Notebook1_Regression_Energy_Consumption_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9e8a1f7",
   "metadata": {},
   "source": [
    "## Notebook 1: Regression of energy consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ddb746",
   "metadata": {},
   "source": [
    "**1. Prepare Problem**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e693914",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\Anaconda\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n",
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "# a) Load libraries\n",
    "import pandas as pd\n",
    "from pandas import set_option\n",
    "from pandas import DataFrame\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import xgboost as xgb\n",
    "from tensorflow import keras\n",
    "from keras.callbacks import EarlyStopping\n",
    "from scikeras.wrappers import KerasRegressor\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.layers import BatchNormalization, Dropout, Dense \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from pickle import dump\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"pandas.Int64Index is deprecated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f0544c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 10)\n",
      "   Relative Compactness  Surface Area  Wall Area  Roof Area  Overall Height  \\\n",
      "0                  0.98         514.5      294.0     110.25             7.0   \n",
      "1                  0.98         514.5      294.0     110.25             7.0   \n",
      "2                  0.98         514.5      294.0     110.25             7.0   \n",
      "3                  0.98         514.5      294.0     110.25             7.0   \n",
      "4                  0.90         563.5      318.5     122.50             7.0   \n",
      "5                  0.90         563.5      318.5     122.50             7.0   \n",
      "6                  0.90         563.5      318.5     122.50             7.0   \n",
      "7                  0.90         563.5      318.5     122.50             7.0   \n",
      "8                  0.86         588.0      294.0     147.00             7.0   \n",
      "9                  0.86         588.0      294.0     147.00             7.0   \n",
      "\n",
      "   Orientation  Glazing Area  Glazing Area Distribution  Heating Load  \\\n",
      "0            2           0.0                          0         15.55   \n",
      "1            3           0.0                          0         15.55   \n",
      "2            4           0.0                          0         15.55   \n",
      "3            5           0.0                          0         15.55   \n",
      "4            2           0.0                          0         20.84   \n",
      "5            3           0.0                          0         21.46   \n",
      "6            4           0.0                          0         20.71   \n",
      "7            5           0.0                          0         19.68   \n",
      "8            2           0.0                          0         19.50   \n",
      "9            3           0.0                          0         19.95   \n",
      "\n",
      "   Cooling Load  \n",
      "0         21.33  \n",
      "1         21.33  \n",
      "2         21.33  \n",
      "3         21.33  \n",
      "4         28.28  \n",
      "5         25.38  \n",
      "6         25.16  \n",
      "7         29.60  \n",
      "8         27.30  \n",
      "9         21.97  \n",
      "Relative Compactness         float64\n",
      "Surface Area                 float64\n",
      "Wall Area                    float64\n",
      "Roof Area                    float64\n",
      "Overall Height               float64\n",
      "Orientation                    int64\n",
      "Glazing Area                 float64\n",
      "Glazing Area Distribution      int64\n",
      "Heating Load                 float64\n",
      "Cooling Load                 float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# b) Load dataset\n",
    "# Load and view each file in the dataset\n",
    "data = pd.read_csv('EPB_data.csv', header=0)\n",
    "print(data.shape)\n",
    "print(data.head(10))\n",
    "print(data.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4183abc9",
   "metadata": {},
   "source": [
    "One of the approach in optimizing building energy efficiency is to minimize total energy consumption, which is the sum of heating and cooling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "848ccc10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 11)\n"
     ]
    }
   ],
   "source": [
    "# Calculate total energy consumption by summing up heating and cooling loads\n",
    "data['Energy Consumption'] = data[['Heating Load', 'Cooling Load']].sum(axis=1)\n",
    "print(data.shape)\n",
    "\n",
    "# Define features and label\n",
    "X = data.drop(columns=['Heating Load', 'Cooling Load', 'Energy Consumption'], axis = 1)\n",
    "y = data['Energy Consumption']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74102799",
   "metadata": {},
   "source": [
    "**2. Dataset summary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae17f8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Relative Compactness  Surface Area  Wall Area  Roof Area  \\\n",
      "count               768.000       768.000    768.000    768.000   \n",
      "mean                  0.764       671.708    318.500    176.604   \n",
      "std                   0.106        88.086     43.626     45.166   \n",
      "min                   0.620       514.500    245.000    110.250   \n",
      "25%                   0.682       606.375    294.000    140.875   \n",
      "50%                   0.750       673.750    318.500    183.750   \n",
      "75%                   0.830       741.125    343.000    220.500   \n",
      "max                   0.980       808.500    416.500    220.500   \n",
      "\n",
      "       Overall Height  Orientation  Glazing Area  Glazing Area Distribution  \\\n",
      "count         768.000      768.000       768.000                    768.000   \n",
      "mean            5.250        3.500         0.234                      2.812   \n",
      "std             1.751        1.119         0.133                      1.551   \n",
      "min             3.500        2.000         0.000                      0.000   \n",
      "25%             3.500        2.750         0.100                      1.750   \n",
      "50%             5.250        3.500         0.250                      3.000   \n",
      "75%             7.000        4.250         0.400                      4.000   \n",
      "max             7.000        5.000         0.400                      5.000   \n",
      "\n",
      "       Heating Load  Cooling Load  Energy Consumption  \n",
      "count       768.000       768.000             768.000  \n",
      "mean         22.307        24.588              46.895  \n",
      "std          10.090         9.513              19.485  \n",
      "min           6.010        10.900              16.950  \n",
      "25%          12.992        15.620              28.750  \n",
      "50%          18.950        22.080              40.970  \n",
      "75%          31.668        33.133              64.335  \n",
      "max          43.100        48.030              89.950  \n"
     ]
    }
   ],
   "source": [
    "# a) Descriptive statistics\n",
    "set_option(\"display.precision\", 3)\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9631265e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking categorical features\n",
    "cat_features = data.select_dtypes(include='O').keys()\n",
    "# display variabels\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fbe7e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b) Data visualizations\n",
    "# Box and Whisker Plots of a few features\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 6]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "raw_plot = pd.DataFrame(data)\n",
    "raw_plot.plot(kind='box', subplots=True, layout=(4,3), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f6966c",
   "metadata": {},
   "source": [
    "We can see the data skewed to high values in relative compactness, roof area, glazing area, and cooling load."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85db6941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the stack bar chart of Heating and Cooling Load\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(np.arange(len(data['Heating Load'])), data['Heating Load'], label='Heating Load')\n",
    "ax.bar(np.arange(len(data['Cooling Load'])), data['Cooling Load'], bottom=data['Heating Load'], label='Cooling Load')\n",
    "\n",
    "# add labels, title and legend\n",
    "ax.set_xlabel('Number of Samples')\n",
    "ax.set_ylabel('Energy Consumption (kWh)')\n",
    "ax.set_title('Heating and Cooling Load')\n",
    "ax.legend()\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000aa0da",
   "metadata": {},
   "source": [
    "The data displays a cyclical pattern that may have resulted from the measurements taken on weekdays (with longer hours) and weekends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf940bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create density plots to check the Gaussian distribution of features\n",
    "raw_plot.plot(kind='density', subplots=True, layout=(4,3), sharex=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cfc153",
   "metadata": {},
   "source": [
    "The dataset seems to be complex as most of features exhibit non-Gaussian distributions, except for the Surface Area. There are several factors that could cause non-Gaussian distributions, including outliers, measurement errors, or the inherent characteristics of the building designs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1cbc8ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Relative Compactness  Surface Area  Wall Area  \\\n",
      "Relative Compactness                  1.000e+00    -9.919e-01 -2.038e-01   \n",
      "Surface Area                         -9.919e-01     1.000e+00  1.955e-01   \n",
      "Wall Area                            -2.038e-01     1.955e-01  1.000e+00   \n",
      "Roof Area                            -8.688e-01     8.807e-01 -2.923e-01   \n",
      "Overall Height                        8.277e-01    -8.581e-01  2.810e-01   \n",
      "Orientation                           4.679e-17    -3.459e-17 -2.429e-17   \n",
      "Glazing Area                         -2.961e-15     3.637e-15 -8.567e-17   \n",
      "Glazing Area Distribution            -7.107e-16     2.438e-15  2.067e-16   \n",
      "Heating Load                          6.223e-01    -6.581e-01  4.557e-01   \n",
      "Cooling Load                          6.343e-01    -6.730e-01  4.271e-01   \n",
      "Energy Consumption                    6.319e-01    -6.694e-01  4.445e-01   \n",
      "\n",
      "                           Roof Area  Overall Height  Orientation  \\\n",
      "Relative Compactness      -8.688e-01       8.277e-01    4.679e-17   \n",
      "Surface Area               8.807e-01      -8.581e-01   -3.459e-17   \n",
      "Wall Area                 -2.923e-01       2.810e-01   -2.429e-17   \n",
      "Roof Area                  1.000e+00      -9.725e-01   -5.830e-17   \n",
      "Overall Height            -9.725e-01       1.000e+00    4.492e-17   \n",
      "Orientation               -5.830e-17       4.492e-17    1.000e+00   \n",
      "Glazing Area              -1.759e-15       1.489e-17   -9.406e-16   \n",
      "Glazing Area Distribution -1.078e-15      -2.921e-17   -2.549e-16   \n",
      "Heating Load              -8.618e-01       8.894e-01   -2.587e-03   \n",
      "Cooling Load              -8.625e-01       8.958e-01    1.429e-02   \n",
      "Energy Consumption        -8.674e-01       8.979e-01    5.637e-03   \n",
      "\n",
      "                           Glazing Area  Glazing Area Distribution  \\\n",
      "Relative Compactness         -2.961e-15                 -7.107e-16   \n",
      "Surface Area                  3.637e-15                  2.438e-15   \n",
      "Wall Area                    -8.567e-17                  2.067e-16   \n",
      "Roof Area                    -1.759e-15                 -1.078e-15   \n",
      "Overall Height                1.489e-17                 -2.921e-17   \n",
      "Orientation                  -9.406e-16                 -2.549e-16   \n",
      "Glazing Area                  1.000e+00                  2.130e-01   \n",
      "Glazing Area Distribution     2.130e-01                  1.000e+00   \n",
      "Heating Load                  2.698e-01                  8.737e-02   \n",
      "Cooling Load                  2.075e-01                  5.053e-02   \n",
      "Energy Consumption            2.410e-01                  6.991e-02   \n",
      "\n",
      "                           Heating Load  Cooling Load  Energy Consumption  \n",
      "Relative Compactness              0.622         0.634               0.632  \n",
      "Surface Area                     -0.658        -0.673              -0.669  \n",
      "Wall Area                         0.456         0.427               0.445  \n",
      "Roof Area                        -0.862        -0.863              -0.867  \n",
      "Overall Height                    0.889         0.896               0.898  \n",
      "Orientation                      -0.003         0.014               0.006  \n",
      "Glazing Area                      0.270         0.208               0.241  \n",
      "Glazing Area Distribution         0.087         0.051               0.070  \n",
      "Heating Load                      1.000         0.976               0.994  \n",
      "Cooling Load                      0.976         1.000               0.994  \n",
      "Energy Consumption                0.994         0.994               1.000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix between all pairs of attributes\n",
    "print(data.corr(method='pearson'))\n",
    "names = list(data)\n",
    "fig = plt.figure()\n",
    "sub_fig = fig.add_subplot(111)\n",
    "cm = sub_fig.matshow(data.corr(), vmin=-1, vmax=1, interpolation='none')\n",
    "fig.colorbar(cm)\n",
    "sub_fig.tick_params(direction='inout')\n",
    "sub_fig.set_xticks(np.arange(0,11))\n",
    "sub_fig.set_yticks(np.arange(0,11))\n",
    "sub_fig.set_xticklabels(names, rotation=45)\n",
    "sub_fig.set_yticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d2fc79",
   "metadata": {},
   "source": [
    "According to the correlation matrix, the heating and cooling loads, as well as energy consumption, exhibit strong correlations with the overall height, orientation, and to some extent, the surface area. The orientation is considered to be an uncertain factor in building design due to limited choices compared to other variables and it plays a significant role in determining the energy efficiency of a building."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a8653",
   "metadata": {},
   "source": [
    "**3. Prepare Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b51f9418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Compactness          12\n",
      "Surface Area                  12\n",
      "Wall Area                      7\n",
      "Roof Area                      4\n",
      "Overall Height                 2\n",
      "Orientation                    4\n",
      "Glazing Area                   4\n",
      "Glazing Area Distribution      6\n",
      "Heating Load                 586\n",
      "Cooling Load                 636\n",
      "Energy Consumption           700\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# a) Data Cleaning\n",
    "# summarize the number of unique values in each column\n",
    "print(data.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e029b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Compactness         0\n",
      "Surface Area                 0\n",
      "Wall Area                    0\n",
      "Roof Area                    0\n",
      "Overall Height               0\n",
      "Orientation                  0\n",
      "Glazing Area                 0\n",
      "Glazing Area Distribution    0\n",
      "Heating Load                 0\n",
      "Cooling Load                 0\n",
      "Energy Consumption           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify columns with missing values and count the number of missing values\n",
    "data.columns[data.isnull().any()]\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ad7798",
   "metadata": {},
   "source": [
    "It is necessary to eliminate outliers in numerical data columns to improve the accuracy of training and validation processes. This can be done by determining the correlation between these columns and the target variable, identifying columns with low correlation, and then filtering out values that fall outside of the lower and upper bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b86a7bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the correlation between numerical columns and target variable\n",
    "corr_matrix = data.select_dtypes(include=np.number).corrwith(data['Energy Consumption']).sort_values(ascending=False)\n",
    "\n",
    "# filter columns with low correlation \n",
    "corr_threshold = 0.05\n",
    "low_corr_columns = corr_matrix[corr_matrix.abs() < corr_threshold].index\n",
    "\n",
    "# remove outliers from numerical columns with low correlation\n",
    "for column in low_corr_columns:\n",
    "    lower = data[column].quantile(0.01)\n",
    "    upper = data[column].quantile(0.99)\n",
    "    data[column] = data[column].clip(lower, upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e41b92c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(537, 8) (537,) (231, 8) (231,)\n"
     ]
    }
   ],
   "source": [
    "# b) Split-out dataset into train and validation sets\n",
    "\n",
    "X = data.drop(columns=['Heating Load', 'Cooling Load', 'Energy Consumption'], axis = 1)\n",
    "y = data['Energy Consumption']\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_val.shape, y_val.shape)\n",
    "\n",
    "# For XGBoost model\n",
    "X_train1 = X_train\n",
    "y_train1 = y_train\n",
    "X_val1 = X_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f87f749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# c) Data Transforms\n",
    "# Standardize the dataset by rescaling the distribution of values \n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_val = scaler.fit_transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f676f9",
   "metadata": {},
   "source": [
    "**4. Evaluate Algorithms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69ee8103",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a) Spot check algorithms using cross-validation technique\n",
    "num_folds = 8\n",
    "seed = 8\n",
    "\n",
    "# Select 3 most popular algorithms for evaluation\n",
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('RF', RandomForestRegressor()))\n",
    "\n",
    "# Define the XGBoost model\n",
    "model_xgb = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 10)\n",
    "models.append(('XGB', model_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "378a8c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neural Network algorithms\n",
    "# create keras Sequential model\n",
    "def baseline_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(300, input_shape = (8, ), activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(200, activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(100, activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(1))\n",
    "    # Compile model\n",
    "    model.compile(optimizer = 'adam',\n",
    "                loss = 'mean_squared_error',\n",
    "                metrics=['mse'])\n",
    "    return model\n",
    "\n",
    "# Build model\n",
    "model_NN = baseline_model()\n",
    "models.append(('NN', model_NN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32711f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a callback for early stopping if the validation loss does not improve for 10 consecutive \n",
    "# epochs (patience=10).\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "099255c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -30.969344 (9.052568)\n",
      "RF: -3.030389 (0.944267)\n",
      "XGB: -2.775281 (0.646914)\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpz5kxcinl\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 83ms/step - loss: 2462.1494 - mse: 2462.1494 - val_loss: 2732.9395 - val_mse: 2732.9395\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2385.2473 - mse: 2385.2473 - val_loss: 2711.2119 - val_mse: 2711.2119\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2331.3684 - mse: 2331.3684 - val_loss: 2688.9568 - val_mse: 2688.9568\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2295.7205 - mse: 2295.7205 - val_loss: 2663.9114 - val_mse: 2663.9114\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2261.5491 - mse: 2261.5491 - val_loss: 2637.6619 - val_mse: 2637.6619\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2241.9094 - mse: 2241.9094 - val_loss: 2609.0032 - val_mse: 2609.0032\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2208.2661 - mse: 2208.2661 - val_loss: 2578.7976 - val_mse: 2578.7976\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2191.2534 - mse: 2191.2534 - val_loss: 2548.2874 - val_mse: 2548.2871\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2180.3074 - mse: 2180.3074 - val_loss: 2518.6614 - val_mse: 2518.6614\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2150.2568 - mse: 2150.2568 - val_loss: 2489.6851 - val_mse: 2489.6851\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2155.2205 - mse: 2155.2205 - val_loss: 2460.3416 - val_mse: 2460.3416\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2137.3074 - mse: 2137.3074 - val_loss: 2431.0740 - val_mse: 2431.0740\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2107.2678 - mse: 2107.2678 - val_loss: 2403.4099 - val_mse: 2403.4099\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2110.6812 - mse: 2110.6812 - val_loss: 2377.6035 - val_mse: 2377.6035\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2101.8374 - mse: 2101.8374 - val_loss: 2350.6453 - val_mse: 2350.6453\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2085.6160 - mse: 2085.6162 - val_loss: 2326.4324 - val_mse: 2326.4324\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2070.7991 - mse: 2070.7991 - val_loss: 2303.9734 - val_mse: 2303.9734\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 2071.2292 - mse: 2071.2292 - val_loss: 2281.9983 - val_mse: 2281.9983\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2057.8689 - mse: 2057.8689 - val_loss: 2260.6848 - val_mse: 2260.6851\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2029.9352 - mse: 2029.9352 - val_loss: 2238.8196 - val_mse: 2238.8196\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2028.9678 - mse: 2028.9678 - val_loss: 2216.3022 - val_mse: 2216.3022\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 2029.4510 - mse: 2029.4510 - val_loss: 2193.6641 - val_mse: 2193.6641\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2002.6680 - mse: 2002.6680 - val_loss: 2172.2795 - val_mse: 2172.2795\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1980.0068 - mse: 1980.0068 - val_loss: 2152.4309 - val_mse: 2152.4309\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1983.5046 - mse: 1983.5046 - val_loss: 2130.0210 - val_mse: 2130.0210\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1966.1537 - mse: 1966.1537 - val_loss: 2111.7439 - val_mse: 2111.7439\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1953.6975 - mse: 1953.6975 - val_loss: 2096.4180 - val_mse: 2096.4180\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1943.3289 - mse: 1943.3289 - val_loss: 2082.5042 - val_mse: 2082.5042\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1926.6705 - mse: 1926.6705 - val_loss: 2071.0115 - val_mse: 2071.0115\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1929.3601 - mse: 1929.3601 - val_loss: 2059.9480 - val_mse: 2059.9480\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1900.0931 - mse: 1900.0931 - val_loss: 2048.1028 - val_mse: 2048.1028\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1899.7035 - mse: 1899.7035 - val_loss: 2031.6713 - val_mse: 2031.6713\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1882.3090 - mse: 1882.3090 - val_loss: 2017.6259 - val_mse: 2017.6259\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1872.7051 - mse: 1872.7051 - val_loss: 1999.2567 - val_mse: 1999.2567\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1854.4313 - mse: 1854.4313 - val_loss: 1981.5779 - val_mse: 1981.5779\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1844.9343 - mse: 1844.9343 - val_loss: 1968.9780 - val_mse: 1968.9780\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1829.0461 - mse: 1829.0461 - val_loss: 1951.3545 - val_mse: 1951.3545\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1817.2952 - mse: 1817.2952 - val_loss: 1935.6642 - val_mse: 1935.6642\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1805.0922 - mse: 1805.0922 - val_loss: 1918.1791 - val_mse: 1918.1791\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1777.0070 - mse: 1777.0070 - val_loss: 1897.6877 - val_mse: 1897.6877\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1777.4773 - mse: 1777.4773 - val_loss: 1877.1620 - val_mse: 1877.1620\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1749.8188 - mse: 1749.8188 - val_loss: 1859.8198 - val_mse: 1859.8198\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1749.0944 - mse: 1749.0944 - val_loss: 1843.4028 - val_mse: 1843.4028\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1737.8920 - mse: 1737.8920 - val_loss: 1830.9353 - val_mse: 1830.9353\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1728.5764 - mse: 1728.5764 - val_loss: 1823.8845 - val_mse: 1823.8845\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1689.2454 - mse: 1689.2452 - val_loss: 1813.8550 - val_mse: 1813.8550\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1683.7747 - mse: 1683.7747 - val_loss: 1803.5178 - val_mse: 1803.5178\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1668.5686 - mse: 1668.5686 - val_loss: 1791.0229 - val_mse: 1791.0229\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1632.5818 - mse: 1632.5818 - val_loss: 1774.0741 - val_mse: 1774.0741\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1632.9835 - mse: 1632.9835 - val_loss: 1753.0988 - val_mse: 1753.0988\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1617.0837 - mse: 1617.0837 - val_loss: 1739.9413 - val_mse: 1739.9413\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1593.0808 - mse: 1593.0808 - val_loss: 1727.6670 - val_mse: 1727.6670\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1592.5360 - mse: 1592.5360 - val_loss: 1708.5841 - val_mse: 1708.5841\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1563.3137 - mse: 1563.3137 - val_loss: 1692.7235 - val_mse: 1692.7235\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1525.1440 - mse: 1525.1440 - val_loss: 1678.2860 - val_mse: 1678.2860\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1530.1278 - mse: 1530.1278 - val_loss: 1661.1597 - val_mse: 1661.1597\n",
      "Epoch 57/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 1493.7245 - mse: 1493.7247 - val_loss: 1642.3452 - val_mse: 1642.3452\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1471.9510 - mse: 1471.9510 - val_loss: 1628.8907 - val_mse: 1628.8907\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1456.5675 - mse: 1456.5675 - val_loss: 1614.6976 - val_mse: 1614.6976\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1424.9130 - mse: 1424.9130 - val_loss: 1591.4668 - val_mse: 1591.4668\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1422.3105 - mse: 1422.3105 - val_loss: 1557.9849 - val_mse: 1557.9849\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1391.0780 - mse: 1391.0780 - val_loss: 1526.8054 - val_mse: 1526.8054\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1361.9561 - mse: 1361.9561 - val_loss: 1498.3158 - val_mse: 1498.3158\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1336.1561 - mse: 1336.1560 - val_loss: 1470.2733 - val_mse: 1470.2733\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1324.0769 - mse: 1324.0769 - val_loss: 1441.3430 - val_mse: 1441.3430\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1287.3314 - mse: 1287.3314 - val_loss: 1418.1000 - val_mse: 1418.1000\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1263.2632 - mse: 1263.2633 - val_loss: 1390.5333 - val_mse: 1390.5333\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1241.2999 - mse: 1241.2999 - val_loss: 1357.3289 - val_mse: 1357.3289\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1213.1929 - mse: 1213.1929 - val_loss: 1329.3596 - val_mse: 1329.3596\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1201.0179 - mse: 1201.0179 - val_loss: 1305.8679 - val_mse: 1305.8679\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1170.1273 - mse: 1170.1273 - val_loss: 1286.3247 - val_mse: 1286.3247\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1153.7808 - mse: 1153.7808 - val_loss: 1260.0688 - val_mse: 1260.0688\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1122.8098 - mse: 1122.8098 - val_loss: 1239.5353 - val_mse: 1239.5353\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1092.5387 - mse: 1092.5387 - val_loss: 1216.4883 - val_mse: 1216.4883\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1090.3864 - mse: 1090.3864 - val_loss: 1191.8073 - val_mse: 1191.8073\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1053.4778 - mse: 1053.4778 - val_loss: 1170.8673 - val_mse: 1170.8673\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1017.5576 - mse: 1017.5576 - val_loss: 1148.9766 - val_mse: 1148.9766\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 997.7415 - mse: 997.7415 - val_loss: 1120.5789 - val_mse: 1120.5789\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 969.2319 - mse: 969.2319 - val_loss: 1099.9124 - val_mse: 1099.9124\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 964.4698 - mse: 964.4698 - val_loss: 1071.3340 - val_mse: 1071.3340\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 942.7829 - mse: 942.7829 - val_loss: 1040.1455 - val_mse: 1040.1455\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 897.6396 - mse: 897.6396 - val_loss: 1011.3821 - val_mse: 1011.3821\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 880.4179 - mse: 880.4179 - val_loss: 983.5693 - val_mse: 983.5693\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 865.5016 - mse: 865.5016 - val_loss: 965.7540 - val_mse: 965.7540\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 809.3765 - mse: 809.3765 - val_loss: 951.8726 - val_mse: 951.8726\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 791.4908 - mse: 791.4908 - val_loss: 932.8680 - val_mse: 932.8680\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 780.7430 - mse: 780.7430 - val_loss: 903.3936 - val_mse: 903.3936\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 785.6189 - mse: 785.6189 - val_loss: 865.5186 - val_mse: 865.5186\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 735.1259 - mse: 735.1259 - val_loss: 829.2845 - val_mse: 829.2845\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 721.9373 - mse: 721.9373 - val_loss: 799.8611 - val_mse: 799.8611\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 706.5874 - mse: 706.5874 - val_loss: 773.5088 - val_mse: 773.5088\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 657.6490 - mse: 657.6490 - val_loss: 750.3090 - val_mse: 750.3090\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 658.5010 - mse: 658.5010 - val_loss: 727.8578 - val_mse: 727.8578\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 630.3702 - mse: 630.3702 - val_loss: 698.8557 - val_mse: 698.8557\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 621.6067 - mse: 621.6067 - val_loss: 673.1757 - val_mse: 673.1757\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 592.0514 - mse: 592.0514 - val_loss: 649.6971 - val_mse: 649.6971\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 559.7238 - mse: 559.7238 - val_loss: 631.2266 - val_mse: 631.2266\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 550.8482 - mse: 550.8482 - val_loss: 607.4640 - val_mse: 607.4640\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 533.8846 - mse: 533.8846 - val_loss: 581.1920 - val_mse: 581.1920\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 514.3947 - mse: 514.3947 - val_loss: 553.3448 - val_mse: 553.3448\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 493.4174 - mse: 493.4174 - val_loss: 526.4984 - val_mse: 526.4984\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 487.9402 - mse: 487.9402 - val_loss: 497.8552 - val_mse: 497.8552\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 449.1292 - mse: 449.1292 - val_loss: 476.2235 - val_mse: 476.2235\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 447.6817 - mse: 447.6817 - val_loss: 463.5132 - val_mse: 463.5132\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 436.7191 - mse: 436.7191 - val_loss: 452.6104 - val_mse: 452.6104\n",
      "Epoch 106/150\n",
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      "Epoch 107/150\n",
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      "Epoch 108/150\n",
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      "Epoch 110/150\n",
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      "Epoch 111/150\n",
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      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 310.5273 - mse: 310.5273 - val_loss: 328.8403 - val_mse: 328.8403\n",
      "Epoch 114/150\n",
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      "Epoch 115/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 15ms/step - loss: 280.9922 - mse: 280.9922 - val_loss: 287.5889 - val_mse: 287.5889\n",
      "Epoch 116/150\n",
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      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 269.9578 - mse: 269.9578 - val_loss: 270.9190 - val_mse: 270.9190\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 259.6517 - mse: 259.6517 - val_loss: 263.8327 - val_mse: 263.8327\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 244.1543 - mse: 244.1543 - val_loss: 249.0445 - val_mse: 249.0445\n",
      "Epoch 120/150\n",
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      "Epoch 121/150\n",
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      "Epoch 122/150\n",
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      "Epoch 123/150\n",
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      "Epoch 124/150\n",
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      "Epoch 125/150\n",
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      "Epoch 126/150\n",
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      "Epoch 127/150\n",
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      "Epoch 128/150\n",
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      "Epoch 129/150\n",
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      "Epoch 130/150\n",
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      "Epoch 131/150\n",
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      "Epoch 132/150\n",
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      "Epoch 133/150\n",
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      "Epoch 134/150\n",
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      "Epoch 135/150\n",
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      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 121.3891 - mse: 121.3891 - val_loss: 109.9198 - val_mse: 109.9198\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 124.9196 - mse: 124.9196 - val_loss: 103.0529 - val_mse: 103.0529\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 121.0232 - mse: 121.0232 - val_loss: 97.5005 - val_mse: 97.5005\n",
      "Epoch 139/150\n",
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      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 119.0279 - mse: 119.0279 - val_loss: 81.5365 - val_mse: 81.5365\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 103.3461 - mse: 103.3461 - val_loss: 78.4483 - val_mse: 78.4483\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 90.0988 - mse: 90.0988 - val_loss: 80.2069 - val_mse: 80.2069\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 97.9793 - mse: 97.9793 - val_loss: 80.3532 - val_mse: 80.3532\n",
      "Epoch 144/150\n",
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      "Epoch 145/150\n",
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      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 88.0356 - mse: 88.0356 - val_loss: 64.3307 - val_mse: 64.3307\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 92.9556 - mse: 92.9556 - val_loss: 56.8661 - val_mse: 56.8661\n",
      "Epoch 148/150\n",
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      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 74.3978 - mse: 74.3978 - val_loss: 50.8516 - val_mse: 50.8516\n",
      "Epoch 150/150\n",
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      "1/1 [==============================] - 0s 122ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpp5p8ntty\\assets\n",
      "Epoch 1/150\n",
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      "Epoch 2/150\n",
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      "Epoch 5/150\n",
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      "Epoch 7/150\n",
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      "Epoch 8/150\n",
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      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 2185.2681 - mse: 2185.2681 - val_loss: 2523.1233 - val_mse: 2523.1233\n",
      "Epoch 10/150\n",
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      "Epoch 11/150\n",
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      "Epoch 12/150\n",
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      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 20/150\n",
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      "Epoch 22/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 19ms/step - loss: 2014.0002 - mse: 2014.0002 - val_loss: 2218.2051 - val_mse: 2218.2051\n",
      "Epoch 23/150\n",
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      "Epoch 42/150\n",
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      "Epoch 43/150\n",
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      "Epoch 44/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
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      "Epoch 48/150\n",
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      "Epoch 49/150\n",
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      "Epoch 50/150\n",
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      "Epoch 51/150\n",
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      "Epoch 52/150\n",
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      "Epoch 53/150\n",
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      "Epoch 57/150\n",
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      "Epoch 58/150\n",
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      "Epoch 59/150\n",
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      "Epoch 60/150\n",
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      "Epoch 64/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
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      "Epoch 70/150\n",
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      "Epoch 71/150\n",
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      "Epoch 72/150\n",
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      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1126.7244 - mse: 1126.7244 - val_loss: 1207.0692 - val_mse: 1207.0692\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1091.6329 - mse: 1091.6329 - val_loss: 1185.8726 - val_mse: 1185.8726\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1066.9313 - mse: 1066.9313 - val_loss: 1162.8141 - val_mse: 1162.8141\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1022.1625 - mse: 1022.1625 - val_loss: 1139.4327 - val_mse: 1139.4327\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1012.7351 - mse: 1012.7351 - val_loss: 1115.5790 - val_mse: 1115.5790\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 990.5905 - mse: 990.5905 - val_loss: 1085.4545 - val_mse: 1085.4545\n",
      "Epoch 79/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 15ms/step - loss: 949.2064 - mse: 949.2064 - val_loss: 1059.8928 - val_mse: 1059.8928\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 950.4922 - mse: 950.4922 - val_loss: 1031.9622 - val_mse: 1031.9622\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 914.5876 - mse: 914.5876 - val_loss: 1002.4808 - val_mse: 1002.4808\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 903.2245 - mse: 903.2245 - val_loss: 971.3836 - val_mse: 971.3836\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 860.6906 - mse: 860.6906 - val_loss: 945.2803 - val_mse: 945.2803\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 855.0016 - mse: 855.0016 - val_loss: 911.4206 - val_mse: 911.4206\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 827.8046 - mse: 827.8046 - val_loss: 877.1699 - val_mse: 877.1699\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 816.2903 - mse: 816.2903 - val_loss: 842.6052 - val_mse: 842.6052\n",
      "Epoch 87/150\n",
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      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 742.6516 - mse: 742.6516 - val_loss: 791.3477 - val_mse: 791.3477\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 729.5616 - mse: 729.5616 - val_loss: 765.7945 - val_mse: 765.7945\n",
      "Epoch 90/150\n",
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      "Epoch 91/150\n",
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      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 690.5073 - mse: 690.5073 - val_loss: 700.3000 - val_mse: 700.3000\n",
      "Epoch 93/150\n",
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      "Epoch 94/150\n",
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      "Epoch 95/150\n",
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      "Epoch 96/150\n",
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      "Epoch 97/150\n",
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      "Epoch 98/150\n",
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      "Epoch 99/150\n",
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      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 511.4836 - mse: 511.4836 - val_loss: 530.0909 - val_mse: 530.0909\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 508.4811 - mse: 508.4811 - val_loss: 503.5656 - val_mse: 503.5656\n",
      "Epoch 102/150\n",
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      "Epoch 103/150\n",
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      "Epoch 104/150\n",
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      "Epoch 105/150\n",
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      "Epoch 106/150\n",
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      "Epoch 107/150\n",
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      "Epoch 108/150\n",
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      "Epoch 109/150\n",
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      "Epoch 110/150\n",
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      "Epoch 111/150\n",
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      "Epoch 112/150\n",
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      "Epoch 113/150\n",
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      "Epoch 114/150\n",
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      "Epoch 115/150\n",
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      "Epoch 116/150\n",
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      "Epoch 117/150\n",
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      "Epoch 118/150\n",
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      "Epoch 119/150\n",
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      "Epoch 120/150\n",
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      "Epoch 121/150\n",
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      "Epoch 122/150\n",
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      "Epoch 123/150\n",
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      "Epoch 124/150\n",
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      "Epoch 125/150\n",
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      "Epoch 126/150\n",
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      "Epoch 127/150\n",
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      "Epoch 128/150\n",
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      "Epoch 129/150\n",
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      "Epoch 130/150\n",
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      "Epoch 131/150\n",
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      "Epoch 132/150\n",
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      "Epoch 133/150\n",
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      "Epoch 134/150\n",
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      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 130.3718 - mse: 130.3718 - val_loss: 99.9202 - val_mse: 99.9202\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 111.0589 - mse: 111.0589 - val_loss: 96.6791 - val_mse: 96.6791\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 118.8191 - mse: 118.8191 - val_loss: 92.1329 - val_mse: 92.1329\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 127.7824 - mse: 127.7824 - val_loss: 87.5129 - val_mse: 87.5129\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 125.8335 - mse: 125.8335 - val_loss: 83.6645 - val_mse: 83.6645\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 116.9757 - mse: 116.9757 - val_loss: 79.1456 - val_mse: 79.1456\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 103.8023 - mse: 103.8023 - val_loss: 74.5919 - val_mse: 74.5919\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 107.6890 - mse: 107.6890 - val_loss: 68.5884 - val_mse: 68.5884\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 106.9904 - mse: 106.9904 - val_loss: 64.9343 - val_mse: 64.9343\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 86.6706 - mse: 86.6706 - val_loss: 61.0915 - val_mse: 61.0915\n",
      "Epoch 145/150\n",
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      "Epoch 146/150\n",
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      "Epoch 147/150\n",
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      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 83.1570 - mse: 83.1570 - val_loss: 46.8303 - val_mse: 46.8303\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 88.9699 - mse: 88.9699 - val_loss: 45.6145 - val_mse: 45.6145\n",
      "Epoch 150/150\n",
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      "1/1 [==============================] - 0s 101ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpeu1kk1h5\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 76ms/step - loss: 2418.3625 - mse: 2418.3625 - val_loss: 2732.5210 - val_mse: 2732.5210\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2328.8093 - mse: 2328.8093 - val_loss: 2711.9995 - val_mse: 2711.9995\n",
      "Epoch 3/150\n",
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      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
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      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2166.3083 - mse: 2166.3083 - val_loss: 2586.9102 - val_mse: 2586.9102\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2151.2771 - mse: 2151.2771 - val_loss: 2559.4275 - val_mse: 2559.4275\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2145.9785 - mse: 2145.9785 - val_loss: 2530.7505 - val_mse: 2530.7505\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2125.3450 - mse: 2125.3450 - val_loss: 2503.2827 - val_mse: 2503.2825\n",
      "Epoch 11/150\n",
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      "Epoch 12/150\n",
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      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 16/150\n",
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      "Epoch 17/150\n",
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      "Epoch 18/150\n",
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      "Epoch 19/150\n",
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      "Epoch 24/150\n",
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      "Epoch 25/150\n",
