transfer_learning_xception.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries, parameters and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\virgi\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_addons\\utils\\ensure_tf_install.py:43: UserWarning: You are currently using a nightly version of TensorFlow (2.10.0-dev20220427). \n",
      "TensorFlow Addons offers no support for the nightly versions of TensorFlow. Some things might work, some other might not. \n",
      "If you encounter a bug, do not file an issue on GitHub.\n",
      "  UserWarning,\n"
     ]
    }
   ],
   "source": [
    "from glob2 import glob\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "import datetime\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_addons as tfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_img(img):\n",
    "    img = tf.image.decode_jpeg(img, channels=3)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    img = tf.image.resize(images=img, size=(img_height, img_width))\n",
    "    return img\n",
    "\n",
    "def process_path(x, y):\n",
    "    img = tf.io.read_file(x)\n",
    "    img = decode_img(img)\n",
    "    return img , y\n",
    "\n",
    "def process_path_sex(x, y, s):\n",
    "    img = tf.io.read_file(x)\n",
    "    img = decode_img(img)\n",
    "    return (img,s) , y\n",
    "\n",
    "def process_path_sex_height(x, y, s, h):\n",
    "    img = tf.io.read_file(x)\n",
    "    img = decode_img(img)\n",
    "    return (img,s,h) , y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_height = 240#480\n",
    "img_width = 424#848\n",
    "batch_size = 16\n",
    "seed = 42\n",
    "epochs = 50\n",
    "name = 'xception'\n",
    "\n",
    "metrics = ['mae', 'mse', 'mape', tfa.metrics.r_square.RSquare(dtype=tf.float32, y_shape=(1,), name='r2')]\n",
    "metrics_name = ['MAE', 'MSE', 'MAPE', 'R2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_files = glob('images\\\\*.png')\n",
    "y_files = [float(file.split('_')[2]) for file in x_files]\n",
    "s_files = [1 if (file.split('_')[1])=='Men' else 0 for file in x_files]\n",
    "h_files = [float(file.split('_')[3]) for file in x_files]\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(x_files, y_files, test_size=0.30, random_state=seed)\n",
    "s_train, s_val, _, _ = train_test_split(s_files, y_files, test_size=0.30, random_state=seed)\n",
    "h_train, h_val, _, _ = train_test_split(h_files, y_files, test_size=0.30, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_train = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
    "files_val = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
    "\n",
    "train_ds = files_train.map(process_path).batch(batch_size)\n",
    "val_ds = files_val.map(process_path).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_train = tf.data.Dataset.from_tensor_slices((X_train, y_train, s_train))\n",
    "files_val = tf.data.Dataset.from_tensor_slices((X_val, y_val, s_val))\n",
    "\n",
    "train_ds_sex = files_train.map(process_path_sex).batch(batch_size)\n",
    "val_ds_sex = files_val.map(process_path_sex).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_train = tf.data.Dataset.from_tensor_slices((X_train, y_train, h_train))\n",
    "files_val = tf.data.Dataset.from_tensor_slices((X_val, y_val, h_val))\n",
    "\n",
    "train_ds_height = files_train.map(process_path_sex).batch(batch_size)\n",
    "val_ds_height = files_val.map(process_path_sex).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_train = tf.data.Dataset.from_tensor_slices((X_train, y_train, [h-1 for h in h_train]))\n",
    "files_val = tf.data.Dataset.from_tensor_slices((X_val, y_val, [h-1 for h in h_val]))\n",
    "\n",
    "train_ds_height_norm = files_train.map(process_path_sex).batch(batch_size)\n",
    "val_ds_height_norm = files_val.map(process_path_sex).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_train = tf.data.Dataset.from_tensor_slices((X_train, y_train, s_train, h_train))\n",
    "files_val = tf.data.Dataset.from_tensor_slices((X_val, y_val, s_val, h_val))\n",
    "\n",
    "train_ds_sex_height = files_train.map(process_path_sex_height).batch(batch_size)\n",
    "val_ds_sex_height = files_val.map(process_path_sex_height).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_train = tf.data.Dataset.from_tensor_slices((X_train, y_train, s_train, [h-1 for h in h_train]))\n",
    "files_val = tf.data.Dataset.from_tensor_slices((X_val, y_val, s_val, [h-1 for h in h_val]))\n",
    "\n",
    "train_ds_sex_height_norm = files_train.map(process_path_sex_height).batch(batch_size)\n",
    "val_ds_sex_height_norm = files_val.map(process_path_sex_height).batch(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    'model' : {\n",
    "        'train': train_ds,\n",
    "        'val' : val_ds,\n",
    "        'inputs' : 1\n",
    "    },\n",
    "    'model_with_sex' : {\n",
    "        'train': train_ds_height,\n",
    "        'val' : val_ds_height,\n",
    "        'inputs': 2\n",
    "    },\n",
    "    'model_with_height' : {\n",
    "        'train': train_ds_height,\n",
    "        'val' : val_ds_height,\n",
    "        'inputs': 2\n",
    "    },\n",
    "    'model_with_height_norm' : {\n",
    "        'train': train_ds_height_norm,\n",
    "        'val' : val_ds_height_norm,\n",
    "        'inputs': 2\n",
    "    },\n",
    "    'model_with_sex_height':{\n",
    "        'train': train_ds_sex_height,\n",
    "        'val' : val_ds_sex_height,\n",
    "        'inputs': 3\n",
    "    },\n",
    "    'model_with_sex_height_norm':{\n",
    "        'train': train_ds_sex_height_norm,\n",
    "        'val' : val_ds_sex_height_norm,\n",
    "        'inputs': 3\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      "Epoch 1/50\n",
      "89/89 [==============================] - 25s 199ms/step - loss: 15.8498 - mae: 15.8498 - mse: 296.3815 - mape: 69.1572 - r2: -12.2290 - val_loss: 8.4890 - val_mae: 8.4890 - val_mse: 104.9153 - val_mape: 34.4348 - val_r2: -3.5048\n",
      "Epoch 2/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 6.5390 - mae: 6.5390 - mse: 59.2110 - mape: 28.7345 - r2: -1.6429 - val_loss: 5.7686 - val_mae: 5.7686 - val_mse: 43.2102 - val_mape: 27.5784 - val_r2: -0.8553\n",
      "Epoch 3/50\n",
      "89/89 [==============================] - 17s 193ms/step - loss: 5.2153 - mae: 5.2153 - mse: 35.5479 - mape: 25.0670 - r2: -0.5867 - val_loss: 4.8845 - val_mae: 4.8845 - val_mse: 30.8788 - val_mape: 23.8723 - val_r2: -0.3259\n",
      "Epoch 4/50\n",
      "89/89 [==============================] - 17s 196ms/step - loss: 4.2916 - mae: 4.2916 - mse: 24.6261 - mape: 21.0009 - r2: -0.0992 - val_loss: 3.8963 - val_mae: 3.8963 - val_mse: 20.5802 - val_mape: 19.3880 - val_r2: 0.1163\n",
      "Epoch 5/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 3.2811 - mae: 3.2811 - mse: 16.0833 - mape: 16.3827 - r2: 0.2821 - val_loss: 2.8612 - val_mae: 2.8612 - val_mse: 13.6316 - val_mape: 14.7753 - val_r2: 0.4147\n",
      "Epoch 6/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 2.5307 - mae: 2.5307 - mse: 11.1949 - mape: 12.6777 - r2: 0.5003 - val_loss: 2.2654 - val_mae: 2.2654 - val_mse: 10.1770 - val_mape: 11.6587 - val_r2: 0.5630\n",
      "Epoch 7/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 2.1329 - mae: 2.1329 - mse: 8.8424 - mape: 10.5346 - r2: 0.6053 - val_loss: 1.9950 - val_mae: 1.9950 - val_mse: 8.3577 - val_mape: 10.1741 - val_r2: 0.6411\n",
      "Epoch 8/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 1.9008 - mae: 1.9008 - mse: 7.3586 - mape: 9.3080 - r2: 0.6715 - val_loss: 1.7951 - val_mae: 1.7951 - val_mse: 6.9608 - val_mape: 9.0458 - val_r2: 0.7011\n",
      "Epoch 9/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 1.7309 - mae: 1.7309 - mse: 6.2242 - mape: 8.4368 - r2: 0.7222 - val_loss: 1.6647 - val_mae: 1.6647 - val_mse: 6.0031 - val_mape: 8.3632 - val_r2: 0.7422\n",
      "Epoch 10/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.5971 - mae: 1.5971 - mse: 5.3849 - mape: 7.7406 - r2: 0.7596 - val_loss: 1.5207 - val_mae: 1.5207 - val_mse: 5.1868 - val_mape: 7.5483 - val_r2: 0.7773\n",
      "Epoch 11/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 1.4778 - mae: 1.4778 - mse: 4.7379 - mape: 7.1176 - r2: 0.7885 - val_loss: 1.4190 - val_mae: 1.4190 - val_mse: 4.5907 - val_mape: 6.9799 - val_r2: 0.8029\n",
      "Epoch 12/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 1.3762 - mae: 1.3762 - mse: 4.2408 - mape: 6.5704 - r2: 0.8107 - val_loss: 1.3367 - val_mae: 1.3367 - val_mse: 4.1566 - val_mape: 6.4939 - val_r2: 0.8215\n",
      "Epoch 13/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 1.2925 - mae: 1.2925 - mse: 3.8610 - mape: 6.1130 - r2: 0.8277 - val_loss: 1.2721 - val_mae: 1.2721 - val_mse: 3.8287 - val_mape: 6.1070 - val_r2: 0.8356\n",
      "Epoch 14/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 1.2231 - mae: 1.2231 - mse: 3.5666 - mape: 5.7227 - r2: 0.8408 - val_loss: 1.1975 - val_mae: 1.1975 - val_mse: 3.5520 - val_mape: 5.6541 - val_r2: 0.8475\n",
      "Epoch 15/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 1.1739 - mae: 1.1739 - mse: 3.3524 - mape: 5.4572 - r2: 0.8504 - val_loss: 1.1564 - val_mae: 1.1564 - val_mse: 3.3727 - val_mape: 5.4202 - val_r2: 0.8552\n",
      "Epoch 16/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.1321 - mae: 1.1321 - mse: 3.1656 - mape: 5.2478 - r2: 0.8587 - val_loss: 1.1220 - val_mae: 1.1220 - val_mse: 3.2195 - val_mape: 5.2264 - val_r2: 0.8618\n",
      "Epoch 17/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.1013 - mae: 1.1013 - mse: 3.0137 - mape: 5.0959 - r2: 0.8655 - val_loss: 1.0959 - val_mae: 1.0959 - val_mse: 3.1030 - val_mape: 5.0951 - val_r2: 0.8668\n",
      "Epoch 18/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 1.0699 - mae: 1.0699 - mse: 2.8732 - mape: 4.9450 - r2: 0.8718 - val_loss: 1.0766 - val_mae: 1.0766 - val_mse: 3.0110 - val_mape: 4.9965 - val_r2: 0.8707\n",
      "Epoch 19/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 1.0424 - mae: 1.0424 - mse: 2.7491 - mape: 4.8136 - r2: 0.8773 - val_loss: 1.0610 - val_mae: 1.0610 - val_mse: 2.9199 - val_mape: 4.9235 - val_r2: 0.8746\n",
      "Epoch 20/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 1.0184 - mae: 1.0184 - mse: 2.6357 - mape: 4.7024 - r2: 0.8824 - val_loss: 1.0414 - val_mae: 1.0414 - val_mse: 2.8226 - val_mape: 4.8369 - val_r2: 0.8788\n",
      "Epoch 21/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.9914 - mae: 0.9914 - mse: 2.5238 - mape: 4.5694 - r2: 0.8873 - val_loss: 1.0212 - val_mae: 1.0212 - val_mse: 2.7118 - val_mape: 4.7645 - val_r2: 0.8836\n",
      "Epoch 22/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.9661 - mae: 0.9661 - mse: 2.4208 - mape: 4.4466 - r2: 0.8919 - val_loss: 1.0042 - val_mae: 1.0042 - val_mse: 2.6477 - val_mape: 4.6712 - val_r2: 0.8863\n",
      "Epoch 23/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.9482 - mae: 0.9482 - mse: 2.3343 - mape: 4.3611 - r2: 0.8958 - val_loss: 0.9894 - val_mae: 0.9894 - val_mse: 2.5829 - val_mape: 4.5993 - val_r2: 0.8891\n",
      "Epoch 24/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.9256 - mae: 0.9256 - mse: 2.2491 - mape: 4.2469 - r2: 0.8996 - val_loss: 0.9759 - val_mae: 0.9759 - val_mse: 2.5198 - val_mape: 4.5362 - val_r2: 0.8918\n",
      "Epoch 25/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.9023 - mae: 0.9023 - mse: 2.1722 - mape: 4.1345 - r2: 0.9030 - val_loss: 0.9638 - val_mae: 0.9638 - val_mse: 2.4579 - val_mape: 4.4782 - val_r2: 0.8945\n",
      "Epoch 26/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.8834 - mae: 0.8834 - mse: 2.0936 - mape: 4.0427 - r2: 0.9066 - val_loss: 0.9447 - val_mae: 0.9447 - val_mse: 2.3847 - val_mape: 4.3990 - val_r2: 0.8976\n",
      "Epoch 27/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.8690 - mae: 0.8690 - mse: 2.0208 - mape: 3.9743 - r2: 0.9098 - val_loss: 0.9431 - val_mae: 0.9431 - val_mse: 2.3346 - val_mape: 4.4231 - val_r2: 0.8998\n",
      "Epoch 28/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.8491 - mae: 0.8491 - mse: 1.9480 - mape: 3.8803 - r2: 0.9130 - val_loss: 0.9196 - val_mae: 0.9196 - val_mse: 2.2647 - val_mape: 4.2968 - val_r2: 0.9028\n",
      "Epoch 29/50\n",
      "89/89 [==============================] - 16s 184ms/step - loss: 0.8277 - mae: 0.8277 - mse: 1.8798 - mape: 3.7809 - r2: 0.9161 - val_loss: 0.9065 - val_mae: 0.9065 - val_mse: 2.2027 - val_mape: 4.2213 - val_r2: 0.9054\n",
      "Epoch 30/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.8137 - mae: 0.8137 - mse: 1.8250 - mape: 3.7157 - r2: 0.9185 - val_loss: 0.8913 - val_mae: 0.8913 - val_mse: 2.1399 - val_mape: 4.1616 - val_r2: 0.9081\n",
      "Epoch 31/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 0.7986 - mae: 0.7986 - mse: 1.7613 - mape: 3.6472 - r2: 0.9214 - val_loss: 0.8799 - val_mae: 0.8799 - val_mse: 2.0827 - val_mape: 4.1143 - val_r2: 0.9106\n",
      "Epoch 32/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.7871 - mae: 0.7871 - mse: 1.7080 - mape: 3.5958 - r2: 0.9238 - val_loss: 0.8690 - val_mae: 0.8690 - val_mse: 2.0293 - val_mape: 4.0670 - val_r2: 0.9129\n",
      "Epoch 33/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 0.7750 - mae: 0.7750 - mse: 1.6549 - mape: 3.5430 - r2: 0.9261 - val_loss: 0.8589 - val_mae: 0.8589 - val_mse: 1.9765 - val_mape: 4.0300 - val_r2: 0.9151\n",
      "Epoch 34/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.7576 - mae: 0.7576 - mse: 1.5998 - mape: 3.4600 - r2: 0.9286 - val_loss: 0.8481 - val_mae: 0.8481 - val_mse: 1.9309 - val_mape: 3.9787 - val_r2: 0.9171\n",
      "Epoch 35/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.7418 - mae: 0.7418 - mse: 1.5520 - mape: 3.3853 - r2: 0.9307 - val_loss: 0.8371 - val_mae: 0.8371 - val_mse: 1.8826 - val_mape: 3.9246 - val_r2: 0.9192\n",
      "Epoch 36/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 16s 183ms/step - loss: 0.7284 - mae: 0.7284 - mse: 1.5131 - mape: 3.3250 - r2: 0.9325 - val_loss: 0.8288 - val_mae: 0.8288 - val_mse: 1.8426 - val_mape: 3.8816 - val_r2: 0.9209\n",
      "Epoch 37/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 0.7128 - mae: 0.7128 - mse: 1.4678 - mape: 3.2540 - r2: 0.9345 - val_loss: 0.8166 - val_mae: 0.8166 - val_mse: 1.7957 - val_mape: 3.8317 - val_r2: 0.9229\n",
      "Epoch 38/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.7035 - mae: 0.7035 - mse: 1.4254 - mape: 3.2129 - r2: 0.9364 - val_loss: 0.8051 - val_mae: 0.8051 - val_mse: 1.7550 - val_mape: 3.7866 - val_r2: 0.9246\n",
      "Epoch 39/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.6975 - mae: 0.6975 - mse: 1.3927 - mape: 3.1884 - r2: 0.9378 - val_loss: 0.7952 - val_mae: 0.7952 - val_mse: 1.7153 - val_mape: 3.7481 - val_r2: 0.9264\n",
      "Epoch 40/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 0.6819 - mae: 0.6819 - mse: 1.3475 - mape: 3.1172 - r2: 0.9399 - val_loss: 0.7896 - val_mae: 0.7896 - val_mse: 1.6750 - val_mape: 3.7073 - val_r2: 0.9281\n",
      "Epoch 41/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.6732 - mae: 0.6732 - mse: 1.3109 - mape: 3.0764 - r2: 0.9415 - val_loss: 0.7780 - val_mae: 0.7780 - val_mse: 1.6411 - val_mape: 3.6861 - val_r2: 0.9295\n",
      "Epoch 42/50\n",
      "89/89 [==============================] - 16s 179ms/step - loss: 0.6677 - mae: 0.6677 - mse: 1.2813 - mape: 3.0571 - r2: 0.9428 - val_loss: 0.7692 - val_mae: 0.7692 - val_mse: 1.6082 - val_mape: 3.6386 - val_r2: 0.9309\n",
      "Epoch 43/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.6509 - mae: 0.6509 - mse: 1.2419 - mape: 2.9794 - r2: 0.9446 - val_loss: 0.7606 - val_mae: 0.7606 - val_mse: 1.5673 - val_mape: 3.6026 - val_r2: 0.9327\n",
      "Epoch 44/50\n",
      "89/89 [==============================] - 17s 193ms/step - loss: 0.6425 - mae: 0.6425 - mse: 1.2102 - mape: 2.9395 - r2: 0.9460 - val_loss: 0.7506 - val_mae: 0.7506 - val_mse: 1.5386 - val_mape: 3.5477 - val_r2: 0.9339\n",
      "Epoch 45/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6277 - mae: 0.6277 - mse: 1.1779 - mape: 2.8708 - r2: 0.9474 - val_loss: 0.7445 - val_mae: 0.7445 - val_mse: 1.5095 - val_mape: 3.5248 - val_r2: 0.9352\n",
      "Epoch 46/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6224 - mae: 0.6224 - mse: 1.1468 - mape: 2.8512 - r2: 0.9488 - val_loss: 0.7360 - val_mae: 0.7360 - val_mse: 1.4822 - val_mape: 3.4902 - val_r2: 0.9364\n",
      "Epoch 47/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6131 - mae: 0.6131 - mse: 1.1245 - mape: 2.8102 - r2: 0.9498 - val_loss: 0.7270 - val_mae: 0.7270 - val_mse: 1.4531 - val_mape: 3.4480 - val_r2: 0.9376\n",
      "Epoch 48/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6042 - mae: 0.6042 - mse: 1.1011 - mape: 2.7693 - r2: 0.9509 - val_loss: 0.7223 - val_mae: 0.7223 - val_mse: 1.4257 - val_mape: 3.4307 - val_r2: 0.9388\n",
      "Epoch 49/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.5973 - mae: 0.5973 - mse: 1.0771 - mape: 2.7402 - r2: 0.9519 - val_loss: 0.7138 - val_mae: 0.7138 - val_mse: 1.3960 - val_mape: 3.3913 - val_r2: 0.9401\n",
      "Epoch 50/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.5877 - mae: 0.5877 - mse: 1.0430 - mape: 2.6963 - r2: 0.9534 - val_loss: 0.7077 - val_mae: 0.7077 - val_mse: 1.3692 - val_mape: 3.3696 - val_r2: 0.9412\n",
      "-------------------------------------------------------------\n",
      "model_with_sex\n",
      "Epoch 1/50\n",
      "89/89 [==============================] - 20s 194ms/step - loss: 13.1013 - mae: 13.1013 - mse: 223.4687 - mape: 56.7517 - r2: -5.9172 - val_loss: 6.5231 - val_mae: 6.5231 - val_mse: 57.0453 - val_mape: 28.7475 - val_r2: -1.4494\n",
      "Epoch 2/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 5.5487 - mae: 5.5487 - mse: 40.5788 - mape: 26.1301 - r2: -0.8112 - val_loss: 5.0220 - val_mae: 5.0220 - val_mse: 32.5252 - val_mape: 24.3889 - val_r2: -0.3966\n",
      "Epoch 3/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 4.2583 - mae: 4.2583 - mse: 24.2730 - mape: 20.7614 - r2: -0.0834 - val_loss: 3.6795 - val_mae: 3.6795 - val_mse: 18.6288 - val_mape: 18.3526 - val_r2: 0.2001\n",
      "Epoch 4/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 2.9686 - mae: 2.9686 - mse: 13.8190 - mape: 14.8610 - r2: 0.3832 - val_loss: 2.4703 - val_mae: 2.4703 - val_mse: 11.2877 - val_mape: 12.7991 - val_r2: 0.5153\n",
      "Epoch 5/50\n",
      "89/89 [==============================] - 17s 193ms/step - loss: 2.2239 - mae: 2.2239 - mse: 9.3148 - mape: 11.0351 - r2: 0.5842 - val_loss: 2.0009 - val_mae: 2.0009 - val_mse: 8.4125 - val_mape: 10.2105 - val_r2: 0.6388\n",
      "Epoch 6/50\n",
      "89/89 [==============================] - 18s 201ms/step - loss: 1.8880 - mae: 1.8880 - mse: 7.2319 - mape: 9.2523 - r2: 0.6772 - val_loss: 1.7468 - val_mae: 1.7468 - val_mse: 6.6431 - val_mape: 8.8030 - val_r2: 0.7148\n",
      "Epoch 7/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 1.6833 - mae: 1.6833 - mse: 5.8721 - mape: 8.2001 - r2: 0.7379 - val_loss: 1.5812 - val_mae: 1.5812 - val_mse: 5.4878 - val_mape: 7.9103 - val_r2: 0.7644\n",
      "Epoch 8/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.5233 - mae: 1.5233 - mse: 4.9185 - mape: 7.3808 - r2: 0.7805 - val_loss: 1.4489 - val_mae: 1.4489 - val_mse: 4.6672 - val_mape: 7.1831 - val_r2: 0.7996\n",
      "Epoch 9/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 1.3884 - mae: 1.3884 - mse: 4.2355 - mape: 6.6511 - r2: 0.8109 - val_loss: 1.3224 - val_mae: 1.3224 - val_mse: 4.0425 - val_mape: 6.4322 - val_r2: 0.8264\n",
      "Epoch 10/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 1.2794 - mae: 1.2794 - mse: 3.7490 - mape: 6.0607 - r2: 0.8327 - val_loss: 1.2342 - val_mae: 1.2342 - val_mse: 3.6342 - val_mape: 5.9105 - val_r2: 0.8440\n",
      "Epoch 11/50\n",
      "89/89 [==============================] - 17s 194ms/step - loss: 1.1959 - mae: 1.1959 - mse: 3.4027 - mape: 5.5952 - r2: 0.8481 - val_loss: 1.1593 - val_mae: 1.1593 - val_mse: 3.3341 - val_mape: 5.4611 - val_r2: 0.8568\n",
      "Epoch 12/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 1.1406 - mae: 1.1406 - mse: 3.1427 - mape: 5.3016 - r2: 0.8597 - val_loss: 1.1090 - val_mae: 1.1090 - val_mse: 3.1419 - val_mape: 5.1599 - val_r2: 0.8651\n",
      "Epoch 13/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 1.0933 - mae: 1.0933 - mse: 2.9425 - mape: 5.0625 - r2: 0.8687 - val_loss: 1.0752 - val_mae: 1.0752 - val_mse: 2.9949 - val_mape: 4.9795 - val_r2: 0.8714\n",
      "Epoch 14/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 1.0547 - mae: 1.0547 - mse: 2.7810 - mape: 4.8807 - r2: 0.8759 - val_loss: 1.0449 - val_mae: 1.0449 - val_mse: 2.8423 - val_mape: 4.8517 - val_r2: 0.8780\n",
      "Epoch 15/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 1.0159 - mae: 1.0159 - mse: 2.6190 - mape: 4.6928 - r2: 0.8831 - val_loss: 1.0205 - val_mae: 1.0205 - val_mse: 2.7225 - val_mape: 4.7383 - val_r2: 0.8831\n",
      "Epoch 16/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.9862 - mae: 0.9862 - mse: 2.4907 - mape: 4.5484 - r2: 0.8888 - val_loss: 0.9961 - val_mae: 0.9961 - val_mse: 2.6132 - val_mape: 4.6345 - val_r2: 0.8878\n",
      "Epoch 17/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.9594 - mae: 0.9594 - mse: 2.3703 - mape: 4.4237 - r2: 0.8942 - val_loss: 0.9808 - val_mae: 0.9808 - val_mse: 2.5352 - val_mape: 4.5545 - val_r2: 0.8911\n",
      "Epoch 18/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.9320 - mae: 0.9320 - mse: 2.2606 - mape: 4.2879 - r2: 0.8991 - val_loss: 0.9591 - val_mae: 0.9591 - val_mse: 2.4476 - val_mape: 4.4582 - val_r2: 0.8949\n",
      "Epoch 19/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.9007 - mae: 0.9007 - mse: 2.1503 - mape: 4.1296 - r2: 0.9040 - val_loss: 0.9447 - val_mae: 0.9447 - val_mse: 2.3700 - val_mape: 4.3855 - val_r2: 0.8982\n",
      "Epoch 20/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.8743 - mae: 0.8743 - mse: 2.0571 - mape: 4.0030 - r2: 0.9082 - val_loss: 0.9332 - val_mae: 0.9332 - val_mse: 2.3042 - val_mape: 4.3315 - val_r2: 0.9011\n",
      "Epoch 21/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 17s 187ms/step - loss: 0.8621 - mae: 0.8621 - mse: 1.9640 - mape: 3.9479 - r2: 0.9123 - val_loss: 0.9107 - val_mae: 0.9107 - val_mse: 2.2046 - val_mape: 4.2664 - val_r2: 0.9053\n",
