project3/project3.ipynb

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  "metadata": {
    "colab": {
      "name": "HandRecognition.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/filipefborba/HandRecognition/blob/master/project3/project3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "Sp4tlgEH60gM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Hand Recognition\n",
        "### Filipe F. Borba  \n",
        "### Franklin W. Olin College of Engineering\n",
        "### Data Science, Prof. Allen Downey\n"
      ]
    },
    {
      "metadata": {
        "id": "gbgSgBT8YwSp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Machine Learning is very useful for a variety of real-life problems. It is commonly used for tasks such as classification, recognition, detection and predictions. Moreover, it is very efficient to automate processes that use data. The basic idea is to use data to produce a model capable of returning an output. This output may give a right answer with a new input or produce predictions towards the known data.\n",
        "\n",
        "The goal of this project is to train a Machine Learning algorithm capable of classifying images of different hand gestures, such as a fist, palm, showing the thumb, and others. With this, I'll be able to understand more about this field and create my own program that fits the data that I have. This particular classification problem can be useful for [Gesture Navigation](https://www.youtube.com/watch?v=Lbma7c55wf8), for example. The method I'll be using is Deep Learning.\n",
        "\n",
        "Deep Learning is part of a broader family of machine learning methods. It is based on the use of layers that process the input data, extracting features from them and producing a mathematical model. The creation of this said 'model' will be more clear in the next session. In this specific project, we'll be aiming to classify different images of hand gestures, which means that the computer will have to \"learn\" the features of each gesture and classify them correctly. For example, if it is given an image of a hand doing a thumbs up gesture, the output of the model needs to be \"the hand is doing a thumbs up gesture\".\n",
        "\n",
        "Obs: This project was developed using the Google Colab environment."
      ]
    },
    {
      "metadata": {
        "id": "l20Rj1HP7LB-",
        "colab_type": "code",
        "outputId": "9c81bcf6-a6a7-46a2-f49d-aef1d7648f80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Here we import everything we need for the project\n",
        "\n",
        "%matplotlib inline\n",
        "from google.colab import files\n",
        "import os\n",
        "\n",
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import pandas as pd\n",
        "\n",
        "# Sklearn\n",
        "from sklearn.model_selection import train_test_split # Helps with organizing data for training\n",
        "from sklearn.metrics import confusion_matrix # Helps present results as a confusion-matrix\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sXBc72Ph89nR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Loading Data\n",
        "\n",
        "This project uses the [Hand Gesture Recognition Database](https://www.kaggle.com/gti-upm/leapgestrecog/version/1) (citation below) available on Kaggle. It contains 20000 images with different hands and hand gestures. There is a total of 10 hand gestures of 10 different people presented in the dataset. There are 5 female subjects and 5 male subjects.\n",
        "The images were captured using the Leap Motion hand tracking device.\n",
        "\n",
        ">Hand Gesture | Label used\n",
        ">--- | ---\n",
        "> Thumb down | 0\n",
        "> Palm (Horizontal) | 1\n",
        "> L | 2\n",
        "> Fist (Horizontal) | 3\n",
        "> Fist (Vertical) | 4\n",
        "> Thumbs up | 5\n",
        "> Index | 6\n",
        "> OK | 7\n",
        "> Palm (Vertical) | 8\n",
        "> C | 9\n",
        "\n",
        "Table 1 - Classification used for every hand gesture.\n",
        "\n",
        "\n",
        "T. Mantecón, C.R. del Blanco, F. Jaureguizar, N. García, “Hand Gesture Recognition using Infrared Imagery Provided by Leap Motion Controller”, Int. Conf. on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016, Lecce, Italy, pp. 47-57, 24-27 Oct. 2016. (doi: 10.1007/978-3-319-48680-2_5)  \n",
        "\n",
        "Overview:\n",
        "- Load images\n",
        "- Some validation\n",
        "- Preparing the images for training\n",
        "- Use of train_test_split"
      ]
    },
    {
      "metadata": {
        "id": "pA0yTcvL_F_Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Unzip images, ignore this cell if files are already in the workspace\n",
        "!unzip leapGestRecog.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jRsNYIIoCmXp",
        "colab_type": "code",
        "outputId": "4c51136b-9992-4064-876a-0c61730cdfa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# We need to get all the paths for the images to later load them\n",
        "imagepaths = []\n",
        "\n",
        "# Go through all the files and subdirectories inside a folder and save path to images inside list\n",
        "for root, dirs, files in os.walk(\".\", topdown=False): \n",
        "  for name in files:\n",
        "    path = os.path.join(root, name)\n",
        "    if path.endswith(\"png\"): # We want only the images\n",
        "      imagepaths.append(path)\n",
        "\n",
        "print(len(imagepaths)) # If > 0, then a PNG image was loaded"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ps9mMyvx_MQR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This function is used more for debugging and showing results later. It plots the image into the notebook\n",
        "\n",
        "def plot_image(path):\n",
        "  img = cv2.imread(path) # Reads the image into a numpy.array\n",
        "  img_cvt = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Converts into the corret colorspace (RGB)\n",
        "  print(img_cvt.shape) # Prints the shape of the image just to check\n",
        "  plt.grid(False) # Without grid so we can see better\n",
        "  plt.imshow(img_cvt) # Shows the image\n",
        "  plt.xlabel(\"Width\")\n",
        "  plt.ylabel(\"Height\")\n",
        "  plt.title(\"Image \" + path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BJoQM_Yk__AX",
        "colab_type": "code",
        "outputId": "ed463c9d-3c51-4c6d-ab24-e8cc080b2915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "cell_type": "code",
      "source": [
        "plot_image(imagepaths[0]) #We plot the first image from our imagepaths array"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(240, 640)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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wV9iKyZ+JMFXx6mjdA8gbsfy5Rb9LXb2T23V6juSKWE/1ooiXM81tz030gc+d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\nhixS9DNKGQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mStukClHZm4p",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now that we loaded the images and checked if it's everything we expected, we have to prepare the images to train the algorithm. We have to load all the images into an array that we will call **X** and all the labels into another array called **y**."
      ]
    },
    {
      "metadata": {
        "id": "LuQLrqJ2ZmOM",
        "colab_type": "code",
        "outputId": "3f019f13-5250-408a-bc76-907f6947472f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "cell_type": "code",
      "source": [
        "X = [] # Image data\n",
        "y = [] # Labels\n",
        "\n",
        "# Loops through imagepaths to load images and labels into arrays\n",
        "for path in imagepaths:\n",
        "  img = cv2.imread(path) # Reads image and returns np.array\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Converts into the corret colorspace (GRAY)\n",
        "  img = cv2.resize(img, (320, 120)) # Reduce image size so training can be faster\n",
        "  X.append(img)\n",
        "  \n",
        "  # Processing label in image path\n",
        "  category = path.split(\"/\")[3]\n",
        "  label = int(category.split(\"_\")[0][1]) # We need to convert 10_down to 00_down, or else it crashes\n",
        "  y.append(label)\n",
        "\n",
        "# Turn X and y into np.array to speed up train_test_split\n",
        "X = np.array(X, dtype=\"uint8\")\n",
        "X = X.reshape(len(imagepaths), 120, 320, 1) # Needed to reshape so CNN knows it's different images\n",
        "y = np.array(y)\n",
        "\n",
        "print(\"Images loaded: \", len(X))\n",
        "print(\"Labels loaded: \", len(y))\n",
        "\n",
        "print(y[0], imagepaths[0]) # Debugging"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Images loaded:  20000\n",
            "Labels loaded:  20000\n",
            "3 ./leapGestRecog/01/03_fist/frame_01_03_0105.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8AjUu9S6d11-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Scipy's train_test_split allows us to split our data into a training set and a test set. The training set will be used to build our model. Then, the test data will be used to check if our predictions are correct.  A random_state seed is used so the randomness of our results can be reproduced. The function will shuffle the images it's using to minimize training loss.\n"
      ]
    },
    {
      "metadata": {
        "id": "NZa2YzKbdEz7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ts = 0.3 # Percentage of images that we want to use for testing. The rest is used for training.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=ts, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "adafxZ8E8_ZB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Creating Model\n",
        "\n",
        "To simplify the idea of the model being constructed here, we're going to use the concept of Linear Regression. By using linear regression, we can create a simple model and represent it using the equation ```y = ax + b```.   \n",
        "```a``` and ```b``` (slope and intercept, respectively) are the parameters that we're trying to find. By finding the best parameters, for any given value of x, we can predict y. This is the same idea here, but much more complex, with the use of Convolutional Neural Networks.\n",
        "\n",
        "A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.\n",
        "\n",
        "<img src=\"https://cdn-images-1.medium.com/max/1200/0*hzIQ5Fs-g8iBpVWq.jpg\" alt=\"CNN Example\" width=\"400\">\n",
        "Figure 1 - Example of Convolutional Neural Network.\n",
        "\n",
        "From Figure 1 and imagining the Linear Regression model equation that we talked about, we can imagine that the input layer is x and the output layer is y. The hidden layers vary from model to model, but they are used to \"learn\" the parameters for our model. Each one has a different function, but they work towards getting the best \"slope and intercept\".\n",
        "\n",
        "\n",
        "Overview:\n",
        "- Import what the need\n",
        "- Creation of CNN\n",
        "- Compiling and training model\n",
        "- Saving model for later use"
      ]
    },
    {
      "metadata": {
        "id": "Lzdh8xGTNQaj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Recreate the exact same model, including weights and optimizer.\n",
        "# model = keras.models.load_model('handrecognition_model.h5')\n",
        "# model.summary()\n",
        "\n",
        "# To use the pre-trained model, just load it and skip to the next session."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Hq0ej5yDZq2e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6e79eba-8386-4095-be6b-da102071a99b"
      },
      "cell_type": "code",
      "source": [
        "# Import of keras model and hidden layers for our convolutional network\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers import Dense, Flatten"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "gq130kMucV41",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Convolutional neural networks (CNNs) are the current state-of-the-art model architecture for image classification tasks. CNNs apply a series of filters to the raw pixel data of an image to extract and learn higher-level features, which the model can then use for classification. CNNs contains three components:\n",
        "\n",
        "- Convolutional layers, which apply a specified number of convolution filters to the image. For each subregion, the layer performs a set of mathematical operations to produce a single value in the output feature map. Convolutional layers then typically apply a ReLU activation function to the output to introduce nonlinearities into the model.\n",
        "\n",
        "- Pooling layers, which downsample the image data extracted by the convolutional layers to reduce the dimensionality of the feature map in order to decrease processing time. A commonly used pooling algorithm is max pooling, which extracts subregions of the feature map (e.g., 2x2-pixel tiles), keeps their maximum value, and discards all other values.\n",
        "\n",
        "- Dense (fully connected) layers, which perform classification on the features extracted by the convolutional layers and downsampled by the pooling layers. In a dense layer, every node in the layer is connected to every node in the preceding layer.\n",
        "\n",
        "https://www.tensorflow.org/tutorials/estimators/cnn"
      ]
    },
    {
      "metadata": {
        "id": "iMiPmBWLYrkG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "385d9b1d-aafb-4efa-f15c-22163b003ba0"
      },
      "cell_type": "code",
      "source": [
        "# Construction of model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(120, 320, 1))) \n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu')) \n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YliBFXpgLZic",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Our Convolutional Neural Network consists of different layers that have different functions. As explained before, the Conv2D layer performs a 2-D convolutional operation, which can be basically interpreted as a mathematical operation to calculate weights inside the image. In order to maximize the network's performance, we need to understand the parameters required by them.\n",
        "\n",
        "The first required by the Conv2D layer is the number of filters that the convolutional layer will learn. Layers early in the network architecture (closer to the actual input image) learn fewer convolutional filters while layers deeper in the network (closer to the output predictions) will learn more filters. This permits information to flow through the network without loss. These filters emulate edge detectors, blob detectors and other feature extraction methods.\n",
        "It is necessary to tune the values of the filters, but it is common practice to use powers of 2, starting with 32, 64, 128 and increasing to 256, 512, 1024, for example.\n",
