mami-t/infoGAN.ipynb Secret

## infoGAN.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Reshape\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.convolutional import UpSampling2D, Convolution2D,MaxPooling2D\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers import Flatten, Dropout\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "from keras.datasets import mnist\n",
    "from keras.optimizers import Adam\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.datasets import fashion_mnist\n",
    "import keras.backend as K\n",
    "from keras.layers import Lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "NUM_EPOCH = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_images(images, height=21):\n",
    "    fig = plt.figure(figsize=(10,height))\n",
    "    for i, image in enumerate(images):\n",
    "        plt.subplot(int(images.shape[0]/5)+(images.shape[0]%5>0),5,i+1)\n",
    "        plt.imshow(image[:,:,0])\n",
    "        plt.gray()\n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## infoGAN\n",
    "- generator z, c to image\n",
    "- discriminator image to real, c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian_loss(y_true, y_pred):\n",
    "\n",
    "    Q_C_mean = y_pred[:, 0, :]\n",
    "    Q_C_logstd = y_pred[:, 1, :]\n",
    "\n",
    "    y_true = y_true[:, 0, :]\n",
    "\n",
    "    epsilon = (y_true - Q_C_mean) / (K.exp(Q_C_logstd) + K.epsilon())\n",
    "    loss_Q_C = (Q_C_logstd + 0.5 * K.square(epsilon))\n",
    "    loss_Q_C = K.mean(loss_Q_C)\n",
    "\n",
    "    return loss_Q_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_model(cont_dim, cat_dim):\n",
    "    z_input = Input(shape=(62,), dtype='float32', name='z_input')\n",
    "    #x = Dense(50,name=\"dense_1\")(z_input)\n",
    "    #x=BatchNormalization(name=\"batch_norm_1\")(x)\n",
    "    #x = Activation('relu',name=\"activation_1\")(x)\n",
    "    #x=Dense(256,name=\"dense_2\")(x)\n",
    "    \n",
    "    #x=BatchNormalization(name=\"batch_norm_2\")(x)\n",
    "    \n",
    "    #z_output = Activation('relu',name=\"activaiton_3\")(x)\n",
    "    y_input = Input(shape=(cat_dim,), dtype='float32', name='y_input')\n",
    "    #x = Dense(50,name=\"dense_4\")(y_input)\n",
    "    #x=BatchNormalization(name=\"batch_norm_4\")(x)\n",
    "    #x = Activation('relu',name=\"activation_4\")(x)\n",
    "    #x = Dense(50,name=\"dense_5\")(x)\n",
    "    #x=BatchNormalization(name=\"batch_norm_5\")(x)\n",
    "    #y_output = Activation('relu',name=\"activation_5\")(x)\n",
    "    c_input = Input(shape=(cont_dim,), dtype='float32', name='c_input')\n",
    "    #x = Dense(25,name=\"dense_6\")(c_input)\n",
    "    #x=BatchNormalization(name=\"batch_norm_6\")(x)\n",
    "    #x = Activation('relu',name=\"activation_6\")(x)\n",
    "    #x = Dense(50,name=\"dense_7\")(x)\n",
    "    #x=BatchNormalization(name=\"batch_norm_7\")(x)\n",
    "    #c_output = Activation('relu',name=\"activation_7\")(x)\n",
    "    x = keras.layers.concatenate([z_input, y_input, c_input],name=\"concat_1\")\n",
    "    \n",
    "    x=Dense(1024,name=\"dense_3\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_3\")(x)\n",
    "    x=Activation('relu',name=\"activaiont_2\")(x)\n",
    "    \n",
    "    x=Dense(128*7*7,name=\"dense_8\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_8\")(x)\n",
    "    x=Activation('relu',name=\"activaiont_8\")(x)\n",
    "    \n",
    "    \n",
    "    x=Reshape((7, 7, 128), input_shape=(128*7*7,),name=\"reshape_1\")(x)\n",
    "    x=UpSampling2D((2, 2),name=\"upsampling_1\")(x)\n",
    "    x=Convolution2D(64, (5, 5), border_mode='same',name=\"conv2d_1\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_9\")(x)\n",
    "    x=Activation('relu',name=\"activation_9\")(x)\n",
    "    \n",
    "    \n",
    "    x=UpSampling2D((2, 2),name=\"upsampling_2\")(x)\n",
    "    x=Convolution2D(1, (5, 5), border_mode='same',name=\"conv2d_2\")(x)\n",
    "    output=Activation('tanh',name=\"output\")(x)\n",
    "    model = Model(inputs=[z_input, y_input, c_input], outputs=[output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dc_generator_model():\n",
    "    z_input = Input(shape=(62,), dtype='float32', name='z_input')\n",
    "    \n",
    "    \n",
    "    x=Dense(1024,name=\"dense_3\")(z_input)\n",
    "    x=BatchNormalization(name=\"batch_norm_3\")(x)\n",
    "    x=Activation('relu',name=\"activaiont_2\")(x)\n",
    "    \n",
    "    x=Dense(128*7*7,name=\"dense_8\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_8\")(x)\n",
    "    x=Activation('relu',name=\"activaiont_8\")(x)\n",
    "    \n",
    "    \n",
    "    x=Reshape((7, 7, 128), input_shape=(128*7*7,),name=\"reshape_1\")(x)\n",
    "    x=UpSampling2D((2, 2),name=\"upsampling_1\")(x)\n",
    "    x=Convolution2D(64, (5, 5), border_mode='same',name=\"conv2d_1\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_9\")(x)\n",
    "    x=Activation('relu',name=\"activation_9\")(x)\n",
    "    \n",
    "    \n",
    "    x=UpSampling2D((2, 2),name=\"upsampling_2\")(x)\n",
    "    x=Convolution2D(1, (5, 5), border_mode='same',name=\"conv2d_2\")(x)\n",
    "    output=Activation('tanh',name=\"output\")(x)\n",
    "    model = Model(inputs=[z_input], outputs=[output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_model(cont_dim):\n",
    "    x_input=Input(shape=(28, 28,1), dtype='float32', name='x_input')\n",
    "    x=Convolution2D(64, (5, 5),\n",
    "                            subsample=(2, 2),\n",
    "                            border_mode='same')(x_input)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x=Convolution2D(128, (5, 5), subsample=(2, 2))(x)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x_output=Flatten()(x)\n",
    "    \n",
    "    x = Dense(256)(x_output)\n",
    "    x = LeakyReLU(0.2)(x)\n",
    "    x_output = Dropout(0.5)(x)\n",
    "    \n",
    "    \n",
    "    model = Model(inputs=[x_input], outputs=[x_output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_model_Q(cont_dim, cat_dim):\n",
    "    x_input=Input(shape=(256,), dtype='float32', name='x_input')\n",
    "    \n",
    "    def linmax(x):\n",
    "        return K.maximum(x, -16)\n",
    "\n",
    "    def linmax_shape(input_shape):\n",
    "        return input_shape\n",
    "    x_Q = Dense(128)(x_input)\n",
    "    x_Q = BatchNormalization()(x_Q)\n",
    "    #x_Q = Activation('sigmoid')(x_Q)\n",
    "    x_Q=LeakyReLU(0.2)(x_Q)\n",
    "    x_Q_Y = Dense(cat_dim, activation='softmax', name=\"Q_cat_out\")(x_Q)\n",
    "    x_Q_C_mean = Dense(cont_dim, activation='linear', name=\"dense_Q_cont_mean\")(x_Q)\n",
    "    x_Q_C_logstd = Dense(cont_dim, name=\"dense_Q_cont_logstd\")(x_Q)\n",
    "    x_Q_C_logstd = Lambda(linmax, output_shape=linmax_shape)(x_Q_C_logstd)\n",
    "    # Reshape Q to nbatch, 1, cont_dim[0]\n",
    "    x_Q_C_mean = Reshape((1, cont_dim), input_shape=(cont_dim,))(x_Q_C_mean)\n",
    "    x_Q_C_logstd = Reshape((1, cont_dim), input_shape=(cont_dim,))(x_Q_C_logstd)\n",
    "    x_Q_C =  keras.layers.concatenate([x_Q_C_mean, x_Q_C_logstd], name=\"Q_cont_out\", axis=1)\n",
    "    #x=Dense(256)(x_output)\n",
    "    #x=LeakyReLU(0.2)(x)\n",
    "    #x=Dropout(0.5)(x)\n",
    "    \n",
    "    model = Model(inputs=[x_input], outputs=[x_Q_Y,x_Q_C])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_model_D(cont_dim):\n",
    "    x_input=Input(shape=(256,), dtype='float32', name='x_input')\n",
    "\n",
    "    x=Dense(1)(x_input)\n",
    "    output=Activation('sigmoid')(x)\n",
    "    model = Model(inputs=[x_input], outputs=[output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dc_discriminator_model():\n",
    "    x_input=Input(shape=(28, 28,1), dtype='float32', name='x_input')\n",
    "    x=Convolution2D(64, (5, 5),\n",
    "                            subsample=(2, 2),\n",
    "                            border_mode='same')(x_input)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x=Convolution2D(128, (5, 5), subsample=(2, 2))(x)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x_output=Flatten()(x)\n",
    "    def linmax(x):\n",
    "        return K.maximum(x, -16)\n",
    "\n",
    "    def linmax_shape(input_shape):\n",
    "        return input_shape\n",
    "    x = Dense(256)(x_output)\n",
    "    x = LeakyReLU(0.2)(x)\n",
    "    x_output = Dropout(0.5)(x)\n",
    "    \n",
    "    \n",
    "    x=Dense(1)(x_output)\n",
    "    output=Activation('sigmoid')(x)\n",
    "    model = Model(inputs=[x_input], outputs=[output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_noise(noise_scale, batch_size, noise_dim):\n",
    "\n",
    "    return np.random.uniform(low=-1, high=1, size=(batch_size, noise_dim[0]))\n",
    "\n",
    "\n",
    "def sample_cat(batch_size, cat_dim):\n",
    "\n",
    "    y = np.zeros((batch_size, cat_dim[0]), dtype=\"float32\")\n",
    "    random_y = np.random.randint(0, cat_dim[0], size=batch_size)\n",
    "    print(random_y)\n",
    "    y[np.arange(batch_size), random_y] = 1\n",
    "\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_disc_batch(X_real_batch, generator_model, batch_counter, batch_size, cat_dim, cont_dim, noise_dim,\n",
    "                   noise_scale=0.5, label_smoothing=False, label_flipping=0):\n",
    "\n",
    "    # Create X_disc: alternatively only generated or real images\n",
    "    #if batch_counter % 2 == 0:\n",
    "    # Pass noise to the generator\n",
    "    y_cat = sample_cat(batch_size*2, cat_dim)\n",
    "    y_cont = sample_noise(noise_scale, batch_size*2, cont_dim)\n",
    "    noise_input = sample_noise(noise_scale, batch_size, noise_dim)\n",
    "    # Produce an output\n",
    "    generated_image = generator_model.predict([noise_input,y_cat, y_cont],batch_size=batch_size)\n",
    "    #y_disc = np.zeros((X_disc.shape[0], 1), dtype=np.uint8)\n",
    "    #y_disc[:, 0] = 0\n",
    "\n",
    "    \n",
    "    \n",
    "\n",
    "    #else:\n",
    "    X_disc = np.concatenate((X_real_batch,generated_image))\n",
    "    #y_disc = np.zeros((X_disc.shape[0], 1), dtype=np.uint8)\n",
    "    #y_cat = sample_cat(batch_size, cat_dim)\n",
    "    #y_cont = sample_noise(noise_scale, batch_size, cont_dim)\n",
    "    y_disc = [1]*batch_size + [0]*batch_size\n",
    "\n",
    "    # Repeat y_cont to accomodate for keras\" loss function conventions\n",
    "    y_cont_train = np.expand_dims(y_cont, 1)\n",
    "    y_cont_train = np.repeat(y_cont_train, 2, axis=1)\n",
    "    print(y_cont.shape)\n",
    "    if batch_counter % 500 == 0:\n",
    "        num_samples=20\n",
    "        sample_range=2\n",
    "        noise = sample_noise(noise_scale, 1, noise_dim)\n",
    "        noise = np.repeat(noise, num_samples, axis=0)\n",
    "        for cont in range(cont_dim[0]):\n",
    "\n",
    "            for i in range(0,cat_dim[0]):\n",
    "                print(\"category: %d, c: %d\" %(i, cont))\n",
    "\n",
    "                y = np.zeros((num_samples, cat_dim[0]), dtype=\"float32\")\n",
    "                y[:,i]=1\n",
    "                c = np.zeros((num_samples, cont_dim[0]), dtype=\"float32\")\n",
    "\n",
    "                for j in range(num_samples):\n",
    "                    #print(keras.utils.to_categorical(i,num_classes=10).T)\n",
    "                    #print(np.array([j/10,0,0,0]).shape)\n",
    "                    c[j, cont]=(j*2/num_samples-1)*sample_range\n",
    "\n",
    "\n",
    "                images=generator_model.predict([noise,y, c], batch_size=num_samples)\n",
    "                #print(np.array(images).shape)\n",
    "                #print(y)\n",
    "                show_images(np.array(images), height=num_samples/2)\n",
    "                \n",
    "                    \n",
    "    return X_disc, np.array(y_disc), y_cat, y_cont_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gen_batch(batch_size, cat_dim, cont_dim, noise_dim, noise_scale=0.5):\n",
    "\n",
    "    X_gen = sample_noise(noise_scale, batch_size, noise_dim)\n",
    "    #y_gen = np.zeros((X_gen.shape[0], 1), dtype=np.uint8)\n",
    "    #y_gen[:, 0] = 1\n",
    "    y_gen = np.array([1]*batch_size)\n",
    "    y_cat = sample_cat(batch_size, cat_dim)\n",
    "    y_cont = sample_noise(noise_scale, batch_size, cont_dim)\n",
    "\n",
    "    # Repeat y_cont to accomodate for keras\" loss function conventions\n",
    "    y_cont_target = np.expand_dims(y_cont, 1)\n",
    "    y_cont_target = np.repeat(y_cont_target, 2, axis=1)\n",
    "\n",
    "    return X_gen, y_gen, y_cat, y_cont, y_cont_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- categorical loss is too big\n",
    "- reduce loss weight from 1 to list_weights = [1, 0.1, 0.1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def info_train(X_train, y_train, load=False, name='fashion02.h5', cat_dim=[10], cont_dim=[4]):\n",
    "    \n",
    "    \n",
    "    noise_dim=[62]\n",
    "    X_train = (X_train.astype(np.float32) - 127.5)/127.5\n",
    "    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2],1)\n",
    "    y_train= keras.utils.to_categorical(y_train, num_classes=10)\n",
    "    #print(y_train.shape)\n",
    "    img_dim=[28,28,1]\n",
    "    nb_patch=16\n",
    "    \n",
    "    discriminator = discriminator_model(cont_dim[0])\n",
    "    #discriminator.load_weights('info_discriminator_fashion02.h5')\n",
    "    discriminator_D = discriminator_model_D(cont_dim[0])\n",
    "    discriminator_Q = discriminator_model_Q(cont_dim[0], cat_dim[0])\n",
    "    y_d=discriminator_D(discriminator(discriminator.inputs))\n",
    "    \n",
    "    ddiscriminator=Model(inputs=discriminator.inputs, outputs=y_d)\n",
    "    \n",
    "    d_opt = Adam(lr=1e-5, beta_1=0.1)\n",
    "    \n",
    "    list_losses = ['binary_crossentropy']#, ignore_loss, ignore_loss]\n",
    "    #list_weights = [1, 0.1, 0.1] count_dim=2 fashion\n",
    "    list_weights = [1]#[0.5, 1, 0.5]\n",
    "    ddiscriminator.compile(loss=list_losses, loss_weights=list_weights, optimizer=d_opt)\n",
    "    ddiscriminator.summary()\n",
    "    # generator+discriminator （discriminator部分の重みは固定）\n",
    "    ddiscriminator.trainable = False\n",
    "    discriminator.trainable = False\n",
    "    discriminator_D.trainable = False\n",
    "    list_weights = [1, 1, 0.1]\n",
    "    #list_weights = [1, 1, 1]\n",
    "    list_losses = ['binary_crossentropy', 'categorical_crossentropy', gaussian_loss]\n",
    "    generator = generator_model(cont_dim[0], cat_dim[0])\n",
    "    #generator.load_weights('info_generator_fashion02.h5')\n",
    "    \n",
    "    #generator.compile(loss='mse', optimizer=d_opt)\n",
    "    y_dcgan=discriminator(generator(generator.inputs))\n",
    "    y_dcgan_d=discriminator_D(y_dcgan)\n",
    "    y_dcgan_q=discriminator_Q(y_dcgan)\n",
    "    dcgan=Model(inputs=generator.inputs, outputs=[y_dcgan_d, y_dcgan_q[0], y_dcgan_q[1]])\n",
    "    \n",
    "    dcgan.compile(loss=list_losses, loss_weights=list_weights, optimizer=d_opt)\n",
    "    if load:\n",
    "        dcgan.load_weights(\"info_dcgan_\"+name)\n",
    "    dcgan.summary()\n",
    "    num_batches = int(X_train.shape[0] / BATCH_SIZE)\n",
    "    print('Number of batches:', num_batches)\n",
    "    \n",
    "    noise_scale=0.5\n",
    "    label_smoothing=False\n",
    "    label_flipping=0\n",
    "    \n",
    "    for epoch in range(NUM_EPOCH):\n",
    "        cat_loss=[]\n",
    "        for index in range(int(num_batches/1)):\n",
    "            \n",
    "            X_real_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\n",
    "            \n",
    "                \n",
    "\n",
    "            # discriminatorを更新\n",
    "            #print(image_batch.shape)\n",
    "            #print(generated_images.shape)\n",
    "            \n",
    "            X_disc, y_disc, y_cat, y_cont = get_disc_batch(X_real_batch,\n",
    "                                                                      generator,\n",
    "                                                                      index,\n",
    "                                                                      BATCH_SIZE,\n",
    "                                                                      cat_dim,\n",
    "                                                                      cont_dim*2,\n",
    "                                                                      noise_dim,\n",
    "                                                                      noise_scale=noise_scale,\n",
    "                                                                      label_smoothing=label_smoothing,\n",
    "                                                                      label_flipping=label_flipping)\n",
    "\n",
    "            # Update the discriminator\n",
    "            #print(y_disc)\n",
    "            d_loss = ddiscriminator.train_on_batch(X_disc, y_disc)\n",
    "            disc_pred=ddiscriminator.predict(X_disc)\n",
    "            print(disc_pred[0])\n",
    "            print(disc_pred[BATCH_SIZE])\n",
    "            # generatorを更新\n",
    "            #noise = np.array([np.random.uniform(-1, 1, 100) for _ in range(BATCH_SIZE)])\n",
    "            X_gen, y_gen, y_cat, y_cont, y_cont_target = get_gen_batch(BATCH_SIZE,\n",
    "                                                                                  cat_dim,\n",
    "                                                                                  cont_dim,\n",
    "                                                                                  noise_dim,\n",
    "                                                                                  noise_scale=noise_scale)\n",
    "\n",
    "            # Freeze the discriminator\n",
    "            g_loss = dcgan.train_on_batch([X_gen,y_cat, y_cont], [y_gen, y_cat, y_cont_target])\n",
    "            dcgan_pred=dcgan.predict([X_gen,y_cat, y_cont])\n",
    "            print(dcgan_pred[0][0])\n",
    "            print(\"epoch: %d, batch: %d, g_loss: , d_loss: \" % (epoch, index))\n",
    "            print(g_loss)\n",
    "            cat_loss.append(g_loss[2])\n",
    "            print(d_loss)\n",
    "        generator.save_weights('info_generator_'+name)\n",
    "        discriminator.save_weights('info_discriminator_'+name)\n",
    "        dcgan.save_weights('info_dcgan_'+name)\n",