      "Epoch 22/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.8354 - mae: 0.8354 - mse: 1.8822 - mape: 3.8224 - r2: 0.9160 - val_loss: 0.8929 - val_mae: 0.8929 - val_mse: 2.1452 - val_mape: 4.1549 - val_r2: 0.9079\n",
      "Epoch 23/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 0.8175 - mae: 0.8175 - mse: 1.8117 - mape: 3.7397 - r2: 0.9191 - val_loss: 0.8787 - val_mae: 0.8787 - val_mse: 2.0782 - val_mape: 4.0881 - val_r2: 0.9108\n",
      "Epoch 24/50\n",
      "89/89 [==============================] - 17s 185ms/step - loss: 0.7953 - mae: 0.7953 - mse: 1.7325 - mape: 3.6383 - r2: 0.9227 - val_loss: 0.8581 - val_mae: 0.8581 - val_mse: 1.9854 - val_mape: 4.0059 - val_r2: 0.9148\n",
      "Epoch 25/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.7724 - mae: 0.7724 - mse: 1.6609 - mape: 3.5324 - r2: 0.9259 - val_loss: 0.8472 - val_mae: 0.8472 - val_mse: 1.9400 - val_mape: 3.9590 - val_r2: 0.9167\n",
      "Epoch 26/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.7619 - mae: 0.7619 - mse: 1.6080 - mape: 3.4854 - r2: 0.9282 - val_loss: 0.8340 - val_mae: 0.8340 - val_mse: 1.8840 - val_mape: 3.8949 - val_r2: 0.9191\n",
      "Epoch 27/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.7362 - mae: 0.7362 - mse: 1.5333 - mape: 3.3667 - r2: 0.9316 - val_loss: 0.8257 - val_mae: 0.8257 - val_mse: 1.8173 - val_mape: 3.8539 - val_r2: 0.9220\n",
      "Epoch 28/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.7247 - mae: 0.7247 - mse: 1.4750 - mape: 3.3171 - r2: 0.9342 - val_loss: 0.8167 - val_mae: 0.8167 - val_mse: 1.7672 - val_mape: 3.8131 - val_r2: 0.9241\n",
      "Epoch 29/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.7117 - mae: 0.7117 - mse: 1.4214 - mape: 3.2578 - r2: 0.9366 - val_loss: 0.7943 - val_mae: 0.7943 - val_mse: 1.7013 - val_mape: 3.7300 - val_r2: 0.9270\n",
      "Epoch 30/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.6903 - mae: 0.6903 - mse: 1.3767 - mape: 3.1608 - r2: 0.9386 - val_loss: 0.7858 - val_mae: 0.7858 - val_mse: 1.6564 - val_mape: 3.6851 - val_r2: 0.9289\n",
      "Epoch 31/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.6742 - mae: 0.6742 - mse: 1.3281 - mape: 3.0866 - r2: 0.9407 - val_loss: 0.7708 - val_mae: 0.7708 - val_mse: 1.6071 - val_mape: 3.6271 - val_r2: 0.9310\n",
      "Epoch 32/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.6590 - mae: 0.6590 - mse: 1.2791 - mape: 3.0172 - r2: 0.9429 - val_loss: 0.7598 - val_mae: 0.7598 - val_mse: 1.5616 - val_mape: 3.5794 - val_r2: 0.9329\n",
      "Epoch 33/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.6494 - mae: 0.6494 - mse: 1.2396 - mape: 2.9726 - r2: 0.9447 - val_loss: 0.7707 - val_mae: 0.7707 - val_mse: 1.5344 - val_mape: 3.6171 - val_r2: 0.9341\n",
      "Epoch 34/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.6415 - mae: 0.6415 - mse: 1.2035 - mape: 2.9373 - r2: 0.9463 - val_loss: 0.7500 - val_mae: 0.7500 - val_mse: 1.4883 - val_mape: 3.5257 - val_r2: 0.9361\n",
      "Epoch 35/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.6268 - mae: 0.6268 - mse: 1.1597 - mape: 2.8723 - r2: 0.9482 - val_loss: 0.7342 - val_mae: 0.7342 - val_mse: 1.4436 - val_mape: 3.4607 - val_r2: 0.9380\n",
      "Epoch 36/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.6127 - mae: 0.6127 - mse: 1.1167 - mape: 2.8087 - r2: 0.9502 - val_loss: 0.7366 - val_mae: 0.7366 - val_mse: 1.4124 - val_mape: 3.4676 - val_r2: 0.9394\n",
      "Epoch 37/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.6058 - mae: 0.6058 - mse: 1.0859 - mape: 2.7817 - r2: 0.9515 - val_loss: 0.7114 - val_mae: 0.7114 - val_mse: 1.3609 - val_mape: 3.3659 - val_r2: 0.9416\n",
      "Epoch 38/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.5923 - mae: 0.5923 - mse: 1.0497 - mape: 2.7204 - r2: 0.9531 - val_loss: 0.7134 - val_mae: 0.7134 - val_mse: 1.3370 - val_mape: 3.3656 - val_r2: 0.9426\n",
      "Epoch 39/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5892 - mae: 0.5892 - mse: 1.0224 - mape: 2.7096 - r2: 0.9544 - val_loss: 0.6957 - val_mae: 0.6957 - val_mse: 1.2934 - val_mape: 3.2940 - val_r2: 0.9445\n",
      "Epoch 40/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.5759 - mae: 0.5759 - mse: 0.9947 - mape: 2.6482 - r2: 0.9556 - val_loss: 0.6945 - val_mae: 0.6945 - val_mse: 1.2702 - val_mape: 3.2859 - val_r2: 0.9455\n",
      "Epoch 41/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.5584 - mae: 0.5584 - mse: 0.9547 - mape: 2.5665 - r2: 0.9574 - val_loss: 0.6786 - val_mae: 0.6786 - val_mse: 1.2387 - val_mape: 3.2232 - val_r2: 0.9468\n",
      "Epoch 42/50\n",
      "89/89 [==============================] - 16s 184ms/step - loss: 0.5647 - mae: 0.5647 - mse: 0.9460 - mape: 2.6025 - r2: 0.9578 - val_loss: 0.6757 - val_mae: 0.6757 - val_mse: 1.2065 - val_mape: 3.2078 - val_r2: 0.9482\n",
      "Epoch 43/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 0.5501 - mae: 0.5501 - mse: 0.9116 - mape: 2.5331 - r2: 0.9593 - val_loss: 0.6629 - val_mae: 0.6629 - val_mse: 1.1868 - val_mape: 3.1609 - val_r2: 0.9490\n",
      "Epoch 44/50\n",
      "89/89 [==============================] - 17s 185ms/step - loss: 0.5592 - mae: 0.5592 - mse: 0.9079 - mape: 2.5800 - r2: 0.9595 - val_loss: 0.7115 - val_mae: 0.7115 - val_mse: 1.2438 - val_mape: 3.4277 - val_r2: 0.9466\n",
      "Epoch 45/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 0.5494 - mae: 0.5494 - mse: 0.8840 - mape: 2.5382 - r2: 0.9605 - val_loss: 0.6505 - val_mae: 0.6505 - val_mse: 1.1631 - val_mape: 3.1243 - val_r2: 0.9501\n",
      "Epoch 46/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5236 - mae: 0.5236 - mse: 0.8467 - mape: 2.4148 - r2: 0.9622 - val_loss: 0.6426 - val_mae: 0.6426 - val_mse: 1.1280 - val_mape: 3.0835 - val_r2: 0.9516\n",
      "Epoch 47/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5197 - mae: 0.5197 - mse: 0.8280 - mape: 2.3976 - r2: 0.9630 - val_loss: 0.6336 - val_mae: 0.6336 - val_mse: 1.1102 - val_mape: 3.0403 - val_r2: 0.9523\n",
      "Epoch 48/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5102 - mae: 0.5102 - mse: 0.8056 - mape: 2.3588 - r2: 0.9640 - val_loss: 0.6265 - val_mae: 0.6265 - val_mse: 1.0894 - val_mape: 3.0109 - val_r2: 0.9532\n",
      "Epoch 49/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.5020 - mae: 0.5020 - mse: 0.7870 - mape: 2.3222 - r2: 0.9649 - val_loss: 0.6209 - val_mae: 0.6209 - val_mse: 1.0635 - val_mape: 2.9840 - val_r2: 0.9543\n",
      "Epoch 50/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.4965 - mae: 0.4965 - mse: 0.7708 - mape: 2.2979 - r2: 0.9656 - val_loss: 0.6135 - val_mae: 0.6135 - val_mse: 1.0468 - val_mape: 2.9437 - val_r2: 0.9551\n",
      "-------------------------------------------------------------\n",
      "model_with_height\n",
      "Epoch 1/50\n",
      "89/89 [==============================] - 20s 196ms/step - loss: 13.1059 - mae: 13.1059 - mse: 223.1002 - mape: 56.6874 - r2: -5.9016 - val_loss: 6.6315 - val_mae: 6.6315 - val_mse: 59.0809 - val_mape: 29.0160 - val_r2: -1.5368\n",
      "Epoch 2/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 5.6216 - mae: 5.6216 - mse: 41.6995 - mape: 26.4413 - r2: -0.8613 - val_loss: 5.1154 - val_mae: 5.1154 - val_mse: 33.7414 - val_mape: 24.8995 - val_r2: -0.4488\n",
      "Epoch 3/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 4.3627 - mae: 4.3627 - mse: 25.4562 - mape: 21.2997 - r2: -0.1362 - val_loss: 3.8225 - val_mae: 3.8225 - val_mse: 20.0229 - val_mape: 19.1227 - val_r2: 0.1403\n",
      "Epoch 4/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 3.1262 - mae: 3.1262 - mse: 15.0784 - mape: 15.6678 - r2: 0.3270 - val_loss: 2.6505 - val_mae: 2.6505 - val_mse: 12.5369 - val_mape: 13.7254 - val_r2: 0.4617\n",
      "Epoch 5/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 2.3586 - mae: 2.3586 - mse: 10.2264 - mape: 11.7623 - r2: 0.5435 - val_loss: 2.1295 - val_mae: 2.1295 - val_mse: 9.3697 - val_mape: 10.9268 - val_r2: 0.5977\n",
      "Epoch 6/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 17s 190ms/step - loss: 1.9870 - mae: 1.9870 - mse: 7.9664 - mape: 9.7719 - r2: 0.6444 - val_loss: 1.8591 - val_mae: 1.8591 - val_mse: 7.4374 - val_mape: 9.4404 - val_r2: 0.6807\n",
      "Epoch 7/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.7676 - mae: 1.7676 - mse: 6.4629 - mape: 8.6507 - r2: 0.7115 - val_loss: 1.6838 - val_mae: 1.6838 - val_mse: 6.1598 - val_mape: 8.4685 - val_r2: 0.7355\n",
      "Epoch 8/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.6031 - mae: 1.6031 - mse: 5.3916 - mape: 7.7909 - r2: 0.7593 - val_loss: 1.5250 - val_mae: 1.5250 - val_mse: 5.1848 - val_mape: 7.5813 - val_r2: 0.7774\n",
      "Epoch 9/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 1.4647 - mae: 1.4647 - mse: 4.6166 - mape: 7.0746 - r2: 0.7939 - val_loss: 1.4154 - val_mae: 1.4154 - val_mse: 4.5181 - val_mape: 6.9800 - val_r2: 0.8060\n",
      "Epoch 10/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.3506 - mae: 1.3506 - mse: 4.0740 - mape: 6.4495 - r2: 0.8182 - val_loss: 1.3101 - val_mae: 1.3101 - val_mse: 4.0059 - val_mape: 6.3520 - val_r2: 0.8280\n",
      "Epoch 11/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 1.2571 - mae: 1.2571 - mse: 3.6518 - mape: 5.9389 - r2: 0.8370 - val_loss: 1.2288 - val_mae: 1.2288 - val_mse: 3.6404 - val_mape: 5.8682 - val_r2: 0.8437\n",
      "Epoch 12/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.1838 - mae: 1.1838 - mse: 3.3450 - mape: 5.5362 - r2: 0.8507 - val_loss: 1.1661 - val_mae: 1.1661 - val_mse: 3.3792 - val_mape: 5.4983 - val_r2: 0.8549\n",
      "Epoch 13/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.1284 - mae: 1.1284 - mse: 3.1083 - mape: 5.2400 - r2: 0.8613 - val_loss: 1.1119 - val_mae: 1.1119 - val_mse: 3.1805 - val_mape: 5.1747 - val_r2: 0.8634\n",
      "Epoch 14/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.0866 - mae: 1.0866 - mse: 2.9140 - mape: 5.0326 - r2: 0.8699 - val_loss: 1.0811 - val_mae: 1.0811 - val_mse: 3.0289 - val_mape: 5.0194 - val_r2: 0.8699\n",
      "Epoch 15/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 1.0483 - mae: 1.0483 - mse: 2.7441 - mape: 4.8448 - r2: 0.8775 - val_loss: 1.0523 - val_mae: 1.0523 - val_mse: 2.8876 - val_mape: 4.8880 - val_r2: 0.8760\n",
      "Epoch 16/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.0090 - mae: 1.0090 - mse: 2.5943 - mape: 4.6587 - r2: 0.8842 - val_loss: 1.0358 - val_mae: 1.0358 - val_mse: 2.8048 - val_mape: 4.7949 - val_r2: 0.8796\n",
      "Epoch 17/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 0.9832 - mae: 0.9832 - mse: 2.4663 - mape: 4.5395 - r2: 0.8899 - val_loss: 1.0198 - val_mae: 1.0198 - val_mse: 2.6933 - val_mape: 4.7188 - val_r2: 0.8844\n",
      "Epoch 18/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.9577 - mae: 0.9577 - mse: 2.3508 - mape: 4.4176 - r2: 0.8951 - val_loss: 0.9882 - val_mae: 0.9882 - val_mse: 2.5822 - val_mape: 4.5869 - val_r2: 0.8891\n",
      "Epoch 19/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.9282 - mae: 0.9282 - mse: 2.2410 - mape: 4.2710 - r2: 0.9000 - val_loss: 0.9654 - val_mae: 0.9654 - val_mse: 2.4888 - val_mape: 4.4860 - val_r2: 0.8931\n",
      "Epoch 20/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.9104 - mae: 0.9104 - mse: 2.1612 - mape: 4.1843 - r2: 0.9035 - val_loss: 0.9483 - val_mae: 0.9483 - val_mse: 2.4190 - val_mape: 4.4049 - val_r2: 0.8961\n",
      "Epoch 21/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.8814 - mae: 0.8814 - mse: 2.0678 - mape: 4.0394 - r2: 0.9077 - val_loss: 0.9341 - val_mae: 0.9341 - val_mse: 2.3473 - val_mape: 4.3393 - val_r2: 0.8992\n",
      "Epoch 22/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.8575 - mae: 0.8575 - mse: 1.9810 - mape: 3.9268 - r2: 0.9116 - val_loss: 0.9223 - val_mae: 0.9223 - val_mse: 2.2690 - val_mape: 4.2826 - val_r2: 0.9026\n",
      "Epoch 23/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.8512 - mae: 0.8512 - mse: 1.9111 - mape: 3.9067 - r2: 0.9147 - val_loss: 0.9073 - val_mae: 0.9073 - val_mse: 2.1887 - val_mape: 4.2495 - val_r2: 0.9060\n",
      "Epoch 24/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.8261 - mae: 0.8261 - mse: 1.8317 - mape: 3.7830 - r2: 0.9182 - val_loss: 0.8836 - val_mae: 0.8836 - val_mse: 2.1266 - val_mape: 4.1179 - val_r2: 0.9087\n",
      "Epoch 25/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.8037 - mae: 0.8037 - mse: 1.7638 - mape: 3.6746 - r2: 0.9213 - val_loss: 0.8740 - val_mae: 0.8740 - val_mse: 2.0594 - val_mape: 4.0696 - val_r2: 0.9116\n",
      "Epoch 26/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.7837 - mae: 0.7837 - mse: 1.6929 - mape: 3.5832 - r2: 0.9244 - val_loss: 0.8575 - val_mae: 0.8575 - val_mse: 1.9967 - val_mape: 3.9993 - val_r2: 0.9143\n",
      "Epoch 27/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.7694 - mae: 0.7694 - mse: 1.6408 - mape: 3.5202 - r2: 0.9268 - val_loss: 0.8436 - val_mae: 0.8436 - val_mse: 1.9421 - val_mape: 3.9441 - val_r2: 0.9166\n",
      "Epoch 28/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.7573 - mae: 0.7573 - mse: 1.5878 - mape: 3.4637 - r2: 0.9291 - val_loss: 0.8325 - val_mae: 0.8325 - val_mse: 1.8874 - val_mape: 3.8909 - val_r2: 0.9190\n",
      "Epoch 29/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.7392 - mae: 0.7392 - mse: 1.5336 - mape: 3.3822 - r2: 0.9315 - val_loss: 0.8203 - val_mae: 0.8203 - val_mse: 1.8336 - val_mape: 3.8367 - val_r2: 0.9213\n",
      "Epoch 30/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.7200 - mae: 0.7200 - mse: 1.4735 - mape: 3.2936 - r2: 0.9342 - val_loss: 0.8076 - val_mae: 0.8076 - val_mse: 1.7760 - val_mape: 3.7822 - val_r2: 0.9237\n",
      "Epoch 31/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.7127 - mae: 0.7127 - mse: 1.4271 - mape: 3.2615 - r2: 0.9363 - val_loss: 0.7966 - val_mae: 0.7966 - val_mse: 1.7343 - val_mape: 3.7401 - val_r2: 0.9255\n",
      "Epoch 32/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6982 - mae: 0.6982 - mse: 1.3802 - mape: 3.1968 - r2: 0.9384 - val_loss: 0.7837 - val_mae: 0.7837 - val_mse: 1.6775 - val_mape: 3.6865 - val_r2: 0.9280\n",
      "Epoch 33/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.6903 - mae: 0.6903 - mse: 1.3376 - mape: 3.1667 - r2: 0.9403 - val_loss: 0.7722 - val_mae: 0.7722 - val_mse: 1.6366 - val_mape: 3.6357 - val_r2: 0.9297\n",
      "Epoch 34/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6754 - mae: 0.6754 - mse: 1.2970 - mape: 3.0950 - r2: 0.9421 - val_loss: 0.7656 - val_mae: 0.7656 - val_mse: 1.5925 - val_mape: 3.6011 - val_r2: 0.9316\n",
      "Epoch 35/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.6557 - mae: 0.6557 - mse: 1.2483 - mape: 3.0036 - r2: 0.9443 - val_loss: 0.7502 - val_mae: 0.7502 - val_mse: 1.5472 - val_mape: 3.5459 - val_r2: 0.9336\n",
      "Epoch 36/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.6431 - mae: 0.6431 - mse: 1.2074 - mape: 2.9469 - r2: 0.9461 - val_loss: 0.7452 - val_mae: 0.7452 - val_mse: 1.5101 - val_mape: 3.5121 - val_r2: 0.9352\n",
      "Epoch 37/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.6284 - mae: 0.6284 - mse: 1.1655 - mape: 2.8783 - r2: 0.9480 - val_loss: 0.7356 - val_mae: 0.7356 - val_mse: 1.4729 - val_mape: 3.4720 - val_r2: 0.9368\n",
      "Epoch 38/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.6206 - mae: 0.6206 - mse: 1.1380 - mape: 2.8460 - r2: 0.9492 - val_loss: 0.7247 - val_mae: 0.7247 - val_mse: 1.4339 - val_mape: 3.4234 - val_r2: 0.9384\n",
      "Epoch 39/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6092 - mae: 0.6092 - mse: 1.1034 - mape: 2.7938 - r2: 0.9507 - val_loss: 0.7128 - val_mae: 0.7128 - val_mse: 1.4039 - val_mape: 3.3716 - val_r2: 0.9397\n",
      "Epoch 40/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.6009 - mae: 0.6009 - mse: 1.0706 - mape: 2.7572 - r2: 0.9522 - val_loss: 0.7086 - val_mae: 0.7086 - val_mse: 1.3694 - val_mape: 3.3520 - val_r2: 0.9412\n",
      "Epoch 41/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 0.5863 - mae: 0.5863 - mse: 1.0357 - mape: 2.6905 - r2: 0.9538 - val_loss: 0.6947 - val_mae: 0.6947 - val_mse: 1.3320 - val_mape: 3.2975 - val_r2: 0.9428\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5762 - mae: 0.5762 - mse: 1.0038 - mape: 2.6422 - r2: 0.9552 - val_loss: 0.6892 - val_mae: 0.6892 - val_mse: 1.3019 - val_mape: 3.2670 - val_r2: 0.9441\n",
      "Epoch 43/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5695 - mae: 0.5695 - mse: 0.9851 - mape: 2.6165 - r2: 0.9560 - val_loss: 0.6850 - val_mae: 0.6850 - val_mse: 1.2757 - val_mape: 3.2465 - val_r2: 0.9452\n",
      "Epoch 44/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5581 - mae: 0.5581 - mse: 0.9515 - mape: 2.5636 - r2: 0.9575 - val_loss: 0.6735 - val_mae: 0.6735 - val_mse: 1.2492 - val_mape: 3.2158 - val_r2: 0.9464\n",
      "Epoch 45/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5535 - mae: 0.5535 - mse: 0.9307 - mape: 2.5459 - r2: 0.9585 - val_loss: 0.6653 - val_mae: 0.6653 - val_mse: 1.2177 - val_mape: 3.1707 - val_r2: 0.9477\n",
      "Epoch 46/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5446 - mae: 0.5446 - mse: 0.9058 - mape: 2.5037 - r2: 0.9596 - val_loss: 0.6612 - val_mae: 0.6612 - val_mse: 1.1890 - val_mape: 3.1503 - val_r2: 0.9489\n",
      "Epoch 47/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5428 - mae: 0.5428 - mse: 0.8925 - mape: 2.4980 - r2: 0.9602 - val_loss: 0.6577 - val_mae: 0.6577 - val_mse: 1.1712 - val_mape: 3.1266 - val_r2: 0.9497\n",
      "Epoch 48/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.5306 - mae: 0.5306 - mse: 0.8588 - mape: 2.4426 - r2: 0.9617 - val_loss: 0.6501 - val_mae: 0.6501 - val_mse: 1.1461 - val_mape: 3.0908 - val_r2: 0.9508\n",
      "Epoch 49/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5280 - mae: 0.5280 - mse: 0.8463 - mape: 2.4310 - r2: 0.9622 - val_loss: 0.6435 - val_mae: 0.6435 - val_mse: 1.1301 - val_mape: 3.0629 - val_r2: 0.9515\n",
      "Epoch 50/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.5207 - mae: 0.5207 - mse: 0.8257 - mape: 2.4020 - r2: 0.9631 - val_loss: 0.6319 - val_mae: 0.6319 - val_mse: 1.1064 - val_mape: 3.0224 - val_r2: 0.9525\n",
      "-------------------------------------------------------------\n",
      "model_with_height_norm\n",
      "Epoch 1/50\n",
      "89/89 [==============================] - 19s 188ms/step - loss: 14.3964 - mae: 14.3964 - mse: 257.5455 - mape: 62.3607 - r2: -6.9658 - val_loss: 7.2986 - val_mae: 7.2986 - val_mse: 76.0381 - val_mape: 30.3347 - val_r2: -2.2649\n",
      "Epoch 2/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 6.0428 - mae: 6.0428 - mse: 48.8230 - mape: 27.5501 - r2: -1.1792 - val_loss: 5.5092 - val_mae: 5.5092 - val_mse: 39.0413 - val_mape: 26.7629 - val_r2: -0.6763\n",
      "Epoch 3/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 4.8504 - mae: 4.8504 - mse: 30.7192 - mape: 23.4887 - r2: -0.3712 - val_loss: 4.4043 - val_mae: 4.4043 - val_mse: 25.2106 - val_mape: 21.6587 - val_r2: -0.0825\n",
      "Epoch 4/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 3.7110 - mae: 3.7110 - mse: 19.1115 - mape: 18.2907 - r2: 0.1470 - val_loss: 3.2098 - val_mae: 3.2098 - val_mse: 15.4178 - val_mape: 16.3740 - val_r2: 0.3380\n",
      "Epoch 5/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 2.6691 - mae: 2.6691 - mse: 11.8517 - mape: 13.3488 - r2: 0.4710 - val_loss: 2.2948 - val_mae: 2.2948 - val_mse: 10.1082 - val_mape: 11.7028 - val_r2: 0.5660\n",
      "Epoch 6/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 2.1203 - mae: 2.1203 - mse: 8.6314 - mape: 10.4427 - r2: 0.6147 - val_loss: 1.9504 - val_mae: 1.9504 - val_mse: 7.9860 - val_mape: 9.9108 - val_r2: 0.6571\n",
      "Epoch 7/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.8442 - mae: 1.8442 - mse: 6.9098 - mape: 9.0162 - r2: 0.6916 - val_loss: 1.7307 - val_mae: 1.7307 - val_mse: 6.4492 - val_mape: 8.6989 - val_r2: 0.7231\n",
      "Epoch 8/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 1.6598 - mae: 1.6598 - mse: 5.7076 - mape: 8.0838 - r2: 0.7452 - val_loss: 1.5795 - val_mae: 1.5795 - val_mse: 5.4163 - val_mape: 7.8917 - val_r2: 0.7674\n",
      "Epoch 9/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.5101 - mae: 1.5101 - mse: 4.8243 - mape: 7.3005 - r2: 0.7847 - val_loss: 1.4367 - val_mae: 1.4367 - val_mse: 4.6158 - val_mape: 7.0909 - val_r2: 0.8018\n",
      "Epoch 10/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 1.3859 - mae: 1.3859 - mse: 4.2037 - mape: 6.6477 - r2: 0.8124 - val_loss: 1.3249 - val_mae: 1.3249 - val_mse: 4.0527 - val_mape: 6.4373 - val_r2: 0.8260\n",
      "Epoch 11/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.2889 - mae: 1.2889 - mse: 3.7675 - mape: 6.1117 - r2: 0.8318 - val_loss: 1.2412 - val_mae: 1.2412 - val_mse: 3.6715 - val_mape: 5.9349 - val_r2: 0.8424\n",
      "Epoch 12/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 1.2080 - mae: 1.2080 - mse: 3.4417 - mape: 5.6577 - r2: 0.8464 - val_loss: 1.1853 - val_mae: 1.1853 - val_mse: 3.4099 - val_mape: 5.6123 - val_r2: 0.8536\n",
      "Epoch 13/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 1.1527 - mae: 1.1527 - mse: 3.2065 - mape: 5.3612 - r2: 0.8569 - val_loss: 1.1265 - val_mae: 1.1265 - val_mse: 3.2185 - val_mape: 5.2435 - val_r2: 0.8618\n",
      "Epoch 14/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 1.1094 - mae: 1.1094 - mse: 3.0137 - mape: 5.1439 - r2: 0.8655 - val_loss: 1.0943 - val_mae: 1.0943 - val_mse: 3.0707 - val_mape: 5.0859 - val_r2: 0.8682\n",
      "Epoch 15/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 1.0715 - mae: 1.0715 - mse: 2.8458 - mape: 4.9613 - r2: 0.8730 - val_loss: 1.0677 - val_mae: 1.0677 - val_mse: 2.9396 - val_mape: 4.9499 - val_r2: 0.8738\n",
      "Epoch 16/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 1.0373 - mae: 1.0373 - mse: 2.6964 - mape: 4.7974 - r2: 0.8796 - val_loss: 1.0412 - val_mae: 1.0412 - val_mse: 2.8156 - val_mape: 4.8316 - val_r2: 0.8791\n",
      "Epoch 17/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 1.0084 - mae: 1.0084 - mse: 2.5569 - mape: 4.6627 - r2: 0.8859 - val_loss: 1.0166 - val_mae: 1.0166 - val_mse: 2.6955 - val_mape: 4.7229 - val_r2: 0.8843\n",
      "Epoch 18/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.9821 - mae: 0.9821 - mse: 2.4371 - mape: 4.5365 - r2: 0.8912 - val_loss: 1.0083 - val_mae: 1.0083 - val_mse: 2.6352 - val_mape: 4.6721 - val_r2: 0.8868\n",
      "Epoch 19/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.9520 - mae: 0.9520 - mse: 2.3261 - mape: 4.3836 - r2: 0.8962 - val_loss: 0.9790 - val_mae: 0.9790 - val_mse: 2.5223 - val_mape: 4.5477 - val_r2: 0.8917\n",
