5. Keras.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/DataScienceUB/DeepLearningMaster2019/blob/master/5.%20Keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "yxTtoHZdQEzw",
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      "cell_type": "markdown",
      "source": [
        "# `keras`\n",
        "\n",
        "> Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow, CNTK or Theano. It was developed with a focus on enabling fast experimentation.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "cGkk9uf_QZ-o",
        "colab_type": "code",
        "outputId": "03916255-68a9-4e04-8bb7-91dbcc33a069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "print(tf.VERSION)\n",
        "print(tf.keras.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n",
            "2.2.4-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-bme-ciBSrmX",
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      },
      "cell_type": "markdown",
      "source": [
        "## Definition of your model and optimization process"
      ]
    },
    {
      "metadata": {
        "id": "KXtqznrhQEzz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The core data structure of Keras is a model, a way to organize layers. The main type of model is the ``Sequential model``, a linear stack of layers. "
      ]
    },
    {
      "metadata": {
        "id": "y2RzbfJIQpHZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aIHoW-hTQEz0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Stacking layers is as easy as ``.add()``:\n"
      ]
    },
    {
      "metadata": {
        "id": "npq3S6jtQy-c",
        "colab_type": "code",
        "colab": {
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          "height": 243
        },
        "outputId": "fffb6f79-6950-4599-8990-553b4aa914d6"
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      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential()\n",
        "\n",
        "# Adds a densely-connected layer with 64 units to the model:\n",
        "model.add(layers.Dense(64, activation='relu', input_shape=(32,)))\n",
        "\n",
        "# Add another:\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "\n",
        "# Add a softmax layer with 10 output units:\n",
        "model.add(layers.Dense(10, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_21 (Dense)             (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 6,922\n",
            "Trainable params: 6,922\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
        "id": "bvZW0RY5QEz1",
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      "cell_type": "markdown",
      "source": [
        "Once your model looks good, configure its learning process with ``.compile()``:"
      ]
    },
    {
      "metadata": {
        "id": "Jb-1515bRtYR",
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        "outputId": "617baa1e-db76-4343-a880-cbe0a71d7ee0",
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      "cell_type": "code",
      "source": [
        "# Alternative syntax for adding layers\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    \n",
        "# Adds a densely-connected layer with 64 units to the model:\n",
        "layers.Dense(32, activation='relu', input_shape=(32,)),\n",
        "# Add another:\n",
        "layers.Dense(32, activation='relu'),\n",
        "# Add a softmax layer with 10 output units:\n",
        "layers.Dense(10, activation='softmax')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(optimizer=tf.train.AdamOptimizer(0.001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_27 (Dense)             (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 32)                1056      \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 2,442\n",
            "Trainable params: 2,442\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
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      "source": [
        "`tf.keras.Model.compile` takes three important arguments:\n",
        "\n",
        "* `optimizer`: This object specifies the training procedure. Pass it optimizer\n",
        "  instances from the `tf.train` module, such as\n",
        "  `tf.train.AdamOptimizer`, `tf.train.RMSPropOptimizer`, or\n",
        "  `tf.train.GradientDescentOptimizer`.\n",
        "* `loss`: The function to minimize during optimization. Common choices include\n",
        "  mean square error (`mse`), `categorical_crossentropy`, and\n",
        "  `binary_crossentropy`. Loss functions are specified by name or by\n",
        "  passing a callable object from the `tf.keras.losses` module.\n",
        "* `metrics`: Used to monitor training. These are string names or callables from\n",
        "  the `tf.keras.metrics` module."
      ]
    },
    {
      "metadata": {
        "id": "88OWboaDQEz2",
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      "cell_type": "markdown",
      "source": [
        "Other examples:\n",
        "\n",
        "```python\n",
        "# Configure a model for mean-squared error regression.\n",
        "model.compile(optimizer=tf.train.AdamOptimizer(0.01),\n",
        "              loss='mse',       # mean squared error\n",
        "              metrics=['mae'])  # mean absolute error\n",
        "\n",
        "# Configure a model for categorical classification.\n",
        "model.compile(optimizer=tf.train.RMSPropOptimizer(0.01),\n",
        "              loss=tf.keras.losses.categorical_crossentropy,\n",
        "              metrics=[tf.keras.metrics.categorical_accuracy])\n",
        "```\n",
        "\n",
        "If you need to, you can further configure your optimizer.\n",
        "\n",
        "```Python\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True))\n",
        "```"
      ]
    },
    {
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      },
      "cell_type": "markdown",
      "source": [
        "## Data Input and model fitting.\n",
        "\n",
        "For small datasets, use in-memory [NumPy](https://www.numpy.org/)\n",
        "arrays to train and evaluate a model. \n",
        "\n",
        "The model is \"fit\" to the training data\n",
        "using the `fit` method:"
      ]
    },
    {
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        "outputId": "9c1d3a35-7eae-4e38-f7cc-94caf96a4550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "data = np.random.random((1000, 32))\n",
        "labels = np.random.random((1000, 10))\n",
        "\n",
        "model.fit(data, labels, epochs=10, batch_size=32)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1000/1000 [==============================] - 0s 155us/sample - loss: 11.6158 - acc: 0.0920\n",
            "Epoch 2/10\n",
            "1000/1000 [==============================] - 0s 45us/sample - loss: 11.5128 - acc: 0.1160\n",
            "Epoch 3/10\n",
            "1000/1000 [==============================] - 0s 47us/sample - loss: 11.5026 - acc: 0.1020\n",
            "Epoch 4/10\n",
            "1000/1000 [==============================] - 0s 43us/sample - loss: 11.4973 - acc: 0.1090\n",
            "Epoch 5/10\n",
            "1000/1000 [==============================] - 0s 49us/sample - loss: 11.4941 - acc: 0.1080\n",
            "Epoch 6/10\n",
            "1000/1000 [==============================] - 0s 52us/sample - loss: 11.4911 - acc: 0.1100\n",
            "Epoch 7/10\n",
            "1000/1000 [==============================] - 0s 48us/sample - loss: 11.4890 - acc: 0.1170\n",
            "Epoch 8/10\n",
            "1000/1000 [==============================] - 0s 49us/sample - loss: 11.4870 - acc: 0.1250\n",
            "Epoch 9/10\n",
            "1000/1000 [==============================] - 0s 52us/sample - loss: 11.4855 - acc: 0.1290\n",
            "Epoch 10/10\n",
            "1000/1000 [==============================] - 0s 50us/sample - loss: 11.4837 - acc: 0.1380\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd48dfe85c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
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      "source": [
        "`tensorflow.keras.Model.fit` takes three important arguments:\n",
        "\n",
        "* `epochs`: Training is structured into *epochs*. \n",
        "* `batch_size`: This integer\n",
        "  specifies the size of each batch. Be aware that the last batch may be smaller\n",
        "  if the total number of samples is not divisible by the batch size.\n",
        "* `validation_data`: Passing this argument—a tuple of inputs\n",
        "  and labels—allows the model to display the loss and metrics in inference mode\n",
        "  for the passed data, at the end of each epoch.\n",
        "\n",
        "Here's an example using `validation_data`:"
      ]
    },
    {
      "metadata": {
        "id": "zd8UjPlyTd5c",
        "colab_type": "code",
        "outputId": "99bb4de8-fd65-4cb5-ee36-f5bd137e4208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "data = np.random.random((1000, 32))\n",
        "labels = np.random.random((1000, 10))\n",
        "\n",
        "val_data = np.random.random((100, 32))\n",
        "val_labels = np.random.random((100, 10))\n",
        "\n",
        "model.fit(data, labels, epochs=10, batch_size=32,\n",
        "          validation_data=(val_data, val_labels))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1000 samples, validate on 100 samples\n",
            "Epoch 1/10\n",
            "1000/1000 [==============================] - 0s 119us/sample - loss: 11.5181 - acc: 0.1020 - val_loss: 11.8104 - val_acc: 0.1300\n",
            "Epoch 2/10\n",
            "1000/1000 [==============================] - 0s 52us/sample - loss: 11.5126 - acc: 0.1170 - val_loss: 11.8084 - val_acc: 0.1000\n",
            "Epoch 3/10\n",
            "1000/1000 [==============================] - 0s 70us/sample - loss: 11.5101 - acc: 0.1320 - val_loss: 11.8098 - val_acc: 0.0800\n",
            "Epoch 4/10\n",
            "1000/1000 [==============================] - 0s 57us/sample - loss: 11.5078 - acc: 0.1370 - val_loss: 11.8074 - val_acc: 0.1000\n",
            "Epoch 5/10\n",
            "1000/1000 [==============================] - 0s 53us/sample - loss: 11.5060 - acc: 0.1400 - val_loss: 11.8070 - val_acc: 0.0800\n",
            "Epoch 6/10\n",
            "1000/1000 [==============================] - 0s 59us/sample - loss: 11.5044 - acc: 0.1530 - val_loss: 11.8057 - val_acc: 0.1000\n",
            "Epoch 7/10\n",
            "1000/1000 [==============================] - 0s 54us/sample - loss: 11.5026 - acc: 0.1520 - val_loss: 11.8053 - val_acc: 0.1200\n",
            "Epoch 8/10\n",
            "1000/1000 [==============================] - 0s 54us/sample - loss: 11.5014 - acc: 0.1500 - val_loss: 11.8065 - val_acc: 0.1000\n",
            "Epoch 9/10\n",
            "1000/1000 [==============================] - 0s 52us/sample - loss: 11.4993 - acc: 0.1520 - val_loss: 11.8062 - val_acc: 0.1000\n",
            "Epoch 10/10\n",
            "1000/1000 [==============================] - 0s 52us/sample - loss: 11.4978 - acc: 0.1650 - val_loss: 11.8080 - val_acc: 0.0900\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd48d34f978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "jRE5P97jTvV9",
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      },
      "cell_type": "markdown",
      "source": [
        "If you have to process large datasets you must use `tf` Datasets API. `feed-dict` is the slowest possible way to pass information to TensorFlow.\n",
        "\n",
        "A `Dataset` can be used to represent an input pipeline as a collection of elements (nested structures of tensors) and a \"logical plan\" of transformations that act on those elements.\n",
        "\n",
        "In order to use a Dataset we need three steps:\n",
        "\n",
        "+ Importing Data. Create a `Dataset` instance from some data.\n",
        "+ Create an Iterator. By using the created dataset to make an `Iterator` instance to iterate through the dataset.\n",
        "+ Consuming Data. By using the created iterator we can get the elements from the dataset to feed the model.\n"
      ]
    },
    {
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        "id": "Qb_E7q45UAbI",
        "colab_type": "code",
        "outputId": "c6eb0971-ba36-4175-d565-4a21f5c06c44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "cell_type": "code",
      "source": [
