWhCA40+dDi7XrYc9iMGHpMOYwCN7pOB9yBFE3+xrRpPdgaFNyuwmHOarvyXrY76m9L2V1VTJ52yuS7+dRZHquqHGj0HMb0+tqELoVDjikTPL7un2tTTCoyoscskiOqKxK/lyIA7BkwmU06wf3bLIZ4vh3Y2pi0r9Uf78LMS07Ec298Hk/DcSmO5+ARX/y6mFE/XHhSgx4t1+3dp7reZEKTPPZs9ce7cM0FIxPWspteX4u31u5IOOb+ldaEvAoEw4Lj+/7yzpdJaUgIVEVM8AK0B3O7DrRi+eqGlN+JxhL3bzIKn4fOt7ndP+vvsqOqKvPu5t3rx+5av2mBwZB8C9FqNUmfC32W7nOjUS/Z9QdId+0XmhqOq3t53HvUn3J4wbOvb23vGjm9DgDQt9KG6mPKk+6PoZjw9dn5MjSXa6fQ1HA+MyHH4+1eHr9JUx7Fpr01HMF5p1YnrIN7w+RRsFsNsjx+KTE/2ik+kLNarQgGgwmf+f1+WK1W0ftoavIg2jFlo6tMeMxahcOMxsbWlPvIdrsKm17w4bDCpk+7XXdGDdDfaQYQQ1OTR9Q2JxxXjj7lFhx1B1HhNKFvuQktLb6021hMetwweRSeeX1zQgVjMadPq82ix+wpo5PGyNms6bdz2IS3c9rTb1dh02PuZWMSHtTnXjYGFXaD6DytqnKI/q5YYiudpiYPXC47GhtbYdLrsGrdroTWo9fe/xo3XjEWT7zcNebqx1eMxWvvd00GYjJok2YmnTN1NAb3TZxFUmfQJowFSxV09AyYGo/6k8aQzaivhc2ki+ebVWD20FZvGP5gW8LxvP7+15g1eVS8u2hnuXr171/Hf7/mGGfSgOsbp42FxxtKCLZm1NfCqNdh2sRaaDUaWAQCnVZvGEOPceDe2ePjE4YsXPppfDxe52+6yswJrdYOixE1A7u2cznN0Oo0aO32QkRo9s4Khwk3XDoqYWzp3MvGYFm31r6e+dw5/vTV975O+M6Hn+5NyKtU56H7EiOdY+SM2lhCmc6kPEZTTGkbFujuE43Gkj4X+izd56FQW96vP7EKce3LgdyPK5vyaDXrBcu/VhNDqzeMeZePQZnNgGNP6Aun1SB4fzRqkVS3dI6RS3XtyIHcz2e+Fft4syqPFuHyaOvlGak7h0mHcocRN08/Cf5QGyxGPYwGDRzd7q1UmuU/FU0sFlN0b9cPP/wQd955J95///34Z1deeSUuvfRSTJ8+XdQ+Eh5UOsa69Qwe6oZXpp+4JNvtgMQp053tCzKLWRA8HzK6GHTA9n2tCIVjCRXM0GMc6RcF1wH7Dnuh0ejalx+wmxCLRXBMH1v67TTAviM+RKKA2xeC02qETgscU2nNbP25LPJULoFcyunlq8vaZ0HsHHxsNySt8XfzjDpUlVvQ1JImDzTAtn2t2LbPHZ+so8xuSgiC50+vg0aDhNkgb55RB4NWG99OcI2zFGk36rW9rhE3YnA5yu3GxMHV6Jg4pfMzmwHbvmlNSMPg/nY0HvXDG2hLPZtnz8kLxOax0ABvgSUybpo2Fi2eIFq84Xi+GPTahOMbNaQCjS2BpBc4/mAbmj2htGk3GbXYurfrfPUpN2PPQW9834P62TCyurx9ps40ac81kGsNRfH0KxuSPr/2eyckLSkg9Fm6z6VcfiDbaz8UjaG1x1iqfKyHJ7TfbPZdqAeffB13VuXRAHy5swW7D3i61QE2WE0G2K0GVNpF1vuaxLpFq9XgSGtQ1hM7lOKDrNwDORiAL3e1YHe3WSsHD7BjRHWZuAnzOkn4TKgUpVj+U1F8IBcKhVBfX4/rr78eV199Nd59913ceeedWL16NSorK0XtI+lBpWP2yaOtAVQ4zOjnymzWyoy3k1DGF4MOONTcPjNnpdOMvuWm9MFYrtv1uMEW66Yqm0AOEJ8H2Vb+PfdvMyQHMBqBfcdEpEso7RD5mZjzLCbtYvbdsR9fOAKrQZdZOeuZ72XG5CBKKA098zTb7cqNaGrO/LwzkBOW7bXf5AnhuTc2J3yWj+MQ2m82+y7Ug0++jjvr8hi/7wZRZjfCZtW3LxmgvCHRGSnFB1nZB3KAIp8DlagUy38qiu9aaTQa8cwzz2DBggVYuHAhBg4ciCeffFJ0ECcoDPRzmrom1BB7EWa7nZJEgL4OU9fAXbETQWa7XR7WQVE8sXkQBVx2Y9cDlNg3eD3333Ptt1j7f0L77jVdKdIu9rO8pD2DdNYMrmy/OWRSznrme0RkGnrmabbbtWV53mVMr9eiyRNK+Mxq1sMXSH46z0erl5qkaiErJqHz15mOvJ+rjvvuqJo+XQ92Kg/iSMZ6lkc1PgeSrCg+kAOA2tpavPTSS1Ing4iI8sAXaBPdqjd76hi0tiVGr5kGfd2Dn9ZQC0KhNlHf7a7nhEBA5gGN0L6F9ptOqy+c1EI2e+oYbP+m/bh6S0eq40uVp0LpEzp/gLQtrUREaqSKQC5Xvc3yqHalfvypSJUvnb/L8yIN5nuijPND6OupdpHJ5ym+6wu24c9v9gj6Ljoh6TMAmDl5FKoEpgFv9XZ1DzQY9AiH20R9t+dvikmb2HSk2y8AQJPi3Ah85Au2Yem7DUkTywilI93xCR2LYPrSnNdcr69025fatcvjlR7Lo3SYv+0UP0aOiIiIiIio1Gh7/woRERERERHJCQM5IiIiIiIihWEgR0REREREpDAM5IiIiIiIiBSGgRwREREREZHCMJAjIiIiIiJSGAZyRERERERECsNAjoiIiIiISGEYyBERERERESkMAzkiIiIiIiKFYSBHRERERESkMAzkiIiIiIiIFEYvdQLkoKnJg2g0JnUyJFFRYcXRoz6pkyE7hciXqiqHqO81NXlQVmbheZFAKV0PmZRHofpRrXnF45IGy2NmeLyFxfIob6WWv+nKI1vkSpxer5M6CbIkdb5I/fulivkunlrziselTGo/vp54vPKmtPQqDfO3CwM5IiIiIiIihWEgR0REREREpDAM5IjyQQO4/WHsbvTCHWgDNFIniGSF5YOKieWNiOSEdVLBcLITolxpgC27W7Bo+QYEwxGYDDrMn1aHkYPLgNKcQ4e6Y/mgYkpR3lyVdqlTRkSliPfAgmKLHFGO3L5wvIICgGA4gkXLN8DtC0ucMpIDlg8qplTlbf9hr8QpKzyNhq/5ieSG98DCYiBHlKNmTyheQXUKhiNo9oYkShHJCcsHFVOq8nak1S9RigpPo9Hg0ZfW4/m3tjCYI5IZ3gMLi4EcUY7KHSaYDIlT4ZoMOpTbjBKliOSE5YOKKVV5q3RYJEpRcfj8YXgDfMNPJDe8BxYWAzmiHDktesyfVhevqDr7fzutBolTRnLA8kHFlKq8DehjkzhlRFSKeA8sLE52QpSrGDBycBkemjcBzd4Qym3G9gqKg3gJYPmg4kpR3rRadjkkIgnwHlhQDOSI8iEGOC0GOC2G+L+J4lg+qJhY3ohITlgnFQy7VhIRERERESkMAzkiIiIiIiKFYSBHRERERESkMAzkiIiIiIiIFIaBHBERERERkcIwkCMiIiIiIlIYBnJEREREREQKw0COiIiIiIhIYRjIERERERERKQwDOSIiIiIiIoVhIEdERERERKQwRQ3kPvroI0ydOhUnn3wy6uvrsXTpUgBAKBTCnXfeidNOOw1nnHEGlixZkrDdW2+9hfr6etTV1eGGG25AU1NT/G/79u3D9ddfj5NOOgkTJ07EBx98UMxDIiIiIiIiKrqiBXL79+/HTTfdhLlz5+KTTz7BI488gt/+9rf48MMP8fjjj2PHjh1YtWoVXnnlFaxcuRKvvfYaAGDr1q2444478MADD2DdunWorq7GLbfcEt/vT37yEwwfPhzr1q3DPffcg1tuuQV79uwp1mEREREREREVXdECuW+++QaTJk1CfX09tFotxowZg9NOOw3r16/HypUrMWfOHJSVlWHgwIGYOXNmvLXujTfewLnnnotx48bBZDLh1ltvxfr167Fz507s2LEDmzdvxvz582E0GjF+/Hice+65eOWVV4p1WEREREREREWnL9YPjRs3DuPGjYv/u7m5GZ988gkmT56MxsZGDBs2LP63IUOGoKGhAUB7i9yoUaPif7NYLBgwYAAaGhqg1WoxYMAAWK3W+N+HDh2KjRs3FuGIiIiIiIiIpFG0QK671tZWzJ07F2PHjsWJJ54IADCbzfG/WywWBAIBAIDP54PFYknY3mw2w+/3J23Xc1siIiIiIiI1Knogt2PHDsybNw/Dhg3Db37zm3jQFQwG49/x+/3xVjahwCwQCMBmsyEWiyVs13NbsVwuezaHohpVVQ6pkyBLUuVLZ3nkeZEG8z1RuvpRrXnF45KvdOXRoNehT5/SuZ+r4XxmQo7HW4r1o1wwf9sVNZD7+OOPMW/ePMyYMQM/+clPoNFoYDKZUFVVhe3bt6Nfv34A2oO9zq6Ww4YNw44dO+L78Pv92L9/P2pqahCLxbBv3z4EAoF4y9z27dsTummK0dTkQTQay9NRKktVlQONja1SJ0N2CpEvYiudpiYPXC47z4sESul6yKQ8CtWPas0rHpc0cimPGo0GABBui+DwYQ9iMfXfz+V+PvOt2MfL+lHeSi1/05XHok12snv3bsyePRvz58/HrbfeGq94AeCSSy7Bk08+iSNHjmDv3r147rnncMkllwAAJk2ahDVr1mDdunUIhUJ45JFHMHLkSAwZMgRDhw7FiBEj8OijjyIUCuHf//431qxZg0mTJhXrsIiIiIiIiIquaC1yf/nLX+D1evHb3/4Wv/3tb+OfX3XVVbj55pvx4IMPYtKkSYhGo5g+fTquvPJKAMDw4cNx//33Y8GCBTh48CDGjh2LhQsXxrd//PHHceedd2L8+PGoqKjAfffdh9ra2mIdFhERERERUdFpYqXQB6EX7FpZOs3TYrFrZWkqpeuBXYeE8bikkWvXyiVvfI5wWwQ3Th3DrpUqxK6V1F2p5a8sulYSERERERFRfjCQIyIiIiIiUhgGckRERERERArDQI6IiIiIiEhhGMgREREREREpDAM5IiIiIiIihWEgR0REREREpDAM5IiIiIiIiBSGgRwREREREZHCpA3kli1bBq/XW6y0EBERERERkQhpA7n7778fbrcbADBy5EgcOXKkKIkiIiIiIiKi1PTp/lhVVYUFCxZgzJgxiMViePbZZ2G1WgW/e+ONNxYkgURERERERJQobSD34IMP4qmnnsL7778PAFi7di10Ol3S9zQaDQM5IiIiIiKiIkkbyH344Yd47LHH4HQ6ce655+L3v/89Kisri5U2IiIiIiIiEpB2jNzzzz8fHyO3f//+oiSIiIiIiIiI0kvbIlddXY2bbroJI0aMQCwWw7333guTyST43QceeKAgCSQiIiIiIqJEaQO5xx57DH/4wx/irXI+nw9tbW1FSRgREREREREJSxvI1dTU4J577gEAXHvttXj44YfhdDqLkjAiIiIiIiISljaQ27p1K4YOHQqtVosFCxbg0KFDOHTokOB3hw0bVpAEEhERERERUaK0gdykSZPw0UcfweVyYdKkSdBoNIjFYknf02g02LJlS8ESSURERERERF3SBnJr1qyJLzewZs2aoiSIiIiIiIiI0ksbyB177LEJ/9/n88Hj8cBut8NqtRY8cURERERERJQsbSAHAM3NzViyZAneeeedhLXkBg0ahAsvvBCzZs2Cw+EoaCKJiIiIiIioS9pA7uDBg7jqqqsAAFOnTsXxxx8Pp9MJj8eDzz//HH/961/x5ptv4qWXXkJVVVVREkxERERERFTq0gZyv/71rzFw4EAsXrwYFosl4W/19fWYO3cuZs2ahSVLluAXv/hFQRNKRERERERE7bTp/rh27VrcfPPNSUFcJ5PJhJtuugkffPBBQRJHREREREREydIGci0tLQkTngiprq7GwYMH85ooIiIiIiIiSi1tIBeJRKDXp58PRafTIRwO5zVRRERERERElFraQE6j0UCj0RQrLURERERERCRC2ua2WCyG733ve2mDuVgslvdEERERERERUWppA7kHHnigWOkgIiIiIiIikdIGclOmTIn//8WLF+PSSy9F//79C54oIiIiIiIiSi3tGLnunnnmGU5qQkREREREJAOiA7lzzz0Xzz//PI4cOVLI9BAREREREVEv0q8t0M22bdvwxRdf4KWXXoLZbIbZbE74+7/+9a+8J46IiIiIiIiSiQ7krr322kKmg4iIiIiIiEQSHch1n/ikpaUFDocDWq3onplERERERESUJxlFYs888wzOOOMMjB8/Ht988w1uu+023H333ZwEhYiIiIiIqIhEB3LPPvssXnrpJfziF7+A0WgEAJx//vlYtWoVfvvb3xYsgURERERERJRIdCD38ssv41e/+hUmTZoEjUYDoH0my4ceegj/93//V7AEEhERERERUSLRgdz+/ftx3HHHJX0+YMAAuN3ufKaJiIiIiIiI0hAdyJ144ol48803kz7/y1/+ghNOOCGviSIiIiIiIqLURM9a+fOf/xyzZs3Cxx9/jHA4jMceewzbt2/Hzp078dxzzxUyjURERERERNSN6EBu7NixePvtt/GXv/wFNpsNgUAAZ555JhYvXox+/foVMo1ERERERETUjehA7s0338TEiRMxf/78QqaHiIiIiIiIeiF6jNy9996L8ePH42c/+xk+/PBDRKPRQqaLiIiIiIiIUhDdIvfPf/4TH330Ef72t7/h1ltvhV6vx4UXXohJkybhpJNOKmQaiYiIiIiIqBvRgZxOp8NZZ52Fs846C+FwGB9++CFWrVqFWbNmoaKiAqtXry5kOomIiIiIiKiD6K6V3W3btg2fffYZNm/ejEgkgtGjR+c7XURERERERJSC6Ba5hoYG/O1vf8Pf/vY37NmzB2eccQZmzpyJ+vp62Gy2QqaRiIiIiIiIuhEdyF1yySUYM2YMrr76anzve9+Dy+UqZLqIlEUDuH1hNHtCKHeY4LTogZjUicqSmo6FiPKD9QJRaeK1L2uiA7lVq1Zh0KBBefnRjRs3Yvbs2fjXv/4FAAiFQrjnnnvwzjvvQKvV4vrrr8fs2bPj33/rrbfw6KOPorGxEaeeeioefPDBeCC5b98+3HHHHdiwYQNcLhfuvPNOnH322XlJJ5EoGmDL7hYsWr4BwXAEJoMO86fVYeTgMuVVdnI+Ft5MiKSRql6oLoPby2uSSLXk/ExAADIYIzdo0CB88MEHuOqqq3Daaadh3LhxmDZtGt58803RPxaLxfDyyy/jhz/8IcLhcPzzxx9/HDt27MCqVavwyiuvYOXKlXjttdcAAFu3bsUdd9yBBx54AOvWrUN1dTVuueWW+LY/+clPMHz4cKxbtw733HMPbrnlFuzZs0d0mohy5faF45UcAATDESxavgFuX7iXLeVHtsfScTO57am1uOu5dbjtyY+wZXcLoJE2WUSlIFW9sKfRx2uSSMVk+0xAcaIDuddeew0//vGPUVtbizvvvBO//OUvccIJJ+B///d/8cYbb4jax6JFi/DSSy9h7ty5CZ+vXLkSc+bMQVlZGQYOHIiZM2di6dKlAIA33ngD5557LsaNGweTyYRbb70V69evx86dO7Fjxw5s3rwZ8+fPh9FoxPjx43HuuefilVdeySALiHJz2B2MV3KdguEIDruDEqUoe82ekOCxNHtDEqWoHW8mRNJJVS9s2XmE1ySRiqnp+UatRHetXLJkCf73f/8XV199dfyzSy65BMOHD8czzzyDSy65pNd9zJgxAzfffDPWrVsX/8ztdqOxsRHDhg2LfzZkyBA0NDQAaG+RGzVqVPxvFosFAwYMQENDA7RaLQYMGACr1Rr/+9ChQ7Fx40axh0WUM4tJD5NBl1DZmQw6mE2iLy/ZKHeYBI+l3GaUMFXpA0ynxZD7D3R02zywtRFWk55dxEi5CtAFOVW9EI0mfi+v1yQRSU5NzzdqJfpM7Nu3D2eeeWbS5xMmTMADDzwgah/9+vVL+szn8wEAzGZz/DOLxYJAIBD/u8ViSdjGbDbD7/cnbddzW7FcLntG31ebqiqH1EmQJbH5EooBM+prsXRVQ7wP+Yz6WrjKzFnlbWd5lOK8uKIx3HLlyXj0pfXxY7nlypMxZGAFtFrp+kyFYhrBm0l/lx1VVbldv9FoDP/atD/pmMePHiDpMctFuvpRrXWHUo8rXVkGsj8uoXrhpml1+MvbWxK+l69rMm1a0pRHg16HPn1K536u1HKaLTker9rrx3w/3+ST1L8vF6IDuerqavzrX//C4MGDEz5fu3YtBgwYkHUCOoO0YLCrmdbv98db2YQCs0AgAJvNhlgslrBdz23FamryIBotzdfvVVUONDa2Sp0M2ckkX0KhNpiNOkw5ZxiisRi0Gg3MRh3CobaEfYitdJqaPHC57JKdl9pjHXho3gQ0e0MotxnhtBrQ1OSRJC2djFpg/rS6pAHXRm0s53xy+8PxB1SgvVXh0ZfWo3/FBFW3LGRSHoXqR7XWHUo+rnRluWZwZU7HlVQv2Ay45oKRebsmcymPGk37C5dwWwSHD3sQi6n/fq7kcpqNYh8v68d2Yp9vik0t+StWuvIoOpC74YYbcPvtt+Prr79GXV0dAGDDhg1Yvnw5FixYkHXiysrKUFVVhe3bt8db7Hbs2BHvajls2DDs2LEj/n2/34/9+/ejpqYGsVgM+/btQyAQiLfMbd++PaGbJlGhHXEH8er7W3HuKYMBTftb8Vff34oBfcbCrsTuBzHAaTF0BTFyeCaKASMHlyUFmPlIW8G7bRIVSUHHuPasF6KFuyaJSB5U93yjQqLPwsUXX4xYLIY//vGPePnll2E2mzF06FAsXLgQ3/nOd3JKxCWXXIInn3wSw4cPh8/nw3PPPYfrrrsOADBp0iRcddVVWLduHU466SQ88sgjGDlyJIYMGQIAGDFiBB599NH4JChr1qzBsmXLckoPUSbKHSYYdN3mDdIABp1W8nFliiJmXE+BAky5jgskylTRy7LYa5JLhxCJJ6PrpdxhQqs3jOVrGuKf8f4oLxmF0xdffDHOPPNMVFRUAGhfD+6EE07IORE333wzHnzwQUyaNAnRaBTTp0/HlVdeCQAYPnw47r//fixYsAAHDx7E2LFjsXDhwvi2jz/+OO68806MHz8eFRUVuO+++1BbW5tzmojEclr1mF4/HE+/ujHexWjuZWPgtBmAaO/blzwp1qnpcaP86VUn4zcvrk/4fbYukNI4LXrBLshOq0QtyxrAE2jD9v2tCfUj16EiSkFm67alrVMySY+MglO10cREdibfunUrZs+eje9+97u47bbbAADf+ta34HQ6sWTJkqSxc0rCMXKl089YrEzyxe0P47an1ia9BX9oXuIYK6WMkSs2sfmXNylulIP62uAJtsFq0JVEEMcxIMIUf1ydD0w9ujsW/bg6rrM9hzxY+f7WvNaPQmPklrzxOcJtEdw4dQzHyKlQqYyRK/r9UIwUdUom2+c7OC3F8p+K6HXk7r77bowbNw433nhj/LM1a9Zg7NixuPvuu3NLIZGCcZ2V3BR77bpUa9JFozGMrqlqv1mq/zmQ1Kqju+PgPjZJy3LndRaNxWS5NiWRHMlyLdcc6xSuA1tYortWbtq0Cffeey9sNlv8M7PZjLlz52LKlCkFSRyRElhMegxwWXFm3UCgY7b6Dz/dK891VmTYvaHY43pkeaMkKiQN8M0hDw4c9hbtuu9+nXEMKpE4ahyzLbsJxWT4HJQL0U+a5eXlaGhoSOpCuXPnzoTgjqjUOO0GXPad4/HM65vj3QZumDwKZTaZzXgos773nXLqg59FhazGGyVRSplc93l8wOm8zt77ZDem19diWbd1qDgGlUhY3sakyYis7rkyfQ7KhehA7vLLL8cvf/lLHDhwAKNGjYJGo8Hnn3+Op556Cpdffnkh00gka5G2aDyIA9rfND3z+mY89OMJgEEnceq6pOreIGnfeyD7pQWycFqNNwAAemVJREFUrJBlNyEEUQGJvu7z/IDT/Tp7a+0OTDlnGAb1s+NYl1XRD6VEBVXApXak4rTqMWfqaCxesSlet8yZOlqSCeFk+xyUA9GB3Ny5cxGJRPDUU0/hyJEjAACXy4Xvf//7mDVrVsESSCR3h1tSjJFrCcJplk/FILvuDd1lsbRA1hWyCm+URKmIve7z/oCT7jrjtUaUmhzXcs2B2xvG8tUNmHxWTfvwkxiwfHUDhvQ/pejPHrJ+DsqS6EBOq9Vi/vz5mD9/Po4cOQKj0Qi73V7ItBEpgsWkF+w2ILcxcpJ0byhgX/ScKmSV3ShJZQrQxbG3674gDzi8zoikI5OxYM2eEPY3+RLWogMgSfAkq26eeSJ61koA+Oabb/Dwww/jF7/4BXw+H1asWIENGzYUKGlEyuC0G3DD5FEwdXSjlOsYuc6uTt3TWdAuhR1dtW57ai3uem4dbnvyI2zZ3RKfECZXnRVyd0qvkInyfd2Ive55PRGpSIHvv5mQU91S9OegIhAdyH322WeYNGkSvvrqK/zjH/9AMBjEli1bcM011+C9994rZBqJZC0SjuLVv3+NyWfVYNrEWkw+qwav/v1rRNpkthp4t65Od806HQ/Nm5DfAb6a9jVwdjd64Q60FXzKYTVWyETprpvu15foB7KO637hT85Je93zeiJSDzlN+S+ruiUGjKwuw72zx+Pn143DvbPHY2S1cic6ATLoWvnwww9j7ty5+NGPfoSTTjoJAHDHHXegoqICCxcuxLnnnluwRBLJWbNXPt0GelWork5CEyVMrytsX3SOdSMVStXF8ZsmHxYty3IikhhwbF87jJpY/N9C3+H1RKQOshoLJqe6RQNs2aWuWStFt8h98cUXuOCCC5I+v/jii7Fz5858polIOlqgyRPC2k370OQNibpCbGaDYLcBm4wmOik0obd/ew56Ct+dQsxCpT1aCqXoWiJIrukiSaXqhnS42d/V6n92DV54e0v+367nsvAvyzORbMipOyOAnBcVz5e8tlR2PC827HOLfl4sBNEtchUVFdi5c2fSOnKbNm2Cy+XKe8KIik4LfLbtSNIUuWNrKtNOkRsMtSWtkzS9vhbBcBuA0hhfIvT2b/XHuzD3sjF4+tWN0q2HI9c1Y3JJl0wGsFMPeTovQstj3DyjDo1H/Xj9H9sS6hhPICyPVn+5XmdExdZRDxzY2girSS9Z/azG9ejyIW8tlVk+LxaC6EDu6quvxoIFC3DrrbcCALZs2YK///3vePLJJzFz5syCJZCoWJrcofhFCbRf3ItXbMK9s8fDZU8dkNmtRqxatythat1V63bh5ONPKVLKpSc0E1SrN4yhAxySdqeQZM0YEQ/0WaeLD8zylM/zItANKRyNYeHSxPKybFUD7p09Pv/HkgU1rs1ElDE51c9y6s4oI/matTLb58VCEN0QOHPmTMyePRu//e1v4ff7MX/+fPzud7/D3Llz8f3vf7+QaSQqisPugPB6cO5A2u2cFj2uuWAkXv/HNixf3YDX/7EN11wwsqQmCUg1mNlu1kvanSLd27eCEDlTWLbpktMAduqS9/PSoxuS1x8WLC/egAzOuwb4pslX3OuMSIZkVz/LpDujnORr4pWmFM+LTb08LxZCry1yfr8f//73v6HVanHxxRdjxowZ8Pl8iEajsNvtWLNmDb73ve9hzZo1xUgvUcGkfFNjN6XfsNssSE3uAFxOM1xOY9Gb1yUl07d/ot++5ak7jNiWiWzfCspqADvFFfq8lNvlu/aR2xeOj4eVY/qIikW19XM+uo3LZUhAnp5VKlLdwx29PC8WQNpAbuPGjZg9ezaOHj0KAOjbty+ef/551NTU4ODBg/jpT3+KDz74ID6LJZGyRfGjKaPxu5VdfZ5/NGU0oOklIlPhLEhZkeHiv06rHnOmjk7qx+60GboC7Tx2hxF7I892/IIaFzNVg1TnRaPRxLtbZ00DNB71Y0Z9LZZ2G4crl/EuzZ4QVn+8K2mc8NzLxsgifUTFosr6OR/3Rzl1OQXy8qwSjUYxa/IoPPv65vgxzZo8CrFY8d/gp+1a+fDDD2PEiBH44IMPsHbtWowaNQr33Xcf1q9fj4svvhifffYZ7rnnHrz44ovFSi9Rweg1OryypiFhPbhX1jRAr9Gl3U523Skozu0NY/nqxHO6fHUD3N6uc5PP8yd6prAs1/ST1Xo8FOe06HHjFWMTzsv0+losXrEx53rA7QvjNy+ux5trd8TL8ZRzhmFQX5ssgqRyhwmt3jDe6pG+oQMcskgfUbGosX7Ox/1Rjc9IFpMB7328C/On1+GmaXWYP70O7328CxaD6KlH8ibtL27ZsgV//OMf0a9fPwDAfffdh7POOgs333wzJkyYgAULFqCioqIoCSUqNG8gLLgenDcQTjt4VbXdKVSg2dP7Gn/5PH8ZtbRl81ZQpl1YS14MqLAbEyY8emvtDhxuDuRcD3SWz2BzJKEcj6guh91U/IeGnrqX+eVrGhLGx7JcUknpVj/7whFYDTrF18/5uD+q8RnJadFj0rdrktb2lOJ8p70LeL1e9O/fP/7viooK6HQ6fPe738Wdd95Z8MQRFVO241BU2Z1CJcScm7yev2IEWjLswkrts9d2Lg/QKR/1gOzrF75cIOrSUT/XDK5EY2Or4q+DfNQ/sq/DsiGjeq/XWSs1Gk3Sv6+66qqCJYhIKtl2i1Bjdwq1EHNu8n7+OFNYSSpUPaCI+oVlnkiV8lH/KKIOy4ZM6r2s+mUYjQqOoolSybZbhIzezFAPYs6NCrvDkAQKVQ+wfiEiqeSj/mEdVlC9BnIrVqyA1WqN/zsSieD1119PGht39dVX5z91RMWWbbcIdneTLzHnRmXdYUgihaoHWL8QkVTyUf+wDiuYtIHcMcccg5deeinhsz59+mDFihUJn2k0GgZyREREJAsajQaxGJ8WiUjd0gZy7733XrHSQURERJQzjUaDZX//GtO/czyDOSJStV4nOyEiIiJSEl+gTeokEBEVHAM5IiIiIiIihWEgR0REREREpDAM5Ii60wBufxibtjbCHWgDNL1vQkXScW52N3p5bkg6LIeyptG0/0dEecI6T9ayWkeOSJU0wJbdLVi0fAOC4Uh80cqRg8s4Va7UeG5IDlgOZc1q1uP3b26BxcRHG6K8YJ0ne2yRI+rg9oXjlRUABMMRLFq+AW5fWOKUEc8NyQHLofx5A2H4gjwfRPnAOk/+GMgRdWj2hOKVVadgOIJmb0iiFFEnnhuSA5ZDIiolrPPkj4EcUYdyhwkmgy7hM5NBh3KbUaIUUSeeG5IDlkMiKiWs8+SPgRxlRsWDXp0WPeZPq4tXWp19wZ1Wg8Qpo5TnxmZQbXmUNQ3wzSFPyeU76wgiSktlE6axzpM/jggm8dQ+6DUGjBxchofmTYAvHIHVoGuvrNRwbErX7dw0e0MotxnhtBmwZZeKy6Ncqb0eSEeoHLKOICJAnXUj6zzZY4sciVYSg15jgNNiwOiaKjgtrKxkpePcDO5jg9NigNtbAuVRhkqiHkinRzlkHUFEgIrrRtZ5ssYWORKt2ROCw2bA5FNq4t0F3vtkN5q9ofaLm0qLpv3G1ewJodxhgtOiL2oFn24QNstj4bAeICJKxrqRpMBAjkSrLDPjoglDsHRVQ7zbwIz6WlQ6TFInjYpNBl1IOgdhdw/mOAi78FgPEBElY91IUmDXShItGonGKyigvfVj6aoGRKMqamdX2UDlQpFDFxIOwpZGSdQDmVDxBFBEJB7rRoVQWZ3NFjmlK2L3NtV3ZZNBK5MksihDcikLRr0WU84ZhmgsBq1GA6Oe76YKLetzL3FX3IIo1TqDiJLk7b6oxrpSLlRYZzOQU7IiF0i1d2VL1cr00LwJ6ghUhWRZhlKVBZvZgN2N3qLcfNy+MH7z4vqkNKj6fMlAVvWAFvh8ZzOeePkz1dw8gRKtM4hIUF6ekeQYaKgosFRjnc3X1wpW7O5tau/Klu5tmlplW4aEysKcqaPx25fW467n1uG2Jz/Clt0t2XdZENH1oRTPlxxkXA9ogD2NvngQB3SVs6bWkKK7t7AMElGnfDwjyWHYQoKOwPK2p9bm596eh/TkMvxFjXU2W+QUrKk1KFggm1qDhXmzoPL1RModJgxwWXFm3cB45fDhp3tV0+IoJOuuIDFgZHUZ7p09Hk3uAModJixc+in2N/ni+8j6LZfIN5JqbyGWrY56YOFPzsGBJk+v9YDbF8aWnUcEy9l/vzqEpasa8vPWWYK3xiyDRBSXh7Vo5TJsoZOsWrDy0Fqpxuc8tsgpmMVsiL/56WQy6GAxFfDiUvF6Ik6rHtMm1uL1f2zD8tUNeP2DbZg2sRZOmzpaHIV0Poh2J+pBVANs2dWCXyz5Fx780yfY0NAYD+I6ZfuWS+wbSbW3EMtaDDi2r11UPdDsCSEag2A5i0bb/3/Ob50lemvMMkhECXJcizbre3KByKkFKx+tlWp8zmMgp2DhcBum19cmPERMr69FuK1N4pQpk9sbxuIVmxIqicUrNsHtVfhinmlk+yDas0JN9aCezc1H9I2j29vPu2adjofmTVD8mCs1KneY8OGnewXrqvf+uzv+vVweDiTrjsQySER5JLeXQ3IKLPMRVKrxOY9dKxWszGbCqnW7MPmsjsUnY8Cqdbtw8jWnSJ00RZJbl4aiyLK7bM+8eu+T3ZheX4tl3dbPid98snwjKaq7Wsfbz/j54QO07DgtelxzwUi88PYWTD6rBlotMLqmD554eQMONwfi38vl4UDSa5dlkIjyRWZDWDoDy57dGaVIUz66sqvxOY+BnIJ1PiBldYFpgSZ3CF8f2IdKhwkuhxGIFiXZsqWo8S4d56/JHYCrzJzb+cviQbRnXh1uDmDVul24d/Z4eAPh9DefXtIupxsHpRaNxuD29zImrWPcWpnNgJ9ceXJX2bAbcNX5I5JmsdRqNVnNeqqoa5eI1Csfz1ZyejnUYzy8y2mGyynN82I+ng3UeK9gIKdkGsBo0ODm6SfBH2yDxayHQYd461xKWmDbPjfCbYA/2IZIJIbm1gBqjnGWdDCnmABCC3y27Ui8e0DnjJFjayqLdv6E8uqHk06EQadpzytNisFJYtIuxRvJjoDjwNZGWE16aaZXznayDimmhtYA/9q0H39883N89/Rq9AtacUCrQZ8KM4xaLY64gyh3mNB41B9fIqLzehrc14YtO1vw4jtfxlvoThziQiDYhp898VFWg9gVc+0SkXrl69lKTtP9d4yHl8tyCLmuHavGe4UmFospNOn509TkQTSqvGw46gtj49bDWNqtO9uM+lqMGdYHFWn6U7cE2rD9mxb87rXN8e1+dOkoDD22DGVmlcT22VaEHW/TjnqCqLCb8vrmqarKIep7TU0euFx2NDa2Cv/dE8Ivlvwr6Y3SvXPGw6DVdB2zVQ+3t0ceQCBfen4mtJ1Q3nXmsTeESocJew55kyv76rKEfUWiMdz+9NrktM8eD5ddojdiqWbC6pH2XvMhhwCsssyMPQc9md8sc5jFK5Py2LN+dPvDeOiF/+LiM4ciGIok1UFvrt2BVm8Yc6aORiDUhvc+2YOTavtBqwVOGl6FL3ceRTQWQ98KK/Yf9uKYPnYsXPZpbmsCdiuPuQT/VVWOlNdexmT0QJbX4yqAXMqjRqPBkjc+h0GvgS/QBqtZD41Gg+svHAm1PuLI/XzmW7GPN5vy2BJow7a9LXjm9a5nqxsmj0LNwAyerfK5jlweeu64/WHc9lTyPTvjWSt1wKHmII50pKWqzAREet+sIGnJ072imNKVR5U8tStclhebN9gWf4AC2vv5Ll3VgJpB5WkDOX+oLR7EdW73u9c2Y8ENZ/Re2eThYiy4bCtCDbD7oBe+YAT+YBvaIjF4/WEM7muT1UXe5A4I9vE+eMSPx7sd85ypo7F8dQP2N/nieWDUaxNaSH561ckItUXjeTXAZcW13xuJ3Qc88TdeNcc4UXOMI3UexIBgOCo42cTtPzgV9//h425pGgOHzYBgcyQh7U3ugGSBnNBEGS+8vUWw619SGcqhrHXfbkb9cKx8f2vyFM8/noBwWyyxboh1BQc2iwEvvL2l6FNDN3tCOLNuINzecFK6l65qwOSzarB8TQMWr9iEn1x1Mi7+9lA8+cpGBMMRfLB+L2ZePAqeQBh7Dnqw+uNdmHjqYPHjFlIFR53dkawGuH1h7D6UQRfNbvsMxTQwapH7NS/HhX1LTHvHAI1qgzmSF1+wLR7EAe112DOvb8YvZ50uOpBz+1NM9//jCXCaM6jT89Rzp9kTgsNmwORTauKzAL/3ye7MxpTpgA1fH8GSlV1pmT1lNOqOr8zo+TFv49vk1HUVyPmFHwM5qeVwsQWCbYKFOhBMP2ul2xsW3K7VG0J/pyn1hjrgi13N2HvQi2gsht0HPBjYz4YTqssLF8xlUcCzXffEG4rgcEsg6W2aq9wMW49Zm6TkKjML9vHee8iTNBNT5wN1Zx7MvPiErslxAOxp9MQnKAGASd8egoNNvvjDeWcLSz+XBdFILKHVrnt3i2svHCFYprqvH9aepo2Ycs4wLF31VULaXU5z7wcupiyILS/dvtcWjSWl/cy6gYILWPcsQ25fOD6JR2ee/t8/t6HvxaPg9YdTpiF51s/kNATDEext9CUE5zfPqEMsioTgYHp9Ld5auyM+cUgxBm5Xlptx3AAH/MGIYLo78yIYjmDHvhYAGgTDEfQpN6P+9Gr8+i//TUi/RgNx4xZ6C46yCZ4KFHDJav2lEmQx6fH7N7cAAK7/nnpb5kg+Wr3CgUarN4QBZSLucQAam4Vf1Da2BDMK5JrcIcHZGTPt/VJZZsZFE4Yk9bqodKR5VuzhUHMwHsR1pmXJyk341Y/OQN8M9qPG8W35uP9w+QGJpbrYmty9T6dqMelTrCOXPj63mlNs18sboyZ3CK2exHS1ekKi0gpN+5um3Y1euANt4tZ3ynJtqGZv+xukaefVYtrE9v8cNkOvU9S2+sKCb9NaCz2FeYZMei1m9JjKfdbkE7H6410J33PYDKge4EjIA4fdlLB+SoXDnFApDnDZBVt5mz3hhPPw+c7mhJagAS5b2nXCOgXDEQzsa0tI+5ypo9u7sHbXs7xoRZQFseVFA2zb14p/f3EIX+w6ilBbFANc1oSvaLVI+eavO48/jAvGHxfP0w8/3YvzTqvGL5b8KzENuvYusQ373GjyhuAJJL9MEcq/nsH57gOepOBg2aoGnHvK4ITtym3GzK83sbTAjn1uLFy2AQeP+gTT3XkD6iwDVeUWTJtYi+snnZjw4qAz/dFoLGl5gqQptzVAU2sIew61YvLZNehTbo4HR53LDGSzDEGhli6Q0/pLpcobCMMbkFf9TeqVaqr+Mrv4YMVk1Anuw2jI7HE9Vc+dJncgxRbCotGo4DNBNIMXI0dSpOVIhmmR29IM+ZCP+48qWuS+/PJLLFiwAF999RUGDRqE++67D2PGjJE6WaKkvNhae+9qZjLqcNX5I/DiO1/GI/mrzh8BkzF965HV1P5GpecbFosp/XbBtggCoUhSa02wrZfmOA2w+1BXl0VfKIJmo67XLotCrR0vvL0Ft11zSto32narERd/uyYpX+zW9PmZ8sHLE0J/kW/TCqJH19tgqA1vrt2RsOyExx9Ga7d1UPqUt79FW7RsQ8K5Mmg1CRXGwSM+DHBZcWbdQEADhCLCLSweXyhhuyde/ize2gcAB4/6kpYfmHnJiVj5/taEfZkMOgyssqWfAUvgDdWNV4yNn8/ONCxavgG/vvFbiEZjabsZ/mb+txEIRuL5ZzHrsLfRk1COb5g8Cq/+/et4N9SageWCb/6cNiOaPF3nwmIx4MV3vop/78y6gXimR7flF97egunfHY6nO7oWmgw6zL18DAa4rPFF1N/7ZHfStTxn6mi8tXZHQv6larnr77Ji2sRaaDUaDO5vR2NzIGmSkXx16TviCeGpVzbCYTOgwm7Ej6aMxu+6dZnpHCPX2dq2at0unH3yICxf3dBeXwikPxiO4sNP9+KO609DuC0Cl8PUPoNlZ/fIHi3APVsiO1sgs+l6U6jpqFX59piIUorGokn14Y+mjEYmfRktJn2K57PMHtdT9dwR1fulm8aWoGD9eDiDFsJUaanMMC3dJ0LzhSOwGnTZjW+T0djlfNx/FB/IhUIhzJs3D9dddx1eeOEFvPvuu5g5cyb+/ve/w263Z7dTA3CwqWscWN9KEyAmOM5iuwpnipu9iDc4ep0GrjJTwgw+rjIT9Lr0r99DbVGYjbqE7cxGHcJt6SubWBSCb2Z+8cPT0m7nDUeg1wFldiNiiLX/byQCbzgCmz518NjiC+FHl54Ik9GIo60BVDrNGFldhhZ/+gIejkTx9r92JASAb/9rB2qry9Om01UmfC4q03U3LTSBrrdzLx+Dqu6BpQb4z+b9+NGlo+JjHyeeWi14rm658uSE3f/3ywOYNrE2vv8Z9cMTAjsA+PDTvbCa9fjZNacgEIzAYtZh5ftbYbPoMO28WkAD9Ku04q//2JYQXP79k92Y8d3heKp7AHPZGDjtBjQ1h+JpB5Aw9rLSacY/Pt2TcP5efOdLXHLmUDjtpnga3vt4N/Ye9iaM57v4zKFY8f7WeDfDwf3t2Lq3JSH/brtuXFLePPP6Zvxy5uk40OSDxaRDIBhKCqx+ePGJ2NPowVMvJx7P6JpKfGfcYASCEVQ4TXjvv4ljAM+sG4hl736VcDzL3v0KN02vQzgchdsbQoXDBF8gnHBNBoJtuGjCcTAY9PFjPuoOCpZRs7GrKo9Govj9WwUaN6dvb7m+c+apiMY08AXCcFqNmDX5RHj8YRxbZUckGsMPLz4RB5t8eHfdLnz39Gq82RGQdi4c3zP9Wo0G9adX46V3tuDa752Ib5p88fFzrd4wbv/BqYItkZPPqsHr/9gWD46yCZ4KFXCpcXY0IkpNAy28vgDuuuGM9ntZmRlf72oCYBO/j47nsZ7PZ9pUM0GnYDa2v6DsOVTE3MsL+55sFr1g/WjNYGK8qnITbpo2FnsPeePHNLCvDX0rTED6kUDJOsa31QyubJ/8JosgTk5jl/Nx/1F8IPef//wH4XAYP/jBDwAAF110EV544QW89dZbmDZtWuY7NAAbvhIYlDm8Mn1QluV2ep1W8GIziJhSVa/VQqsBRh5XAbcvBKfVCK8/BH0vF3yrN4xX39/a3h1L074e1Kvvb8UNk0ejf5o3JP4UY/L8vYzJi8YS+0h35o3Dkf5tTEWZGVt3t2DJyvUJ29UMLku7XSDYhvrTqxNah6bX1yIYagOQOiirKjNh9tTRWNLtoX/21NHoWy7dhC5CXW+XvfsVptcPx9OvJgYUNnNX5d/fZRU8VwePeBM+Gz/6mIT9//fLA7jsO8cnlMfZU0YjFI4ktO7dePkYRAG8+M7GhO+9vKZrcpUbrxiLcFsk4YaEWAwbvz6Kp1ckpt2g12DRsq6JRX40ZTRe6bavH00ZBQ00CWmYe9kYtLQGk1qIL5owBH98q31szOXn1uK3HS1TnXnQsPuoYN58uesI/vy3L+P7tlv1CWmvqrDgoT990iOY+ArTJg5Pavl8s9uYNZtFl1Qerzp/OI66AwnHPKO+Fqs/3hXfrjPo7PxNk0GHW686KamM3jB5FP78ty/ieTWjvhbfPb06ngedac3HuDm3vw3+YBg79/kT3hjPnToadosBj/xlfcL5+fEVY/HY0k/jxyS0cPwNl45Cmc2I5WsaMH70MfjVs/9OanXrPtay+zFptUgIjrIJngoWcMlsYV8iKiyTEbBbzbjrmX8n3BdNRvFBWFNLIOXzWWUGXQibWgJ4Z91OzJ9eh0AoArNRh9c+2Io+5SNg6yM+sAyGIkl19nSBnhXpeLxtcHtDCffq7180Eh5vG+wZtjTmSm5jl/Nx/1F8ILd161bU1NQkfDZ06FA0NDRktb+DTcKDMu+64Qz0S9Myk+123kAbXv371wktGa/+/WvMvXwsKnopVHaTDgadDls6pvLunGHQbk7fTFzhMKHVG453iwPaHxorehl02jkmr+ebg96a/H3+tpR54zCkfjvk9aXZzpl6O7NRLzgOZ8ENZ6RNJyJA3bBK/OpHZ+CoO4gKp0nSIA4Q7np7Zt3AeBAHtB/f068mTiLyP9ecIniuuncZNBl0OKaPLeE7J9X2SxonuGTlJkw5Z1jCZ98c9iXNVtg5eDkQbEO5zQh/WxR3/e7fCfsXmp2xM+3dP/vdysSJWixGQ8L09Km2W7qqATdPr4sfr1BXxFStQv1dtpT5CbQHDELnYvGKjUlp6NzWZNBhyDHluO/5/yR858V3vhJMe/fuqj2DzmA4gu37WvHB+q7WyhOHVGLxio3xLpo986D78eWjS5/X14bdB7zJ53BFchl5+tWNuOP60xK6/HYuHN/5cNF41A+vP4xDR/w4qbaf4HU7+ayalOfslOF94zN5AsgueOqxTX+XHUZtLD8Bl9xmRyPJaDpesHLSFfUKhpDymQUiexFazXrB57Pe5jDoyWY2YPcBD379wn8T9mPLZOZLAEa9DqvW7Up4Rl21bhd+fEWd6H24/W3445uJvUT++OYW3Dnz9KIHcoXqSp+1PLzwU3wg5/P5YDYnXiEWiwV+v1/0Plyuri6YW/a2CJ7ko60BjKrpk3If2W7XsM+N/U2+hIsWAHyBNlHrmLgq7RjU34kjrX5UOiwY0McGrTb925+ysgjmTB2dNFNmzbHlMKYZX3fYm9zd7KrzR8Bo1KVNa7Hz9Kt9bsHtWjxBjE6zXaeqyt7zvZA6y2NVlQNH/W1JD7CpJuLoPvhYaMza9PpaIBbFwp+cEy8vh456E/ev6X3fQOqxWm5vCONHHwMA+OSLAwJBlPB2PfcfDEcSJuhI1RostF0MwLSJtUAMMBt0Sfn34ad7MfOSE/HcG58n5M2ho760+zabkveV6lwM6mePp2H7N81ZHbPQZDHRWCyhvrjpirp4ENd9P1qtJiFgv+XKkzFkYEWvdYOQ7vXjl9+0ZHQOv95zFLMmj8Kz3Vp460+vxgt/24Iz6wZi+ZoG/L/pJ+Gv/9yecgkCaNrP2U3T6hJm8LzlypNRW10peExVGR9ldtsojdi1seSse3nsyaDXwaCPdfxv+38A0KdPlsMs8ujRl9YDQFL39lyo4XxmQo7Hm4/nx+4OuAOCY+T0Ok1Gx9/oOSz4DBDVxDLaTxgafPf06qT0VJVbUFUl7rra1egVzJdwOJLTOc1m21BMk+JFrl308RRCLvcfxQdyVqsVwWAw4TO/3w+r1Zpii2TdF3RMNSizwmFOuxhlttulHiNnFL34pVGDji6RMTQ1eURtM1ag5amlxZd2G5NRJzgmz2zQFSRvss7TVH2O7SbReVqIxUezWRC8wqZPCrqHD65IOc6okz8YwYef7k16i3by8afAqInFy4vNYki4aWg1wpVcz/75qb5X0S2Phc6D2P2bDLqEN1IWs3BrsNB2B4/4sHx1A0wGHcYMOzXppjj1O8fjvY+T3zCeWTcw7b4bj/qT9pXqXADA8tXtwdaM+uGi0975WWfXxGXdWgSF8q/FG0gxrtOc9IavZ92QzYK3lU4zdh/wiD6eE4e6sPdQK267bhwadh9FNNqe1/WnV+OtjglR+rqsuHD8cfGZT4X2e80FIzGyOvmtpdj6LhNqXWhZ7seV64LgABBui3T8p0G4TYtwx0Rchw97JG0J02g0aGkN5DUtcj+f+aaEBcGzfWbpzmTQCY6RM+nTP2cl7SdFS9rJx/fJaD8GDTCwyp6QnoFVdhg0MdH7SfVMVpHBc25P2ZYHY0d3/J5dGY1a8ccjhXTlUfHLD9TU1GDHjsSZ3bZv345hw4Zltb++lab2Ps2GrulNZ08ZjX6u9N0Os92uqsKEOVMTt5szdXT7RCmFFAH6Okz4dt2x7et4iOg+6DDpYLfoMfK4CgzqZ8fI4ypgt+jhMKcfPFvsPM12O1mKAmNrKnHv7PH4+XXjcO/s8ajubxOcgrf7tP4ffroXV5xX27XUwD+24ZoLRiZN01thM6BvpQVTzhmGaRPbZy7tWR7nXj4GA/slLhkwsK8N8y4fk1Ruuy8jIDRVcM0xTsy9LHG7uZeNwaAe+589ZTQ+3LA3/m+rUSBdl41BzTHOpM8G93Xgpml1uHn6SWiLROM3oWkTazHlnGGoKjNj0rdrEvJm2sTahN+bP60uad/HuGxJ+zIbtYJl7f8+3NaVV/1sScc87/Ixgssw9Ck3xffdx2nCFRMTp+Tvua9V63YL1h99yoxwWgwY3MfW3l0kT8+wfStNGNzfnrT8xdzLxqB6gD2pTPYtM8FiNODl1V/huAFlGNTPjrNPHoS31u5AqzeM+dPq4LDoMby6EjaLHjdeMTZpvxNG9WsfiB5FQY6JSo9Go0n4r5C/UUiF3j9lJh/PHhU2A5w9Zix32o2osGfW7c9p0eOaC0b2+gzQqxhQc4wDZ5zQFyccV4EzTuiLmmMcGdW/slo2oFtXxrtmnY6H5k2QbKKTfNHEFN5hOxQKob6+Htdffz2uvvpqvPvuu7jzzjuxevVqVFZWitpH0hu+jtknj7YGUOEwt1+EGcxaWbTt8iDjtxrdp8IXmjo+FYXlqVxa5FLqnD63e59qbeLMj30rTHC3hnvvd93znFYYcehIt/2Ut9+EEvZdbgJiIsqCUDo1AttpRKRdaLtYj/3bDWhq6eU7HTePhM9sBri94cQpjXt+J9VnPfO90pScf0J5JeaYe+5b6FwI/Z6IFzNZt4AYgCMtYfiCbfAF2lBuN8Jm1aPFE4ROo4MvEIbLYeoqb93KQKXD1L5cRKoyKVReinyHUmtLh9yPK9cWuSVvfA6DXgNfoA1Wsx5WswGHm9uHWNw4dUy8FUyj0eD5t7bAYtLHJ+rK94Lhnb/RuW8AeGLFxqS05LL/P69qQFs4UjKLnSuhRQ5Afp7nsn3O6kkG9Wmh0iL3+izf0pVHxXetNBqNeOaZZ7BgwQIsXLgQAwcOxJNPPik6iBMUBvo5TV2TlIi9CIu9nRSigMtu7FrjTmzlwjzNL6FJFDpaWft2TlrTJnKihZ7nNNxjPx1BgdBnvZYFoXTGhLfrNe0ptkv4XkTEd2ICn0WFpzTudTuhfE+Rf1kdc899C52LFL9XMGGg0mrA8OrKhBuprayjO3vnNddtApKE4wJSl0lODkIF0N5wpYkHPN5AGDHE4Au0Jfy9u57BUaaTlaRaiLxnWsS0qnX/buf/9/nD8a6jlJmCTjzT8ewxqqajC2M2zx7ZPmf1JKf6VE5pURnFB3IAUFtbi5deeknqZBAREZGMWM16/P7NxNaxnn9/7Z87cKQlkLKVrmcLW7YBgFBaXv8o8bd7/m/37y77+9eY/p3js/ptapevc0kkF6oI5HKVzUxualLqx5+KVPnS+bs8L9JgvidKlx9qzSsel3wJHYNGA1gtBhi07ePSLEY9LCY9bBYDLEY9/KE2IAboOoZz28wGWEz6+Hfbd4Ku/+34biymie+/U/fPU9FoEJ/mvedv9kxL0m+nSEv33wfaj7ctrBWVHrXQ6XI/zu7nUqvNPe9KsX6UC+ZvO8WPkSMiIiIiIio1ip+1koiIiIiIqNQwkCMiIiIiIlIYBnJEREREREQKw0COiIiIiIhIYRjIERERERERKQwDOSIiIiIiIoVhIEdERERERKQwDOSIiIiIiIgUhoEcERERERGRwjCQIyIiIiIiUhgGckRERERERArDQI6IiIiIiEhh9FInQA6amjyIRmNSJ0MSFRVWHD3qkzoZslOIfKmqcoj6XlOTB2VlFp4XCZTS9ZBJeRSqH9WaVzwuabA8ZobHW1gsj/JWavmbrjyyRa7E6fU6qZMgS1Lni9S/X6qY7+KpNa94XMqk9uPriccrb0pLr9Iwf7uoIpDbsGEDLr/8cpxyyimor6/Hyy+/LHWSiIiIiIiICkbxXSuj0SjmzZuH2267DZMnT8bGjRtx9dVXY/To0RgxYoTUySMiIiIiIso7xbfItbS0oKmpCbFYDLFYDBqNBnq9HgaDQeqkiacB3P4wdjd64Q60ARqpE0REsqX0+kLp6Sd5Y/kiohKi+Ba5iooKXHPNNfj5z3+O22+/HZFIBHfccQdqamqkTpo4GmDL7hYsWr4BwXAEJoMO86fVYeTgMqA0518holSUXl8oPf0kbyxfRFRiFN8iF41GYTQa8cgjj+Czzz7Dn//8Zzz55JP45z//KXXSRHH7wvGbDgAEwxEsWr4Bbl9Y4pQRkdwovb5QevpJ3li+iKjUKL5F7t1338Wnn36K2267DQBw2mmn4bLLLsOyZcvw7W9/W9Q+XC57IZOY1oGtjfGbTqdgOAJfOIKawZVFSYPYaXZLjVT50lkeeV6kIed8l6K+SFc/ZppXcqjvxJBzGciFGo4rXXn0hSKKKF/5oobzmQk5Hm+q8tjcGkQgmvy5w2pEucNU4FSVBjmWBykoPpA7cOAAQqFQwmd6vR56vfhDk3IdOatJD5NBl3DzMRl0sBp0aGxsLfjvV1U5ivI7SlOIfMlkXRqXy87zIgG5Xw/5rC9yXScpm7ySur4TQ+5lIFtyP658lEcllK98kfv5zLdiH2+u5TEQBRYtXZ/0+ZypYxEOhJI+p8yUYvlPRfFdK7/1rW/h66+/xrJlyxCLxbB582YsX74cF110kdRJE8Vp0WP+tDqYDO1rYnT26XdaFTRZCxEVhdLrC6Wnn+SN5YuISo3iW+SOP/54PPHEE1i4cCEefvhh9OnTB7feeismTpwoddLEiQEjB5fhoXkT0OwNodxmbL/pcGA2EfWk9PpC6ekneWP5IqISo/hADgDOPvtsnH322VInI3sxwGkxwGkxxP9NRCRI6fWF0tNP8sbyRUQlRPFdK4mIiIiIiEoNAzkiIiIiIiKFYSBHRERERESkMAzkiIiIiIiIFIaBHBERERERkcIwkCMiIiIiIlIYBnJEREREREQKw0COiIiIiIhIYRjIERERERERKYxe6gQQyYoOONQcxJffuOEqM6GqzAREpE4U5UQDuH1hNHtCKHeY4LTogZgMfq/Y6SJlk3N50QCeQBvc/jYEgm3oU2aWV/qk1JE33lAEbW1RBEMR5g8R5Q0DOaJOOmDD10ewZOUmBMMRmAw6zJ4yGnXHVzKYUyoNsGV3CxYt3xA/p/On1WHk4LLCPESJ/b1ip4uUTc7lRQNs29eKvY0eLF3VIL/0Sakjbw63+BEIRZg/RJR37FpJ1OFQczAexAFAMBzBkpWbcKg5KHHKKFtuXzj+8Au0n9NFyzfA7QtL+nvFThcpm5zLi9sXxrZ97niQAsgrfVLqzJsWb5j5Q0QFwUCOqMMRdyB+o+0UDEdwxB2QKEWUq2ZPSPCcNntDkv5esdNFyibn8tLsCSEai8k2fVLqzBvmDxEVCgM5og6uMjNMBl3CZyaDDpVOs0QpolyVO0yC57TcZpT094qdLlI2OZeXcocJWo1GtumTUmfeMH+IqFAYyBF1qCozYfaU0fEbbucYub7lJolTRtlyWvSYP60u4ZzOn1YHp9Ug6e8VO12kbHIuL06LHjXHODGjvlaW6ZNSZ96U2QzMHyIqCE0sFiv5obZNTR5Eo6WZDVVVDjQ2tkqdDPnomLXyqDuICqepPYjL00QnVVUOUd9ravLA5bLzvORL52x/3hDKbcb2h6cUl3tergexv5dBugohk/IoVD+qte6Q7XHlWF4Kelw9Z610mrJKnxiKK49Cs1ZmkT89yfZ4C6TYx5treQxEgUVL1yd9PmfqWDjNuqTPKTOlWP5T4ayVRN1FgL4OE04c2qe9kuBslcoXA5wWA5wWQ/zfsvi9YqeLlE3O5SUG2E162E36hM8IwnnT8TkRUa7YtZKIiIiIiEhhGMgREREREREpDAM5IiIiIiIihWEgR0REREREpDAM5IiIiIiIiBSGgRwREREREZHCMJAjIiIiIiJSGAZyRERERERECsNAjoiIiIiISGFUEcgdOnQI8+bNwymnnIIJEybgsccekzpJREREREREBaOKQG7evHmoqqrC2rVrsWzZMrz22mv461//KnWyxNMAbn8Yuxu9cAfaAI3UCSphWqDJE8LaTfvQ5A2Jv0I6tmvY585su2LLZ1nruS+tBOW42NcOr9XsFCPfpDo3xfrdXH5HA3xzyKPeciv2+KSus9KdQ9YtRJQFvdQJyNVnn32GPXv24KWXXoLBYMCgQYPw5z//GSaTSeqkiaMBtuxuwaLlGxAMR2Ay6DB/Wh1GDi4DYlInrsRogc+2HcHiFZvi52LO1NEYW1MJRAuwXbHls6z12NcAlxXTJtYm5EHBy3Gxrx1eq9kpRr5JdW6K9bu5/I7ay63Y4xP43pypo7F8dQP2N/mkrT+g8nNERAUj13YD0TZv3oza2lo88cQTOPPMMzFx4kSsWrUKffv2lTpporh94XjlDQDBcASLlm+A2xeWOGWlp8kdigciQPu5WLxiE5rcoYJsV2z5LGs993Vm3cCkPCh0OS72tcNrNTvFyDepzk2xfjeX31F7uRV7fELfW7xiE86sG5h2u2KkU+3niIgKR/Etci0tLfjvf/+L0047DWvWrMH27dsxa9YsVFVV4eKLLxa1D5fLXuBUpnZga2O88u4UDEfgC0dQM7iyKGmoqnIU5Xfk7usD+wTPxVFPECOGuPK+XSqd5THf5yWfZS1pXxoUvRwX6tpJle9yuFalkK5+FFNGi5Fv+f4NsddescpELr+jtnLbszyKPb5U3+vehbGQ+ZIunYhlV3+W2r1bjsebqn7cc7AVBkPyI7bRqJPlcSgR87Gd4gM5o9EIu92Om266CQAwYsQIXH755Vi1apXoQK6pyYNoVJr+C1aTHiaDLqESNxl0sBp0aGxsLfjvV1U5ivI7SlDpMAmeiwq7KW0eid1ObKXT1OSBy2XP+3nJZ1lLta9iluNCXDvprgepr9V8y6Q8CtWPYuuOYuRbPn8jkzqxWGUil99RSrnNtjyKPb5U3+vedbGQ+ZIundBoMj5HpXbvLvbx5lo/AkA43Jb0WSgUKanzViilWP5TUXzXyqFDh8Lv9yMU6urGFolE0mwhL06LHvOn1bXfUIB433in1SBxykqPy2HEnKmjE87FnKmj4XIaC7JdseWzrPXc14ef7k3Kg0KX42JfO7xWs1OMfJPq3BTrd3P5HbWXW7HHJ/S9OVNH48MNe9NuV4x0qv0cEVHhaGKxmKKH0gaDQXz3u9/FxIkTcdttt2HHjh34wQ9+gLvuugvnn3++qH1I2SIHoH22Kl8Yzd4Qym3G9sq7SMkptbcavdK2j3k76gmiwm5qD8bETFjSsV2TOwCX0yy4ndQtcgDyW9Z67stmgNtb5HKc52un1+tBwms134rVIgegOPmWp9/IuE4sVpnI5Xc0QCiqwYEmj2zLbU7lUezxSV1npTuHGZ7fUrt3K61FLhAFFi1dn/T5nKlj4TTrck5fqSvF8p+KJF0rH330UcycORNOpxP79u3DgAEDoNFkN9euyWTCCy+8gHvuuQdnnnkmjEYjZs2aJTqIk4UY4LQY4LQY4v8miUQBl92IEUNc7ZWE2FknO7Zz2Y3xf8tSPstaz31FJSjHxb52eK1mpxj5JtW5Kdbv5vI7MeDYvnYYNbHMt1UCsccndZ2V7hyybiGiLEgSyD3//PO44oor4HQ6cd555+Gjjz5CZWX2g4sHDRqE3/3ud3lMIRERERERkXxJEshVV1fjpptuwogRIxCLxXDvvfemXPftgQceKHLqiIiIiIiI5E2SQO6xxx7DH/7wB7jdbgCAz+dDW1vy7D5ERERERESUTJJAbsmSJfj5z3+OyspKXHvttbj//vtz6lpJRERERERUSiRZfuDtt9+Ot8Z98sknUiSBiIiIiIhIsSRpkTvhhBNw3XXXobq6GrFYDD/+8Y9hMAivl/KnP/2pyKkjIiIiIiKSN0kCuSeeeAKvvPIK3G43Pv74Y4wYMQJms1mKpBARERERESmOJIFcnz59MGfOHADA0aNHceutt8Jut0uRFCIiIiIiIsWRJJD74IMPMGHCBBgMBlxwwQX473//m/K7Z599dhFTRkREREREJH+SBHKzZ8/GRx99BJfLhdmzZ6f8nkajwZYtW4qYMiIiIiIiIvmTJJD78ssvBf8/ZUELNLlDaHIH4Cozw+UwAlGpE0UFpwHcvjCaPSGUO0xwWvRATOpE5ZHaj4+Sz7FVD7eX55zySKgeAesWIlIPSQK57kKhEHbt2gWPxwOn04nq6mro9ZInSxm0wGfbjmDxik0IhiMwGXSYM3U0xtZUMphTMw2wZXcLFi3fED/v86fVYeTgMnU8kKj9+EjwHM+ZOhrLVzdgf5OP55xyJ1DGfnrVyQi1RVm3EJFqSLKOHADs3r0b8+fPx7hx43DJJZfgyiuvxEUXXYRx48bhpz/9Kfbu3StV0hSjyR2KB3EAEAxHsHjFJjS5QxKnjArJ7QvHH0SA9vO+aPkGuH1hiVOWH2o/PhI+x4tXbMKZdQPj/+Y5p1wIlbFt+9ysW4hIVSRp+tq6dSuuuuoqDB06FHfffTeGDRsGp9MJj8eDzz//HK+++iouu+wyLF++HNXV1VIkURGa3IH4DalTMBxp72ZpN0qUKiq0Zk9I8Lw3e0NwWoTXY1QStR8fpT7H0CT+m+ecsiVUxqKxGOsWIlIVSQK5Rx55BBMmTMCjjz4KjUaT8LcTTjgBV1xxBW688UY8/fTTePDBB6VIoiK4yswwGXQJNyaTQQeXk2vyqVm5wyR43stt6gje1X58lPocd+/exnNOuRAqY1qNhnULEamKJF0r169fj1mzZiUFcd3dcMMN+M9//lPEVCmPy2HEnKmj2x+AgPg4E5eTNyU1c1r0mD+tLuG8z59WB6dVHW+U1X58JHyO50wdjQ837I3/m+ecciFUxmqOcbJuISJVkaRFrrW1FVVVVWm/079/fxw6dKhIKVKoKDC2phL3zh7f3p3SaW4P4jjRibrFgJGDy/DQvAlo9oZQbjO2P4ioZbC+2o+PhM+xzYAh/U/hOaf8SFWPAKxbiEg1JAnkotForzNT6nQ6RCKRtN8hAFHAZTd2jYljEFcaYoDTYuga16G2BxG1Hx8ln+MozznlWYp6hOWMiNRCkkBOo9EgEAjA7/en/E66vxEREREREZUySQK5WCyGiRMn9vqddGPoiIiIiIiISpUkgdyf/vQnKX6WiIiIiIhIFSQJ5E477bS0fz9y5AgqKyuLlBoiIiIiIiJlkWT5ge6OHDmC2267DV9++SUikQhmzpyJb33rW6ivr8e2bdukTh4REREREZHsSB7I/epXv8L27dths9nwf//3f1i/fj2effZZjBs3Dvfdd5/UySMiIiIiIpIdSbpWdrd27Vq88MILGDRoEB566CGcddZZ+Na3voWBAwfi0ksvlTp5REREREREsiN5i1wsFoPFYkE4HMa//vUvnHXWWQAAn88Hk8kkceqIiIiIiIjkR/JAbty4cXj44Yfxy1/+Em1tbTjvvPPw5Zdf4r777sMZZ5whej9utxvnnHMOVqxYUcDUEhERERERSU/yQO7uu+9GJBLBF198gUcffRTl5eV44403YLPZcOedd4rez4IFC3Dw4MECprSAtECTJ4SGfW40eUOZnZVctqVkGsDtD2PT1ka4A22A2KUM9cBBdxBb9rbgUGtQBp2WET+W3Y3ermMR+5lSiEl7oY9ZzL54nXbpkV9HfeGufNEpuCwCyWVBK9HxZFu+810XFKNu6fiNT744gP3NAXhCEUAPHGoN4stvWtDoCQK6AvyuyHSJOna5lJtSoeR7HlEPkj9u9u3bF08//XTCZ//zP/+T0T5WrlwJj8eD2trafCatOLTAZ9uOYPGKTQiGIzAZdJgzdTTG1lQC0QJuS8k0wJbdLVi0fEM8P+dPq8PIwWVALM12emBDwxEsWdl1HmZPGY262kqgrWipT5TiWIx6LX7z4vr4Zz+96mSE2qKZH7MciDlfAt9Je8yFSAOv0y4C+TWjvhZvrt0Bg06LK86rTbiOFFMWgaRjG+CyYtrE2oTzXpTjSVEmXZX2rLbLOr353p/I3/j+RSPhtBnx+PLPEuvj4yuBSJ5+N4t0pTx2uZSbUlGMcklURLJ4L/zRRx/hueeew5NPPoknnngi4b/e7NmzB0888QTuv//+IqQ0/5rcoXiFDQDBcASLV2xCkztU0G0pmdsXjlfuQHt+Llq+AW5fOO12B48E4w+fndstWbkJB48EC57mVFIdy7Z97oTPtu1zZ3XMciDmfAl9J5/HLCYNvE67COXX0lUNOPeUwTizbmDSdaSUsggkH9uZdQOTznsxjidVmdx/2JvVdtmmN9/7E/sbf3xzC/Ye8ibVx4eai1cfZ3Lscik3paIY5ZKomCRvkXvsscewePFiDB48GA6HI+FvGo0GN954Y8ptI5EIfvazn+G2225DVVVV1mlwuXp5U1lAXx/YF69QOgXDERz1BDFiiKtg23ZXVeXo/Usl4MDWRsH89IUjqBmceoH6LXtbhM9DawCjavpknI7O8pjLeUl1LNFY4ivHaCyW1THLgZjzJfSddMcMZJbvYtKQr+tUKunqx0zLaKr86uzaJJeymM21l3RsGmmOJ1UeH2n1Y3RN6vtktvVfsfbXqXt5FFvPBcMRHHUHceLQzOvjbGRy7LmUm1K7d+fjePNdLlPVj3sOtsJgSH7ENhp1JXfeCoX52E7yQG7ZsmW45557cMUVV2S87VNPPYUhQ4bgu9/9bk5paGryIBqVpk290mGCyaBLqFhMBh0q7CY0NrYWbNtOVVUO0d9VO6tJL5ifVoMubR65yszC58FhTthObKXT1OSBy2XP6bykOhatJnEwgFajyeqY5UDM+RL6TrpjBpDRcYtJQz6u00LIpDwK1Y/Z1B2p8gsxABrIoixmWyemOrZiH0+qdFQ6LGl/N9v6L1/7y6Y8iq3nTAYdKpzFu94yOfZsy02p3bvzdbxiz02u9SMAhMPJYytCoUhJnbdCKcXyn4rkXStDoRDGjx+f1bZvvvkm3nnnHYwbNw7jxo1DQ0MDfvWrX+Guu+7KbyILyOUwYs7U0e0PM0B8/IzLaSzotpTMadFj/rS6hPycP60OTqsh7XZ9K0yYPSXxPMyeMhr9KqVbPiPVsdQc40z4rOYYZ1bHLAdizpfQd/J5zGLSwOu0i1B+zaivxXv/3Y0PP92bdB0ppSwCycf24ad7k857MY4nVZkc0MeW1XbZpjff+xP7G9+/aCQG9rUl1cd9y4tXH2dy7HIpN6WiGOWSqJg0sVhM0uGdd9xxB/r164f58+fnvK/Jkyfj+9//PqZOnZrRdlK2yAFon9HOHUKTOwCX09z+gCd2EoRctkXpvdXolaa9D70vHIHVoGuv3MUUDX37WLmjrQFUOMztQVyPl3HFbJEDED+WZm8I5TZj/EYl6jOlDPoWOkaByQTEHnNW14OYNOR4nRaCFC1yABLzy25EJAo0Nvvb86XMCLdH2rKYU53YsyzYDHB7JTgegTJZ1UfEcYkpyzmmI9X+si6PHb9x1BOCwaCD06KH3abHoaNBHHEHUOk0twdxxZropEe6ROVlFuWm1O7deT1eEecm1/oxEAUWLV2f9PmcqWPhNEsxjaq6lGL5T0WSrpU333xz/P/7/X6sWLECa9asweDBg6HVJjYSLly4sNjJK74o4LIb4bIb4/8uyraULAY4LQbUDK5sryTEPsS0Af2cJvRzmuL/llzHsTgthvi/AfGfKUKKYxTznbwds5g08Drt0jO/AFR0vg2PKLgsAsnHFpXoeMSUyXxuV6z9pfmNeJ0NAG1AX4cJfR0d9XGxg7hu6RJ17HIpN6WiGOWSqEgkCeSsVmvC/7/00kvzst/XX389L/shIiIiIiKSM0kCuQceeCD+//ft24f+/fsntcRFIhFs2bKl2EkjIiIiIiKSPcknOznvvPPQ3Nyc9Pm+fftw9dVXFz9BREREREREMidJi9wrr7yCpUuXAgBisRhmzpwJnS5x8Ofhw4dx7LHHSpE8IiIiIiIiWZMkkPve976HAwcOAAA2b96MM844AzZb4tTINpsN559/vhTJIyIiIiIikjXJJju58cYbAQDHHnssLrroIhiNpbemEhERERERUTYkCeS6mzJlCr744gts3boV0Wj7nNyxWAyhUAiff/457r77bolTSEREREREJC+SB3JPP/00Fi5cCKvVCr/fD4fDgdbW9rVgzj77bIlTR0REREREJD+Sz1q5bNky/M///A/Wr1+PqqoqvPHGG/jHP/6BsWPHYtSoUVInj4iIiIiISHYkD+QOHz6M/9/encc3Ue39A/8kaZamTbqElkWW0mILQqEtyKJykULhJ4JgVSirCyiLl6qgz3UHcQMX1KI8oJeLIgoitAqCsrpQeVS4si8WylYotLWlpFuWpvP7o83QJJNkOk0ySft9v16+JNM553zPMmfmJJPJiBEjAAA9evTAwYMHERUVhWeeeQabN28WOTpCCCGEEEII8T+iL+QiIiLY35GLiYnBqVOnAABt27ZFcXGxiJH5kBQorTQhr1CP0ipT03pFBhRXGHHq8nWUVBoBmfskrYIE0NeYcbGkCnpDLSDhma6hPfceuty09mxJ/SC07fiOY2/nL0RD3vuOFt7Im2+cQutjn04qMB9/4aodGv52tqgSV68bcK3h31fKDag0WRzrKrRNm5vWH/Jvbpneio8rXzHawklshSWVKCw34MLfVbh63YD8okrojbWoNNY6xueL9vRk23DMFdb6ni2qDMz5ghDiEaJ/R27YsGF4+eWX8frrr2PAgAFYtGgRbr/9duzYsQMdOnQQOzzvkwKH88uwIvsojGYLlHIZZqUnok9cJFDnJq0MOHS6DCtzbqSdeW8ikm6OBCw+id4/SYCTF68ja8Mhtl0yxyehR+cwgHGRTmh7tqR+ENp2fMext/MXwkneEaFKvLnmgOs4hdaHI92s9ERs2JWHK6XV/PPxF67aAY5/y0iLx9Z951BRZUZGWjw6RoUiroOmvq5C29RdHJ5oR2/n39wy3fSDp2NQBEnxzpd/+q4tnMSWX1iBSyWV2PH7BaQN6IKvduZxjrXM8Uno0SUMJy94oD2beHwLbhu7vNrr1JhyV3cUl9VgfaN6BtR8QQjxGNE/kfvXv/6FXr16IS8vD0OHDsXAgQPx0EMP4ZtvvsG//vUvscPzulK9ib2ABACj2YIV2UdRqje5TVtcbmQXD9a0K3OOorjc6NWY/Z2+2sye9ID6dsnacAj6arPLdELbsyX1g9C24zuOvZ2/EM7yrjZa3MYptD5c6VZkH8XgpI5NysdfuGoHrr+t35mH1L6d2X/nF+rZugpt0+ambW49vaUpZXorPmf55hfqfdoWzmLLL9Rj/c48DE7qyC7irDE1HmtZGw6hVG/yent6sh/s8xqc1BEFRVXsIq65+RNCApvoC7ng4GAsWrQIY8eOhUQiweLFi7F//3789ttvreKplaV6AzsZWxnNFpTqDW7TljlJW8YjbUtWXmnibJfyKtcX/ULbsyX1g9C24zuOvZ2/EM7yrjHVuo1TaH2cpWt8exSffPyFq3ZwV1ej2YI6hmHrKrRNm5uWD2/n39wyvRWfs3zrGMZhm6/HbHmlCXUMw44pd2PN2fHuyfb0ZD845CXBjfp6IH9CSGATfSEHAJcvX8Zbb72FOXPmoLi4GNu3b8exY8fEDssndGEqKOW2X6hSymXQaVWC00bySNuShWuUnO0SHuL6R+eFtmdL6gdPt539OPZ2/kI4yztYEeSwzT5OofVxlq7xbVF88vEXrtrBXV2VchmkEglbV6Ft2ty0fHg7/+aW6a34nOUrlUgctvl6zIZrlJBKJGx87saaTst9vHuyPT3ZD1x5Na5vc/MnhAQ20Rdyhw8fxujRo/HXX3/hl19+gdFoxMmTJzFlyhTs2bNH7PC8TqdRYFZ6os1JaFZ6InRa9xNyVJgSM++1TTvz3kREhyu9GrO/0wYHIXN8kk27ZI5PglYtd5lOaHu2pH4Q2nZ8x7G38xfCWd5qpcxtnELrw5VuVnoi9h661KR8/IWrduD6W0ZaPPb89yL777gOWrauQtu0uWmbW09vaUqZ3orPWb5xHbQ+bQtnscV10CIjLR57D17ChLR4p2Mtc3wSdFqF19vTk/1gn9feg5fQqW0IMuzqGUjzBSHEcyQMw4j61djJkydjyJAheOyxx5CcnIzNmzejU6dOWL58ObZv345vv/3W6zGUllairk7EZpDWf0+nVG+ATquqvzjl+wAHWf13tMr0BkRqVfWLhyY8YCMqSoOSkgpBYfs1Sf13C8qrTAgPUdSf4Ph0cUN7XtMbEaFV8m9PHv0QFaXhFXppaSV0ulDx+kVo2/Edx97OX4iGvK9VGhERqqzPm+EZp9D62KcLkUNfJSAfgZoyHrnmR4e5w1U7NPztb70RalUQlAoZrumNUCmDoA0OQqgqyOEhMoLatLlpuerl4fwFaUqZTvZt9lzPlS881xbNGo8SwMxIUFJeA3Nt/cM/qg21aBNW/2ldWYXRNj4PtKdbnhwnHHOFua6+vgZjLdpolb4ZhyLy9bVKc+dHQx2Qtf5Ph+2z0vtAqwrkx1r7hxZ77eqEq/Eo+lMrT5w4gTfffNNh+5gxY7By5UoRIhJBHaALVUAXqmBf82YBojVKRGuU7GsCgAG0wXJog+Xsa14a2rNnbJv6SYJve7akfhDadnzHsbfzF6Ih7+5ddfX93pA3rziF1sc+XZ3AfPyFq3aw/xuAiEb/dqir0DZtblp/yL+5ZXorPif5+sWYZYAOUaGQNw6g0YM6Q5VB7H7W/3u9PT3ZDxxzhUN9A22+IIR4hOi3VkZEROD8+fMO248ePQqdTuf7gAghhBBCCCHEz4m+kJs8eTIWLFiA7777DgBw8uRJrFmzBq+88goyMjJEjo4QQgghhBBC/I/ot1ZOnz4doaGhWLp0KWpqapCZmYk2bdpg9uzZePDBB8UOjxBCCCGEEEL8jqgLubKyMvz00084f/48br/9dmg0GsTHxyM1NRVarVbM0AghhBBCCCHEb4m2kFu9ejU++OADSCQSdOzYERqNBpWVlfj888/xyiuv4KmnnsK0adPECo8QQgghhBBC/JYoC7lNmzbhgw8+wP/8z/8gPT0dKtWNH/U1Go3Izs7GW2+9hbZt22LkyJFihEgIIYQQQgghfkuUhdznn3+Op59+GpMmTXL4m1KpxMSJE1FdXY01a9bQQo4QQgghhBBC7Ijy1Mrz58/jjjvucLlPamoq8vPzfRQRIYQQQgghhAQOURZyBoMBoaGhLvfRaDS4fv26jyIihBBCCCGEkMAh2u/ISSQSj+X166+/Ij09HSkpKUhLS8P69es9ljchhBBCCCGE+BvRnlqZnZ0NtVrt9O9VVVW88rly5Qrmzp2LJUuWYNiwYTh27BhmzJiBm266CYMHD/ZUuN4lAfTVZpRXmhCuUUIbHAQwXk7bkO7qmRKolUFNKzMQNKdNA6E8f41B7JjEaAOx213s8p3FU2VCiEoOo7kWwUo5jGYLqg21aBOmEj9Gf+dvferPGtqq5NzfkEKCKoMZunAVDAYLSvUG6MJU0GkUQJ3YgdqhPiaEeIAoC7kOHTpg3bp1bvdr3769230uX76M0aNHIy0tDQDQu3dv9O/fH3/++WdgLOQkwMmL15G14RCMZguUchkyxyehR+cw95O60LTNKTMQ+Lp+/tCe/hCD2DGJ0QZit7vY5fOI58G7e8Bcy+DL7af8I0Z/52996s8a2mrtDyeRNqALvtqZh87tQjFyQAw++fYY236z0hPRJy7SfxZz1MeEEA8R5dbKPXv28P7PnX79+mHRokXs6/Lychw4cAC33HKLN6vgMfpqMzuZA4DRbEHWhkPQV5u9lrY5ZQYCX9fPH9rTH2IQOyYx2kDsdhe7fD7x6KvM7CLOH2L0d/7Wp/7M2laDkzriq515MJotGDekG7uIA+rbb0X2UZTqTSJHewP1MSHEU0S7tdIbKioqMHv2bPTp0wfDhg3jnU6nc/3gFW+6eqaEncytjGYLqs0WxHWO9Era5pQZCDxVv6gojU/Ls7KOR77leyMGT/B1TL7ud0+WKZQvync1P9q3FVc8dQzjd2PTnaaMAU/zZp+KWS9PaTwe2baSgG0zg9HC2X7XKo3o3lXn01idEWOuagn8sb7O5seCogrI5Y6X2AqFzC/rEYioHeu1mIXcuXPnMGfOHHTr1g3vvPMOpFL+HzaWllairk6c+xnUyiAo5TKbSV0pl0Etl6GkpMIraZtTZiDwRP2iojS89+VbHt9Jp7S0EjpdaJP6wh/71Ncx+brfPVVmczSn/KaMR675kautuOKRSiR+NzZdaeoY8DRvjSmx6+WOkPFobSsAbJsFq2Sc7RcRqvSb+osxVwU6X9e3ufMjAJjNtQ7bTCZLq+o3b2mN498Z0Z5a6Un79+/H+PHjMXz4cGRlZUGpVIodEm/a4CBkjk+yORlljk+CVi33WtrmlBkIfF0/f2hPf4hB7JjEaAOx213s8vnEow2RY9LI7n4To7/ztz71Z9a22nvwEiakxUMplyHnpzN4dGwvm/ablZ4InVYhcrQ3UB8TQjxFwjBMQH+19uLFixg3bhyeeuopTJ06VVAeYn4iB8DmKW/hIYr6ybypT61satqGdNVmC9RyWdPKDATNaVMIeLeHR3ne/ESObww+5+uYfN3vHiiz2QSW741P5Ozj4XxqpVbpH2PTCb94p9cLY8ov6uWC4PHY0FbGujpImYanVoapYDA2PLVSq6pfxPnLg06sxJirAligfSJnqAOy1v/psH1Weh9oVbJmx9fatcbx70zA31r5xRdfoKqqCkuXLsXSpUvZ7ZMmTcIzzzwjYmRNwADaYDm0wXL2tdfTNqSL6xxZfzD46UWVYM1p00Aoz19jsNca+kHsdhe7fHv28cD6SYgcCLuxD3HB3/rUnzW0lfXCThdaP95C5DL23363iAOojwkhHhHwC7nnnnsOzz33nNhhEEIIIYQQQojPtIjvyBFCCCGEEEJIa0ILOUIIIYQQQggJMLSQI4QQQgghhJAAQws5QgghhBBCCAkwtJAjhBBCCCGEkABDCzlCCCGEEEIICTAB//MDhBBCCCGEBDILJKgy1DpsD1EFQUY/NEicoIUcIYQQQgghIqoy1GJF9mGH7bPS+0CrkokQEQkEdGslIYQQQgghhAQY+kTOH0iBUr0JpXoDdGEq6DQKoI5n2iCgqMyIsoa00RFKwPGTeUcyoLjciFOX9dCFKREVpgQs/NNZy+OdTmgdJYC+2ozyShPCNUpog4PA6w4DoXEKJTROX+fNlZfErm+0CugreZTHJy6+sdvvpw6CvspL7QnwGx/e7FMuXMcIeBw3XHHCy7FLgbOF5aisNqO21gKFPAjlFUZEaJWQShlIGCmqDGaEhzYq2xpnlQkhwXLUgYHRWAejuRZRYcHeb18iHm8eS1KgvMqMK/klsNTWwWCyIFKrgoVhYDDWwmS2cI8vXx/fTeXv8RFC/AIt5MQmBQ7nl2FF9lEYzRYo5TLMSk9En7hI9wudIOBQXhlW5txIO/PeRCTFR7pezMmAQ6c50t0c6XqxIzSd0DpKgJMXryNrwyE2Xeb4JPToHOb6hCY0TqGExunrvDnyenpSCqqMtfjfTUds2urr3Xm4UlrtvDw+cfGNnWO/WemJ2LDLTQxCuRofTamfJ3EcI7Pv6w2FXIoP1h9yftw46VNTbZ33YpcCZ69UoLzChN37LyD11i7497fHXI+fLmE4ecE2zkfH9sKmH097p4+J//DmsSQFThVcx/UKIwwmC9bvzIMmRI777uzGvuYs09fHd1P5e3yEEL9Bt1aKrFRvYi/eAMBotmBF9lGU6k1u0xaVGdmLUWvalTlHUVRmdJmuuJw7XXG5d9IJraO+2syeyKzpsjYcgr7a7JU4hRIap6/z5sorv1DPLuKs21bmHMXgpI4uy+MTF9/YufZbke0+BqH4jA9v9ikXrmPkfzcdwcWrlS6PG2d96s3YS/UmmMwMPvn2GEYPjmMXcdayuMZPqd7kENMn3x7zWh8T/+HNY6lUb8LFq5W4XmVmF22pfTvbvOYq09fHd1P5e3yEEP9BCzmRleoN7GRtZTRbUKo3uE1b5iTttQrXaZ2lK3NTptB0QutYXmniTFde5XoBKDROoYTG6eu8ufKqYxjO/CFxXR6fuPjG7mw/dzEIxWd8eLNPuTg7RuoYxmFbqZs4nfWpp2Iv1RtQY6yF0WyBwWjh1XfO6uetPib+w5vHUqnegDqGsR3zEvfHgK+P76by9/gIIf6DFnIi04WpoJTbPo1IKZdBp1UJThuhcZ3WWbpIN2V6Op27OoZrlJzpwkMUXolTKKFx+jpvrrykEgln/o1v3+Eqj09cfGN3tp+7GITiMz682adNiUkqkThs07mJ01mferL9glVBUMplCFbJePWdTstdP2/1MfEf3jyWdGEqSCUShzHv7hjw9fHdVP4eHyHEf9BCTmQ6jQKz0hPZSdv6PRid1v2EHR2hxMx7bdPOvDcRbSOVLtNFhXGniw73TjqhddQGByFzfJJNuszxSdCq5V6JUyihcfo6b6684jpoMfu+3g5ttffQJZfl8YmLb+xc+81Kdx+DUHzGhzf7lAvXMTL7vt7o3C7U5XHjrE+9GbtOo4AiSIJHx/bCll/yMWNsL7fjR6dVOMT06NheXutj4j+8eSzpNAp0bheKsBA5MtLioZTLsOfARZvXXGX6+vhuKn+PjxDiPyQMw7T6r86Wllairk7EZmj8tDpt/VMDm/rUymsVBkRoVPWLuCY8tfKavv5Jc9HhTX9qZaRWxT+d0Do2etJdeIii/kTWxKdWNinOBlFRGpSUVPBPwCPOqCgNr6xKSyuh04XeKF9oG/CNU2LXN2ENT610Vx6fuPjGbr9fiLz+qZWeqDMXJ+PDpt892e58cB0j4HHccMUJz45Hh/lRClQYLbZPraw0IkJj99TKxmU7fWqlBVFhSmiDvdy+PDX52A8QotbLw/OjzXhseGpljdkCSy0Dg6nW7qmVddzjy9fHd1O5ia+ljlNnfF3fZs2PAAx1QNb6Px22u/pNOL3BQr8jx1NrHP/O0FMr/UEdoAtVQBeqYF/zVgu01SrRVqtkX/NiAaI1SvSMbVN/MPBd5DSki9Yo2de8CK0jA2iD5fUn4YbXXo1TKKFx+jpvrrwYu76x8CyPT1x8Y7ffr86L7QnwGx/e7FMuTo4Rt8eNkzi9GnsdENsh3OZEys5BuBG3Tdn2cQJASKME/nQRTTzLm8dSHRAeLMfNnSMdL+wa34poX6avj++m8vf4CCF+gRZyhBBCCCGEEAcWSFBlcPyUIEQVBBm9wyA6WsgRQgghhBBCHFQZaumWTz9GDzshhBBCCCGEkABDCzlCCCGEEEIICTC0kCOEEEIIIYSQAEPfkSOEEEIIIYR4lbMHpwD08BShaCFHCCGEEEII8SpnD04B6OEpQtGtlYQQQgghhBASYFrEQu7UqVOYMGECkpKSMGbMGBw5ckTskAghhBBCCCHEawL+1kqTyYQ5c+Zg2rRpWLt2LXbs2IHp06fjxx9/RGhoqLBM5UBRqRFlegN0YSpERyoBsx+mAwAJoK82o7zShHCNEtrgIPC6xbihzJOXrvt3HX2dTgYUlxtx6rIeujAlosKUgIVHOm/i6mNpfZzW+kWFKQEJUFRmW2debSCzyyuCIx1jl3dE/TabdOFK6Cvs4pQApXoTShv20WkUgIQjdvDIi6vO9unsY+eKk6tPufrdPu+w+rzc1ocrBnC0n8RxjBaX2ZUn5RjHcNzmUB73VxA8Qw5c05txKa8Y1TVmRGiVUKuDYKixoLaOQUWVCWpVEIKVQaiqMUGllKO2tg4VNWaEKOu31xjMCFUroFUHQV9lxt96I4KVQdCo5QhVyrjnMOtxUGVCiEoOo6m2Pg++c54rjY4xEyOBQoqm5yl0LibN0+hcFhmmglIOGM0AmDpAIkW53ohwrRIadRDUQW7GVkPfSSVAmd7ouX6ksUEI8ZKAX8j98ccfMJvNeOihhwAAd999N9auXYtt27Zh/PjxTc9QDhz6qwwrc47CaLZAKZdh5r2JSEqIdL0Q8HU6AJAAJy9eR9aGQ2zazPFJ6NE5zPVJIlDq6Ot0MuDQaY50N0eKt5jj6OOXHr4VxeUGmzhnpSdCqZDhg/X1+/XoEo60ATHu28Cuzpzp0hMRGhyEd784yG6bPykZlYZarMy2zf/r3Xm4UlrNjkVIGGR9dZjd55kpKdBXm23S2cfOlddz0/qhrNJok272fb0RJJNg2YbDNul+P1aI308UQymX4dlpfXGtwuS6Tzn6nSvvzAl9YK5l8L+bjrDb5tzfGzLpjf3a69R4YFi8TV5cbTUrPREKudSmbezrzBXD3PF9UGu5EcOAW6IxoFcHx/rFR3pnMScHTl24jqK/q7F+Z55NeykVUry/7kYfZqTFQ60KAiDBf7Yct9muVMiwZe9ZjB8ejw27btQ5Iy0eHaNCEddBYzuHcRwHE9LisfP3C5jy/3q4n/NcETqPejoP0nRO5vrKagNC1Cp8bLc9OlyFDjq127GVkRaPrfvOoaLK3Px+pLFBCPGigL+18syZM4iLi7PZFhsbi7y8PEH5FZUa2ZMCABjNFqzMOYqiUqNfpQPq3+GznhysabM2HIK+2vUKMFDq6Ot0xeXc6YrL3feFt3D1sYWBQ5wrso/i4tVKdtvowXG82sC+zpzpso+ithY222otYBcmjfMfnNSRfZ214RAKiqps9jGZGYd09rFz5VVttDik+99NR3CpuMoh3ajbY9nXUqnUbZ9y9TtX3gVFVewCyrpt+Ubb/QYndXTIi6utVmQfdWgb+zpzxXCp2DaGUbfHcvdzmXfGbFGpERevVLKLuMaxXrhi24frd+bhWoUJ1yqMDtv1VWYMTuqIFdm2dV6/Mw/5hXqHOYzrOPhqZx4GJ3XkNee5InQe9XQepOmczfU3d9axi7jG2y0MeI2t9TvzkNq3s0f6kcYGEYMFEugNFof/LJCIHZqonLVLILdNwH8iV11dDZVKZbMtODgYNTU1vPPQ6W7cgnny0nV2wrUymi24VmFAr7g2TvPwdToAuHqmhDNttdmCuM6RfhNroKQ7dVnPnU5vRM9Y133hSdbxGBWl4exjfZWJM8465sbbuwajhVcb2NfZWboak+3HOzXGWs79Gs+D9jG5Sme/n31eTUmnrzaxr521VeM+ddbv9nnXMYz7/SRw2EdonfnE4LR+POYPvuznR17t4GSbzXaJ8zrbz2HO5jprHu7mPFeEzqOezsMXoqI0YofQbHzO12V6A+d2fbUJcrmU99iy/lvs8eVMS+jPpvDH+jYej40VFFVALne8xFYoZE7rYRCQxpmCogqs2nLcYfvs+3qjXdum5eXJuJzlJSQ/IePBWbsAwtrGHwT8Qk6tVsNotH33uaamBmq1mncepaWVqKurv+DQhamglMtsJl6lXIYIjQolJRVO8/B1OgBQK4M406rlMr+KNXDSKbnTaZVu+4IPvpNOaWkldLpQlJRUcPaxNlTBGadUcuOKOFgl49UG9nV2li5YYTtVBKu4x17jW4XsY3KVzn4/+7yakk6rVrhtq8Z96qzf7fOWSiS8Y2i8j9A684nBaf14zB9NGY+N58eCokre9bHf1nh7XR3jtM72c5izuQ4MvznPFaHzqKfz8LaoKI3fxMJF6HjkavdILfd2rVrRpLFl/bfY44uLv/enp/m6vkLGoz2z2fH+dpPJ4rQeJpOlyWmcCbS8mpqf0PHgqfJ9zdV4DPhbK+Pi4nDu3DmbbWfPnkW3bt0E5RcdqcTMexPrJ3KAvbe+rU7pV+kAQBschMzxSTZpM8cnQauW+1WsgZIuKow7XXS4+77wFq4+lgEOcc5KT0TndqHsti2/5PNqA/s6c6ZLT0RQEGy2BUmBmemO+e89dIl9nTk+CZ3ahtjsowiSOKSzj50rL7VC5pBu9n290TE6xCHdtl/Psq/rLHVu+5Sr37ny7tQ2pP67YI22zbnfdr+9By855MXVVrPSEx3axr7OXDF0jLaNYVvuWe5+jvTOmI2OVKJz+9D677nZtVeX9rZ9mJEWjwiNAhEapcN2bYgcew9dwqx02zpnpMUjroPWYQ7jOg4mpMVj76FLvOY8V4TOo57OgzSds7n+9MVSPMaxXSYBr7GVkRaPPf+96JF+pLFBCPEmCcNw3PsSQEwmE9LS0vDwww9j8uTJ2LFjB1566SXs2rULkZH8bltweEel4SlY1yoMiNCo6i9+m/CkRJ+lA2ye5BYeoqg/OTThqZV+X0dfp2t4euE1vRERWmX9Bb/FfTI+hHwiB4C7jxs9wTFSq6qPs+GplY3rzKsNZHZ5RXKkY+zyjrR9GmSktv5pifoKuzgbP7VSq4JOa/uURzZ28MiLq8726exj54qTq0+5+t0+73C7p1Y6qw9XDOBoP4njGLU+tZItT8oxjuG4zaE8Hg86EfyOc8NTK6sMtag21CI8VIGQkCDU1FhgaXhqZbAqCGpFEKoMN55aWVljRrAyCGplEGqMZoSq5NCGyNmnVqqUQdAGByFU5eSJfvZPrTTX1ufBd85zpVHe7XShUEgZ4U+tbOpc7CP+/glOc8Zj4/GvVEhgNDEA6gBIUV5pRHioEpoQHk+tbOg7qVSCsgqj5/rRC2PD3/vT0wLtEzlDHZC1/k+H7a5+9FpvsHD+WLaQH8oOtLyamp/Q8eCp8q0skKDK4HjCDVEFQebBE4Cr8RjwCzkAyMvLw4IFC3Dq1Cl07NgRzz//PAYNGsQ7vauPxlu61nYy4Msb7SJ4IUd8pjUdD829UGmpbUX1EgeNx6ah+nq/PD5oIef9hRzXYkmhkEEulTR5seTphZwn28YVV+Mx4L8jBwDx8fFYt26d2GEQQgghhBBCPKTKUOuwWJLLgzB9TE+PLpYCVYtYyDWXVBqYjxz1lNZef2fEahdrudQv4qB2t+WqPVpqW1G9/FdrHI/OUH3F5zQmJ3d5SSTO03A8G8ptGmcCLS9X+YldvpD8nOVVy4DzVkyg/nbMIAFDvEXcWkkIIYQQQgghrUnAP7WSEEIIIYQQQlobWsgRQgghhBBCSIChhRwhhBBCCCGEBBhayBFCCCGEEEJIgKGFHCGEEEIIIYQEGFrIEUIIIYQQQkiAoYUcIYQQQgghhAQYWsgRQgghhBBCSIChhRwhhBBCCCGEBBhayLVSGzduRM+ePZGcnMz+l5OTI3ZYojly5AgGDRrEvjaZTHjppZfQv39/DBw4ECtXrvRJHKdOncKECROQlJSEMWPG4MiRIz4pt7X59ddfkZ6ejpSUFKSlpWH9+vUAxOv3QBLIY9TZvOeu37dt24a0tDQkJSXh0UcfRWlpqUg1sNXUectVPQoLC/Hwww8jOTkZw4cPx88//+yzejRHII9He62lP4XOv4FQ35Y0Hv0RXbtyYEir9MorrzBvv/222GGIrq6ujtmwYQPTt29fpm/fvuz2d955h5k8eTJTXl7OFBQUMCNHjmRycnK8GovRaGSGDh3KrF69mjGZTMx3333H9OvXj6moqPBqua1NYWEhk5yczOzYsYOxWCzM4cOHmVtvvZX55ZdfROn3QBLoY9TZvOeq30+fPs0kJSUx+/fvZwwGA/Pqq68yU6dO9XHktoTMW+7qMWHCBObNN99kjEYjs2/fPiY5OZm5ePGir6vWJIE+Hq1aU38KnX8Dob4tZTz6M7p2dUSfyLVSx48fR48ePcQOQ3RZWVlYt24dZs+ebbM9JycHs2bNQlhYGDp27Ijp06ez7xp6yx9//AGz2YyHHnoIcrkcd999N7p164Zt27Z5tdzW5vLlyxg9ejTS0tIglUrRu3dv9O/fH3/++aco/R5IAn2MOpv3XPX75s2bkZqain79+kGpVGL+/Pn4888/cf78eR9Hf4OQectVPc6dO4djx44hMzMTCoUCgwYNQmpqKjZu3ChG9XgL9PFo1Zr6U+j8Gwj1bSnj0Z/RtasjWsi1QhaLBX/99Re+/fZb3HHHHUhLS8PHH38MhmHEDs3nMjIykJ2djV69erHb9Ho9SkpK0K1bN3Zb165dkZeX59VYzpw5g7i4OJttsbGxXi+3tenXrx8WLVrEvi4vL8eBAwdwyy23iNLvgSSQx6izee/69esu+92+zsHBwWjfvr2odRYyb7mqR35+Ptq3bw+1Ws3+PTY2Fn/99ZcPaiNcII/HxlpTfwqdfwOhvi1lPPorunblFiR2AMT3ysrK0KtXL4wbNw4ffvgh8vPzMWfOHISEhGDy5Mlih+dTbdu2ddhWXV0NAFCpVOy24OBgGAwGr8ZSXV1tU6a13JqaGq+W25pVVFRg9uzZ6NOnD3r27AnA9/0eSAJ5jDqb90wmEwDn/V5dXY3g4GCbvFQqlah1FjJvuasHV7/6+9gP5PHYWGvtz6bMv4FQ35YyHv0VXbtyo0/kWqGoqCisXbsWo0aNgkKhQI8ePTBlyhTs2LFD7ND8gvVkYTQa2W01NTU27/Z5g1qttinTV+W2VufOncP48ePRpk0bZGVlISQkBIDv+z2QBPIYdTbv7d+/H4Dzfue6IDQYDOx48Rfu5i1X9QjUfg3UuPlo6f3Z1Pk3EOrrL3G0VHTtyo0Wcq3Q6dOnkZWVZbPNbDZDqVSKFJF/CQsLQ1RUFM6ePctuO3funM0tH94QFxeHc+fO2Ww7e/as18ttjfbv34/x48dj+PDhyMrKglKpFK3fA0kgj1FX856rfu/WrZtNnWtqanDlyhWHW6jE5m78uqpHXFwcCgsLbS6UA6FfA3k8utOS+1PI/BsI9W3J49Ef0LUrN1rItUJarRarV6/Ghg0bUFdXh2PHjuHzzz9Henq62KH5jXvuuQcfffQRysrKcOnSJaxatQr33HOPV8scMGAAGIbBp59+CrPZjK1bt+Kvv/5CWlqaV8ttbS5evIiZM2ciMzMT8+fPh0QiYf8mRr8HkkAeo67mPVf9Pnr0aOzevRu///47TCYT3n33XfTo0QNdu3YVuUaOhNYjNjYW3bt3x3vvvQeTyYTffvsNu3fvxujRo0WukWuBPB75aIn9KXT+DYT6tvTxKDa6dnVCzEdmEvHs27ePuffee5mkpCRm6NChzNq1a8UOSVS//fabzWOfDQYDs3DhQmbQoEHMgAEDmKVLlzJ1dXVej+Ovv/5iMjIymKSkJGb06NHMvn37vF5ma/PGG28w8fHxTFJSks1/b731lmj9HkgCeYw6m/fc9fsPP/zAjBw5kklKSmIefPBB5vLly2JVwUZT5y1X9SgsLGSmT5/OpKSkMMOGDWO2bt3q07oIFcjj0V5r6M/mzL+BUN+WNB79EV27OpIwTCt/3AshhBBCCCGEBBi6tZIQQgghhBBCAgwt5AghhBBCCCEkwNBCjhBCCCGEEEICDC3kCCGEEEIIISTA0EKOEEIIIYQQQgIMLeQIIYQQQgghJMDQQq4VSU1NRUJCArZv3+7wt/LycvTs2RMDBgzwSFmnTp3Cb7/9xr5OSEjAjz/+6JG8SctnHavW//r06YOxY8di48aNYodG/Fh1dTXef/99jBw5Er1790ZqaioWL14MvV7vsfxnzpyJxMRETJo0CcuWLfPoj9H+/vvvNuO+R48e6N+/P2bNmoXTp0/b7Nt4Ti0pKUFGRgYSExPx9NNPIz8/H2PGjEGvXr2wdOlSj8VH3LOfuxISEpCSkoLJkyfjyJEjYofXZNnZ2TZ1ueWWWzBo0CDMnz8fV69eZfe7dOkSEhISkJeXBwAOY/DAgQMYPnw4EhMTsW7dOrGq0+r54txaVVWFr7/+mn397LPPIjMzU1BeNCe6FyR2AMS35HI5du3ahZEjR9ps37NnDywWi8fKmTNnDh555BEMHDjQY3mS1mXevHlIT08HwzCorKxEbm4uXn31VVy/fh3Tp08XOzziZyorKzFx4kSo1Wq88MIL6Nq1K86fP48lS5bgwIEDWLt2LVQqVbPK2LVrF/bt24d169YhOjoaISEhmDJliodqYFuOSqWCxWJBcXExVq1ahUmTJuHrr79GTEwMACA3NxdhYWEAgI0bN+LSpUv45ptvEB4ejvfeew9SqRTbtm2DVqv1eHzENevcBQAMw+Dq1atYunQpHn30UezZswchISEiR9g04eHh+O677wAAtbW1uHz5MrKysjBp0iRs3LgRkZGRaN++PXJzcxEREQEAWL16tc0YfOqppxATE4NPP/0U4eHhItaGePvcunr1auzZswcPPPCAB6KtR3Oic7SQa2X69++Pn3/+GbW1tQgKutH9O3bsQFJSEs6dOydidITcEBISgqioKABAdHQ0YmNjIZPJ8NZbb2HcuHHQ6XQiR0j8yTvvvIO6ujp89tln7IKtU6dO6NatG9LS0rBp0yZMnjy5WWVUVFSgTZs26NWrF7vNGxflkZGRbL7t2rXDe++9h4kTJ+Ldd9/FsmXLAIA9NgBAr9cjJiYGcXFx7Ovu3bujc+fOHo+NuNd47gLq56/FixdjyJAh+O233zBs2DARoxOmcX3at2+PFStWYPTo0fj444/x7LPPQiaTOYzJxmNQr9djyJAh6Nixo89jJ7a8fW5lGMYTYdqgOdE5urWylbn99tthNpuxf/9+dltVVRV+//13DB8+nN1WXFyMZ555BoMGDUJKSgqeeOIJFBcXs39PSEhAdnY20tPT0adPHzzwwAM4ePAgAGDq1Km4fPkyXn31VUydOpVNc+zYMdx///1ITEzE6NGj8ccff/igxqQlSU9Ph0QiwY8//ojq6mosXLgQd9xxB3r27IkhQ4Zg+fLlAIAjR44gISEBFy5cYNMaDAYkJyfb3PJLWgaTyYQtW7ZgypQpDp+6tW/fHmvWrMGoUaPAMAzWrFmDkSNHIjExEWPHjsXPP//M7vvss89i4cKFeO6555CcnIzU1FR2TC1btgyLFi1CYWEhO/81vrUyOzsb9957L55++mmkpKRg+fLlWLZsGebOnYt33nkHffv2xcCBA7FmzRocOHAAY8aMQVJSEmbMmIHy8nKX9ZNKpcjIyMCPP/4Ig8EA4MZtRM8++yz+85//YP/+/UhISMDUqVOxfft2fPPNN0hISMClS5c82NJEKIVCAQCQyWQAgFWrVmHo0KFITk7GxIkTcejQIXZfi8WCjz76CHfeeSeSk5MxdepUm9vItmzZgjFjxqB3794YOXIkcnJyAAAXL15EQkICTp06xe5bW1uLgQMHYsuWLQCAw4cPs7ecjRgxAp988gnq6uoAcI9hZ9RqNdLT0/HDDz8AsL210n4Mpqam4tixY/joo4+QkJDggdYkntb43Aq4Hp+uzr3Z2dn48MMPcfz4cZv5p6amBs8//zxSUlIwcOBAvPfee2x+RUVFmDVrFvr27Yt+/fohMzMTpaWlLuOlOfEGWsi1MkqlEoMHD8bu3bvZbT///DN69eqFyMhIAIDZbMZDDz2EK1eu4OOPP8Znn32GoqIiPP744zbvtLz//vt44okn8NVXX0Eul+Pll18GUH/B065dO8ybN499pwQA1q1bh7lz52LLli3o2LEj5s+f75V3bkjLFRwcjI4dO+LMmTNYvHgxDh06hOXLl+OHH37A1KlT8cEHH+DYsWPo3bs3YmJisHXrVjbt7t27odFo0L9/fxFrQLyhoKAAlZWVSExM5Px7SkoKIiIisGLFCixbtgyZmZnYvHkzhg8fjtmzZ9tc+G7cuBFt27bFpk2bcP/997Nj6pFHHsG8efPQrl075ObmYtSoUQ7lnDhxAiEhIcjJycG4ceMAgH3TIScnBxkZGVi8eDFeffVVLFiwAKtWrcLx48fx2Wefua1jt27dYDabbd6cAIAXXngBEydORHJyMnJzc/H+++9j6NChuOuuu5Cbm4v27ds3oSWJN1y7dg0vvvgioqKi0K9fP6xfvx5r1qzBggULkJOTgyFDhuDBBx9EQUEBAOCjjz7C559/jueffx45OTlo3749Zs6cCYvFgs2bN+O5557DxIkTsXnzZkydOhUvvfQSfvrpJ3Tu3Bm9e/fG999/z5a9b98+GI1GDBs2DKWlpZg+fToGDx6MLVu24IUXXsCXX36Jf//73+z+XGPYmW7duuHKlSuorKy02b5s2TKbMbhhwwZ0794djzzyCHJzcz3XsMRjGp9b3Y1PV+feUaNG4ZFHHkH37t1t5p9ffvkFUVFR+PbbbzF//nysWLECe/fuBQAsXLgQUqkUGzduxNq1a3H58mUsXrzYbcw0J9ajhVwrNGLECOzZs4d9vWPHDpvvzOXm5uLixYt49913kZiYiMTERLz//vs4ceIE9u3bx+43ZcoUDBkyBN27d8f06dORl5cHk8mE8PBwyGQyhISE2NwL/+ijj2LIkCGIiYnBjBkzUFxcjLKyMp/UmbQcGo0GlZWVSElJweuvv47evXujU6dOmDFjBtRqNc6cOQMAGDNmDLZt28am27JlC+6++25IpTTttTTXr18HUD82nGEYBp999hlmzZqFu+++G127dsXcuXNx22234ZNPPmH369SpE5588knExsZizpw5CA8Px/HjxxESEoKQkBD2FjJn37f75z//iS5duqBDhw4A6t88e/7559G5c2dMnToVFosFU6ZMQb9+/dC3b18MGTKEHbOuWL/XYX/RrNFoEBwcDLlcjqioKOh0OigUCqhUKkRFRbGfABHfWbx4MZKTk5GcnIw+ffpg8ODBqK6uxurVqxEaGoqVK1di/vz5uPPOOxETE8N+GvHll1+CYRisW7cOs2fPxogRIxATE4OXX34ZI0aMwPXr1/Hpp59i/PjxmDRpEmJiYjBlyhTcd999WLFiBYD6ec/6KRkAfP/990hNTYVarcYXX3yBxMREPP7444iJicGQIUMwb948rFq1yiZ++zHsjHVMVlVV2WwPDw+3GYNt2rSBTCaDWq22uf2N+BfrudXV+ATg8tyrUqmgVqvZedI6/8THx+Opp55Cp06d8MADD6Bz587sG2iXL1+GVqvFTTfdhO7du2Pp0qV4+OGH3cZLc2I9uqJphe68804UFxfj5MmTMBqN2Lt3L9LS0ti/nz59Gh06dEDbtm3Zbe3atcNNN91kc3uH9QumABAaGgqg/jYOZzp16sT+23oAWj8SJ4SvyspKaDQa3HPPPbh69SreeOMNPPbYY+zFkvU2obFjx+L06dPIy8tDeXk5cnNzcc8994gcPfEG6wMWrAs6LqWlpbh27RqSkpJstvft29dmIdWlSxebv4eEhLic1xoLDg52uFC96aab2O8jWxd/jb8npFAoYDKZ3OZtvVhxtVgl/mHmzJn45ptvsGHDBqSnpyM8PBz//Oc/cfPNN6OqqgqFhYV46aWX2MVecnIyfv/9d5w7dw7Xrl1DWVmZzafLoaGhePbZZxEZGYkzZ864HMOjRo1CQUEBTp48CZPJhF27drHz3pkzZ/DHH3/YlPviiy+ivLwc165dA8A9hp2xjslAe3gL4WY9t7oanwDcnnu52H83TaPRwGg0AgCeeOIJbNu2DQMHDsTjjz+OgwcPIj4+nle81rxaM3rYSSsUGhqKQYMGYdeuXejZsyduvvlmm0Wbs3eaGYaxuRVSLpdz7uMM17sgdGslaQqDwYBz585h+vTpeP7557Fv3z6MGzcO48aNw8KFCzF27Fh2306dOiE5ORnbtm1Dhw4d0KVLF/To0UPE6Im3dOnSBeHh4Th69Ch69+7t8Pc333zT6cmeYRibCxDrd5ns9+FDqVQ6bGv8UCkrIZ8KnzhxAkql0uYNNOKfIiIi2DcEFixYgLKyMsyePRubN29m3/RcvHgxbrnlFpt0KpWKPa9KJBLOvLnOz43HcJs2bTBo0CB8//336NOnD2QyGW6//XYA9W+0jhgxAk8++aRDHtbjg2sMO3PixAl06tQJoaGhbr/nSfyb9dz6yCOPAHA+PgG4PfdycXX9N2zYMPz000/YvXs39u7di4ULF2Lz5s34z3/+4zJPmhPr0SdyrdSIESOwa9cu7Ny5EyNGjLD5W1xcHAoLC20eblJUVITCwkLExsb6OlRCWDk5OQgKCsKtt96Kb7/9FkuWLMG8efMwatQoyOVyVFRU2Fx033PPPfjxxx+xZ88ejBkzRsTIiTdJpVKMHTsWa9euZd/ltbp06RK++uoraLVaREdH23xpHwAOHjzo9/MawzDYtGkThg8fzrnQJP5twYIFkMlkWLhwITQaDaKiolBUVIQuXbqw/61duxZ79+6FRqOBTqfDiRMn2PQGgwG33XYbDh8+jNjYWLdjePTo0dizZw927tyJu+66i30zIS4uDmfPnrUpNz8/Hx999FGT31wwGo3YvHkz7rrrLuENQ/yG9dw6dOhQl+OzsrLS7bnX2ZsQXBiGwZIlS1BcXIwHHngAWVlZ+PDDD/Hrr7+6fOAJzYk30EKulRo2bBhOnz6N7du329xWCQC33XYbEhISMG/ePBw7dgzHjh3D/PnzERMTg0GDBvHKPyQkBPn5+W6fPESIM1VVVSgpKUFJSQnOnj2LVatWYcmSJXjyyScRHR2N4OBg7Ny5EwUFBfjvf/+LuXPngmEYm9vURo0ahfz8fPzf//0fRo8eLWJtiLfNmTMHdXV1mDZtGnJzc1FQUIBdu3Zh+vTpSEhIwIQJE/DYY49hxYoV2Lp1K86fP4/ly5cjNzfX5um6/qCsrAwlJSUoKirC4cOHMXfuXOTn5+Opp54SOzQiQGRkJObPn4+ffvoJu3btwowZM7B8+XJs27YNBQUFWL58Ob744gt07doVAPDQQw9h+fLl+Omnn3D+/Hl2AdijRw889thj2LBhA7788kucP38e69atw8aNGzFt2jS2vLS0NBQUFGD79u02897kyZNx4cIFvPbaazh79iz27duHl19+GcHBwW4Xcta5+OrVq/jjjz/Y3xubMWOGF1qMeJOrc2t4eLjL8alUKt2ee9VqNf7++28UFBS4vS1dIpEgPz8fixYtwokTJ3DhwgVs3boVN910E3vLPEBzoit0a2UrFRkZiZSUFFRXV9t8dw2oP7CWL1+O119/HVOnToVMJsM//vEPvPfee7zf+Zg2bRrefPNNHDx4EN98840XakBauqVLl2Lp0qUA6r88HxsbizfeeIN9WuC7776LJUuWYOPGjYiOjsbYsWOh1Wpx/PhxNo/w8HDccccduH79Ov1+UQsXHh6OL7/8EsuXL8fLL7+Mv//+G9HR0RgxYgRmz54NpVKJKVOmoLq6Gm+//TZKS0sRHx+PFStWoF+/fmKHb8P6UzAymQzR0dHo378/NmzY4DBXk8Bx//33Izs7G2+88Qa2bt0Kg8GAt99+G3///TdiYmKQlZWFvn37AgCmT5+OqqoqvPjii6iqqkJKSgpWrlwJhUKB1NRULFiwAJ988gneeOMNdOnSBa+99prN939DQ0MxdOhQHDlyBCkpKez2du3a4d///jfeeecdjB07FmFhYRg1ahSefvppl7GXl5fjjjvuAFD/lYq2bdviH//4B9599132B5hJ4HB3bp02bZrL8enu3Dty5Eh8/fXXGDVqFL744gu38bz++ut49dVX8fDDD7M/E/Txxx/bvLlAc6JzEoa+pEQIacHuu+8+PPDAA8jIyBA7FEIIIYQQj6FP5AghLdLPP/+MgwcP4uLFi3RbJSGEEEJaHFrIEUJapLVr1+LIkSN47bXX2CfFEUIIIYS0FHRrJSGEEEIIIYQEGHpqJSGEEEIIIYQEGFrIEUIIIYQQQkiAoYUcIYQQQgghhAQYWsgRQgghhBBCSIChhRwhhBBCCCGEBBhayBFCCCGEEEJIgPn/AwePCC4it1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.pairplot(df.sample(150))\n",
    "plt.suptitle('Pairplots of features', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098535</td>\n",
       "      <td>0.604269</td>\n",
       "      <td>0.545945</td>\n",
       "      <td>0.341420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.098535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>0.035592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <td>0.604269</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875959</td>\n",
       "      <td>0.641523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecoverDiff</th>\n",
       "      <td>0.545945</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>0.875959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.561145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeathsDiff</th>\n",
       "      <td>0.341420</td>\n",
       "      <td>0.035592</td>\n",
       "      <td>0.641523</td>\n",
       "      <td>0.561145</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Month       Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "Month        1.000000 -0.098535     0.604269     0.545945    0.341420\n",
       "Day         -0.098535  1.000000     0.002108     0.027499    0.035592\n",
       "ConfirmDiff  0.604269  0.002108     1.000000     0.875959    0.641523\n",
       "RecoverDiff  0.545945  0.027499     0.875959     1.000000    0.561145\n",
       "DeathsDiff   0.341420  0.035592     0.641523     0.561145    1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(df.corr(),cmap=\"coolwarm\",annot=True,fmt='.2f',linewidths=2)\n",
    "plt.title(\"HeatMap\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treat Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(keep='first').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"traincovid2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_2</th>\n",
       "      <th>Month_3</th>\n",
       "      <th>Month_4</th>\n",
       "      <th>Month_5</th>\n",
       "      <th>Month_6</th>\n",
       "      <th>Month_7</th>\n",
       "      <th>Month_8</th>\n",
       "      <th>Month_9</th>\n",
       "      <th>Month_10</th>\n",
       "      <th>Month_11</th>\n",
       "      <th>Day_2</th>\n",
       "      <th>Day_3</th>\n",
       "      <th>Day_4</th>\n",
       "      <th>Day_5</th>\n",
       "      <th>Day_6</th>\n",
       "      <th>Day_7</th>\n",
       "      <th>Day_8</th>\n",
       "      <th>Day_9</th>\n",
       "      <th>Day_10</th>\n",
       "      <th>Day_11</th>\n",
       "      <th>Day_12</th>\n",
       "      <th>Day_13</th>\n",
       "      <th>Day_14</th>\n",
       "      <th>Day_15</th>\n",
       "      <th>Day_16</th>\n",
       "      <th>Day_17</th>\n",
       "      <th>Day_18</th>\n",
       "      <th>Day_19</th>\n",
       "      <th>Day_20</th>\n",
       "      <th>Day_21</th>\n",
       "      <th>Day_22</th>\n",
       "      <th>Day_23</th>\n",
       "      <th>Day_24</th>\n",
       "      <th>Day_25</th>\n",
       "      <th>Day_26</th>\n",
       "      <th>Day_27</th>\n",
       "      <th>Day_28</th>\n",
       "      <th>Day_29</th>\n",
       "      <th>Day_30</th>\n",
       "      <th>Day_31</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month_2  Month_3  Month_4  Month_5  Month_6  Month_7  Month_8  Month_9  Month_10  Month_11  Day_2  Day_3  Day_4  Day_5  Day_6  Day_7  Day_8  Day_9  Day_10  Day_11  Day_12  Day_13  Day_14  Day_15  Day_16  Day_17  Day_18  Day_19  Day_20  Day_21  Day_22  Day_23  Day_24  Day_25  Day_26  Day_27  Day_28  Day_29  Day_30  Day_31  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "1        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "2        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "3        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0          3.0          0.0         0.0\n",
       "4        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0          1.0          0.0         0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 43)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.iloc[:,0:42]\n",
    "y = df2.iloc[:,42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        [   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        [   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        ...,\n",
       "        [   0.,    0.,    0., ...,    0., 1884.,  883.],\n",
       "        [   0.,    0.,    0., ...,    0., 2188., 1673.],\n",
       "        [   0.,    0.,    0., ...,    0.,  970., 2348.]]),\n",
       " array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  2.,  0.,  0.,  1.,  1.,  6.,  4.,  2.,  4.,  3.,\n",
       "         3.,  1.,  8.,  2.,  6.,  2.,  5.,  3.,  4.,  4.,  1.,  1.,  2.,\n",
       "         2.,  3.,  3.,  3.,  1.,  5.,  1.,  1.,  2.,  2.,  1.,  0.,  3.,\n",
       "         1.,  2.,  1.,  2.,  0.,  1.,  1.,  0.,  2.,  1.,  0.,  2.,  0.,\n",
       "         1.,  1.,  0.,  0.,  1.,  0.,  1.,  0.,  2.,  1.,  0.,  1.,  0.,\n",
       "         0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,\n",
       "         1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  3.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  2.,  0.,  0.,  1.,  0.,  0.,  4.,  0.,\n",
       "         5.,  6.,  3.,  2.,  2.,  4.,  4.,  3.,  6.,  4.,  7.,  3.,  3.,\n",
       "         6.,  5., 10.,  7.,  8.,  7.,  2.,  8.,  0.,  3.,  0.,  0.,  2.,\n",
       "        12.,  8.,  6.,  2.,  3.,  4.,  8.,  6.,  2.,  1.,  1.,  2.,  3.,\n",
       "         4.,  5.,  4.,  4.,  3.,  3.,  3.,  2.,  4.,  4.]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.values, y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y.values, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((231, 42), (78, 42), (231,), (78,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling (If not using Batch Normalization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0., 39., 74.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  3.,  9.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0., 47., 44.],\n",
       "       [ 0.,  1.,  0., ...,  0., 18.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0., 14.,  4.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  3.,  0.,  2.,  0.,  0.,  0.,  3.,  0.,\n",
       "        0.,  0.,  3.,  0.,  0.,  0.,  3.,  0.,  0.,  0.,  0.,  2.,  1.,\n",
       "        0.,  7.,  7.,  0.,  3.,  0.,  3.,  0.,  0.,  0.,  0.,  4.,  0.,\n",
       "        0.,  0.,  0.,  1.,  3.,  0.,  0.,  5.,  0.,  0.,  1.,  1.,  0.,\n",
       "        0.,  5.,  0.,  1.,  0.,  6.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,\n",
       "        0.,  1.,  0.,  4.,  8.,  7.,  0.,  2.,  8.,  0.,  8.,  0.,  0.,\n",
       "        0.,  0.,  1.,  0.,  0.,  0.,  2.,  2.,  0.,  0.,  2.,  3.,  5.,\n",
