AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras import Input\n",
    "from tensorflow.python.keras.models import Model\n",
    "from tensorflow.python.keras.engine.keras_tensor import KerasTensor\n",
    "from tensorflow.python.keras.engine.functional import Functional\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.python.keras.regularizers import l2\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the MNIST data class and call it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original dataset and modified dataset with half resolution is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 784)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c0b60e3280>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "data = mnist.MNISTData()\n",
    "print(data.x_test.shape)\n",
    "\n",
    "ax = plt.subplot()\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.imshow(data.x_test[100].reshape(28,28), cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_factor = 0.5\n",
    "x_train=data.x_train\n",
    "x_test=data.x_test\n",
    "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
    "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
    "\n",
    "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
    "x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 2))\n",
    "for i in range(1, n + 1):\n",
    "    ax = plt.subplot(1, n, i)\n",
    "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 121)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c994211850>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "\n",
    "data_zoom = mnist.MNISTData(zoom_factor=2/5)\n",
    "test = data_zoom.x_test\n",
    "print(test.shape)\n",
    "ax = plt.subplot()\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.imshow(test[0].reshape(11,11))#, cmap='gray_r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_center(crop):\n",
    "        x = test[1]\n",
    "        y = test[2]  \n",
    "        startx = x//2-(crop//2)\n",
    "        starty = y//2-(crop//2)    \n",
    "        self.x_test= self.x_test[starty:starty+crop,startx:startx+crop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_factor = 0.5\n",
    "x_train=data_zoom.x_train\n",
    "x_test=data_zoom.x_test\n",
    "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
    "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
    "\n",
    "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
    "x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 2))\n",
    "for i in range(1, n + 1):\n",
    "    ax = plt.subplot(1, n, i)\n",
    "    plt.imshow(x_test_noisy[i].reshape(14, 14))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "cec58d97b4c05e29e9afdeb102569f20",
     "grade": false,
     "grade_id": "cell-6ee6a27aac2a7e5b",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "# Autoencoder using Keras\n",
    "The aim of this class is to implement a simple autoencoder for the MNIST data and then build an autoencoder that is\n",
    "able to classify MNIST data in its latent dimension.\n",
    "\n",
    "Code partially adapted from [Keras Documentation](https://blog.keras.io/building-autoencoders-in-keras.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "8f093c3123887a615df510d5cb9d3bee",
     "grade": false,
     "grade_id": "cell-b3452809a3ed4ee3",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Autoencoder:\n",
    "    \"\"\"Autoencoder Class\"\"\"\n",
    "\n",
    "    def __init__(self, data: mnist.MNISTData, num_classes=10):\n",
    "        self.x_train = data.x_train\n",
    "        self.x_test = data.x_test\n",
    "        self.y_train = data.y_train\n",
    "        self.y_test = data.y_test\n",
    "\n",
    "        self.num_classes = num_classes\n",
    "        self.input_shape = self.x_train[0].shape\n",
    "        self.input = Input(shape=self.input_shape, name='encoder_input')\n",
    "        self.latent_dim = 2\n",
    "\n",
    "        self.encoder = None\n",
    "        self.encoder_model = None\n",
    "        self.decoder = None\n",
    "        self.latent_classifier = None\n",
    "        self.autoencoder = None\n",
    "\n",
    "        self.history = None\n",
    "\n",
    "        \n",
    "\n",
    "    def build_encoder(self):\n",
    "        \"\"\"Build your encoder architecture and store the output in self.encoder. \n",
    "        The final encoding dimension is 2.\"\"\"\n",
    "        self.encoder = Dense(32,  activation='relu')(self.input)\n",
    "        self.encoder = Dense(16,  activation='relu')(self.encoder)   \n",
    "        self.encoder = Dense(8,  activation='relu')(self.encoder)      \n",
    "   \n",
    "\n",
    "        self.encoder = Dense(self.latent_dim,  activation='relu', name = 'encoder_output')(self.encoder)      \n",
    "        \n",
    "        #building a model for the encoder in order to be able to predict and plot the latent dimension\n",
    "        self.encoder_model = Model(self.input, self.encoder, name='encoder')\n",
    "\n",
    "    def build_decoder(self):\n",
    "        \"\"\"Build the decoder architecture.\"\"\"\n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the decoder!\")\n",
    "        self.decoder = Dense(8, activation='relu')(self.encoder)\n",
    "        self.decoder = Dense(16,activation='relu')(self.decoder)\n",
    "        self.decoder = Dense(32,activation='relu')(self.decoder)\n",
    "        self.decoder = Dense(121,activation='sigmoid', name='ecoder_output')(self.decoder)\n",
    "\n",
    "    def build_classifier(self):\n",
    "        \"\"\" Building the classifier architecture.\"\"\"\n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the classifier!\")\n",
    "        self.latent_classifier = Dense(8, activation='relu')(self.encoder)\n",
    "        self.latent_classifier = Dense(16, activation='relu')(self.latent_classifier)\n",
    "        self.latent_classifier = Dense(32, activation='relu')(self.latent_classifier)\n",
    "\n",
    "\n",
    "\n",
    "        self.latent_classifier = Dense(self.num_classes, activation='softmax',name='classifier_output')(self.latent_classifier)\n",
    "\n",
    "    def build_autoencoder(self, use_latent_classifier=False):\n",
    "        \"\"\" Build the whole autoencoder Model from self.encoder and self.decoder.\"\"\"\n",
    "        self.dir()\n",
    "        \n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the autoencoder!\")\n",
    "        if self.decoder is None:\n",
    "            raise RuntimeError(\"The decoder has to be built before you can build the autoencoder!\")\n",
    "        if use_latent_classifier:\n",
    "            if self.latent_classifier is None:\n",
    "                raise RuntimeError(\"If you want to use the option with the latent classifier, you have to build it \"\n",
    "                                   \"beforehand!\")\n",
    "            self.autoencoder = Model(self.input, outputs=[self.decoder, self.latent_classifier])\n",
    "            self.autoencoder.compile(loss=['mse', 'categorical_crossentropy'], loss_weights=[1, 0.1], optimizer='adam',\n",
    "                                     metrics=\"accuracy\")\n",
    "            self.autoencoder.save('model/AE_model/KERAS_check_model_w_classifier.h5')\n",
    "\n",
    "        else:\n",
    "            self.autoencoder = Model(self.input, outputs=self.decoder, name = 'autoencoder')\n",
    "            self.autoencoder.compile(loss='mse', optimizer='adam') \n",
    "            self.autoencoder.save('model/AE_model/KERAS_check_model_wo_classifier.h5')\n",
    "        self.autoencoder.summary()\n",
    "\n",
    "    def fit_data(self, batch_size=256, epochs=40, use_latent_classifier=False):\n",
    "        \"\"\"Write the fit function for the autoencoder. \n",
    "        Storing the fit history in self.history to be able to plot the fitting scores.\"\"\"\n",
    "        if use_latent_classifier:\n",
    "            self.history = self.autoencoder.fit(self.x_train, [self.x_train, self.y_train],\n",
    "                                                validation_data=(self.x_test, [self.x_test, self.y_test]),\n",
    "                                                batch_size=batch_size, epochs=epochs,\n",
    "                                                shuffle=True,\n",
    "                                                )\n",
    "        else:                          \n",
    "            self.history = self.autoencoder.fit(self.x_train, self.x_train,\n",
    "                                                validation_data=(self.x_test, self.x_test),\n",
    "                                                batch_size=batch_size, epochs=epochs,\n",
    "                                                shuffle=True\n",
    "                                                )\n",
    "\n",
    "        self.history = self.history.history\n",
    "\n",
    "    def dir(self):\n",
    "        \"\"\"Creation of the folders path to store the results.\"\"\"\n",
    "        dir = os.path.join(\"images\")\n",
    "        if not os.path.exists(dir):\n",
    "            os.mkdir(dir)\n",
    "        dir2 = os.path.join(\"./images/AE\")\n",
    "        if not os.path.exists(dir2):\n",
    "            os.mkdir(dir2)\n",
    "        dir3 = os.path.join(\"model\")\n",
    "        if not os.path.exists(dir3):\n",
    "            os.mkdir(dir3)\n",
    "        dir4 = os.path.join(\"./model/AE_model\")\n",
    "        if not os.path.exists(dir4):\n",
    "            os.mkdir(dir4)\n",
    "        \n",
    "    \n",
    "    def plot_score(self, model_name=None):\n",
    "        \"\"\"Plots the scores achieved during the fitting.\"\"\"\n",
    "        plt.plot(self.history['loss'])\n",
    "        plt.plot(self.history['val_loss'])\n",
    "        plt.ylabel('Model Accuracy')\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.legend(['Train', 'Test'], loc='best')\n",
    "        plt.title('Accuracy of {model_name}'.format(model_name=model_name))\n",
    "        \n",
    "        plt.savefig('./images/AE/Accuracy of {model_name}.png'.format(model_name=model_name))\n",
    "        \n",
    "        plt.show()\n",
    "\n",
    "    def plot_latent_dimension(self, model_name = None):\n",
    "        \"\"\"Plots the latent dimension of the autoencoder.\"\"\"\n",
    "        prediction = self.encoder_model.predict(self.x_test)\n",
    "        fig = plt.figure(figsize=(10, 10))\n",
    "        fig.patch.set_facecolor(\"white\")\n",
    "        plt.scatter(prediction[:, 0], prediction[:, 1], c=np.argmax(self.y_test, axis=1), cmap=\"Set3\")\n",
    "        plt.colorbar()\n",
    "        plt.title('Latent space {model_name}'.format(model_name=model_name))\n",
    "\n",
    "\n",
    "        plt.savefig('./images/AE/Latent dimension of {model_name}.png'.format(model_name=model_name))\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def plot_reco(self, use_latent_classifier = False):\n",
    "        \"\"\"Plotting reconstructed images\"\"\"\n",
    "        \n",
    "        n = 6\n",
    "        plt.figure(figsize=(10, 3))\n",
    "        reco_imgs = self.autoencoder.predict(self.x_test)\n",
    "        \n",
    "        if use_latent_classifier :\n",
    "            raise RuntimeError(\"You are considering the autoencoder with classifier!\")\n",
    "        else:\n",
    "        \n",
    "            for i in range(n):\n",
    "                # Display original MNIST\n",
    "                ax = plt.subplot(2, n, i + 1)\n",
    "                plt.imshow(self.x_test[i].reshape(11,11), cmap='gray_r')\n",
    "                ax.get_xaxis().set_visible(False)\n",
    "                ax.get_yaxis().set_visible(False)\n",
    "\n",
    "                # Display reconstructed MNIST            \n",
    "                ax = plt.subplot(2, n, i + n + 1)\n",
    "                plt.imshow(reco_imgs[i].reshape(11,11), cmap='gray_r')\n",
    "                ax.get_xaxis().set_visible(False)\n",
    "                ax.get_yaxis().set_visible(False)\n",
    "            plt.savefig('./images/AE/reconstructed images.png')\n",
    "            plt.show()\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is built the standard autoencoder, *without classifier* here, and the MNIST data class called before is used. \n",
    "The model is fitted and adequate plots are reproduced, \n",
    "as well as the comparison between orginal MNIST dataset and reconstructed images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 121)]             0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 32)                3904      \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 18        \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 8)                 24        \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 16)                144       \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 121)               3993      \n",
      "=================================================================\n",
      "Total params: 9,291\n",
      "Trainable params: 9,291\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0723 - val_loss: 0.0546\n",
      "Epoch 2/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0500 - val_loss: 0.0473\n",
      "Epoch 3/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0459 - val_loss: 0.0446\n",
      "Epoch 4/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0435 - val_loss: 0.0424\n",
      "Epoch 5/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0420 - val_loss: 0.0413\n",
      "Epoch 6/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0411 - val_loss: 0.0407\n",
      "Epoch 7/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0404 - val_loss: 0.0401\n",
      "Epoch 8/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0399 - val_loss: 0.0396\n",
      "Epoch 9/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0394 - val_loss: 0.0391\n",
      "Epoch 10/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0390 - val_loss: 0.0388\n",
      "Epoch 11/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0386 - val_loss: 0.0385\n",
      "Epoch 12/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0383 - val_loss: 0.0381\n",
      "Epoch 13/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0381 - val_loss: 0.0380\n",
      "Epoch 14/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0378 - val_loss: 0.0378\n",
      "Epoch 15/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0376 - val_loss: 0.0376\n",
      "Epoch 16/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0374 - val_loss: 0.0374\n",
      "Epoch 17/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0372 - val_loss: 0.0372\n",
      "Epoch 18/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0370 - val_loss: 0.0371\n",
      "Epoch 19/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0369 - val_loss: 0.0369\n",
      "Epoch 20/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0367 - val_loss: 0.0367\n",
      "Epoch 21/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0365 - val_loss: 0.0366\n",
      "Epoch 22/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0364 - val_loss: 0.0364\n",
      "Epoch 23/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0362 - val_loss: 0.0363\n",
      "Epoch 24/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0361 - val_loss: 0.0362\n",
      "Epoch 25/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0359 - val_loss: 0.0360\n",
      "Epoch 26/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0358 - val_loss: 0.0360\n",
      "Epoch 27/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0357 - val_loss: 0.0358\n",
      "Epoch 28/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0356 - val_loss: 0.0358\n",
      "Epoch 29/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0355 - val_loss: 0.0356\n",
      "Epoch 30/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0354 - val_loss: 0.0355\n",
      "Epoch 31/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0354 - val_loss: 0.0356\n",
      "Epoch 32/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0353 - val_loss: 0.0354\n",
      "Epoch 33/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0352 - val_loss: 0.0353\n",
      "Epoch 34/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0352 - val_loss: 0.0353\n",
      "Epoch 35/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0351 - val_loss: 0.0352\n",
      "Epoch 36/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0351 - val_loss: 0.0352\n",
      "Epoch 37/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0350 - val_loss: 0.0352\n",
      "Epoch 38/40\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0349 - val_loss: 0.0351\n",
      "Epoch 39/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0348 - val_loss: 0.0350\n",
