GPU_trained_flickr8k.ipynb

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    "colab": {
      "name": "GPU_trained_flickr8k.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0T9hdnQ8Yuru",
        "outputId": "da65274b-b2ec-4a0c-e9ea-4fc88a3958c1"
      },
      "source": [
        "!wget https://cs.stanford.edu/people/karpathy/deepimagesent/flickr8k.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-30 19:02:00--  https://cs.stanford.edu/people/karpathy/deepimagesent/flickr8k.zip\n",
            "Resolving cs.stanford.edu (cs.stanford.edu)... 171.64.64.64\n",
            "Connecting to cs.stanford.edu (cs.stanford.edu)|171.64.64.64|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 50286882 (48M) [application/zip]\n",
            "Saving to: ‘flickr8k.zip’\n",
            "\n",
            "flickr8k.zip        100%[===================>]  47.96M  12.4MB/s    in 3.9s    \n",
            "\n",
            "2021-11-30 19:02:04 (12.4 MB/s) - ‘flickr8k.zip’ saved [50286882/50286882]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WKIBANtZP5N",
        "outputId": "14f7e2f5-f024-419a-be6f-a89de521a891"
      },
      "source": [
        "!unzip flickr8k.zip"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  flickr8k.zip\n",
            "  inflating: flickr8k/dataset.json   \n",
            "  inflating: flickr8k/readme.txt     \n",
            "  inflating: flickr8k/vgg_feats.mat  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9BCV74Jc-ny"
      },
      "source": [
        "!mkdir train\n",
        "!mkdir val"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRSyqmJHb9cI"
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import torchvision.models.vgg as models\n",
        "import torchvision.transforms as transforms\n",
        "from PIL import Image\n",
        "import json\n",
        "import pickle\n",
        "import scipy.io"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7QHR_HrQln6"
      },
      "source": [
        "###Feature Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ystpb93PQpW8",
        "outputId": "3574e614-1fbe-4e59-80f0-91f3ffe0186d"
      },
      "source": [
        "!wget https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-30 19:11:51--  https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/124585957/47f52b80-3501-11e9-8f49-4515a2a3339b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211130%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211130T191151Z&X-Amz-Expires=300&X-Amz-Signature=f4280405f525a5866d57bf87c5e5ccbc3ba643652651c0e26c651016b3f426a1&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=124585957&response-content-disposition=attachment%3B%20filename%3DFlickr8k_Dataset.zip&response-content-type=application%2Foctet-stream [following]\n",
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            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1115419746 (1.0G) [application/octet-stream]\n",
            "Saving to: ‘Flickr8k_Dataset.zip’\n",
            "\n",
            "Flickr8k_Dataset.zi 100%[===================>]   1.04G   104MB/s    in 9.9s    \n",
            "\n",
            "2021-11-30 19:12:01 (108 MB/s) - ‘Flickr8k_Dataset.zip’ saved [1115419746/1115419746]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nl6yfriIRdmM",
        "outputId": "866617d9-b99e-45ef-9dfc-e693693b414e"
      },
      "source": [
        "!wget https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-30 19:15:23--  https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/64878964/b0afbae8-5983-11e6-90f4-e3db656bd548?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211130%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211130T191524Z&X-Amz-Expires=300&X-Amz-Signature=2f7515e2f2d000a5f8f187b8fa28a304a02578d897297b01f6f4a13e4da22789&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=64878964&response-content-disposition=attachment%3B%20filename%3Dvgg16_weights_tf_dim_ordering_tf_kernels.h5&response-content-type=application%2Foctet-stream [following]\n",
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            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 553467096 (528M) [application/octet-stream]\n",
            "Saving to: ‘vgg16_weights_tf_dim_ordering_tf_kernels.h5’\n",
            "\n",
            "vgg16_weights_tf_di 100%[===================>] 527.83M  37.1MB/s    in 16s     \n",
            "\n",
            "2021-11-30 19:15:41 (32.6 MB/s) - ‘vgg16_weights_tf_dim_ordering_tf_kernels.h5’ saved [553467096/553467096]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAOnRW8yQsxn"
      },
      "source": [
        "!unzip Flickr8k_Dataset.zip "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfhBZ9UJQ3dm",
        "outputId": "a98d057f-f2bc-43cc-c961-5736e054f75b"
      },
      "source": [
        "import enum\n",
        "from keras.models import Sequential\n",
        "from keras.models import Model\n",
        "from keras.layers import (Flatten, Dense, Conv2D, MaxPooling2D)\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.vgg16 import preprocess_input, decode_predictions\n",
        "import numpy as np\n",
        "import pickle\n",
        "import os\n",
        "import argparse\n",
        "import json\n",
        "\n",
        "\n",
        "def vgg16_model(weights_path):\n",
        "    # this modle totaly has 22 layers with polling \n",
        "    model = Sequential()\n",
        "    # Block 1\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu', padding='same', \n",
        "                     input_shape=(224, 224, 3), name='block1_conv1'))\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2'))\n",
        "    model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))\n",
        "\n",
        "    # Block 2\n",
        "    model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1'))\n",
        "    model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2'))\n",
        "    model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))\n",
        "\n",
        "    # Block 3\n",
        "    model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1'))\n",
        "    model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2'))\n",
        "    model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3'))\n",
        "    model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))\n",
        "\n",
        "    # Block 4\n",
        "    model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1'))\n",
        "    model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2'))\n",
        "    model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3'))\n",
        "    model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))\n",
        "\n",
        "    # Block 5\n",
        "    model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1'))\n",
        "    model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2'))\n",
        "    model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3'))\n",
        "    model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))\n",
        "    \n",
        "    # Block 6, fc\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(4096, activation='relu', name='fc1'))\n",
        "    model.add(Dense(4096, activation='relu', name='fc2'))\n",
        "    model.add(Dense(1000, activation='softmax', name='predictions'))\n",
        "    model.load_weights(weights_path)\n",
        "    model.pop()\n",
        "    return model\n",
        "\n",
        "def process_pic(img_path, model=''):\n",
        "    img = image.load_img(img_path, target_size=(224, 224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    x = preprocess_input(x)\n",
        "    last_layer_features = model.predict(x)\n",
        "    return last_layer_features\n",
        "\n",
        "print(\"Loading Model Weights\")\n",
        "model = vgg16_model('vgg16_weights_tf_dim_ordering_tf_kernels.h5')\n",
        "model2 = Model(model.input, model.layers[-1].output)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading Model Weights\n",
            "Validation_size =  800\n",
            "0.0\n",
            "0.625\n",
            "(800, 4096)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBqNUDipSSAO"
      },
      "source": [
        "train = True\n",
        "\n",
        "if train:\n",
        "    with open('train/file_names.pkl', 'rb') as f:\n",
        "        train_names = pickle.load(f)\n",
        "    print(\"Train_size = \",len(train_names))\n",
        "    train_features = np.zeros((len(train_names), 4096))\n",
        "    for i, file_name in enumerate(train_names):\n",
        "        process_vector = process_pic('Flicker8k_Dataset/'+file_name, model2)\n",
        "        process_vector = np.reshape(process_vector, (process_vector.shape[1]))\n",
        "        process_vector.astype(np.float32)\n",
        "        train_features[i] = process_vector\n",
        "        if i % 100 == 0:\n",
        "            print(int(i/len(train_names)*100))\n",
        "    print(train_features.shape)\n",
        "    with open('vgg16/train/image_features.pkl', 'wb') as f:\n",
        "        pickle.dump(train_features, f)\n",
        "else:\n",
        "    with open('val/file_names.pkl', 'rb') as f:\n",
        "        val_names = pickle.load(f)\n",
        "    print(\"Validation_size = \",len(val_names))\n",
        "    val_features = np.zeros((len(val_names), 4096))\n",
        "    for i, file_name in enumerate(val_names):\n",
        "        process_vector = process_pic('Flicker8k_Dataset/'+file_name, model2)\n",
        "        process_vector = np.reshape(process_vector, (process_vector.shape[1]))\n",
        "        process_vector.astype(np.float32)\n",
        "        val_features[i] = process_vector\n",
        "        if i % 500 == 0:\n",
        "            print(int(i/len(val_names)*100))\n",
        "    print(val_features.shape)\n",
        "    with open('vgg16/val/image_features.pkl', 'wb') as f:\n",
        "        pickle.dump(val_features, f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmJNpd14aaSq"
      },
      "source": [
        "###Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEBPu6hMZTmz",
        "outputId": "c001288a-7c20-4d4d-bca1-ab669b28cc72"
      },
      "source": [
        "dataset_path = 'flickr8k'\n",
        "\n",
        "#Images\n",
        "#image_data = scipy.io.loadmat(dataset_path + '/vgg_feats.mat')\n",
        "#images = image_data['feats']\n",
        "\n",
        "#Captions\n",
        "with open(dataset_path + '/dataset.json') as f:\n",
        "\tcaption_data = json.load(f)['images']\n",
        "\n",
        "S = len(caption_data)\n",
        "T = int(S * 0.9)\n",
        "V = S - T\n",
        "\n",
        "train_caps = []\n",
        "train_names = []\n",
        "\n",
        "val_caps = []\n",
        "val_names = []\n",
        "\n",
        "for image_id in range(S):\n",
        "    if image_id < T:\n",
        "        #train_imgs[image_id] = images[:, image_id]\n",
        "        train_names.append(caption_data[image_id]['filename'])\n",
        "        for i in range(5):\n",
        "            train_caps.append(caption_data[image_id]['sentences'][i]['raw'])\n",
        "    else:\n",
        "        #val_imgs[image_id - T] = images[:, image_id]\n",
        "        val_names.append(caption_data[image_id - T]['filename'])\n",
        "        for i in range(5):\n",
        "            val_caps.append(caption_data[image_id - T]['sentences'][i]['raw'])\n",
        "\n",
        "#print('Train Images shape:', train_imgs.shape, 'Train Length of captions:', len(train_caps))\n",
        "#print('Train Images shape:', val_imgs.shape, 'Train Length of captions:', len(val_caps))\n",
        "print('Train Length of captions:', len(train_caps))\n",
        "print('Train Length of captions:', len(val_caps))\n",
        "\n",
        "# Saving Train\n",
        "#with open('train/image_features.pkl', 'wb') as f:\n",
        "#\tpickle.dump(train_imgs, f)\n",
        "with open('train/captions.pkl', 'wb') as f:\n",
        "\tpickle.dump(train_caps, f)\n",
        "with open('train/file_names.pkl', 'wb') as f:\n",
        "\tpickle.dump(train_names, f)\n",
        "\n",
        "# Saving Val\n",
        "#with open('val/image_features.pkl', 'wb') as f:\n",
        "#\tpickle.dump(val_imgs, f)\n",
        "with open('val/captions.pkl', 'wb') as f:\n",
        "\tpickle.dump(val_caps, f)\n",
        "with open('val/file_names.pkl', 'wb') as f:\n",
        "\tpickle.dump(val_names, f)\n",
        "\n",
        "print('Saved')\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Length of captions: 36000\n",
            "Train Length of captions: 4000\n",
            "Saved\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhjxHr_tewJK",
        "outputId": "20e5aab1-551a-4a08-cd5e-b02f38a394b5"
      },
      "source": [
        "dataset_path = 'flickr8k'\n",
        "\n",
        "# Loading the dataset\n",
        "print('Loading the train dataset')\n",
        "with open('vgg16/train/image_features.pkl', 'rb') as f:\n",
        "\ttrain_images = pickle.load(f)\n",
        "\ttrain_images = train_images.astype(np.float32)\n",
        "\ttrain_images = train_images / torch.norm(torch.from_numpy(train_images), dim=1, p=2).reshape(-1, 1)\n",
        "\n",
        "with open('train/captions.pkl', 'rb') as f:\n",
        "\ttrain_caps = pickle.load(f)\n",
        "\t\n",
        "print('Images shape:', train_images.shape, 'Length of captions:', len(train_caps))\n",
        "\n",
        "print('Loading the val dataset')\n",
        "with open('vgg16/val/image_features.pkl', 'rb') as f:\n",
        "\tval_images = pickle.load(f)\n",
        "\tval_images = val_images.astype(np.float32)\n",
        "\tval_images = val_images / torch.norm(torch.from_numpy(val_images), dim=1, p=2).reshape(-1, 1)\n",
        "\n",
        "with open('val/captions.pkl', 'rb') as f:\n",
        "\tval_caps = pickle.load(f)\n",
        "\n",
        "print('Val Images shape:', val_images.shape, 'Length of Val captions:', len(val_caps))\n",
        "\n",
        "# Creating dictionary and saving\n",
        "print('Creating the word dictionary')\n",
        "worddict = build_dictionary(train_caps)\n",
        "with open('worddict.pkl', 'wb') as f:\n",
        "\tpickle.dump(worddict, f)\n",
        "print('Dictionary size:', len(worddict))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading the train dataset\n",
            "Images shape: torch.Size([7200, 4096]) Length of captions: 36000\n",
            "Loading the val dataset\n",
            "Val Images shape: torch.Size([800, 4096]) Length of Val captions: 4000\n",
            "Creating the word dictionary\n",
            "Dictionary size: 8267\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8rzYkp8aexl"
      },
      "source": [
        "###Utilitary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qgPwA2pZyeY"
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import string\n",
        "from torch import nn\n",
        "\n",
        "\n",
        "def tokenize(text):\n",
        "\ttable = str.maketrans('', '', string.punctuation)\n",
        "\t# tokenize\n",
        "\tdesc = text.split()\n",
        "\t# to lower\n",
        "\tdesc = [word.lower() for word in desc]\n",
        "\t# remove punctuation\n",
        "\tdesc = [word.translate(table) for word in desc]\n",
        "\t# remove words less in len\n",
        "\tdesc = [word for word in desc if len(word) > 1]\n",
        "\t# remove numbers\n",
        "\tdesc = [word for word in desc if word.isalpha()]\n",
        "\treturn desc\n",
        "\n",
        "\n",
        "def build_dictionary(text):\n",
        "    \"\"\"\n",
        "    Build a dictionary (mapping of tokens to indices)\n",
        "    text: list of sentences (pre-tokenized)\n",
        "    \"\"\"\n",
        "    wordcount = {}\n",
        "    for cc in text:\n",
        "        words = tokenize(cc)\n",
        "        for word in words:\n",
        "            if word not in wordcount:\n",
        "                wordcount[word] = 0\n",
        "            wordcount[word] += 1\n",
        "\t\t# print(words)\n",
        "\n",
        "    words = list(wordcount.keys())\n",
        "    freqs = list(wordcount.values())\n",
        "    sorted_idx = np.argsort(freqs)[::-1]\n",
        "\n",
        "    worddict = {}\n",
        "    for idx, sidx in enumerate(sorted_idx):\n",
        "        worddict[words[sidx]] = idx+2  # 0: <eos>, 1: <unk>\n",
        "\n",
        "    return worddict\n",
        "\n",
        "def get_hot(cap, worddict):\n",
        "\tx = np.zeros((len(cap.split())+1, len(worddict)+2))\n",
        "\n",
        "\tr = 0\n",
        "\tfor w in cap.split():\n",
        "\t\tif w in worddict:\n",
        "\t\t\tx[r, worddict[w]] = 1\n",
        "\t\telse:\n",
        "\t\t\t# Unknown word/character\n",
        "\t\t\tx[r, 1] = 1\n",
        "\t\tr += 1\n",
        "\t# EOS\n",
        "\tx[r, 0] = 1\n",
        "\n",
        "\treturn torch.from_numpy(x).float().cuda()\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kiWtVBHaciQ"
      },
      "source": [
        "###Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJdMHbFqZu6F"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.models as models\n",
        "\n",
        "# Image Encoder\n",
        "class ImageEncoder(nn.Module):\n",
        "\n",
        "\tdef __init__(self, EMBEDDING_SIZE, COMMON_SIZE):\n",
        "\t\tsuper(ImageEncoder, self).__init__()\n",
        "\t\tself.linear = nn.Linear(EMBEDDING_SIZE, COMMON_SIZE)\n",
        "\n",
        "\tdef forward(self, x):\n",
        "\t\treturn self.linear(x).abs()\n",
        "\n",
        "class SentencesEncoder(nn.Module):\n",
        "\n",
        "\tdef __init__(self, VOCAB_SIZE, WORD_EMBEDDING_SIZE, COMMON_SIZE):\n",
        "\t\tsuper(SentencesEncoder, self).__init__()\n",
        "\t\tself.embed = nn.Linear(VOCAB_SIZE, WORD_EMBEDDING_SIZE)\n",
        "\t\tself.encoder = nn.GRU(WORD_EMBEDDING_SIZE, COMMON_SIZE)\n",
        "\n",
        "\tdef forward(self, x):\n",
        "\t\tx = self.embed(x)\n",
        "\t\to, h = self.encoder(x.reshape(x.shape[0], 1, x.shape[1]))\n",
        "\t\treturn h.reshape(1, -1).abs()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fG6x2eEahbE"
      },
      "source": [
        "###Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuVS3I9RdvrM"
      },
      "source": [
        "def Score(caps, imgs):\n",
        "\tz = torch.zeros(caps.shape).cuda()\n",
        "\treturn -torch.sum(torch.max(z, caps-imgs)**2, dim=1).cuda()\n",
        "\n",
        "def triplet_loss_img(anchor, positive, negative, margin):\n",
        "\tps = Score(positive, anchor)\n",
        "\tpn = Score(negative, anchor)\n",
        "\tz = torch.zeros(ps.shape).cuda()\n",
        "\treturn torch.sum(torch.max(z, margin - ps + pn))\n",
        "\n",
        "def triplet_loss_cap(anchor, positive, negative, margin):\n",
        "\tps = Score(anchor, positive)\n",
        "\tpn = Score(anchor, negative)\n",
        "\tz = torch.zeros(ps.shape).cuda()\n",
        "\treturn torch.sum(torch.max(z, margin - ps + pn))"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgDb4xHYZkHs",
        "outputId": "1936fad8-3cdc-4fc3-d888-e82a4e89f4d4"
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import numpy as np\n",
        "from torch import optim\n",
        "import torchvision.models.vgg as models\n",
        "import torchvision.transforms as transforms\n",
        "from PIL import Image\n",
        "import pickle\n",
        "import json\n",
        "\n",
        "# Parameters\n",
        "margin = 0.05\n",
        "dim_image = 4096\n",
        "batch_size = 256\n",
        "dim = 1024\n",
        "dim_word = 300\n",
        "lrate = 0.001\n",
        "\n",
        "# Loading models\n",
        "ImgEncoder = ImageEncoder(dim_image, dim).cuda()\n",
        "SentenceEncoder = SentencesEncoder(len(worddict)+2, dim_word, dim).cuda()\n",
        "print('Models loaded')\n",
        "\n",
        "# Adam Optimizer\n",
        "optimizer = optim.Adam(list(ImgEncoder.parameters()) + list(SentenceEncoder.parameters()), lr = lrate)\n",
        "print('Loaded Adam optimizer')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models loaded\n",
            "Loaded Adam optimizer\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wg3AnDdCgNYK",
        "outputId": "f0e101b7-3f37-4bb1-ffaf-e59cb2816561"
      },
      "source": [
        "max_epochs = 20\n",
        "# Training\n",
        "print('Training begins')\n",
        "epochLoss = []\n",
        "meanRank = []\n",
        "for epoch in range(max_epochs):\n",
        "\t\n",
        "\tprint('Epoch:', epoch)\n",
        "\ttotalLoss = 0\n",
        "\n",
        "\tfor batch_index in range(0, train_images.shape[0], batch_size):\n",
        "\t\t\n",
        "\t\tif batch_index + batch_size >= train_images.shape[0]:\n",
        "\t\t\tbreak\n",
        "\n",
        "\t\t# Data preproc\n",
        "\t\tcurr_ims = train_images[batch_index:batch_index+batch_size]\n",
        "\t\tall5_caps = train_caps[5*batch_index:5*(batch_index+batch_size)]\n",
        "\t\tcurr_caps = []\n",
        "\t\tfor i in range(batch_size):\n",
        "\t\t\tcurr_caps.append(all5_caps[5*i + np.random.randint(0, 5)])\n",
        "\n",
        "\t\tone_hot_caps = []\n",
        "\t\tfor i in range(batch_size):\n",
        "\t\t\tone_hot_caps.append(get_hot(curr_caps[i], worddict))\n",
        "\n",
        "\t\t# Encoding\n",
        "\t\tencoded_ims = ImgEncoder(curr_ims.cuda()).cuda()\n",
        "\t\tencoded_caps = []\n",
        "\t\tfor i in range(batch_size):\n",
        "\t\t\tencoded_caps.append(SentenceEncoder(one_hot_caps[i]))\n",
        "\t\tencoded_caps = torch.stack(encoded_caps).reshape(batch_size, dim)\n",
        "\n",
        "\t\t# Real training\n",
        "\t\toptimizer.zero_grad()\n",
        "\n",
        "\t\t# Calculating Loss\n",
        "\t\tloss = 0\n",
        "\t\tfor i in range(batch_size):\n",
        "\t\t\t# Image as anchor\n",
        "\t\t\tanchor = encoded_ims[i:i+1].repeat(batch_size - 1, 1)\n",
        "\t\t\tpositive = encoded_caps[i:i+1].repeat(batch_size - 1, 1)\n",
        "\t\t\tnegative = torch.cat((encoded_caps[:i], encoded_caps[i+1:]), 0)\n",
        "\t\t\tloss += triplet_loss_img(anchor, positive, negative, margin)\n",
        "\n",
        "\t\t\t# Caption as anchor\n",
        "\t\t\tanchor = encoded_caps[i:i+1].repeat(batch_size - 1, 1)\n",
        "\t\t\tpositive = encoded_ims[i:i+1].repeat(batch_size - 1, 1)\n",
        "\t\t\tnegative = torch.cat((encoded_ims[:i], encoded_ims[i+1:]), 0)\n",
        "\t\t\tloss += triplet_loss_cap(anchor, positive, negative, margin)\n",
        "\n",
        "\t\t# Logging\n",
        "\t\ttotalLoss += loss.item()\n",
        "\t\tprint('Samples seen: ' + str(batch_index+batch_size) +  '/' + str(train_images.shape[0]), 'loss:', loss.item())\n",
        "\n",
        "\t\t# Updating weights\n",
        "\t\tloss.backward()\n",
        "\t\toptimizer.step()\n",
        "\n",
        "\t# Logging for early stopping\n",
        "\tprint('Training loss:', totalLoss)\n",
        "\tepochLoss.append(totalLoss)\n",
        "\n",
        "\t# Ranks on test set\n",
        "\tr = []\n",
        "\tencoded_val_ims = ImgEncoder(val_images.cuda()).cuda()\n",
        "\tfor i in range(len(val_caps)):\n",
        "\t\thot = get_hot(val_caps[i], worddict)\n",
        "\t\tencoded_val_cap = SentenceEncoder(hot).repeat(val_images.shape[0], 1)\n",
        "\t\tS = Score(encoded_val_cap, encoded_val_ims)\n",
        "\t\tranks = S.argsort().cpu().numpy()[::-1]\n",
        "\t\tr.append(np.where(ranks==i//5)[0][0] + 1)\n",
        "\t\n",
        "\tprint('Mean rank on val set: ' + str(np.mean(np.array(r))) + '/' + str(val_images.shape[0]))\n",
        "\tmeanRank.append(np.mean(np.array(r)))\n",
        "\n",
        "# Saving models\n",
        "print(\"Training Completed!\")\n",
        "print('Loss over epochs')\n",
        "print(epochLoss)\n",
        "print('Mean rank over epochs')\n",
        "print(meanRank)\n",
        "torch.save(ImgEncoder.state_dict(), 'ImgEncoder.pt')\n",
        "torch.save(SentenceEncoder.state_dict(), 'SentenceEncoder.pt')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training begins\n",
            "Epoch: 0\n",
            "Samples seen: 256/7200 loss: 6537.94775390625\n",
            "Samples seen: 512/7200 loss: 6520.52490234375\n",
            "Samples seen: 768/7200 loss: 6509.48681640625\n",
            "Samples seen: 1024/7200 loss: 6485.74951171875\n",
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            "Samples seen: 1536/7200 loss: 6579.39208984375\n",
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            "Samples seen: 4096/7200 loss: 5777.216796875\n",
            "Samples seen: 4352/7200 loss: 5539.75927734375\n",
            "Samples seen: 4608/7200 loss: 5490.79443359375\n",
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            "Samples seen: 7168/7200 loss: 5071.505859375\n",
            "Training loss: 159523.244140625\n",
            "Mean rank on val set: 203.733/800\n",
            "Epoch: 1\n",
            "Samples seen: 256/7200 loss: 3768.50244140625\n",
            "Samples seen: 512/7200 loss: 4014.772216796875\n",
            "Samples seen: 768/7200 loss: 4515.1982421875\n",
            "Samples seen: 1024/7200 loss: 4181.0283203125\n",
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            "Samples seen: 5120/7200 loss: 2427.745361328125\n",
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            "Samples seen: 7168/7200 loss: 2626.089111328125\n",
            "Training loss: 90592.1455078125\n",
            "Mean rank on val set: 131.381/800\n",
            "Epoch: 2\n",
            "Samples seen: 256/7200 loss: 2493.578369140625\n",
            "Samples seen: 512/7200 loss: 2375.92529296875\n",
            "Samples seen: 768/7200 loss: 2435.216552734375\n",
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            "Training loss: 56330.40905761719\n",
            "Mean rank on val set: 84.856/800\n",
            "Epoch: 3\n",
            "Samples seen: 256/7200 loss: 1649.27880859375\n",
            "Samples seen: 512/7200 loss: 1605.6756591796875\n",
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            "Samples seen: 7168/7200 loss: 1333.5179443359375\n",
            "Training loss: 40916.221435546875\n",
            "Mean rank on val set: 68.11875/800\n",
            "Epoch: 4\n",
            "Samples seen: 256/7200 loss: 1343.7686767578125\n",
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            "Training loss: 34254.11340332031\n",
            "Mean rank on val set: 56.4415/800\n",
            "Epoch: 5\n",
            "Samples seen: 256/7200 loss: 1164.639404296875\n",
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            "Training loss: 28019.028869628906\n",
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            "Training loss: 21418.56964111328\n",
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            "Epoch: 9\n",
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            "Training loss: 18492.231506347656\n",
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            "Epoch: 10\n",
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            "Training loss: 15510.197723388672\n",
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            "Epoch: 11\n",
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            "Training loss: 14730.331604003906\n",
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            "Epoch: 12\n",
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            "Training loss: 13148.200469970703\n",
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            "Epoch: 13\n",
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            "Training loss: 11843.828796386719\n",
            "Mean rank on val set: 22.42725/800\n",
            "Epoch: 14\n",
            "Samples seen: 256/7200 loss: 475.6506042480469\n",
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            "Training loss: 10604.613464355469\n",
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            "Epoch: 15\n",
            "Samples seen: 256/7200 loss: 375.05517578125\n",
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            "Training loss: 9448.19418334961\n",
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            "Epoch: 16\n",
            "Samples seen: 256/7200 loss: 342.0677490234375\n",
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            "Samples seen: 2816/7200 loss: 318.00164794921875\n",
            "Samples seen: 3072/7200 loss: 326.9778747558594\n",
            "Samples seen: 3328/7200 loss: 255.74057006835938\n",
            "Samples seen: 3584/7200 loss: 368.80499267578125\n",
            "Samples seen: 3840/7200 loss: 260.873046875\n",
            "Samples seen: 4096/7200 loss: 267.0641174316406\n",
            "Samples seen: 4352/7200 loss: 298.68865966796875\n",
            "Samples seen: 4608/7200 loss: 244.80517578125\n",
            "Samples seen: 4864/7200 loss: 278.5225830078125\n",
            "Samples seen: 5120/7200 loss: 291.4857177734375\n",
            "Samples seen: 5376/7200 loss: 273.479736328125\n",
            "Samples seen: 5632/7200 loss: 349.90869140625\n",
            "Samples seen: 5888/7200 loss: 277.9467468261719\n",
            "Samples seen: 6144/7200 loss: 339.0272216796875\n",
            "Samples seen: 6400/7200 loss: 261.25341796875\n",
            "Samples seen: 6656/7200 loss: 303.5990905761719\n",
            "Samples seen: 6912/7200 loss: 304.7666320800781\n",
            "Samples seen: 7168/7200 loss: 261.0892639160156\n",
            "Training loss: 8472.885070800781\n",
            "Mean rank on val set: 15.42675/800\n",
            "Epoch: 17\n",
            "Samples seen: 256/7200 loss: 279.56585693359375\n",
            "Samples seen: 512/7200 loss: 282.83013916015625\n",
            "Samples seen: 768/7200 loss: 262.36614990234375\n",
            "Samples seen: 1024/7200 loss: 289.01849365234375\n",
            "Samples seen: 1280/7200 loss: 281.5047607421875\n",
            "Samples seen: 1536/7200 loss: 297.1835632324219\n",
            "Samples seen: 1792/7200 loss: 287.5736999511719\n",
            "Samples seen: 2048/7200 loss: 272.8161926269531\n",
            "Samples seen: 2304/7200 loss: 284.5437316894531\n",
            "Samples seen: 2560/7200 loss: 297.1348876953125\n",
            "Samples seen: 2816/7200 loss: 267.1023864746094\n",
            "Samples seen: 3072/7200 loss: 277.4986267089844\n",
            "Samples seen: 3328/7200 loss: 248.38418579101562\n",
            "Samples seen: 3584/7200 loss: 275.5779724121094\n",
            "Samples seen: 3840/7200 loss: 244.35540771484375\n",
            "Samples seen: 4096/7200 loss: 241.07586669921875\n",
            "Samples seen: 4352/7200 loss: 247.75241088867188\n",
            "Samples seen: 4608/7200 loss: 309.44561767578125\n",
            "Samples seen: 4864/7200 loss: 257.98138427734375\n",
            "Samples seen: 5120/7200 loss: 274.859375\n",
            "Samples seen: 5376/7200 loss: 270.5855407714844\n",
            "Samples seen: 5632/7200 loss: 260.0218200683594\n",
            "Samples seen: 5888/7200 loss: 303.7459411621094\n",
            "Samples seen: 6144/7200 loss: 251.80889892578125\n",
            "Samples seen: 6400/7200 loss: 235.18731689453125\n",
            "Samples seen: 6656/7200 loss: 277.9424133300781\n",
            "Samples seen: 6912/7200 loss: 235.80526733398438\n",
            "Samples seen: 7168/7200 loss: 279.2124938964844\n",
            "Training loss: 7592.880401611328\n",
            "Mean rank on val set: 14.26575/800\n",
            "Epoch: 18\n",