        "# Instantiates a toy dataset instance:\n",
        "dataset = tf.data.Dataset.from_tensor_slices((data, labels))\n",
        "\n",
        "\n",
        "# With the Dataset API we can use the method batch(BATCH_SIZE) that \n",
        "# automatically batches the dataset with the provided size. \n",
        "# The default value is one.\n",
        "dataset = dataset.batch(10)\n",
        "\n",
        "\n",
        "# Using .repeat() we can specify the number of times we want the dataset to be \n",
        "# iterated. If no parameter is passed it will loop forever, usually is good to \n",
        "# just loop forever and directly control the number of epochs with a standard \n",
        "# loop.\n",
        "dataset = dataset.repeat()\n",
        "\n",
        "# We are using an iterator (Dataset). In this case we have to specify `steps_per_epoch` \n",
        "# when calling `fit` on a dataset. `steps_per_epoch` = Total number of steps \n",
        "# (batches of samples) to yield from generator before declaring one epoch \n",
        "# finished and starting the next epoch. \n",
        "# It should typically be equal to the number of unique samples of your \n",
        "# dataset divided by the batch size.\n",
        "model.fit(dataset, epochs=4, steps_per_epoch=100)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "100/100 [==============================] - 0s 3ms/step - loss: 11.4093 - acc: 0.2150\n",
            "Epoch 2/4\n",
            "100/100 [==============================] - 0s 1ms/step - loss: 11.4069 - acc: 0.2120\n",
            "Epoch 3/4\n",
            "100/100 [==============================] - 0s 1ms/step - loss: 11.4043 - acc: 0.2150\n",
            "Epoch 4/4\n",
            "100/100 [==============================] - 0s 1ms/step - loss: 11.4007 - acc: 0.2170\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd48baa8438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "oE3xO_piVH8P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can, of course, initialise our dataset with some tensor:\n",
        "\n",
        "```python\n",
        "# using a tensor\n",
        "dataset = tf.data.Dataset.from_tensor_slices(tf.random_uniform([100, 2]))\n",
        "```\n",
        "\n",
        "There are some other options: from a `Placeholder`, from a generator, from a `csv` file, etc.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "1mAUEQbnYImc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Evaluate and predict\n",
        "\n",
        "The `tf.keras.Model.evaluate` and `tf.keras.Model.predict` methods can use NumPy\n",
        "data and a `tf.data.Dataset`.\n",
        "\n",
        "To *evaluate* the inference-mode loss and metrics for the data provided:"
      ]
    },
    {
      "metadata": {
        "id": "N0x8ULFQYJ1_",
        "colab_type": "code",
        "outputId": "b1bd5993-9aa2-4f16-be40-3daa572688ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "data = np.random.random((1000, 32))\n",
        "labels = np.random.random((1000, 10))\n",
        "\n",
        "dataset = tf.data.Dataset.from_tensor_slices((data, labels))\n",
        "dataset = dataset.batch(10)\n",
        "dataset = dataset.repeat()\n",
        "\n",
        "model.evaluate(data, labels, batch_size=10)\n",
        "\n",
        "model.evaluate(dataset, steps=100)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1000/1000 [==============================] - 0s 163us/sample - loss: 11.7825 - acc: 0.1140\n",
            "100/100 [==============================] - 0s 2ms/step - loss: 11.7825 - acc: 0.1140\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[11.782473077774048, 0.114]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "pSryJ0YXYR6a",
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      },
      "cell_type": "markdown",
      "source": [
        "And to *predict* the output of the last layer in inference for the data provided,\n",
        "as a NumPy array:"
      ]
    },
    {
      "metadata": {
        "id": "r41ioMVWYTBM",
        "colab_type": "code",
        "outputId": "b5b5ffd4-6c93-46b7-a364-2d0d831804c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "result = model.predict(data, batch_size=32)\n",
        "print(result.shape)\n",
        "print(result[0])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 10)\n",
            "[0.09417029 0.09438135 0.09979257 0.09604443 0.08704696 0.12432702\n",
            " 0.09390688 0.0984251  0.12549871 0.08640669]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MZi4CXR7QEz6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Example: MNIST  MLP"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:11:33.329607Z",
          "start_time": "2019-03-05T12:10:46.072748Z"
        },
        "id": "yARoL5izQEz7",
        "colab_type": "code",
        "outputId": "29467a4d-e60d-48b0-fd9f-53960e974010",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1112
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import numpy as np\n",
        "\n",
        "tf.reset_default_graph()\n",
        "\n",
        "np.random.seed(1337)  # for reproducibility\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.utils import np_utils\n",
        "\n",
        "batch_size = 128\n",
        "nb_classes = 10\n",
        "nb_epoch = 10\n",
        "\n",
        "# the data, shuffled and split between train and test sets\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "X_train = X_train.reshape(60000, 784)\n",
        "X_test = X_test.reshape(10000, 784)\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255\n",
        "X_test /= 255\n",
        "\n",
        "print(X_train.shape[0], 'train samples')\n",
        "print(X_test.shape[0], 'test samples')\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
        "Y_test  = np_utils.to_categorical(y_test, nb_classes)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(512, input_shape=(784,)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(512))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "# print model characteristics\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=RMSprop(lr=0.01),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, \n",
        "                    Y_train,\n",
        "                    batch_size=batch_size, \n",
        "                    epochs=nb_epoch,\n",
        "                    verbose=1, \n",
        "                    validation_data=(X_test, Y_test))\n",
        "\n",
        "score = model.evaluate(X_test, Y_test, verbose=0)\n",
        "\n",
        "print('\\n')\n",
        "print('Test score:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 1s 0us/step\n",
            "60000 train samples\n",
            "10000 test samples\n",
            "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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 512)               401920    \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                5130      \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 669,706\n",
            "Trainable params: 669,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "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 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 10s 171us/step - loss: 2.3086 - acc: 0.7583 - val_loss: 0.2949 - val_acc: 0.9272\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 10s 160us/step - loss: 0.5859 - acc: 0.8936 - val_loss: 0.2524 - val_acc: 0.9448\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 10s 159us/step - loss: 0.6096 - acc: 0.9030 - val_loss: 0.3052 - val_acc: 0.9434\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 10s 162us/step - loss: 0.6714 - acc: 0.9065 - val_loss: 0.3353 - val_acc: 0.9492\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 10s 160us/step - loss: 0.6863 - acc: 0.9090 - val_loss: 0.3382 - val_acc: 0.9508\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 10s 160us/step - loss: 0.6904 - acc: 0.9128 - val_loss: 0.3003 - val_acc: 0.9545\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 10s 163us/step - loss: 0.6964 - acc: 0.9175 - val_loss: 0.4028 - val_acc: 0.9525\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 10s 160us/step - loss: 0.7562 - acc: 0.9145 - val_loss: 0.3690 - val_acc: 0.9528\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 9s 158us/step - loss: 0.7430 - acc: 0.9201 - val_loss: 0.3592 - val_acc: 0.9572\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 9s 158us/step - loss: 0.7628 - acc: 0.9241 - val_loss: 0.4533 - val_acc: 0.9517\n",
            "\n",
            "\n",
            "Test score: 0.4533088570899373\n",
            "Test accuracy: 0.9517\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HiGrUqawaMYp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## The `dropout layer`\n",
        "\n",
        "Dropout is a a technique used to tackle overfitting . The `Dropout` method in `keras.layers` module takes in a float between 0 and 1, which is the fraction of the neurons to drop. \n",
        "\n",
        "<center>\n",
        "<img src=\"https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/master/images/dropout.png\" alt=\"\" style=\"width: 400px;\"/> \n",
        "  </center>\n",
        "  <center>\n",
        " (Source: http://cs231n.stanford.edu/)\n",
        "</center>\n",
        "\n",
        "\n",
        "A dropout layer randomly sets some of the dimensions of your input vector to be zero with some probability $𝑘𝑒𝑒𝑝_{𝑝𝑟𝑜𝑏}$. A dropout layer does not have any trainable parameters i.e. nothing gets updated during backward pass of backpropagation.\n",
        "\n",
        "To ensure that expected sum of vectors fed to this layer remains the same if no dropout was applied, the remaining dimensions which are not set to zero are scaled by $1/𝑘𝑒𝑒𝑝_{𝑝𝑟𝑜𝑏}$.\n",
        "\n",
        "Dropout is only applied during training, and you need to rescale the remaining neuron activations. "
      ]
    },
    {
      "metadata": {
        "id": "51CiIGkHQE0H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## The ``merge`` layer\n",
        "\n",
        "Multiple Sequential instances can be merged into a single output via a ``Merge`` layer. The output is a layer that can be added as first layer in a new ``Sequential`` model. For instance, here's a model with two separate input branches getting merged:\n",
        "\n",
        "```python\n",
        "from keras.layers import Merge\n",
        "\n",
        "left_branch = Sequential()\n",
        "left_branch.add(Dense(32, input_dim=784))\n",
        "\n",
        "right_branch = Sequential()\n",
        "right_branch.add(Dense(32, input_dim=784))\n",
        "\n",
        "merged = Merge([left_branch, right_branch], mode='concat')\n",
        "\n",
        "final_model = Sequential()\n",
        "final_model.add(merged)\n",
        "final_model.add(Dense(10, activation='softmax'))\n",
        "```\n",
        "\n",
        "![alt text](https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/merge.png?raw=1)"
      ]
    },
    {
      "metadata": {
        "id": "lUoptiW5QE0I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Merge layers: ``add``, ``subtract``, ...\n",
        "\n",
        "\n",
        "The Merge layer supports a number of pre-defined modes:\n",
        "\n",
        "+ `sum` (default): element-wise sum\n",
        "concat: tensor concatenation. You can specify the concatenation axis via the argument concat_axis.\n",
        "+ `mul`: element-wise multiplication\n",
        "+ `ave`: tensor average\n",
        "+ `dot`: dot product. You can specify which axes to reduce along via the argument dot_axes.\n",
        "+ `cos`: cosine proximity between vectors in 2D tensors."
      ]
    },
    {
      "metadata": {
        "id": "Wdcv_yvcQE0R",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Keras functional API\n",
        "\n",
        "The Keras ``functional`` API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers.\n",
        "\n",
        "The ``Sequential`` model is probably a better choice to implement such a network, but it helps to start with something really simple.\n",
        "\n",
        "Using ``Model`` class:\n",
        "\n",
        "+ A layer instance is callable (on a tensor), and it returns a tensor\n",
        "+ ``Input`` tensor(s) and output tensor(s) can then be used to define a ``Model``\n",
        "+ Such a model can be trained just like Keras Sequential models."