       "        3.,  0.,  0.,  1.,  1.,  1.,  2.,  0.,  0.,  1.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  2.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  2.,  5.,  0.,  0.,  1.,\n",
       "        1.,  0.,  0.,  0.,  1.,  0.,  2.,  0.,  0.,  3.,  4.,  0.,  6.,\n",
       "        0.,  0.,  1.,  2.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  2.,  0.,\n",
       "        0.,  1.,  0.,  0., 10.,  4.,  0.,  0.,  2.,  0.,  1.,  0.,  1.,\n",
       "        1.,  2.,  3.,  1.,  5.,  1.,  1.,  4.,  0.,  0.,  0.,  0.,  6.,\n",
       "        0.,  1.,  3.,  0.,  0.,  4.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0., 12.,  2.,  0.,  0.,  0.,  2.,  0.,  0.,  1.,\n",
       "        4.,  3.,  0.,  0.,  1.,  0.,  2.,  0.,  0.,  1.,  2.,  2.,  8.,\n",
       "        0.,  8.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = minmax.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = minmax.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , ..., 0.        , 0.0178245 ,\n",
       "        0.03151618],\n",
       "       [1.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.00137112,\n",
       "        0.00383305],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.0214808 ,\n",
       "        0.01873935],\n",
       "       [0.        , 1.        , 0.        , ..., 0.        , 0.00822669,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.00639854,\n",
       "        0.00170358]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        , 0.        , ..., 0.        , 0.0786106 ,\n",
       "        0.00681431],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.01096892,\n",
       "        0.00298126],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.00274223,\n",
       "        0.00212947],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.58957952,\n",
       "        0.37393526],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.39716636,\n",
       "        0.30877342],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.04798903,\n",
       "        0.04940375]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1,activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 100)               4300      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 14,501\n",
      "Trainable params: 14,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointcb = keras.callbacks.ModelCheckpoint(\"BestModelPartB.h5\",save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystoppingcb = keras.callbacks.EarlyStopping(patience=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(learning_rate=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n",
      "6/6 - 0s - loss: 6.2281 - mse: 5.2120 - val_loss: 8.5060 - val_mse: 7.4901\n",
      "Epoch 2/3000\n",
      "6/6 - 0s - loss: 6.2194 - mse: 5.2037 - val_loss: 8.4975 - val_mse: 7.4820\n",
      "Epoch 3/3000\n",
      "6/6 - 0s - loss: 6.2108 - mse: 5.1954 - val_loss: 8.4891 - val_mse: 7.4739\n",
      "Epoch 4/3000\n",
      "6/6 - 0s - loss: 6.2035 - mse: 5.1884 - val_loss: 8.4804 - val_mse: 7.4655\n",
      "Epoch 5/3000\n",
      "6/6 - 0s - loss: 6.1945 - mse: 5.1797 - val_loss: 8.4720 - val_mse: 7.4575\n",
      "Epoch 6/3000\n",
      "6/6 - 0s - loss: 6.1866 - mse: 5.1722 - val_loss: 8.4636 - val_mse: 7.4493\n",
      "Epoch 7/3000\n",
      "6/6 - 0s - loss: 6.1779 - mse: 5.1638 - val_loss: 8.4553 - val_mse: 7.4414\n",
      "Epoch 8/3000\n",
      "6/6 - 0s - loss: 6.1701 - mse: 5.1563 - val_loss: 8.4468 - val_mse: 7.4333\n",
      "Epoch 9/3000\n",
      "6/6 - 0s - loss: 6.1622 - mse: 5.1488 - val_loss: 8.4383 - val_mse: 7.4251\n",
      "Epoch 10/3000\n",
      "6/6 - 0s - loss: 6.1539 - mse: 5.1408 - val_loss: 8.4300 - val_mse: 7.4171\n",
      "Epoch 11/3000\n",
      "6/6 - 0s - loss: 6.1452 - mse: 5.1325 - val_loss: 8.4221 - val_mse: 7.4095\n",
      "Epoch 12/3000\n",
      "6/6 - 0s - loss: 6.1375 - mse: 5.1251 - val_loss: 8.4138 - val_mse: 7.4016\n",
      "Epoch 13/3000\n",
      "6/6 - 0s - loss: 6.1300 - mse: 5.1179 - val_loss: 8.4054 - val_mse: 7.3935\n",
      "Epoch 14/3000\n",
      "6/6 - 0s - loss: 6.1214 - mse: 5.1096 - val_loss: 8.3973 - val_mse: 7.3857\n",
      "Epoch 15/3000\n",
      "6/6 - 0s - loss: 6.1136 - mse: 5.1021 - val_loss: 8.3890 - val_mse: 7.3777\n",
      "Epoch 16/3000\n",
      "6/6 - 0s - loss: 6.1053 - mse: 5.0942 - val_loss: 8.3809 - val_mse: 7.3700\n",
      "Epoch 17/3000\n",
      "6/6 - 0s - loss: 6.0977 - mse: 5.0869 - val_loss: 8.3725 - val_mse: 7.3620\n",
      "Epoch 18/3000\n",
      "6/6 - 0s - loss: 6.0895 - mse: 5.0791 - val_loss: 8.3643 - val_mse: 7.3541\n",
      "Epoch 19/3000\n",
      "6/6 - 0s - loss: 6.0815 - mse: 5.0714 - val_loss: 8.3562 - val_mse: 7.3463\n",
      "Epoch 20/3000\n",
      "6/6 - 0s - loss: 6.0735 - mse: 5.0638 - val_loss: 8.3481 - val_mse: 7.3385\n",
      "Epoch 21/3000\n",
      "6/6 - 0s - loss: 6.0656 - mse: 5.0562 - val_loss: 8.3401 - val_mse: 7.3308\n",
      "Epoch 22/3000\n",
      "6/6 - 0s - loss: 6.0585 - mse: 5.0493 - val_loss: 8.3318 - val_mse: 7.3229\n",
      "Epoch 23/3000\n",
      "6/6 - 0s - loss: 6.0503 - mse: 5.0415 - val_loss: 8.3238 - val_mse: 7.3152\n",
      "Epoch 24/3000\n",
      "6/6 - 0s - loss: 6.0425 - mse: 5.0340 - val_loss: 8.3158 - val_mse: 7.3076\n",
      "Epoch 25/3000\n",
      "6/6 - 0s - loss: 6.0349 - mse: 5.0268 - val_loss: 8.3079 - val_mse: 7.3000\n",
      "Epoch 26/3000\n",
      "6/6 - 0s - loss: 6.0270 - mse: 5.0192 - val_loss: 8.3002 - val_mse: 7.2925\n",
      "Epoch 27/3000\n",
      "6/6 - 0s - loss: 6.0197 - mse: 5.0122 - val_loss: 8.2923 - val_mse: 7.2850\n",
      "Epoch 28/3000\n",
      "6/6 - 0s - loss: 6.0119 - mse: 5.0047 - val_loss: 8.2844 - val_mse: 7.2775\n",
      "Epoch 29/3000\n",
      "6/6 - 0s - loss: 6.0044 - mse: 4.9976 - val_loss: 8.2765 - val_mse: 7.2699\n",
      "Epoch 30/3000\n",
      "6/6 - 0s - loss: 5.9967 - mse: 4.9902 - val_loss: 8.2686 - val_mse: 7.2624\n",
      "Epoch 31/3000\n",
      "6/6 - 0s - loss: 5.9891 - mse: 4.9829 - val_loss: 8.2608 - val_mse: 7.2548\n",
      "Epoch 32/3000\n",
      "6/6 - 0s - loss: 5.9815 - mse: 4.9757 - val_loss: 8.2531 - val_mse: 7.2474\n",
      "Epoch 33/3000\n",
      "6/6 - 0s - loss: 5.9742 - mse: 4.9687 - val_loss: 8.2452 - val_mse: 7.2399\n",
      "Epoch 34/3000\n",
      "6/6 - 0s - loss: 5.9668 - mse: 4.9617 - val_loss: 8.2373 - val_mse: 7.2323\n",
      "Epoch 35/3000\n",
      "6/6 - 0s - loss: 5.9595 - mse: 4.9547 - val_loss: 8.2294 - val_mse: 7.2247\n",
      "Epoch 36/3000\n",
      "6/6 - 0s - loss: 5.9516 - mse: 4.9471 - val_loss: 8.2217 - val_mse: 7.2174\n",
      "Epoch 37/3000\n",
      "6/6 - 0s - loss: 5.9446 - mse: 4.9404 - val_loss: 8.2138 - val_mse: 7.2099\n",
      "Epoch 38/3000\n",
      "6/6 - 0s - loss: 5.9372 - mse: 4.9333 - val_loss: 8.2061 - val_mse: 7.2025\n",
      "Epoch 39/3000\n",
      "6/6 - 0s - loss: 5.9296 - mse: 4.9260 - val_loss: 8.1985 - val_mse: 7.1952\n",
      "Epoch 40/3000\n",
      "6/6 - 0s - loss: 5.9224 - mse: 4.9193 - val_loss: 8.1908 - val_mse: 7.1879\n",
      "Epoch 41/3000\n",
      "6/6 - 0s - loss: 5.9152 - mse: 4.9123 - val_loss: 8.1831 - val_mse: 7.1804\n",
      "Epoch 42/3000\n",
      "6/6 - 0s - loss: 5.9076 - mse: 4.9051 - val_loss: 8.1754 - val_mse: 7.1731\n",
      "Epoch 43/3000\n",
      "6/6 - 0s - loss: 5.9005 - mse: 4.8983 - val_loss: 8.1677 - val_mse: 7.1657\n",
      "Epoch 44/3000\n",
      "6/6 - 0s - loss: 5.8930 - mse: 4.8912 - val_loss: 8.1601 - val_mse: 7.1585\n",
      "Epoch 45/3000\n",
      "6/6 - 0s - loss: 5.8863 - mse: 4.8848 - val_loss: 8.1522 - val_mse: 7.1509\n",
      "Epoch 46/3000\n",
      "6/6 - 0s - loss: 5.8785 - mse: 4.8773 - val_loss: 8.1448 - val_mse: 7.1438\n",
      "Epoch 47/3000\n",
      "6/6 - 0s - loss: 5.8714 - mse: 4.8706 - val_loss: 8.1372 - val_mse: 7.1365\n",
      "Epoch 48/3000\n",
      "6/6 - 0s - loss: 5.8643 - mse: 4.8638 - val_loss: 8.1296 - val_mse: 7.1293\n",
      "Epoch 49/3000\n",
      "6/6 - 0s - loss: 5.8566 - mse: 4.8564 - val_loss: 8.1224 - val_mse: 7.1224\n",
      "Epoch 50/3000\n",
      "6/6 - 0s - loss: 5.8504 - mse: 4.8505 - val_loss: 8.1146 - val_mse: 7.1149\n",
      "Epoch 51/3000\n",
      "6/6 - 0s - loss: 5.8428 - mse: 4.8433 - val_loss: 8.1071 - val_mse: 7.1078\n",
      "Epoch 52/3000\n",
      "6/6 - 0s - loss: 5.8357 - mse: 4.8365 - val_loss: 8.0997 - val_mse: 7.1007\n",
      "Epoch 53/3000\n",
      "6/6 - 0s - loss: 5.8284 - mse: 4.8295 - val_loss: 8.0924 - val_mse: 7.0938\n",
      "Epoch 54/3000\n",
      "6/6 - 0s - loss: 5.8213 - mse: 4.8228 - val_loss: 8.0851 - val_mse: 7.0868\n",
      "Epoch 55/3000\n",
      "6/6 - 0s - loss: 5.8147 - mse: 4.8165 - val_loss: 8.0775 - val_mse: 7.0795\n",
      "Epoch 56/3000\n",
      "6/6 - 0s - loss: 5.8073 - mse: 4.8094 - val_loss: 8.0701 - val_mse: 7.0725\n",
      "Epoch 57/3000\n",
      "6/6 - 0s - loss: 5.8004 - mse: 4.8029 - val_loss: 8.0627 - val_mse: 7.0653\n",
      "Epoch 58/3000\n",
      "6/6 - 0s - loss: 5.7931 - mse: 4.7959 - val_loss: 8.0554 - val_mse: 7.0583\n",
      "Epoch 59/3000\n",
      "6/6 - 0s - loss: 5.7865 - mse: 4.7897 - val_loss: 8.0478 - val_mse: 7.0511\n",
      "Epoch 60/3000\n",
      "6/6 - 0s - loss: 5.7793 - mse: 4.7827 - val_loss: 8.0406 - val_mse: 7.0442\n",
      "Epoch 61/3000\n",
      "6/6 - 0s - loss: 5.7723 - mse: 4.7761 - val_loss: 8.0334 - val_mse: 7.0374\n",
      "Epoch 62/3000\n",
      "6/6 - 0s - loss: 5.7656 - mse: 4.7697 - val_loss: 8.0261 - val_mse: 7.0305\n",
      "Epoch 63/3000\n",
      "6/6 - 0s - loss: 5.7588 - mse: 4.7632 - val_loss: 8.0189 - val_mse: 7.0235\n",
      "Epoch 64/3000\n",
      "6/6 - 0s - loss: 5.7513 - mse: 4.7560 - val_loss: 8.0120 - val_mse: 7.0170\n",
      "Epoch 65/3000\n",
      "6/6 - 0s - loss: 5.7449 - mse: 4.7500 - val_loss: 8.0049 - val_mse: 7.0102\n",
      "Epoch 66/3000\n",
      "6/6 - 0s - loss: 5.7380 - mse: 4.7435 - val_loss: 7.9977 - val_mse: 7.0034\n",
      "Epoch 67/3000\n",
      "6/6 - 0s - loss: 5.7313 - mse: 4.7371 - val_loss: 7.9904 - val_mse: 6.9964\n",
      "Epoch 68/3000\n",
      "6/6 - 0s - loss: 5.7245 - mse: 4.7307 - val_loss: 7.9831 - val_mse: 6.9894\n",
      "Epoch 69/3000\n",
      "6/6 - 0s - loss: 5.7174 - mse: 4.7239 - val_loss: 7.9759 - val_mse: 6.9826\n",
      "Epoch 70/3000\n",
      "6/6 - 0s - loss: 5.7110 - mse: 4.7178 - val_loss: 7.9686 - val_mse: 6.9755\n",
      "Epoch 71/3000\n",
      "6/6 - 0s - loss: 5.7041 - mse: 4.7112 - val_loss: 7.9614 - val_mse: 6.9687\n",
      "Epoch 72/3000\n",
      "6/6 - 0s - loss: 5.6970 - mse: 4.7045 - val_loss: 7.9545 - val_mse: 6.9621\n",
      "Epoch 73/3000\n",
      "6/6 - 0s - loss: 5.6904 - mse: 4.6982 - val_loss: 7.9475 - val_mse: 6.9555\n",
      "Epoch 74/3000\n",
      "6/6 - 0s - loss: 5.6840 - mse: 4.6921 - val_loss: 7.9405 - val_mse: 6.9488\n",
      "Epoch 75/3000\n",
      "6/6 - 0s - loss: 5.6772 - mse: 4.6856 - val_loss: 7.9335 - val_mse: 6.9422\n",
      "Epoch 76/3000\n",
      "6/6 - 0s - loss: 5.6706 - mse: 4.6794 - val_loss: 7.9266 - val_mse: 6.9356\n",
      "Epoch 77/3000\n",
      "6/6 - 0s - loss: 5.6642 - mse: 4.6733 - val_loss: 7.9197 - val_mse: 6.9290\n",
      "Epoch 78/3000\n",
      "6/6 - 0s - loss: 5.6575 - mse: 4.6670 - val_loss: 7.9128 - val_mse: 6.9225\n",
      "Epoch 79/3000\n",
      "6/6 - 0s - loss: 5.6507 - mse: 4.6605 - val_loss: 7.9062 - val_mse: 6.9161\n",
      "Epoch 80/3000\n",
      "6/6 - 0s - loss: 5.6447 - mse: 4.6548 - val_loss: 7.8991 - val_mse: 6.9094\n",
      "Epoch 81/3000\n",
      "6/6 - 0s - loss: 5.6377 - mse: 4.6482 - val_loss: 7.8923 - val_mse: 6.9030\n",
      "Epoch 82/3000\n",
      "6/6 - 0s - loss: 5.6313 - mse: 4.6420 - val_loss: 7.8854 - val_mse: 6.8964\n",
      "Epoch 83/3000\n",
      "6/6 - 0s - loss: 5.6245 - mse: 4.6356 - val_loss: 7.8786 - val_mse: 6.8899\n",
      "Epoch 84/3000\n",
      "6/6 - 0s - loss: 5.6181 - mse: 4.6295 - val_loss: 7.8716 - val_mse: 6.8833\n",
      "Epoch 85/3000\n",
      "6/6 - 0s - loss: 5.6117 - mse: 4.6235 - val_loss: 7.8646 - val_mse: 6.8765\n",
      "Epoch 86/3000\n",
      "6/6 - 0s - loss: 5.6049 - mse: 4.6170 - val_loss: 7.8577 - val_mse: 6.8700\n",
      "Epoch 87/3000\n",
      "6/6 - 0s - loss: 5.5984 - mse: 4.6109 - val_loss: 7.8509 - val_mse: 6.8635\n",
      "Epoch 88/3000\n",
      "6/6 - 0s - loss: 5.5923 - mse: 4.6051 - val_loss: 7.8438 - val_mse: 6.8568\n",
      "Epoch 89/3000\n",
      "6/6 - 0s - loss: 5.5853 - mse: 4.5985 - val_loss: 7.8370 - val_mse: 6.8503\n",
      "Epoch 90/3000\n",
      "6/6 - 0s - loss: 5.5790 - mse: 4.5925 - val_loss: 7.8302 - val_mse: 6.8438\n",
      "Epoch 91/3000\n",
      "6/6 - 0s - loss: 5.5724 - mse: 4.5862 - val_loss: 7.8235 - val_mse: 6.8375\n",
      "Epoch 92/3000\n",
      "6/6 - 0s - loss: 5.5661 - mse: 4.5802 - val_loss: 7.8167 - val_mse: 6.8310\n",
      "Epoch 93/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 5.5595 - mse: 4.5739 - val_loss: 7.8099 - val_mse: 6.8246\n",
      "Epoch 94/3000\n",
      "6/6 - 0s - loss: 5.5532 - mse: 4.5680 - val_loss: 7.8030 - val_mse: 6.8180\n",
      "Epoch 95/3000\n",
      "6/6 - 0s - loss: 5.5469 - mse: 4.5620 - val_loss: 7.7961 - val_mse: 6.8115\n",
      "Epoch 96/3000\n",
      "6/6 - 0s - loss: 5.5405 - mse: 4.5559 - val_loss: 7.7893 - val_mse: 6.8050\n",
      "Epoch 97/3000\n",
      "6/6 - 0s - loss: 5.5339 - mse: 4.5497 - val_loss: 7.7826 - val_mse: 6.7985\n",
      "Epoch 98/3000\n",
      "6/6 - 0s - loss: 5.5272 - mse: 4.5434 - val_loss: 7.7760 - val_mse: 6.7923\n",
      "Epoch 99/3000\n",
      "6/6 - 0s - loss: 5.5211 - mse: 4.5375 - val_loss: 7.7692 - val_mse: 6.7858\n",
      "Epoch 100/3000\n",
      "6/6 - 0s - loss: 5.5149 - mse: 4.5317 - val_loss: 7.7623 - val_mse: 6.7792\n",
      "Epoch 101/3000\n",
      "6/6 - 0s - loss: 5.5083 - mse: 4.5254 - val_loss: 7.7555 - val_mse: 6.7728\n",
      "Epoch 102/3000\n",
      "6/6 - 0s - loss: 5.5019 - mse: 4.5193 - val_loss: 7.7487 - val_mse: 6.7664\n",
      "Epoch 103/3000\n",
      "6/6 - 0s - loss: 5.4958 - mse: 4.5136 - val_loss: 7.7417 - val_mse: 6.7597\n",
      "Epoch 104/3000\n",
      "6/6 - 0s - loss: 5.4891 - mse: 4.5072 - val_loss: 7.7350 - val_mse: 6.7533\n",
      "Epoch 105/3000\n",
      "6/6 - 0s - loss: 5.4828 - mse: 4.5013 - val_loss: 7.7281 - val_mse: 6.7467\n",
      "Epoch 106/3000\n",
      "6/6 - 0s - loss: 5.4766 - mse: 4.4954 - val_loss: 7.7211 - val_mse: 6.7401\n",
      "Epoch 107/3000\n",
      "6/6 - 0s - loss: 5.4703 - mse: 4.4894 - val_loss: 7.7142 - val_mse: 6.7335\n",
      "Epoch 108/3000\n",
      "6/6 - 0s - loss: 5.4637 - mse: 4.4832 - val_loss: 7.7075 - val_mse: 6.7272\n",
      "Epoch 109/3000\n",
      "6/6 - 0s - loss: 5.4577 - mse: 4.4774 - val_loss: 7.7007 - val_mse: 6.7206\n",
      "Epoch 110/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 185/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 368/3000\n",
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      "Epoch 459/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 3.7656 - mse: 2.8930 - val_loss: 5.7691 - val_mse: 4.8966\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 551/3000\n",
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      "Epoch 642/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 3.1641 - mse: 2.3359 - val_loss: 5.0732 - val_mse: 4.2451\n",
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      "Epoch 655/3000\n",
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      "Epoch 656/3000\n",
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      "Epoch 658/3000\n",
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      "Epoch 659/3000\n",
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      "Epoch 660/3000\n",
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      "Epoch 661/3000\n",
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      "Epoch 662/3000\n",
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      "Epoch 663/3000\n",
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      "Epoch 664/3000\n",
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      "Epoch 665/3000\n",
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      "Epoch 666/3000\n",
      "6/6 - 0s - loss: 3.0974 - mse: 2.2743 - val_loss: 4.9935 - val_mse: 4.1706\n",
      "Epoch 667/3000\n",
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      "Epoch 668/3000\n",
      "6/6 - 0s - loss: 3.0917 - mse: 2.2691 - val_loss: 4.9872 - val_mse: 4.1647\n",
      "Epoch 669/3000\n",
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      "Epoch 670/3000\n",
      "6/6 - 0s - loss: 3.0861 - mse: 2.2639 - val_loss: 4.9808 - val_mse: 4.1587\n",
      "Epoch 671/3000\n",
      "6/6 - 0s - loss: 3.0833 - mse: 2.2613 - val_loss: 4.9776 - val_mse: 4.1557\n",
      "Epoch 672/3000\n",
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      "Epoch 673/3000\n",
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      "Epoch 674/3000\n",
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      "Epoch 675/3000\n",
      "6/6 - 0s - loss: 3.0728 - mse: 2.2517 - val_loss: 4.9642 - val_mse: 4.1432\n",
      "Epoch 676/3000\n",
      "6/6 - 0s - loss: 3.0698 - mse: 2.2488 - val_loss: 4.9612 - val_mse: 4.1404\n",
      "Epoch 677/3000\n",
      "6/6 - 0s - loss: 3.0672 - mse: 2.2465 - val_loss: 4.9580 - val_mse: 4.1374\n",
      "Epoch 678/3000\n",
      "6/6 - 0s - loss: 3.0644 - mse: 2.2439 - val_loss: 4.9551 - val_mse: 4.1347\n",
      "Epoch 679/3000\n",
      "6/6 - 0s - loss: 3.0622 - mse: 2.2419 - val_loss: 4.9516 - val_mse: 4.1315\n",
      "Epoch 680/3000\n",
      "6/6 - 0s - loss: 3.0595 - mse: 2.2394 - val_loss: 4.9484 - val_mse: 4.1284\n",
      "Epoch 681/3000\n",
      "6/6 - 0s - loss: 3.0564 - mse: 2.2366 - val_loss: 4.9455 - val_mse: 4.1258\n",
      "Epoch 682/3000\n",
      "6/6 - 0s - loss: 3.0538 - mse: 2.2342 - val_loss: 4.9427 - val_mse: 4.1231\n",
      "Epoch 683/3000\n",
      "6/6 - 0s - loss: 3.0516 - mse: 2.2321 - val_loss: 4.9394 - val_mse: 4.1200\n",
      "Epoch 684/3000\n",
      "6/6 - 0s - loss: 3.0488 - mse: 2.2295 - val_loss: 4.9362 - val_mse: 4.1171\n",
      "Epoch 685/3000\n",
      "6/6 - 0s - loss: 3.0460 - mse: 2.2270 - val_loss: 4.9331 - val_mse: 4.1142\n",
      "Epoch 686/3000\n",
      "6/6 - 0s - loss: 3.0438 - mse: 2.2250 - val_loss: 4.9297 - val_mse: 4.1110\n",
      "Epoch 687/3000\n",
      "6/6 - 0s - loss: 3.0408 - mse: 2.2222 - val_loss: 4.9267 - val_mse: 4.1082\n",
      "Epoch 688/3000\n",
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      "Epoch 689/3000\n",
      "6/6 - 0s - loss: 3.0355 - mse: 2.2174 - val_loss: 4.9205 - val_mse: 4.1025\n",
      "Epoch 690/3000\n",
      "6/6 - 0s - loss: 3.0331 - mse: 2.2151 - val_loss: 4.9173 - val_mse: 4.0995\n",
      "Epoch 691/3000\n",
      "6/6 - 0s - loss: 3.0304 - mse: 2.2126 - val_loss: 4.9143 - val_mse: 4.0966\n",
      "Epoch 692/3000\n",
      "6/6 - 0s - loss: 3.0277 - mse: 2.2101 - val_loss: 4.9112 - val_mse: 4.0937\n",
      "Epoch 693/3000\n",
      "6/6 - 0s - loss: 3.0254 - mse: 2.2080 - val_loss: 4.9079 - val_mse: 4.0907\n",
      "Epoch 694/3000\n",
      "6/6 - 0s - loss: 3.0225 - mse: 2.2054 - val_loss: 4.9049 - val_mse: 4.0879\n",
      "Epoch 695/3000\n",
      "6/6 - 0s - loss: 3.0202 - mse: 2.2032 - val_loss: 4.9018 - val_mse: 4.0850\n",
      "Epoch 696/3000\n",
      "6/6 - 0s - loss: 3.0175 - mse: 2.2008 - val_loss: 4.8987 - val_mse: 4.0821\n",
      "Epoch 697/3000\n",
      "6/6 - 0s - loss: 3.0149 - mse: 2.1984 - val_loss: 4.8957 - val_mse: 4.0793\n",
      "Epoch 698/3000\n",
      "6/6 - 0s - loss: 3.0124 - mse: 2.1961 - val_loss: 4.8926 - val_mse: 4.0764\n",
      "Epoch 699/3000\n",
      "6/6 - 0s - loss: 3.0099 - mse: 2.1937 - val_loss: 4.8895 - val_mse: 4.0736\n",
      "Epoch 700/3000\n",
      "6/6 - 0s - loss: 3.0075 - mse: 2.1916 - val_loss: 4.8863 - val_mse: 4.0706\n",
      "Epoch 701/3000\n",
      "6/6 - 0s - loss: 3.0048 - mse: 2.1891 - val_loss: 4.8834 - val_mse: 4.0678\n",
      "Epoch 702/3000\n",
      "6/6 - 0s - loss: 3.0023 - mse: 2.1868 - val_loss: 4.8803 - val_mse: 4.0650\n",
      "Epoch 703/3000\n",
      "6/6 - 0s - loss: 2.9998 - mse: 2.1846 - val_loss: 4.8773 - val_mse: 4.0622\n",
      "Epoch 704/3000\n",
      "6/6 - 0s - loss: 2.9970 - mse: 2.1819 - val_loss: 4.8744 - val_mse: 4.0595\n",
      "Epoch 705/3000\n",
      "6/6 - 0s - loss: 2.9945 - mse: 2.1797 - val_loss: 4.8714 - val_mse: 4.0567\n",
      "Epoch 706/3000\n",
      "6/6 - 0s - loss: 2.9920 - mse: 2.1774 - val_loss: 4.8682 - val_mse: 4.0536\n",
      "Epoch 707/3000\n",
      "6/6 - 0s - loss: 2.9892 - mse: 2.1748 - val_loss: 4.8653 - val_mse: 4.0509\n",
      "Epoch 708/3000\n",
      "6/6 - 0s - loss: 2.9868 - mse: 2.1726 - val_loss: 4.8621 - val_mse: 4.0480\n",
      "Epoch 709/3000\n",
      "6/6 - 0s - loss: 2.9843 - mse: 2.1703 - val_loss: 4.8590 - val_mse: 4.0451\n",
      "Epoch 710/3000\n",
      "6/6 - 0s - loss: 2.9818 - mse: 2.1679 - val_loss: 4.8559 - val_mse: 4.0422\n",
      "Epoch 711/3000\n",
      "6/6 - 0s - loss: 2.9792 - mse: 2.1656 - val_loss: 4.8528 - val_mse: 4.0393\n",
      "Epoch 712/3000\n",
      "6/6 - 0s - loss: 2.9767 - mse: 2.1633 - val_loss: 4.8498 - val_mse: 4.0365\n",
      "Epoch 713/3000\n",
      "6/6 - 0s - loss: 2.9744 - mse: 2.1612 - val_loss: 4.8468 - val_mse: 4.0337\n",
      "Epoch 714/3000\n",
      "6/6 - 0s - loss: 2.9717 - mse: 2.1587 - val_loss: 4.8440 - val_mse: 4.0311\n",
      "Epoch 715/3000\n",
      "6/6 - 0s - loss: 2.9695 - mse: 2.1567 - val_loss: 4.8410 - val_mse: 4.0283\n",
      "Epoch 716/3000\n",
      "6/6 - 0s - loss: 2.9668 - mse: 2.1542 - val_loss: 4.8381 - val_mse: 4.0257\n",
      "Epoch 717/3000\n",
      "6/6 - 0s - loss: 2.9646 - mse: 2.1522 - val_loss: 4.8352 - val_mse: 4.0229\n",
      "Epoch 718/3000\n",
      "6/6 - 0s - loss: 2.9620 - mse: 2.1498 - val_loss: 4.8323 - val_mse: 4.0202\n",
      "Epoch 719/3000\n",
      "6/6 - 0s - loss: 2.9597 - mse: 2.1478 - val_loss: 4.8293 - val_mse: 4.0174\n",
      "Epoch 720/3000\n",
      "6/6 - 0s - loss: 2.9572 - mse: 2.1454 - val_loss: 4.8263 - val_mse: 4.0147\n",
      "Epoch 721/3000\n",
      "6/6 - 0s - loss: 2.9548 - mse: 2.1432 - val_loss: 4.8235 - val_mse: 4.0121\n",
      "Epoch 722/3000\n",
      "6/6 - 0s - loss: 2.9527 - mse: 2.1414 - val_loss: 4.8205 - val_mse: 4.0093\n",
      "Epoch 723/3000\n",
      "6/6 - 0s - loss: 2.9497 - mse: 2.1386 - val_loss: 4.8179 - val_mse: 4.0069\n",
      "Epoch 724/3000\n",
      "6/6 - 0s - loss: 2.9477 - mse: 2.1368 - val_loss: 4.8149 - val_mse: 4.0041\n",
      "Epoch 725/3000\n",
      "6/6 - 0s - loss: 2.9455 - mse: 2.1347 - val_loss: 4.8119 - val_mse: 4.0013\n",
      "Epoch 726/3000\n",
      "6/6 - 0s - loss: 2.9427 - mse: 2.1321 - val_loss: 4.8092 - val_mse: 3.9988\n",
      "Epoch 727/3000\n",
      "6/6 - 0s - loss: 2.9403 - mse: 2.1300 - val_loss: 4.8064 - val_mse: 3.9962\n",
      "Epoch 728/3000\n",
      "6/6 - 0s - loss: 2.9380 - mse: 2.1278 - val_loss: 4.8037 - val_mse: 3.9937\n",
      "Epoch 729/3000\n",
      "6/6 - 0s - loss: 2.9356 - mse: 2.1257 - val_loss: 4.8009 - val_mse: 3.9911\n",
      "Epoch 730/3000\n",
      "6/6 - 0s - loss: 2.9333 - mse: 2.1236 - val_loss: 4.7980 - val_mse: 3.9884\n",
      "Epoch 731/3000\n",
      "6/6 - 0s - loss: 2.9311 - mse: 2.1216 - val_loss: 4.7951 - val_mse: 3.9857\n",
      "Epoch 732/3000\n",
      "6/6 - 0s - loss: 2.9285 - mse: 2.1192 - val_loss: 4.7924 - val_mse: 3.9832\n",
      "Epoch 733/3000\n",
      "6/6 - 0s - loss: 2.9261 - mse: 2.1170 - val_loss: 4.7897 - val_mse: 3.9807\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 734/3000\n",
      "6/6 - 0s - loss: 2.9238 - mse: 2.1149 - val_loss: 4.7869 - val_mse: 3.9781\n",
      "Epoch 735/3000\n",
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      "Epoch 736/3000\n",
      "6/6 - 0s - loss: 2.9191 - mse: 2.1106 - val_loss: 4.7811 - val_mse: 3.9727\n",
      "Epoch 737/3000\n",
      "6/6 - 0s - loss: 2.9168 - mse: 2.1085 - val_loss: 4.7782 - val_mse: 3.9701\n",
      "Epoch 738/3000\n",
      "6/6 - 0s - loss: 2.9142 - mse: 2.1062 - val_loss: 4.7757 - val_mse: 3.9678\n",
      "Epoch 739/3000\n",
      "6/6 - 0s - loss: 2.9121 - mse: 2.1042 - val_loss: 4.7728 - val_mse: 3.9651\n",
      "Epoch 740/3000\n",
      "6/6 - 0s - loss: 2.9097 - mse: 2.1021 - val_loss: 4.7700 - val_mse: 3.9625\n",
      "Epoch 741/3000\n",
      "6/6 - 0s - loss: 2.9072 - mse: 2.0998 - val_loss: 4.7675 - val_mse: 3.9602\n",
      "Epoch 742/3000\n",
      "6/6 - 0s - loss: 2.9050 - mse: 2.0977 - val_loss: 4.7648 - val_mse: 3.9577\n",
      "Epoch 743/3000\n",
      "6/6 - 0s - loss: 2.9026 - mse: 2.0956 - val_loss: 4.7621 - val_mse: 3.9552\n",
      "Epoch 744/3000\n",
      "6/6 - 0s - loss: 2.9004 - mse: 2.0936 - val_loss: 4.7591 - val_mse: 3.9524\n",
      "Epoch 745/3000\n",
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      "Epoch 746/3000\n",
      "6/6 - 0s - loss: 2.8958 - mse: 2.0894 - val_loss: 4.7532 - val_mse: 3.9469\n",
      "Epoch 747/3000\n",
      "6/6 - 0s - loss: 2.8933 - mse: 2.0871 - val_loss: 4.7503 - val_mse: 3.9442\n",
      "Epoch 748/3000\n",
      "6/6 - 0s - loss: 2.8911 - mse: 2.0850 - val_loss: 4.7474 - val_mse: 3.9415\n",
      "Epoch 749/3000\n",
      "6/6 - 0s - loss: 2.8885 - mse: 2.0827 - val_loss: 4.7448 - val_mse: 3.9390\n",
      "Epoch 750/3000\n",
      "6/6 - 0s - loss: 2.8863 - mse: 2.0807 - val_loss: 4.7421 - val_mse: 3.9366\n",
      "Epoch 751/3000\n",
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      "Epoch 752/3000\n",
      "6/6 - 0s - loss: 2.8819 - mse: 2.0766 - val_loss: 4.7366 - val_mse: 3.9315\n",
      "Epoch 753/3000\n",
      "6/6 - 0s - loss: 2.8796 - mse: 2.0746 - val_loss: 4.7339 - val_mse: 3.9290\n",
      "Epoch 754/3000\n",
      "6/6 - 0s - loss: 2.8773 - mse: 2.0725 - val_loss: 4.7314 - val_mse: 3.9267\n",
      "Epoch 755/3000\n",
      "6/6 - 0s - loss: 2.8752 - mse: 2.0706 - val_loss: 4.7288 - val_mse: 3.9243\n",
      "Epoch 756/3000\n",
      "6/6 - 0s - loss: 2.8734 - mse: 2.0690 - val_loss: 4.7258 - val_mse: 3.9215\n",
      "Epoch 757/3000\n",
      "6/6 - 0s - loss: 2.8706 - mse: 2.0664 - val_loss: 4.7234 - val_mse: 3.9193\n",
      "Epoch 758/3000\n",
      "6/6 - 0s - loss: 2.8685 - mse: 2.0644 - val_loss: 4.7210 - val_mse: 3.9171\n",
      "Epoch 759/3000\n",
      "6/6 - 0s - loss: 2.8664 - mse: 2.0626 - val_loss: 4.7184 - val_mse: 3.9147\n",
      "Epoch 760/3000\n",
      "6/6 - 0s - loss: 2.8645 - mse: 2.0609 - val_loss: 4.7156 - val_mse: 3.9121\n",
      "Epoch 761/3000\n",
      "6/6 - 0s - loss: 2.8620 - mse: 2.0586 - val_loss: 4.7131 - val_mse: 3.9098\n",
      "Epoch 762/3000\n",
      "6/6 - 0s - loss: 2.8598 - mse: 2.0566 - val_loss: 4.7108 - val_mse: 3.9077\n",
      "Epoch 763/3000\n",
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      "Epoch 764/3000\n",
      "6/6 - 0s - loss: 2.8554 - mse: 2.0526 - val_loss: 4.7058 - val_mse: 3.9031\n",
      "Epoch 765/3000\n",
      "6/6 - 0s - loss: 2.8538 - mse: 2.0512 - val_loss: 4.7028 - val_mse: 3.9003\n",
      "Epoch 766/3000\n",
      "6/6 - 0s - loss: 2.8510 - mse: 2.0487 - val_loss: 4.7003 - val_mse: 3.8980\n",
      "Epoch 767/3000\n",
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      "Epoch 768/3000\n",
      "6/6 - 0s - loss: 2.8468 - mse: 2.0448 - val_loss: 4.6951 - val_mse: 3.8932\n",
      "Epoch 769/3000\n",
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      "Epoch 770/3000\n",
      "6/6 - 0s - loss: 2.8425 - mse: 2.0409 - val_loss: 4.6901 - val_mse: 3.8887\n",
      "Epoch 771/3000\n",
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      "Epoch 772/3000\n",
      "6/6 - 0s - loss: 2.8384 - mse: 2.0372 - val_loss: 4.6850 - val_mse: 3.8839\n",
      "Epoch 773/3000\n",
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      "Epoch 774/3000\n",
      "6/6 - 0s - loss: 2.8340 - mse: 2.0333 - val_loss: 4.6800 - val_mse: 3.8794\n",
      "Epoch 775/3000\n",
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      "Epoch 776/3000\n",
      "6/6 - 0s - loss: 2.8297 - mse: 2.0294 - val_loss: 4.6751 - val_mse: 3.8748\n",
      "Epoch 777/3000\n",
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      "Epoch 778/3000\n",
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      "Epoch 779/3000\n",
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      "Epoch 780/3000\n",
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      "Epoch 781/3000\n",
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      "Epoch 782/3000\n",
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      "Epoch 783/3000\n",
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      "Epoch 784/3000\n",
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      "Epoch 785/3000\n",
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      "Epoch 786/3000\n",
      "6/6 - 0s - loss: 2.8087 - mse: 2.0103 - val_loss: 4.6502 - val_mse: 3.8520\n",
      "Epoch 787/3000\n",
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      "Epoch 788/3000\n",
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      "Epoch 789/3000\n",
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      "Epoch 790/3000\n",
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      "Epoch 791/3000\n",
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      "Epoch 797/3000\n",