      "Epoch 20/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 0.9296 - mae: 0.9296 - mse: 2.2280 - mape: 4.2786 - r2: 0.9006 - val_loss: 0.9573 - val_mae: 0.9573 - val_mse: 2.4353 - val_mape: 4.4572 - val_r2: 0.8954\n",
      "Epoch 21/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.9059 - mae: 0.9059 - mse: 2.1374 - mape: 4.1609 - r2: 0.9046 - val_loss: 0.9418 - val_mae: 0.9418 - val_mse: 2.3683 - val_mape: 4.3841 - val_r2: 0.8983\n",
      "Epoch 22/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.8836 - mae: 0.8836 - mse: 2.0495 - mape: 4.0570 - r2: 0.9085 - val_loss: 0.9341 - val_mae: 0.9341 - val_mse: 2.3108 - val_mape: 4.3386 - val_r2: 0.9008\n",
      "Epoch 23/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.8599 - mae: 0.8599 - mse: 1.9684 - mape: 3.9382 - r2: 0.9121 - val_loss: 0.9184 - val_mae: 0.9184 - val_mse: 2.2350 - val_mape: 4.2708 - val_r2: 0.9040\n",
      "Epoch 24/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.8348 - mae: 0.8348 - mse: 1.8844 - mape: 3.8201 - r2: 0.9159 - val_loss: 0.8971 - val_mae: 0.8971 - val_mse: 2.1610 - val_mape: 4.1807 - val_r2: 0.9072\n",
      "Epoch 25/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.8219 - mae: 0.8219 - mse: 1.8194 - mape: 3.7633 - r2: 0.9188 - val_loss: 0.8829 - val_mae: 0.8829 - val_mse: 2.0961 - val_mape: 4.1189 - val_r2: 0.9100\n",
      "Epoch 26/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.8030 - mae: 0.8030 - mse: 1.7475 - mape: 3.6768 - r2: 0.9220 - val_loss: 0.8674 - val_mae: 0.8674 - val_mse: 2.0255 - val_mape: 4.0510 - val_r2: 0.9130\n",
      "Epoch 27/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 16s 182ms/step - loss: 0.7818 - mae: 0.7818 - mse: 1.6881 - mape: 3.5785 - r2: 0.9247 - val_loss: 0.8586 - val_mae: 0.8586 - val_mse: 1.9793 - val_mape: 4.0030 - val_r2: 0.9150\n",
      "Epoch 28/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.7680 - mae: 0.7680 - mse: 1.6266 - mape: 3.5166 - r2: 0.9274 - val_loss: 0.8397 - val_mae: 0.8397 - val_mse: 1.9123 - val_mape: 3.9323 - val_r2: 0.9179\n",
      "Epoch 29/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.7491 - mae: 0.7491 - mse: 1.5637 - mape: 3.4330 - r2: 0.9302 - val_loss: 0.8281 - val_mae: 0.8281 - val_mse: 1.8539 - val_mape: 3.8781 - val_r2: 0.9204\n",
      "Epoch 30/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.7351 - mae: 0.7351 - mse: 1.5114 - mape: 3.3667 - r2: 0.9325 - val_loss: 0.8168 - val_mae: 0.8168 - val_mse: 1.8118 - val_mape: 3.8253 - val_r2: 0.9222\n",
      "Epoch 31/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.7221 - mae: 0.7221 - mse: 1.4645 - mape: 3.3087 - r2: 0.9346 - val_loss: 0.8056 - val_mae: 0.8056 - val_mse: 1.7563 - val_mape: 3.7900 - val_r2: 0.9246\n",
      "Epoch 32/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.7036 - mae: 0.7036 - mse: 1.4065 - mape: 3.2236 - r2: 0.9372 - val_loss: 0.7941 - val_mae: 0.7941 - val_mse: 1.7046 - val_mape: 3.7298 - val_r2: 0.9268\n",
      "Epoch 33/50\n",
      "89/89 [==============================] - 16s 184ms/step - loss: 0.6957 - mae: 0.6957 - mse: 1.3579 - mape: 3.1921 - r2: 0.9394 - val_loss: 0.7843 - val_mae: 0.7843 - val_mse: 1.6640 - val_mape: 3.6928 - val_r2: 0.9286\n",
      "Epoch 34/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.6763 - mae: 0.6763 - mse: 1.3153 - mape: 3.1008 - r2: 0.9413 - val_loss: 0.7754 - val_mae: 0.7754 - val_mse: 1.6182 - val_mape: 3.6476 - val_r2: 0.9305\n",
      "Epoch 35/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.6667 - mae: 0.6667 - mse: 1.2761 - mape: 3.0575 - r2: 0.9430 - val_loss: 0.7707 - val_mae: 0.7707 - val_mse: 1.5946 - val_mape: 3.6613 - val_r2: 0.9315\n",
      "Epoch 36/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6582 - mae: 0.6582 - mse: 1.2418 - mape: 3.0199 - r2: 0.9446 - val_loss: 0.7540 - val_mae: 0.7540 - val_mse: 1.5432 - val_mape: 3.5716 - val_r2: 0.9337\n",
      "Epoch 37/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.6425 - mae: 0.6425 - mse: 1.1970 - mape: 2.9476 - r2: 0.9466 - val_loss: 0.7438 - val_mae: 0.7438 - val_mse: 1.5072 - val_mape: 3.5299 - val_r2: 0.9353\n",
      "Epoch 38/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6290 - mae: 0.6290 - mse: 1.1671 - mape: 2.8861 - r2: 0.9479 - val_loss: 0.7365 - val_mae: 0.7365 - val_mse: 1.4725 - val_mape: 3.4992 - val_r2: 0.9368\n",
      "Epoch 39/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.6138 - mae: 0.6138 - mse: 1.1220 - mape: 2.8180 - r2: 0.9499 - val_loss: 0.7279 - val_mae: 0.7279 - val_mse: 1.4354 - val_mape: 3.4618 - val_r2: 0.9384\n",
      "Epoch 40/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.6031 - mae: 0.6031 - mse: 1.0934 - mape: 2.7706 - r2: 0.9512 - val_loss: 0.7185 - val_mae: 0.7185 - val_mse: 1.4026 - val_mape: 3.4144 - val_r2: 0.9398\n",
      "Epoch 41/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.5973 - mae: 0.5973 - mse: 1.0649 - mape: 2.7473 - r2: 0.9525 - val_loss: 0.7095 - val_mae: 0.7095 - val_mse: 1.3685 - val_mape: 3.3789 - val_r2: 0.9412\n",
      "Epoch 42/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.5823 - mae: 0.5823 - mse: 1.0315 - mape: 2.6771 - r2: 0.9540 - val_loss: 0.7050 - val_mae: 0.7050 - val_mse: 1.3464 - val_mape: 3.3685 - val_r2: 0.9422\n",
      "Epoch 43/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.5783 - mae: 0.5783 - mse: 1.0122 - mape: 2.6633 - r2: 0.9548 - val_loss: 0.6936 - val_mae: 0.6936 - val_mse: 1.3115 - val_mape: 3.3114 - val_r2: 0.9437\n",
      "Epoch 44/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.5619 - mae: 0.5619 - mse: 0.9753 - mape: 2.5846 - r2: 0.9565 - val_loss: 0.6870 - val_mae: 0.6870 - val_mse: 1.2838 - val_mape: 3.2757 - val_r2: 0.9449\n",
      "Epoch 45/50\n",
      "89/89 [==============================] - 16s 180ms/step - loss: 0.5553 - mae: 0.5553 - mse: 0.9516 - mape: 2.5541 - r2: 0.9575 - val_loss: 0.6798 - val_mae: 0.6798 - val_mse: 1.2593 - val_mape: 3.2454 - val_r2: 0.9459\n",
      "Epoch 46/50\n",
      "89/89 [==============================] - 16s 183ms/step - loss: 0.5511 - mae: 0.5511 - mse: 0.9316 - mape: 2.5368 - r2: 0.9584 - val_loss: 0.6735 - val_mae: 0.6735 - val_mse: 1.2299 - val_mape: 3.2113 - val_r2: 0.9472\n",
      "Epoch 47/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.5444 - mae: 0.5444 - mse: 0.9111 - mape: 2.5077 - r2: 0.9593 - val_loss: 0.6672 - val_mae: 0.6672 - val_mse: 1.2098 - val_mape: 3.1961 - val_r2: 0.9481\n",
      "Epoch 48/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.5332 - mae: 0.5332 - mse: 0.8789 - mape: 2.4573 - r2: 0.9608 - val_loss: 0.6566 - val_mae: 0.6566 - val_mse: 1.1862 - val_mape: 3.1463 - val_r2: 0.9491\n",
      "Epoch 49/50\n",
      "89/89 [==============================] - 16s 182ms/step - loss: 0.5241 - mae: 0.5241 - mse: 0.8562 - mape: 2.4155 - r2: 0.9618 - val_loss: 0.6489 - val_mae: 0.6489 - val_mse: 1.1592 - val_mape: 3.1144 - val_r2: 0.9502\n",
      "Epoch 50/50\n",
      "89/89 [==============================] - 16s 181ms/step - loss: 0.5293 - mae: 0.5293 - mse: 0.8509 - mape: 2.4405 - r2: 0.9620 - val_loss: 0.6681 - val_mae: 0.6681 - val_mse: 1.1721 - val_mape: 3.2224 - val_r2: 0.9497\n",
      "-------------------------------------------------------------\n",
      "model_with_sex_height\n",
      "Epoch 1/50\n",
      "89/89 [==============================] - 20s 196ms/step - loss: 14.2371 - mae: 14.2371 - mse: 254.1310 - mape: 61.7061 - r2: -6.8612 - val_loss: 7.1142 - val_mae: 7.1142 - val_mse: 70.5821 - val_mape: 30.0620 - val_r2: -2.0306\n",
      "Epoch 2/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 5.9167 - mae: 5.9167 - mse: 46.6110 - mape: 27.3318 - r2: -1.0805 - val_loss: 5.3984 - val_mae: 5.3984 - val_mse: 37.6359 - val_mape: 26.2140 - val_r2: -0.6160\n",
      "Epoch 3/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 4.7007 - mae: 4.7007 - mse: 29.1441 - mape: 22.7950 - r2: -0.3009 - val_loss: 4.1894 - val_mae: 4.1894 - val_mse: 23.2836 - val_mape: 20.7652 - val_r2: 2.5767e-04\n",
      "Epoch 4/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 3.4458 - mae: 3.4458 - mse: 17.3705 - mape: 17.1579 - r2: 0.2247 - val_loss: 2.9024 - val_mae: 2.9024 - val_mse: 14.0962 - val_mape: 15.0717 - val_r2: 0.3947\n",
      "Epoch 5/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 2.5043 - mae: 2.5043 - mse: 11.0944 - mape: 12.5186 - r2: 0.5048 - val_loss: 2.2045 - val_mae: 2.2045 - val_mse: 9.8925 - val_mape: 11.3476 - val_r2: 0.5752\n",
      "Epoch 6/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 2.0493 - mae: 2.0493 - mse: 8.3753 - mape: 10.0855 - r2: 0.6262 - val_loss: 1.9200 - val_mae: 1.9200 - val_mse: 7.8087 - val_mape: 9.7907 - val_r2: 0.6647\n",
      "Epoch 7/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 1.8039 - mae: 1.8039 - mse: 6.7151 - mape: 8.8216 - r2: 0.7003 - val_loss: 1.7100 - val_mae: 1.7100 - val_mse: 6.3086 - val_mape: 8.6086 - val_r2: 0.7291\n",
      "Epoch 8/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 1.6308 - mae: 1.6308 - mse: 5.5508 - mape: 7.9246 - r2: 0.7522 - val_loss: 1.5440 - val_mae: 1.5440 - val_mse: 5.2522 - val_mape: 7.6986 - val_r2: 0.7745\n",
      "Epoch 9/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 1.4764 - mae: 1.4764 - mse: 4.6721 - mape: 7.1274 - r2: 0.7915 - val_loss: 1.4182 - val_mae: 1.4182 - val_mse: 4.4771 - val_mape: 7.0004 - val_r2: 0.8078\n",
      "Epoch 10/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 1.3504 - mae: 1.3504 - mse: 4.0387 - mape: 6.4493 - r2: 0.8197 - val_loss: 1.2858 - val_mae: 1.2858 - val_mse: 3.8783 - val_mape: 6.2138 - val_r2: 0.8335\n",
      "Epoch 11/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 1.2467 - mae: 1.2467 - mse: 3.5675 - mape: 5.8892 - r2: 0.8408 - val_loss: 1.2066 - val_mae: 1.2066 - val_mse: 3.4728 - val_mape: 5.7561 - val_r2: 0.8509\n",
      "Epoch 12/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 17s 188ms/step - loss: 1.1702 - mae: 1.1702 - mse: 3.2359 - mape: 5.4716 - r2: 0.8556 - val_loss: 1.1358 - val_mae: 1.1358 - val_mse: 3.1823 - val_mape: 5.3355 - val_r2: 0.8634\n",
      "Epoch 13/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 1.1130 - mae: 1.1130 - mse: 2.9731 - mape: 5.1789 - r2: 0.8673 - val_loss: 1.0948 - val_mae: 1.0948 - val_mse: 2.9916 - val_mape: 5.1110 - val_r2: 0.8715\n",
      "Epoch 14/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 1.0693 - mae: 1.0693 - mse: 2.7728 - mape: 4.9626 - r2: 0.8762 - val_loss: 1.0582 - val_mae: 1.0582 - val_mse: 2.8214 - val_mape: 4.9302 - val_r2: 0.8789\n",
      "Epoch 15/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 1.0272 - mae: 1.0272 - mse: 2.5950 - mape: 4.7646 - r2: 0.8842 - val_loss: 1.0438 - val_mae: 1.0438 - val_mse: 2.7455 - val_mape: 4.8377 - val_r2: 0.8821\n",
      "Epoch 16/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.9998 - mae: 0.9998 - mse: 2.4490 - mape: 4.6375 - r2: 0.8907 - val_loss: 1.0053 - val_mae: 1.0053 - val_mse: 2.5744 - val_mape: 4.6716 - val_r2: 0.8895\n",
      "Epoch 17/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.9622 - mae: 0.9622 - mse: 2.3024 - mape: 4.4536 - r2: 0.8972 - val_loss: 0.9798 - val_mae: 0.9798 - val_mse: 2.4537 - val_mape: 4.5572 - val_r2: 0.8946\n",
      "Epoch 18/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.9297 - mae: 0.9297 - mse: 2.1775 - mape: 4.2987 - r2: 0.9028 - val_loss: 0.9599 - val_mae: 0.9599 - val_mse: 2.3527 - val_mape: 4.4633 - val_r2: 0.8990\n",
      "Epoch 19/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.9059 - mae: 0.9059 - mse: 2.0685 - mape: 4.1826 - r2: 0.9077 - val_loss: 0.9311 - val_mae: 0.9311 - val_mse: 2.2478 - val_mape: 4.3447 - val_r2: 0.9035\n",
      "Epoch 20/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.8738 - mae: 0.8738 - mse: 1.9576 - mape: 4.0261 - r2: 0.9126 - val_loss: 0.9136 - val_mae: 0.9136 - val_mse: 2.1683 - val_mape: 4.2737 - val_r2: 0.9069\n",
      "Epoch 21/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.8554 - mae: 0.8554 - mse: 1.8747 - mape: 3.9393 - r2: 0.9163 - val_loss: 0.8954 - val_mae: 0.8954 - val_mse: 2.0922 - val_mape: 4.1833 - val_r2: 0.9102\n",
      "Epoch 22/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.8327 - mae: 0.8327 - mse: 1.7858 - mape: 3.8373 - r2: 0.9203 - val_loss: 0.8773 - val_mae: 0.8773 - val_mse: 2.0056 - val_mape: 4.1075 - val_r2: 0.9139\n",
      "Epoch 23/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.8026 - mae: 0.8026 - mse: 1.7012 - mape: 3.6941 - r2: 0.9241 - val_loss: 0.8610 - val_mae: 0.8610 - val_mse: 1.9429 - val_mape: 4.0322 - val_r2: 0.9166\n",
      "Epoch 24/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.7860 - mae: 0.7860 - mse: 1.6358 - mape: 3.6171 - r2: 0.9270 - val_loss: 0.8462 - val_mae: 0.8462 - val_mse: 1.8734 - val_mape: 3.9598 - val_r2: 0.9196\n",
      "Epoch 25/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.7654 - mae: 0.7654 - mse: 1.5576 - mape: 3.5241 - r2: 0.9305 - val_loss: 0.8290 - val_mae: 0.8290 - val_mse: 1.8033 - val_mape: 3.8887 - val_r2: 0.9226\n",
      "Epoch 26/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.7464 - mae: 0.7464 - mse: 1.4938 - mape: 3.4362 - r2: 0.9333 - val_loss: 0.8156 - val_mae: 0.8156 - val_mse: 1.7395 - val_mape: 3.8318 - val_r2: 0.9253\n",
      "Epoch 27/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.7277 - mae: 0.7277 - mse: 1.4329 - mape: 3.3490 - r2: 0.9360 - val_loss: 0.8019 - val_mae: 0.8019 - val_mse: 1.6852 - val_mape: 3.7684 - val_r2: 0.9276\n",
      "Epoch 28/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.7097 - mae: 0.7097 - mse: 1.3731 - mape: 3.2656 - r2: 0.9387 - val_loss: 0.7868 - val_mae: 0.7868 - val_mse: 1.6330 - val_mape: 3.7077 - val_r2: 0.9299\n",
      "Epoch 29/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.6971 - mae: 0.6971 - mse: 1.3291 - mape: 3.2131 - r2: 0.9407 - val_loss: 0.7799 - val_mae: 0.7799 - val_mse: 1.5888 - val_mape: 3.6647 - val_r2: 0.9318\n",
      "Epoch 30/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.6795 - mae: 0.6795 - mse: 1.2755 - mape: 3.1306 - r2: 0.9431 - val_loss: 0.7603 - val_mae: 0.7603 - val_mse: 1.5188 - val_mape: 3.5879 - val_r2: 0.9348\n",
      "Epoch 31/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.6610 - mae: 0.6610 - mse: 1.2151 - mape: 3.0452 - r2: 0.9458 - val_loss: 0.7478 - val_mae: 0.7478 - val_mse: 1.4784 - val_mape: 3.5417 - val_r2: 0.9365\n",
      "Epoch 32/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 0.6495 - mae: 0.6495 - mse: 1.1777 - mape: 2.9946 - r2: 0.9474 - val_loss: 0.7409 - val_mae: 0.7409 - val_mse: 1.4413 - val_mape: 3.5075 - val_r2: 0.9381\n",
      "Epoch 33/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.6371 - mae: 0.6371 - mse: 1.1385 - mape: 2.9383 - r2: 0.9492 - val_loss: 0.7249 - val_mae: 0.7249 - val_mse: 1.3939 - val_mape: 3.4369 - val_r2: 0.9401\n",
      "Epoch 34/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 0.6222 - mae: 0.6222 - mse: 1.0994 - mape: 2.8716 - r2: 0.9509 - val_loss: 0.7136 - val_mae: 0.7136 - val_mse: 1.3578 - val_mape: 3.3837 - val_r2: 0.9417\n",
      "Epoch 35/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.6082 - mae: 0.6082 - mse: 1.0552 - mape: 2.8043 - r2: 0.9529 - val_loss: 0.7031 - val_mae: 0.7031 - val_mse: 1.3143 - val_mape: 3.3332 - val_r2: 0.9436\n",
      "Epoch 36/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.6029 - mae: 0.6029 - mse: 1.0226 - mape: 2.7825 - r2: 0.9544 - val_loss: 0.6993 - val_mae: 0.6993 - val_mse: 1.2809 - val_mape: 3.3129 - val_r2: 0.9450\n",
      "Epoch 37/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5904 - mae: 0.5904 - mse: 0.9886 - mape: 2.7291 - r2: 0.9559 - val_loss: 0.6877 - val_mae: 0.6877 - val_mse: 1.2475 - val_mape: 3.2665 - val_r2: 0.9464\n",
      "Epoch 38/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.5777 - mae: 0.5777 - mse: 0.9516 - mape: 2.6673 - r2: 0.9575 - val_loss: 0.6753 - val_mae: 0.6753 - val_mse: 1.2134 - val_mape: 3.2172 - val_r2: 0.9479\n",
      "Epoch 39/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.5646 - mae: 0.5646 - mse: 0.9241 - mape: 2.6092 - r2: 0.9588 - val_loss: 0.6717 - val_mae: 0.6717 - val_mse: 1.1871 - val_mape: 3.1947 - val_r2: 0.9490\n",
      "Epoch 40/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5633 - mae: 0.5633 - mse: 0.9049 - mape: 2.6039 - r2: 0.9596 - val_loss: 0.6605 - val_mae: 0.6605 - val_mse: 1.1586 - val_mape: 3.1469 - val_r2: 0.9503\n",
      "Epoch 41/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.5551 - mae: 0.5551 - mse: 0.8772 - mape: 2.5685 - r2: 0.9608 - val_loss: 0.6498 - val_mae: 0.6498 - val_mse: 1.1365 - val_mape: 3.1145 - val_r2: 0.9512\n",
      "Epoch 42/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.5474 - mae: 0.5474 - mse: 0.8550 - mape: 2.5327 - r2: 0.9618 - val_loss: 0.6449 - val_mae: 0.6449 - val_mse: 1.1141 - val_mape: 3.0983 - val_r2: 0.9522\n",
      "Epoch 43/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.5370 - mae: 0.5370 - mse: 0.8275 - mape: 2.4847 - r2: 0.9631 - val_loss: 0.6338 - val_mae: 0.6338 - val_mse: 1.0871 - val_mape: 3.0424 - val_r2: 0.9533\n",
      "Epoch 44/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.5252 - mae: 0.5252 - mse: 0.8049 - mape: 2.4321 - r2: 0.9641 - val_loss: 0.6252 - val_mae: 0.6252 - val_mse: 1.0583 - val_mape: 3.0056 - val_r2: 0.9546\n",
      "Epoch 45/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.5230 - mae: 0.5230 - mse: 0.7921 - mape: 2.4229 - r2: 0.9646 - val_loss: 0.6279 - val_mae: 0.6279 - val_mse: 1.0538 - val_mape: 3.0236 - val_r2: 0.9548\n",
      "Epoch 46/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.5100 - mae: 0.5100 - mse: 0.7642 - mape: 2.3642 - r2: 0.9659 - val_loss: 0.6110 - val_mae: 0.6110 - val_mse: 1.0165 - val_mape: 2.9432 - val_r2: 0.9564\n",
      "Epoch 47/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.5036 - mae: 0.5036 - mse: 0.7480 - mape: 2.3351 - r2: 0.9666 - val_loss: 0.6115 - val_mae: 0.6115 - val_mse: 1.0113 - val_mape: 2.9500 - val_r2: 0.9566\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48/50\n",
      "89/89 [==============================] - 16s 185ms/step - loss: 0.4988 - mae: 0.4988 - mse: 0.7318 - mape: 2.3162 - r2: 0.9673 - val_loss: 0.6075 - val_mae: 0.6075 - val_mse: 0.9924 - val_mape: 2.9310 - val_r2: 0.9574\n",
      "Epoch 49/50\n",
      "89/89 [==============================] - 17s 186ms/step - loss: 0.4877 - mae: 0.4877 - mse: 0.7105 - mape: 2.2669 - r2: 0.9683 - val_loss: 0.6037 - val_mae: 0.6037 - val_mse: 0.9812 - val_mape: 2.9195 - val_r2: 0.9579\n",
      "Epoch 50/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 0.4817 - mae: 0.4817 - mse: 0.6966 - mape: 2.2388 - r2: 0.9689 - val_loss: 0.6010 - val_mae: 0.6010 - val_mse: 0.9610 - val_mape: 2.9108 - val_r2: 0.9587\n",
      "-------------------------------------------------------------\n",
      "model_with_sex_height_norm\n",
      "Epoch 1/50\n",
      "89/89 [==============================] - 20s 200ms/step - loss: 12.7889 - mae: 12.7889 - mse: 215.2693 - mape: 55.2462 - r2: -5.6587 - val_loss: 6.5862 - val_mae: 6.5862 - val_mse: 57.8717 - val_mape: 29.0979 - val_r2: -1.4849\n",
      "Epoch 2/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 5.6188 - mae: 5.6188 - mse: 41.5548 - mape: 26.4376 - r2: -0.8548 - val_loss: 5.1066 - val_mae: 5.1066 - val_mse: 33.5649 - val_mape: 24.7394 - val_r2: -0.4412\n",
      "Epoch 3/50\n",
      "89/89 [==============================] - 17s 193ms/step - loss: 4.3276 - mae: 4.3276 - mse: 25.0529 - mape: 21.0196 - r2: -0.1182 - val_loss: 3.7592 - val_mae: 3.7592 - val_mse: 19.5048 - val_mape: 18.8754 - val_r2: 0.1625\n",
      "Epoch 4/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 3.0092 - mae: 3.0092 - mse: 14.2926 - mape: 15.0944 - r2: 0.3620 - val_loss: 2.4948 - val_mae: 2.4948 - val_mse: 11.6572 - val_mape: 12.9185 - val_r2: 0.4995\n",
      "Epoch 5/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 2.2261 - mae: 2.2261 - mse: 9.4634 - mape: 11.0569 - r2: 0.5776 - val_loss: 2.0008 - val_mae: 2.0008 - val_mse: 8.4947 - val_mape: 10.1619 - val_r2: 0.6353\n",
      "Epoch 6/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.8822 - mae: 1.8822 - mse: 7.2615 - mape: 9.2187 - r2: 0.6759 - val_loss: 1.7784 - val_mae: 1.7784 - val_mse: 6.7732 - val_mape: 8.9648 - val_r2: 0.7092\n",
      "Epoch 7/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 1.6715 - mae: 1.6715 - mse: 5.8316 - mape: 8.1225 - r2: 0.7397 - val_loss: 1.5792 - val_mae: 1.5792 - val_mse: 5.4759 - val_mape: 7.8567 - val_r2: 0.7649\n",
      "Epoch 8/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 1.5041 - mae: 1.5041 - mse: 4.8196 - mape: 7.2600 - r2: 0.7849 - val_loss: 1.4486 - val_mae: 1.4486 - val_mse: 4.6079 - val_mape: 7.1527 - val_r2: 0.8021\n",
      "Epoch 9/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 1.3657 - mae: 1.3657 - mse: 4.1074 - mape: 6.5286 - r2: 0.8167 - val_loss: 1.2983 - val_mae: 1.2983 - val_mse: 3.9328 - val_mape: 6.2387 - val_r2: 0.8311\n",
      "Epoch 10/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 1.2548 - mae: 1.2548 - mse: 3.6070 - mape: 5.9196 - r2: 0.8390 - val_loss: 1.2102 - val_mae: 1.2102 - val_mse: 3.5126 - val_mape: 5.7367 - val_r2: 0.8492\n",
      "Epoch 11/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 1.1707 - mae: 1.1707 - mse: 3.2557 - mape: 5.4553 - r2: 0.8547 - val_loss: 1.1416 - val_mae: 1.1416 - val_mse: 3.2120 - val_mape: 5.3372 - val_r2: 0.8621\n",
      "Epoch 12/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 1.1176 - mae: 1.1176 - mse: 3.0060 - mape: 5.1949 - r2: 0.8658 - val_loss: 1.1006 - val_mae: 1.1006 - val_mse: 3.0061 - val_mape: 5.1309 - val_r2: 0.8709\n",
      "Epoch 13/50\n",
      "89/89 [==============================] - 17s 187ms/step - loss: 1.0705 - mae: 1.0705 - mse: 2.7971 - mape: 4.9686 - r2: 0.8751 - val_loss: 1.0635 - val_mae: 1.0635 - val_mse: 2.8523 - val_mape: 4.9320 - val_r2: 0.8775\n",
      "Epoch 14/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 1.0301 - mae: 1.0301 - mse: 2.6113 - mape: 4.7727 - r2: 0.8834 - val_loss: 1.0380 - val_mae: 1.0380 - val_mse: 2.7161 - val_mape: 4.8057 - val_r2: 0.8834\n",
      "Epoch 15/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.9947 - mae: 0.9947 - mse: 2.4561 - mape: 4.6026 - r2: 0.8904 - val_loss: 1.0170 - val_mae: 1.0170 - val_mse: 2.6017 - val_mape: 4.7029 - val_r2: 0.8883\n",