      ]
    },
    {
      "metadata": {
        "id": "ZLz7HfbNeBPP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "c8aa995b-4c2d-403a-99ca-3d92599b61fe"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import numpy as np\n",
        "\n",
        "tf.keras.backend.clear_session()  \n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model\n",
        "from keras.utils import np_utils\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "X_train = X_train.reshape(60000, 784)\n",
        "data = X_train/255\n",
        "labels = np_utils.to_categorical(y_train, 10)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 4s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "X6WK7G-mdiL0",
        "colab_type": "code",
        "outputId": "ae63d5b0-fc1f-407d-c2aa-07ad766c5a53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        }
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "# this returns a tensor\n",
        "inputs = Input(shape=(784,))\n",
        "\n",
        "# a layer instance is callable on a tensor, and returns a tensor\n",
        "x = Dense(64, activation='relu')(inputs)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "predictions = Dense(10, activation='softmax')(x)\n",
        "\n",
        "# this creates a model that includes\n",
        "# the Input layer and three Dense layers\n",
        "model = Model(input=inputs, output=predictions)\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.fit(data, \n",
        "          labels,\n",
        "          batch_size=128, \n",
        "          epochs=5)  # starts training"
      ],
      "execution_count": 2,
      "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",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 55,050\n",
            "Trainable params: 55,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 2s 40us/step - loss: 0.3604 - acc: 0.9006\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.1709 - acc: 0.9492\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.1268 - acc: 0.9621\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.1022 - acc: 0.9698\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0843 - acc: 0.9745\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fce95e42be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "YHiwfriqQE0S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "All models are callable, just like layers.\n",
        "\n",
        "With the functional API, it is easy to re-use trained models: you can treat any model as if it were a layer, by calling it on a tensor. Note that by calling a model you aren't just re-using the architecture of the model, you are also re-using its weights.\n",
        "\n",
        "```python\n",
        "x = Input(shape=(784,))\n",
        "# this works, and returns the 10-way softmax we defined above.\n",
        "y = model(x)\n",
        "```"
      ]
    },
    {
      "metadata": {
        "id": "B8BTfwxVQE0U",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##  Siamese MLP on pairs of digits from the MNIST \n",
        "\n",
        "(Based on: https://github.com/fchollet/keras/blob/master/examples/mnist_siamese_graph.py)\n",
        "\n",
        "Siamese networks are commonly used in image comparison applications such as face or signature verification. They can also be used in language processing, times series analysis, etc.\n",
        "\n",
        "In a typical Siamese network a large part of the network is duplicated at the base to allow multiple inputs to go through identical layers. \n",
        "\n",
        "This example shows how to teach a neural network to map an image from the MNIST dataset to a 2D point, while **trying to minimize the distance between points of the same class and maximize the distance between points of different classes**."
      ]
    },
    {
      "metadata": {
        "id": "mnmUEne_QE0V",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Siamese network architecture is a way of learning how to embed samples into lower-dimensions based on similarity computed with features learned by a feature network.\n",
        "\n",
        "The feature network is the architecture we intend to fine-tune in this setting. \n",
        "\n",
        "Let's suppose we want to embed images. Given two images $X_1$ and $X_2$, we feed into the feature network $G_W$ and compute corresponding feature vectors $G_W(X_1)$ and $G_W(X_2)$. The final layer computes pair-wise distance between computed features $E_W = || G_W(X_1) - G_W(X_2) ||_{1}$ and final loss layer $L$ considers whether these two images are from the same class (label $1$) or not (label $0$).\n",
        "\n",
        "![alt text](https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/siamese1.png?raw=1)"
      ]
    },
    {
      "metadata": {
        "id": "9NVK0jyCQE0X",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In the original [paper](http://yann.lecun.com/exdb/publis/pdf/chopra-05.pdf) it was proposed the **Contrastive Loss Function**: \n",
        "\n",
        "$$ L(W,(Y,X_1,X_2)^i) = (1 - Y) \\times L_S(E_W(X_1,X_2)^i) + Y \\times L_D(E_W(X_1,X_2)^i) $$\n",
        "\n",
        "where $L_S$ is the partial loss function for a \"same-class\" pair and $L_D$ is the partial loss function for a \"different-class\" pair.\n",
        "\n",
        "$L_S$ and $L_D$ should be designed in such a way that the minimization of $L$ will decrease the distance in the embedding space of \"same-class\" pairs and increase it in the case of \"different-class\" pairs:\n",
        "\n",
        "$$ L_S = \\frac{1}{2} E_W^2 $$\n",
        "$$ L_D = \\frac{1}{2} \\{ \\mbox{max }(0,1-E_W) \\}^2 $$"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:22.544377Z",
          "start_time": "2019-03-05T12:11:49.850575Z"
        },
        "id": "tgINnEdyQE0Z",
        "colab_type": "code",
        "outputId": "d9571fd5-3874-4c14-bb7e-6ded5e5d9698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf \n",