        "\n",
        "<img src=\"https://www.researchgate.net/profile/Ihab_S_Mohamed/publication/324165524/figure/fig3/AS:611103423860736@1522709818959/An-example-of-convolution-operation-in-2D-2.png\" alt=\"Example of 2D Convolution operation\" width=\"500\">\n",
        "\n",
        "Figure 2 - Example of 2D convolution operation.\n",
        "\n",
        "Another parameter required by the Conv2D layer is the kernel_size, a 2-tuple specifying the width and height of the 2D convolution window. The kernel_size must be an odd integer, with typical values of (1, 1) , (3, 3) , (5, 5) , (7, 7) . It’s rare to see kernel sizes larger than 7×7. If the input images are greater than 128×128 it is recommended to test a kernel size > 3 to help learn larger spatial filters and to help reduce volume size.\n",
        "\n",
        "Then, MaxPooling2D is used to reduce the spatial dimensions of the output volume. It reduces processing time and allows assumptions to be made about features contained in the sub-regions binned. It is possible to notice in this network that our output spatial volume is decreasing and our number of filters learned is increasing. This is a common practice in designing CNN architectures.\n",
        "\n",
        "Finally, ReLU stands for rectified linear unit, and is a type of activation function. ReLU is the most commonly used activation function in neural networks, especially in CNNs. ReLU is linear (identity) for all positive values, and zero for all negative values. This means that it's cheap to compute as there is no complicated math. The model can therefore take less time to train or run. Also, it converges faster by applying non-linearities to the model, so there is no 'vanishing gradient problem' suffered by other activation functions like sigmoid or tanh.\n",
        "\n",
        "In the end, there is a lot of trial and error to get the best parameters and network architecture. These are some common practices that help reach the best result faster."
      ]
    },
    {
      "metadata": {
        "id": "amOpISRiYw3z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Configures the model for training\n",
        "model.compile(optimizer='adam', # Optimization routine, which tells the computer how to adjust the parameter values to minimize the loss function.\n",
        "              loss='sparse_categorical_crossentropy', # Loss function, which tells us how bad our predictions are.\n",
        "              metrics=['accuracy']) # List of metrics to be evaluated by the model during training and testing."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GMzJWqkGbkYl",
        "colab_type": "code",
        "outputId": "cfae1b6a-e040-4b76-840c-c7cca1f3dabb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "# Trains the model for a given number of epochs (iterations on a dataset) and validates it.\n",
        "model.fit(X_train, y_train, epochs=5, batch_size=64, verbose=2, validation_data=(X_test, y_test))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 14000 samples, validate on 6000 samples\n",
            "Epoch 1/5\n",
            " - 305s - loss: 0.5644 - acc: 0.9133 - val_loss: 0.0096 - val_acc: 0.9968\n",
            "Epoch 2/5\n",
            " - 302s - loss: 0.0169 - acc: 0.9961 - val_loss: 0.0145 - val_acc: 0.9958\n",
            "Epoch 3/5\n",
            " - 301s - loss: 0.0045 - acc: 0.9987 - val_loss: 0.0015 - val_acc: 0.9992\n",
            "Epoch 4/5\n",
            " - 301s - loss: 6.5339e-05 - acc: 1.0000 - val_loss: 4.1817e-04 - val_acc: 0.9998\n",
            "Epoch 5/5\n",
            " - 305s - loss: 1.6688e-05 - acc: 1.0000 - val_loss: 3.7710e-04 - val_acc: 0.9998\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8c5fb09c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "n1zE7Dn4fJt0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Save entire model to a HDF5 file\n",
        "model.save('handrecognition_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JrWvHfD09BUj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Testing Model\n",
        "\n",
        "Now that we have the model compiled and trained, we need to check if it's good. First, we run ```model.evaluate``` to test the accuracy. Then, we make predictions and plot the images as long with the predicted labels and true labels to check everything. With that, we can see how our algorithm is working.  \n",
        "Later, we produce a confusion matrix, which is a specific table layout that allows visualization of the performance of an algorithm. \n",
        "\n",
        "Overview:\n",
        "- Evaluate model\n",
        "- Predictions\n",
        "- Plot images with predictions\n",
        "- Visualize model"
      ]
    },
    {
      "metadata": {
        "id": "TjFW-Rx1lGaF",
        "colab_type": "code",
        "outputId": "e26dbd0e-7f68-41ef-d9dc-6e403a824df2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "\n",
        "print('Test accuracy: {:2.2f}%'.format(test_acc*100))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6000/6000 [==============================] - 39s 6ms/step\n",
            "Test accuracy: 99.98%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "p8kgF7qFhahe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X_test) # Make predictions towards the test set"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-LGVByVLlx0Q",
        "colab_type": "code",
        "outputId": "7047cbc6-3ca2-4153-8243-a718e04d0dcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0]), y_test[0] # If same, got it right"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "0xurvEqibvZf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Function to plot images and labels for validation purposes\n",
        "def validate_9_images(predictions_array, true_label_array, img_array):\n",
        "  # Array for pretty printing and then figure size\n",
        "  class_names = [\"down\", \"palm\", \"l\", \"fist\", \"fist_moved\", \"thumb\", \"index\", \"ok\", \"palm_moved\", \"c\"] \n",
        "  plt.figure(figsize=(15,5))\n",
        "  \n",
        "  for i in range(1, 10):\n",
        "    # Just assigning variables\n",
        "    prediction = predictions_array[i]\n",
        "    true_label = true_label_array[i]\n",
        "    img = img_array[i]\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n",
        "    \n",
        "    # Plot in a good way\n",
        "    plt.subplot(3,3,i)\n",
        "    plt.grid(False)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "    predicted_label = np.argmax(prediction) # Get index of the predicted label from prediction\n",
        "    \n",
        "    # Change color of title based on good prediction or not\n",
        "    if predicted_label == true_label:\n",
        "      color = 'blue'\n",
        "    else:\n",
        "      color = 'red'\n",
        "\n",
        "    plt.xlabel(\"Predicted: {} {:2.0f}% (True: {})\".format(class_names[predicted_label],\n",
        "                                  100*np.max(prediction),\n",
        "                                  class_names[true_label]),\n",
        "                                  color=color)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "f1909f59-4f16-4cb2-ce95-caf53c07c4bc",
        "id": "QSl0leiufKqF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "cell_type": "code",
      "source": [
        "validate_9_images(predictions, y_test, X_test)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PDrFWIb5lGB6VXfV/9cflqmJzGK/KRAzHUfWrdMW644EIp6PSUu2JOvA7z1PhQ/ad+yz7\niag/lfW3sne1zFaw3dWqTtbflB/jcJwPr6ryamRkr5imx3UuE/ku1T89bPabg9iP0eaiMmX1o357\nzJ+zrpgO1oviqRxWnYfHz+rqb9Qt+o00JQrzVL36/+gyDK7L6HryqN7LuJoqI0o2eFZ6RPGq8N+q\nMtMLGdgBsWTOHTsQDy6izuHPskOq3PkzMoxxFVmIVkrUYEg5ZVXmg4MDa7Vah8rAsxRqoBIBkYfh\ncJlw3UcggO+ilS9VR/xc6R3FVWXD+NHAJ6ofzsc/87XlnD7H9/98nSrHKevbLLM08FoeCbdpZGtl\n7RENJvA3gzJ8cTzK9q77M0VulU74TH3OMA3DRgMJFFV2pRdjVmZPWVocvtVqFecDfCZ2f3/fJpNJ\n8a6K7+Hnyoaz8PhZ9SW2eXzPZA/z53SqlIPrW+Fs1H/5czYTfBR/UsvxRF3NzcJtUMYFUDBcNLGA\nYRnTorRQD8Yedc28Kof62RDkP9Hkq+rzSo+IV3JeUToeh3+KgOuQ2ybSqaw+Mv1d8MIIbg/eOeRp\n8G4k9ZM5ZX1D4Qc+j+KU4VqGL2ryu6zdTyIzv61OOVr+rDoEfufRLcbF8yz+jm+hw3SiAYwatESO\nj8vg78vICD9nmUwen4lpt9vyjA7nr+pM1X+Wt+pcTOg4LyVexy5R5y0DxgzIykA/IqCoA7dZVK8q\nLaVjmUEqkFP9RwE151nLbKSMHJvFA/isHaJ2V3YVkYQovhpkYNpViSraJf7GGJY5Ij9RX44cnNJ/\nVv16Mpk+r+mDIvyu4ijnj3Wr9EdRl2eotkafFPkjlYdKyz9n76K+oqQKgaliC7WcjkT+nYlsxq8w\nrvqe9Q/GGtW3PJyKm3GESB/u1+p2yEhUmmrCCe0QBzUKCzjtSG91413EHbLzTQpXI97LaeN3pQfG\n5bbF/KM0FZ4ovpJx0Co4F9WdSi8aHEe2cFKZ+ZkjbNSswczi2brIgfh/bBzVATCOusQA3/ufh8t+\nLwnDYRmriAIoTofrToES1h1+j/LMCEnWGRkgVdgqhqtAAZ8rAhUBeuRA8BmHVe0eGTbXUVY+Liv3\ncS8vAyHrqEhLLacragDDjgnJiYtyQmir6pB+5gSU3SsdM+zD9KI+j+HYwWT9O7M7/J716TIMybCZ\n0+A4mY9w4QmbCMeiW6Qwf9U+KlyEZ5HviUhYlA+nj+XK9FL4VpZH1Cdrma2gTUeklm2fMcjs8Jas\n6HwNtyvHV3assEP1qegCpIzrMdeJsCuKF+3e4LKolX3u5xEm4TsuM6fj4fiCk8lkcui8OcaNMEjh\nQrT1Ep+pPKL3/JzTjbholk+UdvRMXbaT1QvHqxK2qjRmlVCz2Zy0248XopTh+HMUZRSTyeHRbeQI\n+ZlyNHxHPacZzVgwOPkzzhNnBBlQyhwJG1On0zmkf/Q/KrdKG59xZ4/iReXIwFfFi0iDmQYrlOOU\nmdsiIihV8imT48SL6oX1HY1GNplMaiZyQmk2m5NOp5P26wz8y/pf5CxZMkzM4nC6ZeHL9HdMxO8R\naYnSj/qvqqOj2lSWJ9aDSxnuRv5DkRlVJxGO8jNMO/IJHIbLV/Zf6VaGc1E7RZiN5Yra7xfbF2ts\nmoE4d0JCXcYblKj+qnx91J+j75EtlPEbZVtHxUrFyZR9q61/iHP+2eu32WwWK83IX6JVnTLsVXqq\n90rHKA0ljN2K25VxHJVmVsas7avgelm4LD+0hyoc0MPPAp9mvnLEnZb/e4H5mdnh36/Aysn2vaJD\ni5wFdip0TGaP9rH7bSs+2++fVRn9P6/eROXn76oz+nPfT6+ACsuGefnzaFse1xOHreoo8VlkiFWM\n3XVn/VQdRmlXIYdHMWaehcnaEcNFIFimfxb+OI6xlli8r5bVc0Qk2FZVm3EeWZ/B9woHIv3LwnAe\n6jM7uizNTDhtZS+Rjah8yxz6cZwr65rVR5menGZ2kx3jdISvCn8VzrMfy/wN43PkbzHvKn0BMbuW\n2YvXa+aHuN3wP3/GZ+pmxqjvqvj+n/tSFBZ1zXABdUFOomwGwzDPMdMH8yOOpfTLeIi6nCDjOV7n\nnEZ0yQFLZmOMhxHX4bQyjIywCXXHuNnV3pxmFXyJtjr6/wxnWSKsPY7MdOVIzc6aVZtlwDD8K8SK\n5OLzX+QvOwr+98/RGSWOG92EgR30KIRXGRTn48bPP/7K5VUkTunIYflZlKYCX3zHs57Rvv6sDpQO\nkSGUGWNUBj7kGYFlBvgRAamqC7cF54d9AJ/v7+/beDyu2cgJRWFTBrpMWsva/CSAnMWNsIXfqfgq\nXSxPtL0O00b7UXoofInyjATtUsVVUoUwVtEjq48MX/l7FFbpfFTsqPJc5a+wropEfZ3ruV45mp00\nm81Jt9ud8lNmJvsl8iJ1NrtKP8vsWoniPfjO04z6K4aLsFXhQNTPywYCnD7XY8TDVJplfiKqQ4zH\nq8hRuym9WCKcxndZ+aL8omdVfaBKh9tatXHGaTE9tZqaleEX51FPhE8zXTmKFOXKUA3Ejo2vlVRO\nqNF4PNvAHUIZFpJ61QnMpmcEcZZCDTo8L5Vn1MgMMGzEnKfZ41kHfBbpEdUnl9N14DpWaXhdcNgq\nHT0iDaots3Bl7RvVt3I4KlykkypD1s7qXQbgZvHMVFUwqqVcuN+rdsM/JxAYh9Px/1UJSfZdhY+w\nhXXOpKw/Yn7qR5IjB43OKrJztmOsR96Lz7gYnX1QeUQ4FomHxe3WnH+Gr6p8XD/K/1SRTHflRzNd\nyvBXxY+uEle4WMvpiLIrJqjYfzlsWdpViG7E2TivjGy7RDtwHEfwu+I3iNlM1qM+ntlJRLK5nLxC\nwnmp23oVJ1L1xfUQcTCOW4UfqLrkd+ozp6H0ifKKOBu3W1UOiHmqiauo/LPkTDO9rS4rPHeeMkfG\nK0cYvuxKxyoGy8ainKF/z1ax+DmWiTtJpGuj0Siu81bbNfj8VZR3phMbKYMQAxXXpde5WuFQ5Sqr\nY1VHSlQcLhsDkkqD60mloeqMn6nyZoL9VbUBlykqby2zEQZtF+W0eACAcSKnooTjRaQT9eBb5fh9\nZD+KJLH9MfZU0Z8HErzy7p+bzWZxnbaqO+77qk75c+QEIzKlPqu0VbpKV24v3HkQ+buydlZlYT0w\nHcaiDNOid1FZOb9IJ6VPLbMRrG+1bcn7HPpgFG63jAtkonx/5s9RsksRMB2lp+J1ygYUVvBzVfYy\nnECMi96zXlxvXAbkTjyQjXyJ4omcb2bXkd0qrM76B7e1Ki9KdqMnp+XP2BdxumVtrSTjkkeVU1k5\nipych1GGpdJSjoo7n5/RwbyO4ghVA2ZEAwcwPqDxcByeDVa9U2lh+bE+WD888xSRnczAmQCggbMO\nGUApwFbEJeu0mQPnuouAKiIQmAaCVHZuoIzMZO+U0UdpRrehldlHLScTtkX+U7aUXYHKaZY9j/od\nroQrO4twhD9j/1OTK2yfqs8pm+H82NacCPD5UV6FU+c1Vd7+mVfTlR6qfaJ65PKpAWM0cGDsVvEi\nUhMNEqP+wL4oaqfoc+SPym47jdq8jJzUcnzJeEfWrhyO+xnaT+Zf2J4VNyjTL0o34yVltqB4ZfTb\nlZleiqSzjll8ZQdROpyHwnksC/MG1km1fzZ4ZYnwhd+rcqv/KBHfUcKD0SjOUXxplXfHkZlfyJB9\nVp1JORH/f3BwYI3GYeLNAxhVucr5cr7qoJm6EhAHI34eCP/a7XYxuOEVoIx0NBqPBkWdTmcK1Djf\niGBnuipnz+/ZGFU8Dp9dx50BIwJ0BIz+P6s7Thf14LRUWbm9sSxRv8xEgQYOupCIZPGj77XMXpQ9\noJQ5HbYr5Tiz/JSNqv7J4ctIE+qWlZuxmfVReXvY6BIaxtnI4bFDVMRC6Rz9gnrktBWJjwZATCSV\nPopcZCQLP2P78YpTRmhYVLyy8Ki7Skel5b4xIlRVsbGWo0nUlyP/HdkqX1iAfTz7aQllFx6GuUIU\nH9NRtlImqnyYh7pyv4xcZwSfOYkqA/vvqFzRRKeKr/C+jHuh7qwX4ldm78qmlc/A9BnbFU6qzxFO\nRJeOVPHLEefjcs5CTmXlKDIks8P7B5WjVJWmCAcbJlaOOlvEVyBGHdCdA6bDBoU32vmgCK/jLAOC\nRuPwapGKU4W0R3mp+ozIU9TZ/BlvueO8EUgUcGMa/D4iExg2aqeoPjIw4HRVf2OHoeJHhsj6Zduj\nsvi1zE4iwpE9j8ilS9QPI0yK8uZVEUy/LC12VmXYyWc5OR0mRBExU/XAcZTd8CAnIh5ZefA/58P4\ny6QhKiempfRRZAIxDcMr3M2ITibc7qpdongR9rEoG6iiWy2zE2XD/hzf+58ixmrlFNP197iyy/ym\njAewLhzG3ysSzWWK0uIwmL5fVMXlyupO6Y56snj9RH6c9VVtgrpk+M9/HJd1VkcfMHxkt8h1VJ6q\nPApn1HZiTp/LyaJuG1RthZ+ZG6r8ovSOK6f2O0dTmUDDcqMo48wKFjnfyMllnQYdJb73/f7439+j\ncaplQTcibzj/FXfulFhXZYQiqwuPU/bbCFgeJgnRYMnPD3B9oq7ZbzyxjlnbsvGquon05DRUukcR\nL3sVUhH9zlOkcwTGijSNRqP6troZiN9W5xL1VSWKUGfPo/j8OQuX5cF6K+zAvhedXVKCZ2lU/2d9\nIoyNMALTUee5qkgVncp+rBK/R/4ha1vGzSiu+hzpdFSJfBemfdQ8MpzltGZxG1Qtj8RvqyuTzM6x\nT/IkcOZTXXjiUtlR1K8zXbO4Ec5UyScbfGXlVFjAHFRxOtYzqh/WXT3DPCLeyvXE8ZnLReXncnG6\nGdabHV7M4Phcr2WYGX1HXcvCqnT5//+42+pUR2RHjd8VsYxISxnIKwDIyHTUIc0Oz67yZ39/cHAw\ndeYJG9g7L+vhW/FQMiNXTlyBSavVmjJGpZMiUpg/G2BmvBExU0bmf2rGQLUHgwrXPwIa5oOAmZGT\nSAfUNwKLzMmgnmr5nwWdEKYd9d1aTiZR+/MzbusInzDNKC1ltyq9CKsyezY7fFlMVo5MZ0wrIt3+\nTuEVPuf4fDaSy13ldzOUjWKZ+T+GyS6RUTrhZ+Wfsn6k0o98jarLKu1npreNuzBuc1k436w9q+hc\ny8lFnQHjNsjwwMNgOri7RcXFeOxvo5vYMI5KB9PLpIxIs26sR3ZLsfMrXwHibfr4jHkprq5heExf\n2Y2XJ7q8C8PgO8XlsN1U3eDunMym1X+ud+eNVXCQ+4hq72yghulml4qo+olwB/t0Wf88qsz0tjoz\nTZ4zsPawVRyXIqMqTkScFXmISDCnr2YJOB3v1HxWysvhq0VcFwhcbhD7+/tTy99ReXmwoJwk1ks0\n4FHA6d/VsrWqC9VGSpQjZn2jAZkbcwQckY4+WFVxuM8qnZjsVdlmyEvzmGY0u6fspZbZSRUM4fBm\nhwfNVQa+TGKUnSlszMh0NliKSFMURr2LbDMqI5fDRR3o5/6Nq/IKq1QeXDb/zv4G8ZjLHQ2WVPqR\nM85Ih6pDTjuy8QhfFamqQlIU4cnwJfMxZeFqOb5kNs//VT8taw++EOUoeITPUaJ3qk9zmbI8ysJz\nOE6Lb4fz8itfrOzY7PDFX61Wq+B1kY2hvo4/OHmO9c/6YTky/GGJsNP5B3NfTi/7AdaMg2S4kvFL\n5jc80FP4qnTIuGZZnVWVmQ+OXFTnjwA6At6IKEaVohoVG4HDcaVnW6k8HR7wYMOOx+PiGlsXn8U4\nODiw/f19a7fbU/q48XBHVsYTGSPWiQIcVUZ/z4M+H3w4EESdj9NttVpToKTAkrcrsp6YLs8Mef2q\nga/SjyXaalPmVLgPYn/ivsDlUelH7aIczayMvJZYFFZEopyWcrgqvSjdjEhE79jRZPlHekfld4nI\nRFQXEeHB9M6fP2+XLl2yyWRi29vb9tFHH9nBwUFhR7jazucnqhAuFscMpXO03TAjEgo/+F3UBqqf\nRU6fy1dWTgzPOBXVX5Se8tFledcyG4lWgf1/drGPisOcyd+XbZn17+qZ6luYN/s/ZRcR5mZ+T+EN\nx1F9vAq/VLrwZKqyV7Uih/rwBQ2ejh/NUKLqiXmTagPML7Nh5RuUn1AYGLWP2gEWnU3C/LFf404b\n1V5VfNms5dQGR2Y5iVfOB43Xv+PzaFCk8kAAYDCIOowi3gcHB1MDikgn/88dhZdIcfDkz/lANnd+\nrgvMCwWdPZ8F8u98hojrNZoR9KXXAAAgAElEQVRVjX7zRRmZEq4rjoPL2dguvMLC6WX7oj1c2b7Z\nsj5VBq7KEWUXUygCVROR05UMePG/f1YOPIprdviyl4j8uijiodLl8MpRZISZt7Iqe1AEmQf+bLeK\nqDG2I/62Wi37oz/6I3v66aet1+vZ9va2/eAHP7D33nvP/uu//sv29/ensEmdA+B8ojriNNR7TDNq\nB34XtafSJ8I5TKcML83KJ+yiskT6ZOXjZ6q+ovRrOb4gP+Dn/j/qSy5MpiMMYRvB52aHb+zFuFHe\n6jwOx1fcJCtHJIoXReXOSHUV22GbRV7KeB/l4wOhDDs4L5W3mZ7gVfXBE7e8kqYGc6pPKF2U7lH7\nq/Sq4E3E+yI9VB6zkJmeOcpEgSoXIlpqjAwycjiK2HC6TGxUg+IB5axhmGA52CnD8FvteNbBib6f\nY/IZ1eh6cc+jrNMpfRUoqgEZpseDF97Hy23F+qn6UvujlT5cPpUGps91oPqQyk/VFYMv1hv+cVmz\nq4ejvor6z8rAazksakDkgm3C7cP2yo4YP5cBOduYOp+o7DTSG/XDfhkRHNZXYWQ2k83hmYjwZEqn\n07GVlRU7c+aMLS0t2blz5+zmzZt2/fp163a7IZarsnI5VXsehfTzZFxGzlhH1onfl5G9o+ZT9r5K\nXlG/qhK2bPWilpOL8ofKz2CfU+dUlJ9SvtfT8jB4WZST66yfRVjA+Sm/x7hR1r/U5LOnGQ1uFLdD\nPTPMjAaSamDE37E9ojOCygYVD4v4ihr0cZ0qHFV9SOWr8BVXr6riR/QdxdtfDe6Vj6qa7nHlVFeO\nUNzIVAP7liwX5VC4Q2J89YxHn/yMt41EOkcECPVEffEQIP+4F+btgyceBHU6nXSgw2SKgcYFt6qw\nnjiToIwH847qi8GN68HD8aACV8pcT5zpYEONCB2Lypv3WUdEyssZnStT7cx1pUCK9VOrYxjnpESq\nlljYWfgz1W9QFGGO+kPWT1W7choZ3mRnHiPbVekpvZX9RvbHk0VRevis2+3aZz/7WWu327a2tmaX\nL18uft+t2+3a/Py87e7uphgfYQyGzUgL11VGGPgZ42YZZqo8FakqE9V+Cs+VrhFWVu03KNEWmVpm\nJ9lkL/shl4gjKZLv79mOkAsgxvCzKL0IH9Uz7LtoS5gen/VhO1afOZzSVWEZ1gemx/xHlVfljzyD\n64LtUt3yy2ln/DSLy2XJdFY6cjzWpYy/YDrq/BcKh1U/jcD9FsuhZFYTOL+UM0cuEchihSjwV3GY\n/OJ/lY7rlOmoCC0/V50SiQOelYlWndReVP/No/39fWmkGDcyHDROPzukyst6+TMEQgzDIBq1De/T\nbTQa1u12i62EWDe8zM714e+jMwOq7SKDYT2jdseyKrCNwNV1ywBJ1T2DadbutZxcIgfPnyNn4qIm\nDKK4aksn9xkm3WV6RvjD/7ncmZNReMJYh+/RNpVDxP5+48YN+8pXvmI3btyw3d1de/PNN63VatnD\nhw9tMBikV2+zTWKZIxviOlB1EpE+TlvVM9eDEvWc47D+7EtUmlH7so5l5cp0Z90471pmL+gbzabb\nVE1kInZMJpPQ3yufxn0Y25nJKeIS8jf2VTywQh+vyhXZF/YvVaaoD7J/5Tgcn3WKMITrXeEK5qGw\nm+NzeA7D9RmVxeMzJ+S24XpRWK/qpipWoC4cNqpLzD/aZhn5Hswj8x0nlVO7rc4/c8fkMFlHUu85\nXeUIMB5XMFYqAxKGj8gIpoENz8uengenE5Ee78DYidWgQg0wI+IT6W12+HpdLwOmyXtsVTr8jEle\nt9u10WhUGHF25skBlcul+oACHHzHnzlM1L5YD/icCbFKL3JuLsoBopPj+q9ldsKkU81cRviEdo19\nNLMDF57A4bBIOlBPFOX0IjIQORmlQ4TVmAaHyezI88AtPq1Wy37/93/f/s//+T/2xBNP2Hg8ths3\nbti7775r3/3ud208Htvu7m66DTWqh0wv1IXLqMpWpU5YD/9cZdCg+lnUHkh4MFzm+DlNJqbsNxQO\ncTnN9K2DNTbNVtxWfHLR/zNG4WABdyGoFRlvUwzPBLXZbBYXL/lz5fsQp1S/dN0wTxXGb+BV/ccv\nrsJ3atKUeQPWhcdXmB1xNjUg4HpHDoDvFJ9T3Iv/c/7IZSKdVJ0p7McyMHdBHbiusR5xsBWlj/Gz\n55hX1B4Rrqh3XGeqDmYlMx8cZZVXFo8bkbd/YVglVZwH5qXiRc4/MngEHUUwGKA4HwVmHte3G2JH\nV4MLLqcyNH4XEaAISJgAqGdYN/5/Z2dn6gpzLBOWH7cJYvxoViECjshgVR1h/lwPvAqmgEQBoyJg\nqCNuH3Tj9vSq2EktxxPu42oW9ChpqQGLp50RemVf/L4sDxTlSFnPiPBXWdXieIid6jcysF6bzab1\n+3178cUX7erVqzY/P2/7+/s2Pz9vFy5csEajUUwAYTp8XsntJMNpLhvqkoWJRDnfzO9w3Cr9C3VR\neqrv3D6KmKj8s3pQmBn5O4XFtZxccELB6x7tSxFx1RdcynCBP6uwyCP8GQ7QmLNFenkY/H1H5Ts5\njYhflOWpVpx4AFlme5FNZLyRuR8L2xG2aWSL/kzVg8KjjLNFYZR+Su8IS/C98n/MNVUZIx2ZO6vw\np7Xt91S21UWdACuJwVd11mjEGzl7j6NmhTEex8kAX5FzBhQsNzc86sHbUDgPfIazLFxvql65HiNy\nES3XcvlYfzVrlAGYPxuPxzYcDq3b7Vq73bbxeDy1dZDDYz2xXviMy6jSidKPPntY/h/N0JhVu6mF\n66oM9DNCVcvJJCKXHKasX3Nc1W4qfX/GkwFs35l+Ltz3olnfiCSrLcxRfUXlwYkN/vHFs2fP2le/\n+lX70pe+ZP1+31qtlu3u7tpoNLIPPvigiDcajQ5hJ5cJ65j147JxXWZlUrbJ76LPVfwLt69/ropx\nZaJmuVX+mKbqk5ku6FNrmb24f/bPyvegDatVAcWxzKb5i5qEjFZyWB+3adeL7Z3TwsnPRqNRnDE0\nezzB67rs7+8XeSl7i3x01FdVPC8D56OEybYaDERYwaQe69DDKDKv+K8qM0vGozjdsh0KKj+FKxEn\nZX2idCO8qcL1uWyMo7PkTjMdHCnDduFZkKgAXPFYAapBVWNnM4bswDJnkg0kVOdQAxcHAN7Og3FZ\nVwcPdsg4wFFEgEHBDbGsbrhs/BnBNSP5EaA1Go0CAHu9nvV6Pdva2irKxLfhcN2wU2YnkRmpAtao\njBHZyT5HRCmrE2XkiuDUMjuJBjGZM+G25QkClsiJKwLO+R8H1NE+HGsiW+G8ovKqbR0cjvXkVVYP\nc+7cOXvuuees1+tZp9MpbuG8c+eOvfXWW8VECeuDl8lwmlE5okEN6q7KpeqhisPO+hH3mzKcVPUc\nbYtBHfCvyqBH6ROF4XzUbHwtsxPmB1n7Z76Kn/NKD9sA2m7ky1R/L+MCynaZt+Bgi38sNcJlnizh\nrYZsr+hLvU6RR2FaWE7OO+N6SiJMdD04LvNefIaS9YOozhSfYLyJ+g+nG/GtKC2lk/9X9ZBxM847\nKtus5FTOHHHlIZHlRolmyzOnXoVcZI2NYThvRVC5EdjYImeI78oORJbVIYKCmsHjWRwuH9ar/+dt\nd6hfNNBoNptTW+N45kqBhz8/ODiwvb29giRxeVnPaCAXtRnmxxc6RGQE01RGFu0BVnEVYFZZho8M\n/bSM/n+jRHVZRqr5s2oj1f8ULvHKThWCE9krD4S4fzL54WcKb3l7qcJZTsel3W4Xz1utlp07d86+\n9a1v2Wc/+1lbWloq+vhoNLLvfe979sYbb9jBwYENh8NDxARXq7ltMG/Xic9NZO3HEjlv/5z5D7Xl\nO0sby5GFPQoBMct/NybzYWr1SvkEDFM2uVbL0aXReDz4bLfbdnBwcOgMToQJfJkV2wL3YbQtHlR4\nn8Dbg9lHub6oe3T0gfua4j2YZhU8w3KwHr1ez9rttl24cMHOnDljTzzxhC0tLdnZs2et3W7bwsKC\nzc3N2fLyspmZvfXWW/bjH//Yvv/97xf6RfgR8QXFW7j8XPcsjHucf5am8jdc91G7MdfNtlmrZ6rs\nETdSukZbNLPdOJEO7LtmwZ1ObeVIvUNBw40cA6cbhedBCqYfxamivyIBkdPgZ8pgoltFlDChiQxD\n1VdG1qN8OR8+C4N6OYgzgPIKkIO3A73PFHOerFdEIFj3DBBcIkMrM55sBh11V3mVvVOGnQFgLScX\nHhRge2a3KmWAn5HEyAniZw7DK+TKIbtkeOdpIQEuc+CKiPB/joNYgc97vZ59+tOftmeeecbm5uaK\n9P3Q9vb2tnW7XVtaWrL19fVDJF1dzuB6OflTbYA4oGyQ08uIJ5YvassISzJ8zWw+qmeMz88zYhSV\nKdJV4SjbBz6vZXaCKzF4NscHPGp1B+N4u6oJGOYRLtjfImKqMAr5gdnji6MibsR25npgmXg7HKbh\nJNq39vkE7fz8vPV6Pbt48aKdOXPGbt68aWfOnLGVlRWbm5uzxcXF4ucC2u229ft96/V61u12bTwe\nFwOl//zP/zx0KYyXJ1rdiC6Q4fjYBrilEW0W2w3jZu2onqF+KOq78i3ZURQVX+FjhBccXvGySG++\ne4D7GQ+MZiWnfuYoeh8RALP4zEm0+sSNwHqoSosc3mQysW63a5PJRDpi7tRYHtUxeGZHlRPTZKLB\n5UPgVOXhusE8caDi+rCe3W7X5ubm7Pr163bmzBn7zGc+YxcuXLDFxUXb2dmxW7du2f379+29996z\nBw8e2GAwKNJqt9vW6XQKErS/v2+7u7vFStFkMim20SijYufs+qvVH+w7qn0wHNdTFTLD8SND97Kj\nvhE5UbrxoCkiY7WcTNQeeiYjVbCD25DDKvIaYVTmeFR+Sq+or3B/ivbiYxoZ8eZyszPz+u10OvbS\nSy/ZN7/5TVteXi4uY5lMJra5uWlvv/22TSYTu3jxoi0sLNi7775rZocvmmH9/E85cMQ01jeq76iO\nlX2reNH5G46nBqfqfAmWk0kQx0fhsJEPjMrK6bjOODnG28BnTUL+t0uz2bT5+fmirXxXhg8g/Gc+\n1OqLWezneDCifIx/xjwVN8M+geG8b/j//f39kDfhZ+xX+Gw8Hlu73bZWq2U3btywp556yp5//nnr\n9Xq2sLAwdfbJw83NzVmn0ylWjnzwNDc3Z61W6xBn8jOO169ft6tXr9q3vvUt+/GPf2x/93d/Z8Ph\ncGpiCTkUl0dNhrLd4u6aiJuwPfFqP0tm7yxqABKFV7aNOio8U/2uLN3Ix7EfQn6lsBgHzVzGWWDU\nTAdHVSo9IrLcCGXxq+RXJazKYzQaFbqgTmU6RKSf80Ep6+jYMSIj5fRYR3R43qHm5+eL5XMHszNn\nzthXvvIVe/rpp+3555+3ixcv2pNPPlmcGZhMJra3t2fj8dheffVVe+WVV+yf/umfbGFhwb74xS/a\npUuX7OrVq9br9ezg4MC2t7ftzTfftJ/97Gf2yiuvFL9r4gDKdcDCBhE5BqwrNZiJgCarRzZQf5aR\ng6iPK0ehBldlfaeW2Qg6LkUWs36hJjSYTGSENGpf1T8y3SNM5HzYifM7s8Orq/7ZsUIRKcbGyWRi\nnU7HlpeX7Xd/93ftySefnLqlcjKZ2Nramr3++uu2srJi29vbtrOzc2jWOcM0Vbdqe1DUFmzTfMGM\nyp/bA8NHdo7nRo7TdqrcmSjdI8zB8KoPez2aHR50cllrmY00Gg3rdDqHVhYODg6KPu0+mvsuTxir\nXTRm+jcIsb1xRwfbe6PxeFIWsW4ymRSXK2H/8F0luBqF/VLtxHFpt9s2Pz9vN2/etG9+85t2/vx5\nW1hYsPF4bFtbWzYajYq66Ha71u/37dKlS9br9Wxubq6oy0ajUWz19f8+yHS99vb2bH9/3xYXF215\nedn+8R//0VZXV200Gk1dHBXZKNqTwkVF3KP2ZwxT/IXjRG0Vpc068PMIRzg9VQ7mWREHj/RTvoix\nhuuUB9eZfseRRtmgoao0m82Jd0qzeIZTgXUZGaiqY1nDsy4qj6jhFenGZzizonTmg5aq/N4x+PAr\nk2hVBjzE7DfDNJtN63a71mg07PLly/biiy/aN77xDXv77bdtb2/P+v2+NZtN29vbs7W1NXvxxRft\n137t12xxcdEWFhamZn2x06Eh+xmibrd7aGne68WBbXNz0/78z//cXnnlFbt3796h2SoknrwtCMud\nGZ7qK9nABOsY91tzO3scNEzMi42aRZFJPlvBRG5ra8v29/drFnJCabVak7m5ubRv8DP12b+jHUfh\nVNrcP/mMQJlemI56hn3Kn0XYxeVhu1LlYnxze3An9dRTT9k3vvEN+5M/+RObn5+3drs9Zds/+MEP\n7Dvf+Y6tra3ZwcGBvf/++3br1i374IMPzOzwjgFenYnwG+srcvgoiDMKY6L8mECo/Dl+pk8ZJimf\nqbCpChnIcA8/o69RRNZ1qLFpdrKwsDD59Kc/XQw2JpNJMUHrvxHI/Q7/Y/91n+rC77GNfQCgfCx/\nV/wpwjfVP7EvRX2u1WpZu922P/zDP7TPfe5zdvHiRRuPx7axsWG9Xs9u3rxp/X6/4C2evm87dD19\nwgVXOXz3inOiTqdT4NOHH35ob775ph0cHNjNmzdtMBjYhx9+aH/9139ta2trh7Y0Rj9OG4lqv4iT\nMt+owosVF1W8SOFnxMHR1hX388/oO7htuZwqfeSSZvkWOvzMiwYcbn193Uaj0Ynwaebb6rxw3Mhm\nsXOp0mFQFAFnUY0RORaVT9YxFVjg5yrkmPPDJcKMvHMenp4b/M2bN+3KlSt28eJFa7fbNhqNbH19\n3Z544gm7dOmSra6u2rPPPmtXrlyxfr9fzFjt7OwUS9G+qpSRM9fB9/TyAMoHGv5sfn7ems2m/fZv\n/7bdvn3b7t+/P0Uyo7ZURsmzYFzfqu3xP5NBrmMMj+AQEVAvL+qiAAWJhttI1OczsK3l6FKGK2pA\nEZHVbGCUxc2cGEtVLFLvMW21OoLp8wCEcTlyiqx3u922r33ta/bSSy/ZwsJCMdPcarWKVWKfsBkM\nBtbtdq3b7U6tHHHdqdWXrM6icrDe3HYRQeG6yogGY8xRyELkS1hH5R9UvpFPy/bts46MX3jbbLS1\nu5bjC7cZT5xxn0Qs8u/ICzguhsE0VJ/2dxGWqdXaqCyqnJgOPxuPx3br1i27evWqnTt3zprNpp07\nd86uXbtmFy9eLPgGrmr74AdXlbiMZmaDwaAIMxwObXt724bDoW1sbNhgMLDNzU3rdDq2srJii4uL\ndvHiRXv48KENh8OpOlQYpOpB1bmqmwhnsW6Yr+AggtNWO4xUOK57xUEirhSlE+WnPvN3pbuHUWeU\nzA5feIWXiZxUZn4hAzs5NthMIkKLjaMqmkedUYNhxSuio8CnjMQoh1rFUXFZsBxogDzL6Z99hciN\n+HOf+5ytrKzYjRs3bGFhwa5fv26dTsdWV1ftZz/7mX35y1+2Gzdu2I0bN4pVHk4fy+nEhFdSsMx8\nqw6GcfAaDodmZra7u2uDwcB+8pOf2MOHD4ulfBwgqS0CKu2IkPB7bhcWJhwZafHvUVh2JPidyQTn\n4f+xX1YhgLVUF0W+o3b28KrfqEFL5PA4TIQjnI5y7lUIPcbH71GaXKbMGXLaZo8H+P1+3z7zmc/Y\nn/7pn9rKyoosz3g8tvX1dTOzAn8uXrxoV65csY8++miqLC4Km7NtF1WF2yty7KqOVbtW6T9VSIPq\nj1F/cVHtZKYHQi5ZHTIB83fRSn4tsxH3+U70lR9AH4ErJWZ6Ox3aMf48CPsaz0fxtDJupPo0+3H+\nz7zQxfvWj370I7t9+7b98R//sS0uLtrNmzdteXm52M3i3GJ/f7+YgPUtddh3sV7G43GBTT5A8np+\n4okn7Pnnn7d//ud/tn/4h3+wDz/80La2tmxtbW2qzOqMkXPOTqdje3t7sr64ndn+Ml4cDUy4Lfwz\n13s0+aXwhvtOGfdVOqBE3BffMX6zfq6/r/pxONYv2olxHJn5ypEylioEIiMuZvrHzliiWRYGDl4K\nZF2jckX7R3GGM+pknA/HxTrweIpsuyFeunTJdnZ27Pnnn7cLFy7Ypz71Kbt+/brdvHmzAIJut2s3\nb9605557zvr9vp0/f77YZqfIPJ8x4OXjg4ODYv8uDoyw7tGoPK2DgwPb2dmx1dVVu3Xrlm1vbxd5\nIoixZJ08Iwj8PmtXBACua5WHciAoGC+bwUEnFw1AM9Cs5fiCoKyclQpvdtihRMQUwyqiwcJkIsoz\nKgeGR73wQHFG5BkrOV9lB2gry8vL9sILL1i/3y/C4eSL/x+NRvbMM89Yu9221157zT755BN76623\nZP1gPtEqMbYhtynbsqpbrn/OW9V7NIhR6aJw/av/qt6x/BHGqX6lSFOkiwqPfRLrNuuPtRxfsE7d\nXtEvOZHHP4+n+ndEvrO2i2yH33F6aoXX42A/wsnryI4cJ+7fv2937961VqtV7Irx9z7QwzNFXm88\ncPHzT7jy6ZdG+QBpNBrZaDQqLpn6+OOPpy5mcN06nc7UhVJmjzmSD4wYMxUXZL6Xrb4p/xLZL7eN\nslVl7xwfuW6EMZhOtGrG8dTqD2Md68PpHBXrTiIzv5BBOYvM0ajKU2kweFQBaexAkSNSgKDScVF7\nIhWJQF35e+RQFZHCOmo2m3b58mV79tln7ebNmzYajezatWt25coVe/7554vrK30g5De1+NkGH9Dg\nioza2ubl9Dw9jgOKKj/WA95Mt7OzY1tbW/a3f/u39qMf/cjeeOON4rYYLGc0YGVAYcF02KEr8OA4\nTB7xvWpfXs5GIMHyKN0xfdXuqHNNQGYrka0rh2KmHZC/5wFCBuRVMIKfR+mp9DEf/pzFZWE7YAfJ\nxMNx5erVq/aXf/mXdvPmzalzh1yHk8mj26GGw6EdHBzYO++8U5x95HZQW1QVNmTtkrWzao+srf0z\nY5Da4qbIkcpHTeRFfSTzSdE7xJ7M5/h3To/xk+PV+DQ7cV/pn5384+qGqm/V1yL/wWkoO+F+yO0d\nkXg8N8z5sV1m5/sQZ3Z2duzNN980M7PnnnvOtre3rdls2tzcnM3NzU2ddTR7PMnM5RmNRra5uWlb\nW1u2tbVlvV7PzB5v++90Onbv3j179dVX7Yc//KENBoNDZ7T8O17Q4BLtPPLvbN9q9SNrW1Xfqt3L\nBg0cPkubOWuUXuSnFO9i3IwmfLL8MD1eIfLPzlFngU8z31bH38uUZIfFjR8Ri4gMqLT9c+Ygqki0\ndO1GzaJ0Ux0oO0Pjn/2w4te//nW7fv26bWxs2M7Ojj355JN28+ZNW1pasoWFBTt79mwBIDhTg1da\ncl17Z8OVIteNt90dHBwUhzj9BjvXEwHYZ2Tu3Llj//7v/27f+c53pn7TRMVDQOX6UOQmA3ZFNrFM\nipRweTlvnAVX5CZySlh/EYhh3UVgV8vJhPt85CSic2dMviOyGJGOrK+U6aDCROlnZcwGCf7ZiQPm\nwWk0m0178skn7fd+7/fsypUrxZlCVTaXTqdj6+vrdvfuXbt//36RptrujJiqbB0xgttKbfXlesF0\nVTuwLWaH1hXmcP74ndtFDa5QX6V7li/316j+onKyPtyeNTbNVtC3+CAJt8GhZBe44Gf2dWbT/Uvt\ndmFdeAClBjYRUVcYqY4IRDbSbD667GlnZ8f29vaKiRcP43r6AAknbX2QNBwObTgc2sOHD+3hw4f2\n5ptv2uXLl+3KlSvFjXY7Ozv23//93/b666/baDQqVqhcB8YnFsV58bPyCxxW8RqVj+JmyjcoTKnC\noxE3eCBcRSduS4WHqJ/iSlk6/F7tuFID2OPKqWyrq+KAUdhZKSemHL/ZYTKrCEG2baYM6FF3BgsV\nrixd5ajwj+Mh4ej1enbt2jU7d+6c7ezs2GQysRs3btilS5fs7Nmz1u/3bXFxsVgp4qVRRfJcBz+M\n6L8NwOXw23Ncp16vd2igg+DUbret1+vZZz7zGbt586Z96lOfsm9/+9v293//9zYYDApww3aJ2ijq\nPwpUI2euyEiUD0pEYo9KXtnZqbbmPl3L7ETVtwvjDJJrdxTuiLNzQ5gXppc5haxPZ/1bCc94qjoo\nSwvrwu2ZZ2jb7badP3/e/uIv/sIuX75sS0tLtri4mOo+Ho9tb2/Pfvazn9m//du/2dra2tRV3h4u\nI0v43XXhsmUHiHmwkNVVVE9lWBQNRjhdD5ulEz3D8qstmWVE1ckk+gDUS+Eg+4xaZidO5M3skE/E\nZ97WbDP+H/srb2fDsPwZ8zSbtqHoPLBZtYlptqHIn6r+vra2ZqPRyHZ3d63f79ve3l6hH9t5o/F4\ne50Piu7fv28PHz60n/zkJ3b+/Hn7jd/4DWu1WjYcDm1nZ8fu3r1r77//vr388svWaDTsxRdftLW1\nNXvnnXem+AlfnqQkK1sZUS/DJeSHWO9V6p/zwM9ZOtx+jBNZ+koH/qywkftwVBZMI8L6WchMB0fZ\nzAJ+9g6nOo8C/sgxMfBHxqsIjYqnHEWUftYoGE6NwFnPaFYS0242m9bv94slZd83iz+CxttgPG11\n5gD1961vFy5cmBpYIShyXL5lig3P9XBdvvzlL9sXvvAFe/jwof3Hf/yHffTRR1PpY3kzQ2KjVXWu\n4mckQOnu37H+GJCRMGM6vEXQ82cHE5GnmoTMVhQYR33M24nxJdsXzmnguzLMU3apdFcOI+sjGenI\nMBXrgO3O9V9aWrIXX3zRzp49a6PRyJaXl4sZV5Xe/v6+7e/v2/b2tt27d8/29vas1+vJWV8XVd9l\n31EUEeA4ChPwncJ9fK6wpUwXzle9r+rseRsxp636k8LayO/hs6yuapmNKK6EAxv+8/fYB9B3o5T5\nP4/PgyHMG9NiXsUYy1vNM5xE/PPdK74C7OeNXCffueK44Xm5H/bzzf4bRr4V7+OPPy4ub9jY2LCt\nrS375JNPbDQa2ac+9TnspfEAACAASURBVCk7ODgoBlNqe57CAP6s6iP7z+Xn+opwQPmwSDJciNKJ\nBieqHJyXKo9KW5U3iqe4XYRzWd5HlZmvHEXCBASFK7uKE8k6XJaXvy87GKhIRfSd36nlPibIvIqj\ntuyhru1225555pliENRsPrrt7Z133rGVlZXijBH/0jzr47Mv/v7Bgwf285//3A4ODmxhYcG63W5x\niBE7GQ6W8HAjAyjXjYN1r9ezbrdrf/VXf2UPHjywl156yd5//30bj8fpvl31LNreGBGBDJh5tj0j\nSWrroxrYKkNVS/P1CtEvR6q0M7YxO4aordVBfpUuYoGyb8Sh6DPqnTk1zlfVg3LGrI86/9lsNm1x\ncdH+7M/+zJ566ilbXFy0lZUV6/f74a2V4/HYNjc3bW1tzf71X//V3njjjeIg9fLysq2urko85bOd\nmK7aDqTqh6UMRxQORUShLP0Iz7xuI4LBeWPfVe2O5Y328kd9UuFiRFJUnFpmI5PJpPg9I8cfr1/3\n5wqX/D9jBv7AuodRE8QZAWd74DyjcqAtYVr+XtkX5oMDk6efftr89+kmk8fnslhfxjRfQfJ6WF5e\ntgcPHtjf/M3f2MWLF+3Xf/3XbX5+3i5dumTb29u2ublpg8HAvve979m9e/cO2YXiOVwn/F1hcUbw\nua4iLsJl5jxYF9YbJ50i/FT5YRoRfjD3UXWC8TN/FmGp2oId5XdS0Sevjik8IIgqq4yEqkbBcP4s\nGgBFB8o4zawjqLJx3GyQF93gEd0cxUu3XLZut2sLCwvFPf+9Xs/a7batra3Z/fv3i21vagkY63Y4\nHNr+/n5xr//6+rptbGzYa6+9Znfv3i3CqRUjTx+duzI2Lz+DitmjcwdLS0v2wgsv2JkzZ4oLH3i2\nC0EpAwpFhqI+ooCO9eQZL46DZWFh41Wzb/jHz2s5PWGnw/1CtQvPICrHxe2Mtq+cE+vEfUvhAcdX\n4T0MP8d4OODgsmfPPI1er2fPPfecXbt2zVZWVorzjVh/WPb9/f1iz//9+/fto48+suFwWGDICy+8\nYGfPnp1a8c4GDerMAr9HvdUMOtYTthm3ocojI6dR3XM+2K6RfhjHy4H/WT+cuMrqx3WK+rsqE/cf\nruNaTi5ex34BA/75akjUDqrN1HuVp9lhnGDcYwLMwhjJ3Ev57ggHUQ/UbW9vz/b29ortcsPh0Eaj\nke3v708NBLHM+OeTs5cuXSrOaj98+LBId2Njwz766CO7d+/e1ORyZEds3y6RPSteE9UHpx21n/Jj\nCocYR6IwjC3ROSvlbxRms/7KZ1Xtm/xO1U1ZfR1HTuXMEQ4AmKR7GBUvEjbS6Fn2nC898Gcqblnj\nsc7cAaN0MV5GmjjvyWRiN2/etPPnz9twOLTFxUU7e/asjcePfqn8pz/9qT355JO2vLxsk8mkuLzB\nbHpZfjgc2u3bt4u0Dw4ObDAYFGcB3n77bfv6179un/70p+3y5cuHzix5Og7auIKk2kUZsM/+vPTS\nS/bgwQP79re/XQzsVGfnOuT8OLwidaiHIrhReBe19c/DcxnV6gKG5bjRd1UHtRxP3OFjH/KBgvrR\nwAjkvf+o7RP8LJoEYb1QMCz3o0jHKO1IohlH/M9njRqNR/v5/+AP/sC++MUv2tNPP23z8/PFIWnH\nAr6S3gdHW1tbdvfuXdvc3Cy2BC8tLVmz2bT5+XlbXV2dqg8vP96s6Q7b28z1xduJFOaquuLv7KsU\nbnAbqAGQwviMoGJ98wATJ6cQQ3gyigdFmH+kGw+So36q8DGa9Kvl+OK+2Uz/Po/jldn0Nd9qYpJt\nyNN3QbzjvqnaG/uO2umgBleIIRn3Q9x03oL4t7m5acPh0DY3NwsM8h+2bzSmr+TG8vs23q2tLRsO\nh8WFDs8++6zduXPHfvSjH9mNGzdsZ2fHdnd37V/+5V9se3v7UH3xBVbRNkIsvxqAYJpZfUfPoncR\nvnC7KY7GYXmlT+2Sibi12t5btUyqPMyl+Bn7K8U3Z4FRp7KtLnIMmTNSBVNOP2sEJikovPSaGS3n\nxeGVI3TD9r33GQhldeaf0charZadO3fOzp49a/Pz8wVIeBg/YOirSih49siJCu719/NGy8vLNhqN\n7Lvf/a699tpr9rWvfa3IE39HwHXzrXdZXZnZFMHxX6YeDoe2u7srZ1oiUOE6wu9MgvAd/rmeWMe8\nRQfTjEhHNjjDtDKiocqTha3lZFLWZpGDyQA3a1NlC5FEzizSO9JdOW50Kji48Hf4nVeVXDqdjp0/\nf96ee+45e+KJJ4qtt5i3z3A7BvvZgNFoZHt7e/bw4cPirICvgn/88ce2ubl5qGxqMBPhM77jVVse\noPKZr8zXKLzAd1VwKuoLVcvj9VqlD1TVzSyf3Y8wicl4LbMVHgThM/Rf7JP8vf8gatS/kdzjs6zd\nI7+nBuVROtGgH98zRvnne/fu2ZNPPmk7OzvW7XaLnwBxTsSTtz5pu7e3V1zkMBwOi5t7e72e9ft9\n29jYKHT9+c9/btvb24fs2887OUY6z+LfOeK24HIqfCmzIU5bnYHCulVnnBlzOH5VfZjLqAGh0ovT\n5vpFPTL/x+lkA9JZH1OY+e8c8X+uXHZSGLcKWeQOwOkoYsrPMS821ujgdfQZv7darcJ4/IyN/64H\nOr5oedPzx1ki/z8/P1/MlDSbTVtaWrLJZGI7Ozu2vb1tb7/9tl29enUqbyQuW1tbdv/+fXvllVes\n2WzaysqKrays2JkzZ6zf79vKyort7u4Wt+C9+uqrNhqNbDAY2Gg0Kn5D6cKFC7a4uGhLS0tTt9v5\nYUesR2xXT2dra8t2d3ftjTfesI2NjeLXqrlzR8RVkRX13kGNjRrbANubCVY2aOO+GoXFvhr1S9Un\nIzuo5fiCbaH2w6Mox5bhDuMASkRsXSeMi+GiGUhMU2Gr0iUi29yP1ba7VqtlX/3qV+3FF1+0z3/+\n8wUO8UCk3W5PbQPyCxgGg4G9//779v777xerxn6O4Ic//GFxABr1ZwKGWI0DMNdZ1Su3SxUCEH0v\nC8PYwrrw8wgfMA3/7oRXDVwVOVGYg3rg54gsKf2V3rXMRiaTydTqJ9u94lP8zuzx7wtiWHyn2jvj\nbGbT/pDzVX6L3/N5IpUH9kdPbzwe261bt+yJJ56w69evFxMtzkOce2A834o4GAyKidjJ5PF5Lh/w\nbGxs2K1bt+zBgwfFip1PELVaLev1elN16ZM6/t5/yiSqF1Wnqt6iQYVKQ+FYNGGR+ZuyMOpd2YCI\nsSvCHDWIO8rkT4ZnylefVE7td44iwI1Gtehso4FP1Q6VhWfDZDKhZjbUZzULiVvNms1msb8e98Iy\nYKjO5WF8prfX69nFixet3+9PkfulpSW7cOGCbW5u2jvvvGOrq6vWarVsYWHBer2eNRqNYrZjZ2fH\nBoOBXblyxVqtli0vLxfbYrrdrp07d64ApclkMnUOaH9/33Z3d6fSuXv3bnGLzGQysXa7PbXEjbdX\nIWEye3Qt+MbGhq2urk45BW53rAdlDDigxLZD8sRtrc4BISlkcsaECMOwHlm/jGyDnVBNPE5XotlT\nbncW1eYRTqnw+FxJRjCUc8L3/pmfYXkxjOrjjl34rtls2tmzZ+1Xf/VX7dq1a4duxPQ0x+NxsTUW\nbX13d9cGg0FxrtFvj2o2m8Vvj+CZAbZVZUsRseByc7iIGHJ7lD0r00vpgfEY31Q74dZsnjAq63Oq\nvTkvRSDK+ihicdSHazmeTCaTqa1hEfH2sMpG3GdH7ZrZhrIrjK/aG/ED4yr8imyCP7POOzs7trGx\nYbu7u2Zm9vDhw2Kg0+12D2EWch3HI3/ng6uDg4MCl/xyKqwjv0XT67nVahV5Ylnw8qvojCmWz7/z\nUYTIlqI2clEDo4iH8JZMVd+KnyhMU3lEWBTheZSP+s/6qbzKJjuPK6dy5sgsbwgW3HrlEjlBTks1\nGqahPvv3yOFxB4jKohyvg9zOzo6ZmfV6vcI4uRwKnPy5kxA/P+RG5en6s+vXrxff2+12EcaJiK/q\n9Ho9a7VatrS0NJW3gwtunfNnPnByMPA44/G4KI/PpNy9e9c2NjZsbW3NJpNJcSOV64QDxL29PXv3\n3Xft9u3bU9sJsG54S4zni4DMzgHjILlgwufhEeiYiESHxNVWO+wrDCgRQHB/Oi0Dr+WRYH2XzVYp\nJx8RbYyDwv0O0/E0IuLA5FfpGb1TOMdn5rBv+ju+ba7ZbNrv/M7v2Be+8AW7efOmnTlzZqoM/iPP\njt3+zEnK3t6era+v27179+zdd9+1d99913Z2dqzX69mdO3fspz/9abHKjvq4LSKWen3yBAa31VHI\nXVa3qr3Vd+XAVd9QDp/7BGKXr8Cpbb+IgVgvkT7cl1QfqVIfqEMtsxf8HTy22ajOsW3Rh3K7R6S0\nyk4N1a/Knpvl3Cbidezzd3Z27Kc//ak9++yzxQ/b93o9293dLTgN4qjjj9uPcxc/W+R4dOfOnWJV\nCXWYm5srLrvCC678z/HKJ35c2CaxnFwXUb1FwtgWcYkyf+Z6clzWD+Pxd+4z2M8Yg1Rc/s5pqvKw\nPZTV3Sy31p3aVd4ZiWTx656rAAM3KnZM1YGieKwrk2Y2VHZskeNxfdyx7e3tyTA84ELnyp9RZycl\ne3t71mg0ilvr5ubmigEJDx58uZnT9Xe8f9fBAPXyd0iecJDk2/SuXbtm29vbtr6+XhyMdP3MzLa3\nt21vb8/u3btnu7u74bYYdXZMrS5y+3Nb4+foPBq3r0vUxhzX/6s2VWGxDKrflhG6Wo4nGbnL2s3j\nlqWrnIL/Z1Lsz1X4qm2v+gk7HpRsOy/adavVspWVFXv++eft3LlzxSowDoLMprd8ISnZ2dkpDlL7\nqtGDBw+KCZsPPvjA1tfXp8rrv9fm4tjiaWJZ+LtyylV8ABONMruL8uA65/6j2h79hK/GqPOPnE+V\ncnAeEVFlosF4FNVjLbMXJoL4PJMMT1Qe2B8UQY2OFWT8CnVXJJnzY+EzcB622WwWW/sdX4bDoTUa\nj1aOHE+wnjBv5zx+rvrBgwd2+/btYiXI+YOngT8iOx6Pp3DP7NFEt69A4Y6goywE8LsyzMjS4/bk\nuJyHOvOl4kSYyeEVn2TdVHpV8uc+FvnPLK2Tyqn+zhEWkgcg/J4JsTJATNefKUB38Ti8LSvS0/9H\n51AwjLrlCvPAwYi/986ptqZEuvmlCf4jZgsLC0Vcn83wJV7fU+uDEdcVb4NzIMT6bjabxZY9X+nC\n5Wr/bmZTK2BOYCaTSbEP12d0zp8/PzXa39rasocPH9pbb71lr732mn3/+9+fWk3yPqLqBetO9YUI\nYLBdPA3VHxi8sU9yn+XZXNQD0+DPCji8zApcajIyW1Hgjf8jcHZROKTeYxqcv0pfEY+or0RpezpR\n/8mILr7zW+S+9rWv2ZUrV+zy5cs2NzdXkAGf6HFC788RC3xby87Ojr333nu2urpqd+7csbW1Ndvb\n27PBYGAbGxtTtoVECEmNr3qrelftF5F+s8OzpsoGVbuX5cv1ie2nBhr8zCeu/Dv7hjK9FBYqnVRZ\nmNxGOvLzWmYrXP/8vEq9Z22sbITjYX9U2MX9gm2I04sEdeBBBfKRVqtlv/Irv2JPPfVUwW/8Mqe9\nvT3rdDo2Pz9fhMWdNo4Zm5ubtre3Zx9//LGtrq7at7/97UO30uHKENqa+3n/azQaBcdxbIr4hnqm\nMDjjtFF/wLhc99k5VVX/zgUV71EDLtU38Tu2J/OhzGfxQCuqkyw+190scGrmFzJgJUUkNoqLlaM6\nBX5WZFSJatQsDB98dcefxVeORL1TB73LdHXDv3//vp05c8bOnTtXDFx8oOWHoVutlnU6HWu32zYc\nDovnbtR86BP3kPuhajzr5Gk3Go1iGXoyeTxDbPZ4JUsNxIbDoa2urha/JTAYDOwHP/iBvf7664e2\n0WF9KOPF/1iXLsrImVgoAFDb7LBv8XJuVaOLCIYKl9VBLbMRtJfI/vyZAm2zfBad+xw/j8JivlXa\nXDmPDFMiLOL/3W7XXnjhBXvyySft0qVLtri4aO12u5g99RnTVqtle3t7xZlKxEgn+ltbWzYYDGx7\ne9u2trZsbW2tWEkaDAZmNk2M8Hyj67O8vGyLi4v28OFD29zcnJpI8ZurfKImq6uIiOH3iFRkRBBn\njjMcjwZGkS/Ey3s4rSp5oO78zNONVuWj/qG2tNQyW0FCrt5l/QnDcftk55dUeuwzI6KeYVyEa1l/\n9eeNxqNVocuXL9szzzxjvV7POp1OcRmVmU2daWy328X5I/flo9HIRqNRgTdvv/22vfvuu7a1tRVi\nrPfzyeTx+WkzK44FeDznUM6P+Ad3I/xVdZjVBfLosgGoh+Ht09nKMbev4kqYfoRzKNzXsrqIfKiX\nW/VBlW6U1yxwamaDI0Vo1aiubC+tGlChcNyqe3NRByTZ/J7zUk4tK0vUoNEz/I/36nsHOXPmjF26\ndMnm5+ftgw8+sE6nY4uLizY/P2+NxvRsRrvdnrrJxc8D4BYVv53F9fBD1mtra9btdotLFnwGxkGg\n0+kUAyOz6e19mIcfdHz48GEBTBsbG/bOO+8UZ6GYiHAdswHzACmKxzNC7Gh4gOWkjvsWG1+0uoNt\nqkT1DX+GRP04g69aji48QMrIcNQXOGwWv6wtqzjKqC9m2Mh6ui3gYeBGo2HLy8t27do1e/HFF21h\nYaHAjMXFRev3+9bpdAq79u1yTkxwMOPbUPx3jdbX121ra8tu375tt2/ftg8++MC2t7dtd3e3wDa0\nT8ccJ0YXLlyw9fX1gvj0ej3b2dkpwquD1FgHbG/K/jLnr4hhGUHheIp0Rn6G4yv9Mf+INKltM2V9\nPCPb/pwnDNXEVi0nlyp1qvomx1VtqTAFn+EkYXYmM0pL9SX/jP0nwiyzRwORXq9nX/nKV4ozjj4A\n8YlfLDtyGudC+/v7trm5aZubm/bqq6/a2tqara+vT/VZLx/+TpKnx/UzmUyKySCum2ggq8rH9hoN\nELA+I7wq40JR+lXxLOtjKo0IY1jPskGTqi/GZi6bCjcrbJrZ4EiRCDQ4BHQeoEROKeocGAYdj/rv\nYcs6ThXhThE55TJClf33OM1m0+bm5mx5edkuXLhgV69eLVZgnIz44MWJydbWVvF7R/1+f4rA+Myu\nX+jgcX1lyAEAZ2j8MggsgwOID4j816r916ZXV1ft3r179sYbb9ja2lpBangLHbdp1qERePk5173Z\n4YOd0T5q758RyPHAKOpfSgfOSxHuCJDK6qOWowvWLw+Iue2idsf3niY6/4gAK1zAQQvGjbBJYWOk\nKw6glA3Mzc3Z2bNn7Utf+pKtrKzY8vJyMUmC5xdd/Nyg4w7qjlt0d3d3ixnbzc1Nu3fvnr3//vu2\nublZTIp4WXyQ6r955NtjfLWq3+8XdbO8vGz9fn9qW42vUKs2iew08i/cZvg+w2hum+x7Fp6fqc9I\nMnh7MeulCI3SA9Ny4f6F+cyafNRSLqrOeeIP/UrZwMbT5PTxeURiIxyM8lN2laXd6XSKiWCP55Mx\nHpfPpuOOGN/O++GHH9r6+rqtrq7a1tbW1LXcqLfzHD+e4Hngzhh8Zvb49yIjTuLpoY1GHFW1RzbZ\nHw1MODxzDdUn1MBF5al0LePP7BePym0Y86r252wQfhxpzAroms3mBB2q2TQBUIOEQonA6DzsIaWp\nYSLQZuNFkpA1sHKAZYJGEL3nLT3KWHxb3OLioi0vL9szzzxj58+fLwiEh8NfgT44OCgGNbjlzo3c\nzyKZPR7U8D5bMytmahuNx7fTYefEcwW7u7u2u7trH3zwga2trdmHH35YzDAzAcX6Zmfs7/BqzKwv\noC5sfKo9yr4zsUUyiT+WqUCrDCBQOI0qeo5GIxuPxydfH/5fLs1mc+IrnxFhRonAOIsbDZ4iMsDh\nsz5VxbGiHeP/drttZ86cscXFRXv66aet3+/bpUuXrN/v25kzZ4ofk3aM8FVn1B/39Xs+OBPrmDAY\nDGx3d9c++eQTW11dtZdfftnW19eLs0pIcFqtVvGj1r4K7r8Jh1gxmTz+xXufWd7b27OdnR1bX1+f\nslE1gKlK6FV7qLZQBES1F2/hjEhopgfHR924nbN+odJXfbSqOGmdTCY1Ns1Ams3mpNfrHXqe9eOs\nf0b9AP1wGcb5Z/RZPMmY4RLrogZD/t8nZD7/+c/bwsKCXbx4cSoc7mZB3f1q7o2NDdva2ip2sdy+\nfdvW19eLiWC2Kcc650DOjXy1msuAO3Mmk0mBU3zuCPlN5iuOMhBR7cLC7RpxFW6rKjbP/S3jWmrA\nFqVZte+W4VRUrl9Mxp0In079Qgb+z4MEs/KZt0jKHA4DQhSPB0wZqS4rK4fHz7wHFP+7rj5zurS0\nNDWr62njgcBm89HvkPhef9TZQcBnWx88eDBVH3hdtxt9o9EoDjlyHfhg68GDB/bgwQN77733bHNz\ns7hxjoHb/3M7K3KlQBfj4DOeTciIAbeH6mf8Oyu435WX0jEc96uo3RlcyoAxI2a1nEyUo45sXT1X\nwOzvs7NMqk2zPq/iRmmpvPxSlnPnztnCwoJdvXrV5ufnbWVlxbrdri0tLRUDIVwd9p8DQFtzO8Ub\nRbFefHutb2fZ2dmx1dXVgqDg7434fyckc3NzhQ6ISb4a7WV1PXx1yi+N8Wt9FcZjuzEZjEhhVOdq\nMFJGViIyyJ8RAzmvCN+wTB5HXRzDeUR5c54qXI1Lvxxh20b/o7ZiYzhMI/OJHCbykWqA76JINr9H\nX4/PWebn5+3ChQvF1n1cmfGjARxvPB4XF7z4zZf7+/u2vr5ud+/ePfQzAYhzvkXP37l+jkM+QGLM\n6/V6Nh6PC2xSdc92qNqibLFAtSvr6u/VZDO3BaYXcTVl+5l/wrQUFrLenEaEwVXGBRHX5LROKqc6\nOIoM8ijC5DMi3IrAMPmOyIs6f4SNj/lwvkoYVDBdzsPFjXdpacn6/b5duXLFzp8/bz6jxIMfN1An\n8z6AYuP0gc/8/HwBKKPRqLhgYW9vz3Z3d+3WrVvFmaBGo2Fzc3PFfl4fGHm6uLVG3W7DwIFtg3Fw\nkJQNaBgEsr7A+XLdR4MTf6YOWEZOiPOtQpZUP8zSrOX0BMlz1nYYVn3G//zZJSK50Z7sDNdwJafd\nbhc/+ux4sby8bL1ez/r9vvV6PZufn7dOp2MLCwvF5Aum4bOoSArMrFgJxgESOkS8nW44HNrGxoZt\nbGzYyy+/XGylw/r1cvr5Js+Xz0X6CpFfEONba/zHHX1g1+/3zezRj0VyeygyEhGByAljO3E7Rm0b\n9YOsj0U+BnXH94qEsp6cviJGGYEo68+1zFYiH8Ikm48i4Dv0sRyfMUbtuvDnKg6K6kvZFi3FmxqN\nRoFRTz31lM3Pz09t8TezYgDjenm+vo3fV6W3t7ftwYMH9sknnxxa6XIuY2bFgMmfex34oGhpaanA\nPJwgbjabxW8fHRwcWLfbLdLBLXTIM/0CG6xThfOqnct4BtdlmR/zfNQgk7FNSYQZ2IdU3DJcZcn0\nizi/0vM44wwlpzY4ihqybESXOa4yAuNx8Jka4WbOhvXN8oucDH9H8OK00VD9soWFhQXr9/uHZnG5\nTtwIOV81OPHvjUaj+OHW4XBoP//5z219fb24RcrDOkC5frik7YMmNnie1cjAMevAXEZMA0GZ+0nZ\nYEPppAyS4/l/JqjslFTcaN+t543PsrRqOZlkzqgqiEdYpNqW7YHfZ3koG/HBjA8aer1eMUhYWVmx\nubm5qctYfDDkvx/kNz65DTvm8FYV1xsHQ7/Y3lkMVHDw5OcY9/b27M6dO/bBBx/YgwcPpiY9ECPw\nkhesA/8lepfxeFxsi/HvLr6lxf8UDmRtmn2O2khhOtotkxtOIyMtUdxILxVXifKjEd5l6VTpt7Wc\nXJSPwu/oSxQx9P9V+i3bJsdVfQQxjdNw2+bBiRo0ud3Oz88XEzt4GRWXFc854yTJ1taWDYdD+/jj\nj4sbLVE3xxpP28wOnX30vHCg9ODBg6mbe/FIQqPx+IftJ5NJcWyB0/OJn+FweOj3mKrYfUT+MZ5L\ntAuBn6l2j7Azan+lc9mgrKqowSPno8oVcbuTyqkNjrIZiDJCyXGUA1MgwECSDUoip6WIPEuZA4s+\n438nJ/4rz3Nzc9bv9+3q1avWbreL7W1cbzgYUmQdDWAymRTExreq+KqR3yLngyIHAwQ6BA0ERjd+\nJ09qoKJ0VJ1b1T8SIWxHbPtoBg3TKXMS6jnHV+mwc2JDjsBHpa1EXSBRy8kka08XHphkuMFpqrz4\nWaQXvvPJiPn5eZubm5u6tanb7dr8/Hxxg5uTjH6/X2xT8xVov9DFzxHiShGfN3Qn32g0plaLfaZ0\nb2+vmAn1bXS+xW13d9fef/99u3Pnjn300UdyQOT6+HfcUuviz3DQ5r+9hj9ibWY2GAymJm2clClc\nYVJSNgjgNKJzQ8qRRwOYTMoGORFuZH2Y46jnUV6qjhS+1XI6wr6UL+LI2hDbjycNVJ9gshvpovoQ\nr65gGqr/mFlxntm30i4tLdnS0tLUYMPxBgcrODmzv79vDx48sMFgYHfv3i0ugcFJU7+K2+0XBzWd\nTsd2d3fNzAru5T9R4DjV6/Vsa2urwF6z6S12/scTn56n/3l6vhrlK0lmsY+PeAViEdsiXv6g+gK+\nK8MArEfmOhm/Qinj7Goww30rqhN+5vF49W4WciqDI1U5XCFcuGjkxwXNRsleSTxjoRxGGWlnPbAc\nuGztz7OVBOWYzp07Z91u1zY3NwsiMzc3d+iSBP+9IgS07DYVzxcPMzvh8TNDfpMLXl3p6SGQmE3/\n7gaCoW/ri9palTsyjOw9gkn2mwJVSIZq3yg8lwHDRfu+o/BR2lG4mnzMXspIgEtmuxlmZAQ1Csfh\nm82mLS4uFr9n5k7fV4NwUOQDnl6vVwyKeLucunRFERe3eZ9VxUER/vervH0b3b1792x1ddVu3bpV\n6Mp5eNk6nc4UVLDYxgAAIABJREFUbjk+OeFx/dSWF8d0J1F+xgl//FGdC4zaEnEGsY9Jo7LziPgp\nqWrHEQGtGidLJ8K8aBAUfc/sp5aTS8ZpmDyi4AQm9lfsw/jd7cTDIpdhTuFpOv9A23PC73pi/8A0\nPS/HJccpX/XG67mxLnjV2idldnd3i8sWBoPB1PkfMzs0EYP93HEEzxLjGWPEQeZbyhb87KPjJXJT\njOO7gjztiMhHOBD5EvQpGbfGeIp7YD5qYIQ68aKH42c0EPM46txZtICSYRP2byxTGf87jsx8cHQU\ncurPOV4V8FZgrRpJpa+2uyjHrvSPysFLumqA1m637Qtf+IJdvHjRXn/9ddvd3bVut1vMCM/NzRVg\ngj9yiFvc/A8HNj6zigbp4OKDo08++cS2trbs/v37U2cHvHPhLIjP1jQajWJ/LW/fw4EK3jTnaXCn\nRSN1IMHnOPOriCaebULdsZ4xTzZw1ZYMFpFwWRAIFUkuI9v8OSNftcxGsoEoO5GoL+F7j1uF1DJu\nuI23221bWVmxhYUFW15etrm5uWLVptFoFGeGnFD4tjqP2+12Dx00NrNi5tS3zHlZnAw4TrhN+Xuc\n4fQbKcfj8dQtdNvb27a+vm6bm5u2trY2hZtR+X1yxrenOKEYDodTv2iPRAUHamY2hT+YbpVDwYog\ncdvwZ8Yg7htVBheZRP1G1SEP4DBOtF2nqn5Mppn4YLwqg7ZajidV2stxg/soDnbQ5jFNX7Vxu/f3\nHh7FMQUHDI4xzim2t7enfJZzFNTb9fIVGr/wxVeL5ubmpuK6jp6G2/fW1lZx4cvu7q5tb28XvMbF\nJ5CazWYxmcO/i4TlQ76B+Gj2aOLXzzOq3TGeJuqAW/cQVzE/rEevS69PpWdmx9wHooGPGpRwG3Ha\nKnyGsYxJjGnML/E54o86V866qB1hpyUzHRwpgqHCqMpTwK4GOlG6Kn3VYRTgqKVELk/W+IoQ+3//\n7FfXnj9/3u7fv18s7arZ4Ebj0VKvD5LcEBGw3Bh9qRbL57O9k8mkAJOPP/642B7Dq0RcT04g8Edk\nvXO7Xg7EuB3G68YHS1gvv/Vbv2XdbtdefvnlqdkuNSiIDJT7h5dBtQmmUxUA8LvqJ6p9Vd5ZH1W6\nRCBYy+yEcYAHQCosznipOBmWYVvizK4Tj3PnzhV77judjvV6vYK0+IyqbwPBVSHcmoIrQ7jSggQK\n7QsJNp/hwYsPfHDkl7fcv3/fNjc3bWNjw0ajkX3yySfFtbmcB9aB1wPjjJMBn6FlfZHgYXqORR6H\nSQvPUKJOXm5/Fg0KuB0xXplw31LYynpl2BH1UU4neo/l5bpBvaI6Yp2wHWqZrWRt4aSaB/aMP6oP\n4E98mNkh3+9/uKKCN9nyChPGMXs8kHKSj32Xr//HZ74FDfkDYtR4/PjyKD/TuLW1VRwNQCxzrGR+\nyCsTPLmsJrQZt/zPOQ2vNnmb+U4bnBDnSWCuD3+HRx6wP2C7sp1HfAEni13XiEtH/EilneEjxvFy\nZbxb5c3pRatqii8x5s5KZvo7R/6L6idVkCssIjAqHKeTlS9qGH8WxVENzWnh81arZb/5m79pnU7H\nfvKTnxQDDL+Awa/XNbNigOSzHmhQDj6e5ng8trW1tWKJejKZFAcAncj4L9M7oDgxUUuanp+DMQIG\nn3XCm6TcIP1P1R0CamQoEXlTdR0NXrL2VgattuOoNLwuIuel8mEyoUh0pvMvALyerj2hNBqN4jfY\nygY03k/VhEnkRDCeOwe/fQlvgvNzQf7dt9O6o8bfGfP3Pgjy8zf+HvNCm8UBE6/IoLM0e7z1g7fe\nDgYD29nZsU8++aTYQjccDu3OnTs2HA7NbHpGEok32y5ioevql0rwwM915ksbHHt8Vc0nfnyyZzAY\nSDzg8lb1J/ydHT23P0vkpCPSUcVfsj5I0JhslGGgh4vKzeHV6tQvJuVqbJqBNH/xG5HoI3nrPpJs\nJ+toV9gPoq21GAZXPDBt1gN9mA8qJpOJbW9vT+GRD2KQ27jevpLd6/WKgYyvHHm+/h/PNPrtubu7\nu8UFUrg119PzSRbnQK4DY4lP2Hp97OzsHJpg8nDOlfBIA9925/XjA0qchEDs4UGR25jjmteVx/N6\n5AnmSI5i+xFnVhiQ4V2EW2XcGIXTz/yy0jH7/j/ud44yAhmBMBMMfIefIzLMz6KBCpNgzlPpyJ2Z\ndUYQUbq5wfmS7927d21vb6+YbfFBjd8+NZk8/j0h1Bv3yKJz8u0uzWazWIny2V8HFF/+5t/z4S1/\nkf4MwA7MeFMUDthcBx44YN1xnfKAMmpD3tebtV2UNz/n9vawTPi4njLizPmoJWUuG6ZZlTDVUk24\nflGYXETPMkfgdt7tdouVILcFXwVCm/EBgVnxQ79TFyw4SXGcUDOwjD1sPwrbHAdwIsNJADrl9fX1\n4nrcZrNpW1tbtrm5ObXlD/u92qaM9cO2znjm8XF7ruOev/OBHJaDt7xEjpXxLrNZxiIMr/oOlzuz\n3Yw4qHS4LPyZ02VM4vTUwCjSz0kd128ZUavleKK2y/HWfAxrpu0Lb59UOOHpqx/FVn2Mt6Cyv3Yc\nwx9jdjxkn4+27pMxjm2IS7567dvjfPIGjw34AKvdbk/dZsflxAtbsP4YhzC+/8fVIHzm6aK+np4L\n/nabTzTzWSavU/9dJywb1imXSeEZr8ApyThKlbCK12fxVNwIe7K4Co9RTosvzWxwFA1icAY2ixeR\njyqOJvquwIMdiHoXfcZw0XvcQtNoNIob6DY2Nuyjjz4qiJKfNcI9lF4Pvrza7XaLK7MdBBwoHj58\nWACh3/fvIDGZTIrDivhbJVX0RtKCRuh5OUj5oMxvevHzBNjuCDzejgqkMLyHZTKiBiKq7bL+kBkY\np81bUDgct1mkKzsJNUjHz6dl6P/bJXIGETZF5NTFVz7OnDkz9SPKZtMHcN35u91jvzB7PHOLv1uE\nM544YFI3UGF/deeLV227/khynWT4TKxfibuxsVFcujCZTGxjY8O2t7dta2urKFeZnXF9KeLu5Anx\nCO0fiRZu3cMZXSwHp6/alt8rG1QEUQ2Kqg4uIryKJPM1UVqMMzw5iVJ14tLfeZvwKkYtsxfse9jv\nuO6VD8GVJO4ruH0N81HhVD9l7GA9/LOH5TOEKIg7ZlZcce0Twmjf4/F4arcLlsEnkhwDvA7QFrxM\n/g5tyi9zwRt6Mb6v4vtzLKf/jIDrjmVHHR3zPT/ncFE7u56YF5+rwrZhnMB2yETZelX/F2GmGsAz\n/kX83iXCNZUf65th3klkZoOjzCnxe/yuHA8PQDwc56HiV9Upi6f2TKIOKj7q3W637ezZs8Uybb/f\nt8FgUCzTOhC5EfpMCS5x41aSbrdbHFocDofFwMT/cB++H6LmwYmLahcGUK5XrEckXB7GSZkfEDez\nKZBQAwn+43rNSEykG7cHtwunyeRSxVckx4VnpKK8WQ8Om4FELbMVbIeqdcyDnna7XWyHxYkHJwfo\nnHHA4z9w2m63C3vHs0O8xQMdPDp+/G5mUw5eiWMBni3ybTDD4dAGg0Gxlc7s0QUwOzs7tr6+Xpx5\nRFvF/LlPc1/GukYCpvo52xzO5Do+8coS7tOPBhBKJ9W+6nsVp676E688RvlgXMTKqE4xrsKZMh/H\nWIt5qfqrQrhqma2oPsx+lPua2fQEJF9uEHEX7H9uoxgH40b9gdNn/+r68SokfseVbLdxXBn29Hgb\nIG/j50kXdfkTch1/huVGPI3Khm3gKz0eznHOB0Vu24wFyNNUHr6ipmwW0/H/Zf5MhXEcRf0jDFBc\nvAyrVL/JMBpxEMvn7zh97suzHCid2u8cuSjC61JGUlRjZeRROSIkFEof5cAYMCKijs+73a71+327\ndu2ajUYjW11dtYODAzt79qzNzc3ZvXv3iltbGo1GMbO8t7dnvV6vWAnC7XWDwcAajUd7YzudztRB\naU/Dzxj5TIuTrmeeecbef//9YpZGtYmDIc5I4382HJzJcGPH/bJOrviWLG7TqN2ytlBtwxINxFRe\nuDeYAVz1o6hvRf2yCrnh8JGd1HIyiciACod171vlfGXU7dNseqVGDWrQSWM/c9vA3x7y8DgQcn0c\nv3CbmTte15H7DRIOHBD5SpHv5X/48GHxbDKZ2M7Oju3s7NjGxsbU1livF9yGEmEoO1B09v6OccWf\nI7bgcyYQri8TI9SH60aRysyxMwFRpCTCOI7PzzNi4Gmr/5x2RFY5jiJT7Ns4XKS/9+daZi8ROeUt\n3jz4QP9lNn1zGhJ0vDUN2xD7ANqa6meOa71er7iUpdl89GOnOADAVWb88/Bs/2jTOJmDdu4TSa4n\n45O6dALrFusMV6c9D7PpARniC29xxPTwncfzzz6Yw/rkc5vYPpiu7zbY3d2dSpfbDXcnNBqNYreR\nqgeegOHVvQzLVX/huBgGfQD7XYVJqq04LIvKZ1Yy8211+LkKMc6cSFleqjK4EaIGwbAs0WAoi7e0\ntGTLy8s2GAyKvbKNxuPrK31/LRInMyvIijeuG47PGpg9JmJIdvyzryL53txut2tnzpwpBkuKxGB9\noKEgGePDgPhDa2bTsyWevpeTDYxBH4ES6zMaIKi+wMREOe1ID69TZZTKyDgdTkP1Q5V+Vh58lsWt\n5XjCfY9JiNn06oxfGuD26tfs8++JuR1k+ZjZodsmsd/g4IcHZ2wrrDduLUN75lUiHxz5OcS9vb2C\n3Piky/r6ejFY4v6OmMhnDfEdEgomJzhpguXFz2xLbNMeVu3Hx3ZknIsGOGyL/CzCJE9XtQnGdX0j\nh884VzbzGflUJtNV4vozhXdV49dyOhJxHO6b3NfV1ldOF/sqDzZ8ix7iDh4V8Pe4+4WJu8IE1oG3\n/LoO/l9NfPAgw5/xz4iwf0Zeg2XitDgfHkBF9Y9455NcnrdvFzSbPjfJdu9heSDsuwt8Yhrj4nZr\n3+aNdc6+Cv0DCvsQbquoP0VcG3Ev4jJl/KZKnoqnq/52XDmVbXUM0NFgRQk7k6wyIkehdFL6qoZU\nZcnSWFhYsH6/bysrKzYYDGz7/2PvzeMsq6p78e+uqlu35qrupptu6KEEmmZQGbqVhoCgAuHpzxk1\nakxMov6SGJPfS2J+Gd4vw4vJy0vyM4kZNYkoP/McMkh8KohKFGUQGhCwG2kEeoBuoOeuuerWPb8/\nzlmn1v3WWvvc6rqlQM76fOpT556zz95rr73mPZyxsXz5TAgBQ0NDDVljvXlPltwIE09OTjYwu+wx\namtr/K6R3BNBF4emUqng5JNPRm9vLx566KF8Jof7zPSW2SaNn6a7DmJE+Yog642JTEeP1pYTwWV5\n/L0+xHiriNfY6DCvWY4H39fLZ1ghcFDFzqTGwRP2EloHPN6aziJzEsDIb5kpkuSAXk/PY28dQc2H\nq9Tr9TzDp/mFDSo7u8JrOuEhz0VGeWmKGNSpqal8+Zyc+lSv1/P7orMsOllL4bQD5gUhUk7/l9P0\nNJ34oBgpL86R1kF647XomNjMmYU384OnP2JOgfW+Z5ME36JATPMIP7PesRwSdoKsoI3bY8fW6yfX\nU8LSgDUjokHGjGdmADt40PykV0mwjZZ39YoKAcFDkq8hhDwBLP4LBwraEWc51AcTCI4cJHHAoU/P\n1SC+kOwf0jaW/QhNW54VkmvZU6T7oOvTfpfQksdK750CGveVSlnGQY+Rfq7flwS70EJwF3sivqO2\nNVqniL7Vz6QOTS8rsNK8ILiyX2bxkxUEsn7ieq12GQe2j1xPK2BJltWxYmdF7ilaJgwzuJWp997h\nex54jqx+z3tfnKdKpYLJyUlMTk42KAp5V9btyzIdrTjEgdF7DyTbCyBXQJoGtVot/+K8ODo9PT0Y\nGhrC0NBQvmfAMqKWsrIEwKInj6tWokxzVkwC3vh4eHEm1eMNBu18ynsxPrACMK8NjRf3xVuuwP2K\ntVlCa8ELSMWYaIdd9IHe9CsGi4/G1rwpMirltIwDaPjAs3ZELB4A5gynDrZ1WZ1F1AYvSZI8gBJ9\nJMGPJFFkBkk+rujpBev4c61/WR9o/GSPFdPJCliExnomSZfRTo+u33Ju9Ht6fwI/07jr7C73NWZs\nrf5bOsNqk+mgnS9rltAaHwsPtj3A/MSL/NbtNNNX67qE1gIve7KAeVTuWXqOZ2WB+T4P23T9vuhG\nSaBKGS4n/zXP8X1ejaJ1pOUT6ASIbkvw0AcdsM7QulwDJ981Hvo+zzppP4xpLXQWnLRu06uFNA6i\n03R9Wv+Jb8g2RJ/oqfe36r5b9BIchVYaBw6MWQ97Ot6659kzz9e0dBvzpaXbYnW2CpYkOPIE2+pk\nkRNqKXjPudTPvHYt/Pg3424xR09PDzo7O9HT04NKpYKjR4+a2QSZ9ZFoXC+zA+Y+FDY9Pd0gfHJI\ng0ypilAI88qxu/V6HX19fTjttNNQqVRw4MABPPnkk/OOjdSOhJWd0jNT/Fz3xQqOuB1ui2nJzoel\npK2xsYIkxkkvE+DlP97YemOvx1MreAvYueEAKiYPGvelEvQSGrOj2mEQnpfldMJDIre6jC6rDTIf\nNS1tSFl5Lqc6aqOrnRY2TJqftWzpzJ449zLTLCdXyrI5/V/f9xx4OWVJflsyw+9aTpimr5Wd5L2O\nood0Nlr6LmWEJjoJpZc2St16zPRMHTsDIYR5wazOZFuBhG5L6y0r2CiyM+wAMD1igQ7rUU93My7W\n7HZMB1t1ldB64MAg5lxyck7zsJSz7CXXr2druJyUlRkL8UEkeSs4iV/CtpP75Dm9GlftLzAv62O/\nOaDhmRDtK+m62R57q5Q4QcE4cqI+SZJ5S6wt/c/90jKuaaZxFR0m9fDqBf2O8ALzDifiBDedFNJl\nhd6874vHjnWL5+vwOGubwWWssjxGmo6sw1oFS3aUNwu13LOcbn6uhdZqJ7Ze1CrPwRi3y78tA6CN\nqQQ4wqRy/K0YfG18tIPFa0/1b6Axiyp/cga+OCyinCQL3NfXh8HBQcgHeJ955pl81kjjbjGXRy+P\nPpYSkd/WrJ6loFmhcLtM+yJjbPFJDE+mgYWfVqCsFIv4SOPMuFmZLAtHiz4lLA5ECYvy1866GB89\n08Dru/U18wgvf2D+r9Vq+UErIsNSB2/Clf/aAPBSC9ErUrfsKZK9QzKbLPv/ZKZIlsJYMxMafy3L\nnjPPtOAMppZ1/Z7eLM78rR02lkH9Dmd1eRmMHgONFycr+D7PNMt97QhaRthytHgcPWjWoDPOut1Y\nGd1fLtesTdBtlTpp6cBzMFmerDKe3fACXx5vlin9rR09a6J1kciqtmvWyhlPlvm59Uzw0N8r0rKq\nZZ7pIf2y7rGu9dqX8uzEy31rH6V+Ln+c9NL/ta5iSJIkPwxIEuk6OcRldV9Yn2mbo22W1Kt1nlWW\n/T22BXKt37dshr5uhjf1PasO1smtCpBavufIYlC55uUVnpGJKWuPkJ7wMS48m2EtY/DalWVxelmN\nnPzkTTML48nSOWFq7YgIbWSmSGc0tRMEoCFr09XVhVNPPRWdnZ04duwYRkZGMDIyUng2vseImu6W\nI6H7yOOtlxVZAuGNaZHRjRlji4c07oK3doz1e1yH9JGVu4W/1x+v31b5orpbJeQlzF9yoGcFZTMr\nO+Z6OQIvPdD1Mj/xWLa3tzd820P0AoB8Db/gxQZSL7Go1+e+/aG/C8L35LdeRsczH8JjvHSO+2Dp\nC9bZLMN8jxMnnOHU7VtyotvnDKYVjOo6LHnU9er3xRHgcZd3Zex0W5YTUGSLrH5psPRCTJ/odq0+\n8z2r3WaDnmb6VcLCwRqzorJS3pJBtiFWYo7Ly3/tUE9PTzd8r0fqkvr1Pc2rVlafdYkl75oO+oOr\nIYQ8CcT6V+SWHXzGS98TueYgw3L8RT/rIEGX076SntkRnSzvs9zoupkuUrfMosu1vKMTeexv6nJa\n/+oAjQMs9ntYz+jAFECDjmTwdKHH257+jN3z/Ez2UxcDS3aUNxNDC4w8Z2DFW+Q462ee48kDwzhw\nxsTDSZhLryPVa9V1m6yIPOOl35GjMPUHz7QzJvfESerp6UFvb29+1OOxY8dw/Pjxhk2AgqNFB4uu\nHs1Y4chzXttaBDFj7wmlFzgwPXWZZvgs1v9YfzQveAKq37f665XReJQOSGtBK1YdjMghAUCjUdLG\nQC8z0E65vCP3JBEi7cm7eny1PIrB18kWMVjWsjmRf3FU9H0JkpIkaZhJ0ss5pD6tyzRu0j/mWX1t\nySmX4WtNJy3vOhNqLVPxZIYdL26T+6B1lZUM0zjpetl5YYfAM8Kew8q6wgtSipwJDyydajkeMdD8\n7uG4ULxKaA5izqN+bvGV/LbsUZFN1XVZjj7PhmidIvWxLEtZ7fdpueW2WY/oP6mPv2mmn7O86zpZ\nn8T8NMbPqpvppX/r9jgosfwWvZ9cn04sQY60oZfQMb48hpb8a9B6TcZYB7wWH2q9IOPNdtLiP4t3\nYzrI01WebFiBZav005Itq7OYrohAltAUgRaeZuriGZFYWW1Y9RfvAeQnt1gDqAVjamoKnZ2duSOj\ngxZmRO3g6KV4woxAKkDVahUrV65EV1cX9u7di5mZGYyMjDTQQTN7M3QpAmspDguEJxieMowpb37P\nu7Z4yhJw3Z7OAOmyVnaN8dE4ezhpvDzcPQUk/9evX49du3bNK1PCiQOPkZ654GV01glM+rtG1myz\nVtSyHFYSHmJEJicnGw6B4DXfesmdyL02mqIf9FpwuSezU/JdDEsmBSeNO8uL5xhrp1mX8+jLbbBz\nJPe03Hn6WetMS9ZYHhlvjZ+U5yQKjznv49DBsYC3VJbpw33SuFu/Pf0Q02t839KnMcfBct4sWLt2\nLXbv3u3WU8LCwQp42LllO2LNkMjpbVyXZbOlDMujzHTPzs6iq6tr3umZWo514pbbFLz0fWnPsok6\n+Sz9k4SQLqf1tgDPfOh