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      "Epoch 799/3000\n",
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      "Epoch 800/3000\n",
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      "Epoch 802/3000\n",
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      "Epoch 803/3000\n",
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      "Epoch 805/3000\n",
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      "Epoch 808/3000\n",
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      "Epoch 809/3000\n",
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      "Epoch 810/3000\n",
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      "Epoch 811/3000\n",
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      "Epoch 812/3000\n",
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      "Epoch 813/3000\n",
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      "Epoch 814/3000\n",
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      "Epoch 815/3000\n",
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      "Epoch 816/3000\n",
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      "Epoch 817/3000\n",
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      "Epoch 818/3000\n",
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      "Epoch 819/3000\n",
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      "Epoch 820/3000\n",
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      "Epoch 821/3000\n",
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      "Epoch 822/3000\n",
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      "Epoch 823/3000\n",
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      "Epoch 824/3000\n",
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      "Epoch 825/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 2.7328 - mse: 1.9423 - val_loss: 4.5596 - val_mse: 3.7692\n",
      "Epoch 826/3000\n",
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      "Epoch 848/3000\n",
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      "Epoch 849/3000\n",
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      "Epoch 850/3000\n",
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      "Epoch 876/3000\n",
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      "Epoch 885/3000\n",
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      "Epoch 886/3000\n",
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      "Epoch 888/3000\n",
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      "Epoch 900/3000\n",
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      "Epoch 907/3000\n",
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      "Epoch 908/3000\n",
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      "Epoch 912/3000\n",
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      "Epoch 914/3000\n",
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      "Epoch 915/3000\n",
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      "Epoch 916/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 917/3000\n",
      "6/6 - 0s - loss: 2.5807 - mse: 1.8085 - val_loss: 4.3837 - val_mse: 3.6117\n",
      "Epoch 918/3000\n",
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      "Epoch 919/3000\n",
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      "Epoch 920/3000\n",
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      "Epoch 921/3000\n",
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      "Epoch 923/3000\n",
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      "Epoch 924/3000\n",
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      "Epoch 925/3000\n",
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      "Epoch 940/3000\n",
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      "Epoch 941/3000\n",
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      "Epoch 945/3000\n",
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      "Epoch 946/3000\n",
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      "Epoch 947/3000\n",
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      "Epoch 948/3000\n",
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      "Epoch 949/3000\n",
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      "Epoch 950/3000\n",
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      "Epoch 951/3000\n",
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      "Epoch 953/3000\n",
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      "Epoch 955/3000\n",
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      "Epoch 957/3000\n",
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      "Epoch 958/3000\n",
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      "Epoch 959/3000\n",
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      "Epoch 960/3000\n",
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      "Epoch 961/3000\n",
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      "Epoch 962/3000\n",
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      "Epoch 963/3000\n",
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      "Epoch 964/3000\n",
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      "Epoch 965/3000\n",
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      "Epoch 966/3000\n",
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      "Epoch 967/3000\n",
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      "Epoch 968/3000\n",
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      "Epoch 969/3000\n",
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      "Epoch 970/3000\n",
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      "Epoch 971/3000\n",
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      "Epoch 973/3000\n",
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      "Epoch 974/3000\n",
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      "Epoch 975/3000\n",
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      "Epoch 976/3000\n",
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      "Epoch 977/3000\n",
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      "Epoch 978/3000\n",
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      "Epoch 979/3000\n",
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      "Epoch 980/3000\n",
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      "Epoch 981/3000\n",
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      "Epoch 982/3000\n",
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      "Epoch 984/3000\n",
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      "Epoch 985/3000\n",
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      "Epoch 986/3000\n",
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      "Epoch 989/3000\n",
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      "Epoch 993/3000\n",
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      "Epoch 994/3000\n",
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      "Epoch 995/3000\n",
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      "Epoch 996/3000\n",
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      "Epoch 997/3000\n",
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      "Epoch 998/3000\n",
      "6/6 - 0s - loss: 2.4732 - mse: 1.7171 - val_loss: 4.2695 - val_mse: 3.5135\n",
      "Epoch 999/3000\n",
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      "Epoch 1000/3000\n",
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      "Epoch 1001/3000\n",
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      "Epoch 1002/3000\n",
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      "Epoch 1003/3000\n",
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      "Epoch 1004/3000\n",
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      "Epoch 1005/3000\n",
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      "Epoch 1006/3000\n",
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      "Epoch 1007/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1008/3000\n",
      "6/6 - 0s - loss: 2.4613 - mse: 1.7071 - val_loss: 4.2584 - val_mse: 3.5044\n",
      "Epoch 1009/3000\n",
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      "Epoch 1010/3000\n",
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      "Epoch 1011/3000\n",
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      "Epoch 1012/3000\n",
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      "Epoch 1014/3000\n",
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      "Epoch 1015/3000\n",
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      "Epoch 1016/3000\n",
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      "Epoch 1018/3000\n",
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      "Epoch 1020/3000\n",
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      "Epoch 1021/3000\n",
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      "Epoch 1022/3000\n",
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      "Epoch 1023/3000\n",
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      "Epoch 1024/3000\n",
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      "Epoch 1025/3000\n",
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      "Epoch 1027/3000\n",
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      "Epoch 1028/3000\n",
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      "Epoch 1029/3000\n",
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      "Epoch 1030/3000\n",
      "6/6 - 0s - loss: 2.4362 - mse: 1.6864 - val_loss: 4.2349 - val_mse: 3.4853\n",
      "Epoch 1031/3000\n",
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      "Epoch 1032/3000\n",
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      "Epoch 1033/3000\n",
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      "Epoch 1034/3000\n",
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      "Epoch 1035/3000\n",
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      "Epoch 1036/3000\n",
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      "Epoch 1037/3000\n",
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      "Epoch 1038/3000\n",
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      "Epoch 1039/3000\n",
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      "Epoch 1040/3000\n",
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      "Epoch 1041/3000\n",
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      "Epoch 1042/3000\n",
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      "Epoch 1043/3000\n",
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      "Epoch 1048/3000\n",
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      "Epoch 1049/3000\n",
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      "Epoch 1050/3000\n",
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      "Epoch 1051/3000\n",
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      "Epoch 1053/3000\n",
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      "Epoch 1054/3000\n",
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      "Epoch 1055/3000\n",
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      "Epoch 1056/3000\n",
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      "Epoch 1057/3000\n",
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      "Epoch 1058/3000\n",
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      "Epoch 1059/3000\n",
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      "Epoch 1098/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 2.3654 - mse: 1.6291 - val_loss: 4.1741 - val_mse: 3.4380\n",
      "Epoch 1099/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1189/3000\n",
      "6/6 - 0s - loss: 2.2816 - mse: 1.5632 - val_loss: 4.1185 - val_mse: 3.4002\n",
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      "Epoch 1194/3000\n",
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      "Epoch 1195/3000\n",
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      "Epoch 1199/3000\n",
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      "Epoch 1200/3000\n",
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      "Epoch 1201/3000\n",
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      "Epoch 1209/3000\n",
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      "Epoch 1210/3000\n",
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      "Epoch 1211/3000\n",
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      "Epoch 1212/3000\n",
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      "Epoch 1213/3000\n",
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      "Epoch 1214/3000\n",
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      "Epoch 1215/3000\n",
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      "Epoch 1216/3000\n",
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      "Epoch 1217/3000\n",
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      "Epoch 1218/3000\n",
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      "Epoch 1219/3000\n",
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      "Epoch 1220/3000\n",
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      "Epoch 1221/3000\n",
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      "Epoch 1222/3000\n",
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      "Epoch 1223/3000\n",
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      "Epoch 1224/3000\n",
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      "Epoch 1225/3000\n",
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      "Epoch 1226/3000\n",
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      "Epoch 1227/3000\n",
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      "Epoch 1228/3000\n",
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      "Epoch 1229/3000\n",
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      "Epoch 1230/3000\n",
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      "Epoch 1231/3000\n",
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      "Epoch 1232/3000\n",
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      "Epoch 1233/3000\n",
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      "Epoch 1234/3000\n",
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      "Epoch 1235/3000\n",
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      "Epoch 1236/3000\n",
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      "Epoch 1237/3000\n",
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      "Epoch 1238/3000\n",
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      "Epoch 1239/3000\n",
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      "Epoch 1240/3000\n",
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      "Epoch 1243/3000\n",
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      "Epoch 1245/3000\n",
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      "Epoch 1246/3000\n",
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      "Epoch 1247/3000\n",
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      "Epoch 1248/3000\n",
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      "Epoch 1249/3000\n",
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      "Epoch 1250/3000\n",
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      "Epoch 1251/3000\n",
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      "Epoch 1252/3000\n",
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      "Epoch 1253/3000\n",
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      "Epoch 1254/3000\n",
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      "Epoch 1255/3000\n",
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      "Epoch 1256/3000\n",
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      "Epoch 1257/3000\n",
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      "Epoch 1258/3000\n",
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      "Epoch 1259/3000\n",
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      "Epoch 1260/3000\n",
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      "Epoch 1261/3000\n",
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      "Epoch 1262/3000\n",
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      "Epoch 1263/3000\n",
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      "Epoch 1264/3000\n",
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      "Epoch 1265/3000\n",
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      "Epoch 1266/3000\n",
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      "Epoch 1267/3000\n",
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      "Epoch 1268/3000\n",
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      "Epoch 1269/3000\n",
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      "Epoch 1270/3000\n",
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      "Epoch 1271/3000\n",
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      "Epoch 1272/3000\n",
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      "Epoch 1273/3000\n",
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      "Epoch 1274/3000\n",
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      "Epoch 1275/3000\n",
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      "Epoch 1276/3000\n",
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      "Epoch 1277/3000\n",
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      "Epoch 1278/3000\n",
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      "Epoch 1279/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1280/3000\n",
      "6/6 - 0s - loss: 2.2052 - mse: 1.5040 - val_loss: 4.0798 - val_mse: 3.3788\n",
      "Epoch 1281/3000\n",
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      "Epoch 1282/3000\n",
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      "Epoch 1283/3000\n",
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      "Epoch 1284/3000\n",
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      "Epoch 1285/3000\n",
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      "Epoch 1286/3000\n",
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      "Epoch 1287/3000\n",
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      "Epoch 1288/3000\n",
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      "Epoch 1289/3000\n",
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      "Epoch 1290/3000\n",
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      "Epoch 1291/3000\n",
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      "Epoch 1293/3000\n",
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      "Epoch 1294/3000\n",
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      "Epoch 1295/3000\n",
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      "Epoch 1296/3000\n",
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      "Epoch 1297/3000\n",
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      "Epoch 1299/3000\n",
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      "Epoch 1300/3000\n",
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      "Epoch 1302/3000\n",
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      "Epoch 1304/3000\n",
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      "Epoch 1306/3000\n",
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      "Epoch 1310/3000\n",
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      "Epoch 1311/3000\n",
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      "Epoch 1314/3000\n",
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      "Epoch 1319/3000\n",
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      "Epoch 1320/3000\n",
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      "Epoch 1321/3000\n",
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      "Epoch 1323/3000\n",
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      "Epoch 1325/3000\n",
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      "Epoch 1327/3000\n",
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      "Epoch 1328/3000\n",
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      "Epoch 1329/3000\n",
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      "Epoch 1330/3000\n",
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      "Epoch 1331/3000\n",
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      "Epoch 1333/3000\n",
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      "Epoch 1335/3000\n",
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      "Epoch 1337/3000\n",
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      "Epoch 1341/3000\n",
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      "Epoch 1342/3000\n",
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      "Epoch 1343/3000\n",
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      "Epoch 1349/3000\n",
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      "Epoch 1350/3000\n",
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      "Epoch 1351/3000\n",
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      "Epoch 1352/3000\n",
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      "Epoch 1353/3000\n",
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      "Epoch 1354/3000\n",
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      "Epoch 1355/3000\n",
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      "Epoch 1359/3000\n",
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      "Epoch 1360/3000\n",
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      "Epoch 1361/3000\n",
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      "Epoch 1362/3000\n",
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      "Epoch 1363/3000\n",
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      "Epoch 1364/3000\n",
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      "Epoch 1365/3000\n",
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      "Epoch 1366/3000\n",
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      "Epoch 1367/3000\n",
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      "Epoch 1368/3000\n",
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      "Epoch 1369/3000\n",
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      "Epoch 1370/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 2.1354 - mse: 1.4505 - val_loss: 4.0534 - val_mse: 3.3686\n",
      "Epoch 1371/3000\n",
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      "Epoch 1380/3000\n",
      "6/6 - 0s - loss: 2.1279 - mse: 1.4447 - val_loss: 4.0509 - val_mse: 3.3678\n",
      "Epoch 1381/3000\n",
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      "Epoch 1382/3000\n",
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      "Epoch 1383/3000\n",
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      "Epoch 1390/3000\n",
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      "Epoch 1391/3000\n",
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      "Epoch 1392/3000\n",
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      "Epoch 1393/3000\n",
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      "Epoch 1394/3000\n",
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      "Epoch 1399/3000\n",
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      "Epoch 1400/3000\n",
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      "Epoch 1430/3000\n",
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      "Epoch 1431/3000\n",
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      "Epoch 1435/3000\n",
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      "Epoch 1437/3000\n",
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      "Epoch 1439/3000\n",
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      "Epoch 1449/3000\n",
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      "Epoch 1452/3000\n",
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      "Epoch 1453/3000\n",
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      "Epoch 1456/3000\n",
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      "Epoch 1458/3000\n",
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      "Epoch 1459/3000\n",
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      "Epoch 1460/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1461/3000\n",
      "6/6 - 0s - loss: 2.0688 - mse: 1.3993 - val_loss: 4.0344 - val_mse: 3.3649\n",
      "Epoch 1462/3000\n",
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      "Epoch 1463/3000\n",
      "6/6 - 0s - loss: 2.0674 - mse: 1.3982 - val_loss: 4.0340 - val_mse: 3.3649\n",
      "Epoch 1464/3000\n",
      "6/6 - 0s - loss: 2.0667 - mse: 1.3976 - val_loss: 4.0339 - val_mse: 3.3650\n",
      "Epoch 1465/3000\n",
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      "Epoch 1466/3000\n",
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      "Epoch 1467/3000\n",
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      "Epoch 1468/3000\n",
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      "Epoch 1469/3000\n",
      "6/6 - 0s - loss: 2.0631 - mse: 1.3949 - val_loss: 4.0330 - val_mse: 3.3648\n",
      "Epoch 1470/3000\n",
      "6/6 - 0s - loss: 2.0624 - mse: 1.3943 - val_loss: 4.0330 - val_mse: 3.3650\n",
      "Epoch 1471/3000\n",
      "6/6 - 0s - loss: 2.0618 - mse: 1.3939 - val_loss: 4.0327 - val_mse: 3.3649\n",
      "Epoch 1472/3000\n",
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      "Epoch 1473/3000\n",
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      "Epoch 1474/3000\n",
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      "Epoch 1475/3000\n",
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      "Epoch 1476/3000\n",
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      "Epoch 1477/3000\n",
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      "Epoch 1478/3000\n",
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      "Epoch 1479/3000\n",
      "6/6 - 0s - loss: 2.0562 - mse: 1.3896 - val_loss: 4.0313 - val_mse: 3.3648\n",
      "Epoch 1480/3000\n",
      "6/6 - 0s - loss: 2.0555 - mse: 1.3890 - val_loss: 4.0310 - val_mse: 3.3647\n",
      "Epoch 1481/3000\n",
      "6/6 - 0s - loss: 2.0545 - mse: 1.3883 - val_loss: 4.0311 - val_mse: 3.3649\n",
      "Epoch 1482/3000\n",
      "6/6 - 0s - loss: 2.0539 - mse: 1.3877 - val_loss: 4.0309 - val_mse: 3.3649\n",
      "Epoch 1483/3000\n",
      "6/6 - 0s - loss: 2.0531 - mse: 1.3872 - val_loss: 4.0307 - val_mse: 3.3648\n",
      "Epoch 1484/3000\n",
      "6/6 - 0s - loss: 2.0526 - mse: 1.3868 - val_loss: 4.0303 - val_mse: 3.3646\n",
      "Epoch 1485/3000\n",
      "6/6 - 0s - loss: 2.0517 - mse: 1.3861 - val_loss: 4.0302 - val_mse: 3.3647\n",
      "Epoch 1486/3000\n",
      "6/6 - 0s - loss: 2.0511 - mse: 1.3856 - val_loss: 4.0301 - val_mse: 3.3647\n",
      "Epoch 1487/3000\n",
      "6/6 - 0s - loss: 2.0503 - mse: 1.3850 - val_loss: 4.0300 - val_mse: 3.3648\n",
      "Epoch 1488/3000\n",
      "6/6 - 0s - loss: 2.0497 - mse: 1.3845 - val_loss: 4.0300 - val_mse: 3.3649\n",
      "Epoch 1489/3000\n",
      "6/6 - 0s - loss: 2.0489 - mse: 1.3839 - val_loss: 4.0298 - val_mse: 3.3650\n",
      "Epoch 1490/3000\n",
      "6/6 - 0s - loss: 2.0482 - mse: 1.3834 - val_loss: 4.0296 - val_mse: 3.3649\n",
      "Epoch 1491/3000\n",
      "6/6 - 0s - loss: 2.0475 - mse: 1.3828 - val_loss: 4.0294 - val_mse: 3.3648\n",
      "Epoch 1492/3000\n",
      "6/6 - 0s - loss: 2.0469 - mse: 1.3824 - val_loss: 4.0293 - val_mse: 3.3649\n",
      "Epoch 1493/3000\n",
      "6/6 - 0s - loss: 2.0461 - mse: 1.3818 - val_loss: 4.0289 - val_mse: 3.3647\n",
      "Epoch 1494/3000\n",
      "6/6 - 0s - loss: 2.0454 - mse: 1.3812 - val_loss: 4.0286 - val_mse: 3.3645\n",
      "Epoch 1495/3000\n",
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      "Epoch 1496/3000\n",
      "6/6 - 0s - loss: 2.0440 - mse: 1.3801 - val_loss: 4.0284 - val_mse: 3.3647\n",
      "Epoch 1497/3000\n",
      "6/6 - 0s - loss: 2.0433 - mse: 1.3796 - val_loss: 4.0281 - val_mse: 3.3645\n",
      "Epoch 1498/3000\n",
      "6/6 - 0s - loss: 2.0426 - mse: 1.3790 - val_loss: 4.0280 - val_mse: 3.3645\n",
      "Epoch 1499/3000\n",
      "6/6 - 0s - loss: 2.0418 - mse: 1.3784 - val_loss: 4.0280 - val_mse: 3.3647\n",
      "Epoch 1500/3000\n",
      "6/6 - 0s - loss: 2.0412 - mse: 1.3779 - val_loss: 4.0279 - val_mse: 3.3648\n",
      "Epoch 1501/3000\n",
      "6/6 - 0s - loss: 2.0405 - mse: 1.3774 - val_loss: 4.0280 - val_mse: 3.3650\n",
      "Epoch 1502/3000\n",
      "6/6 - 0s - loss: 2.0397 - mse: 1.3768 - val_loss: 4.0277 - val_mse: 3.3649\n",
      "Epoch 1503/3000\n",
      "6/6 - 0s - loss: 2.0390 - mse: 1.3763 - val_loss: 4.0274 - val_mse: 3.3648\n",
      "Epoch 1504/3000\n",
      "6/6 - 0s - loss: 2.0383 - mse: 1.3757 - val_loss: 4.0271 - val_mse: 3.3647\n",
      "Epoch 1505/3000\n",
      "6/6 - 0s - loss: 2.0377 - mse: 1.3753 - val_loss: 4.0272 - val_mse: 3.3649\n",
      "Epoch 1506/3000\n",
      "6/6 - 0s - loss: 2.0369 - mse: 1.3746 - val_loss: 4.0270 - val_mse: 3.3648\n",