        "from tensorflow import keras\n",
        "import random\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Dropout, Input, Lambda\n",
        "from keras.optimizers import RMSprop\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "\n",
        "tf.reset_default_graph()\n",
        "\n",
        "def euclidean_distance(vects):\n",
        "    x, y = vects\n",
        "    return tf.math.sqrt(tf.math.reduce_sum(tf.math.square(x - y), axis=1, keepdims=True))\n",
        "\n",
        "\n",
        "def eucl_dist_output_shape(shapes):\n",
        "    shape1, shape2 = shapes\n",
        "    return (shape1[0], 1)\n",
        "\n",
        "\n",
        "def contrastive_loss(y_true, y_pred):\n",
        "    '''Contrastive loss from Hadsell-et-al.'06\n",
        "    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
        "    '''\n",
        "    margin = 1\n",
        "    return tf.math.reduce_mean(y_true * tf.math.square(y_pred) \n",
        "                  + (1 - y_true) * tf.math.square(tf.math.maximum(margin - y_pred, 0)))\n",
        "\n",
        "\n",
        "def create_pairs(x, digit_indices):\n",
        "    '''Positive and negative pair creation.\n",
        "    Alternates between positive and negative pairs.\n",
        "    '''\n",
        "    pairs = []\n",
        "    labels = []\n",
        "    n = min([len(digit_indices[d]) for d in range(10)]) - 1\n",
        "    for d in range(10):\n",
        "        for i in range(n):\n",
        "            z1, z2 = digit_indices[d][i], digit_indices[d][i + 1]\n",
        "            pairs += [[x[z1], x[z2]]]\n",
        "            inc = random.randrange(1, 10)\n",
        "            dn = (d + inc) % 10\n",
        "            z1, z2 = digit_indices[d][i], digit_indices[dn][i]\n",
        "            pairs += [[x[z1], x[z2]]]\n",
        "            labels += [1, 0]\n",
        "    return np.array(pairs), np.array(labels)\n",
        "\n",
        "\n",
        "def create_base_network(input_dim):\n",
        "    '''\n",
        "    Base network to be shared (eq. to feature extraction).\n",
        "    '''\n",
        "    seq = Sequential()\n",
        "    seq.add(Dense(128, input_shape=(input_dim,), activation='relu'))\n",
        "    seq.add(Dropout(0.1))\n",
        "    seq.add(Dense(128, activation='relu'))\n",
        "    seq.add(Dropout(0.1))\n",
        "    seq.add(Dense(2, activation=None,name='emb'))\n",
        "    return seq\n",
        "\n",
        "\n",
        "def compute_accuracy(predictions, labels):\n",
        "    '''Compute classification accuracy with a fixed threshold on distances.\n",
        "    '''\n",
        "    return labels[predictions.ravel() < 0.5].mean()\n",
        "\n",
        "\n",
        "# the data, shuffled and split between train and test sets\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "X_train = X_train.reshape(60000, 784)\n",
        "X_test = X_test.reshape(10000, 784)\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255\n",
        "X_test /= 255\n",
        "input_dim = 784\n",
        "nb_epoch = 10\n",
        "\n",
        "# create training+test positive and negative pairs\n",
        "digit_indices = [np.where(y_train == i)[0] for i in range(10)]\n",
        "tr_pairs, tr_y = create_pairs(X_train, digit_indices)\n",
        "\n",
        "digit_indices = [np.where(y_test == i)[0] for i in range(10)]\n",
        "te_pairs, te_y = create_pairs(X_test, digit_indices)\n",
        "\n",
        "# network definition\n",
        "base_network = create_base_network(input_dim)\n",
        "\n",
        "input_a = Input(shape=(input_dim,))\n",
        "input_b = Input(shape=(input_dim,))\n",
        "\n",
        "# because we re-use the same instance `base_network`,\n",
        "# the weights of the network\n",
        "# will be shared across the two branches\n",
        "processed_a = base_network(input_a)\n",
        "processed_b = base_network(input_b)\n",
        "\n",
        "distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b])\n",
        "\n",
        "model = Model(inputs=[input_a, input_b], outputs=distance)\n",
        "\n",
        "# train\n",
        "rms = RMSprop()\n",
        "model.compile(loss=contrastive_loss, optimizer=rms)\n",
        "model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,\n",
        "          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),\n",
        "          batch_size=128,\n",
        "          epochs=nb_epoch)\n",
        "\n",
        "# compute final accuracy on training and test sets\n",
        "pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])\n",
        "tr_acc = compute_accuracy(pred, tr_y)\n",
        "pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])\n",
        "te_acc = compute_accuracy(pred, te_y)\n",
        "\n",
        "print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))\n",
        "print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "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 108400 samples, validate on 17820 samples\n",
            "Epoch 1/10\n",
            "108400/108400 [==============================] - 7s 60us/step - loss: 0.1155 - val_loss: 0.0725\n",
            "Epoch 2/10\n",
            "108400/108400 [==============================] - 6s 57us/step - loss: 0.0747 - val_loss: 0.0596\n",
            "Epoch 3/10\n",
            "108400/108400 [==============================] - 6s 57us/step - loss: 0.0608 - val_loss: 0.0520\n",
            "Epoch 4/10\n",
            "108400/108400 [==============================] - 6s 56us/step - loss: 0.0521 - val_loss: 0.0492\n",
            "Epoch 5/10\n",
            "108400/108400 [==============================] - 6s 57us/step - loss: 0.0467 - val_loss: 0.0467\n",
            "Epoch 6/10\n",