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      "Epoch 34/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1736.7157 - mse: 1736.7157 - val_loss: 1938.5129 - val_mse: 1938.5129\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1735.2584 - mse: 1735.2584 - val_loss: 1921.6370 - val_mse: 1921.6370\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1713.8181 - mse: 1713.8181 - val_loss: 1903.3003 - val_mse: 1903.3003\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1708.5714 - mse: 1708.5714 - val_loss: 1888.6632 - val_mse: 1888.6632\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1674.4861 - mse: 1674.4861 - val_loss: 1870.7411 - val_mse: 1870.7411\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1670.8274 - mse: 1670.8274 - val_loss: 1853.6196 - val_mse: 1853.6196\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1650.6191 - mse: 1650.6191 - val_loss: 1833.9880 - val_mse: 1833.9880\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1642.5438 - mse: 1642.5438 - val_loss: 1815.7911 - val_mse: 1815.7911\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1615.1030 - mse: 1615.1030 - val_loss: 1794.5305 - val_mse: 1794.5305\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1612.3414 - mse: 1612.3414 - val_loss: 1780.9277 - val_mse: 1780.9277\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1564.3005 - mse: 1564.3005 - val_loss: 1766.6196 - val_mse: 1766.6196\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1554.5596 - mse: 1554.5596 - val_loss: 1750.3983 - val_mse: 1750.3983\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1544.6903 - mse: 1544.6903 - val_loss: 1732.3726 - val_mse: 1732.3726\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1509.7275 - mse: 1509.7275 - val_loss: 1708.8793 - val_mse: 1708.8793\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1497.3188 - mse: 1497.3188 - val_loss: 1684.8383 - val_mse: 1684.8383\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1482.2129 - mse: 1482.2129 - val_loss: 1662.4661 - val_mse: 1662.4661\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1464.1439 - mse: 1464.1439 - val_loss: 1644.4998 - val_mse: 1644.4998\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1447.2115 - mse: 1447.2115 - val_loss: 1630.6034 - val_mse: 1630.6034\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1421.9586 - mse: 1421.9586 - val_loss: 1612.7419 - val_mse: 1612.7419\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1388.7406 - mse: 1388.7406 - val_loss: 1588.2979 - val_mse: 1588.2979\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1370.5176 - mse: 1370.5176 - val_loss: 1559.5167 - val_mse: 1559.5167\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1355.2279 - mse: 1355.2279 - val_loss: 1526.4816 - val_mse: 1526.4816\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1324.3494 - mse: 1324.3494 - val_loss: 1493.3434 - val_mse: 1493.3434\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1309.7743 - mse: 1309.7743 - val_loss: 1463.4276 - val_mse: 1463.4276\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1278.7343 - mse: 1278.7343 - val_loss: 1437.0396 - val_mse: 1437.0396\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1277.1573 - mse: 1277.1573 - val_loss: 1416.0533 - val_mse: 1416.0533\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1232.7074 - mse: 1232.7074 - val_loss: 1385.8488 - val_mse: 1385.8488\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1217.3212 - mse: 1217.3212 - val_loss: 1354.5446 - val_mse: 1354.5446\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1164.9048 - mse: 1164.9048 - val_loss: 1320.8721 - val_mse: 1320.8721\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1178.3154 - mse: 1178.3154 - val_loss: 1294.9741 - val_mse: 1294.9741\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1142.1996 - mse: 1142.1996 - val_loss: 1266.6918 - val_mse: 1266.6918\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1119.3610 - mse: 1119.3610 - val_loss: 1233.9009 - val_mse: 1233.9009\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1081.2289 - mse: 1081.2289 - val_loss: 1206.7615 - val_mse: 1206.7615\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1061.4932 - mse: 1061.4932 - val_loss: 1185.3788 - val_mse: 1185.3788\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1025.8219 - mse: 1025.8219 - val_loss: 1163.0771 - val_mse: 1163.0771\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1029.6597 - mse: 1029.6597 - val_loss: 1136.4174 - val_mse: 1136.4174\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1027.9497 - mse: 1027.9497 - val_loss: 1118.3147 - val_mse: 1118.3147\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 969.9545 - mse: 969.9545 - val_loss: 1100.6018 - val_mse: 1100.6018\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 960.2959 - mse: 960.2959 - val_loss: 1079.2810 - val_mse: 1079.2810\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 910.3982 - mse: 910.3982 - val_loss: 1051.1187 - val_mse: 1051.1187\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 891.5974 - mse: 891.5974 - val_loss: 1019.3784 - val_mse: 1019.3784\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 881.0668 - mse: 881.0668 - val_loss: 985.5818 - val_mse: 985.5818\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 853.6118 - mse: 853.6118 - val_loss: 957.4729 - val_mse: 957.4729\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 831.9842 - mse: 831.9842 - val_loss: 926.3292 - val_mse: 926.3292\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 803.2485 - mse: 803.2485 - val_loss: 899.8668 - val_mse: 899.8668\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 779.9114 - mse: 779.9114 - val_loss: 885.1300 - val_mse: 885.1300\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 746.9065 - mse: 746.9065 - val_loss: 868.8711 - val_mse: 868.8711\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 729.6008 - mse: 729.6008 - val_loss: 848.4024 - val_mse: 848.4024\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 721.0248 - mse: 721.0248 - val_loss: 819.4003 - val_mse: 819.4003\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 676.5015 - mse: 676.5015 - val_loss: 795.5716 - val_mse: 795.5716\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 684.5983 - mse: 684.5983 - val_loss: 774.1804 - val_mse: 774.1804\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 656.9591 - mse: 656.9591 - val_loss: 752.8680 - val_mse: 752.8680\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 620.8301 - mse: 620.8301 - val_loss: 723.8065 - val_mse: 723.8065\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 605.7662 - mse: 605.7662 - val_loss: 695.1118 - val_mse: 695.1118\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 589.5953 - mse: 589.5953 - val_loss: 659.8096 - val_mse: 659.8096\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 571.6010 - mse: 571.6010 - val_loss: 626.0847 - val_mse: 626.0847\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 553.4506 - mse: 553.4506 - val_loss: 600.8774 - val_mse: 600.8774\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 539.1122 - mse: 539.1122 - val_loss: 582.8516 - val_mse: 582.8516\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 511.5249 - mse: 511.5249 - val_loss: 562.6064 - val_mse: 562.6064\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 485.3427 - mse: 485.3427 - val_loss: 539.9187 - val_mse: 539.9187\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 480.2706 - mse: 480.2706 - val_loss: 520.9145 - val_mse: 520.9145\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 461.1663 - mse: 461.1663 - val_loss: 498.5434 - val_mse: 498.5434\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 452.6761 - mse: 452.6761 - val_loss: 477.7017 - val_mse: 477.7017\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 432.5392 - mse: 432.5392 - val_loss: 460.8062 - val_mse: 460.8062\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 390.5562 - mse: 390.5562 - val_loss: 444.5824 - val_mse: 444.5824\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 393.0292 - mse: 393.0292 - val_loss: 430.3415 - val_mse: 430.3415\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 383.1746 - mse: 383.1746 - val_loss: 416.5563 - val_mse: 416.5563\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 345.4832 - mse: 345.4832 - val_loss: 402.6615 - val_mse: 402.6615\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 360.3122 - mse: 360.3122 - val_loss: 385.4005 - val_mse: 385.4005\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 346.9789 - mse: 346.9789 - val_loss: 360.8593 - val_mse: 360.8593\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 350.0891 - mse: 350.0891 - val_loss: 345.3066 - val_mse: 345.3066\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 310.8334 - mse: 310.8334 - val_loss: 332.2923 - val_mse: 332.2923\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 298.8229 - mse: 298.8229 - val_loss: 319.8948 - val_mse: 319.8948\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 293.4239 - mse: 293.4239 - val_loss: 304.6664 - val_mse: 304.6664\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 275.1153 - mse: 275.1153 - val_loss: 284.8954 - val_mse: 284.8954\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 263.7832 - mse: 263.7832 - val_loss: 268.5450 - val_mse: 268.5450\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 240.8053 - mse: 240.8053 - val_loss: 259.3514 - val_mse: 259.3514\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 245.5136 - mse: 245.5136 - val_loss: 253.3698 - val_mse: 253.3698\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 237.4565 - mse: 237.4565 - val_loss: 243.3590 - val_mse: 243.3590\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 228.5399 - mse: 228.5399 - val_loss: 237.6750 - val_mse: 237.6750\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 213.6312 - mse: 213.6312 - val_loss: 228.3701 - val_mse: 228.3701\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 219.2700 - mse: 219.2700 - val_loss: 217.8945 - val_mse: 217.8945\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 202.1346 - mse: 202.1346 - val_loss: 208.2735 - val_mse: 208.2735\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 191.7027 - mse: 191.7027 - val_loss: 195.7404 - val_mse: 195.7404\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 189.2260 - mse: 189.2260 - val_loss: 178.4262 - val_mse: 178.4262\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 184.1573 - mse: 184.1573 - val_loss: 163.0228 - val_mse: 163.0228\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 166.5284 - mse: 166.5284 - val_loss: 149.0747 - val_mse: 149.0747\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 175.6579 - mse: 175.6579 - val_loss: 142.9795 - val_mse: 142.9795\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 155.1107 - mse: 155.1107 - val_loss: 140.7467 - val_mse: 140.7467\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 151.8008 - mse: 151.8008 - val_loss: 133.6526 - val_mse: 133.6526\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 149.9781 - mse: 149.9781 - val_loss: 129.2106 - val_mse: 129.2106\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 158.8074 - mse: 158.8074 - val_loss: 126.0247 - val_mse: 126.0248\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 134.8931 - mse: 134.8931 - val_loss: 122.8771 - val_mse: 122.8771\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 119.6958 - mse: 119.6958 - val_loss: 117.1208 - val_mse: 117.1208\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 129.7071 - mse: 129.7071 - val_loss: 107.8796 - val_mse: 107.8796\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 110.3479 - mse: 110.3479 - val_loss: 100.1469 - val_mse: 100.1469\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 125.5956 - mse: 125.5956 - val_loss: 94.8067 - val_mse: 94.8067\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 106.5516 - mse: 106.5516 - val_loss: 92.3810 - val_mse: 92.3810\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 110.0932 - mse: 110.0932 - val_loss: 89.7470 - val_mse: 89.7470\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 111.6760 - mse: 111.6760 - val_loss: 86.4509 - val_mse: 86.4509\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 108.0748 - mse: 108.0748 - val_loss: 80.5919 - val_mse: 80.5919\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 95.2147 - mse: 95.2147 - val_loss: 72.2249 - val_mse: 72.2249\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 93.0627 - mse: 93.0627 - val_loss: 67.6429 - val_mse: 67.6429\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 104.4161 - mse: 104.4161 - val_loss: 67.5926 - val_mse: 67.5926\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 93.0690 - mse: 93.0690 - val_loss: 68.8644 - val_mse: 68.8644\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 78.0540 - mse: 78.0540 - val_loss: 69.3119 - val_mse: 69.3119\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 84.2900 - mse: 84.2900 - val_loss: 69.4420 - val_mse: 69.4420\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 84.9750 - mse: 84.9750 - val_loss: 68.5232 - val_mse: 68.5232\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 75.3950 - mse: 75.3950 - val_loss: 65.3814 - val_mse: 65.3814\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 77.6437 - mse: 77.6437 - val_loss: 63.0380 - val_mse: 63.0380\n",
      "1/1 [==============================] - 0s 114ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpeqvfiyl1\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 74ms/step - loss: 2516.8997 - mse: 2516.8997 - val_loss: 2730.9807 - val_mse: 2730.9807\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2436.9307 - mse: 2436.9307 - val_loss: 2711.5225 - val_mse: 2711.5225\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2395.4009 - mse: 2395.4009 - val_loss: 2691.3545 - val_mse: 2691.3545\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2341.1709 - mse: 2341.1709 - val_loss: 2667.6699 - val_mse: 2667.6699\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2314.9001 - mse: 2314.9001 - val_loss: 2641.0872 - val_mse: 2641.0872\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2293.5603 - mse: 2293.5603 - val_loss: 2612.6077 - val_mse: 2612.6077\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2274.0898 - mse: 2274.0898 - val_loss: 2584.6423 - val_mse: 2584.6423\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2249.5662 - mse: 2249.5662 - val_loss: 2554.7539 - val_mse: 2554.7539\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2233.8003 - mse: 2233.8003 - val_loss: 2525.7458 - val_mse: 2525.7458\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2222.4854 - mse: 2222.4854 - val_loss: 2496.9404 - val_mse: 2496.9404\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2210.4309 - mse: 2210.4309 - val_loss: 2468.9761 - val_mse: 2468.9761\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2199.0889 - mse: 2199.0889 - val_loss: 2441.5303 - val_mse: 2441.5303\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2181.6492 - mse: 2181.6492 - val_loss: 2413.9954 - val_mse: 2413.9954\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2160.9126 - mse: 2160.9126 - val_loss: 2388.1775 - val_mse: 2388.1775\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2141.9312 - mse: 2141.9312 - val_loss: 2364.3264 - val_mse: 2364.3267\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2148.3586 - mse: 2148.3586 - val_loss: 2340.5693 - val_mse: 2340.5693\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2123.0776 - mse: 2123.0779 - val_loss: 2317.7244 - val_mse: 2317.7244\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2114.1204 - mse: 2114.1204 - val_loss: 2292.4080 - val_mse: 2292.4080\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2106.8950 - mse: 2106.8950 - val_loss: 2268.7305 - val_mse: 2268.7305\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2093.6555 - mse: 2093.6555 - val_loss: 2248.9050 - val_mse: 2248.9050\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2087.2891 - mse: 2087.2891 - val_loss: 2227.4946 - val_mse: 2227.4946\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2062.2087 - mse: 2062.2087 - val_loss: 2210.1355 - val_mse: 2210.1355\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2045.5922 - mse: 2045.5922 - val_loss: 2189.2268 - val_mse: 2189.2268\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2042.9752 - mse: 2042.9752 - val_loss: 2168.3792 - val_mse: 2168.3792\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 2025.4330 - mse: 2025.4330 - val_loss: 2146.9929 - val_mse: 2146.9929\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2012.4823 - mse: 2012.4823 - val_loss: 2126.0718 - val_mse: 2126.0718\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1994.2888 - mse: 1994.2888 - val_loss: 2110.7480 - val_mse: 2110.7480\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1983.2231 - mse: 1983.2231 - val_loss: 2093.5344 - val_mse: 2093.5344\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1972.3184 - mse: 1972.3184 - val_loss: 2077.4473 - val_mse: 2077.4473\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1962.0682 - mse: 1962.0682 - val_loss: 2062.7422 - val_mse: 2062.7422\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 1962.1700 - mse: 1962.1700 - val_loss: 2044.3596 - val_mse: 2044.3596\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1941.4362 - mse: 1941.4362 - val_loss: 2027.5225 - val_mse: 2027.5225\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1921.4915 - mse: 1921.4915 - val_loss: 2009.2438 - val_mse: 2009.2438\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1919.0024 - mse: 1919.0024 - val_loss: 1994.7454 - val_mse: 1994.7454\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1911.0579 - mse: 1911.0579 - val_loss: 1982.0687 - val_mse: 1982.0687\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1891.6263 - mse: 1891.6263 - val_loss: 1974.6724 - val_mse: 1974.6724\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1872.3226 - mse: 1872.3226 - val_loss: 1967.6511 - val_mse: 1967.6511\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1857.0542 - mse: 1857.0542 - val_loss: 1958.5983 - val_mse: 1958.5983\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1844.4596 - mse: 1844.4596 - val_loss: 1946.4626 - val_mse: 1946.4626\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1829.9064 - mse: 1829.9064 - val_loss: 1934.1061 - val_mse: 1934.1061\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1819.9232 - mse: 1819.9232 - val_loss: 1922.2323 - val_mse: 1922.2323\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1805.2958 - mse: 1805.2958 - val_loss: 1910.7375 - val_mse: 1910.7375\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1782.7297 - mse: 1782.7297 - val_loss: 1897.1185 - val_mse: 1897.1185\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1778.6979 - mse: 1778.6979 - val_loss: 1877.7716 - val_mse: 1877.7716\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 1758.9563 - mse: 1758.9563 - val_loss: 1859.8385 - val_mse: 1859.8385\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1729.5852 - mse: 1729.5852 - val_loss: 1843.6274 - val_mse: 1843.6274\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1719.3413 - mse: 1719.3413 - val_loss: 1822.8521 - val_mse: 1822.8521\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 1705.2372 - mse: 1705.2372 - val_loss: 1800.6655 - val_mse: 1800.6655\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1697.9646 - mse: 1697.9646 - val_loss: 1781.0349 - val_mse: 1781.0349\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1672.6174 - mse: 1672.6174 - val_loss: 1762.1650 - val_mse: 1762.1650\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1656.2856 - mse: 1656.2856 - val_loss: 1740.9264 - val_mse: 1740.9264\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1631.2080 - mse: 1631.2080 - val_loss: 1721.9563 - val_mse: 1721.9563\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 1613.5513 - mse: 1613.5513 - val_loss: 1699.0865 - val_mse: 1699.0865\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1597.3409 - mse: 1597.3409 - val_loss: 1679.1058 - val_mse: 1679.1058\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1578.3021 - mse: 1578.3021 - val_loss: 1664.9011 - val_mse: 1664.9011\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1546.7509 - mse: 1546.7509 - val_loss: 1641.7375 - val_mse: 1641.7375\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1549.7183 - mse: 1549.7183 - val_loss: 1616.7655 - val_mse: 1616.7655\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1519.7137 - mse: 1519.7137 - val_loss: 1592.9927 - val_mse: 1592.9927\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1469.8149 - mse: 1469.8149 - val_loss: 1572.2682 - val_mse: 1572.2682\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1452.2593 - mse: 1452.2593 - val_loss: 1553.3690 - val_mse: 1553.3690\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1442.7885 - mse: 1442.7885 - val_loss: 1535.8977 - val_mse: 1535.8977\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1429.1089 - mse: 1429.1089 - val_loss: 1512.3933 - val_mse: 1512.3933\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1406.8060 - mse: 1406.8060 - val_loss: 1484.3636 - val_mse: 1484.3636\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 1395.7808 - mse: 1395.7808 - val_loss: 1458.4976 - val_mse: 1458.4976\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1332.8912 - mse: 1332.8912 - val_loss: 1437.6616 - val_mse: 1437.6616\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1338.1318 - mse: 1338.1318 - val_loss: 1415.1390 - val_mse: 1415.1390\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1291.8839 - mse: 1291.8839 - val_loss: 1392.8413 - val_mse: 1392.8413\n",
      "Epoch 68/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 18ms/step - loss: 1270.9067 - mse: 1270.9067 - val_loss: 1366.3541 - val_mse: 1366.3541\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1242.1425 - mse: 1242.1425 - val_loss: 1339.3330 - val_mse: 1339.3330\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1222.3359 - mse: 1222.3359 - val_loss: 1314.5135 - val_mse: 1314.5135\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1204.0060 - mse: 1204.0060 - val_loss: 1283.1072 - val_mse: 1283.1072\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1164.3938 - mse: 1164.3938 - val_loss: 1251.2476 - val_mse: 1251.2476\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1137.5021 - mse: 1137.5021 - val_loss: 1220.7037 - val_mse: 1220.7037\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1118.3506 - mse: 1118.3506 - val_loss: 1192.3961 - val_mse: 1192.3961\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1092.7313 - mse: 1092.7313 - val_loss: 1168.0200 - val_mse: 1168.0200\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1076.9674 - mse: 1076.9674 - val_loss: 1140.7671 - val_mse: 1140.7671\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1031.0125 - mse: 1031.0125 - val_loss: 1116.2446 - val_mse: 1116.2446\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 1023.7098 - mse: 1023.7098 - val_loss: 1093.6874 - val_mse: 1093.6875\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 1002.3134 - mse: 1002.3134 - val_loss: 1061.3232 - val_mse: 1061.3232\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 971.3836 - mse: 971.3836 - val_loss: 1028.7203 - val_mse: 1028.7203\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 929.0054 - mse: 929.0054 - val_loss: 997.0929 - val_mse: 997.0929\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 926.7874 - mse: 926.7874 - val_loss: 969.6086 - val_mse: 969.6086\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 914.7904 - mse: 914.7904 - val_loss: 945.4446 - val_mse: 945.4446\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 869.4860 - mse: 869.4860 - val_loss: 914.8685 - val_mse: 914.8685\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 848.2958 - mse: 848.2958 - val_loss: 888.8236 - val_mse: 888.8236\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 817.8936 - mse: 817.8936 - val_loss: 861.3813 - val_mse: 861.3813\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 799.5485 - mse: 799.5485 - val_loss: 836.2216 - val_mse: 836.2216\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 779.9207 - mse: 779.9207 - val_loss: 805.0526 - val_mse: 805.0526\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 763.7938 - mse: 763.7938 - val_loss: 779.7502 - val_mse: 779.7502\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 719.3519 - mse: 719.3519 - val_loss: 757.2595 - val_mse: 757.2595\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 699.0379 - mse: 699.0379 - val_loss: 727.5569 - val_mse: 727.5569\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 690.7802 - mse: 690.7802 - val_loss: 707.5985 - val_mse: 707.5985\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 677.7283 - mse: 677.7283 - val_loss: 691.5245 - val_mse: 691.5245\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 647.5728 - mse: 647.5728 - val_loss: 675.9169 - val_mse: 675.9169\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 626.6772 - mse: 626.6772 - val_loss: 658.2099 - val_mse: 658.2099\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 608.9564 - mse: 608.9564 - val_loss: 638.9307 - val_mse: 638.9307\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 582.8831 - mse: 582.8831 - val_loss: 615.2593 - val_mse: 615.2593\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 560.4824 - mse: 560.4824 - val_loss: 584.6762 - val_mse: 584.6763\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 537.7568 - mse: 537.7568 - val_loss: 550.2944 - val_mse: 550.2944\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 514.5048 - mse: 514.5048 - val_loss: 527.1955 - val_mse: 527.1955\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 510.0304 - mse: 510.0304 - val_loss: 509.4729 - val_mse: 509.4729\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 504.8527 - mse: 504.8527 - val_loss: 491.5417 - val_mse: 491.5417\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 481.0941 - mse: 481.0941 - val_loss: 470.8582 - val_mse: 470.8582\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 452.6005 - mse: 452.6005 - val_loss: 448.5415 - val_mse: 448.5415\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 439.4774 - mse: 439.4774 - val_loss: 427.9532 - val_mse: 427.9532\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 424.4368 - mse: 424.4368 - val_loss: 408.2847 - val_mse: 408.2847\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 391.4281 - mse: 391.4281 - val_loss: 394.1411 - val_mse: 394.1411\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 400.2438 - mse: 400.2438 - val_loss: 381.6636 - val_mse: 381.6636\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 377.8262 - mse: 377.8262 - val_loss: 371.3930 - val_mse: 371.3930\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 366.4669 - mse: 366.4669 - val_loss: 355.9669 - val_mse: 355.9669\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 338.2399 - mse: 338.2399 - val_loss: 343.0867 - val_mse: 343.0867\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 349.7774 - mse: 349.7774 - val_loss: 326.6814 - val_mse: 326.6814\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 295.2684 - mse: 295.2684 - val_loss: 312.7709 - val_mse: 312.7709\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 307.8068 - mse: 307.8068 - val_loss: 297.4047 - val_mse: 297.4047\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 294.4287 - mse: 294.4287 - val_loss: 279.7447 - val_mse: 279.7447\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 289.0198 - mse: 289.0198 - val_loss: 267.0611 - val_mse: 267.0610\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 270.9545 - mse: 270.9545 - val_loss: 251.0787 - val_mse: 251.0787\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 242.9830 - mse: 242.9830 - val_loss: 234.2364 - val_mse: 234.2364\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 242.9083 - mse: 242.9083 - val_loss: 225.5560 - val_mse: 225.5560\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 248.1559 - mse: 248.1559 - val_loss: 223.7397 - val_mse: 223.7397\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 242.4360 - mse: 242.4360 - val_loss: 213.4462 - val_mse: 213.4462\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 226.6906 - mse: 226.6906 - val_loss: 195.6227 - val_mse: 195.6227\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 220.6733 - mse: 220.6733 - val_loss: 180.7734 - val_mse: 180.7734\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 223.2317 - mse: 223.2317 - val_loss: 170.0545 - val_mse: 170.0545\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 208.1272 - mse: 208.1272 - val_loss: 161.3673 - val_mse: 161.3673\n",
      "Epoch 126/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 18ms/step - loss: 198.9685 - mse: 198.9685 - val_loss: 149.7316 - val_mse: 149.7316\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 174.1928 - mse: 174.1928 - val_loss: 143.7943 - val_mse: 143.7943\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 176.4397 - mse: 176.4397 - val_loss: 135.9777 - val_mse: 135.9777\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 173.7621 - mse: 173.7621 - val_loss: 127.6070 - val_mse: 127.6070\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 169.9762 - mse: 169.9762 - val_loss: 118.5735 - val_mse: 118.5735\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 157.6796 - mse: 157.6796 - val_loss: 111.0108 - val_mse: 111.0108\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 135.9639 - mse: 135.9639 - val_loss: 108.0487 - val_mse: 108.0487\n",
      "Epoch 133/150\n",
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      "Epoch 134/150\n",
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      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 125.9718 - mse: 125.9718 - val_loss: 102.6254 - val_mse: 102.6254\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 150.6092 - mse: 150.6092 - val_loss: 101.8109 - val_mse: 101.8109\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 124.8061 - mse: 124.8061 - val_loss: 96.5842 - val_mse: 96.5842\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 116.4727 - mse: 116.4727 - val_loss: 88.7671 - val_mse: 88.7671\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 125.4280 - mse: 125.4280 - val_loss: 84.3523 - val_mse: 84.3523\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 115.6931 - mse: 115.6931 - val_loss: 78.0128 - val_mse: 78.0128\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 119.2276 - mse: 119.2276 - val_loss: 72.4938 - val_mse: 72.4938\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 101.0135 - mse: 101.0135 - val_loss: 69.7507 - val_mse: 69.7507\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 101.2113 - mse: 101.2113 - val_loss: 66.0854 - val_mse: 66.0854\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 104.4397 - mse: 104.4397 - val_loss: 62.5202 - val_mse: 62.5202\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 98.3960 - mse: 98.3960 - val_loss: 59.9011 - val_mse: 59.9011\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 104.6149 - mse: 104.6149 - val_loss: 58.2247 - val_mse: 58.2247\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 92.3430 - mse: 92.3430 - val_loss: 55.1085 - val_mse: 55.1085\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 82.1943 - mse: 82.1943 - val_loss: 54.4832 - val_mse: 54.4832\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 93.5901 - mse: 93.5901 - val_loss: 55.8405 - val_mse: 55.8405\n",
      "Epoch 150/150\n",
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      "1/1 [==============================] - 0s 123ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpin0orkpf\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 76ms/step - loss: 2479.0154 - mse: 2479.0154 - val_loss: 2733.2305 - val_mse: 2733.2305\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2403.4043 - mse: 2403.4043 - val_loss: 2714.0234 - val_mse: 2714.0234\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 2352.2021 - mse: 2352.2021 - val_loss: 2693.0645 - val_mse: 2693.0645\n",
      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2265.5676 - mse: 2265.5676 - val_loss: 2611.7117 - val_mse: 2611.7117\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2254.8342 - mse: 2254.8342 - val_loss: 2580.9653 - val_mse: 2580.9653\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2225.4978 - mse: 2225.4978 - val_loss: 2549.5161 - val_mse: 2549.5161\n",
      "Epoch 9/150\n",
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      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2177.3892 - mse: 2177.3892 - val_loss: 2486.0005 - val_mse: 2486.0005\n",
      "Epoch 11/150\n",
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      "Epoch 12/150\n",
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      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 16/150\n",
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      "Epoch 17/150\n",
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      "Epoch 18/150\n",
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      "Epoch 19/150\n",
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      "Epoch 20/150\n",
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      "Epoch 21/150\n",
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      "Epoch 22/150\n",
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      "Epoch 23/150\n",
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      "Epoch 24/150\n",
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      "Epoch 26/150\n",
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      "Epoch 29/150\n",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
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      "Epoch 32/150\n",
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      "Epoch 33/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 21ms/step - loss: 1906.2950 - mse: 1906.2950 - val_loss: 2044.2938 - val_mse: 2044.2938\n",
      "Epoch 34/150\n",
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      "Epoch 36/150\n",
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      "Epoch 37/150\n",
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      "Epoch 42/150\n",
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      "Epoch 43/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
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      "Epoch 47/150\n",
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      "Epoch 48/150\n",
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      "Epoch 50/150\n",
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      "Epoch 51/150\n",
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      "Epoch 52/150\n",
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      "Epoch 53/150\n",
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      "Epoch 54/150\n",
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      "Epoch 55/150\n",
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      "Epoch 56/150\n",
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      "Epoch 57/150\n",
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      "Epoch 58/150\n",
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      "Epoch 59/150\n",
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      "Epoch 60/150\n",
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      "Epoch 61/150\n",
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      "Epoch 62/150\n",
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      "Epoch 63/150\n",
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      "Epoch 64/150\n",
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      "Epoch 65/150\n",
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      "Epoch 66/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1220.6963 - mse: 1220.6963 - val_loss: 1390.8190 - val_mse: 1390.8190\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1200.0919 - mse: 1200.0919 - val_loss: 1354.8000 - val_mse: 1354.8000\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 1183.9448 - mse: 1183.9448 - val_loss: 1324.1635 - val_mse: 1324.1635\n",
      "Epoch 72/150\n",
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      "Epoch 73/150\n",
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      "Epoch 74/150\n",
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      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1080.1974 - mse: 1080.1974 - val_loss: 1215.4960 - val_mse: 1215.4960\n",
      "Epoch 76/150\n",
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      "Epoch 77/150\n",
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      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1015.9742 - mse: 1015.9742 - val_loss: 1102.7551 - val_mse: 1102.7551\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 988.6433 - mse: 988.6433 - val_loss: 1074.3191 - val_mse: 1074.3191\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 988.9408 - mse: 988.9408 - val_loss: 1049.5690 - val_mse: 1049.5690\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 927.3388 - mse: 927.3388 - val_loss: 1018.8156 - val_mse: 1018.8156\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 916.2959 - mse: 916.2959 - val_loss: 990.5034 - val_mse: 990.5034\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 873.1711 - mse: 873.1711 - val_loss: 964.8346 - val_mse: 964.8346\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 853.6375 - mse: 853.6375 - val_loss: 942.2864 - val_mse: 942.2864\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 835.3444 - mse: 835.3444 - val_loss: 918.3032 - val_mse: 918.3032\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 813.1965 - mse: 813.1965 - val_loss: 899.4235 - val_mse: 899.4235\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 791.9000 - mse: 791.9000 - val_loss: 881.6346 - val_mse: 881.6346\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 743.2219 - mse: 743.2219 - val_loss: 853.5605 - val_mse: 853.5605\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 740.2267 - mse: 740.2267 - val_loss: 825.2833 - val_mse: 825.2833\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 703.7119 - mse: 703.7119 - val_loss: 801.1370 - val_mse: 801.1370\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 687.8699 - mse: 687.8699 - val_loss: 769.6758 - val_mse: 769.6758\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 672.5744 - mse: 672.5744 - val_loss: 735.1497 - val_mse: 735.1497\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 667.3351 - mse: 667.3351 - val_loss: 702.0731 - val_mse: 702.0731\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 632.8527 - mse: 632.8527 - val_loss: 675.7297 - val_mse: 675.7297\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 615.6238 - mse: 615.6238 - val_loss: 655.8155 - val_mse: 655.8155\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 597.5555 - mse: 597.5555 - val_loss: 636.5201 - val_mse: 636.5201\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 590.2403 - mse: 590.2403 - val_loss: 614.6700 - val_mse: 614.6700\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 563.8838 - mse: 563.8838 - val_loss: 595.5904 - val_mse: 595.5904\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 526.6549 - mse: 526.6549 - val_loss: 579.0168 - val_mse: 579.0168\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 526.1591 - mse: 526.1591 - val_loss: 554.0180 - val_mse: 554.0180\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 488.2986 - mse: 488.2986 - val_loss: 524.5745 - val_mse: 524.5745\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 482.9108 - mse: 482.9108 - val_loss: 492.8433 - val_mse: 492.8433\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 466.2099 - mse: 466.2099 - val_loss: 461.8520 - val_mse: 461.8520\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 452.7497 - mse: 452.7497 - val_loss: 435.8235 - val_mse: 435.8235\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 436.3567 - mse: 436.3567 - val_loss: 420.0210 - val_mse: 420.0210\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 412.2220 - mse: 412.2220 - val_loss: 408.5571 - val_mse: 408.5571\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 408.5876 - mse: 408.5876 - val_loss: 395.4811 - val_mse: 395.4811\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 373.8268 - mse: 373.8268 - val_loss: 381.6775 - val_mse: 381.6775\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 368.4181 - mse: 368.4181 - val_loss: 363.8926 - val_mse: 363.8926\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 356.9710 - mse: 356.9710 - val_loss: 346.6336 - val_mse: 346.6336\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 347.9749 - mse: 347.9749 - val_loss: 326.4735 - val_mse: 326.4735\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 329.5240 - mse: 329.5240 - val_loss: 307.6883 - val_mse: 307.6883\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 317.9153 - mse: 317.9153 - val_loss: 297.6277 - val_mse: 297.6277\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 313.4957 - mse: 313.4957 - val_loss: 291.7221 - val_mse: 291.7221\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 291.9988 - mse: 291.9988 - val_loss: 279.9666 - val_mse: 279.9666\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 285.0823 - mse: 285.0823 - val_loss: 268.6078 - val_mse: 268.6078\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 263.2491 - mse: 263.2491 - val_loss: 255.8997 - val_mse: 255.8997\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 264.8779 - mse: 264.8779 - val_loss: 243.1779 - val_mse: 243.1779\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 245.3275 - mse: 245.3275 - val_loss: 230.4606 - val_mse: 230.4606\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 219.8377 - mse: 219.8377 - val_loss: 221.0916 - val_mse: 221.0916\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 232.2963 - mse: 232.2963 - val_loss: 213.0401 - val_mse: 213.0401\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 227.4312 - mse: 227.4312 - val_loss: 204.3536 - val_mse: 204.3536\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 208.4491 - mse: 208.4491 - val_loss: 192.9811 - val_mse: 192.9811\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 206.9283 - mse: 206.9283 - val_loss: 184.1998 - val_mse: 184.1998\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 177.0835 - mse: 177.0835 - val_loss: 176.4869 - val_mse: 176.4869\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 181.6640 - mse: 181.6640 - val_loss: 171.8559 - val_mse: 171.8559\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 181.7293 - mse: 181.7293 - val_loss: 164.9081 - val_mse: 164.9081\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 180.6172 - mse: 180.6172 - val_loss: 156.7749 - val_mse: 156.7749\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 153.4243 - mse: 153.4243 - val_loss: 154.7807 - val_mse: 154.7807\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 165.3897 - mse: 165.3897 - val_loss: 149.4708 - val_mse: 149.4708\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 155.0871 - mse: 155.0871 - val_loss: 144.7831 - val_mse: 144.7831\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 157.9259 - mse: 157.9259 - val_loss: 136.4858 - val_mse: 136.4858\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 141.8163 - mse: 141.8163 - val_loss: 132.6838 - val_mse: 132.6838\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 142.1603 - mse: 142.1603 - val_loss: 126.0924 - val_mse: 126.0924\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 130.4624 - mse: 130.4624 - val_loss: 121.9792 - val_mse: 121.9792\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 129.7603 - mse: 129.7603 - val_loss: 118.6115 - val_mse: 118.6115\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 123.7771 - mse: 123.7771 - val_loss: 110.5006 - val_mse: 110.5006\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 128.9608 - mse: 128.9608 - val_loss: 103.7166 - val_mse: 103.7166\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 115.4826 - mse: 115.4826 - val_loss: 96.6682 - val_mse: 96.6682\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 115.8441 - mse: 115.8441 - val_loss: 91.0964 - val_mse: 91.0964\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 104.5576 - mse: 104.5576 - val_loss: 87.6900 - val_mse: 87.6900\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 100.4096 - mse: 100.4096 - val_loss: 83.1490 - val_mse: 83.1490\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 99.3480 - mse: 99.3480 - val_loss: 78.6421 - val_mse: 78.6421\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 96.6302 - mse: 96.6302 - val_loss: 76.7861 - val_mse: 76.7861\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 91.1894 - mse: 91.1894 - val_loss: 76.5766 - val_mse: 76.5766\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 108.6740 - mse: 108.6740 - val_loss: 71.9120 - val_mse: 71.9120\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 99.7045 - mse: 99.7045 - val_loss: 67.7945 - val_mse: 67.7945\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 100.9962 - mse: 100.9962 - val_loss: 62.3332 - val_mse: 62.3332\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 85.5518 - mse: 85.5518 - val_loss: 59.0370 - val_mse: 59.0370\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 93.5240 - mse: 93.5240 - val_loss: 54.4209 - val_mse: 54.4209\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002729FB5BAF0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 203ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmp3wuokkt_\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 2s 183ms/step - loss: 2475.9973 - mse: 2475.9973 - val_loss: 2732.5405 - val_mse: 2732.5405\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 2395.9470 - mse: 2395.9470 - val_loss: 2712.3274 - val_mse: 2712.3274\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2358.0244 - mse: 2358.0247 - val_loss: 2690.9197 - val_mse: 2690.9197\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2306.5056 - mse: 2306.5054 - val_loss: 2667.7761 - val_mse: 2667.7761\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 2278.2400 - mse: 2278.2400 - val_loss: 2641.0010 - val_mse: 2641.0010\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2250.9492 - mse: 2250.9492 - val_loss: 2612.8142 - val_mse: 2612.8142\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2237.7295 - mse: 2237.7295 - val_loss: 2584.1624 - val_mse: 2584.1624\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2215.5493 - mse: 2215.5493 - val_loss: 2556.7976 - val_mse: 2556.7976\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 2205.0862 - mse: 2205.0862 - val_loss: 2528.8428 - val_mse: 2528.8428\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 2188.6765 - mse: 2188.6768 - val_loss: 2500.0432 - val_mse: 2500.0432\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2159.3103 - mse: 2159.3103 - val_loss: 2472.9631 - val_mse: 2472.9631\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2150.3025 - mse: 2150.3025 - val_loss: 2445.6165 - val_mse: 2445.6165\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2146.4438 - mse: 2146.4438 - val_loss: 2418.3691 - val_mse: 2418.3691\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 2120.0674 - mse: 2120.0674 - val_loss: 2393.1174 - val_mse: 2393.1174\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 2119.8450 - mse: 2119.8450 - val_loss: 2367.9031 - val_mse: 2367.9031\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 2109.1938 - mse: 2109.1938 - val_loss: 2345.2148 - val_mse: 2345.2148\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2084.3262 - mse: 2084.3262 - val_loss: 2321.1450 - val_mse: 2321.1450\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 2062.5513 - mse: 2062.5513 - val_loss: 2294.9333 - val_mse: 2294.9333\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2057.7932 - mse: 2057.7932 - val_loss: 2271.7634 - val_mse: 2271.7634\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 2037.7574 - mse: 2037.7574 - val_loss: 2250.7061 - val_mse: 2250.7061\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 2039.9579 - mse: 2039.9579 - val_loss: 2231.0520 - val_mse: 2231.0520\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 2022.4584 - mse: 2022.4584 - val_loss: 2208.4478 - val_mse: 2208.4478\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 2019.1277 - mse: 2019.1277 - val_loss: 2187.4189 - val_mse: 2187.4189\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1985.4437 - mse: 1985.4437 - val_loss: 2167.7749 - val_mse: 2167.7749\n",
      "Epoch 25/150\n",
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      "Epoch 26/150\n",
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      "Epoch 27/150\n",
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      "Epoch 28/150\n",
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      "Epoch 29/150\n",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
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      "Epoch 32/150\n",
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      "Epoch 34/150\n",
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      "Epoch 36/150\n",
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      "Epoch 37/150\n",
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      "Epoch 38/150\n",
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      "Epoch 39/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
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      "Epoch 42/150\n",
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      "Epoch 43/150\n",
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      "Epoch 44/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
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      "Epoch 47/150\n",
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      "Epoch 48/150\n",
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      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1672.3422 - mse: 1672.3422 - val_loss: 1792.8005 - val_mse: 1792.8005\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1655.2361 - mse: 1655.2361 - val_loss: 1771.9718 - val_mse: 1771.9718\n",
