From 6350b255f97ec868bbe5ded1f8bee8d587f422bf Mon Sep 17 00:00:00 2001
From: ClemW <clement.wang@student-cs.fr>
Date: Wed, 7 Apr 2021 18:10:23 +0200
Subject: [PATCH] add notebooks

---
 Chats_1.ipynb | 1342 ++++++++++++
 Chats_2.ipynb |  627 ++++++
 Chats_3.ipynb | 5819 +++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 7788 insertions(+)
 create mode 100644 Chats_1.ipynb
 create mode 100644 Chats_2.ipynb
 create mode 100644 Chats_3.ipynb

diff --git a/Chats_1.ipynb b/Chats_1.ipynb
new file mode 100644
index 0000000..9dfa383
--- /dev/null
+++ b/Chats_1.ipynb
@@ -0,0 +1,1342 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Chats_1.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JWAmigR04mO"
+      },
+      "source": [
+        "les modules :\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nFoVVZ8Ax0uv"
+      },
+      "source": [
+        "\n",
+        "import tensorflow as tf\n",
+        "import glob\n",
+        "import imageio\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import PIL\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.keras.models import Model\n",
+        "\n",
+        "import time\n",
+        "import matplotlib.pyplot as plt                                                  \n",
+        "import glob\n",
+        "from IPython import display\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x64YqYv0071k"
+      },
+      "source": [
+        "dataset avec des images de taille différentes : https://www.kaggle.com/crawford/cat-dataset\n",
+        "\n",
+        "\n",
+        "on abandonne ce dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "euYpez5r09jJ"
+      },
+      "source": [
+        "\"\"\"!pip install -q kaggle\n",
+        "from google.colab import files\n",
+        "\n",
+        "files.upload()\n",
+        "! mkdir ~/.kaggle\n",
+        "! cp kaggle.json ~/.kaggle/\n",
+        "! chmod 600 ~/.kaggle/kaggle.json\n",
+        "! kaggle datasets list\n",
+        "!kaggle datasets download -d crawford/cat-dataset\n",
+        "! unzip cat-dataset.zip -d dataset\"\"\""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "67phWsfN_IP1"
+      },
+      "source": [
+        "un autre dataset sur : https://github.com/Ferlix/Cat-faces-dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mzIwEaVh856L"
+      },
+      "source": [
+        "!git clone https://github.com/Ferlix/Cat-faces-dataset.git\n",
+        "\n",
+        "!tar xvzf Cat-faces-dataset/dataset-part1.tar.gz #unzip\n",
+        "!tar xvzf Cat-faces-dataset/dataset-part2.tar.gz\n",
+        "!tar xvzf Cat-faces-dataset/dataset-part3.tar.gz\n",
+        "\n",
+        "\n",
+        "\n",
+        "imageFolderPath = '/content/dataset-part?'\n",
+        "imagePath = glob.glob(imageFolderPath+'/*.png') \n",
+        "\n",
+        "\n",
+        "im_array = np.array( [np.array(PIL.Image.open(i)) for i in imagePath ])\n",
+        "\n",
+        "print(im_array.shape)\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mjk8VqWvdxon",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 123
+        },
+        "outputId": "3e44db37-9c1d-446f-f6ea-8b0b8aa8f44f"
+      },
+      "source": [
+        "numpydata = np.asarray(PIL.Image.open('dataset-part1/cat_0.png')) #convertir un png en np appay\n",
+        "plt.figure(figsize=(1,1))\n",
+        "plt.imshow(numpydata)\n",
+        "print(np.shape(numpydata))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(64, 64, 3)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 72x72 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "N_aaYogT9RWF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "50bcc126-97f3-460a-b093-2e9f6699188b"
+      },
+      "source": [
+        "print(im_array.shape)\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(29843, 64, 64, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KCkeI2MiEEzq"
+      },
+      "source": [
+        "train_images = im_array.reshape(im_array.shape[0], 64, 64, 3).astype('float32')\n",
+        "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rzS-oO7XqX2C"
+      },
+      "source": [
+        "on copie colle le GAN de la forma"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OXKj4TnUE4fx"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "def make_generator_model():\n",
+        "    model = tf.keras.Sequential()\n",
+        "    model.add(layers.Dense(16*16*256, use_bias=False, input_shape=(100,)))\n",
+        "    model.add(layers.BatchNormalization())\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "\n",
+        "    model.add(layers.Reshape((16, 16, 256)))\n",
+        "    assert model.output_shape == (None, 16, 16, 256) # Note: None is the batch size\n",
+        "\n",
+        "    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
+        "    assert model.output_shape == (None, 16, 16, 128)\n",
+        "    model.add(layers.BatchNormalization())\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "\n",
+        "    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
+        "    assert model.output_shape == (None, 32, 32, 64)\n",
+        "    model.add(layers.BatchNormalization())\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "\n",
+        "    model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n",
+        "    assert model.output_shape == (None, 64, 64, 3)\n",
+        "\n",
+        "    return model\n",
+        "\n",
+        "\n",
+        "#noise = tf.random.normal([1, 100])\n",
+        "#generated_image = generator(noise, training=False)\n",
+        "\n",
+        "#plt.imshow(generated_image[0, :, :, 0])\n",
+        "\n",
+        "\n",
+        "def make_discriminator_model():\n",
+        "    model = tf.keras.Sequential()\n",
+        "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n",
+        "                                     input_shape=[64, 64, 3]))\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "    model.add(layers.Dropout(0.3))\n",
+        "\n",
+        "    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "    model.add(layers.Dropout(0.3))\n",
+        "\n",
+        "    model.add(layers.Flatten())\n",
+        "    model.add(layers.Dense(1))\n",
+        "\n",
+        "    return model\n",
+        "  \n",
+        "\n",
+        "#decision = discriminator(generated_image)\n",
+        "#print (decision)\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UP7LYmNjofBs"
+      },
+      "source": [
+        "\n",
+        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+        "\n",
+        "\n",
+        "def discriminator_loss(real_output, fake_output):\n",
+        "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
+        "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
+        "    total_loss = real_loss + fake_loss\n",
+        "    return total_loss\n",
+        "\n",
+        "def generator_loss(fake_output):\n",
+        "    return cross_entropy(tf.ones_like(fake_output), fake_output)\n",
+        "\n",
+        "def generator_loss_MSE(fake_output):\n",
+        "    return  tf.reduce_mean(tf.keras.losses.MSE(tf.ones_like(fake_output), fake_output))\n",
+        "\n",
+        "\n",
+        "def discriminator_loss_MSE(real_output, fake_output): \n",
+        "    \n",
+        "    real_loss = tf.reduce_mean(tf.keras.losses.MSE(tf.ones_like(real_output), real_output))\n",
+        "    fake_loss = tf.reduce_mean(tf.keras.losses.MSE(tf.zeros_like(fake_output), fake_output))\n",
+        "    total_loss = real_loss+fake_loss\n",
+        "    return tf.reduce_mean(total_loss)\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1PXmPRS1ru6f"
+      },
+      "source": [
+        "def discriminator_lossWass(real_output, fake_output): #wass version\n",
+        "    \n",
+        "    real_loss = tf.math.reduce_mean(real_output)\n",
+        "    fake_loss = tf.math.reduce_mean(fake_output)\n",
+        "    total_loss = -real_loss + fake_loss\n",
+        "    return total_loss\n",
+        "\n",
+        "def generator_lossWass(fake_output):\n",
+        "    \n",
+        "    return tf.math.reduce_mean(fake_output)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kC6j0KGWgIYx"
+      },
+      "source": [
+        "noise_dim = 100\n",
+        "num_examples_to_generate = 16\n",
+        "\n",
+        "# We will reuse this seed overtime (so it's easier)\n",
+        "# to visualize progress in the animated GIF)\n",
+        "seed = tf.random.normal([num_examples_to_generate, noise_dim])\n",
+        "\n",
+        "\n",
+        "@tf.function\n",
+        "def train_step(images,generator,discriminator,BATCH_SIZE = 256,noise_dim = 100):\n",
+        "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
+        "\n",
+        "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
+        "      generated_images = generator(noise, training=True)\n",
+        "\n",
+        "      real_output = discriminator(images, training=True)\n",
+        "      fake_output = discriminator(generated_images, training=True)\n",
+        "\n",
+        "      gen_loss = generator_loss(fake_output)\n",
+        "      disc_loss = discriminator_loss(real_output, fake_output)\n",
+        "      #print(\"disc loss: \",disc_loss.eval())\n",
+        "      gen_loss_val = generator_loss_MSE(fake_output)\n",
+        "      disc_loss_val = discriminator_loss_MSE(real_output, fake_output)\n",
+        "\n",
+        "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
+        "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
+        "\n",
+        "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
+        "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
+        "    return gen_loss_val,disc_loss_val\n",
+        "\n",
+        "\n",
+        "def train(dataset, epochs, generator,discriminator,BATCH_SIZE=256,noice_dim = 100):\n",
+        "  \n",
+        "  Lgen_loss = []\n",
+        "  Ldisc_loss = []\n",
+        "  X = []\n",
+        "  j=0\n",
+        "\n",
+        "\n",
+        "  \n",
+        "  for epoch in range(epochs):\n",
+        "    start = time.time()\n",
+        "    \n",
+        "    for image_batch in dataset:\n",
+        "      j += 1\n",
+        "      gen_loss,disc_loss = train_step(image_batch,generator,discriminator,BATCH_SIZE,noise_dim)\n",
+        "\n",
+        "      X.append(j)\n",
+        "      Lgen_loss.append(gen_loss)\n",
+        "      Ldisc_loss.append(disc_loss)\n",
+        "    \n",
+        "\n",
+        "    # Produce images for the GIF as we go\n",
+        "    display.clear_output(wait=True)\n",
+        "    generate_and_save_images(generator,\n",
+        "                             epoch + 1,\n",
+        "                             seed)\n",
+        "\n",
+        "    # Save the model every 15 epochs\n",
+        "    if (epoch + 1) % 15 == 0:\n",
+        "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
+        "\n",
+        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
+        "    generate_and_save_plots(X, Lgen_loss,Ldisc_loss,  epoch+1)\n",
+        "\n",
+        "  # Generate after the final epoch\n",
+        "  display.clear_output(wait=True)\n",
+        "  generate_and_save_images(generator,\n",
+        "                           epochs,\n",
+        "                           seed)\n",
+        "  \n",
+        "\n",
+        "def generate_and_save_images(model, epoch, test_input):\n",
+        "  # Notice `training` is set to False.\n",
+        "  # This is so all layers run in inference mode (batchnorm).\n",
+        "  predictions = model(test_input, training=False)\n",
+        "\n",
+        "  fig = plt.figure(figsize=(4,4))\n",
+        "\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.8))\n",
+        "      plt.axis('off')\n",
+        "\n",
+        "  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
+        "  plt.show()\n",
+        "\n",
+        "\n",
+        "def generate_and_save_plots(X, Lgen_loss,Ldisc_loss, epoch):\n",
+        "\n",
+        "    fig = plt.figure(figsize=(4,4))\n",
+        "    plt.plot(X,Lgen_loss, label = 'gen_loss')\n",
+        "    plt.plot(X,Ldisc_loss, label = 'disc_loss')\n",
+        "    plt.legend()\n",
+        "    \n",
+        "    #plt.savefig('output/losses.png')\n",
+        "    plt.show()\n",
+        "\n",
+        "    plt.clf()\n",
+        "    plt.cla()\n",
+        "    plt.close()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9sWJrkqVnqTd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3231f499-ce81-45db-c867-90f50eefb140"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "generator = make_generator_model()\n",
+        "discriminator = make_discriminator_model()\n",
+        "\n",
+        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+        "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+        "\n",
+        "checkpoint_dir = './training_checkpoints'\n",
+        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
+        "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
+        "                                 discriminator_optimizer=discriminator_optimizer,\n",
+        "                                 generator=generator,\n",
+        "                                 discriminator=discriminator)\n",
+        "\n",
+        "\n",
+        "discriminator.summary()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential_1\"\n",
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "conv2d (Conv2D)              (None, 32, 32, 64)        4864      \n",
+            "_________________________________________________________________\n",
+            "leaky_re_lu_3 (LeakyReLU)    (None, 32, 32, 64)        0         \n",
+            "_________________________________________________________________\n",
+            "dropout (Dropout)            (None, 32, 32, 64)        0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_1 (Conv2D)            (None, 16, 16, 128)       204928    \n",
+            "_________________________________________________________________\n",
+            "leaky_re_lu_4 (LeakyReLU)    (None, 16, 16, 128)       0         \n",
+            "_________________________________________________________________\n",
+            "dropout_1 (Dropout)          (None, 16, 16, 128)       0         \n",
+            "_________________________________________________________________\n",
+            "flatten (Flatten)            (None, 32768)             0         \n",
+            "_________________________________________________________________\n",
+            "dense_1 (Dense)              (None, 1)                 32769     \n",
+            "=================================================================\n",
+            "Total params: 242,561\n",
+            "Trainable params: 242,561\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FvL0auzAns1v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "outputId": "470e13a3-4ab0-4317-ae44-c94b9d1b6230"
+      },
+      "source": [
+        "BUFFER_SIZE = 60000\n",
+        "BATCH_SIZE = 256\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
+        "\n",
+        "train(train_dataset, 20,generator,discriminator,BATCH_SIZE=256,noice_dim = 100)\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OldAGytAKhZ4"
+      },
+      "source": [
+        "generator.save_weights('generator1_weight_500_epoch.h5')\n",
+        "discriminator.save_weights('discriminator1_weight_500_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MCJfhI-WezMk"
+      },
+      "source": [
+        "generator.load_weights('generator1_weight_500_epoch.h5')\n",
+        "discriminator.load_weights('discriminator1_weight_500_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jqnEJtaYLZbh"
+      },
+      "source": [
+        "def generate_samples(generator,number=16,save = False, file_name = 'image_sample_model1-500.png'):\n",
+        "  noise_dim = 100\n",
+        "  seed = tf.random.normal([number, noise_dim])\n",
+        "  predictions = generator(seed, training=False)\n",
+        "  fig = plt.figure(figsize=(7,7))\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.6))\n",
+        "      plt.axis('off')\n",
+        "  if save :\n",
+        "    plt.savefig(file_name)\n",
+        "  plt.show()\n",
+        "generate_samples(generator)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "neKkvRA21zAG"
+      },
+      "source": [
+        "for i in range(1000):\n",
+        "  generate_samples(generator,1,save = True,file_name= \"chats_genere/chat\"+str(i)+\".png\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "X-JRjngKL6SM"
+      },
+      "source": [
+        "!zip -r /content/cats.zip /content/chats_genere"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3OFAie_Uqu6b"
+      },
+      "source": [
+        "\n",
+        "#CELLULE POUR PREPROCESS UN DATASET\n",
+        "#PLACER FICHIER preprocess_dataset.py au bon endroit\n",
+        "\n",
+        "## Download CAT dataset\n",
+        "\n",
+        "!wget -nc https://archive.org/download/CAT_DATASET/CAT_DATASET_01.zip\n",
+        "!wget -nc https://archive.org/download/CAT_DATASET/CAT_DATASET_02.zip\n",
+        "!wget -nc https://archive.org/download/CAT_DATASET/00000003_015.jpg.cat\n",
+        "\n",
+        "## Setting up folder\n",
+        "!unzip /content/drive/MyDrive/CAT_DATASET_01.zip -d cat_dataset\n",
+        "!unzip /content/drive/MyDrive/CAT_DATASET_02.zip -d cat_dataset\n",
+        "!mv cat_dataset/CAT_00/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_00\n",
+        "!mv cat_dataset/CAT_01/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_01\n",
+        "!mv cat_dataset/CAT_02/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_02\n",
+        "!mv cat_dataset/CAT_03/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_03\n",
+        "!mv cat_dataset/CAT_04/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_04\n",
+        "!mv cat_dataset/CAT_05/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_05\n",
+        "!mv cat_dataset/CAT_06/* cat_dataset\n",
+        "!rmdir cat_dataset/CAT_06\n",
+        "## Error correction\n",
+        "!rm cat_dataset/00000003_019.jpg.cat\n",
+        "!mv 00000003_015.jpg.cat cat_dataset/00000003_015.jpg.cat\n",
+        "\n",
+        "## Removing outliers\n",
+        "# Corrupted, drawings, badly cropped, inverted, impossible to tell it's a cat, blocked face\n",
+        "!cd cat_dataset\n",
+        "!rm 00000004_007.jpg 00000007_002.jpg 00000045_028.jpg 00000050_014.jpg 00000056_013.jpg 00000059_002.jpg 00000108_005.jpg 00000122_023.jpg 00000126_005.jpg 00000132_018.jpg 00000142_024.jpg 00000142_029.jpg 00000143_003.jpg 00000145_021.jpg 00000166_021.jpg 00000169_021.jpg 00000186_002.jpg 00000202_022.jpg 00000208_023.jpg 00000210_003.jpg 00000229_005.jpg 00000236_025.jpg 00000249_016.jpg 00000254_013.jpg 00000260_019.jpg 00000261_029.jpg 00000265_029.jpg 00000271_020.jpg 00000282_026.jpg 00000316_004.jpg 00000352_014.jpg 00000400_026.jpg 00000406_006.jpg 00000431_024.jpg 00000443_027.jpg 00000502_015.jpg 00000504_012.jpg 00000510_019.jpg 00000514_016.jpg 00000514_008.jpg 00000515_021.jpg 00000519_015.jpg 00000522_016.jpg 00000523_021.jpg 00000529_005.jpg 00000556_022.jpg 00000574_011.jpg 00000581_018.jpg 00000582_011.jpg 00000588_016.jpg 00000588_019.jpg 00000590_006.jpg 00000592_018.jpg 00000593_027.jpg 00000617_013.jpg 00000618_016.jpg 00000619_025.jpg 00000622_019.jpg 00000622_021.jpg 00000630_007.jpg 00000645_016.jpg 00000656_017.jpg 00000659_000.jpg 00000660_022.jpg 00000660_029.jpg 00000661_016.jpg 00000663_005.jpg 00000672_027.jpg 00000673_027.jpg 00000675_023.jpg 00000692_006.jpg 00000800_017.jpg 00000805_004.jpg 00000807_020.jpg 00000823_010.jpg 00000824_010.jpg 00000836_008.jpg 00000843_021.jpg 00000850_025.jpg 00000862_017.jpg 00000864_007.jpg 00000865_015.jpg 00000870_007.jpg 00000877_014.jpg 00000882_013.jpg 00000887_028.jpg 00000893_022.jpg 00000907_013.jpg 00000921_029.jpg 00000929_022.jpg 00000934_006.jpg 00000960_021.jpg 00000976_004.jpg 00000987_000.jpg 00000993_009.jpg 00001006_014.jpg 00001008_013.jpg 00001012_019.jpg 00001014_005.jpg 00001020_017.jpg 00001039_008.jpg 00001039_023.jpg 00001048_029.jpg 00001057_003.jpg 00001068_005.jpg 00001113_015.jpg 00001140_007.jpg 00001157_029.jpg 00001158_000.jpg 00001167_007.jpg 00001184_007.jpg 00001188_019.jpg 00001204_027.jpg 00001205_022.jpg 00001219_005.jpg 00001243_010.jpg 00001261_005.jpg 00001270_028.jpg 00001274_006.jpg 00001293_015.jpg 00001312_021.jpg 00001365_026.jpg 00001372_006.jpg 00001379_018.jpg 00001388_024.jpg 00001389_026.jpg 00001418_028.jpg 00001425_012.jpg 00001431_001.jpg 00001456_018.jpg 00001458_003.jpg 00001468_019.jpg 00001475_009.jpg 00001487_020.jpg\n",
+        "!rm 00000004_007.jpg.cat 00000007_002.jpg.cat 00000045_028.jpg.cat 00000050_014.jpg.cat 00000056_013.jpg.cat 00000059_002.jpg.cat 00000108_005.jpg.cat 00000122_023.jpg.cat 00000126_005.jpg.cat 00000132_018.jpg.cat 00000142_024.jpg.cat 00000142_029.jpg.cat 00000143_003.jpg.cat 00000145_021.jpg.cat 00000166_021.jpg.cat 00000169_021.jpg.cat 00000186_002.jpg.cat 00000202_022.jpg.cat 00000208_023.jpg.cat 00000210_003.jpg.cat 00000229_005.jpg.cat 00000236_025.jpg.cat 00000249_016.jpg.cat 00000254_013.jpg.cat 00000260_019.jpg.cat 00000261_029.jpg.cat 00000265_029.jpg.cat 00000271_020.jpg.cat 00000282_026.jpg.cat 00000316_004.jpg.cat 00000352_014.jpg.cat 00000400_026.jpg.cat 00000406_006.jpg.cat 00000431_024.jpg.cat 00000443_027.jpg.cat 00000502_015.jpg.cat 00000504_012.jpg.cat 00000510_019.jpg.cat 00000514_016.jpg.cat 00000514_008.jpg.cat 00000515_021.jpg.cat 00000519_015.jpg.cat 00000522_016.jpg.cat 00000523_021.jpg.cat 00000529_005.jpg.cat 00000556_022.jpg.cat 00000574_011.jpg.cat 00000581_018.jpg.cat 00000582_011.jpg.cat 00000588_016.jpg.cat 00000588_019.jpg.cat 00000590_006.jpg.cat 00000592_018.jpg.cat 00000593_027.jpg.cat 00000617_013.jpg.cat 00000618_016.jpg.cat 00000619_025.jpg.cat 00000622_019.jpg.cat 00000622_021.jpg.cat 00000630_007.jpg.cat 00000645_016.jpg.cat 00000656_017.jpg.cat 00000659_000.jpg.cat 00000660_022.jpg.cat 00000660_029.jpg.cat 00000661_016.jpg.cat 00000663_005.jpg.cat 00000672_027.jpg.cat 00000673_027.jpg.cat 00000675_023.jpg.cat 00000692_006.jpg.cat 00000800_017.jpg.cat 00000805_004.jpg.cat 00000807_020.jpg.cat 00000823_010.jpg.cat 00000824_010.jpg.cat 00000836_008.jpg.cat 00000843_021.jpg.cat 00000850_025.jpg.cat 00000862_017.jpg.cat 00000864_007.jpg.cat 00000865_015.jpg.cat 00000870_007.jpg.cat 00000877_014.jpg.cat 00000882_013.jpg.cat 00000887_028.jpg.cat 00000893_022.jpg.cat 00000907_013.jpg.cat 00000921_029.jpg.cat 00000929_022.jpg.cat 00000934_006.jpg.cat 00000960_021.jpg.cat 00000976_004.jpg.cat 00000987_000.jpg.cat 00000993_009.jpg.cat 00001006_014.jpg.cat 00001008_013.jpg.cat 00001012_019.jpg.cat 00001014_005.jpg.cat 00001020_017.jpg.cat 00001039_008.jpg.cat 00001039_023.jpg.cat 00001048_029.jpg.cat 00001057_003.jpg.cat 00001068_005.jpg.cat 00001113_015.jpg.cat 00001140_007.jpg.cat 00001157_029.jpg.cat 00001158_000.jpg.cat 00001167_007.jpg.cat 00001184_007.jpg.cat 00001188_019.jpg.cat 00001204_027.jpg.cat 00001205_022.jpg.cat 00001219_005.jpg.cat 00001243_010.jpg.cat 00001261_005.jpg.cat 00001270_028.jpg.cat 00001274_006.jpg.cat 00001293_015.jpg.cat 00001312_021.jpg.cat 00001365_026.jpg.cat 00001372_006.jpg.cat 00001379_018.jpg.cat 00001388_024.jpg.cat 00001389_026.jpg.cat 00001418_028.jpg.cat 00001425_012.jpg.cat 00001431_001.jpg.cat 00001456_018.jpg.cat 00001458_003.jpg.cat 00001468_019.jpg.cat 00001475_009.jpg.cat 00001487_020.jpg.cat\n",
+        "!cd ..\n",
+        "\n",
+        "## Preprocessing and putting in folders for different image sizes\n",
+        "!mkdir cats_bigger_than_64x64\n",
+        "!mkdir cats_bigger_than_128x128\n",
+        "\n",
+        "!python /content/preprocess_cat_dataset.py\n",
+        "\n",
+        "## Removing cat_dataset\n",
+        "!rm -r cat_dataset\n",
+        "\n",
+        "## Move to your favorite place\n",
+        "#mv cats_bigger_than_64x64 /home/alexia/Datasets/Meow_64x64\n",
+        "#mv cats_bigger_than_128x128 /home/alexia/Datasets/Meow_128x128\n",
+        "!read -p \"Appuyer sur une touche pour continuer ...\"\n",
+        "\n",
+        "!zip -r /content/cats128.zip /content/cats_bigger_than_128x128\n",
+        "!zip -r /content/cats64.zip /content/cats_bigger_than_64x64\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TbDY6pZ0uvFH"
+      },
+      "source": [
+        "another dataset\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lZJh3YRh2idC"
+      },
+      "source": [
+        "\n",
+        "!unzip \"/content/drive/MyDrive/dataset cats/cats128.zip\" -d /\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "ERZjImdwaLza",
+        "outputId": "4b73b5c0-37b7-4776-abea-1d18e774727e"
+      },
+      "source": [
+        "imageFolderPath = '/content/cats_bigger_than_128x128'\n",
+        "imagePath = glob.glob(imageFolderPath+'/*.jpg') \n",
+        "im_array = np.array( [np.array((PIL.Image.open(i)).resize((128,128))) for i in imagePath ])\n",
+        "print(im_array.shape)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(im_array[20])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(5388, 128, 128, 3)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7fdbd85d8278>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 360x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8QDx5zrt9nFL"
+      },
+      "source": [
+        "train_images = im_array.reshape(im_array.shape[0], 128, 128, 3).astype('float32')\n",
+        "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rQxIm4E2x4iu"
+      },
+      "source": [
+        "***Anime GAN***\n",
+        "\n",
+        "‘k3n64s1’ indicates the kernel size to be 3 × 3, the feature map number to be 64 and the stride to be *1*\n",
+        "\n",
+        "LES GAN QUI SUIVENT NE FONCTIONNENT PAS \n",
+        "\n",
+        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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MhEp6shvx2Zc",
+        "outputId": "7fa85956-d2fc-40e8-e1d1-c119d935f48b"
+      },
+      "source": [
+        "\n",
+        "## make generator\n",
+        "def bn_relu(inputs):\n",
+        "  bn = layers.BatchNormalization()(inputs)\n",
+        "  relu = layers.ReLU()(bn)\n",
+        "  return relu\n",
+        "\n",
+        "def res_block(x):\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
+        "  y = bn_relu(y)\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(y)\n",
+        "  y = layers.BatchNormalization() (y)\n",
+        "  out = layers.Add()([x,y])\n",
+        "  return out\n",
+        "\n",
+        "def all_res_block(input):\n",
+        "  y = res_block(input)\n",
+        "  for i in range(15):\n",
+        "    y = res_block(y)\n",
+        "  y = bn_relu(y)\n",
+        "  out = layers.Add()([input,y])\n",
+        "  return out \n",
+        "\n",
+        "def pix_shuf(input):\n",
+        "  y = layers.Conv2DTranspose(256, (3, 3), strides=(1, 1), padding='same')(input)\n",
+        "  y = layers.UpSampling2D()(y)\n",
+        "  #y = layers.UpSampling2D()(y)\n",
+        "  y = bn_relu(y)\n",
+        "  return y \n",
+        "\n",
+        "\n",
+        "\n",
+        "def make_AGAN_generator():\n",
+        "  inputs = layers.Input(shape=(128+34,))\n",
+        "  t = layers.Dense(64*16*16) (inputs)\n",
+        "\n",
+        "  t = bn_relu(t)\n",
+        "  t = layers.Reshape((16,16,64))(t) \n",
+        "\n",
+        "  t = all_res_block(t)\n",
+        "\n",
+        "  for i in range(3):\n",
+        "    t = pix_shuf(t)\n",
+        "  \n",
+        "  t = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(t) #peut être une Conv2DTranspose\n",
+        "\n",
+        "  \n",
+        "  outputs = layers.Activation(tf.nn.tanh)(t) #modifier la derniere couche quand ça sera bon\n",
+        "  model = Model(inputs, outputs)\n",
+        "\n",
+        "  return model\n",
+        "\n",
+        "gen = make_AGAN_generator()\n",
+        "#gen.summary()\n",
+        "print(gen.output_shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 128, 128, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0DwPoSElVGat",
+        "outputId": "3a4dfe3a-cb2f-4647-da40-bf6b340a24f9"
+      },
+      "source": [
+        "##second try : 3*16 blocs\n",
+        "\n",
+        "def bn_relu(inputs):\n",
+        "  bn = layers.BatchNormalization()(inputs)\n",
+        "  relu = layers.ReLU()(bn)\n",
+        "  return relu\n",
+        "\n",
+        "def res_block(x):\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
+        "  y = bn_relu(y)\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(y)\n",
+        "  y = layers.BatchNormalization() (y)\n",
+        "  out = layers.add([x,y]) #out = layers.Add()([x,y])\n",
+        "  return out\n",
+        "\n",
+        "def triple_res_block(input):\n",
+        "  y = res_block(input)\n",
+        "  y = res_block(y)\n",
+        "  y = res_block(y)\n",
+        "  return y \n",
+        "\n",
+        "def all_res_block(input):\n",
+        "  y = triple_res_block(input)\n",
+        "  for i in range(15):\n",
+        "    y = triple_res_block(y)\n",
+        "  y = bn_relu(y)\n",
+        "  out = layers.add([input,y])\n",
+        "  return out \n",
+        "\n",
+        "def pix_shuf(input):\n",
+        "  y = layers.Conv2DTranspose(256, (3, 3), strides=(1, 1), padding='same')(input)\n",
+        "  y = layers.UpSampling2D()(y)\n",
+        "  #y = layers.UpSampling2D()(y)\n",
+        "  y = bn_relu(y)\n",
+        "  return y \n",
+        "\n",
+        "\n",
+        "\n",
+        "def make_AGAN_generator2():\n",
+        "  inputs = layers.Input(shape=(128+34,))\n",
+        "  t = layers.Dense(64*16*16) (inputs)\n",
+        "\n",
+        "  t = bn_relu(t)\n",
+        "  t = layers.Reshape((16,16,64))(t) \n",
+        "\n",
+        "  t = all_res_block(t)\n",
+        "\n",
+        "  for i in range(3):\n",
+        "    t = pix_shuf(t)\n",
+        "  \n",
+        "  t = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(t) #peut être une Conv2DTranspose\n",
+        "\n",
+        "  \n",
+        "  outputs = layers.Activation(tf.nn.tanh)(t) #modifier la derniere couche quand ça sera bon\n",
+        "  model = Model(inputs, outputs)\n",
+        "\n",
+        "  return model\n",
+        "\n",
+        "gen = make_AGAN_generator2()\n",
+        "#gen.summary()\n",
+        "print(gen.output_shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 128, 128, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZpjR_n37ohoM",
+        "outputId": "98918140-08bc-44df-c96b-0337777f3578"
+      },
+      "source": [
+        "## THIRD TRY : que 3 blocs\n",
+        "## make generator\n",
+        "def bn_relu(inputs):\n",
+        "  bn = layers.BatchNormalization()(inputs)\n",
+        "  relu = layers.ReLU()(bn)\n",
+        "  return relu\n",
+        "\n",
+        "def res_block(x):\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
+        "  y = bn_relu(y)\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(y)\n",
+        "  y = layers.BatchNormalization() (y)\n",
+        "  out = layers.Add()([x,y])\n",
+        "  return out\n",
+        "\n",
+        "def all_res_block(input):\n",
+        "  y = res_block(input)\n",
+        "  y = res_block(y)\n",
+        "  y = res_block(y)\n",
+        "  y = bn_relu(y)\n",
+        "  out = layers.Add()([input,y])\n",
+        "  return out \n",
+        "\n",
+        "def pix_shuf(input):\n",
+        "  y = layers.Conv2DTranspose(256, (3, 3), strides=(1, 1), padding='same')(input)\n",
+        "  y = layers.UpSampling2D()(y)\n",
+        "  #y = layers.UpSampling2D()(y)\n",
+        "  y = bn_relu(y)\n",
+        "  return y \n",
+        "\n",
+        "\n",
+        "\n",
+        "def make_AGAN_generator3():\n",
+        "  inputs = layers.Input(shape=(128+34,))\n",
+        "  t = layers.Dense(64*16*16) (inputs)\n",
+        "\n",
+        "  t = bn_relu(t)\n",
+        "  t = layers.Reshape((16,16,64))(t) \n",
+        "\n",
+        "  t = all_res_block(t)\n",
+        "\n",
+        "  for i in range(3):\n",
+        "    t = pix_shuf(t)\n",
+        "  \n",
+        "  t = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(t) #peut être une Conv2DTranspose\n",
+        "\n",
+        "  \n",
+        "  outputs = layers.Activation(tf.nn.tanh)(t) #modifier la derniere couche quand ça sera bon\n",
+        "  model = Model(inputs, outputs)\n",
+        "\n",
+        "  return model\n",
+        "\n",
+        "gen = make_AGAN_generator3()\n",
+        "#gen.summary()\n",
+        "print(gen.output_shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 128, 128, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9Flada5emErS",
+        "outputId": "d42a8c74-d3ba-4256-80f8-8ca93682af8f"
+      },
+      "source": [
+        "## make discriminator AGAN\n",
+        "def conv_leakyrelu(input,n,k,s):\n",
+        "  y = layers.Conv2D(n,k,strides=(s,s),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def res_block_dis(input,n1,k1,s1,n2,k2,s2):\n",
+        "  y = layers.Conv2D(n1,k1,strides =(s1,s1),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  y = layers.Conv2D(n2,k2,strides=(s2,s2),padding='same')(y)\n",
+        "  y = layers.add([input,y])#layers.Add()([input,y])\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def make_AGAN_discri():\n",
+        "\n",
+        "  inputs = layers.Input(shape=(128,128,3))\n",
+        "  t = conv_leakyrelu(inputs,32,4,2)\n",
+        "  t = res_block_dis(t,32,3,1,32,3,1)\n",
+        "  t = res_block_dis(t,32,3,1,32,3,1)\n",
+        "  t = conv_leakyrelu(t,64,4,2)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "  t = conv_leakyrelu(t,128,4,2)\n",
+        "  t = res_block_dis(t,128,3,1,128,3,1)\n",
+        "  t = res_block_dis(t,128,3,1,128,3,1) \n",
+        "  t = conv_leakyrelu(t,256,3,2)\n",
+        "  t = res_block_dis(t,256,3,1,256,3,1)\n",
+        "  t = res_block_dis(t,256,3,1,256,3,1)\n",
+        "  t = conv_leakyrelu(t,512,3,2)\n",
+        "  t = res_block_dis(t,512,3,1,512,3,1)\n",
+        "  t = res_block_dis(t,512,3,1,512,3,1)\n",
+        "  t = conv_leakyrelu(t,1024,3,2)\n",
+        "  t = layers.Flatten()(t)\n",
+        "  outputs = layers.Dense(1,activation='sigmoid')(t) #que signifie la fin du schéma ?\n",
+        "  model = Model(inputs, outputs)\n",
+        "  return model \n",
+        "\n",
+        "disc = make_AGAN_discri()\n",
+        "#disc.summary()\n",
+        "print(disc.output_shape) \n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7iRuGCGoVydd"
+      },
+      "source": [
+        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+        "\n",
+        "\n",
+        "def discriminator_loss(real_output, fake_output):\n",
+        "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
+        "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
+        "    total_loss = real_loss + fake_loss\n",
+        "    return total_loss\n",
+        "\n",
+        "def generator_loss(fake_output):\n",
+        "    return cross_entropy(tf.ones_like(fake_output), fake_output)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wMfyG9xXV3TE"
+      },
+      "source": [
+        "noise_dim = 128+34\n",
+        "num_examples_to_generate = 16\n",
+        "\n",
+        "# We will reuse this seed overtime (so it's easier)\n",
+        "# to visualize progress in the animated GIF)\n",
+        "seed = tf.random.normal([num_examples_to_generate, noise_dim])\n",
+        "\n",
+        "\n",
+        "@tf.function\n",
+        "def train_step(images,generator,discriminator,BATCH_SIZE = 256,noise_dim =128+34,epoch=0):\n",
+        "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
+        "\n",
+        "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
+        "      generated_images = generator(noise, training=True)\n",
+        "\n",
+        "      real_output = discriminator(images, training=True)\n",
+        "      fake_output = discriminator(generated_images, training=True)\n",
+        "\n",
+        "      gen_loss = generator_loss(fake_output)\n",
+        "      disc_loss = discriminator_loss(real_output, fake_output)\n",
+        "      #print(\"disc loss: \",tf.print(discriminator_loss))\n",
+        "      #print(\"gen loss: \",tf.print(gen_loss))\n",
+        "\n",
+        "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
+        "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
+        "    if epoch%5 == 0 :\n",
+        "      gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
+        "      discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
+        "\n",
+        "\n",
+        "def train(dataset, epochs, generator,discriminator,BATCH_SIZE=256,noice_dim = 128+34):\n",
+        "  for epoch in range(epochs):\n",
+        "    start = time.time()\n",
+        "\n",
+        "    for image_batch in dataset:\n",
+        "      train_step(image_batch,generator,discriminator,BATCH_SIZE,noise_dim,epoch)\n",
+        "\n",
+        "    # Produce images for the GIF as we go\n",
+        "    display.clear_output(wait=True)\n",
+        "    generate_and_save_images(generator,\n",
+        "                             epoch + 1,\n",
+        "                             seed)\n",
+        "\n",
+        "    # Save the model every 15 epochs\n",
+        "    if (epoch + 1) % 15 == 0:\n",
+        "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
+        "\n",
+        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
+        "\n",
+        "  # Generate after the final epoch\n",
+        "  display.clear_output(wait=True)\n",
+        "  generate_and_save_images(generator,\n",
+        "                           epochs,\n",
+        "                           seed)\n",
+        "  \n",
+        "\n",
+        "def generate_and_save_images(model, epoch, test_input):\n",
+        "  # Notice `training` is set to False.\n",
+        "  # This is so all layers run in inference mode (batchnorm).\n",
+        "  predictions = model(test_input, training=False)\n",
+        "\n",
+        "  fig = plt.figure(figsize=(20,20))\n",
+        "\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.8))\n",
+        "      plt.axis('off')\n",
+        "  plt.savefig('./image_at_epoch_{:04d}.png'.format(epoch)) #create directory\n",
+        "  plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7SwjVvkcWDiF"
+      },
+      "source": [
+        "generator = make_AGAN_generator2()\n",
+        "discriminator = make_AGAN_discri()\n",
+        "\n",
+        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+        "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+        "\n",
+        "checkpoint_dir = './training_checkpoints'\n",
+        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
+        "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
+        "                                 discriminator_optimizer=discriminator_optimizer,\n",
+        "                                 generator=generator,\n",
+        "                                 discriminator=discriminator)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ysKga0frEVgt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 231
+        },
+        "outputId": "ee1080d2-0d6f-4c29-abd3-cd377d169963"
+      },
+      "source": [
+        "\n",
+        "BUFFER_SIZE = 60000 #5388 #60000\n",
+        "BATCH_SIZE = 128\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
+        "\n",
+        "train(train_dataset, 100,generator,discriminator,BATCH_SIZE,noice_dim = 128+34)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-1-3b64f620bd0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mBUFFER_SIZE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m60000\u001b[0m \u001b[0;31m#5388 #60000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tensor_slices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBUFFER_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoice_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m34\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wJ4pmjtJQ6y6"
+      },
+      "source": [
+        "generator.save_weights('/content/drive/MyDrive/dataset cats/generator4_weight_100_epoch.h5') #changer et directement mettre sur drive \n",
+        "discriminator.save_weights('/content/drive/MyDrive/dataset cats/discriminator4_weight_100_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fXaJta6SRDL6"
+      },
+      "source": [
+        "generator.load_weights('/content/drive/MyDrive/dataset cats/generator2_weight_100_epoch.h5')\n",
+        "discriminator.load_weights('/content/drive/MyDrive/dataset cats/discriminator2_weight_100_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        },
+        "id": "XBCXS_eWlLJG",
+        "outputId": "f0ffd16f-ca04-44d3-87a5-52b10f94b538"
+      },
+      "source": [
+        "def generate_samples(generator,number=16,save = False, file_name = 'image_sample_model1-500.png'):\n",
+        "  noise_dim = 128+34\n",
+        "  seed = tf.random.normal([number, noise_dim])\n",
+        "  predictions = generator(seed, training=False)\n",
+        "  fig = plt.figure(figsize=(7,7))\n",
+        "  print(discriminator(generator(seed,training=False)))\n",
+        "  print(discriminator(im_array[:10]))\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      #pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      #enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      #plt.imshow(enhancer.enhance(0.6))\n",
+        "      plt.imshow(nparray)\n",
+        "      plt.axis('off')\n",
+        "  if save :\n",
+        "    plt.savefig(file_name)\n",
+        "  plt.show()\n",
+        "generate_samples(generator)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "tf.Tensor(\n",
+            "[[1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]], shape=(16, 1), dtype=float32)\n",
+            "tf.Tensor(\n",
+            "[[1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]\n",
+            " [1.]], shape=(10, 1), dtype=float32)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 504x504 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chats_2.ipynb b/Chats_2.ipynb
new file mode 100644
index 0000000..9203eef
--- /dev/null
+++ b/Chats_2.ipynb
@@ -0,0 +1,627 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Chats_2",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PCm9Pehz7w05"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "import glob\n",
+        "import imageio\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import PIL\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.keras.models import Model\n",
+        "\n",
+        "import time\n",
+        "import matplotlib.pyplot as plt                                                  \n",
+        "import glob\n",
+        "from IPython import display"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PHYsYgN_-T9Q"
+      },
+      "source": [
+        "def generate_samples(generator,number=16,save = False, file_name = 'image_sample.png'):\n",
+        "  noise_dim = 128+34 #change dim if needed\n",
+        "  seed = tf.random.normal([number, noise_dim])\n",
+        "  predictions = generator(seed, training=False)\n",
+        "  fig = plt.figure(figsize=(7,7))\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.6))\n",
+        "      plt.axis('off')\n",
+        "  if save :\n",
+        "    plt.savefig(file_name)\n",
+        "  plt.show()\n",
+        "#generate_samples(generator)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aeVeAn0k-ktG"
+      },
+      "source": [
+        "Import dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1iESP6_u-kUQ"
+      },
+      "source": [
+        "!unzip \"/content/drive/MyDrive/dataset cats/cats128.zip\" -d /\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fE1mgPjQ-v0O"
+      },
+      "source": [
+        "Load **dataset** and preprocess values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RbMz3DiO-vHu",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 341
+        },
+        "outputId": "bbe66b17-80ef-4cfd-cdd6-b591ec042f74"
+      },
+      "source": [
+        "imageFolderPath = '/content/cats_bigger_than_128x128'\n",
+        "imagePath = glob.glob(imageFolderPath+'/*.jpg') \n",
+        "im_array = np.array( [np.array((PIL.Image.open(i)).resize((128,128))) for i in imagePath ])\n",
+        "print(im_array.shape)\n",
+        "plt.figure(figsize=(5,5))\n",
+        "plt.imshow(im_array[20])\n",
+        "train_images = im_array.reshape(im_array.shape[0], 128, 128, 3).astype('float32')\n",
+        "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(5388, 128, 128, 3)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 360x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3OQHzPd8CNm8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5548af53-171b-44e5-fe7c-afcc9269e1f1"
+      },
+      "source": [
+        "##second try : 3*16 blocs\n",
+        "\n",
+        "def bn_relu(inputs):\n",
+        "  bn = layers.BatchNormalization()(inputs)\n",
+        "  relu = layers.ReLU()(bn)\n",
+        "  return relu\n",
+        "\n",
+        "def res_block(x):\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
+        "  y = bn_relu(y)\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(y)\n",
+        "  y = layers.BatchNormalization() (y)\n",
+        "  out = layers.add([x,y]) #out = layers.Add()([x,y])\n",
+        "  return out\n",
+        "\n",
+        "def triple_res_block(input):\n",
+        "  y = res_block(input)\n",
+        "  y = res_block(y)\n",
+        "  y = res_block(y)\n",
+        "  return y \n",
+        "\n",
+        "def all_res_block(input):\n",
+        "  y = triple_res_block(input)\n",
+        "  for i in range(15):\n",
+        "    y = triple_res_block(y)\n",
+        "  y = bn_relu(y)\n",
+        "  out = layers.add([input,y])\n",
+        "  return out \n",
+        "\n",
+        "def pix_shuf(input):\n",
+        "  y = layers.Conv2DTranspose(256, (3, 3), strides=(1, 1), padding='same')(input)\n",
+        "  y = layers.UpSampling2D()(y)\n",
+        "  #y = layers.UpSampling2D()(y)\n",
+        "  y = bn_relu(y)\n",
+        "  return y \n",
+        "\n",
+        "\n",
+        "\n",
+        "def make_AGAN_generator2():\n",
+        "  inputs = layers.Input(shape=(128+34,))\n",
+        "  t = layers.Dense(64*16*16) (inputs)\n",
+        "\n",
+        "  t = bn_relu(t)\n",
+        "  t = layers.Reshape((16,16,64))(t) \n",
+        "\n",
+        "  t = all_res_block(t)\n",
+        "\n",
+        "  for i in range(3):\n",
+        "    t = pix_shuf(t)\n",
+        "  \n",
+        "  t = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(t) #peut être une Conv2DTranspose\n",
+        "\n",
+        "  \n",
+        "  outputs = layers.Activation(tf.nn.tanh)(t) #modifier la derniere couche quand ça sera bon\n",
+        "  model = Model(inputs, outputs)\n",
+        "\n",
+        "  return model\n",
+        "\n",
+        "gen = make_AGAN_generator2()\n",
+        "#gen.summary()\n",
+        "print(gen.output_shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 128, 128, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QY-D8zLpCRNq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8c047719-a0c9-4b97-a54a-453c1b359384"
+      },
+      "source": [
+        "## make discriminator AGAN\n",
+        "def conv_leakyrelu(input,n,k,s):\n",
+        "  y = layers.Conv2D(n,k,strides=(s,s),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def res_block_dis(input,n1,k1,s1,n2,k2,s2):\n",
+        "  y = layers.Conv2D(n1,k1,strides =(s1,s1),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  y = layers.Conv2D(n2,k2,strides=(s2,s2),padding='same')(y)\n",
+        "  y = layers.add([input,y])#layers.Add()([input,y])\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def make_AGAN_discri():\n",
+        "\n",
+        "  inputs = layers.Input(shape=(128,128,3))\n",
+        "  t = conv_leakyrelu(inputs,32,4,2)\n",
+        "  t = res_block_dis(t,32,3,1,32,3,1)\n",
+        "  t = res_block_dis(t,32,3,1,32,3,1)\n",
+        "  t = conv_leakyrelu(t,64,4,2)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "  t = conv_leakyrelu(t,128,4,2)\n",
+        "  t = res_block_dis(t,128,3,1,128,3,1)\n",
+        "  t = res_block_dis(t,128,3,1,128,3,1) \n",
+        "  t = conv_leakyrelu(t,256,3,2)\n",
+        "  t = res_block_dis(t,256,3,1,256,3,1)\n",
+        "  t = res_block_dis(t,256,3,1,256,3,1)\n",
+        "  t = conv_leakyrelu(t,512,3,2)\n",
+        "  t = res_block_dis(t,512,3,1,512,3,1)\n",
+        "  t = res_block_dis(t,512,3,1,512,3,1)\n",
+        "  t = conv_leakyrelu(t,1024,3,2)\n",
+        "  t = layers.Flatten()(t)\n",
+        "  outputs = layers.Dense(1,activation='sigmoid')(t) #que signifie la fin du schéma ?\n",
+        "  model = Model(inputs, outputs)\n",
+        "  return model \n",
+        "\n",
+        "disc = make_AGAN_discri()\n",
+        "#disc.summary()\n",
+        "print(disc.output_shape) \n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "T1kWYJ1XCU5a"
+      },
+      "source": [
+        "\n",
+        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+        "\n",
+        "\n",
+        "def discriminator_loss(real_output, fake_output):\n",
+        "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
+        "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
+        "    total_loss = real_loss + fake_loss\n",
+        "    return total_loss\n",
+        "\n",
+        "def generator_loss(fake_output):\n",
+        "    return cross_entropy(tf.ones_like(fake_output), fake_output)\n",
+        "\n",
+        "def generator_loss_MSE(fake_output):\n",
+        "    return  tf.reduce_mean(tf.keras.losses.MSE(tf.ones_like(fake_output), fake_output))\n",
+        "\n",
+        "\n",
+        "def discriminator_loss_MSE(real_output, fake_output): \n",
+        "    \n",
+        "    real_loss = tf.reduce_mean(tf.keras.losses.MSE(tf.ones_like(real_output), real_output))\n",
+        "    fake_loss = tf.reduce_mean(tf.keras.losses.MSE(tf.zeros_like(fake_output), fake_output))\n",
+        "    total_loss = real_loss+fake_loss\n",
+        "    return tf.reduce_mean(total_loss)\n",
+        "\n",
+        "\n",
+        "\n",
+        "noise_dim = 128+34\n",
+        "num_examples_to_generate = 16\n",
+        "\n",
+        "\n",
+        "\n",
+        "# We will reuse this seed overtime (so it's easier)\n",
+        "# to visualize progress in the animated GIF)\n",
+        "seed = tf.random.normal([num_examples_to_generate, noise_dim])\n",
+        "\n",
+        "\n",
+        "@tf.function\n",
+        "def train_step(images,generator,discriminator,BATCH_SIZE = 256,noise_dim = 100):\n",
+        "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
+        "\n",
+        "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
+        "      generated_images = generator(noise, training=True)\n",
+        "\n",
+        "      real_output = discriminator(images, training=True)\n",
+        "      fake_output = discriminator(generated_images, training=True)\n",
+        "\n",
+        "      gen_loss = generator_loss(fake_output)\n",
+        "      disc_loss = discriminator_loss(real_output, fake_output)\n",
+        "      #print(\"disc loss: \",disc_loss.eval())\n",
+        "      gen_loss_val = generator_loss_MSE(fake_output)\n",
+        "      disc_loss_val = discriminator_loss_MSE(real_output, fake_output)\n",
+        "\n",
+        "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
+        "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
+        "\n",
+        "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
+        "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
+        "    return gen_loss_val,disc_loss_val\n",
+        "\n",
+        "\n",
+        "def train(dataset, epochs, generator,discriminator,BATCH_SIZE=256,noice_dim = 100):\n",
+        "  \n",
+        "  Lgen_loss = []\n",
+        "  Ldisc_loss = []\n",
+        "  X = []\n",
+        "  j=0\n",
+        "\n",
+        "\n",
+        "  \n",
+        "  for epoch in range(epochs):\n",
+        "    start = time.time()\n",
+        "    \n",
+        "    for image_batch in dataset:\n",
+        "      j += 1\n",
+        "      gen_loss,disc_loss = train_step(image_batch,generator,discriminator,BATCH_SIZE,noise_dim)\n",
+        "\n",
+        "      X.append(j)\n",
+        "      Lgen_loss.append(gen_loss)\n",
+        "      Ldisc_loss.append(disc_loss)\n",
+        "    \n",
+        "\n",
+        "    # Produce images for the GIF as we go\n",
+        "    display.clear_output(wait=True)\n",
+        "    generate_and_save_images(generator,\n",
+        "                             epoch + 1,\n",
+        "                             seed)\n",
+        "\n",
+        "    # Save the model every 15 epochs\n",
+        "    if (epoch + 1) % 15 == 0:\n",
+        "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
+        "\n",
+        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
+        "    generate_and_save_plots(X, Lgen_loss,Ldisc_loss,  epoch+1)\n",
+        "\n",
+        "  # Generate after the final epoch\n",
+        "  display.clear_output(wait=True)\n",
+        "  generate_and_save_images(generator,\n",
+        "                           epochs,\n",
+        "                           seed)\n",
+        "  \n",
+        "\n",
+        "def generate_and_save_images(model, epoch, test_input):\n",
+        "  # Notice `training` is set to False.\n",
+        "  # This is so all layers run in inference mode (batchnorm).\n",
+        "  predictions = model(test_input, training=False)\n",
+        "\n",
+        "  fig = plt.figure(figsize=(10,10))\n",
+        "\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.8))\n",
+        "      plt.axis('off')\n",
+        "\n",
+        "  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
+        "  plt.show()\n",
+        "\n",
+        "\n",
+        "def generate_and_save_plots(X, Lgen_loss,Ldisc_loss, epoch):\n",
+        "\n",
+        "    fig = plt.figure(figsize=(4,4))\n",
+        "    plt.plot(X,Lgen_loss, label = 'gen_loss')\n",
+        "    plt.plot(X,Ldisc_loss, label = 'disc_loss')\n",
+        "    plt.legend()\n",
+        "    \n",
+        "    #plt.savefig('output/losses.png')\n",
+        "    plt.show()\n",
+        "\n",
+        "    plt.clf()\n",
+        "    plt.cla()\n",
+        "    plt.close()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wSyDZ88FCcR8"
+      },
+      "source": [
+        "generator = make_AGAN_generator2()\n",
+        "discriminator = make_AGAN_discri()\n",
+        "\n",
+        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+        "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+        "\n",
+        "checkpoint_dir = './training_checkpoints'\n",
+        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
+        "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
+        "                                 discriminator_optimizer=discriminator_optimizer,\n",
+        "                                 generator=generator,\n",
+        "                                 discriminator=discriminator)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5uT0ikCaCe1i",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "99095891-94be-4d3f-8523-d350d40d16ff"
+      },
+      "source": [
+        "BUFFER_SIZE = 5388 #60000\n",
+        "BATCH_SIZE = 128\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
+        "\n",
+        "train(train_dataset, 100,generator,discriminator,BATCH_SIZE,noice_dim = 128+34)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x720 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Time for epoch 1 is 51.29112887382507 sec\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-19-6e18f9592a75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tensor_slices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBUFFER_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoice_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m34\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<ipython-input-18-dd9eb40c31fd>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(dataset, epochs, generator, discriminator, BATCH_SIZE, noice_dim)\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mimage_batch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m       \u001b[0mj\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m       \u001b[0mgen_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdisc_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_batch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoise_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    734\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    735\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# For Python 3 compatibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 736\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_next_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    770\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    771\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 772\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    773\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    774\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m_next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0moutput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flat_output_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             output_shapes=self._flat_output_shapes)\n\u001b[0m\u001b[1;32m    759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_dataset_ops.py\u001b[0m in \u001b[0;36miterator_get_next\u001b[0;34m(iterator, output_types, output_shapes, name)\u001b[0m\n\u001b[1;32m   2605\u001b[0m         \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IteratorGetNext\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2606\u001b[0m         \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop_callbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"output_types\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2607\u001b[0;31m         \"output_shapes\", output_shapes)\n\u001b[0m\u001b[1;32m   2608\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2609\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eLTrq5viRRlC"
+      },
+      "source": [
+        "generator.save_weights('generator1_weight_100_epoch.h5')\n",
+        "discriminator.save_weights('discriminator1_weight_100_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TlChUVu8RaM9"
+      },
+      "source": [
+        "generator.load_weights('generator1_weight_500_epoch.h5')\n",
+        "discriminator.load_weights('discriminator1_weight_500_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GboQIvA0SCLW"
+      },
+      "source": [
+        "Save on drive overnight"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "gUwJAQ8eReDU",
+        "outputId": "652a73cb-d67c-465b-c822-272a5388843d"
+      },
+      "source": [
+        "nb_centaines = 4 #grand max 5 pcq deco forcée après 12h\n",
+        "debut = 0\n",
+        "autoload = False\n",
+        "\n",
+        "BUFFER_SIZE = 5388 #60000\n",
+        "BATCH_SIZE = 128\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
+        "\n",
+        "if autoload and debut !=0:\n",
+        "  generator.load_weights('/content/drive/MyDrive/dataset cats/weights/'+str(debut)+'00_epoch.h5') \n",
+        "  discriminator.load_weights('/content/drive/MyDrive/dataset cats/weights/'+str(debut)+'00_epoch.h5')\n",
+        "\n",
+        "\n",
+        "for i in range(debut+1,debut+nb_centaines+1): \n",
+        "  train(train_dataset, 100,generator,discriminator,BATCH_SIZE,noice_dim = 128+34)\n",
+        "  generator.save_weights('/content/drive/MyDrive/dataset cats/weights/'+str(i)+'00_epoch.h5') \n",
+        "  discriminator.save_weights('/content/drive/MyDrive/dataset cats/weights/'+str(i)+'00_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1440x1440 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Time for epoch 10 is 58.29913401603699 sec\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-10-df98ed118a3c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdebut\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdebut\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnb_centaines\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m   \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoice_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m34\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m   \u001b[0mgenerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/dataset cats/weights/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'00_epoch.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m   \u001b[0mdiscriminator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/dataset cats/weights/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'00_epoch.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-7-2dd9a9b79c30>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(dataset, epochs, generator, discriminator, BATCH_SIZE, noice_dim)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mimage_batch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m       \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_batch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoise_dim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;31m# Produce images for the GIF as we go\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    778\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    805\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 807\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    808\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2829\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2831\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1846\u001b[0m                            resource_variable_ops.BaseResourceVariable))],\n\u001b[1;32m   1847\u001b[0m         \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m         cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1922\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1923\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1924\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1926\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    548\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m           outputs = execute.execute_with_cancellation(\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chats_3.ipynb b/Chats_3.ipynb
new file mode 100644
index 0000000..8acc24b
--- /dev/null
+++ b/Chats_3.ipynb
@@ -0,0 +1,5819 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "Chats_3.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M-Hn06sNUswM"
+      },
+      "source": [
+        "Même chose que chat 2 mais en prenant l'ancien dataset plus fournis et de meilleur qualité"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "twfN1RsfUpOR"
+      },
+      "source": [
+        "\n",
+        "import tensorflow as tf\n",
+        "import glob\n",
+        "import imageio\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import PIL\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.keras.models import Model\n",
+        "\n",
+        "import time\n",
+        "\n",
+        "from IPython import display"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EQhGz647U2xU"
+      },
+      "source": [
+        "def generate_samples(generator,number=16,save = False, file_name = 'image_sample.png'):\n",
+        "  noise_dim = 128+34 #change dim if needed\n",
+        "  seed = tf.random.normal([number, noise_dim])\n",
+        "  predictions = generator(seed, training=False)\n",
+        "  fig = plt.figure(figsize=(10,10))\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.6))\n",
+        "      plt.axis('off')\n",
+        "  if save :\n",
+        "    plt.savefig(file_name)\n",
+        "  plt.show()\n",
+        "#generate_samples(generator)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZumfUPs_VD8E",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d41515ad-478f-471e-d91f-85fcdf5574e6"
+      },
+      "source": [
+        "!git clone https://github.com/Ferlix/Cat-faces-dataset.git\n",
+        "\n",
+        "!tar xvzf Cat-faces-dataset/dataset-part1.tar.gz #unzip\n",
+        "!tar xvzf Cat-faces-dataset/dataset-part2.tar.gz\n",
+        "!tar xvzf Cat-faces-dataset/dataset-part3.tar.gz\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26528.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26069.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28283.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21742.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24810.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24331.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20529.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25190.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23314.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21662.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27295.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20036.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27741.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23354.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27720.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21009.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26387.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27856.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22182.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24754.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26137.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20318.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28996.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21255.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23836.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28132.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26939.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27790.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24112.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26621.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26055.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23690.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20996.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26464.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23304.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25516.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28164.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24737.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28601.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22783.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27999.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22875.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21575.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25989.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28812.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21403.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20827.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25469.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29623.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22296.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27896.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27766.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23157.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26799.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29228.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26242.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27280.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23888.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21952.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24109.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21017.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28888.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25289.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21324.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24340.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23797.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24077.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21040.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23710.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24410.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22851.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25678.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28214.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26356.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27192.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29733.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26231.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24139.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26628.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24324.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21567.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25572.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21284.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23112.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23640.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21264.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28487.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26696.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21956.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28762.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27255.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27648.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23298.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23423.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20169.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25224.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27903.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26343.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26308.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28519.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28464.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20355.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25857.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21831.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20625.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24143.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26727.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27988.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25758.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20808.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29662.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25460.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26790.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24006.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23293.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22023.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22324.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27853.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27318.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23893.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25623.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27120.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20532.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20575.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25461.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27460.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23240.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29405.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20377.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20366.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22778.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23032.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22034.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25011.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28027.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25405.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24659.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23708.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21999.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29636.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25389.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29495.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21958.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23184.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25771.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21413.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28129.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29632.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24103.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24838.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24579.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24714.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26831.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27304.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27547.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24060.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20674.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20043.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27626.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27150.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23764.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27051.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25101.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21669.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22382.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24826.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25304.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25905.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25137.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22009.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25001.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20871.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21492.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21654.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23391.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24048.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21544.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20823.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22762.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24913.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27202.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27165.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27813.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20925.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22902.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20697.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21841.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21243.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20596.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27167.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23411.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26868.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23874.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22504.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28844.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22576.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23231.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20030.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22805.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23506.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20560.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28484.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23013.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21728.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21489.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27699.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26795.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28828.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28817.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21588.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28808.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26637.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22518.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26824.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22738.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21226.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24730.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23994.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28433.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25163.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28647.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28596.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21175.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23525.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22991.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21238.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27477.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22410.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22907.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24096.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21945.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20192.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20734.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28581.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22855.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20460.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23775.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28266.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20059.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26229.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29795.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23548.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29713.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22814.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20298.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25040.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24503.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29272.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27814.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20202.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23823.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28344.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23382.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27758.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25748.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23633.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23065.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27427.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25170.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28287.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23835.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26742.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29299.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26015.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27188.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24327.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21887.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22130.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22892.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28079.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26839.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26285.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24284.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20274.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21918.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27965.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26286.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23815.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28258.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27542.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23415.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27020.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28839.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20528.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29359.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22866.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28310.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29154.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22082.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23896.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28101.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26023.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20117.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27722.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22376.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25612.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28794.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26670.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21780.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23794.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28892.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28294.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27064.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24219.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27785.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27700.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21457.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20907.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26033.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23072.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23442.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22474.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20710.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25158.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27389.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21890.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22601.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28426.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29641.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24069.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26385.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21550.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26419.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23285.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24847.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28173.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29694.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20725.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22127.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21600.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25970.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29165.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29773.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22340.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20867.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26170.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25590.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21008.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23591.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25315.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23323.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28450.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25075.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24397.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24413.