      "Epoch 40/40\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0348 - val_loss: 0.0349\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAA8d0lEQVR4nO3deXxcdb3/8dc7k8nSNkubpkuSlnQFCi0tlLLIJogCgq0LAhcVlSsXFfFe5QLqRRFX/HnlylWvoiCLIigKFgVBhLILtFCghQKhC03XNG3WZpvk8/vjnLTTME0mpZOZdj7Px2Oac75nmc85mc4n3+/3nO+RmeGcc84lKyfdATjnnNu3eOJwzjk3KJ44nHPODYonDuecc4PiicM559ygeOJwzjk3KJ44XMaQ9C5Jb0hqkbQg3fFkAkkmaeoQv+dXJf2qn+WflPTEUMaUDEmLJP1rivY9MfxcRsL5sZIek9Qs6b8HOmf7m9x0B+B2krQIOAwYZ2YdaQ4nHa4BfmJmP+5vpT09T5IMmGZmNe8oyv2cmX23d1pSNbAKiJpZLNXvLWk18K9m9lCq32swzOwtYERc0UXAFqDYsvBmOK9xZIjwP+jxgAEfGOL3zpQ/IA4Alve3QjrP074kg36n+6sDgFfeadJQYJ/7Ht7nAt6PfQL4J3AzcEH8AkkTJP1JUp2kekk/iVv2GUmvhlXmVyQdHpbv0sQh6WZJ3w6nT5JUK+kKSRuBX0saKekv4XtsC6er4rYfJenXktaHy+8Jy5dJOituvaikLZLmJDrIMN4aSVslLZRUEZa/CUwG7g2bBPL34Dzt0lQR36Qi6bGw+MVw/+f0F0+47CBJfw+XvSbpo33O508l/TU8989ImhK3/JC4bTdJ+mpYni/pf8LzuD6czo/b7j8lbQiXfbrP8eVL+qGkt8J9/lxS4e5+pwnO/RpJR4TT54efkUPC+QvjfqdXS/pNuFnveWsIz9sxcfv7YfhZWCXp9LjyivBcbg3P7Wf6nLdvx82fJKk2nL4NmMjOz8DlfY8hXG++pKWSmiS9Kem0BOtMkfRw+P9li6TfSiqNW36FpHXh7+41SaeE5fMkLQ73vUnSj8Ly6vB85Uq6meCzd3kY53v6nDMkHS3pKUkNkl6UdFLcskWSviPpSWA7wed+32Jm/sqAF1ADfA44AugCxoblEeBF4DpgOFAAHBcuOxtYBxwJCJgKHBAuM2Bq3P5vBr4dTp8ExIBrgXygECgDPgwMA4qAPwD3xG3/V+BOYCQQBU4Myy8H7oxbbz7w8m6O8WSC6v3h4fv+L/BY3PLVwHv25DyFyxYRNHP0zn8SeCJuvu852W084bleC3yKoEl3TrjujLjzWQ/MC5f/FrgjXFYEbAC+HP6+ioCjwmXXECS+MUA58BTwrXDZacAm4NDw/W+Pjzn8DCwERoX7vBf43u5+pwnO3a3Al8PpG4A3gc/GLfuPcPpq4DfhdHUYQ26f89oFfIbg8/lZYD2gcPljwM/CY58N1AEn9/0cxsVdm+xnIDzfjcCpBH/4VgIH9f39E/xfODU8F+VhTP8TLjsw/N1WxB3jlHD6aeDj4fQI4OhE5yHBccSfs0qCz8YZYYynhvPlcXG+BRxC8NmJpvv7Z9DfV+kOwF8GcFz4H3F0OL8i7j/xMeF/vNwE2z0AfHE3+xwocXQCBf3ENBvYFk6PB3qAkQnWqwCaCdp6Ae4CLt/NPm8EfhA3PyI87upwfqAvjd2ep3B+xxdHOP9J+k8cu40HOAd4vM/7/wL4Rtz5/FXcsjOAFeH0ecALuzmGN4Ez4ubfB6wOp28Cvh+3bHpvzAR/GLQSfsHFfTZWDeJ3eiGwMJx+FfhXdia7NcDh4fTVDJw4auLmh4XrjAMmAN1AUdzy7wE39/0cxsU9mMTxC+C63Szb5fffZ9mC3t9JeD43A++hz5c2QYL5Zu9nLK58l/OQ4Djiz9kVwG0J/q9eEBfnNbs7xn3h5U1VmeEC4EEz2xLO387OZpgJwBpL3DE5geCLaE/UmVl774ykYZJ+ETZnNBH8BypVcBXJBGCrmW3ruxMzWw88CXw4bAo4neCv70QqCL6gerdtIfhLrDLJmPs7T3uiv3gOAI4KmxoaJDUA5xN8OfbaGDe9nZ2dp/39XnZ5z3C6Im7Z2j7LepUTfEEviYvnb2F5r11+pwk8ChwvaTxBTeH3wLsU9BuVAEv72bavHcduZtvDyRHhMWw1s+Y+x5Hs73ggSX3mFVz1dEfYHNUE/AYYHcZbA/w7wZf95nC93t/BhQQJe4Wk5ySduQcxHgCc3eezcxzBH2C91ibcch/hHWhpFrZRfxSIhG3TEFSvSyUdRvABmygpN0HyWAtMIbHtBF80vcYBtXHzfTv1vkxQhT/KzDZKmg28QPCX7lpglKRSM2tI8F63EPz1mgs8bWbrdhPTeoL/VABIGk7QRLa79Ylbt9/zZGYvEvxF3veY+9NfPGuBR83s1IFiS2AtcO4A79l7EcDEsAyC5q0JcetOjJveArQBh/Rzfvv+TnddaFYjaTvwBYImuabwXF5EUDPrGew+E1hP8FkpikseE9n5Ox7odzTQ+/X3mY/33XBfM81sq4LLu3f0DZrZ7cDtkooJajHXEjRRvQGcp6DD+kPAXZLKkni/vjHeZmaf6WedwZ7XjOI1jvRbQFC1n0HQPDQbOBh4nKAj+FmCL5TvSxouqUDSu8JtfwVcJukIBaZK6v0iXAr8i6RI2Hl44gBxFBF8MTVIGgV8o3eBmW0A7gd+pqATPSrphLht7yHoJ/giQVv57vwO+JSk2Qo6hL8LPGNmqweIDQY+TxAc84fC2tNUgr8e421i147I/uL5CzBd0sfD441KOlLSwUnE+hdgvKR/V9ChXSTpqLj3/C9J5ZJGA18n+GsYghrAJyXNkDSMXX8HPcAvgeskjQGQVCnpfUnEE+9R4JLwJwTNJvHzfdURNFMm1YFrZmsJ+m2+F35WZxH8HnqPcSlwhoKLLcYR/OUfr+/vqK8bCX5np0jKCc/BQQnWKwJagEZJlcB/9i6QdKCkk8PfeTvB574nXPYxSeXh+W4IN0mUUPvzG+AsSe8L//8VKLgIoGrALfcRnjjS7wLg12b2lplt7H0R/HV0PsFf/GcRtMu+RVBrOAfAzP4AfIegyaaZ4At8VLjfL4bbNYT7uWeAOP6HoJN8C0Hn7d/6LP84Qfv/CoL24X/vXWBmbcAfgUnAn3b3BhZcm39VuO4Ggr8cd/eXeV/9nicFl59eR9DOv4mgFtS3yexq4Jaw+eCj/cUT/rX83nB+PUHTTG/Hc7/CbU8lOP8bgTeAd4eLvw0sBl4CXgaeD8sws/sJfg8PE1wE8HCfXV8Rlv8zbH55iKCWOBiPEnypPrab+b7Hsp3gM/ZkeN6OTuI9ziPoE1gP3E3QL9R7X8ZtBBd7rAYeJLjgIt73CBJrg6TLEsTzLMEFC9cRdJI/SlytMc43Cf6YaSS4sCP+c5kPfJ/gs76R4EKFr4TLTgOWS2oBfgycG36+kxYmz/nAVwkS71qCxLXffN/2XgXh3Dsi6evAdDP7WLpjcc6llvdxuHcsbNq6kKBW4pzbz+03VSeXHgpu7loL3G9mCZs7nHP7F2+qcs45Nyhe43DOOTcoWdHHMXr0aKuurk53GM45t09ZsmTJFjMr71ueFYmjurqaxYsXpzsM55zbp0hak6jcm6qcc84NiicO55xzg+KJwznn3KBkRR+Hc84NVldXF7W1tbS39zfg8P6hoKCAqqoqotFoUut74nDOuQRqa2spKiqiuroaSekOJ2XMjPr6empra5k0aVJS23hTlXPOJdDe3k5ZWdl+nTQAJFFWVjaompUnDuec2439PWn0GuxxeuLoxy1PrWbhi+sHXtE557KIJ45+/O7Zt7jXE4dzbojV19cze/ZsZs+ezbhx46isrNwx39nZ2e+2ixcv5tJLL01pfN453o/iwiiNbV3pDsM5l2XKyspYunQpAFdffTUjRozgsst2PtcqFouRm5v463vu3LnMnTs3pfF5jaMfJYVRmjxxOOcywCc/+UkuvvhijjrqKC6//HKeffZZjjnmGObMmcOxxx7La6+9BsCiRYs488wzgSDpfPrTn+akk05i8uTJXH/99XslFq9x9KOkMMoyTxzOZb1v3rucV9Y37dV9zqgo5htnHTKobWpra3nqqaeIRCI0NTXx+OOPk5uby0MPPcRXv/pV/vjHP75tmxUrVvDII4/Q3NzMgQceyGc/+9mk79fYHU8c/SjxpirnXAY5++yziUQiADQ2NnLBBRfwxhtvIImursTfVe9///vJz88nPz+fMWPGsGnTJqqqqt5RHJ44+lFSGGV7Zzdd3T1EI96q51y2GmzNIFWGDx++Y/qqq67i3e9+N3fffTerV6/mpJNOSrhNfn7+julIJEIsFnvHcfi3YT9KCoPqnNc6nHOZprGxkcrKSgBuvvnmIX1vTxz98MThnMtUl19+OV/5yleYM2fOXqlFDEZWPHN87ty5ticPcnpkxWY+dfNz/Olzx3L4xJEpiMw5l6leffVVDj744HSHMWQSHa+kJWb2tmt7U1rjkHSapNck1Ui6MsHyfEl3hsufkVQdlp8vaWncq0fS7HDZEZJeDre5XikcE6DYaxzOOfc2KUsckiLAT4HTgRnAeZJm9FntQmCbmU0FrgOuBTCz35rZbDObDXwcWGVmS8Nt/g/4DDAtfJ2WqmPobaryezmcc26nVNY45gE1ZrbSzDqBO4D5fdaZD9wSTt8FnJKgBnFeuC2SxgPFZvZPC9rYbgUWpCh+7+NwzrkEUpk4KoG1cfO1YVnCdcwsBjQCZX3WOQf4Xdz6tQPsEwBJF0laLGlxXV3dHh3AjsSx3ROHc871yuirqiQdBWw3s2WD3dbMbjCzuWY2t7y8fI/ePy83h8JoxGsczjkXJ5WJYx0wIW6+KixLuI6kXKAEqI9bfi47axu968ff8phon3uV3z3unHO7SuWd488B0yRNIvhyPxf4lz7rLAQuAJ4GPgI8HPZdICkH+ChwfO/KZrZBUpOko4FngE8A/5vCY/DE4ZwbcvX19ZxyyikAbNy4kUgkQm/LybPPPkteXl6/2y9atIi8vDyOPfbYlMSXssRhZjFJlwAPABHgJjNbLukaYLGZLQRuBG6TVANsJUguvU4A1prZyj67/hxwM1AI3B++UsYTh3NuqA00rPpAFi1axIgRI1KWOFLax2Fm95nZdDObYmbfCcu+HiYNzKzdzM42s6lmNi8+SZjZIjM7OsE+F5vZoeE+L+mtoaSKP5PDOZcJlixZwoknnsgRRxzB+973PjZs2ADA9ddfz4wZM5g1axbnnnsuq1ev5uc//znXXXcds2fP5vHHH9/rsfgghwMoKYzyynpPHM5ltfuvhI0v7919jpsJp38/qVXNjC984Qv8+c9/pry8nDvvvJOvfe1r3HTTTXz/+99n1apV5Ofn09DQQGlpKRdffPGgaymD4YljAN5U5ZxLt46ODpYtW8app54KQHd3N+PHjwdg1qxZnH/++SxYsIAFCxYMSTyeOAZQUhil1YdWdy67JVkzSBUz45BDDuHpp59+27K//vWvPPbYY9x777185zvf4eWX93LNKAH/JhxASWGQW33YEedcuuTn51NXV7cjcXR1dbF8+XJ6enpYu3Yt7373u7n22mtpbGykpaWFoqIimpubUxaPJ44BlAzzYUecc+mVk5PDXXfdxRVXXMFhhx3G7Nmzeeqpp+ju7uZjH/sYM2fOZM6cOVx66aWUlpZy1llncffdd3vneLr4eFXOuXS6+uqrd0w/9thjb1v+xBNPvK1s+vTpvPTSSymLyWscA/DE4Zxzu/LEMQBPHM45tytPHAMo9mdyOJe1suEJqTD44/TEMQCvcTiXnQoKCqivr9/vk4eZUV9fT0FBQdLbeOf4APJzIxREczxxOJdlqqqqqK2tZU+f57MvKSgooKqqauAVQ544kuB3jzuXfaLRKJMmTUp3GBnJm6qS4InDOed28sSRBE8czjm3kyeOJASJI5buMJxzLiN44khCcWHUL8d1zrmQJ44keFOVc87t5IkjCSWFUVo6YsS6e9IdinPOpV1KE4ek0yS9JqlG0pUJludLujNc/oyk6rhlsyQ9LWm5pJclFYTli8J9Lg1fY1J5DLDzJsCmdu/ncM65lCUOSRHgp8DpwAzgPEkz+qx2IbDNzKYC1wHXhtvmAr8BLjazQ4CTgPi2ovPNbHb42pyqY+jld48759xOqaxxzANqzGylmXUCdwDz+6wzH7glnL4LOEWSgPcCL5nZiwBmVm9m3SmMtV+eOJxzbqdUJo5KYG3cfG1YlnAdM4sBjUAZMB0wSQ9Iel7S5X22+3XYTHVVmGjeRtJFkhZLWvxOhwzwxOGccztlaud4LnAccH7484OSTgmXnW9mM4Hjw9fHE+3AzG4ws7lmNre8vPwdBeOJwznndkpl4lgHTIibrwrLEq4T9muUAPUEtZPHzGyLmW0H7gMOBzCzdeHPZuB2giaxlPLE4ZxzO6UycTwHTJM0SVIecC6wsM86C4ELwumPAA9bMIbxA8BMScPChHIi8IqkXEmjASRFgTOBZSk8BsCfyeGcc/FSNjqumcUkXUKQBCLATWa2XNI1wGIzWwjcCNwmqQbYSpBcMLNtkn5EkHwMuM/M/ippOPBAmDQiwEPAL1N1DL0KohHyc31odeecgxQPq25m9xE0M8WXfT1uuh04ezfb/obgktz4slbgiL0f6cBKCqM0bvfE4Zxzmdo5nnF82BHnnAt44kiSJw7nnAt44kiSJw7nnAt44kiSJw7nnAt44kiSP5PDOecCnjiSVFIYpbkjRnePpTsU55xLK08cSSrxmwCdcw7wxJE0H3bEOecCnjiS5InDOecCnjiSVDLME4dzzoEnjqR5jcM55wKeOJLkicM55wKeOJLkicM55wKeOJJUEI2Ql5vjl+M657KeJ45B8GFHnHPOE8egeOJwzrkkEoekL0gaORTBZDpPHM45l1yNYyzwnKTfSzpNkpLdebj+a5JqJF2ZYHm+pDvD5c9Iqo5bNkvS05KWS3pZUkFYfkQ4XyPp+sHE80554nDOuSQSh5n9FzCN4PngnwTekPRdSVP6205SBPgpcDowAzhP0ow+q10IbDOzqcB1wLXhtrkEj4292MwOAU4Cer+x/w/4TBjTNOC0AY9yL/HE4ZxzSfZxmJkBG8NXDBgJ3CXpB/1sNg+oMbOVZtYJ3AHM77POfOCWcPou4JSwBvFe4CUzezF8/3oz65Y0Hig2s3+GMd0KLEjmGPYGTxzOOZdcH8cXJS0BfgA8Ccw0s88CRwAf7mfTSmBt3HxtWJZwHTOLAY1AGTAdMEkPSHpe0uVx69cOsM/euC+StFjS4rq6uoEOMynFhVGa231odedcdstNYp1RwIfMbE18oZn1SDozNWGRCxwHHAlsB/4RJq/GZHdgZjcANwDMnTt3r3zT994E2NzeRemwvL2xS+ec2+ck01R1P7C1d0ZSsaSjAMzs1X62WwdMiJuvCssSrhP2a5QA9QQ1icfMbIuZbQfuAw4P168aYJ8p43ePO+dcconj/4CWuPmWsGwgzwHTJE2SlAecCyzss85C4IJw+iPAw2HfxQPATEnDwoRyIvCKmW0AmiQdHfaFfAL4cxKx7BWeOJxzLrmmKoVf5sCOJqoBtzOzmKRLCJJABLjJzJZLugZYbGYLCa7Uuk1SDUGt5txw222SfkSQfAy4z8z+Gu76c8DNQCFBbej+5A71nfPE4ZxzySWOlZIuZWct43PAymR2bmb3ETQzxZd9PW66HTh7N9v+huCS3L7li4FDk3n/vc0Th3POJddUdTFwLEFfQi1wFHBRKoPKVJ44nHMuiRqHmW0mbELKdp44nHMuicQRDvVxIXAIUNBbbmafTmFcGakgmkNeJMcTh3MuqyXTVHUbMA54H/AowSWwzakMKlNJorgw6s/kcM5ltWQSx1QzuwpoNbNbgPcT9HNkpZLCXK9xOOeyWjKJo/dbskHSoQQ36Y1JXUiZzcercs5lu2Qux70hfB7HfxHcsDcCuCqlUWWwksIodS0d6Q7DOefSpt/EISkHaDKzbcBjwOQhiSqDlRRGqalrGXhF55zbT/XbVGVmPcDl/a2TbUoKozRu96Yq51z2SqaP4yFJl0maIGlU7yvlkWWoksIozR0xenxodedclkqmj+Oc8Ofn48qMLG22Ki6MYgbN7TFKhkXTHY5zzg25ZO4cnzQUgewr4u8e98ThnMtGydw5/olE5WZ2694PJ/P5sCPOuWyXTFPVkXHTBcApwPMEz/vOOp44nHPZLpmmqi/Ez0sqBe5IVUCZrrd5yhOHcy5bJXNVVV+tQNb2e3iNwzmX7ZLp47iX4CoqCBLNDOD3qQwqk3nicM5lu2T6OH4YNx0D1phZbTI7l3Qa8GOCR8f+ysy+32d5PkFfyRFAPXCOma2WVA28CrwWrvpPM7s43GYRMB5oC5e9N3xmyJAojEaIRuSJwzmXtZJJHG8BG8LHvCKpUFK1ma3ubyNJEeCnwKkETw58TtJCM3slbrULgW1mNlXSucC17Lxv5E0zm72b3Z8fPkJ2yEnygQ6dc1ktmT6OPwA9cfPdYdlA5gE1ZrbSzDoJOtTn91lnPnBLOH0XcIokJbHvtPJncjjnslkyiSM3/OIHIJzOS2K7SmBt3HxtWJZwHTOLAY1AWbhskqQXJD0q6fg+2/1a0lJJV6Uj0XiNwzmXzZJJHHWSPtA7I2k+sCV1IQGwAZhoZnOALwG3SyoOl51vZjOB48PXxxPtQNJFkhZLWlxXV7dnUTSth/o331bsicM5l82SSRwXA1+V9Jakt4ArgH9LYrt1wIS4+aqwLOE6knIJHhJVb2YdZlYPYGZLgDeB6eH8uvBnM3A7QZPY25jZDWY218zmlpeXJxFuAr8+Hf7xzbcVe+JwzmWzZG4AfBM4WtKIcD7Zh1E8B0yTNIkgQZwL/EufdRYCFwBPAx8BHjYzk1QObDWzbkmTgWnAyjC5lJrZFklR4EzgoSTjGbyKObBuyduKPXE457LZgDUOSd+VVGpmLWbWImmkpG8PtF3YZ3EJ8ADBpbW/N7Plkq6Ja/q6ESiTVEPQJHVlWH4C8JKkpQSd5heb2VYgH3hA0kvAUoKE9MtBHO/gVMyBhregtX6X4pLCKE3tXT60unMuKyVzOe7pZvbV3hkz2ybpDIJHyfbLzO4D7utT9vW46Xbg7ATb/RH4Y4LyVoJ7PoZGxZzg54YXYOp7dhSX9A6t3hHbcUOgc85li2T6OCLhjXpAcB8HwV/++7/xhwU/17+wS3FxmCz8klznXDZKpsbxW+Afkn4dzn+KbBkZt6AEyqbC+qW7FMcPOzIhwWbOObc/S6Zz/FpJLwK9bTXfMrMHUhtWBqmYA2ue2qXIx6tyzmWzpEbHNbO/mdllwDeAMZL+mtqwMkjFHGhaB82bdhR54nDOZbNkrqrKk/RBSX8guDHvZODnKY8sU+zoIF+6o8gTh3Mum+02cUh6b9ivsQr4MEG/xlYz+5SZ3TtUAabduFmAdukg98ThnMtm/dU4/gZMBo4zs4+FyaKnn/X3T/kjoPzAXRLHsLwIuTk+tLpzLjv1lzgOJ7ij+yFJf5d0IcFzNbJPxeFB4rDghj8fWt05l812mzjMbKmZXWlmUwg6xWcDUUn3S7poqALMCBVzoGUTNG/YUeSJwzmXrZK9quopM/sCwUCF1wFHpzSqTNPbQR7XXOXP5HDOZaukEkcvM+sxswfN7NOpCigjjTsUFHlbB7nXOJxz2WhQiSNrRQthzAxPHM45hyeO5FXM3qWD3BOHcy5b9Xcfx6j+XkMZZEaomAPb66ExeBpuSdjH4UOrO+eyTX9jVS0BDEj0TG8juMcje8R3kJdOpKQwSo9BS2eM4gIfWt05lz12mzjMbNJQBpLxxh4COdEgccyYv/Pu8e1dnjicc1klmbGqJOljkq4K5ydKSvic7/1abn6QPNY9D+x8Jof3czjnsk0yneM/A45h5/PCm4GfpiyiTFYxJ3g2h9mOGoffy+GcyzbJJI6jzOzzQDsEj44F8pLZuaTTJL0mqUbSlQmW50u6M1z+jKTqsLxaUpukpeHr53HbHCHp5XCb6yUl6oNJjYo50NEIW1f6QIfOuayVTOLokhQh6BBHUjlJDHYYbvNT4HRgBnCepBl9VrsQ2GZmUwnuSL82btmbZjY7fF0cV/5/wGeAaeHrtCSOYe+I6yAvGeaJwzmXnZJJHNcDdxM8wOk7wBPAd5PYbh5QY2YrzawTuAOY32ed+cAt4fRdwCn91SAkjQeKzeyfZmYEQ70vSCKWvWPMwRDJh/UvUBrWOLa0dAzZ2zvnXCYYMHGY2W+By4HvETzIaYGZ/SGJfVcCa+Pma8OyhOuYWQxoBMrCZZMkvSDpUUnHx61fO8A+AZB0kaTFkhbX1dUlEW4SIlEYNxPWL2V4fi4HjSviHys27519O+fcPiKpGwCBzcDvgNuBTUNwA+AGYKKZzQG+BNwuqXgwOzCzG8xsrpnNLS8v33uRVcwJngbY08OCOZW88FYDq7e07r39O+dchuuvxrEEWBz+rANeB94Ip5ckse91wIS4+aqwLOE6knKBEqDezDrMrB7AzJYAbwLTw/WrBthnalUeDp0tUF/DBw6rQII/L10/pCE451w69fc8jklmNhl4CDjLzEabWRlwJvBgEvt+DpgmaZKkPOBcYGGfdRYCF4TTHwEeNjOTVB52riNpMkEn+Eoz2wA0STo67Av5BPDnpI92b4jrIK8oLeSoSaO4Z+k6zHzoEedcdkimc/xoM7uvd8bM7geOHWijsM/iEuAB4FXg92a2XNI1kj4QrnYjUCaphqBJqveS3ROAlyQtJeg0v9jMtobLPgf8CqghqIncn8Qx7D2jp0N02I6RchfMrmTVllZeqm0c0jCccy5d+hurqtd6Sf8F/CacPx9Iqm0mTDj39Sn7etx0O3B2gu3+CPxxN/tcDByazPunRE4Exh+2I3GcPnM8X//zcu5Zuo7DJpSmLSznnBsqydQ4zgPKCS7JvRsYE5Zlr4o5sPEl6I5RUhjl5IPGcO+L64l1D3h7i3PO7fOSuRx3q5l9kaD56Hgz+2Jcs1F2qpgDXdthy+sALJhTyZaWTp58sz7NgTnnXOolM8jhTEkvAMuA5ZKWSEpfU1Em6PMM8ncfVE5xQS73vDC0F3g551w6JNNU9QvgS2Z2gJkdAHwZuCG1YWW4UVMgr2hH4sjPjXDGzPE8sHwj2ztjaQ7OOedSK5nEMdzMHumdMbNFwPCURbQvyMnZ+SjZ0II5lWzv7Obvr2xKX1zOOTcEkkkcKyVdFY5YWx1eYbUy1YFlvIrZsPFl6A4GOZxXPYqKkgJvrnLO7feSSRyfJriq6k/hqzwsy24Vc6C7Aza/CkBOjvjA7Eoee2ML9T7woXNuP5bMVVXbzOxSMzs8fH0xfCZHduvtIK99dkfRgjkVdPcYf3lpQ5qCcs651NvtDYCS+g4Psgsz+0B/y/d7IyfBmBnw9M/g8AsgEuWgccUcNK6Ie5au44Jjq9MdoXPOpUR/d44fQzDk+e+AZ4Che9LevkCCU74BvzsHnr8VjrwQCDrJv3//CtbUt3JAWXZfQ+Cc2z/111Q1DvgqwfAePwZOBbaY2aNm9uhQBJfxpr8PJh4Li74PHS0AO0bMvecFHzHXObd/6m903G4z+5uZXQAcTTCo4CJJlwxZdJlOglOvgdbN8M+fAewYMffPPmKuc24/1W/nuKR8SR8iGODw8+x8jKzrNeFIOPgsePLH0LoFCEbMXbmllZfX+Yi5zrn9T39PALwVeBo4HPimmR1pZt8yM79Roa9TvgFdbfDoD4BgxNy8SA53+z0dzrn9UH81jo8RPEDpi8BTkprCV7OkpqEJbx8xehoc/glYfBNsXRk3Yu4GHzHXObff6a+PI8fMisJXcdyryMwG9fzvrHDSlRCJwsPfAXpHzO3g/mUb0xyYc87tXcncOe6SUTQOjv4cLLsL1r/Aew4ewyEVxXz7r6/Q3N6V7uicc26vSWnikHSapNck1Ui6MsHyfEl3hsufkVTdZ/lESS2SLosrWy3pZUlLJS1OZfyD9q4vQuEoeOhqciM5fOeDM9nc3MF/P/h6uiNzzrm9JmWJQ1IE+ClwOjADOE/SjD6rXQhsM7OpwHXAtX2W/4jEzxR/t5nNNrO5eznsd6agGE68HFYugjcfZvaEUj5+9AHc+vRqXqptSHd0zjm3V6SyxjEPqDGzlWbWCdwBzO+zznzglnD6LuAUSQKQtABYBSxPYYx739xPQ+lE+Ps3oKeHy953IKNH5PO1u5fR3eP3dTjn9n2pTByVBEOW9KoNyxKuY2YxoBEokzQCuAL4ZoL9GvBg+CTCi3b35pIukrRY0uK6urp3cBiDlJsPJ389eCb5sj9SXBDl62fN4OV1jdz69Oqhi8M551IkUzvHrwauM7OWBMuOM7PDCZrAPi/phEQ7MLMbzGyumc0tLy9PYagJHPphGDcTHr4GYh28f+Z4Tpxezn8/+DobG9uHNhbnnNvLUpk41gET4uarwrKE60jKBUqAeuAo4AeSVgP/Dny1d6iT3hsQzWwzwV3s81J2BHsqJycYiqThLXjsh0jiW/MPpau7h2/eu2+1vDnnXF+pTBzPAdMkTZKUB5wL9B2qfSFwQTj9EeBhCxxvZtVmVg38D/BdM/uJpOGSigAkDQfeCyxL4THsuSknw2HnweP/DbVLmFg2jEtPmcb9yzby8Ap/vKxzbt+VssQR9llcAjwAvAr83syWS7pGUu+zPG4k6NOoAb4EvO2S3T7GAk9IehF4Fvirmf0tNUewF5x+LRSNh7svgs7tfOb4yUwbM4Kr7lnO9s5YuqNzzrk9omwYwXXu3Lm2eHGabvlY+Sjc+gGY929wxg94dtVWPvqLp7n4xClcefpB6YnJOeeSIGlJotseMrVzfP8x+UQ46rPw7C/gzUeYN2kUH51bxa8eX8lrG5vTHZ1zzg2aJ46h8J5vwOjp8OfPQ1sDXzn9YIoLo3z17pfp8kEQnXP7GE8cQyFaCB/8BTRvhPsvZ+TwPL5x1gyWrNnGpb97wZOHc26f4oljqFQeHgxH8tKdsPwe5s+u5KozZ3D/so1ccvvzdMY8eTjn9g2eOIbS8V+Gijnwl/+A5k1ceNwkrj5rBg8s38TnPXk45/YRnjiGUiQKH7wBurbDvZeCGZ981ySumX8If39lE5/77RI6Yt3pjtI55/rliWOolU+H93wTXv8bPH8rAJ84pppvLTiUh17dzGd/8zztXZ48nHOZyxNHOsy7CCadAH+7El69F4CPH30A3/ngoTy8YjMX/2aJJw/nXMbyxJEOOTnwoV9C+UFw58fgga9BdxfnH3UA3/vQTBa9VsdFt3nycM5lJk8c6VI0Dj79NzjyM/D0T+DmM6FpPefNm8gPPjyLx9+o4xM3PcvardvTHalzzu3CE0c65ebD+38IH74RNr4MPz8eVi7io0dO4LqPzmb5ukbee91j3PjEKn8IlHMuY3jiyAQzPwIXPQLDR8OtC+DR/8eCw8bz9y+dyDFTyvjWX17hQz97klc3NKU7Uuec88SRMcoPhM88DDPPhke+Dbd/lIq8Nm68YC7XnzeH2m1tnPW/T/DDB17zvg/nXFp54sgkecPhQzfA+38Eqx6F/z0cPfhffKCihYe+dCLzZ1fyk0dqOOPHj/PMyvp0R+ucy1I+rHqm2vAiPP4jWPEX6InBAe+CIz7JE3nH8pWFr7N2axsLZldw8UlTOGhccbqjdc7th3Y3rLonjkzXshmW3g5LboZtq6BwJF2HnsPN7Sdy3Ys5bO/s5sTp5fzbCZM5ZkoZktIdsXNuP+GJY19NHL16emD140ECefVe6OkiVnUUDw8/k6trprK+1Ti0spiLTpjCGYeOIzfirZDOuXcmLQ9yknSapNck1Uh622NhJeVLujNc/oyk6j7LJ0pqkXRZsvvcb+XkBA+FOvvX8OUVcOo15G6v472vXcWTeZ/n/oMfYGTbWi793Quc9MNF/PrJVTS3d6U7aufcfihlNQ5JEeB14FSgFngOOM/MXolb53PALDO7WNK5wAfN7Jy45XcBBjxjZj9MZp+J7Bc1jkR6emD1Y7D4JljxV+iJUT/mGG7ueDc/33QQys3jPQePYf7sSk46sJz83Ei6I3bO7UN2V+PITeF7zgNqzGxlGMAdwHwg/kt+PnB1OH0X8BNJMjOTtABYBbQOcp/ZIycHJp8UvJo3wQu3UbbkFr7c+F0uHVnO4qKT+eWbM7n45WqKCvJ4/6zxfOCwSo6aNIqcHO8Lcc7tmVQmjkpgbdx8LXDU7tYxs5ikRqBMUjtwBUHN4rJE6/ezz+xUNBZOuAyO+w9482GiS27mmDfu4ZieO+kYOZZnC97FDUtncf6zUxlbMowPHFbB+w4dx+yqUk8izrlBSWXieCeuBq4zs5Y9vUpI0kXARQATJ07ce5FlupwITDs1eLU3wesPkP/KPRxfcx/H5/yJ9pLRPJX3Lm58cha/fOxARo0o4JSDxvKeGWM5bupoCvO8Ocs5179UJo51wIS4+aqwLNE6tZJygRKgnqAW8RFJPwBKgZ6wFrIkiX0CYGY3ADdA0MfxTg9mn1RQDLPODl4dzfDGgxQsv4eT33iQk6N/pmP4KBYXHsttL8/ic4sPIic3j+OnjeY9B4/l5IPHMKaoIN1H4JzLQKnsHM8l6Mg+heDL/TngX8xsedw6nwdmxnWOf8jMPtpnP1cDLWHn+ID7TGS/7RzfUx0tUPP34LLe1x+AzhZi0SJeGXEMd7TM5u7mg2ijgIPHF3PCtNEcP62cudUjKYh6bcS5bDLkneNhn8UlwANABLjJzJZLugZYbGYLgRuB2yTVAFuBc/dkn6k6hv1W/gg45IPBq6sdVj1K7qsLmbXiPmZ1Pci3RxSwpuQonumcxINPjeUPj02iNbeUeZNGccK0co6fPpoDxxb5zYbOZSm/AdDt1B2DNU8GNZE3/wFbV+5Y1BgdyzKr5p9tE1hmk1hXeCBTJ09mXvUo5k0q46BxRd7J7tx+xu8c98QxeG0NwXNCNiyF9Uthw4tYfQ0i+MzUMo5nuqexuGc6K6IzGF09kyMnj2bepDIOqSgm6nevO7dP88ThiWPv6GiGDS/BuiWw9hm61zxNpC0YqbeJESzunsrinuks1cF0jjucQyaMZmZVKbOqSphSPoKI10qc22d44vDEkRpmQZPWW/+Etf8ktuYZcutfA6CTPF6waTwVO4h/9szgteh0pleUM7OqhEMri5kxvoTJ5cO9ZuJchvLE4Ylj6GzfGiSS1U9ga56ADS8hjJjyWJF7IIs6prMiVsEGG0V9zmhKx1YxvaKMg8cXM2N8MQdXFFNcEE33UTiX9TxxeOJIn7YGeOtpWP1EkEw2voSsZ5dVtlDK+p6RbLJRbLBRtBaMJaekiuHlB1BWOZmJB0xhyvhRfkmwc0MoHWNVORcoLIUDTw9egDqaoWEtNK2HpnXQvIGyxnUUbatl8rZ15La+TkGsObgVtB5YAT0mtlDCykg52wsraCuZio2dwYiqWYypPojxI4u8/8S5IeKJww29/CIYOyN4hQTkhy8guEmxaR3d29ayZf0qmjatoqN+LZHmdYzf/hrjWx4jZ73BC9BhUV6jknXRahqKptIzcgrFoysoG1tJRcVExo8pJ+L9KM7tNZ44XGbKHwHlBxIpP5Cx02Fsn8U9Ha3UrX6ZhjUvEtvwCvlbX+OI1uWM2rYItgE7b0Ghw6JsySmhLTqKWEEZ3cWV9Iw+iPzxB1N8wCxGlVeR44nFuaR5H4fbv7Q1YFtX0bBlA/Wb19Fcv5H2xo1YSx2RtnqGd22lik2Uaudo/Q02nDU5E9lUUE3jiClES8ZSWlxMWWkp5aNKKRs5kmj+cIgWQt5wKCgBv2veZQHv43DZobAUVc5hZOUcRiZY3NNj1Ld0sGLTW7TWLqdn0ytEt75BcXMNU9ofZ0Tb/VDX/1t05hSyfVglXcUT0cgDKBgzmWFjJpMzshpKJwaDSzq3H/PE4bJKTo4oLy6gvHg6TJsOfHDnQjNoraNn+zbqGxrYXL+NLdsaaGhsoLGpiZaWZjpaGynp3MSErjqqmmqoWvc0I9S2y3u05hTTXFhBV9EEckZVUzhmCiXjpxApmwQlEyDqow67fZsnDud6STBiDDkjxlA+Bsp3s1pnrIe6lg42NbXzZGMbDfWb6dyyChrWEG1+i8LWdYxqXk9l8zKqNjxC/iuxXbZvjZSwvWAsseHjyCmppGBUFcPLJ5BbWgmlB8DIaoj4fSwuc3nicG6Q8nJzqCwtpLK0EBgJVACzd1kn1t3DhsZ2ltS3ULdhDa2b3iRWv4ZI01ry2zdR0rSF8c1vMXbTUkrUtMu23USoz6+iecQkOkqmoNHTyBt3EMUTZjCqbIxfduzSzhOHcymQG8lhwqhhTBg1DKaNAY7cZXl7VzcbGtt5vaGNR7c00lC3lvb6teQ2rqFk+yrGdLzFxLY3mLrlcfJWdu/czqI0aDhtkRF05I6gO1oMBcXkFJYQHT6SghGjGFY8kuElZeQUlgYd+fnFwc/C0qBz37l3yBOHc2lQEI0wafRwJo0eDlNHA1Petk5HrJvNja00rq+hfcMK2PI6PS119LQ1QkcTuZ1N5HVsY1jTWorURjGtFKir3/ftyC2iY9h4uosqyCmpIq9sAgVlE1FJFQwfEzSRRaKQE337dE6uX03mAE8czmWs/NwIVWXFVJUdDjMP3+16nbEetrR08GpTO3UNzTRuraO5sZ7WxnraW7bR2dqItW0j2tHIuFg9FR1bqWhYxfjaxRSqJel4YtERdBdPJKdsMrllk9CoSTBqUtAnUzLB+2WyiCcO5/Zxebk5VJQWUlFaCBNHAhMTrhfr7mHr9k7qW4JXTWsH2xob6dpaizXWYq1b6OzsCF4dncS6OqCniyjdRIkxKtbMxPbNHFD3PBP0APlxtZtuInRFCjFFsJwo5ESCGkokinJyUW4eGjaKSPE4IkVjUdFYGBH/GgMFpRDZg6+knrApL8fHMRsqnjicyxK5kRzGFBUwpij+cuBKYEbC9c2M9q4eGto6adjexbbWTupaOljU3EFdUxud29aR27iGgpa1lLbXktvZiqybXLqJ0EOuYuTSQ4Ru8ogxSpsp53XGqIFCdSZ8z/bICDqipcTyS+nOL8UKR6JhI8nNG0ZeVxO5XU3kdjYS6WhE7Q3Q1ggdjaCcIAEVjYfiip0/e6eLxu1MTt7c9o6lNHFIOg34McHzwX9lZt/vszwfuBU4gmA4u3PMbLWkecANvasBV5vZ3eE2q4FmoBuIJbqr0Tn3zkmiMC9CYV4h40sKE6xxyNtKOmM9tHbEaOmIsb2zm5aO2I75mrYuFrd10bi9k+0tTdCykcj2zUTbtlDQvoWCWAOFHc2UdjZT2tpKqdZTyhuMVDMFdNHIcBpt+I6fTRxAa84ItkeKyM/pYWz7Nsa0baV800uM6nmE4db6tvi6FaU9v4yuwnK6h5XD8DFEiseSl5dPXjSX3Jxw6BmF/0hBzamgBApHwbBRUDhy53Ru/tveIxukLHFIigA/BU4FaoHnJC00s1fiVrsQ2GZmUyWdC1wLnAMsA+aaWUzSeOBFSfeaWe8F8e82sy2pit05t2fycnPIy81j5PC8PdrezGjt7Ka5vYvm9hj17TFWt3fR0hGjvauHtq5uOrq6aevspq2re5eyjlgPHbHwZ1cPdLUwomMLxbHNDO+sZ1jXVsqsgfJYA6NbGynXm4zW85TSSER7NvRSLFJAV25R0EymSJhowumcHKQIyomQE8klJxIhJxJFO5ZH4taNBLUm5exalpMbDApaOHI3rzCBDXEtKpU1jnlAjZmtBJB0BzAfiE8c84Grw+m7gJ9Ikpltj1unANj/B9RyziGJEfm5jMjPZXzJ3t13b9NbY1sXTe1dNLZ1sSGsAbW0d9Lc3klLW4ymjhgt7Z20dnTR0h6jrb2DaGcjeV2NFMSaKIw1UEoLJbQwMtbCiI42IvQQUQ/CiNBDzo6XkRtOB+u0kZ/TQzTHyBNEc3rIVU+wjowIRkThuvSQY93k97SS19WMdvc1mFsAJVXha0LwKp2ws6y0GnL27iCeqUwclcDauPla4KjdrRPWLhqBMmCLpKOAm4ADgI/H1TYMeFCSAb8wsxtIQNJFwEUAEycm7ix0zmWPnU1vEcaV7PmwL2ZGV7eFNZ6g9tPV3UNndw9d3RZMx8L5WA/bu7ppbo/R3B6jpaNrx3Tw6gprTD20x4L9tXcGNaf2rp0PO8uhhyK2U6oWSmmhVK1U5HdQEW1hjG1hbGMdY7atZ2zPi5SxbZd4O/5zDfnDS/f4eBPJ2M5xM3sGOETSwcAtku43s3bgODNbJ2kM8HdJK8zssQTb30DYTzJ37lyvsTjn9gpJ5OWKvNwcSgpTdwmymdER66Fhexf1rR1saw1+bm3tZFtrJ/Wtnaxo62IFkCORo+Bn1DoYGdtCWWwTJV1b+GD+3h90M5WJYx0wIW6+KixLtE6tpFyghKCTfAcze1VSC3AosNjM1oXlmyXdTdAk9rbE4Zxz+zJJFEQjjCt5ZzWkVEjl02ueA6ZJmiQpDzgXWNhnnYXABeH0R4CHzczCbXIBJB0AHASsljRcUlFYPhx4L0FHunPOuSGSshpH2GdxCfAAweW4N5nZcknXENQcFgI3ArdJqgG2EiQXgOOAKyV1AT3A58xsi6TJwN0KriDIBW43s7+l6hicc869nT8B0DnnXEK7ewKgP2jZOefcoHjicM45NyieOJxzzg2KJw7nnHOD4onDOefcoGTFVVWS6oA1e7j5aCBTB1T02PaMx7ZnPLY9sy/HdoCZlfctzIrE8U5IWpypQ7d7bHvGY9szHtue2R9j86Yq55xzg+KJwznn3KB44hhYwmHbM4THtmc8tj3jse2Z/S427+Nwzjk3KF7jcM45NyieOJxzzg2KJ47dkHSapNck1Ui6Mt3x9CVptaSXJS2VlNahfyXdJGmzpGVxZaMk/V3SG+HPkRkU29WS1oXnbqmkM9IQ1wRJj0h6RdJySV8My9N+3vqJLe3nLYyjQNKzkl4M4/tmWD5J0jPh/9k7w+cAZUJcN0taFXfeZg9lXH1ijEh6QdJfwvk9O2dm5q8+L4Lnh7wJTAbygBeBGemOq0+Mq4HR6Y4jjOUE4HBgWVzZD4Arw+krgWszKLargcvSfM7GA4eH00XA68CMTDhv/cSW9vMWxiRgRDgdBZ4BjgZ+D5wblv8c+GyGxHUz8JF0n7cwri8BtwN/Cef36Jx5jSOxeUCNma00s07gDmB+mmPKWBY8831rn+L5wC3h9C3AgqGMqdduYks7M9tgZs+H083Aq0AlGXDe+oktI1igJZyNhi8DTgbuCsuH/Nz1E1dGkFQFvB/4VTgv9vCceeJIrBJYGzdfSwb9xwkZ8KCkJZIuSncwCYw1sw3h9EZgbDqDSeASSS+FTVlpaUbrJakamEPwF2pGnbc+sUGGnLewyWUpsBn4O0ELQYOZxcJV0vJ/tm9cZtZ73r4TnrfrJOUPdVyh/wEuJ3iqKkAZe3jOPHHsu44zs8OB04HPSzoh3QHtjgX14Iz5ywv4P2AKMBvYAPx3ugKRNAL4I/DvZtYUvyzd5y1BbBlz3sys28xmA1UELQQHpSuWeH3jknQo8BWC+I4ERgFXDHVcks4ENpvZkr2xP08cia0DJsTNV4VlGcPM1oU/NwN3E/znySSbJI0HCH9uTnM8O5jZpvA/eA/wS9J07iRFCb6Yf2tmfwqLM+K8JYotU85bPDNrAB4BjgFKJeWGi9L6fzYurtPCpj8zsw7g16TnvL0L+ICk1QRN7ycDP2YPz5knjsSeA6aFVxzkAecCC9Mc0w6Shksq6p0G3gss63+rIbcQuCCcvgD4cxpj2UXvF3Pog6Th3IXtyzcCr5rZj+IWpf287S62TDhvYRzlkkrD6ULgVIJ+mEeAj4SrDfm5201cK+L+EBBBH8KQnzcz+4qZVZlZNcH32cNmdj57es7S3cufqS/gDIKrSd4EvpbuePrENpngSq8XgeXpjg/4HUHTRRdBO+mFBO2n/wDeAB4CRmVQbLcBLwMvEXxRj09DXMcRNEO9BCwNX2dkwnnrJ7a0n7cwvlnAC2Ecy4Cvh+WTgWeBGuAPQH6GxPVweN6WAb8hvPIqXS/gJHZeVbVH58yHHHHOOTco3lTlnHNuUDxxOOecGxRPHM455wbFE4dzzrlB8cThnHNuUDxxOLcXSOqOG/10qfbiiMqSquNH93Uu3XIHXsU5l4Q2C4aacG6/5zUO51JIwXNTfqDg2SnPSpoalldLejgc+O4fkiaG5WMl3R0+0+FFSceGu4pI+mX4nIcHwzuTnUsLTxzO7R2FfZqqzolb1mhmM4GfEIxQCvC/wC1mNgv4LXB9WH498KiZHUbwHJHlYfk04KdmdgjQAHw4pUfjXD/8znHn9gJJLWY2IkH5auBkM1sZDhy40czKJG0hGLKjKyzfYGajJdUBVRYMiNe7j2qCIbqnhfNXAFEz+/YQHJpzb+M1DudSz3YzPRgdcdPdeP+kSyNPHM6l3jlxP58Op58iGKUU4Hzg8XD6H8BnYcdDgUqGKkjnkuV/tTi3dxSGT37r9Tcz670kd6SklwhqDeeFZV8Afi3pP4E64FNh+ReBGyRdSFCz+CzB6L7OZQzv43AuhcI+jrlmtiXdsTi3t3hTlXPOuUHxGodzzrlB8RqHc865QfHE4ZxzblA8cTjnnBsUTxzOOecGxROHc865Qfn/mPjfkk+HTv4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network = Autoencoder(data_zoom)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(batch_size=128,epochs=40)\n",
    "network.plot_latent_dimension(\"Autoencoder without classifier\")\n",
    "network.plot_reco()\n",
    "network.plot_score(\"Autoencoder without classifier\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeating the same procedure for the autoencoder with classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 121)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_18 (Dense)                (None, 32)           3904        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 16)           528         dense_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                (None, 8)            136         dense_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            18          dense_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_21 (Dense)                (None, 8)            24          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_24 (Dense)                (None, 8)            24          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_22 (Dense)                (None, 16)           144         dense_21[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_25 (Dense)                (None, 16)           144         dense_24[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_23 (Dense)                (None, 32)           544         dense_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_26 (Dense)                (None, 32)           544         dense_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 121)          3993        dense_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_26[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 10,333\n",
      "Trainable params: 10,333\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.2417 - ecoder_output_loss: 0.0767 - classifier_output_loss: 1.6494 - ecoder_output_accuracy: 0.0649 - classifier_output_accuracy: 0.3733 - val_loss: 0.1756 - val_ecoder_output_loss: 0.0539 - val_classifier_output_loss: 1.2161 - val_ecoder_output_accuracy: 0.0856 - val_classifier_output_accuracy: 0.5364\n",
      "Epoch 2/50\n",
      "469/469 [==============================] - 1s 1ms/step - loss: 0.1600 - ecoder_output_loss: 0.0507 - classifier_output_loss: 1.0924 - ecoder_output_accuracy: 0.0825 - classifier_output_accuracy: 0.6078 - val_loss: 0.1414 - val_ecoder_output_loss: 0.0495 - val_classifier_output_loss: 0.9192 - val_ecoder_output_accuracy: 0.0863 - val_classifier_output_accuracy: 0.6783\n",
      "Epoch 3/50\n",
      "469/469 [==============================] - 1s 1ms/step - loss: 0.1340 - ecoder_output_loss: 0.0490 - classifier_output_loss: 0.8503 - ecoder_output_accuracy: 0.0815 - classifier_output_accuracy: 0.7097 - val_loss: 0.1240 - val_ecoder_output_loss: 0.0484 - val_classifier_output_loss: 0.7557 - val_ecoder_output_accuracy: 0.0865 - val_classifier_output_accuracy: 0.7464\n",
      "Epoch 4/50\n",
      "469/469 [==============================] - 1s 1ms/step - loss: 0.1199 - ecoder_output_loss: 0.0483 - classifier_output_loss: 0.7157 - ecoder_output_accuracy: 0.0796 - classifier_output_accuracy: 0.7686 - val_loss: 0.1133 - val_ecoder_output_loss: 0.0480 - val_classifier_output_loss: 0.6539 - val_ecoder_output_accuracy: 0.0805 - val_classifier_output_accuracy: 0.7952\n",
      "Epoch 5/50\n",
      "469/469 [==============================] - 1s 1ms/step - loss: 0.1108 - ecoder_output_loss: 0.0478 - classifier_output_loss: 0.6301 - ecoder_output_accuracy: 0.0793 - classifier_output_accuracy: 0.8052 - val_loss: 0.1058 - val_ecoder_output_loss: 0.0473 - val_classifier_output_loss: 0.5843 - val_ecoder_output_accuracy: 0.0852 - val_classifier_output_accuracy: 0.8201\n",
      "Epoch 6/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.1045 - ecoder_output_loss: 0.0474 - classifier_output_loss: 0.5704 - ecoder_output_accuracy: 0.0805 - classifier_output_accuracy: 0.8284 - val_loss: 0.1009 - val_ecoder_output_loss: 0.0472 - val_classifier_output_loss: 0.5371 - val_ecoder_output_accuracy: 0.0916 - val_classifier_output_accuracy: 0.8404\n",
      "Epoch 7/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0998 - ecoder_output_loss: 0.0473 - classifier_output_loss: 0.5248 - ecoder_output_accuracy: 0.0806 - classifier_output_accuracy: 0.8447 - val_loss: 0.0968 - val_ecoder_output_loss: 0.0470 - val_classifier_output_loss: 0.4978 - val_ecoder_output_accuracy: 0.0898 - val_classifier_output_accuracy: 0.8534\n",
      "Epoch 8/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0962 - ecoder_output_loss: 0.0471 - classifier_output_loss: 0.4908 - ecoder_output_accuracy: 0.0802 - classifier_output_accuracy: 0.8570 - val_loss: 0.0932 - val_ecoder_output_loss: 0.0467 - val_classifier_output_loss: 0.4649 - val_ecoder_output_accuracy: 0.0837 - val_classifier_output_accuracy: 0.8682\n",
      "Epoch 9/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0927 - ecoder_output_loss: 0.0466 - classifier_output_loss: 0.4603 - ecoder_output_accuracy: 0.0779 - classifier_output_accuracy: 0.8661 - val_loss: 0.0934 - val_ecoder_output_loss: 0.0462 - val_classifier_output_loss: 0.4726 - val_ecoder_output_accuracy: 0.0824 - val_classifier_output_accuracy: 0.8565\n",
      "Epoch 10/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0899 - ecoder_output_loss: 0.0461 - classifier_output_loss: 0.4379 - ecoder_output_accuracy: 0.0768 - classifier_output_accuracy: 0.8739 - val_loss: 0.0892 - val_ecoder_output_loss: 0.0459 - val_classifier_output_loss: 0.4332 - val_ecoder_output_accuracy: 0.0770 - val_classifier_output_accuracy: 0.8789\n",
      "Epoch 11/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0877 - ecoder_output_loss: 0.0458 - classifier_output_loss: 0.4198 - ecoder_output_accuracy: 0.0735 - classifier_output_accuracy: 0.8809 - val_loss: 0.0863 - val_ecoder_output_loss: 0.0453 - val_classifier_output_loss: 0.4102 - val_ecoder_output_accuracy: 0.0775 - val_classifier_output_accuracy: 0.8846\n",
      "Epoch 12/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0859 - ecoder_output_loss: 0.0455 - classifier_output_loss: 0.4038 - ecoder_output_accuracy: 0.0737 - classifier_output_accuracy: 0.8851 - val_loss: 0.0856 - val_ecoder_output_loss: 0.0452 - val_classifier_output_loss: 0.4047 - val_ecoder_output_accuracy: 0.0767 - val_classifier_output_accuracy: 0.8859\n",
      "Epoch 13/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0845 - ecoder_output_loss: 0.0454 - classifier_output_loss: 0.3915 - ecoder_output_accuracy: 0.0742 - classifier_output_accuracy: 0.8888 - val_loss: 0.0846 - val_ecoder_output_loss: 0.0451 - val_classifier_output_loss: 0.3948 - val_ecoder_output_accuracy: 0.0770 - val_classifier_output_accuracy: 0.8886\n",
      "Epoch 14/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0831 - ecoder_output_loss: 0.0453 - classifier_output_loss: 0.3783 - ecoder_output_accuracy: 0.0737 - classifier_output_accuracy: 0.8928 - val_loss: 0.0847 - val_ecoder_output_loss: 0.0451 - val_classifier_output_loss: 0.3953 - val_ecoder_output_accuracy: 0.0744 - val_classifier_output_accuracy: 0.8899\n",
      "Epoch 15/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0818 - ecoder_output_loss: 0.0449 - classifier_output_loss: 0.3687 - ecoder_output_accuracy: 0.0739 - classifier_output_accuracy: 0.8963 - val_loss: 0.0821 - val_ecoder_output_loss: 0.0444 - val_classifier_output_loss: 0.3775 - val_ecoder_output_accuracy: 0.0774 - val_classifier_output_accuracy: 0.8953\n",
      "Epoch 16/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0809 - ecoder_output_loss: 0.0446 - classifier_output_loss: 0.3635 - ecoder_output_accuracy: 0.0754 - classifier_output_accuracy: 0.8981 - val_loss: 0.0841 - val_ecoder_output_loss: 0.0444 - val_classifier_output_loss: 0.3963 - val_ecoder_output_accuracy: 0.0780 - val_classifier_output_accuracy: 0.8865\n",
      "Epoch 17/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0801 - ecoder_output_loss: 0.0445 - classifier_output_loss: 0.3559 - ecoder_output_accuracy: 0.0753 - classifier_output_accuracy: 0.8999 - val_loss: 0.0803 - val_ecoder_output_loss: 0.0441 - val_classifier_output_loss: 0.3626 - val_ecoder_output_accuracy: 0.0775 - val_classifier_output_accuracy: 0.8977\n",
      "Epoch 18/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0790 - ecoder_output_loss: 0.0444 - classifier_output_loss: 0.3462 - ecoder_output_accuracy: 0.0732 - classifier_output_accuracy: 0.9026 - val_loss: 0.0805 - val_ecoder_output_loss: 0.0439 - val_classifier_output_loss: 0.3653 - val_ecoder_output_accuracy: 0.0766 - val_classifier_output_accuracy: 0.9004\n",
      "Epoch 19/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0779 - ecoder_output_loss: 0.0442 - classifier_output_loss: 0.3371 - ecoder_output_accuracy: 0.0740 - classifier_output_accuracy: 0.9058 - val_loss: 0.0796 - val_ecoder_output_loss: 0.0438 - val_classifier_output_loss: 0.3574 - val_ecoder_output_accuracy: 0.0743 - val_classifier_output_accuracy: 0.9005\n",
      "Epoch 20/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0776 - ecoder_output_loss: 0.0442 - classifier_output_loss: 0.3344 - ecoder_output_accuracy: 0.0742 - classifier_output_accuracy: 0.9072 - val_loss: 0.0802 - val_ecoder_output_loss: 0.0438 - val_classifier_output_loss: 0.3641 - val_ecoder_output_accuracy: 0.0771 - val_classifier_output_accuracy: 0.9003\n",
      "Epoch 21/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0771 - ecoder_output_loss: 0.0441 - classifier_output_loss: 0.3307 - ecoder_output_accuracy: 0.0755 - classifier_output_accuracy: 0.9081 - val_loss: 0.0785 - val_ecoder_output_loss: 0.0437 - val_classifier_output_loss: 0.3478 - val_ecoder_output_accuracy: 0.0766 - val_classifier_output_accuracy: 0.9042\n",
      "Epoch 22/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0764 - ecoder_output_loss: 0.0440 - classifier_output_loss: 0.3243 - ecoder_output_accuracy: 0.0762 - classifier_output_accuracy: 0.9094 - val_loss: 0.0784 - val_ecoder_output_loss: 0.0436 - val_classifier_output_loss: 0.3478 - val_ecoder_output_accuracy: 0.0750 - val_classifier_output_accuracy: 0.9061\n",
      "Epoch 23/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0758 - ecoder_output_loss: 0.0439 - classifier_output_loss: 0.3196 - ecoder_output_accuracy: 0.0757 - classifier_output_accuracy: 0.9115 - val_loss: 0.0781 - val_ecoder_output_loss: 0.0435 - val_classifier_output_loss: 0.3456 - val_ecoder_output_accuracy: 0.0780 - val_classifier_output_accuracy: 0.9072\n",
      "Epoch 24/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0754 - ecoder_output_loss: 0.0438 - classifier_output_loss: 0.3162 - ecoder_output_accuracy: 0.0759 - classifier_output_accuracy: 0.9126 - val_loss: 0.0776 - val_ecoder_output_loss: 0.0434 - val_classifier_output_loss: 0.3430 - val_ecoder_output_accuracy: 0.0851 - val_classifier_output_accuracy: 0.9038\n",
      "Epoch 25/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0747 - ecoder_output_loss: 0.0437 - classifier_output_loss: 0.3103 - ecoder_output_accuracy: 0.0750 - classifier_output_accuracy: 0.9144 - val_loss: 0.0787 - val_ecoder_output_loss: 0.0434 - val_classifier_output_loss: 0.3527 - val_ecoder_output_accuracy: 0.0761 - val_classifier_output_accuracy: 0.9083\n",
      "Epoch 26/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0748 - ecoder_output_loss: 0.0437 - classifier_output_loss: 0.3107 - ecoder_output_accuracy: 0.0760 - classifier_output_accuracy: 0.9136 - val_loss: 0.0768 - val_ecoder_output_loss: 0.0433 - val_classifier_output_loss: 0.3352 - val_ecoder_output_accuracy: 0.0790 - val_classifier_output_accuracy: 0.9105\n",
      "Epoch 27/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0740 - ecoder_output_loss: 0.0436 - classifier_output_loss: 0.3044 - ecoder_output_accuracy: 0.0747 - classifier_output_accuracy: 0.9158 - val_loss: 0.0767 - val_ecoder_output_loss: 0.0433 - val_classifier_output_loss: 0.3347 - val_ecoder_output_accuracy: 0.0806 - val_classifier_output_accuracy: 0.9087\n",
      "Epoch 28/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0738 - ecoder_output_loss: 0.0435 - classifier_output_loss: 0.3031 - ecoder_output_accuracy: 0.0748 - classifier_output_accuracy: 0.9166 - val_loss: 0.0767 - val_ecoder_output_loss: 0.0433 - val_classifier_output_loss: 0.3340 - val_ecoder_output_accuracy: 0.0760 - val_classifier_output_accuracy: 0.9102\n",
      "Epoch 29/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0734 - ecoder_output_loss: 0.0434 - classifier_output_loss: 0.2994 - ecoder_output_accuracy: 0.0761 - classifier_output_accuracy: 0.9179 - val_loss: 0.0766 - val_ecoder_output_loss: 0.0432 - val_classifier_output_loss: 0.3344 - val_ecoder_output_accuracy: 0.0803 - val_classifier_output_accuracy: 0.9093\n",
      "Epoch 30/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0728 - ecoder_output_loss: 0.0434 - classifier_output_loss: 0.2935 - ecoder_output_accuracy: 0.0753 - classifier_output_accuracy: 0.9198 - val_loss: 0.0764 - val_ecoder_output_loss: 0.0431 - val_classifier_output_loss: 0.3331 - val_ecoder_output_accuracy: 0.0765 - val_classifier_output_accuracy: 0.9093\n",
      "Epoch 31/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0727 - ecoder_output_loss: 0.0433 - classifier_output_loss: 0.2943 - ecoder_output_accuracy: 0.0759 - classifier_output_accuracy: 0.9185 - val_loss: 0.0759 - val_ecoder_output_loss: 0.0428 - val_classifier_output_loss: 0.3309 - val_ecoder_output_accuracy: 0.0784 - val_classifier_output_accuracy: 0.9133\n",
      "Epoch 32/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0722 - ecoder_output_loss: 0.0432 - classifier_output_loss: 0.2897 - ecoder_output_accuracy: 0.0765 - classifier_output_accuracy: 0.9201 - val_loss: 0.0750 - val_ecoder_output_loss: 0.0430 - val_classifier_output_loss: 0.3203 - val_ecoder_output_accuracy: 0.0860 - val_classifier_output_accuracy: 0.9159\n",
      "Epoch 33/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0719 - ecoder_output_loss: 0.0432 - classifier_output_loss: 0.2873 - ecoder_output_accuracy: 0.0770 - classifier_output_accuracy: 0.9213 - val_loss: 0.0756 - val_ecoder_output_loss: 0.0427 - val_classifier_output_loss: 0.3292 - val_ecoder_output_accuracy: 0.0803 - val_classifier_output_accuracy: 0.9126\n",
      "Epoch 34/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0717 - ecoder_output_loss: 0.0430 - classifier_output_loss: 0.2860 - ecoder_output_accuracy: 0.0774 - classifier_output_accuracy: 0.9213 - val_loss: 0.0743 - val_ecoder_output_loss: 0.0427 - val_classifier_output_loss: 0.3152 - val_ecoder_output_accuracy: 0.0755 - val_classifier_output_accuracy: 0.9161\n",
      "Epoch 35/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0714 - ecoder_output_loss: 0.0430 - classifier_output_loss: 0.2836 - ecoder_output_accuracy: 0.0769 - classifier_output_accuracy: 0.9220 - val_loss: 0.0744 - val_ecoder_output_loss: 0.0427 - val_classifier_output_loss: 0.3176 - val_ecoder_output_accuracy: 0.0777 - val_classifier_output_accuracy: 0.9146\n",
      "Epoch 36/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0711 - ecoder_output_loss: 0.0430 - classifier_output_loss: 0.2805 - ecoder_output_accuracy: 0.0765 - classifier_output_accuracy: 0.9231 - val_loss: 0.0744 - val_ecoder_output_loss: 0.0427 - val_classifier_output_loss: 0.3171 - val_ecoder_output_accuracy: 0.0830 - val_classifier_output_accuracy: 0.9140\n",
      "Epoch 37/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0707 - ecoder_output_loss: 0.0430 - classifier_output_loss: 0.2771 - ecoder_output_accuracy: 0.0767 - classifier_output_accuracy: 0.9235 - val_loss: 0.0746 - val_ecoder_output_loss: 0.0425 - val_classifier_output_loss: 0.3213 - val_ecoder_output_accuracy: 0.0807 - val_classifier_output_accuracy: 0.9148\n",
      "Epoch 38/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0708 - ecoder_output_loss: 0.0429 - classifier_output_loss: 0.2789 - ecoder_output_accuracy: 0.0775 - classifier_output_accuracy: 0.9233 - val_loss: 0.0741 - val_ecoder_output_loss: 0.0424 - val_classifier_output_loss: 0.3165 - val_ecoder_output_accuracy: 0.0810 - val_classifier_output_accuracy: 0.9155\n",
      "Epoch 39/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0704 - ecoder_output_loss: 0.0429 - classifier_output_loss: 0.2744 - ecoder_output_accuracy: 0.0775 - classifier_output_accuracy: 0.9245 - val_loss: 0.0745 - val_ecoder_output_loss: 0.0424 - val_classifier_output_loss: 0.3206 - val_ecoder_output_accuracy: 0.0818 - val_classifier_output_accuracy: 0.9165\n",
      "Epoch 40/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0699 - ecoder_output_loss: 0.0428 - classifier_output_loss: 0.2708 - ecoder_output_accuracy: 0.0784 - classifier_output_accuracy: 0.9255 - val_loss: 0.0745 - val_ecoder_output_loss: 0.0425 - val_classifier_output_loss: 0.3198 - val_ecoder_output_accuracy: 0.0822 - val_classifier_output_accuracy: 0.9159\n",
      "Epoch 41/50\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0699 - ecoder_output_loss: 0.0428 - classifier_output_loss: 0.2708 - ecoder_output_accuracy: 0.0778 - classifier_output_accuracy: 0.9262 - val_loss: 0.0732 - val_ecoder_output_loss: 0.0422 - val_classifier_output_loss: 0.3098 - val_ecoder_output_accuracy: 0.0832 - val_classifier_output_accuracy: 0.9210\n",
      "Epoch 42/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0693 - ecoder_output_loss: 0.0427 - classifier_output_loss: 0.2664 - ecoder_output_accuracy: 0.0788 - classifier_output_accuracy: 0.9268 - val_loss: 0.0720 - val_ecoder_output_loss: 0.0422 - val_classifier_output_loss: 0.2985 - val_ecoder_output_accuracy: 0.0847 - val_classifier_output_accuracy: 0.9202\n",
      "Epoch 43/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0694 - ecoder_output_loss: 0.0427 - classifier_output_loss: 0.2672 - ecoder_output_accuracy: 0.0780 - classifier_output_accuracy: 0.9264 - val_loss: 0.0730 - val_ecoder_output_loss: 0.0423 - val_classifier_output_loss: 0.3066 - val_ecoder_output_accuracy: 0.0808 - val_classifier_output_accuracy: 0.9187\n",
      "Epoch 44/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0692 - ecoder_output_loss: 0.0426 - classifier_output_loss: 0.2654 - ecoder_output_accuracy: 0.0775 - classifier_output_accuracy: 0.9269 - val_loss: 0.0726 - val_ecoder_output_loss: 0.0422 - val_classifier_output_loss: 0.3031 - val_ecoder_output_accuracy: 0.0817 - val_classifier_output_accuracy: 0.9187\n",
      "Epoch 45/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0692 - ecoder_output_loss: 0.0426 - classifier_output_loss: 0.2659 - ecoder_output_accuracy: 0.0785 - classifier_output_accuracy: 0.9278 - val_loss: 0.0725 - val_ecoder_output_loss: 0.0423 - val_classifier_output_loss: 0.3026 - val_ecoder_output_accuracy: 0.0841 - val_classifier_output_accuracy: 0.9184\n",
      "Epoch 46/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0693 - ecoder_output_loss: 0.0426 - classifier_output_loss: 0.2672 - ecoder_output_accuracy: 0.0768 - classifier_output_accuracy: 0.9268 - val_loss: 0.0730 - val_ecoder_output_loss: 0.0422 - val_classifier_output_loss: 0.3082 - val_ecoder_output_accuracy: 0.0793 - val_classifier_output_accuracy: 0.9170\n",
      "Epoch 47/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0690 - ecoder_output_loss: 0.0425 - classifier_output_loss: 0.2652 - ecoder_output_accuracy: 0.0779 - classifier_output_accuracy: 0.9273 - val_loss: 0.0727 - val_ecoder_output_loss: 0.0420 - val_classifier_output_loss: 0.3067 - val_ecoder_output_accuracy: 0.0825 - val_classifier_output_accuracy: 0.9205\n",
      "Epoch 48/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0688 - ecoder_output_loss: 0.0424 - classifier_output_loss: 0.2635 - ecoder_output_accuracy: 0.0779 - classifier_output_accuracy: 0.9275 - val_loss: 0.0725 - val_ecoder_output_loss: 0.0419 - val_classifier_output_loss: 0.3054 - val_ecoder_output_accuracy: 0.0800 - val_classifier_output_accuracy: 0.9197\n",
      "Epoch 49/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0686 - ecoder_output_loss: 0.0425 - classifier_output_loss: 0.2612 - ecoder_output_accuracy: 0.0795 - classifier_output_accuracy: 0.9283 - val_loss: 0.0720 - val_ecoder_output_loss: 0.0420 - val_classifier_output_loss: 0.2996 - val_ecoder_output_accuracy: 0.0791 - val_classifier_output_accuracy: 0.9203\n",