7mgYaB9YlEuDpvgleVv+4/3wKn0BbW1tuD5JkbgZIj6OuX/CQFQ1eIpZn\nspIkmYeDpjWfnCo0svYoebIv+GrdLX1kmut3dLus4zSOlu6xfELLd5TrVumn0KxzXFhRCAcAlBqz\nhBJaBxuSJFn5w0biuQ6lbiqhhJZDqZtaBKV+KqGElsOi9VPLgqMSSiihhBJKKKGEEkoooYTnMtif\n0i6hhBJKKKGEEkoooYQSSvhPBmVwVEIJJZRQQgkllFBCCSWUgDI4KqGEEkoooYQSSiihhBJKAPAc\nD45CwGwI+E4I+G4I+OcQ0LOIuq4IAV/Irl8bAn49UnYoBPz8CbTxuyHgV5so96kQ8EAI+K8h4L+H\ngCsjZV8fAs5xnr0sBNwbAmoh4Fp69pMh4JHs7yfV/c0h4MEQ8P0Q8OEQELL7/zPD6XpV9sdDwP8V\nwW1NCPhCCPjRbJy+EwJGQ8DD2fX13rutgOyAlM9meP9iCPiDEPDySPk3hoCz1O8/CwEvW0ocSyih\nhBJKKKGEEkp49sBzOjgCMJEkOD9J8EIA0wB+Vj/MnOMF9zFJ8PkkwR9FigwBCw+OmoEQsBrAS5IE\nL04S/FmS4LeTBF+NvPJ6wA6OAOwB8C4A/4vaWA7gdwBcBOClAH4nBCzLHv8tgPcA2Jj9XRMCBgFc\nmCR4MYDpEPCiENAN4KcA/HUEt18G8PdJgi9n43Q+gG0A3pH9/gnCq9Xf3ToVwIszWn44SfBbSYL/\niJR/IzAXHAH4KwC/0WKcSmgCnq+JjxOod1cIOKnV9f6gQNOe7q8IAf+RJUv+ip55CZrlIeArWULn\nK6KzQsCbQsD2EPDNELAiu3d6CPhMBK8QAm4JARtU4uapEPCk+t3ZWmosDkLAJ0PA63/YeJwohICO\nEHA0u14dAr70w8ZpKeH5qsNalbx9NoDX5zKxvLjE8rMNQsAHhd7NJr2f68GRhm8COCMEDGcMdD2A\n7wJYFwKuDgF3ZMz+zyGgDwBCwDUh4Hsh4F6kjjGy++8Sgx0CTg4BnwsB92d/lwD4IwCnZ0z6J1m5\nD4SAuzNm+j1V12+FgJ0h4FsANjXRj5sBnJrVfVkI+LgIZwj4oxCwI2vjTzNcXgvgT7Lyp+uKkgS7\nkgQPAOAPQf0ogK8kCQ4nCY4A+ArSIGgNgIEkwZ1JggTA9UiDrzqASibsPQBmAPwqgL9MEszAhzcB\nuCnW2RDw7hBwQwj4DwBfDgFXhoAb1PO/CwE/nl2/JAR8IwTcEwJuDAEnx+pGSktxfi7RzkUI+BNF\ny/8ZAi4D8CoAf5aVH04SPApgTQgoj6z9wcPzLvFRQgNMAvh/ANMZm5egye7/OoCvJQk2Avha9hsA\n3g/gJQA+AuDt2b0PAvhvkfZfBeD+JMFulbj5OwB/Jr+TBNNS+ET5rQQbkgRPATgUAi76YeOyhPC8\n02GhtcnbZzOUieXFJZafzdBU0vt5oewzxvgvAB7Mbm0E8DdJgnMBjCE1klcmCS5EymC/HAK6APw9\ngNcA2AxgtVP9hwF8I0lwHoALAWxHapQfzZj0AyHg6qzNlwI4H8DmLPOwGcCPZfdehdSAC84/G0Kj\nsszgtarub6ryKwC8AcC5maB9MElwO4DPA/hAVv7RJkl2KoC96vcT2b1Ts+uG+0mCEQBfAnAfgP0A\njgG4KEnmghiGEPACAEeSBFNN4HMBgDcmCV4Zqa8K4C8AvClJsBnAJwH8fvbsfSHg3cZrrwXwcEab\n21VdJyMdD6Hl/8ho/SUA/zUrvysrfh+AS5roQwlLB8+LxEfW9r+HgK+HNOv4O+rZDVnQvz0EvNd4\ndzjrz8ezNv8pSyTcltX10ki7vxsCPhHSmZXdIc3y/XFIs5w3hYBKVu6VIeC+7P7HQkA1o+M/q7p0\nBntBtNeQJBhLEnwLaZCkcfUSNADwOgCfyK4/oe7XAVSRJW6yRMdTSYJHPJoAeAeAf488Rwg4I0ug\n/BNSvb8uZDMf2fMfCwH/kF2fHAL+LQRsCwF3hYCtTdS9PQR8OgQ8FNIsbXf27PcyfvtuSJND874s\nGQKeCAFDY6jIAAAgAElEQVR/mPHt3SHgwhBwcwh4NAS8J9LulSGdsft8CHgspBnVn1D8PZyVe0FW\n7oGQztKtDenM3eNhLiPeHwL2hHRGaGMI+HLGw7eGgDOzMqeHgG+HgAeBOdnJ4IZsHP4zwPNCh6GF\nyVuFw9dDms3flsnCSzJZeiQEfFCV++VMJr4b5mYB/igEvE+VyWeCFtPnMrG8uMSyg++3QsCHsnHe\nEQK2ZLz7SAj4XVXu19Q4vz+796ch4P9UZfRM0K+HVOc+EAJ+W5X5bTXOG+V+s0nv53pw1B0CvoM0\n4NkD4B+z+7uTBHdm11uRZi5uy8r+JIANSKPcx5MEj2TM/EmnjVcgzQggSTCbJDhmlLk6+7sPwL1Z\n3RsBXAbgc0mC8STBcaSBDLK6/i5J8HcL6OsxpI7EP4aANwIYX8C7i4YkwR9nQcOvIA1KfjsTzs+G\nYGZo1wA40GT1N2eKJgZnAzgXwFezcfx1AOsy3P46SVInpUk4jFRp/X0IeAPSANqDZwCcsoC6S2gh\nhOdX4gNZPW8C8GIAbw4BW7L7P50F/VsA/GLIlocRnAHg/0WqX85COktyKVJj+5tOewKnI9Vlr0Wq\n6/4jSfAiABMAXp3R7OMA3prd7wDwcwC+CuCiENCb1fNWAJ8O6VK/xdDeAzNBk12fnCTYn10/BeQG\n/n9keL4GwKeQzkj9fkE7PwLgnibwOQvpbNI5AJ6MlPswgD9OEmwB8BYgD5ouCsHV8+cA+PMkwdlI\ndbsY/79IErwEwIsADGJu5ozh8Yx370Rq+96ANJFT1PfzkGa4zwHwbgDDWXufAPALWZm/AfAPWfLo\nnzM8DwPYgZTngJSXvpQkqAH4KICfz3j4N4B8qeRfZv15EVJdqmEbUhv5vIbnmQ5bquTtdCY7f4c0\nafE+AC8E8K6QLsHdjHS25SKkPt17QsAFAD6DVN4E3gLgMyfS5yahTCw3n1i2YCIb539Emhz5WaR6\n7r0hXQ56EdKEyUsAXAzg50PAizB/nN8M4LMh4FUA1iPli/MBXJIFc2JnzwPwamBe8rAw6d3qqbgf\nNExk0405hDTHpp3dgDTSfxuVa3hvkRCQMslHqA13TelCIUlQywb8lQCuRWrEXnGC1T0J4Ar1ey2A\nr2f319L9BocgU0gBwMNI+/yjIeC6ELCRMrUTALqaxEePVw2NQbvUEQA8kCSLN6ZJgpnMKb0KqZD9\nHNLg1oIupH0p4QcLkvgA0qzrPyINUr3EBwB0ArgDKvEBpHs1gPmzMUjl5yeANPEB4FiYWyIhoBMf\nANCH1Oj2I0t8ZG00JD4i/fpKkuBQ9s6/IXU0tyENiN6QlVmXtXGI3n08SVIHKwRsR7rELAlpVn44\n0iYA3Jjx/YMA2jGXlZR3N2X178zufwLA+5IEfx4CbgLwmhDwL0gNza8BuByLo/2iIOt3kl1/BWkG\nFyHgJ5Aa6jNDmkE+AuCXZJwULM8clyJ4NEmwrYlyVwLYFObmeJaFgO4kwbcBfNt553HFy0KnPwfw\nyhDwAaS65ySkQdyNxvvCcw8C6EgSjAEYCwH1ENCXJBh12v12kuBpAAgBjwH4sqrn4uz6IgD/R3Z9\nPeYCrs8gDZC/idTJ/FAIGEIqi/+q+i++xcVIHXwA+P/QOHv0fE88PV91mAU6efsFYP4+wwLQvLxd\nkiAZf65Dqic/l/G46M7LkgQfDgGrQsApAFYiDSr2hoBfWmiflwqSBH8M4I+z9v4BWWI5w++BJJmb\nHctgKRPLQKr/n8hwiy3ts0Anlr+IxY3zg0oP7ULqb14K4F+TJPW5QjoTdlmS4G9CwLosOFuLdGXA\nvhDwa0gTD3qcz0SqN6WeiRDwvwmPQt3zXJ85agbuBPAjIeAMAAgBvSGd8v8egOEwN9X7Nuf9ryF1\nnhEC2kO6hnQEqZAJfBnAT4e56fBTQ8AqALcCeH0I6A4B/ZgzEguGrO7BJEkjdKQRMQxcmoEvA7g6\nBCzLFOnVAL6cKaTjIWBrSKeBfwLzl578PtLMbAWpkAGpsPBm050odtgs2A3g3BDQmeEmAeAOpNP5\nLwWA7Pm5J1A/srEYSBJ8ASktL8geWbQ8E+nyhxJ+sCDr9c9PErw/mdv/YSU+pNw5SYKfaTEekviQ\nNs5IknyG+kQg4d8h4AqkzvXFWQb4PtiJBZ1JrKvfdRQnuqYAIElQBzCTzZY3++6nkWbtXgFgWxZU\nLBXtYwmap0O6fEWW3zXMRIR0w/u7kK7l/z2kqwS+BXvpVi00t99D81sdaFjipscoAHiposepYuAj\nYPFCD9JZlzdkmdmPwU8y6fFn3oiN6WL46AakM40rkGZ8v4G07wdV32WfTd4vp67ne+Lp+arD5kE2\ne/hSAP+CNKiOLgkz4ER5GUhnNq9FGrTLISxL1ecnka1YyUD000ITy29OErwF6fLHjWiEpUosCy1e\nlCT4L03W3wBJuhRwC1I98HoAX1xgFYsZ539BOhvE4/xBGuePN4FHoe553gdHSYIDSA3mp0LAA8iy\nMkmCSaSZmC+GdE0vT/kL/BKAl2cZ13sAnJNlfm8L6ZrIP0kS3Ix0494dWbl/AdCfJLgX6SDejzTz\nd7dUWrD0xoJ+AF/I+vAtpBv2gNRx+UBI9wo0rOkN6TrTJ5DOjnwkyzYjSZdH/H6Gz90A/nt2D0g3\ncv4DgO8DeBQqYxnSNafbkgT7kgRHAXwn629XkuB+3XaW4XlUgtJmIUnwOFLB25717d7s/hRSBfih\njAb3Ic1uxqaGPRhEOu73IzXuQstPAfjNkK2bzaajhzGXlSjh2QXPxcTHVSHdu9GN1LjchpQfjyQJ\nxkN64k90v8oSwcNIaSby+k6ksoHs/4VIl2J9Oru3WNqbUJCg+TyQnw71k5ifuPkAgA9nBrwbqVNu\nJW6kv6ctELc6gCMh3V/TBuQzfUC6rE/vfWhmZcILQsiX9bwdqV7vznA+mPHVmxaCYwvhTswtY/lx\npPyOJF0e/h2kM1yfTxLUs8z1fpn5DAFtIeTJuztUPRyklomn56YOmwctTt5a8E2kuPaEdInvG7J7\nQOpj/RhS/0D2Ry5Vn8vEcvOJ5ROBbwJ4QzY+fUj3mfI4vwmpjw2k4/EzGU8gpHsjT0I6zm8IAV0h\nYABzs+AChbrnOb2sLklSxqd7u4CGrBWSBLfAWFuaJLgJxgkbWeT58ez6aaQDxGXeTr//Aum6Ti73\nBwD+wLhvTlsz/kmCd6nH8zZdJwlug3MaTJLgbjRmM/SzjyHNSvL9bSD6qWc3AHNrZZMEvwr7tCmB\nv0IamOZ7kpKkYTkfEmOvUJLua/oV4/69mFvvru+bU8NJgu8Dc05KkqSbEzOwaHkr0iloAEAIeDOA\nT2fLFUp4lkGS4EAIeeKjmt3+b0mCnSHkiY9xpMrVUty/BOCjIeBnAMwC+LkkwR0hPeTgu0iXon0g\nBJyNNPEBAKMAfjxJcG8IeeLjGVDiI8PPkvG7APwrUrn8ZJJgW5Zg+NkQ8BBSp/1O470lhSTBZAj4\nKQD/HNI9EncjXf+PJMFsSJfKvAtZcNIC2stSigEAnVni5eokwQ6kCZqPIw0UbsRcguaPkK4z/xmk\njsBbVF2nIJ25kWVbf5n14ShgHn/9RaRLi7/fJIkE/m+kBvkZpMky6fv7APxtRsMOAP8B4H0hXUP/\nU0liJsIeQrq/5Hyky0w+miSYCAGfQOrQ7Ie/JG+p4X0APhYCfgPA00j3ewh8BmkiSeviH0Pa/99F\nujTsk0hl4xcB/FMI+E1g3hKml2PhmefnFTxHdZgF/QD+PaR7pQIak7d/HwJ+EcC1SfOHRjVAhuvH\nkepPIN0Pd1/2bHvmtD+ZBSlIEty80D5ryJIWnwOwDOmS4t9LEpybJDgcQp5YBuYnlj+O+XqrIbGc\n/ZbE8gNWYjmkB6uckfkwzdLo8RDyxPJjUInlkB6a8eEsUGhHund1e0gPs5iy/DAHBgH8W8arbWhM\nLH8kBPwKgNcn8X1HsT7cFQI+hTn6/m2SLSVPEtwf0kMUHkuSdDIjSfClLKF4ZzbOIwDentXzOQAP\nINVfwjeyB2sYBUnvkCTejHcJJSweQsC7FyB4zyoIAW8BcFOWLS2hhEVB5gRtSZJ803sJPyQI6bK8\n65MEV/2Q2j8DwL8kSUv3vj5nIMuufxPAqxP7kKMSSvhPC9ks7OYkiX6OoIQTgCzpfY5KpJnwnJ45\nKuHZD8/VwAgAkgSf/WHjUEIJJbQekgT7Q8Dfh4CBMvnxQ4FVSE/3KwOjEkogSBJ8LtgnlpaweAgA\n/qywUKtmjkIISQjzPsfwvIMkSRDrZwhhXhmhsfWepr885/JWGeu+tMv/Nd7WeOv7XpkSlha8MU+S\n5PkvVEsMIQSXoT2Zk3v/GXTasxl+0GNg6cmFtr8Q/flc1relbmoNLMZ3erbqqCJfw3pu+Tsn2s6z\nGTwfTp79sPpT5LdavGb5mtb9ZnxSqx4PTwsvptti9VPLZo5CCOjomKsuFhDod5phBI+AHiG9AWhr\na4u2p99j3OT9er3xm2Bcrq2tLf9rpm9WGxbTxN4tCro8XPl+LICSOi3hqdfr8/AuGvfZ2dmGMly+\nra0NIYQGfOr1el53vV43HVuvf0V08GhgjYPnQDX7O2YcNA61Ws0mYAkLghAC2tvb82tgPs/EDIBX\nJ49XEZ9Z8qHb9XSGo/jn4cL3tb6yjJRurxmjFcNHvxvTuZaceQ6TLmf1VfSs6JKY8WbdwaDHsEh3\naZrq95sFTRcvKRKjTcweNOu8eO94+Gic2traMDMT+35lCQuBEAIqlYop45ZeKrLjcq3va3tZ5KzG\ndCLrOn6m6xdescDypXR9UpeUK9IVlu5g2uj/0rYlG83ae12HfterT/c9hNBAG00L7Uvp95MkmUcz\nqSvmszEOFt5cxhpDTYeYnbR0h66TadOsX1XUti7bCt+ppcvqPEHW9zzD24xDwkzjOSNFgsz/Rfh0\n/axM5H1t1DynhYVZ6uN3mUlijkSsv/I+l7GElukk960xsBjVGjtxPC0cmTYebbkvWjkyLQHkgTgL\nPdPYw0PKWbhIu6yIpH6vTl0HGxoNFp8yjTQNSlgcWEbTMjyW/tIQc0osx4T50NJfntGy9EqMt1kn\naeOt6yxyLmI08N7V5ZkOlmOj6S79tujN9y3Hpl6vo7293dRjus329vZ5QRSPBcu2blv3wws4ub9W\nPUwXBrYDRfagaAz5v9emx5PWmJV6aWmgSPa88ef7nKjk+jx/iH0SbkODxTMaRyvRw7xk6RGRQYsW\nlp1mm+3Rj9tjXWzRRt+zZMpKLln4cr3sA4qPMzs7a46tJZuWTfH8RUkMWf2M3WP/0cLJAm17LP6x\nxoHv87ux9pdKL7U0OLIMAg+UBZ7B4jJF73jEYWfBM166Hi1oFujnjLMYPlYWuq2YQtN4sUB4yo4d\nQIuxGbjuIkYWGrLi8649nC1l6o2HlLMyJDpYEQcohof1TOq16MRKbiEK1frtveMZgpjMlLAw8BzG\nZg0a37Pq1uW9RI4nl57se33RsmjhAsAMpiw5iBlFCx8rANB90PrBMt5SxmrTcgA8faRnn0MI6Ozs\nxOzsLGq1GkII8/SBBFEdHR0ubbUN0EGQpXeZNp6O0/XyNfeP7YRFk2YcHes9/Tvm5Gibpce6yM6W\ncOIQsx1esGHZTX3PCsA934fbZV7VSQiPd61+sC3ndyw5seq2ZEDzp/TX6mMMt2Z1oGUvrNm4WL+5\nfgmG2tvb0d7eno+x3K9UKvPq1isgWCdJ/2dnGw/XtfrIshwL9Dzf3NJJMb2n37N0Cd+z/Gpueymh\npd85ihlejwnlnkV8ZryY424xJhth7Qjz+1Z9Vt36ng6CGOr1+jxhtRwVdvqLlA33Q/rHfWM6cJ2a\n3rExitFc3mVcLHrwFDILj9dvvRyG2xNlbU1T67579bMC8xR7TNGxQWLaMFj0Znx0myW0DjyF6o2V\nNZZeHZZhEYgtl7OudZ2eHmJ+Z73C+MfqaMY5AWznw5JL5mvWN2zkWL5iOtAynLOzs/mfp890nZwQ\nkXta58zOzuY6vMgQF+mPmF3zdLTXf6s/sWf6N8+Ex/rFThHQuNS5hKUBjze8sfLGpMgO8dh6DqzG\ny9MTli6wyjIultxwHV5SWfdB7mtfyvJFPP/E8gM939DSs57fJWV5jPh5vV5HrVZr0DsAMDMzM0/2\nNO2kTnlf3vX0hR4Hy2+09K/VX65XX1t1xfjIa0fj7Pn2DLEY40SgZQcytLW1JVZGzmIquW8tm7AM\nulcG8GdFuLz3XswZ4HLMNFyO60mSJM8IWAyqB17/5uV9WqiZlhbzxpZsMG2aWX9aRAfGwWuH+8Zt\nMY300rZKpdKgDDho4rZkL0Js7HV5rVS9tcTW+1b9Xr+K9qtxHZnCLD2RRUJbW1vCGTfr2gOPD2J6\nyavPeifm/FjlYgbGC8Ri7bAMAI1r/fX7Vn1a7/AyCSlr9ccymhpEXniWyXK8tC7gerTu0EtX9H+W\neY+G3l6hGHj6wnNCvD4KxNr0+ELe88bG+s046P5ndCx1UwtAfKciO8rA/oDlC1h8GtMR3rJxqcPC\nq8iB1jiIjvCW22r8rHdjftxC/EsLmpFBCy+57+krXV9R21abnq/EesySe09/x/Cx8PBsjtcev8c4\nc92WL+rpomZwr9Vqi9ZPLQ2OZBoQmC8wFoEaEGnSudBgDVYzSqVZsAQshoO1qU6uJUiyNiFKW1yf\nx1SWQmnWuMXKWcpW91O3I8B95vX4upy0YfWhSMGFEPLpZzHOWtGKcdFtS1/YmfEczZgy4npjRqLI\n6YvVpXGYmZkpg6MWQAgh0YfFqPvuWGmFzYafDbWlH6wDSoqMjHYePNleiJHyDGLMyFgOeZFDYukV\ndoA80O97yyYsnJqlhzzjoMYzyvzcasvD32ubyxY5QF77Hj083cX1WfhauDBYdGiF81FCCtp3snhE\nQ0ymmact/Wbxo37m6QPvt27bCmp0OxY/N+M8W3V48sj1W3vEm6mDfbUinwFoTOawD8L09fSObov7\nb+Fi0bhIV0kdnABrBmI60upLs3Xpe1J3M3pdX2t6t0I/tXRZnQA7ejzwFlhT/vweGzeuz3OwY85A\nDDfLWOv/1rVWTnJflnzoEzRiio+FOWZAPfCcL92O13+vTY0Pl+G9BZru+r6VVfGmnfUzYG75jG5X\ngiSZgmb663b5TwJW3T7P0vG1Ne7MXx5N+Z61p4HLlNBaaCaoYLkT4BlLqc8yEHpsOdjRPMY8r3mR\n5chaPmr1T8q3t7fPM0B6H46U1zLAtJBrLs846+eMi9cfXS5GU0sWLDxioPWGhtg6du6311fP6bOc\nHn7PowfT0Vt9IPXG2uM6LTvA+AhPMK0W4vyUsDAQX8Gy/QIxXmd5lXq0bGu/RCce2a7xe+xws+3z\nnHV+15KPZmTF8o2sPjOf831Lfi094gUNrNst2eVyzeoCrsN7boE1PoyXNz5efRae1ph7urpIT1h7\no/SyQD2WLAue7tX8tFhoeXDU7AB4TMX3igIAa2AsJ8cLMPS7lkKyDHcMX8ZN/vRaUlYKGnevXS7D\ngYjVb8AOOj3G0kvf9H2mq8bFopkubykIS8CKxkfe1+trRbikHfmzlKCloKw2LOXiBclWnyxhjRk7\nC8cSWgtsGGMGzXpX12HxEMsi85/nMHsG3ZtdlneKjI60YQVyLI9efyWwivGl5VwD8zf3WvVrPWKd\nqqfflfpie148ubH2LFjjyPUW9dkLUFk/FpWzwLI/lmMg115wynUwFC3/YVvQrKNWwokDHwTF8hBz\nsOWeZVs9O6Rtpn7f85s0Pp7vIvesPZHN+A+WzpFyzcqdx/OsW7T+4QCjaJWPVR/Tx7IV3KciKLI5\njG8ssPH8TPbzGGLPNd0se8Bl9TvcP+Z3tmH8bCkPaViSmSMA8waNiRtjXP7d7H2vviJD16wQ8R/j\nwANv4S0b7ywl4zltfG0JBveTmU0rl5ix9JicaaizSux4cB/4AAm+5lkcfXqL5WgyzaVdHTh55fU9\nKc/jypsaud9WXUzzmAPnGTWmZauF/T87sB4C7IyaZfQ9udd18j1dzqqTZVOeFzn/Hn95CQGrrljf\n9D12mmI4CQ5s+Lyycs0yJbKsdRwbeDaa1j5Bdry0I8S4cH8tXC16WXrZMuZeHTHwxsmqK/a+Zy+9\nAxo0/RdyiEMJJw6adzxHUZf1xtSy30XtAmhINnIbjIuWS5Z1L1Dj31Z9rCssGYsdgsW6istYvMv+\nCWAfbhXbp2XRgevyxtTCOaaHmA94uwK3r3HkZ3zfGkfLT7XAW8ap29L1sf9q4R/rlzVuRTguFFq+\n5yhmILSDfiLtxjrO7cYcUX0/VhdfA/O/pSPrNos2Qus+hDD/g7kebaxnMSbUzoI2chZewJzQe0sm\nPKFk+vB/3pzIZbylRZZC1U6nhQu3adHD4wemF5eJKUSLBvye5eDE6tblygMZWgOy4RmIBx8eD+j3\nLP3G9ViyrOthOea135bRiB3mEZNvq5xlqJp53ypj6UndR3YELB2h24nJcqxtS8/qcpbMcv8Yx5hz\ndyJg6RqPvjHdrcvwu0U4evbXc9r0e7pceSBD60DrJ4EiH4r/W+/q3wLeO1zOm2Vn30fX6e25seqX\n3zH/pEjPWjLt8a7F80W+qNUHr+zs7Kw5Sy04xXwmbtP6HStn6RUPmtVpzfBes3uWYjrJa8f6rd+J\njZuc+rdY/dTy0+qAuGDLb4uBvOexAdDOA1+zAOt3dDuegfGYQtdTpGx033R9eu+LBTHHoxkDXWRw\nixwka/w0eBkc3V9WtnJf9727uxs9PT1YtWoVqtVqfrDCM888g2PHjuHYsWMA5vYM8D4d3QfGm/9b\n/Yj13RNWrx7Ghe9b71pOnZQpD2RoDXBwlN1bkPMZk0GLh+T3iZz+xO1zW7p+bi9m6Hm5W8wBjt1n\nfW05bIB9ZD/TnPGQ5/pYbq1HLRp5uLMces90HzSd2K7EaGvRLYYnt+vhWXTfe9aMc+md5sV206Jd\nGRy1DizfCbATvjE/Q/9m4Lo8v8WSTfmzlvzF8OAylt/hPbPa0TJp4aoPUbDq1+9atLF8USvxbdlw\nLTNW3ZaPa9FQ2vR0pEc/r03PdhT5w0Ir62Ahr64iv7SI52I+rwUxXJ7Vp9UBcSfCMqBWmZhB5DKW\noxnDh4EHr8igWm1wfVY/dJBgnaJl9c36XQSew9AM07FQS3l2Hqw2RKgEdEZF9jIsW7YMJ598Ms47\n7zwMDAxgcHAwX05Xr9cxNjaG0dFRfOMb38C+ffswMTHRsMbUwwmY2y8hdUlgpR1jq+88XtynIqWj\n6esZK0/JWmNbBketgRBCw2lQsZPm2JDFFLannC2doD+guBh9p9/TzoA347xQiDnnOqCM1R8LOotw\nFNpbp1t6dRXpbC9r7MFC9ayn3z1nVI+3ZJxj7Vl8F7OtC8U5Rj8PnzI4ah3wqhugOLiVMoAf0HAw\nkbXVMNZah+i62DaJv8IBSDM87jnF/NzrN/fTK69pwLqK++7R0gNPVjSNtY73ktBcp+6H+ElWAMe+\nmKejrfJFtmExfmZsfD0f34Mi3RPDi5+1YtXNkgZHFpyo0faEluv1nutB1IPm1aOVimfErXtSn34/\nZnxkj40oHy1wuk5r+d5Cxs5i3JgDKOWlTX1YAztIlhLTAVQIAZVKBUNDQ7jmmmtwyimnYO3atejt\n7cXAwAA6Ojryr0ELzaanpzE1NYV9+/Zhz549+NjHPoapqal5gU5HRwe6urqwZcsWrFmzBsuWLUN/\nfz/a2tLvHM3MzODAgQPYt28f7rjjDkxNTc3be8L4x5S5NTbWNfOedRgHK0E2VGVw1BpoU99gixnH\noiBFQMYullFkeZLy2uHPcHNnJzwDKPIVO7hB9X2evtD9bKYOq09WfZ5DY8mVpxOTZG4/kTgblp5p\nxuGK3Ys9185NMzTxZmD0NZfznEEPL7YDjFuRgxOjoQUxxztJyuColaB9p5gOEYjpCA4EmK9iTjHX\nbYG8Lz6LhweDZRP5WQxXLmfpP8uX0UEHYDvqsQCGy1gyYwWL3vueHmJ/SuNp+ZExnVEUEBX5s0U+\nujVOC7ENzdTLYyl1Fc2qyXV2evGi9JM/bbEIiDmPFoGsQbfqig1oM4InII6Kt/ZaCyA7spZxi/Wb\n+27hAqROvrUMRcrzBlnPOBYpUgtfi/7aIOslLpou1kZqXU6ODW5vb8eFF16Ic889FxdccAH6+/ux\nYsUKdHZ2orOzEyGE3BESx2hqagrVahWTk5MYGxtDV1cXpqam8rba29tRqVSwefNmnHnmmTjzzDNR\nrVZRqVTQ2dmJJEkwMzOD2dlZLF++HGvXrkUIAQ888AAOHjw4j8axDd0WnS36a3pbjpYeR0t58Lsl\ntBZixsI7SABofnbbu9ayIjwgSRFdFwdJGi/LuWWwHF+LH9lZjxku1kkWrSw6s1Nv0dXC29Jd3tJo\nXtpryY9Hr5hss+6NGW0uy/3XfSlaouLxju4HJ+yKnDn9LrfLffCcEo/fS2gtWD6SNc4W3/D4WPLO\nz2Ky6flj7BPwpwEsHIBGGY7pMH5f+xz8PpfX4Olkbptl16Mf61+Ni+dHsM3X71izdVa9PP7WeBTp\n8FgfGAfPRlr3YjbSopOnO5h3ee+WxeeMk36f2z9RaHlwVCR4zPAsRLx3SKCIARgHT7lLW1YmP+Ys\niRJoxinh/ltMrt+tZ6em6WyM5zB4jF3kSHB7lsG08Lfa0O14BlecvwsuuAAXXnghtmzZguXLl2No\naAiVSgVdXV3o6OhAZ2dn3u/Z2dl8ZqdSqaBWq+HYsWM4fPgwpqenUa1Wcfrpp+P000/HFVdcgeXL\nl2P58uXo7OxsCHZl+Z2cwDMxMYGJiQkMDAxgeHgY119/fa7cYzS2eNRTxPx+TKA9ZWFllktoDXhG\nUj+znAN5znVJGdYbVt0eLjILyvsPPcOiweI56+Qi/dy75znH2lBxfyzZYTrybLfVhqa19b485/Hw\naCF6YCAAACAASURBVG/Vx9fcF68fAlYyiPvSDH30/aIA08PP0reebma6xXS7V7el20vd1HrwAgV5\nFnNo9dhZ8mXJhfy3+EOXAexZKP0XwvzvcPH71h5lS46lvD7ggPFi/LWdtnQN+zwefT25YFytcZA/\nb6Zej4EX0MR4gOW8GXtl+TFcZ0wnxniO3/f8J6sejz+ttmK2yYolYvUtFJZk5giId9gy1PzcM8ze\nAMfqs35bTKYFneu2GN1b3sC4esZHt8mC6OEugt5s0FT0W+rh2Q0NmvFimw71X3t7OwYHB3H55Zdj\n7dq1WL16NXp6etDd3Z3PGHV0dOSHMNTrdUxPT+ffg5J7u3btwo4dO7Bx40YMDw/j5S9/OVasWIHl\ny5ejUqmgp6cH1Wq1ISiS49KzZWl5X7u7u1GpVCBLrISO+ghTLXjSN/1faMDruAE7e2I5dkwzy2iV\nsHTAe+KsMbZ0jS6vZVEglt3k+xwUC+/IgSSegx/DzzpwQesqCy/ew8f08HSG/s3983RKTPdqWspz\nnmXV7TTjYPB1UdAUk3nrt1eWZb4IN/ndzMZvvs88p/eFMH96NIs5jcx7Xt9KaA00My7Mv1q2tTwV\nOalWu5YOZHnj8lre9R5qtnWx+7p+XrLs6VLG0eqP145VNtaObovLxvxBDVaAqIETJpa8erqOx5vx\na2aZcBGdrLHXbTSrF2L2zOI5Bm85pb5uhY5a8qO8vf+6EzHH3mJ6D+cigfCMusUQsUDCMjoeM1n9\n1aAPD5B2LOXgOVaewxFz7Kz3GGdPUHgvkRh1MdJtbW2oVqt429vehjPPPBMvfOEL0d3djb6+vnzJ\nW3t7e/5flhbJEripqSnUajUcOnQI3/ve91Cr1bB69WoMDQ01BFPVajWvR9ZryzeO5P/ExARqtRrG\nx8cxOTmJe++9F/v27cvflXa/973vYefOndi3b19uaDQ9ipwMvS/LUygxh9Ibo/Io79YAn1anedca\nLy0/RZtZLRn1/ks5i5fkmdwTeZJgqSgw8nDQOHJZxoXrtnSg1QYfKqAdc6YfB0GWjq7VankmmmnM\ndLfoGNOPVh+9IMDikdhvj+5eXSeCO78neoxXHeiPjms8rGVQlo7SYPFbK06DKiEFz3eygPk2ez9/\n7jnBllx7vpX1XHSS5V/wO9oXCGF+stnqn9YXHm7cf6aJ1Y+YnrR0t7XU2NvuIEktkUGrjzE66X2o\nlm/I41vk23n09crzewI82y918PjH+NTC0+IvTrZbusizxfo583crfKclnzny/nsDL9eegdb1FzGK\ngLWfwGJa7exzHTo40P+5DsGd67QYRYKiIgZjg8mZQc9psRRpDB/ph55Gt06nYdpKPwCgr68P5557\nLjZv3ow1a9ZgcHAQnZ2d6OrqyvcJ8RI4+S9/MzMzOHjwIJYtW4aVK1eira0tD4IqlQra29vz2R89\nC6RxE/ylzunpaXR3d+OMM87Ig6np6WnMzMygWq1iw4YN+NznPoexsTHMzMw01GHxguYnDhiZH/h9\n65mlkEpoPTBtNZ9bijkWPDBYY+c5G1yfJ68cKPCsly5f5FjrehkXlnN+L4SAU045BV1dXUiSdE/g\noUOHMD09bS6Plvd4abS1h4npZc1OF9Hd+m+9w3q/6Ln892hnjZ9HR37Xa5PpGeNPYL4tksBI2yHL\nsY05cAu1xyW0DmI6xPJJ9Nh4wYV+x/IV9HP2XYqcYfYJtBxYzyz+0m3LKbP8jHUx42TxMvfJ4mVP\nrjS+Hs66TNFBKYwfjxX7YBYNPBti0SOmp2L6VPu/VnkLX4tnmT76N