Red neuronal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99f32629",
   "metadata": {},
   "source": [
    "## Modelo: Red Neuronal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b7b06c",
   "metadata": {},
   "source": [
    "Importo las librerias que utilizare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb55f9b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from keras import Sequential\n",
    "import ipywidgets as widgets\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "from keras.metrics import MeanSquaredError\n",
    "from keras.losses import MeanSquaredError\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from keras.callbacks import EarlyStopping\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "from sklearn.metrics import (\n",
    "    mean_squared_error,\n",
    "    mean_absolute_error,\n",
    ")\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd839290",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from tensorflow.keras import models\n",
    "# from tensorflow.keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bddffb3",
   "metadata": {},
   "source": [
    "### Lectura de archivos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0eb4ce6",
   "metadata": {},
   "source": [
    "Leo arc Shippings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cdc848c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ship = pd.read_csv('silkroad_shippings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f36ecc0",
   "metadata": {},
   "source": [
    "Leo arc Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edef7580",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rev = pd.read_csv('silkroad_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5362938",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rev = rev[['item_id','rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae9c7e71",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(314209, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0bc2c80",
   "metadata": {},
   "source": [
    "Hay varios ratings asociados al mismo item_id, por lo tanto voy a quedarme con el rating promedio por cada item_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bcb36e18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grouped_rev = rev.groupby('item_id').agg({'rating':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76d77163",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12749, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_rev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edf9245a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grouped_rev.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c84073",
   "metadata": {},
   "source": [
    "Realizo el join entre los dos arc que leí."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c786fdf4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "join = ship.merge(grouped_rev,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8727e57d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(560458, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5674f291",
   "metadata": {},
   "source": [
    "Modifico el formato del timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de7945df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# formateo el timestamp del join\n",
    "join['timestamp'] =pd.to_datetime(join[\"timestamp\"]).dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204c851d",
   "metadata": {},
   "source": [
    "Leo el arc de bitcoins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "548dd189",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bit = pd.read_csv('bitcoins.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5e4d157",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bit['Date'] = pd.to_datetime(bit['Date']).dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "511c2188",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bit = bit[['Date','Price']].rename(columns={'Price':'EuroPrice'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69153bbd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "merge = join.merge(bit,left_on='timestamp',right_on ='Date',how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24727ef3",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f67ec27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge['bit_en_euro'] = merge['price']*merge['EuroPrice']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1911b8d",
   "metadata": {},
   "source": [
    "Voy a trabajar con todos los precios del shipping menores o igual a 100 Euros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "546fe09a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#achico mi rango de precios AGREGUE RECIEN\n",
    "merge = merge[merge['bit_en_euro'] <= 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa902159",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Libero RAM\n",
    "del(merge['price'])\n",
    "del(merge['Date'])\n",
    "del(merge['EuroPrice'])\n",
    "del(merge['item_id'])\n",
    "del join\n",
    "del ship\n",
    "del rev\n",
    "del grouped_rev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2cb340e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "merge = merge.sort_values(by='timestamp', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "455a9e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(merge['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f935a0a",
   "metadata": {},
   "source": [
    "Los features que usare son:\n",
    "   * description\n",
    "   * est_delivery\n",
    "   * ratting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df80cd7",
   "metadata": {},
   "source": [
    "### Realizo la division en train y test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7846083e",
   "metadata": {},
   "source": [
    "Tomo el limite para dividir el test del train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ed4dfd89",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_limite = int(merge.shape[0] * 0.8 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f4d31ded",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "limite = merge.index[index_limite]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb74b10",
   "metadata": {},
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a6be286a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train =  merge[merge.index <= limite]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b75a840",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440527, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3bacdd2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "trainLabel = train['bit_en_euro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c20cf9e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del(train['bit_en_euro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fb8ac4",
   "metadata": {},
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2b641d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test =  merge[merge.index > limite]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "43326922",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111840, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9aef580d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "testLabel = test['bit_en_euro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "165d3a54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del(test['bit_en_euro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e74882e",
   "metadata": {},
   "source": [
    "### Encodings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f403a9ec",
   "metadata": {},
   "source": [
    "Diccionario de encodigns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e97449ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "encoding = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db55c53c",
   "metadata": {},
   "source": [
    "Funciones para los encodings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d830d764",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def countVect(df,columna,dic):\n",
    "    if columna not in dic:\n",
    "        dic[columna]=Pipeline([('countV', CountVectorizer(max_features =5000)), ('TruncatedSVD', TruncatedSVD(n_components=50, n_iter=3, random_state=0))])\n",
    "        col_encoded = dic[columna].fit_transform(df[columna].fillna('desconocido'))\n",
    "    else:\n",
    "        col_encoded = dic[columna].transform(df[columna].fillna('desconocido'))\n",
    "    col_encoded = pd.DataFrame(col_encoded).add_prefix(columna+ '_enconded_').fillna(0) #imputacion de nulos\n",
    "    del(df[columna])\n",
    "    df_nuevo = pd.concat([df.reset_index(drop=True),col_encoded], axis=1)\n",
    "    return df_nuevo\n",
    "    #la imputacion de nulos se realiza reemplazando los mismos por 'desconocido'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "edb77981",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def ordinal_enc(df,dic):\n",
    "    for column in df.columns:\n",
    "        if df[column].dtypes==object:\n",
    "            dic[column] = OrdinalEncoder(handle_unknown='ignore')\n",
    "            df[column] = dic[column].fit_transform(df[column].astype(str).values.reshape(-1, 1))\n",
    "    if df[column].dtype==object:\n",
    "        df[column] = dic[column].transform(df[column].astype(str).values.reshape(-1, 1))\n",
    "    df.fillna(-1,inplace=True) #imputacion de nulos\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef59fc12",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b485b722",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['description', 'est_delivery', 'rating'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e597e9",
   "metadata": {},
   "source": [
    "Realizo los encodings en el train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1fcb76e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train = countVect(train,'description',encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bb0557b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train = ordinal_enc(train,encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8438a42",
   "metadata": {},
   "source": [
    "Muestro los encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aded11ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'description': Pipeline(steps=[('countV', CountVectorizer(max_features=5000)),\n",
       "                 ('TruncatedSVD',\n",
       "                  TruncatedSVD(n_components=50, n_iter=3, random_state=0))]),\n",
       " 'est_delivery': OrdinalEncoder(handle_unknown='ignore')}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4de5d57",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2124b52",
   "metadata": {},
   "source": [
    "Realizo los encodings en el validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0114ff77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test = countVect(test,'description',encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8501f993",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test = ordinal_enc(test,encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccc427e",
   "metadata": {},
   "source": [
    "#### Trabajo con el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1403c86a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from numpy import loadtxt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6ad617d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440527, 52)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d62ac7c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(50, input_dim=52, activation='tanh'))\n",
    "model.add(tf.keras.layers.Dropout(.2))\n",
    "model.add(Dense(30, activation='tanh'))\n",
    "model.add(tf.keras.layers.Dropout(.2))\n",
    "model.add(Dense(30, activation='tanh'))\n",
    "model.add(tf.keras.layers.Dropout(.2))\n",
    "model.add(Dense(30, activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(.2))\n",
    "model.add(Dense(1, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aed733c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\", metrics=[\"mae\"]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf76eeea",
   "metadata": {},
   "source": [
    "Muestro las caracteristicas de la red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "609f5a08",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 50)                2650      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 50)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 30)                1530      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 30)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 30)                930       \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 30)                0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 30)                930       \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 30)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 31        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,071\n",
      "Trainable params: 6,071\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff59bbb",
   "metadata": {},
   "source": [
    "Creo el objeto de EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "baefd930",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop= EarlyStopping(monitor=\"val_loss\", patience=10, restore_best_weights=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa922a84",
   "metadata": {},
   "source": [
    "### Entreno la red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6f3d65b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "87/87 - 1s - loss: 837.2511 - mae: 9.8055 - val_loss: 188.3120 - val_mae: 9.5911 - 1s/epoch - 17ms/step\n",
      "Epoch 2/300\n",
      "87/87 - 1s - loss: 790.3436 - mae: 9.5734 - val_loss: 179.2655 - val_mae: 8.9465 - 667ms/epoch - 8ms/step\n",
      "Epoch 3/300\n",
      "87/87 - 1s - loss: 747.2715 - mae: 7.9751 - val_loss: 117.2978 - val_mae: 6.6999 - 636ms/epoch - 7ms/step\n",
      "Epoch 4/300\n",
      "87/87 - 1s - loss: 717.4698 - mae: 7.1895 - val_loss: 106.0748 - val_mae: 6.2280 - 625ms/epoch - 7ms/step\n",
      "Epoch 5/300\n",
      "87/87 - 1s - loss: 708.8563 - mae: 6.9589 - val_loss: 99.4378 - val_mae: 5.9429 - 617ms/epoch - 7ms/step\n",
      "Epoch 6/300\n",
      "87/87 - 1s - loss: 703.6380 - mae: 6.8030 - val_loss: 95.2400 - val_mae: 5.9024 - 628ms/epoch - 7ms/step\n",
      "Epoch 7/300\n",
      "87/87 - 1s - loss: 699.2247 - mae: 6.6690 - val_loss: 88.7888 - val_mae: 5.5028 - 595ms/epoch - 7ms/step\n",
      "Epoch 8/300\n",
      "87/87 - 1s - loss: 694.7429 - mae: 6.5327 - val_loss: 82.5457 - val_mae: 5.2834 - 663ms/epoch - 8ms/step\n",
      "Epoch 9/300\n",
      "87/87 - 1s - loss: 691.4395 - mae: 6.4285 - val_loss: 79.1610 - val_mae: 5.1429 - 618ms/epoch - 7ms/step\n",
      "Epoch 10/300\n",
      "87/87 - 1s - loss: 688.4634 - mae: 6.2937 - val_loss: 76.7935 - val_mae: 5.0476 - 643ms/epoch - 7ms/step\n",
      "Epoch 11/300\n",
      "87/87 - 1s - loss: 686.3673 - mae: 6.2136 - val_loss: 74.4108 - val_mae: 4.8901 - 654ms/epoch - 8ms/step\n",
      "Epoch 12/300\n",
      "87/87 - 1s - loss: 683.7455 - mae: 6.1150 - val_loss: 72.3338 - val_mae: 4.7979 - 643ms/epoch - 7ms/step\n",
      "Epoch 13/300\n",
      "87/87 - 1s - loss: 682.4761 - mae: 6.0580 - val_loss: 70.6374 - val_mae: 4.8541 - 640ms/epoch - 7ms/step\n",
      "Epoch 14/300\n",
      "87/87 - 1s - loss: 680.4562 - mae: 5.9682 - val_loss: 68.9896 - val_mae: 4.6386 - 657ms/epoch - 8ms/step\n",
      "Epoch 15/300\n",
      "87/87 - 1s - loss: 679.4337 - mae: 5.9195 - val_loss: 67.8250 - val_mae: 4.6506 - 593ms/epoch - 7ms/step\n",
      "Epoch 16/300\n",
      "87/87 - 1s - loss: 678.4121 - mae: 5.8917 - val_loss: 67.0221 - val_mae: 4.5400 - 602ms/epoch - 7ms/step\n",
      "Epoch 17/300\n",
      "87/87 - 1s - loss: 677.5319 - mae: 5.8430 - val_loss: 66.2598 - val_mae: 4.5389 - 596ms/epoch - 7ms/step\n",
      "Epoch 18/300\n",
      "87/87 - 1s - loss: 675.9527 - mae: 5.7952 - val_loss: 66.0093 - val_mae: 4.5477 - 601ms/epoch - 7ms/step\n",
      "Epoch 19/300\n",
      "87/87 - 1s - loss: 675.2104 - mae: 5.7635 - val_loss: 64.2106 - val_mae: 4.4364 - 596ms/epoch - 7ms/step\n",
      "Epoch 20/300\n",
      "87/87 - 1s - loss: 674.5689 - mae: 5.7454 - val_loss: 64.7781 - val_mae: 4.4143 - 590ms/epoch - 7ms/step\n",
      "Epoch 21/300\n",
      "87/87 - 1s - loss: 673.5227 - mae: 5.6863 - val_loss: 63.9912 - val_mae: 4.4496 - 597ms/epoch - 7ms/step\n",
      "Epoch 22/300\n",
      "87/87 - 1s - loss: 672.7679 - mae: 5.6692 - val_loss: 62.6460 - val_mae: 4.4263 - 612ms/epoch - 7ms/step\n",
      "Epoch 23/300\n",
      "87/87 - 1s - loss: 672.1346 - mae: 5.6496 - val_loss: 61.9253 - val_mae: 4.3544 - 595ms/epoch - 7ms/step\n",
      "Epoch 24/300\n",
      "87/87 - 1s - loss: 671.6662 - mae: 5.6227 - val_loss: 62.0641 - val_mae: 4.3375 - 594ms/epoch - 7ms/step\n",
      "Epoch 25/300\n",
      "87/87 - 1s - loss: 670.7718 - mae: 5.5938 - val_loss: 60.6060 - val_mae: 4.3036 - 596ms/epoch - 7ms/step\n",
      "Epoch 26/300\n",
      "87/87 - 1s - loss: 669.5491 - mae: 5.5694 - val_loss: 60.5302 - val_mae: 4.2482 - 600ms/epoch - 7ms/step\n",
      "Epoch 27/300\n",
      "87/87 - 1s - loss: 669.2816 - mae: 5.5447 - val_loss: 59.0351 - val_mae: 4.2273 - 599ms/epoch - 7ms/step\n",
      "Epoch 28/300\n",
      "87/87 - 1s - loss: 668.5896 - mae: 5.5208 - val_loss: 58.8539 - val_mae: 4.2244 - 588ms/epoch - 7ms/step\n",
      "Epoch 29/300\n",
      "87/87 - 1s - loss: 667.8737 - mae: 5.4971 - val_loss: 57.8547 - val_mae: 4.1732 - 611ms/epoch - 7ms/step\n",
      "Epoch 30/300\n",
      "87/87 - 1s - loss: 667.2790 - mae: 5.4723 - val_loss: 57.4122 - val_mae: 4.1985 - 655ms/epoch - 8ms/step\n",
      "Epoch 31/300\n",
      "87/87 - 1s - loss: 666.8141 - mae: 5.4620 - val_loss: 56.9643 - val_mae: 4.1629 - 600ms/epoch - 7ms/step\n",
      "Epoch 32/300\n",
      "87/87 - 1s - loss: 666.3666 - mae: 5.4310 - val_loss: 55.9349 - val_mae: 4.1188 - 600ms/epoch - 7ms/step\n",
      "Epoch 33/300\n",
      "87/87 - 1s - loss: 665.4384 - mae: 5.4114 - val_loss: 55.9255 - val_mae: 4.1300 - 618ms/epoch - 7ms/step\n",
      "Epoch 34/300\n",
      "87/87 - 1s - loss: 665.1542 - mae: 5.3897 - val_loss: 55.7876 - val_mae: 4.1293 - 635ms/epoch - 7ms/step\n",
      "Epoch 35/300\n",
      "87/87 - 1s - loss: 664.7285 - mae: 5.3784 - val_loss: 54.6307 - val_mae: 4.0936 - 630ms/epoch - 7ms/step\n",
      "Epoch 36/300\n",
      "87/87 - 1s - loss: 663.6769 - mae: 5.3510 - val_loss: 53.9908 - val_mae: 4.0587 - 627ms/epoch - 7ms/step\n",
      "Epoch 37/300\n",
      "87/87 - 1s - loss: 663.5729 - mae: 5.3403 - val_loss: 53.5732 - val_mae: 4.0108 - 651ms/epoch - 7ms/step\n",
      "Epoch 38/300\n",
      "87/87 - 1s - loss: 663.2924 - mae: 5.3274 - val_loss: 53.2899 - val_mae: 4.0212 - 625ms/epoch - 7ms/step\n",
      "Epoch 39/300\n",
      "87/87 - 1s - loss: 662.9789 - mae: 5.3124 - val_loss: 53.0395 - val_mae: 4.0230 - 616ms/epoch - 7ms/step\n",
      "Epoch 40/300\n",
      "87/87 - 1s - loss: 662.3299 - mae: 5.2947 - val_loss: 52.7472 - val_mae: 4.0229 - 649ms/epoch - 7ms/step\n",
      "Epoch 41/300\n",
      "87/87 - 1s - loss: 661.9418 - mae: 5.2743 - val_loss: 52.7428 - val_mae: 4.0038 - 586ms/epoch - 7ms/step\n",
      "Epoch 42/300\n",
      "87/87 - 1s - loss: 661.9388 - mae: 5.2706 - val_loss: 52.2164 - val_mae: 3.9106 - 600ms/epoch - 7ms/step\n",
      "Epoch 43/300\n",
      "87/87 - 1s - loss: 661.4344 - mae: 5.2557 - val_loss: 51.9485 - val_mae: 3.9736 - 600ms/epoch - 7ms/step\n",
      "Epoch 44/300\n",
      "87/87 - 1s - loss: 661.0470 - mae: 5.2427 - val_loss: 52.1633 - val_mae: 3.8633 - 617ms/epoch - 7ms/step\n",
      "Epoch 45/300\n",
      "87/87 - 1s - loss: 660.5638 - mae: 5.2212 - val_loss: 51.4143 - val_mae: 3.9289 - 601ms/epoch - 7ms/step\n",
      "Epoch 46/300\n",
      "87/87 - 1s - loss: 660.3396 - mae: 5.2147 - val_loss: 51.1233 - val_mae: 3.8739 - 615ms/epoch - 7ms/step\n",
      "Epoch 47/300\n",
      "87/87 - 1s - loss: 660.0681 - mae: 5.2001 - val_loss: 51.2043 - val_mae: 3.8895 - 635ms/epoch - 7ms/step\n",
      "Epoch 48/300\n",
      "87/87 - 1s - loss: 660.3892 - mae: 5.1969 - val_loss: 50.9539 - val_mae: 3.8691 - 623ms/epoch - 7ms/step\n",
      "Epoch 49/300\n",
      "87/87 - 1s - loss: 659.5856 - mae: 5.1790 - val_loss: 50.6343 - val_mae: 3.8586 - 645ms/epoch - 7ms/step\n",
      "Epoch 50/300\n",
      "87/87 - 1s - loss: 659.4095 - mae: 5.1706 - val_loss: 50.4047 - val_mae: 3.8100 - 627ms/epoch - 7ms/step\n",
      "Epoch 51/300\n",
      "87/87 - 1s - loss: 659.2454 - mae: 5.1596 - val_loss: 50.1148 - val_mae: 3.8151 - 622ms/epoch - 7ms/step\n",
      "Epoch 52/300\n",
      "87/87 - 1s - loss: 658.9708 - mae: 5.1454 - val_loss: 49.6141 - val_mae: 3.8350 - 613ms/epoch - 7ms/step\n",
      "Epoch 53/300\n",
      "87/87 - 1s - loss: 658.7963 - mae: 5.1387 - val_loss: 50.1760 - val_mae: 3.8367 - 602ms/epoch - 7ms/step\n",
      "Epoch 54/300\n",
      "87/87 - 1s - loss: 658.6738 - mae: 5.1377 - val_loss: 49.8499 - val_mae: 3.8173 - 600ms/epoch - 7ms/step\n",
      "Epoch 55/300\n",
      "87/87 - 1s - loss: 658.4124 - mae: 5.1197 - val_loss: 49.3742 - val_mae: 3.7790 - 601ms/epoch - 7ms/step\n",
      "Epoch 56/300\n",
      "87/87 - 1s - loss: 658.1590 - mae: 5.1122 - val_loss: 49.1507 - val_mae: 3.7700 - 599ms/epoch - 7ms/step\n",
      "Epoch 57/300\n",
      "87/87 - 1s - loss: 658.0827 - mae: 5.1169 - val_loss: 49.2715 - val_mae: 3.8145 - 585ms/epoch - 7ms/step\n",
      "Epoch 58/300\n",
      "87/87 - 1s - loss: 657.7891 - mae: 5.1082 - val_loss: 49.0163 - val_mae: 3.7693 - 600ms/epoch - 7ms/step\n",
      "Epoch 59/300\n",
      "87/87 - 1s - loss: 657.3377 - mae: 5.0882 - val_loss: 48.8983 - val_mae: 3.7585 - 601ms/epoch - 7ms/step\n",
      "Epoch 60/300\n",
      "87/87 - 1s - loss: 657.8876 - mae: 5.1079 - val_loss: 49.5711 - val_mae: 3.7501 - 600ms/epoch - 7ms/step\n",
      "Epoch 61/300\n",
      "87/87 - 1s - loss: 657.1480 - mae: 5.0808 - val_loss: 48.9794 - val_mae: 3.7388 - 600ms/epoch - 7ms/step\n",
      "Epoch 62/300\n",
      "87/87 - 1s - loss: 656.9547 - mae: 5.0713 - val_loss: 49.2905 - val_mae: 3.7884 - 615ms/epoch - 7ms/step\n",
      "Epoch 63/300\n",
      "87/87 - 1s - loss: 657.4420 - mae: 5.0764 - val_loss: 48.6883 - val_mae: 3.7408 - 601ms/epoch - 7ms/step\n",
      "Epoch 64/300\n",
      "87/87 - 1s - loss: 656.8000 - mae: 5.0579 - val_loss: 48.4475 - val_mae: 3.7020 - 600ms/epoch - 7ms/step\n",
      "Epoch 65/300\n",
      "87/87 - 1s - loss: 656.4313 - mae: 5.0456 - val_loss: 48.2275 - val_mae: 3.7425 - 600ms/epoch - 7ms/step\n",
      "Epoch 66/300\n",
      "87/87 - 1s - loss: 656.2363 - mae: 5.0460 - val_loss: 48.2125 - val_mae: 3.7374 - 615ms/epoch - 7ms/step\n",
      "Epoch 67/300\n",
      "87/87 - 1s - loss: 655.9821 - mae: 5.0326 - val_loss: 47.7860 - val_mae: 3.6957 - 601ms/epoch - 7ms/step\n",
      "Epoch 68/300\n",
      "87/87 - 1s - loss: 655.8809 - mae: 5.0163 - val_loss: 47.7610 - val_mae: 3.7424 - 601ms/epoch - 7ms/step\n",
      "Epoch 69/300\n",
      "87/87 - 1s - loss: 655.8340 - mae: 5.0148 - val_loss: 47.8341 - val_mae: 3.7161 - 600ms/epoch - 7ms/step\n",
      "Epoch 70/300\n",
      "87/87 - 1s - loss: 655.9948 - mae: 5.0204 - val_loss: 47.8459 - val_mae: 3.7359 - 600ms/epoch - 7ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 71/300\n",
      "87/87 - 1s - loss: 655.7139 - mae: 5.0169 - val_loss: 47.6650 - val_mae: 3.6862 - 601ms/epoch - 7ms/step\n",
      "Epoch 72/300\n",
      "87/87 - 1s - loss: 655.6973 - mae: 5.0208 - val_loss: 47.6009 - val_mae: 3.6943 - 598ms/epoch - 7ms/step\n",
      "Epoch 73/300\n",
      "87/87 - 1s - loss: 655.5065 - mae: 5.0060 - val_loss: 47.5235 - val_mae: 3.7076 - 600ms/epoch - 7ms/step\n",
      "Epoch 74/300\n",
      "87/87 - 1s - loss: 655.1845 - mae: 4.9974 - val_loss: 47.3325 - val_mae: 3.6982 - 600ms/epoch - 7ms/step\n",
      "Epoch 75/300\n",
      "87/87 - 1s - loss: 654.9709 - mae: 4.9817 - val_loss: 47.1857 - val_mae: 3.6963 - 600ms/epoch - 7ms/step\n",
      "Epoch 76/300\n",
      "87/87 - 1s - loss: 655.0101 - mae: 4.9873 - val_loss: 47.8549 - val_mae: 3.6833 - 600ms/epoch - 7ms/step\n",
      "Epoch 77/300\n",
      "87/87 - 1s - loss: 655.1437 - mae: 4.9958 - val_loss: 47.7119 - val_mae: 3.6641 - 600ms/epoch - 7ms/step\n",
      "Epoch 78/300\n",
      "87/87 - 1s - loss: 654.6590 - mae: 4.9783 - val_loss: 47.0806 - val_mae: 3.6816 - 602ms/epoch - 7ms/step\n",
      "Epoch 79/300\n",
      "87/87 - 1s - loss: 654.9606 - mae: 4.9771 - val_loss: 47.3087 - val_mae: 3.6997 - 598ms/epoch - 7ms/step\n",
      "Epoch 80/300\n",