            "Samples seen: 256/7200 loss: 243.70326232910156\n",
            "Samples seen: 512/7200 loss: 310.01824951171875\n",
            "Samples seen: 768/7200 loss: 249.9051055908203\n",
            "Samples seen: 1024/7200 loss: 286.9819030761719\n",
            "Samples seen: 1280/7200 loss: 267.8818664550781\n",
            "Samples seen: 1536/7200 loss: 246.87367248535156\n",
            "Samples seen: 1792/7200 loss: 309.2592468261719\n",
            "Samples seen: 2048/7200 loss: 241.84072875976562\n",
            "Samples seen: 2304/7200 loss: 232.91783142089844\n",
            "Samples seen: 2560/7200 loss: 277.3640441894531\n",
            "Samples seen: 2816/7200 loss: 244.9348907470703\n",
            "Samples seen: 3072/7200 loss: 222.87664794921875\n",
            "Samples seen: 3328/7200 loss: 243.78892517089844\n",
            "Samples seen: 3584/7200 loss: 259.7152404785156\n",
            "Samples seen: 3840/7200 loss: 234.0305938720703\n",
            "Samples seen: 4096/7200 loss: 219.77293395996094\n",
            "Samples seen: 4352/7200 loss: 230.74197387695312\n",
            "Samples seen: 4608/7200 loss: 247.2406463623047\n",
            "Samples seen: 4864/7200 loss: 232.13999938964844\n",
            "Samples seen: 5120/7200 loss: 260.60693359375\n",
            "Samples seen: 5376/7200 loss: 208.81845092773438\n",
            "Samples seen: 5632/7200 loss: 266.4788818359375\n",
            "Samples seen: 5888/7200 loss: 236.66748046875\n",
            "Samples seen: 6144/7200 loss: 230.10939025878906\n",
            "Samples seen: 6400/7200 loss: 245.2178955078125\n",
            "Samples seen: 6656/7200 loss: 248.74722290039062\n",
            "Samples seen: 6912/7200 loss: 249.3863067626953\n",
            "Samples seen: 7168/7200 loss: 213.8367156982422\n",
            "Training loss: 6961.857040405273\n",
            "Mean rank on val set: 13.231/800\n",
            "Epoch: 19\n",
            "Samples seen: 256/7200 loss: 196.9568634033203\n",
            "Samples seen: 512/7200 loss: 243.88223266601562\n",
            "Samples seen: 768/7200 loss: 261.16949462890625\n",
            "Samples seen: 1024/7200 loss: 251.93190002441406\n",
            "Samples seen: 1280/7200 loss: 253.7092742919922\n",
            "Samples seen: 1536/7200 loss: 206.8123779296875\n",
            "Samples seen: 1792/7200 loss: 310.0606384277344\n",
            "Samples seen: 2048/7200 loss: 302.73602294921875\n",
            "Samples seen: 2304/7200 loss: 243.83975219726562\n",
            "Samples seen: 2560/7200 loss: 216.44241333007812\n",
            "Samples seen: 2816/7200 loss: 263.50628662109375\n",
            "Samples seen: 3072/7200 loss: 224.8231658935547\n",
            "Samples seen: 3328/7200 loss: 224.8262176513672\n",
            "Samples seen: 3584/7200 loss: 228.95326232910156\n",
            "Samples seen: 3840/7200 loss: 208.8988800048828\n",
            "Samples seen: 4096/7200 loss: 216.15159606933594\n",
            "Samples seen: 4352/7200 loss: 258.16162109375\n",
            "Samples seen: 4608/7200 loss: 213.17555236816406\n",
            "Samples seen: 4864/7200 loss: 261.4729919433594\n",
            "Samples seen: 5120/7200 loss: 240.72821044921875\n",
            "Samples seen: 5376/7200 loss: 203.45611572265625\n",
            "Samples seen: 5632/7200 loss: 249.1664581298828\n",
            "Samples seen: 5888/7200 loss: 240.5841522216797\n",
            "Samples seen: 6144/7200 loss: 269.8455505371094\n",
            "Samples seen: 6400/7200 loss: 253.46751403808594\n",
            "Samples seen: 6656/7200 loss: 250.6226806640625\n",
            "Samples seen: 6912/7200 loss: 212.38461303710938\n",
            "Samples seen: 7168/7200 loss: 200.97158813476562\n",
            "Training loss: 6708.7374267578125\n",
            "Mean rank on val set: 12.49875/800\n",
            "Training Completed!\n",
            "Loss over epochs\n",
            "[159523.244140625, 90592.1455078125, 56330.40905761719, 40916.221435546875, 34254.11340332031, 30757.811096191406, 28019.028869628906, 22825.5078125, 21418.56964111328, 18492.231506347656, 15510.197723388672, 14730.331604003906, 13148.200469970703, 11843.828796386719, 10604.613464355469, 9448.19418334961, 8472.885070800781, 7592.880401611328, 6961.857040405273, 6708.7374267578125]\n",
            "Mean rank over epochs\n",
            "[203.733, 131.381, 84.856, 68.11875, 56.4415, 62.61225, 47.8415, 42.80975, 35.6645, 31.73475, 29.94475, 26.30875, 23.4565, 22.42725, 18.6465, 17.241, 15.42675, 14.26575, 13.231, 12.49875]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NQldbKdfE9Z"
      },
      "source": [
        "###Draw loss and mean rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_Qd4ovWfY4V"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = (15,5)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "B3uNOvWmfDxs",
        "outputId": "5beb4fb7-6652-499f-9723-08ea6c41e76b"
      },
      "source": [
        "\n",
        "plt.title('Flickr8k Mean Rank over top-800')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Rank')\n",
        "plt.plot(epochs, meanRank)\n",
        "plt.show()\n",
        "#print(epochLoss)\n",
        "#print('Mean rank over epochs')\n",
        "#print(meanRank)\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSceA2Z6jbKC"
      },
      "source": [
        "###Load model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFTn3oNvZ0Li"
      },
      "source": [
        "with open('worddict.pkl', 'rb') as f:\n",
        "\tworddict = pickle.load(f)\n",
        "print('Loaded dictionary')\n",
        "\n",
        "# Loading trained models\n",
        "ImgEncoder = ImgEncoder(dim_image, dim).cuda()\n",
        "ImgEncoder.load_state_dict(torch.load('ImgEncoder.pt'))\n",
        "SentenceEncoder = SentenceEncoder(len(worddict)+2, dim_word, dim).cuda()\n",
        "SentenceEncoder.load_state_dict(torch.load('SentenceEncoder.pt'))\n",
        "print('Models loaded')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqQ80JS1hpyO"
      },
      "source": [
        "###Evaluations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhhvf_zXhrfD",
        "outputId": "55bae7db-2d4b-429d-f78d-db65cb3203ef"
      },
      "source": [
        "r = []\n",
        "encoded_val_ims = ImgEncoder(val_images.cuda()).cuda()\n",
        "for i in range(len(val_caps)):\n",
        "  hot = get_hot(val_caps[i], worddict)\n",
        "  encoded_val_cap = SentenceEncoder(hot).repeat(val_images.shape[0], 1)\n",
        "  S = Score(encoded_val_cap, encoded_val_ims)\n",
        "  ranks = S.argsort().cpu().numpy()[::-1]\n",
        "  r.append(np.where(ranks==i//5)[0][0] + 1)\n",
        " \n",
        "#After 20 epoch training\n",
        "print(np.mean(np.array(r)), '/', val_images.shape[0])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12.49875 / 800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhSsv4L0hYrC"
      },
      "source": [
        "from PIL import Image\n",
        "\n",
        "def show(dataset, idx, train=True, caption = False):\n",
        "    if train:\n",
        "        image = Image.open(dataset+'/'+train_names[idx])\n",
        "        plt.imshow(image)\n",
        "        if caption:\n",
        "            for cap_id in range(idx*5, (idx+1)*5):\n",
        "                print(train_caps[cap_id])\n",
        "    else:\n",
        "        image = Image.open(dataset+'/'+val_names[idx])\n",
        "        plt.imshow(image)\n",
        "        if caption:\n",
        "            for cap_id in range(idx*5, (idx+1)*5):\n",
        "                print(val_caps[cap_id])\n",
        "\n",
        "#show('Flicker8k_Dataset', 5, train=False)\n",
        "\n"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxyApNG7hZPo"
      },
      "source": [
        "###Evaluate with Test (Search image with Caption)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "jLdR81V3iZPU",
        "outputId": "ffa75f32-af14-4f94-a278-8dfc3b2bd790"
      },
      "source": [
        "import random\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
        "\n",
        "encoded_val_ims = ImgEncoder(val_images.cuda()).cuda()\n",
        "\n",
        "idx = random.randint(0,len(val_caps))\n",
        "print(val_caps[idx])\n",
        "\n",
        "hot = get_hot(val_caps[idx], worddict)\n",
        "encoded_val_cap = SentenceEncoder(hot).repeat(val_images.shape[0], 1)\n",
        "S = Score(encoded_val_cap, encoded_val_ims)\n",
        "ranks = S.argsort().cpu().numpy()[::-1]\n",
        "\n",
        "print(\"Rank = \", np.where(ranks==idx//5)[0][0] + 1)\n",
        "\n",
        "ir = 1\n",
        "for ix in ranks:\n",
        "    plt.subplot(5, 5, ir)\n",
        "    ir += 1\n",
        "    show('Flicker8k_Dataset', ix, train = False)\n",
        "    if ix == idx//5:\n",
        "        break"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A girl slides down a purple slide .\n",
            "Rank =  3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kp7at9ud2cYFAeIULIj7zs/8u03nMyf27iFXFfhJz+9KIvWFCr58R9TPIAsQwIkoSDl54kRdf+THqnuBv/8J/yItf/hlUehlvFgxHHYQ3WK/xQmKNQekQUwuSMMT7xlU9mecMkoArlzPqoiRQEGsoa0UcCCIJq7NjPv3iTe49mvD6G8d0khE3b91kcpoTJxW+0ggnOb13h+/VlpvPXifwK8YP32Hv2k1slTfu6dLg65wo7iOdQOKYL3PiRPD5l/eIQ01ePOB7RxKpNFFRPbUufSQaPe/9rwK/+n6P11pxMp1T5jmjvSEPHz0ikY2Zejwv0Eoxns3ZTwJ8EHM6mXPYv8zbD47JqwqtA04nC54Z7mOso7KW0hmOH0yRvkI9c4vpfMHJ8RHXDoZU+QprDJPxCb00otvPWK5mKC/oDQ6JFcShxOiUcnqKjAICYyhKQ3/Q53h2TJSkREnIfJJjTUwSpfS6Kb29Q1a5oZMl9IKY06M77B1eJuokzPITDnsSK3oMuinXvvCTBGGycYNKKZGyqfCPDzDrRrLG2lXrvScIgsbaJwRaCExVMT21fPUP7jDoabJUI6VEBxIhBUmkCbREB4JQK8I4IgoCykWOqY7QOkBJjVQCqTRKKoRq3M5KKYQEyVrL2LqxZaObFKLRNAoZIIVGyuDcbSA0Ak1V74jex4UP2jbhSXfrex23uc/askdD+jyNNbhVFNBIHqB2jigKqKuQOIlwNMK/LaXAVid/0bi4XSLx2Lbzspxb+oAL7apxM6/LKjZWkLXV4P24Xn37PE25HEJosk6PMIwoK0OWtvs2+sYP58y9oI/0TyF2T7G87vDJwAdte0p7Dq9rTF3ihQZjWCxVozsPImwdNB4VHFVlNmPIunfdDjYUQmAB42tWyxXdJCaKEsq6wjiF0hm2thSm4my8Quuw1ZM3Gm/vHF6Adx5naqQ3KKWR7SQH7zcuXCEdQkqUBO9t287OSaO1tnHvynXdFa2WXDTBlabpK4RYW8Nbsudbt61QXH3lJ9CD29x96yF2WdEVjiyKcEpSS0GJI5GOIFWE3QgHfO9P/owfefkGv/jf/QP6V18BEeCpqcuSMOwhrALvMM617mpBWVR00gyc53Av5s79FdOl5dlQM0w9o05M5EsWC0PnaocwmpEvLS9//jZv/PL/x9zB+FLG/r7gpeev8dbb9+l3oDfcQ5oZ9+7cJe52uHbtAD8ZY4o5UinKMm+8e0CZ51SFR6iAIOmRz0/xfsazz/X48dMhk39+yrgqiePoqXXpYwvG2Ia3hiJfEQqJcp7J+IxL1w/47qMxURxw5eoB94+OUN0h9+7doZ/GvHM0I85SojCgqip6WYytluTVilBqZqtVo1UTiu++dZ9iOadyAis03379HW5fHnL/eMYL1y4zKy1GCtI44Wy+pLae3AqWpzNENWdVxZiqwkaKsip4dPyIy/tDlpWhriomkzleSFScNh25M4RxxmR6ymI+59L15/AqQNol5dLhdAc57CGjDta7DYmz1m4GImvtJihjbenbDnxo9HjnUcXrwdgBB4eX+Pv/yX/JL/9v/4TybMzkrRMC5RFxB61aC4MMiGMBpmaa11zdy5gvlyTdTiOUp8ZrRRJqsl6KtBXFqibJYmrrQLREMUnRShKnAY2ZXZJEIXEWkiYaIWMCHRKGUaNtVAprdkTvBw5tf/u+SR8C3/g0157cxsonwG+u0VregEVRMghC/PpeLS5a8dYkyV8gdh+E32y7dTcaP87/XrNC50CqJ1WlG8s6HucbbZ8XAiU8zjTttpPF+CikNhY8G/dXc0v/LmT03fGu7/lxS+QT+xrYNlJGyYvkdocfJHiCECyQu6rRkGuBFwoVKGpCtPSUiynFcoIUUeNaxSG8R7YTENmSLLDUtUVamOc51sfUxjKZLUizBDzkhWA2bTTgSmlwMJnMGAx61KYGbwnjgDDSrTYCjHUYYxG1wJlGm621arSuym9F9oLzDXlRWiLY1oIbPBYnFN6bRgsuFE0mGrdpPw5J9qmXuPKFn+b04YLVdIkvanIcd05PmRZzHq1m3FjOeM5d5dnDLh2l+Z1f+Q2q7/4hP/0f/Q2CXobxBUd33uK3fu1X+LM/+Cqz1/8UW3mEk9TO45wA5dBKsVjkBIEkqAXOembzmtl0zjMvDAFPr5tSlBVnpwVJGnH3wR36+x1uXjvkzXeOeXj3BE3JZ1+6ycM7ElNbyqrisy99hq///h9y94032Rv2GFy+QlXl1LUljRNqB2dnJygZMpvNsM6jkz6D3ghfWZbzBV/68oDDvYhXvzHmm9+rn1qTPhFEDxyDTgdjHGfjM0IBSjqEdAwzzYOHj+gkCWkkmZ0t6PUzjsYzjK24dWnA6XhKkgpOxqfEWjLMNLO8JAkky6JmdXofFQaMujHj2RmxltT1AoUniw33jmfMXc2n9y9zcvKIbiyZz5fMx3c56GjSwJAdZpzNlpzdPeHqQOMFvPXmHQYRLBZLIiGJ4pjj0zEWw7J2jCcLisKQlzWd4YCyLAmlxtkK1bmMlBrbsvY1uZNSbix8a5K3JnRrnd6263ZNEgWg10RQSqSWiCBgv3/A22ePSCJN0t8jkB4nAorKkiQgrWG+nDHoDCmrgF4YUlhFJAUn04KkG1HPHf044+T0iF7U5d47RyRZQhbHTPMHmMqSpooAz9HZkkgLTNjnas+RVwXOBsgkQQpPHEVUdfmx1rYd3j8EjSZom4H9Za7C83PFE0yp0ej51sXTbJMSjHdE4ToK/Wk4J0mPxVRsyvr4vvcNsR4U127iNYVsn29L8Oe8x3pwTuB8E7OvlcJYi7EOrSS1g8WqZjTwTXtstbDNOzgnzO/pFuZpGsOL1PdxDuc9WN840+V73GuHTzas85xOc7SQzCsLlcHPPR0kXgY4mWBsTT4/wdsCJSIUEu8VCokWEoVEtf+aYNiA2lqkUMzystFpS8WytBhrcJVlObdEWgMea30jDZKS3JQo5cm6XSprm3biJMoYKEsqV+N9Y81zcj1+SXQoG2ucaNKLKEQjO/Jr6QNopZG0ExTOM0us3bfeN9q99Ooz3P7Jv810AbPxDFuUOGvw0lPTSJQerk45/dYZr333TV6eHPMFnuef/vf/Lf/mTxzyzu85elevsDLf4Zf+i1/i2/ePCEXEM6OQSEQI5zB1ozk0zuADTWUcxgpqUxFFkmVuuPew5MZhzqgTMZ7kXD4Y8c5bD+j2UgIFb7/xJl/5G19g/E9/F1sLVrOc73z7u9y+ccg3Xz1l0VmhooQrlw9Y5jkPHtxh79aLiPkjRD5topplQpYkHB89wDqBkAHlYkIddRAyoqwdf/qN+1y7con8rG70jk/BJ4LoWQc3bt3mzp13kB6uXBnSHQy5VAX0I8/J8owfefFTnJ7co5ckZGmMmi4xdUna63MyHqOEpKwroiBBqUYsGmjFPF+x34tJ0w5aOvJpwdXDIavVkv1el9rUJKGkriTjkyNm8xUvXj/gZDxhNOywmE3IgOXCUiyWAMRacP/uHYrVgqyTsSrKxooVS6pH97kyjJgvzohERdRPEDZntZiig4gwCog7feLe/mbY8niUVJtO3XmHlnqTLgXYVPq1u3ZbNyGAQGucc43rCcHxyQlJqJDSUFQ1pIqsl6GcBZ2ynyQU+ZzV7IRyNcWLK3S7GYNewmxZ4r3g4OCQfiekqpZEWUh/6Oj2MpJ0we0b1zBCoU8DqrKk28vQClBL0n4CwYiOWtGpV+RLiNMM45q8Rorgr7mG7fBXxTpdzwb+g5OGC+7LdnJS1baJqkM2uh2xDm7YvveWIUv4p977r+S1bLmchNaN9ETh28Maq7nz29GEzSFKSazzhIHCGA/OUBlPGID1YFu3rcA32tktUntR99fcw3qxkbM6f267XHuz1uVU+E1uzU0uLtbW0HNX8Yaw8t50eodPBoQQFFVJJ0rpRSmVt/ggQAnFqgjR0T5KV5hyDDZH0kW4JifdOqCpsZsJhG9dqW0gnbGG2tVUtSdU0M26eFORxBmmWpEkjZvV4xgO+zhvMXVFHAdIpdGehtRpgQoiwiSmLCryVY53rskIUZqmjkpFK9pG+sYuZ6xpcsWx3tWma3JrqcXWpFI0nYI6uM3Nf+PnKW3C4vSMelXha4OTzX2CQFOXBdPSEAjJeFzRe0vwlb/zef7TX/pFgqMxV57/SdThJea/90ec3D2jNhYfKEqnibUmJgffkGzhPYW3SOEx1kJpSLTCIbjzyBJ/e8kXP+PpR4rXX7/LwaVDamNJkwGVLXh494i/9fM/yu/91h9xdjynGEvqWc6lS12WJ2d8+9Vv0A8NB3sHKA15DaozQtY1KtRUVU0YxZRFTRSlBFEADlbLGYP9a2TpkiRMSEaXCQf3seO/xmCMDwUvONjbZ5WvWK6WdLKUs9mClZWEaG7euk2v3+fh8RGXr12lk4XcPV0gFMyWJb3hJZZljtYxOuqAr9jbS/HW4N0cKyJu3LrF7OwRRWk5ODzg7j3HsBOi44DRYIUpC4Sv6HW6xFmP2b0TXnhmgDYFBkmoAOEZ7V2mXpxx9vCEg2GP3nDE+O49IiWYnJ1SlIYwSJncPaKfpQRRhFbwxttvsd8bopkTKE9Zrjg+OcbhkVIhZZPnTweKMGz0EVI1rk4pJWEYYpxt9BI4QrXOn0cb+eoJtMYah3eeN994nWEvbCJ5dUSnm+K9IEwSrNNkacb+/h5/8vuvE2Ip8pzu3iVqVzNbLNgbDQjTDlknws8Nxjm6/R5BmNAZjtBBgA5Sgn3P6cmYKJDEWYqxgjgMCaKQMJR0ZUaWCJAetMSUBiXse9eHHT6RuKjBoyU/F3MkXjiexwmb2P7SUCbvkUo2hEQ2JKhJgH4xUEL4VuPnLzhan17W7XI+tu0JPR9rQtREKW5sea0VYpMc9jF6tO1WXkc4egdhFLEqDLFWLPMCrdLN8/tWgK7Wwj4et+J5LE0EsLWOdYKMCx7btdXRe4RqtI6+dQnb1vKqREOkXbtdXYxC4Ukmu8MnDUoIrl/aaxLmejBByiIwBL2Mm5/7cU6mY1bT18jzGdoblHB4Z5tqJZooeNkGSkgBVlg6mYN4iQ4dwmkq2+qtfYEKa4pJxZpnSRRegFKCqlw2xDEImlYi21QuCpQKsMajAkcYhY37Vjh0GrTRtOsMEgJvmnYiRZOTtc3v3OSk9QLp3DobX5vHoSmHGgy59lM/A2rI9OGEYr6irgz4ZtIUheDrktligdaS5WRKGiqWy4jF0R0CHyCOA+zYEw4tg70RB1nCvanBeEdVByShphdpTqsS4yQWh8BivNvog23pSKImY+D9BwURJa98dkS/1yVfLEm7Q0ojUTpgNsnxxvETX/48X/vq1zH5isB7umnG5z/7Eg/vHLFc1OiO5XJ3D1+OEfE+ZVkTSMV8NiWIEpSQmKqiPxywmjzCS8diekS3e0ioLW/+xQPSNMT4p3vKPhFELwgU5fwMURcs5nMGkUeplF4Sky/O+NTzn6JYnDDqdUi6Gcv5hF4agXeUywn9g6vUdYWMIw72+njvWZWSQRZS1zlR9yppmrHIR2SjGB0FxJ09lqsZg70RWkXkqyW5gSzUlLUjTDt0uiOK2RQnEoJYw9yh4x5SCtIkZzTaQ0chUdZDBorZcsne3j6WiuFwRJZl2KqgMjVVXnL1089R5wlCaa7eeI50eBWEwKxz0llHVZWbQdQY0ySMNPY8Ymkdmdv6mjwgZJNUWskm8lYHgnfefpvDZM50NaOrPcu8ppxNqHNFZS1ZaEEd0usmuLri9OQR0eAKq2UNJicJ95jkU0IV4wWEKmCFYGUk/cE+RVUgfYkOMwZ7AVFoiYKIvYMuURQxy0uMFThXkfS6REkHrRTT2YTIFR9nddvh+4D1rPu9JGPnaVEvwtNYs5pUQq5N8t0m4W5Ji+A8DQv+XJsnN3xFvEvKlXXZHiM1m/te/LywvU3zsMnW78GsG1j7ub6381uWM9EYGNdRgr59OVpJvJIsVyWDLGneVZuCAuXXHPk8vUtbXEdL8pzHcf6Mfm3I9K0LuSV7Yvs8tlMXrZMitWXnIrdb33fnzv3kQilJNwkIeorFasmqWJDEAeEwoTsasqoN9VQSugrrDGo9KRAChNy4P5mnQoQAACAASURBVBUSJSRWKPYOE7IrijgSpFFGXhnCQIIUrIolr/7eAiV1E2Dhzq3LRVls8mBaWzVabykRSiJFQ/gImpRKONPWT4czTV4855rk/UjZBFzBZtJzrv1ttHt1ZZrgDyDwGtfpcuOnfw4bX+Pk4SnLRRNMCRVeglJgy5rZ6RhhPflyRSA9lTEcvVHxG//4/yQ4qXlu7xbT1x6QXh6RDvZ5/van+ObX/yVWaEoXkMguiSzYjwMeLms8kkxJlrVD4EmUpBMKeoEgDSFSEpcL7j6oma3G7I32kIsV08mKJEk5uNTHiz7HZzNuf+omfn5M6FY89+wh6d4eX3rhM3z1n/8Gq+WE/uHnSA5vUZw+wNoVqob59IRe7xJ7ox5nZ0uWi5wkjomEaqyYouTgUFLedQx7fTRnT61Lnwiih3cIbwhCRV3VLBYFvWGPSAV0STHe000TdJSRRAF10USRCmsIlSaOUoKhw5iKXppQmRqPZ9CPmM/6BGmEdAWdJGTYG4AZs3/pMunxnOFwQD1ZUZcw6OyRqYIsi8jrPZIoJF+WHNy+SaIdMuiAkETpgCuXcq5dv0E+vcdzL/wIy/Ed5rmhu7dPvTolSjO8ksyrkjrPibMuy7IkjmJsVVP5ELtYYB0orTY6O6k0WjfRrcmawOlmFY8oigjU+QoaWjU6CmjInjeNSXy5mqEk5NMT6nxGGAgQjuMH9+hmAUXlyEJBebpgcjbG1jVJ6LnzxhsE2lOtTlkNImYzh1iFzFeGvBuQG0Gapigcqa55dGLJx/fYu/IccRxhOwrtaxZmycP7D4gCzeUrtzk+WzA4CJG2ojfcQ1S7weUHFReiVte2r40U76Ir8sKvvG1GW+vdvKc2Dm8rpGwShMu1e3brNLFthfK+JVYN6VsTTe8v3vuDQKz1ee2XdSTg+nNd7nXeWN+6bTfxG/L84USrQ0riCGMsrjK49l34lghK17hxW57YuIPbZ/Bt4mbrW1fWEyLH5j/fCtThyRyD3jUCfrF+QQKsWIvzxeY6Yv2uPmQk8A4fLZSUSCuoHcRBjKlLZJRgJKggIu2NsGeSWFdY5ZC2DXajbi1QTXuSwiO9RyIZDAOu3BwQh5owSDmbzghCTRh6gqDHq7/zems0kG1AksAag6stSmvKsiJOmsA6rTVSB81xUqKkwIgaZ8GaVkbU1nOlVCND8KJ1Sep2papmSTAtRZO8WbYrQAkAC70BV/+1v4WPbnJy95h8McWWtgnikJZAe0xZcfboDF836cwEFY4myPHs7pw/vnufK6rDwef6uLqgLh2/87/8KoeXr2O/8XVAUjoBDEl8xUiOCVKFV4JhN2O6Koi14tmDHp2kxtoKYxyVVcyLim/drZFnMSd/8g6H/Yjn9xQvvnBAmh0SJAFRLOhGMf3rKUoUXH32Jayr8U6QpRFxRxPu3UT1b+Pu/QVh0iFIL5GI+1izIksHzM8WrE7HBJcOkHnO3uElDI6b1zMOL3f4zf/7Hnb1CbfoWedZliXLsuLywQH9bofKOC4djKjmguViTv+gz2y8IpQl0jZLoWigP9hnuVyghSVOElbLM/JaooMELzSjgwPuPThjr9PHVp7haI/V+JThoIfKUybjM1I8vdEB8/mCKMvA1KAF3pR0ByNGoyHelfhQ4eqKNFaUZU2SBdhVRJqEmFXMoAujwYClX2FkhMYzcRVVVXNtf5/l9ITD516kXE7RYYLzTT6hdYDFuUWhqefOujZxY5OHzllH2WryWGv2gmaFjEAHKNGkpZjPpuyN9lnefR0QdNIeVb1ESYepaqRXNIuwGcrK0O/1wa6YnB0z6sV4FOPjh1QmZG5gvrKw8uQ+YBWEZJ0ueT3hbLYi0obJ6REOy8H+JbAVZVmwylegBct5j0cnM6yskE5QmJqu3rluf2AgtuIQ/EUL0PZKKK2v84J+7oLr9nHuJZo1NY1zYB1KtUmS383XurlGO6nx57t9K1hrkrA21rYP9HjivL1t/vfbZPH807s2Hcv20k7tH80Sg80yaLWx6EChg4DQWIrakgQaz3l6GW+blQSEBOFFQ/Sca7R/mztukddtr7eHJsLdc55pk40vuVmlAIRbp7VpCOQ6l23zDA1dVFIgW/fuzrr3yUJtDGkWczw5Y9TvEIZdHrBEiQCpYsq6IPAe5TzaejQa4WkSFYvGPdqkMREI1axYM5mt4MGCINLUlWRe5jhXYoH5Et58syAOhmilsMJC7ShXRXMdLel0OkRR2MiN2shcKxzGG3B1Oyk77xe8ayNm24iuonLUtSOMZRsJ3Byv2UppJJrVM1T/Egdf+bco5YijeycUixW2cI1XSzZ9hslXrCZzqrIEa8DlSF8grKeqPbNVSeU9/Y5iOpkS9jp847f/kN/6Z7/Oc//qp1AE1EhyC7WNiJIDytWK2C+okdTWkmrFg2kBUvDFWwn9QWPEWS1rwqVkfn/GJC8JnWQ/Ely7fEiv1yVNUpJY009TUl3SC2b0b9wiOnwec/oGJ2+9RhxBnMWgY4TuYDwk2SV095CDZz5DtVzyxrdeI0OixnOqSpOOegShQKsM7xTdIOeFz3T4xp9+womeB+arJcY4QiW5++AeWdphlcfoIKCeTjidB03Ui46ZekeQ9gl9hQgiWBUQKGSgGZ8ZjicFlw9DVpXi6OSUyXxM7Xscj88IsxC8YD6fEKcdju6d0Ll0SJL2OD1+QB4qlDBUecVsOqa/t4/EsywNZZGjXI3o7BHHIdPJKabIcXWBV5pL165i6gXOS6o8J8oiBFBZgTU5SlQEOkB2eu1yS62+zpnNoChlkxR5O1HlthVlbY6HZlk02Wr8lFwvXA0PHz5CSkFR1CRxhyCNoZIME0FelKRxQmkMRQFxmLF/eI35o7dQqsZ5S7+/j6hPEVJSlwUa3eSaqGBRLhgN+ozHOWnaJZEV47wiECVVUeNMznJ6itQhpq6YjU+YTuak3RAtNSqASO6I3g8WWnfme/CAjU6vrchP0+2trYACgW0Dh6z3hEGwmcDA2iJ4bjl8Kglpb7GJaEU8wSkvFvQiGT1X6D1xycdPbEkUm/I1m9YrnzftEO+xpnFHW+dItGaVl8ShXj/Jxk2L9RvS2iwkc67H2zZ8bgoltrmv36wlul6e0Tm/NpQ2EY8thXXeo7zHb1apaYgeApTcslru8IlCFCmuXAlJ0pj9XsTSVNR7mtN3aFL5mJJAlkSBZ4mj0tCtRbOCRBt0gRBN/lKhUaLgxq0uN16IESqmqgtm8xAhFGeLU+7lNTZ3iMw1gXVt1gdbV81ETAjCMMBYS12U7XKhTfSstRbhLBKLx2LwzYTIObxok+XL1uLdrtneTGx8O0E7n8Qpr6B3mf1X/ial73J8/5R8scAWFZU1IC2R9lR5zuJ00gRk1Dne1QhbgC8QGmpjMa23oDAGLwSlCvi//vdf4ZFd8ny3i/ESLyQOSeFDsqBHqlfUdUVpDZEM+Kkv3+bNN97CeEGaxERRiM4kWQ/2asflg5DVyqK0YjTokqYJ/V5Cv58x6PfoxRZfn5J0b9J58d9GpvsIp6he+zoOT7d/CPMjFvlXKZdjQlK8OiO+/hWqN19lfucBVdjj1t5t6HQoJ0eU3Yru5z5NvZhRnRzxwo9e4ZWvTeA3370ufSKInhZgi5Ik7mCrgtPpnMt7I6r5jByJNjnTMVCvWIaOVT5n1BsS4ZiNH3CwP8JrzcnpGcvJFByk2rOYn3F8OuGwm2DyFd5WPLr/iBdfeoGTo/ssaFyjOgpYrpZURmLKFaqTEkaCqiqRnYTpdM5yOSUIUgoL5BbrJZOTGVkoqaqaZQm9UFFNjxnPcpIkadytcUo9HXN8Mua5W1cwzhJ1DlGyaYjOtevXbg9w26J37zcpV4ALyZWttcgmURbOuialihA8enTEarUgzTJGnZTJbIp0FXE2oCxKBoMu88Wc2sDVWzc4HHVxxZCrcUkYdwmTGLvMkfoAbZfUTpMGKwKjmM5XTBclnV6PZHiTjpwze2eCjmOWq5woSBlcHRGoGFsvKPIK6xXGCGrrCBLFyi8/hlq2w18JWyTjcf52kdR51mGy70b21uRNCMA19b8yhjhsF7hub/BulsOnFIttSihFowBcByecG9227IuidQeLtYPrA5oBz5/0/JpbLmwpJdY10XpOQFlWOLJNIMkahsbqti5B+0oaCd5jz7wu/fq4jUbSe5quwW+sgZ41dV2/leY4teVuXieL3uGTiyhUZL0VWnkO9gKe73WIj1ZMXq2JAk0vlrz+6JSTWclpXlHuSzCNCGH9vxIK4SXCCVCaKKuIUs9qKqjqksv7I5RyPHs9xc1OELJZoMADzgpM7XDWkaQxUmuqssBaR11X1LUhDgPCMGiIZau5Fc30ZxOdXua2WQ4w1FhrCAKNcwa8xHqPEk2sXiPJkOirzzH40k+xWIU8enRGvshxVYWnQqoKGShwhmKxwNWGKl+CK/DeIHyJcDVVbcFUBEDd+I5Z1Ybf+H9+nTeP7mOs4Xf/xR+D1FjfGGNyE2JFlzi5SllNqJmxXFVcunaT0V5IMZ0ikFTOk2UZUgkcDkyMqB1eWMIoZXRwif7okCRJCcOAQAlSnZHcfAnVuQl45OgZrDF4BNJZjt/+NkLFLOdLivEKQ5+q+BbR6SOumRixktwZv83+S88Q2gBf9tF7t8jv/TZVaTi8+a/wlb/j4X9697r0iSB6UgoMkIYxgfcgA7yvCVREWTgSrcirAiUkZ+MxWdZn1E3wVcnZ1CKEp3aGOp+RxQmJlHhfMp8v6GUJ/X5KbWpMVdGJJDVQTE+o4xGduFmX9ezkhCBKMfUMV4b0e10wC6piCuWK5WLFoOvJlxVRKKgWp5hyhc6GaC2hNigJ5WpOVQsuH0TUeHqjq6izFYuyIhtdo1itSPZf2GS9VqpZMHnjvoVNCpVt19h61Qzn3GYlDNV+XyfEXIvbweFMTSfrMdzrMZtO6PSGSKnZP7wCyuOsZzTqI4SgqpboJKUXhUTZgCjOKPySnIRYeGTYJfYCqwbMJ9/BofBCkKQx5ekd9i/tUyzPmE0nVGpGkt1E+Tmr5YxACla5RVjD/OwMHQZEve7HVdV2+BB4krA9yRCeJHuwXm1iXZc3+/3azerAeaq6ppN0tgxtT5Kc9ywfa0vh+rvbIjHbe89dzGsKtPn7wxi21rzpXKKHoIlSdK7RWDlrEDisdefrR7PtEBYf/NZbB3sat6/beqrNMReZLtaLjQUFmgH2Qz33Dn8tkEIQx9CNM8pqyrhUzFcOazTDQY+efZP41gGJmXLaKQlmvqlzqCbiFt+uMOHBOlzoEUpTWomSXSIipM2IGBJUXY7vfY1QTZtEyR7qqqaqqiZyVwmUbuQVgVYor0nCACUVsjVq185hjGnGMppoW6EERV6hdJMnNlCCcD3mbQUiNWlXJPFzP0Ln819hOpY8OjqjXObYstq4a5sl4h2+ctRFQV2t8LbEuwpHCb5sjCLOojAoqZvVgrXiT77zGlNbMXeGwhpWD49wGiyO2juMA0NIoPfp91+kmnyD3JUsC7iyf4067jQyMQ+dLMUJWJU5XtZEaYDGkqY9ojBuPAymRjlPFIZ0r3ya6MYXqasJQbSPWVry+ZQ40Jw+esDJ2ZIo7YDT3H1jzHwueOVz1+nNPPlbcJQ/ZFHn6Krg5pdfYn73ddyf/r+EYU33ep/y7Ii929eeWpc+EUTPekG3v4f1kk6vw/WrV3FSsLQBURxS1nPSNMNUS8rKce1gD6oVXofgA0rfrA0bqZAo7bEoFkzmOdNZwbUbt8l6IfP5FKEisixmenbaBG4M9hHL+4zHU4SzdHs96skMoTN6qWIxV9y9/4Bh4KmdoCiWLJcG70qErYkCRVFVeG+pq4qiqog1jPaGFMspIh4wutzn8niP2emKziAjn52h0r11foTzIIz1yxCCqqqI43jjwl3n0FsnSV5vlwKQjevLe4eWiul4TBTEBErQyXqEoaDTyRgdHrKYL9g7uMbZ6UMqC3ujHovZGAjo9/t4YwiSjCQbEKkcVSXEgUbJlCSwzJea4WgPHXeJ4yGj4ZCi6mPDPZZSYK0mCRX9wQjhVmRpB+8cJ9WSKO2RWdBVwemD44+rqu3wIbFN1JrEnOIvseyxcZNur94CbFyddW1BNNnmw7BJF7SOYN2+tBOtLu09uKZAgGuj/i4QsLVLqHW1Os5J4Nqod9EnegHvyYO2TlsnLt5M0IQkjiPKoiLSkqo2RJF86tW29YxPCRq+kHHGbe7H013qFwhh8937dgF5fOtJ2DrmAwax7PDRoqwr3rp/TC/ukqqY2dwh6wxb5ixnY3RxD1mfcdDxTI+WhLVv5DttIzq3nguElERhwI8//x8zuHQFj25S80iF9E0iE1O8RRDeBekxxmJsjTE1WinWkyXrLOcSVYH3trUon69l24xNzRTGIdCBJAzbZWbasUqpJrrWeY/UAiECBp/5CvrS5zg+zpk+GrPKc1xZYkWN1K7JKKEVxhScPDzBrHJ8XYCrwFV4X+K8QThL4G2jWUeC1NydTShdMxkyVlAgyQErmkyD0Cp9fQq6R6AHjPo1D6d/xt2373LYvUm+zBmfzugO9wnDGCvArVacjaeU04LD0RBc04/ZIodOjMou0z28Df3r+LJGyAWokKNv/hZlLRnt9/iLP3+dk0clUadgNVc8fDsn8QF35EPeeX2GPoX+fsBzzz3D4EefJ/7MpwkevkNy7RK+yBGxoj7+JrbuPLUufSKInneOUTfm/qMxvX6X0yBgkec4ATcPQ/L/n703+7EtPc/7ft+wxj3vqjpDj2zOIi2JlGiRsuxICuUkyiQLhpMgQJABubCRBMlNggC+yT/g+EJwECGAETgQIiNGBiWObFmWJcWiOEpUs0n2xB5O95mr9rzX9E25+NauqnN4jkReJDwEztuo7qreY+361lrP97zP+zxiyIsvPserr30boQv2bYI00LkCXVr2lSfNEtLhlKPja8jdgtN7d0nSkqOrJ6SiIyejaARJOeHOvVOOh0eUwzGr1S2WmzUfeOnjrFb3GE8mqOPnCNxFZSXb7Rnl0YzRqKRrtjR1TZEPSYsRhQ5Ubcd2cYYPHc1uh/QwHs9xtUMPpgQfx9GLPMc0dZwoUkk88Z7T1b2v0EPsx0GnF5m6Xt+gZNT1hd6iwV9kDUohuHX7JgHHeBTNkbuQMZ0fM55dR3GPo6MjbFdhvWKQabbeIULGYHQEdofOSrKioCxeIGwtg7Rms/cMp9eQWtIYQTkY4HDMj6+y6RaIbIYPHaqYMkgCSZpSpBkSR90GpvOS4WRMonPMbg9PW7c/VHWZXb7MMh8sES7XAwMacIFOgniAZZIi+sQJAsZ70iSyEA8MWvRa7XP269CNfBiPiAtN26E5fFnLFidyL/Rw523l74XJetTrPXBzOH/9y+A3+mBKdJqig6duW7IsPf8AHvW04bt+evQbvABtj73Lox93IFq5yP+9YPj6v+/3/nRP6//jGiQn/OwH/0vyZEChRyRK88r2Zb6x//vcfvX/4cXjNU21oq42jLSilElcHK7/G8sIYg7WOkoWlMmcIpngPLjQITwEb/Dec+Pdm2gpUAiMtThjkfRxmwi8MaAOFsz0GtG4fpRSaA0x25b+eFf44AjBoXSCMQYQGOdiipOU4Byj4RU+/Lm/CumHufP+mvVyQ1e1vZWYR+uA0hEwetuwvH1Kt9mDbcG1hHD46gjeIH0AGZA6wYVAF6LVShCqF8MKjPdx8t0reu4TfLQ4k6lE6JJi8FGy6iavfetNJoUhtRWrszVZMsaWjta2aBSZlNxbbrC15/TujmGZUuQpR9OMo9mIdrdjnK/pFjdAKparL/GP/vd/wpUXhhyJkvdu7lieOgg5d9/bwN4zTh3pxpNuW4ZLy4/+8s9z8ks/gZqVGCPo7nTYzS3qs4bjT36KsFvS3Lv72LX0RAA9KaCua4RpCAwJ1nPt+Jjl3lKkEERCOSjwneWZK1MWywXHA8HJvGAZKvIkoZgeY6o1k+MBdb1iUpbMZkcU2rNf1RACR9MhNgimZUI6mkCzJUs0o9GAK889x259m+H8BD2bEzYrKuc5OTphMj+K03FSMPEpz7zwYvQTqpd0omO5XiLzEY1pGRZT8nJIbTYkxYBd06GThKI8xgaBlEMOyZdBcG69oKTEW4vScTrPWkuSJA+AvsP3IZLzcWck1PlFBuDW7ZsI7yg0LO8sSK9fIc81eTHA7CRBKKTSDMdDzu7epKm2bKnIJteR1iBL6NoNWT5iPtcoStK0Q0qLTgPD4ZQrz1xlfXYbrRQim1OOZyTFgLrxzKcjnLdkmUKphPVmyzE7RuMJ89lVbt94G5U8Hcb4YSrZ24I8rJs7GAmHRyCmR7J7XEjlBPF4kjKee5M4UfCADlD0viHy0vMeQI70F/q887ateEjH1t9ZnL/wQ0BGXHrO8IjbLmPURwHaS8Mn5+9Gcg4s45AUmM5hrDt/v/AwqPtui5RLb5mDnPACRD7UMr78+Tykb3zwCQ/fSELweBcIHryM07/yKaP3RFWWlnz4+qdwrotZ4cIzKgfIJmDffZ/iakmZJ9hBgjot0T6HIHv2252DvEPai0xhOE4pihzvDCEUkZ33cOPWTU4XS5QUEBy2szhzEb/pe+cHddhMna+x+F9r7SVSIq4tgsQ5T5pqCDLqQpXEOh+5vqB56QM/xmc/81epqxH3biyoNlvafUcgalyVluhUk+YCqQM3v9ODPFPjXQvBxratN/jQQYhdAqE0XsZuQeQVJdY7dACPx4t+EKQHrQIQQsfziZf0gWpMB8+x3n+TL3zlNa6MSoQ1nC3fYDzJ0Inos3obBqM5AsVqv+f198+ot4aXjlPee/cWL374G3zixz6BxHD/7nvcur/hH//eW/z1/+Qvsq4km7XgtdcsoVuTto7SgEgcdtmQ5QnBSYpPfoLkQ59EVKcwSBhcnaOkpVkZ6u/cBJGRltlj19ITAfQCcG+xJdcKmZTU9RnFM1cxNOBqysGYtq0ZDIekmWZ15zbzdMAgO2GfCMZHM5ACrSGRmkQJTo5mbLZr5pOSMpcEqUHCZt8xnQxJyzHCN5hEkssEmSTIUDE6+jjLzYL5YELnOoZ5Sj6YkJWaaqlJ9J7Z8RxrGtJRSjhbooXDi5w8U0yPr4OCUZnRqZztfsPR0RHjyZim2jK7fp1Dfh89WDtcEM+za4kHju5jzS7Hnkkv40lfgHE+5gf2J27nLKbtwHukXVDtd2w3OclAsttvMaZBJBlFntNVLevtEu8CjXCEEHOGx0rTNg1HRxpf16hkgG13ZOkApQRXrs9RwiMk7Pdb0kSy267Ii4xRCTINnC2XjFwKomaQTxi8cEIIkqALpkfHyOz6D3C1Pa3vt0SPZ/zD7dIHwN6jHngBri4bA8cuazi3bPH9UEbTtKRagRR9K1Sce9wd0NsBVF7grEvP29dl3HnB9F3cdnnw9hxgyR4ShnCJaT+8X4H04ZFgLzyEEON7jeaw1lmSVNN2FoWLNklK8bh6GBwffPy+n6GJPzPXtmdIfc/GihA3m9LTT+k+rSemgifRMurQjKNuNzT7FUEE7r38DusXPsKgnCLXQ8JSo30atXmB86xbLVVvSC5RqSQrhyR6QNfeo+0WjIbHNKbi9//w9zFtR1ZkgMUY33tFBqw1JEkSJ+Olx/t+qrcv19udQTw+vZd0rcEai1IJUia0rcVbUIkgWIvSJT/9uV/iQy/9DKf3PYt7S9p9hek6ED5axCQKlCctNGWpuH/jFvX9Da6twHcoDBaP9za2bIMnCHBS4EVAOAdotIuLPvYPFJYO0/vsnW+7ep17CB4XPDpEu/IyL2lMznK34XSzIVGC2dBAohgWGmEb7D6wCzsaKWhbQ7usGecpKpPsGsub37rB3XfvgnAsK8M3390ihGU+V3R1yutvOupKMXWBo8ZTtIFMuBjccCxpjOPtv/vb0CXoqSecGVxlMJsWu9jSipp3/+EXEMfzxy6lJwLoxZOZRApP0DmDMiPNCmTd4FVBqhTb3R6pYL2rkQjWtWVuHV6NEemAbrMg+ICzXey5S6iN5fT0NsPxEd515HmB9TETL8gc7SocgiQfYExLkefoYsz23Ve4fvIRitAhrEGXJZnOcGXDSCUIEQjOoidj7P37jI9OaFrP8TCQjUqa7QoUWFPjAozHQ6QwOLNDZjMIHtc7hcue1cN77AHwaR2ji/qvhwcunOyNMKWKHkn9rqtp6jj+3nWsl1sIju1yjTcJmblLTkvTdXS2ZberEDLH2Zqqbmnq6KMn04y2WqGyHF9tGcxSdps1WaKwDmbDEbfuvE9bVdStYTbO2e0NIdRM51fYrFbcu3UXP8k4mo7Z1RWjSQHOkxclozJBl4Mf8Hp7Wt9PHcIYzsEWl7RpD8gNLu5zoX87v2P/ZBEYeecxxvReWp56s+Hb77xHWQz40IvPRc1euGhPyh54SdnrhUJsT8V/+mOA3jOs1xByAIq9NILDV/87nVN5FzziRVu0R5KHqDEefPsXv1boAeKhM917l0gZMzHTJCHQkmlJ23XkRfFdcOpxoOxhAP1YQP191OG39SF+TtG7sE/gePiXe1o/0GraPZv1Am8qhO5Ynt7l/bffRzoHC8cr/9d7SGWR25ShKhGZP+xT+uUf+mNXxix1p6g393j33W9x7eQao1kZW7Tk/M7vfzF2e4SnaT3Weax16ESik4QkTRAyDhk550HK3sbH47yLGbX0kXve03UdzgachTRLcMahZBySun79I/yln/m3ybIXufXujtVijW0NzrYIEdCpROmAlAGVKtJMsry75N67t8HERKaAIbgW72sCLQEHIkYWXrSVY3qHFQCRnbTB40LAESfiAxB8nE5WUkX/yUhzo/SY2p4xHHyU1rxNaLckEna15+Wz+0yHBS9cHWJXG9y+I5WQSs18lJNPMvRQoHONqS3v3VqzqQNvf/woNwAAIABJREFULAxSwSeeTzBqwOuvvsOb7xqK4JkrgagCwUaDc+2BRcNQJojXb3D/V34dryTVtsU0gtZ2dK3j+FMv8bH/9BcZfHIOP/u1R66lJwLoKSm4Nivpmj1dvaZra1y3Ybs+JU9mqK7DuhadJOxqw0vPHCOk4N27G567Muf179xkJGu8yvBFA1Kxb2uycsS9s3voYsZiccZHPvgSSqVstivaSnJ1UpColnw8Y79Z4ICq8WAbWi+QUuO1xiPxEmRWoIOjqmpc2zC+8gzOegbjOX63p+vuo40nyzS+S2m7Du8DaZqRJJ5mm6DSjM4YwGOtIEmSGEIt4lTSuclk72F0MJUUxBaa9T5KqXsDysNjtFKc3j/FO0+W56z2S4ZlSb3fcDQ7Yrte0mpBvjyjqw3bquF4PCfsNjTVnt1mR2MzNssNbVujy45ms0DmR7RtoCNns1mQD1qWt++gyxGtaZmOClrrUK2m62ru3LrN4t4tBuqEbaYwLiFJc0zjGEyOGEzGZIPZD3S9Pa3vv8Ihwqgv6/qNyoWC7uBWElm3xwGGA6MWPD70sWIBjGnJpeDNt94DkXDl+ASURCmFEjFSTPaeLEJyvvERh11536u9YA4PqDP++7CpkjJahcvDGxTh0u1RtH7elpUXIC5iwvh8zkceMIQLVjH0cVHOOby3aF3E1xHRPEWEQNu2FEXx4MfxCAuaB2+/fNvDVOXjHvN4tHbZZeZyf/rcjuZp9/aJqURpBoOMZlfz8uuvMNaS03v3o6QhBHztwQiyECUCccEeGG+BVvFgEwh8kKjgqe/eYDzJSDLJarEkVZq37u+5c+8MSCFImjbak0QDcI1OYxrTwafR9+vZOUdwjuA9EA39IbLzaZIgc2ibGM4nESiV8ZOf/hf5c5/811gvBTfvnFKtG0xrCMHE41pLdBJIEkhSQZqkrM4W3HnnfYKxyODBO6yNsW8+dPhggH5z9kBFFj6og8DDQXB4LKEPYouHkSIVWcz27T+7IDTOSVw4YlBcYzISOPMGwnu60LB1nruLinfO9nzgeMKHjlN0axFZghhAOk4pBxrlBff3NbeXhrfXgbPK8vFnE5LhkO1izW/97h3WnWcGiC5QWYH0oEWgSARDJXn+aMRoPuL+2Ya37qzZtLHtnGvFs7MZL/3HP0/YbTj9zdceu5aeCKCHEBQ60AbB7TsLiixhv9uShpZgDVb5OEWTpIylQkhwtkG1js1WE+oV5STFJoKB6lj6FtM5dJYyLHKEq6PcMjR0Tc1INDiVI71kOBoyvXKFt1//FkUx5WxbgbO41rHfVUwmJXXbIUPAecm+2SOCR+Fp9gapc9LBDKqO1ih8tSdNoGkC22rPZrVHvHCFIASbXUNedcikwlqLlPHj1zrpPwbxoBYveJRU0Z9IBYyLGYDWRPBovCNR8ZIVfODOndsgIpU+GuYMBjne7Ui1ILiWLijqzX26ziCFYH5ywjiX3LnfRgPpfIj1LalSVPuKRGZs9jW+3rJarQC4t9yz220ZF2Pa/YLVNmN1tkAeHbOvLVXnUfmczid4BtQWVOVxLtAaifWS4OXjVsLTegLr0OoTgOoZA+fj5Nyh4gTeJR0eD7KAF2AMcNFapTOWYFsIYK0jSxJmwxJhu3iMCUlwDiOigFsI1bN2UceNCL37f3xNLSFRUbStpEQpETVHF2/y0i/1YPvp3FbvUfc9/CgExhi2mzVlWZBl2flzHC4Q8UsjhEDJKLMoi5R9VWON6S84Fxfj72fS9YH3GLhg974XFu4RwDCEyGpIAoeEkKf15JTA01VnrO/f5keee4GzO6/xgeePeUN8u2ePHUGC8O6weyLQb1iCR6NQl9ZaVijy4ymTax/k7s03GOYpi9MVv/p3/1dMB3kmMU7QdQGsI08VRZFebHj6tSt7myDZD1NEX2TfA8EIBtvWkaSgJNiu4/q1l/i5n/t3KfKXuPv+jvVyja0Nru3iZk/FKNAkk6jEoXQg0LHbVJzduE0qOlxisabp9XgG5w2+N2eWQvZOFhDEQQXvItsfJEGE3qDZY3G44B+AelrmCKGjN56IKviARCdDvJfAjJP5j5ONB6zf/xpqvebUS7YOXru1ZvzRZ/nQ8xKZOHQqyQqFAu6etbz8nYr3K0vtHS/NCoTxtEbxlS/e4pW37zJ1kIfAPggcnhMlmErFsZJcHw4QRckfvbtgsdohAwxENNMmKOrVhtu/+TLVG2/RvXb22LX0RAC9SK8mGKo4DeQ9VdMwHZZ0ztJuGmazCYXOUQKEadg1hjRLqLdrRpkmLQtsB7vtGmsDRZ6SJJr8ZMq779/m5PgY6yzSNYhMkqeSYPcEOaBtG1Is5WDI2W7PUSpBOHx9RhiX2HqH94qqbpA+4LqWVDuk6JjPhijpWCzuc2U6pt1tGcxTThtD6CzSx0mjZl+jRODO6X2yJKVpWsrBhBBgNIpRMtZaIHrrBcBYE3NuA+zrmj/+46/ypa98mWE54POf/wU+9rGPRw2fUjhrWC4XhODpOsNwNKPMFZIpnZPkmcaKnHq/o64qxsfPUQzGdGZNUQ5ojWQ6O2G7WZB4gxGCohjSOsNwdoV2V4GwNG00aRa+w5qWzdk9fFfT7RMWtsU1pxTDZ8gSz70br+LzOb6bkiaes7NTNqszgt7+QNfb0/reSxDDyW0Q5/mpIUAiZW9VcqEhe3jiNmrcHmKhECB8zMLsGYCLaC7BM9fmTCdjOg+J86Q6bmRcAIiaVCGiH9zhwmb61rGVgs5KtHUo4SlzhVf95LoL54RHBGEHm+ILY+XDbQdd3Hfr5UIUewNJmoJQD+gQ4+98gagO8YZZFoGeCgHrDEqlD3xOD3zel9Dmd5lNi3AJ7F3k017uuD7wiEML/fDtd72coM9Cu6SB/K639LR+QBUCaFOTJ4rB0Zy2vULTvI8QEqFEdFwIiiDFxZoIEeJ4L/DBAjGuTIhA21ne+867bLYbEulJ8yNunm545dvvoVVGolN2dYuzDkI81pyPObk+eAgiXpsOJETf/iQEgnPnbLExNjKAnWcwnPKTP/ELfOzDn2e3Vrz/5inNvsN2HcH62KpVIBIVGbxSUZQZQVhUKPnmt7/B0ZUc4Q2n7y7pXIO3Bu8d4TBQIeW59jT0LQLZyzsEnhBUf7vH9Y+JwC8eY1oIlEyj1l0EbH8wSCRa54CjGD1Hkg9QmWQ2vsfg9g2eC4FNgNvB8+ob79O2cz7x4hF0jqY23FlUfOOdNbf2hkCgTCQJlq4K3LlTsW2h7Xq/RBeYScEzUpIHB87RCsGrZxtu3l/hQ6AUklIEhNZYGyB0SOD93/gin/qb/xbdjTvwt/+PR66lJwLoESDPU9JmSDbM2W5izlzQKcVwyOruPWQ2RAhJ8NHccG9A5zm7/Zr5bEzrJVJJfLDIJKfIMkQw5HkZwYkM3DrdMikltdXkwdJ2DpFmLM5OyYsBqAxMi1Ka3XaLty37qkWElq4LWOvRSUZRFFizp6trUJrN4i62rSlHz7D3C1TwBJUilaHMJBKHdHvSVPCt17+FEjFN4+TKszz/wktkWUqaxkV3uDggY6SRtQ4pBV/+0hf5tf/pf6SpKrIsZXF6xn/2n/8XHB0d4Z2naRpCrw1q6yUfeu5ZXLNFi8DpumZUFCw2jiD2tF3LSTGMj/Ge6fwEYwWD0ZjWekTVkhcjdCERIuPq0ZhvvvIKqXAksylXjk/YdZLJdE7bGbK0xIfAfnlGKhXj2ZhCejbr+5RpgbUG6TrOFndpqg3b/VPu4IepfO89Z31AS9EzejF661E00IMDARe00znD1/cIAwFnbN+a1aSJxnhIleIwly1FZMWcjXqjOEUY2bjLUC0EH3feIibEaBn1Qq6zuL7NjPWRFVQhXhDO5RIxq1ZJ0bfAeq2Tj+/zAAAPA095nhCNyf+U/ikXDH0cqIom8E3TMhykl7RyDzKJ32Uu/V3P+d1g7PI7kJdvv3S/h5/t4nku5YI8BXlPVEkluXn3NmmeMfSCvRigMxUlDDKuTykkCIWQPdvdgz1/+AP3PpZCgpaKa1dm5KXjdHWfd27W/KPf+wouQKoia940LcHH4TyhUuj188HZCK6CwCFQSkCIzJjrO03OC0wXdbJZkvDic5/gc5/7ZbS6zt33GvarDaZucLaLbLwCnWiUAqUFxShlejRiPB3gqbnz3k1+7hc/w3Z1h7e+8h5tVeFdbLs6HNabyGjSnwOEONfe+ktbTN8reQkeL208dkMUcBAg0Rm6//ziMSH7DdvBBzBjPD7CqGiTN3jx04jbr1Ku7zMWgqsBNi5w9s4ZX3h/hVBgvWdrPLsQE3CUEAgT2Cw9VwYpZ3cativHFSP4oIRPaYVyntZ69gIKAmfGcSd4tsAAyISglJBYS0KUyNTAveWaN37lN/nxv/UfPdlATynwZk/XNTx3ZY7t9mgl2Xcd1wcla+lInGPRNChnkKJDqpwsz0jzkrwoMGiQls4aBpM5dbMn1RmNUyBL6iawbOFkMqbVCk9DJzNmgxl3795mcDRhWVk0hjZIzLZCE3Mqs+BpXINIRwipSVOFC4btdk2QiqbZMkhTuqZmUGZ4swU55t79M565coRzhtDt2e5qXvvmOxRZilIK0xkGgyFlWcbdm1YYF86F5QD7asfrr73G3/+1v0e12YKQtE3H22++wVe/9CU+/y/9y0ghOVucRo8+AomCwWTCpqs4mY9Zt4LhqOB0fR8jhsyOpoyHKev1Cmk9o0lJOczJsgGIhNPbFYPxiOGwQJMwGA/JBhOGqWDd1Fz9yEu4RcUg9Wx2FXmiMV1HkStG45IyzSiylNn8Cmk+ZbPd4L1nv90TrCWIJ2LZPa3vsWyIgKTpLAJN8PEkaZ0nD5FdeED6xaN0YhegKOrWJHmWsmnreNEQAeMi+yWUotD6HKzZSCact38VAt3TgD4ERBC4vkWbpZJUQ1ftWd2+Q7s5w9VbfPBorUjLMSrLUVKTj+fkwzEqKS61gC+88KS6YCzjRJ7EWQsEmqahKCRKyQfA18Oeg1FnG3/WSrCtG0bDIYexX9+3urR8PGC8/HleTDkfknTOucTzttn3VuKCIfxeH/K0/n+t4AO77ZZBgM39G5j1HYq0jMMP0gJxGK+/M/Fv2rcqvQQUiGjSHZAIBe9vBPUCtuaYr7/2Fv/kD18hVTr63HUtxsRkCyUkWmmkiuSKP+SxB9UTwQEXXGTBDoMZIiBVYFge8TM/80s8+8xPsbrv2C5XtFVHaC3BxY2dVPFLJQGtA0mRMr8y4ejqnOE4wZOSD1Ne/do3ufXyN9je2dBaH4keZ7HB4lXAy37veL656TWnQvRQL+DxkcET/mLXGkAGj5YZqSqjZOrSUxySqpTUqCJHDHIkHUql6OGLjP7c52i/8A/JQ6AgMAKeCdBaS2sjuAsIvIgsoRRgg4+t7Mbxgg18vDOUIeBMoPEhdkyIx3grBGchcNpvxAoJcy35keev0263dJ3BBNDG0yrBu6cLxN/6vx+7lp6IK64AqtowHo1x3qGFYpQrrD5GCVBCkqWKzHY01Zbj4zmjcYIzhpMrc8aDjJAN8WaPqTR5IsiLMZvFksYZPvTB51me3eHq8XVmxyWaOWF/m7P7G7puj/eWLE+x2xYVHD7JUTqhyEuCStmvd4ggmM7HyCTDNBW1iQ7dTWvw3vDc1SM2+z2z0YD1eo9XQ1bbPR947hrLs1NEs+LW3RWL01OOpmPSNGW3XHB27zaTyRSCJ8sLkjQjCDi9f5ff/q3f5puv/An77QbvPErrfpTd0zYN//yf/z6f+sxnuHJ8xN07tyMb6BwSg5KCJNXIRDCZXWVcONJsQ5lp5rMRpt6wW2+YDhxZqpGJRucZ4zSh3U7woqAsJuR5TlIGrj3/Erv1glR4ytGAWefJ05R9A8moQIWEYeIZjqZ4mVHkKc57jE3IJyl0FX5bkZcjhHw6dfvDUgFQItDawLZqGeWaLkDTdWil8DKeRES/i74MG85