+s0L7GsdZdlXzw/thXQ8u8c\nech5htsSRl2G6/CYL+aYNONwWvXq+rM+zhN2xkH3NUnm9hXoOtva/CO8Y8Kk64g5WhZYS1QshtVg\nKWUd2AjjhhDQ1dWFDRs24D3veQ/Wr1+PNWvWoFqtoqenJw9mOBssfxK8TE9P49ChQ2hra0NXV1fD\njFRHR0c+06SDMflfq9Xy5XSTk5P5jJG18VGch8nJSczMzODo0aM4duwYvvrVr+L222/H0aNH59HC\nyh7FHB/LCfHKiCLkcS9njloDOjOrwZOvmMzF9JIXwFjy7PGE1Y6+FjnyynvOQjNG0WovhIBTTz0V\nl1xyCdatW5cvd63VapicnMTk5CS+/e1v4/HHH284bp/5XDKsQieL32OGkvW85+jr62Zsm2VzPDlm\nenM7lq2JOYEx2nt8Kc/1CadCMyCf0cl1p+UAis6NOZYeXjJ25cxR64D1k+XsarnRZaRcTP49H0jb\nniK9VtSu/h1zuvVskleecbLkTLep++olEVhXSFmrz1a9lv4ROdKz+3KP7YHGg2nHgQTj6+nCGN2L\n/Eh+16sn5o97cCK6dyHvN9OfZ93MkSeMHlNbQqDBqiPmaMbw4Xr5vRixAXtKT5x03gitZ0EswdeH\nO1gbCLkNnm62HCdt7Kz6rFkJdkq4ft0nrSi18ZXrCy+8EBdffDHOOuss9Pf3o6+vL99bJH3WSlZn\nRWZnZzE+Po6JiQn09fXlwaNuVztWOrMh/ZbgaGZmBtPT06jVanlAVa1WG9qS50mSNMxCHTp0qOFE\nPF4WpOlUlCXyeIzHxTI8zSqXEpoHz1BYcheTRTbA/EyudZsMVr3NQAih4RCHItw9w2fpD23UuR8i\nx11dXejs7ASQJhjkBMnzzz8fPT09uOeee/J6Y8vsGB8NRYGJLsN7LSyjboF+x2tbt8+0s3jIuo79\n9pxObzwB5DPoWu/pJb1Srr29fR6drYCVxznGM+xYltB6YFpb/Gn5VB7/WbbI4ltr/LktPpQhhr+X\n6Rf7yytzYn6a7p+ny2K6Xcrrspb+5jp4X3Ezupx9xZisxGz8QvS0Nf5FgQPja9HZ8ntjvji3U4QP\n+6UexPShVaaVvlPLl9VZxPI6F3NABKyBYSNlEcYqU+S8WM4tCxM7JvqYayDN3AFzsxPyTJz7mBLQ\n/YvRhN9lGuv3uJy+b+194LGynAK5bmtrw/DwMC699FJce+21GBoaajiNTpfThlvalaBIHK2enp4G\n/Cylp/GTPUoSEMnYVKtV9Pb25nuLdAA1MzODWq2WOwxjY2PYsWMH7rvvPtxzzz35uOmNzdyut1G8\nCGLOGCs/K6tVwolDzBGPyY7nLOo6vbIMlrPi4crvaUeal2BKssVaDiy/29rasHLlSrzgBS/AC17w\nAnR0dGB2dhZjY2PYvn07nnrqKYyOjjbgJ7pt//792Lt3L0477bR8aWxnZydGR0fzY/N7enqwc+dO\njIyMNOAsbYcQsHHjRpx00kno7u7G+Pg4HnzwwVz+tWNo6XHLZrB+Y6OuT6TUtODxsnhD6M06UdPH\n06/6mYW/PPecVqsu0WVad0sdeu8C6xG9vE7X5/Gq53hafSuhdWDxYpETyPfYhnv+jufkWnxi7cGR\n69iyMIuHuB2rDzEbGXvOepnx5mRmjP8ZV26ffUEPV2Du49/6z7M3uj5PL+lnDNZSPMvH8H579o3H\n03rfe0/7z55/HfOlYrTyyojuXyws+Z6jZphMC4Y1AJZhEfCY3BrEGFE9/LRgCeNK9oP7ODs7mzsd\nUoeeStYMr4MGi2k9hvT6xIzs0USMpmQX5bdXv65POzqSSX7Na16D8847DyeddFJ+Gp0sf9Obf629\nRRKkaMPPNOex14FOvV7H1NQUkiRBR0dHvuSoUqnkTqPQRJbuzczM5MHUwYMHsX//ftx888149NFH\n89kkacczNBZvsYK1DAcrR4uPrfpKWDxYzjU/0/djm4MtebOyebE2BWJ6Uuq1lr54S2G4bq2DLr74\nYqxcuRL9/f0N8r9q1SocPHgQN910EyYnJ3P9JThNTU1h7969DYkgPYtRqVRQrVYxODiYL2XVfalU\nKli5ciVe9apX5XuWarUa1q9fj/vuuw87d+409bhHE12WE1dyP7ZUiGlvjQdfxxw4Cy+uw3IevPHT\nDoWMnV5tYPVX9LLMLHorF5gGVv+4nCUfJbQWmnE0BYp8Ar7n6S+vbV3O2q9d5K8V9cla7inXjKs3\nC6WfMZ8zvhzEMO4x/9TzGT28LX3erA/Hsygxu6NpoBO47e3tDckmS/94eohx9PaMMY01DbyZppj9\n8/Cx6tHvsWxY47gYWLLgSMATFvntKWC+bzEK12G1XaRUrIFhYbQMrmZAXZc4/NxGzFBrpdGsMbPq\nYCZnwbSu+UAHeZ+VoRjpnp4e9PT0YOvWrbjkkktw8cUXo6+vD0NDQ/msDU/B1+v1fEZGz/K0t7fn\ns0UW7fXGYgmG5L8EQwMDAzmOLKxyNLjMLo2OjmJ0dBTbtm3D9u3b8dWvfhWTk5OYnp7O8WQHK8Z3\nzL/aodHjIMCywAdd6GetFPISUrAClmYcRc/QWI6CVbdl/FjfcHvs9DJu2tjHlj7ItfxVKpV8yWtv\nby9mZ2fR09ODVatWYfny5di5cyduvfXWhtmp2dlZ7N69G4cPH0Z3d3fOuwMDA3miqFKp4Nxzz8Xx\n48dx/PhxAHP6cmhoCFdccQWGh4fz96emptDf3481a9bg6aefxvHjx/ODUIqcCh4ra8w0LTydz9es\nG9vb29Hd3Z1/e83KRlo6M1Z3MwGGlKlUKuju7m6oS07iFPwEdPLJchhjbcUcJL5X6qalAcuf8QIb\n/d96Ju9q+245s/Lf0kWswzy9xaBll9vgfXKWI8x6UusB1nGxg090/UwHppXlA+k+eD6ppqfuk+dz\naRoxvSzfjfUg4yfJX73XX5fzlgXGfFFdJsaLnh7TPrL2cSwbx+16ZfT4Mb7WWLD/fqKwJMGRRVRt\nPCzh0oPB10UOKisAdvwlutYMrB1ZJrDG04rmrb4IaIblpXWeAZLfs7OzDZE/g+csWIqD6/aUi+eU\nybU+cKGnpyefKTr99NPzQKmnp6fh+0HcJx0cjY2NNWSdNU4S/Ehf5T1hdhnDzs5OVKvVBvykP9Km\nBEdyMMPExATuv/9+PPzww/jSl76Ew4cP57NO0p4lVCzErDT4nuXIcllWeFbZZpyoEpoHloNmnVXL\n4eVx47LaMFkKXdcTW7pltaFB877Fd3zvwQcfRGdnJ5YtW5bLUrVazfXO0NAQNm7ciG3btmFiYqJB\nD9VqNezduxfLly9HX18furq60N3djVqtlh+/v2zZMqxYsQIjIyMNRrGjo6NhNlfkVpbo9fT05AGV\npROK9mVa42TNyltOiyfPQtupqam8n5OTk66zaIGley1Hx3pHTuYUPGRPpehHHRxbdtBzcpmGlm7i\n37E+ltAa8JzCIkdbg5Vc9fYaS31em0X4iW9k4alB8zsneqzyzNcx3Cy+tvCxZE7PuMT2vfAx3VKf\n/Pb6oNvha9YH1uwY+xSen2bVLyf6Tk5Ouglej8e4HJfx7jVjE5vR2xaddZ+1T2/V2axdbwaWJDhi\n4+aV8RxDdiIELEfDywjoOnkdpiaydZII42kFFQK89l8rDL20TGZN5B3ZC8MZYF2n5aizw6BpoMvx\nLIhFL0thCMgStbPPPhsvf/nL8dM//dMYGBhAT08POjo6UKvV8j7wJksJTJIkyZeqiaPV3d3dMAM0\nOzs77+hZ3QfBpaurq+HEO16mJ7NEtVoNExMTmJqawo4dO7Bz507cdNNNOHToEMbGxgCgIeDSdNFj\nXMSfnG3S154zZynZGO+X0Frg8bCcVqDxtCSWNX5P18WJEE93xXDishoXMQpy3+NV+c88uHv3bjz5\n5JN4wxvegHXr1uVL3Pr7+zE7O4ve3l6sWLECb3/72/HAAw/g7rvvbpCRr3/965icnMTVV1+N9vZ2\n9Pf3o6OjAz09PQghnSFav349du/e3ZDk0fv4arVa/jFmnSDxgh/LiDdD1yKaa/pKvV5ANTk5CQAY\nHBxEtVrFgQMH5ulXXd4y7tyu5WwCyJcodnV1NSR7hJZyWiA7mlKPdviaaTtGJ5YVXb4VzkcJjcA8\nzjbBsyPs52g/JaYjNL+wXMUcagHmNWt/G9B4QIgF2n6yT9SsPbTKxYIAa9+wl9BiG+HJLuNi+QPc\ntyJdxkEQ6ympX/wgYE5fDA0NYXx8PE8SW/qVacM+sRU4erqYac408MpZ9fB9rqdovFsBSzpzpBnD\nKyPXQHw5CzukzMAWIYH50brHIBborIGFv4CVEWGmFwWhP84nzxgnPdPlBUm63zHmknJMG01Dpl0I\n6Z6i1atX47d+67dw1llnYdWqVfnx4zwOcl8cIWlTgh9ZPqcdpcnJyQZcJLiZmZnJ+y7vdXR05Kdk\nifBK/fLe+Pg4pqamMD4+jkceeQSPPfYYPv/5z+PQoUOYmJhoCIasTYvMT/rgCJ1lkufe0jl2sKyl\nVLptXa6EpYOYQpfnAp6+YHmy3tG/vfo4IPAcE6sOxlX/LnIykiRNVtx1111oa2vDsmXL0NnZie7u\nbiRJmtCoVCo4+eSTcd555+GBBx7IkxwAMDExgYceegiXXnop2tvb8xMhK5UKarVafhCKRVtZllut\nVvOlt0eOHAEw9y0nSVro/ujgUF97YwPMXyYj9LDGQPZfClj2Sn6Pj48jhHSPlegpq6/euPCYMr5t\nbW3o7u5uCIAliSQzbnppn8U3rN9iPBqDMvj5wYIXmMQcWl2enzcz1otxJlmmLJ0lcsa20gruND5W\nQOP5YZb86/ZjutfT0fy+PPeSZNpfsxLxwPwZMXlP70/X48GBp/iQUp7b0uMhMDIyki/LleCp2UBE\nwNKl1n9vXLx72k5pHLisVUcMWhkgLclR3p4x8JwLy9nwmF3XGXNG+JodYU8INFhGxhJ+/SfvecZQ\nB0kAMDU1NS/AsGa1mA6e0DN4SkDjp0+76urqwpo1a/DBD34QmzZtwpo1axpOSdLv6xkj3ZbeDCyn\n1olikf739PTkztr09DQmJyfzGaIkSfKsstQrR2zrYGhychJPPvkkDh06hG984xvYtWsX9u3bh2PH\njjVksSwlLqA3mWu+YiUn9z366rHXQa0V9MSE1zOGJSwOvIDDkm1PqVuyFAPPgGhc5DlvrLUSKNbv\nmMHxAqo9e/bgySefxOzsLM444wx0dHSgr68Pvb29udz09vbirW99K26//XY88sgjOc5PP/00du/e\njXPOOSf/FpnAqlWrctmVb42FEDA+Po7x8fH8EBfZ69Td3Y2Ojo480GgmQOS+eEtSeNxYV3ISQztM\n3I7WI7L8r7u7O/82mwaukx0Lq5zoXfleih570aW6n5o21hJsHUQy7SxaWnzCNGMo4v0SFgZW8GP5\nRbGyno9i8SHrJf7tjTnrQUv2mLc9J5jb8gIX3hLh+Znad/Jkz1rGbNGC+8MrY/R70k+ty/X7Hu7i\nD4kvxTTzZltYVllf6L6NjIw00DnGA2xjpN/y3FoWaL3HdVj/BaxZQquP/J43Dp4ffCKwZAcyWAzG\n9zXECMx1NPPbqt9TBLqdosFkR4Qda11OBw9aeDXI7JRkT3kfgdUnNmwxsPDS+OjAqL29HS996Utx\nzTXX4MUvfnG+bEaXtxhUG3RxdES4tQBrpSBG//jx47kzpr+FJLNO09PT+QxRrVbD6OgoJiYmsHv3\nbjzzzDO47bbbsHfvXuzfvz8POPW+JcaTFSU7V0UGyXK0hH5aMXnKl51gTzGXzkdrwXMgPNkWsPSA\n5ThI2WZnypnHvCUwDCJTug1LHwhvdnR04OSTT0a1WsXExATGxsYwNjaGWq2Gb33rW6jX6xgcHEQI\nAYODg3niolarYeXKldi8eTMee+yxhgNodu7cibVr12Jqagry8UrZOzQwMIDOzk5MTEw09G1qairX\nMaIbRcb1KZGsY6TPvJTMGgtPx2vZ8/YD6Dp4TNnxamtry/sXGzPPpnE/ZZx0Akl/5FrjIO3LPQ6a\nLJ0Sa9/jNWscdHsef5ZwYuA5q5rvrPLW+9ZzwE4UW3h4z+R+jGdYL4lfA8xfYcPvejNRWvdomWUb\n7NHDsvFevwRPLmfxPvdFf5tR46C3UbAPJP/Zl9Dta7/Rmp3ispr23EddRuPPdLX8Ict+eTzn+aY8\nXpYv5dGb+1JkpxcLLQ2OLEPDhLYI6zkUXI9nbJoJEHgALcby6rUGTxuXWN8so27hI//1NCu3wTSx\nhER+a0HXdXBWQgR306ZNuOaaa/C2t70Ng4OD6O3tbcBR2tPCLG2JgzMzM5MHUwKyXp+P3u7s7ERH\nRwf6+/vn7Z+QcvJ7ZmYGx48fx4EDB3DdddfhoYceygMmmSXS9OCxs+4xD/AYeuPMioXb4WV0co/r\ni41tCa0HLXOWIbHKa9BjZtVr8Q4DG2mrfTZGnD3UG/I9x0Bg5cqVuOyyy3DmmWeio6MjX2Lx1FNP\nYc+ePbjxxhtxww034PDhw9iyZQvWrVuHSqWC5cuXo7+/H9VqFWvWrMHk5CRuvfVWHD58GEmS4K67\n7sLhw4fx3ve+F11dXejq6kKtVssPezj//PNx5513NjgDs7OzGBgYyI/tl6BK5FvTmeVPBzW6v5Ye\nZ12tdQvXoTPSrMstJ8hrU5dhsGyE3Nc6i5fLyW/RrbzUWs/OW/pFO18L4XvuK9db6qylBctuabpb\nwZPHg5bM6HeacSRj9VvltF/BwP6K1mG631omdUJFJz0tPrV8tGb0rS5v+XjcP7lfqVTQ39+PCy+8\nEGvXrsXatWvR1taGRx55BA888AAmJyexYsUKnHLKKWhvb8euXbuwf//+hj2LQgf2gS0fQmSeE+za\n1/SCGAt0OQ7ALT2s37MCF9a/jBfjYdE35h9buod1dCthyfcc6d/6uf7Pz2K/PQdW/9flYvgxDjwQ\njFszfbaUj4Uz18sMJO9L5pCnNC3j5dECsBWRCHh3dzfe/OY346KLLkJ/f3++pI1pUK/PfZtI8BHj\nLcZ8ZmamYe+A3mOUJOkyO70ZW9cljpK0Mzs7i8nJSYyPj+OWW27Btm3bcqdLgit5z1uCyPQuUvDe\nux4/sTAz/2gHxlJcOni2HJkSWgN67MTQWIo89h4w38HwlLqU1VlAa0xZpnWb7NhzOZE/kSF+Z2pq\nChMTE/m+IFnG1tbWhr6+Pjz55JP43ve+h7vuugtjY2N44xvfiGq1imPHjuWnpQHA2WefjXq9ji9+\n8Yu5vD/xxBMYHR1FR0dHvi9J9sZs2LAB3//+97Fnzx4A6eyHZFXFwREcRVfIPU0jDlw822AZdb2c\nh2df5FoON9D6jceMdbk37jEDLnixbGvcRf/zcmK97Jd5i2nGNkL2LGkc+NqzUZaTKGVkv1kJrQPh\nIWtJJJeT/x6vAXbm3tJ1uhyPfZH/ZL0LYJ78aH7UII6+9inYFmrb7rWnaSMy7+Fs9dXqj/ZJtH4F\n0hmi3t5e/MiP/AiGh4dx9tlno1qtYmxsDCMjI1i5ciWuuuoqrFmzBsuWLcOyZcvQ1taGo0eP4sEH\nH8R1112HiYkJcx+z6EOZOeY93HpctB/YDF30b6YJ99fTs0V+iVWPFShbY+PxXUxXMf6sJxcDS7Ln\nCIh/s8cKJPg5YG8Gi7Vp3dfEYiJzef6vy2lDHeuLxlvXzRv2PAUj9ejZEDGSFr4WrT2DKX+Sjeno\n6MCVV16Jq666Clu3bs2zxTIFbLUv9UoQI1lgcTTkAIZdu3ZhZGQEp556av6BSGlXnBWpWz7OOjs7\ni9HRUUxOTuKhhx7C9u3bccMNN+Do0aP5t0YEpqen0dbWht7e3vxbRawcLf6yFCcLsGdY+JnmDcup\n0PyjlSwrXa1E9LMSWgssr95zAS2H/C4bNnZ89TNdn+YTPXOhn3tOLN9jow3MHZ0PAIcPH8aNN96I\nZcuWYXh4GD09Pejs7MTy5cuxfPlyXHvttThw4AA++clP4u6778b+/ftx+eWXY+vWrajVarlcr1+/\nHqtXr8bQ0BC2bduGhx9+GGNjY7j33nvxkpe8BCtWrMi/N1ar1fKZquuuuw71ej3/npHIo+zVmZmZ\nyZf4seGXPmudqWlkBQdajrUOlmV7OkgC0PBdJT2OevOypr3nhGicmK88p4V1fkdHB6rVap7wkROn\nAPtAGP2n9TrbUglwRTdz+5qX2FZ6znJRIqqEEwO2Axo8x5DHqxlnVOoDbF/Fsn2e4yzPLR70dJf8\n5hN7pQ3WB9YyN+5HEW9qPc5JF5ZFAZ1Q7ujowBlnnIGNGzfi0ksvxcDAACYnJ3HkyBFMT0+jv78f\np59+Onp7e7Fs2bI8uSRtyGqYc889F1/72tewe/fuhtlf7ovl73FA0EwQwe9KvzT9PJ/GmqHSdGOI\n2S+rjOX3xsCKC6z+WbidCCzpUd6WMDUj4AIxoZDyfM3OjDxjh1nj6jlKzQREGngJnZS3HBt9zfVZ\ndLMYyFI+Hq25jkqlglWrVuG1r30tzjrrLAwNDeXZXWu5nl5PK9keOUVOZ+JlCdyRI0ewevXqfC+R\n/r6JdkAmJyfzAxamp6exa9cu7NixA5/97Gfz47d50zHzlxVw6rI83cxgBZccyGrQ9Iyti7Vw1u8V\njV0JrQMtOzHjYhlI/dyqV+q06tJgyXMzBke/q2VY8792UPTzer2OW2+9FSGke4qSJMkPXgghoFqt\n4sorr8Qdd9yBPXv24Mtf/jLOPPNMDA0N5QkUWQK3YcMG9PX1Ye/evRgfH8fu3bvx4he/GIODg/mH\nZWVf4PHjx/Mj/3t6etDb25sHWzMzM/m3kfTnDVhXWvo35rQD9gcddRZWnnHwYzkontzrcbCuLYiN\nbwihYb+l9INPqgMwb8ZGsu2MI/MJzzyys2XpI8tBasaJKeHEwOM3vZzS4nmtC5i3uT7PGdbPBTw/\nxLpmmStq09LBug6dyODloVZCiWVQz8DFfEypT5cVfSFyOTg4iOHhYbz61a/GqlWrMDg4iKNHj+Lo\n0aM45ZRTsHHjRvT39+enf8ohVNrPkX5Uq1Vs2rQJtVoNjz76aIMOt5JAMv5eYGglThiYZ1jGvTG3\nxpT9LY2HpUdiNs7jZemzNWbim2lcLR5rBSxJcMTMZjn2HCAVAROAB1DXKeXlngWWkOt39fve3mvJ\npAAAIABJREFUQOm+aUbmunQ7MXw4i6Jx8ZRYjHbMrBKkdHR04K1vfSsuueQSbN68Gb29vejp6ZlX\nn3yEUAw0kGZbkyRBT09Pg9NRr9cxMTGBBx98EGvWrMH555/fcESugDhF8i2igwcPYteuXfjkJz+J\nPXv24PDhww3KRNPBynweO3ZsHi95CjdGJ+se8xAbHM5A8dhZDrglxJ7iLp2Q1kGzwQvzkS5jBdnN\nKuMi58WTdy5rLfmy9IOu64knnsCnP/1pjIyMYOPGjajVaujr60MIAT09Pbjooovwwhe+EDfeeCPu\nv/9+/PVf/zVe9rKX4ZWvfCW6u7vR19eHJEkwMDCAkZERvPOd78Rdd92FHTt2oK+vD2eddVaOU1tb\nG0466aT8o7CHDx/GySefjA0bNmBoaAiVSiU/qe7RRx/FwYMH3XGI6U5vtpxtQZLMn3XnWXBul8fE\nAt1OMwFRM3Vp2yHX2hHUe7jkGSfjPNwt/pBrKzAF5u+D8vRgCa0Bj76WLyHlLR7jmQGrLrlmx1Xr\nqFg5Cy8OSgD/O5RWP/g5L0X3/lt46oSq5TPqdnVCpa0t/ch8f38/LrvsMpx++ukYHh5GR0cHRkZG\nMDMzg9WrV+Oss87CSSedlAdD4q9IIkPTUI7gFpze/e534+mnn8Yf/uEf4plnnsm3HOgtCYIf+zTs\nE+kVSRbfWOPNPlFsTLk+GRfPF7d0N9err3kWSH7HgizPBuq2W6GjlnTmCPAzcMyo/L43qID/LQpv\nQDloYqWicRQB95xXbq9o0CxH2XO8i4yct2lP46aFVLet9y6tXLkSW7duxfDwcD7zI+t+BW8JjPS7\n8h0ia4bpwIED2LNnDzZs2JDPFmklLXsV5G9iYgLHjx/HRz7yEezYsQNPPfVUvvxFOzTSByvYtuhk\nOb08dh59mY5Sl5eF0sDLF5nnrLr5XhGuJZw4xMbOKsvjaL3r8ZpVn4Cn27icAC85KdIjGn9dZnZ2\nFrfccgv27duHq666ClNTU1i2bBm6u7vzbx298pWvxNq1a3HDDTfgjjvuwNatW/Pvk0kGtbOzE+vX\nr8fk5CS2b9+Oxx57DCMjI2hra8uX5HZ1dWFgYAADAwM4cuQI+vr68mAMmNtrMDU1lSc3mFaWPubs\nuc6a8kwQjw07QkU6gsfMA35Ht8HlNA7sYMjJplrnaNDfmwLmZsM8R1jT2sJVl+fAKOZQW89LWDzE\nfItmdJbllLJNjMmZ58zG7kmbWj695cYaH40Ty6Ilt0WOO/sMGicBts96lY886+7uRn9/P7Zu3Yr1\n69dj3bp1AIB9+/ahv78fW7ZsweDgIIaGhlCtVtHd3Z37RqIjNT4S9MinDMbHxzE2NoajR49ifHwc\nF198MUZHR/HQQw/h4MGDOHz4MIDGJEgsyIj5KEx/7a/xWFg05XKWf9UKPcBbCZiHNf4WH1t0atYu\nF0HL9xzp/4A9QM0gz0GKZ+x02xykcDnvfTZInHXgd/i/XGujx84MG3XPIdJMzG0xznLNe3hEUPSa\n2RDSpXTveMc7cMUVV2DTpk3o6elBtVptaLNer+fr3mdmZhBCwMzMDGZmZvL65NtE9Xod3/rWt7B/\n/3687GUvw2mnnZbPKLFxFvyOHDmCXbt24U//9E+xa9cujI6ONgRAzPwCLNjekjcPLMPgBVmaZkxv\nby8d0OjIWkfw6vaZn5tZJ17C4sALRDyHVp6xYmZ9YukzllsGL8jhMrwvSbfr9VGX0e2Pjo5i27Zt\n2LVrF8477zxcccUV6Ovry5fHbtiwAcPDwzjnnHOwf/9+fOhDH8KmTZvwlre8JT+opVqt4pxzzsHw\n8DBGR0dx++2345577sEFF1yAFStW5MvwVq9ejcsvvxw333wzNm7ciOXLl2NqairPjo6MjODw4cP5\n0hLLCAvwMl92qrR8eXsOWNdb9OQyLLse3S2HxRsPLqftjugN3UfJJMtmbd1fT89yn7S+ZL6w+uTp\nVMv2lNAa0ONo8XJRoMR8xjrDClI0j3Dyzwpa+B3GU/tNMXm2nlsOt5VE4Dq0/Mg9XdZKaso7IaQH\noFxzzTXYvHkzNm3ahCRJcP/996O9vR1btmzB0NAQBgYG0NbW1rC1YGZmBqOjo/nSV8Ghra0tTyx3\ndHTkH7KXhLGWuVe/+tX5NoSnnnoK73//+7Fv376G776JDojZLu63959p5/k+PMbWUmXdPutlfia6\nytvHyWV1u15/tY62eL0V+mnJZo4sY2QFClye6ykySN49JrrXtoWndoC9gKTISfIcY3awGHhvFDOq\nFkKujwMj3cdKpYI1a9bgFa94BdauXYvBwcF806BM+9ZqtXzpnRhl+ZNTlASfkZER3Hfffdi3bx9e\n9KIX5cf08oZoqXd0dBRjY2P4zGc+gzvvvBM7d+7MFUszjqTuSywDUvR+7PAEplnMobKA+YtpwcqK\nDVGRASxh8VC0HMtzIlhxa1nTsJDxa9bhafYdi58t3jpy5Ahuu+02rF69GmvXrs0Dn56eHvT392N4\neBgrV67E7t27cd999+Ghhx7CGWeckW80lmPBr732WlxwwQUAgOXLl2PZsmXo6OjIj/N//etfjwsv\nvBDDw8Po7+/P5V2SLceOHcPo6GiOu15KZo0H95WdRy/AFVpYhlg/k3saD9ZJMV3AzoXlmFgOiN6T\nyW1Zex+1HdD4afw9HDlIKuLV2Ix4Ca0FbSeYl2M+kudTWXzrBU2WI235N177+rdVrw5SYnvJuQ2L\nDrpdz/HXbVv1t7Wlh0P19fXh6quvxvDwMADg4YcfRk9PDzZv3ozh4bmVNQKzs7MYGxvDxMQEQgjo\n6urKvwcpdUo72v5rfDROEkB1dHRgzZo1OHLkSJ6UZl1i0dyzXRY9rbFlGsf8qZiPwu1YQYxVhv1u\nq85m7WOrE8xL8p0ji5n1b4+wVsBhlbeElw2ZxYjNBEZFTjAPtBcAAfONa6wNz0nTwqaZiY07f0hR\n2m9vb0e1WsU73/lOXHbZZfmX7SWQkTpkOYympza0Gr8QApYtW4YrrrhiHp0nJiZyBTI9PY0DBw7g\n+9//Pq677jrs27cPIyMjDSdqWY6ENg4sxDxe3hhZit1yPnR/teL22mBBthwv3ifA2WA9Poxb0QEA\nJZw4sOMRG2uL5+W+pZs8xyGmv2IOLPOm5WTr/5Y+4qWowqdyTP5nPvMZdHZ24nWvex3WrVuH4eFh\ntLW1YWBgAIODg3jHO96Bq666Ch/96EexY8cOvPe970VfX18+2wwAq1evnsfz4iisXLkSK1asaPio\nqQRIs7OzOHbsWP4dNJb1ZpxEz/ng8bDoL3VbCRF55r2v78eciSKnRegmGWkp5x0Woevy9q3x7Ltc\nMy8yvZk2lo5iXVdCa8Hyg/i3p3OK/Cx5XpTMsWwn1xFzeDXomR9dJhbYSBn9iQLeZ2PRyrLd2scI\nITQsgQPmeFv2QR84cAD79+/Hli1bsGbNmvy7cKIPRkdHMTIykh/PrQ+jYvppvDkA0Kt0gFRfdnV1\nzdtTzT6h3NdbJpiuTCPWA1zOGiN+t2gbi6dLLLD8Mt0eb01hfIrsbSv1U8tnjjwBt54Dc8c56mdy\nbQUDXI9cM7FjByNYAsZldL2sFDyniZnFG6QY7iJY+l2PYbSRZ5B3urq6sH79elx66aVYu3YtqtVq\nLtQ6IOjs7JyHL2/2E4Wjl+vpOmZnZ9HZ2Zl/X+Xw4cO4/vrr8d3vfhd79uzJ9xqwwfdoo8dG+sRK\nxnNyLHrxMy7Dexq8d/mahZIDJeYfPT66HqErn85XQmvAmlUFGuWIx57lOBbsFIEVyMhvTz/pZ8zr\nXlZVP2MdofWXLHG75ZZbsHnzZgwNDeW8JwZ73bp1+IVf+IV835AEProuy7ABaDihUsuWBGdSbnBw\nEAMDA9i3b1++99EKBHVfeRy9oIgdAn2f8Y05biy3HHB4NoSBnU+hDY+bPOeVBOz46eUqSTK374Jn\njyy74jmmUpbvSf/L4Kj1YNkIgSK7Yy0/Y98J8FfEWLiw/tM8ULT/lmVF4+35XF4QYQUJlt70fE2m\nF9NibGwMd955J2ZmZvI9RKOjo5iYmMg/FyLBiMzodHZ25kfke/rFOihBLz/UewyBdCXOoUOHGj6I\nbfEEB3i6bGwbikcj9nmYdp5e1dfc99j7Wvda157dtd5l4LYXCy0PjmKDo0EreRYAy8n0hMhqy1IM\nXvsxfL2sZZFS8NpiR9hzjhgnK5Oo8eXMigh0pVLBm970Jlx++eU499xz0dvbO+9obXmf+6g/6iqz\nH3LIAiuYJElyZXLkyBFMTk5ibGwMTz/9NG6//fZ8qliPt86eWM6eZYS9TdfeNL2lONnB5DFiGnvj\nyfVawM42K3OLjqzkSlg8sGPo6RqLj5gP2EBb+kiXY7ACcAsHq25Lr0kZzdOMs3aiuP/1eh1PP/00\nbrrpJuzYsQNbt27FC1/4QgwODmLNmjXo6urC8PBwg67Qhl3rDh3YiK7QGWBxMGRZytNPP416vY7x\n8XFMTExg5cqV6OzszA9n0XQUndbe3o7ly5eju7sbg4ODqNfreOqpp/Jlu1onMg093W3RlPlBl2Hd\nw889Xc7joQ+t4VOqmKestpMk/Uacbke/b/GYd0+/I+OleY51bSucjxLmgzVjafGQ/PZ4xvJ9vH26\nHt/G7KGFG5djP84Cz9Fn4Do9GgjoFTdadgT0h+q/8IUvYMeOHbjyyivR1taG8fFxPPXUU/lJdL29\nvZidnc0/3Kply0rMWn6Z6Inp6emGD9jLh+4ff/xxHD58OJ/F8nxcqy8ciDAt5HmMt4rob4HWFTyG\nXtAlz3SfPP3s+WisjzRfx5YULxSWZM+RhhhT83WMGFKHRXRW+J4RtIyNfq8Iby8gstq3+mAZTa8u\nq4zllPP6eGlPMr+bNm3CihUrkCRJflIcMH/Gjte56u9pSHaTAxf5q9fnvsmhPzq4f//+/DtF2lmx\nnD0eB4uO3D7ThevhLK/uDwuZVuzWOHs86o0Vl2VnxeIVb0aghNYA85OeBY3txbAMvjd+MaeiGf1h\ntWcZSo2LtG3VyctSRF51PVL/vn37cPvtt2Pjxo15MkN/G0jqkj8B7WjopXtMD9EDEhzJoS6C17Fj\nx1CtVrFu3Trs378/z9yedNJJWL16NS644AKcdNJJ+RG6osOOHDmCAwcO4FOf+hSOHj2a6y7dbiww\n0plwfZ/HxtLrlt7Qz3jctX7WCSrL6ZHyWn9yu7wsxsLD4y3NN7HlT7psqZd+sOA5esx3Wr4tG+X5\nMZr/pVzMDut35JrLWvrS64tu38MjZid12Zhd9+iXJAmmpqawd+9eHDlyBJ2dnRgbG8PY2BgmJyfR\n3t6e6yn9cVod9FhJdGt1AgB0dnY23KvVanjiiSdw22235cGX9M3S/ZaMevRmGlj8oumn3/Oe6TJC\nc89u6bqsPiRJ0uCHWv45+12ctNFltC/cCmj5aXXMiJoQsTJaOKxBjxmvIqXOhoqZObYhzQOLaWIK\nKPacn4lwMc10GX2evjyTbKS8c+qpp2LVqlXo7OzMp2uFIeUgBqlD74uR+oGU0WQZjA6ShI5a8PQH\nIx9//HHccccdeRaEI3yhe0wJ8oyWpbBZMC2+YAdEOxCs0Iv4wFrCaCkHxkWu2QnRhsHihRJaC5rW\n1kyLNXvk6