      "87/87 - 1s - loss: 654.3558 - mae: 4.9683 - val_loss: 47.2143 - val_mae: 3.6741 - 586ms/epoch - 7ms/step\n",
      "Epoch 81/300\n",
      "87/87 - 1s - loss: 654.4797 - mae: 4.9522 - val_loss: 46.7113 - val_mae: 3.6800 - 601ms/epoch - 7ms/step\n",
      "Epoch 82/300\n",
      "87/87 - 1s - loss: 654.5282 - mae: 4.9724 - val_loss: 46.9154 - val_mae: 3.6510 - 601ms/epoch - 7ms/step\n",
      "Epoch 83/300\n",
      "87/87 - 1s - loss: 654.0859 - mae: 4.9539 - val_loss: 46.2852 - val_mae: 3.6411 - 600ms/epoch - 7ms/step\n",
      "Epoch 84/300\n",
      "87/87 - 1s - loss: 654.0513 - mae: 4.9272 - val_loss: 46.3678 - val_mae: 3.6120 - 598ms/epoch - 7ms/step\n",
      "Epoch 85/300\n",
      "87/87 - 1s - loss: 653.1333 - mae: 4.9202 - val_loss: 46.2954 - val_mae: 3.6501 - 602ms/epoch - 7ms/step\n",
      "Epoch 86/300\n",
      "87/87 - 1s - loss: 653.2655 - mae: 4.9183 - val_loss: 46.4626 - val_mae: 3.6147 - 600ms/epoch - 7ms/step\n",
      "Epoch 87/300\n",
      "87/87 - 1s - loss: 653.1894 - mae: 4.9260 - val_loss: 46.1858 - val_mae: 3.6014 - 615ms/epoch - 7ms/step\n",
      "Epoch 88/300\n",
      "87/87 - 1s - loss: 653.4054 - mae: 4.9105 - val_loss: 46.5228 - val_mae: 3.6060 - 601ms/epoch - 7ms/step\n",
      "Epoch 89/300\n",
      "87/87 - 1s - loss: 653.4224 - mae: 4.9016 - val_loss: 46.0604 - val_mae: 3.5947 - 600ms/epoch - 7ms/step\n",
      "Epoch 90/300\n",
      "87/87 - 1s - loss: 653.4015 - mae: 4.9108 - val_loss: 45.7495 - val_mae: 3.5923 - 600ms/epoch - 7ms/step\n",
      "Epoch 91/300\n",
      "87/87 - 1s - loss: 652.8589 - mae: 4.8904 - val_loss: 45.5197 - val_mae: 3.5794 - 626ms/epoch - 7ms/step\n",
      "Epoch 92/300\n",
      "87/87 - 1s - loss: 652.9093 - mae: 4.8960 - val_loss: 45.9247 - val_mae: 3.6097 - 591ms/epoch - 7ms/step\n",
      "Epoch 93/300\n",
      "87/87 - 1s - loss: 653.0189 - mae: 4.8984 - val_loss: 45.7518 - val_mae: 3.5535 - 584ms/epoch - 7ms/step\n",
      "Epoch 94/300\n",
      "87/87 - 1s - loss: 652.7565 - mae: 4.8819 - val_loss: 45.4386 - val_mae: 3.5930 - 607ms/epoch - 7ms/step\n",
      "Epoch 95/300\n",
      "87/87 - 1s - loss: 652.6362 - mae: 4.8806 - val_loss: 45.5974 - val_mae: 3.5623 - 636ms/epoch - 7ms/step\n",
      "Epoch 96/300\n",
      "87/87 - 1s - loss: 652.6554 - mae: 4.8706 - val_loss: 45.5687 - val_mae: 3.5641 - 668ms/epoch - 8ms/step\n",
      "Epoch 97/300\n",
      "87/87 - 1s - loss: 652.4208 - mae: 4.8636 - val_loss: 45.2808 - val_mae: 3.5589 - 649ms/epoch - 7ms/step\n",
      "Epoch 98/300\n",
      "87/87 - 1s - loss: 652.0377 - mae: 4.8625 - val_loss: 45.0996 - val_mae: 3.5711 - 636ms/epoch - 7ms/step\n",
      "Epoch 99/300\n",
      "87/87 - 1s - loss: 651.9188 - mae: 4.8580 - val_loss: 45.1209 - val_mae: 3.5763 - 662ms/epoch - 8ms/step\n",
      "Epoch 100/300\n",
      "87/87 - 1s - loss: 651.9377 - mae: 4.8545 - val_loss: 45.2245 - val_mae: 3.5494 - 608ms/epoch - 7ms/step\n",
      "Epoch 101/300\n",
      "87/87 - 1s - loss: 652.0200 - mae: 4.8435 - val_loss: 45.0962 - val_mae: 3.5767 - 632ms/epoch - 7ms/step\n",
      "Epoch 102/300\n",
      "87/87 - 1s - loss: 651.7765 - mae: 4.8360 - val_loss: 45.0362 - val_mae: 3.5478 - 625ms/epoch - 7ms/step\n",
      "Epoch 103/300\n",
      "87/87 - 1s - loss: 651.9603 - mae: 4.8445 - val_loss: 45.0304 - val_mae: 3.5336 - 623ms/epoch - 7ms/step\n",
      "Epoch 104/300\n",
      "87/87 - 1s - loss: 651.3746 - mae: 4.8262 - val_loss: 44.7752 - val_mae: 3.5231 - 649ms/epoch - 7ms/step\n",
      "Epoch 105/300\n",
      "87/87 - 1s - loss: 651.3334 - mae: 4.8272 - val_loss: 44.7451 - val_mae: 3.5423 - 641ms/epoch - 7ms/step\n",
      "Epoch 106/300\n",
      "87/87 - 1s - loss: 651.7474 - mae: 4.8334 - val_loss: 44.4658 - val_mae: 3.4974 - 590ms/epoch - 7ms/step\n",
      "Epoch 107/300\n",
      "87/87 - 1s - loss: 651.3915 - mae: 4.8297 - val_loss: 44.4737 - val_mae: 3.4875 - 611ms/epoch - 7ms/step\n",
      "Epoch 108/300\n",
      "87/87 - 1s - loss: 651.2079 - mae: 4.8200 - val_loss: 44.3723 - val_mae: 3.5184 - 605ms/epoch - 7ms/step\n",
      "Epoch 109/300\n",
      "87/87 - 1s - loss: 651.0563 - mae: 4.8195 - val_loss: 44.4771 - val_mae: 3.5233 - 606ms/epoch - 7ms/step\n",
      "Epoch 110/300\n",
      "87/87 - 1s - loss: 650.6733 - mae: 4.7995 - val_loss: 44.2789 - val_mae: 3.5044 - 610ms/epoch - 7ms/step\n",
      "Epoch 111/300\n",
      "87/87 - 1s - loss: 650.8701 - mae: 4.8141 - val_loss: 44.0958 - val_mae: 3.5394 - 617ms/epoch - 7ms/step\n",
      "Epoch 112/300\n",
      "87/87 - 1s - loss: 651.3073 - mae: 4.8122 - val_loss: 44.1201 - val_mae: 3.4870 - 612ms/epoch - 7ms/step\n",
      "Epoch 113/300\n",
      "87/87 - 1s - loss: 650.9337 - mae: 4.8032 - val_loss: 44.0520 - val_mae: 3.5119 - 606ms/epoch - 7ms/step\n",
      "Epoch 114/300\n",
      "87/87 - 1s - loss: 650.5540 - mae: 4.7892 - val_loss: 43.9347 - val_mae: 3.4866 - 613ms/epoch - 7ms/step\n",
      "Epoch 115/300\n",
      "87/87 - 1s - loss: 650.9171 - mae: 4.8036 - val_loss: 44.0656 - val_mae: 3.4955 - 605ms/epoch - 7ms/step\n",
      "Epoch 116/300\n",
      "87/87 - 1s - loss: 650.3976 - mae: 4.7849 - val_loss: 44.0727 - val_mae: 3.5237 - 617ms/epoch - 7ms/step\n",
      "Epoch 117/300\n",
      "87/87 - 1s - loss: 650.4882 - mae: 4.7921 - val_loss: 43.8559 - val_mae: 3.4883 - 600ms/epoch - 7ms/step\n",
      "Epoch 118/300\n",
      "87/87 - 1s - loss: 650.2441 - mae: 4.7852 - val_loss: 44.0631 - val_mae: 3.4776 - 617ms/epoch - 7ms/step\n",
      "Epoch 119/300\n",
      "87/87 - 1s - loss: 650.7354 - mae: 4.8060 - val_loss: 43.8854 - val_mae: 3.4887 - 606ms/epoch - 7ms/step\n",
      "Epoch 120/300\n",
      "87/87 - 1s - loss: 650.1880 - mae: 4.7690 - val_loss: 43.9941 - val_mae: 3.4754 - 604ms/epoch - 7ms/step\n",
      "Epoch 121/300\n",
      "87/87 - 1s - loss: 650.2838 - mae: 4.7771 - val_loss: 43.7813 - val_mae: 3.4463 - 606ms/epoch - 7ms/step\n",
      "Epoch 122/300\n",
      "87/87 - 1s - loss: 650.5855 - mae: 4.7772 - val_loss: 43.8191 - val_mae: 3.4718 - 594ms/epoch - 7ms/step\n",
      "Epoch 123/300\n",
      "87/87 - 1s - loss: 649.8157 - mae: 4.7630 - val_loss: 43.5221 - val_mae: 3.4556 - 612ms/epoch - 7ms/step\n",
      "Epoch 124/300\n",
      "87/87 - 1s - loss: 649.6731 - mae: 4.7530 - val_loss: 43.4988 - val_mae: 3.4524 - 596ms/epoch - 7ms/step\n",
      "Epoch 125/300\n",
      "87/87 - 1s - loss: 649.4498 - mae: 4.7570 - val_loss: 43.3924 - val_mae: 3.4503 - 635ms/epoch - 7ms/step\n",
      "Epoch 126/300\n",
      "87/87 - 1s - loss: 649.7117 - mae: 4.7545 - val_loss: 43.6942 - val_mae: 3.4433 - 707ms/epoch - 8ms/step\n",
      "Epoch 127/300\n",
      "87/87 - 1s - loss: 649.6578 - mae: 4.7524 - val_loss: 43.3856 - val_mae: 3.4598 - 629ms/epoch - 7ms/step\n",
      "Epoch 128/300\n",
      "87/87 - 1s - loss: 649.8675 - mae: 4.7569 - val_loss: 43.8840 - val_mae: 3.5105 - 595ms/epoch - 7ms/step\n",
      "Epoch 129/300\n",
      "87/87 - 1s - loss: 649.8008 - mae: 4.7593 - val_loss: 43.8162 - val_mae: 3.4883 - 619ms/epoch - 7ms/step\n",
      "Epoch 130/300\n",
      "87/87 - 1s - loss: 649.6849 - mae: 4.7524 - val_loss: 43.3843 - val_mae: 3.4519 - 615ms/epoch - 7ms/step\n",
      "Epoch 131/300\n",
      "87/87 - 1s - loss: 649.2639 - mae: 4.7438 - val_loss: 43.2554 - val_mae: 3.4704 - 618ms/epoch - 7ms/step\n",
      "Epoch 132/300\n",
      "87/87 - 1s - loss: 649.5114 - mae: 4.7363 - val_loss: 43.3595 - val_mae: 3.4555 - 597ms/epoch - 7ms/step\n",
      "Epoch 133/300\n",
      "87/87 - 1s - loss: 649.6018 - mae: 4.7278 - val_loss: 43.4771 - val_mae: 3.4479 - 639ms/epoch - 7ms/step\n",
      "Epoch 134/300\n",
      "87/87 - 1s - loss: 649.5865 - mae: 4.7402 - val_loss: 43.6483 - val_mae: 3.4638 - 599ms/epoch - 7ms/step\n",
      "Epoch 135/300\n",
      "87/87 - 1s - loss: 649.1088 - mae: 4.7304 - val_loss: 43.2974 - val_mae: 3.4559 - 623ms/epoch - 7ms/step\n",
      "Epoch 136/300\n",
      "87/87 - 1s - loss: 649.1038 - mae: 4.7254 - val_loss: 43.6506 - val_mae: 3.4424 - 636ms/epoch - 7ms/step\n",
      "Epoch 137/300\n",
      "87/87 - 1s - loss: 650.7652 - mae: 4.7981 - val_loss: 43.9068 - val_mae: 3.4621 - 642ms/epoch - 7ms/step\n",
      "Epoch 138/300\n",
      "87/87 - 1s - loss: 649.7677 - mae: 4.7467 - val_loss: 43.4373 - val_mae: 3.4677 - 632ms/epoch - 7ms/step\n",
      "Epoch 139/300\n",
      "87/87 - 1s - loss: 649.2108 - mae: 4.7357 - val_loss: 43.4371 - val_mae: 3.4423 - 642ms/epoch - 7ms/step\n",
      "Epoch 140/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87/87 - 1s - loss: 649.4948 - mae: 4.7349 - val_loss: 43.2377 - val_mae: 3.4677 - 622ms/epoch - 7ms/step\n",
      "Epoch 141/300\n",
      "87/87 - 1s - loss: 648.6568 - mae: 4.7178 - val_loss: 43.1737 - val_mae: 3.4563 - 621ms/epoch - 7ms/step\n",
      "Epoch 142/300\n",
      "87/87 - 1s - loss: 648.6094 - mae: 4.7113 - val_loss: 43.0456 - val_mae: 3.4411 - 613ms/epoch - 7ms/step\n",
      "Epoch 143/300\n",
      "87/87 - 1s - loss: 648.7228 - mae: 4.7140 - val_loss: 43.0798 - val_mae: 3.4397 - 599ms/epoch - 7ms/step\n",
      "Epoch 144/300\n",
      "87/87 - 1s - loss: 648.8099 - mae: 4.7083 - val_loss: 42.8995 - val_mae: 3.3987 - 650ms/epoch - 7ms/step\n",
      "Epoch 145/300\n",
      "87/87 - 1s - loss: 649.0083 - mae: 4.7132 - val_loss: 43.0631 - val_mae: 3.4466 - 625ms/epoch - 7ms/step\n",
      "Epoch 146/300\n",
      "87/87 - 1s - loss: 648.6672 - mae: 4.7072 - val_loss: 42.8975 - val_mae: 3.4330 - 607ms/epoch - 7ms/step\n",
      "Epoch 147/300\n",
      "87/87 - 1s - loss: 648.9336 - mae: 4.6990 - val_loss: 43.1464 - val_mae: 3.4193 - 611ms/epoch - 7ms/step\n",
      "Epoch 148/300\n",
      "87/87 - 1s - loss: 648.5136 - mae: 4.6916 - val_loss: 43.0412 - val_mae: 3.4030 - 606ms/epoch - 7ms/step\n",
      "Epoch 149/300\n",
      "87/87 - 1s - loss: 648.6067 - mae: 4.7018 - val_loss: 42.6579 - val_mae: 3.4192 - 615ms/epoch - 7ms/step\n",
      "Epoch 150/300\n",
      "87/87 - 1s - loss: 648.6596 - mae: 4.7033 - val_loss: 42.7781 - val_mae: 3.4116 - 627ms/epoch - 7ms/step\n",
      "Epoch 151/300\n",
      "87/87 - 1s - loss: 648.2360 - mae: 4.6970 - val_loss: 42.8873 - val_mae: 3.4292 - 620ms/epoch - 7ms/step\n",
      "Epoch 152/300\n",
      "87/87 - 1s - loss: 648.2618 - mae: 4.6973 - val_loss: 42.7487 - val_mae: 3.4157 - 626ms/epoch - 7ms/step\n",
      "Epoch 153/300\n",
      "87/87 - 1s - loss: 648.4154 - mae: 4.6858 - val_loss: 43.0194 - val_mae: 3.4049 - 606ms/epoch - 7ms/step\n",
      "Epoch 154/300\n",
      "87/87 - 1s - loss: 648.1459 - mae: 4.6870 - val_loss: 42.6705 - val_mae: 3.4291 - 605ms/epoch - 7ms/step\n",
      "Epoch 155/300\n",
      "87/87 - 1s - loss: 647.8290 - mae: 4.6783 - val_loss: 42.7500 - val_mae: 3.4339 - 621ms/epoch - 7ms/step\n",
      "Epoch 156/300\n",
      "87/87 - 1s - loss: 648.1088 - mae: 4.6847 - val_loss: 42.7294 - val_mae: 3.4343 - 619ms/epoch - 7ms/step\n",
      "Epoch 157/300\n",
      "87/87 - 1s - loss: 647.8741 - mae: 4.6860 - val_loss: 42.4394 - val_mae: 3.4134 - 607ms/epoch - 7ms/step\n",
      "Epoch 158/300\n",
      "87/87 - 1s - loss: 647.5853 - mae: 4.6713 - val_loss: 42.5390 - val_mae: 3.4220 - 630ms/epoch - 7ms/step\n",
      "Epoch 159/300\n",
      "87/87 - 1s - loss: 647.7109 - mae: 4.6737 - val_loss: 42.5920 - val_mae: 3.4076 - 604ms/epoch - 7ms/step\n",
      "Epoch 160/300\n",
      "87/87 - 1s - loss: 648.4525 - mae: 4.6930 - val_loss: 42.7028 - val_mae: 3.4103 - 600ms/epoch - 7ms/step\n",
      "Epoch 161/300\n",
      "87/87 - 1s - loss: 647.9013 - mae: 4.6733 - val_loss: 42.8222 - val_mae: 3.4410 - 620ms/epoch - 7ms/step\n",
      "Epoch 162/300\n",
      "87/87 - 1s - loss: 648.0752 - mae: 4.6879 - val_loss: 42.2469 - val_mae: 3.4129 - 623ms/epoch - 7ms/step\n",
      "Epoch 163/300\n",
      "87/87 - 1s - loss: 647.8666 - mae: 4.6808 - val_loss: 42.2997 - val_mae: 3.3914 - 627ms/epoch - 7ms/step\n",
      "Epoch 164/300\n",
      "87/87 - 1s - loss: 647.7967 - mae: 4.6740 - val_loss: 42.2311 - val_mae: 3.4057 - 638ms/epoch - 7ms/step\n",
      "Epoch 165/300\n",
      "87/87 - 1s - loss: 647.9128 - mae: 4.6800 - val_loss: 42.5130 - val_mae: 3.3998 - 623ms/epoch - 7ms/step\n",
      "Epoch 166/300\n",
      "87/87 - 1s - loss: 647.7220 - mae: 4.6659 - val_loss: 42.3968 - val_mae: 3.4022 - 630ms/epoch - 7ms/step\n",
      "Epoch 167/300\n",
      "87/87 - 1s - loss: 647.6718 - mae: 4.6721 - val_loss: 42.2846 - val_mae: 3.3990 - 603ms/epoch - 7ms/step\n",
      "Epoch 168/300\n",
      "87/87 - 1s - loss: 647.6190 - mae: 4.6630 - val_loss: 42.1724 - val_mae: 3.3991 - 625ms/epoch - 7ms/step\n",
      "Epoch 169/300\n",
      "87/87 - 1s - loss: 647.4245 - mae: 4.6682 - val_loss: 42.0998 - val_mae: 3.3954 - 636ms/epoch - 7ms/step\n",
      "Epoch 170/300\n",
      "87/87 - 1s - loss: 647.4644 - mae: 4.6559 - val_loss: 42.2641 - val_mae: 3.4370 - 619ms/epoch - 7ms/step\n",
      "Epoch 171/300\n",
      "87/87 - 1s - loss: 647.5091 - mae: 4.6668 - val_loss: 42.0539 - val_mae: 3.3969 - 622ms/epoch - 7ms/step\n",
      "Epoch 172/300\n",
      "87/87 - 1s - loss: 647.3586 - mae: 4.6508 - val_loss: 42.1054 - val_mae: 3.4106 - 625ms/epoch - 7ms/step\n",
      "Epoch 173/300\n",
      "87/87 - 1s - loss: 648.1428 - mae: 4.6753 - val_loss: 42.0361 - val_mae: 3.4049 - 669ms/epoch - 8ms/step\n",
      "Epoch 174/300\n",
      "87/87 - 1s - loss: 647.2996 - mae: 4.6623 - val_loss: 41.8435 - val_mae: 3.3600 - 637ms/epoch - 7ms/step\n",
      "Epoch 175/300\n",
      "87/87 - 1s - loss: 647.1051 - mae: 4.6510 - val_loss: 42.0911 - val_mae: 3.3623 - 611ms/epoch - 7ms/step\n",
      "Epoch 176/300\n",
      "87/87 - 1s - loss: 647.3116 - mae: 4.6463 - val_loss: 41.6681 - val_mae: 3.3717 - 618ms/epoch - 7ms/step\n",
      "Epoch 177/300\n",
      "87/87 - 1s - loss: 646.7886 - mae: 4.6401 - val_loss: 41.8896 - val_mae: 3.3708 - 606ms/epoch - 7ms/step\n",
      "Epoch 178/300\n",
      "87/87 - 1s - loss: 646.9427 - mae: 4.6370 - val_loss: 41.9521 - val_mae: 3.3438 - 596ms/epoch - 7ms/step\n",
      "Epoch 179/300\n",
      "87/87 - 1s - loss: 647.1030 - mae: 4.6380 - val_loss: 41.7374 - val_mae: 3.3624 - 595ms/epoch - 7ms/step\n",
      "Epoch 180/300\n",
      "87/87 - 1s - loss: 647.1630 - mae: 4.6362 - val_loss: 41.6170 - val_mae: 3.3737 - 596ms/epoch - 7ms/step\n",
      "Epoch 181/300\n",
      "87/87 - 1s - loss: 646.8228 - mae: 4.6344 - val_loss: 41.6146 - val_mae: 3.3621 - 595ms/epoch - 7ms/step\n",
      "Epoch 182/300\n",
      "87/87 - 1s - loss: 647.4625 - mae: 4.6580 - val_loss: 42.1085 - val_mae: 3.4054 - 611ms/epoch - 7ms/step\n",
      "Epoch 183/300\n",
      "87/87 - 1s - loss: 647.1628 - mae: 4.6480 - val_loss: 41.9242 - val_mae: 3.3413 - 602ms/epoch - 7ms/step\n",
      "Epoch 184/300\n",
      "87/87 - 1s - loss: 646.9241 - mae: 4.6414 - val_loss: 41.5614 - val_mae: 3.3722 - 623ms/epoch - 7ms/step\n",
      "Epoch 185/300\n",
      "87/87 - 1s - loss: 647.0804 - mae: 4.6372 - val_loss: 41.8120 - val_mae: 3.3685 - 611ms/epoch - 7ms/step\n",
      "Epoch 186/300\n",
      "87/87 - 1s - loss: 647.2025 - mae: 4.6409 - val_loss: 41.6645 - val_mae: 3.3733 - 596ms/epoch - 7ms/step\n",
      "Epoch 187/300\n",
      "87/87 - 1s - loss: 647.3126 - mae: 4.6317 - val_loss: 41.5699 - val_mae: 3.3714 - 610ms/epoch - 7ms/step\n",
      "Epoch 188/300\n",
      "87/87 - 1s - loss: 646.7448 - mae: 4.6197 - val_loss: 41.4884 - val_mae: 3.3608 - 595ms/epoch - 7ms/step\n",
      "Epoch 189/300\n",
      "87/87 - 1s - loss: 646.7215 - mae: 4.6241 - val_loss: 41.1861 - val_mae: 3.3758 - 599ms/epoch - 7ms/step\n",
      "Epoch 190/300\n",
      "87/87 - 1s - loss: 646.9093 - mae: 4.6284 - val_loss: 41.5208 - val_mae: 3.3205 - 598ms/epoch - 7ms/step\n",
      "Epoch 191/300\n",
      "87/87 - 1s - loss: 646.7534 - mae: 4.6159 - val_loss: 41.2446 - val_mae: 3.3558 - 608ms/epoch - 7ms/step\n",
      "Epoch 192/300\n",
      "87/87 - 1s - loss: 646.8627 - mae: 4.6248 - val_loss: 41.2756 - val_mae: 3.3604 - 596ms/epoch - 7ms/step\n",
      "Epoch 193/300\n",
      "87/87 - 1s - loss: 646.5375 - mae: 4.6165 - val_loss: 41.3190 - val_mae: 3.3305 - 604ms/epoch - 7ms/step\n",
      "Epoch 194/300\n",
      "87/87 - 1s - loss: 646.8304 - mae: 4.6231 - val_loss: 41.1593 - val_mae: 3.3424 - 621ms/epoch - 7ms/step\n",
      "Epoch 195/300\n",
      "87/87 - 1s - loss: 646.5799 - mae: 4.6205 - val_loss: 41.4329 - val_mae: 3.3853 - 595ms/epoch - 7ms/step\n",
      "Epoch 196/300\n",
      "87/87 - 1s - loss: 646.8553 - mae: 4.6304 - val_loss: 41.2026 - val_mae: 3.3462 - 646ms/epoch - 7ms/step\n",
      "Epoch 197/300\n",
      "87/87 - 1s - loss: 646.6235 - mae: 4.6059 - val_loss: 41.0928 - val_mae: 3.3295 - 616ms/epoch - 7ms/step\n",
      "Epoch 198/300\n",
      "87/87 - 1s - loss: 646.6365 - mae: 4.6128 - val_loss: 41.2421 - val_mae: 3.3572 - 600ms/epoch - 7ms/step\n",
      "Epoch 199/300\n",
      "87/87 - 1s - loss: 646.5701 - mae: 4.6088 - val_loss: 41.1475 - val_mae: 3.3392 - 615ms/epoch - 7ms/step\n",
      "Epoch 200/300\n",
      "87/87 - 1s - loss: 646.2488 - mae: 4.6083 - val_loss: 40.9399 - val_mae: 3.3455 - 616ms/epoch - 7ms/step\n",
      "Epoch 201/300\n",
      "87/87 - 1s - loss: 646.5360 - mae: 4.6028 - val_loss: 40.8807 - val_mae: 3.3424 - 621ms/epoch - 7ms/step\n",
      "Epoch 202/300\n",
      "87/87 - 1s - loss: 646.2595 - mae: 4.6126 - val_loss: 41.0771 - val_mae: 3.3317 - 602ms/epoch - 7ms/step\n",
      "Epoch 203/300\n",
      "87/87 - 1s - loss: 646.3083 - mae: 4.6069 - val_loss: 41.0851 - val_mae: 3.3155 - 617ms/epoch - 7ms/step\n",
      "Epoch 204/300\n",
      "87/87 - 1s - loss: 646.3071 - mae: 4.5973 - val_loss: 40.7008 - val_mae: 3.3398 - 606ms/epoch - 7ms/step\n",
      "Epoch 205/300\n",
      "87/87 - 1s - loss: 646.1268 - mae: 4.6100 - val_loss: 40.8686 - val_mae: 3.3330 - 639ms/epoch - 7ms/step\n",
      "Epoch 206/300\n",
      "87/87 - 1s - loss: 646.3684 - mae: 4.6041 - val_loss: 40.8869 - val_mae: 3.3184 - 591ms/epoch - 7ms/step\n",
      "Epoch 207/300\n",
      "87/87 - 1s - loss: 646.2574 - mae: 4.6000 - val_loss: 40.7754 - val_mae: 3.3263 - 613ms/epoch - 7ms/step\n",
      "Epoch 208/300\n",
      "87/87 - 1s - loss: 646.0939 - mae: 4.5952 - val_loss: 40.7160 - val_mae: 3.3612 - 612ms/epoch - 7ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 209/300\n",
      "87/87 - 1s - loss: 645.9401 - mae: 4.5991 - val_loss: 40.5941 - val_mae: 3.3355 - 608ms/epoch - 7ms/step\n",
      "Epoch 210/300\n",
      "87/87 - 1s - loss: 645.9384 - mae: 4.5965 - val_loss: 40.6139 - val_mae: 3.3068 - 620ms/epoch - 7ms/step\n",
      "Epoch 211/300\n",
      "87/87 - 1s - loss: 645.6833 - mae: 4.5825 - val_loss: 40.6200 - val_mae: 3.3534 - 626ms/epoch - 7ms/step\n",
      "Epoch 212/300\n",
      "87/87 - 1s - loss: 646.3837 - mae: 4.6008 - val_loss: 40.5933 - val_mae: 3.3354 - 670ms/epoch - 8ms/step\n",
      "Epoch 213/300\n",
      "87/87 - 1s - loss: 645.6771 - mae: 4.5914 - val_loss: 40.3749 - val_mae: 3.3371 - 622ms/epoch - 7ms/step\n",
      "Epoch 214/300\n",
      "87/87 - 1s - loss: 645.8580 - mae: 4.5883 - val_loss: 40.2124 - val_mae: 3.3154 - 640ms/epoch - 7ms/step\n",
      "Epoch 215/300\n",
      "87/87 - 1s - loss: 645.4663 - mae: 4.5862 - val_loss: 40.4738 - val_mae: 3.3231 - 645ms/epoch - 7ms/step\n",
      "Epoch 216/300\n",
      "87/87 - 1s - loss: 646.3159 - mae: 4.5909 - val_loss: 40.4629 - val_mae: 3.3212 - 624ms/epoch - 7ms/step\n",
      "Epoch 217/300\n",
      "87/87 - 1s - loss: 645.7576 - mae: 4.5891 - val_loss: 40.4523 - val_mae: 3.3430 - 633ms/epoch - 7ms/step\n",
      "Epoch 218/300\n",
      "87/87 - 1s - loss: 646.0468 - mae: 4.5876 - val_loss: 40.1285 - val_mae: 3.3193 - 631ms/epoch - 7ms/step\n",
      "Epoch 219/300\n",
      "87/87 - 1s - loss: 646.3174 - mae: 4.6103 - val_loss: 40.4271 - val_mae: 3.3020 - 622ms/epoch - 7ms/step\n",
      "Epoch 220/300\n",
      "87/87 - 1s - loss: 645.8906 - mae: 4.5796 - val_loss: 40.2196 - val_mae: 3.3313 - 605ms/epoch - 7ms/step\n",
      "Epoch 221/300\n",
      "87/87 - 1s - loss: 645.5521 - mae: 4.5799 - val_loss: 40.1553 - val_mae: 3.3252 - 635ms/epoch - 7ms/step\n",
      "Epoch 222/300\n",
      "87/87 - 1s - loss: 645.6830 - mae: 4.5754 - val_loss: 40.0967 - val_mae: 3.2961 - 630ms/epoch - 7ms/step\n",
      "Epoch 223/300\n",
      "87/87 - 1s - loss: 645.8907 - mae: 4.5904 - val_loss: 40.3583 - val_mae: 3.2801 - 621ms/epoch - 7ms/step\n",
      "Epoch 224/300\n",
      "87/87 - 1s - loss: 645.7612 - mae: 4.5745 - val_loss: 40.3376 - val_mae: 3.2955 - 632ms/epoch - 7ms/step\n",
      "Epoch 225/300\n",
      "87/87 - 1s - loss: 645.5207 - mae: 4.5793 - val_loss: 39.9630 - val_mae: 3.2790 - 613ms/epoch - 7ms/step\n",
      "Epoch 226/300\n",
      "87/87 - 1s - loss: 645.8060 - mae: 4.5764 - val_loss: 40.0354 - val_mae: 3.2743 - 634ms/epoch - 7ms/step\n",
      "Epoch 227/300\n",
      "87/87 - 1s - loss: 645.8679 - mae: 4.5925 - val_loss: 40.0197 - val_mae: 3.3025 - 635ms/epoch - 7ms/step\n",
      "Epoch 228/300\n",
      "87/87 - 1s - loss: 645.5654 - mae: 4.5733 - val_loss: 40.0741 - val_mae: 3.2812 - 617ms/epoch - 7ms/step\n",
      "Epoch 229/300\n",
      "87/87 - 1s - loss: 645.7009 - mae: 4.5803 - val_loss: 40.1627 - val_mae: 3.2934 - 612ms/epoch - 7ms/step\n",
      "Epoch 230/300\n",
      "87/87 - 1s - loss: 645.4642 - mae: 4.5779 - val_loss: 40.1219 - val_mae: 3.3091 - 630ms/epoch - 7ms/step\n",
      "Epoch 231/300\n",
      "87/87 - 1s - loss: 645.3351 - mae: 4.5657 - val_loss: 40.0240 - val_mae: 3.3040 - 618ms/epoch - 7ms/step\n",
      "Epoch 232/300\n",
      "87/87 - 1s - loss: 645.1802 - mae: 4.5628 - val_loss: 39.7986 - val_mae: 3.2872 - 639ms/epoch - 7ms/step\n",
      "Epoch 233/300\n",
      "87/87 - 1s - loss: 645.3422 - mae: 4.5747 - val_loss: 40.0016 - val_mae: 3.3309 - 630ms/epoch - 7ms/step\n",
      "Epoch 234/300\n",
      "87/87 - 1s - loss: 645.3408 - mae: 4.5741 - val_loss: 40.0295 - val_mae: 3.2858 - 646ms/epoch - 7ms/step\n",
      "Epoch 235/300\n",
      "87/87 - 1s - loss: 645.8091 - mae: 4.5611 - val_loss: 39.9455 - val_mae: 3.2895 - 618ms/epoch - 7ms/step\n",
      "Epoch 236/300\n",
      "87/87 - 1s - loss: 645.3488 - mae: 4.5612 - val_loss: 39.9256 - val_mae: 3.3047 - 656ms/epoch - 8ms/step\n",
      "Epoch 237/300\n",
      "87/87 - 1s - loss: 645.9769 - mae: 4.5893 - val_loss: 40.2476 - val_mae: 3.3202 - 622ms/epoch - 7ms/step\n",
      "Epoch 238/300\n",
      "87/87 - 1s - loss: 645.6189 - mae: 4.5721 - val_loss: 40.1040 - val_mae: 3.3206 - 639ms/epoch - 7ms/step\n",
      "Epoch 239/300\n",
      "87/87 - 1s - loss: 645.3745 - mae: 4.5697 - val_loss: 40.0279 - val_mae: 3.3069 - 641ms/epoch - 7ms/step\n",
      "Epoch 240/300\n",
      "87/87 - 1s - loss: 645.3454 - mae: 4.5616 - val_loss: 39.9720 - val_mae: 3.3036 - 638ms/epoch - 7ms/step\n",
      "Epoch 241/300\n",
      "87/87 - 1s - loss: 645.3328 - mae: 4.5575 - val_loss: 39.5800 - val_mae: 3.2756 - 642ms/epoch - 7ms/step\n",
      "Epoch 242/300\n",
      "87/87 - 1s - loss: 645.2261 - mae: 4.5607 - val_loss: 39.8986 - val_mae: 3.2891 - 635ms/epoch - 7ms/step\n",
      "Epoch 243/300\n",
      "87/87 - 1s - loss: 645.4660 - mae: 4.5703 - val_loss: 39.5256 - val_mae: 3.2948 - 610ms/epoch - 7ms/step\n",
      "Epoch 244/300\n",
      "87/87 - 1s - loss: 645.3019 - mae: 4.5805 - val_loss: 39.7701 - val_mae: 3.2635 - 644ms/epoch - 7ms/step\n",
      "Epoch 245/300\n",
      "87/87 - 1s - loss: 645.1253 - mae: 4.5559 - val_loss: 39.7574 - val_mae: 3.2896 - 631ms/epoch - 7ms/step\n",
      "Epoch 246/300\n",
      "87/87 - 1s - loss: 644.9131 - mae: 4.5573 - val_loss: 39.6086 - val_mae: 3.2763 - 634ms/epoch - 7ms/step\n",
      "Epoch 247/300\n",
      "87/87 - 1s - loss: 645.2736 - mae: 4.5776 - val_loss: 39.5458 - val_mae: 3.2556 - 647ms/epoch - 7ms/step\n",
      "Epoch 248/300\n",
      "87/87 - 1s - loss: 645.0848 - mae: 4.5610 - val_loss: 39.5276 - val_mae: 3.2654 - 598ms/epoch - 7ms/step\n",
      "Epoch 249/300\n",
      "87/87 - 1s - loss: 644.8667 - mae: 4.5562 - val_loss: 39.5876 - val_mae: 3.2881 - 608ms/epoch - 7ms/step\n",
      "Epoch 250/300\n",
      "87/87 - 1s - loss: 645.1526 - mae: 4.5619 - val_loss: 39.2288 - val_mae: 3.2610 - 606ms/epoch - 7ms/step\n",
      "Epoch 251/300\n",
      "87/87 - 1s - loss: 645.0016 - mae: 4.5646 - val_loss: 39.5681 - val_mae: 3.2547 - 637ms/epoch - 7ms/step\n",
      "Epoch 252/300\n",
      "87/87 - 1s - loss: 645.0869 - mae: 4.5542 - val_loss: 39.2233 - val_mae: 3.2697 - 607ms/epoch - 7ms/step\n",
      "Epoch 253/300\n",
      "87/87 - 1s - loss: 645.5197 - mae: 4.5652 - val_loss: 39.3145 - val_mae: 3.2534 - 643ms/epoch - 7ms/step\n",
      "Epoch 254/300\n",
      "87/87 - 1s - loss: 645.2756 - mae: 4.5551 - val_loss: 39.4101 - val_mae: 3.2721 - 622ms/epoch - 7ms/step\n",
      "Epoch 255/300\n",