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29674.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24234.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27738.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24643.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25285.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20658.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26851.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25412.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21125.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26863.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29311.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22977.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24711.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23763.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29333.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29255.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23048.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26204.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20714.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23667.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24247.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28946.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23364.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26832.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29769.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24452.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27017.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25445.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24260.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27677.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27726.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23694.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21467.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28725.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23305.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25029.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28289.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28797.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28185.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25455.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24040.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20624.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25768.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23190.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22483.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28572.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21585.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23914.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25004.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20767.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22147.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28525.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29102.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29117.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25207.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24527.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29105.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24019.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22745.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26550.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26366.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28441.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27171.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29748.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22407.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24893.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29164.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20965.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28613.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21690.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25691.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23901.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28268.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28182.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22511.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27518.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25969.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26164.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26647.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20175.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24329.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20923.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27258.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28523.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27374.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25332.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24336.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22994.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20338.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24493.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29517.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25133.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27521.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27406.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27084.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24238.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23494.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24993.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22564.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26298.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24355.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23399.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26549.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21687.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28731.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24945.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24904.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28115.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26623.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21321.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23024.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25008.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22523.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21637.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22046.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23628.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21977.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23504.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26972.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23292.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22468.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29043.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27943.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22992.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26239.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28645.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21463.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29439.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28389.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26254.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22716.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28195.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25148.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26907.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23625.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28883.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24338.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23947.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20383.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26770.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24992.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22317.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28354.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23386.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22602.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25047.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28033.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23813.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27985.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25425.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29273.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22611.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25567.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29222.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27270.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29798.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24576.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23206.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20441.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21920.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26084.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26820.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21863.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25394.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28937.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22161.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27712.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26090.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29833.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27675.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20334.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27834.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24888.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23478.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20164.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23351.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28991.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20839.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27015.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28876.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21531.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20295.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23418.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27285.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25165.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28976.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29240.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20024.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27048.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22088.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21011.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22349.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20062.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24264.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23378.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23806.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25324.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29178.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25897.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23544.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24107.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28724.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23150.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28414.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20459.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27126.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24474.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22153.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26561.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25533.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27356.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27205.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26104.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24422.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22366.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23455.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25660.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26709.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23348.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23878.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25312.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20778.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26284.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25891.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29382.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21995.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22487.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28759.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22155.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29428.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26833.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28499.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22065.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29128.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27961.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25613.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20267.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26574.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25611.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28254.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25219.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21090.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27194.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21102.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29604.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22812.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21250.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25664.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22446.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25729.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21808.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22116.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20670.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29542.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26193.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26477.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24455.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20636.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20040.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28313.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25949.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29033.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27755.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27019.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24084.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28346.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21082.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28669.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23732.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26034.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27023.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25627.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27086.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20706.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22779.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23946.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25105.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27480.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23201.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27878.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24947.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21832.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23601.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29700.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21181.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26622.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26049.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22951.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25305.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25367.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22344.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24028.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27855.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22529.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26121.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27858.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24538.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27598.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28733.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22982.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29660.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26098.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28092.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25578.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20633.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21108.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25007.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27964.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22693.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21452.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22299.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25505.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27405.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26395.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24217.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25395.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22830.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25929.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26750.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28314.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26486.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24548.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24093.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28990.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25316.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29284.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29717.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29649.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29267.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
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+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29746.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28335.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29118.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24805.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23980.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28948.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24529.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29682.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22027.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29411.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23204.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27983.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21783.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29083.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23736.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24322.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24868.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24510.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27301.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23458.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29444.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28124.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29354.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21131.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26913.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20128.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27724.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22335.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28956.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21798.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29774.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20646.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24835.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20964.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23743.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28994.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23135.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22956.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28591.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27030.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20546.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22064.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24592.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21514.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23110.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29536.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22138.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27290.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27658.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24489.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22257.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28451.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22428.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25185.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20309.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28728.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28677.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27586.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27244.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26570.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20184.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20604.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25426.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23051.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24499.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20614.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20060.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24792.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20656.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26016.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28511.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21228.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24265.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26266.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26240.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28023.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28644.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25436.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26118.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26866.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26188.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29531.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25146.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28897.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23006.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23123.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21883.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20093.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24158.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21283.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26901.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24020.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24024.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20039.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23480.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22101.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23148.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23527.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27992.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29593.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23118.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28583.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28278.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20530.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28930.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22961.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21955.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29664.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28071.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23365.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26322.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23802.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24622.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27978.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21089.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22985.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24364.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23425.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20256.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21005.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23094.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25025.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20677.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25581.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26606.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22115.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27329.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22894.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27345.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22194.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24951.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20977.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23486.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25379.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29605.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21159.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23441.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28666.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28221.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23257.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22890.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24909.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23885.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23682.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27467.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27698.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24178.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21618.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25942.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25221.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22103.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20054.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22811.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25217.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29785.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28544.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25495.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26915.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28907.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29437.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27778.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22110.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22005.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26313.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24729.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20865.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20758.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21468.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27365.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23788.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28432.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24701.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21972.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22725.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20654.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25189.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26511.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21960.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28506.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21169.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26440.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27459.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25022.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28531.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27496.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29611.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28764.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27674.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24738.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23766.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25853.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22915.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29000.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23652.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27796.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27117.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20519.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27111.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26964.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22109.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29522.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20339.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28162.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25023.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23929.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22309.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23942.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28944.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25617.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21451.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21245.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20014.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27394.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25559.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24916.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23081.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28001.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21170.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23472.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21964.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22450.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21594.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28856.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21679.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26535.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20029.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24360.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24091.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23374.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22585.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27057.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27494.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21303.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28064.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23002.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29040.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21620.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29127.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21848.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21741.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21293.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26375.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28263.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24691.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25976.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23594.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28218.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20425.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22817.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28015.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25885.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23907.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27278.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26684.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28593.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25159.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26876.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22489.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24938.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20106.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28340.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20902.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28325.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22558.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22122.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22962.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20675.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26556.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25502.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27065.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23477.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26293.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20667.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20545.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27268.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26234.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20486.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24316.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27331.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29477.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26762.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29221.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22217.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22690.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26507.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24784.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23526.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23429.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25948.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27201.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29243.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29575.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26493.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23886.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20291.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27070.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25399.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29316.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20064.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28904.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21511.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26937.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24218.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29171.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25114.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21592.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21953.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25310.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25704.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26177.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20057.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29140.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29723.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20908.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23008.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21933.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26818.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26918.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25237.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25881.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28742.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29049.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24239.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23906.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23376.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28249.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23783.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28927.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29402.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22405.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28364.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26403.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21846.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28676.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21590.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29471.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24788.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24967.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22180.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29552.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21824.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25532.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22329.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28490.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28550.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21394.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21605.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29138.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20026.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29057.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26721.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29646.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26089.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27367.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26748.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24036.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26704.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23009.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24341.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20913.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27463.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25631.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24363.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20564.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21028.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21750.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21499.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28957.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23873.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28770.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23476.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25125.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23180.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26433.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21767.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26434.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26895.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24003.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29824.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20593.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28138.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21188.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22941.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26037.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26983.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26132.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21912.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27241.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26150.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26163.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22068.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20781.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28435.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22813.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22026.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26781.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24307.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24589.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27200.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20090.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22734.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22193.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27452.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23092.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21335.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23882.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23928.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26497.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27644.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21649.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24539.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24722.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26562.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28795.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29062.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26430.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20414.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27247.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26283.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26629.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29449.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20784.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25914.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29507.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20698.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24409.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29705.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25200.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20137.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27905.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29826.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27473.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27732.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23276.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28067.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21861.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23408.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20695.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25569.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22412.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27245.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23692.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23939.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21566.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24053.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20410.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21475.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27676.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20601.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27966.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25711.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26275.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29466.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28972.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25428.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27262.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26927.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26753.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23116.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26510.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27946.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27636.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27549.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22125.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21072.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22043.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26909.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21909.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22490.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22207.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20726.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25126.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28872.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25927.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24568.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24881.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26334.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20421.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20516.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21891.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29409.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22802.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22337.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22368.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27688.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29401.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20800.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23407.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26249.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22323.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25103.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25381.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28485.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29701.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24395.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22760.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26542.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29136.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22318.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21346.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20350.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28853.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28549.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27105.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27238.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25873.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20779.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26780.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23495.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24794.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24606.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29468.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22546.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25045.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24417.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25640.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22251.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28836.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25471.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26599.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27491.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28935.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22415.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25965.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22936.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24378.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21442.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22175.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29804.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24398.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25458.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27610.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24570.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24621.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27396.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20934.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22579.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27920.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22864.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29483.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26101.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29142.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21067.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23347.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23453.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27388.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26386.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22365.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28786.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24960.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22123.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27609.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25096.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27551.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26151.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22631.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29508.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22728.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24732.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22204.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22625.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20851.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29671.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20797.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28967.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22549.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25820.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26205.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23923.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20809.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24490.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20469.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25918.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25525.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27783.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25540.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24481.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23334.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25764.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21126.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29329.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20196.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23647.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23696.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22937.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24380.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24626.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21200.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28477.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28019.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22848.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26604.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25544.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22441.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22012.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27371.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29077.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27012.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28319.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20292.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23565.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29233.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25763.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20368.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25773.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24885.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22071.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22362.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24235.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23921.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23345.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21311.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22905.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22120.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27415.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25397.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25182.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28018.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26994.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28834.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25839.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26938.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29820.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20930.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28603.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20761.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23636.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21599.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21516.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29345.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29581.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23597.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22648.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28898.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23102.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28864.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20042.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24186.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20454.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29295.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27093.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28109.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22285.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20103.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27007.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26391.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28016.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24802.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22663.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23449.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27772.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23447.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27763.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21410.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22923.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25067.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28094.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24634.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28113.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22868.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26785.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21479.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22140.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27087.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21060.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28296.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22306.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23735.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22999.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24852.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27822.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28566.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22688.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24088.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23360.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28481.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28105.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20828.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24562.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26206.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20582.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26517.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26212.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25393.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26862.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22777.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28979.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26083.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20653.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21774.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27602.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28997.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26548.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26732.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25576.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25080.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22807.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20403.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20324.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29152.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28427.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28716.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25479.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25580.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20272.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23720.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29635.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22508.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20944.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23244.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29594.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25754.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25751.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25940.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24196.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23899.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22372.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28225.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26338.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22958.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26273.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29750.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27324.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20986.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24564.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20878.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20327.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25750.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21729.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20802.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22599.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25723.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27330.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27734.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26299.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20153.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23416.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20634.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23370.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27119.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21776.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28715.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22989.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24759.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24978.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27908.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26960.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22015.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23809.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21505.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22940.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27627.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26001.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20194.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27797.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22921.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20212.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27928.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20780.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24353.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20496.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29488.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20720.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23047.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20174.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21745.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22519.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20704.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28712.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25003.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20323.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25295.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20639.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25338.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24681.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22209.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20433.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20017.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24367.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29356.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24253.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29691.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21705.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25061.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29391.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27448.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23606.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23223.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29234.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25778.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26842.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22788.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24343.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24017.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24147.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25565.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26624.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21013.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28419.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20130.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26627.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28081.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28075.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20163.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25944.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25824.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21174.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29755.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26990.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26634.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20430.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25345.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28158.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29431.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21743.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23569.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28838.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20098.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25599.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24710.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27919.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21870.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27501.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26885.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29211.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21223.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25641.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26042.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27684.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29486.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21450.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28384.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21097.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29731.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25916.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29631.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25983.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20791.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25582.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25577.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29607.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27736.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26805.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20404.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29223.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21056.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22453.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23138.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21929.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25845.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28327.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29561.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22819.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26840.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26267.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24955.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23623.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27617.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23291.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21796.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20893.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20204.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22933.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25896.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22568.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24882.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26653.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23315.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25566.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23103.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28746.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20891.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20121.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29463.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26861.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25708.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25353.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26531.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23774.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26648.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29612.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23269.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23568.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29030.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23549.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21487.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25098.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20385.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25055.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27159.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23828.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21483.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24209.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28219.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23800.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21271.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23336.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26949.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28043.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24793.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21472.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24922.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27191.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28522.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23698.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27941.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24508.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25370.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27986.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24468.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28537.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26009.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22986.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29364.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23398.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28582.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24342.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28206.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29812.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27818.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25928.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26195.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21203.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26589.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20683.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29487.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20742.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24757.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28671.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21227.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23845.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21204.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27358.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28612.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23974.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21401.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29695.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28411.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22134.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20171.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22981.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26543.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26421.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20759.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25878.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26755.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26423.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25251.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22294.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22591.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27434.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25447.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22785.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26900.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24472.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29218.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21787.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22456.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28292.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23860.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21449.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22945.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24989.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25930.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23149.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21273.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21836.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20473.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29502.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25348.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24585.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28926.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20382.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22592.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29300.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24113.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25749.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26892.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25799.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23588.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21586.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20129.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25722.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29248.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21582.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28961.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22266.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29404.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24697.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24688.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20363.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29727.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27857.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22375.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24175.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20422.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23704.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23089.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21756.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20179.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28989.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29703.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29648.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22108.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22765.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21610.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21132.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22717.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24050.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22800.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20607.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24012.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28046.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27911.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29689.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21572.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24454.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25066.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29166.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26050.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24864.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26468.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27325.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26462.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25249.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23288.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29332.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25530.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24216.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20952.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20180.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23620.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22492.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27826.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22479.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23137.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26024.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22689.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20818.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29443.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26292.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28743.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22566.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23147.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28998.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29729.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21895.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20394.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25421.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20947.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26870.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26186.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28761.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23053.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23227.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26261.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29064.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27198.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23095.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27154.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21052.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29442.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23666.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26487.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28699.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29817.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29192.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26553.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29073.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27052.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22729.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25936.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29457.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21875.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23645.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29735.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21924.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26318.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25374.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28774.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22532.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20992.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24180.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21402.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24126.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26765.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26253.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22062.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22770.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21810.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27687.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21713.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23186.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22146.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27464.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28208.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21363.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26302.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24530.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29569.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21462.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29776.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27809.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28691.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26984.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25012.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24269.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21987.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29206.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23958.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29313.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20914.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20076.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20096.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28692.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20000.