      "Epoch 51/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 28ms/step - loss: 1611.5468 - mse: 1611.5468 - val_loss: 1749.9091 - val_mse: 1749.9091\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1616.4700 - mse: 1616.4700 - val_loss: 1727.0159 - val_mse: 1727.0159\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1601.4579 - mse: 1601.4579 - val_loss: 1710.0645 - val_mse: 1710.0645\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1565.5006 - mse: 1565.5006 - val_loss: 1692.2999 - val_mse: 1692.2999\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 33ms/step - loss: 1547.1725 - mse: 1547.1725 - val_loss: 1676.2992 - val_mse: 1676.2992\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1522.9285 - mse: 1522.9285 - val_loss: 1665.5828 - val_mse: 1665.5828\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1510.0753 - mse: 1510.0753 - val_loss: 1654.3123 - val_mse: 1654.3123\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1481.8916 - mse: 1481.8916 - val_loss: 1647.1414 - val_mse: 1647.1414\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1469.7572 - mse: 1469.7572 - val_loss: 1633.1721 - val_mse: 1633.1721\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1439.4238 - mse: 1439.4238 - val_loss: 1614.0768 - val_mse: 1614.0768\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1398.5122 - mse: 1398.5122 - val_loss: 1588.5060 - val_mse: 1588.5060\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1392.9995 - mse: 1392.9995 - val_loss: 1558.3721 - val_mse: 1558.3721\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1367.3544 - mse: 1367.3544 - val_loss: 1525.6139 - val_mse: 1525.6139\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1334.9109 - mse: 1334.9109 - val_loss: 1495.5836 - val_mse: 1495.5836\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1339.4039 - mse: 1339.4039 - val_loss: 1465.3896 - val_mse: 1465.3896\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1285.1293 - mse: 1285.1293 - val_loss: 1443.9539 - val_mse: 1443.9539\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 1262.9766 - mse: 1262.9766 - val_loss: 1413.4402 - val_mse: 1413.4402\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1254.2405 - mse: 1254.2402 - val_loss: 1390.0536 - val_mse: 1390.0536\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1235.4850 - mse: 1235.4850 - val_loss: 1365.3458 - val_mse: 1365.3458\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1210.0043 - mse: 1210.0043 - val_loss: 1333.8068 - val_mse: 1333.8068\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1196.5586 - mse: 1196.5586 - val_loss: 1311.1030 - val_mse: 1311.1030\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1144.8650 - mse: 1144.8650 - val_loss: 1287.2389 - val_mse: 1287.2389\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1119.0177 - mse: 1119.0177 - val_loss: 1260.2510 - val_mse: 1260.2510\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 1098.8588 - mse: 1098.8588 - val_loss: 1239.3264 - val_mse: 1239.3264\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1072.6587 - mse: 1072.6587 - val_loss: 1218.3840 - val_mse: 1218.3840\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1052.7300 - mse: 1052.7300 - val_loss: 1195.3696 - val_mse: 1195.3696\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1044.7467 - mse: 1044.7467 - val_loss: 1170.3247 - val_mse: 1170.3247\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 993.3562 - mse: 993.3562 - val_loss: 1136.6583 - val_mse: 1136.6584\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 975.4186 - mse: 975.4186 - val_loss: 1105.7035 - val_mse: 1105.7035\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 943.4481 - mse: 943.4481 - val_loss: 1082.6616 - val_mse: 1082.6616\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 931.1724 - mse: 931.1724 - val_loss: 1060.8802 - val_mse: 1060.8802\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 911.1716 - mse: 911.1716 - val_loss: 1028.6274 - val_mse: 1028.6274\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 870.1769 - mse: 870.1769 - val_loss: 997.0457 - val_mse: 997.0457\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 862.8841 - mse: 862.8841 - val_loss: 969.3113 - val_mse: 969.3113\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 849.1669 - mse: 849.1669 - val_loss: 951.1510 - val_mse: 951.1510\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 810.5686 - mse: 810.5686 - val_loss: 931.5576 - val_mse: 931.5576\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 795.1448 - mse: 795.1448 - val_loss: 914.7516 - val_mse: 914.7516\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 760.4162 - mse: 760.4162 - val_loss: 896.1146 - val_mse: 896.1146\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 736.2732 - mse: 736.2732 - val_loss: 871.4725 - val_mse: 871.4725\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 718.2917 - mse: 718.2917 - val_loss: 844.0912 - val_mse: 844.0912\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 682.4692 - mse: 682.4692 - val_loss: 809.0825 - val_mse: 809.0825\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 682.8974 - mse: 682.8974 - val_loss: 770.4812 - val_mse: 770.4812\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 641.2935 - mse: 641.2935 - val_loss: 740.9168 - val_mse: 740.9168\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 641.0268 - mse: 641.0268 - val_loss: 716.5792 - val_mse: 716.5792\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 634.0035 - mse: 634.0035 - val_loss: 693.2450 - val_mse: 693.2450\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 603.0964 - mse: 603.0964 - val_loss: 671.8452 - val_mse: 671.8452\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 575.2855 - mse: 575.2855 - val_loss: 648.9973 - val_mse: 648.9973\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 573.8767 - mse: 573.8767 - val_loss: 626.9668 - val_mse: 626.9668\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 535.8149 - mse: 535.8149 - val_loss: 602.2734 - val_mse: 602.2734\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 510.8689 - mse: 510.8689 - val_loss: 579.7923 - val_mse: 579.7923\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 490.2978 - mse: 490.2978 - val_loss: 554.4680 - val_mse: 554.4680\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 468.6469 - mse: 468.6469 - val_loss: 527.8448 - val_mse: 527.8448\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 449.1418 - mse: 449.1418 - val_loss: 507.2143 - val_mse: 507.2143\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 444.9700 - mse: 444.9700 - val_loss: 492.7058 - val_mse: 492.7058\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 442.4002 - mse: 442.4002 - val_loss: 480.8809 - val_mse: 480.8809\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 419.7439 - mse: 419.7439 - val_loss: 461.2713 - val_mse: 461.2713\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 408.5727 - mse: 408.5727 - val_loss: 439.0310 - val_mse: 439.0310\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 388.4685 - mse: 388.4685 - val_loss: 418.5575 - val_mse: 418.5575\n",
      "Epoch 109/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 26ms/step - loss: 378.2048 - mse: 378.2048 - val_loss: 400.4132 - val_mse: 400.4132\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 348.2789 - mse: 348.2789 - val_loss: 386.7896 - val_mse: 386.7896\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 349.7223 - mse: 349.7223 - val_loss: 370.6584 - val_mse: 370.6584\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 323.8793 - mse: 323.8793 - val_loss: 362.8517 - val_mse: 362.8517\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 36ms/step - loss: 329.1498 - mse: 329.1498 - val_loss: 351.1940 - val_mse: 351.1940\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 34ms/step - loss: 323.0190 - mse: 323.0190 - val_loss: 332.0955 - val_mse: 332.0955\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 36ms/step - loss: 290.6549 - mse: 290.6549 - val_loss: 313.6779 - val_mse: 313.6779\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 36ms/step - loss: 275.6692 - mse: 275.6692 - val_loss: 297.5950 - val_mse: 297.5950\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 34ms/step - loss: 268.3625 - mse: 268.3625 - val_loss: 279.9193 - val_mse: 279.9192\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 265.1908 - mse: 265.1908 - val_loss: 263.4630 - val_mse: 263.4630\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 234.6258 - mse: 234.6258 - val_loss: 249.4731 - val_mse: 249.4731\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 226.8673 - mse: 226.8673 - val_loss: 236.5011 - val_mse: 236.5011\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 240.6985 - mse: 240.6985 - val_loss: 229.0940 - val_mse: 229.0940\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 213.6408 - mse: 213.6408 - val_loss: 218.6043 - val_mse: 218.6043\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 213.5720 - mse: 213.5720 - val_loss: 209.4983 - val_mse: 209.4983\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 188.3906 - mse: 188.3906 - val_loss: 199.7647 - val_mse: 199.7647\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 197.4563 - mse: 197.4563 - val_loss: 190.1878 - val_mse: 190.1878\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 189.7673 - mse: 189.7673 - val_loss: 185.7591 - val_mse: 185.7591\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 180.5226 - mse: 180.5226 - val_loss: 178.7983 - val_mse: 178.7983\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 162.0126 - mse: 162.0126 - val_loss: 170.3231 - val_mse: 170.3231\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 163.4147 - mse: 163.4147 - val_loss: 160.0333 - val_mse: 160.0333\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 155.8037 - mse: 155.8037 - val_loss: 152.9096 - val_mse: 152.9096\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 154.0114 - mse: 154.0114 - val_loss: 146.5727 - val_mse: 146.5727\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 154.7973 - mse: 154.7973 - val_loss: 139.7090 - val_mse: 139.7090\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 141.1871 - mse: 141.1871 - val_loss: 130.9796 - val_mse: 130.9796\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 132.6144 - mse: 132.6144 - val_loss: 124.3510 - val_mse: 124.3510\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 125.4368 - mse: 125.4368 - val_loss: 122.4238 - val_mse: 122.4238\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 124.7978 - mse: 124.7978 - val_loss: 118.1372 - val_mse: 118.1372\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 133.8702 - mse: 133.8702 - val_loss: 114.5842 - val_mse: 114.5842\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 125.5664 - mse: 125.5664 - val_loss: 112.4378 - val_mse: 112.4378\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 115.8970 - mse: 115.8970 - val_loss: 109.6006 - val_mse: 109.6006\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 115.6358 - mse: 115.6358 - val_loss: 105.1609 - val_mse: 105.1609\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 106.4541 - mse: 106.4541 - val_loss: 98.3066 - val_mse: 98.3066\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 105.8521 - mse: 105.8521 - val_loss: 90.2394 - val_mse: 90.2394\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 98.8790 - mse: 98.8790 - val_loss: 80.7324 - val_mse: 80.7324\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 97.7394 - mse: 97.7394 - val_loss: 74.7964 - val_mse: 74.7964\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 99.2540 - mse: 99.2540 - val_loss: 70.5080 - val_mse: 70.5080\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 90.5360 - mse: 90.5360 - val_loss: 68.1337 - val_mse: 68.1337\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 86.3289 - mse: 86.3289 - val_loss: 66.8461 - val_mse: 66.8461\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 96.1357 - mse: 96.1357 - val_loss: 66.4321 - val_mse: 66.4321\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 80.5466 - mse: 80.5466 - val_loss: 63.5816 - val_mse: 63.5816\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 73.6130 - mse: 73.6130 - val_loss: 59.9877 - val_mse: 59.9877\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002729921BA60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 161ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpqgd77_v1\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 2s 102ms/step - loss: 2389.1804 - mse: 2389.1804 - val_loss: 2730.8110 - val_mse: 2730.8110\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 2313.4626 - mse: 2313.4626 - val_loss: 2710.7473 - val_mse: 2710.7473\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 2266.1660 - mse: 2266.1660 - val_loss: 2689.9382 - val_mse: 2689.9382\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2228.1841 - mse: 2228.1841 - val_loss: 2667.1567 - val_mse: 2667.1567\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 2191.7588 - mse: 2191.7588 - val_loss: 2640.8877 - val_mse: 2640.8877\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2174.1768 - mse: 2174.1768 - val_loss: 2612.7090 - val_mse: 2612.7090\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 2153.8979 - mse: 2153.8979 - val_loss: 2584.3291 - val_mse: 2584.3291\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 2128.6167 - mse: 2128.6167 - val_loss: 2554.6255 - val_mse: 2554.6255\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 2123.8787 - mse: 2123.8787 - val_loss: 2525.2996 - val_mse: 2525.2996\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 2106.9890 - mse: 2106.9890 - val_loss: 2495.7317 - val_mse: 2495.7317\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 2079.4453 - mse: 2079.4453 - val_loss: 2467.1489 - val_mse: 2467.1489\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 2069.5359 - mse: 2069.5359 - val_loss: 2438.8655 - val_mse: 2438.8655\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 2058.2507 - mse: 2058.2507 - val_loss: 2409.4749 - val_mse: 2409.4749\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 2043.6680 - mse: 2043.6680 - val_loss: 2382.6997 - val_mse: 2382.7000\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 2030.5773 - mse: 2030.5773 - val_loss: 2357.8291 - val_mse: 2357.8291\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 2015.1886 - mse: 2015.1886 - val_loss: 2334.3435 - val_mse: 2334.3433\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 2024.8262 - mse: 2024.8262 - val_loss: 2313.8816 - val_mse: 2313.8816\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1993.7928 - mse: 1993.7928 - val_loss: 2295.5515 - val_mse: 2295.5515\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1988.6135 - mse: 1988.6135 - val_loss: 2276.4495 - val_mse: 2276.4495\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1967.1173 - mse: 1967.1173 - val_loss: 2256.9434 - val_mse: 2256.9431\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1952.1304 - mse: 1952.1304 - val_loss: 2234.1782 - val_mse: 2234.1782\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1953.4210 - mse: 1953.4210 - val_loss: 2210.4065 - val_mse: 2210.4065\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1938.2911 - mse: 1938.2911 - val_loss: 2188.2334 - val_mse: 2188.2334\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1908.3480 - mse: 1908.3480 - val_loss: 2168.3865 - val_mse: 2168.3865\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1920.4819 - mse: 1920.4819 - val_loss: 2151.2891 - val_mse: 2151.2891\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1905.6553 - mse: 1905.6553 - val_loss: 2132.9048 - val_mse: 2132.9048\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1888.1420 - mse: 1888.1420 - val_loss: 2113.5261 - val_mse: 2113.5261\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1870.7234 - mse: 1870.7234 - val_loss: 2093.4036 - val_mse: 2093.4036\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 1875.1527 - mse: 1875.1527 - val_loss: 2076.8611 - val_mse: 2076.8611\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 1854.7788 - mse: 1854.7788 - val_loss: 2059.8145 - val_mse: 2059.8145\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1851.5447 - mse: 1851.5447 - val_loss: 2044.8801 - val_mse: 2044.8801\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1839.1316 - mse: 1839.1316 - val_loss: 2029.1021 - val_mse: 2029.1021\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1842.4664 - mse: 1842.4664 - val_loss: 2017.0812 - val_mse: 2017.0812\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1811.5900 - mse: 1811.5900 - val_loss: 2004.0671 - val_mse: 2004.0671\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1778.1791 - mse: 1778.1791 - val_loss: 1986.8439 - val_mse: 1986.8439\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1776.7356 - mse: 1776.7356 - val_loss: 1974.0325 - val_mse: 1974.0325\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1778.8168 - mse: 1778.8168 - val_loss: 1959.3123 - val_mse: 1959.3123\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 1739.4436 - mse: 1739.4436 - val_loss: 1940.8414 - val_mse: 1940.8414\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1749.3546 - mse: 1749.3546 - val_loss: 1922.4548 - val_mse: 1922.4548\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1736.1088 - mse: 1736.1088 - val_loss: 1902.8501 - val_mse: 1902.8501\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1713.6794 - mse: 1713.6794 - val_loss: 1887.6028 - val_mse: 1887.6028\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1716.2684 - mse: 1716.2684 - val_loss: 1873.3320 - val_mse: 1873.3320\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1697.6825 - mse: 1697.6825 - val_loss: 1854.2173 - val_mse: 1854.2173\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 1666.6647 - mse: 1666.6647 - val_loss: 1828.6794 - val_mse: 1828.6794\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1673.2015 - mse: 1673.2015 - val_loss: 1812.3154 - val_mse: 1812.3154\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 33ms/step - loss: 1644.4462 - mse: 1644.4462 - val_loss: 1796.3063 - val_mse: 1796.3063\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1643.8362 - mse: 1643.8362 - val_loss: 1779.2922 - val_mse: 1779.2922\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1623.0326 - mse: 1623.0326 - val_loss: 1759.4880 - val_mse: 1759.4880\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 1593.5967 - mse: 1593.5967 - val_loss: 1739.6775 - val_mse: 1739.6775\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 1574.2792 - mse: 1574.2792 - val_loss: 1727.1575 - val_mse: 1727.1575\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 36ms/step - loss: 1566.8353 - mse: 1566.8353 - val_loss: 1710.5917 - val_mse: 1710.5917\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 39ms/step - loss: 1544.0664 - mse: 1544.0664 - val_loss: 1697.3734 - val_mse: 1697.3734\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 36ms/step - loss: 1518.1383 - mse: 1518.1383 - val_loss: 1678.5739 - val_mse: 1678.5739\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 32ms/step - loss: 1493.4468 - mse: 1493.4468 - val_loss: 1660.3807 - val_mse: 1660.3807\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 36ms/step - loss: 1488.5562 - mse: 1488.5562 - val_loss: 1640.4237 - val_mse: 1640.4237\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 35ms/step - loss: 1467.7859 - mse: 1467.7859 - val_loss: 1618.3535 - val_mse: 1618.3535\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 32ms/step - loss: 1463.8976 - mse: 1463.8976 - val_loss: 1593.4091 - val_mse: 1593.4091\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 38ms/step - loss: 1431.4515 - mse: 1431.4515 - val_loss: 1566.9795 - val_mse: 1566.9795\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 1404.7167 - mse: 1404.7167 - val_loss: 1538.2502 - val_mse: 1538.2502\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 1374.7051 - mse: 1374.7051 - val_loss: 1513.9886 - val_mse: 1513.9886\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 1361.8655 - mse: 1361.8655 - val_loss: 1494.4177 - val_mse: 1494.4177\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 1334.8737 - mse: 1334.8738 - val_loss: 1473.2400 - val_mse: 1473.2400\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1294.4626 - mse: 1294.4626 - val_loss: 1452.6548 - val_mse: 1452.6548\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 1291.9843 - mse: 1291.9843 - val_loss: 1429.7722 - val_mse: 1429.7722\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1268.5745 - mse: 1268.5745 - val_loss: 1400.4938 - val_mse: 1400.4938\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 1240.4650 - mse: 1240.4650 - val_loss: 1374.9775 - val_mse: 1374.9775\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1237.8197 - mse: 1237.8197 - val_loss: 1352.7061 - val_mse: 1352.7061\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 1205.2159 - mse: 1205.2159 - val_loss: 1331.9897 - val_mse: 1331.9897\n",
      "Epoch 69/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 24ms/step - loss: 1169.1187 - mse: 1169.1187 - val_loss: 1306.4379 - val_mse: 1306.4379\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1154.6344 - mse: 1154.6344 - val_loss: 1277.2535 - val_mse: 1277.2535\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1119.7766 - mse: 1119.7766 - val_loss: 1250.3458 - val_mse: 1250.3457\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1104.7141 - mse: 1104.7141 - val_loss: 1225.2740 - val_mse: 1225.2740\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 1061.5426 - mse: 1061.5426 - val_loss: 1204.7538 - val_mse: 1204.7538\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1056.0552 - mse: 1056.0552 - val_loss: 1183.5264 - val_mse: 1183.5264\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 1020.6554 - mse: 1020.6554 - val_loss: 1163.0364 - val_mse: 1163.0364\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 1006.4124 - mse: 1006.4124 - val_loss: 1143.2100 - val_mse: 1143.2101\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 978.5621 - mse: 978.5621 - val_loss: 1126.4268 - val_mse: 1126.4266\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 975.3481 - mse: 975.3481 - val_loss: 1099.9921 - val_mse: 1099.9921\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 946.3663 - mse: 946.3663 - val_loss: 1069.0089 - val_mse: 1069.0089\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 887.3120 - mse: 887.3120 - val_loss: 1032.6047 - val_mse: 1032.6047\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 889.6386 - mse: 889.6386 - val_loss: 1005.0959 - val_mse: 1005.0959\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 863.5352 - mse: 863.5352 - val_loss: 976.8326 - val_mse: 976.8326\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 837.5804 - mse: 837.5804 - val_loss: 947.8876 - val_mse: 947.8876\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 799.5237 - mse: 799.5237 - val_loss: 916.5879 - val_mse: 916.5879\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 776.4169 - mse: 776.4169 - val_loss: 887.6888 - val_mse: 887.6888\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 773.9611 - mse: 773.9611 - val_loss: 854.1289 - val_mse: 854.1289\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 755.4388 - mse: 755.4388 - val_loss: 826.1843 - val_mse: 826.1843\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 739.3952 - mse: 739.3952 - val_loss: 800.2587 - val_mse: 800.2587\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 695.3312 - mse: 695.3312 - val_loss: 777.2259 - val_mse: 777.2259\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 671.0007 - mse: 671.0007 - val_loss: 757.8043 - val_mse: 757.8043\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 646.1338 - mse: 646.1338 - val_loss: 733.9255 - val_mse: 733.9255\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 651.5650 - mse: 651.5650 - val_loss: 713.0947 - val_mse: 713.0947\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 620.0638 - mse: 620.0638 - val_loss: 690.1786 - val_mse: 690.1786\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 597.1219 - mse: 597.1219 - val_loss: 670.6819 - val_mse: 670.6819\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 583.8140 - mse: 583.8140 - val_loss: 653.7750 - val_mse: 653.7750\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 560.5939 - mse: 560.5939 - val_loss: 632.8802 - val_mse: 632.8802\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 525.9976 - mse: 525.9976 - val_loss: 604.9070 - val_mse: 604.9070\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 541.6012 - mse: 541.6012 - val_loss: 573.7189 - val_mse: 573.7189\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 492.6629 - mse: 492.6629 - val_loss: 545.3943 - val_mse: 545.3943\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 490.0361 - mse: 490.0361 - val_loss: 520.0035 - val_mse: 520.0035\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 472.9942 - mse: 472.9942 - val_loss: 496.0527 - val_mse: 496.0527\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 462.2693 - mse: 462.2693 - val_loss: 475.6287 - val_mse: 475.6287\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 432.9788 - mse: 432.9788 - val_loss: 457.6303 - val_mse: 457.6303\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 411.6400 - mse: 411.6400 - val_loss: 439.0841 - val_mse: 439.0841\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 403.4628 - mse: 403.4628 - val_loss: 421.7929 - val_mse: 421.7929\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 398.3974 - mse: 398.3974 - val_loss: 404.0292 - val_mse: 404.0292\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 376.1587 - mse: 376.1587 - val_loss: 384.9634 - val_mse: 384.9634\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 373.8005 - mse: 373.8005 - val_loss: 361.1957 - val_mse: 361.1957\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 334.3851 - mse: 334.3851 - val_loss: 339.8699 - val_mse: 339.8699\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 347.7122 - mse: 347.7122 - val_loss: 324.9868 - val_mse: 324.9868\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 334.3563 - mse: 334.3563 - val_loss: 309.5050 - val_mse: 309.5050\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 312.7117 - mse: 312.7117 - val_loss: 299.1829 - val_mse: 299.1829\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 286.0157 - mse: 286.0157 - val_loss: 292.4141 - val_mse: 292.4141\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 283.8576 - mse: 283.8576 - val_loss: 278.2675 - val_mse: 278.2675\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 268.7639 - mse: 268.7639 - val_loss: 258.7341 - val_mse: 258.7341\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 244.5579 - mse: 244.5579 - val_loss: 242.6974 - val_mse: 242.6974\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 256.9134 - mse: 256.9134 - val_loss: 227.8570 - val_mse: 227.8570\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 230.3830 - mse: 230.3830 - val_loss: 219.4454 - val_mse: 219.4454\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 232.7157 - mse: 232.7157 - val_loss: 214.1448 - val_mse: 214.1448\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 226.0961 - mse: 226.0961 - val_loss: 206.7614 - val_mse: 206.7614\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 206.2084 - mse: 206.2084 - val_loss: 198.2545 - val_mse: 198.2545\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 33ms/step - loss: 204.4461 - mse: 204.4461 - val_loss: 187.6856 - val_mse: 187.6856\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 194.8582 - mse: 194.8582 - val_loss: 178.8846 - val_mse: 178.8846\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 194.1391 - mse: 194.1391 - val_loss: 170.9621 - val_mse: 170.9621\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 32ms/step - loss: 167.5890 - mse: 167.5890 - val_loss: 163.3754 - val_mse: 163.3754\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 172.1447 - mse: 172.1447 - val_loss: 156.8172 - val_mse: 156.8172\n",
      "Epoch 127/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 27ms/step - loss: 166.0251 - mse: 166.0251 - val_loss: 148.1180 - val_mse: 148.1180\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 159.3193 - mse: 159.3193 - val_loss: 139.4136 - val_mse: 139.4136\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 153.3521 - mse: 153.3521 - val_loss: 133.8262 - val_mse: 133.8262\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 139.7773 - mse: 139.7773 - val_loss: 128.6514 - val_mse: 128.6514\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 141.9221 - mse: 141.9221 - val_loss: 123.1704 - val_mse: 123.1704\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 139.6886 - mse: 139.6886 - val_loss: 117.8515 - val_mse: 117.8515\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 134.1398 - mse: 134.1398 - val_loss: 112.6873 - val_mse: 112.6873\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 127.4671 - mse: 127.4671 - val_loss: 108.9337 - val_mse: 108.9337\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 121.7170 - mse: 121.7170 - val_loss: 107.4085 - val_mse: 107.4085\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 115.7774 - mse: 115.7774 - val_loss: 106.0642 - val_mse: 106.0642\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 111.6325 - mse: 111.6325 - val_loss: 102.5778 - val_mse: 102.5778\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 106.8384 - mse: 106.8384 - val_loss: 96.9506 - val_mse: 96.9506\n",
      "Epoch 139/150\n",
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      "Epoch 140/150\n",
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      "Epoch 141/150\n",
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      "Epoch 142/150\n",
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      "Epoch 143/150\n",
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      "Epoch 144/150\n",
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      "Epoch 145/150\n",
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      "Epoch 146/150\n",
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      "Epoch 147/150\n",
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      "Epoch 148/150\n",
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      "Epoch 149/150\n",
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      "Epoch 150/150\n",
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      "1/1 [==============================] - 0s 157ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmp17_dypwv\\assets\n",
      "Epoch 1/150\n",
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      "Epoch 2/150\n",
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      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
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      "Epoch 7/150\n",
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      "Epoch 8/150\n",
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      "Epoch 9/150\n",
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      "Epoch 10/150\n",
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      "Epoch 11/150\n",
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      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 18/150\n",
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      "Epoch 19/150\n",
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      "Epoch 20/150\n",
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      "Epoch 22/150\n",
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      "Epoch 23/150\n",
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      "Epoch 24/150\n",
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      "Epoch 25/150\n",
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      "Epoch 26/150\n",
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      "Epoch 27/150\n",
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      "Epoch 29/150\n",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
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      "Epoch 32/150\n",
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      "Epoch 33/150\n",
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      "Epoch 34/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 24ms/step - loss: 1883.8346 - mse: 1883.8346 - val_loss: 2061.8091 - val_mse: 2061.8091\n",
      "Epoch 35/150\n",
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      "Epoch 37/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
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      "Epoch 43/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
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      "Epoch 47/150\n",
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      "Epoch 48/150\n",
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      "Epoch 49/150\n",
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      "Epoch 50/150\n",
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      "Epoch 51/150\n",
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      "Epoch 52/150\n",
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      "Epoch 53/150\n",
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      "Epoch 54/150\n",
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      "Epoch 55/150\n",
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      "Epoch 56/150\n",
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      "Epoch 57/150\n",
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      "Epoch 58/150\n",
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      "Epoch 59/150\n",
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      "Epoch 62/150\n",
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      "Epoch 63/150\n",
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      "Epoch 64/150\n",
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      "Epoch 65/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
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      "Epoch 70/150\n",
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      "Epoch 71/150\n",
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      "Epoch 72/150\n",
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      "Epoch 74/150\n",
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      "Epoch 75/150\n",
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      "Epoch 76/150\n",
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      "Epoch 77/150\n",
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      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 1000.3740 - mse: 1000.3740 - val_loss: 1170.2240 - val_mse: 1170.2239\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 973.9561 - mse: 973.9561 - val_loss: 1142.9352 - val_mse: 1142.9352\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 946.6644 - mse: 946.6644 - val_loss: 1112.4963 - val_mse: 1112.4963\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 943.7589 - mse: 943.7589 - val_loss: 1078.3783 - val_mse: 1078.3783\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 906.1778 - mse: 906.1778 - val_loss: 1046.2518 - val_mse: 1046.2518\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 876.0226 - mse: 876.0226 - val_loss: 1021.6427 - val_mse: 1021.6427\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 870.0576 - mse: 870.0576 - val_loss: 1006.5367 - val_mse: 1006.5367\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 831.4827 - mse: 831.4827 - val_loss: 990.9777 - val_mse: 990.9777\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 820.9406 - mse: 820.9406 - val_loss: 964.4870 - val_mse: 964.4870\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 816.1241 - mse: 816.1241 - val_loss: 930.2694 - val_mse: 930.2694\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 781.2783 - mse: 781.2783 - val_loss: 892.8293 - val_mse: 892.8293\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 728.9940 - mse: 728.9940 - val_loss: 858.7646 - val_mse: 858.7646\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 714.6285 - mse: 714.6285 - val_loss: 826.5237 - val_mse: 826.5237\n",
      "Epoch 91/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 25ms/step - loss: 703.0968 - mse: 703.0968 - val_loss: 800.1320 - val_mse: 800.1320\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 673.8082 - mse: 673.8082 - val_loss: 776.7614 - val_mse: 776.7614\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 648.8989 - mse: 648.8989 - val_loss: 758.7242 - val_mse: 758.7242\n",
      "Epoch 94/150\n",
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      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 650.7908 - mse: 650.7908 - val_loss: 721.1461 - val_mse: 721.1461\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 584.7130 - mse: 584.7130 - val_loss: 700.3856 - val_mse: 700.3856\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 580.9130 - mse: 580.9130 - val_loss: 675.1262 - val_mse: 675.1262\n",
      "Epoch 98/150\n",
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      "Epoch 99/150\n",
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      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 525.2737 - mse: 525.2737 - val_loss: 595.6187 - val_mse: 595.6187\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 504.5235 - mse: 504.5235 - val_loss: 563.1041 - val_mse: 563.1041\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 486.9500 - mse: 486.9500 - val_loss: 532.9493 - val_mse: 532.9493\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 475.8271 - mse: 475.8271 - val_loss: 509.5039 - val_mse: 509.5039\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 449.4021 - mse: 449.4021 - val_loss: 489.6522 - val_mse: 489.6522\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 434.1012 - mse: 434.1012 - val_loss: 473.1304 - val_mse: 473.1304\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 430.5767 - mse: 430.5767 - val_loss: 457.4300 - val_mse: 457.4300\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 411.1290 - mse: 411.1290 - val_loss: 440.1311 - val_mse: 440.1311\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 377.2151 - mse: 377.2151 - val_loss: 422.3704 - val_mse: 422.3704\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 367.0416 - mse: 367.0416 - val_loss: 406.7911 - val_mse: 406.7911\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 360.0701 - mse: 360.0701 - val_loss: 393.0234 - val_mse: 393.0234\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 339.1443 - mse: 339.1443 - val_loss: 377.7250 - val_mse: 377.7250\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 315.8341 - mse: 315.8341 - val_loss: 365.6201 - val_mse: 365.6201\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 324.0587 - mse: 324.0587 - val_loss: 351.7490 - val_mse: 351.7490\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 293.1201 - mse: 293.1201 - val_loss: 329.5861 - val_mse: 329.5861\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 291.6360 - mse: 291.6360 - val_loss: 313.0764 - val_mse: 313.0764\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 276.9882 - mse: 276.9882 - val_loss: 305.0107 - val_mse: 305.0107\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 280.6303 - mse: 280.6303 - val_loss: 303.9257 - val_mse: 303.9257\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 254.2219 - mse: 254.2219 - val_loss: 297.4711 - val_mse: 297.4711\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 255.6256 - mse: 255.6256 - val_loss: 279.9235 - val_mse: 279.9235\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 228.5139 - mse: 228.5139 - val_loss: 267.6159 - val_mse: 267.6159\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 226.9907 - mse: 226.9907 - val_loss: 258.1749 - val_mse: 258.1749\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 201.8162 - mse: 201.8162 - val_loss: 246.6593 - val_mse: 246.6593\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 205.2585 - mse: 205.2585 - val_loss: 236.8423 - val_mse: 236.8423\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 211.0373 - mse: 211.0373 - val_loss: 222.7959 - val_mse: 222.7959\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 198.8966 - mse: 198.8966 - val_loss: 206.4608 - val_mse: 206.4608\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 175.4362 - mse: 175.4362 - val_loss: 193.7921 - val_mse: 193.7921\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 166.6043 - mse: 166.6043 - val_loss: 189.0520 - val_mse: 189.0520\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 174.3852 - mse: 174.3852 - val_loss: 178.2648 - val_mse: 178.2648\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 178.0140 - mse: 178.0140 - val_loss: 167.7797 - val_mse: 167.7797\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 168.1199 - mse: 168.1199 - val_loss: 155.5469 - val_mse: 155.5469\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 153.5284 - mse: 153.5284 - val_loss: 150.5036 - val_mse: 150.5036\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 137.3603 - mse: 137.3603 - val_loss: 149.3427 - val_mse: 149.3427\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 139.5275 - mse: 139.5275 - val_loss: 145.8766 - val_mse: 145.8766\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 127.0074 - mse: 127.0074 - val_loss: 139.6404 - val_mse: 139.6404\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 127.8433 - mse: 127.8433 - val_loss: 136.2134 - val_mse: 136.2134\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 118.1383 - mse: 118.1383 - val_loss: 128.4289 - val_mse: 128.4289\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 127.0674 - mse: 127.0674 - val_loss: 119.2811 - val_mse: 119.2811\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 126.3328 - mse: 126.3328 - val_loss: 110.1519 - val_mse: 110.1519\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 24ms/step - loss: 115.7344 - mse: 115.7344 - val_loss: 107.8610 - val_mse: 107.8610\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 102.0354 - mse: 102.0354 - val_loss: 106.8141 - val_mse: 106.8141\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 23ms/step - loss: 101.9452 - mse: 101.9452 - val_loss: 102.0281 - val_mse: 102.0281\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 28ms/step - loss: 96.5701 - mse: 96.5701 - val_loss: 93.1048 - val_mse: 93.1048\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 101.9732 - mse: 101.9732 - val_loss: 85.5672 - val_mse: 85.5672\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 89.0824 - mse: 89.0824 - val_loss: 82.6912 - val_mse: 82.6912\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 104.3911 - mse: 104.3911 - val_loss: 76.5180 - val_mse: 76.5180\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 84.9576 - mse: 84.9576 - val_loss: 72.2569 - val_mse: 72.2569\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 89.6059 - mse: 89.6059 - val_loss: 68.6202 - val_mse: 68.6202\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 90.5688 - mse: 90.5688 - val_loss: 66.2778 - val_mse: 66.2778\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 91.4925 - mse: 91.4925 - val_loss: 62.5973 - val_mse: 62.5973\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 90.7051 - mse: 90.7051 - val_loss: 63.1404 - val_mse: 63.1404\n",
      "1/1 [==============================] - 0s 157ms/step\n",
      "NN: -49.443072 (12.384181)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "alg_names = []\n",
    "\n",
    "# Initialize the KerasRegressor estimator with early stopping\n",
    "estimator = KerasRegressor(model=model_NN, batch_size=128, verbose=1, callbacks=[early_stopping])\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
    "    \n",
    "    # modify loop for xgboost and NN\n",
    "    if name != ('XGB' and 'NN'):\n",
    "        cvs = cross_val_score(model, X_train, y_train, cv=kfold, scoring='neg_mean_squared_error')\n",
    "    else:    \n",
    "        if name == 'NN':\n",
    "            validation_data = (X_val, y_val)\n",
    "            cvs = cross_val_score(estimator, X_train, y_train, cv=kfold, scoring='neg_mean_squared_error', fit_params={'validation_data': validation_data, 'epochs': 150})\n",
    "        else:\n",
    "            # create DMatrix from training data for XGBoost\n",
    "            dtrain = xgb.DMatrix(X_train1, label=y_train1)\n",
    "            cvs = cross_val_score(model_xgb, X_train1, y_train1, cv=kfold, scoring='neg_mean_squared_error')\n",
    "    \n",
    "    results.append(cvs)\n",
    "    alg_names.append(name)\n",
    "    output = \"%s: %f (%f)\" % (name, cvs.mean(), cvs.std())\n",
    "    print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ba4eeb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "5/5 [==============================] - 2s 52ms/step - loss: 2453.0205 - mse: 2453.0205 - val_loss: 2726.2322 - val_mse: 2726.2322\n",
      "Epoch 2/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2366.7749 - mse: 2366.7749 - val_loss: 2700.9968 - val_mse: 2700.9968\n",
      "Epoch 3/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2324.8918 - mse: 2324.8918 - val_loss: 2675.9583 - val_mse: 2675.9583\n",
      "Epoch 4/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2287.1018 - mse: 2287.1018 - val_loss: 2648.0166 - val_mse: 2648.0166\n",
      "Epoch 5/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2248.7427 - mse: 2248.7427 - val_loss: 2617.1116 - val_mse: 2617.1116\n",
      "Epoch 6/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2222.2319 - mse: 2222.2319 - val_loss: 2583.1042 - val_mse: 2583.1042\n",
      "Epoch 7/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2197.7844 - mse: 2197.7844 - val_loss: 2548.3809 - val_mse: 2548.3809\n",
      "Epoch 8/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2176.2327 - mse: 2176.2327 - val_loss: 2514.9802 - val_mse: 2514.9805\n",
      "Epoch 9/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2166.8035 - mse: 2166.8035 - val_loss: 2486.0830 - val_mse: 2486.0828\n",
      "Epoch 10/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2138.0430 - mse: 2138.0430 - val_loss: 2456.8684 - val_mse: 2456.8684\n",
      "Epoch 11/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2136.2405 - mse: 2136.2405 - val_loss: 2429.3530 - val_mse: 2429.3530\n",
      "Epoch 12/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2103.7483 - mse: 2103.7483 - val_loss: 2400.4336 - val_mse: 2400.4336\n",
      "Epoch 13/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2099.6794 - mse: 2099.6794 - val_loss: 2369.8406 - val_mse: 2369.8406\n",
      "Epoch 14/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2066.2148 - mse: 2066.2148 - val_loss: 2335.8684 - val_mse: 2335.8684\n",
      "Epoch 15/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2076.5051 - mse: 2076.5051 - val_loss: 2301.0146 - val_mse: 2301.0146\n",
      "Epoch 16/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 2028.7365 - mse: 2028.7365 - val_loss: 2270.4834 - val_mse: 2270.4839\n",
      "Epoch 17/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2036.0377 - mse: 2036.0377 - val_loss: 2244.2380 - val_mse: 2244.2380\n",
      "Epoch 18/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 2014.8768 - mse: 2014.8768 - val_loss: 2221.4644 - val_mse: 2221.4644\n",
      "Epoch 19/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1996.9199 - mse: 1996.9199 - val_loss: 2198.6125 - val_mse: 2198.6125\n",
      "Epoch 20/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1976.7747 - mse: 1976.7747 - val_loss: 2178.5779 - val_mse: 2178.5779\n",
      "Epoch 21/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1964.6371 - mse: 1964.6371 - val_loss: 2163.1282 - val_mse: 2163.1282\n",
      "Epoch 22/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1954.0316 - mse: 1954.0316 - val_loss: 2141.8323 - val_mse: 2141.8323\n",
      "Epoch 23/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1942.0780 - mse: 1942.0780 - val_loss: 2113.3267 - val_mse: 2113.3267\n",
      "Epoch 24/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1927.3303 - mse: 1927.3303 - val_loss: 2089.7964 - val_mse: 2089.7964\n",
      "Epoch 25/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1914.1007 - mse: 1914.1007 - val_loss: 2063.3953 - val_mse: 2063.3953\n",
      "Epoch 26/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1902.3976 - mse: 1902.3976 - val_loss: 2048.9915 - val_mse: 2048.9915\n",
      "Epoch 27/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1863.4019 - mse: 1863.4019 - val_loss: 2033.8679 - val_mse: 2033.8679\n",
      "Epoch 28/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1865.1150 - mse: 1865.1150 - val_loss: 2016.9604 - val_mse: 2016.9604\n",
      "Epoch 29/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1859.7247 - mse: 1859.7247 - val_loss: 1996.0220 - val_mse: 1996.0220\n",
      "Epoch 30/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 1831.5392 - mse: 1831.5392 - val_loss: 1971.4338 - val_mse: 1971.4338\n",
      "Epoch 31/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1814.4884 - mse: 1814.4884 - val_loss: 1952.5868 - val_mse: 1952.5868\n",
      "Epoch 32/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 1801.2931 - mse: 1801.2931 - val_loss: 1935.4508 - val_mse: 1935.4508\n",
      "Epoch 33/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1783.4674 - mse: 1783.4674 - val_loss: 1913.1304 - val_mse: 1913.1304\n",
      "Epoch 34/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1759.1438 - mse: 1759.1438 - val_loss: 1888.1176 - val_mse: 1888.1176\n",
      "Epoch 35/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1743.8337 - mse: 1743.8337 - val_loss: 1869.3903 - val_mse: 1869.3903\n",
      "Epoch 36/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1705.9114 - mse: 1705.9114 - val_loss: 1848.8405 - val_mse: 1848.8405\n",
      "Epoch 37/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1707.8044 - mse: 1707.8044 - val_loss: 1822.4608 - val_mse: 1822.4608\n",
      "Epoch 38/500\n",
      "5/5 [==============================] - 0s 16ms/step - loss: 1692.9177 - mse: 1692.9177 - val_loss: 1795.5295 - val_mse: 1795.5295\n",
      "Epoch 39/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1669.1509 - mse: 1669.1509 - val_loss: 1771.3503 - val_mse: 1771.3503\n",
      "Epoch 40/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1648.3433 - mse: 1648.3433 - val_loss: 1741.4526 - val_mse: 1741.4526\n",
      "Epoch 41/500\n",
      "5/5 [==============================] - 0s 16ms/step - loss: 1610.6049 - mse: 1610.6049 - val_loss: 1715.3390 - val_mse: 1715.3390\n",
      "Epoch 42/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1627.1542 - mse: 1627.1542 - val_loss: 1699.3983 - val_mse: 1699.3983\n",
      "Epoch 43/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1575.0331 - mse: 1575.0331 - val_loss: 1678.2067 - val_mse: 1678.2067\n",
      "Epoch 44/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 1557.5304 - mse: 1557.5304 - val_loss: 1653.4946 - val_mse: 1653.4946\n",
      "Epoch 45/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1530.9938 - mse: 1530.9938 - val_loss: 1624.5120 - val_mse: 1624.5120\n",
      "Epoch 46/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 1525.5930 - mse: 1525.5930 - val_loss: 1594.3893 - val_mse: 1594.3893\n",
      "Epoch 47/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 1482.8623 - mse: 1482.8623 - val_loss: 1569.3479 - val_mse: 1569.3479\n",
      "Epoch 48/500\n",
      "5/5 [==============================] - 0s 16ms/step - loss: 1457.6484 - mse: 1457.6484 - val_loss: 1547.1304 - val_mse: 1547.1304\n",
      "Epoch 49/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1425.5192 - mse: 1425.5192 - val_loss: 1521.8674 - val_mse: 1521.8674\n",
      "Epoch 50/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 1398.0276 - mse: 1398.0276 - val_loss: 1494.6028 - val_mse: 1494.6028\n",
      "Epoch 51/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1375.6226 - mse: 1375.6226 - val_loss: 1473.3163 - val_mse: 1473.3163\n",
      "Epoch 52/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1333.5944 - mse: 1333.5944 - val_loss: 1440.9059 - val_mse: 1440.9059\n",
      "Epoch 53/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 1326.1226 - mse: 1326.1226 - val_loss: 1413.2833 - val_mse: 1413.2833\n",
      "Epoch 54/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 1286.3507 - mse: 1286.3507 - val_loss: 1388.3516 - val_mse: 1388.3516\n",
      "Epoch 55/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 1268.1049 - mse: 1268.1049 - val_loss: 1366.1615 - val_mse: 1366.1615\n",
      "Epoch 56/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 1218.6613 - mse: 1218.6613 - val_loss: 1329.3025 - val_mse: 1329.3025\n",
      "Epoch 57/500\n",
      "5/5 [==============================] - 0s 16ms/step - loss: 1202.4777 - mse: 1202.4777 - val_loss: 1292.4738 - val_mse: 1292.4738\n",
      "Epoch 58/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 16ms/step - loss: 1173.4395 - mse: 1173.4395 - val_loss: 1255.2223 - val_mse: 1255.2222\n",
      "Epoch 59/500\n",
      "5/5 [==============================] - 0s 19ms/step - loss: 1157.1633 - mse: 1157.1633 - val_loss: 1230.1974 - val_mse: 1230.1974\n",
      "Epoch 60/500\n",
      "5/5 [==============================] - 0s 19ms/step - loss: 1105.8722 - mse: 1105.8722 - val_loss: 1192.9742 - val_mse: 1192.9742\n",
      "Epoch 61/500\n",
      "5/5 [==============================] - 0s 20ms/step - loss: 1096.0020 - mse: 1096.0020 - val_loss: 1156.8184 - val_mse: 1156.8182\n",
      "Epoch 62/500\n",
      "5/5 [==============================] - 0s 18ms/step - loss: 1046.6588 - mse: 1046.6588 - val_loss: 1123.8208 - val_mse: 1123.8208\n",
      "Epoch 63/500\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 1021.0406 - mse: 1021.0406 - val_loss: 1083.8328 - val_mse: 1083.8328\n",
      "Epoch 64/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 999.6697 - mse: 999.6697 - val_loss: 1036.6130 - val_mse: 1036.6130\n",
      "Epoch 65/500\n",
      "5/5 [==============================] - 0s 19ms/step - loss: 965.5098 - mse: 965.5098 - val_loss: 1005.2000 - val_mse: 1005.2000\n",