      "87/87 - 1s - loss: 645.0482 - mae: 4.5542 - val_loss: 39.6355 - val_mae: 3.2588 - 623ms/epoch - 7ms/step\n",
      "Epoch 256/300\n",
      "87/87 - 1s - loss: 645.2353 - mae: 4.5580 - val_loss: 39.5453 - val_mae: 3.2659 - 612ms/epoch - 7ms/step\n",
      "Epoch 257/300\n",
      "87/87 - 1s - loss: 644.9062 - mae: 4.5469 - val_loss: 39.5138 - val_mae: 3.2554 - 618ms/epoch - 7ms/step\n",
      "Epoch 258/300\n",
      "87/87 - 1s - loss: 644.8547 - mae: 4.5485 - val_loss: 39.4822 - val_mae: 3.2617 - 633ms/epoch - 7ms/step\n",
      "Epoch 259/300\n",
      "87/87 - 1s - loss: 644.8992 - mae: 4.5462 - val_loss: 39.2135 - val_mae: 3.2600 - 640ms/epoch - 7ms/step\n",
      "Epoch 260/300\n",
      "87/87 - 1s - loss: 644.9445 - mae: 4.5475 - val_loss: 39.2902 - val_mae: 3.2522 - 643ms/epoch - 7ms/step\n",
      "Epoch 261/300\n",
      "87/87 - 1s - loss: 644.7993 - mae: 4.5392 - val_loss: 39.4942 - val_mae: 3.2456 - 615ms/epoch - 7ms/step\n",
      "Epoch 262/300\n",
      "87/87 - 1s - loss: 644.4984 - mae: 4.5456 - val_loss: 39.0720 - val_mae: 3.2389 - 633ms/epoch - 7ms/step\n",
      "Epoch 263/300\n",
      "87/87 - 1s - loss: 644.5213 - mae: 4.5416 - val_loss: 39.2719 - val_mae: 3.2331 - 618ms/epoch - 7ms/step\n",
      "Epoch 264/300\n",
      "87/87 - 1s - loss: 645.1219 - mae: 4.5498 - val_loss: 39.2323 - val_mae: 3.2593 - 601ms/epoch - 7ms/step\n",
      "Epoch 265/300\n",
      "87/87 - 1s - loss: 645.0551 - mae: 4.5688 - val_loss: 39.2940 - val_mae: 3.2696 - 694ms/epoch - 8ms/step\n",
      "Epoch 266/300\n",
      "87/87 - 1s - loss: 644.6470 - mae: 4.5411 - val_loss: 39.1591 - val_mae: 3.2363 - 619ms/epoch - 7ms/step\n",
      "Epoch 267/300\n",
      "87/87 - 1s - loss: 644.6049 - mae: 4.5374 - val_loss: 39.2724 - val_mae: 3.2572 - 617ms/epoch - 7ms/step\n",
      "Epoch 268/300\n",
      "87/87 - 1s - loss: 644.5829 - mae: 4.5432 - val_loss: 39.1584 - val_mae: 3.2467 - 602ms/epoch - 7ms/step\n",
      "Epoch 269/300\n",
      "87/87 - 1s - loss: 644.4855 - mae: 4.5411 - val_loss: 39.0857 - val_mae: 3.2309 - 607ms/epoch - 7ms/step\n",
      "Epoch 270/300\n",
      "87/87 - 1s - loss: 644.7822 - mae: 4.5363 - val_loss: 38.9379 - val_mae: 3.2249 - 632ms/epoch - 7ms/step\n",
      "Epoch 271/300\n",
      "87/87 - 1s - loss: 644.1634 - mae: 4.5348 - val_loss: 39.0597 - val_mae: 3.2331 - 590ms/epoch - 7ms/step\n",
      "Epoch 272/300\n",
      "87/87 - 1s - loss: 645.0718 - mae: 4.5430 - val_loss: 39.3145 - val_mae: 3.2443 - 611ms/epoch - 7ms/step\n",
      "Epoch 273/300\n",
      "87/87 - 1s - loss: 644.7949 - mae: 4.5429 - val_loss: 39.0660 - val_mae: 3.2390 - 614ms/epoch - 7ms/step\n",
      "Epoch 274/300\n",
      "87/87 - 1s - loss: 644.3259 - mae: 4.5314 - val_loss: 39.0337 - val_mae: 3.2660 - 628ms/epoch - 7ms/step\n",
      "Epoch 275/300\n",
      "87/87 - 1s - loss: 644.4654 - mae: 4.5342 - val_loss: 38.7921 - val_mae: 3.2359 - 617ms/epoch - 7ms/step\n",
      "Epoch 276/300\n",
      "87/87 - 1s - loss: 644.5071 - mae: 4.5304 - val_loss: 38.7590 - val_mae: 3.2166 - 618ms/epoch - 7ms/step\n",
      "Epoch 277/300\n",
      "87/87 - 1s - loss: 644.5126 - mae: 4.5308 - val_loss: 38.8570 - val_mae: 3.2412 - 622ms/epoch - 7ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 278/300\n",
      "87/87 - 1s - loss: 644.6058 - mae: 4.5473 - val_loss: 39.2553 - val_mae: 3.2567 - 627ms/epoch - 7ms/step\n",
      "Epoch 279/300\n",
      "87/87 - 1s - loss: 644.1309 - mae: 4.5373 - val_loss: 38.8579 - val_mae: 3.2409 - 635ms/epoch - 7ms/step\n",
      "Epoch 280/300\n",
      "87/87 - 1s - loss: 644.2305 - mae: 4.5329 - val_loss: 38.9206 - val_mae: 3.2435 - 629ms/epoch - 7ms/step\n",
      "Epoch 281/300\n",
      "87/87 - 1s - loss: 644.6959 - mae: 4.5540 - val_loss: 38.9973 - val_mae: 3.2380 - 632ms/epoch - 7ms/step\n",
      "Epoch 282/300\n",
      "87/87 - 1s - loss: 644.1569 - mae: 4.5230 - val_loss: 38.9466 - val_mae: 3.2325 - 622ms/epoch - 7ms/step\n",
      "Epoch 283/300\n",
      "87/87 - 1s - loss: 644.0291 - mae: 4.5288 - val_loss: 38.9277 - val_mae: 3.2575 - 638ms/epoch - 7ms/step\n",
      "Epoch 284/300\n",
      "87/87 - 1s - loss: 644.3662 - mae: 4.5399 - val_loss: 38.8821 - val_mae: 3.2255 - 642ms/epoch - 7ms/step\n",
      "Epoch 285/300\n",
      "87/87 - 1s - loss: 644.7382 - mae: 4.5407 - val_loss: 39.2687 - val_mae: 3.2670 - 615ms/epoch - 7ms/step\n",
      "Epoch 286/300\n",
      "87/87 - 1s - loss: 644.4563 - mae: 4.5453 - val_loss: 39.2124 - val_mae: 3.2309 - 633ms/epoch - 7ms/step\n"
     ]
    }
   ],
   "source": [
    "# fit the keras model on the dataset\n",
    "resultado=model.fit(train.values, \n",
    "          trainLabel.values,\n",
    "          epochs=300,\n",
    "          callbacks=[stop],\n",
    "          batch_size=4096,\n",
    "          verbose=2,\n",
    "          validation_split=0.2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b12bea7",
   "metadata": {},
   "source": [
    "### Grafico sobre la loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cfe325f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2656b1504c0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=120)\n",
    "plt.plot(resultado.history[\"loss\"])\n",
    "plt.plot(resultado.history[\"val_loss\"])\n",
    "plt.title(\"model loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend([\"train\", \"validation\"], loc=\"upper right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d95d86",
   "metadata": {},
   "source": [
    "### Grafico el MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3128ee67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2656f006df0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RabPLL7+cuXPnhvUeFRUV3HjjjS0GvtNOO425c+fSr1+/sJYhEpKiXYBuJVXTsoiIiLTVwIEDGThwYNTu37t3b3r37h21+3cm1fBFUrCGL9XqSKFWgU9ERGLWE088gZnx6quv7rXv7rvvxsxYsGAB8+bN4wtf+AJDhw4lPT2doUOHcuGFF7Ju3br93qOlJt3a2lp+/OMf07dvXzIyMjj66KN5//339zp3+/btXHnllYwbN46srCwKCgo4/vjjeeutt/Ycs3bt2j2B7sYbb8TMMLM9TcOtNen+7W9/Y/LkyaSlpdGzZ0/OPvtslixZ0uSYSy+9lKysLFauXMmpp55KVlYWgwYN4uqrr6a6unq/372zKfBFUuh6ulSqD5+IiMSsmTNnUlBQwAMPPLDXvlmzZnHwwQczadIk1q5dy+jRo/njH//Iiy++yC233MKWLVuYNm0aO3bsaPd9v/a1r/G73/2OSy65hCeffJJzzz2Xc845h6KioibH7dq1C4Drr7+eZ599lgceeIDhw4czffr0Pc23/fr144UXXgDgq1/9KnPnzmXu3Llcd911rd7/17/+NV/96lcZP348jz32GLfffjsLFizgiCOOYMWKFU2Ora2t5YwzzuCEE07gySef5Ctf+Qp/+MMfuOWWW9r9vQ+UmnQjKWQ93SyrVA2fiEh39vw1sPXTaJfC6zsRPvebdp2SlJTExRdfzN13301JSQk5OTkALFmyhPfff58777wTgPPOO4/zzjtvz3n19fXMnDmTPn368NBDD/Gd73ynzfdcunQpf//73/n+97/PrbfeCsBJJ51Enz59+OIXv9jk2NGjR3PXXXc1ue8pp5zC2rVrueOOO5g+fTqpqakccsghgG8+Pvzww/d5/+LiYm6++WZOPfVUHnrooT3bp0+fzsiRI7nhhhv417/+tWd7TU0NN954I+effz4AJ5xwAvPmzeOhhx7i5z//eZu/d2dQ4IukkMCXjQKfiEi3tvVTWPd2tEtxQL7yla9w22238d///pevf/3rADzwwAOkpqZy0UUXAVBWVsbNN9/Mo48+ytq1a6mvr99zfvNm0P15/fXXAfYKdxdccAFf/vKX9zr+L3/5C/fccw+LFy9u0ow6ZsyYdt23wdy5c6msrNxrNPCgQYM4/vjj92reNjNOP/30JtsmTZrEa6+91qH7HwgFvkgKDXxWoSZdEZHurO/EaJegUQfLMn78eKZNm8YDDzzA17/+derr63nwwQc588wz6dmzJwAXXXQRr776Ktdddx3Tpk2jR48emBmnnnoqlZWV7brfzp07fXH79m2yPSkpiV69ejXZdtttt3H11VdzxRVXcPPNN5Ofn09iYiLXXXddu4Nm8/u3NGq3f//+vPzyy022ZWRkkJaW1mRbamoqVVVVHbr/gVDgi6TQJl3V8ImIdG/tbELtqi677DKuvPJKlixZwurVq9myZQuXXXYZACUlJTzzzDNcf/31XHPNNXvOqa6u3tPHrj0aQt3WrVsZMGDAnu11dXV7wliDBx98kOnTp3P33Xc32V5aWtru+za//5YtW/bat3nzZvLz8zt87XDToI1ISsvZ8zGLSmrrFPhERCS2XXjhhaSlpTFr1ixmzZrFgAEDOPnkkwHfpOmcIzU1tck59913X5Om3baaPn06QJN+cgAPP/wwdXV1TbaZ2V73XbBgwV7z+jUc05baxiOOOIL09HQefPDBJts3btzIa6+9xgknnNCm7xENquGLpL0GbahJV0REYltubi5nn302s2bNori4mB/+8IckJPj6pB49enDsscfy29/+lvz8fIYOHcobb7zB/fffT25ubrvvNXbsWC6++GL++Mc/kpyczIknnsjChQv53e9+R48ePZocO3PmTG6++Wauv/56jjvuOJYtW8ZNN93EsGHDmoTD7OxshgwZwpNPPskJJ5xAz54995S1pe963XXX8dOf/pRLLrmECy+8kJ07d3LjjTeSlpbG9ddf3+7vFCmq4Yuk5oM2AqrhExGR2HfZZZdRWFhITU3NXgMaHnroIWbMmMGPf/xjzjnnHObNm8fLL7+8Z1Rve91///384Ac/YNasWZxxxhk8/PDDPProo+Tl5TU57mc/+xlXX301999/P6eddhr33Xcff/nLXzj66KNbvGZGRgZnnHEG06ZN44Ybbmj1/j/5yU+47777+OSTTzjrrLO46qqrGD9+PHPmzGHkyJEd+k6RYM6plgnAzMYDCxcuXMj48ePDcxPncDfnY4E6/lx3BnXTf853T+y6Pw4RETkwq1evBmD48OFRLonEkrb8bhYtWsSECRMAJjjnFu3vmjFbw2dmnzGzF82s1MzKzOx1Mzsq2uXaJzOsYT1dDdoQERGRCInJwGdm04A3gXTgS8FXGvCqmR0RzbLtVzDwZZuadEVERCQyYnXQxs1AMfBZ51wFgJm9AqwGfgd03Zq+4PJq2VRSW6fmdBEREQm/mKzhwwe62Q1hD8A5V4qv9TvSzPaeEbGrCAY+NemKiIhIpMRqDV8KUN3C9oZtE4G9Z0UMMrMCoHezzSM6p2j70dCHzyqoU5OuiEjc0+BIaS/nHGbWqdeM1cC3GDjczBKccwEAM0sCDgvu79Xqmd6VQHQmywkZtFGjJl0RkbiWkJBATU1NWP4HXOKTc476+npSUlI69bqx2qR7JzAK+JOZDTCzQcBfgCHB/furOrsLmNDsdWaYytrUnho+NemKiMS71NRU6uvrKSwsVE2f7JdzjsLCQurr6/daJeRAxWQNn3Pub2bWG7gW+GZw81z8gI3/Azbt5/xCoDB0W8T+y6thlK768ImIxL0+ffrsWTe2pKSExMRE1fRJixpq9urr60lPT6dPnz6dev1YreHDOXcLkI/vrzfUOXckkAeUAx9Gs2z7lOQTezJ1WlpNRCTOJSQkMHjwYHJzc0lJSVHYk1aZGSkpKeTm5jJ48OA9y9N1lpis4WvgnKsGFgKY2WDg88C9zrn9r4AcLQn+kSea22uhZxERiT8JCQn069d1J4+Q7iEmA5+ZTQDOBebhR+ZOBq4BVgDXRbFo+5eQuOdjfb0Cn4iIiIRfTAY+oAY4HvgOkAWsxw/a+I1zrjyaBduvhMZHHlDgExERkQiIycDnnFsOHBftcnRISOCrr6uNYkFERESku4jZQRsxKyTwOdXwiYiISAQo8EVakyZd1fCJiIhI+CnwRVrIoA3V8ImIiEgkKPBFmgZtiIiISIQp8EVaaB++QE0UCyIiIiLdhQJfpIUEPurro1cOERER6TYU+CItpA9fIKAmXREREQk/Bb5IazIti0bpioiISPgp8EVaaJNuQE26IiIiEn4KfJEWEvgsUEcg4KJYGBEREekOFPgiLaQPXyIBagOBKBZGREREugMFvkhLSN7zMYl6autVwyciIiLhpcAXaSFNuokWoK5eNXwiIiISXgp8kRYS+JKop0aBT0RERMJMgS/SQmv41KQrIiIiEaDAF2khgzaSUJOuiIiIhJ8CX6TtVcOnwCciIiLhpcAXaU368AWoqVOTroiIiISXAl+kqYZPREREIkyBL9Ka9OGrp04TL4uIiEiYKfBFWrN5+NSkKyIiIuGmwBdpIYEvWU26IiIiEgEKfJHWrA+fmnRFREQk3BT4Iq1ZHz416YqIiEi4KfBFWpMavoCadEVERCTsFPgirdlaugp8IiIiEm4KfJHWrIavTmvpioiISJgp8EVasxq+GtXwiYiISJgp8EVaQgLO/GNPNDXpioiISPgp8EVDsJYvSU26IiIiEgEKfNEQDHyJatIVERGRCFDgi4aQGj416YqIiEi4KfBFgQUnX06kXk26IiIiEnYKfNGwp4ZPgzZEREQk/BT4omFPH76A+vCJiIhI2CnwRYNq+ERERCSCFPiioaEPn2laFhEREQm/mA18ZjbVzJ4ws81mVmFmS83s52aWEe2y7Vewhi+ZOjXpioiISNgl7f+QrsfMxgFzgGXA94AdwLHAz4FDgDOjVri2COnDV6saPhEREQmzmAx8wEVAGnCuc25VcNtrZtYP+LqZ5TnniqJXvP0I6cNXpxo+ERERCbNYbdKtDb6XNNteDASAmoiWpr32zMOniZdFREQk/GI18P0dH+7uNrPhZpZtZjOBbwB/ds6V7+tkMysws/GhL2BE+IsdFFLDV6MmXREREQmzmGzSdc6tNbMjgMeBVSG77sD36dufK4Hrw1C0tglZS7e2TjV8IiIiEl4xGfjMbCjwNLANOA/YDhwGXAtkAV/dzyXuAh5ptm0E8GSnFrQ1DTV8FqC6rj4itxQREZHuKyYDH/AboAcwJaT59k0z2wH8zcz+4Zx7o7WTnXOFQGHoNjMLW2H3ErKWbkWNAp+IiIiEV6z24ZsCLG6hr94HwfcJkS1OOyUkA5BEgMpaBT4REREJr1gNfJuB8WaW1Wz7EcH3jREuT/uE9OErr1bgExERkfCK1SbdPwJPAC+b2R/wEy8fDvwEWAw8H7WStcWeUboBKmvqolwYERERiXcxWcPnnHsKOAHYDdwOPAN8GfgrcKxzLkbm4aunorYe5zQ1i4iIiIRPrNbw4Zx7HXg92uXokJB5+JyDqtoA6SmJUS6UiIiIxKuYrOGLeSFr6QKUq1lXREREwkiBLxr2zMPnB2xUaOCGiIiIhJECXzSErKULUFGrGj4REREJHwW+aAjpwwdoahYREREJKwW+aGgW+Cq12oaIiIiEkQJfNIRMvAwatCEiIiLhpcAXDcE+fEkNffgU+ERERCSMFPiioVkNX4WadEVERCSMFPiioVkfPk3LIiIiIuGkwBcNDTV85jACquETERGRsFLgi4aExhXtEgmoD5+IiIiElQJfNCQ0rpubqBo+ERERCTMFvmhITN7zMYl6TcsiIiIiYaXAFw1NmnTrNWhDREREwkqBLxpCAl8SASpqFfhEREQkfBT4oqFJH756KqrVpCsiIiLho8AXDc1r+DRoQ0RERMJIgS8aQvvwWb2mZREREZGwUuCLhiY1fPWUq4ZPREREwkiBLxqazcNXqcAnIiIiYaTAFw171fDV4ZyLYoFEREQkninwRUOzwOccVNcFolggERERiWcKfNHQLPABlGtqFhEREQkTBb5oaNaHD9DULCIiIhI2CnzR0EINnwKfiIiIhIsCXzQ0mYfP1/CVay4+ERERCRMFvmhooYZPU7OIiIhIuCjwRUNoDZ8GbYiIiEiYKfBFQ8igjaTgoI3KWtXwiYiISHgo8EVDQvKej401fAp8IiIiEh4KfNHQpA9fw7QsatIVERGR8FDgi4YW+vBpWhYREREJFwW+aAjpw5ee5NfQLamsjVZpREREJM4p8EVDSA1f7wwf/tbuKI9WaURERCTOKfBFQ0jg65vtP69W4BMREZEwUeCLhpDAV5DpP6/fVUFNXSBaJRIREZE4psAXDSF9+BoCX33AsX6XavlERESk88Vs4DOzWWbm9vE6PNplbFVIDV9+ZmP4W1mowCciIiKdL2n/h3RZNwN/aWH700A18EFki9MOIYGvV3pj5l69oywapREREZE4F7OBzzm3ClgVus3MjgPygV8457ruxHYhgS89MUB+Vio7yqpZpRo+ERERCYOYbdJtxVcBB/wt2gXZp5DAR6CeEb0zAVi1XTV8IiIi0vlitoavOTPLAc4DXnXOrdnPsQVA72abR4SrbHtJSAAMcBCoY3jvLN5bs4vV28twzmFmESuKiIiIxL+4CXzAhUA6cH8bjr0SuD68xdmPhCQI1EKgbk8N3+6qOnaU1dA7OzWqRRMREZH4Ek+B76vATuDxNhx7F/BIs20jgCc7u1CtahL4svZsXrW9TIFPREREOlVcBD4zmwQcCtzunKve3/HOuUKgsNk1wlS6VjT04wvUM6pv9p7NCzeVcPjwXpEti4iIiMS1eBm08dXg+31RLUV7NEy+HKijf04afXukAfDhuqIoFkpERETiUcwHPjNLBS4G3nfOLYx2edosMdm/B+owMw4ZkgfAvHVFOOeiWDARERGJNzEf+ICzgJ7EUu0ehDTp1gHsCXzbS6vZWFQZrVKJiIhIHIqHwPdVoBz4T7QL0i4hffigMfCBmnVFRESkc8V84HPOneycy3LOlUa7LO0S0ocPYFz/HqQl+/9zzFu3K1qlEhERkTgU84EvZjVr0k1OTGDywFwAPlxXHJ0yiYiISFxS4IuWZoEPGpt1l23dze6q2miUSkREROKQAl+0NOvDB/CZYT39Jgfvr1azroiIiHSOTgl8ZtbbzH5tZnPNbLmZjQ9u/4aZTe2Me8SdZn34wAe+5EQ/AfTbK3dEo1QiIiIShw448JnZMOAT4DuAwy9R1rA22KTgdmmuoYavvrHpNiMliamDfLPunFUKfCIiItI5OqOG71agGBgJHAuErlH2NnBUJ9wj/rTQhw/gqIPyAVi+rYzC3VWRLpWIiIjEoc4IfCcANzrnNuNr+EJtAfp3wj3iT6uBr3Ed3TmrdkayRCIiIhKnOiPwpQGtjTDIBAKdcI/4k5zu32vKm2yePCiXzBTfv0/9+ERERKQzdEbgWwac2Mq+Y4HYWd82kjKCNXmVTbNycmIChw33++as3KF1dUVEROSAdUbguxf4rpl9F2hYHyzFzM4DrgT+2gn3iD/pfgoWKvauHD1yhA98m0uqWLOjfK/9IiIiIu1xwIHPOXcX8A/gD8DW4Oa3gf8C/3LO/f1A7xGXMoKBr6YM6mqa7Dp6ZP6ez++oH5+IiIgcoE6Zh88593XgSODXwH34kbvHOOeu6Izrx6X0vMbPzZp1R/fJJj8rBYB3Vqgfn4iIiByYpM66kHPuXeDdzrpe3MtoHI1LxU7I7rvnTzPjiBH5PP3JZuau3kl9wJGYYC1cRERERGT/tLRatDQ06UKL/fiODk7PUlJZy6LNJZEqlYiIiMShzlpa7WIzm2dm5WZW3/zVGfeIO+khga+ypYEbIf34Vqofn4iIiHRcZyytdgbwAPAxkB78/G+gHFgB3HSg94hLTZp09w58g3pmMKRXBgCvLNkWqVKJiIhIHOqMGr5rgNuAhgEadznnLgZGAYnAhk64R/xp0qTbcg3eZ8f7fn0fritic3FlJEolIiIicagzAt9o4BUal1VLAnDObQV+AfygE+4Rf5LTISm42kZlUYuHnD65cVW6ZxdsiUSpREREJA51RuBLBGqccwF8M27fkH3rgeGdcI/41NCs20KTLsD4/j0YGmzWfWbB5kiVSkREROJMZwS+NUBDVdQnwIUh+84DVDXVmozgXHytNOma2Z5avk82lrBup1bdEBERkfbrjMD3Ko1r6d4OfN7MVprZYny/vr90wj3iU8NI3RZG6TaYOamxWffFRVtbPU5ERESkNZ0R+H4GfB/AOfcIvlZvAbAY+Kpz7redcI/4tJ8mXYBRfbIYmOf7+ml6FhEREemIA15pwzlXDVSH/P0Y8NiBXrdbaBip20qTLvhm3aMPyuc/H2zg/TW7qK6rJzUpMUIFFBERkXjQWRMvn2Vmj5jZ+2a2oNnrk864R1xqaNKtKoFA6/NTH3mQn4S5sraej9cXR6BgIiIiEk86Y+LlH+Fr9I4FaoGdzV6tt1d2d3smX3ZQWdzqYUeOaJykec7KHeEtk4iIiMSdzqjhuxL4G9DfOXeUc25G81cn3CM+Zex7ebUG+VmpjO3XA4C3FfhERESknToj8PUCHnLOac3c9krf/2obDY4K1vJ9srGE3VW14SyViIiIxJnOCHzvAGM74TrdT5Pl1fbd8n3MqN4A1AccT83XJMwiIiLSdp0R+L4HfMvMzjCzlE64XvfRxiZd8DV8A3L99Cx/e3sNgYDb5/EiIiIiDToj8K3Er6X7OFBhZrubvUo64R7xqR1NukmJCVx21FAAVu8o5/VlhWEsmIiIiMSTA56HD7gVuAqYDywBajrhmt1DajYkJEOgFsr3Pxjj89MG8cdXVlBWXcd9b63hhLF9IlBIERERiXWdEfguBW5xzv2kE67VvZhBzgAoWgslG/d7eHZaMp+fNoj7317D3NU7WbeznCG9MsNfThEREYlpndGkmwi83AnX6Z5yB/v34vVtOvwL0wbt+fzExxq8ISIiIvvXGYHvJeDwTrhO99QQ+Eo2tOnwkX2yGd/fz8n3+McbcU6DN0RERGTfOiPw3QxcbGbfNbODzKxn81cn3CN+5Q7x72XboLayTaecPXUAAGt3VjB/Q3GYCiYiIiLxojMC3yfAGOA2YBmwvYWXtKahhg/a1I8P4IzJ/Ukw//l/H7btHBEREem+OmPQxk2A2hU7KjTwFa+D/JH7PaWgRxrHjOzNG8u38/C8DXz5yKGM6pMdxkKKiIhILDvgwOecu6ETytEhZnY08FPgCCAN2Aj8wzl3c7TK1G5NAl/bBm4A/PDk0by1Yju19Y6fPf4p//36ESQ0VPuJiIiIhOiMJt2oMLOLgDeAEuAS4FTgFiC2Uk92P0gI5u52BL6JA3O49MhhAHywtojHPt4UjtKJiIhIHIjJwGdmA4B7gL865y50zj3tnHvdOXefc+6maJevXRISIWeg/9yOwAfwg5NHUZCdCsCsOWs6u2QiIiISJ2Iy8AGXA5n4Gr3Y1865+BpkpSZx0WH+3IWbdvPpRq1iJyIiInuL1cB3LLALGGNm882szswKzewvZtZjfyebWYGZjQ99ASPCXurWdDDwAVxw6KA9I3b//UH7zxcREZH4F6uBbwCQATwC/Bc4Efgtvi/fc2a2v358VwILm72eDFtp96cDc/E16J+bzvTRBQA8NX8z5dV1nV06ERERiXGxGvgS8KNyf+Wc+7VzbrZz7rfAT4CjgBP2c/5dwIRmrzPDWN5968BcfKEallsrq67jtpeXd1apREREJE7EauDbGXx/sdn254PvB+/rZOdcoXNuUegLWNXZhWyz0MC34qV2n378mAImDcwB4P631/Da0m2dVTIRERGJA7Ea+Ba0sr2hKTcQqYJ0in5TINM3y/LStbDshXadnpSYwB1fmEpmSiIA3/vPfN5YrgVORERExIvVwPdo8P1zzbafGnx/N4JlOXApGXDhvyEpHVwAHv8G1Fa16xJD8zP51TkTAdhdVcelD7zP397WVC0iIiISo4HPOfcS8DTwczO71sxONLNrgF8Bzzjn3o5uCTtg4KFw4g3+c1UxlGxo9yXOnDKAOy6cSnpyIs7Bzc8uZs7KHZ1aTBEREYk9MRn4gj4P/BH4Or7v3jeBPwDnRbFMB6b3qMbPZYUdusQZk/vzyBVHkJacgHPw3f/OZ0dZdScVUERERGJRzAY+51ylc+4a59xg51yyc26Ic+6nzrnYTTcN/fgAyjsW+AAmDMjhxjPGA7C9tJofPPwJgYA70NKJiIhIjIrZwBeXMns3fi47sEEXFxw6iDMm9wfgzeXbufet1Qd0PREREYldCnxdSUYv9gw0Lj+wwGdm/PLsCQzplQHAb19cxuvLOl5rKCIiIrFLga8rSUwKhj4OqEm3QXZaMndeOJXkRKMu4Lj87/P4z/vrcU7NuyIiIt2JAl9XkxXsx3eATboNJg3M5c4LDyYlKYH6gOOaxz7lrD+/w4frijrl+iIiItL1KfB1NQ39+Dqhhq/BZyf05V+XH0ZeRjIAn2ws4eL73mNTcfvW7RUREZHYpMDX1eyp4evc/nbThvZk9g9n8K0ZIwCorK3n5qcXd+o9REREpGtS4Otq9tTwdf7SaDkZyfzolDGcOcWP3n1h0VZeWLil0+8jIiIiXYsCX1fTEPhqK6CmPCy3+OmpY8lKTQLgigc/4vK/f8DizbvDci8RERGJPgW+riYrZPLlTm7WbdCnRxq/OGsCKYn+//yvLCnk1Dve4qqHPtKqHCIiInFIga+rabLaRuc36zY4a+oAXv/RdC78zCASE/zcf88s2MLnbn9L6++KiIjEGQW+riYrdLWN8E6UPCA3nV+fM4lXf3Acp07sC/il2L54/3v8/qVl1NUHwnp/ERERiQwFvq6mk9bTbY+h+Zn8+aKDufXcSaQlJ+Ac3PnaSi689102a+oWERGRmKfA19Vk5jd+Lo9c06qZccG0QTx91dGM7pMNwAdrizj1jrd4efG2iJVDREREOp8CX1eTlAppOf5zmJt0WzKyTzZPXnUUF35mMADFFbV87R/zuOGpRVTV1ke8PCIiInLgFPi6ooZm3Qg16TaXlpzIr8+ZyJ8umkp2cPqWWXPWcvqdb/PpxpKolElEREQ6ToGvK+rk9XQ7auak/jz33WOYPCgXgBWFZZx11zvc9vJyauo0oENERCRWKPB1RQ2TL5dFv+/coJ4ZPHrFEVx90iiSEoz6gOOOV1dw9l3vsHSrJmsWERGJBQp8XVHuIP9evB7q66JbFiApMYFvnzCSJ686ijF9/YCORZt3c/qdb/ODh+fz/KdbqA+4KJdSREREWqPA1xX1HuPfA7VQtCa6ZQkxvn8OT151FN+aMYIEg9p6x2MfbeKb//qIr8z6gLLq6IdTERER2ZsCX1eUP7rx8/al0StHC1KTEvnRKWN47MqjOHlcHzJSEgF4Y/l2zv/LXBZt1qAOERGRrkaBryvqParxcxcLfA2mDMrlnksO5YOfncjxY/wgkyVbfDPvTx//lJ1ak1dERKTLUODritJyILuf/7x9eXTLsh+ZqUnc86VD+MZxw0lKMAIOHnpvPdN/N5tbX1jKpxtLcE79+0RERKJJga+r6h1s1u2iNXyhkhIT+MnnxvLC947h2FF+hHFpVR13zV7F6X96m28++BG7q2qjXEoREZHuS4Gvq2rox7djBQRiY867gwqy+ftl07j/y4fumbsP4IVFWzn9zrd54J01bC2pil4BRUREuqmkaBdAWtFQw1dXCSXrIW9oVIvTVmbGCWP7cMLYPmwqruS6Jxby2tJC1u2s4ManF3Pj04s5dEgeZ00dwNlTB5CZqp+giIhIuKmGr6vqHTpSt2v342vNgNx07rvkUK49bSwDctP3bJ+3rohrn1jIYb96lV89t4QdGuAhIiISVgp8XVXDXHwQE/34WpOQYFx+zHDe/r8ZPPmto/jGscP3hL+y6jrueXM1x9zyOre9vJyKGs3jJyIiEg5qT+uqMvMhvSdU7oLCJdEuzQEzMyYPymXyoFx+/NkxvL60kHveXM37a3dRWVvPHa+u4IG31zCwZwYT+vfggmmDOHRIHmYW7aKLiIjEPAW+rqz/FFj1GqyfE+2SdKrEBOPEcX04cVwf5qzawa+fW8qnm0oora5jyZbdLNmym0c+3MiI3pl8YdpgPv+ZQfRIS452sUVERGKWmnS7sqHH+PeitVC8IapFCZcjR+Tz5LeO4vYvTOHCzwzimJH5JCX4Wr1V28v55XNLOOH3b/DIvA3q6yciItJBquHryhoCH8Dat2HKhdErSxglJBhnThnAmVMGALCjrJrHPtrIfz7YwOrt5WwvreZH/1sAwJBeGXxh2mCmj+5NQXYqvbJSo1l0ERGRmGBaBcEzs/HAwoULFzJ+/PhoF8err4VbhkJNGUz5Ipx1V7RLFFHOOZ6Yv4lfPru01dq9Q4bk8d0TRnLUQfkkJqi/n4iIdA+LFi1iwoQJABOcc4v2d7xq+LqyxGQYfASsfBnWvBXt0kScmXH21IGcMr4vH6wtYtHmEp74eBPLt5XtOebDdUVc8rf3yU5N4tCheZwwtg8nj+9DQXZaFEsuIiLStaiGL6hL1vABvHM7vPxz//m7n8TMBMzh4pxj4abdrNtVzoKNJTz47joqauqbHJOYYJwwpoCTxvVhTN8eZKclkZeZQk66Bn6IiEh8UA1fvAntx7fqdTj0suiVpQswMyYOzGHiwBxmTurPFceN4JXF23h/7S7eWL6d7aXV1AccLy3exkuLt4WcBzMn9eebx41gTN9szKCytp705ERN/SIiInFPga+r6zcZsvpA2Tb49JFuH/ia65mZwgXTBnHBtEEEAo6PNxTx8AcbeXrB5iY1f87B059s5ulPNpOd5n/2pVV19OmRyuHDe/GVo4YxpFcG/5y7jozUJL58xBCSEjWIXURE4oOadIO6bJMuwEvXwpw7/efvzIeew6JanFhQVx9g9Y5yVhaWUVlTzxvLt/P0gs3s6+eemZJIeTAkfmZoT353/mQG98rAOYdzfjSxiIhIV9DeJt2YDXxmNh14vZXdRzjn3m3n9bpu4Nu2GO4+wn8+7hqY8ZPolidGrdlRzjsrd7BocwmJCUZ+ViqLN+9m9rLt1NQHWjxnQG46JZW11AUCnHfIQK6aMZK+ORoQIiIi0dUd+/D9lL2D38JoFCRs+oyDflNgy3z45CE47v8gQc2N7TUsP5Nh+Zl7bV+/s4I/vLKc9bsq+OrRw3hj2Xb+O89PdL2puHLPcQ++u57/vL+Bo0fmM75/DwzjkCF5frJoNf+KiEgXFg+Bb0V7a/Ni0pQv+sBXvB62fAwDDol2ieLG4F4Z/OHzU/b8ferEfnzpiCG8sXw7CzeV0Ds7lbU7K3hz+XbqAo7Zy7Yze9n2Pcf3zk7l4sOG8MXDB9MrM4XdlXWs2VlOSmICPTNT2FVeQ10gwPj+OZorUEREoiIeAl/3MOoUeP5H/vP69xT4wmzCgBwmDMhpsu2j9UU8+uFGXly0laKKWgLBvn3bS6v5wyvL+cMryzGj1X6CA/PSueDQQRw5oheTBuaSkqRaQRERiYx46MNXCPQCKoC5wM3Oubf3c24B0LvZ5hHAk12yDx/4FPH7MVC2FcadBRf8Pdol6vZKq2p57tMt/GPuOhZt3t2uc3tlpnDRYYPJSEliyZbdzFu7i+q6AOcdOpALDh1Ej7RkemWmaKCIiIi0qDsN2pgKfBmYDewEDgJ+BIwCTnPOvbiPc28Arm9pX5cNfAD//RIseQqy+8EPlvjJ5STqnHO8u3oXH60vorKmnszUJIb3zqSu3rGrooZemSnsKKvmoffWs3RraZuv2yMtiWlDe3LW1AGM7deDO15dwY6yai4/ZhgzRhdo/kARkW6s2wS+lphZLvApsMs5N3kfx8VeDR/AnD/BSz/zn7+3EHIHRbc80m5bSip5e8UO/vnuOhZsLAEgPyuFqYPzKCqvYd66ojZd5/DhPbntgin0z00PZ3FFRKSL6o6jdPdwzhWb2TPAFWaW7pyrbOW4QnxT8B4xUVsy6LDGzxveU+CLQf1y0jn/0EGcd8hANhVXkpWaRG5GCuBrCj/eUMyqwjLKq+tYsLGE15cVUlRRC/gK3fTkRCpq6nl39S5OveMtvnHsCCYOyCEvM5mKmnoWbCwhMyWR0yb1IztNS8mJiIgXV4EvqCG5xU/VZYN+kyAxFeqrYcP7MPG8aJdIOsjMGJiXsde2gwfncfDgvD3bqmrrefSjjSzavJsLpw1mUM90bnlhGf9+fz3FFbXc8sLSFq//i2eX8NkJfZk8MIclW0sprqjhrCkDOGlcHwIOdlfWUl5TR98eaZpSRkSkG4irwGdmecBMYL5zrira5el0SanQfypseNfX8EncS0tO5IuHDWmy7dfnTOSIEb245fmlTeYJDFVWXcf/PtzI/z7cuGfbc59uZUBuOttLq/dMNN0vJ43LjhrKyD7Z5GWkML5/D5IVAEVE4k7MBj4zewhYD8wDdgAjgauBPsCl0StZmA36jA98WxdA6VbI7hvtEkkUnDG5P2dM7k/h7ipWFpaxu6oOMz+dzLKtu/nXu+t5d/VOymvqSU1KIDkxgbLqur0C4paSKn71XGMtYXZqEocN78mEATn07ZFGWnIivbNT6ZuTRv+cdNJTEiP9VUVEpBPEbOADFgCfB64AsoBdwNvAl5xzH0SzYGE19gyYcwe4AHzyb6itgqXPwDn3+hU5pFsp6JFGQY+mS70NyE3n+DF9qKsPsKGokn45aVTW1PPXN1ezsrCMob0y6JebjnOOh95bz+od5XvOLa2u45UlhbyypLD5rQDo2yONGWMKOG5Ub8b378GGogo2FlVy1EH5DNAAEhGRLiuuRukeiC69lm4o5+Cuw2H7UkjNgWo/0pMpF8NZf45u2STmBAKO1TvKKK2qY82Oct5cvp35G4pZu7OiXddJTjROn9SfrLQkNhdXsbKwlIQEo19OGlMG5XLYsF5kpyVR0CNNwVBEpBN062lZDkTMBD5oOj1Lg9wh8L0F0SmPxJ2y6jp2V9ZSUVNPYWkVW4qr2FJSyYfrinhn5c49fQA74ozJ/emfm84zCzZTWx+gR1oyEwfmcNSIfE4e34fkxARmLyukX046kwflNjm3pi7A059sJj0lkc9N6BuV0fU1dQECzpGWrOZtEYmebj0tS7cx+Qvwyg0QqG3cVlXsa/9iYXoZ6fKyUpPISvX/PBxUkNVkX0VNHZ9uLGHp1lL69EglJz2Fe95cxby1RSQmGj0zUhjZJwvDWLergiVbmq5C8tQnm5v8vW13NSsKy3jso02kP5FISlICJZX+t33sqN5kJCeyqbiSYfmZLNxcwurtvgn6eyeO5HsnjqKkopbaQID05EQyU8P7T9q23VWc8ae3CTj479cPZ3jvrP2fJCLSBaiGLyimavgAHv8mfPJQ021XL9MgDulydpZVs3RrKVW19Tz20Sae/XQLAIcOyWNE7yy2lVbx4doiSqvr2n3tguxUCkurAf/fOseN6s0Fhw7imJH5ZKUmUVZdR0pSAqlJjbVxzjmKKmrZWVZN7+zUPfMgtsUNTy1i1py1AEwelMujVxyhaW1EJCrUpNtBMRf4aipg3TtQXwP/uchvu+RJGD49qsUS2Z/l20pJTDBGhNSO1dUHeGvlDh77aBPVtfWcOrEf76/dxZMfbyI3I4XBPTNYub2MBIMLPzOYB99dz46y6lbvkZRgJCcmUFlbD0BqUgI56ckkJhg7yqqprW/8d294fiZTBucydXAeBw/OZXSfbJISE6gPOFZtL6N3Vip5mSkUllZxzC2vU13X2Jx9yRFD+NaMgyjITo2NydtFJG4o8HVQzAW+Brs3w21j/efP3gKHXxHd8oh0Iudci0FqxbZSbnx6MekpiUwZlEuPtCSWbSvlyY83d6imMFRGSiITB+SwZkc5haXVmMHEATmYGZ9sKAaa1iw2yElP5pyDB/C1Y4ZryTsRCTsFvg6K2cDnHPxmMFTvhkMug9P/GO0SiURNZU09767ZyZyVO6gLOPr0SKOuPsDuKj8Ipbbe0Ts7lYLsVHpmprBuZwUfbyji4/XFe/oN7s9hw3py05kT+No/5rF+V8ujmfv2SCMjNZHdlbXkZ6UyMC+dbburKa+uY1h+JmP79WDq4FymDMqlV1YqG4sq+HRjCRU19SQlGqP7ZpOflQpAz4wUEhJUeygiTWnQRndjBr1Hw8YPYPuyaJdGJKrSUxKZMbqAGaML2nWec45V28v5aH0RH68v4pMNJeRlJnPyuL5sLqlk3toi1u0sx8y49rRxjO6bzewfTueTjcW8u3oXlTV1fLjej2AG2Lq7caGfHWU1LN1auufv1TvKeXVp4zyH+Vkp7CirabVsBdmpnDC2gIyUJJISjamDcslJT2FFYSnFFbXUBRyHDevJkSN6YWa8vWIHf3p9BenJiXznhJFsL61mzqqd9MtJY8KAHCb0zyE1OYF1OysoyPbN1SIS/xT44sGewLdEI3VFOsDMOKggi4MKsrjg0EFtOichwZg6OI+pIWsfL9hYzNsrd7B0Syl1AT/lzJaSKjYXV9KnRxoZKYms3F7Gmh3lNDSu7CvsARSWVvPv9zfstzz9ctJIMGuymsrry7a3XHaDgPN9HWeMKcA5R2FpNRMG5DCsVybrd1WQm9EYeJdtLeXQoXn0SEvmp49/SuHuak4a14eZk/oxbWjPdtVAVtfVU13nn42IRI6adINitkkXYM6d8NK1/vPQY2DUZ+HIq6JbJhFpVWlVLQs2lvDx+iJWFJYxsiCLow7Kp1dmKruralm+rZTy6jqqagO8uWI7H6zdRaIZ1XUB6gL7/zc7IyWR2vrAnsEpSQnWpvM6ok+PVKYMymVwzwwGBV+De2aQnZZETV2AlMQEstKSSE9O5KXF27j2iYXsKKtm0oAcDh/Ri3H9ejCkVya9MlPYVV5DwDnG9e/RZGS1iOxNffg6KKYD34pX4F/nNt12xTvQd0J0yiMiYVFZU8/8DcVU1dYzqm82fbJTKauu438fbuSDtbvITEliUM8MLj58CBU1dTz0/nqG9crkjCn9KauqY9Hm3SzcVEJ1XYAhvTJ4b80uXltaSG56MvlZqSzYVExVbYCMlEQqaupbLENignHw4Fw+Wl9MfTtCZEOtYlukJSdw2LBenDaxHzkZyWwsqmRTUSWVtfUcOzKfEQVZfLy+iJSkBCYNzKWovIb1uyrISEmitKqWd1buID0lkR+cNJre2altLqNILFHg66CYDnwVu+C2cVDX2JTDcdfAjJ9Er0wiEnNq6gKUVdeRFwxZb6/cQa/MFMYPyOHRDzeyfFspXz92OJMG5rKzrJoXFm3ltSWFrNlZzsZdlW1agSUnPZkzJvfnw3VFLNtW2q7Q2F49M1M4dEgeq3eU+6X9slNJTkwgOy2JYfmZbCqqZN66IrJSkxjZJ4tpQ3vSMzOF5xdupai8hsE9Mxian8mQnhkM7pVB7+xUdpTVUFlTR056Cr0y/YCa6rp6VhaWMTAvg5z0/TdVby2p4k+vryAjJYnvnziK9BTVZkr7KfB1UEwHPoAdK6G8EJ77MWz7FArGwZVzo10qEekmAgHfD3D9rgrW76qgsraelEQLhsh6yqvrSEtO4IJDB1HQIw2Aqtp6Vm0vY1NRJbvKa+iZmUJVXYA5K3fw8uJt7Cxv7N+YmpRAgtmeuRX3ZV81lJ0pPyuVU8b34fWlhWwuqSLBYHTfHn4wTEYyuRkplFTWsrm4kqREIyPFN22/vrRwz/RBkwflcu1pY0lKMPr0SGNXeQ2PzNtAdV2Acw8ZyJi+2WwpqSI9OZHtZdXMemctm4sr+crRw5gxuoBV28sAyMtMoX9OWpvmg6ysqedPr6+gpi7AJUcMZVDPjP2eU1pVy5od5Yzr16PJZOMVNXWkJiWSqJHkEafA10ExH/gavPFbeP0X/vNVH0L+QdEtj4hIB9TVB/h0UwkJZgzIS6dXZgrVdQFmLyukqKKWqYNzqakLsHDTbnpmpnBQQSZVtb6GcUzfbJ5buJXbX1lOTX2A0X2yKa+uZ0dZNfUBx87yGkoqa0lONKYOyqOmPsCyraV7wmRyotE/N51NRZVh6/sYDoN6pnPKuL4MzEtn7c4KXl68jaKKGpITE5g6OJfTJ/UnOy2JP7yyYs+Sh4kJxvj+PcjLSGFMv2ymDsplUM8MMlKSKNxdxbx1RbyxbDsfrS+iLuCYPDCHW8+bTG5GMve8uZp/zF1Lr8xUvnr0MKaP7k1ORjJrd1SwqbiCovJaemencuSIXqwoLGPx5t2M7deDQ4fmkRyc3HxneTU56ckd6rO5dkc5f5+7lmH5mVx82JBuN32RAl8HxU3g274M/vwZ//mIq2D0qbB+LuDgyO9CkqZgEJHurWF5vbTkBDJS/GQVNXUBPl5fxM7yGo4c0YvcjBTq6gNsKali7c5y1u2sYGdZDb2zU8lMTWR7aTUvLdrG+2t3MSA3na8cPYzC3VUs3rKbkspaiipqKC6vJTM1iYF56TigvLqOipp68jKSuer4kby0aCuPfLhxr/IlGCQlJLTYRJ6UYGSkJLK76sAmGI+mxAQjNSmBqtp6Ag4yUxI5ZmRvP9CnPkB2WhJpSYlU1dXTMzOVqYNy2V1Vy4ZdFeRlpmAY767eyXOfbtkTyI8fU8D3ThxJZmoSa3eUU1xRS1ZaEr2zUxmUl0FeRjL1zrFwk1+P2+GnPDp8eC/SkvcOm7vKa3jso41U1dZz5EH5TOifw67yGm59cSmfbizhh6eM5pTxfinT+oBj6dbdlFbVUVlTz8aiCjYUVfK5CX2bjOLvbAp8HRQ3gQ/gT9Ngx/K9t590Exz13ciXR0QkTlXW1Pvm5g7ULjnnWLxlN4Wl1QQCjs0lVdTUBThlfB9SkxJ5cv4mqusCDMxLp7ouQE1dgOPHFJCVlsTDH2xgV3kNY/v1ICUpgfU7K3hu4RY+2VBMwPnQeMSIXozp24PiilpeXry1SUj82jHDOH1yf/79/gY2FVdSuLuK5dtKWxxYM7x3JseN6o1z8Pe5awmNDYcOyWNXRQ2rt5d35PFFlBk0jzyZKYn0zk6lpLKWnODgJYCFm0v21Bg3nJtojaPdzeBLhw+htKqON5dvb9L9oMG1p43l8mOGh+37KPB1UFwFvtBpWkL1Pxi+/nrkyyMiIhFRH3AUVdSQkpTQZK7D6rp6lm31g2T69Ehrcfm/8uo6lm0rZUtxFdV19fTKSmVE70wG5jX28ft0YwlzVu0gJSmBgwqyOPqgfJyDBZtKWLOjjKLyWob0ymBIr0xyM5JZtrWUeWuL6J+bxrShPflkYzHLt5X6KXuSEuiVmcqC4ATmZpCcmEBpVS3VdQFSkxIoqmh5BZzstCSOGpHPV44exgPvrOH5hVs7/2G2IjnRmqzH3ZKUxAS+OX0E3z9pVNjKocDXQXEV+AIB2PyxH8QBsPJV+OBe//l7CyG3bRPLioiIRFNxRQ2LNu8mJz2ZYfmZlFTWUlVbz9BemXtqVZ1zLNtWyrqdFeyurGVwzwwKeqRRXl3H1pIqNhRVsLuyjtr6AKP7ZjNhQA5JCcanm0p4dUkh1XX19EhPpqSilp3l1SSY0TMzhYs+M5jhvbOYs2oHa4PXPnlcH3qkJ/O1f8xjS0kV+VkpTB6Yy0nj+jC4ZwapyQn0z02nT3Za2PsUKvB1UFwFvuY2z4d7jvOfP3sLHH5FVIsjIiISy6rr6qmqCZCTEb0VY9ob+BL2d4DEgX6TIWew/7zkaf++6SN44srGv0VERKRNUpMSoxr2OkKBrzswg7Gn+8/r58Bbt8GsmTD/X/Dfi+G/X4Kq3dEto4iIiISNAl93MfE8wMAF4NUboTZkRNWSp+DFn0ataCIiIhJeCnzdxYCD4Zx7Ib2n/9sS4HO/hcFH+L8/+TcUrY1a8URERCR8kqJdAImgSefDiONh/oMw4BAYejQMPgz+eiwE6uCt38MZd0a7lCIiItLJVMPX3WT28pMvDz3a/91vsl