    "        print(\"cat_loss: %f\" %(np.mean(np.array(cat_loss))))\n",
    "    return generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dc_train(X_train, y_train):\n",
    "    cat_dim=[10]\n",
    "    cont_dim=[2]\n",
    "    noise_dim=[62]\n",
    "    \n",
    "    X_train = (X_train.astype(np.float32) - 127.5)/127.5\n",
    "    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2],1)\n",
    "    y_train= keras.utils.to_categorical(y_train, num_classes=10)\n",
    "    #print(y_train.shape)\n",
    "    img_dim=[28,28,1]\n",
    "    nb_patch=16\n",
    "    \n",
    "    discriminator = dc_discriminator_model()\n",
    "    \n",
    "    \n",
    "    d_opt = Adam(lr=1e-5, beta_1=0.1)\n",
    "    list_losses = ['binary_crossentropy']\n",
    "    list_weights = [1]\n",
    "    discriminator.compile(loss=list_losses, loss_weights=list_weights, optimizer=d_opt)\n",
    "    discriminator.summary()\n",
    "    # generator+discriminator （discriminator部分の重みは固定）\n",
    "    discriminator.trainable = False\n",
    "    \n",
    "    generator = dc_generator_model()\n",
    "    \n",
    "    y_dcgan=discriminator(generator(generator.inputs))\n",
    "    dcgan=Model(inputs=generator.inputs, outputs=y_dcgan)\n",
    "    dcgan.compile(loss=list_losses, loss_weights=list_weights, optimizer=d_opt)\n",
    "    \n",
    "    \n",
    "    num_batches = int(X_train.shape[0] / BATCH_SIZE)\n",
    "    print('Number of batches:', num_batches)\n",
    "    \n",
    "    noise_scale=0.5\n",
    "    label_smoothing=False\n",
    "    label_flipping=0\n",
    "    for epoch in range(NUM_EPOCH):\n",
    "\n",
    "        for index in range(num_batches):\n",
    "            \n",
    "            X_real_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\n",
    "            \n",
    "                \n",
    "\n",
    "            # discriminatorを更新\n",
    "            #print(image_batch.shape)\n",
    "            #print(generated_images.shape)\n",
    "            \n",
    "            noise_input = sample_noise(noise_scale, BATCH_SIZE, noise_dim)\n",
    "            generated_images=generator.predict(noise_input)\n",
    "            X_disc = np.concatenate((X_real_batch,generated_images))\n",
    "            # Update the discriminator\n",
    "            #print(y_disc)\n",
    "            if index % 500 == 0:\n",
    "                show_images(generated_images)\n",
    "            d_loss = discriminator.train_on_batch(X_disc, np.array([1]*BATCH_SIZE+[0]*BATCH_SIZE))\n",
    "            disc_pred=discriminator.predict(X_disc)\n",
    "            print(disc_pred[0][0])\n",
    "            print(disc_pred[BATCH_SIZE][0])\n",
    "            # generatorを更新\n",
    "            #noise = np.array([np.random.uniform(-1, 1, 100) for _ in range(BATCH_SIZE)])\n",
    "            \n",
    "            noise_input = sample_noise(noise_scale, BATCH_SIZE, noise_dim)\n",
    "            # Freeze the discriminator\n",
    "            g_loss = dcgan.train_on_batch(noise_input, np.array([1]*BATCH_SIZE))\n",
    "            dcgan_pred=dcgan.predict(noise_input)\n",
    "            print(dcgan_pred[0][0])\n",
    "            print(\"epoch: %d, batch: %d, g_loss: , d_loss: \" % (epoch, index))\n",
    "            print(g_loss)\n",
    "            print(d_loss)\n",
    "        generator.save_weights('dc_generator.h5')\n",
    "        discriminator.save_weights('dc_discriminator.h5')\n",
    "                \n",
    "    return generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "discriminator_model() missing 1 required positional argument: 'cont_dim'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-d7be35ebbf9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdiscriminator_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: discriminator_model() missing 1 required positional argument: 'cont_dim'"
     ]
    }
   ],
   "source": [
    "discriminator_model().outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:38: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), name=\"conv2d_1\", padding=\"same\")`\n",
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:44: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (5, 5), name=\"conv2d_2\", padding=\"same\")`\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'output_9/Tanh:0' shape=(?, 28, 28, 1) dtype=float32>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator_model().outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (_, _) = mnist.load_data()\n",
    "index_0=np.where(((y_train==0) | (y_train==1)))\n",
    "\n",
    "info_generator=info_train(X_train[index_0], y_train[index_0],load=True, name=\"digit0202.h5\", cat_dim=[2], cont_dim=[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_scale=0.5\n",
    "noise_dim=np.array([62])\n",
    "cat_dim=[2]\n",
    "cont_dim=[2]\n",
    "num_samples=10\n",
    "sample_range=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:38: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), name=\"conv2d_1\", padding=\"same\")`\n",
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:44: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (5, 5), name=\"conv2d_2\", padding=\"same\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "z_input (InputLayer)            (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "y_input (InputLayer)            (None, 2)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "c_input (InputLayer)            (None, 2)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concat_1 (Concatenate)          (None, 66)           0           z_input[0][0]                    \n",
      "                                                                 y_input[0][0]                    \n",
      "                                                                 c_input[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1024)         68608       concat_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm_3 (BatchNormalizatio (None, 1024)         4096        dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activaiont_2 (Activation)       (None, 1024)         0           batch_norm_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 6272)         6428800     activaiont_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm_8 (BatchNormalizatio (None, 6272)         25088       dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activaiont_8 (Activation)       (None, 6272)         0           batch_norm_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 7, 7, 128)    0           activaiont_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "upsampling_1 (UpSampling2D)     (None, 14, 14, 128)  0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 14, 14, 64)   204864      upsampling_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm_9 (BatchNormalizatio (None, 14, 14, 64)   256         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 14, 14, 64)   0           batch_norm_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "upsampling_2 (UpSampling2D)     (None, 28, 28, 64)   0           activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 28, 28, 1)    1601        upsampling_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "output (Activation)             (None, 28, 28, 1)    0           conv2d_2[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 6,733,313\n",
      "Trainable params: 6,718,593\n",
      "Non-trainable params: 14,720\n",
      "__________________________________________________________________________________________________\n",
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134b6edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13451a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127bd9128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134b0fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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muFjPkZLjvR988IFXduedd7p45cqVXpkc5891maVcVv3jH//YK5O7/Wrnn39+4HsXCznXAgCmTp3q4tq1a3tlcnxej4/LHXv1nJcVK1a4OIw5Nj/5yU+86/r166d9bY8ePVycz5MGCqVOnTre9csvv+xi3Ta//fZbFz/11FNemfw90HPN5s6dG7ieklwGDgB169ZN+9rjjjsu1HvHmdy5HADGjBnjYr0kXs6FeuWVV7yyhQsXuljn8plnnglcT6lz587e9eGHH+5i3f70vyNJp7eluO+++9K+Vs4n1u1t+/btLtZbzcyaNStIFfejTxWQv3f638aBAweGeu9c8MkUERERUQDsTBEREREFUHTDfA8//LB3LXcQl8MDgP8oUz+ulI999U7HuQzJ6J1i58+f72K9W7J8/z59/EPES2k3Xn1gsRza01sjyFwuWLAg7XvqXIYxtPfYY4+5WB/kmimX77zzTuB7F5P//u//9q7lkNnf/vY3r+z+++938XvvvZf2PXW7kruj5+rkk0928YsvvuiVyXz269fPK/vss88C37tYDBgwwLuWw2KTJ0/2yuQw+9tvv+2VyVMB9FBTGNtLyKE8/ZkhnX322d61PDC7FOh/g1q1auXiv/71r17Zu+++62L9GZbpgPYwPmtle589e3ba15111lmh3zsoPpkiIiIiCoCdKSIiIqIA2JkiIiIiCsDkc8m2MSanm8lx1F27dnll8pTypk2bemXZHtsSht///vfe9bXXXpv2tXJL/mxPNg+LtdZU/aqq5ZpLOadJLo8HgBo1arj42GOP9crkESNRk/MJAODDDz9M+1q5jDjTNglRCCuXQDj53LZtm1cml9fLY58A4KOPPsrldjnR867kfJlDDjnEK5N/h0zbJEQhTm3ziy++8Mrk55Q89gkAli9f7uJ8bwHy/vvvu7hjx45e2c6dO10s51blQxzapqTnsrVp08bFnTp18srk9jL6yJio3XjjjS7+7W9/65XJ+ZK63UYtm3zyyRQRERFRAOxMEREREQVQFFsjyKXpenmtXBKpd2SN2jHHHOPiwYMHp32d3IUb2H8YqZTcc889LtaPauWutnIX+nyQS7jlthZA5qEsvYt7qZG/93ooRT6W/+STT/JWJ+2Pf/yjdy1/7/RWC6Wcz9NOO83FZWVlXpkcMvv000+9snwO7en8yCX/uh5HHXVUXuoUV02aNHGx/jdH7mS+adMmryyfQ3t6K5sHH3wwbZneET1u+GSKiIiIKAB2poiIiIgCYGeKiIiIKICimDPVq1evrF6n5+DIcf5cHX/88S5+4403vDK9FYMkt+Tv3r174Hokxemnn+5iPSYu1axZ07sO4/gHOafnhhtu8Mrktb736tWrXZwp56Wod+/eLs6UT3lUEABs3rw58L3l/XTOBg0a5OLLL7/cK5O/S3pejT6NvpScdNJJLta5lNtLNGrUyCuLetsSucVGpqOHzj33XO9ab71SauScKT3XWH4Wtm3b1iv75z//6eKo28MTTzzhXctcyy03gPgf51TlkyljTFNjzAxjzFJjzBJjzM2pr9czxkw1xqxI/ZnfTVkoJ8xlcrBtJgtzmRxsm6Unm2G+vQCGWmvbAegO4AZjTDsAwwFMt9a2BDA9dU3xx1wmB9tmsjCXycG2WWKqHOaz1q4FsDYVf22MWQagMYCBAM5IvexJADMBDIuiknLZuiYfP8+dO9crk0NKW7du9crkMlq9pPuVV16p9D30TsrSiBEjvOs77rgj7WsLyVq7IPVnQXIpf9Z6KbPczX7RokVeWb9+/Vz88ccfe2XyUbQethk9erSL5cnx+ndKXk+dOtUr08MHcRGHtimX0Ot8yh3tly1b5pVdccUVLtanw+/evdvFelf5vn37uvjqq692sRzSAPxtS/SWKfI99IkKhVTotilPHdi7d69XJodf5DAQANx7770ufvrpp70y+bmrh5pke5dbHuhpEY8++qiL9VSO1157zcWTJ09GXMShbcpdzvV2B3Xq1HGx/rlNnz7dxXIrGwBYunSpi7PdEkPmGQB+/etfu3jAgAFemRyCl5/5xaBaE9CNMc0BdALwLoCy1C8MAKwDUJbm2yiGmMtkYT6Tg7lMFuazNGQ9Ad0YUwvAeABDrLXb5QRFa61Nd36QMWYwgPQ7WlLeMZfJwnwmB3OZLMxn6cjqyZQx5mBU/EI8Y62dkPryemNMeaq8HMCGyr7XWjvaWtvFWtsljApTMMxlsjCfycFcJgvzWVpMVeOepqIr/SSAzdbaIeLrvwawyVo7whgzHEA9a+1tVbxXTucOyLkukyZN8srkOLz+u8jxV73EUx5Xok+0TzdHSx5dA/hzq84777xKvyeGnkIBc3nqqae6eMqUKV7ZoYceKt/fK9PHfkhyXoY+biLdPDedyyVLlrhYnz4fYwegwG2zS5d9n/V67kXduvsWKul8yvaoy2S7rVWrlleWLp+67cvjMvQ8uky/SwVW0LYp55npXLZs2dLFeg5MplzK7Qn0fCc9h+o7Opdy/tZ//dd/eWXTpk1L+30FVvC2Kdvfn/70J69MzgPVeZGfjTqf8jgtvb1JeXm5i+XvSKZtNmT+AGDYsH3Tx95//33EhbU2/b4vKdkM8/UCcAWARcaY7/52dwAYAeAFY8wgAJ8DuDjXilJeMZfJwbaZLMxlcrBtlphsVvO9BSBdr6xPuNWhqGXoYTOXRYZtM1nYNpODbbP0VDnMF+rNcnxc2bBhQxePHz/eK+vatauL5VLsSu6dy629R8d6F+cdO3bk9J6FlM3jymzkmks5bDNq1Civ7JJLLnGxzmWu+UvnhBNO8K4XL14c6vvnQ1i5BHLPp8zT0KFDvbKf//znlb4udb9cbpfW448/7l0PGeJGVuI8rOcpdNuU0xv0kvXnnnvOxXpYKGx6+OhHP/qRi/UpFHEVh7Yp21j79u29MnlCh9z2Igp6m42FCxe6uFu3bl5ZXE8gyCafPJuPiIiIKAB2poiIiIgCYGeKiIiIKICimDOl3sO7lsvp9ZJ2uTT0//7v/7wyeayJfk95nIU8LkMuCy1WhZ6Xod7Du5ZzMb7//e97Za1bt3axPron0zE/8ve7T5998z5nzJhRvcrGUBzmZWRSs2ZNF990001emZzreOGFF+b0/i+++KKLL7roopzeI07i1DY1+Xn5u9/9zivr3Lmzi/VcxGzJz9Yi2soirbi3TZnPZ5991is75ZRTXCz//asOOU9KHwszc+ZMF8d1jpTGOVNEREREEWNnioiIiCiAohvmo2DiPJSQKznMl2mn7aSJ+1BCruTQvR7C3blzp4tjtuN1YElom7r9HX300S7Wudy4caOLd+3aFW3F8iwpbVNvafK9733PxfoUiQ8//NDFX3zxRbQVyzMO8xERERFFjJ0pIiIiogDYmSIiIiIKgHOmSkwS5mVQhaTMy6AKbJvJwbaZLJwzRURERBQxdqaIiIiIAmBnioiIiCgAdqaIiIiIAmBnioiIiCgAdqaIiIiIAmBnioiIiCgAdqaIiIiIAmBnioiIiCiAg/J8v68AfA6gQSoutFKrR7MQ34u5TC8fdQkzl0BFfb9Baf0Ms8G2GVxc6gGwbYYhLvmMVdvM63Ey7qbGzLfWdsn7jVmP0MWl7nGpBxCvulRHnOodl7rEpR65iEvd41IPIF51qY441TsudYlLPb7DYT4iIiKiANiZIiIiIgqgUJ2p0QW6r8Z6BBeXuselHkC86lIdcap3XOoSl3rkIi51j0s9gHjVpTriVO+41CUu9QBQoDlTREREREnBYT4iIiKiAPLamTLG9DPGLDfGrDTGDM/zvccaYzYYYxaLr9Uzxkw1xqxI/Vk3D/VoaoyZYYxZaoxZYoy5uVB1CYK5TE4uAeYzdc9E5JO5TE4uAeazWHKZt86UMeZAAKMA9AfQDsBlxph2+bo/gHEA+qmvDQcw3VrbEsD01HXU9gIYaq1tB6A7gBtSP4dC1CUnzKVT9LkEmE+h6PPJXDpFn0uA+Uwpjlxaa/PyH4AeAKaI69sB3J6v+6fu2RzAYnG9HEB5Ki4HsDyf9UnddyKAvnGoC3NZerlkPpOVT+YyOblkPosrl/kc5msMYJW4Xp36WiGVWWvXpuJ1AMryeXNjTHMAnQC8W+i6VBNzqRRxLgHmcz9FnE/mUiniXALMpyfOueQE9BRb0b3N29JGY0wtAOMBDLHWbi9kXZKGuUwW5jM5mMtkyefPMO65zGdnag2ApuK6SeprhbTeGFMOAKk/N+TjpsaYg1HxS/GMtXZCIeuSI+YyJQG5BJhPJwH5ZC5TEpBLgPlE6j6xz2U+O1PzALQ0xrQwxtQAcCmASXm8f2UmAbgyFV+JirHYSBljDIAxAJZZax8uZF0CYC6RmFwCzCeAxOSTuURicgkwn8WTyzxPHDsHwEcAPgZwZ57v/RyAtQD2oGLceRCA+qhYBbACwDQA9fJQj1NR8ThyIYD3U/+dU4i6MJfMJfOZvHwyl8nJJfNZPLnkDuhEREREAXACOhEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBcDOFBEREVEA7EwRERERBRCoM2WM6WeMWW6MWWmMGR5WpagwmM/kYC6ThflMDuYymYy1NrdvNOZAAB8B6AtgNYB5AC6z1i4Nr3qUL8xncjCXycJ8JgdzmVwHBfjebgBWWms/AQBjzPMABgJI+0thjMmt50ahsdaaNEXVyidzWXhh5TL1GuazwNg2k4NtM1ky5NMJMszXGMAqcb069TWPMWawMWa+MWZ+6hrG7F+v775eWRnlRZX51Lmk2MqpbVJssW0mB9tmQgV5MpUVa+1oAKOBzD3sXIcbKX+yzSUVB+YzOZjLZGE+i0+QJ1NrADQV101SX6PixHwmB3OZLMxncjCXCRWkMzUPQEtjTAtjTA0AlwKYFE61qACYz+RgLpOF+UwO5jKhch7ms9buNcbcCGAKgAMBjLXWLgmtZpRXzGdyMJfJwnwmB3OZXDlvjZDTzTj2W3DZrErIBnNZeGHlEmA+44BtMznYNpMl6tV8RERERCWPnSkiIiKiANiZIiIiIgqAnSkiIiKiANiZIiIiIgqAnSkiIiKiACI/TqbY1ahRw8WHHHKIVzZx4kQXt2zZ0iubNm2ai8vKyryyP/3pT5W+DgC+/vrr3CtLGdWrV8/FRx99tFf2xhtvuPjAAw/0yp588kkXt2rVyiubMGGCi19//XWvbMuWLblXtgC+Oxczzkc7ybM727dv7+KBAwd6r/uf//kfF2/bts0re+CBByp9D8D/PZg9e7ZXtnXr1hxqXBjFkMsDDtj3//Lnn3++i6+++mrvdWeddZaL16zxNwu/6aabXNyxY0ev7K233nLxokWLvLJNmzblUOPCKYZ8HnTQvu7E0KFDXXzhhRd6r+vUqZOL9b9/11xzjYuPP/54r2z16tUu1m1x/fr1OdQ4XHwyRURERBQAO1NEREREAXAH9ErIob1LLrnExYMGDfJed/rpp7tYDj8AwL///W8X62GjXbt2uXj58uVeWd++fV28cePG6lQ7K6W2y3LdunVdPHbsWBefeuqp3usaNGjgYt0m9uzZ42L5uwEA3377rYtnzZrllcmhi927d1en2lkpxV2W5aN/OazarFkz73UyTzqfcij9iCOOSFv20ksveWWy/cv2HZZSa5tdunRx8auvvupi2RYB//NT/9w/+eQTFx933HFemfz8fOGFF7yym2++2cVR/BtYim2zf//+Ln766addfOSRR3qvk8O7mzdv9srGjBnjYjmECwDvv/++i1988UWvbOTIkTnUOHvcAZ2IiIgoYuxMEREREQXAzhQRERFRACW7NYKaG/7iAAAW0UlEQVQch2/evLlX1q1bNxfLJfRNmjTxXqfnSaV7f+3ggw92cdu2bb2y+++/38XXXXedVxbnZbGFJJfktm7d2iv76U9/6mI5dp8pPzqvep5UurLTTjvNK5NLgN95552070E+mc/OnTt7ZXLLA7lViZ5LI9uKbje1atVKe+9DDz3UxXIOCAAcfvjhLt6+fXva96B95GfdiSee6JX94he/cLHM37/+9S/vdTLP+ufeqFEjF+s8165d28XnnHOOV3bLLbdUem/KTH7enXTSSV7Z7bff7mI5z1TOKwX834lRo0Z5ZY0bN670PQB/+yG5XQYQ/ZypbPDJFBEREVEA7EwRERERBVCyw3xyyKdXr15emdwpe/LkyS6WSzMB4C9/+YuL5fAAAHz55Zcu1ss/5RCTflSqh6lof3oYTg7Fyq0lAGDv3r0ulkNEOl+/+c1vXCyHmQB/12U9lCDvrZfnZxpKpH10PmWbkFuTAMCKFStcfNddd7lY/6x/9rOfVfo9APCf//zHxR06dPDKunbt6mL5OQDsP/xE+9O5PO+881ysh03nzp3rYjkcL/MD+DlZsGCBVya3trjsssu8sosvvtjFeqhJ34MqJ7cxAIBbb73VxXJrIACYP3++iwcMGODinTt3eq+Tw+z6lAg5jDhnzhyv7M4773Txjh07qqx7vvHJFBEREVEA7EwRERERBcDOFBEREVEAJXOcjF7eLsfT5dECgD8v5qqrrnKxnjMhx5PlsQaAf2SMVr9+fRevWrXKK1uyZImLe/bs6ZXppaK5SMKRFYcddph3PWLECBfreRlybH3gwIEu1uP4cnm0PpE80/wKeVL97NmzvbJzzz3XxfIE+7Ak5ciKhg0betcTJ050sT45/tNPP3WxnB+n22ampfaZyCOAfve733llLVq0yOk9s5WEtqnzNWPGDBfrY0UWL17s4jPOOMPFcp4j4H8eZ2qLer6WnAepl9LrbRrCVsxtU/4c5TZBADBt2jQXyy0rAH9uovxczHXrCT1f6+9//7uL9Ry4Pn365HSPbPE4GSIiIqKIsTNFREREFEDJbI3Qu3dv71ouq9Y7Ir/55psulo8ud+/e7b0u1+W1chhJDz9m2m2bKlxwwQXe9Q9/+EMX6yXy8tGw3K5CDyXkasOGDS7Wv0dy2Tb55FCCPh1e7hyv25hcGv/111+nfV2uGjRo4OKysjKvTA87UAX5c7ntttu8svLychfrHMml9HLYNNepJ/r7+vXr52K5ezZlJj9D7733Xq9MngKg8/nyyy+7OIxd5fX7n3zyyYHfM0r8dCAiIiIKoMrOlDFmrDFmgzFmsfhaPWPMVGPMitSfdaOtJoWF+UwO5jJZmM/kYC5LTzZPpsYB6Ke+NhzAdGttSwDTU9dUHMaB+UyKcWAuk2QcmM+kGAfmsqRUOWfKWjvbGNNcfXkggDNS8ZMAZgIYFmK9QiHH8nv06OGVyaM/9PEhctmsHPsNa16GfE85jwfwT82OYtuKYs2nzJE+/qdOnTouznTUTBSnw69fv97FekuFs88+28VvvPFG6Pcu1lwCmU+fl6fK6zZQt+6+/5mP4kiQl156ycVyyw3Any+SaeuTXBVrPuUS+RNOOMErk5/Bum3Kn2cUn3VyGb+cu5UPxZpLwN96Rs810zmUtm3bFlmdAL/NxfE4oFznTJVZa9em4nUAyjK9mGKP+UwO5jJZmM/kYC4TLPBqPmutzbSpmDFmMIDBQe9D+ZEpn8xlcWHbTBa2zeRg20yeXDtT640x5dbatcaYcgAb0r3QWjsawGgg/zu5ysfPcvgsVa+03yd38ZXx0qVLQ6mXfFTapEmTtPXK4+70WeWzkLmsWbOmi+vVq+eVyeE7vTWC3MFXPr7+5ptvQqmXHGKUO9sD/hYOQ4YMCeV+WSiKtinzKYf1gMzDsWeeeaaLZa7DGsKVJx7o37MuXbq4eMqUKaHcLwuxb5uHHnqoi/WWI5mmSchdyeVnYlife5dffrmL5fAwEM3vThZi2Tb10J0cgtefk/JnpbcKkqcHPPTQQ2FWEYC/1Uym4cZCyXWYbxKAK1PxlQAmZngtxR/zmRzMZbIwn8nBXCZYNlsjPAfgbQCtjTGrjTGDAIwA0NcYswLAWalrKgLMZ3Iwl8nCfCYHc1l6slnNd1maomhPFqRIMJ/JwVwmC/OZHMxl6UnUcTL6FOs///nPLh4wYIBXJpfs6jHzr776ysXLli0Ls4oAgNatW7tYj/3K+QJ5HMuPHTmnBgBGjhzpYnlMBOD/nOSxFACwcOFCF4c1T0oaM2ZM2rI9e/aEfr9ipedFXX311S6Wc5EAYOfOnS7Wp8NPnLhvZCSK9nHHHXekLYt66Xex0PMSzznnHBe3atXKK5NH/qxZs8Yre/zxx10cxfxQPedNyuN81NjTxySddtppLm7UqJFXJrd/mTNnjlemj54JWxznSUk8ToaIiIgoAHamiIiIiAJI1DDfKaec4l3rnbIlOZSgh5Tko+koHgdfc801acvWrVsX+v2KUc+ePb3rrl27ulgvsZY742bKZRTatm2btuzuu++O9N7FRP+cOnXq5GI9HCqH73Q+165diyhlGkpYsGBBpPcuFnqbGTltQX6uAv5WCXrYLerPukzbzMRxB+1CadCggXctT43INDVC7yof9r+Vmdrijh07Qr1XGPhkioiIiCgAdqaIiIiIAij6YT75iHnUqFFemd6RWpIrczZv3uyVyZ1zo3DdddelLevdu3ek944zOXwwbJh//qccItDDQvJRtM6lXDUWBX0QqPT8889Heu+4kzvOy92RAeDII490sR4akvQw7W9/+9uQale5TIfv6pWipUSu4GvXrp1XJg8h1/mqVauWi3W7lQcRh0EPC8lVaqWcu8rIn5XcWRzwV+zpFaxyJ3m58z0ArFixIswqejuxa1u2bAn1XmHgkykiIiKiANiZIiIiIgqAnSkiIiKiAIpuzpQeF7/gggtcvH37dq9s48aNLt60aZNXJndLX7RokVf2wQcfBK6npHeRlWPBeofnlStXhnrvYtKmTRsXf/HFF16ZHKuX81oAf8x/1qxZXtmXX34ZZhVRu3Zt71rOJZG/b0Bp72AP+DnTv9ctWrRwsVyKDQANGzZ08fTp072ysOdK6Pki0ty5c0O9VzGTn5d6ewr5GSa3QgD8OVMzZszwysLetkTeC/DnvGXa2b4UyXluevsD+Zmm5y3VqVPHxbp96H/LgtKnJsjtLOSu+3HBJ1NEREREAbAzRURERBRA0Q3zySXVgL/r+YQJE7wy+ShTDxvJAzmnTJnilYW9k+ubb76ZtqyUt0LQS2u7devm4rffftsrk4+b9eP8Jk2auPipp54Ks4r7yfT+Z511VqT3jjvZ3gB/2FYPt8oc6nzKXP/jH/8Is4r7eeCBB9KWXXzxxZHeO8704bdyl2y9XP7EE090cfPmzb0yuYP96tWrQ6zh/n74wx+mLct0IHkp0PmUn716mE9OnTnuuOO8MtnGM21dEIaTTjopbVkcp8PwyRQRERFRAOxMEREREQXAzhQRERFRACbs+UEZb2ZM4JuNHDnSux44cKCL9XLJXbt2uVifUC6X8EZxgricB6LnGMh7yyXH+WCtTX8UdzWEkUt91MtPf/pTF1977bVeWc+ePV383nvveWVLly51cdjLrTV9LIXcqkMv5Y1aWLkEwsln//79vesrr7zSxffff79X9tBDD7lYz5175JFHXPzqq68GrVZG8ugMwG+3eg5Y1OLUNrt37+5d9+vXz8Vjx471yl577TUXN2vWzCuTcx/lewDhz01dtmyZd3388ce7uNTbpj4CqH379i7WW1YsWbLExUcddZRXJj9f9fzlsPM5evRo71p+nsTx300+mSIiIiIKgJ0pIiIiogCKYmsEefq8XD4PAJs3b3axHNYD/KE9vTNvFEN7khye0MtSzzvvvEjvHWdy6KRly5ZemVyi27RpU69s8eLFLtbDplEP7XXp0sXFeujn0UcfjfTecSd3S9bLqOXu4ueff75XJtuf3in+9ddfD7OK+5FDPnoH9KjvHWfyc0puNwIAxxxzjIuvueYar6x+/fou1idU/OAHP3Bx1FNK9LYM8+fPj/R+cSdzobcx6Nixo4vlFApg/+E7Sf4eRJ3P73//+961nt4RN3wyRURERBQAO1NEREREAbAzRURERBRAUcyZkvMaGjdu7JXJJZJyfB4AJk6c6OI1a9ZEVLsKerz+1FNPdbGeyzV58uRI6xJnMl89evTwyuRS5qFDh3plH374oYtvu+22iGpXQc/7kEuH9Vy722+/PdK6xJ2cQ9a3b1+vTM7LaNu2rVcm27TeBiPq+Yx///vfXaxzretSSmQuzzzzTK/s5JNPdnGdOnW8Mrl8Xs9r0VuJhO2iiy5ysV4uX+ptU86Bk20R8LdK6Ny5s1cm51ft3r3bK9PzVcNWt25dF+t5sw8//HCk9w6qyidTxpimxpgZxpilxpglxpibU1+vZ4yZaoxZkfqzblXvRYXHXCYH22ayMJfJwbZZerIZ5tsLYKi1th2A7gBuMMa0AzAcwHRrbUsA01PXFH/MZXKwbSYLc5kcbJslpsphPmvtWgBrU/HXxphlABoDGAjgjNTLngQwE8CwKCpZVlbm4u3bt3tlrVu3drHcVRkAhg/f93s6YsQIr2zUqFGB6yUfd8shRcBf7v2HP/zBK8vnrvOatXZB6s+C5LJ27dou3rhxo1cmTwmXw6QA0KtXLxfrISM9JBHU9ddf713Lx94ff/yxV6aHcPMpDm1T7l7+zjvveGVdu3Z1sWzDgL+lwmOPPeaVjR8/PswqekMHANCmTRsX79ixwyuLejpAJoVumzIn+vNM7pgtP3P193Xo0CGKqjl6WPYXv/iFi/fs2eOVzZkzJ9K6ZBKHtimH+WbNmuWVySkVenheyvfO8XKrGb1lypgxY/Jal+qq1gR0Y0xzAJ0AvAugLPULAwDrAJSl+TaKIeYyWZjP5GAuk4X5LA1ZT0A3xtQCMB7AEGvtdvl/CNZam+78IGPMYACDg1aUwsNcJgvzmRzMZbIwn6UjqydTxpiDUfEL8Yy1dkLqy+uNMeWp8nIAGyr7XmvtaGttF2ttl8rKKb+Yy2RhPpODuUwW5rO0mKrm75iKrvSTADZba4eIr/8awCZr7QhjzHAA9ay1Gdes53r6tZyzcvnll3tlcoxVHw2R6e/2wQcfuPiqq67yyuR48oknnujigQMHpn3dZ5995pV9+umnLj733HO9Mn20TZ49hQLmUs6vOOGEE7yyv/3tby7Wp5VLOq/33nuvi++777603yf/r1BvsSG3P9BLrOXvlZy7BfgnrBfAAShw25Q/U71k/s0333Rxp06d0r6HzmezZs1cvGrVqlyq5Z0wf88993hlMvfPP/+8V/bjH/84p/uFpKBtU9JzZUaOHOnim266Ke336VzK99FzYLJVq1YtF+vjfuRRKGvXrvXK8nn0SSUK3jbVe3jXAwYMcPFLL72U9rX656aPRgvq1ltv9a4ffPBBF+/cudMrk/8m6C03ot5OxVprqnpNNsN8vQBcAWCRMeb91NfuADACwAvGmEEAPgdwca4VpbxiLpODbTNZmMvkYNssMdms5nsLQLpeWZ9wq0NRy9DDZi6LDNtmsrBtJgfbZukpih3Q5SO9l19+2St75JFH0n6ffrQpyWX42Z4urpfeyt1g9VL+r776ysWF3AohbuSj/qVLl3pl2W4zoPN69913u3jIkCFemXz8K4eLMw21XnPNNd71tGnTXLxly5as6lgq5O+23h1Z5lO3AZlDnc958+a5WA/ry0f/gwYNcnGTJk2818khZD00JJeJv/LKK6D96c86+Xuvh1Tk0I/O5Y033uhivQWG1LBhQxfLbRgAf+hn8+bNXtmECRNc/Mwzz3hl/NzdR/8s5JYgOtfyc1Lns169ei7WuZDk98ntUwDgjjvucLHciR3wTydYtmyZVybrGfWwXi54Nh8RERFRAOxMEREREQXAzhQRERFRAFVujRDqzUJY4qnJZbPTp0/3yuTRBocddlhO7y/HZuXcGcBffr1u3bqc3j/fslnimY0ocnn44Ye7eMGCBV7Zscce62J5un11yFxu2rTJK7vrrrtc/Mc//jGn98+3sHIJRJ/PRYsWeWVy2brcLkPTS6Bl7uVnl15G/cILL7hYb30S17k0cW6bNWvWdPE///lPr0weL6OXzsu5iXoOjNwCQ87T2b17t/e61157zcXyMxfw52DGKa9xb5syn3PnzvXK5Dwmnc8NG/Zti3XzzTd7ZXJ+Y5cu+7bH0nOy5FYMemsE+fui81ngY9iqzCefTBEREREFwM4UERERUQBFP8yXLX1y/I9+9CMXy0eegD8kMXXqVBcXeOfyUMR5KEG9f9rrFi1aeGXy8bLeBV8u35VLp/XO2nEaIshW3IcSJD1cIIfr2rRp45X169fPxeXl5V7ZkUce6WK5xcGzzz7rvU4PDxaDYmmbelhWTqFo1aqVVya3OZDDegBw3HHHufibb75x8bBhw7zXyWX8xaKY2qYcYgWAsrJ9Zy/r9ie3OWjUqJFXJrcbkr8Heph969atuVe2QDjMR0RERBQxdqaIiIiIAmBnioiIiCiAkpkzRRWKZV4GVa2Y5mVQ1dg2k4NtM1k4Z4qIiIgoYuxMEREREQXAzhQRERFRAOxMEREREQXAzhQRERFRAOxMEREREQXAzhQRERFRAOxMEREREQXAzhQRERFRAAdV/ZJQfQXgcwANUnGhlVo9mlX9kqwxl+nloy5h5hKoqO83KK2fYTbYNoOLSz0Ats0wxCWfsWqbeT1Oxt3UmPnW2i55vzHrEbq41D0u9QDiVZfqiFO941KXuNQjF3Gpe1zqAcSrLtURp3rHpS5xqcd3OMxHREREFAA7U0REREQBFKozNbpA99VYj+DiUve41AOIV12qI071jktd4lKPXMSl7nGpBxCvulRHnOodl7rEpR4ACjRnioiIiCgpOMxHREREFEBeO1PGmH7GmOXGmJXGmOF5vvdYY8wGY8xi8bV6xpipxpgVqT/r5qEeTY0xM4wxS40xS4wxNxeqLkEwl8nJJcB8pu6ZiHwyl8nJJcB8Fksu89aZMsYcCGAUgP4A2gG4zBjTLl/3BzAOQD/1teEApltrWwKYnrqO2l4AQ6217QB0B3BD6udQiLrkhLl0ij6XAPMpFH0+mUun6HMJMJ8pxZFLa21e/gPQA8AUcX07gNvzdf/UPZsDWCyulwMoT8XlAJbnsz6p+04E0DcOdWEuSy+XzGey8slcJieXzGdx5TKfw3yNAawS16tTXyukMmvt2lS8DkBZPm9ujGkOoBOAdwtdl2piLpUiziXAfO6niPPJXCpFnEuA+fTEOZecgJ5iK7q3eVvaaIypBWA8gCHW2u2FrEvSMJfJwnwmB3OZLPn8GcY9l/nsTK0B0FRcN0l9rZDWG2PKASD154Z83NQYczAqfimesdZOKGRdcsRcpiQglwDz6SQgn8xlSgJyCTCfSN0n9rnMZ2dqHoCWxpgWxpgaAC4FMCmP96/MJABXpuIrUTEWGyljjAEwBsAya+3DhaxLAMwlEpNLgPkEkJh8MpdITC4B5rN4cpnniWPnAPgIwMcA7szzvZ8DsBbAHlSMOw8CUB8VqwBWAJgGoF4e6nEqKh5HLgTwfuq/cwpRF+aSuWQ+k5dP5jI5uWQ+iyeX3AGdiIiIKABOQCciIiIKgJ0pIiIiogDYmSIiIiIKgJ0pIiIiogDYmSIiIiIKgJ0pIiIiogDYmSIiIiIKgJ0pIiIiogD+H3uTCadgbfC1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12604c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1279aaeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12777ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136bb59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1279106a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e1c29e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e2fe9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e38a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e8b06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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SnrXW3uA1LZZ0laTrSh/vq+YNO3F9zbv8/6s4GP+RFS+88EITsqkbtVT4tPmDOf30011c6RFCrdbovtnJenp6qj73yiuvdPGTTz7ZjHTqRS0lDRo0qOpz/cfJFAl9c79a1ij666viv7dF/3tTzV+UmZKukPSMMebp0ue+rv4fhjuNMXMlrZd0cXNSRINRy3zQN/NCLfNB3+wy1dzN96ikcpdkPtbYdNBsFR7YSC07DH0zL/TNfNA3u0/Ld0DvZEOHDq363D/+4z928Te/+c3GJ4MktdTykksucXGRpvmwXy31/OAHP9jETJBq8ODBVZ/L0wmKyZ+iq2Wab/fu3Qd8Dan403z8JAIAACRgMAUAAJCAwRQAAEAC1kzV4KmnngqOK92WO2DAgGangwTr168Pjis9lmL79u3NTgeJXnnlleB4/PjxZc9dt25ds9NBjfz1MW+99VbQVmnNzTPPPHPA15CKv8YmZ/5atrfffrvqr/uP//gPF3da/bgyBQAAkIDBFAAAQALTyktp5Z5D1CnGjg2fSblmzRoXx5cyjz32WBe/9tprzU2sBhX2sqlJp9eyr68vOPancN94442g7fjjj3fxzp07m5pXLRpVS6nz6zlt2rTg+KGHHnLxli1bgrZTTz3VxXv37m1uYjWgb/bznzggSffcc4+L4+n5j3zkIy6uZTqp2eib+82YET4R5wc/+IGL4ycQ+FsKFWmar5p6cmUKAAAgAYMpAACABAymAAAAErBmKsH73vc+F+/bty9oe+GFF1qcTXVYl9Evvo164sSJLo7XTBVpzZuPdRn7xfUcPXq0i+N67tq1qyU51Yq+2S+u5bBhw1wcr1mMf+8WBX1zv7ie/lYXe/bsCdqKtE7Kx5opAACAJmMwBQAAkIAd0GsQ72ruT/9s27at1ekgwaGHhj/6/lQQO553nrie/lSe/yR6FF9cS3/7iqJO66G8+O+mv4VFUaf16sGVKQAAgAQMpgAAABIwmAIAAEjA1ghdhtuv88Ht13mhb+aDvpkXtkYAAABoMgZTAAAACVq9NcLLktZLGlOK263b8pjcwNeiluW1IpdG1lLqz/dNddf3sBr0zXRFyUOibzZCUepZqL7Z0jVT7k2NecJaO6Plb0weDVeU3IuSh1SsXGpRpLyLkktR8qhHUXIvSh5SsXKpRZHyLkouRcnjXUzzAQAAJGAwBQAAkKBdg6mb2/S+MfJIV5Tci5KHVKxcalGkvIuSS1HyqEdRci9KHlKxcqlFkfIuSi5FyUNSm9ZMAQAA5IJpPgAAgAQtHUwZY84zxqw2xjxvjFnQ4ve+xRiz1RizwvtcrzHmQWPMmtLHUS3IY5Ix5mFjzCpjzEpjzJfblUsKaplPLSXqWXrPLOpJLfOppUQ9O6WWLRtMGWMGSPpHSZ+QNFXSpcaYqa16f0mLJJ0XfW6BpCXW2imSlpSOm22fpGustVMlnSFpfun70I5c6kItnY6vpUQ9PR1fT2rpdHwtJepZ0hm1tNa25J+kMyX9wjv+mqSvter9S+/ZJ2mFd7xa0rhSPE7S6lbmU3rf+yTNLkIu1LL7akk986ontcynltSzs2rZymm+CZJ+6x1vKH2uncZaazeV4s2SxrbyzY0xfZKmS3qs3bnUiFpGOriWEvV8jw6uJ7WMdHAtJeoZKHItWYBeYvuHty27tdEYM0zS3ZK+Yq3d3s5cckMt80I980Et89LK72HRa9nKwdRLkiZ5xxNLn2unLcaYcZJU+ri1FW9qjOlR/w/Fbdbae9qZS52oZUkGtZSop5NBPallSQa1lKinSu9T+Fq2cjC1XNIUY8wxxpiBkj4raXEL3/9AFku6qhRfpf652KYyxhhJCyU9a629oZ25JKCWyqaWEvWUlE09qaWyqaVEPTunli1eOHa+pOckrZX0jRa/9+2SNknaq/5557mSRqv/LoA1kn4lqbcFecxS/+XI/5X0dOnf+e3IhVpSS+qZXz2pZT61pJ6dU0t2QAcAAEjAAnQAAIAEDKYAAAASMJgCAABIwGAKAAAgAYMpAACABAymAAAAEjCYAgAASMBgCgAAIAGDKQAAgAQMpgAAABIwmAIAAEjAYAoAACABgykAAIAEDKYAAAASMJgCAABIwGAKAAAgAYMpAACABAymAAAAEjCYAgAASMBgCgAAIAGDKQAAgAQMpgAAABIwmAIAAEjAYAoAACABgykAAIAEDKYAAAASMJgCAABIwGAKAAAgAYMpAACABAymAAAAEjCYAgAASMBgCgAAIAGDKQAAgAQMpgAAABIkDaaMMecZY1YbY543xixoVFJoD+qZD2qZF+qZD2qZJ2Otre8LjRkg6TlJsyVtkLRc0qXW2lWNSw+tQj3zQS3zQj3zQS3zdWjC154m6Xlr7TpJMsbcIWmOpLI/FMaY+kZuaBhrrSnTVFM9qWX7NaqWpXOoZ5vRN/NB38xLhXo6KdN8EyT91jveUPpcwBgzzxjzhDHmiYT3QvMdtJ7UsmPQN/NC38wHfTNTKVemqmKtvVnSzRIj7E5HLfNCPfNBLfNCPTtPypWplyRN8o4nlj6HzkQ980Et80I980EtM5UymFouaYox5hhjzEBJn5W0uDFpoQ2oZz6oZV6oZz6oZabqnuaz1u4zxvyZpF9IGiDpFmvtyoZlhpainvmglnmhnvmglvmqe2uEut6Mud+2q+auhGpQy/ZrVC0l6lkE9M180Dfz0uy7+QAAALoegykAAIAETd8aAQAAoFGMCWfdWrlcqRyuTAEAACRgMAUAAJCAwRQAAECCrl0zdcgh5ceR/nzsYYcd5uIpU6YE5w0cONDFjz/+eND2zjvvpKaIKg0YMMDF8ffdr2VPT4+LJ0+eHJw3ZswYFz/xRPg4rD179hzwveLjffv2BW38DFQnXv/g981K30P/vKFDhwZtI0aMcPHWrVuDNv813/e+9wVtJ5xwgouXL18etG3evLmqvLBftbX0fwbi382HHrr/z1Tcx/z+941vfCNou+iii1z84x//OGj7h3/4Bxfv3LmzbF4I+XWqd51S3N99fu2/973vBW2f+cxnXPzCCy8Ebeedd56LN27cWFdeqbgyBQAAkIDBFAAAQIKu3QG90mXlc845x8U//elPXRxPJezatcvF06ZNC9rWrVvXiDQbLoddlitdJo7b/Lr8+te/drE/DSSF0wcLFiwI2vzLzfHr+9MM27ZtC9qa3bdy3WW5Un3Hjx/v4qVLl7p40qRJwXm7d+928cKFC4O2Bx54wMWf+9zngjZ/Kv/yyy8P2p599lkXN6O2OfTNWvT29rr4lltucfHxxx8fnLd9+3YX/+u//mvQ9uabb7r4+uuvD9oGDx7s4mXLlgVtf/AHf+Bifxq/UXLtm5UMGTLExbNmzXJxvDRi7NixLn7mmWeCNv9nwu+nUjjdu2PHjqDNX7bx2muv1ZJ2VdgBHQAAoMkYTAEAACRgMAUAAJCga7dG8Nc8vP3220HbmWee6eJhw4aVfQ1/Lji+1b6oa6ZyUGm9Stw2e/ZsF48cObLs1/m3bU+fPr3sef66AClca1WERxrkoNL38YILLnDxcccdV/Y8fxuDo48+Omjz12ycfPLJQdugQYNcHPdh6ttYl156qYv/6I/+yMXxVgX+Wqh4rYxfW792Urj27u/+7u+Ctmask+p2ft/015nec889wXlXX321i+P1yv6axXitle/nP/95cNyMdVK14soUAABAAgZTAAAACbp2mq+Sj3/841Wd51+OPvLII5uVDhL4l54r8W+lP+mkk4K2o446ysXxtO8bb7zh4ldffbWeFFFBvE3CZZdddsC2eArOv+wfTwdec801Lj7mmGOCNn/6Z+/evXVkjHLiWl5yySUurvREiueee87F8VMovvjFL7q40rTQihUrqs4T1Ynref7557vYr6c/5S6FWwoNHz48aPOndCttkfLd7363tmRbgCtTAAAACRhMAQAAJGAwBQAAkIA1Uwfw+OOPu/gjH/lI2fMqzemiGO69914Xn3322WXP89dbjBkzJmjz19U8+uijQZs//4/Gi9dC/fCHP3Sx/8iK+Dz/Nnl/zZsk9fT0uDi+1f7OO+90cbxlCtLENbrxxhtdPGPGDBf76xClcNuSz3zmM0Hb4YcfXvb9/Ppt3LixtmRRM3+9k7+29KGHHir7NRdffHFwPGHChKre67//+79rzK75uDIFAACQgMEUAABAAqb5DuD55593sX9pOp7W82/ZXbt2bfMTQ838Xaz9W939J5BL0tatW10cTzO89dZbLo6nftgVu7VWrVrl4t/97ncuHjx4cHDemjVrXDxp0qSgzb9te8OGDUGbP/WExqr0+/NHP/qRi7ds2RKc95vf/MbFEydODNr8WsZ9ceXKlS5myrb5/N+h/vYjL7/8cnCe/9SIefPmBW2Vls74Syr81ygKrkwBAAAkOOhgyhhzizFmqzFmhfe5XmPMg8aYNaWPo5qbJhqFeuaDWuaFeuaDWnafaq5MLZJ0XvS5BZKWWGunSFpSOkZnWCTqmYtFopY5WSTqmYtFopZd5aBrpqy1y4wxfdGn50g6pxTfKmmppK82MK+28p9E7s+1x/O5U6dOdfHs2bODtieffNLF/q297dZt9Rw4cKCL/Tn3+Anz/rqa119/PWg766yzXPzUU08Fbe18+ny31VKStm/f7mJ/vZPfZ6Xw0RZxv/XX1vjr4aT2rn3MvZ7xmiZ/bZT/+/KUU04JznvkkUdcHD/OqdLrf/SjH60rz0bIvZbSe7/f27Ztc7H/e3HkyJHBeffff7+LTz755Krf78orr6w1xZaqd83UWGvtplK8WdLYBuWD9qCe+aCWeaGe+aCWGUu+m89aa40xZW9pMsbMkzSvXDuKpVI9qWVnoW/mhb6ZD/pmfuodTG0xxoyz1m4yxoyTtLXcidbamyXdLEmVfnjaKZ4G8KeD/EuZ8XSdP4X0+c9/Pmj79re/7eKdO3c2JM8mqqqenVDLmL/NgV+/+FZp/9b63t7eoG38+PEuzqWWUmfWs9z2Fv4O9vFx3L/915g7d27QVsCtLrLtm/62Bscee6yL/d3QpXBqr9Kt88uXLw+OX3nlldQUGy3rvun3R/935umnnx6c94EPfMDFleoZL6G46667UlNsqnqn+RZLuqoUXyXpvsakgzahnvmglnmhnvmglhmrZmuE2yX9l6QTjDEbjDFzJV0nabYxZo2kc0vH6ADUMx/UMi/UMx/UsvtUczffpWWaPtbgXNAC1DMf1DIv1DMf1LL78DgZvXfe9owzzjjgefEjSPw5//hW+yJud98N4lpeeOGFLvZr5K93O9DX+W677TYXF3BNTdbiunzyk590cV9fn4uHDh1a8et8/iOGVq9enZghqhXX5A//8A9dfMUVV7h4zJgxFb/O5/fHeHsatNbMmTNd/M1vftPF/hZCUvh3s5I///M/b0hercLjZAAAABIwmAIAAEjANJ/eexnZ35nX3yF5+PDhZV/Dv91aKtau590kvoTs18GvUTwt64u3TWjnrtjdLq7nhAkTDthWaeognpr1b7Fm2rZ14hpNnz7dxaNG7X9MXbycopI333zTxTt27EjIDrWKtyPxnzrg72x+2GGH1fX6//RP/1RfYm3ClSkAAIAEDKYAAAASMM13AP7Unj8FWGkqIb6UyTRfe8Q1Gjt2/+Ovenp6XFzpDqG4zZ9KQGv5NZPC6QN/1/pK9Yw9+OCD6YmhKn5d4jto/Z2x/bZaarl06dL6k0PN/NrEd9CeeeaZLvb/HlZ7954UPrGi06bguTIFAACQgMEUAABAAgZTAAAACVgzpffeiuuvd9q2bZuLR4wYUfY1du7cGRx32nxvJ/Pn5P01UlK4xqbatRjx7vW7du1KyA618tdC+btkS9Jpp53m4vjW7HLivvj0008nZIdajBw50sXxjtannnqqi2tZV+O79tpr60sMdZk8ebKLb7jhhqBtxowZLq63nnfccUd9iRUAV6YAAAASMJgCAABI0LXTfP6UzxFHHBG0rV+/3sX+lE88XeC/xpFHHhm0+Ze3X3/99bRkUZG/e/LChQuDtnHjxrk4vs2+nPgWbn864oknnqgnRVQQTwl88YtfdPF1110XtFW7vYUvPm/u3LkuvvHGG6tfV3/6AAALF0lEQVTOEwcX13LZsmUunjZtWsPf70/+5E9c/OSTTzb89btdXM9nnnnGxcOGDWv4+33gAx9o+Gu2ClemAAAAEjCYAgAASMBgCgAAIEHXrpny1z9t3LgxaPPX3fi32l9zzTVlXy/eXmH48OEuZs1UY8VrYPx5/UWLFgVtgwYNcvGHPvQhF1ea74/XCfT29taTJqo0ZMiQ4PiCCy5wsf9op/hcf/1ULbdis21J8xx//PHB8fvf/34Xx9/3cnWI+7d/HH/N2rVr68oT1bnooouC40q/N/0thd5++20Xx/X0/1bG9fTX2HUarkwBAAAkYDAFAACQwLTykrcxpjDX1/0pAv/ypBReevR3WV6xYkVwXnxJ2/f973/fxZ/73OfqzrPRrLXVP5K9gnbW0p9ClcKdeLdv3x60+bfy/t///Z+L4512jzrqqLLv99RTT7nY3yah3RpVS6m99fzOd74THM+fP9/F8e8nf0r+3nvvdfHll18enDdmzJiy77du3ToXH3fccbUl20Q59M14ScPhhx9e9twdO3a42F9CceWVVwbnzZw508XxlJH/O9l/2kG75dI3/ek6qfJ0+oYNG1w8ffp0F3/wgx8MznvggQdcHG9X49cz3iahndPz1dSTK1MAAAAJGEwBAAAkYDAFAACQoGu3RvAfExPz52b9+LDDDqv69T/1qU+5uEhrpnLgPz5Gkl544QUXx7fSL1682MV79+518e7du6t+v2Y8BqPb+WsvLr744rLnxesZb7rpJhffddddLj7rrLOC8yqtmTr66KOrzhMHN3r0aBdXWiMV+6u/+isX33rrrS72195I0r/927+52F/DKklTpkxxcbyeii0w6nPSSSe5uJYtR/w1Tq+99pqLly5dGpznr6uLH+XW19fn4k6r50G/U8aYScaYh40xq4wxK40xXy59vtcY86AxZk3p46iDvRbaj1rmg76ZF2qZD/pm96lm2LlP0jXW2qmSzpA03xgzVdICSUustVMkLSkdo/ioZT7om3mhlvmgb3aZg07zWWs3SdpUincYY56VNEHSHEnnlE67VdJSSV9tSpZN4F8yrPbp8yNGjKj69YcOHVpzTq1grX2y9LFja+nfUi1Jq1evdnG8bYI/nbdnzx4X1/LEc//no0iXnju5b/rft3jK3Z+OjZ9O8PDDDx+wrdK0fcyfOoynjeJbwVupU/vm4MGDqzpv/fr1wfGPf/xjF/s197cwkSr/fn7jjTdcHE9JtbmWHds3/V3rK1mwIBwH+lN7vniqvlI9/d/lcT3j1ymamhagG2P6JE2X9JiksaUfGEnaLGlsmS9DAVHLvFDPfFDLvFDP7lD1AnRjzDBJd0v6irV2e/S8JFtuYzFjzDxJ81ITReNQy7xQz3xQy7xQz+5R1ZUpY0yP+n8gbrPW3lP69BZjzLhS+zhJWw/0tdbam621M6y1MxqRMNJQy7xQz3xQy7xQz+5y0CtTpn8ovVDSs9baG7ymxZKuknRd6eN9TcmwSfz52ErrXvy2Wm4T9Z+MXbC5346v5axZs4Jj/5bcJUuWBG3+mil/DUW8VqYS//8mi7Quo5P75oknnuji+FE+fv9YuXJl0Pbss8+6eOfOnS6uZauLatdItkFH1NL/3SZJjzzySNlz/d+f/hYHUniLvH+ev34qbosNHDiwcrJt0kl9M15n+pOf/KSqr1u0aFFd77d582YX+9tqSOG65KJvhRCrZppvpqQrJD1jjHm69Lmvq/+H4U5jzFxJ6yWV3ywGRUIt80HfzAu1zAd9s8tUczffo5LK/a/cxxqbDpqtwgMbqWWHoW/mhb6ZD/pm9+naHdD9S/2Vpt3iS9rV8nfiLvC0Qkd67LHHgmP/qeTr1q0r+3X+DvbxVEIl/u3X1LIxtmzZ4uJXX301aPMv9f/P//xP0OZvgTBkyBAXb9q0KTiv0tYnv/vd78q2oXbV9qV4ytb/vevXId76xN8RPd69fvny5VXniQPz+5FUfZ+odolDPF33pS99ycX+Eyok6cYbb6zqNWPRwv66XiMVz+YDAABIwGAKAAAgAYMpAACABF27ZqrSvKo//+rPJ9eyNcK//Mu/uLjo2+B3mm3btgXH/q22u3btCtr8x/r4t+BX+wgMSZo/f76La3lsCcrz10mtXbs2aPOfWv+b3/wmaBs3bpyL/dviP/zhDwfn+X0uXtsxc+ZMF1PP2sXfs89//vMufuihh4I2//es308laeTIkS72H/V02WWXBef5a7LWrFkTtM2ZM8fFtWxTUoQ1NkURr1l88cUXXdzX11f26+JHpvmPk/H7X/wYtvHjx7v4L/7iL4K273//+y5u57Yz9eDKFAAAQAIGUwAAAAlMKy9xlnsOUZH5l4MnT54ctP31X/+1i3/1q18FbbfeemtzE6tThb1salKkWvrTr5WmVP1aHn744UHb2Wef7eL4dnz/1uwiaVQtpfbWc9CgQcGxPy3w8ssvB23ltjzo6ekJzhs1apSL42mMWrbFaKVO7Zt+//vWt74VtP3617928b//+78HbeX+9sS35vu1rWV39Hbq5L45bdo0F8fb0Fx//fUuvvbaa+t6/U6cYq2mnlyZAgAASMBgCgAAIAGDKQAAgASsmUpQ7VqdIunUdRl4r05el4H3om/mI5e+GW8H1Cl/5xqNNVMAAABNxmAKAAAgQdfugF6P+JJnp9zWifeKb7+mlgAQ6tZpvXpwZQoAACABgykAAIAEDKYAAAASsGaqBswf54M1UgCARuHKFAAAQAIGUwAAAAlaPc33sqT1ksaU4nbrtjwmN/C1qGV5rcilkbWU+vN9U931PawGfTNdUfKQ6JuNUJR6FqpvtvRxMu5NjXnCWjuj5W9MHg1XlNyLkodUrFxqUaS8i5JLUfKoR1FyL0oeUrFyqUWR8i5KLkXJ411M8wEAACRgMAUAAJCgXYOpm9v0vjHySFeU3IuSh1SsXGpRpLyLkktR8qhHUXIvSh5SsXKpRZHyLkouRclDUpvWTAEAAOSCaT4AAIAELR1MGWPOM8asNsY8b4xZ0OL3vsUYs9UYs8L7XK8x5kFjzJrSx1EtyGOSMeZhY8wqY8xKY8yX25VLCmqZTy0l6ll6zyzqSS3zqaVEPTulli0bTBljBkj6R0mfkDRV0qXGmKmten9JiySdF31ugaQl1topkpaUjpttn6RrrLVTJZ0haX7p+9COXOpCLZ2Or6VEPT0dX09q6XR8LSXqWdIZtbTWtuSfpDMl/cI7/pqkr7Xq/Uvv2SdphXe8WtK4UjxO0upW5lN63/skzS5CLtSy+2pJPfOqJ7XMp5bUs7Nq2cppvgmSfusdbyh9rp3GWms3leLNksa28s2NMX2Spkt6rN251IhaRjq4lhL1fI8Orie1jHRwLSXqGShyLVmAXmL7h7ctu7XRGDNM0t2SvmKt3d7OXHJDLfNCPfNBLfPSyu9h0WvZysHUS5ImeccTS59rpy3GmHGSVPq4tRVvaozpUf8PxW3W2nvamUudqGVJBrWUqKeTQT2pZUkGtZSop0rvU/hatnIwtVzSFGPMMcaYgZI+K2lxC9//QBZLuqoUX6X+udimMsYYSQslPWutvaGduSSglsqmlhL1lJRNPamlsqmlRD07p5YtXjh2vqTnJK2V9I0Wv/ftkjZJ2qv+eee5kkar/y6ANZJ+Jam3BXnMUv/lyP+V9HTp3/ntyIVaUkvqmV89qWU+taSenVNLdkAHAABIwAJ0AACABAymAAAAEjCYAgAASMBgCgAAIAGDKQAAgAQMpgAAABIwmAIAAEjAYAoAACDB/wM/rPrKSxxoFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ed00c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ee69240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f57acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f5f3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126e40080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128285390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136c3f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126062cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ea66390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137177b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126742908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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DtitTNW8ZHszX6qViI0MzkydPVr5LLtm5mtiGUaS2TZs2VT5Z+TjbcEum6eraIHVYtmyZ8smQYxIr9uYD2TfHjdM1COXmsvbzy41mTzvtNOWbOHGit4v93cyePdvbn376qfJ99tln3rbPAhkSLNcwkfwMjz32mPLJjWVtxWwZ3pUbIgPAE0884e18h4F2hdxAW64eBXT4364czvW5njTks8lutH7NNdd424bZ5arct99+W/kmTZrk7ULf5/b977zzTm+ffPLJypeE1dKcmSKEEEIIiQEHU4QQQgghMeBgihBCCCEkBmWRMyXzbBYuXKh8meLwcmn2hx9+qHy55BzZ5d2Zlv1mu4T0wgsvVD4Z+7X5YZkqbJcLMgfmzTffVD6Z72Tz3+SxXLprX5cttYn3Sy2trtu3b/e2zf2RS5NtPlUIWgI6v0QuRQeA22+/3du2pInM/2vYsKHyFaLkQTqsnnKZ+Omnn658ss32HpTvU645U7IfjRkzRvl++ctfertly5bK16BBA2/bciel/C5kiZvLLrtM+WROps3LC0FLQD+bRowYoXynnrpzVxtbZkBWu7d94G9/+5u3i50zJXNs7W9HEgjjiU4IIYQQUiI4mCKEEEIIiYEr5jSmcy72xWx4RC7HltOTgK56a6cyly5dWutr29DTRRdd5O2jjz5a+eQ0udycFdBLTz/55BPla9asmbdtNW9ZAmD69OnKZ6s1pyOKorzUBMiHljbEMnToUG/LzVMBoH379t7u27ev8snl17kiQ01VVVXKt2LFCm/bkEDz5s29bSvvyuXY9nUyZDxr1izlK7aWQH70tPTp08fbcik2ABx11FHefvnll5XPbpicC/I5YctsSC3s80+ea8ON8r6wzwJZ+kGWUADKs29aTjnlFG/L8C0AdOnSxdvz5s1Tvv/4j//wdrFDZplSLWQ5AFu1vV69et5evny58oXSNzt06ODtu+66S/lkCNA+0+TzrtilLjIh+5/tm7KUh/082ZZeyUZPzkwRQgghhMSAgylCCCGEkBhwMEUIIYQQEoOyKI0gsXH3+fPne9vmRcn49qpVq2Jf2+ZeDBs2zNs2Pi+XkH788cfKJ3Nw5LYGgF7OK2PXgP7sxVw+XiislnIbkXPOOSft62y+WC5Yvfr37+9tuQM5AAwePNjbtjyGzGuTS5EBYMmSJd4+77zzlE/m0dn7IxTk1hMDBw5UPpnTJO/5fCHzJOxWEzJvwt6DmbYSkeVUbEkTmbPx4IMP1q6xZYAs01K/fn3lk1raMiX5zpOy/TbT+2fyyfexOXoyn0rmcYbEokWLvG2fd7K/2G2gkpQnJZFbAv3nf/6n8h100EHefvjhh5Uvn9tVcWaKEEIIISQGHEwRQgghhMSg7MN8o0eP9vbBBx+sfD179vS2rEYNZL/EVWJ3ipdLMG2FZxkCtDtcy+XDLVq0UD45xSyX6AKZK4SHEPaTO5TLMCkAXHHFFd5u27at8s2ZM6fW17Lfn1webUM4Mlz16quvKp/UxJZUkJWiDznkEOWTetld60PQEtDhbFv+4Le//a2327Rpk/dry3CdfS5s2rTJ2/a7ls8XGSoE9H0nywEAOhzy6KOPpm1LuSIrvttQiQxr2udsvquJ5/oeNtQrS9nYMJ98ztvK4TbMX67Iz3H//fcrn7y3bWqLDOmWMuRnSyTJNsu0DMvzzz+vjm3Zolhtyts7EUIIIYRUIBxMEUIIIYTEgIMpQgghhJAYlF3OlF0ae+KJJ3pbblEB6HhoPmKjMp8J0PF7G8v//PPPvW133j777LO9bWP5Ms/E5nNMmzbN2yHkYVjk7vOZylzI7yhX7H0kc59siQOpbevWrZVPamtz4zZv3uxtm28nc69yyd8rB2Reg11OLz/z+vXr835tqa9d/ixz1GzunNR30KBByieXkNvP85e//MXbsoRCiNi8oUzLy+U9UOhcQNunZc7pU089pXzV1dXetrlxH3zwgbdlfl2orF27Vh3LXCibzymPi/3ckn3V6nnWWWd525ZzkOWT8lEiKR27nJlyzrVwzk12zs11zs1xzv069fcGzrmJzrmFqX/r7+q9SOmhluHAvhkW1DIc2Dcrj2zCfN8BuDGKomoAJwDo55yrBjAAwKQoitoBmJQ6JsmHWoYD+2ZYUMtwYN+sMHYZ5ouiaDWA1Sl7q3NuHoBmAHoC6Jo67UkAUwDcVJBWZkBOI9vlknLJfD6mmGXYBtChoa1btyqf3F37N7/5jfL9/e9/9/YzzzyjfDIc2apVK+WbPXu2t3NdIhxF0UepfxOnpfw+7Xe9cOFCby9dujT2tez9sGbNGm+/9NJLyierl19zzTXKt3jxYm/LKtGADvU2bdpU+d555x1vx9Ay0X1Tfsfvvfee8rVv397bTz/9tPLlYzm9fBZs3LhR+Y477jhvn3vuucqXKcQxY8YMb9uw7ZgxY7wdYt+Un2nChAnK98Ybb3j7tddeUz75HebjGWxDeVKH888/X/lk+QP7LJU7T9jfjZtvvtnbuS7/T3rflLz//vvqeOrUqd62oTxZ+qIQYT6pry0n0717d2+fdNJJyidDlTZ0f/3113u7kOUcapWA7pxrBeAYAO8DaJK6YQBgDYAmeW0ZKSjUMiyoZzhQy7CgnpVB1gnozrn9ALwMoH8URVvM/z1Gzrka/3fMOdcXQN+4DSX5g1qGBfUMB2oZFtSzcshqZso5tyd23BDPRFE0NvXntc65qpS/CsC6ml4bRdGIKIo6RlHUMR8NJvGglmFBPcOBWoYF9aws3K7i+27HUPpJABujKOov/n43gC+iKLrDOTcAQIMoin63i/fK7xbi0MsgbTxULtnNR6y0YcOG6ljmenTr1k35ZNzWfscy1rxgwQLlk/lUAwcOVL7PPvss7XvWgqeRUC0z5b9J7GfPJRfDlqTo2rWrt5944gnls9uRSOR9ZfPmZA5V3776fzLlru0x7s3dkOC+ad5fHUt9rRZy6X2297l9f1nG4N5771U+uTVRpvvMlh+RffPSSy9VPpmDE0PPxPbNTMjvsHHjxson89VsCYVsvyf5LO3YUY8t3nrrLW/b+0hi76OVK1d6224fNX369Fq3sQbKpm9aZJ6bzFMCdNmIDRs2KF+25XpkX61bt67yLVmyxNv2XsqE3O7o4osvVj6Z0xcjB87t6pxswnwnArgMwKfOue9/HQYBuAPAC865PgCWAbgop1aSYkMtw4F9MyyoZTiwb1YY2azmmwog3ajs1Pw2hxSaDCNsallmsG+GBftmOLBvVh5lVwHdUsxdvL/88kt1/Kc//cnbdhd5uYTUhn/kNPIjjzyifMuWLfO2DOsB+dl1PcnIKdhC70huQw7ye5dlEgDgoIMO8rYs32DP/b//+z/lmzx5srdlWA8o7Y7rpSBTaLY2FcNtOO97bLhOhg9sGQN5bRsi3rZtm7dleAAAnnzySW/LsB5QeXpK5Ge3fUditZNhOdkf7XmNGjXydq9evZQvU4V1mU4xb9485bv77ru9LZ/HQGVrCWgt5E4NAFCnTh1vy984QO8Qsm5djalgAIAWLVp426Y/yFQa+8yQ7bIhxltvvdXbtt8WS0/uzUcIIYQQEgMOpgghhBBCYsDBFCGEEEJIDHZZGiGvFyvyEs9C06FDB29feeWVynfGGWd4W+ZWAcBdd93lbbmk05JpN/ZcyWaJZzaUo5bp8m0AnZdhl1///ve/9/asWbOUb8iQId5evny58slYfbbLhmtDvrQEylNPuYTbPsfksmq7LcXw4cO9vX79euWTWstl4IDOwWHfjI/UT+a12e+2TZs23rbbMo0aNcrbdnuTBx54wNtPPfWU8sncOPbN3LDbLckte+R2araPderUydv2u5ea2T79+uuve3vw4MHKJ7fuKpWenJkihBBCCIkBB1OEEEIIITFgmC9P2J2qq6qqvG2nOeV0dLHLHVRaKCEX7DJ7GTLavHmz8slSCcVeUl0JoYR8YPVs0mTn3rI2zC5LreRSWT8O7Ju7xj5n27Vr521ZbR3QfdWWNCk07JvZseeee6rj448/3tv2d3PVqlXetuWGCg3DfIQQQgghBYaDKUIIIYSQGHAwRQghhBASA+ZMVRjMywgH5mWEBftmOLBvhgVzpgghhBBCCgwHU4QQQgghMeBgihBCCCEkBhxMEUIIIYTEgIMpQgghhJAYcDBFCCGEEBIDDqYIIYQQQmLAwRQhhBBCSAw4mCKEEEIIicEeRb7eBgDLADRK2aWm0trRMo/vRS3TU4y25FNLYEd7t6OyvsNsYN+MT1LaAbBv5oOk6JmovlnU7WT8RZ37IIqijkW/MNuRd5LS9qS0A0hWW2pDktqdlLYkpR25kJS2J6UdQLLaUhuS1O6ktCUp7fgehvkIIYQQQmLAwRQhhBBCSAxKNZgaUaLrWtiO+CSl7UlpB5CsttSGJLU7KW1JSjtyISltT0o7gGS1pTYkqd1JaUtS2gGgRDlThBBCCCGhwDAfIYQQQkgMijqYcs6d6Zyb75xb5JwbUORrj3LOrXPOzRZ/a+Ccm+icW5j6t34R2tHCOTfZOTfXOTfHOffrUrUlDtQyHC0B6pm6ZhB6UstwtASoZ7loWbTBlHNudwAPAegOoBrAJc656mJdH8BoAGeavw0AMCmKonYAJqWOC813AG6MoqgawAkA+qW+h1K0JSeopafstQSop6Ds9aSWnrLXEqCeKcpDyyiKivIfgM4A3hDHAwEMLNb1U9dsBWC2OJ4PoCplVwGYX8z2pK47DsDpSWgLtaw8LalnWHpSy3C0pJ7lpWUxw3zNACwXxytSfyslTaIoWp2y1wBoUsyLO+daATgGwPulbkstoZaGMtYSoJ4/oIz1pJaGMtYSoJ6KJGvJBPQU0Y7hbdGWNjrn9gPwMoD+URRtKWVbQoNahgX1DAdqGRbF/A6TrmUxB1MrAbQQx81Tfysla51zVQCQ+nddMS7qnNsTO26KZ6IoGlvKtuQItUwRgJYA9fQEoCe1TBGAlgD1ROo6ideymIOpGQDaOedaO+f2AnAxgPFFvH5NjAfQO2X3xo5YbEFxzjkAIwHMi6LovlK2JQbUEsFoCVBPAMHoSS0RjJYA9SwfLYucONYDwAIAiwHcXORrjwGwGsC32BF37gOgIXasAlgI4K8AGhShHSdhx3TkLAAzU//1KEVbqCW1pJ7h6Uktw9GSepaPlqyATgghhBASAyagE0IIIYTEgIMpQgghhJAYcDBFCCGEEBIDDqYIIYQQQmLAwRQhhBBCSAw4mCKEEEIIiQEHU4QQQgghMeBgihBCCCEkBhxMEUIIIYTEgIMpQgghhJAYcDBFCCGEEBIDDqYIIYQQQmLAwRQhhBBCSAw4mCKEEEIIiQEHU4QQQgghMeBgihBCCCEkBhxMEUIIIYTEgIMpQgghhJAYcDBFCCGEEBIDDqYIIYQQQmLAwRQhhBBCSAw4mCKEEEIIiQEHU4QQQgghMeBgihBCCCEkBhxMEUIIIYTEgIMpQgghhJAYcDBFCCGEEBIDDqYIIYQQQmLAwRQhhBBCSAw4mCKEEEIIiQEHU4QQQgghMeBgihBCCCEkBhxMEUIIIYTEgIMpQgghhJAYxBpMOefOdM7Nd84tcs4NyFejSGmgnuFALcOCeoYDtQwTF0VRbi90bncACwCcDmAFgBkALomiaG7+mkeKBfUMB2oZFtQzHKhluOwR47WdACyKougzAHDOPQegJ4C0N4VzLreRG8kbURS5NK5a6UktS0++tEydQz1LDPtmOLBvhkUGPT1xwnzNACwXxytSf1M45/o65z5wzn0Q41qk8OxST2pZNrBvhgX7ZjiwbwZKnJmprIiiaASAEQBH2OUOtQwL6hkO1DIsqGf5EWdmaiWAFuK4eepvpDyhnuFALcOCeoYDtQyUOIOpGQDaOedaO+f2AnAxgPH5aRYpAdQzHKhlWFDPcKCWgZJzmC+Kou+cc9cCeAPA7gBGRVE0J28tI0WFeoYDtQwL6hkO1DJcci6NkNPFGPstOdmsSsgGall68qUlQD2TAPtmOLBvhkWhV/MRQgghhFQ8HEwRQgghhMSAgylCCCGEkBhwMEUIIYQQEgMOpgghhBBCYsDBFCGEEEJIDAq+nUyhcS79ikWW1vBJAAAacUlEQVTp23PPPZXvnXfe8fZhhx2mfPPmzfP25s2bvf3LX/5SnbfbbjvHohs2bFC+bdu2ZWo2qQGpl9VVHu++++7K9/rrr3v78MMPVz6p5f777+/tIUOGqPNOP/10by9evFj5HnnkEW//85//TP8BiCLbvmnPe+ihh7x95JFHKt/GjRu9LbWeOHGiOu+8887z9rp165SvX79+3p4+fbry/fvf/07b5komk5aZzvuv//ovb7du3Vr56tWr5+3TTjvN21JjAPjRj37kbfk8BoChQ4d6+/7771c+9tX05KrnBRdc4O19991X+Vq02FnY/bLLLvN2nTp11HnNmu3citD+bt57773efvDBB5Xv66+/zqrNpYIzU4QQQgghMeBgihBCCCEkBmVfAX2PPXZGKvfZZx/la968ube7d++ufIMHD/a2DP8AwBdffOHtunXretuGAGSYb82aNconwxPffPNN+g9QZJJcZVmG7/bee2/lO+igg7x93HHHKd+wYcO83ahRI+Xbvn27t/fbbz9vZwojWp0vvfRSb7/88svK969//QulIulVlmX/sGF2qUXbtm2V78knn/T2IYcconzy+5ZhhmzDFgDw5ptvevvcc89Vvi1btmT9PvmmXPqmDbPLMI7spwAwcuRIb3fo0EH5ZB+X90NtkCH5Hj16KN+CBQtyes98kPS+mUnPvfbay9v169dXvtGjR3u7urpa+WTY1v4Wp8OOPz788ENvX3311Wl9xYYV0AkhhBBCCgwHU4QQQgghMeBgihBCCCEkBmVXGsHmRsg4bZs2bZRPLpfetGmT8slcKPuen3zyibflMs4GDRqo82R+wKGHHqp8p556qrfl0n3gh3HiSsV+7zJvwn6fEyZM8PaKFSuU78ADD/S2jf9v3bq1Rp9dritzeux7ZMqZIjuxeso+VlVVpXyTJ0/29vLly5WvZcuW3rY6yeXRMrfNXlvma1nk/cLl8zVjv89MeTR//etfvb106VLl69Spk7fl/QDo/LdMz8RM+Yyyf9vnAtmJ7Q8y19jmDMu+afXs0qWLt21eq9QmWz1tCaFPP/3U23PmzEn7HkmEM1OEEEIIITHgYIoQQgghJAZlF+az0/733Xeft20lc7l0ev369cr38ccfp33P3r17e1tOaffq1UudN2DAgLTtvOaaa7z97rvvKp+t4lupyNABAPz+97/39kknnaR8cirafn/Tpk3ztgwRAUD//v29LZfZn3322eq8M844I207Dz74YG/bpd+2JEYlY/vRjTfe6G1bmkR+j/Pnz1c+GWbPFO6VffOAAw5Q53Xu3DltO2W5DBnGB4DPPvss7esqCRuSu+qqq7zdrVs35ZN97vPPP1c+WbrA9s1ly5bVeG1bsb5r167e/vbbb5Vv9erV3pb9FAAWLVpU4/tXIrZUwTnnnONt++xr1aqVt2WZIEBXLLdlaNauXettWcXe7k7Qp08fb8+cOVP5ZGivcePGymfvraTBmSlCCCGEkBhwMEUIIYQQEgMOpgghhBBCYlD2OVNymWWTJk2U78svv/T21KlTlW/48OHetss/v/rqK2/LuK1d7vndd99528byZTmEhx9+WPkGDhzo7aTHgQuJXVorS0/Y/Aep5aRJk5Tvtdde87bdhVxu5SPj+DYXQG5RY3M2br/9dm8/8cQTyifz6+zrKg2bAye3VDriiCOUT+Ytvf/++8onl9rbkiZST5kHYs+78847vW1zNmbMmOFtuXUNoHPpKjm30Wp54okn1mgDwD/+8Q9vWy1lbqrNL5RlLmT/lrk3gL6PFi5cqHyynMr48eOV7yc/+UmNbaxEbLkXmZNq80dluYK3335b+WR/mTdvnvLJ56ssUyF/TwHg5ptv9rb8DQX0b4LspwBwwgkneFs+P5ICZ6YIIYQQQmLAwRQhhBBCSAzKLswnp4YBHeazU9OzZ8/29rBhw9L67FSjfE953sqVK9V5sqrsSy+9pHxyifVtt92mfD//+c+93bx5c+WrpKX2tvq0rEIuK/QCevn8c889p3zyu7ZaSuQyajvt/eabb3rbhgDl1PPzzz+vfPKesGFLWeG5EpAhOEB/H/b7lvf5iy++qHyZ+ma2XHjhhd624XkZHjz++OOVT2pv78FKwj5npX5WSxnesaE2WeYiVy0/+OCDtD4ZQjr66KOVb8uWLd6uZC2BH4ba5LHVU4a3X3nlFeXLh56ZXifTZWR4F9DV7jPtcFAqktciQgghhJAyYpeDKefcKOfcOufcbPG3Bs65ic65hal/62d6D5IcqGc4UMuwoJ7hQC0rj2xmpkYDONP8bQCASVEUtQMwKXVMyoPRoJ6hMBrUMiRGg3qGwmhQy4pil4HkKIrecs61Mn/uCaBryn4SwBQAN+WxXQqZw2S3l/jpT3/qbbujuCxV//e//135MsVtZY6FzHuxuTSZtpORbbbxXRmjHjdunPLZHI58U2o95Xfx4x//WPnkcl2bTyWXy9stP7KN3Utd7WtWrVqV9nUyjm/z8qSW119/vfINHTo0q3blSqm1BPR9LpcuA8Cxxx7rbavnG2+84W2ZIwXknoshybRrvcwXsbk08vPI9gPARx99FLtdmSi1nvKzd+zYUfnkd2HLwEyZMsXbMqcGyI+WmZBtyZRHY7eBstuL5ZtSawloPe1Waz/72c+8bTWaPn26t4utp/wNl+232O2jklDGJNecqSZRFH2fzbsGQJNMJ5PEQz3DgVqGBfUMB2oZMLGXOERRFDnn0v5voHOuL4C+ca9DikMmPallecG+GRbsm+HAvhkeuQ6m1jrnqqIoWu2cqwKQtvRzFEUjAIwAgEw3TybkdJ8N5cmq03Zq/5577vG2DTNkez35nplCBxZ57oMPPqh8gwcP9rbdebtEZKVnPrSUU/FWE1m6wFYsvvvuu71d6Klmi7znFixYoHzt27f39uGHH160NmWgqH1Thjlt+EdWQbZ9J1MV5EIj22KXjMuyCdXV1cpX6DBfGorWN6WW9jkrtZTVygHguuuu83YptbT3WKb0kEKH+dJQst9NG86WZUxs+krfvjvHcKXUMxNWzxL1TUWuYb7xAL7fR6M3gHEZziXJh3qGA7UMC+oZDtQyYLIpjTAGwHsADnfOrXDO9QFwB4DTnXMLAZyWOiZlAPUMB2oZFtQzHKhl5ZHNar5L0rhOzXNbSBGgnuFALcOCeoYDtaw8XG3ygGJfLMfYb926db09evRo5evZs6e35fYBAHDNNdd4e+zYscpncwIKyRFHHKGO58yZ422bNyRzNgpBFEXp15vWgly1rFevnrdfeOEF5Tv55JO9LXPhAL2zuV1KX0zuuusudfzf//3f3rZtbtKksIt18qUlkL2edrnygQce6O1nn31W+WSpC1t64tRTd/6myHycYmPLbLRu3drbS5cuTesrBMXum1bL+vV31pAcNWqU8skSNHbLK9lv165dm31D84zdzkhuTzV37lzls1uV5Jsk9M3999/f2yNHjlS+M8/cWQLL5sAdd9xx3i7l9maZxiay7BEAHHPMMYVuyy715HYyhBBCCCEx4GCKEEIIISQGZbGVduPGjb0tK7da7LT8tm3bCtWkWrFs2TJ1LEMedlmqXJ4sq6+Hggx5du7cWfnkNPWSJUuUz+5iXyqGDx+ujvv16+dtG0qoBGRIwFZAl2Uwli9frny2jEKpGDJkiDoeMWKEt+3zJHRk1fNMWtrQT1KeU5MnT1bH3bp187YN51YCcocJGYoF9O+MTY8pZupPJmzahBwHJPFZy5kpQgghhJAYcDBFCCGEEBKDsgjzbdq0ydt2lcl5553nbblSDNAhGLmCDvhh2KGQ2FUmf/jDH7xtp1T3228/bydh88Z8s3jxYm//+c9/Vj5ZcXrfffdVvl/96lfelhXkgR9WSy8kduXZq6++6u233npL+WTV4WJXEi4U9n6VG6FOmzZN+Ro0aOBt2zf79+/v7VtvvVX5arNbQVyef/55dTxo0CBvT506VflkqKuYq4ELhdVSrpK1G8PLDb5t37zlllu8/bvf/U757LOvkFx11VXqWH6eTz/9tGjtKBVWz/nz53vbhsxkeondIPp///d/vX311VcrXzH17NWrlzp+7bXXvG0/TxLgzBQhhBBCSAw4mCKEEEIIiQEHU4QQQgghMSiLCuiShg0bqmNZEf2www5TvqZNm3p7wIAByieXuBd7Kais6G6r1spcjEKUAyh1BXT5eVu2bKl8Dz/8sLfbtGmjfFL3yy+/XPlef/11bxdbS1kB3ObRyNyfQuQBlaLKcg2v87Zcig0ADzzwgLebNWumfLI6c+/evZVv/PjxuTQlL7Rt29bbVjOZp5FkPXPVUubO9OjRQ/nuueceb9v8N7lrg82xee6553JpSl6QVdvXr1+vfDKfqBBlOpLQN6We1113nfLJ30NZJgEA6tSp422Z2wjo39tiP2v79OnjbVvq4u233/Z2IfJTWQGdEEIIIaTAcDBFCCGEEBKDsgvzWVq1auXt22+/XfnkJsi2Gnrz5s29naRlznvvvbe3bbvk9KstB5BtFeJShxIyIUN7tjK1rGZsl+fKEFJStbShBLkJq52WznaaOgmhhEzIUheyHAjwwwrbEllSISmV0gEdnrdhPlkGw96DxdazEFrK6uiPPvqo8kmd7TJ7GQb/6quv8t2snJF902opQ9c2DaPYz9lUG/Kup9zoeOjQoconN/S2IUC5efvGjRvz3ayssbrI0h210TPb3wuG+QghhBBCCgwHU4QQQgghMeBgihBCCCEkBmWxnUwmMm0LI7dmkTkqQLJya9LRqVMndSzj1RMmTFC+rVu3FqVNhWTp0qXe3rBhg/LJPBqbt5CUXc4t8h6zZSAaNWrk7YULFyrfl19+6e2kfrZskFuS2O08TjvtNG/bz5htXkqxkblPsi8CQP369b1tc0lkSYVyeO7UxIcffujtKVOmKN+xxx6b9nXloKUs0wHo7XKsXrLEQrlqCejfD7lNCwDccMMNaV+XlM9snxlST5kPB+hcR5nbCOhtdeJ+Ns5MEUIIIYTEgIMpQgghhJAYlH1pBImdbpZT03a6WS6lTMrUJQCcdNJJ3n766aeVTy4Tv+iii5Tvk08+8XYmTZO8/Fpy6KGHquNFixalPVdO3SYprHD88cd7+6WXXlI+2U5bnf/FF1+s8TxL0pdfSw4++GB1vHLlyrTnyhIgxdylflfIcg5jx45VPqnTE088oXx33HGHt21JE0m59E1Z7gAANm3alPZcWS09SakIHTp08PYrr7yifDJkJHdXAIDbbrvN25k+dzn1TRvm3LJlS9pzGzdu7G1bVb6UyPIcTz31VNrzbLrBzTff7O1Vq1alfR1LIxBCCCGEFBgOpgghhBBCYsDBFCGEEEJIDMq+NILExjxlLlSm8vNff/11YRuWgcGDB6tjmT8j2wgACxYs8Pbs2bOVr5yX0NfE2rVr1bH8fFZLueVBKXOmzjnnHHX83HPPedsu112zZo23x40bp3xJyvvKF3IJcrkgc94AYPLkyd6WeV2AzjMZPny48pXy+VII7NZcmbBbe5SKI444Qh3PmDHD21ZLmdsl+zCQOeetXKnNNj+Z8sSKSdu2bdXxxx9/7G1b/mD79u3efuihh9L64rLLmSnnXAvn3GTn3Fzn3Bzn3K9Tf2/gnJvonFuY+rf+rt6LlB5qGQ7sm2FBLcOBfbPyyCbM9x2AG6MoqgZwAoB+zrlqAAMATIqiqB2ASaljknyoZTiwb4YFtQwH9s0KY5dhviiKVgNYnbK3OufmAWgGoCeArqnTngQwBcBNBWllltjQ0LvvvuvtFi1aKJ+dCiwkdhpZ7sB+9dVXK9/mzZu9bcN8chlnrqGgKIo+Sv2baC1tKEFO47Zr1075ilzeQx1XVVV5+4EHHlA+OYVsX/f44497W5a8qA3l1DdtuEdWR7d6yqXpxUbumjB69Gjlk6E8WfEcAN577z1v5xo6KJe+afWRu1A0b95c+XK9t/OB3PXihRdeUD65y4DcjQAAPv/8c2/bEO1uu2WXZlxOfdPqKUPyDRs2zHhuMZHf/Z/+9Cflk33ugAMOUD75eWRKCKD7u/ztzal9tTnZOdcKwDEA3gfQJHXDAMAaAE3SvIwkEGoZFtQzHKhlWFDPyiDr6Rnn3H4AXgbQP4qiLfL/tKMoitIVFnPO9QXQN25DSf6glmFBPcOBWoYF9awcspqZcs7tiR03xDNRFH1f+netc64q5a8CsK6m10ZRNCKKoo5RFHWsyU+KC7UMC+oZDtQyLKhnZbHLmSm3Yyg9EsC8KIruE67xAHoDuCP177gaXl5UbO6M3JlebukA6LiqXRqaSz6SjcVOmzbN2z/+8Y+Vz+bPSGSMftCgQco3fvx4b8fIEyoLLS1ym52//OUvyie3Q9i4cWPsa1l95FL3Pn36KJ+M49vXyTyhJ598UvmGDBni7VzzEMqpb1qknvPmzVM+WUaiEEvRr732Wm/fe++9yifzbKyesm9OmTJF+S6++GJvx8i9KEste/To4e2PPvpI+WT/KETJj65du3r7j3/8o/LJfBibIyvvK7ntGAD06tXL20uWLFG+bD9DOffNG264wds2b7DQyJw7+5yXuZU2D1nqOWfOHOXr23fnBN/MmTOVL5+lO7IJ850I4DIAnzrnvm/JIOy4GV5wzvUBsAzARWleT5IFtQwH9s2woJbhwL5ZYWSzmm8qgHRTKafmtzmk0GTYsJFalhnsm2HBvhkO7JuVhyvysvKSlem2ZQZatWrlbbs0Vk7vz50719t2qviMM87w9kUX6f/BOOuss2p8P0BPFduQwC233OLtxx57LO3rcqVcdqbPhCxHAACnnHKKt23ITC5nlzuG77vvvuq8q666ytsydAAAxx13XNq2yOtZLeV0+bPPPqt8SdISKL6esk9cfvnlyif7owyXA/o7ljse2FIFt956q7c7deqkfDJcYJ9/ctrf6nn99dd724aU8rFkvFz7pgzL2tDMSy+95G1bTVx+17KEQr169dR5w4YN87YsKwPoSthyxwtAL5e34WIZ6pXpEwDwzTffIC7l3DcPOeQQb8tK8QBwxRVXePuNN95QvnRjibp166rjhx9+2NtHHXWU8sljq6esTG+fn9ddd523bd/Mxw4E2ejJvfkIIYQQQmLAwRQhhBBCSAw4mCKEEEIIiUHF5ExZZCkDu9WMjL/K/By7xUn79u29PWvWLOWTeSA29jt16lRv33ST3klAbrNRiNL95ZqXkQm55cEvfvEL5fvNb37jbZknZbe5kEviZZ4cAHTu3NnbNv4+YcIEb99+++3KJ5foFmJZeDnnZUjsFh1dunTxdr9+/ZSvZ8+e3pZ92H6/MidL5lYBOidE5tQBuoTFyJEjlU/2zSTrWUotGzdurI779+/vbZt7ePLJJ3tb3gP2eSl969evV76DDjrI2xs2bFA+mWtlc7lk/mSStQRKm88oc0kB4IILLvC23KYFAM4//3xvy/ximzMs9Vy3TpfZks9y22/ldl0ffPCB8snf1FLpyZkpQgghhJAYcDBFCCGEEBKDig3zZUu2u4Tb0gtHH320t+UO5QCwYsUKb9vQYaEJIZRQG6R+zZo187Zd9i6XQ9tp6UMPPdTbVks5TZ2PJdW1oZxDCdli+1/Tpk29/atf/crb48bpQtLLli3ztt3hQL6HDVXI+8KGmwpNiH1TPhftM/K8887ztkytePzxx9V5b7/9trflsxPQVc7l0nlAl0Mo5u9c6npB9E27s4d8htq+KVNWLrzwQm9PnDhRnTdq1Chvy/CcvZ5Nxcj0fC20vgzzEUIIIYQUGA6mCCGEEEJiwMEUIYQQQkgMmDNVYYSYl1GphJKXQXbAvhkO7JthwZwpQgghhJACw8EUIYQQQkgMOJgihBBCCIkBB1OEEEIIITHgYIoQQgghJAYcTBFCCCGExICDKUIIIYSQGHAwRQghhBASAw6mCCGEEEJisEeRr7cBwDIAjVJ2qam0drTM43tRy/QUoy351BLY0d7tqKzvMBvYN+OTlHYA7Jv5ICl6JqpvFnU7GX9R5z6Ioqhj0S/MduSdpLQ9Ke0AktWW2pCkdielLUlpRy4kpe1JaQeQrLbUhiS1OyltSUo7vodhPkIIIYSQGHAwRQghhBASg1INpkaU6LoWtiM+SWl7UtoBJKsttSFJ7U5KW5LSjlxIStuT0g4gWW2pDUlqd1LakpR2AChRzhQhhBBCSCgwzEcIIYQQEoOiDqacc2c65+Y75xY55wYU+dqjnHPrnHOzxd8aOOcmOucWpv6tX4R2tHDOTXbOzXXOzXHO/bpUbYkDtQxHS4B6pq4ZhJ7UMhwtAepZLloWbTDlnNsdwEMAugOoBnCJc666WNcHMBrAmeZvAwBMiqKoHYBJqeNC8x2AG6MoqgZwAoB+qe+hFG3JCWrpKXstAeopKHs9qaWn7LUEqGeK8tAyiqKi/AegM4A3xPFAAAOLdf3UNVsBmC2O5wOoStlVAOYXsz2p644DcHoS2kItK09L6hmWntQyHC2pZ3lpWcwwXzMAy8XxitTfSkmTKIpWp+w1AJoU8+LOuVYAjgHwfqnbUkuopaGMtQSo5w8oYz2ppaGMtQSopyLJWjIBPUW0Y3hbtKWNzrn9ALwMoH8URVtK2ZbQoJZhQT3DgVqGRTG/w6RrWczB1EoALcRx89TfSsla51wVAKT+XVeMizrn9sSOm+KZKIrGlrItOUItUwSgJUA9PQHoSS1TBKAlQD2Ruk7itSzmYGoGgHbOudbOub0AXAxgfBGvXxPjAfRO2b2xIxZbUJxzDsBIAPOiKLqvlG2JAbVEMFoC1BNAMHpSSwSjJUA9y0fLIieO9QCwAMBiADcX+dpjAKwG8C12xJ37AGiIHasAFgL4K4AGRWjHSdgxHTkLwMzUfz1K0RZqSS2pZ3h6UstwtKSe5aMlK6ATQgghhMSACeiEEEIIITHgYIoQQgghJAYcTBFCCCGExICDKUIIIYSQGHAwRQghhBASAw6mCCGEEEJiwMEUIYQQQkgMOJgihBBCCInB/wMsLp1hnBUrfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ed67da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f058748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1344f7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e8d3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13dff9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e126be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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lzqfdySCLpXpsV63dwUQKP0Pi7uT43GbgyRQAAEAKNKYAAABSoDEFAACQQsuPmbJL38f9u1b//v0bUR3PjgOxYzQkaeLEiT4eOHBgUGa30nj11VeDMjtuqIhjpuw2MXabllg8XTdrNpf/93//F5T9z//8j49PP/30oKzR9cwbO2ZxwIABZc+Lx9VkvdSFzeeUKVOCMrsNlN0mRZK23357H8f3Zkdy0UUXBcfx1HrLLvWSBfteuf7664Myu4RKvJ2MPbejL3kRb90Vj6GyZs+enWld7HIIdtkLKcyn3Y5K+vySNc3AkykAAIAUaEwBAACk0PLdfC+99JKPP/7446DMdh9UenSZtfgxsp1WHa8Mbad/xt1Gt99+u4/tauEbukYrsssFVMpl3K3QyH97fK2//OUvPt5xxx2DMtsF/cADDwRldgXfrLu1msW+Vnntlo5f+0WLFvn4r//6r4Oy8847z8eXXHJJUPb+++9nULt8qjScIhbfx1mKV8xevny5j+NlS3r37u1ju/RJRxQvW1JJpaWI6i1eemHp0qU+3mWXXYKy6dOn+7hZvwt5MgUAAJACjSkAAIAUaEwBAACk0PJjplavXu3jeNq6XXagPf389Wa3HJGkn/3sZz7u27dvUGb7e7fZZpugrJn/hkawYx5mzpwZlO27776Nrs4GjRs3Lji208R32GGHoMyOjTvggAOCMjuGqiOMmXrkkUeCsq985StVfV8W7H10zTXXBGXHHnusj+NxlkOHDvXxzjvvHJTNmzfPx0UYv1jJo48+GhwfdNBBTaqJ1LlzZx9fd911QZkdw2i3KZHCLVNWrFgRlOV1fF9W2jMOKuv3tl3y4Pjjjw/K7HI5I0aMCMrsEjXNGr+40SdTzrn+zrlpzrnZzrlZzrnzSl/v6Zyb4pxbUPq7sRumoSbksji4N4uFXBYH92bHU00330eSLkiSZLCkkZLGOecGSxovaWqSJAMlTS0dI//IZXFwbxYLuSwO7s0OZqPdfEmSLJG0pBSvds7NkdRX0nGSDi2d9gtJ0yV9J5NaVq6fj0888cSg7Jlnnin7fXaqfRaPde1j5LiLyq70HT82tdN54y4Iu0N6rY9bkyR5pvR37nJpnXzyycGx7UaptDN9Frncc889ffzFL34xKKt0veeee87Hd9xxR9XfV62835vWmDFjgmO7YnG8ArrtXouXAKlF/H455JBDfByvpPzuu+/6uGfPnkHZgw8+6GN7n0r16f5olXvzX//1X4PjsWPH+rhr165Bmc1lPbpf4vfKt771LR937949KLPd7FtuuWVQZneayEIr3ZvxZ9OVV17p43jogn0da10mwd6P3bp1C8rs77y4m33VqlU+Hjx4cFBWaRX+RmlXDZxzAyQNkfSEpF6lN4wkLZXUq641Q6bIZbGQz+Igl8VCPjuGqgegO+e6SrpT0vlJkqyyrcskSRLn3Ab/a+acGytp7IbK0BzksljIZ3GQy2Ihnx1HVU+mnHOdtP4NcXOSJHeVvrzMOddWKm+TtHxD35skycQkSYYlSTKsHhVGOuSyWMhncZDLYiGfHctGn0y59U3pGyTNSZJkgimaLGm0pCtLf9+dSQ3bwe74LoVT7eP+17POOsvH//Vf/1XT9eyYigsuuCAoO/fcc3381ltvlf2+eFq8nf656aabBmV1mkLfErm02wRJ4dTm7bffPigbP/6zMZz/9m//VtP17FT9M888Myg76qijfLxmzZqgzI7hiLfOsONH4u+rxxibVro333jjjeDYTq8/9NBDg7Kf/vSnPv6nf/qnoKzc62bvG0m65ZZbfByPi9p99919HI9ds3mKtyex40cymn7dErm048ok6V/+5V98/JOf/CQou/766318xhlnBGU2lzbeaaedgvMefvhhH9ulEKTwszR+b7z99ts+tuNtJKlHj88m0WWxNEkr3Zvxe/mEE07w8UMPPRSU3XTTTT6O82lfR/u5GJ/3wx/+0MfxWCeb3zifdpzliy++WPb77Fi5Rqqmm+9ASadJetE59+mI2ku0/s1wm3NujKRXJJ1Y5vuRL+SyOLg3i4VcFgf3ZgdTzWy+RySVWy3y8PpWB1lLkoRcFgT3ZrFwbxYH92bH4xq5Wm+5wXZZsV0J+++/f1BmHwWOHDkyKFu8eLGP7ePheNrvk08+6eO4u8DuxL1gwYKgzD5+HjRoUFBmpx3/+te/DsribodaVPjAbpdG59JO3/3a174WlNnX5cc//nFQZrttXnnlFR/vt99+wXlnn322j+2q+lLYRWwfNUvSU0895eN4VexzzjnHx7Nnzw7K6jSVvm5L4jc6n/Z9fumllwZltrt0zpw5QZndFcA+2rfLjUhSly5dfLxu3bqgzHafxytj33rrrWV/pu1OtvdwvbTqvWnvx9tvvz0os+/zuHvQ5s9208ZLWdjj+HPWlsVT9e00+/nz5wdl9rO1HstvxFr53rS7Ndgu1lj8+8jmsNqlCuIuVvt+ibvr7O4T9nevFOY3i27bavLZ/MUZAAAAWhiNKQAAgBRoTAEAAKRQ6DFTdgr0zJkzgzI7VXvSpElBmZ0mb7eNiPuB+/Xr5+NTTz01KLPL7r/88stBmR0rYHeil6Q//OEPPo63PMjTOJtG59JOl164cGFQZsdFzZ07Nyjbe++9fWzzF7+Wtp/d9s1L4diAePkNO+bGjtORpEWLFm3wZ9RLK4/LsMtbxOPQ7GsVT9u2U9rjsTWWzefVV18dlNlxkPHYi6effnqD9Yi/L4vPzVa9N+1YUjveVPr8kjSW/RyslEv7Wv/+978Pyuw4yGnTpgVl999/v4/j91EW205ZrXxv2t9ddhsvKRwLHG/tEy9PUo3p06cHx1OnTvXxn/70p6Dsscce83Gcv6zbMYyZAgAAyBiNKQAAgBQK3c1nH0P26dMnKLPdfPGqunZKdO/evX38/PPPB+fZKdy1vo5x12EW0zqtVu1KsK9TW1tbULZy5Uofx7uQ27zbLlW7QrYUTqvOugugXlq5K8F269iuAymcEh3ft3vttZeP7RTuq666KjjPdr9mtFp53bXqvWnFy8fYz7N46ZARI0b4+JBDDvHxj370o+A82w0cL1uSV618b1pbbLFF2bJ4pXqbz4MOOsjHN954Y3CeHaYRL02SV3TzAQAAZIzGFAAAQAo0pgAAAFIo9JgpfF4RxmVgvaKMy8B63JvFwb1ZLIyZAgAAyBiNKQAAgBRoTAEAAKRAYwoAACAFGlMAAAAp0JgCAABIgcYUAABACjSmAAAAUqAxBQAAkMJmDb7eG5JekbRdKW62jlaPnTZ+StXIZXmNqEs9cymtr+8adazXsBrcm+nlpR4S92Y95CWfubo3G7qdjL+oc08lSTKs4RemHnWXl7rnpR5SvurSHnmqd17qkpd61CIvdc9LPaR81aU98lTvvNQlL/X4FN18AAAAKdCYAgAASKFZjamJTbpujHqkl5e656UeUr7q0h55qnde6pKXetQiL3XPSz2kfNWlPfJU77zUJS/1kNSkMVMAAABFQTcfAABACg1tTDnnjnLOzXPOLXTOjW/wtW90zi13zs00X+vpnJvinFtQ+rtHA+rR3zk3zTk32zk3yzl3XrPqkga5LE4uJfJZumYh8kkui5NLiXy2Si4b1phyzm0q6WeSjpY0WNIpzrnBjbq+pEmSjoq+Nl7S1CRJBkqaWjrO2keSLkiSZLCkkZLGlV6HZtSlJuTSa/lcSuTTaPl8kkuv5XMpkc+S1shlkiQN+SNpf0l/NMcXS7q4UdcvXXOApJnmeJ6ktlLcJmleI+tTuu7dkkbloS7ksuPlknwWK5/ksji5JJ+tlctGdvP1lfSaOX699LVm6pUkyZJSvFRSr0Ze3Dk3QNIQSU80uy7tRC4jLZxLiXx+Tgvnk1xGWjiXEvkM5DmXDEAvSdY3bxs2tdE511XSnZLOT5JkVTPrUjTksljIZ3GQy2Jp5GuY91w2sjG1WFJ/c9yv9LVmWuaca5Ok0t/LG3FR51wnrX9T3JwkyV3NrEuNyGVJAXIpkU+vAPkklyUFyKVEPlW6Tu5z2cjG1AxJA51zOzvnNpd0sqTJDbz+hkyWNLoUj9b6vthMOeecpBskzUmSZEIz65ICuVRhcimRT0mFySe5VGFyKZHP1sllgweOfVnSfEmLJH23wdf+jaQlkj7U+n7nMZK21fpZAAskPSCpZwPqcZDWP458QdJzpT9fbkZdyCW5JJ/Fyye5LE4uyWfr5JIV0AEAAFJgADoAAEAKNKYAAABSoDEFAACQAo0pAACAFGhMAQAApEBjCgAAIAUaUwAAACnQmAIAAEiBxhQAAEAKNKYAAABSoDEFAACQAo0pAACAFGhMAQAApEBjCgAAIAUaUwAAACnQmAIAAEiBxhQAAEAKNKYAAABSoDEFAACQAo0pAACAFGhMAQAApEBjCgAAIAUaUwAAACnQmAIAAEiBxhQAAEAKNKYAAABSoDEFAACQAo0pAACAFGhMAQAApEBjCgAAIAUaUwAAACnQmAIAAEiBxhQAAEAKNKYAAABSSNWYcs4d5Zyb55xb6JwbX69KoTnIZ3GQy2Ihn8VBLovJJUlS2zc6t6mk+ZJGSXpd0gxJpyRJMrt+1UOjkM/iIJfFQj6Lg1wW12YpvneEpIVJkrwkSc65WyQdJ6nsm8I5V1vLDXWTJIkrU9SufJLL5qtXLkvnkM8m494sDu7NYqmQTy9NN19fSa+Z49dLXws458Y6555yzj2V4lotxznn/7SIjeaTXBYnl1LHzWcL4t5sEnvv1+n+595sMJu/TTbZJPhTT2meTFUlSZKJkiZKtLBbHbksFvJZHOQyG7UOg6nDdclnnTQqh2maZosl9TfH/UpfQ2sin8VBLouFfBYHuSyoNI2pGZIGOud2ds5tLulkSZPrUy00AfksDnJZLOSzOMhlQdXczZckyUfOuW9I+qOkTSXdmCTJrLrVDA1FPouDXBYL+SwOcllcNS+NUNPFOlDfrx2s2Kx+9w2pZlZCNchl89UrlxL5zAPuzeLg3iyWrGfzAQAAdHg0pgAAAFLIfGmEjipP3Qdon3j9Edst9PHHHze6Okhp0003DY5tfj/66KOgjPs23+JcbrbZZ7/C4nvTHpPXfLL5i4/jnH3wwQdly/KAJ1MAAAAp0JgCAABIgcYUAABACoyZ2oguXbr4+Oijjw7KzjrrLB8vWbIkKLvyyit9vGjRoqCsUl9+HvuCi6Jv38+2wLrjjjuCsj333NPH8biMp576bHusb3zjG0HZwoULfbx27dqgjFxma9CgQT5+9NFHg7Lu3bv7ON5T7f333/fxrbfeGpRdeumlPl68OFyYmnxmZ8cdd/TxnDlzgrKtttqq7PfZnLz55ptB2Ve/+lUfx+8PcpmttrY2H7/yyitBWadOnar6GR9++GFwfNJJJ/n4t7/9bYraZYMnUwAAACnQmAIAAEiBbj59fip8165dfXz33Xf7+IADDgjOs9M4V69eHZT9+c9/Llu2YsUKH3/yySdBWTxVG+0T53KHHXbw8dy5c31sc7wxw4cP9/Fll10WlH3729/28auvvhqUxY+p0X5xPgcMGODjWbM+24Uj7pqtZIsttvDxCSecEJT97//+r4/vvPPOoIxlMdKJc7nLLrv4eN68eWXPq8R24W677bZB2fHHH+/jxx57LCijmy+9+J7bY489fPzcc8/5OO5mr1bcHTh69Ggf29/L0ud/jzYDT6YAAABSoDEFAACQAo0pAACAFBgzJWnrrbcOjs855xwf237gStuMVBJP2bXL4qO+tttuu+D45ptv9nGlKdaV2HFsv/rVr4IyO+2X8W71169fv+D4oYce8nF7xklZdryMHXclhWOmGCNVX7vuumtwPGPGDB+3Z5xUOfHY1GuuucbHeRhTUzSHHXZYcHzffff5uNZxUlY85nTChAk+zmM+eTIFAACQAo0pAACAFDpsN599DGlXVpWkf/zHf/Rx586dfbxmzZrgvG7duvk47hLYe++9fWxX4pWku+66y8d0+aVnc/mDH/wgKBs2bJiP7aPh+DGxXeYi7q575513fGzfG5J0//33l/0+1Mbm8/bbbw/Kevfu7eNKj/ptt1F8nr3ntt9++6As3sUe6dhcTpvv7j4fAAASvUlEQVQ2LSizwyuqzWX8OVvpnq5H1yFCNp+TJ08OysrdO/EyFPZnxJ+Z9ly7U8GGjvOGdxsAAEAKNKYAAABSoDEFAACQQocdIGD764877rigrFevXj62/fAPP/xwcJ7twz3zzDODMtv3e/rppwdlQ4cO9fHjjz/enmpjA7bccksf2zFSUtiP/9577/n4kUceCc5btmyZj8eNGxeUbb755j7+3ve+F5Ttv//+Pn7ggQfaU22UYV/v/v37B2X2vrJT4e+9997gvJdeesnHV1xxRVBmtxL66U9/GpQdfvjhPo63k0H72VzaMaZSmMtVq1b5OM7Jo48+6mM7RlEKlzu57bbbgrKxY8f6ePz48e2pNsqwWzHZ3MbsONOLLrooKJsyZYqPX3755aDMfl7HY7Lsz/na175WXYUbiCdTAAAAKdCYAgAASME1cvds51zTtuqOp8lOnTrVx7arRgq7g7773e/6eOLEicF5tgswfh3t49B4ZV67cnM8nTTrlV2TJEm/NK2am8uY7V47+OCDg7I33njDx3YX+SeffLLqn2/zFU/Pte+reEXurO+teuVSylc+7UrKo0aNCsrsbgK2u3zx4sVV/3w7NTtemsTmsFXzmadc2i4d24UqSStXrvTxTjvt5GPb5dcecS47derk4/jzv1VyKeUrn/Zzc/jw4UGZ/T237bbb+jheybxaa9euDY7t79T492bWuxVUk0+eTAEAAKSw0caUc+5G59xy59xM87WezrkpzrkFpb97ZFtN1Av5LA5yWSzkszjIZcdTzZOpSZKOir42XtLUJEkGSppaOkZrmCTyWRSTRC6LZJLIZ1FMErnsUKoaM+WcGyDpniRJ9iwdz5N0aJIkS5xzbZKmJ0kyqIqf07S+32222SY4nj9/vo9t/64UTtfcbbfdfNye8Uy2jz5eMt+O2WhrawvKli5dWvU1apEkiatHPpuZy3gsy2uvveZju6yFJM2aNcvHdoufWsXvAZvL7t27B2V2TEgW6pXL0vflZlzGkiVLfBznc+7cuT4ePHhw6mtVymeXLl2CMjuWMgtFuDdjdoxbz549g7KFCxf6eODAgamvFY+bsZ/BdikcSXr33XdTX6+Sot6bdlyUXWJEkhYsWOBj+3uzVpXGwMXbQNmxsVnIcsxUryRJPv3EWyqpV6WTkXvkszjIZbGQz+IglwWWetHOZH0TvGzL2Tk3VtLYcuXIl0r5JJethXuzWLg3i4N7s3hqbUwtc861mceVy8udmCTJREkTpeY+ruzXr19wbB/72kfRUth9UOtSBXa1X9t1EOvTp09wnHU3XxlV5TMvubQrnktSjx6fjeOMH9+PGDEi9fXs4+VKuYwfPWfdzVdGy92b8TRn2x0Ud5HXI582h5Xyad9XUvbdfGW01L0Zv552eEU8pCSeWp9WvPyBFXfBZ93NV0bL3ZtxPuOub2vIkCF1vbb93I3tsMMOwXHW3XzVqLWbb7Kk0aV4tKS761MdNAn5LA5yWSzkszjIZYFVszTCbyQ9JmmQc+5159wYSVdKGuWcWyDpiNIxWgD5LA5yWSzkszjIZcez0W6+JElOKVN0eJmvI8fIZ3GQy2Ihn8VBLjue1APQW8WNN94YHNtpl2PGjClbViu7PUIl8XRhbNwFF1wQHNtxbddee21Qtm7dutTXi8fOlGO3O0D1Ro4cGRzbcRovvPBCUFaPsS6dO3eu6rxat8HoyHbZZZfg2I5jireJeeedd1JfLx5vV068pReqE48DtfdmvLXWmjVrUl+v0rg3qz3bRzUK28kAAACkQGMKAAAghUJ389lHwPHU2EmTJvn4nnvuqfu1q50W/+c//7nu1y66Aw44IDh+4oknfHzppZfW/Xrx9Pxy5syZU/drdwSnnBIOL1mxYoWPDzvssLpfL15BvxxbD1TntNNOC47tkIkjjjii7tertmu9ScuUtLx4CIwdUvHNb36z7tfbaqutqjovj/nkyRQAAEAKNKYAAABSKHQ3n92IsX///kHZs88+m+m1e/fuXdV58Wy+PM5SyAPbNXPwwQcHZffee6+P481O6yFepb6ceGZRFnUpojPPPDM4trO8spiFVY9NdbFh48ePD47t7Kynn3667tc75phj6v4z8ZnLL788OLaz+W666aa6Xy+LYRqNwpMpAACAFGhMAQAApEBjCgAAIIVCj5m6/vrrfbzlllsGZfVYfbeS119/3cfxbum233np0qWZ1qMozjrrLB/Hucx6x/BqV92OVwRGefvss4+P43wuWbIk02t369atqvPi+xYb1rdvXx/Hq8vbeyKL13P//fev+8/s6HbYYQcfd+rUKSizSyNUu2RMexx99NF1/5mNwpMpAACAFGhMAQAApFDobr5Ro0aVLfv7v/97H9999911v7btRow3TrZT6G2XH8q7+OKLy5Zl/Wh4880393HcVUFXUG3iDamttra2TK994IEHli3Louui6K655pqyZXE3Ub0dd9xxZcvqscl5R/Tzn/+8bFnWv6923333smX12OQ8SzyZAgAASIHGFAAAQAo0pgAAAFIo1Jip7t27B8eVpkBfddVVmdbF9tfHu89vt912Po63IGHMxnp2+xhJ2nHHHcue+x//8R+Z1mXVqlU+jse/ffjhh5leu6hGjhxZtuyXv/xlpte2ObNTvaXP36vYuL/9278tW/bkk09meu14WQ1r2rRpmV67qI499tiyZfPnz8/02vHnvnXFFVdkeu20eDIFAACQAo0pAACAFArVzbf11ltXfe7s2bMzrEk4ZX7x4sVBmV3heauttgrKmM67Xtz9Wcktt9ySYU2kNWvW+Hj58uVB2f333+/jeNowyyZ8ZpNNwv+3VZpi/cMf/jDTujzyyCM+jndCOOOMMzK9dhFVyuX3v//9TK89ZcoUH3/lK18Jyk488cRMr11UlfL5ve99L9Nr251DevToEZT9+Mc/zvTaafFkCgAAIAUaUwAAACnQmAIAAEihUGOm2rMNxccff5xhTaSxY8f6ePDgwUGZHXdjd+iWpLfeeivTerWKuL/cjj+K+/TXrl1b12vH43tuu+02H2+zzTZBmR1zE39f1u+xVmK35JHCJUDi6dDLli2r67W32GKL4Piuu+7ycTyubeHChXW9dhHF4xntOM/4tZ45c2Zdr92/f//g+IQTTvBx/L6p9+dCUcWfWytXrvRx/Hn3+OOPp76e/fzed999gzKb32effTYoy/sY1I0+mXLO9XfOTXPOzXbOzXLOnVf6ek/n3BTn3ILS3z029rPQfOSyOLg3i4VcFgf3ZsdTTTffR5IuSJJksKSRksY55wZLGi9papIkAyVNLR0j/8hlcXBvFgu5LA7uzQ5mo918SZIskbSkFK92zs2R1FfScZIOLZ32C0nTJX0nk1pW6emnnw6On3/+eR8PGjQoKLPLKMTTo6tlH3fvvffeQdn555/v43jndPs4+sgjjwzK5s2b5+MsHmsmSfJM6e9c5zJ+ZH/ffff5+NBDDw3Kevfu7ePXXnutpuvZ98M555wTlA0fPtzHcS4XLVq0wXpIn18So95a6d6Ml/ywO9OfdtppQZl9vadPn17T9XbddVcfX3fddUGZ7bqIdxywK6I3eneCVrk349fhv//7v31shzdI0tlnn+3jyy67LCizn2+2qzfuxrcrcl977bVBme2iirsY7c9sdJd7K92b8S4AkyZN8vHXv/71oMwudRF/TtrX2A7TeO+994LzTj31VB9PmDAhKLPvia5du26s6rnSrgHozrkBkoZIekJSr9IbRpKWSupV15ohU+SyWMhncZDLYiGfHUPVA9Cdc10l3Snp/CRJVtn/PSRJkjjnNvgYxTk3VtLYDZWhOchlsZDP4iCXxUI+O46qnkw55zpp/Rvi5iRJPp0Ks8w511Yqb5O0fEPfmyTJxCRJhiVJMqweFUY65LJYyGdxkMtiIZ8di9vYuBy3vin9C0lvJUlyvvn6jyS9mSTJlc658ZJ6Jkly0UZ+VkPnNvbr18/H8fYxjz32mI9Hjx4dlNlxMXYs1EUXhf+8Aw88sOy1bV9+3Cd90003+fiGG24IymbMmOHjjPr5f6UWzGW3bt18vGDBgqDMbs8zbty4oKxv374+tnkeOnRocF6vXp89bY/vCfu/yTgnxxxzjI/te0qSVq9erYxtoha9Nzt37uxjO05QCse63HHHHUHZHnvs4eMhQ4b4OB4v06VLl6rqEY/l2nHHHX389ttvB2UNGHfTkvem/by041QlqU+fPj6Ot2Ky95zdViu+/6rdWir++fbzP+vxbhvQsvemzWe8FMJuu+3m4w8//DAos+NO7T0c//6Ll2KwbO7ja1f6fZu1JEnK77FTUs279EBJp0l60Tn3XOlrl0i6UtJtzrkxkl6RxEZIrYFcFgf3ZrGQy+Lg3uxgqpnN94ikcq2yw+tbHWStQgubXLYY7s1i4d4sDu7NjqdQK6DH7KP/eAXmI444wsd2p2rp8ysyVyN+NG27BNasWROU2anE8+fPD8riR6JYz+bSdhFJ0l577eXjhx9+OCiLp1nXwuYy7rqz+Yu7jFCevR/j+83uZGCXGKlVfG9+8MEHPo7vTdvFkfcVl/PC5jJ+zez09ng17VrEn482lzF7vXhnCXJbns1n/JlmP4erXbqg0u/TOJ/vv/++j22XuxTm067SnhfszQcAAJACjSkAAIAUaEwBAACksNGlEep6sSZO8bQ7xUvSl770JR/bablSbWOm7rnnnuD4+uuv93E8XfiVV17xcaP77quZ4lmNRufSTqe9+OKLg7ILL7zQx3YJhfj7qvW73/0uOL788st9/OqrrwZl8ViMRqpXLqXG59OOZbPLS0jhVjN2+nz8fdWKt5P593//dx+vWrUqKIuPG6lV702bk3333Tco++1vf+tju0xJ/H2V2HE148eHW9nZpWXipStsLlv1c1Zq7mftLrvsEpRNmTLFxwMGDKjq58WvvV1S4aSTTgrKHnjgAR/H45zttm+NHltcTT55MgUAAJACjSkAAIAUCt3NZ9kuv9gXvvCF4HifffbxsV1l+e677w7Os6uq20eQedaqXQlW3A1ruwuGDQt3X9h11119bFdEnjZtWnDerFmzfPzuu+/WpZ5Za+WuhOjawbHtZohXPbbduHZV67lz5wbn2VXx7XTrPCvCvRmzuRwxYkRQZvO3YsUKH7/55pvBee+9956PW2XpmKLcmzF7rw4aNCgos3mywx/i5RWasBp9anTzAQAAZIzGFAAAQAo0pgAAAFLoMGOmsF4Rx2V0VEUdl9FRcW8WB/dmsTBmCgAAIGM0pgAAAFKgMQUAAJACjSkAAIAUaEwBAACkQGMKAAAgBRpTAAAAKdCYAgAASIHGFAAAQAqbNfh6b0h6RdJ2pbjZOlo9dqrjzyKX5TWiLvXMpbS+vmvUsV7DanBvppeXekjcm/WQl3zm6t5s6HYy/qLOPZUkybCGX5h61F1e6p6Xekj5qkt75KneealLXupRi7zUPS/1kPJVl/bIU73zUpe81ONTdPMBAACkQGMKAAAghWY1piY26box6pFeXuqel3pI+apLe+Sp3nmpS17qUYu81D0v9ZDyVZf2yFO981KXvNRDUpPGTAEAABQF3XwAAAApNLQx5Zw7yjk3zzm30Dk3vsHXvtE5t9w5N9N8radzbopzbkHp7x4NqEd/59w059xs59ws59x5zapLGuSyOLmUyGfpmoXIJ7ksTi4l8tkquWxYY8o5t6mkn0k6WtJgSac45wY36vqSJkk6KvraeElTkyQZKGlq6ThrH0m6IEmSwZJGShpXeh2aUZeakEuv5XMpkU+j5fNJLr2Wz6VEPktaI5dJkjTkj6T9Jf3RHF8s6eJGXb90zQGSZprjeZLaSnGbpHmNrE/pundLGpWHupDLjpdL8lmsfJLL4uSSfLZWLhvZzddX0mvm+PXS15qpV5IkS0rxUkm9Gnlx59wASUMkPdHsurQTuYy0cC4l8vk5LZxPchlp4VxK5DOQ51wyAL0kWd+8bdjURudcV0l3Sjo/SZJVzaxL0ZDLYiGfxUEui6WRr2Hec9nIxtRiSf3Ncb/S15ppmXOuTZJKfy9vxEWdc520/k1xc5IkdzWzLjUilyUFyKVEPr0C5JNclhQglxL5VOk6uc9lIxtTMyQNdM7t7JzbXNLJkiY38PobMlnS6FI8Wuv7YjPlnHOSbpA0J0mSCc2sSwrkUoXJpUQ+JRUmn+RShcmlRD5bJ5cNHjj2ZUnzJS2S9N0GX/s3kpZI+lDr+53HSNpW62cBLJD0gKSeDajHQVr/OPIFSc+V/ny5GXUhl+SSfBYvn+SyOLkkn62TS1ZABwAASIEB6AAAACnQmAIAAEiBxhQAAEAKNKYAAABSoDEFAACQAo0pAACAFGhMAQAApEBjCgAAIIX/Bynog5yOlB4PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126a651d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dfb7470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e1cc630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z5jtO0kWSXjKz7Xf73aDePwz/a2aXSFom6b/bUyJajCzjQd+MC1nGg77ZYep5mu8pSdUWrJrc2nLQbjUWHyPLkqFvxoW+GQ/6ZudhBfQG1HqcPssK+qZ6VkDv1dPTk9r2H9fNij1Lqfx50jd3KPuK2WSZ1kl5+vuK9HOoJSugAwAAoDoGUwAAAAEYTAEAAASoe9HOVinSPGijlixZkto++OCDqx47dOjQpJ191DcWZc5y+fLlqe1x48ZVPdZ/U312eYyYlDnPt99+O7W91157VT3WX9Jky5YtbasJzXn//fdT2/4yF1n+vY7Ze3NQDI3k6d8zVbY8uTIFAAAQgMEUAABAgNyn+crspptuSm3ff//9SXvNmvRbAWKd2otFdhXsu+66K2m/8MILqX0xT+3F4he/+EVq28/3l7/8ZWofU3vFdvvtt6e2b7jhhqR95plnpvaVbSqoE11//fWp7Z/+9KdJ+7DDDkvtK3OeXJkCAAAIwGAKAAAgAIMpAACAALm/Tia3k7VB9pUjzzzzTNLO3k/18MMP51JTo3hlRa+BA9O3C957771J+7vf/W5q36uvvppLTY3ilRU7+MsdSNJ1112XtO+4447UvvXr1+dSU6Pom726u7tT2yeeeGLSfuyxx1L7inqPDX1zh+zPWn/ZkjfffDPvcprC62QAAADajMEUAABAgNyn+bavcOqvKi2lHz/ftm1bbjU1IjuV8NGPfjRpr1ixIrWvqP8PrZxK2J5ldvqzp6fHP18rTtdy2SyHDx+etN95553UvqL+P7R6KsGs98tl3+ru/1ku6vciO5Xg57thw4a8y2lKO6b5amVZVNmfJ76iTutltWuab3sf9c7TqtO0TbZmXxnql5jmAwAAaDsGUwAAAAEYTAEAAATI9Z6pQYMGuT322EOSNHLkyNS+hQsXJu2izovXmvvNKupccKvm8gcOHOi232e0yy67pPb5r9Yp6j0anTKPX6+uri43ZMiQ7e3UPv+t70X93pTxXpKsVuU5YMAAt/2e1Oz3ZePGjf75WnG6liPLtAEDBrjt9wBmvxe8Gikf3DMFAADQZgymAAAAAuS9NMJbkpZJ2kPS27mduLpOq2M/59yerfhCZFlTHrW0LEspyXO9Out7WA/6Zrii1CHRN1uhKHkWqm/mOphKTmr2nHPumNxPTB0tV5Tai1KHVKxaGlGkuotSS1HqaEZRai9KHVKxamlEkeouSi1FqWM7pvkAAAACMJgCAAAI0F+Dqen9dN4s6ghXlNqLUodUrFoaUaS6i1JLUepoRlFqL0odUrFqaUSR6i5KLUWpQ1I/3TMFAAAQC6b5AAAAAuQ6mDKzU8xsoZktMbNpOZ/7bjNbY2bzvM9GmtmjZra48uvuOdQx1sweN7P5ZvaymV3VX7WEIMt4spTIs3LOKPIky3iylMizLFnmNpgysy5J/yPpVEkTJF1gZhPyOr+kGZJOyXw2TdJs59x4SbMr2+3WI+la59wESZMkXVn5PvRHLU0hy0Tps5TI01P6PMkyUfosJfKsKEeWzrlc/pP0SUl/9bavl3R9XuevnHOcpHne9kJJoyvt0ZIW5llP5bwzJU0pQi1k2XlZkmdceZJlPFmSZ7myzHOabx9Jy73tNyqf9adRzrmVlfYqSaPyPLmZjZN0tKQ5/V1Lg8gyo8RZSuT5H0qcJ1lmlDhLiTxTipwlN6BXuN7hbW6PNprZMEkPSrraObeuP2uJDVnGhTzjQZZxyfN7WPQs8xxMrZA01tseU/msP602s9GSVPl1TR4nNbNB6v1D8Xvn3EP9WUuTyLIigiwl8kxEkCdZVkSQpUSeqpyn8FnmOZh6VtJ4M9vfzLolnS9pVo7n78ssSRdX2herdy62rczMJP1G0gLn3G39WUsAslQ0WUrkKSmaPMlS0WQpkWd5ssz5xrHTJC2S9C9J38353PdJWilpi3rnnS+R9BH1PgWwWNJjkkbmUMfx6r0c+aKkuZX/TuuPWsiSLMkzvjzJMp4sybM8WbICOgAAQABuQAcAAAjAYAoAACAAgykAAIAADKYAAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIACDKQAAgAAMpgAAAAIwmAIAAAjAYAoAACAAgykAAIAADKYAAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIACDKQAAgAAMpgAAAAIwmAIAAAjAYAoAACAAgykAAIAADKYAAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIEDQYMrMTjGzhWa2xMymtaoo9A/yjAdZxoU840GWcTLnXHO/0axL0iJJUyS9IelZSRc45+a3rjzkhTzjQZZxIc94kGW8Bgb83o9LWuKce1WSzOx+SWdJqvqHwsyaG7mhZZxzVmVXQ3mSZf9rVZaVY8izn9E340HfjEuNPBMh03z7SFrubb9R+SzFzKaa2XNm9lzAudB+O82TLEuDvhkX+mY86JuRCrkyVRfn3HRJ06VyjrDNqg9Im50iLauyZ4k08owHWcaFPMsn5MrUCkljve0xlc9QTuQZD7KMC3nGgywjFTKYelbSeDPb38y6JZ0vaVZrykI/IM94kGVcyDMeZBmppqf5nHM9ZvY1SX+V1CXpbufcyy2rDLkiz3iQZVzIMx5kGa+ml0Zo6mQlnPuN7Z6pep5KqEfZsyxjdlmtylIqZ56x6eS+GRv6Zlza/TQfAABAx2MwBQAAEKDtSyOUkT8d9JGPfCRpZ6eG/v3vf+dWE8INGTIkaWenb9evX593OQjU3d1ddd/mzZtzrAShurq6knb25+y2bdvyLgeBYruloh5cmQIAAAjAYAoAACAAgykAAIAA3DMlady4cantyy+/PGlfddVVSfuFF15IHTdp0qSk3SnzwkU3dOjQ1PZnP/vZpH333Xcn7QULFqSOO+GEE5L21q1b21QdGjVwYPpH1FFHHZW0H3jggaS9cuXK1HEnnnhi0t64cWObqkMjBgxI/9t97NgdC4H7WW7ZsiV13BlnnJG033nnndQ+7qfqP9n7Tvfcc8+kfeuttyZt/15VSbriiiuSdrZvbtiwIWln/071z1fEn9FcmQIAAAjAYAoAACBAKab5/Mt72UvFzV7u86eDrr322tS+U089NWkPHjw4aR955JGp45jaa1w7svSngj73uc+l9l133XVJe8SIEUn7mGOOaepcSPPzzF72b3YKxv86hx56aGrfLbfckrT322+/pD1mzJjUccOGDUvaTPPVpx1ve/C/5ujRo1P7br/99qTt98fsn5vJkycn7QcffLCpOtB6e+21V2r717/+ddI+/fTTk3b2z87q1auT9l133ZXa599+MWjQoNQ+f/mM999/v4mK24srUwAAAAEYTAEAAARgMAUAABCgFPdM+XOute6ryc7511rS3n8E8/vf/37Vff49N/5cL5rTbJbVvoYk9fT0JO2ZM2em9o0aNSppH3vssUl706ZNqeOK+KhtGfhZ1LqvppE8/e358+en9t1zzz1J++STT07a2fx41VPj6r0vKptlrd/n73vzzTdT+/7whz8k7bPOOqvq1/vb3/6WtOmnxbFmzZrUtn8/m3/PVJafe7Z/+/fLZZdF8e+D5J4pAACAyDCYAgAACFCKab5WqHUpeu3ataltfwrIv6T93nvvtb4wtFR2+u6tt97q87jNmzfnUQ6qqHdKKfuY/KuvvlrXcSxb0j7Nfm+zvy/7FoJqWNqimLJ5zps3r67f9+GHHybtWlPG2dxXrVrVaIm54soUAABAAAZTAAAAARhMAQAABIj6nql6X2eRnfv905/+lLS/973vJe0DDjigNYVhp1p1z8vcuXP7/Py//uu/WvL1UZ9W5fnGG2/0+bn/2icp/aqiZl9rg/aqdj+j/9oQSdp1112TNvdPFZd/73Gt10z5r3LLLnXh/5zIvm6s6P2YK1MAAAABGEwBAAAEKP00X61Vzpvlr8rqf83sW6zRWu3I8vXXX+/z8+wlZJRDtakhlM/bb7/d5+fZvu8/So/i8pcuqPXz+5VXXql6XJmn5/kbBQAAIMBOB1NmdreZrTGzed5nI83sUTNbXPl19/aWiVYhz3iQZVzIMx5k2XnquTI1Q9Ipmc+mSZrtnBsvaXZlG+UwQ+QZixkiy5jMEHnGYobIsqNYPfemmNk4SX92zh1e2V4o6TPOuZVmNlrSE865Q+r4OqV4x8Mee+yRtGvdo5F95LMMnHPWijzLkuWIESOS9jvvvFP1uO7u7qS9ZcuWttbUKq3KsvL7SpHnbrvtlrTffffdqseNHTs2aVdbTqFoOq1v+suT1HpV19lnn520Z86cmdpX1NcGdWLfHD58eNJet25d1eN++MMfJu3p06en9vn30a1fv76F1YVxzu30L/tm75ka5ZxbWWmvkjSqya+DYiDPeJBlXMgzHmQZseCn+VzvELzqyNnMpkqaGnoe5KNWnmRZLvTNuNA340HfjE+zg6nVZjbau1y5ptqBzrnpkqZL5blcOW7cuLqOK/NjnBl15RlzlkOGDEnaZZnmqyLqvjl+/Pi6jhszZkzSLss0XxXR9s2DDjqoruOOPPLIpP2Xv/wlta9kfTXqvnnwwQfXddwnP/nJpL1s2bLUvoceeihpF2marx7NTvPNknRxpX2xpJk1jkXxkWc8yDIu5BkPsoxYPUsj3Cfp/yQdYmZvmNklkn4kaYqZLZZ0cmUbJUCe8SDLuJBnPMiy8+x0ms85d0GVXZNbXAtyQJ7xIMu4kGc8yLLzlP51Mu3gP1Zdi39fRrXXlqB/HXjggXUdN2XKlKT94IMPtqscBJo4cWJdx1122WVJe86cOal9RX2cvtP4987Ucu655ybtX/3qV6l9q1evbmlNaN7nP//5uo479thjk3Z2SYx77723pTXlidfJAAAABGAwBQAAEIBpvj6sWLGiruMefvjhpH3YYYe1qxwEqDdLf/rgj3/8Y2pfyZe9iMratWvrOu68885L2t/4xjdS+95///2W1oTmbN68ua7jDjlkxyLh+++/f2of03zFMXTo0IaP6+npSe3bsGFDS2vKE1emAAAAAjCYAgAACMA0Xx/mzp2btP0nf7IvNvZXfPVXQ5eYGiqKl156KWnXytJ/ga7/0mNJ2rhxY5uqQ6OeffbZuo7zM9xll11S+5jmK4YFCxbUdZzfV8s8DRS77JRdNf7P4aeeeqpd5eSOK1MAAAABGEwBAAAEYDAFAAAQwPJcDbgsb7/2zZ49O2mfdNJJVY/zH9+VpEWLFrWtphDOOdv5UTtXxiz9+6cOP/zwqsd97GMfS22/8sorbaspRKuylMqZ57vvvpu0/XvesrKP0y9durRdJQXptL7p32fqL5PQ1dWVOs7/O8q/T1WSlixZ0qbqwnRi3zzooIOS9uLFi6se5+c5eXL67TqPP/546wtrgXry5MoUAABAAAZTAAAAAVgaYSf8lzeuW7eu6nHZqYSiTvN1sjPOOCNpv/baa1WPO/roo1PbRZ3m63Q33nhj0r7jjjuqHnfccceltos6zddp/OVj/Om67C0T/tII55xzTmrfT37ykzZVh0YtW7YsafvLJAwcmB5m+HleeumlqX1FnearB1emAAAAAjCYAgAACMBgCgAAIAD3TO1Eva+e4L6a4vPn9Gt5/vnn21wJWuG3v/1t0q51z5S/JAaK6dprr03af/7zn6se57/qC8WyZcuWpO3/fVhrGZonn3yyrTXliStTAAAAARhMAQAABGCarwHZ1eL9Rzyzq/aiePJc7R/tt379+rqO81fXRjHVO93jL6eA4po1a1bSrjXNt88+++RRTi64MgUAABCAwRQAAEAABlMAAAABuGeqRdasWdPfJaABte5/e+utt/IuB02o9x649957r82VINSmTZvqOm7t2rVtrgSt8OKLL9Z13L/+9a82V5KfnV6ZMrOxZva4mc03s5fN7KrK5yPN7FEzW1z5dff2l4tQZBkP+mZcyDIe9M3OU880X4+ka51zEyRNknSlmU2QNE3SbOfceEmzK9soPrKMB30zLmQZD/pmh7FGHxc3s5mS/l/lv88451aa2WhJTzjnDtnJ7y31s+lbt25NbQ8YsGPuPXtlAAAGnUlEQVQses0116T2/fznP8+lpkY555L5LLLcwc/yd7/7XWrfRRddlEtNjfKzlDovT39qNpunvy/7JvqTTjqpvYU1qZP75rBhw5J2rbdOLF68OLV98MEHt62mEJ3eN6dN2zFGvOWWW6oet3LlytT23nvv3baaQmTz7EtDN6Cb2ThJR0uaI2mUc277d2KVpFEN1od+RJZxIc94kGVcyLMz1H0DupkNk/SgpKudc+v8f/k551y10bOZTZU0NbRQtA5ZxoU840GWcSHPzlHXlSkzG6TePxC/d849VPl4deUypSq/9vk4m3NuunPuGOfcMa0oGGHIMi7kGQ+yjAt5dpadXpmy3qH0byQtcM7d5u2aJeliST+q/DqzLRUWSPbx3SFDhiTtqVPT/4go6j1TIktJ6TecS9LgwYOT9uTJk/Mupymd3jf9+z2zrxnxX+80YcKE3GoK1LFZbty4MWnXWrZk5MiRudUUotP75rPPPlvXcd3d3W2uJD/1TPMdJ+kiSS+Z2dzKZzeo9w/D/5rZJZKWSfrv9pSIFiPLeNA340KW8aBvdpidDqacc09JqnYnezn+CY9EjacSyLJk6JtxoW/Gg77ZeRpeGiHoZCV8xNNX63H6LH9fnt/jnannEc96kGX/a1WWUvnzzE7z+VNDWbHnWcYs/UyyfdNXazq3SDq9b+67775Je9myZVWP86d3pfStM0XS8qURAAAAkMZgCgAAIACDKQAAgAB1L9oJaf369ant4cOHJ+3svRf+XH5PT097C0PDai1zUev+myLdY4Mdsktd+I9cf/DBB6l9ZFhs2Z+XAwfu+Gtq0aJFeZeDJvh/V9Z61dP06dNzq6nduDIFAAAQgMEUAABAAJZGaMCFF16Y2r7tth0L2x500EGpfevWrculpkZ18uPXvjPPPDO1feONNybtSZMmpfbVelS7P3X649e+Y45Jv3XjhBNOSNoFfhtBCn2z1wEHHJDaHjRoUNJeuHBh3uU0hb65w4gRI1LbGzZsSNrZ2y2KiqURAAAA2ozBFAAAQAAGUwAAAAG4Z6oB2VeO+I9fZ5fFLyruy+iVfd2Iv51dGqGouC8jLvTNeNA348I9UwAAAG3GYAoAACAAK6A3IDv9U5apPfyn7PQ2q2IDAJrFlSkAAIAADKYAAAACMJgCAAAIkPs9U9vfAJ69R6Wor+xAdV1dXZL+814y7j8qJ/KMx/ZlXMqyzAdqI8/i48oUAABAAAZTAAAAAfKe5nu7p6dnmaQ9JL2d87n70ml17NfCr/X21q1bybJvedTSyiyl3jzXq7O+h/UoZd/ctm0bfbNvpeyb27Zto2/+p0L1zVxfJ5Oc1Ow559wxuZ+YOlquKLUXpQ6pWLU0okh1F6WWotTRjKLUXpQ6pGLV0ogi1V2UWopSx3ZM8wEAAARgMAUAABCgvwZT0/vpvFnUEa4otRelDqlYtTSiSHUXpZai1NGMotRelDqkYtXSiCLVXZRailKHpH66ZwoAACAWTPMBAAAEyHUwZWanmNlCM1tiZtNyPvfdZrbGzOZ5n400s0fNbHHl191zqGOsmT1uZvPN7GUzu6q/aglBlvFkKZFn5ZxR5EmW8WQpkWdZssxtMGVmXZL+R9KpkiZIusDMJuR1fkkzJJ2S+WyapNnOufGSZle2261H0rXOuQmSJkm6svJ96I9amkKWidJnKZGnp/R5kmWi9FlK5FlRjiydc7n8J+mTkv7qbV8v6fq8zl855zhJ87zthZJGV9qjJS3Ms57KeWdKmlKEWsiy87Ikz7jyJMt4siTPcmWZ5zTfPpKWe9tvVD7rT6Occysr7VWSRuV5cjMbJ+loSXP6u5YGkWVGibOUyPM/lDhPsswocZYSeaYUOUtuQK9wvcPb3B5tNLNhkh6UdLVzbl1/1hIbsowLecaDLOOS5/ew6FnmOZhaIWmstz2m8ll/Wm1moyWp8uuaPE5qZoPU+4fi9865h/qzliaRZUUEWUrkmYggT7KsiCBLiTxVOU/hs8xzMPWspPFmtr+ZdUs6X9KsHM/fl1mSLq60L1bvXGxbmZlJ+o2kBc652/qzlgBkqWiylMhTUjR5kqWiyVIiz/JkmfONY6dJWiTpX5K+m/O575O0UtIW9c47XyLpI+p9CmCxpMckjcyhjuPVeznyRUlzK/+d1h+1kCVZkmd8eZJlPFmSZ3myZAV0AACAANyADgAAEIDBFAAAQAAGUwAAAAEYTAEAAARgMAUAABCAwRQAAEAABlMAAAABGEwBAAAE+P8P4vcLZJw1JQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126066198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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uxTrrrBMd87L8OXPm2I/nBo7j//Wvf418dul0OWy+WNpcDIZzJoDkZc5psbkkn3zySbCTci2sltX8Pnng2WefjY6POuqoYCf9/j179oyO7TZKafjZz34WHVey5LocdrsQW6qkHDbPK0/bOaXFlvIYNmxYsDmnD4jfU3379o18dhulNIwdOzY6riSHsRzz58+PjtPmItr2Jy2fzzP2bwLnoXEpICB+R9vSQ9Xoeccdd0THtegPTz/9dHTMOVNJ2Gcp6xYyjLaTEUIIIYTIgAZTQgghhBAZaIowH1cJt0s8GTt9yMc2dJJ2ietVV10V7HvvvTfVdxqBLf2QVzgEYkMCTNJUcFIF5iS4ev6UKVOqOkclpA1P2N81bTgpD3CIx5Z44N8/Sc9qQgcA8NprrwXbhhJqgW1z2rA+h3eBeBl6s8K/kw1nc1ioWi3fe++9YE+YMKGqcyRhQ7YvvPBCqu8tWrQoOq5lWKit4ef3ww8/jHxcGoZLfgCxhtWWguDrVVt+wF6bj60u/PzYsCXz0EMPRcf2Wc6CZqaEEEIIITKgwZQQQgghRAaaIszXuXPnYHM1dGD1ysoMT3Pa6TzecNSGALma8k033ZSqjXZKcrPNNgu2reCbtNIo6fdJuh5vZPl///d/qc7RCHgKOanCe9L0svUlrYDkVXqjRo1K1UZ7/m222SbYdqPVpI1rO3XqlOp6ts3VhjEbzd///vfomH+PJD0rWd3DWl9xRfkyPUnX47CG3ciVwwe2Xcccc0yqNqat2p43+Pe1zzmvhrbvqGpDQdz/b7zxxhZ/bs9vr3388ccH2/YbTsuwIfdzzz03VRvT9uE8khTm4yr99v1TCz15E3sbGmUN7d/zH/3oR8HeY489It/dd99d9px2RXA5bLi3lmhmSgghhBAiAxpMCSGEEEJkQIMpIYQQQogM5DJnysbFBwwYEOy0O1Xb89g8peHDhwfb7pDOcdukpZNdunQJtt0tnasE33fffZHvuOOOC7aN5aetzG554403qvpeW2Pj7zvttFOwd9xxx8TPpj0nV8O1O6BzZXh+Buw5krTknDpejg8A/fv3DzYvzwVW33G+HDY3JykPK29wbuAPfvCDyFdt/h/nd9jcCK5+v3LlyrLn4OXRL774YuTjPmY1O+KII4L93HPPRb4zzjij5V/A8Nlnn0XHtajgXQ/4Oed3IFB9Hh+/P231cNaSK5Lb52b77bcPtq2yz2227/hBgwYFe/z48ZEvafk8M3v27Oi4marZcz6S3V2iV69ewU7aaSMJ+5xzaRTORbR6cluefPLJyNexY8dg27+9AwcODPYTTzwR+dKW5Lj//vtTfa4aNDMlhBBCCJEBDaaEEEIIITKQyzCfpRZVZ+3mqVwNm5dcJmFDCXPnzg120ua0HDoA4mnrl156KfKNGTMmVVsszzzzTFXfqzc8dVvt9LLl8ssvD/YjjzwS+dJOy8+bNy/YSVrajbVPPvnkYNtKv6NHj051bTud3awVs2tV0mHGjBnB5qXvQPmyIjaUwP3Khn4ZG1ZnPW2VZVspuhw2dFjLKsttCfcVuzF8tXC5DA4tAasv1/8a+16YNWtWsJPKTthl9lzWgMNHQPrNtP/4xz+m+lzesekx1YYrObRnnxEOuzO2b/7+978PdpKe9u/t+++/H2wbfuWUCqstt4vLHtUazUwJIYQQQmRAgykhhBBCiAxoMCWEEEIIkYFc5kzZWOmpp54a7LTLrS02RsyxWpvjUI7u3btHxxtvvHGq79k2n3POOcE+77zzIl/SMmr+HebPnx/50v4O9cZqOXTo0GBXu8N82nuUxFZbbRUdb7TRRlW1ZfDgwcGeMmVK5HvnnXfKfo+XiV9wwQWRL885NlbPQw45JNi87VMlWM04VyltvuS3v/3t6DgpT4qx97pv375lz8H5FjbX6uWXXw42l3JpJrhP7LfffjU5J29LkzYX8PDDD4+O027PY98LnF9nS8e88MILwebyJkCcN/e73/0u1bXzCOd+cl4psHp+WVreeuutYKfV88QTT4yO0+pp+/6CBQuCbcsNrbPOOsHmcjhAXCLDlueoJZqZEkIIIYTIgAZTQgghhBAZaIowX9JS9bR07do1On799deDfdddd0W+008/Pdg81f/UU09VdW0bSuDSCEOGDIl8xx57bLDtUlMu4fCTn/ykqrY0Gl5OW+3u5Hbp64MPPhjshx9+OPIdeeSRweblwbw8txLsNDEv4x45cmTkY51XrFgR+VjnZl5+zVWsqw3B2+eAdyewoXUulcChtunTp1d1bVttnivc2/AEh4Lt9Y4++uhgf/LJJ1W1pdFwBetalbno169fsG+55ZbIxxXzOUR8++23V3WthQsXRsdTp04NNu9wAMThRxtW5LI5zQz/3bT9iP8mVdJv+Tw2ReW6664LNr9rb7jhhtTnZx566KHo+Morrww2V8wH4rDfz3/+88jXlqE9RjNTQgghhBAZ0GBKCCGEECIDGkwJIYQQQmTA1XMXbOdcqovZ0vdz5swJNsf12wpeYsvx5EpyfPi+2iXzvO2I3b2c49A33XRT5JswYUKw7Y7daXX03leXqGRIqyUvWQXiXKXddtst6fxVtiym2twAhp8HXjYNAH/4wx+CzflDADBs2LBgX3bZZZGPt6+ptg/WSksgvZ52q48bb7wx2GeccYY9Z9nvVQvfq2qfEc5p2mabbSIfL/22pRF4yfXjjz8e+T799NOq2sLUu2/a+/dv//Zvwb755psjH+dQVbus3lILLRcvXhxsW+6El9bbvs+/z8cff1y2XdXSiL5p4ftht9ni3Ke0pQpag9+T1fb3F198Mdh2+6YkXfj5aYsxTRo9W/3r4pzr4Zx73Dk31zn3knNuROnnnZ1zjzrn5pf+7dTauUTjkZbFQX2zWEjL4qC+2f5I87/qXwA433vfF8BeAM5xzvUFMBLADO99HwAzSsci/0jL4qC+WSykZXFQ32xnVBzmc85NBTCu9N9A7/1S51w3AE9477dt5btVTT9Pnjw52FxxGoincu20fK1CRV9j7xWf3y6/5OW2zz77bOTj6VAbBuPpUbv8s9bTz/XQ0jJixIhgjxo1KvLxPbRLeasN0VWD3c2eQ0EcBgLidtnwB+tVizCQxU49N0JPrpR9/fXXRz7um3bKvlZhvzQsW7YsOu7Vq1ewbbic+7R95jhk3BZV6hvdNznlwIb51l9//WDvvffeka9WYb9ycD968sknI9/+++8f7CRN7N+Ctk5vyUPfZM1Gjx4d+Xr37h3sww47LPJVuzNFWvjeT5o0KfLZVIG8UJMwH+Oc6wlgFwDPAujqvf+6WMcyAF3LfE3kEGlZLKRncZCWxUJ6tg9S/y+Fc24DAL8DcJ73/gOT8OXLjZ6dc2cBOCtrQ0XtkJbFQnoWB2lZLKRn+yHVzJRzbi2seiAme+/vKf14eWmaEqV/V7T0Xe/9eO/9bt778ku3RN2QlsVCehYHaVkspGf7otWZKbdqKH0rgL96768h1zQApwG4ovTv1Ba+XhU2vs07d9ttOR544IFgjxkzJvLxVgbVxvU5j8dem0s2TJs2LfLxlgRJuVZ2WW4dckkaqiWXRrB5RDNmzAg2L7kHgH333TfYdjuZtHCumt1GZNasWcG++uqrI5/NuSl3TrvLeVvneTWib1p4Wya7xdFzzz0X7GuvvTbyHXPMMcHu2LFjVdfmHBnbj3jrp/PPPz/yJeWvcV+1OTi1zsFsgYZq+dFHHwXb5jMuWLAg2LytBxCXVOjUqbrFaXyvbd+88847g33JJZeU/V4S9SwBBOSjb/K94a1egDj30/YP3uKsZ8+ekS9tH+D3ov27ydvL2L/ZzUyaEcYAAKcAeNE593zpZxdh1cMwxTk3FMDrAI4v832RL6RlcVDfLBbSsjiob7YzWh1Mee+fAlBuOHpAbZsj2pqEVQnSsslQ3ywW6pvFQX2z/ZHLCuiW7bbbLthLliyJfDw1bacgeZrTLvfkqq92WjntPUlaRs3TnHmi3lWWLZtuummw7X3nkGrSUmYb5uvWrVuwFy1aFPnS6sDXS3qO8kQeqixzP7LlQcrtJADEetowHy+957AwEJcycAlVj5PCEfUO+aSl0X2TUyHsM5+0kwDfT1ueZtCgQcG2qRA2NNvS+Sz1LnFQLXnom6yTvU98nJSOsOGGG0bHSXrakjLlrs0USU/tzSeEEEIIkQENpoQQQgghMqDBlBBCCCFEBpoiZ6rINHqbg2qRlo0nD3kZonaobxYH9c1ioZwpIYQQQog2RoMpIYQQQogMtO1236JieJlqXpfki5ZpxuX4QgghsqOZKSGEEEKIDGgwJYQQQgiRAQ2mhBBCCCEyoJypBpNU5l80F9JOCCHaJ5qZEkIIIYTIgAZTQgghhBAZ0GBKCCGEECIDGkwJIYQQQmRAgykhhBBCiAxoMCWEEEIIkQENpoQQQgghMqDBlBBCCCFEBjSYEkIIIYTIQL0roL8N4HUAm5TsRtPe2rFlDc8lLctTj7bUUktgVXs/Qvu6h2lQ38xOXtoBqG/Wgrzomau+6RqxBYZzbpb3fre6X1jtqDl5aXte2gHkqy2VkKd256UteWlHNeSl7XlpB5CvtlRCntqdl7bkpR1fozCfEEIIIUQGNJgSQgghhMhAowZT4xt0XYvakZ28tD0v7QDy1ZZKyFO789KWvLSjGvLS9ry0A8hXWyohT+3OS1vy0g4ADcqZEkIIIYQoCgrzCSGEEEJkoK6DKefcIc65ec65V5xzI+t87duccyucc3PoZ52dc4865+aX/u1Uh3b0cM497pyb65x7yTk3olFtyYK0LI6WgPQsXbMQekrL4mgJSM9m0bJugynnXAcANwA4FEBfACc65/rW6/oAJgI4xPxsJIAZ3vs+AGaUjtuaLwCc773vC2AvAOeU7kMj2lIV0jLQ9FoC0pNoej2lZaDptQSkZ4nm0NJ7X5f/AOwN4BE6HgVgVL2uX7pmTwBz6HgegG4luxuAefVsT+m6UwEclIe2SMv2p6X0LJae0rI4WkrP5tKynmG+7gAW0fHi0s8aSVfv/dKSvQxA13pe3DnXE8AuAJ5tdFsqRFoamlhLQHquRhPrKS0NTawlID0j8qylEtBL+FXD27otbXTObQDgdwDO895/0Mi2FA1pWSykZ3GQlsWinvcw71rWczC1BEAPOt689LNGstw51w0ASv+uqMdFnXNrYdVDMdl7f08j21Il0rJEAbQEpGegAHpKyxIF0BKQnihdJ/da1nMwNRNAH+dcL+fc2gBOADCtjtdviWkATivZp2FVLLZNcc45ALcC+Kv3/ppGtiUD0hKF0RKQngAKo6e0RGG0BKRn82hZ58SxwwC8DOBVABfX+dq/AbAUwOdYFXceCmBjrFoFMB/AYwA616Ed+2DVdORsAM+X/jusEW2RltJSehZPT2lZHC2lZ/NoqQroQgghhBAZUAK6EEIIIUQGNJgSQgghhMiABlNCCCGEEBnQYEoIIYQQIgMaTAkhhBBCZECDKSGEEEKIDGgwJYQQQgiRAQ2mhBBCCCEyoMGUEEIIIUQGNJgSQgghhMiABlNCCCGEEBnQYEoIIYQQIgMaTAkhhBBCZECDKSGEEEKIDGgwJYQQQgiRAQ2mhBBCCCEyoMGUEEIIIUQGNJgSQgghhMiABlNCCCGEEBnQYEoIIYQQIgMaTAkhhBBCZECDKSGEEEKIDGgwJYQQQgiRAQ2mhBBCCCEyoMGUEEIIIUQGNJgSQgghhMiABlNCCCGEEBnQYEoIIYQQIgMaTAkhhBBCZECDKSGEEEKIDGgwJYQQQgiRAQ2mhBBCCCEyoMGUEEIIIUQGNJgSQgghhMhApsGUc+4Q59w859wrzrmRtWqUaAzSszhIy2IhPYuDtCwmzntf3Red6wDgZQAHAVgMYCaAE733c2vXPFEvpGdxkJbFQnoWB2lZXNbM8N09ALzivV8AAM65uwAMBlD2oXDOVTdyEzXDe+/KuCrSU1o2nlppWfqM9Gww6pvFQX2zWCToGcgS5usOYBEdLy79LMI5d5ZzbpZzbla1F3LOhf/qTSOvXWda1bPZtWxHtIu+2Y6oS98UdaFufVPUlywzU6nw3o8HMB7QCLvZkZbFQnoWh1qTkObvAAAbW0lEQVRoyQPiatM/RG2Qns1HlpmpJQB60PHmpZ+J5kR6FgdpWSykZ3GQlgUly2BqJoA+zrlezrm1AZwAYFptmiUagPQsDtKyWEjP4iAtC0rVYT7v/RfOueEAHgHQAcBt3vuXatYyUVekZ3GQlsVCehYHaVlcqi6NUNXFlJfRcNKsSkiDtGw8tdISkJ55QH2zOKhvFou2Xs0nhBBCCNHu0WBKCCGEECIDbV4aodnR8lIhhBBCJKGZKSGEEEKIDGgwJYQQQgiRAQ2mhBBCCCEykMucKbvP1xprlB/zcR7TV199lfqcHTp0CPahhx4a+T744INg77TTTsGeMGFC9LmVK1eWvZ5oGb7vFtavkvw01rZ3796R75NPPgn2EUccEexZs+Itr/7yl7+UvXbSc9XeYT1rdd9Yz06dOkW+zz77LNhHHnlksHv27Bl97v777w/2RhttFPn+9Kc/lW3jl19+Gez2liPJ71n7u9fiXqy33nrR8eeffx7sY489NtjDhw+PPsca9enTJ/Idf/zxwZ4/f37ke/vtt4PNurYXkvbLrIWea64ZDx9YJ9bl6quvjj7XuXPnYNtnYtq0f9YvvfXWWyPfQw89FOwvvviiiha3LZqZEkIIIYTIgAZTQgghhBAZyGUFdDs92bFjx2Dbaf911lkn2EuXLo18G264YbAPOOCAyHfOOecEe5dddol8PF288cYbB/vAAw+MPjd37tyWfwGDncrs2rVrsJctWxb5Pv3001TnrJZGV1led911g73++utHPp4m/vDDDyMfTylvsskmkW/IkCHBHjFiRORjjXbeeedgcxgIAM4888xgc/jBtmuPPfaIfPw7PP3005Hv448/RluShyrLa621VrC5LwJxaOUf//hH5OOQEvdvANh7772Dff3110e+efPmBfuggw4Ktn1e/vM//zPYS5bE+8jOnj072GPHjo18Tz31VLDHjRsX+ew1ak2j+yZrwroCcR+wITMO9drvbb311sG29/Ott94K9uDBg8u2i/uRvfbvf//7YO+7776R77HHHgs2928AeO+998perxbkoW/y31GbXsF/9+0YgJ8DG8rjd+/ll18e+Th8N2jQoBbb0dL1GE6xsX8fuN/av8V///vfy56zFqgCuhBCCCFEG6PBlBBCCCFEBjSYEkIIIYTIQC5zpmwpBM59sjF5jtNeeumlkW/gwIHBtrlWfA1ePg8Ap59+erA5h2n69OnR55KW23Kc+Oyzz458I0eODLbNsxk6dGjZdtViiX698zJsvJzzx2wcn3Nujj766Mh33HHHBXvHHXcse077PI8ePTrYm222WbBtvD8ph4LbxctzAWD33XcPNufb2Dbb/Kk8aQlUn5fBORVWT9Zln332iXwHH3xwsAcMGBD5uJQB930A+PWvfx3sfv36BfuSSy6JPvfMM88Em/P0AGCHHXYI9iOPPBL5vvGNbwSbczSAOO+SczuA2iy9b3TOVFKODWvJ9x2Ic05tmZlu3boFe6uttop8zz77bLBZk3vuuSf6HL/XOU8OiPsYl1cA4ne8zZvr379/sG2+TS2W3eehbzL2byq/03r06BH5unfvHuxjjjkm8m2zzTbB5ncfEOcs8zlff/316HPnnntusC+77LKy17blTvj5fPfddyMfPz+ciwfUT0/NTAkhhBBCZECDKSGEEEKIDOQyzGfhKUobNuLwgZ2yX3vttcue8/333w/2f//3f0e+m2++OdhcAd2eP2n6kJd722X4HH60v8/ixYuDPXHixMjHU6J2+X5a8hRKsL87hwE4BADE4Rc7Zc2VjmfMmBH5WNv99tsv2Nddd130uaSSFBtssEGwuXo2AGy33XbBtr8Pl72wS/zHjBkT7EZrCdQmlGB/f57qtyFQXmJtl1+znosWLYp8vLx+r732CjaHzoG4jIFtF4cRbdVsLoVi4dDef/3Xf0W+X/7yl8HmKu2V0Oi+mQSXc/nf//3fyLflllsG2y5n53tm/9bweb71rW8F++STT44+xyEdqyWnebzxxhuRz5bcYDiFglMBgFjbRmsJtI2enPZi/8707ds32Kw7EJc4sVq/9tprLX7OljHgMFxSSo8tdWRLrzCsE5c9AoA77rijxXZVgsJ8QgghhBBtjAZTQgghhBAZ0GBKCCGEECIDa7b+kcbDsXZbGoHjsUn5XzbWzrH8xx9/PPIdddRRwR42bFjZzyXlTPFS+N/+9reRj3N3bMy4S5cuwf7Rj34U+TbffPNgcwkFoHl2uOd22t+dlysnbXFgv8f5TjY3h3cv52XVdhuRJFauXBlsW37jrrvuCrZ9xjgv6KKLLop8nBswatSoyNcsWlrs79+rV69g8z0E4ntj9eQyA6+++mrk420quNSFPT9j7ycvhbfbjPCyfPv7cO7c+eefH/neeeedYNsczGYk6Vm222hxDprNo+ESC7YEAT8frLNd9s5YLfm+2y1puJSN/X24XIbNt3vuuedaPEczk7RF2wMPPBD5uP/ZHELOb7R//9h3++23B9uWKmBsiRh+RmyuFW8dZOH8aFtu4Q9/+EOw//a3v5U9R1Y0MyWEEEIIkQENpoQQQgghMtB0YT47Xck7zFsfYysUP/HEE8G2Sy653AIv2bWfs1WtyzFhwoTo+KSTTmrxWkA8XWlLO3z/+98P9gUXXBD5eHq0FtW1GwGXGbC/Az8Ddnkrl1Hg0A8QV2fmEES198hWZ+ZSCbxUH4inve0y7R/+8IfBtkuz0z5XeWfTTTcNtq3mz+UguIwBAPz5z38ONi99B+LwKJdQqDY0OnXq1Oh43rx5webnEYjDkVwRHIhD8jfeeGPkq0UF5npj36V8zEvgAWDrrbcO9ptvvhn5WJctttgi8vH9fPDBB6tu69fYsii8tN6+F/j3se/14cOHB/vRRx+NfM0agrfwMzlz5szIx+FsW718//33DzbrDsTaT5o0KXMbbcrG8uXLg21LNrCeSalACvMJIYQQQuSUVgdTzrnbnHMrnHNz6GednXOPOufml/7tlHQOkR+kZ3GQlsVCehYHadn+SDMzNRHAIeZnIwHM8N73ATCjdCyag4mQnkVhIqRlkZgI6VkUJkJatitazZny3j/pnOtpfjwYwMCSPQnAEwB+WsN2lYXjuUAcO01aYm3j/Fx+fsqUKZGPty5hOM8KAHbeeedW22uvBcRxZ5svwnk2dpsNjgXbfI5nnnkmVVvypKfNCevTp0+w7a71H330UbBffPHFyMc7wtsl6za35WuGDBkSHd9yyy0pWrx67h2XubC5TknlHDh/i/PyAGDhwoXBTsrRyJOWwOqadevWrayPl8LPmjUr8vG2IJdccknkK7elBC/dB+J8qiTs/d1jjz2CzVtOWezvw+8hmx/HZVhaaUtu9EzKP7U5YPfdd1+wbc4U57hdc801kY9zqPbcc89g2/uXNr/Rarn77rsHm98RFvse2nbbbYNt38Fpt37Kk5bA6npy3qn9G8SlfOzf2zlzwkQbxo8fH/m+853vBPuxxx4LNm9Pk4UBAwYE+5VXXin7uW9+85vRMed22fdwLfOLq82Z6uq9/zq7bxmArkkfFrlHehYHaVkspGdxkJYFJvNqPu+9T9qI0Tl3FoCzsl5H1IckPaVlc6G+WSzUN4uD+mbxqHYwtdw51817v9Q51w3AinIf9N6PBzAeqH73a56itNP5vKu89S1evDjYDz/8cOTjMgN2KrMcXEk1Cxwq4qXYQPIUM98HWwE9bZivDKn0rLWWXIUYiJdH2+lXnl62FXUvvPDCsucsh62sXS2spQ3n8HSzDQvxdDOHfQHgtttuC3YVS7Hr2jcZO4XOZQcWLFgQ+Xia3vbb6667Ltg2BMPwvbHh12rh89iQjl1yzXD4kSt7A8Ds2bOD3VZ61lpL205eUn7ttddGPt6BgKuhA8BNN90U7KT3WVuUc+EQfyW7Y3A5Dg5TAunDx2VoWN9M2gXgvffeK/tZ+8zz382k8hlcmb5WbLPNNqk+Z9+1HE627xN+drNSbZhvGoDTSvZpAKYmfFbkH+lZHKRlsZCexUFaFpg0pRF+A+BpANs65xY754YCuALAQc65+QAOLB2LJkB6FgdpWSykZ3GQlu2PNKv5TizjOqDGbRF1QHoWB2lZLKRncZCW7Y+m2E6GY7FcUh6IY/m8NBOIcxfOO++8yJc2T4qpxZYHFruTus07KceyZcui42bcQobLHQDA888/H+zTTjst8vGSdV5GDQA9evRIdT3OBbDbJFQLx+BtfkFaLefPnx8dN6OWwOo5RtxXt99++8jH23tcfPHFkW/XXXdNdT1eol+r3IeePXsGOynHx8K+pC1VmgX7DPKxLY3A9+mEE06IfIMHDw520v3jMhS1ul/nnntuqmvb6/HWR7ZsQLPSSomVsj6b/8d9NemeZswtA7D6+3PcuHGpvmd/nyeffDLYtkxRLdF2MkIIIYQQGdBgSgghhBAiA00R5uMpZhsa4mXHdgqPl7XaKfu08LJRni6sBHttDjHaZf5pQzx33XVXdNwsoQRupw0L8dSwDZUceeSRwbZL6dPCYSd7/iR4OpuregPAoEGDgl2JBvxZWwG8WbHPLuvLZUqAuLL5d7/73aqux2FhrujcGhw+OOmkkyLfT3+ariC11ZrfE22xLDxP2Hvdr1+/YF955ZWRLykUxIwZMybYlfQjfrdyKQ5g9ZIj5Vi5cmV0/LOf/SzYbRkWyiucrmD/5qUNl15++eVVXZur39tyKl26dEl1DvsccHmOtvw7qZkpIYQQQogMaDAlhBBCCJGBpgjzMTY0xJsS201Qqw3tcbiCpwhtGIOnQ+3qwMmTJwfbrj7jlWS2anZSm3mK0q7ma0aSVtF079498m288cZVXYMrWvPGnHalCK/Ks5V2H3300WDbqWZe2WQ3Ok6aUuawUC2r8OYJ7i92M+fjjjsu2GlXPQJx/+dK6fYcXAX5gAPi1eh33313sO2muoxdtcbYUBeHLZt1NWZa7KrVGTNmBLvcRtQtwSvleKW01ZLfibzbAQBceumlLX7OYvsivxcmTZoU+e68886kZhcOG7qbMmVKsHkD79bgNArerNyen9+1DzzwQOTjvlrJCkx+n9qV4PV6v2pmSgghhBAiAxpMCSGEEEJkQIMpIYQQQogMNEXOFMdO119//cjHy1irqWoOrJ7/wMvfORZrd6MeOHBgsG119KQd5rlkw5IlSyJfUp4N5xS9++67ZT/XLNiY+MiRI4N94IEHJn62HDbPhXN1uNq8zcvgWL3V0urOcPzf5r8lxeonTJgQbM7fKBJcwoLzlIDke8rYe8M5HNw37fPxL//yL8G2eRlpc7S4KjcQ58RxXgmwet5N0eD7e++990a+zp07pzqHzXfl9yyXvLFa8u4Ho0ePLtuuJBYuXBgd33LLLcEeO3Zs5EvKlSsitoTEUUcdlep7tmwE7x6QVFKCS6HY93xaHnvssej4xBP/uXuPLZ9ULzQzJYQQQgiRAQ2mhBBCCCEy0BRhPsYuf7VTx2nh8IGdpi43TWjDA9OmTQt2UljPwuGm22+/PfKdeuqpwd50000j3y9+8YtgFyE0ZKfo+f6mnb4H4tCoLaFgQ2/lzn/fffcFO20ICogr2Ntq2jvuuGOw+/TpE/k4PF1UeJn8uuuum/p7SXra0NvXWD2nT58e7EpKLyxdujTYO++8c+TjsK0tf2DLYhQNvr8czqkE+z5LG45hLSt5L/AG4naj7SK8P2vFTjvtVNX3bJmYcqG9pHdtJTzzzDPB/t73vlfVOdoSzUwJIYQQQmRAgykhhBBCiAxoMCWEEEIIkYGmyJnifKQRI0ZEvrQ7SVt4WXXafIfDDjssOk6bB2Jjyffff3+w7bLcW2+9tez5//a3v6W6Xp7h/BW7bcuwYcNa/Fxr8K7vvEVFEnZJblotbc7O0KFDg83bathjmzdQxC1H7FYivH1PJTlTXAKkXM6bZe+9946O11tvvVTf4y0wgLikAufDtTfs83rwwQcH2+b/JcHv1nL5bpZdd901Ok6rJW/TBQC77LJLsJUjFcMlhq666qrU3+O8wbR9s1+/ftFx2i2HOOcNAPbZZ59U32sUmpkSQgghhMiABlNCCCGEEBloijAfl0PYbbfdIh+XRqhkSXunTp2Cffjhh0c+rpjMU5JctboSTjnllOj4oYceCrYNMXKYquhT01wBGYinjVmf1uAp6z333DPy8XJaDhfbkhRJ8FL9rbbaKvKlrUSfVNm+KNj+xzvHVwL3ORsimDNnTovXu/POO1Ofn/vV5ptvHvnaW/XrtHDJiEpgLbfccsvIx2E5DutXUk2e3/+9evWKfO2hz1VLtX9b+B3KuxEAccicw8Q333xz6vPzbiQ2DSTvaGZKCCGEECIDGkwJIYQQQmRAgykhhBBCiAw0Rc4U50a88847kY+X21ay/Jpj9LwtDBDHbTlGXMlWBg8//HCwf/vb36b+XtHhe8j3GYhzKGx+RRKs5dNPPx35eJk9529UUnphzJgxwU6bIyWAhQsXBtvmryT1Je7vs2fPjny8BUnHjh2DXYmeQ4YMCbZypNKRtJVOUq4q+xYsWBD5uDzNhhtumOp8Fi5loRyp9HB/sVuy2S3bGNZm8eLFke/NN98M9mabbZbqfJatt9469WfzRqtvIOdcD+fc4865uc65l5xzI0o/7+yce9Q5N7/0b/qMYdEwpGVxUN8sFtKyOKhvtj/S/O/cFwDO9973BbAXgHOcc30BjAQww3vfB8CM0rHIP9KyOKhvFgtpWRzUN9sZrc6/ee+XAlhasj90zv0VQHcAgwEMLH1sEoAnAPy0LRrJFcQffPDByMdhP66gDaSvnGtZe+21U32Op5W5cjkA/OAHP6jq2m2N9/650r8N0ZJDBLZi8U033RRsu5M5hwEqCbdW8wxMmTIlOr7wwgsrPkc9yEPfZOxya64AbyuN87LqJD2tb4MNNqi4Xffee290XElZjHrS6L6ZBKdTvPbaa5GPy4UkaWlDsZ07d664Hffcc090PGvWrIrPUQ/y1jctHK57+eWXI9+OO+4Y7KTwuQ3fbbHFFhW3g8sEAcCiRYsqPkdeqCgB3TnXE8AuAJ4F0LX0wADAMgBdy3xN5BBpWSykZ3GQlsVCerYPUmeGOec2APA7AOd57z/g/wPx3nvnXIvZf865swCclbWhonZIy2IhPYuDtCwW0rP9kGpmyjm3FlY9EJO991/Psy53znUr+bsBWNHSd7334733u3nvd2vJL+qLtCwW0rM4SMtiIT3bF6615aRu1VB6EoB3vffn0c+vBvCO9/4K59xIAJ2994nJJeVG4a3B5Qls+QNesvuv//qvke/cc88Ntt1mJO1Sas4DsVsqjB07Ntjjxo2LfDlecn0HGqgl/5+ZXQLNz+K+++4b+X784x8H+9BDD418aZdS8/ltiYNf/epXwR49enTky7GWa6DBfdOco+xx//79I9/w4cODfdJJJ0U+7u9JsJ5cAgOIy52cfPLJkS/H2zQ1tG+2cs5g2y1+RowYEexTTz018qXNP2XsUv0///nPwR4wYEDks2UackSu+mYL5wz2tttuG/kuuOCCYNut0NL2Tcb2tyVLlgTbbgGUVz29960m6qYJ8w0AcAqAF51zz5d+dhGAKwBMcc4NBfA6gOOrbaioK9KyOKhvFgtpWRzUN9sZaVbzPQWg3KjsgNo2R7Q1CSNsadlkqG8WC/XN4qC+2f5oNcxX04vVIDSU1N6k0J1dhnvssccGe+rUqZFvxYp/hrH5eknXtiGOvFbjTTNdmYa2mHpmrJZ83KVLl8jHJTHsUlteOs3hOjudnLSku+haAvXXk0OzVs+RI/9Zemfu3LmRb/LkycHmEL8NDSVRdD3bWksbVufQD1e+BoCrr7462B9++GHku+yyy4LNJW7s55LewUXXEmh7PW2JA06l6d27d+TjdAgbwuX0CO63HNYD4rCfTaFoZj21N58QQgghRAY0mBJCCCGEyIAGU0IIIYQQGWiKnClRO5olL0O0TjPlZYjWUd8sDuqbxUI5U0IIIYQQbYwGU0IIIYQQGdBgSgghhBAiAxpMCSGEEEJkQIMpIYQQQogMaDAlhBBCCJEBDaaEEEIIITKgwZQQQgghRAY0mBJCCCGEyIAGU0IIIYQQGdBgSgghhBAiAxpMCSGEEEJkQIMpIYQQQogM1H0w5ZyDczXbUFs0EGlZLNZYYw2ssYb+/6oISMti0aFDB3To0KHRzRAJqLcJIYQQQmRAgykhhBBCiAysWefrve29fx3AJgDervO1W6K9tWPLGp5LWpanHm2ppZYA8PZXX331EdrXPUxDU/bNr776Sn2zZZqyb3755Zfqm6uTq77pvPdt3ZDVL+rcLO/9bnW/sNpRc/LS9ry0A8hXWyohT+3OS1vy0o5qyEvb89IOIF9tqYQ8tTsvbclLO75GYT4hhBBCiAxoMCWEEEIIkYFGDabGN+i6FrUjO3lpe17aAeSrLZWQp3bnpS15aUc15KXteWkHkK+2VEKe2p2XtuSlHQAalDMlhBBCCFEUFOYTQgghhMhAXQdTzrlDnHPznHOvOOdG1vnatznnVjjn5tDPOjvnHnXOzS/926kO7ejhnHvcOTfXOfeSc25Eo9qSBWlZHC0B6Vm6ZiH0lJbF0RKQns2iZd0GU865DgBuAHAogL4ATnTO9a3X9QFMBHCI+dlIADO8930AzCgdtzVfADjfe98XwF4Azindh0a0pSqkZaDptQSkJ9H0ekrLQNNrCUjPEs2hpfe+Lv8B2BvAI3Q8CsCoel2/dM2eAObQ8TwA3Up2NwDz6tme0nWnAjgoD22Rlu1PS+lZLD2lZXG0lJ7NpWU9w3zdASyi48WlnzWSrt77pSV7GYCu9by4c64ngF0APNvotlSItDQ0sZaA9FyNJtZTWhqaWEtAekbkWUsloJfwq4a3dVva6JzbAMDvAJznvf+gkW0pGtKyWEjP4iAti0U972HetaznYGoJgB50vHnpZ41kuXOuGwCU/l1Rj4s659bCqodisvf+nka2pUqkZYkCaAlIz0AB9JSWJQqgJSA9UbpO7rWs52BqJoA+zrlezrm1AZwAYFodr98S0wCcVrJPw6pYbJvinHMAbgXwV+/9NY1sSwakJQqjJSA9ARRGT2mJwmgJSM/m0bLOiWOHAXgZwKsALq7ztX8DYCmAz7Eq7jwUwMZYtQpgPoDHAHSuQzv2warpyNkAni/9d1gj2iItpaX0LJ6e0rI4WkrP5tFSFdCFEEIIITKgBHQhhBBCiAxoMCWEEEIIkQENpoQQQgghMqDBlBBCCCFEBjSYEkIIIYTIgAZTQgghhBAZ0GBKCCGEECIDGkwJIYQQQmTg/wM+30u/9gli9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e304b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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SCEEnK/nlyuzlZ/8N5tk/x6FDhybt7du3t7ewBtRzubIeZc+y1qXnrN133z1pb9q0qW01NapVWUrlz3P9+vWp7VGjRlU91n/UfvPmzW2rqVH0zT7ZPjZ8+PCqx/o/Z7dt29a2mhpF39yl1tIIWYMH77q+k32rQSe1fGkEAAAApDGYAgAACMBgCgAAIEDdi3ZCeuKJJ1Lb/iO7W7ZsSe0r0nwv3u+ll15Kbfv3TGWzK9K9GOjfihUrUtv+PVM7d+5M7aNvFlv2fsYDDzwwaed5jy9aY/Xq1altfzmLbJ7+oqZlw5UpAACAAAymAAAAArA0QgP8x3Al6Xvf+17S/ta3vpXa19vbm0tNjeLx6z577rlnavvOO+9M2qeffnpqX/bR3qLg8etdJk6cmNq+//77k/axxx6b2rdx48ZcamoUfbNP9o0D9913X9L2V7aXpA0bNuRSU6Pom7tk+98f/vCHpD1t2rTUvuwSJ0XB0ggAAABtxmAKAAAgAIMpAACAANwzFcB/jLMsj+xyX0b/Bg0alLTL8ug892VU57+Woqj3L2bRN/tkH4/3syzSq7lqoW/uks3Tf51MUe9HzeKeKQAAgDZjMAUAABCAab4uw1RCPJhKiAt9Mx70zbgwzQcAANBmDKYAAAACMJgCAAAIMHjgQ1qr2luhy7K0AHYhy7jstlvf/1tl8yPP8nlvqY9sdjt37uxEOQj03vIQ2TzLsoxLN+DKFAAAQAAGUwAAAAHynuZ72zn3qqS9Jb2d87n70211HNjC70WW1eVRSyuzlKS3d+7cuVHd9WdYj1L2zR07dtA3+1fKvtnb20vffL9C9c1c15lKTmo23zl3ZO4npo6WK0rtRalDKlYtjShS3UWppSh1NKMotRelDqlYtTSiSHUXpZai1PEepvkAAAACMJgCAAAI0KnB1OwOnTeLOsIVpfai1CEVq5ZGFKnuotRSlDqaUZTai1KHVKxaGlGkuotSS1HqkNShe6YAAABiwTQfAABAgFwHU2Y208xeNrOlZnZlzue+1cxWmdkL3mdjzewhM1tS+X1MDnVMNLNHzGyhmb1oZpd2qpYQZBlPlhJ5Vs4ZRZ5kGU+WEnmWJcvcBlNmNkjSzZJOkzRV0rlmNjWv80u6TdLMzGdXSprrnJsiaW5lu916JV3unJsq6VhJl1T+HDpRS1PIMlH6LCXy9JQ+T7JMlD5LiTwrypGlcy6XX5L+R9Icb/sqSVfldf7KOSdJesHbflnS+Ep7vKSX86ynct67Jc0oQi1k2X1ZkmdceZJlPFmSZ7myzHOab4Kk173tZZXPOmk/59yKSvtNSfvleXIzmyTpcElPdrqWBpFlRomzlMjzfUqcJ1lmlDhLiTxTipwlN6BXuL7hbW6PNprZSEl3SrrMObe+k7XEhizjQp7xIMu45PlnWPQs8xxMvSFpord9QOWzTlppZuMlqfL7qjxOamZD1PeX4lfOubs6WUuTyLIigiwl8kxEkCdZVkSQpUSeqpyn8FnmOZh6WtIUM5tsZj2SzpF0T47n7889ks6vtM9X31xsW5mZSfo/SS85527sZC0ByFLRZCmRp6Ro8iRLRZOlRJ7lyTLnG8dOl7RY0r8lfTvnc/9G0gpJ29U373yBpL3U9xTAEkkPSxqbQx0fV9/lyH9J+mfl1+mdqIUsyZI848uTLOPJkjzLkyUroAMAAATgBnQAAIAADKYAAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIACDKQAAgAAMpgAAAAIwmAIAAAjAYAoAACAAgykAAIAADKYAAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIACDKQAAgAAMpgAAAAIwmAIAAAjAYAoAACAAgykAAIAADKYAAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIACDKQAAgAAMpgAAAAIEDabMbKaZvWxmS83sylYVhc4gz3iQZVzIMx5kGSdzzjX3hWaDJC2WNEPSMklPSzrXObewdeUhL+QZD7KMC3nGgyzjNTjga4+WtNQ59x9JMrPfSjpDUtW/FGbW3MgNLeOcsyq7GsqTLDuvVVlWjiHPDqNvxoO+GZcaeSZCpvkmSHrd215W+SzFzGaZ2Xwzmx9wLrTfgHmSZWnQN+NC34wHfTNSIVem6uKcmy1ptlTOEbZZ9QFps1OkZVX2LJFGnvEgy7iQZ/mEXJl6Q9JEb/uAymcoJ/KMB1nGhTzjQZaRChlMPS1piplNNrMeSedIuqc1ZaEDyDMeZBkX8owHWUaq6Wk+51yvmX1F0hxJgyTd6px7sWWVIVfkGQ+yjAt5xoMs49X00ghNnayEc7+x3TNVz1MJ9ShjlrFpVZYSeRYBfTMe9M24tPtpPgAAgK7HYAoAACBA25dGKCN/am+vvfZK2tlpvXfeeSe3mtAcP8tRo0Yl7Z07d6aO27BhQ241oTWGDRtWdd+WLVtyrAShBg/e9U9R9taK3t7epF3GWyu6kZ9hNk8/w5jy5MoUAABAAAZTAAAAARhMAQAABOCeKUmTJk1KbV900UVJ+9JLL03azz33XOq4Y489NmnHNPdbZuPGjUttn3XWWUn7hhtuSNqvvPJK6rjp06cn7R07drSnODRszJgxqe0TTjghaf/yl79M2mvXrk0dN3Xq1KT97rvvtqk6NGL06NGp7UMOOSRpP/zww0k7+7P0uOOOS9oLF1Z9HzByNnTo0NS2/7N3wYIFSXvEiBGp46644oqkfdNNN7WpuvxxZQoAACAAgykAAIAApZjm8x+t3G239Piv2SkZ/9Lj5Zdfntp32mmnJW3/UqY/FSQxtdcMP8tBgwal9vmPQDfCf0T+m9/8ZmrfGWeckbT9zP0pBun9SyWgPn5/zOa5ffv2pr6n3+e+/e1vp/adffbZSXvkyJFJOzuVwNIIjWtH3/SzvPbaa1P7PvOZzyTtPfbYI2lnf66+9dZbTZ0bu2T/3Wz2553/s/a6665L7TvnnHOStr+kUNayZcuaOnfRcWUKAAAgAIMpAACAAAymAAAAApTinil/Dr3WPVLZZev97ew8/ObNm5P2NddcU3Xf17/+9aS9cuXK+gpGVX4O2fswsvnVa9u2bUn7lltuSe074ogjkvbkyZOT9vr166vWhfr5f27Ze6SazdPv4//4xz9S+84999x+vyb7OqBm79fqZu3om/7XZTPJLpXwHv/nr8Q9U62QvUeq3jyz91r59yYedthhqX1jx47t93tk/y79+c9/ruvcZcOVKQAAgAAMpgAAAAKUYpqvFWpN46xevTq1vXXr1qTtXw5dt25d6wtDws+o1mXobJb+9pIlS1L7/LfR+zZt2tRMicio1a+azdOfFpg3b15qX09PT7/fg6UQ2qvZLP2fpdnpnUsuuaTf70GW7Vcrz1q3x/hvE8j+rD311FP7PVe3/KzlyhQAAEAABlMAAAABGEwBAAAEsDwfCTeztp4sO/fb7H/b0UcfnbSffPLJpJ19/HrUqFFNff9Ocs4194xzRruzbJWrr746aftLYGTn8Xffffe8SmqZVmUplSfPxx57LGkff/zxSXvt2rWp48aMGZNbTa0Se9/MPmbvLzWz99579/u5JI0bN669hbVBN/TN7FIIb775ZtIeMmRI0l60aFHquOySCmVQT55cmQIAAAjAYAoAACBA6ZdGqPUYZ7MWLlzY7/f0L12iHO67776k7U/zDRo0qAPVINQTTzyRtP1pPvIsvuwq3MuXL0/a/jSf/0YDtEcr/t3MLhXkL4Ph/1v5xhtvNPX9y4YrUwAAAAEGHEyZ2a1mtsrMXvA+G2tmD5nZksrv5bvbs0uRZzzIMi7kGQ+y7D71XJm6TdLMzGdXSprrnJsiaW5lG+Vwm8gzFreJLGNym8gzFreJLLvKgPdMOeceM7NJmY/PkHRSpX27pEclfbOFddWtHUs7DBs2LGn7c8tDhw5t+bnyVvQ8W63aI/JkWU577rlnv5+XcZmSrG7Ls9rP7okTJ+ZcSesVPctW/Lu5Y8eOmtvv+eQnPxl8rjJo9p6p/ZxzKyrtNyXt16J60BnkGQ+yjAt5xoMsIxb8NJ9zztVaVMzMZkmaFXoe5KNWnmRZLvTNuNA340HfjE+zg6mVZjbeObfCzMZLWlXtQOfcbEmzpeKu5Jo1adKkuo7zV/TNPvZbMnXlWcYsDz/88LqOGzx4V1fo7e1tVzl5iLpv7r///nUd1215ljHLenVbllI58/TfKrHHHntUPS6ifzdTmp3mu0fS+ZX2+ZLubk056BDyjAdZxoU840GWEatnaYTfSPqHpEPNbJmZXSDpekkzzGyJpFMq2ygB8owHWcaFPONBlt2nnqf5zq2yqztu0Y8MecaDLONCnvEgy+5T+tfJtEO9j+YecMABSfu1115rVzkIUO8b56dNm5a0n3vuuXaVg0A9PT11HXfKKack7b/+9a/tKgcB/GVnajn33F3jkjvuuKNd5SCQfy9ULRdeeGHS/tnPftaucnLH62QAAAACMJgCAAAIwDRfP+p9y/UDDzyQtP1pIhTH888/X9dx999/f9KeMGFCu8pBoKeffjppn3baaVWP+/3vf5+0s6vgV1upGfmaN29e0p4+fXrV437+858nbb+fStI777zT+sLQlMcffzxpf/azn6163M0335y0n3322dS+p556qvWF5YQrUwAAAAEYTAEAAASwdrwouOrJSrKSq//E0JYtW5J29ukTfzXe7Itzi7qyq3OuvkdoBlCWLP0X465Zs6bqcf7UT/aluZs3b259YS3Qqiyl8uTpT8EuW7as6nH+z7VDDjkktW/p0qWtL6wFuq1v1pul7xvf+EZq+4YbbmhpTa3SjX3z4IMPTtpLliyp62vmzJmT2p45c2ZLa2qVevLkyhQAAEAABlMAAAABGEwBAAAE4J6pAcydOzdpf+ITn6h63KGHHpraXrx4cdtqCtFt92X4/FXqa61y//GPfzy1/be//a1tNYXoxvsyfKtXr07a2eUPfFdddVVq+/rri/lKtG7um++++27SHjlyZNXj5s+fn9o+6qij2lZTiG7vmxs3bkzaI0aMqHrc2rVrU9u1+nEncc8UAABAmzGYAgAACMAK6AM488wzk/b69eurHjd58uTUdlGn+brZ8ccfn7RfeeWVqseNHj06h2oQasaMGUk7O/3jq7UkBorBXzG71ouply9fnkc5CPSFL3whaf/xj3+setyGDRvyKCcXXJkCAAAIwGAKAAAgAIMpAACAANwzNQD/kd1aFi1a1OZKEOrVV1+t67iXXnqpzZWgFZ555pm6jnv66afbXAlCPfjgg3Ud99hjj7W5ErTCXXfdVddx//nPf9pcSX64MgUAABCAwRQAAEAApvkakF0t3mzXoqiDBg3Kuxy0SW9vb6dLQB3qfXvDunXr2lwJQtWb5YIFC9pcCVqh3jzvvffeNleSH65MAQAABGAwBQAAEIDBFAAAQADumWqRVatWdboEtAj32MSFPOOxYsWKTpeAFoppGZoBr0yZ2UQze8TMFprZi2Z2aeXzsWb2kJktqfw+pv3lIhRZxoO+GReyjAd9s/vUM83XK+ly59xUScdKusTMpkq6UtJc59wUSXMr2yg+sowHfTMuZBkP+maXsXofYUy+wOxuSTdVfp3knFthZuMlPeqcO3SAr23sZAWzY8eO1PZuu+0ai37ta19L7fvRj36US02Ncs4l6zl0c5Y7d+5MbfvLXFxxxRWpfT/84Q9zqalRfpZSd+dZ6+fYTTfdlNr+6le/2u5ymkLf7FMry4ceeii1feqpp7a7nKbQN3eplWf27QRHH310u8tpSjbP/jR0A7qZTZJ0uKQnJe3nnHtvAvtNSfs1WB86iCzjQp7xIMu4kGd3qPsGdDMbKelOSZc559b7/yfvnHPVRs9mNkvSrNBC0TpkGRfyjAdZxoU8u0ddV6bMbIj6/kL8yjn33hsMV1YuU6rye7+PsznnZjvnjnTOHdmKghGGLONCnvEgy7iQZ3cZ8J4p6xtK3y5ptXPuMu/zH0p6xzl3vZldKWmsc+6Kat+n8jWlnvvdtGlTanv48OFJe9GiRal9hx12WC41NeEOkeX7XhkPT8ssAAAFlElEQVTjvw5o4cKFqX3Tpk3LpaYm7Cb6pqTa92UsXbo0tT1lypR2l9Ms+qZqZ5ldGmH//fdvdznNom9W1Mpz+fLlqe0JEya0u5ym1HPPVD3TfMdJ+ryk583sn5XPviXpekm/N7MLJL0q6X+bLRS5Ist40DfjQpbxoG92mQEHU865JyRVG5V9srXloN1qjLDJsmTom3Ghb8aDvtl9Gl4aIehkJb9cWWtphCx/X55/xgOp53JlPcqeZSOZxJ6l1F15+jcBFwl9sw9ZppFn57V8aQQAAACkMZgCAAAIwGAKAAAgQN2LdkLauHFjanvUqFFJOzsv7D9qn30MH523ffv21PaQIUOSdjbLzEJ77S0MTWkkM/8euOxrhVBsZFkO9d77FFOeXJkCAAAIwGAKAAAgANN8Dbj44otT2zfeeGPSPvjgg1P7mNorth//+Mep7QsvvDBpT5w4MbWvbJebu9GCBQtS29OnT0/a48aNS+0jz2J78803U9v77rtv0j7wwANT+8iymPzpu3fffTe1b/fdd0/akyZNSu0rc55cmQIAAAjAYAoAACAAgykAAIAAvE6mAdnXx/T09CTtLVu25F1OU3hlRR9/6QpJGjp0aNLetGlT3uU0hVdW7OIvbSFJw4YNS9rZezaKir7Zx/+5KqXvsVmzZk3e5TSFvrlLtm+OGDEiaa9bty7vcprC62QAAADajMEUAABAAKb5ugxTCfFgKmGX7IrLZVypnr7ZhyzTyp5nDJjmAwAAaDMGUwAAAAEYTAEAAATI/XUygwf3nTI7D