      "Epoch 66/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 940.2632 - mse: 940.2632 - val_loss: 978.9742 - val_mse: 978.9742\n",
      "Epoch 67/500\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 914.4621 - mse: 914.4621 - val_loss: 950.8505 - val_mse: 950.8505\n",
      "Epoch 68/500\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 881.9901 - mse: 881.9901 - val_loss: 927.7124 - val_mse: 927.7124\n",
      "Epoch 69/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 835.3425 - mse: 835.3425 - val_loss: 902.8010 - val_mse: 902.8010\n",
      "Epoch 70/500\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 808.7166 - mse: 808.7166 - val_loss: 876.9815 - val_mse: 876.9815\n",
      "Epoch 71/500\n",
      "5/5 [==============================] - 0s 20ms/step - loss: 783.1913 - mse: 783.1913 - val_loss: 849.8160 - val_mse: 849.8160\n",
      "Epoch 72/500\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 775.0174 - mse: 775.0174 - val_loss: 812.9388 - val_mse: 812.9388\n",
      "Epoch 73/500\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 739.1008 - mse: 739.1008 - val_loss: 786.1859 - val_mse: 786.1859\n",
      "Epoch 74/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 693.6238 - mse: 693.6238 - val_loss: 767.6205 - val_mse: 767.6205\n",
      "Epoch 75/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 665.7607 - mse: 665.7607 - val_loss: 739.7808 - val_mse: 739.7808\n",
      "Epoch 76/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 648.3879 - mse: 648.3879 - val_loss: 689.3113 - val_mse: 689.3113\n",
      "Epoch 77/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 635.5836 - mse: 635.5836 - val_loss: 648.8192 - val_mse: 648.8192\n",
      "Epoch 78/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 601.4503 - mse: 601.4503 - val_loss: 612.5436 - val_mse: 612.5436\n",
      "Epoch 79/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 578.0580 - mse: 578.0580 - val_loss: 586.4237 - val_mse: 586.4237\n",
      "Epoch 80/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 540.6612 - mse: 540.6612 - val_loss: 568.4945 - val_mse: 568.4945\n",
      "Epoch 81/500\n",
      "5/5 [==============================] - 0s 31ms/step - loss: 538.9703 - mse: 538.9703 - val_loss: 542.1180 - val_mse: 542.1180\n",
      "Epoch 82/500\n",
      "5/5 [==============================] - 0s 31ms/step - loss: 526.9637 - mse: 526.9637 - val_loss: 519.6843 - val_mse: 519.6843\n",
      "Epoch 83/500\n",
      "5/5 [==============================] - 0s 33ms/step - loss: 484.0523 - mse: 484.0523 - val_loss: 492.1615 - val_mse: 492.1615\n",
      "Epoch 84/500\n",
      "5/5 [==============================] - 0s 32ms/step - loss: 481.0049 - mse: 481.0049 - val_loss: 462.0739 - val_mse: 462.0739\n",
      "Epoch 85/500\n",
      "5/5 [==============================] - 0s 31ms/step - loss: 453.9199 - mse: 453.9199 - val_loss: 435.4435 - val_mse: 435.4435\n",
      "Epoch 86/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 437.1020 - mse: 437.1020 - val_loss: 413.7538 - val_mse: 413.7538\n",
      "Epoch 87/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 399.1921 - mse: 399.1921 - val_loss: 398.1099 - val_mse: 398.1099\n",
      "Epoch 88/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 407.5877 - mse: 407.5877 - val_loss: 387.5060 - val_mse: 387.5060\n",
      "Epoch 89/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 375.4464 - mse: 375.4464 - val_loss: 366.7399 - val_mse: 366.7399\n",
      "Epoch 90/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 356.6053 - mse: 356.6053 - val_loss: 338.1985 - val_mse: 338.1985\n",
      "Epoch 91/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 322.0897 - mse: 322.0897 - val_loss: 320.1865 - val_mse: 320.1865\n",
      "Epoch 92/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 341.2505 - mse: 341.2505 - val_loss: 307.2637 - val_mse: 307.2637\n",
      "Epoch 93/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 313.3081 - mse: 313.3081 - val_loss: 298.4988 - val_mse: 298.4988\n",
      "Epoch 94/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 294.4835 - mse: 294.4835 - val_loss: 289.3595 - val_mse: 289.3595\n",
      "Epoch 95/500\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 293.7745 - mse: 293.7745 - val_loss: 273.6386 - val_mse: 273.6386\n",
      "Epoch 96/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 262.6560 - mse: 262.6560 - val_loss: 257.2763 - val_mse: 257.2763\n",
      "Epoch 97/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 257.7910 - mse: 257.7910 - val_loss: 236.6756 - val_mse: 236.6756\n",
      "Epoch 98/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 230.5318 - mse: 230.5318 - val_loss: 220.3621 - val_mse: 220.3621\n",
      "Epoch 99/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 219.7858 - mse: 219.7858 - val_loss: 215.3925 - val_mse: 215.3925\n",
      "Epoch 100/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 219.7198 - mse: 219.7198 - val_loss: 209.2955 - val_mse: 209.2955\n",
      "Epoch 101/500\n",
      "5/5 [==============================] - 0s 30ms/step - loss: 200.6058 - mse: 200.6058 - val_loss: 200.8769 - val_mse: 200.8769\n",
      "Epoch 102/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 197.3548 - mse: 197.3548 - val_loss: 186.1504 - val_mse: 186.1504\n",
      "Epoch 103/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 184.3357 - mse: 184.3357 - val_loss: 170.7020 - val_mse: 170.7020\n",
      "Epoch 104/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 177.9305 - mse: 177.9305 - val_loss: 157.3887 - val_mse: 157.3887\n",
      "Epoch 105/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 168.6436 - mse: 168.6436 - val_loss: 148.7361 - val_mse: 148.7361\n",
      "Epoch 106/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 166.0089 - mse: 166.0089 - val_loss: 139.8268 - val_mse: 139.8268\n",
      "Epoch 107/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 146.1592 - mse: 146.1592 - val_loss: 129.7313 - val_mse: 129.7313\n",
      "Epoch 108/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 141.3644 - mse: 141.3644 - val_loss: 121.8424 - val_mse: 121.8424\n",
      "Epoch 109/500\n",
      "5/5 [==============================] - 0s 30ms/step - loss: 140.2147 - mse: 140.2147 - val_loss: 117.3249 - val_mse: 117.3249\n",
      "Epoch 110/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 129.9378 - mse: 129.9378 - val_loss: 112.1328 - val_mse: 112.1328\n",
      "Epoch 111/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 126.6305 - mse: 126.6305 - val_loss: 106.4441 - val_mse: 106.4441\n",
      "Epoch 112/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 125.5916 - mse: 125.5916 - val_loss: 99.3585 - val_mse: 99.3585\n",
      "Epoch 113/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 116.4832 - mse: 116.4832 - val_loss: 91.9240 - val_mse: 91.9240\n",
      "Epoch 114/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 124.8071 - mse: 124.8071 - val_loss: 85.9315 - val_mse: 85.9315\n",
      "Epoch 115/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 114.8048 - mse: 114.8048 - val_loss: 79.9995 - val_mse: 79.9995\n",
      "Epoch 116/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 104.1230 - mse: 104.1230 - val_loss: 76.2329 - val_mse: 76.2329\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 117/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 101.7947 - mse: 101.7947 - val_loss: 71.2892 - val_mse: 71.2892\n",
      "Epoch 118/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 101.0475 - mse: 101.0475 - val_loss: 68.7779 - val_mse: 68.7779\n",
      "Epoch 119/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 104.7995 - mse: 104.7995 - val_loss: 62.4339 - val_mse: 62.4339\n",
      "Epoch 120/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 104.4136 - mse: 104.4136 - val_loss: 56.0693 - val_mse: 56.0693\n",
      "Epoch 121/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 86.5887 - mse: 86.5887 - val_loss: 53.5922 - val_mse: 53.5922\n",
      "Epoch 122/500\n",
      "5/5 [==============================] - 0s 30ms/step - loss: 93.4662 - mse: 93.4662 - val_loss: 50.6775 - val_mse: 50.6775\n",
      "Epoch 123/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 80.5168 - mse: 80.5168 - val_loss: 48.3482 - val_mse: 48.3482\n",
      "Epoch 124/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 81.6644 - mse: 81.6644 - val_loss: 45.7043 - val_mse: 45.7043\n",
      "Epoch 125/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 79.1357 - mse: 79.1357 - val_loss: 45.4215 - val_mse: 45.4215\n",
      "Epoch 126/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 76.1702 - mse: 76.1702 - val_loss: 44.8806 - val_mse: 44.8806\n",
      "Epoch 127/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 79.6129 - mse: 79.6129 - val_loss: 43.2640 - val_mse: 43.2640\n",
      "Epoch 128/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 75.5633 - mse: 75.5633 - val_loss: 41.4515 - val_mse: 41.4515\n",
      "Epoch 129/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 84.0044 - mse: 84.0044 - val_loss: 39.2732 - val_mse: 39.2732\n",
      "Epoch 130/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 73.3616 - mse: 73.3616 - val_loss: 38.1310 - val_mse: 38.1310\n",
      "Epoch 131/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 77.0503 - mse: 77.0503 - val_loss: 36.8584 - val_mse: 36.8584\n",
      "Epoch 132/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 80.4322 - mse: 80.4322 - val_loss: 34.8400 - val_mse: 34.8400\n",
      "Epoch 133/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 80.2216 - mse: 80.2216 - val_loss: 34.0232 - val_mse: 34.0232\n",
      "Epoch 134/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 69.4703 - mse: 69.4703 - val_loss: 32.7737 - val_mse: 32.7737\n",
      "Epoch 135/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 67.2463 - mse: 67.2463 - val_loss: 30.9487 - val_mse: 30.9487\n",
      "Epoch 136/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 63.7661 - mse: 63.7661 - val_loss: 30.2278 - val_mse: 30.2278\n",
      "Epoch 137/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 68.2820 - mse: 68.2820 - val_loss: 28.9658 - val_mse: 28.9658\n",
      "Epoch 138/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 75.7207 - mse: 75.7207 - val_loss: 28.5676 - val_mse: 28.5676\n",
      "Epoch 139/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 60.1506 - mse: 60.1506 - val_loss: 28.2069 - val_mse: 28.2069\n",
      "Epoch 140/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 66.4313 - mse: 66.4313 - val_loss: 27.6970 - val_mse: 27.6970\n",
      "Epoch 141/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 66.3785 - mse: 66.3785 - val_loss: 26.6181 - val_mse: 26.6181\n",
      "Epoch 142/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 59.7484 - mse: 59.7484 - val_loss: 25.7181 - val_mse: 25.7181\n",
      "Epoch 143/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 62.2589 - mse: 62.2589 - val_loss: 24.6448 - val_mse: 24.6448\n",
      "Epoch 144/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 68.7236 - mse: 68.7236 - val_loss: 23.5851 - val_mse: 23.5851\n",
      "Epoch 145/500\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 66.5856 - mse: 66.5856 - val_loss: 23.6041 - val_mse: 23.6041\n",
      "Epoch 146/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 62.3626 - mse: 62.3626 - val_loss: 22.8796 - val_mse: 22.8796\n",
      "Epoch 147/500\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 64.0793 - mse: 64.0793 - val_loss: 23.2771 - val_mse: 23.2771\n",
      "Epoch 148/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 70.0433 - mse: 70.0433 - val_loss: 22.9466 - val_mse: 22.9466\n",
      "Epoch 149/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 67.4298 - mse: 67.4298 - val_loss: 22.0806 - val_mse: 22.0806\n",
      "Epoch 150/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 62.5066 - mse: 62.5066 - val_loss: 21.5907 - val_mse: 21.5907\n",
      "Epoch 151/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 62.8767 - mse: 62.8767 - val_loss: 21.6908 - val_mse: 21.6908\n",
      "Epoch 152/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 60.5893 - mse: 60.5893 - val_loss: 21.3363 - val_mse: 21.3363\n",
      "Epoch 153/500\n",
      "5/5 [==============================] - 0s 20ms/step - loss: 62.0694 - mse: 62.0694 - val_loss: 21.0764 - val_mse: 21.0764\n",
      "Epoch 154/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 63.6716 - mse: 63.6716 - val_loss: 20.7571 - val_mse: 20.7571\n",
      "Epoch 155/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 54.5337 - mse: 54.5337 - val_loss: 20.1976 - val_mse: 20.1976\n",
      "Epoch 156/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 66.3867 - mse: 66.3867 - val_loss: 19.6462 - val_mse: 19.6462\n",
      "Epoch 157/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 56.4486 - mse: 56.4486 - val_loss: 20.1326 - val_mse: 20.1326\n",
      "Epoch 158/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 63.6461 - mse: 63.6461 - val_loss: 20.2191 - val_mse: 20.2191\n",
      "Epoch 159/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 60.1415 - mse: 60.1415 - val_loss: 19.9183 - val_mse: 19.9183\n",
      "Epoch 160/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 59.3983 - mse: 59.3983 - val_loss: 19.7014 - val_mse: 19.7014\n",
      "Epoch 161/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 58.3989 - mse: 58.3989 - val_loss: 19.5965 - val_mse: 19.5965\n",
      "Epoch 162/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 58.9776 - mse: 58.9776 - val_loss: 19.0022 - val_mse: 19.0022\n",
      "Epoch 163/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 65.5713 - mse: 65.5713 - val_loss: 18.1983 - val_mse: 18.1983\n",
      "Epoch 164/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 55.9728 - mse: 55.9728 - val_loss: 18.4452 - val_mse: 18.4452\n",
      "Epoch 165/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 52.5809 - mse: 52.5809 - val_loss: 18.1153 - val_mse: 18.1153\n",
      "Epoch 166/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 59.8962 - mse: 59.8962 - val_loss: 18.1100 - val_mse: 18.1100\n",
      "Epoch 167/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 66.8493 - mse: 66.8493 - val_loss: 18.1108 - val_mse: 18.1108\n",
      "Epoch 168/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 62.3222 - mse: 62.3222 - val_loss: 18.6693 - val_mse: 18.6693\n",
      "Epoch 169/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 63.1291 - mse: 63.1291 - val_loss: 17.8806 - val_mse: 17.8806\n",
      "Epoch 170/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 56.6095 - mse: 56.6095 - val_loss: 18.1349 - val_mse: 18.1349\n",
      "Epoch 171/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 54.8562 - mse: 54.8562 - val_loss: 18.4989 - val_mse: 18.4989\n",
      "Epoch 172/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 59.2087 - mse: 59.2087 - val_loss: 18.0081 - val_mse: 18.0081\n",
      "Epoch 173/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 57.2910 - mse: 57.2910 - val_loss: 17.4402 - val_mse: 17.4402\n",
      "Epoch 174/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 67.7625 - mse: 67.7625 - val_loss: 17.0539 - val_mse: 17.0539\n",
      "Epoch 175/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 55.4705 - mse: 55.4705 - val_loss: 17.2697 - val_mse: 17.2697\n",
      "Epoch 176/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 58.3178 - mse: 58.3178 - val_loss: 16.6643 - val_mse: 16.6643\n",
      "Epoch 177/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 25ms/step - loss: 55.2094 - mse: 55.2094 - val_loss: 16.3765 - val_mse: 16.3765\n",
      "Epoch 178/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 60.3537 - mse: 60.3537 - val_loss: 15.8348 - val_mse: 15.8348\n",
      "Epoch 179/500\n",
      "5/5 [==============================] - 0s 28ms/step - loss: 49.4432 - mse: 49.4432 - val_loss: 16.2180 - val_mse: 16.2180\n",
      "Epoch 180/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 59.5273 - mse: 59.5273 - val_loss: 15.7396 - val_mse: 15.7396\n",
      "Epoch 181/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 50.9396 - mse: 50.9396 - val_loss: 15.5116 - val_mse: 15.5116\n",
      "Epoch 182/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 53.4939 - mse: 53.4939 - val_loss: 16.6554 - val_mse: 16.6554\n",
      "Epoch 183/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 59.4963 - mse: 59.4963 - val_loss: 16.9101 - val_mse: 16.9101\n",
      "Epoch 184/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 60.3524 - mse: 60.3524 - val_loss: 17.9627 - val_mse: 17.9627\n",
      "Epoch 185/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 54.7016 - mse: 54.7016 - val_loss: 16.8600 - val_mse: 16.8600\n",
      "Epoch 186/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 61.1762 - mse: 61.1762 - val_loss: 15.8405 - val_mse: 15.8405\n",
      "Epoch 187/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 48.3531 - mse: 48.3531 - val_loss: 15.8492 - val_mse: 15.8492\n",
      "Epoch 188/500\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 59.7431 - mse: 59.7431 - val_loss: 15.8027 - val_mse: 15.8027\n",
      "Epoch 189/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 63.7440 - mse: 63.7440 - val_loss: 14.9868 - val_mse: 14.9868\n",
      "Epoch 190/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 60.2279 - mse: 60.2279 - val_loss: 15.6355 - val_mse: 15.6355\n",
      "Epoch 191/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 56.9287 - mse: 56.9287 - val_loss: 15.5423 - val_mse: 15.5423\n",
      "Epoch 192/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 54.1688 - mse: 54.1688 - val_loss: 14.7578 - val_mse: 14.7578\n",
      "Epoch 193/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 65.0937 - mse: 65.0937 - val_loss: 16.2666 - val_mse: 16.2666\n",
      "Epoch 194/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 58.8090 - mse: 58.8090 - val_loss: 16.4674 - val_mse: 16.4674\n",
      "Epoch 195/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 52.9324 - mse: 52.9324 - val_loss: 14.8172 - val_mse: 14.8172\n",
      "Epoch 196/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 53.8096 - mse: 53.8096 - val_loss: 13.4164 - val_mse: 13.4164\n",
      "Epoch 197/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 52.8840 - mse: 52.8840 - val_loss: 13.6236 - val_mse: 13.6236\n",
      "Epoch 198/500\n",
      "5/5 [==============================] - 0s 22ms/step - loss: 50.1807 - mse: 50.1807 - val_loss: 12.7553 - val_mse: 12.7553\n",
      "Epoch 199/500\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 59.6545 - mse: 59.6545 - val_loss: 12.9089 - val_mse: 12.9089\n",
      "Epoch 200/500\n",
      "5/5 [==============================] - 0s 21ms/step - loss: 50.6352 - mse: 50.6352 - val_loss: 16.7926 - val_mse: 16.7926\n",
      "Epoch 201/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 53.8207 - mse: 53.8207 - val_loss: 14.0681 - val_mse: 14.0681\n",
      "Epoch 202/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 55.2450 - mse: 55.2450 - val_loss: 12.3490 - val_mse: 12.3490\n",
      "Epoch 203/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 52.1250 - mse: 52.1250 - val_loss: 12.8870 - val_mse: 12.8870\n",
      "Epoch 204/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 51.4009 - mse: 51.4009 - val_loss: 12.5578 - val_mse: 12.5578\n",
      "Epoch 205/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 51.2514 - mse: 51.2514 - val_loss: 11.4689 - val_mse: 11.4689\n",
      "Epoch 206/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 55.4011 - mse: 55.4011 - val_loss: 13.7116 - val_mse: 13.7116\n",
      "Epoch 207/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 48.2581 - mse: 48.2581 - val_loss: 12.2640 - val_mse: 12.2640\n",
      "Epoch 208/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 44.0637 - mse: 44.0637 - val_loss: 11.3546 - val_mse: 11.3546\n",
      "Epoch 209/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 52.6395 - mse: 52.6395 - val_loss: 10.9931 - val_mse: 10.9931\n",
      "Epoch 210/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 45.8005 - mse: 45.8005 - val_loss: 11.3027 - val_mse: 11.3027\n",
      "Epoch 211/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 49.0975 - mse: 49.0975 - val_loss: 10.7667 - val_mse: 10.7667\n",
      "Epoch 212/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 52.2774 - mse: 52.2774 - val_loss: 10.6854 - val_mse: 10.6854\n",
      "Epoch 213/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 54.3708 - mse: 54.3708 - val_loss: 10.3283 - val_mse: 10.3283\n",
      "Epoch 214/500\n",
      "5/5 [==============================] - 0s 29ms/step - loss: 48.4484 - mse: 48.4484 - val_loss: 10.4052 - val_mse: 10.4052\n",
      "Epoch 215/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 50.4880 - mse: 50.4880 - val_loss: 11.3313 - val_mse: 11.3313\n",
      "Epoch 216/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 48.4151 - mse: 48.4151 - val_loss: 11.9682 - val_mse: 11.9682\n",
      "Epoch 217/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 47.4169 - mse: 47.4169 - val_loss: 10.4534 - val_mse: 10.4534\n",
      "Epoch 218/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 54.7926 - mse: 54.7926 - val_loss: 9.6320 - val_mse: 9.6320\n",
      "Epoch 219/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 50.5324 - mse: 50.5324 - val_loss: 11.5881 - val_mse: 11.5881\n",
      "Epoch 220/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 50.3211 - mse: 50.3211 - val_loss: 9.8923 - val_mse: 9.8923\n",
      "Epoch 221/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 51.1509 - mse: 51.1509 - val_loss: 10.8158 - val_mse: 10.8158\n",
      "Epoch 222/500\n",
      "5/5 [==============================] - 0s 25ms/step - loss: 50.0219 - mse: 50.0219 - val_loss: 10.6543 - val_mse: 10.6543\n",
      "Epoch 223/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 47.0456 - mse: 47.0456 - val_loss: 10.1840 - val_mse: 10.1840\n",
      "Epoch 224/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 49.6768 - mse: 49.6768 - val_loss: 9.7468 - val_mse: 9.7468\n",
      "Epoch 225/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 53.6154 - mse: 53.6154 - val_loss: 9.4712 - val_mse: 9.4712\n",
      "Epoch 226/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 56.0475 - mse: 56.0475 - val_loss: 9.3679 - val_mse: 9.3679\n",
      "Epoch 227/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 47.5364 - mse: 47.5364 - val_loss: 9.6791 - val_mse: 9.6791\n",
      "Epoch 228/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 52.4601 - mse: 52.4601 - val_loss: 11.0907 - val_mse: 11.0907\n",
      "Epoch 229/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 50.1474 - mse: 50.1474 - val_loss: 12.2202 - val_mse: 12.2202\n",
      "Epoch 230/500\n",
      "5/5 [==============================] - 0s 27ms/step - loss: 52.1211 - mse: 52.1211 - val_loss: 10.2323 - val_mse: 10.2323\n",
      "Epoch 231/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 50.9221 - mse: 50.9221 - val_loss: 11.0149 - val_mse: 11.0149\n",
      "Epoch 232/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 47.5912 - mse: 47.5912 - val_loss: 12.2248 - val_mse: 12.2248\n",
      "Epoch 233/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 47.0052 - mse: 47.0052 - val_loss: 11.7261 - val_mse: 11.7261\n",
      "Epoch 234/500\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 48.0904 - mse: 48.0904 - val_loss: 10.2319 - val_mse: 10.2319\n",
      "Epoch 235/500\n",
      "5/5 [==============================] - 0s 26ms/step - loss: 47.2286 - mse: 47.2286 - val_loss: 10.5746 - val_mse: 10.5746\n",
      "Epoch 236/500\n",
      "5/5 [==============================] - 0s 22ms/step - loss: 53.0965 - mse: 53.0965 - val_loss: 10.4965 - val_mse: 10.4965\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27298230ac0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve of NN model\n",
    "history = model_NN.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=500, batch_size=128, callbacks=[early_stopping])\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(history.history['loss'], label = 'training')\n",
    "plt.plot(history.history['val_loss'], label = 'testing')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title('Learning curve of the NN model')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9727c181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b) Compare algorithms and the NN model\n",
    "# Distribution of accuracy values calculated across 8 cross-validation folds.\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot()\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(alg_names)\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49996c8f",
   "metadata": {},
   "source": [
    "Of the three algorithms and NN model, the ensemble models outperformed Linear Regression and NN model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79de1ac",
   "metadata": {},
   "source": [
    "In a regression problem, it is common to use the mean error as a measure of validation. This is done by first calculating the mean absolute error (MAE) of the test data with the mean of dataset, and then calculating the MAE of the test data with the predicted data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4d2e51ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of predicted data:  1.2351385281385276\n"
     ]
    }
   ],
   "source": [
    "# Evaluate performance of Random Forest algorithm on validation data\n",
    "model_RF = RandomForestRegressor(n_estimators=10)\n",
    "model_RF.fit(X_train, y_train)\n",
    "y_pred_RF = model_RF.predict(X_val)\n",
    "mae_pred_RF = mean_absolute_error(y_val, y_pred_RF)\n",
    "print(\"Mean Absolute Error of predicted data: \", mae_pred_RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce4349da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of predicted data:  1.412210528551242\n"
     ]
    }
   ],
   "source": [
    "# Evaluate performance of XGBoost algorithm on validation data\n",
    "model_xgb.fit(X_train1, y_train1)\n",
    "dtrain = xgb.DMatrix(X_val1)\n",
    "y_pred_xgb = model_xgb.predict(X_val1)\n",
    "mae_pred_xgb = mean_absolute_error(y_val, y_pred_xgb)\n",
    "print(\"Mean Absolute Error of predicted data: \", mae_pred_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d932a556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of original data:  17.70811874323593\n"
     ]
    }
   ],
   "source": [
    "# Define baseline mean_absolute_error of y_val in the data set\n",
    "y_mean = np.mean(y)\n",
    "y_mean = np.array([y_mean]*len(y_val))\n",
    "mae_ori = mean_absolute_error(y_val, y_mean)\n",
    "print(\"Mean Absolute Error of original data: \", mae_ori)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fc4aae",
   "metadata": {},
   "source": [
    "The resulting MAE values show that Random Forest looks better than XGBoost in prediction performance. For the sake of simplicity, I selected Random Forest as the surrogate model for the optimization process in the 2nd Notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d1925b",
   "metadata": {},
   "source": [
    "**5. Finalize Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b4acd266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 10, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}\n"
     ]
    }
   ],
   "source": [
    "# a) Get best model parameters\n",
    "model_params = model_RF.get_params()\n",
    "\n",
    "# Print the model's parameters\n",
    "print(model_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1e989a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5BH//+9+tfyHIESke1qaKZ9CgQY73uWvXLlRVVRlu+/HHH2PatGlKVIjxox/9SPm7S5cuOPvss/H000/bGkcikcAvf/lLPPLII4hGo4bbbdmyxfC1Sy65RIkchsNhDBw4UBEw279/v7IdP/4zzjhDJQB2ySWX4Fe/+pWtsVshFArh5ptvTlPNd3JuAbIC9uWXX47//Oc/pp9rdrxyRb9+/TB58mTl8eGHH678zf8u/LkYjUZNe7zzx4KnW7duSmSa5/PPP1f+7tmzp0oUa+LEiaitrdVteVZaWoorrrgCDz74IADg73//O8455xzMnz8fe/bsASALoVkR2ZIkKeM2enz77bdKJgognyOBQKqL6+WXX46nnnpK+YxPPvkEF1xwQdp+rrzySoRC8lKuW7du6NGjB3bu3AlA/TuMHz9eOa+OOeYYnHDCCRg8eDCOPvpoTJo0CYcddpij72HGtGnTlLlM+7uzDBUAGDJkiPI3P2Yt55xzjqrzg/a6/uyzz3DyySdnO2zVedirVy8lKg3Ic/G4ceOULBajc9bq9TF27FgIggBJkvC3v/0NS5YswVFHHYXDDz8cI0eOxKRJk9C7d++svxNBEPmHjG6CIAqSkpISDBgwACeffDJuuukm00Ujv6hj8AveTDQ3N6O1tVVJN2T06tVL9djJ4ogfR2lpado+nWDnu7HURi++26OPPqpKwzaivb3d8LUBAwaoHvNOjkQiofzNj9+Nsetxxx13IBKJoL6+HsuXL0c8Hsett96KtrY21eLfyblVUlKCK6+8MqPBDZgfLycMGDDAdj9us9+FN0SdnItaDj30UN12bPxvrlda0qdPH8Pv9bOf/QwPPfQQEokE3njjDWzbtk2V6n3ppZdaagFXXV2terxq1aqM7wHSjctM56yRMWr1+njyySdx4YUXYtGiRdi7dy/++9//qt534YUX4sUXX1QZ/gytY8Hq+devXz/dcWlfY04Dvc/icXqM7MLvR28+5j/Xye/Cf8fjjz8ef/rTn3DnnXeiqakJX3zxhUoFv0ePHnjllVeo1zhBdADI6CYIomB4+umnMX36dNvvKy0tTXuua9euqse/+MUvTA3eUCiU1l+bRVwZLMJkBz4i29LSgt27d+vWtdqB/279+/fH9ddfb7gti8Boo+G7du1Sjc3Jd+OzEo455hi88MILOOKIIxAOh/E///M/lgzycDiseqzNAmBUVVVh7969yth5nIxdj6uvvhq1tbX4xS9+gTFjxij1s7/97W9x8cUXKxkJTs6t5uZmpQUeIPcQfuCBB3DIIYcgEAjg+OOPx5IlS1z5Hm5g9Xfhj0V5eTnuuusuw30aaTLoXb+A+pzV/uYAlBpiPQYOHIgf/OAHeP311yGKIv7+97+r+mdbnWdGjhyJiooKpa571qxZuO+++wzHzNCeI5nOWe32DKu/Q01NDT755BOsXbsWn376KdasWYMvv/wSr732GuLxOGbNmoUzzzxT+d4s+gpAVe+dSCSwfv160+9mNDYe3tC2SqZjZJbRYwf+WOudV/znZvu7ALL+wzXXXINFixZh5cqVWLNmDd58802sWbMGe/bswfTp0207xQiC8B9kdBME0Sk54YQTEAwGlTTgkpIS3HrrrWnbff3119i3bx/C4TDC4TAOP/xwJcV8zpw5+M1vfqNEMZ577jnb4xg7dqzq8W9+8xs8/vjjquc2bdqkipyEQiHE43EAsqGuZcyYMYog1M6dOzF58mQceeSRqm3i8Thef/11jBs3DgAUMTbG888/j3vuuQeALLT173//2/Z3Y0YwAEyaNEkRHGptbVWEotxi5MiReOuttwAAb775Jvbt26c4DZz8LmaUlJTgoYcewqRJkwDIadP33HOPkn7v5NzavXu3KiX9ggsuUKKo33zzDZYvX+7qd8gVfG/6pqYmjBgxIi0FWJIkvPfee7bLKEaNGoU5c+YAkM/zBQsWKBHB999/P6Oh8vOf/1xxdPz+979HW1sbAFnIUHu9GFFUVISf/OQnigNp+/btuOyyy/Dss8+m9RH/7rvv8N5772H69Ok4/PDD0a1bNyUT4IUXXsC1116rRJqfeeYZ5X2CIGD06NGWxmPE8uXLMXToUBx22GGqrKApU6Yo1+Lnn3+uGN1VVVVKFHfx4sWK+NnMmTN1DdFc8Nprr+HAgQPo0qULgPTrmp/DeKNXb440g58/d+3ahXnz5ikp5uvXr8eHH36o2jYbtm3bhmAwiN69e+Pkk09Wro2lS5dixIgRAOT5f+/evejevXtWn0UQRH4ho5sgiE5J9+7dMX36dPzzn/8EIEcrFy1ahNGjRyMcDuO7777DRx99hK+//hp33XWXYpxeeeWVitL22rVrceKJJ+Lss8/GihUrHKmXT548GUcffTRWrlwJAHjiiSfwxRdfYNKkSYjH41iyZAkkSVJqCAE5NXPTpk0AgAcffBB79+5FSUkJjj32WJxyyimYPn06fve732Hv3r1ob2/H6NGjceGFF2LgwIFobW3F119/jQULFmDfvn3YsGEDunbtihNOOEE1jnvvvRcbN25EbW0tZs+erdS62uHwww/HmjVrAMh1s4IgoEuXLnjllVdUtfFucOWVVypGd2NjI0444QRcdNFFisqx20ycOBFjx45Vapafe+453H333RgwYICjc6tXr16oqqpSUqZvvPFGLF26FE1NTZg5c6ZpTXy2GKmXA8C5556bVb3vWWedpXJUTZ48Geeffz6OOOIIxONxrF69GgsWLMD27dsxf/58DBw40PK+L7nkEtx9992KsXzuuefiiiuuAADl2Jtx2mmnYciQIVi9erWyDwD48Y9/bOcr4v/9v/+Ht99+W8l8mDNnDj755BOcd955qK6uRmNjIz777DPMnz8fZ511FqZPn45gMIgbbrgBd999NwBZmXvChAn4/ve/j2XLluHVV19V9j916lTU1NTYGpOWiy66CI2NjZg0aRL69euHbt26Yd26dao0cz5SPHLkSLz99tsAgH/961/YsWMHwuEw3njjjazGkQ179uzBqFGjVOrljMMOO0xxggHq9PXPP/8cN954I2pqalBUVIQbbrjB9HMuv/xy3HPPPYpDpK6uTqVeHovFAMjOT7MsIit88MEH+NGPfoRx48bhyCOPxCGHHAJRFFX3kqKiojQHDkEQBUi+FNwIgiAykalPt9X3GSk6NzU1ZeylDEC66667lPdEo1HD3tETJ05UPbbTp3vQoEGGn69V373pppt0t/vZz36mbLNw4ULTPt16x2bx4sW6fbXD4bDqO1tVL1+4cKEUCoXS9ldeXi7V1dUZ7s/sNzdSTpYkSbrgggsMj5+T88isT7ckSdLrr7+uev2nP/2p8pqTc+v3v/+97jbHHHOMqucx/729VC8HIM2dO1d5j5kitLY/Ms8333xj2qdb73qxoj4tSZL05JNP6u5rwIAB0pFHHqk81vY3Z/BK/wCkkpISlUK2VXbt2iWdeeaZGb/jlClTlPfEYjHVdaD377jjjpP27dunvEerEM4fM0lSq33z5xbfdUHvX7du3VTnzptvvikJgqB7XPl9mamX82MzO0/Nzh3++5xyyilSJBJJG1NJSYn04Ycfqt63dOlSKRAIpG1bVlambGN2jr333ntSZWWl4fEKhULSP//5T9V7nFwfL774YsZzhu/SQBBE4ZKumEEQBNFJKCsrwzvvvIN//etfOO2009CzZ0+Ew2H06NEDw4cPx/Tp0zF37lzcdtttynvC4TDmzZuHX/ziF+jXrx+Kiopw+OGH48EHH8Q//vEPR+M47LDDsHz5cjzwwAMYM2YMqqqqEAqF0LNnT0yYMAGXXXaZavt7770XN9xwA/r162co9jRu3DisXLkSt99+O4499lhUVFSgqKgI/fv3x9ixY3HnnXfi888/V6kKH3/88fjoo49w5plnory8HOXl5TjllFOwYMECnHrqqba/17hx4/DWW29hzJgxiEQiqKysxA9+8AN8/PHHur1ts+X555/Hvffei0GDBiEcDqO2tha/+tWv8Oabb7r+WYActR0+fLjy+KmnnsL27dsBODu3brvtNjzxxBMYMmQIwuEw+vTpg6uvvhrvv/8+ysvLPfkOueCII47Al19+ifvuuw8nnHACKisrEQ6H0a9fP5xwwgm45ZZbsHDhQkyYMMH2vn/yk5+gvr4eI0eORCQSQY8ePXDppZfik08+UQmJGdX7Tp8+XXVszz///DTtBiv07NkT//3vfzF//nxcccUVOOKII1BRUYFIJILa2lqcfPLJ+NOf/oQ///nPyntCoZDSX/70009Hjx49EAqFUFVVhbFjx+LRRx/FRx99ZFg3bIf7778fP/nJT3DcccehT58+CIfDKC0txRFHHIHrrrsubS44/fTT8corr2D48OEoKipCr169cPXVV+PTTz81rL33mnHjxuGjjz7CGWecgYqKCpSXl+O0007DwoUL08p0vve97+HFF1/EiBEjDLtamDFp0iR89dVXmDFjBo488kiUlJQov+X06dPx2WefKVkV2X6ne++9F5MnT8ahhx6KiooKZe4/5ZRTMHPmTMMsFIIgCgtBkhz2uyAIgiAIgsgjTPldy7JlyzBy5EilTv755583bNt2+OGHY/Xq1QCAd99915W2U4Q71NbWKqU0d911l5KOTxAEUWhQTTdBEARBEAXJ3/72Nzz77LOYOnWq0lrsq6++wuOPP64Y3NXV1TjvvPNU71u2bBl2796Nf//734rBfeSRR6rqggmCIAjCLcjoJgiCIAiiIJEkCZ9//jk+//xz3dd79+6NV199NS0aPmPGDLz//vvKY0EQ8Kc//cm0tRNBEARBOIVqugmCIAiCKEgmTpyI6dOnY8iQIaisrEQwGETXrl1x4okn4ne/+x2+/vprpfWSHqWlpRg1ahReffVVnHHGGTkcOUEQBNGZoJpugiAIgiAIgiAIgvAIinQTBEEQBEEQBEEQhEeQ0U0QBEEQBEEQBEEQHtGphdQSiQS2bduGiooKEk8hCIIgCIIgCIIgLCNJEg4ePIhDDjkEgYBxPLtTG93btm1DTU1NvodBEARBEARBEARBFCibN29GdXW14eud2uiuqKgAIB+kLl265Hk0BEEQBEEQBEEQRKFw4MAB1NTUKHalEZ3a6GYp5V26dCGjmyAIgiAIgiAIgrBNplJlElIjCIIgCIIgCIIgCI8go5sgCIIgCIIgCIIgPIKMboIgCIIgCIIgCILwiE5d000QBEEQBEEQhP8QRRGxWCzfwyA6OeFwGMFgMOv9kNFNEARBEARBEIQvkCQJO3bsQENDQ76HQhAAgKqqKvTp0yejWJoZZHQTBEEQBEEQBOELmMHdq1cvlJaWZmXoEEQ2SJKElpYW7Nq1CwDQt29fx/sio5sgCIIgCIIgiLwjiqJicHfv3j3fwyEIlJSUAAB27dqFXr16OU41JyE1giAIgiAIgiDyDqvhLi0tzfNICCIFOx+z0Rggo5sgCIIgCIIgCN9AKeWEn3DjfCSjmyAIgiAIgiAIgiA8whdG9+rVq3HppZfikEMOQVFREfr27YtLL70Ua9euTdt2+fLlmDp1Knr16oVIJILa2lrccMMN2L17dx5GThAEQRAEQRAEUXjU1tZCEATcfffdtt63YMECCIIAQRCwYMECT8bW0ci70f3555/juOOOw3PPPYft27cjFothx44deO655zBq1CgsW7ZM2fa9997D6NGjMWfOHOzevRvRaBSbNm3CY489htGjR2Pnzp35+yIEQRAEQRAEQXRqnn76acUgDQQC2Lhxo+19TJ8+HYIgYOLEia6PL99MnDgRgiBg+vTp+R5KTsm70f2zn/0MTU1NAIBHH30UTU1N+Otf/woAaGhowFVXXQVALlyfPn062traEAgE8Pzzz2Pv3r246aabAADr16/HLbfckp8vQRAEQRAEQRBEp2fmzJnK35Ik4ZlnnsnfYAjfkFeju7GxEYsXLwYgy7H//Oc/R1lZGa655hpUVVUBkCPhy5cvx7x587B582YAwIQJEzBt2jR069YN9913H4qLiwEAs2bNQmNjY16+C0EQBEEQBEEQnZcNGzZg4cKFAICRI0cCAJ555hlIkqTa7uDBg7j11ltx6KGHoqioCN27d8cZZ5yB1tZW1NbWKob6+++/r0rj5tO6+Qg6e44Z/Js2bcKZZ56JmpoalJSUoKSkBMcccwwefvjhtLFYYdasWRg0aBBKSkrwgx/8AFu3bk3b5vPPP8cpp5yCvn37IhKJoKysDKNGjcJzzz2nGuf777+vHBf+u7g9Zr+R1z7dra2tyt9mqnBLlizBd999pzweOnSo8ndxcTEGDx6Mr776CrFYDF988QUmTZrkzYAJgiAIgiAIgsgZkiShNSbm5bNLwkFbytUzZ86EJEno06cP/vnPf2L48OHYsGEDPvjgA5x00kkAgGg0iokTJ+KLL74AAAwYMAChUAjz5s1De3s7jj32WDQ3N2PPnj2oqKjAUUcdBQDo0qULDhw4YGkcu3fvxptvvonq6moceeSR2Lp1K1auXImbbroJ4XAYP/vZzyx/p2XLluHiiy9GIpFAZWUlVq9ejWuvvTZtuw0bNmDBggWoqanB0UcfjY0bN+Kzzz7DpZdeiq5du2Ly5Mk44YQT8PXXX+PgwYPo0aMHDj30UABAJBLB1q1bXRuzH8mr0d27d28ccsgh2LZtG1paWvD444/jiiuuwPPPP4+GhgZlu927d6vqtVkUnFFZWan8bVbX3d7ejvb2duWx1ROXIAiCIAiCIIjc0xoTcdT/eysvn/31b09HaZE1c0mSJDz77LMAgGnTpmHYsGE49thjsXTpUsycOVMxul966SXF4H7ggQdw6623AgBWrFiB0tJSzJ07F9OnT8czzzyDESNGqITKrIqWHXbYYdiwYQNqa2sBAIlEApMmTcIHH3yAl156yZYB++CDDyoG97fffovevXvjsssuU74rY+zYsdi2bRt69+4NAGhra8PQoUOxdu1avPTSS5g8eTIWLVqEiRMn4v3338fkyZNVqfglJSWujdmP5DW9XBAE3H///crjG264AeXl5Wnek6KiItVjbYoB/9jMG3X//fejsrJS+VdTU5PN8AmCIAiCIAiCILBgwQJs2LABAHDppZeq/p89ezaam5sBQCmtjUQiijYVABxzzDFpNo9TwuEw/vCHP2DAgAEIh8MIBoP44IMPAADbtm2zta+VK1cCkI1qZlBfcMEFadsFAgHccsstOOSQQxAKhVBSUqJ0orLymW6O2Y/kNdINAJdddhkqKyvxv//7v1i2bBlKS0sxbtw4HDhwAPPnzwcgp13wtdp8FByA6jV2Muhx++234+abb1YeHzhwgAxvgiAIgiAIgvApJeEgvv7t6Xn7bKvwUVumOi6Kclp8U1MTZs+ejcsvv1z1Hjup69rt2b719KxmzJiBf/zjHwCAwYMHo1u3bli3bh327NmjvM8qLLjJf7ZejfUll1yCd955B4Ig4Mgjj0RFRYWSSm7lM90csx/Ju3o5AEyZMgUff/wxWlpasGfPHrz00kvYtGkTADnKfdJJJ2H06NHK9itWrFD+bmtrU7wo4XAYI0aMMPycSCSCLl26qP4RBEEQBEEQBOFPBEFAaVEoL/+sGsVNTU2YM2eO8rixsRGNjY1KhyYgZZSfcMIJAOSy14cfflh5/ZtvvkE0GgUAlJaWAoASHWf06tVL+Xv16tUAgFdeeSVtPIsWLQIAnHbaaVi9ejUWLFiAfv36WfouWo455hgAwEcffYRdu3YBgOq7aj/z6quvxsqVK/Hf//4X5eXladsZfTc3x+xH8m50r1ixArNmzcL27dvR1taGr776ChdccAHWr18PALjqqqvQs2dPnHrqqUpU+oMPPsCLL76I/fv344477kBbWxsA4MILLyRDmugQSJKEnz73OX7xyvJ8D4UgCIIgCIIw4ZVXXlGMyC+//BKSJCn/HnvsMQCyEvnGjRvxwx/+UAkS3nLLLRg4cCCGDBmCY445Bi0tLQCAI444AgDw2WefYejQoRg9ejRaW1sxePBg9O/fHwDwox/9CJMmTdKtdR42bBgAYN68eTj88MNRU1OjdIGyy8033wxBENDQ0IAhQ4Zg8ODBePnllw0/8x//+AeOPvpoHHrooYqNxsO+W319PUaMGIEzzjjD9TH7kbwb3WvXrsVFF12EQw45BCUlJRg2bBhef/11AMBJJ52EBx54AIAcxZ45cyaKi4uRSCSUlmEPPfQQAGDgwIF48MEH8/Y9CMJN9rfE8MaKHXjl8y2Ii4l8D4cgCIIgCIIwgLX4Gjx4sKrLEgCcd955EARB6dldVFSE+fPnKwb31q1bsXfvXnz/+99HJBIBAFxxxRU4//zzUVlZiRUrVmDx4sUQRRGhUAgvv/wyjj32WLS2tmLfvn2YO3du2nj+9Kc/YcqUKSgvL8fBgwfxi1/8Ameffbaj73bsscfihRdeQG1tLdra2jBgwAA8+eSTadvNnDkTkyZNQnFxMVpaWvDwww8rhjTPrbfeiu9///soLS3F0qVL8dlnn7k+Zj8iSHlufLZixQrcdtttWLZsGfbs2YNIJIKjjjoKl1xyCa699lqEw2HV9suXL8c999yD999/H42NjTjkkENwzjnn4M4770TPnj1tffaBAwdQWVmJxsZGipATvmLXgTYcf9+7AIBvf3cGIiHrNUUEQRAEQRCFSFtbGzZs2ICBAweiuLg438MhCADm56VVezLvQmrHHHMM/vOf/1jefvjw4Zg9e7aHIyKI/BNLpHxhCQp0EwRBEARBEETBkvf0coIg0hHFlNEt5jcZhSAIgiAIgiCILCCjmyB8SIwLbyfI6CYIgiAIgiCIgoWMboLwIaIqvZyMboIgCIIgCIIoVMjoJggfEuMUy0UyugmCIAiCIAiiYCGjmyB8CG9oU003QRAEQRAEQRQuZHQThA+JiaReThAEQRAEQRAdATK6CcImbTERLy/5DjsPtHn2GRTpJgiCIAiCIIiOARndBGGT17/cjtvmfIWH31nj2WfEuZpuElIjCIIgCIIgiMKFjG6CsElDSxQAcKA15tlnxHn1cop0EwRBEARBEAZs3LgRgiBAEATMnDkTADBz5kzluXyyYMECZRwLFiyw9d67777bF9/BDcjoJgibMBvYS2M4niD1coIgCIIgiEJh4sSJioEoCAKCwSD69euHs88+Gx9//HHOx9OzZ0+ccMIJOOGEEyy/hzeQN27c6N3gcoDWCZFvQvkeAEEUGqzG2ktjOC5SpJsgCIIgCMIxoggsXAhs3w707QuMHw8Eg55/bFFREY499li0t7djxYoVeP311/Hmm2/io48+wvHHH6/7nmg0iqKiIlfHMXnyZEyePNnVfRLOoUg3QdiEGcFeBqD59HKR1MsJgiAIgiCsU18P1NYCkyYB06bJ/9fWys97TN++fbFo0SIsXboU//d//wcAiMfjeOGFFwAA06dPhyAImDhxIv7whz+guroaxcXFyvufe+45jBo1CqWlpaioqMAZZ5yBZcuWqT5j/vz5OOaYY1BcXIxx48bh66+/ThuHUXr5yy+/jDFjxqC8vBylpaUYPnw43n77bdx9992YNGmSst3AgQMhCAKmT58OAEgkEnjkkUeUz+3atSsuuOACbNiwQbX/WbNmYdCgQSgpKcEPfvADbN261dJxkyQJv/71r9G9e3dUVVXh+uuvRzQaTdvu2WefxfHHH48ePXogHA6ja9euOP300/Hpp58CSEXrGT/+8Y8hCAJqa2sBAG+++SbGjx+PXr16oaioCF26dMGECRPw5ptvWhqnUyjSTRA2YcJm3qaX80Y3RboJgiAIgiAsUV8PTJ2aqgdkbN0qPz97NlBXl5OhSCZrxU8++QQLFy7E4YcfrhiXf/jDH3DbbbcBAIYMGYKmpia89dZb+PDDD7FkyRIceeSR2LFjB84++2w0NzejtLQUe/fuxYUXXmhpPA8++CBuvfVWAECXLl0wcOBArFmzBitXrkR1dTWOPPJIfPPNNwCA733ve4hEIjj00EMBAD//+c/x5JNPAgCOPvpo7NixA7Nnz8aHH36I5cuXo1evXli2bBkuvvhiJBIJVFZWYvXq1bj22mstje3xxx/HvffeCwCorq7GrFmz0NzcnLbd4sWL8dVXX6F///6orq7GqlWrMG/ePHzyySdYvXo1unTpghNOOAGLFy8GAAwaNAg9e/ZE3759AQArVqzA4sWLUVNTg+rqaqxZswYLFy7E2Wefjc8++wzDhw+3NF67UKSbIGySyEVNN69eTunlBEEQBEEQmRFF4MYb0w1uIPXcjBnydh6xfft2jB49GsceeyzOO+88AEAoFMLFF1+s2i4ajeLVV1/F119/je3bt6OlpQW/+c1vAAC/+c1v8O2332LTpk0YOXIkmpubcd999wEAnnjiCTQ3NyMYDOLTTz/FN998g5tuuinjuFpaWnDXXXcBAE488URs3rwZX331FXbu3InJkyfjqquuwp///Gdl+7lz52LRokW48847sWHDBvzlL38BADzzzDNYsWIFNm7ciOrqauzYsQOPPfYYANmoZwb3t99+i7Vr16LOooPjD3/4AwBg3Lhx2LBhAzZs2IDq6uq07a6//nrs3bsX3377LZYtW4YVK1YAAA4ePIj//Oc/GDFiBBYtWqRsf+edd2LRokWYO3cuAGDq1KnYtWsX1q1bhy+++ALfffcdKioqEI/HMXv2bEtjdQIZ3QRhk0QuarpJvZwgCIIgCMIeCxcCW7YYvy5JwObN8nYeEY1GsXjxYnz55Zfo2bMnJk+ejPfffz9N0GzIkCE466yzAADBYBArV65ES0sLAOCuu+6CIAgIh8P47LPPAEAxJFeuXAkAOPzww3H00UcDgKVI98qVK5XI8c9+9jN06dIFAFBRUYHBgwebvvezzz5TovaXX345BEFARUUFtiSPtXZsY8eORe/evQEAF1xwQcaxHThwQNnXOeecg1AohNLSUt2a9MbGRkyZMgXdunVDIBBQjX3btm0ZPysajWL69Ono1asXgsEgunXrhoMHD1p+v1MovZwgbMLSy720hXkhNUovJwiCIAiCsMD27e5u54ABAwZYUv7u06eP6jGfin7kkUcqRjGje/fuqu34umWzNHY34PfP0s55BgwY4NrYzN7b1NSE008/HQ0NDSguLsaxxx6LcDispJKLFjIYzjrrLKxZswahUAhDhw5FcXExli5dimg0aun9TqFIN0HYJBfp5WKC0ssJgiAIgiBskazbdW07D9EKnB1zzDEoKSkBAJxxxhn45JNPsGjRIixatAhPPvkkfvWrXynbAcCqVauU+us5c+Zk/Lyjjz4aZWVlAIAnn3xSie42Nzdj7dq1AIDS0lJle76eeuTIkcp4p0+frozrk08+wR//+EfccMMNqrF99NFH2LVrl+WxdenSRUklf+211xCPx9Ha2oo33nhDtd23336LhoYGAMBTTz2Fzz//HA8//LDuPtmx5L/H3r17sWbNGgDAb3/7WyxbtgwvvfRSTvqAk9FNEDbJRXp5TBXp9uxjCIIgCIIgOg7jxwPV1YCRESUIQE2NvJ3PKC0txZ133gkAeOihh1BdXY3vfe976N69O0aMGIF58+YBAK677jqUlpZCFEWMHDkSRx55pFIPnWn/rGb8o48+Qk1NDYYNG4bevXvj9ddfBwAceuihCIfDAIDvf//7GD16NGbPno1Bgwbh6quvBgDMmDEDgwYNwrBhw1BVVYXx48fjiy++AADcfPPNEAQBDQ0NGDJkCAYPHoyXX37Z0vdnAm8LFy7EwIEDMXDgwDRl9EGDBimOgyuvvBLDhg3Dueeeq7u/I444AgDwy1/+EscffzzuuOMOdOvWTTHu77rrLgwdOhQjRoxAMAet5MjoJgibsD7dXgagRVIvJwiCIAiCsEcwCDzyiPy31vBmjx9+OCf9up1w++2345lnnsGoUaOwf/9+rF27Fr169cJPfvITRZCsb9++eO2113DUUUchHo+joqICzz//vKX933LLLXjppZdw4oknIhaLYe3atRg0aBCOOuooAHIK+6OPPoqamhrs3LkTixcvxo4dOwDI0fGHHnoIQ4cOxbZt27Bp0ybU1tbi5ptvxsSJEwEAxx57LF544QXU1taira0NAwYMUBTPM3H99dfjl7/8Jbp27YrGxkacddZZuPHGG1XbdO3aFa+88gqOOuooJBIJFBUV4d///rfu/h599FEMHToU0WgUS5YswerVqyEIAubMmYORI0ciGAxCFEU8//zz6Nmzp6UxZoMgeV0E4GMOHDiAyspKNDY2ptVNEIQR9/33G/ztg/U4bkBXzPnpGE8+488L1uIPb34LAHjhqhMw5rAennwOQRAEQRCEX2hra8OGDRswcOBAVe9q29TXyyrmvKhaTY1scOeoXRjRcTA7L63akySkRhA2EXPQp1vk08s7r1+MIAgi7xxoiyEoCCiL0JKJIAqGujpgyhRZpXz7drmGe/x430a4iY4P3UEIwibM2PYy6ztG6eUEQRB5JxpP4JQH30dZURDzb52YE7EdgiBcIhgEkmnPBJFvyOgmCJuwwHPCQ2OY1MsJgiDyT0NLFLsPtmM3ZEdrkGxugiAIwgFkdBOETXKRXh4n9XKCIAhvEEXLKadtMbUDNAiyugmCIAj7kHo5kRdW7zyIq575DCu2NuZ7KLbJRcuwOKWXEwRBuE99PVBbC0yaBEybJv9fWys/r0N7XFT+pqwjgiAIwilkdBN54d/Lt+Gdb3ZizhdbMm/sMxI5aBkW58LbnbjBAEEQhHvU1wNTp6rVjAFg61b5eR3Duz3ORbop6ygdUQQWLABefFH+XxQzvYMgLJGgC47wEW6cj5ReTuSFaNKobI0W3g2aXXdeqoqrIt1kdBME0UmJxhPYsKcZQ3qXZydiJopy+yC9+VSS5P69M2bIasdcqjlFuk3Qa8lUXS33SKaWTIRDioqKEAgEsG3bNvTs2RNFRUUkYEjkDUmSEI1GsXv3bgQCARQVFTneFxndRF5gImR8FKFQSKmX56qmmxZ6BEF0Tn7/xio89dEGPD19FCYd0cv5jhYuTI9w80gSsHmzvB2ndqyt6SaSsKwB7TFhWQOzZ5PhTTgiEAhg4MCB2L59O7Zt25bv4RAEAKC0tBT9+/dHIOA8SZyMbiIvMDuyLVZ4kW4xB+nlMVIvJwiCwHf7WgAAW/a3ZLej7dsdbaeOdGc3hA6Dw6wBwj6fbtiHlz79Dr8+6yh0K3MeYSs0ioqK0L9/f8TjcYhUskDkmWAwiFAolHXGBRndRF5g0dtCNLrZOsPLCLSoElLz7GNywv7mKH796gpMPa4akw7PIlJFEESnI550QGY93/bt62i79hjpa6ThMGuAsM9TH27Amyt3YMSArrhk9IB8DyenCIKAcDiMcDic76EQhCuQkBqRF9jihdLL9eHTy73sB54L5n+7C//5cjtmfrQx30MhCKLASLVozHJH48fL9cZGkQpBAGpq5O04VEJqhT0Vu4fDrAHCPkz/Zk9Te55HQhBEtpDRTeQFlqJdiJFuZRHoacuwjpNefrAtDgCIFXrIniCInMPmjaznwWBQFvgC0g1v9vjhh9PSofl7VKHPxa7hMGuAsA875/Y1R/M8EoIgsoWMbiIvMHu1ECPdbN3lZdRDJaRW4Au95qhsdNOClSAIuzAnpyvlPHV1ssBXv37q56urDYW/1C3DaA4D4DhrgLAPO+/3ktFNEAUP1XQTeSFRwDXdqXTH3LQMK/SFXnN70uguPP8KQRB5Jia6lF7OqKuTBb4WLpTTn/v2lY1DA8EvElLTgWUNTJ0qG9j8vdAka4CwDzu0+8noJoiCh4xuIi+khNQKzxLLSU03Z6EWesuw5nZ50UqRboIg7OKJkzMYtCzw1U4tw/RhWQN6fboffpjahbkEO/8pvZwgCh8yuom8UMjp5Ymcp5d79zm5gEW6Cz1NniCI3MNquvPlfGyLU023ITazBgj7sHOO0ssJovAho5vIC+xG0l6A6eVs7F4uAjtUerlS053ngRAEUXC4WtPtAHXLsLwMwd/YyBog7MPWG/ubo5AkKes+wQRB5A8SUiPyQoJahpmiMroLfKWnpJeT1U0QhE3YXJivHtnqlmE0hxG5hd024wkJB1rj+R0MQRBZQUY3kRdY1CIqJgquZllUFoHefUaca69V6GnZipBagX8PgiByD9O3yNc8yAupFdq9iih8+HNubzP16iaIQoaMbiIv8OsnflFTCEgs3TEWAxYsAET3xy92qPRy+fjQgpUgCLswfQsxT0lRbSohtfyMgei88BkeJKZGEIUNGd0diHe+3om5S7dk3tAH8AZYeyEpmNfXI/HRRwCARHsUmDQJqK0F6utd/ZgYH+kuoMOjB4t0U6CbIAi7xL1QL7cB7xTOV4o70XnhMzxITI0gChsyujsIiVgc1z//GW5+eRn2zZvvSfTVTfgbSVuhRLrr64GpUyFGYwCARCB5+WzdKvcrddHw5p0ShZ5e3hKl9HKCIJzBSm3ylfGjrunOyxCITgzXPZQi3QRR4JDR3RGor0fLkCPQKgISBOybdpkn0Vc34SMGBRHpFkW5H6kkQUqqhyaE5OXDvsuMGa45O2Jix0kvb2ItwxobPUvHJwiiY8Ii3Xmr6XarT7coyvPfiy/SPEhYJkHp5QTRYSCju9BJRl+b9+xXnmqMlHsSfXUTPpJbEJHuhQuBLXLqfiJpdIsBrhepJAGbN8vbuYDYQdTLxTn1Sk2ktH2HZ+n4BEF0TFhNd76cj6706a6vl+e9SZOAadNoHiQsw59ze5vI6CaIQoaM7kKGi74eLCpVnj5QXO5J9NVN+PVTWyFEurdvV/4UhZSxnbYE47bLhniiA6iX19ej+UeXKQ9Fwbt0fIIgOiaiUtOdn89vj3FG95LP7N9Pk45x5rRVoHmQsAB/3u9vIaOb0IGyaAoGMroLGS762hThjO5ImfyHy9FXN0mo0ssLYILo21f5k0W65b8DhttlQ7zQ1cuTDqGWcLHylHLcfO4QIgjCP8Ty2TKsvh7tq1YrDxM/+am9CDXnGE+D5kHCAglVyzAyugkNlEVTUJDRXchwUdWmohLl7wPFZYbb+QV1enkBRLrHjweqqwFBUBnaSvRWEICaGnk7F1D16f5uc+EtypIOIf68VITnAF87hAiC8AeJhKTYpjl3PiYj1O0S72QV7EWoOce4LjQPEhlQ13RTn25bdPQIMGXRFBxkdBcyXFS1qUgn0q2znV/gbyRthRDpDgaBRx4BoDYeE4IgG9wA8PDD8nbZUl+PeHNr6jPm1Bee5zLp6Gkp4iPdOtONDx1CBEH4gxhfZpNLo5uLULeFIsrTCSFgL0JtdX6jeZAwgM/w2Ec13dbp6BFgyqIpSMjoLmS46GtThI90l8t/uBx9dRO+DUZ7IUS6AaCuDpg9G4lwkfJUIhCQf4PZs+XXsyXpuYzz0fRAoPA8l0lHDx/pFvWMbh86hAiC8AdqQckcfjAXoW4PhZWnJb5ExkqE2ur8RvMgYQC/VtrbHKVe8VboDBFgyqIpSMjoLmS46GsTF90+EClzP/rqMgUX6WbU1SFx5JHKw8R/3wA2bHDH4E56LiVJQjwYSn2G3eiKH0g6hFo4o1viauH97BAiPEKb6heNduzUPyJrVK0Tc2lscJHn9hDnZNU6DjNFqDnHuC40DxIZUOnfxBNoeXcBzZlmaCLArx05Ac8PP0N+rdDWUWYUahZNR0/5zwAZ3YVOMvra1KOP8tSB4jJ3o68eIGpuJIUEH3FJjBvnnlMj6bnURoRV/cALxXOZdAg1c2UPqvp3wLcOIcIDkql+baeejv1XXCun+pWWdtzUP8IV+Ei32+nl63Y34fNN+/RfTEaeJQBRldEt6G5nCOcYTzO8aR4kLKB1Nu2bejHNmWZwEWAJwC/OvBG/Pv06NBaAwLAtCjGLpqOn/FvAF0b3vHnz8IMf/AC9e/dGOBxGWVkZhg8fjt/+9rdoaWlJ23758uWYOnUqevXqhUgkgtraWtxwww3YvXt3HkbvA+rq0HTDDOVh49iT3Iu+egS/fioI9XIO3mHgqrhP0iMZD6gXYGLAZnTFL9TVoen6G5WHivPA5w4hwmW4VL8Lpv0vxv/kn/i658B0D3dHSv0jXIEXlHQ70v3jp5fgwr8uwj49RehkhLo9HFE9nXAinJl0jKNfP/XzNA8SFhA1MYm9pZWpBzRnpsOtjyQIaA9HIAkBtHKdVLTbFSSFlkXTGVL+LZB3o3vOnDk444wz8MYbb2DXrl2Ix+NoaWnBl19+ibvuugtTpkxRbf/ee+9h9OjRmDNnDnbv3o1oNIpNmzbhsccew+jRo7Fz5848fZP80hRNLWAPlFT43nPOG6sFlV4OtW6Fq8GXpEeSTy0HdGqh/eS5zEDLUcOUvxNl5cD8+b53CBEuokn1W9OjBk2RUvx8ym2qen8AHSv1j3AFVetEl43u3QfbISYkNOj1Pk5GqNuDYdXTjoUz6+qAjRvl+e+FF2geJCzDariL4vJ5uq+kC/+i/D/NmSm49REfsCjkdZQuhZRFQ6JvCnk3uv/xj38ok8pll12GAwcO4KOPPkIkInuY33nnHaxatQoAEIvFMH36dLS1tSEQCOD555/H3r17cdNNNwEA1q9fj1tuuSU/XyTPNLXFlb8PtMbyOBJr8KmChZde7lHKY9JzKWoi3Y6iKz6hqT11XiZCIWDiRH/cBIjcoBF7SQjyb7++ezXuOP1nSLt6OkrqH+EKcdGjuRapjCVDY76uDu1PP6N6KiFkIZwZDMrz38UX0zxIWEaMysZ2z+YGAJpIN0BzphZVe1eu3V+gcNdRhhRKFg2Jvink3egOcjeeH/7wh6ioqMCYMWMwZMgQ5XmWYj5v3jxs3rwZADBhwgRMmzYN3bp1w3333YfiYjl1ZNasWWhsbMzhN/AHvHFzgDPA/UrBCqlBvfhzVUk06bmMaSPdgYD/PJcWaYlyRjeJrnY+NCl8fOThtaMm4qXhp1t6H9E5iatahrm7b5ZtZTYvtZ92hvo9f/gDRah1ePvrnTjtoffxzfYD+R5Kh4Odpz2a9wMA9mmNbgbNmTKq9q6ptVQ8ECzYdZQphZBFU6iibx6Qd6P7+uuvRzgsp3C99NJLOHjwID7++GOsXr0aAFBdXY2jjz4aALB48WLlfUOHDlX+Li4uxuDBgwHI0fAvvvgiV8P3DSqjuzXm+7YSWkXOQoI/tKLbx7muDuI//ql6KqvoSp5pak85VFw/VoT/0aTwsSyOqz6dCwC46/vXyvXdGd5H2KSDKMR65uBEKnXdLIKudQhLQ4d1nMW6i/z3q+1YvbMJ76/upLo6HpKAbCiySLeh0U1zZgrW3pWLAIsFvI7KiN+zaDTn5tpu1fjlGddjc2Vv0+06Ink3uk8//XS8/fbb6NmzJ/71r3+hS5cuGDt2LNrb2zF27Fi89dZbSqo5X69dVVWl2k9lZWoiMqrrbm9vx4EDB1T/Ogq80R1PSGj1efSYX+cUWqSbdxh4Eb2Nna6Jrkyc5D/PpUX4SLffHUGEB/Cpfkil+v100SuYtG4JoqEiPDjh0tT2HSn1L190IIVYvmWYm067hMVaca1DOKdtywqIaDINwe0SAAJIJA2oni3JSDdf0w3QnGlEXR3EFSuUh4mZz/hvHdVBnKMZ0Yi+vTT8dLw0/HTMOeZk+fVOdA7n3ej++OOPUVdXp6s8/t133+GDDz7QfZ92Ac8/FgzU/O6//35UVlYq/2pqarIYub9o0qSUH2j1d4q5WkitsCLd/MLCVfVynf0DgNijh/88lxZp5pxBtCDrhKhS/VK3m1BCxNXJaPfGrknvdkdM/cs1HUwh1quWYeoOFMbbtcfVi2Caw/SJJp0TWd8PO4sRYgNRSS9vAKCJdNOcaYokpI5J/LiR/jpGHcg5mhGN6FtLUkm+PVjU6c7hvBvd119/Pfbtk3tl3n333Th48CA2bdqEcePGYfPmzfjpT3+KOXPmAAB6906lIjQ0NKj2w9dx89vx3H777WhsbFT+sfrwjgAf6QaARp+LqanTywvrxqrq0+1B5IOP7gDu1zLmkmYuvZzWq52UZKqfWJ1ycgqShF6t8py9u6yr/GRHTf3LFR1QITbGWcRuTrUqY94s0h3TRrrdG0NHIsYi3dn8SJ3JCLGKKEJKHtueJ40BAOwt5SLdNGeaonKu+SlLpYM5Ry3Bib4xbZdEoAOn/BuQd6N75cqVyt8///nPUV5ejv79++Oiiy5Snn/zzTcBAKNHj1aeW8GljbS1tWHt2rUAgHA4jBEjRuh+ViQSQZcuXVT/OgrM6A4HZa/RgTZ/G92iVLiRbq/Ty+OJjpPS2BxVO4O8yAwgCoC6OiRWfas8DP7ndfRcI8/9B4rL0fbOe/5L/Ss0OqBCrGeRbovp5W0ahzCVyOjDjG7H83tnNEIykXRCiEknWY9XngcA7Os30L+CWT6Dv7bjfll7dEDnqGWSom+xMycDAMSLLup053DejW4+xfvxxx9HU1MTNm/ejJdffll5vmtXORJy6qmnKtt/8MEHePHFF7F//37ccccdaGtrAwBceOGFHcqYtkJMTCiGa59KOW3D723DeLuy8CLd3rWxAdRtcrz6jFzRrMnAKGQHApEdvHJ58KQJ6NKlFEUh+bk9I07oFKllntIBFWJjXJqPmzXd6vTyThrpdjGVOxZPitI5+Y06sxFiBOeEYG2vlPTyNhGIRPwpmOUzEh457bKiAzpHbREMIt6zFwBA7Ffd6c7hvBvdd955p/L33XffjYqKCvTv3x8ffvghAFkg7ZprrgEgR7FnzpyJ4uJiJBIJpWXYQw89BAAYOHAgHnzwwdx/iTzDGzaHVJYA8H+kO1HIkW4PFXWBdI9sIRuqfHo5QArmnRl+0RMQBAiCgJ7lskjmroPt+RpWx8Gq8msBKcR6pZ+hFlIz3q7DCqm5nMrNhNQcRRP9ZIT4oaacc0JIACRBXqazlmFNkVK033xr53JCOMTrUkBHdEDnqF1YNmdnzHzMu9F92WWX4a233sLZZ5+NPn36IBQKIRKJYNCgQbjyyivx2Wef4bDDDlO2P/nkk7Fo0SKcf/756NGjB8LhMAYMGIDrr78eixcvNqzn7sgcTIqoRUIB9EguYv0upMYvpgpPvVz/b7cQO3B6eQF/lcz4YcHmY/gbbDCQbINTIc9Xu8nozh6NQmwaBagQy2f9uDkPWk1b12ZhFfJcrOBBKndWQmp+MUL8UlPOOSESQmqJXtXWhJAo30/37TvQcSOhLsI7+bUZhHmjAzpH7cJ0izpjECaUeRPvOe2003DaaadZ3n748OGYPXu2hyMqLJhhU1EcQpcS+Sf1fXo5d7FFDzTJBkqBpJl4nV6eLqSW4TNEUb4Bb98uT9Tjx/viWEqSlJZe7psUL7epr5ejE/xCtrpaVuzsRPVKZvA32KTNTUa3mzCF2KlTZQObX9AUqEJsnDN6xcYDrt0nLNd0p6WXF/j8lSmVWxDkVO4pU2wdZ0VIzUnSmh+MEOaI0B4X5oiwK/SUzT2Zcy6InNEdTIjo2noAu8u7YW9JJfp24EioW2RqDfjqsq2Y9dlmPPrDY9E9GbDyHOYc3bpV/zoUBPn1AnKO2iWezXxR4OQ90k1kD2sXVh4JoUtxGIDP08vr65HYs1d52LZjZ0GplFpdsLmxf8C8pY1vvPM6tMUSaZkABb9o1YNEgCzBFkABIdXWsVcFpZe7CqcQq6IQFWLr6xG/5lrlYWLVt67NbVZVjdMi3YW+SPQolVsRUnMyv+c7Q8PtmvJs78mccyHBHZOAlED3Frnjw77Syg4dCXWLTAGSFxZ/h4/W7sWi9ftyNyhN+ywVBeoctQsrQ6H0cqIgOZiMJpZFQuhSkjS6/ZpenjRQVC3DgkUFZaDw92ZvWoapV3aGKTg+N/a0qeVAB1i0aiERIMuw85illgMU6faEujq0rF6Lh/72FlY/9VJhqhwn57b4/gblqYQguDa3WU4v72iRbo9SuZV0USeL6HwbIW46Ity4J3NOiAQnPhmQJHRrOQAA2FczsENHQt2CPx31zk1m/Gk7xnhOR3KOOiDeidPLyejuAKgj3XJ6uS/7dHMGCp821RYqKigDxeuWYdqbg+5CpgCMPZZaHgmlfuuCX7Rq8ZMIkM8RlUg3Gd1e89pXO/HIuhgeiQwuPJVjbm6LB1LjFgNB1+Y2fk41m5K0QmoFP315lMrNjpPjRXQ+jRC3HBFu3ZM5J0SCO/+DUgLdWuVI994fXlZY13SeyORcU4zufNR7J9tnYf78TtcCjoTUiIKGGTdyTbeP08sNBELawslamgIxUMQMKUvZoq3p1lVILwBjjymXVyRLHoAO6Nn0iwhQAcB+ej7S3atCbnG4+2BbPobUYdmwpxkA0NLu04wnM7i5jTe6lXuGC3Ob1Ui3VuSz4J2GHqVyZ92nG8ifEeKWI8LNe3LSCSEeknJCCFIC3QPy+bj/0COsjLjTw6+d9JT1mWht3tYlwaDsFL344sJzjmZBZxZSI6O7A9DUXiA13ZzhwRvdYiCIOPfYzwaKJEkqR7YXLcPSIt16n1EAxh5LLy+PBJU1XsEvWrX4QQSoQKBId+7YvL8FgMMWTvmGm7PiwZTWK9/nXbudXVS1nqY13R2sT7dHqdwpIbUsD1A+jBC3HBFu35Pr6iAtX648DM6bh27XXQ0A2NsctbaPTk4m7QZWyddhBV59ihLp7oSHnYzuDgBrGeb7mm7O8NAuoJRot2Y7v6Gdt72YNLT1RboKjwVg7DVzWgPM0OpoNnfeRYAKCLYA4gLdKaO7qd0TB1ZnZcv+VgAF6uTi5izeGStBMNzOLvycanbedciWYR6kcitGtwvHJ+fzgFuOCA/uyXwZXmDiRHRLKmzvayYnpRX4pZSeYc0i3QXpnCxgWDo/pZcTBYkS6S4OoZK1DPNjpJszUCTNza09GC4IA0W7qPDCQ8ompFDSOtGdmArA2GPp5WVFIQST4+xwHuV8iwAVEOw85tPLe5QXAZDTzRpafDhnFSib9yUj3X7pTWsHbm7TjXS7MLfxjk2ztjXpke4CPJ56uJjKLUmSki6a7SL6g9W78b3fvo03V+Q4Q8sNR4QH92T+cAYCArqVMaObIt1WyKRezp4TO2PvqjzCnBwdbj1oATK6OwBKTTefXt4a81/kiDNQeA8uALSF5dpOvxso2kWXF8eYLQiZAJlu9KAAjD2WXl7WkdPLgU6vRGoVPfXySCiIqlJ5ztrdRNEbN2hqj2N/0oFRkNcbf5/ghdSEgGtzGx8BM+/TrW0ZVoDH0wiXUrmjnMGSrY9n8Ya9aGyN4eN1ezNv7DbZOiI8uCcnNNlB3cpkJyWll1vDqtGdz0j321/vxPLNDVnvZ0djG7Y3tmY/oBwQdzEzptAgo7sDcJCv6U6mlyckoDnqQxXwpIHCq3ICQFt1/4IwULSdJbyYNNgNoChpdBsu9Hxu7DFnUGkkpBhaHa5lGKMTK5FaRa+mGwB6llNdt5uwKDdQwGmTybkt1rWb8pQkBFyb26z36e5gNd0ewAt/ZuuUyHuNbbaOCJfvyQmNo7J7MjOIIt3WUAkm6tV0J5/Ll3Ny14E2XPPsZ7ju+S+y2o+YkHDWYwsx+dEPFYPWz7iVGVOIhDJvQvidJq6mOxIKoCgYQFRMoLE1hvKI/37ixLnnAZ/+FwAQgoQ4BLS/NQ+o7prnkWVGOzl7UtOdnJAioSCAmPkNoa4OmDJFVkTdvl2uFxs/3hfZAi1Jp095UaqmuyAjb1ZhCzZCF+Zw4SPdgFzXvWZXE3aRgrkrsHpuwOVFjSjmdp6pq4PY5RjgnTXyx/fqLTuyXPhMUZVebmJ0J/t0BwMCxITUsecvh8Ti1o6lFdjxLei0UxfvyewwCMn7Z9dS2ehuaIlBTEhpcymhJlOfblHMb6S7sTUGSQL2Zlmj3xYTsadJdsS0xkRUBP0dT43nWzU+j/jPIiNs08S1DBMEAV1KQtjTFMWB1hj6VZXkeXTp8BdaaXEYB9riaPO/cw6AntHtfaQ748TkU2OvSSWkJj/XkSdZSZKwdlcTanuUIezzm14+SAmpqReKvUjB3FU8iXTX18s9iPmWSNXVcjqth9kcMX7RXFTkmpEvqtLLjbdjQmol4SCa2uMdTwjSBWKq9PJsI90dwOgGXLsnKzoYyTkzEk7dV+KJBIKB/DvX/UzG9HLm5MmT9gX7/Gg8uwWwKnOnANbSLLBU8Ne5A2hl2AFoVtLL5dRyvq7bj/ATYWmR7PdhEQW/o53QvEiPYelBqfRy1z8iJ7S0p2q6AwGmXu7dJLvzQJvK4Mg176/ejVMf+gD3/febvI3Bz7AbrF6kGyCj2y1YuzDApUVNfT0wdWp6D+KtW+Xn6+uz/wwD+PG7Odda3W9b8r5UUiQbNxTpToev6c4+vbzzLsb10NZ0h7i5k45RZvjz0Y813ezzExKySguXeJX2ApijmKOuM86nZHR3AFjLsPJi2YBV2oa1+bBtGNRGZGlyMdMW92H9uQ45SS9nke5ktLRQb65NTL08wquXe/NZkiRhyuMf4QePLEwTP8oVa3c1AQC+25s/w9/PaOsTGWR0uwufXp713CGKcoRbb3HEnpsxQ97OA2LczcLNBRq/L/Oabvl7sftUISxocw0fpcv2+DCnLB1nGUUHIzlnBsnotoWYIdKdbxVtfi0czWJxlOl7+o3O7Fwjo7sDoLQMi8gLg1Svbv9HulkEoT1PhpJdctkyjKWSZVqAJBIS7n/jG7zxVY7brGSghamXFwWVmjSvPJvt8QR2HGjDwfZ43kRm2HWYzc2zI8OiDlph314VcueCXWR0uwKf7ZG18bJwYXqEm0eSgM2b5e08gE/7dHOu5SNbZseICamVhOX7FNmC6cRs/kYfr9uDu15dgVYdoVf2WxSsACDHE/PX4vSHPsDeLLoysMPASnKCAhndduCvV721R94j3dyYskkxV2XuFMAkFWMR/k64VCKju8CRJIkzull6uY97dUNT082M7ixrWnJFLlqGMZEfFunOlLK3asdB/PX99fj9m6tcH0s26NZ0e3Rz48+fljyp9rMyjxgZ3booLcO06uUU6XYNSZLcFVLbbtGRZ3U7m8RVi0n39ssfF7NjxOaV4qTR3RnVdjPBz3dWFvyPvbsWz3yyCR+u3ZP2GttVRzjOsz7bjG93HsTyLQ2O96HNDuIj3R3BMeE1/HpD73il0rvzm14OZGd0W83c8QvUMowoWNpiCeXCTUsvb/Vnejlff1KSrOnOV0qwXbRzhBeTBvMCRsLW6ghZar7fjqHSMqwo1TLMqzmWz5TQi6DkApZOH8uTKIvfMVMvB6hPtxs0tMQUZxfgwsK8b193t7NJ3KP0ctGiMc/m1BJlLnZtCB0GVZ9uCweI3