      "Epoch 50/50\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0685 - ecoder_output_loss: 0.0424 - classifier_output_loss: 0.2612 - ecoder_output_accuracy: 0.0772 - classifier_output_accuracy: 0.9282 - val_loss: 0.0716 - val_ecoder_output_loss: 0.0421 - val_classifier_output_loss: 0.2947 - val_ecoder_output_accuracy: 0.0796 - val_classifier_output_accuracy: 0.9224\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiUAAAJOCAYAAABoXndyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9d5xcV334/7/uvVN3Z3vvq15WXbJky0UGFww2xTbNMcSmmZCQQEg+P0jIJ0A+eQQSQhJSvgEDBtNMB4OxjQu427J6b7va3vvOTp97z++P0Y602pnd2b6rfT8fDz0kzdxypt73nPM+76MppRRCCCGEEPNMn+8GCCGEEEKABCVCCCGEWCAkKBFCCCHEgiBBiRBCCCEWBAlKhBBCCLEgSFAihBBCiAVBghIhxJQ899xzlJeXz/l53/zmN/Pwww8nvf/+++/n7/7u76Z9npk6TjIej4fz588DEAgEeOtb30pWVhbvete7+MEPfsCtt946a+cWYqGSoERQXV3NM888M+n9brzxRr75zW/OWDs0TaO2tnbGjjcfhoeH8Xg8vPnNb57UfvN1gV+MnnjiCe677z4AvvOd73DdddfNc4umZnh4mOXLlwPws5/9jM7OTnp7e/npT3/Kvffey1NPPTXPLRRi7klQIsQM+vnPf47T6eTpp5+mo6NjvpuzYEWj0fluwoLS2NjI6tWrsdls0z6WaZoz0CIh5ocEJSKp/v5+7rjjDgoKCsjJyeGOO+6gpaUFgM9+9rO8+OKLfPzjH8fj8fDxj38cgNOnT3PLLbeQm5vLmjVr+MlPfhI/3v3338+f/dmfcfvtt5ORkcGuXbuoq6sD4IYbbgBg8+bNeDwefvzjH49pT21tLXv27CErK4v8/Hze8573xO/TNI3//M//ZPny5eTn5/N//s//wbIsAOrq6njjG99IXl4e+fn53HvvvQwMDMT3bW5u5q677qKgoIC8vLz4YwF46KGHWLduHTk5ObzpTW+isbFx3Ofs4Ycf5k/+5E/YtGkT3//+90fdd3lP0MjwgM/n481vfjNtbW14PB48Hg9tbW2EQiE++clPUlpaSmlpKZ/85CcJhULx/R977DG2bNlCdnY2u3fv5ujRo/H7qqur+dd//Vc2bdpEVlYW73nPewgGg/H7H330UbZs2UJmZiYrVqzgySefBKCtrY23ve1t5ObmsnLlSr7xjW/E9wkEAtx///3k5OSwfv169u3bN+rxtbW1cffdd1NQUMCyZcv4z//8z/h9n//853nnO9/J+973PjIzM/nOd74zat/6+nqys7Pjr9lHPvIRCgsL4/e///3v5z/+4z+Aiz10p06d4k/+5E949dVX8Xg8ZGdnx7fv7+9P+D5L5KWXXmL37t1kZ2dTUVExpm0jx0v2WYBYj83y5cvJyMhg2bJl/OAHPwAmfs/W1tbyuc99jn/4h3/gxz/+MR6Ph29961tjeoAm+lx97GMf4y1veQvp6en84Q9/SPpYhVjwlFjyqqqq1NNPPz3m9p6eHvWzn/1M+Xw+NTQ0pN75zneqt7/97fH79+zZo77xjW/E/z88PKzKy8vVQw89pCKRiDp48KDKy8tTJ06cUEopdd9996nc3Fy1d+9eFYlE1B/90R+p97znPfH9AXXu3Lmk7Xzve9+r/vEf/1GZpqkCgYB68cUXR+174403qt7eXtXY2KhWrVoVb9u5c+fUU089pYLBoOrq6lLXX3+9+sQnPqGUUioajapNmzapT37yk2p4eHjUcX/1q1+pFStWqJMnT6pIJKL+3//7f+qaa65J2r6GhgalaZo6ceKE+td//Ve1cePGUfdf/vjuu+8+9dnPflYppdQf/vAHVVZWNmr7//t//6/atWuX6uzsVF1dXeqaa65Rf/d3f6eUUurgwYOqoKBAvfbaayoajarvfOc7qqqqSgWDQaVU7DW96qqrVGtrq+rt7VVr165V//u//6uUUmrv3r0qMzNTPfXUU8o0TdXS0qJOnTqllFLq+uuvVx/72MdUIBBQhw4dUvn5+erZZ59VSin16U9/Wl133XWqt7dXNTU1qZqamnibTdNU27ZtU1/4whdUKBRSdXV1atmyZerJJ59USin1uc99TtlsNvXLX/5Smaap/H7/mOevoqJC7d+/Xyml1OrVq9WyZcvUyZMn4/cdPHhQKTX6ffftb39bXXvttaOOM9H77PLXzOPxqB/+8IcqHA6rnp4edejQoTGvz3ifheHhYZWRkaFOnz6tlFKqra1NHT9+XCk18Xt25P3wuc99Tt17773x+y59XKl8rjIzM9VLL70UP48Qi5X0lIik8vLyuPvuu0lLSyMjI4PPfvazPP/880m3f+yxx6iuruYDH/gANpuNrVu3cvfdd/PTn/40vs2dd97Jzp07sdls3HvvvRw+fDjl9tjtdhobG2lra8Plco3JJfj0pz9Nbm4ulZWVfPKTn+SRRx4BYOXKldxyyy04nU4KCgr41Kc+FX8cr7/+Om1tbXz5y18mPT191HG/9rWv8Td/8zesW7cOm83G3/7t33L48OGkvSXf+9732LRpE+vXr+e9730vJ06c4NChQyk/vsv94Ac/4O///u8pLCykoKCAz33uc3zve98D4MEHH+SjH/0ou3btwjAM7rvvPpxOJ6+99lp8/7/4i7+gtLSU3Nxc3vrWt8af629961t88IMf5JZbbkHXdcrKyli7di3Nzc28/PLL/PM//zMul4stW7bw4Q9/mO9+97sA/OQnP+Gzn/0subm5VFRU8Bd/8Rfxc+3bt4/u7m7+/u//HofDwfLly/nIRz7Cj370o/g211xzDe94xzvQdR232z3m8e7Zs4fnn38+Puz1zne+k+eff576+nqGhobYvHlzys9dqu+zH/7wh9x8883cc8892O128vLy2LJly5jtJvos6LrO8ePHCQQClJSUUFNTA0z8nk1FKp+rt7/97Vx77bXouo7L5Zr0OYRYKCQoEUn5/X4++tGPUlVVRWZmJjfccAMDAwNJx6wbGxvZu3cv2dnZ8T8/+MEPRuVWFBcXx/+dlpbG8PBwyu35l3/5F5RS7Ny5k5qaGh566KFR91dUVMT/XVVVRVtbGwCdnZ28973vpaysjMzMTN73vvfR09MDxIZuqqqqEo7lNzY28olPfCL+WHJzc1FK0dramrB93/3ud7n33nsBKCsrY8+ePePOEplIW1sbVVVVCR9TY2MjX/nKV0Y9183NzfH7Iflz3dzczIoVKxKeLzc3l4yMjFHnHHm8bW1tY57jESMX3kvb80//9E90dnbGt7l030T27NnDc889xwsvvMANN9zAjTfeyPPPP8/zzz/P9ddfj66n/nWV6vss2XNxufE+C+np6fz4xz/ma1/7GiUlJdx+++2cPn0amPg9m4pUPlcTPbdCLBYSlIikvvKVr3DmzBn27t3L0NAQL7zwAgDqwsLSmqaN2r6iooI9e/YwMDAQ/zM8PMz//u//zkh7iouL+cY3vkFbWxtf//rX+dM//dNRORrNzc3xfzc1NVFaWgrA3/7t36JpGseOHWNoaIjvf//78cdQUVFBU1NTwsTLiooKvv71r496PIFAgN27d4/Z9pVXXuHcuXN88YtfpLi4mOLiYvbu3csPf/jD+LHT0tLw+/3xfS69qFz+XAKUlpaO6pW59DFVVFTw2c9+dlTb/H4/99xzz4TPY0VFRcIci9LSUvr6+vB6vaPOWVZWBkBJScmY5/jSYy5btmxUe7xeL48//vi4j/FSe/bs4cUXX+S5555jz549XHfddbz88ss8//zz7NmzJ+E+Ex1zIsmei8tN9Fl405vexNNPP017eztr167lIx/5CDDxezbVNk70uZru8yDEQiFBiQAgEokQDAbjf6LRKF6vF7fbTXZ2Nn19fXzhC18YtU9RUVG8zgLAHXfcwdmzZ/ne975HJBIhEomwb98+Tp06lVIbLj/e5X7605/GkwtzcnLQNG3Ur+cvf/nL9Pf309zczFe/+tV4UqHX68Xj8ZCVlUVraytf/vKX4/vs3LmTkpISPvOZz+Dz+QgGg7z88ssA/Mmf/Alf/OIXOXHiBACDg4Ojuswv9fDDD3PLLbdw8uRJDh8+zOHDh+Pd+U888QQAW7Zs4Yc//CGmafLkk0+O6v4vKiqit7eXwcHB+G333HMP//iP/0h3dzc9PT38wz/8A+973/uAWCLo1772Nfbu3YtSCp/Px29/+9tRAUUyH/rQh/j2t7/Ns88+i2VZtLa2cvr0aSoqKti9ezd/8zd/QzAY5OjRo3zrW9+Kn/Pd7343X/ziF+nv76elpYX/+q//GvU8ZmRk8M///M8EAgFM0+T48eNjkmHHs2rVKtxuN9///vfZs2cPmZmZFBUV8fOf/zxpUFJUVERLSwvhcDjl81zq3nvv5ZlnnuEnP/kJ0WiU3t7ehEM9430WOjs7efTRR/H5fDidTjweT/x9OdF7NhXT/VwJsZhIUCIAeMtb3oLb7Y7/+fznP88nP/lJAoEA+fn5XH311dx2222j9vnEJz7Bz372M3JycviLv/gLMjIyeOqpp/jRj35EaWkpxcXFfPrTnx41Y2Q8n//857nvvvvIzs4eNbtgxL59+9i1axcej4e3ve1tfPWrX43XeYDYuPr27dvZsmULt99+Ox/60IcA+NznPsfBgwfJysri9ttv56677orvYxgGv/nNb6itraWyspLy8vL4zJ8777yTT3/607z3ve8lMzOTDRs2xAOMSwWDQX7yk5/w53/+5/FekuLiYpYtW8b73//++BDOV7/6VX7zm9/Eu9/f8Y53xI+xdu1a7rnnHpYvX052djZtbW383d/9HTt27GDTpk1s3LiRbdu2xYt57dixg2984xt8/OMfJycnh5UrVyacNZLIzp07+fa3v81f/uVfkpWVxZ49e+I9Mo888ggNDQ2UlpZy55138oUvfIGbb745/jxWVVWxbNkybr31Vt7//vePeh4fe+wxDh8+zLJly8jPz+fDH/7wqCArFXv27CEvLy8+HLFnzx6UUmzbti3h9m984xupqamhuLiY/Pz8SZ0LoLKykscff5yvfOUr5ObmsmXLFo4cOTJmu/E+C5Zl8W//9m/x/J3nn38+3osx0Xs2FdP9XAmxmGhqpP9RiEVM0zTOnTvHypUr57spQgghpkh6SoQQQgixIEhQIoQQQohZ8dWvfpUNGzZQU1MTL4A4nunXNBZiAZBRSCGEWFiOHz/ON77xDV5//XUcDge33XYbd9xxx7jD7NJTIoQQQogZd+rUKXbt2kVaWho2m409e/bwi1/8Ytx95rSnJD8/n+rq6rk8pRBCCDFvGhoa4sUa58JtN1XT0xuYk3MFIvmjqjM/8MADPPDAA/H/b9iwgc9+9rP09vbidrt5/PHH2bFjx7jHnNOgpLq6mv3798/lKYUQQoh5M9FFeKb19AbY//uJiyjOhB23vjTuNX3dunV8+tOf5tZbbyU9PZ0tW7ZgGMa4x5ThGyGEEELMig996EMcOHCAF154gZycHFavXj3u9pLoKoQQQohZ0dXVRWFhIU1NTfziF78YtWhoIhKUCCGEEGJW3H333fT29mK32/mf//kfsrOzx91eghIhhBBCzIoXX3xxUttLTokQQgghFgQJSoQQQgixIEhQIoQQQogFQYISIYQQQiwIEpQIIYQQYkGQoEQIIYQQC4IEJUIIIYRYECQoEUIIIcSCIEGJEEIIIRYECUqEEEIIsSBIUCKEEEKIBUGCEiGEEEIsCBKUCCGEEGJBkKBECCGEEAuCBCVCCCGEWBBs890AIYSYSz3DIQ439ROMmKwpzmRloQdN0+a7WUIIJCgRQiwhR5v7+c2RNiylsBQcaxmkMi+Ne3ZVoUtgIsS8k+EbIcSSEIqa/OZoG1ErFpAAhE2Lxl4fx1sGiESt+W2gEEJ6SoQQS0Njjw9D04iiRt0eMRW/PNTKo4dbKc128/at5eR7nPPUSiGWNukpEUIsCTZj/K87S0FLf4CHXjxPKGLOUauEEJeSoEQIsSRU5aWnlNAatSyOtQ7OQYuEEJeToEQIsSQYusYf7arCadNx2HT0JPFJxFT0DofmtnFCCEBySoQQS0hFbhp/9aa1nOv00tofYF99LxFrdI6Jw9ApyXbPUwuFWNqkp0QIsaTYDZ31pVncvL6IwkwXxiVdJroGaU6D9SWZ89hCIZYuCUqEEEuSpmn88e5lXFWdS5rDwGXX2VyRw0euXzFhUqwQYnbI8I0QYsly2HTetKGEN20ome+mCCGQnhIhhBBCLBASlAghhBBiQZCgRAghhBALggQlQgghhFgQJCgRQgghxIIgQYkQQgghFgSZEiyEWBKipsXRlgHOdAyR7rRxVXWeVG4VYoGRoEQIccWLRC2+9VIdfb4wEVOhAcdbB3nzxlK2VubMd/OEEBfI8I0Q4op3sKmPvuFYQAKgiC2898SxNiJRa34bJ4SIk6BECHHFO9k2NGbhPQBd02gd8M9Di4QQiUhQIoS44rnsRsLblQKnLfF9Qoi5J0GJEOKKt3NZLnZDG3N7utOgOMs1Dy0SQiQiQYkQ4oq3ojCD61YVYNM1nDYdh00n023n3qur0bSxwYoQYn7I7BshxJJww+pCtlfl0tznx+0wqMxNk4BEXHH85HDAevccne2lGT+iBCVCiCUj3WljbUnmfDdDCJGEDN8IIYQQYkGQoEQIIYQQC4IEJUIIIYRYECQoEUIIIcSCIEGJEEIIIRYECUqEEEIIsSBIUCKEEEKIBUGCEiGEEEIsCBMGJc3NzbzhDW9g/fr11NTU8NWvfhWAvr4+brnlFlatWsUtt9xCf3//rDdWCCGEEFeuCYMSm83GV77yFU6ePMlrr73G//zP/3Dy5Em+9KUvcdNNN3Hu3DluuukmvvSlL81Fe4UQQghxhZowKCkpKWHbtm0AZGRksG7dOlpbW3n00Ue57777ALjvvvv41a9+NasNFUIIIcSVbVI5JQ0NDRw6dIhdu3bR2dlJSUkJAMXFxXR2dibc58EHH2THjh3s2LGD7u7u6bdYCCGEEIvCv//7v1NTU8OGDRu45557CAaD426fclAyPDzM3XffzX/8x3+QmTl6QStN05KutvnAAw+wf/9+9u/fT0FBQaqnE0IIIcQi1trayn/+53+yf/9+jh8/jmma/OhHPxp3n5SCkkgkwt133829997LXXfdBUBRURHt7e0AtLe3U1hYOM3mCyGEEOJKEo1GCQQCRKNR/H4/paWl424/YVCilOJDH/oQ69at41Of+lT89re97W08/PDDADz88MO8/e1vn2bThRBCCLFYdHd3x9MzduzYwYMPPjjq/rKyMv76r/+ayspKSkpKyMrK4tZbbx33mLaJTvryyy/zve99j40bN7JlyxYA/umf/onPfOYzvPvd7+Zb3/oWVVVV/OQnP5n6IxNCCCHEolJQUMD+/fuT3t/f38+jjz5KfX092dnZvOtd7+L73/8+73vf+5LuM2FQct1116GUSnjfs88+m0KzhRBCCLHUPPPMMyxbtiyeT3rXXXfxyiuvjBuUSEVXIYQQQsy4yspKXnvtNfx+P0opnn32WdatWzfuPhKUCCGEEGLG7dq1i3e+851s27aNjRs3YlkWDzzwwLj7TDh8I4QQQggxFV/4whf4whe+kPL20lMihBBCiAVBghIhhBBCLAgSlAghhBBiQZCgRAghhBALggQlQogFx7IUnYNBBvzh+W6KEGIOyewbIcSCcrJtkN8cacOyFJZSFGa6eM9VlWS67fPdNCHELJOeEiHEgtE5FORXh1oIRkzCpkXUUrQPBvjeqw1JK0sLIa4cEpQIIRaM18/3ErVGBx9KwWAgQttAYJ5aJYSYKxKUCCEWjMFAhEQdIroGw6Ho3DdICDGnJCgRQiwYKws92HRtzO1RS1GW7Z6HFgkh5pIEJUKIBWNrVQ4elw3jksDEbmjsWp6LxyWJrkJc6SQoEUIsGE6bwR/tqiI7LRaAaEBptptrVxTMb8OEEHNCghIhxIIRMS2+/2oDfb5YfRIFNPf5+c4r9TL7RoglQIISIcSCcbJtkGDEHJXsaikY9Eeo6x6ev4YJIeaEBCVCiAWjcyhI2BzbI2JaFt3e0Dy0SAgxlyQoEUIsGIUZLuzG2K8lQ9fJ9zjnoUVCiLkkQYkQYsGoKc3CadO5dFKwrkGGy8aKQs+8tUsIMTckKBFCLBh2m86Hr1/OyiIPmhYLSNaVZPKB65aja2PrlwghriyyIJ8QYkHJSnPwR7uq47NtNAlGhFgyJCgRQixIEowIsfTI8I0QQgghFgQJSoQQQgixIEhQIoQQQogFQYISIYQQQiwIkugqhBBCXCHMLsXA/wTnuxlTJkGJEGJOKaVoGwgQiJiU56Thshvz3SQhxAIhQYkQYkZZStHjDWEzNHLTR5eG7/OF+P6rDfhCJpoGpqW4aV0RV6/In6fWCiEWEglKhBAzpq5rmF8cbCZqKiylyE138J6dleSmO1FK8f3XGhnwR7h0yb3fn+6kJNtNVV76vLVbCLEwSKKrEGJGDPjD/HhfI/6wSdi0iFqKbm+Ih1+ux1KK9sEgvmCUy9cAjpiK18/3zkubhRALi/SUCCFmxIHGPixrdMihgGDUor57GE3TSFak1R82Z7+BQogFT3pKhBAzYigQwby8GwRQCoZDUcqy3ZjW2A3susbakow5aKEQYqGToEQIMSOWF3iwG2O7QpRSVOSk4bQb3FJTPGobu66Rne5ga2XuXDZVCLFAyfCNEGJG1JRm8XJtD/2+MNELPSJ2Q6OmNItcT2wWzs5leZRkudlb34s/FGVtSSZbK3Kw2+T3kRBCghIhxAyxGTofvn45r53v5XjrIHZD56rqXDZXZI/ariI3jYrctPlppBBiQZOgRAgxYxw2gxtWF3LD6sL5booQYhGSPlMhhBBCLAjSUyKEmJTmPj9Pn+ygczBIhsvGDasL2FSRM9/NEkJcASQoEUKkrKXfz/derSdyYe5vry/MY0fbCEZMdi6XUvFCiOmR4RshRMp+f6ozHpCMiJiK35/uGlM4TQghJkt6SoQQKesYSrwkesS0ONjUh93QWVGQgcclXy1CiMmTbw4hRMpy0hwEwoExt1sKfnu0HZuuoYAVBelYCnLTHexclkeexzn2YEIIcRkZvhFCpOzGNYXYElRtHRG1FKalONs5TG3XMPsb+vj687Wc7x6ew1YKIRYrCUqEEClbVZTB2zeX4bYbKW1vqVjOyaOHWlBKck6EEOOT4RshxBimZXGq3UvXUIA8j5P1JVnxUvAbyrOxlOKxo21jkl6T8YdNBgMRstMcs9lsIcQiJ0GJEGIUXyjKt16swxeKEjYVdkPn6ZOdfPj65fGgoiI3ncl0fCjAIevbCCEmIN8SQohRnj7ZwWAgQvhCL0jEtPCHovzmSCsQW/W3qc+HM8UgQ9egKjeNNIf8BhJCjE++JYQQo5xqH+LykiMKqO/xYVqKXx5s5mynd9TQjaFBUZaL3DQH7YMBen0RDA00DfI8Lu7aXjG3D0IIsShJT4kQYpRkc2s0oHMoMCYgATB0nR3VeZzv8TEYiAKxQEbXdO7cVka6U37/CCEmJkGJEGKUDWVZGNro0ETTYEWhh6Y+/5heFICwafHi2S4CYZPohQ0sFbv9t0fa56LZQogrgAQlQohRbl5fTJ7HgcPQ0TVwGDrpDhu3rCsm3WEbE7AA2HSNwUCERLmvLf1+TClBL4RIgQQlQggAghGTzsEgGvAnN67knTvKqc5LJ6osQlGTr79Qx9kOL5o2NsDQNLAbib9OdF1DT15vTQgh4mSgV4glzlKKJ461c7ipH0PXMC3FjupcCjKcNPf7sSywLvSBnO4YYnVRBq0DAQLhKKBhMzTeuaOCcx1e9jX0xYdvAAxNo6Y0Ey1B74oQ4sp25swZ3vOe98T/f/78ef7hH/6BT37yk0n3kaBEiCXu+TNdHG7qJ2qpeEBxoLEPm6GPSWiNWoqznV7+f7etpc8XwVKKoiwXuqZRnpNGx1CQln4/GhoKRUGGizdvLJ2PhyWEmGdr1qzh8OHDAJimSVlZGXfeeee4+0hQIsQSt/d876jeDYiVho+YZsLtLUsRsWLByKXshs4f715Gx2CALm+IvHQHpdlu6SURQvDss8+yYsUKqqqqxt1OghIhljClFKGoNal9DEMbd+2b4iw3xVnu6TZNCLHAdXd3s2PHjvj/H3jgAR544IGE2/7oRz/innvumfCYEpQIsYRpmkZhhpMubyjlfSwr1pPisEkPiBBLWUFBAfv3759wu3A4zK9//Wu++MUvTritzL4RYol788bSSc2OMXSNoWBk9hokhLiiPPHEE2zbto2ioqIJt5WgRIglrjo/nVtrikk19cO0LDJd9tltlBDiivHII4+kNHQDEpQIIYDtVblJy8tfzlTw+LE2KYgmhJiQz+fj6aef5q677kppe8kpEUJQ2zWMoWlYKrVA42TbIB6njZvXF89yy4QQi1l6ejq9vb0pby89JUIIeoZDRFMMSCCW6Lqvvg81iX2EEGIiEpQIISjMdGHXJ/d1EDathGvdCCHEVElQIoRgZaGHTLd9UrNwCjOc6FIYTQgxgyQoEUKgaxrvuaoipWRXDbAbGm/ZJOXjhRAzSxJdhRAAHGjsJ5UJNZV5abx5Q+mYMvNCCDFd0lMihACgrns4pRyRfl+EwkznrLdHCLH0SE+JEEuMLxTl1boe6rqHyXTZuWZFPtX56WS67HSnUG7eH44yHIqSIQXUhBAzTIISIZYQXyjK156rJRA2MZWiYzBIfc8wt20oYffKfJr6fETM8ftLFOCwSSerEGLmyTeLEEvIy7Xd+MNRzEvqi0RMxe9OdFCZm8buFfnj7m9oGquLMnDakq8SLIQQUyU9JUIsIbWdwwmTWTWgezhE1FJokDC3RNegIjeNt20pm+VWCiGmyp5rp+Ke8rk52W9m/pDSUyLEEuJxJf4dEjUt0hwGwYiZNNlVKbh2VT4uu/SSCCFmhwQlQiwh16zIJ1E6iAL8YZPVxZnYjcRfCwr43fGOWW2fEPNlsGWQ04+d5uSjp+it65UlFOaJBCVCLCGrijKoKc0ac7ul4JG9jawsSKcqLy3p/j3DIfmyFlec88+d5+B3D9F6oI32w+0c+/FxTv7qlLzX54EEJUIsMcMhM+HtoYhF+2CQe3ZV4UwyuybNYaBJaXlxBQn0B2h8uQkrYsVvMyMmXae6GGwenMeWLU0SlAixxERMK+HtmgampdA1jRvXFGI3RgcfdkPjupXjz84RYrHpre1NeLsVseg+3T3HrRESlAixxGwqzxoTcIwoy3EDsGt5HtetLMBh6NgNDYehc+2KAq6eYMqwEIuNYTdItOiTpmsYDknqnmsyJViIK5hSihNtg7xe30c4alFTmsmO6lyOtgzSMRgkbFoYWuwL+M5t5Rh67HeKpmncsKaQ3Svz8YWjpDts2JIkwAqxmOWvzYfHz4y5XdM1ijcWz0OLljYJSoS4gj1xrJ3Dzf3xKq29wyGOtQ7y4euXc77bR12Xl3SnjS2VOWSnOcbsbzN0stxjbxfiSmF32dn07o0c+8nx2NiBAmUp1rxlNWnjJH2L2SFBiRBXqAF/mINN/ZiXVEuLWooBf5gTbUNsrcxhbUnmPLZQiIUhb2Ue1//1dfTW9mJZirwVudjdsrbTfJCgRIhFbigQ4XBzP0OBCMsKPKwtzsTQNZr7/BgaXD7XJmIq6rq8bK3MmZf2CrEQGQ6DwvWF892MJU+CEiEWsfqeYR7Z24ilYjNnjrUM8rKnmw9cuxyP0wYJisbrGmS67AwFIjx9soNznV7shs62qhxuWF0QzysRQoi5Jt8+QixSSil+fqCFiKniQzRh06LbG+L1+l6q8tNxOYwxEwt0XWNDeTYPvlDLidZBQlGL4VCUV2p7+PG+prl/IEIIcYEEJUIsUt3eEJHo2JojUUtxoLGPPl+Yu7aVkZPuwKaDw6bjthu8a3sFjb0+QlFrVB9K1FLUd/voGgrO3YMQQohLyPCNEIuUoWuoJMvn9fsj/M/vz8X/77YbvGlDMRvLstF1jZ/uayJqjt1X1zU6h4IUZrpmrd1CCJGM9JQIsUjlpjvIdKU2QyAQMfnNkTaCkVjaa36GE0MfWzFKKchJlynAQoj5IUGJEIuUpmm8Z2claQ4Dh01PVJRyFNNSHGrqB2BHde6YoETXIC/dQVm2e5ZaLIQQ45OgRIhFrCDDxaduXcOdW8vxuCYeje3yxvJFMlx27t+9jOJMF7oGhqaxujiTP95dLQvuCSHmjeSUCLHIGbrO2pJM2gb8vFzbgzXOauvLCjzxf5dku/nojSsJRU0MTZMy8kKIeSffQkJcIa5ekY82wSBOotk6TpshAYkQYkGQbyIhrhBpDhu5nvGTVJ8+2UFrv3+OWiSEEJMjQYkQV5Ca0syEs2pGREzF6+d757BFQgiROskpEWKRauz18ezJTrq8QbLTHLxhbSG7ludztGWQgUAYa+xIDQBHWwexgNs3leKyG3PaZiGEGI/0lAixCDX0+Pj+aw009/sJRS06h4L8/EAzZzuG+OieFdy4unDc7JJT7YN895V6lBonK1YIIeaYBCVCLELPnOwYU5E1YiqePtmJ3dC5fnUhN60rwm4kDk1MC3qGw7T2B+aiuUIIkRIJSoRYhEbqjVzOF4rGq7Zeu6qAe3ZWkZGkfokG9PpCs9VEIYSYNAlKhFiEMpKUl1fA//eHcwz6w0CsLsk1K/KxJegxUSiKZI0bIcQCIkGJEIuIUoqXznbFg45EfGGTXx1ujf9/a2UODmN0GXqbrlGek0ZxlpSUF0IsHBKUCLGI7G/o44Vz3SRY4DdOKWjq9cULpbnsBg/csIK1JZnYDQ233eCqZbncs6tqjlothBCpkSnBQiwSEdPimZMdRMaLSC5x6VZZaQ7efVXl7DRMCCFmiAQlQiwCSikefrmecIoBSW66g58faMZSik3l2dSUZaHLQntCiAVOghIhFoG67mG6vRPPlLEbOpayGAxE6BmO5Z009vo40TrIe3ZWygrAQogFTYISIRaBlv4AYTNxiVaN2PRfXQND03jxXPeoIZ6IqTjf46Ox10d1vifhMYQQYiGQRFchFoFMly1pIbTcdAc1pZm8YW0RDptOogGeiGlR2zU8u40UQohpmjAo+eAHP0hhYSEbNmyI3/b5z3+esrIytmzZwpYtW3j88cdntZFCLHU1pVlJF9rr9YV56KXzNPT4cNkNEsUuhq7hdkx/nZtgMEIwEJHy9EKIWTFhUHL//ffz5JNPjrn9L//yLzl8+DCHDx/mLW95y6w0TggR47Qb3L97OfkeZ8L7I6biiWNtrCvJhAR5I5oGm8qzp3x+ny/M6682svflRva+EvvjTSHHRQghJmPCoOSGG24gNzd3LtoihEhAKUV9zzCnOobYuSw3UcwBQJc3hMOmc++uKtx2A4dNx3nhz7t3VCatAjsR07Q4tK8Z33AYy1JYliLgj3BofwvRCyXthRBiJkw50fW///u/+e53v8uOHTv4yle+Qk5OTsLtHnzwQR588EEAuru7p3o6IZYMbzDC/oY+OoeCFGe5aOjx0drvJ2qBrsWKoyXisOlomkZlXjp//aa1NPf7sZSiIicNmzH19LGeLh+WNfakylJ0dngpq8ie8rGFEOJSU/qm+tjHPkZdXR2HDx+mpKSEv/qrv0q67QMPPMD+/fvZv38/BQUFU26oEEtB11CQ/372LC+e6+ZMh5fnz3TT2BsLSAASxAYA2AyNXcvyqO8e5qGXzvPvT5/hpXPdOG3GtAISgFAomjAosSxFKBSd1rGFEOJSU/q2KioqwjAMdF3nIx/5CK+//vpMt0uIJennB5oJmyppb8ilNC3WcwLg0HVMy+KR1xtp7vMzHIpS2zXMd14+T0uff1ptysxyoSVIsjUMjcwsWdBPCDFzphSUtLe3x//9y1/+ctTMHCHE5CmlaBvw0zWJ5FGlLvac+CMmr9T1jilBHzEVT53smFbbsrJdZGa60C8JTHRdIy3dQV5++rSOLYQQl5owp+See+7hueeeo6enh/Lycr7whS/w3HPPcfjwYTRNo7q6mq9//etz0VYhFgWlLLoDpxiOdJLhKCXftRpNSx7/1/cM8/MDLQTDszMU0jEYmNb+mqaxeVspzY0DtLcNgYKikgwqq3OkQqwQYkZNGJQ88sgjY2770Ic+NCuNEWKxC5leXmn7CkFzEKWiaJqNdFs+15R+CrvuHrP9UCDCI3sbU15kbyo8zukXbtZ1napluVQtk5l4i5llWtQ/X0/r/jbMiEl2VTarb1tFuvR4iVkyMDDAhz/8YY4fP46maTz00ENcc801SbeXiq5CzKBjPT/CH+3FVCEsTEwVwhvp4GTvLxJuf7i5P2ny6kywGxrXr5YEcxFz/OcnaHq1mUggghW16KvrY/839xOSmjNilnziE5/gtttu4/Tp0xw5coR169aNu70EJULMEKUsOv1HUIyu3aEwafPtT7jPUCCCOQtRia6Bw9DZs7qQLRWJp+uLpSXQH6D3XC9WdPQaSmbUonlv8zy1SlzJBgcHeeGFF+KjKw6Hg+zs7HH3kQX5hJhBKuHKM6BIvJjesgIPx1oGky62N1XVeen80dVVGLr87hAxvm4fmqHBZalLylQMtXnnp1FixrksgxXDc7PwZnd3Nzt27Ij//4EHHuCBBx6I/7++vp6CggI+8IEPcOTIEbZv385Xv/pV0tOTDxfKN5YQM0TTdPJda4it23spnaK0TQn3WVucSZ7HQZJlbabMbTckIBGjpOWloRLkLmm6hqdIVo8Wk1dQUBCvQ7Z///5RAQlANBrl4MGDfOxjH+PQoUOkp6fzpS99adxjyreWEDNoU/69OHQPhuYAwNCcuIxManLfmXB7Q9f4wLXL2TxOVVQNEi6yN56GXp8smidGSctLI7sqG902+mtft+lU7Cqfp1aJK1l5eTnl5eXs2rULgHe+850cPHhw3H1k+EaIGZRmz+ONFV+gzXcAb7idLEc5JenbMHTHmG2VUgyHoth0nZb+5NN2FckruSYTNi0G/BFy0seeVyxdm96zkbO/O0fHkQ4s0yKjJIO1t6/BnT12ZpgQ01VcXExFRQVnzpxhzZo1PPvss6xfv37cfSQoEWKG2XQXlRnXjrtNY6+PRw+1MBSIYCmSZKLEGJqGInGVV7uhJZxObKnYWjhCXMqwG6y7Yy1rb1+DshT6NJcgEGIi//Vf/8W9995LOBxm+fLlfPvb3x53ewlKhJhjvcNBvvdKPamWJkkWeNh0jTVFGZzp9I66X9OgPNtN+gzUJxFXJk3TYkmvQsyyLVu2sH9/4tmHici3lhBzaO/5Hp460TGp4Zhw1MLlMAhHLaKX7Bi1FP5wlPWlmZxoHYqVgVeQ6bZz946KWWi9EELMLglKhJii4Ugn5waeYCDYQLq9iFXZt5HjWpZ0+xOtgzxzcnIBCYAF+MNmwhk6TX0Brl6exp+9cRWtAwEyXXbKc9xS/l0IsShJUCLEFAyF23i57ctYKoLCwhftoid4mu2FH6YobWN8O9OyONPhpd8f5pVz3USnUY4kUTATtRQHmvq5aX0x2WmS1CqEWNwkKBFikpRSHO/9CaYaXZrbUhGO9fyIwooNaJrGgD/MQy+dJxS1CE8nGplAdIYLrwkhxHyRoESISegL1nKo+2EC0d6E94fMISJWAIeRxq8OtTAciiacNTNTNGB5QeqFr4LBCO2tQ4RCUbKyXSgFylLk5qfjdttnr6FCCJECCUqESFEg2sfejv/GVOGk22iajk13EI6aNPf5ZzUg0TVw2gzeVFOS0vZ9vT6OHW5Hqdj04vbWodhxdA3O9lBZncOyFXmz12AhhJiATFIXIkWNQy9hKTPp/Tp2yj1Xo2u2ceuOzBSl4EPXL0upQJpSipPHOrCssfVOLEthWYqmhn4GB4Oz1FohhJiYBCVCpMgX7R6zAvAIDZ2S9K3U5MXKyTttBqWzXCXTbmjjVoK91LA3RHSCvBbLUpw40sbJ4x0SnAgh5oUEJUKkKM+5Kr6mzaV0bOwu+RRbC+/H0C7mZbx9Sxn2WSxQpdBSHh4K+CMpbRsKmXS2ezm8v4WWpoFptU8IISZLghIhUlSesQuH4UHDiN+maw6K0jaT41o+ZvvDzQOz2h6lFKuKMlLatq/XN6ljW5ai7lwP0Wjy4SohhJhpEpQIkSKb7uT60s9QlXk9LiObdFsha3PexrbCD4zZNhQx2Xu+N2F5+OnSiJWYf8umkpRLyZuTrdhGrBT54IAM4wgh5o7MvhFiEhyGhw1572ZD3rvH3a7PF8bQtVFl4WeCrsGOqlyuWZk/qWJpOTlp9HT5sCbVHoVNFvUTQswhCUqEmIBSFr5IF4buwm3LvuR2RVOfn2MtA2jAxvJsKvPSgdj6MzMdkBiaRnGWi9s2lqRcRt40LY4dbqe/zz/p89nsBplZrknvJ4QQUyVBiRDj6PQf40j39zBVBIVJlqOC7YUfwWXL5snj7Rxq6o8P0RxpGWBHdS631sSGVVYUeDjb6Z3See/aVo4vFGXv+V4GAhEMXaOmNIu3TDIg2ftyA6FQ6nkhuh4btrHZDDZvK5M1dIQQc0qCEiGS8IbbOdj1rVHF0vpDjbzW8Z+s8fz1qIAEIGIq9tX3saUih8JMF1src6YUlOhabPhnz5pCrl6Rj2UpNI2UAwSlFJ3tXhrO900uIDE0lq/IIyPTRVa2SwISIcSck6BEiCTO9T+BqSKX3WoRiPZzrK2FaIIkVkspznV6Kcx04ZxiPoaloN9/MRDSEy0PnIRSihNH2+nt9WNNNslWQW5+OukpFGMTQojZIFlsYkmylGI4EiZqjS0oppTFwa6HaPMfgAS1WTU00AIJgwVd07DbdHqHQ/x4X9OU2mY3dCpz06a079BQiN6eKQQkgNNlIy1N1r8RQswf6SkRS86xvk5e6GzCtCxAY0NOATeWVKNfGK5o9L5Ep/8oiQISAIso28rL2FfXmfD+9SWZPHm8fcorA7sdOhvLsqe070Cff5IzbMAwNHRDZ+PmUhmyEULMKwlKxJJSO9THH9obiaqRgEFxvL8bTdN4Q0k1AI3eF5Muuqdjp9Kzm8KMfN6+xcajh1tjPSYKLBR3binH47LHFuObQvt0Dd62uQz7JIZ+RtauMQwNu8NA17WUAxNdh/Ubi8nNS5/UMJEQQswGCUrEkvJaV8slAUlMVFkc6+vi+qJKbLqOZV2eRzJCY03uHSzPvBmADeXZrCzKoLbLC2isLPTgsseqvaY5bQwFo5Nun65plKc4dGNZFufO9NDRNoRSCqfLzsrVeUyms0PTNPILPJNupxBCzAbJKRFLijeSuAcEFEEzFkSUpG9DTxCvu4wslmfePGqIw2U32FCWzYayrHhA0tDjo9s7tUqo167Mx2kzJtwuEjHZ+3IjbS2D8ZV/g4EIJ491smpNAXaHgWFosd4Tu4E7Sa5IXn76lNophBCzQXpKxJJS5E6nYXhwzO02XSfNFrtwr8i+hXb/IYLRfkwVRseGpulsLbh/wpwL07L40euNmFNLJyF7gkTTaNSip2uY2rM9RCJjp/talqK3x8+1NyzDOxQCICPTybA3xKH9LVhKoazYjB7D0FmxOn9qDRVCiFkgQYlYUq4rqqTFd2LUEI5N07m+qDKe6GrX3dxQ+re0+Q7QGzyL25ZLZcZu3LbcCY//y4MthKaY4ApwrnOYLZWJz9Pf5+fY4TaUUiSYNBTn94dRCoaGgnS0DQFQXJrJVddU0dE2iG84QmaWi5KyTOz2iXtlhBBirkhQIpaUQnc6711ew0udTXQGfGTYHVxdWM7KzNGBgKHbqci4moqMq1M+tjcY4dSFIGCqhoKJ81ksy+LYkXbMFKb6ZmQ4OXq4jcH+QDzh1TfcQ0+3j81bZYaNEGLhkqBk0TKB80DLhf9XAMsA+eU7kUJ3OndVr0t4XyDaR8T043GUoGupPZd9vhC1XcO09/uYeh9JTHGStWb6ewOgJg5INA1y89I4fapr1Awcy1IMDgQYHAiSneOeZiuFEGJ2SFCyKCngVWAI4pfBs0AXcA2xxe3FZITMIfZ3PshguAkNA03T2Zj3Xso8V4273x9Od/JKbU9sWu4MtGPP6sKEt1spBSQa23aW05ekeJplKgb6AxKUCCEWLAlKFqVuwAujLoMWMAj0ApK8OFl7O/4Hb7iVWGgRBQVHe35Aur2QbGdVfLuhQARfKEp+hpOOwSCv1vXM2GrA79hahq5rsVWHNY1VhR6cF3I+cnLc43aU5OansWZdIS6XneGhELqhjQlMdF3D4ZCeNCHEwiVByaI0QGz45nLmhfskKEnFULiNJu+LDEe6GA63c3lfh6ki1A/+nq2FHyAYMfnp/iYae/0YuoZSiqJM16gF+abDbmj0Dod47EjbhYRbhUJx17Yoa0vysdlLWL2ukLOnulAqNgVYNzRyctxs2FyCrl+c3V9Y5KH2bPeYc2ha7D4hhFioJChZlFzEckcuD0yMC/eJibR493K094dYKkqycvKgCET7AfjZ/mYae32YFpgXekZaBwIz1p6IqXjxXE/8vCN+fkDnL289QZqjjpLSa8nKqqSjfYho1CK/wENOrhtN0/AOBWls6MfvC5OZ6WJtTRG1Z3qIRGPvEbvNYMPmEmwy20YIsYBJULIolQAnE9yuX7hPjMe0whzrfQRrzArAo+manQL3OoaDERouBCSXSiHNY9o0DU63O9lW5QMaSEtfyfKVo3vC+npjU4UvzrQJ09XpZeuOcrQLpePT0x0y60YIseBJRddFyQ7sBtKJvYQ64Llwm/wSnkh/qGHCgELDhkNPpypzD76wiTFPF3RLQcTUiOUMtY65XynFmctm2gCYpqKuthePx4nH45SARAixKEhPyaKVCbwB8BObbSMzKlJhWmFqB57AIlm5eXDoGVRkXMOKrJtxGGnkpVvzNqFJA1YVjQwTjf24mqYilKS2ydA4w0vd3iC9w2EKM5zkepwz0FIhhJg+CUoWvdQWbxNgKZPnW/8Rf7Rn3O0yHKWszXl7vHfBZujcur6Y351on7HE1vEpNMBmKHYtG8Lfl053o5v0jDyKSyxsl6wgrOsamhZLvL1covyRcDRWBr+l34+uaZiWYkWhh3ftqMDQpeNUCDG/JCgRi0bIjHKot4PaoT5cho2teSWsyMxJef8O/xH80d4Jt+sLnuNIz3fZnP/H8cBke3UudpvOo4damKEZwEmtKHCT6+llbUGQtjOF1LXrWJaOrps0nG9gx84K7A6D/l4/SkFhcQZdHd5RQzi6rlFRlT3m2E8eb6Opz38hWTe2fV3XMH843cXN64tn94EJIcQEJCgRi0LYNPlB3XG8kRDmhV6Bdv8w2/NL2F1UkdIxevynST7TJkYp8AVyqAvVU+mpJ9e9HIiVkH/iaNusByQAEVPnLRuv4sihRiLhaPx2y1JYYZPjR9vx+0aGn2K9JOkeB77hMJquoSxFaXkW5RXZlz02xdGWwfjsoRFRS3GwsV+CEiHEvJOgRCwKx/u7GL4kIAGIKIt9PW1sySuOr/A7Hpcte8JtlNKpa3ozZtRNQ9MA9+0Oke9x8lpdL+GpLv07ST3DIUCnvzea8P6R1X8vtBiIzbjZuqMM0HCn2RMutKcUYwKSEZE5emxCCDEeGUQWi0K9d4BogrwJQ9PoCAyPu2+77xAvt32ZJu/LE55HKYNIxIOl7AwH7Tz8cj2WpWjq881JLwlA3oXE08nMmFFK0dfrJzPLlXTlX13XKMtOnBBdnZ8++YYKIcQMk6BELAoeuyPhBBgFpBnJe0nO9T/B4e6H6Q/VEzT7xz2Hadro7NnExY+FRti0ON46SDg6Nz0JNkPjDWtj698UFnlINS5RCiLhRFV+R7t9cykOQ8e4cFxD13DZdG7bIPVthBDzT4ZvxKKwJa+YM4O9RNXF4EADPDYHRe7Ev/IjVoBzg09OWCQNwLIMOns2096187LbFb8+3DJBJsrUZToNIgoCYZO8dAdv2lBCnt3GuTNdoIHTaSMcjgIamgYOp41QMDqmLglAW+sQBUUZ4y64V5Ll5k/fuJLXz/fRORSkLMfNVdW5eFwTD38JIcRsk6BkURkGGi78nQtUA455bM/cKXKnc0vZMp5tawAUloIcp4u3V65JOszhDbehY2CRKCiJrS8DGhoG6eZ76e7J4PKCJDO12F4y/ohFeW4af7SzErvNoOF8L4eOd8aDDl3XyM5JIy8/jXSPk+wcN3Xnemlp6h9TAM6yFMePtHHtnuXjDv1kuR3cUiNJrUKIhUeCkkWjF3idWGVPBfQRC1CuZ6kUTluXXcCqzDy6g35chkGOc/zH7TQysRIuXAhZjkqynVXYdDcVnqtx2wqobTtPjy9E9EItEpseq+Mxm2FJ1FK09Pl5vb6P7eXZNNb3j+oFsSzFQH+AquW5ZF/IB1m5Op/+Pj/D3tCY41mWYmgwSFaS3BEhxJVt0GjniZx/nO9mTJnklCwKCjhCbAG+kQuWBYSBl4GheWrX3LPpOiVpngkDEoB0ewHZjiq0BKX3h8ItdPmPU+heh8dRhKHrfPC65bxxbRElWW7Kc9xU56VPKyCxG6klhEQtxaGmfnp7fAnvtyxFV4c31u7BIEcOtl4yJVgIIa4cEpQsCmEgmOS+ILHAZGDOWjPbTMviRH8Xv2g4xWNNZ2kaHsQXDfNaVwuPNZ1lX3cbgWji6bKX21H0UfJcq9GxcenQjMIkYPbxeuf/hz8SK6hmt+lcsyKfe6+uotcXprZ7/Fk947HpGjlpDvQU4hIDyLagt9efdJvW5kGam/o5tL+Fvl5/wpwSAE3XyMiUlaKFEIuTDN8sCgaxnpFkTOAUcM3cNGcWmcripw0n6Qr440mtdd5Y/oSmgakU570D7Otp494VG8hyjH8BdhjpXF3y53T6jnGg65tj8kssZdI49ALr8u6M3/bUiXYCKcxkGc/OZbncsKaQ3xxu5URb8p6sXF1jvcOOhkZvd+KekhG1Z5KXx9d1DTTYsKkk9m8xr5RSeNu9BPoCeIo9pMuUayFSIkHJgucFalPYbmCW2zE3aof66A76R82yiRdMu/BXVFmYpsVz7Y28vWpNSsc1VQhNM+CymTgKE1+0a9RtJ8cJIlKhaeC0GzhtBjevL+Z8t4+waWFaKp5ea+hgWlDjsM/ICr4rV+dTWJSB3SGrRM+3SCDCoe8dxtfji61LZClyV+Sy8V0b0A3pnBZiPBKULGi9wF7G7yUZcWWs9Fo71E/EmvjxKqBxeGDM7ZYy6fAfYSDYQLo9n1LPVdh1N1nOKhRjh3x0zUGea/XF4yo17Rk3SkFt5zA3rC4kO83Bn71xFfvqe2nu91OQ4WRzeQ6t/X4az3ZP6zwjHA6DsstKyou54evxUff78ww2D+LMcFB9fTUdxzoZ7hxGXfI+6qvro+HFRpbfuGweWyvEwidByYJ2mNQCEgNYMbtNmSNuwxbvTZiIoY3+1RmxArzc9mUC0X5MFcLQHJzqf5RrS/6KDEcpJenb6PAdxlSxJFENA4eeTkXGxWEvTdMwLsy6mY4M18WPVrrTxo1ri0bd71YwbBhjpvVOlq5rVFSnviihmDn+Xj/7vrEfM2KCgvBwmOO/OIEy1Zg3sBW1aD3QKkGJEBOQvsQFKwoEUthOIxaQVM5uc+bIxtyiMcFGIoamsSGncNRtZ/sfwxfpwVSxqbKmChO1Arze+b90+0+SwzvwD91AJJIDVgalabu5vuwz2PTReSk7l+VO6zHYdY2iLBe/O97Oq3U9+EJje2hSqb6aiNNlQ9c1DEND1zXKK7OpqMyeVnvF1Jx/7nw8IBmhomMDkhFWRNYXEmIi0lOyYKUSL+YCVwFXTjXOAlcabyyt5vdtDeiahkJh13TcNjtD4RBoseGR0jTPmNWB23wHEw7RBKK97O18kGhU53zn7fgD92DocMpmsPJGF87L0jDeuLaIhh4f7YPJZjyNL2Ip/nA6lqdiaPDc6S7ef0015blp8W0ys12Tnm6s6xqbt5bidMWqujpddmw2+V0xXwabh1Lr0gPQIG9V3qy2R4grgQQlC5YOZAGD42yzjispIBlRk13A6sw8Wv1eHLpOSVoGGtAZ9NEfCpLvclPgujibwR/ppXV4H6Y1tpjYRWFsNlhV/RhHT9+PaRkEIyYvnu3mLZtKR20ZtRQVOWlTDkouZSowTYufHWjmEzevvpjUqpL/ok7mqmsqSUuLVfC1eSShdb65sp0EE7xHNB10Q8eyFMpU6DYdw2Gw8uYrY4hViNkkQcmCdhXwzDj3NwNXTj5B4/Agf2hvoC8UwGXY2JFXwlUFpfELebHbQ7HbM2qftuEDHO75LkpZqCTVW0fRLDI8LQx5q7AU1F1WiyQYMfnvZ8/im+aU4Mv5w1H6fGHSdI0jB9sIBCZej+dyNpsEIgtJ9fXVHG07NmpYRrfpFK4vZMVNK2jd34Kv20dmeRZl20qxu6+8HxBCzDQJShY0F1AKtCW5/8qp6tnm9/Jo45n4VOCgGeW17lZClsn1xYnzZaJWkCM930tpwb0RGmDoF583Q9dQStExFGQoEKGha3jGAxK40DFiWry+tzVp4bOJWKYFCarTivmRtyKPNW9Zw7mnzmFFLVBQVFPImjvWYNgMVrxRekaEmCwJSha89UA7Y/v6DeDKWVTtlc6WUbVJIFaP5FBvB1cXlmHXx16MewJn0SaZq61pFt7hsovH8Ib496fPEIyYaEDYnJ2VbnLdDk4daptyQAIQiVpIrdaFpXRLCcWbigh7w9jcNmwO+UoV4lLV1dVkZGRgGAY2m439+/ePu718gha0IWLTghMFJJnEelGuDH2hxDONNA18kQjZl2ejAloKs3QuZVkarZ07iZoXE04V4A2mVrJ+qtx2nc1OG6HA1M+j6xppadL9P11mxCTsC+P0ONFnKElY13VcWRIuCpHMH/7wB/Lz81PaVoKSBSsMvAJjZpNoQA1QzpU0ozvP6WY4OnY4SilItzsS7pPvWsNkskUbmm+mf2jVVJs4KRqQ53FwVXUeFW47p493TvlYuq6xbEUuhlQDnTKlFHXP1tG8twW0WD2aqmsrqb6+ekYq6gohZoYEJQtWC4kLpyliwzmRS7YpA5azmF/O3UXltNZ7Rw3h2DSdbXnF2PXEF2NDt7O96KPs7/waljLHSXTVsPy30j80e2P8ugYep43lhR6uXp5P0SWL4h09nCwnaGKGobFuQzEFhZ6JN16k1IWZSNosrtnT8GIDza+3xHI/Rm57qRG72075VeWzdl4hrmTd3d3s2LEj/v8HHniABx54YNQ2mqZx6623omkaH/3oR8fcf7nFexW74vlIXs21+8KfEbVAB3Adi6X3RClFk2+IuqE+HLrBqsxcbiyu5EBfBwOhIG7Dzo6CErbnlYzZ11QRzvU/QZP3JSwVJde5Em+4naDVn+BMOruL/5JjTRnoWhfTLNSa0PL8dHYuz2N1UcaYX92WZeEbHm+qcnI2u87Vu6uv2PVsQt4Qp397ht5zvSilyFuRy9o71s74UIhSiqZXm8cUL7MiFg0vNkpQIsQUFRQUTJgj8tJLL1FWVkZXVxe33HILa9eu5YYbbki6vQQlC9IgMJkaGRaxIKaDxZBnopTiseZzNHgHiFzoGXm9pw2D2Eq31Z5sbq9YhcNIfDE+0PkNeoJn4rNuuoMnk56rJvddNHbncKSlJ0FAoogNtEzP8gIPa4ozE9534lgHwSnkkhSXZLC2puiKHVqwTIv9Dx2I1fm48Lr01fWz75v72f0X12DYZy4QU0oRTZI3FPZfOTPYhFiIyspiEwsKCwu58847ef3118cNShbHz+ol5RTwMjDZHAQT6Jv55syCeu8ADcMXA5IRJgpTKZp8gzzZknhlZG+4fVRAMpETvb9iX9tv6fddmkirmHTlsnEcaEz8vAcDEXq7fVM6ZiAQuWIDEoCes71E/JHRJdqVIhqKcurXp2l6rZng0PSL10EsEdWd6054n+cKHhYTYr75fD68Xm/830899RQbNmwYdx8JShaUQaCe1Bbhu5wOJP7iXWhOD/aMuxKwqRT1wwMEomN/3Q6FWyYXT2ghigr2saLqycvvYCZ6SQAG/BGi5tjH4/dHprzgns93Zf+C9/f6MaNjc4CsiEXn8U5qn6nl1f96jdZDrTNyvtVvWjVmto1u01l168oZOb4QYqzOzk6uu+46Nm/ezM6dO7n99tu57bbbxt1Hhm8WlA6mFpBA7AK7OMbGE9UcuZymYgXU3LbRb1GlTCwmVw3V0KNkeFpxu3oIBPOZqWBkhK7HFse71OBgkOYkPSipGCknf6XyFKZj2AzMJIXqlKlQKM4+fo78lfk4M5zTOl/+6ny23LuZ88/V4+/14ylMZ/kblpNVnjWt4wohklu+fDlHjhyZ1D4SlCwoI7/eJ/vz2g1sA6b3xT1XanIKONHfjTXO44yixoQOHb6jHO19ZErnVJaGy9l7ISiZmVwSiFWE3ViWhXcwyPnaXny+MDZDIxgyUVPMqtV1jeUrr+zF2/JW5uHMdBLoC0z4PHWf6aF8R9m426QipzqH7fdfOcsyCHElkuGbBSV3Cvu4gDeymNbAiVgmaoLASwOO9l/MqxkOd3Kw+1uTKik/ort3PcfPvJ/+wUtrlEwvp0QDbLpGZW4aV5dnc/hAKwP9ASJhk0AgOuWAxJ1mZ8OmEnIuWVH4SqTpGjs+uJ3iTcUYDmP86cBTHQMTQiw60lOyYNQTS3Kd7BdwNjM9HDHbDvd2TvgoFdAd8AMwGGrm5bavTHrYBqB/cAXNbdejRq0ZM/3nqzo/nbdsLCU/w8nrrzZOq3z8iKKSDNZvuHKWDpiI3W1n/dvXsf7t6/B2DrP/m/tH1REZkb+mYB5aJ4SYD9JTsiAMEwtIJptPYgCrZ745syxoTjxF1tA0itNiMyNO9P4EaxKLDyoFpqlhWTpNbddeFpBMn93Q2LOmkPwLeQ6+4ZlJSl25OrUyzFeijCIPVddWxpJR9VhPykgiqitzboclzbBJ++F2zj9XT/eZnin3egkhJk96ShaENibfQ5IFbCK2Bs7isiozl86Ab8wCfJeyaTpbcosA6A81TOr4Cp0jpz6CrgcxzZkdBrHpGruW5VGVlx471wwNLayrKcKxxBdzW37jcgpriug53Y2maxSuL8SdM7czyvy9fvY/dAAramGGTQyHgTvHzfYPbMPmXNqvjxBzQT5lC8Jk62ZUAJtnqS2zyx+NEDSj6Fpshs3Io47laOgopahIz+LGkirS7Q6UUhianahKVkJ+NMvSGBhahlIGppk+o23XgI/csILCS0rIh0Mmmja9tAdd18hOUkdjqfEUpOMpmNnXbTJO/OJErH7KBWbYxNfjo/6FBlbdItOHhZhtEpQsCMVAHakN32wAqma3ObOkLxTgkbrjRJWFeclVvNCVzs2l1RSnZYza3htuY3/XNzBVqsMjGoFgHo0te2aw1Rcp4Mnj7fzx7mXx2wybztRmTF3kyXDicskKwPMtEojg7Rgec7syFR1HOyQoEWIOSE7JgpAFLJtwq4u1SBZXYuuI37fVE7LMUQEJQFfQR713YNRtphXmlfZ/xxfpRE0QrOnYWZd7N7sK/5q6hjuw1OzlIDT3+WkfvFgd1mbTyc2bfC+HYdPRdQ1PhoMNm8eu7yPmwXgfq8X5kRNi0ZGekgVjDXCe8X9xK2Il6K9nMcaTzb6hpPe91t3K1rwSXBeKpXX4j2Cp1NaMyXCUsSLrJgDWlQU52jT5tWZSFbUU7QMBSrJigciwN0QoGGUytU+ysl1UL8/F4bDhmWZRsCuRr8fHcKePtDw3GcUZE+8wQ+wuOxklGQy1DY36GOqGTsmm6c+KCvQHOPPkWfrq+mLH3FzMyptXYlyhCy4KMRUSlCwYfcQCjYlyJ7xAOzD9YlJzzabpY9a7GaGAEwNdbM+PLSgYModSDkqGwi0oZaFpOvWdM1c+PpmDjX1srcyhvq6Xxvp+Jjt0MzgQJCc37Ype22ayouEoA02DNL7YwFCbF83QUJYioziDLfdunrMk05q71nPgoQOYEQszYmLYDdLy0lh2Qyo9mclFAhH2fXM/kUBsvR/TNGk92Ia3c5gdH9g+Q60XYvGToGTB6GLigGTEeRZjULIuJ5+jfV1J7++6UJcEIMe5HF0zMFNIcFWYBKJ9DPrS8YZmr5dkRNtAkGO1vfQ19l+4ZfLBRSRiLvnZNiNaDrRy7nfnsEzrYlrVhZdxqG2IM4+fpebO9XPSlrTcNHZ/Yjfdp7sJDgTJKMkgd0XutAPI9sPtsZL6ly5AaCq87V6G2obILF18s+iEmA2LbwzgitRDrHhaqoZgCoXE5kogGuXVzhYeqTvOY01naffHVoncU1xFpj35mi7ZTucl/64m17USPcW42aa5+cn+puk1PEUKaGvqn/KMG02L5aIIGGwZjAUkESthnrcyFZ0nOue0VohhNyjeWEz19dXkrcybkR6toTZvwsJwmga+rqmtJC3ElUi+GReEWiY3BGAQC0wWHn80wndrj/B6TyvtgWHODvXx0/pTnOrvxq4b3L9qC0aCngUDjZrswvj/NU2jLP0qrBRmJKXbCvAGbQwH5y5QU9GpXyRLyjLR9Svnoxf2h2l8uZETvzpJy74WopPorWrZ1xoLSMahLLXoC5h5ij1jVikGQGmk5V/ZSwoIMRlXzjfjohaYeJNRLGJr3iw8+3vaCESjo2bYRJXFs+0NmJaFTde5u3oddk1HIxaMODSdt1atJtNxsadEKcWJ3p+SyjTpDXn30NDjI8EP0VkRy/yZ2kWyuCSDVWsKJ95wkRjuGubV/3yV88/V03Gkg3NP1/Lqf79GyBtKaf+wb+Lp3ukF6Ykv6ItI2dbSMY9BMzTSCtLILJOhGyFGLO5P+qKjgGbgOeApYD+xEvOT+VLSiK13M38FpsZz3tufcPVfhaIvFKA36OeXTacx1ciSfAq7YVDoGv14GgafJ6L8Y45zOR0bkXAFTxxvn5kHABjj9NbrQI6uk5ZSl/7o52HV2gLWbShGH2/xuUXm1K9PEw2Z8aEJK2IR9oWpfbo2pf3z1+Sj28f/GvL3+jn+8+OLurfEnmbnqg/vIKc6G7RYQFK0oYhtf7xVEp6FuIRk2s2ps8SSVEeSNzuAbpjU2iwK2DjD7Zo5bsMOBMfcHrUs6ob62NvTNqoXxQR80QjPdzRye8WqC9uGONH/k5TO5zAy+d4rDZgzeMFy2g2iZiy0ipgXj2sD1jhs5Op6ihcSDU2DwqIMlq3Mw+2+sgqkmREzNn32cgq6z/akdIzSLSW07msl0B9ImHMBsbyS7jM9tB5so3zHzCR4+7p91D5bx2DzIA6Pg+rrqijeOLuLIablpbHtvm3xpQkkGBFiLOkpmTNRYlVbL59NYsIkFpuLvWSdM9WoGbc9vwSbNvZtpYBXulvHFE4bcd7bH/93f/B8SufSNTs+7waCMzxuE7UUd20r5/7dy3AYFx9LFMhKOSC50EZdY92GoisuIIHYonnJngvdSO2rxbAbXPXhHay4eQXZlVmxmS4JuqqsiEXr/tZptXeEv9fPvm/up+dMDxF/BF+Xj1O/OU3Dyw0zcvyJaFry502IpU6CklllEas/0kssMXUmnm6LyeegzJ2VmbnsvFBrZDL0S5JfdS21C3i6sYzDdesmfa6JREyLZ091ca7Ly1XLcrFfuEhucdgnvd6waSoO7mshOlcJL3NIN3TyV+ehXTYcpdt0SremXqXWcBhU7qpg+we2s+bNq8ccb4QVTXXK/PjqX6jHjIw+lhWxqH++YcztQoi5JUHJrOkFngZeB/Zd+HumvvB6mdrKwnNjWWbOpC/e67Lz4//uDUycj5BhW8Grx25lckNfqVEKuodDvHSuhwMNfWwtz6bYZpAxTs/AeLzeEOdrUxvOWGzWvnUtaXlpGA4D3a6j23WyyjNZtudisbFoKMpA8yD+volzhNy5buwJ1gHSDZ3CmqIZafNg82DCj46maQT6F27AL8RSIDklsyJM8iBEJ7WF98YzDBwh1guzYZrHmnlDkRC6pmMmqd56Obdh47qiSgAipp+zg78Zd3sdOwRvIzrLiY+6pVhjs+Hs8rPaPvWPirIUne1eVq+d2qwbf6+fzhOdmBETZSqGWodweByU76wgpyp7yu1KhWVanH+unrYDbZgRk+yqbFbftor0/FhisiPNwa6P7WSgaYBAXwBPkWdUIbCGlxupf64+VqHVVGSUZLD5vZuwpyXuDdM0jZq71nP4h0fixdR0Q8OZ7aRqd+WMPCZ3jptA/9i8J8u0cErZfyHmlQQlMyZMbFgljVgZ+EQ0YjNtBpl+L4cJNAHLL5xz4ch1uJKWkwcwNA1TKQxNw23YeN+KjTiMWI9H3cAzTPTcrMh8O7/aq2Faszskstlpx6lpM1K03ppiANW0t5m6Z+qwrLHFxXrO9bLy5hVU7KyYgRYmdvznJ+g91xtPQu2r62P/tw5w9Z/uil/ANU0jpyqHnKqc0e0720P98/WxfUcqtLYOceynx9h237ak58ypzqF8ZxnNrzaDroGmYYUtQt7QjJSbr76+moHmwVH1UXSbTsG6AuxXYO6PEIuJDN9MmwUcAp4BXiU2ZNNC4l4SBXiAmfnFFwty+mboWDOjN+jn101nx93mxuIqduaXclv5Sj60eitpF6q8mlaYuqGnJzxHXU8P4VnO0cjWNRyahj5DCYl5BZOfwh0cDMYCkmjiaqdWxKL26bpJFSubjEB/YFRAMsKMmDTva5lw/8ZXmsYURlOWYrBliOBQ8jomfef7aHm9FWXFtreisYDk8PcPx2euTEdOdQ7r3rYOe7od3aajGzpFG4tY97a10z62EGJ6pKdk2k4R6xmxuHjlGCQWMCT6Ap34y3xyFkZ3c9CM8sPaYwxExi+aZdc0ytIzyXeN7t0JRgd5ofWfUCnk3QQ5hMXsrYXi0qBI12ckYtcNDZuhs2p1waT37T4zcR6KZmgMtXnJXZYz4bbjiYajDDYPYTgMssoz0TSN4S5fbCbMZTGPMhVDLRNXFE5WGE1Ziog/jCsz8Xs3WZXX4GCIA98+yOY/2pQw72QyijcUUVRTSHg4jM1lw7DLSr1CLAQSlCRlMXFHkiI2hHL5F+il+85U3kOiIMcG5CfYdu79tP7khAEJQEQpXPrYt93h7ocJW96UzmXNwK/lRDI0jbUOW6yHhOnXkSgqziAr20VRSeaU1rrRdG3Ctf6UpbC7p/cxbjvUzpknzsTOp8DmNNhy7xbS8twoc+xzrRkaGcWeCY+bXZmNv2dscmust2SQ9ML0hOX2I+MsFzDYOsixH48//JMqTdMkh0SIBUaCklHCwBliM1sixHoh1pB8uMUi+Ywai6msHpuck4s/WRWxMvNXzfA5pmY4EqY7OPHMCoi1tsk/yHrHxZ6DVu8+eoKnU9rfsnQ6e2eneNxGpx3bDAzXjBRMWz/NYlwFa/M597tz45wIXFkuPEUTBwjJeDuHOfP4mVFDNGbY5OD3DnHdp64lqzKLwcbBWNLpBbqhU7Fr4jyWtLzkuU5nnzxH3e/Ps+k9m8Yk6xauK2SodSjxmjgWDLYMEegP4M5xT/wAhVhisoIWbzmZ2jIP0/X5WTimBCVxdcSGYi4VAk5c+HeiwMQgVu492SqfM/mLPgjcfOFvg1huyvwHJAAD4bEzGZJRQMvwEOuzY0FJf7CBQz3fSXn/QDCXnt5Nk2xhatqjUcpttmn3kDidNtbOwPRVp8fJ2tvXcPq3Z4CLC9Nphoama7iyXGy5d/O02tt6oHVUwDHCilj01/ez+T2bOPu7c3Qc6cCyLDJKMlh7+xocHgeWaY1bJM3mNNB0LWF5eGUqooEoh757CGeWE5vDRvlVZZRuK6V0awlth9oY7hxOPHXX0AgOhSQoEeIKJEEJAP3ESsAnYl64L1lvyQZia9jMRdGlE8D2OTjP5OzrbpvU9lmOi4sJvt7x36QavLn1Ug7UvY3ZCsYaoxZhFWWFY3r5Ci63fcbWtynZUkLuily6T3ejLEVOdQ5hXxh7mh1PkWfaAVTEH0n69EcDUQyHwbq3rmXtHWtQliI4GOTUr08z0DSApmnkrsxl3VvX4vSMHQbJqcqZcL0aZSmCF6bnnv3dOQaaB1n3trVsuLuGM4+fpb++f+w+psJTuDDXfhJCTM8SD0oUsbVnzjB+UBG8sG2iC0A+UAzMTAns8XWM0465FYhGODPYy1A4RMPwQMr7aUBNTqyXpNn7WkqL7o3s6bbewmxOGLOAdtOiUinsU7zY67pG4TSGUxJxZjgpv6p8Ro85omBNPr1ne8dUMlWmIrs6O/5/TdMwoyb7v3WASCAWyCil6K3t5cBDB7nm41ePqcQaCUbi9UlSYUUsOo910nO6G8tSWKYV299S8cBJt+tUXF0hU3eFuEIt4aAkQmwKr4+JezncJA8EzhMLFubCwqjg2uob4heNp1EKoikWSBvxpvIVeOwOlLI40fvTlPeLRJ08fWr2364aEJxiUKLrGukeB8WXFA9baC5fDK5wfSHNr7cw3Dkcz+HQ7TpV11aN6f3oPB4r4DbqbWhBaDhE3R/OE+gPYDgMyraWEhwKUvtMXcoBSbx9liIauuTzqMfyZpSlsKfZqdpdSdGGyQ2N+fv8hH0RPEXp2BxL+CtPiEVgCX9CzxCrjDrRRVUHxqtfUMvcDN3AQsgjUUrxm+ZzRKZQuOy6wop4Lkkg2o+pJl6IUClQSud805uYi7I6FuCcQkCSm+emqDiTwuKMGRu6mUkhb4jTvz1D77leAPJW5bH29jU4M5xsv28bHUc76DzRieG0Ub6jjNzluWOO4evxJUw+tSIWTa82xQOQjqMdseBnBkrJKEsR9oa49i+vxZHumNS+YX+Yoz86hrfdG++xWf6GZVTtrpp+w4QQs2IJByWtTPyt6SCWM5JsgbkBYj0uc2XlHJ4rse6gn4g1uSBMA64vrmTHJQv12XQ3qfT8KAWnat9JMDT7U591IM/QcUwyKNmwqYSCGR6ymUmWabHvW/sJDYXiT3nP2R72dXjZ/efXxBbQ21ZK6bbxF1LMKM7AcBiY4bGv/6U9Iin1jow8xalsauiEvKFJByXHfnKcodah2PDPhYlr55+rJz0/nfzVC2MqvRBiNKnomlA5cB1wK8kDkiBwOIVjzVTcZwfKZuhYc8eGRrUnm215o1eNdRhppNknXgtGKTvB0Nhf7dOVo2ustdtY57CRd6FWRp6usWaSa9ysWJW3oAMSgO7TPUQD0dEBgIolsnafTn2hwMKaQuxue9JVfCdFxYIcR4YD3abHgpQkh1VKjTu9OJHgUPBiQHIJK2LR+GrTFBsthJhtS7inpARoJnFBMi+xGTkZjF2FVhGbOlyfYN9EZqoEeAR4ili71zBflVwLXGk4dCOl4ZuqtEyuKiyjIj0znsNgqgi1A7+j2fsKUWv8ufSmaaO9azszHTuvsBkU2wyMC23K1XX6TAtjkmXl16wvpLQsa0bbNhv8Pb6EvRtm2MTfk2w6+1iGzeCqj+zg3NO1dJ/qRtM1nJlOfN2+KaU7OTOdbLl3M6/+z2uxackJjqHbdZbtWTbpiqsRfyRp8BQennjYUAgxP5ZwULIW6CXW42FysWJqlFiZ+GFiRdR2M/onXDvQyPwknUaIVZDtAN5ArPdkbmmaxvVFlTzZWjfhtj3hAJWe0Rft/R1foyd4DnUhWFMqVmzsckpBa8fVdPfNbKG0NE0bFZBAbIHAXEMnOomX1OE0yF4kdTLSCz0Jh10Mh0F6gqm1SsXKyA+1e3Fnu8hdmRuvvOpId1DzjvXwjti23s5h9n9z/5j1cSZi2HXKtpfSsr91bPJsvN1pLNuznKL1k19deWQV48tpukbeyrxJH08IMTeWcFDiAPYAnUAXY9ekMYGhC/cXE+s5OU0skJlvYWLByYp5OfuldUbG44uOzrfp9p+mO3iaVAI6f6CA7r6ZL5KWo+sJRwl0wDmJYYlwyOTQvhauuX5Z0sTWodYhan9fx3D7MK5sF8tvXDYvuQz5q/NwpDsIRoPx4QxN1zAcBiFvmMHWITJLM+LTfg9//wjetqFYwKhr2Nw2dnxgO66s2Ose9oVp2ttMf10fziwX5TvLaXm9BTQSV2FNoGRLKXkr82h8uQmVIBq0OQ1W3bJqygGEbtNZfdsqzjxxNt4mzdCwu+xUXSuJrkIsVEs4KIHYpaiEWOXWVsZeLE1iQYhGrEDawpiSG9PJfAQlITNKi2/ixdhGWCpKd+AUvnA3p/p/yeXPYaJeEtOy0da5a5otTczQ1IzNXzJNi75eH/kFY3NKBlsGOfjdQ/ELYiQQ4djPjrP29jWUbC4Zs/1s0g2dHR/azrnfnaPrVDcQ6/GKhqLUPlMLQFZ5Fpv/aBMNLzbGSrxf0vNhRSxO/PIk2+/fRmg4xOtf20ckGIkltLZ50e06q25dieE0OPdULRHf+MnfhsOgYF0+mqaRlutmoGlgzEfLshTOJAv2pap0aynu3DSaXm0iNBgkd2UulVdXTjphVggxd5Z4UAKxGTRdJA84Bojljyw0w3N+xqAZ5Qe1x8b0gCRT4vLzdNNnsJSJqcbPHxlZYy8UzqKl/VqGhpNV0J06A8hMsAAcTG3xPaUgFEo8E6n2mboxvQZWxKL26VqKNxVPuxLrZDnSHdTcVUMNcOh7h+ir7x/1lh9sHqThxQbaD3eMGYpRSjHYPEg0GKXhpUYigcioBFIrYnHm8VhFZN2hJy0tH3fJ2pIVV1fQcbxz1HOl6RqeQg+ewuknEOdUZY9ZW0cIsXBNmEH4wQ9+kMLCQjZs2BC/ra+vj1tuuYVVq1Zxyy230N8/thT04nCGWAG18YKShfrYIsTyYebOoZ4OhqPhFAumWeTYnyBi+ScMSEYMeis5cfZeBr3V02pnIpka7HI5yNb1GQ0IsrISD2V5OxKveBwJRokGZyr5efKi4eiYgATAilq0H2pPPp1Xu1DB9WzPuAGHFbZAg7T8NBweR9IZNdmV2QB4Cj1sfNcGHJ7YLBzN0MhZlsOWP9o8hUcnhFjsJgxK7r//fp588slRt33pS1/ipptu4ty5c9x000186UtfmrUGzp5hYovwzVXhs5mmMSPVqSahdqgPU6U2hLUrHywr9RoumgaZnlbS3F1Tbd641jtiKwBPZnbNeHRdIy8/DU9G4iEGZ5Lb9Qu5HNMxUkH18A8OU/f7OkLe1FcEbT/cnjT+tkxFYU0hmjH2OUrPT8futhNNMIvncspUhH1hrv3L3RTVFKHbY1N+dZuObtfZ+K4NsWnAF+Svyue6T13L1X+2i+v/6jq2vm8L9jQpIy/EUjTh8M0NN9xAQ0PDqNseffRRnnvuOQDuu+8+brzxRv75n/95Nto3i2bn4jd3nMTK38+dsDnxL/wSt4ebSpeh1Hn2ByZZZE0zycs+hT8w+dkW4zGYfjbQupoiBgYC9Pf5MQyd0rIsSsuTTwdevmcZJ399atSwhG7XKd9ZPu7KuhPxdfvY/639WNHY2jB99f00v97Cjg9un3C4IzgUpPbpJLOmNChYV8DyG5fRW9tLeDiMGTZjgYShUXPnegBUirNsosEoylTU3LWeoTYvfXW92Fx2imoKE+Z0aJqGO3txzGYSQsyeKeWUdHZ2UlISS9YrLi6ms7Mz6bYPPvggDz74IADd3d1TOd0sWXilwFOjE2v7VubyMRzr62IwOnF9hyyHk0J3OhFrJWqSvVCaBpkZTbFZ1zNIadBpWlRo2pSHbrJz3JNa06ZoQxGRQIS635+P5WhoUH5VOSveOL3k5DNPnB21NowyFaZpcua3Z9n+gW3j7tt1sju+9s3lbE4bK96wHN2mU7ypmNb9rWi6RnZVNmvvWBNfB8dw2kavTZOEI/3CcIymkVWWSVbZ9NYDCg4FOfP4WXrP9aLpGoXrClj95tWyMJ8QV5hpJ7pqE3zRP/DAAzzwwAMA7NixY7qnmyEjs2rmdvhjejxANpAGVDCXvSTH+jr5fXtDSts6jdjQhF13Y9c9RKzUE3ItS5uVWTeWgjKbMeWARNc1nK7Jf1TKryqndHspEV8Eu9s+ashiqgYaBxLf3jyAUmrcxzheLsjIyrv7HzoQW5zvQo9IX10fJ35xkq3v34KmaZRuK6XxpcZx65Lodp0VN62YsdwdM2Ky75v7Y0XPVOxxdJ7oYqjdy9V/umvOk4aFELNnSkFJUVER7e3tlJSU0N7eTmHhzHa3zy4LeIHY6sCLiR+4nrEVZmePUornOxo52Jv6KshV6dnxfc0JKrbGtov9bVk2Br2V9A+umkpTx6UzvZqw62qKphHQ6EnzS6Z0PJuesDrrSK/EeArW5FP3+8TDN3mr8uit62W4a3j0dOCoxUDzAAe/e4hgfxBnhoO0/DT8vX4g9qPEcBjYXDaCA0FcWU6Wv2EFRTUz953QdaIrlhx8SUylLEVoKERfXZ8UQxPiCjKloORtb3sbDz/8MJ/5zGd4+OGHefvb3z7T7ZpFp1h8AQnEvpFN5jIo2d/TztG+yeXeLPNk0ux9jbP9v8VKYbFCTQNLaTS03sjA4EpmY0jq0iOqsBnrOrEsMBV4HONezDUNCoszZrxNU5W/Jp/OY5cNl2qkVPskLS8NZ4aT4MBls7Y0aHqlCV/XcMLiZyqqGGgYACA4GIzlxuwoJ9AfYKhtCMNhULq1hIqdFZPqDQr7wrQdasPfGyC7IouijUUJy8l7k7TLMi183b5xgxKlFB1HOmg50IoyFcUbiyi7qgzDNnefIyFE6iYMSu655x6ee+45enp6KC8v5wtf+AKf+cxnePe73823vvUtqqqq+MlPfjIXbZ0BioVZcyQVbua6rPyB3vYUp//G5DtcHOn9Lp3+o5gq9fVFdE0RCBQys2vcKEbCEZNY/5geNcHQ0C7MflGmBb1+VG5a0nVSFlIpecuy6DvfN/YOBTnLsy9uZ1ooS425wJthM7ZScIL9u06kHnxaEYumV5tiL9eFt8f5P9TTc66XbX+8NaVeJW+7lwPfOYiyFFbUovNEJ/UvNHDVAztwpI1OhPUUetDt+pjARDd00gvSUZai42gHbYfbUUpRuqWE4s3F6LrOyV+dpOtUd3xfX7ePzhNdbP/gtnjpfCHEwjFhUPLII48kvP3ZZ5+d8cbMvhnOoJwzBrCJuU7ODaYw2+ZSyz12OvxHsFTqU4FHeNJbCYWzmJnHqHDbLaKWhsMycGgQsRROY/QQh2boqNw06AtA/thVaA1DY+Waghloz8zoP9+fNJfjxM9PkvbhNBpebKD7dA9KKTKKM1j3trVkXOjpUTNdkfiSplhRC2+bl4HGAbIqs+g500NfXR+ODCelW0riJerj7f3VyVHDUFbEIuQNcf739ay9Y02svZYi0B8gpyobm9NGOBqOD+GMLAaYszyHoz89Rl9dXzzw8LZ76TrVzYqbVtB1snvMcJSv20fPmV4K1y2c11YIEbPEKrou1l6SVcDcr5liQyM8iQuZrvWg1NTqvuRmn6W3f/2U9k3krTV9dJ8vJBq+2LuU8Be8pVCByJhQqKw8i4rqHNwLaHbH5ZVUL6VMxaHvHiIaMuPbjPRGXPPxq3F6nNgcNjLLMhhsGZqVFRPMsEl/Qz+1z9Th646tTKwZGo0vN7Lp3RvjwyyRQAR/j3/sY7AUXae7WHvHGrpOdXH6sTNYERPLUmSWZuIp8tBf3w8aFK4rZM2bV+Nt844KSCAW4Aw0DNB+uC1pO/vO90lQIsQCtMT6L8dLvJyJ9TCqiM2OmWk9s3DM8Q2Gg4QnMXQDMBxpRqUwo+nyWamWZdA/uAynvYfYz2/FdK6auqVoeyGDyJEBVFcsf2jcIYXLLvS6rrF6XeGCCkgAsquyk1dcBSKB6JigRZmK1gMXL87r375+xmYCJdJ7vo/hruF4L4gyFVbE4vgvTmBZFxbGG2fhQ93Q8bZ7OfHLk0T8EcyIhTIVQ61DRINR3vB3N/LGv3sDG+6uwZ5mp78hce+RGTEJe8MJz6UbOs5MWf9GiIVoiQUlBSQeHjCA6Zb+1olN2x37C3D65v5l+kHd8UltrwOh6N6UtlUKwhEng95y2jq3c67+dnr6NpCZ0UF1+TPoeuoVSse0w1JsOtOPqvfCQAjNSD4rRalYwqt22cJvRSULJ7H1Uq5MFyXbJreYnxW1GO4YjvWyKEVaXhq7P3ENq9+8Gs02TqA2xbfcUMtQ4mRZUzHcEZsebnPayK7KHvNR1G06pdtKaXqteez6O5ZiuGsY32U9LPY0B0aCAEu36WSWZyYOvnQo2Vw6uQcmhJgTSywoWUXiEasipv9UWMTW0pkNc7vUeotvcNL5JLkOP7o2thckEdNyc7r2PdQ2vI32rl0M+8sBG91962hsvQnLcjHV3JKingDukIk20mMQGtt7ABcCkrAZ60nJvpjvYLPrrF67cLv1175lDZnlmak/PRr0nO3hxa+8xMv/8Qo9Z3uwOWyUbSul5h3rYyXgR+ixBfWKNxWx7IZlbP/ANoo3F+PKck471UdZatQxau5cjzvHjeEw0O2x8vPZVdlUX1dFYCCQsKNM0zXCl5XUL1xfkHCpaU3XKNlcwrb7tuLOcaHbdQyHgT3Nzub3bsI1zRWIhRCzY4nllCS70CYee565409HBrGgae7UDU1+EcLhqEmWDkYKMy0bmt9AJJpomMtAqeld/fIGQxiX1rPo9KEVe7j0iqiUik0HVqBfVip+1zVVC3JWRtgfpmVfKwMNA3gK03F6HPSe64sNiYwXCCriVVxDQyGO/ew42/54K1nlWRTVFOHKctHwUiPDXcM40h0UbSiibFtpfOZOdmU2kWCEV776amoLCY5Mekqw4N/B7x5i3R1rKaopwpnh5JqPX01/fT/BgSCekgwyL/RQ5S7PZahtCBUdOxTlKR5dSt/usrPlfVs49uOjmBd6aGLr62zE7rZjd9u55s+vwdfjR5kWnkLPuMNHQoj5tcSCkgOQQu2MhUEjVrm1Zk7P2uEfnlSxNIj1jgy05dET+BAlhfsoLjiU6McrAJalMzRcwWx10pmGFrsu6losVyQYRbV74UKZ83gFYpsGl3XtF5dm4HDO/0diJIgYGXYKDgZ5/cF9REOx9WT6G4gle64vpPd8L+Yk1hiyIha1z9RRdW0VmWUZpBemExoKEfFFCPYHGe4Y5vzvz7P9/m1kXAgS2g93YJmTyC9KEiSZQZOTj57Cne0msywTTdPIXZ47ZruKq8pp3d9KxH8xsVe361TsLB8zXRgguyKL6z51Hd722MrMGSUZowIPTdPwFKSn3n4hxIwyTZMdO3ZQVlbGY489Nu628/8NPGeCxFYGXgycwM3M9RRgpRS/ajw96RRTFVZE/DZAx+srozDvGIaR7Fe1RvLHNb3Hu85uQHU2usse69KPmKjBIFpe2oS1M4pKMlhXUzzlcweHgnQe6yQaipK/Kp/M8sxJV4ENDgU589sz9NbGapEUrCtgzZtXU/f780QCkdEX+0nWFrnUQOMA3g5vvOfB1+WLBx0juRyvf2MfG+6uoaimCG+HN2GeyFRYEYvGV5vY+M4NSbexp9nZ+dGdNLzYQM/ZHuxuO5XXVFC0IXmPoaZrZE5zfR0hxOz46le/yrp16xgaGppw2yUUlExvRsfcCgGdwNQvklPRODyAf5K5JCiLyJCdkZ4Pv78ApTSUSjjUj6aZuJz9BEMzWxo8V4MCmw3LuGSNG6cNCtJTCg7W1Ux9iKzzZBcnf3kSpRTKVDS91kzhugLWv2N9yoHJ5eu7AHSf6sbb7h0bkMwA88KiekMtSb4kFJz81SkyyzLJKM6g62TXjAUmwcHghNs4PQ7WvHk1a968ekbOKYSYHy0tLfz2t7/ls5/9LP/2b/824fYLb/B81iyWYZsRp+f0bN5ImN80n5vkXgrDGiYyHMs/KMw/wOb138YwIkmHbwAKco8yk1fZNU47Nc5Yt75+2YlTCQpcbtuU17YJeoOc+OUJrKgVn65rRSy6T3XTe6435eN0neiKBQqXre8SHg6jG9P4mE6j80lZio5jnZRsKY7lmFxyLM3QpnRszdDIXZYz9UYJIRaM7u5uduzYEf/z4IMPjtnmk5/8JP/yL/+Scq7eEuopSW266sIRmNOzPd58jog10S/hkSumFv9/gfEaQ9odeNKbKC/eO24wArHek6jpGn+jSbBpsQL801kpNr/AM/FGl/H3+Tn5q5NJC5GZEYv2ox3kr06t6N3whWJjl7NMi5yyHHrresckfl5K07VYLsolm+h2nardlXQc6yQ8HMaMmJOKBZWliAaj2F12dnxoO2ceP0N//QCarlG8sQg06DjaOe6KwWPaqWlU7KpIeJ+/10/7sQ7MsEnBmnyyK7NlBWAhFrCCggL279+f9P7HHnuMwsJCtm/fznPPPZfSMZdIUNLG+IXTFqK5S8zzRyO0B6aSb6PjdA2Sk3WWipKXU95raLiSmcqXMYCMaVy4DEOjojJ7UvuYEZMDDx0g7B9/WGUyszw8RR4MhzEmMNENnbJtpTjSHbQdTD5LTFkKe7odh8dBZDhMZnkWZTtK6TgSy3NxZjoJ9AfGLb52uZFpuod/eITe2l5QkFWRSc2dNbhz3JgRk0B/kMHmwdgsIIv4y6ppWsKp2OmF6TjSxyarth1q48zjZ1GWQlmK1v2tkx4CE0IsLC+//DK//vWvefzxxwkGgwwNDfG+972P73//+0n3WSJBSe18N2CSNGDtnJ0tEI1gpVJg5LJAwq6FseshqspeiN2b4rUj3d2Fz1865niTZQM2OuxTvmg5HAabt5XhmmTl1u5T3bHpp+M8Zbpdp3RL6oXOCtcXUPds3ajejJH1XfJW5pG/Op+KneUcfuQIocHEAbYZMtn60S2xlYCHQuz9371EQ1FQEPFFJvV0G3advNV5nHr0FBH/xaHPweYhXvvfvVz/V9dhc9rY9sdbY0XNuv3Y3TZ0W6weyP5vHRgTlGiGRnaCADASiHDm8bOj16iJWHSd6qZ4Y9+4qwALIRauL37xi3zxi18E4LnnnuNf//Vfxw1IYMkEJbNRZXU2acApYj89Zz/Z9egkpwCPKHM+h4aVcjBy0cwkTJqAc4oBSWaWk607KtCnULMiMBBIONQCgBbr3SjdWkrOJHInDJvBVR/ewdknz9J9pgdN0yhcX8jq21ah6RrRcJSTj54iGkieiKzUxZWBm15tjLXxshk749F0jfzV+WhGbHgmGorSdaJ7zHZWxKLhpUZW3rQCiK3i6ykcPQRWsK6A7lOjF8PTbTqVV48duumr60vYq2RFYqsHS1AixNKxRIKSmSgjP5cswAscAjYQq1cyS2dSipODqSdkXsqud6QckCgF7V1bae/aeeGW6XfJa0CPaVFkS6Fi26X7aVBQ6ME0LXR9cvsCZBRnJBxq0QyNkk3FVFxTOaW6GM4MJxvftTHhfQ0vNuLr9iXN39D0WAKpzRX7SA80DiZdvC8R3R4LpNa8eTVDbUP4uny0HUm+qnbnsU6W71mWdA2d9W9fR32um9Z9rZghk6zKLFbftmrMasFwIWk2CW06Sb5CiAXjxhtv5MYbb5xwuyUQlERZfPkkI0xiPSblzFbNklMDPYSsya/sa59CIm5sFWCDmZp5o4BoSsNOl+2noL6uj4b6frbtKMeTMbmS43kr83DnuPH1+OI5GpqhkZ6fxto71s5KxdD2I+1JAxLdrpOWl8b6Oy+usuzOdceLiY3H5rKRlpdGxa5y8tfkc+A7BxlqG0LTwBwngTU4FGTv1/ay48M7sLvGDn/phs6KG5ez4sblE7Yhb0VewreEbtcp3Ty5tX6EEIvbEvgZ0jzfDZimCLPZy3O0r3PS+2hAgfPgpPYJhTMIR0ZKuieoQz4FGpA/xZLwlqUwoxanTkzh8esa2z+wLVZh1OPAke6g/Kpytn9g+6wEJEqp5MM2Omx6zyZ2PnDVqGqnVbsrR69rk2hXm0719VVc9eEdFG8s5vzvzzPUGltQzwxb44+yKQgMBGl4sWHyD+gyhsNg47s3xNen0W06ui02cyirImviAwghrhhLoKdkalUvFw6d2XyZuiY160Zh0yJk2Hrx2M6nvJdlQWPLGy67dXIX73Rik6RHrpNOYLvLweQHX0bzDYeIREzs9skdyea0serWVay6ddU0WzCxzhNdSYdiPIUe8laMLdWeWZrJxndu4NRjp4kGogl7WUYWrRvRfrhjUtN7lanoPN7Fqlum/xzkrczj+k9dR/eZbsywSd6qPNzZ7mkfVwixuCyBoGQxf7