      "Epoch 1507/3000\n",
      "6/6 - 0s - loss: 2.0362 - mse: 1.3741 - val_loss: 4.0268 - val_mse: 3.3648\n",
      "Epoch 1508/3000\n",
      "6/6 - 0s - loss: 2.0355 - mse: 1.3735 - val_loss: 4.0267 - val_mse: 3.3648\n",
      "Epoch 1509/3000\n",
      "6/6 - 0s - loss: 2.0349 - mse: 1.3731 - val_loss: 4.0265 - val_mse: 3.3648\n",
      "Epoch 1510/3000\n",
      "6/6 - 0s - loss: 2.0343 - mse: 1.3726 - val_loss: 4.0263 - val_mse: 3.3648\n",
      "Epoch 1511/3000\n",
      "6/6 - 0s - loss: 2.0333 - mse: 1.3718 - val_loss: 4.0263 - val_mse: 3.3649\n",
      "Epoch 1512/3000\n",
      "6/6 - 0s - loss: 2.0327 - mse: 1.3713 - val_loss: 4.0264 - val_mse: 3.3652\n",
      "Epoch 1513/3000\n",
      "6/6 - 0s - loss: 2.0321 - mse: 1.3709 - val_loss: 4.0260 - val_mse: 3.3649\n",
      "Epoch 1514/3000\n",
      "6/6 - 0s - loss: 2.0312 - mse: 1.3701 - val_loss: 4.0259 - val_mse: 3.3650\n",
      "Epoch 1515/3000\n",
      "6/6 - 0s - loss: 2.0305 - mse: 1.3697 - val_loss: 4.0259 - val_mse: 3.3652\n",
      "Epoch 1516/3000\n",
      "6/6 - 0s - loss: 2.0298 - mse: 1.3691 - val_loss: 4.0258 - val_mse: 3.3652\n",
      "Epoch 1517/3000\n",
      "6/6 - 0s - loss: 2.0291 - mse: 1.3686 - val_loss: 4.0256 - val_mse: 3.3652\n",
      "Epoch 1518/3000\n",
      "6/6 - 0s - loss: 2.0284 - mse: 1.3680 - val_loss: 4.0255 - val_mse: 3.3652\n",
      "Epoch 1519/3000\n",
      "6/6 - 0s - loss: 2.0279 - mse: 1.3677 - val_loss: 4.0255 - val_mse: 3.3653\n",
      "Epoch 1520/3000\n",
      "6/6 - 0s - loss: 2.0270 - mse: 1.3670 - val_loss: 4.0255 - val_mse: 3.3655\n",
      "Epoch 1521/3000\n",
      "6/6 - 0s - loss: 2.0263 - mse: 1.3664 - val_loss: 4.0253 - val_mse: 3.3655\n",
      "Epoch 1522/3000\n",
      "6/6 - 0s - loss: 2.0256 - mse: 1.3658 - val_loss: 4.0251 - val_mse: 3.3655\n",
      "Epoch 1523/3000\n",
      "6/6 - 0s - loss: 2.0250 - mse: 1.3654 - val_loss: 4.0249 - val_mse: 3.3654\n",
      "Epoch 1524/3000\n",
      "6/6 - 0s - loss: 2.0241 - mse: 1.3647 - val_loss: 4.0248 - val_mse: 3.3654\n",
      "Epoch 1525/3000\n",
      "6/6 - 0s - loss: 2.0235 - mse: 1.3642 - val_loss: 4.0245 - val_mse: 3.3653\n",
      "Epoch 1526/3000\n",
      "6/6 - 0s - loss: 2.0228 - mse: 1.3636 - val_loss: 4.0244 - val_mse: 3.3654\n",
      "Epoch 1527/3000\n",
      "6/6 - 0s - loss: 2.0220 - mse: 1.3631 - val_loss: 4.0243 - val_mse: 3.3654\n",
      "Epoch 1528/3000\n",
      "6/6 - 0s - loss: 2.0214 - mse: 1.3626 - val_loss: 4.0244 - val_mse: 3.3657\n",
      "Epoch 1529/3000\n",
      "6/6 - 0s - loss: 2.0207 - mse: 1.3620 - val_loss: 4.0243 - val_mse: 3.3657\n",
      "Epoch 1530/3000\n",
      "6/6 - 0s - loss: 2.0200 - mse: 1.3615 - val_loss: 4.0242 - val_mse: 3.3658\n",
      "Epoch 1531/3000\n",
      "6/6 - 0s - loss: 2.0194 - mse: 1.3610 - val_loss: 4.0237 - val_mse: 3.3654\n",
      "Epoch 1532/3000\n",
      "6/6 - 0s - loss: 2.0187 - mse: 1.3605 - val_loss: 4.0235 - val_mse: 3.3654\n",
      "Epoch 1533/3000\n",
      "6/6 - 0s - loss: 2.0179 - mse: 1.3598 - val_loss: 4.0233 - val_mse: 3.3653\n",
      "Epoch 1534/3000\n",
      "6/6 - 0s - loss: 2.0173 - mse: 1.3594 - val_loss: 4.0233 - val_mse: 3.3655\n",
      "Epoch 1535/3000\n",
      "6/6 - 0s - loss: 2.0165 - mse: 1.3588 - val_loss: 4.0235 - val_mse: 3.3659\n",
      "Epoch 1536/3000\n",
      "6/6 - 0s - loss: 2.0158 - mse: 1.3583 - val_loss: 4.0233 - val_mse: 3.3658\n",
      "Epoch 1537/3000\n",
      "6/6 - 0s - loss: 2.0152 - mse: 1.3578 - val_loss: 4.0234 - val_mse: 3.3661\n",
      "Epoch 1538/3000\n",
      "6/6 - 0s - loss: 2.0144 - mse: 1.3572 - val_loss: 4.0231 - val_mse: 3.3660\n",
      "Epoch 1539/3000\n",
      "6/6 - 0s - loss: 2.0138 - mse: 1.3567 - val_loss: 4.0230 - val_mse: 3.3660\n",
      "Epoch 1540/3000\n",
      "6/6 - 0s - loss: 2.0130 - mse: 1.3561 - val_loss: 4.0232 - val_mse: 3.3663\n",
      "Epoch 1541/3000\n",
      "6/6 - 0s - loss: 2.0125 - mse: 1.3557 - val_loss: 4.0228 - val_mse: 3.3661\n",
      "Epoch 1542/3000\n",
      "6/6 - 0s - loss: 2.0117 - mse: 1.3550 - val_loss: 4.0228 - val_mse: 3.3662\n",
      "Epoch 1543/3000\n",
      "6/6 - 0s - loss: 2.0110 - mse: 1.3545 - val_loss: 4.0228 - val_mse: 3.3664\n",
      "Epoch 1544/3000\n",
      "6/6 - 0s - loss: 2.0102 - mse: 1.3539 - val_loss: 4.0227 - val_mse: 3.3664\n",
      "Epoch 1545/3000\n",
      "6/6 - 0s - loss: 2.0096 - mse: 1.3534 - val_loss: 4.0225 - val_mse: 3.3664\n",
      "Epoch 1546/3000\n",
      "6/6 - 0s - loss: 2.0089 - mse: 1.3529 - val_loss: 4.0224 - val_mse: 3.3665\n",
      "Epoch 1547/3000\n",
      "6/6 - 0s - loss: 2.0082 - mse: 1.3523 - val_loss: 4.0224 - val_mse: 3.3666\n",
      "Epoch 1548/3000\n",
      "6/6 - 0s - loss: 2.0075 - mse: 1.3518 - val_loss: 4.0223 - val_mse: 3.3667\n",
      "Epoch 1549/3000\n",
      "6/6 - 0s - loss: 2.0067 - mse: 1.3512 - val_loss: 4.0220 - val_mse: 3.3665\n",
      "Epoch 1550/3000\n",
      "6/6 - 0s - loss: 2.0061 - mse: 1.3507 - val_loss: 4.0218 - val_mse: 3.3665\n",
      "Epoch 1551/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 2.0054 - mse: 1.3501 - val_loss: 4.0217 - val_mse: 3.3665\n",
      "Epoch 1552/3000\n",
      "6/6 - 0s - loss: 2.0047 - mse: 1.3496 - val_loss: 4.0215 - val_mse: 3.3665\n",
      "Epoch 1553/3000\n",
      "6/6 - 0s - loss: 2.0040 - mse: 1.3490 - val_loss: 4.0214 - val_mse: 3.3666\n",
      "Epoch 1554/3000\n",
      "6/6 - 0s - loss: 2.0034 - mse: 1.3486 - val_loss: 4.0212 - val_mse: 3.3665\n",
      "Epoch 1555/3000\n",
      "6/6 - 0s - loss: 2.0026 - mse: 1.3479 - val_loss: 4.0212 - val_mse: 3.3667\n",
      "Epoch 1556/3000\n",
      "6/6 - 0s - loss: 2.0019 - mse: 1.3474 - val_loss: 4.0212 - val_mse: 3.3668\n",
      "Epoch 1557/3000\n",
      "6/6 - 0s - loss: 2.0012 - mse: 1.3468 - val_loss: 4.0210 - val_mse: 3.3667\n",
      "Epoch 1558/3000\n",
      "6/6 - 0s - loss: 2.0007 - mse: 1.3465 - val_loss: 4.0208 - val_mse: 3.3667\n",
      "Epoch 1559/3000\n",
      "6/6 - 0s - loss: 1.9998 - mse: 1.3458 - val_loss: 4.0208 - val_mse: 3.3669\n",
      "Epoch 1560/3000\n",
      "6/6 - 0s - loss: 1.9991 - mse: 1.3452 - val_loss: 4.0206 - val_mse: 3.3669\n",
      "Epoch 1561/3000\n",
      "6/6 - 0s - loss: 1.9985 - mse: 1.3448 - val_loss: 4.0206 - val_mse: 3.3669\n",
      "Epoch 1562/3000\n",
      "6/6 - 0s - loss: 1.9978 - mse: 1.3442 - val_loss: 4.0204 - val_mse: 3.3669\n",
      "Epoch 1563/3000\n",
      "6/6 - 0s - loss: 1.9970 - mse: 1.3436 - val_loss: 4.0202 - val_mse: 3.3669\n",
      "Epoch 1564/3000\n",
      "6/6 - 0s - loss: 1.9963 - mse: 1.3431 - val_loss: 4.0202 - val_mse: 3.3670\n",
      "Epoch 1565/3000\n",
      "6/6 - 0s - loss: 1.9957 - mse: 1.3426 - val_loss: 4.0199 - val_mse: 3.3669\n",
      "Epoch 1566/3000\n",
      "6/6 - 0s - loss: 1.9950 - mse: 1.3420 - val_loss: 4.0196 - val_mse: 3.3667\n",
      "Epoch 1567/3000\n",
      "6/6 - 0s - loss: 1.9943 - mse: 1.3414 - val_loss: 4.0196 - val_mse: 3.3668\n",
      "Epoch 1568/3000\n",
      "6/6 - 0s - loss: 1.9936 - mse: 1.3409 - val_loss: 4.0196 - val_mse: 3.3670\n",
      "Epoch 1569/3000\n",
      "6/6 - 0s - loss: 1.9930 - mse: 1.3405 - val_loss: 4.0193 - val_mse: 3.3668\n",
      "Epoch 1570/3000\n",
      "6/6 - 0s - loss: 1.9923 - mse: 1.3399 - val_loss: 4.0192 - val_mse: 3.3669\n",
      "Epoch 1571/3000\n",
      "6/6 - 0s - loss: 1.9915 - mse: 1.3393 - val_loss: 4.0190 - val_mse: 3.3668\n",
      "Epoch 1572/3000\n",
      "6/6 - 0s - loss: 1.9909 - mse: 1.3388 - val_loss: 4.0191 - val_mse: 3.3671\n",
      "Epoch 1573/3000\n",
      "6/6 - 0s - loss: 1.9902 - mse: 1.3383 - val_loss: 4.0191 - val_mse: 3.3673\n",
      "Epoch 1574/3000\n",
      "6/6 - 0s - loss: 1.9895 - mse: 1.3377 - val_loss: 4.0187 - val_mse: 3.3670\n",
      "Epoch 1575/3000\n",
      "6/6 - 0s - loss: 1.9887 - mse: 1.3371 - val_loss: 4.0186 - val_mse: 3.3671\n",
      "Epoch 1576/3000\n",
      "6/6 - 0s - loss: 1.9880 - mse: 1.3365 - val_loss: 4.0186 - val_mse: 3.3672\n",
      "Epoch 1577/3000\n",
      "6/6 - 0s - loss: 1.9876 - mse: 1.3362 - val_loss: 4.0182 - val_mse: 3.3669\n",
      "Epoch 1578/3000\n",
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      "Epoch 1579/3000\n",
      "6/6 - 0s - loss: 1.9860 - mse: 1.3350 - val_loss: 4.0181 - val_mse: 3.3672\n",
      "Epoch 1580/3000\n",
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      "Epoch 1581/3000\n",
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      "Epoch 1582/3000\n",
      "6/6 - 0s - loss: 1.9839 - mse: 1.3333 - val_loss: 4.0179 - val_mse: 3.3674\n",
      "Epoch 1583/3000\n",
      "6/6 - 0s - loss: 1.9833 - mse: 1.3328 - val_loss: 4.0178 - val_mse: 3.3675\n",
      "Epoch 1584/3000\n",
      "6/6 - 0s - loss: 1.9826 - mse: 1.3323 - val_loss: 4.0179 - val_mse: 3.3677\n",
      "Epoch 1585/3000\n",
      "6/6 - 0s - loss: 1.9819 - mse: 1.3318 - val_loss: 4.0177 - val_mse: 3.3676\n",
      "Epoch 1586/3000\n",
      "6/6 - 0s - loss: 1.9815 - mse: 1.3315 - val_loss: 4.0173 - val_mse: 3.3673\n",
      "Epoch 1587/3000\n",
      "6/6 - 0s - loss: 1.9806 - mse: 1.3307 - val_loss: 4.0174 - val_mse: 3.3677\n",
      "Epoch 1588/3000\n",
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      "Epoch 1589/3000\n",
      "6/6 - 0s - loss: 1.9792 - mse: 1.3297 - val_loss: 4.0176 - val_mse: 3.3681\n",
      "Epoch 1590/3000\n",
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      "Epoch 1591/3000\n",
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      "Epoch 1592/3000\n",
      "6/6 - 0s - loss: 1.9772 - mse: 1.3281 - val_loss: 4.0173 - val_mse: 3.3683\n",
      "Epoch 1593/3000\n",
      "6/6 - 0s - loss: 1.9765 - mse: 1.3275 - val_loss: 4.0171 - val_mse: 3.3683\n",
      "Epoch 1594/3000\n",
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      "Epoch 1595/3000\n",
      "6/6 - 0s - loss: 1.9751 - mse: 1.3264 - val_loss: 4.0172 - val_mse: 3.3686\n",
      "Epoch 1596/3000\n",
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      "Epoch 1597/3000\n",
      "6/6 - 0s - loss: 1.9738 - mse: 1.3255 - val_loss: 4.0171 - val_mse: 3.3689\n",
      "Epoch 1598/3000\n",
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      "Epoch 1599/3000\n",
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      "Epoch 1600/3000\n",
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      "Epoch 1601/3000\n",
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      "Epoch 1602/3000\n",
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      "Epoch 1611/3000\n",
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      "Epoch 1620/3000\n",
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      "Epoch 1621/3000\n",
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      "Epoch 1627/3000\n",
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      "Epoch 1628/3000\n",
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      "Epoch 1629/3000\n",
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      "Epoch 1630/3000\n",
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      "Epoch 1631/3000\n",
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      "Epoch 1632/3000\n",
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      "Epoch 1633/3000\n",
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      "Epoch 1634/3000\n",
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      "Epoch 1635/3000\n",
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      "Epoch 1636/3000\n",
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      "Epoch 1637/3000\n",
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      "Epoch 1638/3000\n",
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      "Epoch 1639/3000\n",
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      "Epoch 1640/3000\n",
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      "Epoch 1641/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1642/3000\n",
      "6/6 - 0s - loss: 1.9437 - mse: 1.3019 - val_loss: 4.0129 - val_mse: 3.3712\n",
      "Epoch 1643/3000\n",
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      "Epoch 1644/3000\n",
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      "Epoch 1645/3000\n",
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      "Epoch 1646/3000\n",
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      "Epoch 1647/3000\n",
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      "Epoch 1648/3000\n",
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      "Epoch 1650/3000\n",
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      "Epoch 1652/3000\n",
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      "Epoch 1654/3000\n",
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      "Epoch 1656/3000\n",
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      "Epoch 1658/3000\n",
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      "Epoch 1659/3000\n",
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      "Epoch 1662/3000\n",
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      "Epoch 1663/3000\n",
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      "Epoch 1664/3000\n",
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      "Epoch 1665/3000\n",
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      "Epoch 1666/3000\n",
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      "Epoch 1667/3000\n",
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      "Epoch 1668/3000\n",
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      "Epoch 1669/3000\n",
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      "Epoch 1670/3000\n",
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      "Epoch 1672/3000\n",
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      "Epoch 1684/3000\n",
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      "Epoch 1685/3000\n",
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      "Epoch 1686/3000\n",
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      "Epoch 1690/3000\n",
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      "Epoch 1702/3000\n",
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      "Epoch 1704/3000\n",
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      "Epoch 1709/3000\n",
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      "Epoch 1722/3000\n",
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      "Epoch 1724/3000\n",
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      "Epoch 1731/3000\n",
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      "Epoch 1732/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1733/3000\n",
      "6/6 - 0s - loss: 1.8836 - mse: 1.2545 - val_loss: 4.0039 - val_mse: 3.3749\n",
      "Epoch 1734/3000\n",
      "6/6 - 0s - loss: 1.8830 - mse: 1.2540 - val_loss: 4.0038 - val_mse: 3.3749\n",
      "Epoch 1735/3000\n",
      "6/6 - 0s - loss: 1.8823 - mse: 1.2534 - val_loss: 4.0037 - val_mse: 3.3750\n",
      "Epoch 1736/3000\n",
      "6/6 - 0s - loss: 1.8816 - mse: 1.2529 - val_loss: 4.0036 - val_mse: 3.3750\n",
      "Epoch 1737/3000\n",
      "6/6 - 0s - loss: 1.8810 - mse: 1.2524 - val_loss: 4.0035 - val_mse: 3.3751\n",
      "Epoch 1738/3000\n",
      "6/6 - 0s - loss: 1.8803 - mse: 1.2519 - val_loss: 4.0034 - val_mse: 3.3751\n",
      "Epoch 1739/3000\n",
      "6/6 - 0s - loss: 1.8798 - mse: 1.2515 - val_loss: 4.0032 - val_mse: 3.3750\n",
      "Epoch 1740/3000\n",
      "6/6 - 0s - loss: 1.8790 - mse: 1.2508 - val_loss: 4.0031 - val_mse: 3.3750\n",
      "Epoch 1741/3000\n",
      "6/6 - 0s - loss: 1.8784 - mse: 1.2504 - val_loss: 4.0033 - val_mse: 3.3753\n",
      "Epoch 1742/3000\n",
      "6/6 - 0s - loss: 1.8777 - mse: 1.2498 - val_loss: 4.0030 - val_mse: 3.3752\n",
      "Epoch 1743/3000\n",
      "6/6 - 0s - loss: 1.8771 - mse: 1.2493 - val_loss: 4.0030 - val_mse: 3.3753\n",
      "Epoch 1744/3000\n",
      "6/6 - 0s - loss: 1.8764 - mse: 1.2488 - val_loss: 4.0029 - val_mse: 3.3753\n",
      "Epoch 1745/3000\n",
      "6/6 - 0s - loss: 1.8757 - mse: 1.2482 - val_loss: 4.0027 - val_mse: 3.3753\n",
      "Epoch 1746/3000\n",
      "6/6 - 0s - loss: 1.8752 - mse: 1.2478 - val_loss: 4.0026 - val_mse: 3.3753\n",
      "Epoch 1747/3000\n",
      "6/6 - 0s - loss: 1.8744 - mse: 1.2472 - val_loss: 4.0025 - val_mse: 3.3754\n",
      "Epoch 1748/3000\n",
      "6/6 - 0s - loss: 1.8738 - mse: 1.2467 - val_loss: 4.0024 - val_mse: 3.3754\n",
      "Epoch 1749/3000\n",
      "6/6 - 0s - loss: 1.8732 - mse: 1.2462 - val_loss: 4.0024 - val_mse: 3.3755\n",
      "Epoch 1750/3000\n",
      "6/6 - 0s - loss: 1.8726 - mse: 1.2457 - val_loss: 4.0025 - val_mse: 3.3757\n",
      "Epoch 1751/3000\n",
      "6/6 - 0s - loss: 1.8720 - mse: 1.2453 - val_loss: 4.0026 - val_mse: 3.3760\n",
      "Epoch 1752/3000\n",
      "6/6 - 0s - loss: 1.8714 - mse: 1.2448 - val_loss: 4.0025 - val_mse: 3.3760\n",
      "Epoch 1753/3000\n",
      "6/6 - 0s - loss: 1.8706 - mse: 1.2442 - val_loss: 4.0024 - val_mse: 3.3761\n",
      "Epoch 1754/3000\n",
      "6/6 - 0s - loss: 1.8701 - mse: 1.2438 - val_loss: 4.0021 - val_mse: 3.3759\n",
      "Epoch 1755/3000\n",
      "6/6 - 0s - loss: 1.8693 - mse: 1.2432 - val_loss: 4.0020 - val_mse: 3.3760\n",
      "Epoch 1756/3000\n",
      "6/6 - 0s - loss: 1.8687 - mse: 1.2426 - val_loss: 4.0020 - val_mse: 3.3761\n",
      "Epoch 1757/3000\n",
      "6/6 - 0s - loss: 1.8680 - mse: 1.2422 - val_loss: 4.0019 - val_mse: 3.3761\n",
      "Epoch 1758/3000\n",
      "6/6 - 0s - loss: 1.8675 - mse: 1.2417 - val_loss: 4.0017 - val_mse: 3.3760\n",
      "Epoch 1759/3000\n",
      "6/6 - 0s - loss: 1.8667 - mse: 1.2410 - val_loss: 4.0018 - val_mse: 3.3763\n",
      "Epoch 1760/3000\n",
      "6/6 - 0s - loss: 1.8661 - mse: 1.2406 - val_loss: 4.0020 - val_mse: 3.3766\n",
      "Epoch 1761/3000\n",
      "6/6 - 0s - loss: 1.8655 - mse: 1.2401 - val_loss: 4.0019 - val_mse: 3.3766\n",
      "Epoch 1762/3000\n",
      "6/6 - 0s - loss: 1.8648 - mse: 1.2396 - val_loss: 4.0017 - val_mse: 3.3766\n",
      "Epoch 1763/3000\n",
      "6/6 - 0s - loss: 1.8641 - mse: 1.2390 - val_loss: 4.0017 - val_mse: 3.3767\n",
      "Epoch 1764/3000\n",
      "6/6 - 0s - loss: 1.8635 - mse: 1.2386 - val_loss: 4.0018 - val_mse: 3.3769\n",
      "Epoch 1765/3000\n",
      "6/6 - 0s - loss: 1.8629 - mse: 1.2380 - val_loss: 4.0018 - val_mse: 3.3770\n",
      "Epoch 1766/3000\n",
      "6/6 - 0s - loss: 1.8623 - mse: 1.2376 - val_loss: 4.0016 - val_mse: 3.3770\n",
      "Epoch 1767/3000\n",
      "6/6 - 0s - loss: 1.8616 - mse: 1.2371 - val_loss: 4.0015 - val_mse: 3.3770\n",
      "Epoch 1768/3000\n",
      "6/6 - 0s - loss: 1.8611 - mse: 1.2367 - val_loss: 4.0012 - val_mse: 3.3769\n",
      "Epoch 1769/3000\n",
      "6/6 - 0s - loss: 1.8603 - mse: 1.2360 - val_loss: 4.0013 - val_mse: 3.3771\n",
      "Epoch 1770/3000\n",
      "6/6 - 0s - loss: 1.8597 - mse: 1.2356 - val_loss: 4.0013 - val_mse: 3.3772\n",
      "Epoch 1771/3000\n",
      "6/6 - 0s - loss: 1.8590 - mse: 1.2350 - val_loss: 4.0014 - val_mse: 3.3775\n",
      "Epoch 1772/3000\n",
      "6/6 - 0s - loss: 1.8585 - mse: 1.2346 - val_loss: 4.0016 - val_mse: 3.3778\n",
      "Epoch 1773/3000\n",
      "6/6 - 0s - loss: 1.8578 - mse: 1.2340 - val_loss: 4.0017 - val_mse: 3.3780\n",
      "Epoch 1774/3000\n",
      "6/6 - 0s - loss: 1.8572 - mse: 1.2335 - val_loss: 4.0015 - val_mse: 3.3779\n",
      "Epoch 1775/3000\n",
      "6/6 - 0s - loss: 1.8567 - mse: 1.2332 - val_loss: 4.0013 - val_mse: 3.3778\n",
      "Epoch 1776/3000\n",
      "6/6 - 0s - loss: 1.8559 - mse: 1.2325 - val_loss: 4.0013 - val_mse: 3.3780\n",
      "Epoch 1777/3000\n",
      "6/6 - 0s - loss: 1.8553 - mse: 1.2320 - val_loss: 4.0010 - val_mse: 3.3778\n",
      "Epoch 1778/3000\n",
      "6/6 - 0s - loss: 1.8546 - mse: 1.2315 - val_loss: 4.0009 - val_mse: 3.3778\n",
      "Epoch 1779/3000\n",
      "6/6 - 0s - loss: 1.8539 - mse: 1.2309 - val_loss: 4.0008 - val_mse: 3.3779\n",
      "Epoch 1780/3000\n",
      "6/6 - 0s - loss: 1.8533 - mse: 1.2305 - val_loss: 4.0008 - val_mse: 3.3780\n",
      "Epoch 1781/3000\n",
      "6/6 - 0s - loss: 1.8527 - mse: 1.2299 - val_loss: 4.0008 - val_mse: 3.3782\n",
      "Epoch 1782/3000\n",
      "6/6 - 0s - loss: 1.8520 - mse: 1.2294 - val_loss: 4.0009 - val_mse: 3.3784\n",
      "Epoch 1783/3000\n",
      "6/6 - 0s - loss: 1.8514 - mse: 1.2289 - val_loss: 4.0007 - val_mse: 3.3783\n",
      "Epoch 1784/3000\n",
      "6/6 - 0s - loss: 1.8508 - mse: 1.2284 - val_loss: 4.0006 - val_mse: 3.3784\n",
      "Epoch 1785/3000\n",
      "6/6 - 0s - loss: 1.8501 - mse: 1.2279 - val_loss: 4.0006 - val_mse: 3.3784\n",
      "Epoch 1786/3000\n",
      "6/6 - 0s - loss: 1.8494 - mse: 1.2273 - val_loss: 4.0005 - val_mse: 3.3785\n",
      "Epoch 1787/3000\n",
      "6/6 - 0s - loss: 1.8488 - mse: 1.2269 - val_loss: 4.0004 - val_mse: 3.3785\n",
      "Epoch 1788/3000\n",
      "6/6 - 0s - loss: 1.8481 - mse: 1.2263 - val_loss: 4.0004 - val_mse: 3.3787\n",
      "Epoch 1789/3000\n",
      "6/6 - 0s - loss: 1.8476 - mse: 1.2259 - val_loss: 4.0004 - val_mse: 3.3788\n",
      "Epoch 1790/3000\n",
      "6/6 - 0s - loss: 1.8468 - mse: 1.2252 - val_loss: 4.0004 - val_mse: 3.3789\n",
      "Epoch 1791/3000\n",
      "6/6 - 0s - loss: 1.8462 - mse: 1.2248 - val_loss: 4.0002 - val_mse: 3.3789\n",
      "Epoch 1792/3000\n",
      "6/6 - 0s - loss: 1.8455 - mse: 1.2242 - val_loss: 4.0002 - val_mse: 3.3790\n",
      "Epoch 1793/3000\n",
      "6/6 - 0s - loss: 1.8449 - mse: 1.2238 - val_loss: 4.0002 - val_mse: 3.3791\n",
      "Epoch 1794/3000\n",
      "6/6 - 0s - loss: 1.8442 - mse: 1.2232 - val_loss: 4.0001 - val_mse: 3.3791\n",
      "Epoch 1795/3000\n",
      "6/6 - 0s - loss: 1.8437 - mse: 1.2228 - val_loss: 4.0001 - val_mse: 3.3793\n",
      "Epoch 1796/3000\n",
      "6/6 - 0s - loss: 1.8431 - mse: 1.2223 - val_loss: 4.0003 - val_mse: 3.3796\n",
      "Epoch 1797/3000\n",
      "6/6 - 0s - loss: 1.8423 - mse: 1.2217 - val_loss: 4.0002 - val_mse: 3.3797\n",
      "Epoch 1798/3000\n",
      "6/6 - 0s - loss: 1.8417 - mse: 1.2212 - val_loss: 4.0000 - val_mse: 3.3796\n",
      "Epoch 1799/3000\n",
      "6/6 - 0s - loss: 1.8411 - mse: 1.2207 - val_loss: 4.0002 - val_mse: 3.3799\n",
      "Epoch 1800/3000\n",
      "6/6 - 0s - loss: 1.8407 - mse: 1.2205 - val_loss: 3.9998 - val_mse: 3.3796\n",
      "Epoch 1801/3000\n",
      "6/6 - 0s - loss: 1.8398 - mse: 1.2196 - val_loss: 3.9997 - val_mse: 3.3797\n",
      "Epoch 1802/3000\n",
      "6/6 - 0s - loss: 1.8392 - mse: 1.2192 - val_loss: 4.0000 - val_mse: 3.3800\n",
      "Epoch 1803/3000\n",
      "6/6 - 0s - loss: 1.8386 - mse: 1.2187 - val_loss: 4.0001 - val_mse: 3.3804\n",
      "Epoch 1804/3000\n",
      "6/6 - 0s - loss: 1.8379 - mse: 1.2181 - val_loss: 4.0000 - val_mse: 3.3803\n",
      "Epoch 1805/3000\n",
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      "Epoch 1806/3000\n",
      "6/6 - 0s - loss: 1.8365 - mse: 1.2170 - val_loss: 3.9996 - val_mse: 3.3802\n",
      "Epoch 1807/3000\n",
      "6/6 - 0s - loss: 1.8359 - mse: 1.2166 - val_loss: 3.9997 - val_mse: 3.3804\n",
      "Epoch 1808/3000\n",
      "6/6 - 0s - loss: 1.8353 - mse: 1.2161 - val_loss: 3.9997 - val_mse: 3.3805\n",
      "Epoch 1809/3000\n",
      "6/6 - 0s - loss: 1.8347 - mse: 1.2155 - val_loss: 3.9996 - val_mse: 3.3805\n",
      "Epoch 1810/3000\n",
      "6/6 - 0s - loss: 1.8340 - mse: 1.2150 - val_loss: 3.9995 - val_mse: 3.3806\n",
      "Epoch 1811/3000\n",
      "6/6 - 0s - loss: 1.8334 - mse: 1.2145 - val_loss: 3.9994 - val_mse: 3.3806\n",
      "Epoch 1812/3000\n",
      "6/6 - 0s - loss: 1.8329 - mse: 1.2141 - val_loss: 3.9992 - val_mse: 3.3805\n",
      "Epoch 1813/3000\n",
      "6/6 - 0s - loss: 1.8321 - mse: 1.2135 - val_loss: 3.9991 - val_mse: 3.3805\n",
      "Epoch 1814/3000\n",
      "6/6 - 0s - loss: 1.8314 - mse: 1.2129 - val_loss: 3.9989 - val_mse: 3.3805\n",
      "Epoch 1815/3000\n",
      "6/6 - 0s - loss: 1.8307 - mse: 1.2124 - val_loss: 3.9990 - val_mse: 3.3807\n",
      "Epoch 1816/3000\n",
      "6/6 - 0s - loss: 1.8301 - mse: 1.2118 - val_loss: 3.9990 - val_mse: 3.3808\n",
      "Epoch 1817/3000\n",
      "6/6 - 0s - loss: 1.8295 - mse: 1.2113 - val_loss: 3.9988 - val_mse: 3.3807\n",
      "Epoch 1818/3000\n",
      "6/6 - 0s - loss: 1.8290 - mse: 1.2110 - val_loss: 3.9988 - val_mse: 3.3809\n",
      "Epoch 1819/3000\n",
      "6/6 - 0s - loss: 1.8282 - mse: 1.2103 - val_loss: 3.9988 - val_mse: 3.3810\n",
      "Epoch 1820/3000\n",
      "6/6 - 0s - loss: 1.8276 - mse: 1.2098 - val_loss: 3.9986 - val_mse: 3.3809\n",
      "Epoch 1821/3000\n",
      "6/6 - 0s - loss: 1.8269 - mse: 1.2093 - val_loss: 3.9984 - val_mse: 3.3808\n",
      "Epoch 1822/3000\n",
      "6/6 - 0s - loss: 1.8263 - mse: 1.2088 - val_loss: 3.9983 - val_mse: 3.3808\n",
      "Epoch 1823/3000\n",
      "6/6 - 0s - loss: 1.8257 - mse: 1.2083 - val_loss: 3.9985 - val_mse: 3.3811\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1824/3000\n",
      "6/6 - 0s - loss: 1.8250 - mse: 1.2078 - val_loss: 3.9983 - val_mse: 3.3811\n",
      "Epoch 1825/3000\n",
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      "Epoch 1826/3000\n",
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      "Epoch 1827/3000\n",
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      "Epoch 1828/3000\n",
      "6/6 - 0s - loss: 1.8225 - mse: 1.2057 - val_loss: 3.9980 - val_mse: 3.3813\n",
      "Epoch 1829/3000\n",
      "6/6 - 0s - loss: 1.8218 - mse: 1.2051 - val_loss: 3.9978 - val_mse: 3.3813\n",
      "Epoch 1830/3000\n",
      "6/6 - 0s - loss: 1.8212 - mse: 1.2047 - val_loss: 3.9975 - val_mse: 3.3811\n",
      "Epoch 1831/3000\n",
      "6/6 - 0s - loss: 1.8205 - mse: 1.2041 - val_loss: 3.9974 - val_mse: 3.3811\n",
      "Epoch 1832/3000\n",
      "6/6 - 0s - loss: 1.8199 - mse: 1.2036 - val_loss: 3.9976 - val_mse: 3.3814\n",
      "Epoch 1833/3000\n",
      "6/6 - 0s - loss: 1.8193 - mse: 1.2031 - val_loss: 3.9977 - val_mse: 3.3816\n",
      "Epoch 1834/3000\n",
      "6/6 - 0s - loss: 1.8186 - mse: 1.2026 - val_loss: 3.9975 - val_mse: 3.3815\n",
      "Epoch 1835/3000\n",
      "6/6 - 0s - loss: 1.8180 - mse: 1.2020 - val_loss: 3.9973 - val_mse: 3.3815\n",
      "Epoch 1836/3000\n",
      "6/6 - 0s - loss: 1.8174 - mse: 1.2016 - val_loss: 3.9974 - val_mse: 3.3817\n",
      "Epoch 1837/3000\n",
      "6/6 - 0s - loss: 1.8169 - mse: 1.2012 - val_loss: 3.9970 - val_mse: 3.3814\n",
      "Epoch 1838/3000\n",
      "6/6 - 0s - loss: 1.8161 - mse: 1.2006 - val_loss: 3.9972 - val_mse: 3.3817\n",
      "Epoch 1839/3000\n",
      "6/6 - 0s - loss: 1.8154 - mse: 1.2000 - val_loss: 3.9972 - val_mse: 3.3819\n",
      "Epoch 1840/3000\n",
      "6/6 - 0s - loss: 1.8147 - mse: 1.1994 - val_loss: 3.9971 - val_mse: 3.3819\n",
      "Epoch 1841/3000\n",
      "6/6 - 0s - loss: 1.8141 - mse: 1.1990 - val_loss: 3.9969 - val_mse: 3.3818\n",
      "Epoch 1842/3000\n",
      "6/6 - 0s - loss: 1.8136 - mse: 1.1985 - val_loss: 3.9968 - val_mse: 3.3818\n",
      "Epoch 1843/3000\n",
      "6/6 - 0s - loss: 1.8128 - mse: 1.1979 - val_loss: 3.9969 - val_mse: 3.3821\n",
      "Epoch 1844/3000\n",
      "6/6 - 0s - loss: 1.8122 - mse: 1.1974 - val_loss: 3.9969 - val_mse: 3.3822\n",
      "Epoch 1845/3000\n",
      "6/6 - 0s - loss: 1.8117 - mse: 1.1970 - val_loss: 3.9968 - val_mse: 3.3822\n",
      "Epoch 1846/3000\n",
      "6/6 - 0s - loss: 1.8110 - mse: 1.1964 - val_loss: 3.9968 - val_mse: 3.3823\n",
      "Epoch 1847/3000\n",
      "6/6 - 0s - loss: 1.8102 - mse: 1.1958 - val_loss: 3.9968 - val_mse: 3.3824\n",
      "Epoch 1848/3000\n",
      "6/6 - 0s - loss: 1.8098 - mse: 1.1955 - val_loss: 3.9969 - val_mse: 3.3827\n",
      "Epoch 1849/3000\n",
      "6/6 - 0s - loss: 1.8090 - mse: 1.1948 - val_loss: 3.9968 - val_mse: 3.3827\n",
      "Epoch 1850/3000\n",
      "6/6 - 0s - loss: 1.8084 - mse: 1.1944 - val_loss: 3.9968 - val_mse: 3.3828\n",
      "Epoch 1851/3000\n",
      "6/6 - 0s - loss: 1.8078 - mse: 1.1939 - val_loss: 3.9968 - val_mse: 3.3829\n",
      "Epoch 1852/3000\n",
      "6/6 - 0s - loss: 1.8072 - mse: 1.1933 - val_loss: 3.9966 - val_mse: 3.3828\n",
      "Epoch 1853/3000\n",
      "6/6 - 0s - loss: 1.8065 - mse: 1.1928 - val_loss: 3.9965 - val_mse: 3.3829\n",
      "Epoch 1854/3000\n",
      "6/6 - 0s - loss: 1.8059 - mse: 1.1923 - val_loss: 3.9964 - val_mse: 3.3829\n",
      "Epoch 1855/3000\n",
      "6/6 - 0s - loss: 1.8053 - mse: 1.1918 - val_loss: 3.9965 - val_mse: 3.3831\n",
      "Epoch 1856/3000\n",
      "6/6 - 0s - loss: 1.8046 - mse: 1.1912 - val_loss: 3.9965 - val_mse: 3.3832\n",
      "Epoch 1857/3000\n",
      "6/6 - 0s - loss: 1.8040 - mse: 1.1908 - val_loss: 3.9964 - val_mse: 3.3833\n",
      "Epoch 1858/3000\n",
      "6/6 - 0s - loss: 1.8034 - mse: 1.1903 - val_loss: 3.9964 - val_mse: 3.3834\n",
      "Epoch 1859/3000\n",
      "6/6 - 0s - loss: 1.8028 - mse: 1.1898 - val_loss: 3.9964 - val_mse: 3.3835\n",
      "Epoch 1860/3000\n",
      "6/6 - 0s - loss: 1.8021 - mse: 1.1893 - val_loss: 3.9964 - val_mse: 3.3836\n",
      "Epoch 1861/3000\n",
      "6/6 - 0s - loss: 1.8015 - mse: 1.1888 - val_loss: 3.9965 - val_mse: 3.3838\n",
      "Epoch 1862/3000\n",
      "6/6 - 0s - loss: 1.8009 - mse: 1.1883 - val_loss: 3.9964 - val_mse: 3.3839\n",
      "Epoch 1863/3000\n",
      "6/6 - 0s - loss: 1.8003 - mse: 1.1878 - val_loss: 3.9966 - val_mse: 3.3842\n",