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21498.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20940.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28304.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29461.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27638.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27471.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27286.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29818.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22188.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26020.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24674.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24005.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20555.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26210.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27458.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21266.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23593.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24974.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27004.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20214.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20810.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24213.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29610.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26745.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21686.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23426.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24013.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21988.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20233.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28518.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23349.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25902.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26184.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22198.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22227.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20051.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23143.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27958.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25840.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29050.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24290.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29335.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22406.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29619.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29393.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27040.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25440.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21154.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26384.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27805.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26263.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29498.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24830.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28207.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28577.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24507.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26822.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26867.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25779.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29412.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26545.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24856.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27606.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21543.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21764.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25721.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28675.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29026.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29793.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26152.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23934.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29385.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26973.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27588.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22769.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27354.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23510.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27497.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22931.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26845.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20369.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28632.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23985.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26534.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29415.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26230.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27302.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26224.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22895.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25588.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20948.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20259.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20478.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27877.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25441.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20511.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28655.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24791.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25849.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27815.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27310.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26182.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25766.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25967.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22512.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24159.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23193.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26959.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23675.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21732.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20346.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27886.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24523.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24694.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25277.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21724.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20526.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23940.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28554.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21602.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22798.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27082.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21412.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21339.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22174.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29087.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27645.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22707.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22704.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25203.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21865.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23331.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26612.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28704.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27531.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25889.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28234.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28436.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21114.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28031.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27570.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27506.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24695.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22609.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28653.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29195.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29343.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22605.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23892.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22278.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27298.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23454.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24898.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20022.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27221.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28710.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29278.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26439.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29698.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29264.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20692.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29100.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29677.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25810.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26129.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21296.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23355.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22152.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24374.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21866.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25972.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24197.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27097.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25323.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29430.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24902.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22462.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27982.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26802.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21942.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26294.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25837.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22796.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25213.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29014.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20443.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21873.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20085.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22816.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25832.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29720.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23071.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22459.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26638.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27416.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22901.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23757.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26361.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29018.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26843.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24843.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25111.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22499.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22315.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22809.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24789.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24484.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29841.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23139.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26153.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22102.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24811.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26559.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25804.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21528.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26235.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22983.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24130.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21192.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27769.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28395.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28070.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20741.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29336.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28271.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26522.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25232.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21349.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20132.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27390.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28322.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29297.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27183.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28504.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29306.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25248.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26690.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29279.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25115.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21398.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21338.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26457.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23140.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24941.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23387.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24764.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28600.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26741.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27670.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25956.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29583.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26910.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25653.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27373.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26116.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24385.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25731.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23716.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20216.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24718.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25999.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21006.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23751.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25059.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22629.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24228.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23373.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21297.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20456.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22589.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21221.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21629.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22615.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29116.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22237.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26999.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26377.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20541.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24170.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29112.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26926.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24982.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20243.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28194.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23371.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27266.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27047.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20301.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25482.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22150.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21374.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26404.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21994.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22458.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28446.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29338.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24259.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24140.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23452.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29515.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25575.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27308.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27892.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20249.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22563.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20858.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25507.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21997.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27989.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20676.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26004.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29008.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21088.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26620.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24133.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26330.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24583.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28170.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23517.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21535.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29553.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25454.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24502.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24586.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23514.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27265.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23210.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21969.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28617.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22793.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29821.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28097.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28089.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24415.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27801.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25699.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27799.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25070.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27527.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24598.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26874.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26707.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25921.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26588.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28205.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22305.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20183.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23055.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28297.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22957.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29739.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20988.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28714.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29560.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27123.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28055.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21138.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22118.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23176.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25638.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24229.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28323.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28231.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29007.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26812.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23511.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23217.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26970.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23156.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23877.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22554.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26211.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21051.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29368.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29070.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28978.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24356.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27850.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29215.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22256.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23214.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27006.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29827.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25406.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28981.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23979.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23880.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23460.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20920.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25818.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28114.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23762.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21697.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24389.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20504.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25939.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21940.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27212.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24354.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20641.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20445.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20411.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22973.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22319.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20475.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25262.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22275.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20424.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24707.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28869.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21205.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29650.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21328.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24878.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26641.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23812.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23215.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27438.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28788.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20220.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20505.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26080.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21664.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21701.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22721.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21850.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26576.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22086.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24844.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25988.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20442.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26618.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20580.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23679.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28851.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21676.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25742.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23913.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28434.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22612.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26687.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29450.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26500.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28449.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21897.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27080.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24991.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26572.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26718.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28439.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29699.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24404.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26227.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26393.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20959.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23833.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20520.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22142.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25030.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21236.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25991.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21855.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23950.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22467.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27581.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25116.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28188.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24026.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21684.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29239.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25732.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28954.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22643.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20235.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23849.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22515.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23825.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24709.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27847.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21033.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28736.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21431.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24420.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29492.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25296.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27945.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20696.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24796.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25414.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27003.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27524.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25018.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22143.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26370.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24224.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21455.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20296.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25267.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24023.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29416.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24250.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22214.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24926.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26226.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29469.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22598.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26536.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20173.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20276.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20975.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23155.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26031.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22387.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26088.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29589.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29808.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22995.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22210.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21490.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21276.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24311.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29005.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20069.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24044.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23707.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26174.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29710.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28104.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29790.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27728.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23241.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22484.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22653.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28050.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23900.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25294.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21443.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20490.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20794.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28697.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26593.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29814.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25753.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22850.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28492.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22391.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22789.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25834.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20785.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22697.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28372.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21332.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26806.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21388.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25828.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24383.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28740.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23715.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22185.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27848.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26202.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20938.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25457.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28332.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21163.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27713.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29670.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24110.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22332.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21786.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22863.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23363.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26878.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22678.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25697.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20845.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21819.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21526.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26222.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24081.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21399.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21625.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21714.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23185.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27776.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24887.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25372.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26093.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24300.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20485.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21556.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25231.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28964.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28013.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20632.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23101.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28678.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23107.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21314.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24207.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27274.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24636.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25679.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23312.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29245.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23199.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22381.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23392.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22463.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26615.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28547.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22225.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25327.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24291.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28244.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25977.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27635.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21319.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27014.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23134.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24749.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26250.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25293.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28822.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20020.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21521.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29626.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27760.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24225.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27289.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25915.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23307.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27067.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26406.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28848.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21931.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23589.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22617.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26691.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26953.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23357.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29742.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25014.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27933.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23573.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22325.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23129.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25128.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27864.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25472.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25659.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23687.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26761.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23456.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23953.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_24756.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25906.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26238.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20322.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27637.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20087.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25720.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29707.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27092.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20136.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28146.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20278.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23389.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23488.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22451.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27033.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20055.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29090.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22067.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23163.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28127.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22934.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28951.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21949.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22443.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23119.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26680.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_22287.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28942.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21981.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26367.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29080.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25141.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_20678.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23358.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_28110.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_23943.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_21453.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_29398.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_26026.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_25984.png\n",
+            "tar: Ignoring unknown extended header keyword 'SCHILY.fflags'\n",
+            "dataset-part3/cat_27294.png\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OzZ7yF66VGoM",
+        "outputId": "767ac99d-405d-493d-b824-93cae8812faa"
+      },
+      "source": [
+        "imageFolderPath = '/content/dataset-part?'\n",
+        "imagePath = glob.glob(imageFolderPath+'/*.png') \n",
+        "\n",
+        "\n",
+        "im_array = np.array( [np.array(PIL.Image.open(i)) for i in imagePath ])\n",
+        "\n",
+        "print(im_array.shape)\n",
+        "\n",
+        "\"\"\"\n",
+        "numpydata = np.asarray(PIL.Image.open('dataset-part1/cat_0.png')) #convertir un png en np appay\n",
+        "plt.figure(figsize=(1,1))\n",
+        "plt.imshow(numpydata)\n",
+        "print(np.shape(numpydata))\n",
+        "\"\"\"\n",
+        "train_images = im_array.reshape(im_array.shape[0], 64, 64, 3).astype('float32')\n",
+        "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(29843, 64, 64, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "69amAxYVVTZ7",
+        "outputId": "87d9f422-9dc0-40d9-8ccc-492002054a13"
+      },
+      "source": [
+        "\n",
+        "## GENERATOR\n",
+        "##second try : 3*16 blocs\n",
+        "\n",
+        "def bn_relu(inputs):\n",
+        "  bn = layers.BatchNormalization()(inputs)\n",
+        "  relu = layers.ReLU()(bn)\n",
+        "  return relu\n",
+        "\n",
+        "def res_block(x):\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
+        "  y = bn_relu(y)\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(y)\n",
+        "  y = layers.BatchNormalization() (y)\n",
+        "  out = layers.add([x,y]) #out = layers.Add()([x,y])\n",
+        "  return out\n",
+        "\n",
+        "def triple_res_block(input):\n",
+        "  y = res_block(input)\n",
+        "  y = res_block(y)\n",
+        "  y = res_block(y)\n",
+        "  return y \n",
+        "\n",
+        "def all_res_block(input):\n",
+        "  y = triple_res_block(input)\n",
+        "  for i in range(15):\n",
+        "    y = triple_res_block(y)\n",
+        "  y = bn_relu(y)\n",
+        "  out = layers.add([input,y])\n",
+        "  return out \n",
+        "\n",
+        "def pix_shuf(input):\n",
+        "  y = layers.Conv2DTranspose(256, (3, 3), strides=(1, 1), padding='same')(input)\n",
+        "  y = layers.UpSampling2D()(y)\n",
+        "  #y = layers.UpSampling2D()(y)\n",
+        "  y = bn_relu(y)\n",
+        "  return y \n",
+        "\n",
+        "\n",
+        "\n",
+        "def make_AGAN_generator2():\n",
+        "  inputs = layers.Input(shape=(128+34,))\n",
+        "  t = layers.Dense(64*16*16) (inputs)\n",
+        "\n",
+        "  t = bn_relu(t)\n",
+        "  t = layers.Reshape((16,16,64))(t) \n",
+        "\n",
+        "  t = all_res_block(t)\n",
+        "\n",
+        "  for i in range(2): #changer en 3 pour avoir une sortie 128x128\n",
+        "    t = pix_shuf(t)\n",
+        "  \n",
+        "  t = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(t) #peut être une Conv2DTranspose\n",
+        "\n",
+        "  \n",
+        "  outputs = layers.Activation(tf.nn.tanh)(t) #modifier la derniere couche quand ça sera bon\n",
+        "  model = Model(inputs, outputs)\n",
+        "\n",
+        "  return model\n",
+        "\n",
+        "gen = make_AGAN_generator2()\n",
+        "#gen.summary()\n",
+        "print(gen.output_shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 64, 64, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z6mEBnnWH4f-",
+        "outputId": "e0e768e9-9f67-403c-dbb8-82e1d99c0eae"
+      },
+      "source": [
+        "## GENERATOR\n",
+        "##second try : 3 blocs\n",
+        "\n",
+        "\n",
+        "def bn_relu(inputs):\n",
+        "  bn = layers.BatchNormalization()(inputs)\n",
+        "  relu = layers.ReLU()(bn)\n",
+        "  return relu\n",
+        "\n",
+        "def res_block(x):\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
+        "  y = bn_relu(y)\n",
+        "  y = layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(y)\n",
+        "  y = layers.BatchNormalization() (y)\n",
+        "  out = layers.Add()([x,y])\n",
+        "  return out\n",
+        "\n",
+        "def all_res_block(input):\n",
+        "  y = res_block(input)\n",
+        "  y = res_block(y)\n",
+        "  y = res_block(y)\n",
+        "  y = bn_relu(y)\n",
+        "  out = layers.Add()([input,y])\n",
+        "  return out \n",
+        "\n",
+        "def pix_shuf(input):\n",
+        "  y = layers.Conv2DTranspose(256, (3, 3), strides=(1, 1), padding='same')(input)\n",
+        "  y = layers.UpSampling2D()(y)\n",
+        "  #y = layers.UpSampling2D()(y)\n",
+        "  y = bn_relu(y)\n",
+        "  return y \n",
+        "\n",
+        "\n",
+        "\n",
+        "def make_AGAN_generator3():\n",
+        "  inputs = layers.Input(shape=(128+34,))\n",
+        "  t = layers.Dense(64*16*16) (inputs)\n",
+        "\n",
+        "  t = bn_relu(t)\n",
+        "  t = layers.Reshape((16,16,64))(t) \n",
+        "\n",
+        "  t = all_res_block(t)\n",
+        "\n",
+        "  for i in range(2): #changer pour avoir une sortie en 64x64\n",
+        "    t = pix_shuf(t)\n",
+        "  \n",
+        "  t = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(t) #peut être une Conv2DTranspose\n",
+        "\n",
+        "  \n",
+        "  outputs = layers.Activation(tf.nn.tanh)(t) #modifier la derniere couche quand ça sera bon\n",
+        "  model = Model(inputs, outputs)\n",
+        "\n",
+        "  return model\n",
+        "\n",
+        "gen = make_AGAN_generator3()\n",
+        "#gen.summary()\n",
+        "print(gen.output_shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 64, 64, 3)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uTW5FE6SVXEU",
+        "outputId": "a733d329-7e15-4b90-813f-cf4238d939fa"
+      },
+      "source": [
+        "## make discriminator AGAN\n",
+        "def conv_leakyrelu(input,n,k,s):\n",
+        "  y = layers.Conv2D(n,k,strides=(s,s),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def res_block_dis(input,n1,k1,s1,n2,k2,s2):\n",
+        "  y = layers.Conv2D(n1,k1,strides =(s1,s1),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  y = layers.Conv2D(n2,k2,strides=(s2,s2),padding='same')(y)\n",
+        "  y = layers.add([input,y])#layers.Add()([input,y])\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def make_AGAN_discri():\n",
+        "\n",
+        "  inputs = layers.Input(shape=(64,64,3))\n",
+        "  t = conv_leakyrelu(inputs,32,4,2)\n",
+        "  t = res_block_dis(t,32,3,1,32,3,1)\n",
+        "  t = res_block_dis(t,32,3,1,32,3,1)\n",
+        "  t = conv_leakyrelu(t,64,4,2)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "  t = conv_leakyrelu(t,128,4,2)\n",
+        "  t = res_block_dis(t,128,3,1,128,3,1)\n",
+        "  t = res_block_dis(t,128,3,1,128,3,1) \n",
+        "  t = conv_leakyrelu(t,256,3,2)\n",
+        "  t = res_block_dis(t,256,3,1,256,3,1)\n",
+        "  t = res_block_dis(t,256,3,1,256,3,1)\n",
+        "  t = conv_leakyrelu(t,512,3,2)\n",
+        "  t = res_block_dis(t,512,3,1,512,3,1)\n",
+        "  t = res_block_dis(t,512,3,1,512,3,1)\n",
+        "  t = conv_leakyrelu(t,1024,3,2)\n",
+        "  t = layers.Flatten()(t)\n",
+        "  outputs = layers.Dense(1)(t) #on enlève sigmoid pour voir\n",
+        "  model = Model(inputs, outputs)\n",
+        "  return model \n",
+        "\n",
+        "disc = make_AGAN_discri()\n",
+        "#disc.summary()\n",
+        "print(disc.output_shape) \n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eeDtt7ZGH_k0"
+      },
+      "source": [
+        "#discri faible du DCGAN\n",
+        "def make_discriminator_model():\n",
+        "    model = tf.keras.Sequential()\n",
+        "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n",
+        "                                     input_shape=[64, 64, 3]))\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "    model.add(layers.Dropout(0.3))\n",
+        "\n",
+        "    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
+        "    model.add(layers.LeakyReLU())\n",
+        "    model.add(layers.Dropout(0.3))\n",
+        "\n",
+        "    model.add(layers.Flatten())\n",
+        "    model.add(layers.Dense(1))\n",
+        "\n",
+        "    return model"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Sgs9SSSMDtVI",
+        "outputId": "33f1230b-0f70-49c0-cf04-567a23a135c5"
+      },
+      "source": [
+        "# à tester : meme chose mais sans sigmoid à la fin\n",
+        "\n",
+        "def conv_leakyrelu(input,n,k,s):\n",
+        "  y = layers.Conv2D(n,k,strides=(s,s),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def res_block_dis(input,n1,k1,s1,n2,k2,s2):\n",
+        "  y = layers.Conv2D(n1,k1,strides =(s1,s1),padding='same')(input)\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  y = layers.Conv2D(n2,k2,strides=(s2,s2),padding='same')(y)\n",
+        "  y = layers.add([input,y])#layers.Add()([input,y])\n",
+        "  y = layers.LeakyReLU()(y)\n",
+        "  return y \n",
+        "\n",
+        "def make_discriminator_model2():\n",
+        "  inputs = layers.Input(shape=(64,64,3))\n",
+        "  \n",
+        "  t = conv_leakyrelu(inputs,64,5,2)\n",
+        "\n",
+        "  t = layers.Dropout(0.3) (t)\n",
+        "  t = res_block_dis(t,64,3,1,64,3,1)\n",
+        "\n",
+        "  t = conv_leakyrelu(inputs,128,5,2)\n",
+        "\n",
+        "  t = layers.Dropout(0.3) (t)\n",
+        "  t = layers.Flatten()(t)\n",
+        "  outputs = layers.Dense(1)(t) \n",
+        "  model = Model(inputs, outputs)\n",
+        "  return model \n",
+        "\n",
+        "disc = make_discriminator_model2()\n",
+        "#disc.summary()\n",
+        "print(disc.output_shape) "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(None, 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Qw9LBkaq8Lem"
+      },
+      "source": [
+        "\n",
+        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+        "\n",
+        "\n",
+        "def discriminator_loss(real_output, fake_output):\n",
+        "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
+        "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
+        "    total_loss = real_loss + fake_loss\n",
+        "    return total_loss\n",
+        "\n",
+        "def generator_loss(fake_output):\n",
+        "    return cross_entropy(tf.ones_like(fake_output), fake_output)\n",
+        "\n",
+        "def generator_loss_MSE(fake_output):\n",
+        "    return  tf.reduce_mean(tf.keras.losses.MSE(tf.ones_like(fake_output), fake_output))\n",
+        "\n",
+        "\n",
+        "def discriminator_loss_MSE(real_output, fake_output): \n",
+        "    \n",
+        "    real_loss = tf.reduce_mean(tf.keras.losses.MSE(tf.ones_like(real_output), real_output))\n",
+        "    fake_loss = tf.reduce_mean(tf.keras.losses.MSE(tf.zeros_like(fake_output), fake_output))\n",
+        "    total_loss = real_loss+fake_loss\n",
+        "    return tf.reduce_mean(total_loss)\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OXJ8m5xW8S66"
+      },
+      "source": [
+        "noise_dim = 128+34\n",
+        "num_examples_to_generate = 16\n",
+        "\n",
+        "# We will reuse this seed overtime (so it's easier)\n",
+        "# to visualize progress in the animated GIF)\n",
+        "seed = tf.random.normal([num_examples_to_generate, noise_dim])\n",
+        "\n",
+        "\n",
+        "@tf.function\n",
+        "def train_step(images,generator,discriminator,BATCH_SIZE = 256,noise_dim = 100):\n",
+        "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
+        "\n",
+        "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
+        "      generated_images = generator(noise, training=True)\n",
+        "\n",
+        "      real_output = discriminator(images, training=True)\n",
+        "      fake_output = discriminator(generated_images, training=True)\n",
+        "\n",
+        "      gen_loss = generator_loss(fake_output)\n",
+        "      disc_loss = discriminator_loss(real_output, fake_output)\n",
+        "      #print(\"disc loss: \",disc_loss.eval())\n",
+        "      gen_loss_val = generator_loss_MSE(fake_output)\n",
+        "      disc_loss_val = discriminator_loss_MSE(real_output, fake_output)\n",
+        "\n",
+        "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
+        "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
+        "\n",
+        "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
+        "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
+        "    return gen_loss_val,disc_loss_val\n",
+        "\n",
+        "\n",
+        "def train(dataset, epochs, generator,discriminator,BATCH_SIZE=256,noice_dim = 100):\n",
+        "  \n",
+        "  Lgen_loss = []\n",
+        "  Ldisc_loss = []\n",
+        "  X = []\n",
+        "  j=0\n",
+        "\n",
+        "\n",
+        "  \n",
+        "  for epoch in range(epochs):\n",
+        "    start = time.time()\n",
+        "    \n",
+        "    for image_batch in dataset:\n",
+        "      j += 1\n",
+        "      gen_loss,disc_loss = train_step(image_batch,generator,discriminator,BATCH_SIZE,noise_dim)\n",
+        "\n",
+        "      X.append(j)\n",
+        "      Lgen_loss.append(gen_loss)\n",
+        "      Ldisc_loss.append(disc_loss)\n",
+        "      #generate_and_save_images(generator,epoch + 1,seed)\n",
+        "      #generate_and_save_plots(X, Lgen_loss,Ldisc_loss,  epoch+1)\n",
+        "\n",
+        "    \n",
+        "\n",
+        "    # Produce images for the GIF as we go\n",
+        "    display.clear_output(wait=True)\n",
+        "    generate_and_save_images(generator,epoch + 1,seed)\n",
+        "\n",
+        "\n",
+        "    # Save the model every 15 epochs\n",
+        "    if (epoch + 1) % 15 == 0:\n",
+        "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
+        "\n",
+        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
+        "    generate_and_save_plots(X, Lgen_loss,Ldisc_loss,  epoch+1)\n",
+        "\n",
+        "  # Generate after the final epoch\n",
+        "  display.clear_output(wait=True)\n",
+        "  generate_and_save_images(generator,epochs,seed)\n",
+        "  generate_and_save_plots(X, Lgen_loss,Ldisc_loss,  epochs)\n",
+        "\n",
+        "\n",
+        "def generate_and_save_images(model, epoch, test_input): #augmenter la taille ici\n",
+        "\n",
+        "  predictions = model(test_input, training=False)\n",
+        "\n",
+        "  fig = plt.figure(figsize=(10,10))\n",
+        "\n",
+        "  for i in range(predictions.shape[0]):\n",
+        "      plt.subplot(4, 4, i+1)\n",
+        "      nparray = np.array(((predictions[i, :, :, :]*127.5)+127.5)).astype(int)\n",
+        "      pil_image = PIL.Image.fromarray(np.uint8(nparray))\n",
+        "      enhancer = PIL.ImageEnhance.Contrast(pil_image)\n",
+        "      plt.imshow(enhancer.enhance(0.8))\n",
+        "      plt.axis('off')\n",
+        "\n",
+        "  #plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
+        "  plt.show()\n",
+        "\n",
+        "\n",
+        "def generate_and_save_plots(X, Lgen_loss,Ldisc_loss, epoch):\n",
+        "\n",
+        "    fig = plt.figure(figsize=(4,4))\n",
+        "    plt.plot(X,Lgen_loss, label = 'gen_loss')\n",
+        "    plt.plot(X,Ldisc_loss, label = 'disc_loss')\n",
+        "    plt.legend()\n",
+        "    \n",
+        "    #plt.savefig('output/losses.png')\n",
+        "    plt.show()\n",
+        "\n",
+        "    plt.clf()\n",
+        "    plt.cla()\n",
+        "    plt.close()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6smXVrioVeNT"
+      },
+      "source": [
+        "generator = make_AGAN_generator3()\n",
+        "discriminator = make_AGAN_discri() #make_AGAN_discri()\n",
+        "\n",
+        "generator_optimizer = tf.keras.optimizers.Adam(0.0001) #0.0002 dans le papier\n",
+        "discriminator_optimizer = tf.keras.optimizers.Adam(0.0001)\n",
+        "\n",
+        "checkpoint_dir = './training_checkpoints'\n",
+        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
+        "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
+        "                                 discriminator_optimizer=discriminator_optimizer,\n",
+        "                                 generator=generator,\n",
+        "                                 discriminator=discriminator)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ny57MVV8Vk8r"
+      },
+      "source": [
+        "BUFFER_SIZE = 60000\n",
+        "BATCH_SIZE = 256\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
+        "\n",
+        "train(train_dataset, 20,generator,discriminator,BATCH_SIZE,noice_dim = 128+34)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6Qqy0eho1ucm"
+      },
+      "source": [
+        "generator.save_weights('/content/drive/MyDrive/dataset cats/weights/gen100_epoch.h5') \n",
+        "discriminator.save_weights('/content/drive/MyDrive/dataset cats/weights/disc100_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qUCuMpFK1k7U"
+      },
+      "source": [
+        "generator.load_weights('/content/drive/MyDrive/dataset cats/weights/MEILLEUR_gen800_epoch.h5') \n",
+        "#discriminator.load_weights('/content/drive/MyDrive/dataset cats/weights/disc800_epoch.h5')\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RVpwcN6QUL55"
+      },
+      "source": [
+        "Je me suis rendu compte que le discriminateur de l'anime GAN ne fonctionnait pas. Donc, j'entraine son générateur (avec seulement 3 blocs résiduels) avec le discriminateur du DCGAN du tuto. \n",
+        "\n",
+        "Pour l'instant, j'ai fait 200 époques à 0.0001 de LR puis j'ai switché sur du 0.00005 de LR après ces 200 époques \n",
+        "\n",
+        "Le générateur 3*16 met environ 15 époques pour quitter la phase colorée n'importe comment avec le discri du DCGAN (la loss part en couille (> 600) pendant les 10 premières époques au moins puis finit par converger)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "D0f7mdYx0joK"
+      },
+      "source": [
+        "nb_centaines = 2 #grand max 5 pcq deco forcée après 12h\n",
+        "debut = 8\n",
+        "autoload = False\n",
+        "\n",
+        "BUFFER_SIZE = 60000\n",
+        "BATCH_SIZE = 256\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
+        "\n",
+        "if autoload and debut !=0:\n",
+        "  generator.load_weights('/content/drive/MyDrive/dataset cats/weights/gen'+str(debut)+'00_epoch.h5') \n",
+        "  discriminator.load_weights('/content/drive/MyDrive/dataset cats/weights/disc'+str(debut)+'00_epoch.h5')\n",
+        "  print('loaded')\n",
+        "\n",
+        "\n",
+        "for i in range(debut+1,debut+nb_centaines+1): \n",
+        "  train(train_dataset, 100,generator,discriminator,BATCH_SIZE,noice_dim = 128+34)\n",
+        "  generator.save_weights('/content/drive/MyDrive/dataset cats/weights/gen'+str(i)+'00_epoch.h5') \n",
+        "  discriminator.save_weights('/content/drive/MyDrive/dataset cats/weights/disc'+str(i)+'00_epoch.h5')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rRukZUjaSbPg"
+      },
+      "source": [
+        "Des tests"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 598
+        },
+        "id": "JKAy0k6hSc6p",
+        "outputId": "0e4622ba-5421-4008-8c9b-2de65e74cce2"
+      },
+      "source": [
+        "print(discriminator(train_images[:10]))\n",
+        "fig = plt.figure(figsize=(7,7))\n",
+        "for i in range(16):\n",
+        "    plt.subplot(4, 4, i+1)\n",
+        "    plt.imshow(im_array[i])\n",
+        "    plt.axis('off')\n",
+        "plt.show()\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "tf.Tensor(\n",
+            "[[7.2202191e-33]\n",
+            " [7.7134906e-26]\n",
+            " [0.0000000e+00]\n",
+            " [2.1600512e-34]\n",
+            " [0.0000000e+00]\n",
+            " [0.0000000e+00]\n",
+            " [2.0437764e-20]\n",
+            " [0.0000000e+00]\n",
+            " [0.0000000e+00]\n",
+            " [9.3234237e-35]], shape=(10, 1), dtype=float32)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 504x504 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 575
+        },
+        "id": "_H5VD6_kicS5",
+        "outputId": "4dd9fb9c-af8a-4d34-eefd-6782fd687c11"
+      },
+      "source": [
+        "generate_samples(generator)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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aFuyFKJ6QWICRxFnVAAczqi2lxF8yukU1opVSGO6R/tFYLEVrRk5/TohNlD9GXLNopi5sRjcRAvoER2QEhDC6hY5Jw/tZMl+lhNZAcpHUZUWLUEsyN064e3GGci74h6vzjLTEJKlEIciqoDrfPHrSYFQii6qsWgNceMFNYs7TeU6WT0VwFURjzUkyOWaGoBQpoJbrTMPYZpRjBwNi6cQtYFKCdSfSgZMvdjJVmgZgzml+MMOCfSozZZm0hnhSrSWjuvxBXGuwSiozqhq4BL07056TUZzBlfv8fkZFM7p7YGzwiM7GMVfyANQVsHP/EemCg4F0I2L3BE8JFlVmNDVR8YHij/8Pn4FOzJWZvymTs7fQWfkIu9SqU9ug7g2XBZOC6B433jL918JutJ571JZkZIlgoBulRl7/QfSHCCwu6Gi4KyaWwcPAsNAFuuPS0ciFggeD0sW4d8HYEb0x2HExmnZECm17xXcFQrfXPX5+vay0MdhGMNXig9v4yig7cg2n0unhlMwpWvjhwwuY88e/+wc+9Z/51F/pXzve4KkuFAFvN8C4SOVSFi7LBVz5+mVjFCh6oe93dttxqxR5Qv0VdUNlJaE07hKsqnmkbkqy+jVA0LoUVAvLEnMbMkFDemG40ceeGjI7onstkQl425G6yAGjG/u+Ya0xWo89hWO5PyO0UDz3vB90zRkUoyWyFZYMxSR0VSZ9moFxWp7UGAYZ5KRkDkhL5KlRnSmhDI6GeTIz+X2N9LfJ5Ow5iQ8C+JhbBuARjKvmxE+/PNNH5pgE4zmZkz46o3dUa7JFqcyZxlGCGR9IZF+AmTKZfrBwMj0HKPMZuMlp1TIOlMwSyaSAmIGsUKpSqqIFRu7vYZbZC+N7pui7IEckkV9P0VQBysE9IR5uCwU0J3Vvp42UoHYt0yih56n0Ho4nQI7juUi8RRQ/zNACpZZgO1KbYw4vz08stVDrktcRLMXmI6muACcnthOWulBKpVswRbUs8bk20mEFmyRa08BYaDdUudQVQTL99u0wM0brAeLMAnQhjHEyLEe6yMHEcBHEnN5D1KU1qPGeKa6lJsgBRBUtSzgqK7Qt8qrqmfXU1IL4ucIfN0cITORgARynpTj2LYek6Ew90m1rWVIXpfRhCfhiM5oQupAqqCtSJpMRz6a7J+LXEAYjuAcLNnxMKHmAh0nvqkaK9eRQNcFGCukS3DzmwkQE1Yi4vAuXy/XQwITxCXAcmkMJIZwroJn6GgefHOkxp9S4vxlQOZkiOhBDgLowhbF58+1jDWGM0Y40bF5pzEFGcPP14hPkKIUC7JnenAYkGIEisQ+L1ogjLYXr+e7xuQGyMyhk2K/B/r/c2PctPjdxjKpTK9Qq9H7BbAHdQS01caHrG6PzevuKSKGUy/FMLIGiljUckibIWULAfSnP9L4HsNMAOIwOFkyLiyMamgbf76cIvEPfYaBQhc6GazA0Q5R2e41UiL8gRRhsiApP1yvsjdcx8L6BbYyxc3NF/hCFH41GZ1BGBG5/89uPfP35xr/9t/+eL3zli3/licIF5bKsLKqw/4gjPMvCpa5cr090cz799MryfGV5eeL1/sq23cA+UFVRvqCEmD3Y0YZZ6IOQgYzOWmApQAk7uT6tLEVZF4mg4B57C4P7/krfbql35LDRZXl7kOMzhd2c3gbbfsdax/dOH2FLhzouymUpCQymS4vgWFSO/ckM2CRY0Yg/5ZAqHMMAmaK4CK400zYzK+yTok8bbmmzfcx0u0Ww4ZG+J0HOwPGZ6plIRwJs9jGVqE4pGizw1Omhkcp3kGEBuIkgsffGGJ1LUbTk/UFKRXKI0Q4BcqTsJYPwEAqTQuKYwBmUSbLycqTlJj8WxQ3HtIkkEA5tkqpkVsWw3undaCN4qO85te8zOVozhJs36PiYEWbAiPQvHP5hBA1r1vNhRUoiqEAHsdRfEFUvhDG2mcuTYFqGDfZUnasqay62CSTu9xuhjhBUleUSTI2mNmaQDI05YoboGaH6QW9NiRSIO+pOFQkDlRGydSP0L7EwxE+WxS1z/pm/rlxQ0RDQmlCXAqkYj/w4LKVQa810FQdTFKkzMOtIKVwvT2cE7eGUy1IyQoiNUIuG4dgjRWgWS2iRJSKPg2bNRSSR0njgKd5kXJYXujTuElUn993DS9ZA9+LkRnZcYh0cqUpCsS8Pq7jbwIAxWkaGk+UJbdTwjKw5MuhhVsSYuyHSd+dzTT4H83Gsili5sWldNEDUOLU5lroH0amniohPZs5cQrcgeAjNXVh84AxEwyn20cLwygwVfT7eTJlCFm+FU5YS+xIeIp74WaQDPZkcSa3S5KoGeMN9j6hMHg1xTSYjK7HksOp5T5Is24x0yYqZNwQ5W8eGsW8tQE7xcKpF2dpO64pWi1voYXgtmT8lquf2cWeMPcBI6tgk2UIbkULsvYetsNBkqIeOwOtAZISQG/DimLYQlo4O6siarJcYG87YB9vY2Gxwv70i9zsvy29ZluUAkvVS8e600TA3Fi2U+kIpz0gJ4FWf17znKAa5rAWRzo//9Ede752fuXOnsclAujFM+OPXQVWBp0gz/P32E3V8ZRl3VApFFxbr1DFQr6zyQzDh4pR1oWAstlBME5hIpO6JFO8SGC6r94xtu7MJ+C1Yz0+fvyIGxZQ+dvaxIR4FIDNg3HfnrYkca5F6HtvAWoetQwNvJZxsehUTxYYGeKjJJh9BkWXwmvtCPGyST70eh/NWCT8y2Vl5CKQm/xBsih3p6p5Zi6iEUuoSRQioHESQpSbu2J9y2jklmXA3hj34aqb9Oz/f0j/jkr59UHjQAyZTNLU9NuwIwB2lyNQnWsomJosU1VAk05MTmCkuYVb+Hpy2Z67D4/1n0CeZdBF3Rgv90MjiJaklKp3LeU+/Nr4LcqAw828+Acs0oglKrJBUXjwI8ZqCrk6QNYapJTr2nNh8IBKEoFhCzwQ5okq3zt4aahI3IpI5vlh4277hbqgodVmosoS5zxTQcE1wEyFwgJOMfi2Xgz6CnHBGOjU508g/lC5PpK0pwDvQtkVEN8Vl3YwhobyfiH1YgBymeFhHFDdoDUV7vt9Iod9a13DSU/XvwWxF2XOAqqIxd2NPJiJBTtHQGkxhoE/nmUDuTcUUwFKu4IpIVFXs3ab2LOZ+zrPlZpEQTCIhwD4ATuZvQiDpjNHj3jKlNUv63WfeOt2wzA00wQdMutYnFJLJ/sR/kdqaeeQ0JKn5mfvpEDjbNCIHWXwCEPHUzsT6sxT6Bf07MlJOrjaFm2faPLRjqmFIbFa65L45puW49wP/wmFCwio6PUFODxJLU5sVFisJrrheHtsoIAm2UxQ75+WNnVNrkRLe7re4zCJ4CTZv2417M5ZFQ0DsltHnTHUCbnTvDGvppHKkaNJH6BL6GKl7jOeknoCuWNg8M6xOBm5ElWlJ/d6a1XA9HNbeomKw4fT7HTfn5W/+QF2fuG8/gTurXjHpdAudS5HCulTWteKXDtWoy4IPi6jWhHVRhM6nn/+R1yF8xWk0GgOxIJy2saEKLx+viAr/ePuM7IW6b1yWCx8uz+zu1OF8uDyx1iudhtHRWikMqtWw68ORouhSCf1JoTKdaczEtvfUajXa1vj5x89RZVVKsPPeWWtoUg72oJ075q2GdYNuoX/ZB7QBXaFXREeAVQs/YxapZ51hjwSgO+xC2v+ee8NytZ0g55FgyMBhBth+fokAzQ+QMw6QczIbgRciiQYzfZX78CiUiX2aSbawbWnT5LgaOT/epyIyDM7I6spJGx8JJXF6j+dLFgDNDI4c4ZQkM8XBoh+infxoSUSrSU1LkiWS0yJ+AsiReCzju7jfHnY/kJvEmlQPMu07Hf++C3K2LzdUImWjAqihw/BUyI8R8bCJIN0z+qxxWUVwTeaHOdG5yEcY3jFGUm8l8rgSPUpoYXQqQqmFooXeLVIbY0N86mqifLyoosNPZxZoJ/J/I/L55oO6LqBypHdCpKVHago4eppMJ9f7ng9tPtxwSE7kpYssjBIi2VpLoH3P3j2HN0yAtibdZltWbyqlOKoDsdAGLGXFcT5//cSRVigGenQrOHUcolmevISwbEyGylO4RaYcJxs0Hff3hVr/3GPPteKRdA2Gwwej70x9ChJ6pD5TTtMYWERQE8jojJRSs2N2pge/STcdz3j+lz/LVBE2w44EAVmVpOgBtCGiWO+5+TKim4Jd68HSjeMz7fg0ZwrXp34t0Edv0aviZFEibTr8LCmPW/eMfGa6MYGfhobGjvsN1vGxivCbNFYKq0VC0C5SkvZN/ZcKURgdq0vcqSW0KzZZi5xQmQGTvL143VlBBqVMINy5986rbbRm9OHsHiX/UioVZVmPK0cQqitSjOaNkqncWipLLVzqQi2VZw16XmVl0NlS1+Xd0V2QJphH2kgoFDTK+d0Rr6gZuvdomdEHy7Lw9PzEl58/09pOZUcRbq+fA0xrCErvKfTcrKPNKV9guUJZgctL2E6CDb/Zzv3e+bS32BdVafvOtjc+PD/z8bpway0c3m4UUV7GNauk4EmFl0uwSe31Mz/dvgBCXS+gijJYFD6sT5lWih47LCXYrr1xu93Z9oa3Oz4G99ZTahC9nrbbjorQtCZwN3xxahnBBAJD31543PcGo2My8ApyvSA9QLMUxTVsvAtIiSIHqRP0Z9XnyD1rUVsWqdyZAjoioJkMONnUQDMcYKNbMIazCjCD5Wk74jMlK/pC4rDUaOEy9oZjWensh4pEkzme9Z4HjPQI7IWs8IJgy81QTjHzUU1N2rbeoEM/+tWk3UofLioc2EdnFVnarChBJosB0fIA2HhkkgNT2Jhhaco1nCRAgihQgvCgpC+Y1agSUoD/0PguyOlbCzCxXLJ8PHL/NiNRH5GbF4lo1sGS8pSks48IOTh01E+DbLOsTVOsRtD/btG8ToGiITrqIww7FshvAgot8TVLn0J4PCcyLb6NwRBhIarFxoMDUYmJ83Q4CJknzumeG5R0YHLGxyqCVs158VxCfrw2PcLxQLUU8J4ReskKLJ+yjfh4DcC0b1u8horiiDqPFYDBMMy51kDReR0BgHLBzJL844qc76yHv8gYKZiDvM8Cnn1r8GBJSsJxMcWz5iou35m543nZB/s22YyZpokb5PDGPjfVgyHNR3JsaE7GIpbFXBezoolJqxzryWcVVK5r6cdTCZo4GZzZe0ZE8j78SJUcxhCSIbWkmM9nOEuF53UfJewpPp0l8fiDiNn5Zp48wcksUT1aG6ikuF6TxLZjj84+PRzs1sOY6/TNQY5mtBgMmDPo5uw9GsrZcEwclRK6LZ9Rbl60x125lmjKOb+XeoIi8afKZCgC5IpLSik8sVUEMUanlksESkfviUg5yxDojjVDF2WtNeLvLFl2g9Y33J22rxHFD6NZZxtb9GUZAyQCnyZrpClFKBqQdIxOu3XqolyflLHHv8uTsVZlz+rEGSSssh4C0qrCUiSauLVOs2AjnsUpdcnZjn4/qoXlKQASRWlsyDBejWjUet+w3rhtPRmIAHxjZBVjBlm4o2Rzywwkw0G97TqyPsBCQO4qyFIPDSQl90U2spCaIKyk/fQMhmymwlO8y2N0MZma9AMz/pm24/i99FkpJJ4l3jbZZH/ghZPpmSkklRTKT0Ags0goyROZJvBhbo8U9PHDg+GPLTKv9Qil43sWRISP8M2llCOMO+53+qV8/wlePNNgcxtO/dXkV+W40Pm5HEDxAEI+S0Ue3kMjBVZHsILK94P274KcfX+llEItoFUpl1R0l5hAVQGLlIFrOJp9bJl22RPhRUpgJLsBQu/hsEdSU2Yn0v02kxJiqXWtoTcRqLqAEB2PA+7gwE7PuhbhWpd4jQfICm2YM3xHXKmXFZliTBF8rhmiOzEqtCzxrT1SdM1HCIEv15j0YRQVqhQsN/N+3zL69bzGs5ePFEVrpe+dfd9RXZGiwXyJRFm1RaM7d8PaHuBEQ7sT7FZsspIkxOl3T65s5kNDkDzSAGdlnHhE/vK2TM69RffYrXW0CEsJNb+dLWhijZUAdVG5E03YYh9Mg+FHdZVMI+HBvyCzqi2d9ME6zKt4NKa57YTjeU1feIAkLdERtRKVg2p0djDotqWuK3N3Yi8AACAASURBVDbwGI8b1rNMkgdjYJmmOu+ht5FdwjUDgYysMDzrEtxafp0PmiyJjvsMwxLtCeL65QBQwqSe0/lO3Y8QzKvD3kIXMiPHovqQZiuUpUSIlqlD5h5+MKpvNcZ0FroQQvOIQKu0qLJzKNm1XNYK64I8pXZpN4oUlrqCLDj1APp9MUxHFEOMBk1xV7bbiPu1LSpYCuzeaKNT12RcszXFes3UcomePSb9UEG0/YaNO6/bK7d94//8+/8L1cL1EgLnn19viIH20DzaUIpUKIXf/fYHfvPhiU9fdlobXMpHXAZUo43BT/cbrQU50BqM4byOQR2d9eMzT7Uiu1Ok8uH5d6BO504bjb//9CcGhe71iJzG/QsqwiJPCIUujVLg6amCF7DQ1CylwiKsvfDj1rjbOBzbsNmLTHN9+iEF2EZnt3FIARYtbw5y9vudg+UXIj1F2v8SxScq0d5Pypossh/ATTPc1QQ/ZPWvIIgLve/fpMW1xDpwm8GsHKDG0p77sAj6Ms3Z20ARljore0mc7vRZoJFB1qzQuq4lfepIpjaKCmTqqSQCHJBMPUeTwKOFhUbAjoambGplRp+Vr2lzJN671iWb2RKvFaFIsGDTP8lDECYS3YqPNZHBxARIkSkiQB8eUoxHOzPjVpsBbRRBRLDPt+b9F+O7IGemNswGmEcGZJa5HsLDs3mbZ17PzI5SchuSkfksjcvKDJ8k+VmKGg9iMh/T38jRBXE+CCHe97zK/CrMopnoiqtkvX5sslmeNydP5wRxzvZ8uPGt3KAzkpdMZ3nMw0yDSDoNNz/K30XlcAiPf4k5CSH0RLTOLOWLlMJE9T7ZAImo04enSv9kHk5UmJt28qNziGRp8+NqelvDMo48s4HN5m3zqeX/ZwSCRlpUZjQjR8Rw9pV5QP0zKnoYcbcPUuXHv8j8/rFqOCIHBzKFORmdqV9BOdb4IThONoBkmw7m5OEz/eFaPY3dcR+ZPpuXNo8/ma3NZ2TlNjUkct43E/cksOLb5/7NdTyIEo/rcoJZiB34QH7JOV8CiGZWTxF56I0j8qar6JhHFbCzmmw+pzC2mXouegQVAuCWWpflSJfOOrQbO43steF53IYJW2uIDyoN1yi8HxYNAMU0tXEPNP8DcHY4mTK3OHpGQIpy3yJFuy4LpQSDIwaMsAvWQ08UosqVWleEDjaopWb6BPCGypb2JD5fSzi6NoyrKrUUhubarDU8nmt2zg2o3Cdb5QLZ5mBJjzPcYAz66NnMUJERKeNFK1ZXaomjHhZCZBwBlqXS4mTw8alztFlUe2pd3nDYSF3b/Ibmzjj262lGZ6PPyc6cHL4cNjledNqgCQgkwcnBBHvsSJGHwHQyr79gaCO7EZ8tpIaJk+GZRyo9NgUsokTvq28S9LkvOH0XmVo6PjPnQjNdx5kyms/vtLP5lsLRvuWYsZzCIyvxMORhvo8Y6vjpsYFOFmr+4lFOPuPHb+2+W7rl8u31/XJ8F+Q8PV1z6xreja01vBTQEm2ix4ieAg5tC93FfY8md2NscaWe6mux44GOhBF7j6qtJXvBXNakB63QOmwpbiZzj1VBU1uw1FT9e0aZi1CKcqmVmlVVdanBllhGwH0g6pTMY5pYPtwJGDgMf2gzJNsk5YMRj3LbqZafL8n/ZodQRLKsMs6j2tuO94bdYVjDXFgFFs0S/QNTOX3s2dsjmwKqHA9ataJVcQlgOcEgNXUZiR51pCiTQjnOPImjM5ZSvyvS+kuMlhUtg1D72x4Mhqge9x8MhaMlmkUVCaMYPQSjEZdZNKMj7vahP0RMUHmIenA/DNIhoOPEnNHlE75xUapxdELR6J9SiS7TNbrF7tsNGx1rJz4Jsfp831AMiQSzgHBoi2bFkkYGP9qaJ9ie50v1pIanKNhnb6BpRLOy8Ne2s+T/Nc/GmvOiszwh14szxZPnvU+DNSuuJkyMPj8a0W1MKT33h2jhbfOeKXpeovDADWwophVTx4ojTxfKslIvlWUtvDy/UEthLTVBx5nWU8/uva+fubc7r9vX6HwrOyBUD2CB7VkFN9i/7mz3dlQ5vt4iVSa1Umrh+fkJHSBuXNaF9eMP2L5h+871b55xEf70p5+jQV+W4tZ1ZfjgdXul98a+78jTM7UqP3/9zNZvlLtRzVk+FrQsqK3s+87Yd5oZuw2W5xfqdcW3V7avNz5qRZZB80rD+dR+iuAQY70W/vaHv+H11vj6ujOZvtlgVfqV0Qf7641unX/60z+gVBa/cLmuXK4rtRSeXp75fYU+OpdFMRv8/NMn9ta5bVuCwtAa9W5ZOBJMwozo31rAHmiS9M2Tocn+M2OAb/gSG1M02pSY7bFThBCa0x+AhxE9YzLI96iy0uwLF3syxe0evXmm8HrqeObZT+7RvX7u5XlMy9jb0b8NCT1f66HjqSU1hO4BVlmy4UYkccKmarA0M8U++xVZNC8tUlIaEqygTR2aOzVLpkaiM82q1MLU6/lJp0zbkXbB0kuWtC/d+gEcPYssgkgImwlQy2MIesbkxykIxP6KDFA8t28Yn18Z3++Tk2yEzjKxqYfK+5rixwewyjwIEibiOit7vs3vSzqimNTHRmpaCsNSkCtTV5BnWuTdFI2D30afBp3DWRrOEGeVOBahk/Npkzp9iP6Esw/JxL+ezNkMp32ek/GtizkQpxk+03Z4RpTpqPzMt07fEwg9UwMeS2FWaJ0aoJjYmaaL12UUlP0WxB+l957RgzN1ON9EuRmR/0u0Uu9ZBTXLs82zs+W0cHIuakumRmbUk3M0q47m4vvGTT+wC3M8AP+Ddfgm4v6lfxZJ1iJZnON3z0iLI/KCsy8FeR25vrP3w8kXHQg2rzXz7jMKUz2qs/jmU+NvckRL+Z5z0X0TucxXPEaZjzfnxzqZxuKbdJac9xLgTR7mer7tTAYTzA9vO2zuE/ED4M+jZSY7U+pCqQu1BrtQy0Kt5dTvZTpvuCXIiUrEmv20Q6dzFPZGirFHZ+O97ZFiHCNbYQijd8yg1ApzdiSCrVILy1pDa+hOuV6gKOv6GiL8bHURTSMNV6KtFRnRe2eMQuvhlNQdevRhqlYwc2pd8NGjwef8T6NtxSyfD3wszJRurGXBXLOpYyhGBc/S3I62nTFGaG6y/Fu9ZnWdgTqjLJQSZwAiwQIUkTgY2WExQ02QHull9XjdXLqWzPhbD5tsvpFpqIOziH1pgOU+scE82y1+Y+47n3FAjl9UiT3uTfM8LugsyyaD/fPX/ZiTI9U1f2YhYJ/tKSyD7bNVxPwjD3/PD0mANf9MYDl77syWIyegyHJvO/21Hu8D5PscLieHnB/3574l15w8zks2H50tWQ5bKBx2k7RRv3y7R/bquFd/eBS/Mv5fSsijnLmuFxzQMVvoz1LSOMnbPNCaIowiWf5VYkJHOvcHOz4NSpUQlPZtx4qyyAVZlOV6xdnjQEWJ3R/9ZCSa5wlxSqk5u/dwgt0YzfjqnWVZWRbh+rxwWa/IiJOabYuuo0s2APMyIpcIOAV3DTDjAYaG52FgZlSxOIXZOoYwtGQukmiBvW/YiHO7lssFrSUWpxl9a5n6UGqJPhOUaIYXjIKx77eoEnpwdJ4VLnMF9HGq7nEiqlMJpC7CqjVShYlsXU9nW2qW1krn+0vin398vX+JJ+4x/3EqcuarNU4KH9Mx5z1qmR21szTTI40TGMIO0JBcBCBHMy7VMtFR0vAPYjgXPB12TGOesabZtlxChB6fGd2+3XaEFjBDSjwziNcETMeJjrTqWaafjQonaJvOs3vkuHsfaAndy/CB9XQWmg3H8KPk0rLlgRw/CQjuk07iTP+d7Mo0dWdkqBr6MLKtwDR6oplvn8KXA/VLpELMompJL6g1xCxEpG+4jjaLlvpmcXkF4iRoBF1WFl14ev7IZb3w9LSyLJXr5SWCjWQGK7B7dIGNAwVD1k+p7OsaYFxCU9FSWHv/urHf79xfXxMAlzj1HkOkoFVZnyNts2gFLSxVKUtlvRZ0vSL9wvryG2S58Pr1Ti3O1/tnxhiM+x2RSn2KA0fJQ2r79hVbHC8Lr3bHxuD2p0g3XfXCsiy8fPiItI3dvjKa0fYbHz8+c/3wGwYbNzewQi0rH3/4DYbzab+xuXH/srNvjW0fqAY78PnTK+7GMsKZf2332Gu0ODKmb9zsRmnr0ZBU1kgNNoL1aB7i7qeXFRsLfbscvco239hp0AYyYk1/J8vwF1tH4h7gSwdlSY0KHB2P1RWxONaBMAmxVw5TnNYjZQoMz7YgEieKewC7cL4WRyNo7Gct4bdkGGqKm2AjdKqtjdCBzjSVWazBW8silYJr9hayCWtmuiksRDTgmz2gohR+rdFa4d57ZglaatASMBSjUKLyeRIsUz+dzM2ynL25gpEL2zMfn+e8xNzMvkLJlPXwQzULekigzcP1T+sWr51SjSQV8lpGnnwwQVuR6G3kyS7/h8b3NTk2mRbigjSs/9n4LJGdky3jU/0sma82mHoAyoMWI4QOZ+O0KdycTEyWBE/mporgWUJsxyRFlKI6kZ1gw6Iz8hgMzQaD2dcGBS+ns+OICKN8PDpIFg5Elk6VpBCnroc0biWpAhsR6bUW3ZRjPkJ7MrxlNBWRZzRZSmaFvIaJmLOl/gFqUid89Os5UDDZTyZBkB9LKu9DsrJkfo7nZotnpocq/+1GrdFWQCzK9gvKbDs+wQaTuvbz+oItzKq68/bjZzyg/iPFMgV2uWH0zMudPWFOqHCwQFP7dLxzxm2enzHX+2Qjc63P6oQjsuOkVONa5GB/jhs6r4hZFn6e+TJJGOcQ9s+7ngnrhzjNOT728dLzo/xIp32rfDgZ1alvikgrkrTCudb8m6n9xee/dSCeDyuY2jOQFjmrPUSiCnHq8qIg6DSmx7PL8l88GkrOgzZxp+/Zdb15dj8PO1SXSinRzmKkRkolTqpfSkWlpGNIVUM3bMv0gzm9bVmuS+hYEohrqQgFdaWfSx8kGpxenxbEBx3lWmJdLCX0F6/b7WjbX4pQi3K5FK6Xyr5Fc0EUXI17vzGZPNVK1RLanDbTKjFX7hrBpxn0vBRKaBxLMOO1FB6Piwk8HPb7qALK1MdptgSkhG10Syf39sNGNKkNPfA4mDaz8/Bo7KyUnfqtgzeYvjDBzyPTCrH2lCNKCs3V9DkPTHDY60d7dR4wO2V+57Y+2Qs3p+OYn/twaqjPwCU+vuj0xxz21iwPTR2WlZ95ZzNtlLZgaiJnJdnMTBSV475nOi6tCAfzciKkufMyyNV5E0xVXJiWqW+ccznfdd5g/FXcj2tl2uXJkH9nOX2fyeng6vRiSClIKYloLSuKhKVE342+RRXCsijmggwYY9AzCixFaSN63WitiMYJvU40+fG8Ge9GZwN1alXWUuKAwGCEuW2zy2hMZCl6UNHNDd/60fq698bwiuqSKvVYcNFzxjHvqGhs+lIpWukWrxdGKqSDKVJq0nmWqFqjZ0Rr3O837tud56draDlyEd2Zq9Yy75n3ayD62HHXWNcFt8K2befqIECTDWcMjk3Rs9SeLG91L6BBFZsmIPNQXc/yZLLjc7Yx/O5j/+cez8/PYdh6id1nSid0NmITYMbzt2Rp4tTxYDympTwlIJMyTbCceeNvUkR66lFsNoVMFhE4nNq5OQag5z5zTziWGhDnMIQ+D2O1sy/OZGEOWCJhYPoI46K/uG6ZwtXRAtimvuwQ2CJHLvvohZNp48c+RwfQedzl7nE/0ezj+FGshWzqNecAh6ymiM+BqDvO95wJb50OLeOroy/I2w23LAGewLGQIIG4X5l/4pT7op4tNcIgmreoWOuN3qNS6t57aAtHx3qcUm42aC2YqipQLjUAR62sNfSCY0AtK48C072RWg3DdsO/9jzN3Lhv94mnuS5X2h4H/tbrUziuW6MnayZUpCy8/PDMb3/3zNd/WOj3EYJgcfpivLaNv/vxH1GJaqfL88qHj5Xfflh5Wlf+ae9soyNX6Nr5v1//RNGF5/W3XNcnfvPyWz7pj3hvGSw5a8kydVkwG7zaK2ZC8csRCNbLSr2u6BBkCFvdGWpIHL1Oz6N54nxFi/OPIBBBpgXNzkMV3xrq9HaPPajRUNH6FjpBl2jM6QmiTYKNTbA8weF82HHMkTwAndDTlRLBpvWwu623OKg6bdGYWsp5FIKAbAIj+reZ5XEPcxxxXDCxe29H6w0k+sjFOAt/Yp9qgJwikZYTZ4xGb42xd8awWGsSjLm6IWOgbpQDtsTZUO5QK7nWygHaNM1D4sLUG8pUqDCLeCIAIcFN2N1IrU4OHh6B4uGfZior/zl/1530iZFyo3x/HX0f5KjGg54syugcB/NllGs2slvjLAWX42cTnc18o2doOHUqk4U5OpPmldo8UwoJYREWFJ5G8y4RoeX5PfNBSUZsWgoueX5KCgZL9pvQ/BpIPXQ9VQpLWR6i7lSwtz1Pig5xVl0WtAglTzs3G2Eo9z2Zp/kYypn/7AnEMlI7gn6Z8zOV+5LppUJd6iH8imcgEU2WFGE/MBUxaQGi5qKfh1dO/+MS5z9NoZc/uMW3GxHdjjyEVA5wd5ZCT4XZoX0RjggLJtMwI4P83QlrjmhKfnFr+ZqH7r4HEEqAMwXCj3M6qxxKiTWnXsGVYduxQeNCYs6PIxIeGi6axLo1Zq4/agn7L/tiWKQkH9OSHNVdsa0lTshNUMzBDM4Nc+b6z2f7yMaQrI67fwtwDtZgoqAH+OsR/YXgWFIbluzTjP7+vy6D/x+j9Z7FAy0/O/ZMsDULulRqXah1Yak1ypzdsdHZ2z32yAhtzXa/0XuL8288zrDy5qknDBAVzUYdJ866KhCsiIT4upljeZr70ba+pPA8m5vtw6GPPJMv9sB2D/p+bxtSCk/PK+KwSrR22FrNdQdtdLZ9Y/1QuTxV/L5hZuwOmEQJNiBu9K1zE+FZjMWEWlZYF8w1PVAEcVqUMRpfvv5E2zcEZVlXtCr32429d2ruO0+pgc1d49HZV21EqrYE8IlURLC1RQuMaMEfzQGzMZ0Y2QmNWY2rs9P2G47ZvwdA8kTsABepYZp0igQjfuwxZnXYRB3ZxiL31hQeh40PJsczo3voLKeOJhvuzYAh0sjxzGMppT1PJklLZD1GBqxx9lSWsWcAZOljj4BOH16br5mgoI+RfnWam7iP2VRV02aRqWGXhwIK1wQ4MyMSf1cmGOQsqmDO12OZztSExj5h/o5P+z6N0APYOYK0BxgkD3KFQ6/56+P7ICcrqaSk1qPtR4dHSaPbe0sKTM4HDMxOrRPZRUP8xHXu4IOaDIxLoMMiUee/W8uHl6xIpjlqFZayUEth37Pjcu8x4VlhVJeFNqIvTx+D1iO6m92JBYWRC4BClYWlLnkP42A++rbR9x0fe1DSl6gAi00Ref3eGvu2BbNEhJZCPZoD2gihbSnrQWGe9KCELsCTuEsthusaAGrfgtKV0CHNA1Ddko4/GJnJCXuCnFDLT+cqGk7YrePHAnrrEexD8w4+m+wN3M7eC9/00II8i+UR5DyMM3x66AUzEcvDZ2LM+sk/YzvyZWcaa37+FH3PlERUXAmVfTSiNjDfYBoqnYA130c0BaTjqLyLQ1wHzTo4qGc3Zfej06kcwucMLCScmB73IKcReABmkWGdqbPMaU+Qk4baPI2K/4pjmdWCc/r8sLFZIeFHKiZaE/25IPAvPVprAQzHfgiGRUDMWcqFZb2yrheW9cK6VpYS8ztG4/Xrz3imjfb7ndvtRms7bTTm+SKFEvHC6CDCUi/44ZRzfxUYApsN9j6yr5Bi2lEVnuoFkYjE932wt0Hfd/remNq7+y26M3cay/XC5Ycr1RUtkTa6bXekRjXM3nZeb4M//OF3rGvl/ulO2wfjHlqOa9pl90G7Nfab8eyDdQjL9YllFfZbj9YUa/QRKqXQ287ty0+QdTjXp2euz1deb3e2e8v2H7PjtTBKtu1wh9GDLLuEHqn2CuaspeISqTgzp+09CkAIftTd6QTQOYsL/gWYnLQ5ZoroCECaTERUx+YJtEKeSK9HlbnNylmiievkhUUkzpsyQ451RJCpM+VjPQOi+L5YMLMzy1FMI7XqZNAK7rHGS63ZhiMYsJHMb0gWa/S2ycu2Yd+ctSUqjBaMWu8jD7UMYmKtevS4caD3cQTgs1Ghpl3tKX63bGxZZAZ0ZJPKaYd9opv5JYafQer50NMhMs8T+EWpOd/+Y+qAs2lNVkhnkP+d8V2Q8+Pnr9Rl4bfLGsbYT+HxcMsJD3Aw8liHpaTz9SiBLolCETkEQnI0s4kLL1kO3vYTDWuVKNVMnWzkFD2iC5zLZcWWQdvC8Y8R/SWWWilLTJqW0OWE9lSi0R5gLUSfy7JG/4hS8bwns4Fb59422p4GFWHmkYeMiLDuG/u2se9blIuXBbdO704payB8m879Ti0VrUtUUblh1sJAzg4I2aq65wFoS41mTjZRbdHzRPfhZ9TtMNIolZIg0jjKJC3kQUfuMqKnt0U6kiHKTKHlCY/M5d2txSmzWcp4chDZQdgD4QcTQZbQTxPp3zTMOr+cjMjs9SITYEPkdR9wysFS9Ml2GVaMoQPXADaWLQIe3yPkB1mVkOyKCEdTvuh/NKPEbGMwiZc5P0ytxLzeucVP1hBmjyb/lnWys0dVgLYHVkcItszkYMrsAWhz/OpEmKeJEX+YPyb4nDlw55vLfIMx10Wfjtck11TeI51FhIuW2HcMOjvWWwRnvWN7izN4RgMblBkwiB7aHb0syV5Mo6zMUtf9vnNvRuvBWusqCaLj4NR1qWgBXQu1OZfFuX195W7RMHKYYeOO9cHlWliXwlIFRhz30UekImI9Ca07++bsrwMdhRd9gQusdeFWNsaXQWsb29ai6agY2EZvX9DrFSmFy/OCDefLdkNGp4tSgcu6sHdnb3EQsuBcy8ryVFjygOTtU8sKnBgmeWBwd0SDqhDI9P0a600vwTrInWGDNvbU4JVoACp29M0qtX7LSr/BmJ3r8x9HoEH2pwkpwSxSiF8wPVP/M5gSDzF1HPEyX+uIxBlkJo6phdohDbEUgRLAmKEheB6d1i2kHMzqOj8CV5kpKRPELBrgiWMyGxKmK01xVB/HrR3vM1Iz2kc/jnIQz9MEJhnh0yac+kA/qrgmzE+m0pNcKEJZ9GgEeMTa8BBQnyBkmsiMx9MOTZ0Nh205DdPxv7yf8/rm7x2g7DvL6Lsg5+evr1wvFz7+8EPmKiMiHNbpnqeljoGPkdqJ6Egc4CwFTGXqCQgk6NlXJFszR3FDVHFs97NqRlPv42bHSeeWOUcELpeKu+KjR7ljn8ClMHlszTSN5YS6hvGeglepwVJ5qVhvQVunMG7vnb03LmUhlR24Cx1LGvlOazt727henlhqlIt2M6rXY8GQaTPBWeqSTFHD+kMqZeoGgNFCy3G5LDHndq4ItdDmDLcJ9iNCypLU3snUQvYE9ujEPPsAR3QCv8DYbzASWPkENwlQJE/xBcrR9+eB2nxYuxFJcgIMfago4sHhPhhNSXA901VHmsym0NgPynVuvgPESIiCRze8jIOSNksD5GkwZ3sFPS82grFIfxj56zM11SP1dJ4CHq+bAEcexNJTmD67jTIb4HE+e/folxFTEUDtiJAzTYZmfbJmetTnjM7rPene89nk0Q4Hq5Om5VtL9majzGZysxgCQxRsmXHgoIiwiCIemr3hg9EbfW+M1thv92TqspggzbaiqX/KwMthHyeAJSPb+7Zzu+24FFw0dQjzN5S1VHRV5AnWhWja142x9YzMx1G+u9bKZSnUGlU02wgnNMuVlehgvO/GfjcWN54uT1QtPF2ufObGT5evsTa26P21FsC3qJoRQcvKsjwxhrG/NnzA0MJTqbwsC80iXdf2HTHjUha0XLhcr7TR+KevP+GMb1R85mA9ztzCB7UqrtFLSVyCic7qnuEWp6tTg3FVj7SV29Er562bdh0s5HTcY258znSSyMPvOClTZjbl9GmnRB7SR3M/ZtCqZ2CJR0NULSXOb5Jg0IZ3fIR/GNknbNoLzc8X1WiXMpKJT/bCZqSi6UM8bP+8xwkCggCI4pgxxkNKSh5ATloDOwGEP8zXIfaXKSCOQFs1KrJUnCL6cPZUmgd/sBf5vW/kRnOez2iLWQ3q+e/5Y3/4J/4wt6Qo+jvG6PsHdG47ONxuN0QkBb2Z0kkGx7tHdVQ2/bvvLT74sPlyRn8kcJllutm3ptaY7KUURndaM7wEpVe1okso+zOcjoc/NG86EFCc2LrydHkJVqDANjwOytsMK4PryxLNqAZILXFqcU2tjii4Hl1Tn65PVF3wHourtZ3/h7i3bZIcSbbzHveIADKrX2Znr7jXaDLpA03//zfJTEZdGcm7LzPdlQkgwl0f3APInuWdlYlkEWM13VWdlS9AwMP9+DnHTeDJwf7cebw/E86bZNNUcYlwbM9sQ3REo+ddtCT5l0C18BM5wpx7uSexK1ZDz885bx7Ojx5OplImZ8Vj0GWeA/OAw0kKT/CqJE3cYPX2otr5mMOzyVkm1OuprmLKobOSkvAmwWPTf/UWQtL12on2GzOfmZmFn/Jrl4mOZFKQ/Ja4yeQlBHChQEYMiTvl2nHTHhwUCzVLPzqWHC1I4z0ygJGoZSb/M1meOZFlpUf28LVoIiWFWuMsTSXeyDkxE8EhP4tKvYLQC3qj6HlO5pZxhqnZ6ko4+ZKdcwX6Mwjp+Vrx07DCLOnxYS+v+Zs86UOOosrb7RbvkQJ1QBuUpcU9VmK2laQOth8Hve8c/Zmt6PhMTra4k3N19hc87i8nku4oqiw9Yw6ONPCbG1kZUcEu7UarLbkJSXgXB3XaEgqpN1kAqB6tt3IvtPvKut7o0nm2kqakwR1CHduE0YW//sV5tManf/p3LLXw9BjG6XLQVviy3Lndbqy3G00GRYzH8c7Y33m3PWNITLQfz4NNBJeRxAAAIABJREFUB7/uB8ODv3fYwTE2bnWlSEUOxcYIkimk1UW2XW1gFrSCoiXbyYKNkKqvtzu1NlSMx/ad7XhnWGf4CI8dST8eCxpy+ehF9LJ2w9dqcuEIp2kIlErSfsIJzmU+Zhr5kVwtCAn9pANMN/SRAIYmeto3Q46B9khwq5aIH2oUwij1fL1MWIQYNBs8p0jaz57NfENDMRP6YScZV6d9xYCOs3Vj64NjJAKpk98arbK1lUiEj3EmdodFIqqJ5IVq2E9fpJIu8Cqe443knABzJnp52kqZRS6ztn0Fq6+EiJfa6aQoZlGTkjPJnxURxrSnSdT63zp+f0BnHxTtHMeOqOKiTEn1nLeRRVG8X796nmUy0vPn5nEyVAvpQg7Do6JNwmaZ/JHYExELF9tSJjqTXRjP8tIiB83LEMPkloWqQi3QvacfgOGuqaTSUDaVmFg8L3hui0w77aUtqCt7ch36iKFtux/sx8G+H1TRkzUvGOTf+3Ek0dAoCFoziZqZd87eYQQZ99xkRM7C5pzC/ZIkJjaWPgWkJPuCX2fmHJBknD+ReL1hQX5sSRD8yGNu+NPGXbNNF+Q+TpjzNfN3m0lsnAPOxOVc/3nDyEtLVs6fARdCNiFZEV5+Ox4z2zGWm7fPmzCSrJi5lsMGLSpxzfdxncULYg4Z6sk4zMTUL+L9eaeTLrxpVKfht2TpyxSBcspPE1VJh2jPDTmecL4XOdfB9c7kTPJOpAqfQjZmzff36yEeP6F9T1Ug8yX5h3Hlf8ARn3CplanUsSKMEptTKSUNoXOXMAuhxIiBuOavnzE9bibTU+CCRiVfK1Jwt5EthXyORLXi18ITaVGlqs4UMZLFtKhQ1SBC10JRpd839qKMFlyLUis+iEG/RbI4tLx3I2Dszw3rxnFEm2S3eD/uA63C2hbun+7cPn2C/o7bzvhlj/b/CCo0NdaKp9fINkB1oWjl8FCXlVJBU2VkMb08QtWkIHgm4VEwDTWO/QCH+/rEG9T6JeLneqfbHmfbR6JLcX3OZN9JL6yPPK6Vf+6wed3Oe+XcefM+8CsRgsnNmbFiFgVkkpPnKNeSptDFelzXae2hmkrJGZvOu1euO9J/bGNPZPXU7QOzTRGcnJei2K/Ox0hV85T3T/dvVUnrgWl9MWNvoI3D5t4SUUBmUZT72ExYpmu7T1r5jONnMThPnudnvy6FzEtyxkbO4us6GxdCDf8Gb+d3jt9NcuoiSHGe23vk3FaCIS8vZNd8N5OgN3trYeOdPhB5IlwkiGilpM11bLzHHvyUtrRQR+UJfD4OWAplqdQaygAheSkYRvQz3Z1lqdzfbnz+6UtsShjNYXhnn0RkF5o23r7e47laSNnRgbQ4bU1ukZitgy4OGv3pY985Rufb9p1xdMa+0dqdpS1hJmgWSgqIBNCNpx1hEtaWMO6rJcjLapQNevZ/bQZTN9xmlR3qg5JTk0MVkX3fHCo3fQ7WpZ6Vv0rYs4dkwE+0Z3J09mP7UC4FwL4/EMKyO0hriniJmyY3JJgbcKwjo18L/3VV/9COkpcvTl7MTCinAXTAHfkDk/PmBE47cDsdoZOMV3NmjDtiI+SOoudzXq0mOZ9r2AhjvzmwkCsmTTfrCfuWM7ENH6FaW6ByI67vlDCKX5+xZHQoUkIO2j2RCDlHWkwPpZMIfSYsVzLzQ8V0ntjzCpxBmzPAvbTJ0tFZRafg/mMOCfTr/ukT5s7eR5ictcpSwl+ij45o8hOcaE8XjTYxBt1frtl0cA4CWx/jbMXPzSE4VIHkjB7tsLJcCV+pUcyVFpvFtD04xo4dxtgHdvQo+msUJ2+fvrCsxi/bd6yHeZyK8tPPP8VsqiPa4d0O/vjzH/n89oaMqO7+9ueNYQ+etrONnV+eT/7w+c4///QHPn35zKevX/iP//L/8Ne/PkEWllb545/+N7QWfhl/i5bdt2eMlBg9bEEWYHfGbuyjMxS0tajH2ooOw7dItmuFUCQlr7CPRGWE7+/f2erBcsuk8+2G+iPQD0KokcsoB9E6T+vZpv64I6aRZNtFw0hyugDDLHAKku3qiaYDEZvdw7aDgGrCcTs+o4uz753RB8eIVva6LFAjrhHNguA2QaCGHijFLIDiHlOGw3NsVAmjS9GSY432VD8N3JPT6dDdTlVu8IyCQnIQqN+xB6lagFuN9myRQMb7HPxMzF83yGGxjmkmMVUpVajVKQVqKayt8LbUbFsFGd6dMHp8sQhwixipZ7iY7aXXCOQvfyYtxT3zuRmx4hx5omQXd+n3j99NcjT5IpNbICNEsSMVJud2M1sAmb1JZpE+JRr+spm7x7TSuTkRmfEcaa9FQ0ZtxuiXNLtozIcZ2ZOf1a5lhhcXoVCWNKrC0NrjJh3zsbHhtGWJttSUCEsSnamnxXstFepATZKLlLD1fkR/NHk208QPf/2ys3o3zTaEzIs4UYUpvdPzwsZ7lDyHlqqdE2N6IWvFxUbiXKsmnPyCQgREYmdWfUrj7WODCoCNfia8QVITMMVNEYk2n8GJMESxPHv28Vnj79f5iG9feC3nYzmJg7NKf6244vzM5ul1nPydyeFRgZE+Q1mxT3j6t+mBhH7kDDLhi5NctHj7zBbTfNGLO3O10+Y//uadnV/ysgDELpJy8JPmOfnhA70kijNS5x9nlpOB+/V3zveR/0+0NUjav5Xuf8zhud4jNjjdw5Ok1hijMFuHkZRmXHFPdVjeQXlvhJLxWklXnMw2oE3CeKJqs0U+10YmtjIT4ug8RqLtQSa1YafjuWcxE5xBiURhiza7JPm5tMayNJZlxQ+wPqg1vq9WcYNfvv3KcRw8jgeHxexAXFjryq2t3NuCmNJ3WG8LS115u31CauGxPwCoLUQKYx9I8xPtFE/434xRpp9YCb8Y8WvdCXlvXIiBA72HI7X0nUKlLPVcm/P6ScbfuVGNiUh+4BEfY8bTfH92yZrBX67v3HqvYv76MxIDV8/1kQ/LdRNKJQuOaLjepmGiny3syQx8jRP5LoFr/Tpz3fq5b4aB6GU0e8Z4rlhjiSSPVFORl7HIxcW5YkSeD+cKIvN9Zkh4/d2iUXRVvXxz5lP9kHwIZ7y4wsVvT2a+5GvC8vL3C2U6t4MfXuPH5/7743eTnFYbRZVWVyDM/8QM9RgHb8OR0kA0N9PpDCxnf3PMRXO+24GNyGJrcgwe24GIRZJRlC/3lW3fefQjyYHCbV1YbkFqYzeev74zeqeIoaWy3lbKUmNB1PBwWEyhDfZfvtGHsXsYQX25/0SrNaYSG2DOrdxCjZWDz9wH7DVIwh7oi/nB4/FEcZqEwn5ZhL1H1nxbV1SFb98fDHdavUWGOw46gvR4/86gZNAsySPBI/gcPeDfkpW4lhIunSOSRodAcmYyUy4HmSYtTa2uBTDMOGzQLdpt5YT9PvKIyv++rqg2qqzsY2frHe1+JhEO6bR5JTr9ZbGLZ6Agb7hsgM2lHpLegpYFxxjW0UTCRMJZ1u3AXRIyvUDiaWwliTIW0XyOCCZ2PkCY8vLrGa4gE1X/jtSGaMXTluAiNM/XDG4ShPPs8CN6/6l8SIA4E6+GaqWUe7wnP0BqcqsyeZrnKdscWpb4N5vkrAy0uVZ+SFJm8zwl7Gc0ZH4fnjFnewzhw7sMfUcUanIgqjZoA2+d9XZjXVZm6mLZTt/evyfi1SkODUmUTsObKFta4WFERPNhiDvFyGQq28maH/9w1nujtcLwmucs5fnSMRs8v38PfsTh2AHW4evnO1++3tn+8mB/HLS0gXgbC7jQxxE8iU8rYwuy8vvzndEHf/r5T9xujcfxjf4cPP/z3xg4bSnoUjnqimlltlBdhD/9/O/5fP+KWqU/O/7YqOp8/sNP/PWvv/Av/+nPfPLK5/qG6sKyNN63LYZF0iOZKwteY8jv0Z889nem39jrPadhaoXtB9+2P1NKw7rR906rd3woQy6e5iFgYuFRdPLOPuYoaY4ppum2b1kD5NBImSqpec9MDp+fG74XUgCSSaoAJqgrawuO1ff3ZxCsh4dTdKvQDR+Dwo2qSWNQxbe4bqk6P8nESw0Je6jPldqU2hrDB49jZ5hz7OMyFZS49kaMI9p7CHK2HOewVk1FVonWd9EzfqmUdNIGGdDlSrQwR0ZBtbCWlVpKdChKpZXGYTHn7Pns7Ns+s5IEN4Jzq0IMxobThuKHI4uw+asTTNaSfMZEm4ICljE7QYA5E+3fOv7B7CrOF+Z833ICFufwSL38TH54tQk7ZFYcP7oQnIibc4PInmK2BYrmV/bbJdUPbp5STDtJbyqSPfkCLxW0lIIacTHzxJp7ZN+FkOY5yPBoCdWCJ5kupgPb9VySC9/jz+AXJRqUFIyr0uPcbCJuOojFpNk0Dysi6acTm7kNy951R3yaAw6C6J0n/CoXf1Nh5HdyVU1nVTrDkcx19MGlE4E0FZ1corlxz/cyv+A0Ozx/82oFXUnF/E6YCoeJlEB8zhjqLfhL7zl+zc/1d5bwJxad1zh9I3570/hrNXeu83mOuRKZKel+/e98nVm7XVhNJDMXge7sPb9UWj94Tp2IhESAdDuf41yD/xV067Wi+vHDva6HPJfTZn2WZzLf6axuf/PcH3IY4hbE4ol8qeIl15bEetCsRE3Ap1JpfoQXlHXeChL/Y3LBorCfSeBlmHaOryERWFWcSHImkhPmd9HamkRzSwg/5pQtlHpQagx+9CRHa4oqioatxegHfdoJEO0wXSZZP1rvUpS3tzvruoIUxoBjC4mx2TQs7FRRBg6ZvLd2o5RnFEPD6SMeU4pEImcxGkdcKRq8uVoLwzPhdflxbZPIQSpzRvI+9v3JGAdIFLy1tbiGWfDMtfrRFdeMF1eS/4IQnCWAZ4wVXu+Pv5M4X//CWWrKy+dykteS6qTY5HCyzTwRY8kHZww40aYTfZ4moDPuTdT3ii02Cy/JYthiT+lJuL/W7UvsmS/NdR/MvXmu+3mKYqrA9Ry1xKgPd7BhHKneClPTOD+KcEHGLxd6VrHnRZmv8VLgvpzbvzteQnEgi7+/jH43ybHRwQqHp1GZFobD7kn5AMSCeWw5WLFqBYl/C/5FOYNlrdFySuIOzz3MiWzE2d77NYq91Ya8CW9vb9zvnyDHQjzen2zPLdpG7tAqWhbWdmcpK4U09vN0Kq2VuixQYpLrcOO7PbkDn2+fKRSqlySExcWb+W19vPNf/vW/4McADlQGS41BY7dFuS2NW72Ft4pa5iCSvhDGLgEnj12pekSVWApSK3W9sbTG++NX+tjZnlvMwRpHBNTWwAea0nUnuBuq5Zq1k5fvgilzTRVl2EEfB+TgNXeJm+z/Yx/zv+fx6e0eAxJLwV3SYylULtMn6Froc1MJlE+y9xrBAsgAEclMJI+WRMiZA6oMYqzDdA5O+bfbya0ZvZ8EO5hLVChVr8S8ENOKZQaSHB6aCcAMPHPMAxbW6WP0tFuPdhyStnJ+TbMHsloOiOD0aHKbr3Cei+SDZrsr32+J+2n0HnLOjEiaxYDU3H5eBiJl/cNUUZ0J9IsJmb+gY2c7jFcfIL1mx3xgwlzosYbkgeiCSIxQKS0Lm+6URSgVvAg2lH0vgbYSCO3+3Ok+oPcYSCiK17jYRypLag238KMbPogZdbkjFo2YVrNlVeqKlEK7xxrevj/Zx+DZc2bVlOt7DBhu9Q37NBgF9r8M9mOj/+U/c7ut/PyHn7nLyk3vfBtQtoPb/R4KrD80alUe//fO+/tON/jy+Y3/4z/8B8QrY6+8/2o8/vLOr78O3g/h//zXf6EtlT/96X+nrivy+Q80Kdx4Y12cpf0ZM+P7979x/7Ky3Boqg9qdvoVwwbRT68KnTz9Rdwle4/QdSqfonq2QDOGZPO30X5/50Y26Npb7Gza28AfryjCjvSRyH3WcCa/EpkohvMvGOPP5uG4XF+dMehJ1Er286lXToWG2Nok21VIbfYQz9rF1ntt7dA+WJWxT1kk+nm1WkBGGlRNW9hTT1GXhuQW3ZgwHi3FHlvJt85HKv+T6mTG2UPFtx8GShcCqIe6RlGlrGmrC9bnkuGLFpFOrwFqEpQiqRi2Vt9sSSXrfeTyffHs88G6nj53glNLiOUZ61SU6c/EVr4TKM8HJOiuLqUmzmL/HqSrm5K7G/fjqvPHb4x+0qyrXAEXwMSV0s/KUl0LvRybBeWTVI1qydy4n27OkZ4tkJJGURh85KTYG4xXa2sLvxUKee4xBWkHQitJqGGvVGgPzRLIXL7EA21rRocnbSYfJWuKLSqWeFUnRGMvQloVhg+W2xiZ8aFiUl5YLe36u4BqNU14br21u2ZYD7wpFqSK0EvJByQzYQ4+ZxOCADaPnqYH05H9x3sMh+eq/2pUpz435TM3jB3PJncf/hCRnVtqTH2RTjjp7qjKl3MKpciGqmRPtynX4giJDfrI4T3nIdZOG7DoCRc+Ojrhma2wGl/ECkkX7MOx8ZiV11qrXB8qEYvaKLTkb+PW4BGZ/eE/zM533xBng5+/OIiev2usGkBXL9OKQsxT98VpG1eb5lH4iFMzq8Dd36RlmBMiGT5q0IzMhEjkVkFM5dKo9PujQiaQSgbouC1qdUkYimNMbavoZjfNKDLvahYIgWmOKts975OLs5K+k7VkEUyFUgAG551n0fL302HEP/xAfTt86QvjwNCIuFYnNs2hA/aoFlRwKmW2z0aFvzvPYYu7UOLCudAM1RbMlUiTaBPf7HbrSh7Dt7zy3d3rfI3ntgfzsz2cg3x6t7VorS73x9vaF7hvDtxDMdo/xJeK47bFugivMOABT6jT8E2ekX4tmi0O5iKZnxfXCQwyi64J7BZRiIzbcD05yLq/avG9edtBXMuwLGPOjkTpnqD2/IhHIwodYUbVEcT8RLu153jzbdPSMQRrjh1L9ijtVC7WE67VWiXZMzp+aX9OzZviLaCPf+yy+ZnwtuUfWGqrcuFR+tei5YtkrgqN5IlRjr65Z4JfJ97SgaVhyz+LhqRCUyduRH15nxu4Xys4JZF3nVphTyF/P93m9MhbNvEZn9+TfOH43yfn09oYb9C33nhw2ONyiT3e+gPD3qdRZkqO10pKxD5y9yEmMjblMYCqMMfi+fWcplXttrLfK25eFrXc4IgHa+o4TKp37snC7Nd4+V2qr6NKSrS4pY3c+1dvpb6C1BilvXWjLSpXKogth1BUVnqL42xulVb78/AfW55PxPhB90NqTok4tkfCgQsd52mAZwafY7aDbYO/hXdA3WFujurAuK2/LLV0on/jeCYw5OBq3GjOylmUOYwwUYKRzbWydad6Ws6DmV8kV46nKMgmvgxlI3T1p/b931f/7H7UGPN1H2F0Zc5RBbMaFgMTDoTq5LYcn8pGb6ktwelVQikRQgB95IjNhmfPJJkyNS1i6O1kBbXFza1T2tbRYi+KMNAqb9D+X7Eu8tL98hCJnRnZPk7pk8pA2TpwjptPGoNSUhU811AnFXWmp+SsaJahme2z0TNjkIj0y31LKW4/LmRlIEnugpDPxuWboZFDTFufRIXFY5qiJ+XlVclIuBx+K5EgIFiBGt3z9+gVnx3kQ5XiGMnOexzPIv3n/jH2LUS4W938rLUgVPtKqwFEqV/MFqoC7YMdlidFSkaKuiAk64vqscsMIdRKbsf3tHZUgRX+6VX66Ldx0UCzmVVFX1rowDXTNBv3xzvt28NdfHzyPJ8/+ZPneafvO15/+GdZKtcZKYy93bssbXz5/RvaImf/p+5/56/u/sB1hhNj6SvPC+5//Gp/5008sS2X5w8Ln9Sv/9O/+V76//5lfv/0XZBfogtYVIawTrBu+CRRlGyBFuemNXivVF7DvuHm0oQTogS739HIRDxd5804hbpm1faaWhef+LTlIV1LxUUdLd/1xjERyo1oOnlEOH5K51XIhnMZV8JObuabE2qfViUT3Q2BZK9WhmbCPAyMGP+9mbH7wZIuhmCjPsbEdexjaCtxbpS6Fdp8q0RgW7dWxI6Q/osFjPDyT+Uwcolk09w3CjX9t3NYaPDCH/RnXqiJnKDNirt5sRhcRTOWcZL6syrIIb2v8jvWN3gfb1um9n5YHUoWaiNGqlQsVzsGjMwyTnCbiJItleZUhiTNOxe9G8j05OTOZmoj+f0OSI1KuEC+OyTir8Ev7Hm/YMju0kj+LXhVaFamClJfqNacrl1YjSU1o3TzB8rqGsVoteM7PKkUxH6fiixFs/WDGK60tlGWltRW3MEJqtUSvuW8Brw6opXFvd27tRiuNIjUWeA48U0KdpeoUde7LG2qF/XjSWg9JoHecI7LJ7E8utaaXUDIkbIS/hQEWkLpWTmvv0Z2eXgSGpX+NoGVE1Tqr+9cWk00OSLZnJAih5/DEIbGKdSahrxOu5AziHx1YLBUm0dJM4bFNrxE5+9hREU5ydWwyUz1Q5jvXC1eZA95KwhzDZyWSSUEiOVIKdWZARyg6eiZ+eD4+Wzg2MdMpMdYIirNXDokfuadvzpSMw3luk29hLqelwhxjMR/ns/eeZY3n+07sO5Gc+Xrzd+L9qijTe2jyTHhJhubvnGiLcPb2Va5E8jomMO35Yj8WLNczkpV8RqoPRHLWJrQCiyTpP9eHUdLMs3DYFm7sexBo1Q4sZ8yFYlJD9t2IC5HSbJyziu5Hj+ttsRJLCSPN3gfalkiYS0FrpbSKlMrQaL/svjPoxBi9SFVVC6U2VG4ob4kmHb85qxIuygw8243qQqOxypL2AMKBcYihFUop1LoiEgqoZf/Our/zt+1vbMc7lY1RjLW9xfoSw2TQbaePJ31/MI49vM40IOdjD4R8HM/w1CotkGo/UE++KoZiFBMq5TKIzCpEZ4oeCzBaIhn3Qw000BYK2vI7LYb/UcfcRF2Cs3cSXjNKkkgpnPUHp1p2Ih1oejK9NJ9zE9Yanm5VoxBVS0SmCXs3tj1Uufu2sbRIriQpx9E+jnggDlULTpj9ecZ+jWpk6mXOSD7NPmOOj8NIg1KJhL3Of864o0RsvS7d/GxBop+oZSmBxtQq4W4t4aLvPv2ZLdq3aZGgaCY00Y4LlChes2THxkVO9Ps1Obn8zrhQtYxTKoLnELEZ007AEH4XEfwHxONg0Q+J+bGmI9QG2ceft6kbp/ugpvIHSfb2IuENUwLR0VLOIFskMr3wp3O8H7iUmEulzlDDS8G1BrcHj6SpOGzhEOkjqv319kZZ7pR2w3p0HW/LGjOg1Ol20AVaa3y5fWFZbqzlxgSdI2HIKtWhWLSYPq1faTz5doRy5u220ns4IM8kZ20VYUW0nO0GrPP+/h1cWOoNpFIW0EWhCmNz9jEw7+CDW73FBqQjkIvTnCzuPXFOKf/U9syhpDYf2zWzXovfycUBAeDIB7ep5jF6SqtHIk/RU4h87GxnXknAtBOIm5IXbDMJpc6JAkFYiksiEKH+mAlOCZ+mWqO6qBDeFJLD7nqGZAGJgDJnoF2zmwpzwtglA833kEM3pznfDzeseyqr4v3rb5ICdzsrlvn9ScKZDxNPR9bgZY0xop+vJYmFI1ok7uH/kUEGZpedDDoTCcq/u5+JZ77Q+Z6vn2RUfE2Kpw/RhH4+cDndm9CqsKqzqFM1/UWs0HLw5PHs9P7k2B6M0VkYyBhs24bUQr2v1AXqCl4EPzQHKeamZk7vOwwoNMShNaXvg2c/KLWxloq2RlmCTCulcRTlcHj4k87OUmF4ti5KpS4rRT8jfEXYs002E8uZZzVcLQjCFIoXbrJy1zeqhBz7ibFhlEq08dsbsiyUpXKzwScT7K9P3vdfsfE9WuO3G1rB1TDtHOOd/Xhne/7Csb3jvWPFcFWe2xYE0h734Lpmzu1btn0FlY7oQQVcY1BytCrqhQCQ8UY1Nj/RREUM94NyS6WQfLjfMScIrLHZBtE8uhPTM8mzdWQnNOxnsS9ZcIkqNW0LPPdABKrWSJhz1ENLo5lPyxvbdvD9/Ul34/n+oNwLARx3NJV5Ce2hQNOCmdJzaJiP4KyWohwiZ32DRGzDJwUiAudMNJoIDTimGSDRYnQLR3OdrXoh20SGaiA9pYZZ4NoKrWqqTAEbCXbEelwl3JkVPWXvc++JxEpOegoIpmEcfILEEwzP8y0vsWcmMa7yQ/n1Er1/dx39bpLz2DfGML6/H5k25oA1jDkuoHiS8aSAQpvnAKV4odAoXineEIuKqyRDdNpPC/EksqwkMoXLwGSwao0q3qPa/nr7RDXnvX+PN6kNlwayIrJSSqOKh0or22KlNsQ1PCfaQi3tlEG+AFKxwcyFnWlwWUq8/9qobeF2f+M4omoRK/RHtJFECiMTof394Ng7MuIzLnVlbXfebl+o0vDDsH0wdqOSFtw2Qh5PbEjGZK4HUhUDBNO9NC++jbzFEiUws7NqMjxUZP6bjWpmTB94jCQnBclXk9OkqLZzA/aXrD1vwxOxibUS7ahp33Ya/b3wU6pI8nCCw1HEUA+vI5dEFDMYnNXLXAQnKW4mhxcnZbY9JtltelCMI6svmxwazrUUoxYCQZktIYibfQ62i3wpnGstx6VMlIcMDBETJJUYnP5O1kFSWSXi1KWFSidzP5no0WkmqCfMyzQrPKdL27XbziQm/opKjBEJ4rafrSulZgX6Mcfb2qh14Xa/UZbGObA3A7u4sG872/6MIZw2gmKUY1VaLSzriovHsMJ9MHqgEhfeKed6qCVZJga1C2qx6SytUlsE/2VpoI3RBQ7B94H3WOtLKZS28um+8PZWWValNaGUBSnw6fYZRfn+jITsl/43HvvB9/dHXvvCUOEQ57E9qGOPdkOrFFHW20JrocjsR9hGdAuE59YqZBL/tI3hwpLre/Tg8bXbgtmG90jaj+GpUBVGC3J/u0eyP44dkUopK+qNRSs2dkwP1jUL4e6JSCb3qduJKhcJR+hzCHIKBcr6lj5jH3eUWuN+HXYmLhDx6OSaSt4BF5QTjzlNMmd2EfeDEN1oJ9Czc/6QAAAgAElEQVQXQViqYubszyORrcKyNEr7Qh873Y5IXl1Y2w0x5VsNMc0g1XFHzyRHTzfjOfoFDUWxT/m0xL91m/HDKEWpYUqX0yAyTmVMGaRn3USrLYqxCJFRcIXxhtBcaCa0MRVhBa8wJHZ7MEry1K4j4vcYgdLP57WcXxicrHj/QwfzHUZcT4Q/k6LpQO92+QyJXVjO/+8kZzt2eh98f2yIQ3GYDdZRZ+8uAmfNDK/m9+YSCgavKPEVdo8arGsJmHmeDJHol6vksEY3oLPodIwJtvnn5Y1qTn+G8zFaQSPJQRtSCkUbpDYfIVpSFNryiVaXeH3VVw7T3/l+xNCzmIRePezXa2sst1vIRW1AF/rT8BpV8t7DWXJ/HBxHbK5FC0tdWNuN+/IpFuo+ord6GFqVIpoIGVNXcyY5nj4LmVZN8CL+tElzzUXsE2p8IeK+1Ip/V5l/0GE2EkmNfncgfnkDyvUYOz8P5I4cCVxkHT9AkrFZp4Yvk7yJWmj2f2MDE7Ae0+a1IJWopFMecSYmmYREBjS3+vwHl3A7nY+xrIL64LLrut77qQwg3/vLiwgkqhmQVEDU8+tF1eF5Pf0F4JFADkutgfSlXEE1DC41W7qehUJEmPmqJd9CXgOZcLJciogrt7mSNY1E3Xnh8CB5/35cHX5fGrW2kEy3SkyizyaDOWJO33b2bQtjOne8xHqYSU5bFo6+h3v5MTiOwVILVaeibvKWiOnKxPkrGoG+FmVpjVrC9XVpBbTx2ByO5O/0aAzXUni73Xi7N97eKutSKC0J3UV5W9/w4bx/D/7Q0x48987jsdNao7XGkEhyntuT2jXQyFYopbGsjdrCWT68k4w+Mt60yrGFpHcbe5hu5ke0HptCWxfsqHhVDnP6cGqN9mxRiyRnLTkXqQeNOrkr1ZWuztBQoyESYyYsMBxzw8sssCyJq8rog34Ocxa4RdL+kUepNew6FJhdibzfX+dCzWLn5K/BiZKeSY6TRciFylUtFClIa4wRfkfmMTR5WW6U2+dAG/vGOMJuYKk3xAtav2M2onllznGkC7d7IjQw56khQlisxT04Ml0bL63oqlCbJkcwv/wKR+MlprhFcjU/oWUrrhCJTvX8sgAbtJagk9SKWRRqYiEP0UwS7bQ/OSNQttkiudGZ5BAkdtepDuEUBQgX7zeS04i/cQoy1smPe8Nvj99vV7U70KEcYcYzUgIrgSIcbnSPqrG0qQaKfxctIak1C78XwlG4ePZmE34KmWoYYZV6VcqlQmuFtoZM1I9InD59/sKyLuw5NM+2gR+dbd9jIyvJyvaAI12g6hqmRG2llpYKLD0DeVS+nK89m52aN4W7c799Qrywlke0Blq2ldSRkp/DN7ARrs1eeWvK0lZ+/vKF5X7Ha+V4RiDGotob42DgLDUJryPnf2S/SSpAcHdUC1WE/SQSz35D/aEdUVQR8zRi1POG8OyLn+2fDzp8TKVL3FQxGC5gVzJJsMzKx2T+p6xZEv0IOnjwpkwTNqVmDueTspSfMVQLEL1fx6haw3jRYZLMg6/R4/zUJN2SQSUfEzOQDnwMJHlTcz7N9GG56oiX83rmkjNaZo0TBkhxs+atLwJSHBmSBLxMnFI5V2p6U2gOkJWKy3Gey2CSvaiheE1rT83EmUhNZUnE6fwsE6X5u1gRq/HcCDIDKqX8g/rpv++hpVBqpZUV0QaqHBkYfR/0o3PsT47jQa1LcFWy519LRYG+7YHydKcQyaKn78y9RoEktxUz5/BowW/H4DChtEppN6S90ccetv0a5NmxDcbzyfGeA1wx6q3x+e0P3G9v1PWNsiyUJjwOY7ODve8cfafn0ETxENEU4n45cG5rWGIsrdBq4RuGq3P/8oV6v7P3HXOw1ijLynp7o5Yl1ocRScf7O2Idnr/AckOXkiqwwrKsNN5yDcD7vmFu/PT2JZISDzSrLcGhWdclNub9oIizFglzU3GwHgtKAlmtrSY3pBBkZkNLSJ/F92j9jI93PD6Llh4DEhVLQQGpmMvRBm4ngju9rWZVNFu+uDNlvsUyBbLgrnJEnKAU3IxjC6Ju2XdcahTnI2aUtRwSfV9WRu8wOgNnfwZlwrTExHKLOESiNVdR7KEeHDFXLNCY4HW5xcDr0f30sGlpCrhUTff5jJ+EqMWwHAsZarxWE30uKRDRQq0LaMV0wexg2BF8xzF9rKAlbF5r7FOhJozTODyGks6KbrKbOh75RMaWiwQ+MzKnajCH/JA0Y/xvSXK0xV1XggwZ5DzCDTSz2QiUwlrSS2NmbX4N6vJsc/046ybualcnvETITSSeV6tSbyn1LsroEb6XJUjJ6+2GIGyP95jQ2mPSbYwzyMWcMF2p9ayAdQ77zJPi5//yLzYJnvHvqkoplVqdVo2mLTx3SsvFYC/J0iSHSUjGS2FdGm/rii4NF6V7OFHGaVX2EYlVtFnkzH4HIwb2qaQktufbLnHePV2ZmZDexLsuBMvizmXCfSKBfPiUNn/QMZOxk/ya8vii6dgw21QTxeBM6JlJRIA54USsaQIX/yyM9KA5VaEvkEQM2bR0eo6AEH33+Bo2sqUUv2wzO8lhnIkRh0fGKXOf5EQ/399EgH6AXeaN+kMykN1mm8TjkDxojpzIT8BclJLKsuwOXze9TKK2JvF2Jjkva9svOH76UJyKhUygrp/NijZff7YRp0uyvHwKIZOtj0xyUpWm0xgUBgGduw98dMYIonFd1miD9BH3g0aTc/TwQ/HhaTQqdIuCIiTOirXGGM7Rje4ek5vz9aWEJ9eebu9jjLAj6APrnZGKNlWnSOG2vFHbPVRLtUARxjE4fDC8J59rxvmJFmRamZPUBcuiIFoT5lDWBamVPnpsmCXcc1uLgZuX7YcjxxExvD8jjus4Cf9aKmUJZ2xwtr5jI7iMtVSeRxBhvYYMfq2VfRjdjSKeMH62MWebapo1aiKHL8M9Jwl5mBJ+JFec/bBjxohQhGTO7uHmD1z3d3798Is/ijk476U4n9MS0IOkehZd5kFop0eCqO0NqTVmQ7nR0i9rrS3aXkfHx4gZkRBiC4/ZU3PB/CgcIPatNKBUstDlin8jH4NDKxFXSrb2JzlvRh1LzoggOacqeTs6zVVjnIOXEs7ug+QMTYSFq9gWYo90P89tEQIJt9meuuTvktv3heO8JDnAHKbtBGIfcvq8bv/G8btJzr4feI48cKL/a94Z3unHnLQaSEFJolLToB8VlEqJ7zVNjTzaV1ILUoU+wlditriEnid7ILpS3+7UsVJtQXM0eV1XnIXt+eQpjf44oqozo/WBH2kSaI4sLRyTkxIV9KsSlbRcngYyF6xz+mn4bPyZhLdKE8qAtsZ9ebgju0Iv2DjoYw9I0QENOfSnzzHAU2+FUZzH/ojJ6EXRmwbn5znwQUovnWFH9laPnE9UcpP108yu20wGSgbGTI9NMQlF+uyxzs5zqLmiF/3RSI6khLvVgmpMZI69+roxbVhyjHJxWyTJfYxYW01PGXy7hdfIXNhHiUAi2aqJ4OKYBs9pWGdUQdvAxwZ0hh50jkQe46nmLJgA8ySC0JSsq5yIXe+pcjoTywh/U+VEmRwYYToU5sdN/k2MkC1ilHLGrdNB20yiaCyZ3XDxdBxDOGLcynr/ATBCuCqgQahHLPxSXIM7ZiMI/D+sgB82mpd/yVaVajmNCk9Ojkyo+WOO9R7FjpdBnh52C8t623dsCyFAa41WlmjbSMfdOPyIwqvk5u7KfVm5LwtWg783jfu8LNH+Gsa+7ww7GBLuwdp29PYE33EZ1E9vlFq5eYkqueTwzkXRt0r7LNh68JDBGI2C8P7sbNuBSKEtjZ9+Erb9yV//9isuwnpfT/WPNoEyGKtRFvj89QtjDB7HO2UfvG+F1pxlubM0aGsJYYYquqyIV25fGm0p7PVAdcN8pwBfq6FNUfnMdryz943728oNZfn8c9yn79/BjLY4ZS0snxrWgvw+91uxhrpSjxpO7nW2TQjSvI7cFKNl4yOKZAGqSXINP+7wkV+inJOg4+aPOOQTdZCrWJqIuV5jEECTJ1cRrXQ7MDcePZKb4Zp81megjO97tJpwoAZEr9EJ+Pr5RmuVP/78ld47//qvB92co/ewK3BlPwbb8JfbUzj1stnScmJwZpU5sNfSeBXCkDKSFPL+2UanKCw5E+u+RFvevCA1xCC15jVq4MWxJa7rxg50YDByzmRwBJ0jFWKGo8pJS5jlqjjIUKRHrI5aUk7A5BU7mymOTpRZ5HQRj5iaAorf6Xr+A8fj2BBVasCQKpkEJBI5MuPz6BXGBO2oJCWh9SLltMoXl+QzhFqkn88/yVCzos+m2wTa/cqeS4kgvdQFaxZ+I2V6yli2e4js0OdiSKRDXt1e515/oQj4RBOubHlWWZC8IeXlvb72Cu0koJJoxUSOTiNDjxkiTs70wnOWlWdrxLERXKOY6WJI57IDd1IZE+96jiuYtt0zuZt8rPn5ZjvonEb+wUe00TSJjZoEWXs5xxeCI+d7TtnmyeO5rg9kgjEBG4lKXdPz5KzWZNqde6JuPbkcEwkLvcQk6kVulev7lG3OJEKuu475uhNShbC7nyiKXmhKPl4TVZs8m5NNNZESnznRhaCcaMq5RuP6zxlxQZbMz5owzOkmmvjzaTGvWXHKDBC/4T/ls8yU7Ydk5zdV0gfX3kAM4FWNAcEgWXCFum1YD6XZvO9EL8TPgTQGnNcxlB6FWiveJDZt74GSWLR6W8lBq0WyOnVEDSkGmi2/Aprcv2kGF/K3QDmkkGh1tADEY8hvT0hftbCsMKycpO5S08VcQnKrKtnHcpal0g/JNtfBsE71kfwGzhZwSeTHCO5NW5RzvhFRSUsJZEVLoXtBTWlNcWL2W5i3RuxexNFWwvKjZxKPRAFISNg1fU00+R82cp2onaiZZPiZlpNzw/vQ41wIcz/ITXXGfvMJp4V6d96nc7uVl9YJnOi0k87DvQfHyYTeB+/PJ33v9MeRRoDzDIQQQjU80QBu9xUh9oxT3GBG2OTMlv5LGPJJKL7iSci44+3NBOiM+SKnmgmfKG78rki0uEyzqE/eoOTvGJG4jhm3zWOPxxk9imhNblxQiIMaUbKoEDxRYV5iaaJmcJKJLzh7choDrQ3u1hVvZywLBPz3F9E/JB4rSuMO4mjpkZCM2IBj48/qNsmZgeYU6ttCqwv1/imTGGN/do7nk/F9D0l6blRLMVoVyucVTBlbxfYNf/6F+9uf4BY3rBelaY+s8/YTRe9sx4a0wvKpIsXZtme2uEpUzQY9GR11qmVyRMBp6T9PsANJoCKdhfuxhWJq2+gpGyceBimh0zHQI/gbmHPTFQeeT+epne/be/CAasxvcXG+/+XJsR1sfWfYoHr0Iq33qHLKbOnkAEDx7B3LuRHWFghJH5rfT65IxMUQECV35MrqPvyIm1coUk7vmmMMjr4zhp2JSUHZLVpPh8Wg0jll3nS25DRRVz+TEEm/iZIVgFlc22OP5wh1VSBc/WmBQh6D0Tv9iJulPiOpyBWBu2ab0K4kzIjzn5FFfrh9pm/EDECBAPnoEcy1ZnAwTMJIbnJ6sIHbgYpTNZxIgNOVV0ZWQhKJmTKImRMV1QqUSJLxICWLgEyzRcmkOs+/Rj/c5igKrrgyW2FkwoR33NP5d46LYOr7fpsI/Y89CitYZRwCxXA6MjrqB+4dl8FaBZcaVagIbtGKaS0Hch4HEEmAS5h4clRcCrquoTRJ9EdtYFKpzzUKssPBCr4LvQv7EAYNpTD6L1h/Br8BxXoO5tyM233l9umOWyjUxtEZew+DPp3JgdDaEk7Myy1I8wqf73duy0L1gnThU60Mh+0B2mNBujljF5TKrQk/ff6E9C/85W8xl+qPy0/c18baVkot3MvCUGcrYc/fagMtVP18ogGyGSLO27KgGmTryQ2pGKsddIxB8DTDEPPIdkphGnFqkUQVsu0uAZ7UHAkBFtfgg48ouGoWBQt9dMaRg1pt4AxcjD5m8RHt4FMxWcmxIJXeYexw7DG/6a9//sZzP3jfd/owns+DMYzjCOLV1VoJN2NR4fm+02rln392Wi384UsgOt8f7/RjcLxvTC7dLArr5N5NnyJLmsiLU3CYrs6Uyqkl9pPJmS4ulAQZ1GJNDYfDlcUFevhQGcreDRFjacZw4Xt4kiSnKVCbakFT2dMe49YKrRS+3pbL304UpGYbPQqIMWKQqRuUmmINTyRpbTHTrU6emZ88YC2xpw35fZXn7yY50zURMjv3ScqcX4YkoXKYc05uVckRDbE4jHDh3I+DbdsxiSRnpLTJizNq2kWbMrZGHSMgQHkw5IlRQJTaw/K6tjsuynq/4yVuKsn+p3oiAicq87LA5+YouWv5+WFjwxzjlN8GhBl9/DGiWrzQnmwhGWdi4eSv5Utux7yFJaW/mSSJsT8O+tZ5jo1hFkkOHgRXgWoxc6pmJh1zmhK9OAnbkWRaJjmInryoudRPEp1dLbiPrsQvq4BE0Sw2+zGu9yUJGZ9jHzIBDVTl+h6J72dyFMnONR8NSMK5c2KYSrx2OtWKhWrAJEiGEPyK8xxmsvRblCkzjfN14vgN4uETWZOXR/y4BucHu6aTX8TfqWbCSaMwTeR0JlCBELhlkjRlVGeWHo+63qbjTH7RfIi9vK/8kh/xnFdUx1/vEzirgo/kdoWPVZpDproNj1k/E9IWLdGiS+RwcqVESxYuE/Z6QXLzPPmUWSayNrKSRhSp4XElWrJzkfefARYodVFBSyQsqKLSgIp7yddLNWeu95hbNNdWJgW1BGE316QmGo5LtN9JdV3RTI447yVPZWIphdqWsM9wo7WwviiqUQR4on4are0jPWEcThQmEnjSb4q4J1x+cCgPxRCTrRo/S1qCi4DpqXKcQyinmDYQ12xpfXTh5RNNyO9n0frb//xCHsKya+53sVYsFmF6pjn73jmOzr51tj1akpHc9ETROe/VGYFnd8NHZxzO9+XBsjTedEmwSSFHf5z0ibm5TPT4/HE8t7x+nvPnM3F4ub4zdOT3561xnpBA6roFmr7vQSXZ9sFA+G6RaAgja0+neoCYhxsmjlmllck/jXa/SsyT8+T9nL4+c88+0Ro9PdSKFkqpGYkCMYrCy060+jcN+B+O3yce97jxTQd9dB77I4avjZ1u0YvTtH13Gk6NDbko2gLyNHe2Y+N9/8b3Xx48vz2pma09jo6586nG6v+XPtIZV6h3oX2Ft29w+/SEuqCl8c9vX/m83PnTv/uJuwD3GBGwj5FujCn3nSfM5IKULYC0MVsYOgO/XAuodxgDtQgukdx0+v6MoY4jWlM2Dratsz0ziywNqwGLv287x+j8sr3HpN+nY945bD9hz7UGuW+3LTYgm6sv22IS5kxFQ55fE7qes7IEaKUm+XM6SUegL23J389g5NA9x1rI/4Qkpy5xnoczhjOOgz46xzHORGW2frqNUykAefOZcxjJvi349uQYx/n80m9I8qAQoWsBrUhbkk/jqAcz65AQW35a7/Ra+WYbw5z9GOEJU0smOXpiqCcBzmbyGL2fiftM6bfAacTlpfwgj/X51yTDh8rAOPqeiZRQ60rVxrCKjJztQ4zDIHlLtSpNCsMN9fR5kTDIFBV6z+BBnMdunTE6e08TOpMzSR5j8mwmBJ/VoVm2FZO/M65k3TWTce+If5xPTlmWSPb6wRxtQRe0g1mNeyaNP72uESRHJkIek1YHoQq1HqZtvUC9gdYw9zR3XA7GGPzyvgW5WBS9v3H7usBwtj6C51crBD2Ht/sXtCy0799oVljKF+7rV7T+kX503n/plHoHWRj2jvug1nBJfn9/cNhBW0Me//bpE89947lvHKJAOSX+ioPC7evK0gp2ZBFTNo7uPJ6GlUa5f8bff8V1IF9uyFLY7R1MeD4dWQvlXvn2ePL4/kAtNo51zflG5Z7J/jvdheMZxeOQAeNgqdAfjm+G9Cga1voWycwSG3TvBydnI5YO0sIcliPWt+/jw8Hl7keY17oBA/UjxB7kZHiFkYmuZVI2rEci0ONeK0Nj+OZhbA9j3yyu1+j8+h7jGY49pfQGKkpbGoPB4R2LLSZtnJyj72Dw7duD1ir/9E9fqUulrStDnJ4JFnbFIiOL+mRd9unb436KWswCyWklODrRVjKqhBBHc2pANICFkfFKM4ty4G/fNx678e15MMw48tGgzPmJRUN5tab698j99baECOc/644S3JylFj7fg59WZ1EuMdh7xktBclpBiakEpbDUFg40A/yIVu1M8O2HjO3vj99PcrISPPvfPk4k5+RQJAS2rgutVZb7wrI0liXtyHNjsMw6bUJ1MrUgjvVYVEeXzH73gN6ArX/j9gRqQ0tF3wfv68Z9/Ym21PQMCfh5zIpfEmKXuOiMJITmhjkVIz7N1Jx0uRyx+oadTHQbsUkkVkaWkdgImK33Hg65okDFEY7+zn6EV84YsbCGDQ7reQlDTj/nxJyjG8gkPRfPHDwoyfEoMsVjkZZPXxnJzxq8V8ckpIGOZrUU4SYAqteK/6OOVB1YkvvmOIsTKZkVDi/tKz8rD5npRMArMfkYv7xg7AAZ1FpBFW/xmpzg14VIxLqIaeMiQWCWOSFcpmPnbNlIQtR5HU51Vd4akyt0fso45nu+1FwT3ZkP8NOAbyRi6BDmjVlWBR8pO9vZwvNudAzZQ6loE2omZnGJCjFuJKz4LY3XbMLwyYmT/ExncnaiHhOp4gdE6uIlZ6U4G/4fmC7/yCCSH/7/OvTXM5EhNzHcGaNna3KqokB7x1UwdWQov35/MPpAS6Ky/iNXS4rSRw+Pl0iB8RLnQmu0cW63BZs2ABJVbky47qj24DAkYhtVbapHRyCRZh7trD5ORFfTsDHUrTnb6L5QU104emcfD3r6taoarQltIkI52FEnitMNqRJS3+GoCfTgMu5udBVEv+Eo5s+8B9bksg18HFg/eD6NfQ8eXHA4Qm6yaLSBagmCaO87RsyLC76IMDKUfvRwTuAUh3hqmc8BqXm/WSI4l/sVSP5OSLANE6UP5+iDPvkyPpVFESdikKum+mc6K/MDvzSonVGMuvjpIfTcdlZ3lrpSXGlaOSwEPyeQI1yKSfJc+kSkss0vyYVNvtgEgOatTvJZJPlJlvtMUCACZd66sx3GMXIOojArz0TjEoVBXiw8kjYw4j2Okbv+Ab1FHCzpGK0lEUT17FaEkuu0Cc7nSx0Ncwblq6fR9an/68fvJjlaADeOsUdFeCY7fgbAZam0pfGHn39iXRe+fg0jq/X+BbpjjwPpNV5KF1SdVitNFfWQ0R3PdyxlbtvY+OXxnf7ujD8bTR+08q+nauBfli/c1zv2OPjppy/8+//w78O4qnT2fWNsG8GVyLRPo6p2B8tZLH6elPgMVQtHPxh9e0lwIina+zPaVBaVq3rHh9F3wvhv32nLnVJjSKDb4P37f+a5P2NsQzLPh8cE9bn5DR2ZBP5IDJ9sJ2zgOZqgQ/az5dSJCelG76S1P7GJq8BwCkqhRrstF6eLhIncBx+We0XYzoTaqdugJ0fFPUYUuKevUlYkcWhwOXNDdYsZO04mo+4MdhBlKWtIad9aJL0itKqsWvGZoKvhBVopMISjQlidRXIgOh2oCdQITVlOIjl5Q88/M+fJemBm/p5kOcheSCTXZ9jMCsQHw49sUynDUyY8wljt8Eiu50BODOSAsXGaaYb6SdFekqu1Z5ALlMU9uGn4JVeeLZdz+Gcm0m5zrnZeN+sglTrdwTPBEQGlfGiubCk3nvPaJEfOKJatH6VLzC/bH4H2LDUSvMfziR0de245JUjYLTxL6hZbzv/1H/8Tz23n07Kw3Fb++M//S4RW25izit7H4H3bKPuBGtzbz6EYXCttUX7+409sW+fbtx2XJ9h7tCLoqBYKwSFSDFq4G93lFomZK/s+GPu3QMl98OmrsKw1EGgb9P6ktsI//fGf4v5/PjkeG49vD45aGLWwLIOlOV8eN/a9gg5MnNWj1XbswbfRxVk6KI1tH+zb4PsRBoLD/oa5sQ1HS+XT/S3s+3WwHTvP7ckxoA+n6IJKoaUj/P3rH2itcluVY3/w7fuWxWusLJXwN3GbIpKPPXqiRz6EpNOercnRx+k8bxL8FHPHx5F+M1dbZWTtbD3UkEKIbJoGV6SW+N2uV9KhHm2i6hKzpPLOq7UgDu9ZeH/79Tu23vipfqJJZW2f+NXe2ekz5AHhR+RqiYSEo3QKP5M+GPFPTvOWzG6qhO1AKTn6Yd5JiYCXyvvx4LkNfn0az31wjEj2lxYBb47Iis5qFk8llX1kUZJtvs0i9vYedgjf6wiSfFVui7JUZSk55HQN4rXUoA2oBg8K8wQMnGH+Y9v4Hxy/P6CzhMrEeniJLG3FasuqMIL2589vrLfG/e3OsjQ+fbrRaqMtNyjGGJEdmgreQ74t02ipRgXWvIaL4ziiFeQk1JayYMseooa8zXzw57/9hcN3lr8ot/XGp7evQVDK2S/m0do5PXHOHgEgNrfM+PNEb/LKDU9iJmfLwq1HwOp7mMPZoIiw1BZogDb2/Z3nc885XoFeBdGxoO4U/bFyOEYO7BBYStr8J0pmaMiEtVLSp2Ka6GluTCOvg9hk93NK46MavXrMs++OfXySE1byyecyo4/kO5zv86rMNefBTBsc0RJZf6kvFcI4R0WcfBXxsDh3IAnmxQsM6FsgH4JjR7Ys4JTle1Z0uXWfHCtBToPLH/bzE7L57S4vKRyYCYSmH8dvHpqVZOA88kInyqhxNdpfPg8XzyarMCTnwxU9k62Jms1J9Wcvn1llXnytyZG6PtBshL6m3YEkneqGWS369VsfcUzlmnoByXlhiXoNc2woj35w+GDbwoZhk1AqPh9PvA98D5GCro26FJZF2ffO3sP9eHRDb5r+TbGJNK2MAfuj83wcbI8D6R115y+//oXH8eCff+9sO3MAACAASURBVP5DeOO0QXPnvlZaE1yyMOwDpGOj0LtHoBbDvdMfD47tYM4TCqAx5g4JgepWraiWsJkQ5Xhm233r+ERw3Fk8VaoqrLcWTumiiJGVOVjVkMyP2A2jRVtPemIvcIye7Zgo7OoutBKDJq0Pej/OkNl7tC/2o1CPxvr9xrouqL4lgP7/EvemTY4kyZnmo2pm7gAiMrOqD5JDyn7a//+PuCI7wtkhOazqOjICgLsduh9UzYHqZmeLjHBjvSU7j4oIAH6Yqb76HicvtEajWnfVER7ZM/2OPvLwMZoXmL4+9kDZqxeX/WFR0CatobVAof2JSUJwLF2dRlfqtVHHoMbarslRuxbNm0daPBRSvtiPA5ZREU5L8WswGsN8BOYZZYmUE2VkX8M6x9p3PJPR6E6E1QLZ1fjZjuT50501TACPuBmYRc4wGBEkukVxMwaseXEwYfH4i7r1UAZ704AILQRG/lehZF8LW/dYFMS5kL6SyFEw+goUY/AWn6N5c77LjorQItaBEWjOAPcCGH9zOvHtcVUSEOewqGRO6ylsP8axaH//u8+cLytpLZSS+O7zKzkXkq5Y6zR2cl9I7YyKj5z22zut7fTiFfCaFh8J3e8utzwIVUbtwcBvTmyubVDrnf/5Y+HlvjLWX/nuy+95ffmelDNLWegmdBSisgTn18yNZeZl+L7iRZXNpzbUAhNhmF34aJXeKq3e6K3C6GRV8nJC8opJ5nq78vZ29RyXocF38FiHeS/VvrPVzf0Lej9M1cric1Kr/ZB6J0mccmFZCmUpJOUgOCLCfd99E42qZoTi4fHZ5sUP6/ej/P7YY7SHHLIPt3Z/wIuTous8lxRoQQ7TqZQebs4EbDkXIwu+0Yixo6X5M1No6QqjNfZWQzkDvUahFRvkiK65m6cDi6VjUXJn7OCl2CwM4EH25XhQZ7YaTJh4ku7BIoz0ga5G4RydiIT6RCYZnkBK4/aTIEMzkRQFmShOdm+Ug1gcY5sRY9Ax5crihZdHNEiMY+YYbr6vOW+PxoBZ8BlY8fdxfPSPvY9smh1qClSqMp2bW4da4Wu7cR+Vet89PLVWRh/UW3UYse2sryfOL8pyFl7PmX//4c7bdXMUpcGS3F24SycjrHnhVuF6rVzfNm63O3Nsfd3/ldMp84f/071o0s0DFAsFzUrXdhQMNpqbf27d17DeGH1n+/oLvTvJeJ7S6UGkJsgYrMsp0CovYG9vu69btTqR3pRlKMWcU2IJLpeV3I1krvrca3VUcnGb/9Fm1pCylMVDTjP0IdzbztaMertjXVDrWFlY5MzonVb3GNPA1jp9EpJTAu2czxc0/zHW+0+IXbG+s40Rm3wD7ZCNx833MUdt1ScQSXBFZKfZTh2bu+i3wYzqbMRIfR/RiyayukGeJkVzIVlBR+LtdmNvja1PnukCNthHPcaefbi33DPBmWhOsyS+XC4AvF2vDOu83a+c5UQ5FUrJDApqnWZeTJnNdkSmIweYc2UGkSI+1W3CMf4u6lliQKxhcyyUqN3YeuO6Ve63nb35/vi5nNzt+JRpvdPbHYnk9C7GUEHTgAj0TCqcYtTa26B3kO7FVeZp1MxD/SUMRjVo6sR4hX0jRli+hmV9RB4R3nmRfvxXj28WOTnFTHF1qeqyFtfPJyebYcLlfPbKvWRSflTmqglLgq6DPJS1JfZ9cWOk7Y6nKnu3JHmJBTRhYxKaiMtHSBL9Qy7LSsmZbGC18fMPb0gtvH/+NRRW5dG5xntM2Rnac8wzIXd3uvWNP6mnkNuEK+MGcN+a6XarAYQkclrwXD6htUrrG/d9Z2uVZu3oBBFoTvrBRqf2Shvt+G8p3o8jE48H3u9XwVIiLYXz5XRwVyT7hpcsR5Hj/IrpdknkbqkI1vU3m3HrLpH9yENSQkMRxQQWJlwrDp0epLMpjw+13IS0H7NmnFz+hEiMKF4nV0y0UPLCy/JC65W93hC6IwAIvU2+jPL6sjLG4LZXr3G7xbl0kuFjPj/CL4oIhvNbwhPNH0DM9M0g3vWDa2W/+fu8/rNjd9GQe55MDs6EpEUzU3oJA7WGSkbSQi6+ANa6u+JIHjlTD75TvFkehQ0yOUZP720SkCcsZL4QTUdo96Kx+DW3go85Wu34UGoWnT6NrtWVH1vt7NuNvVXe3m+05hYBGOgwlqx8+vzC6bxyWU6ctJBRXi4XJC388svGGJV7A5rwKZ99IGbe+csQCsagQp6KR09m7vudROGkZ2RR8pIZKI0U1IGGaaVJJ2W/p+qoDCodt7efPBUbbgOQUqb1yn0zUheyZo/wEENqdSRr7N4k5eSIUfWMKJLSunMC9+s7MjqrhWPzsvqotFWSnsj57DA5g10qo3U0r+SWWJbd5fDdUEm8XF7cCLUbUhv0wSrecdfm98Lt7mv7p8tOypCWjCShlIRICA9wzqTao7D7qKNWL3JG8zLGqESetk8P7KH6atFspLDyWMsJFUjJYqxTPMl+wN47W+vUyOlr8VSJqFMmnDAFYzL+5riMQNEMi+DcXJwbmCTFmMzFCTllNzVlYEGUfwZ25xrvYLAdEvIRPYuOhxilB4o3fPaMi8EH1QZbH2zNfx+xWI/gdh0ddSxxY7jdwQOdcdQoMScO/pwMBpr9+0zd96qUxHrKLEsKDhMuOBGhJW+4Wg9a9FCyzO49sJ/wOhsSqO5fOb5Z5KTYgPKiaM7kUyYXlzlKD+fQ84llWSB8Xfzwh8KyktTVSOTBui20vSHJyZ1Tdi5LwQuIKHKYe8Gjy33caAtLzmQMaueXHze0F66//8pyunA6f8LpzcZMjU6SPCVdOBZxESc/uQyvH4v+YaVtUTh0r1aFBLjvjqDkvNK6Pwj3bWPb7mxtdx6OOVIxgxBdxt2x8eA2zRs9xUUb4Yc9t5mowhiqpJI5nVcn3A7DcpAJR4KhwYV2h00IuV7Ea7iiJp4A09jEP3Zl0TAmc/rzYxOV6Vo1+xGJAlqEPBPK1WMobLSj+LV5fuJjzNGXA2CCSKbklU/nV2rfuGcYtvn3NqWbMMyLkWVJGAO5KbV2rtf6QFqO0aFfzzmCcrQGJyXjlvDzeKBOPN7vgag9/ZJpIsf8RIdHxxxnKkAgd6IJSQs23B7e1WQrOa+UkqNI8vM7DfPme5Djz8eb9H+LSmvyd44CZx42r0+KwngEOmmoxjP2QUevDZNEkjmMFUdp2sOaYr/d2ffK169X9uqWDyrCmjNlWXn5/MK5LLyUk0P2KC+XM3mFUr6y78bW3JH3lFeE4NGJP08ZA2lICUdYPLql7xsDWPU7clLOJ2VvwrVGgj1Gp7r8NZ98sa6+wQ4ZXuaMEaRoQ7MTedtwT53UlEULn17OaJwLaAg7pIQsxthd4FBGIVnyfK1a2d9+RvugrBffeFQdvawVWc7kvCJp+JihXRmo31cpsZRCs07bvQm8nC+0AVs1ht0xq27vj2FhdHq7b9gQ6m3HToqsnkNXiiNwvrULZinyuj4Wyendx9xtVKCDNoaCqQbabAeRuMX3lAQ5K+fLenQzjqQWajcqPnHYqnMNEXl4DkVh4+xaF5M44ZjjObRg1ZpFkZPc103x5qLV6qNHTZ6biGBNIipJolmSJ4DZPGLBLM61TwZK8DmfCdIEdxEcEa9jcG+drXe26ewuToweYqTprzX7oCiCphf0xGcmcjwLMEXQEig17oezronltLCu+aCFdHUlZ1MLHs6x4j9AZh4qULMR+r2/3nB92ycnOuSEFxlLSeSilCKkxY2wsgqMTh+b77d9ZaiEu6wxkncveaz0BiKF+1YxErK5h8B9VH+YktCjq1fBE7gFYLhRVlLOS2HJJR5MWFQRaXy9/8jnYnxePtOH+GYem9EkXYoUUCd6+d3gYwTVHBB0Y1KDn9EPR0qc9JizG4z1IW4EKG6/fbvdqM27wXk1POBtxkSE/XR0xzmM8TJeXZfksm8b5lWzNZasvJ4L51OmrILWzGhQbXeCbvcOobfYkIc9OvRORGZk0iKkGMt4mNoHt0+zYIvXna7V/giGC/VENGJxcO+5WQV4KCXRvWs2kml4FBm0O2aDvd8ZrVNqpubOni4sp8Lr6fe+Keugb5XeGtvbz4y+syy+wSxSuW2Vsc/hHkE8fCT7dhmhjvFjUmYmcjNHSxJoCgfL98EBO+qM8O6RKL4NR3CcwefnIuXirrjl7Pe/nrFRGZaQ5B1yykbKHW3ejdmQUGxkHJAfcf6fUJvjeciB0kRnxF8qMOw3C8vzfTa/8GOOdt9JkiBn777HzZVpKgcSMXqltx2xQcLJ5aUkvnz3wuWy8vLlhVM+cc5nTBqmDdWzO/sugm2de62kfUDbGGJc7cZdM1tZWPOZl1fl631na91Jx8D2dccWsHX3DUM66MKqF9JpoWnhdr0eMTk2YnTbjdE8rDVhSA6DOIHR9sM1vkkDhT0K4yodYYSjvLfGZcmsi9Laxn7duf9653avHqOAUs3FFzruiBrrWSipI3Kntjtt7F7Kp8KSTiRrrPpOUqEkOElmxfOV1vMJM/+5kp0waeJJ5NvmvMU//fojS7twKXAqzje5pBMyEj/dv7K1Hf3/YXQ+EY9jcVcXuW/W2MOk0ALpn/OgcsqUUkjnxRVx4ikASTK30Ri9ujAmJ2R3UmyPiJoRJncWZNlDtYexJo+7EPHoo3VxMn3dnR/Uxp0Vb9gm2sNwUYaKYNMollDPCVGUJFTtcDeez/P8fcR6VJJbbEzFqyu7Gvd7Y987bbhbNYpH2iT3e/IiT0nqBaCGirkgrP4CjojF2MqLjMFmzjXLCUoR1lU5LYm1ZHqrjGFkkYME7rlbFmiymwD6pXMRwpHlpYNvuR5/m5MTa9k0u8pZKUkpOazTNTOZEqOHQ+3I2NCj+x06yCTvDJaV0RPLeqK2Tm83OoNm3Q2EVLAgT8iYHfED3vcT5K8v6ingbhLUuW/vXF5eSBloFmoWAi4ecRs8ddphsOQbzbPK5HH/z182YXsU1YyqufTNQMag9c7eXLs/DtRnjljmiMKOAmdWuUkcpnNoL4U1vM8mVZzsdyoOiack0J1MXYPab8NCyj430/j5psx4gmmmFPfmb87pRx2OfoyjcEyqgTY8J2f7yT4g2AAabKJvoUSTyIvRBEjM+MUXkRYszN5vtJHpNNKy8Pr5xTNYElgbjNa5pkavd0qptNapV4EOb1qPrKq9O/FOYlE01+gzoxHizvQFZ0qNxZ06sT9HcOJcxP+LzN8nHhJ04dGPf5/kxpwLqoWkC6MrPUVnqBqL2fCHY8aqMFGmp/t6FmIcb5wjUoIntRgckT5/4YDMExo0O8QPOlz8gBcQM+BSFJsFJT4OtuHDkKRQRFlK4vKycjmvbm+hCyWd6Nzp1tG0oJp9EpgGre20ptAaIw0qjSZCU+NSMuekvDWjNwI+F/ru3WQtLbhU1RlhIkhR0qLs983TwAMdPFC+MVHXGCWkdFhaTJlsF89I6OJKk3aQov25EISUfCNubxtt36m3jXpv3vmrHjyTYY2svsn4v1Zav7G3O11OIBllAUkk392QpCwpkxAvHBdPI6cbunTQwRbhpFX8vV/vV3qCVM8s2Tk/RU5kW3jbNupEBD4YVT6YrrG5eejpOBB29+FPTEoDClqUVBxRV9UQJ2WyFPZtxJo0GzLxa9ydxO8E2afPOD3Z8PtnSerWKJrIWVyYEgTl3gZZNfYgm9AJWPD+Dlzssa7MR3XuJQfydHzN3A8nkus/u/eQsFf3+GkhAEpxDgL0efyUWJ+yeisF3VVjqCu81F/VuUGOaI4ozmYtkYv670mhT0TKL01tj5gkQ0JtKwf/0af5Iwq8366xf358s8j59HJBEScqlcKynshZKFmw8IRxV9ZYe1rn/Zerz6qXgSYlrZ6+m8qCDM9XWV9PjOJrcq2Vut8QES6vJ3KGsW/ua9Eccp4k4GaDbb9htnNeXf7W9p3b6PzQjSIL/Y9vD3kZ7uFQspK85PT3OuF5nTLY8AqgeJGh5uGO4HCwiDPaNZFOL/RaGWOn7oNt23jf7ny939ha9VRrnzM8YLscWSTaPWKg4wQ2hVwyKkqLBU0jI+xUTpxPK58ubsdON4ef987eNtrobPf+6KgCF5xyZI2fV8dgH80dKMV8HPRkUvchhzpR71QWLPKGDGWIRUGo9BjxpdiYSQUvn93Z10bEgKhLH5MAc1Or/sCaDXqv3G83xBLbcuWcV1ROnEvmvGZv/M3Y1xOjbwz5mfu28fb+M9oBubkd/ZrRnknd0Orml6oFZI5TLXhUfq+ZSJB8/X46Hrrj2ZvQYBTsejDEjv9kYfhHbHqD5o7il4WUCiUlR77TifmDRt/Y75W6u4phzuP7wetJcV844dhrmglBuUmFBalI0wL42MsLTZ+um2TMwvY+FA1D+ze7p//qo5SCmHPXhjmLovbBvTu5WCVxPq2UnDhf1oNfn1LivGSKGFyv9GzUJbGuwstyoq6JinB6UW5VuF2Nem/8+uMb+Zx5+XJBu9KHkLsXU6+vK+vLyio7S4Y//uETospP93fu74P+687LYnx3PpFOK2VJ1HxGtPCWGkZn0eIE1mWnVbhOd/TwVcnJ1SStNTLeTPWqWBDUswrntDrXUD0SYmhm64nbLmx7Za+V83l1F2RN5AQq1UnuKHttXPeNfew0q9yr0bryfr/Re+N6f2ddVv7bP/49aylwSuRhXIYhNFSHe1Kp8FILVjKfv5yovfPr1xv7vvHz//oB+/RCaZ94WYoX7LEx7XU71IcfddhwE1EDujX2dmcfjXsQhgd4TldST04ReThIGM7Hi8wcScnVT4vTMhId643RBnsfgYI6jWPJia4zt8tNPHOJn4277Fv36I+9NvdgG04O38mOyGtCgn8i6sKEbJ7w26M50uTZa+M3jVWMd6Jw67GG3bbmz3j2aOwt2oUCtMl5NBwwqOYBnTqzIwddBx4fgyuJMw4yBE1jM+eXE4TrJMqaE5/OhdfLic+vsY5hjmCGI71zLF3G36Qfa6Pgk5kDII8pyVOs1X96fLPIKdkddRd1h8+cEkXdQbGHskVnR4p3iXVviAxG30g5sZDpJTs5yPxN5pIolskle+deY+adlJHdc8HRwpD9wiGHc16AnxAgVCXGfWyeVTQaMLkT3sEmdTm2w5Bza5kVKU+V9uTD8ATVR3k5DL+zJeBZn2G25gZhNTxxZncrgUD46M0t1GUWUzbt+2OWqlFEiU1lcBgqFkp2sus0HqytOQo2VUpGcKHkILO6OaBEU2FRQMgxWvloE65nVMJM6IGu+TvybtHVQGN+A0AY1oVKyKKqn8o4USe/RnibjAepubVGq5V636h7ozXnO8gIS/wElBMjJRo3cviBiIb3SqB9KWqBPoKT421xjCHD0C/e72FuFkXORMSJ3yebaEafHJ3VU3f2yMgKqHsMZMzIEOPIvjk6RsJvp9F7YqaXP+IiLDrSqWN47nfiHjlWhwcRWWx2eSFT56GqetRu8476mGO+t946jpuEeVucKxWh5ExKzp0ZuMzU7QciL755xtWIgjXnhBZHUJfiqqrbcD7F7bpzUmEhUURYg1xpNsjxOqt1lmSsS3akZNype2O77ZRLw9aOgmdEqSPVKmHy55VudLnBoToUfMHVCC6FDV9vbMwO3DcuH3lnVAqGF2KtGXsdtDZDfufa6cjdEVmD0npna0IL9KBV7+T3q0fZeIq08vLy6plC6uaB2kGzkLJg2dGlJfs4TM7qOX5vN0Z4+9Sl+HMozVEpi2ffnp75jzrmWq9+Lbv5WurPeGzsIfnxhvDxmAybbt+zYfG1W5M3rCkaKDM7uDmzsU4RFuuO84Hmh5eWhOGMdTkia6axbTcPd52oscVneEiv5fhY/nLKzMR7mFT49xJ/8yXG7y8VI9lE+fxzqXiLfuwTswETn2CA8z4NCauPoHOEisKpro62+LZmsX76+ruUxFJ8TNXis06UaCoLnSfk85fJufT3Ik/r6oSkv33Jv1nkLMU30DXn2Ewqs2zykUtiUB2GCtOjvd48dGu/k1NibQtr2WinHUrBcqYsHVVjnDIZaNfkjoptYxnG+vLKVu9cN4ua31gD8vy0lDASdNXT15jV58WQXZG6kteFVIorUMTThlUT5AJxc7kpXfUNIGlsFhuoxyOq4eSt7vAYWV1KHn4a7d7ZbjvX651WOzYinwYhJ5eDk6KwsUHC0OIXf0LBihxoBCHgyic4nU78/R//QFE3RrrtV673N7Zbp23DR2Phu/CQK/siprj/xhzDGRMWnuOsD4aHIWbKRlq8JdKkbPXOrd6YI0IzN+FrIbmsw03tWostPuBaz4DJ/iuk2VkWhubIwRps9zttf6fe/5X3+zu3+xuXZeFUCp9fP3FaFu7bPTa7FvLGE9obY+8uIw/jyJyEPl1uQxbu4OzgEbXMMVqDWbd48T3dho/1QoP1NSaqOKZewTlGh9GVOcfEBm9f/4OkhXU5+zkIyNtHTN4FjiiUXJk16LR4I0+mff4CDy7RmEW1o2YzjDN4+HH/Rg6TzaXWizSVWbB9zLFvu/MR7p1Bpeody4VSVn/WsnA5LyCD63AL+lFHbChO4taOc0joDL3Qywvn9cJFE/94+iOnZeF9/CtbrfzLjz/xXbvw6fsTL4vy6UV4vw5uN2MRIUvmTCIZ3N527/KvhX4d/PLjTvl9RT4bp3zm5fQ79PwTZXR6y2xdsP1GrYO+GzaUNZ+9c1VXe/Y6ImJBYIRVQjW/Jtmf4yJK0oWcX/m63fi6v/OnP/3C119+4b45x8HuHkXTz+7nYl+VXgRb4brvvO3vJFsQO7Pfd+reuL69oRi///0XPn/+wpc//D1Yo21X3t9vfH37CYagyySXKpwyZsZNd4YOSvbHYwiM3rled+rtJ77KL4zSUDVOpzCJ/cBDi2cgGR2JcYwZ1D55gLhIpLscehajJo19vJElkXWJ4qy563VJvF5O0Drb3VGYe3MRwLL6c+Wy7842atAUlEUTphoO14O6NdoYjBqqQEC6k4y7GKTwWAsvNzG3DHDge47YwlOLOZZiAsjOMRRQC9uSiJjZw4F+fkeLF1f0IGFfWyMzOJ+irGqDLEYuXoog6SiWJld1+unto/l0Ys2cT5nz64nlnJHFgrxDUEyGmwAO6Bpr2pOrRfR5/iwLj0Jojgv/yvFtdVWgENMa2uv/IDnhZ89VJwEZ2VFXxljCGM0zMVrzAmnyX5K6CZUVyLl4VVmj201CGjGrc2o2JRWyepZFjogGX+TDpTVm0y6d1khjdp8T1RlgGZW16LHpwKPGPdrVY+OSo1pnfs1TF99Hp/f6JPl9VNdHgCbEdGDCbcKco8vT7ejOtcK6JE7rwrqsIfke1NbZ9kaNDm1Wv7Ph7lMBEtEOLs+0iIB4QHwHSvW3St//6uN47Yf3TbJMHmEOiXsKOUfW/2xMZMOOTfqIE/F91otPI66xRDjpRF8GW93ItytvXwu1FO65uNxzPbHvO8MGSb0wqLu7aQbziqSFIe6k4QaWD26LiJtbzWC4aakuT53WfB9HtxHv+bfozuPvx5/t8UUe7TDodceSF1lTrDnjQOY8ekLLNp49iGDiArMCezqdj9c8irNZPHlbOJ+p5x/n/Kgn5PCDjjGCAzI8IdpmgR+hkMo0RRzk5rpZGbFS2UT5vAgehBfXCFSVxMvpTL1Uj2bAuLed61653nYWU5airmwbg2xQEBbN0b33kPa53Lrtnb53RmswIOHxM7lkUupoC3Xlk7Jo2uR4we+cjqHzHJt3xt2/QhIRDxCrnymjG/vmm+x9q+w1pMx7R01oS7DgmqDRle+1u9N7d95HrY3WY4PVxHk9+Tqk6ty/EY7IvaFh63ZoalQD2R6HN1NKzr1Q8fgJs0bFeZWa5PDE+sjDAUF/bpzPN2LnnKjnJOb6SmDCEdRpo4e4ptPFsxunuigl55iowyAHKjICpRgTYT0kUIFERuzB9GbrT2ue4Mjs6IYkLwIOU9djrYjHUx7P96MqCCRH5Olee5ibTn7ecyEU25MjVBAIe2R9DX8OJV5ogspzTZTxWO58NQkMSRz5OQV6s5RMmuQbm+/DmMjeYdI6HkpgCQTsGZWa2/Lfmk58e1wl7guwRN6GGiRTsqXj4mdxdCJLoidhjDUW5gjv642xK/vYsOoeDuu6ktOJy0ulrZ2BsG93+riy9861NSTB6bSQsqMZvhkKyuqQXFpQ7ZxPbhB0Xj20juGyUS05iipxSaYoluYm5ciHL3K+CPqYVY+bzFEgw/IcmTjEWs27gC4brb9T91+p7UrvG+5rInNS5Df8IeWG0YcjOD64xE2Y3DPnvLxyWhb+4Y+vnhysF+73Kz///DO3unGtG30f7lsRIzryrHI83dzzP2IMhiJ03xjl4dyr+rGdE8DY99iAB0iipMyqS/AOHOrc6hZBlC6B7mN/KnC8vPbHwbsZ36xiYdKCYFjaPfqil7Cl3+nvjev2hlJQKby+/sK6rCwhqDTxCIDRPN+o5BPn04VPL59533eu+x4yfS/vIUVe2kBqZeqR7MnzAnBUxIww0zmKBTuqzefPNY9Q/YXw2LnaHu8gYrQ6lWjJN1Zz8r3/KE9/nt41M55icp4CU3wqcueiaL55IyTNUcB3V1TmjAyXwM6cHE05ivVve1P8Vx+tV19/dJCSo4K2KCxK0TNZT5juDDplFzQMJ0fv1Ps9iuhOlowtjXzvnpjMgKXzh3/6Pa9/98KbVH765Sv/1//9P/jx7Y3tv3cur4lP3xcuNXHuiZeycs6Jz8uFlAt1Sdx75efrv9PHRr9v7Ncrt19/4fL6HdY7ab1Q8ordf2BYheZFWVIFPDbGMDRULq116vCojiUv5ORxCAxHkQrJ+RjdM/v2d+P+C/z6a+WnX++hToF6F5Yy6OZcGGmADnjrVNupVtnfr7St08OB/vP5M5f1xB++/zvKunC9vlO3d65v/85932Fa65vB4kRS+ES1WgAAIABJREFU05XWOm8//Uzr3rmvp4VPXz7RrpX6fuNtb2x18GX5zFoWVj525AkgpcVruipq7I7epxLvxWIPGJDFbSyagZoFjGxsDHqCvk+FJ66MWwpaBKkWhbRhm7l3kWVMIBVFwwF4v1c2PO7AhvOsfpOFZ0643287EoIWgWBN5OOLLNQxBofk2guQifLnaN2ioJM4A6P5ODX4kZqyo1dDSNLDvdlVgC3QHvnaUFWnlCTnFKUoGi32mO4zc0qIabIWTkvmj9+9sK6Fy2V1usXNc9189OMRNrXGuG7z58HE97OcvazO6Own3EsNt1z4Vt/+7ViH43ujmtLHBm7EXDCFmkQU0UEu7i1zINlBhHXFiYX7a3QhySWQ68sJXZTBZ7ZaGbc70hvShFxWUl6OSHZrQaoMsU4pKSgzg94brW5kTm6Yxyw2PAPKgqfzVMrGr4h7D9LmEaYYVSrHl8ohKX5U8Q/meopRXs4pIDSDcBV1Cyxn7uuMH4tRmapwPl9Yy0JZV0SEbd/Y9p297kHADsfMMefqFjwQi+ZfYm6P38QSde/8DLMBRz50zAD8puhzVMkVASmpK2RGqMvUb0g3qwq0YjpZPd/EBg/fFj/Hjvz41dDkvgtq6ovAMPcqks59u9NGo1IQlGG738szQwtzO4Pw5dEgj0/S3iMV1GfXBkzzRQLjtN9ANBP/DHTmQEb8rjmW+T/vwGaHgxzdoDHHkD52OgI85rcEQvnb0+VdkczuKBRiARk8fR6C8xF4wtEZBW/C5gL5eP4/ssiZXWaKXBtZFUqBJVM0uXGaFgaJ3HYwoYu7WKdQaw6cX1VbJW03pCtKpe8ZLoAKl9fP1KF8+fQrrVa2uxOF3YNoIVHYdIMq5JFIpbOPxL3t/Pr1ndv7nb01aquebB7O3Ekzho/Oc04PvkcQSEVTjK7tNyPLef37gZ6507VogvAba6O72+6+0+Y4PUIl9+r38fv17qj8cCEFSegWoY/N34Mv48pyPrOezqyXBUnKdb9S9xuteVixhdX/GK7sG0mQsR8oDmahHlWWlCB1uk5xxGCMxugKOYVC6AOPw8cjnmENich4xj/9Pc0RcO09ntJBwj22UvdxD3OSACzqopxSfGNmhCVIFK4muHKxczRzhh12J4o/lzoXlrgHunUHnPoD/59o6qGWnHC9fwtTlTQBEyD2hMdTOx5fPk9OFEdOpMaEruPgGYHTBzQk22qBMM6fcGjW/e8lmuuyKGV1jz1Nwgwf7n0c8vXD86b7HjdReUeW5FiyfFrD49e8Yv+7SI4mX4J7LIxJk2/ckf1kCCUXT+EVr7AsJ4c0bzuISxtN1F0uu8GopNFIQyjl7Kz/7y6IGH83vnC73fnhh58Z9Ubf3snlEzlfqNudVnfuv/xM7ZXeE1mVL5fVZ77vd673d67Xn8nfnTitgnSQYeTkEtxBWF63FkWNHAu49XhAJ5c0qt7pajtM6Rj7gNqMunkSueIW2UXTkUa+Xk7B+6mIKYk1fDkEteTujVEInS4nSslcXn/nC2Ax9m3jxx9/YN/vbPs7rQcM3lzap/Oe7rO4IqpzR6u8Np/kNj2QHBGJ2evHHiUMrLzogDE8kdnHiD6XzmMgTcla6DbY9zqVif5hIwdF54MdnBhjMIMz3VxSWZaTL8AyEDpCZYj7Fr1vX7F7x7kqQu2eeL9kcU+avHqQ6NUQWVFd3VdJlJECurbmHKOEn3OdIyQOiNUz17wz9KIskE1xf4t+5OzOpQUmsmPHIjXzucI/Z/imp0ehxKOI1YClk5umy5jDbN+MWp8GlERDoligehMu7rb5z4/72ILjxDAXDshTgfRUHH3EoeqmnkteySehfFHIihRFbfEuNy0YQu0N2Xe6GSqDpTi3YEfoo7LdGvvblWyJ+3Jy5PQfPiPryuff/xPnl8ZZV37608/88z//d25fGz/8e+V+eqEtZ24//0xOys/fv6BLYstw33b+5V/+jd58QzrdlOu1sO8dRnbpdYbX0wU1497efSiS3C7CWP09V+eIjYHHOpiEjSAPO4GyIkuinwu1wu2+8XV/5+frL9w25wjW3oJ83ZEmzr2RWKeCa2bm9+ylnDjnlbGAlMyn3/2el5cL599d2OudP/0//8ZoFWmVXivs/h6bCTWsE6i3I/1d4XitEwlNBVbYrVPptL7B3riklxjBfuAx6tFPuBlopvfOXkfEAcnBba2x8W61HxMtV5wIiTuJd855Yc2ZFSdfv148FPW6+Qi8VaP1R9GJEYM+pTOtIqOAz+FHh5O4ex8MGVTbkZrdo0sfdI9p2A9PgK7EWD8eVY95iBHVXCss2qvuhb8XCAMz36+yKhpItGah6eBeG30Y99a86TOfQEh1KXzWydNzd2QRWE/uxv36efEg71MBzNXBddDriNBvBw2GDfbwx5kan5R879KZwybi12GAjiAI/A2x8LeLnPA20ajY5yL8WNzmDE3DlVU4n86+IcsSF9WwqOotJ7BEKR7s7B4XGhfIYdhTXvly/kwvhVYEWMASeVmwlLgsyuiD29UTw40Kg7C3XyjLSs6Lw9KR6+TeQT7qeKbdKDMH46gRCZOK2a4e1fTsbn0c5PNmRVATsihLcpm9psz5cvLzIQUhU+yFIY2uN9SSj/xScmlnyWhWcvER2n2/s+8be63U1mmhIJno0rTKlriz/T0H5ygKB/8lnnKus8gPhdLktXzgsWSXJk+/B9eIyIEKTODgYOebKxaGCVPJhjnSMPo4wgGPApV4kOPEeOgmj/Z/csHQQFYG1vy9qDkErCmRU2ZZVlLKoCm4W4qoj6fmifeRkXk3TbwGw3loxG1j01LQHh8SY6qVDi+lmOkfbCCHvMDiuw8o+oFM/uUfnn/x1Ml5sW7xXubpmKWV8fTWmJ/rMd82/yD+HIWK4oP77uMoSyGhFFlI2Q3FPJDMbSJkkqYZ5JSwnKlaGSY0i0A/3M6/1cbeK9aNWhslF9Y/ZWQZDF2xYZxK4dP5wj98+R33duO9vpFTYhfxmAMbfH27QfJgwtpcxYcpSbLLumUFvFiUPDtSR2+FhFh40uCjkGoRFGsPryjE7RckJM1kR31EMtiCmWdguRM1xIPCRBsnfjgVKjIk/G8i1ygpeS2UZWX5tJDXzHJyk7+37Y2636nb5tl9e0VC8Rd3y+FG8FDm2PE82xjUtocwBUpWTjFykdlsfTAnJ6fAZEaMepOSMTKOKI/gwEz3YwNP9phFzvDmUk1JpvTU2FLmZE5CLylzLvB6gpoGd5wA3ia/bsSTGFWJPO0pJYdXWvAqp+dm7Pn+1MZ7eo6emsdjjQiUyFyZlOShtDKISQCxND14hGNMorLbxrgi1icXqkIbA1W/bl4ImiPm6qL4aYqx5ERKwmXNlOxghPN7/N62YyQbgdUWKrIYzxxo9LHuc8TreG6m79murJxI9l/f1L49rgpHYDFHRDgsnR+gl3WX3iV1U63LywlDaJv5+Gjf3UNEBLQgksmhYKIm+gBtvojm5HDu6dOFNm7so1BvRr0bJZ9ISTh//3eQ4Id/+4H9duf9px8hCevpxOl84fzyyrqc3TPH3LOAHAXO013hCIgrpty1JYEMn5fTgdnNOtxLdhQrRxdUimdpSBOKJKQU1suZshReXl9JJVFWSFJYxvc0aWxypZAoaKBLc2E2qgzaqPz69iv7tnOr7szbwu4aI1wuJyxpDxLsJFZryOVj1p80xjUMWnVI0B+sb131//pjXU7+h5jrtm7YJPyFrPVAOjVsKJKScAQCc8VP6z0g8z8bCfGMhji8OysoSwLFAzsTiqiPA/u+x0PtxW1ZFkrOXNYzHOOpQEXSEiRjd/FWHHpXylG0ua19m/w5j0o45LFPT24UpZA4vtg/Oe5+rBH1Adg0GZyKKOGQcB0XMWTes9D1VSyg6IMgxLHARZH2EJSHG3PM748+hogaGe5RJIj7KxlOAjwqpI85ltUbl5Oc0cXQ1JzClCQ4U0KjYQyWCLutaaeOzrXFCAV1t+v7xrVV7r3xSTdWKZyuQi4byych5cS5FNbXT3z6h5Wv+0/8eBvc98x9941j1M6v9xt1dGQ4J6HgQYsln8npBdVXhOLqSclEwq534ziPLHN2lWe/O49oFgtTc6FECKurUSQbkoo3UOME/U7vV2DExjetDebI/TFYdPd1b+wGkJbEuhTWlxOn85nf/fEz62ll4KrZH99+pd039tu7S+O3SlFlTU4RSI9tgD1eRCMkVwJFut2vzgvRzLq49L72esjiP7pqXop7ltXqCl/NSk9G7Yr1QE9Cut2aF29tg+k0TDdGM2SAjiBei/K5rJxS5pQKy2mBXthr502u1Da47Z1qeAI7c5Q0xznuy7Ou7lK+7dHMxf6dLMba0SSagSQvLmaVeawiokFuBgsKtcYYezbL2uc6QKDMznHs0y9LwlIhZUwyA1ibR1ZcN/fv2WoQp80FLglfy5Mq5yWzLMrnl5WS1OX4CG1vUbz5+W97D+DBnB6AOb4tjyKO+LxHgx7rp3k3y2829L9y/A0k5+l4WqMRN99CA8ERiQ2jk7sgmijJQ+VKdgjZxKs9E6WEb0NZPNq9Wo0PW/1FSgY7oQinAvLqxC8FyuUMKrRXYU830j69YgrnT5/Iy9mr37b7RYBwsXxyqZ03Sx+B9rh3RguvBCEdo4cRrotqkEU5Ld49bf3Est1Z9syoGRlwfllYlpXvPn+mlMKyZoSM9he6dKqcWDWxag6Lbx9N9fC/Gb3DJkgVSmxcPSrW0WeRNrdleUJAHhvlrMQhvILEb/yUvVscox+uCx91yFPXQhReqCDJHJo3gs3vOUpjPNQNOed4uIOPINn9NvqDE9WjAzmwjonNBhxvzTvoIYk1O1JT1k/RiYY5WNYg1M0ASMO6uHeFNXqvXk8wnbh9dh4WF8f47XDjBOcTcfwlCpCJKB1P8fHgC+LSTXHLsNnREcjUnKE/Rnb82TMucW8Tbyqwl0B2pl+OP8YWi4ke6IG/4Pz6sDYIyF5jvOc8fcNmW/tBx+XyiqIUPaEF0mVg0n0M3BU0iNcSW4gY+eScFd7COE0ThOLSvW29YNlksG/vUDe0b2EdYGQtrJcX1tcv/Lf8ynbd2W87y+dP6Lpyv3Xa1vj1339ExuDl4gRPhrKWRL0N2lZpdUPOxZ2Vk4+1leT/SwlsMIb6f8t6bGZZE0lziC+UsQ0nFOO8tVaF2pVmiWbujGzq6lQxv85LcfuMUiILTpSsjphfXldeXyNdWhXrd/bbxgjH9L2+e07WXiEy/LoZdbh/zuMOBkmZrJBS8XszOGw9wo2TuCo3J2VYCuVto32wT04pyZViTRjPI9doeFUgFQHzEScDTJ07YtVHKbPIkQiPDBtqblpjbRAn7YqxnIRMYqXQ6qDuHTENOgEhUfeCZ4m+Z6g/+7u1414oKRrBaGynz9Bv9nbhaHyf0dgjliZ4hyEkI8mDH2PmisMcRF/XWhgphyt/NoYJ67L66DfW7d6hZGHJyiLqe+SayVlZUyjrurOPdpvu0p3e3CbBnv4H0UD9WcEy/64yx3wEqhNfYE/X8T85vo3kzB/yBB/NClCSoqkwTfd6d0Ixwx+kFD4jQRg5TvbATQadpJswM7a60Xtj25tfxJICWVlZs7JmDTmmIacVQ+FlpaYbbO7TKMvC+fUTqZy86Gq7P6xoELwmlB/zyYPgF266Ya7n3JWEtVDwDCf25jjJeVkZ0lk5UfaFsmd6FDmnF5d+f/flE2tZw9dE6a24VFA7p5S55IV9r7TaeN8Huw1sH1gdsAnalSIh1jd9vI+A5g4uhzyuje+JdhQEk+WmcwSUHG8d7UEi+6hjAn8ahYxOBEcd0u4QIyWwvWJ9+puou2tP40mFnKY0sR38l84eCGPArzGa8JPi58GSYimhemJZVj6tZ0pKLGkHMe4yOx/Ho61X2q6RENI9tkSyI2fTi0KA4X4Njqilp/NPBHf6YnQ8xFHgTLq6iDzQQvFiSTQQJOG4VpMECQ+dGdjhGDsHSXb8/6zo530/v+oh+z2sIY56Kb4vRrTT68fNvPy9P5DuZxTq//vjfH5xxE0XdBHSxc33Rjcfe1sKU0wlRZp7WZXeXXothBFkhPVWURp6EM23enVBwf0XABKD88srv//7Fz69fuH7L99Tf/2Z+v6V1//jn1i+fMF+gfZW+R/3f8Z64/s/vjCGcbt5YVNvV+pWaW0nmWft6QxatIhgzB4QS/dnPZV0yI2zZrIWUvhujc0JvOYxblQVWlfaSHTEgwrDRGSSMpfihqLn80tED8CSFl7KhU/fX/j8+wt9373R2t5oWz1Ixdu2Ya1DrVHsOn+iAcnGw5dNPFRSRInoZF/rbQRpfpCCs6Ei9EAMR28PZPKDjpyVLkba/e8dO0xf5/6mJcbR1f3LPJxyHFEDNN87mB5qJtTaPeooChcJJW85qdMS1kLfjXofpOGO9LE008M4PUmnG1SFLpNa4E9bHq7IVYdaHP2eUvfDGTBanShm5xPtrI1olGMPPixHbDbQs8hwN34Tz1As2ScCD/aeMoCKUwZ6d7L1UpSCN+clZZIISsPMg2NtDFp3H6A6Ccd9tnhhQBig7Fw25dg44voE+nXI7OEZjvrr1/xbN8SsqlLAaknTUTnOpdIfQJfFhXsYEwZH9MhOEc3kWNh9sZGjm9WSGKNTTh4YmNMCx2s6qYkIozwenBVGUs7lH3EMKXG5nCnLGROvFpkbRTWQ7gtinLMxBm2vmHka8LZt7PtOyQsp5bghhnOJxMIOXBEyRTIv5Qyv31G0sO0brVcWWbz7SxeWvLKeVoTEqClMo4xFM0uQCqR2XhVaq4yfYdsTRqU2xbbKSB0dMXaqDj06qvRgVRgwpp9JICUWXa13fCMKIAny7cfbAWqKe8WmqaQcC4cIpC7sY3ffodYcFo6iY9s2EAmeTHQ9wSswfHwyqn9Okem/zoHWzWOMxhiD27WzbwqXCyVlsvprjSUFotJpdWO/3RBbEBb2vtOtOpqEPw8PTyJgDFTUeWPDEZyuHZ3F6XAe0VwkiKsn/sP8DXYeHKD4NTcrsx6kx+BlMeHbgeR8FDjIA9adP38qNczCTVQ5eFtzoXsURU6OnooNdxY/TiBHeKxrWxH5uC48X86O5LAgOcZVsvomM8BGD88OgvBtLMkgCydNrrBrO8ogr0IeSm6JPRDV0UegvoFCqLBtlR/+13/w/uuN9182Pl1OvH75A7qekJR4+f4V/SSccqG1nb3d2W4b1/oLte98HZ3T+w394Rc+oawxghYVynnFRLjfb45Y9hPNlBQk12FGyYUlrz6aEMipkDQzdGcfje16Z++NrW30/QptJwucFo8BSSnx5fXMUhbOr1+cQ6ZKTpm1LCyrr8db7Wz3jX7bnXtTfXOqPQQZ+35sjEncFzXFue7N16BS/N9ySQwzbrXF9fBNb8nuej9kuCqn21F0feSx5OzkYgpdGmgLRI8YlQuCI0w6/L2W4pPRIT6eGaFhngCCTZ6NDUY0ayl269ZcoDAsuReOuVJuBGIieGwT5qomM3MjPHn8u7vWe5CSy8L9/RrObX3wZOXwqxsxkhaRUHmBx8AOrHssShuPJuYBsjvKZAN6G1QGwwFQp0vE618idmcqR4e5qnoblTtOYpqhmz3GsBZGpH1yJgPBFxWX6Me/zX37NzuVuHCkTa+go9n6z9Gf5+Pb4yqNRXXGEMiEuv2N6KG4StE4TkHgXGQtbK/dnG9m6MzFSGzmY2SHxIcvvkt23sQchalIyO6Mut9dHVUG5ES+ZIbBPmApCykXuniG1NxOvAsRerjnSnTAozYGjcFOqztt21CSO96GIZ2KG0J1CxmhOfy7pgVbDdXMvm/UVtFdUBI5LeS0OESMYiTIgpzcT6iod16mjZSgtOKLnRh7X5Bk1KE0U4evUcw0Hiw5Kl1vgqb/iQRxN8aCPKpkcFTBxTkf2zmBb6pBq3UzwJwZx8AgRkmhbFNVh+6jWG69xr0Q27vLbNwxOMY6Orq3AIcHUHRak5tjBBzbMdt9IpqMnjwJXlQQFi+8rLHfNq5v7+TUyck8eHXigfbo/J6LRVc85ADQfMxlMsnHCjMxlye05egeJ8Q8FQSPX5Pch3EUt069cHh5cnEeyMp0Ih4xyoyndcSzpF50qqbjHHMMMEecq46rvPz8SrwHO5qXyXH7wCJnXXwBtgVJHVEfoQ1ZMXb/vHNlcqCQElPBJSW3BegdgoeRkoshJLrbGZg5bBaf6l41X9+o90G9w7qspPUF0YIB63ll0YWX84m67/zwp//wzB/9SgPuo/O+7ZSvN9L5hPPe/fylUkgjRktDSWkh9cG0+FNcnFByDi8ti5wq9TVrEEGKndYrve7QK05BSyzrSs6FT5cLy7Jyfv2MqBPqNSmlZEQqRvV4h9oYW2O0Tt+3QFjF18nmdsDuNyZH1y08aBGZyOPLQh9A9b1jxmqUlGgx4ozJ0KSTfeiRNSPq+U2CMkToGAVlqG/6PZppMZdJS3LUYiLQDyrIQ8RxFDphzPrYgh1JkXDKc4K4r9OHmav5ycziDZLhRc5jDZ+YTKwV8VlsIrXiu9pBJyHWBY3Q1wP1cNRpmoy21iDarhTqWy+2Ys0cM/zZDvm7xb6/qAMSSRN7b+yhAB6tU4NysO/1aPL8OzkasYm6J/H4nPgYx/H0UY6/TzR7Npdmj//+renEtx2PY5MulkIVYHHy3BF2+jU4B8eLmCkvn7uwSiJpOubhB5IzKx2EFItMMYdyS16OC+a8EvFvNf8RNga5LGAWGRoQTBwITwGGHsveNDLrUbn2Xt1k61Jo1bhv5jbZ3Zyfk5zwaxgkt8nfm2+27sJspKycWCm60PPiG2jzC7kukHMnaXu8/+zQtDcvg7wYJBhNsK68fDlTdqXpnbRDsxOG+jhcdpoZ1cbBP/GqXTjkhE9FjiupJEhn/u8pzBRnOvxHHhpKtEyQLjWgVDNInrCe1HlQZSmhNhi0qg6ljzB0cxZmqKsMpfkLzJZq7I+1xfjN5/Qv8YwwEdhqo/WB9O6bf5hPdRr7dud+vbIUPK1OBdUVkcWL9DCRHG12dsF98t4KEQ8QPZRWPOTcRCPg3BYfk3rDkJlJ9P5M6fxKxkSsxrzyc9hl4V0l7rrNg1juhpcSnA9HX5LGXFs9p+uoBg4bhacC7DARDFSHWKS7fyrTjnxgkSM5e17dSF489kBme2wMAyaQRxR35XxG08Lr9wv7/c7Xr3/ydaAaicRaBKSTh2HDZ/1Ldy+e19cXehduG4xmXH/5hR9zYa8gfyqkJdH/rvJyPvFaTs5JlE61yq/9xtZujHpnu75xVWN5LfQi6PLMwYL19ELPg14aVRrjHuRKsncxgTQZIKv7rtzfr/58j8y27bxd37jfb2y35mNdUS4ps+bCF10pLNjmaPIojbYP7r92SnIeyHi/w/buiE1wbwaeLK4iXE6L006iYG7dGCHtLZcTOWUuJ6cnbH2jh9IriVCyJ1UruKWH4b5Galgav93ZPuBwabSG6/NAe4+y0o03UNyiZPjGjzi656G9ia6OgDjxmJmUAhDjXX0gqfFzh8G13YneCE2GJv8ZwNF43qsXCr0ZdB/xDVHGGJ6EvsQ6MBstmA9sFEP+HASVKNYPZdAjoNkLlV6dm1ljP3Qw2Qt/puedOBiQZPg/5ZiKREbjPdSpFunltToK31uPMRjska2YJNYrDa8qjqkqc5XW4CXNAjo/PcvRp/qpYgIkR515nO+/dvwNn5zkdvaWAua24E74Ijzfhftz+KIaRal36DI7TT0IWfCQOT9KUokb0DvMnLO/6Tj5D/TBSLg0fA5s0uHboZ71U1ucjN8GsB+/RzWd8M5ExsSl5tt5okLZrKFniJiGERIxu0ykLIzkhFlLftPkJM7cl6jagzQ2LyDE3/0sMUQoSwYZlFIY1sm5kIuRU4PRsaS0sHU/CtiJABzn+jiVj0v+dKfI8Sk/tsgRmfdDwKfKAaH6HEYO4rEmxT1sEgxzOXCw//1qDAc27Il8e6AY4/jjX972T8iLEQqtgbTmi9Fw1KTRaNUf1qHNk+MlUMh5xaamffzlazx/nYr7bvxljzLfsxdAD/Tmgcz4w+zfq4HUqMzP6eiKHA1F/DwmbBscIQkTTNyheMpVJ2JkMmfxdnSZj+7IP6P//zypT9Lk8bFIzjzPI96LjMHkSx8L4Cxw4us1u1P6cvLnM12jJWc4yTzFfB83efNvcsHEmheaCnv1eIZWd263O2TnAWpOfDkv0CvrZd5+PuJugVLCYLRK23f2fUfr7uP76McER4LJ3khJkuPcP+4FnFc2n3qDVpsjuyRa6+ybB9H2UKmq+Dg3x69kvnaYgYnf2/u9uuNyVqxWqDvWgmwuzxiChGlnDCaO58tHFzlnSsmkxd1nrU9Nj0UDFo0WclgraexUjlZ89FrkKJk/n7HZGqEQOm4BRzfmWurbFikJw8OfjqdaApGyw1lvrtGPZ9XJthbPJFHtcezS4imXjoTMCgV7rAOzAJivY3DgOc+7vznSf/xDbMZeKwcaY8HPiWLJR6M8dBA8Yh+eAyDmSx2ByXixM7qbT/awZuhjHMrNKXf/c5Rm3lfP/+JCmbgezHvk8WWP9/P4eEeh8zfuoW97/Et2ZKA7kuOjk7jBNSEpY+FaS5rjq4UjUyZlD8dMQQqMcdXhThibX9IHl2e+zty4j20sqsMkbijUpUIwxf1CCoyA2EVCyjhPmV9dSeYXM4UqLPticLLCJp2RKlr02HBtEFp+Y5rHjeaSXqST85llPWO4xX2rzYucpTxcng8ljcRNGJ/ZBCGBCiaJcTb0/yXubZckSXLkQAXMzCOyqqdnyRWe3I97/4e7Ox6501WVGW4G4H4oYO7V09uzQnKS3lJdH5kZEe5uDlMoFIrR8NX+gjEHohvGx0CI4FyK8wTi+2sP3NubUT2lcq0MEfoUtK5MN0CtiQdQhZckdtCyAAAgAElEQVTPPNZcNHLLOTgBTpN+vc4LqA1B1wbrg+spAhENj4ejtZlrgb8WaogcmUHzhcpdQrANDz2fMJGWD3lw/SHHSfj1FJVmZy7qJjgFwOFChkeap3C6sYUXCu2dG78xUElOjA8AaCxZqbA1teZl7tll0sBZSOcVVFTTe8cyGPW8PtlPJQbRLLEFDbvIzBVFzutWAEuFdHABQwZGMkY7CYjUupnnIFLcns+JQJbDwrFNCSVoL/GJmpzvv31j9jz5bI+HI5Q6CelgE0SNbdGZgCfLU4fgoQN/bb/Cp8NPw1wUktr7d/g8cUx2LElPoW9jNotBLZKdwG8/vuPba6ZuJ/D6t/+GL8fA//Xrr/SC6m/AWvj6VBzR8TKK2305vv/bN3yshceXA603HCdZALPYm4VG4Dm4PiCKt7eBYzwwpyZj2GAeeJkCIRgI+Drxen2jr5YtPHVgaEMYMMPx7WVo7YTb3+BwzO+vZBwYswzA+fEN83xdXStKBudfvjz25gkH1FBNi3j0hnF0fH0bGKPB7MU19E53dl+ONgT96DmQUjCnI9YC0KDBTf2zE67j+Qu8T8hrwqDoekIMiMU26A7Ac8I60n33FM6Ykrwc8wO4fG4CCGG34Q0TWJ6Xm2xAuJOvJalh5TqCJWjKHKJ3luOXlG9PeuSnrg8C9E6iwazsIoAaIQT8dNu4dhBbZ1ruFheTlK8L0BjxTH2OgO7OwqRGAaD8jZT7iCFZLTDWMQ3lZzzaz9qanVABqTPKpV5J206E6zMhS1MBpD/OdYY3mLTLg398/AOQw4dpq7VTk0OEeDE5BVZ2QBa5yhK5if3+qD066jNufYLuDBaJZPe8n7gCsKRh2nZMugNbkY2i6wZeiDJQV3LrILRBe0PzfpUK0nzo72iBpP73AtULVbfG7pPSEt0ZKKCyZDoTV9a9a63Ka9Z6Q4+OMQbMHGPQqirQOUTSGNirjfenlsH7W/6eQKjreE99P+24ZwOlfUjfnvzczVMbk2uAQMDRkslpveXtlu3fREBRZo7A/YT/uER7a7msEoDy56rdfj9l+bmqTWU/aCLXg9kUmp0wu4U8vzf2Gru3eV4Vdibs1036+4y2COhbGlTfrgyCsYNa3M4+/38/hzyNWh/72asad457iN3Oq5km5VrPOv7+7BHUlvxJHfx/9bHmZDA9sTsFofkMZJuPZtmQINUg7ixpZWDufSTEz5KXGPrZYK4s/4RAhABnm4cKdwPiboPbiZXjL358B+Ls+KENNhbGG1+/DYEtujEjs3ozR8wFnczsWyY+hkBEMjlC5pIpLUuNrTW2YedmVsAI4RTDmpFFRrJy1bWY/01fMHGW4eFYi/qlMoj0oJGhmTFVkywt6WUtdx8aeV9leyhJUIthOXoHwXyrpckdq+jX98q1sv8uRv2zD22dI07SiJaGrtTetHyOmlAf0/P56cryf2hQ+ifXpZCMEfctB6gqQD03eUiF30jtV36lYhFuV1eQOkRcDyywY73W8GpPJ3Uvnv4eBXAxb+W35j9Hmivcldazxr/wlVpQJ2a55sWvOM7ORIeGUFdz22fvHyK/VCv0py9Xfefa8uPCE/WzdSK/O6Iyyn+whv5Bd1XLk+9pEQ3qcFpO9kZRf8phWZVllpgza7i0ma4yT2whanidiqVAeewN7jqRn3/3DMTSWJ8MCnAAGP0MbpQeoe+ddLN9Yfk5B5XqEIwxoO0tZ2gwgErcyhpmO/iIANGEtk72Yns5gF5t0JqZdZ5/hCGrYtAaZ5bzh9am/1jk7V0BGXgG28/Pw6AN0BXo+oB9Ccz3CVuO0yciAi3FCNICgZaCNr6+IjUbvGqf3cwAAGidoG/F2sI6w4JLDdtMVC8AkN1CNQpi0CtHHk8sC6wJWstH2taHk8733JiRQBnMKZC/CzjQkh0H2GZkNCpr6P0LH+jRMKci1NF7RxsKNI5uiOGIBvQHr6TrAxGOYfQCQeuoicViBruNUihAH7GA0HyeqpjP/xFkyHUeml8MJ9go0AWAaVgZDpJZiRSm7CRDJH/ulnWC7eoGpWdSOMzO9E/iQTUbw8/1X8aZ2hw/uU9vvd4ZoN8Dogs43/mFAPy0FMeCG0lKPVp6zswPMsRtdPTONaUfCyKGtyPQ9cDzS27Z3uHheMc7JgwWtJV4HsL1qgH9MPgKrI8T70vx/3XF45j42h+I4Wi/NKA1zCCLx6nc3GC+zx8MV4+vQAjes5NJfUGb4C9f3zIlEjzGE70d1GmE4f21ME/D+f6BCMPEwsf6INBTdtpID0R3RAdcA9/8N+pgClE42MabJoZiwJonfaDyTvdkxKadXITSUoTrlCu0hnMumBuaA0MV74vDJ7oqRle86YB2gSpN95oYunKNLYtkLK5u3c86mlBf5NEQLlgAZDlk+Y6Nj46th+nKifMugXnwsftR4EMCJoApOPg0sJ+1KtlUuzcBT7HLns7mtT8kw6+ZDgeT10frMHVMNUgXhAKPQabRssrRcvtj5OBYHA/HCtv6HFsE2fQ6yh+Q2M3Qoyl65xR1yafa3Gh8mhrUyMablhUV4u7AaTnMtxKhuFyvl2c3VYW5ymkb3/Noit653lrX7O7KZie9fga3S0sMmQmYI8HY/0R3lbScrxN0+y3G5WqPkksDErFbf+8sQtw+1K71RgVd+el7+Y/XHwtz1mtcqC4/h8Rl+Z+vWw6K17TnEvLFzmx3dnw3QUOxIvx3XrjYaH3j5CqhBW7I2DeNiyq11Hnj4g/KsZE/mottM0WZ5VSZQRt6azj6gIhDxNAiYBLQxeGDLFvFPrW6HyGp9ynny0TyZb3+mYLR/QFQNWHfrqJ7A3cK7q6W5loW3Kw1S1ctGLhFjVlOKCSE3iHAZh/2E5Eo/9I1VBj7+6rwzpSQWpbNRCYbqbV2sNfM1rttxuT+2X/+XmQWy29PQLLX6PWpanip3F4PItmJWH8PIPIzVRtGfXr5/a/8d71KwJ76GrJpdrE4cTtH1PtfDM7dXVX2v3/SUWU1z4FmOQgSbhRSLpatIFkGCSCcIMeMw1qlZZKSn/660GWcqdRfxU3vk6ddZqJwZq7Ia4Cgydryax3XJOUqF6hm4qNZ6rtFZE/DRY+c+aT0UBLBbizg6V9aQXMyVYYJD+Pr53NfdgWjMylrWuA2agmC/Zc1HPEa+VAJQPknRcbB1jRBTuYT7ginTiey9Z4/e40maJXkZnnEAMA5t6nSUgCfuYIAXHtOMRhUNgHtxnBrBFoAPeNy6WtKp9P1WhuRf0b9XieVJ1jkANkbuTmFx7X/1WerP0QZvvLfapRPrdfagvfbCdLehV2o4cn04nqvn5iQwMXM3eNPbUUbTVyfoZYsp5ynjqdeMwqoxRUVgqNUKkQCpWkuWxh23XXlGm2C65z3no77H25HvlcCq/Lx+veOPxceHwcU5W/CDIpdGZky1cauAU/jH0HOKZI84TCUYWBtLg4aB7bNjFSJ6rZJ7QCb/3yb37RvSN5Fxp4c8uW+PQRYDrldwFatzLyYvhbcJhxnZv1UfAgclgMJVVle0rSJFFHQLC6yXdARMmFqgD7ve9210EV3ryX9YdrVVheJXssePoHQ6AMyAHkCyzumdUw1rOk4MWBmaItUtk9O0vZaSZqmVAgso0/BMksWbP3ZLf+nHG5U3KvQLv1cJx8WD44FcUsfGWE2IZIbCd1ZPRo7a0Lgypo1jfgaIgR+OMwknXuT1YuihDXbpbPr4d7RINgl1Yg05AuukN09SMX9BgkKoVATCl8ZAjaiMQr1IdBkPBAT1LJQA7HjTe4ouyU0gDJb21NgyuupdUAYZDcYU4GgI5CDYkuPpWW7UCVT3R199Ry6TY5cWROeLchRgTX0ijKglotHKTZS47Xh+icdeWvpfxUUiq8TMT9g1hDOJgBRwXx9sEX2VRcrYKEwM6hRh2dO9GIIzBAAHSINfTz5fP94J+tYMiRHttIE1DkipCk7YMwovFRvmKfjt+8fMJvwCWgHogtkLMgABxq3hhadScrH4jWOhZFrtTQ5HM+CbVTKXdaxnMyLO40sh7IhQoW6wHkGjt4xmuPr44AK8JqcIRVwtAgcAN7d8L7oBdNEODVa6EkloE9Ka4Ivx5GAzvAxDR+TLCqg8LYQonimK/PzC52Vwwjc5rT021EMKB7o9DEDsDQ+2wuQW5eCU7YzKQ0zmNOLxgLJYHh2cHIPQwR6AAOOx6Bni7Gznn0USF3OAqjRAQDJcja2s/sKZOmOzSl3wLunJgV3tfKOceAyGUxmtifKqTabNkYmYwBcsGaFOIdKCuElndY2pVReaswZptG7t+d7EYxUp5MiwnHO2OwKBBiaWqPUayGwW72tGjMSlB2NMx9Hp6XAUd3GN58blf0iBJgFB1Q2MGNbOnbFxn/Gin93/CnIGf1BMBKN5Y+WgESpQ6eyvPQg3JQiLldIyXIEW4IV2g9mUwUAt4X8xqx1uxOp1Qwi2VlunU7VNiMzUcvhjb7SgC0qFAfrsFvvgdqqGLTqbQt1JgqXnBUbdgIeV5Ug/69lwJelhAAAt2SzIj92XfriJSqrumXy+ZT7jUpk10z+rkkTArCeXQA5mXtIg/pVM1/FFIVTHxZpcuaewuP6XJ+bP5nRqA3ZNAfcyA9kEKjWeLOc6ZX3t7Lm7DRR5YYjmRHuzRmV9ZawPThM7hZEdjYU9XjfvpDlsQa2Vlc240DWoS1NLPXKAkdOtU8AW11XNdohkGCkDDEqwADYvexZduWvth9kvlQF4vycClSHxmYH8wkvdrKlwL9YAAL7VEBE6W8MLJtd4Itr88oYM/zlqo28TJW4+P7zZx1FeffeATGWZsPhceT6CWpX4KkpcMxquW8VSBuzaSCbDwTSFkQZP+CACjU3ljGkdWbHrQtk0VdH0qSxPdL+YABtAPIIqANqCjdJkSbF/8uMpqQQqAHeyRZJmlsifYx2twsAA8c0rLkSvBj1MzkDbq7FLs6W2Xe71p8HN+3ydOo5vmN6bl7OjV7VU3BAqUGTavwARDsHeDJvhRvj0CGC49kxjraBkSpy02bcXb6SKfIcMREoucJoZHtCP3/EjHYgQhGPDkRHoKGZo4ntPYN6zIy1kbtSsH2fUrDYTIbHVc3YnaO1s9cR1y9KJfJ6VVgQ7H0gAlm6F+6VTlZJRRJ8VEjLta3JVkYkMAL2sNbazHa8KzUUAKEWSXAxReF5j0J3J9wFYC5Fzd5Hc22FB6IxaG1yWmRrnKTRTuBoyrWX09bJPhbQLUDHN96Xsf4fSO1drRfGtMgv/tkq+lOQc4wvRJwufHtdFFtqS3RrG+TQmE3hTmzpOewrkLG8KfrjKwYU2/dBAQVttjkLpeYGFZWePjOR4KluUYDgIhxu7IpZZrC5YNM4kyqQgrxAO3ryjMno7JISy0ySGxwMcGdXjETngpknb35+XnEKi8GRM9lKy6hka+b69kSvDdfySkZhlwrSryYZCjovs5REOnlBZAE6oZSvowXVEM0BqODoA+GBhc5gshYsDDMWYgG+gBVE8Y5L1/HJyRPmXGgKHAevsybCqQDsAAWSToM71ZaiTVygCI0bVXMOmAMI4hKAakKUAKiNKZC7SzH31ypQggtoHAQjrSnEGmL1HLewAHOIBfQgVc2mLkHv9LDwV6UxutdEaY1Eb86jjCTIj56RIld1Zi0sS/mVFkGw2+xRbebJRCJ2eb3+vez1CWY121Mjr9Wi9sYnPM+tBK8QPosXJuQ2W++7L2A+92l99mnHclqNPo+DHYMqCFW4DkS8YH5i2keOCqA56blOUvlN0dD3RosAtHdo72ivBTXD62WpUZqwSAt/AcaDhmdNFe0VWK9Ah0ER0MeApLZgDEC+cG0e8wAW2QGBZYeks6tmAq05nm9pXdDIFItxQ63cyzwQa0IQOF8nznPh/X1ukfA0w/vrRGvAcWSG34GQ1CGB08Id9BIbvcFzynYLwWiCEYHuku7XmmMJNCemc9ho64Lji9Agb9L9d7jir78+8OXtwOuk35R7yRmYeL6f6QsDmr01JNvUFM9jIFoD7ANrM4Wfc8hItvPrkaXCAbO06khxS7WS1/0YQoE49UxIORzHEjiC41tq42/5PkVIoJ71fE6dzEjTu95NroTPiwwSJl6JgkZLs0WtEnTuodnsshYT7Grzp9MxHavlVqq6kkEOw1RhCVEywxfPSUyJj3wFGbet6iRb2rYo2tPtI36qrghwjbZIjdOz655i4JmYFJqqeF7vMzb4qwsTtzO4gM5/JAj9hzQ5mukzmeysXwduwmFSSIgSLGLXIiFVFwdkGaATTVKgnC3eRaNz0/fs4b9YIBHsjFlvLARf13b7q62cIJs168KeNPLOLAnBBQQyRWyCMyJycHFEotOaFxV5wzbXKchhjGzBRAIwSmwoqwVwFblzhRP5+/X9wYCw674giAmn2RnM6VRbCrIFYBFpqwRa1JTZAFRxHAy/4uRQPYvo4pl511P2yUxOaaU8z1vKbCoF2iqc5eIB+DKEOK0C6tLlH0TIrzXtaEoQEdneqRpJx+cugWv9V2JVbMdV+01mMiSvqaL1Ae0BPRy2FLYEsV4J6tnRZG5ck64E1EUzFDipjCNLTxDZc61ck6VMIf4dfbFunzPggPSDAt1s84lXpcCZ5b9gJqVtAxWv5COvMWORb+NBd2NHjtkl0C/wJwXH+boFb/gxsyswCqd9LpOz1kJvDXq8UduiwHqddKN23nIXek61Rv3N2/gKpCWFakPXA8gSlEgH0PB8+4IxnmhzYi3Dx/s7LIDRGywCazlMnG64UOgQdOloAMbzAW0NhzzRWif+W+zA6sphwtYWHIZ5EggcX54YUDSaRNHIE4oRZD3MbF/V2KAnTSdtbauDcDKH4YE1szmhyW79FTC2rLUgwZnnTRS/fBkUHC/HAZaPtafFx8r4C2by0XIdBMvHrYC6Amsa3mPSNDVA9kck19g1wLOnL00LYR2kBfSgnUd7IW0cPu+QZA6kZaNMNLSjYTwbOOUhcvhswMtKBGToeArC1uhGMbLiZ4XjdkMWpqzVWV6JXXneXDPjiuHKuKGAdiKm9ILMFm1A4Bx0qmR76nx4nfn30sZIlXPyF+WKNyZZmCbxz/W8Z/nsBopWMpwVJMrrbfcGBVkk2/49ybar7BJbb1Ia69zH8dM1KzBzXwnVqX7DNvuo5wJ/8LU/Ov4c5PSelZcMohtFalpVF8UWKWCMFLzionASF5sZ1d1r5nDPBDgo4HBrtyuWJg34BBdKLLot1wd8WZapEuyYU29g58UUhF3CweRKckthGccNHjkLKbdA99qUkYuTdfIqjqhKVrs8208TtnvpGIIdWgXvhVOCkcEnrxy9C/xaVLGBDou4mh0aLJoGsORnIWSChqYB6YoWgRYd3hzeAlhKsFSQT+xewfmUgxu37vKjQAlwUC2mmc04YJZDWmukB4DSgCmYvWuCHBcyCte9SJZmlzkTkAIoD3XJks8GOdD9S7Wh9UHWTBzrZBBZ/sHuHaQuyLNsKgQKUXqr+pSlRgx2vgmAJh2RTs5e7F99XiBp/kyf0pkrnBtezHNfy1Cqa69OY0lhAD/DWitfp6Vx5QWm3Y0argQ6qOyNHwSVUW3d2gaE2aYfstu1Pbu6PutYaxEMjyP1gYI1U0vh2V4t5EBbss3P4y1Z17y/3hEr5+wIHdjf3h58fuwDrznx/f0DniAHK6hBgAFqnAc1BroIhioebznR3n8hk23BcrmdOHrH29sXfKwPfNgLfpJl7k/BIXTchTi60gPl0QXnWnhfK+l72doJz+TIfcFt5ebFGODmOM3RBwFF69xANRnjtSYQwkGfQ/D16xvbvV8nKg1sB6efzx8n3Fj2hpBxCOWGqQEaryrtG+w0rDNQqOoYnC1nOWy2KUtfh+r2ZfIWiAboEdAWaPO26X7akVqyVpWDhn40+FuHnCzfWYBguBWHyZ8xOLoIHk0hShBUT0ExNrPYjLiebZqc8llqqgnsbuet+fgl0OlaST9ySnuWD4Wu5U2ZaBiY6ETG0Qhg5h7E/QMX0+s/C4MjX68i1wZauD5aRGDiukeVNKkKE+n8HvNIppUAiLP9aJ7YEuTshDN1T3zB6xJUvicJKomrYn/xYsYYfy7W6B8f/6CFXNNQLAk8rbOvjxLY3SdRYCQZkUilVSzUnBubqT9ogKvllAfWZ+E73LNmGpZsTDI5nmxGTjXHooHWPCeD9iINH/YBm5zlUgPprNN5K30Cd4YsmTk7i/ocApodS+YJmLLNVi3bNMexxWak7GN37vhiZ9dyrq6mlaGvXESXXbaXkDo3mZB6X9ugke8/KdTWQKgjYFjBbClyaOm+2V5blKCPAQyBmpNOTiGgx8TvcPQ//QgwG11zAcJ5Mbm/ggNZBX2w9LlOlhqXLTI3UmGGDJ9F0EQva+doQfAQnIgrflOLqJJSttjUabVNN2kJJmrGE9slj+OA+cRaEy1YboI2mCqqjaQNso9e4sWRooV6EgPw3XaYrJLkpN3sgmB3FNJOAdm4VCC7XI+ro4qhNFJbFcI2dLS21w+yHCmSLfk+4SZwK/KdQnxm2heTiChwdSUQkKvEWpGoEoJAKXH/SYvl3zm+//iGOQ68vT3Qu+J40FxSfxH4F+PzmrGmjU5AlMwdhClxGNvAkSyg2cKaLDHro+NxHPj1L18JABtHgHx9Ol7zhY/zA310tN62E66rQltDH0F9y2PgiI7H1ydZF3uhtyeONYD1zuYMe2GeE9IYO1/GgZzTKYaeMNR06WLc5vqgSBwLIQaD0Zk7qjMu4Etg4TgwCNCc2TktG3LDNMfH64Wjdfzy5Rf0/sI4P/ByCoRXkNmx9HNYyuTuXlBiCRTbTK7nlOod14JruQlLcdqRTDSQynGsDwXEsM5FBuAzD2YGZOZBJlePhrZ6sn4NthTwHEmQiKU1cDBwumRraFW39i9EoGXusDVx28DuemB8CUGKV2kpGZB8rLpmG3d+DeDnaE4zRXOaoXgAc7IU36Lvaoc7B1RrZLKSspBwcGhwaXWAHWeuZCZVenne90noSPChIjCLvJRbqYOmBDVasS7Pt5L4KGa+WsykwGGV6u/vdbtl93uXv6veeOQm+0t/dPw5yMHVqQEJeoUkdN3ZXRm3aWaMkh/aiR6RM3Vc0msGCWjUcXgnqi0wUIrvwI3Jce7duwRxUfluBjM6bIat1Bq8cpLuhDZ2yJSTK1mP3GAkbbLrwjoQqxija7FY+uZQkKbbC+fi1+qz8xfr6bxIEpZbVLk56qb7qo2atVnw+oIsFD/PJRiOvamkx2RR1mXVnisogpuvgl4W2lrqSXwLkNna+sk7FLiB0nae2V8A24VX0vgsEDDF3oD2PC7kPYFnVksxdtkatAQVHinqq8cr73VY1uEFXKPmiArMOXqDgEPQewPmwkq2ravQk0IuMKJKrUtIJEBPZ2JNcB4OWQxwYQlkpahe5QDPO8jJpbR5lBK1o+rPuYpKC2MOulPkWBPkB8vvFTjcJgDAre3zu9ZzekHV2ieNlPcpSa8dmAvkxC43VofVZwKd1wedeuf8gMiAREdrHf0t3axxY5MPljpj1py2fJ5RNgUCrIVYWR7ywPEc6K3h7flgQtXIDj5GoH0Ili2M3tj9l23+UPontUHWoz8Uoh1v+sTr9Y4f7wtNlKWEns0AfsJWYDq7YaYtMiOuiGC5jXwUsIJeNMuyLFfqukh/o928wCSCxnaCEQpPwSl/Y1eieGCtiaENj+ORKb5hvqdbcaSgNplJt2z3ttLbAOVm70HNUg3lRDKRks90mQBeMfYKkBYLgHJ0yufmW0nwJtCpz9kUOtIpX8vhV9iOH4GBoA4yhSKPzmtbZIllohAJKBAsOZUdSDkRe2CXanxiJ3v1lBUTolKy/0zA8lHTjF3uoPYKwiGbtQ8zm9xxXjI20DwQe33dqKa8JLK/rxikshu5WOrSNTK1F48Um5eQWndH1naYqVdL4ntzUhlIuW8VIxM3kLNv1d+zPSisI1fJTG8s1B8cfwpyqjnANt3EV3WwPcwooiDqdI40aJz9DgEVTDoaxci+YDFJ+48A0OEg3VYGf4i0s/dqd6KIObTzIY/AIQRfc7EN1uLEsoXz4wNmL5j9wPl+Yr5OHMcTrR/Q4+Aa8BzG1nmBLZkYgcGnIKZsCnflTI612JUknT4tQ4VeLUDSdJ46H7IxUEfL0ptnuSEZNm5ShV5z+nHJOYp/oHDyuumSzJZTdIAGTapd4FKdMXrdsHxaYqUJlBssAr139NGxVgacTzzGOHiGtwrHVeLkHUDSoNQoBGqtld1NMRyqDsEgCWOLrfcgk4PMUkRHWgqsC31mIHAjSKyhphBm41+eT4zjwNuXjvkyIEYGFMfb84nwI2eSCZRTO9Ef1NnYADfcPhKALhxN4afBLBLQfmcbv5TZJBMHdv1kySzF+yHUJTFgavqN0IQTSG1VajSKx6HDwdXWrSK8BkaXss3W7Lpv7Dgn++ry/y0BXYrMCIqdZQrewwJin5mF85l6+TskHIaDc5/aYGBtILCLgPSRz8fMa20IF9g2caMrstmCLSZkprwvR3sCDehvB5YZPt4/8KaA9EaPGADqJxSOX345MMYDhzY0ZXkK0mEYCDfMjwekG7Q5WgzYoH7G3TB/TDIHqnAAp1uWSzvcFqZNgpzYggy8v15Yy3LUDKd5R96m3hpLQ6oENNkF+DgSbLhBo+FgIMRvrx8sH6vg8TzQxsA5XzSwTOdoDLaWN80yRy7broAITTtb58ifWl/l0nttdKQGOLyYa46xHBAX1Er7tCOMTH615MvYbuV68Pl4ez6ZThr3pmkvxvpBge/zMXFOw+tpWLtcSgb6nOmftd3s+BxSfRAJipIPTdartyztpUO2e5bMEsQqHGsGZgTGCFoQKEHtQ0rLOAvj5M8QrDrYIdg00yivRJH3p4lg5MDs6vqiaLk0p7e0JgK+MpHyq79oGAUAACAASURBVEym2Wo+tjCaUWlsNsv3Ot0NUnIRM8AdaAHIdYIse2UGuK9nBS2t79na1z8+/hTkbJFQ0BivxEt+u1DMMCN1CkSbpaESqZvNXT6KFSkPkaLDEiUyC+aikNvXL9OqUvEzi3HnBhZhWLa298daE+ucaDkOnnqExhIakMJjbO2MisNNk862BHE5gKw2TufN8rwBUmBl54pAzddC3hcKYW9Hslxy22iIZwotX1mU7H+vF0/WozZ7EUCy4S4zcZYP8xNFCVzz+mrSx6H/4Vrm/6pDMxoXtCKhwvILMxKeZDEOBPoXu3CxHFc5RYRdf6G6hbqqgct4w2G7qhK3d769VzJFqoreOnpr9OAx+jhYvo70TgBfAaB8TDofH2m04x9jZAeLoBmz6bUCSw2tvTZ0ixS035kkLVE7UuR/r0dX9rO7nOy2fjKL3kGgViPPc5czUom2r+ltDQjyWZW7Die/OdmAizK8/cwnblD1XuYLFp3PnVLQrY1ru/RH0juvC5FvNjAKEKwzlxu7uKI13o/TJ5nVtMkYxwFZC3NOhABPCGZQr9KU3iPHMTBGp7BWFb0PQBrgBD099VPA2iyApbfSHrSYJU6vpo38e2QZvVpqAWy/q7rXqhfPp2mwdg1EZuhl+QB7vQgAN8cLM/UTSMAPhLOhQUoT1nyPZKhYtYdFSgJdlf1YXeu0blrs7F/usS4qbVF8/pFXrEC+EKRJJfKbZQg0y9LOYqlIIWiNdhgEJoplNIL07ABWpD4z41ok2zOt2rNx3bP8vQFbuwJQ15OpH3/PWLkshz5DdkdlGVMWyKpKBLIE5JHkOW6vKZlCJghpqpt1uwMLQSog4vakVxjY2RG/fzN3cnHAxUfH/bz3vb8SjsIw19IRXEvphnDizu5csep/isk51wkBMOcLfPAuseK+UT39FZomOksNRXAWTLofQdNcTdGIVOeEvU6IOzCCHU3VVu0pXJICPgB8pXhqIiAc9OaOQ8nSvOSEe0O8GrAaNHrWMRfs4x1+TgRy0OPjDRE5zZeiDWg7oHrgNLZh24ueOx+TduUNjgaHfBTFWQstwUWA6NcBaopoFhUZsGgrr/t+XQevJL1WfNeCy+um5u7wwSLA6Y0Ts61nLq0trcorJAoiGiIa3l8fWNPhcxHE7ZXyece6PYCA3ERwfPCacFp3IHb5zrLrYgd0IS3sVh13Whd/P1Tog90MZjABQjo824erTij6BosHRmdbcB8do3ccMjBc0c5FoWV/4MQLJxbkyb1ru1uJorWGr1++MujD0cfA43ijaDw4emO9DOd7YE7OpZ5zQk6C53XSyK26D+vguXAivZU7WKsNoqiw+z1MDdNMsXsCqJo1U/8ViN7BQOtlqpRXTM5PoQY7NLr8BHS2C+snHX2wDr9eH1iNIlZpAfQss6xA7+1yqg4gjoEIx6qMJFGew7JzCOkJoxjSgQDWM4FjV+hUIAy2DKt3WDA2PL7+Z4wx0MYXgtP5QkPDwBsQghaO1g6Mt18x7YVlJ/7Wf9Ch+OQzOh5PBASv84Vwh9pKwSgYjzhIiiyuVUdYu/xoFNBG64i5DF/GwF/6gGqHakPqgFkhisA5X8AynPZKMKIYDRgtGJeljN+E874QsPBtIq4qOLoSzPd2jbuBM77nBr2WXwlXgIMtRYj9smSoUkncTls+79DBz6wL0gUYyT70hlLXqFDfRiLYocZ1ZCu1kvNMA0jDnCu9iygQ/2iT7C1SDG90CW7VwevACxf4phsCk/reaDkC4SwobYC4YYH3cIXDXgvyYum0xrcEeM/MgdN9l3/SuzLFPRfAaeBYGR0NXRWPG0M8zTHN0fgjKW8IMssARra1a45jOI62xchdJI13qyctLlCEW/4JbALl3j19/x6CowRMUsJtSjGKpNg6ors+5w+OPy9XZVlDnaGhyhwbYe0sM/O6C2bdSIi4AmlqfKqkT+PA1AjU+wWz8St/ySBbbEddjGQq9pBNybZH8y0ELudIn4vZcbYsL5279TKcnUzaCdRY3rnGx1dbsgcFgp66IikGIWucuyAQknXm3IgjFTn5OVG1zfvdzwtWfy0B974CtxSclzh5jVTUlxJSoLkPUfPhyFppZmr1s3+AtP6pR+xfskHyfWf/+yUqO8MtiL8//x8ctW9LUZnZ+qlKUOgRW5zm4DT51tg221p9X30Y6mxGa7AcG9G6QAdgr0jvG6RBXM+fdfQ20HpnKcWze6QLtDs7U3qHBYWJf4cz5er8uzKUS6ANEcgfXKXKkARVQ/c6BfzhPc7N55oyfl0/uf/99z9UIesnCvDPxX7/648M0lHMXV6n2yO1td5RMUeAyA5OYSZdrr/bPynTa9L5NPYLSPqUkKFBYyZdtebeGvoxsmtIIK3RFqPAFciO4GiIs22rjUD8nTVFjdigOFfgYPIW5qiaBudf1TXXi/3L554dNxwDI9og0tCUIIeEke9nj5ZR3LQUnLxdoEdSSF+PXsnOpWL4FeqBO7uDAH5iZYpxvdbLLl0l01/f99nHZo+y4xUSGcsvkCNbBpBAuNhmrc6slto/xn4KrrNsNWg5YPVvIFMjnpPkBZm88iKkIoRi3bwemw1R2W3s9Yx6gZggQ66NZ1UMjpepav0998QQzhUTJHOjko7amoOlfSfAFZ5rB8oQnAyQbK8bOiInk5PMOG4x6d+/B7c7f4X4Hev29wlQo2n2nwtfQFDmg/9Iu/6PNTkoob5AW2N3iDZcra98Y0/GhVnUBYiq/lBgKASQnnSoBnUlVvXEy+HYsbBiovUvUH3bV80y2FotQFVIdBzjAXu9w+Z7anrYXdE88P3HD6wAns83iATef/s33sXSsABATAAfHHMvwMePiWWB/mUAKnBkfd8doh0qtO+tcpq57ztnuWGNcaCW8hZ/ZYZtfuIaQwDUlFgGEc1r7ECzCwyCHRCS5Qq36mTDzsSpMOJD0+DAo2OMMiUMfKy5S4ufdhQgtIbIQFLrck84zg26Sny9ZRaLrLlWd4N2bhTV+u+GNlgWiEmNCh2ug118K2BinAyvAjGWk3rr+ZA628VjAsF8fDTF81DEy7BgeHsb6Ifi+/kd5obRWIoY/YBUTRwN0xSvl+P1Mqiz9fYMx0QA7cGOjJPi4WUsF2iOGtkCfqQTLwLaOwBPw6+Ap3kf7oBIC+QYz12Tokm2DJF7c7atXkClgkVmUSI7CbnCTGrN4FnGZXdVFEr7RJQz5weAgaN/xdGfaNJZEuojqfrLR6YsAfrjyQAcwszc6SG01kzLifITEmAcGdsOZodJ/Ygq9Rvh8Mm5UGtNVqCCY0ceXw80bRhNYAacH4D0wOjA+p6syEdgzcD5Y2Gejtf6DghN5iQC3YDAhOMFLEesoAbPBT8+DHPxRu61ohTDqyqeLXIMzICrbEEwN56GFoGOBQXwlmXYEwGA61s9p65nbaO0FJ7u9GJAdcQsp/uyZmIZySTGqs2CPzt6bXqR6lwkFXvL2D+7bg5gQzd98sTAhBcrLiGRUwKBMz/vykG4Qcal3UCANro+V4noEQQixV5zLmbgfFHb6R44NTBFMWdwrhoYnbX2EI9sguBAWetIx2PBd3OcK/DhfN4fo2VlgW7pErZBSoEdy4aNoQ1dFH95DA5NHrqdlJcDr7qHhTqQDupgF10J21Uunc/A9SNXQgQAxerU50gdEFAE/B5aXccdzFRJVDmfiFKEFMXRFytSMwVE+/sE8H78A03OdQZ3BCW4qcKr6yT57ysrKHdWTaSHXS/2NKSTuwwbwF3dXQ6yiZCyVdrBAjsys2Ldka3UgEhD7wdskcWwNQEjzRgB2PkCBLCc3q2+zwJbCOsUX51rsZVyUimv49JNsObdqfUJz0DJlwoFNzPQKwKIyzxtt3tf3VZbCPrzlUd5GNQ6kNQftAw8UU9QYJviSdTwO35WubM2cgOl+rvV9U8+tnlkXetyzPqDQLcz801VInGvZEdEJIi+npj6cz1M23jw1g0U+VQJrvbGSkj5UfgHM+ccdNZK2WGxPB9Mrm0zg8gCjNL1ORdLBytwfpx4fZxZTjXMSJp+LcSyfEJTl1WAgkVtbL1aXpIa/SEpdG8tZ3V57KwJez3dY9PFskiV9OrCop7De8ZUdfhaK1fQop4nr8UtlPx+uOA/+2it0Xm4sSSzQVqaKtLEUylAqFi9GxoC91KfVrIlsUeyFLdSoPESOHquJwKotQzx7hA78YSja0doQ+8D43hjlm4clxHB0upo46a5armPLkDpUI0AS0RBLWAx0OYsedtu+02Ak59PgqWC3oDe6ODMe8fYGiJpG+GbPZpBvHEGdUUqRmZK9yXde31h6WLMynASJXCXvUp2Zyzq//fHmkgaAtnXHqDG8bNxzh5oXCjg/ivj6O4Ey3/bX8cVL5DngiwZ1bMyRufzmbIDlqM8G8ti25VIxgArHVkZgNayA41PuygenT51IoGz0VvNig2M+Oka/l7LJxIEYUJANJpiHCzVR7J9964uBUFMT41Nbl0ELEK3bOp3Ljan4sUGKcDv7v/tL7fkqOQe/Ce5rbOLvaoYueNkPfeBnWxdg4j/+PhzkAPdNzjkCnKFEANJU2k6WsYlDMV45AVryS94CpyUHUwSkMb6MauE4Nz4qDKZAtHRcEDRcPrkUMo0ZIu1CG4mNyY7Aw1P/PL1v+A8J85z4uPbD6zXd2jj0LjX+R1AwHM2C5xsAbxhOv1kdLB+/O21sCzwWIpxDPzLv/4rtA+gHdA+cBxPzPOE2UlmRwlwEDXQzDGUqP61irFJGlQCoo3DJ9dJcCU/r4tSqQCR4sEOHIIeSLdaR9gLZo5ZHVmppjdBZrkHy28lpUcxX58LcrRyFaFvR1nAX8PteASyHTWAuduckwVLx2A+2AKEM3sRZpf83gZEYPmZSN8TDDvcs1yarY4SdBYWY3bSEIAtjqAYD4QM+DTY+YHvi50wrQ1AFO/ff0PXhl+ev8CW429/+wbtA+N44Mf3H/j+4zteHz8w14nnl1/QWsd6pT/N4pgIDTInqg2RQxljcSSJpc+STd5XcW7wx/NJbUD6BAVYVqXvk6ZNe5WWEkoruCF6dXFhJyDFe2ur4MGAe+f52FZ8Azi3oPOZq+jL2xf0PnC8/YJ+UODrFlg40XpH0wbNLj7LUSy+zvzcObctqAfR0SFC3RahjyCE0uxe0dyztBQLK07M+MDHfOH8OPHj24llhq/9CwHCV8HjeIP+pwdB8Fqcl2VAx8Dj+cR/+/Y3CF4YR0B04Qd+gwrw6/Mrlgn+NtnOvpbhyAnNv71O/DjtMl7THFviXDvNDxza8HZ0HE/BeFO4TZqNZjfN6+Mdtgzni8/Et2BH0FyGMQTHITjestRGMnFn7+W9zAQL10RzyaQgsjPWufECSJNXyectmw6M3ThN2I1F00ZATuDnqPfPP7bfmtHvServASAWIAYYExS4ospRuRtyrSDlB43t5xtYBChkR2xrgmXsluptbluSqY45nC7YFrBZ3lbMgQg8CGLfesPz68C7TXzYREfD2Rz/9f3EmRYi7ri6pvwqZUoCnbcHh2E+Hw+01nAManBWsCM6+1JyvhSbU46h6Aqc533+JPXzxbCMpnh0RelOfRMUCQalAPAFmDfAyftRwnitZDYRdn127O8WVD269gFJmYEOvdrJ/+D4ByCHn7SYm3rP+InZuUDP/WMVCNqdVHmmLGuTybG54OoU0tXLJ6VXJ+lZpqluBEun1sp6OXmcD542hbTHzsxemjMycrNoOWTstEW0mnLBrh0ImgquSap4TjozthbQaNA+oH1QoFZZ9z7/rK+n3XrAIOGYzg3InGaA1cUgN4l93G9uYLcV1nUm4CukestCQIV/1W/LFRVAeQKSnvds96sE0DPgf+Ih0vj+hfSTxg74T2K0K3XEBmK7A0oAF7ZaegrgS4MjP/14Aev0tRaWp6QC041dZDaanyXoK9F75zwhIWV8NAZlZv4ss0lTuADf3r/DluP9fEGXYVrg4/WBeX5gTlobzPMFUxqfFVt4N4KsLHdrHIKbcjFSPNJDyFiu8iiQE9fvN40EryI3uZ3Y3ZixfQ/yO/OyXUFkr2r89BoQ+QkgfybIeb4dbNO/WcSXZ8vWw1QOnIxOePU/8rhfHZWGHAKHzc6KbFF7KBmWcTwYk9aEHwSf6+X5vTnEdzkMhh8/PqjT6hxTAg6rRyg3+K6CV8aE3gdEgNcHkxDGDQCaE9LdIA4cQS+eCLLLjHnU4AwNUvUaOem8gJkzjgXBTLijRvPMbOWuzzOaIisC2NOpK2vfaybjevj+WqKtHYtqj6EMlpsu9ZeG8iTbWirw2ftkQjk/YA6X1kjKKsBhVAHqOD33O8ZjQcVjqd01ZznGvhCxM9SoPAGlKwWuEbgOUHycCZ+owrJUX30ntJuTFIKn1KPRs0ezdNYgeJstO63qo/GNmTxSd0NzPsWzNxxDt4fN3Qw0IhAWm2USVIMLn3sFWePSeNWohqubKi+LVOy44oxW3lRciewndLPvFYsqLhW1U/HpHtsl37t1yZ4NxvP20D+tTvwHQE4A2WZX4+Pr7SsZLI3HHeAwTrOTwdMgzcTRlAIvkcA5FyBAG2RaWjJHHp4dWx3LDUUZRwRsZg0122ynLS6q7DY4Rmd5qQfevze4Ah/fTsQKPAZrz9/8HSqKRxt4jo5nf+DEgvnCxzwx3bCMKm7tHSoD8ngjk5MZj91M+jzLI2suCgaVm9BcJ8r2uveOYxy71mgrLdpRQCd9R5Zd4CmCXi0Aa/BZsy3W+DHoSbTQae8eK7VKuXjTrUpKmAnQ4fWTfXKktexucepzpCFicXJuLexE/kj53d7IsuEAWnzfoJlYTIjvlZjxhS/SW88BqgkyzdO2Qvj+yOCL7GAQ6jn6GHg8HzkhN9jBEA+wN8oQmp5Qj45ljv/73/5fehGdyFLiifl6x3q9Y67Fktn7OwDe77seRqJKT7dBlxmo6B9U60t2YnDOD+zBuMDPD5x4/Snj70/QD/W8ShoYyo4wuFB1JS7xBwCnmJ4dqD73+MuvbyxHS7JT7tB+YPQjhfWy2RpRCsDXNqGqXDz2mYg2tAQ21eEjohitp1P6gWgLHYqzdag7nZafivajY54G9QMIgc0X5jL8m/0N4+2Jr//6K8YSyClAD3g3dAEereG7GeI0PN++IsLx7b994/PdaBIpo2H+mHi9L7RoGNJgjZvhj3nCbGH6RMhA9APRBKs5xgjgIfC5sGzhx8cLywwaBBPP0eHOVmaVHEvRFcch9DdBlkAkyOgAONrlYsNu0bx+uXtt0BhxgZtKOiyu57aRmRKXFFfzJ2e7pst/2tEeBDgu4Aw0JeWGBQaNACKBUO45VxWXG94On7WJAzvAVudog2yQ0AJooenKzddRUFTsCZwsiX7+TGrKUiCsTXCIwNFwxMJHM0wPvCYHtVoEPhavaYHX3gSPNnC0jrdnw+iCFUyGZ+4xDUi2ia9hwRlYI+cAeuryFYKjQA4SXNyYPSbZQDlnFTsjhcD0iim7q7ae1wwm98RL7plrHvtbFRhNgdboQN7oV/Y/zORU7b8r6aDWQA+Fm2Job8iWYtg6kVsNPFIk2qTB0ltBlGhOgPQzKWMookptDb0jN0Uq0+/1x+2+WlAyxazLspegdYzHG57Lsc4f3KQ869NLmOlpYNnEx/qOl71wOl12D+14tAPSOr7+9SuO5xNH7xBVvrYXTeg7c/JsRWe2QzzbGp1wj5zV1Tqfao4WsOsc4rqnhWpbgciazI7YdGRlfbuOm0XVfjQursouXCDibOtUgQsfts+ug0MILiHFqCjMFRaWbsCRGqLsIQq5SigksnaJT5UL20cHZkd3g83JToZaIx4p1E4BvNPlWfTKMCsrGNryfT0p18zk1DnxPASHsAwx7WQpKYWncQZ8GebHJEhtJ2ye8Dn3BsCsSa5EoFWjtpTWONdLjqlQMNhJZkUiEJSY0PZaryxmX+LsINrSDWCnTlLgrq5jGS1mZNhupiisSdDo9TdJgHD7Hmxo8DmHO9kTGR3aOserpPHohZBzExburqqk0iV7qSMZs2AujerQ3OecG5MgBe5QyMHr1FRx+MTyBe0/cL5OqDH2LOMsv/l6wZujnYLQA+3xBj0UMgRjfsUTir98BWafCAfNBn3ws+VmYioAnC27OVF+Lk5hR7D883gcOI6BX3994qENX1qDPrhe0RU6FLAOaw2uZS1gEA28PTsaFB0NxwMYD2AEM+MxBNIUo3dezTgvZgKxn5m9tiqxuHWC1tprLduUe04gF16j0j55gq/PtsqRjAe3TBI/B2DKIaRchDXYHXdlD1sveGdz+LOpTA3shEYktpSBb5mlv1YUCBl5qZpRUIcjqtCBnYQUFO+H4gDwZo3Xdhb4DD7uLdnNrjgaAWxvQOuAT35f1/psYIduMtyKQAeHkM7lP8kcqjogYFOLpMA98pJV0wv3pbqW2Ndm/1Eu/YwCP8Xii825nmjGScYvBZsIoMQQBFO6945/7/iHwmOKxcqy+Trbu0YHgZ/0D9WFtGnvSJdZSTfVxpJPsfGy7EaJcoG0xsxDeweU9cpwbvkANRvM2nQHfg+HW55saxjPr0B0fLwHIk7Ex6SO5yRY9+FYOPF+Oj5s4eWGRxsYreHoX9D7gb/+9V8xng+M3hNEWRpbkWUxqy6fBUTpaHjjm3RobxjPR35qh/nJIYqRDNe+RnktRK+aLITgJAcrRgSt0GvDuf2MqGI8+m5JjOAguADt4CPBT+sNnx1Z/DJlKWAPC0v8wkxJa7ZUPiUUasouswly6F/PTioAOg6YGWZjx9gCx128Ps501sxhmJWSoEY5ZOu4AKOxlfS0CQ/aB+xlnmZoQ59QOSAfPzB9QuZiKWICMQ3z44M2MioE+x5ZmtQUfmLTs11lP9iXmPNCJppBzQO75rzLV772NRW0nShgf69sgankhd42B6LbkwJR2RIv7tUYuDmN2+YF6ujCr+d9//55h5tl1t0hbdDJvK4fcCVVkomYCCSHo4rmvyFLnShWrcxJeT2hqWUKlsDQ6KjcxwE/HsihKpD2N7w+3iHrpLh8Daxp+O/fPqA6odMgj68Yzwf6Y3DK9fwFIQ/IXxrWOPHx/R3nWlAbADi82CCpv+FnQSZWaxlOB5Cx+HhreD4f+Jd/ecObKP6iDVMmXjqBLgRVPhAeeA2uwfX+ARXgGJ3xrXX0HujdoTMgFuhHbo5pcjlf5waBjipfUcNWvGH8ZGGL2zPORKUNajw0lHxoGbKC2p//HY7HP2V5tzi6d9hb7EVUM03sb9fbnoP8CeRlkL2GNoZhTG43kBO8bpH6HcaimhTAMpakItiRJciMk2OQUnnzhj6A9kHGsm6BJlDVRrZuKIe2agPvczhaw6bKi0VkEFY0BJoHAsqqxL5MbIeXvE6loakkojVF9ZOwwJH7YNT33QXE2HEYJUdA7kq5HDK35ZrLMToSQmd3vVuj6N4r/r3jT0HOGDlMEMkgLOeCTXaBmWMh2QymefMtrkGThfFqsjfWBAR4e6Mos/wewiybqtiBEEJr/nDhkEknykSk3oSzFIiutbggZY3RHNEe0LeOL/8psM4XXt9+Q0xB/3jCw/CaJyJH7EZToD8gnXbxx/NXjP6A4IAvwce3DwIbYTdHUw4pcwte8DGgjc64JpPLOXhOr/cfeS0WIofuMTMKXI5NdWTrYT4lu8Mm8oY3zbpuXDO5IhBGTYgjsBAM8NIhkr4ejch7VWvxJx4BsGar9O/hKAxhm7w2qASCOxYKgDmYTQkK6RPMlXaGvkix1xuBdIrkUk8T40B0Q5ht0Og51NTM4AKcsxgRQ5jAzpPBZWJnXIZACDfZ1ju+/uUNFoG39YbzNTH6A6dNfMwXOENrbfHwdaW5PumO2zD64xrxkZ1gNYctQ0d2mcWle2iDX4scMBsl9Nc9+bcAeLpeJIOWG/1Oy/nMVsAqT6IrjFeYj/2najCo8xjt8vX4jEM0GZzSGrTAnkOFyg4z0EWBFz5nxbZaWF6L7MSSQLXaOxh4Pfudl88r6bIJ9wkRCi/fxgMNDfPH32AeaGJwBfAAXAyv7+/AbGj2wldpeBxP9F++wN8UH49fMc8J/X/+K/THD7Tv3+FmeJ8LzRs6FLIcOg3vrxMzHB8vgvd2dPTR8OuXr/j65Qv+j7/8l3y2G2J+w5y/4Xk0PJWeYGEB4AeWAyIDrQFvz4YGxYACwZl2itw4cg1ZsvJWs/EytGsrNrA2amw9ZOl3CkBLWjY0pT5JXNB7mk6yzrHnAn7qsetpVWm4YZu89xJk5KuDt3pWGdPj6g5FnvAt6Yw0UCyUoxTiINx3go58ngCrnTyf+HRJjtiEQYGGcJozZqEAzybooRwS6zmWJMGEQpJQCChypMnEjpdk+ZOZq3PIz6we0AiqAOWG//LZ6lIJPBO2kVWZSp5iX2L56d5e4AYbD99vR8GUK9nnOsulwvUZBEMUfGdi4zlq4n9Uk9NSKR8WeyPlB6g2wMqibvxUnWyWVqj25oh2zl8C3Cez9S+/YqRQj943zivYO+5Gge410BJAFbUsrjugNdhOIWhkVhYApXfE8cXQj4Zp79AeaBGIOXF+vMMksCQw9IneDkg7IG2gH28Yg1a3bsCaL24YnaAGTXIuUeTsD83rXH4c1Jq7OeYk02BWgTPK6uJ3AKdYnbzeHtdCj/RTyO4oBh/f3+sgy7SzTamumarr8g3F+y0j/5wjqgylihp65wKUg2vVU/0Gcu46iQI6EEGkXmsv6QCqPdjTMbaCtmgno9VJc0oI9Va4mMZVVG2jhskXbfxNgGPQFyqk1iYgOam8ntbzNRlAznd2uWkgXFNXU1Qlz5XJIQPQaCOn/daIlBIlWyGSvCjgehEKXZEtwRVRypahqF6VEvUl2MkowqT0FpA3bfx7c7bYz7DcptrvtusMd019T+P+jEPS6E5Tp8DhrHeAE1l2zPPYVye5tAAAIABJREFUJWxuGJ7wn6LklhCuQJFsDw+ypJ5+O8gNhkxtV3awHW1A0eH6AzVnTFUgnTHu/DCon3hh4csz0ENxPL5C2gN6fME8T5zfX3AniD6NOgkEcEhjbDPHeb7wY02syWGr7fkL2lB8eT7wl+cX/PXtr0Bv8NFw/nDoeqefSQrp3QwfM71stKGr4DgUzRXdBAs1O0/2zCKB7Ll6fgux1WG1d6XA3igBXJo31IbIsrQqtTiaCQ4QaIMspC77Ofx9wkGn9EtKcYH6ir2+xyjAs5yO6/sj10f+0G6Dj4zRJdjd1hT5S4N6mJZxDGC5LvJ1UOX5uFyjXZwdwAau4Zu/2Uiw+MpY0TUdyLJk1oTtCirGeAR+Bsnn/7ZjE1qJpPCZsbFe40p58ns1dieWSpYl64sSu5Lw0xvcOBDs09+rZINm4Hcgp96zgFCWxYqR3qMs/PZ+f3D8KcjZ/NO2VhVuqDkU8UJPV8dL60eiWIWrwaA55E2z7dlh4FyfiYkw4DEe2To2YGY4X9+h0tCFm9QGTbn4IgJrvRABqBrb8M65f8bngs9F6k4FyybcDON4QtuAYwHnB97X+7bjhij6GHh+eeB5DKAtTH2Hp+nfEmb42gct+p1MyjoNZvQLoCi6NDncQCMc005eJcV+8LUcWMV+d4MyI4fuDZOR1i6wUJtQo1Au4MU2QtDyQU6Nhlu6pXbWNbOm/NlHsWwAALWs4fr1War267weWorjBHOcKkBtTYDTt7UDoyv64w0Rgafxaeot283DMc93nB/fc88LTDXcYhbWZIbeXdPm3VD2XIZ0QjXekzlzwm+WRqdNzLnwvl6YayKylKmi0F7nys1jPL5SnJyzZ6CeGZ4hfHG4pPK8NwjN9dLlAOC7uyqCw/nISmhm2AV2bWdTqK1cqrUc22kVGXQhstt/ay7XnjMkVVDMFwxk6fraFD7r+PL1C1Q6juOgh1HL0pz7DoJ7Hl6NoMiD4E8h+iSjs+xWxkzAEyzrvl4fBJPDoELOgwG574GNIhwL8vWX/wz3hffXN+h54u3D8Xot/PbjhM6Gt+iwETj1xPjF0B5Ak4ZoB576V7gKhihebjh/fMOHCP57PiWa6/ehiugNUMG//Mt/wtcvT/yf/+WJt+eB4wuZpymAyoRiYjoHS77eP2DTWMpwQQ+aAsqZvkLgkEdRmsyl/DHJVCYE4+gcJ7AygVgB7cyklwXccLFpUcA8uxTHkU7hDeIEbU0begi0SyYs8umdnlwQoPhYkiZBifkNWxYtt/gayeTUM7V1oAHcGstrW+ZazPNygiZiwLTRyDKpWmQPwcWZBpjAs6eYRD+tEqg5XFGvzXXSG1/3qYRi614Ozz2bbRO42KZ1lSAZCK5L05W/Qso2gIzcaG0zxZAEHJrgFeD1kPS3ycRp62TyZyTfr84VyNeQ62cA5IiiSuwqwY2NASS7GncX8uaB/vj4B91V2HFMbv+2m+OuaLqPml2liTAj9RWaTxANqvjz5g7IwogjT0QR4BC6nt1cXEtXd0RRpMsslxgFvNMXWgo3y/m0Zetw0aKijQWDLlA3FkpB4FXtqH009KNTS3GzJfesSMruXPIc4rlgK0tBr5xDU6JQxS5DVP1QcnHIvoA8r99dRWbaTBVQjrQbEm9kfFsVt1uh+2GKnxOWqGztk4/9/ld2V//kwE+gqzKi+w/f7z2zpCzX6MViENzwuvd+7OsevrA0Z4ppbIBZYanGdQQoNo79ydLJ2LGzOc9OLQuDheM1ORXaYiXIT8Ozur+5BkQVYzzSo2hdF0RSGJ2/Lh0Az7vExZotxD5/n7JUQCsOIgE7Yq+NnwV5DCY/bSsRe51s1izDtIju0F0/dS8RfeYxxoBIp9i4AQVpSp+mCfYrKbvLRHKxgF19QGQpPeJ+PplIpb+JNz57ZTxIJyVmjtoaRAaG0Edn+gkPYLQOU2QDQoc4Rzr4ihTDMxY2BXp/YPQHBz0qELYw3fEegZ7OtF1YDjDlBPqvb1/wy5cnvn7peBwdrfM6iKf/jwYWE3LMMJb6DdQyIAicyvBuj3CIzeDUupRsr1K5Yk2VTIK4EKXfbdlGE5D9HO20PbWCFFaTwZRQ7G5/1V2K/bzjtqEV3fITHVCRIb9bCt/nM5E/vDd2YH9Fbk9LBav7U6IiW2siKUHQuL9qgqf6WPAMDX6Bkly3xS+oZpeUXHwTX+7aM2rQa50VP1pk+zrulTv6O0FYEsIFZraD9Y4v2A1EjCGXHXApPnWTIHWdYv+tYojKTXSc/x5C4CVyfw3+ZMXna91cz/q/d/w5k1NR4rYjMSi02z9mpnpQXOuWLcpKrNkwbpTmgKjg9f4b1lwwcPOP5dlNdQAAnvqE///svcuOZUmT7/Uz97V2RGRWfX1TnxYgXoIJjJCOGJwhb8AEiRdCiDdAYsAIhMSYAe9xGJ/uU7eM2Gu5mzEwM3ffkVn5tQ580aK0LCsqInbsvS6+zM3+difLw/0menQ1NiPcqCU8Ot73pathJSppELRutOYI3QetCbSCqSHcqEW4vfwN53nQ314p9eZThO2G9d0bzOFT0YsIu25AwVrcuwoVRmzQO6+WiLaF4jVXFM/1CUqi0njQ2ZE3NteEHnNNs1bcm9cVVMVL/iIJdCR/aRmhBDNvKZ7B9K1UT0QrFSklcl++/9T/v6b726sra7bIp/CQmaLh7p1AphmYhcCQ6LYLlACsBdy7Qh19ckrz9dv2T+Eda7TeuX+583Y/eX1Tbrcb+21nK4Z1496+0Hujn5Xs5dTM6IePBfDcmU4rQvbMKk9PlFrYC9QOb294ozNr1GI83baRZ9TOhvZOvT0FuDbQsKyJ8Impg6NIUtfIAN73G6XI6Kdz3u+eFN1bTLveqOJdS7t2Wj9HYnpaWj6xRMhht2otfQRL/Du8QWOOz5T9Fu5o96QWT5Lv+TTsw3mo7s8OkOsewv+MsGQlXJXMhHoPFWfCeSnbyH8SIZqSSgz2izBcL/6Mhiv8DEHuJemlPMUokOpN28y8wV5X9puDnh9++MzLi/BXP25stxu3l08837wSSjZB7eT2fEPKxv6fPPPypfGmf+Lpn5S3X/6Jn+4nP98PpBqlFp4/vfDytPMPP/zI0/ML/9Hf/6fcnm7UvVGqjzwpIrxsgjw/I+WveL6BNvhcn+jHya/3N3prtLff3Ctz+riXTbrnmdFpNcqVu1cCPd/2UUUl4MM+Y3WjZjHCTxq9grwax8ygmvvp30728EB5LsXu/aoM914IsGSpfBi5ZRMlve4hH1WxLK9l/pZ5bpdkDEq94GRYXAPMLF6L2OuY57ckgFCNfJ0eXlwMTZBioDH1vGZeT8/Scu9avBVoKrPK1uAJL5LwpiWwBYAxmfmHa76LxXVguFMAcO8UAThchkgO1jYPUSVo2oZnJcNiMhBX9hYqi9EgZJZQLP4iZwaJezgdTPq59pgKnx3bE0aVIcViuV3EPRpu7+j7ICcBzrdez9sawP1RSU+UHq6o4Y1woUmEHSw604Kg6cqPE4x25BYARyVTFAYlKs3SYS3+8BW3PMhYpmaOixHmNaVuFO0zm11k7IGcoSQRrDcrA/hjM3lXJPJj8Dwb91Y4arVgolHuOwwjG9c8b+T92i5/CHNUSOsLxnIuHONCSGbzqri+HJ42XIgfHq5SsBwz4RadD0QosVknyGFsUmOUQFuifwZ/DLFicwmyzNP0RDW8K+KKsNRKrdVBsLlHRy1LD2Xy1NIQzyz50AIcpyURj2t5fMOyqZ6gK6Pja42EXYYH0DAPwa0hlaiOMs2E2gxT2lgjIasJKmNsSlzfTAwghIL/MBMALQRF7LHhBVzYYbGmBN+moilSzCdKv7/pD6Ic8Js0vZvJE98x5/I6g39KKZ4wy/yIFEdBZTSKTD+RT9r2gggJcGEhF5xPJRLSt/3mpcH7Rr3tbM/VG0tGWTjWEfFS+Lob2xPcnm88Pd143ne+NCW9UKZedXPbKz9+euH55TOfn1/YbhstrOeufYQPEfEO4DGRudYKm1K6RHVP8FN193jydBpLgoN/b3cQYI9pgyfLyJC3+RwI+bha6GX8zhjPszy71aD7YFHkOTduYAjTi0qCmPQUoGMfyQhNpYzKfTBfX87wsBdlLuLQbPlV0tMCDxw87FtLT08sY3g5xObnMk3KQimUEJsWx+6kXmCAtnExy0UVsriDcd1T0uLHjv3jr0cCQrwxr99EJsjR9T7ncceeW1ZwVpIGZljzLvGbyvMVd3Ewp5F/n43+jCcng3V+F6XkbQOSrOreDguhUSXTocVdmj2qq8SBSlejlidk3+ZN45bneZ7Dc5PA6WwnrTcKlSKF1nJDBTMm4j07VhVqT6AM7Yychyi/zrwhNu+0vBWvNFDP76B0Trt70qjHUbxtApFMjERjPbckK5mM51fjaYlG13Psel8x7wnTx2C7yfnl4XFPTvhq+yQ4MYuKBRvn0EDakp626rOF0+uVcWUzv8KPhjgvTztiBcEbt+21cGrj0JPWOud5zMoObSFYbqGQ8v5dqUlOfUulKyC7v+m4/4rqydF+glIpT898ennix7/6EWiIdX77+c7RDpo2unkLAAdZnrReuVHE2OnUpxvb80s0VVTOs6Gt8WYVVa9yg0KVJ7o0rNzpYiiNunslTKTuo0ckIme4qjByXzyFSgibzucYdh0NBCUqsrbb7j0wSgVzvs6E5FpTOUbTTKKLKjaaInrvVe+W66De9+oWILjW8PJlUmARrFe0VdJtKAF0SvRx/SjKHC1PIu6eV2clvFCCyObVi6bRst88p0JSQRDWqo+S0R4GlHjTT6l3791iElUoJ9A52q+em9M3tG2cUimy+S4qPfj22cuBPz/RtXO2k1KNrXjo/TiPAEAb29EwgePtlfM42Oozz88/8Dd/83ec9VeeX33cgJlxu1U+fd75+7/7az5//hOfP71gRfj5eEX7ydvbF6Bi5YbKgXLy5beT+5dGKeGl4Q7V+Ku/+cFLup8L7X7y9ssXehd69wqdLj7nSprnxZWibLsLMKV4Rbt56L9ZjxEFRgnDNMel3Haf07XVSt3ckJRwoyWQl/AmdzuZnfU+iPTuhoQ2rHSEE7J6KrusDjdBytiI5yyl3cOITD9JVq3m7UQjRREHrNo0SqANYqyQxIDJvhh6AFvxRGGL1MoS8SkpPnDap5sXlxOHt+KoRRD1/DpL4BHAofW4rgQ00bF5OCZC/fhpPL905sq4bLhtNRL+U1bMwPYjzLAhP+pSmbA4fFgtqwQ9NrwXM/0g3+t9qnxfZmj24S3j5r5N3wc5i6stf30Y+pg3sCDy8R6JniRl+mZc2CzxvFwkiy6/EdsbjaYkrV8G41n+y80RbsaRNZ5KXWMxyVwOG90lJSy0wdQSJdnqs0yM6BSMuLWPX49MF5FbQ3F8zSZyw02Xp/aHuc7+8dBIPlUcmce9x2KubVMeVjq9MMOIjTWSvNWFZye88tV5j9w/kmK6CXp2qOYhs/c0NmZZzMLIASiyeNtyf4Q3DBslr+cRoDbWdKsbIg6Otfn7ejtp7STzoLZtc6DoZmiAcu/OTe9wnmSZf1qwriB1XmcwnlvG6pb72IVZzjzDkxlaHBSWe1gPfqx1/aKiqNb0Zsg4Zp7FvTxeTeVLt3LPg//FlXmWWxhoCV+NpaUmwwNokVdhungDp33/cTTc1uOFaSG99yjl2udbUrjHvcxqtBy8Gl2gw0rOfe5bMptHhiKkkEUA7tWQwYclgKOWGnv6jGiIROWg0U43+tpxp5+ntxPYbzx/eub5ONlj8jwo2+7z0Oq+U7YIuZnS9aT3g/M88S7igknzSq9QyJmxNDkteLd52bgFr818HFm3XXjBfbXV5oiU2bnWP585ONM7mEUPyxcSFZSx7xMA/As0Js15eJjNBPsgCRngb1y+3tFDnpulnM5oxbc/JA+cOzKXIt/PHl5PDBVON/e9xgdqyI7QJl66P7a5jQKDOGAAl/logJixmM9kvdqYk4inYgxvjiw/j+ucOUiPu09Wjhh6bN2usYjzW17bsnQP6kqYXqTiIbV18Qv2rcc06PuJx2NcQx2u9hIlq+PpxpXUsPRaO1HzHIPhS4s3eUVRNDZSZcMVzBkN7jRKxKqPS3Td0j3pr0ii6KyMiPLDGM6YsUQvudMIL7i/rBioCr2Fx6N6UnEr3YeFGrSzgx303bstl+iM2/G4cy1e2tnbQcGtX43YrYanSEt6aeKOCyzSMpYr+y8MFRg/BINvZTLG+iwC/UqpESee3gDEhhvZzxM9F6wMoeWuzn8BhAOgHT0bX35+dW/Ej59R6d5voRbU9jF7aPeuNDE53meTVdnYb8+k8Pfwp3Ca573czze0N3779ZVShD/96Qe2+szL9gNNO6037l/u3F9f+fLlC+d5cNvcsn758UcPOZ7K/Tj46eefKAJnEeR+IvVLPBvh9vmzhzrOV69IisGrSvdk96M5wCmC1YqVMvpFdesjVCUwrKRqoM2bPXpfIxk5Jv6+4mXBRdg2n3Dce4+eOifZ8bOE9WQSMXhLaJlyYw2ZGR0LR4645xOB7qGc214D5BSkboB35T2th3FS0gL5MBZynZpAR/BeWhFuTKWVwm90QtYAzniYr2zhRZB51Hg+e8wlG+XFaUSYh3pKdQ9OkQ3V08v4bUMUWvsVb2C6U0th359pxxvn26/YKXAI9/6ERdM+MeV8PcGE531n/yxs/+pvOUrhT798GWD4hx//ms9/+hPy/ELfKnfN0TM/0VrjeHWQtNUzStl9DEXdKyc++LeyY6rcv3iu5GFtKJMSSrPkUgQw7rHU7XBv19mcV7aSk6cB0ksWHjZSnrmM6VnlWYq/l1x3QTYQMc+l+2BPjqWbP+I34l08/W+hl7ysyf8+krlSKEcn5JTljkIUCY8wNeQxDCNIolbKU3oEiRCoqCIdD30qw+uL+R6uNWYv9smyRaZ3Xg2e4mjNfGTEal/ngNWat7giB/zRpJ71Mu4czOA6U9RGxV3R7t+JiQWZ4JZAZykAeQAoEEVHsb5xDMvfw72TO7ZFdS2BFyXuuVZG1WFyWj6zLt/v2PXPrq7KVx7645APEUaSUQhvN8hHA3l/f4QcSrSYn9GwtJ5s3HyegTi2mg3EKqHsfS5Um+8WV/oaYQhHoEYecfSUEU9WTjAUJpI36sOwmFs1ciOKejVCHENdHwzkPUG/jQfpzzs/w3xq+QDz7uI4kzPyw6vFtD6Mb4CUgeTzW2zOUArZ12jNd/pIut12mij1hpefavPcAXNX5FamNZzh8aK+uhZurR75A9E0AlQ4+4H2Rj/vaO+U4o3R9u2JIpWjnRznwdv9C8fbnfM4Rgni9nSL2WgOOs/7nfNsXjEnHsZ0MBW9a8zQo0ZljWHVmO3hiyvdUufyGjz09CmuAHKe0vCEm3kjS3FF4GGQEko7eDC8DwksLECXGxs+NWhu0+JCNvPBMLRkaDlydCbunh7E+NkiD65IxsXDi1QKWr0a0Y/7sbw02D+3QLyw/mrjlRSw8f+oGst35LqWWqO5mudHyXqC4o09tetyrPDUmRtZqbizmaJ6cy4yPlblFsMzo7Q/Qj1emenJpd4SJ3o3Fbht4kUS6s3eiha4d0xPn6FmjXYovWn0AjMHZqIupwigke5ksegp5GvwJGmwOm/n0EzyMzbXWsIbXyTz0ULBRjJ7Vowii7fexbhXu0UIRwLaJyidz+L7YYa/BJk2x749ZsNFCoP3/FGvPMubkK+BfO6jOFoYnAGmV9fH0tPm0e9h4y1jpEGZwARcRmbyvz+nxIfyTg/YSOktASA3ZiXVyCIM8CpxB6u6GNfCEp7CFu+PDHw3fH6LDhuv23wv40yEZzTOlyAx3zHCYUPlUaXM6qq42rJca3q5vDFsevPHYb9JfyYnJx9S3JXNuxsLFT9o79P6ExmlX6rrxfjK1boh+FRljaokgKItss5TTkjWoThaDsTt9fmbg5zure7nDCuhd684yYeX2cre5MoFhplFaafmDeCVWG7J9W135dINlYLV0wFaVKeQ5cjRSTiRqhKt+8e6LQAoXKTjQa2LLAxg9fCZBGfkI9AZDhvf3rldWQGOC6GsIpmf+Th6+fxCa8rRPO/h1DPYyLtn11Jo7fD8mNPvbysbCPTq2/M43Roya2jrWDfOqBzp5x2Az3/6a263G8/PP9B659cvX/jtyy/8/Ms/RaUBPG0727bx/MMPlFr56aefOI6D337+98MFX2QfwFdK5zzvMbbj9HlmP754JUL07RHb/U46oQSj2keNsm3RTdabVJ6nh9NSAah5ZWHd9oHf8iH5FpBRweK9IXPz1dn7hvDMiL8e32bCpOoMyYtjMi8Bjo7Aodg8P6lQpJM9c4q463qrni808l7eh4j+wpR8Pm2s2AdljIb05mCZO4cDs+l8970jsSdKLS78JQCl5uDaCPFsm3dg7/chQV2OxWBd9Q7YEj2RtMP5+uYxhmIUq+z1B0xOtJ7YeWKtcz+J0Rsxwfu801R5wxXs8807HkuHW4dyCPxyYLVx2kGjc2ROzAG6Ga0otXSqKC2D6MFMUhUq1OLdop/23XM6us98O+93UvFJyhf1ld4jqbls2UE78kHUJ1UXPCcq2/Ij4p4KhW7ev0ibgNSYHBBJ8xn/GEDn48j6CSNHpkNp4bxbEvR7VFxF2PFrj+WiC/HGq7NjXQpZZa2ZLRFUce4K2FMkkovNZQJgVga2UrMoDX80bh04Bo9LnrrQxaA4YM+O+eBAGpPhlRmDNcIA9qTjyKMpUeof7r1pMiy6NNCsj3OQAepRmbl7cQqJE66AKUkEn0W1YKOaUaJcd5XxDNZ7lpBhmXz8PS768zk5cbEpIX3xdaI7KeP7DN6Fso4LcyWr0Xch8ayRwiirkkpYGel3s7yGAZJ0ACWpvhijH0i8J3MlMovcQsinpwCETG72ELpb5j5SXucDG+gyNrdPEXV0LUSlVxTHpXsy0IuXrhIsnUBwAkUb6xlXKGnDL4y8fCSXIZXeCmoSo0u+ZyBpt5ws7je5aBz3A6kWL6H9048vHOfJl7dfR+lzlY2CzwUrtY7+kzrWKLphn4eXMZs3dtTeUWuoGGW/RVjnRpGNt7eDszfe7m+8vb7x+usrmZuzPX/m6fkZEHrrvH555TgOzrN7qOb25Al80ZLA58a4PdGO8GNbdP00t5Ka+fX04xwpCQSAOMdAUA/NbiXup6UzOTdpH20SMicmn7eW8CplEQiVDMVmFWDm/gwpksnDkrvMhedwd0t07h4gevKfhqehdDdAMll95c4PHw2S9tW4xbgaDakrwd3jfucaFpmf8dcW9RN9iUqpHt71DncelgSfH4XgeTgGw3aWALPNjSbtXrXTgd7IkSTmMVmK9pgLBIjRo8XA/X7n6J2fj4PX4wCEUne2ClqEQxvlNGoXZMf5HUVEiTqvkJkevrRW0S4+SzCfq7incCgpohBEGCAxZcdQvpCpkWhUR5Lnw71FPSpPRWWEHSQG3tZaPcy37WEWRshi8TC6Uv9gsByVtgPZh8dpKOIAG6nDxp6KXNIHDxmW8ZPhOV3NzvRgOdN6DmjiIcqs6PSxIzIqqrLwr3hs5wFTrbvO2yCkTJjJuP5YZHzA++j4ZwT3/ebPaUxldVX02ZznGQoj9s3itfMqp9RH83zk+sUBVr02t+JawCRL1ZeONwZ7Dm9XMd8/noAcz3Lx5vwe/ZkBna7G/VpkKBzPvs8EvrBmgolHBCpupJTZMr1EkC2mRMz3jHyREqMgzuHOd34KN7qZbzaRyAHynAUNNO3hDq+EcX6M5GD1Bl9jYSPGWsYxIk5qeeWTYS3cmQGGQQwt4bEyoWgmhpZgmOm+zEfvcmVh/nSxrWsVj99WBh0Ki+XdkwHm77MibRwtQhmYfNeV9xFUJbwnTz/w+vYbb3cfdHmcd2oxb45Ww5W9hcV8xofDZdsOBzlqZ3gA2/j7bX9mqxv75hVZX97eONvJ2/2V19dXXn99Zdtu7NuNbX/i+dNntHda67z+9oXjcC/SvhVu9YaitN7pqt5aPbb0effxHMfrnVKE2y3yEax4k8DWKJtb9h5+zVbswrY7z2xl83OfLaogivO1NrqzrvNTPMdSZCSyDmsmKor8dyaAtwzJglcpSMy0ihBxCN4iYVFGWGx4M2Ovd1Xv3ty6N99brbMAOGNExAdRhuiGeE5TbswRyqRN/9sEODJAzmhyF8cczqgiEEDbeirrEuWqt6i2bEMOpJg3axiKZsVOBykdmjmoiFAjFIq5MdUMaEY/vPHgeb/zep7809sr/WgYxYf6bhUV4eiN3k+KwPPtBoVR2SYyw1PaG9pOtBvaSwxttMjnKlH5ScjBFKxgpQyDIgeazoyTRYGYPWjKrOvIvA1NPovk7lJ9OHHZ9ghZJMhJgJOG7McKJ9U+c4OjuWH2zZGFMabRzmQY4vrH+s3QU0r3LFIY/pehtBelJwlQwjyZWMaNvfg+woAlDxsDqiU1hH8wQU4xEBFPpQg5kJ3RE7Aac8j1argIDoZydmfKkdWwmZxPOAYgGhSM7unzvXlceff7lCSRk+66Nz5ncdIi3qOnBgJxr0/My9PgetUQAfofDnKcCSO+L7mdlq6oRng41C0W0sMCWXWgmsiYKNUU73Bkcwk8hpnN/2yUo08okJsikgJN8Il4czLuWDWxqKpJWwFPjjMLcGXhnszjCelmtbH/zZkHRqiLsAAxJSc75xyiMU9HZrULFnZPepUWJD3yi4ThiXofzySES84rWZG4f9Dm2uXxv4VmZK7zAFwfTOfbiYiy7Qd0Y6s3mirS2gj5ZWny+FfAUHo7o2PnaHTOyAfYPGF5qxtFCr/ev3ii5HnQW+O4v3HeDxTYn154+fQjst3oJvz880/c3944uwIbt9sLIsKX8yR7Kmk0RJ+DQAnrTummHC0fojcUVHxeULpZMRnP79QzjAHns75YPSU6QY8KiaN0AAAgAElEQVQcrx7DGcQTFS3Ou8XsJilp3sVuNO/7omh4LUNICcxePznRV2JwY+wrMUplVmj4XQZbeWK1YOEiTtstmzR+nIIaSfWr9ZOWx1AyvlYlN2BaeZkkKT4NG7L0vMBDKbxFfyaLZ9fp7U4mApdaKWWnq7/ms8Y0GkKCbD46hW1nkxt7eabhI13O86A1l5+14rPSzNj2wnPZ+LvtE31Xem3s4oJZd8Gqt6sQUeouUHykgpmitUfmcInpzCEOqrB1D4/cbjdkauHJxzHLyucGBstGAUXJ6scIuejwyKcRx3j0HVcyJUY8KETRBp5jpDspY9cSbBFm/ssHkrWGKZQDrHo4mghXZR5I7p8SICRdKxJev6z4TNnqKS8hWTPBxodVRcpPyOeyPIcVEMRyZ867bZGTE3K7FPHJE0QFcIIkMleRoZNF1Muti1Gi+EAXb0d67RwkT4gjElPhRQbI8TVIQBJyZORGerhXTHz0hKrr/uwJN2CLOyJyvzpAiht2fDQMEn9/7kcZbOOgZw1VW6pA9/S8n4z0jv5MTo4t3+dXencsUYBFIq4INcI9ubA6lMPyYIXlpvwBKTZ6lkyFEgL7AeDEAoSFnJbHaAwkkYY9BB0QScry0DJZp8YvgqqgWp3RhWiq5Z4cVzLvhGucokTFgT/H5foIaGYJdBbEbwtjrsAmpPWURyvYe7dwCxh67HfwgImZV7M46T9ONwFw3s8Y7NYwVba6U3ujrAMX0RCqcb8FTwjWYwDi2KJ+v1KpW06mdnj0+nZ3D8xxoK1zvh205h6fut94evkBqTtdjd9++423V+8VIlLZNp9rdLS7r6l6CCk9eYvrkZwMf7boch1jBpwtHeikJTSazulJdn3OIZEQx7W0s/0edWRfBzDNSfchHFzYRhjCEpCUSLqXweKYPeBeG6aTTj4Thhd2AGicNz3vJPa8pAAUrGSY9gMZyZRoAeZAp697YhYArIA+5YVXtMwwk8eHs/JHxz7zfTlDzT7Z/hxHZnhm/O/ao1Kyx84q3vGasnsH9boN8GvmBqFE7xCpGj1TBKmb9+6qXhRRTanWOarQi3F2LxUv9ZZmtsvWGhVyRNVKmcYn4bm7PXm4rZ195IGlSqyl+LTp7KytOZvLr7FYBsQm34naOF+eyuIYIr5nSimU7l5A90zmc1qADiEQP1gWaeu+t08QNax2soeu2LyXKYdt6oSRRJvydr349NiU1OQLYpofYQEW42/pusnDVlnO5e/1aIaMkNpDGbsYEs1WS4KuqaXp6l/+25Rj4zpg7G/JbR33KRA5hf7SMM7SFhgAOR7v0POpn8YdzMtNRbiwwVyXJRYUr4to+i8ejTHfvn7N30E5fxbkSBzLZzHVWKZGVu+UUcqZn3nMmM8NMVCYxSYpUd1uhOcmQYxbSGXsoh5xVGZfAxF6P0JphAu6+nHTez16PayIJICKA40S3hpP2xzDwPLOJRLH9kCwxOBIm25ov+fokWO4QiR6kLCAFPN1yPgjyGJtPq73Q6rXV2Bw3kquw2PYQuYH30sPmQz7ocoJn46OQe93V+DF53vtxYefajTmMzOvdgrF7wotvkOEdyobUTYtO0KlqZfLqrWwsL267mwNK4Xbpxeenm/cbpW3L79ytjtvr2+cZ0O6V0Sd9ophHp4K/nEQWgLUQOuesP6gQFGsN6q4R8lBkMzE4vDkFIumdZs/m9kLKUJTizCoAcqJeVg5R631O6U2UKVW33ujH4oEKMqwaVg8A2RZ7sO1usKvZQ7PeMcXhq+ntRGeSnlghQ+lEkbO7EWSFt80Psq6WTBigqQrZinZazHeY6ASCZ8pvhUfhRhvEXUJ6S1kMRqqby7/pAN3QCnmz93LnaI8mAI8UTa3kG+fnym3jeP1F3o7+NJ/w0x52qJst8QzVP98xbjtN6xuPHVv/ugOb5/+7lu4k3EIQd2JoBoYzoGMdr+f00feP/CupEEUwKtIHfsOXx63B0Wm3iqCDDmzrGfKouLWvBVvSOphFk967WKYdBAH+lK2pefJx1Bvdy9Ei3JpKR3v5Jght5DC0S0dGGGV5B8h65lX2b7PBTGLAgHxCZorz/U03EOeFaBEzmjKbo1cOV20QY1n4G5ghkcmAJI7FXgwXkbBz+94y/x09lXzQLBRnGBx3Rl8GT2mSuYsxbGSR96fQ+b6jeqz8OQNZ0QkOfs1RQRjQkxn8ZLyeDKeRLRojynrv0ffBTkyvmycIL0T602MmPdAMokY3994tqefFUl5wfMnBzpZcujn0/FWIZkwl2Q5d15LXvUDYEw7MC2I4JDhqXGFkypA05rB32uh9GpY0A/Xbs5QGkmpCebGFVvely2uxncqRcZbB4gZt7IcKpPh1vVAMnFwyVl49wQHk5WVUT6GeoT4fDBiJOCJzxDqUaI9PH/ZJyHZfSTQWrzmFRqbxMDFyOPK2U1pWqQ3w1vu1wg1COd58Pb2SmsN7UaNZ9c5XSiXBZAHKJC4FlMNJZPufJcq2mM6M7HZ34GFRyETf11CAMT58ln6u8atMCz32MoqjSJbMvuye2R8yygmuV/Vomw/rmF1l4+LWDMD5rVrSrsEOrKc5IMordJM/EZ07Kk0kObAWgIgZ+PNAEbjvTOh1O8oxaoO+RZSYFR5Rkwi+nOFQZYNAgUHTKazoi2nCYl5p+Gb9+hpB9AVFU+ap6TSMGQzn/2UcmwvWN2Q4qC/c7pXmCmX84Ydf4hHScO7K5Hv5c/Q31lTGo5HHDwkuCK1qdxStpLySh7F0XvZlO9NreahrmhIavN6a3xgNP78QMr+anPnxHMcqH2kRI9Gq6MWRQi9JKNDhx9mPoe5eHG3mU9HeEbNMvjh6yphwi2Lm55aG6pbwqsrDoaHRn94Gt9+zR6X+H2OzPzMlB3zGDaOockMkSAzNGUsTsq9oXlkPfZkDccEMnTAempbzvfVpWXYLPfzuMzMN/x9WfRdkJPJvW5RugaugcDdmxFIzpYy8OHODXWbwzzF3W3+Fx3vsfjdk4fmrFQ1HZ1l1WbdftcEHB4CUlFEhVq8+aDsIOpD4bAcpplho1ibMs9smuGAeT+GxXUTHoZcXh857wqnk07wkaAX1zqZ0E9YFgZ6ACeEQBCG0F09eQ8AhQApEqEdX9xxntXFaI9neCdHFq76MHLFUhYeTk+WdvOEW8v0p7ju9I9qlCqW6lZidaBZEU6Dbp3z8OomzgIdtHmeQdleqFtl2zd6K/z20xuvrwf3o2G2gRR6KKwxGd0mX0aXGGqtCBUx77Ex3MHl5o0pj3u0ue8gJcrBvaR5i7DUyDMbAD4ycMJMKflsF3e2pakY1XpePd7pDZo1aveZSbV4fol7TVOCOscW2SK3qEW+EN60rla/d0sWlGyOwOwmDt2MZpaGKyPevsmHstG2Vc+T6glLJpgdsjEVz9LEND0GaTpmiXP2K/dHEAcxvOVFyCTPK5xeh1ReDsxByhOjqkYMzZkzLQWxUbYbtd7YXp4QhKenndYP+PnGeR7ol1dMYdOCFWG/CUU6RXwsh7bGsFNH97aTKsptr15iXII5IjQkECM6hPPoixdO2OpGjxElceFjvlpWpm6be6ZO2hiRk/Pv1CzkccqmeA7xGIqFJzNqbxDvObaGyi3VYeaWfSSJ8/rAqUYAnBJ906LMSio+Uh5EwwvLLQ6iw1OayezoyRgUa0RVHr65LPfU44ZJOZ0VWJJ1T+GhMM0LFkQzZ9Q1DuoetR5CyztYhdyKJqqp+LfUCcGr0ytDsv0oAEvvikQ7i2wiqNFIt5qXl8u2hH+zpLQAS6Qg9VhZAMoAQmkoDk8q43q9N1O0ryhQilFLJFbnOUfY9dtQb6Xve3KmOZiPYiCy4Rp+B6wmI/OYh8JUHtOSWGHmepnrJyChtz38PY4UDdfmWITFeyRphWc4yZXGaq9aKJdshJT9CsZ92HtjZeGMcTmO4NOTMEHFzBVYke0EOJNNllXPS3/3St5bMBX28CZ7WOtHgPPe4P5gscJovpaKPRh4eD3sgUkWZLZe6VLNxwQjigXAIMp2c4hlwXGRe3y6GtYbran3zHkAhHmmhS9zfYPhCxLHTYDp17RC0sGDZebL5MMbPYrCm2fIQ5uD3LLTG5EPLne2jZdB0Zg/U7RGAudYbVY/oXv+FgZYBEV6VHNPp7diAuZck3UH5DPla8b6C5KEa1/6XDP/ITXV1/JmNRVTgbgcsLmvy5RJ/t9awZUHc0U91y73dAj6aCKXnmrr0DmjArMA++iYLAKlbzw9faKU6t3eu094d4GT1ardS7MHX7uscuAqkWxsQ34NOQYM3ZjXaksu2/JaqochtxYPwWhEKUxg+/hE5mvy9Wu2rH8CnPeSfngEP5rMn7mswn2QfP2V3inx2YQzErB8xpbQDTJytyCHDsiiw2Ro5lwnM0Ze6Qgqrq6OsT+Dn2Nfy7LeU+FP7/OUBlMmQOyVPOQqHwLoTA9yyNpcq5wAOtYqNNoiK32JH6M4X/NT3PfDkeLehZEOIoXICVLf/xH+HAsIi8z+Nv0zw1XzHkaSn3jZdo2cnDxxyduWbMCV5XQedlJb5kxFaXdu3BL5BVly7uEqH0CWCUZlvTLD1VzvkVg1GWIuajJEls45IJHwvCBCDdHw4C4zLzXcqt+zaUJaG0BJtXP08ECFp0hIAeLHz2ZuGZdOYTTjn8E7v8MEySCxqnF/NoCfr/Hi1hybgHGMR0/OR0McePn0yQX35g0ctWen1I1id0o/aDGV2zvCWng8vLJvALsAQ90ywMAEAhgWPU2e9idMjb2Fp8ca1k7s7JwxM8imWy8whOfR9N4GICvBG1W88d4uO0qZ3r3go0r1KoMO0QkN3wuV9BjM5pjRcK+M+smRjiYByMbwS1FvRJkl3mMza8T8PYSh2keVYYl2+6OXlRRypqmFt9QD7ks+2xDYM7I9DHVRsJPoJDU8rjdkJEJ/BJVw4ddoMaCRGN7JIZnFwzrjPvK1HEPjL3tLiVRUMtrseBjbR7P4jUc5dXVl4Y3ZIjxVZJRkY3jVRQhdbZ37a2crJ7bd2fFk4337zLZ/gv0JNeVHKWg/6Z9e6f3kfvwGrVDODbUD42Br7skup+dnHHp6IvD25I0Kz9e01LxCbjOkeUTDmk6vqJQY4Gqc2jBzb55XEWqUnfeQuUIPIC51oxbPMsoRK87XM3hIdpOuaaHXCW7MZ/+5ARk+b/UcRgSPW300xtGGaRoY8TXcXz5M1PDhrDF/gjE+ZA9v3nCCGXae0Pro2SIxlofkI3dPu3cxGtFksrqGLKPW2JIOKjrx+RotW0KeeG8dz3miywxhxXM0IyonQwewhN5CYDp2WfVJLEsCGQFUnCd0Vpj1FqkWVVbY5hQhrPVZJn6dYSyJ8Q7vwFD8yyiFVykTBSnEoFCDzZBuvkbNRqNqiHyh7/RJ+T7ISS9OXlcqyCznYr7+WOKcNC1M8Y/lzhhAJ2TLAAiPGJFhPQ2B8js6+iFPiLS05kEermsBAHk/K+ZFgtMWtD7c40oIvQynTXfwuLMEOWmp28xjWK/j/aC3vCZ7eOcKdNLz8Qi+11Wb+Fa+8fd35/ogSuae2M0ZvtZKrYVea2zApbqK3AD5GBIYz40BEta7VxZkV4WMmVskIFvX2UBwnfgrgxOHVZXrO3g3LRkgh2A6L8YfcvPybYti9TBaHFOW0tlpiWXujH7dCWB4Eea6+H+e2CyYJ40ucsaWvTt/kAEUh5SyKSwGu6c388FTM8TlFFIfqKDGfohyVDemM207/o19t+QLxP0Oa1T89WEMhtVquXHC3Z5qgNjPxlqnGkbc2PezosZwJdaB1hVpB3K+se0HpexeXSUWIVCj3J4pvXphg3eJw8dMROdmLT68yPThnv3WyizzeL/FzYYcWp+jjkGw81l6SkAqpJD7uq71TEEwZiUrNuXSg9aLU3jnXQfVIq40RbLi1vzZfTTIWfURMuRyyta8Hws5kiOERpnzsK5CBnXzGXZ51GSsxesy/2YeK+qCdfOvNWM29+aMOJONax+Ol49w0RnvNeeDJ2fVm2ukIS5J42CjsMdspIlk1VwPI16jz5IXXZqfWy1ZZSpfZmL71w/g2w997jmbfJHXOvIZFtGbmkIf9f97+jPVVX0+/kwAjY0x4/hO+b3mIsUCjR4Dgs8j0mRs8xI+NRKWieV5ZNyJGA+5HLMb5PuFy8Ur80FF74asYHyv/rPse/bxTObwjZzVMeN7WPr5e37l53JT5DVZzIZx96Ug1PmELIVpABNLgRoCPQTatxjYlZ4zg6WQfU8PDLd89uvQ8AeQWzUaLrpSC1WEvQhdb3QROLyTcQ8vzqDY0Fk1YkMbGRITpjbZ0FJBnslS4a6KljMar53+dTaf+bNseikZ/orqueAfNS8j70L0zMGTCEuhBljq0Sa1bnv0VDnJPlJZBt7TCzM7jfnV2+JtELeYrJ/+aDa3hjPGLtmML0vLF/BsXaFD2RJsuYTUGG8wXM8MMR5XoA6oCO+GELs59p14+3SXu4aufZk+GOAA4W11cuFdKKVGXVjcYG8YHesRFo0y8QSpGaERmTkkFIuQFf7HLazNkvtRhgDVLtGR2ueMqR4u4KWAdKTeEDuw2mmmHK2zvTY2+41uxq3/wB5hqlqMKhXZdlSV7XajH51Gc690z8ZncJ5vHpINBbWFEu5lB+sjMdwVVsireG8ORwTfQ7ok6GcCQlNvfJmVsrQwHKp770RiMnoJb6fNKjuXMzZy1STWUtVo0rmfh8+TqxuCT2CXAJklu0l/IIl65+fsz+X2yeagJkamJKjxERQlxqq4AULrcG8THZwdObvnqJWKWMyvK8lsO9l7TU6B14Yd6l+htJ3/Uue4OkwyZfEUgrV8LfZ/KPjpKIj3Tc1Caopkeu0JZvz1U4MTiue8dsw9hWbskYx+9hgafEINnp9d2UFK95lb2SJBZDguCGA0ErgzF0uEzC/NVPXMPxzGZCKbM6IlCpaes7jTmX7wbfoznhzGBU9ZEuh3KVeebjHGwxgqePRdyJuP1R0JR19/CdOa9RDVtLbf1w+NsyYQkmzWt7xhoPV1JZbzLefSoQFdoViCnN7HzJcV/CRaTm1s4x7nRaZe4OH7AohifS0SrlzhyPLBPJhMi1MSxy5CYlFmDrJ4/Pu6/h9ICY9zY2TjRBNPkKwlZuQwFWmOHOjmU8n1IXHPn1GJdcyBry6MJYYqykDCnrMzBfzqXSStl/EcywM3j3dmyGxJWM3OuvkZGVvVr294+BYezBEmI8PNQjot+yfVj19WghIbfLF6BXVYlfr4vNMdvMbPZRYIPPRgemCRrNSDKXpWQL1c2wfqpwH8i2CWibJ+B4TXwhuJRdk4Et692QdnGBADME3rSULJPDYWmk/FAVH8T6fss6FdXDloEaTumJ6otvD4Guf5Gu9plOLTzB2obcuzY/CHFGGU+pXqs8i0DsPPxMi5fhpVixYhEINFfsqUFQKrF97vy8a9TIPLXy88SugQ/MsuXOSs5LlCWSewwnmtq5KDPh1Uu7fq29b+X45Gr6nhpYnLD7CB4WudKQUCIhE+to6I4kPqDGrOrQKpFrl4toAcP7ArZwVpmB141Z56I8eUA1Htu9gwQan7godjsPGwQJgAdx7n8fPBMONU6flw3lk8Oeoem578aGHswUhw7glY0mu66LLUXTDPZVFF5flkbjzN8FRKugmAxpoNqRyyTRZP04MuFYrN6sFv0Z/teEwoi/eKe6z7CKPkJT1+3C3WMhhK1MaFZ9lmzJufXyX9dTEoLIZ0+Y1Oy8rf8e6Ch4BONJAvzO3qOic5I9TN6DSkkSuUHhzPEWnNh4n2sw1wQ2wExhHjQWTS1egpwOyQmUmpg78T4Ew9+tDcb/zwKFC+UmiPt7jcv//84BD6cHKf3yY7tQrbJpza0Q77tvmwzeOOoTR8Qzw93TCM1/OLV3Soe1nqehOxuXx4JDG0Vbj3BmIj7yXDVj4OIj8cVW3aHtZZ0kUfCe0Speqed+ZVhl3CS7Lt6xI7cFv+jZYC43I9PwuTkTjsJlkCuAwXjESRhLYMvi0BFsnLjvyUyMHJ1X5IFn3wGs4OFOteTYEBYH3J2cmzp5VBJrEuLuWPIAGfoxVeLtmQ5gNdwedGdTkw65H/VLwx38P1Z8fmrzYSOdIggY1lYuACWrPc27DhWQSw0lE6p7i3o94+Yf3Vq+6A3o2311+4H78gxxOlbDzf/opSNrabD4PVHs9w5BX59YsJZVOsNAoteoY5nx3SA+D0GCaqIwWkSObVJB+4F6WEU4buQERhjNVZK6j8xpwPdQmfuOyaxpUtfOPexzKN4vAieR6UQzIfmu7DRKXPKdofRZkjUggQ43MS4gaDv6uvWzZaRE9fC4u2ADd1sBj6KJq4MDyki5JH1FsZtBNrbxivqBhWDepGXEAYPg5GsnoTiIhIgcgDVGyMgcBG5MyfgfreTw/PiCqajCopiFCZGWdUCWa0TTCaebf1NG2y9L9Ffx+Rzoaxq8QaFaSoe2eqTpmbQC/1vuA5c2XquSIy8yrLrLgFULKnusVaR+KxY8HFWI7YzX94uCqEq4Xzq0zhOj0qaX3nZ3R8zhOTZaIusjY+FjCqztJyzIRPLzucijyHyQke8ljVdz4eyfebDQdIuqkZsfj5qXzJP75adMEoGk9YfYOK2vjy55LCMiuz5pLlJs+k2VrCg0HinLAQcg0tGSGVXBxjUU7vEYqs+zI36bjhsSrjKc5H9B3I+xciaw5ifUP6tTbTcIk2RDqlGlX9S83Q5o33rGVujO9ECyHrDvsUusGRYWGodZ9Efzb/ao3eW0ysjye8AuEFFGZjvJIK0CLZdKwxjPSMxawQgyJ1JOYKJZRHVt3FB7LBV1pPsS+8AWZBWUJK6XVK/3UqD1n2ywAeM5RlREsFeDAIhk8mPJR5vDF2RGZCteGKtyyJu6sx6IriA3lJwxAqrkgk9lcJL5gVjWcAFiXBg9dHdnFa2nM8zVRI038W+oK0I0eIHjdgptnZ/D1hH0m2txjhA/HnLTr6btlxBznBKrXsdJ49BHQqae8Z2YgugW0cKxql9vtJ1+4jddTCNnQvQM1S3hwH0yMXLJPbo0mdwJCl6cCYqQK+HiMPPau74lehsLI0cY1mfjBx5DSB4qK8DI2RLWUxyD6epCSIifvNmV4DvGvCYyR7HlmLC9YZijF5qNJaW0AAvh+7wtHg6HDXkYJhoewtkEruTdIAkjhtFLuQf8t1i+t2EbXk3sSXBT9O77Ufwyv2bPCpOz9Dplrq++T5kBsL7g1WZGWeEUrKWnSbuYrTqRVALKqkMpVteKkGbggWxPdOekv9/vJ5zd3pSdK//6z/jCcnNwpYLWHZxr3BBCwPLrJc4WhqtWUQIhLPygQpo+1DylsRKIUq3rPBEqGVeWSP7b1LJPYlDK+/vMcDj7txBTowkxUDzBF5QT53RKOpgFIC8Hjy3Ay9jBDMvAz/Fhs7q3QSCBVJA8fm3pfoP5QbjkU2rMcNpZq69SuwZ4+v5OfLhPTx7WMli54tPBAeh+7d+w91VdCOSKNWTQ+wW32n+pyUaOqQfEcAwnSM+YSEqE5wR6srgt45Dwc5PUGO9ulW/Wr1wnYwpVImcI25RQsOima7CRkTNBWEDbEWTyhnPOXGD4Ez/KrLuS39PhGuzGNHfycLkDOqPJBgJFfyUtJlHZUbRF+XPGcwmRDgWL3KJY2W4fJO0O0SMuZ2bSNZdoBkg2LyoVa49R4FdqvHigipODooEiMcZIIcC6Ca7QWI6s7hbdAZWpFgNBN83AF4ArB826hIWJxr7wAieEXxrrpqmHSMgqlwtjuG0btS684trOnzOCnmnqotGldOoZ8eY0W1cX9785wzvLqvIKG4tmFQJbhumYOmKWq8d5jrJ7fqyyj3LQ5sRh6Zf80WqQwZY9hIkBzgR3AvwVDc7nnI8v/ZYmM2tvxokONjJmxRYAR46IxxZujYF/694Zvec3FG7oulzvHDSP5PJngyU/c43hu8dbinoR/7x5jFLAEmxiym4M90z5n576KZLc90BPfcuxK5re5BzFlTloo2DiwG1WyCZKJj+FDIYUTYA5ZxmSSGjVEmE5w4/l94NhLOR5CEDMPOVA154KXgj8ATngeoWPGcwCFvIpc3162f/L8BOfFNzAW9NV9gqWSFSpEEMWkFLTF/kdHV0kNbftGroJ6QZ7HKcOCkcYMhRwBn0LWD54NSz+Ompyg/EwxjuYAhrUauQ1jBQnbc1VBWSvU0LB94BpS9hoxLzTtzQ+Z1zGsTcddxERu5KKEWWcMKDrZkrF+Cn/eI7RsqciFdzv8tBSSLV+EDyXpsVo25lS7ke+Q5eZjPwyMF9+R4eDCsCpEIU7n5UKTEMDl/dql8BLcQPLw1QUWGHvNYKwx8zDrwry4a7vsaoMMHg2bDq77MpoL57J1no3oklKkF82Wul+eNQPpeRyjKLMJd4iWuya8DVQEBLGr4f2sp894AIqyHGF2bCyHJtvIuuNy7nnftx538m4nHDrh800ZCdslp3vn9o7VTXFpaipxeLhtGSCpPv88AQmn9Wjw73hkSHteJEHggmR5SIxJHvZIpnqMRMiKMv+xwbEZBp2xCfVxA6ZRto9YNKw6eWisJfTHr9OPNz90g3QPazZtJHifWHJybdk9Mt+4y1Dp2nN5DJIYiCx5GyblV7laKMROiWXsYssXDHhFpiUGwvjBzVCzx3pQl7wyDdMPjWsxC9s7+LiHEMsaQ9x3HGmMDPpAsAKmDHI29nfukx7BVC1ET/NTemB7dVMgJpD0Jfcp/N1LSU2WtYa2j5wmtQY8EXSk+WahmsnhenCzXmr+/l+U6PASKjiRzER+4OqLk8XwsAMgAEeoAwkzpkUsWV+6vlxW5BQCufj1erq2cKBuwj6kEHZM6AIpfZTTMlehRVSIiU1jkRyCgkDnJt5LxONNoSmioZKflBG5RGBApUr9H3/dtH3kAACAASURBVAU5s+TSH0QxxawiK2p6twnmWDCLG0w8HAJBcsaMhPUtUb5ow/XuGzA3RGz8VCqJFFOzPVxIelf8d2ePNVHSltVIBZPn0QA3LlA8hu0CIq+B6omyPgYgroNMThtLsLBkxG1hxhAJoLeslYRAniGqeYWM60/lvIY73lF+dIAwGImro6zWfufDfznyQY+uvN1jGQCn9eEUHlVo5spHW5tAdhlFkaWuJQU5UWqbXqwENpZejLjnOFZgbxAJDpigN79mTDy//BzZ+V2LLtn9CXAIEDF7QZmjg1iDGQDxT00+tNjMeYKeFzoua/VqzjyhrHzKxFXvS+LvV+3xvs0Vj/ZZ+ht4ZiRGr509x0Ob4xxc9xaQMnPLvoWh/4JkavOa1fNwBojxUdgMJVTy55Sl6ZkIraqQU5U9NCB45WOcbOJpB5vFzz/m4o3QYMgM/DkUyY9p5CnESIfq3Yyj1zUjFq+drgdYia66xfN7fGAU/XyjtyMGvoYhZorQEe1Ya1BLeNpg3ByM/AUfcqwU8b45o0NvytXoP5TXnsoyrEJm7CDWabDQozyejLIk1YM/J0qItbnA5kL9w0FOXoXvccW0jTUROlgLnoi/o3C84cCCoVtGrEkirya9w55ksvBmc3DTGtYbpt1zxcL41TAYRshr0f0pLd6JJ/I5pCzNDm9jCgHhbZGJmSzBv9kY/aHRmyyz9FTynCvIYZwL5oDOFny/x2YZQ5OGXFgqMrF3bGR5G1MHR6rBnKM3r8XDxHkXMmW7ydyG30lg/y7I6dZcHv5eXsjyvcjs7GQw5km42I48haxOGOg/9nt6fLbqmxkf1UB3tDY8Qfkwl3PPV5YFy+sKr826udJ1GtrQjz9cepkIbSNEtoZDCWiSn0FkMrekxZIAzxNhRaDmcLVEQ4v3K7P909IvNZXgutar92Gld6/KN34ewC6V8ceLlS9vv1BK4Wm/kUZT1fCchQXe1QdPEqEUVS/TVnEA1FUw88TRJgpW2aLktSKRve9W1IYnc7Z2Ik3DTesbvIjHnWfr/0xWiIvNXwth0U23urXTV7xHomu0cR9CUiSUUeZ85BeMhMDsfzJ4lrFpfR7TIkhhAaXOoz0sLeuG1bqm6SSm9fdrD6vqHEBpVg46H+ZVWHMFVcLLmLHvYj5Co8AAEhIJg1JkAoUPIA+dhSCPJERCUQ2rMLFMbLPsukQCxIzZaCZRxj6UNCYEqFORD11vHiZbDCTvjO6ySq2DuoFUQlkVLVS2yKUBAmB4F2OJMuXik8URejQazFYVptDvr+h5jIHrLtCjlUIBNjcaejQdlfCMe0jBb1sRTOqw/HtPT7JPGHenxWIVA3NafWaMBgDM+3/w+vl6pBfqIUfOykx1IeSiGTV6/thjGcHH0P4cDhiB4i0HMPGwYgllrF6nnaGrkYtQ414Jz2vAC8HDkYNxIkcGNexo6NHRV29GCkSOSUcpsffTkJ5ZQJ5640pdFwXuACWAxZIk101jXqt7sbt6OoCGAjMSRBhH5j2mB6nOx5jf1Pzuii04FtiqG+uJ587IrVkdTtn01p1hc11l7LOp31bNbcsrxWxITw3eG9q1EU0PAwKN9INv03dBjlp3Eb30+Mj/u84eQZfx89qtON/njkob7u5R2x8HS0Vvo6maC+IxzRn5Ss1LLkpI5bFoIfAk3yDr0vlPI0kuUJYNIPCoUFYk/HBiZQEscZdxbyI2krK8HfWS4Cb4WmYy0gLXZklvvPIgaecd/x5EEZmfn6vzYBaM9f5oOttBKYUtvFklYH1FXPmGB00yO9ISgObvLJaCTadH5hJYyeXHELawJotF0yqyWmB692b5gY71TiANMjyFw2LF3CqztCzM80TAla4zclTXzU1dQjHHO762OBJffQPIjzcsz9LEYr187R4A9PK5VCgjJmDJYx66GR5IZghTynLvNrlTYFia/jlDrKz2xF+ccn6dxT7ypG6bYYLF+zD4/L0PeyjqXBdx+TCschZZkk8h+DFm5xg27JnxVCxVk5/EbaRoVBjeuex5Nf4l0GEbiZUeR+gB8g3tJ9pO9KHKLR7MmOLJUJDIw1XHfkm5ON38xPpkDsUAN3kXQ6bluiwyKm5+VVd5vCEDh9TP6/v2/53RPlgg1S0iTQLFXEEu/O74IYwiQq5GyHl01B7fYo+hY/3ngYg+cd2faesjETsBwWx+Gt/j+VkoL/es2WDjkXM7lN88nsbTyCZ+GTb34ciMs/hrCXKYwNRW3bJUesWpUlZUIaoXXV4mAJfl2gbgtkWfMX8ex5P1PMuBmL6kHKeisfeGSIsoo69jmV7qb9D3c3KyjXLcuFdIFk/MjGqgHNRWYlNs2bhs3JgzvCUQEtCIXVvMjdGyeXm2VN/g1lBRCt3d/+qCI/M3fJ/H4qRBkUppudn3OCEfYskHMJTlxC6JrEuEy6TMREL/8AxPZV+I9XwiHiMXyVyH5atEE6pSZ75JHjbMBW/eFV/kdz/AYML1B3t4gWELxJqMsElI/n8BRw5Vm2dDhJKsS3jEcnAkB1ijRjuBVu4UU07tQzGUUqFUtrKxSWWvsJVt9rNAnW+elENOpB1gJ+fpMeTTOltx71m35kKKtILdnLEALKPBm/RZrqneL2M8iSWu7GMfyih+2Eq2wUgPQA5v1cHD/klnrGDDsSr5E4zIySTDLXpCSGeZ8NA58+d0Nsri6Uq2yT088+gyV8S9XWKeJyQYVWIeWHErXL+ZK/CXJfc2FIQKcvOp3QL0E3qjdG/zQJSOe3WGDdCWXmEG+HAo4s97c2Gg2e4i1kmyeWmG13G5ZIrF2A4pJ6adHuE0xx8bQo0moDI81i+353jPFg/GL2kjLPrekN4QbaB3sDNAiLCXAkXQW0F7GgkJPjyPLB9JjfJtiSon03imFbDId0zZHh5BAmupPQT5ZzgzQiGaWgpIKzATuWvIwLqU9xY0I1nuRCnmJeT/AhiH2wuIYbU7WJPbTEIuzXOpJDKnqy+IlB1QRCJE2j0E5/smIhglnuceRsUpjgDkAGlwvnllbld86uQ0u5ItM1xP6seuM+84vnR8hRcu81o3mU2Bh/KPz4SuyXE4FlXrNfRxjkxJ/DyfiQ0QtKUyCy9jUz+3FG8aKXtFa8UoEVKXCcIjH01GD7gEyymLZmxYIgyWUfHcH55NYqNTNGrY5vu7x6X+Hv2zEo8HJLOJ3T3fZlH4Y21m/sQEtRPgQCY4TVQ3rJBIxiImAXtJbbCCvPfmiDMg8pDslId7eF++/f17von+hj1DWm2834wyUi2+AXKYXt7lz+vvExstgkLmwXMd54eXA+V7h+s4Lm4aaHEXD7bZw41/tFwRUtHMybgzSTyrFhrQyCTJrJqRLNOY3OV/Fy9h9oTuyI2K51WLslWlVp9eWwv0TBAf/LSKDmPmKiTZ8pySvzNGTAjI+e48doL+Kj5Bl/GZyD6a6JN1HMh8LjbOl0/xaxZcQdA75nx3Dwn80xJb3/bgZcRCcD7el6+XDMEztNWHk7z7CkFaA5xpeKiKEHEFf0ZjeWwu25BgMs1KC7k1NEQI3a/O6+SHtThOKkyHqgKYWHQQZlmv7N3EAFcDL0jmHUbejWZOYHoL5OE5SgkFZzPkNLx5Mo8ZSzXZTqZssFgKb9FRpjz+Blncx4NzbNWK7/bK4++ThyT3oNi7Ff0Y8t5J0YxPytz3AtGUjexvRgkPTUl5hcujTDz2hjr+c3aLxsvrvVW6QelQDCkl9ImE0g+JYgmA4wJXNLPss5xrZe/eNlk6NVb+zkzDkEgLIBKL4w1DV6VXPD6cum3t11Zj6rh1oacbU5J/XSGmFzD1PeP3lFSP8ifvY75iC0PIUGvDz2OMcvr1/geG+B2Sjy4nvuiiiy666KKLLvoIKn/+LRdddNFFF1100UX//6ML5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6QL5Fx00UUXXXTRRX9IukDORRdddNFFF130h6Tte3/8r/71f2GmRj8VKRXZd6hgm2EdTAVhBwoKGIZ28Q9b/K62HNHADDPFTKErZgZ0zAxT/66q8btiXdGuPL88s9+e+Id/9R/z8vKZT5+eqFvl5fMTpWzs5YYVg9p52m/cbjdebs/sdWffKkUKsheKFGqpmIH2uGrpmIGZIMUQgVIqIgVEEBGqCCIgAqqCqmDWMVO2Wqm1IOL3qKZgBh3O+8FP//jvOc47r/cv1G2j7DtYwRT+7b/9v/n551/4x3/8jdaUuvm6dVP2/YnPLz9AFaQKx/1OayebbJRSuD09I1UwGiYGVWKJhYpRxWjtoPeGnoqpIWyA8L/9H/+r/CUY6lv0P/z3/6PVWvj08sy27zw/P0MxqIb6wlOoALydr7TeOL+8cdzf+Hf/+O9oZ+N8PTmPxnE/aKq0rpiAYrydjdYbb19+8Xt9OzjPxuvrG613Wnee2Lcbz89PbPuG4uv/5csrqkq3TpFKrTumjd4ORAQBjrPRtAPOG9u2I4BZd94ohSqFWjZUjW5GLc5rn54+sdWNvVRqrfzw6QcAWldej1d+fvuZ3qGrUasEfxlq0LVhGOD7pbfTr8FgDx5/fnritt/4u7/7B14+feZv/uFv2Z92btsTRSr7tlMK1Gq0dudsd562G1vdMTsxlNv+TJFKwe9l3/fBg/f7G69vb5RNkAKbCAUBnsEK/81/999+CB/9m//y35iZ0dVlSu+gNEzO2LsgckOkUiuICFst1Fp5enlh3zc+vWy+r2vlVit7qez7TqnVV9mU17ffMDPKVlHtHO2gtZPzuCOygVTqdkNK5eVpoxah9w5A3TdECrUUeu8cx0GhIhQs/vWuGHCrOyICxTA1erOQNVBL9Wtq/tyl+ud/+/VXtHdUIaQrtWxs9QkEpICpP1ORgiAYAgilbJh1zuMNKIhUzvPgOA/O1uld6c3l7tldDnc9Ue2080DN5bBhYOqCEGHbdmop3LYbRQq3vfqfql/bbXumilBEqFulboWdSqHQDpdJ/9P//j9/mCz6r//z/8wwEDVKUbats92E/Uk4u9EUatsoVii7gYBaRQ1OUz9IEYyKsdEpqBQQl6v380Ct01XoqhznHTXDFFS789S90c7G0U66KkUEBEotmBmtOT+hFs/ZMAPMEKkUEfb9RqmVfXtCRDBxvXm05rpNY1OgQ5/ih8Aw3x/7M6UU6raP88QbUDunHvNP+jcR183W/CVz/kVdlpv5sUWEUgsiwr45rxcqpoq2Hp+FfduopXLbXYc+bTeXp1vBrHNvrwhQ8X21VWErsIkN3kPcV/O//J//1zf56Lsgp2NYAd38gsvTjmJ0FKtAhd5zERkK1vLGEYTCkEKxTskrggODKhu5ac0Mw8GDSePQhtH57csr8vqGauV2e+L5+Uathe1WqHXj6fbiQvx8dQWw7fz93/4dP3z6PATZ87MDo+22Y2q08xjC4TyV1jq3fWerFakWoMUf1G27IbH4Uhwota703rFbwUQw7WBGU0PwB9as8dPrr/z226/800//js+ff+CHH36kFAeHv7x+4afffuGnL7/Qe2ffClIr23ajAkpBO1iH1gU14TRDTLHeKFZ42naKQC0FNRekBShqVATKzql3eutz8T+QrAq9CAfdmaYUBzkYxXEkhYIYbH1HrMDWsW7s+yeKNep+gr5xnCd0gQ739kbrJ6cZqp3zOLAeAlgKSPXNT+dUFwKSStIaqsrb+erCB0OkULRh2tHe2Ledre7IVqhqGA3EKLcbAmhvYEbHkE2otwrdoJs/N4RTwAS2vSDbxv7pE2rG2/03tDMFFCCka9XBFaWEUWCY+V+LCFspfH7+zOfPn6nVN//L0xMvTzdqrVSp7GVDYp0N0A5QqWVn31+47U9suwMXEQEz9DgoRdhuxYFWKD3tHQUoggYY2rT4c/oo2kO2DBZ2wa/gsgIDO3yxi1/j0ZRiFWkF6PSq9FKgFranF2TbKXWjlo3TFKNQth+GQlA9eLsfzrP12a/DIEGClB0pBaH5n4KZFTAplHpzADMUBVAdfPQiAaKdR610P1YKbRFOS6PmhiAhe42ufTBLqUJ9KmhXB1BawIQS12KhAFShdzjOjogiYpytc56dpoqq0fREVWnaUXNjzUzp8V1JZRnQSQQ3M0JXiKEloFUYkYedoQiVulVKKWzlyY2abqxL8xH05WwUEW61sG8bPz7d/OqP5vvYlN4arcPx2lED7YIa3LW7vtoKRgEqXQoqFcqGIZwWe4UELKezjAmmYFpQFbq5IeNqURyQaoDh2PNOvsb5c5FKLb6PS6luLEM4B2zoYjOJo27UksdzDKDWQJxHREqoZcHfEXyj4YhYgz1mfh9mYBUWXkCMIg6UQnrFXhEM53nnWYlDOp/WbeNWN2qBEgBKVdF2ouZGRkGwUvl/iHubJkmSXElMATP3yKruGS4v5IGUPfD//yNe90ChvLcz09NVGeFuBuxBFeae3T01K7Jv8nlLdVZlRka42wegUChgAcOEYw5gALCXABUSsH+8kH4IcnrfSEhkAObIyYW8nheJCG4ghJ4pZNUL9BQ6XA+tL7d7qkE2b3wzfYZZQ/NEOACxPHMMnDAAkyh0OHrriJGY88R5vnC+Tjy9wQF8//4dbetozfHl7Q29N2xvQq6KwMyAMQJzphxbh4OR+5wBg2Pfd3hveMiR9L4x+pmJ8Sbwo0cdQqqnf8f7+xO//v2v+Pb9G779/VfECMwj4BujwffnO14H0X9mAGmwTKJ7AFMLPjK0Rtpa8HOeyHREMyAdxhBvMRBWGyQT3gwBxzxPLd7Pu2ZAG0DrRgwaxJrVJuXmDKL9ScPrcKQ7shlaJ6gNEETGIAMzQ5FoFCNI02tusOBIcK3ytTZkZCIUXYX2YyAwtcF1v2bwRjAw56QRiuCGzWvtEtyD81cPnrgMlqKbGbGcR4j1469pnkGzcY1JXveCBMzhrekP17U3vn5G4jwYdVs6rBkjUjNsaCiD5G7o3dC7wxxcexFADmQAcyZZ2gDcuL/SeJ82uZ7GfAGfCXLcUTPpRjYhwzBldyI1ah9iOf7jPobLMDlgbhz/GOvnVpFz8HeaMWonTQLAboY1y9YJmkbTskg5NEPE3WklYA20dfzditITtuw0gQWWY1KMvdaQbkRsCp99RiDm5P3UeqxFCvDnMbm2EjAEI2+NVDnXWpscD42Ju35Hn5sMCAjQ+d3QcIdBLk3sfs7L7s9gcBYEd3bfK590ba2jGfBoDd1SQfrEzIFXThwZyBPABEYGgYhATiT3vcJx+iNzhAXSGFxNcArMrrmNsgMBritosArkaJpMoKCuvDlK03yb5tyMTHG9TyR9cZEvy+as36v3zgVkrumsdVaffQ+sIDsXax6vG1/hGO/Qou6UAKvuV/eXCyQb109r+uMw4wAEQgTWECM2EXpGT78gV2IF7P4DgAP8E5Dz9e1nZASOPDAjSS8qgojJTRJwRDKyvgbB1uDfaJvlFGhArm/DiOLcG+CBlHE1a8jeAZuY4+BiPA/EOPESivNtw9YazsfBaGZMjHlixom//vXf4W7I7jA3/OnLG/rW8PbThuaGbu0CY5qIrb2hWYcfL2AOvL+/AAD7ly/YHjt+/tPPeOwbvry9YQ5gDmD7qaM9HM0eQDpezxdyDmC84/35jv/v3/5/vL+/8Pe/fUdrO7rv2H76At83/OWvf8X7+ztiTpqGbGjJaN0BjDkRcyJDzIJvmMmxP8/vjOY2Q0tHTi0edxlTB2IgM9A2h23AMQ5EjB8uiv/o6xyBlgSBiMA4D3gAvinKdSCCTmTOE3NOnK8D8xxME7oD3dHN8Wgd2Q3zHIhz4jgPHOfAnIFjDtG0Wlu9wTNgJ43tjInztMU+RgTGpIPjOnTRsFoU2oytNZgZnq8TGYkY59rKFVLHdHBYFZXpHiIJalw+6TgORuS6nxFyaAAGyDRBgGnmnWZmgGFuaPuGtomF2Bt6bxgB4JwY336Fd8drP+HN0R8Nu3e4P+Ce8JbYOrA/HF0pKN7TAOKd6YpsADoMD3RzvD3eSF/HQJwDMQLH+fzcKLzTVDVL5Ew0BHIEEDLwgobL6MrRmJlAbCJN4KIosw4c80REopvfwGmK9ZzYGkFJWoOlwJVegzBkupy2ocUGpha1TmL5AswxkBHw3sjYtA6zxFB6AZCz8xBgF9x1RffGv9tamyD9n3yPOSZiMOXqxvSKyeBmBl7noXER4M9gwOqyxxHInGIx5wItcJB1zkBMW8HXzR8iUE4UCAEf/iMQMWBo8GT6D2PisJPpEtvkqD/v+vmN6bOvZphz4P35Hcc8cMwD72PiFQEP7uIs57QG4wrkZwZGAhPGZxa4RN8IQDYGozNMIFTZjUrpwBfgTKIiAdMKclJBbwX8F8ABKKdw74gkkB5DGRXth/oMGNAa06jQEyxgy+iAa9oqhOBrCpzzdYlcPqN2WAEagSg3XE8liYcT9EeCtna8ZE87em/o24NBmhngA4nAqZRozIFIZkqQwMgJbw0tN4QYo5zMnNRe/0fXD0HO/ngwDzsTHgFEYlpigmgdkxsZQo2FDrPgay2NC+xxlbgGSpOuGJG0FBhNl1OwluiNACjrswCiPzds3dCcb9yao7tjT0dkX7c0IGN4BkYk3udYk6AwcDFThgOWjh4Tlol5TqHyA/MMpkm2HcfbRIbo8yeAbujWYWn49v7k5IwDx3jh129PjHMCiohmAPE6YHNgDOqBaiG2bYc72YqICT8PLaLG6LMnLAyWhs3fGJU3h0sDAnOgNZg19OaYk5FXOpBKD/5gPfxLrl9/+QXb1vFof4JtIPVIMm45qBwHN8IhVuYcmIN/Yk7Mk2DNCjxHwtDQvMMNCCiKzUTcAHfClqOAKF3kkNGJ3ztqhq5rqVIfcwKAotxElJ8U3Vt58nvktVa/8TXwhrSGMBr7MZnZiorCcEVylWKAGAM3J/OjddJb53owh3mHeQfgi1GNkcj54tqYHdMG4IG+cb+ML4GYTDubAZgODMc8ivVrgAVgJ845cQ4Zkwwc7wfGOfAa0lN90rXtTP2MmDBLxEIqRkCfDqYImNIFHHvfuC8agYU3vxyw5sh6Q4Nha52ObXDfozkyGnySkYi0yyYZ37950zdCmIcs5JxT4Fb6Q+RK/5gFHYIRlsWNGTdFyytlUVyKmHFzMXONDrTIpTmmdGVL+SNAxucMpZwA7f1aZxW9U4QI804bkmKqvLhgpp8MtKku9gZ8dADUkzRvTEmZw9MQNpFjirFS6jXI+6Qlho1Pt0XnMTCSuqMZA6954IyBc04cM3BGohVAbNzobSWRyYISbJJ5c3PMBVC4dxNi8IAFdCPiQ0D9cecIYBiwXoQ7HIHYmEqTXixIZVcStUxs+Vlz+gHU14JQNWfu1yfUPWXg93d3AVcrdkdAJvUV7rC1xio9ZQvg135wA4AmRl9YwozqBdN4ld3MyyZasVIrs3ENla9x++Prn4CcN4pzpzaJHP8y4Yq8LRLZZHCibkRIz10gpxDp+h8SU2ku0YLg3wl0NBGdmxu9AWEYh6j1raO549FJm8ISvTU82ib6ShRuhMRggTzJRj3HS/6HVoKMKh3YVM7/4Y5mtkS+OYHDTzy/Hejbju1xSEdiOHJgILA5x+Qv354YM4Ag5Xbmk8K71pDpmJmYrxfma2KMgUhg70qDPR4ADDMnEBNzvsO3N/TtAfQEmrQjaXg8flLqIa78vjeEN7hEfvYiYEo4aVR3pXA+7/rbX/6Kx77jp32HR2JuTRGhrZTf8fpOUKPxj+PEnAPjPBFzYpwnpjFtSlVuokkozOXvyHwxgp7XekszGgNZmzknKdElxvv95jAZFMCUApui+rkqKa4T0JaQfQW2lfqCXQ7RHWgdaA0TDRPACMMIQ6yojYFCGtkBelQJnb3TUYajeyPl7jJevsHbxvlNpW8icBwvRsuvDd0dw194PDa8ve0YJ1OBaz9ngw1gPgW4N0d6Iu2F4zxxvIbWuuP56xPn68B75Nqjn3Htjx0xEymDCYVakEG9VrRATl7CSmsUElu7pZPElrV9g/eOvVH3Mo8DlgFX2jJGx4zAiKD+qTkaOteb0qNQqnGCYOOcJRi/pTW46uBGXUdIhxhWng+a49vfKwCUvrE717n3yzlxvecSqycYlVfqmgAtJZynQ6ET8mVn02gbvG+wpBPMTHhzlIEkyKOtdb9oHGt0gq3RTm5iMpCGHIbECcAYEIavoDgxMX0An2uK8HoeiDnxer5jIjFADdLMYOBDcovwOWluvC/OgoUrjSx5hGG6Y5iT0aFZAmAYQRCLlOO+Mcz1ugUw9Sc13DW24FsRsJSGxp3gpXegdaZaYSvNXTo+xrsN7huDIKu5TAZIEAhCIkroliUZuKXUIKakJqrSXPBr7rxYJl9jJDSstTeugDKl051MFc9wWAOBpRkZaQBdBEQpi7z2eRTIueEJ5A+X0Q9Bztu2Y9jAaUSM5zkpfMvEHCVmNKaVlsNoNyADDd7HiWMEDIwsWEsD79r4aFUXcFVX0SlKSGoBU7qMAj6JI+fAtANWQV6tEwdaOsVxmYgoB5KSrFAa5WB1FW0kjfiwQYZpxmIe/Gjor3dtBEZjlC3y/V9jsgpEYG+CTEAegFtHw4ZpcxmwBsPXtwdaaxiKxZo5q8b6zgau3gAAIABJREFUm1gAKstbA9yp5NcdMxWkZ55gGiSPxHixQsKbUSStarXPzTNg3adwMs5BvOZpwJnM+R0E0w4T2GS1x/N5MFI9TxoiTLyOE+d5YB4TMXCJLlXxV3RtJlNRd2eslDCWfSk9hQErOQ6ogkIbH7k0IXoXAiFX+sPFvDhg4UxjSMMSDqQnrDV479i2jhGGZ5DCb6MDcyJV0VL0K/9KvqIKFiMS4fwzRNr5jcyAKc0CVtYBgXEMhBmGO2bsyDnx9vaG3jv2B6sTcrwQcyB7ilb+TmOIwHgNjOe5nvwcLwxMzJlF1n7K1fqbTLkhcuCMwXSB5tMEAGAGbxfL5t6w7w37tuGxf1nsyNYfaL6je2d6xyAtypB2S3tW6S1zOZLgfjYzRtGZGEmtlknfdeSs2JzgWGLUdEXiRuYNABkOAaKsdFhi2UQD0ypAovUHrAEjOp1Ja4iZGJiIPHHOwcrRuK9UvX/p9Lwt7WAxCsV2ltFsmfACOUktmuMCPt6uwFVbiSCnmIY0AS4HVH0LtDX21B+Kbv0neor/8Gu6GEGunQEyD7sB0UrbJSfduReHiQEXaxCGtU+nkZXKuAUZeRObh6P0LUtUWyzfCvwJPAmaS7NXKacKuFw2hyxuSsgcgwUUy6gxeShpFtduGiFQCrBw7kxVxckqRWm0KlVfTCKn+dKz8VeXWgtlrEKscEF7S1OGJD48I9y51zCZ7bAK8MSS8c6kM3RkBZDBtJabw2QruY64hvwHy+jHTE7vi5JLUJA4pbKPaSplLBETNyLLrrRpUjB2XRcDZDBttpBBpXKci94AKB0ULDNMpacK/WYEo2ChOiqyuWjROMHuHJTNG9xZIlgCV+Ajuua0G0Tu4EqjpZ5d5ZVj6F7fiyxfbFUJVk/RhlERmSK8MQK9TWyNiyIBQPqjt50VYOOgBshVyvv2+EIwl0Hq3f0ykrIRUg0gjMqzDJZNx3miubHM/GTOvsbzM69iWqDKpjESnspJj0SeE3kScHpTNBEEOq/j5H2f0tTMwDlPnONkyetQJDsFWG/Cu6vkVZu0llY9vt00BiCwXXkjGbIE2aOV084yFCa25YparJkEuyzrB1KMCJYOo+8dOclMWlBPU5URy/AXYxSTZfKiiEvwOC1hTkMwBXTCCdpZzpzoDUAG5vnCKDYpEx7A63Vgf51oirqpFznVGiJZLp0Ji8B8DczXWTE/Rd6Za5w/6/K201YYmd6pfV9ulkZUrSI8ON4COVvfsfUNW38AAAKB1na4AI4bA6AoBxyT5fpu8C57lqYKpYSZxLRBRzMVmiBZKDBS1Y1mFE/OoXRzhVJV3s11pOB/rfuyUd6UElDVZttpcapyqvUNMFUMToj9FZjSuFWMuwSr+vfHaB1awwIs2j9eNhyuuTZ4L72JHJM+wZXuYyqkeCwH89P8Xa7sEjdXGfHn2iL6FIKRCWpqNgBba7w3K3BmyKY2FfLeVn8H5OgvUTgFs1wj1KDcfEsCsGIulPK+aVUrFXMFZ5ceBqg01T1d1cFqLQX3FbkZA8oFP0yMMC5fxrek0JdaL85FVIUirtm7i/Gh9X2FjPlBYG2ZN11PIeh5za9sqEmKwdQlCySQKqgxg5uAXdP9QzKVIQ1ujb3F0iy2j278d9cPQc5Pf/4J5zFwHtSTvJ8v4pZU35x0LLHU2lVcvHyAO82F390JJwFFCeEcQ467kKchs61og+IuUoVminKHUgdGRQ8ysQT9yqmeweU4F9Ulx3vTDclHimq7xFtDk1SOc86bf6vHCkZyMbhQWmfUcq4+QLHEZ835yywLdVaOmWGYgIpK/9w70w7P78x3N0ZImXFjOPlcxxzsOXSeKm4zIvzBckkDMI8DQ/0wPru66nxOeEyMd4ONxDEPYHf4o1FAPAIRFLDv6kEzzw7kwJ+/OPvk5BPP84X3eFKkjIYxJo6DbMWcg+K7DEXYSsnUoruFrmlX9RqsXaRjBsLm7c45w6R8G2KqBwmKR6u+SzuZBGuAp8ScZIK6d86dA2YUYuacsKTurPe+WNAhbQW8wWBqrYBVRWEd1Dy0JspZkV3fr7tqO9dnEwsVZEfN6TiP88Tz/Sk9WqL3Dj9VZbjt8Absb2Ba6HjCd/b/2dWrqR0DY074VuP7Ode3b7/SNrQmXYpzfKKAji9dmxkrL33blEZhzyvKQyYCJ1MyERjHKSZMFL5ARGsb2Q+TUP2kqD0mVnuJmBKIFzBRZal7ly0Sv5sEW61RP2XWBHKK6YE0ZjTwqwWHKQ0tQDlPRctnwHpDbx3AwHTqEptYbWCi0nhsxwGKfiEgXsGlpapCmV5CExPm6vvTuDbHZGAHa2QMW5UR3/ZRMcS1Jlapz8UylJ5nqT7i9/qPf/X1PJ5IBNzZ++xtswVTGGwDzXamZ3eCBAIfPlv1xZqRGLOYKYIasvclNL70VZXKAYAPmY36fjmTD8U7EDBo8LZxTanHk7ljisUY+mptE+Nx+SuC6bbAS0V5zvwbYr5o8+bv5+HOKv/mB7o3XMRERTwhAiLLY2veSQOuz0qcvLc0xCRDbxZIN+zeREA5gECTuL5kBglDM7Ye2ZrG9FbI9EfXj5mctx1mZBTOOciMKKfK6fGKOXGhGCFEoUsiSrsN1jVwLC/jwwBQuTbg1WzKKhIhwLGbarbKpCOValoReaLKKBGGVF4Qqc2VBZguqvV2Z8uBVZAxi/nR11lBfsVICWlemDJBpqhfU+OvFHjRs8hwurkWCFcMCYRcNsHE1EScMFPPk1QfjMrjV1Q7B1ZVhxn1UQopaoGHqrTiP8GwjDHRLDBO9qFoxrTnaEmWZtI41Koitc6KpN525m9xAukUbWssYyqtdXu21URyOQpNUtb+1ObTJoWipIpfoFLQIhRrI5s1kJ2pxSvggbv4uK25Sa1DVkEonWh1b1onJsGmemfYbUFyOZcSoNaMCYQ3VLVF9VZJrW82IQQNZ6acapB9UA+UcZ44jwNHb8gZaNp3rfN9W2ePnxgnzChE7r2hNWPjszExc34qyGEzPvYEMjeJJtlJinozMmVut7FyCY41VxGMAMNYMmxBgEKbQ4BUmhX3prkwBl0h8Hxj+tgQFALV5c/JIJf9qJQQ39PXfK6YOW8OBUrhVtq0PktbNkKpiEgGaLUGlpXU72mt6SP0OWwQWBaBzlfBYarvS6u9x3drbUNpxegcm/TdufbXGtt5XA5Qn1mpD90Bnw/XlvxsOwSwwSYvztPenWnwzCWqbeBe9tZgjWXhQCI9UKx0yPYuYXlUkcIVEKeYxuoH96GVgcZq+TT5rwv/yM+5r3Vs1aAWjmo5Ui0o2o1hWj5tzW1oKZfvpJ1afnqxejUyWD+7uAl5xby9ygyl9cn73BcTZffXXmTAJW1hZWKAPIdpH19RZ2kt51pOTJkykGwCh/OfxOw/LiHfHnhlw7Y/sM2JrVH8h+NYyufqXhFqiNXYsvdaTHLi7pWbvN7f101KhxEHjVO/55FpWHqX8EpVJt6IRhGawHq9omD+04UIOeB0XEaGpyKtrPLTWlik3ijIMwAlzhJqh8DIbdG60zCGUQNyKGqq93Wnk/CdGoDmDTEG5njhkAo/nwQy5zjZp6fTYPPuA0e8WFUF4JnsymrK/bt45jDS4q1vcBi2tmGcL5zjBCxQxWSfzBDjl19/wdZ3uHW87Rv+/PWBczQcpV8JlmEGEn9/f2LOgeev7zjHwLcnS8nP5wvHPPCczwUYznPwz6heOacYnBslXA9coGZd3KAuEV7m0Fja+q/KcblDyzlA2gNHaw85DjbL7PsG0siOmAcyJ9q2ofWGkFARnaB3k5AezfFCiZwpfrQ72sHyngS7MCAbHA1ba2hmUHDNe3YasBkn++N8YbMzIIERyDFw5AE7G+aTYlobdD7bANrWsH/dYbvDtp/RE9gS6h5sOOMXjBhkAD6TEJyDwAu5wJU3h7UNsA2wzrQO1Am1GdwCTY4MSGqNjCmvgGGGwVwN3qwp7cfosAyoo1GP09lALwIY56GGfGwAVxbI/DZvkeo3lOp71WC2rdYBpN0NfdsRY+B8fee6attlU2OyAs43WJKF47o+YdOQz8B5nHj//p1AD1fgNIcifOlJWq8Oy0pVlDORozKNbWauhoWlsQgv+zhp92cFdmCHwQT8N0blsqeQ+U3hLukBIjVen2uM/vTYMTLx7WBH8907CwGQC+y4ik0qNdVsAyA2rJyt0lEz2XPrHCmBOn3FLI2dxti9sUp0ngQGzj3sZkpRxkpxWQXDYAVwawyUKIUqwD4FXBn49L4jI3DO5wryy4IQKEhHKD+YmcgZki/QX+Fezi8gklE6nI+2kwHdpeNC9VcCcPVGqrktoiAWYObrEiNCayfVRiTgzbD7pldNyhxwgeOqGGa7MlvP+4+uH4Kc5g3daQi9ECWsiBc+ri2XACCUo9MCT371pb6uqFRipDIOZgulZQ3IvW8FSoCl94OtDXIvCeXbl4nAinQqliiMWveGFBWZ+eG1vIsr2r9ikQtt173i9tpElVveNq6ibQKdLmPHxbS6ikIsVoJCa4s1DqEojCXSfIIpXU2BHDinMVzC3Yry3MQg54ow4zMdk65zsPX++/OJjIlHMwQa0vvKkaMxl/z+ZJfX79+/YYyJb68T8xwYrwMjB84cK1JfjbzilutW108At42W3AiKzHlV5KNy8SqbLYijZlvubc1rXWVYrLQ6xR409UxJNjCEnAwgkaBYnqK8eY9JIxZtsTKrBHN93pQvuj6TLQNU1lz7AzfHomdprSItp4jWSc8Pemu0MLj6n9hkhUcL9qTwvsPEGrRmPHZilZ7chvKTrqsSpCLgYsgaYG0J/WtsVnBrtoywipkv8Bupqpa8Ui3l/+//WVUVyRpE/paWkAHnWrszishrXZVYkjbSmWLiG6LAdF2lNywAezWSlFM6KcA/z5Mp1eb1USsAW4zgjS2gbcBywPV8xXx+2CGq8LPb462f4fY5yzpen3HZ3Ov3frtmftTf5F9x7b3DI/D0YgVdAEwGHLHsRB3HUIVJscS0iyhX93SxOFF6o7wajApcBGwJjgk0UinuAjl5W0blJ2u//2b+KujKi1kiC1JhmF5Tl4iG9Z0MLAHjzZkvJvB/4rKbIaArpZTinvVYiqU74NHeXfAnSwvHZ2IVN3tb3WO91TCxfHBtK+Q/ve8fg5zW0FqgN8e2NWyPjjM24OTZSwBIEXvDZqAzPqV8dkMzx+YbqhJlCXc0EHO+kEj49gDmZCtnIUtvHb0/aCgmcDqpXPMNFxbE6hRblRfERy6RXw2IaOGa5VV5JUBkreAXKt1g2Th8i3+8DBDTDR/Rb0blZaVXklXw1rA93rhIAyUpwnRWvBiMzNH5AmTIMgLfvv2N73dO9Nax9w4VpIoVcbh0SZVFaWYsD1dJfCznvmFrHTBjQ7f5yc0AzwNzDPxlBPbW8fx1w9uXr/jp55/Z9CkCvj1g7nj//ivO88Bff/l3jDFwTuPX1zvgTiFocCzfjxPHGGxmF1cqbqVRbK2AZSTMSqbNzVcA3KyL/p1w72htQ+sbWtsw5oGModLRhm3btcYqNdLW70QOBA70pk2aA/M0HJPdVn1/oBngu6lD90DvjrSdBtD70lFl5fHthLmzIqh1bNuOvnf4BphXFEQmqmOnEZkNvTV86TsCgYHGYwxyAsGqnO/nE0Dgz287vDnCd0QMHH97Yt+/4E9fvyKCVTuuPDgryhrOPD8eV/Avvvr2oCMYY+HVNEc2BQ5gSX5qdmEg82nS8pnDmqO7oTcDJNB02YAcQUlBS/5e9+XkmSYOmDVsvaudgWnsIRpeBLGi2pgT58E0MhDYkjLXGFO6hMG02wOrqRnbTVyBHqsDA3VQwNReGSfPMIO/2Ch0DEDaqczBis1Ku21Mu5GFMCDbSvfBLnhcboLJP1Nwx0TNJv1XZqW74npFMtIu4KjYkdcdvxDVyd8q5eqBz66u+i9f3jAz8Hi0VUI+Azgi2DtnThzGQHyYmt+qDF7NxgBQC3OusxZvuqzlfAVEFApblmSBfy/wyLQyli0CIF1mQ7MNbo26FLmPqjquknFXeXgsDeKtWg8TYSn/5oCC5zGoxZmTDSK9Kuukr6kz+aQs1gPdWB7Wp38EXetrcr8tYF80gBDLAnD1d+lvk0fJFE6bIZaqSQJgBFIGwD3RDXg/J0ZwDf5oFf0Q5FBPUAdQdvS+wdupieGfpbfRTbfmCw+wp0BfY8H1QUFmRSml4meUygEINfxCln5C+pTMVbVggtd37QQ08X8YYt4iC9O/0yQ+1+BZ4kLDVmkvLScDgZQEzstwrocrRkcps9JE2MWCta4Um1rDuzkCNUEFpvTeZNXVdOpmqC35XuqIbIB+htvCKVN/pQdzfcQtf/pJVyqCOfMAYqJBJdWvTsM9J0vKW8PrPHg0xzgw5sQx2EzqGAf7PiBWVcuYg0LeLDH1b55tAdvaYrcNVjnxrH13rediDJfmJer73OiuDshZEVZ9VsT6Y42GLNTOeKat8viKnAv8O3g8aWud46BwOtU4g0JJNVuTxoTLiHRygJoRL2E6bBnbYiRosNqK3DmGpdXg2iqQmAn2pIlY0WlEIuvIJKVnPzdfZaiZNIBHfXgZvruj1vpPW8xXjYOXnsduKhYz7We5+Ckgp46PFUxdn3CbO839xXDcNtv93+AeyKgqFqZUHdc6lPfDKp7gL9VvKy68Iv5iiqJsTDG189JSQL8DYFVUFg6rMa1+Vdom2g8ay7wAY43hQjLI6xEztRf47zUcgk3VwRvL5ZetwqczOV1l8S2pazlH4IzAMeZqs2GmI308lv1Yg6DnmFnVPZC++jomY9mgyhh8WLsEFItXqVSLXdrUqr4rFge3MSrfybGTaBy25pRgphbgxebxVrTG6wih9WQX+CgPadejrjuvZ6rfyfrc9dr691p96zM+ApuL96tXVSl41YqY0plV6GoqJbe09XwJrIKej6qi38z5P/wJgH17wNLxZf8CwHEq5/jL+zsPvouqeQ8MWcCfvvwMJPB6Dbg17P3B8u4Zqw/AOYda7LOF/pcHadt87Dyb6lXtxQ/01tG3B855YsaQc3Fs7QEzKMqJ8uvLvmTU5N6nhINcibBsina8oxTiVd7WGiPiaYeoRwPS4TYZ0ZRWA6XOT5hPeDbsj5+Yx5dY26zj8fYFf/ov/xuez294f37D1ho6HO8qAdzAe51J47fZG1p3+N5WCTkra5z9TVrDpigoTjEzja87C5jlJYyccRJonOenH+tAgDpxzgPTGzIHXjjwa3zD0JEMb19/Rus7nt//zmM58sTMgfeDB4seqoLBqww8MFKdNFViewnaLjN7XSq/XSCY4LGJnV7VSUoBNQvUWYkV6RQQ3/q+5qq28BwDr2/sNwEENr3/ONnscWIiemBMHsRIASebqFlTnGQN26rIU4FKJsGuOzZnIQDSiC+GjgVArmqttklPoINKR5LRm6LN0zt2Y2fwMZ48Mb1/gXvD+/uvgAHb44GA44gXRg4MnPDDgIMtFh6b49w6fOLTrgXaQO1a7w1hYjkEesuFhHR6bWf3cFY1AvvWxTjkalhWfYbmnEo50CZs6SAhTMa2+0MOZrIyR0AXAKLSRDJAWeuoSRidQIzBgzCNKXLqxS4FWCbVf5vrIM6cal8PgS2Q5clEAw/ifc3S4D3QO1vlnxlsECd7Nl5TO8Kl3ZDNSnWvkeNJYAWObmUxryStWcoxVuFCgbF5QRcTQ0Tvw2ecAzEFqvX7y0n/4T79116tg+nx03Ccgb+8v+MYE68xMFGpfTZ7vEy83HRIZoAba8OfrENN17XS2AXonLZEFZOJRE51LbfG8S1JQmlJ1VcGqiJKADk5t73Y5GhAGiYoXWhtB/1hzYvYMkv534mIQ0C4UuOXL2MJet4Ai76rjZJZIMhlNys4vwO6C4QQIwrg+PU5xY4CqlRMtqw4A+qW7XAFca017NsOT0oCEE/MSLUAqM7i/3jOfwhy2Aq980TsWQ2uVNJaBrgeRQivaNGMiTB2RE7lK6EcdUWv5ZjGrBDRpBm40N2FSTXgVm7lHl1o495/ttCpUGzeUGotQBTIKU4kGB0nVr7UOyP5SMDUnA0q866IyovFYnMSHXzoSJ3L0Zq6DzdTTx1bC4/lxYamBn8dpCCb2B/2n2A05M1v6RXcn1QP7isKq0oB82SEX2DwP+WSQYyJIePb8sTItsrazTtanzysVCeBjznVBfkqB2fjrbz0TCuyFXBN3J5zWSHU4rkzMgzvL2ATURudUXFzMZOqkFzdYq1+tRwGNy3zyGX27Fb1V0aRZscN1E9Mv0SrkJO5VUe42MBULxdvVw6ca/B6ND5qYs6BjEt8f46BuuHSswFK7rZGu9NoUC/miE72eVKwDkuMmsN6BjelZz7nYrcJOoQCE6ZKshLRuvq5EJxQQ9Tc4J6/0+gsu8EN/TGVraj3cmSV9uJAN3eoen3t/5r10uCYPqfWX9axCzqDK+YAwlmgkSmymt1ha0IX4XhjcCpgCDkvalgN1Y3DzdgnR6L7VscNQMvAU88nZ1SskaE2T4WAGpCbhtHyup+6R43hb3UjpdWs98ksJ14pnPtnfd71p//9z3i+Tvz3b/+OY6hoYQlmLwYtV4O5Gqea/3LsWGCg3Pl6XjPcW1PcFh709jc/pWwCsKrLK52FNXPF8OH6JCuWkuBz3YMb7gdr1mXlvPVMZW/uvpC25c5Z/va6retFX17zuEgW+dt6bQWHabae4cN93d4/k9bbs3inkP+NxZiVH14s5f8KyKHOwLE/fsI5rzd3+Iqg3XeWloOlaudxICNxHmRdZjuZeslroq1RL/B6PRFz4Plifnp/eyw9TZott5yZUnmBFswukdz15xJy3qnkhQCsFgkR5SrjrjSZ6z0qZaQp7vvGxnTgIiTcBxGvekFUaZ939ijZ9y5V/M6P9on+qB4q3ADpFA9vD0YNjh0EOezu3Dpbd/tGZN5UvWYwaQEoQuSGKNEiN1b3Ri1RBLo3pBsyD2QOVOnz514cp3lORE488xtPyO6OGMppH4HWN5zzROSE2WB+/Hipbf0VCU5RwxwQrkheHJePV67/16a7ctBciypOYw+PyRw2u013bFtbjS+bNwFPGvaGi7Jl5cSLOXTr7KEDR28NmYExdG8COH3bMUBRth4EOvZb0RPQlkCdDr1KeudNIFqs5EgCv+P1gnvDo39BBPB8PuGtU88DoDv30wTgjx3NE7kJbBl1X9NOjDnwfg7su+OxNxwxcMRcva9ab1fZ6idcfWPl0OZfEDNwnifgFERHcI66M1CRCUHfqn+Myk7tqjApoHQPotKUigTXWhRgloZk6Ws6uxYfR/U14phSjHqlTl3AIVPCeGAxQPN1IAw4t65gkmzScR6UB1TUa2p0GrG0omdS62POmgPfDM0BR6A3Msd1gFtTBdiQzsIabYUL0IbWUukd7gCnnAgdoYLaYpfkkEzOvATUS4PiDeZVfB0Fnfn3Kr/G51ui//v/+a/4y1/+hv/3v/03fH8deA0yxKso47fOMkuPriDrlnqr5wWoy8tbvs59A0GDdILlU+o1AnuGWMFNzhtwyABs4Kpeyls1s0uH5jjnk+9dOlRlvW6Hv+PuCkkEGFhZut0eVLNvBqQC8TVBF5hZEZ5dbRLun8E1YbBq6qfKL6CE11efttqH1xpI2V9DeuiwV43xACwSHglHEG1kib3/F9JVSyMDlccdQ630p7rMgo5zGGZTZFGVBQgYmkrHQyVvigKDIlhSXh0R7LEwjhNMFRWlJ4OccWtTXYhzwd6auWtSf/cgBCWrEuwW1REoXIvT8uPiLV1caxTwDfWp4UQwzWHOSJu3wc0/Vc7nzfH2ZScjtu+w4wCjwQa0tu425m3i3WCdwr/zfEdTfxDoGAG3LoagqrAojKvij4BSHFELkT18RpARYjT8edfed5ahRsOcJ14nqZGMqhgzHhegxlBAstw9NAeGRaV/7M0iR5VlgP7o0zkoV1DlWpOQca51jpV6DdPZQFZaqoYEBfjrKAczmNapBQ+Bjdmkv7JrqUauMmIkm89ZGra+oZljb7t6zkyVLTtLO/PS/vAISdMa40OuQh8JGFenbulspp08WycDPidyALbzJHdUg83UnzD1V+Ln82gAzU9Q+7EZNWU5uTO89SuQ+ISrOcv5e9OBmdN5ACR4Ivm9RYUIYZkK2qJIg+VqFqEqJz0/JM78YB8Y8FBcHbDAosVTYx4S99dJ9rUWQ0UJrqj6blGoH9PnR+L5eqE1x7aaQppKY0Hw7Ux7kknSIbUadqZVKThuTYB61hlrXRDcl3M0M52DVs6Jz3s5cWg/lu1bYTnvXkUYy+/p/S9Hdfu51V7rQISksh8Z1upc/ZmXtw5rDRPsf8PUWWlkxJhJG1XC/9pqsQaufFD9/beBYwEGoEZzMbQCAZntGvKiiuu9JBS0DykisZWmfk7qabRamaAq3BJLyKL7WP+ZLT94+ej7PYsBXq+r45rq5x8f7b5sUM+Gsralx6VNKbBfv1BA63eosvxyMNg3J6Ex52SpeX3Goqbs49D/wfVDkFPPMEHl+fE6MV4H4jhXh9o4Ty5dHVFCukwPVMfBx4lMdkqNZLcLApyG5oY5WGUV82C/kb4vpmixFQBMRjjNJLjLC+hULvE+iLe/0XDdKETYqswqa1epsFXuTksGg6H3BkzAZ+UsgUSDdVsNyCpSZwPJwPvxjm3f8PXnn9C2HdtjR3uS8eq+oVlb5YMvvER9q711N4xx4vV6x+5vaP0n2IPsB9U8fp16rDFy0ACPZPt+D9MYstJjVtfTu1L+E659+6J5CrzOJ8Ygq5RKbVoazuO78uHcvFtu2tzleH01V+RyUJ46ges4hH903Tb7Sjnl0iQUXTyM45eQAM4IYHvvMM9VcgoQVbT9wbUZE4ZAzL5SXJUeKYEorSRP8bYw+NsbmZ7tgWO+8Mroo5oFAAAgAElEQVTBc8nU8DCRPHzUqZ9hcFfgx2CmkmhLmMUq0sBIICdGHoABMSbcGiYmPHbsbde4qb26gYdjNUN03ndvOyJPZAxWPUZi237GW3/DaUwdeu+4baZ/+dXUX2pvHdPIbExVHhWgKQHiBXIW5wCIaTFjvp+lvyGhLrDvm3omqXLIycAxkEvAJhSAruKEKUBzTvavaSjnoY7D1pkCXIe2CjiiNECB8f0719eXt7WuMgxzJHzXevUCI9RUlI1y7YvmXZqcjgD3Fs+dMgq7rBqmNnTfUKk4aC+MQc3kkoMWgFngRuv4t4Akr5Stfdh/xW7QltPuDEHCWO8fN6f3WZd5B7xjGntzlaP+2Grk9hS3NZ6r/8bNt1Qp9fL4BZdue0OAb31WCmis/jh1jpegopWdkr8LU/aBvbmaN1hPnkllqfV4Fy1D7L1sne7FjMUqYUUgDKSVN6hTyq/nMvnE8nd1f/UbqzBObpfgAxIHG7r6iAWuA2uBIquLEfs45gVagpsVBvX9ynlVVaIkAjWn9sNl9E80OR1wNeFKUH0+WR4JgDUhzoPoJwYsDb2JAsvBiQ1GLjA1o1LOmbQ7TyctCm9Fl+s/RmAZxh4z1acHhszJOELIhJGXOgybelpUjwGxb6TNsHqqAGQ8ajGXfsIMKl1zmOu+Amhgc7HrPrn5S+x5vA7EmNi2TeND7dLxfFEA+Ro4ngee7+/YvmzYHw+dLGzY5gMjAq/nd0RMHM93jDkxzoD5gOUTgMOmYWAs4SSNEpFzpGHGwDledHZnMkVmiTlPsmm/6x3+r7/CmNjpLdDD0FvnIa+YXCNJQOCKugEs8HfvKXSt61R0WddvowEZ3hof3ECvJ3VKWhsE8UCq3w4q+nfK2mATbWM6cesb3KSjMEfrBqQhRwK9ASmG0mxFLqyGu8Tp53gHbOJ58LiH3ja4O/a+YejQ2TpnJoO9AcKaUvSX015RTk6MnOuco2rbH3loKDhokYYzgOcZ2HxXM82ptXEg0SjMFZPTOtBywnoAnjwX7pyYeZLZ9fhUJmfb9lWdBrCcPat8VCLeJgDqjYJinvjN4GP1JkqsSiM3B1y9PTx1jg8NgudVuVEWqUBypStKRMGz/aRdiUCdl2gqP9eb3aJZibsjMSef6fl8Sru3odZz6GRWW6XLipA9134pgMMlNxcb9DFKF3ivnjda+0X3X16qnL7sr1IOpQ2bOtNmlRenrbFcH5alZeJe8l6MRcJjItKRSv/mh1/8nMvfdtjWwcM0E2MGCXU53RlkRQFb+60ahn4EcgXuGPhMBSBS7MBN5//cgCHBc2lmrk6+qxpKc8zgiye6R/UAm5yfbX9D2zbAp1LMnA/v9DkXW8JO5V4HtibANBSYSlx6RqZ77gFg9QZz89Vgsx55BXm3tBvAvWNoZAybr/cCINZSzQITt1H8vd1eDPh6CvmBNeZYfsDUpqZZ2dc/vn4MclqDzRIAQQLQUHTA/F8zHbSYA9CmAwzR+UsxQ5Rtbd5qGBQEOYt+teW4Vo7NqsMwlgi6FkMF71cZbw2q8u6KutkPgt/rAh8xTqUPLhrWzQRguEiazghJ9RYgWL+1bEdFN2Dk3jvyPRBzwDYZB5WCn8eB3jvmwaZ2x/MFvIEHcG59HZfBni/fGeW9Th4iN4257XxxkU7DwJQQlWmyTdMYMB6iehyIc2IeE97p+CKGqsRiAYnPuiLVldc5Vq11MVCOEsnV3JXuISPXQv5wPEP930BQ+7t9clGYSyQMJarr9+pUcKsjPwKzWEOHFlwgjaR2a+yxsm8PuDvGKfPcnVUkOkvranmRiFPvZ7gpC9mjAhY4zh0bdjYCNHa6TR0OueLiJHuw5GwcTY0AdSFMdYWONPDVumAO9s1paJqDgTETxwj45kpfTKSnTtxO7Psb03PFeNrGA0Y9kC9Wik0fCA9gxPVcn3Bt206gMJTOdFVklFozA80JQtFcYuPSkNwrLbGAAACUfoAgR40kK4DBtc/J9JrAgQz2St9cKfAVoabeE2KcqxJSRzP0jUxlvsDeRK/v2HTWVolZizlyM+59OdYqPnCrXmYNhknBPngPtU6qhP7uWMp2QOcBXSkmk/2NZX/1bbI8OvKk2PSrAue3DqbAnLFtBlEkYjJImwGxD58LcADAtw22dTXyg45zSHRAqUYoPQitg6Sv0/xfwIxA5aP2U+0cALjfgIsMVlVqRlwl1LnsGkFMZZocju4NM4GZQ8yyBPW9X37R5Ox1zlgM7mW/v3aU1oXsHkGOAXNcfkxa0DpE08FCjLQbWDag0roL93xgesQWyaYtn55Vtn5jAw03/s/WkNoHO5+yodfn1XCGRPcNVyHIP7r+abrKHNi647E1fHk8cM6BbfAcIaRhne2VDZgUPiZYqsyyxU2R5YXulxNyW9TdegLkKi2jIdd5PXGJ3/SGWDnGItC0AVtjzrKidZeWYhfLxINFA1e30/rwQAm9DEyjENXynro17J0ngGc6xjgxBsuYcw60/oDbviIwVyXFOAee358ADN13/J//x/+F5h3nnHi9nnze5mQ3So/hjZWDbogZeL0G5vNEYDIvC2okzHSf4IGOERNnvHjo5ZiwkUtUnVB0+cm25Xx/x5mBU9UVvXWkEeSOyWiKjbAc5hsSZA3z2jmKRJWX9urs6qqGUzpAG2tVmC1nUH1jAt0M3Qz7RhD7en9eAnKDBOMdm2/Y+wPb9gbfmMcvX7W1L2QGNp6wnC2RcyLmiRE8dXq6IdHYiXZt9sQ5Dx5b0gwzJ17nC6WrqZ46qcjHdZbU6nALPntrasqW86qoGYNsalTUKU1JXFqvloQ9MxI2gBDLl91p+DpZpWbGvh/lKEMOjn3HVMFTjupzru3BwIbZuIkRT6SFKg6b9gDZjb5vcOc8yyTwUhBVacQal1VwAAVx4OGNhgLfUmi4o5nSASRtlGKme0uV7VYxA8GPypGTstvjPBCROkHc0bYGmw7ECzMCz9c7vHU29gOAcDZwzFt60jd4A7auIGCeC3i9jhfOc2CTTnBVtfBh+HlmgNUByg6PXKnLVRWV7NK7hPopjYrpcNiSHjSWMFvZUO3RVmDICf7DwG6JU881XT2uPm8NAUDLHR6b2jow5dhAKUMzoIuhNBiPt6gFZApauYwWaFxpmBLayqFQ28l9Bcgpa0211pCQfiqKvU76VEsKiDvQH448qWWE0pvzHAg/ufcSsOwE+rPcv690baV4E4NwQiCm5QbDxJnnAqjF8Lmbanv49zGn0mkEJb70nFegzGMwAO8PmG+AWsXMwaNtpkrWq4cXam/c5qV4MRP4Tfl3V/ArekSSDBMW8HXv+IEt+iHIKQrItdB77zwXqW8VcK+GfgXm5qTTH+OEWUOXIDBWNI4CeAvcFAVmckQZ4xqIQpfr54y8F5Ks/xYSdDVO8/X7raqf9D4MQBzp5QDqwLNbBKRJKCo69L3emoaaOftZ3UojKLIDkEM0k9Shc1Ipb88DX758wdevX3EcB8YYTHHNib63GnQuJlcO1w0xJwHVfOlMoo3PM3pZbCJ/bLrXweMh5iy1HNBZxWHagJ95TXVonTlUUn/RmYl5i57KI1HrUGkZDcsywlcklWuN3lsZlFcjpV/HWQh4gvK6uoc666peb3JkzRqad7h3ppR64ynzmSpL9lUBYwaEs2cUvTAdSxgb/BGkc5NPjFWdlZnaJ7qxiowjlvHhwwfWOWoaP9LYc5WpjxgSC2ugRLlHpWV1CjnEPkYKVKksOG9RXAVOObnXLK6xNYmq5z+Jnv6jL28ybifUQmDwMWUIyU1RV8KzfgyeFdFSfxSqDjUj67kQzwqybtR4LTHUusRicu7Bd72HpQ4AzVwamiYb5uB8ZTLtEEHGsLpHe6p/DgJjDDRcjpX9RKZsnnQKErd7mYw5Uce/jMGjHqzvZORaRe+yh82xuGgnY5A+V+kvCRoGFdVuwBR1X+x4W5E5x9PkZK+xNGtSgDc6/EzmhcEWGb5Sd59ri9ik4xLe3629mV/Po/2wHIue/4aSL4BTNsjK19baKv/B4HK9k2zUFHj++F5FAFB752HrfrgEJtsPlH8QsFqgvb631qvqL5NtOlDPWZmMxVLiQ8FEpWUtuOcR9b78hCoAKV/V3OFtg7Ud83yBLTXG6s2zyr01TrdHxeXF6wnUmmAtDdn6LBNJMIfb+/xoFf0Q5Pzy92+ISBxzMhJ3Nj2ar6NSzDDbYNZ0bPtk9Ln6yCRFZ5WnXffUF6K7HuLGmrTS8oQMb4N1blifiUyDdYiluaI1VxRONHqBo9bb0u4AidYLePGz3dg5MnE1LCzx6LazbPc5B6xq86DRBquAGIEnoAP5SBvL/WYixFS4G16v73g+f8XxYqXa2+7o3fFlY0O2L9jp/GSUzznYunoMiWCbSqpzteUuwbJtrAIKTLTWsbc3jPlCzFMMAUsWP9uweHsgcuB1fFtddNOCZYIArLuMTq4wqYRlYQVluHIoCM0LDFidhIwlar9QPReGtTrDCRTYWiLihAGIea5zUgyOvekUbwTmPHGc7+gPio+VvMdWEZ5YmgZgjhOnDcA7fBrSDmBOtE1ar5NAqyWrhGySoRvnZFrDJYIGm0hmJE6bMDd+noxAAgin82O0JmM2OH5Tc8394ejWJOanePqcL2zbA9vW8Hq+MGPie35H7we8UWBPYz6BCYzXifP5xNu+4bF1tMeDexH2qaso1fL9S+84MDHPq9DArcTGNMi90+hui8FgWivVvNBMpbhpq1KuNBUuRgNNDoD1+2iNToZ7sc4mEuBTg7YxdcjrLZ20WOZJLcYU08c0lsOaAIwbLKjTiDGRM9G2DdYrRS/rqXVv1hjUJNZBoDMTOQyYjkO2YZOWcdsfKImQiWkxB6YlmecJnk1mjqEIv9acSffQSgdoPHfu3vY/YgAJyRWolTJQAsCWIKvjJQ+stZsG6hMvJqDtajQMiGGdGJMarNX/aoa64pPdW5kDr3QvK2BXVgDJ/QPhOXDdQs0jIwfmHOjeVqPRqtpbgbs73HeYd5jtgB2AsyKzJf1j5ID7Q/6vIZSqFBQD22wkMCmKnuoMX9qtOSYy1BS20kFK+wDyDoywyRpmA4yBagQLZDJY0bj1Ha4TEWjDD8z5rsOSS8+TC5B8cPmFUG5Ar0z3Cl1v6a3CCamAOOaVVv1RwPVDkPMSmBlzsnJhBTyx9p0V7Lyhwhu4xaWmvwWnRhlfRe1ZD29lyIUY7w3PLhi80HD9DmrBtKth3nX0g93oY71VDfhaFibK2ZWj1oL2omrjGuPbKlioHKkeFlzsxULUGFWfiwwawhknxjERZwD7vgBZ3TPNZ42xGigu9MotxH0R1+e43VqQExg238RQFRPwn3OZykgTrGgZY1RxFGry1mK+Ax1B9AvQl8tIAFE481oTK5gsncQVlfArI/zSregD603E+imSQioCYbzlcgpmpj4pjigQLVDmk5R2wGBsn4qV45KGzULdTCXcRKYo61QEfUWJqxW/19qlMcwoCjdAdgcXgVVHLVh9aSs/D4uroqXAo6J/IHmOmCVmqplcBOaY0uIYprMfKitDPhfkVBjXJDas1hQQW2MGpaghlkPBC4Cw0NEcZSvX4FwsDfAhdVIMIcXvHGeOV1WPYo1xZvWymQIQ/QPrc2ekr9fz6BpfhQAVrdZXns5sS8eQH3wC9L4VSJVAs5ZzMXgRebEI998tJ7+A4H2f1MelQM31zcW+43qeq2SZaxUwWMRat+vZXWChVRepW+O6T7oWSapn8rIxeeOE9fMQAIt67konrV+nrYVhacOqqjJWTku2yEGbgEunZb9zzvXel++qCb/8z6VXxX2+ovoOFfhXH7WoIxwKbNgKGuv7pg2wjpHAzTeaGJPguo8VKeQCWe7Sr0pjS/1n8VS6z5u1+KBjWkvnA7pZ45F5rblrNC+YUcz0b37xw/VDkPNvf/lFizQwzgP77tj2jn3fV5+ckHjUJZiblY+bVa4cjAxFW9aNwZLpnWQUvIxJRU/roULvB2T6LUqS4EgMSd842H3r6+euAfWVmtB7JmntYrxgVymkB5v/+eo/Yvxc6SA+9C5Q+bPMEnUwEavReQkTYybe3594Pl8SKTd429F6x/bzV2xbx+t4IWfgHBIG28R5nvj+/o5mDY/HjhGD7cMlIl49UqyaD7LkGN2x+46H7xiPB0acOOc7q4J+mwr5hMv9AcCxtZ+AfOKMA8uzWAdsE4OWAHvrIlDaEhDwYWp5bzCLpQeJjJUnrg68d8fVrGHDht6B3gR4nd1wkWT1LKFoxNH32sznclItebZW3ze03vDz1y8wA17ni8t1UKMVbljtz2JgjhOWk+uyfwU73vDfLs1Ea0qZIZSjjzr7dgG3NVvqsQLwGIlxvoDHA9vW0Zuxw7YMQ8gS5JwUWOICducc8PGEdVaWjPFERsM4v1DvIpDtOTDOU+f6DHx7/46vluiPHTMu0fhnXDZPOBqTst7xdf/KyDhOzmljNUprjq0xUu6m/RdAmMGVpgBWtp12oIKJu6GdPAn5HHx/nxOV6nDv8JaIm05ixsAxTh6muzU59QZTNd/eNvQAXgeZ4VA/m0pXUkh9zbWBVaBzTAJL/cwBoLGnUTRj0URLeCbCE3mw2V5Xw1O4swPytgGtM/p1Ixsop9Y8qT/xChwbsFoUENRHsrqq2GAvG+I0os0bl5cCgb1tZG2HmhZuRl8BHhfUo+M8c6WKP+t6fRs4nswQdHfszt5W1I86g8VKQ6+o9rpHshhTO4SHJ/e2YSR1UV06SeeLmcZzCeDdgKRu8jieSMzF5hR+vfd4oh2QPyygouAjlhj2doeZCJygSByI4MHF5QdTt/Sh9MQMaJShbNtDAV2yqhmGnqrazUBiIJJFL33/gr517I+dbUrmgREn5hhMJUP+3yD7nEtnc7fPN7NU8er1D9ztywX4KD+6PX9eDNAfXT8EOVPaErcpZEYn4b1JHBy42kpDiE1MwkKCtqKBBK4GU7gH04qafrvgrXKWuKKUmpiKlBbj0hZj46W/WdGJX4Oq5VR3sURmhYgVH1mVYK693C5UmfbBGF2VFer9oJnKKlVXNLbkEhJbl4B2quFa5MSoTqXtlqcVmOOHXpNr6/OhxUKzfelY+Lu+lH9XhPqZVzWCdO8SqbW1m6uD6OorUSfC23WG040415rQzqh882LprtCgxpdduzu2bth3V+m9o/rMsDzWluCuztVxq1LkSn2yMqz3tloEjHlKa1agW+t+sVHaG1ECOluR3mKTEvz8VN+OrJJz0ChUpFmg2UzdlYcON20Ih6JkrexaC1XmmyZgzyFiuTy75rIXDLVbMQfTtnGBHGrNTAArMY5DIKrjoxH6115L/qsApd2a5JVWpDeXHsflKEprRaa29h8368Uw13gt7SAclnN1aMUki0RHaIutACC/k9JLJNJpWZaD1OddLMmNGSm7BpnAvAKxQjUmRub6D9eGrxvA7T3q6xX6L+dWz2emAC1S7N/FboaCzHUMya2SLEJN8m6PxnuTNEA2sOaE103vpvuo9h4t2iUK/6TrfA2miI3N7ta+NQIdMiG+bEixH3rU3yx5+83Xm0+ruW7Q+Nap90A12FwL8ONSgbb88h+LydNNMK2T14B+uG6vk4i+KpD5PlWRev3edVyNo+QMl/ZRc67vc+rYeJLzyDWyOhkXgcAf8bk+MKe1FmpB1PN+LBO/fNwf2ZjbM9/e4x9dPy4hT+bs5ngxxxcUjfW3N8z5xDwmhqoFwibgwLa9ofQTtd1hEvtGUYGc8IihEtyoaQFoSm6ldbr/D+dxJLyTsq7B7r26zTa13/fbg1doHAvl4naHqJJBaAM6oC5gmIOO5tG/YNhATEY0MYoVuYDORGBiovkGQ+I8XkKZ106ulggdQHfD+/fvyEz8tBNczXjBwUZKaAY8/DJwQ6LsJNW7hGIALANzvMNyUyOoiZEvmAfMg2dA6Zk+zzXx+vb+N7g73vYH1mmUDqADz/cT43jBoGZXj5+4Xp7vSJVHAw08NE8GvzaUNRjUq4ArSp+Y8Lbh8fiKrW149A1/+mnDT187kDsyG475d4x54Pl6RwQ1WJmJ83Vg2zY83t7weDzw9vaGre9o3vB4a9j2hseXL8gEXucJR7KV/kxM7GgRcMpsOM6RgAXe1Kn03Z+M7DZVVpwUqjZ09qOygKn8dJ5MS0InS49IYA6c4x0xTsxxAjkwz45qN7dvbNVwjANIR88HyFglqzbcVxVYge/z/YCb4Xz8imHUfFkEMCf2R8fb2wYEHf/zl79jzIn+9cuqHPmM69FZcXG+3gFLtFZHbnW4nFVv/Duamt8n+wCZaVzrjDxLYIrOVyM37g8eoEqf8kSCFThVPeQ+yMrtO9wactJMxMR1JECAY1ei4ABBpoCQqxdOpb3K6hUQYZaaQKJ7knuSPjFiMjCoFGiGevkAYwIzXIdhEpeZsQ+YR2C0QaanObVXEmlnAqaz9Y7Xk3qhk98fitpthLjUFKBkY4JmLMFG5upuO7MO/+UA0PQFMAooAbaZdCkbEp+3hgDgb//2C769fwfaBusTrbFAxnxDpSETLJEvnLa4edny6jh/P+6iQOR5HmTHdqZwtr7TTvUuRvjEeVCuQDBxBVpMa6mMX2zPOHUwdQHnam+BCaQvKYTj0qhlHQNSvqkRTKZaDHDhcu24Obp3zYd6BWUoE8LMDKvgeMba1slcPR47IieO1zsZzTkW9mh+9T4zCEjDlPa7MCP4bY6xAH65/YvP+G1wXgHEQhfw9bM/vn7M5JyMwKeAyFh9cghFuDiI9C5hrz6/Ym+7auErWlovWRRxRSK88chc+oePKJn/thvypNjYV7+RqtFfmh18yAZ+eD5Dga4P2HANIT9TfQ9AYXIo/z5VQpkR7E0TzLNTxDqvqDCTaRRRx9bUNfdGHyYSYzJ6zhkIGIadGKGW8eteSr/EqS1UXhFUMQdQqpAbp76vyPDD033ONaWuPydLCssg5JTuBaSP6+iOa2Fj7YgLmNZmt1ufCNKhMdlosqkZHA8jDMAIPs8wOHSMhxyFeVUa6BNvu3BrHY++YWuNnWUh033LvyeD0nUWl+l8xVxe6wLQZsBj20lbu12N/xTF8zXVgdmAHsvxBei0ShwPkPHhf/WBtcLFHFW/lSW6TWCSija/PTOYBptzoDkdf6otwhzAGA7EAHLiGCfGCORx3KL1f/3Vt4acSXbZiJVLM8eDOp3sjgFYDA7WnkgUeLwMpt0CqapeWkJYMYrNVFFTZzyhRMfQfp8rQnWv6FY37RA7EphhOk6q5kpR+rJ9QKA0UxWkX+nJvNk9B88tijXtZDczLsbFPW/vI93Ch21vWKK2rE8gB5/qTXWdnRYr0FpNDe2uAeT7FKlguiMYq+KQhnSmFLwYDhMA+mRNzvM88Trr0N+rB0zVzy4L+UcmUvde548xC0AbU6Lk6pVXf/4Hce/aG0mSZIsdM3ePSJJV1dPz2IX2StD//0v6IlwIuoIAzWx3F8nMiHA304dj5hHsnakVLu5Q0aiuIpnMjIe7PY4dO0ZUC0k/BVFjVvlS8DTf+6MDPTkz+ZOrO0w0xq/PJn8vA7IZ5YB6V8Fhy9+ZV5uVEY0OQUvKBMJG+nxmiZRC4nV2QX6mMzoDHEw/nPbqDFz4XZnBWLbhZyyX1/37R5Hnnb6Tr//HPu2HQc7jzi6A7nTgfezY9wPoBnXOkQEiixGWr7odAW+e5SLaCGp2uJ2aH0lgnsZoylwbSmmouvJGiCPLGhIkJ1G2RNe2sA7fWsDUKU99dt2MGVzlSvt4fCBhyXQbcLBzAiA8Til58hOOY58y69tBZv5xcOJ67z2CQy5+Tgku0FahtYYk/kVWH4LHsTO6PujAet+pgNw7N48UkAlfYmaSwkPEza6LwQx27FApqFIneTVpTvrpOE4Y79ExxncgxnlQkL8HdyswPKbf1CDxDOCSZDtOw2I6n60AqI2DUI9tAwC0xsnu0ANUBu149Iq+FSxCvSOTHo4vSPMjZMOLIkXu1trw09MzoCtECpoIijt83wBwcryHaKC6oC6K/dBZQvcRzhjgWAVVfF2/wcSw+47Dor6caKYEaTbauEuNkkGP+2EIJDEUfGtFVVI4LUrGSWBfQG2O3geGAXIAMijEOIoF2hEOVOio+3gAqFhKw7CBY+ywbcQ4jQNAx+PR0buh2RFlm885np6WEBYNUr/6NLipsEomRRhoj8QksmuPjHJmuVHSNkvjn3BMcAlKm+3pDMhTNQg4xgF0TA4VNWCUo1oCws8WcTcKPB7d0I8sCQ5m1RbjQ+AwscnJoTahzDVBkx5JnRQoGsSF0hQpSOd7lDHZvSjBp7FSpigpMkgS7kHNrjP30AEjJ8UCnShRvuoxtiK1uAUsgeSAYyrjHsjyMR3ciI5X2rnhgorgpeQgeU+Jg887fts3fN83PELCA7giNTON+YAo5Bmm/EVrDZMXHIKdLHQVCs4qcIxHBD8MaLTkMwW8CLwCdsTau3SDiki03BtggSBe7msGtMnPMx/zHtIT5PiMs1kCxQE12GGTIJ/Ui1k6VSU06nwfA/0OlNo2xegX27LQp9sBc3ZtpayFg/sks7pSLskqIqk87y6/64wNUtggD41gCZHUT/zGUzZC5pxDtgz+42f+4ynkIOw5bESdnq3W5soWQHWKcEnq5eQnyYdoLZeKhOAbIgJjMJG1Q147IbtQbg005xx6GPybElo4pRC5KTlNXP+u4c3o8NxPER1HRv9hnyWyE+iHaGY8kYQBl+FgETS5Y/anXl7IwCl0ErLWHdHvAMh1yqjbfLYbAuHsgND74bUxED+Bu0zEdZ5PQgZxDb/PFiMU+vwwB9OoMeBPiW9Kn+uUCgd8HDODONfMx+ifct4lINmzS4FrwDkCpChaiMKpKpa1oS0VDQsUBX1QBLBWflw/RuwpLhQNuJqIAQ1NrRW1aWS4grWw9Qi0mfIAACAASURBVHjvnMVWQG2d1lY87o/gaABwjrKotcV8IcNhA6ohQRDE41o5aDF5Cu7c+kOjNAqSOKvUKXxX5FRu5loayOKvyNl5xFvKYFNBEqicLW4ABPveYcWgjWtR1SFqcDmgwm6JWjUf5gygP+PwKK+xjdlJrhUw0Yo25eRpcoxM7HuTCcN7dOQl0hDhP5+3cmYcIxaJIDC6uOzMe82Ve9WDPOxj8t0gXCeNKn3wnAck3Meohr5/3H2TePuBYZ4or0WJK8isntwKQ7bRzjbuIDprUaiFRMbkZQEmqZIeqEJk6lpkynV4BGd5D3uQo6fOiXugZhcUI41QIguSvx/IkEp0U5WpvDuRBMeHFfgZx317YNs3DB+cXRZoq0eWNTu+LiZVQwFcYxwMOXMWittXa+yxHpCs9rnKTvPFQLOoZhgxZ66lOrcHsppsGJVLR188h0Rr+I5p4w1TF80NZ/dy+qPzOTJhxokOe4eNY0qozPBEkmcTnxS/n127WcWJJuQTjYdAZ3MI/z7TUkyEJ/KNuKzsaItQWnIn5Htioj4Z9F0H4P6j4z8Jcvgw+6CmSJkieBG1pTqjAz56EPtOp8QHkTVkOiGHzpIFYSaOHDgvnBGxlgIXR6klhNvo3MjFIQenlIJaW7QFK07xpnPZ5bkgkTTPG81zugaB+XqzXPyOWXcNHQ3PSFqVU76jJCRuM2jLd+Mk1TNgQh+x/gWOPgelwT26MBnksOOB94M/Z0BlCE2MudjySq8BjkSglJvuhMCR5ZUfPfR/wpGV02lBxYHBDFdrA0qbfC0bO1IZO2O3JDKexEkaihOWDU2Oyq6a1hhM3G43xtRquD2vuK0L1FaIFfjjVwAdawM6gMNi/ToglWrTWh3achMr2trQagE6dWJqKzj6wO4DYmwlb2XFWAUqrxTTi2C91YalLWhticngxtdbx+4dZjtKLVhatCrD4T10jzSspg0ULajS2HED1uLFBUOTY3NwfcZDLpOEG87F2G2FTDig0KUBItgeG7oK1EdkrQLoAIQChlRC5r3Zjx25rD7j8NHhBjRN22NoRbBUgat+CN3d9ExcVFA1uAszSck9abP8UkOFtsU4EZ/RAO2QyaDdc3DUzbA5Dw6eopBE1dalYZigDwG5ZIJWHUUdu2yJFQEgMjeTrsuaPxOtAZEGDUVeOrwOkmNbGDUQ4S5AqaHZ048gEXe4KEw6UBSlAoj1lfbU+ymU6EI77+Y4epCvMea2zcTrTJY8b+hMpRwCHwIp4exKgRRBBzuDRhCZP1Ewex6v9zfcd3aaMtDxi1POUni8OGaEldbms8mGltRDkww8IshIXyYZY2RZfPoerhFIQUGMa8jAJJoHmKgI2FEQpPAcFHsJUj5m7hZRRsqNnH9yQLDZmP8GLmNPvMMG0PfzfTwcZpKJgbgtkx92wBGgnPgs3XvYSgGgcT5JuCiSK0bm/RQFOD4CYf89XzEDHIn/CU4/fv7s4vv+wfHDIGfv+5zUO29+dHj4rB9b3MpsTHNcm9Qc50Px6PQ4t3hEqhcCIx18nV1cJeDQWpmllMqftZYDDhn0lOC5iKTdOvvrM+Oc9eu0znFnqfya5wPQM56BDmIhmpFHAgOoZ0OHYfEzw8mxmJ8fUfgwCtWZG7kPMXNmaew2yhkchhDicpt3lhAmu2+gp3FITsRJYHTAIvuKjCTvBUouB/9UOX7eZrtsTA9yJYJEmUFxtrxyddgR3S5jhyiREL6O16BwYsByBrpNC0oR3NaGWgqW1lBbRV0bnm8N61oxOscaQG7oHYDvKFKAF5aBdhtEJgcF2TAAXVk6UsInLDd6zOAJNVktXAe1LrgJ24xdokxZKmoT1CaEjp0k2DF23O9vXJ+X1v6iHGZrtrFE5YFUtXWKPBYxUg07VXRlEA3SSiOdek8qFZmNd4/ZOvEomD3a7PbjLTa8H48YEFgJwxeZhNiUXbB+CZ4/4SghsiZWZuBelfN9XFlmyd1rFvs6yjCWs7g+OAGus9yjNqFxOvBzfh7h91Lo6IpEQUAc5gVmjt4t343OfligQGW6mtTOkqJQK/DjmE4ru14y8DydbeTnQV7n+52wfqggMOBxI+HZiK57cARLlD7VuEbQDaVUXo+Tseyddi0RvzluIRBmDL4nlbwBk/O510jKzIN4qgy6AZ6XK58byeEIbkjYxdg/n3m8PV6xH9tEcDTGVEgpyB6+dFnJp8nBzTktfCjRr9wTkjZa9JJQhpczooch0M3nkcN01dmhEIvkDHIz0Eq7DlI2EC4rEZ9cN9NP+amPBI8A5OQEzsxRA/uI+BQIe2E91iPX2gkuZpKaZ4Pzu4nCB3InEsO0ReDHHoEQ13MNxHlkgJZRQJ5fEOU9f5rojXz8vLl/A4Aok7Dx948fiwEeO9EDw7yBV8OWROKERj/c9HmHcif+riwUFykiQClTZpoDvmISchC8cqQE0Rt2qSS0X1Lf4UOZ6swpznO9BDp+BsCprDuRpRkKRGSZLPWIMiniyAUNtxng5GTzBA4dZ5BlGX1HEALEwEHRKG9UVG0QxFQAG7ADYBt7Arr5pNMYxxwRpwLmhOMB8gc8utTyd/0URPz8DCoJ5jY3NIOcC0tPQlukRNYQ2bLZHjyrCHLgCY6QcCyKWheUIihLjSCnUvG2NKzriucvX/C0FtyWgvs+sI8B8QXlcFh3FDEUCDPXbWMOMRg8uJF4p6RBIQWw3JSaEOD6NAekABXkTBRlyUKipFqbolYwyDEGFzYOTp8O+DrXD1vtFao7AxxYGI8FrS5Y2g1V2HkztgPDO9gvY5AYGdGdHRml1GgayKwtngYXCiCC6jEeopC/dt93FKUqbZWC5gXdac5VHVIYuF6d3T/7KJXlo4rCFvhRotlAomuMjggATAJxsc59EKjfRc0UZ8cUZy/l/i8SAyWdQYhhBP+vRJBLpz9cUV0xTNkc4A4O9CJawfEgwJBAfsODUTjN0pciDY3D2MGWjaAX6+7mQQ9IL8gj8ZQe1yRmgSxI7B2jcKI7dETmPkgAbbLAfYePg0GOnb51liwsYrysxAuDp3Hhx+WlTSV446iZDJrcE8ENXoWy7MbZWB8u51OO+/aOYxwBTiRxPcTsMimMa+UjyxJhICG5jjJ6BRPkqTEUnZBEfCKbGCzvaKlcx2nSA+mRf2SQw2fOYCSDRHGctIXfBTrz/5egCemPeM3pAE5SO89zRKACR8gF4DTP4YeuZ0ofJHmq8/nW4MqR8zTmHLgC7jmbv3CJXuabXr4Uxv0Z2AtkaqEBM60gmv0DW/TDIIedPcKY0CmF34OAfHR2W+XDZn3aWJuLS8rbG94hbqDPBZR3TAtjvax31hLlp5CLJuohF2Z+BDVy1oWvSeVVufR6A7l4Y+HEuaQuSvxicIsQhMR8uH5ZcJfgLSNR5XwsCjQB2X1jQTwmOSv1GOJBMmrCvm0Yx4FlJXxsCOOCSyQbn6WhHDUg5zUAGf6fAZHEvXQl+oOzzEfE4BPrDPM471vKxLtybXkuUjk3YlAtA8l1pJom3CLopV5NqQ3Pz19QW0W7MbtdksXvgnVteFoEz083PN1uWOqGox943RVHF3RrMDesTTAqkTXaDcU4On779RUvJlieBLU0SMuZZyyP+QB8jzWwFPjBkpBH61drK263FU+3F7SFpMQ+dvRBkbXaKooyy/ny/Izn20uIpA0MFtKAg05Fna3etS0BBRvEB4pamjp21QigxhKKKot5wxQ1uEUMinPvOkeyGFBCxZhgEJGt3MUBlNJpQXBbGn6UPf2PPu5vBxRE61jwNZLoSzYazNTkQ6enQCCtYIgHGZwJmYaQJx1Hqu8CApKwdQ7/iwxHBlekC7kvVaFyY4dVv09+htnAY9tRKlCWGnstIX4imQKZTRZaNGB6KiBLakz4aSumvci0LYhiNq/YaWKLQgYYlIfZqgJUpRBmawXPtyWcenYZVmCE2OqI51/W2ItjJnnmju4AMIgARalTgODopDGnO9JAphniJUGa3Wrm2e58cQKfdNz3O3qUqhTUVsrRMTaD3XT+ANzRj1NgTwWoIueAzBBdnKUpj+QfQdq2QZ/myu7PIP3OxgooA8QEBi6JcK4WVhbyZ/xbEpXMjqnM1P1qR2N1eCLpYYNjb2j4ZZ0abJiv8ZAC8Mv64yPObkPylHVGbEySkqOrcM6tNIompj0WNRTTQATPQCUrIGdZL8/1JIGkkv25EyIhDoL3Pzp+GOQwclVAGzhIMdqno1WaLdI6Iz9khhgnnqYHweKfV3GJXAXRUieKoo0k74IzyKmFkWE+uHxIH2C4jEbPm5M1xQtxfT7Dy29gGp34mp0G+B2p8sK18cv3ZpSbROtLhB/nkm3xVznwhD9n7VyUXVeu8y1KPNyrUrPM/y4fkfciN0Ned7T4K2TWSUkOk8s9+Kzjdx8Y0C7Lc+mNwsDHxV3NXwqiSZRdchJ3Wypaa7g939Baw/JEstsSk9xtOGpR1AIsreK2rlA3VDHshVoYJfR5qjiqkLPALiaugcfjgbY+QcugHkQpyDq7Bi+LJ0n9EcnSZ6RRJBwvaMuK1ho7mWAcT1DYjlqjsWFtDbflBh8PdCOETL4XouNiQJ2/K9HCpeIxX4cfW2IZamh9SAguisQ4BIAdOBdTkUGZItGGWJ8WpU1LYjcm1FzrigzBP+M4NvKEpCSh2jjjzn3uzkx4slU8ewY0OkdUg/bgyYFjB5pECXiKvznmtRV4UOKiLOEac58c6g0DiqL7lM93B44+ADXMYtUk2htOMxLnqJxplnEZnQHmF7PcMJ2WgKTms4U29/NsX5fTIZcot7Ckz7leIgKXHFbKRMglZmwa1yR9WXBSRkEiVDwMyZmYsU2e+HSg+c+0x3a5DsAnNPF5awhgZ9xIdXWh33HxUPQ9SccnAoIoOQU3D0QvHOfayvtOnpMGsBKBijOYPGMMOX3VtOu4fFisFyDOcbZxXXxLrNH5pp4APzIsOu8+/e4pIXIGD6fv4MtT7DE/62MqzPv1sZEpAIf8O9w7z9jnvUWuE8lGJYkA57w0me94ft6HJX+ebsR7fq63uWn+/vHDIAcloiU54GPgCCTHDOCEccDCYPLB67mIP6gXX57ovD88SReBFBLoHGdQlehIAaDunAStGm2TZ2V65IwU1XkjPAZYKhC7L4lT/M+yH/9qcKaMJ+HE2UHhqYQapSkQbWHbIQBkV1cq1QplauLzuFv0fP/ki8ED7uff8tiJULSA4NHAdla2sBvoYIZlFpW3NIMbmRwACzXYa+/C6CHXH0PZPvcYXPylRNbRL0ab956S+Tq/pZFdN33iRpxziRy3pxXLuqAuT9DaUJ8KpDj2/oD1HX/77VdK4pvg6VvD1/WGUhRfn25YqqKNBeZfcHta0AplEd5e79Ai+Pa0BKy84PvrA29vG46+QQ+B2QrA2D2Dgr7z2ejCGjT5YxWtOp6fn3D0O5anhvbU8PzyBeuywHBg2wXvm6BWwctSsCyNZOmbw9qdpSgXNH8BzPD0lQT2VoDl6RlffvoZ4/0d9vaGPg6M0bG/AaMDS3Cb7IkwfCsrdht4PzonVPcDJco5++jBHwEgwpKWAmVNJ5rWhYlIVUfvmAHYibv/84+3+xuKCCxGWCyN7bjmAowCsUReTnunaY8oboSlVFQImhS21g+DNYoH9h7lipF+JO0GEVZplQMPh3BchBIdUxQ0eWDAZ0urwdkNNg4SlO3sUAqrhFIWQAZGH9Nwk8OBaIkv036zhZgX4hAMJ3V5nmc4PAegVdl5iAq3SJ5KgSwVqIqBg8j40mC9c6Dr4tCiGBs7P5Mz0gIJtuJsBlGPlniNjiMiAXQ6Id1fmHFnEI1hSCX6nHc2oo0Z9o8d0z/r0NrgpkA/gpCtFHFNtAMRoDhCA0mw1lvothm7GUvMBIyhmJFa0rdEvUvDeyfhvRu1wFKNG+CkRA6EzSYKAJ73LmMeB1HES3orQYWYnEvMpFEmRQH0OWexJSNRZAAhEZwJIqCK65dM4Of3z61+LeGVEJYswgA6i5IZvEow1c0Y7NjUBQqbf4nuZJbqT6pIBjV5L0SIUPog+1Vn5eJKTPmPx4+DnPkeEQ0i8Y703RJ24Lw55yn9fzsEuDzA+cZnhJi3LbP/ye/gjZqw4fzckxXDmz0jmY+femG8z7POoOESYp6co3xAPPKOfMi48nqutbjLVXqW6oD5XpmD2uDG8qKTpZ6Rcr7SLLogptHzGegQu9FLhHwSAAHE+TNg+5Fw0j/n8PNJyrmG+CP//UsBxFpSQJGEZJlSDrUSwdGYTE9omMrc/dhwf7yHei3g64KyG7bjgaNvRERAo84xDenY+BxKYaDZ1or7Fp0UbrMc60jym8yuAY2J9Uny9CJY1gXrsqItJD8vy4LWGoY7xojZSkWxxiy4ZVlRq0KLkWvkaTAdaAn/OtZbw/JUMYZi7DIlFewhkEEdn8yYCa0DroJaCB13AUqgO5mRSvT9TmMomafzScytCSBVa11svuIzDjeD6dk+zU6nQEdMQro+LkAjiZkmRaa2hsSz85BskCibsg3/mkfG756Ke/OQKM9Y3GON98gAKcsHkp0pdulOif2qUoienIQX2rHLvs9yD87fji1vUTbMLDk5C3FuGrP2BFGayoDkRBYKqJprTl6ZSyZvxjlfEw3O+wc6LLBDNsvvMxXPgBix2y8ObEppTODBLrb0c21Rin/mGIdrLxJw8QU47X22zfML+iF8aMc+9w9/2c/rvyTSc1Aq/EQ88rb5JRiZviH/n1SE0yfJ9FGXa0NWFDKnTi8R75yozfkLZ1nLz886/8bl+7+7jxHoqCDI2QiUKxOECIyC62UeUEPGadfPkLxmv5qbDw/kDHj8TGQC8rzerb93/Cct5IEMeCZESvRBgTmXKbu2r449Ok7mAvf4eW76MKKpTKxBDlzWhuRiMEcq7KAoCjPEXKfo2vKo42nCiOHIR3Q6SGRiHx5SAMhXQsvE+eJcY9V5lEEJNSYnxGd51AdYf66UwJah6BnZlxhGN9v1LmRtcG5TzjDS+f1xOlNRZhaxgOR6CY5pgDwMJLO+zK64WNioWM8+txzp8Pm1qnni5MUHITqCRgU7pEaWZ3KR554O7hUNAp931q7tuMO74/HgQA0bb7DRse8HxjD0Y2DYA/v9FeOvO355+Ru+tIKlKPpBJ2ePA2MM6DEAFxxHyLEDuNUGPAs85pZ1ByocZYmQbesQBVppKIUcG8T1/HH/M27rE56fK9al4uW5oZSCx8aSwZfnBlkr9MtXaF1R6grBARGDlGe4Kx6vv8HGQAkti24bxr7h8fpXYN8h8iAXCQ2+Orp07I9XjGPg2LgHllJgWmF1IfpYHGqG4g7UCpMKk51PSCvrXZXIY+1AEYcoOw2PjpiXZXjco1z9Sce3r18gAGqaGAdGdzh2qFSIsLNMAk3l4rnYCosSiyqvyQo5xAaMw4Gd+iZJMwQisDo60Du0byjlhlJWziOCw8dBvsEKTqAfI4QgFQpDqcGT8ovhF5vn6iaQ/jE7TtRM5VznDGg4UVqEwayA2mVEQaNMWQTiDJ66kKxcwTK4eoV3wTYGRA2Pew9u1gHbWdrdBodMmi8MwKUGij0iiKzs6BPw3OREaVJk1LKbK1TIPZozcjQBAE67H4NlxY9++hOOBtWCpQEftGcwGMwEC81ATSYRiWGnVLX3YRTJNA6HlVogrujBc8uAQj3tMO9NycTU2ZFWC9AH+4KITOCkSMhZTHYIIxalBIF1MIjPDq1s8LkgUSTIB/E5yc9IDPGkXsiHe5+fGH79onk3E/T5uhDjzQAkAgTJxozLzDgY0NlNA8lzkPTfJ8/Wus+ACMCkeUwxWD9BlpM6ws/14T9cRj8OclRn3Vei3ZocGj6sIZcbleSlGdheA4n5v/gyjdD1X/mMzjY4AZMgE8An8zG3SsbEExj98O5nYfv87u9N8uTiRPz1H47LL8yW0vz+RK/A89IMzwKWjAV6RubpvXMb+IyZ4ZkdC9TSuEXr5Yc79vtHKee9nQ+CC5/T0UnSTCL05LZ99jFbBtNo+xm5ny+af7MsnjkIX+sOuBJO730PvgU37zENDHVLxtQ54maxYRjDcBwDo5Sp6EphRgrtpWigRunUhkElJjBrhWsIT2ohZwwMZgUCCy2nVhngQAqenm4QB56eFK3pnK1WtKCWitYWaCUhUcpChd1YP8MbeRpQKBjYizhnzBRBVXASda2oQnHDXQeGjGkERg8HPwa8sNtu4MygGT8GWqAabakzsgSiO08h0BlYX1qJx+cGOUUrzyy5f4HomhmghhQ4hAvEjE45kwvHdEAy9x8C2SBC2g/jDCg7f27DMI6DGelw1MoSY0L3ox8xQiFUcksKBSYHI9c5LvbwdDCziSJRKJFLl+iZ2SKz3mnS+Bnmp/3M3ymqLMNYdOXF7/XOIKN7pMHikVh1oFPX5vAOg2PvlApxsShrjrBpmPs2yffiFMI8zVtk19MpnujD/MrPff3Zx+wCjuT22hEr4bvmWcXN/TAOwQViv6MxgKUtC9Ivj9PxmoZyBOJDIpgV1RDc1NBZ8+sSib/8NJUXN3KuGzaj5OvnOso1Bf+wPj7+HV+Fj78KUk7eUJxVJsfxdJHPOLu8fm/NeRo6O8jgac3zd2QGKR/9dH6GfHg3SZ/gHz7gcmX/eC39MMhpy40dQ53TiP3oKFJQ6g2HGB/2yDlNAd8lvGt5LjozEkT9dgr3gZAvZTgc277TIVonDC+K0hpqqXh6uaEuFb7TyBZwEvQxNkAFpoSAa1W4MUs5y120SlnrvAYmMAT6E+eaCz/gbB+ExIedi80NiFaDOBiAldDj8NBRcO9IXo9cHoqNMREJj/dNCtNoFaLUsTjn4GhcBfVh3LNmqud54wq7RqSNSg5OnnvIyH928mSRxVkgOH2MM7hVTFIng2OiUyoF7kDfODPJjWROiOH+eAUEU701N/1ya3CwfRoiqGtDe1KsXxUvX77hp5c/4svtCUttGK//DtsfeDzeYGIoLwvK8oSnb19xHBvuj++o2rDeGuryDK0LXr58xXJbcFuynLrAUeBlRasVt7WBcy0dt7/8gShMZWa16sJN2khGllogOlBKEPrdsbZnVK34618PbAf1kswHNnzHWlf8+etfsKyC2xfDcR/Y7oYnfcKChu3X/wP72FAa4QPbNxzD8Diy8+oVpS2obUWN4ZaWJOKl8jlsgJvC9koUFQ3FHLU7SmE9/PDG1mQDdVk+63DudyIJA5QYS8dAEzeAKYAHhPhYoGBsmNi5g6SggyM1Hv3A0Qdev+/Uu4ln8egDNhycczrgdaBIC9QoyOrRLvuHP/4Mdp6zI813lpP2nQF28jKGY8oSZEarVYHhUFPkNHV3qswzKz6Rkox3MiESrxBxFBlRtjMsywu7o7YHxjgw9neM0fH22x1jOPqhcO8wp1SCuGIpC2qpnKAuwDEKgIH9eAMQKttacasrZujbBKgy0eoaiE6LciCEa0qqfEg4uBbDPYzPD3Let3f+I/wPE2njeIRwpr9HCjZ7R2aJEl15VM8e6F0wRNDHASO8DwHRGoASAiqCoYBWgVYqazvYncmxMoKhBhjHAs2QxdJjYUYR7EAuaKVBROEFMKH2GgOnkHDXa2BMVMez1fzDbT+1szJZFtWYK3g6pjmCSELbbAT1QUakDhrdU9FWnuieC5szzNmw5I7u5+dpkVDdzms8S3nMQhCV0LNkRwqHzcv7QXwD4D+bQh4R4SQfiUSk63E/zhrgrPdND3r5ZD8jePqxaHMLZUTrI242AnUYhJ1T5bYYxdZGRUENB2hnXRPZ+RAPEj7JYJn8zBjjdzfkmtWd5GOJyN0DvrXz1fkg4mufd1lwqj9fOsmA6HC6/D74HvxsntSMVJGYTSJVH8/1736RKYjk710exPXzPDfOZxuXzJoSSYhYXT6es8BjQ3JtccHnvc0NOFOZyIIwN0bOTylLmQ/WQS0cTxgrPq+ESF9RvnY/HJBBBzUM6JKFZnIYRFDAPymvX2ulMa8rW9prxRjseCoQuFVo4blV4ee0ViEGHKgnzB/QP53kgO0dY+swOzDswPt9g1XDUR6ouqDICi8OqQyMWlnw5Q9/RFlXvN//HfuxQzfC22tVzqE6OoQCN5xQneihs4BIzZhcNezrGrAp+T8iS53lHM9o/5NWUBAb1SPfDUdhbiiWCYNRFNATB+TJUmE35kzF6hvDw+lbzPfKFu/s0hWcEG8Q4nHloZAzNcSn+F+tDSoGsT6HEc5t6BlA2exa+4D0zPcFAF4LuVlcyHNuVGbpcCRBdviZR2aq72DX1h7k4t47UfHB4HYYuTkKDyHFCsSkaU3x1BGfOtjc0X0AQnRIhbIfdILBz0CcbvJCIrE6zU3s80AgTm21TzzS7kYyJbEHZlyD04fk6yfPMRLMGeT4ee2TvgF2aeWUL4djBCklcF94KSyBB7L68T7IxU9lrSHOO4i72RmW/CsBLl2e+ecjJsL3CFsz0cXw5X/nEUxF8CyzRhVn+vuMeRhRgRzCtOH8+CJx3n6eSZ6eA4HCsmxLPdRYyH5exrwmJP/2XEM+f/QfaxzX48fEY54juwMAqFZ2EcSDjE9DOmkHEpYBNB/IxzfjgDwKbS3rM0QEx34nUnRRi1RlK2M+1P2d4l/fvvyMtiywekTEHJsuyjI57IvA/yUqF9b/LO4w3zc6SBAMqUtg4jHOfnifxi96AyJAS0g6Wg8BiC78xP5G9EYl0MtUaWXUe86ciY8TRY3vz4nKUmm4UwISmJsIrvM8z+9H+6LE8vCT6IdA2sboRETwec4JQNS5HWbkfggwR3GQ4KAhkCan7oTQVBQJDQlnB5sUimqpFgjhBIxOLtS6UodGRdlNtlGu/Hg19KcDw3fYaDAAiy6QUuDLhse24fXXN7QKVHuDO6BWAyF0SHPSr4ajdkA7M7KyrNDSsNy+0MirRGls5Fj3tAAAIABJREFUAI3rQwOlKrG267Lg6AcO39mt0xXRQohjv2McO45fO8Z94L68Ye8P/PX/+gWrFqyvBv/Ln/Dtj3/CrShKUyzfvqA+37D88c/oveP//q//G95ev+O+NywqePr6gsdjwy+//DpLHPtwbGaneNxg8LgG12T4AQs0VG1AffB7GGh1ofih7fjM2VVlVQ5BHTQ0hopuB45OjosIAxgUIqoQieDGKEoWSAfnFSmOzdF3x7Yd2AcHxZIYqYAoWl3hYlA7AFV4pbpykQIwVozRCYZ+31FbxdOXJzgMR9uJnvYIvpSJmxtlCPoIcb9LoDLnzzmQQ0TFBBCNUQFMolQUi1YABu8b1Z2tRLu4zEy5H8CxA6/vd/Sxxx4SqNE+jxEJJAxlXbHUrxjjFfCOp3BKuys5KP3gnMJW2IkkjjZCvyztWZRiLbr7MqkdnWXfchm5Q0Vvw+Pon1ry5MGk/RgHA3tcUIFw6h4dreltLdOw9M6u8dwy8EQgWj5RqxEJnMV9OODwjc98qRWtVJbf1U8U43R/QNyznAA/G0d6h7pjXSh9UJTBpitf5+JT0T7taMoNCFIHiD9P2ZeIRc5jrsGcy5YifIkskrytTgmOKGpTYmMYSjzqotlGz0+hijL34WEMtAfdKfo4gyFEwq+QSBYy9Uof7Od9AuIe/ON19MMgZ0RtdjL9A3qrwqieaqBBdgsj7ik2Fx7WZw090BYRSt7Hw6dkfQ490whGuJpmV5MZjoOaMm/6He1oWGxFKYqlFZTaKMxGGIXOUhSnl48ITC8ZXtxEF58LMtMh8dQDShIWQpMA870kgiloAbzARdEjiKq1ch7HYFDD10fhLM6tlMKNHwSyVLw99h3mhqM/JrkMrkFMiuzAMwO/ZIGS82dIdb+KFn6s3/5uQX/CUesC94ERZYQc8KfZCS9U5oUgVIwFrdKQj5hmX4JIJ2ooTaFVcfQdYwx0dBqC0E0YfUxhRhucvvt43/C+3rFaQ21KIzcotzfAjH2MA/cHJ4wbFG1V1KawIjAFLFpOoRWqFaWx3bxqOTOrIpdsn+8EZ+u+Gz/D3VDKwnU424uN8fMwwHcAHa00uBsqFGqCbRv47bfvsP/zv+Lr7Qu+Pn9BLTegAGM8cPgOrwWoDTlPppUCtAU/PX/F0Q17H9F2brBuIfUwIjbmPt2tIzuYuM4ZREAcA5yFVDXL0J9zkDjMMiwAiHOad67vvP+ZObAaXdg1FGiVOidyDxf03uESpcJBDg9EUGsDQCVjE4GMPvWwUKjdhZqZ6oA4g0IMg+6Un2ha2TILm+uQk+SvSXPYJo9/n5DPaejTigf6KIko5LZP25YaOKp0OS7ox4Z93/j5nmuSpQ4ihDazblOEUDGdRankGDVnyWmMY2bwpRRyyQpH7jQJrlnhuJ2qiXKF347rc5D3huyQ+8xS5/WQwj0puT8zyaXfoKZUjbWdyS0Pn8lh3PezVs5v20lLKBrl9pzgHlUGc873hiPUz08kqUggIuk785QDbTk9aeogKUohB9TtnCeZAVEiL5OL5meclggh5tcn+pa+mTk/186ytDkrkpw8kqdboWSBosD9gHvHWguKChYqA7PtO66/j4H9IBgxkqqZ5+HJ1QIADgEXi0YCFYrbup1B9HymP7ZDP1Y87kecJBeCxEYSpbR6tw7FAkgJHgw5BKfCYqIrvKgiOS2cZAofBxyMRhGlMG6UimEdR9/ppELe333gODYULXjanlBbw8vzC9ZVcLtR1p6TdGMOiV/aM4Esw86HXTLSFoQglsxz7oPIB4JL9jHICUg3shd4SMMry25lWQmPH0cQG2MrSUjRR021lEqNlaIoRTDGwK///leM3jH6DrWKKmu0oqb+gc4FM4O1uLBh7FiI2Hu6WcLsBUI51Ovy+JSjtZXEvBFBDiQUdD26Njimg+3ggKhiXb9AxGH+HrPKWghCGuoNKA34/v0d22PH5hsbYiOb2fdYt2ApdLwfeF/eoVrwZAvaTTCwR0YR9CrvGN3w+v4G1wbXhudbRblVWBUMAbpoZPINUhuW9YlE5CCecm9KqHOH0TCLtmsan2N/MEhoT8y6jCiJj30GOeIPKA6s7QlF2OUig11kb/v/g//2t/8d//Y//0+o/+t/QSsvaO0F2/Ydj/GO0RS+NohSW2cpBcu64EVf8P7Y8Pq44zEMPg4ce+ca34ju7UL1p32MaWgCQKC+UFEMc5gontp6ttV+wpHq4aVVmmxrgBtEDpx1a2aZ2bHBoZssxyioVDugOExx9AewP0LDxOGF84uW5QaIYDcAZZAnkVu/KGStlLBSDvUUY9Boo8PfWY68fXmCwdF1oMd8u4GcNn1qkHwIdBJhjt0rUTvMMvYpqQHapHB5QkOGWiqWUrk+TXBs73jc32PuoEJadK66AmNg2Ij3V/QC9AaS4xGzhwA81RX7odi3bWr1lMY9UUO1fCkL179S32sJTtE9O808dH28Q4tCPIelXmQlPvGQYI1fdWVypiDXO30Ub2yi3hF0fMgOI0MrQa4dAkhO9lbUsrA6EYNSJ0nZ2ewwhqEUIqhJh9DCZz5SaG323p5HlpESOYYUdjP5OZSWUwmikiAKiYHYEgFnBghXFM3n+yO69uIMlAn509MzRyu1BjfH8RhoKliLoJYFpTT4eMBd8eVWsVSOYIE7loPyBIBj6x0sh1MNGrOo4chyLoAJiAAgsX0mATq7uHA55x+Zoh8GObMFerYp5oC4ZEfTiAA9UA87I0EIIJwlBOvsYpELNwVnMJRigIiHwiiYDpxTlyMzyBZ0p9S2m+MhFT4ErdypV1IZQZvE+cTNOpnZjpKiSPG0T5Ei1u4t0ChyWFINJbJDUK3R4BCJaemWdcXBOn/f4N7Rexjmwo6adVli5lYKcQ3sj42P3AfMBvb9wDlQNLpHjNnPGfUGRDkfMomOOuFUIKFTjwDz7A7zHy6If8ZBx+MzGND4WpWSThnYqKaasACIln0k8kVkB05Upy2K5TaonXJ/xegdR6dRmGRuCFwKrArqyxfcfvoDvv35X/GH56/AuMNHx/7rd2yPDfftwDDBwAKRCi0NWhrGqOgHiPT0A1ZPYnipCzMll9Po8IJDjNPZFQgGcxrBnAHYcQBigBr27Y63/R39/itsf8fTH17wrAWP+gY5Br7+y4qiDS/f/oxhd2x9wZe//Bnrt79A6w1mhve3He/vD7y+vWO7b7hvnQbq/SCKtBn24RjGdv1hhn10HKMj5w6ZjNP4yZnjcjtEhu4s+/ZqnxrkqFk4n+jwLBXFAwUz8ms4qsAhrlAnQiFSsNSKMOs0HxIkeBR0B4Z7dI8BNg44OHpgRKIzLEcaOIivE2NvQUBuLdd0IXo0mOUXLSjqaAVs1AilZsBJjr9a5+lvIgMDyZgGm7pMKUpSanDObCCHS0oMKd2OnZ2Encq+swYSfKQ+mJxO5H04+r7h0IpSPEDhEO+Mu6TRIWM+UKVhqQ2tkqzcYhgj4hw5OSY4kcbW9ATCEiGwy5/P5uTYIOrL/JDnfe22uiIoPILweikBFTlnAmYVzkMrx5T+ptYGs0BIPVGgxGKi+9N0NjQJwMq9XD5XlL+XGkQXAb0RHW8pyDjHg+R7eQBNqWUHcDOnJApwrr249omk4ERGa6uotVJ8tVRIEfgArNInbTZgemARYFkVVResT4WdiIOluKVolIIN4wHg4fP9CYgJikap0PIM5ESv8sKCz0aCft4jYGSp+h8cP+bkzBSGb1ikBh/ndD45gyVbMZE8nBBbUo2BbVH3jALmjEjhmMHNqQhJSJG/Tz4/oURDHzv/Pg7YMAgY5Cz6QFsWLE+Ni0iSYxOkXjkvpUSd5OzLoAEELLgroUFhUTMHAK/zfc61EWx298lRNHMc+8Y6ulP6vzTF0hY8P72gtQW1Lri/v8LGHfvGWUpHf0RwJQF3Rs18UL/FLPQPJHMfPa8gDJbl4jZ2oeAa4MznctlHn3QkolRKBDnQEJByLuxYS+y6szAafZ4oBe8qcu5LKQWtFiy3BVaC3GcD/Qj12NgolBFRoFWUpxfcfvoDvv75X/DT159h+3fYsWM3oNaG3943HF1g1lCVNXOUArOC3lkyHb3DRodqgUb2AiCy5WuQgwvREAGISLSk11ANPyLzGzjsjvfHK7b37xj7G37+X/6C55dn2PEG7ANf/rKgLc/49q//in68YXsHXv7wJyxf/wxFgQ3H/f3A628b3t5esW0b7keHoEAbBzDa1uFCXRy2Shu2DHJiHVmSMWONFSXSWYCIL3l9JoJx4R98xsFiSwodsrvSC9d+t4Ncs0iMqA8YPARRLG2N1+3nXoXAUKgp7o4FbEihE1QYyrQFfRiO4ewmOw4mbG6o61doLWi3yP5RoKKwLpAa3Do1FHWoHAAsSrXZrZnZqn9UvAhyNZMchFBtcvaitOuB46pyzEc8q23s2Pcd+zgwfCBVgzw6vI5hGBaOPgK3vm04XHG7VQpshnMuaihu4VAchg4RoNWGdb2htRtKvaz7cOYyDKQhRakzLi0pAOkrziaEzztGoMk5EWDCZS7zfK4xzwz1JfdFcEvmxg4/U4RvqjaDAxtJE0Csu5mWRjIxpjP5MOz0UnadaI4nesHX98FSbWucIaclhgQjlci4zoJ4FIGOzKDBr58Dnt98FhcEsbaKVkORPcqYJg7rCrNjor7qwHNb8LQWLE8cj1O26BYrGqAHcJhktxAySIEQMYI7xqWHf26HiCuGBxLpg/SP6Y9/vIZ+rJNTCXE2aUhdEcOIskAELbE4ut2JiswIgBHr8AMOo6Q/ToRihl6SD5udEepkiw8ZMygi14Hll+qsl2qxCHwGDBvetl+w4sbZNqVEhE7DUleWhOLTkdz3j2rG2SreQ/PggMewvlODLv7hAhFqm0ir2I43HNsD98cDo9M5S62o7QmlFKxtgbjh/f0V2UXR+4YxDuwHeSUpr09J9zBkQPA1yFcaKbo0VyLOm4jk70RsOrVxYhOGjg/LeJ9rWDLYBJLTFSrFcECjZ0kJFWdHTBaAcn5TK4AXgaOg1YalNIwiUCv49vUbtmOB6QYXYHm6YQzD47EHF6rCnFPGX7dXlArs39/Rtx3vf/0b9vuGX387sB0D79uv+Hp7wZ++/mGqhpoxwx27o9cgr8YU1dQPmbzKRNso/TkJfGNsREaVgXFDQd8eOL6/QTfH07HgD3/4Nyyr4uc//RF1qXh/+xUuBceisFLwfvyCpopvX/+Etayw+4b3zvW624Gujr/+8gu2+wY1BsGv79+JIGnFYTv2voeTE3x5emYwE6341oKqb2NWm60PEruDOH8c3Lo3badz+5SDJddSF5od74AXSGlRYZZoaOgY2QXlJD9eSxMFQEtQBJSGVylQb3NvAgj144JSVzg6OTwDOMzR6hJ6NA0DhST0ykDH3PFubyioqFgABVpbsKwHoI5dBmwA2llWGkefwQZmWHzdn3OnQKWyryOEDUuQ91WV5Vof2PuGrd9puwCoNNpTT06GQKWi6hrUAtqecRw4Kjv+ljUCJwhtXjiSokpyfjGsreC2tiiBG8wYZB1Hdq7RwUmiI2FzVDQQNqJun43kJC8Qno41S0Jy0hwuwcVJpwBcaiTiSa4WuFGbKxEgTpkXMhgAaGvwIbAjtJww5lPOAIhrkGglALbAyYnUS9ghzOenqFJQpSCHzE5JlvAjZUYADJSvTVV8l4B6EMmLGgFdOZNsVUFbC1orWG4ry7/pq+rAGESJ3A7s+47eOkZpwFEhJtFo4lhXlpiPTh2vfRzBxQW0cDP2YTFHTuaJzi7JDDgjgeetOPm+LMv9Y1v04yCnkDldy0KCpHVuHXHK35uD8uoClS0c0ryPZ/0Q50NABhjxGgBni7YD5gUDIaUnwQ6XgIORnB5Habxhx0HNjL13aAF6X6HeIFrDwQ9gvXxYLK5JBssy1SXQ4WDSwawkPJfGjeeNVYhUko5LQbeO/XigH3eMPlDXFVIUZbkRsi4VowcRMGZgOXogU9G9FVld6gNpIDmsz+pEa5gtjTgv/fhwZ3IWqE1Ms2VxUYIBJnNxf9Zx3ue4l5DQNImukuho4ZFEufOiqAUClhkmabuiKTOo9Uby7aNzU9Va4Qjn4YBIgRuzn8fxQN0Fj/cHjvuGt9c3HI8D7w/Dth94fX9DQwGeLeGDCKYB6yTrZsfDlFDQaRvm3U5HZUGypDF0iC4BeQvLY/cD0g1tKL49/YTnb094en6CVKDuN1QzaCPX7BgPNF1xu32DumJsR4yrODhZGYa3KFd9rV9pUI4HamkorUSJitk9VHGrDVUFrXHd+RNvmI2dHKIOHNuOzUfoYhi6EYWonSMwPu1wBulaKh2vGfcfBnSkvsyp+SHp5t2hY1zGbrBTJJsbJGwLW6Y0fUnYJ5ZJLRAJN8cQwyILSqlwofCeFI5RKFVhNqJ5IMpmUkNAkuU17UTN1C4zhgJWmOjCTGEzLeMZaYIOgTSokifJNlznKBM7Jtp9XpvAnWX35Cxldm8pdDdSy0cwlB1/ajFzCmwO4UDZmGhelaR20A7nkk/+WQbK7OgJD3tNWl3+/6AHnklfOv08rwyMg6CdY3Vm8igCSIlSSTnLmyHilyNv87Vs2iUCzedFLkrOWzqDHDbyaASUs7IwS/VRMXHgZDGFj5DUJOJ69rn2gan8PZF/mdd9fo1puK70VYIK55ouVVm2CjK1AfBCfzQGAhw4MEaUuget/HAif7U2rq3OxTu7taf9lLnvEAhSnouGPzABxMjlzNedFYozMPp7x38y1kFRtGItTxjoOKLNcsSnqJQZ1pS2Alox+s5MI2DBM4S8/C168bOOFBDM9WcXtCE3YQZCtZBTs9YnAI59ew+V286N2CoaVirBCpny7DDomForSEXiwY4ccOxmLHUAiuF8oCWiRw/iaA/npqJ4PDZs91fcv/+G4/HGxaeK5fYE1QobG8ZuuB9bEG/30+EHzHlG60F6C9jyIBHktLhZl42gxh2hjhoWK1n57AkGwE6LU06er63RtfSZx7G/z2eoohBlSREygvw2UAun0HfjdY9C/peBpToMowFShXmBicJr4wiMougjWl6NLdzjiPJVqSiL4n0cwNsbHscdRYH+24DtA+P9O2wYjgPoo2O3A2/jjr8e/46n9g1P9RuenhrWpjAV7GOg94E+wriDzoF6Nwkte4hJ2vyT8DzpDWzp3/odv+6/wLYDvh3of1wAeeJaHsBaX1DQ0P74DLMdx/iOtQxoecOvv73it18d+9HRx8D79gu27Y5fvm+w3fHyMw3I6IZ1XfHTz3/Gtg/c9o7npYR+zgMOI9IogkM3iDhaXIY48Pr6G3779ReMbWAcRlRjMJPX8nlIzvF4AEsIkBWBLpX6Wt04+d0cCg5OtRGuQhSmii4STkCIHNaKsh5o/UDdFMfgswdYenERapnMUgK7yQYG29KLQaqhVookorCz79gMxxh4PDpaFZS1QkL4sUhDY+gNQImG+6BUv+XA38zeuVczQNbonkwHVAqdbCmAR6LlNuAD6IMddNmBI5UwXfE09UJu2Z6IgoK8YYMuDKi34ZAOsAGDkoutVHy5veDr01f89PITnZLdYeoRTDudXB9zzQN0xK4jEoMMiSIDxzX1/OzjhDYERMQcMZZFawR35Kp5EF1VG782m9dBpIt8qBxhIS6UZZCK2/oNVg+sUqhPNMaMZEWZzjeJ6oX1M+aKe6caPNPgfUphQM9RMA2lxbw8YTnVApkyB5LzZ85S40cV6vMJ0J3nWi/BIdVICkr4Vc7a07Kw2qEHyuGosOjUBHbbIf3AWkd0NrLRxSGw4TgehmNzHN1nt/F0hbPgEM0O0LPhRxFcDc/maUBZZjZ39P+kU+/HYoC5AFAgEoPxAKRAOi43h4vZObsEiKnAmb3bjJjJK01nHcFN6BLkQ6CwEs7vTca1TJqQRHadPJgxqPo4xkHOhBdoLtYgREtCkxP9yLZ2nwjIec1R3jGb+yELXTnksfeObX/g2Hf03tFKnYx2CGDB4di2e5zDmNc0k4MMvCIqT5Ejv1xz3sc8xwh34ycXU5H3L9CzfF4eIlASRlI/GckhmRNIESuqZyaXKDZalHZgFm2Q8btIVMrj+0nyTskBohLQEqgiYgL5WVu3wo6Dbdux72wat+8OPxw49uCV6VQAHj7wGA80f4aBXRCtFWzZNIgrCgjw+Zy188xoT+XsKwct5eA5+6bbDh87vO+w7NKx6GSUSjJiE85pGiG4hoGtH/jtfmDfO46jYztecewb+mEkCYdYhYCq4W29waTDoLg9NTwtFccADCOmLAsKqEV0q5d5RD7Qjwc7hYph2AaVEcNEP89FWe+wUuYaR/BQ+CczWeZ9CG2pDBrCHUUnX5R4qsaAVwl0JO1bZpahXg4GSCwJWeZoGW9Di88stA9H7xQYLBIp75wxJIGicIQGZ8355etrgJML6bKkLv7ptBuxvtNpOW3h1PU6SQsT8VUJaQBFiAGGI1VFVIwJAMcaHvSWKFKw1AVL459uO4YNdqaCr8vPnqjzPOGwVZl5zydyvd5POi5icx+/T895MukmkQWJzl6fTHa4EugNJCLuGQPADIwCu2kLhg0+bzBnLULeTIkFNPy09RLojMS4mWnnJ3eVg1dL4Tpl+ZwCqQ6E4LFM7s1svsG5XnHxp1maFznvRXzBa1dAikf3HiUtHEDJhMLI1RkwGHpQEQDR5I06Zwn26Kzz0C+evhVzD0ggONOzEbakD0AG+zj9pfllt/zH48c6OcFo3p3t2+YZ4Ni5B+NMTDpcDKUWFC9Rixzox8EXuICt40CJIpvFJOE+YlaKMZyz3AaCOdNDNQdb8vIf7yTq7ht1ZWBkWe+6Q401Sy0FtYIqpEg3JLOlfUSXAQGxHl0H0QYehOf92Ino1Bugglodx9bx9vYrtscdj8c9SMYCoMK94P79ldd1bLMEdm6PS1Aj7B5jC/UNVAHtYUgDzUpPDSDrvslvoYZRlrE0DInF4EBu0gSCxBjMcfjfj576//hjPx6xPwWQCgdLieeMKsH9focH10qKYAHXyrCo/RbH4RvufgDlK0wKiZHieHn+irqu6BuH5233N7g42lPjoFU88Ljv2N4l2vEBuzu8O6TvKFC8rF/RtOL5yzOgA/fHAyqv8KF4Wv+AurDkU1rF2hqWUjjY80o2BkXCbAxmtGPgOB4R0HAt9P3A0Q+8vX9H3x641YGjKo62YPv1O962jvZf/gX15Qk3W9G74vH6TsJzF9gouOsNb33DK77j9W/f8fjlPmOs2xJCiT89o7SCb61gqU8Ya8OQjsMP3MVg48Dzc0OtKwBmh1+fvqGWgnVlW3DVFT///B3/8pffsN8PHFvHb7/8Ddt2x8PfYBh//4H/E44HHkGc3VFcoQaoCpb2BLGKgo7348HBjwDIRTgAxDwyBRjGAcUNTQtuy4pSGkRDZ8nP1uZaGAgUCLrSWdSyQHWlDkjRmaDAC9wUfQeOfWB721DR0J5XdnwWR4kxcmtrGFow/MAQgxdm4mNEaVw0Sj1nyZaNHbG3ofAaGLCzhDrsiCCDJHgbkfHCAd/pFGvldcUgpbEqihWIAbe6oJWKpRBJtk7HcYx3OAw3LXhebvj68hPW5wo8HbAH7WUfRGl6OKqgaZBUd9FSU62Qg3pCniMBPp0beDkigJDTY84O1KlODaLKlq8fByRmz2XgaTnGIC4lE5t9P2bZZykFX779TNt03LnO4FjqgiLAdt8wevDgXKLLlMF2zrszEC3kjL1zDt5Sa5DOI7h1neJ7wzYM60xGJIIET0Yq+L1IIEUF0sDkM9qLYEqkrgKGnf79eUVxQKtijIp9WTGOjnF0VO3QYrAXx1gM5UY0dN8H9t7xy/0Nb3vH8EuPdjqoiIQzXJGoaiSp2EGfSNF2xiC1KMyFM/v+e8tVU1xOzkwiZ0hkKS0jefqqRG4icpcz+s2FFD8gTiLZIRAt0Vm2QnTVexYsmPlkPQ8uoZ1D2H+iPhbCXhEcMANLN3pm3bOGl9yVCCUva/Xy2ojiIyMyIcGvHzt63zGirCSxObKV3D3mieDasn2+jwaxK4OdmQ1YIB4f84Z5BXMR4PLjSyaH+aokLPqMkv9Okvgph01kCSHuNGKhEtG5ZnmmoUGUCy6RkUGyu3mP1l4jt0IFbVkoI1AXeDc8Pnx4hOUB56tzY1mnNVYHg6qFjr1IwbAd+0iEJ0wC04uPJLcosRIJcLCdOZCaEeq2x050xsd0pKMfGPuGse8Yx4B1h3XH8dixGXBsD2hVSvAPR9/Z5WMH5ykN2/D+/sD9cce2PXBsG3RpEC1oS0WpDdoatBXoQh7ByI7DnJmmQKkVrbH0p1qw3qiFsSwNRQqqLrH/DFU7iYV9Ry0CDx7QZx3HcYSQXbQ3R6IkE4nIvTSuO/3SVHBmhgKEovqZEbvv0YloEFOcYmoOCe5X/g7XwHluLLNH2Sn+eAxsJHrsMxuVEliBCjk7QVbW5FAE4nRWlGVCOX7ZFvxJ2FdHynvFa87XipGDpZ4Es7DhKtORcPBskFDNT7TRB1QcrVS0WlBbNHCE0OG5PywIqDhta1oeNwhCGgJxHWFX84o//UjAJD/9Yvevptcv/0cGIJ4oc9jVQP+SFpBH6jqJOGUuIrEtpcS9Ch3lGO/BkhI9CZPg82TnOgQiyIlAZ65fibIZkaHz8rieuC88AoZEzy/HBCzSj0SntPqsloxuDMoXPrcapO1aOJm+iFGIUk5EJoOW/ejY4s/RR5T3/Ywrfvf5edWRrZybNu7LxaMxKJe5Wf7u8cMgZ+872fS3hjEEHTu0NLRSoQdg3TEGuzu0lJA63xlVDju1lJLc5Bb8G244KYIiDqkVPgx9IzpjowNG3ZhZrBMGRe/7Ea3EQazTrOEFBhRCSbUWLI16Dj2GiHpQs1MHg1KfnVLZqtB6Qx9bqNL2KF+QfL1oQ3fD47Fhfzxwv38UmWp+AAAgAElEQVSPKcTHDFB6v2MaKSA6bnQGhVn71YAZi2pcI+Cg8ODoB0M6bRP+vDB3Pv7xE1I85+xEwBkGexKotcSgNP0Pa/yffhhgMCJeYhgiU2FWC7Odphw412Xj4g2tlhLI3T4Ghg5IMdgYOLYDa72h1YqXL8+4wWCPgXt7x/vxhtEPDvccBu/R/eEJEYfgWVXcyhNu6w3/8q//BnWB3Tve379j/+WO8pKtsmyftAagAQOdnK0RHX5g2aeLkQklimPbsT92bG+/YvQd7bYQVjaD7QfG68Djbcevf3uDHQY/DH3dcV8apHWsLwtUv2GY4Pvbjn1/4P3tDY++4fV4w/fHO77fX6EHoANY2oKyNnx9/iNaW7E+PcEFnCzdO479F4gbqhrqekNbVtyeXrAu1AOqteLbt58/GE2Z9WBHrVyLpQr27Rn47jj6/mlL6N//9huenzv+9PMGlQXaVq4l7zA1oAmKF7iCOlUpvOfAMYLfhgaREnpFBQbDcmtYxsDr6zs1qhQRNDGwsSIziaMDcZgCHlpPgKMfRIK67+g4YNhxjIL74zsWeSJ/sFBfCULRxxJjQ+DxXqntZBcE5+ptjeiMC4AgWpfK7BwRWI9unNM2nHwlvmHs/ULSZqEYSxGZThIS7ecxQ1A8xhoEMv/t6wueX57w9KXBq6ObYxyO/5e4d1uSJMmRxRQwM/eIzKrunjkih+eFL3zg//8SRShC4Zy99aUyI9zMAD4oYO5ZM1tzZLmT6y3VWZWXSA9zuygUCsUME0mBw3tIDGYesaf3EoyFAuIz5tT88D2feZW0/PB5grGL2y8vRTojO7jfECxUCm2XkSLgTPgxRewCkcazYHJsfMzoHEDrkqqNz3E4HmPAMXEcc/USA0C7jAjaIQjAyxNOakWpFfvthtZ2lBYHvzl8TqCP+IH0aKsBDjj3LKQbbo45AgmnpbcIvDt8GEKdD2s8R95/nRgV0DHQiuBeK1NvtaA2RYWidIWOCT8AH0LMYI5/+svveD4mfv/9wLSEBJFKI0uC9Bm6iqnzmQhYNLCOPvCsR5AoRS6FOX/j+iHIqdHAkOhWCEYwMG3wZiciaklXYtK92XslI5DMnYlnzi0HNQNkhReBlihJDFCTv5c7Ad8Y+2nYx4PaU8rGKFULQY6GeGoao7vMh7L696QlERs6I4+c+PHacm6U09kHZ46xIvYcclwejAQCzb4bEsKyBDmil6oo0YgCrzzNuq3zd3wPTBhGnEwUHDCJXlcU/S04faVTjUr1z7zSZdQdsW1QkLbaTFwj3UVRhrkYgsVjy0sWAbsAM95LUPMQQW03bOZ4/fIF/eh4vr3DZGJa5+JxDUFd9J5SpbHZtkGacvyKQEpBbRvFc54mZxKeJGfzzzO6DkZKLh/5jtcTneEn0R9P5qYPwzgMxzExnh396OjTsfWJ7Teg9wNagWHAH9++4TgOfHt7w3M88a2/43kcmIehaENtFdv9BfW2o+z3WEf0dapSmKr0iaKCJsBeb9jqjlpvUXnFjbPUFtWLiMNzssqiNkDo0qq1oBgNwvzvRFD/mdcM0WZ/Pumsu21IDxBxWdUuEuwORJj2kdDsabA0sVcQJFRsrWGrI0qmYz65wyeLKjzSkdyGWKlk0WDYle7PMhhE9d5pIbFY4tRfAYIQC2voeoSeUFyenFuw0wcIwZgHwlksD8D3o6EjZNqdVZgj7AQ+NHKM/mRTB9zjQF6yWYP5GR17gBuN/VGKoLSCdttQW4VIsOcj9nsPrjiZCDtpEAaV170r10LyIx///VmXhtGsr+Ay7uYCdGTdL3AeCQR9wIkJeKbM9X08o873l7QQ2/R0GoOWMJk0D7H5wPS5OgIsnYnjkqaMmwpNFwXJyehEk2uxj0eEJQekcb8Ol/C5cY21ezIiGSh7nn0BPmw6hhiedmCGM/+oAjRCwAGLflmADUAmLQRMCNLGNLw/Jo7nxJgBcCLN6zGIawYkGxbgh8MeUD+zLYhmnsCZzjqn3d+8fghyXu+vELDZIVRQb3f04w+8P94xn4D1OLTccRwHF1gISufsMdBswV6MEQQXdAn2Iyk9HmK6FwKkQQHmkI7sITJTlBm0v4aPzIp6gsWQArRdcXup7GulGtS2h1PlOXiB0riVeNrv07U4e/dQe+z4/fE7plHwOY4DRMq5QeUZrWsyighKPUVs+aBSrQ4PBTwYSY1xxAIpJwBCpPOQr/MByi4afq1Ez80jy0crqCwqgJ9pG/nBhPhHXCwhJEB0dwwZ7HdSlZSoeAhla1QpCeAVgELdmM/XO1QGu9BPPvv5nBCLLbso9u0rtu0VL19f8Hg88C///G/ozwce798AbAAaXu4NreliAl8rWYxeAUzDVIPsDV9+/hml3DF7ocbLBVpY0QBXRmMWwGXwcCqFJlgi9H9g+SX1R71PjKPjt7/8SvNAFzzeBh5vA7+9f8Nvb39g1xuaNjyfG/atwLd/wjDDP//+B/o0PI6JMQee4xk7sWL7+SvuX17w5b//N7TXG+R5Z6llREX37UakNAb22nDfG9pO5+2X7QWtNZQ7n0Vp29IXuJAB1FZRFTgeHWYHdHNULdiOjRU4n3QZ2FTxj99+wxwDbd/YPqOFJmIIinMDbNL4/cGCTjDKPeYImp0eV61teL11lAn8XgpMgLfeMSGYKEBlGwgoo0U1gZti2kHjxI2BzRGi2+N9rpQ7DfEcPhhcb6WiaYUWwZSCZ6EFvgroD1YLwVMaxKmslNwqHolnTlsJhxalUWHveDyeeH88Iz0ahqbumML1Pn1AVGHlrArk3sv+bQGfAPhKA+/3De2+4+Xnn9CKAN6jwahh9IlhlJhONwaSjuWBNVdNMpAIgEmuNJPN3e2TmZzKfnAng86A8ExtgtpQOdNqxr4vEIxc3Fj5wbASkZB/m3VkOoXZB4Kcx/MbVAW1CGzGPPKDVU/Ryb0U+vBICUPLCYhHx/gS/fK0MjBRQVEwpSzA9Mfqli4m0XQ3g8YCL0EK2Yg6PsFFEMP79WwZRBQhKujPgX4MvM8DIsDb445SlV5KwvL3IH2od8TEMTknhhvmdHz7vWMOBhBalEarfjYvhYddASLRg8A7DqSXMU0CkyjhHFpNbRPo/DvXj6urSuRxowJgRgqHkS2/lCZTayLH4oLnGKZrSCIyZLgFBJ1cQmRrmSdGo1eDajA7hjIFZgVmFXCgNqJSZD8kAfZ9w8vrC/bthqI12NpTl5LwOx1LNWhaSyYoxIy1CsaQ5TaawM1DzyHJQClpwPQQ02BswpqY0dAlImOQluj644NJI7+VfrqwXdfL4fFMfc0E85wZ52bCWMwCUEn8Po9c8eduLBBfpd3UsnsQqhEdyQAaF66n43GJBajGQ6ZysYrS8bgUgYKpFO+AzxC2B8ArrnhpN3Rhqa1ih0rDy+uOtte1IbyAJmfltmE8B/p8g82OOZ/0uNAK8w6ziq0UgoKoRPFoEJniY7YE4cLVEpbo2w6IwN4fgAnG6Mxvm2DaQCtOz5HWsGnFpkyjSRUMFAwII0BDOOgq9qLQShD15bbjtu/RxBMAZqRegSKRK6+K+nLHvlfs+4ZtY/qt7S3SdkxdJLPGNyN0ixbqKSQ0AN2cIklkcPJJU0hkRXHMeDhTBCZB85/3fs7uyyeMjVtFDKqTbwuFxnh1x8vLC0QV49sfZCucGi7vYGpVBcUa1AvZEyet7+KRwgi9IIK5FJpcLiY6WL7Ub7jrGkOBrZJaKFnK8z1j+YgkVZWRN89Z3qv5hF3aNVwvsgITsGgmGhuxXDQcAFaVWqucH6+3HbdtR9OGIjR2nTZDJHsGVR/Y8Ay0olkl2wpE4JVBrgQx8p85Qf6XL78w8PFvgMxZZg8+XHxGSyeZIOiy15pLPMOzrdEJ7IDzRfOs5Jyg3w6iui6CUpHlUm8xpmR5aB1QKllULQRDGl5VnhYkoXB3Rxjj+joazC4VhjnfgHW25X1GnA4RP9nQEPT348AcBGkKBDsZhs+k1xfImcHuzekfxy/YqauOKc+zPKtnzOMcrkt4f/6MXM7XH7DKP27rUMk6iBaYs2Emy+Y4wOJAD2pqUZKDtf66nnM8YHGs9SeGhQwkXGvCzlCg7AsU0QFBzgC8rYUFCLaNaPrxeCN9XRte7jd8/foFe9tQw6V5WOeGlLfiHk6MUU4pEnQrB62oomjedkzas2cDQk2EshyU84C40H4BahydvycfkVztnC5SaPePB8ZKZRFIuSM2Cn7yTGph5VdzUa0t3jNCSWVsCAR9fDrIcUyYj1VpN0Uhc7KvGQ4ACrEGVAd0EPApAY7rBCo7j5dC4FuqErjM8EiZYx1EHH5DM8XX7RVz33HoDZvsaLLh/vML2n2HVzpo3x7fIABmbXjHG76NB3o/cBzv0MYu9wNPDOPfb/uOVgtU5fRmKo3py5l9lM4SZcML9Giwx6RR23Gg94GHAWIde3Xc94KBHS96wyYN7SaQTTDnjikGaQ+oOOoUVJYH4XZX7DfBy+srtv2G7gLvBmAQqN8LCoDaB7Z2w+v9C7atYdvqAjmlsO+QNIQJJeepw7hpKjsOQ5yRpFWMYTieEfsH/f0ZV6YLzYWp8uH0AqxA5l9pFnn9KVm2UQQhA+IFKpM/Y4KiG/at4pdf/oTb8UQXx/M48Hz7xiqZpDlU0MRQtXHzhmMcg2tqYOlqs5UNWdSKFI3mgZj7CcGOIt2Y1c+U+owUiCKAR1QaZuTNgEgwZzT9dDq0TztORjsJXo5MuCSTSVlpc+CDa7WG2d1+u+G2Nfz8ese+7bjVDdM7uoXx5BgwiwA4UmMSB3++x7XZG7UXHql79j8Mzch/Acw5rR+CzwrN0gosJffuOJQBBlfrFWjsubRIsJBCftQXOWYwEYidXs6vh7NvKQ3QhmyIWUC90GGDlXJO8GvuTLNvTK23bYOGHINEhMCHAqWiNocpz0xttkCOOTAnAXbOpxKAai5dV7Ro0GxySysO81Pj5UYj2uNBsMrKZzYlTh3fMyw8YiDOtx3n4JyG1Mtl4G/nqPGMTlIEOFPRkudm0iY8s9Pw8t+7flxdZdGnRwFAsekWbsCRF0tEtnxAAC0bzkUlYdOMiAxPpCthbicQZH+o00+C1JkivCgyNe1nBLC1AhFnNYkqtlqxtYpti4ZxMEwnW+ASRlR+5Kid2EvYrXjG0gPCqEjoMGoRhGnkNqcwr6s10mVCoZiofqfTEQi2IKzOdFWCkvPZyzn/g93i4gixdDBnvoATcF1MJ+ujZ0QmAkrBUjdxlrEbQ5EfPfb/9MtGUrLcYKYHUAYdbE+PDh6mIkCf7xRQSsfsT8jggtVqqK2g1MIWIA7MwjGohaJZ3WTNUbjQM0miOuYJVDfgLpAiaNiAacwZfxt4vrNibnajN8h2x3Z7Rb3dgagsyTRlGlXO9FKahuEOmY5W96CWC7QBR1WUreD+0w59Cp7vb5BpgFbcVFHqBp0VioLt5YZ6q4ABGxzb10r34YfA5oT3A20TbBsF1CKO1hU+FN0ndSRhuVC4pUGVzSFLZNInWMrLhric849BZiqf05iTqeP+hNpB/nw4D3UUlE9kchzK9DEK/45YDRLr20PforrMALkmHW4jNnnAvGPgAVGuWY3ahtIKNt3w5csXbP1AUx6AY0SJdLA07kYthCjEG1diCTak8kAssgG1oNtA8Yriecex9g1QVzgU3XDp4xRrIJanRLojThOEU0YAz5MryARUxlfieunPh2BZ8kXPAyHjAhp0skqx1oovLy/Y94b77YZaC6Z19Hng0TtGn+hHpplC+O0ePfaCucFlLw8rjpM9QATOoF7oHzBXfnRZgpEYb4/gnNPeVwC+/ilnpZDkoKMCGMgmwpL766KnLnqjSP9wVelFfqBhU6Cx1wMSXmCrgGRt84JaN9z2F+z7jlZ55ikYCwLRdNoFU1jIoVKB4pDqbCNiDits09RtIhvuZlumPItyOCzv/XJOIsaEYuyTndKiKE6PKvHQ5FzGPF9Dc/wCALFTAsJ3Ll+N/0/CION9FwSzda77U8JxrWD+6+vvgpx8gaIKlQ29H3GfwX4st2IOSK0EOZiM2qk8R5R2C84y81NpYovaDMo8HpjgtBFH5OTy2hrFV3VPkENBac0IxpnNm6BAEKBhIMdO18hxw8ocaz6RtBXTFUAJLZvWfRZpSJEpvTYKxujnBAVAB1ahBthj8k4gLcCvl3/4u0fOOLcxucQB/t13I9I0OZ5hFiWNIAcd5n2tmYyiPvOycXacT5SOqCLREg3vNAEt0yPH8cD0iWlPdCkYjwOlOrRNtK2htgZF42JuUVrpjboYb7BQB5IgZKlugaA8HWUYtRxQbN7YNPDtDeOt4/l+wG3AzaFSsW93tP0F7XYHKn0jsoSYqYKryF6YCsWEoqC08NQQNmmtU3H/ugPNKNRQAaxir4JXVxwHWz3s9y9o9w0Q9uIq7SfYBI4HMI+O8XhDVYDNqLnB6qFwL+h20BuoIJyBaeqnYtAVfQ5MCNedC783lywkfLccfQzM/sR8vGGPteXDWYpf6oLcn3Olni7Ev84DQdIx2OcCOciWAspUEGaIICNidgdUBwSKJgQspSnQGl79FXvfUMUxx8DxHOhjwHuPg8/obVUUMrNB0SS9fye7pdh5mPhcwl/LuR9MAgXAGhH7dXO/pLWFwlIkYInWC2BfWPhlNZ+G6FHwcC1uIGURh3V+LlMkiPSvYt82tNbw+nLHbdtwv1HcPayjz47n0TG6ox8WGXlf829OroEFypAgJ/ajMOpkuj8Css8WBwIf9iGeRFdmh8Bt3T/OA5o/LEjN5JkWTCYnnwYW45bMBz/HQ93tHJOqFaUoRqxFD+sRXAPROC5b3XC/vWLfCmotSzKRXkMlU6rKe0QpQDG6cQPBBkXF1Thwml9eTxRZ/2cwCjLk8cwWpLaztQ0AqDtmCcCVWADgOStx9iU+vBAV+f4Cw6+7uCLfS9wf38G7levX/84+9GPh8Zc7RBRbqZhuOCYXehHwAJoRoVt42USUmzk3xEaUg5mTmsJtIz0HWWXgiwWZWWUQgytsUSCBMkSBUtg/a9O6fAMkxGJU+gMuISqeHBbxsujK9DfIB8Gih4jIkXRygpr8GAeGVlLNOT2kgGm2yg0s2i344Pu1EKllxOYxiTJPmWzPh+csHKiEhPEOzskDXeAGgco1viYhOM7XUg23ob/GR59yTT9L/iGycO70CUyKOEUEc3a0LZ5nJU2/+c45F+m2OQ1ydPiY2BqrFZoGfRvGihx35op5wOsyoDxGpzP2v8SCeX+DmeE5HM/jSU8UCKPhsqGUG0pr9JsRVinZ6ECkObMc3aZhHgMSVurTDjyHx8Kn6RtaXR4ockxQdF5QW6OLrA/M58TzfWAYKMZWiXsGng/A+oHxONArU2JFOE5ce4Z9v0OL4tbuaLXidr8xzWcVDnriWB+wPjEPVk+gMA1tI3QtGgyPFMBZLTSGxcdoVplB1Cddt9uOqpVanAmke7BGMYF4AZJtcglgYaEJoB5FgzWxOaC1QbWFLsLRQyPT6h1FKcC2aRh37nvbOGBOp+3t1lBqZXpHlBE4WIViEyyhDfEvzDF7hzsrqxhLORs4OkLHNZd2MPNeS88isiLd5D366EjncpsDGBTgUytJLZUWpp5abRDBYlo8Ui+CZCSB1mgLcr/fqfdoG6Yovj06KJrvmLNHZakDk1oLSgKjVNpD+5f9rgRgNVc9g68UGkd6fWkYP/Gas5/gQ2IPdYfYBA1Vo5oYvoCMZMMk7+t1CCDrFWZ+95vi5F7ggAArz0P+FDU5K/jzAA8JIARLd1NbiTQzK5wywZNu/SbsFr9ve3iQgel+SZ0Uf49MoEhlOsiPqFCdecdx33KexTFJNMZpzHg9T+F6aJmQLWH9LJKSBFDnno/LSMURd1FnyAkUoR++F/A15z3/i9ev+uN59EOQs+/RTBCFee3OnJvE5KaAN0ZQMgLJe/L1gYDn8s7zishjHfCRx8yLFg7hMSOMxFN3UQpp47rM9BQAJ8l05qpVA3gtFijZjmxWl7oY5f3nPcb9+mUjyNSaxuSVyJ8KnJ3X5WyXoMoyZwujpwzVMuLPMcnFtODq5bGm+v3sPZWgnhHgWqC5mBYI5teuYOmvWCP//jP/2IulkL4iS5WkSE/X6TEIQLWSLi6FVQP012C1ykQI2Zydj71MQOty/9QwlUxVcVFGIioCMYVMYfkmHMcz/Hb++BVuE1MapnXCSMlIukDLRrO0klRuivFCTC40U/PIa6OQa6AQ1FEiykM09iM9DKBPRuhFIKWh7Dv0namX3iemA23bABWMqPjrHbA+MPqARES5hRhZxbnJVR7AW2XqobUdaX1uLjBhP6s5B44HQeP0twVyqJ8YqKXiVnemEcXDhDDE43EgfOYJtW0tqgQRSybWo2ikcBziSof0D6WwHv2EPFLs0T8IQmYOAEQxnfq8Whq0VqgSINXpKHNAZgPPd2FH5tbQwm1Wg/J/7x3WDdMGBlhy7x4NQjHgSp0cgCCmI+jJ9HUcdkBuCSfIyYjXEXYEoIOXhQHlYmbA95GuuG1rTK2OPILKEozSGJIH41Yq7rcAOcqD5NnT0LLDbJCNt7hfyTRMaDWyZU4Gj/H31LMolKz6OnAJHj7bfT3dpDOURbA3bldNZbIUFoezxr+TldH4zhAky4UBuey2koCBv/kMbpdfW/IS/P8HgJMvGECn1ILWKgsuck5c35dQ19m0MdCXZJYcFoBaS2G1qoQuDYj9bOVHL2divqdIOUoQBPNq4XgRL+e7XAjm4+vkSF/Jxeux94HU++41rl8S0QsQOu/5PwxyeDB7uPsOHMc7xvMA+oQYyypnZIRr2fg7w3RtyWulxIBf8VtuUGfOTYCIxEm9BSeDFMlqAA6PzTwnwHTeC4JlEoSK3IXRJ8JxE4tiWvdB1G5RbqlhGJfeFnwNOsVyQYo7MA2rn0i8nlaCLx++ontHHnTGNBbI5rBM3SBxOOeC04C6mb01u07iADULwFzC6Mu3ne6j7HjNr9FwKruey3+BARcQoy0VSSelt8J6C878toUvTVMneKkNVQS3ouje8fRjgVZtgJYBtwM2JrYwqFItcFW4Ur8BKEor0F3x7Xc2siQQdaB0OIwWCHNgHseKGGpxtM0hMoI9jhhYFSayDk8WAAYE9qgU7A64QbVBpWCvBTKf6Ifg/THx6/OBUiduRdjdt2449MADB2p36CyYnYrWoxyYDjw6Pzcfhps07K1BxMhY3hzaHNudgmJRGrG9zzeoRsrXGiQAjplhoGNg4OhPzDlxPJjyafcCd465zwFBx4xKnrJVoABD+sfN5h987eUFCsHmG6pUlsh6YcqSRhbY2o1tV4xNgu3xBBzYK7WExrp/OAz9OODHQK0NWgomGljGd0YH6XGjWnCrO9wL4AVtf0FtDXVjas8nAc1zRCVKAC8zBw4yLUeZ1F8EcCkJcISgZFV5epahA4DBNcWrueYJGQCgG9tB9GTXohRZpbDEuChu2rivRg+tWul91spGUb8aiy2CLVd36KDOZr7TnHXG4Zt7o8f+5O7s0QUwTXJh3NlIkkArTLaR/bqy6kz0Ez0I4qpKS4cRzTCv5ovmB+CdTU8lK2QzqM1UKZD/AozKCpcoI3eYp07nwsgBuGCh2PMZZE0BxjzoO7TAK3M/popta7jdNry87nj9+UbJkIO9Id0xvcAAbL5BoKi6A1FpZ1kaD8DV4WVHkYpeHeINhxgcTzKgkXU5K5/jJIrAnGy8w1fz1wt7wrdzZgrWF74jNC7RuPzVdwQYyqB+jTW41+M0CVTh86krJbtUaX/7mf+7X4mHsSyd5yVicMRhEqr6VDjn/uCnKOjEeqGX/h6a5SBF9HwCnw+ndyqsTlC0hFDx9kLExgmlF7YkGSY/pSiCDw/I4n26pWA1b1LOD6qLEl6C6sWsyWk+/IGW80tayoPF8ctswErvrUuwWK/16cVUKU7W63wDCYxW5IGgWj1JRHY5zoqAz74W85nj6VFNJohFGIzZinlwpgejeWCpLN1Vn0uEkGWOfHi0XFfxxeBYpvXAPmal0gjNZodEqfny7vAwUvOz1FULUCq4MbufEXC+r3zOcSjJRfyNMGWTMD0UbVAlrT8NeIyBJoIaaQoJHY2VEJK6Q2NtGWaQgTkvOB+qljAPt/C5UdQt+iqF1HX6iOkmq1p/Th74NPUc0R8sGCIVFJMw9MTaJj2WjxQWIogPfG9l/4+8SmnQEJGnLYLEOCQZnLEPzfrmCiKKkKm1Fb0i/LAsIkOsA0HCHE80CysQGytFz4KKKhVFGqoKNDRB2WQzNrIzas/UuTtEFTNSUHlvuROQ4cj9ArldfGAKsP567g/0bDxbLGhEtuGzTvAiUfGlQjZAG7a6B8iZa9xgHnISShAswE6yDdcGmyclI4vphqSwOHVBEWXn/h7PkkFgPr/P3Y9UFGf8eGEfLINErIUtoIs/2bFzY7/6mK1nks/Fz33seoCv97nONj4vs1OPk4dHduIWdVo9tIq28Y/MeEbTgWBnmRIvK8XsDmTPR4uBZ+Uw5SG1NJLOUjElbbTjfLrc+zpZ4pmfZxli/845kB/P9/vh79dn7IJT93T+qMvHn8rxzPkkovEyl8/Ff38vMfHjBp19hP0zmfXXlxtGq+htw3F0HL1Djyf6pDjP4KfRX8ykjE4yFeUukLDwt6iq0ki7uGtIciUYHaagSDlzYChcEyByvW5YOgpz58OPQ28NjgSyrsmSJFvzDEBDKfDMhxA0qypfk6LYDcBEbZr7WER9fqalEpyEXwLAzWzbN/rEGG3N05EUkYflUauXCRWRUPwikQ+P/zK3YpEaKWOJmA8SrJgz+nDMtS2WTLt84pVVeNNT0yVotWBrdVW17RFlSPTH2bQSvQ8HxHAMw5DJyt8x4cNRb/SLaa2hloqt3Vl1dSsBVunwqkLtjqtg2xpsns/j22sdgR8AACAASURBVJNRfy0VqsAUAbQCtWF/2fH16w23rWCrBfftjlpDpRvjzdGc/FysJgHoOlwUGRSWUlEB3F7+hN++dfzrrwe2jXbwX7684H7/M+6zwrZ3uFPjddMCwKGjAFrwU3uB+8SYneNXSkSOhvvLT2jbDfv2QvPEVmEwPMc75ug4Hg+8TVZaeLRNmUGNb8VQwTkuKmim0OF42htUJkQGWqWRmd43jmdnRPxZV315hThQuqO0hlIKam3Yth2qtKjvwVAJqLWpm0PmQHeHzBC/2kCBhHeHYfZBBi+CMA+/IEWDgTU03JQNNgCfgPUnyjZhvbKM3WiEqkaoJBVROfdc+4lN4c9GFGq1LsduunaTAS5C93dAUCvfp1lG0Zkq4rwrqDzEfMCNZqmlVtQLM8MtQaDhXl/KTuuAuiF7rU076LEzQ0QcKeTuGSHLuT/CAZ9ITdQS4cfXPAAiNBkq6nPCvWW9nuTPfnLQFfgBZkzbFFFMZ3ui1H96NEl2YYAkEi1ZavhiZbrdODeWmHm9l4QIVzi6/rEAYVa6Cst3GZyIQIV7YL0V3G87vr6+4svrC+5bC17agcEUuQyK0UVqMHgbU9Hd1q+lwSd9xcwdAwrtHYcZ/KHQ5+88Mi9i6rWyE0FcqvNE8gxOdPi9hUp+zHd/9eGJj2JY0xPMmlzTOqo5FrbOQBFhDzbheTcz7fhh7P/6+jvpKp6mifRp/EP3xFEmdEZzPJNFy50PVFaIckY1+bJ8o8myJEo+hwWB8teNICfO+fH82nqPnq+NhbD5mo7rK/kKnc77uI6/f/c6Z8QSKSNgIfN8mwosJfr5M/HAhFoI3rkscHvlYD5cKyrEevDXsfDFGMT/I5XGYYwIyul0TP6Bh77DUMt1XD/nyt+nwdRthSK6297QjWXXLUAOGTEJUWkATuT8Ekj4PLg6c8taowy9rlL+RTUH0KUYEwFO+TPkS1hZolpw22888K1Fb7GG28sN+7Zha4UNCkthR2CP+rgPAcx3D/0CeLmouXBb29DaBkEBLA9bRka1NrQtXazZZ0cAuBpUC/b9RvM571FJqJizwG2i1R21bCjK9IuUCnFDLUzLmQxuXVlRRfENELOJVecs3xd3lsPPDtXJP6IR0RogwIx2Lp92KSM5Kant4nqk9sRhxUOPA6ZJTMLCnylNgJUtDLYdquPcIJypZQd1dBIrOpO7iLS5R0phzgEZjhnZV1l97rAOAAnfL49WECdrw++zrErNyDkiU+ph+P6SxeTP2tqT8vg8A53zT7Iqy4oDOL++tqczKrfQWU2b7H3kjhHNRdOQLcUDLNjwD+vq3LV9bc9rn0fso9cd+LqFr4D18y7u4QnJzpviAX9VmFx3ZQbNuV/j+3H98PcPTyfON349Hs36wx/zjEsD5JB1o6tww77vuN1u2Fvj/rPOWQvN3wyLE873qtQwzmg/4wFyBVGCDe4rVhy1VIwaeyY8ysqv7/v6Pvnv759XPs6PofNVt4Oceh9far3370DROu+SIfwOEyDShjLXOX5ND/6t64cgRzV/Ga2wR+9hPIeliyAgEFRtMHP0iAJ4wJ6MxYeBMbohnhb+ttgHFeaSF0rMcSGFA5E0B5pYIjcHfOQTtbOmEnKWb4NsCc1uziHz6ZidbFKVikcf6DObqhkpbgdkCpCsgACZj6V9PkEE6wcsDLouJ6A7Vq1ofMxNIjmWXFzZi2MtQx8ZGsXGGRuLF6hs8VtZ1QU7OClU6dGijZGlA4YDjolSOn7kDvmPuG6t8r5Kw61t+Pn+BfeXhpevO377NvHtMdHKDSoFb/MbU2uVIaFYw1YLXvcNz9nxNp6oG1MG272h1ILadkapbQMEONhvBGWy5JvuyTFftUEaJ/4Gx75/QW0Vf/rzL2itYt9bqPYFv/z0M37+8hW31tBKgQjTWY8Rfk+DT84qI3UbHvOBWgk3QORgme7XG1QUP339inE88T9++TP67Bj2QJ9PPMeBWna83nZoY5VWtUZQJ19RSsXtla68qMp5NLkmbRiwNUgpaJVMiwmbMG7lBWgO3F7ZVHHYQvrvo2P6RJ2D962CaR1v336LqiSDlgltE2aFDsz6DhPgeRzrYP+MK3zKWNVUC1IYHrQJIAYtDVBHHZOdiYugiqEK2ZBRCVDqLHA9YHJAowrRnqFjMzKobhPT2ZuHos8Gkw7Tg67BVuBHll2yqS70FmlOZ6PQmAvXFKc5ix6ml5UChEo0qKVLNbtW0xxOUAA/gtC+BmST1XjWs7gyRLwhCFYaxZmwYSOtPCZsTGi2FJjswzcnhcUSgHw4eS3MdLcPA1I3srBZ8IDTgZorIaCBgqwQsgaAjCpS12YZ7l1OsE+6itQTiLlTc+kTjrnOWf5xwAYorX4AIuEbp4BsrOLDeuPruhqz5s6eIPEEwBZfj2BMaIdy2zcybvULWit4+bLhy8sdv3z9ipeXV9z3G1QrIIrRBwG38UxWUxSp2OsdXQaKN3b/toFVNT863Aw7NmqT9oHihreN7I9F2x16TiVnFMLyeI8e5fHZFiOzDR+j9UTTV6sUuQDE0x9JAzhpBqfpdHxJhTOsd2QJP89/jZ5ftgKGf+/6cVuH+L8HfsgB8ED6Frl6gay+Hx+e+hXo5vv77l48TvQs/17of0VUvsYwX2N5GwQj8uH11hzL/LLkb1lflHwIK6IxDmOq2z2V7ulbkBEAYBoLVLIkMu7tgjoX67De7+VRZ+5a44f8Ml6B91YWPH72LFX3WCA1QNIaxPhdWQIsoWc532eieejnR0/mHuC1opUN9/aKrUZnYjy4SGu0r7IA0JD1/vkYBYgO7rVU1BIshxm7wctkbloFszrUL4A4PABEBa0wwtlqitYctVV8+fKKWmkmCSHVftt21NqiV8yZy5esXQTWI8zfxQXoa8Ofg1Vh9TiQJmhta/j6y894PN/x9v4HAJY5CwQV5bxfyQ1Qo8Ir/rRCkKOsKJoyYaUA4YDKg5agWGMuKgRWDLOfc8uHYtiE9gLxAPRQCqudh7ybAWNgcvphwDDgeI7nd+L4f+w1zJjKSW3a4pcDDAtO8B6HiRYNuwpdDJVCYagQHVEafMbcnuPiFNZGtze4KYW2EZKmAapNwUVVu6ooEWLNpRlCBG3uH9ZelnEDsoDQ2i7iIJYLkFw7QQqAP2gkLo7VH9gdXubBNkcqfdpYDM60SSfbDAY99+LklM45vqphY6NLt/ic7+KsVLK1aWcV4okdzjf61/v3Z16OLNvOk8eQFVQc1qiCsr6CcY/De42vgNV18Xoi11HH+r48I06gE3uFytoXauilamtoGzuNb9uOrbUQjLPTAESg0UQ9q9uXeLyw+nbOAlPDVF1n6sj7CasXMtZM+4pESYpZeIxF6m4ZVV6C74Vq4w3FGrxE5zhh7OK/8GHTvJ7p33+dE+kDbsnxzexSbsH53n5UMfxjkGPUDPQeBz4MNjtsHuhz4JhJGSnmCMo23+zsMZ7cYlPHkG+KQr5s4sYo29DhruG1QAHpaY4VtiHx91ysjvRCKR9TTeb0hIgNA3B4HCQlWKbcxN3Z6n6isFzXDI4OB8tlBYJWb5ea/thgMxRbemBf1V/qRNfuweCkU64WeqYgELPHLA3EkhUM59jkEAkA3gtthTS0SAbm5AkCoM72CMJ7yT5djg7IDA+Uz91Z+uiopeBe7rhvX/Dz/X9jVPw80B/f8Hy8Ya9CHUtO58nBdhiGT3Q3lvi2hrY1bLXieD4wD+rB4EAtN5ZP//TKlFjVqGJrNP+rEZtMw/2V+h0UoLaGP//yc8xJRsGsqLnRg6dUQBU+wA1gGDQ2Ohen8FciSuRn4T4gPnE8mOIw+5Wpse2O7esN//v/+X/gt3/9Z/zl//6/oHA8Hg/c2g2tCC3zhSlHESFwiQo+LQHUSgWaQHViDkMvZAC0RofiVmMjA4oUtNKi31OmZgz72BkNHgNzDnx7/k5N0rbBxyAjOQ0+elQ3Oh7jiadNPObnMjnfpqGp4CYVJhVYqdhYC5Bw27VVEKAbu8jrMGBOqDi7zA9hWfdg88PJnBYkZdYOjBkHmVqIlLMFA81NpxkrAUUQ5AAQzspmE5hAmlUqCtwHXAw1WggUUE9Y4uQjiL4YSxrgRtPG1ecnLNjnHIsht0izKEqYOCK0NQnKeGDNKTAFmtJxfAy6OY9hwUpY6BPPoGqBnAioBApoAbtZn4AodYUcgxSrRs+zqgu05z68NBRu/wUoJ9J0K6dGLSnL+blmP6aqAA8N0gpsrQSgDq8fEbJ7Phf+ix/88Dpn9/DsSE8vI63sxXe/39BCW9huG15/+YJ7a+wf1jZoa+ykIg4dBKYaOqGtEuQ0FcigOzizE2kGCExTYBgOnzAFm+/6jvv9FWN29EF2z8YAhCambAVhq/RejQeqqS6Ms4D8OuID0MfpTeDn3w2HnIAZsrRmCXTMAjBGsOCaitMsfuL5vebffxTk1NBIeJPIuESk5wVFHbUAMvmLpghggj4jzTOD5XECnJPE4QrO/O6aeBEh5de0SJhtcaiKJvJdRdZr4iREpsmSJFgl0MEJHK4aAndgRFO9MTtooAeW3tlcoC5/ZlrnBC4XlgpxikBOKhZyejHEPVg0LU0dTm4g8CyHS7YBkEJgNCcnCntPnVEQ6VCD6DzvwbNWIX1E2Em4KQApiBpisOP3O/4KQP2Dr5QXHv3Am7zj17df4Tph2vF+PDBGx/v7E/2YeM6DBcFydgkupWCOnjMHR6E3TJZCZ/O41oAyK+y9wGvFvjvqVlBbBYSeMCoVUkH9SimQYtEOIjYcODehUglKopWDIXULhuxTI4Ub+ao+ivCCT5gsQt0abBZAJnPic1D7EG1P2rYTbOWGoYyyLYCeiGBTvm/0Ay4NbQ/LWzikRj+gKMln81BZG2qJza9oiQPcV5SkaxMK/YdQnKsG/nEBTNkOIe7BF4v1kSn4R19zDjqrzoEBRx+COgrdiKFk+mKuieYhBMANYvRPUuOBpeqh4TKYD0TB9zmmyEj0zPd7dOtdvTIvuhcNcEtG24LJSV1drOGMQOO3pG1+ahDMkkXKn0H445yOtW7JsuCyn8VemgzuddAcSCfTqw4ko/PUGEkC9mDH07U42ehS0uuGBxpU1msQ7J17uUjuRb7iXZMz+IztiR+NRRufeQkSrMq6p/WMrmXL5w+c+3De/yRg9Sg4ECmLff7+Z89ScgqttdCZn5KCYKYbDf5aNN+sO1A3QW1cvxrtauDOczXEziKOWvl7VVPDYhHo0kdJpWIEkK+VLI972LzIhEjBl3nDMQoeXTB7wZT8PqaP3Gecqw6PdkMS+jaNTt0UD+e6OWUmy3fswtsg2SwgDCEzHXoZc3x8Nh6VniwC8GX2SaIgv/dvXz8EOa00RhkQ0tfTMKJh3eZUoc/KiTo6W8yPo0fkQ4Qp8FgdwWaAXgLiUT0VDIV7+I7ESix5yAiiLFgjUpux+ZDVyCokB6jmD7fTBARujFos9ToCDLB3x+is/x84UNRiQhwYc8aGQCbE3dFlspdRuZ2bkUdFVDiAStDi1o/IE0qkU0Y8OwKx6+Mo4WOhYd2OOOQO8+XroZ5UMydCyiEXTRVgssbscANaATZliki1QPYKFMExDGYdn3mpsBLgj/dveB4dxzEgxYFq6MfAHOHRAsVEj3nCaHF4h4qilXoeP86NWWqFKHk5VcF+m5w3c6K3hvr6gpfacLs1jOeBcRjavoVZ3h2lKLR1lFqQvYyKRCVUrVFxJXBMzpM54MMAJ30t0aeNmw4bJnoATwO9erZ7hcDxOH5llN4rZu+YjyfUgf3+BaU10tY0QMbRD+p1OhvW3sVRasUBg+sL7nJfEbBuSt1OpI6q5mZa2O6ktRWFsl/VILMgAvXI10fqtEqBgwxHmUD1ijE7ZheMyvsL6eKHFPJnXKN3QCeeReFW6FD7cNSn0ryvsjxWwEqirJpkVQmAmTb2BDulOEojm4I5VugRYQpoORZJG/pL5EAxnGDJZcyZq2CVIFgCxKYvT2IaShni3gBIAIa5egHpqrpJLxoD134GS9kagq0abLG2WKAr3oXzMBU92wAQz/BGPAF37I0ZM0mtiLMLKqyqS4nxAmWwSHnZYkXOY4yKTKYICeBMz6j7g4D6k0FOQcV5t2fIiQywl07m/Bn3FB6DpfXZ0FAc7BGH0GOeDFcMFhiQEgyUQjBTCsFNKxVVK7atoBbFbd+oMXwRtB3YNqBVhVTqcOhZ1OPsI4hswUBGvgSkEA1eo6mnFRxzotvEZgVagAracYw5sFvDff8J78eB3x4F4+gYj8rAZgIiBjMl2eHOOeNOMLVwneRSwyomCrF+TgpqdzQKQxbaXga6S8skciEDgDUvwSoyVlelbicDrR8zyj8uIQcIEhxkJyJqthEpnNZgRjHTmJNl5Bld+4SADzOZiyVSiolgcIg5UlTrZiGYC9rf51K9ZM0Be8AYIOdGe461oKrS+Mgcbp0CwliIuRHNwTTbHPQR6egYmFDpUTZvkfsHAYoIqmuU0w+kCqsoaeZj0GskbblneFakYVO2g1epKwrDxS+D2+rE+WgtXGz5vRqbafpOIATU50MO6hCnQMyM1fRzdogdANgfyjD+7qT4z75aTOxNKra248vtFdMPdH/gMGdqIPxcsuppgVmE9uui/yDwJdV/ahxowy8wbGEeBSmYY+L59o5xdMzemeITw7AK14rGPBP9YVSjOzfz1JoFm2HQ9nh7xxiDG48IIBT9zd7BRpcN5gPmAzQHFEwq3/k6cHx7/w29dzye7xhzsDnsMHw73lF/3tFaw7bdod4g/g64oEIhk+C3y4HnduRbXnODHifCdgXrSMpUByP10TvGMWDhdGsB+pNybxsF3OrAGAPP54GnGsyPoI596Tj66MsC4jMutmnh8UnTRQFcQpok594SB6v6CWpcygqGJJjfbga1CSmFLETnITYjMgxWHlnJZe4Qi+id9dDQYFrcgume7Ohsw+DT2TMrDnIPIXoyKxAyAxZ+SqfrcdL4p0YIwAIF/FzueIy0qXlzmCA8cUrsF7r2y9WwUzgfzC1SrWf4fC4lX2NJTVPcgAH5r95nzAVfTJKAa2ixXJfnl6qXBTAkIeXnghxKjKMlQcyHxZQ51lrxc1g+XqLUp8UKY6pkLoBZCllZS3GxAlKUmppSUFtZY7W1G6o2tC1YnWhWvGnlXonKViTzYFCnNfaXmd1m13qoQJxZgWQ9JzCQUolwJiAjosBWG8wLhglQFB2OIQ0dA70PBvtRRTksgIhyjHxqsMcRzHMw1q8H8qxOJiuGz7PhtMeUmvHvNI+JKx/AhdWh7CXXcCCIfJ8/mEc/BDndQqQ2mZODGeYwWJ/Q2iCl4ugTJgNjDlZ6zOzDMk9tIEhneh7QmYpYEUBsLJPuxFIb34INLvhVVglkp28t5cMsJDMkUSHimBL3Ogf7bYCHorvjeHakbmf4xGEdS4xsmVbizRvhLBC+FrMPZFVAFYrB3KNjs4RU0Xkol2ReRMPDoHFjc4NUDR1OeLpM9olZ8VwpZ/QUKLhUCk/nQFSCxHcrQmdjSMhnDvRpcOkABg0/xFEq6frPvFqUwtayY283fNlf8RyAPR+AOfqg4C8hXgq/8xKJuRFzqagCUU6peo4Szd2cKZcQSo0x8fj2DTYIzksDRA1jblHVopGGIJWc3iS1VsAY6dPPxPD+/o5+HLi9vnAz652l1m8DpVXIvWJ4x5xPFCsQK4zuRAAtMEz88fgN/eh4PukZVGvB8Xbg7b3j9cXhLxWtFVQxeD8Ac1QvcGPfrQMFj/ZEzvuy0StITSAuLDtPPQ/OtII7WdbjOOjDEroxepUwENn2Hd4atrrh6AekvMExMcYTmEzj2pywOdCPjmmfB3JInXMdaaxV7k1gc03J9ZRrgS0enGE23EENhHA9lzmhc0BriQqneM4fxNSydmcuZTuXGKlVqEWq3YAxycj4JMihzxphug+CGdW0OYi9IprBJrByl/AIyeAnImiX9RzzBgg8JdI+CL1GltUT6FhAiwIJsWaEDpF6kRLvEzg1Fvh4ZCT5k3uv20TvZGCvQ6XiTG3JCdQ+nlzn2ZvVrx9KjT/hmnwaZ6oxkrYEmRag8dSGnGgHvN8wj9QQ+lqA4uxjVqLVx8BgYUC0ZKjbHoUNG89SGFrbsdUdpdEuQ4zp5k0aNjQ0bzxz7RG+RkybD59APwAzqG1QKEqkzZi6DThZnFIFA9aJFoUzCN0e1Zps6DldcMhAlQHRBzAOqLUoPkhGK4T4yiDJJKtIw14i5B381qtong/+Cn0zRj9Zs6Qhzv/nWctvPQHOYnC++9m/df0Q5Dz7E2aG/mR5oYZl9ZIU6WQn5KIoBZglf3nsQAF2FuMSkYFmOXSi+1xwMEwD0A9Ia9CNjfLSGhzTsfpLBWqkeRMwDYses8ES8ON4YoyDNFlOUCdmJBAxdCNizclcs7mlpCqdIzz6CJoxNEZOp8pOXW+IC/mOqhQ+8JlIMzeqeUZ2ESpqVIbkAc9DiuWAbhNjPtckWF4loUuyEHNnhQBS6Q6Be4fPitrC5r/wsNVCt9/PvAY4Fw4ceJ8dvz+/YXqP7san4RgvXwEIgDiEz8qlorq6JtdWV2pJlV19VRVSGSE8jzeWC884+DXuxoE5mLIbx8Y914SgJsZvpUAdOCaB2Lej4/H+jv/3X/6CooJffv4zKhS1K7wrRu/USqnhGM7miZMnrMk73AcUZI88DxcRQCcMHcfxwPubwJVpiOezQ1ywbXlUMa0goL5oTFatKQRNCbi7PTDDC6S4QqJlNe0QnMLbGuyVreEG3FEV3MAc2BCpOGeZ6hwHbHaoOHRUSH/gM43cpGyAMopWFUgpcBjmfKJ3h6Gh7OwztigDDXQTp7SZh0O0otYNmyvEn1AZGDdDjT5NSwQZm9bSv4S+TZXmjFlMpSVShGBVXsGEgyyyGdPDZpNpbTfANNKgIFBcW2boJSxYWecc8dDjnIFxgLwM2NOl2YEqJdKOnMeM9AmECIrCHdkQfeB8lfGahMZEGUAmUyzjeg9zBXHnw2H3a1U2KU5H45we5qDh3rVSBx4+PJ+rD7zXhiHAU3JPzp1XUGSDlIJhj2Wy+beuPMvYJmNDjaCcLAn3jq1WBp+Npo6328sKnrSwr+KuG6qUZSOiofuxMdG94xveAZ1A6djHDXu5UUfqgAxATNDHAbhiSEMV4F7JNEpl2n1vN5Rtg7nhoSwQyr2vRKqtKAXU7o4qgkMA1wpMx3wm+eBg6yO+vlZlyjKBv8nZLmYEoRFspH0oJU8QncA6tUSa3Fh8TxQrXa6ihb25QhPJKehgQc5/kMk5Bntq9KPTWMzZt4nCPpYzVw2Qo6f4iQxZqPQjn5lpBYmJoJB1SGfJOELgNeeEh0lRCijJV1tEBhL0lYSqDYvuZS8Zg42JMQb66As4QD32PEYzMzahOVMo7CECvZyyACdeiFtXKBJuyD7TvC1L6JhndAG1R/lYPZBw5indee8lzLXA6pVaClQqqu70wEiRKzzAoULC00FBujBNvKYbVqdWn/H776hK1o0UdjI+n3eNqACZk5Uq7OUVzzwO+swrrwo2i4V/ATklGnHWSkEwQScrrrRoeI2QpTBM9D6XfqO2ytLrEO2ZTcgsmNNRJoAwyLvswWuTnubo5jjmxHvv+Kd//p9QAbbSsGnDCzb4YHfv0hSlCUY3anTSNdzfQS8XHgKteMQiZ3QyxoHjGeI+OMZgdGYlD9x4dlGFM8eElcj3q0CEc8BNMb1CUOGo50YjoH9OWAwAsvaRXK8uzmilAk0a2thQjw0uhikTYkzjsTHm3xBq/oMuCYH+RBiNi8Rz7Bgm8KhmWikrARLwQ5KFiH9GaqC5Mq0EQdsMOqkLY9FE9lXixk5sIaHDIUuyNGIp1NVofumVjRAL04gsZoiAJ3QOKZS2YHt5nTGsR4ozP/HRb+zc0hP0BLcTz6bk8lmpclx+/Fp+nvsmRFb6gZrSi34m0BzH8KwAkw/vPawOAiDZui9Z7zH1RWtrvTImn3RttWK1dPFM/wGAQqWxQ7eP+Pwa5csrJKcAgrtSUEtDyg1S1Jy2L1oqam0EG1pQakFpQK1AQ7pw+wka3SNzMjD9AHTA2xNqgmoFcwqmC/csc4zO9ioDdHTfs8y9soqrVGo+DZRasBtB9MiKnmahDqGONtKbHYXtUcY8Na7OCmQRoOZreoIcYAhgczBQAuce3Cl3WZPvHMlsyYLFJsdp6QCSsUlWCqwSLVpWhWjRNHb8/2EG+Nvvb6wCGRNVBDVLcrcNba+oTdErQcJzHqjPAxqD1YethY9gTdi0LURpIGvhAHxStMwqyYKq7KAMceprIgpXx7JuJ1XIB569UrjZ05sEKWAVgc04TiK1NKOqYPSOGZUQiAXMnjeCRx8sLY2NsYTRmCgnMoRIHSoQnyjTSQg602UQRptE/QUiBUU2AAOQaIMxjCmXIijbDq2Kn16/okiBzg3DBp5jwxgdYxxsOimCcRyBmkOIOCeyzC83nVStP443goN2h5aK/caO2p95udEmvM/QLaWwW0JTAoUIgWhrbNcxJg+ZWijuLrHpzBlpTACPJ/VS5ds7o8iIJlslw7NvO7atwneH1IIC2tpvreG2s9y8RGXDDEt+uiYHwCpkPHbdoFNxv90wj44+HHN0/D//8y+4tRv+dP8TGHGzlLMVQWms3ur2jjEO/Nsf/woI8MvP/w2tNnx5+RndDc85sQFo1tGtY74dKKH5yWj48XiH1or6St8eiVSdqzPRbsB475hg5/JSBPMx2BF9C72IA6NP2DFD0sYSVjJkACS1UazYsgn0YdHeFRhjYh4dx9Ex54S2KGX/pOvt2+8oCqAr9tog9zte244ddygaBDU2W6a8s1KTE5BCdnapNygMslWCRxeIVkyw27uLkrWplUUDxk3cVVkOrWRzRBSlbXxOEX+R5SWb4l6WR8mctjZ3gsTdVwAAIABJREFUMpO6/s1mxGTplv+ZpBCY692jYuZkTxhgmg5+r8UeUhQoLBVPP5UlAZJgFD2ATDKmmkCIRQqLRV3MzUdAlFemo9J+v4SeRDLNbhL3nn5UshzhT6FyOrd/8iWCWku01Mj3H0wKOkrdoLJDhI2VV/HJTBO7U1/EBtFP1LoxAGs3BuV6QApQ7kxTlQ3QCmDT9NkDwg1bBjMez8cDMMOIdDNmhahByoA0wKuvNjilk6HsT2pV9qKYYvhDJ3xzzBfADsO8Ddx+vqPeKl5kgw3FM4S79b6xe4EadApkB/QdtCDphjYU7zbRD8foZHKa3pG2Fu7O9++chkd/kMVRpvctmqDqclhPYHnOMeR4Ssb+S7TAsTkRMUwnTAUzUool0FeVzAz97evHmpw+IO5sIpilYpK0JG3uIQadirZvMACjMVJd3S9UljAvAx9OtNMBMiuO6Duhy0dgESofVkLyMIg0D5vsadFIbcXCVQ/gwxLYRKgOrNz7zJ4wy6E4vicOYluaBVkL9YSi8uGDR1nuYtDWmEeFQwAd5hHP3GSq7rWxlPB221BQIL2xUaIGVWgzmvslJssyfT8jrMmI32P8RejSOQQoBmhp0LpTof6J19VCnrojjlUSWgSCgrQ0Z48S/qwWdkYubhgZRUZYmmXmM0TopfB5mzW0aihaoUVQLQWYsuZXjTJ0MpMpPDyXF69TTOgCtNbQWoNIhdnA4/EETHC0AQqUATFZbGcpAvOOaQc1LvH8SqnY9htgBNqlVtRWYYOVfTCEszbnW58DVYCGneskjOnO9HSIrt3hBWRsBts3zBKNCUK7kekbVuNktJRzihM3D97p6UVxzjcL8zhVAT4R5Iz+hIsgPIoxbYsy2IJMEizGY4kBT5YkD3oPMAqQou/VUJxOyj4ZebuwPYRpulhT25JsRSIH6k8Uw8b6JdlfhwUFWSCQ+9hlL+F0DDYkNvl1u3wO3/EecV0M5zLKVSCb2YYNe6RnZaUDRC6x7oWpzvvh9ibnokR+TJBz7m3J4OTv0DCslBQdB9N92ezXx/OI+36tfc6lyudTlAaYaTPCm6EoW3WDKzumQ6jhYSGJn2MS74SmmUCNzAKNO0Mgq7JS6xoFDijENpyCgpVqlEhdTsNhLFqRYRAxqBr61lFaYcoZDu8ApmA+4yTZJiysLswJiLoWqCg22yBaWUIuBVZpJ1G3qJoSB4qiacGYBXUotYTqOKrCJtvPiJ+tJxBWJ4BTD6iAOa0y3KnZmV2DDcxy/csDj2lwVgz/rZkQ33SZRp6ZkCQ+VNjq4j8KcrRWKICNpCNMJxQT7DRXACvYCj3yX19eUGvFfB7Q3jHe36mLeT8WeKAw11a6qu53Umoa6Rhn6aYIK4q2WmO9+Um1bw0FiKhMmEYrFff9xt8xJ/bbHaVuqP3AGB3ffn+jKLrzkDxGjyqoGYv3HEUyO2SBVBCpkYqXdmffoN6D0QntDyYjgmj46Ak+LNJ8qmi3G4CBjidgA7CBl9sN2+sLbl9fULdsNMiqETGBz4mqhjsJLVhnSTjNxzZ0V3SnWLnU1F0Q9JnY8sDITOc43tHxjjm3Dzbzn3FZRpAhIjh/O3VNBid75WyWx7LVOxmcwojg6APDWMadm0KuAbrxkjkDgGExhiToYJPs3tCJYTTy69bh6tiwcW7PgX443pTNQz2YmFKiTF0UP3/5E27ljrf/MfF4f8fx+6+0VxgTUgyogu6sOunzgHYlA2eG19efUbeGn/78JxQtbCJsBoyOvRR8uX9hJDR7sIUOHwHGxzu6KY7fOkrb8O3xRFFDVcf9/hX7dsNs2UZFYEPghb9j6oHYHVBUsd9bHIoO64hUGjeb8WQp9VBh6fh4oD/e8fjjnUBHGuZ8Ys6J+8v+qYzg8/131KJouMFLhbYG3SojYwEyjRdlHtwbSlnxRm6yyUAkM7h5hRTBkA4ZIRI2ggWbBgNtICTLrRxkb1WxlYaiBU/DAgJwQUHDiCaPRSr2co+oduIUFOdrkc2opbJgYlqsWVZ98d4jPZTGM8H0AFjBlyibbpbaIkXCQ8Cjyk8L9wg1jaq6SXYIXCCeaQcBD343jLT5h680eIKbErYXqcG5doVGjvkHQBMVNHK6PH+yHAcA8N9/+ROex4E5OsYYGDYwfKA7mXtafzAQZcrvDCLFT3DjWUk8GCgNPVC04sUGBcY794zaFcVpM8AU8xNSyIRwmim2VuG1wN4Hxjzw6799g5hikztUjIafXXE0p34GDjz/P+LebUmS5EjT+9QO7h6RWdWNbixmOENyKXvDK/KG7/8klL2gUMidGQCN7qrKiHA3M1VeqJpHVs+gd2VkUXRIoroOEZnhBzPVX/+DOYfQLs6H5YERY6uh2F1hDKQ3lquRy4U1eei0rf6Y5KrO9lBH1m0pLKM4hzENpCtLEeiCJudhSTTSqIMIp9JUHehAFsY60KHsjwd99NhvPejbcOU101jTOJteP55Ymd/ZaU6JKbmylEquhZx8KpELMN68Jvkrx28WOe872lmFn0hFzBF9VO5GRmpukW8xj9aTR/MOJWE8YVh1ifi0iDnVA+cXXl3PGer8WaIrk6iGJTgGpsHpSD6CKMHuziWhmmJ+6d3oaST0vlOSSZSaHQ6n38hJFJzz5bhY7j4anfAcG00puXlHmCfL3P8RM/fEiw03W0oSeeE9pKbhkzOYY6nnHDwxg1IVd7b0t09iwfGImySUGun8uX2kNb5xkfMEd+M0v6vbT3DyHdrj/8b/XMOXYsSvc+GcjaejLb74Tin0fN9JLn//3mN+mYbM2PxeHoqKMHr3BakYaSID8d61VlB4ff1AyYVb25lGlRZfk7w8ORi+73kwZykRninJk6dRED3RomTF7wMJYl0KxAr8c/SOSSKVHmZYFnyJr+XGhgVZkCDoBwE1uBPu5D3Htu7jYWbnpiYpuSFhyJtjGw0uSiaZUutCLt/uPlLtKPlMyjZ7om8a5EfVDEPw6PeJDvJ0PVcC5EnneiQ5kc1VQYaRNTZi80T6ZHaOjM5uNI73sRvTv0YMbHgbZ/g65YafEeoK7yD5Z1Hgx9fd6LP1knPE89XfnzD3U5E1PbdSnutAXL35zMe6KQnslO8Sa9o75GaOquazaxLAX5zT8/s9n7l/jdzEe5qcnfZ8nk4s59/4XH/Lo+bCyF5sZnG0Yo7xsElH8H+r8u6KiAR3cn6qQLc09pBo1DMenLuVNRLhV+cTkkCdqutsyjD1i8Y1zRFazz7eHIbQSMkoadCjqffCFDySTDDx9PRhehY52DRnDA8kG5h2HyUmSCXcg9NcZ41p2PvkPn6NdMfS9jSoNJ+gzDvYhUhRkBQ8Gma4z5gk98AbMkUyk6IwxT081X7RnI4oWuKWmwBlXIrn+p9EfDRr75+jr4/fJh4/7u6aSHWX0BwuixWf0Y3uqExJXJeNJVfGtrMnNxNq6fCNdUxre5+Vp+D26jjiOriRl5kzvX1R0rhx/ASWUDCMUCiVsAs3cQ7M4+jMObKozzTdzA1eXjdaz3zSAz0GbT84icsB208IWc3XwumXsi5r3ETDz+MkJifnKez7QV2qs817QPkSVyX7RWy6E1fuPLePdmAon26fweD7lyspZfog4PQcnBz3ShGbF9cdVnMkeYOPMwxFzF0HGuajm5JO1U3ug66GFvvXCPjf+AjA90SwZ2059x4Ai011jBao+ezQJ1w+5YJOnM2SvCMSOcM9y+LvNfpARqftN5JsmFXvktV4tIOe1DsCEuM+fDS+Duig6ksV63Ok52ROQS4rZa38w/p72rHz878I2hpj3xkGDZC+RpyHG62ltJBSYq3ubNz34YoJEV/wSnd/lZZIpVKLUPEx8Z7U6+GwDGi7L3p1KSRNJHXUpktn741hymXxRfXRdlJJLLJQamVZlvOk3x8He9s57juje4GNuZswEN2628EUSbxcNrp1ug1y+oip8fqxUuo3HHtq801hLPTWuT92chGW6vN4SUbu3Xln69WbkyWK37k8ZqJpcsXIUCVVICVWMaoON4szI60e7SDHDLJsMb5zIjqYqwYF0rKAGBXQYTRTj7UR5wmWDJoKRmbfDxc6qG8KpVyAMP4U5z1O5GCmfLeQOfceYZunx5ZzjEAd5ao5LAWSr5eSAEfDh/bnjp0yuRT6eNB6I0RVjGB8jKZn80E8g56Zl2IknH0ULOHGe47y/QXvlVVR2jiaG4urxcjlKQ/7dodFYT+z0D5urxwouw3fN1SQ7ArJW9/p6lOL900ApifpNsWo8PVy5bJe+OH6O7Zl44cf/+AiFjWaDu7HzsHdAzNNsQ6jAkUp1flcrx82jpL4XDL7aLy9/UQticviAbOWFsx8066l+r0xPnv0QipAYphQ18yyVuoqbC+VVA2Vjq4blILkC8mUwYGGGa7qE0zIuZLU1Zm5fCEVI5WEhJrTxTqO+r4fSyZ8a8xhXliqlxel+KVuLToNG/GaGB/D6WXn7608HkdcpxbNuSOhrRuH3jAd1KNQsvDykn5zcv6bRc5ZHYn8q+pezS3ui6bwwAnWdcmo+hxSdc6wfXbm6hol56giu8Xm9wwRe3/Tm8w+w7vIlCaJOEi+vIOip2Pi+WKHAj27qHhFXDKqenZec5eVqVaTWal7RpA7x/pCZep9mMqE13whkF/9/v3ikNM0PfKfNr/LUlqKkHP4mOCmYJgratQEs86I0cdpfhZ92Ygu1SIyw9UfzzPixnROgFxqpeSC5UE3Q4vyjSk5J9cF3i+cX/2Ld93tiLGCvbucz753Bm7K7GyYXYA6p1ueIOD8PtNpdZiSdaAq8Xuj6yAj5Cik1MLnadikfjLN0SR5R7ZUV2is20pPgnbfhsqUQZpAETe1jJoyFbdmd/WBL/Sq3RGqETEeMsn6JbKwfGylBWQMkjkXJslUMuYTfbTolNtwBUUOlPMc2U1ZcmxAM2T0fY6Q+964rwdwZsMY6k6tUoJEaKHa+Jbqquh4o/OdXDQdw7tv8ybMkvME3o8zz6da5ERpLEXNkwuu2aqOBHV/1mclnlP2zjV5EyEapF+L6zdRZeTsZEnzHp0mb5k+1FOT1ddArwuea9a5Hj0/MWcS8/zzOZqSJ5rtR2Lmrc2ogFkggYSh3zu0790aK0lO9dT8XrNTtiB0ToQ0yUSJnOsxv05iMc/3iI9wPoOOjM+S51dr9Tc8jn7QtRMCYcCLyZpK9NXxc4phKTMsRQOm7Mx9I86pOEelpMSHjx+4Xl55vXxkrRvXl42cEr03ZBiHCVmda3iGWav6yG74vVZTgbKwrVcYDx6yo2a0ePZzHmQRhkAhRohB/UpR1BYqdStcrivX64XLdaVcVvJWqIuLFuxdfWkazt7BdXFFGGRz9eSy1iiku3PWhoRze1jDzHV6KhC9HPc3mqgfQCBEnNhhQsyp3n4ZHHn2eBnBasWKOjdIB6rBa03F11Abscf7JCj9e4uchEtybcoCwycBBm00hhqpKpYKA/83W81kydx7gpToPSPizPvWXFVUq18cbb6hpeiejn6E7wenP47E73NdPWOot6g6c3T43W9I7HyoiUKs5EpKwtF8c7hsF0oprhSZzs3xknNWH07DBLEpr5E1Yr7o9z7lu355ck0QvByXnfrYQ0RYl4Ka0npzRc3lyrp9ZL18j7Ub9B22Dgx6yNn7aIyh7McIUnKOzTecd804+s2LRfznTsW/XwmjsZQztSyseeHlurJtlcdQOopVvjmSwxwr2LNYif84/QwnIeA0iLQn52CSI9MJx1ck+VjU1Dj67t03xFhpodaElAXLbvbWbWB6kDWR1dO3MfdIygxyRCWYDmQkSnOfIclOO6U4vdVRvEoqwsuHDzzud273Bzm7YmscnXE01vVCqYWuzptyFFnpx51hg8ParC3Y987xGKHeSWzLxVPWa9gU4PN/dqPWwhJeL5YSlkGTRiEOX/Y7AK/rK5AYIa+25kq+nDy+IeWVVNQdmfFgyb2rd2Dr4ryiY6frQesPPlw3rmtlXwZDcR7YNxx7uplkZlkKpcz1yJWdSd13xNSJpNMR2yu8J/H2dEqNjZsEJb0j76syspsdHseD1D2st6fgZzi5K8ZSynHczsiRlDJ5vXqBGcKMWurpQPxF724WGDyQupQzxsNHh/j4DLcNEHI4xzu8LBAGqOkJ1YdyVQ2y1CdnIRAEr/Gi0kvFfb3sYPKHJDkXyLR58xe7XKnRPKTgRtZ6boiz+cu4KaNMWB7OkdcZRRHb3sxS85puPFEc+dZWgPDp9guKsWw+PhrtQV4qdVmfuEL3gvbjuiHi1g6tD35+s3NjlVKQZeFSFtZS+bv/8T/x+t0PLMuFnBKvxdGHx/4gtUErhnU4mkB3V2y6j8lNBAqs2ytrTdh3C1/KF273zhgHb48HWgQrAjVTc6JmR5FqKaScubxcKGlhK69sH1def//CdlnYLiv1dSOtXuAocLvfwnYhQx+0w99rjp9FjK0IVTNr+Yj2wdvjjg4ltYz2wePhI+3eD8YxGM2wiEnsfaDqHBpv7ByZx1Yfx6d3ku9Q3Frs66Wu1JS5XF0lul6KI/ytkfNCrhtzXPj504PWOuvlTsr/Tk5OKX4j55z8poxQLnu3ATkKobO2YPrAzDljFrxrHu5pYdrRkYI0BaesAGOapMwHaaq5ztoFokuaUC0+avKfJn5q8dm3QTcnSk07hpQKJRvr4ot4l/ZuFBLQdlSS7mfifhgTHhAsuDmBwIREfTqWzsXniVgE4z6XyNdxroBpmKrVTE3eLbXeUTVy2hhDKdkJtikLvSut69l5mGWGCNMh2WemchY4uVZqLtRSQISu7sjchtLbcXb93+r4N8PTnnXOuybS/vU/gCdnRRwml5h5TV7V6Xdxvsc7yaLa2TlNJCiFTFj78Gs54CgP78qKGzGO2klFGJIoU+qJ36tTYVjLwig9kqV9wXTY1R2tSy2nAdxMynXHAh9H6hj0oyMqbrEeVvcuuvBux1FMt+uvZaPk7KjM/Bx48T2fjRTnLoWnUM7RfU+xlrgXVM4JYfOfYTx8k334g6JtdkreOUqtzicqC5lx8vG+Ze7QerlSU2JdFsriJpcpu+rJP1uMZyYq/O5emkVOmuQ1kVgT5r3C+fyTvcgoVjAJXl8CS8YYco6Kho7T8mpE85GCWJyXTJXCsIoddvp6jNHivM7ul+AYwSQSR0ni3SwO7jmBZjZvOGrJXIfDmyXnQAt9/YqlOoJZcaIsA1UPHxRzBebkXWGBfk20jKlUnd24wDQ9TNMpWObJJR6wE0J9f/7NvkZvhCknl296DwGBeOForBlp9bM5YgFPEqM+M1rzn7qbqxNrySxl4fXySlkWyraxlcKSMpdlo6jweLv5Z61+gffW6MOb+94Gutu5X6QcgitJXjKqMy63raK68d2HDzz2G2+3A8QzF0mBCJsrH6s48nq5btS6clmurK8b2+uVZV0oayUvG6mUiGDwMO2B74+tK/fH4ePeKlg3rE0OpDKOhvZIM1Aj98EMhk3iHKdUFFH1eiElSu7v6CDi43Dk5Og6x1ROisIkuTvxfvh6TwqlbPb6oTVKVcqAWr3Rua4boyh5icDqv3L8ZpGzVIc9a0BcY/im6QZ1kWxsMWohyIA4Y3v61pQMRxscraP9wLQxtKCSgeqFQZ7kUMfe0nQWjs5C8oQHzR80CV8G8agD37AaE3nXeNh627Hw1cH8NTkl0qvQe+exuwKsNU8yJsW8OSdKXb1TSnGK4hnOBQ8HjIfaGNjBU25uhAeF3+QS7qplqaTFTaP68ca2ZJZaeL1Uak7sj46agCyMAY/bwGRgqXG/H7zddqq4ui0noauydzetysUX+ZQqtRbWbWPJhSUVBsa9D+73g9Ya9+NzLHTf9vi6gPkaTPr1MudITzqhTldx1PB1KZgNZlr8iIJ7jiVkLp7mvAvN3u3XWj2bSgpFCtbUnT81IaLcxmdKzqxLJWli1C2IhgmzGuMI/0Y5glnXZcO6UnOl951j/4yae6SkItTLAk28oG6NgTr8bUKSSusPHrc3lmXjZdt4jE6zgSZlJJiPp4gbHr5+eKEfynHrFJHTVt7MkBweLcm3H080rtSy+POU5TzpNbvyg3XDzLg9hOMQ0ufgK7hcDZFBLZlSryzbRloqJQ3SUPb97SSJf4vj5eN31JR4War7Gy3VF2WxEBpkHw8mt6oX01NIHhOkd8WQuJeHOnnSGxi8KDYjmZAzpN7pYmRNlJEYI5Lsh6v0pHVMB4/jDpYwXailctlWcl+gbuyfB3tT2jhox92RQovCB6BP/oueP603h89CHXGOA2FaOsSlxZ6oHuOjkl1xFmotCaFErRXDeIw7ap2uLsIvUrDuap4QWoX1RoS8ipycNyCavVkwT+Tm3ZgqLDTOh3aOZOKTMTk55hdDCM7CN+bkSC3kktleL74/pcTtfuPLl89Y8lDdNRWKCn/61Dm6YtXFBddl4ePL9/zPf/+/cLlceXl9pSTnkX56Gzz2wT//9EeOdnC7OL2iJ4k9snM8Ov2mSLFwJIaahBou1aI+/nt9qaxLoeTEz59+orc7xqDRsOSqzxEj9QrUkvnuh1eW9cL18j31urJ+f/HopVLJiwseuh7Y6LQEh8LRlfvR+fmXN8hKWkGaIt0NYMyMcdvR3tmbI4rFpZhg5uqxujKS0lN3BLosmB7YDDQ2pxFYcJOGDvpo9DHoZ6K60brnoLXgPRWZ+7zbf+hQSl1Z1isfP35gKVe+e3khS6YnMP566PRvFjk6xz6GdyA6HClAwqa7UywIjcDE3xMOq9dSeF2v3O87X97uNLnRe3Ql6vNRM6MGgXEML3T2fZCHRhflleu6LJTwKzEBPexsIgBk2FNlFRdIxOFVUzcAOyk/Cbe1j3A+Gc8OT9KEtYPnEZoI1YGKz7bdG8FDOadDs0YY37ww7pcRwYCrbzZ1WTzDph88DmNvxr4XEol99yKtLAtmQjv8+xvdCZApgip5FlEvrxtSErXGfHwsjoDFjLIUwYaShrKtiy94uTHs2xY5Z/+W3v1BrG3PNU7O2S3IuRmllIPz4JD5mOq4qZA7S6SpjNPToKqbht/ONC3wyIKhTsx2vweiehWGKI++Y4fAI7MuFzfeKgKi3v1FZ5uSL1LLWvjw/Ue+fPnE29tnH6UOt/CfRZekQAjw2flQZe+NjkItDFH2cefozptaanFpsRO+EHPkITL53LJsDPruhMCUZxcIUp3onGrxvytyooum4XMz1wPffZxQCSzXK5iRxJmCQwclJcp0Ts0F6X7x/Pn/dkVOLjOyY/GU7FohGyYu8x44sik25/d4x5nmveEImucTTTTYOW2GBS8CSvUidNYXy+G8m5QMB7FCtYjnnKH5JGG6VDix5EySClywZmiHuh9hZLl7RyyOSNosKJkqOjkDJNXcNt99RiKH6kRAvfl0gnVm2SrrdUH6DG70h21vB8MG7Xj49ToLkBE3gI+XhgYipkKzQL7M0eGS0xOhDw8oCbR8kgQ0XNsnQDY5VL4OTzXM5CgCsVY+NTPf5rhcVlfUlYJgZO1sJsDm+08Yfap2t0/JsG4u8vjddz9wXV/Y1islL6jCf/nzH/nl00/cHp2jKXs8XDq88E5lPUd9plDygnvJqF9rncpX532d618VXl8vqH1gPx48Hg/240Frg9vYHYUSYVmvQWXw9S2Ly6tzWpj5ZW7PZc8xJ0IRgZLRUrhsK5KNsoFUhe5KVlWQlLE+uN1bcHB9xNb78OfA3B08RQOebLgqFPctM0vBP8QTE8zvsaSJoj6CdasDB1La8OlKGrOADmVW72AZa4Mvnz9ze9xYy4WSMmk5kPTX16LfTiEP0y+X1zl8LZaiyPF0b7MlEJgpk/a5cq2Fmhdelle+fL5h/RceiMv7g4DcjnsQbhcQ33x0gHah1MYYzXfrSEam1nPxscl0DxlkppBLOrs7MDcJSpDG1OYHwTXhC112UqHk5wmasshfFzm79on/MmzQtQdJdUQQ6FM1loorDpImUinkl4WafGauNuht59EarXfoCVPhsXuy9HZdAqqOkVh0aznl4E9MuNp4+XChrMXn+5ZIe2HoYNfmJnjVlWcJpa4XSJlRvn2RMxfmlAJhsefs/mtM5zminBLDlNZwb04ufz8RnNip56JA9MLmLpyYe4IUcDQwzBjdJXt+L/EiJ4peVeVoO12Ufo/3S5nSABmUvLnsNJpVFxEUPuTvPDyzGda8w7IRXi0pAitCKZhLZnRj787VkaXSRwuPHGMo2HZFJg4hMCv7EQuPIc4ra53lslBzRoaPGPLmERdp8e7+lHmbob2jvXH0/nT6xnk6CKzXl+eIYyijDfenSAkp0+nXZe9Dv22RUwKJoy5QMqkUJGRBKj5WNAyxwVB/ELN6Ove0X9A4j2ac0Po8ZgNUcrglu8aaJWXnhuIrQTeNgtmoxZuk3pvzYkgUEkv2RPqcs6vfeqI+dsZw51hsxIjTEVkIY0cLdMmELsPX2ODc6LwXwAtfES8+a6ashfVSWa6VcTP06Gfq+P1xp43GsT/8teEyP1Wq/tyEnYZ61zhNCF1GbJSUzsJkjvS9yIkh1PtJVfQcE9U+CxzkLJT8Be7U+9Xj/w2Oy3XzZrZWZHRS675OyIWjHRy90Y+GjoNcKqUkvr9kri8X/u4f/pGaVqpugNA7/D///C/85//r/+Rozmv67sNHaq0cw9W92/LRP6f5PbHklUHDrKE+J6OrQXI+GN7/U3PmZb0iYow2+Nl+YRyDfkRzhJOeXzUUoZbAMoVCkepFTp5o3Lx2BGfM7VZyzlgtXLeNXKBuuA9OV/rwPa2uC6hS8KlHCT7qw1oQp4dzRWWQrLswIRkko2Rf75skkqoXQRiZFAbBibokSk0kNoRMN28COPxz9S6BoDaOx6DdB5/3G3drbGWlpML2Un7Ts+u/oq7qocX3ajDnFZHwrDAByf6Aa49ZcsCsarT9oFnLgqP9AAAgAElEQVTjwY1jP2jNgyaXdYvXK1eJ/CibMzx/cHqgIhYdVMrCsR+MppS1uophKlDedRUpT4diL1CmCZ37iCT68NA8G0//lMBaJ47gJFWNWHqLmTlOwDMEVNy4rlTvrixTUoT/hb+JL7bgxpeKHgeHdVQPvym6IjmzrpmUPO1aUsEEtosTBPfb7u/k9RZEwvYsx88oC+Bxu5Mkc8lX6lJZ1xeu68LruvmYpHfuQzmGsvREH992ZXF0+wltn+oC3o2qolh5+n1UR3CSd+itHzgBOySYwaOa0Pwklp6k5kBwXFnk99nQzmKVpE569//w86jis+bRBllc2j3koOmDnpyfcByNnDV4W+5cnAVWgdfXj/z44x+4vX3m8faZoz+4Pb6wrCu1FjfpUsOWyH3LOcYRAxsS0nUvinofmB2MoxOJUj4G3SfHK4zZMErxKJMe5OtFXig5UUqOUVMgBmrUXEGNLZCwox1nnAX4jP7cqMKUUs6COzyuxNV5YoUzLPdbHGXxB2pZkJpJlxpKRT25ONNmtJvzHgr4hqrmWVKxqbp5WfDZziLZuXEmHnORF9+kcgpOgCqX0T3VfniRNwuosgTHJ1VySaQFEplFKtqHcxquF0T8WTXrzITm0d2aIlkieioGPTLIfP3qw1U2UnxRstF97LJ8oG4Ly8uFWlequDErltEQSDCMpBFLI0KpC6KGdaWp0qO685FeoI7xHM2YlNkPCN5ETFM8L3LkRCq+Gjy/q38lXiy495Ti12fQ+dZhwevr5mKFR+d4HDw+3xihPtzbQW+dokZOheW6UpbEuvp5/9Mf/8Joyv1tp4XJ3Z9+/jO3Y6fmxJJdrm/DWHXjsqz8/scfSZKxIbTWOXbfA4ZqiCiE49FoMqhsaBYoya/1kslbYXld+CivrLXw2G+0frD3HZVE2VbqZaOsK6kuXvCbI0TWzK91FkhT1CEUq0G8F6QUvr8YpA65udoyKZ2BinpGFsa2NDQnigqjur+UjoG25hyeNujafD8PdLJWF24sa/H6tq5uH5AGfR/0vbtp4FB0PJyLHvdQmpzdkzsHJQupCrsIbXjI8qD7evQbgOBvmwHqcIMz86rbSZ9hgOciNnegVR8zSRB2TWHsPTKsjpDI+saec40+aAQEP3i0caoifP4WqEvHHRVRug2GmHenkk4SXC2OIk1Z5uyIiCLH1E5es4YW/yvDQeB8inFI22f6rgY4u9VQ+ZiGUiCXeMCVnPyh70qMr3SCPhjGaE4qHs1HD2KJWjK5JIpVRBMjOUReamH0jo5b0Ph9cxyhbDh/lgm3A31vvthfjVIz6+uVl3XjdbswWnOfj/uNfhxkeX7Wb3U8Dfrm169FpFNBlc6vNH0GECzmuJ7ifooOOZUjMbeUSVSPY15iR0CiMIpiWocXOGLiKqWkp3TcukIfmHS6HYytoiOfPhklF0hu3jWvw7Zd+PDxe0wP2vGZYQdHe7Bum7vCanS/GVfLpBShd3OjmGM6d9026xzD74lU3UJgNKOk7CGDaSoQfVOeIZDuDOojU3dsniZiPmLJEoGT4VXRu6fSew4NYfalz4tzoh8xIowIAsh80/2pVH+gakGWjCzOM0r5FIhj3c+jUxCfDU7g7oCdkluMcHqNeeU7RNAdtwuW7VnkWBAxdVC7+6yojRiFB0Isfl0ka6Agzq3oS+bYVi9UwjuLMDQcGoZ8ls9iwdPLOxO2HmNHTc/CVkejlJVcQ2Swumo0S2KkgubE6DtnkGbkczlZvsJwh+8ujl7OMcYUi8x4hixPuTqBBnhD6Rd+qiUt4Jx3JQ52PqNPJPFcBc41WLFvXOTUbWG0gX3ZaUfjy+1wh3gxR3H64Jo8CHLZCmXJ1Oqf8dOnL9zvO3/56Scex87tuDvPxIylLO7dhp/vlYVLvvC7Dx9IktAu3G439Hi4DaB6wSfmxQ8ovQ6suOovgxc7S6ZeCsk21lQ8n2eHhzUMIdfqOXm1kkp2OgWEwCEKUhMsGmIfq0ZagBiSheuyoiRGcpTWG0abwJ4XLDk7Ed98WqA4EX9I5Fd1znXFRQmeO/h0Rc4kLU7+KoODBl38nEcIuA49bTjyRPnmPYYjUiUL2YQcaLYOhaT//iJH1WPcjYqKFxqS/E1dLRVVZxJu+xtmRi9buPy69HpdPfju6M4t6drOzCWRQimF7xZ3Gx1daa17xPzpI5LCmREfN9ABY0nVxzphmlXLAgGlW9iSO3fZTc4UDdfSwTF2RleOo5+jBTPv+CUcaQkSH1FYPOfJsUW/WxTSLFBizIX45pnDByCpkkqmXNwxV1Q4xs7jaNijRUqGLwv7MTvP8D0ohd41Mo0c1q6RLHvsjZwzr9sHSsks1wTF2HXn+Pzg55//zP640Y6dt3vn6IO2313p840PC+LhXBDf/U2c33gQ3J3NO1xajDPdqdn/IUwZbZ6S+XfkSF+P/UFr3T1NchK0+KZ8cnby7NoXh+j1cW7sNgx9qJMyi9AuBVfrGiln1sVHPIKehmi1Jr7/3Qvr+nteP2xOWE+ZIpVkiUsKQ712UEbhQ/3gHdl+J+Gz8UN9XDVaR0WcxAf0x4hFozBk8OgOVeeS0ZRQSazrC0kSH64fWZbKZXOfjhrmlSYxPjBXUOgY0VXhc3MIzOjJDzlUQRvSO2n1LurYO70p7TjQ/v46/m2Py+uVnBLbupAz5BpqoARThaYhIw8ajkP1MYrzgtdJ1dqHk69zpkdYYZYUKDWnAs9vKuce7n0PTo1xPzxjzIsTV+FghJGf/15FIRtlFF7yFVkzi1y4f+iUfKN/+Qs6uudr2qDtjzjzzvkzCyd1hTFTsVsUIzkM/1ZhuxQ+vKweoCwetkgaiCqisIXktqcW3JvO6J12HPThAZSpuO9NLb7WDu/WSHnhmdcVSGyg45Pwrqfplp8DPX83VW5xGuev8v7xPy/gNzv6vtOPwe220x6N1DspKRQjLYZW4fuXxLZkLt9/QHLh+OULrXUe97/Q26BkpWRzL7Lqa8PH1wvbtvB3H/6e6/rC3/34d5SSMQ5ujzt//MtfQDKshbIkklWO0ejDVUtixpcvb+RcuNpHb7Y3BxBK7ANWhbFuZIHWDy+8x84YmWM0LAma3ijaadEUOzMjEgYCBX+M3acvY6e1xpcvn0EPZDwYe0cfDmSoPfO6HrubSb4sGwg0HRTJXMrGVhY+vLz42mLObVTMiceEI7kZOrxpqMvKkl/I18x9f3C0g/2x08fgmOhOTHdmKPetHc7lMaEnR9GFiJrIym+p9P4rxOPhC5/m4ArHbSuxYQ2vEA1ibKVUK+GSHCqBIjAcHXHPBz0f3rlZlVLwWs3nzykn59CYFyVqiZldrkEqPXEBizlxVEJulc/T5AyL8YajABaEyqHuleMDb/WxxoRtk6Ij3IXifWczY3PFPAudOFninJP3IXWiiaSODqVcqHU5maOH7h4vELPP85wPJ8sWpmX27IWmOJqTo6MBmW/LFnwkYyToOhjtoN939vsbx/Fgf/gYsA8f+3zLY/pyaIwH7df3Y4yznPDt5091EowbTwzoeUZmREGaFgLvwamwOHcELEVie8D/qucCbTK7akPNO2chgfpGqBKxGuGrhAySQS3Ot9FhkEJSLrBuFeRKLonevWBJEjYMyY0BkzV8LOdW/92iuCuD3gPKHy69kWqBHkZ3lezs/hHBuXaO9tVcwkdmZVlqFMLz+xBqXZl7EXNS61/vEEIcBTUC8jYfndWaEcvu89JGdFHfrshxTo64dDxBSuOrMYrv/naatKV4Pt+BtHHvBfl6EmPnR09E0RKP6Ik8+NlwI78YfQ5Xjkxyr6qdrzVzAYUkX+8SmSVBjzHSdnnBTHh7+4SYcxQ818c7cxNHub3QeRZnhrmcPE2UzknluXghm2L9nI0e+OcpKXsdkXxk26bPV2xi8LREkDCa9OgBOYUc78+T3zvGGbj79YMHyLk8TvTnryPHwlcjrm9wjOb3b4vsqTz9RcSpDpZg24TLKixLAcncutGOQWu7Ry7gBbY314lSM9tWuV5WPr6+8Lp94LuPHwHj0+d7NJlvlGVjXSpCcu6KNn/eY308WqPMiUN4QJlNpC0h2ZVUw/xZ98v8DM0V7WApuGfFg0i9FYsC2oucZkdwGx+0cfDod6Q3pD8Yj8a4+xTGUb5Z5HhRv8R+OEwDSU0urinijTlCT8rA2PfY494przOZlDI1VZa6Ykw7mORFkR6MPNwc1QwZfv9PMr5ZdlRaslcvI5Dlf2+Rc+wPX6RLkBoxUpUw23JvBjXDhoWbo1CJnClLmCRICwX3y1myw773+43WDh73G310vtwOwCjZRwsjChzAyZJDGdmcv2KGlozIEkno3n6p9YDaBmKDbHa6W/YjLKJ7P7X/Y1h0LPbOM8MwBsPlWyQT0DybGCRnal29OjVPjDUdtID6c7bwNIkwjLyQxU0B/eHvjKb0w2Xg27K65H4Yj93VDxqriWaja+P+tp85IgCYsJTi805c4vvj97/zfC5rNPWqd98H7dZ5zSvlutFfPFXil/3NjfC+4TFCxfN+bP9+VAVz8+khb5+2BO+KmyAOp3D5PTv0eC3mShtw8qm35Qmh07KPjvZd0KwcWqjSSQJLfXFjvepkzUspKIOH+T1jIp4UfgTfJysMJxKahGFb708TxuVCqRc3jQzyMcTekxLbZXjHcuwIigwfa5ASF6uoZe6/fGK0hjCQlKhrRQRyUtowHoeypo6Uwod15fXlwrZt1FK4Xjf3x0n5RBqnW7mG4eTQ7uOWSWSOYiUFEd9U3Ha+Q9sPjv0zl76wXAqPz3f2R+N2tylI+iaHhIpsWTyqZdpKOJDgFVsKDtHkGLW+uylfXSYEgqg7SXMWcX6PaRjTORAuNKIYlYRZpqSNkXxdWgsUqaeDsREFQ3/MUgN3YBruu1SroySj8I/LH+it8c9iPO53Pt8+Ie2gD4976N3Dgee9DZBrXMvi8ueXDy8s68KyXEm5cpiSpCM0Du20PnyMUVKglErf1f1awvfEhrv+plpibDLoweUv2bkipUwJ+RQ8wNOfKq4Lk748x1P+Nb3KpnLMP9B8PmNjnyPkb3jcPjmf5tbfKKK8vCyemJ2hZiNn40WEpQs//9NPHM24f7nTh3I3f24ezaX8OSe+u7zw8Xrlw4ePbOtG6gf3Lz/xn//yC613fnr7xS1NcnF07+F7le9X7kp/H3fUjG19QXLm0I420Lv4FGC4FQm1kKmsWXlp7qe2XjfKunAc3QnyWcg9oUf3qI8co9DsfLXJtUsaDbUal+wKZkpCS0fXPus+ZmT6ejuCFO/S7/sx2LnzuD+izzK25ZWlbOTNFXl5+RATEh9v9eL7zng4StPppCxcLpWX64s3DckJ8aqO/LTduU/3dqc/Bv0++LS/8XY86OYKxJlJ+deO3+bkWIBN6tIvU/M5bYkb3+xUykz0YgKWCiSTpzcCU2mSfIMWo7cC6CmX1rkRvntgZqdtqmFmlZCBQ70YlkoUHVGNywwlU+86RwtOjDJ62KoHejI7EjPOivXchM2lqcng9GfU2FDRM6jMiXgTMPf3EVPfiAUkCSV72mq32TcbM7jMxTqexZVUGOaweUkhl+5Gyk6CnCtIXQtrrWApMrYIXoZvZo4ERJRESEAled9ZrSL61zqrv81xBlXOlfHEsL/uAi24VPPX85ik4jBsO8NZmZN/vxZzuX0PV5yBrO9k56J+t3kdFIhhCl8Ved9fOj9iuuGau/A9C2I7IxfDDt4vkKREnqgQ/nMlEkb4OolLJJPOTkicBGhOZt9TcrPH+FwyEcNQ8qbkryvFIybWpXp8R3GibMrpRLvewxjnuTqRsBx8OkcKdESxOCIMdRqCdX92RoN2HLS90fpUqX2b4zShCxI4kh3ZSs45mJ24P3/PjTbFPXcmZcfozk/FE1Z8ckj8/xzJmUnUQkoFy/7fqn4Odfj9MO9tt5QQNEZdAjH+zmGb7+hASbCsK0OVvGeGTuMzPa95/NAnoT5lCbVPocyw1/g+Q9XVLTJOxNLETpWpiQYhVE+U4Hkf+Kc/Zatxrp+I9FyD45S9+4qF89+6Wsx/Lb8Ga97BuLPA/JaHzhzFmP2VXKAkrGRSctFBDnfmsbcYzTa6Kd08CgaMWtwM9OVy4WXduJSFJRWsPxhj8PltsLfO5/sbUjLLdvUmPA0viumnCldlijF8DNhGd/+nBkWVogMJ7Z7J81mIkQUWvBjnGCpMVMcJYi6MjtH65EP5njlFNfMyRPOYJbijgpiP+ZPHfTtlQtyA0tQpLNOUVezA1AnYyZKT6Zlou/PRfILixSLWyebNSxGfFrl/raM7HlDs/FtLRrNBGp3cHxHv44viRF//2vHb6ioAjDEa2pXj0dkumRcpdHHwSXHEZimeoNzG4UZXvWB2oHY7eTypLKRcePndlVo/Mu4faUfjT3/6E60d9L4jFkZEYl5kME8UCBrji0F7fMFyptkWC9p8cP0iY8r9/kZru19kCx6CufTV1K21zyIqFhPfyLwfm6m+03MFFVo/POG8utdAyYW1+kYl2X+CPiIDaHUZZs3FL2rvpEsiXSo5irpmByTjx4/f4+nPfpEXhG6DYzSEQqIgmyAVfvjwka1WRAc2Bl8ef3QjJXXW+f0wtpT4+OFCs4ODgzFW1BJlcZ+jb3lonGMvDOCrRRD//Swev170BBF3nvaHjLM7nD3kLDbevQRJ5VSByBjY0dDa0FGosrGWRG4rbpLpxU4yh57XXCm1UrbVi+P2wMaGaXXvjGThRSQcw8OynM8xd1ZHHpMUck1BIjZ3vTaPk0jDuQ+5DrfFjzGuBtTc6uqz5949NFE8O2qtmbVWXl43LpeNy2Xlu5cPXNbLmV2TxKHwk1MCUZjNcaB7+4gIqVZ677z98tln87c3MFikBG/jDqbUunK0xt53Pn3+zH7fGbIy09K+xVFTokhyuFucHzTlSJos9mgLzltA/HGdnHAdzUbceiMye573jDdpaVoYyNnKeoGRC4gjal/uD+xoyDFjGMzRv3ZHDbo5cl3EwoC0Mror6NbiOM/yUtGspL1QRFnGhrFDi/GsWhhgFvfdyoll/RAmqDWQTUHHoN1u7lMiHW1uRnmMB5jHfagqe38wRqd1RxCd2eodc8Z5bbl6R5/TFAA4Kdv9hWazEkhzNC7pLKSfa4qd4+dp5zHHpPE+UVjN8eu3PNzLpZP1oJbC5XLh+nrl9btXPt/euD/umC6oJlY+Ybrzx75zRM5czYnfbZUf/sPv+Pt//HuWI1Gb59eN8eDL7TOP4+BfPt85htJNkFHYLZPFKNlIi5Kq74fkzKgVG8rjeMTS9oWcEsu9UsSooixlo+aN3nfGaHRrKINb/8UnBuYoz1AlaSO1A6wiVCdHi1ElkwSKKEhntwNkUKo3Mb0ftKPT93YaoaYILe0HoI48lVz5/sMrbTTux4PjduP29uAXOUCE62Vxd+i8kXPmZauUsnC9fsfjuHO/f/aIp6OxN5er9+H3wra59UJdNqd4rIuDC0M8XmrLSC8eKqvZV6AhvymC+G0kh5DC65MxbfYM0/YqPRbQIOgOndD4lGg7g15EGeNwx8O+OKRefD552TyrpzWfy+lxhA3+r8cqz2pXx3B4rneESZl07o0XOYPeOv0YTKn47GD85zshm+ie4v1n9xMFk03viHfRE87LCeVPIDdiLi0n5toIEUXgG0ESn6VbdPRmLu/LJZ9ZN25kGBlMyXk55IJI9SJnEaTG4mFuBjWdbuca42m5g5HtZL4PvJMzc5+W36Si/y2OJ7Dy/IP3hdavV7q5SDJRm3cdpbx/zbxO/p7y/Mtn8fqOJGkSrxsKluO8GWQJngeBCLiQh1OJ4zk0DH+WRu+outuwjxFgknfPTxLwixcW6veF+cgBEpqdtFlyBKzaHCtF0KtEpysWKqlEXQolL6zLhXVdWJclYhvk3Wf/+hyeqKhEp/4O0Ti/0rMAmufrtHYIP6jRfdTcj0Frg87u3i3f6Cg5U2Js6yhofD4DQn2dcgq0y23555z+GSD5FA0QROwZcfLEuSyezVkC5TOUlDCCdE6Oj5ZGH44MdJfTdjWO7h5dmtxYUVJHhysztTrPcZiLIXJOaPYiPiUfT/qt/ZTGP9HLCLuKz60jWlgxUlKS2FNJqO8iT1QDkRwTk2ZGzpyZcMHLSXEvnBwjJuA1kdYnP+drGOfXkM38U3neiw5tv1vXo5j8lkeo4daa2ZbK5bqx1kI1n0TLcITO1Hgczf3MxkDN0ZtrLfxw3fj+svK6rR7lMQa3cXAcB7d9j9cdDINU1kDR/TyouGhh7g0u5S7RqLl31hjqlhamdDG6KKMIoxBFTvcEARukI6Ei1OOgYFgWcnH6iHMMw9MuSswMJPHmYEQhHyyw0+BzDPfDSxioP1zmydQMzEdo4tMdp4xUaq5ni9rbQLu7tueUEO2UMhhUxjgQ1K1mwjdOZrOicU/bAGkkVTThpoPd7x2JNIQsGcvuYVW1viPA/+vjN4ucHlq0Y/dY+rVWLAsPgzUllpTpvaDmH8QYtGOc/jRiFoayBgmO+91VVvug1gvf/+4H6rry448F1c5+3Nj3nV9++cWVJy3mc7FJgMS4QVxxMJS9B7kz5wgMe4B20MG+P5xgFp2+hMHX+yylKI0AC7KyP4tqzqfo00k5TJVcaOBLhUOE7lgpyXitV2rxUZKI0EaKPdW8Mt+qG4IN4wir68vl4rLeMZ7xFBJjiZK4bNWDyfLqi2wS+u2gD2W7ltj8LhSD3AwdB+O4cRejeayuL+QDxNzdMpdvXOT4if4VRjMLnV//6n8ruMotvwsAnFOGp7dGXLe5cZ1dpp6EcsM5Vyq+SWlrDFWkXLyS2dxfaamQs/pMuhhkI5PDmfZA20B1hVT4Mp1g8SI1XVaGiM/OEdyGPwcnLYjR2dWTixXnduBmdqaV1g86O70fHK1x2KAxsDRIIlwuG3UpvH5c2MqF1+U7L26zUHLmLPbC6M5RjXNwN0/aWcxMqHomYufsgabL6qPddt/pY6frzaHl1hhNGAPub4PHo/OlvT0jNb7B8bourq5aVkeGc6wNZieKVkoJ1MZFA+3Y43qkGAmMsziW+TDHCkCMrSdiMdQdyE2noOG52e9tp4/Ofrv5GO8xsFAuHq3z6W0nJ2HJEsVwOteYVNRJyeqF07q6yaGORDdI7eFNmBikCinH5uF3vYg3mdLd60Xx4N08SaDawcyjZ9SFBqoDWiPNQj8KuRIhorOoKSUaLXVIuvcxb5znaEngadgZN9b5q3hXzBPxmeNfwQUTGO5HZX+tLPrbHuNopAw/fP+Rl8uFP/z4e+xtZ/x8g1vHHsauzhX9L3/5idtxsBssNfG7jxv/4eXC//rj75DrFUqhyYNub/z89guf3h785cvO3pW3Nsi58P11JedCzSuaC70WtjVzWdxJPCEcRRjJeNgbvXeOo/l+MBqTErGUnVorox+O3nXnLu5DyUtnl8/UtfBqCwsXtjUh2UiM8xybuGv/COBhJEfpOpPorLQ+6M05rRlBdYRgwfeyo/kzVMaNXBcu64VtXfiYrl4oofz550+87TtDvyAYazgvS/0jL5eFHz5euKxXyuuLT05MOYZz5D59+eSxD+1wIUbrJIPcjbJdqBe3S1jySs0rRmbbMvDvzK7KJWPDEQEiLoCQbXVcvTLL+j4GiIYPTszDAbfq0+BAZBKTAHzn7fbZeQTRuTi3h5DeFdQqQ6NbOffCkFnG5iazG49FPOd8PlUSPiunPT7PRWwec/HyPTK6uff/FnwOGTEAOafIwTHX6/tYMDxt5gLpg7b5BlOCeVIAjFDcpNOLwqV+EosO1CIhFc3xXrHpkuiSIhzUvTX68BFJe6iTZEX8DJmibSYZd9w9s3zzlcX+2jf8CtA5LxowOQHp6z8G5qhK5usJyNzekR2Dx5JLIdfMsi7UxTeT3hs2OtOfNitBLvbCqm41OExu8DbHIG5BEOqX+B6WIlLAFIaS+jhVKucnes87EjeVpBimngNnAMk7tzQ62TIrG9UWcvJO/3r5SK2Z7VKoaSHnJw9t/i/AiXc0p/c8qOd9jVq46Lpdw+jjnIFnSUgKN2FtHMON7FrrdE2eu5X8vpSZCvCNjpm3VQLZQAwlCjzxovlU3cX9MPWJs8J2tUg8axPlY+bmxYeJh9QLoicalyfCEue2pEza3G5+HzvdzMUMw4mlMoSuOZScT/5DVkehhkN1rr7E3DOrZ5cKBxp+ekYl4nnwAt9NAgmpbqhX1DleEh5HEo7Wp6pwFlkySfzue5amQnF+MRVevm45cv8c63vR+OxYfN18IqhzNDpX0DOoEU5k1hfK55r8LY9lce+bDz9cWZYFKwtaBlYTD+18ftw5+u7ZhtpRgQ+XCy/XjX/8h7/nY8mUmtgbfPnzZ+5vn3ncb/z58523R3NjO2BdFiQVDnVSh4hPHjrGg4Rp4lIXSioel5KEqgsiibVDl04b45T8q7lq1oaH52qw/t1MUnjc7h6ZJDulHew6kNX9pKS6nUUOJDKL34vHcKSW44H1gTYXfgzruN+UhNW3IJriGcKL56Mhw9hVvCAyoCZSKqyXDUmZ/XjDdHA0V/xZeEuJKKUMaunue5MEkosDSnVki9EZZq4UVujDaG3nSHA/dh79iPsohzL23xvQeVkcdjoCSUkGDEwbj9ZRyywVVz21AxG4bFdHJKUjIhRJHH2w74dL7iSxHztD33i0N1JOXNcLOWeW4m6wyxLW9KmwH815OCdBcG5obgB0krAyXgTkxcdVmkmtwZiScz9O3ui7w77abOeGEMRD47TmTyIsZS42Rs3++TTjJHSclLgfHTdPTGe4pFvCuwyVIT6uk/KUQM8FenN/nlqzs/6T+jyyGzUlFiot+8MnqaAGt8cb/eg8vuyAhOLMkaK+e2UOzv8oOZPLfwMV67/z8a8LnV/B2KeRX5hKlnq+Mvzhz1c351AAACAASURBVGs0t67nHzmvRcdTkZVSZl1X1uvK5eOFTSqLZO73L/TWeH0VFqtIKzCE+4B0rbz88IpkRVOPIseiFzI0AqsmWXMkd6tluM0BNsjF84+mp9FUgU1spRRBzWXQRTOlZuQA3ZXBQLKwvryQU+a6ZecNbB+jeDK/rt39TjzF17BsUXhLuEuDTQkyPH8NGP7ovoAej8YYg21ZKdOx1bEQHqNxPxrjONj3gyNVeipozSQq2Y7fWlf+ux9b9g15SSUKHJfGdnk2PCf93/z+mMZic7PV0V35mLKTSMPcz0uAqfiZ9xB+JoK4/x7xYPFg037Z6L3zs/3CXaD/DG0oRqNrQof7mfTR/HphlMW9Z7r5/bCECq4sQrXC0lZv+NTbRCORag6I3hVb9IaK0DSh4qMGweLa6686tBQ3gJ7npORMrfm8N55jdS9ObI7bk6tn9+OI8yGBLEjQEfy1crKLHfWR2CqmPFrBTUhPm48Y4f7/cLy8rmwvF/7+P/4DQ4Xb3R3w6Tuf/vTgnz7/hS+PB60PJMNSMv/w++/58Ycf+d/+9/8DHjv7P/2//Pmnn/i//+mf+cvjjU/7g7dj0NR4WStLKXz38ooi/LQPN4s1oQPNYM9uavfd9Tu2JXH94Eo4idDYmjaOY+dLD4ftdiD9cCQ2ZN0+dhL6kZCu7PuBFOPLF/Mg3csGa4Itc3l5oa7r2QWl5LzTHuNoO+5RcBvS3k00TBjdsA6pJ8SSZ3aNwX57METo6cF1KbyslbX69/mQC9o7Xz4b+37wy+NOV6VzIHfjp0+fKblQcuHDdeWyVa7XD5S6sG6Faon90WhDuTVvEo5u2GjY8cZ+u9OPA7HmxGg7+K3O/bfHVYeTiFOWYIzffPabBmbZH0ArlOyutIixy91RS3zEpanSR+juh7p1c8ohA40E3VwjTMxV/ZInac8fdDVBksPSbcw5s3dfvXUviMQTZZe6nRDYcrgBVtvdMTQHedUK797j3zg574qe9M5MbAylHT1cZaebY/GsDiHI18nRJzg7KUZ3F0vxzl5PkGdCO0+CXg4+gHZ3nLSE2+tbQken4caA6j7k7rHR3PNhqty86/SgwZxwVYi3Yq4U6N9WQv7Xj9nxPf/7Gc8wR2ruz5BizPK+WJr/Zm7oSVLItDeu1wt/+P2PLGthuVYqmULivl/p/cDMu9ntsvj37L5gt777+U7GaIq2SHnHA+dyFl8YxDznhSdCN1ULM3JCTd2GgKkIcoFyMi+cUtjmO2fNWHJhDKVEcvhaXQruoX54NyQejpkkewLyNK+c7tDybgwgz2Iw7Jkiby5Q0OS4xuSbqeBF1rJgsrDtC7t2+q3TutGsoe1AtVOWQvlv0S38dzpc1pwcxRLnI8DTxwM4749TPRUV38yuciTh3Mq9MEgEH0r93OW4p0Y4qIvh2/V0gj1LbOfKibCsK6pKqS7E6CpoczM0RV0BRqwhQTKfyryuc7HxBsnNMBWyoeZNVpAfY13Di/6TtBuDrOkefC49c0SfzzUhibjyJU1vkq+fPX83IrbBnaPNIssM/7GY3C4m2X4S174uWux8X//Vf9Z3SJtIuIR8W1j548srda2kbhz3zpd/ubE/PvO4/cLPXx7cewfce+gPf/cdr69X/tP/8D/xenlB7zvt8cbn/san485f9p0vbbAPJ5inIpTisUOtO8o7lU+P/vDNeiiv1xfWurFtL1y2F2p1TyvNYKKUzRG3/f9j7012ZUmSNL1PRNXM/Qx3yMihqruLqAZZADd8Ce654FsQ6CVfgAuuue8d0QvuCHLTz8MGyCK7K6fIO5zjbmaqIlyIqJnfyKxIsLvqFBhwCZy4Z/DBXE0HkV9++eX6Gho4bdrR455fw2EZacEejHd8MWgb6+r4JDAJ/dKY5glqZDlUDxqIuyF9DX4NsQ9qc8i2CtKS8NYzOzFX6KnZ5E73jevW6deN09qo88SDzqgop3JCZ+U6heiit54cX6Ox4TTa1nh9rbxejFqn6MMoGvwolLMIm0j6AYEsqxWw6GnlFsELP5I6/9FdarsuAZeWiW1b+fzlM6VkmVdCj9XPUApOSFNbNk7sFshKV4gYdRtFT0gtWTEQ0HdwTkqUW2KRW64RmfRsqBh9qRxbQ09iQLrbukWrh6xiOmXXV3PndFoAo60buO3aIWhA1y0bOY4lfptZGCbpmGCx1S1tYyqKTiCTRMdoIj1yqoHCLC0i7JYlph1DKTtJtPsoGRzvMgjPCZZ5Rup5AaEELHTbYpL30DfpbY0yvjWEGD1JsypR8WYEyhUHM/t4/dPEUH9sRypn3IEBm8f3I83AgLn9B05pHjjmgbWoCNM08fzhZ3z3sw/8zX/+L6k1ouRsIc/SX9j6wqdPr7RmnB9Pke5anaLCui7RU0YUa9FnjFO8z5RCbGtbbxaV4NEkaSSI0nmNe297GkX3Dd6JlMX4KCqRjuEcrxebXrYv2Z0oP1ITxSI1S9kdpVFePza+MUohjy6IO52+C4zhkYK9bUbbcWpRTs9nKCsP24kvyWvbtmhA2ggH7+npFETfN7JaIgiqtWZ6MDs5D7E0AInD2Xy0VZFd+kJIpCtxwFjvwTsYnR8gSN4RnGTwQTg5UVknR3k+kh2flfPDCceY5gj8zISlB1JkEk7OuI99F9BrWbUZOiQOmI0qriNIGt6FSzo5uTRCayZQp2grEQTogeB4atJojcBnyAvMU91TRqMMt9shBRJPjqhuhEI6ejLlenQGYfhw+Pb1OpDyHb0cPEjZ52dCnOn4va2T8/H5XQTXq9M+r3z+2898WT/z/fX3fH595bJ1Zq2c6sRf/Wf/jF9894H/8l/8F6gpn/7ue67Xr3zavvD9+sLvlyWV/OFhDgQn2tEI62YRpJbgCPZtDeX6zXh+eOQ8P/Dw+J7Hx2d0FLtkqXQpMVdfX+dA9FrLJtC2i1JKykkM57kZ0D2CY1qct+p4gfZypc4FppIK/qEpN0ZeCWmYKhrSFhuB0ppQU5Y2aLGFENhxxATPdPbVnIs59eU1qo7f/ZyH+cTDdGbSyut0QfDQs+kxBmvvrN24vEoqmS/UWnh6OjOViXfzc7TLmWOduUx022h9Qa0kytTp2TX+P1onp2fNPdtGb9vewkAi/MEMXi6fEb7i3hDgfH7YF46KsklDSxDucEXRjGaE0/kxUyealQJreqzZS0eiF4ZKoDixdqLmfqz/Pg6B1unV8DzkC8qkM66NTVd6EaZUfxWvdIwmIbIkZkfqQyNqig2APR1W5mxToZVao7nmqU5MU41SdPNESLIHEgM88ZgwHjoFQ4ANP3gUCEfpqo4qm3G+RyTQet/L2nqPTXlLsnKIGoa2CWY7KdtaamvMFdviNV+2flSW/RPYfsSkgzIiEd2bCqUlMUqTf+HW8vkZhfuoRMrDSpV3z088Pj7yz//qL3n//h0ffvYuexV5dOal0PwZs87z9DV6UU2hobS+RtPE6zZgUFCJcl0tZ6Z6Yjo/UutMaDN0JPAY+hby/IVCF0DCQReHqpVBVMfjcwrpiOYnUtEsDc5UnR4O0QAqguvlCOGADd2ekUIJFIu9S7rfjPWQ4o+BlsxoRMpUzNm2K603rutLOAGtsfVXXuhcfGOxlcuysVw7zYIf4D3E0N7OnMHbw0kJ+UBGNovKr6mOsc6AQUbfrphPoaUVKWR14aDdBD8lXjgO61Fd1PFdm2T45BknRSEAwsMcop+/+OWvuF5f+fxJEBbW6zUELofithujFEPkIM9HmjsO/zoVhjL7kPsXCaRvJN8Gqs0NJ8v9j9EmkUCaNfudRSA47cHF3lPOx7VlECEhMqHDxxqWG5aMuZv+0KFtla+ZUKLszvZ4nwOHPVJcb2u1njA3Xr5c+cOnz/zdH/4DS7uy9AuzKtPzE7/6+V/y/PSOj88fqEz8P//Xb9nWlV9//2terq/8/vPvuFxaKl4MFE5pTSOIIIJoA5qSBSVr3pNCkYnKGZrTrytfri906xSd4zEutC4Uec/T6R0f5hCLXdYFlk9Iu+5zcO8TJeM8tKNC2ENQb7lYKBNUzfMtWrqM7TaqpEJ3S7tQukZQ6AS3K8UDRYRFMwHsTnNnNYs2OM1YDWQT1F841Y3TYyLQekZLodThjEe3crGN7oEu27KiW1BbihZe6hrI7VRAToi+35sDaxGql6SS+K559/fe8x+bEJY9gHxtdDtIlaqa/aeM6/YaC7j3LImL3Lkla1OlUSaYBApTLNN0kGo5UUt06h0dpnPYGa0YIHpQDYchlu9IU+S9cI9D3ceGFBUWVQsmlZqbeZEsMc1FF86a3UR2hBOkN5yPDDymKYT3HqZ5d3LqiLYTguttpCpgqIAOT9vxbBA6IsvB7clPnLnssZEmgAEEabENqNqd3hVzZ92i1N67H60uJETZQzQ6Unh1CkShd4PWQnnzLS3n3w6UfQP5j38TjcgH+3AC0+kcyIlooBLRAiQmdymB5D09PfL+3TN/8cvveHp+4undI2SUO+mJqjMDaXmQmdZWXvhK653elL6tQZLrgRTEPVeQiupEqTNlmpn6EBgcmlAtUgvaY+PoHs7zmGcD4g+4IQ+isdErpoeTM5quHnmH+CfkD4aDlPvrDco1ovLR3uR27EfEvM8pieNHCR7Atbcg9W3XeOHFaCxc6SzWWW1j3RbWZWNtmRokKhrfytyHrPtRSYanQrpFhFuthJSFDydH90M3REnjNUoJtMXGPZCb9he5xryGOB92tI+IdX3gZJo3Y55iz/vw4QPzNLEtF9omlLKxGXv5tg3Jb5FDxSHnhCeykqLusYET8hRFbcdKBmJ1IIY7dJOBwU1wJrJ3Up+ygWcdaDZjdzocE7eDxI58O432a01keRx0R8Pj8ZAx5wO9kZtXGQj7n3jlN7NaZra2cbl85eX1he+/fk/0dOqcnx44Pzzwq1/+io8fvuM0K5jxu9/8ntfrC//3p//A67Lw/eevFA/NpsPJie7fa5L6G31HakPypFG0Mk1KYaLKDC06zr98/crWNh7PHxApqbknqDxxroXnaeKrfEX4ytqXJNrmHLAI8rQymGVxXyWDgm5s7vjm4QllEJb5qTyPw8lps1JMQ8cr9xrfkmJiyQGqASAUL0G2N/YqTeuRKhZbmGrnOZsEI1P6DRulCF0J/o/2JOqHkKZI9JMUES4lnJxaK7UaDw+PUeOfOmWlFIr7nr0YPKI/ec9/bEJIwvCNEKiKBqlRJz/u4HmKfOG6rrg71+vrfnEl4WX3zrJuIcREITt2BGeh1F29c9q7OkugPj7vcDwlqre6pThVLupQOAbrivWNaOqYao/a0QrzaaJXuSklj0E6Me2aIDtEnPwaTZqD5oZhGlUpS9to1mk9nJyqUYG2a9kxNo2h3hyL3Ymda3egbyI5YPfgB4SjRI7efMjvp+qsdbbNEvY+SqSFcGgCdu4oukPr2IDXCw8P5Z8sXbWjC2OHl5vTQ5SUfz4ezIDGg1C7J4QkPrtm88nT05nzeeYv/+LnvH//zHc/e8d0npEaDou0Ttte2ewltJlKYT7PnJipPmPdeCcbvTXWOVpAXJYXtm3l5eX3fP/9bzGHv/7rv+bdu3e8//AddZq4toaL0bJnTGmWlXYGZUrUJg+qm7k9DiHSYc/+5HmG7G5vHLZ5+By9unx3bkafN0l9k33gXPZqmqFa3FqUfgYUHnwtaamHs670bU1YvPP5euG6Xvh0+R2f/vCJL7//zHk+8fjxkZfLwro1el95S2qXtRWTIPMG1XYLblqW0oqw644MfPDoK5naVRqdtUl9GqSmYCCxluIhuAuttyz7DzRopLfVHJfszTdQJQ1i7fl0QtRp/gFUuF4u+DU6Q2/bQC6SxzX4LYneTjUjOUvUJorwwtlJZ63sKNPgJcYN2FEdi35CRaL1SynKPM2olmgkiaQK7Qgao9t5cHaCdxNOS1zj7lYNoUQZey97WbuTkOVN4DQCu7i4seYlIv7dyYt5+2OH0z+GlTqxtMYfvn7lZVkijZP38endA4/v33G9vvC7tdH7QrfO8vLKuq18eQ0143Odwkl2yaKU4FY122jegsFlPYPyEbjl+tUKZcLKzB+uF7qtXC5fcTMqDxRJgnsp1DlKr3sFORUqp6jAc40qOgAJVGaIM+53oAe6z0hx4ti27wo5GPkSRDfxUizI9q47r016phtTHqO3aLJ5LtOusK9EGrdlj7/FXmmbQu3MdeLD/A6qUp6cdd1wv0Icz/QWIotD/qL3ONMWi7YXrBuqGy/LQqmFOiu1CqXAfHpgQqku2I9sRj/u5AzZdMlNRMObG2J3QaYNiHgcwNH4LXggJFphHvyUSEhkszvAlktUTMznzEWHU5OUZnbBvQHBZnRiygFLyyB/JvFubO/eiVbyoDUOUG9Zpq6ajqxmRbLvUZoPgaR6oDgj3WA4LasK3MALkW9P4aUxd5SxbvLmwRGe3ZgPQa5Rcik3EzA+VW5e8dxukbbask1FEHgyYhLBPRK1QyhwT7mNq8rNbD8P39K++eg/CBN3CO0WyrL9aYMwOl5oCPuFDI0ynSfmhxPPzw/Zx2mmzIPb4GAp1tZt7NmUKchx7oqrcToVeulUq6hAs5W2rfS28vnzK8vS+Pj+PerO+3c/C/5YeF4MwTWTII2L52Ek337wQ1vkhz/L8b3fPENIEujNMOWhIiMvr4KWb193T1/44eSMnLWqHPM3hcDGZmi9R0n0snK5vvLy+ZXL1wvLZeXxdOJ0mlhbkGnXxY4U2BuYZSDihB6XyyF6N5BBuRlH3RGzgRbGeCf/ez/YI3sU1VpjCh5cmIx0LXWWkiw87tl4kObr1qoYlfPDmev1yjQXaldK1xANHM8YxFHYEZyieU/c9/fQdBAOBOWYI8ChEZS/k3Et2UqmlhraQRrSF4LsLV32EvpEVvax2ufbCL7y/zJaT+RzPPeqPVDheNbNtDhai8S83f2nb/72dqYa6cLLsrC2LRG1cIzLVKlTjX5tbWNZL6m9tkXfry04UEP0NVKMcV5E41/Pcnvf19txvyKgEQ0HogPXtrJtV9ZtAXeaNlBJYcg47xhnTDbA1EwpiceEsZ0YnyhjxsnjvBE/7vXeKHncoD6uMOkgKowaldD4zTSVg7XBfVO8hHyD5dkU8cOYm3m+u1DW6BagE4gq0zTh7qy1Mrhu4llrNzS+cn51j3O2WwAZa29MU2XymdAYq1GRJlGYcTDI/th+1MlRDSdhnmbIPhZSlTKFt6dECkdEMP+edV1ZuAKwek6M65pltKG86LRcF9EgswPLekUSWo2TXlKoqtK8070zn2pAX9Uo6pwep/3wj702VYOloOKoGDrHRjZrDfGtsTF7qPfoUN2TidGduuUGqsPJyd1jxN1wwIJ4IjMSY9V73PTgTmSn1DyYzGN7vkGVwwGUUZIcUPpUam4+wYDf86ZCtr03ao10XzIUogcSRyfhMimVQmWK5qpV8I0sH97eVMQNyBSCRKnm+N3u0CQJEpA8mANpG6mlLQtpD67FiCCVGPsPT888Pz/z8ekjj+dzVqYYcpVAHLbXILdPNapSpLHku0rq1XACrzHudRLenU48PRRsfWS9/nuWy5X/49/9n5zmX1PkxPPzE6cPDxRT5NKD0BftxKJKwkbqsoDqnnse5FGtSpFozjmQqh+a5WQ5GF6+n61ShjptPLHfOjVOOOMHHAZk92ot0aYl20W4OvPDRJfG19+8sq0rvi28fP3Kb37zO/qywcbgKlJLxaVQJriRvn0DC20j1PaAy3NsB7I1S0ldrkD4QqJhnKgxdCVTep0MFEa6JwXvRvdv6cfQDfzF3KLtjIxqztgRegrgKUopzvn8wPNzZ/tlo34pyBewfqH1Fc1AZ5qm3aESB22y95tyjwA80CYBiaKOEfC0bQkuXu/R92fW6NIuJ06nylQLD6d5R6uAQ0ZDQhTRbAuHZf+Mfqw9jRC/me3PGb7Iwf8Zf/BvHjD2yHHg7us8D9v05Nj9oze20+nMy+XC97/5ntfLheXSIvOgM19+e+XlDxvFJsQ0WuoovH//SBVjU8vKIAOdqdOJtrzS1mt09R5ZAaJVhgzdKaIpbCkC2nhZf0f7/ErIuzjLeo20u32mlBptZWTmpIqUipyi47yelMqZeTH6tuDZfdyddFhifFUE1+gLKW67LIIiOzLsjOeEuUDrZKHECByiShqXvQXSahtFC83i3jtOlURIhT2bYMDLdeW6dtQ+MZ9mHt6/YzpNfCgnrsvCZbni6xpobNITnk8nBNh6qIdfs99k6501tb3WRSlVcb4CyrlOiXL+afuzNaCDoOiWVSG5wwiKeqFOib7USuk9RINyMw5wI7gEowR4n9wjCsbpPbg4bn2HrazGTeoegldmA5Ifsvua/a1Gp2fZRdjGlcdumJC+xMEglsgLNwtwJLs9PFlLHpAkJHPQ5wZ35HiL3F+BIRsPA6v10I3PHcZ2B8ezzle+2STGAT5CyONgG5uFZGsA9EAA4qHCKBMdnKmS/43Q1QjZ7i0Rt7e0b0iGcru5yR8FcjKgjBwfj+lzoBiJ4sQ8Gsz8M4/nh71JJUQe2buHuJX1qM4bFR1YliA76pneUM/FH/pLdVJUT8h85jyfmErh5bLQVmN5uTKXifn5FKlCT15W66GiXByrPXR1esDaR+4+Po96bERSS6IrMQlHNOQ3/w5Bv7F+jqG7OVJ8ONgjxz1QB72JtG74QHlHbgmnbYtWKLSN1lbauuHdYnP05AW5JKG68JanlFvco4Eii2pEnT6CnPiMBY0GmZJO/80EG0nBA/VhX8PHejrW5J4eHQc0x/p0z8g697hwIkYSJ1CUeT4xzydO88xl2iibQfIOD2L50eIgyU77hi9ZjDDakextcrLNwJgXo99U1cpUJ6YpaAKl6CF3P6L7/Hg7e2P/fnx+P5CvHLFjfMZz9yvm5tfffCM/+P/4u3uSqcdf3tjTcUhHcaNtLXiKno0F1oZvjclj7DuGphSH+wjI9l0ZYF9ro0nljuDs/b/iOSKWn9VpbYn9vQuYROd5h62ssS9ldqBap7vgng1eS56zNhGFJhLppKycPW6MjCPpZuDzT3z78/5tzoMBtMm+LmIW7C9vcZZtve8gQM+2Ffvzkk7Se/Ah17aCClO37CEXPJtqPfRz9mrJowISagAHDh3FrOU6NProRZnV0RWJvlZ/j/048dhDdvpUCs07l/Ua/XeIjVtdOZ8fmeczD88repqwr4k2lIiiah0Z8tjgHUYFYeZ4HX/tKSsd5ecqAY81j+Z4kwrVnNI9ImPCM3ZxbC/pVGqJhnWUeAOzKMsUKiJQNaLicHrz4BDHJfgdVYiKLnF0yrvd+z47Am2KSY+zd1nd21gMslZuWNbjPYIDcUOYBIaa7w0FNyZa5hYHbDiUo5Hoe9SLYhqE8OigDdILSpTQuyqmhVkmTnJi8YW1rXy9bqxb48vXr7tT+bYWyFY0/Zs4YuT80gmk4L6CHCR0z01XppIlk5EScotGk6f5xK/+4i/48PED58eZWhzfrph1lm2LynGVUOREoW24d3oJp8J6iFhKzZ5Uy0pRqDrx9OE7Hp8/cvnSOJvwt7/+Pa05l99ekUWx04bqxFTe0frK9foHpk2ZVkWWjVYnFn+le0d7qJkyhyMqCqfTA0/PH4NEl9UvmpWLAKPn1mgsa73l4Zj9i2Q8NKobuhmthXaSkYq5qWg72j+EoxLoa98W1mXl86fPXC4X+tpo68Z6fWFd1yDWz1M0GkX4fN3wxaHD49MpkNU3Ml8jhTyfQo9Ep2yRIpkGEs8KooMMOpVBJh5OYDgk3bMC5Qah6AwHRnApZGeD2PhT8j6GOrSsgl8eYpCS4IR4dvneOmqFx/kd8hSioWKFU7mwXIPX4SOnLZKHbKTljZHmiPBcPEp13S30sMxoWakz1UivnOaZ8zRzqjPTFGXMNQssRnrGgIEEDQejd2NtPdripJ7O0L3xfK7jNCeQI+/74f5tajnLi3Mghzt43LwR3UVPwZFe1HKr1fM29vL6hcvymmNNoDW2UXoGsChehKqV08MTosqn64VuxtogPWx6X9jWL6zbxtYavju4eRZNJ4RANXDL8n4DAkldloa3lhWiycyzFGBsJ/rWcR7wuaHrFho3tTDJQ6SzzhNujS9LqG43yyKUW8/GD8VrnB3BGSO++z9267zH74ZEgMpQuWa/j9addfAEBU6TQN2PNrrHXjQ+0/VyYV03lu48zGc+PrzjPBVO5xOfvn7BMLZ1jZYWJQqFHh8eAeU0B9XlujVab7S+Mciv2bmEl+vyo77yn0FyYiJLqagPqHrk+MOr2toGWnZSb5CGQ1W0FAlYycMT1OFe3rRhEA+kJYi5O3N3Rz9uagoy8oprKjqBBOqyB1HEwZAKWkHazdJtkXifyOv7njJyAVNNwEMinaGeOVE/NgcdN/sHy3Lktd2/7e6dk+KIisYMytTMOLxvvr+NjeR2ssrxu0DWRluHMZlHGbFkwBbRvElA2s07LeX5e2vZJf0N7TayIMf5BxH0GIf9KWMiu4FHemCQt0foMdXK+TTzeD7zcDpn2bYnguOh8CuBkpj6vtH44EeNhQu7jk7wJyXQMoku9PM0czqdKVVp1iJKaZ3ryyuqE00kcvd9oXehrUKbnVIrr9sXujXEglCoD9NOzHcTal2Y5hmyFHyQSGFsSn6Tqw6+T5QfZ5SfKMzgp9h4Dhn1j//U901qoB99i4abbW30bKrnLdLI3v2YqZIk+G6RGvVoYnh7jv1j26BojwadJSsqpATJ2ojqyZQTjTm37zfxGjuymmhgFopngDHkCEYke4tFwHj08S/7vRkKy3vgFNr3gSyVwmmOZqq9ReqgNWFNra8hC7HzjMhkrAM20gt9r2Bxy0qr1GyqVRO5KdH6IkX/hAwibxzhMVduFcT3T+K2ow7f3NYshhhzxlOL6I8RgbEuZf92oLIxLDnvuMXKgWYkhQAAIABJREFU3t4uL68s10hWeyIrTrRbiAY6gWcYkXoKFCN7Ow01bCGLTzLlyeE4HmhptuRwcr9Jp/lmHKLqLkT2TKBZBGCqgovC9ooQgqDqFfHCrSJ3TPMI6EcK327uhzn7fYtbMWC821l9IKExTcYPcQb7/u/+hH0euQSyajaKbDyP9qzyzGrC3iUUvtaFgrDVE0VCN24ulT7NrLrEY/wQPBTxzF7orh8lYsGpTmd5/ww/cs//fLpKC3I+MfUTp/LAa3vlZfuyQ46fPv8elRqiV57emwp1zuaRU40KjrZRJODdwQSPmylUJqQrtg6Oy6iG4IgYiqakfkQYT49PqEK3hd46y3UD8ah0sehsaiP3KLHh1FPB3dFtVCWQbGp2PoOUGumRkpNZcoGHxOMOR5JQc0+o0PebS+hY5M97cJS3Qzk84NiIDNk/601w5GNRjPkcTp8QmjCCRA8r971Tq1l40Rs9y84bS1tY+8b1srBsjW0dC/PtTCk7YjY2gfhQHr1bpIJ33Lc8LKBIOhPWEZ2oKN1boH0JCb5/eOJnHz7w8/fveH56wl4W3Hs4NAb0hAyl0L2xWt/nliUeW8em3IAmlF6SOB6oYNsW5unE0+N79PQbjIZNTvOVl7/9PprSaSALdT7hS8fXjp5CYfTry/dsW8jiixampyfm6cT7x/ecHxe2Bc5Pzzw8Kw+nm43foydZdMSONbOZIz46SQdSY0SFl7XkbGTlTaTj0nV0jUPMDOmxcZnD8uXKcrnQXqJpbr8YfTO2xekrSCt0CbSkWwh3P3KmSOX1et1Rh7ewOXt2zdN0KE+r4yXUo8dBMg6T2GAGWVd+sA3ePMgzKZPOxig4mDRSmy2JzVnekM5WvKZ5bMrFw/GL9Wo4S76fM8+FOj/Rm3OaZ15OhXVd+f3vvse2Dlts0SYHcXUqhVIVa/EaW+t5WMW+VafgJz48zMyn6KQ9SWXW0bfPQ8JN0gGUEGkdvj04pWbz0FRP726ohFBgTwEhQXcH+9aX+Sb4ujn08FjroWPW873kABY40AIhCbBvLGfx67/9D1zWFfPkbxahudFwZimoCJ1AztZL7M3nOebI1pdE7SOo6X3NNJbsSJiI78FRiIoKvQtuW4yT9Z2zMiqwNIOt1l6jTJsNtQvKK/NUufSJUqZo9Jl6bRoaIViJqriuW+7/oaFlPc8/H9vltxSHQHluHBz/9tY64QgPbtWwsTd2D2cbVRoW+1Jyb08lijeuyxKpPDS1dBp9W5HeeHx44vnhHe/qmfe1IkvjxYVr29gsmt1Gq6cZVNG5UDwAlGW7sra+YyE/QscB/qyTE6mZo5AnIpNZTrsinTLy0mPiyx6px+DFxUx1utnAMzsuIzIKifAhZKYEC76k8qxqlE+qZlO5oQvCkVOGcUNtvylDn2SAJJLXpyV+IbEqj5NlNFfM0nGyqswkJbqdgBdHZDI2Vb+dHLeohRwR0+7YyN4qQtITP6hEN9jGLn4YC0f3n4ixyB3GRuTgJFkyIngbHdKtBz9jv0h/8xBqqBWTDl6RaGVwHDtHNDSUnz3Jb1MN8UWpgrQ4REopTKo8P515/+6BIgYWMKbsB5cgXtnxsbxXA8kjOSXWs9x4OJ21ojqEQRv99RJOV5mY5onZO/iKWwk+WIHTuQax7vGJ7XWhXdY8WBraVrStSCnRhdw6k3W0d6Rt+HrFS8W04nKOeVFSFDCvdcz1MgiBA6ExQzQ+mKQzrjKC7/zkmSbwm4Pd01nv/UprV0JSMwTLrK2Rjsnzp6iA1GjNYp0uKyqdMneqvh2UExUndVcN1wxMovQj3a2UgM3q/L26MD71mAi5xtwO/gGDv7MnYzKk8J2PNBIrMtA/H5VpGaUnomv7/gBJ2Io1qyV0UsqEFw9U3Ai5Czuqc8yyU3T3dFr5Zu2IhiJ2rYXTNMX60ES4hgzu2HsGec3HJfvNPpD7j0YaU+wHaPKxhR9ByT6Q+fv9+mDsTePJo3R6VPrsSJAP3yg4Vm9tRULbrCTaNXoUVsmWK+mU2c1caJkRsCGEmfPn2O0P8c6i0X4kep3JoWHmPig5jLPq2yjWE4GPOeHi0ISuhrUYa3ELQnHRfd5OKogrvdRo8iuBggTq6/t2Pxx0gb0Xn9zOrz91K8Zx9M3fbnC4QTfJeS+uezWyDjRHyfPes8q6sazXVHSvPJ4icDlNc+hdSWrOZSDrEv5DIDma53lhcGNViOrSH8lX/biT49E3arPoMio4U52Zppm+XOnrGt4/SrNIHHUteXgLTqRLoifFeS9nNVKTYfSO8o4AUwp5uVtOxhoqjEWYykTVKRU8NWBFT9KX+b7BBVF1pIIS1h4TNlNOJfkd43CFowQuIjnZf97KhrnQbIg6hQfczRm5/wEvW7oeZXdw2L8kSwJvBcqGeFv8nCXTmdIa4E5m2IILJeBD0AtBW1ZYaXQj762n7oTtOfTIY0aDzug7xqjOfkPTHOPYZCZVjFG9kZo3OQ49ETNzQ1V5OD1E2eRZ6JcYn/M88fDwwC9/8YFf/fw9VTesvTByx5F+KNk3KhEkl4D/E+6VEimivgUyEvddKeczBaPSkevK9vWCNKjTmYfHxyBL2WuUPFZnPk98/HlUd3338TtePn3l9fMXLl+/sC1Xui1svSGTUgo8qVGkM1ljagtyecFaNFI1MZAHyjmaOMogZu17aqaJyVLe7jmPcv7rzZ6kGumQlmyTcRCJ4LbgttG2L2zra87Rznb9QtsaVUNmQw1UKkUqy5Z8HVlwhQ9Pp2hE+kYm5USplanOUfWpx3oJhfWIil2DvoDCdLPxuUfjmQEehL/jexAy5Z6Uj45DLiuBItAYeieeaYZYZ+JE8VOiMKE6rmDRT0ew4PxJDf2a0pAJ5tMZ0ZWVTm9EunDf3A0fDe4Ihy4KIQLif3w4M9fK03mOKr1a9sMbGeFmOGejyGKU++7plb0ooqAa45YN1vbUP+qIR/uQ8OUigNxTIvmaAEeDz3hvoSB52I4egekXcPROu3nKG9lDOeEFZhEWIiguokw5fgKsHumXU8pErH2hWQtqRs6FgaoNQn+KFlBriMU+nM9gcB0Cm/Ct02DD8zz+Nl6zN5AUkCxeI2jRjpWG+wS1UrKFkFboKsg8B3rWldWNvuxEjv2Q15s3s32O+zfnwdhDB3Awzrj84/Go27U1FP4FxPRI85eQk2k4WfLJZsaXbWPdOuu6cfruO04Pjzw/PEbRyPXK2hpfXr9idHqHOoXP4UD3gmho5qlGsHeaR4Xyn7Yf18lJJGfLsuNuFsqHkOXAssudq2hW+Yyc8YA2IlpubLtPEV4d2UcnIiHJEvU4lNL3NE8YXuhExOMtcpbTFF0TNTk4xXY4JhaQjLz2uDm+a+LIXl01kBJ2p2LcS00Puw6Kj4UuQjOFJL7uJGIEJKNfZ5fl37vcDOl9xr8jb5vjtodNvr9mjn44LdmuYdzJIcIalS5RgeTm+z1qeX1mRhv5fPeMniSdsLczLZEuaZYcmRKfR0b6Ctm/Hw5izQ7t1YDu+NLQ7kyl8vH9z/jZd9/x7ukj8xSdoFvrqMVYl1qiO7NbyvY7TsE9K+NEcGsgfY8IREa6J5vddacIlEnZliuLbbx/nnh8eEBfQqnzsi6U6R3vf/YLHh+eOD2+w71EWuU0sy1LXN+6cdnipp1KEJDdVgRlmoRpcmptwDUIe+YhGpZ02DiB7MaDyfmQkuuQG42NVGaQs8kULDfoWDdjWxfauqZoptMuV9qypCJ3IH+rNzYa0uOwj5LOxiqOCaxtxXg7J8d6wwr7OEiR5PB1ImFlO4oxYPNxuvjY1L3vQMeA4UeTUhso4Bhi8/2xUXqrOT/igPYBj+TrDBTZM0gbYqXsj++45HXinKeJqkJRoTejljk0sNp27EW5hdZKFnFMwfGZJqZaqGU6quZ8bHY3aJQPR8f3ChwbxOOoZoj1pjtLML52jZcM4uTY5wYSw/hNltyPCx5Oz7j2/Zk+9k3f97pBDXpLK3ICGte2Zfo6+Cyh3hutiFryszbvYMJqW1AuUuRnoD24J+ovzKdAnEd7nr4tGVjYnsJyH1VYOTZ+O+OGszSWeMyj1jvrskZpeY01v+lGPzVKQs6OQ/J7Rl/DI6jJZIUPLTnPXmW+c2lu9a72YuBvILvxR0Kzbv9V7JexRRtjJbUMJDe/EayNNMieqpO+IZvwcvlK0QjKqwhVHBOPQolcb80abbtSZEK1puOdPF7Amv/HOzkqhY6xtiUOpd5CMdWc4pXiJfGiKOke0C04e5dcU7wZ63alSEFc9wHuo3M22Ul5LIlx4CQByU3ANkw7jZhUzFG6LsUTXk8imUTqK9AMz0gsHRo/NoDxu5GmKJJrPS9/QNGjckK70KNUh27chDP5ellaK0BJInAZqbtI6sVjSPl2fljiOkoNj1kV9NJBfBsTb5QkE+OWE8hykTYzWo8WE60lgS6JdOaeZfhvu7OUKXqT+ZZOjsaxExRRzfHw/bOLwFmjOk8tDvC2Ja+qzvz8u1/wL/7qr/nwUDhPwpf1E6231CkpnOaHQLFYbqLwXPiJqJmt4STraU+bRlVXhw59EU7nQpkLqyxc7ZWffZhRmfj96yfWtvL69TMPT4989/N/xul0Zj6dqdOZ0/mB9vyOvm3YuxfauvLr3/6B3jpniQP12q8glekkTNWYpw5caH0JaXWbyWwridfFz+K7Bo97h54qtCNgGJ9ldOYdat4K7hu9ryzXK+t1oVuoBW+Xr3ufpd47GytL72y0KFdehaWtNNtAoZtwXY3J3m4e9bYRrbLSMdVAiuk9eFJ+07cpA6/RfdzHGnI7lBliAe0qybfreV9qHmkqyzVsbgcxPbd3z9dyCITH9+T5cQR4IKme1+k4D6cJs8o0zZjFWt3ayrJeo2VOPxyWqaZUwnSmVmWeA9GJPlTkxM73kdvr8x05YWiKmWfEHftA0YMkmw+Jg8lhiLEOJ0ezknScf57h+y0acGgPJbaRlYCS5JBIsZIBKimE+nZW9AS+8rKtrL2jGg1fVzeEKKkbrvHQTFrXdS8RH0HRmC0ikS55eJw4nSZsCbR/Xa8xDMnTKek89cGHzBfYHSYG0X8fPqxbZBCaMU+G98pG7Fu9z4Hg1axqM3IeeZZYJz+IIyvREmkcTk7PuXFQPr49GQ7H4eCc7o+T43OYe/BmiPEZgXUjEJeYon5zpoO3DXPj86tjduH59MxUKlMW/syl7K1YNttYrxun+shDnSKr4ZLzMXs33jplP7AfdXKmaUJLsJmtd3wFlxYVAuY06/SsoqqjadyOSGS7hX1AHalCqZItU3dYJ6DnfKwImRJI4TSC9FcUqFG6GeTeJM6MXSsXd6Q+oJSMmQxGRKxyoz8x0B5JgbB89kB7BufZ0lkrBAodpaORvtpz5mNS5BQp43WzVDj4gJ75yrxsGZOQnGSDNR8T/ZY0BkHSDm2MHqQ3J/uHOb13ujlrD+HENTuY3/jRMTkFSpGbyfs2Fg5lpkryunHfIwb3JGiqRtPRvAeOs1oDgmz+eJp5//TAh8cTD5Owri/RT2kLQmCZ5lAhvW6xePtYnOHABOybkfVoq+BbIh4hWlmnirVOXzeoD8j0QLcFs1fWFu15L9tXrutGbxfwjblmG5MyQd2QuSLtivaW3Gfl3VnxLpzqKZrxbRvVr9jXX2Ol0ktF/RGRMzJVQvwu4zyLHKOwRQrOFHzJtFMeQBI5GkldFE1xMvdAUC1TKmqGr1dsfcG3jm+NbfvCul7ZttfYhMV33k/3VDL3LfgiRVGFsxbmt5xIYrhaVvcEuruTPHItRjWLMNTHg7gbcwlzrPU4VHLTx2NP0N0hyYfnnBwRaLg4A5NhX1WWr+G5GQ/MBvN9fzEX3ApKDzHGFDzVHpVPWvKQmZ3uE5tN9M3pmyWxtTFPhVKUc52DezPnwaVHHmQ4FMPFGkrGbkNgM9fguOYMmJxDiHKX8k/Oh0siDXZEyoGEprBF+lLxalmxObJ6yZMaHMbwmXKPy8gy5cLe1p6vOFe6rDS2cOQJ9MDZIIs2IjhuES8kyTfQO9sRkRFQmBnLskbQ0DiCcwBXDM92D8bgke5rOy9rnAuQSIdkpbJEYNhsIxOu8bZbQ02prRzInxB9Cn2Dre3O9l6E65aIpn1TebUnO+JB+33x/P3udBHpy2AB3E6IDMNSu8cJHmH3o+UKEJQaO1o3mBsvq0STFg9tqWUL56fUaPmxrZZnW0O4IoB5w7Tv55vaf4KTU2pFzCihjhTpJZPY7Cyg8OC3CkWPSDzDBzKPkt4jlFys0YDGdjhF9vLW1J0QSQLqDTlVB6kp30f7EYHnWB8T5mg9EWfAiIo800h9x0pCFSKrosZjOBAeyWeWcgR7ahzy+GMy77Mk2lIIoJUjnx4gU7ym3uRHfUz2ITAh+9gNyJHB2+kxkXqmflrLkrvW6HZ0Y97Vbxl8W98JvSWJYf8kdpOGlDH5cxgmVeYajV8tDwj3gCqDpDbxMM989+6R5/PEXITX5cq6veI9lqNIxYvga9/vIzYijTwgB7Lhqa5tWyBF6zUWVhG6razbK6UXqp1SHXZhaxfcNpZ2YesNsxXxRi0pm5+cCakV14h2RtuFxznQgnk60WwLwjgrdnnFtIR678mQ2aCfs9x5hMd5xMoCrohXsNf8iofpdI5KSC/pQHbUO9HLjSQvBppIW7HtEk5Oa7T2ytYWel8i0NCye9fmndYjteXEhosKsyjnHfp8A0smdG5rRzooU0LkmtTcCByCs+Sem6tHcKWaewxHOnhPvPm3B/eOyuRUIfb20CGyG+LmIPbnAwYJI4h6eGqwRPl78FuI2xTpIolgyJgwmWiL0VZj3QI9nudCLcop01PUI11wq2QcodpIlY1DzA6ETzyrWznGbYf9h0MY83XHvP2He+v4tY83TScmU1wDppER4PZ9HztSinJ87YSVtzF/WPFtxbTRpUUllQxHbVxvzqss5LhtQBrtRPLsyXFxjG3b6P3A5eMpMeYxa/vNOB+vt18XI5MQE20EpZKbWPfgp4wA2VpDTUKITyT6ShZhKoqWaHa5zwPIuXo47t8sH24uR/KxkijbnuqOP3q+oO3Oy0CfxiNGSlMCaR5npkhw5XJtdQu3+LptmWcolBKNUiEAFixS45aB/CZrAClKVuuOsT5SzX/KftTJWb6GqFpblr0szXtUYChR+dR7C+HGEsjF0/kx4bHIQXaNtATWkRbJE8+qA9VRZZXjm4NTBgxlmQ0TKHFOHRFJiSEdabKRIx+s+NirDq/0dnHaiO5vBmbENeM6Sk6wGEjHfehvhPZBkFgjJWb5mqUk6WrwbYYq7J/46oN0Pd4/MXJJZ8dvxsRzLKIBmrFu0ZywbYHgtLYdDpF7RiJkNJJVA/m+8ySH1P0b2bZECfWsFS2VeTqH45CHKn5oI6Cyl8NPtfKLD++p88zp8YHnhzMfn57oGL/7w29Z21fMVk7lFFV3Jb9qDee6t0jloHjf0vFJFFFWQBCLeeq20TdYviy0tbNdN/DP9GXhy2+/5/LymY/PE1OZ+OVUsGqsHxofPz7iyzWqHqYzohWtZ+r5PUVP+KyBDH250K9X2ssnZCq8//hLfFvoy1dkddgMfYZydvzzbyKy3A/ZDNWKYR3aGqqpvS04BadQz89onaIzuIC3iPqqRvl+1+S19I6tL9jyyvr1yrpsbF8v2NKYTVCpPJ6feN06ra3gxtbCCagOs8Fswvtp5nH6swoU/2BWppByV2TXQsIa9OBquaVTmbhL/D/RMAN6p28LPQOZWitayy5ZMbCNkqrYNzxjyCg1Xt9ALCuDQo3MNcrznYjW265ZFGknNwudMUkypuguPjjCOElSr1LQafROVForx3pNDcgbl2MnEjpEVY1ZpukijTmQ9FHkMaL+W4HcOJVi0wkf5BCs2F89naY8wWKcc8/Zm1CGZ8mIrpKKuw+km0f6Mxs+T9XfHMr523/373lZFvriSBMqZa/mGSKaf3ReDv/v5j/gSAOmLxD7ViJnHk7nVHKvt0RPRmx/8/LjFSc/Xht8pyiIk6wz352S2g7xSBHYiNSj1Sghn1IozwzOAhPK1Y3mqdHkf8S4+OazIsPRkvHj/ufj+JBvxmoXvN3PWtufo0JQVzx7VeXj1xaAifjKpIVS4swa4MxsMR97SWpG2460dJ6zhdtx+2P70V2qb5Ga8m3bJ7j1jm22l1qbJXRn4CUWZNFQOhxRX6R7Ih/uPeHwdF6GZ+i7c+IMYGfXzGFMIr/5OLI7PCP6GmTjPUhIYbXx8+7RDgcCD0hWfOe/7DeS24koZM9FpOi+gHPpMqoFVAezPK9wJ0BKXu/xquO544fhpB28imNaxYTMTcIsDs0eX8Ftio1FCbj6UDX1vdTciWub9O2dHJIvUUuUAE9TpXeLXunJMZGcC6Wk4+swlcL7p0em08z8/MDD6cT5fOZ13bherzRbMG9MZU7+0+gXo7t2UWQ1Ytd17+nkHA6uWIbUtCBvbz3SBc1oAuqNdr3QLyt6mpkonKeKq9MeJp4eJryt0APqRgjxzDLDJDBVrDWKC94DMdJy4nQ6hwDYesW9R5ffbkjv+PI1dE0GGulE5dQk2Ob0a6f3LdQ/ZQKpeJnyI+Xn6w0SPQgEIg5hvOG2JZpzjbRca0g3KlG591gr7spcOk10P1tD0yrO2kmU+c8JVPwDmmavrrEXkAUHkjyb+L19s8ZUbhGWjvdwehwLbSkT0I77QDNut3L2qHafL/t+4olLRI+80XNKsKzIGo7GSGoFV0GJfleGJGE038uHKxv7hZTgHPSucFOWO9Dg8ZG+TQMMx2QEfLnu/WZfyWsnHb1R2nvAMccJd9tMVvYRyTWzb6Y5YjE5js/i42c59mdGI0ZP7pIlxP2fNi/+v9rXTy9c25aE+qhaTc26vUpofKS46sNGOkm++f0Yv5wew5n0OF/KmDMc6fkfOjnJfAq9pf31hS3ntpDj5scsPQL1uKKo1BZ080GT3adtFWECWrZbuAXPfvh5998dHvDhQBy38hsUfn9WPnEPssdcBEyEIRh8gBDJI/LYb2XvQhQOOp40ERmdBhK2Tn7rKG4aWZe/z37UyXl6KgGRZYTQzJiKU6bC1iy1M2JZlRpy4udzjYqBreMeTfImF/zkLFtnbcZUQ9BojFLrORialRNEqfN0k+5RgvR3mlL+vozqpBQFqtFReni2Q2Okq8dgCBH9OXs0MlJUZY+Ojtjl20k+DtDklaiABUYCCV+775VSqunvq34zob/RqMjtaMzEvRnj2JNz93DLRwpI6RTp6BY6K5MYqDPP4fmuLSPL7CtSJK4w9Aqi7Hb2mo7P29k//+ufxYHUelbVNc5VeZwec/NXrutCs87z40zV6C47TxM///hEN3hdF1Y1XiZDrPCA8mXZaH1BH95T6oQUxRXWHs75qEiLBdkjyhwpz2DnhdPjhvkC1tG+MfXoUH/Slbko5aNh7yq//NXHcE6uL4Ah58J0Loj/Hd5f8dYRmUAmdHrFdcGbI7JRnj4h0yuTb+i8MZ0mvHSqBs8HGlp/jS/G8vtP9HVjeh/S5tfPSwxkLfQmbFcCaqxCmZ/QqogaMmUaVmHOlV80VDFMPQm6C61cMH2FsnCqHX2CdoZ1BpXGaf7CNAvlDOursn454SU6Hn9eO2vvfP/lhS9v6Cw/nSTPxDXXTot9oQhIEKYlcGyUOR28CGC8ddQ6ai3WpQjaDVgxq3EQ55o9mhf2VKUYh5KwE3E91nLxoSob+LEmT6XKcTCUaLSXHA1HJfoA1g7mB+rjZmiJdjJRxKA8PMz0frTO0Hz/cFh9r3B1ZC+aEM1De5RtE4FQHTyiPDiLe6KcMMiCY4/zJAu7lP2AVs/zZcSUmfIYTlfsrZYFJ57tHfIzj/ErgWittKQ8vH0N+c8ez1w25ctSuKzOp3XlW97VD1CL8SWDFhwW3FBJPqdjvdG6UyWrrUSAHpVG7pwIDZjt5vVrOiej0WtLJyAFUvbDeVzHuD7IgF9kR086YB68zUrIJygRZ+HBNrqYsXqw/f4+Fssf/S7XgP7A6b0JwfeHDgX+EVjcKqGJ+40PHddWVPbXCCV5SZCEQEc1HjMrTBP0TeiLR/FTpqscWNch6fCn7c9wcpTQV/Fc7KGXU1RoFotsKKzmtVOyLHJokY3oBNU4hPN1DjRhL1w83MQcyfEhd2Qkn7ejO+mk7FHOeL90HOJs992t3TEUObzgPRr65mb9cCT85nl5aGb+dPdsb/7TAaXBDf/Fjyvb85dH7DXwFsiAakcgDr936KHIN2MWj9mFtUZU6AcKtm+T6eG/tZNzfphDeGX13aGdJuF8SnFHCt03aJ3zrEy1IFKZp8ppLqzN8aVl2q5RSTHE4dClQ4kEKc5253FE4mNc9mHgmHeZvvKOeAdvWaIYXaVVjHkCqnB6nDidZjqXOEzPNbpxswCnIC+XHHHpoC1y42xI2ZDaUOno1BDdkGLIFHMgoM810mbrC33ZqK2AKHa9xOXXQm9KXwSmglKgxDyne/BWEhWLuXesq+BZG0iPa5Ce4oLGVHM9TjEPizYmUc5VKU2os+CquBqXHtSWrbWbkOAf34oOJ2cULRwOrKhHd+88hQfGekSbGcAMVHesxIzeRXVnX+5rMx2Z4xPmCvXj+QJ7dVDGTfua3wGM9AqG0nuk6GWnW2miQGN/1dxDR9pIh/fiRCUdZKuS4CvcHsr7+yPc3prgMfoNkjMs95vEsUfhiIygaux5kp/vjyLAW0Jy7l6St2fX78j9Kbdhl5t9awzaG9pcCq0rVVN37AYdgW8vR37wG9n/d2QU9gM/nY1In7DLdLTBPyRG4xYNip1CqHm7Rgw+5ta3Ts0fX9/tnhYzPsZ0ZBluHbPRWmkQov9+l+Bbux2bP7JvptnN2TWu5QevMzSt8GONjNd3H+IKuRJGJwAhkM3ouwyaldA3veVkIJV/32X+2B8DeoEjAAAgAElEQVTvdre73e1ud7vb3f7/am9YHnG3u93tbne7293u9nZ2d3Ludre73e1ud7vbT9LuTs7d7na3u93tbnf7Sdrdybnb3e52t7vd7W4/Sbs7OXe7293udre73e0naXcn5253u9vd7na3u/0k7e7k3O1ud7vb3e52t5+k3Z2cu93tbne7293u9pO0u5Nzt7vd7W53u9vdfpJ2d3Ludre73e1ud7vbT9LuTs7d7na3u93tbnf7Sdrdybnb3e52t7vd7W4/Sbs7OXe7293udre73e0naXcn5253u9vd7na3u/0k7e7k3O1ud7vb3e52t5+k3Z2cu93tbne7293u9pO0u5Nzt7vd7W53u9vdfpJ2d3Ludre73e1ud7vbT9LuTs7d7na3u93tbnf7Sdrdybnb3e52t7vd7W4/Sbs7OXe7293udre73e0naXcn5253u9vd7na3u/0k7e7k3O1ud7vb3e52t5+k1R/743/1N3/jbsayNXBwBFXiqxRKUaAAinrDcXpzzI21bQCoCopQRFEBAST/VRVEwAQc8o+govE9ICqIKKUooooWBYTeHEEoZUJE4jkIiOR7CGaGuyOSL+YG7nSz/Udz6DjTNFOniVIqokpvG+5QZEJEEa3xOgq44B6vb9YZF7tsK713Wltxc3o33DqtbxRVpjJRy0wtM802jIaiCIJ5jIHTcXfMe4yTKE6Pi83PN5eCqoAYiCOqmBttu4ALuDKVwlQKc1EmFaY6U0rh/dMTtRT+zb/9t/IPNov+jP0v//p/ckfwrpSpcn48U2qlzjPWnd6hbx3rxpevr3Qz9DTHuAu0tvF6uSBSEJ0Q1bjPAuC0rcXn94abY83orbFeL3SzuCfLQl9XDMcFHucTpRTmU8wVb2DWaeuKFkGr8vz0zOPjI5pT63pptNb4w9dPMYdc97lnGJ2W93jC3HAcbzFXalVKrbx79xHEaX3J+bPhOZ/whns/FkSJhSII7tABJd6ve8zdSZWiSqkzogUk4xZxHDAccUfdqLVSa4WYNphviBiPT0/UWpnnGdxZlysYeHPW6yvL61daXzHbKFZQV1TOiCj/6n/8H95kHv33/+q/cxFlqie0KDrFmDdvdHNyScca6o57rj+P+WDEeFnOB7N4DCjusLUVM9/3EUfAPdedg0ARQZBYu9bxtgHOVCYEyf3FwHq8TzO6GdbjXgCYxCYnQuyX3jF3Wrd9z3N33A3xuE+lTLknem6eBdFYS0JsSdbjPSFexjzeU6ugWphPZ1Q05qo5tnXcO+ZGUUFV6BZzttY5ppHauGjGbJKcj2Pt5ZaNakFE9umHgUqh1Py9cDM+sdm1reHu/Jv/9X9/s73of/uf/7VrmXh4es98OvH0/A5jpfuV5bKwLSvnh2dKnbFc+EZ8frOO5f7e20ZrS4yLg9BiPFxwc1p7RXBmncCNbhveO94bkgdhyeNENcZlu3zFWqcvcS+31nBRnMI0z9R5jjmoynR6pEwTD+8/AML18sK6LHz9/ImKMkmNe1+V5fpKaysPj4+UqVK1YO5cLhesG20zShGmqrjHvBDXOOckjpTNGyLCNJ1QVbRU3Il1JAZicRZrgRL7c9ECAs1j9isFOOa3eydckMLWV9yN0/R4nP8ueC957sb8bN04nR+Y5hOTFooo7hUQ/uv/5r/9k/PoR52c122FdAJUClVnioKKM82VMhWKh1OBKe7OwkYzoIOKUItSVCgqSE4IzZUxfA+zHvuJx+9NY7NRVUopFFVUSwzcGCjCuQjHQGJyxauRD6Dlwpd8L/UfjIGE0xSHRKFUpZRYkOvW43AUQ7QwFcHd2dYGKCoFN8PdKKWiquFz7IeSUGrBusC6YmL00pHJKBOYgbmgEp/Lablg4hoFBQ+H0d3jY+Vh29TCNSrEYa/g3WhLww16EzaNsd+0UEUp1Sha0tl6YwBPJwRFfEY1Fm03wdY8bMzTc648fvxAd2e5LvG3BjDx+FDjXrvjqrjo8AoRdcTIsYtZ4DgtN/GOsbSVZblQa0VLoVOAwrrFfLEe92TboJowU8Anipwo04yUwrq9gq0gF5AOqnFQYZgb5s5mHUPSCQ3nFXNWM6obUgruxrq1OLNQaqloLfQtrmOsheHXulfcndY3VKFOngddjU2rKPP8gGqlWVwHPbZm1HODFhzF7AgiSq9xiPaZQkGIzbZusVGbQbs2Xr5c6FvDWsdtA4QiHZG3m0e2dkSdTgQtRU9gLc5hN4Se9504rM1R8zjscw2FMxR/62b5fYz11ls6PbE5qeZnyzVuWAQkInFYueEWDlApHk5L3jez+Oo3exrpt1rOz5ipjkmscaPvztnuEnk6EblHjN+5NTDBZDgZ8WoCeYBK3Cs3+uaoGCo1r8Hw7lhzVIVSNMbo/yXu/ZskR3LlQAcimFk9s/skff8veHaSSfaknelKMgDcH+4IZs3bnZXpbuvYVlPTnT9IBiMAh8OBSNAemq6sChVte3SNFbQhY8Cs4FYE5+jAUtdsvEPb5rZBZyCjYEkHGhd6uX7bEXBkGn4/C6sSsAUbBZ8DZh8YxwPXGTg/fxfAMKQbygwwx/CBXz7+CzJPRHwizkCcAcsTqMTxfAIAPtNQGfDQIoYxgIXzOSPhPjHMkHGiEvD8gKPwmIaohNkJ+ICNAz4nbE4+SwfG8eDPeMIMeDwuVBV8HqgyRA3aCBuI+sQKIEKkw5gwFH1pgcAmaEzHMPh80P/AERftwPQHYMDMCYNj2ERlIhTklxmwCnT8BHLzOAA3WJbsWc9916wwmA+YTZjm2AjAjD7B0FiBtsvM4H5gwGCZAObtB/7k+FOQAzB6YAChhWRcGGY3UrECnRQKlhqeYXAjknSDFg+46hv+a+ErKGCE4AYbPMcYRGod0QOKUOq+L9OfarNRjRS1qDLpcHpQjdcFIWJGH7bZHhpEASYjgq8iYyOfCoAPhCZLMMtKYIrfNwCgyHS5D9gw+GRk42NgWMKqMHzwemTcqtrEEeRUkTGA00GaO0YbtNGAqJCK0qJS98AINZIRYsmIrzihq/u2w3zA4AKufFoVQUOsUcQ4NKMJRo9jIiJxZuxRxx5fGh+NWJtYOqSkMSaDN1BIhO7dmkJE3c+5+nt5ip47qd/Wk7SDaAPc2zFoLgtkowxIsgWYA+43sZKVjAKvi85nkR11DhBMz6vndoHXwKBArBBSDGIR2BkY3TtQxtez6MSbgXAiQ65R6LVB9s8JPXmNVgoc+uyBiAUgBdYDsMIVJyISw+NmSL/hqM0a8N5qNevFGbQds9iaEqDJhOYEn1dtoMP3tPFtINSGKfO2S1XF9zsNdFUhm3rV87+hxn3+ey2/vxfYMGaveez3fzXYbZP4fPsECoyRkbf12zbMty0tzd0E7UCb7LZjPcd8X/l9PsD2WkC9X1tt8NXWF+/z4O1/23YW7rc0uNvzuYPSbzqOx49GY6i4cL1+g42Ar9hsXyWRbyYZCYypQCEBm1jlMAv4MGByHRHwBgNPAGN0zNXWnECg4MgKsqn6enQyQJmCMoOlYRQZDAzARgcsZCXh8l9v92YGzOFAmgLuPeG2Dzc32HT+fQ6upUt2uAxWY/s+A32xV/Fa3fY1lyuLYF+m9pudArIcls65ZgBcQXw12DFYiSEEbj9YpXnB++95DDj9LEdRTGSh8s/t0J+CHDe+bE1bouDumMfE5vChX4OLvB/MVFQw5xAmzB2dVg8ULa9oYMDErPjTMcfAHJOOKN8iy5V7kcMAI64TIMGmfjMLay1UJlkWgRt3h88hys3v1Ac4IUlxExCVDVQyVXRdxcjVJxcAKQZd9/2Qypia4HBNuBuyHmSMjoFjPvCYEyM5CaaoaE9G+LHRbo8uwRQqMY8DPgY8Rfv2aSKxClhwGZZApiENWGAUGhlwN3yeQPT1fdPhk/d4+ADkpPIKrHMhh6FkLGwQfrkZnscPXCvwiv9F41Jk98bQ84YjciFBZ2tlyLWQGQgxg2MeWGvhOl+o0jzgiKGU2oLx+3xgLy6mNYKvJxjlFJ3EGHq+lXidTCkOsVNVxdREBg4YcCilO4DreqFW4udvv6Eq8Tp/6jk7HkVqP5bYBTnWjpLmNLmDBMrgAc5ZS5Q70oFVAROw5w0UvJ0raOQKifICxoSNgXEcpJSHQA3I3JYDay2c5++AB56/PlAWCFz4+dvf8Hr9hM/ntzI50HMbSFgU8pUak0JaoEzsTBUiSimD2IAkBVzJ4NCW0HHVBoUb5Ci6NTDVU1GolcA02LiDHoDpCa+ek3otYtuoL0Anq+GYXtN7kvPmPuz2GjBk2QbZwJ2aQ7bdAXxMYNKzMo4k27t06utad7pft8lroTNxuMbAAJtapp3Sy33NDaAa1rWj7Xt8xzslu2PuGNZpq8JCICqR+H4q59d/+8+oDFyfP5Hxws/P/wHzhI0EjsmfBCqNYMIcx/iBQmK9/obEBGKRPT0G5hywciQMWYGMhYrAOHKD523M01HTEJHIlfSr5jcrOGvjYRQw3Rh8jEANrstMBkc2jSBFH6hKuAEfj4NM3VWN5QhoEiQOjoHxPDgFcgIWqNdFL5NkeCs7o+HwSWYFo0GZvQVEUPBUt08GEGIRkfSZNTqNrmArOQGt51zmzkqQJS2kpwgPaE4nCg4YsxHTDREXIrHT0v/o+FOQY4Nsghd1I25K6xwutoM/aaLnURiDF1eKyoc5HMA032C10y5zKvqoEiCasOGww/fDuaTZSAPcCwNaxISW0gVxFkUSJDB4VVSnSMQBMiGi6qutg/EauJQbPYIPQTl7A76wBzKPGiRjFG0Fm4ZRY+f1DY6sqajeMA/H9InpkzeUjuN48P1hjKqappNeieCMyHU+HhhzAnER5AgkZgRiDVieOK8LUYnhE8edF0OnCiMDFt8bPXVEwVRMIuJFsIum9gvzaXAMeKuU5mRq7fmBWAvrfAFQlCOwGouOA15cDG565iGwZ6COwEjDbkZQYNdpdAAZfLcNiKuAcy389vrEA0/moGHYoZoBYzabJPasw+xi/h4LGA/OOW83KKatGUlkwGzB6kVmQmGvmT5hXIeWyYgPBhQNqyXAjxTCpDPTsx1uZIismS4XGE+4DVgN5OI121EoNxzDYc65PH0A/gOZgSOXxrHw+fNBA+bfx+IAQNRCpSOSzrLMG/4TJGzWQAAm3xkL2zbhPTDbLN7gOO4XwDXl3jqYYgCKm+Hjw2xtDdmKyEDmUoq5o1rID8luyIm1UW72tg8+ftmkIjNnYmZ9s+eNXF2BURJ8CRuZt/6BkTpgSjEpGEw6qT43Sak7/VhI4axmaWiDqLuYmDvQ+HoUxILco4wy7LnfWpzsZWItMfi+I+yCeeF4HMhVuNKwcuF6vTDyAV8Bl7aEGaqiVAHOFHcU4vwdGA6LgWkTbhOFC4BsaxJwkNjVLCzaCjL+JfZU49xsLDoFr0B39ANVYN/ssYCCVTNh7Y+4Rm3Q79LPJeakzSBmKsT1EklSGK7VkQyqzGiHrcGZvt2/old8YdCBjcvdHCbmawymWdvbuDIjiQbseTPnSf2Pp+ZgD5ve7BiaN4EMQyCxLqbfz4gbhP+d48/TVU7MPktY35wCysORYcigsSkzGmIDvIrOyrR4ozBQOAw4pmNMGW43PJ8Dvuk9x4+PDy5CB9YK6l/OQFaIETGMXnQNWHQLVcC6EtGpBrwbOQ6sG8FGP54GOGamvHjS4gmtkq7TExEvV3lPLEgQlcpPm7Q5dC6mh2o4qvVJin4KclSFx3xguAPLb+GgGeaQw3XHdOBww3w2yHmhxCQBpTTICceFz0/HFYHpE4cfFF0uibyqsGKh/o6B+lceBLKGvICViXO94DYxfDLqSMBjYB6T2hAz2EER7PMXYJ0v5HVutg5DtKvYCWoyi1qdMgTe9BVWEkVy8TdPtlk84c3Wdk8f2868rgv1+Tt+mOHxkDfU/CkDxkFgmiltkWaaAYrmC/78gG+BHAA5roRSKQuoPFHBtOqdMzcxh9gCZCwDymEYQDKNi0WgWNUuP5UPf+yo2m3AbcKQMEulDA3rvBhFWWBMx3M+OCZOIDR/PAEJbN0K04HPzx906Fg65/cckQtlhrW4LtruFJyRcZLpIsixxvZozcgbzCDtnw0U9JrEtUqmbi2PAUgrRtEgS7ehUoN3kGk848XUGO7U873S3uxSO/n395lvsXqnFTIaWCWQwICKH4zAZQdnmYi6Has5RZ9bYgDgGPMOKizpgJNi/8C7/sFREYDZDkIZdPEat3hdN5KKolsO1zol9H+Nz8CqmpDeesSE616+7wh7wc3xy+MHwgsZhtfnwu+v33FcC8d4YP4yKCPwgg/D43BUAhcGMhbi+o22Zg7UeOAYE8AFIIGwrQ+HA4gb8FolyqiRIiuSsl/FFJMPOf0FFFOQ7e1NWiraKe+8GjrFXAiU9DoOw5guDj8xn/zu4QarRJwng7ThKNd1RSBWUENrBwyBL6lE+TTIj0BX1VKSHZDDMenodqbh9muaE97wv0F2aa4DXnN/kIEDmXK3CRRZ+rgu1DKsizqjV8WfJj3/HOQoou1INbLgARxKH7UwEtLlGCCtShE1Km+obASOOVRlwkX8OKbeU4rOB8pAoJJc5IR3HMjB/+x/YyorWCG1SpGUQjJnBOzehE0zLsbzd4pK0ZUrek5gO6P3yiyz1icpYkZKmCbE7oUubmlab0xVahRpWhdDVFDqzEyVE4YjiNDpkJgSodOj6PkhqtHHoBg3BaiqENfCmmRy5higfMLhNbAqEFlYwWvO77UpAIA4CcSunwtRJ676CbMDZg/UJarWDxxXkg52x1ykuY8x4PMBfPzCaHqInm2hNgxd/2AmJw4+Z4r2gtIoVZ01qN2MDRJe/JYyQ0h4zueYQAbTAc75g2BqIa+u0tO5dCWtTTskcAbIAGIMgjKJxCMWchXWWZiT2qE2FMeDIL5MdEQMRb50tCsDJDBdEZkYzDRS7wVcquib45DBSUQtZC6yjTDNYYpvcxVe+ZOGdXLORjosA5YX9R/Gqgp/HG/sxPcc60qui1rIQYNNTVWpaKdIjkKM4Rubw/Qjx20/KVW0NZcw4UiBJvS6L9zpQzeKkyMwx5C9IDhYQdEnhbWJWHFfeL2BHaPR5j+3ZwCaMHE00IJiqGbgGtDw6t1bF3RtIAFwDGooiGvmZl0NfXU/a+uSYNQJVlCg7zuldMd1bsDhU2xDbmlBQ7RdCLNTW1/nRMKkfzqlWwPlBJUoi//w/n/18X/91/+Kwyb+Ov+C6Ybn/MCvPyaez19RuZhqfrDYIJJBwO9/+58wH3h+/KBfiGPbEGQg46LtN6d2pgrAkCxOYCSz5XPUc2XC4PsZa9rtIKwqEHEiUulXK2UzprIKrSsTi9FsYOvrTH4YbcsKERdQjvnx4DpHAUb/stJwZbPLACZZma1vbIZFOkR7y1b0XHC4dK6ADARfCemyspnNZn+alcJmgvYFlHwYIPH/QsZCrgWfBgynnGQYAeL/abqqWdktewzDjNbIEJGT7XGYFnImc4EkQIyVS4PMxJyDQOfof5t33g1c5BQStS6BJqjBUpeSoUSByQhlAiuDVRUbeVH8TA+3WVcCldFZ5TuXSNbEVG3Rk+Z2oxvI2Ztx13eW1S0Qc6WaHBiP/QbsaNLoCH0QsLhSGTP4jsMP6p6mb93QYzg+NiAEcAWQgTEYUYWfWO6oeMHgOM8k/R6GimTZqpWqN/dK+rZjXSzb/fn5E1kXwl53lHAlLBJjvlBhOAUen0jMOfDrLz/g40A9CmWBtIUIVr7dN6Joxwh2iGkEjoPPpUIMoFKr7Tj6O4Yx7ZoNuvUDRVruWotWKA4/Imqzkh3V7/LQeeAYlIhXgaWVm0VIRoSrEIsuqMfDzGDzoGgZvaaotWLQQfVMp1FLnw6lv7xYaRa1YDUAPNC0dVYgFCUWBprebgd1rQtwg9dEpGEth9XCqIWKZjYHfM495t91ZBRgvHeHScQoQ9qaG8ipdoVaCgRBupyotwCTDreZFtd4dkZwMzqRKFXXRAZinXB7ikkGgEKseCtNT6ykINjNqNGqvIFK3qPWbNDOQsGwrZeA956nO13ANh5kQ6QLbG1UIx6hdPMunLifVpfvajrBhiMqWArW4B62T2dmSl22cxO7oB/rwC3fIvwGXWggqoBRtpDaqQQs7tv6puN//Pd/J6v1BH75eOKX57/hOH7A3XBdf8NaP3E8HvD5xHkyeHj9/A1jHvjxX/4zWfg1BXKBdf6OFRcgu72BW92gVFHE9gMmjaGBRRaKfve4wRxZwCKBh5W4JRFDARfYZiSVZ22ASV/KtdKPqosUKheyHMN/sFq5gkG/G6IDhA1UhTr6WSt7lQ1I2oZqbplQPH2lZnjjFbVQ4NhokukN7X93BrbePlsqeqoiOF/BggMRDmTU/WaI/sHxpyDnOhcMhWlEncMnqhKvqytTDCNdhnKhvDZAMCeyG274mI4fx8TjeeA4hrQRhnkMLkQxRrGEnKJgyWLxKcPeBfsNQq7FFEEsiY2X0gD9FLYLoLNAkaZFOVYs5RiH6DY5oref9sEUuvZDEdgxR5VvwzHhGOUYqSqickYtlwxONaVXROIYpJ7hmJCxnMx7fswn6WY54zGnmByWFWYmHA+YJ+bg4/MYGDiAH8C0F+oyXGfgOhPhF2IkHj5QcMxZ3y2nwO+fFLT6MSm+zAmrAUuWw18WiPgd/noxF+6Oen1g5sQchNjnusjOOVMGXWttELMS6iuilWUAbFD39ZwTCZZAu3Ocj4NtCV7rwrs41JhLvIWqYOlxVIrADfhIkFDyHUXd/kVAYAzYcFU9kfaGAE8V5wIsUVgE5yuUWgFsTIyYeDwmHdiumJPzdKc7zBNpAxTf8/zV+WmbgBeuemFMx/SDKcwx0HVVh7Ri1oCq2OujjIwtulISbCNRVvjlxxPHYXidebOm33HIaY95UDQ9Jtd/9HPZ6ofbaei5ZNUucmhQyDjX1L6CmhYAEiLnZqQ5pgKgWfAyspKVmKpujEutCipU+hrYI6e0vQs4jDF0HjHCAhxbQ1QdRPWVguJwsKoODXyLaV5zU/8xWvqKUtuEQJohri4DP3U+CqqH9EaVEk/jrtTpnjfuBGcLS/ZQ66rDvB0v1RbEtnbDIYAkO9vRPuSorVwO7Xsjrsf6gYcf+OXjF3gF/v2//9/4+PELfv31r/DBdJzpfo9jYJTD8KSk4cWigVxkzI9jIvMTEZ8YA0BNVJwACnM8qYsZjjKy06k+OXCxPkVGkdqb2hV0BmlT/AGfCRuBawVWBNl/NyAX7V520Uz7zkUGbhDOlzumMytQmj9dlYRixeTxnDt9Oac0oEZm2ae0MqU2L6asy1TwXjdThLo4DwDeRBDEUAHSYK61qlTA+mhQLp+db2ugiqAGBqSq0ixIJkyDyQ/EGbtg5+8dfwpySFEX0lLIVZFkJLqg03uaq5zzLqtlHnGYYzjwGI7HpCK9Acsh9iTBktyo2iyRlQkXulJESv2ot0VcuXN5jNJa5/BV4d9G4f5bNTBFwxST16ehqI0qdzSFGypKI97PV5S33YLZ0u/mznWYcqUOx8BQWsrvzznZpGMemwrvnhSHK0vXhq0/Z3JuDtgwPOYH6gCe85NCZqeWyQ0QNSCh2fce16KgbQ49zxw9qqRhvRBxAml0wuXUqmBiTRdTETuyLZUAt6UtNTtpNpGURgpUsl/QCmqnAK6n1r+08K01Be9RQS/qbNADCUEdaoqptIEEw4WOcHwv3NZd7BYFkTvi7YiLYuzaa4rpDgPyII3dOgdpN9yU2KgU8N+zVM7zBn3pC5bsszPcNmg3GObRGjJeywBp8HZYtD+ate6INAYpfiAWXfl3HabgwlwtGcwBSMCN2oCm2ksAb2v0bbz73/VPLLkP9TXCW2n5mwMugQHZpVTqISUOj2wBpTQ1HcK+BVpb42cNE1qovu/wvqgv/3zzPi2BKrvv1+4wWmCJr4WcVIbmw+4jlBg7Vas5hea3JCW1TpHpE/vE1SdBD/LWFnUe3N4K0u22rX2BOx3XTNE3WyOviVGq3F0Lr8/fmC6uv6h1ydBNBXw+aO9DFbXrZBppnRQbl6FqgfpIsqsdfPksZqFcYEPPN6JuG5Ea465a8js8T1MvGqMGLDKB1a+LHUswXaXnBNw2sMe47QXc31gfzt9S8YoPZlbe+8TtpElrUuNtDVkHaWAueL8iYc2m8kogaGwf1R61lGLa4EdzOHsd7JYKnGtc10yvs28U310AU/9fqhO/Hn8Kco7HA6iE58WJGxc1AwWiU5CVqKJ4qwSGphkec6ijI5HjiRdmFHAVG5YBcGNzuCW29LUkoouUjoTUb+RdNt4pl51zV2ScnU8sCQGB7Vj6ibsiludkN91hFPJ1GaSB0XVaKj8uo2fvA3gbnX4+LNUNmKk3SucrFd6XJUGbDTVzIlUrIh1ZrGxhSuQCjKk8Rvp0JlaGK5WmMbFfSdRux6Ci3h2zHMfTkTEQL3bctFGtSb0n5zcezB8z/eE28By/bIf0SGCk4efnT1yxYM8PmDter7/BzPDb3x54PJ74y1//EzIuxLV2lLwUkaQtlCd8GjINGJwjsS4YHI/nL8j8DVgXVgQiCmO8qHtRRGJJ5mOYTLHo3rTAGS/klWLwyLSw54/6pQTBy4oiw+Ps8pmReF0nCoW//vIrzICf10+sSEQMmA88ng+slbhWqCu4oplVuEA6P7E4X+dDwPdA5MLKJaek/P4ecYfXZO+dHMhgY0yziWHOFEwUzvMnqhJzMNI7plhFC6DItLb2LlexMRgM6Y6aypl803Go0KDKFbAyNVRQuiiC12UOeGzwykCo+2m8B2AAV/xiM8elKDdVoacOxN4At3vroKgtMAZ7AOcINYtrOyEu/hAr66qmAbZoWIFeAwi3IT1govsmQfoXywaiDLESbeuM0RMmhbwRgDgqRtS2I2v3vndWanU6DgrI4aNfv+oAACAASURBVAYbE+ZjC6kzWusmUNVpuF6BZuzUXCpsAKCsMIMpK7KT1WznboSAAts2fHfAtfx3GCZ+vg4MTzz+MpHPwm/2O57HE4/jg0Em2DiwKmknsvWMDhsPQHpM2IDjEGvRKWLgOi8BiEFQGovtUfyJBnsdBHclcslfOB4M7OuE1YAjxeA4MgdW2SYgCi/4cBz+kK9QhZSpm1rPkTHwfD5vWQYS13kiYql1Bf2az4nx8YHWrNrQvFG+PuqiznYMsvJQ24HUlG+KT34Vulve2ZvA3Q2Wrl496u+GUosDQ+vYpqvAAhPXFVgXZ8+AIa+FuBKvnxfW+j8EOV8YkUZlxccwFFUBNPxLXRznpChvZ91kIFaWeo+wiihRyFTHxSAjwv4VtYVxnTLgGqkNaG5BF3CXdb9FP2/n/mPDMi5SsSit87Fmi+5v6WjkboKVXwykaYAEhQRwa/8AUL5SkZL6BKCjPIWZRKgbv4NttVvMxUjT0hDW20gk242LZTAHxs6xNQK3vVBdVXFfI9rvPTp6Y2dj7H5CaYZhrMQ73bA72xcNRgJ4JSnzjIWshYiLKT+Tud9VRaVouU8qpsMM3tVY7ncfBjE/O4raIlT7ct10GIEIVyqLz6uF6Hd1iOmcvJTWPjVA7+h4z3FFNJ0ugmjgu4HX/V1RAXfggMvRiysoanD2bG3205rZEWDyDgJ0R0r5skliwEBDkx29e0lsjbvct1kCb7vZiazvOVoUCyhVE28MzltJfrMTAL68fk/9bYnf/l9sRTPJ1eu+Gb63FgHAZg6pddP3WO1vvc/Ua/+rmBfoQKjuqJf/iI062snY/jLN+W199vfs0u/bOO40xg4O39msnp8tqOhz7/NXj94en90YkG9+Gz6Ok/UcsTfg1JBGkXl7wrq5nu9HOd7eWLbymLABxFaeylZbB6lqNQEI5GKPWT/f3R37zX6kKvPMjXqmwGYjd3UuTL/s7XsbRvLc1M2JtRdALj0/M6aYeJ5Wmpk+5/fj1bNtHSjBfG15RxMEpnXtcwqwyL4JYJc3C8V7qRvV9EzfywHAu/SGf3/zx5zbttdHAz7TpPfi6y47QzbZthYICkRiLazFwPUfHX8Kcs7zd6IpGAYcZgcwCjgKf/l44tfHL4iL/Ur+22+/YxXw68cPTAeQFxYS5wqMBRyXwX4UBaSKKl6nenfUwQqIAFZSbxNJnc0dm+BmbLJUgthL0PbDbDFpI0HTaLcm5u3bOP5Fen4by+6Wug27orXghB1vlFt3qsxMlBmOYaA+gjn0nhC7hLL6fLG1J1tQrcqvC0F68dBDj1QEYFixOClNefgpNgmMJPP6RK4F1ER3Gi1PYASuRRBxL7DvPoo5fid1bvPAOD7w4QemAx9//QWrEnG+kJX4Uex4/Hp9AucLv/2P/waAacI5D4wxsJTXHWp8VxWoXJihpm8rUH7hjBfmAI5fnzh/Mh10dLqwmz+3vsTozCMWTXQlHJNsjVIaLO022EoggHixp8qPx0HxsgHpYNv9wY7fx/ODc/H3v8FgmNvQLxyTWzt0KtadpaCPx0QC+P233zC88OODAs/z9aJGqJItCAbbBqAKvzx/0RYqCSTz+CNZaUfSqeA14BBlD8fTDxqXteicpqEEvgp2b09QUDXHwpwf39o3ez4eKIHGjGArBC3wBAiaIWeuao6q7suhxmXWwcNXWt2Mc6LEMDeTYyaA2P1IZIAzesuENsS8kGFDQCkVySrFZmphYKqO2emDAtaSrSNr5+NO57eXSHnA2V3kzcWsBMYYmMfxxVmhcgc5771vDMYeXZ0yy1KqTnvh1bsTb/TBFDxH0r/goCxQH2GGWdzL6FJ/v4TKpEeLR+/7wf7670Y4wC8/HnjMJ/7t17/CjFVfDMoN1++/44y/4fHrL5iPB1PnZpjPv6AqceVPxDqxPl/IckSRzR3PiQ66JV1nYAXhpbK7u3AD7iqCCQxVMLEh3tZLFdgyAgYkNX1TKUUYYOp9Vikg3uncMQEkMk+weZ7e747q8l8xnrEKcSXWp547JsZ44PH4sbVqaSqNN1WHDQk96r0BnzInvjsiAZDso+2l3cAdqJ0O2707wO9z9SeCwMz0gUxHhsPHA08zzEHN0Dp/4vXzxKlS8n90/JPqqqaXGC0MH5heOIaicVHAVcU6fBSOwf04mFMLoIIaGiRp+RGc9G4IEzwppmx6b5mVKVFVbgCz44oNQGqjYB62103tv3115vV2Xzc71BHhW+t3RcdmuN8HoDuWUjNzc619LVlD+2O14vsPkVpfs4xgqaTOpO3oo7UfQnVytq4S+RSKBuk+GI14dUlh5/7vCVeCNgEi/6/pt3/9UaX4ZAwJ2nj+LO7zVHptqAlVVkhbFViXohpFUe3QSuCSben7PAno/on9awvs3CeGD1ahdSQH7Kjg7vli+5r33AgS7bQv7LHDyKfu6LcRdTND4NrVbCCjgjsadh+o7u+wp7Bp/mpe7oBZ6yC7e3WKJq7bWWezntRtoXVCVSj1HhrujCgHRdeGAwA31qO4uC/YdpUYOlUjhqCv/2t67F9/kH5vK2CbFdRFCtS8swT8tbOzO4qOvb7xzou8MxXFmcY50HauK6Swv7vHF91J226gcAdYtj/XkbLJ+XgKCH1hWG/dTp+m4+SOoNteFKgLa2aU00ffraGx8j1mHSXfzQb3GWkrSuyEkE69XVMH3Dw/7iINAceutGFXix4c2us7ppTdtTs4/W6g40at2ej0p1p/uGOnX3pGZYb0kgxCN0vu3VgPYhiw934q+4Pf2eCnnbw0KW2jzWF52yOYsXChtZcGMvbg+drYKS7edupLqxONcw9t6176vN1hOyP3Xnnshq0eXWOgi3Wo/7lv55aAGKzZy70uNK++zpw/HPT3m7Uu+bL9/pu2uH143XPq7YeMvFpg/IlL+yd7V/EmD5+YfuA5PvDLw/DXD8fPs/DvP39iLW5COJ8fbOg3fsE0g40PnHHSQEYCK/BpgUjgMbkpZs0CELiuhSpg1UBk4vM67xJ1TYYbHzQYEfq0weqATslA/R5cAtVqkMD3wtjYKTKwlL8crmaAu8RBG6kCuHpAVXFx5sIcE88HFfcOVmhEJmJxgrnKMOkzTGk90Z/azyqDgrLjsB2hbgNihoVThjeAcFg6ohaiAgc6JagZr9z5bJTnTCWOcaDCcGViAVgovD4/WTL6nUeBfSYef2HTxAq84hM/X/8L52R+148fsHHg8YOL53Uu2Fp4psrCHVixcK4T3Rk6zosaJUVNS7hwzkkBrsZrReAYzLenJ04rIMEmgEX6NpOqPoJ3Q284W1VYSFQuHION/Y6DDjcS7AS7BtIKJwLTHzjGA+t1oVZSl2bA+uxeKgRTx+OBSJaRM46R0XMg1olEID4uANRTGALX+h0JMivdPbbxVQXn6PliZ+i1dz8sdqmdPwAMPMZglPp4wOwDZmCnYxTOpA7MB8EyorBy4Xz9hEnvQWPkcp7fdxzjoDGvN6O7sUrDX0a69zYgfE4ttB0+mTbf1HZXJrHrEop6GxobalpQjoqFWBc34hWVv0+pddhGnoxSbX2Co/fKA9iYUuxtiemDxKJt5ZMBoI+h7UnuDYanPltGHtDmcTsyOYQ5tQluXWIO6EiE6Hdk3Q40QXbJfUhwrs/wS9FtO2jHilsDCFiZQddYGDXvkKqSPXyq7i0uim0Nsu6SfNjxB2Hyv/4YsTDGQQ3kHLD5YCWwFexBfVGq9cN1/gQq2VgTADIwMPB4/BWFQNZFlt0cWb2FEMeg5+DddBJQ9QmsDKMc7gdZPnCeQs9lzgMRgRFBGcVgd3KLS0DHdnFKLmY1uBknMNvpZ3LfKTO1GikAAwjD+fOFFReuz2ClqvR+P375wOPHD/hjsvAjApa7g6rWkgoXbOgZYs+rnoydY3lLYIHgRjrUBHwcGD73y2HNIl7oaLas+CwqKVVYF2KdGI8DY948cqUaJf6D409BzgBUyixjXi0gBQfWB1adFJ9dwEjHywdiOOZkZJjK/2cWLErPuVCuElQzGvoC0xXJZlq84ZtqbcP2dYPOexA50Uip7pt/izx7nN9hKZmq0qJudKj3lMs2dKZWr0l0mBEYUJmNokX2P1GqwN6j4TbAbUBvwWblHaV3tIlu1rUzS7XHkKyMTig9iJV2nN6CaxetrYjEKPZekbiU8vrOw9UgKimhxpyOmWP3BDJ3CfsunLE202Mg2AQ6Kws5BVbEWAW8epPCzqFjf7abWXGGEEqMYTjm3XOoOmLZmils43SXluNmWIysWhvwfKs6eO91M8ZkTYUTivb+SYY3w6DKqK7CaMdok1VzrqaVx5pvTAHeWAQJ9eUMC6VNNUHwV0CXxPtgemtlYGZgVKCFgJsQkjfqlHBF7J2oed23oN68lbTfc9gwsWFAL7jq+d3rvM1BsykyvNsiaO11KT7/Kd+YKqHk92ixy7aVlnKHhKJvYKt5+bZKHVUD2H1p2hQ0mEIDDrsbs/WHdth+f19rurpShvPxXse0q91AVEC9fF/n3XX27saeHU0LREGswK0x6XPrPaKH3lkCF60zbAJlcOnk6C6SvbqqGeZOF76N3xcW619/PI7J0v91CbRNjQd0Uxzb3uG94eNm4qqfr2xOM2gyHzejot9vGbrSPOmtVrpfVzdq5HNoIyDu3QR6nQxy64Wg9d6tJd6+gEw3Gny3wNyAog261ol1XVixZGO5XZPPYwfk2xcp8Np7cEUDmtrX9kczsNlerZN+vZ98z7etcSx2X+4uzvwOfaJ7YCmI66abjltactO1f//4U5Az3XczP6tCrRNnU6PzB8Z84vz5N1aMvJQH/vGJx2Pgx1+eiEqc+WZEQjznYhVL4EIbzSzglQu9wSY3Y5ztNjZ1vPUBWijdVjqTYsmbucmdO+9dxytvurvhYn8/wM8TmJV6hwDdQVeSL9lBphmshiI7TtoM5vWtRGvOKYpY+Wk4HWiXPAOo5SiXVsK4exMMdyqiH/hbVNAgp+qt+R0KNnI7I4JTOuWFwGtdOM/AeZ1vqZnvOeZBqnTFC3YMzI8n3D4w7UD3Pvrtt7/hPE9crxcMwF/+7Ve4A6Nz+pJRTCtAnUk9Aw72Y4JRCcVh4/25q18RAKYpLhwPbh8BdfLsvslj6zUWX4uFlpqWARimcs7CdXEuXZ8X5wr6eSmCccPj+QGHo5xzcH1K5+EDu5EVDAilHJSzJwPBjqSPjykD9+RLy1nFIWaykuxW4i4QPhcjoVC1gbtjIDFqYaXhDIMv415HXRJRDziMfV7AwSb7uljOXoYSUPXU/lfjwLcKj6cKFi5VOzqfcyIRsh8uB1Ha68fE8EGBFnJt4NaIOGJhad1CbG29LbxOBcw5MLXhqiuNya5COkFb2bbrAjBVDC4G2Ddp7wYuoOJDjU29QY6hvLT1Qu2UkLtrq4EBP7g30NVRULIPj7tLtwOkbI0Ztwr4fLFCdvR3ydtk2bZRpRTomNofbPc8IbjjNgPYBQKzwbk7ZlccaTNbiNkYF7vQr3VxHtUu1dAz+RdOmr9z/PrrD3g54vNvcDxw/OWXzWzVtvSlQJXzuzfKBPA1kCrcBEDvOq57c7sb5PKQILyUvjYVRHSKugB2cDWkOQoLZSfgg6n2mnfrgCrEJX3q6pWvEnbr9KTfP/IDJbnDz5+/Y10nlvr9PH95YD4O+ONB1ggAlA0xP9DVc9W/O6Azg03b4Lhv3gSCutijIylWcN0sFJlhXvOcEwPAOs+b/aqiHQ720OuGk+t8IZA4LxEsht1Q+O8df94np8gmrAiMAgZYDsd+IzSmrsgYai/9c104K3D2Q100KA4KtSxNec9Gq3eEtjUyJUce3FWlhJwLd7UHZxl2VRTTTfqO/tPfZ+3oOBGZrqmtgFfegkYz33QNOzd+o2/onZHMd5hKuvlc+zcf5q1fgCpTaJA2kSd0XckKqu6G+96Jtx82cQ3p1D4XxcrYZai7GWOohb/uc0ccDszpeF9633GMwX2oUq0BznUirBCaF14E08cYyElR2xUveAJuAoTJlGL3aurdnjMT0xmNNdPVQlBG4ET6/VyYPn2LMt7KhwqJskXrNciurSRNXCjMxw9GrnqujAKVjoWJemb1U+99xDb4A6/1iYhAmaohMPaE7PbuEakdjJ+YNmBpuhTtmTUHTO0Hetd0a9r6va9Eie0rsKzYipvaXdxhh8JkYEg02g0LyaAmYOqkC2p35qhd2cByU+2Y+51HCzn2o2VTRx9Dee1bt5DoMYk7wstkFUbGDn6qCmuJ2VTk2Fs1fHHEfdrqNFTrYboqhnPMTc0fNyuAOzgZSlnvy8+dgkIWXm0V3r6/2Z4xB9kVLwLtog1NReI8PwXNtFtdBdNsieGYBwxiGK3F0reux4xNB8t6LXTFGYf+blJQ0pr5vh+H1ukOzqQ1EEAz02aNW2YY25Z/N5Mzh+1nmHnh/PkbfEzMoys9+skxjUg91rveqV/jurRme/dtvNl23IHP/Uw1jTdjU2hNC814KQBTvyYQfHI/umOzrOv6lISCKdZQo9PNkLhRezcH/YIZ1ikQrwCP+5ANzPnQvOg0pPQ2Bmx1vzFgTDUeLaGKznRo4e0xgPUYYPsvtB+yZiI5BiYAcAcM0qwW9zpUf2D0GocE/cdzIj1wvf5clPNPQU5V4szCYYbpMh5WCJzMSU7D0x9AXIhI/HaeqLNgP2lMH5O71h6TICezYSv7xZii2fdbBGyLlVMD3Os/3hAOJ4xKzXPdDE+LBW//RX/WtCvWHnyW2ak5V2tyoOZL1o4LuLlKXUdmC+nRO1rvkj39ZxtI5cSzqAFiz5zeRkIGcpdY6ry9ItCXyt2I7y6hN93LVBeQe0+xYM41r50iYaWD+u/gH0+If8Ux5gOV3BhuReL389wVSAcOTNBg+AEkLrZSv37CDHhozycIwDT9WplYwb43rs1MI9lj6BiTC0NiwI4sUIVc7AtBTYoDsd5ATiDBjSD9cOTJFGpdJzwWfvn1B7VkkSgDHsehvjvXnQbAQCUBC4qVYAbgvNiTYsxFgJW+S1Q79I8IrOvCx/GBaRO2OlVMRgfjQFQCYzUPpOhMHXezYLv1ggS2QT1PXMCVAVuLfZ0y8ZgTQ3tkJUAdUQXgi0Lw8aBeYxrGIG29KhFliGgj9U1Hgqm9JkxKLJU74N0Bm0PJjXHV06OAAXZYXevc4v21uD1ISVOyqzrapmt93X2U5YV6q4iiPgUw9sep3vm9RfF0jl1AYAD2LuKaiz1XkbfYl/eqVKocxTwEcpAK0uQAr4WGYO4TNrTXEAoPH+pjw3N3bzBrPQ3oDC3ayBCINBNaWW/9yRiEjRZYoxnQscGfa0uVXWoegmldwWjCqQmg8n7fNwdch/Y+tC4//l+/43g+YPixC2x2S3jZW9polnErKgbAQKUQqkTaXhq7AZ+1JuktMn+36WIArdOL7UBqaV4y11C4MPygxlItKF4/P7UxLSt7r3VhDFdfGYmpD4cdU4AlUedJW5HU1z0fE3MeOI4PCo6tq/dubLPbGVRv7UG9km3qRFIOscKbRbIbtHfmwNQCY+f2dmBpO+XcTJHp36646NdKDWTFDpkXHj8m7Ol42Yn6P+2TAzXPIh0eSFzIVdxfSF0Kp004nKh2GOwx1QRtydGo2y1E1fZNV2FMRaLoyKluoLKPe58qvEdI22Dc7+7NznqQGG3aPgcfLqn3fX/Q5EJHJzeQ4TnzZj7eTv2+nwx9cOpr1AHVTQp50wNXTtpuQ8FIXJeCZKdfsRV/LLckfazeBEGnZHu3PwKjEMjJ4qaU4xj48AfsAB2gGbhRwfeCHJbnAt27wUXgFYrMhBsiLu7nJH3JVGYTEi56P4tKWCa8Ckc3WquQSC5xEy2uEnHbND8r4hjdXxfb3IfYkOMxkTCs5PUgHUfPBRmFa10i0GgF3A+YmlfyOdE5HYPRExmVXuBd8cVrPo6DDeDS2JZhx+4tZL9QEXCRACzj5TH21Lh1WltPs6NPRpDeEo5Bd70CuNal1H6iMLCWhNtxcXyDe1VhkiWhXmoB1g3EAtfrBvHfceh2xCZrJ3UxB5GBK67tZHaPlt0WgCXia61dYFCp1PIGwc08cJS9wfS2MaYKtlsDkUo9NXXv876eNv1dSGADmsS5zRi/mdcwQHPUe5hbcRsdV5Wk6cZYGaOWF9FpMiXPjNt9WLHFIT/UDBJF20gKRt0cmewyT1LzdsI3W+mKsFtFc5fm92c28x5J0Dk1OQeLK67r0njf1YCcT/HVxn3TkVeAXekf1Lg8HDYcKy6GGwb48QA6hQyuE7qbzcPse+cG1M4gu3VPwO1TjBWjXX21Db6OvddiYTv/bjzJ3mDSv6rXV8FVXUowtmtl3oJ6gPdUGFKWd7T/9t4sbFG6949KzOWtuEdUt1+QZKQf2XaVdj9X/emB7PRdbhZHeiIp3pu5Yqm67VJ4ALsPFhvscleEfrk6AFmMbRD4083s/2l1VTMq7qFmbIUrYvd4GQd3BB7u8DJML4QFPs+LDqQoZMu+251CKVgyn9mmsmt+Or3CSXAzMTeJ8xV03H+5P9cPZotrAKYDyjBvEIp+8u+CsbqfDLqM/st3AnsyAF0yZ9sYpATavc8H6WO+Rn/mX6/dakdQUHPF/bLt2G//D1MRLaDUBDMxxMVoGwaM6XiMA16G12tR/KrPfuexrkAT2wZGjzaKjewUZWSy0R+p7MSjxZjrbVZUohDqOVSYWvQdSVlpshefhju7FpVNVtztKpjCCrJcZdws9ePgnk65xAg4t98AgOtiauyKSzaBfWaG9l2JuB3+nEDTzwYKGMnC0VUxVdB75DA9mhZbVQMYsliFyAxmpxZcfof7wRFYQ0Lo1lO0ERPzB0iIWgQ0Be1q7Fir1CMoEYrQ2RyQjCGp6rUBk9kF2MJ5LQrYX7lbC33HsUvvjU4lnMFMJkHFinUTlL14pClZ4NqPUDoOuTU4exm/fZjPSWkv3KAHSkHegZAYCa17H83g3CwFTRDBL5SuBto2YRs4rozcNgAK1MbeCBRkkbIQ1xtrvUETGbg2krHYKsJd+y71mtecJ2ZTSqoBl7UP0qCk2i2UoXe1xps9ZgCgiNwYkc+pvl9luM7CeqkzdXUlGXZT06/2+3uOvJgunmK0/OAzjbiQArxzOoYdW1xPfY6+4D0Fg9r9zmjH2lLrNQOsdyNvTcY+vn6PZrf8XL0BcenNQNBCckEAx32L79+Bjpn2y8K7uLv2lXUghBKYEci5Nxrm75tvaGCHW4bXgT7AeQmClBJDaTuV+7ay3oiBBnbcR6sb3ApYQZkZYAOjXolGB4tMApxceGNC//7xz0GOBiVjaTuGkvJcOd8EjhHaWZz0fByOZ2k/id4mHrUrNTo2MSv5e/0ZjdZsL6Z+gJ3i4cDdht+cLbdbuOsm3VCxLDCrHSzQewSd14Vuk73nm0C2a9KGNgD1HW2Ikn2vPNAAmbi9yj+MDW4tjAEqVcdGJF39ZAnYGEh3Ml9iyUylgqXB2EhZhjrfdv7NfjXZGMmrATobOXaE0JqN7zx6eR27854iSwAVFyIISsZwTNSu7DGUgps354L2C6JIK7X9X6dXgVSTtmMQEMzhGDm5b80shA38/jNw5UKqbwrLPx2/Pn/FWoHPk1to5JWwIIAPpQI/joHeKC+sABAcRAIrTxz2uA1PJjIS53oBWXg+D264OFyAPzAGcMyB65W48qIYPQ3AQLkhl/YSUmTN6MegTnWwMswJ1ARqafzWnX7d45WLzFMWPBOrJIz9ZLuCXGQRDx9IBFb8ZDosAmZMpUY5e1q9Ga/vOAJKM10nmTUxlq1NqYrNevTGAc3oWBcj4A6wXOLlDi57Yi0hN4e/zQuJJVVKHWlKAwPbepk0YdJYsVqOqQPTd9fKrcmbTzlRaVbYL8eA7JQBN0pk7c2AlavChEyxu+H4ePA+k9sHmBtyLZa876owroWRYFDqU2LtZgmA7F2q5cyeB1MDFH7l275xUDXfgq8EyndD3Fed8HSkl9KIB9wcH4/nbt6YEYjShrfR2sPvRTrP569gUvmJwoX4+RvnsRt8PjDHhCWQ68Ju5tj5tvYk5rAu43eXfScQzOsiaH0rcWa5NbYd78q53UJN6OPGIw6rAfOH5gDYfHM81duk8FgP2AQ+6yRe8AKmYz6fe10SdMiOmETf7Ue9sFqwPiZ8TIrquzltJdjjThshdzWlddqoGyAS3KL4byx24U21hqk3bbXOwQIKBAB297+lHrkIbub84FAlO9/7cF3jwHqdiOuC+4ExCmPkZpz+3vFPmgF+RX5XK4sE6swM12L0MI6Dk2eQPZnTEUaDejugVliP3Uxww0V7Z3DszYK2c3ubZM0KmFq+GdDJAPchA5QbAfZC6mgwIkhH7nI8TTwxOm6OgESr+5xgVVk37ttAgYi4qfM9cHqQXarp7ahLG/y5wEtyUjjVfUh3aW9sVw3x+3JHfU2kV/diAHit0dG67luMwk50MMn7/8Oh6NUbcSs6Br5GecaEYfa9oLSpW6GbAd6RRT+DN6btbdxtb01gqMFtUU37RQ0UPpXCRKaMAdunP8YBhGEUwSfHmPMtk9exm8Lta+HeSKHGja1xKQFLpp9iV8qY+6Z02TuFnbT5XGNXAVpT0UqS76VR2FRzp7GsKeJiZc7QHk+lDRrJdLXoMJClDfuqmQGKu4cx9VyKJDNCBh/K7bNLchXtwncdIeYy8ja8vV9U64/uQDHvtQiBYWkGbM81fPnpo/Vt1WEv2tapkqijY2Dboi4kaLNAO6YUPrTJcQPNoK1pVo9IKzf7wW+kNeO13fO7qqtMGZGNg6CXLf7ViiEISnpbEbPY0bmXYZi9jRtXD82c2AQzBYi+P9cj6W47ZZfJZpsdrvT+YL4Wagy4XMucU5LLxKV0To9T6bzfeczjAMoxciDyIihxB0pyC0yg1s6PcuucBjlAsxz3xrXtDPmaeAxndAAAIABJREFU7Q6+HZgYqgLMV7Yvk7AWN8MC3CCnWTqzWydGIHIo+8gKuKyx+wxVi40PbSYqTU/PnkJvEgrsZpCC0Kzesw1wdkFM5fsFoe+w7781RaKPpPW6g4xOd97rq5mf+37bv7bcv7oCSxWEJYCdxs2qxzwQVwDaz48VuH/IjPzxmf/ZhIgtGPpyLyrV5mK7VJ3gsxTl8YRzkP73ya3lKYhTitBLVQj9AJTK2WhR5j76Pbbf6z3hdgWNwI8TVfscRIO7z0kDD+zoq3+GkVlhFRpz2WMM9CZmKGxA0TsNbweLROdHb6U8lO+WQ9ggUTnZZERsPlCuPUnG2/5ZnrAMght3LjSFSjR4otpL+hMxF1D+Mq13pBVqT4oiIxOjCo/hSP/mRoDAjoSHnASKJHsWN+fkGiIrU+tC1cLcvXwEcir3OKd+z0lDsC42MRvyRjbUcTpPVk4MGrSAdgu2wBiOAxO5TgAsva5KTFHOxwGcXEvIPFn+fR6omFjjiTTDuX7ivE58XoqKIYDvDyxVfiEDFsBjHHcBQgGeDrcDj4cj6sJ5nsgYsHhwSwiNh2NgPo/d2dbU6JHgIzAnFR09Nc+lPkMydrVYXYWMu6Q1EomF12qnQwbkMQ+UA5caLsYq5HUhzxNj0k5HsfnYl83Uv+F4nWxyOIZj5YWfn3/bkXEV2zygjW7UFuizhJsgbuy1u+Xeu8VGO5bD+a7uLH4ocs4KCWxd27f4jhcOH7KFF7ryb8wDj8chlrfYnyq02TCAh/SOc06lC04GPOq75G7ajHVQOwLb7Q76Wc+k83IMsU5ALkMGthC5S3jbxrANQuu12v4qoKABQ1wnAzO9Pjg0MNCud7uFDrD4MkE0NwQtLFPD0qEgy+cu/felSp1vZpR5M1R3zHHA1qKTHwabBrcA8kWx7nhgDBUndGsFfL3vBgoOgw3pWfJCFQsGoACzzFF2qDhm7eBmV3lFbHEzioFJtyIh6GAlZmgLDsAx/APlA4ZTN3aA2/lMbDmCy68IuFj9hJXh8XjQZ/sDcx6odAAHbP4AEChbqrh8Z/5VNDN9s1tVPdfUAbrTSkXio1RktLVxCj4S0mMN38iuu3aPgw0ufSqoHYOGxx90sZU45sQ0h62FKxMz767Rf+/4p9s6AO8gqe6/mciPKlV2JCykF5HuoJ1+yYrcnTTfImHryOdmHwhqeL7akYTdOb1Gmx1L9WTwt8is2nrhXoy1g7P3u/xiLBvINM7u79h5zDdDuf+2L9v69mD3N+zzsP8O27knCIToFw3ve1tVX7MMl2ZQY91d2oj3q2hBJKkboWBR6KHdg7ds/puPfux3BebW1fTLtcdfWon3sWuGhvzlu0ZyV4qU/v826EDrFaQjBrw1TKwWYpsCgdhYNGEVmr92f48YEOSgQwhVGK5QSbWaIvgdOd3Xzp+uvrufJX+P4YhFrRuzSzfwb0huPvb+Zb1ZHxkopRkMqLDNiBUU3fScYlKH36V5WoVby4S5dRhkaKgViqCgNNWc0wtYBXDfnv/PZsf/1rFiqXsrmdToHi7A7fXfmNRqNrfXSnU/KR6tleP7bxvXDHN3H/lSJg4ovpJj0/eZ3y0DSqyrWUkgzvctlSFzXWI/G/VMx5dyW9nZbhTYmKK1e235XKA6VWyx2wak5ht82+FmFt5tVN/PPY6ae+rX0qJlzlfZRQHt7fj/gz3hOFDroxaeaohp7vDKPY+tx+JbjzebO3z/+LgLQtqJ915MPebvY/Xlrg3gtgmlyklsUAjcALPbXzSyVtgOKI16B3S1/dZXqrGtQqdIWUxxPwcag9K8aD9pu9xbf9xZADEmwTAc3IRs8jnbH+b8+62275WMo7e26bllZjD18uJ8vwer18tt920/jx5g9xYgy/ptAmDszAU3TNVcstrZo390/DnI0e/8csN8GFNl0/3PEXzPQHePhEgI25qr4UwnHXNSMa0ReB4/OAGMYILUtBHByXBwN1enGNPAjeYAVX2w0qbTHaSjixuaSV/TVSL8DEsfo4GEIhuD2pSvfgC2jU/ttMrXAaI43gEbu+9IZ2PHjqJ4nqjCKLJYudh7ZcQkzTyZ9ywYvNRIythgkt0u7zM3Gdi7wLqqwlPVFg7qg051Tk4kxjHwnIYrQv1zvu9I5L3YDICX8vRnI2Ww6otdeQuFqa6/e8GDVGas3KC4u2GSah2IUMqryMT5ZLPG4SzDxcHxzQJq/oSvC//z89+xVgDXiY/HE79+fMBtwo8H/EwAgcpTDeRIAcdPsnCpSAZ5A7jICz/PT8CZB0/NrzGZHh0f3P7keNLQeDnq54lzkV1JLDgeGDXg9mTrc3/CxsBzDmmuCtx2IDejEiodv4q9Uz6OB+3jg+mLq/FvA6wyVLCZ4WzHpRTqAgOX1ak2BOoK1BkIc7Kf6z0l9K8/zvOT1z4HqhaOY2LFYpsENHjoXh4tkuVPVwVBa8nM2EsK2P2FDjm5FLvloJ0ZojGobKPTG8dkMUXRSaeFRMzS3BQ1P2Oy98xwhw3DCmruIljCXkXCzWE4nNoYgOnrNJDhGRTXcylwPs4x2N/kyW0BqqZSogsvqMKODXMwi44wxFA/j4mIC1e1UzXpELHZavkt7VgNaona1gF7Ld8l1YZu8dCC4rUWWZ/Rkf/kONTE40i4X7givx/kRCmdzJuZxxMmO9GAwo9DfoMNFOd8cBx6zFR00zt6QwyfmVNA+x80AQKrBQ5ikIUfj19g4wHkJ8q0j18lGwsiseqE16QfU1UoDCgvBMRMq6CFwJK23Xzs3mGdBiVeOpgtqBd99TjYI+d4MhVWc4MvixdwGZBT4ExAohxVDkcDoluvNExNJOuTKWV17jVTDyI2cxBIFoNval4okOKjIYnIEkBgWVsyOaijRGLMD1ipI/KfVEH8U+HxPuEfD+s50QulUzwEK67RcrP9yN18GxnrMsU3dmaf7S0ou893v+f9tWqKRvCjIxzf30swkBuK1/1nR3lvd1m2hcRfkPtbpNWnfLvkvditf/dnNEZ3/rK/o8+fUosrEmiKr5U0+3sFmG48jPci1y+Z3feIthehns0QgPrug7ddX/7e/1c3bNuvmBrW1Xpn2rDH/Wv8yBHg2OuZZmu+eqGBY8hkIJmRwGaPSuW5QFGEKJHmMQd3FkeDzVupysWVb3OIzFlmh3K+78lUveA+1NenlRc0mNYVLf34ChvM9Vobg3tkVQaXeYXuE1sT0ZERtesEUSigXB3JDZAKElnUVXQ68Y/6CFNE1WPK6rxAFJQC+n87K/73j0qWmUa0s+cFSvXw9sbb7zbguJd+R8EdCN1ruOfUOwvcc8z+8OOKOJE9lN3FVc8K1IPxvQ2YnCLSbUZE3ddXO9SR7s0aA7tpVt1pJndXGsspShYDzJ/uQ3OvK9pe9RUqVadtU3HbwC/3KlOz5xVag/N+bbZf46WLU8pE+l1xatY2yMnGKd333aYoM1Q5t6jbm+9pPU4I230XZIs6u6D7h7UeVPe+JxjX9E5rAdQHotAdre19gWt9uw+mzbaWB9tvtMvYDCy/VdxsSjvSlXIUkg9ALI/Ywx7jvm5zgqNOZW1mre/Dt43oyzWB3F2BpYsyu9m+L6VXBezMzftKanavF1g1sLfbphu2PKWPbo5I4buq86T5cUZv//CZ/ynIaVV0507fHXdfkUuR/fHjCXfHelHtPQ/t7yMjkCYKVcr0gu8HfwVbUnOTOHbGbYfYlD03PBgSLmPTyRE9EYEpse4Yczsqc8NC58NjU/FAYeSdh25+ZGDgwIFQ/rCB3PF4MC23rq0Mh6l7LBu6cGK5qcqjHxhz65bZdJcQNwA5CwPLqdkUakh7oU7KbkxFDKPOSWmIboLYHUZbb1FgNQ+ks/AioB7Glu7HOP7ZY/8XHBzjWNIYgcPtwBZIUjdL3QGMTA6qtA0Fx8yNY9Dzb2hhrN4EUOLxtbjZJoJCw6M4H8ckUIA5Pj8NlmzCaCoBJ5tG+nMcE7/kE9OB/3l+4nMtPD4+4POBgYEI4PU7N7pbFbAQi7AScQZczJylUmYT/N6HqhgGGJnAAX/A8AHHCYe2UnDgeJYSERdghcfjBzIDJ9gka8VP2BpAOnKxHwmmaHYLDHNMOBvOzQeGc76u4DoYJ9fa8CkQLYcoY2YDsCzYKKwAMgwZF/fAwvUGrL/hkPO+ghqqKsLVhz1YedX9RtrhFLulojhPSgyH8YPo7UXHlIGmCpfMjfQiJi0TAHTKhj3BuJbaVbGPR+IaF51HGqYPTBjnVwUmjNUnbGpAG2TcSyxy4VIzVaSAfS3Us1MCvKdUSf84Jo5jYo4DVYYIQyawFtt7XLE2oFhpcBvcs8kdjzEQVrB64FoXz6v+QVNdb4ccfkHVm62PMyCr04Riv9/LjsH5okQhDIl1ANNoE+fhwGEIH/Cg/f3uY62fMAx2Sv8Y+Pj1ucFNxyruD/ic4FYw2OBmzLkhMudYBzxxByd56N8p8I/zJKt36H0NXtgZCZUX94wCgLjYoRwED/P4AD0L2LbCJ6ubM7CKvG8NsrsryRCtLPhwjEn2hNcfKnZx2BwYjw9WDU7aJT9cfZx6lAywgbKBqotZkHOycv2hMeiUlZpOmjl624jdRP4dYAHblwIDLdxOdXduOA6/A7UE9bKoAOqF8/PE6/NT4LzTbUad1T/GOP8E5AglErTZZmfYK6QRYNsIOlufEiG50Z+5FNygQGmMwUkA30ayo9hmWDYKvkdmR7uber4fhxAl/7Y1NYoiqqsBlKIxvWc3T2p0rollzUg1/VR9j8U2Fy4xGWqXet7hIN6YBtsTujE+HUmLuZsRajFhSXsb6gCpuaYBZ3BZ+GOPnQ6k6u38pe/vSGNHuvb2pm88GhdThAc5pBJXpXmg6HMOrjarrwvk/Z63jelht45BNKr/D29v2yRJciTpPWbuHplV3QNg9154FP7/X0YhKXJ3e1hgpqsyItzd+EHNI7OJ3QHl5FABaXRPd1VWvHiYm6mpqqVSRIWCJtjOkCy6WEsUEanZMsGppVKLX2tpWd97cawUEQuvG6ybPFI1Jdl/GtUFrHELnmsf1LbQxOLEb65rsJT2JuHd0oHYVP1ZCUpRgmSJhAuJSyXXVTVqppSZDAMWodbIa0lTrWUkOGY6Q9u8yIxrXSwER1v6lNnispCPqeQQ+/n5/IOPkq0Bm4v7spIPUomo+/x6Sv58y/ReJkIjLuUqU/NDXhBdLMUD16LLYsIUGxYheCER8/VrYqn6xItabS+RMJ8k/DXzyPLXDFXnWb787CfDahdli6W0TEjURljjES5F3BxEKLQ/QemnNH2u+5IJzPK6kf3Kxdy47s2y2pBj8fIG0rFQnksJesUgoaNzhqaOvxTvUsUUVrL6lcfi2SxkiwvZWCTYZ8y+tGMLFcnv5UJdjQtlWyhJkTI24EVFFKkIHOsHcJGBrzWaLUA3BsfzGdhChxYrasVyz/OaLwBKpADH1Eq7NgVT2lk3nWrtapmZPofVolw/I9dezFzc8dwPE2zKxbC6HnmVM67ve0WjxfcheX/BQmB+Rtrz/X2+rjyRbKGPluqvcaHlgznzrH9Hpfd3B3QGWc0hHk5+tGzf3S8W+XkeuDvf398Sig/Cn1qxOYOtNrbtRp9xITKB0UojQv41cg+97ptIay7pr1n6MmRAMp5zeEhDsxl66O5wdm1Ao59Sx6yPTRh72FMOrP5xVXa45LwRknYDjjYeTxjTXJWkr6w2E8Irh7BF/lOy56HBawoi4jv01dbIc8JCJm2e0KWtxZV8oa573lp9DncjF3KewlUcrcC1yKnXU/366impBbl+ZCqp4C9zq0nQTynitu2ul285iObp+rKUf4kekYHV/JmAz9cNEGMy6DOuxLqYZNKS8sNxCub//v3OdmusKc7FlFh4cWzbsDSSnH3iMZkj6PPk6Af7cVDqhpVNt30K4axbSdnspNWN4oWWG6ioHsZ0F8eiqDLvpTM5Zb7WBr4595tTi+tZkhvT8n4xSeDNEw3MqrnMNaQ1ETzXtVkRSljmpFraISzjMLkLsubTODB646wFKwOOSX9MvENdJPEvOu5vd4jJPJ0xeqKYL8aiuSKerc24/JPGcsmEi8e11tXMALlauCrognp5OpGGgxOaU2qlbk1zhDo5IkLnohizKmmn3iurnLN0Za3FiNAgTXOjVpF0RwxJv+X4RYSeoQ2wNCUao1OscN/eaa1QLTlffeKjQ3TGPOn9JGqTesWBkpu6Oa3IWmBZG8SYGTTU2iwksT3Xg2FsWyrOYl4jMSJcSVmiS47QQKt5F0fO2BrBEN3r2ovdNRpARP+vjUd1k9N4sxtetXlSGla2FHRMRfWrmEAIjgJ+/p7TOLNIVoIoflQxudYThTlOwh/67/7Ii8/W0GUiaIIrwnDftP+xX4kHprY5thJTLeXiDaMwSqJ7pgkCpZj4f7WyZun5UOJRqhDf45S3lLzUXD/fQWQgWTSMPuhnlxkpTgyDaYyr7a49lrreOnU55tR7EJDedgsBA7jc3Jh9alhuiNS4EpjXWgM36nZDb5BTZ2gUR/QUfQhBszZ/j3f8d5KcWjIZeOaDbgs604Mqi/2fcmj55UCMk8tHIDSx1rJijCmzqhl6cGeqPPqZQUuRJltb5XpYkP3S0PbFdSO5eqQiMM9npXKllPn7yyYvSXlmkVazlbHY6UqeZn7OmINlKbbAm1UpL6On5zmkkiEf7XJRVn80rhd7KTmU6c9EfkI8C9a3r1xfPVd8XpXh8miwDPSx4PiE3p/yv+c1vwIiX3UUX+hFXncY7ksTovszOhcZEEjDtxcL/Jc1KEnmqmviQmYWr0Gk9ERSYspx2SdegoHkkcf5yXl+oknLRjE5KCvhUMJrpyZwF1Misp7vSEO9mQ+1lMrWNt7f3mmlqdVGTrPXp6sYj6DTKbjO8RUBLKStwmQxPIsJ4bFSJd1MiCYC3CvbdiOORUxVwlxaUWsgfYKK69pKJruLaiByaxrekUihr3TBL4XnGJXS75gPqg8iDg6Xuukr96fjUOCPdGV+yo8X+rsKqrjun/tSkemfLIR92aqQ4Lkm7fntT8RB7+jId0/xwF5eKamolvOxRVDM2NzZvMhzKZL4PHONlUrEs5JfBZclYhJ1xTINrl1UPoKLUzXnmYKCJiSwOBYFn08UJuZgdtM7NNMfCcVGLOOYy6FYr9qEkAHqhYrPeW3Iq9CCwH0NKH5yC2fei2sWVg4cNlQkP2OxLsdN6M4TCfiaox8dd6g3SZOtLD4c1zVeM77yb83rxalbCArP5Uew9ikn1pDeqbhcSlVsH8/vNS9JqdB+s1DRkTHC0oRxjpfYvdDVyGK7NT3z0RLVCKw02u1Oqbck8FomTzLIjanMRMR58ClKxKLfPC8q3eHD5LtE6POMqz35bFPmd13PNvdoZH/xpLesz14I2Gu7M/lhM2RqaDJm9FIpt1u2ZGHWSd0Gd6+0OjVwdE4o/X8eyWlVSI5Pv6ArZXCm5rUXucm6IH0vhdI0cXp09W6Xv0cgmJf02OhjEqGL6yGvndFPoSoLvbGWFUK56jW/uJxzlV1X9bpIVSP6JY/9qVKw54MgRNZcLqbma3ilcoTVWlp9xvPs6fMwM7nIF994GjHZEwZ9tlBeNmizNGiLC22ayRaLNE30/LwVYLQIdfVzJkErpiDk6qxpw3rV9DUesTiKz3uQ5/A7Ce8/7HBLZMR0js8GaqRqTBbwat0lpDuF7FzoRdZ+Zg4ldM+HjLcsEZ3lMdJ8S8gW9vOk7wfuk1LgHIKCH/tvPI4Pop9S/FnQHFpTAKpecetE8isi10YEPMaQMgStk9Yab/c7f/z2nQVlzziYfeDlpqCYAy3POJmWGyAv6J9D+CRMnj9uRjWnWsFblaNt9qAC+Xx4fbuk+LOozVmb2hiWhMfiWiuVNRPN8GyZhgtVGJlRr2JBQcYoDWbcGKPRSqeXQSDPnN7PC439iuPx+MyE4SUc53oSopC+Ii8JT01y7SV3j5f17y+JfyLiwHO2jLauVJ5lem3PHEpAq4wf9fMmHgqob+bcvHLzjTUrK+122MqGRdc5m13wvmcrzD03mGQh+vSrk1gSBRxjx4pUN+YVWsPi1LiUjAdjpC9S10Y6I61cbV5eOaVowvk4gnGKbzMG+c5ZJjyOebuKRLVE1Rq9bkTorbWX/0GuwTTmtDTSjJd1tlrtX3nsnwe1GvdbA1eS8yw2EtVbyUjGJr+QnKQcrOZocO0D6zBbql+FuVpvzBHM4WAV85z4XdKFmGyHE/R9195TGhA5lUTrWc9h8TmD23bHrHAMze1jTrzd2N6+a+aZV2K9Cjl5fUwlLq1tzOJClyzzUVtE9UzGKFgU+jiIOWlrvEiCG7a6F3mIJmDJd9P+6FazmHgi6+u3K0kq+p5xile37GDq9k5pje3tzhyT83z67G2hHOnz8+DsneBBPF/cvzl+N8lZCpclf9Nmgi4kK9uS00uXadWtVYgkQiW8NkMW0sd5EnFmD9my4tLCcS/MkjyXIvTGqdqke5eqxObVvvGVORqC/JPs62kyGBRmnJe0dlUcRFzSdbAnHJfkrZr9t/Wqyso/p71mzRyBsmm7PgayteU4mvkCI9Lbw/ynAByM3LDtqmhmSqeVlOS2bpG9YgWY1S/vYzKmlFKe6IOAo3mhTUCy7eflzxOYJIRfHFlqrckTCSIcTVlePhQogFf5PWjGV6i3bfMpW2X1ekVSLylhlUq/6WmVklXF6vcHxQqtNFopVF8blZ5jwbi3XGtpibB4C1YNq47fKjUa5nDsWUFPBbHRF6dAbdy+5mplG2FGUGzgFuKlvVZMI4mtJJCT7sVEpZgQ0mVjfrNGtcJWKqVBeZ/M2JmzEdHBh0iCuV4Si0nkJjkFF0dt4AuvsaoqKnlu3sqFQMqFWeMsSjHsVvEY3DkpZ+E8X9uC//jjPI8r6RBFIBJFyaLFFE8iE0TgEg8soYGxYtG4EGBMaMJVmLo+T0ok8ZZ82UpcnjNDyEx73mfdT67PTLg5kQLDTyEo2ybkZZf7fyKCKXBYsccW7UFqtqX2tJKXaUopShnIsMI5OsTgajmtuCvDzaBa4HNwfD4YI/DZcBs4XaZyzVimgG5aD9MMs0gTTbtyAUhUKXkfckhewywXFCaEXyN2pIhx8+TlJWIY8eW2XaXe8pfQjBhnIjVKLHRBkNg8eRG6pmx5swoBt6ttrMetr1vFD17xSn52ZnSJ6secsHhVE65hmcBlaWI1bSryHbSiUtYCrOk8ihIwN8dvb3jascwMNguVI8Bn0++uOW9RbnoG05hjyd9DAoqhGnKketO7kMBFIvdFbM4rVqtJRoiUlkX1SmqSVnDtgBOjar+KU3Fk7XvzBLRWGbJ/iVgxu1D9lm74IgetAveJxv3t8XeTHFgVhHTwYTA90v9BBkKYxjm4O1vVR15mbxGcfcDonL3T+6AUkT+1QQR4zRe7ZpJTr2pmjiTSoTlDc5HvVmAzw6OqjVZexcjOmFxD+VaC8yTNBc+RqgvWlhqsLOQFk+vznDlr5fpycSHccz9Vi009Ti3WiKCH2A1rJteqRIMnJLyclZkL1l7BLZPKbGNh67FGzqXRi3Ftygb4ImWSCdNK8J6L8Sdo/4uOWtLzZiRxdVGOzbiq4FIoq5IiUl0m3pPur4tAPAceSkhaLUmK3JSUFhkGLRn3mIOCs1UlOc0tf1rkeja2qmRp9YR9VfUOJvMS6qy4KUmfPRhT84tGjkyQOVXQo2frzDP4qdrHn8E88rmxCHxJaPcgk5xFEFbiX7ywWaVZodlGrU4rhdErcxRmHOAdrCEcTBVPSUv4ZetvKCDPOa4NuWTFOnKz8ZpVbfBUCa7p7dvAYnDnjVILpa5++tcco58ETikvc4DI5wQ8V3dkJZq8k8ugT8dE/lhXPR688EdXO16bSikaT8OAmv+u/EHqyJYVrWdRa1ciG89ER5K2VD9O2uZYD45MkGeSNC+0yJ+fN2LFs2yjpA+XUKCJ+1RRkOccIzJh02Z1oU4RQvIm9D4ZUfFoJI4AbvLkAS5Sd0B4UdEUg2crnut8NWA5+VuXovRFjVZS1ZaxqF7mdRmffqfF8I86ar1Ta0uV5mCOjtqzWWSQ+4QgFZQgvCY5udoskx3IG7KyW90PbQMTqhNTm37Mnpv4vJ7pKqjJYmkVwZFJDtczzKniSQJf+y5epQitjbLd8XpDooFQR8hg9aPMa3bIMjYUxFHrGtOhrUbXO4fUeiMTMBtDqCbZnUkeLdnyUiiXqtXWNPOMgXjOu0pI0pjibhUjTjRnL+P8SOCjhLyE5vJSmtoPC/4caPzSvVn51L/5zH9vQdxvGwvtWExqc92ciWuTTbfIbdtkkDX0Atzf30TO3DtmB3BeJyOO7Uo4DC9Zj2Wr59V8acWKtXHNlGEfV1AxSplCAtbDzMBDqkIWhLsGWq7Kb5GO18+IqeRlBczFrJdS4rwS/RXvFu+nLiQgf+YYy75aFt4dyerOUCUdiyjw8mA0C8QgN6lY1//i/Ogr+Vo4byJUY/S8jnhWrle7avXbc4GWye/1L/8hR84fcXcivToWeS1Ci7vGyNNaN1kPROR/tQ29FHyKdFfS7IqV5JGVJWAhR+DRT9w7dcr0q7Y3oVszqGVjlIFxAgZ9En2iQiIwBn0/OD/3HBOioZAewVaS5P12Y4xBP3cYndj37LMLBpfLsRySH1O26O1W1f4cZ4pmgjF2RuyYd2qFrYgrsVWNpIghozArwTJis9aImptgMeY8cy22fCcs3zVVQhru2Jn9xM0Jc3o5CetyD3a955akVwuN3FjwRLs1ytawu0ZKjOOme/KVh4Wg90xQ7VKUiZQ9u2LAqodhcT+WolLvbcuxH3Z9FbRqiYpKRq1CK64YVNxwr+I3pFuf5OBGmaGWRA+GG92VTMTszFkl8829ctFTFEGNrVXxDfpVWb0gTD+3lyPEWWj3RqlFcuEDpxGEAAAgAElEQVSYlCJWc61Csi1RYV1/yAwz22DUKgsEa/ic+OyJamj0ScRK2uGz57DSBGfAL2LpaqpZFlESSoDVZfGQ76QCmc5/CmFYU9BfkaGvOrzdsFKFfAIzVkFNmigOSnChUJhhkUmBIiiYNL6LOC0+1kItEp3NTNW8iJvkd3x2Ltd0JpbF/rVW/QZRsHPXs18oP1yKW3MJGER9mrDVbO+IixPFn4iZJ+QcQqNmP5HlqNCgUm6yuJhDvL9Srq6F5gM7fUiZ3Oq3jDnGLJnkmlNsQ/ahA/MNG4skbddYh4h1hYtr+hx2PA7tl8cpCwzLdtc8A8bgGI/MGwr9HPRTitbIxLvUDbPB043vb4/fJx6nbNbzJjGSk1OMPo0RaUrlJodad6LrgdfaNKDzBCxJiqaHfhlV6YpzcTx7tcXL1WdfxLtL8r0QmeQneFYVF4IRpEJg3eDQ+a8fQ1Zu6z+uCjt++hkL81gEwTUNXPSW1abQd2p6TOEC48ZTEorBsGXUtDZvXlCdFdTsGXHJuGCr5XJ1uVmmbcbznsgDYSWOXMRj8XLWCvu5Zfelx7J4z5teqp6H21P+Kv8fBQA9RFkNyNHSGDbTyRhqIhzr0vwiw61KK6com6o0eTkV3Ftm2PKGKanYywXGk2agZHqcUhhEjKfMFr0D1WDzQrdgnJE+SMkjci5kiFzHfciErEaS+ROiVfvrJOi4B7VArVJTlZIDFecUXyeTXkxVDcnjIC0PAqEL7k6pyxtGChdN+wzNxfHV8hm58QrdsRCsnt3Ri6sfaKqymbO5pyw4VSRft4hIyIJloLbW9UIy8ClyJTPf01wfuUGoLbT4e6t9pce/PIRaXqdnnBrj1Fp1mdiVdNbW52d7cxVtK2mdC00cqsCj/BTqIu+tWVrSo+dMfk1kPHB73SC4kA+vSqJn8l3E34us0ldhuFqS69fy73Jmkl99CJ3Zkmh/dsWt2hoB9NDG40laFoyVKrxMUp4hVMXjxRG0VRjzjD9BtjNWwvD1WY5aU+J4yg9Jyf1kvfeZNM9gpvp2ZgHm04g1ZNh0LbFQH1Ym+ILSs2JSxp6hPU4WGlNogTkaN6M1JrHRAhT82qd0C03ojlclFhFCSVyCHy/ZwrrI0SqYNdMmBRtkArJUYpeQJtVN5B6ba/DaCUsVUi7vg2sv99r0Di2xC+lAFcGyPlkigWvbcbm2zzUWZyg+jjnIRpBG56QqUtJ88czEBZSvk/stwYXXAap/e/xuknMcJ8Wdsm1SbGyboDTrtFrYXDC+GZSo2PRcECYIqk/Zvw9dmbt6esdxiBiXsJknSuQzyZYUjn5yHD/0sDHutWAUjt1TWaBBcAbYnPTjeF6nN8IaNivVV0IlVAZWEmM/vWiyr99ZxLIVWSyhQzclXqtdZiZC57ZtjDFFnDIZGx17JhueVdFUW6Wmh5CqrXWXs+YaaYiUVeccibhYQAbKy1wyz/w1cbMIfhootDaqaVkV5FeuOQRfeMyuTXY6mMdlFy/vJZ1z9RsR9YJqi29ApDfIBG6JRPSEmyvDBmHBOTLETD3Hj89P+nnw+PxVs91KsI1g65OWcWXbHOfGsaUE1o3pk927Woe72lNap1M8tK1iNZ/NhNoVHGZXO6xQpXrYGuusCyWRk2VXUAEnugaKbgbUBrVQN+ecXdwbc7wGYZ0eg3FCP3+T6y+FfohQPfZP4jgkkzfjn/70J1qr2UqRyUEUtUyLFabJVBPL9l++F4YTp3hvI1xJ1RhKHIqpRx6LLKnrfiUe/sOP7OmFh5Qv2SKMeG7iIu8/fVwuLxxboXehoitRNmWrbmy+kN2WVUdB5m1T/KxiVAuqDapCD63q86o7UQpv90Z4EM2ZJdQq7+T7PFKKrthTWhHnsmpt4AsnSL5acVHwQxOnzUzjR0g+Uijp9zmpdCoiN2/NiSHuhrlxuzutGZSQwpAhY8wbcBbivKuinodcZq1gVWjgLVtdzEG4MaszD2dkQTYhN2Qotuz/tSlV21YPj2A8I84kFTRqQX91nvPjz79Ra6V8K7m2d92vcVwk8chhlOc8RPT9FPep1XflbiWovtHqjVXE1LoJvbXkyCB1k2U7L0hEcU4lJgawhqVmYusBPjj7g4EM/wLtHzYKUK5xYiQCpAZOpfk71TcsW/cgb6wlBIuA4gMrhVa1J7pH7ufaj+bZsxvSmEPt+Hu5q61fNkrdeLv/gpeGl7esgib0wYzVnprQIWZkQjKZ45BdR6/AxOKg967Bytm6O9OgVINhJ/P8oYS5Jn/LnfMYnMcphaOB+4GbKab/zjr6uwM6l9mTMk1L2bf+bK4KxFn9fX+iDC+GQCvfX4gI+dcLwVjmS8saXWBFEFcitAy0RLIdGIsPsC7uaa2fiIxzVW1j8SMWaes6pUxysv0TsUhOXK2vS1Vgz55ilrd57k7EkIusdSbB2VVJetF5nQFRoaLFXRaZeCX/RBLF8nde7nuCDE8TJlVr10U8y+0rXrz86ys4dLW0vvyI667lbV8PzfIccwothdUsXKoqqTEcs8kcSobLaht4KmuWj8mY9D547Dv93Nn3B14Nb4bZgdHEt4DLrK2WdNHOUxqotcfoaSC5+uexhFNJIidVI2smmyeHRcjRmon0vM7VGl3/yzYIJBqTRUNIAu8IKRkzmGeiW1NTk4c55+PkfBzM/UGcB2fovr6935k0qA03vzYf0qjEi12LYibpdTnCzEQi5lV1x7VOfSAjvrLEB/Gla2lJeM2fMWK9u69/XqDcSw2xXu+r3etYtnBc7a9iciTG0k7ecwzMy/UlfwoSMblMyBJgcrknhxmRihG1WY2wdIKdzxOzZf9gK+4918ZykIVgLpT8J2Llet/1DPxyPBeXqy0Sv+m/Ly8xEjjwoNQ1ysTpc6RqJwn75H0yoWK4MxMSWnH/KqLyXb7mJLGuY6lxkmj6EjZXC0MF3P+a9fH/9+hH1768nWr/ip4OaG7ecpE3M/o8mNE5+669Y6myfTDLDdoiwOr3ElVJOCXbqomoZDssFvqqm3sV31exmvc4Uum0+CnAgve4yMlx7cqAkqslJOBlPTmaNhCrmPGQ8mpKKnHhcAGXYvniHknFWd0v5Lt4w72p5ffqlbXixWrdzmCMnH13HowJ+yFDRIsjfXjO65rX3LRhudfZqcQ/9JKEOf3sUlnlf7slCjWfa+/fOn5fQp5qlcd+UmwQZUHxygCZ0G43aqlstxvuhTMvfIYY3LU1pgUyOdcrVKPBEIRnydR+kj5dP4fCNm8JCYLXoHhgXqVu2Uc+KCEDrd2USUY+uAhqrYQ545GE1UWAWgCOaWEVX8mCXkjB+rppnpmzF9P7PjKwEsxhHAfsR6ePU333CPbjJKZ8WQStV2p1xjh4v93YbrfnhrmkrGUllQczTZfEDs1FF9mWWkEyF7Fad/NKuHRZa0Ne05EvvOhSzH3lUaqqi1Jc1aTnaIx0r4V5uSCT/z9mEo+z6rFqlGqUDWJ0+uycuyzxH58H59n581//wtk7x3FeL1wpRqvO2D45WjC+NWorSQgvGsaX6ygmzCPrIAssBh5n9qVTTRdgXcmNj0QGmpKuNVIBIMZJjEFnJfApdc/EIfdXqpP8AOOtqY3SD42N+O3HX+j95Nw/GHOwHwMjqCTyODpumsSrAZ/Q505rlffbG7UU7q1R6ka7vWFl4qUyzskYXaZgc17nZunlEfW81pyIy+MysxuWmIjPL1XGfPv2C2ZGTclupIeSioDcbKyTzpsX+cWyUBBSog1/Sz8aXDGuWJq/IWR/2mS6YPQR4OfETug+GW5sVaq35RXUEz6vLTep/MxndZJcvEVURwlEBNAHNqYGDONJggfZWIqU3lyE63uivI+psQ1ekqC50ACM99sN2xQjI4w2ihRy5pJL18KY4oFZSNp9dpmr9eTk9HngbtxzkLK7bAbOGdTcXGP1qmIheyODUkZ5m6u0XTID8s2++ILjq9vmQO/iRD6OD7wYFZf30qFRKec4U0EEsyjl/9w/tNfxUBHLyVY37tudfu6McfL+y59o2xvVG26V2+07XhrtrWSBXpJWzDNZdgebzP4JBLX+gtnG9v4H9nNnfH5IYdlkXOjNGbUQXpRARDBX5yHbSMuAliSKK4hKUWvFLu5QxKAfj1yXsm4p+TwBatmwm7G95Rgab3gpjGxXelcbf4yDMQ96HNCD6MHH3jnHYPSdOTrn5w/Oc/Dxud7TcRWyMkltRFFiGE3msHv6YunSGqXeJcOfUhYC7IeQon29vP/O8XemkD8ruomljj2yi5KVTy7Utb6rV4KZPji6EM+XlOSpWBGMfs3MuWDNzGp9VcQlORIvXIQla79eqFU5rGvPPinzBfHIzTOecPU61mcFXCjPlRDwAra8fkeoPJwz6GmgNbNdEKsPbS9VZ7qnXvOmQMmdG8sUqRQN2RNBPa6q9OczjeuZvFzu31BsrgSI1R9fhNWFSH3xcVWhK7uE61qY4pwgntXi5Fyk84WjJL9IVvHZx+2TMQRhHufJsR+aTN1FdC7p5KvNP4g+KF3yfvWsLVtnxnAlxzFgtQmfSAw/IWVr6a2g5StBv5CCdaXPB3Op4PLJrMRT3keoMglhhWNMznPy+UhE6vFBH5P9HBSgEVegrNVSHq71fZ7ynGIatTijd9o2oVQKCmRzRva840KxzAIfM4vFVTWud3Jm50Yax8jE4isJ7F584Rx/UwH/5Ie1Xlriuc7Xq2TPZ7eK4p9X5POz+uiaFn6MJwE4BTUWRhQuo9MVU5b3x3IXXrFopg3F4nVd57xiVaIwPwkheMYqZ5lp6jkX/xkRl1eU0NBalWCdfT69stD1EJ7qzcnM6zJgdF3rSFHEmFPIJOmxZO0iTC8kenEM7eUm2/Osryu4IvRSI8ViInK9V195PAngM9cSieMIeZBiMgdfTP19P9XCjqnG20ln9kF0uemPceJ+Y5whFNYrMQyvjYEMAW/blrdp+V0Fl+mbd3H6SH8aVwuKNO/7eYjm8rCZqVS1VIXZ827bz1SM9UroOjPuRBp6pnt6irGvm2Su+VeL/2UmZVekQjW6UJo+TvrsSpAfwAmP/eDonX48mLPT953RB/sjC9dK8ttEKF7tWSuGV2e5/atGiGc7LdY7munymIw+kjv17z/z301y+ljD2NQ3HmtgW0xlVO6UaSJrjUYF7m/vurGnNnDvQ5VILRzj5BxSu5jBVu+YOXscgqt6h+qU+wbIfty6mNPBqs71ctZaU/7WhXqOkZs5QnPiZEx/vkOB0CXUd9dNyZf0YrivthbqG5OzfrJ6nTE5x2TNJernScwjg4Uxk3nfijwYtm+bkAi/Y9EhPgkLHr3z/f6Ne7txjk9mDA3bm5N9fFzux1f4fQnMQDrpqnWwmmjr6a8grm8XeoLpOswCt6+PLJNnIglyjxbMF/Sja6BmDlWKeUBM8bRWKyKC0UPVZu+X/f15dMbo/Pj8UP8324RmTi0b7+0PzBqM2tl7MM/O8UPzsb5vd6o7rb4z66THDwXoIwOhz5ynahiNglFXv2MeClQzro3C3SiodeCJAk5zZj68W61SDhapD3qXjf85u6SdJqh7EPz1rx98Pnb+x5//O/u58zg/87Ur6V4Mb7fG+32j3u7caqPYrk15qF36lx+/AjJb/PZ25z/EYGsbt3Yjulp7y0NqFikDyxRHYB7ZxqlSUE6UFDGgm5QXXsZLEfGPPxR7LEG3TGDmRKMMBO27iwMh7w05WANKWFHSGZH+OZlcrkG/MxOWOTpnn/z5L5/0HhxHSAlYFc+M4LY1Wi18qzdq0ZiVsMln7KyhoVYqlEofB8dxqCi8fAQi4Xme15MxxUteVoivY25SMsUT1L2Xqng0AJ6k47ebZWVs/PjcOcdQy2VOfj1PDZV9GH2cHP2TnJ4sh+/V0zPDS8PcqTeh4d/u31JOL1TiVjeOM9Fx3eBnO21abuQyEBwmBL2UTZxBU7F30Qu+Gs2pBVIRfM1LyixYnBlnPw7GOJlDCN3IduN4fCoZskwQw6/k8sdvolbcUuVd7pu4N2Xj/ds7/9t/+S+U+k5tf3hOPU9rCbO7UL7eieh4FDwKZWxCX0ziFvnG3WBWWoiTNqLIQoWKXY3nn5P3tWf3JPgeMRMZVqwljOKh8R/T8Al123SO2cmY+YxnbcQIjh8nZ995nL/Sz5PzPJm/TuZn8Ov+G3s/ePz2KzEHtSVX9Zzcbje+//KNbbtze7vTWqO0Ak3mi9VJ42FnzMExT8IKPcEWbSJqqx3HwXn0bMf++1nO7yY5cyEfvmpuyU9i6I2zqflQEUbpUrIIpciFnt8rJUFN2fQi1CYy5lCspUyx0WqjbjfMTB4GixNQGuYifc45Bdl75EKzrNbWpQapmk6k41lBvSYMVy9/fU/EhcLw+g5GPOWfBIs1vtKF6qqeplXCjHGK0DlThSGn6qU+KNSqILLsUmYENZLFvp6jLWJlvFxVxpD8WlsIV56yp1rQeamifrre1Sf+2sAyIoebmnFNS7e8nsyXZyoBZtcLUVaF7LrfNp3RT87zuJ7rzMnYkQtpqzdl+NWp3rjXG8MnZ5HS4JydGJIvdls+PHqmXjxbozIbZHg+i3UfX7ljWvwzN72VMGsEyEyVVj6X9b8LRUjFwhjZntT6UIqtnvV+nBxn5zw0Ifqa1ZY982nZ9x5ofhIGITi8OYyYPJKnNM+B4/y47fQW9C2gT5iTM0eVeGmyf8hNLnARSklOkgU2n8jOQt9+J678rz9+2hTjmQlcLypPBGctkCutvoAvVeZr6nyQM+W4/k6J9KSfUyqq5CnFkFkeWT3PEbQoSjxBHKUXvtATzcufPbWOL4kx+Z+mNeYpy/Ws7uWYa5dBpZEqFVNSIoRIyLqKnhe+jMsJ2abRx86YKgZGh9lNfz6fKEAQl+BBRFoH09oYfeKhqeeEsIYJnEMtrqstsoLN8nGZU9SO61k8E7zrcV0K26879nOnzcLcVGxcp10cH8JJIiXLM/TuiceU/eVFxSADmCcPK9R632VTTEkhyTF+8PGxA4Xb/RfeviHhRJVRoKBbnVtB8cGr46NQaxV6aNn0C6G9mBEifLLGRNiCqGJ1OvKC4+UWr9dlrvUvdGqOTrhGgxR4fs6EmSOG+nEIad93+jn47c87fewc/YdI9aPDRxBH8Nv5wTlOjscDYsqh2MmxT5XtfqPdNmrbKFsV52yrSj4R6llaJ6ZfMwOv1z/3eVaHxJPr9j8rIZ8xn7065Focy1xryo569gduhaCybcEtcsAZaWpWIHJ2DyU3AVMlf5aBF7jffsEp1Cm+xNsvG8fjk0mXrwIn9/sbrRY+90/G7FTkCzJdMuRxvS8yNuqLL+HkQxg8p8iuROzKdNI8ruPkCIlcGTPbF51MsFYLLsOYW+He7txKZdY3Bsa/np+C8T47bkEthpcpEU3d+P7tF8wm03qacwUlLdIFC0uULvM2MdCNC7188g3m+CmB0ffpol+lpJllggkm/Ook5+wzxTHiovimieDTgvM0xmkc46DPznEIQi1T4w3GPHGrNH/j7DvH+UGrm7yUpiOZuXxzvr3f8Vrx7284QWNyRnBE8MGOzZ045PPxeRwUM26uCsLfK2HB4IBRsbhdld7ymioJA8/ShFLKC18cjnWrx8Be/GOKy3eFl9b5nME8z0yaVeF7GJ/nzjF29o+dfpyc+0GMk/smsuI0UqED8wzOCGoPrIqXZu68N7WVPz52znPy4/PB8Tnpu9O2Qt1K+l7BCLmL1vYNd83scjNKLeAD607xSa2TMiseBTiU4MXvQ8T/y485M3mRvJex9GtZN6GAiEWaOUgeG3C1bQxj9MmR7VEsGAWcyY/9oPeRrtb6PLINGUAMUuYanGdQvEM3OWkXIT2394IXqUPcNJuthAwNIltWltn71W7KnCBWm8uLipcxExEWjwiMfcjN+X1rUq/Mg36qpVZaoba08fAiDogN9uNf6X2HocSJbrp3U7GreqFaoYRx9kNGo6YNrT8UNx715H678YdffmF5qo+efjnL4j/H77RbtoinWsaUVAXhRHrsTJuJpo0vT3L++tf/wa3deNtu1MzNlvJ19MGkU8sGVqGfGkZdkzzsjT5P9nMo9uDQDAr4qDCN3z4+6b1TunGcg//6r79RivP+/n/zxz/9E//pP//vvL1953Z/53ZH3kZ+w73y7Zd3rBbKW6EVuI9sozFl7zA1mNkNwluuz0xy1swwEb64ptMS2WqbF82EoVYbU+MUPh8fVG+Mcpdqujld0Ann0egn/Ou//oXj+OTH51/4+Nz5f/7brynlPmkFtrKeMhyc4lsNtVXforJtjT/+4Y3379/54z//k5DOWsWPLE693bFSpEicQx72s9NP7bvjTBEGKcywgrdJCed4ZPfj3zl+N8m5sm97rkVBrEoJl6OuEfi+Mybc9gfF65Utjuw9X5Wwy9E4FrKASJQY9MxULabaPa0yKUxv1K1JKTBF6h0jeRrul28BCwJO1MnmixdJ5CgIe04YnmkWuOzf9XeZQOQFv5hcvsgz0iF0yjjNa6VuN+ptI8z4/Nww5LY816wYW2xz3bdtK9TWRFhGU1hJC+2IuO7PZZceMs9bnkCv0eFvOEmJGl0cp1j/ZM+E5wsPQ5u0XsjnYoxM7JZkXmTaM30/dI1SmY2c1xNs7U4tG+6V/pDEe6x8dRPrfyaZe0wYHY4jOPbOcRwaelmNbFrQZyI3x7ovIn+WUi4vG1FAJgNP7s3yh1oyzuX3k/8WsHwn1hpfFf7MwYaW8G9EIcIZA3GLjgePzwfjHFS/Yd7wLTknp2YenWMhLJ26DZnB1aAU2O6NCOP2kPvz8dAcpXttmlzvJq+ciHQDBhvZmx+qUP0EKxulGdODeQZbEXlykIXn+HkN/qMPXzEkA7dQTwXvqb9iWrAmRyd+oz9Ftqe6krOe7uVumlDewziOIYL5Wp5Vz7qaMSIEfoXl6BctlZ5KPEuuS+9BiVBruASY/KsKzjmm7tvMosOd5REsiexiHCWRF02Rv/g3Jo6VAktc3EgZF0p12rzIF6kHx35wHJ3ZgxjlwlK8KP2rAaVUWsnEzORob9NoRefw0bue8AiJLi71XfpIdSUsQkSC5VJu5kxzGcylAne5H7MAB15Ani88+jiT/9hVZFyI+lKjad5S+KS2Tc+2rUrSpeCcGmnAXHyZ5KxMtQuF9IJHZ7o8sebHg+m/Ela43T+5bd/ZWqeWoG1Cdnr/I6UaXnaYTtveFIM4CdQ2LiYX8sgZH35ZRRjPrSpRQ54AqP6c3LBEnXq248pSc3btVcMVByOMf/3xg8/HwZ//5V/Y909+fPyF/ej8+HwgNfKgF6NX596KiPxplVKQwvP9piTn/f2Nt7c3btsblKKErshQuJQmM1cTSFtGlYP30LwtKz+jsSCApFRLmfy/H4t+P8nJ6v8ZLjLJCS5UI+ni9PlJrZ16exNh6dWgZ5GLBTPg1rDpyNM5mONMqF4vIeFUB98as1Zicw3tNCjszDk59lNoYVVbwaf4DHMI9p+cYo5nvz50d3KitMjR4zwzqcmFYXY54q6EeF7RYaX9GR2LqaVBoWw3trd3vv+yYQY/fnvDwvjt/E1L01PGN8RHGbOz3W58+3Zj7w/mPine0sI6yaBFEGm1xVXJ4GkwPd0kXx/TQqWcyx3TF0KeSIL9f57lVx1rtoomb/ekAmSCQybDU3yb3h8iwtWnZHbOIUXD7c72/TtOwyjs+6/s42Qs1d/thhWne7+Ikvsxefw6+TwPPvuD79/e8FaJvhNzsHf0HKem3m5tozRVGH0YpEFapGQ4zPAmk8JaSs5UQ+ZhY4g8nIaWtiS4xGX2pjaVNieigDVtomNyfO48Pn/j4+PBGJM/vf+RUp1x6zLCGjujnyL1xeAEjXxohteAatzf7xjGt6NRDfqHEpxftjdxj+rgDCWT6x4tVGnmsyAmXjbaJu+V6lDulXrbGCBLhBck5UvW0NqmV6sbUpY9XyD5mSgJShXMryQ65hRRNIIekVV8zoEbwecxGHPSDKmQbmoTbRjnCMaZ6rVYxbKlklRNeg8jTqgzixGD0mbaaxinu9qHecva4oQELEazkmEu1KmZqyBbir3FuUhLAZ/aeC03l+aF8xzMPnn8+OBz3xGtMifZW2BV6jOzSSlFrYIGlKBqIiu/NLVCzo9gJBfLB5Qk4kogMRhnX+N0cZO3jNzXtf69blKZ2ZLXi0S6hNdPtOHrjt5PisshPMLAtkyStV5qbazpfs0nJQbkWKtpA3NoelOYfVKtZmKkhz5GjhKqMP0gmnEcnd8+Tn7bD/7y66+0+gda+0Zjp9J5+1643SuPx3/k9rbx7U93tvrO9/t/oM+D6FprPaaKtLKpXWVKhBfR2LO0/bf4KZF7dox5PcOzHxDQyoaNkN/N2tbYiDD+61/+zJ//+iv/8n/93+yPT377/Gsa3epDPYJaC601ocGt0O6F0oytvlO98EtrbFvlD3/8ztv9G2/3X7AqtMmyHVfaLdtua5REI3rg41XGkfzAqQTN6pTN1Rn/80lO3VQNWkaR5Q5qGSBE8tUvzYiCj48flNKoSSr2JFhZicsnQpvvc7BklMIcwePjg2NWHjcUnHK6alwE4qCPgzF6Qq6LHApzuOyhe5KY9REXp2bt7sv7R//5zFgXMnW5TPK8tkztX9jsJPynF3jY5OTk47NrI4xOqfD+7U4tzvvbnd5PPvcPZu88PnfG9zdl6/6mCdeDdH5MVdriHxFXYrkhF1zLqmI92JlEbu2pa/yDXW234JngcF3T1x1zaLQGNdNxL8TQQu1ILl/udzw26v2m52C61yXdrCTJX4lBxawxrdAxPs4OA97npBUXzI82u14NK53SocXAz56wrcDVtunc4ppdJal4KXKxrrYBk2EhP5xEI0F+KBdScykCkhxrMl+73EO7FCtWmqTopn66W2FEp/fgMQ4+jweB1HZrSBB0YjIAACAASURBVGghLehxRjjnDJqlBH+iBGwaPow4dT3bJkWMbwWabBnKrdFu79yvobUK1v2QQvA4RVKdp12mfzOkTDvGII5Tk8gtfqobv+K4bCPnaj9l0hO8eJtocfd8cecYLA4Ry4dj5jvmxlyKlbTCn4s3UCQeMHLvM3CvFA/cNMOnhXG3kpuOEFYpRoyyKs4wrDitVs7ZlaJ0ZTnP4df2/LVe0MySwpyR3AhxbrJFnm7MzJ7tLCGP5oVzf/A4dz7PB4/zUIvYDLcq7tEUenn2TkleVg3TIMn82tYqQVDPFJxMCH+uxRZpoYCrja8MgACNKxEOhY+J3sQqOkvyoCJL8hgvqrgvOkpahrgnQnzu1NbwuqUFhICriCDGDbsM+wK8EgMexwfRIxFRISpWlOj4TUhpP9QSbPfvTA6Ozw9NCTiCb5ux3ZxzHxzj5OO3k/LpTP6F263x9vGN91uHX37Bm1G2qqGqMdUmG6SgJRVKLObfko3nkevwidou35qd3g/NYwRxbgMpS6sTVPaHkMC//PkHf/3rBx/7g/PcL8rKzJxghIigtgYXd6PZjVYr39/eqaVwq4W2VbZ3cXF8S6uNy0so4036Jol6kcmYywZlDAmfRmSbcyZXx3KMjv9bT1vH7yY5pSnVtjG4uD3GU8YdS7qKILmYfHx8UErjtkkCXmqV6VKs1kNC+ma0NQnWC2cM+v4Js7LviyUe2Jxpuz0Jm5xdrPeV5MyZhlvT6EMv73pxrtZa7jMr2RtpYGQpaXz5pyvkrIBzzcKJ1cpQdR4jwNIpl8EZJ+MxIOdqeAne7ze2VvnDL9/4+PxgPz4kpdsPxpkvgd2w0jjOBzFIy/ig+Epw5sXDKNZ+liFnS+qSxs/nRRhcX3XleZmUfjWUM3omKBfmWJkMDbkEhju1yZMD8qWcB2bGVt+0YeQsqtlPjAY0Bs4JfJwKRHsSMd9yui+OVDFlJkQvtZ/s0cUZ2zZ5jlDUeoyZHjbFKFSqGzOSJ4RQGtJQbxnjeSaOlshmxBrsSq4jU6WEnG5fW1hhxt7V+tj7yee501ytWWvyQhHXKpghMqTWAlcQI8BmmnH2iRVj26rELFvBqjHLpLXG/f17ImtGuO7bjx8PoWgPuZTHGILxr/AeHH0wUGsVjxyd8nVr6Kcu1FV4vKyXEO8L4vIQOc+caFxWNZZTv8dUVWhO89wa3KUsKRrfcKsaGzJ6ZFvAcRcKUNGA4BtyMo8ykm+Vc4NSXzEnGiJbnTKyXR5qsyc/mMScdB2rKHPDKEkoF18LyHlolrzBYPpMqW/OSXOnz85+frIfD/azs11EZLW3OhocfA55mfgobGG0EGpsDjXnT5Uq1+V+hs6pGiUci6oNihxyidSPMeU1Q0CMTimrxXtjuOc9J5Wr8ZOk/quO4pbDpAEm4zwoJYf0pokjU4lzcVEdAnmglWJwDvYjifdzviSRRWqq5ngJdRHCqbfv9PEJttOnxiHd3ODmHMdU8fuZgbs/2LbK7bPzyzu88cnb+0a73clBR6JIDKNsybtao2wyOYCM+y/F/cy9dw2sHX1nnOqIaEDrzHVpzBwE+vH44OPHB3/91w/++tdPPh8PxjyUZMQyDNVz9JgUUmgzoPqdW7nx/f4upLAYbSvc3jchPstPLNt85q62OHFV4zOpMNNl1TJtMmKIXrDsGLLQihwc/u8df8fxWL97cXwxsl0TStdGW/LFG4nD9tHVKpnH5VNiU2RkrwUvTYzt3HQXmRKMt7d3zGVUVdrGtt1USo7Izd/YtjeIyfBHbu4uo6GPhwzl4nnuC4UxX5NyNVlxDg3OfDrv5jfZ8qHNv16LN5Mh0VnSWyE6Kz0ydw2my17nWZTwWa0MmxyPnRiT2+3OtskMcJrxeex87pN+Dj52kZVbFaGstcaYnf3cFURicgwYsfQNccX6BZ8vJMfM06xLiZm98nASRfjK4zw106mfO5YcpYVXV6+CHF0tp96zvUOaaJX1EqvXa3HT1NzRVR3MO/EhSfZf/vu/cNzv3Lf/lENkDx4MHnGyj8F5SNVU3bjd1VK93xpe4MQYIbOwUqXma6VCBMYuldNYyYwyjDVaQGh1kULrBRKOC/5c7iGR5FKZvq/N5vHxwW9/+cFx7IyQRLnUjVpvFC/03nErvN30s8+9Uz1Erg7jNuXJgReiNaIYcZzE1Ny0jozeGifY4La9sbU3qF2mbeXGeQ5obwqE3w7mCMYpEuCcA+aeyrdELVp7rqkvOFbluEiUkY7U88IzLXv1yyx/PlFn130r5rTq2FYRkJcDTcOxs8s7iOT4rHemJcn7GJqaYlKfBc7IMZUlOagjC5LOpFrT/LFqNBqNqa+penOLLyR8DYfMTWq1esxf2u06lypvA9lCEBg1NwUpBmcMPo6d3x6f9JmTrHMI5Mh5eoypIbf4lWxvVrihoa1mdhlY3mrFYjJKpwoQZKLYYibDyq2Z4vip5NJ2jQM4ozMYxNxlpmgjFbLpwZ9tii8Gldm7ZpH9+PGg3W7cv78lYjZFoZh2taCpEnLMtX/QuLvxR/tTuu/KsXdEJ8hxDrVhYWwOcXTefgTUxn7r2PLW2XcevwX92DXfrMscr3DHw4njZOdX/tuf/0/+5P/M23/4P6itUjCNc+kHJZ6jcbSOjOWDttoWsWJQ+tuNcTLOAxtBCePWbnldJh+t+8aJceyTz4+TH7/t7PsHvf/g8lrz3FvHVW3kGzhprXC/b7x/K7x/d/7wH99otYos3Bpv379R66bZaBGZMKkYvDDXyy1Zisd+nupwnJOjd47zZDlm9TE0BDwGsSRq/8bxdyXki2gpi+Up1qHB0j+aiRgkdYkqKtIm3ELDzRzZ3Fdr1LrR+7hMyAKW4SxbawSDGQfVgtKqsEOXp0dMGSs5gmqJoFgj4kg4LhMxuBKXy5gvCWKYXUorr/X5tfk1cTXGdawXn4TIleTEE1omvWn8CbtdWWkCRaPL/bnVRts22u3GNDi6PDnOc7D3gzk7pciGv5SqAG5cG6bs11fHeCVmckQVB0eVt830w/HXa7GX4vdroZw+NAByjIGnhUDiGpmcOe7qOw5S2rvW1gXHQjHN++lopoyQwpYV9+Tzxw9stTAtGD440a8ewTlTpqlyGfdCawUvJF8imK6hlebpHWKCn31yTZePa0OyVF5p/ZTLTyme7wIzf54hImpydizNrGJyngf75we9y0HXimZgaRK2Z6JlbK0x+6R5w23kNitUQeNPCpQms+KQtcO03LhD99c8aK1x2+7QOuGTPh0vg5Omr5uNfp7s7Eoo16y4KU6SYYTlMMAvOmZWprbg9+XpE/P1bVCbKt+XVxXhGgtTTC2l8Em4Zv8QMiJjtVPyM7MyIvoSUASX6yMpNUYoCSks0PcPtcJGSy6X5oZhITv8xGB1vrklRfK91oDVUpRk5tynCCg5KFl0AVciH8kLTpTm7J2jn4lWR75DRec/43LwWFwZTOun4vRc0+lrKv5iopqeBGLpLZL54Y5t8vKZLrn5MU6gy1nYRLrtA84zWPO+UwP6pUnyOsYcnMM5zp5O++KOat/KOL9mBBYltCu2OyrI3uyd4zyJ3ZmjJzlYqS+JTJRi1ChsZTBq0Nqd4GTMXU7o++Py4bE5dF+Su2hjMs6dXz/+zP2XN8JNaJNXxnkwx4msLtJYEPu5kAVeelXXnzVhfEByTusq4maoSPMbZ++cp1Sux97p4xCqTnZwwl6wx+wnGJgHtTq3rXK7O/c35+1b08SDAaU22v1NJOdSrvOI5dycpn9j2FNBmjzHMZ5GjX2Kl+QY55TiKuJZ6vxbx++bASbXY72IgV9Q48wXy3IRXDblaHMSKXniQzQzI7CtsRUjplCPreiFPUZP+GuohVDuFBeD3ZP5PcyI2TkefyVmf5JtSw69G505T+Y89WKjpGySfhKGyE1m9H5eygQuxCMuroEtCE9vhfxdXhaPRkE05jk4z0/OvVFL4dvbRik3+OxMBrXd5S9ymhbATe27UhsyZwp+PH6wH8eqH2miQYlAGEGlZetqSffnswuyziidavdT969Wxxm4PRGr7SZ7bqanIeDXHecRUGFauQjQa6PwlLtmuS0uSvKxIqtK4Zcn4YXwKtv8gNIOypyQ3IVjP/E4+PzxoN4q5e3O5pPv28b+bdIq3LZCLc73+41bK2z3ygw4fpwcs/PoJxEfOMb2/p23t29Uh342Pj4e9D6TNLx68mQsSc+JTLLPrgGNOXuRmzcpCLRD0Q26W1ofKNB45IC/OaGf7ByYVcKqVDq10cJ5OwYRJxFdJOgbtLsSo8fciAF7DA4bUBrtduOXP/5Bv/70T5o/YxOKVBr326Zhk+VUZRVZPb2dnOeD4/xk7JJxioORAe8L+1WZJurPAeeqjF5OQX4az9aMtu9Y6n3FgOTI3LZCa8Y+jXMG5aPgcxKnuIU+LdtDmpE2ykTq6KJZPmbcNime7vfKmJLrjzHpMbhPtX1iNoyNVqpGUowzW2bpk5RDdZ+z9jJ5SLFCtye9u4vsRXgSldc6jCAOiMM4d+PYdZPMSENBv4rKZjJZdc/WXQQnnZhDqA/O5674Oqbcrd/f7rzfN97vRWumFB7H4DwH1nSftu0NJtRWOPvJbz8+ZTw3lrhkML2rTdrV8o2v461fR3HPWUzSsc3etZan0Dn82frDs8gvN64KMUcxuBWaGftjpx9g3ohS+Xjs4k++vTFvTv2Pb9wfzh+2kzE6ZxazQkiF5N3uhebOP//nf0rkQ8+rn5Pj6Pz48Vfe3n/h7e0GriJmBFLZGfyNk/3qTKwwn8lEMa2dAxVXlnPtzGV1UUtj/5zEMdjHg4/+g1I6ty2Ynt2B0Ky/fnSWWvrbvfL9rfG///Of+Odf/sAv//nO9t54f3ujlEbMptFGccOGw1hvZaXvn5pttQCBNCk9jp1znhznUFzNThImKsCIQQ8hQJql9u/Hor9vBhjK3C/YZZEQVnBe1Yytdprl39lPvUElOk+R0hqeaeu/s4KycMnksKunCJ6Ew+RljFNEO3NVc2OkNDcD30ufd7WUgmf1vXJby68wSyM0JpeKIzdXtcRWBZufmYnDRFC+ho117P1GKS7pe8jlOaYRHawUaqtYUY8yOulQe9LHmdXCuoeRhK4854TEx/j/VK25kBeUv/ARGTjNSxaJO3WmG7RnVfyFR++Be+TPtSTeJhKS7c5V2675KpH8r8Xit8wmZEcg7kBJ/oRlwrRsvvuhwFVuSpBbMWarBBt1c2ox6k3+DLh+1pjiQ/U+KXS6H2xIORBjma2dLFPH7Jxk5b7uf/6yhWomX+flnSD/fa3Dn2oQs5d1L5KsrSnYTvpOObWVHBUQWk8uB9fizpiqxHsIwDUX2rXd7vp1v7GggVWRr6nwrT3fwSjyaRGpdnLME0Z/KhX52kJcE8bTaiviWsMGPxWtcc1qSPT2BdBciOtSFW0v97Ek+ZiQyjMmT9TNQ34mlgN2TchQKUap2jgjYCanrs/JKCMTmVRCpWmcT5F5L1FAOi/DOk+77mskMnyBPplAR24u2pzF/5B0/HkOsQr79StbdkuKH2SbaeitEoChturMVsQ0teJbrWytyoSwKsnxPl/MD3PYaRLezYxWVWD1mWWKZKvisiSZ+qv5OJCI3us9nk9TxdV2kce0XfvF4goqTyyaJ7V4YN7B+kUPWGN+SqJ+9V7BJvfjxpiFOl2qtN5TRdTZ0Ay19+/faLXSJ/RzMMdBRHCcO7fxrgdpKVdfiOMLz5VEwXndq/5f4t52y24kRxI0wJ3kjVCqu2Zn++z7v9/OnNmqTEVc0h3YH2ZwXmVXqc6Z3YpmlZSZIUVcfjjhgMHMAKwOh738PX0TuP/hplwsik8gMOHG1mrbyNMyIduzEPjW8Nt7x/f3Dd9/e8Nv39/weD+wvXX0bSNYEVROuTUWIlF3WgrJOcu6CTPm8qybc64ODqLSAcOyHUl1m9a1/P3j10jOOQjZN00Cl4eA3hP5zmwACPeu/0ku7N40n4rBmtO3gZysFPM4RJyjWuDj+YHmjTBw0uyubKb/+P1vOJ+fy/a/Ass1f6e8VoTjsrYOgMQmUPoIA2E+g7wCgHGFNo4GBHkjUVJrJUw6ZTky85frU7IZsjc8nz9wXR/oPnAcO1sgCgwzEj80T+j6uLip94bxvDiiIHJ54SATn1JfcGJtQc73+ACSFvkAunrFP7XPKrOX+iSVaA4lSJb2q/XwLzn++PjEmBu+vT+x9cS+Baw3bH1nuvBy/pUANHRkst9Kn523u5JvDWEdb2/fsfc3/Lb/L+Rz4o88kQP48fGJPSew8QU4HonWG475riCbyHAMAH/7+MAM2vdfY+D8HHR/7Rf2B9Btg28kl46PCz1MNgBATJMcdopJaoQ/NWKgZ3EnaEIHTOR2wtFw9J3t3/PJncYBbw1uO/1YJtVZ7hN9f6KZYQOl4vbeQfL1O7ZjJ9KTjZJi2e0/r/8HMxPv/cC3x3e8f/+/0N9oyTCfgbgSKX5CxoPrpJHU2tE0R8tw7TsejwMf+IHP+QlcH0y+RnylghznJ5kiEXKsHopBMgMtAv4N7hSMT/J2cw5q7d6wdSqRAg1vj443M/z46xMtDB8nA+jzeaFn4jgajjfD8d4RJ9UjLoi+70wuPydNPa3aQSORFhjtQjzUytDGGl5OGg6zwPRcSQg64Bvdc0dMogXN6UWTiQaiiKE2rndHXBMYA8/PJ/74ceIal6676f4kvAVa10bxZPzKrqQnDJs8efZOwn5eIqo2JnDf39/xdhzY3r7jcz7xOT5wpR4/LZvwR35yA/QG2wzH40AbBsOgamdC5PsBwehYhjlfeDweDzRvqzM105ETiPNCcTGsA/CObdMkdUzF243FRO+Y/cLVJctviWGBmRf6AQCOZifcO47jO7J3tmEQGEkun5njuv6GiBPfv7/h2Df8x7/9n3B3XOPEdV74469/wJrh429/xd4PHMcb2t5gWwdyIOfEVHusvNFalmKJsScrrjKlAHKSO2hANqJWR6OQAwkKFbJjOzYc24Y/eodH4N+/f+csPCOBfovA9rbj8W+/4e3d8f7m+Mv3/4Zvb7/BvzW0reF9/wuadd5jAFA3Yk7geV14niQzJwY+58UEMbVXzolrTnw8Txg4mDeDy2fKtf68ICJ3+6UI4tfE45lkw1c5lC9KgFdIwSRfqyxZic6d9EiVogpJ31IgD1ZYUmYWMbVZUI5LPsRFF80kRlSeMuT3TJGOX6r9l6NgdXqzsFIuGeNP17sQg/v7mRisf8M90r3IpODnB3BdUl/0DRChcJmXJSsssTY4AHCQyWovn7WqtTL8S31PwdIvp5w6V7qz1vkBaxRCajHDUNbYa3TFFx4jJlq4fE0gG3oIbq+k7qUKB7DWhAjtlAuWJF7M/NbRAti2jm2nn0hY4vN8Ij2xDSJgpharo9gQtmYJBXxVRBxcSQLphbk+2xsH09FVWIPxrM7xXiuFR6UgVEteXyWnEVhjLcqqH6X+MxPvzdTqTXiNTig0CNW2YhBz0L4/ReZM5Hp/hgw29/0N23FgOx6wLjVbydwdCzXK9ba+IK0i605VYYsQa0zkvhIRpIdeLiWboNm68Vw7QpkWDw/rYhayUygG26PiQLhh2xr24Xh2cgIuQnvoOSUT9wVDr9EN9nOlXHWJKbCFUIKIQj18oXV8T43t7Fc0D1iBcT2Rau1CcXQdDKIka5b30Z151juPQqPrezTjD4CGjP68jgEInaH/zt439NaFnBfBWvc8WFTFT4GdSV1zR2++vMcA3NYb/0VSzyYXabob2TJ/bWUjYvfzXOOAMleb2VHrgIlsnxt6DNkaJMn/wEK5enNg27C9kSs2wdZQ8wbO/Xzit+9v2LcN++PB9aihu9c5QI7qWLyTGuzM8X/aUIVgGrCe9338/JJy7Xa4iTcmNAZweq8Jyemt4dh2HMeuqQQHemeG7gZsCexvOx7f3/H21vD23vD27Tccj2+wg7YMre+0LhDkXbzZADm7VGRPGXQOjJiYSfRqRq593VH7WL5c272i6xn+o+PXSI44HpfQCyqfNNEZCrzthkUdoKsqbp07lT90NMw0XNeQj45r7g4kAW9ofgAIzJNKqZGTcswELB17O0iYy8DHx18JaQne4kKoBVlGbFBQautdIoqjZGltgHWz54Iq1xLJ+5+MkezlkhWupEq97Y/PPzDmE+nf0XvKwySBIY9GAy14MQkTzxsxqtUV4WrLDawsSd4AdJGGAkpiiAu1bdsdeJ3tDM4wcSVNfDkyDee4/hxS/+XHGQMeJntyIEeidbrJlsfCzPL9Ico2k8nHFdeyuTfvsLZhOxra3pXwBN7/8oY8Jv42PjCuC//3X/8nHueOOL6jeaK1gW04+jRYe4P7jiM2ki23NwwEfvjfYMbEZiQw4pNmWXHC93egdeR+MoBNh4fmp8XEmCTRRZR/iHEOi3MkCV9wbmgdHZs1vDX6O3yAXI0uDx7KtUmqn5Moo4FtgnlsQGvwDZKzA3DHZR29BRM8m7hs4GNOHMeB//Yf/x3vv33H21/eccaFj3GREzE4loWtZ9pslWEhgvy4FXxGAujo/YGemv9FY6cvW0NVCxGhZaJXyYuLiDjGCcTU+3+jJ9UanCOBnrAGNNCIsVp3377Ry+OJhucJ/PjbCc+J8Uei7w27dXRIuq9K3KbBp5IbAL4buTWj5jtNXIODDLG/I70jPKD510DIkyuTwWDkbW9Rv5ktlWpTUuYJoUaUw48xMcaFMT8x89Kz4bePKxDhaCpubCqnaTeAROTIED5pP9A7ujm+HRv27cD3t9+ooJ3klYwxOTNuSohhkNQayMHhqDkp1/72bcd1Bs5Pmi7OSPRY83i//NiPN5hk7YyHT+xtw+4cqFlk+szAdV7iq2xKaMlja9sGiwbPDdkd7bHhnCTBXnNgJjkssI7uO/rR8PjLv6tYSXTnGJq0fwfsQhlZZ9KY0Hugd8dbM1zXifPzY3UlatK9vHOZQEMtU1TOk8y2qxjQLzdyGnv7hvCJ8EsjLXbAqK7zK5DjxPf3N3zrHd+/G8Y8kcY277+/7RzntO1o3bHtjve3A9/e3/D+/n/gePxGpWwlhAk4xEe6BmZcmOOpuZQDc54Y48L58cQYA1dINd2ZHJlQhmqfTRtA72jW0Yxo1vj4pKfePzh+meTUZs+bJ3lXVbxVYpp+tVzfoT9ZKA4XVfWt+YrDb5191tOppyEYc3nrqNK9RaKlGJGPQSEaL+jnMmEDBA8S/QFeKtWC9aphr3N2c/kFxJ8wnfXDiQpVRaSqcQxC05xiy0oQCcwxl1y+IMPy9lmwSt3OctBdn3PD8K+nwgXOasKjbOoNnKlkKxlbP/q1MvniJGdV0FUYvaBhrHgXoLGeWbHrh1j4NoObyaQvwgbj8LaAxmrsaDtbPfP5wTlEnxfc2PLJbEA2WiF4cBKvA9dkYPr444nrOjGuCWuJttWz0Rr2RguESccKtm95wi07akQFDFTtpLgx9rOdPderKhNbK56fk4Ym0iGTLfrznD6R0XG1TZUE34MGIO0CTF4VxuuZkQu96ccbvG/015kD1xjA5cghc0vnKAJGSqGK9spMY8LDvV1+MVmYzxdDgii0i7PdKhN4vYesarCQm8XFQSGkWHEjIkhcNCFvnby5SMNo7Idxbg5wgcVYvchmHPJqZhgeQjfKv0OfZyXizXttL1WT2pFBFJZmhvdQB3u5lEQhUSqjcW9wkcGBmzFf2nWGUoiNOWkip69rBCIRFSFBqjTBASWAtSb32g7vhjCKIPKaVNsoyYlhupxXU4sbeS1zQS9FW86XmE9e0lcvoVW0Cvao8UP3H2ZBcfz667b0Ej8Zb7nxt+zYLOEhUn4meUvWsbeG3jr9YbQem22kZThQ5HggkUO+dOjrObTs2PYd3pviJlcTAcCbekCfKAPc1z2uYLpGTziVdtY4vsMthM7dJoIUrhhwbEDr8O3CzAPWO92L3zmdfGpMzLY3vD0eeDwO7Mcbtm3n2lZsJ/9GTX3ZUURM/fvQezjli1Oc0/v9pGioOLC30zJk9hCIFz7u3z9+meS4RikUyXhMOg7OuoFmQBZfR5tCkZmgGUCqVJt1jnrPiRYcYhfLFBzMxPISPNuo/khgeFmpF4HNuAEwr2P1JtieNzeW1t5KsdDlaKoWl8v1srdNGWyjosWcai7fxd2AkKGCcQDoVqc2NO8MepGJ83zifAauMdHcsfdtJXpgYi8DLMipUb4dVt4bqQo01yDHGZVoCSFD3m0bJS7Pk8Z5zR0+GJi7CIP1FpT8ueVr4vY1h/sGt85n18C5XwAQg4hWGCckEzBAIvEclA0+nyf/IICCbo/9wtY7Ht4pc90P7N3w+DZh9omP30+Mz8Dv/+MHKOEOvO8H3jaHNyo8Rgdgho/nE+d54n/9j/+JRMA7cLxt2A7O3YHgXG8N275TAXAmpaMd8HT0/iCicw0pe264+495qTXEZPq6TsASpx24jBbqE+SatGxABMbk87rmDwDA+dmwbR1TIwOas5Knh9cHEsCH1pG3Dt86vv/Hf8fxeMP2/S+AEfk8r4nnORFnQwyalrkZ3hrXTnolbxt5ZwGiW3lyvhEMPVj/31vtFx3iGrSaO2c1E4A+MlG23yLArta50dGmzndmIAZgoJJsgmjz3A3oHe/v79j7xH6xaPl8DpyfEx9xYm8de41HMMNTWUj0XJU/K2YBAi2RnuU+wbulthcnyhu29YqSgxYzyW9cYg/DUAGQjZtVaPjuGYlznDivD4xxikBLcvMV1YI9wY8V/+KgsjMjYFEt1VygsaGhHwd67+hvJPj/wB+IMTGeT3mYBK4LmEPk55eksnklDE0JoaObwTddDsjPXAnjF0ejMUJrQpPEA2umIGwCxR0FRAo3xR4a3hoAzMl5Ye4IcLBvT0ekAf5JI8G2oXvnINDWse8HujfsNezTHDAmBGVfSecTJwAAIABJREFUcZ1l12CAN9ik7cj+OND3A+FAQGojAHAOiAVMXlr3fgzct9Ybp4vndgDW0DawQATbMJH3bLPeHe/fGvrjHR4d07/BWuL9/R2td+wH99kJ8hz3/aA1ysYY72hKuKWQxUTkhREnruvJjkkQ4TzlvDwGk56ZwZEqSerHiInroogk0Fn4QuTknETNpMb6Ve/810iO3Y7GldkupOT1NwvEZOYeVUEBylTZ+2RiIBVUgn+vidugD3G3tVlnsH3TqqpRdupJf4riPNDqv+rKW6WTJc8p2KO+tjgwpkSlvlPtnar5XpPhXH9tqX7WCjJWMy2TAWx9liTEsIUkFXmY+aEqs7q3UXOc7gq52tZ3BbEejBb0Sx8cJuSHaNlMwESYg91OyqoLv/Rw3bw5B4nm6S9eMvJDEGpnIcuBhHgzrLw5lFV+CcPQHIj+hq11zEaL77ftHf3R0b+