      "Epoch 16/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.9620 - mae: 0.9620 - mse: 2.3116 - mape: 4.4478 - r2: 0.8968 - val_loss: 0.9841 - val_mae: 0.9841 - val_mse: 2.4564 - val_mape: 4.5658 - val_r2: 0.8945\n",
      "Epoch 17/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.9322 - mae: 0.9322 - mse: 2.1928 - mape: 4.3032 - r2: 0.9021 - val_loss: 0.9587 - val_mae: 0.9587 - val_mse: 2.3617 - val_mape: 4.4531 - val_r2: 0.8986\n",
      "Epoch 18/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.9015 - mae: 0.9015 - mse: 2.0680 - mape: 4.1579 - r2: 0.9077 - val_loss: 0.9354 - val_mae: 0.9354 - val_mse: 2.2544 - val_mape: 4.3547 - val_r2: 0.9032\n",
      "Epoch 19/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.8782 - mae: 0.8782 - mse: 1.9637 - mape: 4.0440 - r2: 0.9124 - val_loss: 0.9188 - val_mae: 0.9188 - val_mse: 2.1725 - val_mape: 4.2750 - val_r2: 0.9067\n",
      "Epoch 20/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.8512 - mae: 0.8512 - mse: 1.8730 - mape: 3.9188 - r2: 0.9164 - val_loss: 0.8996 - val_mae: 0.8996 - val_mse: 2.0830 - val_mape: 4.1892 - val_r2: 0.9106\n",
      "Epoch 21/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.8240 - mae: 0.8240 - mse: 1.7786 - mape: 3.7915 - r2: 0.9206 - val_loss: 0.8813 - val_mae: 0.8813 - val_mse: 2.0025 - val_mape: 4.1049 - val_r2: 0.9140\n",
      "Epoch 22/50\n",
      "89/89 [==============================] - 17s 189ms/step - loss: 0.8166 - mae: 0.8166 - mse: 1.7106 - mape: 3.7613 - r2: 0.9236 - val_loss: 0.8605 - val_mae: 0.8605 - val_mse: 1.9246 - val_mape: 4.0181 - val_r2: 0.9174\n",
      "Epoch 23/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.7825 - mae: 0.7825 - mse: 1.6256 - mape: 3.6025 - r2: 0.9274 - val_loss: 0.8471 - val_mae: 0.8471 - val_mse: 1.8571 - val_mape: 3.9552 - val_r2: 0.9203\n",
      "Epoch 24/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.7723 - mae: 0.7723 - mse: 1.5601 - mape: 3.5552 - r2: 0.9304 - val_loss: 0.8352 - val_mae: 0.8352 - val_mse: 1.7978 - val_mape: 3.9012 - val_r2: 0.9228\n",
      "Epoch 25/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.7508 - mae: 0.7508 - mse: 1.4882 - mape: 3.4571 - r2: 0.9336 - val_loss: 0.8125 - val_mae: 0.8125 - val_mse: 1.7233 - val_mape: 3.8070 - val_r2: 0.9260\n",
      "Epoch 26/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.7263 - mae: 0.7263 - mse: 1.4257 - mape: 3.3435 - r2: 0.9364 - val_loss: 0.7997 - val_mae: 0.7997 - val_mse: 1.6672 - val_mape: 3.7463 - val_r2: 0.9284\n",
      "Epoch 27/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.7092 - mae: 0.7092 - mse: 1.3726 - mape: 3.2676 - r2: 0.9387 - val_loss: 0.7862 - val_mae: 0.7862 - val_mse: 1.6102 - val_mape: 3.6896 - val_r2: 0.9309\n",
      "Epoch 28/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6963 - mae: 0.6963 - mse: 1.3201 - mape: 3.2057 - r2: 0.9411 - val_loss: 0.7773 - val_mae: 0.7773 - val_mse: 1.5615 - val_mape: 3.6470 - val_r2: 0.9330\n",
      "Epoch 29/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.6777 - mae: 0.6777 - mse: 1.2544 - mape: 3.1223 - r2: 0.9440 - val_loss: 0.7574 - val_mae: 0.7574 - val_mse: 1.5054 - val_mape: 3.5722 - val_r2: 0.9354\n",
      "Epoch 30/50\n",
      "89/89 [==============================] - 17s 188ms/step - loss: 0.6632 - mae: 0.6632 - mse: 1.2125 - mape: 3.0577 - r2: 0.9459 - val_loss: 0.7488 - val_mae: 0.7488 - val_mse: 1.4609 - val_mape: 3.5233 - val_r2: 0.9373\n",
      "Epoch 31/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6456 - mae: 0.6456 - mse: 1.1680 - mape: 2.9738 - r2: 0.9479 - val_loss: 0.7417 - val_mae: 0.7417 - val_mse: 1.4114 - val_mape: 3.4934 - val_r2: 0.9394\n",
      "Epoch 32/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.6382 - mae: 0.6382 - mse: 1.1324 - mape: 2.9401 - r2: 0.9495 - val_loss: 0.7231 - val_mae: 0.7231 - val_mse: 1.3760 - val_mape: 3.4158 - val_r2: 0.9409\n",
      "Epoch 33/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6208 - mae: 0.6208 - mse: 1.0860 - mape: 2.8636 - r2: 0.9515 - val_loss: 0.7171 - val_mae: 0.7171 - val_mse: 1.3327 - val_mape: 3.3837 - val_r2: 0.9428\n",
      "Epoch 34/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.6148 - mae: 0.6148 - mse: 1.0533 - mape: 2.8390 - r2: 0.9530 - val_loss: 0.7062 - val_mae: 0.7062 - val_mse: 1.2968 - val_mape: 3.3336 - val_r2: 0.9443\n",
      "Epoch 35/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.6073 - mae: 0.6073 - mse: 1.0201 - mape: 2.8034 - r2: 0.9545 - val_loss: 0.6979 - val_mae: 0.6979 - val_mse: 1.2629 - val_mape: 3.2975 - val_r2: 0.9458\n",
      "Epoch 36/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5989 - mae: 0.5989 - mse: 0.9972 - mape: 2.7702 - r2: 0.9555 - val_loss: 0.6827 - val_mae: 0.6827 - val_mse: 1.2292 - val_mape: 3.2405 - val_r2: 0.9472\n",
      "Epoch 37/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 0.5791 - mae: 0.5791 - mse: 0.9552 - mape: 2.6762 - r2: 0.9574 - val_loss: 0.6687 - val_mae: 0.6687 - val_mse: 1.1976 - val_mape: 3.1879 - val_r2: 0.9486\n",
      "Epoch 38/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 0.5733 - mae: 0.5733 - mse: 0.9355 - mape: 2.6490 - r2: 0.9582 - val_loss: 0.6644 - val_mae: 0.6644 - val_mse: 1.1632 - val_mape: 3.1589 - val_r2: 0.9501\n",
      "Epoch 39/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5650 - mae: 0.5650 - mse: 0.9019 - mape: 2.6114 - r2: 0.9597 - val_loss: 0.6547 - val_mae: 0.6547 - val_mse: 1.1423 - val_mape: 3.1296 - val_r2: 0.9510\n",
      "Epoch 40/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 0.5530 - mae: 0.5530 - mse: 0.8735 - mape: 2.5578 - r2: 0.9610 - val_loss: 0.6458 - val_mae: 0.6458 - val_mse: 1.1179 - val_mape: 3.0962 - val_r2: 0.9520\n",
      "Epoch 41/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5412 - mae: 0.5412 - mse: 0.8463 - mape: 2.5053 - r2: 0.9622 - val_loss: 0.6343 - val_mae: 0.6343 - val_mse: 1.0846 - val_mape: 3.0240 - val_r2: 0.9534\n",
      "Epoch 42/50\n",
      "89/89 [==============================] - 17s 192ms/step - loss: 0.5345 - mae: 0.5345 - mse: 0.8187 - mape: 2.4759 - r2: 0.9635 - val_loss: 0.6300 - val_mae: 0.6300 - val_mse: 1.0599 - val_mape: 3.0108 - val_r2: 0.9545\n",
      "Epoch 43/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5281 - mae: 0.5281 - mse: 0.8028 - mape: 2.4485 - r2: 0.9642 - val_loss: 0.6201 - val_mae: 0.6201 - val_mse: 1.0472 - val_mape: 2.9709 - val_r2: 0.9550\n",
      "Epoch 44/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5162 - mae: 0.5162 - mse: 0.7769 - mape: 2.3960 - r2: 0.9653 - val_loss: 0.6129 - val_mae: 0.6129 - val_mse: 1.0233 - val_mape: 2.9398 - val_r2: 0.9561\n",
      "Epoch 45/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5192 - mae: 0.5192 - mse: 0.7692 - mape: 2.4110 - r2: 0.9657 - val_loss: 0.6250 - val_mae: 0.6250 - val_mse: 1.0256 - val_mape: 3.0037 - val_r2: 0.9560\n",
      "Epoch 46/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.5041 - mae: 0.5041 - mse: 0.7428 - mape: 2.3422 - r2: 0.9668 - val_loss: 0.6142 - val_mae: 0.6142 - val_mse: 0.9987 - val_mape: 2.9546 - val_r2: 0.9571\n",
      "Epoch 47/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.5028 - mae: 0.5028 - mse: 0.7338 - mape: 2.3351 - r2: 0.9672 - val_loss: 0.6255 - val_mae: 0.6255 - val_mse: 0.9994 - val_mape: 3.0194 - val_r2: 0.9571\n",
      "Epoch 48/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.4971 - mae: 0.4971 - mse: 0.7122 - mape: 2.3159 - r2: 0.9682 - val_loss: 0.6201 - val_mae: 0.6201 - val_mse: 0.9872 - val_mape: 2.9961 - val_r2: 0.9576\n",
      "Epoch 49/50\n",
      "89/89 [==============================] - 17s 191ms/step - loss: 0.4865 - mae: 0.4865 - mse: 0.6867 - mape: 2.2645 - r2: 0.9693 - val_loss: 0.6187 - val_mae: 0.6187 - val_mse: 0.9738 - val_mape: 2.9986 - val_r2: 0.9582\n",
      "Epoch 50/50\n",
      "89/89 [==============================] - 17s 190ms/step - loss: 0.4760 - mae: 0.4760 - mse: 0.6664 - mape: 2.2174 - r2: 0.9703 - val_loss: 0.5896 - val_mae: 0.5896 - val_mse: 0.9223 - val_mape: 2.8471 - val_r2: 0.9604\n",
      "-------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for f_name in models:\n",
    "    print(f_name)\n",
    "    \n",
    "    base_model = keras.applications.Xception(weights='imagenet', input_shape=(img_height, img_width, 3), include_top=False)\n",
    "    base_model.trainable = False\n",
    "\n",
    "    inputs = keras.Input(shape=(img_height, img_width, 3))\n",
    "    x = base_model(inputs, training=False)\n",
    "    x = keras.layers.GlobalAveragePooling2D()(x)\n",
    "    \n",
    "    if models[f_name]['inputs'] == 1:\n",
    "        x = keras.layers.Dense(128, activation='relu')(x)\n",
    "        outputs = keras.layers.Dense(1)(x)\n",
    "        model = keras.Model(inputs, outputs)\n",
    "    elif models[f_name]['inputs'] == 2:\n",
    "        x = keras.Model(inputs=inputs, outputs=x)\n",
    "        inputB = keras.Input(shape=(1,))\n",
    "        combined = keras.layers.concatenate([x.output, inputB])\n",
    "        outputs = keras.layers.Dense(128, activation='relu')(combined)\n",
    "        outputs = keras.layers.Dense(1)(outputs)\n",
    "        model = keras.Model(inputs=[x.input, inputB], outputs=outputs)\n",
    "    else:\n",
    "        x = keras.Model(inputs=inputs, outputs=x)\n",
    "        inputB = keras.Input(shape=(1,))\n",
    "        inputC = keras.Input(shape=(1,))\n",
    "        combined = keras.layers.concatenate([x.output, inputB, inputC])\n",
    "        outputs = keras.layers.Dense(128, activation='relu')(combined)\n",
    "        outputs = keras.layers.Dense(1)(outputs)\n",
    "        model = keras.Model(inputs=[x.input, inputB, inputC], outputs=outputs)\n",
    " \n",
    "    model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.0001), loss='mae', metrics=metrics)\n",
    "    h = model.fit(models[f_name]['train'], epochs=epochs, validation_data=models[f_name]['val'])\n",
    "\n",
    "    model.save('results/'+name+'_'+f_name+'.h5')\n",
    "    df_historic = pd.DataFrame(h.history)\n",
    "    df_historic.to_csv('results/'+name+'_'+f_name+'.csv')\n",
    "    \n",
    "    print('-------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n"
     ]
    },
    {
     "data": {
      "image/png": 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knh3FtewoqWN7cR17yupp73RqBKXGhHJGZiyfT49hUnoMYwdHExrUC41hOjucJFDpFudW4rlvqjriIAPRac7yrxELPkj8xA1xtoXG9nxcvaBfJ4ciggMIC/KnTMkhEXFReHAAoYH+VOkHmYi4LCkyRDOHRMRn1NTU8Nhjj/HlL3/5pJ979913c8sttxAWFtYLkcnJsNZSUN3M2rxq1uUfZH1+NXvKGg7vjw8PYszgKG6aM5TJGTFMTo85PFGkxzTXQOUezy3HuVXlODOBupxaRfgFQtJoGLkIBk2AuGFO8icmAwKCejYeF/Tr5BBAclSIlpWJiOviI4Ko1LIyEfEwxjwAXAyUW2vHebb9HvgE0AbsBT5nra3pyfdNjAxmS2GPvqSIiNeqqanhH//4xyknh66//nolh1xSUtvMipxK3s2tZNXeKso9FzYiQwKYkhnLJyYMZlxqNGMGR5EUGdxzS8PaGqFshzP7p2z7BwmhhrIPjvELhPhhkDACRl3k3CePg8RRAyIJdDz9PjmUGBmsZWUi4rqEiGAtKxORIz2I08n1oSO2vQ7caa3tMMb8FrgT+F5PvmliZPDhE2wRkYHujjvuYO/evUyaNInzzz+fpKQknnrqKVpbW/nkJz/JT3/6UxobG7nyyispLCyks7OTH/3oR5SVlVFcXMy8efNISEhg2bJlbn+UAa+2qZ3V+6t4L7eSFbmV7KtoBJxz6NnZ8UzLimNqViwjkiLx64li0dY6CZ/SbZ4lYVudW1Uu4CxNIzgaEkdC9vmQOAISRkLCcKcQtH+/T5WctH7/iZMig9lWVOt2GCLi4xIigiiu0SxGEXFYa5cbY7KO2rb0iIergSt6+n0TI4NpauuksbWD8OB+f5onIvKxfvOb37Bt2zY2bdrE0qVLeeaZZ1izZg3WWi655BKWL19ORUUFgwcP5pVXXgGgtraW6Oho/vSnP7Fs2TISEhJc/hQDU2NrB2vyqlm1t4r39layvbgOayE00J8ZQ+O4dnoGc4YnMDI58vRnBXV1QtXeD+oCHUoENVZ8cEx0BgwaD+Muh5QJzt/R6dDTxar7sX5/1pAcFcJbu8qx1vZ8FXIRkW6KDw9mS6ES1SLSbZ8HnuzpFz3UPbGivlXJIRHpW0vucH6Q96RB42HRb7p16NKlS1m6dCmTJ08GoKGhgZycHObOncu3vvUtvve973HxxRczd+7cno1RAKeT2Ib8GlbtreTdvVVsLqiho8sS5O/H5IwYbp8/gjOz45mYFkNQwGm0ZrfWaQdfuBYK10HROmd52KH28IfqAg1f4Pz/Z9A4SB7bb4pCu6nfnzUkea6QNbR2EBkS6HY4IuKjEiKDqG5so6vL9sxUWBEZsIwxPwA6gEc/5phbgFsAMjIyuv3aiZGe5FBDK1kJ4acVp4hIf2Kt5c477+SLX/ziR/Zt2LCBxYsX88Mf/pD58+dz1113uRDhwFNR38qbO8t4fUcZ7+6tpKW9Cz8D49NiuOWsocwaFs/UzLhT7yJmLdQWfjATqGSzkxQ61Co+IBQGT4YzPvPBbKCEkQO6LlBv6vfJoeRD7ezrW5UcEhHXxIcH09FlqW1uJzZc/yCJyLEZYz6LU6h6vrXWHu84a+19wH0AU6dOPe5xR0uK+mDmkIhIn+rmDJ+eFBkZSX19PQALFizgRz/6Eddddx0REREUFRURGBhIR0cHcXFxXH/99cTExHD//fd/6LlaVtZ91lp2ldbzzp4Klm4vZWNBDdZCWmwoV0/LYE52AtOHxhF1qr/LGyuhYA0UvA9F652EUEuNZ6dxikRnz4e0qZA2DZLG+mRtoN7S7/+XTPJcISuva2VYYoTL0YiIr0rwjEVVja1KDonIMRljFgLfBc621jb1xnscWlZWXqcaaCIy8MXHxzN79mzGjRvHokWLuPbaa5k1axYAERERPPLII+Tm5vKd73wHPz8/AgMD+ec//wnALbfcwsKFCxk8eLAKUn+Moppm3s2pZGVuJe/trTzcnXdcahS3zx/BBWOTGTXoFOoGdbZD2TYnCVS4zkkIVe9z9vkFOrOAxn7SWRY2aAIkjYFg/d7vTf0/OeS5QqZ29iLipgRPQqiivo3sJJeDERHXGWMeB84BEowxhcCPcbqTBQOve06iV1trb+3J940NC8Lfz1Ch7oki4iMee+yxDz3++te//qHHw4YNY8GCBR953m233cZtt93Wq7H1V3srGli8pYRXtpawq9SZmZUQEcyc7ARmZycwZ3gCKdGhJ/eijZWQ/y4cWO0kg0q3QIfnN3x4IqTPgCmfhbTpMHgSBJ7k68tpGwDJIc+yMrWzFxEXxUd8MHNIRMRae80xNv+7t9/Xz8+QEBGkZWUiInJS9lc28vLmYhYfkRCakhnLDy4czVkjEhmRHHFys4PqyyB/JeS96ySFKnY52wNCneTPtC9A6hTnFpOhrmFeoN8nhyKDAwgJ9KNM06dFxEUJEc7MoSrPVFsREbckRgYrOSQiIidUXtfCy1tKeHFT0eGuu1MzY7nr4jEsGj/o5GYH1ZdC3konEZS3Eir3ONuDIiBjJky4CrLmQMokFYz2Uv0+OWSMISkyhHKdBImIi2LCgvAzUKmlHCLiMue8SBfNRETko+pa2nl1WykvbSrmvb2VdFmnftAPLhzNxRNTup8Qaq13kkD73nZuh2YGBUVC5iyYfD1kzoGUiSoa3U8MiP9KyVHBOgkSEXe0NcGrd+A/8kLiwoMPF+kTEelzB/MAQ2JEMNuKat2ORkR8hLX25IsR9zMf01yyX2hp7+Tt3eW8uKmYN3eV09bRRUZcGF+dl80lkwaTnRR54hex1ukelvMa5LwBReugqwMCQiDzTJh0LWTNdYpHKxnULw2I/2pJkSHsLKlzOwwR8UUBIbDpMQiNJSFivmYOiYh77j8fRiwgMfI2qhrb6Oyy+PsN7B9sIuKukJAQqqqqiI+PH7AJImstVVVVhISEuB3KSbHWsqmghqfWFfK/LcXUt3SQEBHEtdMzuHTSYCalx5z4v1lbI+x7x0kI7VkK9cXO9pRJcObXYOg5TiHpwP71v40c28BIDkUF884e/SATETDGPABcDJRba8d5tsUBTwJZQB5wpbX2YI+8oZ8fRKVAXRHxEUFUKTkkIm6JGwIH80gcEUxnl+VgUxsJnmL5IuJbjDELgXsAf+B+a+1vjtqfCTwAJALVwPXW2sKTfZ+0tDQKCwupqKjogai9V0hICGlpaW6H0S0V9a08v7GQp9cVklPeQEigH4vGpXDZ5FRmD4snwN/v41+gai/kLHVueSuhs81ZKjZsHoxYANnnQ2Ry33wY6VMDIzkUGUJDaweNrR2EBw+IjyQip+5B4G/AQ0dsuwN401r7G2PMHZ7H3+uxd4xKg9oiEiKC2XigpsdeVkTkpMQOgbyVJEY6CaGK+lYlh0R8kDHGH/g7cD5QCKw1xrxkrd1xxGF/AB6y1v7XGHMu8GvghpN9r8DAQIYMGdITYctpaOvo4q1dZTyzvoi3d5fT0WU5IyOG33xqPBdNSCEyJPD4T+7qcpaI7XgRdi+G6n3O9oSRMP0WGH4BZMxSEWkfMCAyKUmek6Dy+laGKDkk4tOstcuNMVlHbb4UOMfz93+Bt+nJ5FB0KhS8T3xisGYOiYh7YrNgy5MkhzkPK+pbGZ3iakQi4o7pQK61dh+AMeYJnHOhI5NDY4Bvev5eBrzQlwHK6bPWsq2ojmc3FPLipiIONrWTFBnMTXOG8Omp6WQnRRz/yZ0dUPA+7HwJdrzkLBfzC4ShZ8PML8Pw851/U8SnDIhMSnKUs8axvK6FIQnhLkcjIl4o2Vpb4vm7FDjuXFhjzC3ALQAZGRnde/WoVKgrISEigMa2TprbOgkN8j/NkEVETlLcEMAyyJYDqJOriO9KBQqOeFwIzDjqmM3Ap3CWnn0SiDTGxFtrq4486JTOi6RXNbV18OKmYh5elc+OkjqCAvy4YEwyl09JY252wrGXjbU2OLODDqyGA6ugYC20N4J/MGSfB2N+4iwZC43p648jXmRAJIeSopyZQ2U6CRKRE7DWWmPMcVtOWGvvA+4DmDp1avdaU0SnQVc7aQENgNPOPj0urAeiFRE5CbHO0o74VqdgaIXOi0Tk+L4N/M0Y81lgOVAEdB590CmdF0mvyC1v4JHV+Ty7vpD61g5GDYrk55eN45IJg4kOO8aysaZqZ2bQtmch712wnYCBQeNg8nVOh7Hs8yC4G53KxCcMjOTQoWVldWpnLyLHVGaMSbHWlhhjUoDyHn31qFQAUkwlAFWNbUoOiUjfi3OSQyENBwgPylRySMR3FQHpRzxO82w7zFpbjDNzCGNMBHC5tbamrwKU7uno7OKNneU8tCqP9/ZWEehvuHB8CjfMzGRKZuxHu401VcOeV2Hbc7BvmdNqPm4YzP4aZM6B9GkQEu3OhxGvd8LkkDEmHaewazJggfustfccdcx1OPU7DFAPfMlau9mzL8+zrRPosNZO7ckPABAdGkhQgJ9OgkTkeF4CPgP8xnP/Yo++erSTHErsqgSiqdRYJCJuCE+EwHCo3k9i5AgqVANNxFetBYYbY4bgJIWuBq498gBjTAJQba3tAu7E6VwmXqKyoZUn1xbw6Op8imtbGBwdwncWjOSqaekfbjRgLZRu9XQXex0K14DtgugMmPUVGHc5DJoAJ2pZL0L3Zg51AN+y1m4wxkQC640xrx9V7X4/cLa19qAxZhHO1MMj17XOs9ZW9lzYQM0BeP0umPllTPp0kiKDKdPMIRGfZ4x5HKf4dIIxphD4MU5S6CljzE1APnBlj75plNPaNKajAoimqlE/yETEBcZ42tnvJykyhIp6nReJ+CJrbYcx5qvAazit7B+w1m43xvwMWGetfQnnXOnXnqX2y4GvuBawHFZQ3cQ/3s7l2fVFtHV2MSc7gR9fMpb5o5I+qCXU2gD73oac15yEUL2nrGbKJJj7bad2UOoUJYTkpJ0wOeQp4lri+bveGLMTp8jZjiOOee+Ip6zGmbrYu/yDYPvzkHEmeJJDKrwoItbaa46za36vvWlYHASEENFaBmRT2dDWa28lIvKxYrOgMofE2GB2lta5HY2IuMRauxhYfNS2u474+xngmb6OS47tUFLo6XWF+BnDldPS+OyZQz7oOFa9H/a85iwZy38XOtsgOAqGzYPhC5zaQZHH7bci0i0nVXPI0x56MvD+xxx2E7DkiMcWWOrJSv+fp6jZ6YtIhqAIqN4LOB3LcsobeuSlRUROijEQlUpAQzERwQFUaimHiLglbgjkvE5SeiDLddFMRMSrFR5s4u/LPkgKXTcjgy+dk82gyEAoWg9vLIHdS6Bip/OE+OEw/RZndlDGLPA/RiFqkVPU7eSQp1DZs8Dt1tpjXooyxszDSQ7NOWLzHGttkTEmCXjdGLPLWrv8GM89uTaJxkDcUKjKBZyi1Ctze3blmohIt0WnQm0R8RFBVGnmkIi4JXYIdLaSGVxHfUsHLe2dhAT6ux2ViIgcoaapjb+9lctDq/IBnKTQmYMYVLkKlv3TWTLWWAHG3+kqdsavnYRQ/DCXI5eBrFvJIWNMIE5i6FFr7XPHOWYCcD+wyFpbdWi7tbbIc19ujHkemI6zrvVDTqlNYvwwKN4EQFJUCPUtHTS3dRIapJMgEeljUWmw/x0SIoI1c0hE3OPpWJZJGRBMRX2ruieKiHiJlvZOHnwvj38sy6W+tYMbJ0TwjZStxBT8C+5d4VkuFg3Dz4ORF0L2fAiNdTts8RHd6VZmgH8DO621fzrOMRnAc8AN1to9R2wPB/w8tYrCgQuAn/VI5OC05dvxEnS0fdDOvr6FzPjwHnsLEZFuiU6F+hIS4/3ZX63kkIi4JNZJDg3qKgGyqGhQckhExG2dXZbnNxbxx6W7Kalt4aLsEH6auJyEbf+B3Y0Qn+1ZLrYQMmZquZi4ojszh2YDNwBbjTGbPNu+D2QAWGvvBe4C4oF/OLmkwy3rk4HnPdsCgMesta/2WPTx2WA7oSafpKgYAMrrW5UcEpG+F5UKtoshIfWsbeje5EcRkR4XnQ5+AcS3FQNZVKjukIiIq5bvqeDXS3axs6SO6alBPDl6PRm77ofCWhj7KTjr25A81u0wRbrVrWwl8LF98Ky1XwC+cIzt+4CJpxzdiRxac1m1l+ToMwEor9NJkIi4INpp0pjhf5DqpjA6uyz+fmohKiJ9zD8AotOJai4AUCdXERGX7Cyp41eLd7Iip5JRMV0snrKB0fsfwmyqdJaMzfs+DBrvdpgih51UtzKvE+dJDlXvJSntXADK6lpcDEhEfFbUYAAG+1VhbRgHm9pIiAh2OSgR8UmxWQTXF2AMmjkkItLHqhvb+P1ru3hibQEjg2t4efi7jCt7EbO9AYadC/N+AGlT3Q5T5CP6d3IoLA5CYqBqL7FhgQT6G10hExF3RKUCkNRVCaRT2dCq5JCIuCNuCGb788SHByk5JCLSR7q6LE+sLeB3r+0iq3U3L6e8w9iDb2GKDIy7HGZ9FVImuB2myHH17+SQMc7SsqpcjDEkRYZQXq+ZQyLigpBoCIogpqMCQO3sRcQ9sUOg+SCZUe1KDomI9IEthTX89PmNpJUs5enwtxgeuAsaImHWl2HGrYfLD4h4s/6dHAJnadmBVQAkRgar5pCIuMMYiEolsrUMQO3sRcQ9nnb2Y8Oq2dwQ7XIwIiIDV3NbJ39/eSWBGx/kvoC3iA+qwUYOg+m/hUnXQkiU2yGKdFv/Tw7FZ8PWp6G9meSoYPZXNrodkYj4quhUQhpLAKjUzCERcYunnX22fwVvHExxORgRkYFp57q3KHr1z3y9/V38A7roHHYezLoVM/Rc8PNzOzyRkzYAkkPDAAvV+0mKDGH1vmq3IxIRXxWVin/pNgL8DFWaOSQibonNAiDDlFHR0Iq1FmPUPVFE5LR1tNG+9Tkq3vgLoxu3k04YFaNvZPD5t+F3qJO2SD/V/5NDcUOd++q9JEWOora5nZb2TkIC/d2NS0R8T3QaprGcQeFGy8pExD3BERCeREpXKe2dltrmdmLCgtyOSkSk/+rqgm3P0vb6TwmqL6C5K4UXUr/B/Ku/xuDoOLejE+kR/T85dChDW7WX5KhJgNO2NT0uzL2YRMQ3eTqWjQhtUEFqEXFX3BDim4sA57xIySERkVO0723s63dhSjaz12byfwHf5xNX3shlY7RsVwaW/p8cComG8ESoyiVxlNM2ury+RckhEel70U5yaFhIDWs0c0hE3BQ7hMiqdwAnOTQ8OdLlgERE+pmK3fDa9yH3DaoDkvhF25eoyb6MP1w5mfiIYLejE+lx/T85BE7Hsup9JEU6X9IydSwTETdEOW1KswIOsrhaM4dExEVxQwhqepIg2ilXO3sRke6zFtb8C17/ER1+wfwz4LP8s2kety8czxfmDMXPTzXcZGAaGMmh+GGQ++bh2UJ5VepYJiIu8MwcGmyqqFQRWBFxU+wQDJZ0U06FkkMiIt3TWAkvfgX2vEph/GwuL7mewOhBPHrrZCZnxLodnUivGhg99uKHQUMpUaaFwdEh7CmtdzsiEfFFQeEQEkOSraK1o4vGtk63IxIRXxXntLMfFlBBhZa5ioicWO4b8I9Z2L3LeD3zW8wp+jITRo3gla/NVWJIfMLASA7FeYpSV+9jeHIke8oa3I1HRHxXdBqxneUAamcvIu6JdZJDY0OqKa5pdjkYEREv1tUJb/wEHrkcGxbHH7Pu5ebdU7huRib3Xj+F6NBAtyMU6RMDIzl0RMeyEckR5FY00Nll3Y1JRHxTVCpRrWUAamcvIu4JT4DAcMaEVJJbrotmIiLH1NoAT14PK/9Mx+TP8qXQP/K37cHcft5wfnHZOPxVX0h8yMBIDsUNde6r9jIiOZK2ji7yVXdIRNwQnUpIcykAlWpnLyJuMQbihpDlX8G+ykY6OrvcjkhExLvUFMADC2DPazSd9xuuKr6KpTm1/OKycdx+3gjVjRSfMzCSQ0HhEDkYqp3kEMCeMtUdEhEXRKUS0FpDCK2aOSQi7orNIrmjhLaOLgoOammZiMhhBWvgX/OgpoDGK57givXj2FpYyz+uO4PrZ2a6HZ2IKwZGcgicpWVVexmeHAGgukMi4o5op539YFOlDkEi4q64IUQ0F2Ho0kUzEZFDdrwED14MQRE03rCEa98OJ7e8gfs/M5WF41Lcjk7ENQMnORQ3FKpyCQsKID0ulN06CRIRN0Q57ezHhtdTUK0r9SLiotgh+HW2ksxB1R0SEQEo3QrP3QwpE2n6zFI+9786thc5M4bOGpHodnQirho4yaH4bGiuhuaDjEyOJEfJIRFxQ7STHBodXq/aZyLiLk87+ylRNTovEhFproEnb4CQGFouf4hbntnPuvxq7r56EueNSXY7OhHXDaDk0KGOZU47+30VjbR1qPiiiPQxz8yh7OAa8qubXA5GRHyap5395IgacjRzSER8mbXwwpehtoD2y//Dl18sZGVuJb+/YiIXTxjsdnQiXmHgJIfiPMmh6r2MTI6ko8uSp6v2ItLXAoIhPJFUv2oq6ltpbO1wOyIR8VXR6eAXwKggp519Z5d1OyIREXe8ew/sfgXO/znfXxfOW7vK+eUnx3H5lDS3IxPxGgMoOTQEMFCVe0RRak2hFhEXRKWS2FUJwAHNHhIRt/gHQEwmmRTT2tFF4UGNRyLig/Yvhzd/CmMuY3H4ZTy9vpDbzs3muhnqSiZypIGTHAoIhph0qNrLsMQI/AzsKVVySERcEJ1GVFsZgOoOifgoY8wDxphyY8y2I7bFGWNeN8bkeO5jez2Q5DEkNu0FIEedXEXE19QVwzOfh7hhlJ/7R77/wjYmpkXztfnD3Y5MxOucMDlkjEk3xiwzxuwwxmw3xnz9GMcYY8xfjDG5xpgtxpgzjtj3Gc9JUI4x5jM9/QE+JG4YVO8lJNCfrPhwtbMXEXdEpRLcVAJAfpWu1Iv4qAeBhUdtuwN401o7HHjT87h3JY8juG4/IbSyp1wXzUTEh1gLz90CbU10Xfkw33pxL63tXfz5qkkE+g+cORIiPaU734oO4FvW2jHATOArxpgxRx2zCBjuud0C/BOcK2TAj4EZwHTgx716lSw+G6r2grWMSI7UsjIRcUd0KqatgfTQNvKUHBLxSdba5UD1UZsvBf7r+fu/wGW9HkjyWAyWmREV5OqimYj4kvUPQt4KWPBLHsoNYUVOJT+4aDRDEyPcjkzEK50wOWStLbHWbvD8XQ/sBFKPOuxS4CHrWA3EGGNSgAXA69baamvtQeB1PnoVrefED4PWOmisZERyBHlVjbS0d/ba24mIHJOnY9nkmCYtKxORIyVba0s8f5cCvd87OXksALMjS9WxTER8R20RLP0RZM0lJ+1yfr1kF+eOSuK6GRluRybitU5qPp0xJguYDLx/1K5UoOCIx4Webcfb3juO6Fg2PDmSLgt7K3QiJCJ9LNrpfDEmvF7LykTkmKy1Fjhu+zBjzC3GmHXGmHUVFRWn/kYxWRAYzvjAQnLLG+hSxzIRGeishf99A7o6aLvwHm5/ajPhwQH85vLxGGPcjk7Ea3U7OWSMiQCeBW631tb1dCA9chKUkO3cV+xm5KBIQMUXRcQFnplDw4JrKa5tprVDMxhFBIAyz8xqPPflxzvQWnuftXaqtXZqYmLiqb+jnx8kjWZIZx7N7Z0U1TSf+muJiPQHW5+GnNdg/o/426YOthfX8ZtPjScpMsTtyES8WreSQ8aYQJzE0KPW2ueOcUgRkH7E4zTPtuNt/4geOQmKyYKgSCjdQlZ8OAF+ht2qOyQifS0yBYw/6X6VWAuFB/VjTEQAeAk41JzjM8CLffKuyWOJa8gBLDkqSi0iA1lDOSz5LqRNo3jkZ7h3+T4unTSYC8YOcjsyEa/XnW5lBvg3sNNa+6fjHPYScKOna9lMoNazpv414AJjTKynEPUFnm29w88PUiZA8SaCAvwYmhhOjpJDItLX/AMgNpOkdicXrrpDIr7HGPM4sAoYaYwpNMbcBPwGON8YkwOc53nc+5LHEdh6kCRqNKNaRAa2xd+Btka45G/86c29YOHbF4x0OyqRfiGgG8fMBm4AthpjNnm2fR/IALDW3gssBi4EcoEm4HOefdXGmJ8Daz3P+5m19ujOHT0rZRKs+zd0djA8OZIthTW9+nYiIscUn01UbT6gdvYivshae81xds3v00DgcFHqmeElKkotIgPXzpdhxwtw7g/Z1TWYZzes4KbZQ0iPC3M7MpF+4YTJIWvtSuBjK3d5iip+5Tj7HgAeOKXoTkXKROhogco9jEyO5JUtJTS1dRAW1J08mIhID4kbhn/eSsKD/JQcEhF3JY8BYFZECU9oRrWIDESdHU53sqSxMPt2fvfwJiKCA/jKvGy3IxPpN06qW1m/MHiSc1+yiRHJEYCKUouIC+KHYdqbOCO2RcvKRMRdobEQlcZYvwJyyhtwrumJiAwgW5+Gg/vh3B+wOr+Ot3aV8+VzsokND3I7MpF+Y+Alh+KzITAcSjYzItnpWLZHV8lEBDDGfMMYs90Ys80Y87gxpvfaVsQ7V6omh1dr5pCIuC95LOnt+2lq66S4tsXtaEREek5XJyz/PSSPx45YxK+X7CIlOoTPzc5yOzKRfmXgJYf8/GHQeCjeRGZ8OEEBfkoOiQjGmFTga8BUa+04wB+4utfe0JMcGh1cTsHBJjq7dKVeRFyUPJboxv0E0qFmHSIysGx7Dqr3wtnfYfG2MjYX1PCN80cQEujvdmQi/crASw6BU3eodCv+dJGdGMEeLSsTEUcAEGqMCQDCgOJee6eoVAgIIYsS2jstxTVqZy8iLkoei5/tYJgp1nJ7ERk4ujph+e8gaQztIy7i96/tYmRyJJefkeZ2ZCL9zsBMDg2eBO2NUJXLiOQIzRwSEay1RcAfgANACVBrrV3aa2/o5wdxQ0luLwTgQLWWlomIi5LHATAttJiccp0XicgAseMFqNwDZ32HJ9YWklfVxPcWjcTf72P7KYnIMQzM5FDKROe+ZDMjBkVSUttCXUu7uzGJiKuMMbHApcAQYDAQboy5/hjH3WKMWWeMWVdRUXF6bxo/jMjGAwDkqSi1iLgpPhv8gzzJIc0cEpEBoKsL3vk9JIykc9Ql3LdiH1MzY5k3MsntyET6pYGZHEoYCQEhULyJEUlOUWpNoRbxeecB+621FdbaduA54MyjD7LW3metnWqtnZqYmHh67xifTUBtHqEBlgMqSi0ibvIPgMRRjPYrILdMHctEZADY+RJU7ISzvsPy3GoKqpv53OwhGKNZQyKnYmAmh/wDnOnTJZsZOchJDu0oqXM5KBFx2QFgpjEmzDhnDfOBnb36jnHDMF3tTImu18whEXFf8jhS2/ZR39pBaZ06lokMZMaYhcaY3caYXGPMHcfYn2GMWWaM2WiM2WKMudCNOE9ZV5fToSw+G8Z9iodX55MYGcwFY5Pdjkyk3xqYySFwlpaVbCYtJpiEiGA25B90OyIRcZG19n3gGWADsBVn/LuvV9/U07FsitrZi4g3SB5LWGsFcdRpRrXIAGaM8Qf+DiwCxgDXGGPGHHXYD4GnrLWTcbq3/qNvozxNuxdD2TY46zsU1LSybHc510zPINB/4P68FeltA/fbM3gStNVjDuYxLSuWtXnVbkckIi6z1v7YWjvKWjvOWnuDtba1V9/QkxwaFVxOflWTlnGIiLuSxwIw0q9AdYdEBrbpQK61dp+1tg14Aqfu4pEsEOX5O5re7ODaG1b9HWKzYNwVPPr+AfyM4Zrp6W5HJdKvDdzk0OGi1JuYkhlL4cFmSms1hVpE+lB4AgRHM4QSmts7qajv3VyUiMjH8nQsmxJcxJ5SdSwTGcBSgYIjHhd6th3pJ8D1xphCYDFwW9+E1gMO5sOB92Dy9bR0GZ5aV8D5o5NJiQ51OzKRfm3gJocSR4N/EBRvYmpWHADr8jV7SET6kDEQP5QkTzv7fLWzFxE3RSRCeBLTw0rYWKDl9iI+7hrgQWttGnAh8LAx5iO/DXu0i2tP2fq0cz/+0yzZVkJ1Yxs3zMp0NyaRAWDgJocCgiBpDJRsZuzgKEIC/ViXpxMhEelj8dlENuYDkFepotQi4rLksYw0B9hT1kBtU7vb0YhI7ygCjlxjlebZdqSbgKcArLWrgBAg4egX6tEurj3BWtjyJGTMgtgsHl6Vz9DEcM4cFu92ZCL93sBNDoFTd6hkM4F+hknpMZo5JCJ9Lz6bgPoiQkwbBzRzSETcljyWxOZ9+NPJ+gM6LxIZoNYCw40xQ4wxQTgFp1866pgDOJ1bMcaMxkkOecnUoI9Rshkq98CEK9lWVMuGAzVcPyNT7etFesDATg6lTISWGqjJZ1pWHDtL6mls7XA7KhHxJfHZGCzTomvJU8cyEXFb8lj8OlsZ5lemGdUiA5S1tgP4KvAasBOnK9l2Y8zPjDGXeA77FnCzMWYz8DjwWdsfOmdseQr8AmHMZTyyOp+QQD8un5LmdlQiA0KA2wH0qpRJzn3xJqZknklnl2VTQQ2zsz8yY1JEpHfEDQVgSngVy6q0rExEXObpWDY/oUrJIZEBzFq7GKfQ9JHb7jri7x3A7L6O67R0dsC2Z2DEAmpNJC9sKuKySalEhwa6HZnIgDCwZw4ljQG/ACjZzBmZsRiDWtqLSN+KHwbAqKByzRwSEfcljATjz5nhJWwurKG1o9PtiEREumf/O9BQBhOu5Nn1hbS0d3H9TBWiFukpAzs5FBgCSaOhZBNRIYGMTI5kfb6ukolIHwqJhvAksiiltrmdmqY2tyMSEV/mOTca07GD1o4uthXVuR2RiEj3bHkKgqNh+AKe21jIxLRoxqVGux2VyIAxsJND4NQdKtkM1jI1K5YN+Qfp6OxyOyoR8SXx2R+0s9fsIRFxW/Z5xFVtIIpG1qtZh4j0B22NsPNlGHspBfVOYvvC8SluRyUyoPhAcmgSNFVBbSHTsuJobOtkV2m921GJiC+JH0qUp519vjqWiYjbRizEdHXwqejdrFXdIRHpD3YthvZGmHAVS3eUAbBg7CCXgxIZWHwjOQRQvJEpmbEArFPdIRHpS/HZBDRXEEETeZUqSi0iLkubBqGxXBSylfX5B+kPDYpExMdtfQqi0iDjTF7bVsqoQZFkJYS7HZXIgOIDyaEJEBQBe98iNSaUlOgQ1qnukIj0pfhsAGbE1LC7TDMXRcRl/gGQfT7jm9dQ09jCPiWtRcSbNVRA7psw4dNUNLazNr9as4ZEesHATw4FBMOwc2HPqxhgSmYs6/J0lUxE+pAnOTQz6iA7S1T8VUS8wIgFhLQdZJLJ1YxqEfFu258D2wkTruKNnWVYqyVlIr3hhMkhY8wDxphyY8y24+z/jjFmk+e2zRjTaYyJ8+zLM8Zs9exb19PBd9vIRVBfAiWbmJYVR2ldC0U1za6FIyI+JnYIYBgbXEFeZSPNbWodLSIuy56PNf5cGLyZdao7JCLebMeLkDwOkkbz2vZSMuLCGJ0S6XZUIgNOd2YOPQgsPN5Oa+3vrbWTrLWTgDuBd6y1R16CmufZP/W0Ij0dwy8ADOx+9XDdIbW0F5E+ExgC0elkUUyXhZxyLS0TEZeFxmIyZrEgaLOW24uI92prhII1kD2fupZ23s2tZMHYZIwxbkcmMuCcMDlkrV0OdHe+8TXA46cVUW8IT4D0GbBnCaMGRRIe5M9aTaEWkb4UP4z4VqedvZaWiYhXGLGA9LZ9tFbmU9nQ6nY0IiIflb8KutphyNks21VOe6dl4TgtKRPpDT1Wc8gYE4Yzw+jZIzZbYKkxZr0x5paeeq9TMnIhlGwmoKGEMzx1h0RE+kx8NkG1+wgL8mNniWYOiYgXGOFMDD/Xf6POi0TEO+1/G/yDIGMWr20vJTEymMnpsW5HJTIg9WRB6k8A7x61pGyOtfYMYBHwFWPMWcd7sjHmFmPMOmPMuoqKih4My2PEIud+j7O0bHdZPbXN7T3/PiIixxI/DNNax/SkLs0cEhHvkDAcGzuE8/w3sT5fM6pFxAvtewfSZ9Bignl7dwUXjEnGz09LykR6Q08mh67mqCVl1toiz3058Dww/XhPttbeZ62daq2dmpiY2INheSSOdIrC7nmVaVlxWAsbDugqmYj0kUMdy6IPsqu0Xh0TRcR9xmBGLORMv21s2V/idjQiIh/WWAWlW2DI2azIqaSprVNdykR6UY8kh4wx0cDZwItHbAs3xkQe+hu4ADhmx7M+YYzTtWzfO0weFEiQvx8rcypdC0dEfEz8MAAmhFZS29xOSW2LywGJiAAjFhBEOzGl76mTooh4l7zlzv3Qs3lteylRIQHMHBrvbkwiA1h3Wtk/DqwCRhpjCo0xNxljbjXG3HrEYZ8EllprG4/YlgysNMZsBtYAr1hrX+3J4E/aiIXQ2UpYwQpmDotn2a5yV8MRER8SnQH+wQy1BQDsKtXSMhHxApmz6QgI52w2sLmwxu1oREQ+sO8dCIqkY9Ak3thZxvzRyQQF9OTCFxE5Une6lV1jrU2x1gZaa9Ostf+21t5rrb33iGMetNZefdTz9llrJ3puY621v+yND3BSMs+E4GjYs4RzRyayr7KR/ZWNJ36eiMjp8g+AlAkk1DoTKFWUWkS8QkAQXUPnMc9/E+vVyVVEvMn+dyBrNmvy66hpateSMpFe5lupV/9AGH4e7HmNc0c6dY3e0uwhEekrqVMJKN1MZkygilKLiNcIGn0hKaaaol1r3A5FRMRRUwDV+2DI2by6vZSQQD/OHtELdWlF5DDfSg6B07WssYKM5p1kJ0VoaZmI9J20qdDRzLlxVUoOiYj3GH4+FkNSyTIaWzvcjkZExJk1BDD0bFbmVHLmsARCg/zdjUlkgPO95NDw88D4O0vLRiXx/v4qGnQiJCJ9IXUKALND9rO/spGWdhV/FREvEJFEY/w4ZpmtrMxVsw4R8QL73oHwRMpDhrKvspEZQ+LcjkhkwPO95FBorFN7aLeTHGrvtKzMqXA7KhHxBbFZEBbPqM49dFnIKWtwOyIREQBCR5zNZJPLyp0FbociIr7OWmfm0JCzeT/vIAAz1KVMpNf5XnIInK5l5TuYEl1PZEiA6g6JSN8wBlKnklR3qCi1lpaJiHfwH3IWQaaDql3vYq11OxwR8WUVu6GhDIaezfv7qwgP8mfc4Ci3oxIZ8HwzOTRyEQCBuUs5e0Qib+2qoKtLJ0Ii0gfSphJ4MJfEwFZ2qp29iHiLjJl04cfIls3qpigi7tr3tnM/5GzW7K9mSlYcAf6++bNVpC/55rcsfpizvGPvW5w7KonKhla2Fde6HZWI+ILUKRgsi+KKNXNIRLxHSDSdyeOZ4beTZbs1o1pEXLT/HYjNojoohT1lDao3JNJHfDM5BDBsPuSt4JzsGIxRS3sR6SOHi1Lnsau0Xss3RHyQMeYbxpjtxphtxpjHjTEhbscEEDh0Lmf45bJyZ6HboYiIr+rsgLyVnllDVQBKDon0ER9ODp0LbQ3EVW9mcnqMkkMi0jdCYyB+OGO69lDT1E5pXYvbEYlIHzLGpAJfA6Zaa8cB/sDV7kblkTWHINqxheuoaWpzOxoR8UUlm6C1Doaezep91YQE+jEhLcbtqER8gu8mh4bMdVra732L+aOT2VJYS3m9fqSJSB9Im8qg+m2AZZdqe4j4ogAg1BgTAIQBxS7H48iYhcUww+zgnT3q5CoiLjii3tD7+6s5IyOWoADf/ckq0pd895sWEg1p02DvW8wbmQTA27t1IiQifSB1CoEtVaSZSnao7pCIT7HWFgF/AA4AJUCttXapu1F5hMZAygTmBO7SOZGIuCNvBSSPo9ZEs6u0jhlD1MJepK/4bnIInKVlxRsZHd1OSnQIb+3U0jIR6QNpUwE4N+IAu0o1c0jElxhjYoFLgSHAYCDcGHP9MY67xRizzhizrqKi7xI1JnMOE00Oq3YX0alOriLSl7o6oXAdZMxkbV411sKMoao3JNJXlBzCYva/w7xRSazIqaCto8vtqERkoEseBwEhzAnNU8cyEd9zHrDfWlthrW0HngPOPPoga+191tqp1tqpiYmJfRdd1hyCbBsZzTvZXFjTd+8rIlK+E9oaIG067++vIsjfj0npMW5HJeIzfDs5NHiys7xs75ucOzKJxrZO1uyvdjsqERno/AMhZSJjbQ77Khpoae90OyIR6TsHgJnGmDBjjAHmAztdjukDmU7doVn+O3lbzTpEpC8VrnHu06fx/v5qJqXHEBLo725MIj7Et5ND/gEw5GzYu4zZw+IJDfTnte2lbkclIr4gdSqDGnfjZzvIKWtwOxoR6SPW2veBZ4ANwFacc7H7XA3qSKGxmEHjOC8sh7d2KzkkIn2oYA2EJ1Ifmsa2olotKRPpY76dHALIng91RYTW7WXeqESWbCvVGnsR6X1pU/DvamWUOcDOUi0tE/El1tofW2tHWWvHWWtvsNa2uh3Th2TNZXTHLvYUVVFep06uItJHCtZA2nTWH6ihy6Ji1CJ9TMmhofOc+71vceH4FCobWlmXp6VlItLLUp2i1DMC97G1sNblYEREjpA5m4CuViaYvbytlvYi0hcaq6B67+ElZQF+hjMyY9yOSsSnKDkUmwnx2Ydb2gcH+LF4a4nbUYnIQBeTAeGJnBNxgPf3V7kdjYjIBzLPxGI4PyyHN3eWuR2NiPiCwrXOfdp03t9Xxfi0aMKCAtyNScTHKDkETteyvJWE+3cyb2QSS7aV0qWlZSIDjjEmxhjzjDFmlzFmpzFmlovBQOpUxtkc9pQ1UN3Y5looIiIfEhaHSR7HeaE5vLOngqa2DrcjEpGBruB98AugKXE8WwprtaRMxAVKDoGTHGpvgoL3uXBCCuX1raw/cNDtqESk590DvGqtHQVMxO0OQWlTiG3OJ4oGdUoUEe+SNZus5m10trfx9m4tLRORXla4FgaNZ0NxGx1dVsWoRVyg5BBA1hzwC4DcNzl3VBJBWlomMuAYY6KBs4B/A1hr26y1Na4G5ak7NC1wv5aWiYh3yZqDf2cLc8IOsGSbOrmKSC/q7ICi9ZA2nTX7q/AzMDUz1u2oRHyOkkMAwZGQPgP2vkVEcADnjEhkyVYtLRMZYIYAFcB/jDEbjTH3G2PCXY0o9QzAsDCmkPf3aeaQiHiRzNkAXJ2Uz1s7y2hp73Q5IBEZsMq3O6s40qezLv8gYwZHERkS6HZUIj5HyaFDhs2D0i3QUMGF41MorWthY4GWlokMIAHAGcA/rbWTgUbgjqMPMsbcYoxZZ4xZV1HRy0spQqJh0Dhm+O1kZ2kdtU3tvft+IiLdFRYHg8Yzs2M9jW2drMipdDsiERmoCtYAYNOmsrOkjnGDo10OSMQ3nTA5ZIx5wBhTbozZdpz95xhjao0xmzy3u47Yt9AYs9sYk2uM+ciPMK8y7FznPvcN5o9OIsjfj8VbNY1aZAApBAqtte97Hj+Dkyz6EGvtfdbaqdbaqYmJib0fVdZc0hq2EmjbWZev2UMi4kXGfpLoyg2MDqlmyTYttxeRXlKwBiIGUWaSOdjUzuiUKLcjEvFJ3Zk59CCw8ATHrLDWTvLcfgZgjPEH/g4sAsYA1xhjxpxOsL0qZbLTWnrz40SGBHLWiASWbC3R0jKRAcJaWwoUGGNGejbNB3a4GJIjay5+na1MDdjH+ypKLSLeZPynAbgtcRNv7CijraPL5YBEZEAqXAPp09hZVg/AqEGRLgck4ptOmByy1i4HTuUXy3Qg11q7z1rbBjwBXHoKr9M3/Pxg0vWw/x04mMeF41Morm1hc2GN25GJSM+5DXjUGLMFmAT8yt1wgMxZgOGymL28v09FqUXEi8RkQMaZnNW6jLqWdlZpjBKRntZQDgfzIG06O0vqABilmUMiruipmkOzjDGbjTFLjDFjPdtSgYIjjin0bDumPq3zcTyTrwMMbHyU88YkE+hv1LVMZACx1m7yLBmbYK29zFrrfmGx0FhImcAsvx1sK66jobXD7YhERD4w4dNE1O1lSlABS3ROJCI9zVNviPTp7CqpJzUmlOhQFaMWcUNPJIc2AJnW2onAX4EXTuVF+rzOx7FEp0H2fNj0KFFBfswdnsjiraVYq6VlItKLsuaS2rCNgK5W1uVpaZmIeJExl4FfIF+J38DSHWV0dGppmYj0oMI14BcIKZPYWVLH6BQtKRNxy2knh6y1ddbaBs/fi4FAY0wCUASkH3Fommebd5t8A9QVwd63uHB8CkU1zWwurHU7KhEZyLLm4tfVxjT/XNao7pCIeJOwOBh+Pmc2L6OmsYU1SmCLSE8qWAspE2khkH2VjYwapCVlIm457eSQMWaQMcZ4/p7uec0qYC0w3BgzxBgTBFwNvHS679frRl4IYfGw4SHOH51MkL8fL2z0/pyWiPRjmbPA+PGJaBWlFhEvNOFKQloqOCtwJ69uUydXEekhne1QvAHSp5Nb3kBnl2WUZg6JuKY7rewfB1YBI40xhcaYm4wxtxpjbvUccgWwzRizGfgLcLV1dABfBV4DdgJPWWu3987H6EEBQTDhati9hGhbywVjk3l+YxEt7Z1uRyYiA1VINKRM5Ez/HWwprKG5TeONiHiREQshKJKbY9bz6rZSdXIV6QeMMQuNMbuNMbnGmDuOsf/PxphNntseY0xNnwdZugU6WiBt2uFi1GpjL+Ke7nQru8Zam2KtDbTWpllr/22tvddae69n/9+stWOttROttTOtte8d8dzF1toR1tph1tpf9uYH6VFn3ABd7bD5Ca6elkFtcztLd5S5HZWIDGRZc0lt3I5/ZwsbDrhfJ1tE5LDAUBhzCdObV1JbX68xSsTLGWP8gb8Di4AxwDXGmDFHHmOt/Ya1dpK1dhJO3djn+jzQgrXOffp0dpbUExLoR1Z8eJ+HISKOnupWNrAkjYa0abDhIc4cGkdabChPrS048fNERE5V1lz8utqZ6pejlvYi4n3Gf5rAjgYuCNjE4q1aWibi5aYDudbafdbaNuAJ4NKPOf4a4PE+iexIhWsgKhWi09hVWsfI5Ej8/UyfhyEiDiWHjmfyDVC5G7/idXx6SjorcyspqG5yOyoRGagyZ4Hx5xPRe1mtukMi4m2GnAURg/h81Bpe3lKsrmUi3i0VOPLKdqFn20cYYzKBIcBbfRDXh5XtgEETsNays6ROxahFXKbk0PGM+xQEhsOGh7hiahrGwNPrNHtIRHpJcCQMnsyZ/jvYVFCjOmci4l38/GH8FUxsXktbfRUrcyvdjkhEesbVwDPW2mOeeBhjbjHGrDPGrKuoqOi5d+3qhOp9kDCc8vpWDja1q429iMuUHDqe4EgY+0nY/jypoZ2cNTyRp9cX0qkijCLSW7LmkNq4E/+OJjYX1LgdjYjIh43/NH62nStC1/HsBnVyFfFiRUD6EY/TPNuO5Wo+ZkmZtfY+a+1Ua+3UxMTEnouw5gB0tkLC8MPFqEepGLWIq5Qc+jhn3ABtDbD1aa6alk5JbQsrcnowYy4icqQhc/GzHUzz28Mq1R0SEW+TMhESRvLZ0HdZur2UupZ2tyMSkWNbCww3xgwxxgThJIBeOvogY8woIBanM3Xfqsxx7uOHs7OkHoDRWlYm4iolhz5O+gxImQTv3sN5I+OJCw/iSRWmFpHekj4T/AK4NGYfb+xUh0QR8TLGwNTPkd60nezOvSzeUuJ2RCJyDNbaDuCrwGvATuApa+12Y8zPjDGXHHHo1cAT1tq+XxpR5UkOJQxnV2kdg6NDiA4L7PMwROQDSg59HGNg7rfg4H6Cdr/EJyen8sbOMqoaWt2OTEQGouAIGHwGcwJ2sq2oTkXwRcT7TLwGGxjGVyKW8eyGQrejEZHjsNYuttaOsNYOs9b+0rPtLmvtS0cc8xNr7R2uBFiZA6GxEBbPzpI6RmtJmYjrlBw6kVEXQ+IoWPFHrpqaSnun5fmNWmcvIr1kyFyS6ncQTjNLd2j2kIh4mdAYzPhPc37nCnbnFZBf1eh2RCLSH1XlQvxwWju72FvRyCgVoxZxnZJDJ+LnB3O+CeU7GFGzkskZMTy5tgA3Zl+KiA/ImoOxnVwWX8Br20rdjkZE5KOm30xgVyufDljOcypMLSKnonIPJAwnp6yBzi6rNvYiXkDJoe4YdznEZDqzh6akkVPewIYDNW5HJSIDUfpM8AvkU9F7WJtfTUW9lrGKiJcZNB7SZ3JzyFs8v+EAXerkKiIno6UOGsogPptdpZ5i1FpWJuI6JYe6wz8A5twOReu5NDqHyOAAHnh3v9tRichAFBQG2fOZULsMbJcKU4uId5r2BQZ1FJNVu4a1edVuRyMi/cnhYtQj2FlSR3CAH1nxYe7GJCJKDnXbpOsgYhChq+/m+lmZLNlawv5KrbMXkV4w7goCG0u4KDqf17ZraZmIeKExl2DDEvls4JtaWiYiJ6cy17n3dCobOSiSAH/9LBVxm76F3RUQDGfeBnkruDmrggB/P+5bvtftqERkIBq5CAJC+Vz0Bt7NraSupd3tiEREPiwgGDPlM5xjNrBh6xaa2zrdjkhE+ovKPWD8sbFZ7CypZ9QgFaMW8QZKDp2MqZ+D0Dji1v+VK6em8ez6IsrqWtyOSkQGmuAIGLGACXVv09XZwbJd5W5HJCLyUVM+izHwyc7XWLpDsxxFpJuqciA2k4omS3Vjm+oNiXgJJYdORlA4zPwy5LzGV0Y109HVxf0r9rkdlYgMROOvILClikXhe7S0TES8U0w6jFjENQFv88z7mk0tIt1UmQsJI9hRUgegTmUiXkLJoZM1/WYIiSFl9c/4xIQUHnv/ADVNbW5HJSIDTfb5EBzF56LW8/buClratWRDRLyPmf4FYqkjPn8JO4rr3A5HRLxdVydU7z2qU5mWlYl4AyWHTlZoDJz3E8hbwfdSt9LY1slDq/LdjkpEBprAEBh1MRMbVtDR1sKKnEq3IxIR+agh59AZl80XA1/hgRWaPSQiJ1BbAB0tkDCcnSV1pESHEBMW5HZUIoKSQ6fmjM9A6hQGv/9zLhoexn/e3U9TW4fbUYnIQDPucgLa61kYsk1Ly0TEO/n54X/O9xht8unY+hzl9arFKCIf41Cnsvjh7K1oIDspwt14ROQwJYdOhZ8fXPQnaKrix+HPcrCpnSfXFrgdlYgMNEPPhrB4Phu1njd2ltHR2eV2RCIiHzXuCtriR/N1v6d49D3NHhKRj1GVA4BNGE5+ZRNDEsJdDkhEDlFy6FQNngTTbiZp1yNclVrJv5bvo10/3ESkJ/kHwphLmdC4itametbsr3Y7IhGRj/LzI+j8uxjiV0rd6odVI01Ejq8yB0KiOUg09a0dZMSFuR2RiHgoOXQ6zv0BhCfyA3s/pbVNPLeh0O2IRGSgGXcFAZ3NXBi0iZc2F7sdjYjIsY1cRH3CJL7Q9RQvrlMnVxE5jso9ED+cvOomALLiNXNIxFsoOXQ6QqJhwa+Iqt7CdxJW8cele2hsVe0hEelBGbMgcjCfj97Ai5uKqWtpdzsiEZGPMoaIC39Kqqmi8u17sda6HZGIeKOqXEgYzoEqJzmUGa+ZQyLe4oTJIWPMA8aYcmPMtuPsv84Ys8UYs9UY854xZuIR+/I82zcZY9b1ZOBeY/wVkDWXm9seprO+nHvf0Vp7EelBfn4w7lOMbnyfwPZanl2vGYoi4p3M0HMoT5jJVS1PsXJ7ntvhiIi3aa2H+hKIzyavqhFjIF3LykS8RndmDj0ILPyY/fuBs62144GfA/cdtX+etXaStXbqqYXo5YyBi/5EQEcT9yQv4b7l+yiqaXY7KhEZSMZ9Cr+udm5J2M7Dq/N1RV5EvFbsJ35Ogqmj9PW73Q5FRLxNladTWcIIDlQ1MSgqhJBAf3djEpHDTpgcstYuB45bBdVa+5619qDn4WogrYdi6z8SR8CUzzK7fjHplPK7V3e5HZGIDCSDz4CEkVzvt4R9FQ2s2lvldkQiIscUmDmdffFns6DmKXLyDrgdjoh4k0qnUxkJw8mratSSMhEv09M1h24Clhzx2AJLjTHrjTG39PB7eZezvoPxC+Svg5bw4qZiNhw4eOLniIh0hzEw53Zi6vbwidCtPLw63+2IROQ0GGNijDHPGGN2GWN2GmNmuR1TT4r/xM+IoJkD//uN26GIiDepzAHjB3FDOVDdRGacilGLeJMeSw4ZY+bhJIe+d8TmOdbaM4BFwFeMMWd9zPNvMcasM8asq6io6Kmw+k7kIJh5K6MqlzI7ooSfvbxDSz9EpOeM/zREp/O98FdYuqOU0toWtyMSkVN3D/CqtXYUMBHY6XI8PSo6axI7Ys9lesVz5BWXuR2OiHiLqhyIyaCh05/KhjYyEzRzSMSb9EhyyBgzAbgfuNRae3i9g7W2yHNfDjwPTD/ea1hr77PWTrXWTk1MTOyJsPre7K9jQqL4fdyLbCqoUdtpEek5/oFw5tdIa9jKVHby+Bot1xDpj4wx0cBZwL8BrLVt1toaV4PqBYMv+g6RppkNL9zjdigi4i0qcyFhBPlVjYDa2It4m9NODhljMoDngBustXuO2B5ujIk89DdwAXDMjmcDRmgszL6dweXL+XRSIb9dsovmtk63oxKRgWLy9RCWwA+iXuXxNQdo7+xyOyIROXlDgArgP8aYjcaY+z3nSQNK3PBZFEROYlrZU+wu1lJ7EZ/X1eUUpI4fTr6njX2GOpWJeJXutLJ/HFgFjDTGFBpjbjLG3GqMudVzyF1APPCPo1rWJwMrjTGbgTXAK9baV3vhM3iXGbdCRDI/Cnma4tpmfv/abrcjEpGBIigMZn2ZCS1rSWzYxdLtWq4h0g8FAGcA/7TWTgYagTuOPqjfL7cHYud/g3RTwdsvPOB2KCLitrpC6GiGhOzDySEVpBbxLt3pVnaNtTbFWhtorU2z1v7bWnuvtfZez/4vWGtjPe3qD7est9bus9ZO9NzGWmt/2dsfxisEhcFZ3yGqfC2/GFvKA+/u5/mNhW5HJSIDxbQvYIOj+FbYKzy8Os/taETk5BUChdba9z2Pn8FJFn3IQFhuHzHhE9SEpDGt9DE2F9S4HY6IuOlQp7L44eRXNRIfHkRkSKC7MYnIh/R0tzIBOOMzEJPJdQ3/YWZWDHc8u5VtRbVuRyUiA0FINGbaF5jXuYry/dvIKat3OyIROQnW2lKgwBgz0rNpPrDDxZB6j58/IXNv4wy/XF58+Tm3oxERN1XlOvcJI8ivatKsIREvpORQbwgIgnk/wJRt4/5xW4kPD+KLD6+nqqHV7chEZCCY+WUICOZLgf/j/hX73Y5GRE7ebcCjxpgtwCTgV+6G03tCpt1AS0AUU0se5/19VSd+gogMTJU5EBwFEUnkVzWSqWLUIl5HyaHeMv4KGHYuEW/eyeNnFlPR0MpXH9tIhwrIisjpikjEnHEjn/Jfycr1m8gt1+whkf7EWrvJs2RsgrX2MmvtwK3YHBSO//SbWOC/jocXv4O11u2IRMQNVTkQn01LRxcldS2aOSTihZQc6i1+/nDVI5A+k8x3bufBWRWs2lfFrxbvcjsyERkIzrwNPwO3B73Ab19V4XsR8V6BM78Ixo8ppU+wPKfS7XBExA01BRCbSeHBJqxVMWoRb6TkUG8KCodrn4SUiZy54Vv8fFwZD7y7nyfXHnA7MhGfZYzx97SP/p/bsZyWmAzM9C/yafMm1TuXsy6v2u2IRESOLSoFxl3BVQHv8MeX3qextcPtiESkL1kLtYUQnXZEpzItKxPxNkoO9baQKLj+WUgYyfV53+fmjBLufG4rr2wpcTsyEV/1dWCn20H0iHnfpysqld8FP8DvF2/Vcg0R8Vr+s79KGC3MqXmZO57TeCXiUxorobMVotPJO5QcitPMIRFvo+RQXwiNhRuex8Rk8P2au7h2UBG3P7mRZbvL3Y5MxKcYY9KAi4D73Y6lRwRH4HfRHxlGAVOKHmHpjjK3IxIRObZB42H4BXwz8DmKtrzNI6vz3Y5IRPpKbYFzH53GgapGIoMDiAsPcjcmEfkIJYf6SkQi3PgiJjKFn9f9kM/HbubWh9erc4dI37ob+C5w3MrwxphbjDHrjDHrKioq+iywUzZyEV2jLuH2wOd5ZPEyFb0XEe912b34x6bz39A/8d//LWNLYY3bEYlIXzgiOZRX1URGfBjGGHdjEpGPUHKoL0WlwOeXYgZP4s6G3/CN8Ne46b9rdXIk0geMMRcD5dba9R93nLX2Pk8XoamJiYl9FN3p8bvwd5iAYL5Y91eeWlvgdjgiIscWHo+57mnCgwN4IOi33PHI29Q2tbsdlYj0ttpC5z46nfyqRrJUb0jEKyk51NfC4+HGF2HMpdza+h9+GvhfPvvvVWwrqnU7MpGBbjZwiTEmD3gCONcY84i7IfWQqBQCLvgJc/y3s+v1f9HUpmKvIuKl4ofhd83jpPlV8dOmX/HdJ9bQ1aX6QyIDWm0hBIbTERRN4cFmMtSpTMQrKTnkhsBQuOJBOPM2Lu9YzN3mj9xw71s8v7HQ7chEBixr7Z3W2jRrbRZwNfCWtfZ6l8PqMWbqTTQkTubrHQ/y4OsfOzlKRMRdGTPx++T/Mc1vNxft/zn/WLbH7YhEpDfVFkB0GsW1rXR0WbKUHBLxSkoOucXPDy74BSz6PXPtepYE3cnjTz3BXS9uo61DNUNE5CT5+RFxxd+JMU2kr/4xmw8cdDsiEZHjG/cp7Hk/5RL/VQQt+ykPv7ff7YhEpLccamNf3QhARpyWlYl4IyWH3DbjFsxnXiY5Kpingn/OkLU/48b/W0ZJbbPbkYkMWNbat621F7sdR49LHkv73Dv4hP8q3nj091peJiJezcz+Op1Tb+aWgFfoWPw9nlpzwO2QRKQ3eJJDh9rYZyVo5pCIN1JyyBtkzcF86T2YcSufC3iN35V/iR/fcy+r9qqTmYicnJB536YmZQ5fabmPfz/7P7fDERE5PmPwv+j3dEz/Ep8LeI2ul7/GixuVIBIZUNqbobECotM5UNVIcIAfyZEhbkclIseg5JC3CAqHRb+Fzy1hUEwo93X9hIoHr+e5V1/HWhVqFJFu8vMj5rr/0B4UxaKdd7J8q5ZqiIgXM4aARb+mffa3uNp/GTx3K0s2q+uiyIBRV+zcH2pjHxeGn5/a2It4IyWHvE3mmQR9ZRWts27ngoANfGr1FWz506W0FmxyOzIR6S8ikgi+8t8M9Suh9rnbqWpodTsiEZHjM4bA8++i7ewfcqn/u/g9+zle26wZRCIDQo3nuxydxoGqJjLVxl7Eayk55I2Cwghe8FOCvrWDNek3MaRuDcH/Ppvm/34a8laCZhKJyAkEDZ9H1ZTb+YR9m5f++wfNQBQRrxc07zu0zP8lC/zWEvPsp7nvhdfp6FSTDpF+rdbpxmw9Bakz1alMxGspOeTF/CLimX7Tn9j4qRX8zV5J2/534cGL4G/T4L2/QmOl2yGKiBdLvOhHFMdM5arye3j5zWVuhyMickIhc79K26X/x4SAQm7ceA2P3f1tSg82uB2WiJyq2kLAUG7iaWnvUht7ES+m5FA/cPbE4Vz41T9zfdSDfKvtVvJbQmHpD+GPo+Dpz0LBWrdDFBFv5OfPoM89TLt/KGeu+AzvLFvidkQiIicUNPlqQm9fx8GUudxY/2+q7jmLtauXux2WiJyK2kKIHETewXYAMrSsTMRrKTnUTwxNjODp2+YTPesznF11B58P+yuVY26Evcvg3+fBw5+EA++7HaaIeBm/6MEEf+FVOgPCmP72jWxY+ojbIYmInFhUCilffI6SC+5lsKli0pLLeP//vkJHXZnbkYnIyagtgOg08qs9bew1c0jEayk51I+EBPpz1yfG8MhNM9jRkcrMDedx7xkv0Tn/p1CyGR64AB66DA6sdjtUEfEiIYNHE/GVZRQGZjHp3a+S89Lv3Q5JROTEjCHlzGsIvX0dm+MuYFrxo3T+aTwNL30X6kvdjk5EuqO20EkOVTXi72cYHBPqdkQichxKDvVDc4Yn8NrtZ7FofAq/eauQSzZOYdPlK+D8n0PZNnhgATx0qZabichh4XGDSbrtdd4PmsHwDb+g+InboavT7bBERE4oJDqRqV9/gmXn/Y9X7QxCNvyLzj+Ph1e+BQfz3A5PxCsZYxYaY3YbY3KNMXcc55grjTE7jDHbjTGP9XgQ1h6RHGoiLTaUQH/9/BTxVvp29lPRYYH89ZrJ/P3aM6hsaOWyf23izrJzOPiFtXDBL6B0m7Pc7NFPQ/FGt8MVES8QHR3DyK+9wPNBn2Dwrv9Q88Dl0FjldlgiIt0yf+4cxn/1cW6Ouo8n22bTue5BuGciPLAI1v0Hmg+6HaKIVzDG+AN/BxYBY4BrjDFjjjpmOHAnMNtaOxa4vccDaayEzlaITqeopplUzRoS8WrdSg4ZYx4wxpQbY7YdZ78xxvzFk5neYow544h9nzHG5Hhun+mpwMVx0YQU3vzWOXxhzhCeWlfIuX9dy5OBl9L1tU0w/8dQuBbuOweeuA6KNrgdroi4LC4ylNm33c/dwV8krGAFLX+bBfnvuR2WiEi3DE2M4J+3fYodU3/O7OY/83DYjbTWlcP/boc/jIAnr4fdSzQzUnzddCDXWrvPWtsGPAFcetQxNwN/t9YeBLDWlvd4FLUHnPvoNKoa2kiMDO7xtxCRntPdmUMPAgs/Zv8iYLjndgvwTwBjTBzwY2AGziD1Y2NM7KkGK8cWERzADy8ewytfm0N2UgTfe3Yr1z60jYKxt8LXt8C8H8D+FfCveXD/+bDtWehsdztsEXFJUmQI19/2c74b+0fKmqDrwYth+R+gq8vt0ERETigk0J9fXDaeH193Pne3XsLY8p/xyMSH6Zh6k9Oc4/Gr4S+T4b2/ajaR+KpUoOCIx4WebUcaAYwwxrxrjFltjPm433qnprbQuY9Oo6qhlfhwJYdEvFm3kkPW2uVA9ccccinwkHWsBmKMMSnAAuB1a221Jyv9Oh+fZJLTMGpQFE99cRa/vXw824rqWHTPCp7aVos96zvwjW2w8LfQVAnPfB7unuD8GGyocDtsEXFBQkQwv/jS9fwi7V5e6ZgGb/0c+8inoF6dgESkf1g0PoXXv3k2n5iYyg/f92fRzkVs+PR7cOVDEJ0GS38IfxoDL9/uLLcXkSMF4FzYPwe4BviXMSbm6IOMMbcYY9YZY9ZVVJzk7wZPcqg5LJXGtk4SIoNOM2QR6U09VXPoeNnp7mStpQcZY7hqWgZLvj6XsYOj+O4zW7j5oXWUtwfBzFvhq+vhmichcQS89XP40yh4/BrY+TJ0tLkdvoj0oYjgAP7+uXN4ffSvubP9Jjr2v4v96xRY+Wdob3E7PBGRE4oLD+LPV03iP5+dRkNrB5f/3xruyhlG9ZUvwK0rYdzlsPlxuHc2/ONMWHn3B7MZRAauIiD9iMdpnm1HKgResta2W2v3A3twkkUfYq29z1o71Vo7NTEx8eSiqC2EwHAqO5xaQwmaOSTi1bymIPVpZaXlI9Ljwnj85pn88KLRLM+pZMGfl/Py5mKsMTByIdz4InxlDcz6ilOL6Mnr4Y8jYcn3oGi9011ARAa8oAA/7r56MiEzb2JBy6/YEjAO3vgJ/H0abHtOY4GI9AvzRiWx9BtnccPMTB5Znc/Zv1vGP3aF0nLhPfDNnXDhHyAoHN74Mfx5HDx4MWx6XBfGZKBaCww3xgwxxgQBVwMvHXXMCzizhjDGJOAsM9vXo1HUFkB0GpWNzvcsPkIzh0S8WU8lh46Xne5O1ho4zay0HJOfn+ELc4fyym1zSI8L47bHN3LzQ+soqW12DkgcCef/DL6xHa57BoaeDesegH+dC3+ZBG/+DMq268ehyADn52e46+IxXHfReVxe8zW+HvgTmkwYPPM5eGChU7Ba44CIeLnIkEB+duk4Xrv9LKYPieN3r+5m3h/e5pmdTXRO/QJ84XX42kaY932oK4YXbnW6nb33V2ipczt8kR5jre0Avgq8BuwEnrLWbjfG/MwYc4nnsNeAKmPMDmAZ8B1rbc+2MPW0sa9qcJJDCRGaOSTizYzt5gm/MSYL+J+1dtwx9l2EMwBdiFN8+i/W2umegtTrgUPdyzYAU6y1H1e/iKlTp9p169Z1+0PIiXV0dvHge3n8YeluAvz8+N6iUVw3PQM/P/PhA5sPwq5XnKLV+94B2wmJo2DMpTByEaRMAmOO+R4iRzLGrLfWTnU7jtPhi2PRxgMHue3xjZTVNvGvcTs5u+j/MI0VMPgMZ6bhmMvAP8DtMEW6TWOR71q1t4pfLd7J1qJaxqdG87srJjA6JcrZaS3kvgnv3g15KyA4GqZ9HqZ/EaJSXI1bBiafHIt+nw0jL+SJQd/mjue2svJ780iLDeu9AEWkW443HnW3lf3jwCpgpDGm0BhzkzHmVmPMrZ5DFuNMQ8wF/gV8GcCTBPo5ztTGtcDPTpQYkt4R4O/HF+YOZentZzMpPYYfvbCNq+5bxbai2g8fGBoLk6+HG56Hb+2Gi/4IYfGw/Pdw3zlOYcf/fQNyXtdUbJEBaHJGLK98bS4XjB3MZ7eM4ebYB2g477fQUgvP3uTMKnzvr85jEREvNmtYPC9+ZTZ3XzWJ4ppmLvnbSu5+Yw9tHV3Oha7h58Fn/wc3L4Psc+Hde+DPY+ChS2Hjo5pNJHI62puhsQKi06lq1Mwhkf6g2zOH+pKukPUuay3Pbiji5//bQW1zO2cOi+fms4ZyzohEzPFmBTVWQc5rsHsx5L4F7Y0QlQqzb4czboDA0D79DOL9fPIK2QBireWxNQf42cs7iAgO4JeXjWVh0GZ472+QvxKCImDyDTDjixA3xO1wRY5LY5EAVDe28bOXt/PCpmJGDYrk91dMZHxa9FEH7YNNj8HWp+FgHvgHO3Uax10B2fOdmkUip8jnxqLKXPjbFLjsXn5aMIGn1xWy7acLejdAEemW05o5JAOLMYYrpqSx/LvzuHPRKPZVNPK5/6xlwd3LeWpdgXNF7Wjh8TDpWrjqEfjuPrj6cYjJgCXfgbsnwLt/gdaGvv8wItIrjDFcNyOTl2+bQ0pMCLc+upFvbkqh9uoX4Ja3YdRFsPZf8NcznIL2+atUl0hEvFZceBB3Xz2Z+2+cysGmNi77x7v8eslOmto6jjhoKJz7Q/jaJrjpDZjyGch7F566AX43FB67GjY8DA1qnCJyQrWehtXRaVQ2tJGgYtQiXk8zh4S2ji7+t6WY+5bvY1dpPZnxYfzgwtGcPyb5+DOJDsl711lytm+ZsyQtYxYEhjkziYLCnb+z50PWnL75MOI1fO4K2QDW3tnF397K5W/LckmMCOZ3V0zgrBGJTkHXNf+C9f9x6pWlToW534QRi8BP1x7EO2gskqPVNrfzq1d28uS6AlJjQvnRxWNYMPY45zydHXDgPace465XPD94DaRPh+HnQ/b5MGiCxjw5IZ8bizY8DC99Fb62iWufLaO1o4tnv3Rm7wYoIt1yvPFIySE5zFrL27sr+NXineSUNzAnO4EfXTyGkYMiT/zkwnXw3l+gap+z5KytCdqboK3RKWo99lNwwS8gOrX3P4h4BZ87CfIBWwpr+OZTm8ktb+Cqqel8b9Eo4sKDnO/75secGYQ1+U4R+9m3w/grwD/Q7bDFx2kskuNZl1fND1/Yxq7Ses4ZmchPLxlLZvzHLB2zFkq3OkmiPa9CySZne0QyZJ8HIy+EEQs07skx+dxYtOzX8M5v4YflXPDXVWTFh3Pfjf3644sMGEoOSbe1d3bx6Op8/vxGDvUt7Vw3I5Nvnj+C2PBTmA7a3gwr74aVfwa/ADj7OzDzKxCgqaUDnc+dBPmIlvZO/vzGHv69Yj8RIQF8d8EorpqWjr+fca6wb3/e+b6Xb4foDJj7Dac2kX4siUs0FsnH6ejs4r+r8vnz63to6+zipjlDuHFWJinR3ail2FAOuW84TTr2vgUtNRCe6CzDn3wjJGR/9DldnqX7mmnkc3xuLHrhK7D3TfjWLqb8/HUWjBvErz45vncDFJFuUXJITtrBxjb+/MYeHn3/ABHBAXz7ghFcOyPT+RF4sqr3w2vfdwpax2fDWd+FkYsgJKrnAxev4HMnQT5mT1k9P3phG+/vr2ZiWjQ/u3QcE9NjnJ3Wwp7XYMUfoXANxA2D+XfBmEudDkEifUhjkXRHWV0Lv1q8k5c2F+NnDAvGJnPjrCxmDIk78RJ7cJLje9+EDQ/B7iXOrOnM2ZAx01mCW1sINQecv8MTYOFvNCb6GJ8bi/57CbQ30fn518n+wWJum5fNNy8Y2bsBiki3qCC1nLTY8CB+duk4Fn9tLmNSovjRi9u5+K8rWbO/+uRfLG4IXPM4XPu088Px+Vvg99nw+DWw+Um1ixXpZ0YkR/LELTO55+pJFNe2cNk/3uV7z2yhrK7F+bEzciHctBSuecKZNfT0Z+D++bB/uduhi4h8RHJUCPdcPZnl35nHF+YO4b29VVx932oW3r2Cp9YV0Nl1goup/gHOkrKrH4Vv7oDzfgL1Jc5Myv0roLPdqVN05lchIskZEx+7Eg7m98nnE+lztYUQnUZ1YxvWQkKk2tiLeDvNHJJusdayeGspv3xlB8W1LVwycTDfWzSK1JhTaGHf1eXMJtj+Aux4EeqLwT/IOamafCMMO9c5yZJ+zeeukPmw+pZ27nkjh/+uyiPAz4+b5w7hlrOHERHs+R53dcLmJ2DZL6GuCFKnwKiLnVviCHeDlwFPY5Gcipb2Tl7aXMx/38tje3EdowZF8qOLxzA7O6H7L2It2C7w8//w9s4OWHMfvPULZ/85d8Csr2j57QDnU2ORtfCLZJhxC7smfJeFd6/g79eewUUTUno/SBE5IS0rkx7R3NbJP9/O5d7l+7DW8qnJaXzpnGFkJXxMAceP09UFhWudOiVbn4KmKohMcdbrT7oO4of17AeQPuNTJ0ECQH5VI79/bTf/21JCQkQwt583nKunpRPg75mk2t4M6/7jfNeLNzrb4rNh1EVOoih1qupwSI/TWCSn49DFsV8v2UnhwWbmj0rizgtHk50UcfovXlsIi78Lu1+BuKFOfbaJV0PU4NN/bfE6PjUWNZTDH4bDot+xMu5yrv/3+zx5y0xmDI3v/SBF5ISUHJIeVVTTzP+9s5cn1hbQ0dnFJyYO5svnZHevs9nxdLQ53T82PgK5rztX0waf4cwoGn4BpEzSD8d+xKdOguRDNh44yK8X72JNXjVDE8P53sJRXDDmqDbRtYVOXY5dr0DeCujqcDr+jLwQRl8MWWepcL30CI1F0hNa2jt58L08/vZWLi3tnVw3I4OvnzfC6dh4una9Au/9DQ68BxgYNg8mXuskzoPCTv/1xSv41FhUtB7+dS5c/Rgvtkzi609s4o1vnt0zSVUROW1KDkmvKK9v4d8r9vPw6nya2jqZOzyBT05OZcHYQYQHn8bSsLoS2Py4U8C6cB1gnQ4g2efD8PNg6DwIi+uxzyE9z6dOguQjrLW8vqOM3766i70VjUzJjOX7F45iSuYxvrfNNU63n10vQ84b0N4IwVEw/HwnWZR9HoTG9PVHkAFCY5H0pMqGVv78+h4eX3OA8KAAvnJuNp89M4uQQP8TP/lEqvc5S3A3PQ61ByAwDIae41wgG7FAM4r6OZ8ai3a8CE/dCF9czv25kfzilZ1suut8YsJ00UfEGyg5JL3qYGMbD63K5+n1BRQebCYsyJ8FYwfxycmpzM5OOLUOZ4c0VkLum5DzmnPfUgMYSD0Dhs2H7PnOchTVKfIqPnUSJMfV0dnFU+sK+fMbe6iob+WCMcl8d+FIspOOM8uwvRn2veMkina/Ck2V4BfgdP0ZdRGMWAixmX37IaRf01gkvSGnrJ5fL9nFW7vKSY0J5bsLR/KJCYPxO53znUO6uiB/Jex4yen8WHvA2T5ovJMszzjTKW6tpHm/4lNj0aq/O12Kv7uf3y4v51/L95Hzy0Xd6/wnIr1OySHpE11dlnX5B3l+YyH/21JCfUsHQxPC+fp5w7l4wuDTSxKBU8SxeIOTJNr7pjNt1XZBUCRknglDzoIhcyF5vJagucynToLkhJraOvj3iv383/J9NLZ1cMnEwdx27vCPn2Le1enMHNy92LlV7nG2J41xkkQjFznFrY8u9ipyBG8di4wx/sA6oMhae/HHHauxyHu9m1vJL1/ZyY6SOsakRPHFs4dy4fgUAv176BzEWqjY5Sy73/OaU6exqwMwzliYMRMyZjn3Mek9857SK7x1LDoZ3R6LXr0T1v8Xvl/Ed5/dwjt7Knj/++f1foAi0i1KDkmfa2nvZOmOMv6xLJddpfVkJ0Xw9fnDuWh8Ss9cWQNoPujMMtj/jtMqtirH2R4aC1lznc5nw87VTAMX+NRJkHRbdWMb/1qxj/++l0dze2f3kkSHVOZ6fiC9Cvnvge2EsAQYdSGMv9KZXaSksBzFW8ciY8w3galAlJJD/VtXl+X5jUX8/e1c9lU0khoTyudmZ3H19IwPujb2lLZG58LYgdVwYBUUrIW2emdfVOoHyaK0aZA4CgJDevb95ZR561h0Mro9Fj15PVTsga+u4fMPrqWsroVXvja39wMUkW5Rckhc09VlWbKtlLvf2ENOeQMjkyP5xvkjWDA2ueenl9YVO0mi/cth3zKnbTY4HZEOJYqy5kKwCuL1Np86CZKTVtXQyr9W7OehVU6SaNG4Qdw4K4sZQ+K6Ny40H3RmEO5e4tzaG50fRuMuhwlXwaBxvf8hpF/wxrHIGJMG/Bf4JfBNJYcGhq4uy1u7yrlvxT7W7K8mMiSAT09J55JJg5mYFt07S2q6OqFsGxx430kWHVgN9cXOPuMPCSMgeawzJqZMhPQZEHSKHWbltHjjWHSyuj0WPbAIAkPhhue49G8riQ4L4qHPT+/9AEWkW5QcEtd1dlle2VrC3W/sYZ+nQO2di0YxNauXCktbC5U5zvKzvW9B3kpobwK/QOfKWvZ8p2bRoPGgNdA9zqdOguSUVTW0cv/K/Tz2/gFqm9sZmRzJDbMy+eTk1O4XtW9rdBJEW55yvu9dHRCbBZlzIGu2s+Q0JlPfcx/ljWORMeYZ4NdAJPBtJYcGnk0FNfxrxT6Wbi+lvdOSFhvKRRNS+MSEwYwdHNV7tVeshdoCZ0lu2XYncVS6DeoKnf1+gc5y3CFzIWuOkywKDO2dWORDvHEsOlndHoushY5WCAxh9m/eYsaQOP501aRej09EukfJIfEaHZ1dPL2+kD+/vofy+lbOG53MHYs+pkBtj71xq3NFbe+bzoyDsm3O9pAY50QpdYpT5Dp1CkQk9W4sPsCnToLktDW3dfLy5mL+uyqP7cV1RAYHcPmUNK6fmXlyrW8bq2DH87B3GeS/68wwAmdW0bB5MPaTMORs8A/snQ8iXsfbxiJjzMXAhdbaLxtjzuE4ySFjzC3ALQAZGRlT8vPz+zRO6Rm1Te0s3VHKK1tLWJlTSUeXJTspgpvnDuGyyakEB/RRzbTmg1C4HvJWOLfijU7NRv9gyJjhdIEdeo4zu0h13HqFt41Fp+Jkz4ustYz60at85swsvn/h6F6MTEROhpJD4nWa2jp4YOV+7n1nH01tHSwal8Jlk1M5e0QiQQF9UDekvtSZUXRgNRRtgPIdTg0TgPjhTrHbURc56/Z1onTSfPEkSE6ftZYNB2p4aFUei7eW0N5pmTU0nhtmZXL+mOSTK/La1eUUcs1/15k5mPumU5sjNA7GXAJjP+VcOdf3e0DztrHIGPNr4AagAwgBooDnrLXXH+85GosGhpqmNl7dVspDq/LZUVJHclQwn589hGtnZBAZ0scJ65Y6Zxnavndg39tQvt3ZHhrr1G9Lm+ZcLBs8WUvxe4i3jUWn4mTHovqWdsb/ZCl3LhrFF88e1ouRicjJUHJIvFZ1Yxv/fDuXZzcUUd3YRkxYIBeOT+GTk1OZkhHbc8WrT6StEUq2QNE650dk3gpneUp4IoxYACMvdGYc6CSpW3zxJEh6VmVDK0+uLeCx9w9QVNNMUmQwV01L5/Iz0shKOIWaGe0tkPsGbH8Odr/q1CkKi4fhFzjdz7LnQ3Avz2CUPufNY9HHzRw6ksaigcVay4qcSu59Zy/v7a0iMjiA62dl8oU5Q4iPCHYnqPqyD+o15r8LB/Oc7cbPKWydNtVJGmXOVle0U+TNY1F3nexYlFfZyDl/eJs/fnoil09J68XIRORkKDkkXq+9s4sVORW8sLGY13eU0dzeSXpcKJ+eks7lU9JIjenjNfEttZDzutNCO+d1aK0D/yBnpsHwBTDiAogb2rcx9SO+eBIkvaOzy/L27nIeXp3PO3sqsBamZsZyxZQ0LpqQcmpX3NuaIOc12LUYcpZCS41TiyNrjpMIHrkQYjJ6/LNI3/PmsUjJIdlSWMO97+xlybZSQgL8uWFWJjfPHUpipEtJokMaq5yuaIduhWuc8yJwxsasuU49t7TpTtMPdYo8IW8ei7rrZMei9fnVXP7PVTz4uWmcM1IlG0S8hZJD0q80tnbw2vZSnt1QyLu5VRgDc7IT+PTUdC4Yk0xIYB8vA+loc6Zf5yyFPa9BVY6zPXaIp6ijp7Bj1OC+jcuL+eJJkPS+ktpmnt9YxLPrC9lb0UhIoB8Lxw7iyqnpzBwaf2ozDTs7oGC1U