boef9xr6lS8D7OJxgesa6baO4ejiY4mVK8GBDZAf8ZYoqiuHQKuAOwbCAFoI0iY789eltNje+QbO8TXAdaZqien2dTzKUVAglaXRCOZ4h8hnFlZcoJkcHVU+0HLmkb/L9AIaSCmsc1jn2fs9oNALUpd/5QdgUwV60ydhXHmcstrdZESFqDXeNoUh1NJHl0S3dh/+/6Iex/mwG8HG3XYDCp0DvySwpBEdHR1Stpa5qBllK3VZIIVFC4E2UPUXyScTBJBmbjsHHDdD5Gg6B043nv/uNlwn5kK5peOuVr4FXqFyj8Ow7G6dVAwQtcMKhvIjpGFL2gCoADh88Igi0iO3S1goSnfHJj36GT3/638R0M+68t6VZNISuI3jbb2gSpHCoBIQWpKWkLBOG8wHjowGDoQQMZ/dWfPl3vsjm/Xu0mz3WWlzosL7DYAjKcT5IBJeEwedChV6K4M9thKKDHpHNCwEZFInMSEQCSILstaohAlmhAzIBkUqcgkSkaJ2QlpriU/8Cb731dbq77zEOgtLF1ytCtHGBPqUHRxJAEdAIRBdtXdIQ39u21z0aAuqSzGILnCFogYkInIjoZVhbz+RoQpoPyK8fk59MCZOM7GPPcPa7Lz92LT0RQK81jtoK5qOSfd3iibuJVsQ4sFQpbt67jQuSrmox1jOb5SwWDUpEIWqqFM6AKjICBmcCZSLorOX4aM5mdQdrKvLyCqaTtEozm89oqy2zcQG2Ji1n2P2bDGbXCFKRJgkqzemajEJLhpMpAkFtdzjXAQ5nG2SSMxgOMb7CNzuMV9h2R9tadpsldd1gdhtOVw3OWVrTIaWgrvac3rtFmudcufoM0+mc1Fm+9kdf5Tf/z99gvVoRvEMK0bd4ZNTsiXhhufneDX7lb/+3/Pv/4X/Aer0my1Kc82gBTV3Rdh2j4YhhWSDlnsH4iFy0TKYz7t1+l67dIgdFnECyFdYk4CXHJxN2JpCHPc4GtMwZD0ckQOkDSVowmUq8bZnPRgyHQ/ZeMtMtWR5QoyHeOBLpUWWC8pJ0mNF0kA+GFMlToPfDUoIDAwdpouK0bQ+anI9ia6ViG9W6QNIHqPoekJ03Bi9jjRAhId5HrWqWcbrYMRsPkSqh7iIzkKhoI+QOu3B6wCXlOfgTSFLtSVOFcB27m6/R3H2DZnGbwWjC5MqLSBnD1NerBZuzBYPRkPnJVZSKu37TnlHdOINrHyY//mCfNCPwwfet19jGiS2w2Ob1JuB8R1PX5EV5abDjQdgUp+YFeZ6zrxoSAattzXBY9vd2pEqw2G65OhkRHrCHvvSRPQbQxevbRdLF5b9clDE+DPIOYPzQQ//u9/y0noza1zXfuXUHGxK2yxXL0zvcen2DCAkSgTuwTkL2E+NxUMK5Cw88JRVKakDh6j2vfeUPMVrSdXuyTJFqRaoigdBZh4jKO/IiRyXxeQutscZcsMjO09QNQmha4WmbwPHRC/zsX/xljo9+jO2pZb24T7c3YAI4h1JReqEJaC3RGaRlymg+YHoUQV5WZNy7dYvXv/FNbrz2LnZVY3c7gjE439H5FuMNXloODpgH+6VDxT1lPFdEeUUEuXARUkDwiABapSiZIYXqh7vAe4ETHqEDKEGSpehEI4QkSTVZmVKOcsbzYz7+1/4GX/0H/wPrW+9wLKAArAjR+owIrmTomTohUEFgbdQHaoAAKZERjEAsni07BPdDYCsCcwQnQrJzjrsI3rhxk7wnv2avJnz8pef5ib/xr9DtKiafehH+l0evpScC6CmgdZJ8NMa3Di9qkkHGPBtRbVak5ZS2rUnzFC1SkmKMzkcMJgoRJCqRWFNjvaWqHSFYmv2eUS7ZN46yrrEeBlqy7wK7xW0aaxmVI96/fZMXnrmKty2bOmGeCLKixHuDSjKCi5Otg/Eouvg3O1R/Vs2TjGW3YpgPUUqRZhmqWmEs2GpJXdVUuxWrVUWCo7UeJaBtLFmS0JmG1eIU5xxNU+O84U++/if83j/7nThpaw0+BLIspdAJlTU470D2J3znefet7/Crf+fv8JOf+Ule/MCLeOuQwXJ25x02O8ugKBFp1PpNpzO67R3qtqNrWmzXYaym6zxSOvbVKSItuDobs95uSI4GVPWeTI8YDBLoalKd4n1CkmtMHTiaTUiyhCR4cqlou5bENmgUo8mYNiTkHqr1fYpBxmBQkg8nP9gF97S+5wohUNt4AsySlNbCKKfXyzlWmxo5KZFIjPHoPDm3q3M+kIjevPTQSAkROHof2ykHXdC90wVKKpJi1EcTQetd793Va+xkBCYOcJaevQjkqULS0d17FbV7n1R0jJ5/icHJNWSas18u0VrT1Q2rxYI0TVBZSlNV4B3FZEYxntJ1Ne3ZO5QnL6BVEttdInC5RSq1IiHBWkvbWqpqj5SSNCvOBfEPlzFdfKwQZKmmsS22g8553rv5x1gBV48/wb7tGGT5nyqVe5Ax/NOmdh9mV8NDtz2qvf60nqSyLvDtV2+QphmLVWzitbsKIR1KKdrgCMEQhIrN2Z49v3Bv6P/GIm5ONIpBqXBpQIScznm0UGRKsW4s1gp8b9cVffPAW4t3HmcNAtFLFRQhRGZbygGf+YnP8xM//q/SNSn3bmxptw2uaft4tYBQoBKFSiVKRtuUwTBjMC2ZnYyZHA+QSvHaN97kG1/4I/bLM8KuxlU1wsWOmQkG4ztsiE3Rx21RLo7VXr0b4HAsxG+jabJSAikSpNSROOkZch88Xji8kGRpTpASlWiEFsgcdKmYnMwohjnD6af4y3/9v+YPfu2/5/6bX6cQCZ0IdNEBnmKUU3pQxiGNRbvoWkAIREOaCPBaYCgiM9viqQmsEBwBRwLu+8B9ETPtJRIZYBoCA2MwN2+z+d++RAiS/KMvPXYtPRFATwgI1oDt2JydIoUnmR6z3ZzStTXVdsdgMmeUC/bNDuMcpmsI7Zbby4pnnnmOqt5RaEE6GFPt1kiV4aXGScvOCMbTY9p2D9mErjsD0zB78cO8+p13QGpIh4ymM/R2CzKJejedY+oKU9f48ZTVYkXoduQqgPBY73DG0BpLU++xVoIz3F1s0bZl39S0bcm+bnoWJKCUojEdczXAW0dd7fDeEnzg1q1bfPUrf0SiE/CeTMdxb2csSkt855BSYZ0hy4Z0pgMCZ/fv8NUvfZGjoxmpThiLju22xnqBa7fsG8+4SMmLgmppWW93nK0d48mc1tbUbUeaanb7PRkJ+9YRpGe7r6jbmu3e4EPOeDJmuWlwItA2DbatUFlJ5zrGk2O2i1vUzY48TbGNp1CCssypu44gocwKhqMR8vHhAE/rCayqi3KKVEs6C84LrGn49X/w9/iD3/9dytGEyXRCmaeMJ0fMZ3PSJCdPMybTKePxlDwv0IlmPp3x4vVrCBGd+40xWNex2Vak2Y7h9ISgQ6+R8SQyiYbh8qC3gc460Iq0b+0iPLJdU2QBNZ5STiax/WoN6fxZdNVgrCEfjrj67PNMj+Z4qdH9oNB6tcQHgc5yaDd4s0Pm034SVzzEpIk+11NjrSYER9s2KK1R/XTKZduWg77Pe49OEoJz7JdL6q4lK66is6s0u7vcvfsa1eQ6H3nmgzyOYft+rFjiaz+drvhhLms6XLugajSu3ZBnAzLR0CgR8869B3xPdkuUTPoWbvSLU8RhDQmooPDS412H6xRKQJ6k0WDfWaz1eNcPBCYC71382Tm8sRA8MtGxBeoAFB94/kf57E/9G4wGL7Fc1LTLPXbv47CFC0gZ255SBHQikRqKYc5gmDKcFCQDxXBS0O4tX/mDP+TGt9/C72pou5h16y3GdbTeYGVMrAjeERW+EcAecNxFfnPcQPoQh1TouxE+RJPkEAQKhRYZKuTIkCJEghQaRNTlIgUykSS5RKaOtNSkRcpglDM5GjEYpzGaLckQyfP82C/8Ff7o5pvkH/kQ2tXkXY1dV3iVwThHhI5quWN/e4kEnIBCSRIfJ5edh0mAExF1fXUQgGcaBGcB3hSBLAgyoAiCHE8hBFeSlKOy4OafvM7shee4+pd/Gn71f37kWnoigJ4PgTLT7Pcbdrs9V2cFzge2mw3DTCER5HlC120oMkmWBrqmYV81dPsK07YUMgYnbzdL2t2KwXBK0+4YZgNK2VE5h3IN33r5T/jAvGDXenCOsihoGoPzLXIkKafHGOOZzMaofIBNNfV2hUwKhNuTl0O0MDwzuc5+v2HaOaazOUFnaG9onODsbMOklGgfqOqOprV4D/eX1Tmjsd3to3haCFRVUVUdt08XtG1DnmX9ZJBHoBhkGc45BnlB8AGVaJquIVEagsc0lsXijK99+Sv8+T//GfLcc+NezZXjOV3bsK5aymRGkC2urZBpQVHmXJvl3D9bs99v0cmc7d6QFy2tsbRtx2Jdo1LNfnWP0fw69a5mu9uQ5ZKu3oE3rDd7Mm3Q6YjNtqUxniZUCFmgMkHiocgG+GKCzCc0dUuUqj6tH4YKQJlKjI0Tt04GWgdZkvDVL/9T3vrmVwCBTOJFxloHIZqgS6IBcRABrROMdfyVv/bv8Tf/q/+Gfd3gvCU4T9MYAmCsj5O99G0ooWMLmDg1GL8DhMAGzo3FiwBJJhDZVRJdEJp1vHM6BtNgQwwt3+0tRTnCJwX7VUUyHOJ1SqZTuq6jbVsWiy36dMOLn/g0Kh8SpXmSmMEe34QQcZevfULbONrWENgxGg4QUvXto4tWbmynOQZlzna7I9E5s9k1lutTuqZmuXyZQX6EdTXtyYtkSTwtX6R3/NklLv33EUO3f+bjntaTV4mWDPMEnaQcTeZst3tMptBa9sxdP4BBBH7ykOwQpyXiJkMQWawQQEjSLEMIRWcrEq2w3tO2LubPBg9CkmWaIBXeWAQyTr73u/O2swyyI376Z3+Rj3zwc+xXcHZrS7ttcZ0l2H6oScdjRmuJ1II0V5TDlOHRkOGkQOkIZN999W1e/fobbO4voGnwVY1Yqu+sAAAgAElEQVTrWrx3OGcuBi9E1OSJC09oJKKfqKUHe+LSgu7Tc85TavpughSoIJFCodBRryuIiTp99nZAgAokpWIwLpidDCmHJePZiHJUEkQvV5EgpEJlJYPj6zC9jugqlLfYQRwy++xnf4TgHV/+nS+wv7tEI9n4wELAEYKEKDs7854sBFIh2YlAiaQhcIv495P9ps3jSRBoEViblm/da7k2n/Lxf+eXkB/78ceupScC6IUAjRWkrcELKDPJcttR7/dMixlFmaNVGr3zBgN0iCfXxgSsdxjTYUPDbDQkrLZYL9ApOKtIdSCYPXXdcjQqODvdsksdxXDEbr9hPByQpZrKWIbKkWpNu2sQeExnaKodSga6rsJZS1oUCA8qS7l7v6IYjhikgqQY4et3KMcDTACV55RFymJV0XSeyTBnPJJ4v2ez3zEZzy9RuIK28+w3u2j06j3D4TCOuXvPqCxo2hpro/aiyJMYtdY5OudZ77akQnDvzm1u37nF9ec1yNjuqpoa20rqtsPUS5zvKLKMk+M5gS3WgTaBrvWkwpOIQNPssM0mTkA6QVtbxG6DVZq23rNceIJpEcJBJkEFlF6wWC7IlaEzBpk2kI/ZVjXDomU4HLBsG5LccuvW6ge95J7W91i2P0M2nUXLhFQLGgOJypgfDxmVjqZtqZuWroWuC2TZiDRL8T6wr1ussQihaDrPeDTBu2iWfKjOWji0meS52R0IieuHNUS/M5f91SsQcAF08FFLm8zjGIVMCOkUhMA1G+rlgma9JAhBZxzj+Zg779/i7bffJisK9nXDfDpmv93EzVc6xjvHcrHgU3/pF0APEKK3bsGfD2Yc2mNaK5q6pe0MaaLJH0q+gHhx0VoTCBjrSbWgrg06HVLmLc+cHHG2XFKt7rNcfZirxx/ABoGWDwmQ/pR6GNzJhx7puRB6P60fjkq04MUTmE0KZpNjVusV39i/x0KuwCkusgmJ7JaKQxrOysgiiwsQGDcAAi8i6PFo2i62eTebJjKBRPCiVEJnfDT89h53YLQ7+NiH/wKf+fS/Tsqcs/cruk2DaTqCczGUQGukVEjtSRKFThVpqRmNCgbjDD3QZHnGbrvjta+/xr23b2N3DVQtpqoxpsU7g3UtxrUYWhzRo08pievN9qWUkcU7J9pFb7MUf4wZ1PH3MzhciNP6op/4F0JGoCw8B6snoSQuQCIDMpFko4Lja3OuPjtjMh+SlwMcMp673AXb31VrnEhJKclHJT/+2U8wzBR375+S1BvIC8bliC5IslyjO8e3jKMUgpFQUVOP4JTAGEnAkwErBBsEQ+HRxLat64/wPEDlAypJUJ//HM1HP0ZYdo9dS08E0BNKkWUplQugciqfYrwlH5Q0aKbTCa0AnRQ0ISUNkl1V41GUgyEuOLCOza5GCMVwNEFJSV4UNMahTDQtlknB0SzlbLnk48++xN4aEqXjIk1n5DrQNnESUAjY7TZ0+wqpUzrj8UJhrMV2HaPS0zZxsEH2bGIwWxqZIpylqg1l0k/ReY9WEmMdOlF4G2haQ9pn2voQ2FUVAUh6HU+eJiiZkDjPtdmYcX6EloKE2JKpTIdAc2/fcFsr0DHjVnqPTIfMJgnloKTemxijs99zf7FmNkrxISFPJK5dEGRCMR6y3u0ZDsuYUegEWhGzSBOoq5Za7dBKEbxltVgyyFMIHc6skXlCvVlRVzWTeYFxHtNWVI1mU1c4mzIeDnDtnulsxGZX/yCX29P6PkpLWFctQioW25rjSYl1hjJRPP/Cj/I1+08xPiCDQCiPUIFtvaPbAAha63G9xk9JwXAwx/Vi6OADSgjatgUfnfEPWbaynxdUSiFVZCZSLVEiDmIcQo2KRJGm6aXBDx8n/LqG5Y1v884rf0y13SHyEe9+5y2ScoDwLa9++y0W2xXLxSkvPvs8y+2W4XjCcHaVoyvX+NYr3yQtBnzisz+P0sm5+78Q8lzwnWcZwXk87TkjmOV5b3/yoM2Ktba3bQhkieKPX/46v/W7v06mFSfHG967cYtnXnyWDz7/CxwRqIxjkqvILlz2JAyXRij6C/CjiD//0M/RduMRHN+jjPme1hNRUko+9VN/gbDfkOmS55+9xvJmxavqJsGFPl/2YO7tEDhESMAHVBD99Gd0nhRS0+KpWgP/L3tvFqPpdd75/c7y7t9SS1f13uyFZFNcRGq3LGss2bLiZQzbmWQcAwGSWeCb5CYIguQul8lFLuIEGQOTxHESxDMxBvBMxrBj2XIsWYKk2KJESSSbe7P3Wr/9Xc+Si/NWsSWLFiPTCgP00+iuqq+/+rb3LM95nv8iJcZ4TBs05YzxdG3AtkeJomlsz1jVGNPhjGFjeIqPfegXOHvmo6zmjv3DOVQdvrVIF+jjSoW/OvJESUScRmRFSjaKKIYJQkqUFtx57Tavv/gay4ND2mWLqWpMVWLaCudD0ab1NS0tlvBaQmVR44UJg90pnHD3MfrFfcg9OFLPc3icA+kFGhG+CoXyAiHCeqOOBZc9KIfOxHEF79yVU5w8vRH20rqj7TzaCjrbIdAoJZnffY3VfIYyHRefeJStwYDVzgGXt88zvX6TeLROc29GqiKyKCLTjnOrmomAQmoG1jBQisp55t6SEJLyifc4IVAe4j6pxHti4YO0ihDwY09Q/OxPM/WQr2ZvO5beE4melII4EcxnK5TKmc3nKKUZDjKyIqc1LcNUUSWeRmg6K2isZTQYUtmYZV2TRxpvHCrN2NjYoFzOcMqzmk/o6o6TZ85jlaGeT5FAlI/oZlPSRKN8gy3WMdWU1XJJlORM50EQufMgnSdOJBGetnOYtmG5WGCdZzhcZ7n/KhvjLZyxrJwhjiSLZUusBG1jjqnpOB+A4EphWkuk9fFAbY0hSRIGaco4jUgjTaEED58YcH57zDCPydOYNEkZDccs5zP2J3PePCgp4ojdqmVSViynh7TtgOH6OqmqWRGhlCZNYsT+jHy0webGBqPYs3NritKGJBtR25piVGBXB3idkOcpxio8lrIyNM2M4SDBC4VKI9ARWkUc7uxiihSnGxpjaJ2gM46qnqGjhFVZc3r9AsvlirppEAyJ4r9a9XgQ792oqpYizwHPdL7CY8DHFIMC6zpMyIIw1mOcpLOe2oAUnq4/5Hjh0UpwcvsU3nuMM0HCRHiMMQgZLMPurzsFwodAyMC0jaQk0QLnBZ11SCEYZNFxL8e5FldNqPfeZOfGdV77zrNUZcVi1TA5nHA4W1KWS5TSdM7TlkuKVHM43UNpjbGWti5p6hrvEl575VUeeuJj5IPhcVUxMHD7FpCSxGkMy4CFCm1rj/O2T1iPEj36CqBgMMiCfmY+4Kt/9gW8aGi6lkgl/GSyyawMmZyxAbYh8XgZ0InCi74aExJkieurNr3MhP+reoZHcexly3dX9Y4SxQfx3os0jYm0pFUGoy2H1Yqqc6FCFwpRvf9zGI/eBbjP0SySsq9eKYUQEgO0jcXJDtf5gMPzHtMZ2saiI4nWEbafW8a2WON49OJH+dAzP0emTnN4u6ZdrqA1COcBidY67OGpJEokUazIBxHZICUdZESpIIo088Mlr7/4GnffuEW9KHF1IG10dYPtKpxt8a7BuI7aNyGRA446st4HOMeRXqQQ8rs45Efas967kN8i8CLMJykCdlYeCUj3jyX6U6TSCiToVBMPY06cGXPl6llOntlk88QYvKJpXZCGUarXFQzs+9WtW8j5Pt1qDkrhyxoaza3XJ1SdYq2siZWkXcuRStKUFRfTBNXU3DQdEXAKTyEkM+8xCFIBjZDEgBUC7WHlHQUgg58C7ZWTnP6FT2MdVNUKYd/+wPaeSPREf2pVKgC+l2XHxjAiL1I60yG9RSpF11nGmxmN6RgNcgaZRtoIowTDLEcnMauqJh/kmG6J9BmxnOB0wng04Obdu4yLgihX1HWN9pJhFrGsU1oUolwRRRFxluCMQWDQzuMspEkC0nJ4MKGIJeVyilIRSsdM5w3ZWk3VSZa1IZKKneWStUzSGYdSiknpQm9fBlXxputIXPC4xXuWqypo+yDBGjZyxYX1jFPjmHEq2BxlDEfrFGsnGJ44xWr3JplwbA4izm1kXNspeXF3gusqdByztb1NO71BFCdoV4OPGAwTEuURvsOi0WlE3qacPnOW8bJGKcmsPggir2oNlKBdLciKITQ18WDEaBwRxxG2rAO4XKXESEhSdJwwzFPm8xlpWtDULWuDdZq6Y39vjyhNOJyZsCE+iP/fxIm1AWXdEgnPyzducuHMWZQyXL78OOPNMdFqRVc5fGu5+r4Rz32nxjuDxRELDS6AnNMk4+TJLaRUxEpRExbo1oS2sJK6XwvC6Voeib06gZdBskUpQSQEUvaLvQTvDVhDuX8dyhk7b1zjpZdfxfsMkphueQgqIRtIojgJhuxI0rwI7SYpEFr3CVLJYnqH8dYFlqsVbdMQp0XAGsm3ErYjdl8UxaRpgrWGtjX93Albz/2ixkoprLNEkcZ2hs31Tc6eeZgXXv46B4dThHTc2rtC23l2Jgsq03Ki2CA0nPoWlQiCrM7BIO51+L6LhOsDoNz30irf51reX//73qTwQcL33orlqmTvjRd46H1Po6MBe8uGVfk6wgEu6DyKvh2JEEEMuNd/lFIgveylgnqFSy/oOosTfbJ4XOUNjkxaH5EXPJ3tWC9O8ZGnP8Ol0x+lXsHkcAoNKBvIDlKAjDxxrEKS2OPwikFMMcqJUoWXnra03H3lDq8+f416tqAtK0zTYtoO29bYNiR31gWdvNYHE7KQ4PXJ29FI9ZpjaRXCeH8Ly3rUug1riDv2sw04Q7xHytDWVmhQAp3E4GX/fURSJJx+aJMnP3SF81fOk8UaJQVdR/+7PshMmQZnHZ1pcVhUV2Nfe543T5/j6X/zZ2maN7n1+i30MMO8cYtTVx9j9/YN9q/f5uGLl1ncvckTJwaM9hbcsY7bnaEVoQJZ0691wqMQJF7wEJKxAO0NCMEqjzj/iWeI45i6WiKMRdrvreO/FT8w0RNC/Bbwd4Fd7/2T/W0bwP8OXASuA3/fez8RYVX7DeDngRL49733z/6g5/A+LOpCRyTKslIJHkmhY/arEmMsw0ySxAVRommMJU1TsljgXaA0KyXJ0hShdcDSpRmmcUgvGI43SSOH8IooUmyun+LGvbuM18egFTJKaZuGQvmexSeou45IymBtHCUUw5x2NUOZkvWt09y9u8OwGKBEy2hQBAaqVswWU9bWct64NyUa5uwuOk6sJ6RaHrdykjSmrlukKIKHIEHzJ01StJaU9Yo2FQyyNdJEMx4N2D55irULVxmfe4R8bYvJq99A+pbJ7j3K1vLYuU3y8ZB7qyVRkpKnMR2gkwHC1HROsDYuWF8bUXc1FQpjDUI4tJYMBzFCKGwaY3VKHBtOjDbZMxWu12myyyX5yZNoITFp8EHMsojlfEqkBRvFmDiWrBWK1bKhc5KiGKGFYbC1hWs6FrM9Tm5v/aAh8SDeQfwo5qbzHkGQ//EoLp47R7lqEELz/ic+RnbiYW7f+yq+s4zGGVcez/jUz4+ZLx3Q9XNbhERJDDl/fi1UEIyl7Vo8YI1FxxFK9xKjtiPRSXgB3gWgtBD9+h3mqHShqicE+K6mntxluXOLrqnYP5jRiRyP4XAyYVlWdDbIEqkoDbIGQiDjGCUFWkqkCnOwMRVVPWE1yxnkA9q27VuvDqVAa32Mzek/b9Ikpms7PC64CmjR77tHbgUyVPGNQUpJ5xyDPOGRR5/ixVeexXaWdBThfc1kesDZ0+eZl1O8Ww+HQ47aqx7vgqhtWJkIG7UIOd4RZEv0XYL7zM0Iqg59AnrfbfA9RI4H2d47ih/F3EuThDMPP0lbL7B1xWr3kNnOLbyzHKEwhfdhjjgBaBAS6cMYPaIDSR8SHWscjWlxwgXXC3lUAQuYNCE9WkiUTrh69UM89b6fJnZjZnsl3arFdx5caKNGGnQkyHJFlAiSPCId5gxGKXGiiDKNcYbZ7oyb126we2OHcjLDtC1t29A1HbbrMF2DsTWtb/B0GN/hsAGWwH3j1XOfLqTodek8TvQNWuHCfYTEiyPHGY/wHqXCXAifiAIUXmqUCJqDUSxRMcRJzNr2kKc+/BiPXn0YGWmUDslXZzukjsHU+F6nsOtM7+urcAii2T6Lr3yJr116nJ/88Sd53Ka4IuO5f/HndNdfhTQhG63j1saY+T6Jgw3nuHTxHLdu3uVe1TBBogl+u4FYEyr4t7xlAqRekgmPHeTkozF1vSJD0jiHdG9Pcvz+6pzfHb8N/Oz33PafAZ/33j8CfL7/GeDngEf6v78O/OY7eHyMDYLJOkoZDgvGowFeSFofLs+qtpSNhzhFyCgwTa1DSI2MYmzbYQkWI1IqVvMpaVpgmgqECxp43qDTGGfCBYuimNnBIUprssEIU69QKiEbjEijKFgiyYhIR8RpirMepQKQtLWeVVUzKAbYZkUxHOPbJVmWMJkuiGXA2g0TyaVTI2ItaF1YgL0HrRSt6XpmbcDweRcqAOVywVBLLpxYY5ilCB2TFSOGW2dZe+hR1h96lGzrLMXWGUbb51jb3GYtS6nbilgRBCWNQ0hJnGZkow0irWiMp8gGxHFE17TU1YqqLEnjmK5pwiakFVGWMxitMRoU5MWA4WhAlCbIKGKQJwyKnDTLyfMBRZqSZxmD8ZjhIKfIIuI4ZVgU5FlG0zWsFzHL2R714gC6BbaqKRf772RYPIgfHL/N3/LclEIQaY2XmulyjtaKxq1YliWN8Zx76DJeeWqvuHNQ80ef2+P2zpz/4Z/c5b/9r3b4zf96h3/yGzv85m/c4/Ofa0iTtUBKaFuapu3HfShNSREYcFEUhVXd+Z54EX40zvc3B3X5SEucNbTzfQ7v3aYsS5qmJinGmM5wb+cuk8kBVVXSdh3W2qCN3/cvQxVDopQ4xhfFOsLbjuV8H+stWsd9OzbIIHw/iRPZP85RdU3c337uqw9CBJFpJQVd25GlEe97/BmEUOg4Yrg2oGxa7uy+hPOWvcNdamODZ2dftQjtKwMEzPFRwil566vsMVvSB+nlI/FWSV8hOPLuJVQDJW/9HCqHD/B67zB+m7/luacEeK842N3n3u4Oo+GAJM0InsuhiY9wKKEQUoOUCKWxBIiBcGHgHI0TZz1N3dJUhqo2dK3DdGCt7F0uBFvj0/zSz/wjPvGhfwdRj5juLDDLBtmBFpJYS/JEsD6M2d7IOLU24MLWBpfObHH57Dqnt4aMRinOtNx88U1e+toL7L1+GzOfoFxDWzc0VUNT1zRdRedaOt9gaTG+6zF5/rhK159vjk8lov/HC47XhWMvW+H733XHvyClPBZ8D6SUnkHc03eV1qAlOtUMtwqe+diTXH70CmiJdS4IKPdJXdu1CAnGmSOwRICUjE+gBag4RczvcO33fpc/+fOXyK5c4d7Nmxy+/ipuMScxLYPpHje+8S262RIXJ6yd2KZ84yZ507LtLZe85wqSh1FcEYqHheSyUFwRgkeAhyWc0YqNsmP++g2a6Yyu69vd3dtj339gRc97/0UhxMXvufmXgE/13//PwJ8B/2l/+//iw2r4VSHEmhDitPf+7l87oJWiXU5JRuukhcTPaxLtmS/mJEmCq5akyQCVF7RVSZFGLEvDsvV0NBgEOoqZTSeMRhuUyxXZaB3hHZsntjDCUXYWZyX5aEAjIkQkaSZLpAMbF1g7ReoEoWMcEEVp0P/RAhtFSJXQ1SuiOGV3MsMjSbOcrp7gpQZbIuOE2bzk9LggikMpXSmJ9hbbBCe7QMzQOBe8PbWCtTRBS+ialivba1zYHAYQejEgK4bIdEwrgnp5s5qjTYPrGryKiPIxPjqkbgzGdYzyjLquabuaNE1Q6ZCqHuIWDYiUyji6tqJqSpQXSB0xX5R4b9jUKSouQnuLFiE0WZogEkkSR6GF6y2dAYkiyiOEjcE2xHmCkoG1dCQ2mcglWZFRNgaVOJSImDSOk5sPBJPfjfhRzE3nPbGSjIqIOzuH5PEWeew4ONjBmDGXTo+5eXkD4yteubYgj2LuvNlw9qGIb34t4HiyVFM7z2J3SBTn4Al2X73sg3NBwV/K0HIUgYKLdfa4DeWlxHmBdUF2QujQhhGuYra/S7VcIIByuaSqapSUVGVJ03VIQQ/LFsGqzR/hlkJyg/B4Z3HOIrxDS6hsg/MeFad0zqNk8K31WmKtR/VakEe+1M56sjzDGEOcAARZFe+P+K6eONah9aw1WisuXXqYYjig8UuEdLQG2uoeBwe3mc1uU3dPgwzyUrWpyaM8JI6ib8WJ447V9+DvxHGZzvf/eVStE4Qk8IiDETgebyV3Dwp67yx+FHOvtY5nv/ENUgyWgJWdzZahsmVBqJC8BPylQ2oVqlaBm4h0oq9iBfyr8za0GZVGKd1XyAKWLxGKDz35E3ziE79KNdcc3l5iyhZlPLHQ6FiSJhFJKhkkmsEgY3NzyNaJIcP1nDiLcNKzqlpeeuUNXnn+Ne69fo9qucK1DU3TUVU1pulwXYcxLcZXWFdh6TBYPKGt7I/gG32Wd6xj2Vf0A+VKHOd/YQgfJXsO78D2enuiZ+MKOJZRkT35QqjATNZak68VPP7hR3nig1fRcURZ1UQ6QhgIlXSBtQYhgiPJUQgpOPn+Zzj8iz/nzOWLTK+/TrnzIgd/+Ht88ZXH6F75CrHvqHGkWc7aI09w9fFHufYHv8/sYEpqLXVnmPcOGmCR/ZoRRKEETsAZ4KQQrOEZeM/tpmXxpecoigSEwmyOSeXbp3M/LEbv5H2D9B5wsv/+LHDzvvvd6m/7KwNaCPHrhNMNwzwhiSRVXbGqIjrbkkcJTduCikmjcJqwpsPaGkGMsS3aSFrTEeNYLCZ45ynLOaarcW2DMy0uyjFdy9TAOHWk6QBjDWvDEbqtKC0crlqKOCZJC6KkQCpB21REUfDTVSi8bWmtJcsG7B3cQqoUrzIW8zusrCBpLauuxRlH1VnSWONEyP4rC7UJ7CGJQEdhkzFdxygt+PTjV0ic4Ds37vLQ1hob45zbOzPuXrvF9oktFqqAtRZ17zbeNEigOrhHXTXM6paD0tB4yPMCLx2SIAsT01KMI3zkAgjUepYzg7AtWEOaxTgEbTVnumyJI01TdwyiCOUaTNXihSJPC7o4Ic8GlJNbdHpAkcVYI8jzIcIKdFbgFUwOJkRSUgy2WazuBnNnoSkGA1Q9D2bVafRDDrsH8Q7iXZ2bZ86e48ZkRqoU0rXs7b6KFzlts8uqmhOPL5KPcqQWDDdqnn465dWX4eH3VXz7awJrQ7K4fSbn5MktlJDUrUFpdXwSl0c4tt7xxVoLTmBNSxTFCKFx1tN5Syk8SgYVFu8stq6YTw6Yz2YgHbOdHbLBGpevXKJcVezu7dA0JV50gD+uBBxh7QB8j2UKGwroSCGNxDRln/mEZFQIibNhdwys2oArVkpjncMYi4y/35LaY/WEpK5r0jSmLGvWx2POnL7Mot0lTXPq1YRzW2vY7oDd3TtYazlcLhlEHVpHeCkCKUPQEzT65M0fP81xG/do8zu+puFtAKFKe0zCeFDAezfj3d0XhzH//F9+gY88dZnT2yeonOPm7SWRDS4JWuhjNrY4Mk72PqD1+wRfIvAy6M3pRHBqMKQxjv2dBQ5JFEVsnzjJL3z23+bS+Y9x7+6CarJEVi54tEpFmmrSLGZQxAzziChSpFlCNspQg5io0Aglme0teP65l7n2nVc53D+gLWu6pqJqm1CI6EwQbHYd1jYY32J9h3MGZMCwu57VHgSQj/yc7xumQuL6P0jbHxYFeBUcLXpv2/BZSiQqYBW9RAiNPJo4KrjceOnJ1guuPv0IH/3Eh9FRTNt1dF2wg5NaB02/o2ftbBCRdqGq2rWG4YUnuPRr/5CX//XvMqgbTm5v0h6+xKDK+PH/+D9i8sZ3+Op/85s88Xd/mYeufphyeYdXRgWXP/sJXv79zzNHYAkMW9cTRfoUl1Z4xnh+6vJZ1p86R+QNh68dcPP562xOa8RXvkFdxJCAVPnbDsy/MRnDe+/FDyHB7r3/p8A/BTi1MfQqSikk7M9KFDFxLFEihr4UHaU5y8kCGQmMaRGmY2UFzrboWFItW4bra8yWFVvFEGsaTNvQJQXStFilsaYhyVLKZYV1gnEuaco5i06xkQsgVMUiwNsGqRVt2yK1oa0N0huieIzoKvQo6GutrQ1544XXeXTdMqkUgzxhXhq6zrNsBU3tSZMYYw3WuWNfQiUlbdfx6IXzxPmAR85v8eb+PmujgpMbY86fOcely5c4deEK0/mCvVvXsYsJsTDEQtCUK55/6WVu7E65vT/h9s4uzzz+CBvjEUka0bUrkljT1iVCRXjAVHOsHqAQRFJSO0smoa6WeAPTwz0GWYoyc7q2pLGCYeIRpCHZ84qma2ltiXAdWeLBCZR2CGkpZxNuvnmTyw8/TKxMcAroDNlwg60T2xzu3EZHDbZe/E2H3YN4B/FuzM2rj7/PR1LStC15OqWpK24f3GQ6ucNwsEFXv854bYwXng98eEhddkynliuJZ31bMr0XNBqTQvOxv3MOHUmWk4ayrGjbJiR1Ryd2jgVSAjM+SsLpW4QEx1pH5y0u7t0nBFTzSWDJCklXLwG4ePESw62zRIMRX/nC57lzew9rW5w3KKlAxCAtzlqU0Ki+ki+dgq4mTlKWxmCdQcpQzfMuJKTGBRtD5UO713uPVMEXe7msSNOIPB8cY/gC/jZsWpHWOBvmxWy+JE8Tnn7mQ7x08/9mUCQMsoLlytFWioODPXYP9llbP0GWbaJ7i6S9gyk39/f49DMf6TF6/TULT/UWzq4v+Ym3rul9r6l/Zd/z84N49+LdmHsntgZ+4Qf8xesz7EsHtJXH7VecJe4ToSOcKEgVPG2FPWKYhgRI+FDRkzpAB/buTajbLlS2O3jk0lP80q/8YxOEyrkAACAASURBVKRb5/YrE9qyhs4RC4WWIRGMlSKJI5RWGBEq9MVahsoUrbTsTlfcePkmb167yZ03b9OWJU1bU9cNTdPSmpbONFjTYWyNsUdt2i6QJYQ8hjUdl6X9/Z+JOz6p+Lckkgmta3XMRveIYxyqlKFNiz8CNATqihfghSdKFEKDziMuXj3PJz/1cZIkoWkMxgRIibMeIyzGGJxzOO8CORTfCzpbOmNQOqK48BSXfyXm7r/631gs5nid8PCVyywmS4QX2FQTbayjIk+cR9gsY3Jnl7LtgmOHkPQcG2yf2EpBSLZxvHzrHs+knjM/8TjnnngYc2uX3fmS/PYC8+UXaD+TUI3+BmSMt4mdo9KzEOI0sNvffhs4f9/9zvW3/bXhPBiVkOLZO5xy6cw2rYckTVjUlqIYsGwD2KSyMcY0FMLTtjVFommNoWpaxiqmqyboUUZV10jAGEvqHdPlnK21EcY7VmWNkIIi0syWC/bnhuGJiDkdsguA53KyjynWKMuGQkVgWhQ1SZzRmYYiLUKrOIVqMWF0aszdWcvaIGVnumKURUHuRQiKPENNwmZ0xCRK4oiu6+iM5cpDF7h5bw8lJFmWcPrMdpgg7ZJlNWXr9Dab6wnVwQ5SSeYHe9SrOePxkE899QESIXn5pee5d3iIlDGxjtnZm5CcXGc5nTBOc5yf46zASUEsY1QGlYe2NcduBk3VEkmPjlNsW+NaD+mYVdmQ5pKqLqnKmk5KvK5RUUrbGaq6whoBq0OUFLT1CpcpkjgDLRkUEZHyjMYxpYuomweCyX+L8a7Ozc50xCJGRyVbG1f5+vNfJhUG1+3TrCq21wX1hQ3euHXIaC3l1h1NV65oV5LLDycs14dcf+OQ1azk4cdOhVOyAK0A77AmyEFIIZDqiPXXO2oQFvkgcBp2gM4Yms6jbcDyuK6jrUsWsyndasqjV59kfPosf/mlL/D8t/+C3f17rJYzmrbEes9oMCCLc5SXrKolbWvROmWQj8iH62iVgzB000M4kjRxwUTc99IqXnisC8zFI+JFlic4b4ljff8e1Uuy2FBdkKH6dyS2PMg1V69+EPX7CQJIBttIfZGXb97CW8nhZBcvErZHQ6x3WAd3d++we3gP+Mh9YrH3Eyl64dhjTF+4i+RBkvcjiHd17lnrkDpld2fFcrlCec2WASUlneiJEX07EoLQsXcBuw5HCZ/qNSl9gA1JWM81w2Kdj3/i53nq6V9kcq+jPJhBbUgsKKlJI02ahEpenidEaUQ2TMhHCVkeEWcag2H/YMZLz73G4Z09lodzmuWKpiqp24badHTG9DaHDZ2tMa7CeYMVBovBowIjtn8vYVC/lbAE6ENfRRcWjyGM7CBf5P190AR/H9O9F4CWQiEQx642XjpEJBBKQKQ4feUMn/ypjzMYpVRVgxOKum7QKlgvHid5BFtG5xzOuL7CL0JyaDqs9aTjs3zgl3+NjXMxL//R5/nmF/6Y99NRT+6Sbq3z/B/+Pt3HfgwVO3RbsvP5a0TeEqTYXQ97oheofgs32wEvtpadF+7w8Gu7fOjqec4MM9atoHWW6PaSW8+9QfnBt6dc/LCJ3v8B/HvAf9l//Vf33f4fCiH+OfAxYPaDcAgQwNVKKWJRIoWg6VZkaY5Sklw7BmnMYrECBDLqsG2LLxLKRUlRFEwPZmwM1rDGoFTMvDFUVclgUFA2Di9brFOkkWS5WLGcT8kHBZ20LFctN27uczbdpK1jrC7BWhbTOWragve0zuNcxzACp5a0xpGlObPDXTJZ4LsOnaQcLnYYpDFvllPOndrgxHCNtfGIrMg4t7XgL6+9zKKucd6TxBHLVcdsPsMhyQcDkjhBRxFZnrEqS37nD75Ilwy5fOkin/nAo1w+d5K1cxcpiiF3XnuRbGsN4ojDezdIpCNPFC5TzJctNEtWdc50UTO4sEnTthSDwMrKUo8xkmZV4aVEyBjlSoQM7MFVuUBJSV0vSBNFR4QVnumkRBmJLFLK6pC4aXDeUC2WpGNFU9ZEsWZVrUiDsjN4xyBXSCVJ4oS1gWQxPfwhh92DeAfxrs5Na0qqeo+NtRMcdg3GVzgVkSSam7dvI9VNnJpz+mzG7euOtq7xbs7L3/Z88JMRX/9yzcc/M+alby/pygLneh9bGQDjofAkA+9CqR44LnuESo+sc8EFQwoBQlEbyCJPSkMyyNFSoCRcfvKDnHvf03zzS3/Kl7/4J8yrBcZYrIypbUPbtSSJpMgSnO+Yr2razjIexDhnsM2SZDDG+wTnLGmSoLQOXruq3yh8YMNpGTCECg0IoigiisJ9vT3S8erj/oql92itybIUazrOnDrHM099lLs7bzDdv8Mf/1//E1tnHiVKR0wOrrMs55zd2iBPE5rWYZ3i8rkriJ6cAh4t+2pFeIXHLVkvjjB4b7V4j6p9D9K8v5V4V+eeANKsoJxXnDl/EqxALae4nr3tbZ8XWY/wARN+JDki8X0lvE9yREhWNIKTm2f5uV/8h2xsvo+710uYV0QGhJNksSZLY5I0Js8j1sYZxSAmGxTk4xiVK0QkqRvDm9du88a1N5jem1LNZ9RVRV02tG2N7Uxoz7oGYzpa02JsgxOmT944xomanjkcQHi+b1v2ZBOOMx48Eofu0bY+4Bd6QWUIZEcp3sLg9eJMx59nmA8SrWN0pNk8u8mPf+rDjDbGLFc1beuQKgoanSrg6W3XoaOItu1w1uGdoGs91geNPqUldAFWUs0nRMAwWWf9Ax8Kq1eSsXX5fYzGI24+9xzf+fM/5cTpbUaDgnkeYUtL7N96fRmSbS3YjgI5ZtI6ls5ReknrLa+1Lf7F62wKSaRTtIela1Dfukk9eHt92ncir/LPCADTE0KIW8B/3g/k3xVC/CPgTeDv93f/AwKF/FUCjfwf/KDHD+8QmrrG+YY8i1gtVqRpRu082tWoZIBcVngd4dsGrQXLqkV4h5KwKjvOn0qx3nJqLWFnsUIKSZrGrKoly6YlSkdIqdi5fRvpFfVywZ4U3JusWBzsIh9Z487uHsI7rAsA9Nl0hUGyqisknmWSYA9W1KUhP5wz29tHuoYsFqw6wWJRc359jY8/8hBbGxuMh0OyQUGUZpw7f471Ycbzr17nzb0D6jjCzh2Hizk3bt7k7NYmWgmkiqhWKx575GEunj3PtBXoJOfsmZNI0SJxwUdQwxf+8hW++OxznB5lPHN5m5MbY5bAcrUil4JYKxbLA+oyBWco8jWWjSZJNMrVIYEu4fT2iNlEkGiDEild7RF5jDGGqm7xymMrSVXXDCOBqSuIcuqqZTZt8a6jEyXeBaC6NY79gwnWS5rOEcuEzkjq1tEZS5Y+EEx+N+JHMTeV0uwcXCfP1qiaJak+i5Q125uC/cMFt3cmbGwdouMOpWJmc8HJD6/TliNi4NOfPs8LL97l8tXbZMmJ0FIRIij7exds/XxwnAgYOAdaIj3H+mDeBxcAeh27trOkszuo//U/4e7J87TnnuL0mTOcfugyXsW88fw3uXvnNrMWZo1lvlxC17KxnnNqcx2t12jNjNncMZ9XWDdEaY+QJVEbo7N1pAgHE60D/ujIRMq7vmJGIJC4nnAlZbCPwgV5GB3FR1eJ+2WMoyjgU5M4Ytm1bIxGFIMx+iCjakqasuXVa/8ns8WC1eEzPPnE32H30vu5dGqbqq4ROuOhUw+B8Nw6mKKV4/T6BkoGook/ekbBdyWXXrxFxuj3U/g+7dwH8c7iRzH3pJLBtm9tk7u7E3xTcd6GayrvI+KExL5nVvSMVSmCJRpCBGcZCbGKeOrJn+CzP/8PKJc5d19dIFYdiYdUK9I8pcgS8jwizyNGw5RikJAPE+JBih5GOCW4d/OA55+9xu1X71AvFrRlQ9dUWNdQ1zXGhHZnYxtat8LaCuMdVh7p3PVtSqePsa6+tzQ71sEjvLljyaBjLKo4LiC8BYs4wvH14/mt3LC/WAH3J4Ui0ileasYn1vjJz3yc7e11VssKayyRjsH2jhre0zZ1/yoIGFxrscZR101PiRZ4ZzGdw1qoJjtc/dBFnLDIOOZj/+6v8rXf+xxnf+ynKDvN0z9/lj/57/5HIqU4fe4M5x6H2e177NzZx/WHMOM9rhMI59iOghduIiS1DxZzlZO82XqWyjNpl3gEHWCokF+79rZj6Z2wbn/tbf7rp7/PfT3wH/ygx/zeiLRinMccrAybo4xy4TDWoaVGK4mOI4RYkeYDfDlFxjn70zmj8RDhDUkcYwUs65rBMMFZQ1EMegBoQ5EmRElEZzsOp0tG4xHbmebO4ZTD6YLRIMU5y3xySJ4lmM5RexcsYmRM6WpMB340YF5WDPKcg507dMsFt5qSphXMVpJzwzHn1zfJ85zB2pisyNE6fMRtZ9jaGHN2lDJSJ3j2xl3EaEhbd9w5nPDZT32C5HN/RtMYtFTkecGp85dolzOSokBoyXx3h3q6j/cO18z5pU+8j8fWCZtgJGmMhyinnR2QZlC3MEg1ZdVS5IFBa4iCXlIkGCYDVq5BxSnpoONUEfP0R/8eLz77RxBvcre7hjcNXdciTb9LaEXbLFnLx3Stp5rv41VKrg6R44v4pEQ7i2lmxOmQsmyoXYPXhuV0wao0bAz+346QB/H94kcxN8Hw1a/9NovVL3Ny8xRPPvwI129/kzfe/CJC1Lx68zonyo4L2+e5cG7JxmjEjdcTnv/2nK9/7ZDNs3tsnE5QYsDZs6fxztO27XErxJjAZDvyuD1KPI7Yor6vUkipeuReWAzTr38O+8LzzHam7Lghlx5+DKEjZBzxmV/9x3g14Itf+RKTxT2wnjTJybM1isEmWsV07ZLOam5Nal688yJPXDjH+ZMFkUoY5utEUYR2jluvv8SZS1fpfF8560kcR5XJ4wplv6s45+i6liTNCDWF72rkIoTs3TkUTWMYFhmPPfIUr9/8KuBIsozIadrG8MK1r3P5kZ9hXKR47/nOK8/x+o3nePTCBVZ1x7U3r/H4xUdwzqPxx2zlt57uCMMV9NbeArQfJXwPkrwfNn4Uc88ay+RwijAw2ZsQS4UT0bFcytGBg56ZHq6r5Gg6OS+QyhPFkjYf8+THfpzBY08yud3SHsxIO08WRQySiCJNKPKYQZ4xGCasbeZkhSYZRMTDGJUoFpXhla+/ybe+9m0W+xPqqqRtGparGmNqGtvRdobOVHS2onU11rVYZ3pkncRxBMnoTx4Edw5/hD04xhvcD9bz+PvHb1+1dj17/IgoJQXHZcJA5OCYFS/QCKlxOLZPrfHZX/kMFy9eoOkcdVUipSJNMsqyRijJsl3gXWjNHrVv26ahWjY0VYuKI5SQwfqwCkST1eEuzl3klT/7MrMbb3Du8q9z6tIFvNY89sTjKNnx5Gd/ksl8xZUf/zG61ZJqsc/nf+tf0JU1qmcOL4XjtoObtaNBgujfL8HWbiEcOy7o6sn+c0mA9dnqbcfSe8MZQyoWZUVTWVwO+XiMMZZqNudEEdFZg3GSyHtUPAi9eicYD3KU9KxtrDEcreHdBB2nrI0caT7E4kljjUwSvK05PDSUVU0cx5TSYozlxu19nrq4xf7hjEVpcFIhnKDrGqxxIB3GSiKpWC5nrFYtaZqxu3OPTDvahWOzyGgOVjz60AWKYRFsysajIAsB4PpTQdeyPkhZz1OM98go+BJW8zn/8nNf5HCxYrVc4R3EcUQ2HHLi0iNIKbnz8nOUy5LTFx/BtB3LxYrzZ84xzDSmNbz2xnV2G3AixVgLsmBRtXgf/HvX1tZIIk3lImpbkUqF0pIsixmP1zncvUNTZSQKNvWQ7Sc/QPO1mxyWK5zwZKqjIWU4GtBOJM5ZPIq0KKg6h1ASIRtGazndbA54ZOQp5wd41+CiDQ5nJcLUmGL4/+2AexDvOLyDcTrn1vXP4dtHudEJNjcvI5TmyvlHQLe8fG3KjdszZlVEGjtUvmJ9O+KZpz7FyTM5RLcYDiec3N7CuDDvnHF456ibJggL+2Aqfn9T0ff6dcHKSRwRCUEK3O4+k+gCB+kG6sY3mc4/Sb7VoruahI5z585zan2dJI0oa4OXCeujAYPBCLqGSChObZ1E6wTlLeNBytbGNnlWoIVmvcipXvgzvvyVz/Ez/8XvMDpxCu/DyV73/c+jJE9rGVI6FzadrutCu1d+N2ZG9OW0rmspiiGI0Dk4f/YxFssagWNV3WE4KPBiF28y5rMDXrt5kyevPMzdu69yYvMiWip2pzN2d25y5fRl7kxnnN9YO65sQHDgOf4sv1/lTnw3QeOYofsg93vPhLGWVIWK3MmtMQqB3GsD4s4FdmZg3CqE0G9Vsnw/9gyApl3fZOszn6WLN9h79QCWlgGSQRaTJwlprBnkMeNxTj5IyIcxaa5JRzHxKEYXObPJkmf/9Fu88txrLKZTmqalLFfUVUPddDS2o7MNxvYCyH2S57ztyRYBi+uF7w08PPRetaEIJ+6r6BGSPhlK5EL0jdwj+q3wOGFDe7fvBuA1Sni8D3hY78UxaxccVlisUxSDlM/8wk/xyNWreA9lHWBK4QBWUlU1UZoihCCKNdZZnHVYY2ibhsVigTEe2XZopQMG0Vq6rqNdLummSzZPbnHjj/+QL/7W73DmA0+RLyZsjDIwMY9+9NOIJCJbG7Hc3SUbjkl0TEQDhOvXv2sqIVgSVDNLBA0BY7jZWx0mwFpfwZ162KNvZX+feE8kes7DqVFM13k6U5M4RZ7GHCxqfOpYzASR0iSRwKsY23XBU04qkkwTVQ2zRUPXGFZZ0K8yxlB1FilgMl9xahRwOauyxbBEtgKDwrgwmXb2F5RlQ5wk1HVJkcYYYYikZrFcsjbMaVctzsJk/5CmbvjwRz7IIB/h2prtU6fwpmW8PiRSGm8sUZJSDIckecGyLIl0TLecI73lZJNihGJvOuOh02e4eW+PVdWwWC0pyxXeWuJ0gJARy/077O/scu6xj5AMRjjbkpcl6cY2+doJJrfewHh4+fWb+HSAjiVRrDmY1ijXUQxHJEkQhpRtkICZlQ26yBhkA7yFu9de59STH8MsF2ycOMXrzz+LixViZcmG6yjvGeYjikJT1ilSC0xniHSGiAxRFhOnCUmeMp1O0EmKkZLJ5IBYWNRggO0qimGO/2sUvB/Eeyu0ijh77pPc3n2V1+/8azaKLYz9DmV3yHduPYeg5vRDUM7OkA8ctdljtL7kk+eusH9H8O0Xnmd0oubihasM8pPgA2PtqNNkXDitHvlyQr9ZHVWjfNCvE0ocn9Jxnmm6xeTcZYpbX2B08RRWRdBVUEU4Y2nqmsFwHaEyhsNgDplEMVGcYE1wu9goEob5WYwxwVs6zdFKk8QZwygiF4rqjess775JsXEKjwsiw33LFkJy17VhcXUukL+iSL/FggCOlu7jz1RrpJQUeY6zhq2NU5wYn2GyuMnh3h7zmSLNcohblqsDqmqPb7zwBvPFqzzz+E+QRAnzsqTIcmarA+Im5fz6GiFPFv1Th+c/0tq7vx4S5Bu+P1BPPMj03jvhPaZrGA1yVmXNaDQiL0HMXKgqe/fWOAt2EKFVi0ArQasV2fsf5/y/8YscTmB+fY+ks2zkBetFzjBPSWJNrCDLNPk4o1hLkakgLhRqECGThL2bU77+p9/gzWu3WK6W1FXDqlz1eq0ttWlDsmMarC2xVOEQ1MsQ0SNIj9qr4a31pIv7GLbfNWXuEz0O93nLQepoRB9h/OhxiELc/ytBJjw484TKl8MRJymH8wV3dvawxpFlGUqFSn1VNQE37IK39tHjdV1HWZZ443De0zRNIMpIiZLBmrVpWnzbcPNbz/PUL/wkb3zpHJNv/gUf/7f+Hgd3blC3p0Jes3mWWGga01Gsnee1b3wJtVoR4bBC0OJZAlMv2BeShfdIPCMBawIGUnIGwZaQbGhHasF6x8oJDu5bY7433iOJnqNsO5QOxuBeeHSWMSgybL3ERIqkd6voXIfUkCca19a0MiaSAkXL5taY/VVDuVixOfa0y4rNccSbO4dsZpqqDri02bJkp/J4lYAPQpIH8yXWCmbLilEsmS9rlq0jkg0nRwnOO+quQ3nNJz/wNI8+fAWJpygK1ra3uf7yi0gU26fOge0CcNxDsbYFUiFWDaapWNvYpGtqktmCC2dPM5/P6VzHo1cu8PyNHWarmqqsmR7sMN4+zWT3Ns4atrfP0RmP7BzVbEacDWmXc5JiQF1V3Nmf8a2XXme2KlkfjnkOyLIUIQXpy9c5s7nOpbPb5MOcVkliXyGKEbPDGebV1zgVj/jIj30CmaXs7Tdc37vOie11nFqQJjnOGZI4CMMm+RCNpaVDmpa1KGM4KnBaE8UpsdL4SGGswzQ1WkdIa4njjJNb29Tz3R84Jh7EeyOMbWjalqab0hlweUYx+hDN7f+eU5sOLyL2dzpeuLHP+5+uOXWi4+Cg5i+ffZkPf3AXlY7Z3FScOWtJs6xvt4hjuaHOHLHoOAKO9d+GTUJK2bs9yH7jCEKo5qOfZnH725guQq3ATnZRZ05gu5pqtcQ6T5Jk1G2L6UpinZPnI5wzmHZFFCf4tiSJJd4XKCGIkwghHHEs+X/Ye7MYvdLzzu/3Lmf/1tqLO9nNVjfVrbZasixLljXeEnuMkRPPZCYI4lzMTYDkNlcJkMsgNzGSAME4QDCTQTIYZ5LYHo3HsRFJtjy2ZO3qbnU32WRzKbKqWHvVt5z1XXJxThXZju34JhYF8AHIKhY/1ne+4vud93n/z39pbr9FvXqR0fplButXcN1GKlVLFDcWAkGbtdsheVK1DVzTGIw1hKoVagjRon+nDLogCDGmIYkjysoz6vVYW3uBh5vvo3WEnTeYpkeSaSb77/Bw4xoHJ/f5xEf/DpUzHM6OeP/BVxkMVimKGSf1EfNzlxjEqh3fSbpBrjh7zg+NkMWHPjz1+dPU9ef1wy4pZetf18+Y5QXGFCgtkQqQ/gwBa98jLbHh1GTYxxlrP/eznP+Jn2dzY4Y5yBlJyeKgz2qvx3CQ0uvFxKFCaiAWxIMImWiIFcEgRCch9c6Mzbc/YPPeNvl0SjnNmZRTyrpoUSxb0FhDaRucLXGuzX71Xj7VuJ2uwfZzz5Nm7XRce3bdOIRwXWP31Gr0p6bJHf9UPFnbp+KL9vAoPqTSl8insp89xwcn/Jsv/zF37tzj/KVLXLx8idFwgEB0WdRh6+VpDFqH7ci2rjFNgzVtFKSxbVKG9x6tNHVZYxqLb1rbmqVLVxkurzC5vcHm5oTRYMjGuze5cvkqx4fHxFELiGw/uMvbv/XbNNZxguIEz6EXlHik8AxxrEvBEEEfTwIkzrGsQu5Zy33rGQjBCEUPxwWpwD7DiJ53jpNZjQpTItWS+fOyppemBCJkXtZUWhM2DUVZIYTCuJqmgdJbRGMorWFplNCUJXjPZHJCUQukShA4rLPsH00w7bGbk9yQDmOSKEAiOb+yzN7eEXEAcRiAlCRxR8AWkqq2XFpY4Rc+89O8+LGPc/vN73Dh8nkuvvQKVmm+96df5aOvv9FGsB0d0F9aAqGIh0tMTw452ttlmA4Y9AfsbG0QJzFSSlaWxsyKggsXLvCJV67xp995i8k0p5jNOdnd5s6tW8ggJolSxkvnGK06pnu7TA53OdreYHFphbu3b/PdWw/Y3zlgPMzwdYEUgpMiZ9hLWRmvspiEHBwcsbWzRxjEBEry9pe+zI31BX7sE6+RvfIq3jbcfv9t6sAzWFhC65A0ToiVY+YUuAZnNaFUSGfpRzFeQJykxP0RVWUIvCRMEqoyR4QRIurj8KhAEKkeg/4I3zxH9H5UypiafG6oyhOqaki8+DLvvfsH5HXJ7vE+3kYczQ1vfHzA44OGL/3RlPWVIZ/6yQFZOkSEO9x7eMT+XsrPfCqgqgxFWWG6AG5nnwSsn5X3GGvOhBvOeaQ83QRaWYRbWOPip3+OOwRMhePk/l1efuVljrYf8/3vfp/bN99hOjliWkyY5DNWz7/E4dED8vyYRHmiOCYOe8zmB8RRH6VT8BZvHdJZBs5Qncw498qPEQ7HuM62wTmoKtturqrdTKw/5YdLwjDAe4d1TzWwnI5J28+DIKCua5RWTPdzer2Eq5c+xte//n/hXUOcKKq6JOllROESf/L1f8pw/AKffLXG1vucTCYcH07Res7y8g0+9uLr5GWJ8BFFUzHuZ+Ah1KJNSJCn6N5Tvnqn5srtn84uNa//4o3ief3NlxAghQfpOLc+JA57iOneGS+r/avWl9ULD1rgrMBkY175e7+GPvdRNm8dwrRkIYpY7acs9jIWBjFJGpP1YsJMIGKFC0FlIToL8MqjtMaeNJiTisPtPWaHh0xnU+ZFSVnXVKahtjnWV9SmbhWq1Fhhu9QK8aHxqXNnzLr2tUmAVk3vnm4In0L96DiI3YC6fY04EJbTJlBKjUB1TW+XhnHKl+JpKkh3L6kbDh7ukZ/U7O3OODmZc/2lq4wXFmkag9QtnUpK3WbZWkvdtKkedW3aGEbaRtOY7rBqDY2xoBJmu4/57V//RwinWHrtVfaPZ7zwkZf4yv/yv9L7lX8XHQ85ePc9bv3ZH3PwaIOTuuAYQdMhlEtC0qOLKnRPfh6Nb21XZsAIy4vSYxzkvkUAp94R/hXHtGei0VMCZNzDGYOIYpSyzGoIgwatJWVV4KOYqDE0VX12yvfWtuNe2zDsD7HWUZU5VVGSmxqlYyZFTZZojo+PmUxzqsYilSQNBXnpGA/7jHuaExOQpVErcY4DaBRSKbwzaC14/eXX+Ad/99d4vPWAR7fe4xM//Xm8s0wby83vfpdeNiQdjnn84C7DLCXO+nzlS/83Tr+JK+a8eP4i2fICVtZIregP+njv0EqSJglRFPHytUt888332Nw94PKlVda8Axmi1j5Ckmb0V85RNjWVjXi4fQTM0d7wzt2HPHy4ySsXV9k5PCKKAiZ5CUKxOOwxyALKqsI4T6RDnCkxVnNlR10GegAAIABJREFUacQvfu4nSdIBo6svM8932dh7wLlzS4Tag6+JwoA4TvC2pPaWujJIGaKVRyiBUw0ybCOkyuPHMBwQygavLY2HxZVVXDUhSnpYL7roqb9OxPLzehZKqYCT2SOKvCYK+rz9wXfohYZRb5EwiJDBlDDI2N7L2dsOubgwIs0SjDds3A0IewEf+8g1bPNxwiBjnhft+LYzSG2MIQ4TlFZwxitrzYiBdmx7OsY9U9lJLAHJjc/z2U/8JLvvfIN3v/U1yrpm89Ej3n33bSaHj6lNSWEqeuNVppNDDvcfgHCUkSaeBwwHrXnX0eE2S6NltEwIswR7ckQj+niRMv7xz4MM2nD0U8EFAt0t4XYyJdpRU0co997jjAXcmWKwezQtAtFSS5IkIwxDvLNcuXQdpUOcL0A60ijh5Djn3ep7XLqwAt4wO/4Gm9slSWrZ2cu5vPYSPZ1T1xMODj8gzpZI0xUe7c3o9YYspDFl4yhNQxZGneVRexm19VTWkQUSfZoQgseY54ewZ6WUFKwsLbb31qbGmIJxINqYVtpc966DAkAYj1lcIPj0J6hFn92b+4RFw1KSsDocsDLK6KcBWRYS9SNUpiEVEEl0olFZiEo1wjjMXk5gFLtbx9x//xEnxzNKU1K7gqopqUyNExVNU3ZUjK6x6sQRdEhjm+f+tKiiex8LwLfCjCcCDDqRyYfr1IaJ08fSxieeCi2eHg+3e0uL+omn/YQ41Wl4pJPU04IT95h7tsaYhsvXrpClKUprwiDAO8+8yPESyqrk6OgQHYRYK2lqS9O0Ag1rDM4ITNMgByOm9x/w2V/5ZbK1dS5dvsjW9gn+4C6TRxv88T/+nxgMh+xtPqS0Bu9bnt15QOGpPTgMHkHTveDUe7IOaa+AuZd81bTiK40gxTEAFoVgSWuw9V+4lp6JRk+IVnkbRILBoM/xNCfylkEkOMkdYSBwxjDLDVmccJIXCBRppNFBxOxgn3AhYF6UmLzEWEttJONYUhnBKEsppzPK2jIpagZpwrmFIbdOBCuDmCQSzGXEpYvr3NnYIkxCVAwKSWPawPK93fv8s9/8JywGIb/49/49Cmf517//r/jmt7/Dxy6s8LnP/TzHB3s8frTJ6MarHJ3M+YOvfYPNh495+dI6P/Nzv4RXnntvfQ2DRYUBxTzHWUuW9MnSjF4a87Of+Ch/+p23ufD4iAuX5lx84QWS9euYOKVGYNKU4cIi15qGOJzzwcZD/uTN9xmEMYN+xsPdfRLvqRuDaic5GOsItWK5FxF16sXl5WVWVs6z9sqrxP1FyjJn9+ARVkDVNORlhRQlxoSoYkZRljQenO4jXEUvkgibUxdTaidxTjAvclQcEgaSEMUsr0h6IywRYRwym+btqO55n/cjU85ZvvPeH9KLA9J4H2NCjo4yrqdXmU2+hxVLJL0HLIwlwzjh8MRy6Zrj4b0rXF19heXVFU7yR6SZRkt1Rstp6qZt9pzHeUcg28XqcZ27/YfriXDAn411ZyJhcvc+0w/uIvIZzkJeFEyKkllZ4FwFYUqUjnAOxgJMcUgchSRJnzgb42xNPj2mqiYI6Vi9dJn55ibq4nXismHptU+iEFigMoIoaPfWyngcllCrbqMTHfbQ5Yoag3MWKTUtn+jDg9JWje8RUmCMZW3pHAvjcxwc3mU+r2hpQjF1U3A82eV49oA/NAcsLMZcPLfM3ftvc2Hlx0jiZba2vsMglhzt3+NAvshwYY1rq6sY72msJ89LrAVLSCDarOBQtq/hcF6RhgGhlgRKEJyG+D6vH34JweHRhDCIuXH9OrbYQjQR052yi+zrEC/h8UpRXVwn+9Rn2DtqmG0ekBnJuSxjaaHP8rhHLwtIY03YC5GZQCRgE4lMQ3QvRGUaXznssSF/NGPz5jZvfe8WOzsTZkVJaeY0rqKxBU1T40QbZ+b8aT5tl0ThXde7ubNxqvO+iyjzZ9Y+eDohBU+Nc5/w9k7JDqdNXvs9n6iLW7HTqeq9fS/Js4mu4MMdYzdBQCAsaCSurCkmBfduPaAsa669cJVev09d13gvmExnCCU53D+kMRYhPfk8pxVU2U5hb/GmjWd1/THldMrKlZcpbMOx0ehI890v/it6/Yy8sJw83EA5Q08oGuHIPeR4LALt24ZMCk8AxAhiwAhoEOR4Jp34YlVILnrBkoReZ4jt/J9vkZ/UM9HoWe8RWCQK69tc2zDVWFPgnaPf77FzPEVphfAWjWsNTh1IGaAFpP0+u1v3kWFI31tOjCDLYko0vVRRz3OSJCGMYnxT019YQBUFkYa5aWOX5k070qmdZDwc4pylbyuCOMY3lhsv3uDFK9f4zltv8qff+CoPt3dxdclL13+WKy99hO/+yR+ytn6R/sISv/eVPyCKPBeurFDrgJsPbjMa9plWFdZWHB4dU+Y51gm0relnMUma8fpr19l4vMf37zxifX2ZF64nTG/9gEuvfop41KeczcgfbQEFW7v73Lq7SV3WxGlMEKjOMFIRa82sqpnMc+IkZGWQEUsYRILto5zjwwPMdEo9nbB+9QWOjrY5FBWNUTTG4JRCGkEQBuggJhv0ODl8TDLsEUUJgauw3tFUc2SgmecFZd0QGUlTzIgSRQjgDKKpKKqCxuQcHe5ysPuco/ejUs5XLA4VdWNIkh6Igu17OW++N+fF8y+S2232jw1JtECWSj77E+fYPDzhl37+H/B49y3eufd/InTM567+Q4RUSCVI4pjjkwnWe+KgjefTWiG8+hCdx3/4Tn12cm9M04ayS49Ye5nBwjmuDiISPcXaVgTlpaKoDEEcUjU1psypiwJhIqzQNBKCoOkyNiWNaUhUQC8dMas3iaKUa5/7POnSeYxwZJGmqlyHLnTjZOupOu6dMR6ER4luU3P2qUbvaQVgizoopTCmIQpDijxnPOhx8dxL7O5/gHMCF3uUmIPXHBzNiFTAnfJ9Pjm+hqVklj/i1p3fZFJ8kxsXP8P9xwX7kw0ePf4dfuaz/wk7k/WWgiI8Wgn2DjYIVq8ydy25O9aq9RVTcDjNScKQfhoin2sxnpkKggB8wxvXFri6VvHK1Y/ylS++ydR3GbcSnHTYSGNfe4X0xhtsPS5odqdkXnJhmLI6GrA07DPoh8SpQvUUPtP4SOIThe4FyCwgHMRIBAfvbrHxrQ3uv73Bo80djiZTpvms9Xg0FaUpMK7G0jpfOK85tXkRHoRvPyJbVb33rem5Q+C8fKqhE2cK4fYrHQIuXIfytXm+HRZ39vupWYpQ4FwNiM5IXbTm5b7l5VnfBSl66Aw4W2skwAmHEy0VxJQ1Te3YZY9QRyT9Hr1ej7Is8d5ztLfP8dGE8WiZfF53vp9topSzFms9TdM6B3gfEQQJP/jyHzF8+UVmm9u88zv/gqObbzITgv1ihnYWj8f61uhZCwi9QAmBxqG8R3vo0x58S+EpgFK2sYsvS3hBKbTzPLaWLeupvaMRHuv/8kPaM9HoKSHb/Ejh27m+hExLXOVJIoUTisWFESeTOaUMOpd6gdAxAk8/jXGydeWPddSqZhqP1hAqxaivyPOQUTPANA1VKTDOEoQBaws9inxCJEEEIU0vI9CSIIrQCqTV9PoZoYu4fuMV3r19k9/5/S8y7IVU1nCu38OWNZPDQ967eZsvfOHvEqQpdx88aHkFUnFne59f/43f4OLyAufGPZxrwHlCHWBsQz9dIw41g9ECzhk+8dEX+N679/iT791kaWWZfm/Izgfvsn7pBcRkSj7f4fj4gMXldVaXDxgN+kxnM4rSIJVEScnyuA/HM3YPD2lMxfrwGlp4+klMXTYM04SrH7mBDBIW185THh8hdo4IxovkRYMRKd5JmukM4RoqIyjmJ8jBKuXRNjpKsU5QFhZdz2hKj6MiM45I9+gPh+ioJIwSXBTjpWciNbasONw/+mEvuef11ywhJOfXhqTxEo/3HL7e4tVrCY8PBUqnPNraYX0lRZMShQN6A8316Oc5mv5zvn3zbWw54pc+94vEqp0bStHGqpVlibcO4xxaqjYXGc7Guqdk7FNidUvEBnxr4sxTlgxJbwxxgKDBilYQJIMYVx7xwb171LVhsR8QSdWSrYk5OdnF7Ah6oyHKW1SQMlxc4cKNH6cu4dxLr3Hhjc93CRhQ1u2NWXQ0ozOkooMerD+9NoG17Zi3aZo2R7cbJT3tt6eUxpiSfi9lOp2SJSFXr9zg69/+PRAWJWLCyJDnAu01QSLQCspKUlYVtunhbM7x0QGH40Mal3B1/bMcHf2Au3e/zHC4yrC3jq2P2dh6l+PpIauLF0nCgJPphEk5YzRaIa+mOBuydfCYUdWnF8U/lHX2vP7fVVU1mah58VKKrWfIKCbp93AcIH0AKEQyJPypnya6+Cpb96dw0jBSmnP9PmvjAQvDjH4WE/QFsqcQaYiNQSUhZBrZDwhjhS0r5psz3vz977Lx3g5HRwXHxQmTcs60zqlMQW1KnDylJTzxtmv5eB0aD3gpOjTvVGzRIdpCdY4qLQ/0VChMN2ZtjdM93p8eisRTlkFPDn1KaawzCKHx3iKFgi7LVnScNiXVGVdQdGRUR4syImSbp20MVV4QJT3m+8fcbWq8Vly5ern7+Vc8erjB8vI6ZVlhGoMxrWeep6WVWOs6tb3BCQmjET/4F/8MPRxyvPMAoSxOSEKpSLxt7UKRaA9hx9u1OJwXWDyBgL6XeAFTPBWenpSsdkzE0lo2nGcgJctBhMYxdZ7cQyXlsy3GMB6mZc1iFtFYT2McOvU0LiKUnsPCMe6l2B64pkJHCoEhiAPqvCRKYuo8R8qQIFTURhMqi3eQDXpMqylF40l7PaRrCAYpIu2TzQyJLjFxjHet0jeJI6bzCXEc0zQ1/f6AWDvube/wv//L/5mD4ylxGjIY9Njen7K6sMxouMTbb77FSTHj+OCIh7u7PNjZIwg7oraU1NZRFgV5LFnsZ2RJyGg0pK5qFkZDpJToQDEYL3F+fY00SPj9r32b924/4Atf+DsEOmR+vIGMAkwzYXV9naW1dR4/vEe/F3MyPeZgckIcRygtWVwcMhoPuf1gk8NJwSBLeOHaFUIpiaOMOBtyUlqK412Oy5LF8RI/duUVwktX+NbXfodSCrydM809timw4aBtoIWnqkvCtEc+O0ZKhaZm2hgimXGye8DS2gKmrqgrT1mcMB732J801LMjiiYgf04D+pEpax3beyDEPkJMAckgjrh2fplbD/+M4WhOEq8xGvTpDWe8936CkF/h9oP3sfWYv/XJf4vNky8h1BjvPdNZTtM0WGtwzlPVNToI8c5QNyXae0Q3xlVCItQpB4c/Jxx9csp3zmOcp2gMMkpQWiOEakeUged7N/e5dv0Ca8sD+v2EJo4pRcD9R49JD4547YVVkv6Q8fIqi9c/ycLLn0FLhfPdwKfj+igpWmuvDrU4Hdb6zgLBeY/tzFuF9E9rHJ68hq6kVJ3NiibQId47rlx8ER0ETKcF47GmKHICGXJuZYn9o0O8k0znR2zvz4lVhBA1EYatR/+GfnKed7ffQhLSD0Ju3vw/WF/9cRYTB36TlfFL1Kbuxsw1R5M7VGZOXe9iScHAo+ObnF977f/X9fS8/vq1stjj1/7+Z3nhxY/w1ne/zvdubbJ9b5/WKy0gWFhj5W//KnJ8mb0PpjCvWUpCzg37rIwGLA17ZL2QMNWIkcbG4CKJ7GlEpoj7MVJAfjhFVYLdNx+z+f5jDg6mnOQF03rKvMwpm4ralFhvsMZ8GGfvOHAeixftlbkOefdnap8OpfMt6n16YHO009721KY4zao9DXH+UKyfaEUnSiusbfOvT9MuTk2iTzE/aG1QPKcWLN338ae68vYCnPMoLHVZIKSmOnbcf+8+zsJoYcjj7U36w4yyrHC2wda2RSg7o3fvfesSYiXWWsBSTk4QvmS+uYeUgtg5FnsRdVUT+NYTz3f8Ooug8W2WraDl4mW0o1qLp49gBFTeceyh6m4mxx4eWsvYWl7VAeekakfmQgLlX7iWnolGzzlPP4lQQcS8nDPNLamGyjoCHdAbjamKOd545pXF5ROSGOojCz4iHQ7xzhNEEUKHzPIZUitUnBGFiqbUWBTGecZZwjDW3J94hv0EQUEYhoSBRqVDqiJnMVnGC4kIIkSYIlVB5cFWJU5rhHdoLajqms/+1M/y6ic+w6//t/81iRDIKOXOO9/HNDWj/oBp1YB3ZKHm4vKQhWHGeDTANxWBgMoYkjhubSFmJ+gwYfX8eTyCL3z+x/mTb77JH/3hl/nxT3yc3njMbDIlGwwYjMZIBVmWsLzUIwkvcPv+Q5QOaIylso7xcIxzj4hCjQ5jLt14gyjOSMYL3L1/jzfv3sRWU4JmyoOTbS6tXmKkHHVV4pViOm9vKPM8J6BBy4iqctiqIp/P2zzhtAfC0xQ56UDRWEMxm5DEipOjY5IsxlYR+XyKNTWT3Heqqef1o1DWGup6F0eCr1LSfoSQF9mbPGSchQSJJoyOiPUVGjfje+98wHIWIN0qn/7kMkfVl9naDnj18hWEbJshZw3W2s65X51FiOG7kWhHSG5z0CRKfbhR8t6jlOLpVEwPFGVBbUxnRt7eT1ZXIl6/ovjBO494r6wIlcALQeHglQtLvP7GKkGYoIOEi9ffwKoYBSjpcUZgrSPQnX2FPDUipsu7leDbnEtHi+ZJFNBuhm1T9YSfd2qiLLqNzBhDGGqkEiinWF++wML4ArP5+9R1hRcGpyoe7+8hhEYGDScTqEyJZpn9wxlN85AkGbCzV7Jz9Igb1wdsnczo64u8P7nJ6vIqyCV6oUPM+2ycbCDlAoGwPNj4LZJE0JQO6y2RDjl6DrY/MxUnPXqDBWb7m3zs+mXef1SxoTXeSMTaZa78wn9AIYYcf3BIWMFikrA66rO6MGBxkJL1Q1QmUf0AE4OPJTLTqL4i6icID9W8QHmNNgJ3XDM9mjMrZsyrOfN6RmEqGtvgMAjhEN7ghcS5Np/6NJvD+VaQJLxAONnm10oF3nE6vPXOti2feNIM4umsYgBU1ziaTsR0CqGDFC2H1FpzduCTUnX/6jTmT+CeavhEJ4ZyZ0hf10NKf+beYr1FUoMG33imBwc8eB82e5rxKKWpA7wB6wRV0RBo3TV4vr2HWYuwXaquzUl0SN0UpJHGNDWxCmkMTKsG1TV4QXdNCEmEJ8MTeEfQHV+taNXIx76NN7MdKmp9S2/PvGNVSZSHbeuovUdah9d/Ofn9mWj0vHNY2+C8QjaGQAoEhjBQDEdDDmcFzXxGmoQM1xY4nORQ5dRVQVnkDIYLBM4jgpAkCRj4hEBAmvZpyhpnLTLQDOI+SktU0FA5x3CY0Mw8Ng4JohgdappSsrg4ZmNzm4WFMWGoKKcFHolt2gUihcRLTWMtO0dbbPzBF/nBBzf55Z/+WS699jr//F//Nk44QtXC7x7oxyGDLGXYSxj1UsqCFtJWComjLudERYLSAVmvx6Cf4YsTfvUXf4p50XD77bdJB2POXTjHeHkVb2vywwO8t6wvDHDDjHPLPbb2JsxNw+L6Mt56Pv3Zj/HijVeptnaJehnj89eZHmzz1p13CHt9ctPgfYOXcOfxHeTOB7gGgqGA6gQVL2Dw1E0DOqPZ3ySIUhrTYL1GaYlu1yzZoM/J9Jh5MSPOU+azkiQJOJlNMdMjVCAp8oYw/P9aEc/rWakoDFF6wGJvThAExOIaItpkcdGwuZ0zPQhIUKy8NqcsX+Duva9izsHlc6u8dfsDpvkcWy3Qy3odt8V2GwMY2/JcWi8w9USeKiTOts2SlC1KRuepd7oltNS4TpEr2sQKX9dMZiVCtSKtMBwgm5JrL5xjJR1wuLVH6dvGcHFhzKXri0SJIY17DEZjBpde5ebtu9x44SqNbZ9DynYTsTzlSidEG+VmHIFqFcLW2TN+oZQKvMFac2arIgRn4+nTnlVrjfeONE3Y3p6yOO4z7l3gMLtPEEqKIiQKFP1+xHiY8HB7n5VVx8Z9y0LfEpRjpnkOdoe8qLlyOeFk5hj1LfvFA46nR0xnBwyHMTM9opzfIe3B7vEcb3ocTx6ytpahfQ/hHbWzzCbPSXrPSu3u7PDf/8YXeeOlc5xfC9g9mNEYD5ducP7f/jXm85Dp1pSk0axlCWujHssLAxaGCekgRPQVMtPYCEg1uqcJegFB1rpL1HmFsApfWNyR5cH7Dzmez5lVU2b1lMrUNLbGnSJk3tPqQzv+rOjen9YjUJ0vuOsUsfKMUwud1Qq6Ew2YJ+bGnX3S6fj3rEkTTzQVUgoCFeBMA3QNoFec9nT+NA/bd4heez6kiwbpxsodJ1AInBd4x1mUnHMWGoMMBE1lOd7bQhUpmmUaI8nS1rS9Lg0uCJCdSbL3HmdtNy6ViCIHX1E7C0KhnUQkAS7PWfCeULSNnkBS4TFtu4fsXneBp/CewkssHi9cm7Ht2w1We0eMYCw8iff0hSB3DuMFWnr+HNb6oXomGj0pBFnS+VhpzSCJScIGo2KG44x5ZXA6wOBaXliek8YhJnd4b3C2wuEYLwyR9RzhIVaWvHZkkcAh6aUJKEUUSiwC6xoWhhnYiGzQJ1QCtGLQT5GBpj9aQPh2E5rUEuEcVmua2qFVwPG8JAkjdo63+erX3sQJxfffv0X5v/1Tbt27gxdtpFPV5eTFkSZJQuI4AiUYjheYHE+IovCMmN02u+2bIIkDxLBHNZsQAufWlkAG9NIEW+ZUxYTp8T7z2YxRFrGyNAKpOX9wwLsPtlBxxtW1mNpp+qmijAIOth+xeO4as+k++/uPWc36VFbQeM1iXCBEhNYOowM8CTJQRL0hbUahIow1VW5Bh9S1JU77oEJqU5H2e6TZgKKuiSNJnpfUtkF4ycFRQV029HTKtJyzPHgedvujUmXdcHjo0T5k3DfQf48P7tY4EZBGLXfNMyTNMmb7E86vrvOZH3udw/wecZoQqpBXP/4qo8Fqm8mqWvK0MQ2maccgvkO6Wj+99pfWAUK0qJ2kTbbwHY/HOcfhJKeqDf0s4k+/9A2quuFvf+oaO9vbOC9RYURgU/AeK2qSJcWCT8BAGEeMlkek/RBQBGFCvPoK//l/80/YuH+f/+q//M94/cYr3cbjznhGOFC69dILlDhDNIRo1arWOVxnmeS7mCJrTYc8PFEMt4ieQMqWYxRHIVIK+lnCC9c+yts3v8xgGLTWMtJzdDxlOm1I44zJ5Ij51BCFR+SHhtdurNA0GZNpTSgHbD0quFVNeeMjn6KqoKrn1GVBQ0JefZdFG0EJthmghSASlspsYWnoJecJxHNI71kpKTy/+gsv4xvP4uoCH//ERX6Xhiujn2R6qMj3ZiROsZalnF/os7LYZzBIiPsauRDhIkETeGQWoAYB0SAhCkOqsqGpc0TlsFNPkAtu/9kt3n77fY7LGbN6Su3nnU61Xd9eyJaGBLhOGeu9wBlw3raHndOmji67llMuq+qaMN8JJBwIg+eJQKl9nlOvzPZk1DqpSIIgwNTN2UFKCEXbujx5rPed5l202bDen2KNsjusnWbiWrQOOlrGqf2RxDuHaSqc7DiEc8XUTZkfF+S9mCgJ8b5LxOlUxKqzSXKmHQNLIdE6pmchdW2kmZvN8d5RAbWHQHi0f2oKgadCUtMZwouWWZgAMdB4Ty49Iw8rQhAhKLxnQttXz0QrZg29+EvCz9p6Zho9pQR50TAvGhYGIc5ZGt8wqyzaVdRNzSBLsdh25Fm1HntaRt2N1eEQ9KKQY2kJAk9/PKCZHyIDjbaCqjEgHMloQN1UhBJmBIwiSd1YnCkJggCPIIgU2jnwFudq1pb7bG3tI0REHFjmtW1za4d95qUhDCQPHm7waGcX6x04wTQ3TGY1bVqSIAgUWgdEcdp26kIw6PdJ4pQ4ylBBiJaKajZFYOmPF1EIhJbIziulLuYIIbCmbmFsZ7h44SJJFqHDiMPjE5I0oBYtkdUQgTcMRprNBxtc/VhFY0qGMdCUpGlEGqVE/pD9PCELIubFIf2gIYwT4n6Ga2rQIVkaYKuaOFCU84IoSxEqRJgpcdInCjRpnDDIIrYf76DDkMoJGmuwtsFaj/eG5YWVH/KKe15/3Qo04CY83hNMZzFuq2J5QXM4S3H5JVYW9xgNJTRD6uaAN268TmU0G1sly0s7HB8t4OQHOF+exQZFUdQ2QV2Dp5VCaYVUGiHVh27eeVHTuNaB3nlHL9I83Dvif/hH/yMXzp1j+dwleklEVdT8/jfvMzl02PkJoyRA6ARhalRToERDthy2Sjcl0Wkr5VAqxhHw3/3j3+LWoz2Wls7xjW+9xeuvfIRT8YjrNjjjQdrWEuZJRixY57FdrJu1rR2g7HhF1jYEQcgpW+9p4+T2gOdaz0Ah0QquXnkJrVOqsgJpKBtDHEgEEVKVzKZtTKMQJVkv5KXLN/jW2++yMlqkyA2Ni7i0fJnb939A7STn1zROxlwavswHGyVxMCbTiqPjfXSU8HDnNqvjF+nHY2j6TOt3fwir7Hn9RZVlKevry/SyAUsXr/Pt70dEy+c5eFzQHMzpecVab8iFhT5Liz0G/ZhoHCB7EhKJDzQy1eieIh6mqEAzPZm0yStVTeAkyihm9w749le+yf58n0k1oTJlZ81zKmA4bcI66oVvgTtOVbTSdUrzDpUTtJm23Xy0VdK22bTeK7wAz9Mm6ad0hs5SWbQHKyU1WgdUVYUS3WHJt/ZgUnQpMJ3KXSLOmkuEbcl/QndWLk+EUEKIFoWDjkvbDRK8x/umTbQhw1aWwuX4WuGcoSo1WseooL0OZy1BEHQWUbITgGgmay8T6ZD6zvdxTWshFSLQAmLhUb7l4BXe03hPJcB0c2WFI/aeCEEsWjxyIgSZ91wWksh7chwTKZkiiFXAkdLkcYJMMkyWwe0f/IVr6Zlo9FSgmOU107wBqYiTkHllyKua/OE2GkFTG+rKEgqJNQZLhNQtZybPp+ggojr8gaURAAAgAElEQVSekPYVxtQYLWicoCoLvAraDUYHBLKEIKY3cCSRxGQJoVY0DXhjcNLTGIMxjgCLtxVl7VhZ6nNwOKPMa6QQ5POcUU+BrJhWDR8Z9TlxNWEaUhSeWVGiMNR1y0fKqxqpJFGa0hsOmRwdEoYhvf6AKEkIsx5h2kMGETqoSJIeOo6Jsj7F5ISmmJMOh4RR1PIbBARhTL/XI+n3iKKQg90tyrIkTWNUnKKDkDAa4WyDijJuvr/Jp/MSYUqUFBRVSZpkgEUKzygL0JGknhiEdGg9wHvJYGmJ/GRKHMbM4wQVRURqyiBLEVJQGQFxRt5USK1R0mEtxIMB3jZI06CimNl8Rhq3RNfn9aNRzntWl/sIm4Dogd4m6eXcOH+Vx7sTXrz8abTa49ajuwTC4W3F/U2ItKapVxgsTIEjAjVuD3QSqqZGSYV1TXcKlkipqazDWUFeGQ6OTnj4aJuHGxssLCzQH46JkgSF48HDB3z7S7/J4q/8Gg/mJQtLS6ysraLilHj9Nf7wd9/kwsBx7fwYIQKiMEUTtW7zskvBlLRqWmv46jfe5nu3j5FRSFFMefx4G2NaxWwYCKqqbewa6zjL1+w2JrxHK9kq/Pypi/+p279/2vafFsV7oigEmM9nZL0+SRxRFAUvXn6BSxfPsb17B6UVcRBT1hVRPCfPa6692OOddycYZ3nh4pB/+ZU/JtaSVIc82K75lZ/796ndMbPqPRbicyBO2NstuP/B12hOCh483OKlly6h1QzpYw5nFVeWzzPN9/DlHjo7/ptcXs/rr6i8qPjGd99nabwM9y4yLV5gb2OCO2kYq4T1QY/zCwMWFzKyUUw8jCBTmEggI4noKXRfk/RjpFLYxtAUFb40KCuRVqBzzztffZPNnR2m5QmNq7HOYpzs9pmWe+fdk7V9NmKVrk2B8Rpv3ZkUAtoHnWJ4rTeeQ+Bw3nZom8AJ3z1eArYzS25dNwIdIISkrttc6jbbVyC8QgrdnqY6yyIlWoVvG/2rAHs6u30KBWyvox0nS87Yha7jDwqJdSVaRogu77a2JbEMaQpBXdQEoWmnDkoipMSb1nDdi1bxi5R4HyCHV0G9h2saIiGJcIRA1HkAVrQNXu1bCxnlHSGCCE+KQAtBjSAXILomLxEtGvowDNl+6aPk42U2XYheXKfyktw0OOGf7UbPOUfgLco64iRBS0djQZRT6PUAxeJCRmUsee64sLJEFUbkVY0pKrI4ZW4FVW6ZapgVNYO0T103lLWh8SB1iPIVUjkOpjWhlJR1hRcBdd16XjUWpJcEQpPGrbFiU5VEQUAUxjgETVMRDfrkRc2lxQwtNU1V0YvGbO5Z4qAm0eB0K/iu6gbjDEezHGtBBwFBEIPQpP2AIIkJk5Qw7RElLarnTIOQiiBJkbKFvaNenyBKW+RDK+qyIqprkiwDoQjTAVLutKTwYIDWCoI2QWReWXASqyPi4RI8jhFCUFUNcdRQFg4lQWUBUejRSUQoBWE2bN90ccD00KKjjP5AoETNgXN4AUpKwigizAJUNccHIV4aKmuJhWiNNZWnFysmRPQjiXqejPEjU8bAlfXrHBdT3r35GIJjLogVpvmE/YMJO3tf5/zyAiocIckZLUwom5JR/0WSxCGDCTu7kiDoUTWG48mUsmjHGnVdE4QhwWCZvTqkmucY6yiLmqIoiOKY9fVVprOcra1HDIZDojhleWmN/+g//S9I+ivsPd6il/XZ39nDYYnilE///N/nO3/8u5zc2uClC0MC75C+JYproQjCECcduycTvv6Dh7zzwRwfRkipsQ7ev/0Bb7/3PtevXcOlMSCRnVGpFGC8AO/QUnQxVRAoSeUcUvoPRbq1quFuvxSys51w0MW5aa2w1jAa9sjznJWlEQujNR7vfkBTe3IqnLPM84pzq4s8fnwCQrG8sMjhZIYUNc6m9JYiLo1Xebj9bcKwYZgskDcPiMKQ44nkYHdOU0zJjx3LmWL5ckTjZlhneXzynXaKISbov8J09Xn9zZY1hr2jRSr/aRL7CkebE+TEMwoi1vtDzi9mLI1T+ksJwTDCRgJiiUgksh8RD9tMVSEExXyKEC2abhqJN20TYadztjcfctDskdvW19V6gUXjRDsBs7bj0AF0HDOP6aZoHudlx0zo+LO4Nq6s482JDllzztL65cmzBAx/qrfwCt81e1EY4X2bJ9vycgGhkaqLhBMKL5+0ld51di7Y7pvpTrDRIoNCnJqwq066Rff11m5FSIH1BXiH9G3EonCuzb5uDHXZIGSAKU1r7K4USEmjDFIrdKhRSuFk+zymnhCbisgL+sIR4Ql8KxQpgAJB7cELSeA9KY5IeEIPWnhqPHOhKIELWuGFZwPFPRVxd3GVydyT9SXeCeK8oDEljffE6i8nvz8jjZ6ncQahWwaAF4qyKFhKQ6YG8qYkSzPqGryrSQZjTC3opZrdkxlKSwIclZkwyyOWBzGN04RNSRQojNEoKZFVjk1SZrkhjQJQCqctk9IQKMmgH2C8pigbhPAsjUfsbJ6AisgrR2MFpnFoHVOVJUG0yLSsSbQmThRxHOGtRUUhcRjSWEcUhUReU1vPZN7m5dV1TZwkCO/RYYCKQ4TsPLmsbU8LSp6xF2QcooMQIXXbwAmPwhOajKjooZRGak2cZvjuJGSbCmcF08kBmcxRUcaNj72BjGIODjZbRFA4agOmOCKNHY1zRI0FFyKFQAvPbD4hzs4hRUOe5+3/l6lRXiBliNQS4jFVZfHGIH2A8Tk6ChBSoqIEYaEfhszLOePR8Czn9Hk9+xVoKP0OxFusrcPRHFZXLPPCIOvzRFGfOw/uMFo4QIcxOycZ2xuO114SXL4EJ7NV5lOHFJqibhFu11pZEQ+WUf1Vdo7mHG/cJU0zrLPk83nHq3E0tjUY1VpzdHSEUiekacZo9RqBUgxGY5zz1Dtb3Hn3+zx8cIs4jOiNxjx8JHj06G2unuuTSkcaBC16qAN2JyXffG+T3f1Zuwk0girfZ+/gIV+59y7f+MrvsX7xGr/8hX+H//gf/oesLA3bfUUKlG/tIpSSHZHct4RxLbC2PaUHUtJYgzQWH7iOV8RZqsdpDGAQRDSmJksT4iQiqytWll7kwea3ODnOscaCaChyMK7k8LBgdXmF9cURdza2SOOYNIMgrJjOcmazhqw/ZTSI6PU1w96A9YU5WjXUM4VZChmNIkZZDOoaKhAE0RwvSnB9kuy5UupZqcpIgugnCKLX2H84R80aFqKU1X6Pc0tDFkYRw5UUlQXYUEISQCYIhiFxPyGKI5y3zE+m5EczwjDE1g5pNKJxSO+pTMN+eUxR1zQWbKtVbblyUmAtnaUIcMqv63JkT5E537HDTvW1VgjEqaXJE10ubZMYd02eR549m8CLEKUtURRgqgZj2/eMQHZcCIlUEqkF3nXCKN+cqWeF7Dh9XRrHaSPXCkMUQnSxi5w+d0u3kIJWDewaAp10Y17b0i2swTQNwuu2edUKpyTCtFQLJRVCSepSoKOwo15prAzwvTHD410UHoXAiVZBW/k25UIKS+gdoYAEie4UJBMgF6JV4qP4qrc8ttAEmmA4IlQhS2urROmA/4e9N42xNDvv+35nede71d7LdPf0rBzOkBpSFDVktDF0RCsUFAvKItiIASMGssjxl2y2g0SAggRJEASJjBiGjXyJETumHSkyFFKiZFK0JIqrSM5wGU7PTPd093RXde11733Xs+XDeat6RiIZBpDEIdIPUJiprup733v7Pfc85//8l3w8Ji0yfBjhRcC1Hde/zb30lmj0RAh4p8jTCFsK4XHOk5UFi4XD+kh8DMIjvKR1Ai/BtD3jPGHedGSpYlbmzOuO2WxC0wYSDCLLqaxBAqkGGQLLqmNWMnTfPUqIKLMOKVo6TLAIqWnqlhAcvhf0tiPBMy5SrHF0XU/iPXe3j8nTBKsyvBR0nWWWpyQKGhtIlECKSOK8u3fI5UubmL6N9hDeI2QcW3kfLQ5EEJEM6h3d8gSpNGiFUDry9AZbCSlEDE6XkjTPcd7TNh3lyoimMoS6Bitolj2jaUpnHHmZ8dLzf8A3Xv4qrSjRsiL0kq5paZXEVjWJSPAEjHWorkESPYMkhq6ZI4MgyxRSZ/RdS986dF7gfY9xEuV7hBgxHSeU5Rjveqg6jo+OEUEiZIJzDxC975fqjeP5r11jNAnMj3Pe9raSw+U+i8PA6jRnOn6EfHeb1ck6Oqm5eWeOUI7XXr/G5cdGCPWTTNef5WOfuY2xnq6PtAgh1xE6RTQG0zeYvqdPUhYnxzRti5QCrSPZ+fjwACEEeTlie/suzfwYlUSbg67raKo5bbNE65Sj3Tvs3LwWG6+soG8rbtzaYTxKyJOoBqxbx0nVY60GUUbEwXcQGuLG1LGY1yy+vs+N6y/x8o1b/PVf+Ktsra+xtjpllGqEkFjnz8xclYzqQFDRxFUlWB8PdWnmBkHGgHh4PyhwYwNbNxVlMSbVCWma8sQjP8Dnv/SP8CHy9hpnKHVJU1ccLSzTlYbb2/foW9jePWRllnF0YtlYD6ytK6bTHhEK5icJbTVDJpKtDUE/E/R9QKSB2coVvnn7BR65+Bwnyxt4cY+V4jx7Rw9Gt2+VKssNxqvPcrKzQNWOjSyPHnnrMza3cmYrJb6UuFxCmeEKTzLVFNOCNM+jfYizuL5HGknfGGRQ4CwaEM5x49p1buzew4RAENEORUgQ3uG8OEPCoiGIPzNJPhUWIU+bugEJHua70eduONCHiLJFlasa/O9E5NrJgMdH94ZE03U9oBBaD9zVgNAKmUTkLE0kprXY3mBdPDwJGU2Lw5BSI4UchBan6HS8KHF6KadWK8Of++BRMh9eZ0ApgfcmCi+I10qIKOjpKdULiVABb+Je7K1DaYtP4rW2K+ewx7uARIZALwJdAI+kFIEUiRLhjKvfI2iBXmo64XkN2CsSFgZkUlCsXWS8eYnRaIp1hqZtEBKWi3mcCmYJ2XeIL3xrNHoSmt6Qlimub1kcRoXbUe1wPpClGdZ5pPToVHCybMjLEX3bUqSSujUEa0iVJ1WSqrMgS1SisI544ggWrRVKgkMhfU+wEuM6kmyC7Rv61CFQSKBIBd7U5OMJipZ62aITgfYKIwxIwdo052uv12xuTKmsYJQkmMbgnccHKFNJkcR/SGcD9/ZPOD5esDKdkaXpYJiqkFLFQHPrcFrhvDsjjHbNgqQoCVk+8CPCkLFnYgM2cBXm+3vc2dsjjKekqWRUOLJsTJHOsS5QtYaXX/kyd2clThBPANYyKnoWpsL0Gccne6SuoDMO0TX0wUexxXJJsC0nvSTB4kOGF56qrvC2I3cBKQJCWISUOAk+KBLVR/RRaQ6PejY2ZzStYVI+GA99v1SZr7A62yJNl4y2Zly40HPnTsba+CJFmTKdWp5+ap3K7nBwTyNIWd8QPHXpaVZW/xKv3Frl2svXubf96wRrWVndIC/HzE8OWV3b4MKFi/R9j3GWgzs3mR/uUeQjdKJROgUhSbOc+fEhxljWV1d5efcuu9dvUS2OhlN7HINOZmsU4ylpntO3FW29BAJtH2j7+9FLsQLQQ7CEoIdx1GkNFg96SrJ6kReu3eRv/NLfZjadsr6+wpXLF/hXfuyHeO87n0RLiT9V/QmBdwGlo9JPKXXW0J0+buQbSSLCoRBCUuQFgWg3Y53n6uVHUXKVNNtFykDoJcYE9g8MEsW9nTkrKyM64xEW6qpnYzrh3v4RP/TudepqFRE2OZnf5pOfu84zjz1KlqU4v8fWukInHfOlR6kF24cvYbuWebPDPdmQ6wdZt2+VEiJjvrNENJa1vOTCbMKFjRU2tkrKlRSfSUKeoicamwv0NKecFaRZgul6rO0JHrrKoIxAWYkMDuXjnrh/e4ff+PWPs2wNIiiUD/jTM7g4tUkRQ2KFxPmBcxfuN1GnMWdn18xpmsUgxAgR/TsVXARiYxbVsRohPHmW44Kj6wxBRMRdKx0npBqSImM0m1JMS9JEMT88YnEwJ9Q2Cj68xQUbYxFDVL7HBjWus1NOoRyQxPut6mmpM4RdSHDOIKQ4a1p9sIQgIzfQS0KwBCkiIiijGATvwFusS1BOoY1jV0jKAIUIdIAZHjIdEEcpolI2ng8lcy+44T13hMfnJdP1i6ynY4Kakm9cQiQpVduhdQGJoM8KhBQkRUIInvY7yG7fGo2ekOhMc1T1XF4taHvLpMhp+xiRlGOpOhU5lkqD6ekXHi0CUibMVkfsbt8l0wqVJAgjyHOBkYq+M/TOkwqBziW1E/TWRid77yh0MvyjakKIKrh4iyYEaqQukGmPb+JHcyoktoNRnjAdpRyebPPsUxf4w1f2SKSM9gqZQkuFVPF0kChNUJJl27C/f8z5zS2UUEgpqZYVx/uHXH74MkmWInIIztO3DRIRoWNlSMpIEPWANYPDvQtIoWjmJxwe7nH3qOfCCLqmR6pwaiCEkBqBZ2VlTOM8WZJiOkGmJXmSkCcpiXRo0UR+UZAxd9gIgjfo5R6o2MQK6WlDjTPQiwrbdBgbGI1SXHAolTLOAj4pIDhcGBZQEiOwrG+p6wfjoe+XqhpB066QJJrHHnuWrHiWtc0ZTQUbKxnzk+epq9coCqjqW8yPc558fJO1h36OG9sXOTzaJU0kx3v3+MYLn2Pj3CXOP3SV+dEeq+vnUFLSW8vezm3u3rpOnmVcvvokHvBNR1XXlGVJmhecHOzTdwWT2RovvvAFFof30FqR5jlpkpJnKWU55T0/+tPcee1Ftm+9jOm7YSN6w+Hi1D0ViKv61IFKEzcHDWlBMpmR5SNc13J4uMv+3jbXrhk++1nJRz/2W3zogz/Gf/oLf4nV6RjvI4k8zRj4SD4mdBCwxqB1dvb090e399W7zvZMp1P63nLp/BYXLzzKzn7FfO7I0pK6qiJPuV9QN461FY0PhiceeYKD+T2ypGDzgiSYnDs7jice7plNFF2fsKyPEbommMDLtxoe2nR0/csganb2X6QoFYdHjvXZMct2+qd+Tz2o765875C1ZS3PuLg64dLGKmtrU8qxRuRRWStLiSsU6UpKMRuhdUyY6PoWLSXtcYuoFcF6hLMDOuUJjeMTv/ZJdnbmGA9IiRcM1AMfx6On0YNE4ZL3UTRxP4H2lH8qztaYAFQALyTeu8hLPTPNkwgUkcVgkTqgVY4xhoCNWfZKoBKFVJI0S5islaxsbTDb3CQrM/q2RWUKYz3WLultR/A+Rp4Ff3+Uixqa1VMvvVN2nog/G9Z/TO9QbzI15+z/wxnqF7BRWBU0QsroxhEcEQUcjJyJnMTgDY0YU0hN5QxBCFQQpIARgn0BRkhGUrCC5jh4dqzlCDjJckRWMl59CLl2BZmN8U7QNx3US0xb40yD1JoqmzDd2ELJBG/d8D5/63pLNHrOBxIFudYY25LohDJXHB30pImkyBUGj/XxRH90siRPNX1jyIoE7zseOrfO0cmc8XiENZ7OWHSvmM97tAyRT6YEJ8sO33uc1fRdh9IZUgiKyQrOtCyrjiRLyXXgeNEh8hIQdKajbXvKVHHSGjZmI4KApjWcP3+O5ZdvsZ7GPDqcJUkT+j4iCD7EPE+AxbLh4OAA4aYEW1DXHSdHh6ytrZD7+FxKKVSaYvsOZAxxF8MYVyrN9mvXyfKSNNGYrqJpW+7s7VGMMyQtRZaTlzNwhqIoGM9GmMOKIk9p7u2SZGO6pqZIS5yUTNfWkO0eMgRMb6hqi8oDnTnEhgwmMSIukfFEZLwnKaYQerxSpColVZqgUoQzkcohLLZ32KBZLhc0TcNcepKywKgHiN73S4WgqZfP8uw7fxgxfjef/uLzfPQf/h0Wx4dcuPp2fvC97+D9P/R+DuZ7rK6+g6p6jfH4R3HJs9y69XW2X79JcAadJBTliGZ5gukbjvbvMD/aZ2vrHIu6Zm/7FrdvXGPr/CVWFwuCEOy8fh2d5qytbzFb26QYjVksjmmbhhAC1dEREJBJgtKK/Z1t8iJlvxjz8BPv5G3Pvp+jvR1c33DjlW9yfHR4xjEKYTA4DvGDP5xuACqFvCAbTxhPVijKMQFomwVd16GVROmUpqn42G9/kt39A/6bv/UfcGFzFeciV8+6KFSSIW4wNpgzVCQEf0ohJ4atx9GQNT1FlmKMIc81ly4+ze2dF7HWkhcCvGNv/wSZgAiCg/2aoiyo+hPS1JJmKQ9fhCwb89w7zlPVx2R5R1nWzOuGqhtRVT3ewrHKmat7TGcKwhTbWy5ubTEql9y8XX/vbrYH9eYKgfW04KHVCQ9fWGd1XJCNNWKkoVQwVvhSk69m5JMRWqnBty4a7daHFb72ERDwAY1CBIPygs996ot89Ruv0gyiAB/A+khnirBeDCATInrEASAM3ls4a34iD84PzWDMcz5F+sKZ4CI2UeJsdCsUsSHF0xsLUqESESdbSqEyTTkpWb+4wWRtyvmL50mygqquaeuaNMsoJ2OaRUfiPFYGjHGxDzs71A1RhEINay/K5YOIr+uNYviIsMcx9+kB7L7Z8/DSB0pZcBYfhsi14fVKkQ7+eh6CQQhPFxTojNybmIYTAomEFEkZAq1UHAbPV13HMYJWCNRsg3LzYcq1iwhR0NYd7fEe7WIXYRp60yJDQIZB7ZvkNEevo0drjNa2GE1G3/ZWeks0ekKAkJLxKKOa7zNdLdDJaRxKwGOpG4+34GxD3RrSVCClR2gd48SyEiQkiWQ0Kjg4qXEuQSnQSlFkkRfTeY2ixhPI8GT5hNaGGAUkFV0nmU0ntKaKXAKdYhsRkQfnmZYbLPdusTLKsXrCbDJiYcA0HUmZo7TG++ja3Rs3HO/BCROf3/QslnPGRYaSivXNNc6d36Qcj+LY00XlkdAJwlrC8L21FuE9oW25/uoNHn38cURw1HXF9v4hJ8sF6xsZ3hqsM5RZhndQ5ILRqMB0fUQZRU9e5uRdQT7eoG2WZGWkJ+lE0HcWZAY64DuLlCldb5jkmt45ghxhTIM53EHrnNZJAh2JsCAEaVLQNobOWJwE5zuarmE8HiGTnHGZkarke33LPajvsupqyUvfPObVW5/joa1X2dnb59bLz2O7hv07N3j1q1/g+S+9jw//7F9ktLrFk7OWZDzmlVdv8YVPf5LdOzfomiU7t28gJDz9rvfRNA37uzsEa9l5/TFOlifs3L5BtTwBLtDUC+68/hqvv/ICV9/2LLPZKrs7d1hZ30QJiZYwHpXsSXCmxboaN5gQt8tI+t69fY00K9BpwWRtHWO6gV+jQBZIFcVYKIU+NWtWirQYk+YFUqcIoG3raITsPUmSkuiUNC/jWrWGP/jc5/mPfrHnv/5b/z4PnduM1BAfqRtSi/icMrrvKzWoceVgx0DcTrVOaOqKYpQjVcy8fuLqO/nUZ/530iQF75Ayeotqpei6nqJMhtek6VpF509o+pLjY7i93Ofe4es03RGQ4o1D6YDzBhU0eXbCzv6cJx95mNb2bKxOEGGdxcKRquZ7d7M9qDeVFpIrmytcPrfKdJyTTVLURMMowY8lcqLI1gqKUYEUGust3lm8tXTLCld5RC+QPvq3CemQQfD8777AJ37jd6lOTR+9wAaPGxozOYxtrWcQG53mz0Z7ouAHQdEpIj00V2cqWobG6NRyaBjZIiLqrVTkt3qh0ImIS1In0Q6sTJlszDh/+TzjtQk6iQlU7WJJ3/cU5QiBoBg1ZON8MCweEnN8GA5tcOrld3atp6NZ3JBf7YdGMHZsp/vuaZ2mZpyJp4a/LWSEDH2IR0OBxHsDwkX1sRNIEfDB0cuMMtQEH2iE4MhFT0EnBAe25xhYCgGTdaZbj7B54TIHc8WyqsDs0c8P6Jo5ztaoIAiJBjUYyQ/Z26Kv8BLsMqUL3z5E/i3R6CkpsdaxupbRuoJxmrDsA+NJgjeGIk2oe4NINT5IpFS0rYuqIRPVJlIqjBUE76jaCiXtfRdr6yAIUuHpjWeUp2RJlF5bF6XTEobcSYXWEXnLUo3DUeYZaVZwa+ceShiWiwXnzz1MhWY6nXBw7zA6/MuILDgcxqW0fXd2eg/ED3sfPItFQ5HNyfMcBOg8wzoXzZFlIAQijK0TxLBRKJ1EEiiGd7z7WbpqycHeNvOq5u7+CdOZ5h1PbXLnzgFlljLO4eDEk+nIG7JOsKxqkIrWWlRSIFOF8jlJKjixI6QwOJGS5gnogLTxup0XWD9EOSWQpuv0yxPSSY5GoJIU6y1lGlC5oq3m9AK8LrDLfY5OFmRbI5wLtL2EpPve3nAP6ruurq157dpXefyd7+P2a9eYbVwiyVewXY0PPcuTbV74g49zdPcm7/zRn2J+tMfB7g7zoz32Xn+Vrq4Hj6sOIVKq5Zy1rTHNsqKrl7z26tc5OjmiWc4JCJbzBU3zTW5ff4nFwS5blyp0kmLrhsPdbc5fvES9LHjo8qPs3rnJfO8OEaF7o5JbQIgonGiWVCd7A2IX72fhDN4EBCnIBJvlyKJEqpTOGtpFG/E9nUaxlBCkWUmWF1H9LgSnsUrOWL72zZf4pf/xf+W//y/+GmuzSfy5c4NdS48Sp8T0N2yM4lSJGD25xMDpy9IEZyyPP/o442KVullGFEUErHFxnRU6qgWD4eBkgZIl59c3GafrfOnrL1M1hlRPWF15mK5qmC+PmBY5o/wcRhzz0LmHGU8WTGewzjoX1i9xcNxzc7tG2AeK+LdKpVpx5fwa00lBOUqRk4QwkoSJgqmmXBuTFnpo8hym77BNR3O8RHQge0Hoo8JUqIAWgm9++ht8/J99iqOmwQSP9cRxpIc8WCYE8jJjt24wg6L8NI7Lnxok82YDZQCG78VAPz07yAiBEhCG3OrgBdYBUqMSFVXwSpDmBdk4Y+PiOhcfvohO05hJXdVYa+K+nKYRjUOQlhmjlRFN3UFnh+dSBB/zOTzR+49Ttbu/P1qGEM3ZEXHfDuFNTd5pnTZ5wws8QwtDCEjkIEBu4X0AACAASURBVOrwg1GzQwgLSKyXGAS3plvs+JY12+AJ9MSkiz4ITqTCjCYUa1fJ1y4jTEe12Kc+ajD9Eb6vsF0bLczyGWm+TkiL+B6EAL7DdA0yOIKzdPUJ4TtkY7wlGr3eeppekDctwsPBooY0x/WWpm5JM01wgdZYeguCgMyntPN9tDEkUgwu3glFPuLG9j3WxiW2XaCTAmcd1jq0TqmbjvPrBaPUYr2hNw7rHImSCAV5kWONoakb8kmGsCZGFRnHyuoMZ3uatmNzsyQEmM+XrI01ZSpjPJLzLGrDNE/jIvGeIKMNQ9eZAaUUNE3H8dERWZqSSEXnA9lohDU9TmmyURkdxAVoqejbFtPVONtTL+csT46p2p7DRUeQnksX19FBMC0Ej73zB9g5OkbojsOjXTprqJueIAKlUmidkqQpSqvITwgW5zukEgQ1GIPJBJmAJ8cjaW1HnqSkqWJpBKkUqCwlJscVmK6hdZasa7DGYEWGUj11VyMDWOPQqSbJSoTZ/V7fcg/quy3vuXfnDluXD7j6+FMIIZmublIf3yWOSBy2X/Lay19hb2+bd7//gxzvXOfOay/i3UC6Dqfnfcf27Zs0bU9bLfF9z81rL2JsRIORgu1br9C3LW21iLwXZ8mLEoegmh/x6qvXuPLwY1x59O0opXjhc59kf+fOfW+6YSO6z6459dsKZx/zCIsI/r73Vx//ljM9aBlVhAKktUil0Vpj3ZJqcYQzhgBkRcl4uoaxPXZxxJe/8jz/+X/1P/E//NJ/wnQ8QkqB8wE5qOv7vkPrgZsqhhGTCNE+AsjyAvCMypJl1TAdjzl/7lGu3/w8Qke1ctUFpII817RtT9d5Rlrjfcut1w94/sXbdI0jLySjtRGbM8GLe4YkUdhgODjeJikVkk2y7B7OBrReQwRBXzcokwLmz+7eelDfsdJEMxtn5OMUpho/UoixQkwV5dqErIi8Tx8czvb0TYs56RCthM7je4MOCpVBIuD6F17mN3/tdzhoGkwIWA8OCSGQEXi4SPnQB3+E3brn//zkp/HC4fB4ESP9PD0MY+EQolde7I88QUR/PCDa1w2jWiAqc0W0GBEqHpyEFCSJRmWSpEiZrk3ZurTJxsUtxuMpfd9huh4hI29OpwkQhYhJmlJOxgQvaec9ru4JKuBPM9oGGsbpFFeIEPl68d1CSokPQ8btt6j7qTeDJczZ928QnZxl+Z6aQeuhmQQnBD7PaYTgYLzOTnuC9D1pmsV0jek6xWgDrUqCSGjaFrfYY5wZuvlezNEVhnx6iWRyDpGlCJ2idDIkfAiUVMhmgW0rfFcjnI9nx29Tb4lGL9oNJMwKTeUFGZrW9pi2pRwVqLSkdAsSranrhkkKXlk2NzZYLCqcEKQyxJOxUjTLliZJSLOCqusIIpBJgSV6eSVKMCoTlp3ACU2eptgQEE4xGhX0y0O0AGRKlqVY27OsG2bTKfPdbbwLzKYzlPLc2d5nc3NCUWacNFHZ670nUVB3ls4EhPT0ncVZS28DaZoMhNdAtVhEG8fJGC8hyzLOaKPDhtBUS/rlCU21wPvoVRRCDIN2wXHh/AqZFlSLI4LUpGtbpPWSYOcYq0F6mnrOaLxGPirxJFjv8EFgncV0liAVSaqpgqRMYvMWPJggkDLBGofPJwityRBUQpCmCT5EMqtOEqQU9J3BW0s2GeNMxaJeMJmtIBUkOqUoU+rDB8q+758KuN5w99Z1vKl59/s+QJZFYc19RUOMD6qOdvj87/zfPPb0e3ni6TGvvPiHeGejlC1EAvTicI8kSwm2hRBoqups1BNC4KRpByXs0JaJQKITssRhsxyC4N72bS4//Djvev+/zLve+yN8+TO/wyvfeJ66riiKnL6zHB4f4awh02qwT7F4b3EmjibTYsTW+Svkoyn1csHhck7b93jnI1lJgFcehKFzNvJvnI/X7TqMlHTzTYqVNaRUNMLzWx//KG9/9CH++i/8O5Ec7j0x5jZGJr1xhBSCi+/hG6LU+r4nTVNSrVlbmXLloXfx8o0vEjCsrCrKEVirmE5G1Ms5vXVQdUgUe4dHzMbr4E9YnawznRlefHmXW68f88yTV3nl+g7Bdbz97TOu3f4ck3HP5fNbJNpxuHidSenpRwlf/doDe5W3SiklyaY5apIQRoowUciJYrwxRqXJGdesaZq4zhBgA8JJPNGSTCcKTeD2V2/xsX/6KfbmSzpvMUPjJYMgJfDkZMTP/+WfYfb4VT71v/wDOjfw2ASI4BDCD1mACh9EjDU7u32jCDHa8w+pEZzd2gRk5OFpT1bmdNaQ6BgLWKyUbFzaYOv8OuOV2RmSJ4QiTTOatiEIcC6mXbjhUDidriCDplqpaBY13jRYZXBIQhjoT54zO5hTw/JTQQhOvgFVv19vRvYEf3Scexql5oM7HRwMCl/iXojHiYAoUpQcMS5WyFPH5Ucf5/XrN3AMhtR6hu2XCCp8c4xZ7lE1PX1bk0zOU248TFATnJBkRTSQtq4nGSynPFCubGGdw/Utvq2w7beflL0lGj2lJKZr6Foip0V7TNPjCORa0rUdWgjOTVPuHFT4JGVSFIwSaCsXu30RmIwzrHeUkzHGOVanJe29fYSUZGnKQeXo+h4lovL0uImjKasiby0ATdPh2jpaJiRjlNYYLyhGDplm7L9+B51IdJpw45XX8cGze3jMaJRzc+eYkRJ4ZwkBFm2PMW7I85QU2Zi671k2LZsrM5yLfoF915KkiiTL8Vk0nmzbGiEEOk3ou4YgFWkxivFuxkQqrBD0NlCWkiA1R/M561vrtF3FwfYBd3ZrEt0ivMK5Htu3dLVBZCOE0HRtj8BHZM+D0CkFYuBQWHwyQmNRKiVkAkkLYowQmtn6hQiVG4+TARkiF3I+r0k1QIyws86wNpsQgkRIjQwa8R0cvB/UW60CIczp5rs8+6/9PM/8wHv40md/n5t8mT/6IRmwdM0xL33l97j42NO887mf5JWvfoZqURHTXwOmbTi+tx15yxAVgG/wvApnY00QaKyxuAERrJZztNZsbl1kPB6T5SXOFbz/Qz/Hv/STP4ezHfd27nG0t0N9uI0KirTIaOrIi7Xe4axBD5m6h0cH1MYx3bzIQw9fReJ56Ztf43D/KDZ2QuCRBG+QQRCUAlfFIYwT2Llj6Xv0eIaUI5y1/L2/9/f56Q/9OR5/4urwMuKwSYr7jfGpxcobx7lJklBVS5IkIxpEC5545Ck+/jswX0KiBF3nyXKP6WImpxTgnaR3lrLTrD00ozEVB0cLqgbu7hyihWd7+zbOG7xN+fKXDnj2XWMODh1Nc5unHr3E+XOXSERPCNf4iQ9e5Z98ZOfP5tZ6UN+xpBTIaUIYR/GFmCYUq+M4vhuajqaqaOoa4cB3HmkkOFAuoHVcdbvXdviNj/wmOydHdN5igwfnUAIy6Xksz/k3/u2/wLn3vI2P/urvcu2VO/SnCJgLg6jIRzc9H3AIgowj1zDwVwUJUuqI1g3rN5wi5lKD0KjMIwUUeY5KFeOVKZtXNlk9t87KyipCgLMOb2N2bmc6OmvIixytk0HhG3ns1nkSrRmvjKjnY4IhHuQ8xDAxy2l+rh/SOXwIhJAQm72hcXuTavhUEX9/7PxGFO9sdIsgnCasBQiDz6AXAUePCxIJKJ2QpCPWLmyy6AJ930KiMU4TbE0wC3x/jK0PaJs5SVky2Xo7YnYFkSWIYAnWE04DBpzHe4utKvCeRbUgH49Al2SrI8oy5+DFT3zLe+mt0ehJQZIqqrpllCf0QWF7S0BFdW1r8EBrHHma0jpYSTRN32C9RwvwQTItc5QK5Coq2frOgveIYFEhY29ZUyhFbxq8hd5aEgl5AgSDEx7vHFVrSZMCITR91xJkSm8tZaoxwZMVGbZq+Mo3blCOC6rasTUd0bX7TMYJXghqY5FIEhUzaa9efYT9vXss6wVV27FiDGpURKn2EKmC8Dhr4tApgDXdWei7yDP61uM7T9d1GGtZtpZF0zMaFWgdfXmqquGTH/09kkzQ9BIRLCJ0yOBJNYjgCV7inePo8Jgs1ZSRqYuWioBGyAytHMI3JHkZvcGkxntQIqU3LbocR+6jzhhlCaYPSCXp3RKlUxLhWFZzinxMkec0bXQ9F4lC5Rvf61vuQf1/qBA8i5N7ONeDECznR9/htz3O1bx+7XlODvZ5+rk/z73XXuL2q9cG+wNJW9cIqeOhLgQChlPPq/iEEH3tYla10pL2uKZanLA42Wfv3l3e8e7nsNbhTItKckoJzzx6kWfOraDVM2itOKkqtg+POK49h4cH7O/sMD/aJcsTurbnYl5w5/UbXLv2EqSrlOGQJ598nI21Xa69+M3TtiyOnoUEoQhnhOdACC2hXmK1ptcaHzz723f4xx/5p/zif/mfRa6tiutGaz1sIsNjchqHFobGT9H3PeNxQZIkGBO4eukRinyVptmOQjQfDY/r1tI2gTzPcc6RpYpUS169eR1rFfsHh4zylOO5IUsjTWRrY8Z4S7F723H3juWhyzOm+Yy2vsvBScHm5lMEfci8fkCreMuUEjBWhFIgporR+oxkADW89zR1TbtsEU4QOovsQ7Qg8x4hPDoojm8e8+sf+Th39o9osRgiMicRKBG4olN+9t/8MBd/+O184/Mv8ju//WkWPgoWTtEwKQXec5YsIaUf+HYhxgoqNUBmAyL2BlBMDlM2mShUlkbB0yhh4/waW5fPkQ17oDE9Wmuc9/SdIUkSvA3kaYmUkjRN6XtDb1uUVnhnqZuGQGA8nWCqOL52tsd7QTjjE0qUlGdcvNOcW6lcRPp4g+DiDejdt278IB7c3lwxDi4+RhAeqRRdtSBYz9rGJZzv2X3tJq47JtcTdDGiWpxguiPccg/hDMV0Hb35FD7bQEqHt3Z4Hz3eRouoIAUi0WSTEa5tMe2SqjoGJCbP6fPi295Kb4lGTwiJcJ5yfY1quYiGikQSp5IaXIPUiuNlQ1Ajlm3HtGkwbcyn61zAhcCFPOVgccyoSNBBcNLGgOZJpsjLgvlxhdae4ByJllTLlul0jFQgpMAYi0fR1jWbl6cYKfDGEEgx1uP7hqppmJQpq7OEujJMJ5q6aRmfX6M1PZ1LsMazaB1Na0EJfuTHPsDbnnyaX//VX+Ho8JC6jd5+LnikFIiBw+ecQ9ho/Bgjk6IZY3AO7zy27zGmx1rHvO7Zn9dsPvYML924xpVL67ROc3Kn4rhRzCaSqgF8wLaC6doG3hmCUiwW+wiZYntLInLQK6hc4lEkiSZRgdZEhVamNfOmJ8kzpFaIUJNpkBi08vTOYU3kQCSJphhnVI0nFwolYDaeRBWyrZBW0Xc5Zfbt/X4e1FuzrOn59G/9KsVsI55MvwPxFyBgmB/c4oVPz3nbu3+cS489zfbN69y9eR1jela31qnmyyjWcPFE/MZx8Km/VUBSVQus6VFKc+/uLZqTQ/bv3uS9P/5TnL90ifnJIV/5+hf40mc1h/MTpFYonbO6tsWlhx+lbRv29/fw3nHpiScZTabcee1lfvc3/i+efOodPDvN+PKXv8Bi9V18+YVv8uRjF7hy9RK3XnuVEHS8LjGEpb/pYz6ANwhr6FxP6DpCMHz0ox/jP/xr/y6zlWlU4jHYrNjIl4uKvmETPFsKgiRJCcFTFhmtCKytzLh08e3sH9yNB1gUaaYoixTbN9jekpUOpRXLpouRg0GT54JqaWiqwMokY1QWJCrh0vmCrTXFhc1HeOLhJynHGsPX2Nxcp6kqXry+zWj8gKP3likpCIVEjCXjtRFpFrdrFwxt1dDVHaF3BOsIPUjv0QSC9Cg81d05H/0nv82t7X0aPMZ4rI+cVSkCl2XKz/7Mh3j4Az/IvVfv8iv/x0fZa2qsAOHcgIjF1KjouBJtQiSDnkjE5kOGqKY9FVxAgkDFxItUolOJyjQyVYzWp5y7uMHKxpSsLKPdyiCTapqGEMA5T9u2OOeGNSLourjvJUmCcx5r/LBvCtCCcpJgTYo1AWN6goNEJjjnzkSZ9ztQB0GCjPnX3215eIPqOP5ZGJD/U+uWfP0y2cqEve1btMtdnBnTNj318h6z2TimTlWHuOqQUC8piwlXfuA59mpJIIsIoWtply0DuRnnzZkgxXYhNtYIgs7i9fQdrq1Q5i0+uu2t48J6QdPXVNbhTY9UKUHAvaOTOGtHoIVFSUhlYH7SEZwnEQHbC1SeYkzLybxlfZxH+bMVzBvDxuqIoshZVMekMhAG3qh10fvGhKjEC0FGBYsNFHlG2zlkMPS2RwpJ03bUteHpq2ssG8GyqTl/boujnSPSUhOsY5InHPcdSgh65xgVE378g3+eO3d3+As//xf53/7u/8xJYzheNmitMNay6ibRXFJJCiGictf14CzexZSM4KNVi5SSpuuZNw4rc5794ffze7/5Cq/ePmFtYwNBxZMPlRyfLJCjKbbv2D8+ZmN9ysnhkpVRyt58yWi2jlbR1NL2LV1XUyYJdaPItASR0pmeHE3VxFODTiXONhGlcAEfLJ4UpXu6piGVE7QQpCoQXEALyIsJoNAy0BtB0rb45MHo9vuxXrv2Ff7R3/5FnO3gj51r/3gJAl19zNc+93HWzl/liR94jre/5/0c7NxG6oSiKHnx+c+xff3aWXsXv1QkNyOw1tDUNabvUUlCluactBU7N1/iDz8tee4DH+b48IjXXnkR4T2bFx5ByoxmGWiqHXbu7qKFQ0vD9p0bXJOarBjjfSAfj3n+i5/lPc89x9bGOjtH1/Ab7+Lay1/gve95H8dHB5wcHxH97lIC9o+97hjf6BDe4fuOECQ3b93k9z/zWX7mwx/CB0jUKV8vvOndiYe50/FtRP28d+RFxmJRMZsUPH71Gb7y1U9gPSipsDbgnUUnYHqHkDF/O04BPF3XxENXXjAVlumqRHnJjdf2uXzlEdY3t3C657C9xvZJy3T1BLf7ZWxTMZu2XLiw9SdyrzyoP4FSoKYZ5caINDsVIzisNZi2wzV9pNzYAJZBMe1JSWj2Kz72j3+LG7fu0jvOuN3Cx8nRean46Z/6AI99+DmaecevfuQ3uXV4EvllPipbbQh4OXDRRETDIttAIryLzZ2IY1Epw/B7GqmiSERJyEcKpCKbpKw9tMr5SxcYjUZILRBSo3SCs5au7xBCRo7sIKBK0wylNG3b4T2YPqBkRBKdcVjjUFqTZB6ba3SakmR+8KoMkcv2huSOU847RISwM9XwXKeTBBfVw28SXbxhfCtFHNvGn5w91hmBpSyofOBk7y7tcg8TAkfbu/TGIAMxzao3ZMWYql6Q5jnF1pPc2T7AoREyRaYFSMVoZSNSXfqGvl4gTEPobXQX6frhcgK4yNePr/fbH9L+Xxs9IcRl4B8A54ifcn8/hPDLQog14CPAVeA14N8KIRyJiHP+MvBhoAb+SgjhS9/pOdxALlzOF6RJyWFTMy1BK0nf1mRCkqgSpTSOnkQKStHQpxmpl5BKnBKYtmFZ9axOxgTb44xlMp1hRUbtEpresbaWooXkeNEjkhQfMnrn6Tobs3CloSxzeh/9hLpe0GNiAsVyyWJZsblyhXlT07aGjemImzd3I28ghIEEKwbyqOPxJ97GfFHx7h/8QULw/NCP/ASvPP8FDhYtidYMyy+SKm2E5Ls2dvMyOJyxMbkjBOqmpu0M89awbHs2Lj/Oww9f4RsrKxwcLkkzRZ8kHFeBx65eoZic4+TkiMmdPfJZwYFd0HWCIBUgSZIUmSRYb0hTFRWHBqT0hGBJkpi7WyYJbd+T4TCuQ8meoDKSYBCpwtgOITzGO9JM44LA9MvIVUSTaIfHMTcdqhek9gGi9ydRfxZr840VgqeafzejvfumQhBwtmHv9Zc43HmdjYce5dLjb2drZYvJ2hrnDvc5uHsrUiTORiuR10YILI+PuHX9RSAKGi5cfpTp6ip3Xvkqd298g3/R1GxsbPHk29/N3t4Br7x6k5WVMdt37yKLFbTZx9pAmmSM169QlJvoBLIiZ2PTox5/Cts3PP7kO9j51EehX+LHj/PSN77IU8/8IJ//g38eR6xIBO5bjG2iAXLwMU0AArY3/ObHP8HPfPgnBwNb3uA39kff0/uk9TTNqOuKUTliWVQ4Z3n0ypOM85Kq6QEXI606g5RQloK+j3wmOSgelRZYJ2n6FiUD23ct1bJhcyPj+o1jXt8+5JFHBBfOXUTR0ZqGMktoXM+Vh1YoJu13ezv8/7r+TNaeFIzWJ+g8j5hXcFjbszieI4xABkkwHmkU2B5ENObvj3s+8auf4JXXtumCx+IwPtpkKRE4FwQf+tH38sxPvx/TGH77Vz7OC6/coCNmxHgRvyCgZFTXEmS8fYWICQxSDVxahRAJQkUQIgoiQajoBStTyXRjxqUnHmLz4gZJmkYxhBCYPgoU67rF2tMEi0iP9T4i3nXdx7Gx80gZk6ucNfR9j3UuquO1QiUalSmUkegw+Pe5qO5l8NILp8Q6An3fx7UbyXrxd4afRUUtnHFqo4z4bOYQVfNn30EIOBnwwrE8uY1Ic9TsHKae03dLgo8RrVZafN8iSx158SQc3nkRnRY4n4Ea+OtJRp+PyMYzkmxEMZrijKVtaoLtsNUJOBuz7q0joBBaofJv70/73SB6FviPQwhfEkJMgD8UQvw28FeAT4QQ/jshxN8E/ibwN4B/FXhi+HoO+LvDf7/D/RzVr0IKykxxIsBaQ5YVSKnQSmK8I8unWGspc81knNAEyMlxKosnXt9hjMX2NVJYlMhiFp4K7J0saNqOLC1JE4/UijyTqCSOTIOArrf05oRstMay6kGn5GWBa1vm84rDozmZ0ozKglcPqwgl5zmOQNN2keMWelKtcCG6ckshmE0nXLxwkRs3b/KX/+q/xy//t7ss9u6yc1JHnxyVoLQG0WKFRykRHbAJZ7Bw1zuWbcui6ZnXBhfgA+//ICqZYJuWzZmgWS65c6/GNfcY5YJ1XeJ9H49W3pErDZlnfbyBTieE0KN1oG4d86MFzo2oe4GWEtu3iCwleIukI2azRVRB6ZREK4JT5OWYxlRI4SLqKjVBeBaHJxSjFbSwVLVF+oBxHud6avdAdfsnVH/qa/NPrjzOVuzeepGDe7d4+IlneOpd70PrhPVLV7n32itRUXdWAYFncXzI9q2XkVJTV/FD8+Klx3nXj/00h/ducePrz/POd76bvYOa63eOEWrC0aJGFauobIW1madb7LK/f5tq/jp5XnLl6hVu3NhjuTQo4VnbOAdiQM2r24jVZzje+QYI4sm/6+Jp/1tWVM8Gdz8aynvDp3//D9g/POHc5tqgynNxYhD8gOTF13iaBSqEROtkcOkPUb3uAw+df4jxeIt5dZvxKMM4j5ABa6M4uDPDpio8WsFklFNVLToYmjqQJYEsFRzPe6ruiPFIYN2IIG/z+NWUWblCmWzQpQfoxBP8A7T9u6w/9bWntCbNIzoWV0O8X4QTcfpoAsJEepHEoxONm1v+xT/7XV586QZVMDiiUTZek/iUFWH4ifc8w3v+9Z/E9o5//muf5FOf+SKtP40BjE2OkIEYoy7xThBsGLh5AeflEBsqIs1IahACKUElApSIEWbjnI1LG1x54grrmxs4XBzTemjbDuccXdfjnD8zKPYeqsUSnWh673D2vgOFdS3OOzrTReqDTqJrhOnoux7rPTJJ0NbhtYsHLxsVws69IQ1H3kf94vRAgnCAR+sEa4e1LsRAIbnP1buPDsZxswsBj8dLiZEJ/w97b/5jaXbe933O9i53v7X2Pt09C4fD4b4MSUmkSK2UZNNRgMhwZMtOHAVBEsRIfstfYCBAjCAGAjiIAxu2ZWgXZYqUTInkiBzOkEMOOTtnpmfrfantbu9ytvxw3uoekjPhCCZpKukHKHSj6tatqnvPed/nfJ/vosp1QrOLNoYs72FdS2EyWrsg2kiWFdR1g84LVjtX6G2douhvsHQaoTVSRKKzRFvRHDTUhykcKieqnLI3IO8PIXhC29LUK3xwie6cvfF99fs2ejHGy8Dl7v9zIcQzwHHgk8BPdw/7F8AXugX9SeBfxvSKPCyEmAghjnbP87olhaCpO8m11pjM0LiWvq1x3hN0wDtD1ViWVY0sNAvpCVIQBMgS+rmkbSK9QiO8QGYZxkiQoKLg2u6cYFs8AW0UtRPkBggW29jOwD7ivCcXEmsdNCtCltP6iK1mtNby1rtPsKpXXLlygHUtAU2mDZeuLtBK0DhJpiKLxqFkClk+dvwkSMna2hpaKX7t7/19fvtf/N8s96+xM2+IEazzNK2jbC2BlBGYGZVwhBipG8eycSxqS0Rx/PTd3P/eB6iWc+67/xiDjU2ef/ocOuszygPPvrLPhi9Z7u/R1pG1cY6OHpTB+8iohCBHFNqyqgO9fh8bJIvlikxlBG/JhaJpUmB0DJGD2S5Ga4TwZKoFDF40NI0Ek4ONjPKAFor5omZjYwOZTdBuF5n3KVROCJa6uh2z9IOoH8Xe/KuUEILBoGQ+f6P3NxCjxdcHXL/4AlvHT6NNxuaRU2iluPjC8wTfOfGTbBmCcywPDhBas9rfJbqK53Yu8vIzPY6eegtv++BHOffck5w4dS8/9f530esPuXL1Cl/76hfR7QUWVaCqbMIIgydGy+xgD4IlugNaDFevXgC3QoiIaA+ISiOyIbP9PcaTda5fvUSyjb3lyXfzb0aATp5aQqruMZ4rly/xxONPsf0zP5n+8hDTeOs7MEFx8wZ36zVMH2WvpGoatjfX2dq8g4uXX2L/wDMa5gQfkapH01gmwwF78wMQDq1huVqRG4OSkVxDVTt8jEQZEyXFpUjCi+dzzp9f8MB7Cu44tsNsvmK1WnD25JkfxFL4/3z9KPaekK/1cEvc8uVsSWyBNiBcTBm2JH60rCNf/vRXePKx51jZgPcCF9L7D5FhhA/fezcf+vVfIHjHn/7O5/jiI99kxzm8ENiO/K9kFbCEdAAAIABJREFU4ue5CN6nDylVp7JVqblTEpRASZJIQBlMplBaYQpFb9hj8/g6x+48SW84TBw2l0bAtrXdOk97yRhDjN00q6rw1qfsXeJNbzjnXGrq2pZDs/IYwdvIYrZCIun1h1SLiihdSs06tPUTMmkdo8AHj4hpopXQOZmUugKUOmyU0u8VY5cGF+NhWuFrV0AyZe4aPVkOcdKgpCJDsVotyfpruKgxRY0kx1vBYG3Ezs4uvrYU46Osn76f+cEKnWmUUgRbY11yBom2wjY1QkSCT95584MMXYzBaKQwZPmAILq/yf2AOHpCiNPAu4FHgO3XLNIrJAgb0mI//5pvu9B97g0XtDGaAxsJrWSUQ2YkrVXYoGlam6TVStMvCkKT0LMmKgodsB4KIqX2LJqWYVnShIi2kTZ4pFTkRhOiJ4SWXCUIu24cw34vkaNli3IRpSJtDIyHPWob0MDSedrG4kLLXcc3eOqFK5w9fgJrHZnRzKuK0bBgVbcomZS+40HOwjmEEEzX1hBCYX3gxPHjnL9wgfvf9V6apuGzf/i7rPauUbuIaQI+NqzahGpI1S3OznDWhYCNCmV6jNY2+cVf+w1M3iMiWTvxDqbb91D0NhDx61y9uKAVBUJn1HFIFRrc3or1vmKxmFFjUXlOkUmikoQ2cfaCt9Q2kpeKy1cWrKmCqvLoGBAaVPRJDRkcUXiUBNuCjoDOcSGyXO0RvAQcPnqsrQiuxXpBNJKm9szny7/Ksrtdb6J+WHvzzVVCoe64a43777ubz3760e5U/Ho8voR7LQ72ufTyc2ydvJOi7HH01N3YxnP94qt4d9gQCZKpsGZj+xg71rKaO46fOsNgssnm9hFiVNz5lvfzpb/4PfK85P0f/nl8UOhyQk9p+v0pYeflhFCbAW2z4PrVqyilujxOhxicAd8S56+Qmq8IesDsYLczMj5U3cmbqN2tv0YhlSb6QAy3xjnOeT7/4EN8/OM/2aVHpcmB9xaZzPVuPWenxhUdgmKdpShyYgiMB33uufMdvPD811m1Nfe9ZcCFVyXXD65DzPHRs72+zv5sl1UNhfYE36ZotDZQZiWRGqMjRkvyrAAalqvIse0R84Xj3Pl9BuWIS+dvsJo/8x+2FP5/WD/MvZdGjgEfWpYHC6gisfEoLxAepPIoFNIrvvbvv8I3vvYkB66hDgEfkrUIQdEPgfecPsrHf+NvEKPms7/1Wb709ce5YZOjRSAmQ+PoO75XGuOKLh4wKkEQAaElWkuiFCijU4OUqc4MPyMrcoaTPusnNtk8uk1W5Am17tb/LdQ7JNX8TU6eomksbeNSKpQQiBjxweO7x7WtI0aZ9m6M2LZmsVjQ6/XQRlMtamTdIpRCSYmXCmNS6pboIjukSIcuIVMTGaJHyIT2eZ+SOw47OpHegNc1TD5M4glEvFC0QWB9w3q/YFaDLiZEIZFZTpZrghQUWZ+DneuENhAp6B+5F9+m9yj6BucDMjhEcPjgITgEERECRudIpQgxkAVL21h8WFH7QPA1pixZP7LO9TdYR2+60RNCDIDfA/5RjHH2XVLkKA7Nc9788/0m8JsA01GPyzdmnNqaYEPABYXUnsFQo8opmVZYH8FZRPTkRYY3JVW1T5EVyMxgrWd31rI26qF6BctZhfOSXhHJM42Nil6R4VpLLTTzpkZrg4+wrKpkxlxZtFJIGbBtpDc0LG2DDwGjJfuLBbP5Cl3m7M0XDEZD6trSK3Ou7y3IjcYHT5kr2lkDMTCdTrubnsCHwIkTx7l8+Spvf8/72NjY4FO/82+ZXb1AVAKhczywrFY47+gVGVmmQSiM7pEZw9qRk/z83/o7HDl5qnP8FhT9DbQxaDPio7/wUzz8xS+zuFDho6BfOOb7NeW4jywyhIVm6VjWjsrW2JARV3NUf0jrWopcYlSOMgNqG7Fun3Ffo02JkwcIneEaR5TptBNqi1SCgUp2GEJG9vYdUZW0LmDdCttEhGjJejmrur150rpdP5j6Ye7NN1cpA/Pa7j6f/fSjiZ/6fR4ffMvVC+c4fvYtKdrLWd7xwEcJzrFz9SJXL73K3o3rOOdZ3zrKydN3c+TYKVTbctdb3s25l57h/nvvZnfXsrG1xeVLL/Lyc0+zXC24cuECOS2/9Lf+Ia+8eo751y4l4k8xgTgl1ldxLiJ0SXQ18eAc6thHGPWHaGWYeUsrJM62bB05xYVXLgLq9ftWqZP1SrCImFBI0BA8D335Ieqmpl+WKV0n+q4Z/I5Xm1tvj8DojKatKcuC6XRE8JGzp+8FLSmkZmM7cH03ktcZO7MaoVtChM3NKUpIMh158tlrCNWAE51dVKJTmSywPo4UQjEoNdduVFSNY22jz9ljjgt7DfK2Iv6vVD/MvXfq1CkCHhcs1XxFs2yJdUB58K1DEZFSoNB86wvf4Ktf+hYza7EhCQu9hxgFhsh921N+4Td+mZgb/uS3PsNXHnuCmbcEkR4TfecGJ2QKDxAKdZh0oSVRKpROEZxKq5RwkSlMbpBaoTPJYNRnuDZiur3BeDoiy00SCMTkKmGtu9ksta2laVwSdyDwzuFd93N1snIJ1mPbFue7xgeN0QrrLKvViqZuyIoCqQUuBNAKtMIrhSPZq9wyKX9tykUgBt8xiEVHrUhiFyGSwOTmuPZ13qN0BI34CKHoEU2PtpnhnadRENUAlfdpqoqiV1BOe7i5ZOfyqzR1Qz7axpTbmDJjNt/HEaH1aKWRxqAy0TWkAVu3BNtishx0hooS6z1aSqxdErF4u0CGA3ZevPiG6+pNNXpCCENazP86xvj73aevHkLPQoijwCFL+yJw8jXffqL73He+WDH+M+CfARzbnsZV1VCayMpGlos5xmjq2qO0TF28NkgZKLRiWGhmvmXYF5iyR+MCGMtsBdtrycAxdLEnmdG0UbE/W3JscwOjIq1NLt7OSYQWBDRFMWS2f5nBtE9oHc5HnNM4L/EOjmwd4eHHnmR9YLBtoFrUrI963Nifc3St4OLVGYNM01qPFgofkgy6PxjQ2hbrHCFCrg0nTp5gb2eHI8fv4O//N/89X/niFzj39OPgK/pGMp1OuHzlKmVZIKVGFz2GW8e45x3v5/73vC8FO0uBUgbvk6jDNZa8v47uO0aTEl6tqXYuk/VG6QYjFeWoR5SB3dUeqIz+oIfwK1oXyV1LdbBPGzN6xlEWEmM0Td3QiCm5XxAl5HmRNnpYJGK4dfQzkRC7ENAiYn1DludUlSXGFqWSS7mWOXlP0djbhO8fVP2w9+abu1GlUYdtI84djja/T8MgAr5d0iwPWNs+xZFjdzBe26IoC9rWMjvYZ7Z7ndV8jxgCq2rJaLzBh+65l0ZlVM2SJx7/Fn/zE7/AO+9/GwTHQyZnc+sOjOqzffQU0ijeefcRXv72EebzBhMco617GfXOMOwNuHzjMsvdq+S9MXeemfLJT/49ZrMZ/+T//FfgVtShoj8cEIVCiIzoX2fdSp1GO6FTXRBJBKrIi+de4uWXL/Kud7wVrcDaFkieXgnV4yaad/h6aa2xtkudCZGqrjl1/CTT6RqNu858GRmNSpZNi9F9pPY4F7lybcZ4UPKB9015+tuStvGYHFYtDJXBa8F4ILDW0x54lBGcONpP6UEHnufqFYslVPVt/uybrR/23nvv+94bg7c0yxq3cogmImyE1iEJqCyp05/70lM89OdfZb5a0bYOHyUuJH5ZRuDufskv/fonkMM+n/rX/45Hv/UMM99iO6FCAqIlh1nQdJYmIqaEC5UZAgFhIkobtFKYXKMyhco0JsvI+yXj9SHrW2uYskxZshEUEusD1t5KhbHWEbxAoNN1wCVvP29dupLEiHeOuqpwLilNjUqgTN3U1HWNlJJev08kpJhRmyxUDjPhhdJJRCLgMCkjjWMPm73ka0no/m4ROp6eJ3Tc+PiG17GQeI9kmKKP0yW5ysiFp1nt4sgQ3iJDhWsshT6C63lM3kvvjcjp6QG50uy1Hp1rgkrG8W3r0SZPriFKIAzkRQ+ERmY5UggUkWb/Kr65gUBRmBwhGvz/y331zahuBfB/Ac/EGP/X13zpU8BvAP+4+/ePXvP5/04I8W9JZNOD78cBaluLDMlWxFUt/X6OQmGjoBQSH2G+v2A6LcmMICs1/mBJVvQxRcZyVtEfZAgRUmfvmpQTGyNBSJbLAxbLhjNHp2S5p1lUCKGxvgEXyDOdPIGiRalEgA2rBlMW6GbByRNbXLh0hdkqcOzEhDoIqlXF6Mg2O3sHqM0+1WrFaFwiBNQhXXwB6qYhxMDu7i7DQZ9Ma4zRrK1NsdZyfSfy07/4K/zkx36G557+FpdefonlwT4n7z3KeDJh/ehx7rz3PqYbWyidoY2hKApAUDct3lqq5RVyt0tb79DMe3z76ZfBlniRrB9WqwX98QDrWqy1SKVxjmR3IgSNjYyVInhNOZywapaYLE9S7uDweLQWqJAiZMpej9AKlMpoY4VWdSdltwQZED6Q9xNk7lygPzCAQot0Xyy2Nr/fsrtdb6J+FHuz+znAoWg0dmbGr+WbpYusUN/dEx5eJF+nV4xg25pzTz/KYLpNa1tidJ1fliPLCta2jlP0+9TLOUIpbLUgLw3FZItt2/DB+87wgfe+nd/79Gc4dvJe7r3vPWTlkDvuOM2Fy9c4uPhtfvqTn+C+u+/iM194mLzsc8fpMzzxzUf44Ic/ysNf+Uve9o7/Atsu2d3Z4dXzL9DvjyikxdkFg/Uxi/kikbWlAPvd9gXppnhTTas6mDtCxDOfH/DQV77Gu995H0Ikikp6Hb/XvuHQs0wIefOGKDqu8Pb6GieP3cnlnRkXL624dmVFURpa2xDbQOsajFYsFw1KeaZjzXwhicKilSQvdEL2qkBvoDm6PWWxnGGdYWQG7C2WmDwwHgy4cPH7obG3C35Uey/S1g1u6QiVAxvAJk6ezhVGaS596wIP/fuvsl+31EJiUTetuHI8x3qGT/zaz9E7eYTP/Ms/4bHHn+UgOBoCIabYrqQM72xFZBLcJUFGSoDwsfPDyxUy1+R5hpQgM0FWFAxGI0ZrE/prA/r9HlprlMoInR+ekIoQI21Td41bl53bodtCQr1qOp5exLmWtm3xPjlOaJUnZwq3JMaAMRkheLx3SKUgSrw/jAWFGCQCjZIR3yF4h1FtUqbX9TAbO3ZNnXiNbQqdWCrRKjql/aEhciejF8IggXb/BqEcIso+Ri9YojFFhrMWEVqq/RucP1fR6+WoLKPdnzHY3EIazaqtyLISqRUNVUISrce2KWPbtS1CF6j+iCLPaOoFvlpgVwf4agbe49oZikGKePye69OtejOI3k8Afxd4Qgjxze5z/3O3kH9bCPFfAq8A/1n3tT8hSchfIMnI/8H3+wHJA8rSWIdtGkCQF10ouATnAi54ru+uGOSKxhcE52m9I7QBHSOt82gpaF1A64AWGRZL65K5Ygwpv9J7S6/IWTUWrSXBWbI8efhpIYjRUTcNk34yldxaH3Jtb87e7j6LZcNwOGLRWFrrGQ0HXL++h48aGwJSa2LbMm88zjuE1DRNw3AwZFGtuHrlCqdOnSITgrpuKXslp06eYG9vnwq4/70f5n0f/mmUTItOSIVtHVpLssxQlL3OaytS1+lkY51ltpijZwfMd/Z4/Klr5CbDB4ttG6QpGUwnRBnw1hFVj1xVCOGQ0qOUYuEHlIvA+PgdzK5dYdALWCSSiDZZgrmjRPsFkVEyslSCLEtwfutLlLLEWONjgTTJHkPrHGsbiiLDWk1/NGGYS/LxiTex7G7Xm6gf+t4sSkVRGJYLi3fdif87bEI6BC+CMDLxXcJ3fe27BAyvrdnuVR576E84ceZ+tk+cZfvISSbrm5jMkGU5o/Ew2RrN9/F719kYDxmd2OTC5cuY/oSnn32BvBji2gbX7nP2ztOURrBz/iqf+OQnaNqGrMg5e+YEg/Em5y++ivMW6yzaaIbDAZ/7swf55V/+Vf75P/+nvHrpajKEdnNO3fkAzz/zBAhFDBb43kZPyMQXEiEmJEFkCUEgEEPLn//F5/mv/+F/jpJJBKW1eo1Jcvcsr1H1AYTgCcHT7/eYLxb0yow7jt/Nlf2HWexbvA8s5p7RqOTG3gpBZLV0ZIXi9B1bDAe7ZFqwv0qHNKlarlx2SJXMdnu9lE366vkZNw4WrE0Krlw/QErF9vT2IexN1g9978UYaaqa0HqETVnL4DGZQuaSvRd2+eKn/pLr8yWtS7FgnUQAISObSvJLP/8R1u86w2f/zZ/ytaef4yDYzupc4oPvzH+7Y5sipUjEdK+MKASgM5C5xBSavJejVBJdZGVOfzpgNJkwHI/AJBWuVhkhQNO06dIQAiKAiAolJNEnAEKSuKzWtnhvOTRL9j52jZsky0pCgLppO75gUvTa2BCCw/rO/DgKvE2Z8iF2ufdCIkVAK/MdHnNCRmI4TM4QpBjGyK3oRZVyfgEO7ZO4lZ4RhYIgkbFBmh4q6zEoNDeu7BKKDVAarSHInDz2WB1cx80UXgekyCEaXFuxWs6IoUCJHBkFvvEQBVIZit4Axoos79E2K6qDa/jFHq7awzdzdJYzGG7RtjnBJysl5H+Y6vZLvPEc5mde5/ER+G+/3/O+tjItuefkNCVDEPEosjznYOmooyG2c0a5oLIQbMuiDigEbeyBLMkKxcqu6BlBZjTWCRQChwIbmDVtBwm3zJcNR9cL2iDIc41dWTKlmB/MUEaghKSqHWvDHG0y9udL5vMlQkrapmV7c8il/X1a5+nlkqZtGA0yvPVsHFnn1XNL+nmODzOkCCilmC+WbG1v8fLLLzEejzFmHakVB7M5a5MJk/GYyXhMDIGqaYjBI0WCz4fjEi1V8ulzHmtdMl50jhA8Jsv4+qMvs3flHP1ScnXPcvLEGkI4fDRUIWfS9+hsQOsbmjbFvxgdsc5RZJLx2pDBMCcvFHNh8fQxJhHMQ4zYViB7LVomqb4RHqM753Mt8KHFrSJCQsgD4+mYYKvO1VwiVJ+NgWQ6MEyna1S37VV+IPWj2JvDieQ3/8fjnH/F8cKzlhtXlsz2YbVoqFcO50LX9wmMBG8U0UNZanp9w/zAU9fNd6BY3/E7hcj8+mW+vXud889/i62jd3Dq7rdz/PQ99IdTpmsbjI6NGVUT7GhCb32Tp545x51H1rgxD1x+8XF+6iMfxQfBY9/wPPLIg4QYMTrjd//4M7z1rrN84/HneeGl51E6Z75YcLByPP/qVa5fusgTTz7NdGObfr/HqdP3srOIqP1X2Dp9DxLYvbGHkIboG74725dOhStiSF4nInYIX0aMDSIEvvHoo7x6/hJ3nj3JYSTToUlyQvAO++ZDmxVBnpf4YBN1Qwi0kZw+9VYe/qairrsbXSsgwqDoY/2C9fEAlQkW1Qxjcg4WNSFE1kaSohiwWj9ASihywYWLC6YThVSBplUEG+kXE67v7DLu307GeDP1o9h7wQdc5Qi1Rbi0zrJMEjNJu2t58FNf4srOPo13tD7ifELoZIAJgY//1Hs5/t57+exv/ylff+JZFiHlRvsgCFF2B/huX0oJ6hanLcokCFRaoDIBRiKNQmpQeUE5KFjfnNAfDsl7JSozGKPJspymTqi8te1N1C6ENAoOhw1XBGcdbdt23L1OeNjtCykVIQqa1naKdYkPMeW+CgFRolVC1aqqxjYtvvUEH9EqIdhIQZQCujSQ2B3GQCOluMWXFYf/7zJrQ0LaQ7yFBALpnnwYpyZSWEBY28IhEMpDNgWdE4JACZFs47KSLJc0+yl32vSPUdUtwx4EJ1B5js4LlJCYoQYCtnFEW9MuFqzsHLvcI7YrRHA3LzGEhma1g+nl2EpgbWA6OMP8xsuvu5Z+LJIxfPD0S0PMcrQUOKGZ9DW9/gCTa5b7nkFZMm8c4xKW5BjRUo57LH0k+orZyjIaTYhCUNsGKRXj6RDpHdfmS8rCQBCsGhDSEIJjvrQYVYDW5Fozmh5h2oeFVeR9wwvnryKUAR+S2EGmoOj5rCH41CwFlxAM6xy6KCgzRe3bzibCY7I0zm1ay/ETx3n66ad53/vfS7/Xx7aWxWpFv1d2z2cYGkPbtiiVoPkQkseQUopIJzN3idRaliVtNWdReZbNCsyUwbAk7/fgYJeoBRcuXeHM8T6CBfWsJZgMI5PnkbMunSi0A++RBAotybKcKBS9Xo9oArkWOK8pyh4uL9Bak2mPixKjNfPZkjIX2HqFzkboLBJ1RgySrISN9U3GJYwGfWyIzOfz/9hL7na9yRJCsjk9io+XeP+7P4opd8Hewe78BV59+WX62Rkef+pFtBFI6cj7FceOTpk1+3z8J95JvfOr/E//6H9guUxK69FoxIc+9BMczJY8/s1HWXVWO961zHevsNi7xoWXnmb75N0cP3sfp++6n43to6xN18k3M/abhrtOH+PKbsXubJdr6i38H7/1p9DssjeDM2dOcenSBZaLi3gXmC8U/8nf/EV+/98pLl25iumVTPoTstLz1necwcoBznk+8+nfZzLZ5MjGFDXtk/czvvrQg+lGmBjafEce780XSIILxOBSbrVIHyKmJI3dGzt8/otf5uyZX0MpfevG+h30H8FrFUpaK+yqJi8KhsM+B7PA9sYxcrNGWTZcvtZQZop+oQlxTmgC69PIopG0tacNDqKlMJK2hc2pZscI6iZSVYHNDUmvF2i9wCjFsm5xwtA6xY3929ZHPy4VQyTUHukFdD6JWktiLXj4Tx7m5fNXWHbJTtFHIhEZIqMo+Mn33MdbPvIePvf7f8FjTz/HXqfkdDH1PSGEWwry7h8BaSolBFpJTJaizVSRIzNJUWZk/YxyOGBze5NykLh4Zb+X4jFJFikhRNrW4kO6p6UYMo+SGmPMzXvYoWgrKc71TZNkIVIcqXeeKASya66IoqNEuSQgoRu+xohSMuXMCoEjxZEJKYlC3FTIJjSuG+0KkEIT8DebzENkU3TjXXHzVTn00JN4n/j9ITqCNrRCEQM4twCtycqcerXChcQFlEhENJjeOria8fYGTg3JxkOyEGirBr9asqxuYKTHLmcI1xJdQ/QtziVRp1IC1eUGR6kgOJR0LA5W4CQb47O8ZXAPF/j8666lH4tGTwqZ1DRSI2kpsxyhFMJHtFSs2oa1tTE9JMMiEoJmoAtGaxPUypHlHqdKxgW4KFNXbzJG/RLvLbVXrI1zekWBdwFlNP1+xztSkkFh2A/JEFKYAXt7i0RwlAVaSVbB432kl+fILGM2T2HK1oEQkfmq6owtJVoJrD2c58N0fR0pJbKLGzty7CgPf+URHnjgAcpej7quaeoUKWWtJctysiyDjhobYhdvhKBtW+bzOVmWkWUZq9UKEQKVdayNSnb39xn1+sSYFD8qLyiMIZNJC7iqFoSwRqZTtEzTOlRcobyiaS3UNa1vkK5CS421jtk80MsyvG8ZUGFKwfxggR6XuAhFUVKv5pS55mBnyWR9C6EypJIUJqcYjLj7rru4cuEVfPT4Lovwdv31KB8sr1y6xt6eYG30Kpf39iHusGpeZOvEcdZGnpmTnD12J6pY0YarHDtScPX6kLo9IKJwMSAyTbSeyWTCr//6bzCv4H//3/4xzzz1ze/4eTEGmtWc8889xtVXn+PFZ77BqbNv44573s6x46eo6pLl7BLtfJe1UZ/3niwJx97Fw19/lLra4ZVzT7FarphOehw9foYTR7b4gz/4A4LPuf+t78L6lu3tkzTtjCcef5a8NJy56wjjvuDCpVfZ2tzg4ivnePqJb+CcSKPYAKnJO4wsu/nbAt3EJ/r0JaWgQ0RElDgf+MPf/yP+7t/5T+n18iTEEOLWQLtDQ8V3gEPyZkanDwFnHZvrU9anp3jplQsc3+xzZDrm1Jk+X/pahVQeDIx0xnzmWS4abhw0rA0TMmLyhtUiYgkEB9euCbY3NSp3ECRNGziYz8kyRXRv7K5/u37EFUD4iOgy0aWOEBXPfulxvv3YOebWJxTPC7xIvLNedLz7LXfw7o9/mM/98YN8/YnnmPlAiBInSKkXnaWJ7CL4hPFp+iklQmqkApVLdKZBQlZo8n6GLnpMNoeMJgMm0wlIgckKtDa0vib6NF2uncMHkCLDth6lDXmWJwqWC9R1MklO04CAVBrrPQGJdyHFfrVtx51TeJcy3wXJz88HT4ih858NiKgRsUPHRUAogQyOqCPSBzAeZBJ9+CASl1YG8CBjh9DF0FEnEqqY9qXsDmRpd/rud0jqeYU3BRKZjKSdJcRkSWOKEr+oiaRIVVP2mR4/y7iM7DWBEATNaoXKFERHNd9NI9lQEf0CEUMSjUhFVvSRyiCkxvmW0CyQStMbjKjmK6ITbE9Pc8/gbrYHkzdcSj8WjR5SgFT4NoCQFFokc0QPPa2J0VDZQF4o8tIQDxrIc6QxBNeQZZq4aBBCkUtBqyWmMCkAvCgZDpdsTfqMhzllJhkOhuSl7IimiiYmqbTWPfLBmGy3weSaspBUjSfTkmo1ZzLKicKwe1BhlKKxDpMZ9mYVg17BYrnCaMWstdSuJVclo9EYKSXOOYqioCwLzBnFIw9/hfe+7330en2USrydum5omoR2lWWBEIK6aboFLtPihy7vMIWgexsQ0ZLnCpH1CLHB2oCQCYUMeBySkRYw6LFXO/LxGGsVRZZORtY5QCKrlojGeQtCkwULLuAVtNbj8hHGB4RyOFuDShujtY7a9fEM0UqQ5QbV22RzbUB/MGS+XKHzgrap8L4Bcftm8tenJFvrOVW1w/kr53jXvT/FU889w/q6YnNzzpVLBXefXWd/d8HsxjWOnzTsHXiGgymD/DTffPI5ZH+AkQo/n+Fc4uKCYDRZe8OfGmOkrRZce+VZdi6d44mvfo7p5lGOn72freOn2D5yktDfZCYly3rOqfvezf2DCdFaLrz6Igc75+kZycljxzlz+hivvPhtvNA0oc/VK+eJ0fL2d72b86/0xGHOAAAgAElEQVQ+z9NPPU2vN+KFJ7/C/o3raayVOjVidCSDiteKT27+liAC0ScD1s7JFdFFDMYspch88/mXefCRx/jFj30oEd19uvHcSsjonotIGt/SEdY9eZaDEAwHPY5s3EPrH6Rx8JZ7wSmLCxYlJQczOLKR4bzHWZgOCgINyggW+y3SGExsEVow7Be46KgqT21riiIjNyUuzNnZv30I+7GpGCE6UBGpDEbmXP3mRb72hcfZc47oEsfOkwQaGZ77T23xE7/wEb7wuUf46pPnWEZPGwOeQPDhJk51CCKHEBCdYbFQGm0EWWkQRqEyiVAxGSCPegyna6xtTxJnPM9QWRLZeRcIztHULVEkYYSzAWJKltFKY62lbRus9YRwS9gghEroXAi4tsU5Swx0ggs4DKmI3aEnRtFtlcTjEyKlPqXki9jtmwhKpuAE1bl2CN2NaMXN/NtDTmwyLf8u6vHhi4SCGPBAkAHVcRxtPsD3x4QoGA8VN6406F5KwTGZRhhHUztECLimpdq9RjANs9kSZQqiysmGE/LBhN7mEdpVSTO7gXQSGQIRRVYkf8CUDqIoJTRCI6KnWq6wref45B7Oju5kazRlOBm+4VL6sWj0vI/Mlyl7NoY0+qhajzEaYzKKMqduHDEYZhraumIphxTWdw7YGcFXaYaPwegSow0hCOo2YPI+mVYIApPxCBdjmp+TIN/gLEYrtEnGj6NRj2IwZBIkee1ZHtSs5kvWRz08kmVtGQxK6jowHo9ZLhr6vZLZwYI8CFaVQwlFnmUMR+ObIxrnHG0b0Jnh+MmTfOnBv+T9DzzA2mRCUeQMh4PupGO7E31Mwc5S3Bx99fv9dAqSqcFyznc8CE1tPWulYrFsKLQiSI02GagBQTZkuabZW9K0A0rlkFoSECxWDVpLqsqhXEumNBFH0LKLW7LE6JAqx/kaITKaZk7e03in0dLgXMt0vU8UfUajAaONdXq9nOX8AGtlZ7tSQFggv4fUfrt+XEsAk2nBdPIAbT1nNM5Y33Bk8oNksuLI+oja7lK7PRbLJW29ztbmiMU8Yt2C5dLT749ofKApfcdxSW4kJ06c4pE3yICFrq2KoRNaNFxe7HP11edQWUl/tMHpe9/JqdNv4djJM1hvefjhzzPfu85w2GdtMuXut93H4y88z1ce/AzRNnzgo7/KeDrk7Q98ABcC5199BWkirW156RsPsTzY6UarLQjf+eLFRPY+tE35DmFJZ8MQO9VxcjhPmdFaJypHb0A2HPGvfuezfOh9b2dQ5sSYrlsyK/hemlfC+pTWnfrYEEPyAj194i7Wp2N8q9ADOLXxLk4fhZcuXUBrDSoyW0p6RbpeCjKObRTMFoLBoKaqHL2yYHM4ZLfaRXlFpkM66MUM2wpceP334nb9x6iUpqILgxKa6vych/7sK+wvlrS+7cavHh8DJZG7plM+9ksf48tffYKvPvUsB4SUBxvpBAopfkxKRRCJw5ZsS2TyWSwUppSYQiB1RhSRrDQU/YLpxhqjtTFZmVHmJSbPk9GyT2KFxWLB/u4V8mKC1n2IEiklSkmapunufS1N8xpRBAopZHImchbvLBKBi11IgPP4cCiSCDdVuUDX8IkkXOKWmEnKhIYfUi6UUlivkqRCpqYwJWUo8HQ8PHHLay8eztJuvQeRZK4sAnilCcMN2nJMcCDiCuEdIRshZYaIAdvUICNaSZxPamP8NegbXDXHWks5HaC1oFkdEIJE5z3kZBsV17GtBZeMz+vVnGY1RwaP1AqpDUZ4bOM5M7mPM+t3szEYUAwmFMPRG66kH4tGD6AsFNIkw+BBGfHR0BsOiTHSzyRBSEqTos206eGCSKPLCIvWkmuNkopl69EimUgSFVXrwAeUzLHWMR5JWieRKmKDxkjJynsGgyE6z2lcQJs0csnyAhcqMqNYrVruOXMswc9ty8a4T3ANa8Oc2aJiPBmwWi2Z9DUrD4ghg/GUzGQ3TRqTzDtibULbto4c4cEvfJ4PPPBBtra2Otm6xBhDZgwBcN6xWq3QSpMXOYJEuG2a5qYwQwufpNuxTWbPzmHKATI3lEXaHN4pSq2QOsHWbYCRNmgl6GcBaRSubpEq0jgoijE+NuSmYVklzqCPgWpZMxxnGK/QCla2wfvAwWyPt959mv6oZDheo9crqasl9WJJkCVRgZaG1qWoudv116UERjZsTreoHDz1wiOsr7Vsrj/DqvFIf5qj6y2vXmrJ85bZoqK2BTv719DhDi5eucRgvEbPB2ZE8jwjxrSuT5w6k7Il3/QoPxK8I1Zz9qsFj984z9Nf/QtG60c5cfY+zt77Ds7ecw++XhJ84MK1XY6dvYdfu+dtBO8wxqBUOrwY4NTJk5w6fpS3v/2dXL98keVij7aa88qL51gu9jjYvcHsYKfjEoXXWDC85jcKHkQiURNCQrllMm6VyiCApq556Ktf448+80X+9ic/niKNArw29zZ2HnyHKEOW5VRVRVEU5HmO946Tx+9gfXqU2XyXM/cEvvhn3+bd7/wgd5+pubx7DiGv8dwLN1jZyLC3wTvuD9x111n+ze9+DW8dR7dyWuc5aHYYT3Nu3LAEEfE2dM0q5Pnr8BBv13+00kqQaY2bwzf+7FEuX91l5R3eQxsSD1yFyIlBj5/7lY/x2JPP8bXHnmYZ2gSHCdElUnRHCCmI0aO0IKrULIkg6fUNplSQRbJeBhK0KigHJZO1Cf31PuWgRGuDzrJkk6KSZ50NluVygbMN0S8pewVZnlBC732y9WrdzUzbw2QLIUISbXRfU9J0qFy4SXHwnRVK6MaZh0jgISpH6BTvnbAp+I5PK9M+jyKCTuphQkDrFGqQQKGuiezGtIkrmPaBREKnTw4d4uilQJ48wdLm6KKHFoKR1ly7dgk93E5iUt9CE8gz2D5xlIVtWS0apIuoXCKyMcKkGNHl1VdwDqQqEEVBFAKli2RM7WucdQQHeX+CX+7j6wo16iOD4c4jb+VMeZa1/oCsP6A/WWO69mOO6AkEQUh6eTIcjtaisx5CBKrGUeSC2kakilSto8wVXuiuq7fMl5Yyi2gtKbUiU5pMQ4iW2cJBDOS5oVo5qipgfURlOS4GMiWpVxWTQR+JxLYRJTXzRY3KCkQ3l181DeO1NWbLCtu2rG+O2Vms2Fzf4MbujMFgnWa1ABFp2mQ/UhYZWqtuNHsLJtY6vezHThwnBs8ff+oP+cQnfpnto0fR3WOFSLFnbdtilMYGT7tcQIg3N4qUkrpaUfY1QivKfk5/w1DPFvSMAhUpeiVKBgoV8TInhDl4gfcNdtGST0ZI7SFqgmsQ5tAg0xOlJNOKSkVMnuGCJ7hAqBoaYVFBQHAURSJyTzcmDEcbRCGoKku1bKjbFQHFYKhp2znBdYaUt+uvRSkFyqyxbM/xzLmXObp+CkHDaAD9/g4Xru3TtpGy13DnnZ4HvzTj7Ol1Th8/w/6eJEZHr+zhQ8C2Lb1SE2JAS8XZs2cZT9bZuX6FN7Jfeb3qzvQE77DVjBsX5+xcfIGnH/kzdN7HZFk6IUuFzkuCT+78OstwbYW3FmLgxJ1v4z0f/lnyXo/pibuYdKjAmXd9LFk+tDXnvv0kj375c9SrXWKzJHQ2EIclbANaE6NNqtrWJTJ1LXFmSTteQ0rY3dnnf/kn/5SPf/g9bG9NCB2HN10WvncsLLpkjn6/YDIZcnBwwHQy5cjGaXb3LvLic3Dlima+eJJJf43ru5d4232KR79ekZk+RS/ywPvv57mX9tDSUW5krG8KXj3vO1GIT/ZSPllxuAbaBvLwfYyub9ePrIQQiCwDb3jp4ad49qlzVN5CFMSuERFBsK0zfvbnPsKzL17iLx95hrltsSHgD6ech4B01yhpA0ELhM5ST68DWalRPUnWy4kiooyhPxgy3VyjP+5RDvtonUGUKetWpJHtsqpxtiZ4j5IKJVVConOw1nYOETbdqxFobToqk03TKB9vijCSpUoCJg4/DtW2HbwPneDjsJI7RTIXbhvb3TsVkYRYCqVABqKAYLvDFAIRBVFKZOchKDpfwZvWRzHymil3Qg2HffRkDXVjD2EtRiy4dB0YbSKQiJCCFnplj3vf9VbyzFOIFBPaOMX+omGxXKIW19kajtkblhw9eYRnnr9KMDlaGeYHM6TOOXXmOPPL12iFSZZVzYp6vgJfcLbc5mhvyrQ/JusNmWyM2RgN2Z7+mCN6UqZmT3VJFja0yRImymRMXApUUORGUVnoFwpUifM2xf4YDbTJyNUGyBRKJauV5qChMILcKJYCykzjLaA80QZMVqCkRBmF857VbMHGZEjV1GR46lVFY0PX+AsODhra2pMXhtnlPU6dMrStJS8Mo15JsKvEfyDS65U45242drcsJlKzRoycuOMO9vdu8NnPfIqf/8VfYTieJFfwusZ5B0Ik8YUxlGVJbjJ4jadPVVcMigwlHEWRkRlJqTICES0EZWmQrqJBYIQmLwuKosS2kjxLETSTfsbSKlxjUJlGKoXuxlSZFuSZoF9mCVLGITX40KPMS5xb0hsNKHsZ/UGfxWKB1hl1u49WPaSIOOfAJ8WYCz0yc7vR++tS/d46k+mv8K2nP81qMeBquMjbzv4sL77wCuvrGYvFKxw/OWF7M6Ou+ji7JJMKIS2njtzDaHCJLL9Bs6oweU6ZJxRASslkNObd73mAP//TT5ECmDTGFLz9fR8gRM/lV8+xe+MKtk1h3bdGKreMmG+ameJpmyVt8+ZylIWQ9Hs9ytzwyovnaOqKotfHe8+Na5c52N9lb3+XLM+I/SlZOURGR717Gb/cu+nBFV2FEHmnzHWkeKUIocU7j9MaXxjqxYJzTz7OH/3xZ/mv/sHfRhrZqQHVzevCrZSM9Dc613mCCYkQiq2NCRvTO8jMt/n2sxVXb+wi9i5T14FjJ3LqakAgMBh4PvLhk+zubTKfP45UAucarl6VVEvLYKRYVQ1ZVuJjjbce51pMJrHtbTTvx6aExIiC+bkdvvmlbzJvLb6z/fAp34ypVPzUB9/Dhf+HvTeLtSw9z/Oef1jzHs9YY3dXVw/s5iSSGkyZlBxZoi1HkYLEjoTASC4EQxkufJPb3AYIYAcJnBsFSJDAiAMbjiTLYWJLliXSlDiJZHeTPbGL3TVXnfnscQ3/lIt/nVNNmm0Lkiw1gfqAblTV2bXrnI01fOv73vd5T5f8qy99g4Ux+CDx6LiyxeFF5OJJAomOrDurJCiN0B6dJySFJhslICVSZRSDko2dDUbTMeUgj4BgmSCQOGfpOos1Mfs8SRRaa4SPudDWNRibnU/0IGrQtdLRbNW0vTwpalKl1D1HLzZ93kPPfO6VEjrC+Psp3ncDxyEIgeunl0J6RO949aLP5w298ZJHV47YEPbQ5rOHLsSj9xZRjuHD2RpYko3GBNcx2pzgDvc4PFkTBlN01+L9CikVtlmSjsc03ZqPvPgsG5ngm1/5FvbeQ64//yIrk2EeGnw64LmPfZKdrZxbq7domprgLNPLA0qdcHp/H68SVof72PoU27UM1JgXdp5iK9+gKobkgwmbOyM2R2MujgdsTcv3PJTeF40eQuBFjguQp5L1WpAkEqkUq3VNIrK47hMSbwMhE6jIOUQn9Dv0/iIZk4oJQaOEoG4d0yonzRPEqSdgUSqL0N+e8C9ktC5PC00zizRu6zqsq0gTRackDslomPHN129x7/5DPvLh67x9d48XP/Q8t+/s8dFPvIisBiz2VhwdL9GJoKiGGGdRKmoCjIkHKwJMP8LWScLm9g5vv/U6t26/zc7OZdI0QeuE0XBEVuQIeGRTDwEtJUrFg7Ku1wyrguA7tqcFRdIQRhPyTGJMR5qMCa2iSCVS54hsiFSKZiVBLOjqjsEoI9SeoFJklqKTDIEkSzWJ0uTDBCkKrG1p04yqlIisYDBMKQYlLmhsZ1kvW9Z1R1YEMg02GES/0pIKTOtwQTB7+PDP+4h7XH/Ecm5FU9/lpz/5K7z51ueou9fpxF0enuyzWE8wwjMcH2Pvau7cs2SZpKwcN242fPCpKU1zJ0b2EafSwyrvXaeCRCv+6s/9R3zhc79N2wIkPPuhj/Gzv/QrBKlxXcfB/Zt844v/gre+9VXWyxl4/29BMP/RSicZn/7ML1CUFTffeYOHD+6hk5SmqemaFaZrsWd3mx5rIFWCHO9G9936pG8zHcEsOWNtxe+sI8KVA97UOOvw6zXe1Pz9/+Pv84t/4+cYjwa4YFAq6af9cW4Q3jVVyIsS5yxFUbC/f4DWiuk0Q+ia05OKovCoRJJkDZ/6xH/Aq299gY2NMX/rlyuubv08//g3f4tqvEJIgfeCdR1IUsm6acCDlC07uwXjQcnbN9ZY71nM6z/Bp/q4/jRLCEFYWr72u3/I/mxBR6A1FusDLkiyYPng80/Tpoov/P7XWBhDEAEXHAEfY72CRKEJeFQSULnA9VBemSjSUpKVus9a1yRpGZu87THDyYhqOIyGBn+WdBTRYdZYtFYEI1jXJtqI0gRrAmmWftc6NJ4bAucdTdPhvYmNZw8b9/5sLRsfdiIWOp7dZxM/gopuVBf19b5vyJyLYQhCgvAigo6lQPp3OXWDiNNBIaLUwkfNXfD+HK4cQj/M8f20VJx54QPBg5+O8KmgW5xQlBW6SMnCDk5qgm9REgKOTGqU8dj1mle+dYsf/sg1nvzgdd46PsTtHTDOOmZmxnhnm65Z06wEwaypj/bp1i3bF7c4eucdnHes6xN8NyOYhs30Cs/ufJjtfIO8LBltjNmZbrI7LdkcD9gYVUwn1XseS++LRs/7gEwUh/OGK1sFlgLhBMoGhI9PyF4pnJMY22C6HJ8FXFyn03aGvIwQZCElwQY64eL41iuSRGGNIZeO1tgYZh7iY4NHYDqDlknMIrcOLT3Cx5PF2w4hPNPRkCAC83qN1AmkaX8BXWBdx3LZEILi6LRlNp+jE83Fy0/QZzb3yBTOYcfRONRDi/OSVEvuv/MG1556hnIwoCwjtLizhq7tAEGSJEgRDz6BxPqW46NDbM/2SVQgnOkxhMKKilS0eK2RSUY1qkgKhw+KTCtKNA1D8jRwMbW0ROe5Tkoa4yO8WlnKNKU1LsbgWEmWl1SDAcPpVhQJm5ZUxVgcpePkkdDhZULXjRlOKqQMtPWKg8MD2tljVtcPSnWd4zvvfJbT/dc4XY7RqcWaBT/xI/8Zr7z8/7K1O+TOw0NWs4J17RkUMJkoWnPao1UMVTECv2C5lFSjCT70QFEp+ejHPsqP/vhP8YXf+xxCKD7wkR9G6wwUaJly6dpz7Fy9zl/8zN/gnTe/zitf+l3u37mBd/aP3elJmfGpn/6Pee6jf4E3XnuVvbu3OT54AAGCs3Eyd5ZdGzzIBJTApykhLyEfEepZRKrEKxDxZnam6zGcOWhDAFPXBNMhcLz62rf5/O9/nV/4az9xrsv71wwexGYvz2LmpZSKLMvo2pphuYO3KUZaOndMkVR4q9jeuMDt28fIxPLbX2j5yR9acnhyh80E1k2H6eJ1VmtFZxTPfaBCSsW6Nrz55hFIiZaCjU3B6s6f4IB5XH+KFfjOl17j5o27dJ3DGhsdriHeuJ/c2WGwscnnv/Q15m2LExEBYmObF93ffbye1gqhHSrVOCMQiaCaZKhcIQUILdFpwmhzzHRrg6zMKKqKLCsiAqxtzhl3EEgSHeUYJm7SlMxBxJi/4GNjKPtnnxBE77g1OG/7Bqrn4Plex2eiqdD3q1shRO+yfXQahl5WcNYcnn9KIRB8/DmFlNBPO8/kd965mOoTQAmFw36PJjYglEP4gOzzfemjG2QAnyhMCsJ0dOs5qak5Op4zfPIDKGB1fILp2ijr6MB0hvo4IJKCN+7N+dEPPsn2lSdx9/e48sJ1TuQR3zptOLzzMs88e53V0R7N0RG7ly/S3L/N4vQhppkhsQSpuTr5AM9OXmCYjsjzjMnWhJ2tDXanIzYHJZvjitGwoCrem2bxvmj0QJBnirYVYOLqLysylAhkWoMUFJkiyxRDl6OTlKwocW5B3TnStCAfjbBth2lbZAAlExpnox7MB+q6IxGO4ARCB3pWI8Y4tErwIfQuN40nxpmd2bnrxlKmAddZmuWKNFXYpmZYZswXhjwv0WmKSiAER5lophsbDIaDSO+WEmMNSkm0Ls6fINquo20atre2WZye8Po3vsz1D3yca9ef487de0gpGAwqRuMRgoic0VKhtYonhXPs3b9FIT2r2iM1JInAGYGXA5AteaJYtq6XN/joarLxCcxJjU4FmQp4JXCywpsWqRSF0AglKFJIsoIKSNME03Rs7ewyGA2oRlNsUyOSrM8nNASv0VLQ1idIlVENJliz4mD/lPnshLLIIwD0cf1AlLGOo1NNXb/BdGPEwWHF1YsbvPGdz3Pv+JjJJcODPc986XBek+sh89OOyzsTNsabGOdYrWeYrkUpGXEhgO/HVlpp/uu//d9wsLfPrVt3uXTtWZbrJYcHezSrOZONTcbjTTZ3LjLa+Ct89Md+hts3XuWrn/sst956hbZZf5dD7rvre48zgVIZP/5TP8/P/PX/nPv3HvDmqy9zerxPaFvwjmAbYqP27szXBEQKNjoYSVPQOZg1j/R1ju925Qri5VXimgZBRBhRVPzOF77Gz37mL6J1XB1JqfrXn+Xe9okbUtGuFhTlgMGgZC08zzz5DDsbl7iz93WsM6xWK564/Ax13bBuOiZl4MYbKT/5EU0QlpvvGJJEYLuYeVtWBdZ03Hu4oF17hmNNlkqCdBR5wmKhAPsnPm4e15+8XGv5xhdfZm06OmMxNuCDAO+ZZCm7F7b44tdf4mjVYc5MCsTkiEBMthDCISToRIDSuCAQiaQcp2TDBBsCSkjyMmc0nTDd3iCtCvKqQqmE1ao+z6cVQvSGiqix64yhF12hZIEPZ/vWXtYQwDl7Dkx2zhKbK4VznMeShR6XEkJk/Hl/dh6dyZ36zNngv+uM9iG+Vpyvc6MD3veJH2cNoXNn5qcQH87OwMj0Vwwf49YIgeDdOS0zRKU6Xkta7xFBEURCY2uCXbA+uEsxGJFUOSpT1KcneBFAinhdOnrIXZExqQr2T2eoJCVt4NsLQ7ox4KnhhOOHd3EmMJhu4tqOzre0i0O8X1PoCc/sfoyrw6cZZUOyvGJjZ4Pt6YALmyMubFQMs4KqyinLlDL/E0Sg/VmUUooEQSL7j18qrI9mijyPuBWtBZ2zZKlG52nMWpVgZEKWRoG3FIFqMET6DlTA2EAQkCaCunHsTjdxKsd6hU6AIFivW7RWEfRoBGmSUK8deZFhbVzZNK3hws4GaMXpfE5ZZqzrmmmVs5rXlIOCtu3IREsgUOQpgypFCkmZF5i2JckyRM8T8r2RIk2jI7eqKhbHRzTLBXdufofJ1g6bWxucnpzQti2z0xlpmlLkBZ5AZz1KBKxZU88esDGB1jiQ0YKtdIDQYX1AKggyTkyN8Sg8Wid4FCqRuK7FBhfDo7UEqUhkwASHFAk+aLQKeBQ2hGiCEQrvbFwtAEoJMpUjCEhdomSKVIFiUAEJtk44PWnY3IiC+K56fCP5wanAG294PvRCxWRomFQSrcbcP3wL4Y9YLSS+y9ne7iiKinYtuXPbE2Tg8KSBIEh1DDgnePIs5SwYXKp4Ybp05Qp/9+/9Kv/X//m/Y9qa/YMjXvv673P3nVf54Mc/zfUXfoiNrR2yLKeoKq5/8GNce+GjHDy4y1d/9zd585Uv0dTLc3ch55MyOItLEkJRVhN+4mf/Op/6zC9wdHjEV7/4O7z6h7+PW6/BGkJY8v2bnJYQDMI0BNdANohbAfP99IBnzWYELAcfhdSIQDrcophs07QmTie+R2/0qM6MW1H4LqWkKAqapmY6HXNp9wq39/+AVCdk6RZae5QZkqcZzbrm6oUXeeudN0hT1wvaM6abVdwmtI5V26BTHx92Q2Bze8TpYkbTBTY2Btyh/WMfLY/rT6/qxYrTesnatHQh4PrmKReBrZ0tvvX2TWZ1jRUaG8djWO/6Ji8gtKEoVNxUObA2QWrBZFqQFPGcyJQkLzPGmxMmGxtUkzFFVWGtpWk6YupEZLlJKfHO03U11vpe36b6yDCJCAHVc+w62+HO0jiMxxhHIEqkvBfvauB8P9ULOBffU/SO9BB8P5GLDL0zzIp3HtG7bQUiavNExLQFE5DexzWzcedyJ2TotwD9lJOARCECON+HEgSP7CfxDuImUSg6lSJkjmkMOqT4sIZgcctT1s2adDQiuI6uPiUYT9toEpkwPzpic7DNwfEJL/zoDzM7OObt/RNuLjuujQLjScnJyYxmccr2hUusTk6xTpDonFxPefHqx9nOLpKnA8pywM7WJpvTEbvTARd2BkwnJZmWFLkmSQX6/d7oxXKkWvcHgybRiqYxZGmCVDlVkbBYd5SlQCiNtyHmrAaFkOCsJ1hHkkCearxQrBpHnmjSRNCaQJJlOB/wIa5niiyjXpxSjcakeUFrLVp4rIO0yHC2xblA0wa2JxXzRcPJ8ZLdC9ucLjvKyYSTVUOe56QqkGclJJphVbIx3UInCaPRiDxNSfMcpRRVWcaJHpGNNxmPaZs1+w/uAIE3vvkSH/vRv8BiviDLImJBKUWapgQC1kTWkNCS9ckDXLdCqQJIUdLiiZM363qSd9DIYEmkJQRFTUZKiFl/wmNDQPcHvLWOXMe4HSX6JyTh4/rZB5zpkCojeIcxCUImSGIsjO0Coufj+WAijsVLtHA4PEVZYFuHlJokl+95FDyu91cpJfjGq7cQ/jKDwYSnrqxIM8XFzecoy2+y7g65vLvB/ukenVWcrhxv3jjm2pMpl3eiY2443kCvl3TNmmpQ4UMgFfLcjS4CTCcTfuW//Nt847Ub/K//26/yzmtfpVnOOT5+wGhvl+V8wdbuLtYYyrKkMZaNncv8lV/8L/j3fv5vsn//NkcPb3Pzrdd5cOdtlrMFxmQTzDoAACAASURBVAYSnTDZ2OKZFz/Kp37mZ9m6eJXDoyO+8sXP89rXvkK7WvWr2jlnk4jvvxOOgfF4A3VDkAXfNxLtvCQIC1hkPkAmGwQl6dYzrj95AanEeezSoxKId6GHhIA0ixPQLIt4qOFgwOXtZ6mXOUI6lqt7eGb8yy//FsiOrtFYs+L+/rd4sH9EXQuKIos3aNNgjKFuPKWKmadVWTGbL1BKMZmUiOQxMPn9Um1rWK8N1jwalAXryIcF908OOFktz3VtIniccAQRM8eTQiMzH8k/UuOcQiWK0VZFkouo7Uwz8ixjsDFivLXBaDImCMViseynavI8ghMEbdthjHmXeQisMee6Uq11n8neR5R5SfDhHN0jEOdu8zP37NkU76y8f/T7gMCFCC1HOOixRILIzxNCxOxbogYvjulEzAh2Pmpjve/fLzahwvdMwbMYOA9KKlxwEARSCkyIbD59+Sm6LKHrVmgR8AaMaRlkU9AOKyQKTzM/jRg1r/F2EeNBQoEsNG234s1vHqPtitnKcv9kje/WfPOl21RFysZ0gyQLLE/36DpH8JLNjed5fut5tpIxKtEMRyN2tnbYnQzYGlVsjgumgwFlLikKidYBXUpI39tI9b5o9Ix1rHuK9MkKdBoFwUcnc4Z5gtYdRS6YL2sGVRW1Ad5QacW6EWQ6oAiYPt5FZRolA7WxVFlCqhRCOqSIIsvgHd6DFgneGRKlSJSP6JYiZ7ZsSIVCCEeWCkxnSfOSOw9OaBvDdDLk3r1Drl65wL2Ht9nZ2STJC05ma2ZLQw584sc/zeHpgs7UbEyfJMnyc8SK8Y62bSnLkqoo+Mo3vkK9XiBUFIu+/tqr/NgnP92vdWQf/GwoshyVpGRJQgiOB3ffRimFDwnGezIi8DRVGucjH6s1MbMwzXIaW5ILR0CipUUSyLVChkBnHFka9RRKSpz0CBkt4wQFQrCeH3CwcOzv3+P+O2/z8U/8MIM8wQjFYLJBkQlEA1kWQ6MJDucFtrME3+Fsi0404jEv+QemvA/84i9s060vMB1c4+HeDYrhSwT/NFpsYs19rLckMmVQVRweLEjyhrq+yLC8gAemm7soIXC2YzgYRcyDIF54RRRqBwRCCp67/gTHe3dYzw8AKPIK7xzHJ/sEZ0ifyqgGA6QUdNZEvhWa4c4TbD/xHB//iZ8jkYLZyTHLxZyiLNnY2EamCXXdcPfeHV766hf52hd+h/r0FAgE33BOwf+3MuQEAo+kAaVw7vu/XkiJSEfxnBcBGTwySMosY71u4gZgUJ5PLt5dka8XH4ZED2RO0zRqXwlcufQEo3yb+8d3SVOJtQ13D28ghEOnOUjBw70G44bsbD6DFAl5rhiNWvb39nnplZt0tUEngdPZitXCc+XyJuNJ4LW3Tv/4B8vj+lMt7wNdG/Be4m2Hsx6tNGtnabsGgaILjuAtUgQcIPVZsoVHJIGsyFm3AZXCcJqTFhqpBTpLGIxGDIdDRltThhsTjHO0TUynMMYgpUIqDSGwrhc9USEh0TmIaLSw1pIkKVpruq47BxXHrZXuV7GxqRMyJinFKV485s+mdY8gxxG14oPnXV4oYmMY4rXjXdq76JyNk3HropwpavoeMSmj+SJq+ILzaNHz+IjnJqIHIveZ1jIITFWxTAMWw9bmlHp+iksrlE0RWlJsp/ggcb7GrE5puxqdSiRDhltPUlYZxwf3Obz1bZCCG986Zu/hgqQqETJDopkdnLBatgyqEW1n8K3h0vAyT5WXGecDMp0xHI/Y3ZqwOx6zOS7ZHFWMKs2gkhRlilCeJFOEVMH7faLXti1Hxws2JkXMxXTxAiq9Ay9QuoREsTaB4/mKNB+ybmpkIVitLSIovBW4AIlvCd5SJpL5Ys10UNDagLcOEwISidIC3wR8CBjvEATyLOV0VZPomElrncD5jiwRGOfQUmB8wDrL9vaEN19/m/H0aVbzOeXT12J2XZaB9QxHJdef/zBJNeYPv/YNpMwYjUcMhgMGwxHGWIIPlGWJdY4/+L3fwTUN67bjiaefRSK5e+cOV5+4ilY5wXlWiwXr1QqtNaPBABEst26+RZrmWJFgsXgRsJ2kHCTkSrNcL5AqkAjN8eEh5WiX40VNlSusd1RZTuM0WkuMrynMCqsKjOhvNEHiAiitkcSmN4SUxeyYk5M5r/zhlyizNOYIyoLxqOD4ZEGWCLKiRIiEyTTGXEmVYJ2lSHK8ehyB9oNSwUsWp4oXX1zzlW++wqgSXL2qWK6/xtZ0QlFoktSj84Lu2LAxjdNtXILznjzNmI6nmNUJt2cz8ixDKdHnPz+CBZ9d5I+ODljPjvrmR7CYHbNz5VlWR6fgLM+++GGqwZDu9IR6vWQ+m3F6dEAALly8zAxBXpQoKciHIxaLBSenN2jahvnpEQ8f3OKNb3yVpm/y0LLf1ubEaL4GQscjnZ3sUQtxPYUIbGzt8OzzL3L37m3uvvPW9/nUFCIZgzMEpcAEbNeSDsd0TvAP/u/Pcjxb8kv/4We4eHGbRGlOThe8deMWH/v4x3j6yS2U6i/NIeZdS6lJtKYVis2NTXZ3n2L/5CFaW5xNerRLwmRUURbw8AEEZTG5xJgZd+/fYjBRfPiFD/KzP/UZbtz8Ax48WIEY462i4x1e+dYey+bf9RH1uP6o5QOYIKPL1guEVjgp8NZju6hRi9DfuNZUSqEzgcwCQXrKcYl1GqUs1TgnyRQ6VaRlgco0w+mYi5cuUo2GLNcNprO0TXs+xYsNk6OzLevlKc54sqyKujVnkVKQVxnWOVrXxIas598FETV4puv6CC+JQMQBhOtf4+m18rHZCkL2iBMf+Y7CRzerj9eC2JOdGTHko6lceFekGgJrwVtBcDLq7wiR2uFj2upZti09P0+EnulCHHR4AhSKZj3HWU/bHnK6CiSTMevTQ9a+JuBQ6Qipc2QyRueBItMMBiOElDy4+Tq2WSFl7Fb3Tg4IMqPt5sh0TFKNSUdTyrwkWI9KUi5Ptnmy2mE7H+GlYry5we7GlO3xkJ1hyWhYMh5mlDmUhUJoj8w0IlWIXCHL7D2PpfdFo6cFzBdzssSzXnWMNzbxrKFdUYscnbYEn5JrHQHEOsF2c2ZOxLw/6UjSlK4OqETReEl9uuLkeEmZ58yXHc5aThc1Qip0muG8xHlHsKGHwmrwIVrJiWaHtu3QWYokoLSmbdZY65BSsWxapEpxnWF7OiZP1tSkcU2qAnlZcumpa2RFwYOHD3jiqac4nZ2yt3/Azs4OVTUgBM+D+3d55atfRHogCC4+8RTXnr7O22/fJMtShsNhhCgn8ecOPq5vb954g/ViRVoWCBkdSanwtCImf1SZQOk4qr55e49qPOTm7bdABQZFwtXLV2jWx6hiF60sThF1QKGiXc0oRhu07ZpcpxgbwDlC0OzuDOiWhodpjrOBcprSGEuSZiA8Wgm6tqVpArPTGbPxMc5BVhaczOcs5x2n8z8a6+xx/flXksCVqxlVPuETH14h/JiT+j5VfoWmPWIwzZiv1wzLIfriZZp6zpGq2Jru0BhDlVcURcm1J67wxpuvked5DGiXEtVHhgkper1n4J/++j/ieO/2+b9/cvAQgmO9nPOJH/0U27sXMdawmM95eO8Oq8UpR4eHFEVOvZijlKRp18xPj5mfngBxJFAvZyxOj6nnp/imjY1lmiJCIE7yFKgUoQsQlmAMwgE6RZRFRDDphDQvyMZbHLc5KnkvbpXHdycIocH2KInhJSwpfn5Ku5zzTz77z/mN/+e3yKuKPE1wq2N+5IXrzB7u8/DaJqnWpKkmL0vyQQn5kHUHwzJlYzJmZ+NivFY5wWrdUmQxKvHShSscH4N1ByiRIqWJEhSv8GHJdDzk4qVt9k4LHu4doNlgEQ546ZU9Vp2nftzovW8qcugsPoDUETQfI+oi5dSFACpy7VQiEElAFQKUJ88zgtQE55lsjpBpzKdN84K0KJhsTdne3SavSharFcZY2sZA4BzI75zDOTC2wwNZniO17nWfnDdaZ4Yk32tvhRARZm4M1kSOHvRRe0S5RvAx1x54V6zZo/eI9WjyFtmUIU7wnXvXZ/Tos4qGRfeuRjX0kW+RHO3PNXj9v+ejHENIoqs+BDwCl2qWYYlf16isYH5yjPEZSTtCDzdpT+4S6iV+NUeojKycMt64RHu6x97RPS7sjtFhiRc+bhGBoHXEtkhFcC1SQLq5zSQbUC+WbJuKS9kO28MSF1I2tsZc3BqzMxozrnI2JwOGZU5RKrIiQCYgVYRMQZYiS41K3xtD8L5o9ITsM+EWNc5r5qsO29WMaGlEircnWBTzoxPqtiGRS9bLBcNhRdsaRKlInEOisc7iTEAHT9t6pLc01jDMU6QIrGvDRpYhixzjQSqF84rO2IgsCZK8yPACrBUYDaPREJ0o1ss1SZbiQ+T7LBdrOgTlqMTUFik8WZoghGS1WLJaLbDOkSQJ88UCEGxubjEej2mahizL+eyv/2Pq5YzRoCQTkieeeIq9vX3+0k98mpe/9SqbGxtcungRFzzrdR0dvO2at2+8QjBLMl0iCJgQm1XROlIRsCHq7Pb37lPm0HSWy5fGCNNwMlvw1o3X2J0UVPlGv/4VeG8RwcSbhhZkYoh3Xa9x9ATvkFrStdHda1xHCC5eINoVXQ1SJwQRI3byTFBkAYRC5pqsrViLrncgPq4fhEpTwfZWxmJ1n9N5x4WdAzZHI9btIcNhi3OOxigePFywNT1iOh0hFaxnOfXakOd5XP2s12RZRpHn0OtszuVwIT51e+95+WtfIXjbr1EkwVieeOpZnn32Azz/oR/Cu8DpbMbp0RHzkyOkhJ2Ll5FC0XYN7XrF4d597t97h+XRId6Y6LTzluD6CUiQsQlDElzvHJQ6XowzDSpH5IKziLKgM0g0Qim8EMxmx6zqlEzlKJ3h7NkE8KwCBEMIUaMg5IDQ1rjFAUIXFNMLHN2+gQ8SkRcoHP/+p17gxWubzB6+yjdnJUWekfXr2qoqSbOcjgQ53GQ4GnD1wjWG5Qa1OeyNUZ5heZnvvP2QLEspywFaBwbFgOFgRAhwvGf5tX/4Bf6/f/o1OnFC11qef/aEL//hMvLXbJx4uH/tKHhcf14VwqNmSgjZs9/i9ibIqGGTSRLRKQWIPJCXOVIqmk5SjgqSVKHTlCTPKQcDJltbbO9ugYTlek3XdnRdB0i01hGZ0k/06FFeWV7Ea3vfyFlrzqVFIQikUFhneo1ej0iBGLXmoOmilk8qeb56pW8mz53mPq5jo3FY9LgVep3fuyb/ffn+jc62Ab5/r/jnfX6tiBM1f8Zi600XkWfbR8CFgBciQmlkoCk1dX2CtB1pkdC0SxCG9vQeyXCKHu2y9ALfnFL20akn995GY7h8aYQ0LVVZ4vE4EwhSIlSCSjKCzKC1LA5PuVSN6eolF/2YS9UG46rAi4zt7W22p0MujUdsjUqGw4zhIKPMFEkhUIVA5CkkMbJO5DEdpDtavudx9L5o9JSUpAGEC+RKE7oG0wZcYsm0Jg0NzjlG2tMFWC+WtOs66scc7C8saZ6Rpin5WNGsO9r2lIe3brM9Sgl+Rbkd7ctlOQAZyJOU1gayNCEvSprWI0VK2xnSLKUzAdsZuiRjZ2uTNLHMlzXT0ZC26djcHDOfLSjSlDIvuHN4iFaCPI2olrdvvMYzH/oI49EYEJRVSZrEJtAHT5ql1PWKz/3uv8BYh3MGVEFVDTg5PeV0ueSjH/4wN27cYHZ6yuWrV5FK0rUNzWpOWzekaYrU8YRUUqFl5AJ6ILEmhkh3hlTC7u6Izasfwa726G7fZODnJFqymB0z2NlBAtZFoLTzkAtQmWI5jxo9KRWGgkGRsNdZhHe4zuE7g8Ki0wSBQieSOgxYL0+xrWO5XFENJ7jWg8tA5kj1eKL3g1J17XntzX2yLGNnO+XB4YrdaYl1MwblDu/cn7O96dHSkRUWbxta61g3grZtGI7GKAnrdU2zXlMU0Ywk+5HAWYMnpOCLX/o8t955AxAxvkwN+MCHPsYTTz7NeDIGH2i7luPDA/Ye3sW0Det6iXcW7yFJM5TWeKLTNxiL77p+rXnGvOuxJ0kvH+jXQkiJSDXoFKQGIZHnMRWCYFvMck3XLuLfkZp8so3MS/zSnM0Ivs8nKAjO4FwNUiNVQn3wDt53IErCsiNJPMJ3vPHmqyRJyqWdXTYmU/Iso16vWM5TkjShyAv08gRVb/Chp6/x5e1LfOfuEUpAmhRY17GuW7pOM69vcnnzOfaPbmOsIE0HXLjwDPuHN1h0D0m1YPui4LXXlhSFwraSue/IsjNL1eN6f5SIx6GSGAtBKKS0vdkOZKKQqcULh9YpedGjeoSmrPLY4GXRDJgVGZONDTYu7mCDp63rc6yYUnFSZ/rc6TNzQ4QOa0BEp7jwMRvZush09TIOSIzpWXUCKSDY2FgFRMyKDSJClkOMBEXG4YqUEFzvwvUukijPNKu9ySR4913Nnjhr7s7Wrd71IOToxlXImJGLj0kZncUb12/rfIxUo8/ZEfIco+IAVySYRCJs2n8LnoBCSoE3a+pZi0xKBtWI7MIVvLHUq1N0onn22adwzYqjowckoy1Ez+GTHqwnXluCZrR7ma1EUhjYDAWXRpsUWYXWGVtbO2xOBlwcj9gdV4yGGWWZUJaKNJeQA5lCVBlSAyqAAzNvEfX7fKKHgNGoRHuHVSmuWdF2liQt8Z1hWBYcHM/ZHRW0taNd14zHJc1qTZ5obFCExtA5TzWSDDLF/p0jcmfIhWdv/4idyQhjHYmUtJ2lKBXrriGREiFj8HiaK5q2Ji9GBNegtSaIlOlEkYqaRGkuXtjk4f2HXL+2y/HRnKrI0MpjO8t8MWNYZkgp+Prv/0s++LFPcv0DLzAcjnpCeJxinFG6TVfT1DV4j/Mw3tqhrEouXLjIjW+/xUc/8hGuXbvG4eEBt269w+bGBt56dCLp2o4i0SRCYxxoGUCqfmyt6LwDB6vVms0LKdOtCwgV8DLlwsVn2Lv7TQ6PZkyu7OK8RSUZjkBnLCrEieDZbc7agExL0jygZEAqwca4IIQcJQPrumZcltiuJZOKcjigXp7gAWMDWbnJcnZEUBVIgX6U8/S43vcVONoPPPXUNfYOZyShY/LkLleGCUmaU+aWxfKINKsQck1TV9QrTZlPaJuWojiLwTOURR4bsT4dRsbdDwbL5377d/if/4f/jsl0hx/51F/l6vUXGUx3KIcDjk+OuHnzBnsP7nH/1nc4OjygXs2BQNs1mKYmhIBUmiwvsN5Rz2d428VpHmdplbL/tQMV11pIHakNInK0Ipk1NoXBezAttDWYJd518e8KgSClnSuSNIlu3H/D5xeTMkREUTRHfeMpIESMiXOBvf2HZElC1zbUdY1UijEjuq5DCEeiM+qioCgKunbNMMn5lZ//JV799gd5487L7C+XLJoWJRK6pmVYbuC9pSqGpIlBiDVF4bh8cRMnJHUz49argYUNqMThjYtSRP3eN4vH9WdbAc5XtN55PIIgI+tNqrhVEYkHDQFJkqUEQCc5SEmSJyitSIuMvMqZbG0y2ZzStisE8txBq7XuUy8i6PtswmaMjVp5GWPXpO8hxi6uSSNuBUJwj8wVPk7LrO04y7dFSWSiz/V1kccXG8UgZZzq+bNp3SO9XWTgWQJnX4tJVj7482cqT2QLCifiqdt/QdDz8Hw/cVQKCOdr3zM02Nnro6NX0Y438SIgncaHFcVwQNMQtbxdDfUaiWJ86QqzpsWGjmwy5ZmrFyi14d7eETKfYpJpzAxWCt8aMplgTEdoG9puxe7wEtOVY2eyRVEOUWnO9mST3fGQi5MhW+OKySCnqBTZQKNSIAUyhSwTRA5KS2zdYVcd2ovvWml/b70vGr3OOPKsxK5PwXUMhyVBrCnTuOsXMqGZL+mKCdZ4mlXDdDqmXh5STMdMBwUHB8ekIWc9WzAcZ6xmc6phRZIm2KYjTTK0TtGJpmk6rLO0XUuZDdAKurZjPBrQGodQEoKhyBNaoSgLRbAOXWRsDzLe+PZtnnv2Q7zx+i02tnbOid2zo2OmRYL3jtnhPl/54ud5+vkP4Jzr9QxRtwCB+XzOwcN7zI+P2R1nZHnOJ3/yp1BS8eRTT3Ljrbc4PDxkOp0wGg0Zj0ccHx8zHo1QKuC9JXgZhwupJkkdSscAZ6TCtgIp1oyHOSenDZvGkCnF0nQIKUi1Y65KgukIMkenAp1AZ9Z01pFaT5F5vKCPtHE0ApyXdM2KPnOGJNUxEkZqIAKpk0KRFgopCryzpCmkylATyFJBlz3Gq/zglCQESZXsotUBW6OKo9NDnrhymc6njMqao4Xn8nYGomA6/Aj3H36Zixc3ONjvSJKEVEdzVZYX5xM31QNYPYFf/7Vf57O/8Y/4a//J3+LKsx/l/t1bvHPjW9z53c9SL+Z0XU2zWmLaFm+72FiFs+8t3mDimkawClHDFM7BrY80OfG26YGonRNp3uuEemufdeBsXCU7g/AG4WuUDiRFgpZ5H+jen++LmpCO3oVJeaQt+r7TvdA8urEQOEvQcB4OT44ZVgXNes1yvWJne5s8zaKOMXQYZWOfGjzOO3Ib9UYffeaD/PDTz7G1McSUJff3jnn95lt8+9a3MSKwtWPZf7jmIz/0FCKZ8fy1TzEsL/Df/09/h+mFGelJQRc6Fk0EzQvzb0LGPK4/84rbRYRQCEVMbPIglUCmAas8QkGiNGhBXlbnjNQ0T0izhLzK2drZZrQ5oTUdIXiMsXjvUUrHX/dsV+cjd871XwcRmXl4ghR9oyaRQvaOWvpggbPGzPUNZDwHAjH6TKjIrrVtvAf2io3z1e2j/+KP7c4Ssc7xLr2mzp+tsx0iRO2edz3wuPdNISyJFvFeHnrTV4jvKaXstXlxohdkwApJnaesjCVLU/BgRUMxnCCVgGKDJJV0/hSReC5cucLaBKSqmA4mvPihZxDNCXffuIkIGZOnnuBwDVlDZNQm8Z6caUNjYCcZMz4RTDd2GG5uIlFsjcZcHA3YnVRsTyrGw4KiUJSVRhUCn3hEqpFFgihlZOAuW+g8/RWMIN97gPK+aPSc9yjXsnCetFSkaYLUmqAThO+QUlPlCpVlqGYRM1jzgq5ekexOmExHHBycIoOjXZ6Q6zFm3TK6uEuzWuI8dF5A3xh1FrxPaJZrdi5uo5Sk7Vy0X/sY7G06Q1kWrJaQSOiSjMFgiBKG2XyNQ3A6W/DEtet03sWwaBxSxLWPR6K14vatW7zwwoskaYbWul9ZBVbrJb/69/5HzGqJHOfgA1vbuxwcHDDd2ODypYvMTk/I8xQpJVVVceHCBYaDAXduvh61NDJlONDMLaRKEDNxYxST0JIsyThYNxTK0qwe0nnBcrZAmCMeHDZcunyNrY2MPBcsuz4PMKnQaXR5hQBZmpzDIx2SQoE3MdsPJFJrEm0QCqz3KNehXEB4QapzAitkP/bXIpBn4Mriz/V4e1x/9HLe0RlF271Nkk554tqSb78150sv3+JjH7yEdY4rFysubxbUi0vI7h3MsmE4GPHgYaTgRzzDGuccq9WacpDFqCIhmZ3O+IN/9Xv8zf/qv+VkuebX/sH/wu03v4ZZLyJ0RQbKKiNTAqkdbXA4ewZGPvtfv/o50+AQ9Ubfn3PnAUswCwgOodL+B20JtoMQo8qUCn1ub0qeZ2RZjpIKHwJdG7EOWW4wARZtgWnrSOEL4fu0eO9uAr/3z+ONa/9wRlPP6FqH94KH+wcMywF5FmMPkYLVuqFrLbpumIzH5FmGaRvSqkQlOcOu5cpGxo9V1zE/9ALNYMid+nUYvIoJ9zmZt9y//w1efilw91bNw/sNPvMMyoThWKGE4NLGLi89eJyB9n4pgUdIh+hZb4FASCRSC0QaolEwlYhEMp6UdMaTFzlJlqBSTTEo2drdYTAcxuSiENOgoh4v7SfGsmfcxamhC462Xcb7sixw3iBVEocKUoGT2D62LHjbN4gei8c4Gx2zRIOVkKLXyAnOfBtnDd27G0QAgsLZ2HR6Fx/Uzk0Vve4ueBDI+MATBCIIFD30+OwSgO+bS4FQgeDiNA8nenfvoxWwUZL1YMDRbB+MIbVbBNewOYrmiaOTOWQDrOkQKqcaa0I1wDWaS9Mhu9ub2IM9VL1ge3ub8sol9vaPeKoqsI3n2DScLmrsvEGmiic2L7K50GxsbLG5vY1CMB2MuDgZc2E0ZGc8YDzIGYwydBGziYMGlaeIQqPKFJ0I6uOG7mRFVsQNom87hHlv0cX7otETQpIqCU7QrRtalZJD3Ktbh0ORKyJyQEe9gTcdhSZm1dqWcpDF8GGpCFISXEdWFViZMRiPKIcFJggyJWgUmK7GtDUm+IhfCQKJ7LlUHet1zXA4RClHqi1dl5EpTaoF46piPptTJZonr+7y4PZddqYFq2GO0gnOxKiXg8NDrOlYLBaU7zqovbP8s9/8DV57+esUuj8ZhGB7e4eHD/e4/syzXLt+nVdeeYnxeEJVDQDfj9gtR/sPSFNJmmWoRCKtRKsO532MM/MdQkqqwYCrVzLmx563by7IkiOOT+Z0reHpp55k/3jB1mZBYx0EiVISpYnTS5niTCDLCoIzJEmCApply8nJmlEpUUmg7hzGAq2lsw4z9wyzlnULiXTUizVbIVL45/MZo2lJUO/lVnxc77dSUvLTf/kZ2voEJQTerlAKiqTk7v0DTmYzJqMxh8eOJy/WvPzNexjvyLIRi8USnZXIHrUQHa7xBqCkREnF3bu3ufDkcyzali9/7p/zzitfxNuaDzz9BL/8y/8pQli+8fJL3Lt3n6OTBcezBS6EiIJoHU1j4kTCvXtSBo8iyb5feaBFWANWIJRASUhySZLKyA4T4J2gqR1NvSZNLVmak2YJm5vbVIMJ33n7JtLUfPqTH6JME/aP5rRO89a33wIRpwbWxqZQyn5p7Dxt234PKBlWa4N3ftP3uAAAIABJREFUcSAflOSdO3d58tJlcq0xvqXtaqRKSZRiUOSsZqdQFSRpyvz4GGVahlcvsHX9GVSa0p4ecesrf8DgeMX2lb/E6Lnr/N1/+Hd4dXGHk6OaznUkA0Hbr7zaznP12oRLO4qX/l0cSI/rj1EBLz1KK5yJjDikiBO8IoAOQEKSa/JBgQ8SmUBaJOg0Ja9Kdi/tkmQJjWkIUmI6GzVy56vb73bPBmexbYtpa1SS4YMnSTRCSnwQvRPX4m10lHoRp3jOO6x3/z97bx6saXqW9/2e7V2/9ex9epnunp59NBptCISQhTBIYCkyjmPABmwnBhyHmCpcFVL5J1SqXC6nXHZlMUkIDgnYBmwMGCKDBJaQQOtImkWjGc10z3RP9+n17N/6Ls+SP57vnBExArkSyaOqvqtOdZ+Zc06fPv2833u/931dv2uRMhGB/IjFOnURQ3a0Ej4ymBxN66Lx4shUEZs4sRjPRcet5yhNJ3yZQzd4iXcuZvUSXr30hVikysS/W8wAdguDhoprWudodcJ8UHIwuombH6DSLnU9Y2OtYL53h50JyE4fZT1t65EucPLCSVqbct/rz5JIR7s94tzGCpNdy+G04dYXn4XgsWnK3LYslQPWTwx4bu8yyTxhOemwsrLM6ol1lJf0i5zN3oD1YYe1fsmgl1N0DWkpkUnA6wC5hkKj8gShJNPb+zBp0c5D7fDO4myNEK9xYLKU0fSslMJLE7UB3qOlJE81DZDnegFB8Kgko3VtjGbxgoODMVmaghNUronGBu/JE4O3FcvDEq0VwQuU0SSqRriWREskChcESWLwwpHmKVXtcUHhhCHRAqnB2YRgHRjDqTNrXH3lNkoKVlaH7N/corPRpV+WKC0gpBR5xstfeIrq27+L6XSCkJLlpWWsbfn4xz7Mz//vP0Ndzck7BVIImqpiZ3eb2sekiTQveOyxx9nd2WUymTIcDvHeMx6NsB4kFu9iZqD3EhV8tLMHgZICV0dwcneQ08kle9NAoTxV5ShKTd5NGJ5YRSiNknEa6ETUDjoB1jsyk8b5R+uI8YPx4gnSkmUZtnWIBQW9tQKdKlzdMhnVIHOUduzsjjltPV53qN0IZ0NcBd+tb4jSSrAx1GyLFWaTMVcva+btiGpa0O2v4cIeTz9/h82lBzm5McVogVQGLZdwfowWEnwTzQ7BHadhSCnweJ568vM888Tvc/reRxjffpmT6wO+5Q1v5Sd/4m+xstyhaj1vf/Ob0NLx+c98iie++CWe+MJLvHLjFoNhhjE9EPEGNh7PUVJR1zXzWUy1OX71F2LhApRoLZFSYIxAKY9WAaWi0LxuLdMJ2DY++Z/aXKPT7QAKgqPTLVlZ3kBqydve8l7e+NjDiGaPly+9wLQGky8zefsbuX7rJvv7e9zaO2TeKlaGHTbXVthcO0FA8Pzzz/Pcc88zns0BycbKEic21mhby53tfUbThvFsSqkVjZ0xPtxB6py87GCrjCwx2Lak2+vjrWV7Z59zZ8+C87R3bjC+epHu+ibJiQv8+r/8JS7/7P/Ai4nC5iXdpQG9zjIvXrpI1dYoLVAaqtmUO9O7ntvXSgUWOiwfo7wQApMoVA4mF9iFO1UlBreQ8GS5RiWGotthZW0VZRQOjwtx7akXGllr28WkzGPtl+FNnKNtZiRG44KGoKLbfBElJhZAY2UU1jp8OIo5c7gQo8TUIos9BHHMzHPOxwYPjw/RfCXCUVLG4o2YgnGk4RWC6JQPsam04VVm3kLHgFyoJALxZyHgOIYtEjUCyAAoRAg472MEmzY0wy77023a0QHeJCRaY2iQjWbkOvjCgAu4ukbiWT13jmx1g2HeRRU5yjfkqymXdnahNlx40+N0Z3O0SXFNgwsWKTSJVuhW074wYjhcYe3kGqL15FnO6qDL+qBgfdBhqZdTdg2mFIjE4bVAZhrRMcheivSeau+QUNnohZaS0LbI0BLC7LXf6GmtmMwt+DiGnk3H9Ls5o9GMctBjPKoYGkHdeJqmpkh7ODvFSoOrm0jiVg6dlJRZybSqYs5sUTCa7tEd9Klbi0kLmrYlM5q6aciSBKkV1jmy1GBtIO8k1NXseBydZQaTKnxlKMoMEeacObXBi5euIJWhyA3eOaajKWDxNkJghQjY+T6/8gs/xw/+8H/KvQ8/SjXZ4/DODX7uf/qHqGZOv8hYGnQY9EqquuHJT3+Mx7/lO9nZ2WZ9Y4MiL+me7TKdTtnZ20VISTdL2Nu5gzGRgn7n1i6v3J5xar1Ey5izG0XqLUXXYIxGywydJwjfcj5/iNFkRKc7oPWGgGQ+swiTkpuA0xKjM6xnoavSBJET2hlKS6RPCa2D0JJkCeDpdbvMmorgNNJILC1FlqO1QiuBayuESijLDGM0Jrm7uv1GKecDbZuhkj6Hs20Igek84eFz58myEY8//ChXr71EOx/TCsczL8xZ7fbplkOcvQTEBw/btkgRjlcxSgguX7nKB3/zV3jj42/mna+/h28/9yNcv/oCP/yjf50kybl69QqTw0NObJxGhJZ3fMd38+a3fRt//uZ1/vCTn+PqnT2eePJ5bt66BQSSRJFknrxM6Q9MpPsLgdIiPvELgRCeGFbhjycIQsRV1HwumEyjWSoogVbRvdgfrJJnCbduXeXmrR12D1sevf8873nnWzi8c4UbO2O2diquXr3K7t4OrXUUeYf7z9/PO9/2Z1he3+T82TNcON+hU/ZIsgwXBHs7t7h9a4ZSitW1PllakBR9mtpxcLDPlz7/FJP9fSYHu7zUVFy6eIm87DAcDinSjGk2YjwZ0y27dIuS/d19eonDByg2z3Lr4gtsvfIU737fe/n45Qd44Xc/jFKKWzevI6Rnab1gtKcIwpKUgYO9lvn0rhnjtVICsI2lcZEFZzIFxoOC1oJJDUGCMRlBStIiI8lT8k6f5ZUlvHQEqaOLVYDRmraxtK39I/F7Me4sTtqsbUAGGqtJTIZUYpEZaxGAkNEFbBcxY62zC51cbMakUnFg7mMKBV7gbdTzHSViSCUXjSAELwhB4lqHbQMIjziaxC9MF5JXPz8iZgTBxa9/xOBDRL1uCAFPQKhFLBoiaveCxKrYjFIUtN0Oc1poHZiULO/hZxMyI7hzex+rO+hERXSSUJhUU01nXNs6oFxR9FzggZWctN9j5jQ3d27xwT/4PAjBeDynU5RkecJkNGYgNcN9TW+4zPrJDYQTFCZlfanHqeUB68Meg0FBWRpMASoFpzyy1ISOwXQTTBKY7U9JypR2v0FIhZQB19S4UINq/p0twZfXa6LRy7KEUw/cy87hDKoZI2fxwpB1B6SqZn9U45eG1JMD5ocj8rWc6XhMZ7hMmaXsH46gheDn5IMhtw9uU3RKjPS0sxnF2VNYqdFCUFcNvU5O4wLCyMjwaxqUVovDH0fNeZ7RNI5OkUZbttLc//AjXHvlBabTinlV0UlylKtJy5IWRdDZYsQcD2iR59x66Ut8+tf/CQdP38v9j7yO/sYGWcwaQ2tJp8hQKt78bl+9RPOmt3PjxnU63R55ljCd1gipUMqQaEk12WO8fwsVoG0F127v84Hf+ihveetjVLVjbbXAu8Bzz1/loQcvsLI6oJNahMhI8hw8aC1xrmI6d4SQYYKl7HVomhqt05haIAJKQlNZ8jLQBHBti1TRPdVYh/ESIx2NsCgVsAtHlvOWPAlkWUaRpQidYKcVtmlxuYjJCXfrG6KETLi1f5X1pfuZzQ84uTZkrf8oN/ZqHhyc5MWXn+Jwv+bW3gHjKmFjs0AzpHWCICTaGIxyKClIjI6rJxlvKvu7O8zHU/7aD34fD967zHhP8dZveRxhHU07w9aBj37s01zdusbSUp/hcIl6NuUzn3uC5y9e4c7uNg899BDXb95kPJoDMf/aGNBaIGVAqYC2sWkTUkb0g5Bxgu2hbT11banqyNhTKk760iTFGMVkcsCVl2tmswolDesbK6yu9rn3RMEXnvwMH//MF3jm+S9SW0dedLj3zDne9z3v413f+V2cOHkaGWqCb5CiBjsjeE8936OuA25WsbnSo9srONzfom4kZXYSRUWxlLD8jkfJOkOcMxze2uPm7ZtcvvQySgiMCFG+0uvjWssrX/oCH/+957lx5hTrSyXXL36J9Ydfz/q99/P0h36H+zdO8Q/+i/+GX/7Ib/B7B4eYRKIM5AOHDJ6mTSjzlHp6l5j8WqkQIpZDKFCpwWlLUAGtNCrRtCFgVEIQgTQ3SKXJs5LBoMSGGq0SkBIZJMbIRbxZlBUd6eLiOlVibYu1NdY1BC9JTLEAGwe8j+tRKdUR0WQBQo6Z7SBQSiKkxnsXk5/c0Tp2ATgOfNma1h83et7HTNoQXnXCSikWLuAoKQpBHDdxzjmEV8gFE+J4/rwY3gsRJViEqOP1RzpeH2g0NKmiURASSWgkKitBGQQaldUUhWH/cI4wIBeTVOcdWdFnPqsocnB1RS8t+fSTz2Fd4HB/hPcuIs60QiI52DnAtjOWuwM6laJTDji1eQKJJBWatV6P00t91ocdBv2CbkdjyoDqSrwMkKbQS0i7BoVkfH0PCHGw1QSSFLytcH6C1A4h46TyK9VrotEDQd4ZcnbYZzaeMJ+MsU2NFgahNUZFurwxhhAkQihs1dIbGkwnR00nBBcwRYfWCQ73D+h1SqqmphpX6LKgbkFIQ2MtHk1txywtL5NoyeHE0u0YQjA0bUAr0GXOpAZjdBRZC0lephSdHta2LPX7DHodZk3LufPnaYRBdE9EppdtaZoKxmNGdcAoRa4C2nsymaKFQmlNmmYURR7Pofe01Zy2mjGbzrl+fYsL954nzwvqpmHQ66Kl5/c//ls0dUWnU0BjESIiJvI00C5SMYyyeO84mI7JexnTacX2zhbdXp/dgylGWO67Z4mnX7hNv5fjmpYz58/SVhahUoqiYLi0zOn1LkZJbBCYROCaOJmxIdDMW0w3PlVl3RTvJLad4JynbQVp7gjOorUiSSVBKppmzmzsSPVXzuS7W6+t0srh3Ix+9wS7+4HU7PNNrzvD5PIXeXHrc1y7tsug0+f02j1c3tri3EnBqD1EyRyjk4hnUIE8y3HVlIX4jZ07V1ju5/zUf/mfszxIeOqJz/At3/o2ZGiYTVuUyVhdHfD93/cfc3h4wCuvXOOFS5d58fI1DitPf3UNlXeY1I777nuI61tbTKdTZtM5VesBH52JUqJ0INGBLIsuvGkVOWR2AefXSiITgRKxQcTDfN4wOoja0qIMnD17itW1eO3v7t7kgx+5ws07u1Q28rkeOnuGH/+xH+E97/0e+r1lPJpqdBvpDhdWQJhOp9TzGuc0QiYMlk8wn01RpmR585H4YDXZi1GOJqHo9qjGFpWknHjkMU4+/hbe8u747xIICywMBFfTTCdcffZZPvO7H+DlOyMGqxt89vc/zImzFzjxukf57Cc/xeb2VRi9Qlkm7G4fcu78KbpFycHoDk07JcsF2miimeVu/YeuAAgT8WuYFpOCNBKtDI5AkqRIacgKg8lSsqKk0+vhcRijkUqjVIIA5tX8WCcX32JTENeqcWLnfEPTtGRpifeeal6hlYoYlUXTZq1dPBApAhLlNU3bxngyv4Ado6Kxw7/qxD3GqvgF1NhHp6w/5uPFqSNEY4V3CxzS8aQurnrjT+XLHLkLaUZcEUdX/dGUz3uPROK0grUBSrQwr7A24HyDCwGVFSR5h3Y2pddZYzZ3qKwbwc++QviYGBV0Qjrs4nxLNdnn2afvgI9/H5mkJGlJVuQE5zi8fQ3ftnSzLmtVyjDrs37qZKR+IBh0c86sDzi13KffTemUEpULdGmwqsEMM1Rp0HlC8J7xtX1CFVAiEGqLNAbbjPF+hpA1QkYDmDSv8axbECRZiTaKTqdPrzvk0osXCbMZXipSXWN9QGEjHTrRiGAxWRafKYRByMi+SlJJ28zJlwZok6GMJE0UdQiYLMHXHqEEo3HF6rKKok+v0FIhkiIKKV2g2+swqStSo2ibGklCIFBmBW02YXV1yFq/y2RUcc89J7mxZ0nSOHQWIZB4h0pKzMEh0wYO9keMp2PC7j6v3Ngi7/VZPXGSpc01XFOTqjFOakbjMefvjziV7Z09kiTiKZrguHPrKpPDHZrWk6Sa8bTFIrEIklQj6ohw0YkgSEFemDiKbxqsh9lsjFGaIi+prKeaO5ScUNdzNtycNjh2bx5QN5b77rOcXOsgE0PwnqZxIDSNnRPwHB7aOC7WnrSNfKXpNGC0pGotejrBuZJZ3RIsGJ3g2wbdKVHhbqP3jVIxWB22d59hbbVE6QnXdy6xtqL44uUDTqxn+EZQdiTdMmN3PGKYLSFkzDZOFrqfskgpkyW2d25ghGV7+w6r3ZTv/s63cfPGLt/xnnczHd1hOosMucI5kqKLzgxCDGgX0osHH3qYj3/yk/zWb38A51uWl9bY3DzDhQvnGU8n7OzssH1rm92dXWbzOV5EB7h1gixNY1B7Gm9itfNICamJ7NW6guks4C3HGp/T92zw2GOPMJkc8OKLz9E0lnldR/2UFCwN+/zA+97Dj/7oX2XjxBlCPaGZHxBUh7y/hJ0LRtu3uPjCU1gHg+Ea/cEySZpyuLdDf+UEKstx1QQnFDLvE+qaIAp2t7YZnrmPfLgCQnwZwJlXk0WAoAymU3Lhm97A2Ucf4Knf/W0+/5EP06Qlv/vxP+Sx8xd49HVv4FO7F/nk1mWaah0XKi5fe47lpRNsLN/LLX+dICze18DsP8hZu1t/tISAJNOQWFQq0akCpfB4Up2gtAGhSdKEtMgpel1UFj9OSkNiYpNXNw3Bh4U2Nt7yvQdr7UI7545NElqZ48br6LwppXCuwbZxaqeEoPEW71pca6MZQgQEEdgfTQ+vTvOOTBjOeZw9cs3GiWX8VQJ2Me0LRImeRhBwWI7ttE5Fkl5YZNXyZVPJeEVE9JIQBKcJHqwOpJsDDqnZv3oZ2VtlPmkQSUy4kouJXSfV9PsZt8eekDhoBYIKQUPZjdg1s9hE2caDlOi8QAkZHdGzhtDMsT6CWwqdse4LhrrD8olNUp0ghKTME06vLrG5NqBXxsQLXQpUz2CVJxQG3cvpDLp455jNp+hE4UNE5tTTFtmO8KIG6Y9/ZlJpkK/xRk8IQV50QHikUpRLA+5/LOPak59mVjUkOiCzAqpRdOupyL2qmxYZJKlJcLbCtw2OOOlKsxTbtqwt9SIjC0g0OB/TKbSKbh4hHNY2SJGhpWLetDgXaNuAlBopPXV9dKQkZbfPaDJhOBww7Ge4oOgNeuzNpoA6fmIhBJSwDPs9RrM5o7qHrWqe/uIX6fc6LK9tsLyyxmBpSDWfo4oeCMHW5Rd47NHHODg8ZNjvo5VESIF3jju3b5Flmt39RRCzswgnGA56ZGVO2rS0rmI6N5RlQVu3TO2YvMgXWb0tzjqKLKaP5LkhuIqm8QgvaauGNEuRWpKkhuAlSkiCr5nXgjQH0Woy5WloGI8nZMvFYk2tadoMLQQzW6OlRytJb9jHK0VQirqyBC+o3V32/jdKpUnCWv8B5geWhzcf4GB2nXo64eEzp/nMFwzzkeGeVU3T7KHMjN3dlnMPlzRtQMk4DZAqUJgGaxJm1YzQNrSu4fmXt3j4YsHSyiZ/+8d/hF6/4P1/7v3ce+E+bu/ucvGJZ9kfTcjzlNWVITpJyDPDd3372zm1uc7/8U9/kSuvXOLW7SsM+ktsbp7mnlObbJ5Yp65m7Gzv8srWJYwSzKsWqRKyLKefFcyrlroZcXg4YjJ22EbEplCDSiSdrMO958+ystJnd+cao9EheZbQ63W4tX2AEJ4Hz53lv/upH+ctb3p9REi0NV72aKwiNDUH+/tcfu6z7O7sM692WF9Zpm0OufjCTe45/whrm2doqxmj2wcYbTDdPiJIVD7klWe+yMa9D5EPV445Yv9uBQIOfINopmAbVDvj8be+kXY+4yMf/gNu6oKLT32Kt97Z4Vu/+U18xnapneDc6fNs71/n9u1bbF2+RbffpXZz0uQuQ+81UwJCGhCJRKcapEYbE8+oSRDSoLUiLTM6/ZK8m5EWWeSkmgSQVFVN27ZftiqNzUrbWpomvg4bo3EuxlJqo/EuauHSND1uJCA2idZabHB4F6PG5CLpSQiJt5HPB+K4wYtfS7zKmVw0eNZGB3Gc/C2Ye/5V8HJMvggxTMD541g0EY5Ax69WjD1bbG+DX0wfIwC9c2qVnXYcv6+0Q0DiZ2OYC1ZWlhgOlqilpKkc26NDbGtxk318fYhvHMvnHkGqBEEMNVBJilYSZxsSLbFNi20rfD1ndHCIURlnzp+j2HOUh5aVzZPkRQchBJ3UcGp5yKm1Zfrdgk6uMB2J7Eu8tpBLdD8h6+Q4axnvHWCnNSI4dNfgJzXStTg5B6L+OESHGUGIBdXgj6/XRqMnFVVTx5QL5yi6GbMqobN2kttfusjGsE/bTqmbijJLEQiMSZmNp+TdHLQhkS6uZOsGoyHNEhrnOLE+pHUBrRRaa0yIlH4p5CLf0WNUjI3xOFwTQ47btkKZHGwTBaYOhHCkRYrWKcvLq6wMDaMmZV7FMToIvFfHCBUlQAl45c42pzfWKco+Lz/9KTZPbrK6foJet0+SaAgWYyRNPef0MGV86VMUa+eZ7XTpd3vM2oZrW1fwTUNXBZQUNM2CJeYqHrhwluUiYz72ZEVJ3inJ0hJrp2R5glAl/qCiDYJ54xgISd3UmCyNRPFZy8GoIvgWF1K8F3Rzg1ABpSRBKJLUx4eq1qKVZ9J4sqLDuILecoZkSpJmaAlyGrMSO3mK6/Yifd0EkkwisEh/t9H7RiktofaH7M4PONUzmOwUh7Nr3DncJksig2taWYwGZx1GGfBldIKbhLZpqNsJ6yfPsrO9ja2mTKYj6mrGaHLIU89f5a/94Dt49PE383P/7Be4vjviwoUHCU3N4697lDwt+dxTz/Li1S2c82Tas7LUZWP9JH/pe/8CH/v47/G5Z57jyuWLXLx4mc3NNU5snqRT9lhfX2b/4CZSCNLMY+s501mz4ILFSX3TeKQm6kalIUsTOp0CowK3bl1lNEoJPnA4rqjmDZ2eYHPjBMPBgP/qb/0Yb3rDw7RVRWftHEFkhKYh8zUvv/wi/+aDH0HjOHP6BIPeKv3lk0zGU06cvUB39QQf//inObkaU3NW1jeQ+/s4mbB9/UW6SZ/B6TPRncgf1+i92uTRzmKCxwIoS4A3vPUx7tx4ha3Dm/yr2zNe/tJTfOj681Spp1OkjKa79DpLtC3MxgckmWG9XGfrzktf7yN2t75CCQkyEZg8RyuN0AIvW/K8xHmBTAxZkZF3c1QagcpBQJIkeOep64qmsceO3SN+XWz6/DFWpW0t1tWRU7dgUUa2njx2zHq/0OUFoi4vxBWtEDE5wzl/vD6Nb/LYPXvU7HnvcT4aQaQ6YtktsClHEz4WusBFxFlw8WOEkHgv8Is4tHhFLJI3jvDHIcTwgoWD1+ea2lfUs0PSIqXdvUO6cpKVfo8yDewcbDOpapydMdu7jbczZDOn1+siyw6dC69D9zciH9YHJCLGj05mKCGZzg5xriFNc7JcMx3XZHmOOqjI9wPLG+t0ux1EkHS14WS/x+nlJdb6HbqlIs0VohTIQtFKR7FUUA4KhITR/ohqPMVkCVpJ6t0Z7NeIEHN3hYgRb1Ia0AprLfqPfZ2I9dpo9EQUcwZatMkIwVNkBU03Q5mUcmWZyfYdpvOWpj9gurdP0umT0jI6HJNm3RgLox3jRf5snpeMR1O6g2UaD0EIrHMkWlPNq0iBD9A4jzYai8QgaF20RPsgyLKUEGYgozMpBIcxKTrJGS4v01/uoyt3HOAchMAYE+3ruIXOYY4NnhvjMR/+/FNcvHGD02fvZWl5CWOS4yeYLE2QruGhkydJtOPg4icYT6bcFJJx9zQyzSg7Jd6DTgx169C6Zl55stTgvMQIwTu+9WFu3hzx4P2r3NkdMeimJEayd2advBxwa3uP0syYTgKdJYe1NfnNnERIgtB0y4xpZUkSvVhhCaSQ1K2j1zNMpCDrDdH1FKHiz+/m7QM2lgSOFIkj0RInPK2tGC4tMRpNSTtdktSAFjTzu43eN0rZtqbbURyMDIYN0Boz6HBz5xqTiWR9JSBlynwSODwMNK7iYD8aMdq6RqjAZ579PM7NuP+BB2msZTzewdqWrCiZ2JTR4Zj3/dlvZWvrFT791DM4Dw8++DC/9cHf5i2Pv573vufP8IUXt/jEpz/LdLbPxctbjGYtW1sXWe0ZHn7gIZ589nlGh1PGL9zg6tU7bG4sMVhaiswvBEop2hBzPSsxZ3PzDFlWEsQOSEWn7EEQVPND2nrKeF7hnOT2nQlCBpJEsr62zoMPPsLy0gqdTPH2d76DUB3SkDCZO+rZHrYZ88JzX+Sf/9r/zfqJUzx875loRtIddGeds6f7jA/H/NzP/yI7O9v81b/4XqQpaUjxzqKFQLeas2//5j+hyYsrquBbhK3iC1lWIJw7NlIJGfiO7/0e/u3zT9NYsFJQWcdqWVK1Yw7GU9Kswz2n76Pf3ef8+fMI51Aarnz+ma/zKbtbf2wJQZLFFa3UEU2SpBlN06KSlLJTkvdKVKLQqUbphDRJo8a0rrGtxcNx7GA0M7THUVlaC7w/mr5ZtI5bnBAEhLgFO9bw2QZC5O8FFxsvvoyLZ63Ftk101NpA21qkVMdr4aOpolgw73zwi0SLheZusY71Lubieh8WOrzoylVSRoA60ZhxlHcbQogO3LBItFnEoHnvUXmHg8mIQgsuf+FThNaydHJAqlP2RnPG4zGpC8wObtCMbrF+8jRnHn8nTbqMJWXnxhZhcogxirpqwDqEs8jgsa1FSLBNQ5jXFN0O5++/H38wody19HtrDJeWkCKQI1juFpxaWWJ9qUuvm2ByTygFspfgEsh7OZ1hD6SnaWvsbI7SiiwvmG80ZgazAAAgAElEQVTtwGiCCC1e2IhRkbERBwguoNAQvnI796c2ekKIDPgYkC4+/ldDCP+tEOIc8MvAMvA54IdCCI0QIgV+AXgTsAt8Xwjhyp/0Z3jvCbbFazDK4FzAmIRuv0NiDK6tUHLB31GC6eEhSydO0pEwrX3EfeiIU7lx62XSPMULhR0fgLkPZzUBT9MqhAy03pNnGiU1s6YlNQkh6Jic4Vq00dQukBuNVoKplUgsrfME45BK0e126PYLlGqpbYySCVIu5ATx8aSta+q6QWvNy1evsXVrm5XVNQbDZfI8QoPdInJJa0WZpiQy4KqKPO+gpOL6aIYVAmktBE9IDEZbgnNoJRmPZuS5xjpPnhsef+t3oz75r7lzMKLsFrTOUfYKktrSGayyoVJ6iQPmzGaW4C2+iY7E2XxGosBmGVmiiT7EuDrO0pQQNHnhOHX6HMvriulkSp5pisEqXV3R+JhbmObbzGZztBGoNMVPJgQfqGuHUjX2K4Js79a/T31drk0k/bLPQenYunOds6dOMJoPqJqX2VhdodvpMp16ikLQzysaPyfPh8yqGp2keCTPv3yFO1de4OGHHsRLcXxzKIqSVhieePo5Hrv/HD/w59/DwWjCleuvcOXKFe698Ai/9aEP8dQXnuEHf+CH6edv4fc/8VnWV9d45epLzOcTLh40KC144+OPceXqNW7dvE7VWC5euo2Ut8kKQaeTkGUFrXXYtsWLwPUb19AmYzhYIQRJlucQPG19SBsCZdkFBIxrijLhzOlzLA2X6Xb6aKUYDpeQtkEmJa5pufTiJT79uc/ymSee4tb2Hqsry6wMS7aub7F5+iz3PXwv/dLwzBMf4Vd+7Td59qXb/PW/8kOMJzXn792grmdkRc71F6/w2NvfhTLqjzR5YZG/y3G0m0c0c0JrEVrF1UFw4B0ugEhKcqX5O3/jb/L0S1d46vplEm0oy2W0SciyGXd2brG7u09ZdsiSmtbus3b6rj7vq6mvx7UnpUBKT5oqaldj0izSDnRC2e+QdQ0m1VG/ZxRlUWCto2ljMxcWjZG1EZIcNXnueJVqj+L+8JG28GUAZEJAKomQjkBcxwoREyvkQgvmfbwn13VN2zZxsmcD3slFExlXiU3TxEQoOHbyxjP8qgP3aOIYJ3evRp9FI4hf4FWOGsMjFy4LjaElEHmuPoQFINmicdG8qTW2UghlSHEc3r6C6a8xWB2g0y4haLpLJ1m9/3Uc+JR6b4Kt9/FVS9BxXphITW0rpFJY2+KcjRGFRcH41i3KMkfuj+iMYJiXrK4vo9AkQTPsFGyu9FhdLukPM0whkB1DKCQuC+TDgrLbQUrBvKoig69ySGWobuziDscga6KOMTIBOWp7Q9RESqmRwnzFs/TVTPRq4F0hhIkQwgB/KIT4beAngX8UQvhlIcT/CvxnwP+y+HU/hHBBCPH9wN8Hvu9P+gMmkymXLl2m1++wvCoxiSZNUlTQ5MkiSFkIAhIjPEoItASRZEhjkdJipERoha1r+t0O+BYhodMvaKZglERqj5aGg4OapU6GMgnM6xgnIwHhsM6iTRKFsAstoA0KIdqY6+dirFPZMRAkWsO4PlJFx6QOscjTiwR8UMagjaHT7bG0tkZaFAvUQ7x4pBIYrdhMS+bjEYJAXVUIaZiSkmmNkCzybVmMy0EbQ9XUDAdRrJ7lhiAkZx94hL3PPR1fFETMwivyDiFAohVKQ5IMQDZkBkyyg0PgEHhbY70iKQZkRQ8vDAGL85JUgLdzjOliCkXdNqAzcIFaaLrdLmV/jZ2kw9l+n/n8gLpy6HIFJSXnfYIMjmY6+iqO3d36Kuprfm1KbRjNr9PrDLHugMzcQScPsLuzxNmTZ9kfbzOe7DKr5rS1ISs9K0sr2MWL9fbONgejfbrDJWwzRylNp+yg+jnGZFhreWlrlze/8fWMZzPe+c1v4EN/0NC2Nbu7u3zbt72TD3zgX/P3/+Hf46/8J3+Z1z9yP1e2bmCMwOgEpRTz+ZzDg22+6U1v4EsvZNy4eYPpZI61UWs7ndYkScyprZoW39oIe53NISiU1Fg7Yz6fMp7MUFJRak1Z9jl5akC30yXLMgge51rmlWc6Nti2wdmWj3z0E3zww3/Ik8++iBeCB+89w/jgGpe+NOJtb36Mtzy6SaIn/Pqv/ib/9F99gEkTuP/sPZzdKJnPZ8xGB7jg2LlzCykSsjxAPYr6G+LkxLuG4Cy4uFlY3OUQUiLs4oXfWVzbEqxFpQky6bB57kF+6od+hL/5D/5ufL1xmrWNE1TVhBACh+M7XLt2lZMnzvKxT15hZf0rr3/u1h+pr/m1FwgkWYrzljTLaL2IpIZeTlqkmDRDGIVODGVR4pynaRqsc8drUBaA46qqjrdHR42eWNyrCALvwHm70LrFzwuL+513R5q3V/WbzlnaNkaNeesg6EXubfz/3juc8xGqLzieKMbsW388KQwLCDOLsx5xJou4DgRSupi1GwQyBIL3+NCCX3yDBHxw+LCQFhFovMe7mmpnTi1mMCzpLK2RpwYjBevn7uPOzRvorIcqIJ1LOkun2Js2sakez7A+PkD5to0gaKWQSsUpuxBIJannMzq9DicuPISqZ6QTWDZDhpsb6FSh0fSTghPLS5xYHjAclGSlQmcSUknoaZJ+RtEvIzPRWdq6xU1rpNeIWUM73gPmSKniojqw+PeKWxNkTPuIhtT/Dxq9EFvoyeJds3gLwLuAv7z47/8X8NOLA/3+xe8BfhX4n4UQIvwJND/vPVe3ruNfqREukA2XGBQZ/X4X3x1QV2OESggqA1yM+JnN0Zg4Pg4CxYKC3c7JyxWCVGRpEoXMwdG6wMsXr3D+9CZtWyNkEhsYZwEXydttg0ctApUN+JbaOpyDtqpx1oOIJPBBt8N4HjMABRGx4DgyCAmkVCidUPb6SClJEk23N6A3WEZrE88xcfStlUIrTSIc27e3WRoMyJKCS3v7HFg4k8a0EIKltZCqqJE4PJgyOpgyHOakU4lH8fLzn+WpZ1/C+5qyzMjKBESCNpamtTjbYlWDaFNm0xmdpQ51Y0EplBLM6xadKdIkvo93OC1QOiEEkLok0YtmMDXkWYZtW/CW8eiA/vpZyn4XjyQvuozHByRZiW9mFL0eiRKM1WtCMfANX1+Pa9M5S7fY4GC8z+VrB6RCUXYEnXzI1u3nObGRcuX6AZ00wUvDYM3jQ4dqNmG5V3Lzxha5MTSzObvXb/Gu73w3r1x5Ca8THJLpZMze6IDf/J0Pc/rEMrd39nn9I4/wwkuX8c2EjZX7OHnyDFdvXOEf/9zP8Bfe+70M+13WV1exzZy6bSNuwjlu3b6JELB5YpPxeMJsNmF0OCfLFMtLK9y+fT2apLQiBIlWKdPZIVJYqspStwLnIUug1x9w9uwFTJrgneXq1StMJ4csr6xz5vRZ6sby8kuX+MhH/4CPPvEkt3ZGzOo5Umqe+eIzrA9yfvA/+g7e8e3vZDpt+Jmf/d/4jQ99FC8Np0+c5J1veZjxzk06q/eyd7CP947nvvACjz7wAHsvPhVv1FIjREQa+cXKLXiBTmL+tZQKKWKUnFAaqTVCSyTx4RKj0MWQP/ve9/FjL7zA//gb/4xrW1fIC02SBoyZIZMJwzXN7ugOdW3Z27nb6H019fW49qSUSJ2gtWZeW7JuRpIKktSQZinaaJIsoyhLmiaeD7doeGIubJyANU1zPA070sq9ilkJx6w4AgShYsPkLHiwrQc0BLlAnkSNe2zkjmDLDoKKEybh8NKBkzS1XaRdRPvEUcPnxQJeftRQhhjv5l18X+nYDBLsookU9Iaaw+1ZDLkIPsai4Qle4IOM+bje4bGwQMVk7ZiT832S/Tm94To7jFHZJpP5jIODirwnCNOWldP30RkMSIuSmzd2qCYzhJaoNNDMJ/gkISsKqumUPM/RIuJjDnf3MFIwXO8g9mAoeiytrFDkHbRXdLOE9WGHE0s9Vvpdup0EnQpEqghdieoauv1ezIIPjqaZU08qmDlEE3DVBEETm7wgWPhLkMRrPwhAiIiHkgI7+8rT+K/qjiuEUMQx9AXgHwMvAQchBLv4kC3g5OL3J4FrACEEK4Q4JI6xd/5fX/NHgR8FyLOUpq3IjALp2d8/wM4UN3b2Wep1qFuLdYImGNomMKo1gxS8gzZIci3j5A2JDJYkzVCipeh2aBeRJ91uweqKBwlKyahBCILWOrQGFwSujeLmRCW0QmOMRIQcbwXaCJyVNNUcFkJWZwMyTUHW8WByFMOiMElKf9Bf5AoGdGLo9pbo9vrHo28A17T4EGi9Yz9LCaTQeG5s32TqBaQFUmk0AimgsQ2KgJCS0dhyMJpwVqyzN56z0ulx7cYeUuUsL6+RlRntbITWka9n2xpjJFlW4IIhMKO1gqyIHLx6PqdpAlLXEeqMxLsKRIZaGDOcs+SZRrQSLc3CnSWxtaWqKkaHI5Q0ZGmGUYb9tCHLNLWdkWcGbSSz+VceMd+tf7/6Wl+bwyXD9t6M4UCxsTxgVO8yniqu3Hyefq9gMMiwreDmbcvZew2NnaNFwrvf/gaUeiMf+vDvs7P1LIe3b/KX/uL30+31OL2+wcF4zBdffJHt6YTJdMJzB7tIPJsnNnj+xZd4/NGHGY9G9IuU8+fPs7e3zYtXrvN//otf4tve8laSvKQsOvST7Oi7pqpqVlY28M6zsnyC+bzi6rWX2Nsdsbt3GHM4haS2ASMlwdbYpqFpBS5ITKIo04QsSZjNJ7RtTVVPub51laqeo6SmaVqatuLJZ6/x2SefZlZPMEazvT/De4nRnpMbZ/jJv/F+3v7Nb+WZL7zIz//Sv+CpL30JlXQY9pbolANWlteZTnYIacGLl0aUZYfnnv8Ss8mEVy49Hx3LSiOEpG1q6noOCPK8w+rKClmZE9dpCqMMSZbRGQzo9PuURY7QBoRBKEUyWONv/53/mos3b/KxJ5+g2rnKuKwRpORJTr9vqBuHEvJYv3W3/vT6Wl97/e4AKTSzSUPSTUk6Gq0UJktJsoykyEmznKqqFiy8RfKEWAQBLFa1R73kEbD4qME71u35GIcmRRrP28KBa51dTP4kwQecdfjg4v1LeJxrokEixPQMoReaMR9om2aRYsFxg7lwcCyiyBbTQx8WfD8BIqBkIDgHwdO4FuEVSnv+3Pvfxr/8xX8LFmwdF5fBERl9Ibp3W+kILkoX1uZ7PLp7kU47BRFww4yDe07zxMWLHKoBndUVRN2ytHkB0xsyG4+5dPEFTp45x733n+X61etxiojCtoucbh9o5zVIgcxT1s6fJyHgDyo2Qo+lwZCi10EJSao1g07JidUeK8sl3UGKKgUiV4SOxCwldAeD45W2JdBWNdQWYcHORwg7Qaq4opVKsfhhgpdxpU1AKY2QCiYzPvFrv/MVz+pX1eiFKBB5XAgxAH4dePCr+bw/5Wv+LPCzAKsrS6FXZjRtg5GSxEvSNGcyGhPIadvA9u4+G8tLbO9PMElKIxP2K8/EGkY+0OkWzA+mXB15Bk6TWUm/u4S1nqYNBG/plyVaC0SQOCEjYw4W/CBJ27qFqLRFpSU+eLRUtG2LDhajDaPpFKk0rQ8UWUrrJUppZHzUWLiHAibN6Q0lRXcQA9cFJElKCIsp9QKZIIQkzXKEgFuHB0ymM072Bxy4QJIkEScjJDo1FLlkPHZkadQ+qTRBa4V1Gi8CWudIXTJrxuR1RcCRKEnVWBIjETpFyzjBFCql7C6TdjKM1kxbFzEvWmOylICI/J5KxrWxlQtcRiBJDLO5xSQGpIzZuiIQhMLahr2DXVZXlikGOUbr49G/VgqNIDV3s27//6qv9bV5732dkJiAUjWDvuBgrEjz53AEJrOWPAeloOx1ubM9wxJ49+PnWFnpcfv2LuvLXb75jY/x8KnvZmV5iXo+RXhLJ9O87r7zVNWEaj6lFeCF4uK129zZO2R7d5/3v/tddDols9ryh5/4JMuDkqIo+NTTT3L25Ckeefhxrl2/xbxpyIuC5eUV6ro+XmGlqWG4tIwQgdmsQWtDked4KcC1TCYtWpcMhwVFUWISQ1XNads5rQu89NIL0cyxiG1yPrBzMMEkW7Rty97BnKp2BFETnMekigtn7+GnfuLHuHBqmX/y8/+cf/OxTzCZzdE6iToeIXFBIoJC6IKXX36JTzz9CvecOcvNm9cxwrN1XaC0Ri4SRIL3ET0RiPmY+7v0yoUUI8tJtGEwHHByMekhTxGLh1FUZK911k7y9/7uf89P//RPcK7zSX77BcXWriPNJM7CbNayvO6ZTSVHMKm79SfX1/raO7VxKhyOZ/QHPXRG5NWpCELO8oI0z6Omy7kFePiIlff/sPeeMZql6Xne9YYTv1i5OkyHmemdnbB5l0mkSC4TZNIQSQWIPwwKlCMgw4Qg0xIMUqJBGCYpwzAMw4YhAbIs2JZowpZMkWDQrpYUdzZzw+SZnZ6ZzpW+/J3wJv94T1XPkjviyiSXs0A/v3qqqrvmq++cOs/7PPd93ZLWGKxtOfWnvnmCF92vDusdwfvYYOkE5+icrtEYoaXCeol3Dutb4qwwpmjEpi10ubkBITQyxCbRmnAmX/IdLNx1WbkhEE0YNuZdO2JSR5z4xdWsROBdQAcJItC2junJmo2tgqObcwQCH1Q0YERfAlZElEtwsF0f8f7Ji/TNiiAF/pErDP7yj7C3tcW1R1/kV//R/8XN5GEuf+j7mMwrtjeHvHD9NYajLeqTY24eHHLl8ccwDo7veaqqQagcqda0bYtSCfv75xj3S+Zv3GGrlfTKkt7OCKUTdFAMy5K9jTFboz6jYU7Z01AKGGj0RkoxKM+aPEfsOUxlEMYTmprgqsgiFDImc3nAxd4lAN5FY6iUgVDX/O6v/Covvv7aW15X/1Y7tBDCVAjxUeBbgbEQQnenl4vAre7LbgEPATeFEBoYEcWnb1nWGrJewXpiYxJFYxBC0LSGTGsa0SIFpGXB/O4Bl87tMhykVMYwKPuYpsKphNuHhxwaTZX2uHnviMce2ebl22uuX7/NqJ9zbn8rghutYzgE0zq0gLbx5Hmc6JnWIIdxH2/qBlmoGG8iAC0iNkInKK0Y9hVv3K27N0y++eeE954kLymkuk/x7k5TPnTBMd1/a51g2prQXeA++HhTQFzHyNjBKxnAC9Iyp17VmLqlzHNGox6r6ZysLBmPC2q7hwprsiwnzzSrVU2eDTA2kKiEskgxpDFY3geKIud4csTdoyVlL2WnGKOQ4B0qiadApKO1gqpqGPYLpIwCXXwMf/cItNIE65HSc3x0iBZbIBVBeIRWZDqhbd2Dx8ifQP2J3ZvOk6aee4cTglBk6Sbj4r1sDxTGneCE45FLl3jlyydsb4y5enmXK5cf607qnrZu+PA3f5Be0Wc9O6FerzCmidME4MmHr7CYz1m3LbcOjlivVxgf8Lbll37l1/mBD38H73hkj93tTWaLhLqNOdYvX/8yx5MJH3jfB7h1t8I0K2azI7RKkUqxWi05OLoLAXq9krZZIQhkqYqTBJVR9gRaZWSZQsroYk0ShTWStl1jW+JqVGm8dRjf0NqWZaEZ9LL4wOrildIs5X1PPsVP/43/ELs+4b/8mZ/h4198jiTNSJKUPM9p6ookyUm15I2bN1lUhpvHc77pQ+9DusArXzph2osRhIlOkFJ0uhtxlhTggPbQcXAyIdXx393Z2mZjcxN8ZFciJKgEkgSRZgShkEKxf+VR/tbf/Hl++md+glt3rlOHGpUVLOaOttN1JZn8N10OD+qr1J/Uved8YDAqyfsanWrSPEUmKYPhiLwoWK3XCMXZtExKiRAi6vRslBhJoU7/H88me9EAEadzMY5QdM+buBL9fZA6WtOePa+ci02Z93Fz5b1ByTh5bpq2+54C50M3EYw5uj4ErGuBgHPRNxQ6Nl4Unvmzb+tPtXudng8C1798g4uXd7l7Y4r3MrL1XHweBtWteF1gu7nH+4+fYegMCEWDo/+ed3HYNPzZR99J8ug16iBZF2OeuXWT5z/xO7jzVzl/4TEot3jm85/jwkNXOLxxk3zY49LD55lMVsxWgXJrk2q1pOgNKJKM1WxGb24oQsH2zg6JTtFB0ktS9sZD9sYDNsZ9il4KucDnAj3U5MMeaZLFH28HgK4WC1zlEG1LsFWUTQkVm2nua/O01JjgUEmCkhLbVnzqn/0mL770CrX/IwCThRA7gOku5gL4PqKQ9KPAXyQ6jH4c+GfdX/nn3X8/3X3+I/8mHQJEeOJqWeFaS8hyilxQpIpxGRuV5RpaF3DGYJyn18spckljPNbHHE0fAvPlil5/AMEzmS3oj8cslmu01qyqmul0xuG0IoTAwdGUvd0RrjVcuXoJv1oyW9Q46xEymgZkCEiVxpxKcZrFJxEyUKYBZxX+NH/5bDwdv05rfTq6i5bybmwu5f0w6RB8XB2rTv8Q4kn+/g1JZ1GP/05j6qiLCI4sUyzXC9IsJmfYPEXrDKVLtnZ6HN96DaMNRSpBgXcGJQpMs8JrTVApwTuM9axWC4JrGI4GbIwS8kJjncCvFmRZHh1RHoKITaoLCuMsQkVDidaCtVfotKAxbWyGJSwWC4xPKVIFaYIgJ9EVSfogGeOPo74e96YxgaNDQX+0SbUqmEwmKLNib3eTxk+oFjmZ+rOsqv+by5dybt1sKL57gDOW0tdcHg8xTUNVV7TVkuVyxrpaAhLZRQFeu3yRL7z0InVb0ZrmTCC+NjX/9J//KsNegbGWyWxKmuZkWYmQiulqwW8//btcOH+J7f4uJ9M5+DWeiFGRnR7JOslsUeGCJ1017O9vEpyjKFKayrFaetLMoxMZtbJpTmsqTBungFIKkrSkrhOUljTWkznY3h4SEJRln37e46/95W/nI7/+S/yTf/FbvH7nID641it6RR9rI/l/Xa9QoaF++Bzf973fzae++Cxv3D7g1muv0rQ1x9NjUq3jSkbAad8mRMy7dkLE1yd11CRLiU40o+GQfn9AmuboNAGlEFoTZBJjpAARJFeeeIK/+zP/Cz/5t36Sz7z2efrlFuvlcdQnOkVePljdfi319bj3lJKUQ0WSaUSiSYqCfr9PORywqtYEJQjeRqMEnOnxTp2sp8+jN0/yTg0ZvGmNC3Qf70B8uOjSNQHrKsAiSLDOdTDigPMOZwNKxoalblZRX6dENGp0Oj4pY1NmrME6T/DyTGfnz5rPLv4sxPUw9n6+bZQSBO7euseH/9wH+PQnn4+f8xEW7KQjeEfiHQ9XN3nv+Zz86gfxpqZeVaiqoioHlHmfQb9Pmqc88cN/nqc/9hGag5s8OUqQN59l8tIXEI8+yTsffRw12kI4GJ/bYXJyhFKwvVmQZDmDR67SLqZoa3FHNUOrGe1vkpYZOkgyqdke9NnbGrA1Luj1FKon8T2BGqVkw4IsSyPsOL452LohrFpoDLZdI4VFJorg4hqeILteImCsRWuNShTOVHzuV3+L555/mSZw/739KvW1TPTOAf9rp0eQwD8NIfyKEOI54P8UQvwc8HvAP+i+/h8A/5sQ4hXgBPgrf+h3CAFvA62FdlEzLNMI6U0yGhNQUtPvlSRpwnjUR6m4O29DZEY1zpPnGgiUZUGW9xn0R2wMCu4dHpIkmraxGO9w3rA93gQF67ZlcjjnYPYS1liWVcNiviBNn2Frc8y1qxfR2QmzyYyHLmyR55EUnkjBamkhzc5s4T6cIhDiivLNNPDTvD/ZTeZOT17W3R+3G2sQQKL12dcKIUiz7OxGTZTCuDWu1aSpZDJZk2YJw0FOta4YjUskgTxJ2Dt3gYO7N7BZSi8fsqoqdNqS5wnGxmSCer1CImgbQ5lK5kpgWktfKpQwaCnReQ9rYqg13oP3KMAZR5IkOCfQCpTsAq6DQWdFZHKpjOnhCWIcHz4+eFItI+vpQf1x1J/4vRmzNAW7+w11tcGyOuLS3gjQ9AavsbDXSYttPvyt76I3NDSrkl5eopRkuLXD6Nw+oZnTTE+4/vzznBzdxlRrXOcGTFzGoMjYGva5O4kn/mq9RMn4QLDOsKwVWZaT5QXe+2hGUIosZLSm5e69WygJ5/b3OT6ZUjcrhAg0zRopBElSIpRCywhqbZuaJElIdMpisWQyWZJkEVFQ5BlpkpLqnH45jGLzTiyuJchEM19WLCtLliguXTxPv8z5wLVd/vE/+X/49JeexzrP1uYeWkuUSmIU0ym/LMCqbvjUM8/xyp17lEXB5tYmR4d3sU3NyaQDysp4uBKdHldKQZZl9AcOsSHI0xwlFcF7tra22NnfY2Nri6zXQ5f97neTPhO8d6ompNQ88u538ws/+4v81b/+16jNnKLIcS4jTep4QH0Qgfa11J/4vSckpHkPoSVZUZAVOSpPWVWrDuEVeXchxHupadvOSHi/ofPBxY+FuCmy3nWRZg5E6JAoPm6lgoxTPQLWOk6hysGDxcXJX4f4Cd4jiC+9aaqIOyGc5dpa10bJT4C2cR0GBU6hzIGAkFGjJwJ4G1e7p/ig+PwUnZu3YbFYsKqPGW6VnNxeRdOtpMOsSC7MX+Xi8YtM5Q6P//hPMHjqGkIqVKpxUnJ8fMQv/fL/zoUrV3nnk+/hsafez/beRaBhKy/x8wUvfPyTfOJ3f4Xw2LspR+cJoUXZlsN7R7zz/R9AJQm3nv0s7WrKRv8Cm7Ocor/JYGODQBx69IuS3Y0Bm8OS0TAlLSUiF4S+JtvIyfMisgCJkzrT1tTzVdxihhYhLVKLbrCk8AZUlwmMBCVjXKuvKz73G7/Fl77wHA0SR8DpP0KjF0L4IvC+r/LxV4Fv+iofr4G/9If9u28uISRBSJJej3pVYWrLYViivWO6rPHOkKiEal1TLxum0yVqs48IKYIWnSQoGU8gF/f3kbRRo6Mls+mCjdGIqWtpW0+9NojNiF1YmIatnRGv3zpGeIdWGXmWcnR0RK/MmVctrz33KrOTY/5nCvsAACAASURBVF5+peT8Q5e5c+M23/LNT3BoGgQtVdN2v5gVUgqQcToXhEIpGV9b6EKg3ySAlVISe6fQGTbiSSk6Au+P4RMdT/ZaS5zt3DU2ksCPDqeUZYoUMf8XL6gaS5ZL8sEAf0cwnS8JyrJczBlvbJLpeEObTrx7MpnQmhaNZmujYLk8jroGr/HaYk0cv8eXFsf6znlOsw2l0tFyLmU8BbYOhEdrjw8ZyECioEhTahM1I6ev90H90errcW9qqcnTBG8Cq+Y53vXOC3zhS5+kSAJZX1PJlssbLfPasL2T8vDGt9HrbRG0iiHbbg0ykOqAcJZmtSRIAd535H1HaAMXdne4efs23lqyLI2NVaYIpFhr6A/GhDu3EQjW61XEC6k0Hgabitt3b+G9Y3Nrn+Uy59at1xFCsbm5RRAp4Ei0pEgV1lmUVixnFZPjJaEDfytnActivYYOCpuoeB9niaY3GJJlilRL5uuWgOLGnSNEO+PVl74IKmU43EFphQ+BYb/ABx1/N7mW+XxKXXvSrASVURvPq7du4F56ncX0IOZ217IT0BMfiiKK1T0BLVMunU9Js5w0zZEiwsw3RkPG4xHFcIgq+4QsB5l2q59TF+39rFwpNY9/8AP8T3/vf+Dv/Pc/xZ2TA5JEspg5tncHPGj0/vD6ujwXpUSlGpkk9AZ9kjyjbduI1IkCrk6D5yM7z8cpmTuVCXUNn/eRned8hyFxUQ7gnH2T7Cg6SRFxqBAbvSi/CN1S1Xkb8WIIhIgmgKZpOAUeu+BouolinGDTfZ/4dwjgfTedkoDodKQdb09JiQuOIO4PSOJGTGBM4N7dY5SO+vrR7oDtvQ2EFnDvDsq23Gky/K03eOPv/898+Kd+ivd9x3eCFjzz7Bd49rkvcePOHcb7F2hbx+OPP4V8SmJdy+997rNUquHdP/LDPPnh7+elV17ii88+z9HJDXYf+ybGFy5zcnSI0p56ccDq9l0u7J0jFZrt89soodBekuea3dGA3VGfzVGPop8iSxCFohiXEX6tTqevnrquWc4mhHWLNy14g1Qgk+RMmxshMwoRLEpKEhFwtuHzv/nbPPOZl6hRGO+xGqx96wHK24JzobVGKUEiElwWLdLSapZNTV/nzGYLhuMt1tWarNcj6w+5dzAj6O4H0q1Gm6bGuwbT9mIkmfTUMRYDITRN60kSBQGyVHN4OGFvd0xwLjZlStHr9bn+2utn0WjGtRT9lJPZnP5wwa3bd3nm5Q3GwwEvP/8CeZayvbUFUtLrlUip0EmKMZ7pZIKxltF4jJASrRXj0RAhFVmaxkmBtTF5om6ZzedcvHD+7AaVwkdgtLM4A4IWiAgUQnTipDrBiQSPohz06Y+3SJRCCs/jT70LEQxOlcynU4o8J7gFs2nV5QrCug1Ua0sua5L+NlJpRoMe4NA6OhGrqkERR81Sxia0qRtUr0RKgdIyTgu0om0aRPD0+yWujSdFSSAIj2sCaU9gwwMd0DdKOW8Yjm8S7CM89FCDWWfU9ohePub552dQeM69C3bGYx7aeAdPPvHvE5RAtEv84g6hXmObhvVixaJag9SkSYpxBkIXiYSgUJJxv8+NgxV5lpJqjW0rglBMFjMu9PrkeU5rDALBbHESY5q6yKc0K7h97x6LVUWvN6Ts9djd24lGo3XDoIjpAqcr1KquWFVtvG4TgTeSJEnY3uxz92CKC53YPDhCa1lVDbPWkkhPkqTItEepwdsGbxrW65rRaAudJGgVEw3SNGO5qBEpHXTWkegcgWC+rmls4OpDF7lx6w63lk18/mmJ1mnMtw6O1sDJkcF5KMuUH/33/gpH1z+HsQYyRVn22N7eJOl+f6EUQmbRcfsHSCmd8xGBUJr3f9u38QvFL/LX/4v/jGV7SORzPUitebuUEAKZJJT9HnmvZF3XOAI6nCZSeJqmw6p0Td5pSSGojUEpdX+4IAQ2nDZQEiEVwdOBdgXeR1ORc7HBch68kKCim1Uq18WcRTNRbBRD58QVca2YQNu6buUqulVwlC91s8LOvKgwnUuYbmOlpMJ6ewZMds7GPF0p8V7hTMZjT1zlW79rn/d/8CkGGwMUAi8Vvl6xOpqwODlkY3+L/t4mLz7/Ap/8xKfwQfAt3/79/Lt/4RzjjQFZmiGF5u7BXXZ2t/jO7/owd964yW/98i/z2Y99jN7mFu+4dg310vO89NFf4qEPfC/eeHTdYkPB1f330Q8l43PbpKVCNJBKyVYxYG/UY2PUo9dPSDLwhUCOFOkgJVEJ4U3sv6ZaYpcGaRzKOzwONEglsa1Hdmw8Z2J/IhWE1vDFj/w2X/jMl6gC+GBx0mNtj42rb+0Fels0ekpJauNJM4mWkGeaxkj6vQKhAuumJbOOeVUzLEsIHoNA+JbGBjaGBcfTBZPJhPxdjzGbn9AvClbLNQLHah2TGdo2TqgEkOcFAYm1gtZYjicLtnfPMSgz8J6NjU2ss0xOZly6uB1P/sGxc+4ceVHgrOONm3eZTmdsb21ycHDAzu4Oi+UyOuusByFpTQMikOgcrSIxXEvF/v4eaZoy6Pe5+NA5nn/+OV546RV+7C/9BZrKUq1qfFgzGm+ymC/IM0mRBNaNA+toyoTpdIIzJfP5OuonWhc7fwW4OFpPdE6/P0R6wcbmNradsrMtSIvoAH79xjGvvXadxaKhVCuaVpDohLY1ZFkWtXlOkJWC9TqOyQOaJNUEDzpRnQ2lO50Fj7WeJElYr1qkEtigKKVAJh30Wj/Aq3zDVAChat64+SXOXbTUTUEIKaum4tHHal55FWR2ncO7mms/+BPorIR2Snv7ZWb3blIvKtaLFbOmwYSEp979JI3x3Lh5i6ZZY4yJhw4CF/Z2uXnvHm0bTUYhwHRR0bSO6WzKxvgc1lYRECoVrakYjTZYLGa0bY2QiqPjA9brFVsbmxR5GYPIVQQdN43DOo9OooFIaxX1qN7jHVjlaFsbyf0+HmxUGlfIRhpCaCKzC8lGkaFEQ12vCCFQlj3SNMGYmizt0zYtvXJEv6+ZziZMJkcED9tbO2yNx7z/XU+wvTHm3U89xn/93/6PHM8CZU+yORxGU1OAxkTDlbHxjUizHuPRFurSe7j32qdorCfNc/Iiwpzx0cEYCFFqcfoGcjq1OcVsKAgBkaQ8/t4P8ff+zi/yUz//N3n94IBHHtnn2Y/f/fpfZw/qD5SUkizPyLKMpo2ECCVlZ34QNE38WJIkCGs7ELHvZEEx39Z5fzbhO22aom4uNmIBgXWW05gyQtxGee+jEcjFTPUgAoHY3CkhcKHpTHUxK7p1jiTRcQPkO61fhz0BibFxWp5mUXNuTJxSx0mfJUiBDTbqc0+NuNJH/b3toth8yvd8/3dR9DOkkkgJTgakN3GtnMDGOy5TLVYs703R6YBrjz3F1UcfZWt3l36vh/eO1XLFcDRma2ODdl1jrWFzd48f+Q/+Iy4/9gT/7z/+h/za//GPSNZR1/vGumL01Dej+rtsj86xOdWUo5Lx7gbCxwCHQV6yMx6zPR4yGuRkhUQUCgpBb6uPThJAnh0ejW3AOZS34COHViQCkeRYF0BIpBJ445AedCLBt3zpYx/n9z73OSoBPgiMFNhQsPPoY1SHh295Lb0tGr3TUXFWKBCaskiYL5dsbQ9YVTXWOoos47BpGF84T5FrJouA0hrtPf3egC9fv4tpG/r9lC9/+Q6PXr1K3Xqc9TTWoJQmzRLW6xVCSnzw5HnJdN4wGg6pK0NbrRGDEp0V2ODwQVHVLWUZ9YGrZcVwMMI0Dct6TVaUuNmMo9mcvD8kqASPpGkil2i8sUl90pBnOUJqghA01mGk5/mXXkVKKMuCZ59/jn7ZQ4uEmzduUTcVt+7cYTQacPvWIdY4NrYGjMoMlaXkScrm+X0OD0/IihzjHdYaPvH0Z/jN3/iXXLx0iQiB9gRnWTeeewdHJFLy8LWruHZJWgzZGI+4+cY96rWnagOqqtE65eDgAFNXbG/vkmcrvGmw4wHeW2pjWdcG5wRJEh1UzoNWOVIqnAs4G+Ot7t1ZkWQJCLD2FJIpyfIHE71vlLLesloKTk6gN+yR5zVbm5Isa5hMQfk+e5sbXL70gxT9hxFmTX3nVe5df53las1yVXFwcMiiaugNh5SDAZujIfNhyXTmEULTmgbpLeOyoJelLNYrjJKsVmtWq4bpsmWyaNgYbbA1LqmbCikFaRJh3YP+kNYEmqZmOBgx7PfRSnFw7wZJkhJEzmxusMYRPCgNZV8wGGg2RgV1Y1iv14Di5HiFE1D0Cqyx9POMIlUcHM/IlMLKnCLLse0Ci8d3D5k0zUhTzWK55N7BHfpln8Vi3uVZg/EB0zpaZ3nk8kV+4sd+lMFoxMc//kk+8/kX8B6yTEfxNaFzQ8a/c9qgGdNQtzUiKUh7e6ybCauqjqLt7uuDaUEbgtBnMNrTla0QEZ0S4q1IQCOSgvd9y7fxc3/jZ/nPf+Fv0zbTP61L7UH9/hIiDhVOp7Ddql50vMMoB7ovwj8V+LuuOZNKRVgyHXxZxDi006l2lBJ1+r6mOdOGx6aqiz4TIh4KpMSYgBQKHyyn11EIDuMcQQic8bRtnNArmRCCRASLtR4hfQQhn6Vl6LPplkwEadax4jy0laeuDNJKjDWMd3u8+0Pv4+Llc0gFSgqkEgjhUV1axcn0kJs3rvPMc8+ytbXD93zfD9Dv9bl8+Qrjrc2zRjnRCb1+n6paxymn0qgAi9Watmm58uST/OB//J/w9G/9Ji99/rPY669RHdzk+BnB6NEPsl1vkqiE3fN7yBAQTpBqzdagz/a4x3hUUAw0qhCEQlJs9kmyDIH8CoahqVts3YAzBAwiEaAlAhWzhOlwMQgSLVDB8/LvfpovfOZzVF5jrI958r5k5+GrrO8csgpvDTt/WzR6rTHY2lAxoShK0qykSCogkBVZxBsEh0RSVxWDckhdtyRJStM0KJWy7Ph2SiiWq5adnQ2Oj4+QKokXWuJQOiMvMpQSkf1mDYEIhkR2Zo6s5IknHkeKaIxQQkdGTm/E8WTGI1u7lKXilVeuY1xgMByjlGZ7e4fpdIL3UdvT7/dIs4wkzfAekkShlGIyWTIejVBady5exbqxrKo5rfN8/tkXEUIyXcyYrQx1VZHqhEfSC9y5e8jlR69yMF0w3N9jWbWU5YDD4xmFNtw7mnF4cJe0GJDnCqk83luWS49pDPNVxfbOhMnJCTqZs95Z8drrrzEcbbCoKqyxbO1sc/2V13kj1YyGBwTvWc4WnLu4S5IUzBYzpsefZKNriJVO2e5nDHZ2GHqBMwYpAr1S0ZqGXjlAAM5LnHfknQnlQX1jVJYqTmaGfj5itdRs7ucspye0Y8d6HXj4Yo/94Yj3vPcHECJgp3c4ePllGjS9rXOIsmZae0xzxMlizfqVNygyHfMnbQtCoJTEBolUnvFwSO0My/WKxXqN8Z6AJISW1XLC9kZJ2zY0TR2F5N6S+IJ1tQJfc273EgjB0ckRJydH7O/uI5MQpw1tdJQ6C6sFXT6nRRLICkFrPDoVaDTjfkrdKhbrhtnMkKY6ume9BbvGyrjaPTVRVdUa63xn/kg4np3gT2JG9HDQI9gGgiZYy7WrD/Hxpz9BOd7ip3/uv2O1rklzEeUceBAa5x1t29C2pwJrwXq15uToMDJGjeTo9l365YCDg0PGwyHaWXAGYVuCUCCTM8H8acXtXsctQ0T0ik75pj/zXfzsf/rT/Pzf/2++rtfXg3rrEkLgOigugOoahVMI8qmLNpytY+83fcH7ONXjFL9y/3P3m7yAUsmZHi+ESMAIXmGM7+QGcRu1WrVAQggtQjpMHaLbV0RXRNsacB6tNUFFQ6FrY3qFVB7noK1jyIHWCoJFBhhtF3zHh9/P/qVdyqKgNYblYsHJyYT5ZEW1bNja2+aJpx4FV1H2BEWhkFojE4VxUTOuU8X27j4/9u73YVF4b7l46SGaTrt4akA5dSUrFfWrzrkouyp7iCA4PjlGIdg5t8/hwQXWzRpz+x7rk7tcPF5R6m2G57ZJigxlQCIYlSXb4z5bo4JeX6NLoAiEviTbKM8CEmIEXPy523WNr5pIw9CAFJ3hOSA79IwSGhEsIvVc/+QXefp3P82i9ZguEs6IPruPvoPJzZuIMmOU9d/yWnpbNHoCQZIonPPMVgsAlusVWS/Hm5ZEZ2ip0TpFScV8uSZNEhKdkCcpOk1IkpzzF84TgkSqmJF7fGTIi4TFYkEgwzcVaZ6yrBoyB5UJqMbgg6CuFqyrQFkU1E1L8CkIR9OuQWgGg5Kj4wnOWjaGA5bLNVpJ+v0+wQtu37rNYhEdbEVRkJdltJh3TmDXAZDzLOuwDZokSRmPRyRpyo2bN0EmzFc11lqSNAWp0XkPrSX9/oDJZM1w2KdqppxMFpi6JUkladrDuSlFnlBXjrpaoXSPQZ4yP6kY9vqcHB8jley0T5YkkSgh6PVKiqIHnLCqGna0pixzvNO01Rrduf+sMSTpEN3hXo6mS3qFp6kPMcMBB/OapvkyztW41rIx7vHyS9e59sQ7yHoJaZbRmngS9A8avW+Y8gEOD1JSbbn+TMOFnS2Gw4bF2rJ3QeInCedHf5H+8NFuESTYvHQFZIJpGnSq2TG7TFcNq9WMyjQ0tolonqje7Mzc8aS/0R9w6+iIdeNYVA7vBXiLa0/oDfZJdFxdFWWPLM2wtmUyPQEfGA9HCKU5mRxz7+DemXOtblpMa89QEhDdfk0TYn5sgKqCEARJIil6CQeHs6gp8iJOGbxl4RcRBEt01zsbV8GjYUpVrWlbx7JqUTJQNx7TQrVa41xL1LwOaU3F7zz9r/lz3/ln+MRHPsv1m7cROjAYZCidxJSB1lDXFdY6jDlt9ALeW6bTE7Y2d/FC8OWb9xiPxzz30suc399BJgkZIFWClBqEjtbN03+howCcApGj7y8QpEalJd/9vT+EUpp/+S9+/OtybT2oP6QEqCQy6jr6CATZSWQiiuQrmrzQvaMhMvhCEJ0Z0J1Fn8Fpwye6DYzDtBGHEkJ01koZMSlCJHjrMY1Fi5TGtChNh9oSZ+QIazvOrIrw7aYxuAAkAnxMzTCNRytNljmcFSASklTwTd/6DoTwPP3bn6OqDfsXRzz86GXG2yO29rbZ296N93+9ZDgoUVnAmhU6ZCT5AJVoQHD5yiWcN51JRHPv9h1ef/0G++fOR4yZac9W16fGyOiEDzjrMSYCyfv9EQfHR7RWRIwYikalbG5ts5ldINElW9vjM1NinpRsDfvsbBSMhhl5T0Gp8D1Bb3t4Zr6IFVEw9XqFbRusrUH4qFkUGuECIkQjl5agQkBqye1nX+STH/0dFs5jbXSytFnG/pWnmLz+OnrQJ0s1x9dfectL6W3R6KnTsXIIKJFgrSXNc5rWsJ4v2NzcwbUWrRUeR93Ei3ldVSSJZr5Y4IVgNB7hnGM4KBEBjLOUZYqpc9I0oWotaZITEDSmZWM8YL5a4dqWXr/HdDbn8HjCaDjsImUC8/mcw5MjvJC4YDk+OmLUl9y6dRspJDpJ6PUGDAYDrly5wnhzEyUVk8mE6ewE6wJ7e/uMxmOqak1eFEwm024cLmjalsl0imlaku6iPb3hBsMR1jvwAqUzqqqGIBgOBsxna/K8oCz7tKZCdzE0acfosa3BGcVi1ZIVsDHepGlWNMaxWLdU7QSlFL3+BlJBv9djNltQV/GGGIwGiGCwxuKCoV7X9PqOIo+nn+lkiihBpRk6AwQkWuGM4Hiy4PbdFU9/6tOcLGdsDUsu7O5RDEq2NoZn7MEH9favEOD24ZrdrZSN4RZ1ZdjZh9c+1aJyw4eufj/XnvqrIDXBrJE6odjYwbQtddUwncw4OppETqSQgCUI0Ym+o/BfqI6+37YI75gtKqrGUbeeEAz4mkTGQ4kxlsFwRJb3SLTgZHJEnhWE4BgOh0ynE27eut2hjuD2vXvUjcOar7zowqnbr+v9YtRnoG08WW4pMkVd2y6eKbZGVWOJ/FeBUDGsPVESKWI6wGplkYmmbk2EmeIJTtDWgqKXMBoUKAEni5aPferzfPpLX8QFx2CYURRFjJ+yLdV6Fdsxl8QIwjeVMSau31pDrz/i8y9cZ2c84pFL53iYgAieVIDwHpE5QpKDSLqfvQDRxSi9ecsT9fLIrOA7v+eH/tivoQf1/7+0UiiV4r3DtK7jKwqMN5yucyE2b64zaXgif1WImEvrnDv7uvt/jtMsay3GdrpUoZBdBq0Q0RyBlDSVJQQTEyhMp+0LnoBBKk9CbArbxlLXJma5h9ClcziCEyQ6JcslpiWaCoQnyMDB4SGH9+aoNCPJA1m6y/nzl5BKRTPkeo5v16SpQihBkmSkmcS0Bm8bjG9QSUyQShKNIOJmdvf3cF7SNi1tW8ckkG6AkmVZZ2CJKR7WRp5ta2zUpqc5ZVHENbZTFEXJUw9/C2GRsrW/hVCCYCEVinFZsrPRZ3PcozfIkIVClopklJP38rPp6Wm2vbERa+bahhAcSsdwhmA9Ah0xKgKUVmBapjdu8Ylf+yiLpsW3nlZ6UCPOXb3GnZdfJNvZJBWag+uv0oi3Tpx6WzR6UgrKXgp1fOGNceRFSds0NC4wyjJMtegaIYn3LQFJVmRR9F81WNNSFhskaYqWima95ORkzdZugbENro4mAT3sE4Jlvqi4cuUq8/mS4XBAVhSURclitSQre+S6ZbVcM94cU1WG0cYmW9tbCGF5/eZdVlXVbUAC08kUBLzxRsbGxgY7O3sMB0PO713gHY8OuHzlCkhJVa25c+s29+7dI82SbgrQkmQ5edEjSZKzWJSt7R22d3bY3BizmC3Y3S04mVYRVTIaMZnM6G9u0BsM6fdKlvM1F0dwMq9ROiNNExKp6PV79Ac9dJqzWioGwxGbOw1l0UcEQ384QAtBnvQYjJYkKkaapVlBojOa2qLXlv5wjNIJqS45cHPKwQCkpswSrIFepqDIME3TRag5dKLQSrJcrnlleRPTNmzvbsZs4gf1jVHCoSnoZzt8/NNvkBR9Hrq4wzsf3+Wh8QV++M//bXTSBxzYCu87mcB0xp07B7z2xi2WqzXWR4BxkC3BanDgnCFyWuN0rDUtq7piVTU01iNDGxNqFHg0i/kCITSJsiwrR7We4KxBJwlF3mcyX3DrTsSsBGJk1HRa0dZn/dz9+n3IqUC30vKB5dzRHwqGI0nbCJaLiI8QUSoFBLyLf7QhsF476jpGO2UZJHmKC4Fez0cNVPAkehQnIMZyOFkwW1b4oBgMUoqyh5ACax11XROCJM8LTo7/IOYk0RprGup6zWg4Yr5a8/QzL/HI5fOUeYZQir4QaOtInEWmBnQakzKkjnlR91EFiHA66YtGKZL8D3zPB/WnU0IIdKLxLmJOZIf6ifKgr1zHnq4gI5T/K1e8EN3mp0aNaLxw3TDDIUQMCSAoArbTcgaa2mJaGw8B0iMleC9oW0PosqG9d2id0NSOpolrXudajLEdc1Wg84wQJNY2SKEJdPnuTeCZz99m91yP3XNj3v3+J9nd3ydJcpRSyGA4vPsqmbC0aYZpEtpVgcpShJZU6zmjrT2c8DRtc38y1mGF0lQREoFOIvKsampWqxWLxYKiiIfD6BAOWBeo65q2bbtkEdM53+Gpx78ZtRyQ9Hr0t4Z4ExBWUGYJW8OSrWGPQT8nKRWUAlEIyo3e2bMcOOMMtk0N1hBMg1LR6d+92dH4BlHX7h3N4TGf+NXfYFbVNAas1chywM7DVzh47iXK3T2UMRzefJk6KXn4qffCv/qHX/Vaels0esY5nJBI6dE6CkgLrdCyYLmq8M6yXtf0BwP6vZLZ3KAUyC46ZbWu8dbQLwvWqzWJTuL6VXjq9RqdZOhEU1ULtJIIoTFVizXxjdU6QYhAUZQsljVplrM1LJkuWx566BJl2WM0GBLOSerljMXacO3aO1ksF6yWy+4pEjlFhwf3mE0nNK09S8go85Ldc3tsbcUItqPDA1rTUBY5piP4t00DIcIml8sVk5Njbt+4gVKKJE0Z9p9iZ3uH8XBEf1ByfLJkf/8C4/EQZwyDwYDxuOCDxQZbG0OyPMObmuJkQVGk7JQl0p2nKDVp0SNRKaZZ0R/2wXuMT8nXA7yzDAdFzNdzLUomPHztGlop6rqhKAKtEcwXK65cvRwhsgoWsxXj7U3a/fPIl7/MYjZh//xldvfOcXJ4D5CYAIvFmkXd/ulecA/qa65EeXrFkOB7fPMHnmA0iG7uyxuP8CP/zt9lMDofRf+mwa8W1KsVk8mUmzducefuEYvVCu+aLovTkG4e0x71sCZig0DgDbSmpWkbjmczZss1Uim0MGgtybMU7yRFUZAoybquqWsb4aOioKoqTiYT5vPFGTOMEGhbQV35r9Ll/cGK27DoUvA+sFrAaEOgE09vIGhridCRYel9iCHnAZwNrONwBZ1INrd6TBY1jfOQKKSMMWp1vcbahmG/RAdHawI6KciyGoGgaSzGtISgIkfQC9rmK6eQQki0EvHn6SxZmqGShMmq5jc+/nvsjvtdLqajHIzw3pM6h5AakSQIncXs2+4VB6VASASn077Y+D2ot0eJLhWhbRvi9M6fxZFFI8N9jZ7ojBhnOrwQQcQi3E9Xik1gvM67jCcQcYXrie7WEEQ35QpYF6kK3rbUTWTutW11lr3a1L57TgjaNuCdIAR75uIVSqC0JwRHU1mUBqk9vX7JeHvI1Ucvcu7cPucf2qY/KuPJ7MwZHjDLJa8/9ykSKQg68ihH27tsbO9RjjYwznHnjdfYv3QZV9dYoWJTqjJEACskUidYHzE03ocuH9Yync7I84zZ5AAlc7zU1E1N01SYtqE1BoRme3uf7fIyq+Oa8w/txG2UAy0Eg7LH7nDMuN8jLyUiD1AosnGJTn5/axXxM029wrdVh3OTkdBhOvh0EKRZgkDg5nM+++sf5eB4QmsD1koYjdk+f5lbzz7H6uXCgQAAIABJREFUcP8irqk4vHMLl/W49MQ7OXrhS295Lb0tGj1rXXTZyBgGXDeGNEmoV8uYrRccjTFs6gQCNLWNLCuh0Sqlrpb4IBmOhhzcvUuaZvi2AplG1VDHftMdx0anGgcQJEWRM53O2NvbJtGKgMBbQwgpdeMpiwznYTqbk6QJLi1JrWdrC9I0ZzgYU5a9OAZ3lun0iF6vR573IIQYOm0ts9mcg4MD2rbtKOSB46MjrDXdjRdt8ub088BUHlMUBcPhiOWq4rkvfYHx8D1cGz6KEpKHzp9je3sbZyzPPfssd+4aVpWjOn+R8ajHRj+jXdecHNyl8ZLgDDvbY46nFYPBgLLMSKqK4BqOZ4Y8L5ienHDrVsP5c7uMBiNa60F5NIJepnDWs7e3T1quuP7aazz5zscYDvooXZKnCVlS8N73fojV5ID984GrD18l1ZrlbIbzjuGwz0n11jbwB/X2qqZ1pGnDdDFHcIJd9ujJx/jRH/qvGG0/FDsk7wjVlPV8xuRkyhu37nDr5h2qpsL7Fnw3PfAtIkxZrdYIM+AU0toaT90a1nXD9Zs3sGaK9iVSB6yJ1P28KGnaCNxe13E16mx80Jm2xTnbaee6PWSQNJX7mpq8WKJbaUZiv3MBYzw6EXgHRU+itGS99BjnSNKY2yxlbI6s8+SZJkslSguG3WHRA/PlGt25GSsTaEwFwpElqvs+9dlKLUsL8iLj6HD2FZpCgKLI+fIrL3Dt0XfgXYuSIka06YTX7835tX/9e4gQnZW7u47SOmxjSNIMnSQIXSNU1DAhJUInCNXFIXaC8a8C33tQf4plIluH+wDhqDhwLubUwv0DCnB2yIkRsQKlMrxrCcHGpIrT1IxuG5XoaMbwISCkA98iZIr3hjQTmLbBGos1IITtIjo9qtvKOOcxresmjkCQJFmCMS3WWLxXGNMyGJa85/3XeOI9j7J7foeiTGMyRqee8MGfMf9OX6+xNTdee4lmNWexWpPolLTXY2d/lwuXr7F74RppOeTm9VfZP3+epq0JWFq/4t7de2xsbCOTHjpJsS6uaZ2PmBlnPcv5EgHUqxVWKIy18XeJcUipSbOSxx9+iNWLC3obW2S9nNY4lIcsTdkY9hgNcwbDDN2tbFU/oRj0otHpTe9jEHGa500LHfpG6oTgRbckEMhEE5RCrhte+FdP88bNm7StpXIaMd5mZ3+Pmy98gcG5q9j5nOnxHUI55KF3PsbRM89Q1SdveR29LRo9ISTrqkEpQR162LZG6hHBB6RSSCmxLgZvK9vSmIaiV8bA9URiTIMSkiJPqdctw9GQyhgSJTHWIYXEtDaOV7UmVQkEgXW+g/3GqWKeRThl3bZYnyOCY7yxyapuWMxWjEY9irLPwfEhIMjzHKU0Za8XafjeIwhsbm6iVEKapfFkQDiTxaxWK0xrImDSGFbrJdYYqqqKSRhJEiPUpMB1q5zlasVqtUKqwL2jCXv7az7y0Y8hlODSlXMYa1BakwRQuuTg5JijkyOKRHE0nVEmGf3RCCsEq7pl1aw5mRyDd+zu7rKz2WO5WnN0dMzJ8YTH3nkNiefOnXssFlPWdcPe9gb9PCHJc2TSI1GSQX/Irbt3qFYDZoslSZIiBMxmK3Y2Njg+OODkaJM0STh34SL5yYx+qXjjtZt/uhfcg/qay1rN8fyIpppzeec8H3rkO/jOb/9JehsX4i8z76Ce0UwnTE5mvHHzNvcOjmlMSwgG5wyhQzo4YahnNbZJUC5+zoVA3TjmVcWXb97g1sHtOF0gcDBb4kLLeLRFXC0G1nUFRKK9NY75fEHT1CSJpGk9TR0QPj6A3L+FFvSsqQp0aQOB5TxOq53zyP+PvTf51fQ8z/x+z/RO33TGOjVQRVIq0hosSx7ajqF2x3ZiODCQBElWyX8SIKteZJNk05ts0ttsskgnMNC2uxM07HiSJZuiRHEUh2JNp06d4Zve4RmzeN5zioZMwWrJZqlRNyAVWRO/6f3e+7nv6/pdrUCovL6dTDWTqWGzliglmUxLTk+2uJhYbhxGKqwLbFqLKQybIYEI+a7WbZk1DTFlTESeoABI6qbGlAXBR9rN8EOP8Rvf+A12FjOss4TgcNYybRrS6Jz85pvvM5vUSC3xIbLTtjSzBWXVUJhyzMg0WeCvDLKokQV5kicK0qWO73k9E/XUaJGIKevosqlCXv2aGu+Pzj81G12iPAQprwyTGDeDTzl7MY28Pe+zAchbYryc2vUkEl3vST5h+3wNFKXOn2PUuL5N4/Tv6X8vpoizfR4lktfNv/2bv8oXv/QyB4czTKWRWo/Tx8sceHDWoUcSRb6PCspmzsPTDaf374JInJz1nK0GdCm4trvLSy/f5stf/xoH127w0XtvcPvlO+zs7pCS4PrBAS6AFpF2vWIYLEIanMsMzeg9m7bFqOwYdtFhvcM5T4ygdcHObAd1XiCDY3GwYPAOIpS6YtbUHCwadnYLyolA1JI0UTT7DUJLPnlGSyLhosMNHVhHCgldFyAkKWb3skLmWMcAH732Xd753tt0UTAkRbV7g8XhER+/+X0WN1/Gnp1x9uQUvzjg9p3bHL/2HYb+Auh/+EM01jPR6Ck5ogUE2MHl03vvsAGmdYHrOrTSedolNDFC33cobShrQ9v1aJ0FpH3M7J+Nl5S1xluXw82zxBRrHVoZqrpGCqiLCsmarh2YViW7izntdoWSijgKUydVTbvu8ulZZQ2EzDsZ6rpGK53RLjJlMOLYsCUytT/GMSFCSGazBSSQSuCcxbmAHSxCCuqqApHwIVAWxZgPGJEErh8t8D7SFJKjw33+49/6bbbdFkHk/PQJZ+fnpBiZ7e4yn8zo+y1CZ7TM8foML8jr3BgoiwqtFCfHx6w3Lba/ADMlBM9ms+Thg/tcOzpEiWz8SFJzsWl58qRlfzZjuhd5cvIErSoWezPuP3iAKScUyrPabNhsOubTCaePj7k4PRihuOBD4vruTQ5u3fqsP3LP6+9ZZRWpS9DC8POv/Dr/6X/y31M0u5nNlhLJbenOTzh5dML9h495fHJG13UjXy7nboYYSNEhlGMYBmTcxflAbwO9j9x7dML3P/wBJ+fnbNoOITWFEXkyVi0oTEXXdXjv8MGNlH+V0yFSoKkrut7RbyPek22BP0mN39IpJYIf/5mESJI4avJSipSlRmuJFHB4bUZMiYtVlxlXwOATSueJTPL9qPlJbNtIYQo2XcyYJUfW1JqcZnF+vskZu598H8qS3/xnv8Fbb77B6fKMvutYr9eYeoLUNVI6fDnlT7/zFruzhrbtOdrbZbHYYXfvgGZSY3RBVTVZ0F6V2XEcSkRRI8qUY9MuJ3vP6zOvT+rsQvBXcZm5QRuNTKPYXwo5+m0yxzQm8NESYhpNGXnNCmM8GfIq+1YkSCHk442TiJiyAzSAG0Ke0pkCYzSDHfL9SmQmnpASofOELKUswVA64pyj0Irf/q2v8Uv/5EtIHRnsBcOQGXtSJ1RRIFWZ408B22ctb/IhM/iU4he/8Tv87//rvyARWHeeIUbW28Dp8iFvf3DMv/vTb7OYNEznc27evskrX/4iN269wM2bLzNbHHJ+tkQg0UWZByftdnSjG2zfkYxku90QhcJZjx08Q9+xWl2wV+5w7+4j5rvXqJqGwQZ0khih2ZtN2Z1PaSYFuhLIRqPnJUVVcXlYujRhhBTwfiD2fU7CKhRKa7wdndGAUAoh4eK9D/nuH/8lG++xSWKObjFbXOP+995gevsW9vgJy9MT4mKPWy/c5sFrf40dVvjoEOInyLr9xygxuvBkYfLIOeYcPmUUMilMYSgGhy41KQWKokLrMqMZgqDvMufqMkV523YUhaEoJEPvUEpmp58oaXsLAnbmM9arNUVVoQuD97mjl0LgrOPibE3fe3Ze2uXk8Ql10zBfzDg5uSDECIIxSkVegUlTzKPwGEJ20vA0x9Y5h5IKY8pR3KoxhaCqNMwytLKua6TKjqYYM5PIe48kMp2W3HyhYGjXNE3F7v419kRgVmvk0T5N0zCpZwxDy+Ach3tTqqrAmJLNJotPZ5OaujLUFoahx/vI3qJi6BNJaYpCEFMOYkeAdQMuJuraoETJRngenDzmxckUWRZ0fUcdC1abLev7jzjYnbOzmCCN4Gy5JsZIu9kgSHRtoB0sH6nEpP503s/zerbKWo1IBV9/8Z/ye7/7P1A0u6NYG6Lf4s5POD0+5v7DRxw/fsIwDDhvR0dtuHIDxpRI0RJFh+0D2yGS1IQH28gf/tU7rNaPMTpilCB4i8AQgibFlvVmTQgjgiVkXVy4+jGO0Fj4Mfa0P3alRE67ITt0Nz7SdRFTeOoqjE1fyf5uTd9HVu2AkhobA+BRyZM8lFqToiM6h40KREFZVlRVg5KC7XbLdvPDJoxXX32Fxe6CbT/wwYcfEaNn6HtuzSd87tUdvEusLwTvvrPkj/78W8T4y5xfrNnbWXKwXLGYTZhO5lTNlHoyYVo3OTqurtG1R8bM66J4bsZ4lkpKOTLuFEqJkQU3XE2+LnPRL/V5PjgEOmNPRph3SjG7cHkaAZjIBoYQQwb/CkUMEoLEDZaUYmbjAQiPHle87bYb77WQfIYlx5hRJTGBKcG7PEn86pduocVj/s//48/5+P59zlfnXDs84tU7X6CqBNeObqCLKVKXKCmomhlFXVGWNcGDSIqv//JvcPd33+ff/N//Vw49iNntHoUhIbE+cnzecny25QcfPeRP/uxb1E3BYrHg6OYtvvyVr3J0dJPDawdcrM+J3iFVzcG1zxFsoO8ig3V43xNiwrnAMGyZVVO69waKVLB/8xo2gYgCkzTzpmZvPmE+ramqEllr5FQxWUyzEYTLb6JscoopEGyHG3pkYdDG4EPKE3SZ+x9lFHHZ8p1/+2csh54+Bcqdm8z2r3Hve99m9+U7bE4eszp9TFgccHTzBR5+99uEuCYmB/hPrL1/uJ6JRk9rTVGWKCNJwTOfT1htVwxDoiwM603HpGmQ4ynXmBxMLpJgs1oSomc23aVQJaWpMNogMWhdgNYsdna4uFhiCoW3nu22Z3+v5t79U67fqKnKgr5rWW9aDg73kWoDQuBsz858wttv/YCd3QW7u3Pee/ceQoJM6krvIGR260mRc1+lyqyjqmzyqQeF9z5HE32C45M1g+lKm9D3fQ41lnKcJEhiSNSNZr3Z4n3IF1lSeW2tE7ZziCQpigapNbNyyrVmSt9uUFKxv7fHjRvXsJ1jMmk42F/Qe0HTlJydXmC7NS4k6rpm8JH5bIcb1w9RBIRILNctZTUluI6ha6mqhqYyeGBSVShZIrQhpoT18ORsjSgM1ndsthui93gSnkjbr9muFe++8+Fn+XF7Xj9GpZj42kv/lP/2v/mfKCf7o2shklyHOz3m7OFDHjw45smT83F9FHP+ZUxX64sUE9F5vBxoVYdlxrVXf4XZ/hF8dI9r792jfc9jt2e48c9tooVks44nXg3ZLh/V+KPgH7K5+/RKeUJtE95G+u2AVJYXPjfj9udu52nl0HNycsrpak1KmkDAaMWkaUZ80pgHKiPWbmnqhsHBxfnFD31hV1XJb//2b3F2ekEiJ4ZAwlnLxXLNrS7wX/8Xv8febMb/+D//C9546y2++fr3+bWvfZlNb7nYbFk0NU1TM9/Zo9aG2XTKfGeH2XTGZDajmvh8w0+fxev5vD6tckOXUzCstYQxH1YpNd43cppFGHWwUopRu5qTMeJ4EALGe40emzib7zVKooXC9gHvJN6OSRZItCpxfkBKGIYuky7KIssmLrXlQhOTR+rMiBtsICWF0YB9zO//qz/hjXd+gPVjYyPe4o/+4P/l+uGCL//cy9x+6SbKTDBlze7+LklI2u3ArRduM5vtYIoJv/mf/R5Cwv/zr/81MVqkVAyDHw94iSTEuGGwNEYxLxT9+TnvPj7lw7feYbG7w8t3vsALL77AfGeXRw9/wGvf/AtevvMVpntH7B3dIqbIetOSth1xE5nLGY/uHjPf2UfXGtsOmKgolWFRG/amNZNJga4VqVGUezWyuIz2vMwiHiUhKWK3m3GQqMkxoplFqLTESIXygff++Fs8fHxG5xRqfoPdw2vce+NtDu58hc29j1mdHiMOb3B0eI37338NH9dEIiSfWYny06/dZ6LR8zEQBczKknXb0pQlwU1Yr8+YTya07YZinlEej0/OMXUNJJQq6IYOomAxa1iul+hCZRhqsBTJYGQeVwcXSCGCFLjeZ9m2kqw3Hdpoakq2naNrO5rKIJREi5LoLZt2YO9QIAn0g0VKnekEMkOHhZKX1IWc/zq6fqfTBcaQGzgpcc4x2I7pdJ71TZensUv+UYxoY7A2TwO885RlgRSOJ2crDo/26PuWzaYjxIjRkiQ8WpdIu2W7CRSFoNsOVIXBVIayMPS9Q0lN1WiMNgQSq4slIgr29vYQqiZGy/XZjLpsmE0rhB/XydOdzGcKFTduvkDXdsyamigZtQzgXaQbm9T1+QrT1Dz4+C6HR9e5dvtFlssz4rJlZ/8QoTXbvvtMP2/P6+9fVWH4z3/3n2Mmh1zGIaUwMJzcZXV6yunFBcvVBut9brnGE2r+ohMo7VAmseojfbA8aQt09SKz/RsobdhZ7LK/f8T56UOebNc4lxNxLiv9SJ3ds9CUpHHal1ivtjTNjNufe4XPv/oLmGrCg/v3+fDDd/j44/c4Ob5P317gXbbp5ilLDo3fbk9wg/6E+P7p6/iNX/9V9vf2uFiuIElIARcTISW2neU3fu13+K/+y/8Ot+r5X/75dX7/D/6A/+1f/ksWs5pXX3qRbd/yWGYu2WJ2yrSq2N3Z4XrX0e/0DM6yCJ46RHTlP/2pPq9/1HrqrgXvIyGkK7TK5f3iEn2NGNeEPuvmEoIU0igBSKNmNUuIBuuJKVIYgbWewSWGbSBFgRQRJRnzcQGZiFGMTaGgaz0hZrOHUhqXIsgsz7DeonRBDAFdJFy75P7Hj+i6mE0/CEgBbx0f33/Co8dPqP/KZJB+hBtHO+zu7nK+6Wn7gWpSs7d3jaZpkDKye+Maj+4+RCdBVB5SQkmQMWf6llLx+Wt7KBFwtcJawfkmcHLvmGklGVaneFmzd3iAKhSbzSnKFDxxEWNKdg73KY2mlJK3v/suJil2ru1inUf4iJQFZaHYmU+YTUuqWqFLQaoURV1eLmxHh/NTBE6wPd5atCmyCSqK0fEMRgpIkbN37vLud9/CRklsFhzdeJF73/8ee3e+wObj+yzPzigW11ns7fPojW/j4jZ/OSYIKb9XP2Jz+2w0es55ur5nZzYnJHAJglK4GOlDbo7Wa42UU3rnqKcTvOsp64bNxoGEnd0Dzs/XTOuCyaTkYrklOI/SarwALLrYgcETfTd6YgTL5Zq9/UW2OW+6rP0JuflMeO4+eIKUitl0Qts5QszUaiBbqMccQJGy2DU3bWNmXUpUVTUiXDRDP2D7gVgHyrK60ltcGjG0kXg/2sC1HMGOnr7txrWRYDadsVy1WXEoE4MLFAJSSDifmNUKyoLBWpTzNE2D1m78ArCZI9Q5ZtOGxxdnSGFI9ChjEO2Gi9UFRu3Q1AIfQArFfFZjTGJ9MbCz2KGsDIPNLulJY9hsemazOcYUvPLKFGTi5Zdf4Y3vfJejw32++gtfp99attsVD+/f5cG9+5/lx+15/Ri1u3iB/aM7+V9SIrmW2K+w3cDZapX1YkMP5LFbYgwyTykbBtQpy+5DVPgC597Tcci01KNIPLBtW8I4qU/xaa7rz2KtVy3Hx4/Y3dnnwcfv8qWv/jq/+Ev/hF/+lV8jBMvZ+Tk/ePcdTo7vs1md0LcrrOvpuo7Vas1mu6YoSoZ+IITs+P/iz73C7/yzb3C22WKtz2HuSiKTRyhFIQ0vvXQdUqLamfLiyy/xz77xH3H65DG//0f/Cq3hhWtH9ClBknRdR6E0rbWUVYUuClSR9UEpBKrnNPNnp0ZJ0GXDcDkQuGzyQowkkVOL8nYpg/YzEzKN+KLs0M1TQEnf93jnkDoRrMD1Ce/yr1eVYBjy30tS+NgjVQSZdfHOOmJyxGQxlULIlJ21ISe45Kg2jykUzrecX5zxuRcOOdu2WJd1rimBNkUGGLuIqiRf/8pt1l3Lo+MlD06WtO34nEXkffFgvFNnc6JWkUoJFlVNrQOzyTxTO1xgM3Q8Pt9Q6AIXPH3n2Q4RGyTvvn2PnaZisbNgc3rKqu+Y7+yxe3DES59/laNbL/Hw/sf0XYuwhrO7Z8ybPXRVMgwWiURL2JlNmE8aJpMKXWlEKdATk/uMkU2Zm71LA4yn7zYjA1GNm45spDFSk5RAnG/5/h//BVvncabh8MU73Hv9dWafexl7+ojlkxPE4XUW0zknb34PH7eIkUWYTTXZRR3jp/Npn4lGTwlJCpHeO7puYL3eMtiBFAK11jyxeS8/DG7M8IvEAIVWrDdtZuBNKh48esTObEIzKVmtM7JAaYP3iaLQCATNrGa1WZGSwChDu16i5A7S5ExI5wJJCpIsqCcCHxNFodDKcHq6yvo1BFKMVnYxGjMSRB/HaDODEJKu7zDGUJbl2PRlOnfbdhkBc2nSGHEqly4oKSTeB6pa0G066jLhY8Lanul0l5BAKZUjVFKkmpdYZ3HbjsGCJJ/ehdGgBJvNFiklTdWQokArTd9tGKyjLCds20BMHlMWLObz7ApuasqyyL+3baFSmELTddmqHyIYDd3aEgKoIgvJtdZY7widI9iB00enTGcLqnpCSjVH14/4wp2f+0w/b8/r71/a1EDm65ISvttw+tHrECfYwbJpz9mme0iuk6QanYGMjjxHlD1bnmA3R1wgoCyRQqNk/p/WgvXqjPPHDwnuh52mP0slZOLx4wdMp1OU1nz4gzd5+ZUv0Ux3SDGxM9/h+s3b6KKE4TopWbSCqizx3tP1jr2DQ0iek5OH/PKXXmS13vDo8QmrjcOHRNe2KCnpnMta5aLg+PgxduiZTAvqyYy6mfDzX/ki333zNv/fa6/z1Ttf4IWDPZQuCEbhlMIfB1KMWOeIKUCIiHhFhH5ez0KNZ54Q/NU07pNltMaOzdzlgCHEEe4tuNL0XWr5rLNIlZCRzG7teqJPKGNoJiV28NmoMCYzxShQSuF8IAZBCJkVqWQBEoYhmwm9zzDlECzGGKwbwCcuXIfEc7g/597DFYica30Z5ZaE58UXD9mZFkwnhqY0dAO8+95DeiszCkiNqR8+gYgc7E544doOksDQtlg70PU257+6yMUgcSmH6sokc/iNCvgoebLsWa4HlISdxZwgVjxaLpFDx9njj6lnO4RgUMsZaQPVixMG74jOo4WhLjQ7TcW8qajrAllIUilHB+3TTOlPNubWd3g35CZPQIq5d5FCoYxGhcg7f/JtHp+t6JXh+pdf5eR77zO7eZvULjm9fx+1f8jOwS4Pv/c6XmzwwhN94OpqvUTGiE8/pD0TjZ6QAqMVIgUKbeitpSgqprOE85GkJAiJ0QVSaYL3DN5TlwVdny3FWiv6tqMvFSnNMyC576gqkdXTUmEHi9ESo1SmdhcqX0tJoZTPjriupykV3WbLjRv7WJfp1SmRY5yIgB5PJ6PkMl4+j6y1K0wBUiCFGvlYedqntMr6CDdgnaUsyhFiKUcmUrxyUaUUabstUsJ0PuXsbENK0DQT2nZAiARCUhUaZz1FM0GvI6YuwSdssPitoAiRZjIBkWi7jrazmKJkvlgwd1kj0A1bqrLIJ7bgmU5rUhT44PAyYJShLDVt51FGEoCqLhmso57UYAPdZoueZpeyMSUX20e0tmcaDEoVELPVvq4nLObzf/wP2fP696zLmKVEGta45Rl3H3yPpniJzbpHpgll0TCY9xHdHaTMUYYxepTOrj5d93yw3HL7VYOTEd9m/VAIgfff/4DjBx8zbFef9RP9icvZwMXFmo8+ep/aKBQQouOVL34dNeZ0H+zvsW239MHhB09KnhR7Qoz0Xc/ZyRP29nYYBsfH9x9SKmg3W87O12zWG07PzhmGjhACushGjidPVtntDxRVneHpe/u8+MLnef/h+3z7nXd5dLZgbzbl87dfoOsiZeGwzrFcrTg9PeXFW7e4fv3oZ3mg+h9cJbKJL0/r0pW2G/Kkb7COJHIzd1kxuYzLkRlq7Lz7xNo2r1+LQjN0A4PzFFVBjGlMqRFXkX8heHRRYsOQ72VKI0UgJYcPedUqpCCkiNRZI6i1yc1f8OAiy2hwvWe96oki52DHdKkVlAiRkSIXyzbrgHykVDozMhFoOUK8BUitkCFw43BBgaXvHCkKQhSgDSF6tjGvlY2UGK3H5xEptWB3VjAtDX3X433Cbls+PDsHqTi+f4wyJc10yksv/jwzd5OyrCh3p/TWk0Keote6YFaXzCcVZQGyEIhaU9TVCB1/OnXN717ADT2CnFkcAjC+vgoFUrB+6wPee+Md+qDZ/+IrbD5+RKw0tYZ77/wAtbvPtes3+fi1vybEjpjs1ebkcq3/SQzPp9Wz0egJMTqCInKEQPZ2wCiNc4FJVZNCwKeANgVS5CmgDRFvc1hyU0/xMVBNpiRgPp/y6Dhn3PX9QKErtn2P9575fMZqtcl4hhhp2w3GSHZ2drG2H4nzid3dHT748JjpdEKMDusHhFBPx+njKSqlSEwJJfPLKaXElEU+ITFyWC+7fCDGQN93WS8XAlrrsSEcPywktFIZwyIFSgrargOxw2w24/yshaSYzybIFOl7iyoMs1lNYRTCCGQQpKQxRuOco+8GdhdTutbhbHbsTUqNUiVCtsSUo8rmsxnddkM5X1AWJdblBpgAk2bCk/MN1fi4jc4ZpTEGtDEMztL3Pc47Nqs1mgybzZE8oEjYlPWYz+tnpxKA67EXT1gvL2hTT7t5ExlfRFfAeo+iHPDyguAUwQ84Z5Gmw/eC8qBnEIHFDjx4fA2ZcgbncrXknXffYrM84T+EAOSUoN06rD2SJq4yAAAgAElEQVSh7VpuPD7mxvF9NuslX/zKL1HVE6QumM9mbFcXhJSB1CI6hIC2bTl5cspydcqT01P6tuCVG3ts2g2r5QVn5yu27Tazt2I2ezXNhIePjtluVpRljVSayXTGZDLj+vUb7O5MCZzzaH3GkCz92z0H8x2mkwlN4xm8pe071pst3bjWe17PRl3iVYAx/pMrM0aGHmc4q1ISrTVt26KUREqV3ejjmlBdmi60xjnLYC3eWaqqwEXyNHc0BEqtcCFRNgXWt0gZgIgyMrtTg8/IF9K4AgZrLSSB62I2aURHGDyhryBMGIbH49ZLjg1KwhSGbht47bUPuXVzxo3ruzhrOTlds97aHNuXYl5Hpmy0qBvDZrPFipg3bzGNr0V+DcqiYt1lU8jBtSlGStqtY71t6fuIDLlXqCqBkLBTKvyYcdu3A/02cOdGTXfh2D04xKeEtx6dBLUpmDcTplVF2ZSoUoIRqNqg9Q+3USll4PN2s0IJgdT5PnzZ5GIkad3yxp98m5WLVNc/h9g61puBw89d571v/RVpus+tl+/w8V/9JSFus7Tlsokcf8hDoXQ10f20eiYaPa0UVVURIzRFjdCCTdeChG7I5oVCSVxIGKOREhSC9bYjxERVGVLMTaCSCjd4QhJjEyZIUeRYoRHyiNJs+pa93Rnr1ZYQPEU1Zdhs6YeO69f2mJcNIiWGvuPg8BbnF2fEJK/WUkrm01WEURuRf857N8YKKaSKl/r13LBdXkxS4p3HuQyJlGNGoVKKRGS72VIWCq0VSsYMO5UKPwz0ts2ns8IwaWqGvqfrtxzMZrTtCu8Cg/X44KirGXYYWCz2KEyHsxusg6os8kmJiA2JvmsZhCI4R11XNM0Oq80aJbMOUsSs/4Ps7pGiHLMDW6Z1jdIlLliCc/RdhmVaaxEyN7REhygyVbyeTJ7fTH7WKjjC+pzV6TkfvP8mhdmjrf4tw8kusbqA6gTlp5R7D1mfz7GDx8ceUkfbaW5fm/CaTrz5xh7rbsrnbjqs7Xj0+BHLs2N8/8M4kX+UEuP//ZSdpsEnNtuWj+5+xN1793jj+9/ltde+yZe//IvcfvELlM0MpQ1Dnw0rXe+IwbHdrNluVlxcnBBDQoqabnfCuu2vwulDCDSTKYmWyXTGYmePTTtw/949JpMZZTVhOlvQNBMODw4p5YInjx9x7XpDAL7z5iOuX1vxuZv77HnHbEz1CT5LYp4P9J6duiQyXL7vl/ePpxOcXH6EJRtjriaAwzDgnKOqCvq+y2vd4PP3soCyMtk5HvPW0bmYDYDRU9ZlXr9KQfK5qehti/e56bRu5OjFhPU5FSN4R7CR6ALD0CO8wHcRI/dYLJZ0p08IKaFNllDFELKmT8K9RysuLjqMUpwse7zUVIVCpRwTFlNid1ZxtFOiE3RdhylLttsBZyNNUyGR4GzWrGnD44t8IAohEWLgyekSLaFSFVplfbuIl9sKg5CBRjaUvsE6RTFrGIYsH1OiwEjFoqmYTkpMkZs8CoEs9dUQB54OdCIBO3R53Wc0KWanppJgtESGwP1vv87DkwvkbI+9oyM++vbr3Py5L/DBX/8ltqi586Wv8v7f/AU2rgkiP7fEpWbTA3l9ewl4/1FujGei0fMhUNUVF6s1k8Ueg+3p2p79/V1W6yUx5VXPdrlkd29B13YoqRisBRKTqmS5XlHXJWVZkMbwcDViTqzLTL409PTOMdc7yJRdTPPZNKNBrKWsDJvtlr4bcNazs1PjfECJiO16fMjj4CQykDI3dCNaRUkG248nr4SUAmNywgUxv99KGQRZRxF8vuiKogAyWFkXBTFlsGRdp5F6Ds7n2LbdvTl7O7scP9jiXdb7RW+zZgLFbDYheMdkYghBZe2c1Djfs1pecLA3JQnB0A2w3dJUilJKFosF1nvOzy1lobBDy2w2Rcr8eAghQ5yrmuAdXT9QFgXTpqE0JULliBkXBNvNhrKuKErDZFLRd7n5i72lKifYdkPRNJ/hp+15/TiVgiV0Z2yXp5ydnXG+6Uilpz7yiOYDkpsS5CO2Fy8wnx0zhKztMfMztq3m7sOWybFivTZspcAYj0wB5wa+/c0/54O3X8P9YzV6klHnlE/AmVkJ0fNTXVmmBLZPRO0RMjBYx2r917z11utUZcnBwXVuv3SHupqjdEWIecXTtS3O5omLUJK2y+k9OSYqf6VrpRGmwlrPdDJjOp2hdMFHH91jb2+fG7depGkmTCcTFju7XL9+k3c/fp8YIr23yFLQFxveuduxu7jg9uENdmYOHwPhVDxf3T5DJURu4i5XttbaPOVLmR6R8SLxCsqf/4xgGCzeW+q6Qgio65rttmdkelAUo4TI5lg0gRo3TVnL7oNF60TbD1lrax1Si6vHE6PHpxyLllfHnhgi3ocsR7ASO7icb6Nq6uqIwz3L47MLYtJI8TTCLUaJEnDz+h7ediQJy3bAW4cQGjMiYs7POnw7sNOo7AqWgvNVnyNKpeLxeUdvPcJopBGEqBGSzJXdWESSeKlonUcrhVYKOYYopJRjDff3jrAt1LMZyWiizeQJKSRlUdA0JZOmpCxkTr8wGlPpq3U6V7FngeAHtuuLTORQihADSkpEIZEo+sfnvPM37zCIiltfuMO977/NwZdf4eEb36FLgi/8wte4/93XcN1pNsAQQbiRsRuQQpDIXGFBIonwVEP2d9Qz0ejFGNl2LT5EkNkF5Lse21s2m46y7DBG0Q9D5uXE7HTdrLdIIdjZ3eHs7Jy6rJg0hvOzFdrU2Z0Ust5N5CALBhvGSLXEdnSgRSRd23Fw7ZDHJ+fZql0XYw5gpFYR72w2aZBXtDHFHPMZMwxWxHwhSiGvtAGJbE6IRISQFEWB9x68QwiJtRbvff49MaK1ImpDYYorInoMATt4BIKTxye8cPs2SEGhFIKEUobBrQnOsd5cMG0qtl1g0lT4FEe9Y14XSTFe3CIQA3gHm80GgWC72qK1QgpNXWu6tsUUkbazTJt6fDxgijKnhsTA0DlSiJRVTjEgKawNlBPNdDrBtyXLYZvziJ2j7wdc31E/b/R+Zsrbnrf/+g/pB4VzBtBoA6uHX6I+ep1y+6u4bslgb7B1Z2ydQW0n2HLJeVfxuN1w998scGmLVvcoy6+SYuDtN9/gW3/5735qTd7l5JynxrfRfTjG8UYQKV+/QuV/ji790DDvUiP7kz2W3DA5m1MJhBQMfUAJR19aNu2Whw/vUlcVk8mCZrJDWU5GfmbAeo82mb05WI/WYoyLEkhlEFLSNBMgZ90qqXlyeoFzNssoypLJdMZ8Mefa/g1mk5ohDWzOLcU0p3EMWnDcbRHtx5x3u1xf7BPmKeuvntczUZeSn8vVnJQS6xxCjRBl+Fv5sEKIkbX39J7yyfWv92F0bQv63pP1t4wrwEhRVlmSIxTbbU8IHqlA6LzqdEOeKtngSARS1IQ4jEkanmADuEByCRkFSmus7YheU4Q9rs8ly67FhuywDwiUiNw62KFInrLUJO+oJxMeHHckBH0/jJo0ydnWshkyXHiwG6TQEBLnj5eEmK8DLSTRBvwQRx6gYlZN83YvRiR5czYmYgP5NZPJcDC/iRgUzeEcHxMxgEoZIzNtamZNRVUZVKGhlIhGYAoDV7aIjLmJMdC1W0hQlBWJRIieqiwILiB85KM//S7nfWLv1TusHx5TX9unfXCP5Wbg1te+xvKD92iXDwnBElMgRU8IAyHlUIP8TZbX6lmr9/QZ/V31TDR6KSWaoqIfAs5HbBzQZUFRlbiQx5V61ARs255usBxdP+JseZ8IGFOwXq2pqwqtNNvlCs8WrQ1oMeoYKpTYYn2OkhFS0G5a6sWclCJd5zg7OyOGxHq95tq1fXYWU5yPtBcnWBeYLgrkJl8gWXeW8o0Drn6uKoord1RZj47FlL9cnfdoo0lSYIfhaixvnaNQ+SIe7ICQWU9h7cDBwZzVcjNGrAnqpkGqFd5bzi9aQFJUBdutYzbfJ9gti8UubrC0Xcu0mTEMHuu21NUOTSOwQ0ffd9RVjTEFZaNYbducXxg9XkBR1hRlhQ+5gdWqwNkBocEOA4Up0FJnvQG54TWj4LSua07dcf5zRZVPnUqDVhi14HT54DP4lD2vf59KSM5OWo63H7IdnmDSdYwvkXVL2wrs8kOSDLihZVrucfvOIQ/uvsey7/jm62+z2cK1oz3cpsXHz9G2in675LXv/A395qdkwBBZZJ7G7i6NjCqpElWRnXd9G5FaoHRC6vx7HYLk0t+eYgnByEr6+0235Bjryd+9AU4po48gL1pi53FKYJXHOctqs0Srh1TlhNlkxmQyBWFIociGjOMzUvS0bY+PgoRES03RmPGQWCCVYrXqiNETY8CYkqaZMp3NOTg45GBvn7P1MbbwTHcFQwuegUrVdLZn7S5Y9T23Xce+3/tJ343n9dOqTzRpV6vaUex/ua69XOl67/OqcVz3Xv5cWZZstxsgobTEe8920wMjH0/m6Z5Wgpg8Qsp8IPcORMJ5T1EYhqHLCQ8hT468DwQXUDJBiATrSNYRbCK6gNQ54tMOHSkO2HU76nYFdaFxSuIjhJA4W7VMm518/ywFQ+tIAUyVhyYpJrQ2VGXFtNS4znPReyKC1VmHEPoKnZ5ICJmxQ1Whxss5kUIgjdxbicibtpSj5EQMzMt9SiZoVSIbgw0eERNKaGpTMGtKmrqkqAyikFBJzMQglboyYkDKTVkKeGcZ0RyEYZzEOocUmvU7H/HhBx9T7h9RBslZ37E32+Pdew/Zv/Mq/uwJ5w/ez0lCKRCjy1nHZD9AntxFYnL59UmBxKhl/JR6Jho9KSVCZ9dYip7kQBcFg3MoKSm0BiVRxiClAiGRSrNuWySJ60eHnJ4+oSwMy7MlppyQQp7cDUOP1iXb7Zq2t8TgSaOQddv1XD/cI/p8wezs7HJx1hJsoNCS4B1aqaxh8Amt8yo4kvJ0IObMTbTIX/bjKN25zJiTI1NMKYWsZL4Q4emFGOJVs5hGJo4pS5ppRKbsmBIiQczcvbPlEmcDIGiammnT0LaOdruhriTSRhpTc3F+zuHBfhZ7DwNNUTKdzlGmpltdUJSGWipW65YpoKJm6HtCiLjBUtYNKXmGfmwW6xJiziicVJLWW2KIRJmD44taoZMaWYGr3MD6jhASQktc9OATtVI5fWM++ww/bc/rx6oU6b0itAt86AjT73F4/VeoZ1sefNiw2PuQITVUC8mqLXnn9Xe484vvYS6ucbFeI0TF8ckF0V+D1BLDx3Rnd3n7ze9cucZ+8sfIVSbt1U+QYd5KARG0lugif/EmkY9nqhAkEUkuIZQYNUOK5N0oBM83mU/8lX+7RNY3XZ6lU3qKVfg00HNKkHwi+nSVpjOIgeBDZuO5Di8LpKnRpsIHx6Qu6F3OJ1VaYUzBbDZnsMOVxne96RlGM0VRVkynU2bTOXt7+xzsX2O+/4TNQcmTkwDJYS0UJjIMnuhbXJV4+/gDrrXPYebPTI1EBinz9ueSx+qcu2LpXYL4nXuafStGJ27W6wVWqw11XWOMGXErORdea0XwWT/uXQBBxnTEkFeOPl9DMXi6zoP0Y1JNQqAoK0lwA84F2tWAiJGUPIzAZRsGRBqwQ0/AgRKEpAl9wNocmaaNxEXFD+4uKapEXRZs1wNHu7sImY0Sm03O191cOB65gERilEFrQ6NLlMjUikuTn0wCGRPy8p4MxKsmD2TK8g2REhGBFJqD2U20KClnFUkrYp/ZeVIISqNpyoK6LtGFRmpIRmDK8hNNXj5gxpgbOus8RVEjk8qAZJ0XxfQ9733zdTpdcuuFF3j/te9x9OoXeP8vv4XZv86snvDR9/6KkNwY75inmSkqUlTE4EDEPM1Ll33D+J7IZx2vQv5gSKXorKO1HdN6gg+eWT3Bx5TZcVIRnEeI0UDQDyNhWuB83vlfv7HHh3ePWZ11zOZzjChHXVxiOp0SQyLERFkWhLNsxyYFYgoZW5AC2yE7etrW4aJn3WmSUKMI8nL0K3KnHfMFE8fd+TBEyqGnHMfgQojcqAqRbeMxgyyVUiSVA6aLokCSM3bLsqSqKoLPwGYlFSHmk8p8McP5DIFWClarC8pixmI+p+ssdVnjBsd0OmWz2RCTZNI0BO+JgPeO+XyO0pHtpsUUhhASZaEpqpKh6+kHhylzyoEUgmk9IcWMYohdx3RS4IND6wIhBT4k2rYbhaYSn/J4PLhAEBC8RwhJO3QIGYnRIOUz8bF7Xn+PGuyWvvMMzjPVe2wuFPfVR5jN+xzd9tz9wQonLPODz/ODDx9x/4GnaAKLFz9k/9p12mXAqIbTjaDbnnJ++oTl6Rne/QOvCAUjtFRQlVXWBkqFEJJhNDYIKVGlQhaSiKCqp0Qk7XKJ9wNCJZQRRJ+bsx+qBMmPkzxJnrZc/scvp4I/orx7OjYMwZEY6IYBqTQpLSnLBiP3UDKvnkptQCjqumYynWHP7dhgJi5Wa87OLrh5s4XJjHoyZT6fs7u7x97OHq+/BdOF5KUXa77z+ikmKbQWOCcwElLw+Bh5693HP8134Xn9BHW5tvXeX/17zk5PV83eZerSpRHDOYcxhrZt2d3d5fT0lKapr9a4WmuWy4t8z4oJgSbGPLlTSuLCgDIS57PuPI2a8rI2DDY3EmrUpns30LcDtssr3hjzRDBGixARoxW29wgNWhSkmI0VIgSSlhAF3g60biAKCZtEjBsEiouLC6TKj08KjZSKRlboJk/QBHmKJ0Qi4iGJq2tBJBBk3Eie6AkuFXkaEEmSxl9PyaNUyc7sOjIZqtk0D2D8iEFRkmndMK1r6tLkw6KRiMJQFOXVe3Up95ACOttjCo0pCvz4PumxJTz//l3uPVlz7c6XOHn/ffY//wKP33oHqwtevPMiH/3ZHxNSlwdS5EjJ7C5OI1g+IUTG7STG756MRfiRkpNn4o6rlObk+AnTaYPt+gxT1TqvF+dT6qKk7VrMKErNUEY3NiNytDcnSq1RJLzP9m9tDNFlS7VzHmEM09mUruuZTJqRLp7G9TDMpjNIj1HSMPjI1uWbwxALtElj13xpZc5f6TnCLK9nBZdRMyFnD6ZIXVZoqXDeZwZg9FcXsFLqynIdvAch6EJ7dYoLPuQPsNKjXiPDkGOMeOupigplCvptRwgROzjK0rBer5lMptSm5Pj4MVorGgq0VHRdS1Vr6rJhNawwRYFzgbKocC6gtEYJSDKbRIL3Y9MaRwdTxGiDdZakNGWZDSbG5ElrXpNtcc7RtS2Lw0OU1JjSEEY6u7f2M/usPa8fr4LoWPcnuZlYrmi7C/pNx+wGWB4wWSwoxM9xePCL/Mmf/zF/8doj/vBPl/zaNxSHR56uvMHDjwN2cHR9pGvdP2iTJ6XM+dMCogBjKpQ0VGXWhQohMEWksz0hwLSaYooCYyq22002WjUV7dZdaV+EHjNHfXwqxxm1wvlLNk9IVKEJLo43nMwZE+kTa7cf9Tp72Kz7rHUVFkk2Yty4tkthJFpK6rpECIMpa+bzGev1BTF4tpsl3dDy8b0HvPrKHRKJoiypqwk7ixmL6TXKasr56gkPVpKuDywWRX4iokBpQUodmzNJtaPZnP+DvT3P68eoNGJULhs9YzLKQ6RI8IEQPEbne6X3fjRKRLbbbZbPnJ5e/V1yXMl2XYcxBTH6K4mRFIqU7GjsULgQEDI7aaUUdF1LEhGSvNKeD3YgpQwyLrTJbl6VD1fSSEiJGAZUIQiR3FAKiUgB4ROFrkketFGUOnNbY0zIcWKYD2XASLrIL8gYrcjTNS0p3xNhnKilhORSrZabJJEkSul8/4wRUoZCByw+BvZ3X6AyM4pYIcuC4BMhCrRSFMowqUqaqqI0CmUkGNB1lkz8rfcrBXxw9ENLUZo8cfMBhUSRiBvLu995E3NwC+0SFo3pHGfnS2599Wucvfl92rDBR08SYdTgpdGAkZOEIFzhdS77jkQa359P/yw9E42e9Z6Tiy0XqzWr9ZLFbI4PntV6S2kq1lpgyoKmniBVHlVvN112LleamBJlUSBFZLnpUdqM42vPYAdMk1MbrM3sIDv0lIWhMJKh79BFzs49v1gBgiQ19x6e8LW9BZOmQqqCshC4EKmqimGI+Yt9jDC7HKELIREi5/5l3YMGkQHDSWQ9hFIZTpmbOfG3LmSlFUYbUkxZWO09QuQVrneBetHQdTnUua4VzrYokWPWQsgr4cF5ptMJXZ9fh+msIUbG5+0pi4qmKmm3LdoUuCE/FpEipdEMw8DOrAGpkVqhy4JgO7bbrPcj5ccpQ7pyKxttsiBXKIId6IYtgyWnfzjP/8/em8X6luX3XZ817uk/nOFONfRQPbjt9hA7JoCTICyiQESMwxBwBEp4QMorEkKgvPEAD0hIjCIkSiRiJLAUoigQkRewAjiJp47dtrvbPVVXV9WtO517xv+whzXw8Fv7f2+1e6i2uzvV4q5SVd1zz//s8//vvddev/X9fYdd3xOnkRQntE50Tf1P8nZ7Mb6NoU3Ere5jo6I93dHt97zx4DE3j/ac3K4I8ZLWr7nePKTvO15/4xKazG/9luUTn7B4fcoYnjCOgqJhpAA6tET/oKOIL2b7IqU0KSdiTrKrdrXwjWIgp8xisWLab6m0x1YepTTGOJqmRanMFGNpkTmmcURhBUF3SuILUy7aOo2uDWEYUWRp6WhNCtKOzTmjrSZNEWNNMVuXkckYa8Xd/rALFwRBUD4Z43jDb33mC3zs46/xyU9+kkUt6vopGZq6YtF1PDl7wPnFOePY87sfusc/98f/GSGdG023aFmvjjg9PqGtF7z9zhM0AZJlsagJQdE1HW3rGeM5U99jqhebsPfNmM3058QkrQ/iPas1OYo/m1imKLz39H2Pc46+7yVmy7lDYTAMPcMghZ73RfCXAqBLZ0rm0K4XOx/nDMMgdixaGWIamIYgDgy+ot8PmARpmkQgYA3eGKZBhB+ojGhCC70pRtQQcNoTM6BnLqzgb6iCiGuhVigUKF3SdlJByUv6xOEVignp8ukMRpkivBDORiaUV5bjYMlKsL0x7okojts75CljF5ZBRaYAVhksmto5FpWjqR2u0kLVcGArU7oGcyydCL7GcSRMA23XMvURVazVoobzz77BxZXi9BP3uP+Zz3P62od441O/yepDHyVdn/H06duQpkPxlnIipkBKslkUVnEip4Qkl1CKvEzICvOczcvXjvdFoZdiZpwiF9tLttsNw5g4v9kRpgljthitWXQNl1wDmTEIRJwVoDLvPHhI07TECBdXO64urgFDVjDsJ+6cVFxtJsZhoOsahmlE7fZ03YKEIaOofENdSwGilWbcTQxDz3q1xOqMMYqnlxvW6zXTtKVo+eT9JzF6nqapmD73rFYCfUtMmsYY2Y1M0yQLUkbCnwvHJgOqcPdmuD7lxPXNhhRhHHsSHUPhajSVBQxDPwrEXJAMawybzQ2uWJ4opRjGPYoRo2qsk+gatKZdNGyur1FKM/UjWImokfawY9wLobTyBu89UwqEyVL5mhAS3omyawwTtvEoozHe8sqrr/CpX/8s3iScr8BaTBjRxjBME65/4aP3/TLGHs6u3oHNXbQfcHrkIx8O9PqS2nnuP+546WPwd/+PX+Rv/83fIuzF6/T26ge4tfyn2O4G+t0bhEnI49Y1rFaJq8ur9/T7n/cO+9qhjDxEq0rTNA2Vb4RvlBLTFMTqKCumMZOtIAbbfY91FTGM4ieJolsuQVmaZkGTwVvPtbNstjfS/tVi6VArxTiNgjwYS920DGyEs2uMIHxaob3BZpgSqOJ1pbSYi4MUuto7QfVRB4X91xsxRXa7HdM4EJ2jcpop6bLwK958+6tENEbDm2+9SYpJkB5d0bUtR+s1d+7cY9Xd4fjkkkV9TIxvEZNmtw+gJ+ps6PeZWy8tmMIAvJif74uRM8MgsYBz98eXdqB8O3N1eYXz7jBPrLUHA37nRLAz56lP44gxRlq5MZKUIpOKANAwjDsiXqxRFPTDVtYzo9jveoy1OA8mJnabDXFIEGGKGV211K1l3EtHylaepCYIc9MUScwwGpKsnNoZmBQag1ZC+WGOUESKmZn3mlClqLLSmi1m0cy8Ow0mAkWdnpUIl0KKJeRAIspSzigVRV2eLctqTWOXWAyuNsUXN2GSRhlFU3maytF6J+ldTqG8pqp9KUQPlwqlIYYJ5z05a3KWbGq0Qm23vPGbr7N85WWu37pP+9IdHn3xc4y+4YN3jvjiP/i/SExFYJGEA1/yjGMSdE8xG0RLEZiyAEE5CZD0vkf0MiJJlsBfKbRSFAPPmCessdzs+qIQKjYfTmwG9lPi9Tfu09QV+njN00cXPHr0mLqqZCefNdOUGYdAPwS0tiglO5WYEqkIFlKCRw+fyK46JkHGli3q7JqYEqfHa95+8JTTEy3GhFk/V80/M1LWWoLDQxDyTtu20jYu1XZVVcRCqEwIpB5zwluHMbYESot7tvf2oOnth4GT0yPGSRaLtm24PO+ZQmC5WjJebzGmYuhHrPcoVVJFmgaFwjvDbj+hxgEqT99PZLQsXCGyvblmcbImZ4WxngxUlWdKImpJWWEVhElsIqaC0Bkt3MLkJbrmaN2wWKxRRtrPt+/cYtG0JKPo9z15CqhsvsGd8GK830aOnu3+EZszw9n1QxgtebJcXGn+lb/QU7U79vsvoZRniAHtM051LLrT4hW5Q+nIftgSitHqvbv3uLm+eW8tzRR/7wNMgbEK5620fpylrlq8ryQZwBjIudASAnVdY63HlwVRiOriC+l8S7s8JmXNYtHRNjV9v+fJo4dcXz5lt9/IxlBpVM74IGrZrBXe16JAnAJoTVQJNUXJEnUanRQxJkiyQz9IA1GlBQc5hG8o3ABQynDr5JgQE+fXN9xaH2NMg7UeYyt2ux1Nt8YaxdOzCy7OH3F66wTnbuOrmrbtOD455nh1B//4ddZq0EMAACAASURBVP7Qj1QYdcrFTc9yWaNRbLbn7K/B1juq6hu7678Y3+PxXGbq3D0ailtDSolQEirGUsBZa6mq6kAHqqoKay37/R6lFN1igbWuiP8SDsPNMBQ/O2n9ZiUCpmEMJbVJhCDWGYzW7IaBfhdQWWNRjGlise7QXhXxncU4x7DfkLLC1zJHxn4UaxctgIG1DpUSMWgSBmXSs2QsrchZo7IS0C9lUZQWRE9as8KhSDnLmk4uYJ/ErCmlUDmisRhdFSRQ0K8xBVKasKamtSsMFZW2GG8YE5AyWYE1WkQYlaPyVvh5NqNqaZdTsEK5RomYJkLoqetFqWMUqnAGLz53nyulOV60PH34mHVbs7noefmTP8zbv/MpUqkXRNCZiVl4eSnIsVGz4nnm6gk/Udq3Sj7/d6LQU0oZ4NeB+znnn1FKvQb8AnAKfAr48znnUSlVAT8P/CTwFPi5nPMb3+zYh91sNjjrZfebM0ZrqdiNtGNyzCiNwLkZSBBCRqvMZDKXV3v6IYByXFxuudkOnN66xTuPL7i+uixw6DsoZdjtdqy6lt12h9bCgdvtepQSM8UxjGgMwz5SeVOMIRO77Z7lsmVz0x988ebdB0qQtWHo2W23rI+OMMaKNYoxDH2PtUKm1ah3kWxVae2mJPw+g6JyjqEfimddL/FkvZimjmOPtRpjbWkny+TOSpOmkSmL07lZGMZpYhoH+bqghk0nNg3dYgFJsxsecOJrpmFgGCbxR1KKqq3QaJbrNdurS5I1ZA2LboHSMJbX9v2euu5omgVjAOcM424rRW1OgqQogzOwn/r3etu9GO9hfHfnJoRgmfKe1986o9E17zw54/Jmxyc/3+HbRKUe8juf3zANiWwUdb3glVc/znp5ymb/OrvdjvMHbwl3Drh58t54a1AEWKXYU1qBliKvriqsM2il8FWF1oKat12Hd57KS9D4OAoC56uK1fqYulmw3WyFz2MqFstj7ty6y927dzk+WuMrT5gi5+fnvPnWV3j44C2uLp+yubkiBkH5h35PImNtjTaaOE4o69gPe0I/MMWM1eCsAaeI4yALU4lLRKtiAi/zW2l1sGD5muvK0XrBT3zy47zx6AKlDNo6bFXjnBR6qoi8lDFcXN/wzuNLPvyR8vNEUrrB2B3LVY2vDYkr1gvN46eBoPbU+hbKKmw7Yq3m+vz7P4ruezm+q3OvzJHnUe0QAv004qtKjJPh0N4VvnjGe38Qbswt31lxa4xlipLEYqxlyommbmSdKWbMUxiLUbPYZ6U4FNEipEloBzEEJibcylN3DVMMpGmkqiv6fiAXHh5aMQ07stIY74ghELWsbeN2X4SNmjiVtmoSXZNWmZQViSwt3hnpy8K9fT7uy2Qgy7qK1IkCxCC2ZiV4jZwFXw8JvF1AUjT1CgW4SoMVHq5KGqXBaU1X17RNg6+MUDGsQlUGVDFbLrvQTCKEQTJ/nacfBgwlnWsYefMzr3P8gQ/y+CtvcfuVV/nyb/xj2pdfZXjygP35kyIomQhhLHx4iEm6dIln7hwppwOKGBKCgoLwAb9Drdt/H/gcMCfS/+fAf5lz/gWl1P8A/HvAXy7/v8g5f0wp9efK637umx14jnTJKknkCRxubK2Ez5PJhUumEQ+ZGeqVVkUIlmDFysQ6h7bS4tzve7yv5CLHyHY3UlcNKSnOzq9p2o6mcWWXH/Deiio0TvzO575KCJocA+fnG4y2nF9c8eqr99jpEUqxpo0ihkhVVQy9WKuEGOkLaliVhWg0CuukVTv0PVlBDLFwLgzW6tKPj3JBUzhwhJTOLJdLdvtN2dkorBcp+jgEjFZiTZMjR8drNpuBoR/Y7nY452hry+XljsSOlCJV7Ykx0NQNVzc3bG5uWK46xtEzBsnUyzEQt4HkGyI92lpiTgz9RMwDdSNmkNY4tLGEkFh0olxUGM4vL/nRrkMZh3MV3nn24xXb7fbbuO1ejPcwvmtzM+kdfhnITyY0cs8Y17AncH6u+aGXW+Kw5vOfe508ZUwlilARVBlB1PsoO1ZmEvV7H7O4IpNFvWc1VeVYLU8kFcY7aifE6LZtabsly26F8xVKi3BBKUNV1VhbYXzF7Vdr6mrBer3m7q1jvNc4WxGT5DbXlWG5XPLqBz7Ew4fv8Pb9t7k4e8x2e8XN9QWbzRX7/ZacNBUVm5sr6m4par1O2mLCTAfnnRR3KZJCFO1dEq8x6y1Ka7Q1ZB0LcpEPG9+qqTm9fUsUeCWSZ99PLH1mnEahWGhp01nvCdPAxfklKclZtlXFctHy6p173Ll1wunREdZcYWLA6MS6q/jcZ97mzgc1yyNLv0vUbQW82Ih9G+O7N/eyqGubpjlYqzjnyDEcOOda60Ne+mGuFARwLvrm+8k4Cxmm7V7ss8joLNzTYS+pTqFwaVNKxfBYWpHjbpAOWBQT/37oqeta7m8ElPGVZRgHtMno2qHR7G6uySninC1IVWC5XLHb9ign702FjFaOlMv8IJecXk3SRVChBNBhjqZCOHKzulZCUeW+V0qTiaSoMLpCqQhoyEaizlwtXFtlaNwROlu8tyRrIBk0GaMUlXF01tLWDuM0yUJ2ULXVoUMHHAqwaZqwWpMUpBCx5TU3X37ALnn8EKm7los3v0pwnlvHC9741d8m5UDKPSH2pDgbIIuLBypCyfNNz/GaY84kLe3arFRpSf8BCz2l1KvAnwb+M+A/UHLEfwH4t8tL/gbwn5Qb+s+UPwP8r8B/p5RS+Ztof3POhDiViyRV8uwtN/NXtNZyrUplSxI7lZxnZOyZzHyGscM0sd1uWSwWRX4u/m8zaXsOiBbn8dKbN3P+neZmG6ibjmHsGW96lNKEELm8vMb75nDitTZkkzHZEKOQYHNK9P2eMIppJYi6OKckqKFPqHFAx/QsCq1cLK00UxxZLDt2O0HEjDFU3hXexZynW3yPQix8pIyKmWGc2G42tG2H9w05RYzVLBYLqsqyvblBIWKJGAKbmxtSVlSu4ubqCqttuXE0Td2gtGLRtpxfXKOyIacJ6y0w8xID09CTsqJbL4hTkOsXAk5bdDF6jGHCWSdq4hfjOzK+23PTOrje7Ugu8fEPfZzVvSVvvPlVwhtf5uGZ56fWJ6zaP0y4/g2yhnrRcbw+paobuTfLovH7HXOAunVa/BqNoavXVM7j2xZfVyy6JXW9YLU+oVse88EPfYSXXnmVuukKx04TY2acJsjQtS3L5YKqqoQ7NBVeTE70+z3O+oP9Sv3hj3Drzj3efPNNnjx+wPLmipurc26uL+h3e2KcJLdTWVnAtGIYhWZCFCskUKLIG0YhUqckrbKoUEYaUa5uCENPCrHYJxkWXUvla3ZDwruaKSi2uz3KbKjqjimMOO9JORDjhDPw5de/WhZCjbaOo6MPcXm84wc+UrMdl3z+i5fce7XjS2cDn/yBDp1hr67xDgateFHkvffx3Z57SgswIG4FsqYZY0hDRitN2zSMJS/9edRvPuTM1Ysx0vc9dVPjnWMbIykXzpwyjONAmiaGaQKjDz6zwzAcggGsEaWtUZpxGLBKUXUVMYiTgtaKy91NoR9FlNWM/YSxGpVs4dtFqkbUtigx2M9JkScBNzRWkLk0K04RhaxKqOIbx4ySZSVetkpwNZWLoWWWLNiQBjQWrcyhvTqvrSShSznnqV1FZTSuVvTWEvssaRhK0zhH5z3OavHctBntLdo+QxMV4pULmTCN2KoSEColkla4KXP/s2+xePkl3vzil7l97yXe/t0vc+sTP8jZlz5LnAbIYqU28/Fiki4hShJIZh4epSWdSmsZJYhnzDMo9ge3V/mvgP8ImJ1uT4HLnPPsk/A28Er58yvAWwA556CUuiqvP3vXTazUXwT+Igg0PY7DrLOZXyAVbblACvmgqYT6ppQgB+Hz5UzI0Ku+tCYt1jqsEz5N3/el2CoFY/kt8bmJkctmYVY4gcI5jzWGOLePlGKcAtvdxHrdljas8AVjmKjqhv1uYLPZiMddXbPfbanamso8U9RClHbr2LPd7Z+JL1IWJawSW5Xz8yt81QrCqDRf/OJbVO0KZzU3mx0nJ7fJemAMGasgxUTbdlhjODqx9HtRyxqjWC6P2O+u2W1GtBXBd4gTlfUs25ama9lvdrR1KzwOpTh7/JgwDqxPjkH8NJlSQsVMzhOVr+mWC8IYmMaeqqkhG3JSjEMv59QalNEEEpWrYUz00wtl33dwfFfn5vGxZ7OfOL39ATa54VO/9evcDD1DnPi13zznp/9E5q13/gHX2w3KGOqqQivDolswhUgMgd+/MbLsVK3VVLXFW0flG9arNUdHx3jvWC7XdIsVTbugXRyxPL7Nxz/xCZbLdcmWltZuVTeHzoAx7269xBAkiF0Z6qoVJCWMqKyo6pqqqlm0HQ+Pjnnn4Tt0ixWL1ZrN5pp+v8V4R7/f0ZhOPMY20nIOaZK2UFbEbEAZ0jQRpxFTeUigrMMoI955JlI5j3Ue5Swnp7f56GsfY4wRozTb4pM2b4Bvri4JMROjKN+zUey2W87OHrJcH2Gcx9WOpj4m5x8gj5/nEx95wn6XCf2Ozbblw68s+ew719xcJ6Zg2G5fbMK+jfHdnXsnp6gSZUZRzmqtccaWzYWmqqp3baTm9WymA4FslpbL5TP0r20Yh4HKGIZhoPKOod8fQIR5/YNiPzZJvm2aFNcXG7yFZlGET4gDxXa7E96pzuQxkaYJckQZi6J8rTRKO1IOIp6cJnKy7Kco8WcjpKAF1SsKXJNK9IwS3p5WkLIqnD1p4SqsvAYxFU95IKeAdRU684wTXgQLWUPKiabq0Fhaa1kuG6asi+LVoJWhriy+Lvw8J+uY9g5l1DPLF2Q9zwSmqcctTtBJgU5opdjef8Q+KvK25+TlVzh746tUt29j+htunj4k5IGsAilEYqB45iWymjBGSVRpmIt38QBOaHKOIkiZ3wLPUeC+zviWhZ5S6meAxznnTymlfvpbvf69jpzzXwX+KoD3VVaUHvvBbSC/+40rqZznm1fl8h9BeqXtMgx473HOHRC8EAKbzUZ278XwWJA4YUaLiiUWXyH1rr9Hi73IbujRxkJxId/tdiyXK6pKfo+zjuAMdd3g656YAjc3N1RVzc36ivXxCVqbg6mlcC+Ep5eRiTgMA03b0ngHKRLHgSlphqGodLVm2yd0lXFaYWzDFDK+lszL/TBijWa761mtjhimPb5eAIq6NgxBUXUL+l1P03n22z1d2zHESWxUvOfp0zOWxyviKD5NIUfW7QKUkMfryjOMgbquSHEUJXBOwmsoPIi6qUBrjBNj2jwmmlsNu22DVpph2BOGF+7734nxvZibr330bv7gSz/E1Ce+cv/XePPtd3j0NFB5jcmW4eKH+cLr14xRobxQEJxzeO8Zp92hpfH7+3wZbUR04X1FUzcsl0tOTk7ouiXL5YKuW9K2K6pmQdOtePmll1gs13gv8YPWGrFe0QqsJWdZLLXSzKQfZyxawzROGO8KwVsdDM8BvPPUH6o4OTnhrbff4unFkvbqnKurcyrn6Pd7Nrs93VKhjSFOIzF7YkxoNRJiwpnMWLyvrPUQ4sH/SqVI3dTEKJFT3WLF+ugY4zxPHj+CGLi42TBNE1d1Tdu0PL26ZppGrFGE4MlJPu9mc8M49jTe431N0zTcunWXKbzMP/6N3+Zn/sUTfvgHn/IjHz7ipt/x6TcU+13Et/CRH2j4jbdfqG6/1fhezL0Pfui1rErRloCh79kX25SjoyMR8uRnzg/PizZm8EApxWq1OrwGkHZujHgndiyzmAOQDY82GCtWQSlGhv2eKSTC3uOMQ3uhFUzTiNXQ9ztJzIiR3WZPShqtLb617HcDWRl0pdCFi+ec5MNqbdkNI3VbYaxlNw1Yq8khQhbfu6wRaxR4hswhiLWs1kUIWVqxCvF61dqSkxVRh1LoWdhCIlM2dXaJTtA1hrt3lzx9MGKSRpPxxtH4irpyOK/Ruoip6gMrDgrvLyU51/o5XqRRBrTi0ecfsPzAS7z1+a+wPFlzue156RMf461f/ofkPKL0KF2PqEkpFLWulri6FIv4VOhqOZd6QRXj5CwGMhTjzj+oYfIfA35WKfUvAzXCRfivgSOllC27l1eB++X194EPAG8rpSywRsin33QoIKSI0bpEhz33vWKSeCjymJ/R8+6FA1I2DNJ6mEUHc2TMNE2SM5uF0ycPW3so9L52CLFVyOMxRuq2Zb/TJeJsLIuAFu6Pc0w3I6vVEav1MU+fPOLy8orLqwu6pwvu3LtH09SELJB4PwzYkhFrtGYqu7WpWKd459jkzL4fqOsKpeTm1oWjESN0iyX77V5sZnKmbjoUsotAa5arNeLmkFHaCUyPpmoWDNOI8Q0Y4RQa67l16zbn509R1nHr9i3GoWe72bHqOrSZ0NbSdR0hbdjve0iBxWJBiPmQ8CEiDrkJCZGhHxj7HlVa4uM0ME6RcXwRnP4dGt/1uWm05cnZWyzdgg/XHT/xp17ml754yRtvRH7qj3+A1dGSj752QrP8TfogXLdbt+9grGN2lp9tRd7rOJiRWzHibpuWpmlYLlesV0esViuapqVtFzRtR912+GbB8ckt7tx5Ce8qvPfFOFmVtpbYE0iRV7Kqkxgia6OpdUPlK4ZBiOdaKWLhOVljBFl0Fl95Fl3Lk7PHPH38kMvlgqurK7a7LebynGacaOqO7fZK2jBTYDQ9tpjZzntZoXtADsIL1EoXArvBWsM49Lzz4AGKxPnFFUO/IaXIvh+wWoOxB0pL1zZC57AG5yy73Z5pGmiy2MC4umK1WrFeHHG0OkXbyNF6wY98csHf/b+fUtWWex9MPH1Hc/b4xSbsPY7vybooSRdildX3PcZY1kdHRbRXlLdlgVeKQzbu7O02/3k2U56LP1/V9Ps9bdcxhQk7Ofp+LyJIhSCIY2TsB3RSTH1k0RnClHCNZ7fbYQ30YaBpG8ZhYtxPAsAkSarYXgeMg8CEM2XTo8WuSAMhy+aiqTyb8wGLeOFGorRwFaicivpYFwcK8cQjG9kIYsg5lG6gKWu2RyuDVsW0OENWs8lLBpWwxlPZFqs966Vj0XhS3IsfrdVYrWi8p/JObNK0QXkDFgF9mLPuJVUrTBPOV6UgzUQS+cmW60mTrm5Y3Tnl/hc+z+r2Hbbv3Gfcbogpk1Uo+oBMjFnmMZFxEM1AjplMKJYqxdBNP0PvclHb/oE5ejnnvwT8pXLD/DTwH+ac/x2l1N8E/iyiMPp3gb9TfuR/K1//o/L9X/xmPASYMTSJBlPGHmTEz27YEkJcLrOYKgqpWRU1nnRkM3PWrPdeMmZLITSOo3w/J+EkpIS17l3h0M+bl2qlhCtQqnVrNGESp/AYJijH9E2NSs/tnpZLFosV19fXXF5e0LUd50/P6LpOuAtloZnNLOc29DiO7HY7uq477Mh22y3GKJGgp4R3DmM0MYo3n2QZynFcJT5BxlqMcThn6PfSIlU6Y5Ut5ziijTisR0Driuvrc66uNlzd7Dk7v6Jql4z9nhAySWk2/chRzIVLGanbhqePHnF8csJ+iKSwpWrERkZipzSuqqi7jqppOL+8omsb9r0U7BPf+IZ8Md77+F7MzUePn/Dp396gbc2XvnTF8euWl1895t/6sx/FLXY82X2VmG5x5+6KswtYrdd4J6o/ZyUNJcb33rrVZQOkraZygs533YLlsmO9PmK1XAua7lsp9poF1rcsV8fce+kVusLHlbkvRZxRWgjTMZCTxngjiJ5RZcNXWjuZYr9S+DIhFnRAfOtSTCjnqJylqWtOjlacPXnMoydnbDY3+KpmGHouLi/FJDlMhGlgHGv6/fbAtRJU0aCsJZaoNNmESlFZVXVBcTR1VZPyFcM4EUNgv++F52sd3luhlkRfkB3PMI5M48h+c8NqfSqbUS+F3r17dzG/W3O9uyGbyNV4SdVZxmHiC5/JuGxYn1pe+Oh96/E9WReVYtj3GKWx3tN5KbCGAjTMdKN5folJhSDRc3cq51SW0nzgjuviZmGdpR+GInRKYMRgWBnNuOsZ+0AIiZxg0XpC2OFrwxQHUohMIeArz+Z6V5BEmFtswzCUzV7AWA9l3VPeoqIiTQG0omkbYg4o6wvSN5JLOoZY5ck6n3OSbm1JX9JGYedEjCwRo5kARMCgVS11hSp1omRTgEqknKlVhVOe2iqOjuQ5JQia2DN5a6grj3dWng86obxB5XRQ/CoFKUdR3I4DrvIlv1quw+PXH1IfHfH4qw+oKssYDSeLhq9+9lOMeiAnSSNJcRZgBiKjWKhMSXw5yWK2Xlq3GoVKmqAzqXyWosflm91NfxAfvf8Y+AWl1H8K/Abw18vf/3Xgf1JKfQk4B/7ctzrQXMzlnIunT1HZabEcgRmaVaJUTQqtcrnoilx2CrOyVmtzkJJrPZXibygmh+bgLj7vfp63OZnnXionNoQBby3TOAJZyKZRVLHD0NM0HZkksvEEbdeyPj7i8vKCi4tzzp4+5eTxI46Pj2m6To5dlMK+IHQz3BtCEOTRWtq2BaXYbndF3QrGGZx3aKWwWj/nCTYdjJgtAlOLU7p46UlsnFzqXJRcdV2LCbIZubzUZCU5vBeX11hXc32zYzsEzq96jk9vcXW9o200bXtEP/SMyTBRsRsGckyMeaRrWrT3TNPE+cU1QTvWp3fk2DHibMbYGl3aai/Gd218x+amsXB9GaC5ZgyJD374w/zgj34CzAOenD3gs799Tb8xtO2a5eRxvqLrFigU09STYzjYRLyXoY3GaIOvPN45mU/rFavViuVixWKxpG0X1FVHVde4qqHuFty5c5fVeo21jpTBlGNJAo1wbHWxoEgp4l0lJG+lMeY5q4YEqppjphJTjIQwiY68qotoTDz5mtpz62jF7eM1bz98Ql23nF+cgdL4wnsah56qLuHzQVTuczfCacVoxDnAeYkcXHQLVqsFKSmGALdv3ebiessw9OS0pfKGvk8SyJ4SGFOUeogh+TjQ9zs22y0n04gvHQfnK9arI7r2lC+9/oQf/KGWm/0Vr3+lZ9cLC2ZxDKvF718482IA38G597wXXsjS8ZnGEWvM4fvz/2fUTilFTpQCLWGsbCxmhW6IUSxaeul8yRoYSVH4tMY5CAV2tpq2qMj7nbx+7DVRBxITFss0BNqmZb/fH/hlfd/TLlqZK8mKU4a4HKOVkY5OUmjrMQbSBmxyaG9BWVRBwkkJnYUBZwionIoSF1QBemaBgogRDDFPGF0JLSontNIolYsIslC2lMf7RsSGDtbriusxkjA4a9BZ47TFlY6CsQqlpTuhtPB7Z9pHigUxDBHVSZrGOA6ofWBzvmdgoluvuP+lz7O6/TJXb34ZwkAugpMUlbRtDzoBaZfHEkM3hemA4s3x2SprFLIJzfN9kJ/nDf7e8W0Vejnnvw/8/fLn14F/+uu8pgf+zW/nuOXngLndPKN5+nARc4zlwW2IKaGSQht1ODlzgPls1SIF35xxV8yYE2SVDpEw8MyjaC42Y0woJcjbNAW0GVEohkFikWTXweF3KNWjiDSlMDPWslisabsFNzc39PsdDx7c5/T0lNeOjhimgOyuIilJbI19LqtwVkkpJXyb3W6H9668vxmJMMTwbjXj8/L7UExdrRUUby71Z3RzbmkrpQ6cw5nbsdlsyuRXjOPEMIzUlfDurq63NO0apQwhgyrHudllFnWNshVZWaYwcr3taZZHtKsjNJnNzQ1DH9j1e66ud9/u7fFifIvx3Zqbxyean/1X7/Ebn37K+ePAOHa8/vpDXn7lc7z5hZ4334qcPUrcOV7Jg7PtsN6TsmDvlbUFXfjWQ2l5WLVtg/cVvvKsV0vW6zWr5Yq2XdB1KxbdEl/VuLqlblfcvvMSp7duYa0gidbY4i0mTB6lMkbLBkgbmSNa5YKui0EsyIJhlaVyBpIUef2oGFUiGaFE2GBlfTGJ5EQxeO/uHRbLJV958wFKa5zztE3Lzeaafr8jFo7iNI2oLPFkKUwYBZMXTzJXFPVH6zXr1YqYMrs+cXpywvHTS4Z+R44BTSYlaWOZEvuWi+pOiutEmEZ2u40Q7H2N8xXOOW7dusXJ0V2+9PbvcuuO4uKRoVtonM+Me6i94ujIA8O3c4v8/358t+aeKqjbMI2Mz3Vv5ud2ziJWiqUlm3OUzXsIxFhQsGlEaynodNaEGAhhPPDKJIEpMIWBsXTShr14qNrKCfcuzYXkhFZBEjKMZ3fdY70l9ANOa6JSbK43dMsFmShzrBSaoNHKCp1IK0wlgpJpnAiDRisvNkMhUCHCIq1BRbFdCxGUFfV8LDzblEt2ehAEU+hdorTNKUkrlyyuDwjip1DoXEkGtso0TtbZ88s9MStslmeGs5qqEsszZRTGiVK3MrboCEpEWQ7kFAgx0CjJ+FXA7p0L1HLJ/q1HJGcJybKsLffPnjBByfbVhCmWmLOZb6mIQfiGIUjqFeW4EhM39z8BxHpODKI5rPNfb7xPkjEEVVPPQcxz4SH4rcDBCnWAMMFgrXuXfYMUMhyKvbmgydmU3rc6WHs8KwSf5dU+/+eqGExOo/xcKlzAGYGbya/DsGcYdkXirpnGgaZpOTo6YrO5YbO55vrmmkePHnL33ssY52S3P/QkQFmD9Q5XBBnjOBZ3cyF32nJMax1G2wNxNsTwLrL43LKeFcbzOUlJDBZTkgxP4XmIkaxzrnwGUTyTMtMo6OAw9MV9XaxSrHXsd4J8yEOGQ8GckXPWtB06K/rdjmmcWBythY+otJx3beWBlV+0br9fxuZm4vJyx7/+s6/g4sTHP/xH+PxXfpG2Hqk7uLpJXG8y/eYpi9UdckxYJ/fnOI3UdXUwdP1mnSqlVUmD8TR1Q9t21E3FcrFgtVzTdQvqpmWxWNJ1K1xVU7dLTk7vcufOHeqqOdhMGKPL/S8GyykJAmZKMaW1KSp+iYmaN5cxBLASK6i1tGkq/12VIAAAIABJREFUq4u7vpXnVIwYo7hz+4iryxuGYWCz3eN9ReUrjNY8dI6maTDWsLOOaRow1rLf7UgpyGI87rFKMU2eECZBCJuGW7eOaeoOlOYEy+07d3lyseXy+powTQw5YZOYT1ilMEqXzyNitRBGlMoM/Ugs6nZjhLu3WC65dXLMEO/g1MD9B/eF9xMzWHh8Fnn11Rdz8/00ckGP5jk0rz1Gl3ZjmOhLTJp1himlYgpcOKFk0iSxWTmLGX9OiZgzUwiM40RWmSlkYtaopMjKMoaA15pxjGw2W6pCGwLhzU09UkClTDaKrBOb7Z66bYXe4y0JKY6MFaRu6EdSVGhnSFmTpgmVTaFriGWad5YwZqxWgvoZKVgNTtYNFUUtP3NnY8JoA6qIUbRH5QDGFZGmFJmKDFp4cFY52QyqjDWa3ZDZ7RMpGtDi2lFVDm/NoR2O00SkcFWHayM2JylMaGdF8asEbbx6eEUcNfX6mPtf+jKLl1/m6Ve+QAp7EhOZ4leYMylNoCZp3QZJ74hxIiVFTras37PRuoAwOSOWK6iiLC7t928w3h+FXuHI6QI7Z6S3nnNxuQa0ttLGVaBUOqBTIBfieXfw55G92TgyHzx4fu+D7FlR9JzgQ8tNPY5jQcEKh8baw/HmYq/ve9brI+H/TdLm6boF69URw7AjhInLywsePXzAh1/7CONkcc4zZxNWVUUYpwO/4uBm7qtDW7ltG4leyuLLl4sHoDbmIPC2xhROlIhaQjFj9pXDWsfQbxl6MTqeC9ppmg4Fo6h/pOA8oIsleN17ybLNhfCrkKi6+XWzaquqah49eEjOsvv0xkmuYoZxjOU8v7Bw+H4Z/ZA4e2fi7739gNOXTviVT/8abz18yCd+ODAly+V5JuwVtjLkrKRgqRpUUa5672ROfzMCiVISiO4NbdNQ1TXr1RLn3KFV27YL2mbBcrmm7Za4quPk9Ba3bt8TGkItLSbFs5zI2TdLCNqKGBTWRDCCMqqZi1vemrOOkCMpZLxTWA21NRidSsZoJFtLyIGjdcut0xU5Z/b7nt2uZ+h77tw55stfeZPzp+c8etJxcXHJfrtlu9+x8Z4xjKQwMfWOnCNVqklxoqocTVOzXixxzhOzYpwy07Dn1rrhi1n4OCmLoaopqKU2trTrZJ6mwtna93usc4Aqz4Ca5WLBer1CN0e8/tUv886TwINHe3K2GBLHtwyvvvoinvD9NGYzZHi2sSZroS0VVO6Zy8TENE7EmAil4zNTlGZbHmM04zgxjiPDKHywjFCJKJsFNNRtTQxRPPOsEbStRG6FfiKlzJQnuqoma8PVzQ5rnnWUVLnvrJKCaL+fRHRU5t449FSVox9GtIWUJnzlIAVpkSbJoEcpjJJ25ZTFJ9YUNX4IE7bYq5ESsWTPW+2Y07OMNlKM6omkxI7NaoM3HqcdMSeudwO7vUSrZZVQKQs1CjnPxkKMohI2h/oilbmYCTEIj68U5WkzMinDzfkVtmsYk2HpNe88fURMe8jSYZwpXFJHzJ6+iplPKIraorAtfEBR+uZnLVsQCgqG9uT7oNCTaKBnJEd5+M5Gh3JyUy5N6syhONGHn1Hv+no+7vzAn78+FHK/p+BTmAIXP9/afabcK6jicz+Xcxa1jXMoBeN+QDViVdJ1S46OBzabK242N1xfX3F29pjjkxPaxZIQpkMRaY3FH1qu0wFtrKqKECb2uy3dckVKiWEQta99Lh6Or/l8uiCTIUx4YwkhEoPA+/0w0rTtwSx6VieL4OXdOZfPnztj393unXeW8wNEOIGeuq6KpYTm5PRUCu2UqOuG6+sN0yRt5Rfj+2NYa7n/qKFqHT/28X+NL+m/R9vBb33G8Tuf6+kH0AE+8SMf4/H5huVySVPVaGOJU8A7/y1/h2xqPFVV0TQtXdfRtA1N3dK2nfjYdYLkde2CxeqIbnEkSF7XYZ2TB62zhLGXjURKKGOKag2s9wdbI6MVRsviYbVBH+wZABS2sjirmB+PTeMl7cZHYsqEoNjvBpYrSbbouoaua8hZ8cor9/ixH/24+Hdut/TDxNXlJZfXN7z91n3Ozs55+PAhb779NldXV8KZmsaiwk8M43Qo2DY3Ox7nwGuv3EXFiWnoS6st41Qp8owkayglZHfvnAg7Uma/3VJ3K/EV1QpjDavlkt/9dGR1e8K7CHbO1jZcXkw8OnshxHi/jGccvFSKC4BnyVHjNDJ7yYUpEUImBHGekM15VY4kqtWU5N4KYSopMOmApuiZe45CWU/MiTEEbOXLOiqFYAiBGCJKG1xdEVUWBa4TvzmllTRL7cwjlIacsRHrarTSjFOga1v2m61w9Cbh20uLuYAxGSym1E4OmboBrSlUp4TVEpmmlPjrjmGD0S1GW3TO6CJTUBowiTBJ8eaMzHlrNENIPDzv2ffusMZbp/BWLJeEn6dIJLRRYij9LsVrJk1Z8uWzeP/ePDwnKoeqPedvPmD10m0uv/oGIUdIlC5kJB02wIow5cK