+NA2D+Q/DePVBbGb3yiYiISKdT4BM47seA+Vq+538Efz4Mdq6KdqlERESkkyjwCfSfCufPgryh/u/idfDAqVC4JJqlEhERkU6iwCfe+LPgqg/hmKv932Vb4V/nQ3VpVIslIiIiB06BTxolJsEJP4cZP/N/l2yAV2+ObplERETkgCnwyd6O+SEMOcp/fv8eWPlKdMsjIiIiB0SBT/aWkACn3w6JqYCDB8+Fhy+B2bco/ImIiMQgBT5pWf5ImHlbMPQBi5+E2b/y4a9h/d3K4qgVT0RERNpOgU9aN/Vi+NZ7MO4sSM1p3P70d+E/X4Rbhvj32qqoFVFERET2T4FP9q3nMLjg7/CT9XDe3/y2ip2w9Bn/eekz8PCXFPpERES6MAU+absJ58LE8xv/zsj37ytegqeuAueiUy4RERHZJ62lK+1z+u1QMBZ6HQQjTvB9+ja8C58+AilZUFMGFbsgOR2mXARjTot2iUVERLo9BT5pn5TMxsmZAb7wL7hnBpSshw8faHrs0mfgtNtg2lcjW0YRERFpQk26cmAy833oS870f6fmQP+DfW0fwLM/gPfvjV75REREJH5q+MzscuBeoNw5lxXt8nQr/SbBlXNh92YYcAgkpcCmj+CfZ0FVCTz3Q9+/77CvR7ukIiIi3VJc1PCZ2QDgd8DmaJel28obAkOO8GEPYMDBcMlTkJbr/37+R7Dg4agVT0REpDuLlxq+vwBvAruA86JcFmnQfwp8+Sn4+xlQVQxPfw/6TIDqUj+1S30N1NdCcpofAJKSEeUCi4iIxKeYD3xmdjFwHDAO+EWUiyPN9Zvs5+978ByoLYe7j2j5uOx+cPx1MPWLkS2fiIhINxDTTbpmVgD8EbjGObexPeeZ2fjQFzAiXOXs9g46AY7+wb6PKd0CT14Ji5+KTJlERES6kViv4bsLWAbc3c7zrgSu7/ziSKtm/AxcAMoKYcQMyB8FiSn+tfF9eP7/oHo3fHAf5A31y7eNPR2O2U9QFBERkf2K2cBnZucCpwNTnWv3Eg93AY802zYCeLIzyiYtSEyCk25seV/+QbBlAbx3N6x5Ax6+BIrWwJb5cPAlfuoXERER6bCYDHxmlgX8GbgT2GxmucFdKcH9uUCtc668pfOdc4VAYbNrhqu40hZTv+gDH/iwB75GcOmzkNEL1r0Dx/4IMnpGr4wiIiIxKiYDH5AP9AGuDr6aK8LX1p0VwTLJgeg7EfpN8bV6od6/F7YvgUAd7FwFF/0XFM5FRETaJVYD31ZgRgvbr8GP2P0csCOiJZIDN/XixsCXNxSK1sK2Txv3r3gRZv8GSjZCz6Fw+JV+qTcRERHZp5gMfM65KmB28+1mdilQ75zba5/EgIO/DLvWQHoeDJgKD5679zFv/Kbx8wf3w7TLYcxpUDA2cuUUERGJMTE9LYvEmaQU+Oyv4LgfwbDjGlfpSEyBE2/Y+/jSLfDazXDX4fD2H/feX18LtZVhLLCIiEhsiMkavtY45y4FLo1yMaQzJCbDUd+BV2/yU7oc9V3IGeSndZlwLqx8Gd65A3Ys88e/eiMMPNSv5Tv3z/DuXX41DwxGfdYHxoIx0fxGIiIiUWPtn9EkPgUnX164cOFCxo8fH+3iSIP6Wh/+WrNxHsw6DeqqICXbD+io3r33cZYIZ9zh+wkCLHkGXvgJTLkQZvw0PGUXEREJk0WLFjFhwgSACc65Rfs7Xk260rXtK+yBr9U75Vf+c01pY9grGO9rBidfBJYArh6e+jYsftLP+ffo5VCyHt64BT7+V8vX3r4Mnv0hbP205f0iIiIxIq6adKWbOvQrUL4dNrwHuUNgyJG+2Tch0e8/+JLgWr4VflLnxBSor2k8/5nvQ00ZjP4c5A722yqL4J/nwO6NsPgJuOJtyO4b8a8mIiLSGdSkG6Qm3Ti38hV46AsQqG3cNunz8OkjfoLnBjmDYNixULoVVr3auH3oMXDJkz5ErpsLteXQayTkDfH7ywqheL3vQ6h5AkVEJMza26SrGj7pHg46Ea58F5Y8CRs+gH6TYfo1fvvLP/cjfgFKNsD8kCbelGzfVLz2LXjxZ9Cjnz++wbiz4ISfw30n+FrB0af6SaTn/Q2GHAXn3u+XlRMREYki1fAFqYavG3POT/i85k1YNwdWvwF1lZDdH77yAjz0eb/aR2uSM32NX0um/xSm/x/s3gwLH4XKYhh8BPQZB6k9IDWr5fOK1kFmb0jJONBvJyIicUg1fCLtZQb9p/rXUd+FmgrYNA96j4Ws3vDFR+D+k6F0sz8+tYcf8fvW7/2AjoawlzsEitf5zwnJvvn4jVtg+fOw5ZOmTccN+kyAyV+AqV+C9FzY8L5fTWTVq5DVB866208q7RzkDIjI4xARkfijGr4g1fDJPhUuhX+eDTXl8Pl/wPDpULLJN+WWboGBn4FLn4UVL/lRwTkD/b7QwSH7kpbrw9+6t1s/ZuIFPmgmp3fGNxIRkRjW3ho+Bb4gBT7Zr7oaH+BCm2GL18OyF2DSBb6GLtT8f/v+fnlDYPDhMP4c6Dkc1r/rQ+LuTbDocdi5sul5iSkw9gxY9vzeTcUF42HqF/3+3EGw5i3f5/DQr8KgaWH52iIi0vUo8HWQAp9EhXOw9Fm/RFzRWj+FzFHf8823O1fBwsd8wFzyTNPav6Q0OOJbMOdOH0KT0uGi/8Lw46L1TUREJIIU+DpIgU+iyjkI1LU+0XR9LbxxK3zyHz9hdEuS0nxfwP5TYPcWXwM48mTI6Nl4TGUxvHM7ZBXAYVdoChkRkRilQRsischs36uKJCbD8T/zy8CtfBUe/QpUlfj+gtMuhw/u98vLfXBvs+sm+NG+WQU+/C15GnYs9/vScvyScx/cC4dc2rjsXEsCAUgILswTqPfXVVgUEYkZCnwiscQMRp4IX3sd3r/HzyM48iQYezq8/UdY9RoQUmvvAlC2zb+aLxH33I/8IBQcbPwANs+HgjG+eThnIGx8H5a/5AOic3DYN/xI5me+75uZz73f1yaKiEiXpybdIDXpSlwo2+5r/rL7wqYPYc0bUL4Dti6AzR/7Y0Z9Fpa/cOD3SkqHE2/wg0hSs/227ct803SfNvz/0O7NkN4TktMOvCwiIt2MmnRFurOs3v4FfgBH6CCOnaugttKHsf9cBMueg4QkmPkHePduKFy89/UKxsHAaX6d4u1L/bbkDN98XFcJL/wfvHqTr3UM1MPSZwCD8x+A8Wf7453z5+5c5fsZDp8O7/wBXvsF9J0EX32p5almair8fISDDvNN0iIi0mEKfCLdRa8RjZ/PutuP8B0+HYYd4wd7FK/zgay6zI8Yzh0EvUf742vK4bVf+mNOvMFPR/P0d/1SdLXlsPjJkBs5eOJbkD/aL0X38CV+FZMG2f0al7LbusBPXZOW45uVj/6BD6m7VsN/LobCRZBZAF9+2jc3H6i6Gj8iur4Gjv6+rwkVEekG1KQbpCZdkXYK1PsBJAv+CytfgerdfmDI8hcBBylZvqm3Idy11eAj/VJ3tRWN2zJ7w4k3+oC26jU/V+GEc6HvhPZd++Wf+1HK4FdM+exvfJO0iEiM0bQsHaTAJ3IA6usA50cTv3ErvP7LpvvHnQVHfttPOv3O7b5G7+RfwCNf9s3DrRk+HVbPbn3/QSfCmXfBhw/4SagnnguTL2y5iXjt2zBrJk0GtQCc/Eu/DN7GeX7wy7izfL/CyiIfLtN7woBDIK2HP37LAv89itf55u5xZ/rjVr4CQ46Cg07QaigiEnYKfB2kwCfSSRomk17ytB84MuEcOO6axmldnPOvhAS/0sgbvw0GrTPhuR/6wRyDj4CJ5/nw9N49PkBWFfvzLRFcfeP9GtYt3vN3kg9pGT0hoxfkj/THfPJvqCnzg00++yvf97CyaO/yp2RD34l+/eOGlU4sAUYc75u8lz7T9Pi+E5uOgE7JhikXwejP+mDbbwokJMK6ubB+Doz6HPQZd6BPef+cg8Ilvmm+YVBNe9XXQaJ6/oh0RQp8HaTAJ9KF1df5aWIqi2DIkX4k8svXw+InGo9JTGnb2sUz/wCHfsVPQ/OPM/y12nP+HsZetYUt6TcFTr4ZHjy38foDDvHzHk4414fC+lofdHv09/0jP/qHn4Jnwnm+2ToQgI//AYue8P0rq4r9toGHwkk3+SC6e5P/OyXT91V85vsw/0HfHH7+LBh6dDu+Gz6wP/VtGHAoXPB3f10R6TIU+DpIgU8kxjgHb/8B5v0Nxp8F038CK17yQa5yF1TsgvLtsG0x1JTC0GP8QI2DTmi8xtaFfuLpocfAmJmw7FlY9bofTJLdzwdDF/DXXfioD4eTvgDH/RjScuGZ7/pgNOhwOP2Pfs7CD2cF50Nsg5RsGHeGP750i699rK/292zQe4yvodv4wf6vl5rTOOhl28LG7ZYIx/0ffOZrsOJlPy9j/ihISoXqUn/9jF6+JjMpFUq3wr/Oa2xuH3myH7G9bRHkDoYRM/zz6sjk22vehLJCH3Y1ebdIhynwdZACn0icCgT8AJDUrAO7Tl21D0eZ+U23l+8IhqWQ8FK8Hoo3wNw/+elvGhz1XUhMhfn/8jVy7ZXdr3GamqoS+PR/TZu3m+s9Bnat8SESaHOtZFuceIMP0O2x9Fk/JRDA6bf7FV460/blvil+3Bk+uIrEMc3DJyISKiHhwMMe+ADRUohoHgDB14LlDvYrkcyaCZs/goNOghNu8OWZfo2vaZtzJ6x72wezKRf5kGgGB3/Z17p9+ggse97XOE6+0A90aRg8AnDU93w/yMx8Hzo/+Y+vZczo6Uc7H3+tnwPxyav8FDftDXsn/wIWPOzvD9DrICjZ5OdgfPUm3xydnOEHxGxb6GsIcwbCjGv9NEDLnvdT9mz+yNcQhtZ8vvZLyOoL793tw3Rarg/BlcW++ThvKAw71vd7XP0mHHmVr1l1ztfe7t4IPUc0/t9200fwwOd8reSiU+GCfzb2P6yrPvAAWFft+0T2nej7ZIrEGNXwBamGT0TCorYS1s3xzcZJKXvvr9gF6Xn7bt507sCaP+vr/EjmjR/A2DNg0Gf8RNgu4INl9W5fjobylm31YWrsTKja7YPbgEMg/yA/QOW+k3zoa01ypu+PuHNFx8vckgv+Ce/eBevn+r9TsuDc+3xgfuBzTacAmnwRnPJLmP0b32w/4FA4/ArfD7SqBDLy/XkDD20McHU1vnY2d1DTgFi6zff33L7UN9+fe68P9Jvnw7NXQ8/hcMzVPuwmJLW8ekxZoR/ENOw4SMno3Oci3ZKadDtIgU9EpI0+/R88ejl7agyTM3wgtAS/nF+o5EwfqjZ/7IPlSTf5WsmGpf4SU/y5lcU+JGb29k3nm+b5voaJqf4+rQ6oaQjCwbJk9fHngS9PaH/IlqT39CPCB34GXr7Oh8aEJL9u9MGX+FrMV29uGl5Tc+DU3/pazt0bm14vMQWmXe77fxYu9iFy9yZ483e+L2neMDjzzzD0KN+H9O3bfO1hnwlw8Jd8aOwMFbvgxZ/5yc9nXNs4Sr5ByUY/AMgFfPDVajYxR4GvgxT4RETaoXCpHxyTnudrAxtqL1e8Ak9806+pfPT3fPhJyfShpqrEB4v178HfZ/rtn/+XDz/NBQJ+8Elmvh8I88r1jfvGn+1r2l69qXHqHIDPfMPXtP3r3KZT5eSP8k3Rocd2RN9Jjc3bB2rE8X4+x9AJxtPz4IJ/+Kbs1hRv8EFtyFE+xNXXwZInfReB9Dy/Os6Y0+F/lzWG7+k/hen/52uKV78O7/21cYJ08IN6Rp3iR5T3HOanSdJckl2eAl8HKfCJiHSS+lpfu7avvm6l23yoCO2T2Or16uCBz/om6alfgtPv8GFn66fw5m8hZxCMPwcGHuKPDwT8iOuP/gEFY2HGz/zygFs/9U2xWQW+FnDtO3709erX/XkpWT6kVhTBoscam4gTkuDwK/1qL0ufhse/2Rgex5/t55lc/jxgsPyFxibnUHlD/VyT7/212WTjBnlDfIgDH76mXOhrOhc+Bum5PsiO+qzvA/nUt31z+sBpMPhwf0zzAUAJST5wh95j4vl+QMuOZft/3n0nBfuZBke9F6/z80ee9nv4+EHfND3mNB8SQ5u+l70Aa9/ytZuhSzlKWCjwdZACn4hIF1ZT4YNH7zGdP53L1oV+upixM30gBB9a18/1k3b3m9y03922xfD8j/3yfOf8tenE1oEArHjRN5n2m+wHo9RX+3InJvtg99ZtMP8h3xR+7r0+OH3yH3jqOyEjqtspJdtPQB4aJvNH+3I0r9lMSofJX/A1eS4AS56Cpc9BxY593yM9r+lk5ZkFcMLPfa3ge3/xUxQ1lOW03/npfNbN8cGx5zAfWuc9ADtX+umDeo/1zd5ZBX5VncoiX4OZnBbsYznaP7OWVOzy4XrNm35A0JHf9r+LQGDv5uvW1FX75547yNe4dkRdjQ/YCQm+X+ziJ2HS5yFnQMeu1w4KfB2kwCciIhFTWexrQEPD4tZP/SCTpc8Czg80KdnoB9E0SM/zNYUfP+hr8QYd7msEJ17gJ+Be+Qq8+2cfZs65x8+d+OS3fCjpMcDPWTn1S34kd3O1wdHX797l/05I9rWIVSWd15TdHokp0Ge8b2ruP8W/9x7tn8/T3/N9IhtMPN/PH7nuHR8AR3/OT/sT2ieyvs6H+J0rfF/PN3/nR5ADHP0DOP46H9x2b/YBdv5Dvsb14Et8OWrKfD/TXiP9s/zwb/DKjf5ap/wSHr/Cd3PI7g+XPesH84SRAl8HKfCJiEiXULrV19TlDfXBbdXrPqTU1/hglzvIN4njILtv599/5atQssFPrp2ZD7VV8PR3/GCdSRfASTf7WrvXfgGlm4MnmZ9Me+TJ8PyPGlewgb2bmEMH1rRbO+aStEQ/SXjvMb5mdd0cH8haM/QYGHmSXw+8pmzf197fyjw9BvrQlze0bWXtAAW+DlLgExER2YfaqqZTztSUw5JnfE3l8OmNc1JWFvlwtX2pD3fjzoIN7/rgOmamrzXcMt9P+dNnvG/i3fCeP7bncN+cXrze9zncMh+2LNi7WTq9p1/dZuBn/Kow2xb6YDn+HD8X5Zb5bftOh1zqy7pj+d77Bh/hA3dDLWBL0ns2DZH9D248ftix8OWn21aODlDg6yAFPhERkS4oUO/7x21dANuX+QFBh3zZN6+Cn8ZnydN+IEv+SL9t88fw/n1+oM/OlT5MDj7M10AOOsw322b1gd6jfPP667/0yzQG6vyE4Gf+ydf2OQc7grWryek+iO5c6bdl5sNnvu7vPfvXvk/kKb+GV2/0/SK//ExY+/Ip8HWQAp+IiEgcautAjl2r/VQ5oz7bch/HtnLON2mn53b8Gm2gpdVEREREGrR11G7P4Z0z0MIs7GGvI9r4FEREREQkVinwiYiIiMQ5BT4RERGROBezgc/MppjZs2a23swqzWyXmc01s4ujXTYRERGRriSWB23kAhuAfwObgEzgi8A/zWyoc+4XUSybiIiISJcRs4HPOTcbmN1s8zNmNgz4OqDAJyIiIkIMN+nuww6gbr9HiYiIiHQTMVvD18DMEvDBNQ84HzgFuGo/5xQAvZttHhGWAoqIiIhEWcwHPuAu4BvBzzXAd5xzf93POVcC14e1VCIiIiJdRDwEvl8B9wEFwOnAn8ws0zn3u32ccxfwSLNtI4Anw1NEERERkeiJ+cDnnFsPrA/++ZyZAfzazP7unNveyjmFQGHotuB5IiIiInEnHgdtvI8Psp2wIJ6IiIhI7IvHwDcDCACro10QERERka4gZpt0zeweYDe+Rm8bkI8fpft54LetNefuQwrAypUrO7OYIiIiIp0uJK+ktOV4c86FrzRhZGaXAZcBY/GrbpQBnwD3Oece7MD1zkCDNkRERCS2nOmce2p/B8Vs4OtsZpYDHIdfrq0mTLdpGAl8JrAqTPfo7vSMw0/POPz0jMNPzzj89IzDKwUYBLzhnCvZ38Ex26Tb2YIPa78J+UCEjARe5ZxbFM57dVd6xuGnZxx+esbhp2ccfnrGEfFxWw+Mx0EbIiIiIhJCgU9EREQkzinwiYiIiMQ5Bb7I2g7cGHyX8NAzDj894/DTMw4/PePw0zPuQjRKV0RERCTOqYZPREREJM4p8ImIiIjEOQU+ERERkTinwCciIiIS5xT4IsDMsszsj2a22cyqzGy+mX0h2uWKRWY23cxcK6/Dmx17sJm9YmZlZlZsZo+Z2fBolb0rMrNsM7vVzF4ys+3B53hDK8e2+Xma2bfNbKmZVZvZGjO73sySw/pluqi2PmMzm9XK73ppK9fVMwbM7Hgz+1vwWZSb2SYze9LMDmnhWP2GO6Ctz1i/4a5NgS8yHgO+jB+e/jngA+DfZnZRVEsV234KHNHstbBhp5mNAWbj1xq8APgKMAp4y8x6R7qwXVgv4OtAKvBEawe153ma2c+A2/G/+1OAu/D/9/pzp5c+NrTpGQdVsvfv+vPND9IzbuKbwFD88zgV+C5QALxrZsc3HKTf8AFp0zMO0m+4q3LO6RXGF/7/ORxwYbPtLwGbgMRolzGWXsD04PM8bz/HPYyf+6lHyLYhQA1wS7S/R1d5AUbj9Ez5wWd7Q0efJz7cVAJ/bXb+T4EAMC7a37kLP+NZQFkbrqdn3PR7F7SwLQvYCrwSsk2/4fA/Y/2Gu/BLNXzhdzZQBjzSbPsDQH/gsIiXKM6ZWRIwE3jUObe7Ybtzbh3wOv7/JgK4oH0d087n+VkgDf/7DvUAPvic1QnFjiltecbtpGccwjlX2MK2MmAxMAj0Gz5QbXnG7aRnHAUKfOE3AVjinKtrtn1ByH5pvz+bWZ2Z7TazF83s6JB9I4B0Gp9xqAXAQWaWFpFSxof2PM+G3/OnoQc557YAO9DvfX/SzWyrmdWb2UYz+5OZ9Wx2jJ7xfphZDnAwsCi4Sb/hTtbCM26g33AXlRTtAnQDvYDVLWzfFbJf2q4E3+9jNrATOAj4ETDbzE5zzr1I4zPd1cL5u/D/BZkHbAl7aeNDe55nL6DaOVfeyrH6vbfuk+CroS/qccD3gRPMbFqwRgX0jNviz0Am8Mvg3/oNd77mzxj0G+7SFPgiY1/NOVrbrh2ccx8DH4dsesvMHsf/l+KtwIuhh+/rUmEoXrxr6/PUc+8A59wfmm162cw+Bv4HfA0I3a9n3Aozuxn4IvBt59yHzXbrN9wJWnvG+g13bWrSDb+dtPxfKw1V3C39F6e0g3OuGHgGmGRm6fhnDq0/dwcUR6Rw8aE9z3MnkGZmGa0cq997+zwOlAOhUw7pGbfCzK4HrgV+5pz7U8gu/YY7yT6ecWv0G+4iFPjC71NgbLDTcKiJwfeFSGew4LsDVuFHgE1s4biJwErnXFWkChYH2vM8Pw3ZvoeZ9cWPUNXvvf0MP3KxgZ5xC4JB5Ab8COhfNdut33An2M8z3uep6DccdQp84fc4fvj6uc22fxnYDLwX8RLFGTPLw4/Am++cqwoOkHkaOMfMskOOGwzMwM/7JG3Uzuf5AlAFXNrsMpfiw/gTYSxqPDoPyADeDdmmZ9yMmV2HDyK/cM7d2Hy/fsMHbn/PeB/0G+4i1IcvzJxzz5vZy8DdZtYDWAlciB+WfrFzrj6qBYwxZvYQsB6Yhx/NNRK4GuhD0388rsdPcP2Mmf0GPwXATcFzfh/BInd5ZvY5fOfrhv8hHGdm5wU/P+ecq6CNz9M5t8vMfgHcbGa78PNNTsP/D8V9zrnFEfhKXc7+njHQG3gI+A/+3wiH7/D+PfwoyPsarqVn3JSZXY3/Lb4APGvNVtxxzjUEDf2GO6gtz9jMhqDfcNcW7YkAu8MLX8N3O34EWDV+FNMXol2uWHwB1+AHbRQDdUAh/r/Op7Vw7CHAK/j+IyX42tYR0f4OXe0FrMX/49zSa2hHnifwHWBZ8Pe+Dv8PeXK0v2tXfcb4EaKPAWuAiuBzWw7cAuToGe/z2c7ex7N1zY7VbzhMz1i/4a7/apj9XURERETilPrwiYiIiMQ5BT4RERGROKfAJyIiIhLnFPhERERE4pwCn4iIiEicU+ATERERiXMKfCIiIiJxToFPREREJM4p8ImIiIjEOQU+EZEuzMwuNTNnZodGuywiErsU+ERERETinAKfiIiISJxT4BMRAcxspJk9ZGaFZlZtZkvM7Fsh+6cHm1YvNrPbzGyrmVWa2RtmNrWF651hZnPNrMLMSs3sZTM7ooXjxpjZv81sW/C+683sH2aW2uzQbDO728x2mNlOM3vMzPqH4VGISBxS4BORbs/MxgEfABOAq4GZwLPAHWZ2fbPDfwUMBy4PvvoDs81seMj1LgKeBHYDFwJfBfKCxx0dctzk4H0PB34OfA74CZAKpDS7731ALXAR8GNgOvDggX1zEekuzDkX7TKIiESVmb0AjAfGO+d2h2y/k8ZQNxl4HfgIONQF//E0syHACuDvzrmvmVkCsAHYCUxxzgWCx2UBq4CVzrmjgtteBQ4GRjnntrdStkuBB4C7nHOhNY4/Am4F+jnntnbWsxCR+KQaPhHp1swsDTgBeByoMLOkhhfwHJCGr4Fr8JAL+S9l59w6YA4wI7hpND4g/rMh7AWPKwMeBQ43swwzywCOAx5uLew181SzvxcE34e08auKSDemwCci3V0vIAn4Nr7JNPT1XPCY/JDjW6pN2xq8DiHvW1o4bjP+39284CsR2NjGcu5s9nd18D29jeeLSDeWFO0CiIhEWRFQD/wT+HMrx6wBJgY/921hf18aA1nDe78WjusPBIL3dMH7Dmx/kUVE2kc1fCLSrTnnKvB986YCC5xz81p4hdauXWhm1vBHsA/fkcDs4KZlwCbgombHZQLnAnOdcxXOuUrgDeB8MwutQRQR6XQKfCIi8F1gMPBWcGWL6WZ2upl938xea3ZsAfC4mZ0WHI37ClAF/Bog2G/vx8AU4Jng9Czn40NlLnBNyLV+ACQD75nZ18xshpl9ITg9THb4vq6IdDdq0hWRbs85t9jMDgauA36BD3XF+NG3zzU7/KfANPzI2R7A+8AXnHOrQq73kJmV46dY+S++6fZdYIZzbk7IcZ+Y2WeAG/GBMRvfH/A1oKbzv6mIdFealkVEpA3MbDq+lu5859z/olsaEZH2UZOuiIiISJxT4BMRERGJc2rSFREREYlzquETERERiXMKfCIiIiJxToFPREREJM4p8ImIiIjEOQU+ERERkTinwCciIiIS5xT4REREROKcAp+IiIhInFPgExEREYlzCnwiIiIice7/Aa16AeVhnJVhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=120)\n",
    "plt.plot(resultado.history[\"mae\"])\n",
    "plt.plot(resultado.history[\"val_mae\"])\n",
    "plt.title(\"model mae\")\n",
    "plt.ylabel(\"mae\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend([\"train\", \"validation\"], loc=\"upper right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81178b39",
   "metadata": {},
   "source": [
    "### Realizo la predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b8d66d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict=model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ffa88109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.890722],\n",
       "       [ 5.520507],\n",
       "       [ 5.520507],\n",
       "       ...,\n",
       "       [ 8.138536],\n",
       "       [16.12886 ],\n",
       "       [20.614296]], dtype=float32)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33c619f",
   "metadata": {},
   "source": [
    "### Calculo de las metricas de medicion\n",
    "* Error cuadratico medio\n",
    "* Error absoluto medio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ddc518a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.17841816792468"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testLabel,  y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f27836bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.677061571027796"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(testLabel,  y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47514ec4",
   "metadata": {},
   "source": [
    "### Curva de calibracion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e28bb74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2:  0.73506899570775  p:  0.0\n"
     ]
    }
   ],
   "source": [
    "r, p = sp.stats.pearsonr(testLabel.to_numpy().flatten(), y_predict.flatten())\n",
    "r2 = r**2\n",
    "print('r2: ', r2, ' p: ',p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9247f2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.05, 0.9, 'r2=0.74, p=0')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SY03E//3f/7F9+3b+9a9/sWXLFsaMGRO7j9xuNw888EDMYioQCNqPsBAKBIJGURSFe+65h6eeeorp06ezc+dOXnjhBd5//30GDx7MXXfdxaJFi+Iqmdx9991cccUVVFZWxqxDjz76KHPmzGn1GHJzc2M+c2effXZc+0033cT111+PLMu8+OKLrFq1in79+vHUU0/xs5/9DDDTnzTFqFGjeOmll5gyZQqrV69m5cqVzJgxg7vvvjtu3czMTJYsWcJVV11FUVERL7zwAlu3buWUU05hyZIl9aJeG+Pcc8/l6aef5phjjuGdd95hyZIl9O/fnyeffJIf/OAHgFmL+Mknn2TkyJGsWrWKJUuWUFpayg033MDrr7+OLMvNHlcijj76aJ544gn27dvHokWLCAQC3HLLLTzwwAMt2n7GjBksXLiQgQMHsmLFCl577TVCoRB33HEH//73v4H657slx5qIuue5pKSERYsW8fnnn3POOefw6quvxiWlFggE7UMyROiVQCAQCAQCwRGNsBAKBAKBQCAQHOEIQSgQCAQCgUBwhCMEoUAgEAgEAsERjhCEAoFAIBAIBEc4QhAKBAKBQCAQHOEIQSgQCAQCgUBwhCMEoUAgEAgEAsERjhCEAoFAIBAIBEc4QhAKBAKBQCAQHOGIWsatoKTE29VDELSBjAyzHm1Fhb+LRyJoC+L69WzE9evZiOvXM8nJSWn1NsJCKBAIBAKBQHCEIwShQCAQCAQCwRGOEIQCgUAgEAgERzhCEAoEAoFAIBAc4QhBKBAIBAKBQHCEIwShQCAQCAQCwRGOEIQCgUAgEAgERzjdXhB+9dVX/OQnP2HMmDGMHj2aK6+8ks8//7zeOsFgkAcffJCZM2cyatQoLrzwQtatW9dFIxYIBAKBQCDoWXRrQbhnzx4uu+wyPv/8c6655hpuuOEGdu/ezRVXXMHXX38dW+/mm2/m6aefZtasWdx6661omsY111zD5s2bu3D0AoFAIBAIBD2Dbl2p5Nlnn6WmpoaXX36ZMWPGADBr1izOPPNMnnjiCR555BE2bNjAqlWruP3227nyyisBOOecc5g7dy733Xcfy5Yt68IjEAgEAoFAIOj+dGsLYUFBAS6XKyYGAQYNGsSgQYNiFsIVK1ZgtVq56KKLYuu4XC4uuOACvvjiC/bs2dPZwxYIBAKBQCDoUXRrQTh48GD8fj9FRUWxZcFgkJKSEnJycgD44osvGDp0KC6Xq962I0eOjLULBAKBQCAQCBqnWwvCq6++mv79+3PzzTfz5Zdf8v333/PrX/8ar9fLT37yEwCKioro1atX3La5ubkA7N+/v1PHLBAIBAKBQNDT6NY+hL179+ZnP/sZd911F+eee25s+Z133sn06dMBqKmpwel0xm3rcDgACAQCSRtPRoar+ZUE3Q5FMb97xPXrmYjr17MR169nI67fkUO3FoSPPPIITzzxBKNGjeLSSy/FYrHw6quv8sc//hHDMLjsssua7UOWu7URVCAQCAQCgaDL6baCsLq6mn//+9/k5eWxaNEibDYbAGeddRY//vGPueeee5g9ezYul4tgMBi3fXSZ2+1O2pgqKvxJ68tikZEkKWn9CRonJcUOgM8Xalc/hmGgqnoyhiRoBVHLRDJ/f4LOQ1y/no24fj2PSGE+fccc1+rtuq0g3LNnD+FwmDPOOCMmBqOcf/75rFu3jm3bttG3b19KSkriti8uLgZI6F/YHZAkCat1G4ahdvVQjgCsAFgskTb3IEkWIpFxyRqQQCAQCARJRa2soHTZEnzbt9N3yYut3r7bCkK73bTq6Hq8RcYwDAA0TWPkyJG88cYbBIPBmN8gwI4dOwA4/vjjO2G0bcMwVDQtXswKko35QaFp4Tb3oCg5yRqMQCAQCARJwwiHKH/rTcrffhMjEkGu1U+tpds62B111FH06tWL5cuXU1NTE1uu6zpLlizBYrEwYcIETjvtNMLhMIsXL46t4/f7Wbp0KSeccAIDBw7siuELBAKBQCAQdByGTvX6tey6/deUrXgdI9L2WTDoxhZCWZa58847uf766zn//PO56KKLsFgsvPnmm3z88cdcf/319O7dm969ezNt2jTuv/9+Dhw4wODBg1myZAkHDx7k3nvv7erDEAgEAoFAIEgqwW+/pujF/xAq2BvfqCht6lMyovOv3ZQtW7bwt7/9jU8//RRVVTnqqKNYsGAB8+bNi61TU1PDwoULefPNNwkEAowYMYKbbrqJiRMnJnUsJSXepPVltSpYLJvElHEn4PGYU8Y+X/umjFV1IpGIlqxhCVqIcGrv2Yjr17MR1697ESkupuSVl/B9/HHCdtexI8k+cy4Dpo5vdd/dXhB2J4Qg7JkIQdizES+kno24fj0bcf26B7q/hrI3Xqfig/+BFv8esvXtR+YZZ+EeMQLF4yanb3ar99Ftp4wFAoFAIBAIjmg0lcoPP6B0+XJ0f01cs5KSSsacU0mdMAGLx4PkdAJtS2knBGEPRCqpwvGnl7G8/xlSpQ99WB9CN84lct7k1nfmDeC4YxHWlVuQfEHUE/MI3nsl+tH9G90k5fjrkAsSWzb1gTl4P3s8fsyFpaRMvoXQz88kdPuFrR9nErGs+gT7nxejfF2IkZVK+KrZhG46B0ReSIFAIBB0AwzDIPD5ZxQtfpFIUVFcu2S1kjptOpkzZ2JJS0NxuTEkifbM+QpB2NMIq7jPuxupsobgby5C75uBdcVmXFc9gj+sEpl/cqu6c/3kUZRt3xG86zKMFCeO+5binvtHfJv+ipHhSbhNzaKbkcL1o5mUzd/i/O3zhK76QfwGhoHzun8gVSevjGBbUTZ9jeuS+4mcO5nQ7y5G2bgT+58Wg24QuuW8rh6eQCAQCI5wwoUFFL/8Iv6dXyVsd48ZS9ZpZ2DLzUFxu0GxohsGtNMBUAjCHobl7W0on+/B9/5f0MYOB0CbcQJyQSn2R15vlSBUNn+D9Z3t1Cy5DXXOGABqJh9DyqjrsP37HUL/d37C7fRRQ+ovqPbjuupRIqeOJfzLeXHr2556F+Xb/S0eV0div28p2vGDCTx5HQDq7NFIEQ37w68T+sVZ4LQ104NAIBAIBMlHq66i9LVlVK1bSyJTn33wELLPOhvnkCEobg+SzYauGwnXbQtCEHZjUo6/jshZE1B27EX5+HvCF0whMm8ioStnoY0ZVm9dfVgfLGu+AEBZuwPP2X9stN/wJdMJ/P1aLP/7FMNtRz3lhFibkZ2KOuVYLO990qggbIjj/mVIZdUEHrgqrk3aU4TjDy/if/Ym3Bfc06L+GuI+8y70gTnoR/XB9o+3kAJh1KkjCdyzAGNwbmwdy/ovG+1D++6fkOLGsu7LuCnryLyJ2B95A8uGr1BPGdWmMQoEAoFA0BaMSJiK996hbOV/MULxJVYtGZlknn4mntGjsXjcSHYHhgGGntyYYCEIuzm2f71D+OpTCd00D8NpQzvpaLQZJ9RfKaJieWc72jEDANBGDcH33p8a7dPITgVA/mYf+qBeYKmfs0gf0hvrK+taND4pvxjbk28TuukcjIENqnnoOq6fP0HknEmos0e3qL/GsL65FT0rheB9PwJNx/GHF/GcdRfeTQ+B20HgoR8jeRNHwTmdVuiTgfxFAVJYRRvWp167NrQ3APJ3B0AIQoFAIBB0AoZh4NuyiZJXFqNWVMS1Sw4HGTNnkT5tGkpqKnJtwEhH5YYRgrCbY/TOIPiXK0BuvKiM47cvoOw6SM2iW8wFqS60CXnN9i1V1WCkOuP3meJoVFw1xP6Pt8BuJfTz0+PabE+8iZxfTM3Lt7aoryapCeJffTf6EFO8aUf1JWX6bdhe/JDw1ac2GQRDbdoZqco8prhj9ph/S96u93EUCAQCweFPaNf3FL20iODu3fGNskzKiSeR+YNTsWVnobhcGLLcYUIwihCE3Rzt6P6Ni0HDwPH7RdiffJvgTfNQz5oQW44WXwM6hiyZfepG4shaA1BaUNUwEMa26APCl8+E9PoBKPK3+3H8ZTH+538Faa7m+2oG7cS8mBgE049RG9wLZcNXcPWp5vE29mtRNdMKGq2L3Vg0sSyijAUCgUDQcahlpZQsXYJ3y+aE7c4RR5N15tk4+vdH8SQvYKQlCEHYzTFy0hI3BMM4r30C26sbCN04l9Cdl8aalHVftsiHkDQX0ncH4tolXxAjtXkRZ3n/UyRvgMhFU+s3aDrOn/+NyLyTUGeeYAqyKLpxSKC1Ar1PRtwyIycVqdLMy+Se+6dmfQiNNDdAfLSzz/y7JccsEAgEAkFr0YMByt/8LxXvvoOhqnHt1l69yTrzLDwjR6K43Ug2e1IDRlqCEIQ9kSo/7ovuQdn8LYE/X0H4urPqNWujh+L74O5GN9czU8z1juqL5f3PTMtZHSukvPsgel6/ZodhfWc7+qDcuAAXqbAUy9bvYOt32F5eU6/N8cAyHA8so/rTxzAG5Ta7j1if5b64ZXJxFWqt1TDw8NVIvsRTvk6nFfpmoHtcGIqMvPtgvXZll/m3NqKJaWeBQCAQCFqJoWlUr1tD6Wuvovniq53Jbg8Zc04lbeJJWFJSkBwdEzDSEoQg7GmoGu7596Fs/x7/MzeinjMpfp0UZ5xIS9jVzBNwPPgalv99ivoDM+2MVFqNZf2XhG4+t9ntla3foU4cEbfc6JOZUJB6Zv6G8IJZhK+chdEns9n+62LZ/DVSWTVGVm1AzCe7kPOLUX9tRkLrR/VtfONaH0IcBtrkY7Cu2Ez4+rNjU8fW1zdhpLnRxg1v1ZgEAoFAIGiMwJdfUPTyS4T374tvtFhImzKNzFmzsGRkmn6CdKpBMH5IXbdrQVuw/esdLBt2EvrRbIx+WShbvqnX3pJgkti6U45FnXoszqsfI/jHyzAyUnDc+wpGmrtegml5ZyGEIvXzD2o68jf7iFyQoDqKzdKoINV7Z9Rrk3cfRCqtbn7c/jCuC+4hdMt5SL4gjj+9hHbsQCIXTm16uwYEbzkP9zl/xnXlQsKXz0TZ9A22R1cQvOtSkYNQIBAIBO0mfOAAJYtfpOaLzxO2u08YRebpZ+Lo28esMCIrpp9gFyMEYQ/D+sYmAOzPrML+zKq49qrKxa3qr2bRzTh/8zzO3/8HDB114giCz95UL0jEefNTyHtL8H5+qCSdVO5FUjWM9MTVTFqK/f5Xsb30YbPj1iYdjXrySJzX/QMA9fRxBP98Bdhadwtr04/D//yvcNzzCq7LHsTok0nwj5eZFkOBQCAQCNqI5qum7I3XqVz9waEgxjrYBwwk6+y5uIYfheJxI1msne4n2BSSYXSTkfQASkri5//bitWqYLFsQtMS1wQ+kvCMvRHf9kcabXefeRcANSvvbFv/tVPGPl+4TdsDKEoOqjqRSERrfmVBUsnIMIN9KipalgpJ0L0Q169nI65f8+iRCFXvr6Lsv2+gB+J92ZW0dDJPP4OUceOweFKQ7bUBIx1ITk5Kq7cRFkJBl2J97n9N5xAUCAQCgaAbYhgGNR9vo2TJy0RKS+PaJbud9BmnkH7ydKxpaUhOJ4ZBh4vBtiIEoaBL0SYf02o/QIFAIBAIupLgnt2UvPwige++jW+UJDzjJ5B16unYcnNR3O4uDxhpCUIQCrqUJqODa2nrVLFAIBAIBMlErSindNkrVG/ckLDdMWw4WWedjXPIUCzu7hMw0hKEIBQIBAKBQCBoAj0YpOKdNyl/+y2MSCSu3ZqTQ9YZZ+M+4XgsHg+S1datAkZaghCEAoFAIBAIBAkwdB3vxo8oWfYKWlVVXLvscpExew5pU6ZgSUlFdjjQdaNLEku3FyEIBQLBEY2haYQKCohUVeL/7BMM3cCamUn67DkoTmer+4tUVVHyn+eJVFZgTc8g57IfYk1rpASloEOIXlM94Ed2urAPGAAQWybZ7eZ6oRCy04Wtb19ChQWE9uaDAfZBg3AMGoyktLzEph4O492ymbIvPkPdvs1MO2K1knH5AiwSlPznBQgGk36s1pNnYI+EMYIh7P0HkDHn1Nh9G9i3jwOPLUT1eiEUMjeQ5fopUSwWUCwQSjC2tHRzeSgEkoycmoqekobsq0KvqYFw/cwN1uNHkTl+AikTTkS29fy8roGvd1L88ouECvbGNyoKqZMmk/mDOdiyspCcTkDqtgEjLUGknWkFIu1Mz0SknenZdFTaC0PTqF6/jqqNHxHc9T00rC8qydiHDKH/Tbe0SBhGqqrIv/O36L4EZRY9Hgbd9ZcjUhh2ZtqS6DWt3rwJtawUQ9eQZNkUPIChRtADAYyokLFazYpFkQiGpsWm9yRFwZKVTcYP5pA29eQmhaEeDlP0/DN4G/Ep63QkCVvf/oT3FXTdGBSFlPEn0mvBj3qkMIwUH6TklSX4Pt6esN018jiyzzwLe//+ZmJpSep2M8NtSTsjBGErSLYgtFq3YRjxRa4FycXptAIQCMT7fbQUSbIQiYwTgrAL6AhBYWgaJUtexrt9G1pFeZPryh4PQ+55oElRGKmqYvf/3ZQwGe2hjmSGPLDwiBOFnSUIo9fU98l29EAQ2eFAUhTU6qpDArCudayxV58sHxKGdjtpk6eSc/ElCUWhHg6z9+4/Ei4s7IhD6vFIHg+p007G6vJgSU/HM258txaIWk0N5f99nYr/rUr4W7b17UfW2XNxH30MitsNioXuKqGEIOxgkikILRYZqbaWrqBjSUkxp4e83lC7+jEMA1Vt4oUv6BAyMlwYmsbBz76uNwXYmum8hlSt+ZCylW+gVlaC1rzItw8dxqDf/L7R9u9+eV1Cy2BDZE8Kwx9+rDVD7fF0lCDUw2GqN2+kZMtmjJ1f1b+OnhTwJe95jSRhOyoPR7/+OAcPiU2JHnjqX3g3ftSjAge6DElCdjpxjxpDrysWdCthaKgqVR9+QOkby82p8AYoqalknno6qSeeiCUlFclm6/ZTwyIxdQ9CCIvOI/qsFta9noehaRS9+x5F764iuP+AOQVosWIfMIC0kyaTOmVqq4WhoWlUb96E5ve3SAwChHbvQgsEEloJI1VVLRKDALrPS6Sq6oizEiYTPRzm4HNP49u0sfGVkikGAQyD8DdfE/7ma6qB4v88j3v0GHyffSrEYEsxDHS/H++mDYQKCxh4+++SKgoT+Y02fDZE/TyD+XvQA0EsvXoR+GoHod27DlmRGyC73ViyMvHt+Azvjs8xfDUYDieEQkgOG66jRpB56unINlvC/Uf3GSrIR7LYcI8ejXPI0HZ90HYUwkLYCpJpIRR0HqL0Us9ED4cpXPgQwW+/TryCopA69WR6XXp5qx6uwT17OPDPJ1C9XoxgfJmpxsg8+xyy550Tt3z/E4/h276txf14xo6j77XXt3j9nk4yf396OMzev/yR8D4xRdujkWVST5pE76uubndXif1GFazZ2aRMmEjqlKnmh+Xzz+Dbvi0m/BSnE0PT0BsRgsgytox0ZLsdQ9NRq6vREpSliyI5HCieFDB0JFlByczEiEQIFRRApM4+JAklJZXMs+eSfvKMDhOGwkIoEAgOCwxNo/DhJsQggKZR/eEH2Pv2I2PW7Bb3rQf8GLoGeussxv6dOwiOGh1neYhUVrSqn9auLzhE0QvPCTF4OKDr+D75BD0cbpeVMKHfqEXBUDWC+fmEi4sI7N1D8NtvY/eNZLcjSVKT4s6SlorF7QYDVK8X1VfTrCXYCAZRQyGsWdnoaoTIN18n3sYw0KqrKHn5RcL79pF7yWXdxlood/UABAKBoCFV69YQ/KYJMViH0uXLzAjRFiI7XUiygkHrfHhD+Xs58M8n2PfwQ1St+TC2T2t6Rqv6ae36AhM9HKbm04+7ehiCZCBJ6MEgvm1b29VN9fp1+D7ZjqGqWLKzUVJSkJ0ulJQULNnZGKqK96P1phhUFBSXEyMcRm8k/Y/icmHv0xuL241W4ydYVITq9bXcLcAwUL3VphWy4TaSZP6LomlUf7SO6o/WtfHok0+3thAWFhYya9asJte55557OO+88wgGgzz++OOsXLmS8vJy8vLyuPHGG5k6VdTJFQh6EoamUfnh6pavHwjg//473HkjWrS+fcAALFnZRKrjk8w2heQyp5iilofQ/kJyLpxPzmU/bNWUcc5lP2zVfgUmvm1b0Zuw6gh6EJIEGKit/A3WJeoLrAeCWLKz44I0JUlCSknFqK5GcTrRQiE0f+L7R7bbsGZkIskSeihEuKoao2EaqpaOKxRq1B+xdmAxsWiEw1Rt3Ejq5Nb7QncE3VoQZmZmcv/998ct13Wdu+++G8MwmDBhAgA333wzH3zwAZdeeilDhw5l6dKlXHPNNTz77LOceOKJnT10gUDQRkIFBUSKi1q1TfW6tS0WhJKikHriRCIlRaihFkaeSxLW1DQzUtLjQauqxPfxduz9+pM2bTqyx9PiKGMRUNI22iMeBN0MwwBJxpLa9t9CqKAAtazUnCZuJGOHFAkjWSyNTg9LFgvWjAxkqwVDVQlXVKO39JnQFAmniiE2KVFHFEb2FxIqKMAxeHD799tOurUgdLlczJs3L2753/72N6qrq3nkkUcYMGAAGzZsYNWqVdx+++1ceeWVAJxzzjnMnTuX++67j2XLlnXyyAUCQVvRA/5WTQED6K0IDgFInTKV0P5Cqj5aj+FvPthBSc+ITfdIkoSSlo5aWop38yZSJ09l0F1/aVEewkF3/blV4xQcoj3iQdDNMAxkpwPPuPFt7iLqCyxZEljWJAmtssJMK5UIScKano7idICuE6msMrMOdCh1FWGdpZqGHugeAY89zodw7969/P3vf2fmzJmcdtppAKxYsQKr1cpFF10UW8/lcnHBBRfwxRdfsGfPni4arUAgaC2y04VstbZqG3u/Aa1aX1IUc7r3/IuQ09ObHo/HgyWlfsSeJEnIDgeR0lJCBQVY09IY8sBCZE/iyD7Zk3JEJqVOJp5x45HbUEpQ0A2RZTyjR7croCTmC6zW+XiUJHS/n1D+nkbFoCUlBUef3igOB6rXR7CouBPEICQSg2A+i2SnqxP23zzd2kKYiIULFwJw2223xZZ98cUXDB06FJer/kkdOXJkrH1wNzDHCgSC5rEPGICt/4AWB5UgSWTMObXV+5EUhfTpM0idNJnyN1fg/eQT1NIS03dIVcFiwZKWhuL21HcGj2JR6n3dW9PSGP7wY6KWcQch22y4R43Bu2F9Vw8lHlkmc965WNMz6tcytlhwnjiRwPbt0E2sQF2OLGPr15/cyxe0q5uoL3Bobz5SSgpSOEykpLhR3z/F6cSSnmZGGPsDRKqrm7bot4c6U8KHltX5/zpt1r79Y7W2u5oeJQh37drFW2+9xfnnn19P4BUVFXHCCSfErZ+bmwvA/v37k7L/aD4tQc9CUUxDuLh+PQd11gz27Pq+RY7d7uHDye6b1Y69ucj60RUY2qX49+Tj++479i97FSQZa1pqo1sZho5ktZDeKxN33Xsrw0Xub29tx3gOL5L5+0u74efs2F+AP39vu/tKKpJExoA+ZE8/mcFnzYlr1gIBtl754/h62R0wjm6bKFuSUNwuMsaPZ8hPr05KUmp15jT2LSslcmB/o4EcksOOLTUNyWImiQ5XVWNE2l7GtDlkux3JakVrzKe4zvWR7Xb6nDKdzOzW5wzsCHqUIHzxxRcBYn6CUWpqanAmmEpwOBwABERkmkDQo8iZOYOa73dR/N6qJl9wSkoKx9z5u6TsU1IU3MOG4ho8iPING6nZvRtLakpCh3XDMNACAdxDzfUFnYNsszHy3rvZ9fd/UrZmbdcORpLMuseahuJ0kjnppEZXVZxOsiadRNnaDk4xUve3UivADED31cSWDbz2Z6QM6I/v+13kP/s8dJA4so8fR3qfPih2O4rTgS0jg8xJJyWtOol/bwHlGzYSLi5O2C7ZbDj79yNt1Gj8e3bh3fk1eiBxupmkIUlY09PQVRWtpoE4b/gcUxSyZ04nZ+b0jh1TK+gxgjAcDrN8+XImTZrEUUcd1aptZTk5rpKi0kXPRFQq6ZmknT8fI6sX5a8vi08XIUnYhwyl/023UB00IJjca+scMwH//gOEyspR0tLriULDMNCqKpHsDpxjxlNZnYSoxMOYjvj9Zf3wx2TMv6J+LWNdB5uNtDPORrZZqVj6CmhJtshFRWA0p5yqgizjPmEUVTUq1DS+v4xLfkjFtu3oneGvJknIbjdKpmk51xQLksVC1llzcYyZSASwZ/cjb+K0ZrtK5vVr7hy1BLW6mvIVy6lc/UHij0WLBXuv3jgHDsQ9ahTusRNwqyr688/i27616ZQw7SBaqURTNSTZgiPvaIxImHBhQf19SpJZG/mseaSdPL3Dnh+HdaWSzZs34/V6Of300+PaXC4XwQSJJqPL3G53h49PIBAkF0lRyJg1myHnnol359cUvr0KPRjA3m8AGXNOTVhXOFlEo5B9H29HLTVTW2BRQNXQg0EzQnLMWFInizynXYVss5E+9WTSp56csD3nB633K22IHg6z954/m4mNo/5mhmH+v2G0yh9OttkYfPf97Lrt/6CVUfHNdy6b4zEMkCQkpxPJ6UT3+Q6b+1WPhKl6fxVlK1YkzCqgpGeQNnky7rwRWDIysPbpZ54XzHPf5yfXoIevrFfL2NqrF/5d3xEqLMCorKz1+7RiSU8Hhx1DNzBqakz/P6sVyWJBxsxveDjWMu4xgvDDDz9EluWEiar79u1LSUlJ3PLiWlNyr169Onx8AoGgY5AUhdSRx9Kn7+BO3WfOhfOx9+uPd/MmIqWlGJqGpCg4Bg0i5cSJ3SaZrKDjkG02Bt7+O4oXPWeWWgsGATOHnux04Bk9mtzLF7R4GtTi8TD8r4+w/9mn8G/ZHG/hkmWcxxyLrV9/anZ8gVZSjBGOmPuMIklIVhuWrCzSZ5yCfdAgwgUFGIaOWllBcNcu1LIyMyXLYXC/GoZBzbatlCxdTKS0NK5dstvJmDmL9JkzsaanI1lt6HpiNxPZZiNtylTSpnSMME6USzC6T+j+YrzHCMJt27aRl5dHVla88/jIkSN54403CAaDMb9BgB07dgBw/PHHd9o4BQLB4YGkKKRNm07q5KkJv/wFRwayzUbvq65GD4fxbduKWl2FJTXNTIPTBn842Waj/zU/R7/yx1Rv2oj/K/M95Tp2JKknHvKxMzSNUEEBWo0Prbo6lvxcSU1FcXvq3Yeu4YfcqKLbHQ73a3D3LooXv0jwu+/iGyWJlAkTyTz9dOy9eiM7HOi6gdGIGBQ0T48QhKqq8u2333L22WcnbD/ttNNYunQpixcvZsEC03Tv9/tZunQpJ5xwAgMHDuzM4QoEgsMISVG6RRUBQdci22ykTpqc1P7Sp51M+rTEU95tve8Oh/s1UlZG2WtLqd64IWG786g8ss+ei3PoMCSHw8w/KIRgu+kRgvDAgQOEw2H69u2bsH3atGlMmzaN+++/nwMHDjB48GCWLFnCwYMHuffeezt5tAKBQCAQCFqLHgxS8fZKyt95O2FqGGtuLtlnzcUzejSKy40hSeasu9CCSaFHCMKKigoAUlIaj5p55JFHWLhwIW+88QaBQIARI0bw1FNPMX5820vjCAQCgUAg6FgMXaf6o3WUvrYMrSq+ZrXscpM5Zw5pU0/GkpoKigXdMIQQTDKSYXTXLJbdj5ISb1cPQdAGRNqZno24fj0bcf16Nh19/fw7v6Rk8cuEChIkG1cU0qZMJXPOqdhycpoMGBHU57BOOyMQCAQCgeDwIHzwIKVLF+P75OOE7e7jTiDrrLNxDByAZHeYWXWEGOxQhCAUCAQCgUDQKWg+H+X/fYOK9/8HuhbXbuvfn+y58/AcOxLJ6QSkbluN73BDCEKBQCAQCAQdiqGqVK5+n7I3Xkf318S1K2lpZJ52JmknnYTF48GQZSEEOxkhCAUCgUAgEHQIhmFQ8+nHlLyymEhRUVy7ZLORPuMUMmbNwpqRAYpVBIx0EUIQCgQCgUAgSDrBvfmULH6JwNc74xsliZRx48k680zsffoh2WoDRoRZsMsQglBwxHM4ZfbvSnr6efTv3cuBR/+K5vejuFz0vu5GFKQeezwCQVehVlZQtvxVqtavSyjwHEOHkT33HFxHHYXkEAEj3QUhCAVHLIamUb1+HdWbN6GWlZq1P2UFa3Y2KRMmkjqlZ9b+7Gx6+nn0791L4R/vqLdMC4fZ9+e7zD88HqxOV485HoGgq9BDISrefZvyt1ZihMNx7dbsbLLOOhvPuPFYXG4MhEGwOyEEoeCIxNA0Spa8jO+T7eiBILLDgWRRMFSNYH4+4eIiQvsLyblwvnj5N0FPP4+JxGAcPh8RQOsBxyMQdAWGruPdtIHSV5ei1haSqIvsdJIxew4ZM2ZgSU3DkBXTT1DQrRCCUHBEUr1+Hb5PtmOoKpbsbCRJirXJHg9aVSW+j7dj79eftGnTu3Ck3Zuefh6bFYNRfD4sAwd1++MRCDqbwLffULL4JYJ7dsc3yjKpk6aQddrp2Hrl1gkYEWKwOyIEoeCIw9A0qjdvQg8E40QMgCRJKGnpqKWleDdvInWymCJMRE8/j/69CSojNEEkFMLajY9HIOhMwsXFlC1bgnfb1oTtrmNHknX22TgHD0W220XASA9ACELBEUeooAC1rNSc3mwgYqJIkoTscBApLSVUUIBj8ODOHWQPoKefxwOP/rVV6xslxUgDBnbb42krejiMb9tW1OoqLKlpeMaNR7bZYu2qz0fZa0uJVFRgzcgg48y5BL76koqvdhDevh10DdntJuPiS1H8NWCAfdAgHIMGC8F8GKL5ayhf+V8q//cehqrGtdv69CX77Hl4TjgByenEMBDl5noIQhAKjjj0gN8MfLA087KyKBiahh4QNVgT0dPPo+Zv5Xh03fxvNz2e1qKHwxS98Bw1n36CHgxiJn6TkF/6D+5Ro8k693wK778HtbSk3nZVH66O76uqirIn/x77W7JasWRlk/GDOaSffZoQhocBhqZRteYDyl5fjubzxbUrnhQyTzudtKnTsKSkiICRHogQhIIjDtnpQpLNwIcmUTUkRUF2ujpnYD2Mnn4eFZcLLUEkZKPIsvnfbno8rUEPh9l7958I799nCl1JMv8ZOrq/Bu+mDXg3rG9z/0YkQqToICWvLEYqK2LQlQuSOHpBZ2IYBlXbPyb/mecJH9gf1y5ZraRNm07mqadhy8wUASM9GCEIBUcc9gEDsGRlE9qbj+zxJJzuNAwDPRjEMWgQ9gEDumCU3Z+efh773PCrlgeVAFJObrc+ntZQ9MJzphg0DLBa6107wzAgEmn/TiQJIxSibM06XIMGYx13Uvv7FHQqocICdj72CtWffpaw3TN6LFlnn42jf38kq0gs3dMRglBwxCEpCqknTqSspAitqhIlLT3uhahVVSI7HaScOFFMdzVCTz+ProEDW7W+1W7v1sfTUvRwmJpPPzEtgw3EIIBEkqqG1VoeVb+fsnXr6DV6Qo89Z0caalUVZW+8RtWaDxMKPPugwWTPm4f76GORHQ503RCJpQ8DhCAUHJGkTplKaH8hvo+3o5aagRFYFFA19GAQ2enAM2YsqZOndvVQuzU9/Tz2v+OPLbMSuj3m8XXh8ejhMN4tmwnu2Y0eCmEfOAjn0KHY+vSl5pOPCZeVEt6bDw4nOjqRwn1EDuwHVTNVns0GsmJWhvDX1HaqmxbBqH9kspFlMAyC+w8cNkE4hzN6JEzlqncpX/nfWr/S+lgyMsk680xSJ05CdjpBkkTAyGGEZBjCvttSSkq8XT0EQRvIyDB9vSoq6gcBGJpG9Ufr8G7eRKS0QYWNEyeKtCItpKPPY2PXL1k0m5zak4LV6eyy+0IPhyl6/hkzvUcypnI7E4sFdB1LSgq9f/JTXMcc29UjEiTAMAx8WzZTsmwJallZXLvscJB+yiwyZs3GmpaOIUliZribk5OT0upthCBsBUIQ9kyaExQ9vQZvd6GjzmNHC8Io3bGWsRn88UfChYWdut+koShgGNgyMuj98+uFhbAbEtj1PaWLXyLw/XfxjbJM1tTJ9DnvXCKudFAsCMnQMxCCsIMRgrBn0lmCQtAxHMnX78BT/8K78aMe76jvGjIYpf8AtKpqDJcbvbQUtboS3efDCIeRrFasw4aTOWMW/q92ENq1C0NRsHjc2AcMRK+uQsnMwpKaRriqkupV76KrKpb0dPpe90sUp5ODzz9DKD8fyaLgHjuenLPnoTidXX3o3ZZIWSmlry7Fu2ljwnZn3tHkzJtHrzHHIdvtR+TvrycjBGEHIwRhz+RIFhSHA0fq9dPDYXbd8kv01uZLFMSwDBjEoF/fJoRhHfRggPK3VlLx7jsYCVwQrLm9yJ47j5SxY5HsDtLTj8zfX0+nLYJQBJUIBAJBN8S3bWtCx35By1EL8vn+/25i2AMLu0wUNudKYWgaNV99RfnK19H8fqzZvUibNQtFktAlieoNHxHY9T16VWVs+15XXInF4yGYv4fQ3vwWVYcxdJ3q9WspfW0ZWnV1XLvsdpN56mlkTJ+B7PEAwk/wSEMIQoFAIOiGqNVVPX6quFsQDFL41wcY9NuW55xMBoamUb1+HdWbN6GWNQi2mjCRlJMmUfnB+5S++gpoh5K7R/btw//p9oR9an4//rJSdn/ysbkgmlAcMw1UtDpM2tST6wnDmi93ULrkJUKJfFEVhfSp08g8/UxsWVkYsixuuyMUIQgFAoGgG2JJTautHtJN3s4WC54TRpE9/7L6tYw1DbT4mrbdidDuXWiBQKdZCQ1No2TJy/g+2Y4eCJr1vi1mVZ9gfj7h4iIqVr1rJgdv145qE0HLMoaqxqrDhPftI+fiS4iUFFPyymIz72QC3CeMInvuPBwDB4JiNSuMdJPbTdD5CEEoEAgE3RDPuPHILy3qNj6EjmFH0een1yIpCrYpU0mbYuZirFizmpLnn+3awbWA8nfeIuec8zplX9Xr1+H7ZDuGqmLJzq6X/Fv2eIiUFCdM79JWJIv5KjdUFSMUomrjR4SLDuDfubOe9TGKvf8Ass85F89xxyPZRIURgYkQhAKBQNANkW023KPGdIsoY8lmI+2kkxL6p/m2bu2CEbWewLffdMp+DE2jevMm9EAwTgxCbSWYBCKtXftUVVMUKorZfyCAf8eOuPWUtHSyzjiTtMlTkF0u08AoEksLahGCUCAQCLopva5YQKhgL+HCgi4dR+rkqY1WZzH07j1dHCVqRetoQgUFqGVm1Z6E9b0j4eQnGK8tE4imJRSbks1GximzyJhzqplYmi7/xhB0Q+SuHkBzBINBFi5cyCmnnMKoUaM4++yzee211+LWefDBB5k5cyajRo3iwgsvZN26dV00YoFAIEgOss1G/1tuNRM8dwVOJzmX/ZDcSy5rNHrVOSyvkwfVNjxjx3fKfvSA3wwgsTQe7ZtUNSZJpn9iKJRwejhlwkQG//4P5J5/IZa0DMTssKAxurWFUNd1rr32WjZu3Mill17K0KFDeeutt7jtttsIhULMnz8fgJtvvpkPPvggts7SpUu55pprePbZZznxxBO7+CgEAoGg7fg//8x09G8swKSDAk8yzpxL9tx5jacxqU2n4hgyJOn77gjSJk3ulP3ITheSbAaQJEKS5aRdM8liMf0GE+xLttvJvfQy87hjASNCCQoap1sLwuXLl7N+/XruuOMOLrvsMgDmz5/P+eefz2OPPcbFF1/Mxo0bWbVqFbfffjtXXnklAOeccw5z587lvvvuY9myZV14BAKBQNA+1OoqwKgvIhJMRSYVSSJSUkTZyhUEvtmJrliQFSuy3YZaXQkRFdXrRasoB7X7Txm7TzwJ2WbrlH3ZBwzAkpVNaG8+sscT70NotYHVCuFwm/dxSAgmOPeyhDUtHeeIo0mbPBVDkoUQFLSIbi0Ily1bxoABA7jkkktiy2RZ5pe//CWff/45fr+fFStWYLVaueiii2LruFwuLrjgAhYuXMiePXsYLOpnCgSCHoolNQ2QwNA7b6eGgW/zps7bXwci9epFnyuv6rz9KQqpJ06krKQIraoSJS29nig0atdpi0STFAXDMBILQUnC4vGgeNxIVhuuY0eaYlAgaCHd9m6JRCJ8+umnTJ48GVk2h1lTU4NhGEyfPp3rrrsOt9vNF198wdChQ3G5XPW2HzlyJABffPFFp49dIBAIkoVn3Hhkh0NYedqAa8KJDLvzT51mHYySOmUqnjFjkSwW1NJSNK8XLeBH83pRS0tRXC5sffu1vENZBkUxA0b0+A8DxenE3isXi9uFFgrjGJ5HysRJSTwiwZFAt7UQFhYWEolE6NevH88++yxPP/00RUVFpKen86Mf/Yif/vSnSJJEUVERJ5xwQtz2ubm5AOzfvz9pY4rWVBX0LBTF/KA40q6foWl4d35Nyfur0QJ+XIMG0mfu2T2urmtXXj9D0/DvyUf1+7G4XDgH9CdQUIjq8xKprMKSlo5itxE8eJBItRdbehqZk5I9PemicsI4SteuOyQGDOPQtHE3F4rW/v3JmTAO3+589GAQS1YmkbIyIhUVhMvLIaKagRF9e9Pn5JMpXr0GtaTETHZtsyPbbEiGgX34MFIHD8JQdao+/hhD13AddRSpeXn4du+m5ttvUX01yIqMOy+P3qfNIWXEiEZ9IDuajGuuouSDYZStX0+ouBhD05FsFlz9h5I1ZQrZJ0+l6J132fvCfxIGg0SJTg8nbLPZsGakIysKWjiM4nQy4Jx55JwyM2nHfaQ+P49Euq0g9Hq9ALz66qtUV1fz85//nJycHF5//XUWLlyI3+/nV7/6FTU1NTgTvOAcDgcAgUCgU8ctEHQ1hqZR9M67FL74Mlqd+79i02b2LVkKaWmMXfgg1rS0Lhxl98bQNEo+WE3ZuvWESkowNA09FMaoTeuhq+qhl3hdvz5ZZs9Tz5AxfhxDfnZN0oThkJ9dgz9/L/69e+uLwh5ApLAQX3oaodJSDE0nUlGBZFHQQ2HgUOk1Y98B9r+8xNwoemyhEHooBEDgy6+I7MlHttswdANJkdEqKpEUGc/QIdTs3Inm86KqGuENG6ncshV7bg69zzyD3FmndLowlBSF3NmnkDNzer2PCtfgQbGx9Dn7LHqfcTrVX+yg8JWlqL4a7L17kXPyyZR/9BHlm7ckFIOOvn3pN/8iHLk5BPbtRw+HcQ8ZjHvo0C4TwIKej2QY3fOpsnXrVi677DIsFguvvfYaeXmHUhtcccUVfPzxx6xevZoZM2Zwyimn8Oijj9bbPj8/nzlz5nDddddx/fXXJ2VMJSXepPQj6FyiX7YVFd2j4kNHYmgaRf95geo1q5tdVx5xNENv/FWnT6e1ls6+fvFlx+zogQB6MNhyESbL2Pr1Z+Dtv0va+dXDYYoXPYfvk0/QA/56QlSy2bAPHoLzmGOpeH155/obtgSrFUtKKpKioFZXmSlS2oqiYM3MwtA0tEAAQ9fMwJaoUG5gOZXsdtImTyXn4ku6vVgyVJXKNaspf2M5ms8X166kpJB5xpmknzwDxek0K4x0MEfS8/NwIicnpdXbdFsLYdTqN3r06HpiEOC8885j8+bNbNu2DZfLRTAYjNs+usztdnf8YAWCbkL1+nUtEoMA+tc7+e6O3zD8j3d3e1HYmTQsO6bX+NBbExEqm1Gd4X2FFC96jt5XXZ2Ucck2G72vuho9HMa3bStqZSWGGsE+eAiWlFTsAwYgKQo5Z80lePAgB/72CGplFUagG7zIDQMlJQXN58Vob1JmTUPz12DNzsHAQCsvN5dLElgs9QM4aku5VW/agH3AANKmTW/fvjsIwzCo+fxTSl9ZTPjAgbh2yWolffpMsk4/HSU9A6BTxKDgyKLbCsJevXoBkJWVFdeWmZkJmEEmffv2paSkJG6d4uLiev0IBIc7hqZRtfGj1m1UWppU0dLTiSs7Bmg1Na2bnjUMUCygRkxrXjicVMEt22ykNpNTz9G7N0P+dA+VH7xP8UuLajeUuy5FjKqCYZjnMkFQRGvRAwEMXTf7i2IY8SleLBaMSAQ9EKB60yZSJ0/tdlbCUEEBJa+8jP/L+FJzACnjxpM19xwc/fphSFJP8RQQ9EC6bZRxdnY2ffr04bvvvotrKywsBKBPnz6MHDmS7777Ls5KuKO2juPxxx/f8YMVCLoBoYICgt992+rtqjdtap0F7DCmYdkxIxI2xUxrEgkbhjlrKUnowSC+bV1Y67eD0xW2BiMSbr91MIquo3mr4wVuomtUm6UiUnSAUEHXlgCsi1pVSdHzz5D/xzsSikHHkKH0/9Ut9P3pz7H3H4COEIOCjqXbCkKAs846i++//55Vq1bFloXDYV588UUyMzMZP348p512GuFwmMWLF8fW8fv9LF26lBNOOIGBAwd2xdAFgk5HD/jbZn3R1K4VLd2IhmXHDF2nTW7WsShgozaxdNdgH1gbwNDVSsJmS3rJNqPW6lh/YSOVXDCtv3o3mD7Xw2HK3/wvu39zG1VrPowbsyUzi94LfsTAW2/Dc9zxGLLStntQIGgl3XbKGOBnP/sZ//vf//jVr37FZZddRr9+/Vi+fDnff/89Dz30EFarlWnTpjFt2jTuv/9+Dhw4wODBg1myZAkHDx7k3nvv7epDEHRjtECAylXvEqmswJqeQfrsOQlTshiaRjB/D6G9+WCAfdAgHIMGd7upJ9nZ9rQQnSFaoqXO9IAf2emK+bwlk5h/XXUVltQ0M4dfK6Zro2XH9IiKIQUxAgHzhd3aF3LUoijJtYmluwbHoMFYsrKJFB00PxY6qMxdczj79EYNJdcKrUPcB5ChG2Bo5tRxrWUwdv0Mg0h5Of6vvmzy/kv0XJAUpV33lTkMA+/mTZS++gpqWVlcu+xwkDF7Dhk/mIMlJQVdN4SfoKBT6bZRxlEqKip4+OGHWbVqFT6fj7y8PK699lpmzpwZW6empoaFCxfy5ptvEggEGDFiBDfddBMTJ05M6lhElHHPpGGUnBYIULjwQUJ7dtd/ocgy9sFD6H/TLShOp+mTt3YNFaveRS0rNZPCYqaTsGRlk/GDOaRNPbnbCEND0/j2pz9u07a9f3xNs35pdWmN8DI0zQx22bzJPI+6hiQrWLOzSZkwkdQpTft1tSTKUQ+HKXrhOWo+/cSMBjbrQSA7HLhHjabXFQta9ALXw2H23HUHanFR24WTJMV8CGWXm6EPLuzSoJ3K1R9QsnTxocjeLnjk23KyiVT7MMKhrrVWWizIdjuKy4U1O6fe/dfocwFAkutMv7f+vgp8/x0li18iuOv7+EZZJvWkSeTMnYclJwe62dSwiDLumbQlyrjbC8LuhBCEPZO6DzQtEGD37f+HniClQxTZk8LgP99D2euvUb1h/aEXaQ9IZ/HNwgdhRyur8ygWhj/2RIsFU2uEV3wKFweSRcFQNfRgENnpwDNmLDkXzm/0HDb3QtLDYfbe/SfC+/cdsoJFLWGG0eIUMIamUfzyi1St/bB9wRe1UcZIEqknTWp1wE5LLdctxdA0ihe/iHfTRvRAIClBHa1BstlA1w9NGXflK6f23pCsViRFQXY68YwZS+ZZ89jzu9uafC4Ah65tE/eVFghQ/vabBL7/FjQdVJXg7l2Ju3M4sGRnI7tcyA4XuF1I4TBqREXdV4AeiWDU1Jg5LxUFJSMD59DhpJ48HdliIVxYEJu1sPcfQHj//qRb4Fv6QebdspnA7l2o5eVYMjOxDxyIJMvoNTXILjfoOsGCvbF255CheMaOI1JU1OSY22v1P1IRgrCDEYKwZ1L3gZZ/958I1f1KrxuVWOenYMnORQ/UoPv9tRYfJS6dBYaB7HKRc8FF3SadRVushKmTp7RItLRFeFWt+ZCylW9gqGp8TVfDQKuqRLJYyDprbqPnsLkX0oGn/oV30wZzDA3TjhhGLCikOXFWteZDSpYtMa95ex+LbchD2KSFKorDgTRuAsb6te0bX2Mkc0pZUbAPGWr+3nTdFFOybIqbrnjtWCxIshyz9CtpaaBpSBYLSDJqaZ1sFY2dh1oxmei+0gIB9j50P5E9u811FaXxCiSKjDUtHcVhR6vxE/F62ybUo79BSUJSFCSbDclmQ1YsLbbAN0dTvz89HKbo+WfwbtsKbQkYqs2hqbg9SEr9WQND05Ji9T9SOazyEAoEyUYLBMyXbZQGKSrqvgTUUjNtUbSGaFems4j63kWqKqn55GM0rxfZ5SJ16jRcw4bX26+kKDD7VFj1Tss6z84m9/IFLVq16IXnTDFoGGC1JhRedXPvxaVwaXgOJQklLR21tBTv5radQz0cpubTT8yXaYMxRfdhWCwQaToFjKFpVEUtaLX57NB1819T4qWucJAkkGRkhwPP6NHkXt7yF1ZLLNcABIMdJwYtFiSrFcXlwpl3NIHd36N5faZlLxw2k11brChpaVgzM7COPB7v8lfrCxm7A9vAAfS9+udY09IoXPggstWKIUmgqhiGUXuNJQxDb7JkW9LRNNPHUJbMXIbVXmSXE62sHFNs1KGxa24Y5vkAU/CpKr5PPiFSVcXu398Ofv8hK2KiY5MkLKmpKC4nRihMqLik0bJ0LaKOxdXQNIxwGMnpBIeTYH4+4eIiQvsLm7TAtxXzA/GPhGuzfrQJw8AIhVBVFUtaWmzMwYK9+L/5msiB/Q0+PnV0fw3eTRsIFRYkNfG7QAhCwRFE5ap346sZNKShZUCS4kRGjNoHfzSdhWPw4KSON+p7V7nxI0Lffxf3gvF+tA7J6STrnPPImHGoNFfe/Ev4Rpbh3bea7N8y8ngG/+L6Fk8Vt1Z4hffvr5fCJRGSZH7xR0pL23QOfdu2mtaDJq6TJEkYdVLAJPKVDBUUoBYdjG5g9qUo5r/oS1eSTDGg68huNxmzfoBj6DAku51IURGaz9vmKa3ChQ82LwY7GlVFTknFUFUC3+xs0mobpc8ZZzXaFtyzB7WsDEtqCpaUFNRAEEPXkWQZyWqeHz0cRquuRpJl5MwMInv3JvWQ6mGYASdE9asaQa9ug1WrgYDTA3723vNnCATMZ0Ijlj7F7cKSkgKaTqSsvMNSPRmhEJLThZKdjVZVie/j7dj79U/6LEbRC88R3rcvOZ1pGnoohKV2zNUfrcOInp8WfHwKkoMQhIIjhkhlRes3akwMRtsMo0PSWUR976q3b0WvaHzcRiBA6csvEt6/n16XXn5IFF50MVx0Md8sWRwnDD1nzaX3GWe1SrS0RXhZ0tPrpXBpFIvS5nNoRkcbTV8nc3A0lQJGD/jRda3Oug22jS6LTs/JMs7hR+E65lhz+fCjEvYbjVAP7tmNWlGBbrHg+2gdeiCI4a9JuE1XolVVYu3XH62srM1W2yjRFD6yzXyhSzZ7XFpE2W7H8LhB01BLS9t/AF2BYaBVlDfqHynb7VjTUs1p6Wovmr+DgjOiH7O1Cbtlj6fdFvjGMD8QP07q1L8eCCABSmoaWlXt77SBCwi03OovaD1CEAqOGKy1JZ9aRVMPvGhwiaK0K+VLIqLl0/Tq6uZXNgyq16/FOXhwnBUgKgzbS1uEl61PXyTZDCBpunOtzefQTOkiNV+7t5kUMLLThSwrpvEoCdf8UIT6O0RKSjp3arQ96DpEwu2y2kaJpvBpdkpU1cyk1T0xOXrUTzDB9ZUsCtb0dGSrDdXnRfW1suJNa6nTtaFGMMJhZLs9KdeyIbEPxGSi62gt/EhqidVf0HqSkpja12CqY+3atSxcuJBXXnklYZ1hgaArSJ89p35uskQkSHTbaNxV7dSQtVcf7AMGJGmUh8qnqdHowpagqlRt3BBzmE82h4RXMy80w3T8tqSmYR8wAEtWNnow2Og5NAwDPRjEmp3dpnPoGTce2eFo8joZtZYb2eHAM258wnXsAwZg6dU7dgztuebRaOWSpYuJHDzYc8RgLZrfb1pt9fZZvqPXXwsEmrn+AQxNp1uVVWkOWTY/fhrxE7RmpGPLycFQNYJFRaheXycE0tRVhMahqeskXMuGqNVVHXM8jYjrhHSDxO+HG+0ShJFIhN/+9recdNJJMVG4aNEirrnmGv75z39yxx13cOGFF1JVJS6YoOtRnE7sg4ccWtBElQNLdi6y2x1zDm/4QotaPWSnk9SJE5PqsB0tn9ao72IjhAsKOqw0V1uEl6QopJ44EdnpQKuqjD+HtVHGstNByoltO4eyzYZ71OhYnd5E+0BVQZbxjB7d6NSSpCikTTwJ2els0TV3DhtO4JuvCe7ZU0+EG5pG2RvLzXRFPfRjWDIM02ort8/yHb3+itNFpKKi0esvWa1IsowlNbV5C3RXE0143UiwkSUlBXvvXkiyTLiklEhlZSem+alz7uom5k7CtWyIJTWtY65V1G+3JdT5+BQkh3ZNGT/zzDMsW7aMY445hlAohN1u57HHHsPlcnHHHXdQWFjI448/zj/+8Q9uvfXWZI1ZIGgz/W+6hd23/xrdV5tCKJHPjyeFgb+7g7I3XqP6o9o8hKpqRkrW2Uay20mdOInUyVOTOsao71Vrv8ANTe2w0lxR4eXdtME8F42ld2kgvFKnTCW0vxDfx9tRS80AEywKNMhD2J5z2OuKBYQKCwjvK4RIxLxOCdLhNBdNnTplKsHCvU1fc4sFyWajetMGqjesQw+GkGxWlN590cpK0Sor2pZ+oxthWK0YwSCOQYPabflOnTIVqayI8s1bGr3+jkGDCezZjWRRkPv0Rd+fpECFZNOEEJSdTqypKWaQWXkFejR3aWdiGszM/7VYkWy2mAU+GdeyLp5x45FfWmSmaEoWsozicreoTF/s49PZuNVf0HraJQhXrFjBscceyyuvvIKiKKxdu5aqqiouv/xy5s2bB8COHTt47733hCAUdAsUp5Mh99zPvocfMpPFNqhU4hgylH6/vBnF6ST34kux9+tvJgkurVOpxGLm+EqffSppU6clPZ1D1PeqtV/gkmJJui9jXVojvOomV7akppM+53T8n2yPnUdJUXAMGkTKiRPb7ewu22wMvP13FC96znQyj+Ysk2TzhdHCFDCSojR5zSWrDUPCnAIPR0Cr9Y3zg1ZZ2ebxdzckVUNqh9W2Xl+KwsAFV+AcOJCi1WsSXn9bv/6E/vVPDFXD6nQR6dsP/cD+rk1gXZeoEExg6ZOsVqzpacgWK5HqKrSaLqzmET1fsozidgO02wLfGOYH4hi8Gz9K2nWSnU4MQKuuQrLbTZ9STcNoEMjWUqt/V1O35Kmuaug1NWBRkHQd24CBGIEAkstFeG8+yAqGGkF2e5AVJVYeFcwZI63Gh1ZdjezxoFVXo/n9qFUVSIaEJSsT+8BBALFE5Y4hQyDnhFaPuV2CsKCggMsvvxyl9kZbs2YNkiQxY8aM2DrDhw9n3bp17dmNQJBUFKeTgbf/rtmKEJKikD59JmlTT+6UWsbRfINajQ/Z7cZopW+MbcCANlsB6tYZ1iUJ36ZNqFUVWDMyyDr3AiweT4uEV9b5F1Hw4H2JywIOGkzuj36MpGlJr2Us22z0vurqdlc1aOyaR8rLqd70EZKqmj523UWsJBtZQbJa2m21rYukKOTOPgXL6AkJa1kbmoYlK5vQ3nxkjwer1QoDBxGJRDAqymPCwOzMTAwNtVP4HTkdWydqNw5ZNoWgzY5WU0M4UNnpFWAaQ7LbMQzDtMgmwQLfGL2uWECoYK8pQtqLoiDb7bExp06eiv/bb4js39cuq38ySPSekG22uHvZ0DTz2VNZSbBgL75Ptpv5O9uKzQ52m5nbspV+yLLTyYCXF7V6l+0ShC6Xq17QyJo1a7DZbIwff8iEW1RURGZmZnt2IxB0CIrTSdbZ85pdT1IUnEOH4Rw6rEPGkajWrxEKH0rI2hLxYbGQdtKkVgusuvsOFR80U9w02F/Vh6uxZOcw8Hd3YvF4GhVehqY1nlxZ1wnt3sWBJx5nyD33t6sUW1PINltSIg7rXnND0yhc+BBGMIQeiRzWYtCZl0fqxJM6JNG6pCgJo1yjvoZlJUVoVZWxijZWqxVyexGpqkSPWmDlQ27vksXSMZHJDdwEGrZZUlNQXC70YJBwSYlp8bTZyL5oPlUbPiKcqEydLOMeM5ZwaSmR/D3JHWtjlUp6906KBb4xZJuNgb/5PUXPP4t3+9a2iZ/adESK211rNT40ZkPT2m31bw+NVQ4qe2M5kt2B5HIhS2AgQSRiBkcl8yMlHDL/tYU2jqFdgvCoo47ivffe46qrrmLLli3k5+czY8YMHA4HAJ999hlvv/02U6cm/+tEIDgcaKzWLzYrBAMtE4SSROqUaa2yAkSnM8rfepPgru/QAoEmH+hqaQm7/u8mhj6wMGYtbCi88u/+U30xmKAsoO7zsu/hhxh4++9aPNauJhrkI9vt6N0wd2AysPbpQ68FV+EcMrRL6nI36Wtax0+tw8fWVGJplwtLagqGqhIuLcOo4ytq69ef9OkzyThldr1axrJiwTN2PKmTJsfEixYIUP7WSnyff4ZWVYWuaWCzYs/ORcnO6na1jJs8XTYbfX5yDb3CVya9lrGkKEmx+reFJisHGQZGMIARCqKnp0N1dY/LJNAY7aplvG7dOn7+85+j1kbfSZLE888/z/jx43nkkUf45z//ic1mY9GiRRx33HFJG3RXIWoZ90xaUpy9q2iy1q+uEyktMV88jdR/lZxOss85n/QZM1v08K9rEQwXFph5vwyaz+NXiyUnl6H33B+3XAsE+P7GXzRdCaaOj9OwR/7WYithV18//1dfcvDZp9ADwcNLECoKksNByoQT6XXJ5R0mHlp6/QxNo/qjdXg3bzJ9DXUzOlbJzCS0e5dpDWxYJccw6gmzNtOEEJTtdixpqUhIRKqrE+bfG/rw41g8nvaPoxvS1b+/riBhzftGLMbdccZAttuZtOTFVm/XLgvh1KlTeeaZZ3j++ecxDIMLL7wwNl2ckZHB1KlTuf766w8LMSgQJJtma/3KMtacXNTSEqy9epNxxln4P/u0yVrGze3vkDUyYJbOaqS6QmOoJcWoPl/cy69VZQF1ncpV77Zour47EEuwrLWj5mx3wunE4vZgy8np0CnF1iIpCmnTppM6eWqcf9bBZ59OHOHe3tQnTQWMWCxY01KRrVYiXh9aTeKPAfvQYYetGDwSabbmfV26oRhsD+2uVDJ+/Ph6PoNRfvjDH/LDH/6wvd0LBIctsanIZmv9OtF9PmwZmaQu+FGb9xetfmKoKkpqKkZpqems3cqHWtlrS+l1xZX1lrW2LGCbygh2EdEEy2oPGnNCJImM08/EfcyxnTal2BYS+Ro2GeHepp3U/t4SWQUlyRSCDiea30+4orJx66HHQ/+bbmnbGATdkhZ93B6mJKVSiUAgaD3RfIMtqvXbzkoDda2RSlr6oQTTbXA+jiSordzasoBtKiPYRcQSLKekdPVQ2odhUPPtNzjzRuAYnPwo+Y4kGuGeetIk0x9Nqn11STKyy41r1JiWd1Y3WrUBFo8He6/c2sTSJajV1Y1GGTuGDWfIPQ90WICUoGtI+LF6mFkCG6NVFsITTzyxTTuRJIlNmza1aVuB4HDlUK3Xjqv1GyXOGim3/VvQkpYetyx99hzKVrx+KHFvMz6E6bPntHn/XUE06KFy9QdmDrQeSmTv3qTWtO1MWpJaKLBvHwceW4hWU4PscuGeMo3I3r0Evv3GDNJqxBfXPnCgKTYdDqq3bUXzB3ANHErqKaegSBKGohD46ivU6sqEKaoEhw8JP1a7qa9gsmmVIPQIPwmBIGlEpyKj+dcSTRsnq9JAQ2ukZLXFcsC1Fs/4CXHLomUBY47YDUVhnYepY8jQbvky1QIBSv/7BjWffoweCiHZ7BgY6F4v6LqZ+NvlQq+u7uqhthlDjXRYNZvOoqnUQs5+/Rh674Oxv9XKSkqXv0rAX5Pwhe4YOpTcCy/CmTcCkDAMSDt5ZsK+3XkjkjJ+QfemRR+3hymtEoTvv/9+R41DIDjiaCz/WpRk1PqNEmeNlCRkpxOtDbnD1JLihMtbWhaw3y9vbvU+OxItEGDPA/ei7c1vcj2DWGWwnouuU75mDQdeeB6togwsFuzDj6LvlT/Gmnb41ITVw2Eq3n2b8rdWmmUIG2DNyib73HNJnTgJZPlIMP4IWkizH7d1Ocwsh0n3IfQns7ahQHCYkzplKp4xY5EsFtTSUjSvFy3gR/N6UUtLkSzJqRoRtUbqwWCsVqglLb3lheSjSFKjqihaFtAxbHj8lHTM56rjklK3BS0Q4Pv/u6lZMdgkktT689hVGAb+LRvRig+aNZcDAUKff8bum2/km+uvJVLVuuo43Q1D16ne+BF7fncbZctfjRODssNJ9rxzGPKnP5M6aSqGJMSgIJ7+N92C7KnjM9xYypn09J7z228B7cpDCKYV4+WXX+bVV19l586daJrGl19+yaJFi9ixYwc333wz2dnZyRpvlyLyEPZMunsercbyr1mzs5OaFiRRzkPN60UtL2txH5LVSv//u63Zqi3NlQVsDR15/eLyjSWDHm41GPLQI0m1FrYmD2Hduq1KaiqK29PiaOjAt99S/NIis9xgAhSPB4vHgxEOE6muNqtK1BHzckoKzmOPpdeFl4g0MnXo7s/PjkILBBLXvJckJIcDyVmnUokaQQ8kuVJJO+iSPISqqnLttdeydu1aLBYLbrebqtovzMLCQl577TW2bdvGyy+/LMrXCQSN0FT+tWRGgiasBqEoZu3TBNNq8QOVsGZnx4quN0VLywJ2JVogkHwxCD1aDALk3/k7hj/8WKftL5osvWrTRiL7CtGCAdANJEVGtjuw9e9P6oknkTol8YdRpKSE4pf/Q82nnyTsX7bbsKSmIWHWpNbrukkYRixISK+ooGb9enatXw/pGQz9w5+EMDyCaarmfZO1jKurkOx2fN9/S+irnaBr2PoNwDNuHPacXAxFwbdpE5HKcpDNexw1giUnFwyD0N49yIoF95ixWHNy8W3aiBrwY/Gk4DzmWIJff4UaUdHLS7Fk5WDLziZlyjQqP/gf/q92IBngHjW6TcfcLgvhk08+yV//+ld+9KMfccMNN/Dvf/+bJ554gq+++gpVVfnnP//JY489xoIFC7j99tvbuptug7AQ9kyO1C/cRDRmjdRDQbSmgiUkCcluJ+fC+aRPn9Fp44WOu35lK16n7PXXktrn4UIyrYRNXb9osnTvx9vM+y/qyF8XRcGSloZnzFhyLpwfE4Wa30/5yhVUrHoPEiQNlyzmdrLFiur1orXWnUmSGLrwsSNeFIrnZ88kJ6f1abLaZSFcvnw5Y8eO5dZbbwWo5xBvsVj4xS9+wZYtW1i9evVhIQgFgp5OY9ZIW9++lLyyGO+Gj9BDwUOO1LW/adnpJHXiJNKmTuviI0gePSk5dmdT8p/n6Xvt9R2+n2iydD0QOCQE61QiMTQNdB3N78f38Xbs/fqTOnmq6f7w+mtovgQf6ZKEJTUVxelEq/ERKq9om9XWMNj7lz8mLNUoEByOtEsQFhQUMHv27CbXOe644/j444/bsxuBQJBkElWDyJ1/KY6BA6netIlI0QEMzcx/aO3Vh9SJ3afEWbLoScmxO5vOEMvRZOma35wixjBMF4Y6hgVJUWJ+WXogQOX7/6Pi3XcIH9ifsE/TT9CNHgwRKi5utz9XY6UaBYLDkXYJwtTUVPbt29fkOnv37iWlp2f4Fxx2RB3YO8pfryfSWb6MHUlrrmv67DliyrgRrOkZ6OEw5avepfzN/0IweKixd28oKgajjtiy201fPMMAixXUiOkf5XDQ+5yzKV71Pmp5BcgynhNGk3vp5ailpWaydKsVzV9bJzhaQQdMN4VaK3VUFIYK9iYcr+xwYE1NxdBUwqVl5vpJIlGpRoHgcKRdgnDSpEm8++67fPXVVxxzzDFx7Z988gnvv/8+p512Wpv3MX/+/IQWxqOPPprXX38dgGAwyOOPP87KlSspLy8nLy+PG2+8kalT25eqQ3D4EXVgr968kfCBA+i1LyLZ7Sbj1DPIOGVWTEBUfPE5JQ8/1LodKAr2IcPoc83PsHVRIFVUFIXKSql6fxWatxrF5Sbz7HlYsrIpfOJx9IP7AQkltxe5F83HffTRhPfv75EiEOpe101ESkswImFAwpqRQerUk0mbdnLc8ShOJ/ahwzomsKSH4ysv57trr0ncePBg/LK6QUnh2v/XdXSfj/2LXqrf9+aN+DZvREpJwZBlZIlDFUQaWPQMiEVtJ0qiLtntWFM8SLJMpKoKvSXBUa0kUalGgeBwpF1BJXv37uW8885DVVUuuOAC8vPzWbduHXfffTeff/45S5cuxWKx8MorrzBsWNNpKhpj3LhxjBkzhrPPPrve8vT0dKZPnw7AL37xCz744AMuvfRShg4dytKlS9m5cyfPPvtsm8vtJUIElfRMok7R5aVeSha/SPXGDeiNOJhLDgdpP/wRlU/+vf07ttkY/Od7O00YRkVR2fp1qN9/2+rtJZcb2WFHVixmypsJExuN7OxMmnNqrxuYoPt8GNHAhOg/iwXn8OH0u/6mWImzKGYewl+ZZc0EXYMst35qV1GwpqYg22yoPh9aTccFPKRNn3FEWwhFUEnPpC1BJe3OQ7hjxw5uu+02vv320AtIkiQMw6B///7cd999jBs3rk1979+/n5kzZ/L73/+eyy+/POE6GzZs4Morr+T222/nyiuvBMzk2HPnziUtLY1ly5a1ad+JEIKwZxJ9oO1evpKSJS+3LMVKEhl8/19bLArbOpUdFUWVWzZBe0qr2e0oLjdGKITsdMRFdnYFzb2QqtZ8SOl/XzfFYNTSFA2IMQzT+iTLuEYcTb9f3hx3LFogQP6D96Hm72n94Hp4zsFuSxPn1ZKSguJ2o9XUoPp8HX7+hz78+BHtQygEYc+k06OMAUaOHMmKFSv45JNP2LFjB16vF5fLxYgRI5gwYQJyw4oFrSAqMpuyLq5YsQKr1cpFF10UW+ZyubjgggtYuHAhe/bsYXAPLOQuSC6GplHx3rudLgYB9vz2VvL+/q8m16k75amWNUhO3QJLXTRas11iECAUQnJ7ULKz0aoqY5GdadOmt6/fDiIamFBPDDYITDBkGSIRAt99S9W6tXFpcxSnk6G//0PztYwtFpSUFOT0dCR/AN3vR/PXoNfUdPJRH8ZEhWACkae4XFhSPOjhMOGSkjbV4W4tlpzcI1oMCo4s2i0Io4wePZrRo0cnqzsAvvnmG+CQIKypqcHtdtdb54svvmDo0KG4XK56y0eOHBlrF4JQULNrF2pZadfsPBIhePAgjt69EzZHrXtm+o0gssOBZDHrDgfz8wkXFxHaX9iopS4qitTq5FiwVZ8Xm8eDkpaOWlqKd/OmbhthHCooMH0Go9PEDcQg1M5YyDKGqlK1bg1pU6clPBbF6aTXhRfDhRc3u9+6VTWq168l8P13qDV+U9AEA51iNZRcbvrf+htKX3qBUHk5Rk0NRkQ95MPXk2hCCMo2G5a0VDAMwuUVGJFIp41p4G/v6Jx9CQTdgKQIwg0bNvDGG2/w9ddfU1NTQ3p6OscddxznnHMOxx9/fJv7/fbbb7Farfz9739nxYoVeL1ecnNzufrqq/nhD38IQFFRESeccELctrm5uYA57SwQ1Oza3XkvkgTsu/9uhv310YRtUeueoapYsrPrCRrZ42nWUhfM30P44P6EyXnbRDiMFghgcbmQHQ4ipaWECgri0tR0B/SA3wwgqZ0mbigGY8gyaBpqeXlSjqVu2h7X0cfUS/atOewYfr95v3VEGStZxjFkKP1+eTOK08mAW26LNSUqTxjFMAzC7anZ3JEkEIKSomBJS61NLF2NFggm2LCDyMhk6J1/FNZBwRFFu0vX3Xrrrbz55psYhoEsy9jtdvLz8/n000956aWXuPrqq7npppva1P+3335LJBLh4MGD/OUvfyEQCPDKK6/wl7/8hcrKSm644QZqampwJqiR6nA4AAgEkucsHvWlEPQsFEVGlhsRCp2EVl1NmtsSF9RgaBoHP96CEQpiz81NKGgsWZmEiosJfLyVQWfOiVm3DE2j+H/vU7TsVbTKyuSOt6QYTZaxZmUiYeCy6KR10f2vKKbbSaLfn61XJkWyjGaYpc4a04MAhiQhy3TIsWTOPR3jzDn49+Sj+rxUf/kVVV9+RWjfPrRIBCMcAV0DqTaAQpJAlrGkp5M9dTL9zj2HQGEhZRs3EygoQHG6cPbri2fECBSbleodOwhXVGLPyqT3WWcmrAvdknspHLekC5GoDSNuuFzCkpqC4nCi+nxEypMU5Wu1mv6kDerSYjFfg5bUVFJHHsuQH/9ICME6NPX7ExxetEsQPv3006xcuZKTTjqJm266iZEjR2KxWPD5fGzfvp2HH36YJ598koEDB3L++ee3uv+LLrqIcDjMggULYsvmzp3LJZdcwpNPPskll1zSbB/t8WEUHD64hwwBWTIT4HYR5Rs2kj395HrL/HvyCZWUoDidjVq3JElCcToJFRfj35OPe9hQDE0j/5nnKFm9Gr2jLCe6TqSkFCU9DcVh75h9tBPX4EHYsjKJVFaa1zbRz90AQ9eRFAXZZsfi6pgXm6QouAYPIv/Z56nYshUt4EdxONEjqjmlrRsgGyhOJ7LLiR4MIksSRFQUp5OUo48m5eijE/adetzIZvffknvJkuJB9fradZztJjY9HN+keNxY3B6zznRJSdIsrJLVyrF/+gOeo45KSn8CweFIuwThsmXLGDp0KE8++SS2OpYPj8fDySefzNixYznnnHN49tln2yQIEwk+WZa5+OKLuf3229m6dSsul4tgMP6FGF3W0OewPYgoq55JRoYL55AhWLJzUYuLumwcVQdKUBrcQ/6icrSIiqQoqGrjLz9dkiGiUllUTjizN1VrPqRo9YfQCdNoWmUVu55dROqJJ3VJGprmohzdJ02lZk8+hqpiyHKcGIoFH0gSSmYWobQcwh30W65a8yFlmzabU7aZ2eg1PjM3niTFLFRaKITkcCB7UlC9XkrWrUdLz8Y5ZGi78kC25F7SLdZkHGbbaMpP0OHAkpoCqkq4LLmJpQEsWdmEM3qLZ3gbEFHGPZNOjzI+cOAAl156aT0xWBePx8PMmTNZvHhxe3YTR1ZWFmCml+nbty8lJSVx6xQXFwPQq1evpO5b0DORFIXMOadRvOi5LhuDJTUtbpnsdCHJZgBJk6hmGTnZ6cLQNCo/WgdJdIdojsDuXURKigntLyT7vAu7VRLrtGkn4922mcDXX0MkYkYVy3I98RGNEE45cWKHjdXQNKo2bUSrqUFJTcUIh2JpUaTaaUlDlkFVUcvL621b8p/nweFAViygKNgyMxMm1FZ9PspeW0qkogJrRgZZ514Qm96UnS403cDwViFVV5sBNi43Su30tGSzIdvtdIBXY8tI5CdoteI4diRGRTnhgwfRw8mf1JasVjJ+cGq3DIrqydQNrFIrK9FDQSRJxjZgAJIs16aucmHt1YvqLZvwbtmC5vdhSUnFfexIHEPNYNFwQQFIYB84CMegwY1eJz0cxrdtK2p1FZbUNDzjxse54AjaR7sE4aBBgygsLGxynfLycvr27dvqvouKirjqqquYNWsWv/rVr+q17dq1C4ABAwYwcuRI3njjDYLBYMxvEMz8iEC7gloEhxdp004mkL8b79o1nb5vyW7HM2583HL7gAFYsrIJ7c1H9ngSTvUZhoEeDOIYNAj7gAFmFZLvv+uMYR9CltEjEao3fETNF1+AprU6NU5HISkK/a6/if2PPUzgu29N65KmmUEmteJQSUnBM2YsqZM7pnqRoWmUvbGc0K7vMDQNtazMbKgdRywKuqlUKcFgTKwFq6sIFhbg3baZftffhB4Os/fPd6GW1v/4rfpwNZbsHPr+8mb2/+NxjApTaEall1FTY/apKEhWK7Kzm/iByTLW1FSk7Gxy5s4juGcPpStehyQLQslqNYX11GlJ7fdIpl61p8JCNL/f9I+ti6Ig2e1mYFWDYL4IEPji80MLap95ksWCNSub9Nlz6n0I6eEwRS88R82nn6AHg5h3t4T80n9wjxpNrysWCGGYJNrlYHfDDTfwwQcf8J///IdE+a3ffvtt3nnnHX72s5+1uu9evXrh9XpZtmwZFXVKB1VVVfHss8/Sr18/xo4dy2mnnUY4HK5nhfT7/SxdupQTTjiBgQMHtu3gBIcdkqKQe9ElKGnpnb7vlEa+ZiVFIfXEichOB1pVZdzvyDAMtKpKZKcjZt0KV5R3fjLkSAR0A93vJ1JchKGZU5OGZqbGKVv5BiWvvNwpueESIdts9PvlzeTMvwz74CEoqWkoKSlYMjJxDhtO1llzOyzBdjRtUNXaDw9NddZNrGwYZp3f1p4bVSWwcyd7Fz7Irv+7KU4MxlYrLWHv725DLW0irZKmYYRCaJUV0BmivYnoHkt6Oo5jj0Wy2rBYbdj7DyR9+kyG3n0/6aedgdSUaJVl5CFDkezN+LRaLFh79yFn/mXkzr9UWAeTRPReL1v5BoHvvkXzeePFIJj3m98fJwYTd2pa8o1IhHDRQUqWLqZ48YsYmmZ+CN39J7ybNphlRqP1sw0d3V+Dd9MG9t7z5w6xLB+JtMpCeN1118UtS09P589//jPPP/88o0aNIisrC6/Xy+eff84333zDwIED2blzZ5sG94c//IGf//znzJ8/n/nz5xOJRFi8eDFlZWX861//wmKxMG3aNKZNm8b999/PgQMHGDx4MEuWLOHgwYPce++9bdqv4PCl+MVFaN7a5M2KcshpXZI6JkUIoGTnkHv5gkbbU6dMJbS/EN/H21FLS9ElCfw1hwSFx0P6hBNxjxlH2YrXqVq/tvWDcLvNerNt9c3SNPRArQ+RJCHb7Ug286XcktQ4nYGkKKRPn0HqpMlJn1qKVFWx92+PotWte+xwQFoGFB2ov3KCmrxtxjAIf/tN0vpCkpCsVizZOUSKi5L/YdGEn6A1MxNb//7mVGNhIZKi1JvCl202ci+4iJxzzyewexfVGzfg+/Rj0HWsvXrR5ydmffDoVKXqrSa4axdajQ/JZsWa0ws0FUmSsQ9qevpR0DaiKbI0v7/1HzjNoSimMAyF8G7aiGPAQPzffEN4/z7zfrJa49IooaqE9xVSvOg5el91dXLHcwTSqtJ1RzcSAdfsTiSJr776qk3brl69mn/84x98+eWXWCwWxowZww033MCoUaNi69TU1LBw4ULefPNNAoEAI0aM4KabbmLixIlt2mdjiNJ1PZOoU3RZUSW7brnJ/NJs8HABzGjUSHK/NO3HHMuA63/ZrCAxNI3ilSuoemN5UvcfxdqrN/bBQwjl7yZSUtK+h7liThPLjkOpTwzDQC0txTFoUMLycO2hpU7t7a32kohIVRW7f/NrU0wfBsjpGRAOYR84CIDgd99haA2smkkWibLdjqNfXwyLBbWiCq2mptuURRQ0T91a8IULHyK0dw+6qiU/Abok1ftItw8/ikhhgVl3PtHzmlpRGIkgu9wMfXChmDquQ4fXMt63b1+rdxClX79+bd62uyAEYc8k+kDLf3MVB595Cgwdydp4tGUsgXVtMuO24JlzGr3POa/FDyjfru/Zf/ef2rSvFiFJph/j+BPxfrSunRYsCVuf3jELYRTN60VSFPr89NqkJrFuiSBsqtqLHgy2SYBEqqrYfcsvD6taxYrHg2SzIykKva/+KaGCAqrWrakNcjGQbTYkiwVdB626EiMUNqfpJAnZ4cDafyD27CzCZeVEDh5AC/gb9fuTHQ5ThIf8BPP3EvEHDgn0Eyd22+o3gvpEf38HPv6SA/98Aj0cQvP5km8hBDMnZO1HiWx3oIeCZlBWc89rSab3j35M6qTJyR9TD6XDo4wPB1EnOHJRq6sAo0n/JiBmKZHtdhSPp0lHfC1gTp30vvLHuI45tk3j0gKBjhWDcGgqZv3aJAgcA8maQOhaTJ/C6NRyZ0YFtrfaSyLy7/ztYSUGwRTOUu11MkIh0qfPIG3qNEIFBfWixoFYBKlWXY2Smori9phtkkT1hvWULn0loRiUbDayzjiTzNPPQLJYSUux49+TT2VRebeIShe0DT3gx9C1+v6xHUFt/7quxdwcml0fo/b5LmgPSSldl5+fT1lZGbqux5ziDcNAVVUqKyv58MMPue+++5KxK4GgzZhpX6RDjsmNEfW1UlqXDqatfH/Pn9u8batI4kNc0zQUS4PHR+25wGrlwFP/6rSowGgtZz0QjBODUJvYu5V1mSNVVei+Lk7g3AFIihJ3z9Ytw1eXRMv833xNyZKXCe3ZHd+5LJM+7WRyzj0POSU1NvssKQruYUMJZyau5S3oPkT9Mxt+HEDdFFlq8yKtPdTOXsiyYvpT131u6QbR5wnR6lOGAZKcMK2XoHW0SxCWl5fz05/+lC+++KLZdYUgFHQ1nnHjkV/6D7q/BsMwGvdJMQxkpxNr336ECwpanA6mLWiBAOxvuytGV6GVl6HkHsrxGT0XtgEDKV70HOH9+w+VaJOkelGBocICBt7+u6SJwlBBAWpZqTlN3ES1l9bUZS75z/NJGVt3Q05PRyuvaPU9Gy4ppnTpEnzbtiZsd488jtz587H1HYBhGIebYfWwpyn/W3XGNHJmzqiTImsPKJaOmTKuE/Sm6zqSxYIRDpsitKF/a+zZYpguIQnSeglaR7sE4SOPPMLnn39OXl4e48aNY8WKFQwbNoxjjjmG77//ni1btpCdnc2///3vZI1XIGgzss2Ge9RovJs2gKpiWCwJo9aQZTyjx+AcnkdZaQlaVSVKWnrcug3TwbSFylXvtu1gOnraphnqVpKoey6IhE0x2IlRgdGpLMnSzDVoMKXdFJHKJNXP7U4oCrrX26p7VvPXUL5yBZX/W5Wweoitb19yL5qP54RR6LqRMP2YoHvTlP9tMD+ffUtLCOwtIGXu+aSeOJGykiIMvx+jg59BkqEfqm6YyOe5jkB0nzBKBJQkgXYJwrVr1zJkyBCWL1+OLMuUlZURDAb5wx/+AMDy5cu57bbb+OSTT9ocoSwQJJNeVywgVFhAeF+hWdUi2hB9uMkytn79yb18AZKixNLBREqKzRdo7XSyoWkoLle7kx23S3h0sSjUAn6oE7ThPn4U1Zs3mg/vBFGBkiRhWCwQieD75BP0cDgpD/G2VHtpDmt6Bt0lrtg6/Cgi+XtaltOtCWS7HcliadE9a2gaVWtWU/b6cjPXXAOUlBSy555D2oyZIMnoDWqEa4EAlaveJVJZgTc7AwyD6uJS9NJSJIcL/86vINjGSjtWK0pGBpI7BYKB2iTgBkpWDjkXzyflmGOFj2IraM7/1vBWUb55C0Z2r/opsiKRtqexagbJbseS28tMjdQcioJj2PAOGceRRrsEYXFxMfPnz0eWzfzWxxxzDM8++2ys/ZxzzmHZsmUsX76c+fPnt2ugAkEykBSFlElTKHt1af2vzlphZcnMov/Nv44Jlax55xHctQu1osKsNhFFlrH06UvWvPPa9fKxpme0eduuRE5JAc0UWI5Bg0g5cSLIClVrPjT9L5uYujUkCT0YxLdta1KiAttS7aU5ci77Ib7t29o9tmQQ+e5blIGDkPw1CZNPW3Jy6Xvjr9j/yF9RS4rjO5BlLJmZ2HJym43uNQwD/xefU7LkZcIH9se1S1YrGbNmk332PCSHI+57RAsEKFz4oOljWPt7qW79ITdNJIJWXAzUP1btwD4OPvwQB4GM8y4k+9TThDBshpb431oyMggVF8f8b3MunI+9X3+qN20k8O03yZ86liSgtnoJkvnR3qAUZXQ9yeUCXadm+1bST54urnc7aZcgdDgc2OtkjB84cCDV1dUUFRXFagifcMIJLF++vF2DFAiSgaFpFL24iOq1HzaadkUtLaHggXsY9Ns7ASi4724zMWo9fzgz8XBwz24K7r+nXf5w6bPnUPbG8tZZ+qQ6ztRdgOzx0P/aG+JqGZe/8xYtjuJOYlRgtNpLWUlR0qb3rWlpyB5Ptwks0fbmN95Wex6dw4/CW1VVPz+cxYozL4+suefgHDK0yWMP7SukZMnL+Hck9glPGT+B3AsvxpKdk9BPUAsE2H37/3WLc1bx6iuEig7Q74c/EiKhCVrqf6s4nfX8b9OmTcfWrz8HnngcLRTCCAXNZ6SiHHpGNiUUZdlcL7pObYlJSZbNCHhNNaelNTVWglLJygZNNSPlay39kiyjeb0t9g0WNE27BOGwYcP49NNPY38PGTIEwzD48ssvY4LQ5/MRCLRxakAgSCLV69dRvX5tszn4Ivv