EZwApma+jmRH8XqQ/cKDxGA6WuFyd1DsvSiUbdDPunt1R8BFhvt9FvWRhAhd2GzvTqk4xY6FNOO460Jw1KKq+pTLpf/up8rvvLawkPhwl5Q5z81SkC/QHQwO62s+GdG0ZNzzWj47wbknRuJcspmQqbyzYqSBJCLD1LICipBprmuxGToF3yZzkw87/EB0IBfttcS2fQl+IeFqWOZ3AZk0+IDQTzOXv+rUx3pDAK+JSiym6L947MRDCRle3GNsGF1YyYhIfDODIcGJNMqp2s4GCQ9iPtDLYOER4OY3MZsem8CRgOA1fW+PkwmqbhzHDizHBy9Z/uitUqsRTuXPeY5y+9IJ3hjgQ9Zxq4sl0EB4Kji7PZdEq3TS/YFEKISy2BoGSx9ZRowB5i7Z75lJ+OwDCP1B1POaNDI0q27RQeeweTySkNhTMZGKoGBWoGeno2OGxk6fq0AhGH0yAajdUX0TSw2w3Wb0g+5VopRe0ztbS83hrvrKraXcmyPctmpXel52wPx352PDaEculzrZOwp0LTNbLKU8u5sEyLgaZBUIqsyqyE7c9dlpM4KFFQsLaArpNdRAPR+CrGOdU5Caf4CiHEVC2BoGRqNTjml4/Zquj6dEtdigGJiYYiy3aGfMdhIPXiaLGpv1cBoNSlV9QpXMiVIkOHzGkEJA6HTtWyXErLs/AOhRj2hnC57eROsHpw/QsNtOxrHZVn0fhKE/Y0OxU7Z/ZibEUtjv/iROJF7y5UStWMWCl53dDRgE3v2RhfF8eMmJx89BTdp7tRliKzLJP1b1tHekE6/Y39HP3RsXgwgYINd9eMKYGftzKPlv2tY9qg23VQsOz6amxOG9FQlMzSTDJKMgDwdnjpq+vDcBgU1hSOSrgVQojJWAJBSfJaCwuTBQwA+cx0T4lpWXSHUpvKW+V6DLvuRdcmV+gsEMylvWsHSs3MW0tTiuVhDW1ys3YBKCnLYNWaQoxLal1kZbvJSiGBUilF86tNYy7QVsSi8aWmKQUlIW+I4a5h3Nlu3LnuWIBxYfbQYMvgBA2CjNIMClbnY3fbKVxfiP1CJVqlFK/+12uEvBenvg+1DPH6g/vY+cBVHPnh0TE1VY799Di7/+IanJdMh85ZlkNmaWZsBs5l1VVb9regazpKKda/Yx0ZJRkopTjz2zO0H+1AmQrN0Dj3dC0b37WR/FVS8EwIMXlXeFCigKms6TLfaonlwVzLTA4/DUfCk9ham3RAAtA3sBKlZi7vQtc0bGlTKyW/fGX+qIBkMpRSREOJEz/D/rHPoxk2aT3URvfpbhzpDip2lsdLqiulOP3YGTqOdoDOxUBHQVp+GmvesiaWMDpBF5a31UvBmgLKtpeNur1lX+uogGSEFbU488TZiz0klz2+jmOdVO2+mCiraRprb1/D3q+/Pnb7qMK8kBZ98lenyFmWg7fNG1uQ78LjGUnGPf6z41z/19fFq8sKIUSqruCgZADYDwTnuR1TYREr+HYYuGZGjmgqix/Xn0x5+97IJor1l9G1yRVW8/kLmanZQhrg0nXSphBYuNPsOBxTf3vruo47102gb2zP0uUl1aPhKPu+sZ/gQDDew9BztoeVN6+gYmcFzXtb6DiWeLqtv8fP4R8cZscHtqPb9eTl64ld9Oufr6dyVwW6TScwEODUr0/TX9+fdB9fty/hzB1lKqKBCJFABE3T4pVg619omLgSrAbdp3vor++LrdWT4P7++v6UV0gWQsycNlXE/zP/co7Odv+MH/EKLZ4WBV5jcQYkIxTQx0wVTqv3DhC2Uj/WsFlNd3g7prJjKSOlJFd/IBevb+aqz2brOpucU+sl2Z5keMUyLbpPd9O8t5nB5sGEvQgjVt+2esyUV92us+pNo2dEtR1sGxWQQKw3pPbpOqKhKM17mxPnilygTMXJX5+i5s71Ez51VtQiMBjAjJjs/+YB+huSByRwYZpvkofYvK+FF/71RV748osc+M5BgoNB+s73TVzXToEyx1+QUAghpuIK7SlpR74xRxsKh4hOYi0UgMHoWgajq7FpPspdT+HQkk8hVgo6urcxUwHJZptOln1yq/eOWFtTlLAYWmAgwIGHDhINRWM5ELpGZlkGW+7dkrDeRv6qPLa+fwvnn6vH3+MjvcjDihuXk1mWOWq77tM9iYuTGRpDrUOxlXon4OvyUf98w8QPTkFoKMRQy9CoFYWTMYNm0pfEvDA8pVAMNA2w/9sHYlOQ/YmnIF8qf3U+rmwXPWd7YismX8pSk1qMcDxhf5iW11voO9+PK9tFyeZinBlO0vLS4km+QogrxxUalIRgEmXBFq5sZuolKnSnY00pUNPRCePQx69pEgxl0T+4nJkKSo5FLaqIUmG3xYYTQlFw2VLqNclIspbNiV+cIDQcunghN2GwZYiGlxtZvmdZwn2yK7PZ9sdbxz2fPS1J8GQpbG47uctz6TrZNX4AocFQ29DEsbQGw50+Iv7wuEM9o6TysiuIBqIUbyqm3dueuGdHi7269nQ7Ta81UbGrgsKaQjpPdGFFLTRDQ0Oj5u6aGcknCQ2HeP1r+4gEIyhTMdg8SOexTjRDQ7fprLp1JWXbyiY+kBBi0bhCg5JcYtVQF2tgYlz4s2VGjuaNhDnT3zPFlgxR6f7thNtFIunM5NvJAhqjJnm6hutkrO3a+gIYZ0VZAF3XcLnHBgkRf4ShNu+YC7QVtWg/1J40KBnPcNcwHcc6QQPd0LDMSw6ugSPDSUaxh5U3raC3tjfeM5GQIjaYOsFb1rAbODMcODOcCVcqng5lKtLy0yjbVkbr/hZ0m45lKtIL03Fnu+g61Y1SEBoM0fJ6K22H2tnxwe2U7yint7YXw2mjqKZw1Iye6Wh4sZFIIDImx0WZCtM0OfvkOdw5aeTOUK+MEGL+XaFBSQ6QB/Sy8AOTbGJtzLrwJ0SsRkkJM/HydAd9/KjuOJEprKarEWJ52q8m3E4p8PqmX9nTTqyc/AgLGO724/JFwFKo+gFYln0xsDC0UT0nuq5RXpmdsELreLkjEyZ2JtC0t5m6Z+qwRnIrLpRjMRwGKHB4HGx93xY0TcOd46b6umrqfl83bq+Fik7cDt2mk1WeRc+5XnSbntIQTsr02Jo5lbsqqL6hCl+XD1eWC3eOOzYj55LzKEthhkzO/u4c296/dcyQ1kzoPdcz7mtjRSwaX2mUoESIK8gVGpRoQA0wuXVa5scAcCswOwWnnmmtn1JAAlDsfAVIrWhaT1/NlM4xwgBW2m30mxYBFF5LUWDo5A6GYOTC1OVD9fgh0wmWQiv2QH5a/BjlldksX5m4PoYj3UFabhq+7tHDUJqhUbShcFJtDXlDsYDk0jwSK/YgsiqysLtsFG0sxpXtit+dluvGsI/fs5FZlslw5/C4SbGubBev/vdroF0ItGYqINFiAUlWeSy4cKQ5cFTH3pOWaSWu9AoMNk1QX2Ua7G47gf7xk9VDg2OnQgshFq8rNCiB2HTamZm5MvuOAjtm/KiWUrRPahXgiwwtQJreMWFAohT0Dy4nak6vAq1Ng3xDJ9/QsYh1PCiAy3MTLAUDwdgwzmVDOcWlGePmnNTctZ6D3zmIZSqsqIXhMHBmOll2w+SGbnrP9SZOnTGhry42e6X7TA9Z5VlsuXczuqGTtzoPbbyhJw0CvQE8hR7MiEloKEQ0OPb9623zTqqtqSrdWsLqN68mGorSdaKLSCBK7rIcMssy0fRYDkeiZF7DMXu1SCquqeTUr08lDdI0XSNnufSSCHEluUKDkhAw/lTJhaWLKZdhH4c/PNVfkRZVrkfRtfFnYSgFkYiLhpabsNn8pLu7iETS8AcLmMxjcQBbnI54QBFfcA9QVVmovgBc3sOgaZDlGnVT7ZkeNo+T+JhRnMHuT+ym/WgHgf4A2eVZFKwrmPQsDu2yYaNRLvRcWBGL/vp+Gl5oYPkblqPrOhnFGclriiiIBCJE2iJzOnFM0zXW3rGG0q2lDDQNcPgHR1BKYZkW9S/o5K/OZ8PdNZRsKaH9cPuowES36ZRfNXuJpkU1hQx3DdP8ajOgsC4Z3tJ0DZvTRvW1VbN2fiHE3LtCg5LZ+TU5exSxXp2pTYFN5rt1x6a0n4aFhkpp2EbXo2xc+110PYzXV0Y4lEWhcYTm9t2YCXpPXBqU2ww8mo7XsmgxTfJ1HWeSk2mGDhsKUYc7LsY5uoa2rgBNH71Pf58fvy+My22nr9dHNGKRneMelfhqd9up3DW9dWvy1+Rz+rEzKW1b/0ID2cuyOfe72qRDIKPMcUBy1Ud2kFGcgWVZHP3xsVHDS5Zl0XO2h87jnay6dSWhoRB95/tiSb1RRcG6AqpvqJ699mkaK9+4gqqrK/F2ePH3+ek83kXIGyJvRS5V11XPWFKtEGJhuAKDkjCwb74bMUkOZvql6A74CFpTS/LVCU/YSzLCZosSNXXO1N1NKJyNUhoa4HAMjQlKPJrGZqcdjVj5eI+uUWQzCE+Q86K5bHBVKQyFQNcg05mwp0LXNXq6h2lq6Me6kIeiFJRVZLNiVd64Qzvedi/nnq5lsGUQw2lQvbuKiqsrEu5jd9mpubuGEz8/gaZrWKaFMpM/hkMPHx738c2W9MJ0/D0+VJIUlTW3ryGjOLao3lCrN+HwjBWxaDvUTvHGYjbfs4nAQIBAX4C0/DRcma4x218u0B8gEoziKUyfcl0Re1psWnXu8lzKd5RP6RhCiMXhCgxKzjN3M250YhmO01XDTA/d1A71TWEvhYZJoePVlHpJRrZpbrueYDAXdeHtpIBQOAtNC6PUxQTeVXYbxqWzZTQNTSnS9IkvVpqhQ8746wApBY0N/UQvy0FobRkgJ8dNXkHivJfhbh/7H9ofHx6wIhbnnqql61QX2z+wPWFgUri2gJxPXUvPmR7MqEntU7Vji4jNhsvecpot1rZEM3ecGU78PX6Sdb+Ubi25+J8Uk6Hd2W7cKSxoGPKGOPKjo/i6fLEeLQ3W3rGG4g3FALHclVPdhIfDZFdmkVWRNeVVoIUQV44rMCiZy1WBK4FGptfnngNMfzrtpZRSnByYbF0SRbrRTK79OG4j9X2V0ukfXInCuOx2G5c/Lx597EVnJi9ElqUSTv21TEVr62DSoKT++fpR+QojBpuHaD/cTunWxK+P3W2nZEvswq7rOqd+c3oarf//s/ffQbZc950n+Dkn83pT3nvzvMV7D3jwJECCopEj2aJEST3slrqpnu6O7Q717s5ObMTMTkxsR8zG9oZ6djempVlJLaqlpihHUhJJCCIJwgMPeN6bqlfeu2vqusxz9o8sd+vmrbr1DIBXzA+iAqh705zMe1Hnm7/z+31/FbJRkEjB3s/u5eb3b7pumhhP4I/6ySVKc4tizdG1e2/lLPJp98iY9Mm1a6wUrTVnv3FuRRCtc+071wnXOZ/BuT8655QV2zaGIanuquboV4+udU328PD46WQXipIP85KGWEnHvMf9DeDwgxvOCjeW5lgq7CTJVRMS47QFX93xuYJGH1pXJixWqmYfMu6fh+2yNLHK0kj5staht4bLipKNtDzWwuTlKacXzYeUF6K1JjWRRAiBdjmpGTRdBQmw1r9n8vIk175zHSHFmqAThkDbGumT1PbW0ny4aUfjmr89XyJIwDGqG35nmKWhpSLrfVspFoYWGf9gnPbHveUZD4+fZnahKOnBKQf+MNDc+wxUhbNsU/XghrPCa5PDO96nOfhWRdspJZmeO8Ls/EG09tPeALHIFMl0E8X9HRWb+z1OWDYtplG0hPOgcYuUSCloWsmdcCNYHSw7eVu5Atfmv026MENdcC8dsdOYsjSXQgjB8V87xuBP7nL39bv3PP4docHKWrSeaGX87HhRTogwBcGqIFkXnw/HdE2RWchw7TvXXXNJup7toq6/jurOrZdV7ILNzLUZsoks8bY4Nd013PiBe+QGIDObIb+cL3l9NXcl2hwjMbpEIB6gfl89hvnwZayHh8fHh10mSjSQ+KgHsQ0CaONBWchvxlaKlFX6R38rBHl8MrPtdlrDnaHPk0y3oLVT0XJ3QhMKBDCkjdYKpU0MqZASCptsNgYtm4AQ1BoSjRM12enyjRDQ0VXN6MhSsa07YBiClrYqxkeX1hJdDUMQjQVobi3vONr3Qi9n/+ic63uZlpsMLJ1HYzOTucLA0is81/Z/wW9ES7aVhqTvxV58YZM7PxxYdyMVTtSiUG6JxJRopXfsLCt9kob9DTQcaCCfzjN9ZXr9TQWZeffPVEiBKthMXJxwPac0JJGGCDVd1VuePz2b5oM/PIuyFHbBxvAZhOvCZJfKG57F2qIkJ92r45bnljn/x+dQSiMNiTQlp37jJOG6sOv2Hh4eu49dJkpu4SypfJwRQP9DO/rNpbkd76Mr/BosZxpJbRAkAEoLsnmDw70ZCpYkkVbEwjbprGR4qrhcUwPXChYBC0JC0CAlLb6dfwX9qQLG4KKTztwQRoR8az1v+vbU09ZexfjYEvm8TX1DhPqGKNIln2WVmu4aOp/uZPitDREmAQTy5I9eYTVx2tZ5cvYStxa/z6G6Xyp7vM4nO6nfW8/MSq+YxgMNhOvCXP3uVSbOTRZvLODQlw8yfnaCuTtzFQffVo3YrnznKuHXwiWdfbXS5JLu0R+90sV3cWTJVZTYBZs7P7zD8twynU924HPpJQRw+S+uFJ3Xztskp5Llc78FzN2Zdz2nkKLIMt+2bey8zaU/v8zpf/FEmQN6eHjsNnaRKNF8uJU394oJPBxvBa01r4wN7Gw0Yplq82pF26aWm9AuVUK2EiwmDQ715BzD1aTB9aHy5aI5DQWt2ePbWVKj1hpxdZY76XFnAhPAeBLfgXpaj7XQ1VOLlIJwxE//3oYdHXvPS/00Hmxk+K1hsktZYl1BBtr/CILFT/0Km8n0eVdRsnB3gZsv3yI9ncYX8dH9bBftj7cjhGDu9hyTF6dcLgoGfjTA8V8/zvi5CYbeGHJdTtnIas7HahnyZuv8jcd223fPZ/vxhXzU761j/IOVe7lpv1wix9CbQ0xcmOD0v3gCX7BYmOSSOZZnXc67xdBrumtIjG0RyXQZ7/LcMtlEjmDc8yPx8PhpYBeJEng0bOXzwBvA8zzItM+bS3P8ZHIIawc5LjFjgKbA24gKhZzPTCOEcqke1SwkTW4Mw0LKYD5hsFWJs4nT5ya0w0oLvZhBpfLo1UlbA1pj35qn+xcPYrg04tsJVW1xjvySk3ics5MMDLtHGgxZOkEujSxx/k8urAmKfDLPrVduk13Msucze7j7+t2yXibpmWXe/J231q9pG7byRNkKIQWdT3XSftJJJq3prqG2v5b52/OlwmTlPIV0gdH3Rt2t+MXOkrylT7qeB5wcGJ13P5ZWH0KptYeHx8eCXVR/J3C66z4KZHiQpct3Egv8YPQOyULluSSSDE2Bt5DCrsiTBKAqdhet17rSbECQWDa4Mx5gPmGylSCRwH6fSeO9JDCOpdYFSfHpSYw+2FyigBGjOtAN+QDG1X58rz+BeWkfRi5Kd+wTjmHaBnV259WBkgiHtjTDb49w5vffJ7OwTc7O/eRMV4hWumgcQgiO/NJhDn3pINVd1a5/DZSlnF4/mwjEAoTcfGMEZT/++dvzZa9R5ZXrfoF4gGDV9iZtHh4eu4NdFimpBcqEsj9WKGAGeDDlj29MDWOVs+0sQ2fo+0hR+T5ag11ootpvsJi79/nTL6DmHp09NzfgWx8cyM2N+x4ABwO/zgd/fBZdkAjbRBsW4vIhxqM+7iy+ijQdD4+mI00sDi+WPU5iLIER+OirSIQhiNQXJ41qrUEI4q1xlkaXXEuL/WWs3A9/+RAf/OezKFuhCk6Dw2BNcL2Z4OqhVj62bRN5tRPN0UojTYkwBIf/0WHPVM3D46eIXSRKFoGRh3RsgdOXZmdVLVvz4Jaalu6h8Z4pKhdvWknqcr9Ijexgb6ejDW4t2JybqVzUCCAuBPsDvnuaZKQUNBxtZvat4RLnVDNgEm8tX/K7HcpWjJ8bZ/LCJMKQtJ1opelIE0OvTELOj1gtorFNsCGzUmarCoqxM2OMnRnb+gTaiTisTrgfFdKQtJ1cb6CXT+d5//c/IJ/OoyzlWk4tTEHnk+69gqJNUZ75t08zdWWa7FKWqvY4dX11ZJey3P7hHebvzCN9knwqvwMVq+k43U6oNkzT4Ub8Yf/2u3h4eOwadpEoufWQj/8gBQk8yKWmKn+Audz2Jb3raJT2IUVl11RnP0WNbMOQYi0Lpr/GwNJwadYRCAbuKcYCqAYOBP2Y9/jEK6Xg6GOtVNeEuJW3GTsz5ggb4Uy0x3716D0/TWutOf9fzrM0lkCtiJ3EeIK523PM3ym/3LBTBIK9P7uXOz+8U7Y0+GEhpCBUE+LgFw8WNbC7/nc3yC5ltxRKAuG+TINz7xJjCQrLBaeEuKdm7VxH/pGTm5NZyPDO//bu2r3dfqySSGOUu2/c5eb3b+ILmXQ920XnU51exMTD46eAXSRKdjIp75QH/XQrgfoHdrSeWPUORYmquOEeQA3HMGTx8oMpBXtqjDVRInEXJRrnk7kfQbLvYCM1tc6yw97P7KHjiXYW7y5ihnzU9dfec6M3gPk78yTGk0WTpioopq/PlHQhvh+UpajrraXtsVbmB+a5+fItsktZpCkfqkgRpqDztFPW6wuZ2Hmb7FIWf8zPzPWZ7b/aGsY+GKP3k71FL9t5m7N/dJb07PKaR8mtHxic/I0ThGvXl4iCVUEMn1G5KDEEN39wc237QsZi4NVBlKXck209PDx2FbtIlNTz8TdOA2f6jgM7K1kth9KaD2Z3ljTb6HsbKSoUWhok7j4VGyt6t5pWu+4x30Magv0HGmlqKTY+C1WHCB3fvilcJcwPzGPnXeSUglh7jMRYYtsS3UoQhiA9myZYFaS2t5Yn/9vTaK15+//9zgMRJf6YHytrFU/+K837ht92ljVv//CO83ma0lmqqeAroGzlWnI8+Nogqen02r2x8zZ2webKX1/l8d88tbadkIL9P7uPK399dW1swhCOUFFOLsrqOKRP4g/7S5KCVUEx9OYwXc903ZcA9fDw+Pizi0RJF45PyceV1afHdqCPB9UV+OWR2zuM42jiZuX3SWnJYi5HTaC0AmIus/2ZJdtfqbYVJHJQsCEeRASdr2UwYJYIkgeNP+pf8/3YiDAELcecjraJ8YTTX0brip/4SxCOGNjIwtBiWYOznXL4S4cw/Aa3X7lNYjyJGTLJJ/OuSzM7EVnSlFS1l7ZCmLw4VXocDcnxJIVMochwrfFAI8GqIENvDZNZyBBvjTv5IhE/Q28Oszi8SKgmRPdzXVz85iXXcWilsbIW/oiXY+LhsZvZRaJEcX/N8R42e4AkMIiT/1KL0/vm/ibda4mdObgK8hWXADtoPpiy+GS7RuIspyitsTV8MLW9v4kGqozykRKdyKGvzmx4QaNbY8iuaiJlqj4eJM1Hmhl4dbBUlAhB0+EmWh9rJTGRJD2dIlwXRhiCS9+6THaxvJW6G9rSLM8tU9VRhZROpGLgx6VlxPdCIB6gqtM57omvnQDg7ht3Gfjx4P0dWIARMGh5rLRLsFtS7Oo+bsRb4+z73F4u/fllJs5NMHlxEmEI9v/sfg7+woG17SINEdcGicIQmKFd9OfKw8PDlV0UC43wYfSgvXcu4ERyCjhT9RzwFveaCzOTXeYvBytzYt2IKSo7n9Zg2yYgqWv5Nq8MpxhMKOYyisElxQ8GCyzkthaAUmvaTYNAGRWklUZfmwGl1380MJGCRJbuntodXl3l2JbN1JUppq5MUde34TwCfGEfj/03xzFWlp3iLTFajrVQ1V5FvCXOk//qNFUdVQhzZ9Gu6397gzO/9z5W3mJhcIGl0fLdiXeClbWKvETsgk1iInlflT7CEDQebOSJf/54iZsrQPORpjWr+41EG6NlbenP/8kFlkaWULbCzttYGYsrf32lqBdO34u9JREl6ZP0PN+D3KHZnoeHx6PHLnr0kEA3cPsjHsdOsHGEyqEd7TWYXOA7Qze2cvR2QdHsf5OYebfiPW4PvUQ224hl+wEf701W5vxqAvFUnpZ0gdqO6vIblmvcpjRVeU30AUdKtNaMvT/G0JuOlbyQojS3QjuT+uaJcXX/1FSKfCrPka8cJjGW5NK3LlU++WtITaV47f/xumOb/oCMSu28zfW/u8HomTFaj7dw+4d3yKfvvVqs5XgLB35+/5bVLj2f6GHuzjzZxSx23kl0Fabg0JcOum6fmk6Rnk2X3Cttaa5+9xpP/PPHEUJQ013D0V85yq2/v8Xy7DL+qJ+e57tpPdF6z9fj4eHx6LCLRAnA8PabfKzQwM6elp3+NoM7ns+qjJtEzSFEhQmuWgsMAZYdATRSZlAqQCXBtY68TdudJSfyUR9Fh03EylOu1np9sttiKGY5o7R7RGtn8pu+Mr2WF1JOTChLcfuHd9jz6X5CtSHS02kK2QI3f3DT8SgRTvKlEPfmO6JtveZ18qDIJ/PMJ+eZH5hfOUll+wnpLIvYOZtoc5Q9n+mneishuYIZMHnitx5n7tYcibEE/oifWEusbM5HLpkrW82Umkwx/PYwXU93AVDXV0vdf3u6sgvw8PDYVewiUXKXB+8l8rARQGkS4VakrQIZe+fVGnHf7R05uIIkm19d1hAoVbnVt7L1uovn4ALiUOPae0VP3/GA6+QpfRLRWcU33xtiOWezryXGqe5aAvdgTW9lLW784CZTl6cq7xmjYe7mHAsDCyhLIU2JslXJWMvmVXyU7HBIodoQT/2rJ+/pVFJK6vfUM3d7nuG3Rpz+NZam5bEW9n1ub5EIibXEyt9/DXdfH/K8SDw8PHZLTokNXPuoB3EPSGBn3gs+IbF3PBlqgnKh8q01LGfryOU3CqYtmppsYj5krk2OYpOt+UaEKaG3BjY+QUuBXRfmb2YT3JhMMjKf5rWrU/zvr94mb+2sA7TWmrPfOLczQbKB1SRUZZUKko+SB2VZL6Sg69mu+zrG4GuDTFyYcPJEcjbKVkxcmGDw9btF2/nDfjqeLN9WwcpZDyTp18PD49Fml4iSJR7+rFHLTqMa2/M066XC26O15k8HLt/DeRSWds/P2KhvtHZ+Mtk67gx9/h7O45AC7P4VsWHILU3IZGMEcawJ2mLQHIH99bzTEqagoG4hy6krczx+bpo9b4/zzt9c31F0Ymlkyclj2CRIPkb6omKDNumT7PncHvwx/72XJW/CH/PTcrT5vo4x8u5oyXhUQTH6bmnLh74X+8o21/NH/K55PB4eHj9d7JLlG5MHljVYlgXgCHAVJzKzcWqLsPNGgFF2KnLOzU2ykN95LoJAo/EBpfuupXdoyOaqGJ14lkSqix21pAf6fAaNhoEEkkojAz7E4yFYSSbdKiwvQj5EVzUAttaYlkVsPsPeoQTGyjBMpcldnuZOVZD+F/sqGld6dhnLUiXKe7Vw/OOwUFBpTooQgtxiFitrbbuP9Dklx9rewiBNQvPhpvtaLtHa8Q5xo+DyuhCC/T+3n4vfvFgUFZGmpP9TfduOxS7YTF+dJrOQJdYcpW5vnVeR4+Gxy9gl/0c/uD4y5dHARaCX0r/0WwkSQeltNoC9Ozq7rRVvTN5bIq+kgE8kt9xGCChY0RVBAjuZsg/5TRoNA0MIhBDEpMCQEmFIxMprlVJVH0Zp6JpIrwmStetQmpF3Rpz8jgpYDhplJ2X1cVAkO0D6JJnFbNkoiTAF0ieJNkd58l89Sbw1Xv6+CzD9Jh2n3RvtVYoQglhz1PW9WLN7g8S6vlq6ny9eMtJKk01sLbaX55d583fe4sb3bjL4k0Gu/PVV3vtPZ8qKIg8Pj0eTXRIp+TATXHfa+C8MtOKU/iqcbsP7Vl6rjIKy+eadK1j3tPCgaQy8U5FhmhTb/YHf1IseCAtBXEqMDSe416fvquogx4618u7CMsFcmfwRDYXlQlFjuXJkYv6y0kp+nNZwKiAQC5AYT6xZx2/E8Bns+Zl+arprCNeFyafzJMcSZSMqDfsa6H+pb+0eaq2ZuzXH+PkJtNI0H2mm8WBDRZ/j3s/t5dx/Ob+ed7PiXLv3c+6i28pZ3H19qOg1rTR33xiirq+OeJu7meDVb1+jkCmsfQXtvM3y/DJ3fjzAvjLn8vDwePTYJaLkPp0rd8ROZ7NGHBGyB2fZx2SnCwfvz0wwm1ve4XnBGavCL7ZPclXKYG6x3B/3UjGySugBVUv4AwYnHnee3H/9qW5euziDL1FqwS4MgS/ibs61mZbqEKM+SeAB5WB8lKQmU66vCykI1gRpPdG6JiIKywUnn6dMcm/fi71FTfNu/N0NJi5OrkVh5gfmmbpSx5FfOrytMKnqqGLfF/Yx9MYQheUC8fY4/S/2EW1yj6DM3Z5zPaayFBMXJ1xFiZW3SIwlSqufbM3U5SlPlHh47CJ2wfJNng9XlOyU1T/OEidKsvNJ/OzcxD3ESDQmS3SFvkvAcJ/QNpLNx5lbcDe+2qryZvkBlcVudG+tDvt5/suHnOqcDUifpPcTlTt7NsSCZLri2JuGbouPV7IrgOF3SQje4qsipEAYgpqeGk78N48VTfSh2tCWFzh1eWrtv1PTKSYuTJZ0SZ67Pcfi8NYeOlprrvz1VW783Q2WZ5exchYLAwssz5cX0FuVBZd7T3wssn88PDw+DHZBpCTJhzvF7KS/jgDub91+NrtMTu2sFHaVat8NAnLrXJJVCgXHJG2nZLRmUSlqpbznZRvTlESE4PyfnCc5kSRYHaLnEz089uvHuf3KbVJTKQIxP93P99B6vLQPy1b83FeO8r3/+BaRZQslBHJlSePjpsbtQmnZseEzyjYBbDjQwIGf248ZKP1fWBqShgP1TF2adj2X2iAk5wcWXCuaHGEyS81KArKVtRg7O8bcnXlC1SE6TreTWcgwe3123YzO1mgcoVLXV4fhLy1dru2vdV1WMnySxg1+NkXv+Q2q2qtYHFksukfCEDQfbXLdx8PD49FkF4iSD3t60ThN9BIVbPs49zu+weTiPe0nsPDLBFqzbT5JNlfF4lIP91rBdC1v8XSwfP7GVgQCJr2tMc5949za5JZPF7j055fY/4X9PP7PTt3TmFbx+0ye//Jhzv/phTVB8rELk4DrmJSlkG7OthKCVQFXQbJK/6f7mb4yUyIApE/SdHB98jcDhmO379IlebXnTX45z3u/e4bCcgFlKRbFIpMXJ4m1xLALpYJZSMHC3QXq99aXvOcP+9n7uT3c/P4tp/eR0sgVQVLTXVP2eg5+8QDv//4H2Hkbu+DY2odqQvR+srfsPh4eHo8eu0CUPNzW9u5UOv2W/yNbKaaU99D72Nk6LCe2FCRaQyLVxsDQ51Ha4F4FVJMhkRVGScIRH1prQiE/bR1V1NVHigTJKqqguPXKLZqP3l/ZKkBtTy3P/ttnmLo8xdBbw+SSuY+nMNmMBGFI2HRvpJS0PrZ1onQwHmTPZ/q5/Q93nMlfa6Qh6TjdXlQZ03igkZs/KE3eFkLQdMTxMBl6Y4h8Or8mXLTWaEs7ibfl2OIzazvRRk1XDROXnGWjhn31ToPDLfYJVYd45t88zfT1GbILGaLNMer6az0HWA+PXcYuECUG0AaMfYjnrMSTxOBBdC1ezufvYf4UaMAmgtxmrEOjn0LpyhJHy9FTgf17IGBy/FQb4XBpb5RkmSROK2tRyBTwu+yzU/wRPx2nO2g63MTFP7tEYovqlI8L/rCfo79yhAt/ehE7bztaWGn2//x+IvXbl8F3nO6gtq+WobeGWZ5bprq9is4nO4u2MYMmx756lIt/ttJYcEUBH/rSQadpIDBzY9Y130MgEKYodWLVUNNTveXYwnVh+nYY5ZCmpPmwt1zj4bGb2QWiBOAokAHmP6TzbVc6awBd3O/SzZ3EAu/Njd/TvgKBEFv3yMkXIlh25T1t3M9DUTlwORqboq6CBCAQ87v6TQgptlyiWGX25iy3f3iHzEKGcG2Yvk/1Ub+nznVbf8TPqd84SWIiwfu//8E92c8/bIQUIGD/z+4j3hLn2d9+hqXRBLlElmwiRy6RZ3lumXDd1m7AWmluv3KbhcFF7IJNcjzJyJlRjn31KLUbEotrumt47v/4LItDi2itqe6sxvCtC00z6P4ZaK1pPtzE1JVpxyBvJVH3yFeOYNxDnyIPDw+Pj1u+3z0i2Gm33YeHxElu3X9fR1Fa8/3RWzuIkljUmBfxy1VhpjC28W9xfEnuL/wdqtAcbX6Lioye53uQvtJKm/ZTbUjDed3KWxSy6yKrkCmwNLrE2NkxLv3FZdLTaVRBkZpKcenPLzF7c3bL8cRb4vS/1I8wPz7h/1UxorUTsbj0rctMXppECEFmMcPVb19j4EcD3PnRHd79T+8x8OOBLY83eWmS+cGFtbwPZSlUQXHpzy+j1KYlIUNS21vrJKj6igVFx+mOks8HAfH2OAd/8SCn/tkp6vc47qrKUtz+h9ss3K2815KHh8fuJJvN8sQTT3Ds2DEOHTrE//g//o/b7rNLIiXXcDxAPmoE8DM8iGWbs7MT5NVOEk9NLB2mM/gDhjOfRW+jN7WGxWQPWkvuxXR9tUh4r7+yr5DhlrC5QtPhJvLLeQZ+NOhMlhraTrTS9+k+csnc2nILgBk0qOqsZuHOAsIU2C4ma6qguPX3t10TLa2sxfTVafLLBWq6q2k93sLYB+MfixyTjctJ2nIqWa599zqx5hjXv3u9aJlEoxl6e5j6/Q3EW9zdUyfOT7hW7ihLkRhLUt2xdZuDVRv5xoMNJMeTjL0/5nQCVppQTYgjv3QYgNkbM8zdmV87V2oyxfk/ucCJrz1GVfuD7hfl4eHxqBAIBPjRj35ENBqlUCjw7LPP8rnPfY4nnyzfmXwXiBIN3P2QzmUCLUBps7H1sRR4EKLk3NzkDvewMUUWgUWj/ww+mdwyyXV+sY/hsRfYSozUSkmPzyAoBMtaczdv4QMs4Ti5tpomwQobyrW0bT05dTzRQdvJNvLpPL6Qb60c9r3fO0M+tR7xsbI2czfnVi+5LBu9MpStmB+YZ2kswfBbw2uvSUMSiAc+FoKkbDazgLtvDLk27lMFxdTlSVdRkk3kyubqqIIiNZnaUpRMX5/m5vdukV/OI4Sg5XgLT/7r06Sn0/hjAWLNUYRw8kmG3hwuTVS2FHd+PMCJf/zYlpft4eGxexFCEI06Xl2FQoFCobBtZH0XiJKH2SE4AhxeOX4UR2wUgNEy5zRwGvftzEvDjfy23iTF0Q2Bpsp3CyEgbE6V30uDZQcYHv8kWwmSeinZ5zfX8kViQnAw4ENybzbyw4PzGIakqUxPFHCWEILx9RyX+YGFIkGyEwy/gZWzyCxkOPfHjg26nS++p0opMguZnVnP3A9u55FONU2wOsjyrMsSl15ZziljUqddgmlaaT74ww+27Asz9sEo7Y+3ub63OLzIlb+8uhaZ0Wgmzk9gF2wO/WKxwV4ulSvbuTk9vdMmlR4eHvdLy/Ic/9dzf/ihnOsPZmY4dWrdtuHrX/86X//614u2sW2bkydPcvv2bf7Vv/pXnD59estj7gJR8jAbcq1GPSLAWRzBsRV6Zfv770Fb5Q8wnd3aWl5QWKmzgSb/W/grNEobGPo0Sm3dO6bXZ5YksFaS0FqOTMbi+pUpCnmb9s7qivZZnrv3SU1Zinf/t/dQtu3Yrpfd8J5P8UAQCI78o8ME4gHO/MH7aKt0gu98qoOZazMlr0ufpMmlGmV+cH7rawZSWwiGW6/cLqmoUZZi+so0e39mD77QerWWP1q+Mmq7RFwPD49Hm4aGBt5///0ttzEMg/Pnz7O4uMgXv/hFLl++zOHDh8tuvwtESfVDPHYeeAfwA1t3MXVQwBmc27oP6L6nsyYKuW0FCQiqzauEjSlCxjRSuM+uSgky2Xo0kEq3klpuJLXc5brtRgIPIf9TKc3gnTnatvGkWKVcc7ZK0LYmu1TJZ/Yh4hJQ0FozP7yAlS64Gpj1vtBLvCXOvs/v5cb3bjoeIcrxHGl/vJ0ql3uUXcyWjV6sslFYbGT83ASJUXf/EWEIcslc0b6GadBxup2Rd0eLlnCkT9L7Qs+WY/Dw8Pjpobq6mhdeeIEf/OAHu12UrDa4e1jxd8XWgmRz29bVvJJrOFGTym3mtdZ8MDvBm9PlclaKWbAOY8osIWZQWgICKYqXKAaGP0si1Y5e8yLZPjQgVq7g/t1BSlFKUyjY+CtIkK1qqyJYFSC7VNqYb9egYXFwgfTscrGFunQSgLuednxFWh9rpaanhukr0yhbUb+vgViZpnex1q3FnPRJOp/uLHndtmxu/uBm+aEqTag6VPJ634t9GD6DoTeHsfM2/qiffV/YS03X/ZsHenh4PLrMzMzg8/morq4mk8nwyiuv8N/9d//dlvvsAlECjjD4qKpvTJyJfvMykg3cZCei5MrCDG9OjWBXLLAMZvKnmc2fQpKhM/R9pMisvZvNVZFIdaD1xo+5fFVOnZT0+UwCwrkirXVJRENrjR5dgsmV8H99GNFRVdI8z21fAASYFXpYFDIFmo42MfzWyMfST+RBkZpNowvF16cVzFydQf/C+n0MVYfoeqZ8lGt+cJ7BnwySmc86icJKu0Zf2h9vXxM7ReOYTG256tj1TNdaPxulFHdfv8voe2NYWQvpkyilkKbEztvc+LubRBujRd2IPTw8frqYmJjga1/7GrZto5TiK1/5Cj/7sz+75T67RJTUAqVr7h8OFuWjDztbPnhreieCZBUNKKp8A/hkpuidTLYWIexNosSdGinYvyGxtaxsUBoy9rr1+WQKvZRDH20sFTC2cmzSV5BS0N5RjaygYqewXODd332PwnLpssZuY7MgWcUu2MwPzFPX524Et5Gpq9Nc/fbVoiUUIQX+iNOZun5fHS3HW4g2RTE3RamUUiRGE2QXs6XurCuEG8L0PN+99vv1v73B1OWptfOtlmbrle+vXbC5/BdXeOLrj287dg8Pj93J0aNHOXfu3I722SWiZOukvofLanKrW6TGPbxejpS18+uQZGkNvkbYKK24CQYWV3xINqMxEESlIKU0NtBllia2uqKBjROXBrIWLGbRGcsRLaaE4SWoCSG6qxEBwxEknTX09NWWO3IRQ28PF/VbeSgIx002v1xwTTAtt8+HWUJ84U8v0vVsF30vlLdk11pz6we3SspytdKEGyKc/NqJsvsuDC1y8c8uOvdZ4Gq9L32SPS/1I4QgMZbg7ut3mbmxtTkd2qm+ySVzBGJbJ1V7eHh4rLILRIkCFj/C8ztdZkqRwIGKj/L21Og9nTtmDLoKEoBQcIFoeIrUcvOGaIlGIngs4MMvBAIYtmxCFfqNoDVsTiBVGtIFmF2GzPoyllzK0iIE/Z/sxTDkjkqJ526691t5kAghePrfPM35P7nAwsA2lVUCWo63MHFu4h5OBM1Hm5m84O49IwyBkMLV6Ewrzd3X7mLnLKraq2jY34DctFRm52zyaffS6eR4+YqsQrbAhT+9UFIqDSBMgTQk2tZ0P9dN/Z56Ji9Ncu1vrruO0/3C3EWOh4eHRzl2gSj5OP/R2z7sDrBsFXh75l5ECVT7yicmAvR1f4+R8WeYWzgICMJCstdnEJbrE1unaaC03rKzK6zkk9yYc/HaEIiAQfBIE4GpFImxJGbQpOPJDrqe7rwnXxMzfH9NAivBH/EjpSQ15W4ytpmpS+X9X8oinN4x/Z/uJzmRdPXuEFLw+D87xbk/Pl/Wl2Xk3VHGz01w8+VbnPrNk0UJp9IvEYZwFQBblezOXJ1xrdKRpqT9iTZqe2uJt8bxhXwoW3Hj725WLkiAQDzgmNN5eHh4VMguECWr3XgfVqKrU9Wy8+MLYArYusU8wOX56Z0PC6j3vUPA2KJ9PGBIi662n5BMN6PzdRz1m/hc/EcqboIU80FiUzWMFFAXRgVNTv6TkxUdxlaKbEER8hvIlfE4JmHOJF0ut+FBIX2SrmedZM+KJlrN9mMS0H66nXwyT2IsgZ23qe2rpe/FXgJRP4e+eJAzv1/sRSJ9kq6nO4k2Rok1R5m7Xb6ppJ23sQs2V799jZP/ZH1JRkpJ++NtjL43VjRG6ZN0P9ddcpzF4UVGz4ySGEu6XpOyFNI0inJZlmeXty0zXjuvKRFScPjLh9YEqVKKhcFFCpkC1Z3Vax2IPTw8PDayC0QJOA6q9xZp2BoTOI3WWeAcoowXiDsaqKyUdSpb2ZN6MTlqfLcq3jqfr0IA7+QKmECHadBuGmuTRiXRDCEEtFehl/KwulwQ8SP21CIMiWFsL22U1vzo2hTvDc6hNfgMySd66wienWD2lhOFCVYHy3qMRFuiTmTjPjSLkIL2J9ppf7wdADNkui5h7BgN87fnefJfnna9n7HmGCe/doJbf3+b5EQSM2RS11dLaibN9e/doLavdktRsnqOpZElrLxVlLDa96k+lKUZPzsOwvmsup/vpuVYc9HuI++NcPsf7mwpxKTPac63ETNkbrkUE22K0niwkcJynmB1kOajzfhXukKnZ9Kc/ca5tXusbU37k+30f6rvnqJoHh4eu5ddIkq6eDiipAatqxkbGyUajRGPJxDC+cNc2d/SypZvav0htneLLWYnf8oXEz2AsbbqYgFDljNBdPh29hUQhkQcbUKvPGFvLAXu6qlBKc3E2BIT404Ep6E+gjW0yMLgAsHqIFNNEd5bWqawki9iKZu/vzbF3ukU9SsDzC4+YNOz1ZulV/+lmb89h36hF2EIqjur721pxoXsUpbF4cWyHh1V7VWc+o2TKFtx9j+fZfrKjNPFV+Da36Ysm/SBlJJ9n9tL/6f6yKfzBGKBktwTK2dx+5U7W0Z8pE9S21dLdWdxX5xgPEi8NcbSaKJInAhTcOiLh2g62Og+TK05/6cXSpalRt8bpaaz2rVpooeHx08vFUftP97UcL+27qVIYC/pdJpMJsv0dBPj4y1UkHqxsm8LUJkj6ZHaUqvw7QiIeddxuEXYx6ZOs/n+KGDEsisOyW9GmLJIkNQ3RGhpq+LS+XFu35wlmciRTOQYuDXL0GyapdEEU1emsX88SHy2uHRZScFIU2V+FsszyxVHSaQhMIOm8zS+8TIVpKbS3PnhHey8TdfTnSUT+L2ibc3MtVkn/0ZrZpM5xhaWsTdFGSYvTJKcSjmCBJweNxUm9garg5gBdzFp+A1CNSHX61kaXUKU6dZshkxq+2o48HP7OfpLR1wjGEe+coRocxTpkxgBA2lKup/pKitIwPE+KaRLq8pUQTF65mE8SHh4eDzK7JJICcAJ4IP72F/g5KZYOGLiIFBDKjW5NnFrvZpfUm7yMFb27aaSXBKAxVyGP759aUcj1VrTGHi74u0LefcmePbKz/18CVo7qmhrryIaDbAwv8ziYga1cQI2JMQDEPNDMo+hoX8kyVx1oEjd+StcPtlJrknj4UYSY0mW59wt+4ffHmH43RH2/sxe9n1hL9e+c73iY5dDK83YB2NM35jh6r4aZm2FWMnZ+fnjbRxodSIQU1end5Q0uhEhBFNXpmk82LCj5Q9f0Ff2q1vbW8uRf1Te+hmcxOAn/vnjpGbS5FM5Ys2xsnb1q9gFu+yjj5X7qAwPPTw8Pq7sIlFyv5bWAniazdENw1i3EcvlAmvLN+4cYCf9bvK2zTduX8Jya/VaFk1EDBEwyueh2LYPjUCgUcpESAvsMpOH1ihAClHehbUMRx9rpa4+svb70mIW5fa0L4UjTJJOCF9qTShnkwmuf/1CFUxQwgC0e5WJG5MXKliSUXDz+zfpfLqjtGPAPaIsRWYxS+OlGSb21SKUojqZ57VXbhF7sZ/2juqykY5KWJ5b5up3rjJ3u4mDv1BZ2bmdt5m5OYttld5n6ZNr+TWVEG2IQENk+w2BWGvMVQg5zQTLR1g8PDx+OtlFomTwPvdXQIbNoiQej7O4uIjWGqUM5udrqK1dcFk68QGV/2EH+ObA5R0KEmecjYH3UMrAMIonGK1hbnEvw2MvEAlPorUkvewsDQk0esMSjgQCwPm8RZtpUC0FgRXfku1o76yip7cO01fs++r3G0hDlAoTpdcdYAGhwdro9Gpr6hfK55EI6Yy+5VgrhUye2RtzD9z/YvityvoNVYoEwhmLqkSefUNLSAWguf6fz5E+1UrryVZmb83ec7REFRRTl6fofKqDaKNj0mflLKycRSAWKBKXSik++M9nSc9sShBeyWPpe7GXmq7qe73ULTFMg/0/t59r37mGshVoR5BE6iO0PlZZNNHDw+Onh10kSrapWqiISaA4vyMQCNDY2Mj09DRCCObnGygUAjQ2ziHl6lp5PXCMndzOG4tzzOYy229YhKbOOIfPcJ/As7kqRsefRmuDVLqt6L24EFhARmv8ArpMg1rD4GKuQFwKgkJUHCXp7a93rbRpaIpy+6aL3b8GVpZQhBSEWqLU1YVZyhSoD5rUn58iknF5gjckoboQ0ZYoczfmmLg44RzrAUU0HjoC9g4v4bM2ykHN+LkJarpr6Hqmi8FX709MLwwuEKoOce2715i+PoMQAiNgsO/ze2k66HyX527NsTy3jNrkWiuEYO/P7NlRlOReaD7cRKwpyugHY+RTeer31tN0qBFZQbWWh4fHTxe7SJSE2WkFSymlxlYAVVVVxGIxlpeXEUIQDu+571LGV8bu7HAPTZAJ6oJX119ZmWOEAKUgm61Fl+laE5OCXn/xEo6lNccCPgwqKwkGqKoOUkgXGHx/lOXpNPGOKtpOtOIL+fD5DA7sb+TyuTH06oRjKcdwbcXGPNYa4/hXj/HsBnO05WNtDL05RGIsSag2hC9oMnVlGq006Zm0q+HYo4ASArNIkKy8XlCMvT9G/0v9DL0xdM+eLEIKfCEfl//qCvO359G2RqNRluLqt68RjAWp6qhiaWTJteRZK83NH9xi4uIkx3/tmJNz8pCINETY99m9D+34Hh4eu4NdJEr6gAnu7xF6AUgCmxNDk0gpiUZ31sumHAVlk99hHomPRTrC/1D06kYdISVUxe8ixkuXNQTQ7NKZd/U5dScCqzbq553/7zsoW6FtzdydeYbfGuaJrz9OsCpIAODS9LooWV6vvAhVh3j8N0+VHDNcG+bAzx0gv5xHK817/+nMg/EN+bBZyYEWUqAEjLVGaB9Lu5ZELdxdZPjtYacaZnOD6R2cL94e59p3rztLIxtQBcXdN+5y7KvHCFYFnS6+ZWzskxNJrn33Oke/cuQeB+Lh4eHxYNhFokRx/zF9DbwPvLDy+wRwbsNxDeAJKvUfKUfG2ukspOgM/d22pchSavb0/A23734BpUxAo5SfqBD4y+wsdxLxsRVjH4wXCQZlKZStuPXKbY78o8NE6iNODkmuuAxUSEHdHvf7lp5Nc+Uvr5Ccvj9TtI8aM2hS01VNqCZE68lWWtI5xv/ovOu2Wum1aJArAgJRP4WMhTAch1utNMZKHo/hMzj61aNYWQthClfD4cxKnk7T4SZu/7B8ZE7bmtmbs9gFe+34Hh4eHh8Fu0iUXNnh9uVKe9M4wmQ/pSXGNvA28DM4ia33Rrrg3t+kFA1o6n1nMCp0k42EZji6/49ILzdh2T4KhQjD4y8yZyvqV6IXYuVnR4IEQAhyw0uuw5y7PQc4Phk9n+hh8CeDRU/mwhS0nVzPc1G2YvrqNHN35pm6PIl+BAMjmwnXhTn0pUPrwuGdUQzhJOm6sdqZV5qyZAkn2hhl3xf24QuZpKbThGtDRBoiJMYSCCmQpsHkxQny6bxrBERIxxQOwBfycfJrJ7jwzYvkNrcIWBuM85l4osTDw+OjZJeIkkV2nk+yVfXGJE4lTjlu4fiY7JypTIo/G6xEQDndh7uDf4NPJivyhssXwkxMnySbqyUanqSx7iJ2IAFobhQsxixBlZT0+IwdLdlorUFpohmLVJkn+42TWfczXYRrwwz8ZID0dBohBALBmf/9DH0v9tJ6opX3/+ADsovZR3OZpgypqRSv/4c3OP6rx5CmZOLCxLaGaMIQ+CI+Ryxs2DQ1leLcN87R96k+6vbUkkvkmL4yTVVHFQt3F7jxvZtr1SxCimKNLYp7+wBEm6NbOsY6uTwPvwmih4eHx1bsAlEyhxO9eNCUb/nunPNVnKhKENhHJeXABaX4r3cuV7xCYZDDb2w1jnUsy8eVG19FaRMwSC83MTN/iPbmN5EIuk1JUEgkpYmXW6ORwQKh5gQtdS0sWvXM3JgtmmylKWk9UVze2bC/nlsv33ScSrVeEx93fjxAaipFZj7z0JvufdisRizO/Zfz1PbWVlTuqy1Nbsk9eqEsxa2/v8WtlwHhCD9lO8s4GwWMVhphCPwRP9rWVHdX0/dib1En4Xy6QC5ZvhfTgZ+vzO/Ew8PD42GyC0TJhYd03K0mFCf64JBZGYPFdsZpdxLzO0iZENiUbzu/mfGpx1F6vfOq1ia2LRmdeHotobXWkM7SzQ6XbYyYhVmrmdGTRD8RJ7YYJTXjREC00tT21dLzfHfRPonRBIVMae6MKiimr83sOkGyEVVQzN2aezAH0+v/3iqqpG1NvC1eNlnV8JUvvw1UBajuqCr7voeHh8eHxS4QJe4W4g+Gcnknm1/TwFWcxoDlJ/y0VdoDZCv8O/BeqY4PMTN/fNOrEluF6DUN2sydLdmsI7DmgtCRRqNIyQSH/8lRCjMWmYUM0aaok9y6CStnPfh2RI8QD9rcrRIMf/l8EDNgUttTw/zAQtHYpE/Sebrjwxieh4eHx7bsAlHyMJ20omy9jLMRhSOQ3O23tdacm5vYwblt2kM/qXhrId2fooNwH4JkBS1Qyik7FkKQsdPUtjYQby3fcLCqvapsPoURMByH3Ht0M/UoRfrktg6pB794kHPfOM/y/PJalKthfwMdG0SJ1k7vnpF3R7FzNvV76+n5ZDeBaGCLI3t4eHg8GHaBKGkFHla3UZutG/BtJkk5UXJhbpLEDqpuwuIupix2brVtP8l0C1LYxKLjiJWKHKUk84t7So4igW4Xf5J7Ya3lj9b4jQomKEMQ3ldP6sp0yVv5VB5/xE++4vvhsREhBUKAMORazk7XM13bWsX7w36e+K3HSY4nySxlibfECNWEira5/rc3mLw0uSYYx8+NM3Njhif/5eltm+95eHh43C+7QJTsRanREg+P+zRcxfEkqQXGKtxe4LjKuvP2TKXHcY7VHbPY2DttcfEpBkePIQVOFxth0df9t0TDc+TzLaSS+9e27V5ZrnlgJt5SO83wAL8RJGK6dx1eRWvNhbNjpKbLNA3UjjB5JFmtp9ZUrlUfINInCVYFOflPTrA4vISds6jprSUYryySIYQg3hYn3lYa5comskxemCwyYtNKY2Utxs6O0f1M94O6DA8PDw9XHnlRks/D8HAfPl+OaHSZUGiZUKh8c7fKEDgRj/1ULkoMSp1g18nYlRmm+cQStb4LoOZZXZpShSMMjh1HacF6OoDBrcFfIBwssJwJroxa40cwYdmklKbLZxCR9ytNNIHmDAJBzFdFd6x/bSnIthVTE0nm59IEAj5aO6qIRPwkEzlSqTzM77S3z8eXYFWA/k/1c+uV21tWsTxIhCEIVgUJN4TxB33YlqKur5amw00YPoPGAw0P9HzJ8aSrEZuyFAuDi3Q/80BP5+Hh4VHCIy9KZmbGUUqSy4XJ5cLU1s7sUJSsma2z/tdYAyngBtACjFdwHBuYZnNDPwBLVZY7EZBzdARfRmCxsXdaKt2K2yGUMkgtG6xnlGpWp8ucUsznFMcCPmIVChOnj3BxiMk0DU7uO4gpJYZc/7pYluKD94bJZq21rsDjY0scPNKMbSsnUiUF2t5pCfLHk/xygct/tVODvvtDmpK9n91LfRkn3AdNsCronqArHGM4Dw8Pj4fNI9+mM5NZzfsAKW1qaxd2sHTjB57B6fC7GQWM4BipVYKmXG7LaxN3KzpCg/8MUlgl4w9F3qN8KYso89/OFQwUdmJp74gywxBIQxAK+zjxeAcB018kSABGhxfJZtYFCYBSmutXpgiH/SitmTwSYrbBz1yVn4LxaEuTnSblClMgDYncohR322MAtT0197w/OC6thWzBMcDbhmhzlHBduMRkTZqSjicebidhDw8PD9gFkRIpxVoUIRJJ7UCQCBxX1iqcSEg5D4idTEalJ1+2CpxfKE32dCMoZ11f95kLGNLGVjtPWi3nwLoRjQaRJ9f0fT7R+2+wbYWUknDEV7ZqZ2YqiXI5ttYaIWAinuNONoRoDSG00zG3cyJFx3QFSzoSBOIjKat9EAhTUNNVQ3IiSSG7szJwcJZtpCE5+tWjSPPeRI1t2dz8/i0mL06ilSYQD7D/C/uo6y8fdRFC8Ng/Ps6Vv7zCwtAiQgh8YR8Hf+GAFynx8PD4UHjkRUlNTQ2zs3NoLQkGd7JsY+IszYDTx+Z+S4slbq6ur4yWb4S2GaX9SOFyDUJQG7eYT0hs5WRZOgmvrg1oi/BtI9I0CjswTK79O/hElHCkMsM2o8xkqZTGsi1uTSu0LdAbnrpHmqPEli2qU1tM1CtJpJU82QtDbGvj/lEQiARYHFq8Z4M4rTSR5jCX/uwyodoQvZ/s2VJMuHH1r68xe3N2bQzZxSwXv3WJk//0JPGW8rlP/rCfx/7xYxQyBey8TSAeuL9ycg8PD48d8Mgv31RX1xOPRxFCI4TaNlLizHUGcHrl3wBt3F8pxaogKU08HEgtVnyUlN1eRmRoDvSe4UhfiqbaPC11eQ51Z6iJ2khRftwS6DC2i65Y5JteBS1oCT9e8VjbO6qRm5Zk9FIW+9osP/qDs2CvlqmsoyRM1heXoJawg6qWYNXH0zsju5StSJCIcktaGhJjSQqZAomxBBe/dYmpK1MVnz+XyjF7Y7ZkDKqgGHpjqKJj+EI+glVBT5B4eHh8qDzyokQIQVNTOz09fUh/YNvIgdI2V+dGKHZADwE76f2xD9iDE2npB54FjrJ5En59crhiqSOwsJW/rKhK5S+ylHsTK5UgfD6H+b0MLTdTRIUFaHysO6QYrAgS06B5C58SjSJf9ybaSCHz9cxdPsDkUGWNDRuaorS2xpFSgNKouwvoazMwn0ELiXSLYAjxQHNLsuU63j4CNBxooOlwaVK0G6qguPXy7YqiRwDZpZxTReNCejZd8Rg9PDw8Pmwe+eWbVZSwmczniG3zIA4wlr7BRPr/yQsd/xNSKOAMTqfhis+GI0y2OkeCM7OVVO04xxPYVPvLL/VoLZkZeJYjgxYiX0DmbcKTNvGpPDe74jQ1Rak3DCytyWtNQAiMbcNGwKAP//xTSKMPOnxcuzzF1DsjHPny4ZJ8hsxihslLU1hZi/o9dfTva6C+Osi5v7gCc+u5IrF0Ae1ybmkr6hcfnJDQ1sdv6aZS/FE/LUeamb46XVESbT6dxy7YmP7t/5cN14Xcl7WE47T7oElOJElOpQjXhqjqqPKiKx4eHvfMrhEli/l5Upafgg0+w908TWvNWGqMnJ3BEJqZzBWawlkcQVJ5714ob6++yqUKk1tBE5TTNAXexRDlJ2wr08LxcA3iIE4YJJmHG3MYlqJvNMmt2hA1UmIKgbnh4p3E09KboZWGhSy+G13OCyKJbomDIZgfWeL2P9xm72f3rm0/dXWKq399Da012taMvDOCGTKp6a6GxeI8GFNpekeSDLTHUBIQAqkUoaxN0/z9esg8+hg+g4Z9DVR1VNH7yV4GfjywVvFiF+yyy1dX/uoqsaYobafaCMTKL135gj7an2hj9MxYkeAxfAbdz3Y9sOuwLZsLf3qRpdElwIlaBquDnPzaCXxhz/3Vw8Nj5+waUSLWyoLLb5OxMlycuwyA1gUyhRkgx84SXIO4eZFsZiFX2eQb5SqtoQ+23sgKUTX1c0hj/eJ0LIDYX4++PI1ha9I5i3FD0m4YTuKMxllNkqJImKwtAUyn0YMblmqkgHQeYgFU3mb87MSaKLHzNle/fa0oR0ErTSFdYPrKjOuQm+ezRDIWk/UhCqakbilH3UKWLVJgPjSkKVGWQppyLVm2/mA90xcrFZL3juEzqO2robbXKfXterqTluPNLN5dxAiYZBNZbn7/Zmn0RGtmb8wyf3ue4XdHOPVPTxJtipY9T/+n+wlVhxh6a5hCpkB1ZzX9L/WX2MrfD4OvDrI0slT0vVieXeba31zn6C+7dyv28PDw2Ir7EiXd3d3EYjEMw8A0Td5///0HNa4dUx2oZT53CyncoyRKK64v3Fj7XQhJVaAZGN722OuTegjH12TrVBxLKSYzZSzWN2Fsm4gKgZlPIVTxk7GQAh3xQdBE5CyUIZy03byFnlmGrIXoq3WPklyZhsQmm3cN2pSIVB4KCpv16164u1DiXVEJsYxFbCS5engyfkko/9E34VudRM2gSU1PDZmFDNmFhxzBkc7SSddTndTvqy/6XPxhP40HG9d+t3MWA68Oom3tWL7r9SorZSuw4frf3eDUb5wsezohBO2Pt9P++MPzFxk/N1GSTKuVdqp+bFUkoj08PDwq4b4jJT/+8Y+pr69/EGO5L3zST0u4Cc2S6/tZK8t42unSK4Uk5q+jOnAAx0a+nEfJCspGJ9OI6sdxDNdWyQMLOCXFNawmul6Yn6o8wVVsb24mrBjCTQhpUH5JLhzkZCRIQAi0qRHtcfRCprzfWnWwVJSETEfoXHO8UuJt8bWJ814EiRt+6+Pl7ppP5Zm6VHlVixuBuJ+WYy1oDenpFOmZZbJL2RKPlWAsyImvPYaswF2388lO2p9op5Au8Nb/+rZrJc/S6BJKqYqO97DY2CNnM1rp9eI2Dw8PjwrZNcs3AHF/N1rfLHlda03OzuOTPgSC9mg7e2v+G4SQOFUzZ1ldwtmcg6FtGxYX0BcuwAtf3RCFubXyI3HiAH7gSWwV4q3pkYrHnLO3d+y0giOIbBOypOsgSJ9BuH89IrL271p3syshBGZjBD21jNbOk7iWwlnmOTfpdKD1SQLxAD/5X15DK020OYqd20a4bcN83E80Y2G6Ga5RXj99nDGDJs/822dKolHJySTv/8EH60swwskVyS3lKl4+kVISiAXWlpo243QK/mjvWv3eeqYuT5XkwMSaoxg+T5F4eHjsnPsSJUIIPvOZzyCE4Ld+67f4+te/XrLN7/3e7/F7v/d7AMzMuOcfPDj8CGGwOfIhhKAmWM3PdL2E8/jWBqyGy5txlmQGgDRM3kBHQxCJOjHziTH05QugJWTmINwAzAC3cYTM6oSRAd5jdPk4hQp73YBGiDxab93VOBe/BIun8Gm9Jkz0St6I3LfzKFVNW5x9n95DfjGLGfKhlWLknRESE0ki9RGmr00zc239s1oado8+7YRExEc6aNIxvfxIChA32h9vcxUGk5cmi9OUNBSWC7z7n94DAaHqED2f6Ka6sxpfyLdlJKr1RCuj740WCRNhCJqPNH3koqT/0/0sDC5g5SxUYT1H58DP76S83sPDw2Od+xIlb7zxBm1tbUxPT/PSSy+xf/9+nn/++aJtvv71r6+JlVOnTt3P6SogxdZLMTHgELDZHbMKeAwAffNlSI2DYYBS64v50gdr/V8Gy5wny9Ty1r1yDGyORm5zKDwIaCYLCwxvYXCqNQhfikTzX6CnP0XErsbSmqiUZV1Vt0KjmJ1dYu71ZcIRPwcONRGrDq0ltd750QBWZif9ciqjcyHLjeawa1TkkRQpAkbPjBFpjGCYBqG6MNEGxylm8uKU69KGnXe+M6mpFJe+dRkE+IImvZ/qo/1km+tpel/oITWVYnFo0Vle05pYS6yoMuqjIhgP8NS/fpKJC5MkxpaINERofawVf4WuwB4eHh6buS9R0tbm/CFtbGzki1/8Iu+9916JKPlw2W7Z5BTrFmNl6PwEXP9LsDfmXAiItSKCq0st5VSEYCa7VVRB86W6V2n2zeOTzgRVawqafSbvLbsLASFACJtwbBQV+RPujj9HPNBLVTIM6h4+PmGR7vp9glOfJ53q4tz7o5x+uptA0DnWzM2HE80ysjbxtIW+Xzf/jwsarKzFlb+8ivRLUE4i67GvHq3cHFhDIWNx6+Vb+EI+mjYku65imAaP/fpxUtMp0jNpwnVhYs3lbeI/bMyAudKsz2vY5+Hhcf/cc5ZcOp0mmUyu/fff//3fc/jw4Qc2sJ2hgTlgq5LOIPAB8CPgKk6Saimi6wVoPALSD4YfjCAEqxEn/tsNW7Xgfus0o6mtn/sDIr8mSABMIYi45bC6TGxSWvS0v8W+tiyoyuILGoVGo7HQokCu+ftgZsm2/A1aFFBaMz62uLb9w3zKbZ/JINX9Gfp/HFF5hbIUiyOL3PzBTRoPN5a3kHfbv6AYfHVwy22ijVGaDjV9rASJh4eHx4PmniMlU1NTfPGLXwTAsix+9Vd/lc9+9rMPbGCVo3HExgxbL93kgNWyz0FgAvgEm2+BkAbi5L9EJ0Zh8Q4Ea6D+EEJuTNzrxCklXmb9sd8ADlNgsuw4BJrhXDON/uJoyqhLmWy5dAFNAWTe2aCC2V2ZCVRwlPqGNHs7fYR9e8lYndyYu8Nc+C4ivYe774+TfH+c/k/30/VUJwuDC/esHLZrkvdILtVUiLY1kxenePbfPcPCwALZpezaks125BKeqZyHh8f9k4o28PrT/+LDOdnf/OSBH/KeRUlvby8XLlx4kGO5RybYWpCstJ0tmmU1TqRkBOhx3yveDvFyIWkTeG5l/ykgAHQznBTk1dg24y2dsJdcKlLAiZYsWf1kVBMx4y4RYwwjX8/yUDPoyqZ3aVXREILDveMYK8Iq4gtztPEg11M207cVeinLzGSKhcEFTv/LJ+l7sY87P7pzT8LkoXftXf04P6YopTD8Bk/8i8eZuzlHYiJJPp1n8uLklnbyWxmheXh4ePy0sAtKgrfyGakG6oG7wOacDRuYxV2UrM56W038BtC98uMYpv3F0HtbjlQj6A2NFr+mNXEpmHaZaTUGi4V95HUdKauTiDFB/8RBsI1txraOQNDfv7AmSFYxpcGeDsn0DQXTadBgW4qRd4bZ85k9aK0Z+NFARef4UPkYCxKAeGt8zTSsYX8DDfudztGxligDPxzAytklHibSJ+n/dP+HPlYPDw+Pjxu7QJRsNTkvAmlKBcnqfmGc5ZeZlW0iwM2V38EpFz5CsWFaKXnb5g9vnq1otG8l63kxPoR/JY8kp2CysOqRsr5so7RBxm4gr51KIY2PZbudpA/i9s4WQUJh98Rcv1/B5UlYiW5oW7M4soSVtxj8ydY5DuUwQybKUhU1mdtNCEMgDcn+L7g3amw/2U7biTasjMXC3QUGX7tLdilLtClK/6f7HkqjPA8PD49HjV0gSjrYevnGZULWChAg6oBXWF/e2XyMSSCJk3tSXgicnZsgbVeSOyAYzTbyQ3GbmOGcMa1AaZNlqxnsAtHgNBqDhNXHTL7YRlwhWfLniWfLN2NzI5sxCUdK70NhGXRWrV+bgEh9mIXBhXtehpE+SXVnNfN35tcs0nc9Alofa6X72S6CVcHymwmBL+yj8WBjka28h4eHh4fDLhAljThmaKNUXGsqJKgCcB62tHnXOKZos0BD2a2uL85Wdl40MZ/Taye1MtS8ijOU+TwgiJsmPRHNfA7mlgVupuxS77xg6s7NOg4encIw1hVCIQ/XXo1CxIC0I1ikIel4spOL37q443OsjU9Kjv3KUVJTKS7/1RXS0+l7PtajguEzqOmq3lKQeHh4eHhszy4QJQLHKr4HeI2KH82FgRNF2W4pxAYuAMcoJ0xst/pd15HaRIzZosoaU6Rp8J9lOv8kMb9GCqjbFAhZ828DarNbLyU5NvkghEIKGw1Mv68Qd/30v1QgEFFMTkW4drkVbYA4BCiNHlrk2Of2YudtCqkt3Ny2IdbilKwGq4Mszy/f83EeNXxhX9n3lFLM3phj7vYs/oifluMthMu0AfDw8PD4aWYXiJJVYjhN8eYr3F47yziikluQBc7gmK+Vht392zRFEyuiIuYbQIpc0XtS2MTNOxjGQar9UbQGQ8Khas2FScnyTBCVlyCgxg8BYW6to7IWenCOZ77yN1g6yPJ0mKtnn2FqwmDqogE+gTjRijCko8tWMPfXE++oYvZ6pVEfd5ZGlvjR//3HaOunYd3GwfAb1HS79zBStuLsN86Rmkxh522EFAy/PcKhLx+kcb+3hOPh4eGxkV3WW/wQJa1J3aIYWkFm2FnG2QqtwU7D4uuQvgrKPZm1yucevfDZkr65GMcmazk2WUv7dD+GFS/ZTgpBU2CGxIKPd6+GOXMtRHLRJDMRRuVXKm20YDEnuJYvXm7SK9enlUbbCn17HpJZht7vIxzMUn/413j+//QcfZ/qRfok1Lk72mpgZipFvDVWUh2yE/Kp/E+FIDECBobfIFjjdP8t179m/NwEyYnkml+JVhplKa7+9bWifjZaa8e1dTa99pluJLOYYXF4kUL23qNYHh4eHh93dlGkBJypNQ4srL9UzoUsNw7mAfDn2TIXJTMEKge5MchPQ9XzIJ3Qu1IKIQSDqcT66VdzRjXsnY1jKolYedGfq4PRX2G56/dBrooLgV+2MzPbz8ySyXzCEVVTC6UfjQYWlCKrNEG53hVYaw0LGfTdRcjZaAzGLnez53NfANPxWul+pptQdZgblyawXCZQrTSFgiLcFqZhfz3T12d+KsTFvSBNyb7P7yPaECHaHN2yMd7U5Sn3SiQBS2MJarqqWRxe5NJfXMbOWmggEAtw9JePEG2MYuUsLn3rEovDS2vGdB1PddD3Qu9H3pDPw8PD40Gzi0TJPPAuW7u6riIgfBx8L+I08Xsfx51182YCIvvAToGdAG1B7k1mRx7j5g9ukU5kuPlCEKpyVJlDSFFg2W4jp2qJZ/0YWiKwsaK3sIPjCKsaM9mPmdqLFb+KFCHqwj+DIER10Ka7JUMiLXnnagRVxkZeAFmtCW5YwxFCoMM+yK1fu7L8a4JklaZDjQxdniSlNGy2QddQXRMC4NAXDxF/b5SR90YopAtgZ6mKTFEdmyaZriOTj5BarmX3BNqc6it/JEs+7QjOaEsUO2uTWciUbK0sxc3v3eDJf/3ktsLA8JW5RxoMU5JP5zn/Xy5gF9Y/u8x8hrP/+SzP/PYzXP32NRaGFp1qqBUdO/LOCJH6CC1Hm+/paj08PDw+ruwiUXKVygQJjtgIRHH+yseBvcD5MhtLCPVB6hygmB8Y49K3fdiW4vYLAcyaMTqCrzmHRaF9F0lYvZB6AUSWTMefoc0UyAIog0LtO5gJx8uiKvg0hggjNiwjVUUVe9pz3Bh2r+RQQMhtqSBY/FH6wj5yyRyB2HrWrJWzSN2ag7116HgAsWLypW0Fi1miK31vhBR0PtlB55Md6OUZ7B//DwidXws62bbB1Hw3N4aeLH+PHxSVOrjek9OrxvAXaDs0TOeJ25h+i7Erndx67QhV7VWYAZORd0aKlllWUZZm9L1R+l7s2/IMbSfbWBhaLImWmAGTWGuM4beHXZdrlK2ZujTN7K3ZkvJsVVAMvzXkiRIPD49dx2551AUSO9xe4ljEvw9comz2qBDgq15JiBUMvN2CshTLtZJstaIl+DpS2EhhI4ReSVwdRAWmKNS8hzaXHEECIG2QeazYdQQGAaOpSJCAk+Ta0eieNyCBBkMScHs63zTp5VN53vvdM0U5CKuTq742ix5cRC9lnZ87C6gbc9x65VbJYfWdHyCFVbQKZhg2TXWDmMaH0K9Fw5rT3Hbb7RBp2hz9/Bn2PHuVQDiPXTAZOdePEJqxM2OMvDvi5Iq4nF7ZisR4cttz1O+rp/WxFqQpkT6J4TcwQybHfvUoQgjyqbyr6NG2JruUKRuJyS97uSUeHh4fb0ZGRnjhhRc4ePAghw4d4j/+x/+47T67KFISwPEUqRQNDOG4vm6HhNgJyNxledG5ZZkqSdicdPUSEViY4YvYcgakSz6BLADlS0jFSrmOCUSFIKE1BtBqGnSaRukOtkKPLJW8bOUsJs5N0PlUJ+BUiRg+A8u2YDqN3uQhMvreGN3PdROIbqhJXrq7YjYHSgtGJvczNr0PS/kIRAysjfOygRPKedBmrg/JHFZZBhf+9jS9p2/QdeIOt984SC4dRK94waiCKhuBEYYg1rx9vxohBPs+t4+O0x0s3l3EF/ZR11+HNJ1zVHfVMPbBeGnjPgn1e+sZe3+M/GbRIqC2t/ZeLtnDw8PjQ8M0Tf7Df/gPnDhxgmQyycmTJ3nppZc4ePBg2X12UaRkp+WVisoECU60xIhA9DDRBmeCCKQV5R7PhQApdUm/mfUNnG6/eXuhJHRvK5ic9REQcDzg42jQz7OhAE+F/HT5zJInZ20p1PASTG0QGAYQdCIjixvEyvk/uYCV38osDgZf3WQvH2tbq1K6PvgkdyeOkitEsG0/y0mnAqX+iVqqvh6g+reCBJ82Sgqg7pvQw9LOAmWZDLyzn0LWx/SdVrTa9L/EysezubpGGpL2J8o1bCwlXBum9UQrDfsb1gQJQP2eOiKNkaLXpE9S11dHvDXOvi/sc6qmVkdsCMygSe8n3RtJenh4eHxcaGlp4cSJEwDEYjEOHDjA2NjWTWt3UaRkK1OxIJDDeewVrD967yTmL0EI+l4scPaPJQvNBst2M8LlGEqbtOh2fKqVBfEeeqNr7EqFjm2bzKeWaK6qW+t5ozWogsQ/FeWJgLEiQMqPUWct9PmJ0kiCDbJWoAsQrnMSNxPjCZbGlraNOszenCv6vdD8Isbou+RzQWYWulB6g+LQYOdsluYWCRtO5EdsaaJyj6TyrhELX9SHnbPvu8+OMBSjl7qxC+VFZNORJqavTKMsRbw9zv4v7CMYv38HVyEFJ772GGNnxpi4MIkwBK0nWml7rBWAxgONBONBht4aJrOQobqrmq6nO4tyhTw8PDw+CmZmZjh16tTa71//+tf5+te/7rrt3bt3OXfuHKdPn97ymLtIlERwHtE3hcGVDcsDYI2BvxZkG/hPgbyOe6O+MqxEKKo6FN2/UuBcOgxCMJF7jtbAaytawynHTVmt7OEIUptk9RgZxlivFxbMLnSRyRzkRH+8pGLZkIqINlbOt3EWdpnsbbW+ZLIZC8wGSfupNgCngqOCEl8jsD4xZxNZ3v2jYaLmizRU30UIG3TpxK2Sei2CY3ZJeGvb02zLavkrUCJGIo1h+j/dT11/HZf/8gqzN2fvT5howdjlLrZypVscWuTpf/sU/pC/rCfJvWKYBp1Pda4ts20m3hbnyC8dfqDn9PDw8LhfGhoaeP/997fdLpVK8eUvf5nf+Z3fIR4v9erayC4SJS2UVOBoBYl3QGUBDdklYBjyIxDbv8UcZLKVYLkTCKxVEKftDgYyX6LHHMWmwKLdQpX/fVAagUG7/UWyYoqsmMLUMUKqkw9GbfZ1ZREiX3RcIQADzJosKmegln1sNVHKiB8lBHrzrC3B7BR0H/RTyBTwx/zM3ZpzP8jG85uCjg1LEndfG8LO2SxmGlhMNlA2auNzfE6EFBjVEv8xg/y5rSuhhLTRWiClBqExfAUKmdDa+24NAYUUHPi5/bQcb1l77fCXD7E0ssTc7TlSM+l7cqQVBuRSofIbaMguZrn5d7c48hVPHHh4eHhUSqFQ4Mtf/jK/9mu/xpe+9KVtt99FosQAngbO4VTiaMfsTOUpnkxtsCZBHwaxOS9EAKdxoi6XcapzVl8HaGUiPcPFheKwva1D3C7swY+g3pjBlBMk9HWq9GEkJkHdRFA3obTNtJ3j+eNZIkGFmzu9kBDqSmElfGQGqre8YmlKmj7dxPjfT65rKANEEEKnBE2+b/PuH6fRKlDkg1GOcF2Y8XMTDL8zguGTpOeWN7m7ugskteh4aGjTESbhp33Yswp7xF3ESNNCawFKojYMK1q/SGrW5Zql0+iv5VgzzceaKWQKjLw3yvztOQJVQad8+alO3vpf3972Gt2ObQYiWNnctptOX5tm5L0ROp7oqOjQWmmSk0mEFESbtjZZ8/Dw8NhtaK35zd/8TQ4cOMBv//ZvV7TPLhIlAFHgOWAYuOyYnrl6lxSgkILA5goYA6eHTgB4fOW1BE7vm2mWrRG+OVhNucnZIks48DJCwKzxJkG7mYCuBQQaRc6fJBTBVYysoh0fL3JjW1R2aA0aWmtD9BzpJhuZJnleo5LO8knoMLQHLyMti+rWCaZvtZU/1gpCCpbnlsss8ay+5n7dOgWJb+UwGiRqTjvfKmOLXBglSxJKlWUijVWRuCmpVEpO/sYJ4i1x8uk87/7uexQyBWesowlmb85Sv7eutIKlEhRkl7YXJKvcfuUOTYeb8Ie3boy4MLTApT+/vLasZAZNjv7yEeKtW4cuPTw8PHYLb775Jn/8x3/MkSNHOH78OAD//t//ez7/+c+X3WeXiZJVcoACI4xrngkm+N2SGi3gNk4PnVXiOGs1I7wzHXRdwJBkaQy8S8wYWntNiwIjxjcJ6Vb8uh5laCLRI8ht+u0IAXYO7KwB6NJqG61hIYseTzLywTgRKTnaPc104xyzVi/5TITChI8lXzOxuqm18uJtEZQIEiFt+p++SuvhYWYHm7j2w+Moy/0ro5fAWqosr0MaCntzlQuQzwTwBfMUssVJnMpSXP/bGzz268cZemuIwnKhaHlHFRQz12bvq2dPpQgpmB9YoPlwEwB23kZrjRkwWbi7wPDbw2QWsyzPFkeZ7LzN2W+c49nffgbTv0v/t/Pw8PDYwLPPPutqDrkVu/SvYxwwwN8Ey7dBbxQlAswqyudqjFMsSgDuAjaDSbccD01H6GV8IlHaZkdARoyTYZzq4DMlRmnlkAEnz0KsVGybRhafmSabiaGuLcLKk70Cbv3gFs3/h8fIDr3P/FQHSvlWrqKK6YV+rLHp9eFI4Tpx+6N+8ql8yeuHPnOW+u4pDJ8ilwyXlsveI67fUaGobp1j+nar6z7J8SRv/s6b+MJ+13yTtcKqct9/sZI8+wD6+RimJJfMcfU711gYdPos+aN+CssFVyO0NTTMXJuh5ZiTE6OV04RPmpJwXdhb3vHw8PipZ5eKkkYg5OSMxB+H9DWw5gEBvgMQ2Q/C3QFWa8Olh5/FUMrHkkvJaEhOYoo0cpuIRN6eJWh2VzTxaFsgkAhhs6/rRzTV3EFpCWiG6WHsUg/5tJPXYudt0slWRheP4mvLIn1p7KSf/FyQQiGArq2BdAIE9L7Yw9Drw9gFG6204zJqCNpOtjL05hBq44QtFHPDjVS1LGD4ssQaF8tGOHZKrGGR5Ez1etRFKAyfTbxxgcnr7hUozrUqtHJfatFaI0yBLpR+DkbAoP+lftCaOz8cwMpuU3UlwPAZKFuVCiABNT01vPe7Z8gsZtZEUC6x/RKQshX5tOPEOj84z+W/vIIqKLTWThO+XzlKtMG9i7OHh4fHTwO7VJQI4BngGhjjEH8CdBOwH0QIGMNJiC1GaxhJCwSDtEc2CohW3p4ewy26IkXB2VGA1EFq1HHCqhtLpFiUZ8nIcQACiQEs8STZ6TjaMpBBi2BbCjNSKOpkrG3ITTneIr2tb9NYcwcpbeTKElTPqVt0n7jN8kKUyy+fZHmpitn8NUJ7nUiHkGBGC/gbMqRu1EB1EEYSoKGms4a2f9PG+LkJEuMJok1R2k60Ig3J4Ot3N90MycS1DmYHmjn9a69S0z5LpDZBYqqa+/XcW5qoo+3IXZLTVeTSIapbZ6nvmeLGT45vu68qF+lQ0HK8meRkiuREEjSYIZP2x9vpeb4baayP+ebLt0oiJp1PdTA/uEAhXaCmt4beT/YwfWWagR8PIjY0Lzz2K0dZGl0in87v2NpeSEFNdzXZRI4L//ViURnzahO+Z3/7maKxenh4ePw0sUtFCTg27kdXftikJ1qAAcBxO11dTshYkpmsD8kUpjRpCa9WWXSxlJ/CjbTdzlD2F+kK/IQe9YsYBBEYFHxV1AbqKLCILQvo5RYy41FYtTBf9rF8u4r65lsUGuIoTBBQmA3QIq/Scug6UirEJhMSIUAYmkhtkpNffpOpoceZ8QmEWE+8FAYgFP6GZXJT6xPczI0Z+jv66Xq6NBrhj/jJJzct4WiJlTcZPtfLnmeuEapOr4iS+0UwdqkHfyRLtDZBcroGI3aa6u78PZX0rjJxYZLn/t2zSJ9ESIF0ySiu7aktERPCENgFxenfeqLo9a5numg+1sz8nXkMn0HdnjoMn8Ho+2M7zl+RPkldv+PSOvj6oOv+ylLM3ZqjYX/Djo7t4eHhsVvYxaJkKyROJGWUrHWJgKFRGgKG4mjtErcTMaYyExtEiUG1P0baSrkey9IhAoWfxTAcQZKJJbDNgrMMQAyhIDW1LkjW0JLCWJD20de5FvoiyjJ4rP/bxMLTGIYTGSmXIyQkSFNTs/c6M7rJ9X1fdZ7s+ErjNrFulZ5N5NC2IlgdXIsGlZtktTJYGK0nmwoyc7uVB9mZIJ8OMr+yDCWGl4qs1u8FrTSDrw0yc32WfDpPrCXGns/soaptveLl7ptDJdeqbc3E+Qn6XujFFy6uyApEA2s5IKvEmqNb2cesIyFcE8YX9tF2opXmla6+uUTeNS9GK03OJbfHw8PD46eFn1JRAs7kWoPf0E5rm5VJxkCzryrBxTm5ljU8lFoikd9qspDUiygSk4I/uyZI1lCAcp/F0qKRGb0flTOpjo0RDc+uCRKgJL9lVaQI4fh9GCrnNJBzOXw2rx2LdhxBEogHeO933yM96zi/+aN+Dn/5EL6Qj3h7nLkbbgZrmkAkS3ImDpVW8twD6Zk0obotDMwqQcPIO6Nrvy4NL3H2j85y6p+eJNYSA5yE2XIN9pbnl6kKV217mnhbnHhrnMRYYj2xdUX0CSGchFql6Xuxl84nS6NStb01TF6cdC1hru7c/vweHh4eu5VdKEpsYAJIA1U4Sa/lnsBHXB94BbCvOoUGvjt8g5FUgoLeutw1hw2YZEPpEoEgDDCieexkab8SKSym5SEQgnhkGim3TsIsFimC5HCMQgCMBl1sf65tlm4VqI0tk02G0Apu/N3NomNlF7O8/wcfOJOpUe7RXzA72MLsYEuZ9x8cmfmddHmuDFVQDLw6wLGvHgMg0hAhNZ0qESba1gSrKxNFQgiO//oxBn9yl4nzE2hb0XCggb4X+7DzNvlUnkhTpGzpb/2+esL1YdLT6TVRI32Sxv0NRBu37zzs4eHhsVvZZaIkDbyJI0xsHI+SEM5SzWajNICCS6WNM/EHDZu7yXFG0hsFSamx1yqDdpp4wALpHk0ItKRZTvlBr+8vsMgTZlU0ZfNRlDKRRqFo39WGfSVouP72KQqZAtGf9yOjzramkacr+w6PnZhBPyaw8ibXf3yM2cFm12NoW7ssJzgubk4/H7d79xB4SIGY5OT6slv3s13M3JgpSjKVpqRhfz2B6NaGaBsxTIP+T/XR/6m+4jciEKrZWtxIKTn5T04wemaMyUuTTsfhU200H3P5fDw8PDx+ithlaf4XgDzrZmk2jlC5Xmb70lyMVYSQTGSmKajtBIlGYBEIvEcumi5zrA399cTKZG/YaGOl2c2K4phd7EUpg40pD1v5zihlYB6shtZakv81T+o7eZb/oUD33BtUhWeQplNqG4jkOPwzHxBrWCx/sA1EIkkENkLYCCGoikyylWII1yTpeeIGvU9eI9ZY2Tk+TMK16yIh2hTl6C8fIVjlRK2EKWg92crBXzz4oY7J8Bl0Pd3J6d96gsf/2Slajrd4PiUeHh4/9ewiUWIBCy6vaxxDNDeacCIpbmiWrZUkzBJBogEbg2VA0RJ4jZZIo7ONy7yiNVgJv/O+hvCeebQSYBsrR3OEitImZ298iXSmfkWcSDLESeo6V3GiEeTsKNSGoCWKPa3xLSSI1y9gmJuqdgyb/S9coLFvHMRWduyKdDqKxkRrH0qbJNL1lBMl7UcHeOKXf0L3qZt0n7zFyS+9Sf8zl4u2kT7J4//8FE//26cR28TmpCm3WEpavRZBpClSUbKpNCU9n+hZ+33m+gwXvnlxzVpeW5rpK9NF0RQPDw8Pj4+GXSRKtqJ8vgQ8hbPMsxEDaGdvVSvmJhfWuHmTvvCfsif8J/SE/4qe0F+itI+AufWTbn4mvPJfmtxwpKgSR2ywI80Uqjg/9fOcXfgFziV/nmv6swzbR1BaFAkT2zYZHH/CMXszJGIlkTMYzaJcDM6khFjDEnueu0j3qZuUj3xINt8vjbk2vo0EIhn6n7mK4VNIQyMkGD6b9iNDxJvn8YUzBKuyBOIBJi5Osjy7vI3JnGPotvcze5Bm+XupbU1tby0v/vf9HP7CFNJ0F1nBqiCHvnyImu4aALJLWS7+2aUSj5J8Ks+5b5xzvEdWUJYil8yhVGXW+R4eHh4e988uyikxgVpgnuLJUwJbNaQLA8/jLPHMrhynF+gmYmbwCcHqHFbrO0+d7yKwsiSDQoosphhE6xMgSnMvtIbCog9trQuFxthtZqx+LDuwto0jaAQogTUfIrkQJHp4Dokmazdw8f3n2HvwEoFImlwhwt2JU0wv7F07pjCceE5yLr7S2K50HGNXOrn5k6PoNRG0U4ojRvXdU0U5MqtI0+b4z7+NL2CTmKrizLc+wdjCGBPnJlAu268eO1RtcuQrj61ESsp7xht+g8a+GUTyr2nssUgd38vQuf6V6w7gj/g59tVjhOuLrdtHzoy6Hg8cETJxYYLOJzu5/cPbjJ4ZA+1EWvpe7KX98fbtb4+Hh4eHx32xi0SJxqm22VjWKnE6B+913WOdCHCy6JW8bfPNgSvklA0IAsxQ57vknnAKZAoDRPwHXaMlYpMAaKgeIBrNkCp00lv3Xb537jeISomxmngiINCSQprOpCxNm1j9Atd/eIDCvmNksqWRkLqGCNnWGMnJJCPne+k4NojhW/c6KWR9DL673ykfXrtflQsTM2JhpYsjSpujN2vXK8AXcM7tCzrRB600dt7G8BnYWJvObSMMQWbJaVrX84luanqqmR9YKEnAFYYgVBukqvZ7oJ1E5b6nbtB+7C6JyRr8sQ7ie3/V9XNITSTLXp9Wmsxiljs/usPombG1RFhlKW69chtf2E/TocYK7pSHh4eHx72yi0TJLWBo02sa2I975U05loFlbidy5NXKxCqWaA+9TLkndyFACvfcFCFABosbAlZHx/Dl8mSjVfh9ea5aGapFgCafpKE2T7AhixFZKQ3WCr/M0NN7GXoEyXyaCzefQyuN1iClwDAl+460YBxt4fJfXGbwzCHSCzE6H7tDpDbB0kQNV//+BPlMsGgcxcJkC5FiQvC5AOmXrSIRMjvQzL7nL5W9k1qDGbAQUq018xOmwOfzo6wC2lY4qyPGWs9EK2tx50cD9H+6j6qOasbeH6OQKSANiRk0aTneQtfpGsTynxedKxDO0dA7CSK5njh8c5bbP7xDZiFDqCZEuC5MOYSE6o4qrv/N9aLKHHDKigd/MuiJEg8PD4+HzC4RJQq4w3rVzSoaR6xUMplYwAc4kRbJ/mqbvArwo4kI9f5zSFTZKEk8cJqQz73ZnlZgL6/fZiEUQmiyuTAZU0IADtUPcHGmn5msQX1acbzVRiobKSAkFukTb2FIJxm2yrzME0/+KmOjCZbTeaqqQxgLWS788TnsvE3TwUb6P7OHWy9X895/7aD3yWvMDjaRTbk0ehPaCcxITU37DPOj9Y6pyobbKKog8qIff5PNsqGKlqEARi5103n0LgiNNDZFNQQYPosTX3yT2cFmxq924o+GeeLrTzB7a5Z8Ks/Aq4MUlotLoFVBcff1AZ77d5+g57nu0nHrvKMd3RDOdU5fm+bKX11d8wFJT6e39EEJVoWo6a5BlXG2zSWyZff18PDw8Hgw7BJRkqd84malVRWXcASJAhRLCyHyNxs5ngqgjc+Sr3kPu+p8STAhYLQS9vUgROmSinZ0BPmVBntCWDTW3EZrg9HpY6QTUXgcPtP7LmPJRpbydcwnTC7+EH752deJxpP4RWbTMTUh+T79HUmQNVx/JcTExfm1p/uhN4eZujLN/p/bx+LwEkMf7KGhd5zkTBVaFS+/SKl58h//A8FIFq0F737zOczeAOm7YUQIAsdNfJ0GApvw0hRLct1ttOvELXpO30DZEqUE65df/DlIQ1PdukC8aZGuk7eZm/sa0pQ0HnCE4o3vFRu6rZJPW5B5BUIvlb4p/OA/DvnzOGJyFT8EXwDg9j/cWXdbXUFZCn/UT345z8aWQlVdVRz/6jEMv4EZNCmki0USQHQlkdjDw8PD4+GxS0SJHyd/xK1SopLJZNUF1tk/sRTk4rl2lJI4TiJhItlnyRkxktHXN5iOQMR/GOFS56o1qJwkMxSDgkLKApHgHH1tb3F79BmWUm3ogoVdMAgYFr954gbDha8wM/M98n9fhZHN468qfbIXKFj+DpBH46fvMc3CwLMsLzjXqWxFNpElM5/hwOfD3PhBlpmBJsI1jjhLz8UBgTQtmveNEoo5EQBVEORSYfbvP8Po6Sex8KMASYEAKTqD5xm3nQm/um2WniduYphqrfRYa3eTN61gYayOQjZAXfckTZ0/Ag6vvR+qDRVFMJylHkGoKg2Z1yDwJMhNn6HW4DsMheug06xVTwWfdbYHMgvuUZF8Ks/z/+fnmLkxi523CUT9BOIBDJ+BEII9L/Vz/e9uFJur+WSpSZqHh4eHxwNnl4gSCfTjLNXYm17fV8H+Nhuf8Afu1KFWetWEY5qqGo1ConmMhsJxRs1/IBJuxG82I10qbmBl6SKoCHWmsJdNgmYSNSN458o/RilnnwAp1EwBXVdLoO5Z9uS/S7/vLO9HnmH0Qg+RFy5i+tavZz2fY6WfDXnMABz6zFnO/Nkn1rZTBcXUlWke+9lXaPxnM6AFQmiUklg5Hzd+cpiq5kU6j99xrr4gGb/ahbYNbr18CGWniT82S1V/kphvjhhTiBB0nbzF0Nl+2o8MlpThCrEixJRTfgyOILHyPi7/4CTKNhDGYZ74yuuEfN8Hoxb8x9nzmX4u/8UFGnrH6H/mGoFIlkLWTy4dcD6/wm0IPAbA3O05ht4eJr80S13nEJ0nCgTCK+Eo2QqhL6ypIn/UpesxEIgF8IV8BKJ+Lv/lFae/kXbMzI5+9Sgtx1rwhXwMvDpIdilLtDlK/4t9xDc09fPw8PDweDjsElEC0IeT0HoLyOFESA4CddvuuZSbJGQq/CumXcupACDw+TTxGo0UArlh3abdeolFMY3czgkMMEI2KmewNNyMttaXT6TO0ydfRY7kEWOT6EO/iwg7zQFPfPFNRi70MHG1g9ZDQ0758Yp9/eZIhJAQrUtgBnJYufXeOguDC5z55j6O//wivqCzHGFIhWHmOPr5DwBHNChbsDBex/D5bpQtSc5UO/fgxxHm3q7iyV+9xWoOb+/pm1Q1L+AL5Vzza+y8QTYVIlztONum5mJcfeUEhWwQw1/g5JfexhfKo7MvI/DD8ndp6P+3nP71CwQjI2tixh/K4w/lcdxuneTc4XdHuPPDOysRDIPlhS4mb7Ry+quv4g/nQU1C/gIETjhj/UQPN1++VRLx6PlEN9lEjovfulT0np23OffH53jut5+lfm899Xvrt/1sPTw8PDweLI+kKNFak06nyeVy+P1+IpEIUkqga+Wncq7Pf4eBxI+oDVRxqukEUkjCkTy5nA9fXJWU84ITU1lOhwlUVdBmXmvMWAEZsrFTEoRGKE2H/Q4N+rrjOq9A37QRx50kTcOn6Dg+yJk/e57UfJy9z13e0nRMSI3Y5E2ilSY5U8W1Hx7j6Bfed91PKUkha1LbNsvJL7/N0kQtA+/uY3khhlYGhRyMXOyh9/R6EnFd1xL4DkHhCqsRm/VxwPt//jxCaJAaK7veS6b39HXCVetlzpAHXYDE/4dwbKnMldlg7sHO2xsEyer1GRSyMHSujz3PXAMKzphWREnriVaUrRj8yV2srIUZMOn5ZDetJ1oZenPIfaVPw8yNGZqPeD1oPDw8PD4KHjlRYts2IyMjFAoFtNYIIZBS0tnZic+3s8Zxifw4A4kfoXSB2ewsb46/RW9VL01tJosL/SipXUWJAPL2Fo3yijbWCG0T6Z2HLMTGh+maewPfpgmdrELnFCLghAukVDT2jxNrWCqxjHdDWS55Lcpg9m4TdsFY8ywpGppQ+EMFpKEJRrP4+8ap65rizLeed4SJbTA31EjP6TsI2e4suQQ/AWYnJP4j2NM4wkSgbMnN1w5hF9y/Us17xzYIkrURgi4nSGDVtj81ky7ugLzh+uaHG+GZayuvrHfYFULQ8UQH7Y+3YxfstZwRgMJyAWW7GMwpXVIJ5OHh4eHx4fHIiZLZ2Vny+fUJXWuNbdtMTk7S0dGxo2NNps+jtSDs20fQbMNSaa7ODdCd+wS1DbCQATvkfpNisfyWgsQRLJq4Hic7EkUtag5Zf400oZJ2uEIKep5KowvunQA2CiJneWezVf7akVCWLBIla0mp0hnjKlICpk3H0QFu/OQYoEnOVHPz1f30f6oDI/ZZZ0OVBv+p9URT2cKtt5oYv7qFNXxZJ9ctEDHAwB/xuXQxBtAEIk5Cq1bw1v8vhrLeoPloEz2f7MH0mwghMP3Fn2Btby1j749hF0qFSU1Pzc7H6eHh4eHxQHjket8kk+6unJlMZsd9SgQm9ZHPEQs8RsBsJezro9n4WUASDEFLrcG8KmBpjdYapTW21tw1koS2kHPrgsGii7MoZZJSTWhhkq2vcXNmh6Bci5I4mIjYP2N5rgnb3l471vfYrt5nvmAesSHSsposK8t88nbex513Dqz8JtBKMn61m/f/JOv0hrFGYOl/hszfgXUd7ClQY1T37EX6yn+dJm+0YVtu72/Rl2glcTVUHSLeGiuJlkjTpvOxAQAKOT/ZJUE+nWfkvVHO/udzThKrC7V9tVR1VBWN1/BJmg43Em2Muu7j4eHh4fHweeREyYPEZ3YiRXgtYVUIiWH70cJCr/xTK33cVVmGVJYBleWaTlAf3zpK4qDo5R1MmeNo//d48sR/JXGqn4X2PvJttdim6cRLJGCC2LvJbTX8S2DUMDrZzNjsAWxlYCsDpaTrslHfk2/jC5kb5ngNQpHP+Ln52mGWpqtJzUXRytk3Me1eTTJ+tQNlbbKTtw1Ss1He+J03sBf+EHQWWF3myIM9RWP3JcK15Touw+B7+0nPx7DyJloJNAEg5rizuQmT4OcgeHrt16O/fNQREiYYvgKGr8Ce565Q2zGLXTC489b+teNoW7M8t8z8nXnXsQghOParx9j3+X1Ud1dT21vDgV84yIGfP+C6vYeHh4fHh8Mjt3wTi8VYWirNQwiFQivJrpWzbGWRwpmA8zYMpQRz2Qwd/tfYq38WTQHtg6boMpoN1S/bKBKBJqJnqJYTa5sahk2tHnV67rWb0BZlbrqJejGEqDdXmtCt4oPgEwBofzV3RnoZnzlKbXyEzub3CfpL7UyD0Qx7Ph1j6sI1lJKEq1O0HRrCHy1wk09yI9uP8cE0Uf888YZF0vNxYvWJEj2QmK5G2S5LQVpgGHnQiy5XbKEyZ0jPPV/2ntgFkzN/9jy1nTPEmxI0HNxHvOsZUIuQ/D3n30I6oZzwP4Lg40X7+8I+Tv6TE2SXsuQTt4iEX8YQk+RzVdz8cR9Tt9o3nc8mOZmirt+9+koaktbjLbQebyk7Zg8PD49HjVg8ywsv3fhwTvbfP/hDPnKipL6+nuXlZSzLKkp0bW7eecWEsRIhsRR8MAdxn0VDKIXfKiC0gRYFDOXHl1MUgpkdNdZtzNxCRwRiQ/7ImkDBAgFVTQskaaNKTG/ae325pam3h+mLs2Ry1YzNVNNYe9NVlGhsEkMXOfy5Um+TfvsN3vrGCwhVzaJVgzAsmvonsC0D01+cABupTSKkXeL+6iSd6rK3oJBRhONLNPROoLVk+lYrmcRma3vB/HAj88ONpBI1HOs2waiHqv8e7EmwxyF3Bpb/HDJ/C8HnIPgiG+xiCVYFCVYdAY4AMHt+gtmhm2xuMWD4DELVQTw8PDw8Hh0eueUbwzDo7u6mpaWFuro6mpqa6Onp2XHlDUBjqAWJZDFn89XeJX6ld4lPNC7Qpb7knIsQUhkEMmECqe1zDVYTSJeHwviNzJZlvADmSuVKMQJ8B9d+q2lqpKkpjMQCrRibOlKSY6IVZJZqaOgZKRIk4AihgFim+8hNTHOlY69tMjPQ7Jp82npweKXap3TsVjZAeiHi2hlYK5vHv/IaPadv0Hv6Oqd/7ce0HRksf/EbD6LToBKQ/paTp0LeqcrJvAzpPyt/DKDpYCPSlMW3UYDhN2jY37Dlvh4eHh4eHy8eOVECTk5ANBqlrq6OeDzuumyjs4uoa99CvfE/o87+J/Ti3ZJtqv21NIaaebIpSY3fxm+AnW5BCF3UXE8gMCxz26IZIUAoRXvdeUzTffLetAdh1vMetNJogmD2YxWmmMtOM5+boe9wG8ef6KGzp46Zq80Mne/DtiSq4ORP6LTNle/uIVzt3udHSEXX47d45p++QtOeUQDsgo9z336KXCqAlTewciaFrI98JsCJL7/hNOtz4dLLJ11fD0ZTGD6FlE6/G8NU7Hn2ilMds3YsTTC2TLA6R/OxFqexXvIPYfH/BqnfxTG920gB8h+AWl+u01qzNJZg8tIkqekUht/g1G+cpKotjpACIQXVHdWc+s2Tjljx8PDw8HhkeOSWbypBZ+bQr/9PYGVB27A0hJ66gD7+z5EtJ9a2E0LQFqmmYGtEJoWeGCOV3YP0t5UcU2xT0rq6lIQhGbU76bp2Bg75wNhqP41PWGiloaAdiShyzKXPM4RY8Ugx0Wi6ov20t1QxPJpk8O4+fJOLtHTdRVoKMopC1sQX2jypr14nmD5nSejgS+foPHEbEExeb+Otb7xIpC6N4SvgC+Y58rkPKGT9CKnRNiBsDFNhF5xIVD4Vdk2pcU2z0dBx7A7R+gSDZ/Zx8KVzBCKOG6zwX4ZUFbpwE1HUVG8zJqowyfKSgdaaa9+5Tno2jRACrTQ13dUc/eWjnPrNU1g55zhmYFd+rT08PDx2Pbvyr7e++R0oZFjPzdCg8nD5G+jm40UdfbPZJObYbfTNy2itqamuYqG2D2TxcpBAIJREG2XKjrVg+U4cGS1QL8chnUNfKUBXAGIr+RkrM7dGIrRCLuWc6MishWjzQ9hpfzfEcTTGSmBmpftv6jYH4sfXTtcUH0KmLKg1ES0+WnN3izxHyiGkJt6YABx7+rYjQ9x5az/N+8Zp6HMSc32BPL5gnv6nr9K0Z9yxuNeglCAxWbmPh+FTdJ0cwMobPPYLb2P4Ntw7ewzssW3TdLS2OPP7A2SWJrELdkm0auHuIgM/GaT/U32eGPHw8PB4xNmdf8Vnr+LmI66tLDND17H91cRiMQKBABOD43TdvQxKIYCqxEUWa58smvs0GiUVWroLEq2hsGRiJQOQ8rNAFzYmRtpCX93QrbbKQBwIIewA+uIi5FZM4PwCQhIhBQu63fUcoElYC7Qcb2Hi/ATCVIgj4bX92muHK7o1GyMaQmgyi1EidSkKWR92wcT0WwgJp37pdfyhHNJYuRMCDKmpbnMvs3VDKcHkjXZq2mbwh0ujOJqtc4e1NpkbqiM1E2BzIuvaOSzF+Nlxr4uvh4eHxy5gd4oSXwSyC6Wva0UyY6HyKVIpJ/+iKjmA0mItuca007SN/lemmr5AwV+LRmKbBbKx5JYzaG5sxfdDCwo6wqQ8TJs6X7xR0plYhZlBNx2B7FWcxJB1CeRESNz67Sh0/hx7P/s5wvG7GK1OZGU198X0UUEOyzp2weCDv3yG5cUodsFEmha33zrIyS+9SbQ+STCarXyZZhPKFmgtuPHqEaZutYEWHHzpLE17JoqP5bLv6jVoJVma7efSD3q3P5+1M9M8Dw8PD4+PJ7sqE1Ap5eR29H4WDH/xexikw70ow61MtHh6DOYm6Rj+Q+TQB0yMwGK2gCqT5ao15OcD6MJ6Ca0SPubkvtJTNPnWRIRou4PoDSD6Q4g9YVhxK61mpKiMeH13RZX9PjL3Mp2HXkZUG0XJuFCZYFjl7gd9pOZja71qlGVi5XxcXklk3e5Y5QRQIWcw+P4e3vrGp5m41oWyTJRtcOPVI9uKJq1AK8HMQBPn/uY01145iirTS2cNAXV7tu8E7eHh4eHx8WdXREoymQxTU1Pk83mEEMRjPTR0fQru/gNIE60ssoE2ppu+ULJvKrqXutkfuxxVMksfWgtyE2GMWB4zVGzlrjVYSUFueLM7qiYU94NtgmXDsoZ6E9EZAEApEMJ2nfiDIk2jvsEM+1ArmlGgaOQ2IbEA2VfQWrEj0xQXmveNkpqpZvbuRn8XwfJShFzaTyCydQdkIcAuyOI8EcAwbTqPDTJ5vbgP0VaCRNlOQz9p2EhD09A7RV3nLGNXJknPVyENm7mhBjJLxWXZ0pQYfoM9L/VXdM0eHh4eHh9vHnlRks/nGR0dXetzorUmkUxixZ+i9dOfg+QYy5afiaVCUS8UW2um7DzNRpSZhpdomHkFZ6J3Mh1ma57FNGox0holNIZfuegATXakmuLsCE13y7t0t14AqkDnHMd3KVBaMqYOM0Mf9foObVxGitKlh3Z5hWo9wbzuBKBWDBMV8yglKIggfpZ3FBVxI1KzzOHPvs+tNw8xdqln07uVHlyVWN5LAxAF+p66xpWXT629rpVJNhkiFM8UHUED2VQV/lACuSEf2PDZdBwbdGzxV+J5yZk45//2GUI1dQRjAeLtVbQ+1oIvtEOPGp2H/FnIXwejBgJPg+F5mnh4eHh81DzCosQC5kilFnGmtvWVKK214/pKI77avYSVQiQG1kRJXitG7CzmykJJsuoYy5FeoqmboBXp6B7yvhghM0e4VpLzZ1Bm8aO+1pqcNUa0yyQzWINtKaTOU1dzl87m84jVxEwhnC46WnNXn2KRdjQmfpF1FSSrY4yKeaJic1KpxtZmWc3g1hNnKwyfYs+zlxm/0rni4KqI1CTxh3NrkY2tjidNZ8lFbDJ/lRJqO2eKx6YEpq/AZgQQii+6Hl+I4mPHGhI887XXkXX/A0L6XffZFp2Fpf+XY2tPHgoSsm9A9J+C/+B2e3t4eHh4PEQe0ZySceAV4BzV1Xfo7R0gFCq2XhdCUCikgWWkFLS1ta2ZrF210nQZIW6rHNmVKh3bjLFUfZKlmsdJR/2ka+bJR+bJxubBn3c1TpNZSSxfTWMrtNQvccj3V+zv+BGG3OyqKkALknYjekUHJnWDIzA2oXAsS9YjP+s/QkBYJra8MztJdgXH6Kzt8F3HpyRU4PBnP3DEgNhe4AhR5ABfhC9QIFK7hHPjNPW94/hC7n4klZxrdTvDSCMKF4vf0NoxWNPZ7Q+SeRXUPLC6PKWAAqT/xFFYHh4eHh4fGY9gpCQDnGe15HfVzLWtbYyBgV6UMjAMi5aWSUKhGzjP4n5CoWM0Nzdzd3yMGulDAifNKGiNQiOEQmBgmTnyoWUQAmU4ywJaKaStUKZzu7TWiEyGusJR5+Taoi32Q0Idi2W9QmzbQNoFWHnyzxJBIZEbeL4dBAAAIGZJREFUohtaayROzutaQuwOKmCEcPJVttrGbZ+9z12h87E7+MM5DHNnqkYrAVK7nm/Ps1c5/92nAJgdaGV58Sbh6vSOju9yRihch8DK0lDhBqS/CSrpvOfbD5FfAxl23z1/HtzM2rTl9N8xW+9zfB4eHh4e98ojGCkZK/tONJoCNO3tY4RCyysCQQFZ4H18vhzzqkCD8CGAamkSkSYCJzqRZxnLN1d6V6REC9bCEAJBNONMXkLlCYsRwjVTyDKTM4C2JTnpJGqGmeeA+CEG+U2+IWLt516RcmdLOOBEO0Lx7I4FCYBS7l8hIaCqZX35KVyToJCtXANvGfHJX3VydewpSP4+qAUcoWE7giX1e+X3FYEybygQ97gk5OHh4eHxQHgEIyUF3IzRAKTUhMMFfL6Cy8Ss8PsnCApJCFk08Qsh8CEZUJrGoHvSpBYCaedRhp/Gscv///buNTiu8s7z+Pc55/RVd9mSdfVVxsiyMRebS3ZgsD0eWCBkJjDBmewWlKG8VcPukqR2h9S+ybKT3VCZYjbUkK2tVEiWmiH2pKgUJhg8k4SQIoSbGXAAAb5gGUu2ZV0tS+pWd5/z7Iunde+WWnJL6m7/P5TK7u7T5zw6Nu6fnsv/wRcrJeGUER4+SVlJKzphQ39ydc4yZ+z8WoOXsDn2u414K2NYq4Ks8r+DrVIXA8s3Smk8V6UMNImYD2V5bLr9MFVrzs1pwdDMwWoYLvwtWCswfx8mciHRAYmz4NROf2vwZhg6y/jwDZjKcFVmx2IhhBBLJg9DSTXQxtQKn5alqK7eCFwE2pkeXDRaD1FGecqzKhR1IS/9LizKwlM2Rd2fUd/x9pRT29ARZWx7nDaFviKIVWpub3zEZsOtf8DzPqLfqifEBQpFYsShr2M5VWvOTVoerDV0fbaCxqs+Y/mqzrRzTwwf08PFLLxu85WSnew9SRFK/NdB4iSMvMXYWJoVhuIH53Z9IYQQWZeHoaQSE0zOMx5MbKARKE7+fnpPiudZfP550cxDIzapf0TXGjwXOz5M08lXp78+kGzHaGeBp+HjCHpzGBW2CRSZYRqbBFWcyuSbzBuBohg1V5whEbenrdipazmFl3CwZh0WmmMgmdUIONM3VQRM44r+AoI7INEGVik469LP2BVCCLFo8jCUKOBa4BxmfokFNACjdSZCyccdjIYWz4Pe3jIiMQ8YwcKf3IF3nE7+l2pdrfJclnd+TEn/KfrKV1PZfwo12zIXD3RXHLXKvuSaIvnA8U1dcQS2DZY10w7AC2mWm24vM19CCCFyRp7+eKgwXfNbMQGlmskfQpvRuplYLEA87nDhQjnne8N0Wq+kDSQjoSFcf8x8mk4IHMpNEB7qprb9XcJDPfRVb6CvYnWaovOYzfXqfLDMTjf15bIy5XYuEp+Z8CqEECKv5GFPSSYUkUg1HR3jVVx7rVcJeY1pjlZYrj0e0UY/SbXH8vMfU9f+Lkq7gI1n++iq3UxZfzu2l2LYIaahM26GctbY01+/TM21sNulkZU0QgiRjwo0lMTQuoOioosMDYXR2sKnKygjdcVOjYftDlJ/6i3sxAgD5Y30V64Gpag7fRgrWVTL0i7RYCnackhZTW3U6EjGyRi6zIcK5GmHVJYsfm9JApxmcLtBOWCVm6d1AmLvQewIqCIIfgGcVYvZMCGEEDMowFDSBrQSDiuCQY1Sms8+W02ZtxGL1D0XSnusPf5POIlhFJry3jaqzrVyfMNtKK0n7WzT8v7POH7lbST8AaxoYuaZCzo5r6QhXW0MsTCCMPA4eKZuDdYKKLoPhp4D7xxmObAy+9+E7wZfC7inwSoDe9VidukIIYSYoMBCyUWgFfDGJloaCivdt6o96k//I77EeKVR20sQGu6hrv3wpF11VPK1dZ/8C7aeJZCA6UxJLPqEipzkuckdj5XGsvUCD+fEwJtQct7rgIt/N+UYDcRh+OfA85j/FTRYlVD6V2ZVjhBCiEVVYOMK0+uT9PaW4Hnp53YEox0EYj3Tnre9BMvPf4KVYpjG9uKTV9/4FDT4UeuDsMI3flctUBUFlvvmybI9utuq8LxkVdwF7YyYy8k1ZrxtBBNmzsPgMwvTLCGEEDMqsE/MyctPo1GH7u4aZvqQ0jO8ZmWyQVuxhdpo9llRtoIKB+r96A+HIWyhS6w5fUQWKqVgRVPnYlyJqYX15saDxCmzl45VkpUWjYwkGBqMEQr7CIVSVwwWQghRcKGkBtNbYj6U2tvTFNCaIBqsw7X8KDf1cMzE+SSjjz1s7OQ11LqgCSNJylZoBWp1AN2XQMVA+/Wkk1zK3jZilE3q8GEz3vsxXwp0LP3LWsPI7yD6KughU3wt/EWwa6Ycpvmk9Tyd5y5iWQrtacorQmzaUottF1gnpRBCZEGB/cu4HFOzxMZ1FZ7nZ8aufK1R2uXEhtvxLAfN9DU1ruXgKhsPhWv5cJ0Ax4puM8c5CoLTb6GyFCwLoFYHITC622/6jfa0XopaHvkuXejQkGZC83QOKf8XUGEztySd4QMw/AvwekBHIf4RXPjf4E4eBvy8rY/z5y6iPY2b8PA8TX9fhKOfdGXYPiGEyG979uyhurqaTZs2ZXR8gYUSU+11eLg5o14SlAJlUdH7GR9c+zXO1F876WUXi+Gi5Sg0Cs1IoJhPN9xJT2gDGsuUk097aj8qUGbCiDV5879UzQDzk/Xo1yidYVqZ+r7Lkw9Ct2dYqVWB/4bkhNbRIRXL/L74L9NPevGGTS8JU3tS4hD59fjD2IdU+v4vN256mk3rDlIUNIHF8zSd5y6Oza0RQohC9sADD3Do0KGMjy+wUGJ6HNrbY4yMhMlkwqPyEhRdHGDNsV/jjw/jKbOHi6sVrhOgePA8lvZQQDDSz4ZPD+K70I+HY+bURp0UU2EtCNRBIMWGcOnaoSb0pmjQMQ/tpv7g0lqjvfEQoj0NevZhoYIPLlYFBHeCO5jBwckhtbJvQehOsyw4cDOU/TX4NqR/m3fe1D6Z/gK4bea30d/D4DOUhM8T8EVYXtbGtVc+NxZM0FpCiRDisnDLLbdQWTlDz/MUBTanBAYHM/lAmkBZ+GN9+Ib6UIwP37Sf3UBD3XGsCat5LDQkYlzHM6jk87o7hFrtgTeSPEqDbxmEVqP729BHI9CfYGxsyK+g3o+q9qUNEcpS6LhnapzUpahMqgFXQ18CbZu5CmrZ7BMoC34ui9cLsXeB4QwO9oP/KlBBCN0K3JrZNawKU4RtGgX2CtAuRH7BxE0GlQLbSrCm/k0+PHEnobAPxym4nweEEJeZrq4utm7dOvZ479697N2795LOWXChpKtrDuP1XoJA9Az+eN/YU6PBJBy6aDboq/aZuSMDCRj0kkuEJ/yUe74HNt0GehC8CDglYBejhwbQb/3BhIeJRjS0jaBjGtU4Q1E1v4JzMUgTSpTPQpc66I4YqtE/aYho2uEpaoIsbtn3RRQ/yeyTXH3gbwHnirmf3yoD30aItzJ5tZdjemm8gZShRSkoLerEshQbmlfM/bpCCJFjqqqqOHz4cFbPWVChJB6Pk0hkuCut1gSjZ6g7+/NpLykFFQ1d+DaGTEpRgPabnolj0ckHx2Pojz+AjZvBLk3WVE+gj30wPZCM8oAzMXSdf9LKnfGmaVN0rcKBIQ9dZJn3JE+nT0VhrZlEq9YGZu0BSfVyQQYSLLBnGTKz6iF8F/iunP9NKP53MPRziB3GFFwrh/C94DQkV+2k/nP3dBlbb1xJUZHsyyOEEKkUXCjJmFJ4dgDlpV766Wv2oZwpuwlXOLDcge4pwefzNrTvAqpuFcQs9LkzcH6WHhsPdNRDFdlm+CXZ0zE650OFbFgVNLN+XI0+G4cLLgy5sDIwtlY5kyGZy6enxDXLcycNxE2hE+B1QdwBuxzsqrlfRvmheDfoe00IUaHxm6n8ELgeRt5h4hAO+AhV3gV+CSRCCJFOwYQSz/Nob2+f03viPrNKY2otEoqtlJ/YylboUnt6KAE41Yc+2WdOVjTzypwxFxNQZJuAMpj8vTUeNEZ7UbQN9MYhoqHGByucGYdrprV7jj0lWutJYWfq49ylYfDvZzmkM1la3gYscBqh+CGwwnO/nHJST3oNf9n8OvLO+HGhL4I/syVxQghRKL761a/y6quv0t3dTUNDA4899hgPPvhg2uMLJpTMNZAAOIkLDFFFmJ6xiasz0XEP2kZSvzgxpwx6k0tl+BVUOiaw9CYgngwsvQn0Cj/YyWBkzdDzYSmos1GNsw/XZNvo9cZ6cXI2oHigIxke65qvxCkY/Aco/Q/Za4ZyoOgrEP4z8IbMsmOVae0UIYQoHPv27ZvT8QUTSqLR6OwHTaC8GMt6XsNHBBdr0iobBr1kNbMpwzdnY1O31klvdK5ljQ+1KjA+mrA6gP4sCl0JuOCh30yuFgoAVxenX8UcUKjG4IyXnEuPxkzDN2lXBeVsGLkULiSOmR2FreLsnlr5wZbhGiGEyFTBhJKMaLNyxnaHWdb9KkWDn+JhYadIGvqTCDqgoN8FW5mN9voznEQLZi6IX6FWBqYPtawNoi8MQWzCEM8I0JNAL3dSF1hbHzK/pq0Ka2qVaGYPJoVcqmR+rGQPS5ZDiRBCiDkpiFAyMDCQ0XFKJ6js/i3lF94d65BIFUgA01syWvLE1XB6hr1QUvGAZb7UPR8as7Kmc/LEXN0WRZUUoZPV8cfmlswQMkZ7R5RSGQUSc77Mv43cF8CMnV3CXjfKB1YmVWCFEEIspIKo4HTu3LmMjy0eOr64u/amuphK83wC9HtD6E8j6PaRtBVdp9IRD30mlnbBScr3FER1VwWlf8XMgUSBtRzwMz2DK8AH4a+YCT1CCCGWVN73lLhuBj8haw3ao7zvHXyJCwvfqFG9Caj3p94frneGoaB+F/pddHcCvSmM5Uv9gam1hkEX/VEEbFA1s1d1HVUQ80Os5eCsAqvalH9PSYNdB2X/Dbxu0HGIvQfxY2BXQnA7OCsXtdlCCCFSy/tQkumqm8qe16jsf2uBWzPFaA9Gnd/0SSWLwerPRybPJ0knquHDYfSWosl1TEbfGvfQH0fM4wSm4Jp/6cLGwi0dTlV3xILALea34T+HwaeZvARqAq/f9ITY1eaxk8FmjUIIIRZd3oeSkZE0S3Qn8QjGOhe8LSm1x9A9CbMkGKAnbsJGpqIafT6OqvGjh1308SiELQhaqJAFtX4zNyWu0W0jsC6YskrsYlBKLUAwsUk9PKMg8IXkIdUQ/FOIvpTiOAd8zVlsjxBCiIWS16Eko6EbwPLihIY/n/6CrwTiF7PcqhQiHnTMcaLsRGdieBU2fBoBD9TKANjKhI9yDbV+9EfDZvWOG4ErQzkxPKOTy6pNW+Y7fyXdGmwXiMPFn0D8k2QRs9HJOqPvsUGFIXjLPK8thBBiMeX17L4TJ07MfpD2qDnz3PiuvoCLD9cuhpprF7aB2TKi4V+HIapNIPGpsd4QZSvz2bsuWcOk34XuRFYnsc5lUuzEMDQeRubbFp/ZlTcVaxlEXjSBhAToKOO19yvNHjjBW6Hsr8Eqmuf1hRBCLKa8DiWz0pr6z58hHO0Ye0oBFnG0TqB7jy9d2+arYnodE6WUKW1fZoEDWpkQoL3xX7Wrxx7P2bAH0Uyrxk2WeY9Nqk47DaE7gKkTeH0Q+jMYeYvp80hcYATKHoXwF7NfEE0IIcSCydvhm6NHj2awq5zGH++b9qwCHC+KHjqzYO1bMN70SrOj1JVh89KQh77omg38Sm0z4fZsDEKW2cwvZJlJsXGNCk9fGjQ6L0R7yYJsJ6LmvBvDCzRfRWHqjXiMD734wL8ZAltBj8DwASA5BOZrAlXM5A3vJn4DmcwzEkKIwtMZcfi7D5cvdTPmLW9DSSZlSZ34BdQMQwczvZardGccvcIHZ2LQnUCtDkBlcoO+ZF7QIcvs0XM+Pv4dltlmmXHf8PjJLNBrA1hV46XQtcaEmhHPhJnO+NhKIX1kCK4umtNmgJmzgSDgmuGWwB9B8I/B7ZocSADiR5PDNmk4axagfUIIIRZa/oYSmLU0qacclJ5Dafh80B6DrmRQUIwHkgmUraDWhz6f7EkIKjP8MjWDecCpGHq5b/KuwJ9FYSjFcE0my5hTmH1FjgZGq/L6wX8VhHaYh9FXST1Ek45/fJdeIYQQeSUv55QcPXp05gO0Bq0pGfiQuFOGp5xLmm6Zc0aStUpmGkrxTXityDbvSSWu0TEzkVUPeegTmF6VqX8zpgYgew2okhmbOXGCbOYTb2MQ/R14yVVR7lky3wXRgpKHwanN8HghhBC5JH97SmadQKm5UHk9FypvxBfroWTgA8r73wXcxS0zv5DiZl4IgSm7GXvazCcZNTzL0ul/HRoPbAELNofMMuaJ5whbqLWjuxQrCN8NOoIe/AkqxdwO7Wqwpk90HZ+vUo7uGoKz/Wbjwno/qiz511E5kGgHf7Op2Jo4RWZ729gSSIQQIo/lXSjxvAx+alaTN5eJB5bTW7Wdi2VbaPj8/2HrNBMk85A+HkGHbOiNAwqWO6ag2ukJkz0jc+gjintmg78rw+iIa4ZxghaqeMKEWFWJHgmhjzwL9iCs8iUrySqUsk2vSDxhnkuRAHXMQx/pMBsdag0R0Bcj6FUBrBo/aA+sMnNw8I9h5A3z3Fh0sjG9JxO/Lx8EbgAdM4HGXjZ+jmzSHsRbIfY+4IfgjVKmXgghsiTvQklf3/TVNJmK+ysZLN5A6cUPC6e3ZMAzXwBoOBc3X1moVaZCNoQmrs7xgVWKDj+E/u3jEBsENLonZoKgE4I/ug+cGnTn06iq6YXplFJ4Z0bAnTLHxQNOjaCrAyjfCnDqzPNWOZR+w0x2jR8HFYDgzUAYogdhdM6Q/3oTGPr/O+avdQJ8LVD875OF1bJAezD4YzPRlhigIPYOhP7t+BwYIYQQ85Z3oaSnp2de71NegpKBDwhFThdOIEklgzAy8ZBU90J/PAzNYTOvRKnkSJkPgjsgdDuceQvc2OQzaQ2eC70eqmENXPFf4MLfkHLYpd9N385IEThRuPCECR/+bWCvgJK9048N3gTegFmtE/0dRF4muQmQeT3eCsPPQ9G9s9yRDMU/nhBIwHwTcYi8BIFtYM08x0YIIcTM8nKi69xolBul8fOfUNX1K/yLuUtwjsl4su+gB13mg3d8Skgcoq8kP+i7k6FkCncEIiY0KqccVXw/pvDZxL9mfgjWpWmgAmcYvC5wT8PQczD0T+nbqWywK0D5k6t0pg7LxU2BNT2/wm/TxI4waWnyGBvin2bnGkIIcRnLq56S+Qzd+G1Fw/H/g1VA80jmK+MeIguo9qeoRxKHyD9D2S6w/SaETGQHoHTV+OPAVeD/G1NTRMfBbgB7BWp9K/rdH0wONsqCEgsVmBggYhA7DO6fgD1LMSAdSfPCaM+JP83rc6CCpN6xGBOMhBBCXJK86imJx+cWLAKBACvXNGGpglkMvDicGeKL1wtVLVBUA9aETGs5UFQN1ZsnH6+C4L/aDG84taAsVNUm2HCPCTFOECwflJagrggynQ2JtgzanKZgmlWVvcAQuIHUOV6B78rsXEMIIS5jedVTUlZWRn9/f0bHFhUVUVtbi2VZeKWroD+DzfuEEdPpx3nsRpSy4KZH0cd/Ae1vABrqb0Kt/6J5LQPWmj9Br7wZLp6FQAlKvw7R35JyDopVOvsJw38OA9/HDOF4mB4NHxT9RUbtyYhTb5ZCDx/ArADCjG8V75WeEiGEyIK8CiWBQADbtnHd9DUrLMti5cqV+P3jHxJq7Z+ij/x4+nCDSEt/PgKrAmN73ZhthnwQvsMcYNmo0kZ0TQzCVaiGm1BOqp6O9JQdgPLV5oH7BYi+xuRQosAKg9M0+8mcWij7rxB5BdxTyV2Cd2a/bknwZvBfYya8Kj/4NoCaumGgEEKI+cirUAKwdu1azp49y+Dg4NhzlmVh2zZlZWVUVFRML9gVqAR/GUS6wLIBC7xUExYvH6MdIWkHajrj6N6EOS5kQYUD9btRTiM6Poz+/f8yE149M6SmP/kZevMDWI3/Zn4NspdD8R4YetbUGkGDXW2ey7D3xZzjK/O7/lxYxRC4duGvI4QQl5m8CyVKKerq0qzeSME7+gIcOzDhiQT4S6F6G5x9J/UqkstARpNe48noEndhwEUnWlEVN6KPvwhDnZNXtWgP/vBjdFE1qnL9/Brlbwbf/wC30/RC2Mvmdx4hhBB5Ka8mus6VFxuaHEhGxQZMz8nGv1z8RuWzjt/j/ct/hlOvpl1mq1v3Xdo1lGWGXCSQCCHEZSfvekoy5fV/Bh/tT3/AqVeg5hpMLstSHYuCpyE+NPMhF9rR/W3oYJm5/9F+aLgJa9Wt5gyRXuhuNStvqq9COYEFb7UQQoj8cEmh5NChQzzyyCO4rstDDz3Et771rWy1a960l0C/9QT0zrKTsBuFjjcWp1GXFRf9+v9kUtDrP4738c9g9S44+c/JOSLJAaRt/wm1TJbTCiGEuIThG9d1efjhh3n55ZdpbW1l3759tLa2ZrNt86JP/mr2QCIWWIqeJ3cETrxoJsa6IyYUulH04b9Hu1LYTgghxCWEkrfffpumpibWrl2L3+9n9+7dHDiQYv7GYmv71VK3QMxV90dL3QIhhBA5YN6hpKOjg8bGxrHHDQ0NdHR0TDvuhz/8IVu3bmXr1q10dXXN93KZ89LXMBE5SDO2rFgIIcTlbcFX3+zdu5fDhw9z+PBhqqqqFvpyUHf9wl9DZI92YfnGpW6FEEKIHDDvUFJfX8/p06fHHre3t1NfX5+VRl0KdcWXTB2SqewAWFIKfOkoEz7s0dU2ltnzZuNXUb6iJW2ZEEKI3DDv1Tfbtm3j2LFjnDx5kvr6evbv389Pf/rTbLZtXpQvDDu+h277NXS8aTaKW7UD6rahuj5Ad31ohng8D2L9EO0D5cCyK6H7YxjuBBQ4IVNoLREBPHOMHYLExVlaYJNy/5ZL/85IvyHNpbCTp57aZgfzfSSvafnBV2yOS0SSw2QasMD2QagSShvBclCVTeZdR1+ARBQq18OWB82fTfdH6HPvgRNCNXwBVZJ5ITwhhBCFbd6hxHEcnnrqKW677TZc12XPnj20tLRks23zpmwfat3tsO72yS/UXIuqkfLgi0EBNN48/YWqTWaXYCGEEGKKS6pTcscdd3DHHXdkqy1CCCGEuIwVdJl5IYQQQuQPCSVCCCGEyAkSSoQQQgiREySUCCGEECInSCgRQgghRE6QUCKEEEKInCChRAghhBA5QUKJEEIIIXKChBIhhBBC5AQJJUIIIYTICRJKhBBCCJETJJQIIYQQIidIKBFCCCFETpBQIoQQQoicIKFECCGEEDlBQokQQgghcoKEEiGEEEIsiEOHDrFhwwaampp4/PHHZz1eQokQQgghss51XR5++GFefvllWltb2bdvH62trTO+R0KJEEIIIbLu7bffpqmpibVr1+L3+9m9ezcHDhyY8T3OIrUNgLa2NrZu3Tqv93Z1dVFVVZXlFomp5D4vDrnPi0Pu8+KQ+5xeW1vbol6vpXEVP33gPy7KtSKRyKTP9L1797J3796xxx0dHTQ2No49bmho4K233prxnIsaSrq7u+f93q1bt3L48OEstkakIvd5cch9XhxynxeH3OfccejQoaVuwiWR4RshhBBCZF19fT2nT58ee9ze3k59ff2M75FQIoQQQois27ZtG8eOHePkyZPEYjH279/P3XffPeN7FnX45lJMHKcSC0fu8+KQ+7w45D4vDrnPIhXHcXjqqae47bbbcF2XPXv20NLSMuN7lNZaL1L7hBBCCCHSkuEbIYQQQuQECSVCCCGEyAk5H0rmWqJWZG7Pnj1UV1ezadOmsed6e3vZtWsX69evZ9euXfT19S1hCwvD6dOn2b59Oxs3bqSlpYUnn3wSkHudbdFolOuvv54tW7bQ0tLCt7/9bQBOnjzJDTfcQFNTE/fddx+xWGyJW1oYXNflmmuu4a677gLkPovsyOlQMp8StSJzDzzwwLQ17Y8//jg7d+7k2LFj7Ny5U4JgFjiOwxNPPEFraytvvvkmP/jBD2htbZV7nWWBQIBXXnmFI0eO8P7773Po0CHefPNNHn30Ub7xjW9w/PhxKioqePrpp5e6qQXhySefpLm5eeyx3GeRDTkdSuZTolZk7pZbbqGysnLScwcOHOD+++8H4P777+f5559fgpYVltraWq699loASkpKaG5upqOjQ+51limlKC4uBiAejxOPx1FK8corr3DvvfcCcp+zpb29nYMHD/LQQw8BoLWW+yyyIqdDSaoStR0dHUvYosLX2dlJbW0tADU1NXR2di5xiwpLW1sb7733HjfccIPc6wXgui5XX3011dXV7Nq1i3Xr1lFeXo7jmOoH8m9Idnz961/ne9/7HpZlPkJ6enrkPousyOlQIpaWUgql1FI3o2AMDg5yzz338P3vf5/S0tJJr8m9zg7btnn//fdpb2/n7bff5pNPPlnqJhWcF198kerqaq677rqlboooQDldPG0+JWrFpVmxYgVnz56ltraWs2fPUl1dvdRNKgjxeJx77rmHr33ta3z5y18G5F4vpPLycrZv384bb7xBf38/iUQCx3Hk35AseP3113nhhRd46aWXiEajDAwM8Mgjj8h9FlmR0z0l8ylRKy7N3XffzTPPPAPAM888w5e+9KUlblH+01rz4IMP0tzczDe/+c2x5+VeZ1dXVxf9/f2A2b30l7/8Jc3NzWzfvp3nnnsOkPucDd/97ndpb2+nra2N/fv3s2PHDp599lm5zyI7dI47ePCgXr9+vV67dq3+zne+s9TNKSi7d+/WNTU12nEcXV9fr3/0ox/p7u5uvWPHDt3U1KR37type3p6lrqZee+1117TgN68ebPesmWL3rJliz548KDc6yw7cuSIvvrqq/XmzZt1S0uLfuyxx7TWWp84cUJv27ZNr1u3Tt977706Go0ucUsLx29+8xt95513aq3lPovskDLzQgghhMgJOT18I4QQQojLh4QSIYQQQuQECSVCCCGEyAkSSoQQQgiREySUCCGEECInSCgRQgghRE6QUCKEEEKInPD/AclfvW7gHRUAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = Autoencoder(data_zoom)\n",
    "network_with_classifier.build_encoder()\n",
    "network_with_classifier.build_decoder()\n",
    "network_with_classifier.build_classifier()\n",
    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
    "network_with_classifier.fit_data( batch_size=128 ,epochs=50, use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Autoencoder with classifier\")\n",
    "network_with_classifier.plot_score(\"Autoencoder with classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "789093bb736187c46b4a361b2c83e244a48ff9bb7ad40e66e5ffb40455c8edff"
  },
  "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": 1
}