            "108400/108400 [==============================] - 6s 57us/step - loss: 0.0424 - val_loss: 0.0446\n",
            "Epoch 7/10\n",
            "108400/108400 [==============================] - 6s 57us/step - loss: 0.0396 - val_loss: 0.0425\n",
            "Epoch 8/10\n",
            "108400/108400 [==============================] - 6s 56us/step - loss: 0.0374 - val_loss: 0.0420\n",
            "Epoch 9/10\n",
            "108400/108400 [==============================] - 6s 57us/step - loss: 0.0354 - val_loss: 0.0407\n",
            "Epoch 10/10\n",
            "108400/108400 [==============================] - 6s 56us/step - loss: 0.0334 - val_loss: 0.0393\n",
            "* Accuracy on training set: 99.43%\n",
            "* Accuracy on test set: 98.50%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MGe9Re3U6k8t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import umap\n",
        "\n",
        "digits = pd.DataFrame(X_train)\n",
        "\n",
        "embedding = umap.UMAP(n_neighbors=5,\n",
        "                      min_dist=0.2,\n",
        "                      metric='correlation').fit_transform(digits.iloc[:20000, 1:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ivd7Jrdm69ni",
        "colab_type": "code",
        "outputId": "394493b8-0b84-42fe-a1b0-6f19df4a4d7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 10))\n",
        "plt.scatter(embedding[:,0], embedding[:,1], \n",
        "            c=y_train[:20000],\n",
        "            cmap=\"Spectral\", \n",
        "            s=1.0)\n",
        "plt.setp(ax, xticks=[], yticks=[])\n",
        "plt.title(\"MNIST data embedded into two dimensions by UMAP\", fontsize=18)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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43TTNl3ZX3juJHAcELMuaUEN9CdM0P0Xl5uUC+zolTUXw3aiG1ypv1ZummY46EXxuWdb1\nSctDqFH3tdqXdVG3TMtRk4fP3U3ZfZVpmmbQShqxzc7e0i2o0ecARl1tMU2zXvsUFaw7tZQ7strv\na4GMeuyDrfWs7+dWMWq5GWpGh2RNULm89WZZluPlcvZD7Yt8YL63vCItIB9Y76Ve7Gsbkv+Gyb2A\nP+d+rEgbsPfnZ937PH+OClL/hLp4/jsqfWJiteIV37dm7KquHuv6tGMqKtXlLlMNwvwGNW/0yN2s\nmouapaBC8neywqvezwDUbfnJSfnztanI626LuvCpwsuRz6Dq3Zvq3kANKDsNNRisriDyCtQdleuo\nOaf8bVRO82vVlq/4BY594ldKelhFsi9RPTsPow7kuzyRpB4eQPW8PbW7gl4qwDOo0fNH1FJsECrn\n6ae9aEt1y1En+ioj9U3T7MnOExSoA63Grgn+f0Jd5BlJy9ykdfZ03Sq83M4JwDDTNHOqtfG3pmne\nVdto571goC4Ekg0CClD5cOtRgc+5Fbl4SW251RvhDfXcp5aaKusnYKhpmkZSuSPYOVK+wliga7U8\nPUzTPN00zYe8Hh5Qk/739W6LVpQJs3/SR3Yn+e9YkauZPEgI0zSPQQUc1XNDd1dfRZ3Ho9IBJifd\n0p6MCliPp2qu7r60oeLhCdVnsDizHu3eWzNRgwsvqv6ZNk3zQW+gYL2ZptnTNM1XkvPdveNLRU5o\ndvV1vO/bRNRnMrXay2ehPtf1nnfWNM0Gpmk+a5pm9c/zeFR+7S5tqMHp1X4fhLrQS27Hh6ie0D+g\nBny9Xo96v0Id2+/0BkNV93dvO7UO3LIsax7qwucuVC5yjekA3t/zStScqs9ZlvVN9R/UXaj+3qAy\nIepFelhFJcuy4qZpvoe6Kn6nnjlX1esoNU3zZlROan1uCf4JFShPM03zCdSURwlUr8elqAP4XXuY\nmlBb22zTNJ8H/mqa5rNeG5uhDtZLUSPEsSwrzzTN+cAF3gjfDaic3vaonMLOpmkORQ2Cqch1u9E0\nzcmok0m91q0hfwuvLZNRI4TvRJ3UB6FyFZ/fj4MO8lD5qTneez8Z1av+cFLe4O2o/Luxpmnej8rX\nOxuVV3wr1H+fev7t/bxrmuZzqJPeSFSvTnJw+iBqBPho0zRvQfUe9kalWXybtN8qpjj6xDTNh1HH\ns7+i8gxr6nndXzYCA72gapllWdNM0/wY1ZtWjgouWnrvbRU1T7heV32zUQHmf1BTTCXPWTkZNWdo\nRcACwD624QvU5O8PmKYZR12EnMyuM0vsN17O6d9RF7YfeQNrDFTP3IXU8mSlOmxC9fq18o4jG1Cf\ngVtRvfvja1mv4vv2qWmao1CzTpyNCt6rzElcD3moqcZONU3zbtTnOsTO6dE+qEcdF3i3+md5dV0O\nfGAlzbtqqfmpP/BeK0d95+pkWVahaZq/Q+X0TjXVXK+rUReV16COMbd5QWld3kR9P6dalrXL3Nme\nwah5ke+oo56XUQN1r2RnHrYQdZIeVlFdxRV2Xcn0dbIs6wPUCXe3vYGWmky7D6qH6PeonLyJqPk6\nE8Awy7Lu39u21OAu1JQwI1CB8p+97Va/TXaht+xF1MCzIOqE+DDqQQDvoEbHv4uagus6r15jD9bd\nhaWmNjoB1UP7Omo/Xoh6CMEN+/bWq8hDXRBchtoPV6PmT70rqS2foebHBBWIj/badrllWf9Kqqte\n+9SyrGdRJ6e+qNu296Cmx/q6Wrk1qBzAH1A98BNQ730USXlvlmVVtLsp6m7A46ig4AV+XnegBqW9\nxs4HalyI2n83oKYWewT1uehXw4wPu63PsqzlqAn4s6k6bdA01IVgBrvOhrBXbfCCspNRQe6TqIuU\nFlTrrd3fLMt6GpUL2RwVNH/qbfcMqx4P2KhW13p2pko8h9pnz6BupfdPnkmi2noz2DnDwtuomTOO\nRX3Gn9jDNrio/Mwx7Mxhfwc1s8cZlmVN3E0VNmqg5hDU/rgT1RP5uxrKVtxK/189Pl8V7fsI9d1b\nhvpsjEddMBQDQ6yk6dDq8F9UT2xd54ervDK19vxaljUflat9mRegC7FbmuvKLBFCCCHEr4Wp5rqd\ng5qvua55nYU4ZMiVjRBCCPHrchcq5eDr3RUU4lAhOaxCCCHEQc40zQzUnNQXo3Kbh8lE+uJwIgGr\nEEIIcfDrjMrN3QRcYlmW9K6Kw4rksAohhBBCiIOa5LAKIYQQQoiDWp0pAVu3Fkn3qxBCCCGE+Nnl\n5KTVOh2m9LAKIYQQQoiDmgSsQgghhBDioCYBqxBCCCGEOKhJwCqEEEIIIQ5qErAKIYQQQoiDmgSs\nQgghhBDioCYBqxBCCCGEOKhJwCqEEEIIIQ5qErAKIYQQQoiDmgSsQgghhBDioCYBqxBCCCGEOKhJ\nwCqEEEIIIQ5qErAKIYQQQoiDmgSsQgghhBDioCYBqxBCCCGEOKhJwCqEEEIIIQ5qErAKIYQQQoiD\nmgSsQgghhBDioCYBqxBCCCGEOKhJwCqEEEIIIQ5qErAKIYQQQoiDmgSsQgghhBDioCYBqxBCCCGE\nOKhJwCqEEEIIcYhyXZfE5i3YGzdS/NTTuI5zoJu0V3wHugFCCCGEEGL/iC9ZSmLtWqLjxuPv3AkX\niE38ltDZZ+EaPuwdecS+m0xk+FkHuql7RHpYhRBCCCEOAU48TvFjo0DXcLbvIDp9JrHPPgdNw/7x\nR3zt2xMdP4Hy0WOIfj/lQDd3j0jAKoQQQghxkHAKi3Bte7fl4j/9RHz5ctxEonKZZtu4uk5i6XLC\nF5wPjgPBIESj6NkNCXTuRHzGTAJDBuPv2qVKfdEpUyj612P7/f3sLxKwCiGEEELsIbe09Gept+jB\nhykf+83O7TgOsRkzcV23SrnYlB8oefZ5Sl9/s3KZFgqRdsP1GA2ycLduQQuF1As52bihEEZONsFh\nJxL76mu0cJiyr76m6IWXSGzciK99ewLHH/ezvKf9QXJYhRBCCCF2w962jZL/voXRrDnO2rXEFy0m\n66nH0QKBfao3Om0aicUWuODv0R2nsJD4T4vwd+xIyXPP46ak4Cy2CF90IeFhJ1aul3L1VdgFBeiB\nACWvv4mvaxfczZsJnXQi/vYmbiJB6KQTif/4E4kVK3B37ABQr516MpphUP7Fl7h5eTjLlmPn5YHP\nh96wIf52bffpPf0cpIdVCCGEEGI3il55jcSMWUQnTMResxajVSu0QIDyr78mtvBH4itWUvLKa/Wq\nq/SDDyl9620AEitXoaWmoUUiOI4Djo3R5Ai0hg2wN23GZ7YjdO4IfEc2J7FpE/m33o6zYwcFt90B\n8ThaOExi+QrK3nqH+GKrchuaz4eemUnw+ONIufQSQpdcTMnbb2Pk5oILZV+OBsNH2qOPELn2aigv\nh4ICYnPm/Cz7b19JD6sQQgghRA00yknXvmf1TaNxNmwCIONfD2GvWEHJs8/hlpWpwU3fT0Fv1Ag9\nLR2A8gkTcTZvIXLBbyrrKhvzFVo4QmhAf9x4nNjUafiO7oCzcSOBfv0I9u4FgP+hB4hNnIS7eQuR\na66GsnKCJ/Sj+LkXiM+YidGlC25GBsGTh6FnZWFv2oSr6wQHDyTY73h2XPcHUq75HcEe3Su3XfrZ\n/4ivXI09Ywb+ozuiZ6ThpmfiFhayaMocXjZyeeja30EoRNnrb8D5O9t9sJCAVQghhBCiBi5BSkub\n4OTlo+U2Rj/iCPRgEL1tW0JDh0IwSOTCC7A3b4ZgECMzEwAjOwf0nTexi195jcS6degpqYQG9Cc4\ndAjRr78hvmQZaX/5c9Vtbt5C4qfFxOcvQEtNxchuSOHIe/C1bgOZmdg//oi7YSOhQQMAKP9uMs7i\nxbjduqJFIvhatCQ2bTr2suVEfnMuBffch7N+A25WJnrLFhitWhHo2oWixx7HOCKXNkc04JJmRxJs\nmoXrugdlOgCAVj2JN9nWrUW1vyiEEEIIcQiKTvkBLRjE36Uz9pq16DnZ5N9+J/5ePUi78rc1ruOU\nlqJHIrssd12XghtvwsGFvHyCJw9Dy21M+UuvYLRpTcY9I3Hy8yl66BHS7roDPSVFrec4xFetpuy1\nN4hcdAG+FkeihUK40ShaMFhZd+lnnxOfO4f0u+5ENwyiU6dR9smnhE47hVD//uy4+DJo0xrWb0AL\nhTCaNSH99ttIrN+AlpqCkZHx8+3IPZSTk6bV9prksAohhBBCJHG2bMXeuo3YjJmUvPAieno6wd49\nCXbvXmP5xOYtFNx0K07SzAH2xk3k/fF6Cm6+FcfQMVJT8ffsQWzaNMrfeZfgBeehBdUofi0tDT07\nm8SSpZXra7qOu307zpYt6E2OoOSlV3BjscpgFaD46X8THTMGd8MmNO8JVr6jOxAeMZxQ//6Ujx2L\n3rQJoe7d0BvlELnyCpztecRmzcbXtMlBFazujqQECCGEEEJ4ot9/T8Oh6ZSlHoNLiEDfPgCk/PaK\nKuVcx6H07XcJ9O+HvXQp6ff8A3v9RsomTSLl6ispfe893JQU/N26Eho8CF9uLnZBAS6gOw56Vhac\neSYAmmFgtGyp5k1NEuzdi2DvXkSnTiM2Yyb+vr2JTZhE2q03A6Dn5sLyFYQvPB/N7weg+PGnCHTr\n4rXRJXT66YRO6Efk7LMo/s8LONu2YRzV8ufafT8bCViFEEIIITwJawlO6xxIBTsvj9LnXyT1zzdU\n6dkEKP/8C2KTvkVvlEN8xgxCQwbjxmK4ukbhE0/jFBYSHnYS4SGDK9epqUez6Ol/Ex5xNpFzhtfa\npkDfPvjbtcPVNdz8AgBKXnsDp7wMf5vWGI0b49o20R+mYi9binHuCADCw06qUk/KlZcTGjYUo0GD\nvd4/B4qkBAghhBBCeFKu/C3luadTPmUOJc8+j3HkkTjFxRTcdgdOcUlludApJ5P+4P2ETxxK+h3/\nB4CvWTP83buTWLgQZ/ESnK3bKsu7sRgAsXnzcZN6UuPz5hOdOq3ONsXmzSO+ZAlGZiahoUMAMI44\ngkCfviSsJZSMeoL43HnE5i8AF8rHfFVjPVoggAsU3HMfJW/8d6/2z4EiPaxCCCGEOOyVjRuP5roE\nhwxG0zR8nTqCbhA8pg+u46A3bUJi7VoCHdoTmzkLf5fOVXoqi554Cl+rVpRPnASlpYQuvpDIqacA\nEJs9l9I33yTjoQcoffV1wheej89sh5GRQdbzz6JptY41AqD8w09wtm0jeEzfymWhk4YCYA8aQPmn\n/6P4gw/RS0pJufUmfM2a1VmfnpmJz2y3t7vqgJCAVQghhBCHFae0lPicOQR69qx8fGl8/gLsxRaB\n445Fi0Qw0tMxjlH5q5quozfIwi1Ut+PLPvoYggH8HTqArqPpOr6unYnPnoue3ZDAiUMInzyscnv+\nbl1Izb4Rze8nc9S/KHrscewVK4lc8JvdBqsAGfeO3OXRrADxZcvRcnLQ0lLRsxuSfu/daL66Qzv/\nUUfhv+FP9d5XBwuZ1koIIYQQh5XY3HmUffY/3B15RC6/lED3brtdJ75yFYn58wmfdWblsqJHR6Hn\n5hIc0J+iRx7F360bgW5dMFq3xkhPr7J+6bvvE1+8mMiFF+Br0xo0rTJYtQsKQNcx0tLU73n5RL+b\nTGjoYLRQSM0Y4Dhouk506nTs7VtJzJyDflQLYuMnEr74IozmzQi0N/fjXvrl1TWtlfSwCiGEEOKw\nEujWlUC3riQ2bsJo3KjGMqXvvEfgmD74WrYEoPyTz7A3bSJ40ono4TAAkcsuITpjJq4L7rbt6M2a\nEahl6it/p07YmzfhlpdT8tyLhM8/D6NBltrWS6+ghYKERwwnNmsOZZ98Co5D/Icf8PfqSXTSd2ia\nRuic4WiBAI0HxIi1yKW06WkkFi0mvnw55Z98in3SiYTPOmP/77CDgASsQgghhDisFD74sHqUaR91\ny9+NRnGKijCyswGIL1mKvW4dbrw7pe9/gOb3ozfJJT59OgUj74VQEF9aOsHBA4lPnY4vN5fUv96C\nv1PHGrfnFJfgug5pN1yP6zjEpk4DXSOxfj2xH6aR2LQJCovwd+tGYtkygkMGET77LJySUmLTp6M1\nyiE0eBDx2XPQGzQgb1sJRuceGFkN8HfrRnT0aPQ2bUmsW/sL7cFfnswSIIQQQojDSuD444kt/InC\nR0dR8uFHFNxzH8XPPFs5er/8q6/RGzXC37YtbiKBnZ+PlpmlbuPnNEQLh0ksWkR8+XL8XTsT6NEd\nLRyi9JXXqmyn5LXXKfvwY2JnIKutAAAgAElEQVQzZlD+yaeAyodNveZq3IJCCu++D3vdOlJvvAFS\nUzBatyLY73ji06YT+2EqZW++SfmXY3DWrSM+dx5+04R4nJJpa4huiFP06GP4u3RCy2qAu2MHoRMG\n/OL78pciPaxCCCGEOOQlP9I02Lsn8SlTCA0ZAg2ycDZsxM7Pp+yDjwgPPwt71Sq0Tp2ITp1G9IvR\nAGQ8/QT2goWk3nQjxc+/gDZwAPGZswn06gGAFg6jVctbDfTsCZEw/latCA0aiJtIUPL6G2hp6Tgb\nN5L+j7vwNW2KU1ICsTjRSd+SmDuf9Lv/QWzufNxwCOOIXJwNGzGaNcONRdF8PjLuvRu3vBx76RL8\nbduSctOfKbKW4u9ccw/voUACViGEEEIc0hIrVlL4r8fIeuIxNL8fLRQi7bZbK18PXP9H7G3b0VNT\n0Px+QqefTuk772Ac3QEtMxO9VUuMzEzSbvkLAP627SifOBFnwwaMJqcD4GveHF/z5gCUvvc+xlGt\nCPbuWbUhjoNbVEygf3/clkdSPOpJ0v9+J3p6OllPjsKNx4lmZ6OlpeFs2UJi9jz0nGxSb/4LiUWL\nSHz+DraWQuTiC9FCIVIuuxSAr0t8jIvlMOoX2JcHigSsQgghhDikJbZvg0SchLWkMs80Om06rqET\n7NIFLRDAyG5YWT543DGUf/opzvoNuCUlpF19VZX6QkMGoQUDaOnpBLp03mV7WiSCHtr5ZKziZ/5N\n6LRTMXJzSb3marRwGNdxcMvK0VJTd67n92M0bkT0m/FEzh2Bs3EDgYED8R/Vkuj48WiBAL5Wu86f\nenK7Rhx7ZNa+7qaDmuSwCiGEEOKQ5uYXQixG+eTvAXBKSih54SVKX3iZooce2aW8FgyS8egjhI7t\ni5aaQmzGzCqv21u3EjjuWAJdOuO6LmVfjsEtL698PXz6afg7d6r8XW/QEC0lhZLX36TkhZfUNnSd\n0An90fSqoVh0xiziPy0CIPX6PxHw6km96kr0XgMJDBiyS3t9uk7DSHCX5YcSCViFEEIIcchJrFxZ\nGRwmFixEz84h9eorAXDjcYyjjgLXIXLpJcQW/kjRv3beUC9+6hmi34zHOOIIUq78LVqjHOwtW4jN\nmUPx629ScMffKPNyW3Fd4rNmYm/eUmtbIheeD46Lm0gQ8dpQGz0lBV/bNjW+pqWkoIVDe7IbDhny\n4AAhhBBCHHIS69cTHTcBLRyGgJ9A9274jjwSgJLX38TXoT3+ozugp6Rg5+URmza98ulUsTlzMY7I\nxcjNBaDoiachJYIWDhP7Zhykp0EsToNnn66yzZIXXsbXvRtBbyBWMnvbNso++Qy7uJjUiy4gOnES\nRqtWBHv13KVsfeSVxrh97E88Mqwj6SH/XtVxsKnrwQHSwyqEEEKIQ46vaVN8x/al/NPP0FIilDz/\nEgV/+wexBQvQcxqiZ2TglpYSW7gQIysLe8lS4tZSAALdu2Hk5qKvW4T/u7cwGjfC3byZ2KRJkEiQ\ncsnF+FodVWV79rZtxGbPRk9LAaDoyaeJTvmh8vX4/AX4B5yAPWcubkkpWjiMHtz73tK44xC3HVwO\nj75FGXQlhBBCiENSoE0b7BEjiE2fhVtWiv+E/iTWrEFPS8ffri1lX35JYtlyNH8AuyAfvWHVgUuu\nroGuEzihH/H580m79WY1sX/vXgR798LevoOS554n7ea/oGdlEb7gPHzt2uG6Llpamko78MRmzIQf\nfyLzmSfRU1PxHdVSLZ83j9jCHwmfdiqUlxOd9B2R88/b7XtrlBri5RG79uQeqiRgFUIIIcQhpeTF\nlzHatSV0Qn8iI87CjZ1C+Tfj8HXoQMnT/wa/n+BxxxI+9VQA4osW4W/duvJJV4lVq3ATNv427Yk3\naY+RSBAcPBBfmzb427at3I6emoKvXVvw+9UgqgFq4n6noIDE/AXo5wzHSSTQfT58LVpQ/tXXFOcX\nED7/XPztdo72Tyz4kbKSEoJDBuPs2PHL7ahfEclhFUIIIcQhpfSLLyl/+10iV1+FvXw5eqMcwmec\nXmv52KxZGC1aYGRnkz/yXpzt2wj27IGv49HE58wj9Zqra1lvNkbTphi5jassj69dp2YfyMyA5Sug\nQQMiF5yP5jOIfjcZDJ3IBecTHTuOlCsuw7VtADTDqLWNrm0TnTCB4ODBu8wscKiQHFYhhBBCHHLc\neJz8m24lsWZNleXhU08hdM5wAn174+/ZA1/SFFP21m0U3PX3ymmonMIiSp59ntiMmbiui7txI0bT\nZqRccTnoBrHpM3BLS72yhRTcdgdOUREA0e+nEF+wkOiUqdjbd1A+bgKFDzxE2bvvwo4d6BnpkJvL\nD6268PHUn3A2b8Fo1hzfUUeB44CdAFSgWhGs2hs3kVi9etf3WlREdNwE3LKy/b8jfwUkYBVCCCHE\nr5NhQMCP6zhVFpf+921iS5ZCIACaBrF45Wt6ViaBfv3Ua4AWCRM8/VRCJ52IpmlkPfdvfB2PZsdl\nv0XLbkjqn2/AAQpHPY4bDhM8aShaihpYlXbDn7DXr6d8zBgSloVdXIjeoCFuWRRf715k3HwTWQ8/\nQPeNy+navQPhs88k5aLziYwYjq95c1Ku2nWKq/Jx4yh+6t+Ufz22ynI9M5OMB+5H97Z9uJGAVQgh\nhBC/Om5pKUVPPkWgT2+MnEZqWTxOyRv/JTphIs6ixRTe+0+Kn3ya2Jy5letpPh/hk0+qvK1ub9tO\n9OtvIBarLGMvW6Zu5ydsAp06Uvb+BySmz6T4nw/i5OVVuSWvZ2QQueIyino3oHTKeLSGDXF3bCfQ\npSulr79BYvVqSo0UFiyv/XZ/spRLLiZ8zgiMli33w146dEjAKoQQQohfH11HT00jfPpp6CkRAOI/\nLSI+cxbp992Lr1NHjDat8bVuRfjcEbVWYzRuRNptt6r5Wj3pf7kR35HNKXnqGdxEAr/ZHtLSMBrl\n4O+881GssdlzSCxfjr9VK1KMXIyGjXC3b8Pfuxf+Pj3BccEfINS7F6H0SL3fWvDYvvjbtd19wcOI\nDLoSQgghxCEhbi0hPnuOerIU3mT9731AyrW/q3NAE4C9fTvFjzxK+t/vwikpITp7DtFPPiN4ysmE\nzzgNolG0UNV5U+0dO4jPnYeemkLZ6K8IDTsJrUFDAu1qflJVfbiuS8kLLxE+6wyMxjsHc5W+/yFu\nWSkpl12613Uf7GTQlRBCHCIC415A27QMSgsPdFOE+MXFV66kbPSYylH11fnNdkQuPJ/Chx4h/y83\nY2Rnk/qH62oMVqMzZhKdPp3ysd9Q9Ogo9MxMgiedCOEwRQ8+gpGeTuTKy3FLilUaQLVg1Y3Hcbdv\nJzR4EL7OnQkOHkjpa2/gy25QtVwiUa/35roulZ2Ihg5Ujd3cRBx7Rx4lr75Wr/oONRKwCiHEr4gb\nVAMuIq/fRGDMM/hnfFK1QLwcfdU8tI1LDkDrhPh5FT3wMGXvvo9bVAyoYLDwX4+Rf+ttlLz3Pvl/\n/T8VIPp8+I4+usY6fixax3fbFxP79jvKvxiNv0cPgkOH4JSWEl+/AaeoiNA5w3EKCwn27g2xOMUv\nvEh06rQq9cRmzabkpVcA0