9R6z6tQletsTx7vLC8duQhSJunHTz+lsUj6g9zyev72Vi4vbS4m0N+Pa2dkcMtZQ0mJ9pJi0V2dToHrQ8t089+D5mpnX0iMswwtfbozw2jQRAiPdzVcb+SLY9Gr20q59ZH1/O+2OYxLje7FyETkZCg5JP1WQXUTz6wv5Jn1hRTVNBMS6MeZwxKYNyqJeSMTSYsN6/ugqvc5s4n2vQP5Kz+4mhY3zFmjHz/MmVV06BbWSx3ZvJi3nQQZY9KBh4BkwAL3WWvv+bjnaCzyXtZaNhXU8Mz6Ql7aXEx9SwdpsaFcMSWNK6aknd64UJnjJIp2L3GSRrYLIpKd7obZ853Crfrh029421h0KjQW+Y79lY38fVkuz28sostapmbGsnBcCgvGJrtzvnM8XV1QuceZUVSwxumOVrEL559XnCYAg8bDoAmQMsGpXRSV6tNdI31xLHr0/Xx+8Pw2Vt85n0HRIb0YmYicDCWHpN/r6rKs2lfF0u2lvLW7nILqZgCGJ0Vw7ugkLhiTzKT0WPz7qkbR4cA6nc5n+1c4V9PKtjttZDniuxWdDsPPd+qaDDnLJ9rGettJkDEmBUix1m4wxkQC64HLrLU7jvccjUX9Q0t7J69tL+XpdYW8u7cSgJlD4rls8mAWjkshOvQ0Cr02VjnLz3Jed2pxNB8EDAyeBMPOdeqQpU/3ie90f+VtY9Gp0Fjkew5UNfHcxkJe3VbKrtJ6AManRrNw3CAunpBCZvwp1F3rbc01ULLJqd9YugVKtzoJJNvl7A9LcJJEgyfDoHFOsigyxUm++we4GXmf8MWx6J43cvjzG3vY84tFfdNsRkS6RckhGVCsteyrbGTZrnKW7S7n/X3VdHRZEiKCOHdUEuePGcTc4Ql9v/zskI5Wp5hj9T6o2ussSdu7zCmAGxDqJIiGnev8qBw0fkC21fb2kyBjzIvA36y1rx/vGI1F/U/hwSaeXV/EC5uK2F/ZSJC/H/NGJXLppFTOHZV0emNCVycUb4K9bzpF6wvXOh0O/YOd7/KQs53vduoZA/I73V95+1jUHRqLfNv+ykZe217Kkm2lbC6oAZxE0cUTUrhoQop3zSg6WluTc9GsZBMUb3TG0IqdHySMwCl6HZ4EsZnO8rSMWZAxE8IT3Iq6V/jiWHTXi9t4cVMxm398QS9GJSIn67SSQ8aYhcA9gD9wv7X2N0ft/zMwz/MwDEiy1sZ49nUCWz37DlhrLznR++kkSE5WbXM7b+8u542d5by9q5z61g5iwwK5bkYmN87KJCnKC6aydrQ6M4tyljp1TQ51AgkIdX5MHlqvnzoVIpNdDbUnePNJkDEmC1gOjLPW1h3vOI1F/Ze1lq1FtbywsZiXtxRTUd9KZHAAC8YN4tJJgzlzWMLpzzJsrYf8VbD/HedW6vmnLjDcKdQ65CynJtmgCeDnUqJavHos6i6NRXJI4cEmFm8t4X9bSthS6Cxpn5AWzaxh8cwaGs+0rDjCg718Fk5bkzOjqL4U6ouhrsS5r9oLRRugs9U5Lj4b0mdA8jhIHuvc9+Mlvb44Fn3l0Q3sLK3jrW+d03tBichJO+XkkDHGH9gDnA8UAmuBa463FMMYcxsw2Vr7ec/jBmvtSfX+1kmQnI62ji5W7avikdX5vLGzjAA/w8UTBnPTnCHeVQyvttBZp1+wxlmzX7IFutqdfdEZTlHHtKnOiVHKpH435dpbT4KMMRHAO8AvrbXPHWP/LcAtABkZGVPy8/P7OELpaZ1dllV7q3hxUxGvbiulvrWDxMhgLp6QwiUTBzMpPQbTE3UwGqucGmT7lzvLTCt3O9uDozwJ4OlOEjhtqk/WIXOLt45FJ0PnRXIsB6qa+N/WYpbtKmdTQQ3tnZYAP3M4WTR7WAJnZMa6N4v6VHS0OrOLDqyCA6udGZpNlR/sj0h2Eu6HZmAnj+03dYx8cSy68v9WAfDUF2f1VkgicgpOJzk0C/iJtXaB5/GdANbaXx/n+PeAHx9aqqHkkLgpv6qR/7ybx9PrCmhs62RqZizXzsjgwvEp3ney1N7sJIgK1zq3ovWe2kVAUCRkznK6ovWTmQjeeBJkjAkE/ge8Zq3904mO11g08LS0d/LWrnJe3FTEsl0VtHV2kRoTyqJxg7hwQgqTeypRBM5V8f0rnB85hWucpRWHllIkjHCWTRxaPhE7pN/8wOlvvHEsOlkai+REmto6WJ9/kNX7qli1t4rNhbV0dlmCA/yYmhXLmcMSOHtEonddJOuuhnJn/CzbDuU7nOLXh5Lv4UkwbJ7TMTYmE6LTnM6xXlgHzhfHovl/fJuRgyL5x3VTejEqETlZp5McugJYaK39gufxDcAMa+1Xj3FsJrAaSLPWdnq2dQCbgA7gN9baF04UrE6CpKfVtbTz1NoCHn3/APsrG4kODeTyM9K4dkYG2UknlbvsW3Ulzg/LvBXOj8yqHGd7QKgz3Toh2/mRGT8cksdA4iivSRp520mQcX7x/xeottbe3p3naCwa2Gqb2nl9ZxmLt5awIqeC9k7L4OgQzhuTzJzsBGYOiycqpAdrB7U2ODU3DnX3ObAaWmqcfRHJzqyilEmQMtG5DYDlpd7A28aiU6GxSE5WQ2sHa/ZX8W5uFe/mVn6oqPX1MzO4ZGIqoUHecb5wSmqLYN/bsPet/2/vvoMju647j39vN3J3A93IOQ0m56ghRbEYRDGIQRJVXqVaqmwVvSXTklOtRdlFyZTX9nq3bLq82lqxKK7olYqkSUnUkEsxiGEp5kmYAEzCzCADg5wxiHf/eI0wwxkNwAHQ/Rq/T9Wr7n7vdeNeDPrM69P3nuvczh5dBJCS4SSKstZC9qwtrShiifjlGIs2/80r3L05nx98bsMitkpE5mupkkN/iZMY+uNZ+wqstU3GmHLgdeBma+3pSzxXUzlk0U1OWt4/08nPPqznlapWxiYs20tCTlHHjXnRUZvod+lrceoWtVQ68/U7TkFP3cxohHifswpI4fbw9JWdEMiNSFOj7SLIGHMd8FucGmhTlTC/a6198XLP0Qey5aN3eIzXwomid2o6GR6bwOsxbC5M47qKTK5flcXW4gVeDXFy0vn2u/49p3ZR8wHorJk57s9x3s9Fu6Bot3M/IYoLz0apaItFH4dikVytjoERXjzSwk/fr+PkuQECSXHcu62Qr+0upiI7EOnmXZ3JSeg+C31NTtKor9G57amDtuNOPaMpialO8r1guzPdt2C7s2raEiSMllssGh2fZNVf/5o//fQqvv3plYvcMhGZjyWZVmaMOQj8kbX23cu81k+AF6y1z/6un6mLIFkKHQMjPLu/kecONnG8tR9jYFdpOnduzuezG/NI9yVEuolzM3beWRWt9Qg07XOGW7cemalfFCx2PlgW7XLqF2WvW5L6RcvtIkhix8j4BAfre3inpoO3azo41NDDpIUMXwI3r13k1RDP98G5o9ByyKm70XzASQQDeOKcKaWFOyF/izPKKHOV6+qRLTXFIpEZ1lo+PNvFTz+o56WjLYxNWDYXBbl3WwF3bcon5JZrn/kY7ob2E86UtNajTlxtPTpznZScDoE8Z3U0X5ZzG8iFnI2Qtwn82QvSjOUWi1p7z7P771/jbz+3ga/tLlnklonIfFxNcigOpyD1zUATTkHqr1hrqy46bw3wElBmwy9qjAkBQ9baEWNMJvAecM/lillP0UWQLLWatn5eONzC84eaOd0+SLzX8Jn1uXx5ZzHXrsjAs5CjBZbC2HloDdcvavgA6j+AgVbnmCfO+ZYsrQiCRc6w64wKZ5W0jBUL9u3ZcrsIktjVOzzGWyfbebX6HG+caKP//DhJ8R4+uSKTaysyuXZFBqtzAosXJwY7Z97LDR84U9PGhpxjccmQu2FmKlruJmfqRFzi4rTFhRSLRC6tY2CE5w428ez+Ro639hPvNdy4OpvPby3g+lVZ0b/q2dUYH5lJFJ07CoMdMNge3jpgZNZCpoE8J77mbAjXNCqA1DwI5DuLC8zxumm5xaKjTb3c+a9v87++tp3bNkRmFLuIXNrVLmV/B/AIzlL2j1tr/4sx5mFgn7V2T/ic7wNJ1trvzHretcCPcKZweIBHrLU/vtLP00WQRIq1lmMt/Tyzv4FfHGiid3iM4vQU/sPOIu7enE9hKHnhitUuJWud4tb1H0BblbNSWm8j9DQ4w62npqUlBZ0h1lMjEzJXOQUeP8bIhOV2ESTLw+j4JB+c7eTV6nP89lQHZzsGAWdU0e4VGVy7IoNrV2RSmpGyeLFicsKZftZc6Ywwaql0itmPOjVF8MRD9ppwwmhL+EPNekjwLU57opxikciVVTf38YsDjTxX2UzHwAgJXg+7V2Rw85psblqTTVH6MpvSer7XGYXdcsiJry2HnGnAU9dLU7yJcP8bToy9guUWi/7fyXbue/xDnv1P17CjVCt0ikSTq0oOLTVdBEk0OD82wctVrTz5YT3vn+kCIC8tiR2l6ewqDbGjNJ1VOYGFrUESCRPjTqHrxn0zq6S1Vc9cAHniIb3MKXqdtRpufmhO35Itt4sgWZ6ae4Z593Qn757u4N2aTlr7zgNOrLimPMNZTroik/zgIq+cM1Vzo+WQM2qw5ZCzDXU6x43HSfbmbnTex1lrnQL2odKYn5amWCQyd+MTk+yt7ea1Y+d4/XgbZ8IJ8DW5Ae7anM/dm/OXX6JoysQYDJxz6j/2NUF/+Pa6P3NGEF3BcotFP9/fyJ8/c4g3/+IGSjOX55cTItFKySGRq1DbMchbp9rZW9vN3rNd0x8AE+M8lGX6WJHlpzzLR3mWj+3F6RRnuPzCaWQA2o45SaOOU07Nk84aJ2H0wN45vcRyuwgSsdZS2znkJIpOO8tJdw2OAlCe6eO6lZmLswra5RsEfc0ziaKWQ870id6GmXO8CeGVD1c5SaOp24yVEB/lBfrnSLFI5OM70z7A68fb+PXRVvbXdQOwrTjIPVsKuH1jLtmB2IgTS2G5xaJH3zrN3714nCPf/wyBpfg/T0Tm7HLxKLa/LhRZIKWZPkozffzHa0qx1tLYPcze2i6OtfRxpn2QquZeXqpqZWLSSbZeU57Blz9RzK3rc0iMc+FSsYl+KNrpbLNFYTJZJFoYYyjL9FGW6eOrnyhhctJy4lz/dGHrZ/Y18m/v1eH1GDYWpLGjJMSO0hDbS9LJCixCjSBjIK3A2dbcMbN/pN9J+LafgPbjzm3LITi2Z2bEoPE6I4umCl/nb1nWU9NElqvyLD/lWX6+8alyGrqGeP5wM3sqm/neniq+t6eKgmAymwrT2FiYxqaCIOvyUwmlxLtzCr4sqI6BURLiPPhjuXaVSIzRu1VknowxFKWnfGRY9ej4JLWdg7xS1cpTexv41pMHCaXE84Vthdy9OZ91+anEez0RavUC0cWeyJx5PIa1eamszUvlG58qZ3R8kgP13bx9qoMPz3bxb+/X8djbZwEoyUhhW3GI9fmprMtLZV1+KsGURVo1KDEQXsZ5+4X7x85D12knWXSuykkYnXoFKn82c04gD9LLnammobLw/fCWlLo47RWRqFCUnsI3b6jgmzdUcKK1nzdPtHGkqZcjTb38+mjr9Hm+BC/5wWQKQskUBJOpyPZz24Zc8tIWeXqtRJWOgRGy/IlKFIq4iJJDIgskIc7DqpwAq3ICfPOGCt6u6eCpvfU88W4tP377LIlxHjYVprGtOMTW4iDr89PIDya7v2aRiMxJQpyH3eUZ7C7PAGBkfIKjTX3sr+tib203757u4JcHm6bPLwgms6Egla3FIbYWBdlUGCQ5YRFHIsYnOaODctbDhi84+6anplXCuWqnrlHXWTj1m5kVEKf4spwkUajMqWU0e/PngMflyXERmbY6N8Dq3MD0456hUY429XG8tY+mnmGauodp6hmmsqGHnqExHn6hml2l6c50tA25hHyLlPyWqNExMEqGX//OIm6i5JDIIvB4DNevyuL6VVl0DIzw3ulODtb3cKC+m8ffOcvYW870rHivoSiUQnFGCqUZPrYWB7ltQ647p6KJyLwkxnnZXhJie0mI+6939nUMjFDd3Ed1Sx9VzX0caezh5apzAHg9hrV5AbaEE0WbCtNYmb3IRfEvmJr22QuPjQzMJIu6TkPXGed+7W/h8NPArGmocUlO0ihjhTPqaCqJlF4GqYUxXxRbJNYFUxKcumorMz9yrLZjkD2Hmnmusonv/vIID/3qKNdWZIaT3s6UNNUuij2dAyPkpOrfVcRNdDUmssgy/YnctTmfuzbnA84qaFXNfZw8109d5xD1XYPUdQ6xr7abn7xbS6Y/ga/sKuaru0v0n6rIMpPpT5xOLE/pHBihsqFnOsH83MFmfvp+PQDJ8V42FKSyPj+NFdl+KrL8VGT7yfQnLP5Q/kS/s/pZ7saPHhsfgZ4G6K6FntpwAumsU9j+1KswMTJzrvFCsGhmxFHGCkhfEU4glcZMYWyR5ao008e3bl7JH99UQVVzH3sONfP68TZ+e6p9upRhTmoia/NSyU1NIiuQSFYgkexAIkXpKazLS9XUJBfqHBhlXZ6mG4u4iZJDIkssKX5mtMBs1lrerungiXdr+dc3avifb57m9o15fHF7IVuKgqQla6UHkeUow5/IzWtzuHltDgCTk5YzHYMcaerhUEMvhxt7+Pd9DQyNTkw/Jy05ntW5AbYWBdlaHGRrcWhpk81xiZBZ4WwXm5yE/mZnpFF37YVb9XMw3D3rZANphU6SKFgCoZKZ+wXbwKu4KOIWxhg2FKSxoSCN796xlsGRcapb+jjc2MuRxh5OnhugqrmPzoERJmcNPKzI9vN7Owr5/NbCxSneLwvOWkvn4AiZ+vcScRUlh0SihDGGT63M4lMrs6jrHOT/vFfH0/saeP5QMwDlWT62FAXZUhTkmvIMVuYErvCKIhKLPB5DRbYzQujzWwsB50K8pfc8NW0DnG4foKbN+ZD1v9+p5UdvOSuQ5aclsakwyNq8VFbnBlibF6AolIJnqeueeTxOwietEMqu/+jxoa6ZqWqd4elqPXVQc1GdoweblBwScTFfYhw7S9PZWZp+wf6JSSex0N4/wpHGXp7Z38jfvXicf3zpBDeuyebebQVsKw6RrdHVUatveJyxCUuGakuJuIqSQyJRqCTDx1/fuY4/+8wqDtT1UNnQTWVDL2+d7OAXB5yCtZsK0/jidmcltEVb1UhEXMEYQ34wmfxg8gVT0kbGJ6hu7uNAfQ8H67s52tTLy9Wt01M5UhK8rMz2U5rpoyTDR1mmU/+sPMsfudGKKenOVrj9o8fGhqGnHnobnGltIhJzvB5DdiCJ7EAS6/PT+NKuYmraBnhmXwM/P9DEq9VOHbasQCIbC9LYkJ/KluIg167IJCleNRujQfuAM3U406+RQyJuouSQSBRLSYi7oMCjtZamnmFeqTrHs/sbeehXVfztC8f49LpsblmXQ2mG8wEvlBKv+fkiQmKc11ntrDgElAEwNDrOyXMDnGjt41hLP6fbB9hf182eQ83TSSOAwlAyG/LTWJ+fyvqC1Ol6IBGNLfHJkLXa2URk2ajI9vPgHWv5i1tXU9nQw9GmXo409XK0qZc3T7QxacGfGMct63K4c1Men1qZRUKcVkiMlE4lh0RcSckhERcxxlAYSuH3ryvj968ro6q5l5/vb+K5yiZePDIz3cKfGEdRegoV2X52lobYUZLO6txFXtVIRFwhJSFueorqbCPjEzR0DXG2Y4hTbf1UNfdR3dzHS1UzscWX4KUsy0d5pp8VWX7Ks3zTt/rGXkQWW7zX85GpaMOjE+yt7eL/Hm7hpapWfnmwidSkOG5ck01hKDk8Cskpcp0XTCY/LcJJ7mWgc3AUQEvZi7iMkkMiLrY+P431+Wk8eMcaznYMUt85RH3XzLb3bNd0zaJAYhzbSkLsLA2xrSTElqIgKQkKASLiSIzzUpEdoCI7wC3rcqb3D4yMc6ylj+Ot/ZxpH+B0+yAH6rt5/vDMSCNjoCCYPJ0oKs/ysyLTuc1JTdQHMRFZNMkJ3ulVHn/wuQ28U9PB84ebebemkxcGRpiYXd0aCKbEs7EgjY0FaWwqTGNjYVAJowXWoZFDIq6kT4YiMSDe62FVToBVFxWpttbS2D3Mvrou9tZ2s/dsF//9lXbAmdO/Ni/A9uIQW4qDrM5JZUW2j8Q4ffsvIjP8lykae35sgjPtg5zpGOB02yCn251i2B+e7WJ4bGblNF+Cl+IMH/lpSeSmJZGb6tzmpSWTF0wiPy2Z5ATFHRG5eglxHm5ck82Na7IBp7h11+Ao7f0jtPWfp6F7mKqmXg439vKjt85MJ44yfAlsmEoWhVdUy01NWvqC/QvIGHMb8C+AF3jMWvsPFx3/OvDfgKbwrv9hrX1sIX52x8AoxkAoRYsGiLiJkkMiMcwYQ1F6CkXpKdOrGvUMjXKwoYcDdd3sr+vmmf2NPPFeHQBxHkNZpo/VuQE2FKSxszSdTYVpxHs1b19ELpQU72Vdfirr8lMv2D85aWntO8/ZjkHOtA9wJjyqsbXvPJUNPdPTDWYLpsSTl+ZM95gqrJ0fdO4XhpIjX+tIRFzJ6zFkhaeUrePCWHV+bIJjLX0caerlSKNTw+iHb7QzNdAoKd5DaYbP2TJ9lGf52FwYpCLbH/XT9I0xXuCHwC1AI7DXGLPHWlt90alPW2sfWOif3zEwQiglgThdP4q4ipJDIstMMCWBG1dnc+Nq51u18YlJznYMcry1n+OtfZxo7aeyoYcXDrcAkBzvZVtJkE+UZbCrLJ1dpemu/iZNRBaXxzOzctonKzI/cvz82ARtfSM09w7T0jtMc895WnqHaek5T1PPMPvquukdHrvgOf7EOCqy/azK8bMyO8DKHD/XrMjQSEcR+diS4mcX7HcMj05Q3dJHdXMvtZ1D1HYMcrKtn9eOn2Nswska+RK8bA7XbdtSFOTaikz8iVH3kWoXUGOtPQNgjHkKuAe4ODm0KDoHRshUvSER14m6SCYiSyvO62FlToCVOQHu2pw/vb9jYIS9Z7v4ILz9829OkuFLYO9ffTqCrRURt0uK91KckUJxRsplzxkYGaelZ5imnmHqu4aoaRvg5Ll+Xj/exr/vawSg+uFbl6rJIrJMJCd42V4SYntJ6IL94xOT1HUNcaihh8qGHg7W9/DoW2cYn7S8/CfXszo3cJlXjJgCoGHW40bgE5c4715jzPXASeBPrbUNlzhn3joGRsnwqd6QiNsoOSQil5TpT+T2jXncvjEPgN6hMeq6BjW1Q0QWnT8xbjppfbGuwVFqOwdVUF9Elkyc18OKLGeVxi9sc6bpnx+b4GhTLxXZ/gi37mN7HnjSWjtijPlD4AngpotPMsbcD9wPUFxcPKcX/qff28zYxOQCNlVEloKurERkTtJS4tmUEox0M0RkmUv3JZDu03QFEYmspHgvOy4q1B9FmoCiWY8LmSk8DYC1tnPWw8eAf7zUC1lrHwUeBdixY4e91DkXK8nwzaetIhIlVCVMREREREQkduwFVhpjyowxCcCXgD2zTzDG5M16eDdwbAnbJyJRSCOHREREREREYoS1dtwY8wDwMs5S9o9ba6uMMQ8D+6y1e4BvGWPuBsaBLuDrEWuwiEQFJYdERERERERiiLX2ReDFi/Y9NOv+g8CDS90uEYlemlYmIiIiIiIiIrKMKTkkIiIiIiIiIrKMzSk5ZIy5zRhzwhhTY4z5ziWOf90Y026MqQxv35h17D5jzKnwdt9CNl5ERERERERERK7OFWsOGWO8wA+BW4BGYK8xZo+1tvqiU5+21j5w0XPTge8BOwAL7A8/t3tBWi8iIiIiIiIiIldlLiOHdgE11toz1tpR4Cngnjm+/q3Aq9barnBC6FXgto/XVBERERERERERWWhzSQ4VAA2zHjeG913sXmPMYWPMs8aYonk+V0REREREREREImChClI/D5RaazfhjA56Yr4vYIy53xizzxizr729fYGaJSIiIiIiIiIiv4ux1v7uE4y5Bvi+tfbW8OMHAay1f3+Z871Al7U2zRjzZeAGa+0fho/9CHjTWvvkFX5mO1A3xz5kAh1zPDeaqR/RRf24eiXW2qwI/ewFoVjkaupHdFEsugqKRa6mfkQXxaKroFjkaupHdIl0Py4Zj+aSHIoDTgI3A03AXuAr1tqqWefkWWtbwvc/D/yltXZ3uCD1fmBb+NQDwHZrbdcCdGjqZ++z1u5YqNeLFPUjuqgfMl+x8rtWP6KL+iHzFSu/a/UjuqgfMl+x8rtWP6KL+rG4rrhambV23BjzAPAy4AUet9ZWGWMeBvZZa/cA3zLG3A2MA13A18PP7TLG/AAnoQTw8EImhkRERERERERE5OpcMTkEYK19EXjxon0Pzbr/IPDgZZ77OPD4VbRRREREREREREQWyUIVpI6kRyPdgAWifkQX9UPmK1Z+1+pHdFE/ZL5i5XetfkQX9UPmK1Z+1+pHdFE/FtEVaw6JiIiIiIiIiEjsioWRQyIiIiIiIiIi8jG5NjlkjLnNGHPCGFNjjPlOpNszH8aYx40xbcaYo7P2pRtjXjXGnArfhiLZxisxxhQZY94wxlQbY6qMMd8O73dbP5KMMR8aYw6F+/E34f1lxpgPwn9fTxtjEiLd1rkwxniNMQeNMS+EH7uyH26iWBRZsRKLILbikWLR0lMsirxYiUexFItA8WipKRZFnmJRdHJDLHJlcsgY4wV+CNwOrAO+bIxZF9lWzctPgNsu2vcd4DVr7UrgtfDjaDYO/Lm1dh2wG/ij8L+B2/oxAtxkrd0MbAFuM8bsBv4r8M/W2gqgG/iDyDVxXr4NHJv12K39cAXFoqgQK7EIYiseKRYtIcWiqBEr8SiWYhEoHi0ZxaKooVgUnaI+FrkyOQTsAmqstWestaPAU8A9EW7TnFlr3wK6Ltp9D/BE+P4TwOeWsk3zZa1tsdYeCN/vx/lDL8B9/bDW2oHww/jwZoGbgGfD+6O+HwDGmELgs8Bj4ccGF/bDZRSLIixWYhHETjxSLIoIxaIoECvxKFZiESgeRYBiURRQLIo+bolFbk0OFQANsx43hve5WY61tiV8vxXIiWRj5sMYUwpsBT7Ahf0ID/GrBNqAV4HTQI+1djx8ilv+vh4B/jMwGX6cgTv74SaKRVHE7bEIYiYePYJi0VJTLIoybo9HMRKLQPFoqSkWRRnFoqjxCC6IRW5NDsU06ywh54pl5IwxfuDnwJ9Ya/tmH3NLP6y1E9baLUAhzjceayLbovkzxtwJtFlr90e6LRI73PIehtiIReD+eKRYJIvBTe9hiI145PZYBIpHsvDc8v6dolgUHdwUi+Ii3YCPqQkomvW4MLzPzc4ZY/KstS3GmDyc7GhUM8bE4wScn1lrfxHe7bp+TLHW9hhj3gCuAYLGmLhwNtcNf1+fBO42xtwBJAGpwL/gvn64jWJRFIi1WASujkeKRZGhWBQlYi0euTgWgeJRJCgWRQnFoqjimljk1pFDe4GV4QrfCcCXgD0RbtPV2gPcF75/H/CrCLblisLzJH8MHLPW/tOsQ27rR5YxJhi+nwzcgjMv9w3gi+HTor4f1toHrbWF1tpSnPfD69bar+KyfriQYlGExUosgtiIR4pFEaNYFAViJR7FQiwCxaMIUSyKAopF0cVVscha68oNuAM4iTPv8K8i3Z55tv1JoAUYw5lf+Ac48w5fA04BvwHSI93OK/ThOpyhiIeByvB2hwv7sQk4GO7HUeCh8P5y4EOgBngGSIx0W+fRpxuAF9zeD7dsikUR70NMxKJwX2IqHikWLfnvW7Eo8v2IiXgUa7Eo3HbFo6X7XSsWRb4fikVRukV7LDLhhomIiIiIiIiIyDLk1mllIiIiIiIiIiKyAJQcEhERERERERFZxpQcEhERERERERFZxpQcEhERERERERFZxpQcEhERERERERFZxpQcEhERERERERFZxpQcEhERERERERFZxpQcEhERERERERFZxv4/Nasria//XCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_with_sex\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_with_height\n"
     ]
    },
    {
     "data": {
      "image/png": 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2rARSv0SRDmEMsMVauw3AGPMqcBawdxFoAPBz789zgHdbM6CIm+obGlmXXcoi7yqfZem7KSivBSAiyJ/h3aM5ZVAiY1JjGJ7SieAAP3cDV5dA/kbI3wB5GyB/PRRtdwo/9ftMIQ6McFb+9JjkfI/r6xSLjnKVUpsoAqXE7BkTX8HI7tEupxGRjiohIpiGRsvuytp2sydYRNqYyiIiMhaRElqnfokiHUMSkLHX75nA2H2OWQmci7Nl7BwgwhgTa60tBIKNMUuAeuDv1tp3Wz6ySMvY08B5bVYp67JLWZ6+m2U7d1NR2wA4F0mO7RvPqO4xjOweTe+EcHcu3JblQs4qZxVPSSaUZDjfizOgsuCH4/yDncJO1+EQeRpEJkFkIkR0hU7dICLxqAs++9MmikDJ0SEYoyteIuKuBO9KxLzSGhWBRMQd2SvglYs4Nupv7ChKdDuNiPiG24GHjDFXAnOBXUCD977u1tpdxpiewJfGmNXW2h/tJzHGzARmAqSkpLROapFDqG9oZHlGMXM25LEsfTfrskopra4HwGOgb+cIzh2RzOjUGMb0iKFLVHDrBrTWKe5kLYOsFU7hJ3sVVOT9cExAKER1g6hk6DIEortDfBok9IdO3cHT+iuT2kQRKDjAjy6RwbriJSKu2rMdNa+smgFEupxGRDqkuL4ADArOY67eF4l0BLuAbnv9nuy97XvW2iyclUAYY8KB86y1xd77dnm/bzPGfAUMB35UBLLWPgE8ATBq1Cjb3H/E0SouLubll1/m+uuvP+zH3n///cycOZPQ0NAWSCbNraC8hrmb8vlyQx5zN+VTWl2Pn8cwKCmK04d2ZWDXSAYkRtK/SyQhga1YQNlT8MlbB9krYdcy2LX0h4KPxx/i+0PvE6DLYEgc4hR7QmNaZDXP0WgTRSBwlnalayWQiLgoIcK5uqAJYSLimoiuEBBKb082u4qrqG9oxN/P43YqEWk5i4E+xphUnOLPdODivQ8wxsQBRdbaRuA3OJPCMMZEA5XW2hrvMROB/2vN8M2luLiYRx555IiLQJdeeqmKQD6qvqGRFRnFfL0pn6825rN6VwkAceFBnDywC1P7JzCxdxxRIQGtF6pqN+Sucwo+uWud73nroWbPgBjjXJTpfTx0HQFJI6HzQAho5ZVIR6jNFIG6x4YyZ2O+2zFEpAP7YSWQikAi4hKPB2J7kVSfQUOjJbuk+vtpYSLS/lhr640xNwKf4oyIf8Zau9YY8ydgibX2fWAK8DdjjMXZDnaD9+FpwOPGmEbAg9MTaN2PXqQN+PWvf83WrVsZNmwYJ554IgkJCbz++uvU1NRwzjnn8Mc//pGKigouvPBCMjMzaWho4M477yQ3N5esrCymTp1KXFwcc+bMcftPEaC6roGvNubz0epsvtqYR1l1PR4DI1Kiuf2kvkzum8DArpF4Wrqfj7XOFK7sFZC1HHLWOAWfsuwfjgmOgoSBMORCSBjgFHsSBkBw290V0GaKQCkxoeSX1VBZW09oYJuJLSLtSEigHxFB/loJJCLuiutL9I5FgDM5VUUgkfbNWjsLmLXPbXft9fObwJv7edx3wOAWD9gK/v73v7NmzRpWrFjB7NmzefPNN1m0aBHWWs4880zmzp1Lfn4+Xbt25aOPPgKgpKSEqKgo/vWvfzFnzhzi4uJc/is6tuq6BuZucgo/n6/LpaK2gZiwQE4Z1IUp/RKY2CuOqNAWXu1TWwEZi2DHPMhc7Gzrqi527vMEQEIa9JzifE8Y6HyP7Opz27mOVpuppqTEOhPCMoqq6NclwuU0ItJRxUcGkVdWfegDRURaSmwfgta8TRC1pBdVMtHtPCLSsXz8a8hZ3bzP2WUwnPL3Jh06e/ZsZs+ezfDhwwEoLy9n8+bNTJo0iV/84hfccccdnH766UyaNKl5M8phsdayo7CSrzfm8fWmfOZvK6S6rpFOoQGcMbQrpw1JZHzP2Jbd0lxVDJlLYOe3zteupdBYD8bPWdEz8GxIHAZdhzmre/w7xuCXNlME6u69yrWzsEJFIBFpfQ31UJ5D5/AArQQSEXfF9cFg6e2Xq8mpItLhWGv5zW9+w09/+tMf3bds2TJmzZrF73//e44//njuuuuu/TyDtJTqugbmby3kyw1O4WfPYKcesaFcNKobx6V1ZkKvWAJaovDT2AhF22DXEkhfABkLnT4+WKdpc9cRMOEm6H4MpIyFoI5bU2gzRaAUbxFIE8JExBVL/wuzbie111t8m9P6oxxFRL4X1weAURGFZOh9kYi0tiau2GlOERERlJWVAXDyySdz5513cskllxAeHs6uXbsICAigvr6emJgYLr30Ujp16sRTTz31P4/VdrCWkVNSzRcbcvlyfR7fbi2guq6RkAA/JvaO5dpJqRzbN57u3l09zWr3TmdLV9ZyZ1tX9sofGjcHRUG30TDwHOg21mncHBTe/BnaqDZTBOoUGkBEsL+KQCLijsgkAHoGFvNOaTjWWkw72x8sIm1EbG8ABgfl8ZzeF4lIBxAbG8vEiRMZNGgQp5xyChdffDHjx48HIDw8nBdffJEtW7bwy1/+Eo/HQ0BAAI8++igAM2fOZNq0aXTt2lWNoZtJemElH6/J5uM1OazIKAagW0wI00enMLV/AmNTYwgOaOaLpmW5sH0ubP/a+V6807ndLwi6DHIaNycOg6QRzmh2jyZnHkibKQIZY+geG6plzyLijiinCJTiV0RVXQgVtQ2EB7WZU6iItCeBYRCZTG9Pti6OiUiH8fLLL//P77fccsv//N6rVy9OPvnkHz3upptu4qabbmrRbO2dtZbNeeV8siaHj9fksD7bWXEzOCmKX57cj5MHdqZXfHjzXiAty3EaOO/81vlesMm5PTgKekyC8TdCyjinebNfK46Pbwd8/xPM/Edgxcvws3l0jwljnff/cCIirSoyGYDOFAJJ5JVWEx6vZaUi4pK43nTNz6Skqo6SyrqWn6giIiIdirWWVZklfLI2h0/X5LCtoAKAkd2j+f1paZw8sEvzTqcsyYQd38LOec73oq3O7YERTrFn2CXQczJ0GQIetWY4Gr5fBALIXQ3l+XSLCWX2uhwaGi1+Hm3DEJFWFBoD/sHENuQDkFdWQ08VgUTkIIwxtwDXAgZ40lp7f7M9eVxfYtIXA5aM3ZVEhUY121OLiEjHtbuiljeWZvDSwnR2Flbi5zGM7xnLT45J5aQBnekcGdw8L1RZBFs+h21fOSt99mzvCoqC7uNh1E+g+0Sn6OPXNsoWbYXv/6+ZkOZ8z19P99ge1DVYskuqSI5uxqqjiMihGAORSUTV5gFoQpiIHJQxZhBOAWgMUAt8Yoz50Fq7pVleILYP/vUVxFNMelElg5JUBBIRkSO3IqOYF+bv5INVWdTWNzKmRww3Tu3NiQM60yk0sHlepGAzbPzY+cpYALYRQqKdYs/Y66DHROg8SCt9WljbKQLlrad77ADAaUSlIpCItLqoJEKqcgBnJZCIyEGkAQuttZUAxpivgXOB/2uWZ49zmkP39mSpX6KItIqOMBTDWut2hFZVVFHL+yt28cbSTNZmlRIW6MdFo7px6bju9OvSDCPU62ucnj6bZsPmT50R7gCdB8MxP4d+pzij29XEuVX5fhEovDMEd4K89XTr88OY+AnuphKRjigyGb/tXxPo5yGvrNrtNCLi29YA9xhjYoEq4FRgSbM9e1xfAAYF5ak5tIi0uODgYAoLC4mNjW23hSBrLYWFhQQHN9N2Jx9V19DI1xvzeXNpJl9syKWuwTIoKZI/nTWQc4YnERF8lD3mdu+EbXNg82fOVq/acmeCV+qxMO566HsydEpplr9FjozvF4GMgYQBkLeerp1CCPAz7NSbHRFxQ1QSpiybLuH+2g4mIgdlrV1vjPkHMBuoAFYADXsfY4yZCcwESEk5zDfEEV0hIJTB/vm8rvdFItLCkpOTyczMJD8/3+0oLSo4OJjk5GS3YzQ7ay1rdpXy9vJM3l+RRWFFLXHhgVwxvgfnjUwmLTHyyJ+8pswZ2b51Dmz98oeGzpFJztj2Pic7BaBA7eTxFb5fBAJI6A9r3sLPQHJ0KOla9iwibohMAttIn7By8suO4j+WItIhWGufBp4GMMb8Fcjc5/4ngCcARo0adXh7EDweiO1F77IsrQQSkRYXEBBAamqq2zHkMGWXVPHu8izeXpbJ5rxyAv08nDAggXOHJzO5XzwBfke4DauxEXbMdaZ4r3sf6qsgIBR6HANjroWeUyG+n7OgQ3zOIYtAxphngNOBPGvtoP3cHwW8CKR4n+8+a+1/mzVlwgCofgbKcugWE8rOoopmfXoRkSaJcq4M9QkuYU5prMthRMTXGWMSrLV5xpgUnH5A45r1BeL60nX3fHaVVVHf0Ij/kb6ZFxGRdqO0uo5PVufwzvJdLNheiLUwqns0fz1nMKcNTiQq9Ai3e1kLhVtg9Ruw4hUoSXcmeQ2dDoPOhW5jwT+oef8YaRFNWQn0LPAQ8PwB7r8BWGetPcMYEw9sNMa8ZK2tbaaMEN/f+Z63ju4xnVmRvrvZnlpEpMkikwDo4b+b/HJtBxORQ3rL2xOoDrjBWlvcrM8e24eoNW/j31hDdkk13WK01F5EpCOy1vLVpnzeXJLJZ+tzqa1vpEdsKLce35ezh3ele2zYkT1xdYmz1WvLF7D1CyhOBwz0mgon3A39T4OAkGb9W6TlHbIIZK2da4zpcbBDgAjjdAgLB4qA+uaJ5/X9mPgNdI9NpbS6nuLK2uYbVSci7YYxph/w2l439QTustbef9RPHuUUgZI8RRRV1FJb30igv668i8j+WWsntegLxPXBYOlhckgvqlQRSESkg2lstMxel8t/vtzM2qxSYsMCuXhMCmcPT2JoctSRNfGurYT178OKl2DHt2AbIDDC6esz4WZnoldU++ub1JE0R0+gh4D3gSwgArjIWtvYDM/7g7A4CIuHvHWk9D4PgJ2FlSoCiciPWGs3AsMAjDF+wC7gnWZ58uAoCIwgwRYAUFBeQ9dOuvohIi6J6wNAT5NNelElE12OIyIiraOh0fLxmmz+88UWNuaW0SM2lP87fwjnDE86sj4/1sKuZbD8BVjzFtSUQnQPOOZW6HU8dBsDfkc5NUx8RnMUgU7GmXhxHNAL+MwY8421tnTfA49qCkZ8f8jbQMrYH8bED+3W6Whyi0j7dzyw1Vq7s9meMSqJ6Po8APLLVAQSERfF9gagr18WOzU0Q0Sk3autb+TdFbt4/OutbM2voFd8GPdfNIzThyQeWV+43TudHj+r34D8DeAfAgPPhuGXQsoEZwiBtDvNUQT6CfB3a60FthhjtgP9gUX7HnhUUzASBsCKl0iJdj5waRKGiDTBdOCVZn3GyCTCS3IByNOYeBFxU2AYRCYzsDKf9/S+SESk3aqoqeeVRek8PW872SXVDEiM5KGLh3PKoET8PIe55auyCNa9C6teh/T5zm0p4+H0+2HQeRCsCbjtXXMUgdJxrrZ/Y4zpDPQDtjXD8/6vhP5QW05oVTbxEUHsLNSEMBE5MGNMIHAm8Jv93HfkqxKjkgjKXglAXln10cYUETk6cb3plZmti2MiIu1QeU09T32zjf9+u4OSqjrG9Yzh7+cN4dg+cYfX76e+BjZ9Cqtec7431jk7bY67EwZfANHdW+6PEJ/TlBHxrwBTgDhjTCZwNxAAYK19DPgz8KwxZjVggDus9TbMaE4JA5zveetJiYnQmx0ROZRTgGXW2tx97ziqVYmRyfhVFhBIHflaCSQibovrS9KORaTr4piISLtR19DIq4szeODzTRSU13LigM78bEovRqREN/1JrIWMhbDyVVj7DlQXQ3hnGPtTGHIhdBkCR9I4Wtq8pkwHm3GI+7OAk5ot0YF8PyZ+Pd1jprBgW2GLv6SItGkzaO6tYPD9hLD+oWXaDiYi7ovtQ3BjJYHV+ZRU1hEVqsadIiJtlbXOtK9/fLKBbfkVjOkRw5OX92f44RR/CrY4K35WvQbFOyEgFPqfDkMvgtQp4Nccm4GkLWs7/w8I6QQRXSFvPT3iTuOdFbuorK0nNLDt/Aki0jqMMWHAicBPm/3JI71FoJBS8kpVBBIRl8U5zaF7eZwtYYNDo1wOJCIih8tay9eb8vnPl1tYunM3veLDePLyUZyQltC0bV/VpbD6dVjxMuxaChjoORmm/AbSToegiBb/G6TtaFsVlIT+kL+efv0isBY25ZYzTBPCRGQf1toKILZFnjwqGYCeQcVsLFcRSERcFtcXgF4mi6355QxOVhFIRKStaGx0Vv48PGcLq3eV0DUqmHvOGcRFo7o1bdpX3npY/JSz5au2HDoPghP/DIPPh8iuLf8HSJvUtopA8Wmw5GnSEsIA2JBdqiKQiLQu70qgbn5F5JeqMbSIuCyiKzYglN4N2WzIKXM7jYiINIG1lo9WZ/PgF5vZlFtO99hQ/nHeYM4Znkyg/yGKPw31sOFDWPQk7JwHfkEw6FwYfS0kjVCfHzmktlUESkiD+mqSTR7hQf6szy51O5GIdDSBoRASTaIpIr+8Bmvt4U1nEBFpTh4PJrY3gwpymZuj90UiIr5uQ04pd723lkXbi+iTEM4D04dx2uDEQ6/8qSmDZS/AwkehOB06pcAJf4Thl0FYyyyAl/ap7RWBAE/+evp1iWG9rniJiBsik4lryKeuwVJcWUd0WKDbiUSkI4vrQ2rhd2zKLXc7iYiIHEBpdR33f7aZ5+bvICLYn7+dO5iLRnXD4znExcTiDFj0OCx9DmpKIWUCnPw36HcKePxaJ7y0K22rCBTfz/mev57+XabxwcosXYUXkdYXlURUznYA8spqVAQSEXfF9iG27m0Kykoora4jMlgTwkREfIW1lvdWZHHPrPUUlNcwY0wKvzyp38HfPzY2wrY5sPhp2PQxYGDg2TDuBkge2VrRpZ1qW0WgoAiISoG89fRPvpCXFqaTVVJNUqcQt5OJSEcSmUTYjvkA5JVV06+LJi6IiIvi+mCw9DA5bM4tY2T3GLcTiYgIkLm7kt++s4a5m/IZmhzF01eMYkhypwM/oLIIVrzkFH92b4fQOJh4K4z6ibP9S6QZtK0iEDhbwvI2MGC086FrQ3apikAi0rqikvCvLSGEao2JFxH3eSeE9TZZbMhREUhExG0NjZbn5+/g3k83AvCHMwZw2fge+B1o61d1Kcx/CL57COoqIGU8TP0dDDgT/INaMbl0BG2wCNQftn5J37hgADbklHF8WmeXQ4lIhxLpjInvagrJ15h4EXFbXF+s8TAoYBeb1C9RRMRVm3LLuOOtVSxPL2Zy33juOWcQydGh+z+4vsZZ9fPNfVBZCAPOhmN/CV0GtWpm6VjaXhEoPg0a64iozKBbTAjrNCFMRFpblDMmvkdAsVYCiYj7AoIx0akMq8jmfhWBRNodY8w04AHAD3jKWvv3fe7vDjwDxANFwKXW2kzvfVcAv/ce+hdr7XOtFryDKSiv4YHPN/PyonQig/3590VDOXtY0v771zY2wKrXYM5foSQDUifDCXdDkvr9SMtre0Ug74Qw8tbRv0s3NqgIJCKtLdIpAvUNLiGjrNrlMCIiQEIavbavYlNumYZmiLQjxhg/4GHgRCATWGyMed9au26vw+4DnrfWPmeMOQ74G3CZMSYGuBsYBVhgqfexu1v3r2jfqmobeHreNh77ehtVdQ3MGNON207oS2z4frZxNTbC+vec4k/BJkgcBmf+B3pNbfXc0nG1vSJQXF/AQN4G0hIH8sX6XKrrGggO0Hg8EWklkV0B6BFYzLIyrQQSER+QkEbcxo+pqKokv6yGhMhgtxOJSPMYA2yx1m4DMMa8CpwF7F0EGgD83PvzHOBd788nA59Za4u8j/0MmAa80vKx2z9rLe+u2MU/Pt5ITmk1Jw7ozB3T+tM7IXx/B8Pm2fDlXyBnFcT3hwufh7QzQUV7aWVtrwgUGAoxqZC3jrQBV9JoYXNuOYOTo9xOJiIdhX8QhCWQ7CkiX0UgEfEF8f3x2AZ6mmw25papCCTSfiQBGXv9ngmM3eeYlcC5OFvGzgEijDGxB3hs0v5exBgzE5gJkJKiKVSHUlFTz2/fWc17K7IY2q0TD84YzpjU/TTl31P8mXsfZC6C6B5wzuMw+ALwaBGDuKPtFYHA6QuUv4H+iZEArM8uVRFIRFpXVBKdywrIUxFIRHyBd7t8X5PJxpwyJvWJdzmQiLSi24GHjDFXAnOBXUDD4TyBtfYJ4AmAUaNG2eYO2J5szCnj+peWsr2ggttP6sv1U3rj2XfqV2MDrHsXvvkX5K6BqG5w+r9h+GXgF+BKbpE92mYRKCENNn1C90gPIQF+rM9RXyARaWWRScQUr6W8pp7K2npCA9vm6VRE2onY3mD8GBqUzQY1hxZpT3YB3fb6Pdl72/estVk4K4EwxoQD51lri40xu4Ap+zz2q5YM2969uTST37+7mvCgAF68ZiwTesX97wH1NbDyVfj2fija5rQyOfsxGHy+ij/iM9rmp5Yug8A24CnYQL8uEWzI1psdEWllUclE1HwJWHJKqukZv5/93yIircU/CGJ7MbQ8i/dy9b5IpB1ZDPQxxqTiFH+mAxfvfYAxJg4ostY2Ar/BmRQG8CnwV2NMtPf3k7z3y2Gqrmvg7vfW8tqSDMb1jOHBGcNJiNhr2211CSz5Lyx4FMpzIHEoXPgC9D8dPB73govsRxstAg1xvuesJi1xGB+vydEkDBFpXZFJBDRUEkkl6UWVKgKJiPvi+5NatoxNuWU0Ntofb08QkTbHWltvjLkRp6DjBzxjrV1rjPkTsMRa+z7Oap+/GWMsznawG7yPLTLG/BmnkATwpz1NoqXpsoqruO7FpazKLOGGqb247YS++Pt5CztlObDgEacAVFMKPafAOY9Cz6lq+Cw+q20WgaJTISAMclbTv8uxvLIog9zSGrpEqQmiiLSSKKevYqIpJKOo0uUwIiJAQhox6z/A1lWTXlRJj7gwtxOJSDOw1s4CZu1z2117/fwm8OYBHvsMP6wMksO0YFshN7y0jJr6Rp64bCQnDezi3NFQB/Mfgq/+AQ01MOBsmHgzdB3ual6RpmibRSCPx9kSlrOatDRvc+icUhWBRKT1RCYDkOK/m52FKgKJiA+I74/B0ttksTG3TEUgEZEjZK3l2e928JeP1tM9NpQnLhv1w+j39AXw4W2Qt87Z7nXSnyGmp7uBRQ5D292g2GUw5KyhX2fnDc76bDWHFpFW5F0JNDCsjHStBBKR/TDG3GaMWWuMWWOMecUY07JXqxIGANDHOyFMREQOX019A794YyV//GAdx/VP4L0bJjoFoMoieP8meOZkqC6F6a/A9JdUAJI2p20XgWrLiKreRVKnEDWHFpHWFd4FjIfeQcUqAonIjxhjkoCbgVHW2kE4vTymt+iLxvYCTwCjQnPZqObQIiKHraSqjiueWcTby3Zx2wl9efzSkUQEB8CWL+DhMbD8JRh/I9ywEPqf6nZckSPSNreDgVMEAm9z6K5s0Jh4EWlNfv4QkUiy327SCyrVnF5E9scfCDHG1AGhQFaLvppfAMT2ZlBFFs9qJZCIyGHJKq7iJ/9dzLaCcu6/aBhnD09yev98fg/M+zfE94dL34bEIW5HFTkqbXclUMIAMB7IWUP/LpFsza+guq7B7VQi0pFEJtHZFlBZ20BBea3baUTEh1hrdwH3AelANlBirZ3d4i+c0J8eDTvZXlBBTb3eF4mINMWGnFLOfeQ7soqrePYnY5wCUHE6PHuaUwAacTlcO0cFIGkX2m4RKCAE4vo6E8ISI2hotGzJK3c7lYh0JFFJRNXlAZBeVOFyGBHxJcaYaOAsIBXoCoQZYy7d55iZxpglxpgl+fn5zfPC8Wl0qskisLGKrXk6L4mIHMp3Wwq44NH5ALx+3Xgm9o6D9R/CY8dA7jo472k48z8QGOpyUpHm0XaLQOBtDr2a/l2cCWEbtPRZRFpTZBLBVbmAVV8gEdnXCcB2a22+tbYOeBuYsPcB1tonrLWjrLWj4uPjm+dVE/oD0NtksUl9gUREDurrTflc+d/FJHYK5u3rJ5AWFwizfgmvXeI0fL5uLgw+3+2YIs3qkEUgY8wzxpg8Y8yagxwzxRizwjsB4+vmjXgQXQZDaSapoTUE+XvYoAlhItKaopLxNFQTZ0o1Jl5E9pUOjDPGhBqnYdjxwPoWf9X4NADS/DJ1cUxE5CC+21rAzOeX0DshnDd+OoGuDVnw9Imw6AkYdwNcNVuTv6Rdakpj6