75jx468S0EAM0seec1mR5blY2bJI+nZVyGVIc8y3lfTLmaWLPWxffv21D6yLB+/b2bzLPPrV2LDlSkAAIAADKYAAAAC5D3N93Zvb++rkvaW9HbO5+5Pt9Vx4MCH1Mc59/a2bdvIsn951NKyLKW+PLdu3bpR3fVnWI9S9s0tW7bQN/tH3wxXlDwL1TdzXWcqOanZfOfckbmfmDparii1F6UOqVi1NKJIdRellqLU0Yyi1F6UOqRi1dKIItVdlFqKUsd7mOYDAAAIwGAKAAAgQKcGU7M7dN4s6ghXlNqLUodUrFoaUaS6i1JLUepoRlFqL0odUrFqaUSR6i5KLUWpQ1KH7pkCAACIBdN8AAAAAXIdTJnZTDN72cyWmtmVOZ/7VjNbZWYveJ+NNbOHzGxJ5fcxOdQx0cweMbOFZvaimV3aqVpCkGU8WUrkWTlnFHmSZTxZSuRZlixzG0yZ2SBJN0s6TdJUSeea2dS8zi/pNkkzM59dKWmuc26KpLmV7XbrlXS5c26qpGMlXVL5c+hELU0hy0Tps5TI01P6PMkyUfosJfKsKEeWzrlcfkn6H0lzvO2rJF2V1/kr55wk6QVv+2VJ4yvt8ZJezrOeynnvljSjCLWQZfdlSZ5x5UmW8WRJnuXKMs9pvgmSXve2l1U+66T9nHMrKu03Je2X58nNbJKkwyU92elaGkSWGSXOUiLP9ylxnmSZUeIsJfJMKXKW3IBe4fqGt7k92mhmIyXdKeky59z6TtYSG7KMC3nGgyzjkuefYdGzzHMw9Yakid72AZXPOmmlmY2XpMrvq/I4qZkNUd9fil855+7qZC1NIsuKCLKUyDMRQZ5kWRFBlhJ5qnKewmeZ52DqaUlTzGyymfVIOkfSPTmevz/3SDq/0j5ffXOxbWVmJun/JL3knLuxk7UEIEtFk6VEnpKiyZMsFU2WEnmWJ8ucbxw7XdJiSf+W9O2cz/0bSSskbVffvPMFkvZS31MASyQ9LGlsDnV8XH2XI/8l6Z+VX6d3ohayJEvyjC9PsownS/IsT5asgA4AABCAG9ABAAACMJgCAAAIwGAKAAAgAIMpAACAAAymAAAAAjCYAgAACMBgCgAAIACDKQAAgAD/H0WHUFdBewSnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1345cf898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13e05d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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A4IHBFAAAgAfWTPWCNRrhaJQl6zeqjTyrh4wQEu5MAQAAeGAwBQAA4KHoab4NklZJenet3W59qiPHXXqL+v0Y38J/V6WzzHmKoYjfk1ZmKfXU+6bKkaWU4fewoGki+qa/stQh0TdboSx5lqpvFvo6mehLzR51zh1f+BdTR8uVpfay1CGVq5a+KFPdZamlLHU0oyy1l6UOqVy19EWZ6i5LLWWp41+Y5gMAAPDAYAoAAMBDuwZTs9v0vWnU4a8stZelDqlctfRFmeouSy1lqaMZZam9LHVI5aqlL8pUd1lqKUsdktq0ZgoAACAUTPMBAAB4KHQwZWbTzGyZma0ws5kFf/ccM1tvZk/GPhthZgvNbHnt1+EF1DHOzBab2dNm9pSZfa1dtfggy3CylMiz9p1B5EmW4WQpkWdVsixsMGVm/ST9TNK/S5ok6SIzm1TU90uaK2la6rOZkhY55yZKWlQ7ztsOSd9yzk2SdKKky2q/D+2opSlkGal8lhJ5xlQ+T7KMVD5LiTxrqpGlc66QfyRNlvRg7PhKSVcW9f2175wg6cnY8TJJY2rtMZKWFVlP7XvnSzqjDLWQZedlSZ5h5UmW4WRJntXKsshpvgMlrY4dr6l91k6jnXNra+11kkYX+eVmNkHSMZKWtLuWPiLLlApnKZHnO1Q4T7JMqXCWEnkmlDlLFqDXuJ7hbWGPNprZEEn3Svq6c25LO2sJDVmGhTzDQZZhKfL3sOxZFjmYeknSuNjx2Npn7fSKmY2RpNqv64v4UjMboJ4/FLc7537TzlqaRJY1AWQpkWckgDzJsiaALCXyVO17Sp9lkYOpRyRNNLODzaxL0qckLSjw+/dkgaTptfZ09czF5srMTNJtkv7hnLuxnbV4IEsFk6VEnpKCyZMsFUyWEnlWJ8uCF46dJelZSc9J+q+Cv/tOSWsldatn3vkSSSPV8xTAckn/I2lEAXV8SD23I/9P0tLaP2e1oxayJEvyDC9PsgwnS/KsTpbsgA4AAOCBBegAAAAeGEwBAAB4YDAFAADggcEUAACABwZTAAAAHhhMAQAAeGAwBQAA4IHBFAAAgAcGUwAAAB4YTAEAAHhgMAUAAOCBwRQAAIAHBlMAAAAeGEwBAAB4YDAFAADggcEUAACABwZTAAAAHhhMAQAAeGAwBQAA4IHBFAAAgAcGUwAAAB4YTAEAAHhgMAUAAOCBwRQAAIAHBlMAAAAeGEwBAAB4YDAFAADggcEUAACABwZTAAAAHhhMAQAAeGAwBQAA4IHBFAAAgAcGUwAAAB4YTAEAAHjwGkyZ2TQzW2ZmK8xsZquKQnuQZzjIMizkGQ6yDJM555r7QbN+kp6VdIakNZIekXSRc+7p1pWHopBnOMgyLOQZDrIMV3+Pn/03SSuccyslyczuknSepLp/KMysuZEbWsY5Z3VO9SlPsmy/VmVZu4Y824y+GQ76Zlga5BnxmeY7UNLq2PGa2mcJZjbDzB41s0c9vgv56zVPsqwM+mZY6JvhoG8GyufOVCbOudmSZkuMsKuOLMNCnuEgy74x232jodmlLnkiz8b22it5Hyie586dO4suR5LfnamXJI2LHY+tfYZqIs9wkGVYyDMcZBkon8HUI5ImmtnBZtYl6VOSFrSmLLQBeYaDLMNCnuEgy0A1Pc3nnNthZv8h6UFJ/STNcc491bLKUCjyDAdZhoU8w0GW4Wp6a4Smvoy537bL8lRCFmTZfq3KUiLPMqBvhoO+ma/0mqn4OCaPMU3eT/MBAAB0PAZTAAAAHnLfGgHJxzalcj6Ki2zSWcaPd+3aVXQ5aLH+/Xf/lZjup+165BrNGTBgQNQePHhw4tzmzZuLLgeeurq6ovaxxx6bOPfoo7u349qxY0dhNcVxZwoAAMADgykAAAAPDKYAAAA8sGYqJ+eee27U/uAHP5g4t2TJkqg9f/78xDnW3ZRPfL3FNddckzj3yU9+MmrHM5ekxx9/PNe64C+d2d133x21//73vyfOfehDH4rarJ8qn4MOOihx/Pzzz0ft7u7uxLkJEyZE7XXr1uVaF5ozZMiQxPGWLVvqXnvZZZdF7VtvvTW3mhrhzhQAAIAHBlMAAAAe2AG9Rfr165c4fuyxx6L2xIkTE+f+8pe/RO0zzzwzcS7vPNhlue9OPPHEqL148eLEufij9Iccckji3OrVq3Oti12WmxPfPXnTpk2Jc0OHDo3aS5cuTZw75phjcq2Lvuln5cqVieODDz44aqenZQcOHFj3XCvQN/0tXLgwcTx16tS61+6///5R+5VXXml5LeyADgAAkDMGUwAAAB4YTAEAAHhga4QW2XvvvRPH733ve6N2+hUk8UdxebVM+V155ZVRO/5KAyn56oJGj+6iPPbdd9+ovc8++9S97uGHHy6iHLTI+PHj657bunVr4pitLcrv5JNPrnsu/d/NPNZJ9RV3pgAAADwwmAIAAPDANF+LXHXVVYnj+BvL07ck49smoHzS07JnnXVW1I4/Vi9J27dvj9q8ib4abrvttqidzjPu8ssvL6IceBg2bFjUbpTlOeecU0Q5aKH0koq422+/vcBKsuHOFAAAgAcGUwAAAB4YTAEAAHjgdTItkn7UNj5/n/49HjVqVNTesGFDvoWl8MqK3n3jG99IHN944411r50/f37UPv/883OraU94ZUU26TVwu3btqnttvB/HXxVUBPpm38W3Jkm/0isu/voYKbnWMQ/0zea8+OKLUXvcuHF1rxs5cmTiOP1aqFbjdTIAAAA5YzAFAADgga0RPMSn8ho9lnvzzTcnjoue2kPf/OhHP6p7Lj09cOGFF+ZdDjz97Gc/y3zt5z73uRwrga/0dF2jqb3169dH7byn9dAajab24tPzeU/rNYM7UwAAAB56HUyZ2RwzW29mT8Y+G2FmC81see3X4fmWiVYhz3CQZVjIMxxk2Xmy3JmaK2la6rOZkhY55yZKWlQ7RjXMFXmGYq7IMiRzRZ6hmCuy7CiZtkYwswmS7nfO/b/a8TJJpzrn1prZGEkPOeeOyPDvCeoRz89//vNRe968eXWvS7+Z/o033sitpt4456wVeYaWZfz1P43WV9x1112J44suuii3mnrTqixrPxdUnnGN/o5Lb2nS6DVQeaNv9q67uztx3Gj7iv322y9qt2MLGvpm7+Lr2qRkZmnXXntt1P7+97+fW017kufWCKOdc2tr7XWSRjf570E5kGc4yDIs5BkOsgyY99N8rmcIXnfkbGYzJM3w/R4Uo1GeZFkt9M2w0DfDQd8MT7ODqVfMbEzsduX6ehc652ZLmi1V/3Zl+i3WP//5z+teu2zZsqjdzmm9jDLlGVKWaX/+85/rnotP97RzWi+jjuybaZ/4xCcyXTd06NDEcdFTexl0fN8cPnz3Ou1G03rXX3994riEW9DQN5XczqLRtF76v5tFT+31VbPTfAskTa+1p0ua3+BalB95hoMsw0Ke4SDLgGXZGuFOSf8r6QgzW2Nml0iaJekMM1suaWrtGBVAnuEgy7CQZzjIsvP0Os3nnKs3r3F6i2tBAcgzHGQZFvIMB1l2nkxbI7Tsyyo+97t69erE8dixY6N2+vcxvr4q/mbzduPN9D3mzJmTOL744ovrXnveeedF7QULFuRWU1/xZvrdpkyZkjj+05/+FLXNkr9NGzdujNrvfve78y2sD+ibPYYMGZI43rx5c9ROv7Yr/vduo1d6FY2+uVs6l23btkXt+FYkaYMHD04cb926tbWF9UGeWyMAAABADKYAAAC8eO8zFbpLL700asen9dJuuummxHGZpvbQ45xzzonajab1nn/++cRxmab2sNvRRx8dtePTelJyai8+rSBJBxxwQL6Foc8OO+ywqP3ss88mzsWz3LVrV+JcenoX5TBhwoSo/dxzzyXOxaf90stjHnjggajdzmm9ZnBnCgAAwAODKQAAAA88zbcH8V2RN23aFLXTTyXEX4ibfplxo5fltlOnPTEUnwp67LHHonb6Ca/49MHhhx+eOJe+TV0WnfjE0PTp06P23Llz614X/3vtu9/9buLcrFnl3N6n0/rmr371q6j92c9+NtPP/OEPf0gcn3rqqa0sqWU6sW8+88wzUfuII3p9f7MkaeXKlYnj+HRvmd5GwNN8AAAAOWMwBQAA4IHBFAAAgAfWTO1BfFuD+Buu0x566KGofdppp+VZUst02rqMrH++//jHP0btU045Ja9yWqoT1mU0WtvWyJIlS6L2iSee2NKa8hJ630yvK92yZUumn4uvdTzuuONaWlNeOqFvnn322Ynj+++/P9PP/fWvf43akydPbmlNeWHNFAAAQM4YTAEAAHhgmk/vnMrLunv53nvvHbXffvvtltaUl9CnEtKPWMcfv45L/7mPv3Bz586drS8sB50wlfDWW28ljgcNGrTH67q7uxPHAwcOjNplesS6kdD7ZtYc4i82lqRhw4blUU6uQu2b8f9WZv3v5KOPPpo4PuGEE1paUxGY5gMAAMgZgykAAAAPDKYAAAA89G93AWXwwgsvZLruJz/5SeK4KuukOkm9NVJpo0aNShxXZZ1UJxgxYkTUrrdGKi2+Rkqqzjqp0D3//PN9/pkqrpHqFFnXScX/21jFNVLN4M4UAACABwZTAAAAHtgaQdmnBNI7+L7xxht5lJOrEB+/PvDAA6P2mjVr6l4Xz3mvvar//yNCffw6vh1Co2m+hQsXRu0zzzwz15qKEGLfzPp3a//+u1echDDlHkrfTE+5/vOf/8z0c+k3F1QdWyMAAADkjMEUAACABwZTAAAAHjp2a4TRo0dnui4+51/FNVKdYOXKlZmu++lPf5pzJWhGen1F1u0QQlgnFZrjjz8+87Vbt26N2iGskwrRsmXLMl97yimn5FhJ+fV6Z8rMxpnZYjN72syeMrOv1T4fYWYLzWx57dfh+ZcLX2QZDvpmWMgyHPTNzpNlmm+HpG855yZJOlHSZWY2SdJMSYuccxMlLaodo/zIMhz0zbCQZTjomx2mz1sjmNl8Sf9d++dU59xaMxsj6SHn3BG9/GxpHtmNP+LZaMfd++67L2qfe+65udZUhPgjnlXNMr2tQdYpgtAf161qnt/+9rcTxz/+8Y/rXhvf+mLcuHG51dQOIfTN7du3J44HDBhQ99qurq6o3d3dnVtN7RBK3+zL+CC0v1/jWr41gplNkHSMpCWSRjvn1tZOrZOUbRESSoEsw0Ke4SDLsJBnZ8i8AN3Mhki6V9LXnXNb4qNQ55yrN3o2sxmSZvgWitYhy7CQZzjIMizk2Tky3ZkyswHq+QNxu3PuN7WPX6ndplTt1/V7+lnn3Gzn3PHOueyPeSA3ZBkW8gwHWYaFPDtLr3emrGcofZukfzjnboydWiBpuqRZtV/n51Jhi/Tr1y9xnPXN5B//+MfzKKedKp/lnDlzMl97//3351hJe1W5b8bXvc2aNSvzz4W2TiqlkllOmDAhajdaIxXfCkEKb51UXJX75h133JHpussvvzznSqolyzTfFEmfk/SEmS2tffZd9fxhuNvMLpG0StIn8ikRLUaW4aBvhoUsw0Hf7DC9Dqacc3+WVG8l++mtLQd5a/BUAllWDH0zLPTNcNA3O0/H7IB+9dVXZ742/jgoO/OWQ3xa6DOf+Uzmn7v33nvzKAeeRo0aFbXTW13E9XXrFhTviiuuyHTdiy++mHMlaEZ6S4OPfOQjmX5u4cKFeZTjLf2/p6i/Q3g3HwAAgAcGUwAAAB4YTAEAAHgIes1UfO70q1/9auafi79qZseOHS2tCc055JBDonb//tn/2D700EM5VIO+Sq9jmD17dqaf27VrVx7lwMPAgQMTx1/60pcy/dzatWt7vwiFO+iggxLHw4dne/fyW2+9lUc53tq1zpI7UwAAAB4YTAEAAHgIepovfrvvPe95T+LcypUro3b6TedHHnlkvoWhz1atWhW1H3zwwcS5qVOnRu2lS5cmzsV/Du2TvvX+i1/8ImqfeeaZiXPxXbQ/9rGP5VsY+iw99Rqfvjv00EMT5+LLJKZNm5ZvYWhKevufeL7pN4ds27Ytaq9evTrfwiqGO1MAAAAeGEwBAAB4YDAFAADgwYp8jNDMSvNuiK6urqidXgMQ8nYIDd7/1SftzDL9+pGRI0dG7U2bNiWMDraHAAAEE0lEQVTOhfw6oFZlKbU3z6FDhyaO99tvv6i9YsWKostpm6r2zfi6msmTJyfOvfzyy1E7vk41dFXqm+ltS0aMGBG1v/e97yXO3XDDDVF7zZo1eZZVKlny5M4UAACABwZTAAAAHjp2mi8+VZT+PQj5TfVVnUpoJD5l293dnThHltmUadp20KBBUTu9yzJ59q6dWca3tZCSWW7ZsqXoctqmyn0zPm37rne9q+65+JtCQsc0HwAAQM4YTAEAAHhgMAUAAOChY9dMdaoQ1mWkxR/tDXlNTVqV12U0El9Dld62JGQh9M30Y/bxLEPepiStSn0znVn879D0ufiaqZC3EEpjzRQAAEDOGEwBAAB46F/w922QtErSu2vtduu0Osa38N9Vmixrt6XbXkdMEbW0Mkupp943VYLfw9rUXlnypG/2QXqavTa1V5YsJfrmOzRaGpE+V5vaK0uepeqbha6Zir7U7FHn3PGFfzF1tFxZai9LHVK5aumLMtVdllrKUkczylJ7WeqQylVLX5Sp7rLUUpY6/oVpPgAAAA8MpgAAADy0azA1u03fm0Yd/spSe1nqkMpVS1+Uqe6y1FKWOppRltrLUodUrlr6okx1l6WWstQhqU1rpgAAAELBNB8AAICHQgdTZjbNzJaZ2Qozm1nwd88xs/Vm9mTssxFmttDMltd+HV5AHePMbLGZPW1mT5nZ19pViw+yDCdLiTxr3xlEnmQZTpYSeVYly8IGU2bWT9LPJP27pEmSLjKzSUV9v6S5kqalPpspaZFzbqKkRbXjvO2Q9C3n3CRJJ0q6rPb70I5amkKWkcpnKZFnTOXzJMtI5bOUyLOmGlk65wr5R9JkSQ/Gjq+UdGVR31/7zgmSnowdL5M0ptYeI2lZkfXUvne+pDPKUAtZdl6W5BlWnmQZTpbkWa0si5zmO1DS6tjxmtpn7TTaObe21l4naXSRX25mEyQdI2lJu2vpI7JMqXCWEnm+Q4XzJMuUCmcpkWdCmbNkAXqN6xneFvZoo5kNkXSvpK8757a0s5bQkGVYyDMcZBmWIn8Py55lkYOplySNix2PrX3WTq+Y2RhJqv26vogvNbMB6vlDcbtz7jftrKVJZFkTQJYSeUYCyJMsawLIUiJP1b6n9FkWOZh6RNJEMzvYzLokfUrSggK/f08WSJpea09Xz1xsrszMJN0m6R/OuRvbWYsHslQwWUrkKSmYPMlSwWQpkWd1six44dhZkp6V9Jyk/yr4u++UtFZSt3rmnS+RNFI9TwEsl/Q/kkYUUMeH1HM78v8kLa39c1Y7aiFLsiTP8PIky3CyJM/qZMkO6AAAAB5YgA4AAOCBwRQAAIAHBlMAAAAeGEwBAAB4YDAFAADggcEUAACABwZTAAAAHhhMAQAAePj/qfvIl1cF/P4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136bc3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 0, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136d5fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f3a89e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "info_generator_fashion=generator_model(cont_dim[0],cat_dim[0])\n",
    "info_generator_fashion.load_weights('info_generator_digit0202.h5')\n",
    "info_generator_fashion.summary()\n",
    "for n in range(num_samples):\n",
    "    noise_input = sample_noise(noise_scale, 1, noise_dim)\n",
    "    noise_input = np.repeat(noise_input, num_samples, axis=0)\n",
    "    for cont in range(cont_dim[0]):\n",
    "        \n",
    "        for i in range(cat_dim[0]):\n",
    "            print(\"category: %d, c: %d\" %(i, cont))\n",
    "\n",
    "            y = np.zeros((num_samples, cat_dim[0]), dtype=\"float32\")\n",
    "            y[:,i]=1\n",
    "            c = np.zeros((num_samples, cont_dim[0]), dtype=\"float32\")\n",
    "\n",
    "            for j in range(10):\n",
    "                #print(keras.utils.to_categorical(i,num_classes=10).T)\n",
    "                #print(np.array([j/10,0,0,0]).shape)\n",
    "                c[j, cont]=(j*2/num_samples-1)*sample_range\n",
    "\n",
    "            images=info_generator_fashion.predict([noise_input,y, c], batch_size=num_samples)\n",
    "            #print(np.array(images).shape)\n",
    "            show_images(np.array(images), height=num_samples/2)\n",
    "                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (_, _) = mnist.load_data()\n",
    "dc_generator=dc_train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(X_train, y_train), (_, _) = fashion_mnist.load_data()\n",
    "info_generator_fashion=info_train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (_, _) = fashion_mnist.load_data()\n",
    "info_generator_fashion=info_train(X_train, y_train, load=True, name=\"fashion02.h5\", cont_dim=[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_scale=0.5\n",
    "noise_dim=np.array([62])\n",
    "cat_dim=[10]\n",
    "cont_dim=[4]\n",
    "num_samples=10\n",
    "sample_range=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:38: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), name=\"conv2d_1\", padding=\"same\")`\n",
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:44: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (5, 5), name=\"conv2d_2\", padding=\"same\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "z_input (InputLayer)            (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "y_input (InputLayer)            (None, 10)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "c_input (InputLayer)            (None, 4)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concat_1 (Concatenate)          (None, 76)           0           z_input[0][0]                    \n",
      "                                                                 y_input[0][0]                    \n",
      "                                                                 c_input[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1024)         78848       concat_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm_3 (BatchNormalizatio (None, 1024)         4096        dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activaiont_2 (Activation)       (None, 1024)         0           batch_norm_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 6272)         6428800     activaiont_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm_8 (BatchNormalizatio (None, 6272)         25088       dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activaiont_8 (Activation)       (None, 6272)         0           batch_norm_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 7, 7, 128)    0           activaiont_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "upsampling_1 (UpSampling2D)     (None, 14, 14, 128)  0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 14, 14, 64)   204864      upsampling_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm_9 (BatchNormalizatio (None, 14, 14, 64)   256         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 14, 14, 64)   0           batch_norm_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "upsampling_2 (UpSampling2D)     (None, 28, 28, 64)   0           activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 28, 28, 1)    1601        upsampling_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "output (Activation)             (None, 28, 28, 1)    0           conv2d_2[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 6,743,553\n",
      "Trainable params: 6,728,833\n",
      "Non-trainable params: 14,720\n",
      "__________________________________________________________________________________________________\n",
      "category: 0, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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GMLZlXwLgDgCf78F2dZm999472pofw3FhjYtz3JaXWwPtc100ls5xeM2nuuGGG6KtW4lccMEF0dZcj96mynqyRppjw9del8EOHDhwjedQOMdGtyPgfCddKvzjH/842lpG43vf+160NSekt3MvqqwlkF5HzVvgfCrVgo/b5bwBqZ6aA8db0uj5ub/rVlKXXnpptHVbCs3H62mqpKeWH2EdND+U83G0tAuPb9qnuR9z/imXUwCAT33qU9Fevnx54rv66qujveOOOya+H/7wh9HmpfpA2t/Vp3lf3aFKWgKr50zl9OT7XvOJeXsnzW1jfTmHSXOyvvKVr0T7Zz/7WeLj/KqDDjoo8Z1xxhloB98XWlpDSzj0Ft1NQB9UFMWbrV8BYFC7F4YQTgNwWjc/x3SGLulpLWuB+2azcN9sDu6bDab0ar6iKIrcRoxFUUwBMAXonQ0bTc+S09Na1gv3zWbhvtkc3DebR3cfplaGEAYXRbE8hDAYQJ+tGeblsBr+4ZCP7lx9yimnRPvEE09MfPfee2+0uZLyxz/+8eR1p59+erR1+TVPW++yyy6Jb/LkydHmEBUAHHfccdHuid2uu0gl9OTQgk61c5VsnZb/u7/7u2gfccQRiY+rafM09Gc+85nkdZ/85CejrVXOuV3bbrtt4uPSHAceeGDi4ylsnh7vZfpMS57aB9L7V0M3vDu8VqMfO3ZstPfdd9/ExyE67mPnnntu8rozzzwz2jNmzGjbTi6hAAC77bZbtLUy+3/8x39Em8srAKuHEnuQPtFz++23T4459UFTE7gKOZcYAdJ0Cl5yD6RpErvuumu0OawHAN/4xjei/a1vfSvxsZb8WQCw1VZbRVu15FIrWrrm29/+NnqJPuubOi7yWLhw4cLExyFB/d3kMNwhhxyS+LgU0aRJk6I9ZsyY5HUc9rvkkksS33bbbRdt7ovaFi1/wG3+yEc+kvg6FebrbmmE6QBObtknA7iuZ5pj+gjr2RysZbOwns3BWjaYrpRGuALAvQB2CiEsDSGcAuBrAMaHEBYAOKx1bGqA9WwO1rJZWM/mYC37H11ZzXdiG9e4Hm6L6QDWszlYy2ZhPZuDtex/1G47GV36Pnjw4Ghr3gLHhTfccMPEx/F03VGccwB4+fU222yTvO5LX/pStDXOznk9nNMDpDteb7TRRomPd9HmfK0mwjkvQHqtFc7Z0Hg552KolgcccMAa38exeQD4/OffWqH85S9/OfHxUuHTTksX2PB9pduNcNkOzt0CmrllheaTcX/U/D8+1h3gOfdF+ztvLcJ9R7ee4Lwb3ZKG83rOP//8xMf35NKlSxMfa7bDDjskPt1ao+5w7iYAvPrqq9HW0jF8XXQJ/ujRo6OtpRG4D/J4rHk6nH9z1VVXJT5+32c/+9nEx2OBlr/hnKlx49Lnm17MmeozPvrRjybHvIWTlrPgHFHVYq+99oq29o/dd9892nvssUe0NZeSddcxY8iQIdHmvg6kOdGag8q/m3xPdBJvJ2OMMcYYUwI/TBljjDHGlKB2YT6dMuTltRo20lIJDIcFeAobAJ5//vloc7hOw4hcEflDH/pQ4uNpR60wu3jx4rbn5DDSv//7vye+3PepIxoSaLeLPJBOPWsoj6eUtdI2a7TZZptFW0MO8+fPj/YxxxyT+HjqmSsnA8CcOXOirTvT83JkDU/oPdEENJTOFZJ1yTz3Ve23XBqB+yIALFu2LNq8TFv15DCAhgs4zMD6Aem9pdXXOaXg0EMPTXxNC/NptXcuVZLTUiunc1/ScGu7vqm7SfziF7+I9oQJ6Q4tHG599NFHE99vf/vbaGt/23nnnaOtoaYmsmDBguSYS/IMGpTWDuXfWO2bhx12WLRnzZqV+Hjs5TIJrAMAXH/99dHm0kYAMGrUqGjPnj078XFYX8dhDkfyby+Qpgr0ZnqFZ6aMMcYYY0rghyljjDHGmBKETq4q6omy+Fox/IEHHoi2hoY4hKY+nsrUFUPtfBpi5PCghhn4tbpagqexOWwBpNXStaK2bqTbHYqiaL8T8DrQE1pqhdtrr7022rrCK7diiEMzOS15taBOX3NISvXie0fDiPxaDUnxlPV5552X+KZMmYKy9JSWQM/oqZWHv/CFL0Rbrzfron1T2tX2fay73i+8QevKlSsTH1dc19Ash7NU6/e9733RvvvuuxPf0UcfveYvsA5UqW/qijYei3QFMh+rzrxqU39r2oV6NVzMq5p1vORK+poGweEl3bCX+6aGoXSlb3eoWt+84447kmMeC/U3lUOu2v+4T2gYnENt3B9VT664rn2Tw7a6ups/W8O2Oc34HunuriJd0dMzU8YYY4wxJfDDlDHGGGNMCfwwZYwxxhhTgtqVRtBls5w3octyc/F63rlac6H4nJwLpefnfBltF+9SrrFfzq3R/Bw+5p3vAeDTn/40moTmGPF1Uk04/01j9ezTnA3WknPc9PxcEVhzKLgCut4DuRwb/rxTTz018fVEzlTV0Jw+zrfQ68YaakXtXHX0dnpyfwbSnArOhwPSHA49P59T86n4M7i6fRPRiuFjxoyJtubR5PJQWFvtmwxrpGMp903NTeXX8nJ89WkJGtaSc4SA9F7tbo5N1ViyZElyzGU+FL42Oa01F4pfy2OojqestfZ9/v3jKvVA2h/1NyCnJ/8Wa+5cT+KZKWOMMcaYEvhhyhhjjDGmBLUL8+m0Ky+r1nBabrqWp311CphDBBwG0ClPnmrUDSFzYSkOVeiyVK64rtVhm4aGZniaXq81hwhULz7W0AyHbXhaWquo83SzThOzT8/P2vJ0MpDeO1ytG+hcVd5OomFOvh66bJ37pk71s2Ya8mkXqtVwHfd3DWnw/aIhiNyG2oyONbnK6XWEw9fA6n2CYR20b2oaA8P9n6+nhoS5f+S01BAgn1/vD74ftc08TuTaXyeee+655Jh3jdDwK19/HaNz4Vi+/nx9c+FdrVaeKzfEWuj4zf1ddzThUjoO8xljjDHGVBQ/TBljjDHGlMAPU8YYY4wxJahdzpTG03PL4hmNmXOMXvM5eFd0jttqLgR/nuZ9cP6Ibr/A59StUfj7qK9pS3ZVL47Pq6+rMXiFc9k430nzlDhPQrXkvBpdfs16ac5U7t7kz9M8rLqi9znnVOj35+uv+UfttgAC0mvM59fXcb6T5l60291ez8njAJDvm7xFjW6RUUf0Xs6NZ7ml9DxmqUbcNzkfJpf7qn2F36dt5n6by4PU3wbe0uSRRx5BXWEttIwBX7ecnjpO8rXS30PuL7m+yflOmnfF59R2cYmMQYMGtX2ftpnLmCxduhS9hWemjDHGGGNK4IcpY4wxxpgS1C7Mp6UEeApYw3U8zamhG/blllkyGqrgcISGEjgMoKEEnt7WcAFPV+r7+LVa1bmODB8+PDnm6WDVgDXSpcy5kC3rwtO/qjlPX+tyYL7uGkrgKr2qF6PhaQ4LaXXiujJixIjkmPuY6sl9R/sm66lT9rzsmbVWPbmv5kK6qieXJlFfrl18L9c1zMd6bb311omPv7vqxVpq32H0HuDz5MpJsLa5kimqFx+zrkB6D2i7+D6uc5iPr5uGOXPjHV9j/V1j9Lpx6DDX97nf5nTX30Zuc05rvT9HjhwZ7Wuuuabt55XFM1PGGGOMMSXww5QxxhhjTAn8MGWMMcYYU4La5UwdcsghyTHnomh8V/NnGI6/5ra64FwBzcvguLDmxHAeAdtAGqPWuDDH9nUZMOcxzJ07F3Vn8uTJyTHnP+iWAKyR5quwLroMV4/X9FlAqqXqxce5cg6aM8VxfM1L2GWXXaLdlJypY445Jjnma6XbcrCe2v9ypS+0n72Jasa66/n4WJfF57aB4qXa2jd32223aN93331rbGPV4Wt28MEHJz6+TpqvyWNYbixVX7sctJz+usSfS2DovZHLyeLz6PL8UaNGRfvqq69u25aqw+Mi5w0B6TXWLZX42qhmfM5cn+Nrn9seKFcGQ8/PGmrebG5Lox133BGdYK0zUyGEoSGE20MIc0MIc0IIZ7b+vmkI4dYQwoLW/wf0fnNNWaxlc3DfbBbWsjm4b/Y/uhLmex3A2UVRjASwH4DTQwgjAZwDYEZRFCMAzGgdm+pjLZuD+2azsJbNwX2zn7HWMF9RFMsBLG/Zr4QQ5gEYAmAygLGtl10C4A4An++VVhL7779/csyhvdyUvS6pzS2n52lIDs/o63i6W0NPXP4gV+JApyT5PDqlrRWZu0NRFA+1/t/nWu65557JMWup3z23RJe11el89vG1zu2GrlpyeCenZS6srKELXardHarQN/n66nQ6h8L0+/Nyab3efJwLM7Ceen+0K6EApKEhvV84XJBbtq3fR8P13aGv+yZf2y233DLx5SqUsw6qZa6kAo/XrJ9qye/TZfY65jO50ji5MintQsnrQhX6JqPjFlcT12vDaN/h653Tgvt+7rdXteZ+ldNB35cbMzq1w8Q6JaCHEIYBGA3gfgCDWjcMAKwAMKjN20wFsZbNwno2B2vZLKxn/6DLj+AhhI0A/B+As4qieJmfNouiKEIIRZv3nQbgtLINNT2HtWwW1rM5WMtmYT37D12amQohrI83bojLi6KY1vrzyhDC4JZ/MIBVa3pvURRTiqLYqyiKvXqiwaYc1rJZWM/mYC2bhfXsX6x1Ziq88Sh9EYB5RVF8k1zTAZwM4Gut/1/XKy0UNL7LcdVcng3veg6k24Ao7Zbp5vKicks89X0DBry1gEPjzhpfZnoizwYV0jK3IznntQBpXoYuzdach3afkdOScwr0fLnzs5a5fJFcOYfuUoW+yd9Lc4xYQ81tYQ11aTb3He0PnA/B59Try23J9SnNr+BxQXM2+Fj7rS6v7yZ9qiV/J/1+nDeo9y6Ps5yLA6R9QMdI1ojHbr2PWFtdLs/tzJWy0PfxWKDa9USpkir0zdy14fs8NxbpWMt9SfsV/zaznvo6bpeO83wv5XId9ZzsW7FiReKbNWsWOkFXwnxjAJwEYFYI4c2Nis7DGzfDVSGEUwAsBnB87zTR9DDWsjm4bzYLa9kc3Df7GV1ZzXc3gHb/tBvXs80xvU1RFNayIbhvNgv3zebgvtn/qF0FdJ1+5uPc1F+uqrWes93STQ0l8PnVl4PbqUtWefpZp8y5yvL111/f5c+rKnrdOZSnIVteeq7L0jn80tUd7fVeYR1US35trsK6tpk/W7UcOnQomoaW+chVQea+k6t4reEJfi2fXzXLlSpgzbRdfE/qOVjP559/vsufVxc4rKKlQ3JVq/m6bL755omPNcpVumedc0vicyF4HeM5zMe2foYunV+XsbzK8DXVHSU4RP7iiy8mPi7Bs9lmmyU+LpGhY2i7fqz3C98HuXFBx1MOTXKKjX6G6qe7L/QW3pvPGGOMMaYEfpgyxhhjjCmBH6aMMcYYY0pQu5wpLQ/AcVuNp3McVeOmHEPXpbEcx9V4L8Ox31z+j8JxYX1fDt7NvAnkYum5HcNfeumlxMfXU/Mf+Dxd3SZCczs4b07bzHkC+r5crlUTc6a0j3Ff1a2QuH/kSiPothGc68Haav4GjwU5PTUniz87p6eiOTl1R/No3vOe90Rbt5phNDeQr6Fe63ZlLnK5OOrjc+bKj6iWuZINuW2h6srChQuT40GD3iq8ruMi9yvNQeVro7+33Ke15AHDeuo5cufPbQGXK6fCvxfq68n8OM9MGWOMMcaUwA9TxhhjjDElqF2Yb/DgwclxbiqXyU3l6hJMfi1PeeaW5eaqwer0Nr9Pq7VyaETDj3yeJkxN5yrv6jXj75ubqs1NL7OWuow3N+3Py8Rzy7uXLVuW+Di0pUuTeUq8CVoCq4f5OISt36mrVeu1dAifh6+b6pnrm9yvcmFb7Zu8TFzDj/xdm6Cnhl5ZB01N0PGTYS21fIT28TfRsFMu1YK1VJ059KqlLDjMpWUg+LgJWgKrh21zpWA45JqrRq+7GvBr2c71Tf3sXAV0brPenzyeqp4vvPBCtHNV+MvimSljjDHGmBL4YcoYY4wxpgR+mDLGGGOMKUHtcqZGjBiRHHMMVHOMOHaqOSsct9W8DM4ByOVecLxXfRxP1tgv51MNGDAg8XFez8qVKxMfx361za+88grqxlZbbZUWoFHOAAAgAElEQVQc57Tka6bLdTnHQpeos5a5/LdcLgTneuS0HDhwYOLjz9Dd13m5bhO0BFbXk/uA6pnrO7kSGZwT165MApDPheBtKfR1nA/EeTVAep9p6RPWU/P29LvXAS1/wP0otz1H7rvntnDi8+dyq1QvLbnR7n16f3Df1LGAtdR8MP0dqQs6NvH30rGJ+5+WIuLfVC1Dw/2D86S4vwH5fGLejkjzm3K5sfxa3WqG71fN39LvUAbPTBljjDHGlMAPU8YYY4wxJahdmE+XY3JlXg3/5CpQ83SfTt3yVD9PAeuSXf48PX8uNMTn5PbrZwwZMiTxHX744dH+/ve/n/jqGBrSKVee4tUwSm55NN8TuUr0PE2s0/65SvS5pcKsl06J8/fj7wYAEydOjPZll12W+OqoJbD6NeTQgvZNvo4ams0tnebjri5T19flqh7zfaEheNZT2zxu3Lho33DDDYmvjmG+p556Kjnee++9o61act/MVdPWMF+7sFAuJJerfJ0bZ/V3I/d5++yzT7Tvv//+xFfXMN+VV16ZHB999NHR1vuTd2fQMCeH7FRPvg9yYVvWKXdP5Pq3/nawvnp/brfddtFesmRJ4nOYzxhjjDGmIvhhyhhjjDGmBH6YMsYYY4wpQS1ypjgW++tf/zrxca6CbueRWxo7c+bMaF988cWJ75lnnok2l63XGC7niORyQnQZ51FHHRXtPfbYI/F95CMfifbVV1+d+LhUgpZNqAscI7/uuusSH3/3RYsWJb5cKYt58+a1PSfHyHkJsOZgsU+3KuCcAs29mDRpUrR33XXXxHfSSSdF+9JLL018ixcvjjaXvKgzX//615Pjz33uc9FWPXnpveaTPf3009HWnBW+73mZti6/XrVqVbQXLFiQ+Hic0HHhsMMOi/aOO+6Y+E4++eRoT5kyJfHNmTMn2rrUvI6cd955yfGJJ54Ybf1+PAZvs802iW/p0qXRZk2A1bf9eBPetgdI+7DeDw899FC0tU+PHTu2bbt4rPnud7+b+B544IFo58pA1ImrrroqOX7kkUfavvYTn/hEtPfcc8/Ex1ssaUmJdjnKmhfMv6+PPvpo4rvrrruirTmYnLenvwF8f15wwQWJj++R3sxf9MyUMcYYY0wJ/DBljDHGGFOCkFsm3OMfFsJzABYD2BzA82t5eSfob+3YtiiKgWt/2dqxllk60ZYe0xKIev4e/esadgX3zfJUpR2A+2ZPUBU9K9U3O/owFT80hJlFUezV8Q92O3qcqrS9Ku0AqtWWdaFK7a5KW6rSju5QlbZXpR1AtdqyLlSp3VVpS1Xa8SYO8xljjDHGlMAPU8YYY4wxJeirh6kpa39JR3A7ylOVtlelHUC12rIuVKndVWlLVdrRHarS9qq0A6hWW9aFKrW7Km2pSjsA9FHOlDHGGGNMU3CYzxhjjDGmBB19mAohTAghzA8hPBlCOKfDn31xCGFVCGE2/W3TEMKtIYQFrf8PyJ2jh9oxNIRwewhhbghhTgjhzL5qSxmsZXO0BKxn6zMboae1bI6WgPWsi5Yde5gKIawH4LsAJgIYCeDEEMLITn0+gKkAJsjfzgEwoyiKEQBmtI57m9cBnF0UxUgA+wE4vXUd+qIt3cJaRmqvJWA9idrraS0jtdcSsJ4t6qFlURQd+Q/A/gBupuNzAZzbqc9vfeYwALPpeD6AwS17MID5nWxP63OvAzC+Cm2xlv1PS+vZLD2tZXO0tJ710rKTYb4hAJbQ8dLW3/qSQUVRLG/ZKwAM6uSHhxCGARgN4P6+bss6Yi2FGmsJWM/VqLGe1lKosZaA9UyospZOQG9RvPF427GljSGEjQD8H4CziqJItmHvdFuahrVsFtazOVjLZtHJa1h1LTv5MLUMwFA63rr1t75kZQhhMAC0/r+qEx8aQlgfb9wUlxdFMa0v29JNrGWLBmgJWM9IA/S0li0aoCVgPdH6nMpr2cmHqQcAjAghDA8hbADgBADTO/j5a2I6gJNb9sl4Ixbbq4QQAoCLAMwriuKbfdmWElhLNEZLwHoCaIye1hKN0RKwnvXRssOJY5MAPAHgKQDnd/izrwCwHMCf8Ubc+RQAm+GNVQALANwGYNMOtONAvDEd+RiAR1r/TeqLtlhLa2k9m6entWyOltazPlq6AroxxhhjTAmcgG6MMcYYUwI/TBljjDHGlMAPU8YYY4wxJfDDlDHGGGNMCfwwZYwxxhhTAj9MGWOMMcaUwA9TxhhjjDEl8MOUMcYYY0wJ/DBljDHGGFMCP0wZY4wxxpTAD1PGGGOMMSXww5QxxhhjTAn8MGWMMcYYUwI/TBljjDHGlMAPU8YYY4wxJfDDlDHGGGNMCfwwZYwxxhhTAj9MGWOMMcaUwA9TxhhjjDEl8MOUMcYYY0wJ/DBljDHGGFMCP0wZY4wxxpTAD1PGGGOMMSXww5QxxhhjTAn8MGWMMcYYUwI/TBljjDHGlMAPU8YYY4wxJfDDlDHGGGNMCfwwZYwxxhhTAj9MGWOMMcaUwA9TxhhjjDEl8MOUMcYYY0wJ/DBljDHGGFMCP0wZY4wxxpSg1MNUCGFCCGF+COHJEMI5PdUo0zdYz+ZgLZuF9WwO1rKZhKIouvfGENYD8ASA8QCWAngAwIlFUcztueaZTmE9m4O1bBbWszlYy+by9hLv3QfAk0VRPA0AIYSfAJgMoO1NEULo3pNbDxBCSI432GCDaI8YMSLxveMd71jn8//xj39Mjh9//PFo/+Uvf0l83X2A7QmKoghtXOukZ19qqay33nrR3mmnnRLfO9/5znU+36uvvpocP/HEE9H+61//mviaoGXrNZXR821ve2vCfMcdd0x87373u7t0Dtbl97//feJbsGBB2/epvp2kiX2TtdRxdqONNurSOVjL3/3ud4nvySefbPu+JmjZek0l9exq39QxMqfnU0891fazK6pnpMzD1BAAS+h4KYB99UUhhNMAnFbic3qEDTfcMDneZpttoj19+vTEN2zYsGjzQ1jupli4cGHiO/DAA6P9m9/8JvH94Q9/6GKrO8pa9ayKlvzwBADvfe97o33FFVckvlGjRkU7pyV31EcffTTxjRs3Ltr60Pzaa691tdmdpFZ9kwdoIP2RveSSSxLf3nvvHW3WUwdaPr7vvvsS35FHHhnt119/PfGxnn35oCzUpm/qP1r5B/biiy9OfAcccMAaz6H/+OTje+65J/Edc8wxbd9XVy2B6uip5PTcf//91/ieP//5z22P77zzzsR3/PHHR1v7NP9uqtZVoMzDVJcoimIKgClA55+weZDWhymesZg2bVriGz58eLTf/va3LtHcuek/Hlj4bbfdNvHdeOON0f7Sl76U+K699tpoV6iTr5W+1JIH6Xe9612Jj689zyIBwJ/+9Kdo8/2waNGi5HU8EOy+++6J74Ybboj2j370o8Q3derUaOsPc9Wpip4bb7xx4ttvv/2izTPIALBkyVu/QzzYPv/888nr+B9EOsiznrfeemvi++53vxvtF154oW37q0ZVtOR/2ADAscceG+3tttsu8bFm/AP729/+NnndZpttFu2DDjoo8fFYqg/NV111VbT1H0hVpy/1ZFTPU089Ndrve9/7Et/LL78cbf5Hp84+ceTnsMMOS3yXXXZZtB955JHEx9Ee/UdzFSiTgL4MwFA63rr1N1NPrGdzsJbNwno2B2vZUMo8TD0AYEQIYXgIYQMAJwCYvpb3mOpiPZuDtWwW1rM5WMuG0u0wX1EUr4cQzgBwM4D1AFxcFMWcHmuZ6SjWszlYy2ZhPZuDtWwu3S6N0K0P6+XYryYmcyKrruraYYcdoj1kyJDEt2zZW7Ou7ZIkAeC4446LtuYDcF7P/fffn/iOOOKIaGtyXm/TlVUJXaG3tdRE1k022STaGsffd9+38je32GKLxMerfTg+r7k4kyZNivbmm2+e+Djfju8NADjkkEOizfk8naCntAQ6n5ex1VZbRZv7IgBMnDgx2ptuumni47yYD3zgA9Hm+wNIE9U1J4vHAs3POeOMM6Ld6byMuvRNhfXjfDcgTRB/z3vek/iuu+66aHMfHjx4cPK6PfbYI9qaL8kJ0bpq86KLLor2WWed1f4L9AJ17pucM8rjG5DqqXnInO+01157RVv7Ny8K0pXz/Juti3tuueWWaH/oQx9KfL290q8reroCujHGGGNMCfwwZYwxxhhTgkaF+T7/+c8nx7Nnz472wQcfnPg++MEPRpuXdAJp4Uf23XvvvcnruGiZ+nbeeedoDxo0KPFxeHDOnM6Gy+sSShg/fnxyzNPBHCYF0hCdLsNljbhOiRb723rrraM9c+bMxDd06NA1vg4ALrjggmh/9atfRSepUyiBQzV6fNRRRyU+LmWgU/1cBoNLUaxcuTJ5HYcHdVn8lltuGW0ONwLAXXfdFW0ONwK9X8akLn1zt912S465r3IYCEhDOhqG477EYRoNvXKYVkufcMhfx1kuzjp69OjExyVTeoM69U0Nw5100knR1r7JZYP0d1PLA72Jjskcmn366acTH4f5Bg4cmPhWrFgRbQ7jA8Czzz67xs/uKRzmM8YYY4zpZfwwZYwxxhhTAj9MGWOMMcaUoPY5U7yEXreU4Ni77qnGORW6uSLH9rncwo9//OPkdbw8k5eCAmks/6GHHkp8t912W7T/7d/+LfFVYYlnV+htLXWvw1xOBZcyeO655xIfv5bzrm6++ebkdRMmTIi2buDJcfxZs2YlPr53TjnllMSnuQI9TdXzMnj7nocffjjx8TL55cuXJz7OadJr+OKLL0abc2kefPDB5HWcd6W5HFwaQbeI4n00eesMYPV8nZ6myn2Tx8G777478XGuEue1AOlWMLon6UsvvRRtHo81j4ZzWDVnkUslPPbYY4mP8+2+9rWvJb7rr78+2r0x5la9b66//vrRvummmxIf50Xpbyr3Od0+i8daznfSfEbOU9SyRDxG6/ZA/Pug4/c3v/nNaL/yyivoaZwzZYwxxhjTy/hhyhhjjDGmBLUP8/GydV4KC6QhGa00zqEErZDM4QleQq+7yO+6667R5ilrABgxYkS058+fn/h4mnPp0qWJj5f5r1q1Cj1NlUMJfF10OTtr+5e//CXxcZhPqyxzeILPoeFAXmrLoR4gXTqsoR6eztZ7jKsuX3PNNehpqh5K4PDajTfemPg4JKDXjUM3ujyamTdvXrRVszFjxrT1cd9UPTncpNWZuSL6mWee2bZd3aXKfZNDbRdeeGHb12nJAR5LtQwFh9c4DKznOPDAA6OtYaf3ve990dYQPIcYdVcD3pXi8MMPR09T9b7J5S2+8Y1vJD5Oa9DQLI+9GqLjsN8DDzwQbb4HgHRXEQ0Lc3hedw5hPXXHkWeeeSbau+yyC3oah/mMMcYYY3oZP0wZY4wxxpTAD1PGGGOMMSV4e183oCz//d//HW3OjwGARYsWRXvkyJGJj/OkdPfrDTbYINozZsyI9rRp05LXfec734m2LsvlUgy8CzeQbkvBOV8A8Pjjj0ebY8T6viby5S9/OdqsAQAsXrw42vvss0/i4+0JeMkvkMb/OXfthz/8YfK6H/zgB9HWfC3OudFtUTiH4NVXX018l19+ebQ32WSTxKd5Qk3k3HPPjbb2Md7qiXMogDTH4u1vT4eodiVHpkyZkryOS5VoLg3n3WhJE15Or3qedtpp0f7CF76Q+HpjOXaVOOOMM6LNOW1AmnfG+U1AOmZp3+TxjUuhfO9730ted+ihh0Zb80+5jM2ee+6Z+Hi7E81pPeigg6KteXmaT9kENG/pox/9aLS5VAiQ/m6OHTs28fG4pTmFW2yxRbS5HILm2PF2YPfcc0/i4/IHum0Ra6jlFjg/k3OZgc5t2eaZKWOMMcaYEvhhyhhjjDGmBLUvjcDTjvpdfvnLX0Zbd8bmKrtaZZkrMnN4Saf9eQkvL9EFgMGDB0dbp5F5OlrDPTwVzucAVp+q7g5VWn7NU7pAugxXl0f/6le/irZqydPSeg+wZnz9tMoyh+t0ejm3XP79739/tLUiMIdDxo0bl/i0inR3qNrya702HB7lcAwA/PrXv462LnPmMIuek/VkrTk8DqRhDa2AzuUztP9xSF5LcHDImEOYAPCtb30LZalS3+Sq5kBaIkZLTTzyyCPRHjZsWOLjPschWiDt46wz92cgTZnQdvHYqqVkdt5552jrWMNhZy55AaTh3O5Stb6pIVDehUNL/nBf0orz/FotdcHXmPXUHQ54LBgwYEDi43PqfcD3lvZNDlXyvQqk5Ra6i0sjGGOMMcb0Mn6YMsYYY4wpgR+mjDHGGGNKULvSCLrEk7ck0DwbjtHrcl5+7dSpUxMfx955mSUv6QTSnI1tttkm8XHeDW+dAaTLqDkPQ9Fl4U1DvztrwnkSQKqlvo+vk5av4GvNW8ZojhvfR5oztf3220db89Z4+TXnVgHp/ZHTuSnwbvNAmhuhuUl8rbi0BZD2l3vvvTfxcb4Oa6jnYDSPh/XU7Sw4X0vLlrCeqnXT0HIVvBRd89i4xIFuzcV5kA8++GDiY5253ImWmWDNtcQN509quzhXh7cQAtL8Rs3DaiLHHntscsw5o5rTmxtrWU/OlQPSPs56LlmyJHkdb6Gmv6m8zZv+BixbtizaumUM51L21VjrmSljjDHGmBL4YcoYY4wxpgS1iyPpsniertXl1zydr+UPeJrzqaeeSny87HK//faLtoZ/eBmnLs3mJaS6u/aLL74Ybd0FnadYdUqbSz00gQ9+8IPJMeunerHuGs7lsIBWxuVQDe8Or9XyeVd5noYGUr24yi+QTj3rEmCePv/Qhz6U+H7+85+jaXzyk59Mjvk6agiG+6aWlGB01/rRo0dHe8yYMdF+9tlnk9dx2J01AtLQni7vnjt3brQ1PMHhoKOPPjrx/b//9//W/AVqysc+9rHk+OGHH442lxwA8lryTgbab3lsHTVqVLS1b3KITvsYa6RhWQ4rap/mvqk7KjSRY445Jjnm35Lx48cnvtxYy+ky2jd5NwG+J1QzLhOjYXbufzvttFPi436s7+MdJrTUCpds6M1SUJ6ZMsYYY4wpwVofpkIIF4cQVoUQZtPfNg0h3BpCWND6/4DcOUx1sJ7NwVo2C+vZHKxl/6MrM1NTAUyQv50DYEZRFCMAzGgdm3owFdazKUyFtWwSU2E9m8JUWMt+xVpzpoqiuDOEMEz+PBnA2JZ9CYA7AHy+B9vVFi1xwEvadZsDXmap5Ql4ybXuEs45Uxwz5vg/ANxyyy3R1vIKHDM++OCDEx8f65Jx3uH6s5/9bOLriZypKumpWwLw9X3ttdcSHy9F13wq3ppEtxLg3DVePqt5bLzz/a233pr4eMm/bk2Syy/gdh122GHoaaqkJbC6LpyXon2TS5xo7gVryDkUQJqTw1v5qJ7cp1lbIN2mSdvMY4b6eEujfffdN/H1RF5GlfTUnJRDDjkk2vr9OMdJt9zifFRdSs/9nfNcNOeFcyI5fxFIrztv/aXn0XH2oYceiraWgeAtvfR9XaVKWgLAggULkuNDDz002ry1DpCWmtE+MH/+/GjzNQTSbZq4j2nuL/d3LX/AuU+aB8k5VLo90OzZcQJwtd9bbouesyfpbs7UoKIo3swqWwGg+YU6mo31bA7WsllYz+ZgLRtM6dV8RVEUuY0YQwinASi/c6TpCDk9rWW9cN9sFu6bzcF9s3l092FqZQhhcFEUy0MIgwGsavfCoiimAJgC9Mzu18cff3xyzFOGzzzzTOLjHak1PMhLtbUaNod1OIyjy+J5+nDWrFmJj5eJanV0nkbV6rs8hZ6r6tzDdEnPntbyb/7mb5JjLo2gS5m5yrIus+dQL1esB9JwDIcV9H7gsNP111+f+L74xS9GW5fSs5Z6f3DYT9vMn8ft7wH6rG/qEmsO+WgonUuAaPicr4eWQtl9992jzWFi3jUeSMPEV155ZeI777zzoq271nP4QPXksUY141CJhqhL0id9c//990+O+V7W78fXXndt4OuiVch57OO+z2VKgHRcv/DCCxPf+eefH20OMwGpRlqxnsdkDTVxqFfLY5Sko32T7+U99tgj8fG9rKE8vm7aN7m/qJ58H3DJH9WTQ46q57/+679Gm0N+2uZ10ZN/36sY5psO4OSWfTKA63qmOaaPsJ7NwVo2C+vZHKxlg+lKaYQrANwLYKcQwtIQwikAvgZgfAhhAYDDWsemBljP5mAtm4X1bA7Wsv/RldV8J7ZxjWvzd1NhrGdzsJbNwno2B2vZ/6jddjJHHXVUcswx0JdeeinxcbxX8484t0bjwryMnZdj6jYHnMez9dZbJz5ewp3bsZyXzwNpHpbGmvnztaxAHdlzzz2TY96SR3OmOIdDSxAMGzYs2rpkdu+9944250JwzhKQxud1F/WFCxdGm7cwAdI4Pm9fAaT5VZqjxfeO5hPVCb7eWjZixowZ0ebl7UCqvS4/52uj+TnclzTfieH8DS29cN9990X7wx/+cOLjPC/Ng+QcH82B47wMPn+d4D6h49lll10WbV1Kf8IJJ0RbteQxTO8Pzk3l12nf3GijjaLNpU4A4MYbb4z2qaeemvhYS17SD6R9XMf1iRMnRnvKlCmoK/y9eOwDgP/5n/+JtuYfnXjiW8+BWuqCxzQtkcG/Xfp5DH+ebqd29dVXR1u3p+Lfac1l0/wqZvLkydG+6aab2r6uLN5OxhhjjDGmBH6YMsYYY4wpQe3CfE8//XRyzNOOuRIECldP1mnll19+OdpcDVfDiNdcc020eSoRSMMY2mZul4Yxcsu9eXqdlxLXlUWLFiXHHDrZbbfdEh/roMvSeUp51ap0tTEvw+WKyPq6a6+9NtrHHnts4tt5552jrVrm2sVLdPUea0qYj9Frw6EbLXHA102XMnNYbuXKlYmPdy448sgjo819Fkj1/OAHP5j4uC1aNZv7mIaMeQk56wesXoG9jvA9yiF3IA2vcjgHSLXUZfbLly+PtoZNly1bFm0OLWmVcw7lHXfccYmPQ4eqJYcHtf9x+IpfB+TDx3WCw3xafZ5LjGj4la+V/s7wNZ45c2bi4zDcxz/+8WhraJbD//q7yW3RUB7roqk5rKeG4DXForfwzJQxxhhjTAn8MGWMMcYYU4JahPl4ul03weSpYg0l8KovXQG28cYbR/uggw5KfBw24ilsDRvyhoo6lbnjjju2fR9PSeqqGV6VoOEPbktd4WuhoZFHH3002rrSj6+FhhK4mrautuOVfhyG0xU8PL2sU9scrtLpZb6vtDo6h4y0zbyis87wd9TwD6+C1LAth/J0E2Re7cPheAAYOnRotPka6vXk1bu6yTnfL7rKl8PsujsB+zQUpf2/jnAFcf3uHMLl3R2ANFVBV2fxtdbdCbj/8/2gevF4qeFcbguP6UDaN3WFLvs0zM6/KXWG+5F+f96kWKvdsxZ6bfga6+8tj5Osma7M5mNNc9lnn33W+Fl6Tl3pzuOrVjnX9IPewjNTxhhjjDEl8MOUMcYYY0wJ/DBljDHGGFOCWuRMcaVVLU/AsVJdms4xVs2p4NwdrejL8Odp3pUeM5wHovFdbpeeo6s7Y2ueSV1gLTWPSPMhGH5tbtd6zWnie4Lj/1qRm+8BvVf4WmteEOun7eLP5twRABg1alS0n3zySTQBrTTeVT21yjJroXkTvGybl91rDtMWW2wRbc2B45yc3M70uf6tfXq//faL9h133NH2fXVBx1K+vlr5Oqclj7OaH8o5oJynpDkvnCer/TY3zrLuOtbw99Px/wMf+EC0ufJ73VCdGO47upsG56fq9eZzcv6i8swzz0R7xYoViY/HPs2x42Puw0DaH1VPrryvJU00J7q38MyUMcYYY0wJ/DBljDHGGFOCWoT5eNpOw1sc+tLSARwS0A04+TxauqDdFKien9uVm2LWsBG3RT+bp581TKJVfOsIfwedQualtrnq9Rp+4WXpOS352mqVXD7WyvPcTtWA74Gclrp0Xu/HusJ9Qu/X3Cbh3Ce0BAhrplq0u24aquGq1urjdmol/Nx9x3pqeELDjHWEr5N+n1122SXaqiVfFw2pcgV7rTTOYyv3K61YzccawuE0DNWSQ/4ayuL7T8PTqm1d4eumoTYuPaO/azze6e8tj2MahuO+yefU30bWWq89p2KoDjwW6JjBn633oIaNe4v6/zobY4wxxvQhfpgyxhhjjCmBH6aMMcYYY0pQi5wpjn1rfJeXSOqydV6mq8tEOZ6s8VcuScDn18/mXAHdAoFfqzkGuaX2fE7NteKtUnQX8LrAW1bo9eRrqNdMS2K0O6eWJ+B4PecCaAkFRrdQ4HtFl35zzF+XCnPsXu8x3gbj+uuvb9uWqsN5E5qHxvkyuVwX1Zp3h9ecCv48vl/0s/nzdDsLvl80b4910q2rWHttF+eH1RXOP9VtdjjvRLXk3Bbt06yl5i1tttlm0eb7QfPWuK9q7g+3U3P2WK+dd9458XHf1Bwb3hZKv2uu3EDVGD58eLT5WgPAkiVL2r4vl7PIv416LXjLmtx4zbl5en358zRvj/OiRo4cmfj43tL7gMvS6Fij92QZPDNljDHGGFMCP0wZY4wxxpSgFmE+nprTaceuhmB0Ob1O9zEc2uPX5ZZj6pJtXv6pIQgO5ekyYP5+WuVVp6rrCH/3XHhHteRroVrmQqp8Hg4XaDiQ9dPpaz7WZfYcstUQx5ZbbhltDnkBaUiwzqEEDpFo2QgOYa+Lnhzm0b7D52Gfhus4RK5hI77een4OWeW+j+5Ez+EPfZ9WE68q/N31mvF3z4XTtF/xeTS0zvcO96vczgg6/ufCNNw3tU8NGTIk2lq2hMeQXEmPqsNjjo5bgwYNiramqLAu2je5v6iefB9w+FX7Jt8jugMBX1+91qyT6s7V9TWEmStF5DCfMcYYY0xF8MOUMcYYY0wJ/DBljDHGGFOCWuRMcbybY91Afkkk5y5ozDy3taWSXfIAAA5HSURBVAi/j+O2uTwrXUrM58zFaTWXZOONN15jG4F06XJd82w474yXrALpddFtDPj75Za3ar5KO800v4nJLdftaq6dvk/1YS31/qjTVjOcD6H5f5xTocuc+d7WfA7OqdC8Cb7+rLXmb+TOz+3Ua8/HmuvBfVz15JwNvQ56X1SVXM4Uj0u57Va47ASQLpHXnFPWjG3tw7mcF9ZEx3HevkZ/G7idOs7yuMS5cMDqW9ZUmXbbogHp2KTfia+HbhnD59RcM77veXzVvCvu3zoO87XX/Dgu76B5XvpbwvB4zv0UAJ566qm271tX1jozFUIYGkK4PYQwN4QwJ4RwZuvvm4YQbg0hLGj9f8DazmX6HmvZHNw3m4W1bA7um/2ProT5XgdwdlEUIwHsB+D0EMJIAOcAmFEUxQgAM1rHpvpYy+bgvtksrGVzcN/sZ6w1zFcUxXIAy1v2KyGEeQCGAJgMYGzrZZcAuAPA53ujkTylrtOCuWW5PF2poRueEtZQAocFeKpfp5i5LXqOdqFC9enUN0/HamVeDn3pVHhXl3gWRfFQ6/99omWunTydr2G+dkvi9VgrU7OWuYrZuR3JWT9tP99j+tn8fTQ0wlPkek93NcxXhb7JU/gasubrxuEBIA376RQ9a6Hn5HAQL/3mkI4ea5iNNdS+yXrqeML6Ll68OPHxvZULIefo677J971WsObrsmzZssTHYSKuHg6kY6aGe1gHrrCu1br5/tAxMTeWcj9SLVmvOXPmJD7+jFxYP0cV+iZ/Z9WM+9GiRYsSH4e+9thjj8SXu7c5lJq7J1hPHev4ftHUAL5/NKzP73vwwQfbtkufA3qSdUpADyEMAzAawP0ABrVuGABYAWBQm7eZCmItm4X1bA7WsllYz/5BlxPQQwgbAfg/AGcVRfEyP+EVRVGEENaYAR1COA3AaWUbanoOa9ksrGdzsJbNwnr2H7o0MxVCWB9v3BCXF0UxrfXnlSGEwS3/YABrXOZQFMWUoij2KopirzX5TWexls3CejYHa9ksrGf/Yq0zU+GNR+mLAMwriuKb5JoO4GQAX2v9/7peaSHSuKrGxTk3QvNsOF9G4/V6zHCsmWOzuXixlkbg82ucls+jS6z5PLrb/U033RTtEltUVEbL3O7eGi/npcyau8bXWnPJ2m0no8vl+X2af8N5NXoPcE6Fxv95OfmsWbMS34033hhtvae7StX6Jue9AGn5h3nz5iU+3k5H89f4emhuBPdNvg90GTXron0zl4PJeVG6ZHz77beP9m233Zb47rzzzmhrSYV1oE+15Os+f/78xMdbWd18882Jj/NqcmUuVAfOW2It9XWspd4PTG5rIF3GP378+GhfffXViY/vVR2HukoV+ibfyzNnzkx8e++9d7Qvuuiitj4dC1lPzXXkHEbWQl/HGurvGPdN1ZN10e8zadKkaF944YWJj/ux5rX2JF0J840BcBKAWSGER1p/Ow9v3AxXhRBOAbAYwPG900TTw1jL5uC+2SysZXNw3+xndGU1390A2qXAj+vZ5pjepigKa9kQ3Debhftmc3Df7H/UogI6L6FdsWJF4uNpyMcffzzx7brrrtHWqUZe+qvLX3l6kcM/+rpcJddcZV5e+qsVgzkkqL7nnnsOdYendXVJ8sCBA6OtoYSJEydGe/jw4YmPr4vqwGEH9qmW7XawB/LhQd59XUNNfO9wiAhISwWUCNn2OTwtf/vttyc+DuX97//+b+L72Mc+Fm29NryMW68p9+NcCJ5DyDldNKQ0ePDgaOsyfA738tgCANOmTYt2T+5E30n4PvzpT3+a+FhL/q4AMGDAW3Un99lnn8THy+5VB+7vrJ+OlxwO1LATa6njJZ//3nvvTXyss76P00XqtBuBwnpq/7vjjjuifd999yU+rnrO4xsAPPnkk9HmNAYgHZdz4Trum5tsskniYz21+jz/bl555ZWJb+7cudHWtAn+vN4ca703nzHGGGNMCfwwZYwxxhhTAj9MGWOMMcaUoBY5U4zGvkeMGBFtLXfAcXjNY+DcpNyO8xzn1zIG/D5drs/n0HwOPo8u1eTX6jJ/ziXRcgvatjpwxRVXJMe8/FqX2fO11u/K10JzYDjngbXU2DnH9ddFS86j0S04tt1227afp9+vCUydOjU55u+vuY5M7t7VXEfOh2Ctc3qqZvxazedgdMuYkSNHRlvLeugu9nXn5z//eXLMuS363Xls1VxEvtaaA8Naci6ijtW5cgiM5jNyP9bSJDwuLFy4sG276jiuronZs2cnxwsWLIh2rkyQ9g8e7zbffPPEx9eU7wPVk6+v/sbl8hn5fbzlDZB+H81z61QOo2emjDHGGGNK4IcpY4wxxpgS1C7MpxVpeVm8VkB/17ve1fY8udANTwvy6zT8w1OgGmbg6VD15aq28xSl+riibxPQUg88Ta8hM16+rFPDTK5KPU/ZaziCP1uniXPT4NwuXjYMAL/73e+irWGGElWyKwtXmwfS8g+6XJmXXyusofY57lesp4a9WU9+D9A+HAGkS8E11PXoo49G+4Ybbmh7ziag34dDexo22XLLLdv6OLSe67c8RqqWPI7r+VlbDfNttdVW0dawEFd45/QJoDmhPUZ/g3KhTO4DXP4HSK+xnpPHydw4zPeEas166u8332e5siV9pZ9npowxxhhjSuCHKWOMMcaYEvhhyhhjjDGmBLXLmdJlshxz1TgtH+sSa17KrPk5HHPlkvm6tJdjxpqXwVssaCyfyS3N5pg/kMakc0uQ64LG4/lYtxng46233jrxcfxc87A494P1y21LodeW7x3Vi7XVrRc4L0q1rKNea0Nzy7jsh15vLpuwww47tH2f5rPwZ/CWILnyI5qrk9tWiO8z3r4CAO68885o5/p0E8jlgOq1HjVqVLR32mmnxMf9T0tNsJbap5ncUnr2aZ4X59zo+Mz3VRNzpBT9jqyvXtMDDzww2rvsskvb9y1dujTx8fjNeqouPF7rFkOcJ8xbGAHpb7F+nypo6JkpY4wxxpgS+GHKGGOMMaYEtQvzXXzxxcnxmDFjoq3TzwcccEC0NczAU4Y6zclThlyFVXcX5xBjLpSQq1auPg5jasiKv0NuuXddePzxx5PjlStXRlv1euKJJ6Kt4RdePq8+XkrNmquW7c4H5Cvd55bk8j0wZ86cxKfhJaaOWgKrV/Pn76Hh0V/96lfRHjJkSOLj66jhXg4zsJ5aLTlX0oRDdNou/g5c2gFI++PTTz+NdjRhd4Lcsncde6ZNmxbtXXfdNfHlQr18ndinoZ/cknvWXd/H6RtcpkS/Q6cqZFcJvif1+1966aXRHjt2bOLj9BjVgvtVThcO82nf5JC/9ml+XxX7lGemjDHGGGNK4IcpY4wxxpgS+GHKGGOMMaYEtcuZ0t3necsHjb8OGzYs2hMnTmx7Ho3lcx4Fb6OgO8M/++yz0V60aFHiu+eee6Ktpe+33377aGueyXHHHRftCy64IPFxW+qaV8NorJ63CtJr/bOf/SzaWqKClzlrzhRryddaNeF8rRdeeCHx8Y7rusUPl8CYN29e4hs3bly0b7nlFrSjiWUSgFRfLYMxffr0aI8cOTLxsZ66Mz3nZbBmuvUEa6jb3HC+k95nnMejW8Zw/pbm4GieVJPR+/WXv/xltHWc5byzgQMHJj7OpWEtNVeGr7WOGTyOa4mbJUuWRFvvAc5xbWr/6y6PPPJItH/xi18kPs4b5O1dgPZ6an4ql4zRXOMFCxas8XVAmmdZxf7mmSljjDHGmBL4YcoYY4wxpgShk0sMQwjPAVgMYHMAz3fsg9vT39qxbVEUA9f+srVjLbN0oi09piUQ9fw9+tc17Arum+WpSjsA982eoCp6VqpvdvRhKn5oCDOLotir4x/sdvQ4VWl7VdoBVKst60KV2l2VtlSlHd2hKm2vSjuAarVlXahSu6vSlqq0400c5jPGGGOMKYEfpowxxhhjStBXD1NT+uhzFbejPFVpe1XaAVSrLetCldpdlbZUpR3doSptr0o7gGq1ZV2oUrur0paqtANAH+VMGWOMMcY0BYf5jDHGGGNK0NGHqRDChBDC/BDCkyGEczr82ReHEFaFEGbT3zYNIdwaQljQ+v+A3Dl6qB1DQwi3hxDmhhDmhBDO7Ku2lMFaNkdLwHq2PrMRelrL5mgJWM+6aNmxh6kQwnoAvgtgIoCRAE4MIYzMv6tHmQpggvztHAAziqIYAWBG67i3eR3A2UVRjASwH4DTW9ehL9rSLaxlpPZaAtaTqL2e1jJSey0B69miHloWRdGR/wDsD+BmOj4XwLmd+vzWZw4DMJuO5wMY3LIHA5jfyfa0Pvc6AOOr0BZr2f+0tJ7N0tNaNkdL61kvLTsZ5hsCYAkdL239rS8ZVBTFmztxrgAwqJMfHkIYBmA0gPv7ui3riLUUaqwlYD1Xo8Z6WkuhxloC1jOhylo6Ab1F8cbjbceWNoYQNgLwfwDOKori5b5sS9Owls3CejYHa9ksOnkNq65lJx+mlgEYSsdbt/7Wl6wMIQwGgNb/V3XiQ0MI6+ONm+Lyoiim9WVbuom1bNEALQHrGWmAntayRQO0BKwnWp9TeS07+TD1AIARIYThIYQNAJwAYHoHP39NTAdwcss+GW/EYnuVEEIAcBGAeUVRfLMv21ICa4nGaAlYTwCN0dNaojFaAtazPlp2OHFsEoAnADwF4PwOf/YVAJYD+DPeiDufAmAzvLEKYAGA2wBs2oF2HIg3piMfA/BI679JfdEWa2ktrWfz9LSWzdHSetZHS1dAN8YYY4wpgRPQjTHGGGNK4IcpY4wxxpgS+GHKGGOMMaYEfpgyxhhjjCmBH6aMMcYYY0rghyljjDHGmBL4YcoYY4wxpgR+mDLGGGOMKcH/B1l+BUBpGPcpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b0c9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 1, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e12c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 2, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1337a3128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 3, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e280e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 4, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ddd5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 5, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fb6fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 6, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134ac2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 7, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1367167b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 8, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136a870f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category: 9, c: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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