a/adUrKEnmOPLpFIiFhe4PcjSEbH6y2plsQBGX+pEh3Ziz36c7TPZsfXzaBp0zf008kEpIyjxaCg8BtyOgucNgCSxCA0uTCgAmpNfo0vVwV6Q4XVqRbO6F5Mb+xz7Ba080WKn4LsCotwyKplmFeeTbbOCG+1jw5HyjSbU4m9fKGlpjuQpywDh/lBlyIGI4fL6uUa2sCGIIA1NTI23lA3KP0cqs1kOy+VOqSkNof3lyFn7/whaeCkrkmarNlmFk9p9I32eeGQyZ2N7Urzggxixxapaabu/7I6LaOKgJsYnRn8xtlgyrSnU1NtypzJ6sheY5Kp6MTnsNkdBc4SgpvUUgR2+hS4vP0cu5CYzXdfovSGpGL9HJmtEWYenmGj8h3ipQR7NwsjQQRSM403tV0p84fVkuea5Sa7gJxIOUapf2NJtJdWRJGOCg/x3qNEs5gyuXFST2IrCOGwaDcFgxIN7zZ44cf9qxftzq93KtIt/5+JUlSrmV2n8p2vn/qow14/cvt2NrQmnnjAiFmM9LNHCl6x72jRLp551c234UdIpXRLZDRbRXeANVNL8+zhoAq0p1FBx/+e/o9ZVvlSPX5WL2AjO4Cp4kpl0dSLdf93jKMLVyCAUFZHLYVSMuwtPRyDyZrJdIdshbpVnpN+uwmrBfp9sqzyZ8/+XLgNFGk2xStEi9DEAT0LKe6bjdgImq13csAuGSo1tUBs2cD/fqpn6+ulp/3sE933KZBZxUrRjeffcXSy7NdJHbE/rTqPt0ZNhZFiAcPyn9+/U2a6j37LfzmQLbLtgZ3OgjotVlkbcMK3THhGqIILFgAvPii/D93Tqn6V2s7zyQkZT1H6uW5QyVi2QmXSmR0FzgH22XDmtVzA3LkCPBxpJurU4qEWHp5YUS6c5FezmqCWaQ700KPvey3VB0l0l0UVLzzXg2xrT11rrcsX+FZCyMzUunl/vod/EJKSC39tZ5dkgrmB9pyOaQOB4t0D+heCsDFhXldHbBxIzB/PvDCC/L/GzZ4anADHgqpSZkXfnzkKdWnO7vP9auDNBuicfMUXoX6eqC2FvFN38nb/u8DQG2tqs87mzrzVWPrFltdMrrZeconmQSDFOlWSJ5TmDQJmDZN/p87p/jMFO1cKJq8livcUi8vJCE13pHqtzVrLiCju8Bpbk9FExl+F1Lj22BECizSrectNcTEA2sGqy9S0sszRbp9mJIXExPKTaQ8ElIWDZ7cEOrr0f7Di5WHLX98KG0xlwuoptsco/RyAKlIN4mpZQVLa2WRblcX5sEgMHEicPHF8v8epZTzaI0vtxZp/H6NI93yfB0QuE4SWcxfkpT/yJoXWEovr68Hpk4FtmyBKCSzBgIBYOtW+fnkXJ3SJyns47PVpfRyrXo5QOnlCtw5pYI7p/hbsXbuUNdB5yvS7ZLRrYoe+/u8sNqusaNCRneB08Qi3ar0cn/XdLMJIiAIKC6wSHea0W00aWTwwJrBFoRFIWsLPSV64qMJrKU99Xvy6uWu39ySN962/QeUp9rCkbTFXC6g9HJzjITUAGob5hYsvXyAF0Z3HjCLTmWDKu3U4Bix9PJIKOhK9wWxgy42eWNB914lisCNNyoHL54U+BCFQOqAzpghp54n/Hcvc4J7kW75fz0htbjfFbO8RHNOqeDOqQSfaq75Hfi5JW+RbpWQmvM1sFkavd+IUaSbKGR0a7pL/F3TzXtvCy/Sbf4YgCUPrBnsBlDECamZCfiwictPE1hzUsysKBhAUSiQqul2c4jcjbctXKQ83RIuTlvM5QISUjNHrz6RwRTMd5HR7Ri+R3dtMr288I1u9bXk1vdR9ek22CVzBEfCAQguaFIUUt2lHTJGuhcuVN0LxUAyVV9ILj8lCdi8GVi4MO/CVm7BR7rdqOnmp8yQ4sB2vNvCR3NOpZE8pxLfrFKeSnPg+SA67FZ6eSGpl5OQGlHQHEwu9PmabiakdrA97itDjMEmCEGAEukuVPXytEnDogfWzBBkC80iLn3TNIvdh9GBZk65HIA3LcO4G297kDe6ZQOOX8x5TVxMKI4jqunWR0+Jl0GR7uzZfbAd7fEEAgJQ061jGN3a8bs1fagMYIOdsus5EgogqHRfcP6ZiYT+34WOWkhN5wBt3656GFeMbiFtOz86kJ3glpBaqk83F+kOUqRbe04ZkWhoSP2tXbupIt35OZb8eZ5N21z+q/lpHagHf6wL/f7kBDK6Cxxm3PCR7oqkAS5JKaPcT+hFugu1T3daBNqiB9bMEGSeQHZs9D6Xh70kSf5ZrDQnlcvLiuRz0ZOWYdyNty1UxP0dMdzOK5q5dHpKL9fHSL0cIKPbDTYno2t9K0ssizD6He215Fp6uYV6ThbpLg4HuUwdlyLdBf678ETFDMeyb1/VQxbpFgOBtO0SHSDS3dgaU627sqrpTp7+VNOtQXNOGSFWVqb+7iSRbr+fF3ELc29HhozuAkcvvbw4HFQWXX5MMWfXWZCr6S6USHfGlmFWDTyT7ZT08mDq8jRb7CV8uJhjzqCyZKQ76EXLMO7Gq0ovLyo23M4rmqLqRVZnvJlkwky9vBcZ3VmzJalc3q9riaoGuZDPRbOFslv7NRRS4yLdghtGt2ph3HEccxkj3ePHy+3lkscwFelOZnIJAlBTA4wfr6iXF/I5y6eWA9n91in1cr2a7sI9RlmjOafSSJ5TicFDlKfM5hJ/1HS70zLM7zXdlF5OFDR66eWAv9uGpdLLBRSHmZBaYSxCMrYMs2rgmWzHFjFWI91+9HKmjG75vBS8qOnmbrx8enkri3RzizmvadZklMQ60KLaLUzVyzmj20y/gDCGiajVdC1VHeNCXthoSzXcOjesiJrxQmrscGarXp76fMe78R0qITW97xUMAo88Iv8tCLKAGpJCasxoevhhIBhU5ohCTp3mRdSA7H5rxVHJrdRDAWtdTTo0mnNKBXdOJcDNgyaijHnr0+1SpFvywXexiloDIo8DyRNkdBc4epFuwN9twxLcjYRF5NsLJNKtXXSlLQItemDNDEExi0i3X7ycTEiNnZfMCHC9hVHyxtsWTqWUt4aL0xZzXtOkNbqprjsNM/XyHsmWYVEx4cs5qxBgImo13UrURrfPF2FmeBXpVs2ZGdLLIy4JQfrROeoGllqG1dUBs2cD/fopkW4xEJDvlbNnK/3e2fsL+fBsSzO6szem+DkzQJFuGe6cUsGdU/whSptLxPxfj7zRmV16eepvvztj+PO2MzrYyegucJhxU6GJdPu5bRi7BwW4SHehqpenTdYWPbBmhqC2ZRhgLrzjx8VcU7LGubSICanJz7s+ySZvvO1duytPtYSL0xZzXsOcX4x4Z3ThZsAs0l0cDipz1u6mtpyOq6Oweb9BpNsnc4ITvKrpVtUVGuyS3ZPkmm75uWzmLz+kgDa3x/GHN1fhyy0Nru0zmim9nFFXB2zcCLFIdrAlrroK2LBBNUen1MsLd/7URrqzMY5Fbq3ECHnhwC5UkucU5s8HXnhB/p87p8x0FPhzLF8ODLf6dIsW5jO/kLEcpYNDRneBwxb7TLCK4ee2YbwiZ0pIrTAj3boTnAUPrBnsZhDhjG6zycmP7SJaDNLLPZlk6+rQfuVVysPW4cemLea8Rptenk19VkfFTEgNAHp1kWvxdx2gum4nbN4nL/aru5Z0mPTytHIely6rhAVHJR/pTrUMy+YzU3/na5G/4Nvd+POCdbj3P9+4ts9YPHPWAEMKBMA2FwfUpjmfU+rlrg0v57CabmYnu6NennrOk6yxQiYYBCZOBC6+WP6fO6fM0q4TPkjJ5ufmbEos/ajrY0TcBxkG+SSUeRPCl4gisHAhDu4+ACCI8iK1/4S1DWv0odGtpJkG+JZhhXGX1S4qDCMWdXXAlCmySvn27XIN9/jxllKd2YIsHLRW080PwS8RAqWmO+kMCnpR083Rxi38WkvKcpJSzkPp5ZlhhyRoUHrRszyCtbuasLuJjG67JGJxbEtGuqu/XY5gdap8RSzgc1FrnLoVIeZ9YkbRa6WmOxxQDB231MvzlQLaksyM+3rbAUiSpBLockqUa3+ZacHPf229Y5DoAJHuLclI9yGVJdja0Jqd0a306SYhNSeYZQHyx6/QhdSslMv4BSudIzoyFOkuROrrgdpaYNIkNO1pAACU102Rn0/SpYSll/uvPlJJMxUEFBdcpFvz2GzSMPHAmsE8gaGgtcWeaSsaUQQWLABefFH+36Q/uGuIIprXbQQAlO3cBohiqmWYR5Msr37fEs39uZRmdBeIMGAuMUsvB1KlCK52MsjH+Z9r6usRHTxEiSBWnnsWgocOUl72e+TDDK3x5Z56eeYUx5R6eSq9PJtjaSW67jWJ5ML+YHscW96Y78r1wEe6M32veIbjzvxDBZkolJxrtm7fBwDo361EfjqrSLf8P58dRJFu65jWdPugm4BbQmp+LDE0gtLLicKivh6YOlXpBd1cJE/sFVs2ys8nDW8W6fZnern8fyAgIFJokW7NJOHFpMEWJqGAYKknp2F6OeecwbRp8v+1tSrnjOskP7P5tf8AAMqeeQqorUVg1255fB5NsnxqVmseRPnS1MsLctXoLan2NzoviiIC+/bKf6761h3jOB/nf65J3g+i23cqT4XFGIStWxFIyMfQ74swM+KaKL1rkW4LSuLM+VMcTgmpZfPxVhTTPaW+HuIvf6k8XDnj165cD/xcl8mpqjYO0l9n7y+4lmrJuabt+6dhT1xeVg+Y9xqALGu6ddLLQ0qku8COUR7gz8e0tVuGczEX8GPoNOnlPiyHzCVkdBcSogjceKNy5xeFAJojpQCAsnY5tRAzZgCimKrp9qGQmlLbyUW62+JiQSgZWqrp5vhs4z7U/fkjW8I1bFIKBYRUhNhun26Nc0Zh61aVc8ZVuM9sDsv1uWXRVmDrVgSWLZXHl4NId2teIt3qz6Sa7nRSfbo1VndywRpc+IG83UMPZ28M5OP8zzXc/SAWTFWKhUURkCSEkisaMWYj28lnmQHp6eXu7NdWenko6E6f7nymgCavB7GhUXnqm14DXbkeLAupIbNyMbs/FFTqNDfXbO/SEwBQEm1D993bAADimjWOd82OUVAn0u2XTiV+hj9G2nPKD5FuVXp5J1Evp0g3UTgsXKhaRDLDBgDK21tkY3zzZmDhwlSfbh9GuiVu8c0i3ZJUGHWwWqMxk6Ng7tKt+OK7Bry5Yoflz0ill6ci3VbVyxMJKc05oxmw/H/SOeMams9kGRhlsVZAkhCU5C8geWSM8pkSrbHcO3DSI93+P5dzjW56ObdgDbLIrBDIzhjIx/mfD7j7QTQkz/dhMYYAkk5NKXk8F39qbX8+zAzQdgHwomWYFSG1VJ/uLD4zXzWk3PUgCakl39e9BrpyPagj3ZmGYn7c2e/id8NBQTPXbE0a3f0O7EJIlO8J4sKPHB9bPkDBUGq6C+0ekweHnlkvbjFf1yOHKr3cpZpuvxuynV1IzZbRHY/HMWvWLFx++eU4/PDD0aVLF3Tp0gVDhgzBZZddhpdffhnxuP9qiDsM27erHjYlo9xhMYaIGFNtl6qV9l/EjU0KggBFvRyQo91+J61kOsOk0dASs7Sd3j5DgVRao9lEyu86npDSnDNpcM4Z19A6hJjRHZVFZQLJ1Vhi9Wr3PpOD1wQQE1LOI83pQmr+u+7yjdL+RimQVS9YmWNGDASyMwbycf7nA+5+EAvIke6wmDoPgyzSvWtX5n35NDPAKyE1fuFnNDWnhNSCXJ9ulyLduVwYc9eDGEjdb7/pNVD+I8vrgY/QZUp55n9P3ZruQot0a+aarZW9AACHHNiNUNKJGG9pcXxslVK8Qm8ZlieHHn+Kmdd05+dYqvt0O1//qtPosxmR92jniIJxsLmEJaNbFEU89NBD6N+/Py6++GI899xzWLNmDZqamtDU1IS1a9fi+eefx7Rp01BTU4OHH34YYqFHEfxI376qh8ywKW9vgaDZLpi8ufpxYmZjCgYEVVus9gKo67abXt7QGgVg73dgBlswICgGitn70wR6NM6Z1lAEjZGy9DdqtssKzb5SRrfccznADKrGRniBVhMg1ynmJKSWmbT0cs2ClRmJCSEpOOjUGLB6Xrt5/ucD7n4QDbJIN2d0s2uuRy/z/fg4M0AbzfMi0m1kALOSlUgooMzDWfXpztcinzvPRS7SvaWqj/q+4PB6iGkcGGbHKJNysRLp9nm0TkFzzPhIt3LPCwQdH9uUennqOUVIrVCOUR4dembXnCrSnaesAdeE1ApIvTwtzb9QzmOXsGR0Dx06FLfeeit27NihTKi1tbUYNWoURo0ahdraWgDyZLtz507ccsstGDp0qGeD7rSMHy/3ek4uWg+yeu5kNBGCANTUAOPHc2Ib/juh2TUWEAQIQsrwdlW12CPMej3qsb85Gem2MbGwzwg7UC9PSFKac+b8S/6A8T/5J1rCEfUbNdtlhWZfLXxNN1JGd6JLpXufyaHNksi1mBr16c5MWnq5ZiGqLFIFzW3J7oLV6nnt5vmfD7j7ATO6i7iMJyXSPWKE+X58nBlgd751sl/j9HJW051KL8+u/ZP+53sOd54nAupra1XPWt3t7KCd68y+Wib1cvZeP65bdNEcs61dZAdXdeOuVKRbCDo+tux816vp9mNAJY08O/TMnGvxfDnBONxqGeaHqL1VvHKkFgqWjO5Vq1YhHA7j/PPPR319Pfbs2YP169dj8eLFWLx4MdavX489e/agvr4e559/PsLhML799luvx975CAaBRx6R/xYENBXJRnd5tDUlCfzww0AwlQ7nxxNaqVNK3jyKw3JkqxDahtlqGYZUn3Q73kdVpNumermYkNKcM2u798eB4nLsKa2SN+KcM66h+cymZKS7NGl0B1kEY9Ch7n0mhzZLItdtw6hPd2ZSSrzJuUqzEFWll/PYXbBqzsU0vDj/8wF3P4iFmNGdPA8FQV0jb4aPMwNiXCcHwD21Wyup3krLMFV6ubef6Qnc9aA9F77uPSjr60FbSmP9XmX8uiT5P2IHIG2uYUZ3vwO7U9dfRYXjY8sOgaCq6ZZ/w4Ko6c6zQ89MR8G01WqOcEtITZVG7/PIsXa+KJisFpewZHRfd911WLduHV555RWce+656Nq1a9o2Xbt2xbnnnotXXnkF69atw3XXXef6YAkAdXXA7NlAv35KTXdFe4s88c+eLb8O+DrSrXhvk/eRVKTb/9FBu+nl+1uS6eWOIt0CggH1c7pj0hrdGudMnJUaBIJpzhnX0HxmCyt9SDqEBBbpNjKEskTrsMlXejn7elTTnU4q0p18QrNgVSKzgeR56dQY0JyLKrw6//NF8n4Q7SM7JpRId3U1gl2rAFhwvPo0MyCRkJTFJLtHuLWgFMXMBjCbU4q5SLdb6eU5vS9z10MioD7nlbruLK4HrbFgtohWtwtK3y7vbdXsoplrWE13v8ZdCDEn4ojjHB9bvY4PoUJSL8+zQ48/xbTnWyZRv1zgRZ9uvzurvOpIUShYMroff/xx9OvXz/JO+/Xrh8cee8zxoIgM1NUBGzei6dd3AQDKRwwDNmxQDG4ACAZZhNR/i/+EJuJVWJFu6+mO7XFRibjascFiXBouu9ma3V/5SUuZfJOLcalfP2WhJQqBNOeMq3CfyVLZS2NtsgEwbqx6fC7DHDZscZyv9PIuxXLEkYzudLQZLtoFqyoym61xzDknVXh5/ueLujrEXpoFAAgf0geYPx/YsAGhMtkpm/Gas5kZkKvOADHu3lWUNLq96NNtdIts4yLdgguRblWqa65XmsnrQaysAgB0a5G1Nb6uPiLr68FxpFs3vTz/hpBt2LGtrsb2ih4A5JruYGUXAIB4iPW1sxZ2rfHJP0EfB1TSyLNDzyyazT/WdknIFfz5no3osR+i9lbxqiNFoUAtwwqVYBBNtYcBAMoP6Z22MA35uK2EosippJcnldYLItKtfWx8fBtbUvWVdhZZfE03W+yZTaSGE25dHcR161NjmPlMmnPGdZKfyQz9ovo5wIYNCPTvD8DceZANzGFTVVoEIPfp5c3JPt3dyuTPz8Zr3VHR7dPNGccsvTwRcMk5lHROYv584IUXFGO0QxncSaLJ6yrcrSswcaJcYpS8u2dcnNvIDHh5yXcY+bt38OWWBpdGbgy/GFOMbreE1CxEVNUtw1xQL8933WVdHRI33QwAOLqXLKC2uucAxKacm9VutaU0ZvcqlWp8pkh3IS3G6+qwe9k3iAdDCEJCr/+bheDvfgcgO+NYt2WYj0sH08hzqY+qT7dJLbE/1MuzSS8vnEi3dr7w+3jdJuTkTd999x1eeuklbNy4EW1tbarXBEHAP//5T1cGR5jDUlrLI+mRICsCXPkidSORH7Ne3YXQMiwtRclkwmjgeqRb9T5KkqRSd7cimqJqF6EdH+ciF0eOzElKbZTT0i+aOF6tMeDB+SgmJGUiryoNY19zNKfp5YmEpFyLVaUs0u2/6y7f8AKKKurqgClTEPjbO8CmOMRLLgWunOnOuRoMykZoB4ct2Iq4bhChgI3oMHN+3Hijugazulo2uJOOig9W78He5igWrtmDYdVVbg1fF/4aYt/LrcVxpjRnQC2k1pI8rFlFulWGvvP9ZANb1tcecyiWLt2KpvY41u9uxuF9KhzvMy293GKkW++8VGVt+XDtYsbWg+0AgD5VpQidPAmhJZsBZCm+pzNnsixGPwZU0mAOvalTZQOb/01zUOqTMDnf/NanO6tIN/dWv9uwaSryBXadZ4tto/udd97B2WefjWg0argNGd25IWV0p/+MbMHlxxQkrSIni3R3tJru/c2pa8SqN4//vcIB++rlaWq/eVDM5RdhRckCXuZg8cIJxKvedystwno0ozUWN3mHu7Rwn981GWmn9PJ00tLLeYJBBGtqgE0bINbUdIx66xzCDNRwMGV0s8NseXGedH5g4UK5xrJvXzkCxf0W7Dfc09TuzsBNUEW6gy7XdFsRUkvOY8W8kFo2UUvVPJ2f+UHk7r1H9q3Ako378fX2xqyMbjvp5Wr18vTXVenlhWBUcmzZL4uG9usq65m4oTKe1vEBXJ/uQjFWLDr0vIA/9GlCar6IdKc+Nyv18gIqy4h18j7dto3uX/7yl2hvN77hCkZpJITrMKO7TMfo9nNbCW1NN4t0F0ZNt+axy5FufoEcDAqWWtWYpUryi5xcZT0wozsgACHF6M5+0WoE7yFmkeZcppezeu6AAFQUy9ciGd3p6KaXcyhzVoEttv0AO9+Kgg4j3YxgENJJJyEmSqqoOYMtXPc2GTvd3YLV/gWElDPBrSnMStSZ79MtuJ5e7ng3WcE+Vza6u2DJxv34ZvtBnHes831qjQXTUqiOJqTGsS/pZO9ZLuuZhIIuGN3JY8BPmamOJgV0j7Hg0PMCs/NN5NqUiQkJEMWcO3vdUi83i+j7jbQ0f5+P121sG93ffPMNBEHAxRdfjIsvvhhlZWVkaOeJZtNIt39TkNg9OiWkVkCR7jTlRROjuyW1MLWaccAbySEuvdzMWDUTn+Hvy7nKemBGsCrqpkTs3f88tjgOBwXFAZXL9HI+44QZPdSnO5009XINwUKL4PgIvfTygEPBpeue/wJLNu7H/FtPQkVSGJDB5ppcRLrZuEMBTtvCpQmE34+RMFwqvTzoSqYO/9Z8LYz5LLPDeslCX19vO5DVPmNp6eXG22bqjZzWiaOA4LuOAKn1TTwL49hMvdyPWYym5KHUh7+2VfeV+nqIj7wMnHiZvB2ARO1ABB55OKeaH26plxeSAGFnF1KzbXT37t0bmzZtwpNPPomKCucpSUT2sGheSZFxTbcfT2hterkS6c6x4rSW7Y2t6FEeURmLWuyklzc4EFLjnSQhrk+32dv5OUz7e6si3blKLxd1DAALEXunMKO7OBREafJayKnR3ZYyusPJ7xyL+++6yzem6eWAJQcToQ+75tiCH+B7W9s7nks27sOepig27W3BMf0qVa+x3zA3ke6k0c23TnQrvdyCccfuR5FwwJ0+3T4wKNnnCgJwVF/Z6P5m+wFIkuQ4eGJHSC1TJFulKF1g80BKi0U+WUMurMH0dDBYTTfNk5lRpV2z87S+Hpg6FfFhp6m2jW/fgaKpU3Pa3cK19PICyhCJac7bQkrYcAPb6uXXXHMNAGDJkiWuD4awBzMsSnWMbpba5McbF7tZKEJqLNKdR8XnNTsPYszv38Mts5abbqed0MwiFvs5o9vqjZf/vYKc0W02kapa0Zi0xcjVQo+lukY4o5sZVF60G1IiUuGg0n4uly3DmrkyDxbppvTydDKll3spttfRSUW6U/eCgMNFP9teX+gqaXQ35yLSLX8ndetEl4xuC/OiuqYbWX++H+ouFcNQEHB4nwoEBGBvcxS7Djr7PSVJSjMWzIxBO326C82oTGVmyCeLG4EPPUdlwUa680CaMJ8oyrXlkiS3puRQHs+YIW+Xk/Gp5wSn50pe2xHaRFs+1tnu95Yi3f/617+Uv3v37o3evXujrq4OV111FY488kiEw+oUtMsuu8zdURK6tETlxX5J2Di93I91P1pFTmYoteUx0r1hTzMkSf7fjLSabovp5VbTCdlCMxQQIAjW0svNagXz4QFVDACVqJN3BhVfe8kcULms6ea1FVikkYzudPREgXhSi9ScDanDENOJdLM/7S7O2ZyhtwBkz+1rjkJMSIa/pRuwcataJ7p0bqjTy9NflyRJpV7uSp9uH0S6mdMgGBBQHA5iUM9yrN3VhK+3H0DvLsW296d3bpn36eaF1NK3458qNKNScWgkLzw3a7r5y6ygWoblmYR2bbRwoSLmJgbUwap4IADEJWDzZnm7HKTCa3/DaDyhm7maeT/6f/sRrZBaZzuPLRnd06dPT0s9kiQJDz30UNq2giCQ0Z0jmGFRptMyLFVP5L8TWlRuJCy9PNmnO4+RbjEp4hbfuw9YsMBQ5EMb6TBtGeYk0s2lVALWolVmERR15MDSELJGt77UhUWrEUwLoDgcQGlR/mq6K4pDSmkC1XSnwxzcaS3DklB6uXNcE1JD6hrVe18qCg7sb4miR1I0ygvYXGi1daIdMjkj+XtRJBRQDJ2shNSM6ktziPbe27tLBGt3NeEAJ/ppB1WnilAA0XjCcp9uXaeOD7IBnMLWWuxccWMNpueoDPq4M43fUEeSE7KIG3sc0ES6eSOc285LtMsEp0a3Wbaj39DqTPl9vG5jOb1ckiTVP73n+NcI72kxSy9HMloRjclGZI7SZaygvZHkPdJdXw/xup8BAMTNW4BJk4DaWrn2R4N2IWB2uu/nIt1W9ex48SAgFa0yTS83Ua7Ma6Rbp6bbiwmWqd4X5zu9vChldFOkO51MkW4/Owr9jr6Qmvy/80h3+mv89eu1mBrL+glz6eVuzR+Z+kWrje6gciyzahnmg9RpXr1c/j9pwDkUXOXnueLkuZdNn24/1L07ReTKIYDUPdztPt0hqulOY83Og2jUcRyl6d307Zt6rIl0q9LNue28RHsNtDtcpxeUerm2ZZjPx+s2liLdd911l2cDqK2txaZNm0y3mT9/PiYmUz2WL1+Oe+65Bx988AEaGxvRt29fnHPOObjzzjvRs2dPz8bpRxQhNW16eX09grffDZx3P+LRmGxEVlcDjzySU2VGI7RtMPIa6U6KaohHTACQTDECgK1bAR1RDTvp5fxNwOoNkt242Y3Vrnq5WS/KXN2k25VU1/Sabi/GwCLd+UsvZxknIcXoISG1dDIJqSnCX53sJuwGUZ0+3Uqk24P0csB7MTUlchgUUkavS+dGphpI5siT25UJ7qSX+yCKq3V8WXHqmhHl2rqxuc800m1iVKe1dMrTMfrV3K+wbHMD6q8bo4i8WsGTmm5NZgL/NzknZb7b24JTH/oAYw/rjuevGq16TdJec+PHy2vhrVvTa7oDQXlRWl0tb5cD9NLLHe3HB3OLVdKEF30+XrfJu9FtBaaS/t5772Hy5Mloa2tTXtu0aRMee+wx/Oc//8HHH3+M3r1752uYOac1WdOtinQnjchQWVcAnDfPwIjMB6Lmxp+3SDcnqhFPHifleEmSPAHPmCH3l0ymmttZGKgi3RYnFjYhsRu3XfVys/HlanLTi7q5sWg1go90K+rlsbj7H2RAsyq9nGq6jdCrT+RxKvxF8DXd2bcMYws43Ugk95TnkW7mSAgEuN7EOUovVxx5QQgC30Uim0h36u98GUyixuEdzDIaG+OcPVZ+I3WkW39setvmkrlLt6IlKmL97mYcmVR4t4Li0PC4ptsNVfSOxNaGVgDAtoa2tNfSSjqCQTn4NHUqxIDa/IkHk48ffjhn/bq157xTozuRYT7zE9qWYT6UnfIUS+nl9913Hz788EO0t7t/k924cWNaevpbb72lvP69730Pxx13HGKxGKZPn462tjYEAgE8//zz2Lt3L2666SYAwPr163HLLbe4Pj6/IkkSWpJGaimr6eaMyGBCfk0MBOVEc3Yh5lCZ0Qg2FJYyyNLS2nPdp1slqpFMs+NTjiQpJaqRxGnLMKsToah4y+Xx2FUvT6vpzkMdoV59qdLyx4OFQrtS082ll+dFSC1INd0mKK0CjWq6s4y6dWb0HF1OW4aZRbr5fe3xPNLNqZe7nAWRSeuCOfJYZ41UeYzzz/RDCqhiGAosBTo7A44XzUxlMxlvb6ZebtZ5I1e0xUQlS8ruMdFGur2r6Sajm4edN7rzFfeUcl7W1QGzZ0OsrFRtKx7SL+dBKe014DTbU/09/X1epGVjdrL7vSWj+9e//jVOOukkVFVVYfz48bjjjjvwxhtvoLGx0ZNB/elPf1L+Zob0vHnzsHnzZgDAhAkTMG3aNHTr1g333Xcfiotl1c1Zs2Z5Nia/0RZLKMYrE4/ijchQImV0JFgajY4RmQ9S3vakkFrSUGILnZyhEtVIRrIFnUuC285qenlrVFRNoFYnQmawBjUpapbVyzXj4Wv1ctan20RIzQvNh7a4n9TLqabbCOrT7R0pR1d2aaiZIibq9PLcRLpDXBTVragI/9305nC+ZAVwZ/7yQwqo0rbPpRRo5bwLBbjzzfhHMlMv174tH51X9jXbz07Tbu9mn272Vl7ImIxuNaZGN/ec6rysq4M44ybVtvEF7+c8C9Sb9PKshuQ5Wv2IznYe2xJSa29vx8cff4z//d//xVlnnYXu3bvj2GOPxQ033IBXXnkF211Q/Fu5ciXmzZsHAKiursZFF10EAFi8eLGyzdChQ5W/i4uLMXjwYABALBbDF198kfUYCgHWLgwASpJGK28cBjmjO65RacyVMqMRSsQrOaxi1qc715FuTiwjLuhEunW2S2gWLUZGd0OrOgpkN9IddqhebhY9yLWQWkTH6PayZVhxOKhcC7ksVWDp5eWqPt2d60ZiBXZIDPt002LSMVHRONJt55ozm0uA3AqppbJ+BCXS7Nb8oSrJMRFSY5kz7qSX+yDSnfxY9n2CDksQGGyuD/ORbos13WmlUGnp5Y6GlBW80W33mGjVy93s080ljXG/mc+tqxyR6qhgPl8lJLXTTCu7oq3xzgXaMTvNkPNDFo1VOruQmqWz7J577sFpp52G8vJyVRp4IpHAl19+iSeeeAI//OEPUV1drRjATvnTn/6kXBg33HCD0gN8586dyjZVVVWq91RyaSL8dlra29tx4MAB1b9ChUXyIqHUzY43DkO8R1lrSOZImdEINkGkWoblKdLNRDUEAQltTTcgF77V1KhENRLcQhAwvqHub1YraVqPdGsiERYWm6obieZzzIRrvELPAPCyZVg71zKsJI+R7vJICOEQ1XQbkbFPN/WfdQxv/DCURb+NczGTBkQuhdTYNRQKCq5nQWTqmd0eS91fgVR6uRv1udnuJxvShNSyPK6KlkCIb+tmvL1ZVpb2mOTDqHSiw6Jsr2n3yf7PJr2c3dtV6uUF7Jz8ZN1evP218RrdCWaR7jSxPu6hHzQEXIt0F5DR3dmF1CwZ3b/61a/w5ptvoqGhAZ9++in++Mc/4pxzzkG3bt3S6rHXr1/veDC7du3C888/D0AWT7vmmmt0t9OmePGPtf3Eee6//35UVlYq/2pqahyPNd+wlkhlEU4MgjMiVZFuIWlI6hiR+YDdlFlkK2+RbiaqgVSEW4l0s/NII6rB5ge2uDWa37KPdCdbhtlML0+rOc/DZKxvAKSPxy1S6eXBvPbp5tPLnd48OzJW08s7W42XG+gJqaWOp/X9ZDS6+Uh3s7dGtzrS7e65wRt0elNSu5Ktk4x0B1h6ufPP5I9n3oTUNNdgtpFutZCa+jP0UPfpVr+WlqWV50i305rulMOcGcfOv4ieenkhp5df9/znuObZz7AtKX7mBuw80o90qx/z172omRjz4eTRzs2OhdR4Z4LPz4u0SLfPx+s2tvIpAoEARo4ciZtvvhn/93//hy1btuCJJ57AgAEDTI1dqzz++OOKWNtVV12limDzquQNDQ2q9/F13Gbq5bfffjsaGxuVf6xGvBBhKa1KajmgMiJDkibSbWBE5gOtImdxKI99upmoRtduALgUo+pqXVENdhNUemUapZe3qCPdVoNNMU3LMLvq5enRAuNFjle0c+I6DMGF9EwjlPrLcEC5HvLRp7ucarpNUepJjYTUCngxmW/0dBScRDEzCS+qhNQOepteHuNEJVOpy+7sWyU8pJterh/pdi29PN813WlCas7mK10hNZNjZHYM0vRICqymm31vbcuwrITUkm/VM7oLsWVYY2sMkgQs/a7BtX2aRbq1ATr+lPJDpNs9IbXCiXSn1XT7fLxuY6llGOPgwYP4+OOPsXDhQixcuBBLlixRjGR2cpeWljoaSFtbG/7yl78AAILBIG688UbV66NHp/rvrVixQvW+tWvXAgDC4TBGjBhh+BmRSASRSMTR+PwGi+Sp2oUBihEZuPFGCFICkhCQo7fV1bLB7aM+3UFFSC2PfboBWVSj61DgrdWIF5cA8+fL2QA6zglJc2M1mqiZ0V1aFERLVLS8yIor6eXuqJer0yhzc3xjJunlXtiiSsuwUFBJL48nJETjCdUYvKI52ae7PBJSFqGFuCDymlRqq/7rbitUdyZYxLFIJ9LtWEgtQ6R7b3M7JElyxeGuh8g5IFNCau6cG5ki+tqabtf7dOfpFE+VdsmPAxZSws3g53o295lGuk3Sy9PUy/Mwh+5X1XTbOyjaSHcoy3ZsgP6cWajOSUmSlOvnyy0NmDzMnTJHdowyzVcA+02Dutvn456dll7u8ELMR2tYp6Spl/t8vG5jyeieMWMGFi5ciC+//BKJ5ETEjI/KykqMHTsWEyZMwIQJEzBy5EhHA/nXv/6F3bt3AwCmTp2KAQMGqF4/9dRTUVNTg82bN+ODDz7Aiy++iDPOOAP33HOP0rf7wgsvRJcu1vsqFjItRkY3IBvWU6Yg+Os3EZcAcc4c4NST8h7hZiiR7oC6pjsvke4kbBEkCgFg4kTj7bjoC2Ccbshqw7qXF6FlX6uN9PJkmqhD9XLtwiUfkW79qJv8vyfq5TqRbkCOdufC6D7YJjtYyiIhHGyTo96UXp6OXqokj9u9mDsTUb30cgfZJZlqA3k7pC2WQHNURHnElu/eMsyREAoIrovs8fvROzxtaTXdbgippf7Od6Q7EHAp0s2dd0qrOdNIt3FHj3T18twfo71ZqZcnnUTs3u1in25BVdOdvTGfD/jxLtvc4N5+LbYMAzSRbh8Yf6716S4o9fLOLaRm6W756KOPQhAESJKEXr16Yfz48YqRPWzYsKw93ZIk4aGHHlIe33rrrWnbhMNhzJw5E5MnT0ZbWxumTZumen3gwIF48MEHsxpHIcF6dJfoGd0AEAwiGAwgHk8gfsJo3xjcAFfTzfp05zvSjdSYMhnHbF5m6d9G2ze2yoZYt7IINu9rdSykZmWxyU9aZj0Qc61erhvp9lK9PBREUSiAUEBAPCGhNSqisiTs+ufxSJKE5mgq0s1U5ym9PB2tgKKWQo3g+AHda45Fum2EVTO1ntH+Nnub2j0zuvkWTExQ0os+3WaRbm2f7mw+3hdCasmPTVPYdvjFUloCAmJiZgdx3MSp44d0X15IzbZ6uSZLzQ1hSO1aSd4/e62w5kn+eK7Y2ggxIRmKatrBzNljVsLgB6NbOz43jG6/G7FsjSsI8nza2ZZKtsJAgiBg4MCBGDRoEAYOHOhaLfd///tfrFq1CgBMo+Unn3wyFi1ahPPPPx89evRAOBzGgAEDcP3112Px4sWm9dwdjRYm3lRkvODxq8oli3YG/RTpVjI4zBcN2vRyowmOpan1KCuS9+9USM3CYlM14WpvJPno052cRSPBdKPbiyFoU0FTCuZxw/e4+dnsNyuLBBEOUcswI5QFfyYhNZ/NV4UAb/wwHLUMy1B3rN3XHg8VzOPcd3Ij0syTyRnZrvTpVrcMc6P9E5C/8pM09XIHvdx5eAE/K8fITL08U413LnCnTzeS/6eOrdMMr9RaKfUcM+oLrYSJv3aboyLW725yZb9st3rJGumZfwnu7/wb3ewcZ87SqMMOPoWVXq5uKet3J4HbWHJRT5kyBR999BH27NmDxYsXKwrmgiDg6KOPxvjx45Xod18H7agmT55seVIaPnw4Zs+ebfszOhosvdww0g3/Cm6wSYH5a5ix1B5PeFojaEZcsyAqMjAMlPTyIJsw9PfXoES6i1TvywRbxNiJdKsnXM1reYiu6KmXuyFEZIQ2FbQkHMTBtnhOxNRYOjkgO8CKSEjNkIzq5Uo2RM6G1GHQ01EIOkgdNjOKgJRhVBIOojUmetqrm6+Rzbb2WAtv4OmtPRSdiLCb6eX5j0alqZezjC2HF51dITV1n279sRk9zgXuqJfL50yIm+cSUsqBbge9kpxMDn+/ol2HLtvcgMG9K7Ler1mkO13jhv9ba5DnL9JdEg4iGk9kUdPN7dPn5wXLCImEgmiLJUi9XI+5c+di165dWLlyJf7yl7/g4osvRnV1NRKJBL766is8+eSTmDZtmit9uglrMINCt6Y7iWIY+uyk1iqoshQ+IH8p5laNUyW9PEOtdYNS0x0x3S5tHEqkWx2JMHu7mSJnPluGZaukbJVUn275WmDXRC7ahjUrGSdBBAKCUnZARnc6WgFFLV6eI0bM/nwLznpsIbY3utfCJh9EdToGWOmbrMWs5hFIzS+9u8jzmpe9uuNcOyon9emm+84QGWrTRrpZy8MsPl7KgwNUi6F6ucPjGmUCfqEAl05t8vkmx90P6eX7mlNdR5xGurU13U72xWBv01UvLzDvpHZe/3JLo8GW9jCr6dae1vw5lh7pzv09mw2BadE4TS/n5xa/G91sbcSCJJ1NvdxWevmRRx6Ja665Bs899xw2bdqEDRs24LHHHkP//v1d6dNNWIelzpaapJf7NdItaW4krGUYkDKgcg3v6TdTLWUTWjhonhrD1Mu720wvZ21ylEi3hcUmv28z723OIt06UTcvW4Zpo1IlyWuiJQdGN9+jGwD16TYhFWXTf91tsSwrzF26BSu2HsCi9Xtz9plewPdLZjiKdGdYvLHfpleXYgDIYaQ7OSaXzg21MzL99fSWYfKxzEYIMtOxzQVaNexs645jXFYT+43M7nWqPt2a7bTHNtfrFkmSsqrpFjX3bj7S7dzoZpHu1HOFWoajPZ7LtzS4sl/VtZzJkWNy/uXDicF+Q5ax6nTdkI+MRqew8bFgm9/H6za2FVB2796ttAz74IMPFEVzJrRG5AZT9fIkfq3p1qa4hYOCIqogL3a8Fb/Sw6pxqvTizKBMup8Z3eUsvdzaOESRtclJLvayFFJTt6nJf6TbiwCwNipVkpzMc5FezvfoBsCll/vrmvMDGft0e+iYMYIttAo9MaHdpWsuU20gW9T2Thrde700unVqut2aw8wclQAvpMZqupPbuqRenq8oJfvezAmabX0wX9PNVLXNO20Yq5drz1O7DhZJ893scqA1rjn/7U0K2kg3H53mW1XZQVuDz/+djz7m2aD9Pb/ZfgDtcVG5b5uxYmsjbn1lOW7/wZE4aUhP9X653YqShAD4tH5jI1xbUpEXIbXkePgSSyeYlRj6DRZYKsoQuOqoWDK6n3vuOcXIXr16teo1raHttE83YY+W9sxGt18j3do0U0EQUBySawTb8hTptqosqxjdARb5SN9GkiQ0tqrTy63ewOOaG7cVpVKzFPJ4HoXUinRquj1pGaaJdLPsj1ykl7NId3mxOtKdMb1cFIGFC4Ht24G+fQ37wnck9BaQPCxSlsv5SqkHLLAFrBbe+GGkopgOa7pN0st7Vcjz2p5mD9PL+Ui3l326TYXU5OPpfp/uPEe6BfX9xelxTTlYrbV1M+vTnU3fZEmScOk/P0V7XMSsa090ZHjva1Gfy7bVyxNqPRY3I92Cbk23o13mDXY8w0EB5ZEQ9rfEsGr7QQyvqcr43ne/2YVVOw7izRXb04xubZSX6xqq49hJzYVp518erkkl0p1lB59CUi+PK5mQrF96PkeTeywZ3Zdddply0WsXzVVVVRg7dqwipOa0Tzdhj1TLMCvq5f46q/VSporDAdnodqjemC1WI91sglB6ZepMcM1RUYl0drcppJYyutVtR6yml5vVyeWzT7eXLcO0Nd3s/1xEups0XQRYLX48ISGRkPRFw+rrgRtvBLZsST1XXQ088ghQV+f5mPOFtkewFiuRMreJK0Z3zj7SE2I6ji4n7aCs9ulWjO6DXqaXs77Hqe/kWqQ7w/ds06SXu5GFkUkZPhewj1XUy12KdBcFU23dzPt0G9+rtMfWzrFuao/jw7V7AMgR68pS+9ly+zQOJLu/kTa9POiC0c3mJX5fgQKNdPPHZ1h1Fd5fvRvLtzRYMrrZGtYs+wbQO4e0+0kfj9HjXMCuFSW93OGNKJMWh19IJCTlN1HUy308Xi+wnF7OjO3evXsrBvb48eNd6dNN2KdVqem2EOn2WaormyD4xXeqZYIfarqNj5fSMixovAhjImpFoYBS62t1XmFeQCVFzZJ6Of+38Y0k5+nlOWoZpl0glyotw3KRXi5/hlLTzTkaYokEIgHN9VlfD0ydmp4isXWr/Pzs2R3W8GbXvbGQmvx/LiMOHSXS7VZJRyKDky4lpJZML89BpDsUEJR5w635Q51env66EukOq1uGZbNAzMdcbDSGgCYa6zjSzYvdWdiXWr08Q6TbxrqFOT8B58dWa3Tbj3SrjW5BEBAMCBATkmNDSNIJUCjBFJ+t6zKRyhIMYHhN0uje3AicmPm9KbE0nddMHDmm66Eszje34NXLAXdquv0c6Y5xk21nFVKzZHRfeumlmDBhAiZMmEDq5D7BWk23P4UK9NtgWByrR2m51tXLU4sMQD+9nImodS0N2xaqURaattTLjSfcfERX9ITUvFSmZi3D0tXLve/TzWq6K4rVNd2AXNcd4WdYUZQj3Po1CXIPvRkzgClTOmSquTYSpMWNXsh2SUW6/TVH2iGRkFSpm4ysW4bpvE8RUmORbk9rutlcGFDG4sb8kUhIqktQb5HK5rBIkKWXs22z+NwMdeS5QFvalYqaZhfpDocClrKZ4pzVlCm9147xwLdudBoB3q8xup2rl6tLPERIjo+v3lrJSQaLH2DHICAAw6srAVgXU2OnjW72jYkDzWw95KdId3GWRnc+utQ4gXdsMIdmId97nWBJvfyZZ57BlVdeicGDB2PhwoWm21533XWuDIwwp1kxugtPvVyroCr/bSFlqr4eqK0FJk0Cpk2T/6+tlZ/PEv7CNzteqfRyYwOBKaBWlRRZUnTlURaamki31fTy9DYYeYx0q9TL5f/dviFIksSJHsmfl8v08oOKern8mXxNbUx7A124UJ1SrkWSgM2b5e06ICkRJ/3XvXLMiAlJUaPWwj6rwIJGKvjoQdZCaiaRbv53YerlDS0xz9rj8cJUbirbW6nl1EaErczDGT/X4j3GS7QdBLIVW+W1BGxHujWbZaNezhvdTr9LtjXdek7FbJXG2dscBSh8BjsvQsEAhlVXAQDW7W5SZSkYYZpebqKVYCakpl1rOr4mRRFYsAB48UX5f9H6uoMNobSTqJfzx1hJL/exk8ALbLUMA4BzzjkHy5cv133t2muvxV//+tesB0Vkxk56ud8uwoSO95bd/A1TfFhartZoYWm5WRreVlVL2cLArGUYi3RXlYZtGxJx7sYEWIv+mUVQ8hrp1m1flD6GBd/uwhff7Xf8WezrMxXU3KaXq1uGyaJP8mtpxsj27dZ2anW7bMhioeAUdu1kjHS7fBO+8K+fYNIDC3QN77iymMvCcMzDseThF2rZCqnxc0RaSx3ute5lRcp5ro0QugW7fkKcerkbU5h2DpKkdIMvXYk6ta1TrIp1eom2g0C2a4RUKZE1x4jZ/Ug7XdoZE2+4OU0T1qaXO450B9PXNY6Nbl1DXv7fb8GUTKQi3QJ6VkTQr6oYkgR89a/6jPOmWaTbrLwuvabbOCru6B6QZSAoJaSWZU039z39XCnFZ7owB7Hf7BOvsW10NzY24owzzsDatWtVz1999dX4+9//7trACHOYQVFSgOrl7J6oMrrNWnBlSssF5LTcLBa7liPdGsNBz5BlNd1VfHq55Ui3uqbbrnq5WcpUrs4DMyE17WFoaIniymc+wzX/+tzRZ/Fq90qfbhbpzqHRXc5lnCi9urU30L59re3U6nZO8TBjxAwlEmQQ6s7Uhs8pyzY3YFtjG3briH6larod7jxPx5KHb0+nL6RmfV9mNbf8gjcUFNCtTE4x3+1Rijlv+LrZTk4/RVX/s5VIt9tCanlWLw8E1Ea303sDX0rEfiOzfdlRL7dldGcT6U46zfZ9tUr1tF3jXVvTDQDBYHbHN6VennqOid8VWk23ypFVX49hX34MAFj+j1kZ502lvCRTerk20m26HlKvU2yfNy4EgpT08iwj3WbOUj/BXyOFqsKfLbaN7srKSuzatQunnXYatm3bBkmSMH36dDz11FMAgKOOOsr1QRLpMIOirIDVy9XeWxMV1Ryk5fKpRuZRZfn/lJBa+japmu4iZXGjF03RH4faW25bvdwknSpXHkV9ozt9PICcFigmJMVRYRcWvRSElMHBHFG5TC9nLcMAk17d48fLKuVG+dWCANTUyNt5hccZI2ZoDRktdvUPrCBJEmdYay0rEfGWVvnPdevsO+3yeCx52PXGp2EDzmq6zRZv/PELBgT0KJc7M+xt8ibSzWf9uJperrMPo64P2p7LbvXpznekO5AW6Xa2RojpCalZVC/X3hPT2l3ainTHHL2Pd5rtX7IMAFAkyvuya7zoORWzndP0UtaVdZ2bxlUOsnWU79LWCkydimHr5YzZr/oeJm9gMm+mhNTMr91Mivjqcjv5f6bbYOu8cSkQ5JqQmskx8BMseynIO1J9PF4vsG10v/XWW6ioqMCmTZtw6qmn4pJLLsG//vUvSJKE4447DgsWLPBgmIQWK0JqTmr6bOFwola87XqKnHoXYA7Scq0uiBQhtYBxevn+pNFdWRq23asz1eszmV5uYbHJr5dM08tzVdOt26dbf0EWj8lGq5hIOLrZ8/10WRcFpnOQj/RyIKVgnpZeHgzKbcGAdMObPX74Ye9E1HKQMWIGOxWNIt1uGlbazwQ0i6rkYlvcuUv+zKeethehzvOx5NHr0Q04Sx1Wq3obO/ACgoAeyXaIe95+35OFOp/1YzdjyIxMdaH8NmzeYtN4wqLzVA91RM7RLrJG6SDAIt3KcXW2P6ZbEQ5yQmom6w1TzRGtwWTjIDmq6dY4zfaWdgEA9GzaL+/n628sfz6Q3qeb/9upuBs7ZfiswGzF79LIUbaOkiW4dw8gSahp3AkA2F1aJW9gMm+aZSSZlW2YOQ7FbCLdLgWClJZhSaO73eFivVDUy1n2SJjX6cgw3u2Nrfhub4uy1ip0bBvdxx9/PN544w2UlpZi1apVeOmllwAAY8aMwbvvvovu3bu7PkhCTSIhKVE8s/TyUDC7Cd+ULCZqpaZb9+akcwHmIC2X9/Sb3cxSYiDGXv2GVjnyw0e6AWsLRjbxhzWLIrP7gVkKeT48oGbp5QmN4SOefDIAQIKAxKSTbd/stcrlAFBSFFC95iVKejlvdCfPDV2vdV2d3BasXz/189XV3rcLy7OQWyb1civnutPPVP3NLbbjyZZuYiBgL0LtI1G8qGJ0q4+rI6PbohEUfPVVdH/nDQDA3mde8GShzrcMS9VU59boDmoi3fIYsv/cfPVY1qqXp0o6nI2Hd7Ayn495pFv9OWZZWk6F1CwdWx2n2f4S2eju0Swb3fF337XlSDKr6Xb6c+t3enHROcnNhQ3F5VD26EG2jnJNxeT1UXHy//ZQJLWRwbzJ3qubXm4SWEir29YRimSCXracGC4FgpSa7izTyyUTx4OfYNdmKBiwlgUiirjzH+9jwgPz8dqs+TlxYnuNbaMbAE488UTF8AaAU089FW+//Ta6dOni6uAIffi0WfNIt0dCBVmmVbLJTl9ITWfSyUFabtyicaptGWaWXl5VElZF9azceFm6HqsFU1RzLYxJ+zegVYvNzWQcE9ON7lTqYfKJ5DmU2LVb2ca24QMoyuXFIc7oDucu0t2U7NOtNroNIt2Mujpg40Zg/nzghRfk/zdscGZw28k2ybOQm6jjbOPxQviRP+fjopS22BaZ0S0E7UWofSSKl3Jyqe8FTo6nmRKwKr38wgvQY882AMCe0kr5SZcX6nFlLnQ5vdxErVz7WJteDjifRzP1Bs8FWvXybK+5VMswwdK+tHXS/HHQOlTsHGdVn24r30XHabYvaXT3aton76epxZbTTHvOAHxNt1OBLGZ0p57jj3NWTihuLvymZy1GXP88/t+pP5Ff8yBbRzG6E0lHeVzWgmgLF6VvrJk32a1UN73cxOA0q/HOKtLtUiCIjSfVMszZsVZnNDraRU7gW1tmXNsmA3vRxZ8CAIr+9/c510vxAktGdzAYTPt30kknoaWlBQDwzjvvoLy8HMFgEKGQpdbfRBbwxkRxyCTS7YWQmgtplVpvO5Ah0p2DtNy0xbkBbPI3W2CkhNSKVFE9e5FujXq5yXvN0qvMRNa8IJGQFMcBn16uahnGnUNxIfWbiULA9s2eRbNZuzAgt+rlrJaQTy83rOnmCQaBiROBiy+W/3dy7trNNsmzkJuixJvD9PI0Z5pmsa2KdAPWI9R+EcUD5+TSRrodpA7zc59permUQPfmBgDAnrIq+UmXF+rKAi0guFoqpWf/aE85rf6AEDDe1vrn5j8apdVTybbmOKVeHrTUacMs/Vf729oRMmtSRbotvE9j1EUDIRwsLgcA9Eye1/FA0JbTLCUSpddBwDyaZ+Q41VUvVzmALA8vHW4u/KrPYUgEgljVc2DqdZezdRSjW0o6yuPyOqktpGN0a+ZNMyE1/rxJM7Il9VpEL7jCup7YWie7FAhKSy933DIs9befa6RTLXEDhjo/AFSBvWgwDCCptZBjvRQvsGR0S5Kk+8/odcJbWrl6bqOoEeBRyzAX0ir1FDlZdNBwrFxa7t2nXINfnZbsB+9SWi5/gzc7Xuz8Nk0v51qG8RESK78DLzQh/y8/bzaRqlNC1a/FTV7zAl6xW79nsNrwSXALFBZ1tHOzZ+rlqkh30ujOTXq5g0i3GzjJNsmzkFsq0q3/uhcCQWkpvdoIStKaSgiaQWVabPtBFC+JXmYJ4Cx12LTnLXM4JiNVPVoaAAB7WU0m4OpCna+RdTO9nO2XT8c3Ump3NdKdMD62uUIr9pWtenmMK22wIqRmq6bby0i3xqhrKKkAAAQSIrq3NsqfLwRsOc10I92Zjm8Gxyl7G3/+BbnzNqsyBW6OO5B0OIh6k7NL2TrKuRcMAILAGd1cernBvMmWZ3q/rTq1WvuZ8v9hnc4Y7G82b9oyVl0KBLGfL1shNclk3vYTfBtIww5AmsBeNMQZ3TnWS/ECS2Hp/v37KyJFRP5pttCjG/Ao0u1CWqVebaelm39dHdp+cBZm3v02AOB/bp2KypMnuCI8pYp0m9zItEJqevNbQ2tKvZz/jlYmdSXSHVQv9swmUjOxNLPUcy/gje6wjpCaJEF1brBII5AygBQsnGtMvbyYi3SzG1hL1HvhjSZFSC31PcKhZE23V0Z3pmwTQZBvSlOmqK8NtlCYOlXeRpKQgIAAJM+F3GSHrPx3wCjS7YF6edoiS7OIZucffx4CyLzY1jmWCrkQxeNo58SseJwcT7WgpOY1FiVNzo89tJFuHhcW6qm5MGBpHrRKQlmIBxBLLtyMynK0QmqA85puX/TpTjCHt7qm22l0TFEvDwUcRbrN1cutz58H2jj1cm2EXBRlJ9D27fJ1PX58ymm2dSsgSUpqedfWgwiL8pwe79LFltNMOWdUSuMmBh1znGpPKOY4nT0bYqKnzj7tlawZws1xDczoFnTmK5eydZR1X3U1AJ1It8m8KSavU3HHTjkbYPx4ZRszxw07v8KhABAVdfVvipzUdAOpQNCNNwJbtqTupdXV8newEAhSIt2sptupkBq/BvRzpJtzTBnO6ZrAnirSDagduxMnej5mt7EU6d64cSM2bNhg+R/hLVZ6dANcdNHNxb8LaZXsGtNtg5HhLhJD6j1tJ5zo2qLWau0zu58b9RROxOJoaE4KqS1bgkBC5N5rJdLNHBKB5P+ZFzL8S9obh9UIvlvwnlo99XJRUhs+vKEt2jV8kIp0R3Qi3V6nl7fHReX7VkTCyvNKpNuh1zoj2WSbcBkjPz33dky65q/yosdjITdVPbCB0W3XQWX3c+MJKS1CzTItlEi3nQg1dyz3lnTB7GNORmsokhtRPA6+bROPE8ElM/VypeuEJF9X3VvkiOBeVtPN48JCXdVexoOa7qAgKOv89Ki+2jGsyljit7WhqeCHtj4Jzb2XfS/HfbqV9PJUyzCz+5ydSLedZYthpNsokvzqq6pI5b7kOdy19YCS/iyOGWtrfWEr0q1xnD489mJcN+WXSEBQRfPMarrl/WZxj+HmwoZiOdKvinS7nK2jGN3duwGzZ6O4R1cAnNFtNG/W10P875sAAGnVqrRsADPlbvaakkmpM79FlJpuB8cyqc/y0r/mYfjtr+Hz+nds6bO4JaTmhywaK6Qi3QHjkiFt6UfS6I7EY6bbFQqOhNSI/KKkl4fNExU8iXRrFq0NxeUpIR3A0kSt9bYD1tPc+HnRzfRh1eLcpJZM0kziqkm+vh4HDz8KbIiVZ50OYeBA2fsJq5HuVPoNkDouZvNowmyhnOO0o6gSdVP3DE613FEbPryhrdzwbdzszWq6vU4v33VAFoIpCgXQpUQvvdyj451ttklyofDukWOxqesh2DL3DedCbhbhT8tMfboB987VtOiiJi1QFel2EqFOHsvHH5yFWyffjDn//Lfnx1KLXrcAwFmNvJU+3UEAEAQl0r23tCqleuziQp3P+rGSumx9v0ljPsj3itVso6l91k0vt6mp4Ie6yzT18izFVvl2dYbpohxm6uXpCvLWDZAmPfXyTCU4gOI025dsF9at9QCCFXLUN14zwPLnS5JkmsGXdnw1jtOnRk7Bf48Yhw3dDmE7BDZvRqKpSd6PTu9v3f3agZsLG0uS35ndjz3I1onzx6euDsWLPwEAtIcjkN57T3/eTP6GYrscyFCc9FwZlZlWAnuNBQD4bbOOdDOCQXwc6oGDCQGfd6+1fLz4sbDyOKdGtx+yaKyQquk2mdM1Dtu0SLfBdoWCJaP7iy++sL3jpUuX2n4PYQ2WNlsaMb+4vRAm4idqSRAwefojOPXKP6M9GLI8UesJqRlFjrXwnl0W5XQDq1EIba2fagE2dSoa9h8EAJRGWxER48DWrQgm09WsGBJ8+g2QckxYTdkzUuEFcrPQS4k66fcMTmgMH96znhACtm/2ino51zKMOaNiouRpXfXOA20AgN5dIioHUpHXNd0uZJu0JYBocnjxkSM9T4Pmb6xGLcP4IItbc1ZcZ5HFItSJftWQkos4MRBwHqEOBrG5VG6Vub9fbU5SynmMrjlHkW6zuYRF3YrlGszurY0IJEREQ2Fsr+jh+kKdz/oRDIxjJyhimEIqxTEttVn5bJ308gQcaSr4oe5Sq17O/ncspMbpCTjp083/nulGt/VxqCLdy78EolFrgq9TpgAbN2L/7f8PANBt5HCE7rg9+fl2tBBSf1uKdGscoilBR/V1k4jJ30svQKG7X7sk58KGyh7y5zGj1oNsHa2IX3FxSkCtfZxOmSCXDcDWCSrBS0DOBjBxFLKXUu1z0508ESfq5RrYe+2cs/xYWaQ7npAcrdP4t/g5vVy3ZEg7Xk1gL5oU51aM7hzqpXiBJaN71KhROPfcczFv3jxTIRNJkjBv3jycd955GDVqlGuDJNS0cEJqZngS6QaUiTpW0x9bK3tjf2mlnJ5lcaLWT5myFh3kL9BWryLdFgzcEN8yjLs5HCyS2+iVt8vK/pAkBJI3b9HCeBVPYHL/KQXizI4Ave2stkJzC6Oom7JoZkNInkNir97KNvFA0PbNXol0c59XXJT6281zRMv2Rtno7tOlWPW80qfbK6PbBRGvxlaTGkgPsJte7ta5qtciBgBQV4f42nWp7c46O6sI9b5mOevB9bnWAnzbJh7F6WrDwDMVtmKRqkiRnB7apxeG7pCP4aL+Q11fqPN9j63Mg3b3GwgI+kanKCLRJl/bwS8+B0RRHekW4446ePgjvVxt+LgX6RYs9unWd+TIr2m3tTh/1tejafd+5WH813cC/fpZL8EJBrHvkFoAQNcB/RB0oGbNBwMslc0ZaUtoaqoTwVDaPgXBRITKCXV1ODBqjDzO6ursWliaoES6k9dSMXfP1s1K47IBdAUvk7+huG2b8pRRpl/YNNLtQL1cA7sO7Dhq+GuhhAsaOFk3qHuV2357zlALqcnPpc09mmw0JdIdj+VcL8ULLKuX//vf/8aZZ56Jvn374oILLsDvfvc7/PWvf8Vf//pX/O53v8PUqVPRt29fnHnmmXj11VdJxdxDlJruDOnlnvXpBoC6OoirVisPm1+YZXmi1rZjAazXdPMTo5vpw9b7dMv/q6JI/M0hefMMc7XcrEYs8emnFsaRUO3frnq5UR2mPHbvr8l2A6NbN82urg7is88qD8WXXrZ9s29ThNRSEzBfX9jqYV13KtKtNbo9jnS7oJzKG925OC9U7aYM7jp22+tZQRXp1jgXeD2BeK8+Wd3E9yc7FuTDmGrn6mp52OLWjlNFNHHg8bXQLK1+zNijAQAfXXOb6wt1ZYEWECzNg1bhM63YMVK+ajJlPL5nr7zN1VcDtbUQ5s5Nvf+TRY40FfxgdBuplzu93vj2kFbWG+l9uo3vXZaMoPp6JKZegKZwSgFbDASBPXsyvxdQIs7MadatLJyexWYB/juH+JZhRnWrGsepqG1dmHScJkpKAKgDFAB3bbt0HjUkhejE8grnLSwzwH5fFnUOBQPKsdbNWuSyAZTjoxVbBZBobkltZ5Dpp1fTnWoZ5kC9XIOTSDd/fvFGt5O2YWbztp9ItYEMcHOvzng5vZR2Pr08x3opXmDJ6J4/fz6GDh0KSZKwa9cu1NfX46677sJ1112H6667DnfddRfmzp2LXbt2QZIkDBs2DPPnz/d67J2WFpvq5V7d4OPcgr95+AjrtSzJ4fDRA6tRea8i3XoeULPtQlzbDmkbf3NIRqh1jG5x1+6M49Cml2dS7ZUkyVxILdeRbq7Gj8eo5U+Cm4ISJ5xg+2avtAzjaroFQeAUzL0zuncYRbpDHgupAaqbkgqLNyXW1g7ITXSWv74MI918NNGlMZkZOnx0KlvHw94mH0S6DdLLnRoPaX26tc7SYBBjTj4WAPBJcwiSkTfFISlhKi693IUFJV9bqppfuZRxFlELSglg61YIF0yFwLQ5duxU7W9jVV+8NXg00kamSSHOtb6GFv5eIWiMbqfZLlFOOd9KNoJZ+VN6anCGMSUzzJrDEaVMBNDpRGBGMuK8LzkfdiuLKOe3nWPCX/eqSLeS1qy5F2jK9EQ+vZxznKbaLKrnTLfbwTInrJfnpbYjAJByluuu5XQEV9PEVgGIJaWpvzXjZw+LdNLLtS3Dspm72XudRrp5TRondd2Fol7Oi2NmzMRKOnajpbLeQPi5Z3Oul+IFlu6SJ510EpYuXYq5c+fitNNOQzgcTuvNHQ6Hceqpp2Lu3LlYunQpJkyY4PXYOy0seleWoabbkz7dHPxNqZmrqcpEKsUt9ZxS053hRsdPjO2eRbqttwwDgETfPqn3JtPDQpzRraSX9+iZeRxKerkmEmHwG2rnK7OWYbkwCAxFnQwWZLo1tzZgLcN49XIgVSPlZaR7RzLS3adSbXQXeS2kxkjelDB/PvDCC7bSAvlIdy6cMaLBopTHk/Ryk/PfrchjTEzgQFLIKe5lb3ajz88gpGbnujIT5FH6dHOL5pEDuiEcFLCtsQ2bdh+0rORtBcUBqUovz2qX8j54o5vNr3F1yrjKecruV8kDIPXqpdrfL34wA9fW/Rorew1Sf5AmhZg/npLkTs9xO/A/Zyq9PLs1guJkDQUsZSOkXYMm51tGozeZYdYUKVU9rRcNTUNTgrM/2XGEj3Tb0kIQ9ec30zZqTFsi2UILQJq2BLvmtG0W3QyoJBJSyuj28J6l7QgApJzlulmLOoKrqvRylg3AXY9Ggoh66eWK0c2i4FkZ3QnV51mB/7iAICjzt5P0clVDBT9HukWdOd3k60qBAKLJrxMZN6ZgU8p5LPXpBmTP6JQpUzBlyhS0trbiiy++wPakJ7dv374YMWIESpKpMIS3tMSspZd7VtOdhN9vsw3jJlXTbUFwREMuarrNJgElvZyLdItjxsm9J7duVbzsAYmr8WLp5SNGZBxHKtKdXPRlUC83UhjW7k8eew6NboOewdqfN1sPbbtOpBtIZYG0xrzr1W2cXu5xTTdPMOioV2XOa7qT554gqEWBeIRkCydJcjG93KRlnltZIPtborr7zBXRDEJqdq4r0WS+0Cp6A7Jz69j+XfHphn34+KJrUbvgpdQbqqvlaJ7DyARfauNmDStf16xk4Hz2uSplnC3y2dytaHMEghCPP0HV43lvUvl6d3k3YNd6+SSvrk7TVEhrH52QVPcRr9HTVcgmvVySJJWInxXhVq1D26z8KeP9Krn+bCpSG90ZI906JTh7WZvP0iLsaZL/tlfTndqW9ylmXIPV1SE++Wzgrnnydo8+DkyZqIxLT3QWkJX37Y7RiIPtcWV9kYtINz9/MGe5rtHNsgGmTk0XUuN+w0TMuCxJW9OtN+dH3Ih0Jy9uWw5OjSM6EgwgGk9kHen2tXp5IpUZE7Awp8cTUipbIWQpRux7HH2LkpISjB07FlOnTsXUqVMxduxYMrhzSEu7tfTyoKII7s3in7+47US6RR3vrVVBF/5179TLM0e6Q9wCNxEIpNS4g5pItyAoke64hcstztUxym83X8iYpesB+p5dL2GLsEha1C19PNoxObnhs5t1sTbSzdLWot4ZvizS3bfSWU13PnUvch3p1ouS6mHUwsnx5+Yg0r2/ObfHUotRn263hdS0yteMMdFdAICPu/RXv2Ci5G2FVHsZawad3f0GBM7o1JT+sGgpXyYkMOcpN99DEJBIZje1hYpMNRXS5r6cR7o5wzD5G2aTDSfyC2KL6eVmkW7bNd3JTIKD2ki31ujuqckw0ynBYZHu7mURR1FkvquJWmk8c70wH70Vhw9XnTcJzlnJE8ywLrBDYws/f2W9O0NEHQdCKtJt8MFMcDUi2xd66upGjns5C1f+O6xTt83Or6z6dGv2ZcvByV+PQsqodGR0m1xHfoIvn8xUOgmoj0WnNrqJ/KIIqeVLvTwJb1Q0R22kl+t4PNnfsYxCaqnX3UwdtprmrG0ZpjyXvDnEe8qpTspirboawa5VqvdaGYdV9fJM0QFV3WoOAq9G6eWKMWUQPQOcRVyZ6EhEE+lm10aLjfPSDpIkYWeyT7eRkJrZ93lv1U4M+808zFu5w5PxZUIV6c7BiaHUJmYyupU5y50xmZWNWO1YkIm9SRGmbPfjFKNrTjEe7AipqZxg6tdScx/3OaKIMU8/DAD4pP8wdV2ziZK3Ffj0cjbdupGtw38PJQOnRw/1Z7NIN3fOBCRuYc1pKrDoW2s4YqqpkMlB6jV6bftSNd32rzc+kyccspaNYFSyAOhnApiSTD8+GClTPa3qNV1TI2cwmJTgSJKEfclsla5lYUeOCL0oLmBtDcbPdennCHT36+Y8qXbAencvEFk9b5A3upMOq7jJ/FBXB3HYMABAok/ftN/QqESBP5Ssplvkr+EkkeQYssn4Yr+vk0h3IJn9xebvdrNjkWFfQGGkl4eD1spReBtDm8lVqHSMb9HJYOnlGSPdXqqXa/brpKabX39bXSCqIt0OJqdMY9J+Rvrny/+rje7kH3V1SDw9U3594EDl5hAsLcm4Xwa7iaYWRcnPsBjpTo/mcX/nIr1c1DcAjCL2/E3eyYJaiXSH1ddCKr3cm5ru/S0xxdgxSi83i3QvWr8PB9viWLR+nyfjy0QjlxKdm0i3fpRUS2rh7s7nmhnWZqnUduAj3fmo6TYWL3QQ6TaJmIjcIlFh4UJ8b/mHKI61YW9ZFVb3GKDeoYGStxX4rB8jp50TVC3D2H6/d6xKTVolpAbIGUssFZ2b77Fxo2wIAGi77XZTTYX0PtS5XRzzn8e+d+q42t9fLJ56k6rvrsm+0vp065xvlo3eZPpxWk23RowMRUVyCc7FF+sqczdHRWUu71ZWlFVNd8jAODbtiMLfozXbSQbOypCL82RDa27uBey8UEe65d8ikz4Pe69YVpb2G/IZY0YGuDaTkj8PmTGXzdzC1jFOIt3sHIlkEelWXUf+tblVLcPsRLoDgjq7tJDpGN+ik6EIqRX5qKa73bpxo5dmFLJYo6RqGeZmpJtbLJuNgU3w/AJXlcKK5GKmZ3fl5mAnzTPlCZTfk2li0t500xfKxl50L2iP6xsARrXposmCwwqZ08u9MbqZcnn3sqI0BwP77mY13U76erqJOtKdA6PboDZRi5v9mAHzdGm3arr3cZHuvKSXs2tO06dbEad0WGdoVKqiirpt346iRByjtnwNAPh4wDD9HWuUvK3AZ/24mV6eWuxyRpGgThln0euAJCoGXKBYbkulWpwHgxCL5OdbBx5mKvRjFuXNBfznpUW6HQyGzW+CoK67N5vTUi2c0n9P9rvYEraqq0PTjFtUT8U1YmSZYKnlxeEASotCnACh9WOidZYzrGjVmEa6FaNb/R4nYzQiV6VG7LzgHRMZ08s179Uzao3Sy/nrNBxSn1P8a66olzuo6U45MQXVODqyejmvWWTFIWXUgraQ6TjfpBPBosqZ0suDFqPHTuEnfDuRbjbf8W0wrEbl1ZFur2q6TbzSOmJCegtVvV6dViZDbZpapveapWvLj4239QJjITX9MWTrFDBOL5cdUl61DDMSUQOs1XTredxzSb7Uy7Wtb7S4aVxp95PWp9slh9S+5tw6MLQw4ydi0KfbVsTOJDNGt0QgWVc7ZtNyAMDH/Q2Mbo2StxXiXPTQSIjRCex+GAwElIAoXyKU6FettKAKJRKKARcoKkpty+8v+VhXDEpnO6PHXqOuIZW/uBPHDINvVScIQsZrV5IkzugOpI3JzCA34+BR6nNOvO7ntloL7WPK5aXy7+so0p0hvdxqyn1aJoDBvOmmejnfPtLLc1LR8uGNbjMhNdV75XHpjc9ozlIZ3ZpzShXpDtlw8hig1HTbUS/XlA4o6eUOsqX4ofs7vVxHHNMs0m0gElrIdJxv0olotZxe7nGkm28ZZsO40Xr4AId9ul00qKzWFitGNVeXxA9Z6UXJXVl2Fr9xTaqoovBo8NY0tU5ty4wcR7rttgxzK9Kd1jIsaYR7lV5u1C4MSH13Pv1SCxO/ypfSaN4i3RmMbis3YiefC3TgSLdBermT+d9KernqN0zW1Y757isAwKL+Q9UtmzStmezAz7VKmY0b6eVK1oXO+VZXB3HdOmXb4Kv/pxhwRo5DdpwyGQ7pQmG5DXWr08vl/7MR5IppFsSZWgCJOoaOKjKZ/Fsxgiz+1k0ah3984EBbrYWY0d21TDa6nVw3KWe5/WvQ7JpTAhRa9XIXjW7+XuDlKamnh1NcZNPo1hmf0RzPHxpt9gS/naJenkVwSjHm7ehnaLK/2BizTS+XpPyKtJoR41qGmbbTS5JaTxZ+qzAGGd0FiF0hNb+pl+stwK2kpmk/M9NEbQerKtrsJT6SrRcl51+3lV6uWdhmmpjM1MCNxuYlMYOabiPngVmfViu0GbYMkyPdXqeX60e6M9d0s/M8W4M3LiawcM1uHGyLZd6YI1fiOanPkP/PlF7Ozne32pjxx1cr0mjWTswO+7hIUSbFei+IxtXGCsNJmy2Vk85ApFFldCfrao/euQ4VbU04WFyOFb0PlV8zUfK2QoxL2bWyQLMKnyavV9PMOw2CE8YrYzdse5g8LpnmmkwOUq9J1QenNDaC3Lxsd6EeVUqJ2L6Q3Jf+fvTqaPWMpLCd9HIAB9vUaw+7mX1KpLuMRbrtRz5T930j49j4x+bnISN1d+286WSMRuRKVFPpGqAX6c5gaCoCaDrnlrqeWX9O12ZW6DmAsjmWSrmYg04RAU2kO9v0cr3HfoHPdEldG8bbs2Oh7YZTyFju061l165d2LhxI9ra2tJemzBhQlaDIsyxWtPteaSbm6DtqETzqo0MJdKd4YbJfxc3o5hmKsc8CW7hEgwIkNumpN80VQ4FG22QlJrugDp6YLSQ0U7yXolFWcVokkwdA834RONFvhWY0mdapFtRL/c2vbyPSXq5WU133KVI91srd+JnL3yBy08cgN9MOcby+3Lep9tienlIG3nMElXpR1p6uTVHWyZYTah2n7nCSEjNUaTbJPNEr9UjAKCuDqFXZuGEOavwTs1wfDxgGIbvWCOnZT/8sKM+3YkE1+7HYv2fVfg0efZVjJyTvLEjGMzF7LzKJOyZloWUp/Ry/t6kdR7b6RuuPe8yOUZ0DR0dESy7RlCTxui2u97Z36I2up2ca0bp5VauQSOjkX+sveQCLq7t+JZhzPkiZHCOOoH9vvo13RkcVib3S/W1m3qeX5dpHTnsf0Hge3g7dzjoRdAzoXVismhutpFuQD7Wjo07D4lxdf1WxDGNhHkLGdu/y+7du3HZZZdh3rx5uq8LgoB43Js2PYQMM3AzpZe7vYDVwi/WtSleZujVBlpdIPIGsVt9uvkFXqYxKGMPyGq2IqCbHsbfWJSbuIXfQZu+ztZEVhYygLlCbk7Syw1qcAxruvkIk4uRbkVIzaP08u2NLL08kvaalZpuJy1G9GBp7qx9mRUkScp9n26rQmou13SbtQJ0K718L2d056OmOyWkZhDptlNnaJJ5YmRYAADq6jCq21q88+a3WDHlEuD3P5dTyh1EuAF1VkKQS0V041amTltPdwaqap+5Q6rMYQbGc2vUepaW9jNzgV5ZF//94gkJdrI4WaooWxBnOt/06mj5Y8mOh91IN1t7BATZaLR7LSvp5aXOje645r7NsFLTrZqjRO05AtWYtPt1w3HDq5cD9p0vVmFrN/78U1qGZRJSY5FuneOop4Avfx5ndIfUv6kylwkCdywtfhEdnNzP04TULDjrM+2LkSd91owoOh2cOKap0W2gEVTI2P4m119/Pd56661k43n9f4S3NFtML2f1RV5FsvgL3U5Ekb2Nn3yt3mj57+JWenlanbHJ8Upw0R5BJ91QN9JtI82T9wQCmdWc09XAjY3uXKzxogbq5Xx6uarFR9ZCahlahnnUp9tMSK1IMbpNnDfKzT+7u2MqTd36flpjompsuTAUTQ02DsXJlAP1crccUvmOdDPnjpGQmpOFoPZvILPjpKJENlqi/ap1WzPZgf9sXkjNjfOCX+zqpYzz87+qTMgo0p18c6ZId7qeRW7PFT0DzqhMygppNd0ZjFVVpFtPSE2jXm71vD2YNLqrkkaz3vsaWqL4fNM+3fVpVCPG6SxDRH9+U9ZgZo58C9ecYU23C2s73gELeJeBoddu1XKkO/lePQPNSIiOP5Ra4TlebT4lAOj8Xsy0eOyV8rBzRn6cXcsw7WN/2mEpzaKUToe1mu6OY3TbjnS/9957EAQBPXv2xPjx41FWVuZJKgqhj5iQlBOx1GLLMK9u7jFuv3Yi3XqtZ6y2LvGipttMYEmLKr1cJ2Va5CZzhlVFZj7iHtIsZKz26U5L386yZtouRi0e+EWDJIFL60xt48jojumnszMj3Kv0cjMhNeZVN4t0s9eydYgx49nOAjFtkZULo1vJEDHfzqgMwSmqdn5p13n2IoOSJCmRMu0+c4WS5hvSX5gzYR0r92mzcpRUiYD+e5W2jy7Utcc0hq+T+nQjeANJz5hXq3wj7W/tepYdp0wtLNOF1HK7MNarD+bvU3aNrZjGwZopvTyuRDpT54pelFJPZM2MpqSeRVVJGPuao7rG0//M/hLzvt6JV382FsNrqjTjSkbfksfCybpJMeLSjGNk3JdZmYvRvOlm6SCvXg54FyXVWxtZVy83rplWOcx0UvWDASGtO06CcwBYLW00w0mkW+vEdFrTrdtGzadGd0xn7jVzELRrdCM6AraN7mhUXmAsXLgQgwcPdn1AhDl87XS+1cv5m1uLjT7dvOHKsHqj4ycTt1KH7YhQ8J5nvZRp/Zru5H4zTISqlEompGZTvTytxtslsSirGNXg8IsRUZIQQLp32Yl3ts0g0q14jT0QtmqLicpCxbSm2+Tm6aQGzGw/dhYMWqM7J+rlOqmterjdMsxMq0GV2upwkdIcFVXnWC7q47UYZZeoDCqLKaNWMgOMshWsdqCwQnqkO/m8i5HuoKDftoZ3CvOOCiNBTKeR7lxHo/g6VobqHLF57rZrnD2Z0st5kVE95xp7n+1Id7Kmu7I0bPg+Vg60vbENw2v0x8UMMye9y92KdPPbyZmj8t9eqpcfSLsfJAC4rxatiGmqIt321Mv1fhI9BXxAP0DCrkHFARQQXDmWbN53EulWhNQcppfrOiLykHFlBb47jyX18g5Y0237m0yaNAkAUF5e7vpgiMwwEbWAkFnRL5senFZQtQyzU9OtmWwAOzXdqdfdinSb1XpqSXA3QT2DWE/F1GqURq22KSifo31NNZ4MYzer0fSCmEGkm+8ipB5T6nm7i3UxISkRMa3R7eW5z1LLI6EAKkvCaa9bqelmHt+s1cs16XJW0EY27KbVHWiL4Yn5a7Fpb7Pl9/CGjhnZtDDSg78+zCLdTn+HfU3qesh89unW1r3xi1ur41JHijSvZXCcsOwcNxwPbHEWEJL6GS7qk/CCYqk6bR3HqeZ76kVmJElSjlNG9XIbzl0v0FOf521Eu+eu7Ui3yB13HUOHTZfa+ttMsCy7quRcrPc+9t3MXlMi3cq9w9LHq/dhUNNt9l1UjkGRd0KntklXL3ev3KKhNY+RbiW93FqGo26fboM1Dj9fKcr6zHjnRN3cUIJPpa1b30e6kFqyT7fNSHcmcTk/wV9rqXWx8fbUMgzAH/7wB1RVVeHSSy/FypUrIYrepG8S+rB67tKiUMZ0QSceWzvwE0xzNG65np9txt9IrBpJ6ppu94TUzB7rvWbUxkbrNQeMe1Rr4VMq2W9nV73czAjPRcqRoQHA/db86ZhNTXc7F1lKU0v3UM9gR2MqtVzvGrRW051Ml8vW6Bbt7yfbSPdry7bhgbe+xZML1mXeOInirMpQ021HdNAKZmJpqlRqp0Z3S7oIUa5R+nQbCKkB1o1VIyEiQG2s6uFmOVNqcabp4uDClK+aw3WNP+YUVr8vlV6u75jIdD/K1N7Ra/SMbsEg2m8FRUhNW9NtsBveIa3nwGB/2xZSa8tc083mSb21kNYYdNJqNaFz3+f3aT29nNunqszBKNKd3QURjSfSSrC8Wi/qzR8RFum2mCWidxz1otuAuse5NuOAz0h0457Djpmda0jbRjOiGN32bCq9j/RrerlKSM3CurgjCqnZTi8/4ogjAADz58/HsGHD0l4n9XJvYenlmUTUAO9rurWiFW2xhKVx6auXZ44OAn6IdLOx6y/CtF5zwHoqmCrSnTwemdTL2f1REOTJ18wIz0XKkVHLMP63VkW6dRZdVuEXuWmRbg/P/R0mImqAxUi3UoudrZCapNqfFdJqum06JlhkSdsf14x8qZebR7qNX7PKvma1anx+1Mvlz0wTUnMS6TYxuhXDwuA3dNPJm1qcqVOX3Tgv+EwrPSG1VBRMfw7Ty2wCMt+PjDIHcoVRpgJrfWk70q1J/cyk1K0YOkGBy2hJH5+eyJoRiYSEpigzupORbp35zKycR3vPZsfHzvHQu+8D1jL41NecvhPasKY7S6cyuxew9QPgncGml+1kN70ckH9z3nlrdD3yKf/aSDf/WrY13YlEKtvFzj7S0ssd1nTrp5fb2kXOYPeGcNBaWr8iEtqB0sttG928IAspleeeViXSndm4tVJPlA1aL2tzNG7N6NaJJIQtLqr47+KVkJqZ9zhVF8dHCNJfDwbTje5MRiXzxgtCaiLO9F4+OhCNJ9JEk4wEgrzCqL6UX+eJOk4KwP5Nj/3+/ATOCLloAGhh6eV9dUTU2HgA89ost2q6mbFtZz/pNXz2xqBVgLXzHquRbtNrRRSBhQuB7duBvn1NW1Opzn+zPt2OjW75WBaF5OvPDRExu0SNIt2q7BKLRreJE4xvl6gHO+/duN/ENdFH9lXcSC9XpTjqRFzZ69qvqYzBILKW0XDgIn1iQsp5NMqs/VQU9p2y2rk+kx6DKtKt06WA/R220ae7JSYqxmJViXGkm+ml6L2mrcd2km5sXNOdOSKtl2IPqCOYWkeJW+1gmdFdEQmhJSoinpA8TC9PXxsVh+yllwNQacLovcbge5xrHSl6ug5O7wFOswndElLjx20UfPELbL0ii2PKz1lqGdaZje7+/fuTWnkeaeHSyzNhRTkzG7TRteb2OHqUp/ct1qInDuKkT3drTLSsymu6T634mMkYlNR4TmRHTwiFX/AGdLz6erD3hjlvRKYaVz46wCYoXjRJ9IuQGrcYkXgBK4MFrBXalah6usHlZU33jkY5uqknogakFo2mfbpF40WgHUSTxaQR2aqXOxFvSxkc5ttlvFbq64EbbwS2bEk9V10NPPIIUFeXtrnVPt1OF68s0t2zPIKtDa15SS+3IqRm9fwwSy/PHOl2saabzYXaLg4uLCb5xW5AZ+Gn110DyBzpziTsmeDm6taEmPs+3ToZZoCz1nIA5+xh2QgZSqF4R4qezgn7O2IjvfxgUrk8FBBQFgkm35c+ecRNnJPaKHXQgfPISAfAirigYaSbO45Gxny294/GZI/uqtIitMXbgITkeTmi3Ug3r5sAyMeLT2wz6tPNl1Noa+BVWRdZHkunzlu3It38d2bBF78LqanaQJqM1ahcsZCxbXRv3LjRg2EQVmHp5bYi3Tno0w0AzRYVzNNqe0QRoZUr5D8bDsiRLKPIlWpSlQ3/olCWRrdJBEyLbsswKf2m6aRPNy80w7CqXs63VBAlSbmwRYMbkldYaRmmFj5JbWP3psdu1EyMhcfLLA+lR3dFEbBgQVrEVanpjht/tpmwjx0UQTYbEVatkJrdY+SoPUoGg41hGnWorwemTk0vYtu6VX5+9uw0w1uvnZ/eY+fp5fKx7FkhG91eppdLkoR3v9mFIb0r0L97qfK8tl8yQ0h2WEhI1q99M0eEUTTv/7P35vGWXVWd+Pecc+8bqiqpykxIZWBuiBAG+RlFIQGCdqsEisCvTaNElLbpFhLAARSjtt2gDAI/RG21GdqGKELRAipIAzGRIaKBQECGkIGqTGSoueq9e++55/fHOXvvtddea+997rsvkOKtz6c+r967556zz7T3Wuv7Xd9lbJ7sEj4XzlNgz7wqpeL4uXpPOYkhlRMBGWgdmauPjL9z9PIggLMJyn73zWoJWKTbP452/EEpKxfPUtNt6rm3LA2iAW5szjLrf1X5NPl+AVQXTFRycBx7/zRBR/r+8WlzXuUWJgG7dXmIuw+udoyHNe1SNSmZZYLumHhYkPxT5qX2/3S79mdZhEkeuh7NUsNPbayUBKSsJr4kMLt6Ob0eQ8Na+S5FuqngYM4zrPmT92c7es7ke8QO96CX35c13UBLL88xSvvBzp3AWWeheuWvtvv8xjeAs85q/55xzHm0DeNOYjQrTdACq3ybqOnOFVKbCAu3RVeUMZm/U5RLy7zeF5QjLQCgfp6UpAD6JwVM0C0i3fdBTfcDfvXlwPnnAxdf3P7sntucmm4TVKw1IVbPQC83jtZQ6JWbdUyLrq8fvTw4n7puEW5prOZvl13WbkcsinSTaz+zkFqHdJ98zKI87jnal2/bj5//X/+MV/zVF7y/O3ZJeG0HPZNPsW4H5nJp93CeLcNs7Z+ll8/vfaZIt4Sgu+DU/55UskOfG8O8koy2fzIO5H1OL2dOvjE3V/bbH6/p5m2ZuKXEq2ZRLz/Q6UtsWRzYoFlEs43gpHCSZgwW6SZzUG4JpZQwp/vM0YkBdA2WsP/3fAAVk4DdtmlIGA/xB6GZTPDVD30Ck/dc0SadM8WUpXaqTr1c30fYag/sd9nHoRoGOUJqMyPdM7IJeRJikSPddd1e3yvi17km/rRW4vGJr96JV+384txKMmc1KqSWYsYARye9fKYz2bNnD17ykpfgzDPPxNLSEs4880y89KUvxb333jvv8W0YMxN0Lw9zkO68YG9W4+haTtsw6nxUf/u3LUK1ezcG0/a86rJyyJUQePPJZHUOkwh/6aPq5cTxlFBoO5F6Nd3tz9x6dU+ELeHImF3mBN33hbiGNkkWRSHWZWrtPnLM0sslpBvd4rpnby/HIMfuuL2d50655ev+B91zO/z4/wUQz1gbx2btSPfs9PLjjNpvT8fNOi49vpctpKY971df7VHKr33gI/COJ/wk7FZNA+za1W5Hjxuh/s0jIWWQ7pOPbYPulBDkWsyo5t+2d8X7O2/dRC2FPnLLE1KTvzvPko4Jm0cdyrzmXbsAqXIlQlLP9lBIzXzu/safGw2x80Qye6pzz8u0xFeZGWxx09TLtfWT1nTH+nTPgnQfszSMI91WuDL8jDMAPC2EzFs0ZYG7sRw0j86jXJzW2HrXdB+7PLTvWnSfO3fig+c9Fz/2qSP4//7ow16yOWXSNcqhl8fm7Xa/5P/C81QWzgfjSPegLO17PmvidWakmyQFAEYv78AoKanPzWo1RDoRvPUTN+CKf9qFz954T/b41sNoMjXF4gQ2gm4AwIEDB/CkJz0Jf/iHf4jdu3djNBph9+7deNvb3oYnPelJOHDgwHqMc8M6M0JqmxfTlQHrjXTPQi+nX6l+7VXWi6q6oHtcVtnIFTAvpLuJ/k6tIRN5jJoo9ulOCqm5LKCxXMoenZSo73SftwyLTJJS2x/p2uWapZdzpHvnTgye99x2/7ff0csxSNl0PMG3j7THfcABtoAZp/G//TaABNJtM+7zUS/vQy83jtbxmxe6ffQUbok4sOp3DKU3k14eOEC33+79evkFL8ZvP/0XcN2pD49up4n28d/7Krgbc0h3W9+/noHU4e5554wiTUcB6C8KJalJ298TQmqDedZ0d/sYlnkoah+rSQLIJBCk5yRsGRbO4/w51YIHuv/vVNCtJb5m9RNWNSE1DekW+nR7CQy2luXca9NJ4ZjFQTTA7dOnmybMc+dnCcVtf08zTXKS0HqiZG3PkEW6l4fp2v6uvGd33arE7976gPbvEZCEmrn/9FyWbdDdg16emTylwoGcbeje8WKmGn71+H2QblZmaYPu2+6wYJRnynWm87KmiWKub98e4PO2MUW6M0ovRp3/fzTVdM/Up/urX/1qh1i2F8v8/+tf/zpe97rXzX2QG+bMIt1ZNd3zq7GTbBZ6uae0eOut9v/DafvduuzOS0Gu+KQ2j17dqUyq9FlF6OWNsFDKQmqJoNvQy3sE7A4dcN/R6I/3qZCahLqJNfBhZjrXzL33aro7x6C6ow2+JuZ5ynQMUnbPJ67GuBqgaKY4+ZDA7GkaLOzeBSDexism7NPHYgiOZka9/IQtutpv9JjT/sd0jkF8O1UB+dRTvV8PLLY1zfds2hrdLorcKs5uH9tz2NV0A+ujIWDsSDe/HlyZeGuvec5EpFsIKmMWuybS3EZtri3DmDZGX8Q+ZpTWKdVpa/oD4nzPrpGWBKaXxDjX93XdpaZenlId14zXdKd6qVNEWUqimP8vkKREit5thNTSNd0x9XL/WaPrb1+GCGdHpNqo8THR5B9N8HNbM6DSUZf3Xddq6WxdGsSfA1LeY9bUiTnXCEhCTUr6GJbaykQvzdCSf9LnMtJdBCwcWlKwVr0ImmTss47wPt0LnY7R6jdu6FVGRecrzdes58SsW6vREspefbq/l5HuD3zgAyiKAk9/+tPx+c9/Hnv27MG1116LCy64AE3T4AMf+MB6jHPDOrNCahn0cotyrJOQ2iz0cupoVETGujKLYsnOiyFX64F084ko5jSaTYtCztZLIjy5oidUZMJ+N1O9vCpLS9+OocfrLaYWmyQlx9lzOHr66qsTVtNNHANXrtDPMUjZnbu/DQA44dA+DKfyfoZ1l0CaNup9izmBfWyW/ey1SPdsdcjmHe6DrmuK0Nwk5BFAK1K3fbtVFDLzxMGF5fbzogBOP73djljsXdBonX3s3kOt+u99UdNtEq6TaWMRC5rYEdklmSKOxjx6K7u9qXto5q15It2G9WMctLnQy8l5FAIypKGWEhU9NwksI90zDH4NpqmXzxrA2bKGgV8CoK2f1OGOqZcvKKVSkh1YITXdEUGsKNJN2hjR86DfS5lW0923T7fETJPet2otpRyEurzvyk8BALa96fUYrLZlK+LtI+U9Bhipqa+mgCTU5Jruyn5dK8dKId0S6AH4iV4ejNYEdaf3aJY2yL4Pk//9KRkfQJDu2MQgXGeRRs/Ow4xxPcufcsy9aw60il2zke1Q8z0cdN94440AgP/1v/4XzjnnHGzduhWPfexj8c53vtP7fMPWweoah2/+FgBg0+27k8HDvNpKaMb3a5zCmNHJoCRBt1fTTY0hV7l0vj7Gz0ObBOixNXp5bZ3F/MDZjoMt/kC+enlZyMfReu2ul3FxHWqS4xxTl07ZKke6iWNgkzhFP8cganWNO+7aDwB4wEG9NmpYO3VwbZGrI05gH3OCbHnXrmkaSy8/YfPakO6ZHIxZ1curqm0LBgBFgUnZltccXNjkpH3f/Oag68Ek8nxpCEmujeupvZaGXr6eTg2dXw2tljpoErukr4r/VHBa+e9Jevkc1hta/wvMV5+E0jolxFVvGeZ/v93W3/cRZQ2kz9rCOrYzjJl2XrMqYZtnfZElRrRXIJUgtqwtsnak7rd5D1qkWy5vaBHz9v99+nQD+fMC34exnGurrdc0wc9tZtFC0wGiWyf3LW0BAGy981aUd32726dwAwn4YZHuQgB+GEhCTbpGtDRsZZROWKV+p/9vyJrDfWGKdFNmwyyvpN8Fo0cimiVVrJBaNUx/mVxnqtWgiZPNy99Yq7lSjlx6ua5Xcn+13meS6om80cN7nazLTh7+m48AADb92Z8k61TXs1extN+DGUi3V6d06qnWYTY13XYiV5Cr9UC6+UuvLWQeSk8mOIluKLX9ylYvp9+loi7CuKgjJR0nN6EwL7MtHqQAQHBw14J0r0xMy7DumSEL0aBRkjhsu2zr3r87/vIDAIR6bmNFgeGpD7C/akHYeAZauGR9A+BDo9pue4Kt6e43BpNY6hNgak4pt6iTumNH2xbstNMsg+HQ4nKLgAvtwgD/neHO+Fr1Dkw9ZFEAx2+Z7Vr2MZpgNKyiManRkxyTXBFHY3lCalrQPb/1xjxbZg3LLdHJMSpg5Igw4bMQ9LMWSn34c2PmJG68ly4dx31l7rz9v88adHOHOFUKRWunZdV4ePvLGZMTUtNruuk8FUPBzfNLr0820p0IumPBmNZhIfa+ufehx6IpdIDYu3wMAGDrkQMW+JhKzzABP0SkW9guOLxwjYaVQzy1dydV/ue1CRP8CdoWzHxOS2VmqeGnFmMHxUwVUiNB93sffQF+8+m/gCnYM0Cuszm85wNyn3ZO/sZabWLnjA16ebY9+MEPBgC88IUvxPXXX48DBw7g+uuvx8///M97n2/YHI1kJ48MW0Rl03glWae63kg3r1k9nEUvd/8v3/jG9j9F4dOBI8gVX2TmoV6ejXSTP1M1bkn8RFIgn6VPN11wpcnJ7LIsQkXYKcnwu+3Xd9KNTZKSevla6moN0m2pR2Qhskmcno6BaOT9u/OYEwAAp0hId3eCwze+wf5Jq+ueG9LdvQ/jzP0YZHahKq0Y432JdOf26Vaf0x07gJtvxmTbcQCAgz//n4CbbhIDbkB+N93vbi5pmv5ot6GWH7dpwSaZZqUo5hhFug2t1gQUtD6ZWm8hNTZ2CflO0cvngfbzeZQecq0lMs75T4hhMpl2Sy8nhw/o5RrSTa6rTYbfxzXdGlOhLxvC2GjSbm+Q6VSCR6zpFlBeunYkg24ipKahv7ESE29c3X0pirwewtQ09XIX7EW+qzButBZvdL/8VZvUU9xzcFU+EOsAARCke+WgY4d94brwu6S8x9V0k7VVAUmoSUF3URRJBfMw+ed/rvkQtDWtJqRWcaR7hqnLL1OaPRFtnvvVxWWgKNAA+J2n/hze9YSfxA0nng57Muw6p1og0mPNo/RnLSYJqcWWixiIc3+13mfyrGc9C03T4KMf/SjOOeccbNu2Deeccw4+8pGPoCgKPOtZz1qHYX4PG8tOHlpog+7l8UqyTnX91cvbF8L40Qdz1MvJWKodz7bIlQ2SqkEUubovarr1oJuMnShF0s2ljHeZMbnQ4w4F9XJtXJ4jw+635NStZ6azaZqokrJEJ/KRtX4rnlUvN0g3cQzEcoUMxyAw9v7dueV4AArS3T231XN22HPVke7OwVljgNJXkG3fYdciZmiZMD3Vy02g32MBt+rlCaQ7C9GsKtSGXv6A7UFizj+u7nAHznnPIMgF3cM1UxRzjAbdBul2CtLydbU6CrlCapFrklKg7xvgx8wkkaQ627UmDinN2Tmp7vOUkJqWNATSSHdZuHNab30NbQyqennP68qF1MpEcpkiyhIry9yXBSoKmrhGXk23wuzzhK6EOSvWcWR+Nd39kW5KG+ZWKXP3pX/xBfzAaz6OXfceDg8kMLxM0L1t5YAt96vvEdY2Ut7jkO5ucomAJNQkQAKgbcMya7p5YlCYowCfBchZn5KYIrB2pLvPcs4T0SawHB1/IgDg7s3H4UB3f1aroXqdzTkVBU1myWPsu9bP2yibM6erzwbSDeBXf/VX8fCHP9wqltN/D3vYw/Arv/IrMw3kK1/5Ci655BKcccYZWFxcxHHHHYfHPvax+M3f/M1g2+uuuw4XXXQRTj75ZCwuLuKss87CS1/6Utx1110zHfu72lh20kO6gWid6n2lXn5Mh5gdzlAv92q6C1jkavC//7zd56bNceSKIwv3oXo5H7tMkfOVUNv/d58lnBqKWrnvxp1NKctpjiOdx3o6ehOCrMfVy93f6hkXLMA5uBbpJo6BEemb9HQMAmPv356llo53/OF9/nZvepP33JogaJTo27t2pNvtJwdh3XukDRS3Lg8cwjVjn+4+Y6+Zg6FZLsJkeqOmxBtjdOnc917ecY17P/d5AMAJkyMYuI7h61bXfWTVaQUc/OfPA3UdBD7c1op0S0gkR4CNrVWQyD8uo5eTeXCtCDF9FqVAUSuFEJOs7N05kqhLjVFA19ssK4qflxlPz3nA6newXura/aFrY06f7vY7iaDb1nTrfbrHXs2tlLgO191Bz2tSC6VhdJ+5Nd3++9b+lJJcYnuvusZXb7wDk2mDmz7x6RCIYQyvBj7Sbenlx50gD7Qr75kcc2x7bJPQjoAk3nkqZRtL3fqdi3Tnq5fDHo8ncyk44rEJZ3gnaSDbJ6jliWhb0z1cAN73Ptz88MfYbSdlpV5nytyRtBLouL7z9HL3juck2GMaQfdX630mW7Zswac+9Sm8+MUvxmmnnYbBYIDTTjsNL37xi/GP//iPOOaYY3oPYufOnXj84x+Pd73rXdi1axdGoxH27t2L6667Du9+97u9bT/xiU/g3HPPxfvf/37cddddGI1GuOWWW/DWt74V5557Lu68887ex/+uNpadPGyD7tXodsDstVq5Zl6gY5fbGpSsmm5C+bH1/1WFwQ/9YPs5ymhQFCDdGeJtyTElEDBpu5K0DJPqRiWKeCrgdUg3QckTC4LLcob3O7b9ehgNMGX18vB51ERkcswJqZHnpXMMBief3O6/p2MQGHuv9ndOyjEjhiSccor33BrHUQrAmqYJBF1mNQ0l0cy0C9u6PJyZCWPVy/uIxiiBDDdNCIabGfPBRKKvF9Kdex26+v57f+/3AQDHffYfMTj7kf3308d27sThD/+d/fXgb/0OcNZZGH30YwB0dddSccI0i6FKvAaR27AHQpmyMVG5BfxkzVpzyO5ZDGmnQDro9tqLcaQ7ETi0ZUD+3+4r0xJfM4ly1TXGd7Ygx8I3vwHUddKJ9pSLBfYXve65z+1B0zIsol4eS7wBvriTsb6AhdS1xN/PLEG3PmcGc3c3J41ubder+ldfFWr+sA4Qh4dLGHf1wxTpnpx9tn6iO3agvvg/tMd41NnAJz8ZBUmk8+RJu/70cv13SVSNCqlxpJsirsBsQSllfK0lEW1ruidTYMcO3PTH73LjetsfqtfZnLJKL69rTEbtezL5+g1hMuY+NKkcKkdI7Xs66AaAE044AW9729tsgLxr1y687W1vw4knnth7XzfddBN++qd/Gqurq9i6dSve+c534tvf/jb279+Pz33uc3jZy15mtx2Px7jkkkuwsrKCsizx7ne/G/fcc4/d5sYbb8QrXvGKWU7pu9dYdvLwsG1Ns2l0JLodcB8E3d1+t3ZBd5Z6ORG3oDbIXOQC9XKFztfHZqnpLpWWYWJNt0GbUki34Oz5SLc0JuqopOnl61lHSANMGeluf9IFwQsaeyItFunm7fN27ED1uWvafVaDXo5BYEp/6GNXDka3c0F3PPGxZqTbEwlK78vUdG/btDCz5sMsoiwxqiS1MnNM5vN+SLfujAOZ7wap77+36xF+/JF9qAgbYu5oQnfMIySpdXBhE3DrrRj/csssmxvSHZkLKaIi2VqdV+m4Zu70BCXnRC8vSXAn1YXy5IKkScGvV6pPdxsA5K0HM1nXfxlXXNH+lPr5cjQ2M9FlrQvwVv/pcwCA4e++FjjrLFQf/Uh0Pz6tH8G2nqp8JuXdJPqPperlHOmmc6S0JgrP9UAKXiKmvRs5718q6JamzIrul8xJ46rT6SjLUPOHdYAwKPewHmN5MsKge0inCTZSV8qPyQknAuedl80c05J2Zv1e0VhhHNmO/C7WxJehLzwhc8AsNfze8Wdcz/n7yFuG3bLH+fiTxzxWvc4OyBL6dHfv6uRwy4yd/I//kRRgnpf99RduxS++51ovmUKBJUlYl5ttGfa9XNM9b3vLW96Cw4db1OgNb3gDXvCCF+Ckk07CMcccg+///u/Hf/kv/8Vu+/d///fYtWsXAODJT34yLr74Yhx//PF4zWteg6WlFgF+73vfi3379oUHur8ay04G9PJInaqtHWvWh1ZsnFgTdPfp060rw8bHyhfUedLLzeKmBf4U4SgLpWWYdRxo1rz9mUa6Q6poUr3cc1T885GocetZ0mMmyLJw/XWplYJzF6uPTJlJ8kgo32DQOh8NCkyf/JR+lHJq7P07sLgZAHDMaod0K+/fMCIq1Redjhl99nJozfso0j1jd4NZRFliDiQ1rjIr7mvqyhiMerFmfZT8k1RSVt+/Z7mlWR5/eD8GtRtHPU7Pg9lGjnmkS7gCnWp702DUOdma0ExfKnNMtCglhkfnrTWr8hOVW4BpW8wp6PbqCgXHmSNykvBPoF6uoXWReXpuRvov4+KL25/EwdYSX72SbzTA63QVhvUEuPVWVL/wH73jcPPUy6W1wEtMdGNKvJPm/d8SUS/31mepptuy0ySkO+8epZDu2L3214MwiSoxS+zcXU+9Ocnck0k5kDV/SAeIvUtGufwgiu3bUT70Ie13E9eci5Hlmnvv/GtkWn5m08sDNk44NsDXMOABHm0ZBqxNdDiV1NGMv48LJFE/nTa4+W7HpouBUZQh4iHd5F31FOcTAszzsj+68pv48Bdvx2dvdBoBVqujKp3eSAzp/l6t6X7Qgx6EhzzkIfb/D37wg9V/Zrtc+9jHPmb/f8MNN+BRj3oUlpaWcNppp+E//+f/jHvvvdd+fs0119j/P/rRj7b/X1pawsMe9jAALRp+7bXX9hrDd7Wx7OShhWUAwPJ4NVmnSjPa65FVN5OzDbozarrdROP/nU7EsbHyAHkefbrNPhe7npGa800nB7VlmIB05zq+Y4GaTv0jOUtvtgsdGbq9mbTWE+leTUyQqZrIvtoDX7/zAADgQSduDj6jbUDGa8k0sPdvfxd0b109GH3/TAAySgTdzRoTYuMZke6ty8OZNR/M+GdSak2gKDmig/T6pUpa6gj1rzfSzer77+mQ7uOO7EeJBoWhZ376M/H99DFyTFNaBHRIN4BR2c69w9Uj4XfRX0wzUL2V6OVK5sRbb9aokssFKatE8rGPUSQ7xlbiwY6YNAySwCl6eYZC/yzG+i9bIw62O29/ExPAJa8rSzoZVHVYT4CmQWmQ0gZiTb+dAyq5rt2jA2ei71RIbaAE6mNvjdHHJQqpZdd0h/ug+4m9f5oQmPmzFHSbv01uv8O756NBOx/YemtJ86fT0dn3R38KANh6yvHATTehOvGEYDySzZJ0pd/jvp/p1T2zernKFGh/FkUopFZ3F9o8hznJXs1mRrptErP9nfpNo3qKm+4+ZH+PJQMkhsh04r+rRttmrCVj1sHMu3mAJMZtMpWMNfa4HY1B9yBno1tuucXW39L/c2uapnef7ptvvtn+//d+7/fs/2+77Tb80R/9Ef7hH/4Bn/vc57Bp0yavXnvbtm3efrZu3Wr/r9V1r66uYnXV1ULv37+/11i/Y2ayk5deatGOTeOVFoF785tV2ixdAOppA87CXatxevmhDPVybSGpqryxmmNuXhjgwOpkPkF3N6iFQYkj4zrSp9v9X2sZZhYi6pjm0GgAORNcdLXjGgPAU+HkCwt19DLrytditv5GRd3gjQvQHQ7R6rp1Hm6/HSsnPwBfvb0NNB592tZgU/7sr8m696+59FJLLz9m9XD0/bNZa4EyxxXLJ9MGCykIWLFYzbJke4l6+aw13VGni9wjnHpqywCoKrVdEbecd4UG+6lEn/RuSvsBMt4NVt+/Z1OLdJ/QieoN6xqjQYnJnd+O76ePkWMe8YLuNvnqBT6CrRXpFmtMVfVySi+fjyo/V8aWxug+kJ+9YDMyZ0q1w1oAZX6VkqzGVHp5pAxozSb0X7bWNG1y8LLLMH3vVe0YlGRCcv5gSSfz7C3W7ZxC2R7TxgUTxjz18kQtfQ7yOJ02VtNhy9LAMp/4dZ0khK6kjiOzlmXo6uWR+cxLDIZJ1FhN9/Swry0y6pDuoIc21/ypKux7xKOAz12LbSduA6oqq4UTPZfe64ZFl2WkezVTvTxKL1eep7D0Dt1Y1o50S4rzORYg3SzovvkeEnRHEhy0M4I9zy9/2b6rUxSYcsV5mow577zsMfexA53eggm6p9PG+tCDqrQPWpRevlHT3ZqkXD6rWul47BRZH/vYx2LXrl3YtWsXzjnnHACtqvnb3/52cQza71rg/9rXvhZbt261/04//fSZxvwdsR07MP7mjVb4YvP735usU51njZ1kZtLoQy9PibkAcZqsWZS2LLWLy3yE1Lq6kYGh48vXyjxjZqgxNW4pa57u051QQI3UaNPAmgfdUjux9bBUVlJiBtDnMnp9GHXyqz/9nzCZNji+GWH7ddcEGdu5P/s7duDQ126wi9cxH3hf9P0zwYJ07JkFvATzUJwY8tDVeu772jcBAFsXK8zan9c8p5Npg+aTn3TXPkJvpWIvMct5VzykO0UvjzhEoXOeuA6sbv+e5TbZc9yRNnlr2h7WJ50c308fI8f0kO4u+WMCn4XFofj1vu2gcmq6tcQJrY1cM72cBRz0mOKuE9Rqah69XEBUtWBH7NMd0MvltUusV57XXMyC4QMLy/jYQ/8frHbPhnGw669/A0B4/1zyLRFtscBt1Pkiwy7oNkJc7b6EtYqqlwvvubtGefPA4XFt55Vjl1y5TCCYmES6w3V3Vnq51qc7WtPt6Qm4v9M+09zs+JaW7d8aOKR7zINuQfOHsp4Aqogefw5qq+nRL7Gm1b1bITVFnyc2J7Wxh/4ZwNgl1jfqnsXCD7pnaalFny+N5SFZIKRGLsyte454GkmxcYmCcffucZ+TJMcklYyZkzWTiV2bD37la0Bde4zDgcJ24fY926d7Op2i7hyr6XQa/Vf3pCucdNJJ9v+XXHIJtm/fju3bt+Nnf/Zn7d//5V/+BQBwyimn2L/t3bvX2w+t46bbUXvVq16Fffv22X+mPvz+YofJpV0+/ynJOlUP7Vsj3U8yExwb9fLVyTTZd9iJW+hBd47oyOauTZkmvtHHzKS5OIwHIbQuj/70g8iw/YiU1ZfMKvYyiCCGjFAqJJ/ERERnHenlo8QEKSUpPOV37b4L1MkvPqAtJ3nMjV9E8dSnBg62V64wp2f/wNg5Z8tPOy/6/g0H7bmK9PIE4trHfKRb2Q8JSPZ9/ksAgG2v/lUMPvuZYB9J27kTNSnzqZ/29Hbfv/IrUXprff2XAfTo0x0rMSHXL8WuiTEB+H1IXgdW38+R7kHTjmXyhO+P76ePkWMepjXdHdI9GiwAABa2bRW/zhNxKYuhSOZRjinQzy3oZjXddN9BUjSDWk2NJg8KKehWdEfMaUt1yMY0pJvO03MPupnj/IfnPg8ves7leP/3Pc37+7TzkXjiKxfh5IHbiLEsKhJ0S4lrEekW1Mup2FzsOTJO/aAssDgo1eRBComUkW7lWVPMBnGqDkDMp5GRePMVsWWY2e8JJ9n5oS5KNEWH0JvgKqL5Y1hP2zr/zZYZJM55ZqTbXmffP1juq17uIdt8W/J/IRh1pVH+PZ9Jwb8zvu5ml/KwMRRFYQELUzrnjqHvc0rmZTtHbTvOjYcE2pOSkZuFZMyabedOHHnoI2CuysG3v6sVc/vAX9tN6BwA6Mm1o5Fe3vtMrrrqKlx11VVzG8ATn/hE8e80kNm0qc3qn3vuufZv119/vf3/ysoKbrjhBgDAcDjE4x//eHGfi4uLOPbYY71/9yczfbAHZZH1EPpo3/wVtMzkYoJuADiUQJ4pFYZaLjLJg+55IN1mEjfBohakmWEZR03qichpS0C+U5OqC5PZgwRB4WIhZlIv8tozrNVSPRVjdXyAstgr1MnrTu2C7ju+3v6BOdhV6ej/82J5GJrUMUuDZBnNMEYvn3GRlkwS3vGMBST7F7u+rLfvQvXffqfdR25SotsXFQqblFW779e/Xqe3Aqg/0iobpxLWOeJu9H6O6qnaCx3gQWP8uifvA6nvb4oC91Kkuygc0t2zxCr3mJRefmBhE1AULvBR37n2Z74jyH8P388YW8HV1a6RXi4EQmICIUWtBoLaRXoeojiamYeV5Gcc6Y6rl0vz9JqNOc53bjkeAHD35uO8v0+P3WrHQC0b5WNJJ8O6G05N0B0PbmurWaKpl7vx5CDEB7u+9Vu6+VgL1OmzGK/pFoTUMufGyVR+N7Lo5TQxWAvvm5DksvttGjs/jIYLbj9lldT8MUi38d9smUFKSG3Gns+arodRL1d73POgO5JEkZ4nGnSbz3nA2/d+x8aXe114n27AqXR/7Q4WdEfG5WlUmLnlEf/GvqsTL+hOJ2PWZJ2PcPBup8V1YLHttDH5mRfYvw3K0u+PLsyFTdNs0MsB4LzzzsNTn/pUeWdlaVWDc+2FL3yh/f873/lO7N69G7t378a73vUu+/dnPOMZAIALLrjAUsKvuuoqXHHFFdizZw9+7dd+DSsrrZr38573vPtdMJ1rhm6yvJBXnL3WVggpM5PLpmFlEYlkCx9lIckdqznmlsX2GqzOsWXYQifoodZ0s4QBVVw3VgtId27Aa+g3XPk7C+n2KHvdZxKl8b5AuhM9gz1mQKrHJaNOGvuSQbpvv8HstP1JHOxZa5Y128+clJjFWoaFSPdagm733eBYQkBi2sRsPXIQAxMk5iTkyL7qgiziVcZ83zSYHmhbrCWF1DKQWZ60iM05dYRaGtD8c96Nrr7/0JkPtlTOEw7vB7Zvx+DYVg1Yuudrsh07MHrv+7xrfXBxE7B9O0YvvQyA/s6tR8uwGFshCzGKtLUyZu6xr2/R/vSSc2x+2Le4Gdef/GD3uSAkJc2ZUp12gHQL22YLqQmo29wSoCwYdmJa3bXrHOz6wQ/2zsNYNjuBiUra0oZ6AhQFyobQYRNIt5RAmU7DaxR7bvcTETWzX+k72Ui3wKpYa013DnVfSmzRv0tTptsv7Jw03n6G/XxSVu0z8b73tZ8L79xe2z5y6I19/ZFu/4SserlGL8+s4eZj8vRu2PMWIt395klqfD3PZkcISUwzjwdBdwzploTUisK+q3TdqMsymYyZ2YiPYIQ+gS7obhov+B9Whd+RQnkvzaX8nqOXc5NqFszf+tZ2P/OZz8QLXtBmQL7whS/g9NNPx+mnn44vfOELAIBnP/vZeOYznwmgRbHf+c53YmlpCdPp1LYMe9Ob3gSgVVZ/4xvfOMsp3S/MoLqbF/ITG/Oi+0lGEQGDPB9OCBuZtUdCCnPGao65ZY5It9mnVfjWgm42SVrkQ8hUz0IvNwGCJuAj13TDjokH1jEhkfWw1RTSLVyHWM9bAGLN0aHhEm44YTsAgnQDgYM9qzq3ZhTpTplr/xFXLwfWiHTHkhZCwmLfklNfrzpa6OTgISSN7GtCqftFeK+lszHbJYXUMpBZ7uTEFMwloSbt9+wgaMcO7PmnzwMAlooGyx/7CHDTTRgsLyfHPqsd+fGf9H4/9IhHATfdhPFjW1aX1qe7t5BajF5u5z/9+yZhqB4vs/ZaqpHlSUUAwfxw2U/+En7iZ/8/fO3EM/3jku3ovkW2khIciC0ig6A7jtZVZZGl0N/LeDBsxLQKH+2cokP1FJZZ1jNCWk6Z4yzUY2D7dlR/8Rd2M+ldEq+BQuvP8QUOsqBb+066plt/1nLXDq2muzfSLbDApESle4a68e3YgdG1n3f7eclLneaI8s7t+8ZNAEhNd09G3sxIdxB0z65eHke6XdKC34cpu19rASVSyVzNONoOkKCb08sjN4WCQV7ipHtXJw/c7vZTDvxkzDyN+AhGcwRwnTZM0F0VPshmx8uMMti+55Dub33rWwGt/Oqrr7Z/u+qqq/C2t70NAHoj3QDw9re/HX/4h3+Ixz/+8VheXsby8jIe97jH4S1veQv+6q/+ygvQnvrUp+Kzn/0snvOc5+DEE0/EcDjEmWeeiZe85CW45ppr1Hruo8FMvVgu0g3MH+2jZgKKqixsIuBgosYyRlG0Y40gRWYBdTXd82wZZuhpmhhO+7O0Qbf5e9xhy+3T7XoYygt3TL2cTrhmIaY1VKLDOmczk6QaAAj0zKTyp1BzdP0pD8G0rHDq/rtw8qG94Xc6B3st2WvJ9ndqnMcoolXUDPNDbhmm1xz2Nbqv4LllAckUhUO6Vw7aGsysvtJkX7RGbMyQ7kPDJTz5F/4Mv/pjL/H+boLupJBaRqsgfj+jQbfi0La/z34f7llp553jj11u1V+J+u96JDh5cvHgYAmoqmRJR++WYZFEhOQkascTxTB71F5PrL4FofxKyUs2P9x27EndzxP9Y5DtPGQooaJNzc337m/8XmtJYHrMbOGyPkaD4cr0avbRTg3B782G61pOjbZuAwAM3/mOtuXUjmfbTWKIsidgpyGTGairee+PXRra/UrHjqmXN00j3u9Z35uKrX1mDYqt/dI1aMfW/hT7dAtzzahx29UPenCbjIm8c/uu/SIAgnQXec/lWtXLg6DbtgzLo5fnIt219DyxhAFvGTaLr8CvV27yVtKOMPP47j1tZ5alhM5Qe3x3LoFWwo4dqD/3Obvt5Md/IinAPLMRH4Ei3Vb0s/MbBgjPW7ruXtB9FCHdWRHyO97xDvzX//pf7e9N0+C8884LtiuKAtu3bw/+nrKyLPHiF78YL37xi7O2P+ecc/C+972v93Hu7zYWxGVS5hQp5+8I0lqozR3d+3CCXm6F1IRTMBNjrK+yOeYx64B0O/Vyuf0dp1fG1cvdJJGNdAuUSiCehaWtmPiES9VCc8ewFksJqYmKtUJm3zNDnbz1VuuFfPHUhwMAHn3HDfJAOgd73kGQoTMeu5yeNocxpJsLeK2BjuwFlXw/LCA5uLBs1de3rhzEbce0gUkWRZzsi9LEeGuab5x4BnZtewA+WZHERFFg2rV0TAqp9SgxMRall0eSOr1ruokZVGbTort2rh59/pktziAyAUeueGHuex+j3PehlwfXMrOtFS68EKgqEX2UUGk+P5hn05ZAFEX7OaldpMGnuT6eArKSGHbb6k5/SoG5LNZxLt6xA7jwQox+/++Be6ao//2/B170Z5ZCqrXtm0lEqqowQglgiuEPnQtUFQq0l7tplLWKiI2VQlDtgrK8GluLdC/5SHc9bbz120O6Ayqw+/96qJdXiqK69F3+/9j7JiUlqH7IZNoE79ydW47Hex99AX5g1/V44u4vuwRsB+LwlqOa2cC1J1WjFliAAG0Zlot0U/9B35YmLfj9dO3LOl8u43nTjJcT9Ue63d/4PP7gE7fgK7fvj94TSaPCY18SNlp98inzpZRTIz6CaWkJAAcWWnadEXEbMnZBO95wdwawKIuw5PL+bNlnwtuCaW3DDFV8w+ZvEyJEkmtZE2lGjZ04HrLQbLJId14LH2khGaaoiXCTpUW6lexoH+NBtzYGrWWY1wtYRLrzFrOJSi/Xvy+LAvmZ6AFButcjIDCWW9M9Fa5XOzbh+lDqZGdffMBDAQDn3P51f1smDrJeNd3HLGUg3YOYkBpfpGe7J03T+C3D+HmyWk/jZC2OV7E0GWHQId3ThUUkjezLQ7pZ0G0CeBvIm2f3vPMByKgNtRy1bX69DkTmHM5CifVYzq3Fo9+l76pDeeef2DJ6HubyHVydeEIzWiK2b0DFURoROYrcQ7PeBNeAlTpMUeDzpz7c9hXmpSEmWTUQ6my9EjZGrTbBdkxISqZ6h4lAzjiSasr5M6Qi3QLqNjd6ObWqwujYbQDQ0krJeZtha2JffWvMLctCYCNIU5qHdAvXgCZDkoyXusaBL38VALBl3z1AXfsdK7xAVhebpJ/5SHcaofaGo7AjcuYzLTEYAyjM+GiQSFlVk7oJ3rm3f/+FeOOTfxrP+w+/h/P+45/gpuNOAwBs/fpXvLGm5gquAJ5rPNA1tpaWYTy5o3Uh4EkKkwCYD9Ktjy/nexLSDbTP0oNObAPWWBtdczgPeKHXgTwj4xnOj+woHicQH4HSyw8stgG48QmqBZ9Zwcdr7GhULgcyg+7HPvaxeMELXmAD6qIo7O8veMELcMkll+AVr3gF3v/+9+Pyyy9f1wF/L5vk6KUsOZn06G/KbUIy11tsTXeKXt7+jCpyxujlUx50z09IbXFQBX+jxulAMREesRYxU6BEazsifV0SCzHHcRQqJryyTmYWhsUe6uUxJVJrhjrZsWi+aJXLv+G2ERzstSiSSmZquo/NCLoXIkJqKRXtXONfC5AHFpBQajlV285ynsi+aB13bYKbogB++ZcxPvkBAEgw3tFbpw9pEyWphHVOkpDfzxjSHWsLNmstHt1WctTXpaa7m+dO2Lxoj7E6mSbp5X0DqsCJlVrwRNafoXYNWKnDG57803j2z/w+rnjsj4nbxebRwEEj1GqjNzCpKrV2sY7MmQCZNxWkm56a7XzRXf9VRUk/FgDM28wzoQUDMwupsX2ZzWk5kUUMhajbK3cSrrtNCpSJmu7OZznwZ+8AAGz52w8DZ52F6m8+ZDfR0OOYnobHTivl7TVbi3p5KuiWfaVwe0rHrafT4J3bTwKhW457oA2Mtu69G0B+m7RZa7o5pduYFVLT6OUxnYnI/aRJC5784AmAtei/cGZmX/0Mej3oPL79uGXbxjYnadOyaMIxxMorsi0nTiA+QlDTTVTUTYKYnrdILxeSekeDZdHLL7zwQlx44YUAYFXF3/GOd6zfqDZMNCn7n7LoZNLV++xb2IQPPP4n8ONf/UecdHivq7FLiC0Yh3ZYltjUUZRSSPeUBa7UcrKNZtLYMseg2xxvIYF0m0toJotcEZ5cETPbm5YxGeLq5W5MHOme2oWl7C2oNIul2jvMVNNtrKNO7v34VbjlE4cBAI+5nQTd27e3ATd5XueNdB8wNd0ZQmqxmu5Z6WjhfjIWexOQXHop9pdd0L16sFXbfs0bget7XJ9uX/UnD7oxGGGW7tpPvnYn8I5/xmTTZuCTn2yz31WF+v+0LR5za7qjfbrZeKP08ia81ia3FqMtpsy8q2uhpPYxk8w8ccsC7j64CqBNAqV0FPqOKVY/mYV0a+sNoR7uW9yMdz3+JwAA39r2AHG7sVDTHZ1Hu/mh/p2PAisN6t/4TeD/PV+kUlLxSYl9o9WuS9uasWxZHODeyUhFuj10XaA9z9PMvKAFmPxRsahujyQAnXs8dC4T6Y6tnVGFd1Oj3DQ4+LDWsT9mdBi49VYM/sPFwMvfH+w3JjZJr9FaEmi2NIz5ZnR+mE4bMWGlBt1R0dmw1peuNeNpE5QXGYbSSz79F3jwPbvxf84+D8cdOYAH/+iPA0C2wN+sNd1TZf7oL6RGEzV8viLbCUmcacPq+Is5IN2RxG7MpOtBA8yzTthsfcHe6uVKYmKmOYe8c4eHi/jXkx6Ex932NZRSnND5CAf+5G/t1w8sbga2b8fkv/8+8OWwNR9noBlzSPc60eG/Q9Y7hXDTTTfhxhtvXI+xbFjCZkO6lcWD1Pv85TnPwG9d8J/wP5/YJla0/qbaeKqSIt2Z9HLhFHKyjWbSsOrl47q3Yr42JjrhSZMcp3tJLcPi9PL4OKTvAqTXbqSmW6KXm2tFM73rha4AGTXdwhiS6uXeDip88azvAwCcefwmbP3Ih4H3vKcN7gRxEFdPNx9439V092kZpiM+2u+5FnMgPeuEj/b+XtvZYetDH9QKH/3oBfHvKfuaPOCBbgzvfJd37SedmM+kKK3AGEDencTclYPM8vHGxBtj12juSPc61nQf6ebVTQuVnfsOrU5Eii+1vkg3R25zkTdjJkgOnDtCPXz3Y/8tDllxHVKGQEpDnL5FmLxUp7CqwqTrVTx5xL9RaxdpUC0lI7V5WLqW5nsm6axRZCUhtfWai808HAg8JWrV+zz/FNGnCZ+o/gip6ZW2S6qXsxplg6Yds3oIaBrL3OHfiyLd5DldU023Uq9MWWvavqTyNPp3CWORnqGxh3Q3QXmRqak9ZvUQnv2VK/Gu9/023nzde1E++cndcfLmLyrU2sf30t6rxUG/oFt6ZoxJejH0eTL7c2MpvTHNshbP2o0khXQ/6MTNzoeJBMsUyIrNZ9JY04P037nffcrP4jk//QZ8/KFP1OOEHTtw6NJX2F+PLCxhcsM3MX7KeQBYyVAkyW7mMY05eX+13mdz5pln4thjj8Wv//qv44lPfCLOPPNMPPGJT8SrX/1q7NmzZz3GuGGdWXXrPjXd2uJB6n32LB0LANi3dIz7nNXYSWYz11WBTYsG6c5TL58d6W4/M/TyabP2Gkpb0z1MIN3MaZFahknOYrYqqGEOcHq5cBw7Jkot+g4j3asJ1E2qiZScjJh96dZ9AIDHnL6tDep+6qe84I7avOm+/ZBuPejmf5sVGQ37fUeer6rCvge3AnTbTj0RqKqZs/v0Po0f/wTv2ptzG9e+QxbrWkDN1aTp23AavRFUEscacbLXgnTXwly8njXdhl6+aWFgRSsPrk7ssVL08r5CaiaIl5JiMwmpddTD1WqAd3z/M+2fx5Vce+3WOil5mV4f4poAJOiWxDCVZ7UQtjXXJ9XC0jKSFMd4nmaeCS0YCITUMgW0/GPQoJsmRvxjUZNqulPq5d5+WI3yoeESAGDTaKX9HnH8fXRPp9eaMRWFf136Ksy7tVZeu4NzEcbAt2kivpIk/OXpe9RNUF5kSi8GdS2XY9nnQDnJxHhjRu+zJqSWq17uvauRgJcmej0qc9MEImZr6tM9YxcMc53pM0JLHM86YZMVHYs9h3RuqYQ5KtUyL2rsnbt168kAgNuPabtEaHHCQTYPHpq4Y/vlKOYchKA7wZy8v1rv/l633HILnvzkJ2M3uRG7d+/Gtddei//9v/83rrrqKpxxxhlzHeSGtaZRmGKmOkGk3se0GBlJKsZCn2Rjtqa7JH26U/RyM9FEarpjTqs5Dxr4HBnXa3ox6WRg1FelAMZcQuN8xYJIj15uHd+8cXClxpiTRrOl5rEwf6M1VFUELZ+XpYQvJEdK+79m1+3aCwA4Z/vW5LZzVy/vhNSOzenTbYTUxJpu7ijMhowGi33iAdtnxs/6svZlAsScLv4Zd+jngXQH9PIIu4ajiX6LNXYf1ojarGdNt6GXL3dI951YxcHVSTLRlSPkZKwVQ0W3v/B7U8FJDI4Xe+d27MBf/8F7cddNjikyYeUJxiwqKjlokfs0UajV1Pzgzv8b/5yaTC9vf5r1b3UyFWnEUh35es3FDumWn2+tVn2WoHtYFR79OVavXhN/Qex5btkASoKF+SKrg5bVsDgZteeBBkUzRVOU3ntOxaO0RITWXztXA4Ujp3w/7TZTAEK5g5IIpKVj3CS/blRTpL/7MikvMkj3YFqL71w+0u1fzxz2r0bjB/oLqcXUy8UkYeHfXwnp7lvDT41/J5fBIqmXU1T3rBM341v3tq3DYgJoYqs97x6toaabvXNGq2XCBFT5djwRfmB17MpjBfaSqF6eYE7eX6332bz85S/Hrl27ROXyXbt24eUvf/l6jHPDAFXdOmaqmBSp93F9PYVgQuiTbMwsFsOytH26Yw4w4CYkqU6pj3r50rC0TpDWaiLX5B6q4Rhc5rT9XcoqWuVbr27F/0yziYCS0+NITlqsXcSUOBQxtHxellvT3QjXC8hz+r64u0O6t29Lbjv/mu58ITVb0y0IKwU13TMio31pbSbo3rps+trOFiTWtbygAz4CNvacyfZnjJoMxIWY3DH98cZ0JGJCahrqlWNSAvS+6NNN6eUHVyZpIbUeKCbdRpqLaVCk2dDSIYWk5bTBnxw6DgBw2lKXYP3RHxNLQ8z9H4oOWjohE3t+aPDpkO544tQ7Pjk876YByGJqnqhT5nowq42UxIOWTJhlnhxPQtSK7jsb6RaDJCUxwXyR1UE7jy3UY3cuHcXce249pFtOwgXXpGepiBa8+zXdyncVunROe1W6/WiiJBe68qLxk5/Sjulll8rlWJmsmFiNvGb0PmtB92qukJqAZkufWWE+8p6bbabsfq0lYcprunPXc6nkis7jZ52wOYuFIs1nmthcbxYWe+dMrDDlbFu2He8ocmBlIpZg5NDLjzaku/fZfOxjH0NRFDj33HPx0Y9+FF/96lfx0Y9+FOeeey6apsHHPvax9RjnhkHPpsZMXQRJvc/Itvkh2StWYyfZmCzim22dYTwAjjlufWq6q7K0k/WRNQbddDKIOQ1c+CLqsFWhs5ia0NVen2UYrAZjEurkKNJ9X9DLx4lJMkUvT2WIv71/BXfsX0FZAGc/8NjkeOYdBJmgO6tl2H1Q083PK9UO5M59LQ3zpGNaBexZr49XI8YWcbqoU1XXXHp5jso+v14xenmIdOtj7yekFr6r69unmwTdSy7BmRRS61GvSx0fqaabBkWaxZ6pT37t27jh2wdxzOIAP/XktgPB+KSTxdIQURtDCHq170VrIMm+pXIGHek283ATbLt5wZ2DtB7Ra1fOeV7iZtXLlZ7U/P7NwnjREqxx0U+zNpYiA6Mh4xPfJVajPKraeXjRBN1FgarbiVrTqiTheMle3zp3taY7QLpDU4XUIhoKMtJNkgv8+a8q1Mef0H73kbLeQS66r13bmNHtwpZhhl6+9ppuKUlYFIWKdJt3cS2+wqxIt1TGYlDdQVlg+3HLpGRJvylT4lNLqvazlANYU3QBJl1rRi1O4OKmB1cn9thStwNpXKsbQXdrBqH8y7/8S1xwwQV4+MMfjgsuuAB/8Rd/4X2+YfM3R4POv8YDTUyK1PuMu8VrREVtgKC/KTe7YFWFdTpiSsKAc1gk57tPTfegLLBsVS/X5uRa2ltRRDOe3GkxE4aE1tLzy88gd+NgDnQRdWRgx8ITBvRa3SdCaqZlWELUSctUpzLEN959CABwxvGbPGRJs751eSnbv2Lo2Wur6Z61BizYT6BeHj/P3Xtaqtrpx7UCROb6NE3PemYlacLHJCEiSXp5xnPKr2m0ZVgkwRGjxqfM0YXvm5puSy8fDiyr6ABFupU1oY+QGn18jPMnIW9RITWrths+i//jqlaA9eIfOAPHbW6pwVIfe4DqW+Q5aMb61nTLdEz5PGNJw2FV2msmBQ+ikNp6Bd0TGel2z6y/fV8qNQA12ROjl9Oksl07yWa1cF+8NYHVKBt6+cJkbG/OoOsBrKF72ju/VvS/Vp8Zp5CfU9NN58+Y/o3ECKLvEm9jBUCk91KrMtfLWMsu9TtkO34+S4M4eBKnl+sBuR+M+kE3bxm2Fl9h1vVcSu6ZAPP04zdhUJVZzyEVjJN1hvonSayxd27cxQN1WUbjBM4+o6wsiR0mzRdHa8uw3mdz/vnnAwCWl5e9vy8ttaIWF1xwwRyGtWGS2aCsF708Qpvp6n3GW1rU0DSv1/qbauMZUKQ7qV7e/pTbYKSzjXSRTLWayLWaOETm0kpjMNfQDN0upt33m6YRF/FcardeX+YfhxqloHGUgSYActuBrMVSdCCJXt4H6Tb7X17Ik6JQSytmsEk9tYFPDtId69Mdozz3GhNHuhPnuWtP22pt+3Ht3J2jrCtZbBEfKw5kTZ7TmPUJrIzF6OX8nYsJ9/VCuoV3NTrXrtE89XKDdK9m0Mt7IDj0ephgSnLeokJqitru7j2H8U833YtBWeBnn/Qgu39tXBLtN5WQofNvTl9kOt9LHRWCnstFOIdSx9kIccaQbh9dX5+g2zirqCUfeAABAABJREFUuerlucEWNU01PwvpLkktvYBalrEkMenJ7pDukfVZqsVOvd57biM6Duqam27VRE2jqbf7ju/LCyK9JET7MyY6S2/ZKEKjBwg7J5kUFz8W953LjqDf4dd6ecH5cZIa+qxINw1Gi4K0qWsaywTgSPcs/tGszDUzRdL7a+bxs07oEuOJeZIez2M0KolxqewnaeSdM90mJmUVjRMM+8wAhPtXxi5BSRLVsfnCJva+15HuN77xjTjxxBPx8z//87jhhhswHo9xww034Bd+4Rewfft2/P7v//56jHPDoC8QMTN+dayd0PgnfxIAMD7ncWr7Jcmc41laRd0kvVzJCJv90G3k77tAfyni5PQxel0HQi2jsabxx85r/OhXRNXdVFApoDtAPGingXWAdJPxrrd4D6CjH8a4szmdNl4AnlqsqHhPjs1T2OoAoTCvtU93KjN+14FV/PDvfQJv+b/fQMz6BO+jyRR37G/p5dsZ0p36LjXa47QdAxdzkwWMNMouN8kZ58avVyzRFzrZoWNqqdQ93g1HlXXnM+gR4PY1LqQGIE9ITUASmqbBf/+br+A913zL25ZSUoeD0BmSWDzcNGTG6Akcv3kBD9i6ZN8PjTYpdXKQkGZv/EqgxY0Gn1KSR0sumN8bKVAsKPMqTi9fS3uilDVN01+9fIaAQ5uLs5FuwdmeCmuZJsiHm2/G6KFticLi637P+izS8+cj3Xy+Sl2TvHtkNpN8s9T99sUdJaQ7/I5UEkDfpRhoMFTm4PwuK/HgXjIP6eb08oFpK5knPBoVUhMSGOZ49J7ypOlaSoP4mLOfGWFNPL5jAD3iAcd6n8WCZXP4qpC1e6Q1r7d179yke+fqS342GieYmu5Tjm3B2IOrGtKtj+toFVLrrV7+8Ie3bWc+9KEP4UMf+lDw+VlnnWX/XxQFJpM48rlh+TZOZColM4FHDMUx6v7jk04GzvtB77ODqxNc8vZ/wo+e/QC86MkP9j6j9cu5QmpN1kKSdrjninQTZySGjtpJvPCzo+ac6MRRSRSaxIQ3URyZGPo3jaADtZB9X08htdWkkBr88UWy1JKNlaSEZvOs6TZB9/Kwyjq+yc5K9NlYIAgAX9y9F7v3HMFHv3wHLn36w9Rj9KG13b7vCJqmVUc9cUu7sOco63JLUbK99iSCcxarB6af5yDdS8MSK+Npr5ZhUn3nYlViNJn2ckhEpNs4butAJzGJxeWhC7opvTwVdNP7css9h/GnV9+EbZuGuPgHzrB/92q6hZZhfejlXF+A08XtdlrQbed5KkiZCl7kQEvft9wyTNPWkIJ+StmOrUf02sUC07VajEqt0ZVnQbqNU81ZR7EAPle9vCwz1syqwuriMnDwMBa//3GW3io97zFmjop092yjFke64/uil93/v/6+Se8CFe0MarrhKOfa+5sLDtTCHJqyGGBEW7WuTMJONL2E1ERhPnQ/CwBt0lhr/zqLrzAz0i0kwZ7/A2fi2KUBfuIxDwRAhCkj+2zEucV9vqaWYdSqCuPlTcDBQ6jPOCNaempKvk7duoTde47g4MrEJldo/BJjL406Nfvv+T7dDcuYm3/S3ySqyIbNblIP6JTlBB7G8ZG2uW7XXvzzLXvwl/+8SxiPy5w6IbUEvTxSpzSIKN8ao+rgMWShj1EnK7ZA8rFbupJBlsl3vLYImYuZE2dSkO4IvdxHuuH9rMr7Rkgtt2XYVLheQMb16fn8z9J/VrP9PXp0A4ma7qC/tv+7Q6pSiAMPbPTtTT339uOWbWkHfc7ynUs9yAb8WkJ67jkBGx1TznxlVNgPRtg1gZCa4IxLomEpkxJaw4y5dlaj6uV0rk326RbmDoOa83nTm78s4yf8PCqkpiQeODJq6eWK027f9cz6Pz7+nBpIygDykgtKsCMF6HTbmMYITdjOs+yF2yiCdmoss1mSk7vvbUtVTtvmlxnG1hm6vvF7SdvVJZHuzhwS5px/EemOIH1asNxHgBBwQS5fuwEkhfN0pLv9KZXiSawa/96Hz6D119ZIL/cSl7nXR1DqNrY4KK0vJbJEFEHAdqz8GXf/5/pBlJLPywDX0umEr/H59HJ/fACwddMQP/2DZ1nNC1vmkNFGtyhCNiGQoJfXNXDllcAVV7Q/67gvHYsV6HjMGvOAre38cHB1Ivpvsflio093Z2ecccaGWNp3yBzSnX/9cwIP8yJJAYJZ2PhnlGZK1csPJ+jlHC32xtoD6S49ZGFtyJIo4CL26XbHBqhDi+A7YluE2DDrGuM9ewEAw3/9MnC2U/WVRGf42H0hNb+mryoLh8avYyJMq/Mzxp3WvhniFKrHzVyPmPJnrjkRtXQ9N5Co6U6ct13YEk55n5Zhu7t67tOP32T/Rv2fbOdJcVqNjUnbGnru5msp9fKcdkpmDNuWF3Dn/tWZhdTMZ7ME3bGa7vWmlx9TO3p5ioInzWeu5pc7s2Ew6gsTtT9j6482h3OWlqWXa4GIoA5fJObR3GCAdtAQ6eXmWQ2C7vanWIdckHKnUaymO12bvhajzBotIOFr76DsP55vdUH3GWQ+AeLnJgX95m90qLkUfFNaQZHSShCOjSF9ek13P/RfE2Sj+9bOxUduW9+qKAoxKHPjC+essdYyzHyuKKy7feadc24Zh/edSLvboiiwOGhZS1LbsEB7I1LTLX1m5g0qPscTa2uZu/uCB3asZE7QLEtc2ANe/H0DkXlx507g0kuB3bvd37Zvb4XTFNq4eZckJoUxqrHygGPbTikHVibKmqnPFzah/L1OL7/55pvXYRgblmNugejfMiwHOZICBPsZo8n6LSBKp14+mthFQ7I4ZSqnpttdg3m1DKPOQMwBocIcAA2Gm2Dcknq5Gkh0k9/4ab8EPOChGP7aq4BfusNOflJ7FTd22DFxsTSKdJvT+U4i3dzBnZ1enol0zxHdd+3C+iHdYk13UActB7LSd6P7iQbdDuk2ZlqpTKZN9jXi90wbO/9MqyflliMyZc5z66Y2AXJkXGNST8Wym6iQ2tR/XnsF3aR1obF594WndnhskO6BPfbBHkJq9FFxidTGm6slfQhRSC1a0y07rxzlMPdKVS8X1jrz2q+1ppuehxhIK+inJARJ1zObBJ7E6eXryTqK1fU2JNlAzQYjsSRfXQNXXw3cfjtw6qnYde8WAMDpx/dAuonTbT41h+Q1vzlBt5RwknQ8YgyIlHp532RkLhXc+66wDg4qlyiX3D3JJxgRlFJmGoTsEXmc4scAfLFCoD/SrSVdl4YVVsbTqB6C9Dtn0/IEBkADa+evcYbDWubuVLmYZjHWp7FBQvsC8OcgWaNCSMTt3AlcdFHYh/bWW9u/KwJpml4ENRN0L1Qljt9Mgm6hPDDWBnKjZdiGfcdNqzWLWU4rBCPgJb3Y5iUb1frkNqgc0j1t4shzzPlOLXRcHTzV3zHXaLa7T003b89Ax00X32ggYSa/3btbRUgAg+nETX47d9pFN0ovJwiKOQ5d6HJ7ha/FjBOk1eBwBzdo95EYW0p9lds8a7r3HzH08jykOyYUlUa6u+cpgXQHDmRkYd51r1EuZ8hUX+cyIRqT7NOduHU5+gfmmFsJ6+CQgC4CeUj34gxCarGa7vWgDUvq5RTpTgqpUedcQUPd3KwgwJHAwpjmvPLkwFBAJKnZObmHNoZP000/PxLNmX7OneFoHXLhgu4Y0l0WlEWgDnFm8xWsGe1VcfKTAcfOncBZZwHnnw9cfDFw/vnYdfXnALj2g8ZinTbs81OFiBy9/rnCn6tdcoM65dJ8Nq79570R7nVQ0mXLJPJu0lqQbq3cKJbkqoRaX2/uFdaCJL08Q0iNn0J+TXcX5CoBv6n3lXzHGJLsuuF046NBt71+7e/UvzSnaP7Wt4afWti6M+97a5lPqVm/tJQ7I0xq9ozUdYtwS++W+dtll0Gimo8VlhQ1wzzbsjSwIMXB1bEopBallx+lQXdvpBtog5/Pfe5zuPnmm7GyshJ8/jM/8zNrHtiGheb6OOcH3VlIt+nrKQbdpoZDrtEzx6DZ5oOrE9sGgltUkTMhRERPgfbpXivSTUV7YllpPnYXRML7TlUWHtKvOots8ht3LdsGdd3+rSiAyy5Defl7M8YUolM1eV7Kxt9+Pcw4fKlF3YyhDz0aSNPXucWU6PuaQbqPzUW6jZDaDDXdk0gSjFogVtUT6Qba92gV/Z1LNwaO2ieQ7pSQWpl2ts112bRQYVgVGNcNDq1OvCDc2NQ6mu12EyHrP6+abufUrb2cgRullxs7tDoh71wcvfJrP918OZk26PxdH+nudif2vZ0BmbH08m48w0j5Bf2+V/+XCMS8hEpGDWTbatbMmeHnPLldsvmr3RZ2W1vTLaD3UgnTLErJKaMJFa27gYbgR5PC7LNdm44HAJx+7aeBRz3b/j3WaYMmqhwbyw8wzT4GiaB3OnUq7TTJKwUp0nUYsKBVQ7r7UoUlQETqqe19V6FI8yS/NL6cZJqxfCE18WMA4TlkM6QM605FujsARWCJxFo70kTCaDIV5ytz/WnSzJwHVzafJUE/K9LdR5gyq2VYIb9/Aep99dU+pZxb0wC7drXbnXee99HExgP6eIyvtGXRBd0+vZy2DEMwXmMbQXdnN954I575zGfiX//1X8XPi6LYCLrXyaQ+dynLaZs0C73cQ7o7WsvmhQqHRjUOjyYAFsVjxRy31MTn1UxXxdxqumltTay2h1PjuROmOTVq9p9NfqYH4sK0RVXN5Fft3evtnxo9JneeaCuJKdt+PSw1SVoH1yQAIhlsyaRMaczm2cLJCallIt1GnXki3TP+LnGauHkfU7V1+Q6QC7o1pDuTEpdIlGiiLTlZfYAIGGUETVVZYMviAHsOj9W6bvNMLVQlxnUtIt0LMyRnJKQ7x0Ga1VbGTkjNXKODKxN3fso7J5XLeIGZgNp6KrgKEq5ZCuke2pruuHq5hECm2Dq5Kr2p2nWNjcWTrHxftt+wgHTLgpfzf05y1MuDAFND+RREbN/iZuxfaunl2y//FeCnnhnoj8hrVRj0SZ0syjKdYKGIfgrpDksdaKJJXlNySt34PunxqSWRbmUOzVEvn0xdiUhMRA9w74fmP/ZhGcWOE/ueNv/HlP9DdpX7/5TM77wDBdcwcOfnfKMB8+VmSYSt9Zrk0Mtj67PYAlETUps2bYlIjgnbjdmzKZmhl29ZHHjtLaXuPLH33CSHj7aa7t5n84u/+Iv4yle+EqiUbyiWr7/F2lJolqOUGhNSG1vUTZ9YzDE2kRdMM/M1qeY7hcrTeWdAnZw5It2xBZKPXavp1hCSYJ9sUpt0jsuA0Xqq0Yp3HH9MbjHjzhNVu19PR89YWtQJ3hj6I92hIx6zeSJKFulezq3plhE/II1Qa+9csB+uHK5svzqpceeB9hk6nSHdfZ+LkPEiB1eA7xjnoKRAnqgTTT6aspYDypxjKeSdU+fXdHefdR54L6RbKHWYp1o+N4t0D516eY6QmtQKZ9VDQ8k9iiTwgDxkxgQrwXoRtAyLr0uSaKg5rvZo9K3p1oSHaLKSmtSnm/Z5jpU72QDAS2ioQ5zZOJXaH0P7O1971QBTQcR2bXsAAODEQ3uw+aYb2u3MviJCahTp4ts15FpURbyLCOA/w+b9bfcdzvkxdNZpM7Br0le9fBo+r3ZfCb8muE/dmMy1kaZM+myar8dYDvT42vubQuTFsc4p6F6MAChBzbswJ5nrLrY4ZEH3ZDq1z8dckO6Z+3TDG5dkOf673DZWHs+kboBTT80an7Rdjnr5ISnoXplE5/QNennEPvvZz6IoCjzhCU/A05/+dCwuyojmhs3ftKAuZjk1ITFxBPOSjeqpJ7pD1WXN37YsDnDXgVXrIMbOQfIRh4nsMl0MyqLA0mA+Nd1eXXRk8tXo5TyI5M6amkFmk5pBugdTP4Aol5aA/bVYK0QdKR7cU0fPQN33Bb1c79PtO619F/CJ3f99j3QfMOrluUj3QBdSS6LFSklHej/y9rfvXUHTtAHb8V0rEmN9VVtTx/T7dIcBbqrzRY7IFK0N3ZJoVWjeOYtmd2Oi3Rfmp16edpBmsaZpXJ/uhQpL3e4Prk4spVkr6bDOqBZ0J5BusaY7cg+HNvHgPxecBp9GugV6eQ/EMKYLYj6iZUCSkBoPoGzdqOLYx4Q9PTQqo155VqP3lpd+aBRfNcBUELFdW08BAGzfd2ewHRfz9I/vnq9pw9YqinQTxE67jzTAHApOfIz1ICXeuE+Vordzi/fpbp91DR3Uapaj6uXknCfTKaqyioroARCRRn+c+r3TxprNkEok7GK+XMDYEO7fUGArcX/NE1Jj5S59a/ipzUy5zxFSy6KXtz9z1MvraQP8yI+0KuW33ipnMIui/fxHfoQdx7X1iyU1D67Qmu7WX9q/MhHZSzH18pRG0P3VegfdS0tL2LdvHz72sY9h69at6zGmDVOMt13Jsax+lwqFvD2mP5FzARw6iW7qkOcY0t1EJpqUEBGntC8t6MI1fYwqEceRbn/h4FlFTSxEdbLY5GeE1Bbq7vp1k1910onAt+9U2rDAjoUjyXY8RWE5LXPonqVavnq5P05jSaRbmLRjZhGcOQRB+4/0Uy9fiAQV/G+aU8jVpVP70ZDuXXuMiNpysK++qq0aKm9/VxAmrQ0TN7cIR8ZA6n03J4Juh3T7zgvd/yxCahJys1413SvjqXV2Ni2452/aOAG5VKLLaxk2ctdq8o+fAi54ClBVHt0xJqQWU6DX1hsughhjgkjbt+PqxpFR0609z17tcCmLmmm0T6lPN70msaCbbre+9HLyzgX9jc0z639HHY+CiO3a1gbdp++9M9iuEp43YzTQqad87SRBd0m6iKhId3uN2x7P9B0Mg69Q6EpONFHrIzA5nZJEzgwMPm0dNI95rKa7PX7702c5CNd/Gr5T3jgzKNZ92Wl8PDPRy5WkBODTy9vfXcs1XtNN1dk5Ct63hp/a7Ncknoig48oplylLed72dFam07YU5C1vabUaisIPvM2z9uY325IRY6mkjrEDq2FN98HVsfUNRJ0O4ZE7Wvt09z6bF7zgBWiaBldeeeU6DGfDYjYL0p1DsbX0cmEbTRGTZxgBZPXqjjluKaV1Tmm3ipdK25lco9c1T0it3aZkjoGmLq+24jCTHwAUBcZVmxUcTGtv8uM0dmlMUv9XG5BXRZT2Ny8zk2Suenmsx6Zk38ma7gOrPZHuiFBUKtmQu7jlJi00ETWgn3MJpBXn/fmCOEcRhgu1HJV9SpM0SLeh/2tj5XXbNCgwi3qsTk0bw31R032YBMnLwwqbhlVAOU2pl9tHaudOjC7/Lfv5+P/9qVaZeudOzwmUWhLFAgtjxqHniVMugpgSUhOZBIwpE34nTq8FGKJaFrY+XVa05kF3+1PqBVwVREgtosDcsqn8v83TYnMHX7+MqYk3kxRm2xuk+/R9dwKnn+4hYjHkynu+2DUIEtqJd0lL8Io13ZG2hhL61v6e9pvsPsi5SgnhVJIlVdMt+Ur0HtoWkyl6eaRXtjfOyGMZsrJyA0z/GNzipRm6n2D2S9F7l8hpf9qabpIQ4lT7vjX81Mw16MuYyunoMVCYQ9RoklDSqJiwOXw6bdp2YO97H3Daaf7Otm+PtAvTS1eoUaTbrM+0BztN+mTRy4+ymu7eSPdv//Zv47rrrsNznvMc/OAP/iAe9KAHYTBwuymKAv/zf/7PuQ5yw1rrG3QAkYCP7teql4cPPn3RxpMGWDB/D7N0tld3BOnW6uXovlIZYUMLXJ4T0k1pPjHhCj6Jc7qhlrmMOSJ28rv0UoxNy7B60k5+b34zsGMHyvdc6+2fGnWkgj7YBOlu1tHRM5ZqX5Sil6eClUndbxKeJ6LUF+m2SJ7IHok7LhOW6NKuZ0oF3djuPXK7MKB/HXKIYHJ6uYJ0K+ght5x7RhN+MXo5dTwc0j0N9m+epz7BssRq6UtJzTVTrrM4cN0VNi8MPEZRuk/31CpRj57oHKq6rGx7wvqd7wew4HdCEILRWZAZvnYNK/9+cLN9vcX2MvKxPXQn0YrMjFVsA6YEO7xFJP1/Vbp7IAUOvpDa/LoqcIspWKfWp2A8CiK2a2tb033GvjsDRCx2jxzSWmBc87XKXZ92TPI5GFu11FMfjZPms2jbQK2mOyMAlfYndZZJa9U04u/OLwm/QwNnc3za1lWklytlE3ycseRjjKofswll3QkWE8WN0ctdTXfpbV+Vrs+5C6zN+YUidWtJ0JvxLQoK6jnfi5Vc2URuRk13VcpzlORnLZRF63teeGGryXD77S1j5Ud+JEC47fcyhSoPjUhNN/GX9hweAWDlIBEwaDXhT95frXfQ/fnPfx6f/vSnMZ1O8elPfxqf/vSng202gu71sbXVdMeQ7na/VAnTGHWiR0Kmi47FUj1HOfTy2FjjGWEzUZrs6KrQZqKPUVQlhrQFNUIBvVzOmifRu27ym7z6I0ADDP/yCkv5BOKBCHWk+PWj1LkGcqA7T0vSy7m6Ohm7qReK06md05Zj61LTLbSlkswsFHJNN6c7Mpo4+T0mppYKgI3lIN19KXHaGFSkO0OEC4CIsHKj88DmxS7RF+mNDEhIdxiQrxXp7ssayLUjRLnc2JZFFnQnKaONVaIeDdwzPKkq255w+ua3AM/4ZQyqQqT92SAgh16ulFBYITVLL5dLKCTRp1TvZqlWl5tHY1aUxFUhtQS9fDmDIuuLHc1/Ls5BuntRqUlS2IiqfcvQyy/9BWDHs/19mUS0uFY5eilP6rjrY8YUT0xo9Z6yejlPDIbPic5Oy0C6WSKH28zq5eY5FPZJ/2aDbuIHSceSWjZRyxJSC0TD8liGKdGw6LvTuHs0mTbenDRl8woQPlPmNaZCaiHSPXvC1KzXi8MSB1b7r6Ux5lDOmuL6kVP1cvd5lP5eVbj5+56IP7jrBrz00Q/DGUrADfh+SWytpC3DhlWJpWGJlfEUew63PpTXkSKSXDtahdR6n83LXvYy7N+/HwA2lMvvY0vV5EiWomk1TeMFBtzJHzHUzY0lzJpuXojXVwI6ikDHqgUaUzZJ2T7da0W6CWoVC9R4PTpvGaapg+ZkkKdFaSfK4Q8/ycs2xpw06khxpJuOZ73p5dNpY4+XUlLmSQqa+YwtWLztUMqqDGpWrpmFJLume6C/dym0gCPdmiUFXOoauPJK7P76twAAp29bCvbRtw45OXaF4purXu4QpkjQTWq6tyy2AaSkI+EF3ex+UOdqNqTbvFtEvbyHo97HzPxG67lNssGYKo5khNTuvdcGTaPK7ceIN6JpUN91NwB46tGeaFiGkzhUmBNji1p0SDe5bpJTaA47FBy0HEGqnA4YVKWdPm66Nke3D4Vevhir6fYYSeF452UjIaDkv3O2SXJ92rEDuPlm4JOfxPTd78Huk04HAJz+zB8NNo29v7RLCE9Em80t0p1IsKwqQbf0DnJ/QnpO1ERERhAmdXKR9qXdb61kZ6rcL6BFR/l+tVJAs425lBpo4+roxY8BCAmMvki3Si83pYJ6wsqs+9L7RwMzV7LQ/s7Vy6dNQ/qql95na0K6e3bByElEWw2liB9A5xYp6RVL6gPAFZ/7Ft73L7vxex/5anS840ykm7YMa3+2a/SeQy3SLamXi0JqR2lNd2+k+wtf+AKKosCFF16IZzzjGVheDtGTDVsfMy9eCi2ilqKPSjRA+pB79PI6dKhp1soh3XoQbA4nLSSa8i0fq1kcFiMTdR/zxcj0DLunBk5+JunlmjosMToR8qRKjiJsIdTzTMn14mqx8zatbyo1rqxpxrJQlZZWNpm6HqrceNuhlM0L6W6axvbp7lvTXU8bS3czRmvrJtMmuCcT4T2TLEov37nTolO7//O7gGNOwPafeR7wm6/w6rX6Ul1jauX8d6kGLIaStuNJJ6j8mu5OvFGo6fbQbOYQ0c/sveILf12r1DupPtL8n7eEW6sZerlh9gDAFvIcloWeiLVCaisj+7dRRZDu0r1sddHuQxPkcUi3PlYtyctFEIcD/30YkneeOvY08OVJTm45NYf0HmtK4prKt2sZ5v5GaaoxtI5ul8PmmNX8Huz+u2p+1YLuaOKtqoDzzsNd+1cw+uLHURbAqUISL8bqoqhygHSzhHaqVKNPTXeMnaMp1fcRmPTo5dGyubRWDd1frGWYOVYNl+yOlRZMPP9C3qEkushNS+SkLBVgLtqabp1ePqwKHBmzOcn6ECRx3/jXj/fprqfumpu5bC0JU7MWLPas6c4pucpJBtB6eSmIDZ4v9k6tdOvLx796Jw6tTqwfz22SMb8CwMHOVzLU8mOWBrj74Cr2HumC7jJvTj9ake7eQff27dvxzW9+E3/+53+OzZs3r8eYNkyxCZl8ci310gZKyoETrQTdQoBpqZ7RPt0OGeg7Vo5AGCG1eSHdgzKOdCdbhin1YRbZiKJ3NAiQg3YR6fYSBn7A4haWAtU0jSCuxXKCbj7Buix1BWDifSbZmCCcOTav2snVydRmebNrukGCz09eieq8J9uAzYxnaVjh4OokeNY0dgm3EHXofu9qd9E0WKmGuPOYEwAA22/4cvt3IpTSNzEREyXi46X/t6hDMuhuf8aeU4p8DIbt/qQ5xxNS40g3DQCEtlo0aWFt+/a2xnXHDhG5qVhbsnnZkXF7bhTp3kKQ7lgSyqJXC65VnBp0G9RH6ITQMtlgP08dT3suXJ9uN+ZRPcUy3Di8uVBoL5NDHVdV0btxFUWXXBDQa17vaUxsL0aSyE69XAoc3HY2oFxnenkQICnCTY7VkN7/rntbfYhTty6Lz10MufKYV7wUiiXlUm3VTEkZX2v6qpdT9N0/j3z2C3+muPV5bun+Yi3D7H5rinTrpQX0ndLo5U7BW/w4ud+Yab6RMSuKGynNMH4CfSYcek/o5SxpYdXLiTq7rQVnSPcsU7e5X/2F1OAdu/2yn+gdPvgxyX3S2nUxWZrQfjH+xsp4iv/7r3fiwscycbXOfKRb90sOdULKBuk2PtPeQx29XBRSk47XsVmOspru3mfzyle+Ek3T4IMf/OB6jGfDIkZbW+VaKmM7nvh/59QTWic0vuZz7aRA9ufRy62oUQTpZhnGPmPlSLcRUpOyo33McwYkB7wzLXNqJv5UfVgUvfOCbo50IzkmKqJhdkWR7pwxrMVoll2ll7MJ1tGyQmqYZAYtu6+R7v1H2sWiKFwJRdR27sTwUf/G/jr+dz9uFaIBspgoi/SEvnOf+ax957iJDlBd29pdALjt2JMAAJtGR3Dc4X3thpddZvfZt5aNO8ExurmENqWE1Pqql5s5R6SXk7G6Pt2GUeGC5oCJYpIWNOAGrOAYVfqWkO75q5e7Ht3GthA0IiYsaOs0jznWKlGv0ppuQy8vCtSnnGK/E3ZCcOcUFVJT2j6aZ3r49a8CV16JISibQ6es0mOlkO6cmm6apKT79/vayoyyWIA+qBzSvRoTUivznvFZbRb18pxaXmPf6oLuM44PRRnpvlJINy814khoyhdI1nRH+3SHiYmgT3ckeRA7L8lS58LblpnhmUNr7CC+X1+9XAdT1iKktmakew1CaguCbyb5oUEix4AkAtJtps61IN3Wj+nOIXf+r9kYsHNn6yecfz5w8cXA+eej+vF/ByCu7UJr1yWAJvb8A/6z8qHrblePk6tefiCglw+8vw8l9pKwv6MV6e59NldffTVOOukkPP/5z8cP/dAP4Wd+5mfwwhe+0P77uZ/7ufUY54YhPblLlqI2caEnDyHYuRPjv3iv++w//oINHiYC6pinXq4736nsMs+WzktIjSIbUaSbZeM58qH2/Exk7QH/PoStavTvS8rrZuGwSDcR2Jh3QGBsRGo2NSE07rS6DHZm0M3qQlO2FnEUavtNPffiIEmPNgHbcNe37J/G1VAM2JbsIu2/c5P3v99995IXegE7NXNe3jN79dVesLi7a++zfd+3UQCtJ7drV7sd/26GxajwAC9BEejlicvXx/EblAXpA6rXdFcl7UrQBPvwegKzpIVn5m+XXSaW+vQRX+pjh21NNw26XeAcc0o89KprT7g6cKj3uKosf3X6Cy+239H0IYB4iYAYYOzcifGfvR0AMPzrDwDnn4/iQQ/CoAj3DfjPjUxFlI9dRwItuw1Dv5zwEAmku/+q8zi5vTSZtLzQ3odYn26JkTRPo4FX02hK63KAmTNP7rq3FWU8/Xi5rFCjzjdN472P/BrwpFwseAdcTXeIdIff4wmgudd0J1DcGOOKXhenLTH1ttcSlXxNH8WQ7qnuX2j7kyxG1Y+Zdp2N2ZZhkZpuCUmmImvmMpm/New9pr24awZglT3uNzdzbgaRzX2vvfurJHoHd7RBcL2you+HJDTEPt2JkjC6Zv/D17+NfZ3gGbdc9fKDqz69fAujq3tCahHfdiPo7uxd73oX7r77bjRNg2uuuQbvfve78a53vcv+e+c737kOw9wwAGJz+ZQlkW4tI9pNApOVVbdtNbDBw6Rz2inqHhPDMKYt/Dlj5ZSgeQmpybS30HE2w3JCavDGWyv3h9YCpnrMtotHPlJOrydHUKZkQaoiGcU1W11j9KnPAAAW0KjIrIae5QqpmWt0nyPdXY3SMal6bhKwVc0U1bS9DuNq4AVsKtLdvXNjUn87Ji2deOBtkP9FSp2+3c9U7956MgBg+747/bF22/VVL08Jqa1ZvTzjno1JwBsTb5QCHY6CUEqelLQIrEta1PfcC8B/FofVfJ43bkfEoDuPXl7S+axToh5tOdZ+XpeV7c1an/uD9juBPgSrhdbMzM1j/kwfaZ3GQfdO4NZbMRi1z/mItdWjCRoa4KfaSOUh3e6ZaI/ho4vtfjSk28zjctC2GKPIkmA/RZ1ei/EkundNWMLBWI54obFdXfvB04X2g4COENP7MSjL4PniqF8+0u0LgEjzB2fvSc+Jrl6evibcL+FmzkkuWXP/N8HFlL1z2pTJr1EMiaTPqZYUH0T8DL4fd5y8BKOmd2NsKcISMc+IpL0haSWYIVG9G3rsejoNqPt9avi52ZruYRw04mZp4U2jJnoHdbuuTQ6vqH6V6AOSXaXYCVSDZFw3+OhX7hCPk6tefpCAFAC8tmEAF1KTxwQcvUJqM52NeVg21MvvW5OoNCmz6LGSweNB97ieesHDmCjdjsqhnRjq//EnAPyAybwc3ImixgNXf6xxp5WLX7ggf7qmZ48qWcZEzxxFqhsvQ17MNQ6cGnKuqsMYEQnLRbp5fQwdTx/Hqpd1lKjRJT8LAFg4sE9FZgNmgF0syiCBIZlrGZapXj4n5NEolyfbhbGAbVi3wfqIKERj1y7U97YBm6WjMVo4rbPlATtdeM15mfegnk5bwS9iBuk+nQfd3XaxvvSSBfVhEQ2ItfTpjtE6qaMcpZdHGCxuLi39ZNDtOr2O2mR15I23/X98rp3VLL18SGq6iSOThXSbW7FjB0b/9sft55PXvR646SZgxw7iiIaJvr708noqryPDzolE09j3YzL2753WpSOFEMfQzWDf3b4kOianoBszv3rbEufd0P+lJDAN9vsmuvoYLxfzRadgx0Ctz3hMTffpSXq5/3evZKAiqOTU+JL+2JJIt3HI2TPC2V7SPqTnREW6M+bFFIor1Zm775KyrIE/f8QACvp3kV4+9f3xHD2UWYTU+lOpE0h3VEit3cYTMiT+JGetcEo7bYNo71llAvL+HSyMmeu1yJImKbO+23WfVxO9Jkk5QWHZadzoNeBitYCwRnO9je65OW1by1758BflNTC7T3dXXmrWZi4+6/Xpjqz3ls1ylNV09xZS++QnP7ke49iwDLNBxzxruoOgu/GCBxswAC4AbxqM79kDwJ9EzcsRC7pj9PJUewTnbLfHccFGg3HdYGGgLygxo8F8TDWV18QFwmBK1pwG4XXTiC+dyepJCZWY2ARVVA+QZIJ015F9zGxEsGt08oMBAAv1xCGzRLBLOg9PzKosMK2baFLAiTHl3ed5Id0HLNKdmC5ZwDasJ1gZwktcAeiQ7IGPdJN3bkLeuQkL2HH11cB557Wfdc+oeQ/GddMqbG/f3t6DpsEuQi8H0EYO27e32yFOfZQs2vOTfb7efboHVWnviaQj4dNZ/fOkpReutVwTJC00q4cLAKZiTfe8gympTzdVmI29D1Q8yNiI3qNHfR+MwJ+HmLB3lfppOWq7fB0xz/Fw6gLsoWGC/NM/Az/5VPt3SRkecEk7vU+3nPChZpM/3b6lQFoLEFJ9umkSmBsVdVqv5wQQmGvTKYDw/lLrM0/u3pNHL48j3bp6ubnHqTEZRHRxKCdmPG2JSKKwVlgNs6iXawFtLKlB/8aZT+ajFDLtkO7wmvPuNVHRxSp8vrmlqMqaJYPuDCG1YYReXhYhm48zBWjdNme8zAXpHvSt6W5/VnffrW4z6ObLSVWpCeGanKekF5FKlJjfL3zsA/GHV34Tn7rhbtxzcBUnbFn0tsup6V6d1NaX1ejllUQvl5Duo5Re3jvofspTnrIe49iwDNPoyzFLoccjLqRWT72Xe1IJQTc6WiIbi0W6I5FdjDI1a0030FLaZ305aTAfu158EeQtw7SMN71GWhI5hnQ7wbZwTA3J5nJ0IFZDt2Zjta+jTpxpoR63fyuKFpm98ELr1JvL0LAkhaNchu2zqPVtGWbVpNd4zvuPdEh3KuhmAdtCh+qNKz/TawK2JUpHI+/cuGJINzX6bnbnZRzPetq01/otb2mTHkVBarrvdBHGm99s78laa7pFpozw/5TTZcy9U+1zIjmcdF85QmpRpLtkQmosaRFYl7SYHHMssGevWNOdyxrItSOj9tyokNoxXtCdQy9356K1FvISESxY91ptxZBumtwgz+qII91w1MnxXXd5+5hMJu7zK6+0rdo4s4ibdC7cePJHSmhq87gVtJSQ7tKVO40mU9RTv00gbeuTQnHXYnztlQIU/krlCruNJlPcts8E3f2E1Lg4XqzcA5CTRd5YNKRbUi+f6tckWdOdcY80hpsbU17QbWuWuUZMsqa7PT8OdtD2m2MF0ff2dx8g3ZrvmtOn2wipqfRydp0tAlz6/povpMbngX7vZNM08jqcYXbuOOkkdRtTojYpKzUhLF2DqJCaUtP9sFO24NGnbcWXbt2Hv7v+Djz/3DPF7aR9GqOtO03pF6eXD4U1k1+zpmk26OUb9p03jXYXsxStWHSaycs9VoLuiQ263Vg6HRmM7r63dZaEGpS11HTziXuhcrTklfe+Xz1mymgrlRgVjNedcUq0VtvlOV+J+yAtSpyKJ42pLMN6Oq9udd51hIxKbdoQLXSUUS7Y1Y7Rv7+U6pmTae6raTBvpDvZo9sEbN21NqiefW+KAjj9dEw2HwOA9Y4m79xEYpcYo9t1z8wSz7B3tbs47TS/prur3ZXZB3nXKObAtmNqxP9bByhFLyefa0Oi74ptUziaBEkpKenERQaDhJRJWgBhdEKSFlKpz3rVdDt6uYx0cwVnatJ7RZ1zWs9HA1JO1aTfj71+3jvnPdPdelFTpLtDcU440e1g505Mnvb0dtsD+1sl365cJZU4zKnp5olbyUnVWBmuptv9jZZNeElghthRqmsqebAWkwIvOwaG7hmLrXnUbtt7BE3TJrtPYiiYMa1G0ytPKHz18qZp9GSIMqTVjobcB+nmQmV0XKF6eX4AlQooYwJlXtBdld52Zj7T3je63tMgxZj0PsRYMa6mW91k9pruCMMRcPdRKs2wQbehbwvnRdv/8TnL9n4XkqLmb7Mi3XTzmft0P/EJnt9Abdj5s01RYvqkH47vpwzZjtJ4Qh0Ws56W+Mlz2jn7Q9fdFhzHZ4goQfeqaW9Z2XeRMwS9lmEKM6Ytj2j/v0hAiKPBegfdVVVF/w0GvcHzDcu0lEqmZCkF55CO1njBA6WX2/6uRYHJSa0zb53OnTuxcPG/b7e79TbPWaLG0WJprHpNt08FKz7wASyNWoGelZdeph4zZVTJMpakaNjCwZ0wtU93Rk0372NLLaZ+LilXmnH4QUe7/dyQbkZ1Ms8GRbL4dmqbGEV1k1vsGknmFGjXhjzuz6WXs4DNXItRNfACtkl33g7pnnrv3JjWdJd+wG5o4e332H7oee7YgZVvfBN3bTkeALD9f/2prd2l1jcxwRfHcbCgyxTfVH2iMa8UI5F8q8oCx3Qq3k3jglO+XVlISLcr5wgcfJK08IwkLaT+vutV0y0LqfVEusl981SOFTYCV+qmDCWN7goQjYDaf6YdvdzdI0svf/Q57R+MeOe3W7qlDdC7cpXimzcE50Itp6Y7rPP0/86vAzVLbxec/qosbPILCINuzzFeR6Sbr+f0GFoyoaryxmNE1LYft0l9BnQnuh1XWfjXoN2Wvqvt36TabGoO6fYdcil4irVo1Pt0598jTXgvHJNer0zHFyi6K/ulCutSSynq81HxSc1yzjnV81n9npCkpBZtGcaF1KRnuiAJNAaCWJCk+5wmpjgK3pelRN83L4meYXZNHAzURG/VkASp8s6Zw+Wql/PncEwYhD/+mAcCAP7p5ntx98FVtl26fOcgaxfG/w/4z4CmAeG1oP1eR7ol8bQNMbX7xlIZVclSTjXPjI/rqRc8ePTy0gUPk//wfADdRNc5Swu3t9kxG5wLqsscLfbGmlhoqRq3OeZyF3QfGS6px0xZLuJK66fpTwlZpuY5GAmURspE56iXS4rD64p0M6qTueeLk7G6naZertGiuPWll88P6c4UUgO8gM0IRY3LgR+wsRowjxYO/52bkJZOlBYOhDXd/Jm97UAr9rV5ocK2C87zvmvMJeXynI2w/Y7/PQ9FpZnxBFXSGJ3bVLRy6p6DpaFju3CKOZ0vTXbdOI00QSYK7u3YAdx8M/DJTwLveU/7kyQtpLl4vWp15ZZheUG3hF75/XzlAJIn6XLLAzyEkDzTAb2cJKXGTQFPSNCUPjRd4GqenSuvbMek1nSnkRh+HrEayEBwTBJdY4lD4yDytmGSSN260MsjSLeqXp5JL7ftwo6T67npvjWk2zwfXFyUJ+VSlHeDdHOHnAai/NhSH2XNp5pNvTweHMuaLC7ZwEEHc2gNHaZj5MmWdj8hHTimB+To5ZE1OMJgiBlHnbmZhJXU/tXRy0OhMqqzwwETrsFjnrlV8o6sFemWkibZ14SuiUqid3DqA8RjUfOuQYZ6uSZ+OqwKnLZtGSduWUDTIAy6abJDcRcMvZxSyjlYMaSJaiVJR+exXA2f+4v1DrrPOOMMnHnmmfbfaaedhqpbJBcWFnDmmWfOfZAb1pqlgfRSL8/LGLtjdA9/NwmMl1zt1rhywUP9uMe3YykK6ywZarENus2LRFSXOdIgjVVVnq3dAmWOuTRpA4sV03tWOGbKqDMSGwOvR7d0pm4yUmsBya9Jevma1Mu7cfJM73oo5jIqtVfTDYjILBctog5wToAcE5uTbDCnmm4TdCeRbmNdwLZw5hkAgPGb3uIFbMYZWuItRrp3brLknNpRNRRp4fR7S4IzCThxsWOXh0kxntlrutkC7qGNIYqa0oDMKcWgz01R6HXdseeLOtvqu1FVrWjdT/1U+5MmPIR3fb1qug93AdzyQn/1cqlOc3USOuOAwjxRqJqaOSG17hjmmd60GYATBsL27Ricsd2NzRNdM1R0Mn83Dar9+9pxagF1BhLj5np45yNRxrV5XKSXdx8uK4idWHu6DgBFgHTTEg9TV86TCZlzgEG6z1Dquem++blxFhidB6ZNEwSYyZZh3bPBSyskVHls50m93nseNd2zIN00GOaUdvfMyMelGjhy0E2TCy6w0swJqennvH413Rnq5ZKQGgFCgpap5jP2TNGAjpcz9E3Q0+291p0ZFqyJQqJ38NV/tdtLbAZ/P4XTnfCYHvF7NqJgRl1joQNORtd8zvOhtc4k1MwafIyHdPtghYx08zE5Zkyfctr7g/U+m5tvvhk33XST/bdr1y7s3bsXL3nJS9A0Df78z/98Pca5YQizxTk2SNQZBi8ko6iOH3W22/YVv2SDB7OQDfbcY52lRRN0D8hLxmp7zYQuBQGDhJCaPf+DB+wxlyZtNu7IkNSYCfXEMZMd8HBS4fTyijlsmtBdUbh6IxXpzhBSkzLl1pES0GyxbnVejh6jUhsa9EI9VpFZTi8XkfhYlv07hHTvP5LZp5taVWG49VgAwPj7Hi0GbCIdbccOjM9+tNv2118t0sLb/fi0SU6jH9kMtn69ZlUv13QGaLJKqxeOWU4phjlPc38N6st7dVMhNR5YU1ppLtIXGwP9//z7dLs6OWObc5HuKpw7/NZCYQBOFbZDqmb8/g0lqvKOHRj/QNsDfOElv2hZA8PjjwMgiHd2c8lg6idNSyPqpuQ0vIRPag0xiKvg9KXo5R4VvfuveYZc8KDTy3OFy2ax0MGWyweo+UKf+pi+lWgXRvfN98ODLp9e3gQ1v8mWYSrSHfoQE0Yvz0G6Z1Mvl9/DnJrusnTBV83eOS3RZcfYNPadLgvSekwIvLKE1CKsJ55M6Y10q0F3Wr180fbpdp9JwILt0608UxRoMt/pU8NPjV4rqeY8ZuKcyhK9A9ImMoV0V+WM6uVmLfv0PwJnnYXh7bcCAMb/5Re9cs0c9XITdNP1Kajp9hLV/jkYO1qVy4E5Calt3rwZb3rTmzAcDnH55ZfPY5cbJthsSHd8MompDwO+4zx+8ENt8GBQhWplxX5uM2SVEJx0ThVVceWWclqtozwa2b8tjdug2yLdwjFj1jSN54zEMp58EuctbGL9OnMF7WL0cql0gy46NlPN0LzcoLa3EUrUKkW6FWSWO2QivTwCEk4i10iyeaH7ll7eJ+iGG+cqp3xaWrhcA+w5Sw9/hEgLp9tpSHdOizWz6PXNzmuB/pgiTIJKdi5SCqRLMcy2W3oh3V3gRiihs6hJS++6ycjPvabbIt2yenlMSE1SgV5VSgCo0BZP4GmU6+B4yvxpk7RPebJlDZhkwbhumOha+/cg6O7qG7XEoedoplCh7jSkmvdaSTDYxGkjXLMA6Y4IqWUGubMYn2u8a2IT3v53aCAUmwd23+tqujVL9ek29eM8ucavYxrp9gNpY7xF1nTqUHRXhhOWV1RsjuxX0x0PaHPUyyWk2zzC2pxpr3XtKz0PyN/tGDMS1k6YTd0kACNy5zpawifZMlnD+JrC68Eb4f3zklnd5w0JRgE3d3lItwFQ1kgvr0pXwtS7d3lkTqUCcRq6LFHsffXyVE139+z8xquB3bsdY7UceuWadK1Q1cuzarrdM2iBGLa/o7VHNzBH9fJdu3ZhdXUV11xzzbx2uWHM1qOmW+zTrfwutQwYblqyfzNqtGLQ3TlVMUXOKlHTbc9/yQXYyx3SvTIU1FQzeu7Sy1IV8ZruKVsE+QQXuz+poNf1YA+/q6GK9G8lyXJyCq2P9ImHn906StTol34FALDwQ+eqyCx3WmthsYhRc0f2Gt3HSHeukBozrYWeQ6jlWmy/7ZY+dqtergTvLlusq3/OinRLPUnraeNRb6kKL393NKOPf4pebhxISy9fkYPusnBzixUZJMiPQ4yiQxP3TZ/F9a7p1tTL40mVcEwjUjdZK0go/54NGhMJr6GSeJASQOZ6jRXRtYoG3UWB6pgt3li45SDdYe1w+/eGOOpagkjq080DLhPYBTXdJDigTva8WRGiMGpnqT7ddJyS3X2wTXafcqysXA7ISR6634G97jTxECKhUjKEmpnbeNDNkz70/G1yUqj3DpDuHp0INAaBG5MejElstAl75zR3j65v5noMKxd0jz0Wi0yj98eZnr9Smh6apYQ0l8jcxvvcx4TUaI9qPn7z04AjZu4y16oo3HPGkzW55t0/pT5ZMm9NTPjzlgGaTCTmqZeHPn4H5nUaG1YAdjD0yjUndN1QxpJT003fNa100iYCIr7L/dV6S40/9alP9X5vmgaHDx/Gl7/8ZdR1jW3bts1rbBvGzKIFs7QM0zLGgfCKjnxLQXf1gFNsX1uDdE+qAaYoUKKxfW1NbW+MZmQXEa2m2xzz+OPtMV1NN3EE2DFjRs+3qoroAmmzqgYlsUEkvHPjSqjt3+JIrhmHdG9j6uWikIgUdPdYEHpbVWH04IcCX/kKhqecrCKzsZZhWUJqUzMR90W615Zp6CWkRswsGPwdM+fvqZcT0xJd3BzqHEe6F2J1fD0TE1My9n1HGJ1cYc14ia2Eg1F0z3E91Xu2a0j3oZEmpFYGAbH07PV5Tlz7Rnc+s9YFpkxSL9+0UKEoWp8oXj4gBN1KUsdSpYX30TKUZkS6JaTN0mDrxpWrXHQR6m7+MElck3Usf+xHgVv1tSynpQ1Xq+alBfRrPBAz07qEtPGgm9emUho6TVzMez6OqZdrYoY5OgoARZd1R1gLlnnQx49JheaAtC+wOpHHwt9zev691MuV5IFka0G6KcWYM8FSgSp9R817vDgoA38EcNcx5jvmMH5mrelOXSOaPFkZ1x46ao65INy/WI/qACRhSDd9D3JE5MTzImBJn/mfgz0xq8oCqPX74pUYCvcwJX43PtICV0b4dYEKwAK2XHP8jW+q+zQm13QzITXaMkwBg7Sk2tFgvYPuK6+8UqzHNQvRM5/5zLWPasNEWx+km72AEz0rNpIyxMRZWpg6p3c0GGLJiGqR2t4Y4pVKEEynZOHojmno5bamW6kn1oyuqVS9XFQKZ7RDTo1xC0t4nFjgDNAAKRawh9+NBdb0eZmFQtvHKL1Ns6BlmJTlj8CNhq6cj3TPh+47M9Ld3cuwQ4AfLIeOTEh/lMzuxwoEsZrujLqovgFniHSHiTg+dnp+KQfDbFMjEnQrNd0HV2Ntmvxk2lprbKW5WOoDOw+T1MuLosCWhQEOrE7y7i+ZdzT1cvo+8muSqsk0pl0Dqd2fh3QDtlxl9Pq3t58bpHv7duDNb0a59VHArV+P0MtJMiEhpGaGUbA5iT4D/FwlVIaXHC0rSPdUmKf58eZhOUh3oF5OA+DofJNOevJWc3a/JAEGMEbLNKSXp5LE2tzGAx/6HEhlOJpP1SeASvllMb9G6qLAke4cEUyKdFuUX6ADDyPv70CYK4LxJgI4zWICugCs8v9oMlX1ECT1cvr+mVML2DmGXt5tYHyVSpi7+yPdbi2K+WncYvMMt0FVAGM9AS/Ty+VjtWNmv09qAKVt4bhAkW5i4337AbTMVu0ZMQAFZWINqhLLw8rOiVKiOqjpzvAn76820xlJbcKWlpbwohe9CK9//evnPcYNg0976xN0p5zqsIY7E+mmtVCds7Rwysn281VFdTk2+abELKjQjzmmRRZMTbdST6wZdQ59mrMUdLc/zSKotwwLX6tU0Gvp5QIq6Zy98HuUWsSPISmbr1vQnZGZ5I6URM2KLfiG6THMnIjnUdM9nTY2e9u3pttci9FEdiIC9fLOfDGyNNJt2q0E9PIMIbX+6uX+ffbqzxXWDF1Qc/IlXFCIG0dNVHo5qdsOke5p8Bm/1ON6ik/dcLdFmr0xSI4ba0s2LzPHp+rlgKPw9UG6J/WUOWSEXi6yZtrPUk6zMe0ajC2TJ7xeXuJ3xw6M/vCPAQALD3+Y16otzRZy+2kaJXE69c+D11fTZy5Auovw+CHSHRdSaymt5O9zRrpDVo373Zya1goNiCeMxpP0fMI7aLj9+tfJExcVyk9SQa9pLZVSL6dzooR0a9TwPsJaaXp5zKdwc1TY8tP/vrbfejr15vqhPV4/ejlNtGmtf1NUZc00RgG1pYF5d2S2hoh0m2daYgqwpIUNuidu7jeWU94WO69BVfbaB10TU+yvVEKAUuylBLLWIszYuGivq6GVB0i3+d6WY9wxlbEYMdMtS/JaBfgtw7SE9+gorunujXTfdNNNwd+WlpZwyimnzGVAGybbxHMG8h/EFNoXBN1S327hMzOx2Mzpjh1YeOYzgVd/tN32rz8EPP3JAdocpZcn6qgCdfAdO7A0eTBw7a048vwXAG94eUspz0C4+XjMfmO1PW5xbX/nmdWYWEiKPu1oSv2Q7oYsOlwIhQqQrKdiLhBH6o0Z387SvwSkO0tILTPpNGudFrWDo4m9xvOq6R4zmma0pnuij91spwmpZSHd5hr1dJ4WhTryMGHnOz9A2sEA0igXr+nestief6BeXru5hjtE1FEuFWfp/f+yG6/c+SW85KkPxSue8QhxDFJN9zzp5ZPaOdSbhv68ZpINeUyGdkzBsygGIOE9SAUWxrRrYOnl5HqZuYJf91H368JppwLn/T/277HWiXSM9pjTBgtsvHz98VBnQnMGwnMtheeSI+dG7C4IuimN2KtnnnPQHTjY6QCzLAtbqhBNemaIcWn0cgkwqMoC07pNdHDGQAo11JFuf14yz1ZRyKre2ppd9ajpnggJJX9MOvBhgQRSdmBEamP6N/5+KQuhRAPfHwHk8g5unop946j+0niN9a/p1rdZGlbYvzJRE1a2ppsMgZb8heKP7TY8wWbbUXkJ0xmRboFenqOb04f9lRJo8xlK4f5dGVqJ1ck0LEMrSgBTyywywbcRxzXlmuPtZwA3fNPus2magIUh0cvN73cdaFmplYd0h+MFjm718t5B90Yf7u+MeS9pD/Vy27dPo2lF6GiAXrMpZS6LwQALVYlRPcXoB84Vg9+YOIir45JnLakuyKA/K2c/Gjjv4eL3YuZdV0KrlLKVqZZhsRqsVNAbq1fmiLo3foJA8eCBouA2c78eNd3oS2VuvLH41DqFFjp1aEiupsE8amxNu7CFqvTEXnJMo5fbRVBDuj2nMAPpFvrPAnlOcg6lUDymRC9XBFo8Kl0GvTzFClGRbq1lWBGiBdTZ1oTUbt/Xdma4Y98KuHE1ZjqeeSa2DhMHlKqXo66xZbVVk17YvQuoHy7Oty54buevVYYiSb3UpdpA3t9aM+09tvRyMr8NmLCRsZGSwNNQVHsuQUCgJ07NPS/IIaZNE31WzbROXxU6xwLuvQjUywnCTteH9RZSk2q6NT2Vca2XdDRNY5NqUaRbUSOWelm316yR6eVJpDuvppsme6Tgt1YQ4FiZGbcYw80fk/RdF7DzhBVPROj7nXr0cqmeXdKg4Eafi3raiH5MoF6e+fymrhHgkserjBlm/B6pJZdfQuTf+8BfiyDds9LL6XzSp1SLrrepOTWlb0DfawncMe/t0rDC6mSq+gkL0wlQFDboHpcDr1xzzA5fT5vgeTqoIN3HeEg3WTOVBPvqURx09z6jm2++GZ/4xCfwla98BQDwla98Bf/u3/07fN/3fR9+9Vd/FdM517NtWGt0Me1X0x3PksVquJum8YJyuabbH4tF9ybycxDr95paaKVsuQk4VoX+jjnmnKaWhpSjXm6ye1rLMBnp9o/HbRSpV5ayl8H4KTo19T8blGXSYV2rrWZQD0uWpLALsdLqghp9/nNbhs1DTdqJqPXOT9p3gbbxaZomqIsOEDpPRyFC97RBd9X9Lgc6MfbBzOrlQqAf0tja3+mwspDuJCvEd5rMAh8g3d7zzxxa4gSWirNkrl+sv67kuMVKAvraSkctLwtCpd25EzjrLGy57l8AAMN3vcPrp0qtYo50LMlKg24+X3FatmYaW2ksMHm0tUkrVemPdIf3IUBUFRXtdnz+uUp9ugN6eZcYOTLyj00dY49aPW+kOyjxCMcqvYKphFGLbLX/z1LLV5Bu+jzS95wzEFL07tyabq9mWhDL0oJRuh+Nas33oV2WWDBG0VgegJhhqi3DyPlQpDvQSkA4Z8bG2Y5r9sRW7HtxpFuml9t69Ih6OfUheNLBTDnm+pqg3n8W436yZmaurypaHpD+Hn3vc9toqi3DyDMU69MttfmkJTWDP/0T4LTTXMuwgV8iysEw6VrZmm6lFArwQRMtwa4lXo8G631Gl19+OS644AJ8+MMfBgA861nPwkc/+lF85StfwRve8Ab8/u///twHuWFxZyBmqcU01qdbU0MGfFoNNY1Sa8xMEJI4SG5NN82WasI1ueYC5bLbtx74c/VXHhxoWXP6nVS7GykTHftujno5bSe2bkh3jpCaMr7Ko7/L36X3IxbYU5u1TovaHftblPOYnvXcAK3pDtFEIKJenimkVtsMdrsfXseawz6YWb28SxiMY/Ry1hMbSAdtOWPS1Mu1Pt1lGdIHPfVy5dkz8x2fI2niRKrp1uqJZzHaLqwoijawvugiYPduh3TXY6+fKjXPkW6aMCijQmqUNTOzkJq7BvRZlxJAhtXDnTntuZVadnnnktFDmAd/XusqjnQH9HK3nd0fC+LNe6EKqbG1Y97zsbnO5rS0AIUbV3EP95s3/2qMLomCTVkYXL08peugIWHhe06QZKGURkNgvWRM4halUNyYT0FrrQMhNWGOoUbP1dXAhn4AQDrfRGDVAZsrJNNq9VPmwJY00s01NGxNdxX6Qea/pTBnTXmCjSHdXtA9Y/mdpb6XJbkfGUg3OU5qTRym6OUJITUzD0plaHTNHl74k8DNN2PhaU9tP/svL/Hav8ZYNMbUmm5PWC2NdB/N9PLeZ3TttdcCAJ7xjGfg+uuvxw033ICyLLFp0yY0TYMrrrhi7oPcMH/Ry0GLjKWoKYHaqRBYS9tSISJqQ4UyaCy68M+AdC8OZTpfrvFsaKy2h9dYcbphrC1GmQokbL2ygHRHEiceJTSoaXILbR9lzVls7+G2dVtMbIw7rQ4hoAwD+bmhegK5QXcfMRzJDqyM8V8/1DJ6ztm+tff3JaSb3n+LdDMnkK4/sZZhdjElFEu6iDpxnQxkasaa7hykm26TEXMnSzF4jd/mbiwHb94NXHklULdzgRMpKoNaT1M3WVV6xwIz5/J7QDeTxHiA+dGGD1MRtboGLr3UTjgPu2cXAOCsPbd7/VTN+fMx1dPGexbbcYYJIY+qaeaSbCE1eg2EJK3Xp9skaJnDpdLL43OYVNPNLehAwUTNcphYdLdc7Gp5waB1ek03kH7GZzXzrC4zB7sVvG23kRInqbWXJtGj5SpCYATIgSld13iAmZq7VxU2BE+0UpRUOsdUTTfdl2aSX0Ityp4jz4+9dixo1F45ylKiSW+JbULnO81oAkpbD8w+JVGzmNG1XjNbmqEIj5rnTupBLVGrzdB4yYJVLydj6VPD752XoD6fc018enkm0p3q013KLLFYGRrd57AsgapqtTQAjM48yytZGrPzkhIzrqbb9wMpaDFU5gBqG0E3sdtuuw0A8KAHPQhf+tKXAACvetWr8KlPfQoA8PWvf32Ow9swY3Ri11pISJaaCDgiPYpQW1M13YAcaFCLiYMkVRoFtMUh3bOhmTQwBeJ0W378QkGDokJq6n3oFhahpjtGWzK787KcZjxE5EcTuJmX3bq3RYRPO25Z3Sagz9GEgb338ndNMFkU+UmntdR0N02DV+78Em66+xBO3bqEy3/y7N77kFgfdCySennIPNHHzgNgwH9u1wPpdlQ1g3S78WoJPKrjkDN3pUT1vOTWzp3Y8pL/DAA49LVvAOefb6nWxqEoy7BsxFxW2k5Pq0OOJR89pFujZ9Z1mwy44govKZBjR8atE7NpoQKuvhrYvdt+dtk/vgcf/9NfwI99/dPtH5oG2LWr3U4YH0XE3LmEzplHdbWoW7g/yaRrQMuUaMBmERz23Ggoptm1yoRi90najtcWc1GzWOJUpJezYFqr6TanaAXcEs840NJgNX0T/Tt+0G2SmPRSiJ1DTECjIt155T060h2ujTRAoOtY+1n7u7ZeGYpwgHSz95zebymQl2jvfJypIIoytiSL+RQU6ea9orWx8THS93pYlaJ4bp+WYYB+3QOqci7Sbc9F32bRlgrKiGqSXs4YS1w/yFxHU1JZVeHc3TcJ5jEVeiTScufTnLF5uj6SenmwZsv+hnmvzXUORRnZfRF8E9NBJBvpVvw+0/Flg14O4MCBAwCATZs24Wtf+xqKosDjH/94nH1265Surq7Od4QbBsC9HDEhDMlSiuAxpDsWANQCcgHo4lH2e0LgbCxFB5YW7sW11nQzpykWhISOge+ExdpipCZkV3MlfFcJ2OnvEj3NBh3CgjRvu23vEQDAadv0oJv3xO3TMsyKgvVS7u+H4lL788/egr/54u0YlAX+4N8Ax3/w/b0DJknfgL5fUk13rKSDm9kXRbqlAD6vpVTeg2HeTan9Tth+MM955JYqhbDvykf+DrjoImy5vQ1EDy5sajfoqNb1P/9ze9winFtqgdbJX03z/gQZfvI7fV99pLu7Fl39Nc4/H7j4Yi8pkGNej+7bb/c+q5opHnLvrQiuKtmO94TmiVQtAOFJupSokz0euQZjgelAAzbzf/7cqPRyAcXRzgWQ1xHe+synl8dr1zlTB3DPou3TraiXa/Ryba0bTaZ42hv/Ac/548+In2tmke4FH+mO0ebp+HUmlgl84kl/zYmW5gCaRAnVy7v5RZm7tbp/XptLu6xIlH4V6e4RdMe0XOjfY4l8qa0hpU5L5td0u8BUYo3R1laacSE1yUI9ktx1Q/eNjDkatTxHWSE1MjQazJcsUWN9TUW93EO6ybVP1fBL56UxKTTLZQ4BzsfWtELMcL21zJvX+ZpNfXzfhwR0XSYOAPDzbJoGB0emT7cvcEiF1AbCHMCv+UafbmLbtm0DALz1rW/Fhz70IQDAQx7yENx7770AgOOOO25+o9swaxyRzbU0bdfPBsZRN/KyKvTyhW4y1mq6zXsrTTYO+dCcqrAWZ1hGMq4ZCJNW4ydSwRR6uUOD5EQEkEaa3eStfzeg7LFaWT7h+vRaOXCfh62Ma9sOIhZ0c9YFRYlS9VCWft8j6TQr0n3drr34nQ+3tPJXfu69eMKOp88UMEkJKDoWsYVNJAnGzSHdJOgWsti9ke7Ie+Ooas6pb1jSiY+dOz8pi7FzplOHilWv/nWgabBp1CZ8Dg8X2w+M0/UXf9ntrwwRMOLsasdzSDenZIeOCuAnNyZ149Vfe6bUX0vm6OUVcOqpye0BeNulkW5CLydiX5xyn6ovNUaTYuZ6avXA1pnk9HJNJCtTF4Mf3/9b+9PMqTzQiAVQYp/u7hBm+yVFY8SuHUbUKZFAuPvgKnbvOYLrdu3thb6Za+mQbn+upcemlkLTxpGkMDXtHknXla61gXp5JPFGmRMppNtcD1pG4ieaQp+Cn2ca6Zb3YSxWWkZBCE615TXJ3Hyk2yH/Io0+Q0iNnkOKbehEufKezRyk25YmcsZK4wJbPjbai5v7ODxpYZ4pqaabt0vLNYlenuNjWVHDDHc+lYAy14MKNEqJJbGmm5TymGSauc4x/58e19jhUe3aqzJ6uUG6q9JP2mkJ9qO5T3fvM3rCE56Apmnwy7/8y/jCF76Abdu24eyzz8aNN94IADjjjDN67e/KK69EURTqv9/6rd/ytr/uuutw0UUX4eSTT8bi4iLOOussvPSlL8Vdd93V91TuV5bT8kGylCqjeZE2dWqDHhU2QqvU0CuzCPJ+38aaRp9sUoqh0sI9UNCSXISJ0w2jSLcSoEvIbXBugsNGLYZKarRKr1a2DB2VCZnY11NIzbRUWh5W2LYpXdNtgzRy7VNCalb9uMckPGvvzcs/+GWM6wY/9rVP4+f+77v8D3sETJKQGhUgHArj4wFIlF5eOwfIrGMUJchZuCruyCTeG3pMY87B9Y/H+3T3LQuItcgDgMHutqbZtjipyLPXNKj37uv2FyZgauosKYk2g3DHaMtS1h4AJpOJV3/tmVJ/LZlBTDctVMCP/EirJqslL4oCOP30djv7J793a1RIjcyvAdJNgoOYmZ7PgHsWKUIz8JBuGdka1TK1MNVKLkyaSEFOGHzQeSl2nq77Ag3a/Gd7qTu/lW/d6iWsAhHOxHxHy7M01lh4bm78FukW2AYxPZVUGVoq6andIykwpe85v+6xGttx7erTecswXv9qqcm0gwGlXQuq+gB7lzORbg21nBXpdui/fNyKrB+27VNViuCFWz8zg26VZdTN/0qbSs16Id1MXdvda6k8wI1bE390+g3tT/NuJVlKGUbnk15Id+KZoTa0+42zR6VrQP8vJUokrQ0N6Q40W9h4Dh7u+nCjwdKnrvLWNlPTrTFKNmq6I3b55Zdjy5YtnTBHg1e/+tUoyxJ/8zd/AwD4oR/6obkP0tgnPvEJnHvuuXj/+9+Pu+66C6PRCLfccgve+ta34txzz8Wdd965bsf+TpsmXJayVAbbLKZukZ4Gnxmj7cV4Gwdji7YeJEEvF9XL49nGmmSt7XckdLwHwuTQYH+xlxBXMyaTqXNBN5vkJWpiYjFz5QM6NZ0HIR56URSB4qtzotcX6TbU8gduW4pSD3mSglI9U31Rx2tCuvtx6m/d06pCv+TTV4TU3R4Bk1zTHdKaJx7dK55dpkaTPNJ7bnUCMvp0T6ZN1ntjF/BhSGk376CZS3if7hwHw5wP3R81L+Cdttd/2P2clL4DPu2aMA9KV+fIke6YAI5JHHJan7lfZeEHZ17Lwc98NryO1IT6a8mcevkAqCrgLW8xB/M3NL+/+c3g/brtuTdN0APXE1uiKrgsKOTq0jGz7CNDz6ciiMTRtYgKa1uZUi/X8lAaLVX6G71vNNCIJYjMJfZIIYQdgJ07sXzZSwAAR778r17CKkjYJtZlep/4PdOMzhVLDOmm6470GqYChhzWTLuf9meAdAvdTjykmz1fWr9vwJ9POb08rOk266os1umeB/8YFDlNIt2JgDa2H1rexgESs7mWrKQt0Kj4oHQvc5mS9r1PsA1tfXCmP5EzfywICCt9biUhNepD8IQBF6Iz99+8T9IcAPRL0lMwbBYhtVQSk45Nezcb4Rr4JTB+ooTuR9LaWFCArCjSvXMnDvzwUwAAm1cOoXjqU71k/ZZOYHI4rb1kpJZgN77LRtCNNqj+13/9V7z//e/H5z//ebzsZS8DAFx88cW4+uqr8cpXvnLmwXzyk5+0wbz5Z5Du8XiMSy65BCsrKyjLEu9+97txzz332OPfeOONeMUrXjHzsb/bjSOyuZa7mG62jnITfGbMR8FlSlVun24p6E4phpqJii7cJgtox8oUfj0TAiaLBnfjiam988xzyRyMGNKdCiptzZyEsCiTOf3Vr03l43HO0Hog3bfaoFunlgORlmFlEaXgAbSmsE9Nt6sBy0421DXGh9rzWZqM5G0yAyYnKuicZnoekrBPjF3CzdYrVk4kyOvxnZEtto5CPc16b3h9WDsO87x1CTxGYzOnl+NgAHEasSdiZoJu01e08sVbTBBOlW15javUw9udl6GX+3+PObB2X3d+O3KGxFidNjePXg607Vve9z7gtNP8DUk/VW5mmJNaQLq9AMSdQ1CqEpnbuHHncyK85wCp6eZIt1qv2/7Uai5zarql8iYa/MXWWYktZK9Lpy+wdGd7P1cHXamD0Rc4eKjdh0nuJjQ+Vkn7u9Wr/jGZ4AP89XkTq+nm+h/cBorza8wkRlLzr1ae5eb6EF0Uke7IekD1WzSFe8eycMeVWH9Z73Iq6E4weXKQ7vad8//m+qqngvmpE1IbyMkFrRxQHWuihEOqD46ZA0yU56euMfh2C5qNv/FNxxAh10xihtHnJhB/ZPfFPFuuVEIOuvuUo1GmRJ+gu0k8M9SSZZckuSDpk5h3YUmow6c+hDG1plt5p02y/tC9+wEAx3StLG2y/ld+Bcf8pxcBAAaHDnjJSG2+2EC6mZ122ml49rOfjXPOOcf+7ZGPfCSe9KQn4dTcurOe9vd///fYtaulEz75yU/GxRdfjOOPPx6vec1rsLS0BAB473vfi3379q3L8b/TxvtJ51q6Vqv9+3JHL5d6cbttwwyx2qdbC7qJY6eNVRuvhJgZZNhOCEzhNzAWMFl6UOUv9tLx+URZMeSW74taCtmwQijCJKOpl3viOEUYWEyJw7FeLWoA4DajXJ4Kui1SFDrjKSG1Ua1fW8287HVOsqGjVo9X22B7oZ7Et08ETAYNkGq6JeE7QEJV9XE7inTpI9ZmXxlCavZ799wbvDeTgnyve29MMLnk1ZF3Di5Duo0oTV96eU6LPAAYPPBUoCgsvXxSkqC7KDA9/nh7jlqfbilZZcxk20OxSf18rIN04snJ8wSQrNM+0gnTbCLXGzt2ADffDHzyk8B73tP+JP1UubnkUyikRud4SgEO2g9mCqm1x/OdW8nJpeMKaroVVDU1h+XUdEvJA0cbD4XWqJnvePRyc11+4zeApsHyuKVYHmH6AtNO8yarT/fOnVh99nPsr6v//uIsLQk6z3AHO0Uvt0lPrbXoND2XAPr6GVMvr6cE1WXXBwgTppTqzhN5/D2vaWJSCNpyEuUpIc4Uiqx1RwDoXOKSAkFNdzKYp6U9laiV4ND4RNIk4afMWtMdFQ7r1t3hhz8IABi94132eafjcEJqJOlFgBDOJrTJXgaomPeEXlevhr+H8KqHdPco4bMJzoz5NJX8iVHsqQaKQbo9YE1Ipg0VtmqgXj5tPJDr4GIrZLpl1AXdTdP+e/3rccyttwAABiZ52AXk1Rc+752DMa3E6GiwQXqT0A4ePIi//du/xc0334yVlZXg88svv3ymwTzvec/D3r17sby8jHPOOQcvfvGL8VM/9VMAgGuuucZu9+hHP9r+f2lpCQ972MPwpS99CePxGNdeey3OP//8mY7/3WyxgC5mdBJtmiag/3Kk20PKMlqG8UncvCSrCkLHxciopbKNUl2QydDZCSERCFnrtuOTH6ViBmPvtjXXUGsZJtd0Q90voDumdH9J9XLiPDYNaX8jiIzM027LRLpjLcMsrV95biYZASQ3nsQZVpGNDbW6aTDuENNhKuhOBEyykBpFp4mzP21QlkXgxMTqOakjKwnR9UK6V/yuE3/2xGfhTU+6GH9xxavw6Du/af9eH1kBMPT2yenlNCAf1wTFyqaXd8eK6DoAQPWmNwHPvQjDpl2gzX0zfMLJhc8CdvGElAlC3PuWopdrwVzsXZ084Qkt+nzrrTJ7oCjaz0n9tWQB0m0PVAHnnRf9rjEzzMk01afbbC+0H+yBdPPghtabUgvm7s40LYJk0J1Q16V/k2qL62m8ZZhLGrY/ae/r6tY2YbXYsWNs0N1uGFw/NbnbzUOrZ55j/7QyXARu3dXOTwqbAfDXEJO85WwTQA7iksn5iZu3YqaxVOz5k++bYVB6OVeaNuewQH43LaV4PXf7Pf+8bS2zkuTMeZdTQVTsmaH7jtHLB0Li2c2b8nFpKZyp0GiRboFBFTlPb5+5QTcrX0iZep3Jujt8hEmeVo4h8hfvA9C+S45e7r5OE7oc6ebrDgdJVD2OHj4SXUf6aMjkamQAFOGP+9Q0gQy014E+u1K3lLEQV1hdpkiCFuiuEwG5DnTdQ7YYpJvYmXtux8JkjAftaVtOo2mAokD1F1cA5/5MmPA+ipHu3kH3tddei3/7b/8t7r77bnWbWYNuI4Y2Ho9x9dVX4+qrr8aXvvQlvOY1r/HqtY2CurGtW7fa/8fquldXV72WZvv3759pnN8Jo4tHH+N10nzNNA83r8MEQjE0miGTarQAh9SqLcMiiAmt9xPp3ULtVEC96anwS2ts6c9o/RVzDHhN9yx9uicRJCFHvbwswrokO94q7AE6T7ttX17QndcyTP6u6zPaX0gNaJ/rJS3qJtnaBk6Qy9CWhRPJCpiknvWe2qlXTtE6lsFCF7lfE7JgVgJq6NAPfc6wtPThgvf3T5/xGBxa3IQvnPpwL+ieLC4BqK3wz2TqKLm2XdGQBuRTEnCow/AsVs9J37HiOS3Vevgrv94eqxqgAVBs3w68+c2ojzsb2PU1VGWIWonPXoDOGXq5fE+kvrz2OCjb+uuLLmqfF/ruRuqvuXktw2Y02oM5KqTWuPvEEx99gu6gbZPCUnEquf511/p0xwT22uPFnUT6XYowuVrtMDimxvt0e+hx0wWCXdA9ZqUOdcca0dpNthu5eWi1cu/jajW0Tiouuwy48ELxuTGo1cIgVOtPsU1SASZtSRUzDSmNId2SgJ0XPAQMFN0hD8TICAotBb98/Zf3FadQx9Z9um/p2tLA0AZt3bU2m6v0crKmm7lSq+nObTmbHXQLomYxE4NMVgq40K2342pgn/f6134duOgN7eeikJq7fnzsQbcZdn/8OcCtZ9r8ItmYrOd92IQxRg03c16aqCpNWHktIsl7BSjq5ZPQ7xwKYAEQgnD1tPFAroOLrf+3uesmQu2kw3tx9f/4ORy7csj9sWlQ3nO32xc9llJidDRY7zO67LLLcNdddwW11+ZfXzvllFPwhje8Addffz0OHTqE22+/Hb/0S79kP3/d615naeXG+HHo7zEhp9e+9rXYunWr/Xf66af3Hu93ynKFMLilVBmdenkojMEdfgmxC2q6E326Y7WBkvItNSmjbKmMZvueCr8aAiE5bA2fxI2zZpzLCKKXUg+PqYtSRICaOW5RdCrFpT/h0t6wKYd1LXbrHiekFrOgTpRm+RNJiVno5dltX0i2dkwoyiLS3SNgkoXUQieQjq9Xn+6EQymJpHCzjsqxx3rvzWjQJh6sInj33tTbjrffC+onmSgj0AYCqdY32phk5WI27+zYgUFHUQOA8cc/YanWtLxCr+nWkW5HL2fJrhykezqdqf7aP1CNI99qn8tNt34LfXrEU6Nzj5mXpfme1t1qSsA599Ah2O13tOfQlgbl9ulmSTtuWTXdgvNP58ZYcMrney/p2R1rYdrOGaPK7+JgRP1sfak0p5N5yLx/ALA66ALwhJYEvc7a8645+bQ+WLKcUhW6/7AUKvQX6HPJE3P03eLzgEG6JeopP28abEaRbmFdya3pNs+5TgPXqdhS68IJe75SLcOmU19IbSAwSMx5Zt8/FRzwg+7cJL44Z7JSQCOMad+dpkFNgrohSR66/XbjLmnHBXjbmesa68VOf++HdJvnR9ZoSX0vizmU2C9lRNCKMMrcAWhJQOiPUDBj0SLd8fl0Mm08kOuQQbqFoBsATjl4L5YnPqOu6vbZJ7F2f7feSPcXvvAFFEWBpzzlKXjOc56DzZs3RwPdlD3ykY/EIx/5SPv7pk2b8PrXvx7/5//8H9xwww2o6xrXXHMNTjnlFLvN3r17vX3QOm66HbdXvepVePnLX25/379///0m8M7NVHJLBR7mxdrc1XRL6uVl0To6Uk03p5ulhdTQ7VNfSCgllZrU7iVQLzcKv5kIE5/8YlQwPnbuBOYs4Do1UndqdPVy/3PqUE2nfpIiRR+c1ZqmsUJq27dtim5rnVZW0y3Vo3ObRUgtmzJGFnaKUBkH2rMORU0GTJDfhXoaOoHt+KYAqgBV1bLbbblI+39KbaPvaB96+WQK770xjs+4GnjvzWSPc56GVYnVydTeG3ONlym9nCDdfYPuFNJtbGHBBSjjJ/0IFio/o98i3b7jQgNMtQ6V1STbv2egY3Y+2rGjRSavvrp9zk49tU34JRI22LkTuPRSHHncvwcedR6W//htwG/9XHuPMp49auaVmdTOOd+0MMDhUe0FWVRhWEON8pBuPxmjvbs2OA9ahslBVXIOZX+P6oKQXVOl9hwhNfPe0WFXDzwVuOVG0r7O1xeoB6ZPbeRcyDy0WglBt7AdtRFBrTTEV8vZx5LNQH7LMC14T/bpVlhkAIIaW1PvuTgMTyZUL5fWQCkJGgm6kzXdemkY3U9KYyAov4qU4rXfcYHvyNTmDjR9j7z3N8V4cEj3bPRyLzHBnmPp3ZlahgjEmmkKhJhH0yX10X1WhMeGHnTPVNNdht1jYubUy9PHUNvidiY9Q0DH3CHnIrV5s+/1wH1PQ7pDQdGpA7luvRWrXaJwUROgFaxsTLkXe8eP4j7dvYPuLVu24NChQ3j/+9+P4447bs0DqOsaleCAeA3UyxLnnnuu/f3666+3/19ZWcENN9wAABgOh3j84x+vHmtxcRGLi4vq59/NlqIwaZaqk+bolEQv37wwwIHVif8ZEQChtqjUg/Dz0LLCVRd0y+rh4TmJdYEGYbr0Ul8cSgiY+HWNBX987Jz2PVGuCd2vqg5rHVPB2VMWbd52gl4Xiq56WeA5B933HhphdTJFUQCnbI2/W4HQScTh4DZLyzBKGYueN8nW0gU/QLrf9CbgJS9JB0ydSawPWiYiJcTCPt3ye0QDFb8dDnW00uiU9z3y3hinf1JW3nsz/bPPAuBIt3n+faffUM97C6lFWCGSlgRNckl0aUmhnCbINEfTXL9A1Vzp7UvH5X2nR/01AK/O8fD/07JHNo1XnRpsDkpOx0SF1Mycvljh7oP+9aK9y0Oku92mj5AaRxv5u6vRy3WkG/Y8JNMozdI29N5R2niM9slbRNJ3sHr964DnXWTFF0elry9Qb9oM1JReLqBXZB6igfYqQ821EipznRcHYU/qFNskf/7NpZf7f6+FtZGuiQG9nNFkqWUh3axdYYv+h4izNC5juchlqqY7FnTHhDVTHQOo2vmYJKqkZyu35Ww6sdU9Y7b9VJx6b0z0X9lzbOjltPWj+X+lBLWUDcD9i8A/Ys++jnTnnRPgnrOqlGvpNaMsxJQlKf+UXk6TVdPGOxeTKKHzorSWWb8l0r2o3T88kMuwBA1jIWlFger448Rz00qMjgbrfUYXXXQRAOCmm26aywCe97zn4fLLL8cXv/hFrK6u4o477sAv//Iv4xvf+AaANpD+wR/8QVxwwQUWlb7qqqtwxRVXYM+ePfi1X/s1K+b2vOc9D8cee+xcxvXdZo5+3O+WeRRWIZA1L9LmRaNeTifq9v/LgsialgSQKLXUUtnb2MQlUdTM9iN+bpkKvzrSHY4/qBFiTmAsMVIWsjNizIngxAJ2Nh42cdPrMmZBNxcRmZcZ5fKTtiyKwjbUrNPaDY0ihml19zynj1sWZYyUJJi2U+W0tnWatiShR8ANuAQUfReo0y8h8WFdqhJ0k+dda3uVQ9EKvte9N6NHtOyj0Qt/3ntv6CLNkUo6R9HsPKUA5lhUV0F4x+j/JSp/WYRJCa1PNy1V4rXqfL/xOlC3n5vuPoQn/e4n8OefuVk5Y3qCfp3jkWEXdI9WevWIp2Zbhk0pvTzsVkGd1ECvgtR7p4yzj7S1i6ucG1OD7kTiMkBiom0f3b2jwUssKWydfps0JJ8961mtvsDJJ7bnMBiiAWwpwdTMK3au9vcFwJuHaNC9YkTZWGkUN5rc4OtYLr08xTRK9ulW9BhEpJsE6Jy1FSs1MyKtMtLt+w+0VZZc062v2SnU1xgtk5Isdm2n5PhmzuRIrcYk9ZFug1iWIhCRK6SWYsTNXNMtJX1YKeBwapBuV9JUd6U5lTAn0eNL2hy8HJDP12E3hf7ABKVn92nLmgKfqA3JfZbMvjskWWr+TtcqiVlEkzX2eApbNRRS6z7vkvWTbW0APTAMwdNPB375l039oz9oMw9ecgmAUGt0JNSaHy3W+4wuuOACbNu2Dc961rPwB3/wB/j4xz+Oq666yvvXx+655x78zu/8Ds455xwsLS3h1FNPxRve8Ab7+e/+7u/i1FNPxXA4xDvf+U4sLS1hOp3almFvetObAAAPetCD8MY3vrHv6dxvrE8NCDV/8QpfWid+JCDdLCAXa1M5vTxV050QkIgtUBL1b2EQyU4ahOmnfqr9KQRMWk23TC9n23J6eQTRS9Us28lbElJT6qxoLQ8dD+Bffx5YzNNye3TT8bkkhRufRImjNllj0ilKGTPZWgDjTsDIotw9ari5SfRyWiZSSMFggHTHnxezL6mXpxVWykK6KVe2wmipvZ/j08/wzttzchiaZsUAy8JzFPhzmrIYKySo6UbrlC5YhNmdh0OKQhSDBiEVc1SMmflO79Otv+d0HP900z24de8R/P1XdJFPa6zO8XAXdC+ZWrhEXa9kFumeNrZnPO/jbD4H4GlAWHp5j/WHP9NawswqbCstw4I+3YnEZU5NN30mjFHaeE7ilM/3dvsdO7Bw7T+3+ypK1ERfgK8NYnBK5qEA6c6Yh6ijysXszGHUuuOE8rLV1Ejcf56YMCb5L7ajx5ToPnif+0laO5YI9bSygYVZY5yfEqvpzk2gSRZjuKX2YxODJRU7ZXOUds+IT2FZDoqQmhMzzGUqxNcdCTWNmRj0k+edtn4ckZKm+jcubzctZMFL6pPxpAVPNPHrGNLN+9WpA+TeEzHTHHp6tIUaM/tMa0JqJLlMz3E69bsxSOUSUtnqgmUgxQEA7xnZsQOTS18GABg+7WkO5Hrd66K6JkXHYObzxUZNN7ELL7wQRVFg7969uPTSS4PPi6LAZJJotUPs1a9+NR72sIfhM5/5DG677TYcOHAAJ5xwAs4991y85CUvwdOe9jS77VOf+lR89rOfxe/8zu/gH/7hH7Bv3z488IEPxDOf+Uz8xm/8Bk466aS+p3O/sVx6kGSxOmnjmG+SWoZx0R0adAvOLyArNlNLZW+HkcVfRrlkxy3XOGoVQ9pTLcNi2eS0kJpOn9aCEE6foosIvVeDMqRezctMu7BUj27AVwkGfOZCKilhr0/P5z+bMtZla8e//t8AkHruHjXc3IxjsurVdPvPSFV2NGwWoCwNS6yMpzq9nN1f6TzHGQuXluQyi14M5R0wJ4WKAVIxn5np5cKpa0HRoCowqt185o+1DIIKeh6eAOG0UZMJbr+ho2JMcmzM/Y+1f7PG6hxN66lN45XodjEzp+cj3QJ7idyntQipmWtgUEbaJo+aScyoSDcLLkspUCVmjmOkPHLbTkqCXiLSbYNz/5rQ7YdUX+CHn4xBdw7mEeJzdbDOdPPQ6p9+xP5pdbCQNQ9RR5U/76l3MKW83FdILQfpFunlhf95OzcypNsqG4fJhzDhY44bXpN2XLpflepdbixV+hdn7uhIPEdq1fFNG682VwryHc0+/v6mhNQCpDvT71KTdqSkaWBqusuBfd7rH3kG8MV/8ObpaQPb/pb6ZNzH4r6mhmzz3/sAE+L9y/CxzLj7JTHl9YPWh9Pd1aSme8AS4cakrgQGyArp5e4e1tMmmF9N1Df4N48AznuU+yCia1J1iWi1pnsj6G5tFpVyzZ7+9Kfj6U9/evb255xzDt73vvfN7fj3F5sV6QPg6qRj6uWGXi6IjBgHbdo4p1SqjQMyhNQS2VvrsAket+QwpUQmVsY1FqpSzfDTuk8gzJRL20p0pbbtSTsG6ViUwihZrHzA7E7r0+2hB929odeftpKYd59u16M7rlxOx2lula3RFxZMbuNpuDjkmLmeWQvpjh0Y/+DTgLf8I4ablttsbY7olWK0xYl5b/h9HpQFVoGgldXysIoG3RQFkRBzwDmn0ZpuJcllnp+YcjfvDU7nKKdM3V+9POZkSDXdgDnHWqTy+0i3n/Ch9fCAn9ga1+4aGCfP3294XYfC82ZqUGNK9NZYnaNBuoOgO7c1Inyk25YTGeHMqXS93DUxp9EncWLfOU4vFxIl7ee59PJunIma7qVBhSPjOpq49YTUyLsTT5y2P2PXhI55VE+xjMobc8UCAHFe2rEDq5seCVx5IwBg9eW/BDz/qcl5iDrQmoZBSglbQ/nGhL4cM42SbROsXp9udw1i90VFuoWx0PNumsYhvEJismkaj5rLLRvpzvRpZI2B9mdZhiVWuerldL0fsrnXmKbBo41Ve8fMHG+o/eNUMtt8L3aNuqBs4YpPANePMPnhHwHe91+BqkJ95wH7Pc5Iqgr/vaKJe+rnmL9zv0wTVpuFXj4oSy8JkrLUvaVm5u+xsN+2a1T7/6oouuRDe32mJGElqeMDLnlPkzFWa4PTy7t9LQ1KHBrVQcJF2pc1RdfEvO9an+7Fo5Be3jvofsELXrAe49iwhKWyqTFrX9qpOJk4J0ynl5v6P/O3qqxUerlVPtSChURtYLymO7wGllYrbH9wdYKnvO6TeNQDj8Wf/9wPiMfj7T5imV5Ojae3YtrQelcdAdPrEXUkVwtIp2Sypcepp42HrnrKmnNGuvvQyzlSNCXPUIreqPX6TVlWTTexUbfZcPNyP/ErwTwHfDLF8kKlljPwtlubFgbYc3isoixjth/Z0Yosgp1J4kJmvED4HkuK+GMbXLlnmFLU+pbGaGJM7d/kd0yqV3NBkWvn0hhnhPZLJ/uSauLNcQcsIRenl7v9rIxbSrdWKuAZUYNF01ia8eK4U4Qtiqwe8dSoM2jmhU2LIdJNg0jugKZqgqkNlGc6VC+X524tqEqJfVnq67DEkXGd3afb3MYmIaSm9emm23r6AgLDxczDqSTjKmVKPOjBWYk/SrvW1Mu1dbciiRnJzLObUhOepU+3JmCnsZRMiYTUw9dPoPllcNo1ab8XC+DjgWUuDVxKIkpIaVASkCjFq6esZZhwPI1tou1Tew4c0t1PvTxZnlJVGD7qkcD112G07TiE3WVK+PXKDSoUXrkIfafoHGFBEnYdtRZivejlwvOVA2z0KteJ+Eb0Tza5UBQte4QwSKjOCn0upLlZ02Uy8+nSsMKhUR1cp1jbW800hs0GvZzYO97xjvUYx4YlbByhQaUsNpnwwDpGLwfal2FpWHkZZGoO6ZaFfsxkqIuDxLLC4URFF1KKRgHArnsP455DI/zzzXvEY9F9SurlfH9mSJxebvYTW3xT9D1b0x3J3geKsNaRI45Kdxw6YZYkS9w0CM5rLdaHXs6FRowjUBYhnZWbJPiRY30pYxLdalajYzVBN3d+OBI/tgtbInlVy88tPU+tNpaadn0s0s2y3b6TqIgW0V6x0/70cqk1jDGpphug2XmSxSdBkadwTt7VQemLz3jUUyYqadisVOWbm+TwrlrWQAYqxFoeGmG/xXo8s74ApT0G9HLhfGNKwH3okE7VXn6nzDvAn7FVxeHKDwjSiVs6Z9JAkQfH1FxwbphN4bZGX2BUO5aKh7oV+vtKjQbsK+M8NJGWk/CAMa2lgm57LcmXF7RpgYfEDpGCbq1/OrUcpLs9l6nHvuE1u/RcK+G88pFueU7iYxKfRyHRZVuGZQbzk6mr6R4OtJpuM9/lId3ac2DG27dPd+oaAbSbgcxWokOvpw2GlY8YUz+Jrh1SdxdAD8J7Id1CxwfJd+QWK2PhFrShFPYDOH+07KBuytyh5WASA4Iyx7SWYcafWBoa5itPrBnfIN930t6N8VFML1/TGd1000347Gc/Ozcl8w3TjQqC9LWYs2JeOrFuu9t+mQTd5mXQ6EKLygtrzNayZFCmuIlBN5kstHZLR8a1Oh5O8+GZ8tjxPdGKLrPY7iMWOMedrD4oeQwdGLP90bH2WVRSdmunXp6DdFukyGTyu1uiKctSmyWLCvTPXs8a3EtGHdTVTm2ai+7w8U14EizhBJvrJiqTZtDLNTqhFRFTneewNzgNrigK3qfdFB1TrE+3GnQrQmoD9vx79d7KuyGxfoA8pJvORQaZ0xIogZk6x9NOs/3SF+qxFZ/pqy9gg8qatAyzSVaJGRCWo6wFmdF6PLt75q5VQxID/B0sIskYgCDdXXZEor5KTAkqkKaVTQF8vtcDWeMommeGjpevHRpqT5lKq0oCmxu9zhqqqwUCqXZHJpmVFOJS7pGoXk60G2rBL9Ao7/Gabje+9j0PkWQZ6dbf5dR6WZN1TLLYtaWJQa4Hker04pJFU3t/FqtSFNVMUeCNpc7Z7Mc844Y5lDKzSTzoDufOWrg+7f7MNWp/p0H3lFCuzWfSsbXf+7QMM8nVYcnLlBLfs3NH+hicOURtKs0tRICQznfSvZXYcFrLsIkNuuWEi1v/8300jb10NCPdM53R5z73OTzmMY/BQx/6UDzpSU/CQx/6UDzmMY/BNddcM+/xbVhn48xMpWRa4EEz+y7oJi+kXdx8J9p8Fwgd+vyWYfGssDTBSNlS+oJzJImey/6VsXg87gxUHiLm748Lm9A5m4rBSAtLmhqpB0i56uX0/+b6SyJr86KYr4xr3H2wVVXOQbqpSjBAkO4yv2XYzOrlmQtpTqCaa0VRBBoHVOHbH1973iYAMd0E6mkTDT7N9ZBo4jkLl5TsaNVw/YApPK6kvO4WcOr4Sc9pzGLPgqbyPxBQUxpYcwSMtvnhQmpA+65LSBHdRnrPpZrulT413cZM67alTQCAhSveLbY8zDEP6WZJHRrw0rpfXo5Ca09TVjFERdMjGbKkDf0OEEO65ePOinTTuTmGQPmsJ31bc17mnafjsPOxQcWUkgMv6M5EuqW6Xh7AaUFP7vybSkaqrCyBHSLV4HoMBI16mo100xr9sHe5h3QL1yW377LUypQabdkXfJeoX2tJAVVx3r7XJOGiIt2Z9PKkkFrInspJaM+MdDfu+kiMJJoMlDQC6DnlBt09Ym5S5lWK64hmVHE8ZTFV9dh5ThuOdAvPhVlPaZ/ugT9/m+OYry1Z30T2t/v4aNq8s6okXo8G631G3/zmN/G0pz0NX/7yl7si/vbf9ddfj6c//em44YYb1mOc3/NWM2Srj2koIp3cNgnIB6334BOiRvO0L+xEm7TNQqKMNSJ8Favpbseun9/+I3LQbSdtQ/el2Uo2+dKsKv1pPpNamhmL1anSsUvBnoaKcPVyui1Hzj0Rkh6LSszu2Nei3MvDCts2DRNbh7RhKqQWoxQD7vr0nYS5o5UyqgI7D+PMD444aEi3xy6JCCCa+ysp3+ZQ5d31cceggXYopuKcDF5HPrbvJ+nTPRO9vP0pJag0avdCFToKPtLNEDC2nwF7xzTWDEBodDH1cjJ/WaQ7R72c2LQoMe52s/DDT+pFKffGRFBri3QvhjWZ5pQHZViO0qfFzdA+i/5awd9dqfMEffa0lmEpXQzjFIp0TKmmm7w7DlkL98+TrNpzzXVNPDSqkJ83bhTd1jqBcKMUYz4fpJDGNNMoM2hTzktGutufNXm+6JqqjSm3pruuGy/YjCHd0nMdC5bFc1OuTVSnhib/2BpoLmG6pnuq1PNLCci8+5dah807pp0XtxTDEaBBt5Dg5Ej31P+8LAtXvjb1a7rNIVNB90Bgi6XMlnkxQc5kokbw3TSjnUC4SedJ3ytaBirtx/V3d+NYID64K3tz39Hm11k6LLn5wv/70axe3vuMXvOa1+DgwYNomgYnnHACzjnnHJxwwgkAgMOHD+O1r33t3Ae5YVR9sn8woCHdIy/oFpBu4kTb7L2hzAmLKOBe2FUluuSBK7dY/Yq0cNDj8+CEThT7lKCb0rv4vvnkyzPPXNhDuyZ0/xriavugCgu32R1HPBvB6TNZUTNpSec1L6SbKpfn1IibTYKWYVRITRUOmy3plItW2OPMsaYbCJkfPLnCHSTz+TJxaiQBLv6sWSaKDXTcd+JId3h9qJPP0VnJCeJId+uASEh3ZtAdyexr1G4a5NuxEiE1ujmv6QZ8sbH2J5tLBERYUgKO13T3e+/o/JxSjY4Znf+tkFqXZDVaGICPGvFylD70cr7eSL1gAXdOY8EJBCR6uRuPZH2QbnoeJZmX6DPDjT6/TSNTooEQKaLDqNjzpiVhKbqdSy+P9WpOsU1SwZajrmfSy9m1l9Bguq3YP10JukeEgRccX0G66TNt3m2nKSIHP/lIt/7MmGMD8rWl8ykvy0glumhCmd4ftxa448WS+tI+c3UTgLwgde013b56uWsLRtYjkpSjQ+K0a2OhkFo/XwHw16M+Pha97ymLdRYQz5PcQ6mmm56fVIdN1xpbOka+Y+jl/DrlPmPUpPmiIYysjaAbwMc//nEURYFXvvKVuOOOO/D5z38ed955J175yleiaRp8/OMfX49xfs8bp5P2MY1iS1EsG3RPp9YJG5MsGK9T01r3JFuGJVCvWA2uVG9H2yUFmTePXi73jucMAo6ISWM3Q/czr000m5yNdEecvbANSzcOr6a7/WkmLYPge9SsGXuac+ujXN6OzSwG3TjI+JMtwzJrCrVj9q3pnlfQbZwT40SrSLdFi01Q5IJuKcMdtB5T6miBODtAapE38oJuGbGqyiKoI6c1XZQ63BvpJogFN815k9qcuIQagrZqfD+8hpkzdeg96FvTbdXLeyLdufcwZRR95Eg3Has5L1ruAehIpGa8plRLZA1L94wYowwdraWPhg6bIGNRqTkE5KCb7td9Hu6fJ1lpeQw1WxMp0csL/3lT1cu9mu5cpNtd5wDpTiRNUrTitQZtyT7dPZDuFArmvedeG0MW1Ar+hHQuuUh3SqdGqn+mATsvEXKis/Jx6Xk6Eb0CFWvZ144xL2mdOmcXdPdDuu1aHzm+6Q+tBd0SfZv6ZDRJ46uXh8ADILQM637tp17u1hEv6E74WLO0YIwxd4AQYPHUy8u4wB59lzwBWAsWEKR7EIpwAnpyNWZSnXkrRNf+f3FGdtd3s/VeyW+//XYAwKte9SpU3QUpyxKvetWrvM83bL4Wc/RSplFsaf9UKozBKSULgjiHNp6Uenkq4x5vryFPVGq/1wx6OV8MOCJGjdO96LbTJl4CwJW7udlaX4HWrNUaxeoT7wuk+9YeyuUAFSwyTo+7XrZfo+b0Tc2z2O/5T7Ui4zZPITVAQrr9hYlnn2kfVONsSfoIHKnlDhsN8KJ9uoVFjx4vVtPNj2nZGqVfjhKrgZYshrZoWhJS+ykeaFCng4+JXwd+3jSIjpWRiH26lfZrKRtHUN8+VpL538wLywI91F6vgqFKChKpmVYyoQmpTcmak1Ovq73KPCCI0THpvXMCj/FArGDzvUaZ5Yid1DM4hShSdNskbVK2SpLkji3SBf4pLZXKPSOSUWX0mGnJBPu+kQfID5K67wtBuS6klgqWp16wGaiXJ9iDuXogqfmNo+/ydwFePjFlvgk3i8x64oNVtHY3FRClnksnpKafk2Q5QaZV1xZa7XHhUd62j3ZooUkcQAZJ2uPx32dHuodVKSLxmvXRyIiVfnjJBevD0uSyu+9DiV7OytTac3H/l1qHmqSmVkIiAUeaSSVDHttpA+kGlpdbB/vGG2/0/m5+N59v2Hxt1j7FgD6ZUFSPIoiWFkgcEFqn1jS6aFiqT3e+kFoeSgG4l1zLvAE6vZzT3gqBNmu3ZWPv0zKMI2nUaP1tVPlcUS+nhzMTLy9HoNv0WVRidltPpDsUZyILZoRSDMwupNYX6XZiRP3fM8lCITUeLGsBSumea6nUgs0HQ7sff5HkGXhu9PiG4TLKoZeXRXBMinTT7HxfenmM9aA5ypIo14Q5bNZ5qUNWSopeTn+PJ9fC5211FiE1+GrUOc6ZZpKQ2uZF1y10zAOzkiXppnGBMe145hkdk2SMt50ggjnqVP4lZ8scWpq/6JoUa2ckPYtmGBQZks6TCzlpbdRM4tSWYkUCAG1eGs2EdAuBFxM+TZV1pejlqaS/neNzkG6RXh4G3XzNnA3p1mu6tXNyfoD4sbXUfmK1vpLYo+vTnXnPprRlWEijB9xzkBLipW2vJKNJqT4ttia172dJJnUzcO+YGZ8/DpqskZDuonB+GncdNCZNL6SbnFdZFjYxl6LcuwRn+hixWnMnyOb+VnpJRJp0EpIxAoPFtD0EpM4kfukYtVmQbl5yCBz9QXfvPt3f933fh8985jN49rOfjZe//OU488wzccstt+BNb3oTiqLA2WefvR7j/J631MQes1RNN6WDAu3LszSsPHo57alKJ1me1VpM0csTWeEYMqnRtzWk26eXJ2q62WJPs4R27EKQa7aNJSIAktWXAgl6PcWabiUJIDmQ3XEM04AnEzj1ai12W9curC/SbcSZzOm0wk3t