2cfDZp80ycR2cJV9xOrEPGUqobgWM5mj4JpblDquyEo/EoLv+gjUtPqKvdVHcyY0KbFjtRXY4wKLUm9Ju7fT+oLv+0iPyEBqhEHt/d9uh+6Duh4saIbDzK4q9Xgbe/ruE4kitqqLsjddM626CY3UMGULuJZfiGp7XpJ9g4cIHu4UYjOJfv47q4Ud1WeWcnkBL/W8lxYIeidTzvzVCIVBkZLsNLRwCi8WsHQ6g1/H/i3VyyC1HsljqrROtOV53PUM7lHbGMAwkDGS3J35wrfANFjRNuwThGWecwT333MOtt97K9ddfz4gRI8jNzeXRRx9l4MCBlJaWsnLlSgYL1S7oYgxNo3Ljhhb7vPg2bzRrqnaCP1z/m25h123/h1HTypepLOMYMpReP/kp+b/5dYcLF8ntZvAf704oBqG1UdyHogLNesh7CO3NB4Mmy45pgQDlb79J4Ntv0cNBJIcTvaqSSCiMrGugGxi6juqvQbbZUOyO2JR2dKo0tr/8fJDAPnAQ9v4DCO/fX8/qaU1LY8iDD3dtpZLW+IkmuFeb+nDRI2Eq33uX8jf/W5seqD6WzExyzr+Q1JMm1RrFGx+HFggkTkXTxZStWysSYDdBS/1vDU1Fkuv738a2jajx92lCa6M5MxCdDj703wbr10n7FS0pKtVaDxOShNRfApN2CcLLLruMbdu28frrrzNp0iTOOeccrr32Wv7whz9w1llnAeYD6eqrRY1BQdfi35NPaF9Bq7YxgoFO8YdTnE6G3vuA6Xu1e1dCC2DO/EtJOfEkqj74H5HKCqzpGaTPnoPidBLY9X1SA0w8p8zGqKkh8NUOMAxsQ4bRZ8GPGhWCsWG2Kop7NJKiULn6AypWvWumuqidPpIUBUtWNhk/mEPa1JOB2unIP99FpAnR0dAuqQeD6KmQPmZczDJY+eEHVLxXf3/Rl49ktZmBF0r9cnd5f/tn4lrGsgJ6B6TeqEsrr2nDOzXRh4tkteLbspmSZUtqAzLqIzscZJ56Ohmnn45ksbVoCJWr3u1EK3XLUfP3iKnEJmip/60WCGAfUN//NrptMH8PkmLBiIQPfQjWtVIf6inmLhL/wSjF9hWtTCK5XEg+nxndXvv7TDS29qb+EhyiXYLQYrHw6KOP8umnn9K7t5l0dP78+aSlpbFy5Ursdjtz585l+nRhshd0Larf33orT+30RpMkyR9OcToZ9JvfowUCVLz7NqF9hSgOJ6lTpuIcflTMwpF19ry4bUNN+Je1Ca+Xfj/9eZs2jYvirvtiqBPFnXPJ5RS//CLVG9abU08QsxIYqkqk6CAlrywmvG8fqQsuZds1P4NQG+pMV1dT8uILBArykTQd78aPEgoXQ9MwwmH0WittpKQY/1dfUvbmClImTiLztDMY9pvf19vm4AvPUv3h6npjNzvr2nRADct81f1wKX9rJf4dXxBM5KIgSaRNmUrOBRei1CaWbindNk2ProupxCZoif9tpKISxemK87+NbhspKULz+2M+gYaimH3ULf1Z+3esHJ2mHQoWAbMCSnR9SUJxuWv7kGL+hXVti9GxJSP1l+AQSalUMmrUqHp/n3766Zx++unJ6FogSAoWVxunE5p7K7bBH65hSTe5Vy8OPP4IRo0fZAnX+BOx9+2HYneglpfHnKgBvN99y4EH7zMtbZKEY9QYXMlOudCOIgSyzcbA239H8aLnTItUtFKJJJvTxKNHk3v5ArwbN+DdvNEUg7VF7eu9iMJhjGCQytXvs2392raJwTr41q5p03ZqaSkVK1dQsXIFSnY2qVOmYc/OwTNufM+LEDd0yle8nrDJdcyx9Lr4EmwDBrYpsXS3PReyLKYSm6E5/1uL20XmiRNISeB/G93Wu30bWiRS67eqxvsPApLFCi4nkqZh1AY+STYbkiybzwnNFIWSzWZWOistRXY6UbJz0Gsj7JPhGyxoHMloRSZRXzschj2eBM6gPYySkvh8XILuT0aGC0PT2Pqz69DL46fIGsViMYVXAh9CqJ3eiESQXW6GPriwWR9CPRym6Pln8H3yMUY43LIpNklCdjqxDRlGcMfnLR97O8hdcBXp005udz+N1TI2NI2Cvz5I8NuvzRXriEFDNwMhujuyy43ruOPwbd50aGGyrYRtcQOQpHoWQiMa7dnIvWbr1Zvciy/GM3pskz6CzaEFAnx/4y+63bSxZdhwhvz6dmE9agZD06j+aB3ezZuIlNavVNJrxsnkzJxOZXXiGZbottWbNhIuLEQLBUHTkRTTImhORYMeUc2AM0lGtpq2KEPVMHTTOm9opq8gNiuyYjHdNk6cSMrESXg3bUg4tubSKB3J5OSktHqbVlkIx48f36g/VVNIksSXX37Z6u0EgmQhKQqZc06j9OX/tHgbz9hx+LZuaZE/XHNiUAsE2HPHb9AqWjm1Zhjofn+niUGA1IknJaUf2WZL6FcZKihALTpo/lHHR7OniEEA3V+Db+sW05oRrrVeGi1IudPBSLX1pY3a1EjoekJRKbs9ZM+dR8YpszASJJZuLYrTiX3wkNZFy3cCWVOnCbFQh0S1iiVFQVIU0qZNJ3Xy1Lj2zOymhUV025SJk/Bu2Uxg9y7U8nKUjHSsqenYBw9G9/mIeKvxbd+OWl0JERV7/wFYevdG0nQwNGz9B2DJziGw4wuMcBj7wEGkTDjRHLckIbk9WCQZe7/+eMaNazaNkqD1tEoQTpgwoaPGIRB0OBkzT6Fs5RsY3uYtvbYhQ+l95Y/Ze+BAs/5wuZcvaLIvQ9PY+5e7Wi8GuwDPxEkdXgJKD/jR9Tq+RVF6iBiMoeumw3vUkgzJ9R9sQ19G7biIpuxoiKKQccossubOQ3G5kzrc/jfdwu7bf42eoLJJV+CaMlVMJdbSVK3iaPBUVBi2NgDH0DSq1q2h4r13iJSUtChRtY6Z87VJJImi554G3YA6k9CBL7+g8v338IyfQO8FV4mSdUmkVVPGRzpiyrhnkpFh+hBVVPhRfT52/fpXEG7cJ01yuxl674MoTid6OBzvD4eE7DjkD9fcA6ni/f9R8uILSTyiDiInl+F3/bnDH7DBPXvY//gjZiBObYRvT7IOxqj9OJBdLiy9ehPO31N/yjRRdYXmurTbMSKRtk29NjPF7Bk1mtz5l2LNze2wuBctEGDfww8R3L2rS6ePM86/kOw5olIJNF2ruK4fXs6F8xOer7rPz4R9L36Rqo/WYyRIXdTRWPv1Z9Bv7xCiMAFtmTIWgrAVCEHYM2n4QIuV12o4vSVJOIYOo98vb0ZxOus1NeYP1xyGprHnD78nEi0D1hn1hz0eaKW/r2PcBPr/+OpOebAm8iFsys+t2yMr9P7Rj3GPHhOr31s3LVC4vJyip58kXFGBpGnYhh+Fs1dvUqZMw7t+bdz6desAR5drgQD5v7/9UER2XWw2071h40ZI4M6vpKbS9+qf4jr2uEaTSiebusfgFrWMu5SqNR+aMyOq2mgVH8liIeusuQmTeDclCKvWfEjJsiXoNTUddwDNkDp5SpsLAxzOCEHYwQhB2DNp7IGW6MXbUAi2laivTnD3LkqWLTn09dwJgtB1/AlkzzsP/4H9VL7+KnowhJLioc8vbkS22Tj4738SPngQ2W4n9ZTZZE6f0elf2LEXSTRdRU8VgwCSRPYFF5F5asdnVoiKS7WqCiUlBWfe0VS9v8o8jw2Q3W5yLriI9GknY7QndLydNCUoBB2LoWkULnyI0N78hLWKgVhEr2PQIPr98uY4Md3Y9TP7fpDAN1936e9XcroY9tDDwkrYgA4PKhEIDicUpzNhXr/2EPXVqdiwnkjBXnP6r65PTSd8f8lWK9ZevTC+/w4VwOdF93nZe9fvsY88DsnhwJqdhTU9g7QTJwJQtX4dob35SLXJpe39+lPzycettoi2lNQpUwkW7qW6i6aakouUMO1QYw78jS1vCbbMTPrffCu+7dsoXbqEiv++ET8am43MU08j84yzkKy2hClABEcGLa1VLDscra4HHCooIFJ0sMtLOxrBQLsKAwgOIQShQJAkDE3j4KLn8K5b26UPSd+nn+C7PkFi6UiE0Ccfx/4MAb7t2+JWq3hrpfk/0SAaJOSX/oN71Gh6XXHIZ7KusJHsdnRVpeazT4iUlKCHwkjpqejFpUhWK4rdhmvCRLSDB5AsVizp6WSfewH2fv0pe2M5WlX7Ent3JZLNFivDB4078FuysrBkZKJWlKOWlTXq2N8UwT17KFnykmmViRuIROqkyeRccCGWtPSufk8LugEtrVXclnrAesB/qNpPV2K0vzCAwEQIQsERQ0stMy1ZL7BvHwX3/AnqWLeUkcejdWJ6mEZJ1kM6GhAhSej+GrybNhAqLGDAr2/Ht2VzreApQa2pwQgEmhXB9cRnbX5F9wmjsPTqjR4Kmf5xPVDFuEaMqCeSEznw6xGVwHffmlNrsowlNRXJYsFQNYL5+YSLiwjtL2zUsT9SUUHZa0up3vBRwnPkPCqPXpdcin3Q4NbGsQgOY1paq7gt9YBlp6t7+GpKiS30gtYjBKHgsMfQNEo+WM3BD9Y2mXLB0DSq1q6hat0a1IoKwECy2rDl5MTWCx48SMGdv024n24hBjuC2tJRqCrhfYXsvfuPZpm3QABdVZuM2G6U2vyK3k0bzWoSKalYs3MIF7au3nRXI9nt9L76Z7G/q9evw/fJdgxVreezZajeQwJb12sFsfnylT0etKpKfB9vx96vfz3Hfj0UouKdtyh/+81D+Q7rYM3NJfeii3GPHmfupwuEYENLMYARCsU+pgRdR0trFbelHrB9wACsvXqjVlV16ReI5HDWs9AL2o4QhF2MHg5TuXYNVR9+gBbwY83MImvuObiPPqZ7fH31cAxNI//Z56nYshW1xl8v5UJdy0zWvPPY/7dHTStObT1N85+MVl1FqOggNd98jW/jR51/EJJkpiPpKl87XTcFs8UCkQiRgweRPB6zjFR7cyvWVtIwAn6ktDQsub1Qi4uSM+4ORrLbybng4lggkqFpVG/ehB4I1heDhoHmrzGP1WIBTUOvqUFxe2KJuZW0dNTSUrybN5m58yQJ78YNlL62tPbjpD6yy0X22XPJmPUDDLlrnhN1p8YjpSXmFGKtaJVsNhSXC2t2DuqMaeTMnNElYzzSaUmt4rbWAzb7PolQwd4ujTJOGTNGBJQkiVYJwmOOOaZNOxGVSuLRw2H2P/0v/Fu31Fseqqhg/8IHQZbJOv9CMmfPEcKwHVSvX0fFlq0YaiQuyi5qmfFu30bNF18QKS4yrTeKcigaWNcwDAnd5+tcMWi1IrvcyFZL7bSPWQReCwZQi+oLJqVff6RQELW0tGPGUmvZkiTJDFAwDGSrDa3GlzTLgBEKo+u6GQsbTUPTVVitYLGYY6m16Ml2h3nsmoqhaigeD56xY0k7+ZA1rzEHfiMcNhNY14o/Q5IwVBUjEkaymRa1uo79VevWUvXhB4T25sePTVFInz6D7HPOQ3a5uyxgpO7UuOYPmEm6I+HY/WBoGoaqotXUsG9pCYG9BaTMPV88y7qA5moVt6cecOqUqYQKC6j6aF2X5SFsrjCAoOW0ShDm5eXFmZwPHDhAVVUVKSkpjBw5krS0NPx+P1999RWlpaX07du3zULycEUPh9nzpz+gRnPTJVxJp+yVxYQPHqD35QvEg7QNRC02WsCPPTcXTav/+oxaZsIHDqCpFebLrEHd4mgt2GZ9cJJJbRoWWVFMK1IdFIcTMrNqnbkNZLsd2WJB83fw+BoIPyMShkgyE0kb6P4aZKutS8q/JapHnbi+q6PRGqqNOvBHy8dFj0uSMAwDIyqAo/vDtNYUv/BswjG6TxhFr/mXYO3Vu8t9BOtOjctOx6GgoNoSj1FBKLvdGGqE8s1bMLJ7JcxzJ+hYJEUh58L52Pv1P3Qva6bPoGPQoHbVA5YUhZyLL8HWr39tpZLi5H3MSTJIJE7urih4xp9I7wU/EtbBJNIqQfj666/X+3vr1q1cddVV/OQnP+GGG27AVufC6LrOv//9bx599FHuuOOO5Iz2MKHoheeaFoN18K5bi2voMPEgbQNRi43idNaKvEbeoroWK0XX8INHkiQMRUmy+GmGWuuk5q+J8/sxDAPD0DHUiBnsEVGRnZhWtU4Y06E/k59PUa+pQc5xm8EW0Tq8nUSietRN1XdN9PJs1IFfluufv1pra9Tqa+gaWlUVWnV1wrHZ+/cnd/6luI4ZWXv923+87aHu1LiSlYVaUmwem6LE7lVJUUwrqN+PpU9vwiUlselw8XHb+bT2Xm6MxgLu0qZOwz5gAME9u4mUlaJVeZEcNiwpadgGDCBcUEC4rJSaHV+Y7iGyjH3gINxjx6FVVxM5eADF4cRz4kloVZVoPm8s3RVA9eaN+L/cAbqOtVcfnMOHY0lJFfdSkmmXD+EDDzzACSecwC233BLXJssy11xzDZs2beLhhx9mxowZ7dnVYYMeDuNNkOqjUQyDyo/WiwdpG4habGSbtdF1jPChaa7GLFOxqdLOIhaIoGKEw6b/oGGg+3yo3up4cSoR80frELVQ609pxASahOR0mtVQkrG/qIiwWNDKykxR1f5eW0xz004tre/amAO/ZLOZIjccNi3OhoFktYLFilZdjVpVmVD8Kqlp5Jx3HqlTTo5ZFVtDe/IdNkXdqXEikXrT4fWITo2HIyhOZ6vz3AmST1tqFUNtveI1HzaaSilSXo5aVlrPjxSrFUmWzZKf0Vrf0f6A4DdfE/z2m0MlHgHvlk1YsrLJ+MGcej6NaZOmIOkG1Zs3EcrfiHfj+lanbBI0T7sE4c6dO7n00kubXGf48OFs3bq1PbuJ8fnnn3PxxRfz85//nOuvvz62PBgM8vjjj7Ny5UrKy8vJy8vjxhtvZOrU7lfY3LdtK4Ra52sRyt8jHqRt4JDFRm18peh0XiMYhhH3MOtQog/H6H91HcMwCJeWQIJqFABaeTlaMIjkdGI0sk57x2ROndeKFkVGttlNX7tEpdRaS+35130+cLlNHzxdM18sHWwltOTkJq0WamMO/JIkobjcqJHIoXvJaiVy4IBp6W3Yz/+z995xblTn/v/7zIy6tlfXdaGGXg3YBgyEkITQQgkklDSS3703N+GSAglJLrkkkEKAhJv65YYOBhOCCZAAocXGhWJDKKbZXu/a25tWqzoz5/fHSFppV7urtsVm3q8XL7zS0cyRNJp55jnP8/k4HFSd+jFqTz8DHPnPaywdxIkunrkGkBlL4yOXwzPeSCKINU2EqmHG43np3NnMDJKNeT0bNo7wQk6TUhKKtUqhx4eXeOPxiW/sEiU5gHWe0XXiHe10PbiS2M6d1F1wIcCYXsy5SDbZ5E5RAWFNTQ2vvvrqmM/rus66deuYNWtWMbsBIBaL8d3vfhcjS33ClVdeybPPPstFF13EokWLWLVqFZdffjm33347Rx99dNH7LiUFCWjmKRg6mSQ9fWM93USbt4OUqF4f5UuX4dlr7xn1g0xmbGKtzWhl2W18pDJc15UeGMrECW1KURQUj8e6ozYM6wSrKMQHBsYMBlOEQkiXCxTVWgIvJaZpzUdR0CqqMJEYgQEUrxezFAFhOqEhzNAQzJlL5YEHE23ejt7RRrzYbuZs1Nax4NrrSu7Akq2AX44ImszB7DaYZUcdTd35n0Grqi5o/8lmj8CrL2MODSGEkrgoS+K9PYQ++ICeJ5/Ae8BBKA4NR3UNzlmz6V/zT8L/eg0zHkeoKsLlwlFRie+gg6n+5KcyPqP0pXHhdI6dmU4sjaMoyFgUoeSnc2czM+h69rlEY16mlJIRHEy7oTYhnlBn0LThc0Y+CJHQ5tSR0SiBDessGRxJViknGF+yySZ/igoITz/9dP7whz/w/e9/nyuuuILq6uGTWHt7O9dffz3vvfde1iXlfPnNb37Dtm3bRj2+bt06nn76aa6++mouu+wyAM466yzOOOMMfvrTn/LQQw8Vve9SUpCAZp6CoWYsxuBLGwlv30a8qxvF5cBZW4/vsMPwLFxUUNBmxmJ03HUHg5tfhfBoU/rAi2sQbg+1Z59D5YknzYjAMJmx6evpIt7XhyirGFWPZ4ZCVuAlAWlazRqKMvXBoBCoPl9i+aXHknEwTYxIBDnQn9s2olHUqmrM0JAl8lzCuSkeL/5DD6Xuws/R88ifGXz1Fcwxat5Kws5W+qMRFv/wf/CEB+h/7TVa77mvZJvX9t2fBV+/ouQF6WMW8CsC1WddvLLhbGhk1he/jHvx4qJW4fuff46Bfz6fWrYbtSnDQG9vJ9DePuY2ZDyOjESIDgwQ3dFM4MW1VH/yU1QsPx6hqhlL46rPl7EcnrFsnFgaF04HxkA/rnn56dzZTD/SMOhZsxYjHEKtTgvGpMRISs2ompUZhOFa5kIy+wmFB6FpyHgcMxxmYP16q8duhJRTkmySTTPh2rO7UlRA+O///u9s2rSJBx98kIceeohZs2bh8/kIBoO0tbUhpeS0007jC1/4QlGTfPvtt/njH//Iv/3bv/GrX/0q47lHH30Uh8PB+eefn3rM6/Vy7rnnctNNN7F9+3YWzKClVv8RR8Ldd+a1bOxqWpDTidSMxei4808EX3nF6gJNYwjo+/vjqOUVVH/qDCqPPzHnH44Zi7HjJ/8zoWiwjITpuu8eYm27qL/wczPih1m+dBmip4PejS+NKbng2ntv4h2dGMFBa3l5ioNB4XCglpWj+v1IsDIpXi9aeTmxYH76XhUnfxT/fvvjaGig/8W19Dz1N0jK0TgclpexaSKDgxj9/eh9/SDHP3m7Fy1m7n99KxU81Z33GYyhEMGXNyInq24RoLubrvvuZv8rv45v8SK8Kz5G3xv/outXN41/wUk0awCZ44TA/4nTaRyR8So16QX84a1bCax9gcGNG7IKS2uVldRfcCH+oyxP6WI+SiMcpmvVysKEwsdB7+ul++FVxNp3pZblkkvjZiJTbCQ8u2WisUQmtDyF14ve34/q8eatc2cz/URbWoh2daU15lnIeMwqfxCCUe16hVrlSAmmBEWkSmbiu1oRmjYpXsw2oykqIHS5XNx55538+c9/5rHHHuOdd96ho6ODiooKli9fztlnn83HP/7xoiYYj8e5+uqrOfHEEznttNNGBYRvvPEGixYtwuvNzKAdcMABqednUkCoOJ2UHX4Eg+vW5vYCIag8bumEJ1IraPsRsdbWcccZgQG67r+X2K5d1H/mopxO0O133k5s5/jbTWfghedxzZtP5Qkrcn7NZCFUlfmXXoxn/nw6nnshq+RC2ZJj6f7zgwQ3vYoRCKQ1T5RqEiJ15+ua34T/6CUMvbSRaGsL0jBQvV7QVIxgMBWklh19NLXnnEfrL39BZDD3TFzk3XeoPc36zdWcfAo1J5+SdZwZi/HBN78xYTAIENu1c9Rjel+fFchW16D3dE9anWXgpZcwY7FUAFd14EFU/eH/JmVfpURKyeDLL9H90IPovT2jnlfcbmpO/xRVH/2YlWEpdn+GQcsN15U8GExiRiIMvvpKalkufWncCIURmiMVJEiwAgWHAxmJIHw+qo8+irICdO5sphfLr9hEaJnHqEzUNo+r3lAQEhCpMoTkjYXiHrsxECjIi9lmNEWfiYQQfPrTn+bTn/50KeYzit///vfs3LmTP/zhDwxmqbvp6Ojg4IMPHvV4fX09ALt25SbvkgtVVaWpf6n4z/+P11uaiU4QvAHUnrSCpk9OLE79wa/+j9jO0RfurBgGgy+uoXq/vak/5aTxh4bDvPvyxvzu+EyTvscepekTH50RGlGqqjDrY6dQt+IEQtub0UMhNK8X74Km1OdaffkX6Hp2MS333GsV/5cCIVCrqnBVWmUC8b4+zL5eysq8LPqfH9L17PP0rF1LtLPTOuk6NbxzF1GzdCl1K05AqCrtmjLBTjJRYpGcjtPu51+2/IdzwAyHkVtep+qE4wEY+mArZn8Pms+Loqnok9l0E4/Rt2ED9SeeULLf32Qz+M677Lj9DoLvvjf6SUWh/uSTmHvRZ3CUl5dkf9Iw2H7bn3L//Re0E4kMhwhvejl1PqpK/GZ61q4l0tGBMRTCTASkqtOJ6vPhaqinbvky6k86kUzVRZuRSMNg6IOtDCVKo7xN8wFBqNkSKPctXIhvcWElP4XibKimW1UwdR2Hd/hcZGgaepquZpLUinKhOxQiYxuKw4GiqSBNtHHOhVKaCIdGZUM1vt3kPDETKYl1na7rrF27li1btjAwMMC3v/1t3nnnHbxeL/OKqBl55513+N3vfsd///d/U19fnzUgHBoawpOwjkrH7XYDEM7xojeVKE4nB//8Bt679Tf0rx3D/UJVmXfxZ5n1iY/nlB3se/mVvII2Mxql55//TAUeY7H9tj8VlP2J9/Wx86GHmXfhBXm/thCkYYwZ7CURqopv8aKsrxeqSn97G3qedXG+vffmwBt+jB4M8q9vfYdYdw+q14vi9+PwDi+zmLEY8UAADJNtv/s9/W+/jZASPRpFcXvw77MXZfvsQ8UhB7PzwVX0btyIq6YaV30twS25z8c9Z3ZO46K9fbkfL1Ja4xPoobSswRRoBcb7Mxux9GCQ5jvvZnDL20TbO1NWg2qZH6koOLxetMpKPA0NuOfMJryjFdOI466ro+qoo/DvvdekXFSjnZ3suPteesf4TVcccgjzL7sE7/zS1tF1PfscvS9OsouOaaI4nUQ7Owltb04FJvWnnJRxo6W6XCDAiERTv0MtKftk5HasTPRblobB0NatDG21AqfpCJRKiTQMOv/xDO1/fYxoVxcyrmf+NhOyT0JTcdXV03j6J6g/eWrqtL0LmnDV1xPcuhWtrCx1PlNcToRDw4zGMjRQJQmN0kJKSZLSRZJUZtAzby4CGNq+PWP/6UgpMcJhfIsW4V3QVMS7tSk6INywYQPf+c536OjoSKWQv/3tb/PEE0/wxz/+kf/6r//ii1/8Yt7bNQyD7373uxx11FGce+65Bc9PUfLLsIxHX19p09H1n7+c2s9eNq6XcX9g4uaAwLoXMQoIfIeaW2h//Z1UzYUeDNLz8CrifX04qqqoPuNsel/KQzMxHSnpeHE9wY4e9AFrezVnn4vm90/82jFIdjjrgYGUaKlQ1QklNqprrQ7jsb6/dx9YCU8+UdCcRFk5fX0hItt3IKVAq6xCTXQ0G4bEjEeJj8xSS0nfc89nPBTevp2uJ58uaA7pOA87KqfjdKg/e5freOOT2w3HJGZcx4xEUVyTnwHWyssxDJOulk52XPff2S36pMQIWO8p2j9AdFcbQ2+9PWpY219WF1RHOx5GOEzv43+l/6m/Z5U4cs6eTcMFF+E98ECiEqJjfD+R9nba/vcWjGAQ1e9n1r9/HXdjY8aY+MAAXffcSby/D0dlFbWf+Swdz/4TPTz5tmFSUTHiOv0dvcSqM+dFdSNUQ3rIFwNigShVVdZnPNFxOZFcTtkxxxJYt5a+p560nk90sQpVxVFTS+Upp6YaX3YXpGHQef+9BNattRrBxujWRkpkXBLZtYvmO+6i772t1F+QW8lPsVQvPY5wWzvRnt4ML2TF67MCQl0flskyTEvIX1Hy7zJWrI745G9I8XjwHnE0SAhl2T8MezELlxvPYUfmdL38sFBXl11ZYzyKCgjffvttLr/8ctxuN1/5ylfYunUrTz31FACHHHIItbW1/OIXv2DhwoWcdNL4S5Mjue2223jnnXe499576e3tBUhlCMPhML29vfj9frxeL5EsHorJx3w+XzFvMS+CWz9g109/MvxDUFVmfesqyvbae8zXKE4n1SefQvUYtV65oAcGCirilYaOGQ6hB4PsuO5a9O6ujOcHnn+u4DkBGLt2EkirPxt4/jm02jrmX/PDvALDZIfz0GubLUmWRJ2Jct/dqOXlGNEoRGNj6lNVXf4FyznBMAhv28rQ5s1IPYZrXhMdzc3wzFMFv8faz3zWmmMW2zIzHhsdDE4mQqDkcByYsRjhLB3749H3j6cwQkOoLheRrVsxhoJIXccMT/IFyeGk+thj0INBtn7ziuFuxiIopI42G9IwGFjzT3r+8meMLHWeqr+M2rPPpuL4FQlNvuzbibS3s+MH383ItpqDg+y45ipQFOb/6CeoHg/NP/yepdWYIAoE8xG5LxJLHnNypGPSvZGzac1FO9rpe+YpqwY42UGfJuAe62ina9VKortapyxQKgWBtWsY3Ljeek8JiZ4xA6nk+41GGdywHve8+VMis1K34kTCO1roXr8hszEvqZea1CHUEjqEul7Q9cgKeq3ft3C5KF9ybMpfebK8mG0yKSog/NWvfoXL5eLPf/4zc+bM4dZbb00FhCtWrOCAAw7gjDPO4E9/+lPeAeE///lP4vE455133qjnbrvtNm677Tauv/56Zs+eTVdX16gxnZ2dADQ0NBTwzvIjuPUDdv3kf0Y/YRi03fBj2oBZV31v3MCwGLTyioJS9ELVMIVg67eumLLOWr27i63fuoJFP78pp6Aw1eG8a2fixJNcjjAxQyFLNkZRUBOSBEJRUNyeDH2qrmcWYhoGrStXWRfuUnXFCkH/k09Qd95nRtuWSTm1wSCgVlePe7HOCKxD+XUvEw4TeOYfqT9T+nOl8i0dg/KjjkJxOnn9G/9VkmAwRcKn2D2/sIvq0Jtv0PXA/VmbrYSmUXXKR6n51JkIl3vc7UTa263AbyxM03p+Mru5c8QMh3DPWjwp0jHp3sjZtObiXZ0YfX3DFpNpTQ4pv/EpDpSKRRoGAxvWY4bDEweDYL13TUvo0oYJbFg/JTIrycY8WdswwgtZw7PXPjiqq4j39hLvHu1UoigKRhankuGND3cUg/XbcdTWUnnKx6hYtjz13ibLi9kmk6ICwldeeYXTTjuNOXPmZH2+vr6ej3/84zzxRP7Lcd/5zncIjKjnamtr47vf/S5nnnkmZ511FnvttRevvPIKq1evJhKJpOoGAd58800ADjrooLz3nQ9jBoMjaLvhxzBJQaH/iCPhrtvz7jB0zplD5+23Tb3mXjzOjh//iEXX/2zCoR133WEFg1JaVkjpywXJ92uaGF1dGAkvYqFpKD4fSlk5encXO+65D6NUNmtpKP6ylCBq+XHLhm3LfL6snaWThqKA04mrvmHMi/WowLpIZCxmdZEWIkCbK7W11H/uUvRgkGhHZ8k3b4nf5ndRje7aRfeD9zP0r9ezPl925JHUnfcZtJranLa34wffzXGy02xgDBCP4zv8yJJfeNO9kbNpzQEZjkJiRFd2ym98igOlYom2tKB3JPQg0/T9xkRKS+IlcXMQb2+fMpmVibyQ011uhMtlTTcaRfF4cc6eTbS1hfAHHxDZ0QzxOI6aWnyHHYZ7fhPR1haiO5pBgqupCXfTgqz136XwYrYZn6ICwmg0OkruZSSqqhItQCT3wAMPHPXYBx98AMC8efM47rjjADjttNNYtWoVK1eu5NJLLT/SUCjEqlWrOPjgg5k/f37e+86HXILBJG03/Jiy/3d7yeegOJ0FyU14DzmUnpWlE/vNB72rEz0YHDdLaMZiDL222boYjAgGMUecOJN1NlgnIjMaBaU/Je48imIyLoqCc/YcUJQMQdSkNlu8uyvnDt6SYJoI3UCrrBpzSEZgnVzmKRKp6winM1XLVUqUvfZmUUL7sOWeu0u+/SSxnTtzuqgag4N0r/4LA88/m/Wzcy9YQP2Fn8Wz1945H1aR9vYpacopGaZJ5IP3YUV+qz0Tke6NnLVpIBYbUZuZkCZJQwgxLYFSMZjhEGbSVWgMjb1RJB1vpMQ0p15mJemFnAwAw+++MyoAzBakuZsWJF6voPf2oVVb56poawsyGsW9YNGo140XZHr22dcOBCeBogLCxYsXs3btWkzTzNq8EY/HWbNmDQsXLixmN+OyfPlyli9fzs9+9jPa2tpYsGABDzzwAO3t7dxwww2Ttl+wsoP5Mvj+eyXPEgbefaeg1w1s2lTSeeRLz8OraLj4sjGfD77yslUzmOw+S0Nms2dLX36YKHNVRMbFNW/4JiNdELV86TKirS0MvPD8OK+eHKShE9zyFsqD94/y9MwIrDUHGCWSiUnW/IwMrosION2nfIy553w6Q64o2tNb7EzHJFlHOxZmPE7/M0/T+9fV1tLeCLSqKurPPR//kmOAsesEs9H2v7cUMOPpZej11zJ0IUtBtvrbzAEj/MbH8U6erkCpEBSPF0VRrUacsd7TSIRI/bYmq55zPDIbf7owQsNLxMLptDKCdXWphj6AgX++QO9Tf7dq1Eeek1UVxe1G9Xpx1NalmocG168jsHED8e6ujGVo4XRmjB3Ll9umMIoKCM877zyuvfZarrrqKq6++uqM53p6evjRj35Ec3Mz3/ve94qa5ETccsst3HTTTaxevZpwOMy+++7LbbfdxpFHHjmp+2276RcFvabsf39f0nm0FzAPAP3dPPRMJoGJ/Gkt3+cxTpRjdeOZ5tRmXdIEUYWqWiezjRswglO8DA/ISCTD09MIh+l+9BEG/vkCJC+QpazDg5TdFDB8sk+KEo8sRVBUtMrKVBc2WJk3oarM+sq/jZnRcdUU5uubC0LVsl5UpZQEX32Z7lUPEM9SoyxcLmo+8UmqTv04wjGBaO4YGGkNIrsLZiRC8JWXKT/2uJJtc1T97agBSuY5YKzAKXFOmI5AqRBc8+ahNTQONwVOJNcihKXvl3je0dg4pVaAmY0/YaRhWL/xxHykYSB1g/DQkNXQ19qCaRgE1r8IY60SGgbm0BBS1zESr+t79h8YQ0HMcARM0xI8z9jH8NjortZRN8A2hVNUQHjhhReyadMmVq9ezaOPPoorkdY96aSTaG9vxzRNTjnlFD772c+WZLKLFy/mnXdGZ8N8Ph/XXHMN11xzTUn2MxbRHc0458xNHXwF+cVmWdo1wmH6n34yJSVRecqpqFm0FcdkqmsAS4SjauwlTkj6PovRbhrjWSNNcjCoVNdkPqBbxc3JC5CMRhEuJ4pWhdk/fsBbUhLBsBEKE1i/jp5n/oHesmPq9p/MCibmIbN8P4rLhTKyRCAHh4F5n72IzhJI8mTDOWfOqItqZPs2ulbeR/i9d0e/QAgqli6j7tPnopQV4Euehur3Y2bRVp2xJFwprBu10pHujaz4/aP9ap1Oy982lV3KrkU3XYFSoQhVpWLJMcRad1jNcaY5cZexYYCUKF4v5UuOmdJAKL3xR7jdmAMDCX1EbbixR4+jVlQgdZ3AhnVWwJhDOZOMx1F8PoxQyHJBcrlQPB6MgcSxpmkpS0Sp6yg+H1LXM26AbYqnaB3Cn/3sZ6xYsYJVq1bx1ltvoes6wWCQI444grPPPptzzjmnFPOcEbTf9kfU8vLhVLXLlX+tWNpSixEO03rTL4hu35YRyPQ8+giuBQuZe8U3cwsMHY7dMiisOXt8fUn/EUei3HcPZmgozSaJce+gU5/jJHVlqo7M7kYzEsHd1JS6ACWzHYrM1GSbCqSuo3pdhN+ZxsyvpiFUDTkyE6koKOXlo2vERgTUWTfp9+OsryPWOTpTVwzC6cq4qMZ7Lc/ewXXZRZ69++1H3Wc+i2tuaYKNWf/+9fE7jGcaUoJQEjdqpSPdG9kY6B+lNQcMZwmlRBp61i7j6QqUiqF86TIirTsIvJjQIRzvhjbZVONyUbbkmCmVWclo/KmpId7VaTW5JL6HjMaeUAitto54Pg1spok5FEKSuKE0rL+R0rL9TBwPQlWRuo4MhdDq6tF7elI13LvLdz6TKYlTycc//vFxPYvD4XBWN5HdDTMWJbZ9eypV3fj1/7K6h/Ng1hXfBKxgcNvV38rQFRvekUl06wdsu/rbLLz+ZxMGhY1XfJP2n12f1zwAyr/wZUKr/zJKf3Aq0OrqJ5SdUZxOfIccyuCGdZZHauIucaIlo1H/LiEiUSubFES1fIeXpE5G6dkOrXEWenvbpMwjK1JiDE3xMmR6ZlCxggXF70fGosR7eqwbFSGs+qLECsLwdEcH1NmQhkHFwQfT9fQ/xhxTCI66WsqWHIsZidD79yfo+/sTw53r6eMaGmj4zEV4DzqYbNmpQnE3NpaswWeqUNxuS9WgxKR7I2fXmvOg1tRaDVvR6HDtKkxroFQsQlWpv+AiXHPmWqtE3d1WA002pxJVTUiynErFsqkV4A5tb041/qS0BkdmchONPVLXkZFw3udgGY8NL5sbaZ7YI8/3iX2gxzNquGd6E9HuQFEB4cknn8yll17KJZdcMuaYW2+9lXvuuYd169YVs6sZgZFI6xuhUMroPV+SDSWtN/0iMxhMP+gTPyQzOMjOm29k/tXjL4WX77Mv7XnPBBqPW0r8gAPZ9q0rpvai5HAw/3s/yGlow8WXEm1tsfTe4nGrk3DkCUJVrf/Sl1rS7ygT2YOiURRM3YDo4JiCqOnZDhkOTW1QOAW6gKN2qWmpOiLF50st+QmXG7WszNKOAxSvN6vDwMiAOhud/3iGnrVrS571jXV10XH3HYTefBNjoH/U84rPR90ZZ1Fx4kkZ9lylZP6PfpJblnAG6BCiKPgPPXRS/MmFqk6oNVe25FgC616k/+m/p56HdO26qQ+USoFQVSpPWEHFsuOJNG9PSbA4EzdJsZYWEOCan12SZSrQQ8ONPzJREpJVHiiVxS3wPJTWRQ1YN0xZ9iGltCSvcig5scmdvALC1tZWgmlBzM6dO9m6dStbtmRfoorH46xbt25G+gkXhJSpuxgjHiewYT2zrvpezlnCWVdZzTVGOGwtEyfJcgeU/EFEtm3FCIcnzhJ+++q8soTlX/gyAKHXNkNZGQRKKNg8DlpdPfO/94OcnUoUp5P5V19D5913ENyc5lSS/Mw0Da2hEUVRMKNRjN6eYYkKRUEtrwA9nrdHcTZUnw/MiQVR07MdZiCAVl1jBUD9w/7B6qzZuBYswOjsJLazFRnXUWtrkIqK2bF7yJEItxutrMw6IUejSN2wbnISmR0ZiaTkIsxwBAORt8OANAza//o4ZiQ63KximgX5a48iFmPwxbWjH1dVqk46mZozzpr05gR3YyPzr7thlFNJigynkmswg9NUcygEzjlzqf/cpZO3ixy05ipPOJGKZcutwKm5edoDpVIiVBXPosV4Fi3OeNw7SYYG+aB5hxt/FJfTygYmdBEzSAR0BX8XybKfZCA4RvNg0oRARmMTlpzY5E5eAeFrr73GlVdeObyeLwQrV65k5cqVY75GSsnSpUuLm+UMQaRnoqQk1tqKQ3Mw+7vfn1CPMN2ppP/pJzNr3bLuTKQK9PuffpKaT5057vbL99kXcgwKvRddTONxS1N1IUpcR5s3H8MwkAP9VtGwpiEqKlFVlVhH+9hdYmOw4Ge/RHE6M7yRC/UyVpxOGr/w5QwvY9VfRnjbB4T+9Tpmby8klpiEw5nhhan6/aiaguJ0EuvtLTjQUo88itmnnjau1laSbNkOTANHbZ3lyzoikBx44Xl6HluNjETQKioxq6rQ+/rym2sJPbsn3FVZGRVLj8e7//6oPj/O2bMZ3LAuS2ZnAf4jjwYkwZdfKshhINK8nWiiyzdVNzaJNy7+ww6j/oIL0WrrJ20fI3E3NrLPH/5vQi/jvW7+NfGBATrvupNIazPG4KD1u0y/eE7CZyM8XsoOO4z6z106KdnBUftLaN2N93y2wMlm8vAuaMoQ3kfTRjWLJBt7hMOBcHvyzmoLh9OqIYxGQU00kcRjo7ORiX2gOTAHAhOWnNjkjpDZ2gHH4ec//zm9vb1IKfnLX/7Cfvvtx/777591rMPhoL6+ns9+9rNUTdBRujuw/nOXWZkQKa0MhaYx5z++ju8AS0Q7Vy/j9rtuJ5D0CR5Peyrx1ZSfcCKN4+j1paMHg2y98uvjLx06HCz6+U3o3d20/f43SMPIkAIZSVIapPbCz9L15wfRW0fbdQGIBQto+rf/xFk9eTIh6ciE/VgqEDENy1PTMDBjllG84vagOh1IQ0cfCiE0B1pVFe7Fi3E3LczNy7i6hgVXfa+g95UurjpWICkNg64H77cyiuEIituFGQ5n1b0bE02zlm+ziXCXENfivag96xy8WYRhx3uvuXwO2eh/9hk677OEqZMBodT1kmdQXfPmUX/h5/Dss29JtzuZSMPIWGJ0NTXhqG+g/x9PM7T9A2I7WpDpPucuN0RHHB8uF95lxxN6/rlhSSJVxXXMsfgbZ+GorLKau4oIBKuqrOxNX5+9rLc7kvz+tj/yhHXjquugqqkOYKGqw409QqBWVCQ6jnVkPJ61LncUioJaWWnJ2USjmV3GaY0lyaXo5D6EplFz+hl2l3EW6urGvqaPRd4BYTonnXQSl1122bg1hHsSyYAQSBQ1KzR+/ot5a3L1PPoIPY88bP2RQ0BYc+bZE2YIk2y96ls5NYlodfU0XnIZ7bffNmHK3QiHwDBovOyLePf/SE7zmEpGBhsjM1YCaTWDuD0Irw8jNGT5IMdjqF4f7gULCG7dChPV+iU8k53V1TR84fKSBr7ZglsZjWHGotZJcKwAX1FQvT6cc+fiP/wIuu6dPFePDFSVypM+Su3Z50xq1qj/uWfovPduEMOWZSUNCBWFyuUnUP+5i62biRKQLfgFUsucUpooLjdaZSVKohQk3eYrtmtXhjuDGQ5jBAKo5eWoPv9uZ9dlB4S7N8nvr7d7MO3GdbQOoVXS4Uyckzz4DzkM0zQIrBtHhzCBcLkS1yEPitc3pg5hsmwkOdZ/2OG2DuEYFBIQFtVU8swzzwDQ29tLLBajMW15Y/Xq1RxzzDHU10/d0stUI1QFtbw879dVnnIqPY8+MtydOZ7wsqJQecqpOW1XDwZz7hjWuzotA6jxBGFTgyeWBplOsi0xJWuRIs3biW5+me7nX8Ds6Bj1Wr2/n+CunbntyDQxOjsJd3ay/dv/hXC5aPqf60sSGI5VP5X0AY3uaEaaprWsErfuuJNBRTJIAOj966MYJdaJy4ph0P/U3xhY8zy155xL5fEnTspJ2TW/ySpkj+tIRY7fZZ4PQqD5fOB0ULZ0WUmCwUwXByuoF4oCqoYZCmGEhlLlJsk5JN11hNOJoihIM82aLJHlHvbwVVFcbpxz51J+9DG2S4PNlJKtFMYIDWV3KkmUgwC4582n76m/WwLvYzqV+FLlNGVLjk3d0Me6sjmV+LKW3tgUT9GyM7feeiu/+93v+NrXvsZXvvIVwGomueqqq1AUhe985ztcfPHFRU90JpFMWytuN6ov/5o41ePBtWAh0aT13cigMC1p6164KGeR6p6HV+U1j8GNGzIEYaWUyFAoVeclEp2heiiEs6qK0NtvEWvblfMSUnrNn1ZeMep1Ez1fCgbWriHwz+cnpUlDRqNs//Z/seBnvyxZtjBbcJtrvdTAC8+Pbf81SchwmK777yXe1kbdBReW/OTsblqAq66OyK42axlKVccX780BxeNBcTowhkI4yspSPqvFkOniELF8eVUVPTAwtoB9MtgzTct9YaJ9mCaGrhN+/z3iXZ3jujQUukRvYzMe2W5cJ/IyTu+gjmzflvIyds1vSjSGjH5dvvuwKQ1FBYQPPfQQt956K/vuuy8HH3xw6nEhBDfccAO33347P/nJT6ioqOCMM84oerLTTVIlHQBVxTlnXsHFrHOv+Cbbrv72cNdgNmcHfxlzvnFlztucyApuJPpAP+VHL6Gro41Y267R4tY9pLIVsbYw3Q89YKXs770L/6GH03Bx9iJzMxaj4647CG7eNKxHJQTivrvxH3IYdRdcSNfK+xjc9Coku4YRcM9dlB02/nZzCSCTF8PA+hcnLRhMp/n7V7P3CDvC5Fzj/X1WvWlFJQOPP4oRjiAUFVFbi2LqaGXleD9yAN699i6qUzLW20vXnx/EHBoaLcEz2RgG/f98HuecOVSesKKkmxaqSuPpn2THnXdbHeZFvK+kD6oxNIQeDoPDQeUpp5bkwpLu4qDV1iKEwAgOjrbvK5ZE/bLe00P/008TeOcd62YkHMFRV4taXone003o3XesmztVQXW6cM6aZWcVbUrGRI0/2cbn2wiU7z5siqeoGsJzzjmHaDTKww8/jDPLhTkWi/GpT30Kn8/Hn//856ImOhNYf9ElmLqeqJHwFl3MaoTD7Lz5RiLbtmYGLYqCe+Ei5nzjyrws7Druup2BZLNKDlSccCK1517A1u98ExkaymPmFo45c2n63g9GZf12XHctsfGWYScIWLQ5c1mQtt1kgDn0WprsDALF7cZ3yKGpADJ9yS7e3YXe0z1l2m3JLGHWYDhH1Pp6ak79OBXLc9dSiw8M0PzD72UXOJ9ihNfLop/emJ/tYg5Ulrto/tMddL3wT6vRJs9uWqGqlk1cNGLJ1yRw7bUX8791ddEBkjQMWm+60RIjTwSDSEmss2PSm3zyQXi9lB9zLPUXXDSlQaFdQ7h7Y39/uyeF1BAWVTizfft2TjjhhKzBIIDT6WTFihW8//77xexmxqB4vdbFxeudUD8tF1SPh/lXX8PiW/6XmjPPpvyEE6k582wW3/K/zL/6mrwvrBNZwWUb337X7QUFgwDxna203/GnjMfab79t/GAQJszy6Dtbab/T2q4Zi7HjJ//D4IZ1mKGhYV9jaWKGhhjcsI4d11+HEQ7T9cD99Dy2muiOZozBqdFVTNLxf38Ynuv6F5HhUN77Nzo76XzgPjpX3puTsGt8YIBt37piRgSDADIUovXGnxcuSjsGQlVp+vyl1J17Pp599k01Ykz8QoFWVoZwudAHBjKCQYDK5aWpe4y2tKRcHJLyGDIeK312sEhkKMTA2jUMrHlhuqdiY2MzAykqIPR6vbS1jd+Z2dPTs0fY1gEoThfupgXUnH5GSTubVI+Hmk+dSePFl1HzqTMLzrBofj9abV1uY+vqUZxOhl7aWNC+kgRf3oiZKPg1YzGCL71U1PZS231pYyrbFtu10wquHA6rw0zTLB0qh8PKxOxsZefNvxhesqupGbtua5LQBwYy51oosRiDG9YTeHHNhEObf/i9GSdgHW1pZmDNP0u+3WTt0twrvkn58SdOOF71etH8PvQhq6N8FG4PZUcdXZK5meFhF4ckMtkwNtOIRul/+smSB+02Nja7P0UFhEcddRRPP/00r7/+etbnt2zZwlNPPcURRxxRzG5mDI1f/DJzvnElFctPmLF1OPOv+aEVKI1HwjpuYP2LxV+0DIPAhvUAie2VKEAxDPrXrmHotc1W0JP0MU5DCGEJpJomkW3bMMNh1IpKZGxqg0EAtax8eK5FdsGaQ0MENqwf96IdHxiYMZnBDEyTgTUvTFrAEe/uYnDt2MGy4nLhqCjHjMXQB4NjBszlhx9esgYmxeMd1a0vFKU03dCTQKyri0jz9umeho2NzQyjqKaSr371qzz77LNccsklnHnmmRx66KH4/X6GhoZ4/fXX+ctf/oKUkv/4j/8o1XynFdf8pumewoRofj+Lfn4TO378I/SuztHPp1nHBV95uST7DL31JpXLjyf4cmm2l2Rw3VqrZjCbwXkCIYRlgp6wOxJCEO/vL+k8csF72BFEkqURJcgMxdvbxzVs77rnzqL3UXIS35He21tys3l9KETXqgfoe+rvWUsOhKah+XwY4TDxgfFtCrUSW7C55s3L6NYXCT024XDMzEycYRDd0Ww7fdjY2GRQVEC477778tvf/pZrrrmGlStX8sADD6Sek1Iya9YsfvKTn/CRj8w8MeM9Gc3vZ9H1P0MPBse1jpNGCfxggaShpTRLtL0E1lL0GDqNGfsfYZE0xcuowuVCMQ1Scy1BQGiaw4btya7p+EA/Q5s3YQwOEnr/3aL3UXJS35Msmdm8NAw6/v4krfc/kN2PWlXRvF7MeJz4wAT6i6qK/6ijabzk8yWVNxKqSvnRS+jp6sAY6EetqEQIgerzocdiM25ZH7D6smxsbGzSKFqH8Nhjj+Wpp55i8+bNbNmyhUAggNfrZZ999uGoo45CnaFLqzOR0I4dtP7oB5kPNsxi4bevwlFRAVidyW0PriT0wnPDYzTNEtZNCBbj9zP3v76Nd/586i64iIF1LzL40ga2/uC7kH5RLcBXOBvejxwAgGfxPkS2bCnJNgFcTQuI79o18TL0iABMaI4pLehv+p/ribyzBRAlWzJXFBXhcjHwwvP0vriG+Afvz8yatCQJP12hKChOV0lEzIOvbabzvrvRu7tHPykEWl0dakUFsa5uq5tX08DpRFRUIvv7wdBx1NXhbJyF78CDKV9yzKS5qpQvXUZ0Vyv9/3wBY6B5UvZRMlQVV9PMX+2wsbGZWoqSnfmw0dU1OCnbzRoIZqOuAbpGu21MK6rKXr/+LYrTiREO88HX/r9J209WAV4pLf1ERUFxu62mGtMk1toyOfNII92pxIzF2PrNK6xO6BJkCd377o+joZ7BdS+O1oecaQiRcPqQCE3Ds3gv5nzjyoLrbCMtO2j/4+/H7FZXPG4UzYE+NDRx9k1RUNyeDHmiyaDvjX/RdfONk7LtUuNobGTBtT+esjpoW7Zk98b+/nZPJt267s477+TQQw9NiVDfeWfudUwfFr/jfMk5GISZFwwC/iOPSl1kVY8H54KFxLZvK/2ODMOqx9I0q2CfRDCY0IV0L1yI3t+fWrJDUUq3VOdyUfPlrxJ6+u/oAwNoFRWjvIwVpxPfIYcyuGFd0ftVfD60ygqreWIm1qCNRAiSwaBaVkbZ0UsKCjb0QICev/yZgfTsd/puHA5UjwcjFEIP56jvJ2VKnija2sL8q68peVC4OwWDaBpVHz1txjbF2djYTB95ZQj3228//uM//iPVJLLffvtZRf0TbEIIwdtvv13cTGcAk5EhfPdLl5V8m1OFc85c5o8QpjbCYbZe9U3kUGHahjmhKMPixIqCc85c5n37anoe+XPCeD2CcDkx8nRuGYl36TLmXPL5nC+eZizGjuuvs7KThWYInU4qjlvG4CsvYw6O3xwxI1AUK0BXFNSysoLM5s14nP6nn6L38Uct4ekRJLU/jUgk/1IARbGE0HUdhKD8mGNp/MKX89vGBOw2v+GEdE/9hZ+1haltcsb+/nZPJj1DeP3117P//vtn/G0zjDQMhra8Te+jqzHCQzjrGqi/+NJU/d9IQjt2TPEMiyCtsUN4PJQdehj1nxu9BKd6PCy64ReWA8vWDyan7s00QVFRPG78hx6amsdI43VRVY3e35f/HISg4pRT8R94ENGWlpx9MxWnk/lXX0Pn3XcwuKkQp5IGaj52Go5Zsxl47pn85jyVCIFwuRBOF0JY+pyFmM1LKQm+8hLdqx4k3t2VdT+qz4c0DPTBAm/GTBOhaUhNg3ic4ObNmLFYybKEfW/8qyTbmVSEQK1roOZjH6NiWe4uODY2Nh8u7BrCPBgrQygNg96nn6TnoVVgZpHE8PtZcO2PRwWG71/5DcyB/smYaukQAqWqmqrlxyOcznE9hEfS+dCD9D/x2KRMq+KTp1P3yTPG9TI2wyEUj5eapka23/Z/DHV0gaIRCw9BW9twsKYo4HLjnD8fT9MCYjua0Xt6LLFhRbWCnaOW5OUDW4yXcddDD9JXyOcmBI7GRpyNs6g44STiQ0H6//oIZiSKVlFO41f+HcXppPOuO4l1tqN6fVSdcSZmXx8dd/xf7kvdqoqjupqaM85Gq6wsyGw+vHUrXQ/cR+T997LvwucFRcEIDhV/U5EoM5DxOAiFxs9/kfJjjytumwmmPTvoL8O9eHHKy1irrEKrqiK2axdmJIJr/nw8ixYX5ZNdLHaGaffG/v52TyY9Q2gzGmkYtN99B4P/HNsOSgaDbPvmFSz8xU0ZQaFZoGXclCIlwtDx7LU33v3zkw+azPc38OTfaRjDqm+kKbqzyss+3/rmuCc0aRh0PXA/wVdewgxHLBsyzRIbjjQ3E+vsILqrNeflUMXpHBV01Byfm+/1SIu1XFE8XmZ98SsZ771qyTGjxs35j69l/N379yfyC7pMEzMWQ6uszPuYiPf20P3QKqvWMguK12M1jEQiVjNNKe5Xk9tI1DrqgQnkaXYjVE1l/te+Md3TsLGx2QPIKyAstDFECMEdd9xR0GtnOoG1axjMxapLmjT/8Br2uvnXw4+p6szvIAVAFCQj4qismoS5JCjx5xZYu2bY+q62NkMIW/H7MQb6CW56FdecuVQszy2wKxS1QDkg57x5uObNy/t1WnlFAZ3R+R0TZiRC798eo+/vf8taB+icNYuqkz9K79+eIN7bk+hcLo2mY6rcQUoQivV+9xCEZt/T29jYlIa8ziYbN47ve6tpGmVlZYTDYSIRqwvQ5XLhcrkKn+EMRhoG/S+uyfmiZQYHiQ8MpLKEZStOZnCSllRLhhA4qqoKCjQqTzmVnkcenoRJMbE9Xx5IwyCwcQNmODIqGATrhkatqETv7mZw44a86uQKwb1oUf4v0jQqjjm2oHn5jzgScc+dlpZfrrurrs7pmJCmSeDFNXQ//BBGFuFo1e+n5syzqUj4Ew+89JIVECYahkrRKS4UxWp8k9KqOz3iyKK3maKuDrqy1D9OEf4jj5q2fdvY2OxZ5BUQvvTSSxl/79q1iy9+8YssWrSIb37zmxx44IEoCUmQ9957jxtvvJG3336b22+/vWQTnklEW1qI7shPhLbrnjuZ/W/Wkl3lYYfP/IBQ0yhfXlghuuJ0guYAvfRZ0PoSLpNFW1rQe7qtZeJxLPIUt5t4d3fJbdlGopWVI1wuZDT3pePypcspP27ZhOPS6yuFy4U0TWItLTgbZxPdvjWnfQlNy6k5IfT2W3Q9cB/RltGakELTqDrlo1R/8lMZmcaKJccQa92BGQqVRjYoGQwm5In8hx5aUtmZpv/6Ns1Xf6tk28uXmk+eMW37trGx2bPIKyAsK8ssUrz11lvx+/384Q9/wO12Zzy399578+tf/5pzzjmH6667jttuu62gCW7evJlf/vKXvPnmmzidTk488USuvPJKamtrU2MikQi33norjz32GL29veyzzz58/etfZ9myiS+QxWCGQ3nrxMX7h6VQ3E0LwOuDmVpLKBQ8e+1NxbLjC3p5pHk7k+WRpUxkU5YHZjhkNZBoEwS9moo0jJLZso2Fa948XAsWEnn3nYmzz0JQe8GFVK04edwATRoGgbVrCGzcgN7ThREKWT7RppnfsqwQuBfvRcWy5WMOibW307VqJUObN2V9vuyII6k97wIctXWjnitfuoxI6w4CL661AuJil43ThMudJfYwBnDVjX4PU4WzqQnV45m2/dvY2OxZFFWAsnbtWs4999xRwWASh8PBcccdl+FxnA+bN2/mc5/7HAsXLuTrX/86gUCAP/3pT7z88ss8/PDD+BO1VldeeSXPPvssF110EYsWLWLVqlVcfvnl3H777Rx99NEFv7+JUDxeqw4wj6Awva5OqCp1Z51D1713Tcb0ikPT8O69D7O/9o2Cl0ejO5onTVi5lI0BiseLUKwGkvF3aiBUtSS2bOMhVJWKJccS7+rEGBy0slsjgyIhED4fdWd/msoTVoy7vVTDzOZXMcNhpGHklX3MQFVxNDZmfcoIBun56yP0P/tM1u/d3dRE/YWfw73X3mNuXqgq9RdchGvOXPqffpJ4d7clSF5ItlBRsGodM+WJSk3V2efS9/Cqkm93InLJCI9kZAf+WN3h2cYBGdllABmNjtrOyNfK8n0LPoeM9GOvPuNsjL6+CedvY2OTP0UFhB6Ph7a2tnHHvP/++6Myi7lyww03UFFRwb333pvaxgEHHMBXv/pVVq1axWWXXca6det4+umnufrqq7nssssAOOusszjjjDP46U9/ykMPPVTQvnPBNW8ervlNRMeQzshG3WczG3MqTziRwMsbiL77bqmnVxgOB67Zc6g4/kQqli0v6mQrjRHZp5HZnkKzP4qKVl6RknbRAwN5yeGMxDVvHlpNDdHt20BTUVQV4XBmaC9KKTEjEdxNTQXVU+ZL0hs3uOlVjFDI+h6kBCGQhoHq9eI/7PCcsrfpDTPC7cbs7y98YppG6F+vE2hak2qukbpO/7P/oOfR1Vk7y7WqKurOPQ//UcekXGbGQ6gqlSesoGLZ8USat6N2W+eYmLeS4Ksb0fsHcFRVUXP2uShOJ4GN6wm99SYA3v0PwH/Y4YT+9XrRx0UuSMMg8vZb4HJBoUF2vjidIBRCmzdRdeJJOf1GMzPE3WNKKmUfp4BqXSqkHrduKmKWb7pwOlE8Xpx1dVY9o4TBl1/K2Ed/YwM1y45DO/TonM8nejDIjuuuRR+hTznw/HOgKGgVlQhNK0gSysbGJjtFBYTHHnssjz/+OI888ghnnnnmqOdvv/12XnzxRT7zmc/kve1YLEZ5eTnnnntuRkCZzPi98847ADz66KM4HA7OP//81Biv18u5557LTTfdxPbt21kwSfVeQlWpPG4ZHR+8n1Ngo/jLRmkRClVl3je+ScvNNxJ9951xXqzgP/oYghvXl86STVHwHXMcroYGhGniWrAQray8ZHfdSnrmOBlcjVGjl3oulwDR6ST4xut03nePteyJBATKfffk7VmbvADq/f2YsRhmV5flf+twoPh8qD4/EjAG+lE87oJt2fJFqOoooe2Mi3iOItAZDTM1NcS7Ogs/fhQFIcEIhRncuIGyY5cS+tfrdK1aSbxjtK2icDqp/sQnqT71NIQz/8Yyoap4Fi2m6oiDAEsHzX/ggaPGVS47nsoRgXGpdAYnIll/qvn8iKpq6/OdTLtBIXDW1GBGYznXs2ZmiMeWVKo95zy6H3owc5yqogcGUgHgyEYfaRhI3SA8FCS8LVGDKhRUjye1j6FtW4m0t+N9b2tOsk16MMjWb10xtpKAaaL39aJWVRUkCWVjY5OdogLCK664gvXr13PVVVfx//7f/+PAAw/E5/MRDAbZtGkTO3bsoKmpia9//et5b9vpdPKHP/xh1ONJC7xZs2YB8MYbb7Bo0SK83sxlvAMOOCD1/GQFhGBlckLbPhhXhxAAodB07XVZn1KcTuZf+W36X3ie3icew+jvGz7pqiqOujrKjltO758fLOnc3Xvvy+xLc7dmyxetshIU1RLrTmS3xsXpHM6ypI8dESQqiiD40kbrM0rJk5h5e9amXyiNUBjhcCLjMUtnLxrFjMUwgsHEMrEH/2GHF7RMVygiYTVWftyynJb5spHeMIMez9/6LWNCAmnoqC4fsbY2Wq6/jmjz9qzjKo5bSu3Zn0adTOmhGUBG/WnyN5v02zZNpK5njBeahjTNhNuOYv1tGFYQqarWsZzlNSiK9Toprf/nUc+aq6SSMRQi/O6WjHFGcNB6D8mbNcOwyhUcDqtZxzCQehzhdiND1lzUqirUtJt4VS0n3teXs2zTjuuuzUlWyhgcxDl7zpRKQtnY7MkUFRDOmTOHhx56iF/+8pc89dRTvPfe8NJpWVkZn/nMZ7jiiisoLy8veqIdHR28+uqr/PSnP6W2tpYLLrgg9fjBBx88anx9fT1gdUKXiqRi+6jHv/b/0bZ4AS133ZM1O6CWl3HIzb8c08IuSfU5p7PwzI8z9MFWhrZtA8C3aCG+RYt4+eLLip7/SGqOOozq2sKW83PBOauWDr8XPZBweBkv+ycEqsOBaZpW0JJtrBCWp23YsoQTDgdCSVvWNSVS14ntbKX/gXtY/LV/B0BVrWXKkd9f59P/IPT6ZoRp4JnVAIA+OIgeDCLjOtI0kPE4rrpaZn3qU9StOGH6MhC1+QlAJxloNRFIFJeDCcLxiTENUFTMiLVkqKc1SCUp+8hHaPr8pfgWLSx2bynG+v5mAs6GarocVlCnuFzoCfFrIUBmuwESgtQXIYQ1bsTTo478xLjkv1VNw4xFEQ6NyoZqfON8LtIwaN/0EjIawVVfn7WLXqupJtLRQej1zQC4GupTHvXxkOUUIzRtOEtobRlFEUhhZQFlOJSSCpLhEGplRca+HFVVRDs7CW96maZPnjrm70gPBkctE4+JrqMp1vxz2bZNYczk359NaSla1bShoYGf/vSnXHfddezYsYNAIEB5eTlNTU1oJRJN1XWdFStWYBgGqqpyww030NBgXcCHhobwZOm0Sza6hMPhksxhPISqMvtTpzPrEx9n4F9vsHPVQ+jBIdyzGln01csnDARHbsu/z9749xkuvI/19mJOQn3SzvsfYO7ZZ5V8u0m8C5rwzp/P4DvvWlmGMYI8VBVnRQUoVhZCKgpmLJYxXjgcVC5ZQmDTJsufdkQwCFh/axoyHqd3w0Zqlh6Ho6IC37w59GzYSLS3H2dlBdXHHoNQVXrWrMUIhzIulI7ycrSyMsxYDGkYxPv7cVRVTVkwKA0jdUMgdYNoVxc9L71MvKtrdEewEDjqanHPmoVv3nxcjfX491qMb9Gi1Fw1rxehKlb9oNNZXLmBxMoIZbnpcTU2Mv/Si6k66sgxpXv2RLwLmnDV1TG0bRuq349waJjRGCjSCqrSyyCSn4sph491Uw5rLkoJKJmlE0JYn6e0NB0VlwvhdGAM9ONbtAjvgqZx5xfa3ky0q8tawh1PUsnhRB8cRCsrS40zozFkXLeynenHXULTMTk3KQQYJqiqNfW4jhmLoabpzwohUD0eop2dhLY341ucXWuz5Z57J/7Q04j19uKqq8tp2zY2NuNTMpn7WCxGIBBgYGCAww47jIGBASryCITGQ9d1brjhBhRF4cEHH+Rb3/oW7e3tXH755RO+VsmhiD1XcvJybNqb2VdelfozaAJFekC23HhzUa8fE12n/d3mSZXO8Bx+NKG2doxQyFrqSl8KcjgQioLq9VL5idMRipKql1MMKzuneDx49tmXugsuZPDllzDWrAWsi6OUYrhJIbmUlsAMh3n3ZzciDX1UEPTBrb9Bq6lFDw6iKCrxoTAyHkPq1tKfcLqs5WbVgeLxEWnvpP31dwrWHkxvflH9ZThqa4nubEXv60OrrMK9cCGuufMIvLiWrr89juzuJie5HimJd3YR7+xi8LXXrcdUFUddPVUfPdVqOKmoQ6msIbqjGaHnKTGTA4rXS+2ZZ1NxwgqEptHfX/obsJnupeo57ChCu9qI9fYhPF6Ixa2smapagV4ygE40BKEoKC43ZjSSWo5VPB7MSMQ6hke+ButGASEQHo+1H5cbz2FH0h8Y/0Yx1NGLEdetWkB97JsBE6zfEKTGmbqOlDIRkGYeN1IyfIgmn5MSKazg0YjpSNUSj9c06zdqCgXiOv0dvcSqs3eqB9vzE/k24zq6bua0bZvCmOm/P5vsTIuXcXd3Nz/+8Y956qmnMAwDIQRvvfUW9957L3/+85+5/vrrOfLI4pwB3G43Z5xhCbB+4hOf4KKLLuLXv/41559/Pl6vN+WKkk7yMZ/PV9S+ZwJ6CTX3RtL++/+l6Zr/zus1RjhM35N/J9raYtXWHXMs8c5Ohja9ghGPoXl9uBcsxFlTi/+oo4m2thDYsM6SOkm/sMTjCI8H/yGHUbHseMxYjHhvL8LjQXV7KF+6DM9eeyMNg4677iD48kZSV6HEBXO88EbGY9mfME30rk7rn4A5FBw1RGgO1PJyK1sZGqL19/+LGbQ6aLXaGrTySusCHgnjbGjEd8CBOGbNIrJtG+F3tiAUBcec2cQ7uwi/+QZmOGzNNltApijWfyNqxwrCMIi3t9H1wEpiO3dSd8GFlB+9hJ6uDvRiuotHIgRVJ59CzafORPEVZrWXC8mMqR4KEYoYxDs6MAIBzFg0tV8jOIg5NITiduM75FDc85uItiZE4yW4mppwNy2YtAxvZkd4GKE5rGNv5PeZrBt0Oq162VhCZ1FRUFwuq3Y1EsnMKEpplVAk6/YikbzqWXOVVBKm1ZiVkZVXlNTS8ajucCmHg8WR9b6J9zSKHGSbHFX51ZymvtMpkoSysdmTKSog7O3t5YILLmDnzp0cfvjhRKNR3nrrLcCSpNm1axdf/vKXuf/++9l3331LMmFFUTjttNPYtGkT77//PrNnz6Yri3VUZ6d1wU8uLe/OqOXlxCeQ9ykUPRDIeawRDtN608+JbtuWceEYfHFNxrgYEEqKErtcKF6vJUEDWRtL9HiMHT/9MdHt2zOyeYHEdtW6OoyentJ1V+eA1OPovT3DDwwNy6noLSF0ht034jtbGXr15dEb2fxqbjtLNhmUEBmNENiwDte8eVbj05a3GBzhNFQoittN4xe+hP/wElrAjSBd/sTs6ybePzCqjCAbfU88NhyQJMYKVUWrqU1lTUsdGI7sCI91dVnNJmnSLEJYTSFCUcDpQFE1XHvtjaO6mnhvn9X44/UmmkzMRGZQQto2VK8vrw5zSEoq1RLd0Yzi92ddNpZSYsbjKG43Mh5PBXrC4QRNg2gMyYjjM6ENKZNdx0IMB7wOhxX0jtxHDrJNNWefa0nL5IhaVTXlklA2NnsqRQWEv/rVr2hra+O3v/0tK1as4NZbb00FhJdddhn7778/X/rSl/jtb3/LzTffnNe2t27dype+9CU+97nP8YUvfCHjuWDQyui43W4OOOAAVq9eTSQSyRDIfvNNS5fsoIMOKuIdzgy8Bx5C5J1xJGmKQMux4ccIh9l29bcwg6OzaeMSjQ7XPzqcqF6PlQ1RVHA4iPf1Elzzz3Ev9EYy4M9TBPzDjjk0RGCD5b1cvnQ5wddfy8uveCRC01BcLqvkbRIzMSOFtM1oNL/vPdkNC1aTg64T72in68HhrOlkBIUjO8JHijc7Z8+2MpfNzUhpprranfObMIJBzKEQituF96CDUTQNMxy25JCiEYRQCsp0ClVNZYiNgX7UisqMoFBKaT3u9eDZZz/C727JGKd6fejRqFUjOJK0rmM8Hkg0eyle76h9xPv6cpJt0vx+1JpajJ7uid+cpoFQplwSysZmT6WogPCZZ57hox/9KCtWZHdKWLJkCaeeeiqvvPJK3ttuampiaGiI+++/nwsvvDDVODIwMMCqVatobGxk//3357TTTmPVqlWsXLmSSy+1bKlCoRCrVq3i4IMPZv78+YW/wRmANAzCb74xadtv/Mq/5zSu9aZf5B8MjiQewwhamQWtqtq6kOSQ9UlhB4N5E2tvI9rSgurzo7o96IUEhIqlK2dEIhhDQ6hlZZMaEKbLpEgo7nsXwsq66ToyGk1lTSdLnkSoatZa0+GM5/pEwBi2urazzLfvyb+h+vwoXo/VxGOaWUWkcyV9SVvvTkgQaSroBmYkYjm5HHa4pUP45wczxuWSlU3aAyYDYDMcwUCk9qHHIqgeL75DDs1pmbvqlFPpXjlxc4nidFrzTMx/KiWhbGz2RIoKCPv6+pg3QYq+oaGB3t7evLetqio/+tGP+M///E8uvPBCzjnnHCKRCPfffz/d3d389re/RVVVli9fzvLly/nZz35GW1sbCxYs4IEHHqC9vZ0bbrih0Lc2Y0jqyOF0WTVHpUTVcmooMcJhy8WjFEgs5w2nEzze1JKYzeQgIxHMcAjPPvuiNTSiD/Tn1Viier2Y8ThG2pK5Y/bcSVuaGymkbexsLW6DZqL7NdF9bobDqazpVGWT0jOeen//+AFuIutmBAYwAgMIlwutvMJyRClQhDmryLlh1dy5m5oylqBHjYtEUkoAAivbl+oyTkNxOKk5+9MIRRB8+aWMfXgXLaJm6VK0Q4/KSUh96LXNKF7v+JlhIZDxOO7Fe1O+JPcldBsbm7EpKiBsbGxMLRGPxeuvv07jGN6nE/Gxj32M3/zmN/z+97/n5z//OQ6HgyOOOIKbb745Q3vwlltu4aabbmL16tWEw2H23XdfbrvttqKbWWYCKeFbReTSd5oXTdddn9O4/qefLF2Nm3VVwRwaQpSiicJmfESisUBVqVhyDJEd262lvQlQ3G6rWzSU2VkonE4qjjlm0i6+6ULaZjhU/HGXJpGSrCuMd7Tl5PBRKpIZTyOU//uR8TjSNKysbJqIdL4izLmKnKePG9y4ga5VD4CUqJWV1k83HrM6oUWyu99qhFGcLjwLF+FesICKZcdn7KPxYMvLOJcu1eHv34Ojrh5pGhh9fangUqmsBN3AGBpCUVVqz/k0nkWL8/pMbWxsslNUQPixj32M2267jfvvvz+rPd2f/vQnXnnlFT7/+c8XvI+TTz6Zk08+edwxPp+Pa665hmuuuabg/ZSKXM3jcyXZJTihy0eezLnmhznLzcSzCBAXhRBIXR9Zpm4zCWiVValsnu/wI+Cu28cdLxwOFIeGEY6MziQqqlWPOIlLc+nOH0W5qqSRknKREkwTIxwmvPX9klk0jrvvVMYzbEnH5Cv7Y5rWMr3PaghRKyrRu7sZ3FhYlnOsJe1s47TKSkuGSVVTNYHC6RolcC6lzHBNGbmPfOaY4fwCCMVqCMpA1ZCm5e4ip8o/2sbmQ0BRAeFXv/pVnn/+ea699lruuecezMTd71VXXcWbb77J+++/z/z58/nqV79aksnOZMYzj/cfeRRabR0D/3gaIzyEs3E2tZ8+D80/sVxHskvQGAqWJkNYUcGia3+c076TOEppP5a4sEhdL43Mis24VJ50CkJVMeMxmn/0gzEDEqGqKC4XRjiMMUYgplZVUX/hZyc1iMqQSSnVfpLWcAlkJELXA/cz8Mw/qDzlVCqWl77zOEky4yUcjmFbxjyR8TgyHrOCMSFQ3O6cfYyLIVfJmlJKvkzHPm1sbCyKCgj9fj/33XcfN954I4888gihxPLSX/7yF5xOJ2eeeSbf/va3S2JdN5MZ2zxeJ/T+e4TezlxWj7z3HoF/Po9aW0vTNf89bnCW7BLsatsF5CgMOnsOjR//JNGuTiLvbgHNgbOmhpqzzx21r5Rocn8/Uo/jWrAQraw8I3tSecqp9Dz6SGmWjbN4tdpMDo7GWZQvPz6x9LcSI1strxCoHjdGNDZqeXgkRk+35UCRxRmoVGTIpOSpSZcXhkGso52uVSuJ7mql/oKLJiUoTGa8MtxH8iUhup7KzOXhY1wMOUvWlFDyZTr2aWNjY1FUQNja2srcuXP54Q9/yDXXXMO2bdsIBAJ4vV4WLVqEc4QW1Z5KVvN4KYn39Ixr0m50d7P1m1ew6Bc3jRsUli9dRscES30ZtLVRfuxx4w4xYzFL7Pm1TciEXARgdWU6nbgWLKRiybGUL12G6vHgWrCQ6NYPcp/DWIxlYWdTUtSqKuo/ezGtP7+ByAfvZx2jeNxIw8QI5e4u0v/0k9R86sxSTXMU6TIpemfH5OxEUVISRjIaZXDDetzz5k9K5/FwxksvvOxDiExh6CnKjmV8F/19lpyMKYfFtWGU5Is0DMLbtjL02mZkLIZrfhMVp54IQGDdi+iBAbTyCvxHHGm5AY2zz/FkcmyZGRub0iOkLPzqfNJJJ3HQQQdxyy23lHJOM5aursFRj0nDoPWmG4nuaB4OBrHcE/SenlHjs6HV1bPo+p8BEB8YoOueO4n39yH8ZaheD7G2duLN2/Oaq9Y4i4rjT0RIc9QJ2IzF2PGT/yG2a+fYWT8hUKuqKDv8COrO+wxmLFaYDmE6to7g+JTi83E48R10EJiSoTGEsRWnExTFcsXIk/ITTqTx4suKm+MESMOg4767CTz37OTsIOn6kaZX6NlnX+Ze8c2SBxjD54ftmLpeUFe9cLtx1jdYtbdSond3425qYs43rpzUgEgaBqEP3qfzjj8R7+ocFqBONOgIQLg9OOvr8R50CLGuTkL/eg1zcMR5UhFWE0rauUbxePAdchgNF186KjCUhkHXg/cT3DS84pJNJiefTmubwrGt63ZPpty6rru7e0LZmT2d9K7I1J2slOgjT4rjoHd1Et65k50/v754rb/kNtvb6HngPusPIVDuuwffIYfScPGldNx1x+hgMG3uyf8bg4MZHY0Lr/85rTf9gui2rYVl+exgcHwMA1Fbi2/f/dE72hFOF7oeJ771g5yW2avPPBtjcJCBZ57O+rzQNITDkWmPliclrScdA6GqeBYuJvD8c5OTTU50HgshkMlsfnv7pNTkpWe8ZCiU2l/OKAqqz5cKBqciOyYNg4E1L9D75N+tLG36fJNd26ZpeSwHB4kEB4mMt3pgSiDzt2+GQgyuW0u0ZQfzv/v9jKAwH5kcGxub0lFUQHjUUUfx4osvEovFPjTLwyMZ2RUHCQ/dPDskW374vVJPbRgpMUNDDG5YR7RlB7Hu7uzBYPLfyQuArmOEQqmORtXjoem738/qZdz78ENWoDhymyMvfsXUUn0IkN3dxBsHqDphxfDy2jeuJNbby45rrhr3tb2PPJz9iYRXrhmJWEuXRVB5yqlFvT5XjODgcDZqMiwLk1I0iePRNCevJi9DGDoWy+vGSDgcoKgYg4MZ2bHJ6vSWhkHXynsZeHFtUa42uRJrbaHjjj8x68tfyXg8V5kcGxub0lFUQHjeeedx3XXX8bGPfYzjjz+eOXPmZNjHpXPJJZcUs6sZS7auOGmaMzPoMU1irQkP3pGBWbb5SolQ1FEdjarHQ+2ZZw1vNhajoyNR7+VwZBaCmxKwiuKZqZ/LDCP6xuu0v/G6VeuGld01Q0MTvi4biseNGdctZ4wicS/ea1IbStLRyisAAXKSxIlG3LAoyuTV5KVnvAIbNhBt3WF9H9kCw8RyrOr3W/MxDOuGc4qyY4G1awhsWD8lwWCSwZc30nDp58esKZwqvUgbmw87RQWE3/jGN1L/Xrly5ZjjhBB7bECYrStOKMrMz4RNFAwmMIeCmKaBMZS5lJ3UW9xxz52QnhmMx7PL42ja5GR69mQSNVuFBIOW57DEDJfowu7xMOcbVxb88rH0Od9b9SDyb4+VZo65ksgMplw3AEdj46R2rI7MeBlDwZRPMViC3zIaQ6gKrvmWZzEwpdkxSzNxfUluHvIisd/KZcdP7X5tbGwyKCog/MlPfpJVFuDDRNauOIcTHI49x5YtHKbvuWfw7rc/YGUROu78U37bsKVmCiPPmwrhcFi6g6XM8JSVM/tLl2fN4EzEWPqc8b4+0EsjPJ03ijLcUCIlitdL+ZLJc19JJ9+M11Rmx6ItLcQ72q0/pviGNvTWm1MSEJqxGIGN6wm99SYA3v0PoHzJMaljO1uXdNlRRxd07NvY7G4U1WX8YSNblzFk74oz47HR3Xa7OXWfvYR4exv9/3hquqdiMxJVQXG6imoYmXAX5RVUn34GlSecmFPwNJY+Z7yzc1LmlzNJ1xJAuFyUH7d00nQIdydCb79F2x9/hxEMZvUrnkz8Ry9h9uX/36Rt34zFaL/j/wi+/NLopXpVxXvEUXgWLqT/iccxBgMZ7104nfgPP4KGS7Iva+/p2F3GuydT1mXc1tbGmjVr6Ovro6GhgeOPP56qyRSRneFk64pThBPpciOjU1eLM9l0PfJnNJdruqdhk44QqG4XRjQ26Ut9RmCArpX3Et3VSsOFn5swgMqqzznVCGHVtUKGdZzQNBy1tZZTybLJcyrZnUh6Xk8H3v0PmLRtm7EYO378I2I7W7MPMAxCG9cT2rg+69MyFmNw/Tqira2jOqJtbPYk8g4Ib7nlFv74xz9ipN1lud1uvvOd72T1M/6wkK0rTrhchN5/j56HH8q763hGEgyil0gWx6Z4FJcLaZqW7/BUYRgMvrgWT9OCcYWchz18IxnBYDRPPc1iqfvSV/A0NOKcPZtoawvR5mYQpOr07EBwGNe8eTgaGtEHBqa2/llVKV9yzKRtvuOuO8YOBsci/eYl8VnEdrbSefcdNH7hyyWcnY3NzCGvgHD16tX89re/xePx8PGPf5yGhgZ27NjBM888w7XXXktTUxPHHnvsZM11t2BkjZBn0WKqT/4onX95iIEnHi/hjgQNl1xGxz132fV5HzKE02mpsRTojVssMhZjYP26cbtds+pzTgODzz9H1bctuR7PosV4Fi2etrnMdKx66GOItuzADIWmLCj0T2KNnhmLMfTapvxeNPJ4TdZTSklw82bMWMzOEtpMK9GuLtp++7/Ee3uQsSi43Tjr6ig7+lgwdOLtbdRd+Z95bzevgPDBBx+kvLycVatWMX/+/NTj//rXv/jc5z7HPffc86EPCLMhVBWHP//1/PEoW7ac8uOW4d7vIzRf/a2SbttmZiJUNSUsPd2Fv/GdO8cVcs6mzzkdRN7dwrtfuiz7k04nzrnzcM2dixmLgWEipYnicOBuWohn0aIPXRaxfOkyoq0tDLy4ZkqkZ7Q5c2m85POTtv3gKy/nX0qR1KjMghkJE3zl5QmtQW1sJoNoVxfN3796dBIoFiMWCNDzgSUQr7hcwCQHhO+++y6nnXZaRjAIcNBBB3HiiSeyaVOed2IfIixdtRLgcFB95tk4ysrpeuRhBtatLc12bWYuQljC0tHo+BfpyRRyHsFEQs7Z9DlnHLEYsa0fEMvishFc9yIIBUdDPVUf/diHps5QqCp1F1yIc85c+p/+O7GurlQ3tjVAgOawrOTi8dxWJxyJy0zcACQgwO2m/PDDqf/caOu6UqIHBkq7QSlLv00bmxyIdnVNevInr4BwaGiImpqarM8tWLCAZ555piST2hPxH3Ek4p67kJHc71ZdBxyAVlaG0dOD4vZRcdJJDG5YT99jjyKnWivMZlpQXC5MXbe6hxsa2euHPyLW20vb/96CEQyi+v3M+vevE/3gfTpuv23KtB4nEnLOps+52yFN4u3tdD2wktjOndRdcOGHJiisPOFEKpYtJ9K8nWhzs5U5dbnRKitRfX6cs2cT27ULfTBAdPs2hOZAq6zEe9DBDL22mWhLM8LhxHfIocw+/ECkYdDy9AvD7jtp3uqTScluxJMIUfpt2tjkQPP3r570feQVEOq6jjrGCdHhcKDbtWxjojid+A87nMFcM3qqSsVhR1Kx3MpMmLEYO37yP8R27pw89wabGYPidFrC0ml1go6KSlpu+SXCoeGcNRsZ19Gqqxlc9yLh5m1T2gjgmDNnXCHnDH3O/n6kpiLicZTycsxAYMrmWQpkNEJgwzpc8+aN20izpyFUddy6y2S5gP+ggzMer1i6DBi21hOqah0P07DM6j/iSJT77rZqInNlnJsXxe3Bf8SRJZiZjU3uRLu6pqRXoChhapv8aLj4UiLN24nv2jnxYNOka9VKortaqb/gIqtTbpcdDO7pCE2zbgCyNIzE390yDTPKgsOBp2kB0ZaWVFCYdN+I9/YS27WLeHcX0c529N6+PeKYNcNhAhs2TKptnE3pUZxOfIcclvuNOIyuIUxbLvcfeqjdUGIz5bT//n+nZD92QDiFKE4nTdf8kLbbb2No44axByZs3mQ0yuCG9bhmzWHotc0FLQcKj4fy408k2rwNRdXQFu1F4NG/FPwebCYJRUFxOjAjUeRMz7QrCoGXNzL46isomoZEog8OIqewM3XKkZJ4R9u4jTQ2M5OGiy8l2rJj2Mc9F7Icx845c6n/3KUlnJmNTW7oU7SqkndAuGXLFv7yl7+Mevztt98GyPocwFlnnZXvrvZIFKeT2V+8nNb+fsLvvTt84lEUK/OQuDOVQoBhYIbD9D3zdMGiwzISwTN3Hg3nXZB6LPDXR/bcC/fuhsByGInFMCPTICOjqtaNRi7HgxCgqqheH0JVMQKD6LHoh+ZYksb4jTQ2MxPF6WT+d79Pxx1/YvDljVmdSnxHHoVnwSL6/vYYRiCbU8mRNFxymZ0dtJkWtPJyjN7eSd9PXtZ1++2335jF4cnNjHxeSokQIhUw7s6MZV2XL5Ht29n1v7dYArBCjLkElXRWEE6X5XhS4IW39rwLqP7Yx1N/h3fupOWH3ytoWzalQ3E5MXVj9AVqKufg9dL45a9iBAIMvf8ukQ8+AD2O8HhxzZ6N4nKjd3cSbm5GCIFWWYUQAmNwEH2gP/dgslRMscdu+n61ykpm//vX7Qxhnswk6zPbyzh/ZtL392El3w5jxeXi2AfuzXs/eWUI/+M//iPvHdiMxgyHrGCPLAG0YWQuDQsBSAoWnsvSFeeZM4eKM85mYPXDBW7UphiEwwGAGY1N80ysgn+trBz/QQcnmgEykYZB6003Igwz5TgipcQIDU253601oWnKRgqBo2HWuI00NjMfxemkctnxVC47PuvzQlXx7rU33r32nuKZ2diMjauuziolm+RyIjsgnAbSPUOllJbPqq5nrxGUEhmNWlpeBdjfCU/2rrj6T54OgX4Gnns2723aFIZQVYSmzohAMIXTRbS7i8HNrxJrbwcBzoZGPIv3QisrR5oGek83wuVCxmJI00QaBnJPsGLMA8XjoXzJEruhxMbGZlpo+p/rJ12HMK8l4w87Wx97Gt+hhxW9fGBlXX5B+N13pjzL4vvcpcw5cUVqHv3PP0vXn1fBFLgSfGgRAsXpnDaruYLRNMvbtqvTKglJP1ancZl7OvDstz9zr/imHRAWgL3kuHtjf38zhzGdSkZQ6JKxHRDmwbrPXAxC4DvkUBouLk5hf+CF5+l66AHMoaESzjB3RgaG4fffY2DNP4n39KD396J3dloDp6tmaw9BcTkx42Nkf212G5x770PTN79jB4QFYAcUuzf29zfzyMXL+KACvIztgDAP1l3wWcsxQlFwzpnL/KuvKTgolIZB5313T+uSbXpQmI4RDvPB1//dDmKKQDgcVlZtpkvI2OSE4vEy98pv2w0lBWAHFLs39ve3e1JXV5b3a5RJmEdJef311/nyl7/MkUceyUEHHcRZZ501StomEonwi1/8ghUrVnDIIYdw3nnnsWbNmtJPRghQFJCS2M5WOu++o/BNqSruhdkdAKaKobvvILJ9O6G33yKyfXuq0UX1eHAtWDitc9tdEZqG4nRYNXZ2MLjHMJZ3szSMrL+hQsfZ2NjYTBczWpj6gw8+4OKLL6aiooIvfelL+Hw+Hn/8cb7zne/Q19fH5z//eQCuvPJKnn32WS666CIWLVrEqlWruPzyy7n99ts5+uijSzch0xzOmklJcPNmzFis4CyhESyNjE0xtNx8I6rLiVAUFJ8fZ0MjroULKTtuGbFdO5F2bWFuKApC06zGi+mei03JGendLA2DwNo1BDZuQO/pRpoGQlFx1NZSdtQSypdajia5jrOxsbGZbmb0kvHll1/OSy+9xN/+9jcaGhoAME2Tiy66iC1btrBmzRr+9a9/cdlll3H11Vdz2WWXARAKhTjjjDOoqKjgoYceKtl81p1/0ajGgMYvXl6wR+fA2jV0/On/lWJqRaH4yyyvT3NE1sKuH8wJxeXEjMXtz2oPxrXPvsy/8tupIK/rgfsJbn4VMxxBcbsRmorUDcxIBMXjxn/Y4dSecx7dDz2YGBe25IYSvykZj6N4PPgPO5y68z6zRweF9pLj7o39/e2eFLJkPGMzhIZh8NJLL7F8+fJUMAigKAof//jH2bRpE1u2bOHRRx/F4XBw/vnnp8Z4vV7OPfdcbrrpJrZv386CSaz70QMDGX9LwyDa0kJo21Z6/vwgMhZD8XiY9Y0r8Y1YhhUVmfqA04U5FMwezNgBzrgoTiemYcwsGZnpxOmCWIk7qWfCTYmqUnnscamgLbB2DcHNryJ1PaXNmETx+zEG+gluehVzKETo3S0YoRDSNBGxWEqoH1XFCIUIbnoV15y5VCw/YbrenY2NjQ0wgwNCRVFYvXp1VmeU3oSFi6qqvPHGGyxatAiv15sx5oADDgDgjTfemNSAULg99L/1Jp033zhmE4YZDLLzumut8X4/amUlRk8PskA7upIz3Rfc3QyhWT8bM2YHgimEYK+bf03wlZfRAwMoPh9IMENDKG4Pse4uom27MAeDaBUVKC4XQ2+9CaaJ6vEgE4LXMhxOuPM4UXw+4gMDMOKma6rfV9nSZZQfZ4l2S8MgsHEDZjgyKhhMvcTjwejrY3DTK8i0Y0RKCUIgTTNVX6pHInT/7QmibW1gGrjmNeHeZ1+6V95PrKsd1eOj6pOno3o8xFpbkIaJaegEXlyDHgggVBXXvvsjE+fEeGc7psOBq7qaqo99AtXhQPF4cc2bt0dnIW1sbIpnxgaEQgjmZXEFCIVCPPTQQ/h8Pj7ykY/Q0dHBwQcfPGpcfX09ALt27ZrUeXbddXte42UwiB4MTs5kbCYXVUUoyodOlDkXFI+bmoZKaj5xSk7jpWGw5X9+zNC2bTj83uHAqsw3PEZKTFPHdDqQsan/zBWvh7kXXkjjxz6aCqaGPtiK2d+D5vPgcAwHWFJKYgMDlgm9boy+yUpmOkc+LiVGRzv9T/4t6xziQNstvxxzjhIIr39x1OORri7a3nkHAGddHe7GRmqWHUfdihOnPDBUVat3Mbn0aLN7YX9/Hx5mbECYDSkl11xzDV1dXfznf/4nLpeLoaEhPB7PqLFutxuA8EzJwtnsvgiB4nBgxmJ2d+gY7P/D7+c1XqgqNcuWEmnvIN7Xh1ZZaQXahgmqgnA40Pv7UT1e5l5wPjvuvLv0gbii4Nt/f+adew6+RQtpf+xxQs07UD1e6lacQNn++40KnvRQCGmYqSwxWOUt0Z07kfFxusqnMQsf6+rCGBoi0t5OeEcL8y+92M4W2tjYjGK3CQillPz3f/83jz32GEuWLOErX/lKTq9TlBmvrGMzgxEOB1LX7eXhCYjXzM6r6FwaBnpVPY45cwlteZt4f3PCtzs5QCIcDrQ5c2j+0x2Fa2KqGs7ZsxCKFQAZg4MIVWXWV/4tpSloAoNx8J16Osn8pAH0B0bXQ0Z0BYnAjMbBaWKaJvFdO2e8c4sRCoHDQff6DcjahimtWbSbEnZv7O9v92SPaipJJx6Pc9VVV/HXv/6VQw45hN/85jdoiTt0r9dLJIs0SvIxn8836jkbm4kQDofl22svD0/I7O/mnh3MlGHpQh8ctLy6kxm0tEyajMWIb9tW3ORGBmqa1SWcTVMwF1zz5qHV1BLd0Yzi96P3dM/4YDCJ4vNj9PczuHED5cfZcjc2NjaZzPiAMBKJ8LWvfY0XXniBo48+mt/97ncZQd7s2bPp6uoa9brOhPVaeoeyjc1ECFW1Cv/tQDAn6v/rW/gXDQusm7GY1VjS348ZiyE8HvTeXoxgABmPE925E32gH2GaCNXSbbSCwMnqJpbE2tpwzZlr/akbCDVTUzAfhKpSfvQSero6iPf1zpzGsBzQe3tQvT7i3d1EW1ps1xUbG5sMZnRAqOt6KhhcsWIFt9xyCy6XK2PMAQccwOrVq4lEIqm6QYA333wTgIMOOmhK52yzm6IIK0CxA8G86P7D76i48WakYdBx1x0EX9uU6hQeDwlIEkuyky0to+tI0wChYEYiuJuacGVpWMuV8qXLiO5qJbB+HTNYxnUUUteR0sQMRQisX4s0DdxNCzIyhdIwiDRvJ9rcDAJc85twzZ1HbNcuzHBo0jqWk3JdxlAQIxBA8fsxg0HU8nKURI24jEbtjmkbm0lkRgeEv/71r3nhhRc46aST+NWvfoXD4Rg15rTTTmPVqlWsXLmSSy+9FLA6kVetWsXBBx/M/Pnzp3raNrsZwmFZzUnTDgbzxQwOsvPOP6E3bye2a1dhtX5TEFTFe3tRNA3F46bs6CVFBRRCVak77zPofX0EX3l5jEEzQD9xJLqO0dcHQP8/nmbg+edw1NRSecqplB+3lMC6tfQ99aTlqGIkOqUVxcqoOl0IpxOhltZlJVlCMLBhPfGdrRiRcOYSvBCp/xSXC9XrxVFbZ7u82NhMAjM2IOzp6eH//u//0DSNZcuW8fjjj48ac+yxx7J8+XKWL1/Oz372M9ra2liwYAEPPPAA7e3t3HDDDSWdk3B7AAFR285tTyDZMGJnBYsjtO7F7JIqMwgZDiOqqvAfdnhKU7AYhKriP/RwgptezQyC0xtjZmJQmERKpK4T62in68H76Xv2H1YgmO7EJCUYBtIwMBIC+4rHQ6S5mVhnB9FdrUW5rCQdXwY3vYIRCGRag6bPIYFpGEhdxxgaKsn+bWxsMpmxAeGmTZuIJTo7f/SjH2Ud88c//pH6+npuueUWbrrpJlavXk04HGbffffltttu48gjjyzpnJz19RjRqFU7NDj9PsQ2hSE0zRJBtgPB0lBoB/AUoni91Jx+RkmbKfxHHIly3z2YoaGSbG9KUVVQFCvgi8WI72y1Hk96chvGqGYZMxpF8XrRamtTbizFuKwkHV/MZIlBDsGz1HUUnw+p67bLi41NiZnRXsYzjVe/eTVmLGZJTbTsmO7p2ORLYvnLDgQ/fMy+4pv4Dzgw9XeyZq3Yuri22/7I4IZ1u0VQnIEQkNRSTPs9CIcTibScVJKXhrRMp3C7cdY3IAG9uxt3UxNzvnHluJ9dNtkSaRi03nQjkebtYJrIeGzizzCRfVVcLrS6evSenpz2b1Mc6d/fWL+bbI8D49ajikQ/gIxGweEg3mE1asV2NIPPi6vaKmdQPZ5RNaZqeTmqz2/Xk47DHis7M+OIx627693tIvBhRQgr6xGPW7ZhNh86gptfZXDdOoTbhYzH0Lu70Xt7kaaBUAqvi2u4+FKirS3EdraOeT5QvF7MaHRmydMkMnICgRz5ODIzGEx/Oh5HxmMIpwvF7S64Yzna0oLe043icGCEhoDRFoBZ56woSF0HPV7U/m3yQxoGAy88n5CL6k79btTqahSnC72/D3NwEKkIFFUFVcMMhazv1jQz6lGF02mVLMRiw+fjLL+NINDzyMNotXVoVVXE23ZhJLPJioLq9uCcO5fyo4+x60lLhB0QFkLyIFYUhNOJzKKDaDMzsOsEbQACzz4z6jHhdKJVVCY6awuri1OcTuZffQ2dd99BcPNmzEgEpGndhDid+A85jFhfH/HWFkRVFWZ/H3KowCVmh8Pyfq6uQTgdKZHtpJexERoi3tpijXW5UD0ejIGB0cFdxqLQyAWiCRaMpESaphW+FaHpaIZDVue3IoaD0ByRiTkUqylpkxvSMGi+/U56NmzEDEdQ3G5QFfShIPGuzuGBiYDPkDLl1Z1Boh5V5inyr3d3oXePkJYzTYzgIOF33yHe2WnXk5YIOyAshDT3E0dVFcLhJNraYmcMZxBC02xhaZtxkbEYen8fzlmzUfz+guviFKeTxi98GTMWY/CljUR3NCOcTnyHHIpQFNr/+HsUt9sKeQo5RzgcoKooqopQVISUzP78l3HNm5exTCdNg7Y//A4zEkZoDtDjmY0tI6uDdH10ODjyQp7Fk1kkz39FaDoqHi9CUS27PyHyiQcRiTnIaKwoTUmb3Oh69jn6XnoZqetotbUAGH29oxMhiUalMX28JwPTRC9BPauNhR0QFkK6/I3mQEo7EJwppISls92hftgp5YlZVUFRIb57W/rJeJx4TzeO2jrUikr07u6CnDykYTC4fl3GktrgSxsRmkq8vy+jTi9v4nGIx0mdZQZh1x9+Ay43MhBAShOhKFYXbjA4qZ3NwuGw6gylLErTMen4Emnebul/mjkcR4njV2gaaA7MgUDRmpI24yMNg541azHCIdTqWoQQGIODlhVi8uZmvJuOsR4rlBElDMlOeGNw0HbgKQF2QJgHZjSKKSVmYCAllqoP9GMGAtM8MxurTjCRcbAZjduDo7KCeHt7STYnXC5kaPdx6RgPc2gIaqyLXSF1aUn5FKtjNmIVy+txq95pkm5M9M7OiQdNAqrXiwSMgf6iNB2Tji/xrg4ruEjqDU4UPCgKwuvFCAyURFPSZnyiLS1Eu7pQPR6EEEgph+sCkyS/sqnoT5VydFCI9Ru060mLxw4I8yDe1YkZjSLcbrz7fwQMk6HXNk33tD70pISl7WBwbKIRZMxTsiyhDIXA7YY9pH7WGAqi+ssKqktLyqdIXUetqcHs77OCnD1QwEEfCKBEoigeN/5DDsM5Zy6ht98qqFM76fgy+OorGPF4Tsem0DTMcBjhcOCorSPa0U7fP57GvXBhhuuKHgzS8/Aq4n19OKqqqDn7XDS/v6j3/mHEDIeQhmllZQEZi6bZTSaZAce5lJixqF1PWiR2QJgPic48GYkQfvNN0NQ98qS/uyAcGlI3Jr9OcCYLDOeI4vNhjjqRF4eoqUHu3Fmy7U0nZjhsBYR51sVJwyCwcQNmOIJWW4sZDA4Hg3uiEoGhY8YEODQC/3qdwIZ1mLpVB6j6y3DNnoN7wQKMgX7U6hqc1TWUr1hKuLWV/n+9k5IfSQZvded9BtecuQQ2rCfWOr5TiXA6U7WDZiRKJLCFyDtbrHGqilZbR/nxJxB49h/o3d0Z0x54/jm02jrmX/NDOzDMA8XjRahWZ7dwSKtJabqP6aT3uRj9mF1PWhy2DmEerPvcZdby0nT/ID7kJBtGpux7EAp43BDafe8+tY8ciP7WG9M9jRmLcLtx1DfkrK2XJLJ9O22//w3SMFD8fuKdHVYGRVWtwMY+VwwHdck/VTVlmVex/PgMHbuxvIyladL3tycIvfVG3l2qGTgcLPr5TXZQmCPSMOi49SaGtm0Dpwujv39mHtNC4FqwkPlXfc8uIUhg6xBONrEcxFNtJg9FsepYprphRJoQ3r3r5exgcHyE5iioLs4YCmLGoiAEZmjIOjaFsI5T+1xhke5CktARjHW007VqJdHWFsqPW4qMRlE8Xrz77Z/1sx944XnC772TGQymupPzyGnE4+z48Y9YdP3PinpLHxaEqlKzbCmR9nZivX0z9vonVI2KZcfbwWCR2AGhzW6B0DRLT3C6JmAn0vdoZCyKUlaWs9exNAwCa9fQ/8JzGIPBlPYgpmktFduHy9homqVJF40y8MLzBF9+CeFyjikQbi3Lr7cs7tIp8Depd3WiB4N2ljBH6lacSOBfb9Dz4rrpnsqYOOpqqVi2fLqnsdujTDzExmb6SBUz2zIyNpOIVlVNzeln5CRum+wq7nlsNbGODktcGYYDFNNE6rb+ZVZME4EYzhqahtWNrSgpgfCex1bT9eD9lp8yVqdrvKO9pDdlPQ+vKtm29nSEqlK7YoWlrDETM3CKglJeOd2z2COwA0KbGYnQNFtPcCKEgIT8kU1xVJ58ChXLT8hpySm9q9hRV4dWXjEsVp/VDcQmHWmMFC+WCFVDLStDq61F6jrBTa8SeHENkOx0La3tX7yvr6Tb29NxlPlRvT6E05XfC12u/F+TjhCJgK/csrxL1qMmXMLU6mq0ykqM3h6iLS2F78cGsJeMbWYYQlUtayo7EBzG4WDxL3+FOk7w13HX7Qw8/9zUzWkPwzW/KadxI7uKhRAofj9qPJYp1mszNiM/IylTjwkhRgmEW52upc1MOaqqSrq9PR3vgia0mlpLZD0PtIpKVK+HaHNzfjdJSf/5xI2A6vGgVFYh45b/sVAUhMMKEI3EDYMtOVM8dobQZmYgRKrb0L6ojsAwGdo8vt6lnfEonsj27YTefovI9u1jZqSiLS3oPd0obneiccTA6O3BjMetoCWR2bbJg0TGZ/jPTIFw17x5OBoaS/q51px9bsm29WEgKSSu5Lki4T/8SByNs/OvqVUUFO+whIxQlIT0kAvF7bGyjsnjQTcQim1hWArsDKHNtJNqGCnxstAeg2nQftsfaL/tD6mHlIoKZn/9Srzz5wN2xqMohKDj7rss8W7TGNXcAKQ8g2Md7cSjEQiH0Xt7Sj8Vf5llQxcYyP1FLheqx4OzcTZlRxyJcDrRAwOg6yg1tQSeeYr40BDmgPVYRtfvTEDTrOXAjMeGBcKtYOQYotu3YZZACF2rq7cbSgqgfOkyIjuaGXjumZzGK14vZjhE+N0tVp2tmeMxp6qoXi9KWTnG4KD1mObIOrRYC0WbTOyA0GbaSAWCu9vy8AwQqjYHBmj90Q8AmPuDH1Fz9rn2knGhSEm8bSdqeQVKQuw80txMrLODwZc2YJoSo7eHeCwKgcCkfffqrNks/P5/A7D9umvRd40v+q3W1dH4+S8hDGNcp5Cq45Yy8MLz9Dy22nJTqajEHAqi9/dbAxRlWAx6soPFkb8dIdB8fsTI7N8IgfDypcsYevtNgi9tLG7/Dgfzv/eD4rbxIUWoKq6588DptCTYxkNV8R50MKE3XgfDQJs9B6OrCxkdP6AXLjeq34fi9SXsCa2MpBEYQK2ozDhOpJRFWyjaZGIHhDZTjlBVpGnuloGgSGQzzKGh6Z5NitYf/YC5P/gRWm0denfX1OxUCMsyUNdLu8Svaag+H6auI4TAc8CByK5OQjt2TJovMJB6D8kARPH5iHe0E+rrs25c3B7IJ2uXQC0vt7pox3PTUVX8Rx1N4yWfR0lkyhZc80Pa7/yTFQCNzJyrKmVHHk3DpcPjxyNb3aPq8yNjibrH9GBwskkPCIVA8flQRmTr0rM+ztmziWzfjhkOUXnyR4ns2IHe2ZF9rqoGLueYAvJaXT3zv/cDOztYINIwGHz5JYSqgd+JHBoa85hRy8uJ9/Vn1to2NGAEBzGDwdHnDU2zmlZcLjAM9J4eyx7xiCMRCIKvbULvtko10FTQDcxIxBqTo1SUzcTYAaHN1KEIQOyeS8NCIFwuyo9dihmLMbj2n9M9owxaf/QDFt18K1uv/ProAKKUJIJiR20tlSd9lMDLLxFp3o7q84JhYASDhQduQqA1zkJRFGR3N6558/HtvS+RUBBHZSXxEXZkJUVKKyPh96MoyrDItGlaTjVDwYI2q5SXI3UD1+K98B+1hKG330zZqjlra/EecBDlS44ZFdgpTiezv/QVzEs+T2DDekJvvwmA9yMHUH706PHjMbLuEbA+66rq1M1NqmRDKGg1VaBqGH19yFi0oPedwQinu3j6qQAASllJREFUkpR0icOBWlWdNesj3C7Uyip2/upm9J7u1FK+UFVwOKwAOy2wtLQfpSUgryjgdKG6XSg+P55Fe1H7advLuFiSx5Hq8VhOMk4XxtCQJbEkZeLc4EAkLF2Nzo6MY04IgVZWjvSXWQLjpmndLEkTR129FSiaVmbY3dRE2dFLUoGea948BjduIN7djTRGj7Gzg6XBDghtpoTk8vDuqtirVVVTtuQYfIccSmDd2umeTlZivb0suvEWtv/Pf2P2lC54ci9dRtnivUDXEULB1TTsRStUFb27M7UUKVTVWoo0zbwzTorXi6IoqWUgBPQ+8VfMcBhzKmwDDQNzYAClshIjmQFW1QmXucYj3t2N5vWh9/TgaVpA1Qkn5vV6xemkcvnxVC4/vuA5mOGQdaHVRlw0E/7Dqs+PjMcSF2dJw8WX4d1vfysAGAwQ2boVPTBArKvTqps0JWpNHcLUibW0YEYjVjONoiCktLzFNQ3n3LlUHb+CeScvJ9zaSlfCy9g5dx6DG9cz9NpmjJ4e5Iisj3C7UH0+wu9uwQxHrKBCU5G6jt7Xm3nDkfSLTrsJUtxuhNuDjEaRahjF5Ri3Q98mN9KPIyEEalkZit+fCu6SUjBmJIyMRDAB1Tm69k8kbq4BJBIMg9qzzkH1+THDoazlDxXLT6D8uGWpWt7xSiRsCscOCG0mld22TjAN4fdjRML0P/0k/U8/mdEROZPYdcsv2evGm9nrp79ADwbpemgl0W3bUZwOvIceTmDL2+jvvzdx/Y+modXWMvs/voG7sXHcoeVLlxHd1Upw06uJJR0XitOZf/G/EEjNYW3D48ZZ30C0o90K0qIlyFLliBkOY3o9VtAxMrNVCJEI0uNBGnLaZDEUjxehqEh9jMxxontTRGMIVUX1+a0MzIIFAPgPOnjUS5I1icKh4aydmzXLZ/b3g2mgejz4996beO2c1BjPwkUE5s3PmvVRK6sIv7sFqeup5UYAIzg4vMyoKMPB4Mj34vFYmajEPIKbXsU1Zy4Vy08o+DO0yX4cpQd3KRL1n0Iy9jE3Yqzq86eOt7FIPyZtJgc7ILSZFJIaUrtzIJhEJrNTUubvnTqFmMmOPEDz+5l16Rcznq/9xOkl36dQVerO+wyuOXNTF3fF6019/6auW8HVRHWGQiBiUVxNCyg7agmBjRuQkShqVRXGQP61e4UiTQMZiyGltGRlSlBXZ/T1gaoS2vI2nn32nfKshmvePLSaWqI7mlH8WRo4yK9bM1tNYjojtQTlJ08d9Z6FqmbN+jhnz2bnr24evW0ph7O2DgckAkiZrElUVSt4Nwzr9+ovy6ppaGeUCief48g1vwkpJbGWHSU55mymBjsgtCktiezZnhAIpjBN64KjquM3B0w3pkF46wep5dyxkIZB+P33GFjzAvHubmLBQYRpogiBNmsO5tAgMh7HWVVN3WcvwVFRMe5ux7u4x3btwhgKEti4nqFNm6x6I01DUVWE04Xq9WLGYxiDgwhNo+yoJbibFtD3t8dR3G6Mgf4Sf0i5YWkMpmWjim2cMQz6X1yDGY3kZI9XSpIacj1dHRgD/UV3a2atSRy5zzQtwdD2ZnyLF405t/SsT2T79qzblvFYKmsrhECmuxgpiqVTB6nHZTxmZT1HaBraGabCyec4Kl+yBCT0dHeW5JizmRrsgNCmNAiBSPiR7okIVd0tgtzWX/4cV9MCKpYcS/nSzIyINAz6n3uG7ocfQmZZ0jWAeHt76u/Ytm0EX30Fxe+n6dof5xQYjrzguhcsQBoGvU88DoCjcdaoIEJxuRFOF3p3N8GXN+KorU3VKsnQFH7miSVi4XQiVdUKQFQVfP6COoxHYgaD07Z8OXppv/BuzTFrEkeS0BLU86j/TNnUSRMzHE41k0hdT2VtAeu7Sg/YkySyutI0SR1laZqGuSINY8x6teRzxlAQIxBALS9H9flTY7K9Fsh6s7S71cPlexyV6pizmRrsgNCmaPZ4YWlFsVaJdwMHFRmJEHlnC7HWFiKtO6i/4KLURarz3rsZeOG5vJs9zGCQbd+6goU/v2nCoDAb+WaUjEBguFZpql0/hMCMxqzmCEVBqCpaRQXxoWDx3du6jj4Umpbly2xL+4V2a05Yk5gkUR+meXNzkJCGQWjL2xiDAevmK1meISx1ApDIxI1nsqvVemHa8ZwIGkV6kDhC03CiOQTWriGwcUNGd7Ojthbf4Ueg9/Yy9Ma/0Hu6MWNxMBK1ppqGVlWNu2kB+kA/ekcHpmkAVlc+ejxhuaaCaabs13A6UFQtQwh9JgeG+R5HpTrmZjp6MEjPw6uI9/XhqKqi5uzhrnYjHKbn8b8SevtNhATvAQdS/fFPYoTDdPzfH4j19iJDIWRlJb76hpxWZSYLIUtRJPMhYd35F01pgftMZ7hzeA9H06yLk7F7vVfhdFJ58qnUnnU2gbVr6Lj3rqK0/BR/GXvd/Ou8Xxd6+y3ab79t/IuylFYgYBhUn34GwVdfIbpjBzg0y2FjMnE4Ul3RiseD6vWh1dSCgFhHOzISxQQIFa89qfj8qF4vs77yb9O2fDle9ivX17fedCPRHc1ZawjBWhLUu7txNzVx4LXfR6gqfX1jZ+ikYdD1wP0MbnoFo7d3/AkkM7kOp9WlGo+DanW+Sl1HcTpx1DeksoXJecz5xpUTllJ0PXA/wc2vjupuNrJp5000x4kurYqC4nKlOqKTGbOpLimYiKoq6zc78vvL5zgq9pibqejBIDuuuzar/qtWU4vudsPO1ry3m+uqzHjU1ZXl/Ro7Q2iTN7utsHShmOZu6U8rYzH6nnyC8Nb3Mfr7ihZ2NoODxAcG8j5JjZtRktJaehsasuQrgL7HH7OWlwQlsSqbCCEEamUlnsV7Ub50eWr5DyDw4hoGN24gumsXpch/SyTSzG/5stQU261Z6ppEgMDaNYlALJzVzWRkFhAhUH0+APSBfquZJDFW8flSwWA+80jOIaO7WUr