dau9NJOhFcQWAkWgzYDbwKfTi8/mJcYkA7g1bf8O+HXhsb7B84Dr5fD0qMDsWn0P2+lxGqpaKG6aqrfeqmlJIJLlJ45pUWFwXMoaCf9Jfo/Pn53Lmps/NeT0XF6SBJEMYVTLdDN7AYX97x97pKXR+fIAE9ifGxaCRQxONg8qJMLbEPFdNqSSNyVeEoUz4YLZ8XG5+2r/+uBFXJTer4MBSa/C9aJXnLGQktDnpW4CYODMx5sBuTpO14htORwPw2HcM5/O65kQMmpwOcVDWORhIrMXNl2Nbiagc0ol6TBYwVWTrDcft5aMI5DpvN8Ud8ekUZ6qQLJQhp2KHZuIZ+EyzWtVWUZGx9rb3DxLMIZWi0XW89ca5gWAbLJGY4Dys8bzWezsG14UpHtIK4jbvrPhmSdWod97jqfXPk78Vh191ZBaHUet429BaXzsAzLHYloqdN/uyrXPOlAzaO4ncwkIOf0MmuT/NG7ZtR/eOre8cMK2ZdgZZAejZhCWiBzwnZpKqMDtRHisT27X/umKqrX3Ofr5IUSiUPI0LM2heGKVYQiCcfBdDU9wihyLFOWzN4f2Ad8Pj8Y1zqXZm5WGS429EpZqXCxILiwRIIhbthElMITk08kyEPMkq7tiiflTiGcXhsljNntoXVkxSEfyPjn8yhXzds3szBmo/5SGPlrAkOVMQpTm9cSK4yBkwhzZgstj3XRE7mIB444bYO6WduJoQIqBmThWqRLJUInuHPNeECxX1E1hZSoJfFaTr3tcLbwt6l3osZSNdZlovic66Fwqg5flS7eW2bQsmzgSuQS+BDZxxY7oXfMEazLoy25C5mT6jKsRUMSTSVtXQpkrjzw/25snJl4KZDhbBDmyFfaUdP6AwlmWzz+nr3nhdnCWUy+I9bRKRy5u4DiWM13niej4x4sLEwPX4dzw6J9rCHd8f/4bcJi5zXOeJP37/A1ewevicwHMCHgNAYD4uhAWujw+kCOCH73h7/Afejgfe93dYPvD8ZELWemBOTQ2HE21sTXB3yMjt9iOpFl2HVE8xuTYzEJNKu+u8ME+aexkGh9M6sIEGheSKJWCcPv75nCvgbtaIKqgqNEvAHY+3A/tjw9tBZODHX/+KiYGBE3kN5BiAPWB2oDfyJg5vMGxA7lorRBkyDDFJSJxzIianUltr2I5jSWu/5GjcgMy73u+hogUiUwPdm0ZyqKtXa6dqAfA5IALndSGQePiGbh35qY1vNzQ0HG/vGGPCW8fHeUr5YTiLYAqqj9a8HSRacBinO9/3ng6bDTGda9wY91Lvw89cybwTA9kiWBVDYFGouRW0hkgDhmmG0mTLatLiYGtAa20p/246AFtbIcSI0njDvnV6Bx0bWueGmgh8Pp9KyC/MERzvACh22k/lUu8s8nyTZb+1l7hKxKeBPLXx5Dvgy6H3Kw+uDBjb3Mfjm9rOKbSV7ZusRM3L06Uhkw7XJVjgD9ngbSsYDBs2IY58ll0k4+KTNtQMMig2J6KoG3Yh0mE52JZPFRxIJss5QW+bJgK6IealH9UWvhqKl2PIc+b6xJgXrif3X6igDhto6Nght34MZPKXm1EhvW/wreHbt+/oW0eXRQDQfrIQqE2S16F1moERQyNEBq4Y8gi6yKGFPG4KuZxUAXOET6igDZhD5i62khxn758dICn8/tHx6yRHCElJljOrYfITNXft/PfXhF4oszcnxDTmwDUuuCyws8aoc/kL1iehDlEPN/Xz+HfGdVJ9pUrq5ja8Jjd1FuQ5hEy0IFnd3flT/uhy4TRj1qxWWyV2P/++2EgoX54add86s/TQ9zGfseVivGaLWDlJr3qOm1pqOFwUAfHuF7s5H2q1m4ps8KfDwcv06mNWr3PdmK+mHUOzdbSGQJMxq6FuAF6rpuVJ0bQdWCkhUKChMvjE53NgjCfhUTNs+yWUA9i2Dd//8hdyUWKAnq6cxgx3oYwJ/01k42uitx3vj3+nWWJyrsznM5GNbt3XvMS7mC8oGhbMl7iw5iiZKnNxdcp7JBsRzYJnw50VXXcq+xLYtwcSiceeun98ZqPalGHYW8PRO/JihbNtG++z0cfi83cS+K5zUrqKCcxEjoC3i4Hd2a6dGQysV22q5AR4AiMvBKbcb3fKmS3Rt/alSQ6N6v7krJ43t6QJXXI39CaEpBxLQbFCRS6GZP5zruKN/DkbdxGQZvBjw9EbfN8gqSnBIIM8g6DxqYCH/JRgaGo5Fxr+6nDFJeNq2+B+H02tSHGfENX61LsQ7UaPVOXSD4mkUbivwZ/WtZELuS5l4TBVyxM4NpoiliIsMpg0yfLxutjSy/K3UUsfIOm9oiggqTrLa8anF6fyimPNDHvrNHAtsssX5zje+hLCcDTFJN9u27GifAQpCOA/4UGyeuNsOwtK+dMbVULItd9ZkdKV6G5tR41yuZEiVOWnX4wlbaFfBzwnncuh+K1isOTnRXbHusNCoNQXXZyzMVcBqeCx7Fbi4rvNQakDEU+ceeKMgW2faC3QdyU2rZPPs3isvvY0bkPigeXEDBoejjgx5onr/CCaNOlxN8aTXLJgsluDqFGDQi0xkoUdDalf9+v7jnFWZcXif7yr/RMkp26jslYrWhxuNKc29JXAGao/6+4cjgf2M+e8MOXN4d4I86aRTwNQXhvGnayguWpT+QH3jiufy8H49QVZisQ6c+MDNxOREwp2IgMKsJPrMZZZ0Qsuw/xNCpK6wa80R0Itsup3DRxteGkzgBI9pxIoBGuzJ3z/LKIr+twR8m9IlIy8kqUK8Gy5aTcHVhZrqACJZXoVs74H67O+unai0dpNVZ0RUiE1PbNKcoRwIbXxqqqy27rAEitzf+bApwHn+QFk4u0x6Dx9bNi2HW/vb+uNaBUcjk3EO27ijyQh/mNyLXQ/MJ4feP7xVypWzgHbDeGxxpEYimSsTUgVWaaqZNQ7k5I9q+efQBrD+6qoKd/gpiRdd983ts4m21LbTnj46RCibDh6w2PruD4uxDnR+05jRAA5J57PTwW6S+eU4LRyB/oF60lPDLBCHJQDofw1CqEkdB50LDWqg2BA77be7a84zDuDN2608nXEhltji6A5WlPQyyG38kIdeL5uRDYNpjxIBYUlMFIWLsbAvu84YHiELY3A5eQkTBHf+0tIUP9VgXeuz0xI6gpu/d0NEAG9Yp0xG4E3bnjVdrBSmUhKC9JoMJJ8pAl650DxtrnRAdsde/I5drUYPpFSfTr2o+E4GsbnhXleiBFqaw9E0CsI0DzaZdEBwDSYwWuEBsQj1LsBoDYEjsRhstm0QWZLUGv5deq8Oujd1tB8J0I2J3Lv8L4x1keupG4WJ8cnFW3OeF+PtcQFLEgbmvF9ojUB73/37U4GXtbCvXnezGZ3yAx3h2MiWyBiYCJxZS6ui1vtCcC9R4uqUVYhiBeCrwjxeobzqq8NjEic88n7MC8Mn5htYvSJjkDbO7aDiqoyIlx2Fy/XxEsSJWWemDFkdEvOGJHGiTkucnMgrpqSnNTepLSOogukUEHdteRuSAk6rUk4MujXFIxfJjn740HfJj2PDFYkEzc/Jyuzlxtr61xE+373ICM5mfvGQ5hdjjkR6di9rc8kJ4fSzrCBtJoHFMjJKcgl68NsuNaLwjovkQsVgBKDcvmEC+9Jwb0JIIzmb8pyK3uiSiJeqr5E8SzYUy25PCe5mmn+EYCtNvWq4sQcNGWpiRupYZ+1/HNU8Rnh5kI7oIDuZa39ZxRnKbTu6zaRq0lyg9oq4oZ8cZZTlam3jQlYE6ldSzPAAZucOivCeCPp3bVnIIJVzLGxkr4K3ZmsiBE4xydGNAQmfA50pRsIw3Hs2PcdfXO0o+Fob+je8K11VrY/nuL+GJ4z8VdwIKefAD479iABPlMOm5mUy6pCpkPnIKcqmcA1c4y8gJBU17iBYIYM0ibaGNgyAW/wvhGJeBwk+aFrww3ADW+Nhl11Dg4g4xMXLiKlY2D+TYRamWY69UOIxaXhumjuRMUGcMWAG1El84R1uid772h+oG0dPZzDA9VL791LbfuFR97+ILXTIwTf31OZGXxJ+nUzlMsuzXaYxHm2lwKwhgUqPli1FBI2Bgou7q1RTbPRN8c1jWmXec856H11Pp/yg5qc+2SGLZO8B5RTVsWf18urAobTxavyn9fQtcqNWQxMuww+A31ObMm4vDt5NYeS3oesCYrHs8mGggA5kzpOni/rfNpXxKtaxQsVB1iQcC270aQNoJlgA1sc1sSVNIdZZ4UepYgNzMZ4xe3pH1fg/4pjJu+l+QPeG/pDLToYkCy6zFmIEk0AYJwIf14XLDt672wJpSQ45ivW12295yjq+tLWGitQABZ3smKF2gskQHE5N9B6tsEwlnHs0LgMclYpIijX5jkTMDnet0R3KUuNXY1t3xHB0QlIJtHjfOL6OPm+WMPRuvyRGlqXiqwUWrWBVOGsKx/ziTlPnOOJOWmdMMfFggBEusMGQupQVtzcGGdeGBHiDwX6VhYhlcI4p5xHrPrBlvHha5Xxn49fJjlbF4SnlyrXRU0E7KeHUsGFsLGjN/YNV/ZaqMSCniYJjag+3rprCxLjDbflgMzJwnkjQSiy8KrzsXAKIyHTXhICc7XcVpDEgv91kigSTipz9MqRX35GIStudv95JSyRsK5rV3uhJrRXTzFQFY9xwnBCee3L6bvTUTpfwoDfaMhKXRMLnbHXJGehabxHBa37fTu+7ljn1KR4u2WB632RvXc2BoCtiXSX65Gzb9s7fCR8sFpBBqpROYNjBwKAZdCLo/qZvcGRTLC6ox879r7j2Dr5CZnAYNtqOPCRg2Z4E9gHB7oGXmCwgNQDQHq1BWtBmZKQe43Vt5naJjHBxG1MuS9orllr2I43tN5xtB1mbBmZUcVVQYUV0IQ3St7HvBBz4NJk4wmSULuIqgGD7YYaXO1COhGCtpUUWAOahxCdpnERHa5ih8Wg1JR3bfI1x9o8AKV4KKWK+/3OLTRKZnvpzsDo9/eAeSPfuxS2m/fnrC1+zpW4WmNLonXaCWztAbeGo2tDez5hF/kHORMzjTP3vBLytQ3q9/qaLkcxB3ip1Jdk/CXJkQUFpsMmLSQ8k8NancnY3roM3VR5ax22UfE3yZcYbFfEFLcsxecrCoLe0eKorEhblBTxQ8peoHlf/45CbhKAk38HvZeZP+d3X3XMGHdsbA2971Sz2Y32mYJOWmXBfFfo2yapNsACdF3rnz5ISU5AZnZqsU4Z2iJTHT0mKveNVUAH1BYiAraWjv7suoi2mXU0LyRDRroWq5vAvbJzl8qAZ1DpmkIuVTCZTcxT3wDy27q3NZy1numfOVS53p1AzEu+NydG1OiKsbomgamCLddtrX0/MjCThoCZCe/iHEUlOqJrwLR4KvXBXdz/g+PX6qpCby7C9HPky4nxptc1W8ploeJ88oKYvYskKC5KZr4s8FBAvoPTNZ+8cQpe5Q+RMMymvLDR0dW3jhyarirYurJot9t12HDP6JmYK4iTq7GjWljkAk2hNxUU2e5gYbUyI4YKI//CnYmdwRCD1a439tBdzle8L9wMm5Ik5jgpFKB8ANZvAHgddA7Nl9aTrSDoUro0qxTolp0TzKFyiD/r1/3Lf8UxYwAZGNbkyZIi61F+iaZF7QAHoyZGKQFSw0nJOCd/AJJ1C7m7Jp9Je2kbJgzXD66yTMPTBhIXPq4Bb4b4t8SxH4iNbZDrnGt21bwGfAY277S674ZsnOUDFO/JYe3Be+ydg1rnuUjw9KuZ4ktwnSQo++VLarjGxPO81lrrZtia4/1xYDsOHH0HMvH8QRUarku+LWP5TZyff+A8n4jnycQnCPNSIc8knNUo71tHA2Zi5kBvBxOx1mHu2Dv/iW3nexQbzNXtF5xLX01H+vNL1xE5JymElWvce4dbXw6yBpkylgRBt3pySJDyCG1kLMHRVTjMi+9/17u8N023v14k4FJ+4Jw0tNwnJ4FDFTqnrTLhEheoxi+UMirkArr3XQULY5KhcXPCBH2UfMXuqCTVuFk8r4uJyWlrNEw5EJsluge6pyaIM6WKS2NrxtTMPLUsJjlmFkyWAPKtGJMqitCXZaIKO8A3JsDb9uAsJ6Gzx34ImRCKCsjLZ8L1DpcdQU451X/hccYFOI0wOcOMVhFuABoRz1pj+0N+NVL2NefsQbeOEkW0dhcC5hz5gpya+g48NciyLdVWFc5F8AfS2f5biikw4Yy4uMFKTeRwKZZeuJrlXygBTwxeZ+0rqGeh60qA09cBvD24H1Xqel0DH88/8PH5A+39N3R7rMQk9DPurV+Jfai4jAvP5+84rx/4PD/p6H8xyfnx/OO+Vhgd2itrq3gStHJpSprQ1eUcVdjwa25+UzmKQx4vrdS/c/wTM0AH9ALTcyHEM1l16V2h8sr1S3+/kIhVfbFVki8nVP3DwnwYK5jAFOxAhKIY6b4emlW5U/7qdU6v/17npSDP78s7qRKxtbDr1CNccIn8cGA/J4yRL/JPfbEqyTnngoQNBitkvSSkf+cguSxfeDd3AknXfqVcCdTQvqqyKEusJKdmVNVpyaGZUAj+Wf/yX3GU8RdJdKAcG6/3jveIwAtDf0wSgvPPN0zX7X6zmuoHmdWfSUQzFGgTmCNwXSHeCXDuJywMp4irMXKtcyTns3RjVVwKv7UOsvrRXShCx4yLfessjk79EhJp989PVWN8pyYJ0YYV6LoGSG5bBwIY1mA5ua7GRJwXyqE7xoU5TiIOetlN71mhe6/vqMGExsS6l1VxUyRAbgfdsu1lveaNjKLe4S92PK4rMb1XRl5QsyaibnlY1Z2/v9fW99ud6KAI+hQPmAjjZWCXIN+CLW7cBVxUgA8hzfc6NeCl4pVXj1AP/g0xtlycoFCKa+3+vEKNK8aJhLxUK1Hy2pKELxhA515O8/X8VZRFIOb4mZBaayZzxUpyre54vMAMMMaUgsyao21dyYKeR1d7fD00Y7hWcWiZ6z2tjfIrj6IHLJKw+yKIm73E6rRV2EIJcY3msPU/iGTuizRcLnIsZgEYC+b0W20Gq1Y9b25ZBAB1w+v9CsWKdVKKH1Wk1znxZyVqv639Rh/xMnix3hsKYtr62a13WO/I08j1Qlnt5fq1zHn1WZV8zBgYceGaJ6554pwnW56TMWuogK+xILW1opIwLdZMg2ntRaE9whkKWSSBld972ywl8It19Msk59jfKG2emoackxcvySwfvAKMcoXIAGbiumIFoTpuSV6w4pIaYQ5WKsuSyBq8G1qXp8jggqnWD5+ba40MqR6wFkCInWjyEQklLfd91WJ1JnEzPpXBaOmmgSRSkZoykUPzWF68cADmSMsF2sb6O5V8RNJRtyoCE7E5sirDuTLirEWeuWKaG2jdnrZaTZXU5FJnVYB9kRPDVMmGXlK+FK3lT6nBlxzl7uqJ1g1tl1twpioUsNKQTDjhyBiwhO63afPm4NOm6iKNpFkouaObMHANztaZ+h7Dhudz4nN8yovN8OE/EP1E29Vv3tkKspzoHnjbGx6PHY/HG4wiJFy6b+99W60OU8DfWqLbWEqIynTOz0QMA05W2nPSgKu1DsyAIZaaLibYNpgDmBun1rth7BvGMFzjiWsOfD5/iFQYOJ+fGOeTm44UHbBUa87Q9k2OoJR/j0uomBm24PTl9hK2GZBN75Jm5sTkWlPQZCI4b0j8C44MbhZNxU7CsWoUFTvhhtupXJu2G7Z903vAWGFJWXwkMJL2AY5N9Q7dyS8F1VDwRSZyOnIY0oGwwDwvpA+gMTl6bAeyJfZuOFvCc+BohgN8hylS4DPt3uEALiNSu7VWWx68G7ynZMiGt3lgOpDjxIiJz5hMyLxiYRKR8o69O3bF3IgE4kajMwPXvLg+hfYmODHbDCKXVlKW2EwxV898JNHNrXeuETMiOB2ownAMmrcR8bsRiOYdQ5ui6Zd/cYID0Leq94a2b/Ctw5ooESrcXQnOShKQS51blgREfogE2aS6h4NWX/hPcqM+50nFZ2NHwkUQT2srDje87FPQJp419wdAJOa4aDZKETp5rEZ/IwOAFS9jvasrEZMyKqKtAhhItl/BPWZGx7ZveNg32E414bCBM56IQd+faPcMgNBsvzkHzvGJ53jief3AuJ7y4yl/G87BmjHwHJ/8XHCAsTXDdCZU06WwAuNkccJWi7TiTmCJO2AsIIsw/g+f+a8WhC9Y6T5UeGuThfqwDVZtkOSWS8dhBhSd7f0zqqBKvmTLupmfiuJRuBZb/SpFBKyCcCUsVWFWpsmDlNC/h1sUolOV6us1vtZ/gsVMmTmsrCp+OupLy6rc2svPwcpiWR2mCKOvlf29gawb9J8CwM8oECvwXKe8+DiqFFaSu6pfGVZVdfiFRz0fVk93hcoY8tqC+M8Hpy7/jEVUQlpfq7ZiPcvIkqbzv1mRllyXHxzXwExgtiKiiu8FmfJ1kUvbjRiWcqScwCsQms5za5vQqtRcMjqrZpUkCqD6cSRWu9Zglt6g0Ih5J1EiFdb5peqs2yb/fg/y9Va93i+VPaGqFDA5aicwKFedHqykJoM6zz8wba7bXfc45zJZ/9LDilcjJNVeloMA0BWDKtDX9SZe/qJB96Sq5ddIwX+7aaPrDdd6hcDfAFtLgWzlgcVNvZSl7jLutDsO17Na/47K0xi0i+vnWttNMF+Yg8wwtXvkipyt3uu7ZVtnzQGHUGJ7v2eF7hYabS9I1DqzihWKvS219t2WV321VjL1XIwbERERJvqsf++f53YjQv/ovf9XHe6FVmofef3Duni38lzkl1difRfK2pD09UQh/tZuBMwykTFJ+tfzSbvXZq3PtW/Up5T3Vn3OuseBEr3ciq36tqgPXa97tavXNa8f97LnKBi5O1rfsCEQnvek8vXZudCpRMrYj95Mz/GJj/MD5/mJ6zqZ/EdIqEDPnplT8xgDtQtS5Ri3+vNeUSuxWRf4p4L+/roK+f/dsQ5Ni2CME4tkDINbX5Hl6Dt660g8eQJ6ofnBykr1MBTjyRBvQE5WiiMoVZx2S+A8G1o2DeJi0mBmZF0j8fn5g712YzDZDOpPj/Ua1w3z6m/XC718a4ocLBWUWhdrU8YNB1e7adTG8yItqVyDieadtAG1gbHaP7YHR2PMgVNs8goVZRDFNcjNbLmmaqEFlBQZk63W1d7Y6JnOZEFJzkoiqGxbAOt/RWAB1S+9daqrMNHMsDVgnCeez5OtErtdPXPwnPe+M3a+DE7NmchBZ1CaJMrnKLmOFnrFEIKGKXg4dC6OnCeGDTztkDeJYGYEWMLuiI1DCx20qE+INKeREPOUx0TQiXt3Gou5G65nYCTdPulDYSLrE6ZvcaFZ4HF0GTdOWJ+YHZh4YiRwpWDt7YTbRNtOeF5suQ0RtDfKVBnjEueChrmOezCIjAB6c01SZ2V3XcnhkZPE5vPtjaKB/VYUjS2x7QnNPOcIBST5AvmFEmCnv8nmG5BEvtwCzWNtIJg0+kPc7xTf4VoLgMoqdWlyEWdDLadb88d3bSaJpkw45lLOxAQdp7nzA24yOeVhDfADVGJ1B5Yxp1SVSlbK2I8tHbVPYPB0tMl26FzKVJexXCOSAoBMcldbYGLbjKNm1E4c+l73C0Bg25jcDnpirrjG2MU2u03Gv+78/N42ZCZ60qF2ArgiblTdOEvM3bHt5CEyJiq+aQ9oSy0k2bDNX+1N/5LDtw7rjUgcJhC+ivHlAdP4tLurcMgmNfC1Mp+IRI5xtyZpMoXUWIXenSVAjJVMBegllE6PHiIsQE5qhCuBH/NU+LrLVE51x9pXvMaBlCJOeyhnkzGtrNmMlaTVAOAqsMNzxc7eD7z/1jRVvSMnE1TTXgLRKYY6HM9xYownnp8/8Pvzd/zt86/4/OOJ6/OCzUEPtEJfMFAybSbiAg4cSOuANUnMAzGoGkudc3kCubyVZiR5PAnAaFwY48Ry/Pw7x6+nkE8mDbzRtXmqBOXKXTyUyvDY/9W/m1AMPbCVfGnxeLC6KrLtzf1Ze5SeMMl3CYNpk7gHc75WJ6pxFmqhBYySldYPVDKQTMrsZTHdgAifQknN79qoLkKf/MrjldttvFSYua4L9F14qTJfjzoDM6INlYmro47FC0BVrEHI3bDuww2x8mdUT552ZVroXsjH1x0/UaaExt2Xrww9WVHPlwpmPQ83tE43aksn2hDQ84HIsQww0DXXI6nKMZ0Jd6siTi6bK4Gt0kF+Nq+tkATbWGxhK1jjvqYqim7vCAb+ZsX5kLlB4kablpT3XntECciXaDYQ/QSaIa2I7OKgNG6CkYAjEWbLBt0KatcNKD+8iMS0Qj8IOpOr72pHO9qkZYNHJeimIeXJhCYNE7JdF8Hyq46MUFf65V2v1oDK62W4We9AJczrtS2Zr6mivlu+qzjTRsCUtqrHP51L/lRvrsVCxSfYWqtNvXrML5V7jTm545faa3arWNqLxYK71o/hPqP1brMl0LQG26tSKBWq66oNQnm4hgI+SodmAAAgAElEQVRAlMFYmSfi/owIeYDVWepdeY2/GXGP70mOH/Eq7mA3fxAsGO9hijd37EsPrZeZayW8pLW1QrD+uzZ6OG0H7vuuolaO/0vFa+xg0B2dLcpCDO+AISgQr50GISbFKTNjYSf0tvapii/cZ+9fr7fR115dxn33Hgd9Kv/P782ctzLYKLKZU2IH/fwMKqonLsycuOYnrvHE8/rA5/kDn+cHnueJcQ642qkRfJfcxeUFkfwMcKabxsTweUytcZWasnNRiX/fv7V1iUgtUcF/7nzcxy+TnN8//sYbKNVQZevWKN/OJCcn4dzAETd8jFwXA+An7kWpgaB5QHRozsX4j0EH2zFKnz8glgFKis6qo4JYJQCO5uUr4Au29XqoRfhEAEnlvjl7lqjcbaUbHYu0u5yNoQdWkGRiqE/IDVmJHRK3hJxJ25UTEefKaAkRNgWZ24+nghb9CABUxZeaH6wXJ2AI8Z5YTYksWaixOeAdrXXCglrkm6PSyi87HOQNxBiwbsthVScKc3JwIgKXSNsrOWmAdcPx/ZsQnMCwAAl9NGxsSFYO0UBtsAKpT5EjN21JHFza3bEfGyFZ7d+mygUz4Ag0pCYJGJD0ntn7htY7Hu6oGKO8FrXf1LblTWbw3TDDNHxPihYkEFRgRPKzLVzuxYFhn0CfOHLSZXXj+u2bA+jA2BBbw5wdOU5kDAaYOTEuBdYJ8EmTSHgpSAXEyjbAJy34U1wuzCRXxCoR5mgAOuQyEJ5RRnhP5BcmOeN8qspONGtLyQizlYyYO7rRbK78Xmqz5e9lNoDFwVh+NZ1b3UQNF5RMBfUeVgKE5RMWShpM6q5jeyAzcA56FGXS1DGd8S2MPjZUVZJvscbg2OAgxa0vP57KT8INmI7nRfSBipdUfONz6o0oAW1rchmP7o1Xfo3BeUOd1X5PwzWA64LiMFcHsrAmIOeEBe83uZFDXiWqwt3gzkGXTkMnmrMZ4xUTvLZaLeMKEvy1mf1XiCAA1kdnBHrIpdnYtZhaHR7Fc+Gz2TYV89UGrAQvpYqVlNzM4GrVDe3mnlMxWt5pzdQanEpi+HwAYMynEl8X8r0zAZkXlimFFc2Ce1hBYa4xGpY814a+AAlbqAjjCMOkaBM5McYTIxLXCPg20TowwHmHMSbCgAsX0oAnflBJNf7A+fzEH3/8Db+fv+Ovz78hPgPxGehyQJqxAQY8DhUg2NabmDkQFrS+wMSlToU3IhxjqGDjTgskcwBbhURiXoF5DtgMTkn4B8cvkxzC3sDeN5TbIWPCKik0YmFo9tJLkmNsqyDUBw31gCsDq4dbGZ4lMq8b6agqwRioIT7Lct601968UIuXUQBW+bnli1deVe2xPrsiSa7Kqr4ofsRPGWIKmbrNwlavO6tqr82vZOG4HZqTc5kyCq3QdaLuhaG55JsxXlAwrITJXv63wIco9ZJ4UM3U56SfBkEpJl0dt+fPlx3KcKneqXSjoGqdj1HlsKUJKRjiYHFzWcGgDKlaQxPfKxv1eawFiitGkrU3I0wKwEApfm9O8mGncormlfSOsMGf0hDk5bghZqWg9pMhmnVNrx80KtxaX8hdzEAMGVeFIQYY4OPmRiwc03LJQE3KAWj0AyyZgKnisUyqIsxlc+OE3Cc3xYZOK//GYJm2w+aQ1UEXugnUK7wUk0qSDf5T5pZCgdhC0alF+ar848Dy//8asrXpM0kUmuHlWiy/EbA1iLQlQFh0ySo6ltoP2nDyjme6pljcKSHHKrMqQgBq09g9pX4hwqkE140KPdfYBQkuYLgRJFRiXGR1JgQZjhrHEgPLOmDOgaZn0zQMs+j1PUGjNVO8UQXMeG0rDpIfw3NILxaYiSj6IkwQRHnra2IhUyZklBt9ciM2FTC6ZpghfcAhTlllTwUNvX7WFx0RAcyJcV2M8rGpMC3BRr3phUzxXebdVFKfumdFvs9aP4k1Xf0n09W1G/H5V+Khd2l5HUCIiyC7GsewIr7VcE9fe5XVjLCl+rrbVLW31s+++XpCVPQeJb9EhLjRAyokivDGaxlxIRA484mZF8554YyBwUgOpv4huwzt87U3yxzX/Ua5IQJ2vbs6XUjRwWehYpf7MN8F5gN6LkkLAov/D0kOwJ712/HGE2rOgYpjrOm3Y7JHV+Mx8aKnN71ArYGGDTppVg1YD5XzoujfUIGTa4bTzSkt4w1bczhcHgYoSSblaeY850pUbshQG4slh3oRwlkvMZO1iWrDlUnYXZsrOcGEtZKYVjDg21sPc7n3Xuda/wYFlKTPDxftS9tIYwd63zTX6MkvrwVe1bXs0WHkEaB8QAAEA2vb1LTUTBAiC6zat50Ugi89VHC2nUjLxEmzPhHZzEC0MF1wZuL355PPeuo+tb6Cq7UOD3J9DAabE2EcSBcITJPCRsRPeGn3GratYe8N+7Gj9U5OS2t4e9sRkWgXEGoFmVRsFlLYVBtHclnbN5qx5UUDtr5hTFYh1xi4npPKqjDMUz5TmHyBHTAjalTZg3WTEzSfJ/llvtQXtTZ760BTvHRDzMa23WzYw7Hga2uA79wYn45IQ+Rt6vUcJyJCZoSGbhsaGttX7hr4bTLLVFIfgdRU7a9Mcqo1w7qA0vvaEGKqJe+FZvPrk/0YlNSdJG8halnt71ykW25esTYQ8geVOK3/VVEGdHds3ji9uzkTy5loWfC843DHZg0weoFhKlh3NW2FDqWs90tlGUFUZEYiLyBm4vrk3L691okGVQU4sLWb4wzNajMAlksyTeglaepoRpSyMZke2jSJVFBtVakhV6bUdcXhKGWqcQjxHJPIlxKYIhZXYg5w7+relknsyljt69YQwGQxY+DEDyAe2I4HVYmKsaw1iJrMIXl9XAASkUKtzFHmdkojUO2SnDVfjNcWbhpnwdhHfiQk6gFqbBCAZYvBw8jTWyUw29/FTSz80k0DM71SqbbaWoASmaqGwdKqJtyvIp45Eno3TmXvDY6J2QPWiYyf4xMzBz7nHxg58IwnzSQtETZBe/igQagXAsM2eN+6ikmXdoJDssNJT0BMFaO2YiE0RsXqHNXKixCaKR5SsAL4309yet8lSb2z0BhkSdcLubJZgJuQ2N9e31aFUvnQZMJcUkW71VKs5H3JgFNEJ1hQGqcIVkocCy2wujarU6gPVka4vqaqJtme+rPXiqFa0uuElejwvLtz4UMby6pCEhKuaAMEW3hc+FpoWmdU/YjgWBVSVZAzNLeLQa7gahLOdG9GiAsBLLkhmMDUQr4nw2rmUzLNiVRLZ9ZQwi88rKoj6Nw6mlA1OudWn1k9biSOfVfPdVImaKsWQndHtEbUAgZshmiG0EbSVKWErMwdgHknBHzQ9r7v9PcwObOmq9VlTpM8EZ6RiWvKKVXVPYJTHL1XYNpfUAQFFoeqID1/Y7tiDCY5vRJcu38xj2LW5wUJZgCpdnHv97Nfie6FaGxF+UxEqRnLXwMMghGTBH7rq1DwucYkIjNxXic8HC0M5hPNA8MSp3HjdiTOGas1/ZUb1Lg0BEDqo/IscbmVF3KRqyiBxqBgyc8BxxoMGERrXi2QeLjiEhSj6h1ejMNViVvvsK3DfVfiqOS1b2ypCTmbMlIdI9fATAJtVYkDAOdQxXVhWqBZ4JJx3xxES2YOXps18dR2QKUPwQDFnuaIq1DxWmeCT5quqzl8li1GwrKxnRRRoVNCDMZPx41AcAixWnY1DmKpRfmOh+7dMtp7QQVNXB/yJb82GJk8lUzE7ZgT6Q2p178G967EF0x8kXdSn8BaML5+j/t9VRJc8Zv7YPE0tZ4iFfde4gDqe2kQWa1Wys5VaMFe/ixRk+19rVshSIUqvqxs2Ov3ArxoAyopb6RQeGvYrGNLw7ATtOu9cMXEc9LN+COeuOaJz/GB5/zEGScL1U2UCUvsuyavb033QWptm/KXIlWhwdhCcyXeL+doCaTMBkfSpXtkai9MzPznxuu/HuuwHeKzvFQbGrJV4EhoE7W1kbGHvQhv2ihQXARLFAP9hvM0I1eEzpL7hqA8S/n0cHUosagkpynJYBlxb5f6VTC3/pufWaTpO9udi0BcyZgCW0zlYLebiDsICeun5vo5uv4XFnwdc95IVfXzmVgpOET1p1lFt76hOXvFvXEe2GUDGJDZYNWUkAkdtFjv667AdrfiKED54riCAmvvpK2DzsuaRwVH1TAjSTA92o5MzlTJdAR8JRDNGvZmaPKMSDOMlKtoJprTXZTLh5uze4e3Ddve0Lem6kKvhzf+UoBpveF49DU6YWqStyn6Z1J95H2DJTk+mOKSpRJK50ZS9X8YFTVjzKXAQq0T5SNUG0CryAUla2NvrITzJXF3d0SrqSt02U3cM18IHcqXJyfMN7S+Y54X5pjaYJkURCae4wmfpoGqDsfgeSnJsZjQGDa0Hgve/4rjPAcraHeZ0UlBqDjN26hCRpwz8wZLQ4DPizxCYARbEEw+XTB6FQ1KnhxKcirOrWoGi+vQOdixtZ2eNhg8l84Zba3dReGYfPYdFRv1Xiw39ETGwBiDMgELzItmamcFdMRqe5g5WtsVgICcpTzhkODiN9KywO51BtAfpXWYp6rrakaNpVLh3Zj3eRrIX+GHkzspsvtKdHBbeISx1dtggNYTIKpcFaq6x1950E/qrsBjDKRva6tCFSna+CvZ04YA/RXeBkCJ2ovQoVDUxEqSmb+kxkdUAiMfGc0oK7k2Cxz9ndck02Q8qXbrnancQ2B5Aro2SyZQ+frHBeELlMgODdFbRb9Lgdn6znOdhisu8vpy4hwXrjzxEZ84xxMf8wdJyHFhc8d2mNqlVF43a8qrDZRUB3JRFnx5wbXGd9larj1baQNbosG94XTGoDmS73HWWIp/vI7+absqYZjWyK5+seF+dULkA1s5Jm4lE2+nv8JiZigcojgX7rJhxw2FcmS7EqNMICd9B9RCajr1GzLsWMsv2EGvFs/N4heUKEivWk6ZSSJi3NLrOacehBZz/Whl181vMpcr0XPJXEvafGfMxeW5X5BUb76svKd4GmOwQp5B7kjbG8ZkBotImDn2ndd6niTE5iCRtAluJmrDCqpbA4fk8cIcX5/l9M62UZRsLoV0dLBFhSZUKzAkyYaJhDu5ccGKFoi1jtYQQyQsgLaR2LbJ1OxOVANb39BbRzuoLOmdZNHqFRP5d+zHof4wJcgbGrBPDAPOODEi0XEwaA+u67OCUWtADlhOwCfQAtuuhOFHAIOjIyKI8rADwBlldFjmOXU45+MI1RxzYrbEzBPl0v1aIZt1jFbFBt+7a/A5J2TC2Ar94H1zD/TWtYGxMcGZV8AIJlnbzRDn+sZNSrXXl+ILjjlPJiQwIntot0KtspxKpdWaKb7E4n69bMJmlbAmxnzlJXH8SaSt4oZ+gQG2DBp6J6R/bA/s267p0wzuUKsmcuIcJ9tDQgkTE+eUsq03GAIek35gSkYTvoip1wzMSVKpe+K6SDu+9D4fbdN1Tr0ToU3UcHhHwnGNQXGHfI0umyjbQSSI1GeIiGpCfohktEq45ZcWU1wIEehjSr7fK64CJTlm8VLIAqMwu4ehVgQTuS/OcZS1sNXt6GhtE4G7lE52m61KuWPexD261pohiqJCCozlaUVBuC1GYk7AnTYfVggox5EATGTcO4uaKvy1ybfWcVMmTEkQl3oO7qF1rrVNAhAiArbCcpEq1uUX4lZFGU80KXwwA2Tw6G7YbYOHYesbSflomGEY40LMBKLDsLNojY45HG0nenwFuZddcdqV5Jk1dCTSAtccGBFIL/UUgY0yxxzqPuRVPFcS2OeVi/Njm/2EvP35+KfEY9jtHzrl4lgVdRRJLm9CnksWFsoaV283saqvIt9WIr8sv6uScmO2r3SOVTlhznpkrbJsyfHq03hy5XpakGgtxJIAakHptSZhStWZziJM1eB9VloLudZdtYVqddWQwDEHSjK8Vl4W+fXnxbZkgaoYa5hn5pQKw1dLsAaets6FfV1DXgRCGVoF9+rDM4g1bwVewaq1+IWHaxJzBhEsIKgCUGXsZpgpDG1VzWxXxkoGhbDhRjGqiio/JnqeMIGpzT4iMGei945t2+CdChRv/BlboQJOHLB7hxwZ0CQL3lqHZeB5fkrqqJlVk/d5Bvv4rZlUIyIMW9LuPh3mAZhk3rreBuN9COoBN2xscdQmFJI6636EDfIH3DgWIO/J9O5UajWpD6zQPpO5oaZCVyCtjajuP6CZWFnMtBf0VEcCNNA1knq/coOiqVoiXcWHqEwRJvVn/cVKe/Fz4Afwc7jnfSi1G4uZXJtUvmx6gHiBpXrpncZpfUdvG5Utyz3OVmt5TM4Q60INMoXmAixCIFJ3OjLaau+wsx6ISbPGXRyqaglNS7bpNO6h+BkpTpJro4bi5kwgQ9PPfa5CvinB93W1rs1KPL5K/NyXvBhywS4JeSm8ClFrVlSAegAqbGD8ueIJGUAexX9BkkOgwxdxvIxkDSo8VAjUeB0XSylyLD8qAKuQun/oa8JdCF2JXCrRBbBUQhNLyFLtnLofxl2Na/1OdOrh3eg8b/wK61r8vgq8uToNqD1J/1xjc4QQ5Uw01TVMbg0dHKrcW0eLsiKx9b1W8x99BwYodEGHS4bOe+OrMHV3xaJcNYnFwADU0udfb124rIoNaIbVnMFER0Vcobn2i4X0yyTnvC6YNWwgP6K8OF6Qu5ckQL/pgl4Ri1Jp8UEYWmrTs/o7sxLs+yE7UPNjFpoCQA0/Sa/58jKpultm5XBByedN3VpZcsGBCLizMjPN2KjP2Te5NV7MPrz3OllVhyFiKgmHlMxNMc35QF99d03Opxb6ucVlsjuxY/a+0jVEDPz4mER0mmNqQuuVY7033vpakPvWualfNKliRq/+c8kfzdcd+apjxAlHyX4TyMkespNT42pFWRp7uhlAGf3J9aq5aV4PpztHYk0D3ntHGsltZsDmDZGB5xjcgFhOY5rQw0i0MTk9vBGGPwDAZdCFDmQjDJ8B68aJvM9N1v58QNu+y0J9EnI1Gq4BYAI0E8guIubORERcnf6+k5+RTELcE7YB6BrcBw5YxEriRDQFAxLEnSD83vHYNsRWm0ggkgqJ6yov0UqAh5R3JrIfoJ7UQjEqgX/FHjW8Wg6+YJ/9KwnsFRCNV3NFIEciMdG3HX3b1uZj4Ebl2waASS4MMDgiG3p0TPFdEFOtFm1A4or0vq3PbE5DvL7vJGYeO3rv2I8dW98Qagu7RMhQ8eLuiDlwjokYZEfbpNoznyystmpAhQzTEBrt4UihrpdRaTlPFTXzQprhmp9wa5qxRMM622j0mEJOLQOuPMMhZV5tcmpp20YcPHyyjTcLo9cNCMav5g7Pjo6GQ/FXtlJ0fUbi9rCo7Vwz0MJgjVzE3tgibP120v6qY9sPcHL8g/vLZPuE78G8E5XWiDhBvlwpZMVZmBC1izsRqjirZJCkcG7S5QNjJkKFCn2a6vK8KEMHWHRwf6qUCID2Mce4npjz4v5nhukvfF0HzINWAkqIWByT8BJyYX9eT9l1sIXWDl8JvG0d3snLcW9KYgK/Hd+x9R1jDnyOjmmfOK8TMZ/iM27YdkNLGY7CEOfAzMTT2aLfO1V3W2+FbmBvDVtMRF5wBGZeMAe2bcdEYsQTGTSfHBHKQdRRqj0w/e9OIqjjl0lOSGVR8JaeZ911rmXwwdxwX/33z3+XTr1SQoUzY7P623/vDIvFrgprfbUqq/tz6ufXf1TeC6kx7vN56U/XtRik7GGLp1Af09TumNwcyiOBVtVzqSBQPw83+W6Nkr9vjf7dmAOmL9m3Ive6B+Xpk/p5c06YdfJMoiyw+W29FbfJlSHfMOtPxmgvXFRTlfqVR8iXJYIvYDaib5WUFgclzUiNURDmtcrR2LnBGeJee4qwTWhG7ySldqcK8FqO0kbiHrNkYFVHsWKy7vxyySWRky/UIrkaW0sI8gncOGHcpZJa8Hw99NRnZ1kaVAsEaN2Z4EjoZw7Ck01JDU9Jib2plWLrvty27wwoNAikQ7NJZYeqgrQWfkIWtTbuGk9fBhYKtiSndR3AuoYaWfBVx30GRXANzABsGlrPFYsSxYNI3MICx73ScPudACgxub2+o2qZm95DNyV3koqXzNY1gZqE0DvSpH6OG5vDHKhJZHJNv5Gp9Aph5SqaUTQJJdAiJKe4L2mASZlYKk3vWAZzTpQgIqSIqhjD6tk9X5JkMZEIY4oro801VQypqicyyZ/DZ0Gj1OIUTmh/UF0XVrQFoMpzN37dleR4u/lJX3UQbW9rajhvvk5e72xqs3tFB5ZQRYakWchNURFKFl4dgJ9aYOw4tEKN6t2r74lCqevv17aca58rp/Q5JfwRATxnLufiZYdQR23YBGvYtgxQ/Rt6f/RcbK17W+e1nJLh2PqOBHD0A4mJfVKN6q0sDYAOR4eCWYL8rqTCzJoh3cnPamWpQMUY3NFDbde8i7cS5rDgr6G0BbTk2td+CmR/5/gnnByg2Desbt4x40TkJ2+42iSvQZ1Jca6e5M/bqV6iNfYA9wM3v83ZUiTk3pYaYWVZbcPNZgfMKt/lAoh5oZQl2cq8cEONSOCGWVWt2lxzAOOiTbcWYkGJxUjO9PtGmsG6CZGhhTU5DWo0nCRybX0DQOdoRGDEIA/E/XZSVjE96yq02HrTYEXjWMhzyJysxgsYSNySwybM0K2T9FWJAhgoQ+24CmF/fir/6oNtkImPmNi2jm+/HURyRrWNOh7HhtYarQkAwGiW95SJ3pgXp5fbhCcHS3JSebJ95w37sbHqdkfEhHdWt1BvOC0QTZXV9cSc9DExdHgc6N6xW5N8usPnxOiTFURMtKNTEWFT0OobW1JGL51x/b/EvdtyJMmOJKiAmXuQmXU553TL7Eo/zP9/2cqI9Mz2pSqTDHczYB9UYe6sqVM1MrPN9hJWZpKMCHe7wACFQsHqptkdox0458T4PjGPgfd54MTEvrPceAe5JdEJzfZu6O0VzXfASco2I9o3BlMMftIQRAZ6MspKGZRKeb74C+BAm4lowGtzzKTQ5lBX4DMDI8gXKFJl7TsAN4SmSpDJ3+nese1sU9AflzjaZ1wxBgDneWAiHaj6sTWWRG/bpvYg6/YB0JYkIP4ECereN2xtwzlOVVvp2RfKULw9wfbOdWre8Nh3bFuHtwRsIgf3lCsSTwDdd/Rtx1u84UTi7XScT+DFOhpM/XwCZwy4AZuQl3kG1yO6eAqJ93OIeMx7e+wdtjvQJ1rreLQN80ySyZWwGoPr5lSVlfBvWIjbCGPvoYjLJkHOsexYUzRVncVLzffiOkLocuAM2W5pQmSXrQWdbaI+ClenSjyFNn/qFQZrjv6ys8JMzxbGCtrmtjyvSmKa+DOGS/HYmGpY9mXBfjKt/JYcbC1Ic4f3faXKi7tKLl9ixpP/7nIiZd9Ttu4cJ57ngXG+owpM5jiUUmeFn6dRCBA7bYOF9NoS52GYkXjDOwKBPTrbJxnT6eajjmMszSCd490fQG94ffkJPjYcbaD1E+YHpg2En3htG1H0oEhhvMpZVEFM940q8G4wOZv09ieOCSAHNt8BI2EgI3Ccgfdj4vsxcJ5D1Z3ar/INYnEzf//6c07O4neUZ1fRES4ohKOBj4dnfWjBl7+z3soIZTH3FT1pzZhfG6QqJqp0FnmloXS3oDOiOsAMeo7l3NqFmqyXVVBbf8lyDYAqHf8t6pHrWew3Y0SyM0pWX155kUGziKRWkSjW4QS78QhWdFdcHbvdnzZLrUTewAePHTLK5fitW9UhyEjkc50ckwM8JwWnigwck/NuRqe4VWiXrM6wCMyemNNoTI1OLcxJ9tZ4uJzZrj+b0lY9ha5NOUUQX2zxkxYktDaJlRFzcWmWkI/G9bICt3Qrn2dOqoNOKBpWpBfBM9lh6Ftb97dQg2aqvLgnf+XIM8TWOOo1981042Ddf1JBhrfO56y1ZEAGaaAR4gCsFLTXi+vNBS5IyMuvPz9WR37CVXssQshX8XAq+i5NLNxszYVPFX55oWlcZ7M6b5c18XJsHFWcQA6UHAGhpqvXXBIP0lB9+Niyl4kmBE58M+SyAyFuoqd6Sk2q8IbpBUYl2hmBCpnXenHdb3NkNmSSe1d8jUsJPikzIUSGh+YFYFzVnXx/d0b8KZvpQqBpk6/DGckDK8RX4oHG36v0uMUFVxWaWEgc9af+/10mf3ZxHquMnPNIu1sRp6/Au86DhbDczo6ywVZHlBWT7mavoaAVharbbR/exvF+/shg1zuUiYqcLJ+eJ85xMv2ZQO9S1d4pO9LF0TEoDWe5noHVcEFem5Ffi9lwDqJrzZruv6POHz6vifwObG1HIvGSX2AYyNgRdmLaiUfr2JSizUwpjyegVh/dutL6CTMGporE4RhEyY1rdEgBeQbX/hxKVVUlX517srELdf6d6497V43BBdFZumipkq4PRpiTUgJP1/HKxeKtJr0qsZIW36ThkgnMgUxDczaa5AATFUozHnhpt5SU6T2U8BHHYLFrr7tCVRskdHBWqkoHbbeGhgaKGGLBr6oFULLTkE3eVw2w0Q+yIFKTOdE3Vt24DqUzBhGwbcMsiHAEBm6kKS0md8LJKfbtPA+YFTmQJeQkewWLxAzrkI8YmJY4gwq+tl99b2r2t97IwZi4zdvnXPtG4uIIqgkfZ2BOxzgbXneCc7F3BBz7/gozx3G+w2Bo3G/wyfzuWavPydJPGPZ9g7tj39oyXJGBbEZkaAZmUBzOWyI94ZuUQvcnvBH1iEb/GC0APwmPgumwZq30Tlk67kxQxZwYz5PCWONQbydgG4ktGqYb2uZ4fXlFxInjPZb8grlj6x3NAgAVjgOHHLlENQ3c9l32N+TkN7CfFVVGC91IAM/jWPPO8vqG5uyqnrkDYZg7RebO+UoDMgdmTDzfnzSqC7Ssg/hmtBHImeoo/EkLSJ/MT5/obtg3E1nLTxoAACAASURBVOGXKz3mxIEBm0DvDzojN4fCQDidjkEixevqLkRWJeXN6qyrNPCG1hxba7gq2xjFn1VVWqj1SjsALMk6AUts246+dcQ0fP/+HTkDr/sOADjOIRNTtsswCrrn0YT39wMjAvvrhG8d7fGKvpPj1rcG3zuwGWxvGOcQms3+Qd7oEL68/CCbMTHGwHGcK+KG0uAukcveNwApBwWq/MHH3JKcp7QGqphVaqEKJxIIsBGonKhpOrjOSVumqtDPvF6/flVARH5NgvytcmwSQFPT1omhRw05whfsdDXzTMCLY5mq/KssobwWc6BtUuudSO/SUmJ6fc6Q0GwJ5pKX94xneVB4ize8z1/x/f3f8Pz+hn/9b/8D5/uJl5cX9P2BH//yj9gfO374+hVuHW5TwSOEHjeM41eMcWL/gT3G3t7/Hacl3k7a0NfXF/y1/yNe249ELcUNMrBK183hj58wc+LL/hUjJo6XiTOfOPIA8oTlxLlV1ZnkJJNVeGyuG8g4F1824onMUwE4u8YmAiOfGDlxxonzOHH8emI+J+b7JPgDE+cxMfz/wMlRjMIoFYAF5fbn6sGDD2J8KwdbuV5gwWp00GS8b4x24AZGVBSgNyQc3riFArgoyLh9tolrApGZ2/U9RR1Ykafu73fDB92L5KQrR+7y7FfkpnLUYofnesbSginsRxFAYjlnJoLiclivIOfDONWTXreWi4yKG4hTQxiKCOdkXcym6iJDcThqTq4I5TOvxenCNc8zyfwfOdH05SXTvV5Jkmu6s0GncX4ugTYu9tbJWSKio3FOsKFesS6kMzFxS80ovWqFggVRGUbzjYKFDsR0duUdJrLriVS1UoyJ8+2dEuMxKOgHYJwJk5bDRC6yXGmXMHpp6L7BjLwfBKP5so9XNAnwQKkqDDn0xu7nK3YszhKubu6wQjdYAl371J2vhTEQaNT353UPTBI3JydLiQBonytFQEE6Vb4YVkUY9xOVsysdXdvqQrW0t27f15ZYhzWW7cJly+zqFXQ/CLn8cs0VqvoVFQfFskelAdV9Q7QdZ9J5HmPqtWIdZslcmPZ7SSZUyjAlfdCw7zv2fUNvG3vTdeqZJExRqNL9LnV0Y+HCHQEk3w9gyzYtst8O3G/m974vL5Totr9vqGIGyBeRMnZGLpG4cgQ/2QwBkMjqzaE0PY/d/s7r2oB2G5Rlp2+vuDDCm226vS7rVDC7ziRcZxL1nwo8kD1fGRRg6TPBgXDkdBzPiePtQA6gbxO9/4L53NGeJ/r2wPb4ikq8UPjQlLIemO9PzBh4nk+cSLyDhQT79sJqwVJ6t8rY8N4dQNcuQwOaBRoGejh6NmRuQE5UfWjZEKo8V4HJxA1CgEkaxGzKhQxWsCalHcY51fMMWD2Bo4AKruM0pkL/3vXHxGMAlolzHDxMZxA1yEML3JDVCExsbyuBtVoktzI9AyGykn4ryeYE9+ZMl1FPCRM1RWsNx3lgzpupSrJYrPRqMgDvsF7pNENvtcEV4d/GoRww2ishJKgeN+qyDODxeKA1Rksk2DEZYUnVgJmBre8qkR6sFBP7ziLWoQdwg0USDvO14Tl0bRlUEr1P1swhMynAFnPxA7h2lXoAHbI5Au8z8Hjs+PLySl6BUatgyCEtI/TZVzXdFIUYiQ2BqWc8MbIBs6l5n6/qAoBcC28d3iQ2b9czhJBAKkKbejpx3ENCWxBiZzNhqjY6z0A7B8dx4xqxk783fODhD+z+itYSG4C358D5HhjfHPMATv83WAu0r68Y7wNv//wdAMm42RzZHL9+P3CcA7NRdRfnd+QcOJ8HDA1b37H5jpf+I6KdCB+MwhHwuNIBlkRNrKkfELl7MkJU1W3wpYZLBLga31l55TSzaUD6qpB0o4aObxsMjq+PL9yvm6/XcI1jVf9BJbRnfEN8YoPOrW3SfQL8ZmMiT0ROWBIF3Pq2CMd1QHnbFnJb3nYFE2lVqVEVl6Ads0p9NvFx7OYAxGVgA5hjwNPw0jbETDzPk9VX+0YRxQx86T9ijxdMB458x/uvv8KQeEgNlpQ/MjHY1iWqGxDCJsyBl9cdr68v+Ief/oJt5/v7tqM/XnGeAxPsx2Q+4RPAmOjeOG5eOETD5g0vfcNxPvE834lglnRF/Sd0q/yu5SjKloeef8ZcQVYm0dp6n5hBIU0FWr03Ck3C6BG2vkLCz7p639YZUrzIdXbJka5S9wrYyymptdHUUgi4BbAg/6ZUtiuY5e9QGqS1TjX14tFpxlszmOgVGYExzuUUVSTdbQPaVzxjoo2G8d7w/i3wPH+Fu+H89g0dhl8n8PWnv+Cv/+WfMHbH3A1n/oRtA97OgeM88K//8t+oO5WG2QzPMPzw5S/4v37+r9j7jq3tuKNWfD7QTTEQHc7OTuLOtRp5iYNGneGKIkacmBk45oEZAwPtCtwNQDZ0A5Cd/FGcOA/D8xn4/u8H3r8PzHewRc70RaCOEfIbbpH/7835Hy0IEoQvtIUqoRK64zyiuoJe+UahFet7tYYY6/ht4swrDSWmf15RDdUdb3VV91KE8pytEJ1aUAb3fkUOt+oSzlR+HIsVMqsTsHRm5D2tz0mNReWSVyfwFbloMFsnfDnGimiKUwSUd5+Xg2O329BXa5cGSY1xRZbIVOM0bqSmMsUsRw/iTNT7GRZkWfHTymV+4kWEzVjFZoZznICRRHxiIOMQSdvQ7MF8vslsSGhtnVsGOaWJGSUdUOF3jYEqQ+xaS2ZUgk1v2HrA2oQlsD3aqrjw1tB2SvVDTqI7sD92mCW+O/s/HUcSGcp3xMlo1VpDWqdC8nngeH/HcQzMjRWC8/lGrZcAtt7xZd+xPTa0vcE9EI2E0/AkDAvOHVJj4JAcAJ32kuJ0d7h1IobQfaRSW8lOwlyHk8R4Vk3T2ZYd4/50NNvgLS+hS32t6gtFcNTU2LA4PJ9wFY+piRNEIKv2c8ErKbdMQZUch+vHtlJRJSsPLyQUDOTyoz5OvdgqUBIvya0ws1w6JENNZdmkMFgE4NRDwtbo3u8dGQ3zZEr1OSdTrXXIVk8j2Y8A4G2Dm6P3F7TtBemUTCDknyopZvqSoyGjm445CzgUjVYTKhwRm+0XAV1Fy2XrTMdD8bs+eIeMX3lQh/a4RqzsIn/dhP5VNV7tbSy06lMv3b5LY+sjACBe2zozlDkoe3l79vWXvD2TPEIzlZiDqb6qpvTWccErQnis8A5WdgYS55xr4zHjygOhtYaXL68wNPz015/Rt4bx7R2ZgSE+WgSdgflkIc4YwPfxDdbf8f7+K87xxPtBxPnlyyv2fcdPP/2An3/4K7a+kxepdQVVdF047q3KmdtNa+PKkhhI4i7nL9eq4PlPrkVb32clM2DYierkwb5tGXT05QxVO6TIXHu79vXKVvyd60+cHG6aq5s2QFLHXOgEU1CUWr+XnS2tGuiAFlQHXIiCgdVTqTreOWMtlOIPFzm54pzCRRYpqs7sybJvbx1zkrF9F97hbclzvOdWxTMj8aojpep8EdK40OYphCaqTn8ILReZzgz7/oVR5Psb2fBTRidpZOid65zWAmigEXA5Wr1T4+WsYqrS13EgTupYOKAUjkiSWzlkQG/cnFWyzCyInFL1mPlsJ4d9uBzwHTNOvB3ftdwNcTxx+kQDkZxmk+Tkxjnera+DvcqWU3OAFZlrNVapdeOcu0r5I0IlQxtekiW/cbDpXn+hztCcQN869q+v2uhC12Bo/QVjdPz7f/+GeDvw/Z2Cj++/fIOn4RUdjg3mrxhvv+B4f8P7t294Ow7E1jCRePv2CxKJ7eUHfOkNLz98xb5v2F4qIgp035DDyF3KUHlLYhviRHSKrXXbEGBljLeG5htMHDXAV7nxnNSWiJwInBiDfZCYQpDTZA5LVRXufh1K5igV1qxodnnOgI+x7MOnrCHnwbTUrKWW62ksm7bGtFUkqrGqb7egKMuRk2amN8oOGLkxVVY7JEvA/ano3R3onSKAJWSGRJvSIHIiIc8hPpQB6RRvrK73W2vIDXg+d7hPnGNDjBO/Pt+lc2OwtmHfHjActDOgGNzWX9D7hsfLT9j2B2Z7kOtntNF5VuB5HUMWDTaBM0js7xaLU+uqHuzd0Tbay5i064UKcwRaYR6IlDYRru2GyUDLHMDgPmMLywvh4J6Vhkw1vrVCWE+sgpJPulJBUe9Xs1f+CRSyzn1Bm1AIoMEAb/qVy9mjk3Id9FdpIs+a5g2tNWx7v9BAJ8qccpTFVl1o9XE8WVbdO3+Wc5Hf219+xpefAPSB92/f8Mt//wXH+4F//favyAlMNIyz4/yWGDZx+sQ7fsWBgWN8x8yJ55bojw1//flv+OGHn/BP/+W/4rHtePTHGqd1X/ej4jc+3uKTGpB5axGjlTizHBMQRa4uU7brE+i8Oejce/J8mzhx5sCZA2GxvgplrCo/HoQmOZcP3uqH6w+dnClZaK0OOiMmwqyV8yAvrhbyQl5qNMoJ0EqyYtjj+t069TFRyqJr4SGUG8wP71eWKOVklJ6K0b3kIS/nqnmVu4q0Ks0Ic5aTdm8sA88peDgxgl5+yWZb5RKTqSK3XurxnNhMPM9jaZmsTYCkkyjP3K2cFkWYUmj1gsR1sQ0CjWfJzafUa6M8OzN6+/MyFCMCxzgFtbsqYgiDFrz+2cHT1l4UHW/w2US8nWBfsAY0x1TZYKDaEBSfCutQCVU6mQGtN6rdRsDRuCZ9DQkAXEKUQvmyKvMA9F6S6eXMclgiQ2qoKZ0Sjv3mjp/++hP2/YG0gbfnG779+z/TIS0RQB94P37F8+0bzql+MMnn+PLyI7w5vv78FV9eX/DTzy80gO6YaJjowLbBesMuteJoPMhfFA2mhK96OmaeCHQ021gy6pzYCSIcfTZB5xMziOg0B7LbVYIpxCwH93nkCUTgHEJt4kJBckVOdLhm3mzDJ1yZak8gzpW3BpdadBGFdHbwMPdcZdF1n0ULnqVbA1z7VCF9K2cOUAq09HLYudyzInvtv+VbujgHQne1nok2hRzzwCbJ+5iB4zhwTDE2EtJ2GhgpZ8Ed3Qwv2wu2bce+v7BMHhMIw3jSJoZzPmbM4m4iBj9zBZ+4ENHFJRHizcNKncjtfrglFblRJkM23wxuIcHBMt86B5pkPwoNjFw8lCAlU2gXFdw/2RRdCFkJsQplKnQJUFrTrkIXWxwVX2lbE5RlXjZG6EWJdEqKoCo+i+PSvNJfazhRVXYRdAjMO9KU+ssbSGBGUrQ7fvrpr/jy8hVfHj/iPE58/fYD8pzAO8Uc/3X8K21sBs4OTN3L1jq+/PwT9tcH/vLzP+DLyxdsvatKTkImt7N5cftw3fJ6fhQWUxwr2enl3MT6+8pslG+Ccg7JoW0KLCwNFg7PDZYbMMlDWsRbg3wNlr0jA1V88PeuP3Zy5qilwfwltCEklAUdoK7JZ4WHXcavQAMdIAW9fcyY1IQLZQAWFBbBEuOVxro5Raw0AiBBKhZwUyALxcMpT7MQAH2ig06Gd3na1mE5YUFWe4wlEUYHyMC84zRxGXzB1ZMSpMhIHIrkVlsB3ecc1XW3yRjfOosXP0J9lGoBuZOkVWPucCqrWijnSSMDd2TVg4KO6fvxZO7bHfu+M42mnlDKtH3qtXe2QQBYSeRoOMeBIw5G4a2RrItAqP0BeVS2oqtU1/kZMhyNUTkj2qb1uGJvvsaYYnUv4XtdxmojQ+LMKzpIk/aT4NFmxsNSKbOf/vZXvH4Z7Ij96y/45//xz8iYOFoicGDgO97ev+P97Q2b+lBBTURfX75ie3T89R+/4PVlx89/eeVcjGS0HQnvD7htcIlozcZ7/7o99HycOxv8vEhqQlkyyuQioPQ8Jvlf1gxzGsbQG1gj50Ml+XyFehHhQEYDTv6AlJuEWbBhcYmX3RyBz7oyB/dk8NBprcHVY4t1qY7WqgJYsgS31G2KVM5UjJ73tiRa25Uu4ivot4gv4YmIgXCuP6AC8tvhKCJ5GfcEuXDlZFHFGOj7A1vfYAn0fuDb+7GqJmcmjnniSHZ/35wVVK+PF+zbAy/7C5WmEcBkuTmzUoYzBs45gFH8M24d79Vs9bKHZV8XsouiGdDZY/uCsuHaf6jIHZJRqIMXyzkCTKXqvEwBBlEQo/L0VBUrWLGIlQr7vKsc3QoAL92fqzLszl+k7S7dGlNFnSy1GcwSWZ2P9ZrmHd6o7ns5Kf4hwC2jzbXIUmmeBR0zJ855qvRawSrIFTPrePz0N97rX07MOfD92894Pk/8+usbfvm3f8G//PP/gzaCTXv3Hdg2vGw7tseOH//2f+Px9Qv+9vOP2LcNu9T8ZxCHy4psYDz3ysVVQG5WvNAaTRaSiNhxCURWxdmFwcBQgYRJN5E0lUrAW9CpsdzgscGm09GROKU18HsVzQgZXI7j71x/XF0V4/qHcVGWjatBQID53jlhN72RxReoiFzJM8VAuNDBK+vH4FJpLb1/5XYrdxor3XIn06pqBczvVUv6ps7hUYlpoULmbEbnSbh7a46RBhjz5tEMiJKCcqXqaLwYnZWnKw6MSZhtULEx1H6B3ApufFWfVuBIIaq7I6PNMUd5wozie29y+JgeyBDK4QbyLEp48eY5JlQ6PYFGMqoLsTjGsfpjfdblSglZdGRy4/lI+Jms5HEgMDFG4O3tV4y+wdoPFKrqHN+tb1B5DYDK+wNIbsR6fopOfUTE0nwJv55y9u6NZmsdxZyI53f0tmPrr3QmG0g8NUPbuEZ++McvaF8Sfzt/JGT8P/6F0XRrmCdg1jFBaP7FKez48uVHbHvHS3+gm2O+h5BBx4wNMxqQnQKBg+nW8+R63SYdPlMPsGYO9odJWHRYNiSegAW6qdVEOxnlgPLxzRvlCwY7XY+YTFNkIA5yyOAd7pMcIybqGN3lQKkz1PYL/+A2/odfVyWKjGtUBRPRt5iBkdyPLQh/h/gzzU0i0sVP06GVFzpcRQq29psaacZcyHXOucr/0y7F8YpWmeYJEbLpfIbxHsLYMd6QTEG/7Nia4YfzR5zniff37yjNbPSObo6tbRTq6zus7xhoyDDgjTZjZjKFIVSrBYUafTPkOJEpIcsMjPNcNEN3g3ehn11BaV7BT++bHC+mdBHiMhkDysuO2/p/1dqtqkAhXJcLk+ggkbsSDyeuhs+feZXj5Qul0tMs/72Cba0JRekh1GD1h6vDO7F4WVWJV0Hm0v7S2tVQ6Eyr1wvpk45NFb0076uiEMVPLedIulqt7+i5oe2Gl+NE3zcEDvz7rw8c395wPJ+L4/h1/xGvr1/w9eWBfd9XReU81c7CcDurbT3h7bBmoCGkMkDAgJkUMNjH5RxGXA4P7g4v/IZ66BwF4Am0DHQkXtwR7tjcMRupGemGnIBHZ9eAMRe6/0fXH3NyVPpVk7dSLA7dnK/nZ++NK442rXar06XmN6/82eXy1P8LrbleU8iwzjOlIIoQnSh3h+3njeJtIO/ClS9NRW2mLsF3Hp2j0k8iErYuR4mRCAZ5QFFE47rzrDLN4iNhISXnPGUMOP4UUOMj3YIguLP3yEpLJxvyFcmKAZAvOesl+mb0hkuIzFCCZsWRsCtVE7W4OIfztok+62qPzkh3Smwqk/2+PEhSAxTJAOfxjoyBx3xFNxP0rTYIykXVM1xpTw3gTLBPlYTPNAGLhGoQQU/zmHzlKoeNwDzZFybVabwZxdmAVBddx+uPD9g28eO3L0Ce+H//24EJzmUG4NYvTR1v6K3j8Xhh6W9rcADz4GEXTnmEmC5dHBN7cGJMOm2nTR5M6YA3oVDUdnLfYNmI6iDIKzOhQGFUGwXRzBmHjCjlBs7z5DiegsW7oRqimnoiRUzMeV7jN3i4Dqk+f9ZVZF8GFeXQQj3NGAFX7t9y6tB2OTRtWRYA114vRErv4+bsWbTMklDkoB1LpbgUTknwzlSQIaS0fiMMmY18K9DZIQdDQP3W0JzkTzwN355vKLG87h1962hOJ8f6BnjD1M9tqJLJgO7k9SGldts7U7lGMnWc5BHOWAME785qq6YgYNXzAhUcssP2rUu1AXZLe9c3L8kM2cz6iUHoQ40lUOKIVX0zP5G4XlfhcMsZ4wrhLZZtrt9dHFM5OZEKkLGI/utQL6RGZx710i5l4+tYW3ewDv9Cja72H8xGeArdzxppW9gCFDS3xv5/26Nh26mO//b+ir7veHt/4m2Su2iZaNuG/fHAy74zRQU5OfNyckrwkiNzndAG3GZaDh+qPP6yw6shqcYrbwGoZz25LoEF9CGSvFQkGhKbGzadrU1p47gHKLcU4H1mf+/6E50cPlSVNrt3kMuSK4fNg6KagF0TScEswpGcwLHyvCssXF/rlEc5FPynr0jgcrb4W2MyNZR6/xknIig+VfDVnCfMDGNe5N0iwTHyATwTYZK+zyB6lYFTmi15iqS5xsIvxwUclzIGrOhhuiozcfJm1KytaIEmYaTG1g2jHCg134wL4luclCQS490kyc4xHyeftfXKt/c1WvXnDCqmhhZx622JfH3W9frg2tm3H0D21MAYJ8Zx8NCMxPdff8F5HEhsKNmB5g37Y7+4KxkYU00Eg5pAVsRTGPpCywxFeCu7EnUAaSxLpRRBvRJLRyYPxYyNDP9BPoWDrTLmWXLiHd1f8dMP/wSbP+Dffj7x9nzH+9ubSHSJ1wcF4H78+gP2fceXH35QVZSaLFYlhQqFkRMxpfE0nkpBquloccosGe6o6iLkmFka2v4CaxAaADzaC9HNUY5NYm8v+PpqdG4mReEiAuf5ZDmweGsux9nQ2Qw16kAEzoP8nrfzXQja51xfvzzgZnh0NmzNRh4dJpu9ZgCBIU0fQ8NEZCOawhp4ctR0kPG8/xhmBabEpakjBECFMEyLVaquwqsRV1ABvUfFpeVSTZGhVxFOpbDA+9j3hsiOvnc6JQm4Nez+UCWZUzbfVMUXYHWiG6w3nHni+/OpwCjQs6MNh43JdJScwVCAGhmwQe5P37o4Pi7bqH0zgxSzdKGeWJFZyTUEUiXkwTYrcKQnvwxA4yHY1MXay0EWIX5GfKqT/OGy+p/hvoK5p8ThrAA5DVDPxabGkoFYUXcdg120ja6eWCZOTgXUpnOj7JGBnS0CLOOZYIUUz5ErGcQbdPUCWz3AEUMK8EkLVZzHl8dP+PlH4J/+C/Dr4xf88vgF7fUrfN/xt798xZfXHS+PB9rWEDH4We3+/FB5D3mmhdB1uwoSkNXSCausiWyWAGViqo2Kdtc9L5zl+JSPKfxy0hlv05HR8cW+wNzw2n9Eng3f55MK2WHI0xHvQI4GkZj+yMf5X+ldlbeclzxJHeaXk39l3JafZrVQDFX18NsbuQQB680E/ZU3qLjnQxsDgw76Isfx/VNIC5ByRMhfqft2M7WfZzfvyidP80s6WtVTkNMRSSlpS9zEtOxCY1BoU0r/R6RsFEG7ftfk3N5wKsGaa7xu3vzlMRdaw99q5nAHRhZRlGkuV5rmGntb0dPSsHCTQOLd+/2ca9sa3DteXigJHtbRT8fpBj8DY5CEWBFLVYZRtI6lu+mJGKm8NdGYBSuj+gxVxFHX7W9ZhO36pfsY1Lxe71h7c0ZqDKUJMhMk4Hfs/Sv2bWJ7fMVzApHvC/HY+kbk5pUITn9QSwnnrFheZGvpLlXmOyslgKqPU/7ZANNrtV0WbyASUM8t3r0Qjwh2l46ExRRpsaG3wd5JqiQ8jo6IiVPHtyX7RCEbBUAz0RqlDxjFC+HMxGddm/RkttYWAkaeWUXGHDOmAELwOUs0eXDYtecc3G8lQbA+JRccsdLKFcxcYZYCFqjcXN+DlpYOP0bq3PORU4edHO91kOXFz1N3cBZkqiO0037wgCPx2WAXOd5NFXQD7LU3kXNipsOHouNK+98yBMEwmwRPwf2tnLcyHStlgUUZWJwe2JVxwCAh2xX5u84Gpx10dJHAARNvpYTu/lNcHPuf/sJ/3Y6ijwG4loRDhTV1eOsHWixeDmClPa3OyuLtFJ9lvetysAmyXfXI15mge2FeCAsjSAA6/yhImctuttaw76/44evPwGyw6GivX9D2HS8vXba4tI4qoL7m2iIAx+LizBQs4aRtNK81rbytiNbcNtU+hyBIzfA6urUfrgfBCtoLlmfKyrCh47QNWxVWCKnMMMxhIthzoS29u79z/aGTU5PlvS0EI5My8HZbBEBVPLGUs/q7AC4S70U6+s0HoJwZA7kTXDshKCvlLDkq7QABQVbto1Mbq63Vosmj+FLlU1OpoDRgGmWlpzmGE/2pWvyaFJbHBvUdnV585d3Xe0n4bzmBImdDDtDW222URHLLRIyB05xlvjlVGUZYz00ld/dxkkPTvMGa46GqkVN15p4JV7oOIoCz9xIPpXGOlZct0cPPvL5+/QvcHFt/kAxsgeEGs0D6QPTAz//wEzB/lMPmhDarGmNMHPPJ6Q21WWjkmVhVoQElJMzLHL0X7MrN6yJ00nkZy4EEoEoqIWZa61Al23MMDBjnPjh/85x4//6OcQb21x/xiMTL2zf2BXDD/vozXl++YNt3+NbYzR6ATZWOth1IgA20J7INNX10+EnH2tVYtvdKC+RKe85MjCGJ9DiBX3mA7C9f4a1j70QuXl9/UuoAKyqPxxRfjKtsjAOREhCMwHGcTPGOgWNQbdRSAe2cwAzM54kxP28dNZDjgkK/C/aAzIizvL6Z0rMJ5JgIC0xvrMTwgUwnF0GPb6hDTE5Q4wBHMGApHfOKOqtwYn2uJbAE8Ih8IFglGmrqSzFIburWWF2VccheNaTteHmQm5PziYTjTPb6aa2tNcUPSWxGvZWcHZt1bI8dEQdmnPydUhkuNNMq1aFnBLlJmMAZJ4puOrQn3Jr2ndD34Pgw2UzyOiupfFXNFpLP+IEVqjy4AjmBcQZTiWhVzwiy2T/f1eF+IHplkpu4pBOwgieg6A0VGNZ9X8UzvXfJODCAKC/GRWqugUK4bAAAIABJREFU4Hi9Pi/nrtJTFWTHHOzNNEmF2PomJ8gLwMWIqRS+5kBBH5egAdkQ5nh8fUF72fD1H35eXezTA08DzuMJDNpfuMFyoCq34HzeJbSqooszeOaHbGrEdVYWR6fSYoXwVtf15tTumjmL2rMaBpsWWFOxBabDwzEOQz8M+wD2NDx6w3ifON8n8gl+VVn5n2Q9/8TJqY1xsZfzNlEXIPA/R8UVDS92dSES9T5CH7Rq7o7zcnAyKcUPIRZ5X2hul+ddwI/fPrO+7HK4633Z3dtBXo8af4kEtyKV8mBvUVz5zGXlGBkV2fq6tzpoyzyaor/rV26ozW18Lkb/zTW6OzxWj86NVE0G75EBPwtrbC4BpRsR6ZOv3jpKWn6R26IjOzVkUlC5JZBn9YtRSkaVB2NKgaOiJPfV1BJIaRFdDiWXA3VGGFCxk0otOc5dSg8pP4ASRWafUdOVmGCpfgYd3TkmzlFaNZ1qpr0TMXOD9w2tb+KDqIoOUD82YGSV51bEjwIzaVjLKTZbM1v3XaTrzHKNIXJ9wkfAc8IRcO+42hIA4XnBu36lTbw3IAwdgWmG1hM52fuoZYNPdgu+CJJzGefPutjdOiloaFh2YQWGVvZIu7E2/s3eFFSed6VbYKGtHKfKL/zm8zVBTC0KoXbJE6zqT518UVH67VDjxGH1qyzPHOTqsGkkEZ+Uc/1B/7Suu41cprBs9A1dWz5R2cTrWS844eKmFSrAPVFDa/odW3PAwDMWz+ly+Oz2scaq2NtN5m2d19nw2Yhy3d9yOux23wt1v37vbilrDu+2omx77bGF9t3f9+ZcXufQtSbuArOriCYDFokIac+sz03SMyxg2VfaGikEsOY6WFpnjYFOipdDtX1gnEMVY7nS4NVawtKUuhfzTOXZCRbCSONQaOI1NpxrlcFjmTHcuijefjnXfsiSWVhfysjrqxEL5DmcWDk+brlEHXd/dP2xk9MuAhWShM+1SNWYjX3hWz0Rljx6aDIzlfoZcO9LkRUQWmKAaQCXImaa1GHFdZHXTZdNSqK9kBxqZXgZfgOrnKq/VgImclY5EeccitoaPHjwziCE33uHe9PGrrLtWpDXJmXCwWHYZCpUaifnw/QCc8O271xk56lxUrSYSY2bmHg+RVYW9NlVZorQuKhzYM2zAVTQ5EjKXhrHhhYEFoaq2iz13g8RySddLDCg0GEtneyG3DuGok02Fgyc70/MCBxITAzk+cbc8JjY9g2Plx3dG6MOXYTqE5sizFalvQaUmzomkJP9f5g5aAghYpmJAVauzZwwO+GDaanMlTzTBgtK6I/A8Xwi5sTWHa+PB/DDT6C4fmJ/dPSdkVZE4ky2+LDO2TrPN3gCPQ2IAYsTb7KymzN90LvIz4oK+6YIOwyGjt58dTyPJ/fT23e1XMkTZg39caA1x75fVR7nPCgPISRyce9qvjaD9462dfRzQ7OO9/df8DyeeHueOMfEMQeF8z7pej7VOkNjZOokfcnoG4qATt2kUiemT8dD4kQm102g1ITJoWirBUxpush2LYdO6aCcbDPijnGolcYSOVV6OZQEq4M0gSLcjPOdAFSjjpLJUbW+vBJkPxEdQA+g9VX+DW9IA56DSFEOpgrcLtWRQtJb59FzHOLUlJZUl2NsVLbeWlvork1qRzF6B3JTK5tJx/Y8B9gYdqCqjkqVvky3SZCToICvMl8PYBwHjnkKqa72LZ9ri5pTXNRli4BUgUFDrtRxpT9qXqGquqjjie0+pC9TvqiBaR0i5nIRhQhaM1R8XIFnBPfynFOcUu3HYBviI6acjobME5GDFVfmGBq2LqSIbBBncUIMIJ44TuAYHOsEgMlWQdENcEPbGs/4zqARnZIFrXWlfYHNyFkcala61dlThGIU9eOyKyFWdPZOHbrmeo2cuDlgQQemnDvLQFMAZRF4BYs2fnx5QcwTPg02DDiAPA0YCnQc8L3R5v+d64+dHL8ThDXb5arevioAvQJOuyKK316Xp7C8sBWNV8rnt/erwVGivb55v1Pe64oq5orUIUJe3HzzyqXbQnpyVW0RhrvyiV5dyGPFPRe6BVYIARUULhcQpZ+wcrNCH6BoaJGyUJFOoTh8r7pH1/jWYZwfPuXDENzm4IpwL1Fg3b2XpsXnXexBdmlEmCc3ntGohu4PAKx3CTMOJHKR8cJycYpuiW0gVRas6GnpUOAPIsWPwS6/VRIBWXnlUrau3zRg8KA6j1MH38lDAFCJsqEQQm3/FeWHuZSDL1KfQfn9pFNeJaIzDdWKIr1SBEnuA2i0uKR87UJ3xjtMowY1STARB52cTKNQW3f1G6JzjQzM21hyD7Yb8gpqEvWOLTacncdAG7jCuk+5dD/Aui/ecmo9pHREoKalWKhNxir25Z6wIh6Dp68ItlAT11RafCFXkatShEhHRTx6zyUehxXYfbCJoK0pxKR4VAlQVA/VpEN2IQKmIC0sLnvCkrBlhyqaDjk5VfxgIspWdh+wi4om+5BJMzGVtlw/y8sqrdqSWmW3/XShjzduTeo+oUqata6K7yO76YrarU6Pz7uu7ATKUK/v8W4+WIXbv69D60o/3X4vc/G77uYJH8bpxkfK+4rEQh5zaRcBsIkMznuUWx6DlZw1pnE52AZjU+IcsAjMCcQ0qQXb4pum4EQTQMD9AdgU2o25TF7Uuqk1F1LcF5fA79mY+5lWyNTvnFhcI9WhXGCA/rzyDf5B5sUD8Hkr+y+wwIL0hf9dJ8f7xhsrp61udTk/fFiTnYb5bWPcHorDz0lUp+xFKrX1rigSRtQGMF+fknPIUdAvL7Eqvtp7X5ArYiLnQKak/zFx77GBVJSRfJ8JEUyDDxKLgJDItl9QpODs1hj5IajaS/g8kZO6EsXOLygTkgVvrWNiMPeqxbx+d+UVU0b1FNGy15rh4Wkcy+v3ZXxUBQMQ5oM23eKHzUSmodnnlo8DwL7vrDywTSXeATTJD6jSYjZyk/xlByLR5sSMwHOcAMSVaGWciqTHQ7ZLSLHdcuLA+vEHY1KGxMRbgNGBjYN9h6Z0jmJMnHPgHAMlvx5HIGZinJzn3QHAMLNjzMBzJvdGMxwqzXSjzkprL4AbovPOfUqnBUIsq8pLvhSlxzqb9nXF8p5Kh7FqkVUxXD/7C3v/nsGy8PnrwJyBt2/fYJZoHuiPHdvLY1W7lP2ZdTBPpWsbI8MyVK0BX758AV5fsT2eOMaA93fMz+TkGOe4N8L08CYUqtAbw2Nr6M1RQu3nPKn0bJeybrghpgFCe0gWbphDLWzUNoJES/KS5mRHZKMPixXh1+G20mKqRoo6xJTWMlvwe8rLmoPOiHeikM8MnGPgOIJV7EEzn40imWZAx0YUwmS2q0kxXM966qxJbFtXS4EHquVHGGQPORrjJEeidNXIQ7w6zWc1YGW5FBvQQiTZSeduSfjrTihvUL69yrOryssb2u5oFQAPGafPvMpfQy7EaeE24n7GjMVjAZj2KZtCcKotJyeKeIsJt4Sroq8aVYec5eXx3oLhUsG2lCbVZGXbHHQjDFL5npP36Y7neVIfJlmJ+S5FbosEJVSY4vJy5AHhUwCS+luWTS7EXOdG9YHLmJiTAfnV4+0avLCD/5ZIamsbqAY+lea05fxbcN1SVVum2UlHn3Mi5wkTilhjwTZHDWaBkQ57n/D3wH44OXaT4zA3QzQA7nhs/ydITjkmy23Hh/CXe/tW7ibsARqsZViqfr6gzeXmZ62425teSIgrOVwHVCqnCMMiG+UFXwBgaicE75MHoagMhWDIL61Umibj0oO4kBeIdFxS9qhvJ6QXoa8qN11RmzghgjLX5+uXSpclcRG46r1rw4VQM2JQgobrvbLKo3kzVwbKFopQg7NcAfltNlmK+JlXEwkda/wrdRjri8TGjqnGnXEKVdPUVixwdbTXexlWBL94T7g5NlcAxqVJdPXiACky/kgsZAiTMuRjqlncSSd6nAcdgMY1PWcjFH8eXC8TkuoZRGLkmMIco3KqwVLUdtvgJXRXEXjalEExEgLtGju67pchW96souUusbcQajPixDwCRwym7LxEBaEeMwDAcnRGebn2MA82ilEe40nCe8o+fNLlrcjg+HBQFLqcybYIFanm7WdAsUuACnRWHK3xKz2vsiRrzsTZ8vUOufRyCh1dBRGKRiOuewswy+kIOjniE0w5FImpLGgCcmwsjeThwaaqUJ+uCvzCNc8oFEJcBjOkVSUZsBjYJg4iZDMWciGStnSgKqh0patqXPmMXMd5e86Eo9C8XGPNYRKFik5eMtiaKWdIMJr9TxUW//HXShT8FhHG7f4BoMbw8opu/9Yr6sy4Fg2ug/L62UIdAVXPCp1OkeeZt9J02UVkX2fOhYcUzQJR6FjqTGWguxpgG53qWiMEb0gVuVJnFeVAgSPIJ9Rpcy+n541QAsD02rZQVD5T5HXKGYg0XuNTKLQtdJw6drzv6z++oJmhgy1sWhLN0jKF3c4B6My0D9WyH68/KSG3daCvxagFXh44UYW2JtmUZ418rkWwFHyVh00r3Y8Pa2Z9JMxgccH+9VlZXnHdCsprhDZzIUSObNT4ZbomUSRTIFSKmURi1oGvqh4rAScZuta1ShmRi1uOORMxB3V1VDZ8SZjrIK0pu0UrrL7gs9CuyQFUqqWy1Et4LEMHn6PO7JkJj5vhkSLptbxyFXoV42KuVN+UuN3nXeTPXIcIowciGDOI4LTW0boh5gmMwKmGiddByu3TtGnrMOf3cHNuuD743kNrrqOcQZZBk3eCBIYcHeumklqsihEEeQ/H8x3HOBGTQmbjeEfmxNPIf5nDMJ8T4+1E80T3BPCOPinMZu7AMZDmeKp6xydh+95BdLP2UxGxDJg2AE/qc6hqDrq1mYlwLEKzBaMi3zqsOfaXHTknWpt4Hid+fX/ifE6cY6JtDa03vOwv6N6ufVo0hboftdKAO57Hd5zjHc+3dyn7foXh4kX9R1/WywTKqbnDy/L+z/PAiVRkbEKsCiXlfHtV1MR9Xxq5OcbmqHxk7kSmiRJs7SDUbbJYodI4CtqlWKuWAaD9mHPgjJAoIxSkAHOQz4PzlB1wZJhazLA1Q8RA+lyVf3FS9wvqZu1q++CdKF4zW2gnvxKwIQeHz0cdJ5WsZ6VG6HmVpEW3QioqxcITxoSy50y0Oujq5EE9lzqLT6zxYXQfGJPaQhW8tLyOtU+9zFSdhHXvwBUcppU7qOq4co+Xc3h7GdaL8NEBmvrdqq6CCgYC5xyYwTZCFkAM8nFc5aE5xXNZjr2tSiLvRIqmJFNWzY2wX0fnPVuip6GlIVzp/tyQ2ZBSJq62CxZ85syAN0nyBQAYUvwzl9M81AWhuG6uyiwXFQPJe0ECI8jdyg6NJ7B4a8rk0M9LVCNIE6fUpHj8yI49GnwCJtEeT/kZ0iHygNIXv3/9CZKDm8f34bu1rJHL4eHCnUObY7Gzbb3uipb182zXh5Qnnbbq6a/X+oomlvOs35GHs5xpLmCHd6rO2vXLa7HWva0ii9vTpaKfZf5kQC+BuXKybq/S71RkN0u/Qx5zPVqr0AhXRWg934cr69mvA/0aH0j4LBVdmJw7jsJy+EJjVRtHXvsq7/zEq55lOWC5ZgtihQrtA86YGDmRzujAjKnB3jfsW1NZuNy5G1J2McMkZpZ5Y/9L9deqMgeokK4lI+FH34R4SGhxsklnUb2ad4xTpZ7GXluRguIRsGZo+47eEr0B7dHgvUkWwRZsHZBSs9CeaaBTEYNE8mwwlW1iVhB0xTnl4DS1/WGlbq6UQI7BdaGWIr2/IODYZ8AwYHny80di4ET4XGPTnIdnM6VqKq8+BjDFVQqJuc3xG7vwH3v58hDkfwF0BqI0hA2VV286VCraa1ahCVaev7L/RKzU78tKvVxQfdZBfXFWMrH4ZXMFcdeftQpN770OxpRe2CDBXfjHmtcmDzy9aO7FWalDt44JKGC8nF2LqiDM1bDUjYceb4hrenFRAhcqoedVuMrPKE6A2vRc/Jri2lxIeCGxerel9xN56bhkaE8CC/WKmUsP6jOvSu9i2aAKtut75MCZ2Rr3lclYR5lpHcoG34LZjERaoBpP5m3NTqWjMoIpqGpO6UTOppy/3roGk2fhSvsByhJwbtOqTzxPEK7Lts4FB38eSlVG0NmMKLI5e98RaC8uWi56BRcZ0SYqttAJSaQqTbGELhO2BBFRMjL6r7R0GMgD8Ea+mQam1I45tg2VvvKUegKSavYT8LmsPCkyFeT9wTr6EydHB2M5E4UcrNmunwXUzY8bPxOiXUqGnY7H5etersWCmSoFsfwA09TFtdkBGfrEDdApHwMpRXbUZxXrduWhrxx6dcDlEZWragNFcF1PpwnXIBJduAwQQ6yCvgueC8q4l5OSeRkQXOklr+c2oBydkaHnrAjA14Lj50WtuzVLVtFbvWnBwEUwg9F7Ty72z0ZyVlkubjYt6dXXfzMHm9LpK4xpQGsJ74aXvWPvHb37JQCnKQs9r2ktZlXlAcsBXyBsOTlghMlNxJLvIn9OTIzJho9sJ+E428CBA2MMHN5YiTXLeQxYa+gvG3o3bBvQNq75EC3MddDMObAapWqdW7LSidaGSsMOh7oTyHixwpEVYKBTpcS7Ra4oMdVuzrTG9/0FbMqZaDjgYYwk58A5D5jRqKY39L6vfjsl1zDnQLA0DZYqoY/E8xhEeD/pchOanEsxSATdklCD5pk2h4iFKhXNbj8lebEOeJKLsTiF27aL9yL+yd3WJZHhvqmqa06Rha+Ai1Oq9ZlaWyr7ZZUgqwiz8TDqkIK6GdFt8R2X7c3yL2yt54wkwpMBhONUOosOSJMOUJWUy47NeatSTTqtpe1SbQjK1gD60LLZTDSkPt91wIfWQ3EHyO8xHVzkMpG/M8nf8kRVuZez/JmOMsDqp7s91wYD4FoEuQJH6Kl/TyGeY0GNNCiNx9gygCD/8spmBXKy/QgbtYaMAjMai+Ojhru9bTqHchn6WgNsVHlpx+G2PgsdqZL2ajcUxqA4pq09GykeoCnDUTpquCRWmFEpzhF0BpFDE1VtyBpYTAMe2wN731CNpt2aUJ3KJ9BB696o4h20KRWQUkxXvKhBJ2czoCVgI+ETaNPUKoWNrxMc+PwDU/THvasEWzNy4A1nuaUopEDqqCING9j6IXIokrq6sEITVWWOCy4sR6e0bz6490Xi+5i6umDOy21CMCqrqW+NxM0Ypxyd+hhFKh8EbUTidBd9xK4fXbey/khN9uUmFe8I4k9AZYm850JPMq6mZXcPtFXDtTLiKwq8co+phVwhZcWN1ePIdBBa42Jy8xX1VoyycmWfeF0aSzqkg3D/OSeOcWLMgZM9yFlSjmSppDteHy/s/bRt6PpeSmisaKdXiopaLkMR0XISb1Ha+lMbsaLvOiQSAW9A3wyZbIFhALbWsFnDnBPmxjRbPLQ/ytA3uAWaBwnWljhNaTeJeG0m+H6mqpbYo8aQ2Do7xjcdNlH57xlgL73q3yIxsshVKRFTXJCFfrIvWBxSag5CwNu210zIGQRLhCOB93f2q2od5RzD2PE95slqjHSKkeVnLyOu8eaqAKl9Vq7/QgW0z5T2WyiP2ZLEqGqzBPky5QgAoawD9zQVzw+Yyve9OapxJ8DxdMkOXMjA2p7ce3IIkAZHw74/OHb9IowqaFfA0zQfagwqYTZAUXsFM+XqWVG2aD9676sMHrhFuAY+XwKZvoJO3NfMSsfwcyoMrLG9NL30/MDSolooeVxiqcW9qKB4RC59JTqHn+/klE3A4lnmbR0rMOXmQrWLuXNvCnurqruOEpDU88yAiX+0iPIrYEicJ/vQ9b6h0u3nmDprqymLa55kvOue3VaAW7zKxU/UNHrTGZrJZtKFDgBySkLIS+jopYNHRJqpzUVsjzp39AHIxQFCci+MkJMDoFnALbG3zvYWsCtYyFRxhVE2YaovWiQ34Typ79NroPl51WC3u+PMwQKkZE+35XgXAezvXH/ShXyuAVsebupB18ooAp0OFqsjtTR1GLHUgauZwSVOVwPIyfz4vZSh+OjgcA/ax1fVoqgspUEbWVVQ+DgIy3lJW5vb28UtArA8UvzmtRd6VcZG/bYWbM28YTkkde9TzkvMuN17GWrXONntXg0l1V+fXNUMue4DmKjUTC5Hrek5soxUPXTmcgo+96rNR39zBvtQHeeJ5/nEAKNi8pkM7h29Nbzur2jNsbW+4FhWtMVyGGvuR47FiQCEgKBYPXfnLlHrcuXbATrpoaCmAxlso2FJx6OBTs6VyrneLldOfvArS7pARG/tme5sM5DKY7fedY88oLoaeHKqqj8MtbjNSyROa24Gg4o0VeTdYgkINo8idwOmysByBqvp7KyigPPANLbQcG9ovcPUe4hVG1XWqQDhM88n4vasvqgJo6YAoW1ct1PkW5ljMO1JFMNbVU7RrlyUkvptvYeCu8iBhk71aaeDQO2cShFV0MF7Kgcg0xYfsMJMg1NbxQzYtOZuJdwrVcRT4UovycAVIlrPRIlK2ozVELJJA6aeS4ec1QvX3tfvLBXNskOynYbr31lWB5dTj3pfW29eaHZkIYt186bxDpwxFx+qfu8zr9Lxgd3J6XWW1L2uonncTvkLVdPfVzPbNDUapogfjNW6TagoytkTCtqc2jFlfaguXmcU1+6av4U63oifNRe2phe3aeD9BUGHyCLNA/fU4joDsrSOaA9Wg84KGLQ8VrZi7RMi6nPwXJtIxBb6vIauPoq0Y1ozMZf2GyHuXKRr8hLnVTQQl8yASzfMQUfJrX8g2qsU9O/O+R+XkGs4lhchh8azhomR5JJ0NqwjZY33irAqzSR4HWpTIMh4nX765MuJud/8NflZ+jU1F7WhIYEhQdnlBVvpmKO8eVtVOV4D6bYOiQLFr4hWGzMqDSdVyOIoxFwy1q7mbIWExZhL5ruqK2SddA9KN2nzuaLOso9VjVWg39L4KFJGaW7ImCCo9xHmFzlN+2POvLmHn3ONoY0VjEpnTIwxMY6pMnqD+a7SIi7srXf01vG6PdQUsFpkkHCbYN+hmXM5HmcccrTJaeFeZTkoxA3IldoSY2tVwtRhFatIq7uMsANNrRVcZYvU+WEa6DkPcTtIFp1xADFoSHohbNvlPyeAgcuRUDqrdQncaUHb4Po5T6bbzjiEWCds29H7poibwYVJ0DAzMfMJGCjAWOtF+9C8wRutl0XIIQasqdGr8SA/vh/ganFs3dD6A4+XBu+BMx1jfN4aojBraW+YUJza8jTU5NaXq6K0rlV6lvM8EyRC3p0AA8xIzCU/x1i6r4KAQKrlRUrKAai0dso+Zl7IWPXeE74IQL3Z7CJsFlpc6XpWs6gQwaCy5UIqdSj7hX6zSzxJ7a0VM4NrOOZA8WxYvms8GMrWwFY1n4l/NZXSd3N+NsBWMOUIAQvJCcyyiriQHVvr7PJbEpUJmCoyKIc7UaXJl2P5GVekOEx1MGasysVybIvLtPSG/HISij7hzeBtA1Jkda2ltjVRNIjivx/vINeL7PR939AcDHzKVs9QJ3veY7MQkAChPb6cEPIvfRXMrCAtGQjFKDHDy/muU+zS31mUcP5Z5y+AFOp7oWyaH1ffMX2HZ1Zgd0dLNgB+wLCbKbUc67O5DCZivBMNRsel2SMyP29sCRbmOZBjoBmwueNlaxitscdasLFwyqB6/+OK4T8nHn/4S66FrVkH3fLy9m/Q3ofXfbwya99ow32wwteL6r5XtmG96c39WQOZ68dLddRcUTQuGBaF0BSPRQ6PqwxvfcL1jJf2T5X6GiqSXWSzyKVKXE5LihNUTgoNa6265U1dX9DicUNrTVF2rs+5oqWPg5vl/d+dQOB3g+1c//u8a1ZeOWhEmPfNFQ27ULRUPys3w75t6N6wKUW1nEq+E6oBHFegSrxFYql0Dx+10kkph5HGxDWpvsbL1rqpVWA64nXewBuQDvTK13uHxcRxAihHalYPNEYmKFh6IZfL6tDJaY1OTjA3TvRReWpTqenkU6b8smxAekP2TmjdynGNC4FQuiO1EFKcjiWG5rU3DCbHsuD5Ch7GmMuZbE16RN3QLeHtInZ/znUdtJXWqR1wdx5Tzjx9gbJJsWB/mHRB7nbmt3vWpHh8swGsrGL07sVtWVE152BFrKh4lzdV92dmq/Ly2u/rCVDzBwOqGaRO2+XY3V/HYMpZfbf8uFj3UPee9Yy4nvsel0M2xeUotgSWEJfdnLj672bjLzHAet/fzhrdorx5QKusHHfU+vOuQrdNz6Ik1c226gyouahB1/oCNJ/uyHGNx2XK62+qtLMk98bYZsGtiOw6H5auUq3lC2lnEG4C/JS+Xu9+H71yUnKdafX9UvtfKJyM3pqxvOcGyn5UkY2s4a2sPSFAwJlyx7wqX5vdVsKyrQkKlJ6rCszuZ2Ot2fUlXqVssRuwNQr+1bhx9VxKyn90qP1xCbmijXVjICt65lTuNa70Qivy1PVhZo6279f0q0OtdzoUMwdzt3PADOhi5qNy3PJkE1gPzdJOEmnrYDENPiGOyyutWMrvGhHAkmGHs91Baw2tOb1zWSBWSCWKsDuVa84ZkHQFKkqxxlTDJpnvkNddkt5t64hMHHMy6u63AzpxqX+aw1KcdN9F+hoYqEhLcZ1EzKpvVu9CLpSCYbpZz5gVlVyRyX+CXVmfyc3AuY7NwZLGTqVjGW03Cru5Oba2r0MhYR8qWIAO5MTIwEzDsE0f1jETlEWHwUho+XAz9S6K1ZaRmxhrDbNvFrs/Uw8t1BxPCaVGQb/djZo6IsKxV54B4ejbA9Y6sipdKuprjM/cgmJsURNnKAzVu6oQ4oZg6H7DGPmX6nLgRDauzSLIZpLoyooMNdkVX8sQeo4EdS6ACi6JDjheXrrEySYwT4znQFhHmiPbid8M6n/sNaUInRyjD/WI9uGP36zvxAgSXhuAu5qhAAAgAElEQVSKfn5VdRbfrVCT6gsWy6PEImMv26JqlJlE60IK62iXg0XOEDj2mVrXN82RMsz3dHTtUwUx5ZKURau0g3msoM1NB0ulYIIH3VJUl4O90BnI1twkPFYH7BRmEHRz27KYeu/b0EYdYmvcdUBaORFCFjRevRP9rIZwicSR81at+jmXqwURnWD+zzSlSEPcnUEnhSBmOQLX3IyTh/YuRJU8wcDz+ZSDwDPx9Ue2ZumgGvl5DmydaePzVA+8k3yd0uUfxReT4rVjrj35W7J2VEpbFWFuTvmMal3khtDKOeeJSDadpgPGCskFZi/3Cas4whAy2iVay9L75gY0IlpmEzEDbhssO7ptaL6h2lRMlcjHQRRwxvsSoGQfNMM8D+QckoqQrTODt8S2Gb68bPj+7R1zsj1Ud8O+sVDimG+XNtXvXH/aoPNuMO7RzzIS9RNtsmsx+QXz3f6PMioQi1uOwqXShMvrv32UrXdYJ94teqlZqlzQb16buYzL+niFfqsVwC3/ff+8VSapih3cDtmL1GeCKOtT8/bMOri0saHy09B73+7+euTbmH+Ef6+HMtgVddilsLxufj19vf42P7d5+pzLLuOt+3Q3pUwaSJZWT5nGw6DZVR2z1kVZpitIRcUk1Hng5wQMEDfg8vT50uIu4fY9/rv6qCi1lrk4Kx9i1fKo5ZNQvJk8m7itpfqFynGntWsyDUJW6OTQoN5uB3VS3g7hxEIx6v7If8AqW73AZK3RhHg7jlbVGve9ofe+l8ouDSIvyQOoJFbk3pVzwW2d/cdftv6sfWy/2Yf5cXmXUFQtlxUl8nez5vDDu1/jlivPX2mM2xrIIJK40mfX7+LmpCwbU+t/2Td+yMVnuR7y7qiVk7OGWj+/BN/q5/UsV2Rf9ut6bUXBH43JhebUv7h3ELbkD37PXGTWM+DDq9f++PCLuCglCoQ5xv8JxOM7jHa/yjaVds9vHjqtuDrXmbjsgtly8K6u4pfzyUeus+PjuroCmOsdr4yIHG7cq3Nvn30fujsEhdrL9buhz7vxSLN4U3lVJpV9/bvng1absg1lo0iav0kW6E4VSuHeHoU+b16msPaLEJyrpbittd7csXVfmZba1IVy/dn1v8DJARBTJLFzHbC9kzVeTQzrg1trTLdsGzISpwSsyIvpJI6OiZnnLQ9MXYAYE5c10HsuwyXBIVxOx1osSCBvbO1aSGuW9aeUQr0qG2DqcqpIP+twSekAKFcaeZGwNca4pbZ6Y0XDHPSUfZEcOLycTENvDd35NSvaqgVWDk+SFT9Ock3O44BZQzfKZ5e/nwDc+bdCP7xt/HnM9cw1GjMAS1uQ32deWxea5JdD4gk0FjAQjq04Qgd7a03nKe/2Q+RoqSz1wJkn4JxaVa9iBMdpxglHI+cqqfOCqmJICQnW0i1jcy+LnikotowZq9c4mAAshG9ugDWgs3FiDX9CyE+R8I18rUxgDK1F30WgjjIJPDwSqmZURUUEK7uSsG2MwMiDKFOaovuATRphW+t3gMJZQPhkGWkZRLereicTFvo8Vd54T7aRsIZARwDo/RXditPzeS1CdvGFWtvqpMeMXFo1a34gpC2xUk5FJPUGkrjPsQj6WanE4l8kgDSWYQPgG30k78+TlYArnrqrlodhCXQZkYM7eFPCqgmJxbU6OMqpacvxuBy7dQavaL0MfFXXxJxM1+pVXry0alpbtvKDp8VLLDD+/i2Qq8KrBdAnFqxklb6qAz+qsQPvuKMjEDgLaZBvTCoGqyvHMUh6/+xr3ZBspOyPcoDXoV/HjzsM+SGF2beO/eWBVfBQ54/sGbrWGyZmJN6eJ7bWse+7ArEplDjQN+p1neHLBn1ICQJq4hnwxubE52Bvv60TvY7zVLm+hBK8w1QCSfsiUVbLFczFiDWvpiDdijRmiXufw1xsf/5pLmLbDDTv6DtL8OcxEO1EbKwscweezxNjnjjPJws4Wqd9RqIJgdq8KwbhSeAA7VPf8Pj/2Ht7JkmSJE3vUTVzj8is6p7dw+FW9g+BBAsWHAgcceAAEsSRoMGAAH4CBAx+ET5uDzc7012ZEe5mqiBUzdyzZqYWcrKTEGlx7cnJrAgPD3c3M9VXX/2w1Xi537kvb6wO2x7svYrixSNs9gOj9mOQk57I2bbDQMMfkewxeZwjidMPd+JkWi03BhxdfWOTy5GbMxDidIQO9mW+Mb5nXFhOvsEKAQf3ftxPoMsDXScQzXNYLupDaYyQ0/TkppGV6cWIDMV4KIIRE/XM+J0eVYbezuzAfCZTW4RRbK2lYbNgiXL/lMOTOHmIeXcjWSxyXUIjeRrB8Wx0ALlPlFnmSCrMUQujh6KexmiwUukhHOSlJACACDqM1zSngsPo5zELqTLU6aPiLcdwNpw8HCBOYzwZNmcyLscAM4+T8duOmxsUsXOqJmQoi+EJZh7A8K7ndcT3DK/vQ9HAOMgtNt3z6Nxtcz5k3sOs6MnvcGKtjTTYYS3nSTldQ3yh9T6LOs7tG85m90P310+Q2RyT45H8U/Inq+zsSY+xG162cLrH0zhjMFpkjLOOpEw/jxuEw3VUrczXJko4VCIJEIJlDr13Lgg4XJTj3x+rreL+Bij7wDZ9r4/zXkT8/OHj/IMF/pMH6JyOYjyh46+P78TxOnX/nIfT0x+sUNyE+dgQ+fPEfHTeH+M+7ukAbJMfO+n8CTpPj2lUkp1zLnvvmZ9ZhjpKJznCmmad2ZTUR6+hnE82jh/XkU94sI7CLJeebOEcx5i38/LOdpBxzHgrKy5HEcZgBM8RjZyNE+ROQ5MOdV7QbE44GiDlea31ZK/jEsbPWDSSOlhGs9r5TNOpzQ3oBrmx1JW1Lqy10vYIj1nPUPFpTfw5+SHIicqMaD6G5XbuWa9/NBOSROkR57bWcMn+OtP8RtOp8G47rW/RdyNbjXffOBowOeYdySSt0SArnnv0mgigsB0TD88s7UwMns2tRjgqGYPsdlnmtg0j9S0+Oyi1yMHp07iereGhZk+x9AHWEuBUDS9qtM0eSYMqhW6dve/TKxtVEDPWOrpD9n0ClkJMslpjwAMAeZQb5wCbO+0ZG0emH4agdBfMFbPYnA3p6CeXkBd05jFCLG4Vp5ziqJIGu2SYs5y8VEkArD76YkZPmvAlOs33pH3JhmsjbBPz1yxKw0OnRR6N2XcAY4Qjh6KB6amP/IbZrXiEtFoAERkVhhIJugslGRaL7j/eqEMxEImIRUdJcJkJy2NT2NajvDI6lA5l52j3bCjWQSofWpmXqPqYu0rLSOoOpWb7lhor83I0r2U8f6K03Azac0M19r+a1VrpYRV1RCOPqHzerg4M624TuEdCe++p9CeIhJFc3nOeD1sfzkWsr+j5M4z/YH0y1TyV8VhHpGEcCY9DwgaekWjkUJicqByS/ZuwoMz7OAX7J4gfnMo5tjWAjY4wIYNFicRqrEzDNZvT5XkkC0Pc2uyJApyY6JMneTzs6ZzN5zeuX3zeycm2MnuE5ZwdZfaW4dwiSpfYMkNUwITdWsz1T5TWtxkOD3Ym+rlpqbQMNS3ZFNMTsPTcNqcuR0FD7zu9NdxjfCMvErZv7/TeWe5fEFc8mVWs06zT941bvbHUFWtG3xtta7H+025MW6MBBsyij5qK0HuPtgaZm2r7xphnUTzjEQHYdyiRxzPDQTlHLCuofN8jRWCp2T0+We+SuV2exTg594Swq0w2cURoziG+YEq9dTR1bLWa1ad7PL0OZcmmgO7MxqoGZLXftm0IznqPnkJfX77y/vrgb3/6QvvjN973Rutt6uIPHWm+k3+iT05MZI27pEhNgJExQhmdUQ8/oOj4t8xMbEhcOIxHekJzGaWnM/bU8FS+cf1nr30onXGBCXtnLsUojcv/MnZYiiZjEMBCRwqfWxozmyBnoPKZ7S3MAZxod5ybk9fkdngpDmO/mXhpxFRP7I8c3unEUYzvPSmRfC3A10Duejy7Uy7TEW+O2OZU2qdRUDld8yfJTPQehigf7WgVnhef4SlO7NTheQ4vQFOrikQnWxcmwR93G4Ym9PbwsHKeZKggWA7h2NQt59U0HN+zOsNzj+/2BKKSCp1Oxsui/Lwgo7KfcYvSLT0fjrnvlr0iEtzqYCSPqovhEXsP8D8MjCPZF0cn4xmP6DginhOn5l+Sz0TBYg65jETqOJfKoH4jadlJJeQRohnJh9Byo93PkVEddviqH8M5OVhZRs6hI+b7YywZaDf0jOeqlBEKn9+YzypeCNb5OPF0eU5rNZi0Y85NOJDPHZhOTbxPztWYRzrypmRqsNMTEEYjpAHoINp52KiqM5BRuwEMFmo0OJzPaToPH55w6q1jMp0JoXHX4xyzy8tHrMMHOiKQ6KFTzWYrh/Dmf+iA/xXlAJGDKbbe4j5E5tzo2RJAtX7AgB/KlVNPdIueRZ6eRWyCS/SksvPedGn/ep+JuWAJ1PMJJnlQ9WCDJUPLU2WexuZPJjyc5ojNex15e8GORLm7SKRvRJ+lghTPnRWOfCzJ7wdmmxSZ24/IB0yO5Ia/xL3H+9HUb7mtMeY28u0j3G8ShURmLR/Q2PuNXBfRJmFZbtzvK/XbE3yLZqlE88UfaaIfg5wWH1UpFKksrDRrPG2fPRosF/dAVEtudBdDF7+Djk3EHnefcWxj0JgQinSO3Eh5HzHgRKvDm56x4xbK10YPnDEAmcRaikbVkxyJY1VqTOyMD3t24hw9cOI6U4noML4juXcYljR406gwj3X3oKH1WMTDqI3Pjx3KT1Avn0GerQ8wNPY9iiTXeDQZupqTPb63ZLfLqAbKrq6eYaqcqNEN9XOZnD+h2cPGhLLL11SE0Sn6gyS4EU6x/fFW/t2ICgSTyB3zsZNbniCSc/sEOqFcFfnwJQmWScXjh8Gb5iqn7ZhrxUuMc7Nw2GuU36pLhJIGEwRRVSUHAAeOZPaeYzk+MPNp9lCIbXSqzbuSMGzqFYi9ruZTGWX6Gt8XyY8DxOWHfahFm5U/wZgWXJxFg3bf9p1R/ru3J9129vaMslGtszndp4gfJnYq3jMYHuE9O4MQTlbUh8/CALoD+MH5x0+fy9U5gMuBlvKP8KZlsIhHXmcyYMymkeN6VWt8Sx7Y+wAAkVswohw6WBfRWeUa+z9JbNKYY+rjXBOMSeK17Gw7cvCmUefYmmoaynE/crq3wQWlvmOmr87f8bgznyvHwmRUxvbIXWvRs8vVkhnJ17qR9VafLHHjo7LQPaqRrLcofiiR7efe2RL4fLlnHo336RQI6eQQWZK9ZUVtDd2y3qKR6N4ec1LEDAtWrZnRbKP7HgBHI8dy5PhULbHvnBtYP/aqU83quRihkVJiaV/d0pnOEm8faDWBhGOxoasolTIm5VFBqIA6Gr2cmaH6nC8lc7xGvfJZR4751K1nP7QR1uqUqtzvP0VOXNtxb8HsaOyk8LSN1h6QrSG05Lw1EApLvfHy8spPP33h3//xAR5d3j0rJ3/UzuKf2IU8lnzrHVSoUQiT3mEssoHoRilgPMjhHeXDTU0/wgnDyIRk1vUHEJAlu9js7iqZwDcz0ZPBGTHRcMayBFlLALOszhE0Y3+JlHswP91OpfDuHx7ULNc81nz8O1mnjxOXqTjjfu3wbuT4zJFXILNjYzslvLr7kWxK/F01Z3cmFwdlOJS9n747Fetp4GIh+kyEPLDj5/pP8xrPStXPs9LTezof50yWalgOPuaBjMq40eBg5EGMHjjusVeM9dgFOhgcjnO6R+XQeP7mRwLhYPn8SNSdCzrB8PDtO9HF1dpgHEESWB77r5NeSXxyziEboDhfJ0Oludv199Un01R5hvDG+gCYvYOivHU8s5mQODw6SO8qT+UjpJEM2GBPhWQ68twddm/QBK0HO/EZcnCRTMBxMGKDATue0feVRgkZwE+5Fpav5QEfGJn8Vj8dK9O9lGNOQzBjftrT6ZxbkQUNPqanx35Bkw1M6mX01Orm0YRPDw9PPZP1c/PFEdIuHxifMHaK5JiNysJ8BodDD5ZGEMLADnBzUgtHqJiJrmea43AAnPndwQqmyffMlzCLnaiJ0uett9iAN0OgilI/VxXhoyt621Pn5D536Yh7dzyTwUsdQ98m+wyx/s2N1lsMkXmmIwh9psLokXcynmTquFlV3DPlw0/jKMJSakZERu4e+fdhw0ZUIBJ1OXaVH1GSXNtxvfF9KiVARXYjHvZqVPyOaAeSuascIVLPSEXRw3pIfj6A7mCrMrw7mPD8TKSPZDRThG1/8nj8ylrvlLIgtVK4zQ1M295iTHLKqyxh+4gtHeKcwzQKP0LLP2ZysmV66x1cqcWzTYVMDyX2XMrNBDN5lzkM4YmM8E8gr5gFOunhg94dfW0kKZDo8DhAFYyZFqDmYGDi20Z/megEqlqPLsae1BtDCfVM6h0xPZ/G0lPJjZDUWNz5JTP+MDyxU7uJKWOfqjhHInA5lJFmwEwc6EeobH4mp3QRjfbYOkBOfE+R2aj7BCD91CtgeKCCdJDRjVo4UYWfJ6Nf0ABe+DDs/vHFYTUG/SjnkBJz8Q0TFgl8kb8z4aZHDtDYeNGyQk7dPmbgT7Ym+uKMvVRsvIcfnaUHgB0f7QPk9FSCWY7Zxjw6zQk5dtiN6ZPBJj8qG4bHP1RqtxZ0dnZBHduLjA6fkwlKNiLyMuJknrxma1vcFwOonedgPG/PUNeojPGxU7QGU1A0P2sxDtYlkv5w6npucPgZIh8ByGBNJrgZFvjE9J3A9DQkuQbyjAfoZcy+1Ekca3w4QJ6egqTjNBImJfXG/LoEOpqOx2j/jwqdnnouYZvqmAA5lgYl9jIbx3jqotkjrEcV6tE88mBAP4S5PAC/5PwYjiOq0fU3r4lhSIcjJiM1gCOfI893ytPNl0dIQ/LZBQsSm3L2CdjNhM0ae64dJ/RY+eTQee8NFaMNB0oD7GuRCFkZuNSsEI77M98ge03hkffWrNH6HvoUQXTJ8jGJ+WXZt8ZzDzLR1BPGsVFnbMwrsyNRpBOUWmdYa9gYJUFFPr0RmRhVelpX3AMcHKkSGTbvsZnu7XanJFsFkTcVckrnYFioJHVKsD3uDYgtKUKG0+jQe/SMSpAjifIzvhGd1CWcIpGYh4/9wR/ffs+X+8/cllekLJSy4O9vmBvbviMYUok8wBL99oRKkdz+JlMbtDLDaX9OfgxyNHM6xHE1TCOTWbQiPap/LBdtp09AMnwul8SZPvrMZFb3h1yIs8c1WlJnAt3oP+I+GaIDBgTLM5Ioy1KnIQ8yfw9FJJLe0kguHLkNGdYYwCk/PBJYZ0L10IIZKxq9WmYTJjmYkblJ4NQ4MnRzpAGLTkU5wm4992yKvgNyfB8ZdhtJosMgCdPwT+UyQKLEXkkTNOrhCcw+FaeuqZ8lUbIr6HoYxaN52YjTJxOYyMIylFmyVcGg7I+SBRAT1JXFKupKM4l4cM+9sVpHulP7UP0ZfkROIOfEqvhwv0+Aayj5MbYErRwKP+ZuJUHmd9R9YJI0VjImwkhwtvl1bo73HC9hXsP47CzYyQRIEnxY9jIx+tFobg5v7qic9tWFdADqpKSdUFzDBpeqecsdRVl1nbceOTkt9uMd4/WJTM5ca2NYEiRHQ8wMD+fcH6BwJIwnNsxOs6dQrftkRw+wmFDRI0F80P2zApB00ITI409gOXRfDHJjEL7h/ZK64xwiyzcJI6jF51SLdgDMsRtaT0sWepznxmDTfTynDP/nA5PRh2kCkVwHA4rnRQ6Qgg8H9IDDiX9yLQx0OdZJPDPy3gcL6BIdGLY+nA3n2Tb2vjP2tlhu+v+pz8k/p3xwwr1j+44ssTeZESxqGOnhmnhsukmE+mF0MYa1Vqw1emuoObhSpIfOloa70DOxeVkKvTf29oitriWKMTQLFEjdhENre27iedaXJfad6+kkjUIfO/JKx1rQ3E1K0uZkDJ1SjVL6BLBaR1uUAboNLTWAUConzXDWEdZNm+1jX8t4RiJC7xvWWxobDegmjNQwyHCYaDbY3Xfe+i889REb4IrgLRhsya0heuvZf2in9R6pQlWRpeQ+dFBW/Y8HOQj4WAvpI6IKVXMShOfhg47lAAUTxqQiMoudwCOB72MeyvnPgVqj3yZM+nN4DjJQYQJxFaQqZV1yASaYMsel4OjYTBXLNvmj6gkZHWd8drocm4/OrewlDZWM53AoQRVm2XksoFBUo/Z/0JOeisOHKySeXUbjPxeObHUd58uFUIYXlzVRJwN6qK00+qIxMWwo96yymfHchIj/P4AcLQrrGHefYzjKX8n4KjP5O7xVl5LU6wmcpY4NOlgzNyaUhSR7Qxdas8Cmdni7ammwJqaJxSyDnTu0+fRizzkPMUeHKxsGs6SpU89NRt0+zNF56UJWuTnqWck3E4/TYAyQkxT6yOWKy9IEuZ7sjKbCDRQb8e8secpwbnxtVhiKRkVUiUU22yRM8BY9N2IHZUVlmSxs7VGp1yy5qMlmfpKIJJAhxoSRp3UGLiNEk0r89PHI/0iDP6n0YZRzYmV4IY7PUGFWaMrg4KZTkw7FSZtFk89Q0McznSs0pq0PqDSuuB9rIV8Pj9cyDHu8rmrfgYLTOmI4P2lR5vzzA/hmEl/MlEQmflRsTVAz18Ch246ll2wNsSbm1UwVedyvMXYej3Dp3na2tkGNsMla19zB+/PER6I5Ma97b5RSEtiNwpcE0MOOt9gLrJbU2z1yTGpRtu7xuWwqNHIthWiW1a2fqMWO2U6kXpR0ymU+T5H43fuG+3kH84iWyLBF7tlpWFGJlI1mmV/HYAQjTBggO8ayFMsw2BjPMY4wAu9F4ntncrhkQU1GZKZjOMFOfHIwwr3taF3CZjFCxOR6i3uJPnIBYLq94/7O7b5QiyKxMSEjDG3WcdHYHcGjoztF0CUbxxaJ/cJ+YNR+DHKaRdFIJiF5c7w1fIt8ltjiIU+ky/R2IErznJE0O3JfkmXInBW+U5LD4zSR8HhHdQwj1+EAT5rWQ1Vhd9yeGW449h4KqiwoPfNsY51e84dFOhTMcK5IZCy59Cfw4vj/MbgTII3zSAKsBEpkI7Ei1IXJSvUsVxxhsVqSeh5K0YPB0ZohuFMlW8nfkcA12v5bJPrhUR6b3XZBEItxFItNUT872W9/POI+bq8fYsUgB4CxXOS9R3LwvscgS4mW4tTR0XAaKU0WqJgjkbEd+RDd41DL3aYn1xYGMXJ1NtyNZdCslg0GTyBnJPeRHr1kU7gZWkx7aTMePdgFItdHOBLMRz+QGY8f4dZTW/XB7vS4Lx1e8/iV41iqRGxb8/rMo0TdibJK60jL60ngHA0ws9S9N+ihKIsWjq0MMrF1hqH6DEe3pngrmEaQzj855jnGcDahi5I2RsCIaZ5On7HRGG06nBzuZ+gHEvgMqDLBCCPH8NBZ6Dgm/1+Y+ikMZlzHUVYL6VYMyJAhweH8eFRESWz8Okty04s58mY+VtDNkNIwfCozxHtUb5FmK+60zIU34M3JVUorNZblJGbm8zzpt5znY+4e1aXxVt+D4SdDnCoK3sLD7x1apzfDpdFE8E8GOUVSrQgoGsxmd9r74wCw5qB2rIO6TNvj1mn7G2aKW8Hd0KpZKeXcb68ULbnlh4N2TBpb23AnSsd79EErKKUsrEsM7rY/8jyNAKqVEaY0b/TtSdGKaMF9j3FRpfgBlCL6qTg1HeckJcSpNdmSEU6yxizUEc2tmRQyxBb2deQrNQZaDic/QmDeWuqO0MGFemxUrYP0iHlj7UmA7IpsYG8b23OntYb+7idkXYM/cUnGGHxLrtT3aJHSGmqd4sb+3DHg6+vLD/tZ/Hhbh9TLOr1Dj7BT7nnhmZw5F4sc1Nug44ETg8P0wj6qo9N3ygA6MMJXkJPvtBAHQhweimVfGx/0qwhS4oCRjDXyLmY838/ZeHFtzKqnvJa8zukr+vjS4zOefTpC6XgywMKZyZlZe+kBj6TSURaoo0QwQ4TmY+uDTK4dAUiixwMOaIuQxQy9HKMR7bAjn+Ks0jSJ6s8U23eiZ01Q9fORy+nXqBYZFVA92waPuHE5jNPwPrAAFToSPZNxKU72xQmwaOkJjdLQQeMcTd1O4/TdvPQPfwcQ6j42jhvNBn0q/3GcnMYicjZOx/j5Z5QxM/NqxEbins857pDdk48miVF6GeM+Oj5jPeL84/tF/iSpcFDbpdQM13g+zuMzce0RNi4KVaALVE0m7JNBzlG0PNiK73SOjMc1jmECl6Er7Dzf3Odzzbf/RCtNMDNfCB10uDfEnJrzJ0FihkQlkcGHfDwbeVJ+zNm4nDS8xzilymOwMlOmdz9+OHoPzvc/3MhxwvEE5OPb8x05gNyZXTofM0DdMecTVNnQa4NBjDkZ68EiDyjBujvY1kNHf6J8yJcjQ3MezesoJfd2y3WXjqqcbY9blkZrONQl9gWL7vVGyYrefY9QsEY2Ot06KpUiK05sKFy0JLsaYNufmYg8WbYYg0hojmrLKKoZNi2cF+ZzHkMtOCXXvDL3jNTQ/TaKLTgSQCJ/dQCFHNMTIJys5tS/nvfROdoCZBGIDObY5xqYoGpUg5oH4N029m3HXl/xMlg04pwdPDu0t95jjCyq8grOs1sU7mSDxL8kPwQ5S1aY1cyN6TSaR5LRQfqFWH/gItMrPXwXwrBMmPBhqcy/h1r182rNRa8ntCn5BZKaq/t+VMWMxZvl3t7aUaabwCt+z6vmwCwyXuJDaGRe7OF94T4zywksgyCUrHayNOZju4VaZNJ5Y/M8sbFRZQCZqoMazA3kSjBjlOg9FEVWSUN7lkGXEiBGI0GcEgvDzKhCXI+GYR6hj2gq9aNR/+cX8XfEK97W7Nw8OmQmPyYgNe5DMdwUL1meq1n6/Rxbfgy3HOYgtRZs1VwgglFAVyBa92d8C/caz6jUrKjKRL1MzLSRNzZNns/XJMOcJRm6kcsRl5Lg+AxsCTYmjsqy9oqV4fMAACAASURBVFQuVhwTp4x5hMxwaqmeydqpwDzWVc+Qlo4cLS3sbQ8F6tGnYxGL/LkaXvZaR4J/jzh/EXoqLdHIHRlhMzdABS0xf81ArVBcuZVGucF6i60Umn+3Vv/aUuL7XEdOCxxUykQ48fIAwQPU6HgdBnM6shYnoMtjbLDMHOBy/s6Q78hPCKeK+f1jN/dShFF2nqTL3JMtIsojRxFm56OxpmWwsAcwHXk1ccmxuSP5+nBdRuHVUF3DwRxG6sgYHrotAdI8gAmY5MPdM9mzcY+lxN2b+OHwjgoe3ZMp6AgdZcPpFBqLRg6leae5sz2fn56TI71nWAjIzS81m4+iPqN3WTYTz0Jz37m+Ua1xV4YXxSIRtmpWMJRiLfRz31FVXteXAEZty5FuUaWsJZy54aTh3MRxEaQu4ZS/vyO1wFLRTFJWeUQ+jp/4uJnqUBCNLRM8CQIFZqV46qjkKMNzcSJJmnSiJD412rBIGfZoZp4GQG1R9GESm1nbABvulBqhpMgnHY5IRIHMHrR9Q+1XXtdI7WhLZVWjWMvkeqf7E3C8Kq1HPldjx9i4rcrvXl/Y+6+5wXfnRy0lf7ytg57QuCQlNRPO4kEdUzQSig9oc37/DHj+aeR+JktEx+AMylRO3zl8jknNAIN8FiLhOa9getHnzx5/HbT3n78gHxfGcRqZk+JYGDLfn7h6enWzasM+vj686bM3NRRi3vLsPHnoBJ+sz1RWRKgsDj1ycqJp25GA+dnNAIVRwh1z5KiqOqo6gNlIC/yUSDaeeVT4DQX9YRYlqhjPfsyVoJvDMnl6P7GLj6FE+G/kYDG9lXxyPmLboezMQwmYz8DJ9JAH9ycD8foxTjqMw5wP56TRMdGPz1g+l7F10jDKMr3xfD0N9PDghvepM1k2HmXJ4h33ANXK6dLH9ZxYJwCVyJuLio44TtO4eb7mxscx+GuLDO0xgj9phDNmOO7nL83s76f8CPl8/+b3uO0MdAIAxOfcz8ccazPy9wY4OYGcxOeqGSBzZh7jdJwgfw+GZoSDfGKTmWd3KIvjCvUYw/Pbk5E5H5sHyekZfNQNJ/AoJ6Dz4VmcmdEjIVmmUxusxPgZjSYnRDSL1IRPlPGMR5JurMcDCJ7X8jkfKtaooVhWcyaDKrGuxidl2EjPZOVU4rPfWY6lM+bamLNHAzwl2z5YzzYEYcvUPZOimeB+bAsy5uXoSDzn7JibOhhNP4ClHPNgXJSQUYWzAzBs3NC9WalgaZdi+oZDHefPhqLnNQBRdewdtw2ho+JUlcixke+1euYLaXyfMZxPS+Igk9bdzyT6nx/zT00evOSSSy655JJLLvkk+ez0jEsuueSSSy655JJPkQvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblAvkXHLJJZdccsklv0m5QM4ll1xyySWXXPKblPqjN//tf/evvbfOtz8+cTG8ANrj7+5gzv32Qi2VZg3HUQF3Z+sNEaXUylIry7JSa6WUQq0LpRSWuiAqEP9DRBERtCi4A+D5n4ogAqIFEMR9fgaI9wARwd3zx3A3RATJexIccBBBRREBFQCNk/g4T/7TBXdwt3EC3B3c8kxxbTBedyzfy8Pj+9wxcwSLz1pc47gwFc/f8QyKFBzHrLPvO2170ppj3TDruDtrLRSFuoKbsz87KsKiFS2CFsGaYc3wBm6wu2AO/9W//R/HI/mry3/xn/9nLgiLK4KggBSQamgtSFFu6wtVFwqdIsJPL3cUp/dvWGv0xxMzp5vRu2MGUhQEtt4wN7oLLqAKZs7ee84ejp9uOTdiXHvPsbIYLBFBRSgx0DlOMR2bGyLw03pnqcrtvuAOe7c8xomZpjQzzEFQRJWy3tGiSI5LXStLvXG/faEUQTW+3yWu3c15bBtmRm/Q3dj7I+d0p/dG7w3cESfns/DTlzvrsvDzT7+jLpX7S6WWylKXuO/eebz/ke3xjd///hcej433t1/pvbGLYd3YH4YK1CKsFj8vFe4qrOuNUhf0dgNR/tv/+X/7lHn0v/wP/8YFqG6UqtR7iXWKU8tCKRVlQVBcOw5Yh9AV0HvjsT8oZaHUOyaxDv79P/wD375947F3ujtmBS3K7cuddbnz9f43mO80f7C3TjOjlIqo8ty+0XvDdsOtsz3fwJxiRqkLy+1O2zt9NzADh9/9/DPLsrCjPPfGv/+H/8Bz2/n22Gits22d1/WF1+WF5cuNclv4F//J33C/36m3F0op3IrSeuPX9zfe3jb+8R+/4SK4CGsxiji/fPs9+75BL7g5z+eGCCxLZbnfuf/0lW/fvvHtl194We6stfL0jY5R2krvxi+//iN723l7PnF3ujsqSimKWCwot9CLqoIolCoogrri7ph3HJv6zx1upbKI8rouFBX+p//1f/80XfTf/5v/2gHUCmVZWO6vPP3Bw9/BFXHFe8E7vL/9gdZ2Hr2FHmg7IlAKdIPN7LAnVUEF2ztmxvP5wLrRWoNcz60ZrXVac3rzaSNebndUlee+0a3TtkZ3o5vTemPb97BhIizrSikFt9BjVWXqLBGhlEIpSl0XalFqKXjvuBuLLCiKmYM4VmG53fjd3/4Noo5LQ7pCV3QBKUJREAWpYSfvReh749d//Mb2vvH26/vxcFO//fSystaCSig1L55zJexZ743WndYNSxyx3lfqUvmXf/svud1vfPkXLyzryuvrT3Ff7nSE7kIpRlFHLMYLewEv/Jf/5r/5s/PohyBn7xutG8++AY5i+O7QjbootSgLRvFO6QoORcOsVC8UrdzqLdCCCSuFRZZQSrVQlwURISwMTEjTe9j+A5mElXEC3AgM2CJnUHK6ds/3QAZemueUVHwyDhyfkHFcACpwxPw4yOP6JAGUACqxqBGhtZhM41Sqmh+LCa3Dijl4c+gO0uN7dAC8fA7eEjRB6Y4biAnuhZ4G9aYLqkIxw82QHotO2UGhq2BN8C5YF9ygWeeAYJ8jvSccdEcRqhTEnLJDXxoUsEdDEKzF2LeXO+LG/nwgOAVnKcq6FLw71hPkqvKiN8DZ3BIYGr0b+w5LLfGZVEebN3rveCBbeoLKpYRSbmYUERZVrMe59havV1VUldf7wlIK67LQu9H3PYxqQJqYrolaal1QLazrCiq0NHZm4CLokugmn5MTQMPNoSuYBDo1wDTnllEoCYYDNE6A5gW6sj873oVFFqRAXRxUEanofqM8Oq+uLNpYi9Bk480edDEKoZjFoJvw5sMhUKreUF1oe+XjivvrynJ/RYFVFC2FequIg7oHoFeQJtAD0GCGtgFUA4TQDKoF+lFAhGqFm6+UReg4b88NN6O9b1RfWH5aaB1aa3iHvjkdAEe4U3F8CYu/1DtFlJdSkKUgt0rfO2033t7faa2hP31B1wVaxx/wdGN3R1Wpi6K18rJ84XX9ieXnn1leX/j5X3zlfqu0PcD0vvf49vudrTu9CntXtl35qoW1wPLyt5S7saBYM9q/+8a6rvzd3/8rHv7gD+0PvO/w/lx5vf/M7eWVb7/8O577O8u+Iy7c1q8U3dk3Y++Ntu0UFSiw1lgD5RbTwFrM28fbkyLwpVaaOw8Lx6uoxpw2ZzOhqfC6aujEz5RYQjQcKUq5rdS9U/cNUQVVXBVTx+m4N9TShogi4hTArMG20xNseHrqRRUFaq+4O0tZkBqOu3Wnt87eGnsLIO4Ct3UBgfZtxxvx3e507zQ6zXqAF1XWWxAG2+OJmaFFURWWWtOXHrbKQs/Zzr7t9N6pmvY2n4E3WG1nuYX9cvHQMabI5og69/pK0YKUDZV4ZuJwKy/IWmg3Z1kW1mUN71KFr7cAWJhh5my+gxmC0U0wM8okDYJc8OZ0OlqcWoWlFJaiLDX0qRjYvmHbhqRjrL7GPe92QIg/Iz8EOTKgRDIivVkqP8dT/5pbIPdSEBe0xIAXF0oR6hKuteQiLlWQ4iCG0wKdcDAtB4NzgJL5frIrfDjiACfxSnxekhk5L6EDHPl3zA5/UV9/5AHGK+M74vOeTA1yvB5/fHcNfjBT5zMOAOCAe+d4IgebJRrP1sb5xu0rM+goVcCCSTpeHM/Nk6zyT49RxsJwvAW7sbujknOohZejCOJC6w0B6mTSOgosKpQS3gUWjJY7iFmwe6fxK0WD0fHwLDgzeZpA8/TvOc9kzHgJTCHxvI3wQEsJL7ZUDbZRA4RqEVqyTBaPGU/Pyr1jHveBS44NdHPUGmY9jkMwdyxZSHBcDDTgmaYvIDjeAc9r9dNaIbxlE6P1BjjbrrjXeHYUVA9FWMajWJTFCm6VRgcJI2rmoRQlTZF7eOsFGnZaRX990VJRASWApogea3cwoxbXaD2AindNdRLgESUUuYdT4OJoccoa+kkTeDugBbTGb+udZ9tiTi2FosEYtecDN6NWYrLWe4DOdHvFwFoyOS6IJLvRjW3f2bY9mJ5m6QjEnKkJoNd1oa4La6lUqZjvcZ8saYA74k4FdmtYc3oRuhAeMsrt9RU343bbUBXe396wYtyXlV6MVhK4DB3W8/k4dAwD1uWOsyH7mBcxh0w8WQHSAUxmXQLwB8sd2qabpPMoseaE0I/6uSBHa1h4Vwn2whqQbH9ek0sDccoiuCgdT6fEwiaX1P0OlYoi7L0FU6WxKnQZPGOw87WUMO4V2OK4brHeLRexzWeYFiAdF1FBNcB9hhKCuTYLYJY6IMYhNYFZ+tPHOV04zpn3KgTwgtBzmOIuSEt7Vp653nZEHK/hXCnhOJalokuJ+5UBluJcZsGApQbF3Yj/PK5Fj/ngJdZiwM8NtxtuQtu3YA81zhvzJea+eziAZuD2l63aD0FOEcUx1Dq9d5qFASoITcKjKdWQIqxLRTUAjcugNYV6E0pRtNQwDio4HZOOy06EL5YzzAlQJswwkIiHAZroYQCTM0w4FmqEwL4PUQWlGgdLgqmPUIN5/HHWA5Kc+A8ZBx3AZLyu52c9ULUGA2WWnrobFm7o/P7E9XgPRqNomYaLYkiJ82gxvGUIpTqmOTk8FlPvxt7Se5Jy3H9eb67xT5W6VtyM3ja6GW0zShWWpbBtPUBPzoC9B4P12DZKEe4vwRhaLZRFYCUWS2t0GwZ+CSaMoG2XNWjMPrHPGPehVCMc6skKIoJNTBheW8Mn6NhTEd0WZVkq5V5RVZwOApVC2xqPtiPmqEMp4W20bojFPeCShsPZ9wAw977GtavmYh3g1rHSwT3YPTPUHQuSgnG1FhYnmIwTm+ibU4rSZefmC1ruqBZKWTA6pkZRo1TnZam4w705W+n84yMcmt07RWP9ltDCLMVYl06T/qnzqKxrsIAeYZEcUMCwvhMUToCX1sLL9T1YMlUN0FkBcYyG9Z1Op9yMdQk+vpuz7QFw9UWod0FW2LaNP7z9kdeXV+5fXiLEqCt//L8etL6zUmK8v35BKBEiag17buzfOo/3JyxCqRVvndY73749eH88eb5vAXJMUA1ncS0LX+831teV+mXhrivVC31vmAvUF/Cdrb0h3bi5sbUHtj3YUaQLvd1RWfn693+LCvS3B4/3B//3//l/8PXnr/zd3/+n3G7Cctupapg3pHe0G94D3DzoqBS+fPkb9PHO89mmcd1TH+57w9xZpKKiaF0RYIunSJFwAPYerEYtlaKNIo4Xx/QHLvhfQdZ1yZkTqQO9P3BarDEZzsWOaWP5opS+0H/dwTrdWzDAtSBd0EV4qTfWsvLr269s+546HmpNhgKhiHLTGkSBCbI5bMb27PjuEfJM5sYIe6omqClFnVqVWiuqC3gwQnsPm4wU3IVaExxJRt77Ya8UQbVOpmWpmo64oQr9Gdfdezh1nkBKHN5li5NYRwWet4VaC7fbPcJZr/ewPyWep6I0Or3Dvr3jbqH7gE4yU3TQYIeLJKlaPWwbb/E8+kLbOtadWheW2y2A0VLAHrhtuO+4F/Yez+AvyT/B5IDiDJDmEjeuQBk/7qgFIhSEUgPJd0LJLkUppVLqMnNj7Gx0nPAcGCGi9KVdEmnG3DvCUZ1xVNinA/gMUCDiH/Jwhvyp0+A//tsTYI0wlXx8f3q3IomWT+87Rx7POM4N8Q7JTgQuy+D2mFgkgk7q0XuwXgn1E8wk/OoNbEA4T7DlBwYbf6gEovb0ZD7Ze9pb0JX0yOVyi3DTTsT53R3RI/yCBFtQNG474uFGE3gkKFlvNfJezGMWiyPE4rV8hrVmvlZ685DnN5lgwpJdG0Mez5b5uovltQQoW5eSysTD6RWS3hZ6flc3x0usDykFRGnEfe4ZUnORyH95Bv2qtcRUOE0Zk6ly8p74wFrG2IbnbSfvLKEajtN7Z9/gKULRJRSSQLlVMPDWsZ65Py64C4sopcKyeOTzlIq3Bq3z/mg8m/EmRj85GX9tkRwYs348I2uIdWbCWU/G2WJczyyxiYCUYObM8Pw3GiCxJWhelpoeY0Vd2J9PxJyXdY3wDAIWLPT6urKsyrJo5grWCAnvLTxpgCqUm2I5bj3DrPseYYS9dcwdqRVEwyFaF5bXL9y+vLJ+uVNqiRGtGnke9s7eN749wiDUtbD6ws2hlGBI3Q2znW+//EJVZVnuuCuvLxtrLVjvEWrtjm8bYh03CYdhqYgb9viGS6fRcLe4Dot8SDzD0D7mfBjoUmqE8DXDKGvFXTFX1EDMk+UxNjeafy7I2baW4em0Jr7NdXbYo9AlS1mgCPK601vj/dkD5GXejXRn94b31FlVKDGdJvsb32LsfZ+5ouYQA90RjVCfJ2sarAmYaWgsVVRDh0AywZYOuxRKVYoGwxzzPVhzybzRNGLh9KtmvqEm+eZhZzmY4CoF0ULRcBiNyHFtbQudosTaSfY8bG18d5E4Pxa6J5y20GPpWkYqQF6LqHJbKkspSLEMBVbElLbtmBqqRteNvgWTagK3YiwaOtxN6OiBKf6M/BjkeHili8QzjnyUmNSrxOtqjvZ4qCoeSFkyt6EoSynUWqnLEsYtvfYgOXoOfGfwLSIaFGdodnSwYOdwEAPkHH8xznCwZh9u+2QfAk98f7Mek3H+eTp2Mir+4WA4AZ8z+8MwkEMbOxGTdEeso9Y/8FYjVBUGLe/Rw4BbD8uXT+c0KR3PuG4sgIwhc0rSJo2wDM4JylIiTvGJ8tw3xJylW6KHSLxrdnqkOWZVZSa9Rogm49PdeLpiZrzeKi+3yrb3SF4rx+eBINkFFk3GZlDqAosI2gUKuAvNI8zRW4xl0aDjzT1CG+KRGCzKfS0sS5lsnREL3ovgGn6MeT8RhMKSSaq7xNzfWg/GSZW+O5s80bWiXubaGrPA5JjzEbLk40QeICdDRz5CSxLxdNxoLTxzmlFqp9SV200p6wJesWZsjwfdjWaKubGoIgXKIqx1YS0L27uzWefb287uzjdtcX2fJTmHm0UYLh5Qg54AZ4KczJlJbl4+PKwSLKr3QNBFcOuRNLx3zJz1toQHLgVceL4/EIyvtzuqNRJqewTrXr68fDi/udKss29PxmDKGuGn1jy95bCC+3Nj3zb23kACKCHJIt1Xlp9+4uXnr9y/3Cge9J1vlb7vPJ6/sG0P/vgtAMx6q9z0julKt4ZZw2zHe+OX3/+eWhd+/vK3lHqDvqG10PYAWHszxJ6gjndFuSHrHbzTf/015pBvkbi6LJhF4UO3MPaS4dTWGo6wak2DKqy3hS9f7hS9oXrD3x/Y48k3UzY6mz/SEH+ePB4RnrvdV8w73VqCWoWWBTWZf3a7vSKlcC9O25/07cHeNx77M51GZ2dnk05ZnLooEuQrj5a6mNAnDZt6wTJUMfJjzGLuRfgmgIKZp3Man9mbZTFD5FSKClUyBaRoMkf5Y0APW+P0yVaLFFSDXROgn0AOBAipqqzLyrpUSlHMo+jh8ch1owTQ0ZJj3+OatVLSSe0eIN86kT8pEW4vJYtqSuQ+aSl8ud95ua3hbAB0gQ7tsSVjux22XhQvSn29cbuttKZ0Exo6KY4/Jz8EOVUUE2XRGgNSIxlxIMmIvVmGFwRtGgs7nJrwijs075htM0N8aGobqDmR4LjOiUTlADMiSWjkxz/q+/Qo8rrHceOUk9WY2OS7RzK8+cnQzGj/jIgd4bDx7xN2TO95etnJyMy8IA9GwCw9cz2uyzIGfnxhAIHBcMzP5MQ+x8NGcrRZG7cRlyiRiKklcl4ApMRE0LUGm/OJsm0ddYfMvzHjiMcOgOfgJlhCNbNYGLVGPs5SInFx2zsjjjxz7BwQR5OyHXNs5sYMJ0cSOGhURyAxn88s3AhRRWKzZLKtzqq3Ywok2HRQF9Qjmd09KrZUoxLrNasJt9YyHh3XGPSzRKiuWySQe+RL2dCH3iFzNCBYVc3wZHg1EewVP2ajaNxj66HcVAZzVwJMm9G3hjTDnwGAlrweWRVTpXvB3Gm+82wRWns+d7bd+LY7mzm+OD9giP/ZpXtDukOLuY5KKnM51rhLMA3JborLHEMzoUtPI9EpsqJaWYtQ5YbVd3o3qlacrAmQqFbZuvHsjRddWeoN1+FM+fxezPA9wu/r68rz8c772y9RzVQrz/2ZeRuZLJnXVUodtQisy8Lryws//fwzrz9/5Xa7UWvFdpsVd94dezba3tl7gIt96zy3xvPZub+s3O43+nLDHWrql3/8w+/Z2863t1+oVbnfa4TL2jPWogpt77iBeuj0MmjNzMk0i1yv1tqxDtIKqwTbJJkG0KQEU/n2Dr4h/hZVZ915WFQP3UpBKZ83iQDVMPTIgqqzliVuURJseKd7pBRsz4aoUYh1fVtforKOkg5soxEVPzpYV7FkSMj8yGRUxQJQeNgyLaHqlQijijklvRgVRdzodDBBbOQYHF7/sEujmviRcy/YCI+1kkC71oKWSBPp4vSa+W1SYq5L2CStgovx5EG3gqLsLcOT7RmRiR72+ttjoxblfitZ0VW41cpSNMNvRh8VyL0hSrBEhC2KnMbIJWNzSoK1ZYn3lnsCsmXFmtPeM383QAfdnOfeac3oGuU2f0n+SZDTVWd+iJQMn4y8Vg1j7yZ0y5JxjRLPUoK6tQ5Yx6wHlVnGB9PDhkgOjb/iV1ovTXQiySCNf8MBco5XBk0v8zVJRQVnsPKd9+nf/c6D5fvXJvA5JR7nzzCy55N6oteR+xPJnjbP/QGgTRzqx/HW5wI58ockgdNh0MIzGDlBOgiEiHnqYYgiTylK+qV+LpPTWkedLCQbgDQqK3xiQznyHj2SHnWAlYyNBsPSw7sUoWYSXCgXQXUogTJBi+aDHscNvY34rF4b4dBgFUcJt03avWhUNsgcpXHFcXJxmQZ1eGrWDZNY2GupET/3KE2P5NLIE2luEX8fOfgECDRz8J73VRgzVwQononNo2JPj1mf5+hu6FgzonMeRenpTvcGe4fuwSKo4lVwVborexe2LZgyb8Zj72zNeWvG1p0lwxGfJWYtDGhLODdAjskc24kW7DACMbCxUMwbI5VcChQvqFZcYdcd9T4Vv7kF45YJQK0brEotKyY7o8YKkn1zx9sOpVDvd57bG4/HG6qv3NYl8rD6KKcexk4ppcw8iFIXvn75ysuXV+5fX1lWpaqwuWAuSVh5gJ7daD3Gk9bYnjvb1nh5WVnXFdNIDJW3d9rW+OXXP/Lcd359vrEuQvPCvjV2a4iXCBG0YDVL6+E0JAPgCXpiHKLSSgnQb6mb1uEI5DxrCN6N96xK8w53jYqZPVmOu9c07J8nZYKcEjlQOovAabaHesqKqZYOlVQAZ1lXtEXOFdJAG7t5ssGW5fSpz1OPWc/wtGZKhiVI0Wx14UCGkDVDEKMKVx3EBO0lxoBgjNF05IFUObT2cT6KRW6PlKj4opBJ6x0rBTTzBPF0yqCWyOnsZnTv0IWtNXo3rLc5D9yc3t5Zl4rZLSu/Cr4uWC3hSAzHXZxufdRoBOtUMpqg0LxhrbOWFVfhViWqq9YaFdi3O+3ZsOfw/IaO9WQiJStZ/zIj+EOQc1sLrrCsld3hIS3ieq70kTEtI2tbk2nIqpBuGI3dNooWaqLfkbcgPnIIIMIxMvNoTmRPeqPjik5Jxec40aACRuxxvD5+f8jbkblgB5Dx0+Hux3Hfg8MozEhORw7w9SeP9wSSErIwK2TGtaeBdc0ww2QlMpE0LzsM8qDrAih5PzFNnMCixHN0GXFziWz+KmTjgyjr23806v/8sj13CkGvKhGG+gD0cvIOChY8KqMEVC2ratLwD5YrnZsBhCQ9k/BILJSFRk8Ic48+HjLxMyPvO4bbs8RxrEuf02aEac2NZqGcTU/zz2VWF97u62RyzCORT2qhrAtrJmx6Ktn1tkbPjOeOdYEimTciWWEVeQ5jEsVdjioFn3PaxOAEfMfE0EICpHBEmkelQ6VjxSPP6KUSmCm+o3gBUxa5sfQnpYRBdi/cvm3sj8brTekikQD4iSCnW492DpI5eacKne6duQo1c6JIQ+BK0QjoNUl2SzUYzbXQ+p5FFVEO/PqyxnprWXJgOwXjZVXEdrbnL9kTRSnzWReMyC/xbvgjQEjhTn86b/s723Oj7T08fhe2Z6Ob89PrlyCkVPny8oWXL194fXnhZV2RaoiGFx1lwA/cWyamKlUrj+c7v/7xlwDTWigS4PgP/88f2J47i8ZcsRdBV+VWBTHYdmNZbry+fA2WqbVIgPZg8czO4QAAIABJREFUrRCh3qO30uMZxl+kslSiHDqr2SInKquOxClRxkfbI8xQM1+kLFGQ0oiiFIXsc/W5DteiUeTiOUcQUjcoXoLtrNIQI3KQVDE2orVDhOfuZaEb9GZRY+ROa5G301pUWZFhyb7bVDZuoQOGw7zvkReFjeTgUGqtBViqmoZ+XSitRFFB/mf7cIIlQUeE6Eu2wnALECVoFEN0i6T1NCoio1go8ski1SOcmtaMbYt76XsPhy1BVLDv4WyLO23b8RKJxLs3aDpV0VrCOdu7zWoqb8be2lT+NymISuhUjF3BTam1gIH1HduikKJ7i/CiK1sTns90xlqJe/sL8mMmJx+Y1oK40QhwUjxq5bvbBDlaTpYLJuIzGx65HlhkWpoD2HAyLIzfcvr3iVYZWeSTCzn9U3T6tAFCPljTsHLywdrxgbGRaUT4E8B0JJKlgnVysM9Pzb8/+Dj/yJVJ4OkDtEWm4MePzvMf9zwSZD0BDHKwSvODp2eIZ18DjUQv0Nm08TOldzvQqmQIZTQqGvHpPFYT0OkALuRQuB/AMJP3JOJeDCZngD03yxDoOeSXzIMfQw8y5+IJRx8AbM6nUUVnpxAr4yqzAaFms8uW8z3AlWjJxPuOmNGwpGtLUMo2khEd0zHOkrgrlF1rbZBzDNhMHjedAc018meH1tMTZAJ8F6BkE7H04CVDobVWVAyRJasWFHl2VDu1RpK11c8FOSNp88OaxhP9+nFf+KyDPMZZGUnkotFWINZEwW3D6JE4axYVjAMcegAoEaeKgPdIttUlwHQ2t5TTNZp7Ngc0VBasN6zt2N6zaikZkQwjr2uAqoayrivr7cayRgWLa5S5m/cAYxbViYduCs/78Xjysqysq06993h75/39wct9QavitzuiymISpeZbVAveb7fDeCRQ69ZAI4wXvv94igHmlQCSZg7Np9EcYS8PpB95FywUUdZa2LJ6qPqRW/i5mohZcTrpG0L7RDqLxNqMWUatw+mIcB0aTmQpBfaOmWSBbKZXWDRe7XbSVTaYHc88lZbrOxoHxvthB0dYjDactOgvVGuwcjAa3Rq7RCHLMKrTJnxPjA11Md6fFozJwNSy4HI0TjUT3Fsmpme7hR4PS6dzEWypmeXqkugD5JlULBqYgNDHyQyc9FpchKnjJRLhUegtk6JtOBl9thmxbtG0VKPoY29K68Jzi3D0X5Ifg5y1RuMdKSy+U1DaZuxb0NWj6gqBnpN2YB31WBSLLDGhW8YfpczHPLxhRafSCmzgyaV8Z+k/jJycjieYpPyZluzPegkD4ARICLtwGDr3j8BBToo82J2JunICnUIg4w2bavb4GYAuT+Fq+fWJvD2617rJzNnJeGCsF/epTkevFyeSXD3vMxoUxr31FspRRraqAKJR+vnJmqWWCNOUzO4XkdlnycdwYUGVl0wAFpkMjgObBRtTamHkOg1ANKIUz+6owZKl2psco6DFKEVyviRjJM6uhntek+UzzAmcxS7zmofuimh1odQoXew94tmvpcSipGfOj/Dy+oXX1wg9tG7wCEDBBtKh6I3mxp4elGDUWzQQ649QaMzy4pwL6YUaTOVMHwAgvKfHvoexXtcIuRVHqkMJo+XNefz6jrcO2fm7Vgsd2Tu1ws+vr/Snsb8Zjx2ez+iq3QFfB038SRJYC7IHkhSCAUt3fESDZ6Z9NPECFNcS7DIHKIlOqQotGpHt6bnKH99DOZfovyRrwRpsLbrTLBBN0SRKotUVb1skPbfIsdtaUKWvL8q2w74bWkig5DHfNDrCvnz5XeTLLc7vfv6Zf/V3f8fL/RXUsiWH0GlstvEfvn0LNsgK+97Y3oznW+Pb24Ot7LzVB+vLV+7rinsBKXy9f6WUyvu2IhjLurLx4Nf9G303fmmZR0Sl7d94bhuLFNQLxZZIJl0re9t5Pt8oS6GuKy/1Ri0Lj+cbve+pVp1ti+68t5eXuT5b7/zy9j4d1fceBSedeyShfqIIwV7UW40xWSxBjDNaemgWLGxtS6C7ha3Q6OPRUZBKyV5v5j574bRs7hpFsQFEunX2LVs7eDp8ItlRWGltj+LT1M3NIuyuHoxZtLzyU/qBxhq0YMycyGmJCEkPMFqDOTPrSPaWUokiiFUqlUqVF9yM9+cjHbjG1lrkuuwtmkgmMxUMvEwHQUskMFcNgqNoNDptvbNIpYjzbGGPmkU4LMrfo8VJhK00cv+68fz1LTDa1y/IbYUvMX+jOWOnqyf4qjy/PfG+sQENePTlP57J0RKVGlqHZ5r9Tnqf9KSmdy5ZOjeqoYKS1GRqxn/nDpdpsP8CXXlgzgFp5MfMZk6cGeP5+DV/RoY3/NH7/XP2X8b5zyeeXrV8+LrZcXIcfrq8yQAlhPMR1srjz999hON0luTFDB8A7XT+098OM0cgHNMMOSRS7/LZPA6zK/QxRKPAeQTyfAKviU3nBIjnkBHt8CRIujT/G7B05NiMqh87AWTRrFj4MDxy+itA70HynH30+EQ6Tcd8TjQqCUDFE8ilIUZ0bqlgXnHpqEYoK65FJigPZWpEe4EII5kH02PqQd2e8Lv5eCZjKgZotlTUwThETgDpCPTBIKbn2bcWICe7ptaa67VEOeztvkTfImnzWYS3RlZYfOJMyqEYuUjxfI/x+ngl+mH+zKqzZBom8Pfpp8wxaa1FEnnqsJLfLTL4xcG69vRqZW61ArnuWoAa1aE3w5PVZG2jVFvDeVxvkZO4OOvtHts31CxjJzo19/zZW6M1Qy0Sy+eWI0jkTnlsG9Bbzm3JfDKpaM8WDSU60S9LwbpEPsZpHqmQOWgl23gMzzyBZDI2Y3ueGzfMSm5DYJSebEet85wyqofye8wynDudwc+TySIMsJvr3Ka3HA6z4tAHQzicqnxSPmtgv/thrs/wiIIFiSrZDC95AJhweEtqH09HN5pXGqfogPvBBs01ML4ykL0TYxYx7rS/euRLDZ077lVdJtx3j15H0e6k0VvHcm7hgyHyYHkzgiKSjVElq2FLgB/bbX7n/8vb2zZXjiNZmo87APJeSZGZVV09NtNm+2X//29asx3bnZnuma6sjAxJlyQA3w/uAKmo6ui1tikxTamQdC8vX0Dg+PHjxwdVYePzmY8jZ/B/bqO9Tm/unN3qmPGDHZVBfCjWPD3XhDC+7P9xkFOWFLNpQywBq/dKKkSpYvNeJep1/RBlaYIjSwkTLnWBn+QEKXmawWxWCnmlgbgnAWMR+XgRxuQ2o7YYoDFST3Q1fp45rMsFHX+P6I/QsDgzEnR4PMwiwzAphrMkn1R7m8eBpNh9Px+GeEjmAIE5qFXU0Xw3Bos0FWpBR5yppvEYntjmA8MEc4Fs1uZ5DSo01IxzkG7Hg2qCrDkMaD5vK0EBDx8FVfXJu3Y6Lqoc931441Q8sMrBnLXQqysEjXsuVi0iQ8FPu15ThQMU9nhIYgi0WCB6uLuOvjp97APvAJA62HCsik0iS6axaJaiMy/uNuveo03VnWtLzhxW/Fjt3XskmeuKRBM50vb75lGNHAfSJACK0RoInW1vJE0seZnivnqcrskASY9gZRLdYI/S3tYVCf1OEtd8LTi7cCtOiT8/P1FS4ilnyqrcXzKPP7+h2+883Tz1wtaQBluA6E/bMsHOjCfapjWCJEVS9pvW8fQ6Es+lYC1B6h69o6h4lOhPk5fbD7auWnWxZ/doVPdKKZnlZUWjV87j/ZX6OCg/30lJeH886L2Ri9Cp1PfNn/WeOI6D/X1HlhVNhax3MOHb2+ZGey9fXDvWGrfyTFmevTgjwevrN/b9jffXN9fXmELvvL79RgfyLXOTlV/sH3h/e+X99ZXfv75hLdHxfmm1+5ylm9Ck8q1/Y7kV/vSn/8Tb1wffvj74/e2dx/7g5emZn9PPFBbMjK/7RuuVyk4XIS03DK9w1L5hR+cf//QH1tvKX15fqa2Rnt2b6dv2Pq0gXB6g05un5IQJ1BDXf+b29dd/JuXC/fiJsmRult2zCgUrJIYw2Ui3SHP2B67Lc/akNTh6n0yPAHttHEdjr67dGef6Hn2/AO9D1aoTAJq8/5I4Ewxh3mog3Vm8IhlMaHUPM1j3hekGfQ9z2cPnztvqZoO7dW//kJXaHPQo6u1eqoMiGXmxdlCPg2/fXlExSvb5dlmUqkJriho06ZScgz+oJFFuJflr1T21NAlVoDVvvaM6pACGZQ90F1W6ydQNYS6KLprR7GlE67A/Dv78P/8VzZn1/jJ1q6pGXnwtzrnM1jn1zeAHw+jHTE5WL3dRj3TU3NTKF4oUyF8mcvS1pceCnhzkXMpvT4O+EY2NpY3x2/h+/n9648j8H+cvLgj18nVSKx9RwywBnuBFJkFjMnDjiZNObDgTZxdGZkSCH3gcBrMwt2vkP2OZy/mNf9qH33z8s52fezb1lFl+HSw4M096iQI+sB1D99ONz9zGfZ9+QDYAhs0UEBD0qp/b0Ox0u0Y1crZNYCYeUFGGNTrEwkf8GNolj9wtAviLcHsydOMzvo8xxn5iFAhO18rJCqgoXUa6KMy7cJA/morOWFkcbPwVnzawbnipSA/QO7QLQTmYECDJo9IRKTIx/Rm5jVM3cypZAvl2jfL8lKNaIyEphYGbV+ClFBNx8l45t3X1CE8qqRq1X57Fz9jigbxeNY9V4t7G+fplHgybRmGAePNANSRSwN061lpcw9MuwOeQYe4n52QsiuaCkuHx5tF5734vbTBCUU6tfnSDRXNn14xEOwgzyOE+XUr2KDwXSi7zM3sjGnYezs60huFB42EHWAj5RchlQXXz1GnvtNq8L1PCm6l2cWG1HVSp5D4MLQHr3iy53Lx5abTnsQ7DhNR6Z1Tw+fhWH5ezAhTW9c7SjWSNWit7ayDeSLGnsOXvnuobQbfJXz9rf+9tO7ZooLphuL+NSZQgD53gCA5HJawOScFg1E+WY+hVWmteSHDUyey13qeIWCXu6whwu3ktiPi8MtLo8N16EUBhBBQWef6heaWf+0Ciu0CMYVUjWZ/zwpSCxLG35lqs1j1Itu4BaEkp1g6jJU/FOcjxCkHnU+I447M1WEIJ7aOLnk/yQOfrPMb2Bsk2g7PBG4z515pTUtYbZm5tMXpGEjYg15D/RzPRjzU56+qUXWvQ1B+0avS9I5ZIZFIsQiMSrvgEm7V4TlvLBA2TrYjLpGFgYn24GI9k1sm+TA+dywkRGooJeoKOPn8mJvsJTU74MiZ+Iu4fK1hQ3KNL+EAPg+KDsdim+dmjEurcnMGROE+bT/FIh5wLs//WGCpksXGEnkv9sClBQwouT4uePGGkpeMBDSbCKU6i1F99AWuGYkP0/6mb52M9AkwikDxPe/RRTuvtQwb+UsFdMAW6drIKWaPUVhylWhLX+uhoTmfsR53kGIwRdAFJIZ4aIIAA2j5nBVgYcCSGUeha6UT/LFVy8aoHp+QVsQzWaZxpnx7M0ev77lbuBcwSudxo2qbBGsGItAr1MP+KdPASJfITYjdnZ7aoyfX+LXHfBxMZX9tWEcGra8zL2kPiQcvi+3/K5KRIWZGc6OGLZZLoXegPyHIjv6zcloX+8xNvj41tb/zXbweP9pkDaQHMz3nMD66eZhiJjdYcDjK8fx4isEbVXcqumeqV2t5pDfpevWoGL9dOYVKWyup6geoeH1IT5emF5fbC2/agHwfHY6epa5oM49jeweD5trAfjffHQSGz3hZSfkbSyu9vD2ptfLk9s6wLT/cby7Jwu92dwTk2N2VsyuNt57HvvL/vzhJI4dDOu+1IM257BzKlPJHTQdIHPoM1llJIS+ann/6BXhv//V/+L1rbKc17EC3fCsf7Ttsbf/zyJ263Z377+ivb8eDRNgyv7FIztLspnLXGPd95vj1x1Ae17fzl99/J28b/8U//J+uy0torx7GTdKH2na2+ucQhgYSR4D6CHdLHqf0Ttr+8vYb30cGyZI52c4BDJi86xcZIpH4Fluyp6Bpi62qur0kdtm1j2ze2t439qLy/ba69C7Z2P2owq96OwbuPdJoZRRNFvTLPGJWi0RJG5EMwWmultkYOLZfV+iH1ZhFgLdH+RC167GVnh3qLlhGIu3V1Y9uiqq5VUKX3jOrCutwQ9UqrFK1lbsXZvdc3P6Z+eFq7iYNARSllIa2K2QF4Q2XM2Lb3aefi7YbGHIw3j/boDo8I3JNv9AdUIl1VXbfa7ECt4kUAgmPIhR8FXD9mciRHzkBc+azFvSS65+bNIqqNxpBmkAK1DrO3sWI4UpOJ3OLe+LfJysTP382dE6nOXwzgwkwrfSg/j52f/z5f7z/9dTrselgzNTaiY3HwMcDT+Z5xp5ggYyyc5/vGSfHhfVdp9QRq9vF149Dtco1gEhKXMxjw93INLoH2yOk6kSB89sxyrfrp5iWFLQDaZChw0BcVls424OC5i3hDSzlBh0a0ZZ3ZhHNeQy4A58NxXO6HBd8jzAhrlG6P+36ydv47j1DCRl1T2O07yOnWsCNYTD31O6PXj3sCRarOvMrDU2s++Xj6qYU/i8dJk50agPbSkG8ElF41NqJMzvES1Vbj1l9sNRgidnev9QoKFU+PNTN2DEuQUbR72i0tBS3GmhKyNNb9nX58Zr7qEv7Y+bNHqefChMbzZxHEAF5lF54lsUBdrMB9bo0UhYbdvIQzrGY3f2u10etBazspJ9Z1ic/2snCT0CoipBQdoWOguq2BV3W5ULWxLDfyspDS4gxR9tLrZs4K9Fbjq7k78dHoqFdtMfxoPKoV8cof1EhLJt9XKK5B+vb2Sq8t9JWJlIylLDzfn6mbYbJNxgjr3rQ1nG69cir6ysnZsscrNH192B4b277z66//yrquLEXovYUYtbCkZxTv9dREaDF+m40mr5+7ue2Hs5re58z7gbn7sVCznHNkNOIkBO61u26utkbbD459Zzu8o/gR6akxh8xKu7h+wJlpkHMct4tOsE8dhn+NwGUEL5Gx90MbUo+g+a+ZkmuQdp0TB3gac2APP6BZxaXJxzwRLOGGkD6mPRjM0fwwwTBbd9G9MJ30Jws2l52xLo1n2OmnUUAj/XLS4z4FqzxOQeOZlq7hv+eu+a3LhdD429uPG3Tq6jd5bag1MEXygaWNfXeRkizZe3bgF0EHW3LVMITOT9PQUVwYlAEmRBgamYkQLtqKD+mjuab5zZn7GH+OBe/qJjNmszP1892fzrVvgooLRkOi9HBQ0idoUEan6bEv0Qv9aOfOjUv5pznQcZ2OT4Zj8vZjuPr5jB3HtQ3x6N+aIITwWxBAnPYeoi4R10p9Zunv91szY2vtMqaHGZf/17rbtWmARVNAOkcTUjKy6IhF6GEENz1TcKH8cKI4IaRPEAli3Em4mgYU6m6bPu7rSP3NhTTAYc6JkhMlFS8LX5aoklnp7xv9MfQwglWfwJo1qlWSZpChVROoC0evVDP3WOk7rR9unFUKmsTdSa3TdvxBppGAIqMfUJ9upr1VJDprI0YpeCrr6AP/Rnn0Sc+3Wjmss+eEtcwhRqNTbWPNgt4Si9xRfUbWguTC3RJrM162P5Men2i4FJik2wkgiYlfLqWxft9c82F2xDjzdiBt23EvF5Ce0a5zisk5x7iJCTYncims9xe2twdvX7/5M8rGbVVut5/Yf/fqtCVXqnl1lqLe2mCkMoZ52wqyQPv9QaOSbz9R7nd0fUJL9qjYjNqbe4PUSq0PWt3Yojlm7JbCDfD2DWj1eVUPTCvl5cbTH3+hto1aD/7f//b/0I/Ol/sXVFckbfz05Sf+6U//mVb/hT9/feXb+xv27Z2npbMmQ2WJ6/WGmLlOJWdSKhz94Pf9d9Z8p+Qbv//6P3h/vPHrr7+ylJX//F/+iWUprCVR8spt/QP9/Y2mr2z7g6PuFPN2Km+PN2r7XE1OwUh4o2LBU3t7a2ytzmzVaCRdSva5fDAmKhG0VB7bztvbg1YPem88anVwGs9Zrz1S1eaaTBQVI2vYUXavovIkhmchjjZUw+Gj4wryGONGxtsmiMCaM8NKw0Qge2qWFtWh42TGAmbKsoRW0NwPqAZRsebMkhK3nCkqaO+kYIlHD7IcBrL3nlzwXv2YdJgDt46lRk+nIa2Zty/SYHBUfc6q1s4gccQiNkJQiXswtFE+v5fkwaQ02PaDfbPwMnUH+B+taD8EOa16WbNULy9r5k3GUsrk5Gkdo7mtdcDesazIdYH4HvQgHl1dD+279NJ4s3Ai1OvLz2ntXMzOz7+CotjnRAT24bsMoHX95CtgGkf88VAv+zlvzmQkImd7ZZ8mYJHzPZ52ak4CydCtxPc+wE2AwiuTEwBvnP8YVM4gXNIbIh/KoInI9LNBznE0rzzq17w08SD40uIBzeimfb533iUJ4Dd+HuyEmbeMwPP+DiUvdI7FpGI+sXhG0abw2JALixN7V5k6MvULGxUlzuAggqlEhnSA+vC+wZA2GBIXEmvr7nSMYubReD0iH96qd5XvAhY6N1wX0Y4auXPmZCEms+WHhe+Eu6NGpjz0IGJ9ln1q8l5Vnq4yhgfbIubBV9iwbzSyeim5VwF5VUNXb7ariyIsdFO+/HRnWX84ffzv3SyqDIMBHM+Rn36b7BWcz5+Fr8ko3XVtwNBQedPDKV7OiURUo2HeSFYMO9z3aLndUIXWKoPV3Wul10rW5kyjev+x2g6MjhaFlLyBq/iiMK4nYdKZUgDPhus4mjMEdd95bAfb4Tff+/7489Kj0a2aNyY0NVLO3JcbrVbevn0jrT5mPdg29l5ZcubLz79gOfHPf/kzW925Pd15vLsDdsV1M+/1ndY7bnXn1WJNdmpzE7csSq+VzTpJhLVkb/lQN3777VdKKdxvN3JZWDuk1km58GVZEIHft8pWm5cWt/p5Ywg8PaOKbJsH3HrQMG8UGuyFVl9zei/OnPZzPmm9stWNbT943/api2u1Rbl3D384O5kZIl4Xb9HSYk1UnMUwGeP1rLQ8t7PyFHCfopSQ1qJCcwS/UbEx39bn+tnHwRszyHeQczA0ns7yabR082Cvm2sDzVy0bozea+f6449hzMvxuzS82YJoyPpdWtJcwD/YZGtevSUQc5sz4OBA1L35IoBPiUOHvchokvxdhui77YezVD126OYX1CrVNiB7VFoaIs2rCbqRdRSlxaQ/dTP2gaYb6y9yFpPPuxjeIhNuiNPI07qfU2g7YdSk+C4A5xLDf7jx51X+G7+PATOhl33Yy7iGHwCOAUPYNY7OmA/F/L1d4Nz82HH8zPSBn4YGnR7nFNdp3HQGQBx5mwt4uvr1mM+qHuXn4VGjpFI+oohP2I5jXCOdVK5dL+EAapysyRWX+kIWMwPCtPaO1yQjyhkjiqCNW0nHo50WAId2Mka+c39Q2kVfMiptxoI4fCFyyt6uJKmDHDnBr6hf22YdqtCa93zaawNtrBYC6Z6wZuF4G80Um2HN9VYOZHyxPPYeKaWYIHU8ru0cjzEsUtSXz5YdduDAD8pSuL08BdgFlY5KI/pHIPWgA+8HLFl5es6+IDc3KOyxyKY1o+kGZP7wh516fCKTE5VVjhZijEQU2PE2BGnOASHs1uAHQ1zsk7Oi6otXbxK7NFIek26L1BaeRtw2NBVuzy/U+k6tG27JIDz2jXpUVgBzIXfvnb1udHEvFpOESaJhYI2WIoVYQIqFL5RFr9FKPXa2x4Pt8eDt2Nir+x2V7LqfUeZuHZJ5SbOJsZSC3p85to192/n5H38mZR8v3TpvbUNT5qc//SNvr7/zX//lv7OUG88vzxz9d7a+Uy3RuvDt7RvNjHV5Buscdfd0rFXW5cZtubPtO0c9KEkp6cbr+zv1qPzrv/4LKWWeX35iXW88185TmBz+8vLC8+3OP//ljW/vG0c7OOr+eWMI2EIjU4M16eBESvLBZNZdWGpQ24omb9QqHaT5tfi2fWOvjcdoMRL337pR9zbLqG2w7/g0nUa5dYy7sQx5M2DmEmQhBNY0qo99PRQRNGVyLjQORLq7gBMngWHhrGzWZ/HPOBYZIMepIfa49kndeyxnr24dfbjOjunut2O4W7GIePPjCxMwKp1ElBJOykMmkRnrqs/dSvKgrINVB4Zpkage8zZR1aLfoTUygjZnYDV5L8EqvuIK+Pv+wyCnufdGstE7RTxOtiPyxkaf645fbC0hHJoTsgSjoOcFmSjwhBP+N78IA8i4gtouzMqFUdH44FDrf2jz8B1bM38fucK5An54nXy3jytzMhiEASgkWJtLykjFJ+D49/iIWUlAgJkB+MxABrUQx2ZOx6Pq5fZx7TCvVhsrf4uBN/QZI7odZzSBvQhICNU0uSYkZz7Qap+wDcPLOsBbRA+O/k8o6seuwxgZ0egAriNm92vn0anMqm43BjZ3tBXPV3e8p0w3OQHOJUs6mmCioftpNtmkgrh1fyDHET1pchuEVAqpZHLxtFENXxLRhIl6g1EiDdUq2oTj2OmpUErxks41uWX6Hs8Nycu+sy+6njY6tTYekfn9d3x7TlxuDuhjoEyPHb+GKfszo9E3qZrhdoBeXSQY6+rGXPdSWLJS7oWiiZwSqp6SI2dMi5e905FVfSx90jY1/iPiEIHhujvmgnD/GIFS7+FuG4uLStD1OXlzxBAj+75co3A8KiZCThWkI22IvJ2paHXndn8ip0JddqoIhC3EAO9tiDnCOkFGCs3yNMC8L888Lc/++ZinpixKj2vlqAfWui8EPTrUi2smUs702kI0Db36s7I8CZYXSJnXt2+YdX76T78Awvb+Ts7C+9tXtv1BA97eXnk9vqL5xsv9C0txJrHFYlZrVJ9FtVXrnf046LhtQ+89DMuNZb07iNSGaqakjCDUevBaK9v7O/vx4L4Wfv324H0/eBxv3kPtEzdJoxT6Mvtbd2Hr+G8IhzcLRsdfnMCrqJrPKxIFE2NeNvPS8t4tSueVElYTOFby+y/RmXuwF83f4000/Xh8uRoLSOgARecBMArZAAAgAElEQVR82KOYo3Zn1xZdGJWdtVb2rbnz+0Xn06XjEnsHdNO7SDSMCw1M6SKzl1Wt3n5EojP3bB1kber8rEto3YSOeFot5k3BZqWUasJacwNDtwz3ixpVgWLeykJEqUcnmbDmaABaCuLNvEglUVpmEUMaHKbYf9Qnx42iQj8SUcMQK/YWD3asNGOuEU1zUXB0y4UKGWhlxu3xbSDWcdUIq/zLgV/olLOyyhel8a8TnFzAyxW3MKjA7wHO+Ixrgul60S5l4XIp476chl3fEo6ZQ75jgWlGIfEwxoNwjTWgt4l3iet4HmIsuN2XzqlxTjFj1qhM4iMT4hN+QjT7oBKBlAPdf+5mZtR+Na+TmVqZs844/qA5vSzRUy6XWzn356DIJyGLFheqwpJT+OL0EBVyVlyNMdmZ97R3975wQ75RAuyAS6I6TVIKv6cAOTmTcvYKnQ5mHmV5081L9+HeqE04jgNwDwolIcUpWztggLeUHJBudjj1PVNoElUGLmweupQOSOjBhiwlxXM41/7w+JFISdVLl2VrFcEoTzdkEW73wpIyZS1kcdM4lYx4LTLeEAugI0XdAPaTNruwlvN310dZIyq6MMTu+nsGFhpl8ZITUjtID5CjWD28r87WnKW7NXdJroA6pV7bQe/uc7LkQs1eXbofLfyWagDlFPHJyQZ2Et080ECF23LnttxRTS4FYPd99ObW9ccRJcISHmU+CpK4ZmSYsnbzFg3lScmrovcFcuH3//5njn3nT//0n8ml8PV/urxge/weVYjG+/bO4+s3/vDH/8LT8zNlVUSh7o1j2/h6fI3KV09ZeFPESsU8g8753K5l9fJj9UbN0dWSehzszb1j9vbOsiiv7zt7bfiePt7Tv/emyXkFb/QbjIu5ds6fqViUzVkZgKP6vJWilNxT3xKLvQczI5o9IlWdU/Hy/iiKiCJSnD0aZdA+9/XaglkeBQc+x0t3YAuuZ8kphzfXoGS8n9PInqTsLIph2BbCYphl5Z0BVmJ9moFSPFvdCYOOA5KMsdfNP6M746jq85GZu+03nAToFyLCKKCdlLyyug1zzZJQMRe0SwADxfv2NWeVixZEhBYsWcKfWV0yPmkbmpVcEiXW4Vrlh64oPwQ5x364iZsWj94svvAI23RYW0NOJUrJYoaNiobTvIIAEUGr23V4jys0XhcTU9BfH1Tj4/V2ee/4su/2M+D1pH8uv58/Dlbluoga15/AwsBPPr4v2IEr2jkR+Ie3n0c6qITh/xPXaGhpVCbUmYcw/BiYadc4rxaR/XgQ0Nl/ZV6rUH2bKCHT5LO3FADXonXuNXs8fElK6FtS3PdRSeAtFHBXpgAsvYfhJBJiVGdwVJVkQjadTIpF9Z3hlUPAya4ZtHDprOZpoW4dDT2Ns8WKkklkii4UzSzhJ5IiYncvEMGaW7y36hUYR23oUb18O1dqV8rdsCSUe6FJhy0hyf0sWjJMK23vXq4aE1GSMLdDXZhtYdXOqZsZYLFZC7bAx1gX8QqQt51qjaNX1sVZo6dlYcnKzz8/s66Z59udLIlVE1mVJeUA3GD1nfq1zTtjBNP4adtpuClhdjkEx6oZ0R65f/FInIHymOzgMGELajAChU5KxuNo1F45rPn8tXdUlEomFyGvC9lcdNn2xqNvzkjnHMJUCV2B4W23BZpXWpW00rL74fzh5WfAxemiHbRhrXHUzr7vvG/vvG7vvB/vhAMco8ny++s39nrw2+tvCLDeF9IYCdm1Ie73qBRZQGHfNgC+vPwDrR68ffuzN1/cDm7pzvPPX0jlzt47WEK68fXtG9vjwbFvznJWr0rrvbOkzJqXmXaW8CxZyp2UCrfFF9Dj2J2x4HBWIwxAH9vBtu0ctTnQ/+zpqAawEU/B7Pvh2ro05oVAI6fZmKdQ4BQgj8BMJFLhUREX1W6ahFS8w3oWpVlnPyo5J3LK5ORMaougygNRv87g/koqPmcnVUqIcB1kuM9M7T7/WwATM2jVEKuxH6VbDc2QW1KoCNZcdG1mJHKcXxS7BMM0PkfFKCpIFx67z20ljXlaA8g7iCopI+b92eoOauK6XQEV93+iO1OYU5nyiVtRZFHetyP8xFyPmBcHdSLF01utRzr3oG5Gq8qxHRy10yzHfPS3tx+nq6q7G1pcDJmGZxFdTg3EKE+7MASTLDlpjmv52gcmhAt4CcbGdTeXthAXEDIjCLm828bb/wag+f8RLExQ8Tdfax++ffjcQavYX7/R+Yjrey9P9Dze8U85MRpnKgZxRD68OHzF6QESz0l8gBzkpAn9ryM6uIDNH1B7f49N57HPu8z4h0e7rqeZLULiPkZsHsyXTmO7wcHMcaBh/mcDCJ0VauNzpqLrcjuMs9Fgt1O/0/vpiDyAuZJIEg3tJM18t4ozjtJO0XnvPfbr5dnQOY4GGg0+RTyqOXzBle65k+FIPMTZ4z+BCYRdY+J56hPfB80VkZlftBGV+eRsu1cAHb2Rs7BIYi2Z25J5uq0st8K63kiiFEsk9bSIr5qd/l7p2wYWJnn57uzgJ20T4Iy7F3lyjfEzyvuD8JxCT2AuSNNzSyRS0O6iijggbja6vDvzYHhvMlUXP0qkDnrzgosxWCezLDKiFr9GkWbOkhFN9KTc1zuI6zM81e5wvLXO0SqPY2dv/qW6BnBymcDj2Nj3ncf2IKfE7b56pC8JrDpF6beLpAnTQo/Go/flzmFCrW6Z32tnzTeelhf2WDRTRPSPfWPbH9hsHdHDENDZ1aIpOr1bADWXJyQt7kRrndYOv5ahlVJxn5nWWkgd3C3/yp1/yjgaOCXS1LU2X9B9NT69Q01mID6lB1hMYWPyDkbVvCpudBh3G4noF4V6SrFH7K/JCRwdaao+57re25yYxuc6w3u6788kxhDXx3zmxRw9ZCU2Y/fR+FIs2o/gFWJYrDKD0YlTijN2bSBOBHfxVGXvHmA4Q2wXkBMVvS2MMVu0W7Duaasc2rEZlDhjrYTOUdWdoiOQhSjySAlhaKI6Vht1q146XoV2+HMTT+2/ec//nXSVC/qqHO6eOKKWdi6ZKTuDk7ILWjvDK8R3bXM8jNLPPvc/SsuvIMj/cBn6sSiMr8F0XPHGAECXLBYzVwTMUnJr57+vo+k8ohO00PnrbY6Ev/H7ieqm0L0Hyp7vGak7LgeN8ledsOcxj0VugDgP6ZRYHFMsiGPJtyGSTWdaZtwpu3z/7OgJJmgTOQmw6+WYh4l/pfCqMBuiMp+EJPQXJufEMbuP2+EPW9dou6CzcsKrpXQ2nBt5dxm5YS4TjMV+vYkTPSWvyCgraVmi35BGesHcILN5I74xidf94NgbJS+jzxxKZz92d5jNBVuMttzZtbIfDctuwNYfR/Qriqqg7qmuboKIkaR/mGBSSlhQ4PEY+nDrndfXgyyw3pQvy411zXx5WbjfCy/rwlIyTz+9eORZnKG6L3cHcGgYgVaSFZCVTo1Fef+bwP7vtc1WJZHytLDEN1Uku7X8EBzPnj2RXhu+J75weG7StW9uUTBmE8GNJUXx1I8mUlroZmz7byQpZDIlvG1ev/5Ga43l5ckt/L/92UFE60hy/7CyFJ6e73RZ6ZL59fGVZk7lZ83UerDvO29v79S9Yg/HK5h7gVCNbrune/ad4zhIZFQSo1VKTiXKByuPr+8cbeP2ZSXfnvjDH14QEX79n/+NY9s59lfElC/rjZILS4J9f9C2g6/v4dS7v0M7XGyMgHnUranQu/H6/vCUZzdScV+o1r6imnhvrlV7+fkl0s34574/uK0rOT2R9Y29HuzNx9JnbmPEzsIkJEwf20xr2qiQkuZZdbloH0fkBRH0R8+45sUMSwr9TPaFvF3Go6OnTl4yS1pobxvtOPDu5lCi1Uitni5rrdGCAYsZzg37xFPjiiLHQQ9gmlQhZzrdi1DC00ejwa9Fm5PHaJDqnT9jXc6k8Yx0z0y7H5jQo5qpY+5kDdAaKQlLce+jkjI5eyCIGk0O3ruSUF6WlQ5s+4bCBDbe3oGYn91sVdWZoacnT+XS/f7se+fYK8d+UI/mTV/fH2y1Yavwo9z5D0HOXGYtzH7UF9IZbX9nnuUYwReh703/rmLdD78bEfb8+YQmXL4PEBOj628Cg4FBPoCOqVaM39t3r/+r931/ET7SN8Z1f99/+HnNJgq0j+czMBtik73x132MaUQMu/zOpgFioISINPwjHC6o6Ywi+kQQ4/rJGSH8jaP/e27nbfOYdJzyh+zf3P76+rqeiQ/s1hw3zKBmpndq66h56ssu5zw+3WyUrDNJTrt8ln+NI3Wdh5eRn7ocEQm2x07RKUOv1qLRXPtQnt7NG2eKeMmyaiaX4jnr0BWNdNykyO3MrQ+x9dTcj2dy5DPgNP4eQ9BAs1Juhft95eVp5SVAzlMp5Jy8PDqFF0rO5NsNj23dWdUORYpF2edOp6EfHFc/YQv2Ua7DQxz0avgkndb7zKBg4GOI+4qjf/f/9l+OwgVNSh4gp4T+KhdqPWi9IsHIqCan64M5TJoxaugVG32ImsNvJ2U34hNLPleOKBpfeJzxq9Sj0ap5+TpEg8jRfqKGmSFIFBNIzLNuDOdfvXrH6/sfF9bbQimeYqvHRq07HSOLM+8pqXdct05vB0c9fOGzFgv4uY15xZ+zPo3kuqNEWoAiDk+rm4Akdwe3ZlhpLCVTcqL2hhxK3dpZ5fhJ27ChGOyFn0OnSz9BTjzPLSoVhindKO02znlsPKTjWR3XabCGLs6+nKNEWj1lNB1o9Idylsgf3BGkuNjezrRgfKBzAvLhWW9DLK2nvxqcy994LiY/bGexgkXArSL0mJTd1ya+5PwaPmZm0XbBoiBGvPIpJWc8BSJIc6+vwXCLyCwf9/XPPy9pQhiMdnweJ+hqvXIclXq483NrpySA0n4YuP8Q5Hg5qkymQAUkKzlHo0VwUeKgivGc3xgQ5/IaJ3TeolinYv9DkBpVSb2HX4oqF4bZ3xcDrvfw8AmR75isRi3eSSteYMmH50k+RKIWM6N8+HukipBzoI/y8MvbpwCSEUGfNOJIkfx10DtK7sabg/6xfe5sfkYgAqfFUwjFmOcoA7308BtIZ/oEYqELfXO1z3cZnczcpQos/sAV/PlCDphT5wbUJg4Iew9rfpkgI0sK7xKP4CWo2t/fd1Td/MqvxzKt1LMWJMHbY5uNPacDME7ba4+KrT6i2BxCQlcISTw2tXlX6MfhehfD2Gvl9bHz2A+Oo8PjQW6NpolSE+REKYWSVlKGp19e0PcH8v7g6+tvvD82joc3Lx2lrK2H5kg8pSdZyLmwlBua3VG3HS4stTADSwmWNfOHn164P9/4+Y8/8fL8zJeXF263O0tZURritcjuA6QLmhJ5WRkqOGsNWypyO5BeEa0gRgn90Wdtcy4RnxPIEt2y85wQW8Xbn5in10d1pkVpsLWOhJN2643aQZrPI+vtCcH9c0QEXYzhXl0PRR/N+zMtz1D9wXz58otrMtQBSO0b1TrVlLWsrC/PpFuGxRfSZp37bZljrjdDrEB3b5zHdvDtdcdwBqXujXZU9mP3MaDF9QzRlqMhXg5f1hlc2v5Os8qtrNzzjeNtjCVPbZUv2V9rCdVC0gV7vHL0xvZ4pbWDsmQWSShQj87rsaMps96e8Hi+MXqqzQWs+tyYVGmPyl/++Z9Zb3e+/PIPPD/9zMs//BOtPWh94/bLz3Qz/sf/+r/Z9vdPG0MA1SmOWCQr78fDF94UmkZiRhVxUCv4w2QeZFyBRgcanrhENEDdGlYTC70bj22bDJCKN91MUlAKt0XIuUX/qM5eD5p111UCqoaocVBZcmLJiZQN0Sipo4Xbeace4QfWDk+payZJYlkWRlajdx9ZOXsWYNi85JLIayYvxfvi9e5mp5rIyb107utCbY3+8Kqt/Wj+nBVFmiJVuC0L96X4E9eNx/s7rXb+578+SJp4Wm7OytjhwVQW7i93ltvqpffNeLzv9Np4/csbmjK3+8rj2PjL62/YY8ceezBBydOuvSOHIT9oMfPv14AKEOZXfi9lLlojkpgLMCcbM0DMJBMCcfpENRbo8Z7xYRb7un527HOOr8HQfM/12GQqZIieufgYzO3fuBjfMTY/fMn8d1S/2AkoPgKaC3M0o+sL9Js5/AHd7eN7xxtlROiO+EbOdZRVT4gw74PMaz7yvXa9Lz+CvX+HTVSiWmAOgg9j5jzKgMJXmide3i8gxjBUjBbuvc0uw8KYeoEa1YGjs7mN6F4dnAt4WRVhjRDHcl48X1Q91ZUGj8K4X4ErT92C9dmob+TbW/cy5FYbqm0yP72ag9HkPbDKUpA3mdoHLGhyCc2PjOogT4PkUsiloKk7ULYMvZNTaJyKUUrmy5dn7s9P/PTTTzw9PfP0/MxSPFVBrz4BawtGYHFQKGNasGBDvMu3H68zHakTJoyfs43C1yszcxI6ct6TwdZc3jmZvh5pdpFpGzDs67VkZ9ZS8fGabQwBwLBWyWUh54XWm5vxxRhqvboWZlSJRqbcn9eoSjVv3TGs7FuvmEAZYubqY6TWuJ9KpECd/esRNIpI2EsQrFGcB2NO8RSDmCHdnLHqHwMBMU8BtN6odnggd5ERjOgdl9vMZq2ahFmmPy+qBweaUhxB8K0xlzU7TS/dF6qHzVUnZw0m4PO2KR220OJ1BxJeQTw4mghA9ZwRrjLGMfdzmXeGtnAsboMxOfVhPh5Ux3zibA6qIYb297qPjcz17ZRijFT9GN/+npy8KGE/PCLyMS10GVqbsznm2QngrBAW8VLxofvRcRz4OuokdlSJWTA7NmgLZmWWXI6XUU0lXgBy1EYTI4tXgnmjTd9frY3UeqQPz4C396H9jfV1MNvz3p0taaT/2DX7hyDHS57dW8WJDO+do0WRhjMH31EtGrkxSYNxOVkViQs0Jm03mLq8PWrwU8qM/OUQ1Y7X9CNclzX8LeawHQvopQrjsqB+AANXY715Wf3mnAJZ41oZNsRa53Z1ybXLniwYqXgCAiQOCnKcx/zSIeDxz28TB/T5+TodJENEPMDQpUZ9lEhfgaFei6mCLcspc0n8fMqWs5vLuXDYu3JrME49KNlBURLeEZOO9zP0KoRmAZTOSVkuEwsQt9cfpGaVnI1CJF9E6AqWhTXdoRv72xY6mu56Mo2FfH4lUl5cW6MrSPZ+KWJRiu5RhPs/bOyPjff3B7X6BJeP6n16ts1Fe+YP5f72RiqZcltYl8T99sTr6595o84KxmVdSCrkNaFJ/RjEmYml3FjyDbWGWENuPimsRUhZeLoL9/uNf/xP/8i6PvP88gs5L+RSYvIgSmU7EiJCjfJNX8C6R4veFyTK6Au6FiRnVJ7hBxUN/7u3pGVW3Xpw0Tx1zhlUjIq6WaJrIzjzcVarWwyIQds7x6Oh2t0TpKyU5cbt/uLPr1QHJ6L0Y2PNd1J+QtOdbm90djT52LVXQ5pySz8h7WCvr1jVaKiaaXizytY2LBdMhNf3ryRRvpSf6fvG9rqzbY3j8Og8JTh65egdS75S7vsBoiy6YHTasdMOox9CxkgGiyVKFuyo7LJxe/qJDry9f4PeuR2uMWrW2Lt7AB1bR6rNdiXuZg/H4emIp1voygpkyWTJHG2jtRqASHkuPyEk3g/vgL7e71hubPbG9ttv/Ou/HKx5YUmZR3vQrfLy00Janj9tDEF4dYWutPdYXCOtMpyKz6KCHu0dPHPRqs0ga1hEWLCsy/TV6jQqx9CBhy7SQUoiL8UrrDQjlFjcLZYKB4lJE4SdgEY7mSxKMjewtGa0AzDhy/OTs0Dt1V2XjxY9t8TTO7Wxrt5QOI8qrRZixu69qL6UlSUlwuEp+lUdWBdKEhKJ1DtVYTs83JCcAkw3Bxm1Upuw9452b1y8lBXVxl/efay8vX0jqbCor/EpJd6Og7y8kvMaVcIVkVjfNWHSSMl4WguWEpYKR2jTtqPyqI3ONisp/9b24wadKTw9JE1EN5DgRJcBcgZD8b3KeSDR+OPl+8lCfLRklu8ok3jVKEsfr4nv5zsDuMzc0eXDJmNyMivnJ3zco+/C5m/+ili5np4xj/VMisl8aK77HXhnlPKNTuWYTaxms4IDZs5+LPZ2YTsGgPpwHGOd9wvuDSHHIfl1thFdfjLIGYAvDdg5epJEXrl3V9IPDwg/5BTn7ZSw2Fm5MC717N0ymD0575kEzVJbx6hk9YmC7voXuoOukjKJRO0HLXLo5wUNEHqJzk6A6ZOYjSqq6mWiRx2CYcbFxxdZ12vU6oJd0eQVA71PLxVNKSajjGpnXQspK8vNHURLXvwaJaHkhZwWb5hrHVVP8Xp5uPDyvLDeVp6ef2FZVkp4sngQMsLLGKMa126aS/rkPctCGAJ4v/DiF4bPBDnXJ3SMmykotsGkXSLvwSrEYJimZzgI9uDB036aNHyP0hk5m8yiAU0FFpC0IupMFmoc9fA0VTAu1/s9WBVMwzjO0z8tnvke2iy/zqBRwdqdcsK1ZS6wHOfWewOMehz+nFy0Ij3sPeZ/5vs69o1WG2vKkOBWbhzNhe69dnrdserPVMruwxON6900EQ12CyS7XshF8aPPnAdje90RSd57TRKit4jmE6a+8KbszKWaYtE37ONq8fffanX0od2m95I/zjbHzxhqY8SPqqnrquJLyqlhkaSTKUFif8GEALME3FqjS6Jpw73nYNIg8cESc1mK+aCk7FVKcgVEcH3rUpRKZxsl8nPBcaKhtU6J4x7nMZt6joIO8UDB1yR1Yf5Ig8p5PsM1OWfhtiRKEXLxnzWdrRp6c6YliT+rFpkVH+POoGtrSHVA6Y/rMDD0AKV1T8uapqh+9flIBcqSaao85G+XCY3thyBnWRYSSjZncrxgyif1YWgnOlq14hdnLGTdr6j6HfdofY6QHhWgg2qIQ+zjoR1RulNapuN3Lr2UoIBjqoLIuc+FaZ7Bv4FMrtHq2MKPZ6YL5PsJXCY4Gz44A2uMx0SDOun1CJrwwiAN8JLUH/w+Pn+grsjDer3fubgGSyOB9BG85Ph6bkOjMyMGp6idVXLNVB/YUdKFd/qcrdaGGqxwPjyKe7mQMBVyzkGLD7ByTuBqhvUai9QJDo8oj21xD/LoKxWn1ztY8/5PSyksuVB2RZvQmzeP+2m5+1OQHuyHiy+H2F6IySmOtVF9tPYFw0sY69GpR2PbDl7fNx6bG52NqMz9G7zkVmrl7f13Sl7p6wtop1QXzYkpuSyU2xPKDr3z/PJEKYn12fVARW5emfHkxmspys/FjKW4f8btvrLkzE8/faGUhdvtKVJQA2Iaw8HXxYyucTon9h7W6h1CryIBkKMe/Xx25DOFx2OY+3Oas7O9HXcI7q1xTnXhNGxpAl/R5NXvbmziJdaLcr/fKcvCeruhmn0ecXGDaw2LkvJCLi8Q7Tz26nqsb1+/cuwV5O7VNbEQEADHAWWidSWxkgUedfc00xYA7TDoSklPJHmn982N6azy2A6vZjG/D1Z93602Uk7cn9eo/OlR9dbY7aDSWe2OduPb11+hw8/3Z1LK3G53fn+887Xu7Edj+7azhFX+cndQXR+JblCeXAP0tL5MFvbt/Xe+Pb66+NNclYLBY/vNr7wISylgC0myN3