GeBh4Dn93enMaYT8AgwzVqbboxJaLZ0h+JtDu2Xt4Z+XSJYr+bQItKaolMBGBZeTLqKQCKyF2vtQmPMm8AyoB5YDjzR4i8c0xP8AhkdlMfHWgkkIrJfS3YUcfWzS+geG8qL14wlasu78OGtToP9Ga9Cv1PcjijSYg65EshaOxcoOsghFwNvW2vTvcfnNVO2Q+v8w4Sw/l0iWJ9dhrW21V5eRDq42N4ADA8t1HYwEfkRa+3d1tr+1tpB1trLrLU1Lf6ifv4Q24cB/lls1EogEZEfWZFR7GwBiwrmpYt7EvPZrfD2NdB5EFw3TwUgafeaoydQXyDaGPOVMWapMebyZnjOpgmPh4hEyFnNwK5RFFXUklVS3WovLyK+yRjTyRjzpjFmgzFmvTFmfIu8UHR3MH70C8hlp4pAIuIrEtLoVr+TXcVVlFXXuZ1GRMRnrM0q4fKnF9IltJH3hswn/unxsPJVmPQLuPIjiEp2O6JIi2uOIpA/MBI4DTgZuNMY03d/B7bIFAxvc+hh3ToBsCK9uHmeV0TasgeAT6y1/YGhtFQfDr8AiO5Bd7LJL6uhsra+RV5GROSwJPQnsiabMKq0GkhExGtnYQVXPDWfi/y/5hPPbUR893foORluWAjH3+WspBTpAJqjCJQJfGqtrbDWFgBzcT50/UiLTMHoPAgKNpIWH0igv4cVGbub53lFpE0yxkQBxwJPA1hra621xS32grG9SajNACCjqKrFXkZEpMm8zaH7mF2s3lXichgREfeVVNbxy2c+4fnGX/O7+ofx75QEP/kYpr8EcX3cjifSqpqjCPQecIwxxt8YEwqMpTWmX+zRZTA01hNYtImBXSNZkVHcai8tIj4pFcgH/muMWW6MecoYE7b3Ac26KjG2N+GV6Rga2VlYcXTPJSLSHBKcItCo0BxW6n2RiHRwtfWN3PXsB/yz/A76BeTDeU/DNZ9D9wluRxNxRVNGxL8CzAf6GWMyjTFXG2OuM8ZcB2CtXQ98AqwCFgFPWWsPOE6+2XUZ4nz3bglbvauEuobGVnt5EfE5/sAI4FFr7XCgAvj13gc066rE2F741VfRmd1qDi0iviG6B/gHMyY8j1WZWgkkIh2XtZb/vPoev8u9jYTAOvyu/AAGnw/GuB1NxDWH3PhorZ3RhGPuBe5tlkSHKyYVAsK8RaDj+O+3O9iYU8agpChX4oiI6zKBTGvtQu/vb7JPEahZeSeEDQzKVxFIRHyDxw/i+tCvZhfbcisoqaojKiTA7VQiIq3u7Q/e5+rNN+AfGELQtR9BQn+3I4m4rjm2g7nL4wedB0LuGoZ3iwbQljCRDsxamwNkGGP6eW86HljXYi/oLQKNCCtgZ6GKQCLiI+LT6FKzHYDVWg0kIh3Qgi/e4eSlM6kLjCLsZ5+rACTi1faLQPD9hLBu0cHEhAWqCCQiNwEvGWNWAcOAv7bYK0UkQkAo/QNyydBKIBHxFQn9CarMIZIKVmYWu51GRKRVZSz+iGFzr6XIvzMRP/sME5PqdiQRn9E+5uB1GQxLnsaUpDOsWycVgUQ6OGvtCmBUq7yYxwMxvehek03G7koaGi1+Hu0zFxGXJQwAYHJ0oZpDi0iHUr3xcxI+upKdpivRMz8mOCbJ7UgiPqWdrAT63+bQW/PLKa2uczeTiHQcsb3oXJtJXYMlp7Ta7TQiItB1BAAnhG9Xc2gR6Ti2fYXfqzPY1tiF3ee/SXxnFYBE9tU+ikAJaWA83xeBrIVVGXrDIyKtJLY34VW7CKBeY+JFxDdEdIa4voxoXENOaTW5KlCLSHu37WvqX7yILQ2dmTPuacYO6ut2IhGf1D6KQIGhENsHclYztFsnAFZk7HY3k4h0HLG9MbaBbiaPdDWHFhFf0eMYupauxI8GbQkTkfZt+zc0vnwh2xri+Vfivfx02mi3E4n4rPZRBILvm0NHhQTQMz5MfYFEpPV4J4T19uRoTLyI+I7uE/GrK2ew3041hxaR9itvPfbli0hvTOB6vz/wl0umqj+jyEG0nyJQ1+FQkgElu75vDm2tdTuViHQEsb0AGBpWwE4VgUTEV/Q4BoAzIreqL5CItE815fD6FZTbQC6svIM7p0+mc2Sw26lEfFr7KQL1nOx83/YVw7t1oqC8lszdVe5mEpGOITQGQmJIC8jTmHgR8R0RXSC2DxP9N7JSF8dEpL2xFj68DVuwiZmV13PelFFM7hvvdioRn9d+ikAJAyEsAbZ+ybBu0QDaEiYirSe2Nz3IZqd6AomIL+kxkZ5VqyivrmWHzk8i0p4sew5Wv84zAdPJjRnDrSf0cTuRSJvQfopAHg/0nALbvqJ/lzCC/D0qAolI64nrQ+e6TEqq6iiprHM7jYiIo8ckAuvLGWB2sEp9gUSkvcheBbN+xc5O4/hL2Wn86axBBPn7uZ1KpE1oP0UggF7HQWUBAflrGZQUpSKQiLSe2F6E1eYTSrWaQ4uI7+g+EYBJARv0vkhE2ofqUnjjCuqDY7iw4CpOH5rMMX3i3E4l0ma0ryJQzynO961zGNatE2t2lVDX0OhqJBHpILwTwlJNDjuLKlwOIyLiFZkIMb04PniTmkOLSNtnLbx/E3b3Tv4W9ksq/Tpx52lpbqcSaVPaVxEoMhESBnj7AnWipr6RDdllbqcSkY7g+yKQ+gKJiI/pcQyDGtaxbtduXRwTaYOMMdOMMRuNMVuMMb/ez/0pxpg5xpjlxphVxphTvbf3MMZUGWNWeL8ea/30zWzjLFj3LhsH3MLT6V34xUl9SdA0MJHD0r6KQAA9p0L6AoZ3CQRgRcZulwOJSIcQ0xOAgUGaECYiPqbHJIIbyunZsJ1Nubo4JtKWGGP8gIeBU4ABwAxjzIB9Dvs98Lq1djgwHXhkr/u2WmuHeb+ua5XQLaW+Bj79HQ1x/bhq01gGdo3k0nHd3U4l0ua0vyJQr+OgoYak0hXEhQeyXPvfRaQ1BIRAVDfSAvO0EkhEfEsPpy/QOM96VmZoS5hIGzMG2GKt3WatrQVeBc7a5xgLRHp/jgKyWjFf61nwKOzezqvRPyO7vIF7zhmMv1/7+zgr0tLa37+a7hPALxCzzekLpCaIItJqYnuRanLUGFpEfEtkV2xMT44J2KAJYSJtTxKQsdfvmd7b9vYH4FJjTCYwC7hpr/tSvdvEvjbGTGrRpC2pLBfm3ktl6onctbYzM8akMKxbJ7dTibRJ7a8IFBgKKeNg6xyGp0SzLb+CwvIat1OJSEcQ25vOdZlkl1RSW6++GyLiO0yPYxhjNrA6o8jtKCLS/GYAz1prk4FTgReMMR4gG0jxbhP7OfCyMSZyf09gjJlpjFlijFmSn5/fasGb7Is/QX0NjwRehZ/HcOvxfdxOJNJmtb8iEDhbwvLWckyXBgAWbNMbHhFpBbG9CW4oo5MtI10TwkQ6NGNMv72asa4wxpQaY251LVD3Ywiz5Zj8dVTVNrgWQ0QO2y6g216/J3tv29vVwOsA1tr5QDAQZ62tsdYWem9fCmwF+u7vRay1T1hrR1lrR8XHxzfzn3CUdi2DFS9RMfxanlhjOH9ksppBixyF9lkE6jkVgEHVSwkP8ufbrQUuBxKRDmGvCWHrNZlQpEOz1m7c04wVGAlUAu+4FsjbF2gM61iTpb5AIm3IYqCPMSbVGBOI0/j5/X2OSQeOBzDGpOEUgfKNMfHextIYY3oCfYBtrZa8OVgLn/wGwuJ4zJ5LfWMjPz22p9upRNq09lkE6jIEQmPx2/41Y1Nj+G6LikAi0gpiewHQxy+HddmlLocRER9yPM6Enp2uJYhKpqFTD8Z51rF4h1ZIi7QV1tp64EbgU2A9zhSwtcaYPxljzvQe9gvgWmPMSuAV4EprrQWOBVYZY1YAbwLXWWvb1glgzVuQsYDKSb/jmSWFnD6kK91jw9xOJdKm+bsdoEV4PNBzCmybw4Sxd/DFhjx2FVeR1CnE7WQi0p5FpYAngOHhhczKUhFIRL43HeeDmav8Uo9hXPG7vLqtAKb0djuOiDSRtXYWTsPnvW+7a6+f1wET9/O4t4C3WjxgS6mvgc/uhi5DeKZ8PBW1W/nZlF5upxJp89rnSiBw+gKV53JcjNPYTKuBRKTF+flDTCppAXlaCSQiAHi3b5wJvLGf+1q3EWuPSURSTkn6Shoabcu/nojI0Vj3HpRmUjPlTp6Zn8HUfvGkJe63r7WIHIb2WwTy9gXqXryQ2LBAvtta6HIgEekQYnuT3JhFflkNeWXVbqcREfedAiyz1ubue0erN2LtPgGAQXVr2ZijvmUi4uMWPw0xPXm5oBdFFbVcP1UrGEWawyGLQMaYZ4wxecaYNYc4brQxpt4Yc37zxTsKUUkQ1w/Ptq8Y3yuWb7cU4GyNFRFpQbG96FSVjodGNYcWEXBGN7u+FQyATinUh3dltGcDi7br4piI+LCcNZCxgPoRV/HkNzsY3SOa0T1i3E4l0i40ZSXQs8C0gx3g7Tr/D2B2M2RqPr2mws5vOTY1nLyyGrbml7udSETau9g+eBprSTb5rFNfIJEOzRgTBpwIvO12lj38Uycyzm8ji7e3rd6wItLBLHka/IP50DOVrJJqrlcfM5Fmc8gikLV2LnCodwo34TQdy2uOUM2m1/FQX82U4C0A2hImIi0vcSgAU8IzWK++QCIdmrW2wloba631nZnsKeOJo5isHeu0QlpEfFN1Kax6HTvwXP4zv4C0xEim9GuFLbMiHcRR9wQyxiQB5wCPHn2cZtbjGPALIj73G5I6hfCtmkOLSEvrPBD8Q5gUslPNoUXE93j7AvWuWsXOwkqXw4iI7Meq16C2nPXJF7A1v4JrjknFGON2KpF2ozkaQ98P3GGtbTzUga0+BSMwFHpMxGz5nIm9Y5m/tVDTMEQ6AGPMDmPMamPMCmPMklZ9cb8A6DqcgXYT2/LLqaptaNWXFxE5qLh+NAR1YrTZyCJtCRMRX2MtLHkGEofy6q54gvw9nDyoi9upRNqV5igCjQJeNcbsAM4HHjHGnL2/A1t9CgZA7xOgYBMnJNZQWl3P2izfWZEtIi1qqrV2mLV2VKu/cvJIulRswN/WsTFXzaFFxId4PHh6jGes/yYW7VARSER8TPp8yFtHw8irmbUmh+PTEggP8nc7lUi7ctRFIGttqrW2h7W2B/AmcL219t2jfd5m0/tEAMbZFYD6AolIK0gejaexjjSzU82hRcTnmJQJdCebrdu2uh1FROR/LX4agqJYFD6VgvJazhjS1e1EIu1OU0bEvwLMB/oZYzKNMVcbY64zxlzX8vGaQVwfiEohMuMr+iSEqy+QSMdggdnGmKXGmJmt/urJowEYF7iNddlafSgiPsbbF6hLyQpyS6tdDiMi4lWeB+veg2EX897aYsIC/ZjaP8HtVCLtziHX1llrZzT1yay1Vx5VmpZgDPQ+Hla/wbEDfsVLS7OpqW8gyN/P7WQi0nKOsdbuMsYkAJ8ZYzZ4Jx0CTn8yYCZASkpK8796ZFeI6Mqkuh38O1vbwUTExyQOpcE/hDH1G1i0vYgzhupKu4j4gOUvQGMdtcOv5OPH0zlpYBeCA/SZTaS5NUdPIN/X50SoLWda1E6q6xpZnl7sdiIRaUHW2l3e73nAO8CYfe5v+f5kyaMY0LiJ9dmlNKohvYj4Er8ATPJoxvptZLH6AomIL2hsgCXPQuqxzCuOpqSqjjOGJrqdSqRd6hhFoNRjwePPkOrFeIz6Aom0Z8aYMGNMxJ6fgZOANa0eJHk0MbVZhNQWsbNIY5hFxLd4uk+gv0lnzbZMt6OIiEDmEihJh+GX88HKbKJCAjimdysNEhLpYDpGESgoAlLGE7RjDoOTovhOfYFE2rPOwDxjzEpgEfCRtfaTVk+R7AwlG+bZoubQIuJ7uo/HQyMR+csoqaxzO42IdHSbPwXjR3WP4/hsXS7TBnYh0L9jfFQVaW0d519W7xMgdw0np1hWZBRTWq03PCLtkbV2m7V2qPdroLX2HleCJA7DGj9G+G1Vc2gR8T3Jo2k0/oz2bGDJTm0JExGXbZoNKeP4Kr2W8pp69SoTaUEdqwgEnBqylvpGy5wNeS4HEpF2LTAU03kgE4K2ayWQiPiewDBIHMJYz0YWqS+QiLipJBNyV0Ofk/hgZTZx4YGM6xnjdiqRdqvjFIE6D4SIRLrvnk98RBCfrs1xO5GItHfJo0lr3MyGrN1uJxER+RFP9wkM9Wxl+bZct6OISEe2eTYAlT1O4IsNuZw6OBF/v47zMVWktXWcf13eUfFm2xympcUyZ0M+1XUNbqcSkfYseTTBjZWEl++gsLzG7TQiIv+r+wQCqcOTvYyqWr0nEhGXbJoNnVL4LL8T1XWN2gom0sI6ThEInC1h1SWc3zmXqroGvtmsBtEi0oK8zaGHezazPrvM5TAiIvtIGQ/ACLuB5elasSgiLqirgu1fQ5+T+WBVDolRwYxMiXY7lUi71rGKQD2ngPEwqGoxkcH+fLJGW8JEpAXF9KIxuBPDzBY1hxYR3xMaQ0NcP8b4beSrTflupxGRjmjHPKirpLLH8Xy9KY/TBifi8Ri3U4m0ax2rCBQSDclj8Nv2BcendeaLDbnUNzS6nUpE2iuPB0/yKMYEbFNzaBHxSX7dJzDGbxNfrc92O4qIdESbPoWAUOY3DqSuwXLCgM5uJxJp9zpWEQigzwmQtZyzUi3FlXUs2q6JGCLSgpJG0dOmsz1LKw9FxAd1n0CorcS/YD0ZRZVupxGRjsRa2PwppE5m7rZSQgL8GJ7Sye1UIu1exysCDToPgImVXxIc4OETTQkTkZaUPBoPlvDCNWpGLyK+p8ckAKZ4VvLlhjyXw4hIh5K/EYrToe9JzNtSwNieMQT5+7mdSqTd63hFoJie0G0cAWteY3KfOGavzaWx0bqdSkTaq6QRAAxlM6t3qS+QiPiYyEToOoLTg5arCCQirWvTJwDkdjmWrfkVHNM7zuVAIh1DxysCAQydDgUbuSi5iJzSalZmFrudSETaq9AYGqJ7MdyzhXmaSCgivqj/qQxo3MSWbVuorK13O42IdBSbZ0PnwczNCQJgoopAIq2iYxaBBp4NfkFMrPgcf4/h07W5bicSkXbML2UMo/y38u1mTd8RER/U7zQAjrVL+G5LocthRKRDqNoN6Qug70l8u6WAuPBA+neJcDuVSIfQMYtAIdHQbxpB69/imJ5RfLo2B2u1JUxEWkjyKKJtMYWZmyirrnM7jYjI/0pIw3bqwSn+S/lyo7aEiUgr2PIF2AZsn5OYt6WQib3jMEaj4UVaQ8csAgEMnQGVhVyRsIXtBRVszit3O5GItFepkwGYYFaxcJsmEoqIjzEG0/80xpu1LFy/UxfGRKTlbZ4NITFs9O9HQXmNtoKJtKKOWwTqfQKExjK+/HOMgU/XaEqYiLSQ2N7YqBSO81/JvC3qCyQiPqj/qQRQR9/yRWzIKXM7jYi0Z40NsPkz6HMi87buBlBTaJFW1HGLQH4BMOh8grd+yqRkf42KF5GWYwymz4lM9KxlweZst9OIiPxYt3E0Bsdwot9STQkTkZaVuxaqiqDX8czbUkDP+DC6dgpxO5VIh9Fxi0DgTAlrqOHa2FWszSplS56ufIlIC+l9AsG2mujCZeSUVLudRkRagTGmkzHmTWPMBmPMemPMeLczHZCfP55+0zjRfwXfbMhyO42I7MUYM80Ys9EYs8UY8+v93J9ijJljjFlujFlljDl1r/t+433cRmPMya2b/AAyFgJQmzSWhduKtApIpJV17CJQ1+EQ149xZbMJ8DO8tDDd7UQi0l6lTsJ6ApjsWcm32hIm0lE8AHxire0PDAXWu5zn4PqfSoQtx5OxgOLKWrfTiAhgjPEDHgZOAQYAM4wxA/Y57PfA69ba4cB04BHvYwd4fx8ITAMe8T6fuzIWQXgXlpeEU1XXoCKQSCvr2EUgY2DodAJ2LeLSvo28tTSTqtoGt1OJSHsUFAEp4znef5X6Aol0AMaYKOBY4GkAa22ttbbY1VCH0us4Gv2COMGzlK835budRkQcY4At1tpt1tpa4FXgrH2OsUCk9+coYM9yvrOAV621Ndba7cAW7/O5K2MhdBvDvK2FeAyM6xXrdiKRDqVjF4EAhlwIGK6KXERpdT0frtISaBFpGabPCfQhnU2bN2r6jkj7lwrkA//1btF4yhgT5naogwoMw/Scwsn+y5izPtftNCLiSAIy9vo903vb3v4AXGqMyQRmATcdxmNbV1kuFO90ikBbChjarRORwQGuRhLpaFQEikqG1Ekkp79H77gQXl6kLWEi0kJ6nwjAwKrFbM4rdzmMiLQwf2AE8Kh3i0YF8D+9PIwxM40xS4wxS/LzfWPljel/Gknkkb1pCQ2NKlaLtBEzgGettcnAqcALxpjD+pzXauejzEUAlCeMZGVGMZO0FUyk1akIBDD8cszuHdzRK53l6cWszSpxO5GItEcJaTSEJzLFs5JvNmtLmEg7lwlkWmsXen9/E6co9D1r7RPW2lHW2lHx8fGtHnC/+p2CxTC2diHL0ne7nUZEYBfQba/fk7237e1q4HUAa+18IBiIa+Jj8T6udc5HGQvBL5DvKpNptDBRRSCRVnfIIpAx5hljTJ4xZs0B7r/E24V+tTHmO2PM0OaP2cIGng2dUpia/wJB/oaX1SBaRFqCMfj1OYFj/dawYHOO22lEpAVZa3OADGNMP+9NxwPrXIzUNOEJNCaN4mT/pby7fL+fFUWkdS0G+hhjUo0xgTiNnt/f55h0nHMMxpg0nCJQvve46caYIGNMKtAHWNRqyfcnYxF0Hc687aWEBvoxPCXa1TgiHVFTVgI9i9NN/kC2A5OttYOBPwNPNEOu1uUXABNuxj9rCbf2zuPd5bsor6l3O5WItEd9TiScSqq2L6SuodHtNCLSsm4CXjLGrAKGAX91N07T+KWdxkCznUUrV1Fdp4EZIm6y1tYDNwKf4kwYfN1au9YY8ydjzJnew34BXGuMWQm8AlxpHWtxVgitAz4BbrDWuvePur4GspY7/YA2FzA2NYZAf21MEWlth/xXZ62dCxQd5P7vrLV71gsvwFlm2PYMvxTCErik9k0qaht4b4WufolIC0idTKPxY1zjMpanF7udRkRakLV2hXd7xRBr7dl7vV/ybWnO58oT6ubyxfo8l8OIiLV2lrW2r7W2l7X2Hu9td1lr3/f+vM5aO9FaO9RaO8xaO3uvx97jfVw/a+3Hbv0NAGSvhIZaSmKHs62gggm9tBVMxA3NXXq9GnD35HKkAkJg/PVEZn3DGfF5vLggXdN7RKT5hXSiMWk0kz0rNSpeRHxTbC9s94lcEjCHt5dqi7yINJMMZyfaGo+zS3Zot04uhhHpuJqtCGSMmYpTBLrjIMf43BSM/zHqagiK4vbQD1mfXcqKjGK3E4lIO+Tf90QGe3awduMmt6OIiOyXGXUVyeRSv2UO+WU1bscRkfYgYyF06s6K4mAA+idGuBxIpGNqliKQMWYI8BRwlrW28EDH+eQUjL0FR8KYa0nJ/YJBgTm8pAbRIm2WMcbPGLPcGPOh21l+pPcJAETnzKO0us7lMCIi+5F2Bg3BMczwfK4t8iJy9Kx1ikDdxrIuq5SUmFAigwPcTiXSIR11EcgYkwK8DVxmrW37l7XH/QzjH8wfYz/ng5VZuvol0nbdgtNA0fd0GUJdSByTzEq+VL8NEfFF/kH4jbyME/2W8uXilW6nEZG2rjgdynOh2xjWZZeSplVAIq5pyoj4V4D5QD9jTKYx5mpjzHXGmOu8h9wFxAKPGGNWGGOWtGDelhcWByOvYETJbOIb8/nPl5vdTiQih8kYkwychrNC0fd4PPj3OYEpfqv4cEWG22lERPZvxBX40cjIwg9Zm1XidhoRacu8/YCqOo9kR2EFAxKjXA4k0nE1ZTrYDGttorU2wFqbbK192lr7mLX2Me/911hro72d6IdZa0e1fOwWNv5GDPB/Xb/m5YXp7CyscDuRiBye+4FfAT47g930PZkoyqnZOpfiylq344iI/FhsL+q6T2GG/xzeXqIt8iJyFDIXQUAY6xq7YS0M6BrpdiKRDqu5p4O1D526wZDpjC/+iK5+xfxzdtvf5SbSURhjTgfyrLVLD3KM+03q+51CQ2AkF5gv+XRtjjsZREQOIWDsVXQ1hRSu+JC6Bp+tq4uIr8tYCMkjWZdbCagIJOImFYEO5NjbMY31/CfxE95fmcWaXVoGLdJGTATONMbsAF4FjjPGvLj3AT7RpD4gBM+w6UzzW8xXyze4k0FE5FD6nUpNcDyn133K1xt9cLKriPi+mnLIWfN9U+iokAC6RgW7nUqkw1IR6EBiUmH0NQzJ/4CRITn84xN9SBNpC6y1v/FuXe0BTAe+tNZe6nKs/TIjrySQepLT36WgXE3oRcQH+QXgP+pypvqt5MuFB1xgKSJyYFnLwDY4RaDsUgYkRmKMcTuVSIelItDBTP4VJjCCf8e+yzebC/h2S4HbiUSkPek8kKrOI7jIM4ePV2W5nUZEZL/8Rl2JB0vXbW+wu0I9zETkMGUsBKA+cSQbsku1FUzEZSoCHUxoDEy6jZSCuZwesZV/fLIBa63bqUSkiay1X1lrT3c7x8GEjLua3p4sNi/53O0oIiL71ymF8m5TucDzJa8u2Op2GhFpazIWQVw/dlQGUFPfSFqiikAiblIR6FDGXgeRyfwp9FVWZ+5m1mo1cBWRZjTwHGr8whia/y7ZJVVupxER2a+IidfS2RSz9ZvXKauuczuOiLQVjY2QuRi6jWFddhkAA1QEEnGVikCHEhACx/2emJK1XBu9kns/3UB1XYPbqUSkvQgMoybtPE7zLOSzpRvdTiMisn99T6YmsgeXNr7Ls/O2u51GRNqK4h1QtRuSR7Euq5QAP0PvhHC3U4l0aCoCNcWQC6HzYH7ueYWswhLu/VQf1ESk+UROvIZgU0f1slfcjiIisn8eP4KOvYVhnm0sn/chpVoNJCJNUbDZ+R6fxrrsUvokRBDor4+gIm7Sv8Cm8PjBSX8iuCKTB3st4el525m7SWNSRaSZJA4lPyKNSaUfkV5Q4XYaEZH9GzqD+pA4Lmt4l//O2+F2GhFpC/YUgeL6sC5LTaFFfIGKQE3V6zjodRwnF77AlLhSfvHGSgo10llEmon/6J+Q5slg0bez3Y4iIrJ/ASH4j7+OqX4rmTtvDiVVWg0kIodQsAlCY8lrCKWgvEb9gER8gIpAh+OUezEeP57kT4RX7uKOt1ZpWpiINIvosRdTZYKJWPuS21FERA5s9DU0+IdyScN7PKPeQCJyKAWbIdZZBQRoMpiID1AR6HDE9YbL3iWgvoL3Iv/BmvXreWlhutupRKQ9CIogo+spTKqZy/rtOq+IiI8KicZv1E84y+87Pp63iJJKrQYSkYMo3OxsBct2ikBaCSTiPhWBDlfiELjsHSIaSnkn/B889tF3bMkrczuViLQDiSfeQqipIfODv7kdRUTkwMb9DI/Hw0UNH/L0vG1upxERX1W1Gyryv+8HlNQphKjQALdTiXR4KgIdiaSRmEvfpLMp4lm/e/jdS19rbLyIHLWIHsNZHXsKxxa+QeYOTSEUER/VqRtm0PlcEjCHN79dw+6KWrcTiYgvKtjifI/ry/psNYUW8RUqAh2plHF4Ln6NVL887tr9W+595zu3E4lIO9DlnL9ggfz37nQ7iojIgU28mWBbzfkNH3P3+2vVI1FEfqxgEwBVkalsK6jQVjARH6Ei0NFIPRa/GS/Tzy+b89f8jPe+Xe52IhFp4+KTezM/YTrDd39K0eaFbscREdm/zgOhz0lcF/I5n67cwdvLdrmdSER8TeFm8ASwoSYGa9FKIBEfoSLQ0ep9AuaSN0j1y2PQ7IvZuFlbOETk6PQ69/cU2EjK3v816Oq6iPiqibcSWrebJ6Jf5O73VrGjoMLtRCLiSwo2Q0wq63KrADWFFvEVKgI1A7/eU6i+6A26mN2Ev3wGpdlqkigiRy4lsQtfdr6K7mXLqFj9odtxRET2r8dEmPo7Jld9zt2eZ7j5lWXU1je6nUpEfEXBZojry7qsUiKC/UmODnE7kYigIlCz6dR/Mhmnv0x4Yzl1T51MY8FWtyOJSBs26Myb2dqYSO3Hv4MGjWAWER917C/hmNu4gM84K/dh/jVbK6JFBOe9S9G278fDpyVGYoxxO5WIoCJQs+o/6jjmjn8aU19J1eMnwsrXoFFXxETk8A1IjuXDztcRXbWT2kXPuh1HRI6AMWaHMWa1MWaFMWaJ23lahDFw/N0w9jqu9v+YiO/+xrdbCtxOJSJu270TGutojOnNhuwybQUT8SEqAjWz00+exmOp/2FbTQS8MxOeOg52fOt2LBFpgyacehkLGtNo+PIeqCp2O46IHJmp1tph1tpRbgdpMcbAtL9TP+wKbvB/j9Uv/57C8hq3U4mImwo3A5AblEJVXQP9u0S4HEhE9lARqJkZY7hlxpn8POp+7vbcSENpDjx7Krx6CRRscTueiLQho1NjeSvuegLqSml4/xY1iRYR32UM/mfeT3Gf87iu8RXmPn4r9fUNbqcSEbd4x8Nvt10B6BEX5mYaEdmLikAtICzIn0cuHcXrdZO4MvxRGqb8DrZ9BY+MhY9+AWW5bkcUkTbitJOncV/dBfitfxeWPe92HBE5PBaYbYxZaoyZue+dxpiZxpglxpgl+fn5LsRrZh4PnaY/wfZu53BO2cuse/RSqK91O5WIuKFgM4TFs608AIDusaEuBxKRPVQEaiF9OkdwzzmD+GZHJf+qORNuWgYjroClz8KDw+DLe6C61O2YIuLjpvRLYGufq/jODqbx4zsgb4PbkUSk6Y6x1o4ATgFuMMYcu/ed1tonrLWjrLWj4uPj3UnY3Pz8Sb3qv3zZ5WqGFM4i//EzobrE7VQi0toKNkNsH9KLKgn099A5ItjtRCLipSJQCzp3RDLTR3fj4TlbmZNl4PR/wQ2LoO/JMPf/nGLQwse1xUNEDuquMwdzR+P1lDcGwZtXQV2V25FEpAmstbu83/OAd4Ax7iZqJcZwzDX38XDkz+mUt4jqJ06G0iy3U4m0WcaYacaYjcaYLcaYX+/n/n97G9CvMMZsMsYU73Vfw173vd9qoQs3Q1wfdhZW0C06BI9Hk8FEfMUhi0DGmGeMMXnGmDUHuN8YYx70npRWGWNGNH/MtusPZw6kf5cIbnttBVnFVRDbCy54Fq6dA50Hwse/gs/uVCFIRA6oW0wo048bw83VMyFvLcy+0+1IInIIxpgwY0zEnp+Bk4D9vpdqjwL9PVxw7R38IuC3NBTtoPHJ42HnfLdjibQ5xhg/4GGcFYUDgBnGmAF7H2Otvc3bgH4Y8B/g7b3urtpzn7X2zFYJXVkElYUQ14f0oipSYrQVTMSXNGUl0LPAtIPcfwrQx/s1E3j06GO1H8EBfjxyyQjqGyw3vLyMytp6546kEXD5+zD6WvjuPzD3PneDiohPu3ZSTzJiJ/Kq/5mw+ElY/6HbkUTk4DoD84wxK4FFwEfW2k9cztSqEiKCufLyq5lRdxeFlQ3w32nw/k3OB0QRaaoxwBZr7TZrbS3wKnDWQY6fAbzSKskOpMCZDGZj+5BeWEH3WDWFFvElhywCWWvnAgf7r/VZwPPWsQDoZIxJbK6A7UHP+HDuPX8IKzOKuejxBeSVVTt3GAOn/B8MmQ5z/gILHnM3qIj4rEB/D38+exB3lp9PTlh/eO8GKNzqdiwROQDvB7ah3q+B1tp73M7khhEp0cw463SOrfgbCxMvheUvwUOjYeVrWgUt0jRJQMZev2d6b/sRY0x3IBX4cq+bg70N6BcYY85usZR7804GKw7tTkVtg1YCifiY5ugJ1OQTU0d2yuBEnrx8FFvyyjnn4e/YnFvm3OHxwFkPQ//T4ZM7YMXL7gYVEZ81oVccpw/vziXFP6UBDzx3JhSnux1LROSgZoxJ4Zyxfblo+6l8PfVNiO4B78yE58+CnA6zQ06kNUwH3rTWNux1W3dr7SjgYuB+Y0yv/T2wWacVFm4Gv0C2N8QBqAgk4mNatTF0uxuFepiOT+vM6z8dT21DI+c++h3fbS1w7vDzh/OehtTJztX9da3Xs01E2pbfnppGXkASd0b+GVtT6nyIKstxO5aIyEH94YyBjOwezXWf1bL+tLfgtH9B9gp4bCK8fgXkrXc7ooiv2gV02+v3ZO9t+zOdfbaC7dWgfhvwFTB8fw9s1mmFBZshphfpu2sBjYcX8TXNUQRq8ompXY5CPUyDk6N45/oJdIkM5opnFvHW0kznjoBgmP4yJI1ypv8selLLpEWOgDEm2BizyBiz0hiz1hjzR7czNaf4iCB+dXI/Xk6P5pNhD0NZrlMIqihwO5qIyAEF+nt49JIRRIb4M/PFZewecBncshKO/RVs+QIeGQ9vXv19LxER+d5ioI8xJtUYE4hT6PnRFWNjTH8gGpi/123Rxpgg789xwERgXYsnLtgEcb1JL6oEnAEXIuI7mqMI9D5wuXdK2DigxFqb3QzP224lR4fy5s8mMKp7DL94YyX//mwT1loICodL3oBeU2HW7fDWNVBT7nZckbamBjjOWjsUGAZM856b2o1LxnZnSr94bvk2gO0nPwO7d8AL50BVsdvRREQOKCEymMcuHUluSQ03vbKc+sAoOO53cOsqOOZW2DgLHh4D8+7XhTARL2ttPXAj8CmwHnjdWrvWGPMnY8ze076mA69a+z//eNKAJd4G9XOAv1trW7YI1FDnvC+J68vOwko6RwYRHODXoi8pIoenKSPiX8GpKPczxmQaY642xlxnjLnOe8gsYBuwBXgSuL7F0rYjUSEBPHfVGM4fmcwDX2zm1tdWUF3XACGdYMZrcNydsPZtePI4yN/odlyRNsPbpH5P9TTA+9WuPk14PIZ/XjCU6NAArvoqmKpzn3e2Urx0AdSUuR1PROSAhqdE85dzBjFvSwH/96n3/U1oDJzwB7hlFQw4Cz6/G2b9EhobDvpcIh2FtXaWtbavtbbXnibz1tq7rLXv73XMH6y1v97ncd9Zawd7G9QPttY+3eJhi7ZDYz3E9iGjqJLuMZoMJuJrmjIdbIa1NtFaG2CtTbbWPm2tfcxa+5j3fmutvcF7UhpsrV3S8rHbh0B/D/eeP4RfntyP91ZkcelTCymqqHWaRR97O1z2DlQWwhNTYdUbbscVaTOMMX7GmBVAHvCZtXahy5GaXWx4EA9OH87OwgruWJmAPf8Z2LXUWRFUXeJ2PBGRA7pwVDcuH9+dJ+Zu4+E5W/h+4UJ4PJz3DEy4GRY/Ca9dBrWV7oYVkcNT6N3SGdeXnUUVpKgfkIjPadXG0PJjxhhumNqbhy4ezqpdJZzzyLdszfcuYug5Ba77BroMhrevgRfP06ogkSaw1jZYa4fh9CgbY4wZtPf97aVJ/diesfz8xL68vzKLV8uHwYXPQdYKp0dQ1W6344mIHNCdpw/gjKFduffTjdzw8jIqauqdOzweOOnPcMq9zvaw58+EikJ3w4pI03nHw1dH9SS3tEaTwUR8kIpAPuL0IV15deY4yqvrOfeR71iZUezcEdkVrvwQTroHMhY7jRNn/Qoqi1zNK9IWWGuLcfbAT9vn9nbTpP5nU3pzTO84/vD+WtZ3mgwXvQi5a+G5M/TBSUR8VoCfhwenD+N3p6bxyZocznnkW7YXVPxwwNiZcNELkLManj4R8ja4F1ZEmq5gC4R3JqPSH9BkMBFfpCKQDxmREs27N0wkKiSAS59eyIo9hSC/AJhwI9y8DEZe4SyRfnA4LHgM6mtczSzia4wx8caYTt6fQ4ATgXb76cHPY/j3RcOIDAnghpeXUd7jBJjxijNh57nTobztrnQSkfbNGMO1x/bkhavHkl9Ww5kPzePLDbk/HJB2BlzxgbPF9fFj4dsH1CdIxNcVbILYPuws1GQwEV+lIpCP6RYTyqszxxEdGshlTy1kefpeWzrC4uD0f8NPv4HEIfDJHfDAUJj/MNRWHPhJRTqWRGCOMWYVzljVz6y1H7qcqUXFRwTxwPRh7Cys5KaXl1Gfehxc/LoznePZUyF7pdsRRUQOaGLvON6/8Ri6RYdy9XNLeOjLzT/0Ceo2Bq5fAH1OhM/ugmemOSsNRMT3WOsdD9/n+/Hw3VUEEvE5KgL5oK6dQnh15jhiwgO57OlFLN25T2+PLoPg8vedxtGxveHT38K/B8HX92pEtHR41tpV1trh1toh1tpB1to/uZ2pNUzoFccfzhzInI35/OWj9dBzMlzyptNc/vHJ8N6NUJ7ndkwRkf3qFhPKWz+bwJlDu3Lf7E3c+MpyKmu9fYLC452truc+5XzAfOwYWPAoNDa6G1pE/ldlIVQXf18ECg/yJyYs0O1UIrIPFYF81J5CUFx4IFc8s4ilO/fpAWQM9DrO6Rd01WxIHg1z/uIUgz64FbKWu5JbRNxz2bjuXH1MKs9+t4PnvtsBPSbCTctg/A2w8hV4cATM+zfUVbsdVUTkR0IC/bj/omH8+pT+zFqdzfmPzmdXcZVzpzEw5AJnVVDqsfDJr50tYls+d1YfiIj7inc636NT2VlYQbeYUIwx7mYSkR9REciHJUaF8OrM8cRHBHHZ04t4et526hv2c9UrZSxc8rqzTSztdOfD3hNT4LFJsPgpjYsW6UB+e2oaJ6Ql8McP1jJnQx6EdIKT74HrF0LqJPj8D/DIWEhf6HZUEZEfMcZw3eRePHPFaDKKKjnzP/NYtH2vC2GRiXDxa3De01BT6kxOff5MXfwS8QUlmc73qGTSiyq1FUzER6kI5OO6RAXz2sxxjO4Rw58/XMdpD85jwbYDTPxJHALnPAa/2Ain3udcGfvoF/DPNGcfvSYFibR7fh7DA9OHk5YYyY0vL2N9dqlzR1xvp2H0Ze84vz97qrOdQlfQRcQHTe2fwDveYRmXPLWA91bs+uFOY2Dw+XDjYpj2D2ci4hNT4M2rIG+9a5lFOjxvEagxMpmM3VWaDCbio1QEagMSIoN59iejeeKykZTX1DP9iQXc/MpycksPsKUjpBOMuRau+waunQP9T4VvH4QHhsDnf9R4eZF2LizIn6evGE14sD9XP7uY7JKqH+7sdRzM/Br6nORsp3jzJ1BT5l5YEZED6J0Qzjs3TGRESjS3vbaCt5Zm/u8B/kEw7jq4eQUc+0vY+DE8Mg6ePxs2f6aeQSKtrTgDAsLIqQ2mtr5Rk8FEfJSKQG2EMYaTBnbh859P5ubjevPJ2hyO/+fXzN96kNU9xkDSCDjvKbhhIfQ92ekHcv8Q+PIv6gsi0o51iQrm6StGU1pdzyVPLiSvbK9/7yGd4KKX4IQ/wLr34ImpunouIj4pKiSAZ38yhgm94rj9zZW8vjjjxwcFR8Jxv4db18Bxdzrns5fOd7a+Ln5ahW6R1lKS4WwF2+1cfNJKIBHfpCJQGxMS6MfPT+rHZ7cdS2JUMD95dhHzNhcc+oHx/eD8Z+D6+dD7eJh7L/z3lB/27opIuzMoKYr//mQ02SXVXPrUQooqan+40+OBY25zJg1Wl8CTx8Gq190LKyJyACGBfjx1xSiO7RPPr95axUsLd+7/wLBYOPZ2uHU1nPskBITCRz+H+/rBu9fDjm+1BVakJZVkQqdupBfuGQ8f5nIgEdkfFYHaqO6xYbwycxw9YsO46rnFfLWxiaOfE9LgwuecVQAFm53R0TvmtWxYEXHN6B4xPH3FKHYWVnLpUwspqaz73wNSJzlbRxOHwdvXwoe3aZWgiPic4AA/Hr9sJMf1T+B376zh+fk7DnywfyAMuRBmfgVXfwaDz3NWPT57Kjw4HL6+12kk3VB34OcQkcNXkvl9U2g/jyGxU7DbiURkP1QEasPiwoN45dpx9EkIZ+bzS/lifW7TH5x2Olz7JYREw3NnwoLHdHVMpJ2a0DuOxy8byZa8ci7/7yLKqvf54BPRBa74ACbcDEuegWdOht0HuNIuIuKS4AA/Hrt0JCcO6Mxd763lt++s/t8VjvsyBrqNgTP/A7dvgrMfg6hkmPMXp5H037rBM6c4wzM2fAT1Na32t4i0O3VVUFkAUcnsLKokqVMIAX76qCnii/Qvs42LDgvk5WvGkZYYwXUvLuWTNTlNf3B8X6cQ1HcafHIHvD0T8ja0XFgRcc2Ufgk8fMkI1u4q4Sf/XUx5Tf3/HuDnDyf9Gaa/DEXb4fFjYeMn7oQVETmAQH8Pj1wygquPSeW1xRlMuXcOz8zbTl3DIZpAB4bBsBlw5Ydw2zpni/zIK6GhBuY/Aq9e7FwU0/AMkSNT4p3gF9WN9MIK9QMS8WEqArUDUaEBvHDNWAYlRXHDy8t47rsd2Kau6gmOhItehKm/gzVvOk0UHxoNX/wZslZodZBIO3LigM48MH04yzOKufCx+eTtb8Jg/9Pgp19DpxR45SL44FanZ5CIiI8I8PNw5+kD+OSWSQzt1ok/fbiOUx74hrmb8pv2BFFJMOg8OOXvzsWw32Q6q4SylsHTJ8HuHS2aX6RdKkl3vnu3g2kymIjvUhGonYgMDuCFq8cytV88d7+/ll++uYrquoamPdjjgcm/gp+vh1Pvg4hEZ4rYE5PhwWGw/EVobOJziYhPO21IIk9fMYodhRWc88h3bMnbz9ScmFSnj8b4G2HZc/DwONgwq/XDiogcRJ/OETx/1RievHwUdQ2NXP7MIv46a33TL4TtERDsrBK6/D2oyIenToRdy1omtEh75R02Uxbchd2VdXRXEUjEZ6kI1I6EB/nzxGWjuOX4Pry5NJOLHp9PdklV058goguMuRaueB9u3wxnPgShsfDeDU4D6a1zWi68iLSaKf0SeG3meGrqGzn3ke9YtH0/2x8CguHke+Dqz53eYa/OgDeuhPImNqEXEWkFxhhOHNCZ2bcdy6XjUnhi7jb+8P5aGhuPYCVz9wlw9WzwD4ZnT4NNs5s/sEh7VZIJGHbWdQIgRUUgEZ+lIlA74/EYbjux7/dNYM/4zzwW7ziC/e1hsTDiMrjmC2fffE0JvHA2vHQB5K1v9twi0roGJ0fxzvUTiIsI4tKnF/LRquz9H5g80pmwM/X3TuPUh0bDN/+C6tJWzSsicjBB/n78+axBXDsplefm7+R3764+skJQfD+45nOI6wOvTId3fgYbP9bURJFDKcmEiETSi53hEynqCSTis1QEaqdOHtiFd2+YSERwABc8Np+p933Fra8u57/fbmdZ+u6mbxUzxtk3f8NiOPHPkL4QHp3grAjIWtGSf4KItLBuMaG8dd0EhiRFceMry3jwi837/9DkHwiTfwnXzYPkUfDFH+H+QfDlPWqiKnIIxhg/Y8xyY8yHbmdp74wx/PbUNG6c2ptXFmVw+xsrqT9Uw+j9iegMV85yLoZt+MgpBt3bG968Cta+o4KQyP6UZDiTwQorAa0EEvFl/m4HkJbTp3ME794wkZcXprM8fTfztxXy7oosAEIC/Pj7eYM5a1hS054sIBgm3gzDLoHvHoDFzzhvhHpOhWNuhdTJTsFIRNqU6LBAXrxmLL95ezX/+mwTa3aV8M8LhxIRHPDjg+P7waVvOb0yvvknzP0/mP8wjL7KGS8fntD6f4CI77sFWA9Euh2kIzDGcPvJ/Qjy9/DPzzZR29DIvy8advijqoPC4YwH4JR7YftcWP++UxBa8xaEJcD462HU1c6ADRFxVgIlDiO9qJKYsMD9v48QEZ+gIlA7FxUSwM+m9Pr+95ySalZmFvP0N9u55dUVbC+o4Jbj+2CaWsAJi4UT/wSTfgFLnoEFj8LzZ0HiMBj1Exh4DgRHtcwfIyItIjjAj39dOJTBSVHcM2s9Zz/8LU9cPope8eH7f0DSCJj+EuSug3n/cgpBi56CUVc5xeKILq37B4j4KGNMMnAacA/wc5fjdCg3Hd+HoAAPf521gbzSGh6cMZwuUcGH/0T+gdDnBOfrtH/B9q/hu//A53+Ab/4No6+GcT9TEVw6tsZGpwjU/3TS0yu0CkjEx2k7WAfTJSqYkwd24YVrxnDeiGTu/3wzt7y6ounbw/YIjoJjboNbVjlXyuqr4YNb4L6+znLpLZ9rophIG2KM4apjUnnx6rHsrqzj7Ie+5bN1uQd/UOcBcN5TznbRgWfDwsfggaHw8R1QeoAeQyIdy/3Ar4Aj2JMkR2vmsb24/6JhrMkq4dQHv+Hrpo6QPxA/f+h9PFz+rtMrrdcUZ5rq/YPhnetg21fOh2GRjqYiHxpqIaob6UWVdFc/IBGfpiJQBxXk78d9FwzhV9P68f7KLC5+cgH5ZTWH/0QBwTDySrh+AVz7JQy/DLZ8AS+eB/8eCHP+CiW7mj2/iLSM8b1i+eCmY+gRF8a1zy/h3k83HLqnRlxvOOcxuHExDDofFj3pFIM+/Z16BkmHZYw5Hciz1i49yDEzjTFLjDFL8vOPskAh+3X28CTev/EY4sODuPK/i7jv041H1idoX12Hw4XPw41LYOgMZ6vY82c5BaEv/gQFm4/+NUTaCu94+PrIJLKKq7USSMTHqQjUgRljuH5Kbx69ZATrsks5++FvWbOr5EifDJJGwmn3we2bnDdGnQfB1//nvCF69RJnxLyukIn4vKROIbxx3Ximj+7Gw3O2cslTC8krbUIj1NhecPbDcNNSp6H8/IfhgWHOlfK6qhbPLeJjJgJnGmN2AK8CxxljXtz7AGvtE9baUdbaUfHx8W5k7BB6J4Tz7g0TuWBkMg/N2cIlTy1k1ups1uwqobS67uiePK43nHG/897n/GcgIc055z00Cp6Y6mybLzvEqkqRtq4kA4ACTwINjZZu0SoCifgyY+0RjM9sBqNGjbJLlixx5bXlx1ZlFvPTF5ZSWF7Lnaencem47k3vE3QwRdth6X9h2QtQVQSxvWH0tTDsYjVTbCeMMUuttaPcznGkdC46uLeWZvL7d9cQFuTPg9OHMaF3XNMfnLvWuSK+6ROI6ApTf+v82/f4tVxg6bB8+VxkjJkC3G6tPf1Ax+hc1DreWprJne+tobL2hy3rnUID6B4bxs8m92LaoGboaVaWA6vfgFWvQ84qMB7oOQUGXwhpp0NQxNG/hvgsXz4XNdVhn4++ewhm/47lM1Zwzn/X8dxVY5jcV4VtETcd7FzUpJVAxphpxpiNxpgtxphf7+f+FGPMHO8I1FXGmFOPNrS0riHJnfjo5klM6B3Lne+t5cZXllN2tFfHAGJSnUbSP18P5zwBIdHwyR3wrzT46HbI33T0ryEiLea8kcm8d+NEOoUGcOnTC3nwi81N30rReSBc/Bpc+RFEdoX3b4THjoHNn4NLFyBEpGM7b2QyS35/ArNunsRjl47gt6f257TBiVTV1nPdi0t54PPNHPUF0oguMOEmuO4buH6h00OxcAu8ex3c2wfeusY5DzbUN88fJeK2kkwIDCenJgiA+PAglwOJyMEcciWQMcYP2AScCGQCi4EZ1tp1ex3zBLDcWvuoMWYAMMta2+Ngz6srXr6psdHy2Nyt/HP2JrpFh/DwJSMY2LWZp33tWgaLnnDGrDbUQq/jnF5C/U51egxJm9LWr3jpXNQ0FTX1/O6d1by7IoveCeH85pT+HNc/oekrBq2Fde85E3V2b4eeU+GkP0OXwS2aWzoOnYvkaFTXNfDbd1bz9rJdnDYkkfvOH0pIYDOuWrQWMhbCyldh7TtQXeyMmh98PgydDl2GOFvrpc1r6+ciOILz0auXQOEWnhv+Gne/v5Ylvz+BOBWCRFx1tCuBxgBbrLXbrLW1OPvaz9rnGAvs2dsTBWQdaVhxl8fj9Al65dpxVNU1cM4j3/HE3K00NDbjVfukEU4T2dvWwdTfQ/5GePMnzmSxD26B9AVaJSDiY8KC/Pn3RcN47NKRNDRarn5uCTOeXMDqzCb2ETPGmSB2wyKY9nfIXgGPTYJ3r4eCLS0ZXUTkkIID/PjnBUP5zSn9mbU6mwse/47skmbsZWYMpIz7oX/QRS9CylhY/BQ8fiw8Mt7pJaRhGtIWlWRAVDJ5ZdX4eQwxoYFuJxKRg2hKESgJyNjr90zvbXv7A3CpMSYTmAXctL8n0hSMtmNMagyzbp7E5L7x/HXWBi58fD7bCyqa90XC42HyL+HW1XDZu9DvFGf//DMnw4PD4bv/QFVx876miBwxYwzTBnVh9m3H8qezBrIpt5wzHprHLa8uJ7cpjaMB/ANh3M/g5uUw4Uanb8ZDI+HF82HzZ2oeLyKuMcbw08m9eOryUWzPr+DMh75l8Y4WmHDoHwRpZziFoNs3wen/dvokfv4HZ7Lqc2c6xaHdO5r/tUVaQkkmRHUjv6yGuPBAPB6tahPxZc01HWwG8Ky1Nhk4FXjBGPOj59YUjLYlNjyIJy4byb8vGsrm3DJOeWAuz8zbTmNzrgoCp0lsr6lw7uNw+2Y4+1GISITZv4d/DYCPfqFRqyI+JMDPw+Xje/DVL6dw/ZRefLImhxP+9TWvLEpvei+NkGg46S9w6xqY8luneepL5zsTdRY+DjXlLftHiIgcwPFpnXn7+omEBPhx0ePzefCLzc27InpvIdEw6iq4ejbctAwm3wHF6c57nweGwn9Gwsd3OEXyuiYW26XdaUJ/1n8bY1Z4vzYZY4r3uu8KY8xm79cVzR6uthIqC70rgWpIiFBrBxFf15Qi0C6g216/J3tv29vVwOsA1tr5QDBwGCNkxFcZYzhneDKf/Xwy43vG8qcP1zH9yQWsyChumRcMCnemB131Mfx0rrN9ZNnzzgfDF8+D9R9AQzM0rBaRoxYZHMCvpvXnk1uPZWDXSH7z9mpmPLmAHYezajCiM0y5wykGnfsUhMbAx79yroZ/8Wco16pREWl9/bpE8NHNx3DG0K7867NNzHhyAVnFzbg9bH9ie8HU3zgrJW9cCtP+AdGpsPRZp0h+X194/ybY/o1WTXYg3v6sDwOnAAOAGd4erN+z1t5mrR1mrR0G/Ad42/vYGOBuYCxOi4+7jTHRzRqw1PuxMKobeaU1JESoF5CIr2tKEWgx0McYk2qMCQSmA+/vc0w6cDyAMSYNpwikd+7tSOfIYJ65cjT3nj+EjTllnP3wt1z61ELmby08+ikaB5I4FM5+xNs76HfOuOnXLnUmi83+vSaLyX4ZY7p5pxWuM8asNcbc4nam9i41LoyXrxnH384dzNqsUk6+fy6Pfb216VPEwNkmNuQCuOZzuPpz6HEMfPNPuH8QfPhzKNrecn+AiMh+RAQHcP9Fw/jnBUNZu6uEUx74hk/WZLf8CxsDcb1h3HVw6Ztwxw645E3ofyqseRueOx3uHwyf3Q05q9VHsf1rSn/Wvc0AXvH+fDLwmbW2yFq7G/gMmNas6YrTne9RyeSX1xCvIpCIzztkEchaWw/cCHwKrAdet9auNcb8yRhzpvewXwDXGmNW4px0rrQtVhkQtxhjuGBUN7799XH85pT+bMgpY8aTCzj/sfnM2ZDX/NvE9giPh8m/clYKzHgNuo2FBY/Cw6PhqRNhzt+cUatVu1vm9aWtqQd+Ya0dAIwDbtj3ipk0P4/HMGNMCp//fDKT+8bz9483cNqD81i4rfDwn6zbaJj+Ety4GAZfAMtfgP+McIrAO77VBx4RaTXGGM4bmcyHN08iJSaU615cxs9fW0FBeU3rhQgIgT4nOkM1bt8M5z0NnQc6vRMfOwYeGg1z/gp5G1ovk7SmpvRnBcAY0x1IBb48gsceWe/WkkwAGiKTKCzXSiCRtsC/KQdZa2fhNHze+7a79vp5HTCxeaOJrwoP8uenk3txxYQevL4kg8e/3sZPnl1Mj9hQZoxJ4fyRycS2xFhIP3/oN835Ks9zxqyufh3m/h9Y74qDuH7QbYyzvz5pRPNnEJ9nrc0Gsr0/lxlj1uO84VnnarAOonNkMI9fNpLZ63L50wfruOiJBZw9rCu/PTWNhMjD7BMQ1wfOeshZCbjocWdLxPoPnFHKY6+DQedBgHoPiEjLS40L462fTeA/X27msa+38vn6XH45rT8Xj0nBrzWb4AaGOmPlB58PFQWw/n1nddDX/wdf/wMSBjgFo27jnItmYbGtl018wXTgTWttw+E+0Fr7BPAEOCPim/zAkkwwHgpNLI0W4g/3v/Ui0uqaqzG0dEDBAX5cPr4Hc26fwv0XDSM+Ioi/fbyB8X/7klteXc6i7S0wUWOP8ASYeDNcNw9+nQ6Xvw/H/R5iUmHde/DkVHj1EsjV5/6OzBjTAxgOLHQ5SodijOHkgV34/OeTufm43sxak8Nx//yap77ZRk39Yb8vhchEOOEPztbQMx5w+oK9d73TN2j273X1W0RaRaC/h1+c1I+PbzmWQUlR3PnuGs595FtWZ5a4EygszrnodeWH8IuNcMq9ENwJ5j8Cr86Ae3s6q4Teu9EpoNfXupNTjlZT+rPuMZ0ftoId7mOPTEkmRCSSV+lckI1viQvBItKsjFu7tkaNGmWXLFniymtLy9mUW8bLC9N5a1kmZdX1HN8/gT+cOZBuMaGtF6K6FBY8At89BLXlztWyKb9xGi5KszPGLLXWjnI7x76MMeHA18A91tq397lvJjATICUlZeTOnTtdSNhx7Cio4I8frGXOxnwSo4L56bE9mT4mheAAvyN7Qmth+9ew6EnY9Ak01kPXETD8Emd1UEjz9ryUtsFXz0VNpfdFbYu1lvdXZvHnD9dTVFHDqYMTuWJCD0Z1j8YYl8dj11VB1nJInw/pCyFjAVSXOOfGQefDsBnOOdPtnO1Uc5+LjDH+wCac/qu7cPq1XmytXbvPcf2BT4DUPW05vI2hlwJ7lscvA0Zaaw96pfawzkfPng4NtcyZ+CI/eXYxb18/gREp+u+wiNsOdi5SEUhaRFVtAy8s2MEDn2+mvtFy49TezJzckyD/I/zQdyQqi+DbB5xx0w01ENsb4vs7S6UTvN/j+upN0FHyxQ9expgA4EPgU2vtvw52rM5FrcNay7wtBfznyy0s2l5EXHgg10zqyaXjuhMe1KSdyftXng+r34AVL0HuGvALcpqnDrsUek0FTyuec8RVvnguOhw6F7VNJVV1PDJnC68sSqe0up60xEiuGN+ds4YlERLoI+efhnrYNgdWvAwbPnLeE8X1g36nOFvGuo1xVhVJs2iJc5Ex5lTgfsAPeMZae48x5k/AEmvt+95j/gAEW2t/vc9jrwJ+6/31Hmvtfw/1eod1PnpgGCSN4LXuf+COt1Yz746pJEe34sVfEdkvFYHENdklVfzlw/V8tDqb1Lgw/njmQI7tG9+6IcpyYel/IXsV5K/3Thny/v8+qhuknQFpZzpvhDzaIXm4fO2Dl3EuwT4HFFlrbz3U8ToXtb6F2wp5aM4WvtlcQFRIANdPcXqMHfHKIHBWB+WsguUvOb3CqnZDRCIMnQ7DLnH6C0m75mvnosOlc1HbVllbz3srsnjuux1syCkjKiSAc4Yncf7IZAYlRbkd7wdVxbDuXVj1OmQsgsY65/aYns77oO4ToOdU6NTtYM8iB9HWz0VwGOejxka4pzOM+xkP+V3GfbM3seHP047uv+ci0ixUBBLXzd2Uz93vr2V7QQVnD+vKXWcMJCYs0J0wtZVQsAmyV8LGWbD1S2iohfDO0P80ZxpRt3EqCDWRr73ZMcYcA3wDrAb2zCj/rbfB/Y/oXOSelRnF3P/5JuZszCepUwi/OKkvZw9LwnO0TVbra5xtYitehs2fgW2A5NEwdAYMOlfbxdopXzsXHS6di9oHay2Lthfx/IKdfLY2l9qGRtISI7lgZDJnD09y773P/tRVOe+FMhY6BaGMhVDhnQoV29spBvWaCqnHQlCEu1nbkLZ+LoLDOB+V5cA/+8Gp93FX9njeW5HFyrtPavmAInJIKgKJT6ipb+DhOVt59KstRAQHcPcZAzhzaFf3985Xl8Lm2c6Ejc2fQV0ldEqBIRfBkOkQ19vdfD6urb/Z0bnIfd9tLeBvszawelcJAxIj+c2p/ZnUp5lWDJblOFe8V74Ceet+2C42dAb0Ot6ZOijtgs5F4muKK2t5f2UWby7NZFVmCQF+hvNGJHPbiX3p7IsTlKyFvPXO1rFtX8GOb6Guwjlv9j7eWTXdb5oK6YfQ1s9FcBjno8wl8NTxMONVrluUwNb8cj77+eSWDygih6QikPiUjTll/OqtVazMKOa4/gn85exBdO0U4nYsR20FrP8QVr3qvAGyjZA0CkZc5jRTDAp3O6HPaetvdnQu8g2NjZYPV2dz76cbyCiqYlT3aH42pRdT+yUc/cogcD7cZK90VgetfgOqiiCiq/Nve/hl2vrQDuhcJL5sQ04pryxM5+VF6fh5DNcc05OfTu5JRHCA29EOrL7WWR20cRasex9KM8HjD6mTYeDZznZ6FYR+pK2fi+Awzkdr34E3roTrvuW8d0oJ8vfw8rXjWjyfiByaikDicxoaLc9+t4P7Pt2IMXBM7zjG9YxlbM8Y0rpENs+HvqNVmu18WNyzgiAwAoZcACN/AolD3E7nM9r6mx2di3xLTX3D/7d35/F1VnXixz8nudn3fd+TJk2bLulOVyqlBaooIpsg4+g4zqjDbI7601F+Do448xt/buiIiDiKiBZsgVIW6QaU7k3bdEnSJM2+782e3DN/nNs2VLBJm+Q+z833/XrdV3qf+7Q5J+R+ee73Oef75dlDNfx0TwV1nf3kxoXw2XWZbJ6XiI/3JG3RHBmCslfhyC/h3B/NsZwNUPig+eqQ9rZ2JLFI2EFNex//+WoJLxyvJzLIl79bn819y9LwdVh8C7rWUHcUzmyD09ug4zx4+ZiYOfejpsi0b5C7R2kJdo9FMIF4tO+H8NrX4EtVrP7hURalRvC9exZO/QCFEFclSSBhWTXtffxo5zn2VbRS094PQKi/g2WZUdy/PI01OdHu3y6mNdQegsO/gFPPw8iAaa2auhwiMkwxxcgMs4XM28J39KaI3S92JBZZ0/Cok5dO1POT3eWUNl0gKTyAz67L4q7FyZPbZbCzGo7+Co79CnoawCcIMtdC9k3mw0146uR9LzGlJBYJOzlZ28W3d5xhX3kbmTFBPPxBNzTOuFZamxb0xc9B8fPQUw8+gZC0CCLSICIdwtPN17g54DuzOkXZPRbBBOLRji/BsafRX64m7+uv8uAN6fyfW2dP/QCFEFclSSBhC3Wd/RyoaONARTu7S5tp6h5kfnIYn1+fw02zY92fDALTcej4s2a7WEuJqR90kZfDLI1e+feQuMBdI5x2dr/YkVhkbU6nZldJM4/tOsfR6k7iQv347Nos7l2aOrndRy62UC59xdQI66w2x2NmQ8FHTcv50ITJ+35i0kksEnajtYlv33zxNOfb+tg4J46v3ZZPSqSNkiZOJ1S/Y26SNZ40K4QuNF1+3TfYXBvNu9sUmPby/K5Rdo9FMIF49Mx90F5B96feZN7Dr/HVW2fzV2syp36AQoirkiSQsJ2hESfPH63lx7vLqW7vIy8+hM/dmM2G/DjrtJ3U2lzotFdCe8Xl9tRDPZC13iSDMtaAFZJXU8juFzsSi+xBa82+8jZ+8EYZByrbiQ724zNrMrh7cSphgZO8Ak9r00Gw7HVTC6PqbVBekLMRCj8BOTdLQWkLklgk7GpwZJQn3qzkRzvP4dSav1mXxV/ckE54oIU6iU3EUJ9JpLdXQOkOOLUNBrtMHbZ5H4PcWyF+nseuELJ7LIIJxKP/Xg3BcZy7+Slu+u4evn/PAm5fkDT1AxRCXJUkgYRtjYw6eeF4PT/adY6Kll58HV4sSY9gZXY0q7KjmZMYhrcV6gdd1N8Jh5+E/T+B3mazbWzWJogvMI+wZI9LCtn9Ykdikf0crGznhzvLeLOsFV9vL27Mi+EjC5O4MS92creKXdRWbraLHXvavK+D42H2ZrNlLH21FIy3CIlFwu7qO/v51stn2H6iAYCM6CAWpIRfesxNstg1z3gND5hk0PHfmjpszhGTWI+ZDYkLzerp2HyztT44HrwsXh/pKuwei2AC8eg7GZB/O/vyv8Z9PzvAbz69jBuyo6d+gEKIq5IkkLC9UafmzbIW3ixr5e1zrZxt7AEgPNCH+5el8enVGda6YzY8YApKH3zctFvF9T7zDzfJoOgcVy2hLPM1Ih18LNgudhzsfrEjsci+iuu6+MOxOrYV1dN6YZBQfwe3zUvgIwuTWZwWMfkF5keHzVaxY0+b7oHDveDtC6krTEJo1ibz3vawRK9dSCwSnuJ4TSdvnWulqKaToppOWnoGAUgKD+ATK9K4Z8kUrICcLr1tUHvQFJmuPwb1R6Gv7fLrjgBzTRSZATG55mZaUiGEJtkmtto9FsE449FQL/x7Inzg62wLuYeHflvEH/9xDdmxIdMzSCHEnyVJIOFxWnoG2Vfeyo6TjbxyqpFgPwefXJnOp1dlWu/CaPCC6S7WeNI8moqh7ZypL3SR8jJbxxY+AHmbbZUQsvvFjsQi+xsZdbKvvI2tx+p45VQjfUOjJEcEcMfCJD5SmExG9BR0rBkZhOr95q72uTeg+ZQ5HplluuTk3gIpy2Xb2DSSWCQ8kdaa+q4BDp9v55mD1eyvaCfAx5s7CpP45Mp0+3/g1hq6aqG1xGyv7zh/eZt9W5lZNQQQFGMSQmkrYNYtJkFk0aSQ3WMRjDMetZTCY0vgjp/xRNdiHtl+huPfuJmwAItdhwsxQ0kSSHi0s43d/OCNMl4+2UiIn4NPrsrgr9dkEuRn8Q9ffe2XL3SaT8HJ56Cr2qwWmncXLLzf7Jm36EXORXa/2JFY5Fn6hkZ49VQjzx+t4+1zrTg1LEwN585FyWyelzh1F6ddtaaodMkOqNwLo0MQEGHqCOXdBtkfkPbJU0xikZgJTtd388t95/lDUR1DI05yYoMpSAqjIDmMgqQw8hNDCfS1+PXPeA0PmBtn9cdcK4eOQstZ81pEukkG5d5iurU6/Nw61LHsHotgnPHI6YS+VvAJ4N/fqOWpfecp+bdN1mjkIoSQJJCYGc40mGTQjuJGUiID+M5H53FDlo32JTudULkHjv0azrwIo4MQnmaKTGd/wKwU8g9z9yj/hN0vdiQWea7GrgG2FdXx/NE6Spp68HV4cXN+HHcuSmZ1TszU1dYY7IHynSYhVLIDBjrB4W/ey3m3QdYHpNPYFJBYJGaS9t4hfn+4hoOV7Zyo67q0ZcxLwbrcWD53YxaL0iLdPMop0FVnEu6lr0DFHnOthDI1FyPSL28li8wyq4Uis8AxveUC7B6LYOLx6B+eLeLQ+Xbe+tL6KRyVEGIiJAkkZpRD59v5ly0nqGzt5f7lqXz5ltkEW31V0JX6O+DUH0x3osq9MHQBlDckL4GEeRCR4aoplGESRW7cPmb3ix2JRZ5Pa82p+m62HKlla1EdnX3DxIX6cffiFO5dlkpCWMDUffPRYajaB2e3m0d3rTkePcsUlc5YY74GRU3dGGYIiUViJmvqHuBkbReHqzp49lA1HX3DLMuI5PPrs1mVHe2ZqzOGek19toYT0FHp2k5WCb0tl89R3iYxFJNrajKmroCUpVO6MtPusQgmHo8+/sR++odGef5vV07hqIQQEyFJIDHj9A+N8l+vlfDztytJDAvgP+6cx0q7disYHYaag2ZlQcVu07p6sHvMCQpC4s1dsEuPFFfHjYXgPbV7s+1+sSOxaGYZHBll19lmnj1Uw+7SFryU4qbZsTywPJ2V2VFT+0FJa2g8Ye5eV+6F6ndMghcgbu7lhFDaDRAQPnXj8FASi4Qw+oZG+M2Ban72ZgVN3YPMTw7jixvzWJVj0+ugiRrsMV0dW8tMraGWEnPt1FoK2mkSQ4kLLieE4gsgPH3SupLZPRbBxOPRhu/uITMmiJ8+YOtpC+FRJAkkZqwjVe188fcnqGjtZVV2NA/ekM76vFh7tli9SGtXPaEK152vCuisga4aU5ekq9a1PBrwDTZ75dNXQ8ZqiJ8/6YVq7X6xI7Fo5qpp7+PpA9X87nAN7b1DZEYHcfuCJDbNjWdWXPDU3zkfHTa1Lir3QOWbUHMARgZMofiEBZC5FjJvtFy9C6uyWixSSvkDewE/wAFs0Vp/4/3Ol1gkJtvgyCjPHanjJ3vOUdPez+Z5Cfzr5nziQu3TfGJSDXSbm2rV+6DqHag7bOq3AfiGQNwckxBKXgyZ68wNtmtgtVh0LSYajxZ88zU+OC+Rf/vw3CkclRBiIiQJJGa0geFRfv5WJb/eX0VD1wDJEQE8sDyNuxanEBFkobbyk0Vr6Gk0HyjPv2k+XLaWmNd8gkyr1eQl5u5X8tLr3oZi94sdiUViYHiUHcUNPHOghkNV7WgNmdFBbJwbz6Y58cxLDpuerRTDA1B7yLxvK/aYDyjOEdMyOW2FKyG0wnxIsVEHwelitVikzC9NkNb6glLKB3gLeEhrvf+9zpdYJKbKwPAoP91TwWO7z+Hr7cU/bpjFJ1ak4fCenJUvtjU88O7urY0noekUDPWY12PzTS23zBvNCk3fwHH9s1aLRddiIvFocGSU3K+9wj9tmMUXPpAzxSMTQoyXJIGEwLSRfv10E0/tO8+Bynb8HF7cuzSVz67NIj7Mwz9Q9TTC+bfMHbDag+ZC52Lb1ZRlUPgJmPORa9ojb/eLHYlFYqzm7gFeO93Eq6caeae8jRGnJjHM/1JCaHF65PStJBzsMe/bit1QvutyMtfLYe5YJxa6krpLLd0uebpYORYppQIxSaC/0VofeK9zJBaJqVbV1svXt51iT2kL+QmhfHFjLmtmTWGRfDtyOqHppIm55Tuher9ZXR2TB597z7fun7ByLBqvicSj2o4+Vn1nF4/eUcA9S1OneGRCiPGSJJAQVzjb2M2Tb1Xy/NE6vJTi7iUpfHZdFknhU1gg1kqG+qChCKrehuPPQluZWQpd8FGTEEosHPcHSrtf7EgsEu+nq2+YP55pYkdxI3vLWhgacRId7MvNc+LZMDuOFVlR+Pt4T9+Auuuh9rBpk1x3FOqLYLDLvBYYZVYJpa4wq4amYOun1VkxFimlvIEjQDbwmNb6S1e8/hngMwCpqamLqqqqpn+QYkbRWvNKcSPffOk0DV0DxIX6cUdhMh9blExmTLC7h2c9Q31m+9hQL+TfPq6/YsVYNFETuTY6Vt3BR368jyf/YjHr8+KmeGRCiPGSJJAQ76OmvY8f7y5ny5EaAO5clMyH5idRmBaOn2MaP9y5k9bmTtfR/zEdyUb6YcXnYeO3xvXX7X6xI7FIjEfv4Ai7SprZUdzIrrPN9A2N4u/jxcqsaG7Mi2V9XiyJ051Edjqhvdy8f6vfMV3IOirNa74hZvtCxhrziJs7aUVPrcrKsUgpFQ78AfiC1rr4vc6RWCSm09CIk51nm/j94Vp2l7Yw6tQsSovg/uWpbJ6XiM9M3yp2Hawci8ZrIvHo1VON/PWvjvDSF1YxNylsikcmhBivPxeLZtZtQiGukBIZyLfvKODz67P5ye5z/O5QLc8crMHfx4sl6ZGszI5mZVY0c5NCPbO9KpgVP2mu1QO3PAont5gPjEKIS4L8HGyel8jmeYkMDI9yoLKdXWebeeNsE2+cbQYgNy6EtbkxrMmJYUlGxNQnkr28IDrHPAofMMe6G0xC6GI9sLJXzfGACLP18+IWssRCaUs/jbTWnUqpXcAm4D2TQEJMJ1+HF5vmJrBpbgLN3QM8f6yO3x2u4R+ePc5/vlLCX67K4J6lqQT7yUcF8ec195hmJDEh0sBACLsY10ogpdQm4PuAN/CE1vrR9zjnLuBhQAPHtdb3/bl/U+54CSvq6h/mYGU7b59rZV95K6VNpn1zelQgH1ucwkcLkz2/ftAE2f2Ol8QicT201pS3XGDn2Wb2lLZwqLKDoVEnAT7erMiK4ub8ODbOiXdfEfruepMMqtxrCk23lGD+Nw2Ep0HCfJP0jZsD8XPNMZsmvK0Wi5RSMcCwKwEUALwGfEdr/dJ7nS+xSLib06nZXdrMT/dUcKCynVB/B/cvT+OOwiQyo4PxktpB42K1WHQtJhKPvvt6KT/cWUbZI7dIsXEhLOS6VgK59rM/BmwAaoFDSqkXtNanx5yTA3wFWKm17lBKxU7O0IWYXmEBPmzIj2NDvtnT3NwzwJ6SFp47Wst/vlrCf71WwtpZMdy9JIV1ubHTWw9ECGE5SimyY0PIjg3hM2uy6BsaYX9FG3tKWthV0sLOs818dWsxK7Ojua0gno1z4gkPnMaEUGgizL/bPMC0SG44DnVHTG2hxmI48yKXEkMX2yRfTArFFUBc/jUVjRckAL90XUd5Ab97vwSQEFbg5aVYnxfH+rw4imo6eXxvOf+9p5wf7y4nxN/BgpRwFqSEszA1nCXpkYT4+7h7yMICWnoGiArykwSQEDZy1ZVASqkVwMNa642u518B0Fp/e8w5/wGUaq2fGO83ljtewm7Ot/ay5UgtW47U0tg9gJ/Di6UZkaydFcPaWTFkxwZ77paxP8Pud7wkFomporXmVH032082sP1EA9XtfTi8FIvTI1iXG8vaWTHkxYe4P24MXoCWs672yMWmRXLTKRjsdp2gIHoWJC+B5EXma8xsyxWellgkxOSrae/jnYo2imo6Karu5GxjN05ttpOtz43lQwsSWZ8nN8XGsnssgonFo089dYj6rgF2PLR6ikclhJiI660JlATUjHleCyy74pxZrm/0NmbL2MNa61euYaxCWFZ6dBD/vDGXf9gwi33lrewuaWFPaQuPbD/DI9vPkBjmzz1LU3nwhnTCAuTumBAznVKKuUlhzE0K41825lJcZxJCu0uaeXTHWR7dcZa4UD/WzorhxtxYVuZEE+qOO+t+wZC82Dwu0ho6q0wyqPGk6UZWugOKfm1e9wkyW8kS5kPiAkhYYGoTeckHQSE8SUpkICmRgdy1OAWAvqERjtd08drpRl483sArpxoJ9nNws2sV9dKMSKKCpTbMTNJyYZBYqQckhK1M1m08B5ADrAOSgb1KqQKtdefYk65ohTpJ31qI6eXtpVidE8PqnBj+Fajr7GdvaQuvFDfy3ddL+dmbFXxyZQZ/uTJ9erd9CCEsSylFQXIYBclhfPmWPBq7BthbahLJO4ob+d3h2kurhG7MjeXGvFhy3Lm6UCmISDePvNvMMa1N97Haw65W9cfgyFNwoN+87hMEUZmmrlBYCoSnQngKRGWbh7ckx4Wwu0BfByuyoliRFcXXbstnf0Ub24rq2FHcyPPH6gDIiQ1maUYkyzKjWJsTQ1igvPc9WXP3ILlxIe4ehhBiAsaTBKoDUsY8T3YdG6sWOKC1HgYqlVKlmKTQobEnaa0fBx4Hs8zwWgcthJUkhQdw79JU7l2aSnFdFz/cWcYP3ijjybcq+cSKND6+PI2k6W4dLYSwtPgwf+5aksJdS1IYGXVytLqTXSXN7DrbzLd3nOXbO84S6u8gPzGU/IQw5iSGkp8YSk5ssPvqLigFkZnmMe8uc2x0BNrKoL7I1BpqL4e2cijfBcO9l/+ul4/ZUhY729QYisyE0CRTsyg43nJby4QQV+ftpUwX1exoHvlwASfrujhQ2caBina2Hqvj6QPVBPh4c+eiZP5yVQYZ0VJbzNM4nZrWC4PSGUwImxnPVdchIEcplYFJ/twDXNn5aytwL/ALpVQ0ZntYxSSOUwhbmJsUxk8fWMzZxm5+uPMcP3EVVCxMDWfzvERum5dAXKh0FxNCXObwNvXFlmZE8qVNedS7VheeqOvidH03vzlYxcCwE4AgX28K0yJYlhHJkvRI5qeEu7cWh7fDJHZiZ8OCey8f1xr6O8yWstYys62s+QzUHIDiLe/+N5SXSQRFZkBMLsTkXf4aHGfbbmVCzCS+Di8WpUWwKC2Cv10HI6NOTtZ18czBap49VMOvD1Rx0+w4Pr0qg6UZke6vhSYmRUffECNOLdvBhLCZqyaBtNYjSqnPA69i6v08qbU+pZT6JnBYa/2C67WblVKngVHgi1rrtqkcuBBWlhcfymP3FVLd1seLJ+p56UQD33zpNP+2/TRL0iNZnR1NYVoE81PCCfaTO+BCiMsSwwO4Z2kq97iej4w6Od/WS3FdN4er2jlU2cH/e60UAF9vLwqSw1iUFkFhagSFaeHEhlgg0awUBEaaR+LCd7820A2d1aZ9fXet+dpVZ1YRFT8HA12Xz539Ibj7V9M7diHEdXN4e7EwNYKFqRH888Zcfv1OFb/aX8Xrp5vIjg3mlrnxbJobT35CqCSEbKy5ZxCAWLnBKYStXLU72FSRLhhipilvucBLxxvYUdxASVMPWoOXgtz4UBalhbNxTjwrs6Lx8rLXxZDVumAopZ4ENgPNWuu5VztfYpGwo47eIQ5XdXDofDtHqjo4WdfF0IhZLZQSGcCStMhLq4syooPs8yFLa7jQZLqVtZRCSDzkf2hcf9VqsWiiJBYJT9c/NMrWojpeKKrnQGUbTg2pkYFsmhvPyuxocuNCiAv1s0+8eh92j0Uw/ni0p7SFB588yO8/u4Il6ZHTMDIhxHhdb3cwIcQkyIoJ5qGbcnjophy6+ocpqunkaFUHR6s72Hqsnl/vryYpPIA7FyVz56JkUiID3T1ku3oK+BHwP24ehxBTJiLIlw2ubjwAgyOjnKrv5mhVB4fPd7C3rOVSkdboYD+WZURSmBZBYWo4+Ymh+Dks2sVLKZP4CYmHzHXuHo0QYhIF+HpfqqHYdmGQP55pYkdxI794u5LH95oqEmEBPuTGhTArPpgl6ZFsyI8j0Fc+riilNgHfx+zKeEJr/eh7nHMX8DCggeNa6/tcx0eBk67TqrXW48usj0PLxZVAsh1MCFuRqCqEG4QF+LB2VgxrZ8UAMDA8yuunm/jd4Rp+sLOM779RxsrsKJZlRJEVE0xmTBAZ0UHurf1hE1rrvUqpdHePQ4jp5OfwNtvBUiP49GrQWlPR2svByvZLj+0nGwCzhWxOUiiFqRHMigsmJcK0gE4I83df0WkhxIwSFezH3UtSuXtJKt0Dw5yu76a0qYeSxh5Km3rYVmRujgX4eHPznDg+vCCJVTnR+MzAGKWU8gYeAzZgmvEcUkq9oLU+PeacHOArwEqtdYdSKnbMP9GvtV4wFWNr7hkAkMLQQtiMJIGEsAB/H28+OD+RD85PpK6zny2Ha9laVMd3Xy+9dI5SkBwRwNL0KD4wO5bVOdGE+Evb1WuhlPoM8BmA1NRUN49GiMmnlCIrJpismGDuXWp+xxu7Biiq6eBYdSfHqjt5+sDlgtNgOv0khPkzOyGUJekRLEqLpCApDF/HzPvQJYSYPqH+PizPjGJ5ZtSlY06n5nBVB1uL6nj5ZAPbiuqJCDTnJYUHkBgeQFJEAEnhAWTGBHn6aqGlwDmtdQWAUuq3wO3A6THn/BXwmNa6A0Br3TwdA2vuHiTYz+HpP38hPI68Y4WwmKTwgEvbxvqGRqhs7aW8pZeKlguUNvXwxzNNPHe0Fh9vxdKMSG7MjSUzJohQfx9CA3wI8XcQ6u9DkBScfl9a68eBx8Hse3fzcISYFvFh/mwKS2DT3ATAFJxu6BqgpqOP2vZ+ajr6qGrr40RtJ6+fbgLAz+HF/GSzhSwtKpD06CAyooJIigiYkXfkhRDTw8tLXapr9vAH57C3tIUXjtdTXN/FrpLmdyWwfV0dFtfOimFtbgw5scG2ryt0hSSgZszzWmDZFefMAlBKvY3ZMvaw1voV12v+SqnDwAjwqNZ662QNrOXCoGwFE8KG5FOiEBYW6OtgTmIYcxLDLh0bGXVyrKaTN840s/NsE49sP/Oef3d+Sjj3L0vlg/MTZRuZEOJPOLy9SIk0W8HIevdrLT2DHKlq5/D5Do5Ud7DlSC0XBkcu/10vRVpUILnxIcyKC3HV8AghOSLAuvWGhBC25Ovw4qb8OG5y1UDTWtPeO0R95wC1HX0cre5gT2kL33r5DN96+QyJrhWN/r7eBPh44+/jRYCPt+lKVpBAqGeuonYAOcA6IBnYq5Qq0Fp3Amla6zqlVCawUyl1UmtdfuU/cC2rpFu6B2UrmBA2JEkgIWzG4e3FkvRIlqRH8uVb8mjo6qepe5Du/mF6BkboHhimpWeQbUV1fHHLCR7ZfoY7FyXz8WWpZMYEu3v4QggbiAnxY9Pcy6uGtNa09Q5xvrWXytZezrf1UtZ0gdP13ewobmRso9FQfwcxIX5EB/sRE+JHTmwIi9MjWJASLisUhRDXTSlFVLAfUcF+FCSHcUtBAl+9Deo7+9lT2sKekhZqOvroHx5lcNhJ//AofUMjDAw7+fq2U9w8J56PFiaxOicGb3t0ZK0DUsY8T3YdG6sWOKC1HgYqlVKlmKTQIa11HYDWukIptRtYCPxJEuhaVkk39wwwNyns6icKISxFrsaEsLmEsAASwgL+5PgX1mezv6Kdpw9U8T/vnOfnb1USGeRLdLAv0cF+lx6zE0JYlhFFSmSARyyfVko9g7kTFq2UqgW+obX+uXtHJYS9KaUuxYzFV7QB7h8a5VzzBUqaemjo7KflwiCtFwZp7RmiuK6L7Scb0NrUHJqTGMqitAiyY4OJCvIjOtjX9WHOlxA/h0fEICGEeySGB1zqPnYlrTXHa7t47kgtLxyv58Xj9cSG+HFDVhSRQX5EBvkQEeRLZKAvSREBzIoLsdIq6kNAjlIqA5P8uQe474pztgL3Ar9QSkVjtodVKKUigD6t9aDr+ErgPyZrYC09g8SG+E/WPyeEmCaSBBLCQymlWJEVxYqsKFp6Btl6rI7zbb3mw9mFIY7XdtLSM0jf0CgACWH+LMuIZGlGFKtzom3bol5rfa+7xyDETBLg601BchgFye99N7h7YPhS6/rDVe08c7D6XfU8Lgr2c5AeHUhalKk7lBYVSH5i6Lu2wwohxLVQSrEgJZwFKeF8bfNsdp1t5rmjdRyp7qCjd/hd213BbHnNjg1mblIYcxNDmZcSTmFqhFvGrrUeUUp9HngVU+/nSa31KaXUN4HDWusXXK/drJQ6DYwCX9RatymlbgB+qpRyAl6YmkCn3+dbTUjv4Ai9Q6PEhsp2MCHsRpJAQswAMSF+/NWazD857nRqypovcLCyjf2V7bx1ro2tRfX89ZpMvnLrbDeMVAjhaUL9fViXG8u6XNOxeHjUSXvvEC09g7T1DtHmWjlU19HP+bY+iuu6eKW4kVGn5rZ5CTx2X6GbZyCE8CR+Du93bXcFGBwZpbNvmLYLQ1S19VJc30VxXTe7S5rZcqSWgqQwXvzCKreNWWv9MvDyFce+PubPGvhH12PsOfuAgqkYU3PPIAAxwZIEEsJuJAkkxAzm5aXIjQ8hNz6EB1ako7WmsrVXWkILIaaMj7cXcaH+xIW+/xaC4VEntR39aC3N+4QQU8/P4U1cqDdxof7kJ4ZyS8HlemjNPYO09w65eYTWkxjuz/a/W/WeJQmEENYmSSAhxCVKKSkeLYRwOx9vLzKig9w9DCHEDKeUumrSeqbyc3jLdl0hbEpu9wshhBBCCCGEEELMAJIEEkIIIYQQQgghhJgBJAkkhBBCCCGEEEIIMQNIEkgIIYQQQgghhBBiBpAkkBBCCCGEEEIIIcQMIEkgIYQQQgghhBBCiBlAkkBCCCGEEEIIIYQQM4AkgYQQQgghhBBCCCFmAEkCCSGEEEIIIYQQQswAkgQSQgghhBBCCCGEmAGU1to931ipFqBqnKdHA61TOJzpIvOwHk+Zizvnkaa1jnHT975uEotsz1PmIvO4fhKL7MlT5iLzsBaJRddhhsYjmYe1yDyu3/vGIrclgSZCKXVYa73Y3eO4XjIP6/GUuXjKPKzOU37OnjIP8Jy5yDzERHjSz9lT5iLzsBZPmYcdeMrPWuZhLTKPqSXbwYQQQgghhBBCCCFmAEkCCSGEEEIIIYQQQswAdkkCPe7uAUwSmYf1eMpcPGUeVucpP2dPmQd4zlxkHmIiPOnn7ClzkXlYi6fMww485Wct87AWmccUskVNICGEEEIIIYQQQghxfeyyEkgIIYQQQgghhBBCXAfLJ4GUUpuUUiVKqXNKqS+7ezzjpZR6UinVrJQqHnMsUin1ulKqzPU1wp1jHA+lVIpSapdS6rRS6pRS6iHXcVvNRSnlr5Q6qJQ67prH/3Udz1BKHXD9fj2rlPJ191jHQynlrZQ6ppR6yfXclvOwE4lF7iWxyJokFk0/iUXuJbHImiQWTT+7xiLwjHgkscia7BKLLJ0EUkp5A48BtwD5wL1KqXz3jmrcngI2XXHsy8AbWusc4A3Xc6sbAf5Ja50PLAc+5/pvYLe5DALrtdbzgQXAJqXUcuA7wP/XWmcDHcCn3DfECXkIODPmuV3nYQsSiyxBYpE1SSyaRhKLLEFikTVJLJpGNo9F4BnxSGKRNdkiFlk6CQQsBc5prSu01kPAb4Hb3TymcdFa7wXarzh8O/BL159/CXx4Osd0LbTWDVrro64/92B+qZOw2Vy0ccH11Mf10MB6YIvruOXnAaCUSgZuA55wPVfYcB42I7HIzSQWWY/EIreQWORmEousR2KRW9g2FoFnxCOJRdZjp1hk9SRQElAz5nmt65hdxWmtG1x/bgTi3DmYiVJKpQMLgQPYcC6u5XlFQDPwOlAOdGqtR1yn2OX363vAvwBO1/Mo7DkPO5FYZCESiyzje0gsmm4SiyxEYpFlfA+JRdPN02IR2PA9fJHEIsv4HjaJRVZPAnksbdqy2aY1m1IqGHgO+HutdffY1+wyF631qNZ6AZCMuYOR594RTZxSajPQrLU+4u6xCM9gl/fvRRKLrEFikZhsdnn/XiSxyBokFompYJf3MEgssgq7xSKHuwdwFXVAypjnya5jdtWklErQWjcopRIw2U7LU0r5YILL01rr512HbTkXAK11p1JqF7ACCFdKOVwZWjv8fq0EPqSUuhXwB0KB72O/ediNxCILkFhkKRKL3ENikQVILLIUiUXu4WmxCGz4HpZYZCm2ikVWXwl0CMhxVdX2Be4BXnDzmK7HC8CDrj8/CGxz41jGxbWX8efAGa31d8e8ZKu5KKVilFLhrj8HABswe2d3AXe6TrP8PLTWX9FaJ2ut0zHvh51a649js3nYkMQiN5NYZC0Si9xGYpGbSSyyFolFbuNpsQjs9x6WWGQhtotFWmtLP4BbgVLM3sCvuns8Exj3M0ADMIzZ//cpzL7AN4Ay4I9ApLvHOY55rMIsIzwBFLket9ptLsA84JhrHsXA113HM4GDwDng94Cfu8c6gTmtA16y+zzs8pBY5PZ5SCyy6ENi0bT/vCUWuXceEoss+pBYNO0/b1vGItfYbR+PJBZZ92GHWKRcgxNCCCGEEEIIIYQQHszq28GEEEIIIYQQQgghxCSQJJAQQgghhBBCCCHEDCBJICGEEEIIIYQQQogZQJJAQgghhBBCCCGEEDOAJIGEEEIIIYQQQgghZgBJAgkhhBBCCCGEEELMAJIEEkIIIYQQQgghhJgBJAkkhBBCCCGEEEIIMQP8LwyOrBsbH3BSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_with_height_norm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_with_sex_height\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_with_sex_height_norm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['model']+[name.lower() for name in metrics_name]+['val_'+name.lower() for name in metrics_name]\n",
    "results = pd.DataFrame(columns=columns)\n",
    "\n",
    "for f_name in models:\n",
    "    print(f_name)\n",
    "    df_historic = pd.read_csv('results/'+name+'_'+f_name+'.csv')\n",
    "    #df_historic.rename(columns = {'R2':'r2', 'val_R2':'val_r2'}, inplace = True)\n",
    "    fig, ax = plt.subplots(1, len(metrics), figsize=(20, 5))\n",
    "\n",
    "    info={}\n",
    "    info['model'] = f_name\n",
    "    for metric, i in zip(metrics_name, range(len(metrics))):\n",
    "        ax[i].plot(df_historic[metric.lower()].values[5:], label='train')\n",
    "        ax[i].plot(df_historic['val_'+metric.lower()].values[5:], label='test')\n",
    "        ax[i].set_title(metric)\n",
    "        info[metric.lower()] = df_historic.iloc[-1][metric.lower()]\n",
    "        info['val_'+metric.lower()] = df_historic.iloc[-1]['val_'+metric.lower()]\n",
    "    results = results.append(info, ignore_index=True)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_t = results.set_index('model').transpose()\n",
    "pd.options.display.float_format = \"{:,.4f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>model</th>\n",
       "      <th>model_with_sex</th>\n",
       "      <th>model_with_height</th>\n",
       "      <th>model_with_height_norm</th>\n",
       "      <th>model_with_sex_height</th>\n",
       "      <th>model_with_sex_height_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mae</th>\n",
       "      <td>0.5877</td>\n",
       "      <td>0.4965</td>\n",
       "      <td>0.5207</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.4817</td>\n",
       "      <td>0.4760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mse</th>\n",
       "      <td>1.0430</td>\n",
       "      <td>0.7708</td>\n",
       "      <td>0.8257</td>\n",
       "      <td>0.8509</td>\n",
       "      <td>0.6966</td>\n",
       "      <td>0.6664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mape</th>\n",
       "      <td>2.6963</td>\n",
       "      <td>2.2979</td>\n",
       "      <td>2.4020</td>\n",
       "      <td>2.4405</td>\n",
       "      <td>2.2388</td>\n",
       "      <td>2.2174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>0.9534</td>\n",
       "      <td>0.9656</td>\n",
       "      <td>0.9631</td>\n",
       "      <td>0.9620</td>\n",
       "      <td>0.9689</td>\n",
       "      <td>0.9703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_mae</th>\n",
       "      <td>0.7077</td>\n",
       "      <td>0.6135</td>\n",
       "      <td>0.6319</td>\n",
       "      <td>0.6681</td>\n",
       "      <td>0.6010</td>\n",
       "      <td>0.5896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_mse</th>\n",
       "      <td>1.3692</td>\n",
       "      <td>1.0468</td>\n",
       "      <td>1.1064</td>\n",
       "      <td>1.1721</td>\n",
       "      <td>0.9610</td>\n",
       "      <td>0.9223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_mape</th>\n",
       "      <td>3.3696</td>\n",
       "      <td>2.9437</td>\n",
       "      <td>3.0224</td>\n",
       "      <td>3.2224</td>\n",
       "      <td>2.9108</td>\n",
       "      <td>2.8471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_r2</th>\n",
       "      <td>0.9412</td>\n",
       "      <td>0.9551</td>\n",
       "      <td>0.9525</td>\n",
       "      <td>0.9497</td>\n",
       "      <td>0.9587</td>\n",
       "      <td>0.9604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model     model  model_with_sex  model_with_height  model_with_height_norm  \\\n",
       "mae      0.5877          0.4965             0.5207                  0.5293   \n",
       "mse      1.0430          0.7708             0.8257                  0.8509   \n",
       "mape     2.6963          2.2979             2.4020                  2.4405   \n",
       "r2       0.9534          0.9656             0.9631                  0.9620   \n",
       "val_mae  0.7077          0.6135             0.6319                  0.6681   \n",
       "val_mse  1.3692          1.0468             1.1064                  1.1721   \n",
       "val_mape 3.3696          2.9437             3.0224                  3.2224   \n",
       "val_r2   0.9412          0.9551             0.9525                  0.9497   \n",
       "\n",
       "model     model_with_sex_height  model_with_sex_height_norm  \n",
       "mae                      0.4817                      0.4760  \n",
       "mse                      0.6966                      0.6664  \n",
       "mape                     2.2388                      2.2174  \n",
       "r2                       0.9689                      0.9703  \n",
       "val_mae                  0.6010                      0.5896  \n",
       "val_mse                  0.9610                      0.9223  \n",
       "val_mape                 2.9108                      2.8471  \n",
       "val_r2                   0.9587                      0.9604  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results/'+name+'_results.tex', 'w') as tf:\n",
    "     tf.write(results_t.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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