      "Epoch 1864/3000\n",
      "6/6 - 0s - loss: 1.7997 - mse: 1.1874 - val_loss: 3.9963 - val_mse: 3.3840\n",
      "Epoch 1865/3000\n",
      "6/6 - 0s - loss: 1.7990 - mse: 1.1867 - val_loss: 3.9963 - val_mse: 3.3842\n",
      "Epoch 1866/3000\n",
      "6/6 - 0s - loss: 1.7984 - mse: 1.1862 - val_loss: 3.9964 - val_mse: 3.3844\n",
      "Epoch 1867/3000\n",
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      "Epoch 1868/3000\n",
      "6/6 - 0s - loss: 1.7971 - mse: 1.1852 - val_loss: 3.9962 - val_mse: 3.3844\n",
      "Epoch 1869/3000\n",
      "6/6 - 0s - loss: 1.7964 - mse: 1.1846 - val_loss: 3.9963 - val_mse: 3.3846\n",
      "Epoch 1870/3000\n",
      "6/6 - 0s - loss: 1.7957 - mse: 1.1841 - val_loss: 3.9962 - val_mse: 3.3847\n",
      "Epoch 1871/3000\n",
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      "Epoch 1872/3000\n",
      "6/6 - 0s - loss: 1.7946 - mse: 1.1832 - val_loss: 3.9961 - val_mse: 3.3848\n",
      "Epoch 1873/3000\n",
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      "Epoch 1874/3000\n",
      "6/6 - 0s - loss: 1.7933 - mse: 1.1821 - val_loss: 3.9962 - val_mse: 3.3851\n",
      "Epoch 1875/3000\n",
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      "Epoch 1876/3000\n",
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      "Epoch 1877/3000\n",
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      "Epoch 1878/3000\n",
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      "Epoch 1879/3000\n",
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      "Epoch 1880/3000\n",
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      "Epoch 1881/3000\n",
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      "Epoch 1882/3000\n",
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      "Epoch 1883/3000\n",
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      "Epoch 1884/3000\n",
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      "Epoch 1885/3000\n",
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      "Epoch 1886/3000\n",
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      "Epoch 1887/3000\n",
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      "Epoch 1888/3000\n",
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      "Epoch 1889/3000\n",
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      "Epoch 1890/3000\n",
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      "Epoch 1891/3000\n",
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      "Epoch 1892/3000\n",
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      "Epoch 1893/3000\n",
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      "Epoch 1894/3000\n",
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      "Epoch 1895/3000\n",
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      "Epoch 1896/3000\n",
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      "Epoch 1897/3000\n",
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      "Epoch 1898/3000\n",
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      "Epoch 1899/3000\n",
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      "Epoch 1900/3000\n",
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      "Epoch 1901/3000\n",
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      "Epoch 1902/3000\n",
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      "Epoch 1903/3000\n",
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      "Epoch 1904/3000\n",
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      "Epoch 1905/3000\n",
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      "Epoch 1906/3000\n",
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      "Epoch 1907/3000\n",
      "6/6 - 0s - loss: 1.7725 - mse: 1.1652 - val_loss: 3.9952 - val_mse: 3.3881\n",
      "Epoch 1908/3000\n",
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      "Epoch 1909/3000\n",
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      "Epoch 1910/3000\n",
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      "Epoch 1911/3000\n",
      "6/6 - 0s - loss: 1.7700 - mse: 1.1632 - val_loss: 3.9948 - val_mse: 3.3881\n",
      "Epoch 1912/3000\n",
      "6/6 - 0s - loss: 1.7694 - mse: 1.1627 - val_loss: 3.9947 - val_mse: 3.3881\n",
      "Epoch 1913/3000\n",
      "6/6 - 0s - loss: 1.7689 - mse: 1.1623 - val_loss: 3.9946 - val_mse: 3.3881\n",
      "Epoch 1914/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 1.7682 - mse: 1.1617 - val_loss: 3.9945 - val_mse: 3.3881\n",
      "Epoch 1915/3000\n",
      "6/6 - 0s - loss: 1.7675 - mse: 1.1612 - val_loss: 3.9945 - val_mse: 3.3882\n",
      "Epoch 1916/3000\n",
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      "Epoch 1917/3000\n",
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      "Epoch 1918/3000\n",
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      "Epoch 1919/3000\n",
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      "Epoch 1920/3000\n",
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      "Epoch 1921/3000\n",
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      "Epoch 1922/3000\n",
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      "Epoch 1923/3000\n",
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      "Epoch 1924/3000\n",
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      "Epoch 1925/3000\n",
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      "Epoch 1926/3000\n",
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      "Epoch 1927/3000\n",
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      "Epoch 1928/3000\n",
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      "Epoch 1929/3000\n",
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      "Epoch 1930/3000\n",
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      "Epoch 1931/3000\n",
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      "Epoch 1932/3000\n",
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      "Epoch 1933/3000\n",
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      "Epoch 1934/3000\n",
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      "Epoch 1935/3000\n",
      "6/6 - 0s - loss: 1.7552 - mse: 1.1511 - val_loss: 3.9940 - val_mse: 3.3901\n",
      "Epoch 1936/3000\n",
      "6/6 - 0s - loss: 1.7545 - mse: 1.1506 - val_loss: 3.9940 - val_mse: 3.3902\n",
      "Epoch 1937/3000\n",
      "6/6 - 0s - loss: 1.7539 - mse: 1.1501 - val_loss: 3.9941 - val_mse: 3.3904\n",
      "Epoch 1938/3000\n",
      "6/6 - 0s - loss: 1.7533 - mse: 1.1497 - val_loss: 3.9941 - val_mse: 3.3904\n",
      "Epoch 1939/3000\n",
      "6/6 - 0s - loss: 1.7526 - mse: 1.1490 - val_loss: 3.9940 - val_mse: 3.3905\n",
      "Epoch 1940/3000\n",
      "6/6 - 0s - loss: 1.7524 - mse: 1.1489 - val_loss: 3.9938 - val_mse: 3.3903\n",
      "Epoch 1941/3000\n",
      "6/6 - 0s - loss: 1.7514 - mse: 1.1480 - val_loss: 3.9939 - val_mse: 3.3906\n",
      "Epoch 1942/3000\n",
      "6/6 - 0s - loss: 1.7508 - mse: 1.1475 - val_loss: 3.9939 - val_mse: 3.3908\n",
      "Epoch 1943/3000\n",
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      "Epoch 1944/3000\n",
      "6/6 - 0s - loss: 1.7496 - mse: 1.1466 - val_loss: 3.9942 - val_mse: 3.3912\n",
      "Epoch 1945/3000\n",
      "6/6 - 0s - loss: 1.7489 - mse: 1.1460 - val_loss: 3.9941 - val_mse: 3.3913\n",
      "Epoch 1946/3000\n",
      "6/6 - 0s - loss: 1.7483 - mse: 1.1455 - val_loss: 3.9940 - val_mse: 3.3913\n",
      "Epoch 1947/3000\n",
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      "Epoch 1948/3000\n",
      "6/6 - 0s - loss: 1.7471 - mse: 1.1446 - val_loss: 3.9939 - val_mse: 3.3914\n",
      "Epoch 1949/3000\n",
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      "Epoch 1950/3000\n",
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      "Epoch 1951/3000\n",
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      "Epoch 1952/3000\n",
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      "Epoch 1953/3000\n",
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      "Epoch 1954/3000\n",
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      "Epoch 1955/3000\n",
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      "Epoch 1956/3000\n",
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      "Epoch 1957/3000\n",
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      "Epoch 1958/3000\n",
      "6/6 - 0s - loss: 1.7409 - mse: 1.1394 - val_loss: 3.9937 - val_mse: 3.3923\n",
      "Epoch 1959/3000\n",
      "6/6 - 0s - loss: 1.7403 - mse: 1.1389 - val_loss: 3.9938 - val_mse: 3.3925\n",
      "Epoch 1960/3000\n",
      "6/6 - 0s - loss: 1.7397 - mse: 1.1384 - val_loss: 3.9935 - val_mse: 3.3923\n",
      "Epoch 1961/3000\n",
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      "Epoch 1962/3000\n",
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      "Epoch 1963/3000\n",
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      "Epoch 1964/3000\n",
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      "Epoch 1965/3000\n",
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      "Epoch 1966/3000\n",
      "6/6 - 0s - loss: 1.7359 - mse: 1.1354 - val_loss: 3.9936 - val_mse: 3.3931\n",
      "Epoch 1967/3000\n",
      "6/6 - 0s - loss: 1.7353 - mse: 1.1348 - val_loss: 3.9935 - val_mse: 3.3930\n",
      "Epoch 1968/3000\n",
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      "Epoch 1969/3000\n",
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      "Epoch 1970/3000\n",
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      "Epoch 1971/3000\n",
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      "Epoch 1972/3000\n",
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      "Epoch 1973/3000\n",
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      "Epoch 1974/3000\n",
      "6/6 - 0s - loss: 1.7311 - mse: 1.1314 - val_loss: 3.9934 - val_mse: 3.3938\n",
      "Epoch 1975/3000\n",
      "6/6 - 0s - loss: 1.7307 - mse: 1.1311 - val_loss: 3.9935 - val_mse: 3.3939\n",
      "Epoch 1976/3000\n",
      "6/6 - 0s - loss: 1.7299 - mse: 1.1304 - val_loss: 3.9935 - val_mse: 3.3941\n",
      "Epoch 1977/3000\n",
      "6/6 - 0s - loss: 1.7293 - mse: 1.1300 - val_loss: 3.9935 - val_mse: 3.3942\n",
      "Epoch 1978/3000\n",
      "6/6 - 0s - loss: 1.7287 - mse: 1.1295 - val_loss: 3.9932 - val_mse: 3.3940\n",
      "Epoch 1979/3000\n",
      "6/6 - 0s - loss: 1.7281 - mse: 1.1290 - val_loss: 3.9933 - val_mse: 3.3942\n",
      "Epoch 1980/3000\n",
      "6/6 - 0s - loss: 1.7274 - mse: 1.1284 - val_loss: 3.9934 - val_mse: 3.3944\n",
      "Epoch 1981/3000\n",
      "6/6 - 0s - loss: 1.7268 - mse: 1.1279 - val_loss: 3.9935 - val_mse: 3.3946\n",
      "Epoch 1982/3000\n",
      "6/6 - 0s - loss: 1.7262 - mse: 1.1274 - val_loss: 3.9934 - val_mse: 3.3947\n",
      "Epoch 1983/3000\n",
      "6/6 - 0s - loss: 1.7256 - mse: 1.1269 - val_loss: 3.9933 - val_mse: 3.3946\n",
      "Epoch 1984/3000\n",
      "6/6 - 0s - loss: 1.7250 - mse: 1.1264 - val_loss: 3.9935 - val_mse: 3.3950\n",
      "Epoch 1985/3000\n",
      "6/6 - 0s - loss: 1.7243 - mse: 1.1258 - val_loss: 3.9933 - val_mse: 3.3949\n",
      "Epoch 1986/3000\n",
      "6/6 - 0s - loss: 1.7237 - mse: 1.1253 - val_loss: 3.9932 - val_mse: 3.3949\n",
      "Epoch 1987/3000\n",
      "6/6 - 0s - loss: 1.7231 - mse: 1.1248 - val_loss: 3.9931 - val_mse: 3.3949\n",
      "Epoch 1988/3000\n",
      "6/6 - 0s - loss: 1.7225 - mse: 1.1244 - val_loss: 3.9931 - val_mse: 3.3950\n",
      "Epoch 1989/3000\n",
      "6/6 - 0s - loss: 1.7219 - mse: 1.1239 - val_loss: 3.9930 - val_mse: 3.3950\n",
      "Epoch 1990/3000\n",
      "6/6 - 0s - loss: 1.7213 - mse: 1.1233 - val_loss: 3.9931 - val_mse: 3.3953\n",
      "Epoch 1991/3000\n",
      "6/6 - 0s - loss: 1.7208 - mse: 1.1229 - val_loss: 3.9931 - val_mse: 3.3953\n",
      "Epoch 1992/3000\n",
      "6/6 - 0s - loss: 1.7200 - mse: 1.1223 - val_loss: 3.9933 - val_mse: 3.3956\n",
      "Epoch 1993/3000\n",
      "6/6 - 0s - loss: 1.7195 - mse: 1.1218 - val_loss: 3.9930 - val_mse: 3.3955\n",
      "Epoch 1994/3000\n",
      "6/6 - 0s - loss: 1.7188 - mse: 1.1213 - val_loss: 3.9931 - val_mse: 3.3957\n",
      "Epoch 1995/3000\n",
      "6/6 - 0s - loss: 1.7181 - mse: 1.1207 - val_loss: 3.9931 - val_mse: 3.3958\n",
      "Epoch 1996/3000\n",
      "6/6 - 0s - loss: 1.7175 - mse: 1.1202 - val_loss: 3.9930 - val_mse: 3.3957\n",
      "Epoch 1997/3000\n",
      "6/6 - 0s - loss: 1.7169 - mse: 1.1197 - val_loss: 3.9929 - val_mse: 3.3958\n",
      "Epoch 1998/3000\n",
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      "Epoch 1999/3000\n",
      "6/6 - 0s - loss: 1.7157 - mse: 1.1187 - val_loss: 3.9926 - val_mse: 3.3957\n",
      "Epoch 2000/3000\n",
      "6/6 - 0s - loss: 1.7150 - mse: 1.1182 - val_loss: 3.9927 - val_mse: 3.3959\n",
      "Epoch 2001/3000\n",
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      "Epoch 2002/3000\n",
      "6/6 - 0s - loss: 1.7139 - mse: 1.1172 - val_loss: 3.9927 - val_mse: 3.3961\n",
      "Epoch 2003/3000\n",
      "6/6 - 0s - loss: 1.7132 - mse: 1.1166 - val_loss: 3.9927 - val_mse: 3.3961\n",
      "Epoch 2004/3000\n",
      "6/6 - 0s - loss: 1.7126 - mse: 1.1161 - val_loss: 3.9925 - val_mse: 3.3961\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2005/3000\n",
      "6/6 - 0s - loss: 1.7121 - mse: 1.1157 - val_loss: 3.9926 - val_mse: 3.3963\n",
      "Epoch 2006/3000\n",
      "6/6 - 0s - loss: 1.7114 - mse: 1.1152 - val_loss: 3.9923 - val_mse: 3.3961\n",
      "Epoch 2007/3000\n",
      "6/6 - 0s - loss: 1.7108 - mse: 1.1146 - val_loss: 3.9923 - val_mse: 3.3962\n",
      "Epoch 2008/3000\n",
      "6/6 - 0s - loss: 1.7102 - mse: 1.1142 - val_loss: 3.9922 - val_mse: 3.3962\n",
      "Epoch 2009/3000\n",
      "6/6 - 0s - loss: 1.7096 - mse: 1.1136 - val_loss: 3.9921 - val_mse: 3.3962\n",
      "Epoch 2010/3000\n",
      "6/6 - 0s - loss: 1.7089 - mse: 1.1131 - val_loss: 3.9921 - val_mse: 3.3964\n",
      "Epoch 2011/3000\n",
      "6/6 - 0s - loss: 1.7083 - mse: 1.1126 - val_loss: 3.9922 - val_mse: 3.3966\n",
      "Epoch 2012/3000\n",
      "6/6 - 0s - loss: 1.7079 - mse: 1.1122 - val_loss: 3.9921 - val_mse: 3.3966\n",
      "Epoch 2013/3000\n",
      "6/6 - 0s - loss: 1.7072 - mse: 1.1117 - val_loss: 3.9924 - val_mse: 3.3969\n",
      "Epoch 2014/3000\n",
      "6/6 - 0s - loss: 1.7065 - mse: 1.1111 - val_loss: 3.9925 - val_mse: 3.3971\n",
      "Epoch 2015/3000\n",
      "6/6 - 0s - loss: 1.7059 - mse: 1.1106 - val_loss: 3.9925 - val_mse: 3.3973\n",
      "Epoch 2016/3000\n",
      "6/6 - 0s - loss: 1.7053 - mse: 1.1101 - val_loss: 3.9926 - val_mse: 3.3975\n",
      "Epoch 2017/3000\n",
      "6/6 - 0s - loss: 1.7047 - mse: 1.1097 - val_loss: 3.9925 - val_mse: 3.3975\n",
      "Epoch 2018/3000\n",
      "6/6 - 0s - loss: 1.7041 - mse: 1.1092 - val_loss: 3.9927 - val_mse: 3.3978\n",
      "Epoch 2019/3000\n",
      "6/6 - 0s - loss: 1.7035 - mse: 1.1086 - val_loss: 3.9928 - val_mse: 3.3980\n",
      "Epoch 02019: early stopping\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(X_train_scaled, y_train,epochs=3000,\n",
    "                          validation_split=0.2, verbose=2, callbacks=[checkpointcb,earlystoppingcb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'verbose': 2, 'epochs': 3000, 'steps': 6}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_history.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 37,
     "metadata": {},
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    "model_history.history"
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  {
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   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "          loss       mse  val_loss   val_mse\n",
       "0     6.228053  5.212002  8.505991  7.490143\n",
       "1     6.219394  5.203680  8.497508  7.481991\n",
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       "4     6.194484  5.179749  8.472036  7.457499\n",
       "...        ...       ...       ...       ...\n",
       "2014  1.705927  1.110648  3.992495  3.397284\n",
       "2015  1.705299  1.110135  3.992620  3.397521\n",
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       "2017  1.704114  1.109159  3.992657  3.397762\n",
       "2018  1.703473  1.108623  3.992794  3.398008\n",
       "\n",
       "[2019 rows x 4 columns]"
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "hist = pd.DataFrame(model_history.history)\n",
    "hist"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist['epoch'] = model_history.epoch"
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  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.398008</td>\n",
       "      <td>2018</td>\n",
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       "          loss       mse  val_loss   val_mse  epoch\n",
       "0     6.228053  5.212002  8.505991  7.490143      0\n",
       "1     6.219394  5.203680  8.497508  7.481991      1\n",
       "2     6.210801  5.195415  8.489076  7.473888      2\n",
       "3     6.203500  5.188440  8.480375  7.465508      3\n",
       "4     6.194484  5.179749  8.472036  7.457499      4\n",
       "...        ...       ...       ...       ...    ...\n",
       "2014  1.705927  1.110648  3.992495  3.397284   2014\n",
       "2015  1.705299  1.110135  3.992620  3.397521   2015\n",
       "2016  1.704737  1.109681  3.992536  3.397540   2016\n",
       "2017  1.704114  1.109159  3.992657  3.397762   2017\n",
       "2018  1.703473  1.108623  3.992794  3.398008   2018\n",
       "\n",
       "[2019 rows x 5 columns]"
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     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2Mbp164FOp6Nv3+d4663XuX79GsWKFWf8+E/w9PTKl3zvR1ELUS8lCQk3MZvzJtyAAA/i4pKyTVPTbpL8y4eg1ePW8yMUp4L/NpUb98qlMJI87Iuj5AG2z+XKlX8oVqzMI6/HHq5554Xc5pGYmEhMzBUqV/731qznnx9Aly7d6d69Z16GaLW82Cf3+nxoNAp+fjlfgpDT5ndQnN1xbvUCalIsaTsX2DocIYQQd8jIMPLqqy9w8uQJAHbu3M6ZM5HUqVPPxpEVPDlt/h+64Mo4hXfDuH8FGSXC0FdsbOuQhBBCkNVg7a233mf06PdISIijWLHijB49gZIlS9k6tAInxfsenGp3wxR9nLTtP6ANqoDGM9DWIQkhhADat3+M9u0fs3UYNienze9B0Whxbv0iKBpSN36FmouxVoUQQoj8UqDF++DBg/Tq1Ys6derQrl07fv75waPN2IrG3Q/n5s9hjjuHcd8yW4cjhBBCWBRY8TabzQwdOpR+/frx999/M2XKFMaOHcuJEycKKoSHpg+ph75KS4yHVpMZddTW4QghhBBAARbv69evk5CQgKqqqKqKoijodLr73p9nDwyNnkHjE0za5tmYU67bOhwhhBCi4Iq3j48Pffv25Z133iEsLIxevXrxxhtvUL58+YIKIVcUnQHnNi+jGlNJ2/Q1qtlk65CEEEIUcQXW2txsNuPk5MSUKVNo3749Bw4c4NVXXyUkJISmTZtatY773bCeGwEBHlbOWJWkTi8St3Im2qMr8GszIE/jyAtW52LnJA/74ih5gG1ziY3VoNPlzbFSXq3H1qzNIykpq3MdDw/7/Sw+6j7RaDQP/fkssOK9bt06Dhw4wNtvvw1A/fr1eeKJJ1i8eLHVxTu/e1i7r+J10VdtzfVdv5HuXhJ9SP08iSMv2Lr3qLwiedgXR8kDbJ+L2WzOk57R8ruHtddee5lSpUrz5pvv3vXeSy8NolGjJgwYMDjH5Zs2rcvcuQupWDGUdu2a8fXXcylfvsJd802cOAY3N3dee23kA2Pq3ftxpk2bScWKofzww3ecOXOaMWMmPlxi93H5cjS9e3djzZrNufqCkBf7xGw23/X5tJse1q5cuYLRaMw2TafTZRuSzd4ZGvVBE1SBtD/nYLp6ydbhCCFEnure/Qk2bFh3a1jPf507d5YTJ47RpYv1Q1+uX7/tnoX7YV27lmh53r//oDwt3IVZgRXvJk2aEBkZyeLFi1FVlaNHj7JkyRI6d+5cUCE8MkWrw6XtKyh6A6nrZ6AaU2wdkhBC5JnmzVtiMDixffuWbNN//30FzZu3wtfXj+++m82zz/aiXbvm9OjRiSVLFt1zXU2b1iUy8iQAp06d4PnnB9C2bVPeeOOVbAU5KSmJceM+pHfvbrRp04T+/Z9i3749AAwa1BfIOurfuHEdc+Z8w7vvZh2tZ2ZmMmfON/Ts2ZlOndrw9ttvcPlyNAD79++jb98nmTXrczp1akOPHp2YM+ebXP1OTpw4zquvvshjj7Xk6ad7smzZv7c479y5nWee6cVjj7Wkf/+nWLt2FZB1JP3FF1Po2rU9Xbq0Y8SIYVy6FJWr7eekwA57K1asyMyZM/n888+ZPHky/v7+jBw5krZt2xZUCHlC4+aDc9tXSP19EqmbvsGl/WsoGse4BiWEKBgZp3aQcXLrQy+nKAoPO5aUPrQ5+kpNrJpXp9PRqVM3Vq/+nTZt2mfFmpHBH3+sZuzYj1m//g/++GM1M2fOxt8/gB07tvL++6No3bo9/v7+91yn0WjknXdG0q1bD776ag5//72XUaPeoGfPrC5NZ836grS0VObPX4JOp+Obb75k+vTPWLBgCd99t4CmTevy9dffUbFiaLYCPGfON2zfvoUZM77B3z+Ar76awahRrzN3btaXifPnz9KsWQt+++0P9u/fx8iRr9KmTXvKli1n9e8uMTGR119/mYEDhzBt2pecOXOaUaNew8PDgzZt2jN27P8xduzH1K/fiL17d/PBB2/TvHkr9uz5i127drJw4VJcXV2ZPHkC8+fP5Z13PrB62w9SoOesW7RoQYsWLQpyk/lCVzwUQ+NnSd8xH+O+ZRjq97J1SEIIkSe6d+/J00/3ID4+Dn//ALZv34KXlxe1a9clOfkmtWvXxd/fn4SEeHQ6PSaTicTEqzkW7yNHDpGSkkK/fs+h1Wpp0KARTZr8287p+edfQq93Qq/XExNzBXd3d+Lj4x4Y5x9/rOaVV16jRImSALzyymt07tyWY8ey+uRQFIWBA4eg0+moX78hfn7+REVdfKjivWPHFvz8/Hn66awzAKGhlenV6xlWrfqNdu0ew9XVjfXr12IwOBMeXofVqzeh0Whwc3MnPj6eP/5YTePGTXnnnQ/Q5PFBXuG54Gxn9FVbY064iPHg72h8S6Kv0NDWIQkhCgl9pSZWHw3fqSCGBC1WrDj16jVg7dpV9O07kJUrV/D441kHKCaTiRkzprB37x4CAgKoUiXs1lI5nw1ISEjAz88PrVZrmRYcHIzJlLVMfHwcX3wxlXPnzlCyZGl8ff3uu77bEhOvEhRU3PJar9fj7+9PbGwMvr5+uLm5YTAYLO/rdDrM5of73SUmJmbbBkDx4sWJjY0BYNq0L/nuu294552RmM0mOnfuxtChr1GvXgPeeOMtVqxYxsyZ0wgOLsErr7xO06bNH2r79yPne3NJURQMTfqiLVaJtC1zMMWdt3VIQgiRJ7p3f4K1a1cRGxvDkSOHeOyxrLZJX389E6PRyPLlq5g37yeGDn3tgevy9/cnPj6OzMx/x4iIi/v3yHr06Pdo2LAxK1eu55tv5lq29SBBQcW4ciXa8jojI4O4uLhbxT9vBAUVIybmcrZp0dGX8PX1Iy0tjbi4WCZMmMSqVRuYPHk669f/wcaN64iOvkRoaGW+/vo7Vq3aSKdOXfnww3ez/Q4elRTvR6BodTi3G4bi4knqus8xp1yzdUhCCPHIGjduSmpqKl9/PZM2bdpZbqFKTr6Jk5MTWq2OGzdu8MUXU4CswpmTGjVq4ePjy5w535CRkcH+/fvYuvVPy/vJyckYDM5oNBqioi4yb9632dan1+u5efPmXet97LHOzJs3h0uXokhPT+fLLz/H29ub6tVr5irn+Pg4YmNjLI9r167RsGETrl+/xuLFC8nMzOTUqRMsXbqYdu0ew2TK5J13RrB+/R8oikJgYBCKouDp6cn+/Xt57723uHLlCm5ubnh4eOLu7p7t7MOjktPmj0jj4olL++Gk/DaB1PUzce3yNorWvrt8FUKI+9FoNHTt+jjffvs13377g2X6kCEvM2HCR3Ts2Bp3d3dat25H+fIVOXv2DFWrVrvnunQ6HZMnT2fSpPF07NiK8uUr0rx5S8v777zzAV98MYXZs2fh7+9P9+49+eqrGVy6FEWJEiXp3Lk7b745nGHD3si23r59B5KRkcHw4S+RlJRE9eo1mTbty1x3ud2v35PZXtev34ipU2fw2WczmDlzGt99NxsPD0/69OlH9+49ARg/fjJffz2DiRPH4ebmRs+evWnUqClms5kLF/7hxRcHkJKSQunSZRk/fhKKouQqtntR1IdtumhDNu2k5QEyzu4hbcMsdJWa4dxiUJ7upAexdQcUeUXysC+OkgfYPpcrV/6hWLEyj7yegrjmXRAcJQ/Im1zu9fmwm05aHJ0+pD5OtbuReWobGUfX2zocIYQQDkyKdx5yqvM4urK1Sd/1owwhKoQQIt9I8c5DiqLBueXzaLxLkLrxK8zXY2wdkhBCCAckxTuPKU4uuHQYDkDqH5+jGlNtHJEQQghHI8U7H2g8A3FpNwzz9Sukbvoa9SE7BhBCOJ5C1DZYFKDcfi6keOcTXXAVDI37YLpwCOO+ZbYORwhhQxqNFpMp7zroEI4jI8OIVvvwd20/VPG+/Q0hISGBtWvX8s8//zz0BosSfdU26Cu3wHjwdzJO77J1OEIIG3FxcScp6RqqKmfhRBZVVTEa07l2LQ53d++HXt6qcn/48GFef/11PvnkEypVqkSvXr2Ij49HVVVmzJhBq1atHnrDRUFWF6r9MF+7TNqWOWg8A9EGhtg6LCFEAXN39yIxMY6YmCis6bc7JxqN5qH757ZHjpIHPFouWq0ODw8fXFzcHnpZq4r3J598QsOGDalSpQpLly7FZDKxe/duli9fzvTp06V438ftLlRTfh1H6h/Tce0xGo173vW9K4Swf4qi4Osb+MjrsXVnM3nFUfIA2+Vi1WnziIgIhg4dioeHB5s2baJly5a4urrSsmVLzp07l98xFnoaF09cHnsd1ZRB6ppp0gJdCCHEI7GqeHt4eHD9+nWuXr3KgQMHLGNy//PPP/j6+uZrgI5C61MCl7bDMF+LJnXjLFSzydYhCSGEKKSsKt4dOnRgxIgRDBo0iKCgIJo3b87q1asZNWoUXbp0ye8YHYauZBiGpv0xXTxC+s6FcuuIEEKIXLHqmvd7773H/PnzuXTpEn379kWv15OcnMxzzz3Hc889l98xOhSnKi0xX79CxuG1aLyL41Stna1DEkIIUchYVby1Wi0DBw60vDYajVSuXJly5cqh0cit4g/L0OBJ1BuxpP+1CI1HALoytWwdkhBCiELEqsr7zz//0LdvXw4ePEhqaiq9e/emd+/etGrVioMHD+ZziI5HUTQ4t3oRjV8ZUjd+hSnhgq1DEkIIUYhYVbzHjcsaaLxEiRKsWLGCK1eusHbtWp555hk++eST/I7RISl6Ay4dXkMxuJG6djrm5ERbhySEEKKQsKp4//3337z77rsEBASwYcMGWrZsSdmyZenduzfHjx/P7xgdlsbNJ+sWMmMKqX9MR81It3VIQgghCgGrireTkxOZmZmkpKSwd+9emjdvDkB8fDweHh75GqCj0/qVxqXNS5gTLpAmg5gIIYSwglUN1po2bcr//d//4ebmhrOzM61ateKvv/5iwoQJtG7dOr9jdHi60rUwNOpD+s6FpO/6EUOjPiiKYuuwhBBC2CmrjrzHjh1LtWrVMBgMzJ49G1dXV44cOUL9+vV577338jvGIsGpWjv01dqTcXQ9xoOrbB2OEEIIO2bVkbebmxv/93//B8DNmze5ceMGL7zwQr4GVhQZGj2NmpaEce9SFBcPnCq3sHVIQggh7JDVg4guWrSIb775htjYWAD8/Pzo37+/FPE8pCganFsOJjU9mfRt36MY3NGXq2PrsIQQQtgZq4r3nDlzmDVrFi+99BJ16tRBVVX279/P//73PwwGAwMGDHjgOn777TdGjx6dbVpaWhqNGjXiu+++y130DkjR6HBp+wopqyaTtukrlI4j0QVXsXVYQggh7IhVxXvhwoWMGTMmWz/mderUoUSJEkyfPt2q4t2tWze6detmeX3s2DEGDRrEqFGjchG2Y1P0Blwfe4OUlRNJ/eNzXLu+i9a/jK3DEkIIYSesarCWmJhI9erV75oeFhbGlStXHnqjGRkZvPnmm7z66qtUrlz5oZcvChRnd1w6voni5ErqmimYr8fYOiQhhBB2wqriXbFiRdauXXvX9NWrVxMSEvLQG124cCHOzs706dPnoZctSjTuvrh0fhPMZlJWf4Y55ZqtQxJCCGEHFNWKcSm3b9/Oiy++SLNmzQgPDwfgwIEDbN++nRkzZtCqVSurN2g0GmnTpg1jxoyRe8StlHYpkssLP0LvU4zi/caidXazdUhCCCFsyKriDbB3717mz5/P6dOncXZ2JiQkhCFDhjz0ae+NGzcyduxYNm/e/NAjkiUk3MRszpsxsAMCPIiLS8qTdRWEzKijpK6dhiagHK6d3kTRO1veK2y55ETysC+Okgc4Ti6Sh/3Jr1w0GgU/P/cc37f6VrF69epRr169bNOMRiPR0dEEBwdbHdDGjRvp2LGjDCX6kHQlq+Hc5mXSNswi9Y/PcXnsDRSdk63DEkIIYQOPVEF3795NmzZtHmqZQ4cOUbt27UfZbJGlL1cX55ZDMEWfIHXDl6imTFuHJIQQwgYK/PD30qVLBAYGFvRmHYa+YmMMzQZgunCItM3foJpNtg5JCCFEAbP6tHleOXjwYEFv0uE4VWkJGemk7/qRNK0Tau/XbR2SEEKIAlTgxVvkDacaHVAz0zHuW0bCWnfUOk/LSGRCCFFE5Fi8U1NTH7hwenp6ngYjHo5TeFfISOPG/tXoMxUMDZ6SAi6EEEVAjsU7PDz8gYVAVVUpFjakKApO9XvjrFe5sW8Nis6AoW4PW4clhBAin+VYvH/44YeCjEPkkqIo+LUfRGrSTYz7V4BOj6FWlwcvKIQQotDKsXjXr1+/IOMQj0BRNBiaPYdqysS4ZymKRo9TjQ62DksIIUQ+kQZrDkLRaHBuOYQ0Uwbpu34ErQ6nsIe7B18IIUThIN2cORBFo8W5zUvoyoSTvmM+xhNbbB2SEEKIfCDF28EoGh3ObYeiLVWd9K3fk3Fym61DEkIIkcekeDsgRavHpd2raEtUJW3Ld1LAhRDCwVh1zfvq1avMmjWLiIgIMjMz+e9AZEuXLs2X4ETuKTonXDq8Ruq6L0jb8h2qasapcgtbhyWEECIPWFW833vvPQ4fPky3bt1wd895iDJhXxSdEy7th5O6fgbpW+eCqmZ1rSqEEKJQs6p479u3j1mzZsntY4WQonPCpd2rpK6fSfq277MKeNVWtg5LCCHEI7DqmrenpyceHh75HYvIJ1lH4K+iLV2T9O3zSD+42tYhCSGEeARWFe/hw4czduxYDh8+zI0bN0hNTc32EPZP0epxaf8qugoNMe5ZQtquxXe1XRBCCFE4WHXa/JNPPiEpKYmnnnrqnu8fP348T4MS+UPR6HBu9QLpTm5kHF4D6TcxNBuIotHaOjQhhBAPwari/cUXX+R3HKKAKIoGQ5O+KM7uGPevQE1Pwbn1iyg6J1uHJoQQwkpWFe/bDdXS0tI4f/48qqpSqlQpaXleSCmKgqFuDxRnd9J3LiR17TRc2g9HcXKxdWhCCCGsYFXxNplMTJ06lR9++MFyn7der6dHjx58+OGH6HTSRXph5FStHYrBjbQ/vyXl90m4dByBxsXT1mEJIYR4AKsarE2bNo3ffvuNSZMm8eeff7JlyxYmTZrE1q1b+fLLL/M7RpGP9BUb49JhOObES6T+NhHzzQRbhySEEOIBrCrev/76K+PHj6dTp04EBQURFBREp06dGDNmDMuWLcvvGEU+05WuhUunNzGnXCdlxQRMidG2DkkIIcR9WFW8U1JSKF269F3TS5UqRWJiYp4HVRDS0jNtHYJd0RUPxbXrO2DOJPW3iZhiz9o6JCGEEDmwqnhXr16dhQsX3jV94cKFhIWF5XlQ+e3khUSe/XANZy5dt3UodkXrXwbXbu+DkzMpqyaTeemYrUMSQghxD1a1NHvzzTfp378/e/bsoVatWgAcPHiQS5cu8e233+ZnfPmidJAHnu4G5qw6zkfP1cNJL/c536bxCsK12/ukrp5C6pqpOLccgr5CQ1uHJYQQ4g5WH3kvW7aMJk2acPnyZRITE2nVqhVr1qwhPDw8v2PMcy4GHa89VYsrV1NYvk1OD/+Xxs0H167voA0qT9qmr0k/+Lv0xiaEEHbE6nu8ypUrx9tvv52fsRSoWpUCaRVegnV7LlKrgj+hpX1sHZJdUZzdcen0Jml/zsG4ZynqjXgMTftJb2xCCGEHcizevXr1Ys6cOXh5efHEE0+gKEqOKyms43n3blWeY+evMnvlMT56rh4ertLL2J0UrR7n1i9g9PDHePB3zMkJuLQZKp25CCGEjeVYvFu2bImTU1Yxa9Uqb4aQjI2N5aOPPmL37t0YDAaefPJJXn/99TxZd244O+l4qXs1Jszfx7e/H+e13jXQ3OdLSlGkKBoM9XuhePiTvv0HUn6biEuH4Wg8AmwdmhBCFFk5Fu9hw4ZZnjdo0IBatWqh1+uzzWM0GtmyZYvVGxs6dChhYWHs3LmT2NhY+vXrR/ny5enatWsuQs8bZYp58HSbiixYd4p1ey7yWIO7b4kT4FSlJRoPf1I3zCJl2Ric272CLriKrcMSQogiyaoGa/379ycpKemu6RcvXmTkyJFWbejQoUNcvHiR//u//8NgMFCqVCnmz59PgwYNHi7ifNAqvAR1QgP4ZcsZuX3sPnQlq+HW40MUF09SV32KMWKDNGQTQggbyPHIe9GiRcyYMQMAVVXp1KnTXde9U1JSqFq1qlUbOnr0KJUqVWLmzJksW7YMg8FAnz59GDRo0COEnzcUReG5jpX56Mpevl4RwUeD6uHmrH/wgkWQxqsYro9/QOqmb0jfsQBzwgUMTfqhaOX3JYQQBSXH4v3kk0/i6uqK2WzmvffeY+jQoXh4eFjeVxQFV1dXGjVqZNWGrl+/zt9//039+vXZuHEjZ8+eZciQIQQEBFh92tzPL29HMQsI8Mj2+t2B9Rk1YxuLNp7m3QH17ttIz978N5f85YHa930Styzm2o6laG7GEPTEW+jcH73FfsHmkX8kD/vjKLlIHvbHFrkoqhXnPffs2UPt2rUfafSwb7/9ltmzZ7Nnzx7LtOnTp3P27FmrxwtPSLiJ2Zw3p2kDAjyIi7v7UsAfey6weNNpnm1XiTZ1SubJtvJbTrkUhIyze0j781sUgxsu7YejDSiX63XZMo+8JHnYH0fJRfKwP/mVi0aj3PeA1apqXK9ePTZs2MDp06cxmUyW6UajkYiICObMmfPAdYSEhJCamorRaLS0Yr9zXfaifb1SnPgnkcWbIqlQwosyxRzn22F+0IfUR+NVjNQ/Piflt4k4NxuIvlITW4clhBAOzaoGaxMnTuT1119n9erVzJo1i40bNzJ//nz+97//ERwcbNWGmjRpgq+vL5MmTcJoNHLy5EmWLl1K586dHymBvKYoCoO7VMXD1YmvVhwlVQYweSCtX2lce4xGG1ietD//R9q2eaiZRluHJYQQDsuq4r169WomTpzIypUrCQ4OZtq0aWzbto2WLVvi7Oxs1YYMBgMLFizg4sWLNGvWjCFDhjBkyBA6dOjwSAnkB3cXPS92CyP+Whrz1p6QFtVW0Lh44tL5LZxqdiLj+GZSfpuI+UacrcMSQgiHZFXxvn79OvXq1QMgNDSUw4cP4+TkxLBhw9i4caPVGytVqhSzZ89m9+7dbNu2jcGDB+cu6gJQqZQ3PZqXY8/xWLYekvGtraFotBgaPIlL+9cw34ghedloMv85YOuwhBDC4VhVvAMDA4mOzipgZcuW5fjx4wB4eHhw9erV/IvOxjo2LENYWR8WbYgkKvamrcMpNHRlw3HrOQaNZwCpf3xO+p6fUc32175BCCEKK6uKd9euXRk1ahS7d++mefPmLFu2jGXLlvHZZ58REhKS3zHajEZRGNI1DFeDTq5/PySNZyCu3d5HX7klxoOrSF01GXPKNVuHJYQQDsGq4j18+HCefvppUlJSaNCgAX379uXTTz8lMjKS0aNH53eMNuXl5sQL3cKIuZrK/1Yey7Nb1YoCReeEc/OBOLd8HlPsOVJ+GU1m9AlbhyWEEIWeVfd524uCuM87Jxv/jmLh+lM8Vr80T7aukCcx5JXCcM+k6WoUqetnot6IwaleL5xqdkRRsn93LAx5WEPysD+OkovkYX/s7j7vmTNnWr2ROwcxcVRt6pTkSkIKa/dcoJifK81rWneLnMii9S2JW4/RpG2di3HPz5hjTuPccgiKwc3WoQkhRKGTY/HevHmz5bmqqhw7dgwvLy8qVqyIXq8nMjKSxMREGjZsWCCB2oOn21YgJjGF+X+cJNDbhcplHr070KJEcXLBuc3LZBSrSPqun0he9hEubV9BG1DW1qEJIUShkmPx/uWXXyzPp0+fTrly5ZgwYYLlvm6j0ciYMWPQarX5H6Wd0Go0vNS9GhMX/M2Xy4/wf/3rEuTrauuwChVFUXCq1g5tYAip678kZcU4DA2eQl+tna1DE0KIQsOqBmsLFixg6NCh2TpkcXJyYsiQIaxcuTLfgrNHrs46hveqgaIoTF96mOS0DFuHVChpA8vj9sRYdKVqkP7XIlL/+BxTimNcAxNCiPxmVfF2c3Pj1KlTd00/dOgQPj5F79RxoLcLw3pWJ/5aKrOWHyXTZLZ1SIWS4uyOc/vhGBo/iynqKFHfjiDz8klbhyWEEHbPqoFJBgwYwPvvv09ERARVq1ZFVVUOHTrEkiVLeP/99/M7RrtUqZQ3AztWZs6q4yxcf4r+HUIL1RCi9sJyGr1YRTL+/IbU3z/BqfbjOIV3RdFY9d1SCCGKHKuK96BBg/D29uann35i4cKFQFY3qZ988gmPPfZYvgZoz5pUL87lhBRW7/qH4n5utK9XytYhFVpa/7IEDvqUqF+/xPj3ckyXInBu+TwazwBbhyaEEHbH6gG6e/bsSc+ePfMzlkKpZ4sQrlxNYfGmSIr5ulCjvL+tQyq0NAYXnFu9QGbJaqTtWEDyLx/g3KQvuopN5KyGEELcIcfiPXnyZIYNG4arqyuTJ0++70pGjRqV54EVFhpF4fkuVflk4X6+WhHBO31qyxjgj0BRFPSVmqAtHpo1vOif36L75yDOzQaiOOfcYYEQQhQlORbvI0eOkJmZaXmeEzkiAoOTluG9ajBx/t9MW3KQd/vVIchHbiF7FBoPf1w6v43x8FqM+34hOeY0zi0GoStVw9ahCSGEzUn3qHnockIyHy/Yj7OTlvf61cHb3ZCn68+Jo3Q1mFMepvh/SNs8G3PiJfRV22Bo+CSKrmB+t7nh6PujMHKUXCQP+2N33aOePn3a6o1UqGBffX3bSnE/N17vXZNPfzzAtCWHeLtPbVydrW5WIHKg9S+Da4/RpO9ZSsbRdVmN2Vq9gDbQcUe0E0KI+8mxsnTp0gVFUXjQgbmiKJbxvQWEBHvySs9qfP7zYWb8cpgRT9VErys6vdDlF0XnhHPjPujK1CLtz29JWTEep/AuOIV3Q9HKFyQhRNGS43+9jRs3FmQcDqVaOT8Gd67C7JXHmP3bMV5+vBoajbQNyAu6ElVx6zWOtL8WYdz/G5n/HMK51RC0vnKbnhCi6MixeJcoUeKBCxuNRiIiIqyat6hpGFaMpJQMftwYyfx1J6UTlzykGNxwafk8GWVrk75tHinLPsIpvBtO4Z1RNHIULoRwfFb9pztw4ACjR4/mzJkzmM3ZuwJVFIVjx47lS3CFXbt6pbiebGT1rn/wcnPi8WZyjTYv6cvWQVusEuk7F2L8ezmZ5/fh3GIIWv8ytg5NCCHylVX9T06cOBFfX1+mTp2Ks7Mzn376KSNGjMDV1ZUpU6bkd4yF2hMtQmhaozi/7TjPpv1Rtg7H4WicPXBp/RLO7V9FTblOyvKxpO9bhmrKtHVoQgiRb6w68j558iRLliyhcuXKzJs3Dz8/P7p06YKfnx/z5s2jY8eO+R1noaUoCgMeC+VmSgYL153Cw9WJepUDbR2Ww9GXrYOuWOi/18LP7ce52QC0xSraOjQhhMhzVh15a7Va3N2z7jcrW7YsJ06cAKBBgwYPdUtZUaXVaHixexjlS3rxv5URHD9/1dYhOSTF2R2XVi/g0uF1VGMyKb9NIG3LHMxpjnE/qRBC3GZV8a5ZsyaLFi3CbDZTuXJltm7dCkBkZCR6vT5fA3QUBr2W