RS+Zh6VdzV4vMvtWjrJc1gs4+YZHJYFHTjV63Xs27cP3/nOd3D22WfnpyzJaU1yuIpe\nXqK/4SGblEW3yDIVCaZk43F/fz+OHTuGdevWYc+ePfmMj+iZ3t5eXHzxxXjd616HU045BUNDQwDQ\ncCiMxv/QoUM4cOAATjvtNDz44IN5MFHk5Gl68TjHdIx3XdSW1gXT09O5XpSDcQDM08PA3KyZ1rns\nROkDdDTOVuCk7SzbFq2bLVtdwtICJx418G+L/4oCI2vmT9s9r35djvWRXMecW8/Bt+4V9Z39CO3z\nyH/LTrN8avzGx8exfft2nHHGGXj00UcxOjqK/5+9Nw+3LbvqQn9zn76999y++qqkihCaxFhpaF7y\n9D1A8YFEDTwbFFAQlacoivI+FTtUogif2ICAD+ThQw0SwACmgATTGKRIQipVlVSSe+vWrbr31u1P\nf84+zZ7vj73Hur/9O2PMtc89+5giWeP7znf2WmuuOcccc8wxfmN26+677+47nEqPCudBIl31YvaO\neW21Wmi32/jkJz+JT33qU/iFX/gFPPvss3juueeqQ2q0rZRnlj/nrR+4LsnaWz7O5GFkLl/1JWpP\nD8fqc85bebD/JV8X6d0w7Nah7TliisCqvsMdW8ECCzpyZh4P2gk8IBLx410P+k5EUdCk8vFAjfHO\n9edlK6y4k5OTOHbsGI4ePYrJyUlMTU1VJ01ZegB7lsuxk9WNfzYzxJ3HDAQ71VarhevXr2NtbW1P\nkMDGPwoq1PjVBTme4VAjXtduHkiIOrWW6aX32pnL4LbzOvig+tTQYOTpmkeRE4/aMCrLc+77Sa/O\nwQP70fvWB3lAwqujt/RsZ2cHTz75JI4cOYLjx49Xo6U8oGJA3WZ27L2cc7VcxI77N/4sCBgdHcXR\no0cxNTWFJ598EqdOncJzzz0HANWJTd/yLd+Ce++9t/qeiJHtd7IgbXFxESsrK/jABz6AZ555Bk88\n8URVpi7p48Ci5NgjR+zJ0WurklP2dNBsru0DZX61rc0mKy8WLLFucptG/s/q5QERD9R61w0Nl9T/\nlHRKnw0y++eV5/mq6HkpsNHySuVGoNzz5fqcsY5XnuejS0vyrT+88MILOH78OMbGxrC4uLgnYNnZ\n2en7xqNXrqVV7LS7u4vR0VHs7u7irW99Ky5evFgd2721tdW3rNbre6oPXC8P55TaivF2tH/Mw9hc\nhg7ORzqiNozT6UyfPo941+WBnk8cBh3KzFHJ0XuC1EbRPNTIl/4zecrmNbYX3arQvcbzFMJbWhOt\nSfXkEzmlKDDgs/e5zLGxMZw4cQLT09N9wZEdvMBKZiOtLAPrOHyCSqvVqk6XYiNg5VtHs6N+p6en\nMTY2tsdxR3VnY6XTtRy8sRy0LaPAxJMpv6N6oDql7ablmHw8w+B1ZpVJSX8bGh5pX/aWMOoMtr5v\n7zJ5Ni1ycnXE5Q4yq+31AesrXh/ybDAvVXnuuedw8+ZNHDt2rJp5Hh0drXixQMiO/rcPxfKAS6fT\nwcbGBgBgYmKiz061Wi1MTU3ha7/2a/E1X/M1uHHjBnZ3d3H69Om+wRfbizQ6OoqdnR2cO3cOV65c\nwWOPPYZnn30Wly9frvqSJx+dbdHgQWXNgMN7xvLS9lKw5MlZ87F09lFsO1mUeedlkfabR/+tXF2S\nq3X0dMrTUc/G8T31bw0Njzx9iXyDkadfCpqBvTOIJUDrAdiI1xKWU760LlEw4PUd1nEtrw7Y63Pd\nj2S0vLyMxx9/HI8++ihyztVHWe0UX7On29vb1ScLNLhge5TS7e/72GDN5OQkXvayl+G5557D5uZm\nNVtustYly57tjzCU96x03xsc47SltuU61gU0+lvbwpvpKtU7slWlut4JDf0obw9cloITfZ/JcySc\nNnI8JQDjOY5B3/c230Z1UEfm1V0j95Ic2BhFRoQd5cjISLWczkYnrEyum9WJ+fDWwptR8OrPaW0W\n6nM/93OxtraGD3/4w9UyHE+ZmeexsbG+I8ZVFrwvyj5Kq2An0guWnzdaEullNC0c6bYHOqMlUp4e\neO3f0HDI9Ezb3Yh1UR2Hl5c95/+l/mllRHsrNT8FvWp/OF0U8KhdUXDN73NZa2trePbZZwF0l7ON\nj4/jyJEjfaB6bGys+qCr2RsNEmxvDXB7b5Ktu7evzE9MTFR9m5fu2UcWL1y4gGvXruFd73oXLl68\niOXl5T4QUeegtY9GabTfla69NuB0g/g4TmcDVLb0kOvllaP2W/ecWRovcOR8PdDK8vT4bWj4pG2g\nz6J3gHjglPMtzTwbecFExBf7b74u1YkDB30e+WMdsOSy9L9ikkGxGAc17XYbKysrmJmZwdraGmZm\nZvrkacvseMBld3e3milSfnXZfEoJ999/P6ampvbM+nhL2bytJlwHztsjTx78vqZTeUb5lWyG935k\nTyzgLOmRlst5evo6LDq04Miu7b8aZCPPqXijFV6j6AifCijKm9No/nytDeUBJk9JWFk9hVdZ6bI0\nVTIGRlHn4BkXCy52dnawsrKCyclJbG5u9r2js1lcJnd+2zNkZeoeBQDVXiMDR7u7uzh58iS+9Eu/\nFDdv3sRjjz2Gp59+2g08UuqOriwsLPQBL34OoAJMy8vLAND3DQLjxfjWUbNoBF2DEm5HnhHznAuX\nre3K+qvfLohml5gaEDJ8GsQA81IITaP2INKbOnATjcRanh7IVf71mQdgPNCrwInTcp7Wj975znfi\n/vvvx1d/9Vdjbm4OnU4Ho6Oj1d4fIxuosGBobW2t6oe8p2Zqaqrvmz5WV1uKd/XqVaytrVXfG/n5\nn/95XL16tbJdDHa4L0XyL8nIA5ZRP+d0Xt4eRUsaNT8Fduvr61Wg2Wrd/gaS1iHypewTPJ/o5aEA\nQwePFEA2dDjEfp4pCpo8vKO6ws9VB7zygXi5k9d3VP84b+/aSDFh5Fe9d6OlYFo3fqZLSRkrqf5f\nvnwZIyMjuH79OnLOOHHiRLWs2AaEdHbH+hsPLHv12N3dxdGjRzE7O1vNmnC/4v+Ms7ytIB6u5rpo\nWpW7krZByQ56GKW0l0ntjuefOG3JTyovqkPDslGH8hHYuvsesNCOEhkEy89rvFKHA/zNiEaRs+Iy\nvFEMLpfvR0pQB6Q8PlTR+b+3/nxnZ6c6Tnt9fR3tdrvvzH5e287O1/LjIANAXyfmchSwcJ2M5ze8\n4Q3Y2trC2bNn+87wtxHlY8eOYXp6GnfddVcFCPiDtnbAw9LSEtbX17GysoKdnR2sr68j51zxakGc\njv7bM0vHAZHO1lin5GttF6+O2s5ajteJ1SirztYZhob2RzqQoo41AhCeg7FrTq9l6QyVlsN5eOVF\n6Qc1/BGA1uesr/pse3sbL7zwAl544QWcOXOmWj5nNsOCGj50YXd3F1tbW32HuLCtsJFXXm6yvb2N\nnZ0dXL16FSsrK3j3u9+NJ598EteuXavsTgkIqIy8a08GKt8o4OV0JX3w7IKW7aVTQGcHXPD9aNmc\nNxLP9kf1mcnzHZqX/S7l09DBqSRTD6CW9D4KitgXeXZLfVIEjDkYGIRnz9bqYG8d7mG9V1vFtkHL\n9/ww9xvLz5a95ZyxvLyM1dVVLC0tYWpqCuvr65icnKw+fm12TmVufbe0WghAhW+0/pE9A/oHeLne\nKmuVZaQn3hYCzqvOd/E9xlLaXmyPPH6jdvcGMiN9V387LPt0KN85Uub0OgIOdVTXYb1OGDlHbjhL\n680qRQ6vxD+Dc6+x6hx2ZAQUjLMC6RKTdruNF154AS+++CJyzpiYmMDGxkY1ejs5OYmUugc3WH1s\n87V9HNKWeFhQZXnbaDCAKugy3iwQsyUx7XYbjz76KF796lcjpYTZ2dlqBMb2K1ig1Gq1qv0NvLzO\n+OPAyQxUu92ulvasrq7i1q1bOHv2LK5evYqnnnoKi4uL1ejzxsZGNQPlyZxlH+kPP9PnfM9bxqAG\nWt/jtmwAyPCpBKLrwJ/3rjp2oH+Ur2R7vHy1vEF1IALmni306hLZTLMHb3/723Hs2DG85S1vwZEj\nR5BzrvpvzreX0q6trVX7kLa3t6sBDAX/Zl82NjbQbrdx7tw5XLhwAZ/4xCeqZ5aWZ49Nvh4QNN41\nwIlGmdm+RnZa5amggtMoLypLfqa6pwMyKd0+zY5HuT2774Er+x0tH7Y6e/WM+GMbrICroYOT51eY\n1JdE79vvCKdE2CYCqVqmB1yjtBEeU/2K7OcgttPDbdoHorrqihyrW7vdxsWLF3Ht2rUKA9nebRv8\nsRkkoL//2j4kj2yA9jWveQ0uXLiAJ598sm+VjteXjU/FDSoTr524vTx/pwMtXn6aZ0n3PDuj6T3f\nxPWKML7mrTPj0Yz/QejQv3NU5+SjDu81SCRofsajlQp4o0a3NFG5yqc1bsmARR0kKl/zifKIHKJe\nb21t4fr161hdXcXs7CzW1taqwAgA2u12n8M1ILS7u9u3R4j/8yZhG/m1b4rYyMva2hq2traqQGRq\nagrb29vVsjsLtmyvwcTERF8QZkt2xsbGqnJY9vwdpZxzFdzNz89XHf7Vr341dnZ2sLS0hFu3buGZ\nZ57BlStX8MEPfhC3bt3CCy+8gM3NTbTb7b420KDE68AKZOyeGpoIGPEzdjRWnhrzhg5O6miAcn8v\ngb+SY+J89Vp1IQIO+p7nAOrI09uI32iETkHEysoKNjY2MDExgfX19WqW1/qc/W+329U7ZkfMttjA\nRrvdxvb2Nl588UU8//zz+NjHPlbNNKl99eyuVx9PtpEPULARtYMHKj3d8HxcpBN1wFbLsmDT8uCR\naU7v+RVNp7amrl5239o3WpLe0MGp1Ec5jdcHgG7bqq9UMGm/uRyvfI8HxSQlvKbll+pQspear4d3\nFA9FdSn5W+OD/fjm5iZWVlYwNzeHzc1NpNQdcB4fH6++F8mnYXJ+FnRpHc0G7uzs4O6778bY2Bja\n7XafrWX5MI7VLQCeXFkG3FZRO0RLfxVvD+JzOJ1nT6MAz8OznEcJL3Obeu8elIZ+Wh3/tgaIOkpU\niVJApOQ5M8/Qe4rgOQgu3xO41/DMQzR9GNWxZECUB+Vb8zJHZtc3b97EJz/5SWxublZH6Nrsy/b2\ndmVMbZmadkgbBbET5yYnJ/u+XG+BlC2j2dzcxOrqKoDujJIBIXPo7Xa7Wrs7OTlZ/TcltxFo+6+j\ntOzs7RkvC2TDvbu7W3389gu+4Av6lv3Y6LQtOzx79iyuX7+OD3zgA7h69SrOnz9fzYDpN1M4IDLS\naV02PHX7IvhajUZDh0OejBlga3tHYMW7P2if9pyhR55NVcDPMx91fNcFWMzLPffcg88FxgywAAAg\nAElEQVT7vM/DxMQEjh8/jqNHj2JsbAxXrlxBp9OpTmzi47Z3d3extLSE7e1t3Lp1qwIDq6uruHLl\nCtbW1nDp0qU9p07yUt8jR47sCQg6nQ5WV1erg2WM16g+OirMsvSAV+SEI7+l7+o72g6Wb8nue2R2\nS0ES5xcFcV5d1A4pYCzx2cxoHx55fdtLA+ydqQH2Hhbl6aBnS5RK/kf9W6TbHq92T3nweFGd1WVZ\nVt9o2XrEu9Y9mgEDbn/Efnx8HBMTE5ifn8fExARyzpiamqo+cm33eCDXk7OdSLm9vY25uTm8/vWv\nx+tf/3o8/vjjuH79+h4Zql0pBTFKLJcSBlU98eRcKkvlHC391fJUbzR/tklROm+20P4Psl9pEDqU\nAxk80AHsdSrRzIga4Tpher+1E3vTq9EIYik4iQxYtNygrrPqEgnNW3lkOZVAkI3CPvnkk0gp4cSJ\nExgdHcXCwkL1ngULm5ub1fGUljd/jNBmhXjvke0VyDlje3sbnU736F37bYCm3W73fbHeOqN9ddpA\nkM0UWZ2tfOZhEFBi7cAfbzO+ebmgfXdgbm4Ox44dw+7uLt74xjdicXERTzzxBM6fP4/3v//9uHHj\nBpaXl/um0Fl+njNSHfLazu556aO+0dBwKbIlgyyJtd/8XinIAfypf0+nGQB7wFevIz61jDrwxfmO\njIzg0UcfxcmTJ5FSwszMTLXU1WyILiuxfs36Oz4+jo985CO4dOlSNQKrDo4BkJ3iZN8UsU8QbG1t\nVQMrNtqq8ovstmdLPTlGslBbUwIeEQCt6/+WZzTaq8sFOc86cOj5Xy27VP/SdUPDo1JAEgUbnI7z\nKQHcQfxLhEs8MMrveLrpYS7P10V6zGCX+7KVU7K9Hu+enLif24yQ4Zn19XWMjo5WK2LMxukJwIxR\nePao3W734Q/DKTMzM9VAsA0gW710hqsk98jOsLwiDOu1leeTvN9K2iYeeTZa2y7yy6wfVpY3+cKH\nYB2Ehh4ceR2zpLRRMFHXuflaf/N1aakUdz5vlJ8bo6Q4Kd3+oKeVrfuFVB4eoOZnXuBWCpI845Vz\nxtmzZ/H8889jc3MT9913Hy5evIiRkRGcOnUK8/PzGB0dxY0bN7C5uYmRkRHMzc0BAObn5zE+Po6Z\nmRmsrq5idHQUy8vL1UzN/Px833rZlZUVbG1tVQbFntkyOwuYrl27htHRURw/fhwzMzOYnp7GyMgI\nZmdnMTExUe1BsjrZDJK2gxEvDTTDZsGPtauN6NhMmAVNJmMbne10OrjnnnvwyCOPYHd3F9/6rd+K\n7e1tXLhwAefOncPP/MzP4PLly7hx44YLVkwWXlvX7Wfjdra2a4DI8EjBgDdK5gW0niOJwK1SHXj1\nHEOUhp/rfe0Tg9bDSGfVR0dHcfLkyWp/oO1RtODI0vOyuZy7G5m3trZw8eJF7O7u4umnn8alS5f6\nPgarbaDl2oCKBWiLi4vVff5YNfe1CJSpLCKZ6nv6OwIqXl7W1738VO5st6K+bnnzBnDeh+XpoLa7\nyr50r1Q3e6cZuDkc8uRt9wE/KNYlXCVMxPdKdk3Tqy5HNomvozSezunhIdxnIhutWIv5jGydly6S\ni8302H+gi4larRbW19erpcb2DSSbRedvKBnusZU1FlQZvfKVr8QzzzxTzRyZDIwHnjHW9uX61y0r\nK+FxfqekQ57t1nYo+Tm+r4PKyqPaTG9QkevO5Sv2PggdyrI6r5MBtwGJ/Y/SRo6llLcHFKJ73Fkm\nJiaq/TdR+RxElZyt/ecPW3md2JTKOgDnv5+61qUz5dne3saHP/zhqlNboDM7O4vTp09jZWWlAiFr\na2sYHx+vTmhZWFio9gLl3D3YgWeRrGz7ntLS0lJVro2I8AlWtlTt1q1bmJiYwN13342RkRHMz89j\nZmYGu7u7GBsbq9ba28ZvAO5yOwtMdfTF+LKZKRtN4Bkp3uMwMjLSt8E851xtupycnMQDDzyAe+65\nB0899RTe9ra34fr165XRrDMw6sS8mVHWt8hRNXTn5IFJzyZEILUOvFoaDxhosML5efbNC270PbYf\nTN7sSF1ZbKtarRYeeeQRvPKVr8Ts7Gx1+Eq73cbk5GTfN4t4kGF3d7f6aKt9K+T55593R/FUx63P\n2f5Bu28zRDMzM5icnMTGxgaWlpYwOjrad3CD1ieqexRMRvKLQCmXp+VoH458WYlXzpvb12Sdc64G\nkDQvBRURcIx0KuKpJLOGhk8aBNg9TVMKHqL7nm0q2a2oP2g69cucJ/s9zcebLS1hn1If17RRH9V+\npfmZ3eO0hl9sD7UNDtmJutbfzEYa9rLVNDawbPsyFxYWcPTo0VCmxhcPvEf4wuubpf7q6ZfKzrPT\nLB/mQ/1WyadG+FnTesEW8+Thb+AlfpS35wj4vtdBAP+bQF60GXW4yOh7PFp5m5ube06X8pQi4tfL\nn2ckmDfv2jNgnjwiuXmbc7UtOp0Orl69iq2tLdx///1otVp44YUXMDo6iqWlpWqToR2XbQHB1NQU\nFhYWqn0FFkDs7u5ifHy8GlW2o3iXlpYqg2CAx5bqWb6bm5uV3HPOeOqpp6p6Tk9P49SpU5iZmcFd\nd92F2dlZLCwsVHuT7HtKlq/xw0bfAh2TDRvp0dHRaq0wy1A7lYE+SzcxMYFOp4O5uTl84Rd+Ib7y\nK78SFy9exI/8yI/gYx/7GG7evLmnLT0j5gGvCFA3wdFwSfu09sPoHfuvxl5tgwKSEvD18onSGK98\nj0GF52A4rc66al1ZHidPnsSrXvUqPPzww1X/t/5l+4Z4dNM+2GoHLFy+fBlbW1vY3NzEM888Uy2n\nNfKAnNXBBqrsw7DWdy0Is+8rzc3NYWpqCu12G8vLy9U6fk/u2p+8PlZaZ8+k7/K96F1tHw8QRM5e\nlzWpHfEAKddfy9HlTJGf0Hz0uk5ODR2c6sAiU2lrAJNnv/ja02Utz9P1iE9vRN/jX58xrvFWYkSA\n3LuObLOlZezEz0dHRzE7O4tHHnmkWslitqnValV7uG1ftWEO/f6blW91seV17XYbm5ubuHLlCpaW\nlvq+m2Tv6KC51/88TKpUwhd6X/HpyMjIni0RWrbWlfPicpQi/YvyjuruXQ8LOx36aXVMdUEMO3Bd\nhsQV5qi15JwG7ZBTU1PIOVfHUvM3c6LO593XsvW/OjhvHw1f6ywIdzTd/OYZFZ6pmp+fx/T0dDUV\nPDMzg42NDaytrVXrX+1DjdaBl5aWsLi4iMnJSUxOTlazNq1Wq9oDYMvmLl26hJQSbt26hdnZ2WqZ\nnL2Xc/dwhhs3bmB1dRXXr1/H1tZW3/6BnLsfXLMRlZQS7rrrLpw4cQIPP/wwZmdnq03hBtzsBDye\nVbKOrWuBbaSHAyqWK+sMd3ILtmwkaXZ2Fvfffz++6qu+CidPnsQv//IvV6fXeG2t7VLSzUifGhoO\nRf0yCkAUTEYAYVA7xL8V2JbyKZVf4ovL4dlcdZTj4+N47WtfixMnTlSHpdg+wJxz3yCI6bEFLnbA\ni/W/sbExTE9P4+bNmyEos/9so6wf2wirlQf0j55ub29jfHy8stt8vL+2mxf4MD/qS7x2KgFOlb/n\nrzzfxbxwkBPxqjzYe56v1Dy8QRldSj4I6OV6NjRcKoG9O82rzpbwtYLYQd7TsvReHT4z8vxgzrf3\nDWtduK+VAq8S34D/0dtOp4OJiQkcO3as8vdTU1MVBrJvHZn9M3vK/Yp/p5SqQ2lstnt9fR1ra2v4\nxCc+gcuXL1d14bry6qNopQnXo66/qh1mPqNBEbO5hoGiyQDPV2qZdYEPX7O/0nqzXfdWSXh53ikN\nfVmdKrAnlCgwGiTgsXc0fcSPvudF4aurq0ip+80f+wbOoEalxFsUqfO1t/yO5eHdi+rOiskHExw7\ndgxveMMbcPbsWWxtbWF5eRmnTp3C9PQ0Njc3sbS0hK2trQpwjI+PY3JyEu12G5cvX67KWF1dxQc/\n+EHcc8892Nrawoc+9CGsrKxU5UedR+XCaZX/69ev9wWOPPtjRmNsbAwnT56sZpgWFhbw0EMP4ciR\nIzh69ChOnjxZBWY229Rqtfpmm3janL/lpDxzW9lyu/HxcQDAo48+ipe97GVYWVnB448/Xp3UpyDJ\nO8xBdcIzAg0Nj7S/1BnQCByrPnN6JgXZpQEUtgMRL5bOAyNGpl886OIFYjY4YM9f85rX4L777sPp\n06cxPj5eDTjYniAL/A0E2AEudhy+LTF9+umnqwNYIvukspqZmekDEKOjo5VTtt/2TqvVwuTkJNbX\n16t68PIyXRqtMi/1KW4j9U+RrkQ+TcvnZwzIOKjhGW0vP68OBg4MpHlAKJoZq1v3X1fnYQGQhvpJ\n9bWuHbwA3QvMOR/VMy9AjkCn18eUHw+jKPZisud6FDkHC1qeh630PtuFSC7Kx+zsbIUlWq3uATGn\nT5/GxMREtbx3dna2whJmuw3MA/1L94HbH33d3NzEzs4OLl26hPPnz+Oxxx6rVtRoPSM98Po419V7\nR5c7qiw9uTGO9LaJRIGJpYl018PkXl1K9tuzW3WB/Z3QoSyrKzHO6QYFKvqugolSGUaqeJ7C8WlK\nnpOMFKPkGL17nE8kE0/5SntVdFaCO8bdd9+NCxcu4ObNm9Wx3MaHdXIb0bD8eAaNO/vi4iIeeOAB\nnDhxAtevX8cTTzxRlK0CA69OXB97n79XZfWw9DzjdO7cOaTUHd2Znp7GzMwMHnzwQRw7dgwPP/ww\n5ufncfLkSYyPj1cbzMfHx/cszbPgSTc7s67ZqLntn8q5O6J++vRpzM7OYnl52W0XDoQ9oOvpfwM+\nhkuqd5GR9tKpXnu2Qyky2NFMRsnRDJpOnRLXR3Wy0+ke0X/ixAk89NBDexw+cHt5KX+nyJbR2RLZ\nzc1NrK+v48aNG30ffWWwE9XZBjomJiaq5ba2xMSCB9vobLNJFqSZfbA/neXnvqWjjHxd6meRjde+\nG8lZf3NanUk2WXGakl4p2I0AhfeOXnszbiXf7PWThoZDKv/I5jBF/d1rwygQ9oIaTs86qfeUd/6t\n+7Q5rb7r7U/06s0BfxQ4cBneXifuZ3Y9NzeHM2fOVKtobImvzRzZdyLZRnI5Hq7jgaX19fVqwPmZ\nZ57p2/Osy+q0baNrlgunGSSd5udhZsuL90urjD3y9E9xe11cwDyU4om66zulQ1tWFwk9Ag2lZ15l\nPSdYElLk3JRndjL6rgegvHIjhfZmDjxelUfPUOoeLK/clLqbme+77z586EMfqkDNyMhItSzO8rIR\nWDtxan5+vgJE9v2i8fFxfN3XfV0FiE6fPr0HmHj8eMFCyWDzf9UPK8eWyNlM38bGBm7duoWUEj7x\niU/0zQSZQbPlQseOHcPs7CzuueceHDlyBKdOncLs7CyOHz+OqakpzM3NVTNOXtvaJsubN2/i1q1b\neP7557G2tlYd4MD7MqJ6eYYDKH+4raHhkPZZWzbg9U0N6OsA9SDgNuKFdcJz/BFg8sC51y+5r09N\nTeGLv/iLsbCwgLm5uWoZHX/7jE+dtH2C29vb2NjYwObmJm7evIn19XU888wzew4n4d86K8a8LC8v\n46677qqW7Y2MjKDdblcfezZe+LQ2e9c2Ot9///3odDp4/vnnQ5vJZat9iQKHqC9G/ZqptI/N8mY9\nKe0fY/JALC851P2zWmflQ/XKK8/qU6f7DR2cPD3k+6U2UN2PbEPJLnm+KcqLwS7zxTPYHtbhZyW9\n4oDJ01FPPvq7tH3hNa95De666y5MT0/j0qVL1T7qnDOmp6dx9OhRLCws4MiRI5iensbCwkJfwGWD\nRtGyNhtAtW9BLi8vY2lpCe9617vw7LPPVgfQ6IqTyCZxGpaBN+jM76uMPTyr9iDyg5F+evL12oHz\njPLSAKpkfyM7Pww6lNPqBu3IXsfVZ3WNX/rPeXllR07I4zvKzwM2pXooH54sLL+Iby1XOxXfn52d\nrb5Rsr6+js3NzWqtvqXjJTM5d2fQbHOhBQIve9nL0Ol0+maX7KQ5M2AeIPA6npInM+5M0Sirvm9l\nWXrjhz9C22q1qqWATz/9dLVvaXp6GidOnMDs7CwefPBBzM/P4/Tp09VUuhnNnLt707a2tvDiiy/i\n2rVrePrpp6tZR5Z/CfB4hknl1dDwyev7umzAA5JA/Yyxve89Uweg6fWdyMZ4/ZzTeqBD8z1y5Age\neOABHD16FOPj431LV4G9M0YWIJmTX1tbQ7vdxurqKpaWlqpZH9V1z9mqk93d3a3W3dt6fu6/rVar\nWuZnAZQtt7MlfBcuXAjXzXsy8Jx4BLRK15HN53L5vtkmpohvT18i4KR1jfiMqK6+Xh6NfRouef7B\ne17yE5HvqNMrjxePSvav1Nc9/WN/Hs3s6G8up26/CZerAZL1Q/tcweLiYrVszmynzRzZPiPec838\nKpA3e5lzruymDSotLy/j5s2buHLlSjUQZAdW8fue/CN94BknfR69Y2Xxe/yfZch+sa79texSUKM8\nKUaM2llxZKSrw7BPQ585KjmKCPRFHVadQR2QKAUqajgiR+iBEi7TWyKioCAK0LSOpfw9mZSAtCqM\n/bY1/a94xSswMzODCxcu4MUXX8QDDzyA2dnZav+MfYvERo/NmBw5cgQAqlFk2yRtAcr09HQ1gusp\ndAl0eu3A7zOA4rracj8DUmoo7LkZKV4ux4dJGJnxvHjxIgDgfe97X197mUymp6er+7u7u1hdXa2+\nZWDGkNtNN0p7pP1Cdbih4VEEZr0Ao+TYS85b87R8vHXvEXn2wvqZ9gkjXmoSOZ/Z2VmcOXMGr3jF\nKzA7O9u3TMSW0VpeOeeqPDt+f3FxEe12G4uLi9jY2MCzzz6LjY2NIsD36qVys36zsbHRx68FbsaT\nzRLrMle2IRp8DMpP1DbRwFwdefoR7bng8ktBWqSLWqan05pfCVxowFin7w0dnLzDWbznDMpVdxVo\nlvTNbFxUnpcn/1f/bu/wsnQ+9l8pGiguAWFOpxTJIAqgcu5+m/HEiRM4ceJE9Q1Hsx9HjhzBkSNH\nKjvJh9MoDuMT3axtDA/YTPva2hrOnj2Lj3/849jY2Kh8gh0uZfuR2D6q72A7ojLz+OJn+puvoz7u\n2U5Prl4QMyh+8QKdUj9gvfN0Y5j46VCW1XlKHf32HLmm84QVNTSTllPqLKVTjTx+tby66WHNS/lR\nhYycOjtQde5enQx4LC0tod1u46GHHqoCIuvAtl+HNzfbMhkDKDZbMjEx0cfnkSNHsLy87AL8qP6D\nOH9tq8g4A6jqqfJVmUZG3a7NEGlH397eRrvdrsAb884GUevijSoZD2rQ9LS8BoAMl3RfBZPXpiXg\nES2j8MqMyjA+PAfn5c37Ia2f6vcvdBmYvT86Oorp6Wncf//9OH78OKanp6slo9of2LnzXiPbX2Qz\nRsvLy9V307iOUbDIOq7E8jaAwODNPlBt9eZDH3TPUWQ3I9l6YCpyzlaeByo9x1zixyvHa8NInl65\nXpkeRTx5dYrSNDRcilYZeHoK7LUX7F9K/dADvlyOZyMjTBJhGqD7wVTbG2yfCrFDR0p9ginCDSWc\noLKx/94+pevXr+PUqVN4sLdS5PLly+h0OtUeI/tUiVF0Gq3J3WyQzXrbN5FWV1exsrKC8+fP49q1\na3vay1avcHDE9bCyIjsX2SP+7y0RjnTAy4dlXadjUVtEVBqI1Dw8fdB0Hh93QkMPjjzw7jkOTm/k\nrUmMhKadU4VX1yAKmKNOpnx4SuHVE+hfZ6lplW+rv2cASnXm37Z3wtJ2Op1qtDelVAF8W1oHoPqo\nq/1NTExUndSCAtuvMzk52ffV53a7jZe//OVYWlrac2wvkwcSIz1ReXJbKMBSMKGA0fIxo8KbuJk3\nPplLgyxLEzmvCChHIEWDJk8ftPyGDk6l444jpxABFCVtt8ieeHmW0jOZ7trBBAAqx80js2xL7rnn\nHszPzyOlVO2ps+91sIPm0+V4WYgdvmDH9V+/fh0rKyt48cUX+wYKgPhUNK+OVpYH7G2vk43UWv9t\ntVp9tirnXA3U2NKVaImJ2holr9/qqaZeu3nvRhTZRuXD05mSLnm6ozocAYqoreqoBGQaujOqw0ce\n9lCbpnt9vCDC0yPOP+JL05d8dM4Zi4uLFX/2vRw9jY7zL933BlU8nVccFuHPyclJHD9+HOPj4zh/\n/jw6nQ5Onz6NV73qVWi1Wrh161Y1qw6gmtWxASXjiW0t70EyG7a2tob19XW8+OKLuHz5Mj71qU/1\nBUT2sfuUugdjGb6K/I0n+8heeIM4ns1SG+L5P9UFxewRhtW0EUWz/VH9vbzr4ow7oUObOQL2RnKD\nBCwsbK9zWz56XCKXy2Vrw2sZmm90ugrnq0CgBII8mZQ2yOnMRcloaHChDp3LHhsbw+bmJlZWVqqP\nmnU6HYyPj1eHG1jAw8vRbD+GjVzbFLLtPxgdHcXCwgJu3rxZBB+RkvOoSx15MmVApI6Ev1TNcrFD\nKeyerQ1m2epoRskpMNjzwAfrGfPi6Z8no4YOTt7MnuoMLw0F+m2WBsNe/41I+25kG5gv77ktLbO+\nnlLqGwww3iYnJ3Hy5MnqqP677roLExMT1aELbOdY72wQwJy8fdzVPlq4srKC1dXV6gPOTHyaUalu\n2gaq+znfPtbaZops0EePu7b3bT8Sf29Ey4vayLOnEeiL3vF+e+9F+UXkASCvzLrlesyz1waev/GC\nqpJfbOhgVIeNvMEHz4/y/VI7lcorvVd6xwPYOWf3sJYIeLOfZN1W3+7x69k0rWdKCadOncLCwgJm\nZmbQ6XRw7do15JyrZXR33313NQhjGMcOheEBfB5ozTlX2Mn2aa+urmJjYwOLi4u4fPlytSzYeDWb\naRir1br9qQJtwwhvRn3XfBnjyWiLwyC/PXnrc89+Mq5WfuvKsWeeHR/Eth+UDuUo7+ja6xAlJxR1\nJq+h2eEree96wZMHcD0aRLlUOZg8AMYbqT1HpCMBnI+3tMTyX15extWrV7GwsFAFOWNjY1hfX69O\nhrKT3ezQAuODP9DKmwe3t7exvr5e7d156KGH8Oyzz1Yj0J4B3Y9SewZQ71kb2qlW3Pb2zPvYpf3n\noKrOUZg8vHYvBU/eMw1+92MoGjoYqdHWPmikdsfToZIeRECZ00fAOtoHoGksiGBdsxHRhYUFrK6u\nVh9+tjXzPPKpAwY5395AbE5+Y2MDOzs7uH79OlZXV7G+vo7x8fG+IDLqp3WOnGXD9i+lVI2e2uwR\n7+Xz+pbZAdsXGbWj17c8Pj2KAgR1/pxfBNAUxNSBVE5f0sVBgjGWo12XlpRHvxs7NVwqBdn2vCRz\n7RvRuxEm87BWyZd7ZTEPmr7Ox7I/jGyI4iHtj2bXdCbC3p+cnMTCwgLOnDmD8fHxvg+6Li0t4dy5\nc1hYWMC9996LycnJavvB4uIiOp3Onu8mal15MGlrawtLS0tYXV3Fhz/8YVy8eLEa5LFBHxuEMvtl\ng1+Tk5MA0Dcb7s2Kj42N9QVTNhPFOMyTu2eP6rC4976Hp4zXkp3Sdi3phlLJlu03rzo6tI/A6j11\nbkC9IYhmUbwgSxsjAqmc1ptB8vLXd9UZlRrHc6asTGoMPB68+thzC2qsPnx4AdCdFVlaWsKZM2ew\nsbFRrdff2trC2NgYNjY29nwU0vJN6fY3jlJKfWuGrWPPzs728aAyi4iNX6lTclsxmYHik7Z4dC3n\n3LchVDc1RuDO0nu8eJ3e03cP8Hg6yPx48mloeBTplacPnjMZJO/IfpR0Rssr2R7ll5+1Wi2cOXMG\nx44dw40bN3Ds2LFqbxEfsGIAgsuzYMcOW7FlIbbPcG1tDRsbG9VpTiW5ePe8/XxcT53x5hksz7bY\n+wqobAQ28kFc36iNDWRMTk727bs02ZTAZeSUFchxmSXgUnL+dSDAsy+Rv/XeKc1SNHS4pLqyX8AX\nDUIYRRiMrwft5wfVixJO4/pwH9KBYs0jwnR2LDd/2xBA394imx23fjc1NVUt511fX68+12H4iJfs\n2/cPbW/m+vo61tbWcPny5WrQ2XjhwXCug53IyQGT/dm7nI8NUNvpnZY3l6OD1iXcwT7CWyLnBTgl\nO+YFMtpGg8x862+9V9L3O6VDnTnie6UgiInveaP1lod2pMhplcCIKUmpY5bSRwaiBLzr5MT/OR9W\nLl7vanLS77QY7ezs4Pz587j33nurpW/j4+NYW1ur1r0Ct5fF2IktNnqhbbCxsVEZgpRSNcp85swZ\nXLp0qQ9AKN8leXkHIXBHV+PIaYz0UAN7x0ATA0FLz7NtbHB5/ba2qwagagC0HbRcr71VHg0Nl6K2\nKt3T56UAS9OyPkd5WbpSe5ccvj2fmZnB0aNHcfz4cdy4cQP33ntvNTJqQMD0VvnK+fYpi3bqpI2A\n2iEutql6YWEBFy5c6OOrDrSx8+N3GBREMxeWxoCMfs+Hv8nUarWqjzvzsj9tOwVa3JZsp2xPlf1O\nqfvNOBsZjoKP0u863dI292Qb+RW9p8s/S0GRx2cEoEp+raE7p/3anug9SxulGaTsaB9tXZ5RWm+l\njvFptmCQOmjZqqPRqoyJiQkcPXoUp06dqg5asHJHR0cxOzuLycnJ6lCqmzdv4tq1a1hYWMDs7Gx1\n3/Z7Li0tAUDfzDbPuNunDq5fv45z585V9oj/jNQu8SogL8AzeZrdY6zDezCZLPDjsnivKcvXs1X8\nbuQ3+Z3IT2pbehhI9Up/e3pXpzt3SkMPjgZlTDuJvV8yBqUOWzIIUeDDz1QhvU2NnN5zOvupO/Pp\n8R+9w4GROlANmtiIrK2todVqYXV1dc+eIt6/ZdPG/BVom+5lAGUd0sDImTNnsLm5iatXr7r1UoOm\njls7NK/HtbwsCLRy62TEU9LcrpbWrhWw6ZIjzZ/3Mnk8mGy8/VDcNvxO6bqhg1FkR/RZFHzU9XF+\nz9N3tXV1DiZyLF6/GRkZwfz8PObm5rCxsVE5ceu/als5sDc95wMYeM287TUaGd6YmMMAACAASURB\nVBnB7OwsUuoOhhhfCmw8mZVkAZRn79lWGL86y61gwwZ82E55Mrd3vOXKJiO1JwZ+OB+dDasLPjxd\nikBxHVhmKvkPlZem5YAxSuPVoaHhkQcCebY30quSfSjlben5XoQr6myg51e5nJJu8bvKq5E342wy\n4f4bzUofOXIEMzMz7moTXiZn2GJ6eho7OzvVsriTJ09Wg00cvKyvr/ftpbFBpY2NDWxtbeHGjRsV\nHrI9k1zfKLDjAS0OdnQg1+qgMvH6uz3jYMr2n3oz4tzWrC8l/6f3VB8iUv84KGnf0HoelA7lQAam\nSDAaLQ6al3ddCqgGFXrJgfPz0obj0nKqUj3s3Wgak42eBhReJ7L8AFRA6cqVK1VnsH1G5uwnJiaq\nI7ttvazOGNlSM55RSSnhxo0b1RHfjzzyCBYXF3Hq1CksLy9jbW2tr0NrvZjMOGint1Fh/kBkHUBQ\nWeiyHAaKQP9HZa0MbU/Oj4/05LLrQAi/o4Ba9aih4VPUt7mflZYSREa8BB48vdAyTed0L4+m52v7\naOH4+Djm5uYwPz+Pa9euVctcjWzJB9suy8OcogZHq6ur1ZHdnU4HJ06cwPj4OJ577jmsra3tsbtG\nPLvjBSSefPQZcNtmGW+29Je/yWT8Gs9cTxvc0fqpLI1nk43eM554WYvXHp4dUn9RSs/vDfK7BGS8\nd6IyPPtTqo9RY58On1Tmnl3Q5ajejKhSpDv6rE7PWXe8PEr4LsJWXpq6PlPS4YmJCczOzmJubq6a\n/QFun1A7PT1d7SOyE2st+DFMtLu7iytXrmB3dxczMzOYnp6ulhfb3+rqap/9tJmjnZ0dnDhxAouL\ni8g5VzbMVt2oDI13m6Eyu6Uz/VZPz1fx4Q4RZjV8Y8vxzNaaDWUcZvny/6jN6u5zezL/ke1RH8lp\nIuw0TBpqcBRFxHYvAhoeeek171I+XoPWOTNN7/EUgScur2QM9Lkprze6wkrEgQHzwe8yuOK6tFqt\najS40+lUa2wNzNgaV09m3JEsbzu9xYyA8TQyMoKJiYnqi/cGTLiutmkwkpECFBvRsd88cqOAh+UF\n+EGMGn0zQp7MIp3TttKZJk8XPL2KOn/dUquG9kclo17SQ8/21NkHBi68lFP1S0GM9nkmXS7SarWq\npSA8ysgOletizlHBlPVPc4a7u7tVwLGzs1M58tHRUaytreHWrVvVaZUeb2aL2LZ43x+LgI85d+PT\n6mI2QweDuK5Wf91PBaACPpaXtru2EefH9oGDyKjtOe/I1/A7UR6l/DU/BQqeD4neLfEfAZKGhkva\nJ+weX3Pf17aNAhJ+19uHFAFV7avsp4w838g64s2mRkBX/aDWVfn1dNG7Pzc3Vx3Jbf0eQN+eI60r\nDySZHbeTfe1obtuvbYPNKXUHcW3LgZ3sub29XX06YXNzs7IfZhtLfp9lYfWyQS/9GLbZYA9Dql3k\ndvTkzQNPit9UHyN7UMIunp0Z5H3PL5eWaw6D0rBAWKvVyvzBrKqAwOGXSIXudR6vU0b5qOC8DhqV\nP8hz5qNUDy+dGjZvfb51DFZk+68dREc4x8bGMDs7i/n5+Qr87O7u4tixY9VpLLYBmTuJGRAbxTCe\nbFbJAi1r8/HxceSccfPmTbzwwgvVulx7zzNmfM/KnJ6edo1jSv0bDXmqmevLRs3S5rz3tDkPwOj0\nPMvZAzL8x2V5baudWJ2I59ja7TY6nU6DRg5Iaps8WxA5eaU6gFgy/qr/njOMyjH9n5ubw9jYWDXT\nax8QnJ2drZZznDp1CuPj41Xf5b4A3O4jPDKZc3dvzfb2NkZHRzE3N4cbN26g3W7j+eefx9bWVt+S\nPJYh91cLjLzlLpae7ZTZkJTSHhvH+XuyrJO1ylCXp+hyW525szpxewGogiML2Or8W+RDSrx67w2S\nhvP05OKli0CrlsH/e7rT2KYhkNmnUvvW2Z0SlXSkhJsiGhTTefmU3o18ZF39Nc+pqSnMzMxUtnJu\nbg6jo6PVnsSZmZlq5siW4VqwxIOxljdjsk6nUx28sLOzg/HxcUxNTVXpbeZofX29+j7S6uoqLly4\ngNXVVQBdrJRSqgalTVY2UGV1NUylA8FsW1NK1SCUh1ssb/6vA0iMp3jZoe1LsgH0uja9E58ZYfpS\n2pLPNuoFdweyT4f2naP9dDyN+LznJUcYPdNAQ39r+VY2N4CnZKXGVKc6iEy4bB5x8dbDc9kqN75n\n1yYDWy5nexNWVlYwOTmJqampvtFdXZJjS+9shsiAgQGhlFLfN0bGxsYwPz9fTSV7TpfrZPcsOLP8\nGXBFpPuiWB68vEf3aZVGHFS2KmNPznVG23tXy9dyGxoeRQAjauMoHdsDTqP9lvtdSTciZ6bEyzx4\nPyDr//b2NmZnZ7G0tISVlZVqjxCAvtkWdpDmeM0J2ul0U1NTWFxcxK1bt6pvn2kQrzbQ7AAvCeE+\nzAEI90nd8+j1V+OX5c+jr56tZmJgw+3Hezq8JdFWLp96yWks4N7a2tpTHtMgwYunn6prUZoIKLDe\n66qOyIfwb0+XS3ra0J3TIEGs2gx7r65NIj2qS8M6UsJfEengZgnMcr6q556d9p6bjk9NTVW20maM\nzK7w4I32f5WHvaOz32YPcs593440DNNqtaoZq83NTSwtLe0B7my/oq0CbCvZVhuvxhcPCHvtqnpj\ngTjPCnkDdVZPtn+D2gz1j9rmak8GiRW8fOow9UHoUIIjBrTKvDbgIJ3b8vGuteNwmZ4Ao05aB069\n97WDlq69KU+vPpZOZ+EYQGg+9psPCrD71gHsmyjm7G2Ew9acrq6uVmW2Wq1qvS0fgNDpdM/6N8Ni\newFsCtY6/czMDCYmJqqjcG3KV42LjXqzMfZAQGQodapY9crI6q5trvkxYLJ0OkOlMuT6abkeLxEp\ncGtouKTOz1uGUJJ71F8tL7UxTNH9kt0zp2j7iqx/e/al1WpVy1zn5ubQbrerL9SPj49Xy+/4XV1G\nZ7rfarVw9uzZyvHbTLEXQAKoTn7iwAjoBxDW/yx//pgiy12XhzA4sP7mtRHnbfVisMBLWPSjucan\nzkrzf7NrbKNZjlNTU8g5V7Nr9iyyYx5wKOmD2nq+Z/Xw9FFBdPQ7sjv2TG1TY5+GS2qDIj3wBvW8\nttN7innU53llR8ET82v3dZCH9VLvRYGOyiOqiycfs432baLZ2dkqEOL9i2ZHrC/bbIvxykv5gduz\nKFouz+5YoLG9vY2bN29iY2MDZ8+eBdCdIbJZJq6DLVm2o7q5bfmzI1tbW2i1WpWNZVtvZXtbKmyW\nRw+W0TZkG836YLICUPE4OTlZrRiyvNlG1w2ecf6eLkW659kgT1f4f2lAfT90qDNHJaqL+Oo6OD/j\n9FH+agAiRxDxXnevBI45fzUYJWXRpWGmcDx6YM8YSHh8mFHY3NwE0N3Qbd8zYZDAPFrHyDlXJ7VM\nTk4i54xbt2717QGw0+ys3JmZmapzemt8GWhwkKMBngaALCdtA21DbX9dcue1lTer5ZXL+w7UKHBb\nR6Ay0l0GdQ0Nj7S/ecBRnXhkjOucgL4f2Z4IcOScq32BtvRVgwl+d3d3t3LS7XYbMzMzAIBbt25V\nG4PVGXNftQEOS6/r7plX66McxFhQZPbHlovwTBHL2ZOlpde2UJlZuTrDzrZS7bLZx2i5rP1me8X9\n0ECUDvpp8GR2zuqv7VTnE0ug0NOR0rvedQlYK59eP2hs0+GQB171maZT+8JpPNvF/cLTDfbNHg+q\nNxF+8cr0dC3iv2SLFRPZPf6em+EUCygsLV9rMKHBgb3j2Qx+lwMEHmyyo7tttQ0PwHAQZHUwe2FY\nSrdH2CA1H5zAOE35YZnp7JjyYr+NdACKv20JoBo09+xShIM8f6dt7+kr60Pd5AJT3fNBaejBkadw\n+pz/e0JhJfXIE7ym95zgIOVEDsPuRTNfHpj36q38eIbGHLGNEGjn5T+v43KZ3Cnsw45AtxPwF59T\nSpienq6MC2+MZvCwtLSEjY2Nar0tr0m1UdPt7W3MzMz0GQMOvnh6lgGJdlImb9pbRxM8IxvN/HAa\nbkfVJ89w6rvcZpq3p2daFt/z6tXQ8Chyunxtv7XPqqGPDD8TA486MJFzN9Cwk+jUcQJ7T1ezfHd2\ndqrRvZWVlQoQ2BG0PEtkm4btHtsE+2/p+JRKK5v54YGPnHPf4S4WVPAX4Tn4UHkwYNH+r3JUO8LB\nibUJz3xbHbhcbXML+nT5HNs/AxyW3vhmm28zfTwyzXVVO1UHEgchD3B6edv/aNlnyW+qzWrocMjD\nClE7cXpgr5/08FcESEttq8+igQbPR3o2NtKl6L7a41arhVOnTlWndALd5XQ2Y8TLdXlGiG2nnRrH\nnz5gH258su3mP7ORNgC1urqKF198sS9YAm7bITvAwcq1j1zboJUFUxwocUBk/Ns2BrOBdt/y172h\nLDcLdkwuKl898Ebb0mTDB3RZHSP/xrg2am8vsNKy63TC89MHoaEHR8qYFwywMOqCCLunBiACLJa+\nNNIXGXzPkCiPEXCNHFwdn6o0HnBm0Kz3NL11bgYItpdA15Wurq5Wy9rsRBbrIPYlaOvkZmg2NjbQ\nbrerGSTrnLzEzIyGjUjzaA87ZR15YfDEAEtJgSG3leqWF4Bwe3hp+Toy+ExsOKKyo3ciZ9LQ8Miz\nNR74MzKwXQcM64BHqS/r+3ZvYmKiL5iwZzzqB6BvCazZOlvywH3Llr8Ctx2Y9Vk+NZJHNK3/Wl4M\nHKxM1VFeKscBAQMSD5Crgy6Bc3XWbE84L+3Ldl/3DrBd5cDPSPdfer6M3+HlOhzIcbsYRcEMP1c5\nlACllz/ny2VFdskD414+DQ2XvMDBS6NAXXUo0pGS7VJf5+klYw8P5A6iT0aKQbzBZv2tem2HK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kp0W6n+wayF8O5A8C+b/05PUYkI/VtOVzQJ6Te3/X5NW7frjXtq+ldmce/qjVrddupgNVewH5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J6Q+BJBkZ8PnEyon5USywj1PBP4RoznQ8CZEu8B/oDu52JxVN67CEryz4BhwFrggUyONcBp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\nYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BJJlLR0hPYp1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(predictions, axis=1) # Transform predictions into 1-D array with label number"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Tdza8t63PB7V",
        "colab_type": "code",
        "outputId": "ab5c6770-09d9-40cd-c92c-142c41af6819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "cell_type": "code",
      "source": [
        "# H = Horizontal\n",
        "# V = Vertical\n",
        "\n",
        "pd.DataFrame(confusion_matrix(y_test, y_pred), \n",
        "             columns=[\"Predicted Thumb Down\", \"Predicted Palm (H)\", \"Predicted L\", \"Predicted Fist (H)\", \"Predicted Fist (V)\", \"Predicted Thumbs up\", \"Predicted Index\", \"Predicted OK\", \"Predicted Palm (V)\", \"Predicted C\"],\n",
        "             index=[\"Actual Thumb Down\", \"Actual Palm (H)\", \"Actual L\", \"Actual Fist (H)\", \"Actual Fist (V)\", \"Actual Thumbs up\", \"Actual Index\", \"Actual OK\", \"Actual Palm (V)\", \"Actual C\"])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Thumb Down</th>\n",
              "      <th>Predicted Palm (H)</th>\n",
              "      <th>Predicted L</th>\n",
              "      <th>Predicted Fist (H)</th>\n",
              "      <th>Predicted Fist (V)</th>\n",
              "      <th>Predicted Thumbs up</th>\n",
              "      <th>Predicted Index</th>\n",
              "      <th>Predicted OK</th>\n",
              "      <th>Predicted Palm (V)</th>\n",
              "      <th>Predicted C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Thumb Down</th>\n",
              "      <td>604</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Palm (H)</th>\n",
              "      <td>0</td>\n",
              "      <td>617</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual L</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>621</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Fist (H)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>605</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Fist (V)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>596</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Thumbs up</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Index</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>568</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual OK</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>586</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Palm (V)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>591</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual C</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>611</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Predicted Thumb Down  Predicted Palm (H)  Predicted L  \\\n",
              "Actual Thumb Down                   604                   0            0   \n",
              "Actual Palm (H)                       0                 617            0   \n",
              "Actual L                              0                   0          621   \n",
              "Actual Fist (H)                       0                   0            0   \n",
              "Actual Fist (V)                       0                   0            0   \n",
              "Actual Thumbs up                      0                   0            0   \n",
              "Actual Index                          0                   0            0   \n",
              "Actual OK                             0                   0            0   \n",
              "Actual Palm (V)                       0                   0            0   \n",
              "Actual C                              0                   0            0   \n",
              "\n",
              "                   Predicted Fist (H)  Predicted Fist (V)  \\\n",
              "Actual Thumb Down                   0                   0   \n",
              "Actual Palm (H)                     1                   0   \n",
              "Actual L                            0                   0   \n",
              "Actual Fist (H)                   605                   0   \n",
              "Actual Fist (V)                     0                 596   \n",
              "Actual Thumbs up                    0                   0   \n",
              "Actual Index                        0                   0   \n",
              "Actual OK                           0                   0   \n",
              "Actual Palm (V)                     0                   0   \n",
              "Actual C                            0                   0   \n",
              "\n",
              "                   Predicted Thumbs up  Predicted Index  Predicted OK  \\\n",
              "Actual Thumb Down                    0                0             0   \n",
              "Actual Palm (H)                      0                0             0   \n",
              "Actual L                             0                0             0   \n",
              "Actual Fist (H)                      0                0             0   \n",
              "Actual Fist (V)                      0                0             0   \n",
              "Actual Thumbs up                   600                0             0   \n",
              "Actual Index                         0              568             0   \n",
              "Actual OK                            0                0           586   \n",
              "Actual Palm (V)                      0                0             0   \n",
              "Actual C                             0                0             0   \n",
              "\n",
              "                   Predicted Palm (V)  Predicted C  \n",
              "Actual Thumb Down                   0            0  \n",
              "Actual Palm (H)                     0            0  \n",
              "Actual L                            0            0  \n",
              "Actual Fist (H)                     0            0  \n",
              "Actual Fist (V)                     0            0  \n",
              "Actual Thumbs up                    0            0  \n",
              "Actual Index                        0            0  \n",
              "Actual OK                           0            0  \n",
              "Actual Palm (V)                   591            0  \n",
              "Actual C                            0          611  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "sVd6-ROyUY6e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "\n",
        "Based on the results presented in the previous section, we can conclude that our algorithm successfully classifies different hand gestures images with enough confidence (>95%) based on a Deep Learning model.  \n",
        "\n",
        "The accuracy of our model is directly influenced by a few aspects of our problem. The gestures presented are reasonably distinct, the images are clear and without background. Also, there is a reasonable quantity of images, which makes our model more robust. The drawback is that for different problems, we would probably need more data to stir the parameters of our model into a better direction. Moreover, a deep learning model is very hard to interpret, given it's abstractions.  \n",
        "However, by using this approach it becomes much more easier to start working on the actual problem, since we don't have to account for feature engineering. This means that we don't need to pre-process the images with edge or blob detectors to extract the important features; the CNN does it for us. Also, it can be adapted to new problems relatively easily, with generally good performance.\n",
        "\n",
        "As mentioned, another approach to this problem would be to use feature engineering, such as binary thresholding (check area of the hand), circle detection and others to detect unique characteristics on the images. However, with our CNN approach, we don't have to worry about any of these."
      ]
    },
    {
      "metadata": {
        "id": "TrqIdPIhknYZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Any doubts? Feel free to send questions/issues on [the Github repository](https://github.com/filipefborba/HandRecognition)!"
      ]
    }
  ]
}