0vl5LSief3EkuY00TMxZD83hRa2t3u47ofI6jPbFDWA8G2fqtK8asK9eLkP4qdlWmUGamfobNzCQhAGvV+HyIEsu7wVLxmBgGka0fEO/sLMnmuu65M+/XJLsTzUgks2s3cbHV+/uR0ai13JfoHI0PDFgdylPQxOOcPYea08+g8YuX4zvgQNwLhjUWK5afwJxvXEndueej+v1F3xgIBELJbflyJlO+dBn+ww5HaBp6dzfG4CBGOIQxOIje3Y3QtJzrw4a7lsOZjTypAVnONaaJlCZSVRCaI2W5JzQNFDXveaR3TqcHuMZQ0HJzmazznWliJDLPakUlZjhidWbvqeU3exA7rrt2cs9PpknzD6+ZvO1nYbcKCH//+9+zdOnSrM9FIhF+8YtfsGLFCg455BDOO+881qxZM8Uz3HMRqqU+v1sHR8WwGwfAwuUq2fzj/X357z+RUVI8VtdwMijMuNgmbjbUsnK08nIcdXXWRXmSjzettpZ5376aiuUnjJlBEqpK2dFLcM6dV5w2oRCYhoGjtna3l9lI1pLVnH4G7qYm67NLqw+rOf2MnJc+kzWmwuFAGIYVDCa7+rN91poDNA0BCNNELSvD2diIs7ERrbwCzPznkbXONSl1M5nHoJRW/XUsNqoj2mbmogeDU2ITmlyVmSp2myXjZ599ll/96ldUVlZmff7KK6/k2Wef5aKLLmLRokWsWrWKyy+/nNtvv52jjz56aie7B7HHN4zkym4cCAshSqac6Kisyvr4RDVC2QSs9cHB4eV4IVC9XoTPhxmNYEajU1KS4DtqSU42cFZQewzRlh2Fu6ZoGprPS9nRSwBLYsUYCqL392NGIykXGNfceURbW4g2N4MA13zLGQYYs3s12fEqvF6i27ejBwcxQmHARBgmSkU5ZjhiLWMpKu6mBWgV5RiBAFplFe6FCyeUK4IsHbaVVZQdc5w1f0XBNX94/gMvPI+UJnp1OY6qaiJSy9qJG2tvT9jmpek+mqZVt5tcLk6X/hGAquGYOw+tqhJnVQ2Vp5yK4nQSad6e9XNLftZGIGDZrgWDqf8Lr5fBlzYSDwxYc0BavsjxmJW5nmzSb7QL6Ii2mXp6Hl41ZfvquudOZv/b16ZkXzM+IJRScvfdd3PDDTegj3GBWLduHU8//TRXX301l112GQBnnXUWZ5xxBj/96U956KGHpnDGewbJk/aHpk5wDyZdsLpY6j57Scbf43VlpndNjuxOjLW3WV27CZkXxesFKYm37bIen6KMbOCF52j49Hk5jS1fuswKdF5ck1rizhkhUPx+/IcchjRMWn75C2KtVkCUcbOhKKO6Z5PNOMLjSXw2iQ5VVbPEfYPB0duZgEjCGzmFqqLV1lF96seoWHb8qMAw+T0PbFhPfGcrxoj9CVVDuFyWlVk4bMnH6DpISVdi+4rbg3PuXBzVNcR7ezF6e5CmAYaJkfSKHuvGM/29xeMQjxP+1+upz7b3yb/hnDUbNAdmX6+1XaGgaBoSiRkcsrJ9cuLPyJiCutUMpFVXipR5dURPJ2PdAObSPLInNJjE+/JfKSl4XwWsyhTKjA8Izz//fF5//XWOP/54enp66OjoGDXm0UcfxeFwcP7556ce83q9nHvuudx0001s376dBXtYQeukkZB0+NBnBG1GofjLMgqcx+7KNIg0NxPr7CC6qzW1ZJcuYD3wz+fpXv0XS6DaX4bZ12cFBVO8NJ9PBkioKnUXXIhzzlz6n/67JaMxUQ1RIuB1LVhoZRhbW+h5/FHLfSXbb2xkUKcoyHgcIz5gaSE6XWjl5eiBgNWEU6rPyzDQO9rpfOB+Yjt3UnfBhRm6e6kO4EAga8Au9ThSj2MmA7ss2zeHgkTe2UJECFBV1LJyFIeGYZgTf47jISUyHCa69QMr8K6otLYbGESP5Rm4Twemid7TgzE4CFLiXrTXjC0pGOsGUKupQauqzgj0R94YAjndPO4OOKqyr5RMyr7GWJWZDGZ8QNjW1sZ1113HueeeyyWXXJJ1zBtvvMGiRYvwjtC7OuCAA1LP2wFhDiTqgGxsRiEETddel/FQ1q7MBIrfn7VrMpkdQIKiqJgSxMDAtASDgNXNnAdCVak84UQqli1PLU+a0SjhD97D1HW0ygrcTQuR4QhSj+NasBCtrBzXvHkE1q5h6PXNVidtoSUI8VhG9q3kRKMENqzDNW9e6jvL6ADOd5/Z5FeSDSBCoHi8SH0QMxeZllyQEuIxK5DW8+wMnk6ktG5OFAWK61uaNMaT5Qm//551TCfqgBWHlnFjGGndgQCCr23O6eZxplNz9rkMPP/clOxr5KrMZDLjA8JnnnkG5wQ1Ph0dHRx88GgD9vr6egB27do1KXPbU0jpCdrB4IxBeL3DHsozgLL996NuXn1G1qh900vIaARXfX1WLTqtpppoZyfhTS8z/7ST6X7hn/SsWUu0qwupGyndwenkI//9fXxVBS7P1R4ERxyU01BpGHRsegkzEk5pHubEyMBRSszQECBy07orADMUIvzqSzR98lQA2hPzFgkHkAxymUO2MaaJGR7CUVFGPDxU0vdhhkKYu1MwmEQIFIeG3tWJvvkl6k85abpnBICqWr2n+uaNhF7fjDAN3I3Dv/l4IDAc5AOqpuAos0SZpSwn3tdHcOMGqwnIoWW8Nn1M6LXN6HsvnjHve1yqvOxsqCfaURr1hrHQysupXzBrUveRzozvMp4oGAQYGhrC4/GMetyduPsPh8Mln9eegNCs+wG7TnCGoSjUHHsM5QcdaEmdTCHC6URxu1BcLlSfF29TE866WmK9vYS2N6fGhbY3E+3qQvV4xnUfUd0ewjt3suW662m5byXBrVuRumVnNiq4mAZ8CxdOyX6Sn5ficGb+3nLtWE4fJ0ldgCeLyK42QtubR89bSQvu8um2HjlWEci4bulPxnVQS3gpkhLik5RBnWQcjY2YkTA9a9dO+81SOtIw6FmzFiMcwlFVlfrNSynRg8GU5I80TfRgMNUcJIRAq6zECIfQQyG0yspR5wshBI6qKoxwaMa977GQhkHjJz4xufq0isLBN984edvPwozPEJYCRZnxce+U8qETlt7dkBJZUY0RDGOMcTOjVddQ8dFT0bw+Qh+8y+DGjVaG1zAKXo5UvF4ctXUZJzkDMIUCcZ3+jl5i1Y0AhDp6MeI6QlXR9Sz7SwhO64EA6Drx3r6Uu4QeDluC00IBOX0n/8pPfmpc94xSkvy8LDe2AgKVKQ5ujHic/o7exL/1RAwqybtdPd1iLuPxRDAxFJ6cG4NSB8zJLGc2u7ziN07iA8EIhhBON+G2Dtpff2dGiDlXVXkZ+mAb4fZOhNONYUiSB4KMRa2APinHJARmXCceiqC4XACWu1fi4zIiUUynK+t+Ztr7Hov0pXPF47HOZVmOYbWmBtPjRbbmLyGk+MtouvY6gqYDCjxHfWidSrxeL5EsnWHJx3wJRfsPPYkf7O5wB/ahRgj6n/mH5S4yBnpvD72PPEzFccuoXHYCkbe3YOo6Qgj04CAkXD/y2adaXpH94p2l83Ei9xG9r9fSGExfHlUUZDyGjCZOng4HIlnrNdW4XMS2fpDyVp1sUp9X8uI5w0n/vjPmXUgNYTYSF1AzFMqp87cwEoFWKUh0fytutyXDFYmUbsk+bZrSMBBOx4yTnrEy+gZCG9ExbJrIRD0oMPyZpAdIaf+Wpjl2ieRuIrmTWTtt6aVK08Do67PE9A0D1V9GzelnULH8hN3Ky3iPCAhnz55NV9dokcjOhDtDQ0PDVE9pxpGqE9wNl1E+dAgxbjCYREYiBDaswzlnDlpNLdEdzai1tTh8PuLtbfmp6DucWfX4pJSYkQjupqaMzsek+0h0RzOK35+xDJTV3UFRUpnp1AXCNIfFh6f4uFQcjpQA8FRkI5KfV6R5u6Vxl7wpm6G/R0fDrNT3nZy3UDWkER3+vsbK/mVjrPepCEriCTgSIUobaEqJ0BxIw8RRXUOsrW1SAlmhqjNSekbzZr8BFIpiBUSJ+sF0kfkUaf8W463WzcD3PZJ0R5v0Rjqr07rWGpPwzx7cuIHy45ahejzUf/o8IFPeSvV4mPfNq6b6LYzLHrGWesABB/D++++PyhK++aaltXXQQbkVfu+J2HWCuyF5ZHDNoSG6/vYE/sMOSzmBCCY48WZDmoklwYS3cGAAfXDQcpDQVNyL9soYPpb7yCh3h0RWOjmfjPAhkV1gOko65NR6Cic/L9XrQajKjM4SKl4v5UuWpKSCkvNGEaO/q1wC2rHGJLbPTO8qTS6HKgLV66FyxckoyZr1UnyP6TWZiSXImeZm413QlNV+UjicoGnW7ynNPlCk31w6HMP/1tL+nUbyxnOmve+RZHW0GcHu7DizR2QITzvtNFatWsXKlSu59NJLAQiFQqxatYqDDz6Y+fPnT/MMpx5bWPrDg+zqpOu+e1Dq6lBUjXhHu6VRlw/xOLGWHdmfUxT61zzP0L9ew3fQwVR/8lMoTmcW9xG3FVgm9y0EistluY4kMwgZzRGJC0gycziV2TIxOZ7CZixG8JWXiXZ1Enl3C6gaqsuF/9ilOOfOxX/IYQxufhUjFpuZXf0uF+VLjs3w/k1+z4OvvoKRr17geN9ptq7lUpDQUi3dkq4VECoeD/7DDqfi+BMIb/2AwfUvlvSYFV4vcjCA4nFTdvSSGSW/krwx6OnqwBjoH/Z7FgLV50OPxUDXLdkZry+j6cQMDKQCaCMwkOEVnRxjDPTPyPc9EjOcfel8FLvJ8vdI9oiAcPny5Sxfvpyf/exntLW1sWDBAh544AHa29u54YYbpnt6U0viDt6uE/zwYXZ1YZaVW+4M8Xjm0l5RGzYxBwaIDgwQbdnBwItrrPqYZcdnuI/Eu7sxo1bAIDTN0iPz+Yh3dWYEiSMv1DJ58VY1MAtvismZRB1tMdmIkW4LjoYGOu+9m8FXX4YsYtfBV18BRcGxaDHeffZj6IP3Mbu6KFmNW7EIgaOhgaqPnkbFsuUZF+V0l5nAhvXEWlsxIuGMgFaoGsLtQvGVISMhzNAIrcSk9ZyqpurwMIzh+jO3G6GqVoF+wsEm7+PW4UAoKhh6ygpPraqyxLSLuTGWEq2mlupTrc8GwL1gAYMvbShuu+moKtIwEW4N/2GHZwTkM4WsN4BawuM++f0mlo+NcAh0AzMSQfG48R9xJAJB8LVNma9NHzND33c649ZOp7MbLH9nY48ICAFuueUWbrrpJlavXk04HGbffffltttu48gjj5zuqU0dtrC0zWAA0+WygjG3GxQFIxrBLJXVkpQYfX10rRx2tEi6j0RbWohs20rvY48ihUAts7rcVJ8PPR4fbuBQlMzjNHHx1srL0INDEJtk/1hVRfV6C8pGZHNqQAirm3qirKxpEn//PeLvv5f9eSHA7UFIEzQHisOBAEzTwBzL4D6xXJfxeU7UCatpqF4fqApmOIxQNerO/wzlxxw75ueR7jKT7mWc9ANWy8tRff5UgJ0U7ZbSpKy6nFj/AG2P/hXhdFr7ltJqMDItGz7hcFq1s6EQMhbFe8ghVud8NDp8E5H4DLO+L0XBUVVtBZvxmNWdLyWNl34B96LFtP/hd4Te3TLamUbTrMzc0NDY504p0fv7iO5sQRoG3atWMvDi2uLOtWldy8LhQCkrx1VfT9nRSyg/bmY6doy0n4x3dyd+0xqevfbBUV1FvLcXvacn9Vt3NzWl3hNYtbSZr80cMxPfdzrj1U4nGavuendASDmV6zS7N+vOv8hqoZ9hpBpGbGwSOOrqU/7Asc4OZCxmNQXE81xKHgfhdlN33meoPOHE1GPSMGi96UaiO5qHi67Tu46Tp5sR/r3C5UI4HMO+y5N4WhJlZVQsOSZvV4SxnBrifX35NfCMh6IAAqGpOBpnpT6/aGtLKgOTUXOmaQhE5vearNlKz9AlX6couObOS2Vy9O5u3E1NzPnGlZN2MbZkS7byzs9/iTSM1I1CNozBQYSqUnPGWXQ/9CB6YMAKmpJzM+VwV3r6Z6Gq1jGfkDpJbmfWV/4t1TRkxmJ03ns3wVdfRkqJo7YOoSgYwUH0vr7MYzJR3yiTwbaUKF4vvoMPtdxmQiFrXppmKcbo+qgmE+F0IhXVqhnVDYRDo+qTZ+Dw+9EDA6j+MhwNDRCPz2hP36qEcHu6RNOH2ct44IXn6XlsNVLXx1z+FpqW6jKeLj60sjMfVpKBoB0M2owk3tWJmDUb+vssiQxAmqULBsHqcu59/K84amsRLhfxjg7ifX3EBwOY4TCxHQkha5Fmx5XFqxeny6oxMhJdx6qaddm1VFQsXUbd2efmfRHKZtUnTbO4YHBkNi+17KYQ7+21GjliseHPLT1QlhLi8dGLztnmkxQKdjqtxgBFYA6FEA4NtaKCgReex9XUhLtpQerCnp7lU1xutMpKVJ8f5+zZxHbtwgyHEC4X0jSJ7mhG7+tDq6zCvXBhajtJkk0JuWZW/EccSf+af1oBYaJhQSQaO7ItJwvNkWpkGCtDI1SVeHc3IHDU1aWC7YwmqCSmiUx20CZWXsxwmODmTchoJLHPRMMew7p8GSgKrvr6jOA7/Ppmqicx+C4lRjhM/9NP0hcaxFVTjWvpCtRELaBQVdSqKnoff5R4fx+OyqqUXIpr3rxUwBdtacE1bx7SMAi+8jJ6YACtvAL/EUemVA2S+0lup/KUU1P7yYY0DMLbtjL02mZkLIZrfhNlRx2dVSVhMhhz6Xw3W/7Ohp0hzIMZkyFUFTAn16nAxiZnNK34Wiq3G/fCxVYjxiQ3mLgWLKTy+BMpXzp6iWpk9sI5ezbR1hYi27fR/8zT6H19OOobrK5pKYn19SEHA5M21ylF09BqanHPm0/o3S2YweDoQEkoiaAs8f/4GBZxLjf+ww+n4aKLqZ1dA8D2R56g57HVmPE4iteLMK2MpXA6MU0TvduSDvMs3gvfoYcx+OKLRLd9kNPUhdOJTC4tx+NWMNa0AP8BB6YCjMj27bT9/jcZWUoZixLr7Bxe/k0PNjUt1R0vDcM65yYDv4SMEljaeqnjf0TW0llfj3COnbWcCuIDA3Tdc+eowG0sjHCY1pt+QXT7tlGZfNeChTR84cu03nCddXyMQLhcOObNx+zvR8ZjmKZExqLWTU0qW60gXE6cCxZiBgLE23aNkqhyLVjI3Cu+mREYSsOg//nn6P3raozBQMZrhNOJ//AjaLjk86MCw2Sjl97fn/IXVxPaxDIaTd3UxFotj3XnvHmWPmo0Om6WM/DimuHlb9NAKCqO2toZs/xdSIbQDgjzYNoDwoR8h90wYmNTOMLlRvX78B92eGrZeFRtoGEgYzHMWNTKwI+4MOJ0ZmbubLIjBFVL///27jy+qTLfH/jnbEmTJt03KS2L0I4g0Aq0BVp2GeCC7AoiRQUUWXzJIqB4f4URL14drciVkYss10F0FARGxOsMooKjwgwisnsFWsraUro3aZJzzu+P06RNk7ZJlyRtvu/Xy5d6ck7y5DzNyTfPeZ7vdyC6LXwGRUWVuL4xG4b/+z/7hSMe+ApSd70XYf82HgW7/mw32V8yGmAuKLBPk+QsIJQk261j2y3s2o85CwhZFkJkJNiA6hW2hkpAFBHz+Bxo7+vR6u/ZXFKC3KzVTgM3VqdDp7WvOASGosGAKy887/QYt7TAfHZWp0eX9a+B02iUWuC7dqL0uyMNPq+qYxziX/x3sCoVJJMJt//8P6g4dRJS9ZxSG4apyYFaOzeq7cVZsOoAsFotVJGR0PdPdekHpC/d/qaAsJV5MyCkeYKEtAw2KBiSxQyO5xGcMRRh/zYOd3b/BWUn/gXJaFQSEJtN7qfuIfUSoiKhiuuMqiuXIZaXKbdkrQGWhzAaDRiNFizQ7kcIzSUluPL8koZ/sLAsuryebRcU5v7Hy6i6XGtUtm6aKA8LuLcb4l94CSVHvkX+Rx/YfyadtY1hEDRgIKIem42r//EyTDeuN/1HG8Mo0xBYDpxWY/cDsi2gOYTtEM0TJKRlSaXKil0RwN3//RzFR76BVFFze9RhJSppNnN+Acz5BcoK7+Bg8Do9TAX5kOup1d0aZIMBsiQrqW+q5zEyggqMINTcdam9CKdWLj1lZBBg1BplDqG1yk7tfeuMdjKCoKyehudXnuZmrW48EJIk5Ga9hG5vbQSgjA5W5VypebzuPE9PVRSq9TrGK5dhKS9H8Y8/1B8M1j5GllH+888QRal5wSBQvRLeDC4kELLFgvKTP0Ed29GrC0VaW7uoVNIeMdXD2RQIEtKKRBFSWSnd+vUUUYRYUgLz3UKPBoM2VUbIKqGmuk51YmWHCiy1SrLZVhlrNNAlJduSLFuvzUzdUm3Vx3OBgTV5+TyYeNlcUuLyLV+pvAzm6pRGxYf+Zn/rvC5PjRDWLokoSSjcuxuWG9drHm+kOoxkNKDi5InmfaZrvb5UUancVTAYUXb8WLueskUBoa+pvrh4+nYKIYS0uupgSKr0XgUHITQMDM/DcucOxLIygOXACHVKqkmSEvBVp+5h1GroU9MQPWs2glIHgFGrbSNIDnNMoYwOguUglpXBcucOGN5zCacLPni/SfubXaif7g3moiJIkhtBmCQDJnOLlYeULWYlNVAbLUfnDrpl7EOsk9spECSEtEvVtW69OT9TCA6BbvAQuxWifFAwwHGQDJV2aWgYnocQEYGQkaMQnD5YSc78yAyoYjui+NCXtgTL1kUKXGCgsmBFFJWVp15IvOxuYGfdXwgJbY3mNJsQGooqloPLISGD6lFGtunfpXXTO0lSmy1H5w4KCH2AbZ5gOx6KJoQQ261Va6kzL+C0WruqK7VXiAI1VVbAAOr4Tg45FRmOQ8iQoQhOz3C6LwCvrjwVQkLhzixYayAYMnIUCj/bX5P2yVtzCAG7ldrhk6bCePMWxF8v1DzWwOgfIwiQTfWkQ3JV3QTwLAuYTG2yHJ07KCD0IobjIFtvTRBCiL9oodt5TRE0SLlty3Cc09W+mq73QtP13kafh+G4evf1ZJ7BuiJnZiq1s93YHwA4jQbqzl1qVhnXDby8scq4S1fwOh1C0gYgP+dyzchyfW1jGOiS+6Li9C+QKitapA0MLwCCAKm0tE2Wo3MHzSH0BpaleYKEEP8jipBrj7x4GKPRQNOtu8df15OE4GCwOp1L+7I6vV3amY5LloPV1UpXYl057YXvKVanR+xzywAoQbx+4KCald31tE0V2xHRs59AYJ8kx4U+7qg1QskGaiGVlnhsUZA3UUDoSQyUP2hniTAJaSs4DuquXZVayYS4y7oIo1ZyZ49gGERMmtKuv9CtOq19pfFzy7LotHad3SZOo0GX9a8h4N5uTldOB9zbDfHrXrUPGu2egFMqF7lCpbIP8Oq8jjUpNaCMxkbPeAyR0x8FFxzs8GOCUamgTxtoS0odPWs2VLEdm/f3xTBKfXWD0aOLgryJElO7oTmJqSmxNPF51uz9QM08IuutGVnJ3xbYqzdi5jwFTqNB8Tdfo/CzfZAtFkgVLXN7xu8xDCAINT8aa1+e1Wqw+iDwQUGQjEZYSoqV+s+CAIZllJrV1XWrHZ6T58FFx0AVEgLzjeuwlJbWVN7wJOsXOcOA1WjAarSQTFWQyspaty08j+D0DETNeMwvAkLAWqnkJUjlZQ6PsTo9Oq1d12gJu+JDfwPrpJax9fnrlsTjdTqUfHcUJUe+genGDcjmOouHWBaMWg1WGwgGMhiWAx8WpvwtBOmhCg1vsVrGksmE/J3/g/Kff1YWgrhbqSQgQClfGRnpM+Xo3EGVSlpZUwJCCgSJ1zgrH2WdIF37Yy8I4DQa8EHBkAFlno4kKbVqASW9hskEsKxd2S1ZFFHwyUcoP/kTpMpKpTxUC+KjohD/4v+DqjgfF/6wrnVG1T01Ub66sgUToIEkWoC6pbRYFnxoGMJGj0XQoHRUXctrcGGDM9YyWmJFOcTSUrA6HaTycnBBQeACdbbFDbIowpibA2POFRQdOgRLUSG4kBCwHAfz3SLA1MqJuXkegckPICR9sK1dksmEa5s2ourXi80ueWaH4xHQtSuCBw5qc1/oLcXdWsZ1hYYqdwKKilxfXWv9W7SUlSrJrnkeQkgodH37geE4jy66aY1axm0BBYStzJ2AkOE4Za4M3Rr2G7EvZcGcm4Oib7+F+eZ1wGxu1vOpf3cf9H37w3zzBkSDATLHo+rXC5AtFrC6QOj79gen0YARVJCqjBCLS8CHhYJVB9hWC1pLdDkjliklxMAAYNhG93VWdqt2kXfT7duw3C1s1nsGAHAc9P1SED1bKVQfGqqFpbwcp5avgqUg3+3nso0CWC911nq0ggBWHwROJUCWAbG8DHJVFWRRAjgWjEqlrFasO8phxTBKJTNGKYUW+tBESBUVKP/XcUiiCFVUNIIHD4VYWOiwCtUakFnuFoEPC0VA5y4uBX0tyZiTg5ubN0EWRXB6vWMZt1bC6nSIfXaJw2IMWRRR8t0RFB/6G0x37thyADpVPcLDqNVQRUYhOGMI9CmpqPj5pJJGxWKBunMX8PqgNvuF7iuaEhAS76PSdb6AYZRfF5RCpu1jGECtVoJ7sxlQqRA6cBCE+C6488mHkAwGcFot7nl2KbTx8coxnbsgOH2w3UgNFxQESRRx5+BnMF+/rowOOcFFRYEP1EM/cBBCBqU73AJxlSyKKP3he1RdzbWV6HLYp7qUlrq63VVXrza6r7MVdgzHOaTwsFRVoXD/XpgLCgBZAgIDgYoKJWGsWg1Or4NYXgFG4KEKjwQfEW5L6aDt0RNBKWkO753X6dB1/WuwlJejcO9uVBUWQjJWQTabwIgihE7x0HTqAkv+bTCCCtpevcHyvO1XvhAdjYqfT8JSWgJOp4cQHa0km62VbqRun3GBOhhzr+DuwQOQjFVgVCql3JlKAMBAlkRIlZVg1WqEj3vIVtIq4t/GN9pHrq5kbU2SoVLJlcfXqsnb2uMD1SOlzsoDKulchiE4fbCSzuVqLmRJAqsOUD5D1aOdtkohTkZwggYMbN32E9KOUUDYgiifoAdY51i1dmJbhgUToAYboIFcVQVOr4cu+QFETHlY+eJKS6v/0HrSWeh792nFBtu/flBKKgoLbkMsKQYXHGIX6NUupRWUmgbIQGFBfqP7NrTCru57DkpKbpX3xut0iJ71eJOObSxYcNZn6rg4mG7eUG6LG4xgAgKUwMkiQjIawWoC2uxkc1ajBcNykC3K9Yph2dZf+SsrQ6oN5XJrKJ0LIaT1UEDYAmyBIM0VrMGyYAQBfGQUOK0WQmQkTNevKfOimjEKwWq1iJg8Deq4OBguX0ZVbg5MN6+j6tYt5bZgcwJFhgHD8YBKAMtyyr85HkJMTJubVBw0KB1VN66h/ORPsNy5AzYgAOC5egMZd/b1JwzHIXLadKhjO9ZUthC9U4Gipanj4sCHR9SMJAsqMLzQ6j9ohdDQdp3LjZC2igLCZrDOE6RAEMqqQZ0OjEoNQa+HJiEBun4ptnlRxd9+jfJjx5p3S4rnEZQ6AMHpGbZRBFkUcS37DbAsC6mp8zVZFnxYGCKnz4QQEgpVhw4w3bjhtUoDLcHdQKa9Bj0twdlt8bb6d1Gbs5FkThcIi9nUenOfOQ5BGYPb9HkjpL2igLApWBYMw9Ct4WpRsx5HQKfO9X5RyqKIor9/qRQJbyqGgRARiYipD9s9d1VeHiyFd5Ri8k1d5SoIYBgWQkio7bahNysNtBR3Apn2GvS0pPqmArRljiPJarBqdYuvGAcAMAw03RMQnD645Z+bENJsFBC6yXZ72NsN8RGqXr1tI3b1MebmwHznTtNfhOPABwcDogjTjRt2X8rWifHNSh8iScoCgXZatNydQKY9Bj2kfs5GklmtFgzHQzJVKT96W+KHL8tCk5CI2MXP0Q8MQnwUBYRuotvDNbSDhyJ25qxGL/BVubnN+1KpTqDrLGirmRhvaXpQyLJg2PZdtJyQ+tQ3Oqzq0EHJh3g1F5LFAmNuLoy/XoCltAwQLc5vK1srQ1g/hxwHVVgYOkx8CELfARQMEuLDKCAkbtMOG4nQpCRwga7Vy0QLLFyULRawARqHoK1mYnxO01cfMwyEiIgWnehuTczq7q3Xph7XlllTyZiLiiCEhiJ80lTwLtZibU883ffOEhbXHR2uu9q3dhtljoPh/HlYSoshhIQiZOQosCqVLWUMZEDdqRPuSeoBhuMoj101V/vZm5+L2m1URYdB27mTR17XV3mqL7x9/aeAkDSOYRDQPQFCeAQsRXdhPvMLbv9yEgzLQYiIgL5/KoIG1b/oQB3fSUkQ3IzRVdlshtAxzmkePOvEeKm01P0nZhjwgYEtVrRcFkWU/uM7lB4/BkvhHSXPmwvnqanHtWWW8nJcXbcWljsFdttLvv0GfEQk4l/K8ovA0NN9r5Q0Ww2pvNy2rQpA+U8nwOp06LT2lXorWdSdUhCYkOiwT90gsr393TaVq/3szc+FszYWCDzUkVHQJPdrl9ehhniqL3zl+k+VStzQnFrGbZogKLeAJElZkRsUVD2X0j4tSeS06fUGOzlZq2G+datpr89x4ENC7JL/1n3+mzv/B+VHj7j91GxIKIL69au37e6QRREFH3+E8p+VnHVsQAAYnmv0PDX1uLbMUl6Oy88vabiaiyCg6+vZiIyLAtA+KyV4uu/NJSW48vyShlcRsyy6vJ7tVnmzhlClC9f7OXTMOFxZtdylz0VLB4X1tZGRJYgGAxh1+7sONcSda1Rz+qK1rgFNqVTCun0E8SuMIIDX65WA0BoUMkpiWU6vBx8RAdliQfnJn1D6/XfOn4PjEDry9wDftAFpNqDhPHgMx8F4/pzbz6tOSETkhIktdoEr/cd3KP/5J8gWC/iICHB6vUvnqanHtWVX161tvLSf2Yyrr/zBMw3yEk/3fW7W6sZTykgScrNeapHXIwpX+zk3a7XXPhf1tVEICoI6KqpdXoca4qlrlC9d/9tVQHjjxg0sWbIEaWlp6Nu3L+bPn4/c3FxvN6vNYgMDIcTcA7GyUgkGqwM6qaLCNmmcYRhwwSGQDEaUHT9Wbyqe4IzBSroJN4NCLjQUEZOnNRi0WcrLYXFjFXPEnKcQ/9IaxC9bgeCMIS13q/j4MUgGo0O1D6D+89TU4zxBFkUYc3JQef4cjDk5kEwmGC5fQvHXh1H8zWEYLl9qUnuU/ipofEcAloJ8WGrd2mxPPN335pISu9vEDZHKy2AuKWnW6xGFq/1sKS93uX9a+nPhy9chb/DUNcrXznu7mUNYXFyMzMxMlJeXIzMzE2q1Gtu2bcPMmTOxf/9+hIeHe7uJvo9lq28JB4PT6QCGgVRVZVvByzAMZIZR0u6YTWBUagDKHy0bEADznTuoystzmraE4ThEzZgJdVw87v7tf2EpyG94pEKlQsjwEYh4aFKjNX0L9+52622af/sVAS1c89SaD5ENCHBaDxhwfp6aelxrcpjPIoqQTaaaNCTVo8QMx0EIj0DIyFEIdiPZsLv9lffBLnR5+qmmvBWf5um+L/jgfbf377BgcZNfjyhc7Wd3Fe7d3eQyjnX54nXIm9y9RjW1L3ztvLebgHDHjh24du0adu/ejfvvvx8AkJGRgYkTJ+K9997DypUrvdxC36QZMABxc55G5flzuLVjKxiuTvoVa8F76x8rwyjVWSTJfvEwz0EWG87lpxSvH4rg9AwYc3NgzLkCy90iMDodzLduKsXrtVoEDUqHplt3lwMMc1GRW+/Z3f1dYc2HyPCNtLnOeWrqca3FcT6LkqTYWaJi2WKB6fYtFOz+C6puXEPUI4+61Gfunv+qwrtu7d9WeLrvzcVufk7c3J8453I/u1kdpiWvY752HfI2T32n+Np5bzcB4YEDB5CUlGQLBgEgISEBaWlpOHDgAAWE9bjnkZkAHAvd21gL3lvXHskyGIYBw9aZbWARHYPJerR08XohNLRV93dFveevrjrnqanHtZa681mkinLnC6mqR5MhipCrqlB27EcExMU7XfRTl7vnXx0e5tb+bYWn+14ICYU7S+KEkJb/nPgjl/u57jW1ES15HfO165C3eeo7xdfOe7uYQ1hSUoK8vDy7YNCqZ8+eyM/PR35+vhda5uNCw2yro6z5/CSjEbUXnjMqFRieVxJDVy8sYXgejFBzG1eWZUhGY4vn8nNV+KSprbq/K+o7f7U5O09NPa41OMxnASBWVNSMXNS+pWHdxnHK1AKDAaXHfnRpjou75z9u5qNu7d9WeLrvI2dmtur+xDlX+9ldLXkd86XrkC/w1HeKr533djFCePv2bQBAdHS0w2NRUUrKips3b9r+u11iGUBy76LS960/gtfV/OKwDMvA9d0FkMtKwIeG1sxp0OtgLipW5hKyLHi9HrygDHHLsgxzUTH4QC2ihw5GWIT7S92bLVSL69FRqLrdeNCvjo62pTFpafWePzR8npp6XEuruHQZUnEh+EANBIGDaKxyXGVnHS2WZTCQwbAsZJmBLMmwFNyGuqQAgfd2bfiF3OwvdXCQclho+xuV8Gjfh2qRp9fDUlbW6K58UBCiOt/TvNerxnHKuEN77D9XudLPqqAgSEajS/3TGtexxtool5V49zrvSR78TvGV6z/QTkYIKyoqAAAajcbhsYCAAABAZWU7nvPAscoonhv6vPO2Q+6kyGFDEZbSHwwvoCo/H+bSUlgqKpVfLgyUhQQsC8gyLBWVMJeWoio/HwwvICylPyKHNX67sLXc/9qrYAShwX0YQcD9r61vtTbUd/4aO09NPa6lWSorIYtSzd+SJNXU7G5wzrvyoGwRYXHxc+YL/eULPN33vTe82fitSZZF77feaJHXIwpX+7n3hje99rnwleuQr/DUNcqXznu7GCGUqm9fNbRSi3VzfoYvYAIDwYWFQywogGx0nNQPhqmZywVG+X+GReiUaSj+bL/TY/jwCMT/+xoY1DoYnCSK1T80BXJEtK3QvWQ2g2E5BNzbHUJYGMx3i2AprNmujusEfUoq9APTUVzqzaTdSjLdq6/8QVnBXIc6OhqxL/w7ysws0IoJcus7f42dp6Ye15KMFhYyGEhVZkBVKxgEABkOQaEsK9tttzoYFpUWFpJL57fh/uIjoxC/+v+hzMyCF5XPd3tNbOzZvhfQ5fVs5Ga9BKnccSSK1enRae06lEtCi31OKDG1wpV+Lpc4lz8XrXEdc9ZGTuAR2LUrNMn9fOA670muX6Oa2xetcQ1oSmLqdlGp5MKFC5gwYQKef/55zJ071+6xP//5z1i3bh327NnjdI6hOzxVqUSXNgBR02eC1+kgiyIMVy7jevYbkKuMtjlbAGypYIDqL2WzGaw2EF3/mA1WpWpW/cX6aip6u9aiK+q+725PZoLX6Tz6hdQWaxnLoohr2W+g6mou+IgIMABM+bchG43KDrUXFzFMTU7J6nmDmoREdFyy3O32NvZ36i8BhS/UMm6p6iS1+Uv/uaqt1TIOqa5l7D+BoKO2WMu4KQFhuxghjI2NBQAUFDgmkrQuJnE2v9CrGMY2HC3LslLnl2EQlDYAMU/Oq9mN46Dt1h26B/qi7NgPymR+nneYZ4Dq+X26pCRb3j5ep2tynqq6NUsb2+5L6r7v2vMkPaWp58mb57d2XWixpBhccAi4wEBYTKaa9ENW1hF3UQRkGaxWi6DUtCZdvJrzd9qeeLrvheBgyjPoBa72szc/F7XbGOjHcz+tPNUX3v5+bXv3UZ3Q6/WIj4/H2bNnHR47e/YsYmJiEBkZ6YWWNUCWqxM8m5WJ+wwDVWxHRD022+nu0bNmQxXbURmZMZshm81uHU+IK4IGpUOX/AAYnleqv8gyWLXacUdJUn6EyDIYtRr61LR6SwsSQgjxfe0iIASA0aNH48SJEzh3rqam7a+//ooff/wR48aN81g7NL+7D/rUAeCCg+3TdACASgUuIhKMRgOw1SMpDAtWG4igtAGIf+GleqtysCoV4l94CUFpA8BqAwGGdet4QlzBcBwip01H+LiHENCpExiOB6vVgtPpwahUNVMWGAYMz0MVE4PIaY+4nJSaEEKIb2oXcwgBpXTd+PHjYbFY8OSTT4JlWWzfvh2CIGDPnj0IC2t+ctuG5hB2XLbCYa6d4cplVJz6GbLZpEwO7Z8CVqWCZDKh/MS/YCktAR8UDF3ffm4Fcs093t/QHKamqTufRdWhA6qu5aEqNxdgAHV8JwR06tzqgSD1X9tG/de2Uf+1TX67qMQqLy8P69evxw8//ACVSoWUlBSsWLECcS2UzLGgoPH8UMT30AWtbaP+a9uo/9o26r+2yW8XlVjFxcVh06ZN3m4GIYQQQkib0m7mEBJCCCGEkKahgJAQQgghxM9RQEgIIYQQ4ucoICSEEEII8XMUEBJCCCGE+DkKCAkhhBBC/BwFhIQQQgghfq5dJaYmhBBCCCHuoxFCQgghhBA/RwEhIYQQQoifo4CQEEIIIcTPUUBICCGEEOLnKCAkhBBCCPFzFBASQgghhPg5CggJIYQQQvwcBYSEEEIIIX6OAkJCCCGEED9HASEhhBBCiJ+jgJAQQgghxM9RQEgIIYQQ4ucoICSEEEII8XMUEBJCCCGE+DkKCAkhhBBC/BwFhIQQQgghfo4CwkbcuHEDS5YsQVpaGvr27Yv58+cjNzfX280idfzyyy+YN28e+vXrh169emHixInYt2+f3T5GoxF//OMfMWzYMPTp0wfTpk3Dd999550Gk3qdPn0aPXr0wMaNG+22U//5NqPRiOzsbAwfPhx9+vTB+PHjsXfvXod9qA990/nz5zF37lwkJycjKSkJjz/+OE6fPm23D/Vf+0YBYQOKi4uRmZmJH374AZmZmViwYAFOnz6NmTNnorCw0NvNI9UuXbqEWbNm4eLFi5g7dy5WrFgBjUaDlStXYvv27bb9li1bhm3btmHEiBFYuXIlRFHEU089hePHj3ux9aQ2k8mEF198EaIoOjxG/ee7JEnCggULsGXLFgwfPhwrV65ESEgIVq1ahY8++si2H/Whb8rJycHMmTNx+vRpPPXUU3j22Wdx5coV23XVivqvnZNJvbKzs+XExET59OnTtm0XL16U77vvPvnVV1/1YstIbfPmzZOTkpLkW7du2baJoig/8sgjcp8+feSysjL5+++/lxMSEuTt27fb9qmoqJBHjBghT5482QutJs5kZ2fLPXv2lBMSEuS3337btp36z7ft2bNHTkhIkHfu3GnbJoqiPHHiRHngwIGyJEnUhz4sKytLTkhIkH/66SfbtpycHLlnz57ys88+K8syfQb9AY0QNuDAgQNISkrC/fffb9uWkJCAtLQ0HDhwwIstI1aiKOKf//wnMjIyEB0dbdvOsizGjBkDg8GACxcu4LPPPoMgCHj44Ydt+2i1WkydOhVnzpxBTk6OF1pPajt//jy2bNmCZ555xuEx6j/ftmfPHsTFxWHGjBm2bSzL4rnnnsOMGTNQWVlJfejD8vLyoNVqkZycbNvWqVMndOrUyTZCSP3X/lFAWI+SkhLk5eXZBYNWPXv2RH5+PvLz873QMlIby7L461//ihUrVjg8dvfuXQAAx3E4c+YMunbtCq1Wa7dPz549AQBnzpxp/caSepnNZrzwwgsYOnQoRo8e7fA49Z/vMpvNOHXqFAYOHAiWVb5SKioqIMsyhgwZgkWLFiEwMJD60Id17twZlZWVuH37tm2b0WhEQUEBIiMjAdBn0B9QQFgP6wej9qiTVVRUFADg5s2bHm0TccQwDOLi4tCxY0e77ZWVldizZw8CAwPRo0cP3L59u8G+vHHjhkfaS5zbvHkzrl+/jqysLKePU//5rmvXrsFsNiM2NhY7duzA4MGD8cADDyAtLQ3vvvsuZFkGQH3oy+bNm4eOHTti2bJlOHfuHC5duoQVK1agrKwMc+fOBUD95w94bzfAV1VUVAAANBqNw2MBAQEAlKCD+B5ZlvHSSy+hoKAAzz77LNRqNSoqKhrsS4PB4OlmkmoXL17Eu+++izVr1iAqKgplZWUO+1D/+S5rf3366acoLS3FM888g8jISOzfvx/Z2dmorKzE0qVLqQ99WExMDObPn4+1a9di0qRJtu1ZWVkYMmQIAPoM+gMKCOshSRIAZQSqPtbbI8R3yLKMNWvW4PPPP0dqaiqefvppl46jvvQOURTx4osvon///pg6dWqTn4f6z3tMJhMAZaRw7969SEhIAACMGTMGs2bNwrZt25CZmdno81Afes+GDRuwadMm9OnTB48++ih4nsenn36KP/zhD5BlGTNnzmz0Oaj/2j4KCOsRGBgIwPmvHqPRaLcP8Q1msxmrVq3CgQMH0KdPH2zatAk8r/yJa7VaW7/VRn3pXVu3bsXFixexa9cu25xP64iTwWDA3bt3odPpqP98mHXUKCkpyRYMWk2ePBnHjx/HiRMnqA99VGlpKd577z0kJCRg586dUKlUAIBx48Zhzpw5WL9+PUaOHEn95wcoIKxHbGwsAKCgoMDhMetiEmfzKYh3GI1GLF68GEeOHEFKSgreffdduwtUhw4dqC990NGjR2E2mzFt2jSHx7Zu3YqtW7di/fr11H8+zHruw8PDHR4LCwsDoNxupD70TTk5OTCZTBg7dqwtGLSaMmUKvvvuO5w4cYL6zw9QQFgPvV6P+Ph4nD171uGxs2fPIiYmxrb6iniXxWKxBYPDhg3Dhg0boFar7fbp2bMn/vrXv8JoNNrmvACw9W+vXr082maiWLlyJUpLS+223bx5Ey+++CImTJiAiRMnolu3bjhx4gT1n4+KiIjAPffcg99++83hsWvXrgEA7rnnHvoM+ijrtdI6Tao264IgURSp//wA3fRvwOjRo3HixAmcO3fOtu3XX3/Fjz/+iHHjxnmxZaS2jRs34siRIxg+fDg2btzoEAwCSl+aTCb85S9/sW2rrKzE7t270bt3b8THx3uyyaTa/fffj4EDB9r9k5SUBACIi4vDwIEDERUVRf3n48aNG4dLly7h0KFDtm0mkwm7du1CWFgY+vXrR33oo7p3747o6Gjs27fPtpgSUALEjz/+GDzPo3///tR/foBGCBswZ84c7Nu3D3PmzMGTTz4JlmWxfft2REdHY86cOd5uHgFQWFiIbdu2ged5pKen4+DBgw77DBgwABkZGcjIyMBrr72GmzdvonPnzvj4449x69YtvPrqq15oOXEH9Z9vmz9/Pr766issXboUM2fORGxsLPbt24dLly7hjTfegCAI1Ic+imVZZGVlYfHixZgyZQoefvhh8DyPgwcP4uTJk1i8eDFiYmIQExND/dfOMbJ1TJg4lZeXh/Xr1+OHH36ASqVCSkoKVqxYgbi4OG83jQA4dOgQFi5c2OA+W7ZsweDBg1FRUYHs7GwcPHgQBoMBiYmJWLJkCVJTUz3UWuKKS5cuYezYsVi0aBEWL15s207959uKiorw1ltv4dChQygvL0dCQgIWLFiAYcOG2fahPvRd//znP/HOO+/g1KlTsFgs6N69O2bPno0JEybY9qH+a98oICSEEEII8XM0h5AQQgghxM9RQEgIIYQQ4ucoICSEEEII8XMUEBJCCCGE+DkKCAkhhBBC/BwFhIQQQgghfo4CQkIIIYQQP0cBISGEEEKIn6OAkBBCCCHEz1FASAghhBDi53hvN4AQQppq48aN+K//+i+X9o2NjcXhw4dbuUWuGz58OEpLS/Gvf/3L7WOPHTuGzMxMZGZmYvXq1S3arsTERPzud7/D/v37W/R5CSG+jQJCQkiblZKSgkWLFtlt27t3L65fv47MzEwEBQXZtuv1ek83jxBC2gwKCAkhbVZqaipSU1Ptth0/fhzXr1/H7Nmz0bFjRy+1jBBC2haaQ0gIIYQQ4ucoICSE+I1jx44hMTERu3btwtKlS9G7d2+kp6fjxIkTWLVqFRITE3H+/HmH4xITEzFhwgS7bbIs48MPP8SkSZPQu3dv9O/fH/Pnz8e5c+ea3L6Kigq88847mDBhApKTk9GrVy+MGjUKr732GiorK50e8+GHH2LUqFHo1asXxo8fj48++sjpfmfPnsWCBQuQmpqK3r17Y8KECfjwww8hy3Kj7Tpw4ACmT5+O/v37Izk5GVOmTMGuXbtcOpYQ0jbQLWNCiN955513oNVq8dhjj+G3335Djx493H6OlStXYv/+/ejevTumT58Og8GAL774AtOnT8fmzZsxYMAAt57PYrHgiSeewC+//IL09HSkp6ejoqIChw8fxtatW3Ht2jW8/fbbdsd88cUXuHv3LsaOHYuMjAx89dVXyMrKwrVr17B8+XLbft9++y0WLVoEQRAwatQohIWF4ejRo1izZg3OnTuHl19+ud52HTx4EMuWLUPnzp0xadIksCyLr776CmvXrkVRUREWLlzo3okjhPgkCggJIX6noqIC+/btQ2RkZJOO/+KLL7B//36MGzcO//mf/wmeVy6lTz31FKZOnYqVK1fi0KFDUKlULj/nl19+iVOnTmH+/PlYsmSJbfvy5cvx+9//HocOHYLBYIBGo7E9VlBQgE2bNmHEiBEAgEWLFmHWrFnYunUrJk+ejK5du8JgMGDVqlXQ6XT45JNPbPMqly9fjueeew4ff/wxRo4ciSFDhjht13vvvQetVos9e/ZAp9PZXmf06NHYuXMnFixYAIZh3DuBhBCfQ7eMCSF+p2/fvk0OBgFg9+7dAIDVq1fbgkEAiIuLw/Tp03H79m18//33bj1njx49sG7dOjz++ON223U6HXr06AFRFFFSUmL3WEpKii0YBIDQ0FA888wzkCQJn3/+OQDg8OHDuHv3LubOnWu3yIZlWSxbtgwAsGfPnnrbJcsyjEaj3a1wnU6H3bt346uvvqJgkJB2gkYICSF+JzY2tlnHnz17Fmq1Gh988IHDY1euXAEAnD9/HkOHDnX5Obt06YIuXbqgqqoKp06dwpUrV3D16lWcPXsWx48fBwCIomh3zAMPPODwPL179wYAXLhwAQBw5swZW5s3btzosD/HcbZ9nXnkkUeQlZWFWbNmITExEYMHD8aQIUPQt29fsCyNKRDSXlBASAjxO2q1ulnHl5WVwWKxNJgUu+5oXmMkScLmzZuxfft227Hh4eFITk5GbGwsLl265LCIIyIiwuF5AgMDAcC2CKWsrAwAbCOG7rZ1+vTpCA8Px/vvv48TJ07g4sWL2LJlC6Kjo7Fq1SqMHTvWrfdJCPFNFBASQghgu/VZN+gyGAwO+2q1WgQGBuKbb75psdfftm0b3nrrLaSkpGDevHm47777bLe1586di0uXLjkcU1pa6rAtPz8fABAcHGxrKwDs2LHD7YUuVg8++CAefPBBlJaW4tixYzh8+DA+++wzLFu2DN26dUNCQkKTnpcQ4jtovJ8QQgAIggAADuldrl696rBvYmIibt26hYKCAofHvv76a2RnZzd4G9aZAwcOgOM4/OlPf8LgwYNtwaAsy7h8+bLtv2s7ffq0w/OcPHkSANCzZ09bW4GaW8e1FRcX45VXXqm3TJ3JZMKf/vQn7NixAwAQFBSEBx98EOvXr7fNVbS+HiGkbaOAkBBCAHTt2hWAEtBZSZKEd99912HfSZMmQZZlvPzyyzCZTLbt+fn5WLNmDf77v//bNjLnKrVaDVEUcffuXbvtmzZtwvXr1wEoqWlqO3r0qF1Alp+fjy1btkClUmH8+PEAlNE9nU6H9957zza/0er111/H+++/7zToBQCVSoUDBw5gw4YNyMvLs3vM2qYOHTq49T4JIb6JbhkTQgiAcePGYcOGDdi6dSvy8vLQsWNH/OMf/0BpaalD0DN58mQcPnwYX375JS5evIiMjAxYLBZ88cUXKC4uxrJlyxAfH+/W6z/00EP4+eefMWPGDIwZMwaCIODYsWM4e/YswsPDUVhYiOLiYrtjYmNj8fjjj2PcuHEQBAF///vfcefOHaxZswYxMTEAlFG9devWYfny5Zg0aRJGjhyJqKgoHD9+HKdPn0avXr3w5JNP1tuupUuXYuHChZg0aRJGjx6N4OBgnDlzBj/++CNSUlIwaNAgt94nIcQ3UUBICCFQFmi8//77ePPNN3HkyBEIgoCMjAysWLECTz/9tN2+DMPg7bffxgcffIBPP/0Un3zyCQICAtCtWzc88cQTGDlypNuv/+ijj9qqn3zyySfQ6/Xo0qUL3nzzTajVaixcuBDffvstkpOT7Y6xWCzYuXMnCgsLkZCQgLVr1zq8/pgxYxATE4PNmzfj6NGjMBgMiI2NxYIFCzBnzhzbQhRnRowYga1bt2LLli34+uuvbQHywoULMW/ePFppTEg7wchUe4gQQgghxK/RTztCCCGEED9HASEhhBBCiJ+jgJAQQgghxM9RQEgIIYQQ4ucoICSEEEII8XMUEBJCCCGE+DkKCAkhhBBC/BwFhIQQQgghfo4CQkIIIYQQP0cBISGEEEKIn6OAkBBCCCHEz1FASAghhBDi5yggJIQQQgjxcxQQEkIIIYT4uf8P3xcbKdRZe1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.dpi\":120})\n",
    "curvaCalibracion = sns.regplot(testLabel.to_numpy().flatten(), y_predict.flatten(), color='r')\n",
    "curvaCalibracion.set_xlabel('True labels', fontsize = 12)\n",
    "curvaCalibracion.set_ylabel('Predicted labels', fontsize = 12)\n",
    "curvaCalibracion.set_title('Curva de calibracion', fontsize = 12)\n",
    "curvaCalibracion.text(.05, .9, 'r2={:.2f}, p={:.2g}'.format(r2, p),\n",
    "            transform=curvaCalibracion.transAxes,fontdict=dict(color='red',size=10),\n",
    "                      bbox=dict(facecolor='yellow',alpha=0.3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb1f6ad",
   "metadata": {},
   "source": [
    "### Residual Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9ec42f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(41.87500000000001, 0.5, 'Predicted labels')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.dpi\":120})\n",
    "jointp = sns.jointplot(x = testLabel.values.flatten(), y = y_predict.flatten(), kind=\"reg\", color ='g')\n",
    "jointp.ax_joint.set_xlabel('True labels', fontsize = 12)\n",
    "jointp.ax_joint.set_ylabel('Predicted labels', fontsize = 12)\n",
    "#el de arriba es el label, el del costado es la prediccion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b777a349",
   "metadata": {},
   "outputs": [],
   "source": [
    "info_histo = (testLabel.to_numpy().flatten()-y_predict.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aea88af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribucion gaussiana del error')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.dpi\":120})\n",
    "histo = sns.histplot(x=info_histo, color ='orange')\n",
    "histo.set_xlabel('Desviacion', fontsize = 12)\n",
    "histo.set_ylabel('Frecuencia', fontsize = 12)\n",
    "histo.set_title('Distribucion gaussiana del error', fontsize = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451ba46f",
   "metadata": {},
   "source": [
    "***Conclusión:*** podemos observar que en éste modelo hemos obtenido mejores resultados. Dado que la métrica nos da un resultado mucho mejor y se observa lo mismo con la calibración del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfde6da4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}