13rVIMjXlS+WHeGfK1JQ8ouuTC3cnvwYfY2OZJzaSfLidzAe/xNVlaFbhRCOwariPWLECJYtW8bcuXPp3r07p0+fpk2bNrz++ut06dIlv2N0GG7OekY8WQt3Zz1TFh/kQowU8Pyi6J1xbvgUrk+MRetbkvRt35OyYjym+PO2Dk0IIR5Zjt2j9urVi8cff5xOnTrh6+tLSkoKaWlp+Pr6EhcXx7p16/Dx8aFjx44FdguUvXePaq3YxBQmLTpARqaZN5+uRemgRxvIxFG6GsyvPFRVJTNyJ+m7F6OmJaGv2hpD3Z4oBrc83xbI/rBHjpKL5GF/bNU9ao5H3tWrV+err76iefPmvPzyy2zfvh0PD49bwQbw7LPP0qlTp4cq3EuXLiUsLIzw8HDLY/ny5Q+RjmMI9HHl7T7h6HUaPvvpIBdjb9o6JId2e6Qytyc/Rl+1NRnHNpG85F0yInc+sAdBIYSwR/cdmMRsNrNjxw5Wr17N+vXr0Wq1dOjQgR49ehAeHv7QGxs7diyurq68+eabuQrWUY68b4tJTGHyrSPwUc+EUzIwd0Ne2kMueaGg8jDFnydt2w+Y486iLR6KoUl/tL5519GQ7A/74yi5SB72x+6OvLMW1tCsWTM+/vhjdu7cybhx47h+/TrPPfcc7du3Z+bMmVy8eNHqYCIiIqhSpYr10Tu4IB9XRt06Ap/84wGi5Ai8QGj9y+L6+P9haDYQ09UoUn75kPTdS1Az0mwdmhBCWMWqBmsATk5OtG/fns8//5ydO3fy+uuvs2nTJtq3b2/V8iaTiZMnT7JixQqaNm1Ku3btmD17dpE/bRnk48qoZ8LRaZWsAh4nBbwgKIoGpyots06lV2qM8dBqkpe8R8a5fUX+MymEsH8PPZ734cOHWbNmDevXrycxMZH27dvz8ccfP3C5uLg43njjDfr06UPbtm05c+YMQ4cOZciQITz77LO5TsBRRMfd5N1ZO8g0mZn4chPKFPe0dUhFSlrUCeLXzMYY+w8uIeH4dxiM3re4rcMSQoh7sqp4HzlyhDVr1vDHH39w5coVmjRpQvfu3Wnbti0GgyHXG58zZw5bt25l3rx5Vs3vaNe8/yvmagqTFu3HZFZ565lwSgZYdw3cHnPJDVvnoZpNZERsJH3fMjBn4lSzM061OqPonB5qPbbOI684Sh7gOLlIHvbHVte8c+we9ciRI6xdu5a1a9cSHR1NlSpV6N+/v6Vb1IcVGRnJmjVrGD58uGVaRkbGIxV/RxPk68qoPrWZtGg/n/54gFHPhFPCygIuHp2i0eJUvT26kHqk71qMcf8KMiJ3YKj3BLryDVAUq68yCSFEvsrxv1Hv3r1ZvXo1nTt35vfff2fZsmUMGDAgV4UbwNPTk7lz57JkyRLMZjNHjx5l/vz59OzZM9fBO6Jivq683ac2Go3Cpz8e4FJ8sq1DKnI0bj64tHkJl86jUJxcSdv0DSnLxpAZddTWoQkhBHCf0+a7du2iYcOGebqxv/76i08//ZRz587h4+PD4MGDH+p6t6OfNr/T5YRkJv94ANWs8laf2pTwz7lDEXvPxVr2mIeqmsk8vStrpLKkeLQlwjA06I3Wv2yOy9hjHrnhKHmA4+QiedgfW502f+gGa7ZUlIo33Crgiw5gVlVGPpVzT2yFIRdr2HMeqimDjGObMO5fiZp+E135hhjq9UTjefcIcfacx8NwlDzAcXKRPOyPXd7nLWyruJ8bbz9bG71Ow6RF+zl18ZqtQyqyFK0ep+odcHtmMk61upB5fj/JS94lbedCzKk3bB2eEKKIkeJt54r5uvLus3XwcjMwZfFBDp6Ot3VIRZri5Iqhfi/cnp6EvlJTMiI2kPzjW6TvXiJDjwohCowU70LAz8uZd/pmXfee+csRdh69bOuQijyNmw/OzZ/DtfcEdGXCMR5ak1XE9yzFlCJFXAiRv3K8VWzmzJlWr2TYsGF5EozImaerE289E87MZUf49vfjJKdm0q5eKVuHVeRpvYNxafMSpvCuGPevwHhwFReObUQX2hynsLZoPANsHaIQwgHlWLw3b95sea6qKseOHcPLy4uKFSui1+uJjIwkMTExz1uki5y5GHS83rsGs387xo8bI0lKzaBHs3K2DksAWt8SuLQdiulqFMrxtSQf3UDG0XXoytRGX7092mKVCmzoXCGE48uxeP/yyy+W59OnT6dcuXJMmDABZ2dnAIxGI2PGjEGr1eZ/lMJCr9Py8uPVmLf2BL/vPE9yagav9alj67DELVrfkgQ8/gYxNXtmtU4/vpnM83+j8S+DU7X2WZ29aHP8sxNCCKtYdc17wYIFDB061FK4IWugkiFDhrBy5cp8C07cm0ajMLBjZTo2LM3mA5eYsvBvMk1mW4cl7qBx98VQvxfuz07F0GwgZGaQ9uf/SF40kvT9K6SFuhDikVh1CODm5sapU6coX758tumHDh3Cx8cnXwIT96coCr1bVsDdRc/Pm88Qn5jCKz2q4+osR3X2RNEZcKrSEn3lFpguRWA8sg7jvuUYD6xEX6Ex+urt0PpK2wUhxMOx6j/9gAEDeP/994mIiKBq1aqoqsqhQ4dYsmQJ77//fn7HKO6jY4MylCzmyReLD/LJwr95vXdNfD2dH7ygKFCKoqArWQ1dyWqYrkWTcXQDGSe3k3FyK9oSVXGq1h5t6RrSf7oQwipW97C2bNkyfvrpJyIjIwEIDQ1l4MCBPPbYY/ka4J2KWg9r1goI8ODPPf/w5fIjuBh0vNG7JiUDC9+AJo6yT6zNQ027ifHEFjIiNqImX0XxDMKpSkt0FRujcfUqgEjvz1H2BzhOLpKH/ZHuUa0gxfvebudyISaJ6T8fIj3DxNAe1Qkr62vr0B6Ko+yTh81DNWeSee5vjEfXY445DYoWXZma6EOboy1VHUVjm0ahjrI/wHFykTzsj90NCXonVVXZsGEDR48eJTMzk//W+1GjRj1alCJPlA7y4P1+dZm+9BDTlxyiX4dQmtcMtnVY4gEUjQ59+QboyzfAlBhNxsmtZEbuJPP8fhQXL/SVmqAPbYbGu7itQxVC2AmriveECRNYtGgRlStXxs0t++hWcu+qffHzcua9vnX4asVRvl9zgpirKTzRsjwa2U+FgtYnGG3Dp1Hr9yLzwiEyTmzDeHgtxkOr0fiVQV+hAbryDdC4525oXiGEY7CqeK9du5YxY8bQu3fv/I5H5AEXg47XetVg0fpI1uy+QOy1VIZ0qYpBL/fkFxaKRoe+bB30ZetgTrlG5pndZJzZTfruJaTvXoImMATdrfc13sVsHa4QooBZVbwzMjKoV69efsci8pBWo6Fv+0oE+bqyeGMkk2/sZ/gTNfByN9g6NPGQNK7eOFXvgFP1DphvxJJxZg+Z5//GuOdnjHt+RuMdjK5sbXTl6qDxLytnw4QoAqy6L6Vnz5589913ZGZm5nc8Ig8pikL7eqUY9kR1LsUnM/6HfUTF3bR1WOIRaDwDMYR3wa3HaNz6TMHQuC+KqxfGQ6tJWT6G5EUjSdsxn8xLx1DN8vcqhKOy6sg7KiqKzZs3s2bNGoKDg9Hr9dneX7p0ab4EJ/JGeMUA3nm2Np8vPczE+X/zQrcwalXwt3VY4hFp3P1wqtYWp2ptUdNuknnhEJnn/ybjxDYyIjaCwQ1dqeroSlZDW7IaGldvW4cshMgjVhXv0NBQQkND8zsWkY/KFvPkg/51mfHLEWYsPUzPFiF0alhGTrE6CMXZPatVeqUmqJnpZEYdJfP8fkwXj5B5ehcAGt+SaG91FKMtVglF52TjqIUQuWVV8ZYhPx2Dr2fWuOBzVx/nly1nuRh7k+c6VZGGbA5G0Rksjd1U1Yw54SKZURGYoo5k9ex2eC1odWgDQtAWq5T1CCqPYnB78MqFEHbhoe7zPn36NCaTyTLdaDQSERHBnDlz8i1AkbcMei0vdgujVKA7y7ac5XJCCq/0rE6gt4utQxP5QFE0aP3LoPUvA7U6oWakY7p8ksxLEZiuRGI8tAYO/g4oWUfmxSpmPYIqoPoXvl76hCgqrCreEydOZNGiRYSEhHDmzBlCQ0OJjo7mxo0b9OrVK79jFHlMURQ6NypLqUAPZv8Wwdi5e3mhWxg1ysu9w45O0RvQla6BrnQNgKxiHncW05VTmK5EkhG5k4xjmwD4x8UDxa8M2oByaALKovUvh+LmI5dahLADVhXv1atXM3HiRLp3707btm2ZNm0awcHBvPbaa9mGCRWFS43yfnw4sC5fLj/K5z8fonuzcnRpXFY6dClCFL0BXXAVdMFVAFDNJsxXozDFnsEp6RLJUZEYD64CNWvIWcXZA41faTR+pdD6lkTjHYzGuziKk5y5EaIgWVW8r1+/brnPOzQ0lMOHD1O2bFmGDRvGq6++KiOLFWKBPq68168OP6w9wa/bznH+chJDulTB1Vn/4IWFw1E0Wstp9oAAD4hLQs00Yr56EVPcOczxFzAlXCAjYgMZpn9vRVPcfNF4F0fjE5xV0H1uPZw9bJiNEI7LquIdGBhIdHQ0wcHBlC1bluPHj9OtWzc8PDy4evVqfsco8plBr2VIl6qEBHvx08ZIxn6/j6E9qlE6SP7xClB0TmgDy6MNLG+ZpppNqDfiMF27hDnxMuZr0ZgTo8k4sRUy0/9d2OCGxjMQjVcQGq/iaLyCUNz90Lh5o7j6oGhl/HkhcsOqv5yuXbsyatQoPv74Y5o3b87w4cOpWLEif/75JyEhIfkdoygAiqLQpk5JSge589WvR5kw/2/6tqtEMxnYRNyDotGieBfL6pq17L/TVdWMevOqpZibr8dgvhGL6Uqk5Za1bOtx8URx80Hj5pt1Pf2O55pbrxW9XJoT4r+sKt7Dhw/Hzc2NlJQUWrVqRd++ffn000/x9vZm0qRJ+R2jKEAVS3rz0XP1+ea3COauOUHkpev0bVcJJ7mdTFhBUTQoHv5oPPyhVI1s76mZ6ZhvxKEmJ2JOvoqanHjreSLmpHjMV05BevLdK3VyyV7QXbxQnN1RnD3u+oneWRrUiSJBxvN2APmRi9ms8uv2c/y+8zwlA9x5sXsYJfzz9z5gR9knkkfuqZnpqMnXLMXdnJyImnw12zQ1NQlU071XoNGiGO4s6FnP3Xz9SDU7ZZumGNyzGto5udhszPSHJZ8t+2PX43nnpRs3btCtWzeGDx9Oz549C3rzwkoajULP5iFUKOHFnFXHGDdvL/3ah9K4WjE5shH5RtEZULyC0HgF5TiPqqqQkYqadhM1LenW4+YdP29aXpsTo1HTkrh2ItnSYv6edE4oepd/i7mTa9bpeidXFKdb03V6FJ0ha16dE2hv/dT996cBRavP+qmxavgIIR5agRfv0aNHExMTU9CbFblUo7wfHz1Xn9m/RTBn1XGOnE2gX4dQ3KQ1urARRVFuFVVX8Ay0ahl/fzfiomKyinr6HYXemJr1yEgFYwqqMQ3VmJI1LSXR8j4ZabkLVqPLRdF3ujX/f5bTOZFm9MZ0Ix002qyzDBrdree6rLMHWt2/7ynyxcGRFWjxXr58OTdv3qRSpUoFuVnxiHw8DLz1TDird/3Diu3nOH3pOkM6V6VyGR9bhyaEVRRFYzllnhuqqoIpAzKNqJnpkJlx66cRNdMIpls/M//7Mx3VlJH1M9OYfbm0pKzLBP+Zn/tcyUx9qKSzijjaO4u8FhQNaDRZxf32Q/Pvc0WjBUW5Nf3W/Ipya/q95r9zPdbNf93DFWNyhmW68p95/p2uBc2tWBStlfNrbsV/j/m5vS5uPb99FlH5d5rledZrez3TWGDF++LFi8ycOZOffvqJIUOGFNRmRR7RaBS6NC5LWDlfZq88xqc/HqBDg9L0aBaCXiff8IVjUxQFbh8lk3/dxqqqCmbTf74M/PtlwctNy7XEm1nzmE1gzkS9/dyU+e9rUyaYTVnDwt45r8mUdfng9sOc9VP9z2tUM5gys2K4NT1rHtOt56rl+Z3rUlVT1pcP8615VfM9v4yk3yN3+5e9qN9+nXTHFwBdqRq4tH+1QKKxqnjPnDnzntMVRUGv1xMUFESzZs3w9fW953wmk4m33nqLt99+m4CAgFwHe7+L97kREOA49zEXVC4BAR7UCA3iu5URrPnrPKcuXmfks7UpXcwzz9bvCCQP++MoubjaOoCHlFXo7/gCYDah3v7SYP73C4BqNt36eee8/06/c/6713PHF4hs6zFZvphkTVctj3/baqv/fsFQ1azXtydb3lMtk7PNz53rAUPxCrgX0OfMquJ97tw5Vq9eTbFixQgLCwPg2LFjXL58mVq1apGUlMSECROYM2cO1atXv2v5WbNmUa5cOdq3b/9IwUpr83uzRS69W4RQMdiTuWuO8/q0LfRuWZ42dUo+0ikmR9knkof9cZRcHC+PHErQrQNbCsFJvTv3SSqQmkf7J09am2u1Wnr06MHYsWPR6bIWMZvNjB07FqPRyMSJE/nyyy/55JNPWLhw4V3Lr1q1itjYWNavXw9AcnIyY8aM4fDhw3z00Ue5SEvYg1oV/Rkb3IC5q4+zaEMkh88mMKhTFbzdDbYOTQghHJpV32vWr1/PkCFDLIUbQKPRMGDAANasWQNAt27dOHbs2D2XX7t2Lfv372ffvn3s27ePSpUqMXr0aCncDsDLzYnXetWgb/tKnLxwjQ/n7GH/qThbhyWEEA7NquLt7e1NRETEXdOPHDmCu3vWYf21a9dwcZGRhYoiRVFoXbskowfWw8/TmZnLjvD9muOkGTMfvLAQQoiHZtVp88GDB/PBBx9w7NgxqlevjtlsJiIigp9++onXXnuNqKgoPvjgA1q1amXVRlesWPFIQQv7FOzvxvv967Bi+zlW//UPJy5c4/muVSkf7GXr0IQQwqFYVbz79u2Lj48P8+fP56effkKv11OxYkUmT55Mu3bt2LNnD40aNWL48OH5Ha+wczqthidalKdaOV++/f0YH8/fT5fGZejSuCw6bSFofSKEEIWA9G3uAOw1l5S0TBauP8VfEVcoFejO4M5V7jvMqL3m8bAkD/vjKLlIHvbHrvs2V1WVDRs2cPToUTIzM/lvvR81atSjRSkckquzjue7VqVu5QB+WHuScfP20bmRHIULIcSjsqp4T5gwgUWLFlG5cmXc3LKPLGWvXccJ+xFeMYCKJb35aWMkv+04z/5TcTzXqQrliudNxy5CCFHUWFW8165dy5gxY+jdu3d+xyMclLuLniFdqlK3ciA/rD3B+B/20aF+aR5vWk7GChdCiIdk1bnLjIwM6tWrl9+xiCKgVgV/xg9pQLMaxVm7+wKj5+4lMuqarcMSQohCxari3bNnT7777jsyM+W+XfHoXJ31DOxYhZFP1yIz08wnC/azcN0pklMzbB2aEEIUCladNo+KimLz5s2sWbOG4OBg9PrsYzkvXbo0X4ITji2srC/jhtRn2ZazbPw7igOn43iyVQXqVQ6UthRCCHEfVhXv0NBQQkND8zsWUQQ5O+no064SjaoV48eNkXy9IoJthy/Tt30lgnwK2/hJQghRMKwq3sOGDcvvOEQRV664J5+91oKf153gly1n+ODbPXRpXIaODcrIeOFCCPEfORbvyZMnM2zYMFxdXZk8efJ9VyL3eYu8oNUotKlTktqVAli8KZJft53jr4gY+revRJWy9x4rXgghiqIci/eRI0csDdSOHDmS4wrk2qTIaz4eBl7qXo2m1RNYsO4Un/50kEZhQTzZuiJebk62Dk8IIWwux+I9f/78ez4XoqBUC/Fj7OD6rPrrH1bv+odDpxN4omV5WtQMRqORL41CiKLLqmveAAkJCZw8eZKMjLtv52nRokWeBiXEbU56LT2ah9AwLIj5f5xk/h8n2XoommfbVqJCSRmtTAhRNFlVvH/++WfGjBlzz/u8FUXh+PHjeR6YEHcq7ufGW8+Es/tYDD//eYaJC/6mUVgQvVpWwMfDYOvwhBCiQFlVvOfMmcOTTz7JiBEjcHfPeZQTIfKToig0DCtGrYr+rN71D2t3X2D/qXi6NilLu7qlpFW6EKLIsKp4X7lyhf79+0vhFnbB2UlHz+blaVq9OIs3nWbpn2fYejCa3q0qULuSvzSiFEI4PKsOVZo0acJff/2V37EI8VACfVx59YkajHiqJnqdhi+XH2HSogOcjb5h69CEECJfWXXkXblyZT755BM2bdpE2bJl7+oeVe7zFrZUrZwfVQb5sPVgNCu2n2P8D/uoVzmQJ1qEECi9tAkhHJBVxXvPnj3UqFGDtLQ0Tpw4ke09OUUp7IFWo6FV7ZI0DCvGH3susHbPBfafiqNleAm6NimLp6vcHy6EcBxWFW+5z1sUFi4GHY83C6FleAl+236OzfsvsePIZTo2LEP7uqUwOMnY4UKIwi/H4r1lyxYaN26MXq9ny5Yt912J3Oct7I23u4H+j1WmXb1SLP3zDMu3nmXT31F0a1KWZjWD0WmlZboQovDKsXi/+OKL7NixAz8/P1588cUcVyD3eQt7VtzPjVefqMHpqOv8/Odp5q87xZrdF+jetByNwopJT21CiEIpx+J957Xt/17nFqKwqVDSi3eerc3Rc1f5ZcsZ5qw6zupd/9C9aTnqVg5EI203hBCFyCOdOzQajRw4cCCvYhEiXymKQvUQPz4cWI+hj1cD4OsVEYyes4c9x2Mwq6qNIxRCCOtY1WDtwIEDjB49mjNnzmA2m7O9pygKx44dy5fghMgPGkWhbuVAalcKYO+JWH7bcY6vV0RQYsd5ujUtR53QADkSF0LYNauOvCdOnIivry9Tp07F2dmZTz/9lBEjRuDq6sqUKVOs3tjmzZvp2rUr4eHhtG3blp9++inXgQvxqDQahQZVgxg3uAEvdgvDrKp89etRRn+3h70nYuVIXAhht6w68j558iRLliyhcuXKzJs3Dz8/P7p06YKfnx/z5s2jY8eOD1xHbGwsw4cPZ+bMmbRo0YKIiAieeeYZqlevTlhY2CMnIkRu3S7i9SoHWo7Ev/r1KEG+rnRsUJpGYcWk33QhhF2x6j+SVqu19GtetmxZSwO2Bg0acPr0aas2FBgYyF9//UWLFi0wm81cu3YNrVaLm5tbLkMXIm/deST+UvcwDHoN3685wdtf72Tt7gukpt89qp4QQtiCVcW7Zs2aLFq0CLPZTOXKldm6dSsAkZGRd3WVej/u7u6kpqZSvXp1Bg0axLPPPkvZsmVzFbgQ+UWjUahfJYjRA+sx4qmaFPdzY8nm07w5ayeLN0USfy3V1iEKIYo4RVUffGHv8OHDvPDCCzz//PP06tWLLl264OTkREJCAr179+b999+3eoNms5nMzExOnjzJCy+8wIgRI+jdu/cjJSFEfjt1IZHlf55m55HLoKo0qFacrs1CqBbiJ10ECyEKnFXFOykpCa1WS1paGr6+vsTFxbFu3Tp8fHzo2LFjrv95TZs2jZMnT/L1119bNX9Cwk3M5rxpRBQQ4EFcXFKerMvWHCWXwpDH1RtpbNp/iS0HL5GclknpQHfa1i1Fg6qB6HVZXa8Whjys4Sh5gOPkInnYn/zKRaNR8PPLeRhuq06bd+vWjXPnzuHr6wtAQEAAzz77LJ06dbK6cO/Zs4eePXtmm2Y0GvH09LRqeSHsga+nM71aluezV5rQ/7FQMs0q360+zpuzdrJ861mu3Uy3dYhCiCLAqtbmAFYcoN9XlSpViImJYe7cufTv359Dhw7xyy+/MHPmzEdarxC2YNBraVmrBC1qBnPsn0Q27L3I7zvPs3rXPzQLL0HjKkGUL+Epp9SFEPnCquLdqVMnBg8eTMeOHSldujQGgyHb+88+++wD1+Hh4cHs2bMZP348M2fOpHjx4owfP5769evnLnIh7ICiKISV9SWsrC8xiSls3BfFjqOX+fPvKIr7udK8ZjCNqhWTIUmFEHnKqmverVu3znkFisLGjRvzNKicyDXve3OUXBwlD3dPF1ZvO8O2Q9Gcib6BVqNQs4I/DasGUaO8H076wjEsqaPsD3CcXCQP+2Ora945HnnPnDmTwYMH4+LiwqZNm/I8MCEclYtBR/OawTSvGUxU3E22HbrMnuMx7D8Vh7OTltqVAmhYNYgqZX3QaqTzFyHEw8uxeH/55Zc888wzuLi4FGQ8QjiUkgHuPNO2Ik+1rsCJC4nsOhbD3yfj2Hn0Cp6ueupVDqJBVbk+LoR4ODkW70dtoCaE+JdGo1C1rC9Vy/rSr30oR84msOtYDFsPR7NxfxT+Xs40qBpEgypBlAzM+VSZEELAAxqsXblyhfT0B9/6EhwcnGcBCeHo9DoNtSsFULtSAKnpmew/Fcfu4zGs2XWBVX/9Q4kANxpWDaJ+lSACvOXMlxDibvct3r169brvwqqqoigKx48fz9OghCgqXAw6mlQvTpPqxbmRbGTfyVh2HYvhly1n+WXLWcoV96BWBX9qVQygZICbnFoXQgAPKN4//PAD3t7eBRSKEEWbp5sTrWuXpHXtksRfT2XP8VgOnIpj+bZzLN92Dn8v51uF3J9KpbzRaaWxmxBFVY7FW1EUQkJC8PPzK8h4hBCAv5cLnRqWoVPDMly/mc6hMwkcjIxny6FoNvwdhYtBR7VyvlQP8aNaiC/e7oYHr1QI4TCkwZoQds7L3WC59Sw9w8Sx81c5EBnPkbMJ7D0RC0DpQHfCQnwJLeVDxZJeuBis7jxRCFEI5fgX3qNHj7t6UhNC2JZBryW8YgDhFQNQVZWLsTc5cjaBI2evsm7PRdbsuoCiQOkgD0JLeRNa2puKJb1xd7F+6F4hhP3LsXh//PHHBRmHEOIhKYpC6SAPSgd50LlRWdIzTJy5dJ1TF69x8sI1Nu2/xLq9F1GAEgHuhJb2JrSUN5VKeePpJt21ClGYybk1IRyEQa+13EsOkJFp4mz0jaxifvEa2w5Hs/HvKACK+7lmFfLS3oSW8sHHQ86yCVGYSPEWwkHpdVpCS/sQWtqHrkCmycz5K0mWI/Ndx2L482A0AIE+LlQq5W051e7vJfeXC2HPpHgLUUTotBoqlPCiQgkvOjUsg8ls5kLMTUsxP3Aqju2HLwPg52mgeoUAivu6UK64J6UD3QvNgCpCFAVSvIUoorQaDeWKe1KuuCcd6pfGrKpcikvm5IVETl68xuHTcfx5I/3WvAolAtwoV9yTssWyrrOX8HeTgi6EjUjxFkIAoFEUSgW6UyrQnbZ1SxEQ4MGps/Gcu3zD8th7PJYtt061KwoU83XNajQX6E6pIHdKBXrgJY3hhMh3UryFEDny8TDg45HVDztk9f8Qfz2NCzE3uRibxMXYm5yOus7uYzGWZbzcnCgV5E7pQA9KB2V9GQjycUWjka5dhcgrUryFEFZTFIUAbxcCvF2oExpgmZ6clsHFmJtciL1V1GNu8sf5C5jMWZ09Oek0lAx0p3SgOyUD3Snh70Zxfzc8XeUoXYjckOIthHhkbs56KpfxoXIZH8u0TJOZ6PhkLsbetByp7z0Ra2nhDuDhqifYz43gADeC/dwo4e9GsL+b3IcuxANI8RZC5AudVmPpRKZJ9axpqqqSmJROdEIy0XHJRCckcyk+mV0RV0hNN1mWdXfRE+z/bzG//fB01cvIakIgxVsIUYAURcHX0xlfT2eqlft30CNVVbl200h0fFYxj7712HUshtT0TMt8bs46ivu5EeTrQpCPK8V8XQn0yXpucJKW76LokOIthLA5RVFuNY4zEFbO1zL9zqIeHZ/M5aspXI5PJuLcVXYcuZJtHT4eBoJ8XAi8VdSDfFwI9HUl0Fs6nBGOR4q3EMJu5VTUAdKMmcQmphKTmErM1ZSsR2IqByLjSErJuGMdEODjSoCngSBfV4J8XLOO3H1d8fdyRquRcdFF4SPFWwhRKDk76SzX1P8rJS3DUtSvXE3hekomF65c56+I7KfhtRoFfy/nu4p6kI8Lvp7OaOT6urBTUryFEA7H1VlPueJ6yhX3BCAgwIO4uCRUVSUpNYPYq6lcuZpCTGKKpcifuJCIMcNsWYdOq7l1Gj6roFtOxfu44u3uJA3nhE1J8RZCFBmKouDp6oSnqxMVSnple+/29fWY20X9aqqluB85m0CmSbXMa9Brs11TD/B2zvrp44Kvh7N0SCPynRRvIYQg+/X1O+9XBzCbVa7eSCMm8d8j9tjEVC7GJHHgVJylMxr491R8gLcL/rcKe4CXy63Xzrg56ws6NeGACrR479ixgylTpnD+/Hn8/PwYPHgwTz/9dEGGIIQQD02jUfC/VYz/23DObFa5mpRGXGIqsdeyHvHX0oi7lsr5K0ncTM3INr+rQYe/d1ZxzyrqzreKvAt+ns7oddKATjxYgRXvy5cv8+qrrzJp0iTatGnD0aNHGTJkCCVKlKBZs2YFFYYQQuQpjUbB38sFfy8Xqtzj/dT0TOKupRJ3q6DHX896Hh2fzKHTCWSa/r3OrgDeHgb8vZzxv1XY/byyjtz9vZ3x9XMvsLyEfSuw4n3p0iW6dOlCu3btAKhRowb169dn//79UryFEA7LxZBzq3izqnL9pvFWcU8l/nqa5efJi4nsikhHvWN+rSbr1H6At8utop5V5P29s356uTtJC/kiosCKd926dalbt67l9bVr19i3bx/du3cvqBCEEMKuaO64zl6plPdd72eazFy9kUbc9TTir6WSkmHmwuUbxF9L5ciZBK4nG7PNr9Nm9WDn5+mMv1fWUbu/V9ZrPy9nfDwMcl+7g1BUVVUfPFveSkpK4oUXXsDT05OvvvoKjXyYhBDioaVnmIi91UFNbGIKMQlZP+MSU4lNTCExKT3b/Jrbjel8srqVDfR1JfCO5wHeLuh10s1sYVDgxfvcuXMMHTqUChUq8Nlnn2EwGKxeNiHhJmZz3oR7+75PR+AouUge9sVR8gDHyeVh88jINJFwI52E62nEX08l4UbaredpJNxIIzEpnTsrgAJ4ujtlO1r393L597mnc570Ie8o+wPyLxeNRsHvPm0cCrS1+d69exk6dChPP/00I0aMkE4OhBAiH+l1Word6mDmXjJNZhKTsor7fwv7+ctJ/H0y+21wkDXim5+nM76eBvxuF/lbxd3P0xkPGfmtQBRY8b5w4QIvvvgib7zxBv369SuozQohhMiBTqvJumUth8FbzGaV68nG7Efut47kYxJTOXY+kfQMU7Zl9DoNvh4Gy7V3X09D1s9bxd3Xw/qzrSJnBVa8Fy5cSHJyMlOnTmXq1KmW6X369OGtt94qqDCEEEJYSaP5t0Hdf3ukg6xe6ZLTMrl666g9q7hnPb+alM6RcwncuGnkvxc7Pd2c8HE3/FvY7yzyns7Sat4KBVa83333Xd59992C2pwQQoh8pigK7i563F3097wVDv49NX/1dmG/kU6K0UR07E1iE1M59k8i6cbsR++3b4m7feT+71G8M363XrsYinYHoUU7eyGEEPnqXqfm72zkpaoqqemZWafjb6SReKvA3y72py5eIzHJiPk/batdDLp/j9Zvnab39TTg65H108fDsXurk+IthBDCZhRFwdVZj6uznlKB925dbTKbuX7TyNVbBf7qjbR/nyelcTb6xl3d0AJ4uOotxdxS1O947u1uQKctnAVeircQQgi7ptVobh1ZO1OBu6+9A6QbTVxNyrrWnngjPev5rZ+xiamcuJBIanr20/O3b437b4H3veNo3svNyS5HiZPiLYQQotAzOGkp7udGcT+3HOdJTc9qXJeYlM7VW9fhbxf4S3HJHDmbkG1Md8jqBc/L3clyKt7Xw4CvhwGfWwVe0eswmc0F3nOdFG8hhBBFgotBR4kAd0oE3Pv0/O3W87cb2F1NSicxKe3WkXw6F2Nvcvh0PMbMexf4OqEB9GlbqSBSkeIthBBCQPbW8zldf/9vgc9A4UL0dRJvpOHr4VxgsUrxFkIIIaz03wJvq65eC2czOyGEEKIIk+IthBBCFDJSvIUQQohCRoq3EEIIUchI8RZCCCEKGSneQgghRCEjxVsIIYQoZKR4CyGEEIWMFG8hhBCikClUPazl9cgu9jhSTG45Si6Sh31xlDzAcXKRPOxPfuTyoHUqqvqfEc6FEEIIYdfktLkQQghRyEjxFkIIIQoZKd5CCCFEISPFWwghhChkpHgLIYQQhYwUbyGEEKKQkeIthBBCFDJSvIUQQohCRoq3EEIIUcgUueJ94sQJnnrqKWrVqkXXrl05fPiwrUPK0Y4dO+jZsye1a9emXbt2/PTTTwAYjUaqVatGeHi45TFo0CDLcqtXr6Zdu3bUqlWL559/noSEBFulAMDSpUsJCwvLFu/y5csxGo188MEH1K9fn4YNG/LNN99kW87e8vjtt9+y5RAeHk6VKlUYNGhQodknhw8fplGjRpbXj7IPoqOjee655wgPD6dt27Zs2bLFZnkkJCQwYsQIGjVqRMOGDRk1ahTXr1+3vD9q1CiqV6+ebf9cvHjR5nncK5dH+SzZ0z75799K9erVCQ0NJSYmBrC/fXK//7d2+TeiFiHp6elqq1at1Llz56pGo1H9/fff1bp166pJSUm2Du0u0dHRanh4uLpu3TrVZDKphw4dUuvVq6du3bpVPXz4sNq4ceN7LhcZGanWqlVL3bt3r5qWlqaOGzdO7devXwFHn92YMWPUTz/99K7pn332mfrss8+q165dUy9evKh26NBBXb58uaqq9pnHf0VERKgNGjRQjx8/bvf7xGw2q0uWLFHr1Kmj1qlTxzL9UfbBU089pX788cdqenq6unPnTjU8PFy9cOGCTfJ48cUX1TfffFNNTk5Wr1+/rr744ovqiBEjLO937txZ3bJlyz3XaYs87pfLo3yW7Gmf3CkjI0N95pln1GnTplmm2dM+ud//W3v9GylSxXvbtm1q06ZNs017+umn1cWLF9soopzt3btX/eCDD7JNe+WVV9Tp06erP/74ozpo0KB7LjdlypRs/7RSUlLUsLAw9dy5c/kZ7n09+eST6u+//37X9CZNmqjbtm2zvF6yZIn61FNPqapqn3ncyWg0qh07dlQXLFigqqpq9/tk+vTpao8ePdRvv/022z/Y3O6Ds2fPqmFhYWpycrLl/ZEjR6pTp04t8DxMJpP68ssvq+fPn7fMt2HDBrVFixaqqqpqamqqWqVKFTU2Nvau9dkqD1XNeZ/k9rNkT/vkv2bNmqX27t1bNZlMqqra3z653/9be/0bKVKnzU+fPk358uWzTQsJCeHUqVM2iihndevWZezYsZbX165dY9++fVStWpVjx45x9epVunbtSuPGjRk+fLjlVNR/c3RxcaF48eI2y9FkMnHy5ElWrFhB06ZNadeuHbNnz+b69evExcVRoUIFy7zlypWzxGlvefzXwoULcXZ2pk+fPgB2v0+efvppli1bRrVq1SzTbty4ket9cObMGYoXL46rq6vl/ZCQEE6ePFngeWg0GmbNmkWZMmUs0zZs2ECVKlUAOH78OFqtlg8++ICGDRvSo0cPNm/eDGCzPHLKBXL/WbKnfXKnmJgYvvnmG8aMGYNGk1Vy7G2f3O//rb3+jRSp4p2SkoKzs3O2aS4uLqSmptooIuskJSXx8ssvU7NmTdq0aYOLiwu1a9dm3rx5rF27FmdnZ1555RUgK0cXF5dsyzs7O9ssx6tXr1KtWjUef/xxNm3axBdffMGPP/7I/PnzLbHd5uLiQlpaGmB/edzJaDQyZ84chg0bhqJkDdtn7/skKCjormkpKSmWWG6zdh8kJyff82/p9rL55V55/NecOXPYuHEjb775JgDJycnUrVuXV155hW3btvHSSy/x+uuvc+LECZvlATnnktvPkr3uk++//55mzZpZvkyB/e4TyP7/NiwsDLDPv5FCNZ73o3J1dSU9PT3btNTU1GzfjOzNuXPnGDp0KBUqVOCzzz5Do9Hw7rvvZpvnnXfeoVGjRly+fPmeH460tDTc3NwKMmyLgIAAFixYYHldpUoV+vbty9atWwGy7Y8794W95XGnbdu2odFoaNmypWVaYdont93+p5ObfaCqqt39LWVkZDBu3Dg2b97MvHnzLEdETZs2pWnTppb5OnTowLJly9i4cSOVKlWyuzxy+1myx31iMpn49ddf+eyzz7JNt9d98t//t7d/1/b4N1KkjrzLly/PuXPnsk07e/ZstlMi9mTv3r08+eSTtG3bli+++AKDwQDA559/zpkzZyzzZWRkAGAwGKhQoUK2HFNTU7l8+fJdlwsKSmRkJF988UW2aRkZGRgMBgICAjh79qxl+rlz5yz7wt7yuNPGjRvp2LGj5RQgFK59cpuXl1eu90H58uWJjo7O9o/Lln9LN2/eZNCgQRw9epSlS5dmO8rbtGkTy5cvzzb/7c+gveUBuf8s2WMuBw4cAMjWCh3sc5/c6/+tPf+NFKni3aBBA1RV5fvvvycjI4NVq1Zx8uRJ2rVrZ+vQ7nLhwgVefPFFhg8fzsiRIy2nZwFOnjzJJ598wo0bN7hx4wYTJkygZcuW+Pr60qVLFzZu3Mju3bsxGo1MmTKFKlWqUK5cOZvk4enpydy5c1myZAlms5mjR48yf/58evbsSbdu3fjyyy+5evUqUVFRzJkzh27dugHYXR53OnToELVr1842rTDtkzvldh+EhIRQuXJlpk2bhtFoZNeuXWzcuJEuXbrYJI8RI0ZgNptZuHDhXadxzWYzEyZM4PDhw5hMJlauXMmBAwfo1KmT3eUBuf8s2WMuBw8epGbNmtm+6IL97ZP7/b+127+RPGn2VoicPHlSffrpp9VatWqpXbp0UXfu3GnrkO5p4sSJaqVKldRatWple0yePFm9evWqOmLECLV+/fpqnTp11BEjRqjXrl2zLLt27Vq1Q4cOaq1atdQBAwaoly5dsmEmqrpz5061R48eaq1atdRWrVpZWminpaWpH330kdqoUSO1QYMG6tSpU1Wz2WxZzt7yuK1mzZrqgQMHsk0rLPtk165d2VoEP8o+iI6OVgcPHqzWrl1bbdOmjbpq1Sqb5HH8+HG1UqVKarVq1bL9rTRr1swy/4IFC9Q2bdqoNWvWVHv06KHu2rXLLvL4by6q+mifJXvZJ7d99NFHd7Xivs2e9sn9/t/a69+IoqqqmjdfA4QQQghREIrUaXMhhBDCEUjxFkIIIQoZKd5CCCFEISPFWwghhChkpHgLIYQQhYwUbyGEEKKQkeIthBBCFDJSvIUoAK1btyY0NPSej2nTpuX79nfv3k1oaCjJyclWLxMaGspLL7101/RTp04RGhpKVFRUXoZo8c477zB8+PB8WbcQjqJIDUwihC2NGDGCnj173jXdngfG2bx5M2vWrKFjx462DkUIcQcp3kIUEDc3NwICAmwdxkMpUaIEEyZMoGnTpnh4eNg6HCHELXLaXAg7MWPGDF566SXGjx9PeHg4zZs35/vvv882z/bt2+nduze1atWiVatWfPvtt9zZw/HatWvp1q0bNWrUoHPnzmzYsCHb8r/99hutW7emRo0aPPfcc1y+fPm+MQ0fPhyTyXTXkI53at26dbZhX6OioggNDeXUqVMA9OvXj6+++oqXXnqJGjVq0L59e3bv3s2PP/5Is2bNqFevHuPHj8+2ztTUVEaOHEmNGjVo3bo1q1evzvb+r7/+SocOHahZsyY9evTgzz//tLz3zjvv8Oabb9K7d2/q16/Pzp0775ujEIWRFG8h7Mj27du5fPkyS5YsYcSIEUydOtUydOLevXt54YUXaNWqFcuXL+eNN95g1qxZLFq0CIC//vqLN954g+7du7Ny5UqefPJJXn/9dU6fPm1Z/6+//soXX3zBwoULiYqK4tNPP71vPF5eXrz77rssXryY/fv35zqvr7/+mjZt2rBy5UpKly7NsGHD2LhxI9999x3vvvsuCxYs4K+//rLMv3XrVnx8fFi+fDkDBw5k5MiR7Nu3D8gaT338+PEMHz6clStX8tRTTzF8+HDL8JMAK1eu5JlnnmHevHmEh4fnOm4h7JWcNheigHzyySdMmTLlrumrVq0iODgYAGdnZyZNmoS7uzsVK1YkIiKCRYsW0aNHD+bPn0+zZs0YOnQoAOXKlePKlSt8/fXXPPvss/z444+0bt2awYMHAzBgwABSUlJITU21bOvDDz8kLCwMgB49evD7778/MO5u3bqxYsUKPvzww7vGYLZWgwYN6N27NwA9e/Zk27ZtfPDBB5QpU4aKFSsyc+ZMTp8+bRn3uVy5crz//vsoikL58uUtR+p169blm2++YfDgwXTu3BmA0qVLExERwdy5cy2Fuly5cvdsXyCEo5DiLUQBefHFFy3jAN8pMDDQ8rxKlSq4u7tbXteoUYOff/4ZgMjIyLuWr1OnDlOmTOHGjRucOXOGrl27Znv/5ZdfBrJam0NWobvN09OTtLQ0q2L/6KOP6NKlC3PmzKF169ZWLXOnO7fr4uICQKlSpSzTnJycMBqNltc1a9bMNqZy9erVWbNmDZD1ezh06BCzZ8+2vJ+RkZFtfPQ7tyeEI5LiLUQB8fHxoUyZMvedR6vVZnttNpvRaLKubjk7O981/+3r3WazGb1en63g3cvtdT2sUqVK8corrzBz5kwqVKhw33lNJtNd0/R6/UPF8t/3bud3e/0jR46kVatW2ebR6f79d3av35UQjkSueQthRyIjI7MdgR45coTKlSsDEBISwsGDB7PNf+DAAfz8/PDy8qJs2bJERERke3/w4MF3NXrLrUGDBlGmTBk+/vjjbNP1ej1JSUmW1xcvXnzkbZ08eTLb60OHDlm+NJQvX55Lly5RpkwZy2PNmjWsWrXqkbcrRGEhxVuIApKcnExcXNxdj2vXrlnmiY+PZ8yYMZw9e5bly5ezZMkSBgwYAMCQIUPYtm0bs2bN4vz586xevZrZs2fTr18/FEVhwIABbNiwgQULFnDhwgV++OEH9u7dS7NmzfIkfp1Ox7hx44iOjs42vXr16ixevJhjx45x6NAhpk+f/sAzAA9y/PhxPvvsM86cOcOcOXPYvn07gwYNArJ+Dz/99BM//vgjFy5cYMmSJcyYMYMSJUo80jaFKEzktLkQBWTq1KlMnTr1run169dn/vz5QFavZk5OTvTs2RN/f39Gjx5Nhw4dgKzr4TNmzODzzz9n1qxZFCtWjKFDhzJw4EAAwsPDmTRpEl9++SWTJk0iJCSEL7/8kvLlyxMfH58nOdSqVYunn37a0sId4I033uD999/nqaeeIjg4mPfee89yrT23unXrxrlz53j88ccJDg7m888/p1KlSgC0a9eODz74gDlz5jBhwgRKlCjB6NGjLQ3YhCgKFPXOm0SFEDYzY8YMNm/ezLJly2wdihDCzslpcyGEEKKQkeIthBBCFDJy2lwIIYQoZOTIWwghhChkpHgLIYQQhYwUbyGEEKKQkeIthBBCFDJSvIUQQohC5v8BgbSBP0U4dfQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(model_history.history['loss'])\n",
    "plt.plot(model_history.history['val_loss'])\n",
    "plt.title(\"Model Training\")\n",
    "plt.ylabel(\"Training and Validation Loss\")\n",
    "plt.xlabel(\"Epoch Number\")\n",
    "plt.legend(['Training Loss', 'Validation Loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PU+5Z7c6kqKrqXteKy8eFCynYbAVTbkREAAkJjj8Rs146RdrMYWgjK+PT7hWPGbwlr7Z4ImmHe/GWdoD3tOV22nHmzFGKFYu6xxXdGbmqmKO8tpVGoxAW5n/DZaTb/BrakEiMdXtiPb4dy66lri5HCCGEyJOE93X0sS3RlqpG5rrfsCaecnU5QgghRC4S3tdRFAVT074oOiMZS79FvYNLtQkhhBD3koR3HjS+wRibPInt/FHM//7h6nKEEEIIBxLeN6AvWxtdxcaYt84j6+QuV5cjhBBC2DktvOfMmUN8fLzDrXLlyvTp08dZJdw2U8NH0QQXI2PpN3LtbyGEEG7DaeHduXNntmzZYr9NnjyZoKAgXnvtNWeVcNsUvQlTq+dRzRlkLBuPavOOnzYIIYTwbC7pNrdYLAwZMoQXX3yRSpUquaKEW6YNLYmx4aNYT+7CvGXuzRcQQggh7jGXhPfkyZMxmUw88sgjrlj9bdPHNEFXvj7mzX+QdWq3q8sRQghRyDl9hDWz2UzLli155513aNGixc0XcBM2czonf3gNW0YaJfp9hs4/2NUlCSHEDe3cuYvISPccYU04OnXqKLGxVW5rGaePbb5y5Uo0Gg3NmjW77WXv9fCoN6Nv/hxps97l5LRP8On4KopGWyC13K3COPSjO5N2uB9vacvttMNms7ntEKTXDin60kvPUapUaYYMeT3XfM8+24f69RvyxBN9eeyxHqSmpjJ9+hx0uqvRNWHCtxw4sC/X2Oa3onv3Tpw5c5qff/6N6OjyDs+9/PIANm3awPTpcyhePJL09HS+/noM//yznJSUZIKCgmnatDnPPfcCen32BVQaNaqN0WhEo3Hs1K5atTqff/7VDeuw2Wy5tqvbDY+6ZMkS2rdvn6txnkAbWgpT4yexnt5D5vppri5HCCE8XpcuD7J48UIyMzMdph8+fIg9e3Zx//1d2LLlX3Q6PWFh4SxfvqRA1x8UFMSiRQscpp0/n8CePY6HSD///GPOnDnDhAmTWLx4FV9+OZ49e3bz4YcjHOb75psfWLRopcMtv+C+U07f8962bRsvvfSSs1dbYPQVG2JNOIzlvwVoI8qgL1/f1SUJIcRNWfatxrL3H6esSx/TBH3Fhrc0b5MmzRg9+hNWrVpBy5Zt7NP//HM2TZo0JywsnLFjP6dp0+aEhIQwY8ZUWrVqW2C1Nm/eisWLF9C///P2aQsX/k2TJs2YP//qScq7du2ge/dehIWFA1C8eCQDBw5i8eIFuV7TGZy++3vy5EmKFCni7NUWKGP9XmiLVSRjxUSs54+6uhwhhPBYOp2ODh06M3/+n/ZpFouFBQvm88ADD5KYmMiqVSvo0KETbdq05+DBA+zZU3ADZ9WuXQezOZMdO7bbpy1c+Bft2nV0mK9581Z89dUXjBz5PkuWLCQh4RyVKlXh5ZcHF1gtt8Ppe95bt2519ioLnKLRYWr1PGmzhpO+aCx+XYejmG58bEIIIVxNX7HhLe8NO1uXLt3o1asr588nEB4ewapVKwgKCqJmzdr8+uskateuQ9GixQBo27Y9M2ZM5c033ymQdWs0Wlq0aMOiRX9TtWp1Dh06gNWaRYUKMQ7z9e3bnwoVYli4cD6jRo3k8uXLxMRU5pVXhlC1ag37fAMGPI1W67hf/OKLg+jYsXOB1JvD6eHtLTS+Qfi0foG0OR+SvuRrfNoPRvHA4/hCCOFqxYoV57776vL33/N47LEnmTt3Ng880B1VVZkzZxYXLpync+fsrnKzOROz2czzz79ESEhogay/bdv2vPrqywwcOJi//56fa687R5MmzWjSpBmqqnLw4AGmTp3MK6+8wO+//0lgYBAA48Z9lyv47wVJm7ugLVIOY6PeWE/uxLxxhqvLEUIIj9Wly4P8/fc8zp07y3//baNdu45s3ryJpKTL/PrrDCZOnMzEiZOZPHkGZcpEM3v2zAJbd6VKVQgICGDDhnUsXbqINm3aOzx/+PAhWrZsyOXLiUD21SfLl6/A66+/jcVi4eTJEwVWy62S8L5LhkpN0VdujnnbfCwH17u6HCGE8EgNGjQiPT2db775kpYtWxMQEMDs2TNp0aIVERFFCAsLt986dLifP/74nays7Es2Z2aaOXfurMMtMzPjttbfpk17vvpqNCVLliIiwvG8rDJlylKmTDTvvPMWR48eASAxMZGff/6BIkWKUq5chQL5P7gd0m1eAIwNHsV26SQZyyegCSqKNryMq0sSQgiPotFo6NTpAb7//hu+//5nLl26yMqVy/nii3G55m3duh1ffTWaZcsWA7Bhw1q6dXPs6h4+/P3bOiu9det2fP/9Nzz66OO5nlMUhVGjxjJhwrcMGvQCly8nYjAYue++Onz55bcYDAb7vM8+2yfXT6G1Wi1//738lmu5FU4fYe1uuHqQlvzY0pNImzkcAN+uw9D4BhXYa99MYRyAwp1JO9yPt7Tldtpx5sxRihVzzxHWrh2kxdMVRFvy2lZuN0iLt9L4BOLT9iXUjBQyFn2JarW4uiQhhBBeSrrNC5A2PApTs75kLPmazNWTMDZ+CkVRXF2WEEIUOtOn/8b48Tce2ezzz7+iatXqTqyoYEl4FzB9ubrYLp7AvGUumtDSGKq2cnVJQghR6PTo0YsePXq5uox7RrrN7wFD7a7oouLJXPsrWScLbiQgIYS4HR50SlOhdafbSML7HlAUDabmz6AJLkb64q+wJZ1zdUlCiEJGo9FitWa5ugxxExaLGa329jvBJbzvEcXgg0/blwFIXzAa1Zzu2oKEEIWKj48/ycmJqKp3nNXtbVRVxWzOJDExAX//4NteXo5530OawCL4tHqe9PmfkrFsPKY2L6Io8n1JCHHv+fsHcelSAmfPngDcq/tco9Fgs3nHl4q7aYtWqyMgIAQfH7/bXlbC+x7TlaiCsf7DZK6ZjHnTLIz3PejqkoQQhYCiKISGuucVHL3ld/fgurZIeDuBPrYVtgvHss9ADyuFPrqOq0sSQgjhwaQP1wkURcHY6HE0RcuTsfx7uQa4EEKIuyLh7SSKVo9P6xdQjP6kLxyDLT3J1SUJIYTwUBLeTqTxDcanzUDU9KQrQ6jKzziEEELcPglvJ9NGlMHUtC/WM/vIXD1JBlEQQghx2+SENRfQl6+XPYTq1j/RBBXDUKP9zRcSQgghrpDwdhHDfd2wJZ0lc/1UlIBw9NH3ubokIYQQHkK6zV1EUTSYmj2dfQb6svFYzx10dUlCCCE8hIS3Cyk6Az5tBqL4BpO+YDS2pARXlySEEMIDSHi7mMYnEJ/2r6DarKT//TlqZqqrSxJCCOHmJLzdgDY4Ep/WL2BLOkv6wrGoWWZXlySEEMKNSXi7CV1k5eyfkJ3eQ8bSb1G9ZNB+IYQQBU/C243oKzTAWP9hso78S+aqH+U34EIIIfIkPxVzM4ZqbVEzUjBvmYtiCsBYp4erSxJCCOFmJLzdkKF2N9SMZMxb56EY/WUQFyGEEA4kvN2QoigYGz6OmpmaPYiLyR99TGNXlyWEEMJNOPWY97lz5xgwYAC1atWiQYMGfPHFF85cvUdRNBpMzZ9BWyKWjH9+wHLkX1eXJIQQwk04NbwHDBhAREQEa9asYerUqfzxxx/MnTvXmSV4FEWrx6fNi2jCy5Kx5GuyTu12dUlCCCHcgNPCe9u2bRw/fpw333wTo9FIqVKlmDRpEnXr1nVWCR5J0ZvwbT8ITUAR0heMxnr+qKtLEkII4WJOC+8dO3ZQsWJFvvzySxo3bkyrVq1YtGgRRYoUcVYJHksx+ePTYQiK0Y/0+Z9iSzzj6pKEEEK4kNNOWLt8+TL//vsvderUYcmSJRw6dIh+/foRERFBp06dbuk1wsL8C7SmiIiAAn29eyoiAPNjwzn18xtkLviMyMffRxcYdvVpT2pLPqQd7sVb2gHe0xZph/txRVsU1UkjgXz//feMHz+eDRs22Kd98cUXHDp0iDFjxtzSa1y4kILNVjDlRkQEkJCQXCCv5UzW80dIm/sRGv9QfDv9H4rJ32Pbcj1ph3vxlnaA97RF2uF+7lVbNBol3x1Wp3WbR0dHk56ejtl8ddxuq9XqrNV7DW14GXzavoQt6Rxpf49CtWS4uiQhhBBO5rTwbtiwIaGhoYwcORKz2czevXuZMWMGHTt2dFYJXkMXWRlTywHYEg6TvuhLVKvF1SUJIYRwIqeFt9Fo5JdffuH48eM0btyYfv360a9fP9q2beusEryKvkxNTE36YD2xg3Ozx8iFTIQQohBx6ghrpUqVYvz48c5cpVfTxzRGzUwhdd1UdBYVU7OnUTRyrRkhhPB2MjyqhzNUb4+vScul5b+SodFgatJXAlwIIbychLcXCGn4IKkp6Zg3zSJT0WBs8hSKIgEuhBDeSsLbSxhrdgGbDfPm2aBoMDZ+QgJcCCG8lIS3FzHUegBUG+Ytc7MDvNHjKIri6rKEEEIUMAlvL6IoCoba3bIDfOs8UBSMDXtLgAshhJeR8PYyiqJguK87qs2KZfvfoNFirP+IBLgQQngRCW8vpCgKxro9QVWx/Lcguwu9Xi8JcCGE8BIS3l5KURSM9XqBarsS4NmBLgEuhBCeT8LbiymKgrH+I2CzYdn+N4qiwVCnhwS4EEJ4OAlvL6coCsaGj2WfxLZtPigaDPc9KAEuhBAeTMK7EFAUBWOj3qBaMW/9M/skttpdXV2WEEKIOyThXUgoigZj4ydRbWr2QC6oGGp1lT1wIYTwQBLehYiiaDA1eYoMwLx5DmpGKsaGj8pIbEII4WEkvAsZRaPB1PQpMk1+WLb/jZqRjKn5Myha+VMQQghPIZ/YhZCiaDDV64ViCsS8YRrp5jR8Wr+Ioje6ujQhhBC3QPpLCzFjXAdMTfpgPbmTtHkjUTNSXF2SEEKIWyDhXcjpKzXB1PoFbBeOkTbnA2wpF11dkhBCiJuQ8Bboy9TCp/1gbKkXSZvzPrbE064uSQghRD4kvAUAusjK+HYaCllm0uZ8gDXhsKtLEkIIcQMS3sJOG14G3y5vgN5I2p8jyTq5y9UlCSGEyIOEt3CgCSqGb+c30PiHk/7XKCyHNrq6JCGEENeR8Ba5aPxC8O38OpqIMmQsHod5+1+oqurqsoQQQlwh4S3ypBj98O34GrqytchcN5XM1ZNQbVZXlyWEEIJCGt4XkzL48c+dZFokjPKj6AyYWg3AUKMDll1LSV8wGtWS4eqyhBCi0CuU4Z2SbuH3ZQeY9c8hV5fi9hRFg7HuQxgbPYH1xI7s34KnXnJ1WUIIUagVyvAuXTSA9vXLsGjjcQ6evOzqcjyCoUpzfNq+jC3pHGl/vIf1/BFXlySEEIVWoQxvgCfvr0JIoJEf5u/GkiXd57dCV7o6vp3/D4C02e9j2bfaxRUJIUThVGjD29ek58l2lTh9IY0Zy6X7/FZpw0rj22042iLlyFj+HRmrJqFas1xdlhBCFCpODe8ZM2YQGxtLfHy8/TZr1ixnluCganQYLWuWZNGm42w7cN5ldXgajU8gPh1fRV+9HZZdS0j/cyS2tERXlyWEEIWGU8N7165dPPXUU2zZssV+69q1qzNLyOWhFuUoGeHPhHm7SUzJdGktnkTRaDHV64WpxbNYLxwl7fdhZJ3Z7+qyhBCiUHBqeO/cuZPKlSs7c5U3pddpebZLLOYsK9/N3YVNBiO5Lfry9fB94C3QG0mf+xHmnYtlQBchhLjHnBbeVquVvXv3Mnv2bBo1akTr1q0ZP368W3zQR4b78Uiriuw+eom/1h11dTkeRxtaCr+uw9CWqkrm6l/IWPE9apbZ1WUJIYTXUlQnpWdCQgKvvPIKjzzyCK1ateLgwYMMGDCAfv368eijjzqjhHypqsrHkzax5r/TjHyhEZWiQl1dksdRVRuXVk4nceU0DEXLUrT7a+iDi7i6LCGE8DpOC++8TJgwgX/++Yeffvrplua/cCEFm61gyo2ICCAhIdlhWlqGheETsy/EMfypOviadAWyrnstr7a4UtbRraQv+xZF0WJq+Sy6klVvaTl3a8edkna4H29pi7TD/dyrtmg0CmFh/jd+vsDXeAP79+9nzJgxDtMsFgtGo9FZJdyUr0nPM51juZiUyc8L9rhFl74n0kXF4dd1GIpvEOl/fUbm1nnyfymEEAXIaeEdGBjIxIkTmTZtGjabjR07djBp0iS6devmrBJuSfkSQXRtUpYNu8+x6r/Tri7HY2mCiuH7wFvoyt6HecN0MhZ/hWpOd3VZQgjhFZwW3kWLFmXcuHH89ttv1KpVi4EDBzJgwADatWvnrBJuWfu6UVSOCmHyon2cvpDq6nI8lqI3YWr5HMZ6Pck68i+ps97BeuG4q8sSQgiP59SfitWvX5+ZM2eyZcsWli5d6hYnquVFo1Hod38VDDot38zeKcOn3gVFUTBUb49Px9fAkkHaH+9g3r1cutGFEOIuFNrhUW8mJMBI346VOX4uhenLD7q6HI+ni6yM74Pvoi1eicyVP5Kx9BvpRhdCiDsk4Z2PGuXDaVW7JIs3nWCrDJ961zQ+gfi0H4ThvgfJOrSR1JnDsCYccXVZQgjhcSS8b6JHs/KULuLPD/N2cz5R9hTvlqJoMMZ3wqfTULBmkTb7Pcw7Fkk3uhBC3AYJ75vQ6zQ817UqNpvK2Jn/kWmW498FQVesIn4Pvou2ZFUy10wmfcForKlybXUhhLgVEt63oGiIL/27xHIiIYWJf+2WvcQCopj88Wn7Msb6D2M9sYMT3w0i6/h2V5clhBBuT8L7FlWLDqN703Js2H2O+TL+eYFRFAVDtbb4dh2GxjeA9L9GkbFmsoyNLoQQ+ZDwvg3t6pamTuUizFxxiO0H5QS2gqQNK0WJp0air9oay45FpMlvwoUQ4oYkvG+Doig81aEypYr48+2cnZw6LwO4FCSN3oipwaP4tB+MmpFC2qx3MG9fgKraXF2aEEK4FQnv22TUa3nxwerotRpGz9hGcpp07xY0Xalq+HZ/D12pamSum0L6/M+wpV5ydVlCCOE2JLzvQFiQiRcerM6lZDNjZ/4nI7DdAxqfQExtBmJs9ATWM/tJnfEmlsP/urosIYRwC/mGt8128+5Ki8XCxo0bC6wgT1G+RBD97q/MgROXmTBvNzY5A73AKYqCoUpz/B58B01ABBmLxpKx4gdUS4arSxNCCJfKN7xjY2O5cOGCw7Thw4dz8eJF++PLly/z+OOP35vq3FydykXp3iz7DPRZ/xxydTleSxNcHN8ub2KIux/L3pWk/j4M6zkZslYIUXjlG955/Z55zpw5pKam3nS+wqJ93dI0qRHJvLVHWbntlKvL8VqKVoexTnd87v8fWC2kzR5Bxrqp8pMyIUShdNvHvPMKakVRCqQYT6QoCo+1qUhs2VB+XrCXnUcu3nwhccd0kZXw6zECfUwTLNv/IvX3t7Ge2e/qsoQQwqnkhLUCoNNqGPBAVYqH+TJu1n+cSEhxdUleTTH4YmryFD4dXs3eC5/zARlrfkW1ZLq6NCGEcAoJ7wLiY9Txco8aGPRaRk/fRmKKBMm9pisZi1/3EeirtMCyYyGpv79F1qndri5LCCHuuXzDW1GUXF3ihbmL/GZCA0283L0GKelZjJ6xXS5i4gSKwQdTo9743D8UgPQ/R5Kx6me5VrgQwqvp8ntSVVU6dOjgENhpaWl0794djUZjn0dcFVUsgGe7xDLm9+18O2cnz3erilYjHRz3mi6yEn7d3yNz40ws/y0k69g2TE2eQleyqqtLE0KIApdveH/44YfOqsOr1CgfzqOtK/LLwn1MWrCPJ9rFSI+FEyg6I6b6D6MvW5uMFRNIn/8p+kpNMNbtiWL0c3V5QghRYPIN765duzqrDq/TomZJElMy+XPNUQL99HRrUs7VJRUa2mIV8H3wXcz//oF5+19kHd2KsV4vdOXry5coIYRXuGl/bkpKCn/88QcpKdlnUKuqynfffcfjjz/Oyy+/zPbtcv3lG+naOJrG1Yvz55qjLN18wtXlFCqKzoCx7kP4dh2O4h9OxrLxpM/7GFviaVeXJoQQdy3f8D527Bjt27fn3Xff5dKl7AtDfPjhh4waNYrw8HACAwN54okn2Lx5s1OK9TSKovB4uxjiyoczeeE+1uyQ4HA2bXgUvl3exNjocaznj5A64y0yN82SwV2EEB4t327z0aNHExsbyxdffIHJZOLSpUv8+uuvtGvXjlGjRgEQFRXFl19+yQ8//OCUgj2NVqPh2S6xjJ6xnQnzdqPXabmvUhFXl1WoKBoNhiot0JWpSea6qZg3z8ZyYC2mRo/LCW1CCI+U75732rVref755zGZTACsWrUKq9XKAw88YJ+nYcOGbNu27Z4W6ekMei0vPliNciWCGD9nJ1sPnHd1SYWSxjcYnxb98en4GigK6fM/JX3J19jSEl1dmhBC3JZ8wzs5OZmwsDD743Xr1qHT6ahTp459mp+f3y1dfaywMxl0vNy9BqWK+DNu1g4ZRtWFdCWq4PfgexhqdSXryL+kTn0d838LUK1Zri5NCCFuSb7hXaJECQ4cOACA1WplxYoV3Hffffj4+NjnWbduHSVLlry3VXoJX5OOQT3jKBbqw9jft7PveKKrSyq0FJ0BY60u+HUfgbZoOTLXTiF1xhtYjvwrYxcIIdxevuHdrVs3RowYwZw5c3jrrbc4f/48Dz/8sP35TZs28cUXX9CxY8d7Xqi38PfRM7hXPKEBJr6Yvo1Dp5JcXVKhpgkqhk/7wfi0ewVF0ZKxcCzpf36E9fwRV5cmhBA3lG949+3bl1atWvH++++zbNkyBg0aROvWrQF47733eOyxx6hVqxZ9+/Z1SrHeIsjPwKsPxxPgq+fzaVs5djbZ1SUVaoqioCtdA9/u72Fs9Di2S6dIm/kO6cu+w5YihzeEEO5HUe+wj3Dfvn3YbDYqVapU0DXd0IULKdhsBdOlGRERQEKCa0PzfGI6H07eTJbVxv8eqUlk+J2NAuYObSkI7tIO1ZyGecufmP9bCIoGQ412GGp0QNGbbml5d2nH3fKWdoD3tEXa4X7uVVs0GoWwMP8bPp/vT8Xq169/yytau3btLc2XlJRE586dGThwIN26dbvl1/dG4cE+vPpwPB9N3swnv23h9UdrUiTE19VlFXqKwRdj3YfQV2lO5oYZmDfPwbJ7BYb7uqGv2BhFxqoXQrhYvuGdlJSEzWYjPj6eVq1aERISctcrHDZsGGfPnr3r1/EWxUJ9GdIrjo9/3cInU7Yy9NGahAXd2h6euLc0ARH4tHwOa9XWZKz7jcx/JmLZsTh7qNWSsa4uTwhRiOUb3mvWrGHp0qUsXLiQsWPHUr16ddq0aUPr1q0pUuT2BxqZNWsWKSkpVKxY8Y4L9kYlI/wZ3DOOj6ds4ZPftjD00ZoE+xtdXZa4Qlu0PL6d3yDr0EYyN0wjff4naEvXwFj3IbQhJVxdnhCiEMq3/y8oKIiuXbvy9ddfs3r1anr27MmmTZvo0KEDDz/8MD/++CMnT568pRUdP36cL7/8kg8++KBACvc2UcUCeOWhGlxOMfPJlC1cSs50dUniGoqioC9XB78eH2Cs+xDW0/tIm/Em6Uu/lfHShRBOd0cnrJnNZlavXs3ChQtZtGgRZcqUYcaMGTec32q18uijj9KnTx/atGlDly5deOKJJwr9Me+87Dh4nncnrCPY38SIZxtQJFSOgbsja1oSietmk7TpL9QsC/6xjQhu+CCGcBnzQAhx7+XbbX4jhw4dYufOnezevZv09HSHQVvyMm7cOMqWLUubNm3uqMgc3na2eV6KBhoZ9FAcn0/bxqtj/2FIr3iK3STA3bUtt8uz2qFAtQfwLd8C87a/SNm1hJQdK9FF30exlr24rIS6usC75lnbI3/e0hZph/tx1dnmt7TnbbPZ2LhxI0uWLGHx4sUkJCRQv359WrduTcuWLQkNzf+Dql27dpw7dw7NlbN0U1NTMRgMdO3aleHDh99yYwpDeOc4djaZz6ZuRVEUhvSMo2SRG29Ed2/LrfLkdtjSk7D8txDzzsVgyUAXFY8hvhPaItGuLu2OefL2uJ63tEXa4X7cMrwXLVrE4sWLWb58ORaLhSZNmtC6dWuaNm2Kv/+NX/Rm7rTbvDCFN8DpC6l8MmULliwbg3rGUbZ4YJ7zeUJbboU3tEPNSEF/eCWX1s8FcxraYhXRV2+HLioORfGsn5h5w/bI4S1tkXa4H7f8nfeLL76IXq+nTp06NGrUCIPBQGJiIrNnz84176OPPnr31QoHxcP8GPpYLT6dsoVPpmzh5R41qFgq2NVliXwoJn9CmjyEpVwzLHtXYv5vARkLx6AEFsVQtRX6io1QDPkfZhJCiJvJd8+7RYsWt/YiisKSJUsKrKgbKWx73jkuJmXw6W9buZiUwYsPVie2rONhCk9qS368sR2qzUrW4U2YdyzCdvYA6E3oYxpjiG2FJqioiyvNn7dsD/Cetkg73I9b7nkvXbq0wAsSty800MTQR2vy6W9bGT1jG891qUp8xQhXlyVugaLRoi9XF325uljPHcK8YxGWXUux7FiMtnR1DJWboS1VHUWjdXWpQggP4lkH4QqxQD8Drz0ST6kiAXw1awfrdp1xdUniNmmLROPToj9+j3yGoWYnbAlHSF8wmtQpQ8jcNBNb8nlXlyiE8BB39FMx4Rr+PnqG9Ipj9IztfDdnFxlmK83iZIQvT6PxDcZYuxuGmp3JOroNy54VmDfPxbx5LtoSldFXaIiubG0UvYyyJ4TIm4S3h/Ex6njloRqMm7WDn//eS2JyJv26Vnd1WeIOKBod+rK10JethS35PJa9K7HsX0PG8u9g9SR0ZWujr9gQbfEYjztTXQhxb0l4eyCjXsuLD1bjp7/3MGf1ETKyVHo0LYtWrnblsTQB4Rhrd8VQqwvWM/vJ2rcay6ENZO1bhRIQjr5CA3Tl6qIJjkRRFFeXK4RwMQlvD6XTaujToTIhAUb+XHOUs+dTeLZLVYwGOfHJkymKBl3xGHTFYzA2fJSsI5ux7FuNectczJvnoAREoIuKQ1e6RvYeuVbv6pKFEC4g4e3BFEWhW5NylCoexDczt/PJb1t4qXt1AnwNri5NFABFZ0Rfvj768vWxpV4i6+hWso5txbJ7OZYdi0BvQleyanaQl66BxifvQXyEEN5HwtsLdGhQFq2q8u2cnXww6V9e6RlHkWAZCMSbaPxCMFRpjqFKc9SsTKwnd5N1bCtZx7aRdXgToKApEo2udA10UXFoQktJ97oQXkzC20vUrBjBkF5xjJmxnfd/3sTAB6tTrkSQq8sS94CiM2Z3nUfFoaoqtgvHsoP86DbMm2Zi3jQTxS/0avd6ZGUUnfTGCOFNJLy9SIWSwfxf71p8MX0bH0/ZwtP3V6F2pSKuLkvcQ4qioA2PQhsehbFmF2xpiViPbSfr2DYs+1Zj2bUUdAa0kZXRFa+EtngMmvAoGRRGCA8n4e1liof58cbjtRn7+3bG/bGDHs3L0a5OaelCLSQ0vsFoKjVBX6kJqtWC9dSe7GPlJ3eSeWxb9kw6I9qi5bNv4WXQhJdG8QuVvxEhPIiEtxcK9DXw2sPxTJi3m+nLDnL2YjqPtamITis/JStMFK0eXalq6EpVA8jeKz+9F+uZfVjP7MO8eQ6Qfa0AxeiP5soevCasNNrwMqjhfi6sXgiRHwlvL6XXaXmmcywRwT7MW3uUhMR0nnugKv4+8tOiwkrjG4zmyjjrAKolE9vF41jPH8V2/ijWC0cx/7cQbFkAHDGY0ISUuhrq4VFoQiJRNPKxIYSrybvQi2kUhQeblqNYqC8//b2HET9tYmD36kTKHpUAFP3V7vMcqjUL26WT2C4cw5B6itTjB7JHftu5OHsGjQ5NSAk0QUXRBBZBE1QUJbAImsAiKL5BMhKcEE4i4V0INKxWnKKhvnz5+3ben7SJ/p1jqV4u3NVlCTekaHX2E+DCIwJQE5JRbTbUpLNYzx/N3ku/eBxrwuHsn6iptqsLa/VoAiPQBF4J9IAIFP9QNH6hKH4hKD4BEu5CFBAJ70KifIkg3nriPsb+vp3R07fTvVk52tWVE9nEzSkaDUpwcTTBxdGXr2efrtqyUFMuYks6l327fBY16Ry2pARsJ3aC1ez4Qhotil8IGt+Q7DD3D0XjG4xi8s++Ga/8awoAg48EvRD5kPAuRMKCTLzeuxYT5+9m+vKDHD2bzJPtK2EyyJ+BuH2KRmfvMr+eqqqo6ZdRUy9hS7mImnoJNe3qfeuFo6hHt+YOePuLKyhGfzD6ouhNKHoT6E0oep/s+wbTddOvuW9wnB/5jXuhp6pqdi+RzWq/qbke59y/Ol295nlUK2rOc+rV55OO6TEnpYHNiia8NLrIyk5pk3xqFzJGvZb+nWMpXTSA31cc5OT5VF7oWo2iob6uLk14EUVRUHyDwTcYbUTZPOdRVRUs6agZKdfcklEzr7lvTke1ZIAlAzU1EZvlTPYylgzIukHw5y6GVIMP6IwOYY9Wl33ynVYHGh2KVgda/TX3c6brHefRaLNvigYUTXYPgUYBNKDRXJmuXPP81fvZ06/cuLZnQb3u37yZVT+sl1Jz/gOv3GzXPLZlB45qc3xeVQEb2FRUbI7THe7bskMt17JqdqhdWTbPeRxeI69lc6bZSDDqSE832x9js9nvq9fcv/Y51WE+K9hy1nVN4F4JZPWa+9nLWG/tb+UOZF5zXxtZWcJb3DuKotChXhRRxQL4dvZO3v1pE093qkJceTkOLpxHURQw+KIYfCGPvfebUW02yMpANWdcDXhLBqolHcw59zPBko5JZyM9KenKtAywZGbft2WBNQvVmpV9lr01C9Vqsd93N2muLiA/OV9Icr7I2L+0ZH9ZUa75UpOm1WJTcfySY//io7ny5Ui55suRFkXRX52W8+VJowFFa/9X0Wgcvlxdne/KNE3ONK192i3PY3/dnMfZy4SFB3EhMT27Tr3Jaf/dEt6FWGyZUN5+sjZfzdzBmBnb6dywDJ0blUUjx8GFB1A0mqvhfxPhEQEkJCTf1uurqpq952a1ZO/NWS3ZgZ7TfZqzx6de3aNUr7mPw/2cvd7r9lYd3mo3f98FBvmQdDn9yuw54aZcXdYh1LLDUkHJfsy1YapxXF65/rkr968sq6Dkfu76YL4NEXewPdyVLiAATYbzo1TCu5ALD/Lh9cdqMmnhXuasPsLh08k83amK/B5cFHqKomR3l2uzPybd4Sutf0QA6V4SeuLuyOmcAoNeS58OlendNoZdRy7y7o8bOXImydVlCSGEuAEJbwFk72U0jy/B64/VwqaqfDDpXxZtOp7dDSiEEMKtSHgLB9GRgQx/qg6xZUKZsng/X878j5R0i6vLEkIIcQ0Jb5GLv4+egd2r06tlBbYfvMDwiRvYdzzR1WUJIYS4QsJb5ElRFNrcV4r/610LnUbDx79uYe6aI9hs0o0uhBCuJuEt8lW2eCDDnrqP2pUimPXPIT6bupXLKZk3X1AIIcQ9I+EtbsrHqKN/51iebF+JgycvM+yHDew4dMHVZQkhRKEl4S1uiaIoNKkRyVtP1CbA18CoaduYsng/Zsu9G3ZQCCFE3pwa3suWLaNTp07Ex8fTqlUrfvvtN2euXhSAEhH+vPlEbVrULMGiTcd596dNHD0jg0YIIYQzOS28z507x8CBAxkyZAhbtmxh9OjRfPDBB+zcudNZJYgCYtRreaxNDIMeqkFqhoURP29i3lo5mU0IIZzFaeFdpEgR1q5dS9OmTbHZbCQmJqLVavHz83NWCaKAVY0O472+dYmvGMHvKw7x0eTNnL3k1pdOEEIIr+DUsc39/f1JT0+ndu3aZGVl8fTTT1OmTBlnliAKmL+Pnue6xLKuQji/LNzH8B828lCL8jSNi5QLnAghxD2iqE4e/9Jms5GVlcXevXt55plnGDRoED169HBmCeIeSbiUzpipW9i6P4EaFcJ5qWdNIkJ8XF2WEEJ4HaeH97U+//xz9u7dyzfffHNL81+4kFJgx1W96ZJ07tQWVVVZse0UU5ccQKNReKRVBRpULXblsoX5c6d23A1ph/vxlrZIO9zPvWqLRqMQFuZ/4+cLfI03sGHDBrp16+YwzWw2ExgY6KwShBMoikKzuBK80+c+Skb4MWHebkbP2M6lZBnYRQghCorTwrty5cqcPXuWiRMnYrVa2bx5M7///jvdu3d3VgnCiYqE+PK/R2rycMsK7Dl6iTe/X8/K7afkKmVCCFEAnBbeAQEBjB8/noULF1KnTh3efvttRowYQZ06dZxVgnAyjUah9X2leKdvHUoV8Wfi/D18Pn0bF5MyXF2aEEJ4NKeebR4bG8uUKVOcuUrhBoqG+PLaI/Es23yS6csP8Ob36+nRXM5IF0KIOyXDowqn0CgKLWuV5N2+dSlbPJBJC/by8eTNnLkovwsXQojbJeEtnKpIsA9DesXxVPtKnEhIZdgPG5i/7ihWm83VpQkhhMdware5EJB9RnrjGpFUKxfGLwv3MWP5QTbsOstLD9ckxEf+JIUQ4mZkz1u4TLC/kRe6VWPAA1W5nGZmyJh/mLxoH+mZWa4uTQgh3Jrs5giXq12pCFXKhPL3xuPMW32YzfsSeKRVRWpWDL+lwV2EEKKwkT1v4RZ8TTr6d6vO/z1eCz+Tnq9m/ccX07dzTi50IoQQuUh4C7dSLjKIt5+sTa8W5dl/IpE3v9/AnFWHsWRZXV2aEEK4Dek2F25Hp9XQpk5p7qtclN+W7OePVYdZveM0vVpWIK68dKULIYTseQu3FRJg5LkHqjK4Vxw6rYaxv//H59O3cfpCqqtLE0IIl5LwFm4vtkwo7/SpQ6+WFTh48jJvT9jA9GUH5Kx0IUShJd3mwiPotBra3FeKulWK8vvyg/y1/hird5zhwSbRNKxWHI1GutKFEIWH7HkLjxLkZ6BPx8q8+XhtIoJNTPxrD+/+tJG9xy65ujQhhHAaCW/hkaIjA/m/x2rRv3MsKekWRv66hbG/b5fj4UKIQkG6zYXHUhSFulWKElchnIUbj/PXuqO89f0GmsZF0qVRWQL9DK4uUQgh7gkJb+HxjHotnRqUoWlcJHNWHWb5llOs3XmGtnVK0+a+UvgY5c9cCOFd5FNNeI1AXwOPtYmhZa2SzPznELNXHWbJvyfo1KAMzeJLoNfJUSIhhHeQ8BZep3iYH893rcahU0n8vuIgU5bsZ+HGY3RuVJYGVYuh1UiICyE8m3yKCa8VHRnIqw/HM7hXHAG+BibO38PbEzbw795zqKrq6vKEEOKOyZ638HqxZUKp8kQIm/clMPOfQ3w1aweli/jTpVFZ4irIcKtCCM8j4S0KBUVRqBVThLgK4azfdZY5q48wduZ/lC7qzwONoqlRPkxCXAjhMSS8RaGi1WhoULU4dasUZd3Os8xZfZgxv28nqmgAnRqWIa5COBoJcSGEm5PwFoWSVqOhYbXsEF+74wzz1h7ly5n/UTLCn471o6hdKUJObBNCuC0Jb1Go6bQaGteIpEG1YqzfdZY/1xzl2zk7+X2FibZ1StOoenGMeq2ryxRCCAcS3kJwtTu9Xmwxtu4/z1/rjjJ50T5mrzpMq1olaVGrJP4+eleXKYQQgIS3EA40ikLNihHEVwhn/4nL/LXuKH+sOsz89UdpUj2SNnVKER7k4+oyhRCFnIS3EHlQFIWKpYKpWCqYEwkpLFh/jGVbTrJ080lqV4qgVa1SlCsRKGeoCyFcQsJbiJsoGeFP3/ur0LVJNAs3Hmfl9lNs2H2OqKIBtKxVkrpViqDXyXFxIYTzSHgLcYtCA030almBBxqXZe3Osyz59wQ/zN/NtGUHaFIjkqZxkUQES5e6EOLek/AW4jaZDDqax5egWVwke45eYsnmk/y1/ih/rTtKlbKhdGpSjugifui08lMzIcS94dTwXr16NZ999hlHjhwhLCyMvn370qtXL2eWIESBURSFymVCqVwmlItJGazcfpp/tp3io582Euirp2H14jSsWpzIcD9XlyqE8DJOC+/Tp0/z4osvMnLkSFq2bMmOHTvo168fJUqUoHHjxs4qQ4h7IjTQRJdGZenUoAzHLqQxZ8VBFqw/zl/rjlGmWAANqhajTpWiBPoaXF2qEMILOC28T548yf3330/r1q0BqF69OnXq1GHz5s0S3sJraDQK91UpRpkIPy6nmlm/6yxrdpzm18X7mbr0ANWiw2hQtRg1yofL9cWFEHdMUV10bcTExETatWvHe++9Zw90IbzVkdNJLNt0nOWbj3MxKRNfk47alYpSr2pxalUugq9JBoARQtw6l4R3cnIyzzzzDIGBgXz99ddobnEM6QsXUrDZCqbciIgAEhKSC+S1XM1b2lIY2mGzqew6epENu8+x7cB5ktMsaDUKlaJCqFkhnLgKEYQEGJ1ccd68ZXuA97RF2uF+7lVbNBqFsDD/Gz7v9LPNDx8+zIABAyhfvjyffvrpLQe3EN5Ao1GoWjaMqmXDsNlUDp66zJZ959myP4FJC/cxaeE+yhQLIP7KKG8lwv1kIBghRC5ODe+NGzcyYMAAevXqxaBBg+RDSRRqGo1ChZLBVCgZTI/m5Th9IY0t+xPYsv88s/45xKx/DhERbCK+QnaQVygZjEYj7xkhhBPD+9ixY/Tv359XXnmF3r17O2u1QngERVGIDPcjMtyPjvXLcCk5k20HzrNl/3mWbj7Bwo3H8ffRU6N8GPEVIqgcFYKPUYZpEKKwctq7f/LkyaSmpjJq1ChGjRpln/7II4/w6quvOqsMITxCSICRZvElaBZfgvTMLHYcvpi9V77vPKv/O4NWoxAdGUjlqBCqlAklOjJQBoURohBx2dnmd0JOWMubt7RF2nFzWVYb+48nsuvoJXYduciRM8moKhj1WmJKB1OpdAiVooIpXSTgrrvYvWV7gPe0RdrhfgrNCWtCiDun02rso7o92LQcqRkW9hxNZNfRi+w6contBy8A4GPUXjmeHkSFksGULR4gF08RwotIeAvhwfxMemrFRFArJgKAS8mZ7D1+ib3HEtl3PNEe5jqtQpligZQvGUR08UDKFA8gLNAkJ40K4aEkvIXwIiEBRupVKUa9KsUASE4zc+DkZfafuMz+E4ks2ngc65VDT/4+esoUD6BMsUDKFgugTPFAt/mNuRAifxLeQnixAF/DlZ+aZe+ZW7KsHD+XypEzSRw5ncyRM0nMO3yEnDNfgvwNlC0WSJliAdSoVJQQHx2BfjIeuxDuRsJbiEJEr9MSHRlIdGSgfVqmxcrxsykcvibQtx04zx+rDgMQGmikdJEAShbxo2SEPyUi/Cka4iNntwvhQhLeQhRyRr2W8iWDKF8yyD4tPTOLpEwrW/ec5ciZZI6dTWb7wQvYruyi67QKxUL9KBnhZ/99eolwPyKCfWQgGSGcQMJbCJGLj1FH6ZIhFA28egzckmXl9IU0TiSkcDIhlRMJqew/kci6XWft8+i0CkVDfSke6kuxMD+Kh/lSPMyXoiG+MqiMEAVI3k1CiFui12kpXTSA0kUDHKanZ2Zx6kIqpxJSOXMxjdMX0jiekMrmfefte+oAgb56ioT6UjTEh6IhvhS55l8JdiFuj7xjhBB3xceoo1xkEOUigxymZ1ltnLuUzukLqZy9lM65S2mcvZjOzsMXWf3fGYd5g/wM9jAvGno11IuE+GAyyMeUENeTd4UQ4p7QaTX24+HXyzRbOXspjXOX0jl7KS073C+m8d+hC6z6z+wwr7+PnvAgE+HBPkRc+Tc8yGS/yeAzojCS8BZCOJ3RkHcXPGR3w+eEekJiOucvZ3A+MZ1jZ5PZsi/B/jv1HMH+BsKDfAgPNhEedCXgr4R8aKARrVx2WHghCW8hhFvxMeqIKhZAVLHcwW5TVRKTMzl/OYOExHQuXM4g4XI65xMz2H88kfW7znLt1Ro0ikJooJHi4f4E+eoJDzYRFmgiNNBEWKCRkAATep2Eu/A8Et5CCI+RHcbZ4VuxVHCu57OsNi4mZ3I+Z4/9SrAnppr571ASl1PNuZYJ8jMQFnQ10EMDTIQGGrPXE2AkwM+ARoaRFW5GwlsI4TV0Wg1Fgn0oEuzjMD3nyk9mi5VLyZlcSMrgYlL2v9n3Mzh+NpltB85jybJd95oKIQG5Qz3kyr+hgSb8TDoZJ144lYS3EKLQMOi1FA31pWiob57Pq6pKSrqFi0mZXEzOsP97KSmTi0kZ7D9xmUvJ53IddzfoNYQGmK6EvJGQQCMh/tnd8iFXHgf46CXgRYGR8BZCiCsURSHA10CAryHPY+6Qfdw9KdXMhaSroX4xOZOLyZkkJmey59glLiWbHX7jDtm9AiEBhquB7m8kOMCYff/K4yB/gww7K26JhLcQQtwGjaIQ7G8k2N8IkXnPY7OpJKWZuZScycWkTC4lZ3ApJZNLyZlcSsrk0KnLJKaYc3XRK0Cgn+HK6xuyw/1KyAf7GyhrtqFasvD31ctx+EJOwlsIIQqYRnM14MsWz3seVVVJzcgiMTnTHuwXkzJITDGTeOXx4dNJJKVZci2r1SgE+Rvs68gO+KuPc4Lf1yjH4r2VhLcQQriAoij4++jx99FTsoj/DefLstpISjVzKSUTVaPh6MnLJKZkd9EnpmRy9mIae49dIjUjK9eyep0mO8j9s7vmHcL9mj16GcXO88gWE0IIN6bTauw/j4uICKD8DY7Fmy1WElPN9lDP/jd7Lz4xJZOjZ1PYduACmRZrrmVNBu01AX/NHrzDY4OMZudGJLyFEMILGPTaPH8md730zKxc4X4p5er9/Seyj8dnWW25lvX30V8X7td21WcHfJC/QUa1cwIJbyGEKER8jDp8jDqKh+Uecz7Htcfjrw/3nNA/eT6Vyym5z6rP66Q7h656fyN6kwGbqspJd3dBwlsIIYSDWz0eb7OpJKeZSUwxXwn4q8fiE1PMt3TSXZBfzh67kWC/7D33IP+rP50L9DWg0UjIX0/CWwghxB3RaBSC/I0E+RuJIu9j8eB40l1ishkrcPxMkj3ozyWms//EZVLSc4e8okCgb+49+SB/A8F+RvtZ94F++kLVXS/hLYQQ4p669qQ7uDpc7fWyrDYup5hJTM3kcoqZy9d0119OzX9PXgEC/AxX9t6N1/yU7tq9++z73nAxGglvIYQQbkGn1RAWZCIsyJTvfDl78ok5AZ+aE/SZV6aZOXYumaRUM9cdkgfAz6TL3oO/Nuiv7MUHXTleH+TmP6Fz38qEEEKIPFy/J38j1x6Tz9l7T0zJ3qvPeXzm2CUSU8y5xquH7OvO5wR8rj34K8fojb5GVFV1+mA4Et5CCCG80q0ek7efXX9dsOc8vpxq5uiZZBJTL5Bpzv07eZ1WIcjPQM2KRXi4VYV72aSr63TKWq6zfft2+vfvz9q1a12xeiGEEMLO4ez6iPznzTBnOQR8FgonzySRmGImJMDonIJxcnirqsqMGTMYOXKkM1crhBBCFAiTQYcpVGe/rOyNTr6715x6yt2YMWOYMmUKzz33nDNXK4QQQngVp4Z3r169mDlzJlWrVnXmaoUQQgiv4tRu86JFi97V8mFhNx7p505ERNz4BAZP4y1tkXa4F29pB3hPW6Qd7scVbfGos80vXEjBlsfp/HfCVccp7gVvaYu0w714SzvAe9oi7XA/96otGo2S7w6r5w8zI4QQQhQyEt5CCCGEh5HwFkIIITyMS8K7bt26bNq0yRWrFkIIITye7HkLIYQQHsajzjYv6Auye9MF3r2lLdIO9+It7QDvaYu0w/3ci7bc7DUVVc3rgmlCCCGEcFfSbS6EEEJ4GAlvIYQQwsNIeAshhBAeRsJbCCGE8DAS3kIIIYSHkfAWQgghPIyEtxBCCOFhJLyFEEIIDyPhLYQQQniYQhfee/bsoWfPnsTFxdGpUye2b9/u6pJuaPXq1XTr1o2aNWvSunVrfvvtNwDMZjNVq1YlPj7efuvTp499ufnz59O6dWvi4uJ4+umnuXDhgquaAMCMGTOIjY11qHfWrFmYzWbeeust6tSpQ7169fj2228dlnO3dsyZM8ehDfHx8VSuXJk+ffp4zDbZvn079evXtz++m21w6tQpnnrqKeLj42nVqhUrVqxwWTsuXLjAoEGDqF+/PvXq1eO1117j8uXL9udfe+01qlWr5rB9jh8/7vJ25NWWu/lbcqdtcv17pVq1asTExHD27FnA/bZJfp+3bvkeUQuRzMxMtXnz5urEiRNVs9ms/vnnn2rt2rXV5ORkV5eWy6lTp9T4+Hh14cKFqtVqVbdt26bed9996j///KNu375dbdCgQZ7L7d+/X42Li1M3btyoZmRkqO+9957au3dvJ1fv6J133lE/+eSTXNM//fRT9dFHH1UTExPV48ePq23btlVnzZqlqqp7tuN6O3fuVOvWravu3r3b7beJzWZTp02bptaqVUutVauWffrdbIOePXuqH374oZqZmamuWbNGjY+PV48dO+aSdvTv318dMmSImpqaql6+fFnt37+/OmjQIPvzHTt2VFesWJHna7qiHfm15W7+ltxpm1zLYrGoDz/8sPr555/bp7nTNsnv89Zd3yOFKrxXrlypNmrUyGFar1691KlTp7qoohvbuHGj+tZbbzlMe/7559UvvvhCnTJlitqnT588l/vss88cPrTS0tLU2NhY9fDhw/ey3Hw99NBD6p9//plresOGDdWVK1faH0+bNk3t2bOnqqru2Y5rmc1mtX379uovv/yiqqrq9tvkiy++ULt27ap+//33Dh+wd7oNDh06pMbGxqqpqan25wcPHqyOGjXK6e2wWq3qc889px45csQ+3+LFi9WmTZuqqqqq6enpauXKldVz587lej1XtUNVb7xN7vRvyZ22yfXGjRun9ujRQ7Varaqqut82ye/z1l3fI4Wq2/zAgQOUK1fOYVp0dDT79u1zUUU3Vrt2bd59913748TERDZt2kSVKlXYtWsXFy9epFOnTjRo0ICBAwfau6Kub6OPjw/Fixd3WRutVit79+5l9uzZNGrUiNatWzN+/HguX75MQkIC5cuXt89btmxZe53u1o7rTZ48GZPJxCOPPALg9tukV69ezJw5k6pVq9qnJSUl3fE2OHjwIMWLF8fX19f+fHR0NHv37nV6OzQaDePGjSMqKso+bfHixVSuXBmA3bt3o9Vqeeutt6hXrx5du3Zl2bJlAC5rx43aAnf+t+RO2+RaZ8+e5dtvv+Wdd95Bo8mOHHfbJvl93rrre6RQhXdaWhomk8lhmo+PD+np6S6q6NYkJyfz3HPPUaNGDVq2bImPjw81a9bkp59+4u+//8ZkMvH8888D2W308fFxWN5kMrmsjRcvXqRq1ao88MADLF26lDFjxjBlyhQmTZpkry2Hj48PGRkZgPu141pms5kJEybwwgsvoCjZl+1z921StGjRXNPS0tLsteS41W2Qmpqa53spZ9l7Ja92XG/ChAksWbKEIUOGAJCamkrt2rV5/vnnWblyJc8++ywvv/wye/bscVk74MZtudO/JXfdJj/++CONGze2f5kC990m4Ph5GxsbC7jne8Sjrud9t3x9fcnMzHSYlp6e7vDNyN0cPnyYAQMGUL58eT799FM0Gg2vv/66wzxDhw6lfv36nD59Os8/joyMDPz8/JxZtl1ERAS//PKL/XHlypV57LHH+OeffwActse128Ld2nGtlStXotFoaNasmX2aJ22THDkfOneyDVRVdbv3ksVi4b333mPZsmX89NNP9j2iRo0a0ahRI/t8bdu2ZebMmSxZsoSKFSu6XTvu9G/JHbeJ1Wrljz/+4NNPP3WY7q7b5PrP25z/a3d8jxSqPe9y5cpx+PBhh2mHDh1y6BJxJxs3buShhx6iVatWjBkzBqPRCMDo0aM5ePCgfT6LxQKA0WikfPnyDm1MT0/n9OnTuQ4XOMv+/fsZM2aMwzSLxYLRaCQiIoJDhw7Zpx8+fNi+LdytHddasmQJ7du3t3cBgmdtkxxBQUF3vA3KlSvHqVOnHD64XPleSklJoU+fPuzYsYMZM2Y47OUtXbqUWbNmOcyf8zfobu2AO/9bcse2bNmyBcDhLHRwz22S1+etO79HClV4161bF1VV+fHHH7FYLMybN4+9e/fSunVrV5eWy7Fjx+jfvz8DBw5k8ODB9u5ZgL179/LRRx+RlJREUlIS77//Ps2aNSM0NJT777+fJUuWsH79esxmM5999hmVK1embNmyLmlHYGAgEydOZNq0adhsNnbs2MGkSZPo1q0bnTt35quvvuLixYucOHGCCRMm0LlzZwC3a8e1tm3bRs2aNR2medI2udadboPo6GgqVarE559/jtlsZt26dSxZsoT777/fJe0YNGgQNpuNyZMn5+rGtdlsvP/++2zfvh2r1crcuXPZsmULHTp0cLt2wJ3/LbljW7Zu3UqNGjUcvuiC+22T/D5v3fY9UiCnvXmQvXv3qr169VLj4uLU+++/X12zZo2rS8rTBx98oFasWFGNi4tzuH388cfqxYsX1UGDBql16tRRa9WqpQ4aNEhNTEy0L/v333+rbdu2VePi4tQnnnhCPXnypAtboqpr1qxRu3btqsbFxanNmze3n6GdkZGhDh8+XK1fv75at25dddSoUarNZrMv527tyFGjRg11y5YtDtM8ZZusW7fO4Yzgu9kGp06dUvv27avWrFlTbdmypTpv3jyXtGP37t1qxYoV1apVqzq8Vxo3bmyf/5dfflFbtmyp1qhRQ+3atau6bt06t2jH9W1R1bv7W3KXbZJj+PDhuc7izuFO2yS/z1t3fY8oqqqqBfM1QAghhBDOUKi6zYUQQghvIOEthBBCeBgJbyGEEMLDSHgLIYQQHkbCWwghhPAwEt5CCCGEh5HwFkIIITyMhLcQTtCiRQtiYmLyvH3++ef3fP3r168nJiaG1NTUW14mJiaGZ599Ntf0ffv2ERMTw4kTJwqyRLuhQ4cycODAe/LaQniLQnVhEiFcadCgQXTr1i3XdHe+MM6yZcv466+/aN++vatLEUJcQ8JbCCfx8/MjIiLC1WXclhIlSvD+++/TqFEjAgICXF2OEOIK6TYXwk2MHTuWZ599lhEjRhAfH0+TJk348ccfHeZZtWoVPXr0IC4ujubNm/P9999z7QjHf//9N507d6Z69ep07NiRxYsXOyw/Z84cWrRoQfXq1Xnqqac4ffp0vjUNHDgQq9Wa65KO12rRooXDZV9PnDhBTEwM+/btA6B37958/fXXPPvss1SvXp02bdqwfv16pkyZQuPGjbnvvvsYMWKEw2ump6czePBgqlevTosWLZg/f77D83/88Qdt27alRo0adO3aleXLl9ufGzp0KEOGDKFHjx7UqVOHNWvW5NtGITyRhLcQbmTVqlWcPn2aadOmMWjQIEaNGmW/dOLGjRt55plnaN68ObNmzeKVV15h3Lhx/PrrrwCsXbuWV155hS5dujB37lweeughXn75ZQ4cOGB//T/++IMxY8YwefJkTpw4wSeffJJvPUFBQbz++utMnTqVzZs333G7vvnmG1q2bMncuXMpXbo0L7zwAkuWLOGHH37g9ddf55dffmHt2rX2+f/55x9CQkKYNWsWTz75JIMHD2bTpk1A9vXUR4wYwcCBA5k7dy49e/Zk4MCB9stPAsydO5eHH36Yn376ifj4+DuuWwh3Jd3mQjjJRx99xGeffZZr+rx584iMjATAZDIxcuRI/P39qVChAjt37uTXX3+la9euTJo0icaNGzNgwAAAypYty5kzZ/jmm2949NFHmTJlCi1atKBv374APPHEE6SlpZGenm5f19tvv01sbCwAXbt25c8//7xp3Z07d2b27Nm8/fbbua7BfKvq1q1Ljx49AOjWrRsrV67krbfeIioqigoVKvDll19y4MAB+3Wfy5YtyxtvvIGiKJQrV86+p167dm2+/fZb+vbtS8eOHQEoXbo0O3fuZOLEifagLlu2bJ7nFwjhLSS8hXCS/v37268DfK0iRYrY71euXBl/f3/74+rVqzN9+nQA9u/fn2v5WrVq8dlnn5GUlMTBgwfp1KmTw/PPPfcckH22OWQHXY7AwEAyMjJuqfbhw4dz//33M2HCBFq0aHFLy1zr2vX6+PgAUKpUKfs0g8GA2Wy2P65Ro4bDNZWrVavGX3/9BWT/P2zbto3x48fbn7dYLA7XR792fUJ4IwlvIZwkJCSEqKiofOfRarUOj202GxpN9tEtk8mUa/6c4902mw29Xu8QeHnJea3bVapUKZ5//nm+/PJLypcvn++8Vqs11zS9Xn9btVz/XE77cl5/8ODBNG/e3GEene7qx1le/1dCeBM55i2EG9m/f7/DHuh///1HpUqVAIiOjmbr1q0O82/ZsoWwsDCCgoIoU6YMO3fudHi+b9++uU56u1N9+vQhKiqKDz/80GG6Xq8nOTnZ/vj48eN3va69e/c6PN62bZv9S0O5cuU4efIkUVFR9ttff/3FvHnz7nq9QngKCW8hnCQ1NZWEhIRct8TERPs858+f55133uHQoUPMmjWLadOm8cQTTwDQr18/Vq5cybhx4zhy5Ajz589n/Pjx9O7dG0VReOKJJ1i8eDG//PILx44d4+eff2bjxo00bty4QOrX6XS89957nDp1ymF6tWrVmDp1Krt27WLbtm188cUXN+0BuJndu3fz6aefcvDgQSZMmMCqVavo06cPkP3/8NtvvzFlyhSOHTvGtGnTGDt2LCVKlLirdQrhSaTbXAgnGTVqFKNGjco1vU6dOkyaNAnIHtXMYDDQrVs3wsPDGTZsGG3btgWyj4ePHTuW0aNHM27cOIoVK8aAAQN48sknAYiPj2fkyJF89dVXjBw5kujoaL766ivKlSvH+fPnC6QNcXFx9OrVy36GO8Arr7zCG2+8Qc+ePYmMjOT//u//7Mfa71Tnzp05fPgwDzzwAJGRkYwePZqKFSsC0Lp1a9566y0mTJjA+++/T4kSJRg2bJj9BDYhCgNFvfZHokIIlxk7dizLli1j5syZri5FCOHmpNtcCCGE8DAS3kIIIYSHkW5zIYQQwsPInrcQQgjhYSS8hRBCCA8j4S2EEEJ4GAlvIYQQwsNIeAshhBAe5v8BpQm4bNOqxTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(model_history.history['mse'])\n",
    "plt.plot(model_history.history['val_mse'])\n",
    "plt.title(\"Model Training - MSE\")\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"Epoch Number\")\n",
    "plt.legend(['MSE','VAL_MSE'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-0.05297512, -0.12366547,  0.07736905, ..., -0.09062039,\n",
       "          0.06347909, -0.20646802],\n",
       "        [ 0.07098177,  0.03261359, -0.15519755, ...,  0.05719507,\n",
       "         -0.15749949, -0.10113611],\n",
       "        [ 0.04887106,  0.00890229,  0.0305215 , ..., -0.03756858,\n",
       "          0.22047883,  0.05023079],\n",
       "        ...,\n",
       "        [ 0.00669095,  0.08665477,  0.21863142, ...,  0.11498241,\n",
       "         -0.22320448, -0.02883197],\n",
       "        [ 0.07771438, -0.00941532,  0.1424937 , ...,  0.15572394,\n",
       "          0.01894218,  0.06721682],\n",
       "        [-0.18693943,  0.13739987, -0.16098392, ...,  0.17147072,\n",
       "          0.15384378, -0.0786418 ]], dtype=float32),\n",
       " array([ 0.02286501,  0.05595629,  0.00629237,  0.04838721,  0.03751145,\n",
       "         0.0583783 ,  0.00977572,  0.00928267,  0.03593256,  0.03126948,\n",
       "         0.0205111 , -0.00657973,  0.04227478,  0.02382563,  0.01991147,\n",
       "         0.02659311,  0.02421512,  0.01672214,  0.03921797,  0.01986597,\n",
       "         0.0157018 ,  0.02551727,  0.02921482,  0.02828117, -0.03395299,\n",
       "         0.01385191,  0.00488556,  0.02775309,  0.02842942,  0.01569038,\n",
       "         0.00961349,  0.04344428, -0.01884883,  0.03810513,  0.04133326,\n",
       "         0.00993282,  0.01908422,  0.0247392 ,  0.01945011,  0.01810755,\n",
       "         0.02757343,  0.0003002 ,  0.01042781,  0.03299244, -0.00842451,\n",
       "         0.03537451, -0.00486113,  0.03444086,  0.01632731, -0.00494784,\n",
       "         0.02895826,  0.04090858,  0.01624953, -0.00232494,  0.03320777,\n",
       "         0.02880607,  0.00932558,  0.01108106,  0.01900231,  0.00590762,\n",
       "         0.01495663,  0.03224392,  0.02279348,  0.00956912,  0.03479084,\n",
       "         0.03201027,  0.03735344,  0.03164657,  0.02834768,  0.02609827,\n",
       "         0.01470054,  0.02500943,  0.04311519,  0.04978966,  0.04149166,\n",
       "         0.00990919,  0.03031854,  0.02596855,  0.04233734,  0.00772767,\n",
       "         0.01478912,  0.02874257,  0.03725343,  0.03459123, -0.00632979,\n",
       "         0.0195595 ,  0.03106309,  0.01895577,  0.04748554,  0.0296518 ,\n",
       "         0.02844439,  0.02226294,  0.00356059,  0.02874099,  0.00481089,\n",
       "         0.02929419,  0.04231519,  0.02573755,  0.03442017,  0.02246939],\n",
       "       dtype=float32),\n",
       " array([[ 0.01932846, -0.06379972,  0.03528106, ...,  0.02721103,\n",
       "         -0.06627891, -0.09619927],\n",
       "        [ 0.04493451,  0.00656852,  0.02879464, ...,  0.10298236,\n",
       "          0.12347642,  0.0790211 ],\n",
       "        [ 0.10854605, -0.0068546 ,  0.07407635, ...,  0.0044572 ,\n",
       "         -0.00207024,  0.08878914],\n",
       "        ...,\n",
       "        [ 0.1396535 , -0.04051942, -0.00445042, ...,  0.04680633,\n",
       "          0.12956887,  0.09798314],\n",
       "        [ 0.02053706,  0.02908778,  0.10291904, ...,  0.06987229,\n",
       "          0.03162135, -0.09036664],\n",
       "        [ 0.11015532, -0.02761587, -0.03520921, ...,  0.01708783,\n",
       "         -0.06405288,  0.05709064]], dtype=float32),\n",
       " array([ 0.01630714,  0.00099641,  0.02342422,  0.03041459, -0.01091548,\n",
       "         0.02370039,  0.02377683, -0.00821587,  0.02551295,  0.02527285,\n",
       "         0.00426583,  0.02821421,  0.02721922, -0.01164966, -0.01868976,\n",
       "         0.02700692,  0.0074912 ,  0.02741081,  0.0164098 ,  0.02065276,\n",
       "         0.01861774,  0.02307676,  0.02213335,  0.02518474,  0.0012639 ,\n",
       "         0.02137186,  0.00907708,  0.02548635,  0.01295699, -0.02586252,\n",
       "         0.01023637,  0.01955277,  0.02945013,  0.02716623,  0.02647329,\n",
       "         0.0240985 ,  0.01169613,  0.03748401,  0.02509284,  0.02338381,\n",
       "         0.02692194,  0.03857735,  0.03489181,  0.02900954,  0.02710829,\n",
       "         0.01242332,  0.026138  ,  0.03142025,  0.02358344,  0.02807993,\n",
       "         0.02781942,  0.0327286 ,  0.02216413,  0.00988645, -0.00147242,\n",
       "         0.01393692,  0.00290914,  0.02329304,  0.01355721,  0.02473922,\n",
       "         0.00133924,  0.01345306,  0.01188673,  0.01756324,  0.01164875,\n",
       "         0.00410176,  0.03048343,  0.04753087,  0.00755803,  0.02925803,\n",
       "         0.02488935,  0.00831269,  0.02454482,  0.02858614,  0.02305795,\n",
       "         0.0321452 , -0.00792221,  0.03532981,  0.01933076,  0.01506962,\n",
       "         0.02606578,  0.02109972,  0.03459637, -0.00016614, -0.00102375,\n",
       "         0.01814243,  0.00190724,  0.02770859,  0.01932088,  0.01561605,\n",
       "         0.01171505,  0.02421564,  0.00732737,  0.02337352,  0.02996152,\n",
       "         0.01547399,  0.03777861, -0.00568226,  0.02551151,  0.02648873],\n",
       "       dtype=float32),\n",
       " array([[-0.22588977],\n",
       "        [ 0.16904639],\n",
       "        [ 0.05371038],\n",
       "        [ 0.25027236],\n",
       "        [-0.07141335],\n",
       "        [-0.2988813 ],\n",
       "        [ 0.17238206],\n",
       "        [-0.11384952],\n",
       "        [ 0.09266353],\n",
       "        [ 0.25406265],\n",
       "        [-0.21250758],\n",
       "        [ 0.08650818],\n",
       "        [ 0.1537262 ],\n",
       "        [-0.27764058],\n",
       "        [-0.07687674],\n",
       "        [ 0.08438861],\n",
       "        [-0.12691034],\n",
       "        [ 0.1382819 ],\n",
       "        [ 0.30734628],\n",
       "        [-0.27995613],\n",
       "        [ 0.25349355],\n",
       "        [-0.16406277],\n",
       "        [-0.08648181],\n",
       "        [ 0.17945851],\n",
       "        [-0.20591398],\n",
       "        [-0.20706494],\n",
       "        [ 0.29878476],\n",
       "        [ 0.30075517],\n",
       "        [-0.27687076],\n",
       "        [ 0.03653288],\n",
       "        [ 0.04586389],\n",
       "        [ 0.28601742],\n",
       "        [ 0.23488227],\n",
       "        [ 0.12337863],\n",
       "        [ 0.13417761],\n",
       "        [ 0.23447405],\n",
       "        [ 0.2104664 ],\n",
       "        [ 0.06035161],\n",
       "        [ 0.16009642],\n",
       "        [ 0.20422143],\n",
       "        [ 0.17106517],\n",
       "        [-0.3100447 ],\n",
       "        [-0.24016683],\n",
       "        [-0.1563393 ],\n",
       "        [ 0.31357655],\n",
       "        [-0.10226043],\n",
       "        [ 0.12418597],\n",
       "        [ 0.05983278],\n",
       "        [ 0.13532527],\n",
       "        [ 0.12617171],\n",
       "        [ 0.14278556],\n",
       "        [ 0.18430334],\n",
       "        [ 0.2419832 ],\n",
       "        [-0.23127195],\n",
       "        [ 0.06561269],\n",
       "        [-0.10979528],\n",
       "        [ 0.0478153 ],\n",
       "        [-0.2688555 ],\n",
       "        [-0.25366443],\n",
       "        [-0.16682777],\n",
       "        [-0.2234427 ],\n",
       "        [-0.0988427 ],\n",
       "        [-0.28043368],\n",
       "        [-0.31948337],\n",
       "        [-0.24627833],\n",
       "        [-0.19419248],\n",
       "        [-0.3210379 ],\n",
       "        [-0.09730595],\n",
       "        [-0.18817177],\n",
       "        [-0.22419067],\n",
       "        [-0.2218875 ],\n",
       "        [ 0.2602077 ],\n",
       "        [ 0.153298  ],\n",
       "        [ 0.11866332],\n",
       "        [ 0.24079604],\n",
       "        [-0.31990212],\n",
       "        [-0.2111475 ],\n",
       "        [ 0.06587303],\n",
       "        [-0.27023652],\n",
       "        [-0.22321294],\n",
       "        [ 0.28342387],\n",
       "        [ 0.14152497],\n",
       "        [ 0.3060754 ],\n",
       "        [-0.23415774],\n",
       "        [-0.23870659],\n",
       "        [ 0.2080592 ],\n",
       "        [-0.12197859],\n",
       "        [ 0.20669596],\n",
       "        [-0.10314799],\n",
       "        [-0.19274594],\n",
       "        [-0.11509818],\n",
       "        [ 0.05561753],\n",
       "        [-0.02352788],\n",
       "        [ 0.2887438 ],\n",
       "        [ 0.1723902 ],\n",
       "        [ 0.07997457],\n",
       "        [-0.252106  ],\n",
       "        [-0.27027738],\n",
       "        [ 0.16019955],\n",
       "        [-0.19936158]], dtype=float32),\n",
       " array([0.02274327], dtype=float32)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 998us/step - loss: 2.4563 - mse: 1.8615\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_mse = model.evaluate(X_test_scaled,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 1.8615323305130005\n"
     ]
    }
   ],
   "source": [
    "print(\"Test MSE: {}\".format(test_mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.2393335 ],\n",
       "       [-0.07362135],\n",
       "       [-0.10825673],\n",
       "       [ 0.4234009 ],\n",
       "       [ 0.45786145]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [-0.],\n",
       "       [-0.],\n",
       "       [ 0.],\n",
       "       [ 0.]], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.round()[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.051282051282051"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test,y_pred.round())\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4322297480788657"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31488801054018445"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = r2_score(y_test,y_pred.round())\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(X_test)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = X_test.shape[1]\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5072463768115942"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adjusted R2 Score\n",
    "adjr2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
    "adjr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.regplot(x=y_test, y=y_pred.round(), ax=ax)\n",
    "plt.title(\"Plot to compare actual vs predicted\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"dnnPartB.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a model (regression or classfier) first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_regressor():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=1,activation='linear'))\n",
    "    optimizer = Adam(learning_rate=0.00001)\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KerasRegressor(build_fn=build_regressor, epochs=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=5,shuffle=True,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  1.5min finished\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(estimator=model,X=X_train_scaled, y=y_train, cv=kfold, n_jobs=-1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.08172989, -3.07708287, -4.10006762, -2.8819921 , -3.49925804])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.5280261039733887"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.501070195491264"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5280261039733887"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = abs(cv.mean())\n",
    "mse_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8783040499273245"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse_mean)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a regressor or classifier function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_regressor(optimizer):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=1,activation='linear'))\n",
    "    optimizer = Adam(learning_rate=0.00001)\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KerasRegressor(build_fn=build_regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'batch_size':[1,2,5],\n",
    "          'epochs' : [100,200,300],\n",
    "          'optimizer' : ['Adam', 'RMSprop', 'SGD']\n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use RandomSearch CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearch = RandomizedSearchCV(estimator=model, param_distributions=params,n_iter=10,\n",
    "                                  scoring='neg_root_mean_squared_error',n_jobs=-1,cv=5,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearchcv = randomsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearchcv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearchcv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = Sequential()\n",
    "model2.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "#model2.add(BatchNormalization())\n",
    "#model2.add(Dropout(0.2))\n",
    "model2.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "#model2.add(BatchNormalization())\n",
    "#model2.add(Dropout(0.2))\n",
    "model2.add(Dense(units=1,activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointcb = keras.callbacks.ModelCheckpoint(\"BestModelPartA.h5\",save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystoppingcb = keras.callbacks.EarlyStopping(patience=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = SGD(learning_rate=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_history_2 = model2.fit(X_train, y_train,epochs=200,batch_size=2,\n",
    "                          validation_split=0.2, verbose=2, callbacks=[checkpointcb,earlystoppingcb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_history_2.params"
   ]
  }
 ],
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