/1V6+Yy11r3Vy9erpnviL14aLZzrKIxqOVjjASV/x3OTB7zNSVX47y5fWOk8FCQMiAI/DcgL+AmGlOUg3XwelHqLcnSWOqMc0eQISkgvJ47K1L+H1KTnbaV7F6dN66TlJo601ll9jc4fFun2yhc6B5Yg3R6VeopkfSk15yT6z7TGTqDbxlRry0jiks6NS8NQJI5vJ9KcydwovTb0+Y3N99ZhnfhId+kFAPq5ALOpl48nUuDlB3AqGpvQHMmdF7USgFxaf+GtZXLyJNYyjO6X1nQPhaCWllPE9pONdCtzbYxlNiXPo3lvzN9oEkwymmA396dlJYb+Qk5NtRmHNE5pP1VZYEIC/pjZ4Dgyf4j08tqfd9rntAmRbg8skVkovOZe0wXJaYFmx8eT6EWBCaspF7/Xh17OEmip/VCQhiapJfBNE0g0vkfgt5D3SKvpzl3nvbGS62U77xT99nV/sd5B9yWXXIJPf/rTuOWWW3DppZfav5tm8Jdccsk8x7dhnc0ix29MC2QpZYwGz1aNWKCXj2s/GOULTbqmu/2pZ28j9SuKwz1gWTljPr1crumeCvsclAVGCOtypIRBWQA1ugkuEnRbtVopmUCOI91f7uxZOmRkwh0JmeWqKFCjEZ3RWawv0u0pqDduAS9JAKfS2jLVc4Nj9sxeUwXgeZgVFrT1nT5iFKPits9CLQpwuQC3/T4Xouvb4sfssyqrKNJNnS6tdpfWT47rhqAU/YJu6Z6NFedxIDjoXBTLou/T8F3lJTgxUcZYAjSGdLf7aaDECoHNS8GVIlLmOVwaVCgLlwgA3PVqSwf8QKGfkBovmYjTy+k2efTyeOIyC+km5+Hm0zCZRY2e+rTRA1nO9pIc45SGxVr6dC+I9cvdcRNorMo0muTNi1ptuKxe7hxuKamjlWStppBuUuJBu6zMinRnq5fnIN2KRkZZFh7K3HYFardJId2rpPygbRkW+lDzE1Lrjt0xB1aRiXT3EVKj9HL27vBSOwqEeEJqAkuAB/zB77Mg3SwBaxIRKaS7D/srp2UY3Y+ZunxxX39NNqYl0xYGJQ6NasJAcnO4dp1ibW/1cwvn9KNZuRyYgV7+8z//87jooou8dmGGlrhjxw686EUvmvsgNyxOaUyZ9pIYNGdYld2k3/7d0gLJYkLr1Oh+NPVyLehOtUpwi7+E7skT90IVLjJmrMZS6uVS262Qzt3+pNn4kjhssXrAWB9U6uDLSLf7P/2+GHQX/sTqOXoK9W8Wa5pm5ppuwFcYlertuFH9gT7WX728WzjmlGHlNd1j9oxwZNQFrv47xy1seeVT83LVPyXhQEPxpfvh21Daeoh0U3o5remODsVaTGRKq+mOi/DAjrkdE3FGKodQALRlGE82NMH/xeSaMBfRoKlPXXcquMg1ikwYBszCoAwSFdSB43NOPyE1c539Z5rfs6osAt2CmMMVC1Tp++3Uy8NrTdsU2nGQ/caeVbVPN7skXHRUZiSF46ZGA6n+9HKK6k7teAGdzpoKtty+4/e/tE60664BhGVc7f9hxyZpBtjkDVvXYy3D6DHoe+4FC1YMNh4I5tKnU326eWLT/65b1+jx6CFT6uVHyLNCW4bRY+VSf1OBZ4qurFmqPr0dmwN23PFY0M3mAK9Pt3f9jF9Hzo2du4Z857LiAIoAy0kBzfoIqXHBUmrmFZN8wFC9PDw/rWzPzNVm7qEBtSrMPIOPJrE4j+Ye3cAMSDcAvPe978Vf/dVf4YMf/CDuvPNOnHLKKXjmM5+J5z73ufMe34Z1xp31PqaqgDKl5mFVYnUydQECyW7TlmF0EdSE1LT2OFINCrXYRE6zrN75KZNSDr1cynZrk4pYQ61McNxigYQnehUJ2M33zUtr0QuBXm4mLsm5nAe9/N5DI6xOpigK4JSti1nfoadWk2xwSw1r/55y+voi0BzBTFlua5xcW+hgTa5kbIM9JXClbfpi7fOGjB5tg/cuodYP6W6/E2sZ5i3iSnA1KEuiTN2gLPMDNjqmGCtEQ7olh80cN6pezubIWAIvhpBJiOHKWL+eMZvXs+gF3bWh5rbIx6gO637bYNgvR8lRH+bHS7UMA9rnd1RPAzE+kV4ecWg9pHsYtr80JrKVukP5lHH5mht2gB8oyklg8w5KAYdG0TRG38FcIbWRSaJLfboTTIVUAKWVCAT7YWwmqwgt1BTT91xK6mglR31quj32DUvAppHueCLYWEq9vOiSWDQxb0xKYvISsJT+zeERQbq9oDucC3PVy1NCamVReIyClOWop9sSxq6DTlEUQdDNwQv63NDnZSr5RgGy7R8/Vc8eO6+AMZVZ052jkRHzYWSkO1znBgpCrQnUcj+eaiNIlHC6r5mQbjIm934ffcrlwIxBNwA897nP3Qiy70OryUPf17QFntdzmKDb0UXdS0RRN4rMBg4HQfYoFdoY7asoWaymW0P7bV/KiKOsq5eHk5+22Gr0cr5tjHYqBhIkESFNwp4jQyY6CWkxky+l1dnPMjP3OWbquU/csqjW1nHT+txSJWy9ptsg0P0CEKm+LWZj4rjOwzjzg2fGuSPo2CcO6ZbQUZ7g4QG6QatT5yFRHz16Oaltnk4d5ZEK6XBqvIfST6cYTH2HKWWx9nqpmm5JSI3XbU+mU49l4X/mB392X5nq5VIdaCyJEbN50ezo/E+Vn3lvd+7A0XKUPvRy/lzE2vBx3YJYD+YcBgT9rqwLIsyLBG1JsTLKosC0o/5q6L9FiRi9XEqO5gTduUg3TdLwNSzFVIiVPwEuMZwqb/OVuttyFSBELAHHHKgbWb1cYymlkDDKVKMoNG+PaOdQxafKR7rTvtmgSy7xZ5fO43QOosfUljyOdJt9SD6UBW1S96+HkFpuQpvqt8TWAHM/my45MajCoJvTkWkykPpYkp/Jr2OAdPf0Fei2w0oen/o987znaGRE2Hq1AATReZKuVbwcDHCJuqD1L/NbPNFlBZSazBCjSHM6TQwfjZYVdO/evRs7d+5EVVV48YtfjJI9rHVd44//+I9R1zWe/exn4/TTT1+XwX4vm1bLmGNa9o07Q0HfXTKhUMocR0SoUQdxVE+9gIxOvvx7bqwyVZyOR3NytJZogB50y/1DIe7PLhxCVpE609IiHqNGpnpD8/pKN57QkbE13ZPwecldEHLMMAeO37SQ/R1+HtYhIki3tuDZZ1FoqZZzTF6jq9m8W4aZ9hqG1ssz/lotMa2LllofcfVy7mSPSOlIzIziatPQ78pBoqflUBYBldrRiJl6eRVPtHGLMTK0edDSyyNCajlItzlmHOnWURsJZaL0ci7QFrN5PYt2TqinNmmyMHCJVKcEzFElAHV/ejl/Lqjyrbyt0y2I0ssJHZmbY82QciNhLkmhzqmkMBVy0oTUTKJrzNgtkmOszcX0mcmml5Nrp9Uva/4DV+8P9p1JL6f7p1NuTL18SpI6Um1qwNAziZmhnOylQQH1YfSabmXdzQzCUuyI9rMCqCWfwt2XAZmD6GORSpRwwcUqwvpJlU0NEj4CTVQMy37Xh+5fMrpWGe0Lfm05Hbkh8xIFFcx8RS9d0D2BjSXme2rmyvj8crEUWj5LEnMszme6D+i3QJS78mjoNC+RoSVCGijFk9w5Jr3jue0J76+WdVZ/8id/gpe97GW47rrrgoAbAKqqwuc//3m87GUvw5/92Z/NfZAblk8PkowL2xjjD/fAZrd8B8hDuidxGjV1EHldN53HZ6mj0hZJKxARET/av6IIqTGxJbr/gAomOJ7m//RcJYdNo+S044wHSLxVDR+7lAQQg+5CH0NfO7TaXs9Ni/kUIHpZvJrIsggWU25a26GUzVzTPaeWYYtBbZSfWebPO0XCXdsOPQHlEHN/PzGaLjcufqT16aaOmF+D6GfDfWr8tFf9GhBnZDjGD3egDLJOHE32bktIUoCCM5aPMa9Pd8TJFmu6x/R65lGFgTjq28fM/T08cnPgwsAhVbz2mCrxAnrNrWb8uYjR5F1/dX/bWE236X5AzUPgKv/43naROZwmnlR6OXESdSE1/7pK28We8em08Z631Ux6OQ2M+fPcp/1UbN996OU0QWyDJzKv+oi0WwvcmDSku9MlSCSpJzUVUvPVyxuGAsbOJRVA5fhm2vWl1HSb+CBzZnR8ynM3ZO9fqrMKtRQbzgviIrXG3ncoVT4z6A5ECLuv8eQ8bSlG56tGeOe4X8bHkko4SKaViyUTET2SmLH5TGKIUAYJDYRjtf6ayKVj6LlEswaQzCJ2KwFB8+ra8d1qWWf1kY98BADwsz/7s+o2L3zhC9E0Df7u7/5uPiPbMM9SCpkx07JvHA3jfXd9erlDS+zL1TPoppNvShxEWug0Oph1uCfcUXa/p4TUfNVUeQw2q0reGooi8b9Ri9ep+qil9l06BkBGL3j2e73o5aaObPNCfoUKvcYNy8JK6tPUxhG0LHrMTAqcsfnXdMuLV1gD1gXdVLBESSa1+2HBO3P0c9WG/TH4FF96HDpG8x0e6DpUqfQ+k7QHYhZDC7R5UKKXc6owdeID9XKGPAb08mm4X+l9lWppvSRGD6R7bvTybpxHRm4ctCNF0MvZIt0hcpSntusjM/xZpeZaUbbbxKjDlZJ8pMegiGHu80NZSKnkgheg1/I14W0zpXk6RpXnz94s9PIQ1e3GrwZw8eTkKFP9mut2GJtFvVxbryz9dCiPhb7nFOHl7RHTNd16sEMtBkLwc+HPpGXNMCSePhfaKxeyfSrv7668gyRLE/cv9u7w4D03oZ2PdLvPJrX87vB5mpbEePOVsObwPFqIdOclWajx51pT7+eWKmOhZkrqYjXdfqu99ueUPP8VYZ9NG+eHxtTLgVAANoZ0z9JhSbpeG+rlAG655RYAwGMf+1h1m8c97nHeths2X3OBWf8HMbumm1FKzEvltwybRheZkixu3Hmg2axCOY1hDKVQFkmNTsjp5RwdASDWymtZafOrj5J0xyKOkbSwxOnlLhspGd2dhHRLzpxU0z1PIbVDHWq2aSEf6S4ZCuKJoJCFQrLJjMFwbl2esVxEJ9f44kXpjgCtI/NpXLQuWl5sfYozP09zHosZ5xGg5Aq9nAso8ho4mumWke7kULrt9ASVSukVqPhBjTJFuhnKxR18fs2l5EMsucaRW2OzCKmtOejuzv/IuH1ny4KVL9jAzA98KAqRi5QB7t0J29cJSQqWWIrTy/2giZpPo9Tfm5guB2ffSFaSOTTVMiyuXt49b8IYeV/u1clUXLu40WvHn0PXg13+brK8Zwakm76/s6iXqwKwmTXdk6kvpEaPPZlOs9XL1xPppu8V9REa8gio+jeBH+SvKTyZlhqjGQcQn3vNfnKDVK8+PZK0KwrnO47Z2M2xaHs/Os6qLEgiizA7BOaEHYuCdPcBJYIkepV3Tfq1YNT3KZXDUD9v4s2L9Pn35+Ywid358Kame+LWdp21Ie8rZlK3g3l17fhutayz2rt3LwC9Dpfavn371jSgDZNtLfRybfHgAQaf8MwLN6gKry2XQ3rkx0fr1T3NWEgqNgZjMYoUR/n4+QHtuR8RaHpSD1UtSSE5T064zG0rO+PtT1lITXdKgfa9M4f0KXvuczue7jhGxEdC8OeCdK92SPdiPtLN6eVWzKoqokkJYLYsKtA/e21Vv9dZSM1Rnv2EkWORlDbDLbYMY+9gUNNt1d77L+oj9t6YZ5aivRRR5HVfvE93n4ANSCSolIXdBTr0/fCTWTTBkaLnR+cSxVGR9sOVp/sE3asTf36e1azg0si1C6N/5326OapUT/u1uOE6CrHOGzyxFFOu9eqFNUGqiFNI/ya1iKQdKLR11sxhLVLafV/RGDHocEx7Q3rGpb7cOWi3J3bEEmKp+5du2ZhHHdX0R6R6zxSTQpsHbK95Dekm504DAd4eMYl0m/0kWFLm/YlRp1M+RVX4SUyPkp3QvzFmywS5OCeZt1MiVzEhtZDplOdP9An6tYQVZ4a559qNm77Hzlcj55ZAuh29P98/skwKsw4TnYLo93owh2JisNLa6iP+Lingiaay5DJnxPH74FTQS/VZzu0FT413OwDc/Ke93/d3yzqr448/HgBw1VVXqdtcffXVAIDjjjtuDsPaMG6zBh0AzarLjqQJDLgDJKmXj+qpmh0zpgXdOQuJRlmiv3InR6qjpOM3JlHMOboD6AtkrGUYRZal5FSOkFrs3rrJnIxHQC8s3V2kl0MdQ1+bBek2rVMA4+C6xEBKOXTWWmuOJKds3jXd5l1YZVRTi3RzhNUG04UNmGN9us33OdLbpy6Kt37h722obu2jEm7soYM7Jkh3fsuw9qeItijzYIxebp57WmrAnRWOdM/ap7siyQYgDJb69OmeV22bGacpCbHzPQu0bJsd4ZpICUfNQp0Cl7zlxhNL0ZZh5Ota8EIZGFKtqaxq7MabJ6TWoeJKIJvXp1t3zs0zQ9fXvKC7SxjOoF6utefi+0451DRB7CPdQuKhoNcdwed6WZx5RuS1hyYRpVZH9rNEMtA+xykl6jkh3ZXwHAKRkgAl2cNZDtQvSnX/MO+OxIbzg2e/Rj5m9HuppB0tY6Tf5bocll5OEO2SnLcZPr1GPOkX+JH2+ufP0VzYM/eazIJ0S36AuQ70VDSkm86/NiFKkvzUeImMJ5KaAPH6qJeXXtDtJ6qXjtKWYVmr+eMe9zg0TYNLL70Uu3fvDj7fvXs3LrvsMhRFYWnmGzZfWw+km9dphdkt9zkNAFJjWSABOjV/IZHHqk1aHsrGRR/YImOMo+X7j4RiatLCqzltZvgSvVzqi00thjJTSrFmpeAASE4fF3bTnJy1mq3p7oF00/HxNjEpITX6LPaxvkj3utV01/4ipy3SNAEj9Z42FtSSKWh1znnwunfu4NtenQFy7I+PZrrNeU/qfu2mgHiCSkMiJbaL+b4dL3kHORWOO9j8mku0dVEYjCUNOVVYupeazbtlmGH6GBSZXzNeV0vnC4vqZtxDrWWYFEjzxFKUXq6IdLXHChEdaS6R5nsXSNPP46U+TaM/11yESBRSizzjBunZvDgg60taTI0Kn0raAtJYjVWsJCDYd4/5RApWeZKQjoVeSy8xYZHmkG4PxGq6XfAkCUm1+3THVNXLc+tzM1ggNCD0vkuDbvIcSkhtMD5F/IrTy80xiyKOxrfH87/rjZWJu9n3PJFI5MnamKlId2HG5+/TAQ+FB0w49fLQV6PnIP3ep6a7Zs91LJnmfa9HIjpWOx8XafSZdR7TIwDW5GdpzPyWYaULVVofbQb1cnp+K2PDZDk6g+4sj/m5z30uPvKRj+DrX/86HvGIR+Diiy/G2WefjaIo8OUvfxnvec97cPjwYRRFgec973nrPebvSZsli2RMVS9nYkuqGBPtu0trupWx6PRyEnQnVVTlxYluY39n6JI9P7YYmDZX0pjoddUW2xiVR6qhpsYztNQmBKHQTKItSQgip/xqtKO1mlUv74F02zEQBMKMMUV9HwnoT47Nrl4+56CbI91CIAiQwLWkNd0S0u3PB0H7rh5CarGabruvxRjVz0cpqSjQeDrt5WD4+9XRlgBdjNDLza20LJB6GiBr/J3n11xWLw/PhztuKyxY4tc2ZvNqncLp5YsD+dlT72/jt/dLHo89tzGWFk8sRft0k+vdsMtISwliVGmxxU733xyVdk/8yzIp+LPIkhlCwsI94+ExnNNZYjSpcGRcB8kbyejzwp/DVK16qn7Z7Tt9/01bNfr+imtnQZ+vcHy0RtfYhCT9c2q6XSlcEbRHTCHdKco9P7dYZ42kennp09+lpLm2T2OcXj4J1pT8tUBEupmmRzbS3aM0JRB3VDpQmPfUId3una0VvQXDtJNYFeac6D5zjCei858ZeQySaf6ttx+hpttXLy+8pAMX2Qv6dDOGHk2aaar+M/XpJoMy98wkh7Wk2v3dsoLu5z//+XjrW9+KL3zhCzhy5Aje/va3e5+bAvjHPvaxeP7znz//UW7YOtd0F91P5gAR5Ip+lprE1aCbHL6vejmvJ/LOTwlOAnr54TDodv1dQ4eIj0Gi8nBkWbs/MSQ3J6EiBe2xHo3Soj1gC9ZabBb1csBlm7naeyro1vpJpqx3TXdGAqSPmT71lqalBDauT7dbBHnrIWoB6hygi+3PHCE1TkfUxL/4HDTkiAqZF7w+3Ya2nBl0x5FK+T0TkW6GWpprRecl8zfeLzSkl4f7FWu6GQoQIt396eXzaxnGgm4yp0+F+ZVeE+PvpZAyAEH/3ljJRlAWEUk0xJBu1y+XInCCkyqI+nmU8URwWpJ5mLczMsbZLRJqyRNW1CySO6iwOJy2QXcO0k2uc5BQMfdPpZfrwRbQr17TBUbk+1JymyQeJFq/lICnc1OOejkvhRuUhdWZqIUxefvJrenOYPKkarppqzvAvTMxZJjfS7Nmacm0nGAolpina2hZ5quXp1qzUTPMl5Be3h2Xis01jcc+pD7WVHjngPY6m32HSHec7SGeG7u2NPCPWR+NjBjtXZqvzLzd1ra7cy2KVuB0XDuml5l3ub/DY4Ex8b+0Z9nqrcxIL+c6KMtHKdKdtZoPh0N88IMfxNlnnw0A3cPu/gHAox/9aPz1X/81BoN+TviG5dmYOZB9zEzmPNgKa7p951tsGTZJq35q9PKaTdqS8fpS+10vy8omCIvuypk3YxLSbYZIHWi9rlwPci2ynGj7JfncOT2orZCagB54Ey6rMdda46zVZunTDTinasyD7sTYZhHpAOIBnGT2mc9AdHLMZYzrbhwMoWYLqq8AHqOX+++ghjr3ooNqSLdBTBjDJRCBm7prR+eSvurlUcdvhppursbtJXzsffDf+ZA1437PQbpdTffsQmp9eq3HLKSX80TN1E9qMnp53z7d/BrE3l1739izt5aabp5EkrbzWwm5uTmV3DZ/9q+Jv20OvTy2HtBki0mQrGQg3ZRe7pJ53RjsuyB/N5Wc7FWuIszlvNsCQK7llCY73H5K4RrRuSmnT7dD6HyklD7zKXZaLGE7nbrAL17Trfg15LxpwsG8O7GYTEO6uU+SYuFRi/Um5+9Gdv1yQieBmp0PlHZ7ft27P24KTJjPuJ9Jn68gcdszQQ+498uNLy9w76WRoXTnAUgbW7Ibmnzg3VJ4kn9k11P5WQpZr6V4nWryHvShl3tId/dqb9DLO9u+fTuuvfZavPOd78QHP/hB3HTTTWiaBg95yENw4YUX4gUveMFGwL2Oxp31Pta/pts4SzK9XKN4GuMiDMakCUIbK3d6fRE2/zvOcfOPx4P+/aKQWod0Cw5RiHSb47ttzX/HCWcthiTQ1myaSUi3hF6Y7QxaIrap+Y4i3d39nTTe31K1UJS63Mf4IpMy6rjOwxaZA87bw4Xq5e6d1FT56faOpeI/s33qgflzIdLLETpdNLBumsY7N0obTqGHwXgi74oWFNHuCnxb4wOY71D02fwtJaTmB/M6q4XXdPNgqQ+9fO413Uy9fEgSnJLexqxCakOWOI2VbAyYkx1rF0PpobyFVm5Nt0QFps9bqhSC9unWtuUJIKkMKPaMm0TN4rDE4qTq/pZPL18clEGQJ7VQojZQEvMAunc7f/6VkmZSrTzVKZH6dEsIn1vX5HIF/3tNgES212XaPdNxdlqMas3PC9AT7gCt6daSRX5LMxsoR963sKbbzMv+ve+jhxJ7DniiN1ekNIcJYGxY+j6oFnRTVgrgC6nF5itz/813qM1SfsfLxVKChPx7OW5GDnNHFGlsaGtMyvaakmdDnpuDrivkPTLlQ/QZGQuJ7Byjz7c5Fyuk9r1MLzc2HA7xohe9CC960YvWazwbppgkRJJrGrU5bBnGslsivXyaRB15pt9YTm2PRrujky+nXGnq5ZQ6M6qn2BcRUvOQbmXhyaGXa+cWm9Cz6OWkltCOR0AvzHGkIDVWV97XZlEvl8YH5NWH5dS9S9ZXkXS9+3Rzqh9XbactsbT+84AvkEJ/ugx2H6Tbd9KCoNsKK/qJHJrQoM/1sCq8hAFHnFOWQ3EMS0zC58o85xzlogGMq+luf3dCav418Pp0s2tPjT/HIdKd/+7NvWXY2Fcvp4g0vdbWqSaJsD51mRxpjtEOFyp/21SioSoLTOsmcGrp+hBDhmREtf1JW4ap87iI/ssJIC1waI+pP+OrBO1fZEyZmNH1mvcLloJa6bw0doC53Dnzr5ggFtZZqlNi1abFa+T2PSLU+9Tx27ptBxyEn8XvdQ6S6703kflNVS8n7xUdh5nHYkmuFNI9mbZM1D707ljLMH4PY8EgtT5CmlZYkXXMMNe2IH4Qfb48ttzUMXD5PaFDmAfSbRmoCmNKsz7ofwwskeYWTb0ccHOwnZsn/vthLEC6KRggXCf6/35CaiTotonqDXr5hn0XmNTnMtfUmm4rtmQypGYSbZEr6rhTRyK1WPFAw5h5qWJ1StpYY8fkdYH2/LqJ4oQtCwAUIbUI9Y+OgdYPSaIVqcy0bYMkBLw57eAkJ01CL8z1GwlIdykE7rPaLH262zG0PwN6eQLpnlVIMFfYxB1ntuBeM65vwHs882fNUy+v/HtJTXOAbPA+A9KtBYpBwsAg3eTa0utL69EntSxqEx2PIKBkbKIEcBK93M0Z8L5Dz89RzxP08llrupUERo7F6pv7mI50u3lTaufodRrokTjhzrhhtUjvlK2zZ8ki7f0rlAQyRWJiSHdKSXwaubft8c1+3DEDITXmsIprjEnyRILuxUFlKZY5SLdl6Qz6q5fHrhl9F3ISQFKSX1q/bTBBUEvqG8hIt/8Mi+dC3sEQ6XNjSwmgxeru3XmFCbz4vjgw4K4L/bqZv2PsII1hwfsx28RDxvwbQ/f5PdQEevXvpZ+dgF7OhdTs+8fYOQVDeAWAhI4dCK9tzv3m5pDubs4s/b+nvpdScwfc88yZnACSySoelAfaLwoLYmjXSr9EYVjJ+jvU956VjRi0DNsIujfsO2l9xDC4aRL/Wp9ujny09PIuAKinSVoWVz40JgWtwVi1jHAE6bcIPfuOceCO39wF3QK9XMoCS4s93bVEL7dIt3J/Yq04LI0/tmgLdEQJveBJAKlFyzzo5bMi3bzerCzahYc6YNyahtYm9US6E/2/uc0d6VZpWqETCFB6OVEvFxZbnoSgauEADV7yF3WLdCvJq7Cm2y3gNJgclHI5SraQWoS2qKE2XNSNng9nA5h5qSicg1IxZymKdEcCmFRN93eCXm4TcSyIpyrHNNnFE0J1T6SbI81aooT+zfWXjwdVtC0QNRpc8XtATWI20ZZhqVIIGlBqDA7z7JvnTBIStC26pKCbqPdapHscR7ppknyh8tXLm4aIGSZQXWk8dD7IE1JDsC8pUSVeSyUZYswlJGKlWO75GzNfRar3TiLdESSXPoc5fbo1cdZB2TL4eHld7HXTSmzoe0YTorGkvrEYA6NW1q5cdfecJZWXOPLv0uS8Ry/nQmpCMNr+7gahId25edGmCZ/b3kh3ViKk7I4XghJS8K6pl9Pj2YSoAmYsVJX3uQMLSrHun861fdm4vHOIUy/fCLo37DtoUhCVa7k13ZQSyrPbpvXQeDL1UAXJeKBhzDnf/ccaE2/TkG7z+wlbFgEA+8Sabt0Z4EIR9nMhkE2JocWCSi3bKH0/pV5u6dtCpjylUNvHjszYp7tgSQFJIIXbrNQlut/+fbr7v2eSxVpv+OPzaVyDsgwcEGoB0s1p6n2Ej9g10ujlvK8tDZj4fOEFdIoDpFlM6Z/X0NFj0rHSbc1zz2u6JfFE826E7VAEpFsKIlM13T2Q7lh/6z6mqRxX5P5J3SE8YaIEUkpNE+aT5kZei59DLzdjokadyyy1X6kG0kM/44FYo7QlAtzcEfQaFuZiuU+3CyyNQncK6ab3b6Hye1JPmzS9PDr/ekF3+v7zdUYKTuj/KWop1XTTc8tJRHk13UwoSka65XPKEeHMRbq1YHbCnkdXflUn9xmUNbBkmjlen9LEaOcIlqSoMhPaKUYBtQU2j/PEqc9Kcd/z2HIkOOVzH51K+bvQF+n22F22C0a/a5KXxHTbcLRb6qBA5zMr9Mbo76adniZ+ZlXkWdcVqjVTK+yvHPSeGmcHWCG1NSabv1vt6Dyro9D6TFzc1D7dzDGnNaQ8uz0kn3FFUG6cXmcsJeYCuCwqV3/MoXRqisMnRujlnL5E9ydRuQFZKXKcypqT+jVuhkoVYzHI6uXms9CZGwsLbS71KccM0t237saMbzRhC3gk0PJQ1J7B8Ozq5fOZGhdZqQVveRUg3aQGMdYyjJckBDXdhtLbq0+3QolmKD3P6k/I4l4U7ec0EdYnYKPbSb6PhjJLCQpev+tqukOH1godseDPGGXRxBKAPFDgCOV3omWYpkw7JGM196go3Hzi9aTOmLv58ZwWQPcsikJqJsjwGRraOdt5UKnpTulDiIlK4sinhNQovVxCsIEIvdwL9P3xUPNahnW1y6k+3fR5HQ78+uDJdEqCEPn7sWtG15Ich5oHHnTupT6DKHxFgyISPBjLQ7rdGhj0USY1rfPo0x3TmqFGOwVI3+dJATN/x/ZphAWNDe1aQO597ebmtbYMo6i8f06JALMJ761mfO7QkG7KvgHQqZfDfqb5mpWQbLPH7pmg955rIakT/277M0u9nIyT71eaz6ivqCfnp95axP0dnvygtd8SA2TW8j8gTNLZmu6eLMr7i20E3fcTG7OMbR9Ta7oZqkdrSHl2m76E2TXdQZ/utPOtLk5CcOy+Iwf55vcTLL08FFKTqE88gAEcNR6Q280k+3RHkO6cHtSiOI3QiomPR0MW1mL1tLHZyLXWdPN2TjGnD7gP+nRH6k9nsaCmmzlZ/FmjLbG05xpwAbKrr/bfm14tflJIN3OeA1GWehqwNWgP6FQgwy3W2k6jKktK71xIzSLdAvJKawXpccz9o/NhDDnipTxrqumeE72cX3cTyFEauFiq4gVFCD7XjKOmHG2kxkUwYy3D6L55sEqfC63tJADxWSzJvU8hUJ6QWiM/B5zpJQWUsWfcqpcP8oXU6HO1QOYOc/xU0iSGdPctueHrjKbw7ZztfG0V907oDjnXNWnH7icK22daT57R/eSol6cSitray5+3XI0YN0Z3T3grwHZ81F/LWAsiQmphTbeb/2OmlWFIptLLmZ/Aa7qLQi5X4LfFe/54QD5jgp5+N8ZopNanjSa9xzkddeg14veeBsy8JIwa12WSNDPo9bflfzOAgpzZtjIxSPdG0A0AmEzCwGXD1t9SddQxsxNBgAT7Tp1DrqZBdpsqktcEjZNMaxkmTRDaWEN6pz5JOcdYPr8YvVxSIpaylV5W1UNJ/GNp50aVgLk5ZcjIdRFE0CSFTl6/KSI6a0S6D4/cHDCrernWZ1psVdKT3kjtu0a9PKCX+w4Sp4UPPLRYeGZYAMzfmz5CaoF6OU9eqUi3GzvXJaBJgBTKxi1OdZWdXJ6Zp+O1LcOYOIzv4Puolkm+GCaHVNMtMX24iBinl/dRL5+XkJrmULmxkrp7ek0ylLrl47lryQU5ufH7lqSXk0CNGqWhRlvssKQXPSdKL9eDbnTH11vh2VKs7vhiy7DIM24ZDsMyW0jNXGPDNPGRbpm+TS2npjt37uVrlY9002vQ/pxOSQmKQvs31gfpXqGCicI8mVSqjzxHxnIDSu2ZDIJKljxMvW507Ob9KkuHgNfTUME9Z5xyu0aZpZVf050+fkAvZ2ALZd/QdYXqwtA+3QGFPIJ09xVdlZ7rWNJC+m5fpDt4fqS5hczbthzLtJOjegcRMMMlDn2/ZFiV4nXqw6bgVtr71v6+MtoQUvPs9NNPx6tf/Wrccsst6zGeDVOsT9sHbmpN98RHp2jwGrQTE+q9VaTbvLCKenm0DYZSJxSj13MRKWOOXt4G3TH1clFwjNZPs6yqMd4yTJt0okj3jOrloqPMF5JMR6+PmR7dVVn0pr7yPt1mTDHkpy+9kZrEWojZvGu6HVLl08slJ5D+pCUdUh2wFY5iyDJvGdanxY9KrbZUP3/sFM3mdHcJ6c6nl7c/4326/fMaMISEtiFyCQ7jSNTd72EAYJMf3bmapNJYeO8k0cQBc0jWIqQ2r5ZhPCg0zyQtyzFTJ50vaDlKH7YCbUtD5xrpneJzd6xPNz0XrTbWp5dLDJEwSCpIIJ+LdDfetv42GtItobjSXOxahlX2Xq0khNRoSx8qyAW0c0VavbxLvInzb7/kD0+sSnoBAFGiV9TL40i3Phau3QCEyt7zq+mO78OYdr9DIS4WdCf2S49LrwkVl5VKzTSLtms04m527dIZJdR6tQzj9HIGtngJMrau+EJq8nxFxxCsIT39I1EHI9LnnFqfNbEsC+tz8jktputTT+P+xoQkdrRyLeNH0HhAuk45HXg04ywQkyxbXjg6idi9z+rOO+/Ea1/7Wjz0oQ/FT/zET+DDH/6wRdw2bP1sVvVmgE74sjO9wBakUe3qPYwDRVuGperL1T7dGYiXFiTFstJan25OLz+4OhFoieHkLE0qDTkVKavIhcGC8xKQaj7OeaiXh4sMpdDqWew+dmjVKZf3Fs3oNndIvB8AxOiNM5VWfMfVy9ugjTvgPJlVTxtPcIjSZEXFe9YGRkPM+9V0+0F3we4VT/x5SDdDU6jz1Cer3+4/dt6yAxk4a6zej45bRrp9Z8k4EQ7pDhH0mL6EOWeOUPYRUktRrXONoz0Ss6mOJR+nMwqp1XE0BXCocG6yiCLN1Gjwor3zDUHA/ESl20cqECsEx15TL+et9nKRbhMseurlKaTbIMAE7XSOeo56OdTxTGp/zkoZV5in+5Q6bWjPl7QOU+q9evzuuaZIt8QIStGuY8kbY7EEnLyveNBtk/h13pxJ7yftVGHfgQx/jVos8AzKizKR7j70dp48DZFu2L/z90qinvND+sGp/Fl20C2AAblId1+dk6GyJqYYSrGa7igDiQgn0+MOykJkgPRpS8ctoJcb9fINenlrxx9/fOsc1jX+7u/+DhdeeCHOOuss/Pf//t9xxx13rMcYNwz9KDrc7ETK5gEuGjUkTj7vV0zVgZPq5amWYZFz0Cb9mDM0JMiKf37td0yf7qYBDqz45RFT4VwkoQjJiQfCwCSleivSy209YhrpltTLJQdS+r3MXCRTZpDuzQv96rnpGFySov17jpDaLPVCfcVR7HM/JyE1XhvFWSLUsfMClLK0TpRUB6zW15ma7h4oaVDT3R3P3F9Ok+VO13g6DWjzdC6ZtU+33Cu2e26UPt3mHOhzZOnlDAGTAkxOzzdiLr5Am55g4/fBORC+E5NjfRFGzcJ2MIxePnX1rXQu8Woke9QgVva5aDz2kbReDNl8kEsvjyHdensm93/v3lMnNYHoUxaGRi9fGPjnJPfp1p9xr6Y7k14uCUDStTRNL9cTXX30IYCQ1WXehbKQWVn0uvtMi/Be54gL2jISgnSHiUKXDNT8mBzkM5Wkcftqxxuq7suU7bGQGIyNEfDvj7+u5Ceto0g3C541MIdbn7mDd6HgbBI6J5lLaf4msRj4e+yDK5wt1TPoFtaB3HK2/oloeWzuvXZ/o/eQ3zPHpGiiybQA6bbvynog3fD2d2SDXu7bHXfcgb/+67/G8573PCwtLaFpGuzatQuXX345zjzzTDz3uc/FP/zDP6zHWL+nzQVm/YNu1xNUQ7rbfVK0iCN+tE+3bUOgLAg80DCWQ1HUhNTiisF+dpSf36aFgUWsOMVcQrr/f/bePNySqjobf6vqnHOHnm6P0NBAMzoiar4omgCNRoNTiE07QAaNfg6JnwIao6C/GOfERAU10ThEcQAHvBoV4wyCCjhAGEUEuqHnebh9p3NOVf3+qNq71t619q5d55x77+nuep/nPvcMdap2Ve3ae6+13vUubjIx0cvlArPgmljFoRwiCdLDSwY6TgEzp9bJRbq7NLplpHug/KCYqb1HSptoXo/OnJEsjw6MjzLeazpZ9zqnW+xbz7HiosWAGulm6w1rfYYauUA5mrze38XCdt6AVqtTp0KSRUpTW9hxFGMX5etkO8j96jCJmMnFWls1dJJt1UWiVC9nRZ1UQ0HSy9lId76P6LmbwlhaMFjP7acIMyWkJiPdpM9IoTQu0s1ElWyoB1mfp04Gzmmm9/Gic+acjwAQkvFXUqVzbKmsLbQveuTeF0WgFHp5QaS7Jdkt6m/p8W308kS9PHVgF9DLMycb50iibBP+97ZcXpfqGhS6Mrsp0lqkXs5HuoufCT2nW4lEkufTtU63TRTLti7h9qWKs+bLKeo53UWPGz1ug3G4tCPKnnJwwFqc37qDIQt22OfWjCJefHzd0Uy1GpL/4vnL6y/Q/mQK8KjBFfXYLswGCm6tQKndNpR1RNcM15rXi8jal2cnkLHZskaQTmJNvbwR8Ok73eR059TLNYf34YbSs3mtVsMLXvACfPnLX8b27dvx2c9+Fk996lMRxzFarRZGR0fxjGc8A+eddx727ds3A00+8sDlJ5aBpLxoi5B8ne5sAaR7R+vkISzKYypSL7edgsnbGDGLQv03JvXyeuBh4VAStdPF1DhPNUcF00U7BPSSYUV1unkhtWIDSc97oa8DZqGS/S6/j17ldHcS6dZLmgWa8cm1r7sa9erkYYNSQqPHdbqB5HnI1yfPIrTUuK4FXs7rT1GUG14mSpqLdEujW0S61QWQniPdZrzm1BFWRvmabsctdk0LeOoUpOcCMJFuTr1cy8UWY6PwttMFho11lM/pFka3yhpwQVH5LFeYhNRoWTeWAk2cdGXokNQRSR01nNNF7+NF5+x7+XEQMOV083MIPTfa3iQH0n6eLgZ6VjIsNTot47Td6C4vpEYNAEWpu0C9XFxXW/UI1zQH3XAzGbeUhs4ZIZyjukkcEiZkjJZ8aUBOvdyc022O/gu4UrdZcVbyWo7jmkZM0ZjJCakBqkOtTJ1um6Mhz67inVs6uFrSJuRSM7TfKo4aaXCmbWe0GfT0N/W599nv3FPR8n3WNdItnS2OcyIVvaTg0kboGJlfb2TPuS06raeIZtUhskg3vUxdqZdTRk4Uy2NWdboZbNiwATfffDPuvvtueF7iTYzjJDfxhz/8IV73utf1qp1HNIooekUwTfAtjYKaUTSj3EQrc7rbxZ5TTkkYIHksVvVyPrpn8yg3An5ypIbAwjTSdMBgdBflkomXeqRH0svbeXqmel7p8axCYbZId96DytZ/teV0ywnVeBgniBrdZZXLgfz10iPdSft0o7vz6HMZR4NqdPdmwB/QjO684Zq1T1Fp931j2gRA+7bqtNDzsl0Wyqbfzh9QDUV98Uw93m3NcaTU6S5BL6T75RxUJkqoTi+nv80i3WnUUDyrZBd6dE6czzBDL7ctYnWnoaCXC6O7s5zu7jz+Ofqz5hhR1K39/O9c6ldT1IlhU1RKkfYToPicqYFMQWmUNcNzU1SBgoue6RAfRzFhSRlYF80wSqKZzD5twprCWCyT081VK6AL7KK510VTo1N6uYmCTedEzgjh0gRcBCJ17Qaedu2iXg6l/Rz0CgkmcKldXH/UmXNFz1tRpDuMshSPUkY3O/byVHhXpW6XgJEM7qRCq6E+50hHTD4vWtGgMBj6XIqH/l1Z9XLqoHeNdJdlf5mU1W26Pmykm5b5tDxLuhObivFxke6u6nSTgBLVYRj85ihwww1AaGf5HGoovbKMogjXXnst1qxZgzPOOAOf+tSnMD4+jjiO8eQnPxnXXHMNPvjBDyKOY3zve9+biTYfcaADYDclw3Lq5QYKOU8vJ5HugshjNyXDOsnp5iLdeqmaRUOp0W2gl6u0ymzRmLWdX3SK3xXldNvKdUmDpWZzRpg95YqX0xBNoPvoll4+MZ1GukvW6KZtyHlgSTt1G1M36MqgTE53N/XAbccXpzYdhjk9BIXuqEUF64G6AKHI5Wppz0CZ3HR9Qp/O5XTrVD9hWGfOrpa2sKKsGdOzY4LNIGkZnG+U0hyRBXVyXKRtEotxQTvlaIGxcs6ckJrNMMvuZ5QeS3dgzD693FgyjJSk4xbGtMwhRz83IQiyZ07cB5ORJPu47iwyRrqT/7b5wShaRZ5v1gAmi1STcarU6TZsS8cOKjzKsQi47pBF+8url9PrzOV0F0W67Ua32/Nb0+YZOVZpv+eMA069XCkZRhwSRccX14waAtS5UJSPrZdS5FA+0p0fR+jvs/WEm1GmqJczzoWWQ+46307O6Fb37erQ5lIHTKClJgHKElGvDy0z52uO+zB2Uy/X+2OnoqudRbrLOaJN5UPjOH9tMyOWSQlTnE5mQzlX6jTKxmUufacMm0IHHQunvvEt+fngX/8lcO65wOrVwOho6f32K0qvmlevXo3NmzcDyG74c57zHLz5zW/GmjVr5HZXXnklNm7c2JtWHuFoGRYLrjANJhkFVTyQFno5zeku8BCb6OUu3j1TXo1VvZzxAtJzTejlidGdp5fnjWXOUDNNHLkcZWOk22zwulBzOOEdNp/H4ycSup0tR80F3US6TdeLXrfk3mf7zvKFO4h0l5hIaZs6ec44eJ6HRuBjuh1JgRAgHy3gooKmUngAchNmrtZ2CSE12oY4jvP0ckOdbiVqpEe6mUWfc/6aaA9XZ9kwudN+3tJqg+v5nEJgiVssZfT85P9Q6nhQapJaFrH6olUYCcLoLlMyjMvT7QTGkmHkHnERO4X2VyIHkVIhi3L9pLOoHSnOEqOQWrrvnO4DSVcRz0KcRsM4Jw4bGaKCXgWR7jg2U7Ypu6WlMD3sBqWA6J+Nmo8ocqWXJ/2MjeoacvYpbMZWWaaRPs+YI935Y3LOb/rsCYegPdItHBX5uZ2PdNv7ms1h6zq2cQwNLgUmY/rl2Ti2/QLqM6PoKjgw6bJ2mPtBfs4pvj50X06RblchNUbIUEmHEQa5dgFtayXXEmgC3Hm5rrHKaGQkbePXxaxDj0SO9bxzeo56yWAK/T4Ix3/N92VfVefEztdoss/99KeYuvi1wN9+Fo12C4EoGbR5M7BuHXDttcDataX3328ofYU2bdqEOI5Rr9fx8pe/HHfffTeuu+46xeAGgLPPPhtnn312r9p5REMxIDvJ6TY8sHl6eWYQ6RMtjWoViU2ZhNQyI9Hc1uJIt5kKQ6NRetRSRronVfVybtLkFgO6UqaAbkQWRrqZwVgXoWJ/L+ivtGQY03brJONAl3OBzOnesxO45ppSFKBMvZz3YANcpLvcoo+iTKS7V0aODvE8jE8ToztQz7tNDB/5zGliJhS6Z5kuECnLwynSTbzo9LmZrwmp6Q4q+tzp+WHiHOLYnNNpglV00DAONPToYpzfLpfTbRFSy0QYzerl3LOeRZiS7YUoTCdCatMl7qEN5kg3YQdYUlXCuNwikeb8FdUarxPDis4X5kg33zfoPaH31eSI5UrsCCMdMPfVrGQYifxZIt00pYQz9LnUEVW93Fc+M0EsoE0U42L1cpvRXY5eru/LlBqmV7LQ26enfABqOTUTsuc8z2hh1cs7cEQIFO2DO64AlwJT066JqzEPmB0uZUSuXITU5JzNrLs4lKFS12XARnXYZOuEbJ96n5bpAIrwo7p/rkqN/M4xci/AOV/LOiJc50TqRCnaD2WQ6HMvnbN15XyKAU2Xggo5cwyQMrXgdci592P/jqkgmScH2tPZBmKsv+SSw4JqXjrSvWjRIrzmNa/BxRdfjJUrVxq3+/znP99VwypkEIOa57nngFCYBhNdSE0tGaZ6lBvEAOAEJCjEfnTvvAvNVAqAaG11qdNNF8Z0AVcLPCxMcyp1ernN6Obyr/RFi/iZ8AQWRbq5+SmbFM0LCY7+x6niugipda1efuc9AGoY/ubXgR9/Kvlw1SrgyisLPZGieVmd7rRt5BxMyvWdGMMBs9gxodc1ugUGaj7GAEw0M4dPvm5mfnFkqs0J5I1Pem2S6EYnke5IeW5EpFuME/qzokaNtCg9aY9Y/LrWdLc9K0U53YCIsOYjt1muZ15gyUQvF0a3UqfbosarL46mczndbs9eTERwujW69TFLj3RTRypb0ikqW6c7a6+g+BqNblJei84X5jrd/FxG5weTKKMpWs+X2DGM4wy9XO+Lge/B9xKnQyuM2Ii4+A03FFMhNbHepOWvOHAU8IzmX1xBwGZ0l6mEQPcV5SLd6j0NNId10j66n/xaIMvpNrOsxPFlpJtVdHe41w5GWNE+BLicbi4FRndEFI2ZdAyqs8ZfsfCtuj8Xh6en/Hell7sc3zXSnaS8aPMRdZ6ZnnXrWsnNYBbgqvi4rrGKnGA6srQ8db9Spd1RvZyeo21+yQmpkfQV7jpla7Tyc5Voerh7DybrAwCAwXZT3SiOgY0bgZtuAtasKX2MfkJpo3vjxo2YP3/+TLSlggHdKAMC/OQF5I0MOuDp9PIa87Aa1cu1B1bAJVpiGshNCxwAuVqvgLpIrvu+kV7O53QzXul0dNObrud0F9LLLSXDGpZFDff7OFYnJO74QQfUJytGRzHxnR8Af/ACzGtOZp87UoDkQkvWIU3a5/tJ7jMtxSPQbLtP2jq41AMTyqr0ukLsb4Khl3Pq5bohzYlv6canSs+PSwmp0UmUPrN6HrK+6KLiZaYSZkBmRJRdYPB1uvnFVED6T5NQeq2RbmoE6dG5nHp5frFsi3TrVH2pXu5IL08YC8nrgS6F1PRrped0t8KIzU9madcukW4yjk1Ko5v/Ha0uQPueafuMXq5+zuV0A0mEZgjqPcwvxJP/TiXDCOPIJi5XT1NKmkXXlhmXaMmwWPvMBC6dhFUvN9w+U6lOoAMhNS1f3Xzd84aEOg8n/+k44BTpzmk30OecsDssawraFju93L4Wyo6bv9/UaBTGtdhOzHlFfg4lp5uyHKiuQokayjajUV+/mdaVud+VEWHURBD1SDeXty12qzz3ofiON6zpvvTvytPLeaeOy29dg2hGBmicP0/qGNRZJorAniU6rbNVqT2gazYAhHnXTWDE9zFVS4zuodY0v/HWraX3328obXTfdtttxu88z8OyZcvwmMc8pqtGVVBRloqiw6herkWz6SIsRy8nA7oUKDE4AUw53abaiRQmjx5XT1uAi47TAcX3PUIvV41u2+Kc7i8TrOAXDlKN27RYsyyyMhE2l0h39hk3meWEQ8jbriPdYQhcfDHGT78AADDcnMq+i+Nk9rvkEuD88wGDoZCv003b6qEdx3l6eRde1DIT6UxFusXzQCPd3CSoR/Rp2RdTW3UDGCj2Yusw5ZXrtTrzVD/yO4OTDiBGt+NldREd5BYK9cBPy7LFZLzIvhft5sYhPdItzoer020TJtKjrLp6uSu9nI6dXUe6daM7fTbrjFHGGT00YuLi96X7yKKNhkg3SW2gKRGmCJ/YtTnS7SvjKNUFMAmfKTmQBQYCrdOdGej57Rq11OhuR2x+qW1ckvTyuo84NbuLhdSy6Lh+DEW9vNApnMx19PrLNDTHfqjPM6ZIp7hNSqRbidjlx78y6uVc31No1wU1tl1EsTJGjX1txuULsyw7bX4sMlTpcVUhtfR4YVYarV7QRtoWzmjsNNJdJtKeRVjTvqM5+2hZMD2aTa9Ftr5Qj8k5WgVMaZgmcKlOrmss0aWdS4aJtbkhp5s+Dh6ZP/W5iisLbMvpbrYjhXVV802R7vy1cIV0pMLDdK0BgIl0C1jY1YcKShvda9asKaS8HH/88fj0pz+NZz7zmR03rEKGbuT4AXWi4fYrKH514h3VH0g6oE8wqqAUppxuF++eaaKzeZRpLrp+bqKNsmTYlJrTzQkIcQsiU/6eeFukXm4bjF28hNzvbdR4+Z7ct64j3TfdBGzahIk/GAQAzGtNqd87UICyhRbfdkpVFujGGK4Z+j6HpvY89AqZ0Z1FXnRxL7VuZvKZqfQekKdsKl7+NnWauSy0MqcVVY+ukwlafC/aD6jMm2nNEacaXsl3rqIxtsVclkfHLBR8D00khjlnZOTE19hcTzXSndHL6QLDvFjXGQeZenm5nO4ZNbpr6j1qMVRN+joRJnJ3/NLrPFlELyd9XNZgtqXZ+PwYlumMeIqjhcvp1s/BtkjVQet02wz0Bnl2uIUxJ4wpIKO5lF5eVDKMGSNpny5imdFrEkYxmxfvygDS55miOt3Uwc4qvCuR7g7Uy5kxgKPe6nCLdLsZlDZxVq7CiDS6HfcL8Pn87SiS+hImwTgKmzMoFIasXnmjSKm7RNCoiF5OxyR9TaakqBmE6DjBMQGbgCcHTv3bNdJd5pok22VOlKL9UCeirp1D19c2rRzal5QqFLVMM4P2kW4qzMjxYvkKTO7eBQAYbGuRbs9LUhjPOqv0/vsNHc3moha36e/hhx/GC17wAtx///29bu8RiTI5MRxq5CEUiMkDqVNCm22u7i6JdDfzOZEUeoRMwEVIzTRo2XKnaC66gL4IWTiU+JcovTwiFE7bApy23URXyoRP+EeKW0Bk51bsvecMZi5H3qVkWAktJxUptWe8PgQAGKb0cmY7DpIZYM2HUn9TxoDUUSrSXULxuwwGaonhNs48N0qkW/TZXBkwm/HpKfsBoNS6LB3pToVKGjU/J+SmO81oyRVhXIlKCJ7nyfs1XcAC0WFd+FnGQtpeTpmYo6QL6M+XOGdBLxfvqTIuL+qoLlamNXq5q3o5fT46ZTgJFNHL2yGvMM8pBbtEZhSHS7OAXk6MU5cSaVShmII6QmjfUxaGhjmERs+KzlP8VNnWMicp6uXMWMwtzrmSYYVCaswCWol0x+pnOnRnEdce1/WHLthpuk763Klvw40DbpFucc3MRlEYF6uXO5UMc3RGcarg3Fiml34suuT0uJzDhassYd2fjWWkO10dK4O45r3TfZrYVVb1crJ/U8k1m5Aa7Rsu4JxJ3FqbQ1l6uV5KTUCyaJixJYryfYymHdi0Gujz1WxHcm1U833ZR9Q63WZneBHkdf+/r8KUiHRTerk4tyuugIlBeSih9BV62ctehlWrVgEAnvrUp+IlL3kJnvKUpwAAjj32WDz3uc9Fo9HA9PQ0PvzhD/e2tUcouunQAD/BczWJaf6rPokHfhZBmGQ8yBSCvpiv0128cDNNdFZKZ2AeBBrS6M7Ty00lZER0mBfhUY8t6dJMnqiynbYQoWg63F8uv40VP7LQqWy5sk5IqT0TDUOkW9uOQ8a6YIxug2OiTE6aDjrJFGHGcrrThfNkSi/nF8ZRzpDWvf4UOs3a8zLjjOaOuzgQ6OJWUDIbgZ87voleCNDyPNnxpKCiiHQ7LjBM0cw4tosf0cg8F13Uxw7u2cgLqWm1yumYURDpDsM8vdxVSK2XSvp6dFP0b64iBbd4CyNzCR4Onpfl/RVHurP+72J0+3IMUz83CwZlz44pWk9VstsFi2Eq5GSL/lO2V5FInQ4a6R6U6uVuOd28ejlv+FPQc9DnByESWppeLiLdhnrAnHo5vZS56HAYYnrnbgBA4/7fwaRkbGW0lKjT7VJu0iQSZ2oTR8mlzsvMKe1mqNL7yeV0u4gDKvuzGJ55Ic1svWhDmahug4xJ6jGh/E8CJmoQh45dWaRb63MMk0LA6mQJw6RKC6nWItk1VDdHrm8KrkmHQmr6tbalGIbMfCnX+KE915/2pVaYsSVqgUee7+zetkukMOTOTYypT30qpi59EwCNXr5q1WFTLgzowOh+4QtfiC1btuBTn/oUbr75ZlxzzTW45ZZb8MlPfhJbtmzBK1/5Snz1q19FHMf4yU9+MhNtPuLQbaSb8xjTiS5TKM8MIo6uJl5TmiwHc53u5L+NZhoYjCTd40lB64sLmOnlxOgmx6ATHzdBRoa262IwxTlzjAfZIX2ALvZkm0T0wqLISRd6tlxZJ5x1FrBqFcYbhki35wHHHWelAHm560UMULlQMNHLy/d/l7y8/HF6bHSn+xMlw1TPeEYbo3lTSTtUBwUFR7MW+6X1wF0cCLS/07xaPeqgL544GjFXPqW0kJohmkn7LRvpJot4TqtBfzYVNemc0Z38H9KE1IraQM9xuh3K35XN6RbXrBcOIH1c0enl7YivJc0KXZVcJBY5aKmjhLIsTBC7MTnmMhplnv1kolhTynhUMNf6ZFuZz8sKqaXGUztiRehkNJhxLCklw1KWjLt6OePQM6jTU9gi3br2SxH0ecY0d+fzlzVBKOqYGB0FVq9G8867AQAD//SPwOrVyeeWcwF45xo1Oopzus3zpWs5RGt6GDN/Cyd+UUoOfba5nO5WgXHl0k6BbnO6nVJTBNvSFOkmx9QNTiXFypDTLbbxPLOQWs6JkPY9nHsucNFFyf/Vq9G65dbcceU1cY50WzfL9mtwAPH08uy7UNOJUdPZzM81DbJR5msj0DQzJDPMHBQrAg1ITT7hSQCAwTOfAlx9NXD99cD69YeNwQ10YHS/853vRBzHeOlLX6p8fuGFFyKOY7z73e/Gn/3Zn2H+/PnYuHFjzxp6JKNVIPhRBM7rTxd/UrhJGajzho54OEX0JjAM4iajW9KhLb2ubhjIrbRStk63ughZxKiXmxbQHMXKVO5MDkxF6uVWcajiCCtHT+OEfHJ0KmYw7tjoDgLgyisxUU8j3VRIzZEClLtepLnZNVJ/040x7OqNp22aqTrdEzLSzSwCI6oA7qX/884kAb1ON31NDR2XyCR1dnA53ULURl90cXnbdS7SXZJe7hsMEnoP2Zxuhl5Ox5p8BIwYmNrzJcbKIS2nm7aBOx+fLFbGifOjbE53JoTXPZ3OVDKManhwmhXi/Og47kyHDAS7wx7plkZGGDk5GkxGgTnSzRg52u6VeuSFQmrJ/yg2R84BjV7OLIxtpc/ERwO1QOYuT7VDGdnjkC2gGYeeEpXnf08X0vq1Lev01J38JiGtjF7OX0d5D/fuTSpjbNqEpqjjG7ayihma4Z17ztlrUlynO6ulHhuvfU9yuhmngKuQmolenq2jolJRSFvaQ9aPNaddD6O6Rnq5lretpLyk7aCnJ/qUvtaUUXqmLUGcHnOqKaPZGB2VfU/B5s0Ir/wIALV/uQY2yii6A3xgybQfPtLtK20No3yFIh2UrUPZtjRApWugdEMvj+KMGTa48ijgwgsTbaDDgFJOUfoK3XvvvQCAH//4x8rnN910EwDgt7/9LQBg8eLF1kmigjsykZjOoh40miYgDAzPI5ErMuBxOWJiUVscvUg+nzbmdJsHGjph0/7jol6uRrpVQ1bQy6daEaa/lFCE2q1MVI2jVXJ1uvVBXG+PmV4uBhbkngvhVLFN3HRhmGsTE0HRj6vso1OjGwDWrsX4UccAAIZbJNLtSAHSoxsBQ80yKRN3oozp6o2nbZop9XI2p1uhAYpJUJ0gW1GU6zNcuoU414kCQ0eHEukmhk9e1Ca9Z+kxKY14iot063TSkpFuqhCdHN8eZc4MOKLWrDwbWqSNeTaiVJdEj3SLZ5SOoabnVdy/8elsfJnfoXr5gCOl1wbdmMnqdBM6PjO+FeXcuhxT6AuYDDZOSM0e6c7uE0WeRpl/7k1UYFkGLCoWUqPHt+V/U10TcfmMUVwCSiMfqGeR7jjmnW8C3LWj81iRkBq9rflId7n1hy4SZ8rptlF/6fbRlq2y5IBQNx5oN7MyBJdcolDN9eOY8txd1cuTc2E3cY7iZuua7P66Gd3W3RqF1DhnbuDgNJmRSHeJ4+vioXrOvC29w/Myh6fJaSGNd/3Cjo6ids7ZAIDmdDOJZp9wAvDqV+frEwJAHKPlJ89mjWEqFF2TskJqJsYe50yja+hcTjeZI4uea6rxRKul6BU6gO7o5dRRIcY/Me8ejiitXn700UfjkUcewYte9CK84AUvwAknnICNGzfi29/+tvweAHbt2oXly5f3trVHKFoFUdQi2HK660FWnoUOeDy9PNlusgS9nJYfcVHApQuidhTnxDqstFImki8WTwu++214cR2x52Ps1X+LgYn9CE9+FLDug7n9Um+gQGxwGJgoSjrowkxXh83aWi7Cwy2kbE4AU65sWUz4dQBtzPuvTwMHtic53Ged5eSR9HJGd76t+oTVkgvK8v3f5rnXUabMVhlkOd35Unsq3Uvt4+J5jGOmzzALRtF/RO6463nQcjbU2NOjDlzEqhYkivNcRFM3btwXGCp9zUf+HhZFF4U/maP+ce+5+wBk6uXiHlC2kG2x3kRmdDcCnxhhca4kE4dmD/ui3k5dSC2J+uVz8vXyfoBbtArI5gohpGYuGZbRwMsY3UWaH5zOR5HjtMiQpr+N41g6QLnoP12w2hbG+rg03TKnhky3Q+O1aTLzNZe/bDK6hSYE3VagdJ1uOY6nv0+d27WdO5IIYjpXFBnhuv4HABnpboQpY42pmKE7TFj6r4ODhRqJ7ShC4Ofnt9CRhWhz5HNGt2tON71mdcbh2SblE+sOTutMJCs/X+ZzuvPPGAcuvcIEEdgRhrpunCp1ug3OrCg0p35wqVEimj08uAAAMF0fQAQP/ubN9vNK+0Owb09u/z2PdBtEVSPLGjBicrrpXF8ksDdQ8zGWHjNTL/eVPik+74mQWkQi3ZbqBIc6Sp/Za17zmkTQpt3GN77xDVxxxRX4+te/junpaXieh9e+9rW48847MTk5iSc96Ukz0eYjDl2rlzP5IC2GykfrAnMPpKQMFpQMGyDGFx0kyuR06+21eUvrmnGSHJcsFEZH4b9oHeZPTwAA9g/OT/a/bQcAwEPMitxQ2pRoioleLlAU6QbM+Yg2+p5NvZybtPXf0bZ1XKcbyWJzPDXqhs9+emkKkOhuQpyHW+Tr10eKeMx0pFsIBvVavVyLfHLRaWrQSWFDTQlbwKS6L+59V5FuYuyZRW3yhjXHftH7s+vwRW+zOgZkizs+zSRrr62USvY+f+10w1oY3YCIWmb7NRnO+n0YIFR9IF9KkUOzhzndRqOb5D3LsZkxUGh7XR9BPafbdB7UsVOmBnM+0q0aP5woUlGdbkrtNtHoMweyvU63NLpD3pA3nQel2Pu+pzAdbGJq1pxuJirIwWQwlKaXS8dIBIyOIrzk0uTzu+6U+bAYHc2NByZ6eeSRa1BL6eV6HV9SMUNntJicnDb2XPI7fi1CYYrim/alCqHmGW5ZSkfy3BQ555Sc7lp+XKbn6eL0lKlGbJUVLWrqKFLq6pig+25qjl4ppEZS0DJjPvu97ig09Sn5fIchcPHFQBxjmChmT9YHCtvaTvtlfTpLs3MXl1PbUwSzwHDyX9HusUS666T/FznTxOdTrVDZjx5AAtx0iUygzBjhvB88jCPdpWf0t771rbj44ovheZ5SJsz3fVxyySV4y1veguHhYXz2s5/FO97xjtINOu+88+B5nvy77777lO/vuOMOrFu3DitWrMDAwABWr16NN7zhDdi5c2fpYx0q0FWNy4KPdHNGdTpgRRFLPaEPYbJfvvvQwZ8u2FxKhtEJSM1/MlOwuaixUmYqHVQXTR0EABwYmJfsMx00gyhS6Gn8BMm33baQN21nLollW2wi16bIslDm3pvo22Uw3Y6kwTevUZook5sUlbYbxEK6qVNvSlfgMNM53ZxhSqMFmdHv5dpBnyPK6KDXREyo1NhzgbxGIV+nO7cA8vL3TKiXU4Vj3evtmg+sPCvMM2gyeKV4VUjEq5hSgNx72k/EfQCynG7AfQEr9nswdbIM1APlXthowgJSvbwHNeNzRjeTvpAZpNl2mbo0YRiUzEGcbNmfXUVIrVSkW/1c1zjQ84WT34hIN++YLHLqqMePWSeUPC/BbCBCai6pPtNaWoHneaRsmNnoLlYvV9vPwdQmLopug5xn7rkXWLcO7X37k/ZE6Ryb5mL7P/qR+jvD3NomEeYs0t1WNyYVM1yf8yLlcfrcmOZMU756fl+qA5O+5hz+Ms+9MKebrM/Ysqf58q/W/RlELMW+aBu5c+IQljAwzXW6BXMp2S6O7Y5V4ajXL18u0n3TTTJfe7A9DS/N6x5PK7TY0A6S9U8wlG1LRcxsKGLU6DA5ONhyhNQxoUe6if5RUTAj06Iha+PUGSgOJ5ydZdgUOigzRqQjHc5Gd/lVM4APf/jDeP3rX48f/ehHkkb+J3/yJzjxxBMBAKeccgpOOeWU0vv97Gc/i+9///vG73/yk5/gec97HqamMs/Sww8/jI9+9KO47rrr8Itf/AJHHXVU+RPqc2QLis6iHly0j5tIVSVZM71cGomGQVQxutsRkDoNXfJYlImOLpgcRGuA7OGXE83BMTmoLpweBwAcEJFuYXSHbYWexnkrzUJqutHNn5fiNdcjuQ6TIqdeztG2THQqul039HKap9pJ3o0YYLnar6ZFX9shAmYCNfx0iraOmcrpFotmGelmIsVhFGe5/aKkE021YIyH5Pfk+kkD2J5Hq8OU063XjJXKzkykXpaGUiLdGr3ccYFB+7AS6S4YP+iCrR2pizTud5Q1oxjdxKlB+3g7jDJWis3o1pgNeqS7RcZEE3qpXk7P2/fy/SskOd3ceNEixp5zZEZLRTIKqTElw2zOIs75mPxeXZzbc7r5MZwa6EaVby87vo02S1O1uAi7rvEhnEjyGhB65UDNx3Q7ks81B67cIZfT7eIs6jqnO90s/J/vAXGMsYFhAJBMM8Qx4HkI3vse4M/elWuvfC/mvHo9sZ7iWM3pBpLPV61SKmbo++FqM9MSjeY0EXXu4GBzvCjnIq4JM39zYpiZU9q6W9kf64FnNN7LUH9t6VguugkcykS6GyQNh+5b9AWbejndToxZJlaLbAthSHhIxGEPDgxjoj4EYJ/9vERO91EryP7dIt2mVBcTuFQLgA9kidvcVJyI+XFRrpENjt2sWlG25pMOW99TSnN2kwIrSQdxVrL0cDa6S83ok5OTeMUrXoFXvvKViKIIr371q3H55ZfjVa96lTS4O8WWLVvwxje+Eb7vY3Aw72VqtVp4+ctfjqmpKfi+jy996UvYvXs3Lr30UgDAQw89hDe96U1dtaFfYYvyuoDLveEmUvpAyrrTDL1c3y93PF0QBMgGGhtlSo10R+S12aOs/CY9nnQatDIa2sKpxOiW9HIxEEWhMvhyk4mJDpQ3cvlHymRI0DbbnCp6nlzSpjy90USnoq+7oZcLr+dwI3COXFLkIt3MQjQf6e6c6WFiTnAoWxrHFbrHmF8E5tXLfcNzRI2DGhPpEMdxz+kW1z2S4oeUXq7ndKs5kmkUrpXP3dWNftcJ2cQKKUqz4enlNPKuLcYZAzOKY8UJRtuS5LYVLy7EfoVw3kA9yYMTP3ERU+tpTjd5xjixpVZkqtOtttf3iumu+r6LHECco7cTITWzejmT021IEWopi1S+veK3VOSPGy6E/oSxTrdhPhDlwugYNFAvLhvGsXTKqJfT9pnmJ+c63eIeHUyYZfuGFgIARqbGso3iGMEWNWfWNHe1R5YkH3iemtMtrqFWMUO/d6Y1TrFoXvbaNHeUjnQzTiBOxFX0xaLnTexXX5uJcTg5T3eDSB17yz9jHDqKdKfrT90ZSAMHHHvD19qU09zRhdQIQwIAhlpJQE9UaDHC8yQDo0YqTJicgjo6FVIzldLlSsOKFAUgczDTtIMseGaIdAvnsRLpVq+frl7eCUtQ7CuOY+m8r3K6UwwNDeGaa67B5z73OazUOmu3eO1rX4t9+/bhjW98Ixut/sEPfiBLkJ199tm46KKLsGTJErzvfe+TRvpXv/pV7N+/v6ft6gd0Y3QAfD4IJxpFJ9UJJhcvP7Cb2yN+R8vNiMPbIl5C0EVvb8hQsQT0hTH9Xx/MQko6vVwMmn4cKYOvHLiVnG7eYaA3xzT52iYzQfOxLWq4KDXnxLAJu2U508bDFELmc3dALQcyz3xWJib7ziik5uCUMB6PMgwKje5yER1X5IxuJrIRx9mzokbCGeeVKdKdvi6b000N/4ymGpASXGrUgTumrU63QFn1ckDt70XedEovz6jwZL829XLG+VHzE5FJmnecLdRt0djU6E4j3YPpokyn69vQIvehW9D+1GCcIu2Q1DUP8mNJUTlEDmIhJxkQprxB0jbpLLJcW3OFAy2nO8jPeaZob6CdJ2AXHAPUSDdfpzub/1gWQcD3cUkvJ5GejF5ujnRn6QhMpDssFlIDisffhuP6Q94jLzmHvalA1eLJMXU7zVgzsbTCgUHg2msRHbsKzRoxug0VM+yO58wxV+RAM61FKGSfKrg2tpQ1zgnbNERqTfvNrc3oeCbWFyXUy8VvKXQ2iS5ya4I0+l2E1EjKC923HqGOIju9XAjRmfqUbMtZZyX9KH0vyqBOCHq55wFLlybbUKxahfZf/hUAfT50pNyXEJcDsvFM3y9LL9f6EG0jZRYV5nRLAdgs0p2poKvjq87SKwNFvbx1+NPLS1+hxz3ucQDQU+P2S1/6Er797W/jtNNOw7ve9S52m1tvvVW+Pv300+XrwcFBnHrqqQCSaPhtt93Ws3b1C4pyj4rATRxiUafkdJP9Z7TA7Ht9MWRrT4PJQ5NKi0WUKRKBEbB5lPWFMUC888uWyEFV0stTozsSkW5AoaexkW5DPrqrejn9OC+klrbVsqgNvOQ30V13yxqSbFkkG728B5Hu8emkX8wb6GxQlJFuWbvZz31nEprrJOpXJtLdyzxaikYahRE0LTox0cUaV3ZLFzNLXmeRR45SOCnrgZeNdPMlw8RnXKRbVjRg6nTrE7Cr0cbljIn2Jcc0UZW5SLfZCcA5D6IozkUNa8pCvThCISPdMqc7+X3GHCh+/lxExVxBx1tqzKnCmRaqpmPNYAo90m2aK+izJtXeHZyP+qNsKo3DRRZzziDGsVU0jkexPVpF73XROE1tTxHNphR7l5xujqXDRXVdy3VSNMNy6w9pYKbb7x1KjO4RzegWdZEFTJU3wigC1q5F8/cPyO8G/vsbwPr1bIlKvZ11xhEYRrF05FtLdRoijAKczoV1P0yakCLoqKU6FA2ZYv7QnxnKXirjtDbpaQBZ4COLdpajUpfL6VYdveK3NHBgi/KK9YV+SD23GUEAXHll8trzZBnU8cZQxqT45CeBDRuA668Hrr46+b9+PdqPfkzuvFxT+Ez6EiaYSoaxLBrGiZhjJ4QZo8tYpzsQ81hmCwino/5c6JoaZUDHHUEvP5xLhpWe0d/5znfC8zy8+c1vxuTkZPEPCrB9+3ZcfPHF8H0f//Vf/4WhoSHjdgIjIyPKd4sWLWK30zE9PY0DBw4of4cCui0ZlnlZs4WCLWcb4A0E3RixPWC0bJiAS51uut+wYIKiqGvGifTi1bJBVRjdgl7e9lMhjAXzFHoaR5syqb/mFgqGAYzWkNTLfom3Ri/h6Cj8//pssv1/f0sqwEZ79ubapN8TJdJd4LV3wUSXkW65wJVRhuw7k1OA1ogsC8VzXxBhnPE63dNMnW7yWtY0ZijR1CAw1S3PcrDK0csp5UwVUlOPbVPbnWxy6Sh6X3RqTrKt1DDIPisyeCkd3laOR4BTfFVp/qlTjkRfXCpJyJzuZpbTDWSRg6bFeBLoZZ1uU6SbGqa2SggZK8X9+avpzhjDuEjbJp4PO708+a+PEfnSOGYjxySkRgX0jCXDSKS7bVk403vNlRZTGDhKpDsvgiiiPk5Gt6FWc1fq5UwU3QY5zyxcCHiepJcvniTrLc+Dv/Jo9vi5/aTtmY6z7xtrzoapYoZLilVZUUSTo9qVus058m1sHE5olIPop7pzLmOx5BWsXfantxXIG8/uOd3ljW7hKNIdW3SNwKmX6ykxpr6gPN9r1yaMiWOPxXBKL5+sD6hMiiBINH9ItRbOCVzEjBAwVVIwoWZwcHAUe10fgrarTiLUMphhmJgzhl5ei0Y/zyKHuA2KenlFL8/j3/7t37Bo0SJ8+ctfxjHHHIMzzzwTz3jGM+TfM5/5zFL7e93rXofdu3fjDW94A/7oj/7I6Te6CjF9b8uBef/7349FixbJv+OOO65UW+cKWYfuzOjmqGwclZYOUBzVzzWnm/6OettcB1/O6C3KnZI03EjkdJPzSwfVkUayjRBSa6eUcqo+SffF5XQX0cudIgkMZRYwGOxpDclgbH/SDrH/zZsRrl+fHJMxHvRjAu5eWBtkpLvRaaQ7+d9ioiemqEKriwHdhSIojzPDOd2cAULvzxSj9Kx7/gHiVTbkTHOpITYoke4wW/TLKLvmzVYUc9NtuJxu3SlQJlLK1ZQvzunOnATcWGOLdCt6FprzhTr0ykW6VaqcniNvg4uStyuUSLeiLp+Ns5yarq8t3lwXiEB2DaYKUh04R2/DUn6Q6xdAnqrLRYY4KiZ9n+XRmo0dpU63ZeHMC6mR/RhzuoWzJU8vtwmpSZZOkL+/zurlzLwrziE5J7f7L+eZNecCIJFukdMtomXvVlmNuj9dn4epEWEb2/LaDdw1KVYvB4oj3eJRLjJo9VxjgAhTMu0rKmemty8f6c7G7k6E1GgbBMw53b00urNtWkqaUPK52AWtRsKyc9Lf6es18VQFB8ckYxBAskbcsAHDT3wCAGD8H99pZFJk7TM7KgsdESWi/0C+Xwiwuj76uO3nI9Qtwugqqixh1aLRglydpMAq6uUtoYNy+Ea6S4erfvrTn8obuH//fvzqV7+S31EVThf8+te/xte//nWMjIzghS98IX79618DAJrNTPzqnnvuQRRFSp73vn37lP1QqrtNvfyyyy7DG9/4Rvn+wIEDh4Th3SoxaHHQvZf1gDcwBE27FcZZXiiJbpvEOjhwke7YYeIHTEav/Rroke5c3cC1azFy1JOAb9+Lfec8E3jrBYhOfALw8Ztzky5nHEeMV5Vrj23yTc47Vs6LDqK5hQSpIemnx4+yVZ+sX0ppejnPLiOM1J2QWrIwHurQ6NYnJU5Exijk00XJvJBENkyY6ZzurD28YTrF5L/qJVSAfP5qtq/k/WTpSHc2oXMlw6SoDTfBi2NKQ988XpQZv7jSNUWMnxpxULDUzZxDKk/rjOL8YoTm1buIWuo53VmkO10MlRBS60X5OpOQWs0n14sra0XOW/+uCLn67RYGUM330I5iWWLNhV6eHyNUA4YzCLiatkC2kJe56wWaI2JftuhxGQcQJ6SmqpcXR7qbTNRKiXSXieoaq2uUc+JFJ50MXHst9l2fMMxkTveqVcAVVyA47/nA7T/MfmdIjRL3kArt2daZ+jlyTk4X9XL6nU7rFZCRbsec7qJqDLkcZOecbn4uUJ0Lxc+vQi8vYJO4R7rz+zZBr0QjjVMiLgokazNbjWpWvXx0FMGnvgeccT5qmzYC5/6/pC9eeaWMZs87ejmwaxsmTjrVyKTIzitvaLpeE+kEcxxTOeYOAFZXIov2M+MOccTpjC4dDc3oNqWuAN2VDKNzr1gDHc708o44ojSyXFT71oaDQt1y3z6cc8457Dbr1q3DGWecgfe9733ys7vvvlu+npqawgMPJLk+9XodT37yk43HGxgYwMBAcdH7fkMoF4GdGQOc9zKjX+sTlI9WGLKlXvI53eYBgxNSswnPKPvVImz0tTHSrVFhOafCwnlJuZF9S48G1pyJ9iN7k/bkPOwpBYcRUtPbnvOkOkzgdP5uMWIXEqSGpBCdEWXOAEj1df/uu4FHHcW2j5vQu4p0N0WkuzN6uX69FO8pGXwpWl32/5rvoYniibBsPVpXDOjUP+WeZJ/LSDf5sE6ikQKZMI7uBFONbleDLSATMVcyTFwXtva19qwqaura8TvJCeaEsMxR08xBwVE3decaXy89T7urk3NsO1CtM/VyYXSrQmotB3r5dA8j3bStinAmjSq1mUi3r97/7ujl9shkO4qd0iJEG/R1h25YUJqqQNvgMNFzuq1jOBVSs0QjqaYJ59Cgr2kbubQCYYBP20qGMRRwKnaUPbvGXbACokB5Z6Qyz6xdi723fw9ohVj8vncCq49O9FOCAP5Uiz2+gJwv07aL8y9KucinWPHGglOqSEE01129PD/3urBxXPVvTDndrTAqFYWkh89HutXnJzME7eNZmeo7emlFnU0i/seGkn06W0MeMmUM+k9dl3wu5tO0ZrygkYu0OarYbULG1ss/172ml9e185L7SQ/DrfPEHKIyurLgVFGpWjGWyFKnXIlNyZztRaT7yCgZVnrlvD6ltM42nvWsZ+G4447Dxo0bceONN+Kaa67Beeedh3e/+92ybveLX/xiLFy4cE7aN5NwHdhNULzqoUrVMqlecnkcZUoAyUh3mA1e2SLFrb3qgrsgyqXlvDQZCu7IcGp0T7aU/Zsi3WqdbqRt1w0JrR2WQYejd9OawLlzI2XMgji5jhE1utPX/p7dxn10Qn2yYSIdgIc7FFLTJxku0t1tndjcMQsWTvI4MySkNlDn+xegRvpETrdC0ZbOK2I8GAw/0Y8nHAwd9XfZYptGk3Q6NLdIzVG2qddfO36ZSCl3z4rrdGeLQFZIzTJ+0VzBPL08268LJVWc90FJL0/elxJSk86P7hcfQk8iivlIKJBFWAPGCdadkFpxxL7u+5hCJCPdNqOKRpopdPYHFxkyRabL5K6Lr2JSsoiPdHP08vxxaTkvgHe2lBFS46O6vVEvLy/MmLB3hONl5CUXAEN1uV1RVNcU6S4yuu2RbuJAc8jHNkUYBYq0ZgSosZP/bf6eCbjSy/V7Q5XFTRogHEQ6lt4vadvFuZryjHWEhrUTh8DPxqomwxKhBhqrXq4L0fmewhisRekcm/4XNeNxySXA+edjuCFET4uNbu7eO0f/S9fp5q91xDzX4nrIMd3gXC5K2xPBgqzqCneeyTOUpbx1EOlOf5IY3Yd/Tndpo/uEE07o2cHXrFnDRspXr16Nhx9+GADw29/+Fo9+9KMBAJ/73OfwvOc9D1NTU7jooouU35x44on44Ac/2LO29RPKDJoc1Ei3iATzD5xe3qhBDBD9gbJNwhm9PM+KcM3p5sSjihbcOfVy0saRdMI/oBndpkUYF2XTb0F+oWC+JpwXlEbVcpQ5WsYspZCHZP9RWpIlWLZUfuai0NyN0d1tpDvnpDAYPhSiz3ZKtTUpf+qYsZzugucmSI3uaSbnO4uy5unlutq9pHo3i2m63O/CKCY5pRm9PIpV4RVbZEahxuvt68BoY0vsGMcA4eiL2Zw527NBaYu6157S1stQUiemDZFul5JhPazTDSRzRzOMtEh39nqaoWNKmqKIgpdoiq1f6KjXfGCa5nTbIt3Jf9ecblX9Pvlvcpy65J5mJcP4dAt5TtToZiLdQHKtQy3dKFMvz5wtLkJqnEGaOdMidnGuQ8xd+rUtiojl95ON4/smWvKzhYPqnGFzwNLvRU107tpwyDNaCiLdlvPiyq1SuKqX+0jn7737klzis87qidEtlmeN3bvkfhEEipHmcp4UXL8E8v1dOOiKc7rLRUHrgY/pdsQKgWWBC1rGNfutzlrxPU9hDIpUPJ8q58cxsHEjcNNNGB5IGINi7LYho1QzaywLmzCOY5lq6V4yLN2vTi+P8mOLZCixkW5idIvvC5hjE8x6QndGmdYkLjjS6OUdz+j/8z//g4svvhh/+Zd/CQC46aabcOONN0rK+EzgGc94Bm655RZccMEFWLZsGer1Ok444QS8/vWvx6233mrN5z6UUSYnhwMnJmUyMMQkM81EwrsXUsvaYwMf6bZfg5q2oOXOb1FqdO+baCr7LPKwA2Z6ub6Qcsn1pIaESRQLgFJDMpD08mwwEqJq/hPPyNpjaR+th9gpuo1054ToHKhZYtHfeaTbzSM/ezndfJ/J6OV5dgnrgNL6jNjWpd6xevxsYankdJN2KzWqLdFj23dlfIZcXy2u0y2iSXwerY0FQism6P2AGnBuC3X1Psic7kBEIBxyuntILweya99gjLKkTfmoiFT1dsh11qEv5GwREHm9HNTLKb2UQq8hzI7hhjnElpKjg6qX26Kc4p4n+fJq2+W+SIRHgFMvdxFSazHztRLpdlIvh9xe2XcX9PK96Vw7MlRnxn7tdwbmjthX09ERlRtfDQJQLk6WIiNKL6PFYnQUtZe8JNl+y1ZZfST8zW9yx7eNUdx+gys+DABo3H2n3C9GRxWHS9nqHybnvP6MlVUvd2XKiHmL9nedwRJF/P3T2TmBD4UxKGjlMtJNsXWrDCa40MtFYCdg+pftmtDvXFN2dKFguS+OYi+E1Ji0PCWnOxJrZL4NsuqKjHSbz7OMWJ8OhV7erujlLP7iL/4CX/7yl+X7L37xi7jssstw88034xOf+ARe9apXddWoDRs2GL8744wzcO2113a1/0MN3eRLCAjKUC6nW180W5Q/9Yez05JhRc+lnVpq98plaor5yXBkODG6x5thUsrFMOlyk4lJBK7oPfedksNnmxBFDcl16xCkDYjF/j1PRr2DWvYYm86FfqfTxsqg20h3blFBrpeR3lgiJ4wDpU/bMFM53foi0URz5up029TLddESfT9lI92mkmFAcm04OqapbFlyHlr7SkW6k/+s461ACVsVr6JGpu5gZBxScZwT7qNji8sCVlwfQZeW6uXSEHM3untRMgwQ5x4Z6eXieHT80nP6ywmpqdvalK/F9XURUvMMjkPdIURVqgVMJb7KiFeJn0Yk0sZtT2vcm0TMxD3h1cup0e0upKY8f0pUV7Tf1m9T55s2TpYdF6lgpzS6h+v57XLODvV7+riGUZzVcS9oh0uVgoSBID4rdgiZjKhCZ4aoPrLyUcn24libN6P9yU8Bz/47do4WMN4usd8zXwQAqIdtuV+sW4fax74BoKZGuh29nibD0aReLpTETcGUskEjwXwRTmh6LIVezjx/ui6B73kKY1AY23qNeADAypUYTtN5BFvMBu68nIxusv4qK6Sm7zdm1tS+dEwi1z6a5tD07f1Cj3TXlLlDDWS0S7IZKGgOuji/wQI2y6GM0jP6Jz/5SVxzzTUpRSLrAK961asQxzGuu+66njawQveRbvpbWX7DMJHqE1rDql7eodFdGOnOU7pMUYqsbcJwUCPdtM0LButyEts/2TJGrXQxDtoWk/Jt1nbbokbdFz2GcaGZljvzFiZlzuSkvWoVouUrkv0ylNCsfeUmhCJkdbq7KxkmQK+9Seit2/rZruddlkbpiiIBQjGZcSXF6CJRwKheruVglc3pDiMSTQp8xag3RYZsOd0m+rsLOFG9Im0LhV4uWSxkn/pzzhig9Brk6nS75nSn+8qpl5egl9P70AuIS0ZLsQg9AYDQy5nFm4vAmA6T3gCHMgwNU+QxX6dbdcLS35iYTab3FGL8V4TUmOZmfTEqnDvUOt2p0U3uU5bTbSsZlne00YWxS2pXwLQHyCJ6DUetCzqO70/p5UJPRTmexQELaJHuKMYvHky0S047eoH1+LnnnDGKKAPP5X4X5XSzYxKTS9z20/saxwjT1/SnORE4bq1E9huk+22ELblfAKh9/WuyfVwAwgaTBoo+75gU+HPNNfR/E2QVDhrp1oTUTOrlYuiQjkLPUxiDglYe0Ei35wHHHQecdVYpITUu7dNUXYGCBqudhdSC/HhGj+Mpjgf1t6acbulcNqw9ZaqpYCBZVNpNgQAXyHQs4ujQdXAOJ5Q+s09/+tPwPA8vetGLlM9Ffe577rmnNy2rIJEJvHTeEfWBVFJ2C6JwCr1c39bSnky9PC+kVkgvZ3KFinIpM+NEj+SrC+uFg4nHff9ky1gLUxyfDo4ZvVw9bm7h4CCkxtLLbfd27VoEb3kLACA8Zw1w/fXA+vUIB5L64kpOk4M6uIOOkxGyTvdAhzndliiTSSm2qLRFEUw1LnX0Oo9WIB/ptkeolYoBtfxkaxIt0Rcr5SPdkRLp9n1PMfrZPGnbeNGFkBqXalBEB5X32WDo2Ba0dBGhX1+xkHDN6dYV3QdyQmruke5eOYBEm3IOIEF5b+Wj2TmBsRJMhTIOWtG2cQctApPuQ069nOk/pohzR/RyGmljtqeshrBg7qDnwil0Z+rlNiG1ZB9KWR+SB+qiXm7KXy5LL6cOrL2p0b2YiXR7nsfm42bvs9ftKMb37t4GAHjO44+2Ht+mJ6D3d257bl+FOd3cPphcYmFoA0CYnnxt1075Wa5vFuy3JnJpQxKZjWMEu3cBEGNWhzn5BRUC6Pxjm1vLCgGL+8XRyyXThDx/dLdijMoi3cgYgwBqwugWkW7RAa+4AggCzBsQQmouOd356C4XsNFBnVqujkzTfrnUlbzzKntPBfbkGtnQBmFkj7ORbvW56Eq9PG2vWFt6Xu8YXv2I0md27733AgA+9rGPKZ8ffXQyEG4l+RMVeoOwSyEpID95FOV0c+9L5XRL9XIa6S7+ndrWfKTZvOBWjZPMUFO3F3nd+yebRi81N8CZovS5ElgOXnOWXl5wb+v1xMhtHb8aWLMGCAJ20ZdTYmci3b2o0915pNt8vUzt6za9wjnS3WXuuAk5ozsXBRRGN5ODxURmivqtmNOdI91kItbprZJi3I5YB1GeXk4NcnNfLGwTc8+KqN3UqBULGxt101TqJRsbxeIyGw/K5HQL6EJqTQevV6Zi3RuanXju8n1RjaIq10uL2nSiPi9gi16L6yL6rc3oznKq1c/1aLx0GDCl9vKGDX8M/viQxzdpggDZc6DQyw3GPRvpJtdg0IFebnIyA+7q5aZxkssNtYEabRm9PB/pBoqMhex492zej0f2TGCg5uOc05Y7HV/uh1Evp6wBl5xukxFlLYdF1sK11ChuU6M7fR1MTRrbwt4vst8zH7kTKw/sxDMf/JWyiYish1Ek0wVcgzYm7Rd9DeYa6bY5pziI5581uskzwznRjOX/UsZgsGhh8rmIdK9aJcuFAZmAl4t6OScsmj1D5t/Ra+Wa555V0FCvM6vgblgX0NetkNTpdox0mwTZ6P9ObBQxrIi15WAtKAzMHcooHa6K0kFGL821efNmAN3V7a7Aw6WUSRECzYttiurpDyCllOl5ebYHjKvTbYoW59sqBoY8DdssoqROjib6/MhwHY/sAfZNtIyGvJjsozgZ1Hzfk4u8nBiM1hyXCZyLdBcZSFwpC86JYROnca0haUPXOd2dCKl1G+l28D4DM1inu4AhIu6fWAhSzzNHKzM5IXRqly2PVm1Pdt11Aa964GOqFWl50tSwNi+Uy5QY1GGLdBeprbaimCw0zYt6jiZPjW5xLlS93OVc9O9E+ZM6k3JjgqtolCvE+eX6oibuxuVHCpSLdOuGTzG9XMBudCf/zfmmqrOI5iebIt05Y8/y3FAhNVOOOMA7gEwsH/pscwJ6MtJtoJdHUdYWTigvjMx55RSc0R3HxfV8dVCjTYiWcpFuIL0mBocAbep1dyWG5jmnLS9kWdm0G/R0Cv37/L7yczaFjKhy+yC5xMOtpKztZG1AfiZKfgbDw/KzvNHNHJTs9/TtD+Lmj/9Nvt2Czh7FpTVRTNF93eFI22qNdJdcv4p+NkXukfipZAtGMWGQ5Md40R5lvbZ2Lf7onGfjiZ+8CWvXPBb4u+ul2ruA6FvC0KTYdXAaO8em8ZiVC5Vj1H3+mTOBBhXcI90qe0ruy6LgnrUp7yQPoxhN2PuFzOlmGHj6WKGXkysDcc/GtRKbhys6Khl2//33K7nb7XYbb3/72wEAJ510Uu9aVwFA8WLTBbrh0TRMpDrVpGP1ci6n2zFiwtHcCtXLffV4pjzgTMG8xU4egLoIC+MYPjx3erktkiAXI9lnumiTCcLonmxltCcuemEVUmPo7WXRrXq5fv24BVFONbXLXGvd4WTCTOV06yVuTIrfXKSbywM2RrpLGC/K78iErteHprWluSivLaqkL35L1Xlm8vuLaIoy97rNU+F9P6sBq7eV5qG2tAiAGBPbYSQXNy4LdQFx/8vQy1uM8dUNxHUwR7oZenlBpQaX4wlY63RrY/SAZZ4zUV91RX8uL5VbpAN5R6qLkFpMI202IbUwliF8E+2Tq9NNxwwppCYo0WGYUIy3bgVWrkTzzKeT4+afzbbBQNHBXrMoK2/kqi/A0ctdIt36ep3WjBbU8vMKqOUAN8fkjQVqdDtFujvJ6Ra5xJs3Y6g1DQCYaAwiggcfMUI/WX7XVmbnVKQ3oO8X3FzueQgWj8h2l03PMjEechUC6DrJJdLtOP6L+zXVzCoqiGc0e/55J1rOUai9P2bpfHzzsucYjy3WWeMMvfyVV/0ad23ah5+++Vwct2SYZc64BDYUITXHIbXIEeLsXCapdkWMODFXiGPwY4u63u5Ed0pcMxHpPpzLhQEd0Muf+9znIo5jXHjhhfKzZcuW4eqrr4bneXj+85/f0wZWKJ8Tw8GUg6E/cPlcPLPRbc3pFuIv1FhwmPhpW8vkdNcD9TcmermY/PdPtoy0J2q8iOtlihTk6OW2KAkzcLa0CI0JHO2JpRZZI8nIHb8sJrqt06178mn7DEJqTYbWXAamXHEdM1anu4Bens/pzk+S1FAz9ZkytZG53ylCaoz4F/cM6tF1Ln9SoMzwlaUaZJ8VpZjQtpqov9wCXD+e7nyR9yAql9MtIKLLgjXUKhPp7pWQWrobk6ifLBlmceCVaYpN1T6/rbuzyDc4DnP5phbHrd4UGx1Th1Kn2yGnu2lwAAH8fGArGTbdDhPV6tWrk/JQF10EnHsuWo95nNzWpF4uniOb40Q6AZh5V9+3DTQtIYt080Y3bQ53HcW+do83UfM9PPPRxWVhaYlUQHcEpv29lTfo2HMpMKKye8tcG5JLPK+dRLpjz8dUvQF4Htrpb3xDRQFxLrb95uTN0/f1l/01gMQochmzlN0b5kv9GRNOTHEcE8oeXzg7p7gyhrRPp83jIt1y+5JLZhHpnmTo5et3HkQUA/dvH0vbkGecuZRRk8Enr1jfSO6XWQcAvEPD7ggXTqTIaAMI2GyBXKRbGuadR7r1ah+HK0pfobe+9a04+uij0Wq1ZIc5cOAA4jjGscceize/+c09b+SRjraBTloG+kCaCfXwdEMBagAUia5RSCGZNqWqpW1xFFKjFB2pXm44Jq3RC5ipwouGkkF132TLSHuii0BxvbgBnjsXG72GU7ZsOQomCVVNOhlwAj22uuFc9LAshAd4XseRbkv7DE6BTOG9s/4vJ4iCXFo9wtkrFKqXa9QxTkeBUpu5yV7/HdB9yTAAqKfXnKowK/fMUkawK3o5F+ku8KZTx5spD5mjlNPXdJEqHAo1MraUUS8XEIuIUurlM1IyzFy+jhNS64ZeXsYBlKug4WB000sYx3llfc7Rlm2jMbY6EFJLckrTz5jtVQcQvx0Xtc/Uyxl6+eZtwLp1UkRLoLkjE+JSS8Jl44pJuV05N+aaUS0J5zrdYryNYRVSo9sCvDOe3ounn7IMiwz7sf3OFuku6tMmA1SgsLJMmks8uGI5vFS8a7w+lFQfefFLcr/NVVIxtS/dL449Vv08zVEOnnYmAJVp2K2QGmc8m8T3KFxSGyhEWpRwQnN5/1FsoJfrjq2SucEiuKFHuqMoxlhqFG7ZnzhQWkwwwBQ4oHANPlEURbrpc6TvltM1aUfFaSO51FPOeNeCeB0JqclId+pwrIxuFcuXL8fPf/5zPO95z0MQBIjjGLVaDc9//vNx4403YsmSJTPRziMaM1EyjFM7BZgFEI10l1iEyZzuMK9eXkwvF964vMfdNFDpeZcmL97IUBrpnmjKScUUeQQyQ03Sy3ODOt92DlQEJDsvtzziISanm6OX68fnJqSuhNTSvJvhGajTbaKBOym8W+Aa6TY5orqFq3p59n3ecG3TSHfI91ubkrgNdGyQAl5CuZvklIu+SvuUazqKV8KrD2TnwkXdinK6aXTRVrOXGl9ZKSjSD1KHQ4NeA4f8RP07vWSYi5Aal9vbDUST8gsplV7OOenkPkrMPzlnjJVern5nVy9P/keMMQ1k7Q+Y58YU6da7pZ2CnfyPY7shK1kNoUVIjXEgCOcHSy9/4EHpud60cDl+cfzpiAG0BE05CmVJJECLdMv5y3hqbD5qqyOjLfmv1uk20MsZg0AiDFGLs/nuvMescDo+YDZkdQdnkSFYZFQ65SuvXQt/w3oMC2Gqr30dWL8e7dMelf42b7Rl7y2NW7sW2LAhqWZy9dWyqgnWrs2lLBW2kTmmqTwVd89M9Hugg0i3VC/P6/hQtl7EPM/dGt0i0j3VipR7Pt5sy6DRln2Tsg36MakoqQmdCFOKftjS9stG+w1OfSC7trSUnJFebrENRJ8V90BS0LsoGSZKbA4d5jndHZ3diSeeiG9/+9sYGxvDpk2bMDY2hm9961tYvXp1j5tXAcgetJ6UDEsfNGNOt0UAhz50vmcfNLicblPplHxbzdRAk+El8y7TBYOZXp7mdJOSYUU53fT4etuLcojU75L/dMHoWkOTE1KTzAFLdIpVaO4w0t1sR7LfdEov1+dAdQJP/pvVcztzOhVRBAVmrE63ZkTYjGz9+Lac7lzEzmDsFYFeH73eryIIFeXbq7dBpZeTSbrk4keKTLFjgMHxRiN7BoGjgPHW6+2bkkrevrJdK+Kj/fl2aPdBCKmViXT3UtQvDFGbnAAANNY/lOQEa22dZqmc6m5KCanlRP0szACLo1cH77jMXhsj3WGIaMOGZJstW5RrUPQ8Ukh6eUQXz/nthCYC1Rcw1c+l1FyOXj4ohdSy83zd+Zfhogvfj0889QK0gmQsrrdbwEc/Ks8tEySNnFhmbPoTcfC5Os1UIbU00j2Pj1Ab1ctTGr0/llB5vTjCs//qucnnDjAxWmysNtt+Oo50yx0FGB5ORNTGn/h/gCBgnUA5HQyH/WLNGuDCC2VVEyB7pqaI+J67EzY1qHKR7rwR7OLQLkoL0qGXDOPS58KYZzOVSRXhQKuy0LJhB6ay11tTo5tT7C5Tp7vMeBpIY95AL6eOBwtzh9oBRQEfk/5H8lq9723HdSwHKaTWFEJqVaTbiIGBARxzzDEYGBgo3rhCx+hFybBcTreM5tijcCZ6eZGoG6deHjtQ3AB+IC+s0y1zXuyR7qxkmFm9nMtVEvNP3hPtPolzzoSivBoBKaRGJgIuV9A64JIFYyeg1PahDkuGObVPn+y7zG+V0dqCE5+pnO5kwUrbY18YcGqjLeZZKCOCaG9ftt2kVidYlgwLI7Y8Tt5JR76zOIOKwDlKisYAGl10i3ST16TdItrYkNeARvuLF5D5nG4hpJZeS5ec7l5FuoXx8uCDSVuu+FCSE5waL+I8xPGs+ZFlIt0lDNlSkW5mjKB9RPR5ZaxNr0H4xS8l3339WuUaFI3p3Hd0buIcwTQto6hkGB2WbEJqU/UkWhx6Pu496kQAwL+s+Rt86YnnJceM2sCll8pzqzH9zdpvmXnXdX7izsspp5u0Rxr1o6OSRi9UuP9w4z1Y9tB9yecOhrca6abrFrMTzrYfEztMsn8cno150nGezOGc87TbSK1AFunOO9iKYBIDCxmGlckYVH5n6P8miPFgkjG6fXI/uChvXiTR6ZASAzVfrv3oeufAZEu+FvRysS5htUFicyUnmxaECcKJaWIfKNfAMvZSB3JRwCev98Td91juz7YvG/T+VhndDL7yla/guc99Lh772MfipJNOUv5OPvnkXrfxiEfXJcPCELWJcQBA+3//FwhD42Sao5SQCaFhiBJx4Op0u9JqbNRA00Nd06JITQN1hqqXmyKGQJ5WZsrDKYp8K+eVfqXmqbp5yiW9vBXKwbxIvdz3VEqva8TXBJHn1Aj8jo2B3PVTJiyV+geogimdqve7R7rtdKtO4Xkem2spYKOJ1xnxLW6y5/brnoOZvZZpJ7qQWpsK8pgXiUr5lC4i3ZxxVZzTTejlBYaOvh8l0t1S65JSin+WT2+J3GrtE5FKqXNRItLdlQOIMV4aYStRPU6NF51ebqP79lNOt0nsKxfpfvAheQ1kXeQ4Uq6BPvxbU4Q0ZyzAXxcu1cFkUNH5gC0ZJoTUgsRw3bJwOVpBFjn+xJkvSn7TTg2D9NyCXyW1m+kcbJ2f/PwzZ2LE2SCu377Jphy7xbybO6YyPyGJ0l98sfRyC7r8efffnHm+L7lEYSrw50IYev0Q6UaWkiUietl4mj9e9r5wtywycc7y9HJjFRE2p9t+fYDyRqaVXk41FVgRMXVfZZAWAucAAKaHSURBVNfMnudJFt+4yejW6OUmcU6j+J6BNWlDkbida+BFzFvUH2AWUjOP4bQ0G61w0Am9XL8Oh3vJsNJnd8UVV+Ciiy7C97//ffzud7/Dww8/LP82bNiADSmFq0Lv0E2+hPDyB/f9NtnXZZcDq1ejtXU7ALvIU+B7yiTN1ekzgS0ZZogW6+Aj3byhIdtGaDPJf97zRtXLbROmuNRtPadb27TMJM7lVLccc7rFhB3HyQKZ7oMe0kVUpCi32QRBbe+0XBhQIPTG0u/L5xTqcM7pFgvMHgupAeoiuijSreRFGxwRQHFOt6tjhIvU6TndSskwQxRJbwNXYsQVnNJ+ltNtcLxRernBwaeI3hgW41NaXVJZjjCMjfn0FHmavyqk1nQxuruNdBuMl0bYUowXsXuuZFiZ1BkdOcaUVXhOYwYE5vGFinQJ0D4iFoMyt/LmW+T5irrIfhQp1yDQIlI2w5Srrc3Ty/PPjVFIjbR/mhHQkzndA4OA52HD4qRO88m7N+KCu36UHTNKDQPB8vjsfwFQ52BrvjozP7Q1J5wLxDF2H0yi3EP1wBi9yjl5brpJEYo7fdsDWDh1EM+/76bkgzgGNm5MtrPAxGix6bfY2meK5JYxKHW2WkYNztN2BTqNdMta18SB6JweYDC6Mw2cPBPLltPdKb18mhNSI+wQF3p5J9dPrG9EjjEAjBF6+fYDU4iimJ2P6PFN6w1T1RwbTLniHAPTtibljukqpMartEfKGq0jennOSX14R7pLJ2b+x3/8h5E2UWFm0DJEtgohIh1xLCMdbT8ANm9G8557geMeX1AizGIMONLLaT3MyNHDx0UmxZxXFOUSRqyJKixzuieaVnG2ZDLJImZc/jTA1Hh1WLBxke5CIbU6zTUKEQwWLypM+U2d1ukWtLhO87kBhhlQIKTWSckaHS6R7jguVvPsBgO1AGNoK+0RsJX+4gy1jBZmj5iXVaul4HK6bZEO+d5Qp7vs0MX11SJdB0W8ykEk0fSsiAiLuH400i2FuiwnVJzTXfz8da1erhkvok7w/GYSpRHGS+2gKH+TjxrlDIAyi8QChxCFXpXATi9P/quRbkGNz9ooHW1T09l2NNINyGvg33KLdgxzW8VXdKFpe35s9HJOLVzmdHPq5YsWAwA2LE4Uq0/cswX//L2PYu/QQvzklKdguDmV7TyOEexMHOsKvdxybnJhT/pnN/TyPZJablYcp4+y73lJ7XGCT46+B+2ghsF2U/2htp2pDYDGuCkYe037MRlQZUQ+h1ORrvFpNdLN6a7I9x0a3WIulQKJJeY0sal+ztz47zK3dpzTzehM0JxpTsiwlBCdAUmAY1rS2wHgwFQW6W6FMXYdnGaZV7QfmNZZZa8HPYbOkuIYmK4VOwQ6E1IT96H7NZp+zw53o7v0Fdq0aRM8z8NHP/pRjI2NIYoi5S8soP1UKI8iajX/IzXSEaRGd+T5QBxLxdO6pw4MDSVSVfzQmcBHut0GG1uku0i9vC1zunljluZ02+iq+mQr7kHOyM7ldFsWjMwE5VpqIfA9uQCfaLaVAd0UncrTjJA7fhmMS+XyLiLdlhx40T4lp5D0n07V+8W1LRJ7EZe01zndgGo86VE/W6S7Rgw+AS63mtuPq8HGXdeMXp58ZyoZlhOB8/Nt59pWhICJoBTqOpDfiN/ZmRX8syIWezLSTfoPt1DOtcOQ0y0YFKXqdHdqdGtGySU/vxqv/NU38Ucb/ldta0s1ZhQDIDfWuR++jJJ+mVJ3mcq83Rkj+w/5bKqeaM8Mt4hxCsDfsc3admVbL7/45eYkRSXY0G85Gi+nXj4oIt1+Dbj2Wmw47lQAwOq9W1CPQvz7f/8L/vbmr+GyGz6r7j8SqVaUXm48tWx+YujlZdYe4h6J3ZiUywEmirlypfJ9LY7yBjeQ206HiXGj9zXXtUgRVdhlfMvldDNpg/lId+FuWZjKUrr9NtlWNxq5nG7XuRUoY3Qn29nUy6M4lsE/utueRLob+Ug3pZcDSV43Nx/Ry1yUklCuZFg+IAHw5dj0y1wzzMnZ93w7rCXDSC4/XZt0skbLRbprh7fRXTpkdeqpp+Luu+/Gy172MsybN28m2lRBA1e/txBapGNk6iAAYOPIUQCQKZ7edy9w+jFyO5NaOaBGJFxzuhXVZVd6eZAfYIocD3ppJZMxK4zuKE4MbwCsmIo+2Zro5fp728TCRe/K5BEPNwJMtyNMNkNFfIfL4Qmj2Jjz22nJMLFYEB77TmDzwnJ5vIK54HkdMD3kMYQxZjZ2aPSx1zndgDqBFaUk0D7bYKKjJgEUm5PMBt9PhN7EZfe8rE1qveG8IVvTJnv6HXVelF388PoHjjndFiE1dYGrUs3FNZhuqdE9KWYXRnKcsQkwmZwfwqFZlNMdEnp8x31RM0rO3Hg3ztx4d26z2sAAcDBzlNuVgMss3PW+aXNSqMd0YgsxFGjOeBF53AAwXh8EADUiDMA/+mjg7oncMTiIY9DnsYiyKSi+eSdGfrxj6eV1EfmLgLVrsWH/KuB3O3HC3i0AgKH2NN5y41X5tqaO9pZjpJtTXpaCq2Ui3doxTMrlgOYw9jzgrLOSWtObN6uJpwKel3x/1ln2NpicawVjb34/vLEjUCYgoud0s0KoBY5UV+jtKec0Sf675HTPaKSbUy8nayiWXl5CFNEEweSjlWIovRxI8ro5lqIS6Z4BerluyLuol5vYZ0AyTpnSDnIsWCZ3Pan3bR8Li6C3d6jR+/VXP6H02b3lLW9BHMf4r//6r5loTwUGpvxkK7RIx1PSBdfNxz8BQGZ0N/buUbZTKeTmRX1RWzghNVd6uU29vKhckIgqmOjlg/VACjWInDNuIaJPJpxHEeiQXk7W3G1J33OftCeaoWKMmASPTJ93WjJMLBbmdRXpVt+rE2pqHDML6rrvl6rzTFEUrQDUfjojRrdNQdch0s2VDCuK2pSJktLnqhFk1zrLr+O92VwNUL3tQOfq5XydbpNnPrtWpmiCdYGbbptFuj3lP1UvL1UyLFen2250U3ZQx5FuYbyYnhnPA447DrWlS5SPeyXqVEbUjz4bRSwTe9pB/t62h+fLazDRGAIAzGulFPv0GgR/9HT2GBzEVwq9nLnG9L5NMxE7IG/cRFEs+4aa050Zfu0wwoY9iYPgxFrLen9ry5YCUPub3SmcH3+lsGIH9HIBW6Q7pzwdBMCVVyYf6Ocm3l9xBWDJ+wf4FB39c66tpv0Y6eVlIt1prvDEtKpebksJ63bOM723/5Z3NHAOhuz6OKiX98LopvTy9JBqpLl7p4XI6aZGN6WXA6nRXRBl7m2kW8xBPL2c9hPbNbBFwXVYI92kj7RJdaVO+qve3irSreGHP/whFi9ejEsvvRSf//zn8ZjHPAb1eubJ9DwPn/nMZ3rayCMdZRQyJbRIx9MeuRMA8MtVj0PLDzJ6eToxC3A1grn3RVH3AaZkmKuHL2AGmIyKxR9Xp+G2DXmvADAy1MC21hR2jzeN7clHupPPbYt407707zihMBcWwxCp1U0nxLwiOIAwf61MHmxXiMXCcBc53fYaksl/7vr0olyejQLXC8E2G+gElsvhtlC0udrOLQO9vIxitI7A93LK5ck+kn3S/DY1X9I8Xiie8ZKTMad/YKs2QI/XJuJVucgWQ5GTx/Q9IIpJTreglwuHXoQw8q1t0I85UMscGFzKDQfF6O5Yvjg1Xtatg0JjABTjpXZQGyMs+ZGdRGZM703fFTkZsuhw9plc/DKU4vAJTwC+DMDz1Eg3uQZ+TR3PbOcp7iUdSziHEn2GhdFrUlYW8yI1jgdIXiOlmk+0QmzckzgNTnjj3wJ/8WPj/Q1e93fAfVn03PPsRhw7/joKfVLo/caW080qT69dC1x7bZIeR9h6WLUqMbjXri1sg6nCgs5QKerTJlExgTLlsHKRbsb46lWkO18ho8T9M5wzJ2ZbxAQAYGQdmSAqdkwKZxVjUCZVTfLXXr8PnfgshrU0AAA4MJm8TqcIbN0/xaZ5eZ4nmYa9jHSb1jBcze98yqPavnrgEYaluQ22nG4qyltG14CDPrQc7jndpVfPV111lRy4b7/9dtx+++25bSqju7foqGSYRtN6zI4NGJk8gH1DC3HX0aegmZYcqT/5icrPbJGrMmrEdWaBacqL1sFFJosiTHUZEVQXMNygsmiojm0HprDHYnTLsmXpqBZJj6K6Xd5zaD43zpAQkXlXejkATLbaRvVygEa6tc+ZKFEZyEh3N+rlloU8N9mbRMPKwCXSTY37TqMLNihGd27hZzbCaZRVQDqgcoaNlg5S4poJ4UBAyz/Xog7ZtszrHDPGfbzQYY10m8YAKl5lpJfbaf5NZKq54npmUQa3SDd9lrlrWUQvb/bKAeRgvNS/9BvlJzYmQCeRGQGbejm9XkVGt2iCOjfkF7/iPrdWHC2vwUQjMbrnNSeVa+BrThCbAZWpl9vFTX3fQ833VOM8N/ZlTiJAZZM0DH1ow65xNMMIjcDHMS95ITBgvr+1PzgHuO9mOQcXGYZspLtd3umpXxNTjW7Aks6wdi1w/vlJmtzWrUkQ4ayzCiPcAkqk26IOXsTYK4p0d5XTzUSO8/Towt2yKHueFAHzjNG62JzYolW9XOhrOI4fUoSXq9NNnG7cmqwnke6GKngHZJHu1Uvn4aFd49i6f9IY2Ak8DyFiS58p3zYxRprqdNuj/XlGnEtaY169PD/Xh1HsrEtkQl5I7fCml3cUsrKpl8/EgvVIR0f5fVqkw49jnPnIXfjeo/4IN59wRpbTXVe7QJ3kbVvp5QUDhhg4uZJhxZHu/ORfVJqD5l3S/5zhsSj1vAujmxVS0/LbOKVMoNwgzwmZlaGXCwXzQnp5+j5HP2Zy9sqgF5FuF2VNTmiuJ5Fuy8Kg1e7gGSuBhuXZsdbpZijJ4rnQDRl9P2WoyfQ+0LYKQ3ayaYh0K6/Nk3RZJzjngOGEhyiysoGRk5BaziAX9PKWSi8X17EdRYVt0L+jXnsuP58DFVHrej4tMF5yCzJLxKSTyIx4bUsvUIzuDujl1pzuKJbXYPx93wfGYwz/6z8D568h10A7T6uQWvJfOsIt96dR89E2PDfJb5P/YiyfTkVoPU8d73zfQyPw0Qwj3LctUZs/bslQsj/L/Q027gOQ9SfXUp3dOj31Ta1CamTbXPuCAFizxvm46n5JX7A4/4p0CrI0M95RVpTyQjEkBbqEenn+vvRCCIxrTxmWpIxex/n1V7KvvOPSpU63ayRUbFdML88/93rf6yynWy3tBmQ53Y86egEe2jWOzfumjA7YwPeA0LzOMukD2WDKneccD4WaMcTBblvvuATdEnp5B0xcAr29VaRbw/XXXz8T7ahgQaugRrURWqTj6Q/fmRjdp/4hWouXAG0mb8Ma6TZHtXSwOd2Ogw03+Rd5lIVxIKil4qc8vTwxuncdnDbuU59MZD3EgknRqU43I6TmJsSSGd10sDXlletzXKb8mzjOyi7oZyKnm6OzdlJSzYZawcIJoMyIGTK6LUJquZxuJXqc93CbatbbcsOLoOR01/JG0KQS6eYXtLpjpMYslsq2R4m6RXYHjDjfKKZpG+ZrZDK2pjTxqMyhVz6nm5Z+co50CzGtXvVFi/FSt1wfffwocw9t2iA6lOtVRC+3zA28MSDCSgEmagMApjDvKf9HiZiamEIcMlplajBZmptcAyJSZxinBZsjUy7PO1sGaonR/bvU6D5xGRGxNdxfcV2zucvc1uR7s9OzDGumc3p5lw4mApNzzVae0bYfc9TSLu5IMW9AFegSw4CtGkTnJcO0sbgDpwn3jAGqU8plbs0Ez9yOLwI/YhzmxqQwjlmadi+E1Ia0NAAgi3Q/6ugF+J+7t2HrvkmjiF6RuJxJH8gGqe2iXWeXa1AmxYoin9OdDxyoke4O6eW5SHdldCs455xzZqIdFSzIIt0dDMDEE/609duB3wG/Ou5xqWcssnqz9IdILRlmf8DEAzvN5HQXq5erC5s4NudoyraRRVZRfq6o1T3NDOqyDVquUiYCpw1oHQmp5Rc1TnU+08mAqpfbROBMkW4gMUzKdqdeqJfbvLDcZCXzl7uKdOeZEzo6qUdbBhzNWMCuXu4p7QOoI0I3stX3nUa6aQ6pXr7F88yUUJuaemkhNUkj5Bxv/HnVyfmK9tqcYqbnI4t0p0Y30YsIHRygRdfSVUitYxG1ErCJ+uWFv9zvIf2tjVoOlKWX5/uFrX48HUtECaBhLT3G81T1fusY7ovnsTh6pz/nJoetjHRL5fL8onOg7mNsGtLoPmFpceWYfGS9vIFpS9NyPa4rvbxTA7OoDQrjRnewFPRpYWCa8nPbBWMShZ4rzI0leSZd4W5ZFLGgbOCE1OhrVrDQFulmnGI2CAePVb2cRLptNao78ePoaQBAVjLs0UcvAADsPDhtHC+KrglXW7sI4trFcXLuuoOM9ht9tzZmnc2ZZnWik/V2Jnbb2fObE1I7zI3uns7qjzzyCB555JFe7rICKH2uw9uVesJPefmLsWz+AKbbERELMntEG/oivkt6uWv+k75gMg34XLtbYawZ3Qy9fEj1vNsi3Tq9PB+pVV/bhWryC0Fxb10W2IqQmmgP03YxmOfaSj7ohGIuaHHdRLptau8svbGDkjU6aP6RCVlEp3cLP4pSkW5GsLAVcUaGZjQy5UBcYYp0y5zuVNFbXxQqEU3L8UsLqcm+kH1WXMEg+1y2t0QkVxxzSuZ0J++pXoSk9lv6IzVkaX5aWSG1mXIAUeiOVVv5nXKRbrMzxrats5AauYR8/XjV0RbHsYwwzmPSY1wjruKrlqRsm9uqjyUmB62MdKd9lov2C0P8d9sTo3v1smKjO1fzucjA1NoDdMY00o2JEcdIdy+NbhOl3PM8a4pJbj+FkW53au28XMmwfPt6RS/P632474djPNDzVxxqQd5A15GtVdyOL/Yp2FVcyksUx6y4bTcpMQIiqKDmdCcG+Opl89Co+YpuoamKiEk7p6yau75tm3GGWEs9Whyr1ki3VUg56yNSU6PD+Urv40OV0Q0sXrwYS5eqKteveMUr8MpXvlL5bPXq1TjppJN617oKAHihmE7geR6edrJ6Hxu33gKE2eBiVS8vUadbLBxaLL28aPLPDGjAPOArbSPRcUrF5RYLeo4Zdy66oRYzAzygGpFFnlxbHWqXeztMco1s5deMkW6ycWkxtTDExOakDN3www8pfaYMbJOibbIvipZZj+ngjZeR7hmKLnKK4AI2T7RoD621a9IB0CfQUmV+DIaPmEinmsVGrC3SXVpIjYt0F+V0k+NNGco0uVBNRTcR14/qRXD5wzroftVItyO9PH22ZiXSbYmG5qJGpRaJZmaHjnI53cl/JQWFiRjqAk/NMJLPvx7pBuwLd247l36g378cXV8bl2SkmxESEvPpzrEkJWr10mHjcfX9m96btldLhpUfFzuNdPfSx6T2P/N1KLwmBToobcboMWE4VzKM67