hdErIoWm5oLshDPT1nm+uFPnF7f3+gwCrMVh8D8E/TyJmd8Gn7sde4pw6CR8m595HzVGQqbtQXrZ2CUzQk27TIEIy67xhGU2947fFZj+UlUmW9kpLwfItGwHlBaAie8qSPht/iQnGB56fMvhvbThijRrNehb0727LeFlSgSvfqRCJ95sMUS97eprcGZfHSb/FH/RA3b5QcZewdllX55ZclAgRlWZ0t+rYfHEdje/e595YUS/js2TtW+3ScTzW5f5pUEGXvrm0skknW0So0lJqKpz73nUJnScLLy41FhON1j55zf3v7sfB4/GMG/xHRhQ+J8HGAOKiJbt3XnZih5qhQxv4gFiSZtPNYnT7YuESaxnUHp+Zk5PY+HusAFB+BzjBXPGmV+crL9rdiigu6Z6Srhm7me86Ky2tPpms8KVfN0uyaLEOwPSCbgRb/OfTUzYhUjwVjcWqWxv57GwvlAJ5jb3Ipu45F4oMW5nO2QSaZ+bGV7MDG77MbnqWUopuy3xyvbmIubt1k7mdEyT7hRLoPGNSVpACGRLVS69TesVrpCslS2K67O7G/1Z2Ny5J9Qhpfk8WJPkd2qndICk3d8ZPQPUz8OZKlQWu3sMt/7FjBfUUotOh43FMniRvw9QUwYylKLsq6LGTN3NKdVBL55o7LuWQ0QE6JKOq+LOSUWJaVlAua82ReIiZzJkPj+7Qk5wQw4wEZeiRC8KyC+1ac1TyftY2QBtxzydmyNg3P+ggqxiABRlNaSYmRF+/1cCv8sMkXTQFG5QO4HZoCt8Jwk8URRKS0kJdOyg9qg8fr5pErvgLqon6fkk/6Kefo1mzcoiSdHAFhaG2WVSnNu9zXo3KMtgmtz15EoyTQ3XkTzZKDvgYuf/XqqG6uKsmi6PLkz33zztPvjzeOYyd1uC8r958znsAW73sUUXpCKKuf+76/RYbY031ffnqJysEeTJN5GhQ4jj0AuCHWaHXDpHlJcq1oA/YKrbPkNJmOz9rc/8g4GMyYb2dz3rBl0EQz//s0o40qvpQH4+IhfRd/9mSwivhUpBaVVCqspcwybKF7oBqPmjNjbTKn0nsEdCd3NABYsjAO7JxMlJkDBYOSggC4PAqjtYgE65ZFHQCJhaba99M7ISfx6sEkwgwdnGNAMYo467eWHDq1TMoFM+XYg+9NQl41GCWfV6QKvQu1GpJ8jNbe3PU9mCtC51Zbc93i9kbDeLRKf+y0/SCtiVLUdYWi5C1S1P/G9u+CnJNwcWzqwEYGlmCKeyfKPZvCTdtoGYtOYjY1PFdqRtKpTTQ2FuUeQq0xuX4HaiYaGmmzy4le01YjJXTRblgM0MmHXLnTkd7xvxsrnxAAACAASURBVExQc6Ka8zhsoqWxt8HgnKcYNap+rnI1OBR3yx9pAyHcin3i6wFuiAfAmex+LvSz4qv6MQw60Wdo32eQZUmjfFpOm8DP2sYD540ehVLCSl9GdOSaE183fRKqAyxI+Dt0p3Z9PJ4liCpGiuu+H3Guyf+uBtbcbv9onaN1GkZKnZeXG5qV/QgrKXWG7aYl0kdplox7ubFGXUmn0/w+5oy07iDH3CvDwjDr5NliAmmOWDc2WmkkMcRu7ssSwyOJcnPeF8FYF8hFebqvLGnhJX9BFkFu3hW7rCspGtlpgKVbWkiaWNdwIE+ZawggBiEmicHbmKncyWIGqEgZlYLK6pUeg6dmpKo+b4WaHt7q/YJqrbS209s+6rUdYFwCKI3yeskLAInFK4jqO6qZFBVzEuXk4bKDQPh9OMjp1qm9kkyc2V7uIJl8e3B0eHz7X/Rm5KcnLLvBWV7d3baUQl4KHA1ap5QVECx6C1kILNenxNET9/eF17p7X7PawvL/0ofIPF3UJVEsWHYTjEq3nXo4+EhmrCkht2cM5fFeOfYHr69/pnWjmHB/euJ+X1z42jrf3h/sR/UmjSo8P63Uo/Lrt7949VQTvvz8ws9//AOjF8r7wx29X37+gij89vVXN6sU6P3g2CqaXWeS94q2A3b3gbk93Uj5c1FOb56uctA6YnAbUw0ApSRKSux1CHfPwFCTkEsElxb2LJfgmdD5uM+ez8Mlu+Fm640jmOU+TRY9hdbDj0p6R1r3NWGYT4bvjWjydKJwAuCjR+WeO3UvOUeqMoArDlY0qsqsdxaNqjG8cwExv/ZqFFVK8gBg5FmagTaQ5lWvIkJZMre1sKw3ci6kXHg8Ovve6CiSjTV7i4m9H1gFOTyVddTwS1eiTYi7tCfL3NYbiPDYfwfr1L5Tu7BV6NuGPXZu9xvpKVHyipJZ3rqvgf/G9mOQMwLkaGmglxSOI0RHt8O3xZBzvgzA8rGjtIs1Z+pyIOQxCfeoJhGZRmtcmAnPJ46Dugwuf7dHmCFY/pBelUseM953TvtnRZNN92KmGYnnw8cglo/YKQ7KItpKyZuTtnCudFJC0KhWGb1L5jmJotldVy1SUKcQuTEXHr+QY44PmUSwZ2bzGutkh0YFVojokkQOXKK9w+eCnJLduGxFKCmxLHkuShZl2k1t9p+yiCqMmIC6awGm8aF5PncwKqODdOC8WDgkSr9tmnmBLxBHrTzeHqSUuOkCIjRchGuGlycnT3018/J1mmtnYIjpvXKqdgsA1diP6s37/KP8tc1BkUdxwtE8sj1qMFEi9LZgdcVac88T6+4Gmt1gS+Xm5b6pkLNS1kTJN3LyFIiKgxwRKKOCI4V+Z4bKJ5BmuClLA6KNAc40OCs7gHhCxI3nGG7UU6Ok87n9jM0N0nARtBjkIczPwbiEdUIEUSLhvaFeGeZahoamTMorSQopLAHo0I8aYybOXxPWO+2Svqw7tKNRw2mVqkhPaFo8bdarA2HzXlXEGPI5qHj6K3urjHW9eTWVNe91Jp2ucCSlJqWl5GJ4iT5j5umKQGCx0kWULBW1TsJN6AxFm9K3htiDjrO9SY0/vPwxynh9DHuqxN24by83Frxs3MyoR0ek8A//+F+8ge3eMGl8e/02U5pHVFfV3//sItkSJoqqUd7byEVdvJsadkR36d7Z9gfyuf05WbKnJOd80izYtkQlMrI4g6Ej0xDvHY7aTqpFU80wYB2UQImegxZMvCVvayNmbj4RrukpKc088Fst+VA+WsQevri03b22pFfvt5aUFrNeFmcIO15+rrWDGjlHt58eIUjtE9BEcpEuRlZhXRY3bBx/t+btIui06mn90aj12CutNXIwLs+3O+uy8qQ3cr5Typ1MZS+d3nefo2tUBfadLv75pVRKOnxc06F7hienTEkleFrzrmZmzrw243GYg0AEyYWy3Kk9zfTgj7IT/04Jub+x96B+GaqjIesbSOe7xd98UZoRIXON4fqDjgkJCYY8dCnBjowo50LABNC5HqFc9jkInBiJWAAc+wBw5o6u7+eitUE+7O8EOfH3QRUGwOvNsBBYoeNaeJmcBFsxwugJ7obtd3IR5dQHTVBop84omKKxXg07bonzOEvsPaWgQjREjxJAUfp0jtJPhjhQUoo8uJKTU/IkdY1DEy/goYf51qheYFLIFtbGvffoMXUlJ4Ui5WTNCICnHvlkxaukAMx43d6otfJonazKevf39pln8nSihrmlL1o+KWtUCo6KwRaCvtqdJTpq87TYZYBad/2Z41zhaE4t1+YN58wa1u8+Mw0sYT6ZpuTAJukaIMfN1G5LIaeVojdy8iamRLFhCuY0pTLHxSnKn5fIfxDXfsyHMw7cqfdhOBfGZsFqgY/fS+3ip2y1uRZBDvM+oRonIi7KzkkjNRunKF7WrJowzS7K7R3VRC4ryRJplC8b2OGlzTnhol7xkvxa9zlXuJDS6C00b01d9xONCHuvkdIUOooNvaKAGwlmr5QUY1lWqlb293cXs6vRFFpSmipNXdTaxV3kzRyQ+txRgh13kGNSnVsziSrAjDawvWOyuR6ve/PDn59+pgE7je14ZzveQTqijfXpjqTC8TDa0Xj79o28FH7545+8zH07+PrtV377/etsO9KjKe37tzdyEv7xT3/yasrkukPNnZIlhLG7N8DtCtXY9wef7Xhcirv2N6KDu7m3z7p6367DPP3k3mLDjT7mhQA8o/S8NQfeOaWYooWsrs1xTx2PdEUci7rBnrv9DmNFxMjRA6+pO3GPILYeXokp/Yjq0DTNRZeykFSpPjDRpqBRjezEHGrm0tJYW0Zi2rU6wrIszk9EQ9LWm0fmXZxRAvbDwc0xmMjslWRPtxvrsnJLN5b0xFK+oOmgWKO3DeuN/gjTSEl0hZ6cSVvywV43trb7deqeHizRIb5ZD8Bp9Fa9r9vRke4tQiQVUlmxPdLWce3/re2HIGe938GE1LPfrORRjtfK+z3s9Fj0h4wcHxCzUsN/bQQZEdGrDCpZZDIQIilyzy60c3lPuC33HlR7LD7RosEBgoV4hTgoC9A0mJw4hqk3iMF7ATo+z0d+M/42Ffd2WQc65+9sMFNus+2LdGBt8cmy4SVuKWXyujoNqGFop4LXDl0e9JFnbYrrb3yBd7raJ/HenL4e6blx/dJIBeLBniVBdBig6bkw/WBA/D22+72QEJ7MWSXSaadfzbvU7mZ+C8VBZo/jNOsO0DQSVPNJHb4JxhELkYaRW8ZTGiaHW9cvKyPpVKlhlujvf90fINFFVxx7iUGxU4fj46KDuWAuVxci7q2x75XH7lVVw63kZArDLK3FmCOOvRl9P2ipoa3zODpVd2dj0jA5FI7FF8TSOotasDZO52m+NAZPoU1SGTHHd5vE8VsQNsPA7cDaAdIiuFiIjjJMEbtk0MyphRsP9UXL8ylbgLey+MJu1eeMJGjxNgvOTIXmQUBTQURjLPmFSZG+s2q0xiwXdhYIUO+LYZpprbI93kN/0jiOTq3GsbXoX3VgdHJRkMzb68ObLiLYk3naKhdKLqcNv45qNUCUZVnRprSts6hwS0ZNiSOpM3Rhzd9bhShpXvPi3k+10ZN/XqsVaZVcOpoKuxUHT7t3n9/3jUMzUsMSZCmUYAY5MtYO2nZgVtG2ICi//PTilWf9oFejbp2ncmP5wz+w77trh7rbO7TkqRSOTu8HZu8IiWwFq5XtUREaKh33jxWylE8HOSPwKzljCkXBUmdrDbMoj8ZCkeFjbglQ6eDVA4OxBoyiEA+SwyBTFC3+HvPpjqzBpJs/YTolH36PAW6aqCa8OXTxYKu7TrG16iAmWh+0fmCmNBomxnor1Np4e9tAhFWUmoSKB5GjgEPNq+KSQs7eDuTxiP5W3VDtIIqFyN7heuO+ZoTMfU0spfByy5T1xu3lZ/KyUJaE9Ezr8PYmVKssxUXMx1t1n7b1RrdGWzdkE3Rr3k+ti2tz2o7uGm02fI4S8YBYU3OrjZx8fFfh9e2Vt33HNZ3/QZCTyuI3sfnMb9IYjcdkshUDCFwYm6B8J3A42TynmgetnE4WApEoq8W7qA7WxSI9EYDCFbnDvM1OVUC3sw9N0C9zsrc5rQTL46vphDgyylCvzI1dAlyb5+anbfN4TCTMnYSOg7O5DuvQ1jRUckSWGjR6fG77mFozs2AtLseG/X/Evd2yHUmOnfkB7h6x9yGZWdUtmelO7/9IupkraWw0PV2dlSTP2RHuDswF4LE3S13ZspGaE2VM1uH5ix8POLCwsFZUsatPm0nP2pzQum7hBU1eKNq65+EbgKjx09tVrVBd2CiYpGAscYeNIP/mJC3rEn2hg0SgkOwph8BUBIX1LObM6YQSQcpWEusWiUGpaNTWlFooLF2MMEX0hKWXREJZeXK2J51FAI9n1S30nMaY9JQXj8o2jlew0HM9hP7G8xn7nCnIluaQDMpWQlStxlqpszOnoNMIk8iVpHMl0YsMeSUh+vz9zpPc+lyzyQVYdgFzgvQL/bkQQVlTJopoGvRed51n9fIzD4niwNevVwkOm5Y4x0StcmgsJz/XlB5XjKlIPDMioKPlQqQjyVGMaDufR0/344PzjIqyP07mmLQ977/eENVQYHWPpNwDSYrWRKU8t89o9eTsb7SXwmm+ilElHOCLLlm9WMdrutQ9OBYlCwBMMAlNFcaAolTNTUMUt5EJ2onK5OEPqm3hsVTDj8otpu3meWAjWlRalP22J6o+Y6OcTm2VfXujoJx+wpjo0r2SQLQDbTjDYytNQoed1JoFVyJcS/frZx6e67iIRrElxFyav3Qn8v1aW1BdibEbYi8aaHB9j2Wjyz3+X1nvZMmfmyi68UxyFhdo8c2ehOBXcGDtB+FW3zTas54K7pY+drUoyyKkqNJKtIHCsJOLrnElZBLt3MXfYSbrxImhhELukR78wRZk5P22sbfCrVXKtlH3G7UVyhat22rC4xGcwqIxcKQecURrtHUFB+vIKJhKIJhEO7kMu4ZFXAibChG0BAp324JrOKZznAfHcSDlfg3X/GvHv6l4nLHhOZabOUe0SxwkpmKCmZ+IA0KpWyIS2RxUT+Z4VLxzif0hqJfYhMuBjcn46CHg2tbMgwVEPATxmCTyJP+M5BRY8ieXT1dUzSvNXnD9H71Qa2X7FcdhjZQ/kZw10h1TZvnjU9K/5IsbrsMBoYsIXmoG43jYkbclB8RSkG2e+fG6z9l/XyejOVWRo5vuTxNL8vl4cpHkhyvKrWkliVyZ0E89XMAkRxLFL7VK9xz4FclphDi3eeXNwVFy8QsGVgriwsipGs9nUpOb0coWFU8B3OlnGLnZNI7xiKpoRlAqOVI/Pap/K4q6YjWqCemG2kwCd4hZzhZR4zwO+hmGn9PDoBM8OWXR5l1x3GUlccHx6UkkHWbgg0sjqUCbN2qpnPVgH5XdQyPnvR/sY+NzuzHbG74ZNVWQa4uX39Psb7WWnJnBL9FRCwjYfTJ7wMqljOB6qaDeQOq1+S8JgmvFJC9P7OU1+RmHJNFcBaeAbNmSjN0opOhbFBF1JTlx1mUhWRBN0cX1y0QRdXx4VMbJp/YaEgXHcfD+/p2vv/+Vb98O3t97QPhmbHsImn3aP4MIhxulbbx9/hO3T79S64bUgpeoTvHg+EQADw5FxEdHtw0/c8pGIkFCNQcFFFyvaxDLjViDPaLDg2xBwW1wjsE5DkK/JrhGb7cbpcA5Hxx+8Pv4GkKTqtAHDOMxBXPl02Y0FzhjLP5kRNJ3L5wymZwYnSA7H7hPZIv36fvxDRFoTaAKbMZ8dPr5ASbRPtaYVBvTfqo1SDz/GOboI/kdRKzetEUBYE4h0L2riFw6Sx6FUiVMKF2zgCsVaqwjs3j3v50HeHDkVqFebxvt851qM6xjUk6gtC3awwzKDC+y1QIXwmYmKkNBa4z7r3OlRnxcsbTWaIfVWrJ1SiC/GK3Gv2tNyoBGEfj5XiMRdaOKUEXCLqZN6r6h7FRuqChve2VrhS+/3KDszC3eIS9Bzu9m9PN7EM7DNTfcxFW5qzCl8aAhe0DQb5tzm87DToKyXHOfGhdiXCRIxluquT/e3/lmxtfvDx49kPk/GoL4t20drk0xc2B/ZoVRTK2y8lpF+S1BIbqQBM0e8iIow5U5+GJqSpDZbCYJteRonHhW1c9gRWKFbtGrj0r5ZUx9MaD/IBSvTP3Hf5MLuVlf5BeasyDG+Pg1gZCENqMSiKqtyJr6WFVmBDoknWU9EhzckNQuWZPqnswxycTKM1CvRGbVEc/zXHDQ6/W+YAtPYtFP3p24TtNXsvw3nI74pxc+FhGzU3IkP5/oWyIuZi9I3EsVpskhudAMM6aHj9Qck2HReuDyIIs1YjZZ5LVpdvXdLWEDSaIhkoJtHlL8lroOS5DNnxAkrI9W4RcwzmXCGejoE0W04bGWPYKcnILb5CEfjDrpCFYG9XDEokKydD12d0pZfXlizRHB1TxIsfFLSO2giY34Wwl1X8/q/1ogF9frb5+lvFzUTzpWnCHXipYrgSRRP5WSnIf1pZkU5EW4KytArZHchej6ah/k854eYnzn2TkeB+/vH3z//uDbt5NzhuDjPndarTQ6qooVj9HbFtohy6gxwNdY8ZdKelZIC/9bYnIrvqmsJvrLs1hFml+XfYlexrOV8GPD0tfHOXqgLFtrWaWfDJwuRhWlqSLDYMJpIbh2K8HxsaGYOF1mrFOFSZClo7CLeUPPCT3HmWOhgkE9cFkxyxZRMN+5FI37ybFooTBy4Zx5e1/ifjbGWaPYa/+Jpb8QTs/2dkyGhmp43A2b4WGHg0gQnacNWlNi845nZj5C9V9Wi5RAAKug9jzXS1KNhcTmH54xcB3B95EryVdVXKOrUrIgKktjS0AIEnIIoaZxjXBN2G2pkrwRisTbFuPbdauYBBHaPIrWc4SB5tnPEKz0ADGmx1RWzomhEuimbXu0x6Zz9pkkYrn2iPUsghxdchbIGTY5e7/sc64hnb9z/PF0VTLFQwufZyXBM0GI1zCXhbD0jehpXa+lhFIiFr1zvYZB09Yhs1AMtRMQ6l6ZdtI/Hvju0KDUGC2b8kFMBORvn8sNd4KFeaIk4evZdlowlD4DXt7HlTBYQodr7H2Zql0b9PLOuHInv16Ta7tejgkWs8yGIV5gSowW2sBSb0QtzBGnBidCMwlcic+cIVtfvF0VeByK5mTAGpddCVe0KgJNElmk5eCESGYYPn/yOANR7ejSVhIJkug18RW6KyXHly+Q6QeRsEhwIld0eh+pHZIJjj61YOJbc02ckUi6jSSMcpFWI2nkSlLm0hqawW2pvcEepOTiQXoTi8S99yBVHkk6DnQgxn2XgetKjCNhTw6ZpLWnwbm8WYQruB6jM21S9J2ihY9WaaUw7o+YCmp39rfK598bX+6f+eXtM1vZqaWx3VqoJbcwMpUS7sCrPTW9Bzw2Hc8JqcVTosyguo0PSjWq3qM60tRx0Vz04izBThu8rMmfccS52DiRWmi3LYqJHPMWLdS6pYdStg4sHU6FLBRApAWvh4mIXdNiQs4Ca3BMPo6D92/f+ef/5194//7O73995/0YPI7BacG9aLc7SKPJhggc44HV+LmqQistEGxiQiqsVVZ8WqO/Jwksxf3sPadIBngHO0MNVnKTEsHGjNaBbLin/tJw+mnMc8TETX9uiCD85eOvIIq5xjlVYy9bWi0IRYXDQrnZzneKKPcjNl9LxHhNsW1F6DPPf0asovdYZ0fyAUfBqtO7ItbZlbSeCBsKJ4ixzybvzzlmn6Fbc9sjPA7h9Ng0l5XMTFQ1GCkwljeYxBCHaEu7hSDMqgh9doZNvn8PkcXH0UHgbEEWPs+BvD+Q375SYhXw5dbYW8HmmbSEeM73XYlwNIOpgTMttpX+GDD84ptJktFbifOwEhNacxhFhbJVrEaMKZmwtFoiBktMMXc7Qy39nkCEGRs7m220LSbm3soXVCpDC1Mmfz2+4WPgh3D4g4c/OM/OGJ2vXw96H0hqSxW9h/6TLvL9Tt0b7fOnkFYYxvxm6HnyMGOKo1uQ+VfbbQylqEX7r4CYJL9tcj48pnT/zvE/h+Sw0JyrS/xEMa7/rrGGtfPGQl4MkDWRkYMvBMRGsPZHJDljBmzv07NKCGt3JqgFacpW9XPFXbnGGVft7OpIRo6XevpKyq73an3g68O1QfnL160K76q5ni/mqgqWvDyRbEl65swlLObZa83pIabHFCgeRn0+YUa7Bl86CjPFzBKWfJWNX0ExYYKXGveHxOwJZ/AjkvOTj5pK0GIrAUsXewmOgafkvWfLaBH+lgaRQLaqiA1ptY5EcCk8Rftye0vxtCCMDswG/oSRfjy5zHTXmnKiAp4r4DssZaGrssok6oc/vHC6cqXFGso1lZwqy0rl1SBvfe8YkzEHps6UibhjOnmXmMqQ0zlNmVqZ52Sek63stNLY7kEirS3uVWnpnJxraVqPaWMjfaiUvRaqCiXRvzlCVdp9skiSUeEK13CtJ9Q45bmkfsJxWTKQU5nX5NJKdILPtAYeFmL6hD1IBWQFFGoWAokQxTqURL0sSLXn4Dg6xzk4h4EU2hbSsIajpSKiWU0+eVwrBtq06PxdZda6gkSSEumRFEtb72a0RnhysC7kRq7N0ExCnsCcMcK+ZFggUIZfSM6C3dcUynQNLte0GOyYpLZP8HiW2W2g0IluahD6i2gQkWfYN3QPvo+t+Ot+TR76DJLu0E5hphJRvCtzceD/fofh3+0wfM3GXMiYewjy+UpiiH1ltZc9EYR4VyV3kniqc4bi+jFGtjc7fdjFKXEJ1LaPQCwYQWsYomyp5yUz5QtSDLXk+WnJgWrxK9mJHMSvQv6HvYBAbzD/4d28pFoWEp7Jan6WxeWMN2MhJ4GKl1JCgyurz2MMzDuTM2LQd+PhDx72YIzOnJPHMdIcOXig+JHO5yUKshYTWpvsOf0lyfVd046px/OCVIlGcr7a/jMLX1HNfff/I5KzOkNLFVRe4NL1l1/QZZxMWeJmGi2ZPk5KU6SW4C/0ce0z/ZAQSXt8MH3w3R/hCXgIty87n//xzuMcjMdkM6cwkT0qbc4I4iV77LPHpIHXFNN3kJxWWVFOcvEEgdmucyZHsq8V72uj5IL9x7RslZRIPLDopw9D7zvaKqO/hxCW3kCUMyenyrQ4l60wHg/G8eB+r2xb4egnc3bm+wyp7M2z7VLQrKDVBJnBRbCFYAHR1PHcuQh34tValJiMiWrWXr5nXfPPO95uG24wxzO4KxFjzcLbBw3zygB8n3nZIvSqJ+vePEmDJQjXni8AZBvB6b1H8J3nlTBuZaOlJ1SGC5A1/qlXgJ4zNo1+GuU0yha8BBVPK4EwTlwtW/EwMp8WfCG/OEQEImc/3nvTUFF1SS4SlWUrGQJ3k06stWlRjZ0jEE5zRQqUv0AtjVYre2k0rexvhVqVvd6opXB/i4AxpjHnoPeDJpVNGnWv1K3yH//0J95uO/J5DxXX8Y57Z7MddItK25X0Q0CwbLM6Ntq1SfyMo5aWAV1ChLHsJFsvWwYlVbMLc4SRZUlh0pDZzfbUVbh4JoHx5zwGc6xNavDt68H7t5P3j8H76by78qdf/8SvX35h1MEU4+O3g3lOfvv6NZ7PbYdZQG+cA37//pU7jVvZgqdXlW5n2JlIoAWbb4w5OPsjZL5KQUaJgsA0kMXV4oK85skxJ49vRyQpkyspHTajOk/e0NW+lucGuZLrh3ZUH7TUtbnI0DlNpz0GSArhnbSV4AihSrfBdKNIrPqxyNFmmAgbxIb3frAV2EsYC6s75zCmwXbTi4P3s46RG2LpPZHyykjfORVDRbiXDRXl7Mm11BK2bgWQpBibB4/pcXIeJx89kpyRIXlvsfYeo+f+5FAcijFFUYG/fDtROq1G++j+tl+JjqhQ953VspQe/EBMmS7oDG6faziOR3cseIsR+0PuYGRBLUSCqqLpe5fyECrcbhuMiT8Gm0ZM0VJC2mnf0K1wJP/wv/7zb2HpMeH795N//pfv/GBRJHB/u1Fb420Pde9/+svvmBtS4G1v/OnLG3+a/8A/1E95bwu6RZeneAIaj+TaDoOilL2gMpjqPMx4PwdSK3upfPQjJpv/zvHHLuT+THijV6bZS1wSMc+KNSqY0A5Zvb4wmQs36D4646Mzjo55vIgfHxJthMc75pOHn/FgDtgfG596yImLwa5KFWXzcOWuc1X4OTa6xMmy0gydrJf2h8Ayf3xWb+tKX4LIjyBNXgeQ02KX++xVtYPOmHSaAyxdsg3jO6EGat2TCKucx8H5eHDfK1tT5jyiZXLG5ldrJDmNgtZK22eIfBF9Xynl4hKEeWciPJnBrbbNaqHJtfHaRZj+2VhOLTV1GhfSJC8o2xMalgttWQgHQa5M3Zhoy1m0JCRZ+ysMeKB3cpGanr9fLoG8VSk8pfu30nB3ukRrI1RIw0xzjY3HxsL1c/1aE3Gvl8FmqDdne2eu9uYLh4xIhuKk4tpV4ndaIkjPdehpFhqzG0601iQLwqnOLIYVZ5TJ8KgEDx0UVR7H07dpzkEfJ1tpjDKpVmiz8L0VfHbKrjRCLl49zR4tuXIBYcU5eBp2xouQCM/POV49qEjk7ipSXvg11x8g9KlWqZtlztpU8znGjuUvVfdk9Mnj44wNrHcA9m2n1oZIwWZn+kjwRQg/uOdo/nPtLffziWhJCayQN3hej6RQ3NNCZFXblyijxOTSsiG4qlbx1OtZfMUgwIfmU1TfUTTknZKQGvDrBsRLFu3+J+qz6AAjE1sjWpyBUAuucv0eXVoiqzjMn9VDZQFPwKG4XJtNtDD8qcT7E4+ZUiNDgvg/bRlwrlgiieR6FF+JOJlLWkJAzgLhmUz2nsiNp7YOkjy52IPimxJ1yVgXa86zHWYXkqsmuExKVWoNNKepUDw0wnx6NgQi2ZJUN0L5xgAAIABJREFUgo9xa3lOW2rYeBRfV5WIsy29HrmmPePEJSwVtGZrfEP3ikthuGLj5OzjQqrGcB5HZ4yR+Y3HviNCk42bbrTUx3nb74w56XYwp/P+MWjbyb5/sLUQPJ0zikzVChq8JGaYzobP4QtiZbk5rGnY8io7/T8ef5jkzKx69JrKqRGMfVLzpbQ19jZDoXjMeBGLRq+2n5N+fnB8fOfx+8Hx/eBxfnCOzm/fR4xkHo/UFwg+xaPPHBkTvtxufL7d+HRT9k359T9+Yd83Ppc3qgq6B6RaaoirYUu3R3gyVyPZ0bLK7+fo4iKNLvJnvKjyTGLcczOK3urIhW4s3QQY56CcE+uKu2Z/dvBP7984+uDb+4FMQ8bk/TF4fwzuRdgkphtUoG6xOXaM4vBmQt0b25edbdvY9p1Pb7cwWSsxSVOoLws1qovwXKrgoZQZoF8sgjX99rPTnH3bAxLWZwsz9k+PADIzMc57jhMyAw6aPCrNsXCzidZA8ao0FI1MfloIoIkgMjOPem5+pW3UtmHjwBOVK6p83t4i4T6D3T+TJDhnx23E5j6TrmqOFk/H6UDOCkItjVLmpcECmmPlkcxfLasfM6/Yb1MZ9oL9L55CTJ+I+eV0vDY36VDFMbXgPZSCv4+oOldOVYPD8WnbMYxuk71V9q2Fa3CBfnzjtldk/8/c73dkj5biTE8NTR8ZhFAaTgTLc/P+2YeIoOkafiGz+e/JlOSCKYjnE18VJxvt0NyoY/YkWgFGiu7B6J3z44O//uUrH+/f+f7+wf3+xn/40y+INMZw3t+/c5wfCG8IBd3uidodwU8xQ1xR3cJJ/HGylxgNR9dYPrHpaSC1VcZFrowR4EotGsq46Ts1xhk8mATDq0ZmZmhsypacPpy93hEpnON7/lsgnqW0/BnjUnxWcmI019fSbemZXCuDqYrVGglP3uJUdYgCjigso+WV064Sn/N8B02VpoVtE0rR0Fb5uTkOY4S1ixnBTxurAK1XATZGOq0TxdWggBv9HJFcJKXAcebpzBFRdhNBb7dQfE7UbM4z1tkSBUSQxZNVDw7YjOlAeT9B4j0PTRhl18rnujHUGHVyHgdjpLK6AyMV7LuExtatBvl97Sv69MbynBXXErzITaNw6T6CrLjvUDZUG/uXT9T7zrc56H1yfpwcj5P37z2MXc8Z8SmnY2LlbOAbf97+xC+3nUHHEb784z9wjpN/+frfeXTjn//SOc9vHIfx+Zcv3N/uHGdMmtXbPTpH20R8QD+Z4vQ1MzChmNBM6aKYCLdtv7pO/9rxx+0qfwmuWdFFpeuYxIuTeVDGEcmRXC510DGM3uEcwjmDZ/R+Do7z5P0IT6FFkJvZDz9nVKzDQFyZ0zm6sjXhAez7Rv21sTXFJHyESgqoFdWnWm4+1GW3AE/tHkh0Kv7PVQmx0KlV3VhEI5t2TcVkIZukO8NcGC7MEcHfj5Nzdv76/Z3HOfj92xGj9XMGQXA4psIpwt4qTYUmSzI8WjLfjklzwVrQ1FpdzyNeSC3B6g9txDXdtUrvdU3PbTXaM85qZ/3MQ7PqzsLoqmJDN2JxWmIsfK7zzqTTlswAwWWpUrgmJBYh0ksE0p5tOwmZcCmrfae0WilSIinPEcuiMY3kFpopLleNeyUcaw58mdzhMYUEskBDpETQrrUGiqgvSNRVyP2Pic6qBi8dm+tYPfTkLuX0lKffznUv49KQknoY7sz1+C2S8T6NUgr3fWffCrc90aZEkqY5Z++UWrnVDTe5FL3DGiRgbc+O/YWe/EHl9O9zxLmURDI9ZNhTVDO1u16QkDUJ+szS8v56vAMrsZxrs0CBis3CGMr70ZkOb58+07YNqnCend5P3t8f9H5w+3Sj1sq9veFmjPcOEsnInFvI7VtU42bG9HFpaj3RlHiuNkfIIJRUeSw1NHZkaZLxgnYuZC9SEFvIIoFeCan74hORimqYucpKSmziKewqoqFzUjQEBy2mriD0oC5U1CUQVSwRRscEpq7CevHa5Il2kuRigVWNOcZ0BTN0oRo/8RA0E8lYE2b+WvMCUWhj5N4CtcS7dfbwvYIZsUf1iRQuSxoBkyW+Gm28iC25Am3xXjQ1liTycxH2Gnvnowe35XiE0vHWUr+qaHobJg/FeGnBPidMV/m+/isq1NTEEZEEAiQNSC0R9shaSw0CP1Oxh2NntHHnKdhQBiVkPETRvD+TySA99DAes1On0PaUdCnOZsov8kY9Ot1PtBjDOufxADeO/sG0yT7SRkZmrN9xMkXpxWF2GCcOtG2jp09YqEv//Wf+P5Hk5LBdlthrg5kWMCZXqyFub92iD3fMg9Gd85wc3Xl05ZjKacr3Y/L+OPl6nExzNioOHDP6yH0m2jDg7M63R6cVCQn7vz64bY1f/vMn3u4NPCqSJo22CdseL725JdyVENjCvIiKLl/BZxC8Xubn5rrgbPMwUVuHmVyE6T4spgKncFpsGv3rg+M8+b+/f+Xj6PzLX49rI1MJA79DohL7LMqtFb7oRilwAH1OPj46+wRToUjjbROwWMalxoRIratojaoUj3Nc7rQ/brAZdnTtjj/veDqwR0C4vF4uCDtePnOLZMYNSTuOeU0HhjliXE62kkoEmioVGcb5CIx8CS7et50l7Lb8coaG+Fq932hFwXpsMvnEL1n0aVeis2D+tbn7WCG84ihas7XYNtwj0V0jqZfKdl7n+n/X+3SRmLl+vuQ907RWqKqESaRdVXREakGaIFWCCGyLrB2I43SnzMbbtvPl8y/cbsptl6hm58QZDIPHeSBF+NR2ZlGWmLbN6PFTSl6rZwtCXrK4n3eIhB6Q4wzrhPVEDbG9Uq9JKbykXAXXOa/DiX3JxkzeykpydkQKczTOs/D7R8Scf/zzP0RbQSbv3x58/f2Dx8c7c5zcvnym3pVff/0Vm8b38RURp58fnG2j90nTmCy0nB7U0l7OxRGbMGfwiIC93cJZ3ib1SnJWKzYJAplkTLiq6MRgWGaSIxHbrWzUUrm/3RONeDBzrD3eRaXtG9tWsX7gc1It+DOnZ6PUuXiCsccvByVLAuhzIRR97hmQ3cDkeq+zHDaZRDx7EmB/zqEUisQ00iIFu0xcUkYh0SjzgcvIdz9i6sfjJEaujdJCI0lS4m1NRoUKnFEkx++Tw7Zon25hIyFaoKbIXd63t73F2P95Mrvx/WsMAJTbxrYmolr8Qid+1hqihVjmV+Mmi3ST4B2qSBpgRxz04XSfoaq99NZUKK3S9h1OxT6c+d6ZswdaOCsnFdMYOzcPjRozp89If5TJ1/HB6J3/8MsNaQWasSF8+vwrX98/OD14osM6H+9fOd+Vj3EyzPhU4t7RYk9Tm5goo4zQmOonb283bm8b399Pen8p7P/O8cftqgy6elVwT8a4ZMKwqmnxgOoXuz8WdzzMUp22Gf0xQVOJUxab2jOIJ/diQfKsvCOq3a1s7LUkQRX+8ttfef9o3P98Y2tKuwUf6Bx2DTEViUmC1X4KTCDh0/wtz350VvDuiSxpokvx8Zy5kkp8Te/Z2pCc5DHj/QF9GN+/fqOPk9khBLA0pnV8bV6SqJOw7xv73nIUEUqtOMJ2m5QqWBFohbJX9LYj+x1qiTaPLEg4n4U/UQ53Vr+RZ3IaY9c/eW+i7fd4IRlxj3Xdb8HFQ8/KUtsmJwMcu8YC89SBeKaelbfUyuIfyYvuSHDGQoRRVcN1N+IMc0nyz6jUe5KDVw99KYgGsXwyx6Cl2qfPQBsXw98slbiJNmFpW/hbzZm8EY2A5OsZvNyUl7w6PpsJzuJgIPluvVTJf/PkhDgRn4E0hhBnysebIFVprXG73fjyyy/c7oX7veZ0iyA8UDHq/Q6tcWYieBo0T1cVeJGcyqHaGNHijwS4/ncfA6P4ZPQHS+lYS6G0dk05LQ6LxfbP4n4t0u0ldXG1oychFagMN8Y0Pvo7x3hQtKIVrErG/5pDU1H9IrlhmjIeB2ZG0yC3t1oRjN4fIfSme8ZLzaThuTnFpJXSamM2oW0wx5HJtebEWE0iabQ0RRa5fUlH6AXtRXt02VuEA/ZWG5/rjT47H+NrIuaDklYT6hLcbMI4dy9RCHTIuOXhD1YJTkmprHwypkOzqFy3PF4kVhfx6YIdy6hIPIfRnWVN89OOWnHJkQ0PzZWQM6uJ7gUp2pIzIxKSFQJsW0tkJ3zlWtt5WGfKSH2ptW9JeGAthDDJ2cQjRxVKibinIjStVFXaPe77Z78xxuRxPqI1dIwsTDdEGqUqzQcmMQG66qOlZSQS6skisX/NGSaw5FoLQm28B6qFvVXMoB/gTbFScI3Jzq+9836cfPSD4cnrNOPbeUasFWNRPmKHnagoRWoMEQFqBanK9knZ3bndH0HtGOndKHEeKvB+PiIOnznGMQ1ftBiLmBxt1lizzaMj8tzT/5VH/kfrYXoG3ZwYWe2pEC3KQLysGSySHGNcGaRLvBAF2MR51IFLjQCZFyZ4uEi7IXPBzS9Ic97AVpRbq6y87bff/sq2bfxpb/i98uUWxL/Zwy1aE6VZ9hJrvDNuxvOGXCTi/HuJwK2q2maw421C2oozPJynLYWupg1sDj4O5zgmv33/xhgD03DZrpIj5WIsl2RNcuG2b9xuW2iWSDD5AXwPnycrQNNIcvYdud1i99HgEST3FSH5E7kQPJOzxc6PMftlhvFzj7rvSZhblgyBXqxVBcszKuBf8xhE8JSwNluCAvnslix8KVci8ZISs143S0n9WkIFVBCKfVA4QzvHIrUIwlsw+6dmOu85Kj5nGkjHS2YuqTvBpXAsV5Kzo2OCnFzjvtkj+NdSlNfzfb6jktW4XhD/Irs/f8JCxsIo0vDLVXlV0SvAriTn05cv3O+Nt7eG6xJ0ekcYUKMF1Ami9bDg5tT8ler+cvLPKQrn7weW/91HEL8nYzwuJ3EtSmk1AmS2rfLGXIE/kOw1tv8y/uuL4yR5H41pnUd/55wHqhWNafFINiDRxXm1KAoFdaUfB7jHxFtptBpxrfcPNlNU9nxJn5YkP2iVSjynOYVtE85HnI9orPEwi13kTENkcb8CmY21Ey1FS12oRV5utbK1jbd64+GBSIefnlHcKShqhLgkkcjtNSZKDw/bErOZvJXF8KuUGolLHzFFNeMGXwhynloCjtlGSdR8tVXO8fNjEZnAmdsP05rKEy2did6vhzS8U0oJQ0vN1rQ29npjnM5JEJAugNxDaynW19KFm/meh/dUqZnsaBgYt1Jo90yUZuHxOPn28UEf2baSMP7UGrYOLXlPs+e0pnJNWhUN2shCbfocjDnRUqlp15CEwExy7sGdPQZ+DzNn05gg/DY6Xx8H3z6+Y+7c7jvuzrf+kVpQifBa3C+RQGKKlDReBpmhY9X2xmbO7XZjOIwTNLRUYv8y5/08gseWEEsOo173FYStjOAEo1QRziUv8neOP25XsWAbyWDB1YvUNVK+JpaeeRB4eLIgknweiUpdjrBcV8PUaTWCzczEiFwEl6gd0DS8WEoKlv1a71SUR3+Ad/zjG4hRPt0BZ5b1vfLc+5KUGG2E6279K/SUZ+RZbQQ8NrGiniRFi/FnjaA3xTn95Oidr49HELJkYDVHzgW0QV1j4O6MOVLfR7E+oUzYYnTwpgFDS4u9fNuVvQFl0hrse8h/C4sYuojFhs+DlQR4+inpCvLpuWI/P6wQla+jNcObEI6/tvSCSOJjIAVrVNqcuP/XjhDrMR6LZ9soJqViKjdesmhngc0Dk4GX9hT48ol6MPIVD4n8ZLTFb1mU6Jziq42mjSYlf67DqJEw95y2kEAhd61YaVhtHBrCWbEpSibOcYhovB+XTsfabMsFH8eU3Kr+srmq5XoHVeL7Z3pnLW6PZmW8lcZWGntpVK2BwnhBvFEJcuHt7U6p4CU2KBlH3BdtCCW6n6nkmmceCxNwb7w4x/27H5HXK7VsaK3U/RY+ZOnc/ppvLb+3pdkiuuQvLBHdpTzMhaiZxUDF1BnQuIzwrPr9DMSGynlYbDYSAqd7q7ztG/ftFgXE1wdqwng8YINStqjwtSSKmkXOim7Z9g9O2ETU2ZpSiuNMqsKtKqfkFpknrGmtsRArJ6xezAhdKDdaiQGFun9GtfL18Z2zH8nLKFRt2Q4LblrVKArI9pWI88bk7IOzjyjmJpC+e7VUWiuB+rjR04vNZrx7lHhvtdi1SqLCzyJWQH7i+llHKyGuanbi6LVWus0YATdj9fIkE4tPX95CiHGLltG+VyqF5pUxQ+V3SLS7Cxthp3HiLjEkQU4yWZCUsTDNLCnYM7K4L0k2f/tcKCr85VugSN++fiBTqKbUO9QmeGt4qYz+YHgkZuFtGGt5shBkSemMcml3RuNf4/ercNgInuLthhfhmAcffXJ249v5wWMcfLrvqEpysIS2RYK/0Vhio1UqjcZNN+6lIT2sRbZf7pRdOUrnfXR++5eUiunCbb+z3Tb8jMmxulfUlKppC3XOVJ5PlXpzPsZJPwZTgh9kdbuQtH/t+Dc4OVxBQFgJSKI3vqSDViaxMtkkQGmJh1ws+ioXfL+IaFBKbFjdAvbi9fcQaEdVoRWlqCM6easx7noMB594P6DXhLWIMWvyBVpZjQrh/UKSKTMmvhSoz5p6fWQXnyIq9fjaaSHUVWS9CwF5HrPzGA+OPoKUHZccSU46HAsauiVmTJOoDENkJe6lQBNQcUYJwttelVriukoNVnwUkpb6RXFtWI4cS/Z7s1p9XtEqsv6W5PoTjmwlaU5XhYCT5USG5x4a/f8lOrXG412eiEdcRCYMibrhWSRnLn1xlFxw66zpuJUcRdJKjuw+T3F9Lj9KRDGIriWT+ukxweSWP28m90DjTShSYuPQ8jSynT+2B5+CXOX5LPJEYr0oS9rpb+vctbmtxBXSguJ1tMDjHsbGVWglpi3WSycUCo0qG7e9UfenUKUdngTeSvRl5QfBtgtJE8G98DNHyAvP+1ZKpdR6ydT/DTh7nevTiiU/KWsN8DfvxqrsA52NPyEkeR4P1CslN6jFk0I1qu9a2LeKTajSwcKfSkvLCbSAWoM0vtpjPFtmKVwJIYRZykoCLKt8uQqB69pywYvLde4XCr0ScQ3xNS0bSOHx+E4fMe0S66NeY+q60q70KtNWUZxWagga4olcBtqsSTUIYrQgHjyTacZY9zjGERGdFxIYgoLyHEBYm8tPPGIwZtlEyoXO2SX++SL3l+//fruFuGaF2gr73qheaKZs76FKvvaKQqJsfmZs0msLwheRg2sdQu7dFu93Lcq+Nc7eox04J31MbnVj1oFshdIArVdr6geD6YwpoZGUO6HGsEGfdsVQWBITTvfsfmQrb8zJY04+xuQxOufs/PJ2o5TCh49IkKpSrFCsUuhR+KOoVJoWWq4HzCitIU0YcnBO4+OjZ9EtNAFaDX0pN+pWcAu/NzcPEdMcnfecnu426CNG7SNRrfwvGHT6FRCCH/AkX5LTFtmEBQknbF1qiym7rCUgtFmM+6eNPjc+zoZ7peTP7zO0AmTMqEovuDW8M/YWaMbewOkMnxzJ/L8Po4/BOfozA19QS5Z5AePChfHIszpeENiCWlf2Cy+X6ZIV/7otuUKT4Pt4DL6+Hzwend4nt1tm5NvOGOH0PCGgUV0LL37+1E4HBlv8MtOsyJTaGl9udz7dPvF5/xxtL+vJQWjPSj/NPiNoGD7PCJyiLA6B24g21h8/8H+nY62ZZyKrC8HJf5X8uKiivnRZIti4LpXU+FtLoD5zRktOa0xY7fc7Uk7OfuLpV2VemT6TeSGc80wTxCDjScoCPI7j4mO1WoLnVUPPw7xHICiJEsxAe0pNldxcPoMeSsL7jb0n2dzi2V9TJLKS/ed6Wp5npYWgnSxJ2MTdXJZApF7L1cxCYdTGEyGQEM2TUtjeNvZtZ7vttK2lnUUWG6388KeUGuTWtA1o+5ajxdkzL0TiIyASCfR8+GVh8VOOHL2eEgWHJj/PF1JzKR3Dms9erXTRNLe0Fa5WCz4TluR5zQmPQzgOSfI+8a6NAjNaFO1WuN1CqPP2tlGbYqMHZ686E+OYJ7deKWXgaU9ic4IFWuOXpUmgMEjESbEQN9Qabs+iSukb+vUdKWHOCI7WHBu2mPRbau+xNKKC00Qnhz1wrUwtUDc2CWfxmBhSJnB4bPDVoy336NGGfQyjG1g6nwvBtSgaaN60Sa0a7QmPtdnTI02W+F0r+DCsz1d+Mi5wTPvpUgR99rxXITlijKuYkDV2LXts1rtTm/Lp854JwB6UAgZaG+gdvQ30mPjxEejMvkVy5B8h0mhPEEC1oZuiNaQojIn7YCsaDu2zMkz5/dsRwwA1ooYN4xydj+PB/f4F9RvqNbXgTrDwwJKcPTE8lZoDUKhp6hmn8tzr0BTUtDNQKOtgFfEStkp+8v744Pg4mJ/fqLXw69svDLMgJz8M+WZoGczSkOpQOrMd9Dp5/z5QE+4TOJ1vffL1a+fsB9u2s+87nz7t3D/tbDV0iPYW989cETMk7TIe4+T993e+//aNLtFwPc4RYMHjj8nH/waSszAcnslA/lkMCV65B6wqA9Ywm5ineZnQWmXbGq0VWi8pEb9qeqEmCWBOvRCdkgTdWpbhavRS5zL/ygx8zshGJUCRH6rOdfqv4urx39daWV7+5Mfycv1JFJKFXq35YQvS5xx2TeQsDZatxjxQS80Xy7HFleREJr1UfldV/6xyVJWtNlr+WWOP0dfVH9Cmxe5/9sX9yt0iU7ULDfnZQA6rYmJV3X61hW09KfcLxjaSOCxPBAoyp1yoIWT7jYvUWGqhWKqTehKDp1yTE4UwMJw2cqxdINs9M2H2NZ1SiyZiEt5U4uSGCE+GkD4TzQse5qpyNX1tLBP2+PTLvKpcj+niUIj8uAYDzXq9k3mPPLQ8LNsgr0a5qlBriTH5EqOqC6m0C0mNByA5hh1jrZZ8gcbCfTTRW3LkFVabbfxURFBSAv/5WsvFWVqo2w/v82oLrbgEXG/W+lxyDq8Ql4mOzQXzLMFJTZJmIEdaJOonjd9p2RqOFCcMLKc9pTFW9IlqOwj1T57jtQBYpVfwPmJg41lg8mzjrwmmpBK8ogKIPEWOAfMwhHQB12xUJGJkLqQjBdNCSh8PdXeSdzjm2kDWvVrIgSVXLBJ2zbZPoIaOJrHOa76301MYL9llHlSGn5knQ3BlFkqzYvoTUMpVr2tqzyJBqFF8TFq8IxITu9q2QL1qQbqyxIHWaHgMlD1/8lqnqhaCoR5rQpKruYqi4xyX9yOrBZV+hlH4FeJJxoP9QdfNIs7mjpBvxNqTn3Hzyd+L56MeO5Gun5P/m9PCbsYmzZ3WKsWdMbcomrUHer0ABTGmDAZh2bjyhGlwnJN+RGtVVNi2jdpaSG9Mw1W53++hSTfjbKt3uk3KqNgxOfTBJEfzV/vvNU/5V44/Jh5b9PhNnjck1DmjpSKr0pP4Why0Vi6dCnO8lNwAjDm/oKXR+3eKTH777Ru9T7wIUpTPf6rMPqnvWaHMSRRswqda+bQVzo/BGIaNxXUYzHly9gc33bnXTxEg1vCHXPHqmhHzBeUlxPX0vcgX2cnWigQylWnI6puaj1AqvngQxDiyC9OCUIc42py9KP/46c4xBu9nx4biw1kiXDeg+UTHQIpSmiPV2RU+3Ruff/nC/rbRboQCpoBoTqn5Itk6l747ZFXvLKdgrhcsVTt/cpZTStomZFtlTZSEwm7A4cu+x/VKT3GileWSTVEPZGUlrCrZstJgCrbiUIX7HtMJ59EjgTlGjhQH/OkX6kUqlUbCULWw1419r9zvDdR49A+0bjF9MQPinctjzdNp2UPZOm7/2oWNUnJz9pg6fL3va3Is/httxzl69uxflbmJFsTaxVYAypd8fd3yo7m1ytYan3MseGuGyODRH3gxrIyIeSV0Ntyc2m4xPUP83fada+DAE4FY+XkGUutBdv5Zx/72KX73JCeOUicpJ4TiNsQGPnMxLfRs8ZVWPgFRrKgHvrcKNMew+YHNAxkxkUcfNFW2Vuk+YhonE5nz/Qhz0xntgY/zEah1jQS9UTL59MhQFKwPXGC738CFAYnGTNygEsnpdisYwXNBDZcwXtVMepZdSfiS2ZUth+q2Jn9LmPOBpdYX4hQlhOC2ypzCnFlVGDAEn87pB9Mm34/v0Q6dhraGli0T5nmpc3cP0+FlCdGS4F9rIPjdIx61ImgqZie1IoPZT6+4XsukiNkOQ54ot0qIMGoJKYrdAvE77aS2xuf9T5St0W4b47zR54M+P1Jy4gNzod0r6so88mKH49aZ80GV2HhbCRTVUwT2MfPM4lFRp0bnHQ9BXnPUgyFWdGZcGLiPiPWp1ybCxdYSCOKxSaqtS4yo20Ra6HqNMVEttBrieuZQy8Zt03STh7/89Sv38+Tzr79QcHY7GcXob5OtNn45fuHb8c6jn/z+7cGxV758+RP3/Y0vn24cj4P/67//C+fjgzmN/f6Jf/yP/ymQ0qkh/IfhUjAt2DmpItxvn7n75G0e1Jth+4kPZ4xBubbtwh/xu/5NJGclCAsFkHwIES1WybBQnbXBxia2+AorPtZaaK3Fn9oAuVoEgU5UqOGS6gaURTyOrDsUNZ2e8Ky8nOdM4tuq3i506YpqLxvMVem8DvatH/h88UIkaVV/6wvkh9+7NknPCCpEJWgayI4SFV8tSqvLTMxQ16yU8/6urF6jpVVbZb812t6oW0lPk8Xt+eEpcc3MLx1+eWbi4s+c/v/P45m4vFY1+UnPpGx9zpfdgTzRqER5JEfgo0WUcvfEBlBUUugvlvXsEVRnJgRuS/smEz/nIgSXUqilsrfQpGi1BQpjsbHA832IZ/WsniyF88LSZEnx80QMLoIjWb2tO5FXnMTqC9rOtqmknombX8/ziQjkBcjiD0SiU7Rezsiaidx6P+acF2plS78DefneZX9Rr/NeSdVzSm+VequEKXYdAAAgAElEQVSC+jmHpL6TuF6S9Vc8It9Kkb/33ayAz/V1UbWv93mpga/EAV/tuOAL2oXUrVajpkCo5fRTIIcmjg+Fza/3dXHS1gvw8n+fCRi5llgKvMHrilac5Jhztt2v5bPWytqwM29YSVs+9/Bre41e8vIx1/2wRJoC7ZxpmPzkqKyfuSwgVFJywSX9uMK+IYHVaJf46+VnXL60ifzvP7J/p+NCk8n77Xme8ck8sghLhG1OErkCT2HOFW9VobRsNY+YSrvMd4ni3S/ZBSdFdYB859J6RH4Y6+alqEiuTd73KHCueuPlIeYFZJwx/PqUv3xOyJzWSVHHdckCKaMxR6CQ4hFTVYVzDPRU+tnDC8uCM2lZyNUqlF4oc/G1Ku3WKK1w9oPj/GAcJzYnWp/aVu6CDY89053Rs6Aak6lpfuvhExn5xJKKiInsl6v7u8e/4V01ucamLqg1dBvixviT26tLhihuv0gEJqsLZla2LTad/bZzzht1U7o5348THUIrUWHf7i3638WpBBHzODvfPzpnsq09A3J4awgffaBtcPeBU/HUuUD9Esaq8vK6vqwSpeBrKkdW8MzWRF5fTc+XSW4oRbGzc34cnOdJH50iRlPHxuAw+JffAh24bztSC2+7BFv8HCEV6hqKkApd4mXbt8K+V/786ydub3fefo1+sLaWlWe5pngDrgwlZbfnhI2zCM2Ti/C4kDZNmPtnHn9bSSeqlivlQinWNhSPZY3+J+pWlks0l29OqYXQpDDEjFt1poSH1BzR6+598Dg6fcxQcL3OKf7aUtRr3zb2tvPr7UtMgbXUCLGYqKla6JnQiCdZeqwfVZg2GPNAS0OlwSJnLv+kNCgc/WRVkAKgUclPT7MfuO6HvBCKozX33BBX0q2aPJoMHPt+Y6sVTPERJrgyDfGBSKEUoxejuOGfFfGSCU6hbDGFEe2qfFe84N7ATtw6oXVkL0/t5xw2ByKVtr2BOP0YlC1I+QvpC1VsgTVtyEp+SrQErmsSKC2cyHN6bPbBODujd8boGJ3pxrCBzMGjn7S608pOrRWtKQZqk8//8Euoxz5+x8ZkmGP7G+zgMdgGGj48VaO4cYuJ0jkf8Ty1MH3ymEdIH9y/UIZSx1PrphME1dj07JngXO+XX7PJ00Z4nM1le5Ptby34dMYZJGXDUN0RLTz6A5uD8wzdnzk7KsKtNUJzJThGNiZbTmedMzWD1LL7M5Iblpt2iU3M+2rwVqb38GH6yWEICI2xldC60WePCeGiz/gpGlIn6kyH335/xH5WobWTyUnrO/u4YXayb8L8/Im233n/+h4KwRmHVRRXmDIyp85iFSi6U8tGrUapcN8b4DA7/gH2LVqHxxisMHKacZhTLHhZXnIMVSwLx3oVXmVp2Ekg0LVlMquVgXKcg1KEti8y/05/HJy/n3gLAv5tE+yt8NevD/o5+O//7Z+4tZ1fPv3KoPPQSW3KVgqfeGOvO//hz7/w9mmj/fnGsMl/+T/+C+ejcz46ujV++fOf2VuDjw/O4fQJnY5jPL7+FQG2bUMEPt6/RbfGDh6PwYc4XpQmjUfvMU14FSx/55H/8Yp4TRPlh3Qp67rrgwvJ+JtvuVon5EihOlur3LY9HJDdOc4gf9m0rD5jQ0g7utjIRph9moBraM+0ouy1stVVvSb5cCm/Xb9/XcuzN/njiT6vRsiglJcm8opc5VdLoCNO8BvKdS6FitBzb5ojeo+jGOrhN0RWalHRrWpPKCUktfdtY982bvsntm1LsbOYergUm4XYZILZFsnZ2gBTmNAz8XlOvz2rxZ/crcqq5InWxFIKtMZW4kNC3jynkS4O0cujiqcomQTF12p+QYmZzPRvCrl6ZI2DjyBseojcLL7M5czbCq0EahZc1PS3WVX2y7XEfU4TvmvjXO7CLxYDRPLvhYDqM2H7YSL7h3Qhrl8XXOcv/yqkqngidIlWrnW/+CKLvzHdEQsHcsSRKfSh1KH0cVJKZc6BjRrEVbWr0v6b8jBPIjfTbCOH3PvPW0h9zODDaKB2T2uQ9eZ6vhc8X+L1wQvyEfdSriKMhU4ZMSyR7Z4VEVYrKNALvSZFlRyyEOfrxwfTBo+zJ1KYXlKpMBsmnLE2w64oJ1GzXehO+EWZZ+st9Wgy+Wxto22DSgxojJ7moC8Iy/NYusi5shLlsWTHW6LTS07C3THtiASRfXmorSr/iUZyoaCGh+bXmm1wWNNhkkjJYAkeKjKDozPzHQ9O5bNL8DOPC/Raf+Jf87Nxpcvix1Iw57BI9mpy8HRIyN7lvidS0SLoDFTPxPA5MsmJn1vaRkxxheFmFaFqWM0sU+EZkBHnCH2idY8u5ImcArSJeF1LOs88b2WiZmRKudry60tXLHai4yDEOLsQS9tSI+hSdbeIuCsWPo4Opmy1M3Rmohp+iSXEzZjuHGMwPw76HDyOg3HG+IUSiTbunP28PNd06b0ss1J5XIySaYM+D/rIvS3/8Mp5+wNRyT9Mcpa78tr1V2C9bjhybQKWEx41BGTysytrNYQefUQxvtzu3FXZTuN9/+A4BmfvzMeLqFRCfEtIaiTEWVtoCHxujVst/MPnW2iZ3O5sLSe8BEQtvIt0deyDLPZMel6qoRUMfJHS1vhpBlOB4S+LRAzzE5OJq3BvlYZzT8GuZV72/jjpMhE/KQreku+pUaUHHFipRbjfGtvW+POXX0Kh9ss/RqXahOW8fNkZSKR+Ps7wUfLOkgQ2m1g/o402LPI6XUt8tWv+6Kn/7z8WnO/Xc5Ur7xKVtffj2U9+DUQLfr3e1fye1/AegzMSonaqbDqDdV9hc7hb9J3ntKsN8LZ/pmjFdMSMxeyoCzUTFnygNpExMK/g5dpcu4ey8G3bgiyMcI6oBIMPppwS70jZtnT07sgcyBhXMvqagLv7BVPXUlFJaf7VIuP5HvvaOCV0P4oUSpFMcgzXmJgZJtBDC8gpTE6cM7ghMvl4VFQn+60lh2vP5/ICua1nYTMCdz+wc3B8/cY8n1Yn/97H98dB0YFZ+DBt+x4VN5BvA1crRl/2Tll3LDgsF9RdFhE9zCp9CIxCLTu1xsY/5qTPgY+BnZ0iJyqN/dNG3Sq9heDa//nf/itjDmx0VAvbfuN2Tvr7AdNoVUEjuQ7sEabF+zyH57Re6Dfd2Zh+MnxgGqO3nz5/QWtlfv/OcXbOIwRKY9ru2XKDpd8Mi4gdEzzOHEnyL9nqzNZ2KGSf1/25YiGE/IBqqow7o3eupql3RlGKZbw0Sy57zHWdGackv6Z6vDeOM5Yk4t8Ujz/j8B6BxUhUDCLum2WCGxyUMSfyOBEsBChFuO03yqhJqO2YfOfTp0/c7zdUO6WEOrowOb8fYeFQglz79uXOGOF9diuFXUsg8y6chFfVx3vwq/769aD3yTFiyq2VaGE5XObBS+7es/ckKZthw1AtVFkSAQL5XCk5MJTv9DCCVzcdK45vFhwtFD8MHzC60qcyXbDpfP1+8qiT3mdYQNxaoNd1pzWlyODr44N5GvLbYE7j/XtHTNi10bTyRsH6ydfzQSlOKc6uOyqFM4eIHsf3iG9bZdjgoz+wLlhXpp0pwBumo6L/C9NVenELYE30RBh56dNmEHkSubIOSZZ9HKtCcmK/jsmT1m7cTPnz5184x8nsH8zpnGd8j0tkktEaj9942za2VvjcdrayKvoCFERbKqHGjX9uiiWvISGtK4FZE0rrHK+UOcdOL5iBNA/AXBDprKy0iLPXRlNlr8H9eNQIkKqR2Wpeu4SvbSA/VakI99tGbYVPn97Ytsb9fmPbd0ptS9g3znXBSRKTDWAXmfaycjC7kp3ELJ+XuzbM+fOTnIVWrA1dMmhGlZjwMJatwidPau23K9FZ+krX6WcCUDV8YpCAzY308IHkfMX0kRVLVEwjKZAYIg2CdrjO4CU1MwbDgouh6x7b4hjkep/R3pSiiXKE7YMNY45VEWcvPTkw6174M4vjtaQVlsR5oS0y0JIdSP+oQBCjx18JywnS5XhVewE2CYNcfy+cJBtnmMnOOzYknNkFfNlbe7nKXE+xOuYIg7weLrveO9Z/XpLTx4zxcR44G9u+Rdp+ZYCrG5BBWp6l+gX65Lj4KxHEV/uHaHPupWF1hoBiseAFSMoESPBzjvNBH3DWiCfHeYDH1E3A/5W2CaUaRZ2FC604KUT7bZplUbXSMLkm39RDcVkMWtuZBrdpaKmcZ2eMifQga84h1/h/3TZKbcx+JKL7XGuCMDLpecLTyzttcfd4FnpEi3Rk4WmSiWQiNleS4s7wvM4kk8z0WfNpFCcS7sU9y7bNH3QY/t2OS6U8ESlSMX2howlH5d6WO12JbbLPHpyrUxcti3GeHEFOCtJ4aeCw758SdYjC9HEcF0xiBH9v+kjUwsOSoYNZ7DGQ6Kx4TkhGcj6n0UfHqobC8drLLhQqn6VGjFh8wIiFkC81qxJIsY5AmeYj+XohN2IzEukqlV1LTF9pFKrn6IhPTh+cOmjluKxlzjEwdUpy27a2IShNaugNlRhU6hb8LzVnZDtz9nLtx/G8/NJDi5UcE6UzCewx+OM/ILt/e/xhkrPgIpVnW0Uyqblkui80zHK9+wtEJtkvVMjAHZX0iaqxb18oMvhP/+iMcfDx+CvHOfn968A88v2IrZLJgvD5/om328af9zuKc54PjILRkLJT/l/i3m3JkSRJFlMzd48AMrOqu2dnlityhC98oPD/v4YU8olyLiJ7yO2d7q4EEOFuxgc18whkX2bPCic3SrLyBiADEe52UVNTW68oS4PUimGEj2trs0TEe9zj/R26P2kcE6NC8nc8YLZUjTWFYocEV6gJUFfKuOefuG839DFwvShbUnclkc8NBcpotjRcSsPr24plbXj97g1tbfjyxgCnLo18gQxmLO6BFi7CWEC8HYTcffQY5UBH7M4oPTk6dFinIOGzjwl9c2Gya0+jjHUuUx2f5cQhylld7kmoZpWhlhDJcxoJB0dBVKVGTrcOqQDcoWkwDIAP2NgwYo6uu8JMsPeOR99DXJHZd4otuluUHBxuGxE2acyCx2Dnnxm2vqFbD0PnsD6iu+yDMeKFwZxXJYqiC+cKBXKqLYKecqWjhcF8R/cNxQVioPMCeXNmJOhrcDGKsFODk8UHRr+jbzvGtsKqwbY1kMErzyeupxsiuBnA2IDxgG8d/ujwxwb/xCBni/lBY9vh/oLXtxdeV25Prosw2hnEmOX5cX1pjrOQcO7GkqQNAcDE67WtKGZ4r40idrVi84HRMcvUj9s39LHPv9WELdkv1wVtKbi+rrhcK5bFUIuxFOUjgmOWv/u+Y3Qje0iJRZknXVQhaDFqQbGsr6BsesG27dj2jn3v2OQBgWPsG+UTWkNbr9C64mY7zHa+pjmSbD/GmFeExzE8NJPZIlkWZ9izWZ8JLcmy0VI/r65j9FAZD19DJ+Rz3tGcJqCBxqfi5ScfDHKInMGZUOSUcMbAIRMg5HdRnfwC94HH4xvUOhZ3DosuVKPuNvCqVzQ0WF2oGl4vGKPj8f5XdBu4fXugCks1A4ZNeC7DnfMJVSCd994iAKmF12lpAnOWxlPTi0tZMcKgKwgq2IjgRsinJR3waPFnCSpHk1S4eKgjD3h32OiwPqjxMzi6ZNUFVhdeO2XV5rFziOh+dxQIWvhSh5JuKsDihlYKvn73lZw/AKUKvDrGbnj4AHoHbLBb2hwiFxQteFm4uvZxCDSm5lsO8bZTOe8PYpy/FeTEVpiZUPxnwao3UHBLgXRS2ac/s3bnb9yYXWlVLJeG2gDRDaUL/HHBGAXLldOf374YgwLb4UNmrVxV8fa6otWCutOxa1uhtaCsSjEu7wCCta41RJH0+X2UOgMyCDsZJuzjBvEyH5ttslSoDQ5IKajLCjJPO2pxtgvzGRxI6Y7XfiWLfRRKxqOjakEtBZe6ommb02Wvby+cw7NUSC2QhZmPq2cIDsCjluwTcvQZ4U5mAh1uZO2qH0zJfwApJ8LI+CaMqeFor010Jsm4mRF6BslABgcqgIWB9Qg2BnwG16LUMBF1yPDk402yNjd5DhbkzwnaEzpmhsVMK4OPMWgYho+JVNJlcHCeGbuWuvXZldLNouZdYy9ExhwOZ2pVeL5XZlfmDukPqO2ctyWKKg1FjQT5GYQPhvaloDaNgYAWQzV98n4yo5budOYWo+urolvH3jds/QaIYdkqzBpEOsQM6Du87/CdcpUAyza2d+z7hv0Ty1Uj9KdcHS0wh0kHAZ73wtHAEqjE4Y4zkITIRAZRBHUhIlwbVVyXS4M2QVkFdSM3jkN7SVaW/ogEUPBSLihFsS6Vgn3BLVzqglYqaikYcc9TR4yjIQSmQZuMNTockany2paiWBZ2vGm9oNWKbX/Dtu24CcsYwzo740pBFUWBYBPOIJrFTslSPFG6nKEFHLwRCVgpE9oMwTUlxfMZDnSz0I5K8OPoRpp6XxnIx7onNwmBQOSb/txAZ+pjpS3AIMqukWiEnymiKHUBB6N2uAEFRGl6H3ATWB9o0qAQ3H3D5slTcQBsBPjy5QtLUduDTtxyPQZ6nf30cS1dHEUpvOmykINWBpPVwSYaDEPrA6KhnRN2HmD1QwXs3hWl5gxC0VlAvhHI1awhcqsSTRHGrrphnf7agHsfGJ2lsCoV67IAohioDNZ0RFjGAAvBhxQXVFDziVPMB4YIS5cdsN5R9j6lNYoIRBW1ch1tfQPAOEMlxohogVSglh1LL0SNpv7bv5OT87T+5nrkDRmBE/NCynyIjYDNs/Wal36SbBUCvS6AFaA66gDkcYFbA3qd1mnvHXsPCWmQ/a0a+jfmuP3rDaMP6ELl2bJw3tFARzArAOGcFZeDyZ9KynAHRufCVkRrbDLpMK1nzudhkIOQW88gB4AYWnOUbDgDsGCd5D9AYOD0YRd2BJHkekHVBmx0SJfXCw1k481Gy+eHqTHQtAeqAzuHNXm6h36GR3vdrIicy1qfHeQ8BcmYayljnLMBlUS8IVMYMNfEdGpyiE8CzNiTk6EQlAqIETESITHZEAG4E1GxkBxINKwg2ic91ZUPpI8t4sEkEMBA5M7BbCK7rjIw38eOPogqNQ35dO9x7e3J+E/vjBgZIg7fH+B8II26OiYxGADRJHWoGpZa0aqS2OkAuh2UmjBcBXRINmKyqLIrY9hgkLPfABnYd4VbQxGDjA5s7/Btgz0ekFAYtm2D7YNBzn6envf3PRKWH0DMCcok7EABn5wqwvqk40Z8Pi39XEtQoCzs1CytoBqHCRYvWHTFvm1zgOYYgqED6IIqjiqKr+07Zt2VtqbbBnUGOTkc1iTWoGSwziAH2pGdnBLBqXU6Gom5UstaUMxRekFrA/sY2LZUNQf62OM1FVUKCuikh4ZkhWRYzse4OFGqvA6nWOMQpQRyIaVkfmbOcy+BHat0QgI5X9O49pPjE6iyRIZjBRB7ClM/5WBAliUhvhsGORHgSIyr0ILW+JjR90BESB7uY5/DJ4typT06ddNkYct/aQxuv1wY5JRH5YyrLe+3RNAcYIH51H7SGohjrQB2QO5EX8CBoiYG7Wy17sHxSzR7egInCqjCZIZBNq3WcGKFKWPigeaNPrA7PzxQxW1nkFOcEhOv7QopFaYX7GpopYOc0I6xD3g31FFRjB4Q6lTBBphMGOC7AL1DRyfXB4KmiqKC1ljcfb8RhYQCrQYoIIolRucs+477tqEHDeCPOvX+MMgpSrehEQhkqQBwSLChBR5ICx1C6jtNf5bfT4dGVAZFsNQLTBtgJdjoNSB1R6kVTTpqWVBKg4ecvGY79Cu7QgYe8ZoxjVgqHEROWALgvB4gzyl1HJyvmciCctCYIAMBdsPMqeAemZUIUAWiFUMcoyhqIWmU709xqSumAqtTa0VUIJXiShrTsyW0SQSCEkJbM+OyuHinfHVqrcTmdM8SVHwefbLpBAHfR9cGH/P5AQ7PO/ofA7WQEM7IsiARnCNYO5/ik1bYdF4AcLyXEqHocJnPVwCmDDCHUWGUxplr2jJY9DTaOf8HnMUjEnya7GZIXlF2cQlJgObYQy3ZDOTijAHrCGPSIhEAIIIhDHgnWDXf3+FpyP+ZjQ0Yw6Y+TYKpbkT5HndHr4StfYbkmEFkKQoRi8a8DlhHMaANSvGrCfrtDt82tG1HFYVXJdn29gD6gOyDXC4z9PcdfRv48TawfaIYIMAsfGkrijY8+o5FBFo4KygDY0CmA6ZK8hEe+ywXhhNwTu3WqWasaG1BH4BKZ2AgVCTvFqGUcm04OBesSvC7FNDKv7ksr3j98orXt1dclhVNyaWSmDHnUYLOEre5k3wcvENaLJYozBRuJTJ+6mtd1guqkme1lMryqVK8rlWOJJAV2MYOf2RgzTluY3d2q/THXCcsdycHM5Gcaa6RqINF0KPAlDMwoehgFtqeVoQopBgJ8YbJTxvOzqUMOD7z6J0T480HUT3UCGycaJlWcOCokbQvKR/i85rVyeUMftZOVEXEUawCQn82TPDNt0DHKvXOKmkPDkG9cBBqztGrYd+2GDCMGF3ThwZvBNSYqakfQ10ZP+XnpXAKwOY7ZIzwQ+RwzqkEzragPWqLCkQ3s0/blB12w5K7xI8bBjmlC4e6ahH0DozutJFu6ObozqDMh2O/71AFWhN4qew0KwrThmuILuowDjANTTM1R1XF68sF69rw9vIC7wNjZyOICvB6oc989GPA928dfyPISeZ+LsXUx/CZJR9W2k8BjTwFNqm+njooDD4ArQ1uBdYBl8qMOUhg7oRea11R68q52y6A7YAYlrUilENYV+8lnCm1BiwJSQCmHDn/epyDMXrP94GAkhMBiQZ2jSDHfOfzVFGdrbsDTsl+DT6OM9hb2oXE5UBSvIPqmUtjzTcGujkITXJGUIvzGEdAAKehmPlTmpEoT2X7uHPDzda6s6kJZAfBx/kPOZLflJn3zBqRiXicsj+bPD9/HwFOZpdAcBiOMPAIcLJkGtmkHFfvDB6FJFJwnuxUyYvOFadwXgJoR9s2H5OGwEaHuZA0aIESGdcDyapRmovzQZxbIi7PcZw/fT01PQRgRxZmm3iK1Q076u21HGUDBjo+g0qAmknqYGbmSlXWvcO7Y9spZidisL2jv98hA9DusK3D94792459M/zSgccnxjiJzLSyoEhBHwOlpBhfoJ9hIzyfgLhnp9eInz4L1MXl4jiFSh4NGjjGsXP8gx9nks0DVeoxjLUItBhKU7Trisv1gsvlgtYaciwq12fHabVGwIVj7bkg9X5sSvYL4Br7QTniRTT0jwRjHyzZV0WtC1QKRnXU0YGGGeSMYdjuG3oXmPe5IdyYFWSnIMrTluS7zgQi9u9RJo9kaj40LL1kwqZQyzLRmOR9vuYpG/6kY3TacQiHpSZZXVXnKBbrnAfXBzvGVuX92GL/qZbg/jl63+E+0NoxKoPNPgMmwLYPiJZQFY+5Y5EQ1TlklklQS95f3w/0q3d0Uxo5JVWiqNLmDKMGUfgSnRwjD94ptcXqlFw47hLALjcBMKJ06ZCJgnrYMY/kyeJ+7tHJ2DR8WhG4DeSseWqZ7RNd4XVkE05LIT8naRqF3VOtKrDtwBgRtPHVmipe1xWXy4Kvbxds941z1gbLxkutGIFW4w8Srr9Rrsr5tM812ZkxRWuCiU8xwCQJaSkzi0rDrHJk8uRPUNyuqPKituhsiayR5aR4zLCIviOCzVdXRnMlJ6iC8zc0nad6EMhYckLcVoGiVE4+hY/D8cZ7rWXJ285zir1RomTBjL6ioFLtscQickdNx1Iqb+qikdUcyJJrIGF5bi1b72P+SWwGeLbHHkx9G3tkCtHBM2ZsdoRD4UwPc5pL+/MPC+KiZnSCgGZ5is8xXZwkG60kCNR53rFxMuA5wYWJ7pg7B1wCM4hgFxVl0DNTMLEY/DnmNfO4gAGkJI0nHBAJdgiiL4OcCLC9Ur+kd7aJ9h2kWipQHArjrBV43P+jS8XjRCesj3C8SbYeRt6GYKJBM4YREoqLRIeEUFVcVdEqtTuqpNqyoNYXLJevuF7e8HK9Yl0XLFWpjzIG3rcHpO+UZ7/d8dNff4JvA/6gkJ0A8MH3/S9esX2impsBMNGQgWDi0ceA7xuuekXVRqREBN55T5dZKjwHj5muJYGbCEUGPEUbSnHUZti2Dd9+/hk9dEssA2brGNZRa8WlFXz3ckUrCl03LOuCt+9/wOvLK9trKwB0QCyCzbzDMTLWWYZUBfpw7COgfS3skMEgwuJKGQokaix4ebniZau4vjHhAxyX6wV1afg6Xkgp2NhW/H7/hrEPir31He/3FgiOxz21qYI9FcrCkHAmIJ26Bjqe5yChe+DRwZqorML4HpV6Ojq4NkXYiWUA+v75nZ6cLYjoFGaJqpSC2shVc2ESTbvLe74JS7VlWQFQJK/UgkUqeqdgn4VdwmODiMAqNXPWReFj4NG/xV1XNiuoBpGd1zsTMRcD0FFEOBVgVDy6sDbm7LAsWjmypg9snfeMStNKsnAhtyXaj2h1cv5ZOjpnucxi5aiASsYRgSXKKNMyKgnH9zt6ISeplYK1KHQMaDeMfcc2eqDHgIFBzRuI+lxXBkIjGzLEsbnCuqJ6jL8xiib+8E/fYV0aXt/esFTy0oZwDpuqQKtCd4cY7fq/G8lJIah5pBGehiPz68NAexAEJVNoOYBMEZ+OSSK7FQglJIwlBCrcOmCDhGEPxyI25fUzyODXvJBQbvRh54DlcAbHc/K8cKj/zgnjJyej0fUTRuhcW8igTeLcSlUqfAZUIpYlMJ6bap0nQkE3jTo4vfmRncf55InPlCm5BkRuUhDJsi18BgJHKJMxXt6nWNend/l5R5YJ5jnNLPbXZ3Ki2jyJ5sEP5NDPj833Fv9LfDzPKhIQjjkRfjO0kSOIej6bzKKP4IcIDOYFPTRs+LhUSE6VWe4TwsQfg5j8f76bM4ITn3PYq3NfQlMAACAASURBVFvy3M7XSQKsYeKQs9UgguJADWGu+U9KOHCSaFtrqLVBi4YytmHsBt8H7H3D7f2On375BrsP2D3ImXldAPyChv0T03CfSJhM++KIVmX/uLdpnxIxTn7gsc+BtFnHeshtpzFWAQCMXVCWpZq4/wqIOocGV8HSmI2W1rGsFS/XFZd1YVecylyTGdTnHj/WZiSSyNE0mEgINAIw52McLFOKgEJyoRFqMf/qcm1o64JqUbmuLHcaOkahoKDuwvZlyodj6IANQ4/xBXOqeQZkcnyEIY3rOnGwaa9SFiLfq+qh5kMeGQ3ScMcYjk+dZA9g2vikMkiOzgjZgQ8JWCZAEKIoQHL9FFVLlDtpxx0e5UiB6Qg7RHSEI34ClS+Hr3Q50F4TORAcIacmXBDO60Q8lN9jXc5GFDjEWMHwyM5SGJCt1mmTEL7mfF2OfZWx+OSFx2u7p2ChoJcdagb3wlLroH8yt0kiFmFK10TRCmd1DSOyQ8kQNmd0GFRa+Hfyki7XhmVdOBpCY+0U4WR2E6hLUGhm+vu7d/yPgxxNP8xb7RP0Pz0mLvKcg5GRcowDT/VQRECRBjd1ObngKkQd3iTEpWJp+SAsaACsRzmGwnca0u2iW/Qlcm5I8wrIgMhAqRUaE5XldLM0iHQSBiQDGI/FD2HJQwTR1lawXl642gaFs6BGJKootFFcTHJRRtbLrFMhUrMXfwZGKokJExnKoaHcSI6p3x6LBz7YEu87gD10UoLMGq3NzMAcwIj22LlDplFNZOIzjwOt4GYTPZER08H4PM3jSZ7Qts8NO78/fcon2dz0wUMaMQyQ6l1QL6gAhygqS0wa1QMZFhmpki8R125WFwCWJHJ+UGzGFOp09xnkWNQoxQceYBkg585MdAh4Kk09X7CEeo9ATUQw5Pj6qH55GMo8TYOrQMVgxonHrTWs64q6cDxIu1yxvL2hLSuqCnZ70AjWArMKs4pumpI42LpjG4bdDXuIcu4lxEA/6VhfVhRl16FURb0sSKJgh+HWH1jLQjg/oPPzDKe81Bbo84hRGpkFTj2Rxny7XK8oTkFRgUF9oC0X1LagrcDwDW+XhrVUjgRQro+lVby9vWJdX3C9vIKt4wMjkzgkIsIgRqK1HILZlu3RyafSIFJCiNSmzawlA/SO2gwXie5Qc1wvC9pSse2UM7iNHegDSy1wFerZBD+LHTSC+3bD3jdULXBTCNgAwg5ER49WtsNmH+tWghyfbqaUEvSRwd/ViuLUBDPfWSZzDz8/Pj3I0XDAtbHsWYWDeG08oLVBS+VQaEe0VqctBmBMWJd2AceEDFQoRBtUNggGrDN4UCEP8v2XjYluaTDpsa8V6IrRO1EJUC4ig4ohDIcEO/bHgHXuzbpWrtl9x77vRJDCT5UoeQWmi+7OifNQ2kUBmnIeX6vRUDGFx8oMblTJVWKXH9BGh7rBvMPnvwHBjmE73h/CIrgIoIZFQfFC4b0XCOroEBP0XbAPx+3RURvbya0P+O4YxQBVvF0am3OUHh6B+mzG1vYighrjQgrYVNLKoTD/W8ffRHIAHGgCnjPt5yPT4YxSHYniZISX7JJZH/TDWLuw15/A2OmPDMSNPLIFznwi3KZK+I+lnnnimMqmcvBuPgSup/cyCwiHHhD8JMDHrMkD4pPwPMnIp77CEWVPxzS1hcI7hThgJpYnqOEUQAbCEUIFHh/IQMYPN3mgO3bco6fPNr8n/yRRi89zTnxvHtf4RE50n9+fblz8nwWp+KU/PQjn7wisnDKjCDgsPwZLPqkMm5d8gjkucZ9xZDgf/0Ku16ffsSNDI2hOFeb8lwhQwrKTv+BZ9/5o3E+w1G9dQ8/sUo4oVY4a+mwInrEjH6MqqLXgclmwrA2t1YP4HhmsaoXrAISJA9V4CyA18scwbQZs5ujpAP7AsPz/fWhwX/i+caA6MVojO3/4WH2+lDMTPXGcPna55T4ROkJNVe3z70AInw6l4nK5YK0VFZVJUQQ6tVXOtyoFGBadkWfENU5K/LkrRAKFPP1sJmg4kBQSohVJnqmhpiwK8kqgUVYXVGXXKbSS7FtpL/Ze6MgQ6FSi7Hrwjw5tsUCoBUjsZu6OsG/pGzIAL1pifZXZxXP4CCY11YRSJJ94nFEpUY1us0zgY1+nzQ4ERbUc5332Q5k5fmjZO1UlOetOLdAe/iw7LOmTSH53CHkqACw4f90R9gOx1iWS13EgOPO9yNyrPI+TjcjTpRM6lJAn0qszSGYL+oHk5eytHAsSL4ncimYW3cOn9SEy95FAIgiOrTBvNxNGjwHXkOPeaKDXNoikKozihJEcFCFHSk8f5Q9M0R8HOTgFOnkPPcMBIIMDR2opYDphGyMuMMMWByXvoYXZiwjY6i0HPJhCqwPTGGgGm0YGeE4j9gmPLrzYsQrpzgoEjUqvwGxxR8yqMRvxHrLrx4K4yK4uXvw9Sm9xHWoJGHecbgh1COAdIkGADDa7CKA1RdXYpQEloVGkwq3DvdOxiMBsB40AoQNxcnDMomNqZDRtQSIOwmt0AMEj9A6V3bBgJHO7U/cEwNNk9k88cvDpaemEzTugYQCTDMx4ZEYiR6xzenq+RlaNPYTPeqcR6J1o1hhOh61lStdbDyfXM8tPC8QAvRt5CaUEtwZUi0WQ4ooKLssKB1BXR7kLZ5WB7bl9zgAixyLMBrLk8NsBze8HOnMdfryu8Mi6/HS+fGCpBV+/+4K3tzf8+c9/xtIuWJcrainUvqkrRAra+hWldGzf/ooxBNveYHbFZfkePja87Bu+yR1VNo5fccNmA5/pnhSNxMqAs8YI+FpqOPyjSUKjYzHRUHHugz5YlhljUBMERN58xDowtoS7CJZu7JrZOtw6xugYskHNoaGc/Zfv/wmvL1dOTB4P3B4/Q5rj8nJBLSFAag5Hx4BhxzEsOPke1iLzN2qNyEjERHleNmjARQ/PEmtEUcG2OXJkivK1bDgDC1WsX1a4s9V4jIF7eeB2f2DrhtEf2Lc7eqdMfiIKw2J8Qwllco85VTjSkEzcpASvJbMGd6gUvKwXGIA9AvpuQFtWrPUKM85CUn1nAveJR4peJ2VOSvgTBwVoxwC0QiGh1SL48vInCiSOHWPsePRbiCJW7GPDPjbkgBHt5B8uS+M6JZEKZh21VKzrgvtjw74/UPWCCkW3PVRBDh863NAHJT6xIEimYdtGn63jy9LCySfXb5yMRAQ1AQSo0N9q4dy+iqRTNHg3jI3EU4cBhRyy6gIZgt4j2FciNMvrFWY8R+o6HAEwEXGZ+aGWlZo2jwccjrqSx1qrsoJsRlS5KjXG9g77dovgvqOIYikV69JwWda5DtdS0evAo+SMxt8+/saATsx635HBng1xZKTuISF9ur6ZXcNndwghsWjhdqXQ3Sk3OILPU0x6ztoQwY1jbniJbEgjm1M35MytA0Xxp9eF6NO5JjE5a83zrYKltfSwfEw5bl4iRTFKnERq8OsJUZ0Cf3z8nAGHT2Rjumw7uqYOFOdU9w2kItnvCOQmM9YZ9BA+gJ9u2+/jBX+fYwYyvxNdzcz1FAR9LOX4sQhx5CuOGQpFcJJt8h6dCmMwyJHQmTkUYPOxR0D9fDIzvML5Jx43VFTQlgaIoLjDTbAvdFS7G9BJb09EZ6JrSCf34WbM2PP57wOnACfW5yxV5Z5Cdm+B3JCiuKwLLpcV1+sV1+sLrtcXtHrBsqwMgN1nPdxEYCKUr9cBAvGCLmDQroX7FULFb0FwRD7vYKLJQKVGsjI38emCzMz2dP0yqPQgkz53IiIcS+CNWXZaGvY12nV3mRw4c0OrDXXh/Kx1XWG6Q4djYMWyLOFM9AAujro/cq/TtiTP7Fi7mdWn0ZC0i3KgoAfCnvZND0Q8939cNE21vspzatUxmmG9rLQjfcCkA8PY9QJB8PbnOs39q5OkwWTu2JPxGEk6QnBLJIbVGjWdiipLoruEwnDBM5T19z/m+nCZqPtcS55E6NRLIbpuRj01zavhx/BMhwXCmDZcuLeCfnAYtPNyjWQ4khNWOqIxwz0Cv7RtNv+mnYQLa9HoIJaDawMuLckwOa+/hnaPEgVJRedEWgCQWF0Lg7zpeCMoBDsxzakaXkuZyUQJ8cRahNPTfZz8aFhPPb48uz2KSQoQGjmq7MTjg7O7mT4eyMSeFQ4VQylkqejHvqgPxx8HOTOJnmB7XHiNkzj9LNt5p6zL4T1slmgcrgaVhdt3Kmvy5g7vYbRLOOUDgs55R3MNKpn6kKxvl4im7eC9FIoBWrRl12wzjf+PgNdnaQlwjkCIG50cGSItgGqbi4/aPBVaFmYHHiMYsgfzTGg+ESIj5wZb/FIJN41xGuGdWSgSWo/JwD7miAYbWapCGO+BWbkKVIfX7lhZecc+80hnkmWrWb7L85oBcf4sgpyPJzo9Vzw+OmOm/kYfRK5AsuW+UcdmDIcQRIQNcgJsLs9Ta+w8YZ4cyX0OjQ4XqEXdW1BKwZevbxBRdAPWtqPJitv9Hbd74YTdnF/lhtG3aayeSnG/d8zA5mwYEwI+/HqJn0sM6Hy7LliXii9fv+J6veLP//AXvLy+4fuv/4BSV5R2wXb7Bf3xwGPvkN3hK+9PbVdoE5gU7AJ8Q59tr1tRdBOoNCzm1B/6H14J//5jF+6tsW1YdcVVV6QYYBr+1LpJ5NZG2q7oooq5Nx7aRzYofU/EOfZKFdRa8d3yBl0cP/18hf0yYLeOYdSkeXv7gpcvr3j98gXXdUHfHnBUXOUfsa5XLLIAHh2joB4ICqDukJ3JiEgFO/YGhjn6xgBu7zv1WkqJ5J2cI5Ym6ixNHOU1hTRC3eY213W06tBaC7C0BaZOXlprKC8L7usNj7bithVs447H7ujD0W0jGnw4AFA6IUuDDARFAdsDta90rDW6bHvv0NpQ6xWlDFQfVHNvBY9BbhdqAz4VD8Qsh6iFT3IiFprkbR+0FaIosVcf979CpaDVxg5NBCLYB9bLgnVtIf4J2E6097712LPsOkvyNRBk7NIY4PSO4WP6JCBHTzA2sDhHsm86O8FqQS0LUk8rjSm5sUT0KgrV0GtAVyK4RJt7KQxISwwjtT447qYtGA+qHPI+C6Sy66opKzirrqxYOCBF0UpFKw1LWXB7vNPujURQYyFKhyhQq0awFk1E3VErO6VqYZeoVhp5rezSWi8Ll2EH3Drut4FSDVoMyzIw4CgP/6MO8n8DkjM/J2qTnoabdSIQWXM8GJB8XtSK3YxeJrMWichM2d+ft4oLMV4egbCoUMFWNDqiwlkiMk3glBHI7ETIv8Wu8I9wioS4sZ/2sszXTZZr/iVJcp9EgBQ13emwgRN682HjusfPUzsnAkQPefW8JhPNoePO+Uxu8T4ccJf5vcf3Z42XXOhI+DieP0/zP0BKPfOF+flISY9znBkhPydgd6yr+WKnoMhxVp7MujRbII+H0+fl7Kn0ArxXnvFnLoO4B3l92Uab3TUsQ5Zo065RQx9GB1wFaEVhrUBQ0fUgonePgMsOftTzNYoARjL7xszIS5YG8v05QpIBka0JlrWitYIfvr5gXRZcXonefPnyFet64TkXpjxaKmqL9ndkGw7LHHDB2lZsY0cfzkJ6NwxRCFYSSsGS7/iAtv09j6rUidHC907UNh1HLgDkzT6+zz3hLFPZCB2j0aNs7fOBLojSEPf7ZV3x3dfveJ+tQ2qDlEqVaS10MNnaKwXr5QuW5QKtC9yc4wDStoyQvRcB6/LxN0/ciez4yf+p1H4ki0jpDcw4/NgP01DHnsjHpQ3xEWs3NFoMqItirJTB0L1ARgitpuJxBjjxuhb7RtPGGtEHF59rNwPxVmogCM7s3FnONXPsW8feDdSq+dwj30uKG/bubCJBNi3wEhpClTpsgclAj+7+qg0WRHCEThGi6eQjophNLu4UutMYEXHwMunDuMW5mlPfbSKmdrKLGq/pnEWnpURSN+Y9Ujh9Zoj1zc7EIMnXhclAlQp3wWDPTrweA9gS95iCqg6pPKcaFIwOj+Y/kvh32bkOik4/1GqL6+CAOkrMhytaJ5LWGuVXigRrMvi1ORusVZaetVAKwcRIDA9kqgrRqaK/b4v+ZpDD7P8MsPs0HBkWAME5MJYOp5nJrHv4hHhEEEhJyI370QlxtCke+zeRHtcyfwbH0eFy+ltpUJJw7MN54UHDmIMe50LUQiQEfQYRU9o59HAyslFZwHbjZ80S6MnAno1tGp4nRxBEUd9P7d/xJwavouGMzsRLGZ6EW3wEqhPfz6DGMu7002mcgri5kT45yEn04fR1Ajh5Jn76PzezBOLCjBUnLaAsGx0XSQBmAQYIx54gbAFBNYQY4kQDU7wKmDOF0viZzflVBMQGHOzuEwWu7cpZKoWGZusDOgwVwKUoylpRi6Cb0xm642EPPPYcwnicel4f6lQwYGmN3+9RN2ippqlxvgbkIMFSCPV++XrF9bLgP/35T1jXBb6uuFxf8Zc//yMz/+QWwlHaglIWPPYbkS2LFtA+ICZ4u75hGx2PDozNMB4dtVVoWaG+odhA3zeW5T7pWGNuildmkGNQxTqu4Gw+OI5jRdkkMTK48U6ODVXR2emZ21AT2keBvryg/dM/4ZefL7gsgt2B7sB1abhoYfv1TqXW2hq+vn6PujTU9QW9P+C+5UzBmfmjlOg0pcBaBtM5Z6hqCAyKYBeDyXgKNKbkBACUfP5HqkAmZrHuHTE2QFBXAOYYHbALBxtXaeiyA1ugWSjI5guScpWDbgfVbqsWSARsEtesZAkE/P2lrdQv1CgBuWC7d2yjTzn+w9Z+5hGlftsBZ/egKKCGmbgOI/qRei97DOu1vaPUisv1hUHL4HiTbdunPtWAwGfTS5Brnd1OPthJ5EZBPA1UTsMgagQ5JQKDIoCDM8g6DEMMWkPiIMo6ta58P/09uveM5VEVtlsXBSr3hxQG6etLRS2KJhr3VtH3Hf1+53VoisWpTbAj2C7KqQdtbeju2B47EztzDB/YfGN4XgJJdWC9XLiOB5WiS1MsteC6FvigSvIaYoD98Q02OkqpEAWW6ihFsLYSa65i33ds244GdlYtUjDEZunu944/DHJc0lcfxZ2TGwJcpkPlNWej43nZzpDFgxzp0Z4Li5vtQAgHsup5qqefbFg6yCP69eNBmQxNUxWdSjLmmfMjxQCT7OrzO1JvPGqQoHd8Cu3yOWWWWqfrFr4WnZfPv3YYWp0R2NHOmu8zyxeR1cVTj7lGJz5Ofj0v6flfdBHFdRaPLrVES+JLmz/7xEN+9QW/+63T8OOREzT8sCCOYJcLNKjeOHctlCJYlopuzinaTq5Bch+y9DzLe0jyISOp5DKYJ3fDoJWdNevSsC41gidHR8djbLjd72Ab/2CraSko0khorQtj0kTsZkR+dPPUCFxajaw4ultqBDkj13JkaFoUl7WhLRXff11xXRdcXy5Y2gK/XNBqw/54cK0oIIXidJqEwwjwLD7vtkFgWKrBC1CkojtnJd1BFedt39DHwG1wHs5nHT44bly1TO4CJprjH0L3zHgiwI1Sr9k+5Rb4sIMzlUFBygeI0mCX6wWi30GrYN8HRu+oy4pSiVR4VO6JmEQiFYGo9eD+RPCcdgLwqVPjT2QCLnbGBCd+UTjBLL2dMss4zhtF5h46o6PmDrF0oAW1rhwcXBV73WDDcJcRpbER3Sw4OtFwNHAkWnBWFHf3OJ2OPgw3aifAq3C6dQf23tFjXlqOC/h91/T3OsLDGK/XOXHmmwOGs5NHcKApuaRsOAm0zlJ27yQjmxsRxhy3gKBRpFxJBEzusa8RvxMiwcQIsiGH1zr5pyIa901JFgaAojFYlvZlv5MjVVJHZ7CzlEqoEbSJoAogQX7fw4KJOWwbeNwH0VuAjRM+YFIodDg6gTflzLW0VVVxQvH4XgsaVBXX11c4gP3dWTseg2tj65yvphW1AcsqWJfXkKPZacftMRNb02yuASU0Aw1nTAAsVc+b/1fHHwc5uVmAQEFk/nwGPh7Q/ilzyg/WIk/OKp48jNmBD2qmaA2HgiAXB/rAUtdx9pLvSmLLTwTkFPBkhh8QZLj1+Mjgx4+VHVBrBg7HuzzSZp+vLSHslycU/ymQXVqez33avoLZFQUEny3+mDMos5PT41Py9TLzOAKdDG9mG6wwWx2z5JVv8XCWMs8Xf7gg/j7HKVqdUWp8zCDj+Yo92W3/jZ/HbZ4/FhzdNMpgpFRK31sn1AmfDA0KpM01KXCUGSC6gDVqOfhQcM4sKkUY5DR2+3R37DJwHxt+uX9DUUdRYFkX1LpCscAMaG2Fg7yhKbaG1LggkTBRmRJkO7XM7DDXgYJy51ooQvn2uuJyXfH91wWXteF6XdHaCqwXqBRsj3s4TUddKa0ONMQ7Poy3Aw9ju2atACrAOXAbtjFw847HGNi2HX0MfG5vFeB7n+3u5Kqw60fFnoIcLg8ukPya3C12KuYMMD5E533QTM6yvA0n/2GhINn6ekW/P2CPLdSrASlH2+80IBEsuxHlcQwYoisy7AQDrx7Cp8AkJZ8sd6ZmCp3mpGeilHlLfJHtwCx9x7tOGxrv0Iy8Ezci2K0u7CCtgl4v8OFQvUOwAyEsOMIGZ1CYna3mwYOiB0bGnHAHwyRy30QV6EL/1rn2xzB026lTxN6hTz14idNf5OUnapwcJnJiBignI6CbjPc+HJs9gq8HmO3RARtzouKWKgyuCmrrMCGZfkiPAMcRwfCpTT/LSNlBTNvDZIegrASlU9AuS8wBY3dVEZZCe3csRcJ4sHEggxwMg5tEyck5TmLreDws5lcxyOlmrKxCMKJW58rgvgSPplUNNR6BxRq+lCtqWXB5e4PB8ct2h/UN3qnts4+BtS6oi6I2YL0olnqlntj2DTZ2PG7sxPLkWDKTQIkyqRh5PSpstjjHCR+Pv0k8PshNoSUQpN5pHU+bG3Ac5tuno01nNToht6EK1RArUkA1VJLnMDHkrqExi8DEJ+HuWKCH07NpIOa/SdSL8MmDeCw0H0R26PZ0vuHT6zmAkJDXCd9kAMMg68wfmkTjXLDTgYw434Cpw6sccV+gEGCrd7ZCwyPq9qwVRxBj0SEUEvsjnpOQXfI6cqhkOkd671Og8clHxqgf/7xE3Pm0lCIePZf7jkwInCx+CoYRAbEi+FOxBkw6JLkXmdEPP/zcqZPE3LGPWIsuweTPieUDF1lRtUFbhRfFL/1Byf8bpwvvY5CfA4OUDghLIz6IODQtkLpOYcCcP1ILEzMzKsGyKVjwenkWCbO4r6oFtSiq6hzPcK1XLKWiDO6hHRs7IeoCFRJcfdzQ9w1aXqC6Yl0qvBbcbt8AG6htAXbD/r7h/X3Dj7/8FffbA+/jgW10QuaRBc45R591jAEFsGoJXgJCO6dx0ne0xcZK4T11CwkBfvboDNMCdB/Bq8jdbNOkCdgmq1pQKjkry7Kil4bRdjz2HWMMauGoQK6NrfvC0mZ/vGPb7njst2mjRmejgJRoj9hIfOYkd9oqZELiDhlHKd9BxGcMislh8NqXyPQDQ4rzZ2BOZfUjPxgechKxDzJIrEXZ7gzg9WVH0Yp9dOz7Dn/s0XqfjQMy/7fgAhaNNarJK+GqqJliusc15wL28AnUp/k7r5nfOI4+T4n/uV/oUGPYZaAl992gws4wAPCRQebJ7obtZYnxlJeFKrGYQbSi1SDQhliodQaK+QzOdCLKyiGrmLo4HkMxi0TipsC+cxTL9ss7xhh4PDbYGBgxVqG1it43bOKo1VGkYuuccyfa4AC+7RtgDu0D7iSw90fH/XGPQBtAjBlSKdNHCwAZA6pAK5WLaxjb2IUq0N02eB/QIvj+7QrrBeMXCz/Ot937wLf3Gx7bDde2oWpFdQ7GLbVCiqMsUfYTwQiUEDC4cjL6Uhpei570d359/JuQnPw6AY0EQX4dhseF8cOBp6/ij6O04okWRnllxLDMkI+dcDLwK67OrwDOcIRPwkiJyEjKqgfEF0EOXyq0KHCCqsVnpucn1vocUyER5GRAEtyeifTMqOV8PU7kQhxZM9LgzETwVMbIv58o2fmzexjsnJ1EIz4dfby3aV3dn0/pjKZ80iEfPp7O52SJ00fNip8fP3v6+cnYehh2Bpxxn6b8OgdR6kTaIrg0n8s3yxUA67rD2Z2nHgYm4HozBspFC6AKE8Hed/R9YN8G+m6RnbIDrvUBVdbxbYT0OwRSKm1dBDlSDUUdtTgem7GVF6xrry0G+MU6kRq1/qJoWlBVcV0WLHVBKxdUqaHAbBjYme0VdnZUDBIExw5og0hDLXRu+y0CfSU03TfD9uj4dn/HY99xsx09tDu4rw6Br087bFCpNoXMJKQilMFIlWc1Xs+9h0BBszwhCa8fAQ5tg8/M2gGK2BUqmhctqFqwi2JohUPQpbMlOrJyLRGQ2Aitjwf2viFnPVnw6FIQ1XcGOaPvgBSUUg8pi3CGfD8KD8RjBOxpRg7PbNc5WUULp0tGzbF5cqYgR4T4RMNUgFoq0IDLcgFc0B4rs/ctGiRyv8jZrBhckif56yAnJfrz7xalYree7PlUff/UI533yabHSbA0jWmoulGzrE0jnUFiqJtHaYqvVk5v5ZnewfdfZ0mTY1QOtedD5JEBdsmZa2EpIuxmkBMdw71vc6Dl6DZ1kMboAApaEQwT7IN6OCqCbgYzQW+0QbftQW7nPlBLw9ouMOfMNg3iOGKQdVF98sPiAwryZRw5VJNzJm0cyvMqBW/XBbYD9yido5IDtI8dj0fHthnQHK1UXGhe0VYlPaBlKCrwAmSHNtygld1815Iq9L99/Bt0coJ4JEpUI647m5tlLlKbHVKB6ITg37FrnWQo6ITqoIw7ug9IzJyaIUm8bl5YC4VADXQGFshNdizZsUC1lIAH6bCKxnl5VFonEhS8m4xfiBUCkNn+Sz6GwNsyiWTTGp5JynCm42cP7YYU65qcGr6pAMNOA2yZvgAAIABJREFU5agojQmClO3JIg4koecgMpu1/ixhZRQ7jUYEXkkj/DVL6nMti05dIsHp1vISngOc09cJiT+9TgB9yRE/v/6BAoUTceMSccdS2QkjToPgUXFnzGPguBGSdNQy8BTAQ+Uq6snbbQd24F7fUUpBjzks77cHHo8N295DjXQA/o7t0WlS3bHlZN6QNliXFVId3hwVjgrDZSF58bIUVOXMFzgHGUIFdSnoveN+e2CUaPvUCpcKvd3Ri0LXC9t7QYGwRRywDX3/CevbF3x5+YpleUVrV2h1QBxr/Q63bzf88//xv6PfNiw3R79/g5YRjMws9aXR9ino+VlHKryqUPCwrVdyjEICfy4IwgzcW1FGKApev1KmujU7NRPByaQCoPyDBi8HwR1g27VogS0LHolybD3uC2fTbY8NgGH4Tq5GiKX5YJgyANRAZyzahNflGl0+G53Vzu4bc+GICZXZjCDR9JCT5S26RnV2bTrVq08IZ4ZKEvwf2jCELaa5UlGUUnFdFlQRuHyPPjr26w192/Ht2w3dDLuN2XrvJkTIRjiuCD5LoTO+lBamLzRVRFAthh4vDabBS/p1pvx3PTIYzQG47hY6i+SDkiNFu36UQOOrJH1Hyzk7epIHGIruEQyURnTxGoM/t32fts8Cts6ASTjdk+UvdXTdiCrbjlYKLkuFYcfDHG/1jSNaisF6x/7eaVvAZKzbgMbr2QB2AGt3oDiGVXQUlAeHs+IOwBw2BF07bP8Z77cb3u93EqlLwfryQn5jvUJLQbuA574vRFtkhdQBaEeVK4osGH6HqOFyXVCXxlJ931CNZP8dFOOVziGktQCL1iDf8/paJO42wHshgO10avvD0XeDLAU1pDy0/H4o8zeRnMx2zxl0MnDOyzODh3MEy+fFJpMjuziFAWBAZDNrJ+HqN7JEP0h3MwJ3HEMn7ewUFZoCgP7RzR9/XYN/cUYZpiIw0zvkTKl8KxPezABuwg95iU5/Kc7vfI7zkFMJKq5mLvokG+fnnBA8kgQYmWka/unh57V5ujEfYprPD3IOZxgrQDJx9nnO55bKLAPOciCef4546FEulJmQTSZGXFtB8HPUZrcE12asvigFYl7LYwXPVR4/H32gu2B/bBhaMNyxj4Ft37GPzvsTGd4mO9x8tn/ntHMaT+XU+qJAcRTnhPNlYdnjuhKlkRhgODrCORUAg911PLPoAGMpQ6AUy0Nwe4y8G3ODjx1FBNflgrasKK3ROIlDuhJx6jdYfwBDod7Z9dAFuwrbxWP5zoDyUx1UrG/J9tEaWlk69/dxSnmivH/ZyjvLWR4lSc2kJLGQo9SN+TJZ+uY9O+tpca5cLNQgoZLx1YO4GYnIGBjCciPlBmL9eSRNUx07kxkyCcUkukITOfbT2eX6fE4acj9lE8P5csi0NScb4Lm2yAlzK1jXhjKoE7OrYt861EaQqaM6MVHlQ8GZu5uJ1pBEurMdXaaWESoHdZ75Pp92yPnePv2CtiET4tkGk/aLvosBjkzk2EMFPVYaZjwUmk2llNifQSpWzHWZNodMCzY5iAlGrIc+dhRh56dFGZKvLWi1kr6xJd+Kr+2n9+OOifSPPjCMCM4ukVwOktGZOFKdfXQiz1ZYslfNqea0Ta3ZDMhUyPURGHKsETLpiD1XCsfKuDh8Xaim7QZ0BUoO7iQhnl3QuY+zesFVNSS/Zx/JOGnsXNYramu/e8v/MMjpnvor3MgiFvy4k3ONnXWeN0W7kYhHBgK5ZAAfQklz79zElfV0rWWKH9GwMDq20Cc5swCSLEx0gxlGGrL4DbI85ZbncA5MhOVGpbPJV7a+cwjaRmTIEPN9pE7omcEI0lPHRjWE8A48pzpOpCXmVAS3aQY4mP4zNEqcE13dMBWPrQesfTjR2YKfqpAn03yEE6eAJ87zPyC+mScjiIAzCDhJHH+iUYT9JVKXzicDmQh8jFB4Lj03QFsYoRN8nB3FFjoTVpVmuPK1vSdfIzfO/OOx3iOgFAa+DsG9dzz6wPjXHwNdZHnrsfdpUFKY8L4/sMXQQxFOCRcBWg0xSdshnXeuqmKJun1ZSApmzZ/cj/ftBoegjCV4FAJtirJUlMaSCrtGDeP+DYqCt/UVbQC67lA4Slnxcv0Bbz/8z4SiS8EYd7gN1NJxvSj+l//1f0O/P4C/vuPr+zsu/3LFv/58w4//esP/8/4NPz3uXHECLLVNDZDPOJbrK2opKI1aH4aBIiXI2gUlApapCwMuHNGC1pTDEXsY8sHSVxGdLdi6kFxaNEtGPbrwotwZ5FMqnDtQBjyQiLHt6O543+/QoljWFcM69v0BtyhdGNXitsHFy+xd0Ps3DAPlBYajDwZTVZVt7oPcIQiwtmtI9HN97PcHNNCTwylw18yyUYbDp2whS1o+eFql0BlvCqA43DpcO9DiQjagakFtBb45/BGO0x3b5IJEwDVoA/vYUZaGupRpmWptqLVAlgYvgvftxymy+llH7xvEidClLxhBhlWhra/LFUUbbNwADCS7KTuPAEz3VrQeStBOnknOXdrN8W0HMlnWoqi1hEDomBxKDxQsyfzScQqWDY9toDbqYN37Hdu9489ffsBSG+7tjrrv+L4Aj8eG95/fiVBWIdl7G/jxcYeIYakLOVeXC4oqWovSZQRVA45aKl4vVyzXirpUvH7/irYsuGCJDi/6oK0wsfv2yzdUdVR1vNs7hgmR6KL49uM/M3H703dEXL77E4Zv2PovsIvB3xx7/4YxHvDB0Ra1FHC4KUu7tSww63hs77AhsM5gqtYGSIGj4OXte6zXl9+9538Y5MybEFn34dwxDcrHhC6NS04cPxL05K6csot8HfPoUjgZTTnc9lmq/dircn6R+dePmPt4lMxH5+tpIC+ZE+XLRNY0yb7HCT874+M853v7ED34vCbn8zt9NRGEfPA5cPRJaHseVfDx3CJ4efrb+RfO1xIzvvOPD/+EI+KaZxTmlH4+39fTO5iP4YOeBLT8iFfP2ko4rbj82VQ5VZwCYZwyqZjYPuEgrl+fF4t/yIKv9dj3WE/saOtjzL82eVPw+fwM1ueAybkScWqoUWRLPLN9ixo6uTAeQVxBCK+lXHsG3k5HU4ZzWvkAdJCgLaJAaah1Ra0XSGHh242dMBoCYa9fvoetG0wW6HpB14LSvkHwMzYMdNvRBTARXFs7vZ+//yFa4iM6KU7cjkOE83mvxdWnHUouz8fFL2kH8jUzqGYZ0yLZOIyXzJfIn5szE963jQFn0UhMQlHZQyrDMMvqGuurj45hwNhlqhWzEj9m6VOKHGtYZGpH0W7isBs8q49v77m7dT7MDpslB8oigQjMp+SllcPCHYjR0eo80Y18TKA07P6M66tci1oUXk5CdZ955G2ciK3Mn/+KCH1OriDAHCZKG/OrKy1p7Q9bPk7dvXzqgRamXeDttHlObNnnhxkFC6UoiheWCEF9p6INpQ00Aa7XF1St0A44Biz0icyYwLl1iAuGGqQWoCoWqcHrCrRTFK0KmlaslxXLWnFZV9SlYY3Zcb3T9qlKzENMTDkTM8fwhVPc9x1dnNpUZFJDQMSQtBoSofsgIijGsi6T1wEXRZGjizhdaYnmA68KibLaH9miv1GuOl7YIsBJo+yxc55EeNJ5xE3P32SJJfU5OLfkIKsdjt4gWoIQyYVSSkUt9UBH8rD8K0GCVMUh8w7MLj1BZGcyO2kkOBysDDnYm08n48gb1/lmPVGe7N6KkoeV6YSVMqxHJhVdYgfh+KTiIQCCd0MC2qGTQ62dnDyenUDBC0rIxzEDnCNbk6cg4bg2mJEChxYCdJOfe2Sg8dTynZ/9+fvfMtKQ2dl7CmcBTOPqT1l8/j45hFkCdRjWWgnP7nEP+kByZTy6m2aNMQKoyd2K+3Lb+/H3/MMeiGPkPrGUdw9HB/ZuNKmz1ALhnKj+2CAPwOP1pdEE8rkKC0SSVLAGxQLFguIVt28bfHS8VMFSgL12VG9YZEVpirIA6+ULSrtAlIJ02DcKer2+ACJoL69wN8g/sYz7nyD4+cf/jp/++3/D//1//l/45//8X/BeFV0V1/LKdtVPOvZAXNEapBaUpUJ1gWqbQW6WKoGwC0dqxkNJKFcrk0h9jv4P6a0Meg0j5ha1VuEeo1hMAVSWp8zQ1TBGx3YjP+Xx4HXFMFRZUGSJwCwzd8c+NlgfeHy7Ywyg75zrVNaGvm24jX0Okf3yw/dobWWJ0x3WOzt3Jrafgf5hd453zbWbHKDJjxNAotNFFBBVLOsFpVZ02SFd8Pi2wzpJyvs2sL1v6NtA3wbRTQHbr4VyBhCZfBOO1BjwfkPTFaVc4NXhZQCNcKxmB+snHqUu3Lu9z3telO3QpFk7tu0BwYYiHPkgNSdH9wh2ZMpA9M4rrTEo1mIkkEb7uce4laUc3UkqBaLhaRx43N5Zbg6Ef8thzRgYEDwguAyBLQI0cvr2jfTRWhS1rnh7/cLW8NsD7/2Gv+6/4HEzPO6O2zuHzb4PookFDUUqalO4AWM3tFKwtopluWBtF7y9fcFlXWF1wNUh5QVjGH78f//KWV6VROHXpUK8Q7xzPuVwIEjmW6eEwvZ4YPSOXR7Qwm4vHx22b7Ctwzuw3Xf4PvC+hT1WNhas7YHSBPVC+1nLwMvlFWt7gS2AF4H7hm3/ffXsv91ddUqO/PjilKkeh8z6QxjnGehkpnR8yWiX2cREGE7O6vSs6dyBI2vP3+fBYZkGkZhIGkRlh0fQobOFfL7eAFwslJctgpyBZIvnOfl8P8fFSANDJ5zRrJw+H/G7KPUTPLLG4zPP5RTPz4/J0/FTgBO/4QA8RI3+dP1/9TUN68TEJuLxudnT5BE4kGJoOGV9Ob4i144cy+z0GjjGL8j56p7+ygei8vP6SC4H2zA548jmuSSyxoByLtKnjFVmKStvyQnt+9XZxleJNkSgHQOB4zqQ20XEYPraA8HrB0+Lmd3gILyqSHK62YBRzAI5NJNkZyIIJOYqyYhD2AYrTuIlyRWwxw5EpxKELdauglIqXscOWMdffv4ZxQ3fAHQXqNVPXUcS85yS65DjLH4Nzpw5KzODyl9xD2gEnHJ6qgPne/i83wNFRd77g0vB/INlz9HtQAP6gG0dQw1VOkUYNTpEIuhg2Ujmn3Uz9O2Bbh3D9pgWXdnGXsuB4qbeVp5/7p8Pgf7TmowkI68Ok47sxuRjGfhQXdYLsK4rA9l9QLvyfJXk2DOTJlZybKUjEZMBoDNxFe8QraHIwfBTZwLxece0gTFiSJ4C4/P/J6TQk5NZP+A35+dF4qnyAWWOj1Oe63L4vtPSRNILSijxDx9z+VqMBRHl/h2hyk6eYcxtk4L6UoFe0DdHE8eihqoD26b4+XaPjrCD6ymO0OgSlCZYLwteXl5wuSxYWyUSIwxc3AVePGwEW+YLKti/NaDF0Luj1EBaOnlpY98hbizHu8eQ0AKrDX3v6N4D5fSQ94jEvuRlZQCv6lGe5YdLsMHmrMnfPv6HghxySegMkm42b858TnqA2IRhhXij5QiC3Ck2BEBqBaCwwZueQo2iCrcRzRKMs2vMw0hHM2dIFQDhCLK7YgwAJigroW6ARtzAjgfsgInBikewAXTfMNBzmQNIoqBNx4ZZz81L8xvOVQA5t0iKnfRyAC+Ehq2P4Oxw8YnY4QB9BIJ1bKQk62pkBbwQ2UcVzCg9BRanAOcobfzRXf87HbFuzkJcPLuPR7wn+fXjckxDvsS8tBG3Fpy7yoK743REpXDtLWuBF8G27Vy/G8c1eLD5Pevuclwnccr8m/pRygCnjOd7+3hMMmdNnYdQCK2AFoNLZxwCCoxJd5RWQxqeFfntQR7WiE5CFeCytGj1JRF9fwCQgaaKUhta7TFTq0HQsCggJvC7wm+G/r6hXBq0Ffijwx8btp9/AUSx/OVPkKrRYFQhy4rXf/if8Pqnv+CHv/wj+vtPuP10x3bf8eNPP2PPYT6fcLTlhVlracEhqWzhzluASDwATLmAuREjoAmJ/dzrGqhtbukMdmcErg4p8Zjs9nCFWSe3xwQ2FFtwp7Ytnjsc2/sNt7/+jBpdOMtKBerleoEWRfedga2unBMkhm274f2XnynHXxUvbz9gfXnB5foCFcXtp585f0sQjQ9lIolZ0g7Dc5TdYhNlkK6QOeaD50rl4Zx/Bwgu9YK1Ol4uXzB6x335BY/tgV/ev+Hx2HAvG/bBddmDK+iBFBHgd86/AG3sXgYeZcdFrlh0hRaFoqCh4Tlc+pxDRLEsCzl1gTaY9dDZ0hjCXFDUkN2RKoLSuO98bEDMXkyEPDuGVRogdPAAhUHZzWbTF+5jw7CO4hwpUpBNBUzWa20Yo+N9iyDHhPypPlClcK2MHTIKrjE6wXpHqSuuX/6EZWy4bF85QqIbbv0rHv2G//Jf/xvebzdIDIW9b4JWCt6uKzsyF+Dlhyt++Id/QLWO4h2yx4gb3emfVu75VleUUrG0BbUKahX024a+D9TKtP1ffvwRY3Q83n+BtAUvX9cYTmocEbNWoP8IewxYJmBA7EMORV6yHIXKUlplZQbYMfaBAUC8QvTf3V2VAllkUB9Z7+kx+X/8SiMw9pkZpfYNLYlqajakyN+HfFyytJK19qzBM/qf9WKJoTDTUHEEOxdflCpMImMdRHFkRPZjMzagsBwmATGRhgNJpeGIOJ27OODd6Xjd4KCmwHwfMyDKLMtO53tEq6lgnAGM5XmcyiFT2yXKYZnxP2UecmQCmdw+ZSmSJS15QsM+4ziCrTxrnz/5/4h7uy5Jkhs78AJm7h6RWVXdTVKURhrNSOfsw56z///H7INedrVazYhik1WVmRFuZsA+XMDco7rZreUZ5nh3VWXGp7u5GewCuLhIOHY+1/muNNLpsrrj0IM8pyKCoH56W/J3HEzvRIU4Cqj6Wab2Q47n4VLxboR0QD58RlQPHKBvMQ6fL6VMUS5VSq1nxkXACorsCBmVqOhtwIUCcayuoCth4XFrEQxz3PcOrdwoeqHEPrk6wLqu2MqKy/qE7XpFfX5mhEZX6GVjU8rdQeIOHQxZdo5zeIkwg7eB0fYp9AU3EkcXg5vC/QvJ8u90REZ45qAZSdUJ9HODltxkgYl+c43NuzYdL0IdALQfOK8pRvSyCaPZacWJclPyxvdNfS++hBpLhtZ3dDGIMj3j6MCdTQytBOcnahIosmgoVbGsK+q2YtuiRcdwpMa0CyDsIjpXi+dlna4NknZn/hWHTJvC6yuMVIg/BDATIxYp2LYrxRGrooUuVApftvTWrR9RJI8xiHJ6D8Gm7bpiWRdooaNqrT1WnL7jMaUjQuvH9Yilj9m4lXyaomm5okFvaF3VGu+Ye7NMo6AlAEyh5EkR7ns2eZbHzsKydkVqgbjxey7rBgAQVxQhWNKSVXBsODsCnEnOfRkoVXCtK2w39p7TBdDOruQSJPXhaI1ka96TglJL6PmA6X1ZYIUcsGVZoeZ4Glc4gEULAVnwY7L6s0jMLVBdvaBgqZRxoSYUO8AbBtxJml/Xjcr0taL1zijToH0upRIUFzqMpSqKVKgU6EBUrjnzo3/h+FWQQ3tBzzWVx/ncsVHOlgTAoT/AFwEeaqPuQKHQWXJjlgeV0tw/yPRPHotGxIPA52hXz6aJAyrRZ6VTo0LE2JgzOEAqChn95KEJpNAumaVCbmg+WAMK9XqsxzkLYUkN78kRaP/sLXlMnEkwiutBqFs6IzWsKKJkOLUmMoLjs2ItycWIP+4gL6fb4fOcbVl6bngEPdNa4XgtJHkK7wty5mnJgUASv/ylMzk4POzJcmCQRwLk2dBLgB2dX8jvNCDy3Y4an7CWAlRHVYEl5xdOnuhDtFKOE01jYhJFcjRMmUuf562KZVmwLJXVBdGxWuCows2gtRGeI8PmCsH93rCPjta4ZnLIED2zIAV9GL683LFsFSuAggGpFSoDqoqn63d4Xq/4dPkB2/MTLr/7DWTdoJdnWBsY+4Dvb1AbKE9X6GWFjzee++JcGPuOvjfcX15htwF/61h/uGD5uKAsK8wVvRn2+/tVxkiJNFqU3ifpOuH8WTLiAKEhWWEGmEOhp3uaQIdAdzi5ABkZytioRKNCj891IIQCF8Be4aNBq8GVlElxQXFuQvf+ho7G9gXacMWC3m6s4HlaACjGThHKBnr667bi+cMnPD19QN0u0Frx8vqK3jubPKqiLlTHtX44UdOJ+abxbDqcHtokyWFzoyVL1XeEPpQrImXglM5HwfX5E57EoPIB7sr7PwbGGOhRTdjszvTp4OfWsqD7jvt4Qe/AvjvWlZtUtxqE2PuMbr/n4e5o3qEoqLICReEa19Q73BttsmRp9sp5YR2qBUsNLaoikfZhXzc3oDsjxLWsTB/VJWYaK2VHb1HAgGlTSg3xv52gr/UdtRZ8/PAcNAzl+0bHshaUpdBJajuWbpBSsMqVwFZ2rGvBdrmgvQ20twHUFdoH1qViL4reOwyOt9GgpWJ7uqLqiiobCjaMNqALKzC7LDAHPlwvNN8XRg/LYCTBO2DK7IugQN2i+a1hi67n68ak5L6/ojrXTjTXQakVz8/PsHWFD8M+7tzb728ElHVhn74q06ZWqVAUqHfoMLQ9m1H8/PEr1VUBN/0oEJ9S+jlh5lbDBwOLsuZ/2hKPSXNKWwGBAEN/YqaxKH4nERWxMGSZP57dXUsh0DKBSYM7a/z7fkchnsIa/TXMAdcaHAwFSoEby9FcPBqAdcA7rDeYDPTOHGRZ2RbeR8QNqsxQ75F/zlx5kFkRM/48NjhzjRQqs2AwNuJMT9mMTtDqxKYuApkk5fQoHyHCQwPTBKQJEDy/6pQcfqcjTwM4/M8zYwvfnlF444mjJcbBE/gc+C1gTRj50xdmc7vkfMEFGpo0Lghww55R7o5WgmwaSrMZFTI/tsR5JyP8x1x9eNUh156aLJfrBZfLhrnLxL/n/mIakaI2SDBvY2Bk6fp5OIIUbcNRtWCtTNWIKYquWJYVT9uCrRb4GNj3G27yAl1jI1elZykFUhzSK8U3+4DvHWI0A+OVnZllb5Qp6AprBtsd++tXlssPxz4MVnf48n7pqrWs7JWjC8uZdeGmjRTCm9hm6kjNOaIKc3q9GW45uDw5f6I0PN7FIgmJUl/akKzOq7VgjApDVK4gKsQHwPS2YV02/Nvf/Tu83L/gbf8KH8DdOq5rYRNF0Hsvkx7NCMGyXlDKAhfB3tjvJxta5tEbSbPWfTo8s0pPKUhnUawwIyXTnJz4fqFtc2jdcDxqKbPxKEQYNVeHa1aAGaQIiheSXd2huoWNYhf1bV2jsIxE5f0+ZkTr9TZIFLU3jP6+tqjoRmyhMnV/xDwcaIXrCp3yJ3cA1Jgh519Ib4iqAlEPATuZLTM8ozFOrmfL1KhQK8dHVltJ2DGfFRKUgnA6RSLYe0cRxVIWtnxwiZkjuC4Vy1qxRZRE3Blpwg72qooCFnSoDCzKVHdfN3xuHeZOMT8oorMIahEYHK3T4RfpuHkD8xQdKsC9N+5z90a9L1c0DDQfWJyRouW6RbTxwvVm0Q9tJ4C5vREc92GwPuCdsT6ZUUqHRRXsUhVlKVi3lWnqWlAL2+vIEhWobzeM8ddGclJ2Ojbn1L3yKLf1dMfntnwSqAspck2CruIQg4pXkyzF0D4NlJ1ADnDsaDRKKgIvUZVSCsQLxGt88x2tGd5e7qhmWADoRaGLYgyhUjIGXULZYKB+znAn8UkGIB2GBvOObgoDe9jABd6NEQUFgREi+mKn9NPI9NOYY8JdI92pBHpZ+zFi9wyPYJbKZVwC8R5EHxMaKZGT55avwQEe87FZhTUfFjxUer3bcSZD8jzkZFQ9x+dnj4AXKtNL9wiHJnmbmMijk7wcmPt03Qn6ai0QEyxF4YMN5gBg6R4dmAmQFMJS7sSNiRUd3O3cUTwD2pHMHcwjF1Vcrhc8f3hGu9/Yc8h76KDY6Zx4rn0Ybvt+Wjs5QvHKWG+jG+rKPDgjXfSY1+WC5+sVl1pgt89oveOGryhbmcBWCqt3qlSCm2EYf/oT/L4Dhaqs46WxrcTeI6mqsC7oO9C/vgD7C26qaABGuQPb+/EptrJR26NQlKwqic8Cphdw4Pso6T7ApITgHtMQOEVhjwgc7dRMeqJE1DZBpyHTBUCpFTWFyajcNzkF7gO7GJ4vF3z3w2/x45d/xp8+d9xed9xax7WuHG4yMXinI7JZ64r18hR6YY4WHd9HODe0RQQ56UslyGE0GCHHjyDQH6XvUtnq5CHqDmptpdBc2iZG0jm3HWwxKgK4RorYWdKsUiCDm8KyrVBVLPKMWiueny6otWLZNvR7Q3u7o/uO7h0/fnnF6+2OMdZ3Bzm1rHSsa8HodAjUo8mrFEipKLKArTjIiWwtwHxJRyqrnxxrXbCUSsh4EjY055z0wfcSVFk0QmWGYto/i32hMqpWnMBzbw2LVramcQ+AQw7Pda3Y1gVLtWkTqWO3A05+q/nAQIeKYRHBZV3Rt4E/f33FGI5lZRuG3WIv0xB57APN6fDdcMeQgTF2grwxYL2jv9xQpWAtC+624247Pm4fsSxXXK4XLNsCXSvcDfcvX2FtD1XvgXbbsff4c2sYzbCt1FC6PlMLykLOQBdBXSq27UophcJmx0u5oHqk70cPDthfuOe/OitmuPPwmmNVnbbK+P3h8fg7HYnZW8bm7sMUuwSI4gY8oxOSIChPIrwQiciqcoVrGO+yFGgTiAz03tB3ErNGpbS9ykKNEKU3y0qGju6G3QyODkfDABG41ydorViXCyW6ZYWYwtvIUp10648tPMCLR+QBljwmP+g4bKEaZRmY0RuxVJ+0473Bfs9rZ/ri2ORnjCFKYe3k7XHYaEDD1ySBTQdStPDdjoyyzGE6NhfEZv0IgzKW8hcmrhwwKez7L8A2CbAtcBVUdagzD+0iWMcCaQNtOA02LDZJ5r8TmAJgZQIwxb+0RMVLAIIoAAAgAElEQVTJ3qBKoP78/ISn5yc8P12x1oJ2SyOm0AKoVwA+OVt7G+Q1pEOBjEpRmC7HrihJgkWoKyHRk0qUodxhDXvruEYaAl4wmuHtf/yIcr1huXXUD89YPn4ESoWaYzytMBnY/+m/o7/d8fLnwe7R/ormjpduuK6f8Lz9AFmMZa/7DjPDVlYs7ziNKJAX1XFy1ORwbDSizjGG4RBBlc5D9xjPGsaMa1ilcE14CnYCWSxAL5gRHcSm46bkUJWCWh26FOigtLyNgdYLdZN8oGvDbbzi+uEJ10//gD/94Y94e3ljtO4+8JvfPEO14G53Qp0i0LoARdBGgzUqrHiA5rhS0rgKeB2TRxL2Ja5DnBtfpuKAdEyZ7sojre7sjRdjJzjmPHCYC+84FOENgJCQCxEshQrU3DYB8cE8RndUUdRtBcoCL0CpG+73hqdV0fv7igGmLRxR5FLrCtEBKYwojNGj8AQM85z1OBI0I6yKkDbR4KExlDQGQZFyZB1UUUudTT2jdekUDi1RqUTHXiBSqUHTd6bLCu0dK0vZu/3eXmBe8LrzXq1Y0Ipgb4J1WXG5XDDajtF2Vl06+PgV2K4vqGPg6boRjBT6/l4MrmwX4qzaYaQkAHfe9gLFh8snLHXBZbuiy0DXgcWEwGN02L3jqT6z39b1ClwuwNMTRm/o+w23+w1vt1d8sRe08YZmxrYmr+TkbqVAtELXdbZsYLS1QpYVui1QKSgOPPsP6P2vTFcx0xGetJ+2nPSaIgqRyyVv/Bnk5BtUTvTSudlFmspzK2KbeGS0Q08MkgjheywuR5QglkIDUaKkVB3DOsa+Y3e2dxZRah5sEbYHb6LdbyTPDYOBKS9TgSsJ0plbLnWZQ2XDoH4iO/pxqUfCJC3L4TXFAxFhGRM5knw8yNkIQ5UtHeDkl0zvS0hSnV5qGCT+nx2Z5HQ+R+THQ/cjK7fe/ZDTn3l8C2y+fUO+5vzzEeUQOcCfn0b/Z79cQsAqNsga3nJdKtNXO1scWJaIW/Bww4i7REQlI2MiVBseEh6fQaTger3g06ePWOohUDWjb1BoOaJ6Gdlhu47k1MrhUesB+lUES0Tq+rB4jpFF1RNZ0uJ+O1NN989fUfcON4WsK9aUggaAbQFGw/76Z+x/+oIv/2/HGA2in3EbHZ/3jt98/x/w9NtPs+Tae4M1i03r/XRy0vvNzWSOS0QnVCSWlM01ICqAHbNDpTAuMe9rsLcsOH7ukFJjMwsictxDCvMVYgsJrmAtjI7VCivsLYZh6Eai8H3c8eHpA54+PKHdGnw4vn55AcxRlg2lFrS3TrtWyYFAAcYYaG0HygIpbIKoonAPfa+sjIpWIQ5Ggjl/PdJH1ELKcbKIKCTPkEe6rvlr2DQ9bDQQxRUW6TgBpFASg0Wm/I6qJcjYEsUnwSns5L2VpaKshYrjqNjWDlWPhpLveEiCOs4T1QoU4dYzqIYdUp5QWNAkco/zbz9qasCRtxNzVCSqoPj5RQuWssDg6O6Adbg1qqBH6xdJWQkRiC4QDLiOEGfMlBfAptaO3m9wF/QRAEg7OUIG9G4Qr7Cxw8YdTJAqaq1YV2BZK9QE61oZzQkBSCjLxdMxZnYhWjhEihTRqmK7XLGuG65PV1gBvAK47UBruI03Otz2hFIFZY2+j1JhvaHvr9Agb7/eduDOiuZhjrY7ihQsF/JytbJnHE0uK990qZCV5GNAsfWC+ldHchJ8zLj/N1ELSWQsyNTS7AU8xafyOXY5VtWpfpglszmHmOsFtNIrNthJzTAAQpKY+074Y4APh/mGZQE+fVDs8oJdXmD3V9xuO2RTmA6M0aAo2NYPMDE0vaPZwN4bylJQ1gXlskKWBfX6AWXZUC8kT/msVghWPni+vE5ukBqWwUK4MJ0mn5Vph3IjJfs7fLCXR2+N3Vt7O4AdPChEHI8RG2KJD64nAxwYaK6+RP4Tb2VIKK3Tex6es+aIvpwNxpla5KcfJpDGo9E9jgPNyRHQwpyPnkZYg9gNmEe1QFEABU9jwa6KvjnKYHuE4KmiZ/l2fHeJ7r1ZBSFiUWYcsvdVcX2q2C7sBdV3i+uIiginJ0ddlTG5EAzzR4RCEtSGsqfwnAFBswRpoW+hgA9Du9MLkuGQ1rEqUHvBWAt27xjLDf0roOsTrr87hk7rArle8eEf/xP6716B+gfsry94+9Mbem/48e0zluUjPq6foeUVMu748uULbq1hexJoWf+q6fDXHVHtJBkBnovrAL0AGA3mPPcowFRUcmt6n/bK481JnZPK9FFqLbmU+MRIKQFhf4zlwWNwDq0VVRe4VTwt32GXHe3Otfp6G9j3r/j85xf01ztkAHYf6L3jn/7v/wvLsuDj9RMggn1/Q11X6o5opMmF190aHSOLc2OEkfdeFJBKiQIxnb2wLdLm2ThZVX/iR6RbMNdZEOs9CD4HXYGvrVF67oIQFUyHFEReAXAmyDbAvKEJMIqzKsYVPjrEBjkw8uvJhH/Jo0gNe3s/wE4BpDgj+31QH6go+rgDnq1YAEfITXQLfkg4wySThBPDNdptcNzM0F2wW5uRiKhCYFRN+N2AYd95TtAGUVYeqTpQkjPk2T82KONKPo4wgyFCQrCNgc/3e2jqGDZRVBEMF7gatovATLEIsIhgqytqXVHKBc0G+u3G1B0WFFnZYk+5L2VV89e3L9D9Kz6/SVTeLXgqCzatEFfWL9x2WB1YNrayuL19hnUGIO63N9zfXlAAfHy6xGwU1KpBuv6IWgvWleRtClUW6FLZPBROxfYAdeMXKj1/mZMDPKjUAsi4zukBQXJI8lcgK43mw4HETh6UyCw9y35GU5F4VrjEIstGetHTgiWQA0leJT9aIbpgWa/wvcPXhvv9NjcqEcCsQ6VicbLLDQPDO4Z3FKVXJusK2VjCWWolESoyU5Pn4gZ4iHPZACSFBzk6HiQwBAh8IGdHFCWF3LKaagx+lhkz4VP/I4ZLlVUYkkq8QJQOxiYojyDnASTM4I7/TDTlb3/kPMpZ8hC/yQdi3pBkfLxvvu70m+P4oKl2e/7wjA4ek47zzTlXUuXU3VFrhUNQKz1KM4NlNacELHN+gWikIhPcS2YuFaUKmf+VzTHNHT7G9EDOTUrpeR8Jugz5l9wwoqou01YcI6FHpbnpxLkNpmP63SDDcG9MR/aFnc1t8fi8Bgs1UYmJIUUBr1i++wRdN1x+fIOo4f6FxEeujVDtMYOMgdEaRtthnQTbdz1y3ktuznEv0ukADscMaZdi/HEokOe4P8yvdKSyHOCkij5vXTpZ5uRWZEpd+dqiK4oyNW7OqMAYDXd0aO/sBD4Mow28fvmKZV3xcfsAlyA4R2ogTmiu02zC6lGMIbNCijyaBDZyao0jAdRn9DPm1s8ufcmxTFt8cl7hcy3lcwSaGZdPVMM/MgOFEn4p7baJU+Fdoqln6vL85b3pb3IkIPZsoDrGhLF5XskTHREFzOh/VuZ52ONSyhT3tLz+GOdZIpIyLDBoqZNonntkSmEIGMHLOcrIi84Ij8AnN5aHpQ8c9y6qdIPD1axDK1tnmBpTn855oyXBa6RxI1KoooAPRtdO2QxAQL5PVC+7Ye+Ul3AZbBdTNqwXwbKUudWN1AqqQLeB236D9YFxb2j7jt4aOTY1q6kV21qw1IrL9RpAEpSuWDdo1ShzJ5juFn3CzGa6/+eOXwU5qTLLOXvqw5ETOI6MDUxtmKhSmbNe0ivSCCEDXQZUjDnBMObuhvvtjTfMHb5UjJ5leEFSLgLHAhDrMKcuyd4H9Lpgq0/oRtJUj+oVtQBQYwfM0bsFwXiD1I9Yrp8g2wqsFS4F3YTETJFoC0BgJKLQ0Zl39swz8fPhDhfmcoscTPv0Am002Gj0Bs0i1TFmceuwTo2UntVpoe5YmXZbaokyR5/RslJ0jk9GjI4ljXDSFBCNRfWveUx08k145put54GM7A9v/cm/+RxwAjgBnOdXyCyvhQDLJqhuQCnkxAxBHx33yi63vD2KYVlpITChochKCubVo4uu8t6gC24vN6ZOhF5OLXFPzA9Ni1pRAeiCB6NvDvJFgBlVIBXIo6mgAuWC4UBrgrs5/HZnl3M33G83VABvYvj04QO+//f/iFouWMoHaF3Re6dHFP2rZKlA/YCyPeHpPy8of/4T3u5/xvcfCtbvC374u7/H7/7hH2Hjj7DxBfVScL816PqJJ/9Ox1bX0DYqRxQh72zc8wSej7sndwKtJDAOY/sLj/K5ogtECnqUkFP7xw8Sv6X8fvaOIujo5hBdUItCnST1ve1wBT5+9x1Gu6PdXvH2+pVcnP2O0TraoHNmo8C74PNth5QK0QX7XdDsfgCOEJiD4gT+Hd2iJYEx6jeCx3feBEupB0fk8HJwLJoolkggiAQnfgzdDEX7sReE05VgLDkoFrIdBkFxYClGOymKMIzoo8GGo912igy+vp2I+O9z3G6feXmKycseRqdUC4m0daXCtIzCeaKMfqutKJVd2h0U9EzgWCMjYZogJaK4xoolCjb22BeZBrPoX2YxPhltK5Wtd5KvVETQjfyY6gWqwiIJMUYqVbFuC2CGfd8BUaZdwZvWBtNkbybYh+HtK0vVS12g0jHaTgmGxbGIQYuj91eM3YOzJrgH+XwT8hDf+g74gKOhtoFFB7w3vNWK7bJAq2Bvd0gDxu2YYz4GWnuDo6EsQAtHKrncumxQBeq2UvNr21CCwJ6wWpW72Nhf4a2h3ai0/JeOX44VnjT0z95zjN78N9fGTxZJfgwOYHR2j2bH4ODZiCiysVvuTj4CWOQ2LizRndt4LtSojDDedUitKMuKunX4a4sGYNxYR6BrG1kdo4BUiJA8B1GMMCg+6C1NCWkQTHlMVHXjIsgN1Y/IjZ+M7aTZRIdaxMaWHYHl5HXl8HIf98iHU+RpRr5Or03vdo7xyZOaHly8b+Bk797xeIQzCVrk4Aj4z7z6fKLfAJsjgPjN1Zwek/kdCXqOdIcUhbtQ0EsE2zpQBj0iNroExtDw5vgnvaERICd0tVA7P7+k590HZht0OZ0X4j6oTE84daIcmPIM+dqfLNtTWNUhTL2ZhVAc5+TuJCa+jlcsVtEQ6VTvWIz6TEwZ54LVyd0oTxtq37B82CDNUFvH08cVy4cKawusr9juV6gusLIA75hqKBExOZh9ORCPo5SrL3lP+SgA5F5/qJcjQE6kaXCAYjnfuHjMZsozTNiMYuQ3k/BbaoGgwq2i7IwQ+x7evYDpeiVgHQColB+tOvqYnCMhuonmxUeExNLmxllbnJTHeQgALzlrfz588xhhjsE5HfLw07FeD2cCD/aFQ34GWnQCHAHABEf37dExOhuY/lLp79/iGB5q9snHCuf6iILJtKXZAiS1asRLRDu+BY1y2uTi0TkomP+SOyOhJReTKO1+tE7g/Iw9wy32DEEKwnKKhaSECBtdxhcQpEcKU5muZUQZgDmaMQXfOzmgpqw4HDZQokIQiMiyZ2PaiACFvRBVpsomH89QIlI9bZgyfTqiPdIIUdMOUO9pNFrTjEo7qPIN9kDTKhQ7rQV1XRlJK5WRJ6fzxz+xblNb7i8cv0w8nn5/oNS8pw5MuRVDhL3ORyi0igRvJJaTkzswjVVMllkVNAYXkkYzM5fkr2Ggw2HoXaNSJibIMKiynn6MTiEloWDT8w8LfHR8/X/+Gf3tBh2gYRk0E6yWKJBlgYNiUMQrHb1HuWVwoU3P1+aADhQUVMRNUZqX3MQAhY8kiUQUyBkdgCva6BRlapTL1hjvWlcaihINOo1tH0YfWByoC5VHBcqIDkKwKtfKNI50uSRCnTnm/7qRnPMO/gtncQYpc8M/jPERtJHTx/h81g5IDECiIkQjssJ3pzJIWbi5bGsNHg71clpjCSerChLkcA3sNiBFcX1aCZYbcN/veH19gXU7+q8AU6UaYUDqQnDl4SHBGZ2QNHoG3NoeOifcRGtuLgq679hhcY/b4pA6sBUK4r26oZmh6w2jK/7pxz+yem0Av792/Lt/+yGWGKN/tLdcW7I1rN8rfvO//w52/wr7OqBbh93/AClA2S748PsrzB33t/6g3fK3PpZSocKx+vaYvaRi/DzC2Of92d0pqDks1F47xjDo00pSc8Si+6D3LMlhCTIot2xDtwYgBSAjXW1cWeWSvatorJfyhLouuH73CV//+EfcXl7xJl8xxsByWUkEvSxBKqYGy0ADjCRjnojMXkAl7CIEEKdez2zrEPYwo7/ccIIjM9eMTy0vw1Eyjvnsccz3SE7Dg2+Tz0PI0xFRdKe7WLVChcRt8yg9LgqRQg2y3tFulP+3fUx+23sdk1BugDpTTnVdsGwLbnvH3sjdUieZWqRiWy8oQd/d9xt+fP0DShXURVGXBbUu1HuBYyklKh4rhf3azgiFKLbLhqdPT+jtjt7uGPuADYeuCyBA2zuGDbTbDSrU8hoO7E4/Wo08vbUAIdyDJRqAsqG0sRZADPAd992w24APilneO1XU741hkzrYIbzuiu4F3QqWdcWyLKEdB+jCgp41+LTPZUXRgt+sG4ow8tOhGFBE5yVs64KijOSYdbR2w73d8eNnNve00XHZKp4uC7ZlhVbB0+UDlrpiESpAr5tgWRXrh2dOwkjlmXeKcVZFuazwotCXG8T/cpXer1RX0XWegmvA5AMcXnW6NcdCQi4K5CLC3FrpgXOzY7mjBCrLj5RwnmJ1BZYxSUVRPwq6EMbLhCWx4cUK2DlXA7HLskCjAsYgc12JFpgWeCkwoWZORnySfHqWOj+QHJ90RI55ZFThKJOXh0gOP5diUPSmU/F4NiNDKPXG4EqIIUnCXSRPx6eBET28yAcPBMfJJtjMffJnnLq/+ZHBmuNMTz9/E+k4uYVID4kAOcM3hxeVkRx/+NQzDALmVavMcXNkJD76izsAUPCtOgXeinq0ZQjPDZhF7Ys7pAgu1xVwgS2OUgUQKgr3No4QNTy8A48U1rH3nqYxN82cl5MbEZ7KVEUMPo2Hl+yphcHZ4w7quphBhqD1jq9vL3BjdGn70yc8/+FHyA+fUNRZzZPRNDhJkcuK5ekTrFZWWpQnoKwkZyrXjLpgjDfoO6YaHnSh8shI8AQ5+fj865hj4a3LKTJ3xtlcZhJ8l4cvju/g92TZ7zlV7wGks2JkNgAFBSirCGpdsSwDY+uMyi5LlA4zhT+/I4G7H4UOrGuQ6cWbGsS5AZ4CTfN8j/lzrAXP8ToJTc5oxeNIn5yI86PT25gR+5+ssgyvOg4xxowkANQRs9Pr/xUsEiPw8hA592hLITBUxcGHjHswK66GRvNUAls1h1vhnEhBxWjhcrhcYDSoVIKRvK9h10V9ptYlAWkapbl9RJTj5Mxm5GaAOJHip8ZAAUAHaLD9hoThyTZCHjfBYw/uvQMenJv4z8fx3TCJeens8QWHWHQr0GN9IYpzLFrWmDPCPLWmAmBrRHoAmfy2oiQdr7WilIJ121iBWBYcEgfBbzLuo24U8IV1/NVtHXJaAI8GmTuPIbUZOKb+sDgYzpJAsQlkNEAZvajed4iQeKTT2/Y4eczNhfoO/DgLer9HKQ3Z+c5ctztMOMjDDOKDJXofr9DrhtvLDaMbbp2RjXW9wGuBhdz1cIM2ghwXi+9jw7XiEcEJlEfhuYFhLA11oR4Erz2Wb0wocxq2vtOTGT1ujhurq8zJxo9B1kIJa4kyRLjCrWCqlT5UWTg/ByxX5EKJBTKiV5KmGc2UyPtDHW7LB9DKDuw5sw6i+k+9yjQKCTTTPp3fM22WC87VY1NLSGSuxtSuQIJIsPEbkBsZbYXL4DzwR5tDNTedvI2MuI3+Cbe3G+63O/axo42Bl9tA687qOXP04bPtiMdmIOD3sMtHaj/p/N4Rm1sthxOAQaDd1WFqKLZAHbjfO0PWXXHb7/hv9t/Re8fb7Y4vtxte/vAV//h//D1+/4+/w/L0A0pdpzetyzOAZ5TlE43Gp36Egr3BMSB1CfD1Gloa73OkEFpIwBzgF2mGWPg7pSaQwndHybcuBS4VYp1qtZpRWYsUIj3X1ICJHWpOxtzIyD9xtpgQhe0CR0GtH6Lajo0f+71DIKgQXLZnFFRct2tsqo0XgY33eHQMAYYmcdPhsdP0zi7L0wnS5OBpXFsCG/YSUkmpDMnRIefIjPcsnFDRMjten0EbkMswiM6JwHQcjujZ0xCW/UIOINgaT5bVgg3eHOJMdahWoCoKmwb+C8+UXzmiDl4QQNQd9/2Gvu94Xq/4uKwwFZgAb6NRYqS9EtyNKCiQpC90DCuQQb6Mu6OUqHQdLBJRJb9nfbrA3PD2+kqbRKErMO580DNEBctKfg1652cAWEvBmlEyi/S0O/Ygq/doF6w2d2EMG+hm2EoJhXRjBbAde0EfhtvrTt6PCvoysKyD2YKiQO9wCHolaH4R0Ga+FSxSsGkFlgWoKxB709gZjLDKdGW3hmFsLlpKwWVh+k9dMN7uaKNhrFdIqXj6/rdYtwu2p+84dmWFR/sQGWwWavsbzG+ztci4vTIq+BeOXyUen3/JfSIrTlhFFD/HpM8sVroYjCB7VGJwm6OxsOltZNt5z0USiDJzuUDOAU6uuT86kM33LG4ytTFYvURkSjXLzHmiSKbDic6dYV0ZBviIzVciUU6AAcHsBD3zgUYkXMSCD8GcI88tcqvZbMwHbLBjr4/Oqpsg6XiKCQbCIRJ29N6hKgGYIscZ7xFgAh2Epz+jOB4k48APM+ccuwLf+0t3/V/+ODlNOHzCcwIqX/dAJ0WesBx+5PGBGfp7fDUy+nE8fgI5OIrzvz1kRnZkcuVZMHH4nBlRsTToclyFQCCVBECWcipWM0gh8b3tFTYMeyMI6cpQde+cT4qI/2XgSBiahoOGKVK/6YkVqSScB5673xp8WOhm0B11BXbrcHGUrUA3AS4OQ0NvdxKvs0UFMtIFQGt4mllPT+KleAe08D6VivecSOkgHSD3MYpjDxwczHsyJ0l4w1T0reg9bE5u4Hk476nmfIkI0bC0YadqjgTVuXYjbS9KcmipmIBlWS9QXTD6nSH3HqDdS3xW9Oab0zo4B9Pe+Vy7nP4EfakkTw2R0BDK6itk7U6OjRx8n5OGznnEchA45U5gJoYpKwEfBiyhpp/Wnp8+Jx5LAcwERtC/tBr/dodFJGcgymDUGCEJovaAoAe4gZEDkoRgVl4BIhWqsZ85Uz4eqtQi5JBc1gvcHLf+gmGOfb9FVB04nDABVGdEP6kFFulQVZlRD3dWE2Huo7yvnMd0sgXctjIL4vBZfSXOKiqBY42U7+oF4ppeFJ1pMZiQp+OK2UKmBIswaRVaIgouoSekB8gw78d9hkONduZ6uUDVUdUnQF+KQuSCbdvYKLRW2hbNkBrniqjD9SjyYRSKMizDOsfmLxz/SyDHT9vPBB0IItTk4yTGD9NinPjumORaD82R3ER0XSAqMGsxHplLJhFXI/7A/ey0AM8/ZSkgGooqqlZ64BEigxsqWPomhYZhWfMzGtwVPhjyZ1ZBWDZewxCENzPk0P2R+LxaSJAa3uE+JkehFKKwtt+QzQGHh9plgq8AHMyZR+hSMtJiaK2hBpO/zLRVlBZ7hjgRIEAngFFQMPC4I2elD17/O/tO834lTUL9/MQj/fHAQo/m74BGcb188CFFwSVxgJLjcw6A822InMYl0ppZGooIAYPk3qIVk+vkYKNEp9w9o4cOK5HCKgvWrc7c1ofRCXLuDb0b3m47xjDsd8rF3247N6tMPxmmNs/zdoFA8aZBlkTFsI7d7ljKhstyhWmDoePHrz/ifmu4rFe2rlgUJsCrdaxLwfPTFdtvV5TfKUZt2G+v2D56CG7qsaHlvIpKLkkKjHdgilg6tC7RV+59jm8J9+7HzEnpBQ0EkA7Y7Lg3eYOhRFuApknI5vM5Kc/Rn5miGsb2DbGp9Z7fB5zTp2YdihQsK1S5HZTYv354AqC47V8wRsPYd7gZmCkIBwrsJp+grUYHa/jRW4rk6BHXomlAQoPsaHicNhc41o4I2PZhpkLjODkg5wcyQwwc1zjlP3DaCzLCDKrh5otTmkvAyGk6fCm2R0fyEZj+rY8kXLtZiPQBdSsolw3WKu5NcL+9ofcdpV7YT6yuEDiG34EQpENE6H04OhpV8oVgqS4bvvv0EWN0vL18Rm8db82w1QVP64aEn1LYdbbd73AbLFYUB0aHimCN5tRQQR8D+3BqPdmx0beoKloq6Q2DFwl3RY3efAoCpTVT9aGzdMFCQnKzSD0J3CmWeUdHh6E+XdlWATWindQCqitbjGA4pBgjWHHf2YSUhRwCoBg7kl++/w7uA33cCa5c8Pzhgu2yYN00dHA2oC6sZhZCPheDFKMNsgZ3cu6a7djbHfu4o/+1xGPIeaLOXQSHO4V5Ycdiz5z2uTwxIjKhTSL5oWOEGFcamAzbeUQ4ol/UMHgsrty4PfPYIyau0LsaPcg0UZ7Nm9iBESV7IIk4lGwm+CLgEJIIRRj2iwgJz6hNw8r9gPll64BJoPj4HOuchKM1ZMXBzLPHNeZ4+kTcfJ1WCZTrKCXIfgkKYxOaBMCs8MQBcKbxCdDLkHzcR3G+6l8hXTWhxsmyHlEQGkJ/gLL5zPHbfGR686fPO82Nh+99dCynUc3Xyvnx9DRxwCSVQ0cijb0bCaZlEkY5Hw2WDekjJcWomhXHjoJRDWup9Ow2Vjncr+xr5d4wBp2q0XkuS1kACNYIqIxOD68C0DIguoMCZdT6KMqWAAMD6wDWteKHDx/x9Lzhh9884/sffo8ffvvv8OnjB1yvV5SyYi5qD+/jfPU5phEFoIfIyI7g2FDf43iIOuRamv+dXjdfqyFcJjO6Ol8YlRk6040sIuDaSm4T74Gl3TvQE7IHUQKMnEm1kEBqUZE500YKiBe4K9a6YIhQZT3mopidfHOEzbAZnZ3KzHM5cKWXjDAEFUCQgAOTAH1oBoULOrk6vFc+axMAACAASURBVKZMw/LVjytITj8c4//4mtOSAkTChgsgJRxVXhdlEjwi+wm7FO8OckLpuGjl+oViDIftHcULlqK4fv87qCpGu1PDDMwyrJcr1ah3EnfFSZItqkiZydvthrbvsLETEFv2cAwA4I5hjOyvlxWKMqNAl3WDiKPv4QhPLh/30smpQWg0iUAWzhGNvWJu6M6KxFqUZfHsKQMA2Bb2fVuUlXrqg4Cv1pN9jVSoOKidA8BPhUS9xfMFxQ1qbPrJhsYRHYuoelnJs1m2jUEHL9PZXZYaejgLQQ4QEdM2gx4zlRd/bN8x2hvaywva/c5+e7/guf8iyNHcRey0GeD44ewXZ0gsnz/3ksmwG+2EACB/xjsF2NJDs2iglweJm4n8+S2TNqc1Jm2cjZIwaj1CighV2WHATiEuZdtOoMispMm+USoCEw1SmgCD551N6cx2boDRAwaKIDkbJk/H+L1sYRggK20kMuVBj5D6Q7Gtn0LRh5S/h+hRtK9QRM8emZZlSrZHJH/aXGCGuUUz5efIXlY/hQJ/2+O0DyD97+OJY25NYT94gMsoh01gjUeD+LjvHWW1j+jn9Na/YFD5/THHLMf1SBMWUfII5ncx7ZpK3GaZFEigFMWz4lijed+i7F3jF3J+mrE9QxuGPm5o407uTndY81hzhR+2MHowXhsUBi2CIgOQGwBGLFUpJnjrO2CCzRSbrvg3332HTz884/f/4Qd89/3f4fsf/iPqsqIsCw6i/Lcw4bzUD1BID4EOBCNf79fWYW6u51P1oyniOUk551H8yyBoECrjQkRDj8STDEvDrxpE0slTCb9bHd6da3qEHYgUp0d+MT+vj86oSuHnMQJIz1WxYqiyVFyoiq5GZy6J6uYOtYMAPVt9JPE0sguaypA40tyTW5PO0bQXQPINDoDD9535lPniOZb5l5yclAjxHMmLGOfQkIpfZiTNkMrhcU0JnqVMruC7HREFX5S9pRzRiNM6yrJirQW/+e2/xeXyhJc//Xfs+x2fb3uAnBXtfsfbywsyOlVriSo3Tovb2yvMBr58YRXxdbtOQAIBmpOKMHrHsipKQUS4HJdtRRGgeVTUBnfVzFipO3wGAVSi6qmcKu4C5ATEJwArBZeyYNEChCqy1i1AOEnBaoJaCtZlDWHaAUFBFabzHEbHS5iShxusMSpYirLB6bCp8aZlCXFNhUKxLVuQiS8xXzcoDBWUSyAtY4mu6AiaCaul3AbXDgr3ZhDk9Nev2L9+wX7fgV6joODnj19t6wAAWeEjWbYd8urc2P1sEaMcnOAk3s3Oprw3D95GORE3AfbwODyvYGQ7uTIWi2m2TsikS6g4DoypQSOBsj06LXpEdxi+Zd7aANigsVOJCl8H1Bjem9ERY4uGHqzyGQJPdDntbjb5Iwhyd4xO0FakgJEtm28tERjQEuObjEpJA3YY5bAL4a1Fr6QTATPHeapl/uR+n4DNO9uUXzseTvXhNH0+KDhAjD88lh8iJxCeJOYDOHJQHxk/58/OM3m4v/N5B8TgMiZuyvFNsb5ss3Fwq4Sku8kbAUoSx51S+zYKhhu6DbSu6H3B3sjfySqUEQD+4gVjGNaVZP399go3w90MZVNsS8XH5yeIC/7nn7+iqOJ/+/vf49PHT/gPf/+fsF1WPD1tuFy/R6nLrKrKtMWBAtOICpiaSSVvZ8jSDR4tS87k7nc7cjyBEJ7zIx2evCHg2Ohj4RCsCrIWwoaFjFGAVKd+B4TtGBJscE5lVUnC2ENKAKCjkiRi7TpTTXCDIcTiElw7u5oPt1A2MgAF5ja/P29FgjEazewmH157tgM4RXLhyZeJ+yUnsCL87gQ2+VDy9eZCOvJPOKIP8WcOqYQCPcJrj+sL7qLMMdeYRX6klNO4zXX0bRzub398/PQJAqB6gQ9g7MCH7z7gu9884e1lx+3W8M//479xnEaDiGB7+gFsy3UDquLD9UrOSCnA6BiNqehhjrVeIBVo7ZX3agxIKXCtMF2gumHbBLopzDpe7w1VC0pRXKIf3T231JANKKIYbWA0I3UBwFZDFDPtWhglBZ3hmm0hSsEi1PDyECysS7y3EAAUp0O91MrU79AQv42ej+JolRVWixaWeK9rpPIWTJJKEwg6uTWlzG7u2fpCLLlGsT7MQq07iIgmjBdYZCVyHdiAW0O/vWC/fcbL5x9x+/IZr6839DaI/X5i24/jV3VygLSD8pBqOEP9w/8GMn8801tpiPw4jRlZyrYEIwOYh/FNJc8kWiZJMBf0FE4DAVEbHed0ECxCWBxPwOk5QxQqNc7JUELJNEt+qytgwkiqhcazY0aZjqaLFuCE0REawgjlK++ORWhUw9uyyKULQIEnZTqDonIyx5rp0Qz0hsflMsue0xKenLYY19ikT0Ahk234hUnwfkcaWP/m9wO8/NzrIQfBfZJJfy5VMp3W9DRxen9+S5acP7yNHh7k2JDk+Ick+eMxlccXzD5KsU4gCiusjImeniiRx+I8FrjVCO8O7K2gtQW17uiRYnV37CPbO1SYAaU67jeFjYbWGvVWloLlWvHD00csWtDFUVXxj3/3O3z67gf8/j/+R6gUSipsl6mEK1OJfJ54gJgc92lhWJHiI7yqjHC8tw9+grcn8HjWyJmRNGCCNRZEclFpiSIIK7HGENdDwTKIT6HHvFcadmdu1pKjE6keARVbHXCbBCawFCLLZfMxnyH9LFfIZohmrHpSzWoun9ecgIbTKyIoAszqp9OaP/+Yw5a3mCm7I65/8LC+HeX5ip9ZZ9yUpm2ZoPg4ZUFwmnL+g9/N9cUKWuRr3tnpuj5dCcQ6YDs3hw+XZ/z+t7/FP9sfcbvt+PPnP+J2u+OybVjWCz58f4UWoI8btArKusGLwmtFe31F63eCkOG4bs8oWmD9BriRdKtK/pqwuq8Wx1aAL29vuLeOD9sT1lqxqM7904CYszK7zptTWbmIYKuVQYLkopy6y1cVrDXUwQuZrZKFN0J9HxVlVEYES1Agqmrod7EzFrl63Dx1cbgAVSL1dKEEQq2FqQQT1HDy11pRakXVDZwNUS1tdC4014EZVLjnI+ghMJD74wyssBXFgPUdvb2hvX3B7eULXr5+xf020LvDtcB/wen65XRVvG+mrSSqO07UVSo4Atk7w6LsUSjjORdYMscRqJEXRu0bjc3BhGWO1iK/NxzAgCBFtshLOPANFUKzFA6T3MwojvGhSEUKUBa6Gi2Fg+iVZSUsbXqkh2K1MnjFSI4CcC3TW1KkDSAR2fqhviwIVjkirw+HiYFVp7F1xnWwb0iAmxKgCZmHy1RIQBgBtGrYFgvAx1el3N/ZVHlOcPe5Cf80ovG3PVIfQh2YWi8B3hIgixzQI6XXAJxee/wMyNFuBDgq4hMETW7JaQ7mEeP8ABAlzgsx5xOsID1Vw8lG4JEjkdyOg8cCEXpC7hBw45vCebLAHeiz+XLFooZeDY4LAPbKAVjl4XCUwpTY/VZhGDBvsCifLAu9po/Pz1jqgn//DzuKKn7/w3fYtivqskYEUGf595Ei9TnH5ngBmGHXJK9SaCfKb0e8hOHo9zwSkyWIyURJYejzACGdDk/esKyi9Ewtz3uM2UG+1ODkKFt1ZCWjWWdZr0bjFTPyL2LzSoDsDqDEmg30Q3VswCTAhXMeGIBSLhAx9LYz8xW2SUtEmM2R8gZH2JzreaAH1Dhm9rcr+oh1/v9DEXL+6yf4hg+YW3BtDrchXYcUVUwCdwS1CNILUzzde+im/LR0/W992I1cGWuGRVZ89/wBb69v+D//y3/B68sdt9sO2ILLtuJyXVBrQb994Ya+PcOXjoEb7nvH28sLrLWYC7QdY9zhJjONaoU2fQvRUR873todb87WQgsKlqViWQp2H6Et0+bIZsp8KRXVjURipdikqKAE1yztSwaYagWSZ2JBp6gLbdVSAmwsBaIaYpMldJ5ID0naxqwaK7zbGmVpVP13pmah5OYsnMfrUkmaj1nResPoA/tb9m4IQWAVXDZjO6ZCkVvydyqkFn6fAKN1mN3Q2gvebp9xv7+h3Rvebh2tJ074K0FObipHZcthZX66Diy0A+x4TnDamDw+ISIg4UURP8l8r/lA70Hy9ahukiPMmoJCkAgjjwMQeURSMp8+QnekxGTh3hSfJaDx93RuaPAtU2aa1U6IEHRqRMRn4AhpS5a1WXqAAXSmlzNiBG2mUs4wBO5Er3p4bIkj5/cF2MpKmAQFLsdrHw4/fjj5WqcUxfsdZ9Ji4oejUeRhjuer/DjF+dw85RM98ifo5fjx/MORUjzemBGb8+ckX+nhMaQRD6Gs+drjK7OsdkbRRGAhOifyuPhEhdNeDTOsLw7VTKEAZVkB0UiDGEpxuCn2daExrX6kaKRCpODp6Qm1VggcRRVP1wuqsk0JG4SWY93hnEI4AQfkc7Fe3afDAD9KqPPPe25QP52xaU9wmhxHVAdzfn1z7+U8HzkPSegsR2QjbAGHwSBe4t5Sh+vnHAkehvMcZESIUemstThaQtAqHeno4NectLgeUopyllFgJd75Or5dDzL/9p/M/fOLfmoKfsU2nMb02/F9mEcT5KTSu6NEq4kUdzt4T+93ePT9622g1ortsuJzu+HHl88YjYr8l/WCWjcsCykV3hvMHWVbARRAmXbc9x0pQicxr7I6CbnfaFQjKflKbSTXpmMthZGZwtc0I8jxiAyyE3iQd1WBguhMLpAiAfrpsJBLePBfU26JYD/2O1UoC7pQFPEzK6VEC6RQ+d8xpmCiOcGZFrrqwmqKaUfSWIsaaRlKIjajdLRRZoPR5/1Gp0kMZVlQlzUyIsx+iJJHRIXsAPUOkN/a0Psdrd3RW+rsGVqLirNfKKb5X4jkyIxmOEYEdILZrUKNgOTLO1h+DVDoCTgh/oiwAOTIwClM5U4vDI4eisCjdWgpU7ac7G4LqfswJFGm7eMALQzL5BeGkUsAA+YLufGkV0TAVUAvjQQ5vqUMTiILHYIEiiN6erCjeYSiQ1QsIxMlJhXDcSSEepDvyLUI9coIN7oAVRdOzrSf0eEi6TrkHAhw4nqGUsHsQnxayqcfc6Zk1RZzre95cBrJBLSZTpqQIkEz0ts+oxKfHkHuUo+b8sMe981mgHmv4ze+/2TsJw3z/Lq8r+eU1zwdCsslwslXyMx/8I+W+WEcgyTpxj1dCsPXIhU1NjomOBylbhAt2Mp3JDdqbIiQKShIqQCFewGSJOhA3Wgwl0xLBTGR+inBlfPjPB8uMDecGb1Jg2vpCYATUA7H4J2OeSdGRDVtxH06pbHztaXSW4/1mKTPBDgSEZ7hCE0bivH5AGxEmkW5vjTEbjqOeCqjZwu67TMawb5TFBUspcxWCFlR5PBYinTC9p2Vl5n+NiHXyQd4XaXMi+J+cuav8GJmgddp/qf5mDwJSdD2c57QaR7gKGQ4opySH/sN6OejB5DK/eGw9wrMCPsENH2HGI5NqvV3BznN+N0vtzf0FXi67kBRPD09Y1mp1fL9x7/Dujzhf/y3/4r9foOoo9sdf/zDnyneORy9N9zbHbUwJVSLoqiQw6KC5ekZpVY8Xz6iwFFHx80b2njjPasLy6e3iroIHAN//vFzkH4Hai24XKn227qRt7PW6VD3kLuesXCJdb8W1EWxbAobTL8STAlKrSRDrytUFaYVpS64XJ+5NtzQB3vz1WVF0co92ZztjwSzJ+8Q2sGlZrsVhTW2TelR1v35y1cMswnw9vuh/XZ5fsLzd7/BUlnmvtYaYxnOvg+47ei3H9Fur3j9/Efcvrxg/9rR3wC7K+wNGM3w1lk5/ZeOX+HkxL+5UIBjozrbR0+v2KdBEQFms7ZYoJLWKMK7noSFlMQeI4CMnz7zhEjnQv3lhXFsL98Y8QcjcXzutAMedTPurMaQgxQ9vSbB6fx8pk2SD3CYGf7sp5Uv4rMC7TAWmLo3Oc7nU/6pt3T2IE/GP36aPKqHwfiGYPuvEcmZQMsfvl/87E1y0c6qM+CI+pwMsn97W+WbH87XJzl/UyeHDx6383hP4JqHET2nth6vCRMjyOnfhzsrx+4r37w5Fb4l0yynzYIVBwWlrlAtjMjEl5k5RAyKwrw1C8pZ4QAwnJ1VPRKRpDm35GGOJs36SPfF3M357ae1mCkZHO9/zyPtgJ/PDxmd5SsSUBAjy3Sqzmed8yHMECYyiFdEpun40vxeP6K3M01lcT5Z4RT2LeF4JnFO1mCeekYxSggRZaI5Qf3x/T+5gocTcznSVnN8cjzOr/25AZ2T1k+vO9n6b+yEfDMu+XMC8PmdzhSJuZPrFGMzzKHxGAUVjwrc9zpEFYjWHMMH9tGACqzLZfZt2i4XrPUCiFKWBCwQ2O/3CIywE/c5ugzEpRRMoFtLRa0LxAze23QWSqkkBK+VejPST0KhHbUgIov5Fp+SABPQZ6QMx94hIrH2ZVbiKRj9qYWgpITqsAZHSIVpqtyrVUl8KFpQSmWqUT0qKRmR4jqSKFFfqC0mgj5CqqUPtLbj9vaGMQzbepCTswhAtaDWJarTNCqJoxHqsSHDrcHGDmt3WGtUNh7OYMZwoDNjM35hHv1K76pcB7yJJDDFLRX6nhqMk6QhSslcWhD6Wkt9wlntNNdhVgdECDH1PrSUAAYt+jxxckzFzPx6B6RhMrJJyEtiZ1QAAPEXN5Vg+/IULHt/kAZoAqhbVEwcReucLLFZBBLVcsiha27J0/aHaQsJ8Zywa7YSyK3WGeXRJJwluTjuMU+X6QemHR7HrcQDoSUW5+DHA8HvmXsvQHG398U4szs3cpoLieSWwC+e5Tw6YFyet0Rq8ugk/u0FPHq4NiNV53LwMOXyuLEneDg5+0faSnCU7XJvg4jzemI6yTQyB/BBAiMHmGrNSpXzmTInLTEXzYFFwhhIhYCy51NYNSyWQLGG2jCNWoWWihrSA2GLZmWMRMXGeXzy5Jh+Oo1+tlMR4dwd/Xib5fMZ2Vzx7hVWJ8OOTIujzOigxeMTMvipFw+C3AsASGG6FlooA+u2oqjCQ6zOYAFighw7Iym0U6zkHHNeCmZCM9KRRzRwRrAd1OryIODCsXuMcamgIGSfk6QUNkVk+v4MPGacZv4dA4SM7p7CMaf34RtseiIn4QwIJ8t5vufs5J2dK9FK0E6xKPIg3TG8YZij731Gzug8UifGM+3xzsbo48cPjCR1gQ3HP//4B3z/d7/Dv/mHv8Ptyx37646XP3/FHW942b/gNt7w8saUpjkb82IFlrLiUi6w3WD7wN4Y63vertRGGlzX3Xf00fDy+plNP73gw+UDnj88wy8DXgduP76h3XaMO/lgZQHnrjItQYDIfVDBCqW16EmYlnIIpUTDTuHeU6SgLhVrFdQiKGWDamVaWwSwAkWBjONWL8vCiqlQQ66yILsQkDNGHs8qBVIUuuikuWE0jP2Gly8/4u32ij//4SsjXz88YV03fPz4HeetM9LsGKjrB2zbE8oScgp1ifVE/l/xqFBrd9jtjv3rG3AHlgGU4dDhQLOfD1TG8esNOs+erSfIOemC4OQMgDeHdjTClOcd+MEB9MN4ZB4dQPagCU7t1H44Pw8g2NjHsjzCnoe39BNwJ4/Pm51IsMiw8xEt4qbL91mAmQyLzTLik5823Zjpbeb3HfwAAkcPsMPolsYJzUoQHON+tlFzm5yhDD+dw8/RiQ9yZnzkeSDe7chqJO6fR6UTO80DR71TlsH/9PzCpEJ+dmM9uZiSQCm+I0bR53geUZyfjtkp0jP/ldNzMfZ+3Jvc4HJdfHskZpi7wzzfMyLKn/T4PEgQq8+gTnDwa/i7Ti5XpGH02JBynL/10+eJnX+IvP782dNDiDed66YfZub7HB6VX35e3LPG9PxCTFDBX32+71w+PV/78B1pa3A4YBmdmaj1WNvfzIys2sZ5/kxolJGgjGLnvYl08hFQOVUcTftIszp7PpyHPn//1tbNpwSPb3pwIU6/nj704daeVoCcKxOPuXsep7PWj2cqz7ju3T3rU3j9D+mAdzocgCtF+BDFKpE6G4NFLPf9jm4yQawN6uGrBlG3VDqgGT1RVhfNHUNKvJaduHvvaKNjjcqkEikui15grXWCpNxfhfM2WxUwCwJ+RxRBDI1vE0ADhIgLhoUNNCDbZvAzM9qDaR2TMJ6OgWqZ1dypaXRs2RYzKQqMopE1m0WyEnrsd/KXrM9KTMAp01KAWkOu1mWKF6rim+jTAd8570lJsWawRhFOGxE/cIGGNtOwn1kAcfxqg05xarlOACA0pGb0d2bgISqGfLREEOy83UcQoXS+F/F5IzyUmaYRRlVapz4BQ4sZ0uRNHdHYsKRqaQF8OIbtwHlwZhNPYK6kDCuPCFcGyJEiR+sJOdb7t8DE4OiNao5eNIT6ECWRPps5Uvk4JxJ4s/NUgrtZMnw42DRN3aNS7SBdSbyekaLs9SVI2Cop8xjAgES1b45Mj+X3j/c3LCU8Q1ebUYWcOZPE+WAsHek5nzk14rlIAJwiQHxSkJ7pQ+h8bjePpv7hvbN8/wxqAnA6o3gCOch9hqliKwFCHgCO59aQsJQh4GMZ8ne4RofmiMIgekblORnvt9TsN0TjWYJz4m5hGMJQOUGg8CO5YpwcHGFH2RjnPLcc62zXIPHvznk6K8Bi8FyYlBehgu87ziNuOHGZfnD6XAxR7jGjZxY8PY9QdKZGzA6isgQvSUuFq8zqlN5jbc07avg2q5LAoUQFHTL9BUZuVab2ONwd3Q/iMY20hZOm0FPK65gnPP8+DDryeh4BugeicmAq4z5in/NvCUbiPT9z3+T809lE+PmaZV675lozbnw2uF5H2tyZa/EHTSN4bGgiodf0Kzf+X/h4+zo4D7xgkYpLVYzXjj/8138KzkrBn1/eMHaCn2IS7RwE2/oMrRXLuqKNO/b9DVUK6rrARgANqRBZsD1fYDbwP3/8n5y73bE+Lbh8vELWii4gTcM6Xt5ecXu7YxX2PNPKUpUv7YaiiqVogIYABSIYzSAKbJUVt94HigksVJulFqiydx68TIFc/mHj2AIPPbkCXRaU7ToLGvJ6hnS4OmpUVZoBNjra6yvEgWqOvjf0e0NZK7QW6GiocKwb58H2QbEuinXNeaTYFsVWBbUatHSmzM6EZhDgjLeG/tpx/2Job8DYBbfdsO/+/7V3ZU2S5Lb5A8jMqu5eSeuwwm/+//9MfrAOa6e7ujKTBPyAg8ze8cihCNXDRmGipuvIg8kD+IgTRRgrM5ps2Po/CXIseuMcS5AihDSFP4WgDyCkaiuPXHC7mn9sHv03Gtc0dXJERnn0QDLhcayfkLsj49sa8BeRU+aUEAx2jEZItwxhar5AGO1mb37w++l+psnye4pAWR206eAhqhkhFWNW2MBQlmjQ0SeRmTjlTmRPzjQmZ5uraYD8ZolcTGPBCRaHGWJouQMo/kCv9y8i9nIegE/yCNeedsMhc20uOaAIpj5FQg2lBp3Pnd//ynlgishKOkOOvN7E4IcJK/rePzPhtDNKzQnGhJnuMtqZOiyHd4Go/XUCS659ynXzHa2TC9PQiI3H0hFeryHu3OZFU4sCFXjah2HbHRuL8SANI6yfrE7PIwWUDqA6Fov/FKqOL3xCpOcgykmT4wA6LozBt2IkQ1OrEiAyyHfs01odP2k2LRSyEb0VQx31f8QjS+1ghUXlqGdRN9Az8zbKyRUTlAafQuqFsq/i07kcA8a5pyea+fs4+rSU8uDQLLqJrkfSy9G/1m/unhBtIII098OBRwGRPnYOAbher2itY98aIpJNtUFbh/QGEEMOWxLMjMILVv7JOCdZwefQwEMF5MDk7fJmLgz+rIccdj0Ry1HF5qPDi6UgkWYRR10tk34kvyM1v02FKRIYBtZDI8aFTOYwbJl6nTvTnnnhUQfWXMws2w4zTZv5U9zthACsgCr6saGIQGQUeLU8NkDrnn3ZA2627YC0hv1+x0KM11JwbDuObcOKKyoW1GUB12J+MqRY6oKlVis2zcVMZuuCUhffmIcvzpT6o++gvoPRwb2B7wf654H7x4GPTbAdiosFatkc/GdrV4XDE5VgbkhOPnLE2GeKEHKPxqCCLEVA7r8zlpKpCdkBBRw9di8+Fg3WeV16A7KUQZqxHJsw+3fTbQKk+P/d0XDPFNCjFgicuWgPQWs3lklgmMMUABmCJyulxqR3xsMEVGdsxT+H1ssQuYXMUXEzQ/W+5qGhShDIisw6Hbe0J0pmxABKPDB7/4FBEupiT66U4WePoyjpISHL/QFUwpfLRyDAJDDCNGYKX5nZYvUV6VAw/mHuScB3uo6Tfuc7ByOhOMs+T2ATYfxfAM73moUANWM8NUaMOFMWqE438zYoWWmIXPo+zaLQKWG0xTIt0BC0hsr9OAMyBPKQydAAugYHrlruPjc41uvEOCg4/wJwAZUBEx5GJ0wxksrBTePZd55Rte/NeEOxYoLiGVdtL+B8yiM2u1hOIjMFOsjRyb8r7wXHJS58WEEIzZodMxu8e/CzYmNbFtO2Cg4TYkzoHWj7YW5PgDthApmPq8QcCS1TyeuHtlOdB1H+YjRM8t8BLNOx3zO1nhdafIcTH9ZuGZxPS1U9KlX9uh7WfBwde2tYnTGGO+oj6ec//Bu2bcP7+4f1HpvswiG4Hx1HUyxYUVCwvL6i1Ipy+R16P/Dt258chCyIvNfMZob547//B15e3/Dnb3/DdtzxsX+DHM009Si4lCsu64pyrcChaPuGbb/h6BuoWyI+bjZ12ZeliDlwQ8InCLb5L5yuI6aA9fFmoHWyUkSloHjOHOoFrRTUqmAu6Jfupm1fM+0O1oqKHevrC9a3F0g3TeK2H1aVvW849gN//a+/oLWOo3f8dF3xx9+94rjfcdw3vKhg1Sv+8PPvsV4qXt8u5idXTPtcV4vmqpcrluUFdbmilBWMCkL1YAxbj3LcgHZD0R3l2FC/faL97Y6/SG7EHAAAFHhJREFU/+UTvxyKW1P8BKASoX/dbHyhH4OcYIzTFUKgSu72kPZqmnZDWWMpBC8BYV+2Y9xGq5rqZRI98ejcTcAZulL6LUtIRf+ChKIKe+b1CPuwxgbIH0NcvTsE7FQyznfFodItkw8eOf+3IuWmsOX0YwihE/VDXB3o/cUK27nEtWDXK0hlUH7P0X8AKM8579JsQzbEDAGeLhtWPBC2A41QVhsnZ4w/8tL6V5CqO52L+zSq52EwYYQIGydvdYDbLxN39B19B1VEz8G0DOMMnPen/v3kjJt9SJNNmPy3vBXlmJ03x9OETTgzWH62gwaQse/Y512G3jnjjBsM67SFEghAdXocMiHEgsjaaQIvAPrQ4NhsUAO989irORJDR+SHS6jBZcnvA9fkKPk4DkH3CJLeci8xbD9Dg0q+QK0PbB1YqL+DXuc1gGTET2FCy3wtPnpeMsHylVAOZQiU4ExEHomiZOtN1bP82jESSdTI2+FjItINybhDuDZPngqegDTcaGU0/IHiiwEqEr5rdk4c5L9PY5R8UL98dZ6z48oBgE6n23jo0B6RfwaCvwCRvHRef8bLTPMjYvXYHh3pue837G23osoaJkzn/VqxktVwKoWh3NFU0Nu7JYUka/u+fwIFWK8XXOsLLvWKZS3gqhYN1A7o0UECrJcrmCvKcgGvBYDnwpGOrR3Yj8MKSIuCqUA5SqOpFaZm8uAYr2lGZqVgnyfq6nB1dwZlQJih7PXsrGQVpCha2UDE5gwcLhdsgQyW879bSpYGLOsLVi5ALZ7Mb0EtB37+2fL8tNZRAByHYm/A3qxURsUCYAGwYllXh+ENzFa9vZQFtVjUqO23vPwJh7m2AdogR4MeAml2j4+t42MX3A7FR1PcugIMLFA0Om3HfkU/BjnukKTqk5F4mFB0CEuCNTaKXZoMmcwqgIMOOMJwZuQ7MO0GjjgZyuwONYEckN9XU6Mu7tXPaiGkLDAHMg/9k4mpe7TmCF3P9o3P5hsxVNKF3AykvkjFqk9XsqN4ihaLiRfAJbObqJuqHOhwOITBHKfCkya81I0fDgGb2aK9HzGZzSjMW6kx0/xrUSReh0RjNzhSrT+KYnzHC1YUVWAMm7oL0gFNkgJspLQBBpignCv5YwC6fD/Dne+w6+kY5DFhuqK85my6sqkxwXANoDO38XxVDV+rBFjm4RdA1O4lef/RLmMSoIDkzgi4ZmRLomUgHaxD5HzVoNIcf6+HefDphkjMaaarfRKaNpPtocd3Kq6qfRD1ZvWhcuPj80BVs0xFaCkDA9ZaEgSFadvClw2wWSSjeo28EMQGOjNDtcrQlvmmCQ60mSyCszloGuVh3GEVpoGNQq7hxKqqoOIm3OPwcz23Dlt9PsLQZotr3IrnWtKIEkubGgbgTppWxRkR5TS15/XZcjqGRt9O59m5lIhvLsapWRxU8x9IJ7Om8aXCFkEmXXBMAOhR9Hn/xSvAW7SU9AZSAqNgwYqlXLG+VJSF8bG9o2kHDi/RQAYC7m3H+nrFy+sbXupPeKmvWC4VZVFAduhxh2wdhRjXt1dQrcC6GAhSy1je+4Ft3/C5beZfA2C5WNRTj0zQYrz6iM+qOOByBjYMUUkkkvAKmSawee2qwgSqakVhYRoc6uYsLVWxlIqXi5dvoA5tAu3A5fKG9eUVeltQ2w45LtB24IWrZ1tv2G533P7+jr0B905YpWLVFaoXkF6yIGdv7x6VvFohzrJ6GDsAbdCmUO6ur7BoR+wNcnT0A9h3xbd7x/sm+LYr3rviQwWdYHW58AXMf6F/6Hhsc3UwSVsPaos/TT2AVQ5VTwao4C/ZUYEOcrse1G2NsRNjTziEUfcqVZ3zA5ABK5nMVOmYqg5IBFaAU5I1oztjiQca5ikgxIZDgNw9x3fFnU7ZGYr51gxxQzquGWruERbtO8op9DiSd8VurVCDhRMXN21F0TUDkgQDVrl7hSbQDIqyFTyBv3Tqgdvu1aCXdvphuN2/grLURiaNVJPG6biARLQpf7+aByatR+whv3pLnMDOadLT+QgKW5jOJw2AkbedQZMDK4r22JhqtiNahVRMzRJibFjHbh8ILBE7c3JfYxeqDAc3XjOKAFVzWIaHtMKr+CI2FbFTSH8bTzIXz6PkampB1j0JJwSJgThcVx7aIx3LmMwFH9Lw0BBy5zuqdn8iT+wJNhO32etsbAJTz8OeGxEGuKLp4ZshwPKCDCBjc2uylwa/AZDMhpB5eYIZWUkbeL4PyvlDrL7mpvmkHaQWiiwkOGSzCJXmYxobKwytbmAaCVA9RjUfc1owRt8zzQYFcKNxpeHDhbHLLGOjkGPhSf4sOy2Dehs38E2Apve69WnhDlTxLLqUrmCPpF0AdfDR9gPb7ROVGetiKUG67th7B4GxXAiVGG1v7ohr12AhLJ1xaQW8MPrC+Hb7Bfq+o283FG14ezVtxbpWqxF1sY7p2nH0hqMdVh6DAC02AFtvKRcICrD42FPyQ199xmMwXgq/Fpm1g0EQT/y5K0Nc8DATutSM1CMG6iuspMJS0WTH7f4/wCehYwcTsC4FoAuwrNDri/XTvmNZPoBDUdcLlpcDP7294eV6BReF0gEt7nuqplWqi4KLAtxcADK0bVBpQDP3jd4+zWy+deixo338Hfv9A5+toanAiqBbQtRNjF/+XBn1B6zoH5irHIXPtt4AOS6saDJbBbq09ffVB6fnK+R1hjdTT7tiXhspHqI1QyAEcwkmMjAQsqR4OvwYE8gqwq4FGV41X0GOT2S/s80Nyr/mnxFCNhZzHGux/gPgaDY/HY/JJhbntYcjmDklxm5e09E51OzRL5wMbproXtwMno47BKyGYJujeR7MWKIqc5pjYlyUTyMNxTC3KAaDjm6cvWtTZeG9PWMaHZc8E01AysHG/3HcaS5Pp5v8cLMQgsFMgiGkIQFz0dVTGwMJfQFjEgdl3gICyOEduzBPXDjMqlZQLy4ySXQVAzk53t5oFRB5UUlVN0/NmY2bmbWkI3bjUTzPOpBhdeceh5ZtE5QhCoC0THioocp3T38rMgjXbqW0d9DBLjgO3/wAgCVfNIOhR3xOc1IdOMVnZ4rWknnzpJIaVdMHs0V/zX1I4RNlF6zlCkFDL4SuVqaGUECl+roeYDvGePCvAGU2j8a6PwMbnc77PtBBzuu4Jk3nquLEY5OfihjIYcqRSf5Js7uA8S/ijgIxLaAwssbFA+kQ0/ytlxUEwv65ewRTQRNFk8Oi38B4ezUtnDZ1swpc6wMUJSzdfOKkEu7vH2jbO9AaWIHL9QWFPdKpEFAtmvfolliw5cabHG8oDouLNtnAihJBLcn/nGv4ZjaOHfzFbS9Mp5XZlCHdTFfqLh/skarEBWUllAuhvhS0jwP7+46yLUBRrJcXz7i+2iZgXQAVyL6BldGudxTpqNrxcrnisqxuemoWcc0Ak9WqsjISpjEys4pAcYfI3YBNF7T9Hdo7ys6Q/UC/v6Ntd+y9W0FtjxrtMPhsGwXCZSRj+xX9EOSkMYpDJ+KLSyUn89CoDIASEVe2SJxx+kEU4EPVNeRe+VcBjTxWcbcYXbL3uZuKtZ1QBVDm1GKEh7t5XZPtHDAcj+HPFYvSBJqOkhx+Dxpcxq9pO5dQxZhKeghOQ9jWRwlooi91fGYAqGx+De4tH2UFpCTIHc82D4o32Bz6NLGA+QBRjonN5FBzEkDGgLnyw81VwjzMl6QwLQIQlWfNZBPIN1Ch5vMmc4/EfM7QU1tCOG1YQWcNz1dKEIrRtxkxMp043sf8NIFA8Xc6LmthxeSV6XnC34hsR+aw20wsFPs2neabg+mob1UN7Fr46KSBJDgods3D1KYELH3WqEaHOmgObU3fDAxJxIz7QutiY8UNud0HbO1OEXyPoNZ375eKCFzQjgxNVtXJAdEyRKdMD/8R5xtW91eBbuZ1doBiw2Z9NTv7xvoKf7YIdY3SNlqtXzPzsCf8M17lIb7kpSLEwsSJLlC18FtLXxFrgS1vS9+xVCvNERE04hxZJiY5a7Lt5jh//n8OUa6r+bvcHTu/VnWNsyVEVWY3+XQ01xgu1QrQSoNHDHnyDRUzc0jPhHKqATIfR7ePDzAR1lrRurkz3PeG+95QSjWTYLWIn/dfDhAUlS2C6vUPF5AyihRULtAqaPoO+bzh2G7oRwPBoprK5YrCFVCLcOq3A10ETRQ4FlRUA1zdcr+QGnCyqdkjw4DJCx9T8nEnqGUwJl+iMN/RwIwqCmXzUSUGKqlbH4xH7Jugd2D1KK99u2OBgmvFulyw/vyTtfu2g2g1TZQWu3bbwERYyoqDrdZVIUUhRfv8RPv4xOUFKAvhhX4Cl4LCo+AnhKwoeT/Q0dHvH5D9E+3zE3LsuP3tHbJ3lI3NJ+loOO4d12OF9oYCwh2Kjo4i5v3ze2VcfzDR/4G5akgR24VOzsbwhe+TNDcRmMQHDTttanscHAzINGmHaDoXBpdCkNkAztEeA3al2HKgZY6nX4SYnzC3c4CkybwUxwdEji7AED5nThB/CaH6BfxxaVw1NT4U1zUveYu6QPZxgJ1wns2nyInux063j81cPkX6iNiPcf8J0eGRNOpCGXqfJ0rkK/nVeEUIUTyx74Tzr104+yOPVTLwmLeg03gNEEv+K02fv1h2Q5MZ32YSJTpdixx1JxNK4Yq06AToHw8+xIr62EVEX2SCNYsjZcg6x2cfwwk/ne4R0zXa4qgLaW7LRSvjJa658QsNRay6H507Tvu15rF6BHWxIAgu7qAdGyUHJzbn7XUORx1E5ObA+CLMwWo5vxQmIKxP5/A+H5hJa0MgN0mZMHLU40LGDo2NDSOVcta3hnhd+O+pDUpXczHfDZSCmKUg/bJC7IxZy4K8ysTZfvX7fNxYe2MOjYl65ufxTgcPgyLrUsWWmH3+T/xLnL9HskWC+SUKeyc9kNpxWD3BLtmu8DFZFwJXB2YqaJuDnAt5GQYrg1B0cdAsEG1oh5qfl1oQABVGqYsBpgaLjDp6WoNJTMtHyh4w4+Po/EP8PYebXHQ3NDde7lM/5ti0pImNfwbQ7x5FFXEEvbvfpiAzd8erVkZdrmj3BmkCFvXalaYooL6bOXVZvTQEZUT73hra0WBFDxiyX0HhhxyNRbijmJZZ9x2ybWjfbmj3DZ///YF+7+CNUmvWu6AK4+J3eiXG3TdZBcACwvKDpF0/BDmFh2NnCCHLAXMSIVBFakEkfHMc2IQXhSkA7fgW5gt28MNxLM0BU0bBX3BWjgdzH+zWtUsc31oSQ+muLhZLumfam2CSw3E6BsqiMMLhGGl+ckVOJt6a2zYEj31Zig/8HE3FDCvSShaxFfn306/BDGSj+qoDRB2p6YesjRHwdoiNhHBFIH0VC1EOIa9lgVKFeuzXY6k4+LSJKfG4S3bgEKg5Wd1gGHWCwk+Folo0XJBFn1F+jkHLXqIBZgbImZpHdv0ASQFshl9FHOIjMflngdwBNdhRMCsAqWcmgMhVdDFRPMcC+V+da0x5oqVIh8/uYBy+OuyVxQEeOXvi+SLLaIHN7+rCWbuDnxkFsU3oIhalFWUdpmdRPycD2X18+vHYCtL77RNEZCnpPTQ2gJt2E7ZlrVacM8S8hrbFimyKiCeiY1yuKwoRtu2G3o4MeebFSinUpQCinusjxs00khG9ZrXDkOue2DZwXRoiKZZqRxbXJbLQe7X6TariaSYESg1cCSuvONpu5gzqljfHQXskQeScRsEQog2DIqz8KwA6vw+zLE+fxmGcMMVuQFQsjQZMmySAl8HxKtIgrI6ypViqfe0KpQ5BRymCTgrSDWiAHMfDQc7lauH+/WhQgpU8qAsWuiLMiL1YAryIcv3pwiiVsJDlj2nN6l3RAmDvQBO8XQrqa8WlvqBwxbUWiCq+6YaOjsYdSyG8lYJtO7DvDczNKoJ7f/fumYS7+deUUAgEWww+BFu2w6w/2CbBXe0AFDUwZIEyBhqoG6iiTqhCWJRx5WrJHXtHqYKFgeXVoMH6toCXBVxeXEFhlhhtDVw3EH2YaY0Ey0UsD0+Fmaz2w4KBStTvuoPIi96SwLI3f4DlBt5v4PuG43bDdm/YPxWkhEs1eVcq4xXAKxW8vjD+U1f8aW9474K/7vJlnp/px2Ud8szz7sF+09QgpHz372cfnVgnow2hnPdjHMCctg2ntTh9mDZWAa5GvEVsWabQRnInYVJPIDv8b75e00TctKMJ3obzC6rTbocS4GQzaeKJvwITzuh4CLPh6sz5YCmO1WayIXnG/Kj2d2JysJ22aRHIP7vPC02NivpdD6WAZIRwHKDEd9PqVZqO/tKhpz4aGWXP9awYAySMq8RQJBCIbjvNM56AZFybMQBVvOb7Ie83ItowOerPYDjAWbSPMYMc8sRI4xgyDEKUJgLAfHNCUxHA6ZSrZwZ0CkALRuSUYmTJ9mdWa9vQeE0qIF9kw1fDx1GBiFZ6FEViUpFum6GU9JrPF9qVfDx/E6B0HsvCDCnmtN1JU5jYAQaO5vQLeT6Nq1NczE+yCC+x3Ec+LqInDuVh5pb0L8xodri/d0dydrB5Aion38dp8n5hM4Mvx7z4MlBfnmm+Xv4/86HpNs5ZBi/0NpriO6CwzR2d/sEdY0MKmy9YBx4IlAErVik99HY+F4ixkJljrIahmc/NpcR8WSrTcPHSaV3DNIGFGUtlrMuCyhULEXqOtOWQIyLUAhysADoiwMSD7zzrmd/El2DccqYc+2kvpT7/Z87Do5E4Jwz1zZqGb2gUnPWgIEKWieHK4MqotfiurpjWRxTEAkK3u5I5Dmd+N4Kty0gIqbCoKYaNO7tvTvjpSgcmjdLRPPCH4M7t8MoJVu9Rofhzt5pp+2C93yV6dKn7Jz3pSU960pOe9KRH0KO39E960pOe9KQnPelJD6EnyHnSk570pCc96Um/SXqCnCc96UlPetKTnvSbpCfIedKTnvSkJz3pSb9JeoKcJz3pSU960pOe9JukJ8h50pOe9KQnPelJv0n6X3LopMci0+XjAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 720x720 with 16 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CdEbVVZSTSJC",
+        "outputId": "5b0653ef-aac9-42a5-bfa5-327b4f8c36ea"
+      },
+      "source": [
+        "generator.save(\"./model\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO:tensorflow:Assets written to: ./model/assets\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
-- 
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