1DYLGkskqFkrGs8Lu2T/PqGqoBE3gJ3/6E/z8//uFr3svvS8KPXI+hP/K12WXrYCZp5cykFClop69cbTSZPVMeD2TJOWwz8wjJXqsuEofCr783FtIgCmmlKbAws9SOWaiKzzjus3oYQwiAElFAu+9x9U1q9WaxXLFOE6EMHF2dsZq9ZhXPiQmrCIaEUm68x4zjgzDcAiufpbgEWkaMXJ1RsKUpzBhnBXZd0HZjBFTRw7FmS1qwXgogI0WM8hhkCSR2em8Wyzw3kt2Z3zW3qYgf1rrw+mapkng5JLYMXsX5pIscH19xWa3YblaSPdMCyl+t9vR98Ph+C/G+3/EkBlT5GN3T/n4Sx/n8WvX/NJn/k9+6Zff4uw8E6dMVRsePX5EszilrsT2JGXhSjtrn4Vwl/sghmf2OtparLM0XkyJV8slXSuKWu+rwstbsFgspW27XHN0dMLp6W3qpsE4e5jbCotvF4xjzzT0VL4qfniJxWIp4q6cca4o30OgKnm5go4Xa4QYxP6kIPhaBLpIZJKlbuq5b3Jw9oIy7comzFpHVVWsyNy+e4oi8xM//kms9YzjwMXTpzx+9JinT8955/4DHj16xGa3pak8XVNzenrMYrHgtQ9/gJPTU/7kv/Qn+W/+8l/jl37ll2EaMcZKmo7mIBBTSuGcxRlR405jzzj2tM0CrYX6sVgsWXYtlYOTI4+3A3WVuL5OaKfo9+/e7L0Y/wSH4l0WYAc0z0jRFnMsyJ6Y5ccD/y6WDpgSpE4hytAwHmg3MyginaEM2hReXy4UADk2So6hZnVv1hjnUMaAhrHwyLqmIpd5bl3h0ReLMlLGOycxoOMkdKR9JIWEQayOtNKECCRJj3LaYHIua2SYSx2cM8RQbIqyrOFamcIJBKcrSGJDknJC6wQqk3MgqYDJFdbWqGyprGI/JS7OBmxjsKYcT2usFV5eVUnsp9EGtPB6VSkhVSm2Ykj4yglvFsX+asf+ZsS3NdebLSe3jnnw8CExjiImSXJepLCbivI2QOlGqFKkkiEqJGeYgnEJEfHA50MFdBX5Q3/sQxLh9g3G+6PQoyByRsuHZ5ZWy/NUaYM2iqRyUc5ZNLILEVXRLEdRB0UbcED/YBZ5zKiXFHZam3ehVs/ei9zgUwiHluQB2Zq5gyAqJVOMnovUO44jKQa0EeXsarli7Ad2+x1XV5c8ffqYdtFyfHJaVLNSNM3w+/OImRRvupgte1E/hcBC14dzNMPzzpWdRi4KI+8w83l97rP5ylM3Yj0zTRND34vtg/W4yh9sHua2tILyuqEU0/KftiAp6TmhByisc1xdXnFxccH2+oZpnHDWst1uGYahmFi/KPS+X0bOgIt89gvv8M7rP8/1OJFzxflFIseMSYrVkefo+Ih+kAgfZgsClYtCMKOLpYkUJQFb+KEzktc2NV3X0jQ1i0VH07QHZXjXdiyXK6zzdF3H8fEJddNgvROk2VgkFkmQhlng5ItiMWchXKtC4rZGeDbWeWkPaVGx5yykbq2dEN+9xWiZPymJK33TCH/n+fG1z49nlJKSv2lmBaCcH+8rbt+9y+3bp2XByoRpoN/t8XVF6PuDStdZQ4yJH//xH+W//2//C/7W3/rb/LW/8T/z4PFjiTAsNhtJyxysK8mUFv8vcQvIDVS1x1hL0yxR+Q77Tcu433J5tZclS8O9WzWvvLwArr8Xt9aL8S3GzEUTYEHU4TnBlGJZI8RTLsaMdYbtrqfvx8LxVNzcbNjvtmhtefz4nNWqEhWvE8+8UkscOlgxBciaaZrFE7NAcSJMQidS2hLTKJxXY6hdhdGyVhCQVqvTYGVe9MXhQSlFnKRgtNoSpy0uWkIQZb9OQHLEIvwgZREtRQpPbcR5U1rSzyCaGbCJecSprtCXiu1aFhTUmGLJohVG2dLxks3bdopo66gUTMoACW0SxoCv3EHZKoWxORgvP7tIhb9fouLC1Z4ULDHuGDfXtCe3uLn/FjGImGpu9wLCyUuBaZLYqWkIz6G4cw3zPDBV/qqoL1KeUGbgAx+tWZ84fuyn/nngf/+699L7ptCbIUzpQ88FWVEOofjJn/yj/NAf/iF+9Vd/jSePHmEweO8Edi7IXj8M9NsNkQInlEJlNmxV7/pdHCYRcIDGZyUTPHMbn18jMLQUgSpnKXSSqIGtc8QgrdgUI0pJ4bdcrRnHCaU1l5cXbDbXXJ6fYY2l7Vohxob4LpRw9v078AqLyaQxFkWWRWfmHT6naDRGgtTDNGG9I0XxCLMzb0IpFt0C57wYw4YSpI7sHL17pip+HnWzxrLb70tRK++lrmt5v0qMnAX1E8XjOI5MU2C3k2QEFIQgYhD594Wq7/tlxARvv3nD5jrxJ/7ZI6Zhx+9++oukEfKocVHTtZam8oxjOvBYdr3wPI0u9gjWYpSSeL0UqCovFATrqSvParlgsehYLJalhSvK26pqaFqxJTLG0jSd+Ek6EV7YEnk420oYrcjaoLQv6lrxddSIkk1rjSnotDYFTdDCA5JgdEBlOb4tr0UKPZUpGc8yvplB6bu/P3sD2gPCorQmxXkTJsbGVSUh8WEYyChizDirsE7oD23T8G/8mT/NH/nJn+Tn/5e/yf/zD3+FzW6HJAQpVIaToyOstayPjqnrjrHfk9fyrLJG/P9av+K336jJJrHbJeoGXn7FUdvMetF+B++eF+M7McSJQfhbIUThdYUoRZqKpAhPH15z9viKqtZMnRSCm+uBfidm3Fodc3V+Td1CtQiCqmeg8PNiznAojES8mJUSznfOWO8OQImhcMFD8abMWXJq5+KwiDRCFDsh6RJldvuB5UIiM1MGlTLeWJKOgrpNstbpKEhfihmVNVlHlM7ECKmEAGgl0YZjPxFz2UDpNSopJBdXNpvaijtHSBkmg3XCuVVKitkpJVY6YY1hn8BZg8XircWXeTfbumU1CzDyM1FoAVeMkSSM3eMrhiHhfcXZV99hce8ub3zx00QTySkQo3DopyK2iEEDoSCoBkXhGua5VSsjlYJzFoVCQuuIXmZeP7tk84UtL9/71De8h94XhV7OmZgiplwwjbRjSdIXXxnPX/i5P89P/ak/yuX1DY8ePxRVXtswe+XknLm6uuJ//Ct/hS/8o1/leuoZEaURCFFg9sZLKRc/LUmSUCUD0Dl/4N3F/4+994rVPL3v+z5P+de3nDZ9dmcbl7tsYqdoUTVqtC0hRhIhthIHSmIkQmAkVzYQJ3e5MJBcJchF4gvFiC05cBxZki1YJGWTomRRYhNF7pJLcvvOzsyZdspb/u0pufg9//ecXXKtQlKYBeYBFjNz9rznLedfvs/39y1ZthkBnxg/NFoZMDrNzSMhemxmCV7MFOv1SuIVatHcFEXJZDbfJJu3bcdqtSTPj1CI9s0N8tixxWMMJg7eM7g+uQntxmShrMU1jYxkrYxyjVJEazBK46M4CLXS9G1HlmWviWJpm24j/HTOb9iV03q9cew9grvg5QDU3pJnJUVeouJJPE2WZZRlSVFK1Zw1mt0ze+RZRt/1tG3DarVKjt77QO/NstzgKTPFlbddQRWWW1dfZH28wOmI9Yp6bkErFqs1NpthswxjLSG2aSQUMUpjTIYhphG/oyoLjDYYm1HkBXVdUde1sHv1hDwvKItKxrhlKeN/m1PVU/KikBBZMwK3FLQcFZvBY2LuZOQqNx+FaIGIkTzPMCqkiBK9MWcQ5ZpjtCLLM8Zw1HGTZ7M/+ZJ5WhN88qdMIBI9iEp6m5BGXH0r2VpKhXRdsNg4DojknAfQWcGDD17i7/2d/5afefob/INf+kd8/blnRdZhLWfO7LG7u8eVRx9H64y7t/dxfY+xOUVZsLu7w/nze9xcnGHd32I6zVmvB/b2FM8/M2DU/XPzXlqjmTCMWrQotVgy9ot4pem7geVhR6228SvP4rghosnNjDwzuEGiV5oh527TcKYoExESUATGsF2pc5dJV0QAnlJKRr8qGTUSgxZCRHk5NrVSDL2wUrkWcBSBzGQQxFZwcGfF1k5NJOJcLzWZuZxzxlriYHAqqQmVQkeZHwU82gZ8EHe4MIxRKsKcQwIcYpIwSW2aSSw6gM4CPg4M0ZHZXK4VgImQa4XykUmZsUZAlFaaLFrqqsANApLH7ZrSJ2BPjUCPlP0bAzrAerGmX3doDKqe0hzdwg0t0XtcSIYqAs6LOUXMGB43frSJXDoxkJ7IywLj80tI84VHay6/7QwPPP4BdlTPF//gE294HN0TQA+iuMaCJziPtp7ztuLxbMpDtkYXNU888iA6RM7ubLO3syWOOU5FNii4dOYs//3f/Ts887//Eoe//zle6FY8P7S8GDoO04c7jnm1UkSlNlLA03q+0xq10bUk1K3ofYy2GKOIOjIMaxE7W4lJ6LqOw4O7aK2oqhpjFEWey01rMsE5l7Lxlul7Knwav24o3RA2DNnYxpEZjUFs5947OWhCII8RvMeBCFATQFWRTUG1c24TuTKGM49aqa5raZsWtaM20TFjlt8IcOVmpCgLqYlquwaUtAaK7ko0TsvlUk7yEMjKgkcfewtVXdO1h6xWa5qmoW3bTU3d/XXvrzzXrNclH/nAj/G5L3+eg8NbNB5KbWk7x0HXMdsq8SpQqZAAVRJKozZuemNFAJ1lGaS8L+lPLsmyQtisLKcoSgF6mXy9rqcURcUwOCbTOXVdY6wVFt3aBPQErAmzIGNSlcaxIptLIeIICDNWY1Qgs5K1Z61ObLSMx5QKSX4hMo7x/iamhzcyc73RSurhU+kBERFjG5vRty1Gp8zMpZSxC0OgyZXF9w5lRJekMmEYdVkSBse73vYW/uf/6e/xq7/xMT7xyd9h3XbsbW9z6aHHyKup/Bxt6NqG6bygLEusLbh46Qw37hQsXM6lcwUvvdLT9j1ZmfPCS8vvwVF0f/1513jNBpekLwPOdcSYtNgu4LoeqyIqOKn+oiTEgTgI26cIRO/JVI6JHoMmKKk7816l4Vfq1E1AwxorzlAf0SojeokmUUoYJWWEtQteNHghBNlkKWHwldFp2qRYLhvqSYW1Gu9kBDtkUh2WWakTA51GrgqrhB8MOmXEBQ0+ovSomc9Q0SbZVYDYo8gE7J2I+9E2oIzGBxk5Z9qiQUBkBAECAZspQu+JRr6WlUBi1UaT4gh4R5f+SE5FfyIbc6se34OLkfbokK2zu7z81LMSU+bEZOmDx/meEGBwybXrnOT7ElA6ioEsTSMTOhF8krwIMQZssabaLjm+c8DW46/w3DdfZXLlncDXvu1xdI8APRFO+r7nvM340ekZPlSe5aGtHdr1kuuXLzM/vwduACP5UVLym0ab4y9XBfb2zvC+/+5vcTevee9Xn6dveg5XC17p13zTrfimW/Oib1m5gZAMHaOryXuHMScNEhuA5xw6ZfJlWUYk4l1Ao+naltlsujEjlEXJ0dGhHCB7SpiJsqRrW8qyom2bNMZsWSxGo0j2GnPHyahY9HYmLxmcjLv6vpcdUKpFCd7jo07jJzGi6MRQnnbwdl230f/FxMANQ58MEtJvqbUmy4oTij7pE0MItE0rNnUfyBIo9V4S/Z0bNgaZLMtYLZdYY9nd3k4EhqdpGpxz9G17n9F7E61hCFzZOsfRnUNevXaNGCM7s4Jc17x49w7ewXSyxRAKtmcl1lg2uZcqBZAbQx4FkGXGoLOMPGnzJnVNUZQyks1L8rzEmhxrC8qyoixL0eHlZRrr5q8Z145Gn7H8Xdi7MScvJcd7NoyXsQarkZ29Uiij0EqigrSWMa/Sehy2MrrCxnNxTAb406yT7ECVBPAJIGqF0ZbgxOGqtIEU+D70jqzIWK8astxuJhZKJyfjoMjLmlV7KNmUIfKXf+KH+at/+af4F7/5capqzmz7TGIMoSgr+rSxms9mbG9tc/7sI2zt7NAeZXz4A5eZTK+yf6sjyz3FrAIOvmvHz/31na1REyYSG5U27h6ps1cMg+f47po4GDQWHxyRkNqztDBh0eHCQNQibbhz64Ct7YwQPT4IWxdj2DzHycTrpBp03PADKaw4nryuMN6HxZhAYsGCjmK+C4HpTExNMRUeRCUaPGUkGqT3DmMKdMwwg1SVOSWGED+IUVJr0c0rJDYoUY2E6DEqE1wZRT6C0gTbQ+qiNcGQWwtOomLM6GJWGufE8KAtmDRN7Dsnsg0VN5u0qEQloca/g5yDMRCtZjhu6HtPVhfceGWfudWsj49xsTupWA1BRuR+jFcJRDRS3SY64JAq38QaJkkBADoCDFAN3AoNq1f2WS80B36LLXvE7Rf33/A4uieAXoyQx8iHii0+OrvAo8WM3dkW1facz199kbf8179AtrqBv7nGPPAI1DUxXW/V6LgY/1CBYj5l/h/9NOFX/hXz/bvs+V0eHgY+3Pc0bmB/veRrqzs80y95JTr2Q2BNJCjwTvR00ocbN3k3NrmGdBJdDs5T5LmwY0qaKqwtqOqSts1ZrVZEIvPZfHMzOj0WXSwWFIXo8IqyoiiKzQg6eL8RyTrnmSbReDcMRB+xQQ6YxFozjqbhJDFbQKOwEyPAa0eRd99RVVWKa+mT3i7Q906CmRPYHaNaxBElB+q6aYQtyXK8CwQnLE6Mwu41TcNqvaLvOpr1mklZ0TTtpqatTdb/++vNsYrS4qoFX/raV2maI9rWE6Li6qt30bW4cuvJFB9LHn8sUNhIDJGuaTYxCXnayFgtmxqjoSxLyrJkUteURZlE4qNYPCcvcqqqShVQGXkxYT6bSzZcYtdfazQ6GZcaLQXmXddRFAV9PzCZ1HJhDu7ErY/0UsZTj1c6XdiTtk/JLAkVv30G559mfavZS3bpKn0eaAlqFV2twVjLMDRyQwhjfRMy1vGBMEiFY3AGZRzzrS0m0ym/+Iv/OcGN4zbRERdVxXo9tt1I5mdZlTTHW8xrmMRtyuIlHrlcMpnBufP3xC3h/krLakM/nETemJFpS8HH6/UK10VsyE7iUmJMXbHi1hVEM6BMwHceNyjW1lGUwi6jx5gOlWKG0lh01Ikl1gmlUkZlkjIg7FOivqSWL2nolFe4fmC1atg9tw0h4NKkqB8Cmc0J9GSFIfSAiZhcy3xSK9GpaWRzlWmUj/hBnPXifBU3LVGjVYFWFnBSFabAq4DONH5wODdgsAQf0BhQBquUgFw0TevpExuoo9qEKo96XpVutCLvkHNZKssC0TmpHQ2edrVi3baEPpBlBUe39gnBEeI6aSzT6N2LkTOkcGuBzzExhVrYvYjoFtOIPSJxK7pyzB7KuXjucc6fmXLr1dvcOr7G1661XHlo/sbH0ffi4PyzLqvgb2w/yPdXZzibVcyqCeV8itGW7ckMddzhu55sd44bevKYs0F6AIwXYbmAgmby0EMs/9OPEv/hb5ItGqrtbdy6YR4jZ+JZ3skjtF3D4XLJ1XbJHx7d4LPDiju0RC31LMoYQu/RukoXW3lOCQl2KCWden3foYwR4WgyN7TrFW4Qp+oYveKGATfa2wE/OIa+pygrJpPpJqdujDPRWm8y59QYIZMO5BBEwIr3KJ2D9wStMSaidLoQkAKVQ9gYILSWG1zTSA3ZYnG8cSaGECiKYsOUmM1j+8QSJubTBYbe0bUt/TAkvZUmDJ7F0THr1QqXGMWjo6M0ru0Y+iGNIu5HOLxZVlFkrFYlutgnBk/fOY4aKKcZFji8PRBcywc/dJlmvY3JxCkbUyhqbRWZNYQIuRVHLFFTVxWTqqauasq8kKiUXABfWUq473QqTHlmLbPZjKIsZASc2INNtpic8nJsj1FAQJ5LC0ZZms3IVkZbYaPRVUFJ3h7So7kZDmwu8IgUIgWwfqfrdEOI0gaTQLDSGrRBR2Q8a6xEXkQJTlUR/CD6rHa9YuiFmS9y6bIt6gm2qBMTsHkybCa1cM4NZFlOWZbs39xH6zkvvTDjPe9esuodud3ii//2Fu9893f+Hu+v794aUyFsJr3u1ipiNPSdxzlPbnJMGJDoyBMjo46p8SQgznJlGNoBHS1WGYa+YTKV9ojBeUR/nqGssHvOuU3O28jmjbWCITljRScYNrKI8fWO06nF8ZLpbCLyI2VQSl5Xrg0ueMq6RCtF3/ZkZYbuNa4PROWIxoMOZDqnG/pUpJCMVQmQjvI1rVJ0GiLZCIpktkhK+ugwtiISNoycUI5CjPgQTnXJJkCnX7exG82aoyZwo/1VKKtRg0e5gO9hcfuIyfaM2089TXQ9wSlCQPR4CezKZ+rTNDGcAtUnTylB88JaKgai9YRpwFYZN16+SWgbet9ycGfN7qVznDs7ecPj6J4AemdswQ9OzjIxOUVZks8mFEWFLQre9uQ7OPz473GrnnPhZ3+EejaXihAiUZ3yWQPjLDugGCJMH3mM1X/wo+jf/Cy2C5iiQsVAGMRRVOgtZmc8V4D3d2/lp6+9xOcObvKZ7pCbvqf10G5AXaQoyo1Zo+87jIqo6HBoHSkHAAAgAElEQVTrFWWQTj3frjBDh+saVgsJOTU2l+5MN4geLoWXGmsZ+o71YsnQSWMFCWSJ/shIPyaR4BwxjWS1kuJ3pcVRHIae3OaU3uNjYAiamEUymxODZB1pZShKS9e3FPl48Rf9Xl3XacQqDQDjCAxO6wU1Q99vwphXq5U4HIOj6yJdW1KVFUfHRwzDwNbONiFGuq5nsVjQ9z0u7UzVd+GGeX/9xazc1Jy7kHF0tKCe5PhgyPIJl3f3ePBRzzNfuc0v/Pz7OHv2ISbFeZyb03QTbt66BUn+YIzFKkWRZ+KYNTl1JWPZsigpE7jLi5qirKjLSoKXqzqFcNdsb+9QlOVGHK31KLuI6WJp0iZGlkKdAmuJ/VOp+3Z8czESg2htZXd98nM345ooPdonbPmfbb0+YP31bLZOmtiRQdTZCCjHuisYugGHwxjF4Ab6ztH2PbPJRHIs6xqTSR919AEVAyTXv9KaPC9wXU+eZBnz2Tbnzlzm2Rcv8MKrf8T1q57IDQ4O4JVXVn+Od3l/fS+WHOd6AwpgBPHSZhFD4PD2EhWm6ZjRG8ZNQ9JqByHJKLBYtB4Y/ECz6umnkbLO6BqH0hadq2QQCBtwNxIAm4apNE2KSSakABVjAktiNlJEvBtEO4tDuUjU4vxWSsaReZFDDAQnrLMbBkI/4HxE5x6dQZYrQhjIUIi6aBzXhjR2jSOZmLZ+lqBDknEEiaIZumTKUqigRfaVpFpBKwprMBrQloiRwOioUckMIikbCKuvRpAowM3FgAsenRcMg6fvPLkRJ3CZD/h2JYxe0ITYEtVA7xw+hhSKPKCUT7E2KeOXcUSeIpnQUt1WanYe2uKFu9dg7ThatVx/6pi3PnGBH/zxj/Dx3/oM5tIbh9PfE0CvUIaZLpiWNZP5DJNZQtfTd+LO2alLho/9G65/5Vm2f/anmL/3cbKtgqDB63By6R4ReboZ9INn8s63s+495mNfQEdFjBoVJG0arTFZTuwayiLnyYcf50w15UOvvMzResUyDHx9cPxxu+TVrqdVmioGJsdLLvjA5Q5mbDGPmqkrqIqKWM9pS8/tfI/nD2/xtfVdrvWHHHtJtB+6XkZXRSW5YFmGj5HuyNEuDTovKPMSk/Lshral73pWywV9lqW4h0AMnmLw7PWBd7SWJ3e3Uc++wMoqji7tcLADB7ljmWlclJgLpSB6cTb7IOGMEruiCSmbKc/zU3U5JrGKA9ZKT22GRLGQboBi9/csl0vqut6E4VZVzTA4XJQGkLZpGVzPMPT09zV6b5q1WKxol+c4PKzpuoHdnSn/zS/8bW7ceYVPfvJX+aEffStveewJpuUOB0cZz3zzgGhcyq3SKB3JshyIFHlBUeTkmaFOI9uqKimynDwrmE2ExasmcyZTMWHkRcl8a5eymqR+SgFvkq0pcUKjyQJIIa1sAJ4WBbVsVoJY7kZZhDUC+2KIYE6AWPSBqE/MV1qd1B6OovXXZ1v92de4mTJi7CpSi0ECglmUG7mxkgQQEfcygEvXklXTcP7SRbJ6itI2gdsx13N8GkVZ1azXa2o1oyhKiAdcvHSJC+eu8Mr+l+mdIUTH7k5Bbu7vwu6ZpdQmMHnUeUqvsYC+1XKF7y3ai8Y9OLVhgAbviV4YozACo0RJaRSGkqPbK+orBXVVsFq1BK1wMeBdSO0NosMewaaAtHE0HJIeXAm7HEGl8HEiWGOwZYUKA9FFdJ5tNn46hS2rYBhch9Zi+nKdZzCOWARsnaE0DAu512llBdwSiBgUdnNexNGaqTTaaLQfIFN4WlxoqG0pAcRoFJLlp3TEE8hSxFKIG9yIZnTaxo2DeEN+yLRYgCIxdXtb3KqjXfdCPhnL4tbNFPsSGHwgRIXzEpbs/JA6eGPSAMq4WOhCn5jH8RV4gm15/P2P8P4ffhv/7Nc+wdB3XLqwx42bC1zIWN6+yqVz29jp+Tc8lO4JoKeVYlZJtEIYBuLgCCh0yoCLKAqtUfsvs/y/f4Xmtx+k+v73Mv3QO8jOTlEGnJFft4RMin6uaTsGZSjf/XbccUv2mWc2mdabpg2liINj+fKrrA7vcnxwQLc8pu0bjvqGPaP4q9MtFmWNP/ZMjhZUzrOT5VSlwVQl+ZZkfxkxVQtIzWreV8xYrs6z3614xXd4FbFD5LhdMskKnjj3AJf2zqGV5qBdc7U95ktHt/j84R2cMmQa4jAQ7t7Fth1bOmMvaC6rnCuD5oK37JqKPGjyecnVazfJYscH6y2Cj8RMsw6RA9dxI3RcLxW3a8UytxwDLu0KAYZemMvMmsSQ6E2si7UWm2X0w8B6vWZS15K7l+cpmkZy+4qyZHF0RNu0TPd2BESGmHoVR8fYwODdv/N4uL/uneVjYKsqKC+f5xvPv8QPvP8h3vbk2/inf/9XqPI9didnsLogz2Z889kbYCeJnVJYa4huIMtEklCWBVVVUhY5dTVJTF5FmZfUkwmz2Yz5fI7NKyaTGZPJNkVZMpnPyaxscsaxrDYnf98ArhSyOio41EbKkdiHdLNTis3PUlpGQUafZrEj1iZDVBA2Qr5+Uhv451mvj10JPqQRkeRwjm0dXduC0pJVlnR5eSauwmo2F/CHwhY5+WQm4vO0dNq0jR+JQurf/PGhMPZFjks6RsOEV6+WZNZyeNSxXPZs73wn4PX++m4upVIQ/fgnJ25PazMO77ZkbhsVvMiG0kbEe5ccoWMWqpgXZdqpUcqSa2j7nuO7A/W0wGQ9gxsIcTQgyePG5x1Hst57iRZJ3+JCIkxSDNfI8oUoY9KgouTxOcnuy8qCoIURCy5grCEzGhXhuFtTTEpxvpuMvmtxA2S6BOU2+ngBvul1atnphRjQNqIC6esQnZQrKK2Ino30SSd3rTWRIcDQB2KuTk2agtS2mVM8/mj0OpWhl9kcp1qMUgzdQLdoWC8biumE21efJvhBxt9eE6OS6UEcDS4jAD/dcTu6bSMRn7S2A+VZR++v8plPXaM57pjUPfNyyu0soKJjuRyYzQ3NwT1uxjBKUSRaV9xvIoI0eYbv/abEWPkIQ0N47hnWLz1H88nfo/zwB5j+pXeTnd9GF5qgFR7piyvLgq7tweYM73kC/dRLcLxKyDxCL6PVoxdfZv/553j2YJ+XhgWHcWAgcrGac3myy8WzFzAYurYlKk9hLcoHVNOKVTsrSDV2YpbQmmlVYWczLpwzPJEXeAOLg7vceuUaZn6OBx99nJ0Ll8lmU7kVOU+3POYnF0v++MVvcOvwNnMfmWaG3a5k1lrqLKeyOSYKE6mqUhy5qx6c465riK6lu32bHIjKMpvPmDSWS+sBpRVDbljGgZux5yU6nvWWrh1YK9FKKg3OD0CUNgGVqHut2dneZrFcSnhycvJWdZ2ynWRnsjxesG4bLs2uYJP2KYTAkMKSVZQb3P315lh5prlxcI35rmZru2Zn+g6++tSz/P7vv8QH3nWBvrf88j/5FHfvQrV1mbc8+naKMifGgFGyU65SMn5dT6mqgrLIqUoJRM7yisl0yvb2NmVZU1ZTjM2ZzbaYzndSk0S+MQYxums5AWbjv6MazRgm3Qfi5gI99B2ZlZDmsdPaGJUMUol9VBJrohOQi+mm8Z2bh04//gRIaW2SjkHME5KzpwgpnFxyt06cujbLsVVFVkrEjNRD/smg0xhpEgrBU9c1eVYyqSeURcXu1gVu3PoGPii2ZgrfS5TH/XUvLJU0ekmqoxSr1ZKmadm/sU+IhsAAQScNm4x4fZDrd1QyDnRBgH0MXrLwNKhoyJiyvNVxeHtBVjlMFolK8u1O61Kdc0TnZYKZKkRJ41KUBOYTI9F5eQ0x5UQm964KQiQURYZCCgm01rRDS1VKdZofBKQp47GFxQ0NMXTkWcWwUiR1Q/IeCkkTFfJeUKDkHhU86EyBhhCUHPsqdbkrI+keScsYUq6dj2ocAaBjil5LG0QZgbPZIIo7fqx284kd1LhmTQjQHi/RsxrXryB6wpCq6bwQHT64BMiTW3dzbRHgJ6298p6idlBHvnFjxbV+4Ec/8iRv2/IcLjvu3D2E6HjxhdvYmKJqZjtveCTdE0APFG5wqCCdclFrdJGjjKaYVZg8J5tWcmHUBpyTD6lf4j72cY7/8IuU7/s+8ve8lfytl7FlTtBy0BVlQdf1UNX0Z+bUdxdp3q5o7t7lzje+wQvXXuaPF/u84lsWIVBozdsnO2yVNXU1ochLvA/kJiNP/bRaWUyeYcucosippzPyLEeTIiRSbRtGMzQ9R9df5fD2Lc6dP8fZS1eYXXkATJ5cS6BnU8q9bSql+fG3PIq/eYt46y5huYJ1S3QBjEk7FEvUGQpNaD2giEZxY3nAXpHTHx6RFxV6Z4uwWGKqqZQxL1uyw5ad4Nh2jifrmp80Nc0tOLaB6xiuVprbbuB68CysPqnH8R5rDZPJZHPjbdbr5DrWaKXJrWW1XNK0DVVVS22Okiy1vu83vbtjnMv9de+vwTnmO3MsBX1/lYcefIDf/K1/zbJxlMWEV165zb/94nXqco+3711JxqM+XeglfLWqaozWEqidF1grHctG2+S+raiqmqyoyIuaqp4ymW6TlyXeeTIrVUSMblXFa0AekJiEtOMnSNaenH4orSimNSm8PsWzKHkd5qQ7e9TxkXRJoySIZJY4rV39s6/UTXl6bQxk6cKuFNpoqslUtLnei5YJxK2cS1+tMmZzk908/pTJQz6O+JrPSGtD13XU1YTppKTvKnZ2dqnyPVTSAlc1LLr79YT32hImKgGR1BdblCV1bTm6c4yJU1SwhOg3LBGAeDFkhuVdTNo0Nk5uaYgp0DHHr1v6sMbHFlsqTGnQ2ejwDehRnzceV6OpIcaNVlx5OUfHr+uUY0kIRC0RKYNzkMvUqKoKMSf0PVYbsiJJK7QYF6zO6Htx9ktGXhpBpycfI05iDBgrrFlA8i9D9HjfYbMRHSbGE6QaLUS63uM1hKiFQdMehd/o/EMyfKgRWJ5+fIyEQdpFXIi4FoK39EGhj+/igsc5ab2SPlvRA8v99GRzRzJhBLyAYIA4iE54D84+vkt7vWdrVrBsWu7c7qi3S0KAS+e2ucUxw6CoSstHPnKJj3/yuW97DN0TQC8QWQ0dJhiqLJfxCxAGJ3bo3ArQmZZkRSm7h4Su83RBti8/D/v7dK8+Qf4D70LPC4KJDP1AURY0a4979AL+mRdQ/cDy1WvsP/tNntt/mafXd7nme1oiM5vxQDGhtBl1MaXKKhSK+sw24c4xXSPdsLZQ2LqmnEyoyorS5mRWumJVUaS074724IhrL73IarXm4gMPsffgFbL5FsFF4vGCOEicS1gYsJZoFKHtAIPe3kbPZtD2xKbBtz1xCERrib2HviWue8zuLt1iybX1XablRXzwhK7DRNB5hmsbTFXBbknYv4O/eUhoO9R5g5pU1DFSD4ZL1ZT3tgG/7Fl4uJUHXq56rnfHNCGyLHJUdtIvWtc1eVEwDENyFXmOj49pu47JdEJd1ywXC5p1w9BLeHPX97jkJL6/7v1lbc7QOxZdQ7cKvPjyi/z+H/0hszl86pPPUk0sVx5/GzFUWGXIc+mdHcZsxQTwtFKbPuciyyjyXIwWhYQlWysxKlleMJnMyYt809TymvGVF8ZuZP1H5U2IJ53VKumI1Nh4oUCRwpzVyObp14C20XA0YiW5MEsDgcrETHLaifed6fNOxrcjuIxRxkIxgs0LTJbTa4sehJ3Jcql8O5EjnwSqvv6lfMtrUwpjM2ngmSjqyYTlquHcufNMSjG9hLhmtYZkxr+/7oGlSM1DMcLQb8Z+VVWlys8GH1sihbDnSVe3yZdVSuJ4QgAfkjwhbQJiCkNOo0ITK2w0eDraVUv0jmLLbjYMSmtImrOxA1fKByJhkCpNFSWwHKVS+ltMkWECjrwLGCMse0xstVZJJx8iKoc8Nwx9i7UG12q8lw51rUkatoDWAuRUCmQWlt6Ii9U6dG5xfQA8StnEnhk0MDbjhBjwg4fSMrhUtZZMUKOzV5IrEqhMesWTFeX3krI6Q9cR/YDNKxb71yQFw6cNIwOD84QwpPiak+Yp2BCU+ODTVEFhtxw7lyv6/oh3vuUit/bv8rVv3KBvFOfagr3Ss7dV8cDZS3zzxQOODhc01xZveCzdE0BPAbmxmwu5xxGdtEV4KxfbsB6wgyPYhqwsUHnK08kMoFEhoHyD+vJTNMs15Y9/CLtTM0RP2zTYzOIfeZDjxW+x/sZz3H3lZZ5f3OKF9pglgVwpdkzBdpZT24ytyTazyZzS5qhuwN06krqzukR7j85zinrCpJ6SF5VUKqUOTeU8vulZHdzh8NoNonM8/Nhb2bp8SVi4w4WMRLteasyA2PaAkuLjdSO9eK0weQFPVpRJ8JqhixxT5eg+I2QWSsv1my9y6BucivRDj+971NGCWFWY2QTfNtjJFHV2C/oOrjaoIUg+kU9z550KvVgT76zYMoptq3liqXBLxWIx8Go58OyWYX9e4MuQhMKeEAJVVeGcp2kbtNZM6pqmaei6QfR5TqJV3DBsImjur3t/TSc1Q39M7waafs1vf+rTrNctyzuRbtHjTOTu7SN29mqUNmzPZ9y6c4R3njyzOBc27K5SGq0NmbUUZUlRVilDr5SxpM3J84qirDBJkyfX3vHiq6USSY+dmuokUDYxCDrdkEgS7RMWLjETyqQoA153sT0JhB3/3bUSj5QnY9TpcfG3W6cZtW/9vtfGs3xrtt4I9tLEWWvKuiZ4R4g+aaVGdmLUHgIpFkaNBMFr/v/Jc+RlyXJ1DJHUgKPZ3t5mZ+sM03IX728zrCL3k4/uoaUkVsUHcWebFNrf97JhttZQb+Xc3W/IfY8mS+yUcEPeJ3CiAkqlIN507R3dsyCboJBABmgyPSF0K/AynXKJARzPjzHyRQdhu0ICeYqID27DPsr3ksgrQ0CjYyA2ndQfJnNRSKaKIq/wvcMqGfE26z5VfsLQ+c25LOfHSXa5D0CUgGedCzvnvejslAZ8ctInU0pI1wJjFS7Kf5kao5XSaDYIwyYyjpMAdWndiRtJk9IyFfCDx/kBWxUsVoeIYVK0di44IuCil/caxjFwyuKL0gqm8YTMoa1jEVa0i5LMdGxteRqnuXh+QpkpLk86Fo2jrjSm1PyVn3wHd6+9kExv337dI0BPsuzFUafI8hyjNVlZoqz0YdosSwJshe86GCTqQ2dSpjxkDkNBVljUH32ZZt1R/exHqHZnHLeOvusx8znXcrjz0nPcao7Ydyt8jORK6lq2TI5VhrPTXXam20zrGTmK4Adc6zDWkk0KlM0oq4JyKp2cKgR8l8SsLtWndT3tYkUxm3Lu3Dmq3T10WRM9xKER0JhnsPbSnWsMymp864SJyEp0KZEJRCQjy2p0ZmSX5jyxyFBVhsozwk3Hpa3zeAXLviW7dYdZVNA5Bu9E77gCW05QF8/hXMA1rXQIeE/MMhSG0Af84QI7KWVHY4AFbLcztjPNk8uKm3ccz+0OPL+Vs8gMxmQiwA2edRrnHi0WdF3LarWU0TkSBD0MPd19Ru9NsyKR3hXcvnOEG+D67QXrEIhWkU2hrgvOnb/I0EeUMnRth7GGqCLWZtJXrS2Q+pCLgrIsyHKpOysLyd2zWUaelxRFhUnh5GMdn1L6BLSNkSmnxq2jQ3zjhk0zV4mGiCl7Us6jEMF5ufsYk8TVjKCPDSgEAUdsfh58y+j19Od0CjB+qyt3/POExTvN6I2gbAPyNk+j0CZDk33L842RT1p/q0bv9WNcEEMGCwm+zXNpF6nKgkk9Z1ruYaxmdRAx9+Wz98yS48HhnDQrdF0no1lkY97HgaA9bX+btmmY1A+RqTlBeZwbCFFY6NGXOrLAI2BTWgw/Av7GNgZDjB7vB3zriFaT5YWE9aeN0WjQIIIKMZUehs1hPhqWxmNb67G+K6KCEjOJsjjvMFqYSOnsFJ0f2uKaSJ3ndL0TfGA0w+A257yw7x5tTiKVRBecMumCNMpYNM4HdDJRqJimA0aRWehdRGEEHAtylXDoBPp8aqCKkeTFlc9KETGZZVj1RB9oGkez6sFq3NDgvSOSQpED+OgJKukoo/TbirZyBN8Oco8pAt284gM//GM8euUs//If/zLNsed9Tz7Iv/7trzC5cJn/5D/+ee7e+jr+eJ9PffKrtDeXPPGuR/n1X/3sGx5L9wTQ8zFw6FrKaKl1Bs5jCgF4WV2ix07LPEPbjOA8vuvww0DstXwtjmJGjaky1Ne+RqdB/cwPMNmesFivMHlO+6638NL/+//QOQEb1ih8lBKS1g9sF1WKephRTqaovmdYD1STGl3moqMJAe0jmc3IihzfNLh0EuIDsXegNfOzZ6nOniWbzoj9AJ2kiyut0e0gUSRBYSYVIS9RWpOVNT5vUZlBWUOwo+BaJ92TA+chOoICbQu+eOc638gi6wsX2Yqa2hXowbNetfza0av8uHqU89UEZQMhz1F5gdraErpaQ+wHwmLB/s6ClXU8WNf4vueqW/OFdp+36Zon25a2zMnuHHJ+WnHxeMa7z1i+Oun55nZPa6DvS4auRynF1tYcayzGaPquI3hP1zQMfTeq3O+vN8Ea+g6TdTzyUMGNVzV3DpcUGQxGc+Wh8wQiu3sXuHv7gFld432gXbcEJ1V5buixxmInlkktgch5XkivbV6SlyXKyLleVVPKqkxub7MxTYlJSyVT30nROvAtLBywobZCRDoyIyhMqm4St598v7AA0pMLJyzZCNJCGuGEjWnjT1r/Lsbv9P977fed/vq3f+xpYHjSg/n6n3Wa6YucZvWMNWRZhveePE9NJZllZ3ubaX2W+TSnWXd4/52NpO+v7+7yPjAMA13nRMaQupeNNfi+Y1gsMOo2g18xuAtYEwgq0PuWGMSIIBuc17JyjH7SiPThagXKJIOAbK7aZcvZy2f58A9/gM9/9o+5tX+XETaqBAwDJ6PisUr0tLtcpXGxTlrZiEIrg3NefsZ47AYYhp5cFbilOHSdEW3qMHiUOumZlr1NgGhwLqKt6BZ1Js7bgCbEgFUa5Tw6yr/ljJbzw1hLjArnHTrLE7OWjIKjjjEB25DwATGeOrtOddE6CA4ZF3dLTAi0fmBwHd5Jnp/zjdTR+UCIPaPpQoLaA9FEbOa403c8/uAeP/Khd/P0Z/4ZT7ztHD/yo+/meH9B+KGHeO9P/C3e8f0/xfWv/iOODjIee/uS/VsrHnko47/4r36AX/6df/5tj6N7AuihFLOiYlKUVPWUoi4xVYnKczE1AAyO2Hvc4AljPEcI+MHJB241qg84H4hViZ2WqGe+TlMV5D/5fqoyY922FE88zismUg7ienFEXIw0IeCVZAxNywnTyRQPKGvIJxOK6RSIuEbGOVZrfNMSqxJlMuKqEcZNKUyWkVUVxWQqrEbfQzdsjBeoRDPXNQweTI62irBsZFdVFKi6giIn25oSMyPF0QlE0fZoJ35xFSJtd8gPnn8Xq6HnYy9/lcZU/NjuZf7Pp/+AhY28T3kmMXLcrNjtHfbMGYozW6BhvVgSCstyveZ//cbv8O898HaulDlfuPkSz4YjHu01H1fXmeU5Xz+4xRPTXS7GEtX3zG4e8W4VuXhhzo3zkbszB9Gzt7PLuTNniTFy/fp1vBukBWToU7XO/dHtm2UNruetj1t+99MdyyW4weD7gDIwnw90bUFV1uztKCb1HJtZehdQypJbhdOaLBPglhdF6ouW3LyiKDAmwxhh86q6FvY+NcQopfHxxHyhAYwRcMbojGMjkiac0uklo0KIEbzclLSBDLvJAAtGxkEmnNLLpfetdYqWOGV4GEc13w7MfTsW7bu5vp3ubgRyr9X7nfTrvu4B8rvpOqbzmfRvdz1b8xmTco9ZPaGdDByvAvdTLu+NdbqRYowostaSqDTcMNCtegwlDUtC7JAqAdA6o3U9OTrVksloNiDnZjhdq5dMDpIj52QkGkqIlrvX1/zB7z7NxQfP0neOwztHCO0bk6Y04uNJ7eD4uk/0siAhlYroIhgS2RGxWkKYjdI0TUtuLf2BQwXIKk3XO8mXUzbl+SWmPh33QQXQI5iMZKUmQMIEHqOMMI7JKUvq2I3pMzF6HMVCVJoYxOGhDYwxKpqT8OnT7H6MYlSLCuIg7tqgFM3RAYOXsfngB3z08n2blpHUeTsibAJYhzKBdWhx1lHEFb/xj/83zpyZ8uRj5zi6/TKPPHwOU76dCzuHfPY3/i6rVtE1K977ngd4/rnrfPHTX2Tv0Ufe8Fi6J4CeVoqtvCKvKmyeY/JCfjFtjypyMEaqTZzsNEyRiZZv3aAA33WijzOWOLQMbUe5uyM6tmdfoHvPY1SPXMQfHnH7+j77Q8dOCBQKhgDLEDgOgZmGWV6xO5mjonw4JsvFgaQ03WqNazvyskD5iO4dw8GScm+buM4g02gfUdaSZYW4Y0Mkdh1xEO2CZHkpCZAMER89msQ6WEPoAirPoCzR8wlxPhHrYO/RRthOkKon1fS442M+mO/iAvzTl57m0u55/q+nPs2v3XqW+fYWmfP8m6zl1dtX6ZTjR2YP8LEvfZa//+G/xn4d+cLBLVrX85Xlixxajx0CcbHAr4746bzkH7rr/KHu+Slt+P8Wr9KurvIT6i2YI8WLzREH2nH7qy3vuvggP/f290Nb88mixWqNjuCGgclkwvVXr6YL132Q92ZaWgd+73c6Vi10QyQ3lq73mOj50tN3efiBSwx9ZGs2oSpzikLy4AbvsVmZtEWWzGbYBOLyNLbN8lxAX15QFhVFUQpzNmrtlCbLJWwZNQ6fUhtMDKhoZJceZYQ5/j+dwNjpMajoZYStMKPbzUeMIVX7pfEupADVmNx1Jzev6CPavHFQ8utz8r6XS42uwE3+w2vHxadHwvJveX9D16DUnLIoOVbHTKY188k203qbA3tAkUfud2PcW0trqQ8b+8L1CFCMYXvvPPs3liiToS1z46gAACAASURBVJSMKZWW/uSIJwSHZOeNMgZDjMKsBT+gvGyAIiIH0CaKXjtk6T+4ee2QK4+eRSHkiEaGMj7EjWFkc0bEk8y/ccQqo085H3106OR2BzmOx/ekuoB2lmxqGGKH8TmEDOfiazYx8j7j5vHBR7QOia3ThKHDKJGCEUOqJg2bTVtEyVg7Sq2cjvrEEcFJvt1oNiHIz1DJdTwaXUaxoGsHwgBGG9rVMkXcRLyHoEIyilmU6gk4NrISowULMFCd0Sx6zcOXznN4+xbzGtZHLZ/7zFUefegcjz98hknmeOozX+CBC5HPf/VZ7iwqPvLX/iYPvS+y//xn+d1PP/uGx9A9AfRUhFWzZrVeY5Ui01aEmgryqsSUheRIpV5HMy0xZY4aHHHoiSES+h60QzlH9IHBGJjWZFsV1WRC0BHV9kwBM5vIqEnJ2HYRPQs8lS7YnW6TG4NK1U1GSR3K0LQE5zajJFPm8vX1mmpni2JSM3QtuEg2KbFFDih0jASt5GBJo/8R0cd07KkUiqp8gMyipjWqKqEsoJMMI7oempawbFDOE/qe0PSYzNJWhn91/Zt83+NvJ8bAbDKjKGoKZTBFzu04sHfpErcWB/zWcIMfeOf7uFnDvou4acXW2vA/Xvp+/iDcofMD/WLJO2PG2vXs2IKPljt8cnGdwfW8Glt+6cZXIES2qxmPbp3hsdk2P/fg93E5Vlx6y7v5EaM59CVfCyvqIufGnbv0TUsMA2P9zP315ljOBYIvKa2FvGN52HLlYkHbw927LbPZHFtUVJVFmYym6dBGQntNqvLLbEaW5VhjKRLIq8qSsqjIi4KymlBWNSYTLdpoqojIxTOGccSTHH3RQ5SL9abwOwGf053Usnc/zXQld63REj2Rytm1FqfbhuVIF3KVxlHWauLYrf2nwHB/Icf35vUJSyBavdc+7+v1fjbLCGFJjJG8yNFaM5/P2dqaM6v36IfnWRx871/6/fVnWxJPNWw6UiEKs6cULkDwZyiyc+RmG8IJuAnB4Ynp70qMgN4DYzBvctOmMb/WBp9Al0XkESG0hNDy8nNfp107CD0MGVEbolGb8OQxa3VMHX6NcUKRxrzI/VSHpLtVm6iXUmtcA1jwtodeg88gKGm1SE014iqWmCTG1xiDNFIEwIS08ZNzNyTDgz6dN5kkIGMQX4gRRUivPeDHqVM8eUCM4oodpwgKRZ7n9E3HsOoYugGdQKbzncjLfCSEHh+TtjK6jd5PVkCHQKgHlgRU8Dz9lZeYxJ6zZ+HBB/aw+Q7z8xf4+tdvc+Xxt/LK9Ze4cVNTbW1xcPUGn/j1X+dnfu6vc/biY1y6+PwbHkP3BNADsVlbJR2w2piky5PSbzur0AEYPKH3qIVDT6eYIidoCL00MKjo6ZOT1R0c4LoWH0B97WV88TA3vvIMN37r0zy2e56jvODw1h3WbcsqelyMXKl3mJc1WoFrGmJepBMjze5T8GRRVWRZTntwhEYRO4cpctRihdSwGFRy94V1l8rTU42NShR5oqTxA6FphD0wFlNmKCJh3RK9R/lIHByxaQlti+ocwckYWJcF+uwWW9sz/spZGb/4VcPBY4f87vVv8jc/+FN86uWn+C/f+RFMXaCU4aur23zm+WfYt/CDT76bD15eUg8e/8I1fuJVLVVMeoWOka0B/ka+hVMTvhgagpkRhgDVDv/Dw3+J/+OFz/Lvv/P7+fCDbyUnYs/sEH0kLyz1zRXnioqLxVl+dfUCjJR1kFHa/fXmWNFHFoeRtlvzyCMzju4sKCYT2qElep0qtRRXX73KfLbLvL4kI1fv0k5fkycJhvTWVtRVTVkKyMvygqKcUNZTtBWBtrjiYrogB5QRbZ1JF/euH8iKE+ea3PAE/Emd2aigee0Ic9NIkc5FpZTondINQ4jDUxq3KBVuvnUUZUGMkexPg/T+glZMbMNp7eIJm3harycrywvq2XQD9EBRViXbW1vMq3P4LgPuG6XupTUMQYxzLuWQbuJTZEN0vFpiVE2mawhJXpAy40hu1hhJm4JR5iBBv2K+EEZMaYkTGc09UaUIFjxFGWnXA1mZ03YevIASNk5VNsYF0bum5xYchY4C1hRyDgsJLedc2zVUWYE7WmNCiZ5KawYD6KjxITF/Y52i1gwubgClMCXCQio0wfXyd5WB8qKBR2FGeYcKMLZR2IBPenGrBeSNm0Eh92LS6gUUZnM6ySUnJWxoxdA5hmEA7fFR9IUhdoQwyGvFEuPYOmIAT4wetIPC8eC7zrGm5+pLnlsvHHNoFUw0cVFzbjLhmadeJN+6xJMfehidvcClB87w4Y/+bZ740if47Oee4qVnX+SFpz/L7ZtvnI10TwA9T6TzjiorBdgVBTbPyGczFIpsZ44tcmIjoCm6QfR561ayexJ6V9Zii5xh1RCalr5pcb2j/fWP8+w/uMZXn3sarCf0a6blhDv2kGV0rGNgz5Y8Otkls1b0ZOsWHyVoVQXJ3QqKlPllGRYr6AdUnhO9QwUr6eG5QQcInUdZw+gORGt8J9liWAs2Q9kMrUq8Vags2whAw6oldj3kGcoYonPQdChr0VuTJHLVRGtQVQFDYOoVygdsWfCRJ9/LUZXzxaNX+dn3/xDTbIqaluAD7588zDt2LpG7gM4UWZ6BEUOI3ZnjD45x+wUOTRN71OD5jXDE2/MtfnH2AJ87LDmaX+SRvbP8L5f/Q2LTYZcN0RjczbtoZQjzCjObotct77Q7XHnsQ/xq+AL/8vjLHL2mmuf+utfXvJrwsx98nDurm/hqRuM9q1XL/gtr4QFCpGtbTFZushS1irjBSSq9McLmWUueFxRZQV1PsHmOLQrKcspkOpP8ycRSEXWKFz4ZRcoGO6bzScvzaBl9yGPSzSUEGSnppOZJ4DCeAj2jfEDpFEThUkQJCj2OuICoPRFP33ZoYzfjL23+dOPZ7yWzN7onY4xoFdPrOxmZbXRAp8Gu0lT1BKIiy2wyxGTs7u4yn56hribcPbgP9O6VFaNkOSoVU/uQ9NDKxFDTd4Fm2aMJDK6jMCd1eCoGrLa4IWKwyQQhpogQBOzEtJmSJ5PzTmIhhCUzSqFUxvq4p/ADQUXKUtMcdwQ0ykqch+IEdElnrIxsx1Eqp4/HGIlRopKcc+Q2IzhHDJZgDFH3MMhYVSmkjzazOCJyY03nZzJkaa3xzglL74Xd14lZHLWHEsG3UfaBEn2e0slokQSKwUdiyusbP+ewUQTCeJmR96FSLFnErTp61+Ndiw+9SLK8/K7G2rmIMKwxsYYoBzgefsc5HnvPI3zp81/m0kPnme7tcfvGDUrj+YX/7Of55pc+we9/aZ+//tGPcvTSU+yUOf9/e2cWY9l1nedv7X2mO9XQzWaz2U02SYmmTMoaKFmR6NhREFlIBAWBARt2giR+8FP8krwEkBEggR+ThzzEkOEkkAMEMQInsBNPChRZYuwkgikpsii2JM5kc+h5qOlO5+whD2vf20WKlDg0uy4b+wcu+tSpqntW39rrnLXX+te/Pv+bf85HPjbmjpMFo40+t99xH7cc6fPwl/7sddfSSgR6VgybwzXKqtRoP0TivMO5HaL3+MmUemOERLCDPnbQR6zgd/bodsZE54jOEXykGNSYGHGzlnY2x21vM790ia3TT3H5wvNcdi2hLCj6DRcmu7gYKDDcPzzCRt1HRKdBTL1jVKQdS1rIZVkpp2A6IwZP1WtU6HA2p0udcEVVK6cw1fO913EwIqLnRImftqjwUefRLjlFRJ0KMmgIgx6mSAPKfYB+ra3eRaE8PaOcAb83Q+oG01RQWqSquHN9jV/Y3GQkBWWMGO8Jkzm4gFQFlUtyllGQukJixK/1wd5KcWgNU1lko4ecu8D48iVm3rEx6/hOM+VrcZdf7d+P7fWUG9FY5o8+idQ1FILp95BRD9kY4Z1H5o5mPucf3PkRPj68nf/w6J/zP/eeOKillvEmcXl3zF+89AQShMl8m3MX5oQp+Ck0GxZbGUL03HbkGIc31+n3e4wnM4J3VEVBUzdEoCgKRoM+vV5D3e9jbUnTjBiubdDrj7Tz1vCKjtIFnTuGRaeddsdqxtzorExr041Y9MaOJo0lkbdj4uMFTWtgSMfoA2kxt9aFiBX9XRbZB1TWoen1lfuEak4W5Ru7bb5SYuU6Q0ilAVXaXyhepCuzKM+xL1jW6DUwm7U0TcOw38e1U9ZHQ9aHPUaDNYpim5a8EVsFxBjoXKtTUsRgbYEYFacnRmbjFgkVkUiQSBc6jFHOO1EobYV3Y81uAd4n31p2yfoUuGg/KnCN1yYGYgfoGDHn9yhcSVkN6TV9Zq5LfnSNd62ZugCSsnr7lh0R9c9gFvEe0XuGTY/dK3uEDsqR0InRDGPaeBWFvkFpZDmyzBaGaJR3F/FE8drBqoNlE+3DqzZgLIliSMPZSK2dy4BwsV+LQe8eIXXaRlQOLIZrE3cI+6oCSTzaIoRpi5SGbjIhxBYfHa4LOBdVNNkvaFoC0RFFY4Ijdx3jlpMDpuMx95wYIeWA7z71AuM9x+Pnd/jCb3ye9c0Bt29uYq/+BS+PO8bjHX7x7/8U585MuHL+Cmthi9/8F/+Uz/7Cx/mZh3qvu5ZWJtArjQGv9fGiqvBth2+1C9WOJzoKzGsZp1pfo771MNJU2OCJ4xnRh2VMJsZS9htIGj3eOZqywiK00XFpNqGb7eJR4vVtRY/j/XVKYzGRJPoL3WxGdJ6iVB0/EcEnpfoizd3EB0LX4boOgKLXsNjjiNG0sZ9Ml2Kt2mKuizzOO+Lc6aLrN0hTYZpKdyjGgHOE6Yy4p/IkxhpCXWmmsDJQVxSDHqGpkUbneEYfiNMpmz4SZxPirKWLAakrbE+5jouqTpjp7Fkdtxk1WDMG896T1Hcfp9wb0zv9Ij/33Gm+ceYZ+tM5nzIj6rkjFgWyu8X88eew60N923lMXESHu7yFNA2xriisIZy5yn2Hj/LrD/0cP/vsKf7On/z7g1lsGW8K3kcmlwNFT3jumRYfoIiBUAlFbej31qkKy7333MVoMGJtfcSLZ87jurmK/vZ6FGVBWZYM+33KSkWRi7JmOFqn119DbIEpiuQ1Me3GZZnFs9YSJeUNYkzSCIvKkZamFvIRxGsBlg+JvSf7OD0mib7GgLXFMjO4eDBZI2lvFTRbKIvMIksy92vh1Rm+12rMuL5Bnwap3VzFVrvYUjVG7wH7Ajz9LK79VnCOdjal1+8zGDTs7Fo2Dg34sfc3bN55iOefGfOF37h4He3MeKsIIeBcR5eSGN6rKK8Pjtlkwu72FkYKCmnwopqVBIdISfAqTt7Uwny6g8g6C05bjB6DICaNz4xmmQ32+0vD0WAkYgmUdYkxBcEYqkGBcYbd3QmLebi6n0qZ8KgDvay51qSxaKRKzEAmkymjfkM7nSHRYMqIFB5pwXhDYUvNzMcI4nVuL6IlZStpwye40BFNwATdpU1kxqAYQBe0ASTRNLRhN2Cidi1rF79mCBc2LZqwQto86T1Ig2AjBSyCtkLjhkKsatB24Jww293DeY/vPG3nldMnkRA7nYoRW6AjAL0+fPYXH6Istwlhws7ugBdOv8CHHvggD30CmvUjPPu9U/zBf/86972nz3/8ne/y0QeP8tWHT3H8ju+yNhgh0VCNau5+4Cc4c+YlHnt0xUu3nXdcuHKZNVvR9HqU/Z6WK0uvRX2nJQopLWE6Z37hMm5nTLk20JZrH8AayrrSkmbqRLPDgY4wch7jHE6U3G1jmnsnGsXf1d9gUNRURcmkndN6T2Wt1t2D6uUto3rQYKosIWrU344nWITR8ds0fT2ZYmypvLuuQ+Yz8BCbSndMiyxf63DziYollwXSlPjJFH9+C/o1FJbYeegctrTEfgNVhfQq6KUxa7bQ68wclCWxdfqazpEQ9P9vBDvsw1oPypLCFknzz8GsI847TG01rz9tiaUjzuaYXh9zxzFu++D9fObx55k+8wzPvfQssSyJZy/QPfm8Orc1mF5DmLWqI7g30w7jWuUoYlVjigq/PaYa9vipD34McqD3roAxgo2GT7z/fexNnuXyzjZNJWyPI71BwcbakPX+BneduJ2mN2Ayn+PTMPO6qWkkzYY2FVWl3eh1M6TpD2iGQ81am2vdeaRsBELapesOftEFSwiYotBRTNg0h5JlIwbo2Cdr0MxCas4IksSRQ1SR1gCCX3anipg00uhaM4iI3thD8On8KydxLEWWuRbQRRLN4wekXl673Hst+Nv/vTcSEOqF264juiTmXpWI3WcfCx6WZi0lCmKVcB9DpBn0KGxBrznEfOckTz56K9Xak29wZWTcGAhGCmJo6boO7x27ezuIF5p+xaz1uC5gpSBKEh7GEKNDMBTSQ2rPrN0lhBorBhVZCYQ4T9k+rUmG1J0U0wQIE3QiDKZiPp7TK4SiENzcMR5PUtfqYv2jZd9Fg4dcEzJe7JEWQubOOYqiwIgw61qCF/rrPa0eTxyWUjN1VpAgWGPw0QEBSSLn0eukHSTqTPkQCLHDo9xBE30SO7aJEhKWLIbUu59oGmFZWl5o7Kk6hF9m9JajEIN29S/YrzrVKoWvEVw7pnNzOqeyMDEuOICqTxi8GmDNnA988sNsX3iW8xfOcvfdG1w+6/i/XzuNs+f4ax9/D7OrM65eER761Cf5zKce4Iv/7cvcftca7/vgbdx7zwnueM9dPPv9J9ja2ePUt77FF5+/QmVePxO/EoGeFcPRW4/SHyohu6gbbGFx0xkiEZ9S16awhO093coXVlWx92bErsP0G7y1FKIZtDieUd92GNYHzK5c4creFufaMa1AJTog2eHZMBVH6gFFYRmHDu8DvaIipHvtok6vpXUdFROcozSNqpWPx0osHw6xvR5x7tBUYcTvjQnTMYgod6LzqhXsHW7WYXoN4gRTV5iquBbQFjq0PFqDCZFYFDDoIWtDTFWBNZiy1AYR0J8XvYnHImIEnYVbCMV6BU0Jwx52fUh0QbmEpoC6p2KS48mSZ+GLAgmROLbE7QnMPebWI9g7j1OMP8T7nzlN+/gL+N09MNp9jAuE6QzagN/S7GW9uUacd9gGgggxeHAeswheM94VsFY4e7nlkVMvcPH8GN8Kc4N2koulNzzEkc0jnLzzbsqm4LnnX1Y5CGOoq5KibOicx3nBlpa6N2SwtkHV9LBFpRSERSAEYAQfAmKszqlFvw4hIGWJiNWsQ4jKsUkBldKOlFS+mOWp3aj6EDPIMputZRrdsAlxOW1jmb1DqwBVVaUnWCAEw2itwhQmlaoibZuU+kEflIsi8j5O0kID8PVm5L6yvLtgA+3j1f0wpBKY8/qQq6N2GV/jEaWMyOL9RMveMWUtqrqm1/SYty2HDh2iV5/gj774g5M2Mg4GIoaiaPCuxfkpndN54VWp2a71jRFV17J3FbwTLBbQzf3CL7wrgDWKwjPtxvhodMIUuqGJoSQGIci1TtyU5tL9lolEV9B1NZ2bMjIddV0CgaZXM592+/ivVikWaWPlJOnrJd5eXEgE+8Bo1MN1c62URbC1pZ11yu8TCOJVCsYtGokX87O0+cJIIOKI4jEmEpz6gPZmqKi0iaTRZ2gwJyzn45Leh3QuxohJ95ToF4Lq+jmEoDy+mDixMX04XeeoSELqBjo/xruZZl2dx6cGRJVq6RDxRBy3nFjn03/7w8zGz3HfT95Hu7fLsRMtvig5efcxaC/wZ1/5Lr1exY/f92M88+g3+euf/iSPPvKnTKdzXjj9POOdM2yub/DE5W2ePQNrt9wG0xm8jjjSSgR6hRGGRU09HFH0qhTMWMrRgOg8bm9Xs04YuuChshSJw9bNWsBqVm88x7eOOJvhZ1O6uWrebV+5yunxFluhQ/cFES/QxsjxasSwanAh0HpHZTV7R4AoghfwrSOI8uWCc1qx7Rx+OqebzRkMBlSjgd7gBaJrcbMpcT4HA9aUyzKvlqB0yoWpSqSpoSgSL0HHoRVNjRvP9KbdeqTfR0YjYl0hwwEURrt2re6eYoRodGFH57VDca2nvNqqQOoSylKzHp0jzFpVWO90YKB2K3nNzFW1lobLCmyJ39rFX9hBNtewR2/Fnrgd++AHiBcv4Z9+kXj2InGs0i9uPIa9KZRCGOv/zTuHtC1S6ygnP5sr9SPjXYHOBU6cbAjWc/we4eXHoSeGOIj0qyF11aedjbl06QKbhzfZ2Z0gRjl0GvBZHUdkC4wpGIzWqHqD1EWXBIlF9gVDBh8DJmr3YPCB0pbLmcqLbrfFjTlCugGnJosYVJIlcYe09KJcvUXQuJiyASgfCRWA3s9VIkR8q1IUEDnctwyHSstwzqluX1xcg+UYKWP1AbDQEoshJPFlq1kSa34g6HtlefUNBnkpc2KSHlnXOVzXJX2yJC3hQ6LlaeC8+L2mrtUWKekP++zs7XL48C0cPnyEI0du4exT229lqWRcZ6hPeA1ggo6nMwEKsZhSS5r9qmLr0hihSZt1bTJQH9HnjXeWGAuMDcxmW/RsD2JJcA0xFogE/GIIAQKpvKmbIV2RRhqC75hP5pRlyeaRTQ4dOsSzTzzHoKmZTGZgChCb4iida2uWci46b9f7juFgoHNnEcRYeqNSy6T+moiyttjHNLY6gIsUYjWzLwYvWn62YpEqMm/BY7BS4YNPXH+UkrToPpa4DEojS08nGvBWu4NdjLgQmQefJFHARIsEwEVC51SkOWi2UdAgyoSAa1v8PNB1njZEfDSp/O51olV0lFXgvT9xC9XGYY699yc589RXqEbHefhP/pAzVyOf+vRD/PF/+l84I3z4Iw9y5cqYcjDijqN9HmMMBk59b5u1Aexsn6PcKDly5CiHDw/ZvnjlddfSSgR63jleev5ZjmxtMbrtKHZtSLk2Uj05BFNYZqdfpt3dI85mlBul1v9Fx6JFI8RZiw8RWsHPZjCdEvbGzOdzXjz/Es93u8wItDHQEmgDNMZytBlijAaQvaKkEKGLbkkE1y7uQHQO7zokjUtq98a0sxlFYamGfWxZ4PfGdHsTwmyOSaRy2/QXOoxaGi0KZO4JEjAharZNLMxaMIY4n9PtTlP50yL9GgZ9/SyGPahK9UWvpewIMGkxO7PUvRQwVUGsaqIBKQsY9jBVeY00W1daAkM0w2cFqiLNGTREI1CXYEfKl9zeI1zYIm7vURw9jGwMkXvuojh+HHf2Ily4gr94mfLqNv7SFnF7Rx+046lyIUY1zAttQhn2idOsvf9uQWGF+byj7PXZugqmEupGuHTJc/sRSwgGbyJnL1xCyordyUw5cSkICUn+w3uHd8JsNseU0ySErEPDTdpRL/ICWnJRfbtF44Q1qua/GEdmEn8mpodBJOKDZyEMLFHwHmzqkFW5CJacPCHCQlsravfcNXK2ZgxNcEsmuU+k9ZAarEKIy868RcAXgmrulZU+m0JImTOTHpyLjMEyKEwl1VfIouzP6r02r2//9ItFI1cIAd91hFLlqWJQ3qLFEINbbghFhLKucF1LVVZUTYVYw+bmJoc3D3PbLcf4Ds9c72WU8RYR0vQEnZ2aNjhi8LEjgg4HKIV52+KCxUih89YTp2CRqdaxZkJZNszbOU2V5kbH/bpukNrUIW2K9NBoQMWI6a5DaFmv+rzw3MtIjMznU4ipVKv7jFSuFUg8tUW3a1GUlGVJJNLr99jb2k78cxVntoUhGBVcx+iUCr+Yd1sY8D6VWtVGjOC8JjeiQWlWixL0UuycFPhea86KMaZSatQMCRoYhxAInuTjEIIQg2CiLD//FPpi0A0lhWU+mxI6h3eetuvwQe9dfmFL0CDg/T/9QT77K3+P9vL/46WLT7J78QzWtjz62Gkubs/5o9//3wyPPcBHTk6wwfO+B+7lya8/zLOnzrNz1fGxj9zH5uGOxx9/mvXb1+m2LzCMjhO3jbj35Dpf+L2nXnMdrUSgZ8qKUBY8dfUCve0rbBQ168dvZ/C+9yJNRejXtJVlcv4iWEOv16NKQ5JjpWXWdjrF7ynfzXUttC1uPGF7OubU3iXOx45pjMwJdEQ6In0pMUXBzHvqoqSLgdYH7epTMR42ihIvwridQ4w0Tc1sOsO5GUaEejigMwa3u0vYnRAR6tGQtuvAOexsiojFSqGL3qfFknYA1AWICrXGtsXvjTWj3C/xhRCbAkY9ZFgTe9oQgk/zCKedLqLdKVzeJZYWWe8jlSV0HbI+QMoSXdnJQUjCzFa5GmJEg8x+SSwFKYskTuugVxKbEtYbzPaYeHWPePYicu4SbK4hox7xlkNQ18QjG5jOES9cwD3yGMxbzKEBcuJWxAfClZQlmAnzLqf03i0wpmJvx3P/vce5vL1HWXv29lKQURpmbcfxzSF33nmcF89cpm3ntO0MIzp/0idl/iiWKJGqrtW/Fp3mITGGkpzCItwLi8BPVKgUJMkqJF6Q7OfJXfsdoo5mUkmKCC49xVIJNXjNnunPpvUPsFTCX5R3lzkNALZ3p6yPKsq6SrIm+p7B7+f1BLwnSdH5ZUDWRoctbAomJZWYkzZnKh2bJBCvn4BmM38UdCSWZRIDMQQ656ic8mJDcKns5HDjGc365jJ+tEXBfDIh9AYU1mKMZdgfcOuRI9xx+0ng/7ztdZPx9qEyWhHn9X65yBKXZblcb5aS0dBQmsDujs4/F6vTHrquQ+VNwNhIicGYHjvzCWY+oa6GyjmTxbg/Up1UM9uL6RXXRupVFBLw8ymXzm9z58lbmeztMZ2OCf5aQ8eCvkBcTOJY8FWjlo1FKGttlqyrWjnvooLe4nVmrC0L7YAXbQgrjAVRqZkoYSlwXhSS5jNr57EtCmTmtWJl9bFnjVFun9GsZTIFIwUiTt8jVXOXE3aC3iuCj2mKBirt4v0+Xq7GCEVVanOm9zjndKMarm3IQuyIOIrKc+8DBWsbP868vo3dv/xtjt3R52tffYoPfOAo3/zei3z7scd5/wPv4eruWU6f+i5P0AAABLlJREFUe5JP/HTHk89tce7bU67sevbis/ytv/Fhzp1+nu+fPse9d63xjW+9SGsDxQ8plckqzB0VkV1gFTU3bgEuHbQRr8Iq2gRv3q6TMcYj75QxGdcH2TffFFbRJsi++a5E9r03hVW0CW68Xa/puyuR0QOeiDF+9KCNeDVE5JurZtcq2gSra1fG20b2zTeIVbQJVteujB+J7HtvEKtoE6yOXT+6PpCRkZGRkZGRkfGuRA70MjIyMjIyMjJuUqxKoPfvDtqA18Eq2rWKNsHq2pXx9rCqf9dVtGsVbYLVtSvjh2NV/26raNcq2gQrYtdKNGNkZGRkZGRkZGRcf6xKRi8jIyMjIyMjI+M648ADPRH5myLyhIg8LSKfu4HX/W0RuSAip/adOyQiXxaRp9K/m+m8iMi/STZ+R0QefAftukNEHhaR74nId0XkHx+0bSLSiMjXReTRZNOvp/N3i8gj6dq/KyJVOl+nr59O37/retuU8c4j++YrbFo5v0zXyb55EyL73itsyr73drFfdPRGv9Bp288A9wAV8Chw/w269s8ADwKn9p37V8Dn0vHngH+Zjj8D/A9UJ/HjwCPvoF3HgAfT8Qh4Erj/IG1L7z1MxyXwSLrWfwF+KZ3/LeAfpeNfBX4rHf8S8LsHuc7y6y39zbNvvtKmlfPLdJ3smzfZK/veD9iUfe/t2nqjLvQ6H9QngC/t+/rXgF+7gde/61UL+gng2L7F9UQ6/rfA332tn7sBNv4B8LOrYhvQB74F/BVUCLJ49d8S+BLwiXRcpJ+Tg1xr+fWm/87ZN3+4fSvll+ka2Tdvglf2vR9pX/a9N/k66NLtceDFfV+/lM4dFI7GGM+m43PA0XR8IHam1O6H0Z3CgdomIlZEvg1cAL6M7ji3YoyLadj7r7u0KX1/Gzh8vW3KeEeRffN1sEp+mezJvnlzIfve6yD73lvDQQd6K4uoYfeBtSSLyBD4PeCfxBh39n/vIGyLMfoY44eAE8DHgPfdyOtnZCxwkL65an6Zrpt9M+OGIPveK/Fu8b2DDvReBu7Y9/WJdO6gcF5EjgGkfy+k8zfUThEp0QX9OzHG318l22KMW8DDaEp6Q0QWY/T2X3dpU/r+OnD5nbIp4x1B9s1XYZX9ErJv3kTIvvcqZN97ezjoQO8bwL2pS6VCCYp/eID2/CHwy+n4l1EuwOL8P0zdPB8HtveljK8rRESALwDfjzH+61WwTUSOiMhGOu6h/Ijvowv751/HpoWtPw98Ne24Mt49yL65D6vol8mu7Js3H7Lv7UP2veuAG0EE/BEkxs+gXTTPAP/sBl73PwNngQ6to/8KWi//CvAU8KfAofSzAnw+2fgY8NF30K6/iqagvwN8O70+c5C2AR8A/jLZdAr45+n8PcDXgaeB/wrU6XyTvn46ff+eg15n+fWW/u7ZN6/ZtHJ+ma6TffMmfGXfe4VN2ffe5itPxsjIyMjIyMjIuElx0KXbjIyMjIyMjIyMdwg50MvIyMjIyMjIuEmRA72MjIyMjIyMjJsUOdDLyMjIyMjIyLhJkQO9jIyMjIyMjIybFDnQy8jIyMjIyMi4SZEDvYyMjIyMjIyMmxQ50MvIyMjIyMjIuEnx/wEd1thpcOWA+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1440 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWQOcSHSjhaN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}