dan+k0pzs37rlfXFt6H8A7qOSaKwyBG24ArrkGuOEGRK22XDs553Sn94uLJNMUuYymnf1Wv5+daGLQNL6kHTHGUnr5oqE6Vi4aZNurt8GkIdOJejntw7Q8G7cuzTnGLUr2trFZKJ1z2/rkHNslUiQ55NXLZ37em0s4nd3+/fuxb98+5bPPfe5z+NznPpfb1iayVqEzmHJHOsHT9z+svK+/8M+B1auB0dHkvZK3bfF0OeZ0R3FmJIixomiCypfropFuvsvSvEsaheAGNj3SzbVHX4BwHkW6nf6aAxe9KyNUw5UMs9HLc20l27KLCG3ClIb16CiwejXGb/4lAGDeB96v9JkysNH7OEOrKfOFOu/7TnW6Z1hIbUCJdNsj1Co1N3MmCZhqw6q1acupsNPnSq2bnOxDRB3yZcnMTjqlTnfHke68cWVyUAW+J/sXp3ybvDc7AvQ2ivPJHHod5HTT/PhaNkbZMD2b9HJL38s7yMrs19wvctuWoJf7nDOGFVRSx+8JErUaZiIp9DLY5idxScQcYxvz9fM2ORxF+7Ocbo5ern622oFebksv4rcX7SHlCTvI6daviXOku8ugAoVrHneRs7soNamTSLcw5jjHjckxUxZlc9cpWG2VKFtT+cz1C6NYrhNw7rnARRcB556L8OST5bauTJncc2MYk1oy8GDuQx1FujV6+VQrkmP2gkHG6Dass8yRbuTaXQRjpJsR+C26BmpOt/25bhjW/Gqku7vASF69vIp0V5hjuOSPOWF0FE+7/HXKR/WonZQVW7cOGB210su5kiwm0MWTMJxMFG0d+oLJJdIthSbCqFAUS18E2NXL1ZJh+qb0feEEzuUjlqA1D6dK8xOtTEjN5jDI0wuz16E+ITATJlavBv7hH5K+sWmTLLkz3JxS+kwZuDgt6P3uhZBaTlSJwUzndFvVyzUxMmos1xlDzViqpISho4P+lqOXTzoYsXp76HhVduiylg10UFydMjgJbAtuU23prP53+ZxuuoCQke4Cermslz4L9HJbFNDmtCuC7gBybYOz0c2xYVj6Y3Ktx9MF9EDNZ/sPXfi5lH3MHLsljO6CxbmdXq5Gbo8ZGTIeV+6fq0NvQWZsZZ9Jp2cHRlvyGlg4aDa66bhQNgXFBo71wH1XtI6RjDfGURa325nD5+abC9lfotSm6ItcWcN8pNu6S3O7C8Y2G2xCaiYnXfs3v5HrBIpw2/ZsW8c26OOeaUxqMZo8vSi5pqcBCBE130tS6/Rnr3ROdwf0cmrQKuVDDSJ1rv2/KA+7bmDoUYX7bkuG6dehMrorzDnKeFPNOwmBiy/GCXu3YOWBnfLjetjKOCqXXIKGZ6Zme54nB8Sih5UOnCJHMWa8chwyandqrIfFRjetZ1xkqOVzuvPb6QOnGD9tJcOKJpVu61Bn9PLQSlESbbRGuukiYnRUTpgRPMhvNm0C/vVfZf8YryeTzXBrSukzZajmNiE6Pue9+5JhZep0z0ZOt028yhShUITUDONBGUEqHUU53aJkWI79YpnAu8np5vqCyziY5aDzRlEQ5PubqY2SXs7kdNsWGPQ73VgC+ktIzZoukHPalTC6S0RTOikZFhakHeQi3Wn/nW8QgXTP6Vb3W45ezl9P0R1s9532o+OWDDst2HP3r+An+rwLkFKKHdLLFw3Vrf3GVi6rG4hz1yOzgNofiyPd+f4GABgdRURSKWt//meF7C8hhDohS4aJPmR2TnZKL/d9TxHUKuOElQYV4/w2RU3Dr14LMJHdEOT+WpzeFNZINzW6GcdXUXUZFwxrJcOEiNrCoTo8z8Mxi1SjW4/wcnMXReS4Dqag95MGD0zrQGU809O+SqT+KOVDmeemHcXGIIAr8urlh7dZenif3WECFypbIW66Cdi0CR6Apz18p/y4LgymOAY2bkTtvt/K77iHSCwcXfLDxLMkFhNiECqivWaRpXyk23RYmi9ZVNt5ZEjNMeM203O6ZQ61ZVAvGkQ5D7KptBGHIUJ7Egs17piijTYPp1xEpM4YxDHano/n/s1H8JcveQ97/PFGMtnMa00mH6R9BjfdVNj2rA36e7vRnUVauol0q/2Jw4xHuqnRbaM7GvKiVaO7uBxWzyLdNTVyXCanW63TXW7sspWPs1N6PaW9uetZwqjMhNQExTQqVFBP9pt9p9DLS5YMmx31cj2y5u6UsO63xMLO5PDh4CqwJ85L3C+Tcrm+X6DI6E7+u0S6cxE7g8NRGLmZejljdJPoz4kO1HKg/P3jxt+O6OXkOLZ8br2NXdekZ/bLrWGUOcexnJ1iQKVO6vaWrdl+orCQ/SWMuWa6RsnGEno88/NYFtS4Kifalfx3cXjKufXAAfkZnWXbftZvg5t/7nR8K/uGfNVi1mS9KBmmC6kJETXB2Fg5ktHL9WoeQLGTv2wJNYE64wzhxOT0fdvWCcUil/y6RY4VYSakVqmXu6FU7Z9XvOIVTp9V6C2ykmFdLMK2ZhPE0x65E6OnPxO1sA0f6sBQ27sHwLz0ePmHqJbWHi16WEVUfLodycWEjBYXPJwm9fKab85TlYZ6FMvosSu9PGAnZtVQo3WxKeipdCLKIgesEpFumtPNXQ9xHH0CoqVxJD0zdcYAwI75S3DfihMBAE2/hkbUVn5/YDDpFwunxtUDkr5VBJt6OR/pLh9p0eGU090DGrsNNE+zjLEsDT6GUpYzcqnxUvI8lDxkJsVECpNp948uam3lB8tHupP/EfOsOEW6U6M7XxvZvBA1MQeoKFfZnG66gNB1LkyRASGkVlSzuhew1unuQglY7dPuC7tiennyPy7I6daF1GzK5XS/+n5y20nmiYMDqGa/foF07CbvrSXDyHVxKRcGlGcqcPNTK7I7sDnQBb8tn5seU3/dLcS5syKpFiNNh85soE7qHfMzUdp6FCYTq+cl7K/zzwetBQ9kkW5A02WxjEndBFoC34MoE19OSI2JdBuCDnJuTY3r/QPz8Ny/+QjWPPQbvPcH/4HII+e2bZvT8W1aCHRMkvRyxVGo7quT66eXDBP08gWDyf2j9HI2NdE10l2ybeJ+svRyi7NBdxCrJUkLIt0GejmNdGdszc766pFGLy9ldF911VXytVg8088q9B5xHDtR2QqxcqV8+ccP/y/qYQvLx/fmNmssWwqsn0pes5FusRAtHsQbtcToFtHKbLCx/y6f0+0iWiMWWVFh1HKhTi9nJl45maRt5gQrAPdcQIBEaah6eQljj6eX57cT14m7XjXfQyuMszYQg3myPiBfjw0MY+mk6r0eG0iN7mnN6CZ9qwi2usmsunvYnRcVyPcnDrI02Syol9uMZZNQGaUkm2q7K2JoJc+jKKfbVILL2naFNlnu/tmU7F1yuk0Ovhp5NnQHUE5IzRf08jRCrUS6zedDvzPVZ2+FMRjbCsDslgzLlZOxRLo7pZeXUS8vcjTYHJdcmSjxnYx0G8od2pwNFGUcEfr8aUqtCR1yuulC9MRlxcrlXNtc2GmApl7egcAkHYeKIt0zrV5uM4pM31PkKPfESf2Rp78UAPC0h+/AYLuZfE/ZX2vWKPtq1HzUg2T+TdhqeQeefo+68UOUEYyj4FhG5kh36oRKje57jjoZmxcdhe+d9nS89wf/oUS6/ZVHOx2/LL2cbt6LOt0i0i0cdZJenka6Kb2cWwcXGd0mQ7kIevk66pC2sVqsuiZFgsgGp3o2P0elAkccckJqs+Bsnks4G92VKvncgD643eS14qyzgFWrgM2bsXJsN77+xTdjXnMy+97zgFWrUHviE4BfJSrV3AMpIl8ug/hAzccYOqCXk/JfQKZ6bo0uSepmXEgvXzBQg+9lC3MXb6WpZJjiUSy4P1mN2XzOsouXcIion9rygmxGd9LeOOtXxGCerGe0qQOD8xWje6I+KL3ZC4TRnfYZnHVWYdtl22w58dyirwcsj3qg3ksOM57TbaBpAaonulSkW59MS3iwdZgMdvFcZTnd5mNyNZ/Fc1Y60l1i4UdhoyYC2TPKaiHoToM0Utkg98Ap0k1zuhkhNSBxogyBNwAlvXw2hNR0OitdwGqn2Cm93NWwAYrPmXNcchFDPT1IiFfNM+R0e47OU/0r26kVCqn56nwg1cuZSE9nkW47vT23PTNOZiJJ7n1RjXQX0MtnWL2ca7fNyNUh5yQx/qZO6vuXHY+vP/4ZAIB/+OlV+R8a2F/DjRr2T7YwPh2yxpeNBVUW6tjsvh8by8jkxAxHlgCeh71DCwAAe4YXouUHiNLOEEQhvLPPdjq+tcQbjXSnfZM+u7b1hStEdYPpdoR2GGFM0MuHkrGD0suta8eCOt2ljW4SWALU4ITLuiprs3vqj1ImuCjS3WFf1VlRneaGHypwMrrf8Y53zHQ7KhjgotzthCAArrwyyTnyPDxh2wPZd+KBveIK1OtZl+AeSBEJdGmLWEA1dXp5UW5ZSnlvb90O3HAD2o95cuExqeBUUfTY9z0sGqpj70TLuF/RdlF6yEQvt4lg5c6LXdSIthZfTxnpbpEJmzOsLUZ3ttBLPyDOmMlaFuk+MKAu7MZSanktbGOoNa30GZ1GZ4NtIa8vQoFyTgkTsrIv6UmHYRKJ2Lo1cTqcddbs5nRbIt16P+Byuk3ie8rkVSt3vWjX5UqGTTNKsUnbffZ38vvAR7MdlR67WPVylxrZBXm0Ntqp6dyokJqLejm9JoMGep4tr3tWS4bl+mJ2TM/zFOdkqUi30qfdI91F5yyuO13PsjndmgNP5GcaI92Oxl+ZSLfOmskb7Ori3JrTTT47cVlnOd3O6uVsTncJo03J6S6glxsMqm4RWIIDZXJalXEoDIHtiRr3B87+a0R+gPN+93M8aev9+R8a2F/zGgH2T7bUSLeltGI3Od02Z67L7+ja0xT4kE6J5z0P+MGnsXd4IQAg9nzsHh6Rjvog8J3XCfpzo7BQlEh3PvCQ6/Md5XRn7ZxohYRenvTlhYN1zB+o4eB0m+0/WcCGH+M7p5er94Ua9Z6vb2teU5TRfjFFuuVaLe6tkNqgif51GKEyuvscdODr2iBYuxa49tokJ4mWdli1KjGe1q5FffN++bGVXl7UljBEozUNAGj9+jfAMWsyMTLbQDg6itq/fRY4+7UIf/c74B/fgvCxfwi84B0F9PJsQHLxzo8MN6xGt8hF25NuY6KV0rdFYygXvStj7A3Vs1wju3o5LN9pbSDOmMlG5sEdGxjODOu//3sc+O4NAJIotwcofaYMcorqQX5C7fT6mJCrJcr0/+ZlHwNQmyUhNfMkmCu7RWpEx3EMz/OMEd8yqqQ6TJHuXO1tS3SdW4DUfQ9N9Ipe7p7TbWqTZIFYGCICslIDYd7IBYbFwabQy0nU0vM8SS+1Gd2yZNis1OnWrw+09x4iZnFbuF+tDJ4N5UqGJf85kSfOARrKnO400m3I6XalHOedrub20vnT9/IMLz2ybC0ZJkT9vBgr//dW4JyzC42Y0urljLFVVH6TA72Wi+eVoZf3zugW5861mxOEMkFc4fCee4GVzwN27sSvj30MfnTqmfCjEH9/4xfUHxSwv6hIV9sh0t2N0V0mok/BCanJSLfBYRw+6tHAtddi3ye/J7/bOW8EI4uTyHdQc89kNamB0/dJfei8A9RWHcUVjcBHzffQjmJMNkMcmFSF1ADgmJFB3L/9IPv8Z0Y3v/9O6nQDecYeteltaY+2nO4yehucqFqbCql1GBihzRs0OEUPJxzecfzDAG3y5PZkUlq7FtiwAbj+euDqq5P/69dL46lwEa2JC7FIaz43Ht4AAGhe8kZg9WpEBxKD3rhOSVVBazt2AMiUL9u7dgEAglbTeMi6jEZFTt55mtedu65hiKW7EnrYnvvXA2FoppeXiXRzkdyoeBEvoAqp8e2hx+G+44wZ4YyZPCrzzh8YnJ8sHq69FvjABzD2re8CABaOLMj1mTKwTYq6+BFQjglggvTGb9nK1hLF5s1of/7z6XFmZkgcYCjb2Xvzoou2R/QVU11MuiAqS00uyunOtjMb4VwfFuda9vaxNe0dcrr1Z96kIcClgpjo5eK8Wu2IjU7pMJUMAwhzoW1OdegX9XL9fZkupdDLC9XLKVvCvujiorFcpFvmmsqc7jTSbVAvp6dtFVLLRbrNbaXPoDnVhxrdhvs+OoqBT/0nAOC4XZtQe+YzCstTAVl6h6ntOnimUfn0Hj/O1iwjG9dbS0rSx2gm1MuL0kis8+7oKGpvvBQAED7yCLBzJ2IA/3LOywEAL7nzhzhlD5lLHNhf85QKJPl+q5f66mbJV8a4ouBKhpnqdItnux3FwNq12Pu6N8jvdn7kE2jf8NN0n+7HzwkQGtYMYm1gc9x00qU8z5OVYsan2zLSLejlALByYRKgqE1PAjfcoPRxvSqBjtBR20iHHNPS+Z8GJ/KOiey1Pae7INJtCBbQ9MuWgyPaBjouHe7lwoDK6O57UK9zV3W6KYIgEfm48MLkf8DnHXITrVjUGgdRUvO5ESaD1XStDmzejHDTFgCGyZ+oggZxMoCFXpD+Tw39/fuMEzjNfXURJxshRrdyXVOHwZJPfBQAsOeHNyQOg9RQs5YMc6XvsZHc4nsr1E8nmzQfLL+daIeNWhfp+UZr12LyYx+Xbw+87R2KYX2gmbRzwfKRXJ8pA5u6KGtodUldSn6bXvf7fsfWEkUco+Un/aExQyOimtOd99wL2BTA5WRrYF1Qsble1+k2tV1VL+ecdAXjhQGcc8hFxMxGuU/em52GeSq6Ri+PYiPLQP0dMbq1/FxxbZsWQ0R8Nxs53UU5pCZqZxEUJ07hws6933LjVzvMR7z0KHKZSLfNOLUJFumgxgPvHEXWxjBEc/3DAICBLZuzeS6dTwf27gYArN6b5goXlKcSKBXVFc5Jhl7uLDA5Oorg8Y+Xbxe/751WB4FactPtEC7IcrrzO+UE93JIr7u/O3H4iwDAdx/1R/jVcY/DQGsaF//8GvU3wkltcUZLY05RLzf3qa5yuksEBLjjh4zD05jTnX6/dzKreLJj9Wly7VbK6NbHcN2Rmn4txE8Vx1LBeOaKeUQ/R+R0C3o5RkdxzHe/AQCo7dwBnHuu0seLIt2RwzzGQb/WdAy0BTNMcyDgMDYbI93ZWCFTADu81rR9RwK9vDK6+xx0YO6lJ9iEIoqq+Iw1EonhDIAY3Q0gjhGn7Q84w4eogs5rJurp+wfnA8gmvKDVNNaEpuJrpkggBS1lIgdq4jBYkoqI7R5elDgMbk3E5fRxhb4vyg/jBuMyRqWYsNtRLKmINk++S3SFYpJEmA+ccJJiWEtv76A9R68ItpJhnKHVSZ1YHXIx2WwZt2mm51rf4VbWpCyoMV+76UbFeeRSpxvIPPtiAWQTauoq0q3k2OoLHpvXnDO6k32VHbusz4plci8SUqtZno2cE0Ov0x1GMnLqmtNtinQ3HSLds1IyzFFhW39dvF97v6Aoo7rvM445mwq0zOluipxuU8kwaqCY26tfApsjgsuBpJBOxgceBFavxvQ3/hsAMPCFq5JF/Ne+JufTJ275HWphG+c+9Ovkx+L8L7nEHkkuQTHmdBRKVY9I589g00b50cjkmNVBMFP0cmFY6FRl/TjsvSbrmFqUXNuJ+hDev+Zv8P/OfwsA4G9+8y0cfXB39psPf9iJ/SWNuek2iXiax6xu1nxl7r36u+R/xEa6+baKsXnfRDbH7hybdnKU6iiqby+eVTkW24TUOrx+ghEzPt0m6uU12cdXbkscZDURzSZ9PBt7DJHujoXUUpaUKGMbUaNb3dYmDMmV/jLBVDKMRrozBlpn15peh6GKXl5hrtFivPgzCSslKQxRH9uftGfTpvxkTwxnAFhxcA8AYHtaz1J4Pf0778gfmKh9HnMgoZdvW7AMEbws0h2FRlVQSQFV1MtLRLo1h8HSieQ89wwvBOIYUTow+JrDoEykm6PvlYt0ZwOSoEuyFHLh3GANi+Q/a3Q3s/spvLsCB6S3t1SVwfzxHejlXJ3Yblgeei1RAPjSGefhxyf/YXacNNJdn9DKofUCo6MYePlfy7eBRg9VBG8s9G3RV8xKsnaHmQ0mSnRhpLsgmiL226l6uUovdykdWBTp9oxt1RfjYuEizjGKM1q4c063ZkQ25MLJnNM9qyXDCiJDnRpFZcR6aBTV1ehWnDGcenn6Oo6T8XZiWqiX84s6W7SMO76ArV/Tc+G2k07G738f2LQpcU4DGGg3k0X8i18s59M162/D3R9+Ef7q9u9mO6DlqQxQqMsFzyDn9Gy60svJ/OkTY2NkcszqIJhp9XKuzxbmOpN1TJAa3Xcccxr+86kXIPZ8XHDXj3CJHuU+6ign9hctR2WsQtEjR4TKWizz7BLKeIriSHdyz/dOZCmAOzo0ugvHcCu9XN1Xh4znLJWPCKktHAhkHxdrVMHKpH1cXOpe1+nW10d0/7Zx3PZdEYNFiW4zTkRXDSUbqkh3hb6CeLC6qVNcBkZ6eUq7rv/6VwCA2pe+kKeNaQbxqv3JwLRp0QoAQJiOgP7u3ciBqH0ePbYbQRSiWatj1zyigBmFRlVQOqlMtYoXrYv0nG7NYbBEGN1DiwAAkaBJPfB7ZT9qTnfBosZC23JZYNcDX57nwWmzCJw1n40xZgQmW9mCSHh3BcZ6FOmmCz/PU68fK6QmjJyuIt2poeMnC56NC1fgbef9P1z6/DdlxwmS7+oL5nd8HBapZ7yxLXk2amE7EaIjnnFbtFiIbwFZNCE0RHyV0kul63Tzhk++vJnN0M/3N+FEKLvA4KiuocOz4uokKIp0mxYbpnrlyjHIdRjU6OVUe8IEIV41Gznd+j2z0c3LRGaK+oXSBuqkKHjOOXo5Z7zQe9CO4sJIt5rmYj6+TdNDhyKkxhl/6X8xv03XkrF1IGyyaTCDoYGpY3BEAyo1t6NIt3AAFfVFaqiSnO7FUwljzOQgUAymGcjp5vpeYaSbXE8R6QaA5Qf34NPXvgsf/O4VWV1uAcO6RAfN6Ra03FzKmvLMOe2WhWp4laCXs5Fu3vmtG+jGSHeHIozJMfjrwxn0vVJ/H5aMhDCLdN//W9nHT92VsDmWju/LfpT28WDfPqV9Ojq5JoDK5gSydZLn2RmE1mopHUa6pSOapFx1aqPQ6zBwBOR0dxe2qjDjaDmUyekl6kqkO30ABO06jlFPJ6FaFGaGg8hj0iaeVfuT8hqbFh0FABm9fPnS/IFJ6apaHOHosd3YvGgFNi1agbZYuAeBURWULoxF/p5tsbeI1A8NfC+3cFk6sQ8AsHdoAULPl0a3t3+/sp3iqS8Uqkn+R8yixjWSO1QP0ArbMhJtM6xZejmzsBKYoka3Hume1PKaOgSd+00GUaREsbqvny0Xk4ODgOdh40jSHw8MzsdEfQDDrWlpdDdWH9/xcXIg0R+RaiEiJ4jjZLa85BIE//2LrK0Gtd1WGEpDrSWNDLNBXF69nDfYdXq5zdBnlYLT78tGsWyig/ZIt33RZSslpFCMDYwf8YzYqHS2SLekl/dLybBcnW7z9SszBxX1C3Vb90i3HEMZ3YeAWRQCyRgic7qNkW5ynpb26pfAmtOtqZfn9vXIBgAZA2w6EJFucxoMC4vBp0RNHYXUQoZdUjj+kvkziCP89W++g7GBYRw9ttu4XXLM7PXsRbppxI651+R6nrH1fjx6x3o8bvuDePtPPoPFU2PqtgVq5TqEMTfeDGUpPluku1fq5V1HupmoMpDXTthHIt07D04bKfQ2uLKVBOjbMpoLNsyTuffZWmvBvl3y+zO2/R6fufadOG3nw7nf1ppTAOpmozvmr2UR5H1J74VJ3Df5LP872b4C3SYKTrE82WfmADCVMHWFol5eP/wj3ZXR3edwie70EjlKkka7rqfGQy0KFcMB55+vGM6IYxybUnA2LUwj3akwmv+kJ+UPrNURP+bATmxetAKbF67AyNTBZJOVRxspXHSCETRpV3p54Hu5hcviyWRyjfwA+wbnS3p5MLJI2Y4ObkU5LayQWlSOSjrcqOHAVBsHU7okl/MlI92W77hI94RCLzdEuoe6GzJ8i5NCvKe5UN0O6ACZII5LDOodCzKnz67hERx/YAeaQUov7yW9iUR/GulCuk4iJ8IzXvvdffIjzltcDzxMtvJCajYPtm4sF0HN6eaFFfXt9DaYHAaAPXrIgRVSc8jpLhJSc6WdNgyLDcGisRr+Sk43L6TWCvkFGf1uNoTUdJEiU11zoNwisYyIU5mSYWK8CwuicPT+tKOY1Okuzum2LdRtESUdhTndY2MABqXRfWAwqb/dMEW0840pNPjU/GD77gLmmXOml2vz57t+9Amn7co4rctAOteYdtuigACUdcyyif343mdfzx/EQa1ch3D6TEy3jekynbJLdNQMY1gRJD26gE0CZG1thTGiKMZ+wpDbMTYln82ucrotjkD9fdG2rhBpAJPNjF6+aOVyZZtnPvgr9rf+4CBwIFSuH0XUaaRbY3/ZIuY2NkcZ9fIBU6SbOFvaDhpKNtC2Dh0BRvfhH8s/xDG3Od1+jnZdD9MIK43YCdqYMJwBwPNUernnZXnRppqNoo74scdKg33LwuVor0gik7UlI8Z200XBRMvB6CZCaoHnZRNt2sZ6FGJRanjvGV6ESFDjH/1oZT+eMuC7USNDxoPsbnQng5KMdDPdQhrdHLVOGLbMol+ll5tyuruLdLvkGtEgYE/rdC9YBFx7LbYde5L8bue8xcCqVWj9ybOS4/QyukiiOoJmuSh1IFEE+/ZmbbWIkQljrGVQC+1GSK1j9XIlH513GAAdePUtDirbWFi0YAssdHfTYpyLEtkMf/rc6SVQMiE1W053ql4+G/Tygki3Srt2v4ee52UU3wIHEO1jRf1WpscUlF2kr8MwJurlhki3hYGjHN9CBdZB7x+rXr5wIQAg8n1sm78UvzwuUf1+4tb78zvTf+9o8HWmXk6cnq76Atr8mYPnAccdl3MQ2PJxu0FWp9v+7LLXRFvHGOGgVq5DiXRHfBs6FUDTUehcKPidQi83RKxpTveBqRZocHfn2LST+KWOnBhmztHFtxfg0j+cD6tgODX+9k40paN14dlPd+rjtRXLANjo5aJtnRrdqvOdO0ebMKTKiCsamz25D26t2yZCapxooQvo/a1KhlWYc8x6pFsXY9LoYKfsTnJZTtqzWf2h2I4azqnRvXd4EQ6uPhnRyGIABQNhWkf8mBc8GwCw+WWvRviZ/wJgN2oDP6tJmkW6zQMKzemuBR470Qoxtd3zRjJ6uWVRXzSx6FHmOC6v/CjUHQ+a6OVhCH/XzuS7rVtywjVc3rTAVJPSy0053d1GurPXRaIsQFYOqCd1uqMIWLsW2/7uUvnd7g99FFi/Hq1Vx6XH6eFzRqI6K8d242P//c/4yLc+kNustmRx9tpSMSAX6daeB8/zyGKz3HkoBnuJOt2Bn9WVtbW9tJAam1dYPBYWlcCylRIy0fPpdTXt19SGXKS7hJDa7NTptp+XqzHKQRrdhZHuMvTy/PjFMT9oU5VI94Ahp9tR0Ev/yjYsUUo2m+pzSuL8a/sBrn7inyL0Azzlkbtw2q5Hkg2Eofq1rwHHHqv+2NHgK2PAcek9LpVAkp1bDFWLg2DGIt3SuVYQ6TZdE7KOUbB8ecLsu/56J7VyHcM0p9tBELObWEvN5TwZcPRy05xDtTf2pvncYoybakUy8t1puUH2/SxEuuel48T2A9Pys/nDA0593GfqnFN0Wqdbp/JLQbaiSLelIkrROkGMx7l5gjCOnMcIA2jfOBLo5ZXR3edwUeztJfw4kguJ+n33AitWKN+//hdfwc//42/wp7+/Rf0hpY2lhvPC71+HRUEyMGz+2a8QNZKctcIFeBDg2DMelfxueAlCTUXYBLHod6KXk9y+4PbbE+NUm2iXTCZG955VJyJ8/OnJtrlBnTS7oH16uS5KMS0b6T6YRm6USSUVuwt+8uOkPel7KnaXRYry+7YJqYn3Xed02yZIhgnQ6sBTrkOfrLaPZRPprtWnAUFASpP18DnToj/Pv+9n+IMtGZVcLKqDJ2R1bblosawTLdTLQ7OjRnzWVZ1uS+SR66bWqFKHdbrFws+lViyFzlTILdiQ7mPPXuCGGxSnlI0pkKfy2x2AArowjIj6ugipzUbJMBv1EHA3Rm37dimlKA5TTC9P/tP1LNcvqKMkjLJI93xTTrdjxDWnEWDri0pON/Ns1ISAWgPXnHEeAGTq5NRQXbcO2LAhMfSuvrqUwVdKvTx91toHx+WzUapko8lQtTgIehXV1VFLn/P6ti2559ypTjcg1zHKdd+6NSkPtmaNM6WcQs7f0zSnW2eb0NfdzHvuLAcKzuHZNmgL0WdMKJevWDiA+anRuu3AlLKdCzzPU/pbEbvElrLWaZ8S92l72v75A7VkXw59nBMkpOicXq4y3sTubRVsAN5RLuCa021KM0vqdHcXGKTXoaKXV5hzdELP6RipkVZrJkZJ/W2XAy97GbB0qVwE+Ihx7NjO7DcG2hiCAFizBquOSnKgN+2fJnSY4nM5dmQIALB536Rz2QlhsBTSy0dHsejsp2VNfe1rMuOUTLRLHnsaAGD3v16BeGlCGbKp1zoL1aTrbUrjc43kDqX0tFykm9QYD4SjJo5yNVK5SJHAhKVkmHjfy5xu0wKfzlWSXt6FAaJ77sVECgC7DyZ9XdRN7mmk2zH6UyPpFly0WCxARPvbljJqYmIuH+nmo40uxqb4jGuPaEd50Zjkv/TqR7EUjrGNhdYar6OjqF1+WfL5A/cD556rOKVMQmpAPlprjXSTZ2vw179UFvxu9PLZKxmm9zfr+FY2MlOCdSGur2uk26WGMHVWlanTbWNU6Y+xNdWBlgzjIt3pzn72qKdi5/wlWH5wD/70/puTL3VDNZ1PceGFpQw+Z6N2dBS15yaGf7h/v3w2WmNJCUXnvsgZqhYHwYyol4+OYuhtyXM+dOvNuedcLS1XHADo5LqbICKoVDMlr6Ngd9a4olbCuKKwR7r1ZyzbVoioLR5uYMWCAQDA1v3JXFv2PLia0AI2w1rfttPLJ4xu0X6F4VfQx21itUAWoe7UiRnm6OXM2GJhOdiqpegQY1guzSzIxuFuy7rStg5URneFuYZJNKnnIEZbI0rVv8M2sGULsHt3JppG4ZBXtmpxYjxv2jtp9czpoEZ329EzmEW6Lerl6Xkueigr/RVEmnGaTrRLH53Q//ZMtLOBUtulqnpbYHRLozIf6XYVOhG5RkJIzfe8nNidqB0pxe4AWSOVW7QK0Ej32HRbmTQO9KhkmC3/issp7DZfCEDO80wpY7tSo7sXueMsHDzjRTlW0nhoR4iiOIuQWESCSke6Het0c+kMNdHfHnwgl84grn3Z+TjHCiF9wvacGWng6XMf7E4UaGUpIEPpNtMiI/ve0IbRUQQnnYiRyQNotFtY9OfPVxb8en4+B0kvnw2juwSVs+zCuSajJMW/E9u45nRzCtsmmu50O5TXdJ6pZJijkFqnkW5WQyCNxk5GyXcXPmE5Gl/8fMfUZQ5Oolzi2UhTxISwGzZvRmvPnty5FB/U3VCll6Un6uXpufzprdfhFb/6Jl5769eTzw3PeZlSWr2ArskC5MczhT3XhdHdeU538j9yYBlRnZq948kaYWS4jmWp0b0tNVrLrl/rFodV/j3/Guj8+gnnnHDQLxzS1j2WPq4LnukILbRwGwJtv7Z63/Ry28RPi8Zb8b1pnqCR7p7U6a5yuivMNVpysJvBW6UZbbWQGN3C2F66tKO8slWLhwEAm/ZOWHNQdBybGutjU23pQS0auMXCbcJEL6clnKI2hpuTSXviKGecAsCSeQkdfs94Vvoit+iyRG516B5QSjF1jXQPazndvu/lxO5EpNsXhgoRu3MtGUaPAZBId5dGN718poG8o5xCC+gEEUWxEuneNd5UjtNTITWBAs94UURC0sqiGO0WWaz94uc5I9fVeNGhUKINiqV6WyUz5sD+5Jj/9q+5dAYxEXe6wBD9lPZXmwOGpZeT514Y24oQJABccgl8jzjBcka3Q6SbOC6/8JX/D1/4ytsxvzmpLPgz9XIbvTxpx+zU6dYj3er33dB/y0S6hVbFkEHoTLYv3WccJ5oYQLGyMi1/aNo/7Z5l6OU2R4S1ZNjoKIK3vCU7ZhTion97MzAw0JOIqoBKpWY2IM+GeCbaad1wxDFafjLeNzzegOgWPc3pJueyaHoc//iTT+Px2x9MviPPeWCZg2YaItJN07dske5uLglXV9kFusMzec3Pw9TA3MtGuieVfbq3nRjduWeOby93nM5zupNnYE+6PlhQQstGrNW5wAb9vOx4Kq6JMHJlpLsopzs3LpqZbDqMkW4yP7dlBZ7OrjVt3mAvq8f0KSqju88RdtmhnaAZbWJhWk8j3ojjJNr9uc+VzisTkW5KE3cZB4cbNSxOFcYf2T0BoHiQEpOVMLpznjftPM+/9wY8dvuDWL13c3aeQokdwJJ5ycSxe7xpjNKrizV7+3QPMk0d4Ep/cZBCatNEvVwTu/PjdIKMVYMMW7ey5UAEJpvq9iK63QojeU3LTD4clAnS5DWnitW9VC+PYuyZaCoe6F1jaqS7pzndFBbPuDpB5s9TGJKtm36Ou5/6TACAF0cYeM6f5nP2paHTmYEEqMae53k81Y9JZ6hFbZUxEoaop5Flf8vmnIPABp0VQu+ZNbrI9Sny3D9mx3o02k08cQtRiE6f+2Dbtuwa5Ojl5oUxgJzj8vTtD+Kpm+7J9g8Al1yCRrqfflEvVyOh+ZJYrqW0OGQ53cXn8Q/nPRqvOutEnLRsnnU72h7RJbJ8U35hKAycRuAbr6nqXDC31+aU0DFgitZprAsAePbvb8HRD9yjpAL1AkERVZk8G2LeF6KhANAKkvG+dttvetYmip6ql2vzew6iROOO7eSYs2t0s5FuSyS3O/VySqN3v7g1hl7eNgR/MudohH2pkNri4TqWp0a3YJWVdW7ULdfARjfP58d3dv2GNEZMmWCD6MfGSHeH6uWmSDe3G5t2Tpm0g7qBrURZiZnuTmcPMG1rkdP1cEBldPc5WgYBi55CM9oWTI+n/yfU7XbsKJ3flEW6J+Xa0/Vcjkkp5o/sSdpRNHALkSIRsc0ZUNp5vv/7/47vfu5iDIRtdrulMtLdlFEV+4Bvb19eSK18FHdYN7p9L1f7dOHUuPJfYuVKZ3o5kBndNOLdrdFt98Bm1yfWHBM9US8PI0l3E9iderKd69HOAJSyW1zJsLT94x+6Em/+w4sAAH9+zw0YDFu5nP2azI0tN3mZ1MuTNmmecc3AFM45JZ3h1a9OouDXp6J+X/tazkFgg87ICJVUDLfoojwv8tw/etfDuPPKl+ItN16V+21tMnte9AVEYaTbccFf35lUdGg6CKnNTqTbvsBXjPKyi0TJuij4XRjixQcfxNsO3AHvpz+1Omeo4a+zIEzjiVBQHjaIqNFtk9fmpuYchc6RbuHtJJHlOOsDf3Xbd1i2VbcorLNOno1GOg9O1xqSYt5Mje76rh09aY+Onka6tfndeMzJbF0z42l7GkR6A51rbSkevcrp7ohe7pLTLQz0MMa+yWQuXTTckEa3jHSXNbot9HJbiociQtfFrdVLC+bo5RaIaxIxgQ36eWknZpCtYwAYS87pn+XnMmp0F6ypBb3cMP/1uk53RS+vMOeQZXJmkl6uGW3v/f5/4P/78Sdx2q6Hrdu5gOZ0myjaJhyrGd3FQmpqpDtnQLm2P91uCTG6TVF61wgJ3VY3usvcWymkRnO6NZXs//urb+IdP/pPvPjOH0A2OhW7k4a/JdItzlHU6hbG93Aj6DhvR4BeP9sCNoqRqOfu2w8AqN97T8cLUTHphIRaLlQyZU73LIpX6bDm3oUh6il9+8qnvwQPLj0Oyw/uwTt+/Mnke22hLn7fVaQ7MBvdeuQYyKja9VBjxmzaJD/jRP1c2qPndHuefQGXWxx4eafUYLvJ/tafl0VZdZq6iVYp4bjgr08lY5lTybBZFlJjxb7o+FaWIioF9iznISosnHsucNFFOdErHZTmPH1DYqCb8k1FvxWRblM+N6BG+O2Rbs0AsDqAGIcGeXYWTR0EAJyy6xE87ZG7ku81tlW3CIruH3k2Vhzcg4HWNNpBDZsXLgeQRbrrR6/I/7YHUCPdXRrAjvN7bd5w745ZEsNMJM/E+Eq+6/xYNYvhZQMnpNY2UJmVnG4S6V6xYBAAZI3r0pFuy7hkUzMPLBHeMtAFF8sEG8Q1Mke6uxNSE/u1radta4oykW4TvVzJ6ZalPDs0ukn7K3p5hTmHWJzN6OSgGW1P23gXXvnrb0Ee0aRQ7gCRm00NV9fxX/w2i3TbfygGFyO9XDvPHLTzFEa3jV5Ob0vRGtlEmS2TRywmbSWfR1PJXj6xD3/zm29j0fR4TuxON/wphPddRPiFwmqv8rkBew48nYTC0W8k6rn3J4J3tbe+pVSkVDlmkE0Qgu726JULAAD7JlpohdHMCak5gE6KSh8XOdO3/RoA8NDSpJb4+7/3MYykC3YAykK9UyE1k3o5wOTXaQZmPRLCfRpjhHznR7xuggl6PXlX5yNLg3N87mvHrcr2oxtwWp5lbsHkuOCvz08W/H1Tp7vAKLMJ8hRBpjqYzoOkKCgwOWdGRzF42slopE6TfRe8FFi9Gu1HNibnYqBAipxuzuCR29LztAqp6b+zGN1ctI48O2evvw3v+NF/4lOj70FuL45OnCKoomFMW8mz4SPGiXu3AAAeXHocQs9HnEa8G09/Wv63PUChU6AMHJ/zYFWmTdMzxXRH6HXi+QoUxOjuJtJN55UuhdTMke7kfUtTLxeR7myfM2d0mwxt15Q9DvM0VkyZtQ9V9ubQqZCaWM/m6nQz19azrLPKpB0IJ2d9316l9J74XRxn81WnulNKne6KXl5hrjEr6uWOpY06EXdZOFjHIo2a4zrYiEi3MKKLPINiIBDq5TlaY8nzXDo/MT73jjdlbn2+JEVWY7ZoABPtb2uR7jJeYH3hKNvjWCNV0su1SHccx9LoFl5qsVjNanR3Ry0H7KIn9DqEF/0FsGlTFmkJ26UipRTCgAqjWNYNffTRC+S12DPezHK6a7O7CAPUPiWNRlpNgKQ/rL3rx/iTB3/J72jzZtRS6mTjvt+WYgaYcrqVNiGdWDUDM5BGd/549TDpO0JR3zWSJ1WqRXk9xzQb2nbfSxcfjs+9T8Y3m3o5+7w6LvgbqWHfj+rl3PjaDb1cqpdzv9NSFBRwzpn0efA2bcLI5BgAYN/QAmDzZrRv/WWurUA+p1s3eChUrQnzOZWJdDc4ejl5doI4wt/85tvS0FXQAauMQyFVWXs2Tt6dODAeXLoKrVo2b9cb3Ttc2fZZND7K78y1RCOvpzEb0OsQ95pdYtp3mTVG4KvGHX2tK62L92EUKerlK7o2us3XwBb46NW109dZZUqlFkW6MyG1cm2SkW5JLzfndNucpfTaWtc7o6N47Euej6HmFJ5803UKC4nuc0qwBDt9lsJQtnfw7rt6llrTr6iM7j7HrNXpdjTaOoGgmAuUpZcLFOZ0p0/upK1Od4nzFJHupAZlMqFwTRfnUyikJiLdot5yB3nE+qStLFQcaqT6mjEjMN2O5Fr3qIXJhCkWqwdkje7uF142OiF9H4qJyydGd4c5j5QKtT3N6V65aEje351j09IImpucbhqR8I3VBI4a253RyjlcemlStgtA4+2Xd5RDDdjp5bXAyxmYSyb2AwCWje/L7fdZv78VJ+/eiHMf+rX6RUEkT9ceMEVadFDHV6A/G4Wl27KP8/RyOw3bdcHfqCf92SqkNos53YX08i6E1ITTkz0Pxxx43HRT7nlYPEWM7jhGmKpt68viLNIt6OWW0lWOVNx8epH5FNg63SXZVt1CjXQbNiLPxsl7knvy4JJVaB53vNxkpsZFet27zukGSpdonO2c7sD3lDmce+Z6FulWVKpL0MsZ9fJ2QaS7HRZEurtRL9cZcdquTIZ2N0vmPL28TE53/vpRdEov14XUbPux5XQXCbcCkE7O4+/7X9z+kQvxT2LdkQY+at/5ttx0umVglrogZfMFrWSMHnz933XMaDxUMOdG92233Ya1a9filFNOwaJFi1Cr1bB06VKcc845+NSnPiUFlQTuuOMOrFu3DitWrMDAwABWr16NN7zhDdi5c+ccncHMwqQaOSNwMNo6Qc7odhxsjtV+V6heHggBC/V9Do7nOVALMD+NjgjBLW7yEM0qMgikV1jW6e5ESE2dDHI/LaiRahJSo+XCskh3S/nfm0h39tomICOEfKZqiWHcSCOmneQ80ollSyrsctTCAUmj3z3elIbOnOd0+17OIDln/W+wdHwf/u27H05SBkzYuRP/Z9NvMW96Ao/esaGjHOpG4OdoebncVM3A/MD/fAQf/8b7svI8BGdvuB0//vTf4g8236d+URDJ07UHZC1mx7KBsq0UBc89HWN1r33DlAKg779gwS/6l0lILY5j6QDqB3o5HZtKTUFhiL9aPImzF7TxlM335p1krvTprVtzz4OIdO8dWggAaKcNCx64X/mpNLonBb3cPH6pFFVzc2wlI3WwZY9mkFXGITA5oXSkz8bJf/s3AIAHz30uWrffIb+eqeopakSuRzstUaJxtut0Ayp1uUgEy+uieUrubgkDT1wSrmSYyVGu5nQ3sGS4oendlDW63ZzzgJle3g1zQtd/KEMvt5VlBey52DbIkmFaTje/JiXXpGxOt+bkHBRrL0B+FvzDm+VH0+0OhdQIm89PmXBDremOGY2HCrpfRXeJe++9F9/4xjeUz/bs2YMbb7wRN954I+677z588IMfBAD85Cc/wfOe9zxMTWUKxA8//DA++tGP4rrrrsMvfvELHHXUUbPa/pnGrJQMoxBGWw8hFMwFXMfCYzqMdJveK3A8zyXzGjg43bZ6FZMBLnaOdIv1difRVZ32VHZiMQmpCQp/I/AxMi+ZYEQud09zui2ToqJM7AeIAeweXgQgi6ZKlMh5pJPO5n3C6B7E8gUDuG/bGHaNTc9tTreiXu4Dm9Rzu+iO7+PCO76fz/tk8K4ffQJvu/7TmSK/5yXMgPPPty7kbbngSqRbT2e4+GKs3rQJq/elbV61CpicBPbs4anDnpdsUxDJ051DWaSlKKebMXSUHZufe7p9vi5pCePl/PMTQ3Hr1sS5cNZZ8tqLCgstQ6SbGuOzE+m2L1I7inSPjgIXX4x1mzZhnfhs1arE2BSOzTKiltqzLunlg4kug4x079+nbCfuoVAv1/M0KWi3shliNhEnHUqqA90leXaUaP+qVYnB3aWTm4LOmYU5rkGAk8/5Q+DOn+HBZg2t2JP76CY/1oaeR7oFbM95h7TrXiFx/jRzbRHoVZ57YHNAWlBj6OWmSLd4xiaaoWQYjsyrw/c9LJvfkBoqvczptqV42NLXykAvXVWGXq4LnumQ9PKS7cuqsCS/F9Nr2TrdRdVSXFhIwSMPy7ciYFNK7Fkz7Idb05iqDyYVk+LYed1yKGLOI90nn3wyPvGJT+D3v/89JicnsWnTJrzsZS+T33/mM58BALRaLbz85S/H1NQUfN/Hl770JezevRuXXnopAOChhx7Cm970pjk5h5nErJQMm2HokW7Xc1k6r2GudcpAXyj3IidS5HULcE1wppfnDInyOd36ZFB2YjHVkBQT5lAjkMa1pJfPUE63VUjN8zHWGEYzjXQvn9in7qhEziM9zpbU6D560SCJdE9LI2g28mh15OiOzLkZ7/Ly5bmPlBJ4jswAGelmDD2WJgvwEaUNG4BPplS0LiJ5OpXONc1GKdNUmr6Xvc4pvhbldCs7MrNNRP8yCalR2vls9EW6UOLOq3S0ylUcrQzNWnseFHo5gLaXXN9g8WK27Vn1BbecbttiOEcvt0a6LdduhlhlOgJLbiyHk5YnCv67x5vYOZYYTDPpiLTVE54pFIrLzTCo43xGhdT0tCVHiE2pYz6U61CeqrzzYGZcL0jZgZRiXvY6NyxGt41u3k10XTl+zVee33J1utV1ng4h51G6jJqMoEfpf0sgyHId1JJhTL9wCGgEUTZPdRTp1gz79/zgP/C2n3wGx46ljOUeV3HoJ8y50f20pz0Nr3nNa3DKKadgcHAQxx57LN74xjfK7xuNZGH8gx/8ABs3JiIfZ599Ni666CIsWbIE73vf+zA4mNBhv/rVr2L//v35gxzCkKq9c2AM9Ar5SLfbw+l5npLXXZzPqQ8uPTC65+lGt3lhWjTI61HmTqKrOSG1Dr2l+oQgyoUN1QOZu63Ty3uS013giZb5UEGAXfNGAADzpyeyMk8d5Dwqoh9pCZOjFgxi2fxkUbDrYDNjHcyBkJpKA/SLDRIAWLIE+NGPgA9/2O0gjjnUnKFnjR5zBmYP9CF0lX0p5FOC7VI2ihXojANlv1pee4eoS6ObX5DNttHt+5kQJDu2lRG6KiOOVoZmrT0PI5MHAAB7h9RId/1xj1F2owupWXO6OxRSs6U7sEJq6o+tqUC9QFkDc7hRk3PufduS6zyTLLtCobcZOabd0TTToHN4kY5CdyXDCiKaBsiSYWSMKsrpFuPWyFBdsiJEmlqyz3LXuVaGXq708ezzbm8tddKVCTjoDmMdWaS7XHvEfWnp9HKmj9iqxKi5/kwjHAIaPmJpPMpId5kT0tYjz/3dz/GqX32jcLvDAX1lyUVRhI0bN+JDH/qQ/EwY4Lfeeqv87PTTT5evBwcHceqppwJIouG33XbbLLV2dtAy5NIcSuhUSA1Q87oL1ctzC+Xur9kSB6M7Uy+3H083eMXCu1xOd4/o5Yac7iTSnUwwOr285zndzHnLfCgvwK75SeRKCnR1mPOoG4qNmo+R4TqWCqN7bHpuc7r10i5FBonnAZ/6FPDMZ+YNWxMKJlJxL3h6OVnUuPbVLiN5pvJ6Rc+KjZZYeExy6vrY0WnNWx3i+ppyupukokHXas6OENHurpWUy4ijAe7OGe15WEzp5Z6Hdmp0BzVN70JGutOcbot6uZO4EDrP6Z4tg1JHJ0atiHbfty25zjOZ5tCrusplMNeR7nmkHxbqKPQo0l3mPPWxFzA7PfX3I8OZY375fBLp7kZILadWbn7fK3o5oK61ygQcuOtH4epA1iH6hWAd2GjqYtcBkxpCj8s6dl1LbKbtEWvHUvNimfSiwwx9Y3SfeeaZCIIAxx9/PK666irUajV86EMfwlvf+lYAwPbt2+W2IyMjym8XLVokX9PtdExPT+PAgQPKX78jlHm/h67RXVYQTfltiUh3Pqe7++69ZF5x6Yss0m0/nk7b2pFSnZfs26nUQLRhSBdSK3mKppJhIqd7sE7o5VMqvbznOd2WyFr0iY9j16qTAQDLJvYmX3aopO/7nmLsH71wEJ6X5JwBkGXEgD5RLwfcDZIeqSGL5UVjfCzXF9mcbhd0EcnTxXzajuX1ujO63SLd3SzUs0i3nV4+G/ncAmLxxI5tZYyiMuJoAq7OGfI8jEwRIbVVqxCedlpyHgYaZdlIt11ITX1vuybGtIxZhJN6uYaTl88HkEW6u3EyFWHGcrotcHWwzBSUSDfneO5ER4FBUEQjNoATUjNHutX9Lh7OghQrFs4OvdxEKe+l0d3LSLcUUitrdKfbi0Cc2D2nt+D75jG9MO3AkYUk5stMuLjE+cxyFYd+Qt8Y3Tra7Tbe+MY34gMf+EDuO13RnL63CX68//3vx6JFi+Tfcccd17sGzxBaHXrF+gl6re4yp0LF1IqMWn0C6IUBpdPLc90rDOGn5Q5q6x+yGs5KlHl0FOs/8FEAwIk/+o5SA9GGYb3OZ4f0cj3SLXO6674UDRGqv72MdNPn05bP1n7Wn2LXvybU6WWPO63rnEfaN45emNDeBL182/7M6J7rnG5FZdbFIOmFGvLoKGqvex0AoPHIhlxfVA3O2bk+QTqmR5OTwA03IGy7edMVWmLZZ4Nsri9GSuV0WyAcg6aSYXNidKfnw51WqUVsp9ELV+dM+jyMXP4PAIB9T34KsH492gtHcm0Fsr4iFr/zrHW66WvzeerrC9u2vcrN7Qb0eXVd5J8sIt1bE+fGTKbc0MdstobeXuX9dgqqjM1Gukmbuuk2VK+hWyE1kUecEz/NRbqz9VI3Od2lhNQM0e1u760YLwZqPgZqJZh1tpzuMES0c1ey3f33d1T61CXlSvQrdo2lOGMM16hk6b1kXyUe4Fmu4tBP6Buj+5ZbbkG73camTZvwzne+U37+9re/HTt37lRUyfft26f8luZx29TLL7vsMuzfv1/+iRzxfkYoxbb65lZ1BEExT9ixMxPp1j1tM04vT2sM+vuSSKz/Hx+zGs5yMB6fANatw/pGUvLmxD2bkw0cSiXkhNTKRvO0vHIBQREabtRkTcqZyOkGKDPA7KENoxi7xpPjLnvMyV3nPNJjCQ+8MLpFGTFgbhgl1nqqLgZJNznUqfDV8LaEFrxgeiL5nPTFbvKkO8LoKGrPfhYAIDwwBpx7Ltp/8ZcAihdSymKt5L1UaXfmiE5XAj1FkW5Ro3sWnT+iz3FzjE2QJ4fZiF4EARY/5ckAgH21QSAIjDXc9fc29XLX6GdR1I3C8zx5H+dKkqUTw19EukWZzNkSUpuTSPccjPfDhSXDkuvtl1wr5fdDnbnu95AVUkuHq6JnbDGll5PXwbatpQxMe063ui39Wim31uWtFQGOhWg7MxFpG4RQLsIw+f2llwIrVyK8JUmV9f/1A6VqUsuSYYJebikZZltj1ZDd1/rPf2Y+r8ISm/a+UIgeaL8ciugrSy4IAhx77LH4x3/8R0kZb7VaeOihh3DmmWfK7e6++275empqCg888AAAoF6v48lPfrJx/wMDA1i4cKHy1+9wVe3tdwiju+zESqnpRYs+faHaE3q5pl4u20BrDArHSBRZDWcZ6d63H4hjrF+cDDYn7k2Nbl1siMFAzXeOynAoElKj9PKxqTbiOCYlw3pTYVC03yZKF8Uxdh5MFn1LNYp/J6DPj4x0L0jurRBX87y5iXzQQ3Zs9HeSQ02Er57+8J14w8+vwT/89KrkO9IX62UMr26RPlf+1i0AIPN1w72JY6t2YJ/15wotsfSzYaOX9+YaZHW67UJqcxLpZiMm5HVRk2YpeiEW9qIucLughrCATb2cGjd2ITX1feGcVDPny88GOik/dfKK+cr7mXQAzUXUec5zukk/5Bw84nJ364RQ2DkdCKmFYT7SXfSMLRZBitFRrHj5Rdl23/xmRwYmdwzVUaM+uz3rT6OjGP7ZDQCAhds3OzMRASIGG0EGZnDuucnYt3MnQqGhUbBe1KEb85l6eX5bMZbn7IbRUQQXZfel/qw/sZ+XxelvKh9XCrNUxaGfMOdG9yWXXIKvf/3r2LBhA5rNJrZt24b3vve9MnodBAFOPPFEPOtZz5J08BtvvBHXXHMN9u7di8svv1zW7X7xi198SBjSZSCocWWjNv0GoWBediF87ILM6A0e3mD1NuYj3b2nl/seciq9fuo5DKLQajhTelDLD7BxJGFlnLh3S7ZRQakEz/OUxWPZicWnEwKBUjIspZeHUYyJZphFunuQ0w1kkyS3EJATSxhjV1qGZNmC7o1u+vwcvSgxunUWQz3wZ6werQ1eFEEwOOt331UqIqCgbA41Eb5qRG288Wdfwh9suS/7Pu2Ljd275Ecz6vwjz1UQp3lrXhoZTstC1Tasdx4DuisZZqGXd6Nenhphsk63iIJccw1www1oNhMH12wa3XVLNLZ0zu0sRC8WDWdMnDCKs0i3pcwboBo7OpR7X0JIreiaCGfNXNHLlUi34/OwYsEA5hMq/kxGuoO5jnTPgdFN52/W0SUi3V22TWFQldgXx4Yz5nRrz9jIcF06Tpev/122zzgsZWDa9BBs7JueMCcE+yt18C6cGk8+d2y/XGNt2cKWTxRzWhBHToEWgRpZPwJZpNumjaOkg6XnVd+eaGp4cZS0ocR9UY5R0BfcdzTzVRz6CXNudH/zm9/EunXrcOKJJ2JgYAArV67E29/+dvn9ZZddhhUrVqBer+Nzn/scBgcHEUWRLBn24bRkzoknnogPfvCDc3UaMwZZMuwwopc7Y3QUR//B4+FHyWAUfObTVq9cPqe7+wlVN8w8z8up9AYy0p0OmgbDWUZxfR8bFx2F0A8w1JzCUWN78ge2iBJRinnpSLeBXj7RzHK6h+pBVm5nqkVyuntEL/fMC1Gq/CmM7uUa26AT0MXCijTSPVALlOj9XORzC0940EzOtXbJxaUiAl3BUfiqPjUhX8+oojZ5rgR7REQFRFmo2tSktXZnx6JvUPujnV7eeT9R6OU0CnLRRcC556J54V8o280GJBXR8jzS7Qoxw9GLkaFkPIjjRCRNOqZzNYTV98M2erljpFu/REW3KXNozFGkuwONA8/zZF43MLMU7FLpCz2Cjbo8G6BpDjZdk26b1mmdbk5ITeYPB/ozpkW6B2vScbpcVB1BukYqYWDamEW0H9uUuTu6fsTxO6+ZpJ0tmE6Nbsf2S+P4t/ex5RPD1Oj2U8eya03qmpw7NHo567jRIt3UoZ2uU+thu9R55dqTo5cf2jbKbGHOr9JrX/tarFmzBitXrkSj0UCj0cBxxx2HF77whfjOd76Dd7/73XLbZzzjGbjllltwwQUXYNmyZajX6zjhhBPw+te/Hrfeeqs1n/tQhcj9O5SF1ABgVRpdDKLQLT9GeOU2PoKjDiZGaS2ye0tnQr1cpzYHnpczVrx08BSDmYS2naSXez7WL0kiQav3bpGRcgUWUSK1zqe9/TpM9HJZMqwewPM8KZq2bf+UnHBFBLxbiK7MLTgoG0BGuuf3INLN0Mv1fc96PjdJURAOm6I+3lM4Cl/V5g1nr2dyHCLPSxbpTvq6LAsVhcCPf2wcP+g9LOuQUqPZ6oNFIy/dXINGSmlo7h9joyDNfQnDqzG2v+NjlIU4bz7q1uH1nMHoRaPmy0js3ommMae7rr23RboViqrlPMtGuiW9vA8i3WXmCpHXDQD13btK5bSWwdzkdNNnefaXwEMFdbptTrAy6JRqzQmpuaqXj2x4QI5pQ+1pabAGBQEJHbaSYbaUia6ZE8TxO9RK1h8LhdENOLVfrmGaLfb7SNLL7etFHXqkW7AVbWVs5bUi51XTjW7A+b5Q6OzbQ7nC0mxizo3ut771rbj++uuxZcsWTE9PY3p6Go888ghGR0fxvOc9L7f9GWecgWuvvRY7d+5Es9nEhg0b8JGPfATLly+fg9bPPGSk+1Du0KOjOO7CPwcA1McPFufHaPTtYw7sBFBM387X6e6+ew81AgzVtciyZqz4aZtq+iCqbScN3qAmje6TRD63gIPYEG1P2YlZGv6aB1bkdIuSZEI0bXNa1izwPeW43UAs7m2L/HYUY9dYktPdC6ObUy/X9z2r5cK0Pi6MzHrU7tjzXBqOwlf1VRlVeEadf+R5EdcjF+mOQuA97zGOH0qku+SYSRcvel/oVR6oLBm2Zy8bBWn6yfPXKKiE0EvIOt2W0jNAfzl+R0het4j8FOd0dy+klqvT7agzMls113V0ol4OACfvfFi+btxzV6mc1jKgj9ls2b/9lNNtM7p7mdNdZv0oI90Odbpz9HKNtbc8DZiI8VyiwMB0zenOU89h/M4JpF0LmgnDa9HUQet2OqTRnUa0YwC/OfbR+NyTn4+3P+tvcf+y45Pt9GtS4AQX11oE4qyR7vQayftO2iuN7qid+51z2Ufk54t+mh/6GXNudFewI1tQHKK3Ko3mPeruX+Ivb7sOb/jFNcnntmieRt8+bVeyAFg6kUZ/DF45PbLRK0cFpZj7PnLGihg8JV3IYDhLavfgINYvPgYAsHoPyed2FBuii8eyOchiLssJqZFIN5Dlb2/eO5m+r/Us31lMmrZI99hUS7apJzndCr08299SQl2fVaNb6+My0h2Wiwh0BUfhqwZxtsxoZIg8VwPtxOES+gF+v/Q4tKjRDRjHj25KhtFz08eOWhe0dQoppBZn+zjYGMJULemHzSB57hoT4zN77wmsSrd9bnTvn2ySCh92g8C5ZFgPhdQkvbwfIt2ubRgdxckfzUq1yojYDDBwelWTugzmPqfbLdLd25xu93FbbBvH2TrBHOlW3y8+epnyXlDMiwISOqz0cuqosTyPHa1XSLvOv+cG/OnvfoEL7/i+dTsdcp2Xzlk/PvkpuOAv/w3/9KzX4otPfh72DS2EF0dYtW+7aKhTVQehOh5u3wHccAOitIwm101yYzpp77H7d2DB1EE8Zsf6UudlOobArK6fDmFUV6nPIRYUh2Skm0TzPADv+eHH8cpffyv5zhbN07xt//DTz+MT33gvnnffz6zb5cWPetO9qWHme17OWBH08loUWg1n6UH2A6w/5zwARLkccBYb6o2QmsHobiSNFPRyEenuVT43YF/ki892HEioXYN1H/MsESpXiMXByHAdg8SIpJHu2RSv0vvuurt+hD/ceA9O2b3Rul3P4SB81Svl7kKQ52pkehznPvgrAMAlz38TpurJfVJoikBu/KC50N0IqeXUyzvMj9QhI91B8nztH5iHs17zaTz7Ff+OXcOL0Ew/b4Stmb/3sk3m53Eu6L8uWJzWA947TnO6O490+4ohZr6/nucp/qlCo7vWGwOqU5ROD0jn7JPJONRt7qdr++ZEvXwO1lXU+WMr99Tt5ejUuUDbJKLdzurlf/xUJSCxfDwtp1oQkNBhi3QrfdoaBbceggdx/J6yZxP+85vvw+O3P5h979B+GekeHAQ8D79e9RgAwCm7HsHf3vw1fPA7H8IPP/13ePyOh9yrOoyOInj1qwEA7QceAM49F+Fll+fOWSBTL/dz57WwOYFffPxv8Pmv/mOp89Khj5OHeoWl2UJldPc5WoYFxSEBLZqXgymap3nbFk+N4bz7b0ZDp8No2+kL5V6JESmRbjHAEWMlkDndkdVwpuWwNnhJnuyJ77q8tNhQUU6YDSYhNUkv1yLdm0Sku0f53EC2mLDRonaMJRUJls0f6EmEXRyLUssBPdI9i8+Y1ncvv+Gz+NrVbyns4zOCAuGrbsTJOmpL+lz9y/98BCOTB3DP0afgP5+StEWJmDDjR6+E1PJR0x7ldGtG92+OfQz2Di/CI4tX4m///HKMNxLByUbYmp17j+zcbE4woL8WVSPC6FZyuvVFoLpwH7A41RTnQsG0USY621d1ul3uXzpnH79vqxQwrYckN7XHDBxVwO7Ii3RzKTCiTd2u+TLl/HLXlvZ/8WxlpWvNzxgAjCwYVAISZ224HY12C2dsvb9U2UBnerlVSK2D69eDsofS6F59IgDg98tOAAD89W3X4S03XoUL7vkJTtmTrotdAi0pW7SxYxsAYKqWOKCj/QeS423dkvtJpl4uFlvqeS1oTqIu5tIS94U7T9P7Cjwqo7vPIWol9ipqO6twjdTo2znmmupeubyQWm8GgRy9XCA1VvzVqwEAtfe8y2o4i4GwFcbYsj8xKk969lmlxYaGu1EvL4x0i5zuNNKdGt0LBnoX6RZttuWQbjuQGd29gDjvFZrRPWc53R328RmDRfhKWQDNhmMifa5WXPo6vP97HwMAbBo5Ojm+TlME1Hy1Hgmp1TUDrVfRfsGmaAV1wPNw+7GPlt/96rjH4WNPe0my3UB91u69LYfUFlWaS4ha3fsmWsaymtSgGm4EVuddUMKQVqnoBZHuOaaXq6JTDj9In6WBsI3jU/prveCZ6waKZsAsXSPdGTPbUCLdFnZJt85mqWBdcl6j10cvT6XvijZ/uBFgoBYojtOX3PlD3HXFi3DuQ78pVTawbkkTUh1kmuHXC2ZOl2UPpS7NohHg2mvx+6MS4/vUXY8kGyxfnrBFXAIthC26an/yPD48shIRPETp+fn/+7855okQ563t25uJIPa4nKOuGTAX5VYPRRyCltyRhbYhX+2QgGukRt+uQ2+jfo16NaEu5SLd8iAB/AULkpdnPMFqOOsTxKKhOhbPK18Oqxv1cimkphvdWqR7gYx0J2IivY10Cy9svvHiHm4/0DvlciCbII5eqO5vzozuHnjUZwuUMTJrxkMQAM98Jp5z/y+w9q4fy49ZA4CMH7S0YjdCajpLRim/01VOd+p4C2qI4OH2Yx4FAHjO734OL46wa/7i5PhPftKs3XtXenk/RTJGUqHHfZNm9XJqhNuUy4FygnGuSudA5mSZK4dF6Zx88iydnEbjFJVjZrtu0HVksstjzoV6uTJ/M+csI93dCqml51aa8UMj3bGa063P2Z7nyf2LlA8ACntq4AufL1020JVenjPIlf7kdCgeXZQ9pIGNyeefj40LE5HnU99zebKfrVuBD3/YLdBC2KLH79uGWtjGZGMQWxcsy0qPjR9UmSejowiuSEopBw/8XhVB7GE5x7l+jg5VVFeqz2HKVzsk0E00rwOvHPXoNgK/Z563pcNZlDf4xS9yXsUTliRU8eOXDMMGfYI4cdk8w5Z2DNWzBWSnke5Io5dPaTndgl4+nhrjM5PTnf9OTJo70kj38gXd1+hGGCI4mCiQHr13u3L/lhF6+azX6e6x53mmoJTSms1xKB0//unHn8Sx+3cA0CLdzPjh+17H6r822mnNsggsAxpBn/7K1/C/aaT79T//Mt5yw1Xyu8ZJqzs+RlmIBRMf6Sav+yiSkdHLW0ajW4l0W2p0A9kC3feKI4z0MK7q5XMW6S6rcUBzWtO8biFqCKDnDByfKDj7P7txVhT7a6REp//TG2atSoBAkSaLrO7RZZcR43bZMVuJdIcip5t/xoDsHEaGtTVCF2UDXenl3KnZNGNKocP2U6P7wZ0HEccJW3LZX7+0fPlEwiipRyFW702o5A8uXZWVHoujbLuUiu7v3w/AIDzao3KOdEwr6+A+klEZ3X2OQ7pkWLfRvJJeubpGd+kJRkex5J/eJt/6f/aCXOmUK176RNz0D+cqtU056Iuekzo0umeCXj6RGtdCZEyPbC/sodEtmsyWDEs/6hm9fHQUWL0a9XvuAgCs+I8rlPunRLprc/CM9dDzPFOwLYBmFOn4sbA5gY9961/wxC334c9+e2PynWX8sEVurYcj29vo5b3I6QaAe59yLg7WhzAcAKdd8V685p9fhz9/QkKjX7Xx9zNWG1lH3bI4V3NuZ7wpzlg8LxmPdo1Ny8/0aAuNyhVFunM5kBbY6K066pZ8+dlAafVyMmdfeMf38Zzf/RwvufMHyXe9ZuCMjiJ44QuzQ//Jn8xIWbLcMS9IxtYgCuE94xkzf0wNRerl4p51r16ePtclncn0sDLSbSjLR4+jRLq7hD3Snb3m1xDJZ3NFd6a6OfdvHwMAnLLCvjY0QmOUnLw7iXo/uHRVFumOomQ7QkX3pc6QXXi0Gyjz5aGY/jpHqK5Un0PU5DtkS4Z1G80r4ZVTSmT0YhBIvYZLN2+QH3lxnCudMlgPcFxBlBvITx4dR7q7EVKTRrf6ualkmIBQM+8FZK4ZO4En923nWA/o5en9w6ZNOGFv4gl+wrbfK/dvzkqGUfTI8zxToIbirHu00/HjSf44vvmFv8c5629LPreMH+I+ljWOqVFSt4pydd5PaB/75fqkhu0ZJyxF7aIL4e3diw++9YX46pfegv/79xfOWG1kHbbI0Fzk3LpgZCh5bnePZ1HYopxuG3zLNdBRJv+7Psf08o5y8tNnbvU8Hx//5vtxulBv7iUDR0Tktm/L2hpHM1KWTD9mbXsyFwQF5QdnCgM136mCR7d0e9s8a4PnedLwFiXDXCLdi/RIdxdoEAd4vha3+bvk+/S7ORqvxBwZRjF+vyNh2J12VIdGt8YWFSkfDy5ZleV0Dw8l2xEqeiCqHlFh1h6LIM4ZA+4QxyFqyR0hCEOEe/cBAOq/vXfWaVA9wyxF81Rhhy67NvEaLhH1wZEuDDr0GuqTwOpeRLo7NCxM9HJBfdON7IVDsyWkhrR9yf+OjW5y/wDg/d/7GG78xCvxhG0PKPdvfi1TNa68tTzqygJoDq5RacaLmS5tgyqk5hm/62aBEfjZgvaX63cDAJ50/Ig0CIKNG/GUTfdgUChGz4JBIByUtqiRaHu/QFBZdx2kkW7zPbPV6AbsY5IOuol1yAhDNHYmaRH+I4/MyfytqJeXeR5mcs6mEbnUOPDiCB4wIxE5/ZjC2K7NYBTQBs/z5BxuM2K7fd6OWzSAhg+cEo+XZs0IJ6NIb2wbSoYhDFFLc/4X793Zs+unODl1hfICp5f4bK7iVFQ35/dppPvUFQs625nGFqWR7iitAx6c+dRkO0JFVyrq6OiRCCJdC1RrJ3dUV6pfkdJiW/fcCwAI/r+3zzoNqqeYhWheQ8np7nKBSLyGS4nR7YsJugOvYa8i3UVCLDYY63TrJcOGZi7SLS4DZ8Dp14jmXJeCVq6uEbVxfKr+CUDeP+9nP5PHaGzeNGuU3kMJs1oyzIQS44ekS3chpKZTlRUxuW7GljBEI/35rx/cCQB40qpFioNIwSwYBKIGOXdaYrzwvLmja3IQVNZ9E1k5K33soH21yOgWt9eJXu5iyKbzd+O730n2+4XPz8n8rdbBLvvjGZqzaUSOMw56HJHTj1mLNOrtTB3TApHuwDm6auSZ6xijo1j2+EfhF1f+BT77T+tKs2bE8BfqkW46SKR9vLYncR4u/soXe9bHbRUzaJ/mrlGWEz9Hke704tFI96mdRroBhS0qIt0PLD0O4cgIAMA/8cRkO0JF92PNsUTRIxFERVz0UEx/nSNURnc/gtBiQy+Z6OpROOs0qEMNSi3dbj1vxBu4fHwfBltTGG5OqjVLte2K0Dt6ORVSK/dbMTbSOt1xHEt6+aAmpCbQy5zuTL2caZ9m7Cxb0GGk2/W+/Pd/Y9kD9wEA6r/42axReg8lzFlOd4ewCYPZQM8tp17eC8eD0BeYSBZiYynz70lf/6ziIMphhg0CMSb4D2/IOZ1k1K2PDG6Azx/N1+ylOd1FQmru0cVCRXcyf//hpnsw2JrCk7bcNyfzN3UezZURkgMTkfM5h1OPInL6vpaP70UtbOOYsV0ze0wLhLAfH+nuUnyP9L9lE/sxINTnS/Q/ajgCjHo5OUYtNfBGJg/0rI83vKw/BD/7mTImFT1/vaLndwpx/PHpNh7Zk1R/6TjSLZAyT0666hMAgJ3zl2Dfa1+fHi/dhlDRA86x1GMRxLmud3+oojK6+w0aLbYVJAZWEIWzToM61KDXDewKxBs41J7GVV99Bz73tX9CLY6M2xWBjktHLRwojL6YMFzvgl4u1MtJpHu6HUk6d1YyTBdSm52SYfpt65he7npfrrgCS/clFNB6B4uTIwGNQ8zoFmWauhFS0z339H1H14AsUhukBNPxe7diGRFqtGImDILRUdS/9lUAQO2nN+ScThlVs7/u+4LBmlov28u3Uc3pdqSXOxnd5DVDt6Xz99p7rsfdH35xUqd4DuZvNdLdJ/eQRuQiYXTPXERO39eSyQP4zucuxue/+o8ze0wLRKSbZ3sl/zsyGrX+p6BE/xNdRTjnlZxu7RjCwFs8OdabPj46ivoLz5dvg/P+VBmTVPVyM718rvq7OO6BqTbiGFg8XO+csafsOMDCZ52Lo9Kyp7/fOQ6AXANCRT9qfG/y/2CiGzITZUgVDZSKXu6M6kr1GzRabChqLdL8o1mkQR1KqPcyx0QTsHjqpnvwlE33ZN934DWkAiWrl3YW5QZ6Ty8X+dyAmV7e05xumbOW/44uQhqB37mxX1SuDpCTz7KJfQCI0V05txR0bXDOMnqiXq51zq7GFm2RSsVtnrTld+776bVBIISlDiTpM5LiS5xO8lntlyhpCt/3sIiMSVydWHo/5zmWDHPpM5Rmn4vwaPM3ANVRO8vzd2n18tkAGZvFukahl/c4IqcfEwAevevhzCCZqWNaIMRQ2Tkw/e/v31c+3Ynpfwoc+5/unFdK12rHEPdw8eRYqWOwEGOSzoYgY5KiXs7qwohId/nD9wL6GHLqigU9Tc0RVXJ+vz1hTCnXIKWiP3viEVx9zeX4hxuvSj6fgTKklPZf0cvdURnd/QYtmtH2E6Mjl5sxSzSoQwk9FVLrttyZabfpgHzS8s6N7q7UyxkhNUEtbwS+pNEuGKgppz17Od3Z66XzG51PVi73L13MHJ3SDOc3J7JtKueWRF/kdJdArUN6Jt0+Ty/vwvGgLVLrITW67yv+/UwYBMQRIOYWWTOZOJ1ETeN+dLZQijnXPsp2Kop0l6HR00Pltnedl2dp/qYL475hK5Cx+bgDO3DOQ7/GhXd8P/luBiJy+jF7OZ93inlSSE2bA0dHEbzxUgCAv2F9+XSnHvW/QBNSi6jRrf32MTvWo9Fu4tE713fWFgEyJjUiotUQqeK19Iqx9HLBzpljerlAV/ncDITRLcqq5q7B2rUI1q/H06+6EvM/95mZEy5W6OWVKemK6kr1G7RoRlsoFOpG9yzRoA4l9LxOd7flzhiIiaDTfG4AGCbKyv59vy3lCffTRXS4abP0ok/KGt0k/8/3MJ8sVHuZ050tcM3fAT2o0W27f5dcIt/+5e3/g9f94iv469u+k99H5dw6tHK6w1DmTNe2bytXXcBCL++qTrfWhxpEF+LJutE9WwaBIizFRBtTp5P/UFIuqh9v+8gwjXQzi2+a010U6RbRsZIlw3Lbu87LszR/92WkG5Bjc3DMSlz1tX/C26//TPL5DETk9GP2cj7vFMNpapliGIoKBrsTJ7DIdy+V7tSj/ieG/HxOt5f77Ue/9QH86mN/hZVjuztriwAZk4Rj0osjuWYRY1LwcGbcsxUX5lxITY9099roVteO7Jw8C8LFVU53Z6iM7n6DRoP66tVvwU//8//icTseSr6fZRrUoYR6L4XUBHpcOkUMVCcu63AgHh3F8DPWZPt75z+5e8JHRxH8/ZsAAOEdd0ov+sT3fghAjaADKqV8fg8j3SJ6zYugZPetJ3lQpvt3fpYztmJ8L9580xew6sDO/O8r55YialO79Zb+pdwLobJ77gIA+N/+VqkokY1ertbpLrnA0PqQWFAOtKbx6B0bsi/e+c7ZMwiII0A4AZSarikCQT3vw0XVCI10Mx68TnK6XRaPlH2Tuy5FaS2zPH+rdbpn5ZDumKVSonN+TB1hiHm7k7mmtnlTMp4yzBOPVkoB3NKdetT/dCE1JadbO0YQR1g0PV76GDmQMWmgnY5JITMmjY1lr5nTlHW652jM0h0Bpx3VpYiahpM1I36ufGmVenln6LdhuIJGg1o5thsn7NuGwXZzTmhQhxJqWj5wz9BDr6EQT+vI+5l6woc2bpAf+XHs5gmXXvTEGx2Ja7V5M6YufzuALJ9bQFDKh/0Y9Ztu7JmxJcbnIiG1riPdcqfM/euzxXHfYnQU9ec9R74NLrigP9XdiVCZWKgFUVQqSqQa3Wq/6KpOt9bXhJF7+vYH0IjaWV9729tmzyAgjoA/u/eneMYDv8QL77k+t1kwsij535dGd1GkO/usSL08SA0b/+BYYR4tHbZy0bQ+ozH3pXo5xSxE5PrimAKpY3DhN74GABi4+gvJePre92bMk3T8UlIKXdOdetT/ZMmwOAbCEO1WOqb+8tbki5no42RMOurgbvzF7d/F63/xlXzbFi7MXluE1PrBGAWAU2aIXi4wVwyWqk53Z6iuVD+ij2hQhxJ6Ti+fAfzruifgXy44HavL0suJJ3y4NSU/9uOo2BNOfivSFFqpVgDiGJO1xLgdpEb36CgW3vEbAMCC/Xt6WkrLVjKMeok7Lhfmgj5bHPclUkO2vmWz/EgXtekL6EJlwuiOy1V8ENHQeY0gpyWgUuxLTptaXxPte/Lm+/J9bbYMAuIIePSuh/FfX38XHr/9wez71BHgP+o0AP1psBXldIt8dAAYvu8e8/0fHYX3D29OfrNpU+FYV1gyrI/m775ULz9SQRyDF97xPbzkju9j3V0/SsbTd7xDbvbUjXfjnId+jb+6/bv5fbikO/Wg/wlnTXT9DcDq1QhbiaOw9uIXJ88G0Ps+TsYkD8B7f/AfeP3NxOhOx6TglJPlRxy9PBNqnaNINxkfRobrWN6r4EGKlYsGVTHdOVNpz15X9HJ3VEZ3v6IfaFCHGHpap3smEIZYs/VevOSBn3elSjrYasqPZR6mzRNOfrt48gAAYM9w5i2erCWL1+GplCKWLg4WHkjKTiycTnJke2VsZZNi/h6RdHUs27ZxZqnMfbQ47jtQURtC8evL0oW6UJmeo+wYJVoyGOD9j2vggyvz0c5at/lrpK8J1eSnPXLn3PU1R6dTEAiV5f5bVC0etqiXj44ieO1r5Nt5l76BN6S1PFrfIY9WMbpNzog+mb/7Nqf7SIPmGDx5z2b8y/c+ihP2bcuV91o0PY6rvvZP+PN7b8jvxzXdqcv+J7pN+x/fAWzapGoLiWcD6G0fdxyT/Jq9eovo8/2Q033qivk9VS4HkvQWGu3u9f5dQcfcvlxv9ymqK9XPmEsa1CEIOtj1lF7eC6S0Mpx7LnDRRV2pkvqIMdRMlSv1uuGcJ5x8Jhb82+cvlZ9N1hNP7FC7qSwOFqZ5WgumU1XvXhhbYZiUQgEQ/O4+dT+jowi+/GX5dtkH3jPzVOY+WRz3HRShrczo7svShVqfr6f07Zz4pC1KlD6fF/71s3He370493z2REwu7Wvv+btn4/MnjWPNp/91bvuag9NprkWJbFhkinQLhsaO7fKj4eZU3pBWGEDJOBrQ/g2wYx29FFbSQx/M32pOd//dwyMGRaW8itBJulMX/U9WOYGHCB5ij5Supc8G0Ns+7jAmKdUDuEj3HKuX+2RNdmo4NiOOaSqmNnf08v5nlvYj+swyqVChc9CFcV8NAoRWpqALVVJBMfd1o5vzhJPPVqRG94HB+ZhKI9yT9UEAwNDQgLI4WDh1MP1PRFK6rcG5ejVW/OwnSVsu//vMsEmvkX8wE0lZPr5vdqjMfbA47jsQA7WuR7oN280ZtD4v1bhdng3A6flURRq7GFuCAEuefS7OfvWL4Z177tz3tQKnU5YK0kfjaQol0i3uCZNKAwDzWpN5Q5qMdV5KRa8xCu76WEcXuf14XSi6KnVXoXcoM072QbpTMJk42tt+IKPcgOaUmimna8GYRJ8/zt7MHIW9b1ohRkdRO/3x8u2pn/33GQkc0Ej3XMWXqpJhnaG6UhUOGyhiR/0S6dZoZQq6UCUd0o1umyec/Hbh9DgGWtMAgB3zFgPIjO7Bo5cri4MFad3qBVSZVKCssUUMm3f98BO4+prL8bSH70wMmwsuAF79akW5FQCWTezrPyrzkQJioM6fnsBwcxKLJsckdZvbbs6QEyoTOd0Oz4bj81lDZowddsaLxekUiDrdDgJjsw2a0y0XgEwpNCCNdAOqsUDGMGFM5JyYQG6sc6KX9wnowvhw67aHFFzHydmsYGBBkKqHh76PkBjdtXiWnK6WMYkyNvgKKHPkKBSpKps2yo9O2/XIjAQOqIL5XDFYbCU2K5jRJ5ZJhQrdQ41090nXLqKVdahKunx8HwBgZHKs2BNOfut5nkox97yMXt6oKYuDP9h0L2phG0/ZdE9+n2WMLc2wGZk6iKc/cmdSf1MYNqmqOl30LkvPsa+ozEcKiCE7GLbwlavfimuuudzNkJ1taM+GoMMHUVj8bDg+n3Wh2osjSDRmdBT+a18LAPC3bumpmGIvQNXLpT4EY0gDaaSbYutWZQxbMpFoXSyd2Jc/kDbWqfTy/u4LQb+rlx8pcK2WMZsVDCwIGsmzFXk+QlousQ+crkGB00v081nNdaYl38K2THE6ddfDMxI4oJHufshdr1eRbmdUV6rCYQNKKW/0i+fN1RNcUpX0X773EXzoOx9MFIddPOGMkNOO+YuBVaswdeFfAECiiEkWB2vW34a7r3ixqqLaibFVIp+N5laOTI6pX/YDlflIgWbInr79QTx253r5HkB/qbuT/n3arkcAACfv3lT8bDj2qdr2bdnrI2GBIaI2u3YAIKJ0faRcP8JFuokRIKJyXhwp4pNyOzLWPXXj3fjP0ffg3T/8eLaNYayji9x+d8DUCqKCFWYJZaplzHW6UxhKhkvbDxB61Oiee6drkU5BFumetSapDJs4wj//z0fxz//zEayYocDB6mXDkrkyV2wbv4p0d4QjYPVQ4UhBX6opunqCS6qSnva1q7D2ba+CV8YTnv52xR8+AQCw/a3vANavx+TJpwIABhtBbnEw2CaL1U6NrRLGsvCkLx3fl0TCKfqBynwk4VBTd0/79yv/5Q245bFjeMHH31X8bDj2qdoxR2evD/cFBiMwJhkofZTusViJdKf3hBjStbR9w82pbCyhxgIZ63wP+NPf34KVY7uz7QB2rLPW6e4zKAZKn7f1sMehMJ6muivB3XcDSCLdNx+frBe8OEqYTnPsdPULUiaEETqr/V1b41xwz0/w0jt/ULhdpxjwgOOH0vN86ME5GYuVSHe/rLcPAVRXqsJhA7VOd590bVda2SypkiIIsOLk4wAA249aBQQBJprJgD0k6nT3enFQwlgW9OVllObZT1TmIw2Hmrp7EMA791wc/dcvdXs2HJ/POul7h33EkERtVownZQOXp/8B9E26x1A9QKOWqirLsE9mSA+3E+2KkVQQkjUWOhjrCut09xGqSHefoZ/HU6K7Ipzf333UH+H/nf8WAMAFd/8YHjDnToIiIUPhFJtVo7vXwRUbUsfIKXfcAgBoXPnhOUn7oakr/c746SfU5roBFSr0Cp7nIfA9hFHcP/RysQhcty5Z9FHBpjnyGB+1MBFO23kgWZROtjSjG0gm1PPPzwSHBB2zk3YKw2bzZl6wyvOAJUuAPXtQE0a3oGX1I5X5SINw8hyOcHw+/XoNvgdE8RGwwCDRmMdtfxBf/PLbcOrujdbt5gKe52FkqI4dY9Pq4js1pE+5+GK86cYv4FE7NySfr1qVjCO6sVByrDuUjO6AsIWCX/8aeO451Tg61+jH8VTTXRHO728/9hwAwPn33IB/vvVLwI9+NOcVPoICpsmclDl0WeOsWtV94EA4RuIYrwu/giWT+/Gnv78ZmNiffD6LzhBFvbxfglyHAKorVeHwQRiiJkq/PPLwnNMfJfqMVnbUwkQ4bftYoug7JYzuhjaR9iq3zCWf7ZOfBK69FsG8YQDE6O4n6l2FwxMuz2cYoiaCqffe0z9jy0yARGM8AH/88B1SB8K03VxBKJjnKP9r18LbsAGvf99r8ex3XVwcUSwx1lE7u68p26OjqD3pifKt//KX9ZUQXoU+gqa7QgXTXnLH9/Gh6z6E2u5dWc75HKLI6ZWVOZy1JpXL2e8UmmPkSVvvxwdE3vgcpP1Udbo7Q2V0Vzg8kFJu6ml9yfqnP9lfC4w+opWtWJBEureLSLdOL58JuBg2a9fiGf/5fjxuoY8Xnn9mf1HvKhzesD2f2thSu+yy/hpbeo2ZSImZIYwMJ2S9YPv2fEmzGRKkOiQi3Uz5Ij+O+koIr0IfQWOtrNqfCCi+7Dffxvu/97GsakUfiJnS5y/n9ApDBHuTVBh/06bZdY7OdHClV5VwegSlRO+RIC7aI1T08gqHPgjlRpRqqIftbIHRL5HSPqGViUj3jgNJpFvmdOuR7l7DgcZ5+vFLcN3lz5nZdlSowIF7PsnYIuo+B1HYf2NLL9GHKTEsRkex+Kf3AMc/GbXbbwPe/q5kgXvllTN6T/q+TjctX0QilkEcJffS85KI2Pnnz/09rNAf0Fgr//jjT+Ivb/8uHrNzPTzLdnMBozjg6Chw8cUInvoK4JSnwL/6auA9r5zx8UBBL9PydPSyEk4PoFRxqCLdzqjcExUObWiUG7HIqEdhXynt9hNWpDndB6bamGyGGb18JiPdAnNdDqVCBVdoY0s9rf9dOxLGlj5LickhdYaM7N0JgNBhZyGSq9bpnrHDdA4SEaM0YT8iCvR9IIRXoY+gsVsGwxYeSw3uPmK3+Io4YPqCiMD5aZ+fM2bHTK1xZlOszQFKTne/Mn76EP04ZVSo4A6NciMWxiLiXS0w8lgwUMNgPXn0d4xN8UJqFSoc6dDHljAZW6Qhc7iPLX2UEqOAOENGJg8AgBRgnA1nSN/Ty0mkS9ZVRiaOxW1X4QjHbOQk9wg59XKDCJxkdgCHh3O0z9J+gkpIrSNUV6rCoQ1t4SBqs1JaHbfdkQzP86SC+Y6x6czonml6eYUKhxK0MWOgnaaupI4903aHFfqRmUKcIUsnEqNbOlmBGXeG9H2dbhLp8hFjuDkJL44w1Jo2blehQt+zW1LQ58/zvLwIXGp0e8LgPlyco33mGKkpJXr7cBzsU1Q53RUObWgLh1q6IG6Ebet2RzqOWjCIh3dPYPuBKUw2k0mqMrorVCDQxozX3notfnHCE3D6tges21WYYRAnx3Pv+xluPe7xuPB/v2/drpfo+0i3Vr7oQ9d9CPsGF2Dx1Fjyfa/KF1U4/DCTOck9QqBrKmjPuUijOCyZHcIxcvHFqqiaqRziDEKt013Fb11RGd0VDm1oC4y6yOkWkY9qgcFiuSgbdmAak83EQVHRyytUINDGlpfe+QO89M4fZN9XY8vcgDg5jh3biU+Pvrtwu16i74XUNCG88+6/Ofuuz6jCFfoQfSL4akLga04v7TkXxravG92Hi3O0Txwjap3uPhwH+xSVe6LCoQ2NciPyLmtRWC0wLDgqLRu240CV012hAos+o/NVSDHHuY1irel5qqhTX+EQoQpXqFAWvq5ero0HItIthQP7SASuZ+iDtB/qfKzo5e6ojO4Khz7IAuPUXY/AiyOcvGdTtcCwQJQN27R3ElGa+jRY0csrVFBRGS/9hzl2hojFZl9GuSn6VQivQoUuoNbpRm48OGV3Upv+pD2bK+foDEJVL69MSVdU9PIKhwdSys2//vRGXLZpO1Zc+4W+y0XqJ6xIje4Nu8flZ1Wku0IFBn1C56tAMIe5jZ6IqPVrlJuiz6nCFSqURU69HFDGg7+99Vqsved6HH1wdxLhnuVc5yMFQSWk1hEqo7vC4YMgQO0Z52LFXLfjEICglz+8ewJAMmjWq7IPFSrwqIyX/sMcOUPEOr/vI90VKhyGUKoHUMdXOh54N92Eoyvn6IyjinR3hsrorlDhCMSKtGTYwekkB36winJXqFDhUMMcOENEdK0vlcsrVDjMoQip6Y6vyjk6awgqIbWOULknKlQ4AiHo5QJDcQiEoWHrChUqVKgAZDmllc1docLsI9BzuivMCWh0u2JJuqO6UhUqHIFYcN23MdSelu+Htm0GVq8GRkfnrlEVKlSo0OcQa/4q0l2hwuxDUS+vnsE5A7Wza9V9cEZldFeocKRhdBTei9bhqAO75UdDremkHvG6dZXhXaFChQoGSPXyaqFZocKsQxFSq3QV5gwBiXTXqki3M6orVaHCkYQwTBR/4xgrxvfIj4da00Cc1g675JKKal6hQoUKDISt7VcL/goVZh1BFenuC9DodqVe7o7K6K5Q4UjCTTfJEjsrDmpGN5AY3hs3JttVqFChQgUFVaS7QoW5A/V1VY6vuUOAOHt922+qQI0jKqO7QoUjCVu3ypdHUaOb5Hfr21WoUKFChQR+pV5eocKcQVEvryyYucHoKGrPfrZ8W3/lKypNIEdUXbZChSMJK1fKlzTSPdiaMm5XoUKFChUS+JWQWoUKcwZVvbx6Bmcdo6PAunUItm6WH9XCdqUJ5IjK6K5Q4UjCWWcBq1YBnqdEuocFvdzzgOOOS7arUKFChQoKJL28WvBXqDDr8JVId/UMziqIJlAQR/LjehRWmkCOqIzuChWOJAQBcOWVAJAXUhOLyCuuSLarUKFChQoKPFGnu1rwV6gw66gi3XMIogkURJlhXYvayYtKE6gQldFdocKRhrVrgWuvxYp5dfnRUHs6iYBfe23yfYUKFSpUyEHSy6sFf4UKs46qTvccgmj91KIs0l3TI9uVJpARldFdocKRiLVrcdQvfy7fDl70UmD9+srgrlChQgUL/CrSXaHCnEERUqscX7MLovVDI911EelmtqugYs6N7p/97Gf4u7/7Ozz5yU/GUUcdhYGBAaxevRp/9md/hp///Oe57e+44w6sW7cOK1askNu+4Q1vwM6dO+eg9RUqHLqYP9zAcCOhkQ+ddnJFKa9QoUKFAmRCanPbjgoVjkRQQ7t6BmcZRBOoptDL09eVJlAh5rzLfvGLX8THP/5x3H777dixYweazSYefvhhfPvb38Yf//Ef41Of+pTc9ic/+QnOPPNMfP3rX8fOnTvlth/96Edx5plnYvv27XN4JhUqHFrwPA8rFgwAgDS+K1SoUKGCGbJkWBVlq1Bh1kEfO696BmcXRBOoHieGthdHqIetShPIEXNudPu+j3Xr1uEHP/gBDh48iE2bNmHdunXy+8suuwxhGKLVauHlL385pqam4Ps+vvSlL2H37t249NJLAQAPPfQQ3vSmN83VaVSocEhixcJBAMBQvRokK1SoUKEIFb28QoW5Q0Uvn2OkmkDzly/BJT/7Ev7+xi9gIGxXmkCO8OJY6LzPDQ4cOICFCxcqn+3cuRMrVqyQ77du3Yrf/OY3eP7znw8AWLNmDa6//noAwNTUFBYvXoypqSnU63Xs3LkTixYtcj72okWLsH///lwbKlQ4EvA/d23FF299GB9+8ROlAV6hQoUKFXi897p78amb1uP/nLAY1/7t0+e6ORUqHFGIohgnXf5dAMAVL3ki/vxJx85xi45QhGGiUr51a5LDfdZZR3SE29WerM1im1hwjZuYmJCvh4eHsXTpUtx6663ys9NPP12+HhwcxKmnnoq77roLrVYLt912G84999yZbXSFCocJnnP6Sjzn9Er0okKFChVcIOiB/q6dwA03HPGLzQoVZhOVenmfIAiANWvmuhWHHOacXq4jjmP8/d//vXz/mte8BvX6/9/e/YdlVd9/HH/dNz9uuEFAQCgVwV9D07XE+eObmeSvysvUFM2cTte18srS/MHKtEZbTs20zLzMmkvmNdSmsTbdlqZSkWXbXCWsXGOBGuAvQMAfCML5/kH3iVu4EZa39w0+H9d1Ls/9Oe9zzufc94dT7/M553P8nJ7XDgsLc1qnbs92Y891X7x4UWVlZU4TAADAFaWny7L+FUmS77/+Jd1xhxQXJ6Wne7ZewHXEcYs5OTdaGq9KuisrKzVt2jRt375dkjR06FAtW7asXtzld8TX/dzYwArLli1TaGioOcXExFylmgMAgFYrPV1KSpL1m4v1Pt8MJKT8fCkpicQbuEYcz3LzTDdaGq9JusvKynT33XcrLS1NkjRmzBjt3LlTNlvt6MrR0dFm7JkzZ5zWLS0tNefrxl3uySefVGlpqTkdO3bsKh4BAABodaqrpccekwxDPkaNJMn6zb9yXPSfO7c2DoBbWa2Of0m60bJ4RdKdn5+vwYMHa9++fZKkRx55ROnp6QoMDDRjBg4caM5nZ2eb8xUVFcrJyZEk+fn5KSEhweV+bDabQkJCnCYAAACXMjOlr7+WVPuKHEnyqan5drlhSMeO1cYBcCt6utFSeTzpzs7O1sCBA3Xo0CFZLBatWLFCa9eulc9lA5OMGDHCvB38/fff15YtW1RSUqJFixapoqJCkjRp0iQSaQAAcPUUFpqz1m96ts2ebhdxANygulrWmto7SqzZWdxdghbF40n3ypUr9fU3V5ANw9Djjz8ui8XiNL377rvy8/NTamqqAgICVFNToylTpig8PFwvvviiJKlz585atWqVJw8FAAC0Njd++4YHR9Lt1NPdQByAqyw9XYqLk7W8XJJkXfgEAxmiRfF40t0cQ4cO1YEDBzRhwgRFRkbKz89PsbGxmj17tj7++ONGn+cGAABotsGDpY4dJYvF7OH2qdvTbbFIMTG1cQCuvm8GMtTXX5t/ez5GDQMZokXx+Hu6U1NTlZqa2uT4H/zgB+bo5gAAAG7l4yO99JKUlCTL5beXO54rXb2a93UD7lBnIENJstbUGVfBMGr/BufOlcaO5W8QXq1F9XQDAABcc+PHS9u3yxrSRlKd28s7dpS2b69dDuDqqzOQofTtXSaOC2AMZIiWwuM93QAAAF5v/HhFdOwrpWcrYsj/SQsyam8pp3cNcJ/LBij0Maq/+bem0TjA25B0AwAANMHYhI5qE+ivW7tGSnY/T1cHaP0uG6DQvL3cqG40DvA23F4OAADQBDZfH939/RsVSsINXBt1BjKUpNGHMxV/Kk89TubVLmcgQ7QQFsNwPBRx/SkrK1NoaKhKS0t5vzcAAADgbRyjl0vmgGqSvh3IkHEV4EFNzSfp6QYAAADgnb4ZyFAdOjiXM5AhWhCe6QYAAADgvcaPr30tWGZm7aBpN97IQIZoUUi6AQAAAHg3Hx8pMdHTtQD+J9xeDgAAAACAm5B0AwAAAADgJiTdAAAAAAC4CUk3AAAAAABuQtINAAAAAICbkHQDAAAAAOAmJN0AAAAAALgJSTcAAAAAAG5C0g0AAAAAgJuQdAMAAAAA4CYk3QAAAAAAuAlJNwAAAAAAbkLSDQAAAACAm5B0AwAAAADgJiTdAAAAAAC4CUk3AAAAAABuQtINAAAAAICb+Hq6Ap5kGIYkqayszMM1AQAAAAC0JI480pFXunJdJ93l5eWSpJiYGA/XBAAAAADQEpWXlys0NNTlcotxpbS8FaupqVFBQYHatGkji8Xi6eo0qKysTDExMTp27JhCQkI8XR3gO6NNo7WhTaM1ol2jtaFNwx0Mw1B5ebnat28vq9X1k9vXdU+31WpVx44dPV2NJgkJCeEEgVaFNo3WhjaN1oh2jdaGNo2rrbEebgcGUgMAAAAAwE1IugEAAAAAcBOSbi9ns9mUkpIim83m6aoAVwVtGq0NbRqtEe0arQ1tGp50XQ+kBgAAAACAO9HTDQAAAACAm5B0AwAAAADgJiTdXurs2bN66qmnFB8fr4CAAIWHh2vUqFHKzMz0dNUAl959911ZLBaX0zPPPOMU/9lnnykpKUlRUVGy2WyKi4vTnDlzdOrUKc8cAK5rlZWVeuKJJ3THHXcoJCTEbLeJiYkNxjen/RqGoQ0bNqh///4KDg5WcHCwBgwYoN/85jfiKS+4S3Pa9IwZMxo9f+fl5TnF06bhCR988IFmzZqlhIQERUdHm+feMWPGaP/+/fXiOU/DaxjwOmfPnjX69OljSKo3Wa1WY8uWLZ6uItCgjIyMBtutY0pJSTFj9+7dawQEBDQY16VLF+P48eOeOxBcl0pKShpsj0OGDKkX29z2+9BDD7n8u5g1a9Y1OkJcb5rTpqdPn97o+Ts3N9cpnjYNT5g5c2aj7fS1114zYzlPw5vQ0+2Fnn32WX3yySeSpEmTJunkyZPas2eP7Ha7ampqNHPmTBUXF3u4lkDjMjIyZBiG0+To6a6qqtKMGTNUUVEhq9WqtLQ0FRUVad68eZKkr776SgsWLPBg7XE98vPz08MPP6zXX39da9ascRnX3Pa7a9cuvfbaa5Kk+Ph45eTkKCcnR/Hx8ZKkdevWac+ePW48Mlyvmtqm64qNja137jYMQ3FxcWYMbRqeYrValZSUpN27d+vs2bP6+uuvlZSUZC5/8sknVV1dzXka3sdj6T4aVFNTY7Rr1868spaXl2cumzFjhlm+bt06D9YSaFjdnu6MjAyXcTt37jTjEhMTzfILFy6YV6X9/PyMM2fOXINaA/X99a9/ddkr2Nz2m5SUZManpqaa8ampqWb5pEmTrslx4frVWJs2jG97umNjY6+4Ldo0PKW0tLRe2cmTJ516pQsLCzlPw+vQ0+1lcnNzzedM2rRpo9jYWHPZ97//fXP+wIED17xuQHNMmjRJ/v7+Cg0N1e23364tW7aYyz7++GNzvm67DggIUPfu3SXV9ib+85//vHYVBpqoue3XVTzndHijgoICRUZGys/PTx06dNDUqVP173//2ymGNg1PCQkJqVd2/vx5c95utysiIoLzNLwOSbeXOXHihDkfFhbmtCw0NLTBOMAbnTp1SlVVVSorK1NmZqamTJmiRYsWSaKdo2Vrbvt1FU9bhzeqqqpSUVGRLl26pIKCAqWlpemHP/yh00VQ2jS8hWEYSk5ONj/PnDlTfn5+nKfhdUi6vZhx2UiJdT9bLJZrXR3giqKjo7Vy5UplZ2fr3LlzKiwsdPqP4YoVK3Ts2DGndWjnaMma237rLqetw5sMHTpUv//973X06FFduHBBBw8eVEJCgqTaN6osXLiwwfVo0/CUyspKTZs2Tdu3b5dU24aXLVtWL47zNLyBr6crAGfR0dHm/JkzZ5yWlZaWNhgHeIuePXuqZ8+e5me73a7nn39eb731lnJyclRdXa2PP/6Ydo4WrbntNzo62rzYVDeetg5v8uMf/9jpc0JCgtasWaPbbrtNkvThhx+ay2jT8LSysjLde++92rdvnyRpzJgx2rp1q2w2myTO0/A+9HR7mS5duigqKkpS7ZXlI0eOmMuysrLM+QEDBlzzugFXUl1d3WB53avDVqtVAwcOND9nZ2eb8xUVFcrJyZFUO+quo5cF8CbNbb+u4jmnw1vU1NRcMcZq/fZ/GWnT8KT8/HwNHjzYTLgfeeQRpaenKzAw0IzhPA2v44HB23AFCxcudBop8dSpU8aePXsMu91uSDJCQkKMoqIiT1cTqGf8+PHG008/bXz22WdGRUWFUVhYaCQnJ5vt2c/PzygoKDAqKyuNmJgY893zmzdvNoqLi4158+aZsT/60Y88fTi4Dp06dco4deqU8cYbb5ht8dZbbzXLz5071+z2+/bbb5vl8fHxxn/+8x8jJyfH6NGjh1n+zjvvePCo0Zo1pU3n5uYa/fr1MzZt2mTk5+cbFRUVxsGDB42+ffua64wePdrcJm0anpKVlWV07NjRkGRYLBZjxYoVDcZxnoa3Ien2QmfPnjX69Onj9PoDx2S1Wo0tW7Z4uopAg4YMGdJgu3VMq1atMmP37t1rvrbj8qlz587G8ePHPXgkuF411n4lGSkpKYZhNL/9PvTQQy63OWvWLA8cKa4XTWnTubm5jca0a9fOOHz4sNN2adPwBMer7RqbHK8s5TwNb8Lt5V4oKChI7733nhYvXqzu3bvL399fYWFhuuuuu5SRkaHJkyd7uopAg5566in99Kc/Va9evdS2bVv5+voqOjpaY8eO1Z49ezR//nwzdujQoTpw4IAmTJhgvp4mNjZWs2fPrvfcN+Btmtt+169fr1//+tfq16+f7Ha77Ha7+vXrpw0bNmjt2rUeOgqg1g033KCXXnpJo0aNUlxcnAIDAxUQEKD4+HjNnTtXhw4dUnx8vNM6tGl4O87T8CYWw7hsSD8AAAAAAHBV0NMNAAAAAICbkHQDAAAAAOAmJN0AAAAAALgJSTcAAAAAAG5C0g0AAAAAgJuQdAMAAAAA4CYk3QAAAAAAuAlJNwAAAAAAbkLSDQBocZ555hlZLBZz+tOf/uS0fMaMGeay9evXe6iWterWNTU11aN1+S52796tQYMGKSwszDyet956q9F1Pv30U913331q3769/Pz8FBoaqs6dO+vOO+/UokWLrk3Fr5K8vDzzuBMTEz1dHQBAC+Lr6QoAAPBdLV68WKNHj5bVyrVkdygpKdG4ceN04cKFJq9z4MAB3XHHHaqoqDDLysrKVFZWpry8PO3fv19Lly51R3UBAPAq/N8JAKDFy87O1ubNmz1dDa9y/vz5q7atL774wky4R4wYocrKShmGoXHjxrlc51e/+pWZcK9du1ZnzpzR2bNnlZWVpfXr12vo0KFXrX4AAHgzkm4AQIvm4+MjSUpJSVFVVVWjsYmJieYtwnl5eWa5q1vA4+LizPIvvvhCI0eOlN1uV0xMjJ5//nkZhqGdO3cqISFBdrtdvXr1Ulpamsv9V1dXa8mSJYqLi5PNZtPNN9+sN998s15cbm6uZs6cqS5dushmsykkJES33367tm3b5hT37rvvmvWbMWOGNm7cqN69e8vf318rVqxo9LuorKzUypUr1bdvXwUHBysgIEDdu3fXnDlzdPz4cafvbNCgQebnd955R/7+/vW+w8t9+eWX5vy4ceMUGhqqoKAg9e7dWzNnzqz3SMD777+vsWPHqmvXrgoNDZWvr68iIyM1YsSIerexp6ammsedkpKi5cuXq1OnTrLb7RoxYoS+/PJLlZaWaubMmYqIiFC7du1033336eTJk07bcWwjLi5O//jHPzRs2DAFBQUpPDxcP/nJT1RUVNTod+hw8uRJLViwQD169FBgYKCCgoLUr18/vfrqqzIMo17dBwwYoJCQEPn5+SkqKkr9+/fXrFmz6sUCAFoJAwCAFiYlJcWQZEgypkyZYthsNkOSsW7dOsMwDGP69Onm8ldeecVcb8iQIWZ5bm5ug9vbuHGjWR4bG2uWt2vXzpx3TBMnTjQsFku98v379ze47Q4dOtSLtVgsxtatW834v/3tb0abNm3qxTmmhQsXmrEZGRlmeWRkpFNcSkqKy+/vwoULxm233eZyH9HR0UZOTk697+zyqe53eLnhw4ebceHh4cbUqVONtWvXGgcPHjSqq6vrxb/44osu9yPJ2Lx5sxm7cePGRn+X2NhYY+DAgfXKR44c6bRPR3lQUJBht9vrxd9yyy1GRUWFYRiGkZuba5YPGTLE3MZ///tf48Ybb3RZ78mTJ5uxf/jDHxo9xqqqKpffJwCg5aKnGwDQosXExOjhhx+WJC1ZsqRZzx03x6BBg3T69Gm98cYbZtm2bdt0//33q7i4WM8995xZvmnTpga3UVFRoczMTJWWlmrJkiWSJMMwtGDBAlVXV0uSHnjgAZWXlyssLEx79uxRRUWFjh49qsGDB0uSnnvuOWVnZ9fb9unTpzV37lydOHFCRUVFmj59ustjefnll/XBBx9Ikvr06aMvv/xSp0+f1tSpUyVJJ06c0Jw5cyTV9qZnZGSY606fPl2GYcgwDMXFxbncR3JysvmMfXFxsX73u9/p0UcfVd++fdWpUydt2LDBKT4xMVF79+7V8ePHdfHiRZ07d047duwwl69cubLB/ZSXl2vXrl0qKSlR//79JUlHjhxRVlaW3nvvPRUWFiomJkZS7WBwdXvxHc6dO6epU6eqqKhI2dnZ6t69u6TageCuNPjdY489psLCQvn6+mrbtm06f/68Tpw4oYkTJ0qStm7dqj//+c+SpH379pnrffTRR6qsrFRhYaH27dunn/3sZ4xJAACtFGd3AECLt2jRIgUHB6ugoEAvv/yyW/axbNkyRURE6J577nEqf+aZZ9S2bVuNHTvWLHN12/VDDz2k2267TSEhIVq0aJE6dOggScrPz9fnn3+unJwcM6E+c+aMhg8froCAAHXq1EmZmZmSapP0Xbt21dt2t27dtGrVKkVFRSk8PFydO3d2eSx//OMfnerfvXt3RUREaM2aNbJYLJJqE9S6g6A115133qnMzEzdfffdstlsTsvy8/P14IMPaufOnWZZx44dtWPHDiUmJiosLExBQUFO3/Xnn3/e4H7Gjh2rkSNHKiwszGlU8bFjx+r222/XDTfc4HR7fEO/ja+vr1544QWFh4erV69eSk5ONpft3r3b5TFWVFTo7bffliRdunRJEydOlN1uV3R0tNOjAI6Yrl27mmVLly7VCy+8oAMHDqhbt25asWIFSTcAtFKc3QEALV67du00b948SbU9waWlpVdcx6jz/OylS5euGN+tWzdJUmBgoFO5I7mtm1i6SlZjY2PNeYvFYvbASrXPBZ84ceKK9ZBqe7Uv16dPnyYnbXX3U7dObdu2VUhIiKTa76S4uLhJ23Pl1ltv1V/+8hcVFRUpIyNDixcvVmRkpLl869atkqSamhoNGzZMq1ev1uHDhxu8W8HVd+r4XSTn36buRYcr/TaRkZEKCgoyP9f9Ti5/DryuoqKiJrUdx+/18MMPa9q0afLz89OOHTu0cOFC3XvvverUqZOGDx+us2fPXnFbAICWh6QbANAqJCcnKyIiQsXFxU63JdcVEBBgztcd3TsnJ+eK2/f1bfgtm67KG3LkyBFz3jAMHTt2zPwcFRWl6Oho83OPHj3M27gvnxp61Zbdbm9yPerup26dSkpKVFZWJqn2uMLDw5u8zcvVvfARFBSkxMRELVmyRKtXrzbLHQOVZWVl6dChQ2bdsrKydOnSJbMujbkav8vp06d17tw583Pd7yQqKsrlehEREeZ+2rRpo4sXLzb4ezlG1vf399emTZtUUlKiDz/8UGlpabr//vslSXv37tXatWubXGcAQMtB0g0AaBVCQkL0xBNPSJL5fPTl6j6D7Li1ef/+/fVGx3aXDRs26MMPP1R5ebmWLl2q/Px8SVKHDh100003qVu3burdu7ck6fDhw0pOTlZhYaGqqqr01Vdfad26dbr55pudksL/xZgxY8z5X/7yl8rJyVFxcbHmzp1r3gEwcuRIp4sUzTVu3DhNmjRJ6enpKigoUFVVlY4dO6b09HQzplevXpKcE2QfHx8FBwertLRU8+fP/5/33xyXLl1ScnKySkpK9Pnnnzs9Pz5y5EiX6wUEBOiuu+6SVPts+QMPPKC8vDzzWH/7299q0KBBev/99yVJb775pl566SXl5uaqZ8+emjBhgoYPH25u7+jRo246QgCAJzX9MjAAAF7u0Ucf1erVq1VQUNDg8mnTpunVV1+VJC1cuFBLly5VWVmZgoKCdPHiRbfXz9/f3+n5YoeVK1earz57/fXXNXz4cJWVlWnVqlVatWrVVa/H7Nmz9dZbb+mjjz7SwYMHzYHDHKKiopx6pP8XVVVV2rZtW73XnDlERkbqsccek1Tbq9+7d29lZ2eroKDAvDX8e9/73neqQ1MFBwdr8+bNWr9+vVP5LbfcohkzZjS67po1a/TJJ58oPz9faWlpDb4yznEh44svvtDTTz/tclujRo1qfuUBAF6Pnm4AQKsRGBion//85y6XDxo0SGlpabrppptks9nUrl07LV++3Hwe3N1+8Ytf6Nlnn1WnTp3k7++v3r17a9u2bZo8ebIZ069fPx06dEizZs1St27dZLPZFBwcrO7du2vixIlKTU1V+/btv1M9AgMDlZGRoeXLl6tPnz6y2+3y9/dXly5d9Mgjj+iTTz6pl4g315IlSzR//nwNGDBAHTt2VEBAgGw2m7p27aoHH3xQf//7381n2n18fLRjxw6NGzfOfK58woQJTqN9u1NERIQyMzM1fPhw2e12hYWFafr06XrnnXfqDQJ3uc6dO+vTTz/V448/rptuukkBAQEKDAxUly5ddM899+iVV15RQkKCJGnYsGGaNm2aevToodDQUFmtVrVt21aJiYlKT0/X6NGjr8XhAgCuMYtRdyQZAACA64RjpPbY2FiXI84DAPBd0dMNAAAAAICbkHQDAAAAAOAmDKQGAACuSzxhBwC4FujpBgAAAADATUi6AQAAAABwE5JuAAAAAADchKQbAAAAAAA3IekGAAAAAMBNSLoBAAAAAHATkm4AAAAAANyEpBsAAAAAADch6QYAAAAAwE3+H4agZ7ANjmABAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot the actual data as a line chart\n",
    "ax.plot(range(len(y_val)), y_val, label='Actual data')\n",
    "\n",
    "# Overlay the predicted data as a scatter plot\n",
    "ax.scatter(range(len(y_pred_RF)), y_pred_RF, label='Predicted data', color='red')\n",
    "\n",
    "# Add labels, title, and legend\n",
    "ax.set_xlabel('Number of Samples', fontsize=12)\n",
    "ax.set_ylabel('Energy Consumption (kWh)', fontsize=12)\n",
    "ax.set_title('Predicted and Real Energy Consumptions', fontsize=14)\n",
    "ax.legend()\n",
    "\n",
    "# set tick font size for both axes\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ea7bdb3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# b) Save model for later use\n",
    "# save the model to disk\n",
    "filename = 'finalized_model.sav'\n",
    "dump(model_RF, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b4d9c4",
   "metadata": {},
   "source": [
    "**6. Conclusions**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d153e8",
   "metadata": {},
   "source": [
    "- Exploratory data analysis shows that most input variables exhibit non-Gaussian distribution.\n",
    "\n",
    "- Among the three machine learning algorithms and neural network (NN) model, Random Forest and XGBoost attained lowest Mean Squared Error values following cross validation process. The Random Forest algorithm was selected as the surrogate model for optimization process in the 2nd Notebook.\n",
    "\n",
    "- The Mean Absolute Error values indicate that the prediction accuracy for the ensemble algorithms is excellent, whereas it was unexpectedly low for the NN model.\n",
    "\n",
    "- The performance of the NN model (Keras Sequential) is worst than all ML algorithms. Further tunning of the hyperparameter may yield a better prediction score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa7b9e67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}