MNhQv37k/nEY+gNdgassdlzKbjt/3b7vuIrV5F/6+3kXXUNztatpF51\nJUZjlZeb2LRJbSMUVlNoeY+XPdzIgwOEEOIAC338APH2x2N3OEEtcBzCr96I3aIrblpD4n2GV5aN\n97sI3ZqCVl6E35pMtMcZla8lJn5B2uJP0RJlJI4eSCynFaFXb8TJbUfs9BurbFMr2IK+aSm2efwv\n8h6F2BeFDz6Mr1UrtKZNMVJTKHrnXdi0CXvlKnzdu0NKKvF5CwiPGI7m8+Fr3hx/1y4AxJcsJTpu\nPKm/vxZ7/Xoy584k1q8boTNPx9m2A6NhA4yGDSh5513iX46mcOYsAgMHEP/+e7RIBL1ZM4ItWxD7\nfgrO5i3ojRsTPKYPwWP6EujTGwB7wwacgkL8HdpXtjm+YAH2li2k/vH3tb4v17YhkcDJL8DdsgXt\niFx07+EFAE5eHkUj7yXj4Qdxtm/H1749wX7H/Ux7+eAmAasQQhwIhdvQt6xAcx30zcsxUhvgNGiG\nsWkpiU5DwLFxMnNxMxpVXS9ahpG/kfIBV2GsmkWiz3C0HRswVs+j/McFOK6NgYZWtAPKizCKt8GG\nBP7xL0E4Da20gHi3Uwh98k9wEpRJwCoOYrF584hbSzBatsDXswflY78hUVam5jA1DDgiFz09Hc1n\nYGRnE+jelbLRYwifdw6apm6p66mp6DneE6w2bCRjzWaaZrSEDHBbO5RP/h63uJjozNnQsgUZt95C\nYvVq4j9MJbF8OW55OanXXkNowAmUffElmm9nekHFbAGxqdOxt2ypErA6hUW4hQX4WrWq9f2VvfcB\n9uo1RK67BqO9iWYYlH36GeEzz8ApLkFPSSFz1L/QwmGM1q3QmxzxM+zlXwcZdCWEEAdA8JOH8G1Y\nRMl1L+H/5nl8K+dAOBU3kILTqCWxwVfVuJ62YyPBCS8RHXw1oS8fw/WHSTTriFG4hejAywlMfA1c\nl3j3k3Fz24Jjo22wCI57CfwBtJJ80HQSzTrgZjUh0bf20dNCHGilH35EYvES/F27ED79VBJr11H8\n7HM4K1ditGhG41sGkjcljpaZSeiE/iTWrqPo0VH4WrQg7S83VK3r3ffAtolcdCGu6xK3LBIbNxL7\nYgyuYYCuETymL5Gzzqy63vsfkFi5imDfPgQHqLsgruvi5uejZ2Xt0/tzy8txyspJLF1C6auvQyiE\nFo5gNGtKfMZMgkMGEejVk9JX3yD9n/dWBuGHqroGXRkjR46sdcXS0ljtLwohhNhzrgN2HLt9f+I9\nTgPDj563CWPzcqJDryHRpg+kZOJmNal5/XAaiaNPIPTVM8Q7DsK3eh564Rbix5yn1nEdjLyN6v92\nAlIyIRDGt3IW0UG/JTDnS4iVEBvyO/TCreC4uBk5v+w+EKIe3LIyYlOn42vVkvCZ6rGqJS+/gq9N\nK+wtW8l69H78KYB5HHpODon1GzBysgn26Y2WmoqvWTNc1yU6fgLGkc2xN29BCwTwt21D6TvvERvz\nNXo4TPotNxGfMYNA715Ezjxj14akpKAFg5S++V/skhK0SAplX4+l5IWXSKxYges4+Jo336v3qPl8\n6OEQ9pq12Bs2kHrdtegZ6cRmzIDyKGk3Xo+RnY2WnrbX2/g1SUkJ3l3baxKwCiEOL7EyIm/+lUTL\n7hBK/cU375/1GYEp75PoNAh0dWvROaItiY6DcBs2h9Ss2oPVJPq6n/CtXUD5RQ9g/PQtvvWLSBw9\nAH3LCpxQCsaq+WjxMpwmJvgCJDoOhNQGxNscg920Pf41P+JbPBmnaXvchs1+5nctxJ4r/OeD6JEU\ngif0Q8/IUAttm0Dv3kTOPhPNFyJBDqBRPOoJykd/Ba5LsFdPfM28z3Q0Sumrr+Hv0oXEkiVQUoK/\nc2eMJk3wd+tCqH9/Ve2GDThbthDo1atKG9zycgr/9g+Cp5wMuk5s7DjiM2fhbN5C+IzTiH4/BXfb\ntsqe1z1VMPIe9AYNIRImOvor4kuWQiRMSoN8jEYRnEA2viOPxNf88PiO1hWwSkqAEOKwsP6Ll5lW\nHGDEby7GWDYdu3Vv0A/ARCmJGNqWVegFm/k6O4uWkWxap+TWWtw3+wvs1r3xLfoWN5iCm5KJtm0t\nesFmEp0GoxXtIPjd60SPvxi700CCX4zCWDYdp0EzlQ834m+EPr4fu3Eb4oOvxP/9OxAtId5nBKHP\nHibW/2Kc5p1q3b4QB4qdn68eQ2oYaLpObOYs/F06o3mPVE3mlJYCoIXDtd42t/Py0CMRtGBwj9pR\n+OgonM2bQTfwH9OH+Jy5pN1yE9HRYwifM6LW+V3Lx08gPn8BaX++ocbXAaKTvye+bBmJH39Cb9yY\n0OBBlI4bT2pWHrE8ByfUjNTr/7BH7f01qyslQAZdCSEOC02at6BXQp2o7LZ9D1xDfAH04h34lvxA\nau4IwkbdJ09jg4Vv9TwSrXriZjTGt/g77Ixc7LZ9cZp3JPzvK3AyGmN3Ggh2gujxFxEw/GjFO9Dy\nN6GVFuIEI/jnj8XIW0+077kEpn9McMo7ONlHEpj2EeUSsIqDSNxagq/VURiZmTj5+RTc9Xcy/nk/\nZR9+DKEQgU4dd1lHj+x8ilThgw/j79qV8CnDAG8i/n//h8T69TjbtqGlpJByxWUEunatUocbi1H+\n1deETju1yqNXA506YpwznPisOTi2jbNezQiQWLoM4nE1+GtnLYADGPg7d0JLTwOgfNK3JJYsIfV3\nV1fZZrDf8RgtW+D26wfBAL7GjfHPmUPx0u2Ezz6LyDEH8Fh1kJGAVQhxWNC6DKHdu3/D1oqIdz/l\ngLbFbncMdrtjqDwVFe9Az9+I02znidj/w/toZUVET/szgfEvYh/VEzc9G7tlt6p1dRwEBdvw//A+\nev4m9E1L0UrycdOzKbvqGbDj+Lavo/z0m/FtXorbuDWkZhE99jcQK8NYMw9cF+wEoQ/uJnridZIi\nIA6o0ldeJTz8bAJ9+6BlZKiJ/9NSyXjgPtxEAtd16xx8FD77LPSmTYjNm4+/vQmBAFp6OpFThhGd\n9B1uWSkau67v5OURmzWH0LCTIKkX19+zJ0Z2Q4wGDSi4/U4yHnmQ+MIfcUtKdnmYQJBVhLRVFLiD\nMHJyMHJUfrivZYsqQXCyivSFgr/fjb97V5wdeaT87irK3nybQK+eaD4J1UByWIUQhxG7iYl9ZKfK\n3NFfkrFqLoHv38Zud+wur/kWjsO3ZCq2uXN+RTeYgutNa2W36gnBCKGtkzAKVuKb/DluOA03PQe7\nZTe0ou3oZQW4qZlEh14HoRSix56Pb8kUnCbtiB89ALfxUdhHdgZcfMuno5XsIDT6SXyr5uBbPp1E\n12HgJHB9Qfw/TcKNZKj95Nv19qsQ+1N06jTcvAKMxo2w8/KJzZ9PZPjZaD4fmqZh5ORUBqiF99yH\nWx7F37b2R58a2dmUvvgyZW+9g2MYBDseTaBrF3yUk7Z5Mr6Lrsc4YtfpofTUVEIDB1S5xe+UlFBw\ny19JrFxNoHtXNRdrYRHhk4fha90avUHVWQJsMoiTg0vVOyd6ZiZG82bYGzaie72u1QWPPw5f61Yk\nli4jsXI1vnZt8JvmIT8zQLK6cljlSVdCiMOG26DpAQvAnKwm2E071PhaosNAYoOuBEDbvILAN89D\nIorTxCT46cNoO9arOkoSsGQR+obFGBsW71y/1xnEBl5B/NjzIZxGvOcZ6EXb8K2YqQqEk06Qho/o\nSX/Ev2wmsXbH4TRuQ7TfxaBpJLoOU/1O8XKC376Of/5XP8euEKIKe/VqEuvXAaCnRPC3awt+PwCl\nn3yKvW1bZdnIlVfgM9vttk5ft+5ojRrhLF8BqMeo5v/nZTZ/thi7rGy3629+5jlefX88WiRC2l9v\ngUiY/L/ejp6Rib9LJzS/H1/rmuZX1XGoOSCNz51L4d33Uvzft3Gi0V1e1wIB9HCY8PCzoEQFxYdT\nsLo70s8shDg8uTZov1xPq5vRiES3k3d9IREn5ZXrcVKzKLvsMQIzPkErKVC9m5qGm9MCN5IOgB1P\nxVcSRUvEiHc5qc7tOc07Ut5811w/AHSdsov+qTZfw3pO847EHRs06dMQPy/XdYmc/5vK37VAgMh5\n51b+7qxZi7M9D8N7+pNm2xQ9OorMxx9F8/txXRdisSoDqZy8PEpfeAGtWTMiF54PQGyxhb1iLfqR\nrSkZ9SSh004j0KNqek2yWR168tkmm4sTNv52bfG3a4tz3jnomZmVZeJLlxEdNx5N19EyMoicf16d\n7zXQoweRC39D6cuvEV/4I0azptg//kjayH+g+30753S1bfTsHEkFqEZSAoQQhx1/4SLS1r1LtMEv\nM6DBP+0j9PwNOI127ZEJTH6LWO+zifc+C33bOhI9T1NTXqU2AE3Dbt4JfOpk7DZsht3uWOIdBxL+\n6H60LSsrp62qiW/WZ7ihNPQd63ENA23HBlUvoOVtJvTxfSSOHoB/1ufoW1fh5CbdZtV0kN4d8TNy\nYzEKbrkN39FHo6Wri7KiZ5+j7O13CA4dQvmYMYSHD8d3xM5ZNPSsLEJDB1fOFBD9Zjylr71OaMhg\nVafroofD2Pn5BHv3ItCls1q+bSuURUn9w7U4RUVEv/ue0MDap6Jq0/IIhrVtROL++4nNmYu/cyd0\nr42VYjE1FdZxx+FrfdSur9dAb9SY2Jq1+Js2IT5xEvh86M2aUvzY44ROHIrm8+Fs305iwUICvXvt\ntr5DTV0pARK+CyEOO/G0dhT70vAtHI+d20bNf5ocnJXk4Vv0HYleZ9ZeST345o3Fzm2Nm5qFG67l\nZFa4A+PHCRjhdHzrF1F2ycM1l3McAmOeAtfFaXQU2vZ1+Iq24ls9h3iPM0hUDCRzXSgrgkCIwKzP\nicXjBBZNItGoFcaOdZRd+i9VLhFDL9yGtuYn/FPexUnNgkRsn9+zELVxSkpwY3GMLNVLqQUC+I49\nlsL7H0ALBtBzc6G0lODQoaBpxOfMw9+hA/pRR1WpJ7k31d+zB9Fvv8UpLUWPRCh56mn0xrmkXnF5\nlXV87dtT9tEnODt2EBkxHEYMr7OtmqaR6dcpP2ko8UmTcbZuQ0/dOW9zYtUqol9/Q8o1V9dRy67K\nv/gC3baJz5lL8NwRGLm5BI7pS2zsNyTWrlUPPDjySFL/cN0e1Xs4kIBVCHFoKNqOf+qH2M2OxmnR\nheC3rxMdek2NvY/a5lU4wTD+NQvxz/wMfEFiPU/H7qAmETc2LiU45V2cRq0Ifvc6Zef8A3z+KnXp\na3/CWD0PY/MyyoffoW7huw7B0U8SO+Y83AZN0betxg2nkug4qGoDykvQoqW4GTlsmbCOjBPaoffo\nT3xA1ZNsMt+Cb/CtnI0bScfYtJREh4EYa+dhpzZAX7sQXyKGhouWtwHfup+IHn8hdrOOJHqeTqLH\nKQS/fIKEeXxlfW5Oc0qve5HAt2+AP4SbmlNrjq0Q+0PZ+x/i5OcTPHEoZf/7AnvzZsjKBE0j0Ls3\nWjiElpZGoGcPcBzS7/y/ynXtrdtIrFhOoHt3ykePwdetG6WvvkbaX2/B36ULWiCAm0hg78hHb9ly\n143rOv4e3TEaNap3e2NTpxH97AuMRjk4hk5s4U8EOh0NqKBZS0mpd12u46DpOuFzzyGxYgWlb76t\nZjPwBnhl3DOSsjFfE/t+Chn3jqx3vYcTSQkQQhwSApP/i3/hOPxrF+Bk5KIXb8c+qge+nybhAqTs\nHM0b+OE99PyNJHqegX/eV8T6nK1G7xvqGt5t0JR4l5NwGzRRgWg8SviTB3DSstWUUcEw+vY1aAVb\ncDMbE5j6IXZWU0hriLF5OXazjhAIY7fqoXpvKyRiGNYUwh/di3/WZ5C/hXC3tjhDLoG0hpW9vNrq\n+WiFWyFj58nVN28Msb7nopUXY2cfibFpKXbbY/AvnoxWVoSxfR3GhkXoG5fjpOdgbF6G3bwzTrMO\n+BaOx7dmIbHeZxH6/FEV3M/8FK0kD71gM7FjfkNg8STsFl1wM3feftUSxaDLLAFi//B37oSvQ3tK\nvxqLPXcu5OfD9u2QkoIvOxtfx6MJ9e9H0X3/BJcqg5riM2cRmz4dn9mO0nc/IPbNOLQjGhPq359A\nx6PRdJ343HlEJ39P5NwR6BkZuLEY8XnzSaxeg695M/zt2u6SF2pv3kzpy6/i790LTdOILViIlhKh\n9K13CJ1yMr7OHSn74CO0lFTiU6cS7Kcu+vS0NPxeusHuxH5aROEdf8No2RJfbq6aCiseJ2C2IzZv\nPokVK/A1b46vdSuC/Y6v9UEEhwNJCRBCHJICE17GDaYQ7zOceLt+uL6gGikfCGJ3HACAvnk5ri+A\n3egoSMQJfXQfuA52ShZuZmNKr32hsj593SKcnJbYSxYSmfBvotc8RaLzUHAcYl1Owj/vK7TCrSTa\nHku81xnoxXk44TR8S6ZCJAM0jVj/S2psq7FwPL6Vc9CKtqHFy3H9YZyGzfGtmoMdLycw+S2czEYk\nep5J6NvXwXUpu+zRyvXdjEZqmquGzXBtG3DQt66GcBrlZ92Gb+lU9G1rccLpOI1b49uyjET3UyFa\nhn/+WJy0bPzTP8bYtpZEy24Y29cR7zgIbccGQp8+AIEUjFVzMVbPJ378Begl60jf9D75bf8iQavY\na75Z/8NNbYBtqkDMKSzCnjgJHEcVCIXQNA29SS5GwwbYBQWEzz8ff2c1YNDeuJHyseNIuewSXF0H\n2ybj3pGUvPUOge7dqoyiD/ToTla3rpXznRaPegI3kcDXoX3tDQwG0ZIGUpW9+Ra+Y/oSW7IEY8ZM\nnB156I0aERl+Vq0j9kveeofQ0MGVvbf29u3oGRmVwbHfbFcZEIN6yEH4VJXC42zahFNUpNZbs4bi\nJ58h48H70bxZEsRO0sMqhPjVcgMhnJwWhL54nMC80ehF2/HP+gz/vLEYGyyMzSuIDbwCY9MyiJbg\npudgrFmA3agVekmeejwrgJ0gOPY/+Kz/Z++8w+OqznX/W7tOUZdlVas3V1mWezfNBEIxNRDCTT0p\nNycJCcklPYcQQgrpIaEnQOjVgA02Nsa4994ly5YsyZLVNXWXdf/YRrbBdkJykkCY3/PwgDR79l6z\nR0LvfOv73ncFItSDFczDrF+FsEIoA50oXc2I9kZvICKjAKd8AsTC6Ntfw5pxg9c/6ks6ZW368keQ\nwXRPyALmqiewq6djjbsYER0getk3kPnVaK17cUpqQTNwhxRDMA17xEzsUecOVnwBL1TAn4xbMAI5\nbCT26PMwVj2JXTEJZ9Q5uEVjcIbPQO3vRNu3ChnMQJoBZGY+6rEmcB2s2otwhpbgjLsY0dmEiPYj\nwr2oXUeQyZmIWBgx0IN2eDva/o2EJ34WFB2URG0jwd+HcrQBzKD3Qct1kZEwMjkZp74eUTsWc+YM\n7PoG1PR0fLNmEluylBVhI50AACAASURBVNiri1DS01Fzc3G7e3D27kMfV0v/D24FVcUYW4MxtgY1\nJ/sd1xNCEPrzw94XqoLv8ssxx9cB0Hfb7aAoaIWFJ9bn82HUjBkUk8a0KdhbtuK2tRFfsYrgJz+B\nVlqMdlyMSilxGhtx+0MoqV5fevzNFWglxYMOAv0/uoPuaB8bMl1KAkMRioI5vg41+8SOiYzFCD/8\nF/yXXoIxejQyGiX8zHP4zpmDVnRifR80EhXWBAkS/EfiHu+5FOEerOrpuJnFqA0bUOJRpHQR7Z4H\no3qsHi3UQHTYKMIf+jKPbWvmygk5+KMh8B3vQ1MUohd9hcBT30cNbiN2ww9RjzYgA6loWxehdB0B\nAXb1DNwCr48teuV3zrg2YcfQdi1DxMPEz/ss0ctv8R6wYmj7V6P0HiV67Q+JXeDlhDsltYPPNV+7\nF6dgOHbJONRD23BGzj7l3MbrD6IcOwymD2vaR7zXuH8txob5RC77BnbxWLTdb6DuXYnScZDYrBvB\ndRH9nfiWPUioqAYnrxp9+2tIzcRVTNz0fGIXfRlj7WP4OjfQd+H/EOxYBkCo4Mp/6H1K8MHFPilV\nztqylfCfH8ZVBLgSuW8/TjjkpTkdF3/+iy8Cy8I+2IgxrhatcBjaZz4FQNI3voZeUTF4PhmN4rS2\noZUUn3JNJT0NkRTE3rcfraIChmYhpcS8cK6XfHWmtba10XfLt0m66csEbrgep7kZJTWF0J8fIr5z\nN3rRMCKPPoGULhgm6T//ibeukwakIs/PR68di3vODNRoxxmvJR2H56M+qo50M9bnErrrjyiZmRhj\na874nA86CcGaIEGC9zeOjRjoQmvcih3MwJp+Hb5nfohbeiluttcDJ7Oy0VIdkBInHqVo18sYRxz8\nPU1I3cSaOA9pBNB3LiU26+Mo3UeQSUOwj/efikgfTl8nhHu8qurkq862ItTDOxDRfqwJ81D6j536\noGMjVROctzugnsCumIybkY/v2R+BPwn1yC6kLxlr5scQve24wXScYDpa005Eyz7U1r2ox5pxVR3R\ndwxz+Z+wi2rRjuzGzshDX/ccSvNuYpfejJOSReCxb3niO6cCvXkXUgH18HZ8z99BbPwlqKEjYKYQ\nyks4BiT430MdPQqRm4t/zGiEz4eSkY6SlopzpAURDAwe57/8stM+3xh+YijQOXaM+Nr1xDdsIPX7\n3z3x/c5O1GHD0MvL0f/f1we/H3n6WZzGxsFq6+mwtm5DqxmN0HXE8Uqs3dyMc7ARxTSJ7t6N78Yb\nMKqrQFGQ0ehgNKt1PKBAHZYPQpAfHEJ+cMg7riEdh4Hf/wHHlVA1CUfXUTOTMKZMxvehCxNBAWch\n0RKQIEGC9x2i4xAYAVBVb4tv6yLsnArQDfRNL+MWjQF/Ck7JWLSti9DXvUy0+FyU9kaCi35PUXEJ\nStEorPGXIf0pOMNGgx3DyS7Dt+gupJnkxZjqpleVfP4OZPIQrBnXI/racXMqQT81elEcbcBY9diJ\nNoN4GH3bItSmndijzhlct//527EmX+W1KjRuReltQ6adGhMpM/LBl4RwbeIzPoY4dgj90BbsUeeh\nr30GY8MLqEd2IRwLtW0/WsNGojf8BGP1k9452xtBN4jPuAG1vdHrm1VUZFoOdtkEsGPEPvxVtANr\niVz+LZySseA6oCgY655HdHZgTbnaC1b4F4YrJPjPZuDue3HWrUebMY3Ys8/hdHUhw2H8F85FzfGG\n/ax9+xj4ze8wZ886q3jr//FPUIYNI+lTnzjluPjKVcTXrcOcMhkpJdEFC9AKC1FLS1Dy8rydBp+P\n0P0PopaXIQ2Thq4wGQGD+OvLMCZMwBhbQ+i+B5BItNJS3LY2lKFZyP5+sCzMunFIy6L3q19HnzAe\nJSlIbMEC3OZmfBfOPW3sK4Dd2krvj36Mu/8Asr2dMaNKyOnqwNq40YuiTYjVs7YEJARrggQJ3vvY\n8UEje23H6/gW/hp93TNYI+dgvvxLdh0Log3JIXh0J07BSKzJV2FseAFnaAna4e24gVTUlj1YU6/F\nKRyDsfwhcGycEbNA1fG9djdaw0bczGE4FZMQXUfwLboLYmGcYaMQHYdQu5pRm3eh9LTjZhcj/SkE\n7v0crj8FObQYYcdQelpxC0eDL4ibV4XSuh+ltw0nsxBj/XNou95AOHGUgU7sisloB9Yhwr2DrQ2n\nIARubiVoOm7mMNyUoait+zA2zsfJrcSqmomTVYxdMQntaD2i/xhSKOBPwZp+A64/GWPl46jNu4ld\n80OMlY+hNWzCHjkbZ9Q5oPvQDm5GpmbjFozAzSzAya3E2L4IOz0fp+bsSVoJErxbnNYW3GiUpBuu\nRx9bg1pQgFpUNOjLKqXE3n8Ae9cOzHPPI7pgIVpx0Wmn5s2pU9CrKgcHrN5CKyvFnDL5+AUdIk88\njVZdiew4RvSFF4k+8yxKfh7Wxk2o1ZXsjavcubKei0sz0CorsHfsRMaiCCHQCofR+9WvQzAJZ9du\nAjd8FHPWDIQQuNEYzSOGsdQ4xqjkYehjxqCPGYOitSCUPqT7Tt/l0MpVuMvfhIJ80n/zS7TSUiIv\nzAdNQQSTUDMz//dv+vuMRA9rggQJ3peIY14l1ffSnVijzsMeMRPpCxKfcBnGqifRNr+C2nqAvr58\n8tsOIJxenOKx6NsWY5fUIbOKiZ/7Gc9MX7rgOui7l+FmDkPqPsTRBtR9q1EPbSVy4X/jVk45kS6l\n+9D2rERr3omdU0l0ytVo9RsQRhARjyIGunAz8jHXPUu4bDwyLQdr6nWI9oNem0KkH2EGcYeNxnzj\nT+A62GMuwNZM1IYNoOpea4Ed99oDThqw0rYvAdfGrpyG77kfeWv3JROd9y3s8gngT8FY/Ee0+g24\nwTScWByrpQPT6sQtHI1TNg7R14Gy7EEEoNavJz5hHkKA7/kfE/nEb0FRcIcUIpMyvPvTexT/oruI\nnvc5RKjr3/aeJ/jPRcsvQGg6QtPQ8vMhP3/wsdiWLYR+9wf08mIy01qwjjYTX78BbexY1LRUlGCQ\n6KuLcJqaCX76k4Nb8WdDaBop3/f6zKNLlqIWFmBMnYw+fDjRF14k9MijlEyezE9XLSbUXoa9dRvG\njGmojoN9pAVr7z58n/g/+CaMx9q+E62shOgL83Fa20BKshWFUTdcfOpFlTh4RnoAuAMh+m/7Ecm3\nfANr/osAJH/pvxGKgrRthKaDIwndez/+a67EqKt7hwhP4JEQrAkSJHjPYqx7HlXtQ6kpwzm4ncCy\nB4nc8DPksFGorfvR97yJo2is7CwkJ+MIwdLh+F79PURDoCjQdwy9YQNuWg6xed+EeATl6EHsohqI\nhQg88R2kBDs1G7V1L27VVO/CVhRr0pW4w0bgmsnoWxei1a/HqrkQ87U/om1/DTc9F6doDFZaLphB\nlNZ9+F79PVbpeIQV9cRkei7Red8GKwqGH1QN0XYAY7XX7yZ62vC9eCduXhXxug8j+jtxh430Qgqi\nMQIP3XS8ijrVs+lSvAqqOLIXEQ1jl45DPbwdS88h0LIGIQSOAAuQKVnELvgCTvlEzMV34+RV4RTV\noGsvI/o7kalZWBMuB0A9sA7z9Qe99fUfQ9/1BvbEK/5N73qC/ySSxVpsmcrAsSHIgdCgndPJRBYs\nxNq3HyUnG2vHHkLXX0n0rgcwJk7AaWggNP9F0n72E9SqSkRq6qAJ/7vhrejW2Nr12E2Hcbq7IBQm\n1tsHPd2oVRdib9iA6g8gI1HE0CzsnTtJ+vxnEaaJOXkiAMaUybjdPZ5HrBCMPMl+auCe+wjMCKCN\n8QJI4vsOEFu6FGPmDPp//gvw+6G3Dz3PaxkQmkbyl7+I3dxMOBQm9MCfiL3+Bsk3fzUhWk9DoiUg\nQYIE71mc8kk4yflYKeWo9ZtQQl3oO5dhl9Zi18xFPXYIxQoz4eJJpMePYDRt9zxKkzKxK6egHa1H\n6W4Fw4c9+lzQDOzhM73K6Bt/Jj7yHGIfvgm1pw2lq2VwGt9c9iBa8w6sCZfjf+J7KD1txK74lte3\nqpmorXtxRsxCaa/3Uqw0ExnMwMkq9ix8ktJximq85CpFhXgEc9mDXpBB8w60+g1IVUXoJqKvAykl\n2r7VqD0tOOWTcLOK0OrXI20LISUi3Iu+4zWc7DLMN/7kuQz0tWNNuAJr0lUw/jzsYaMQ8TD2iHPQ\nt74K/Z041dO8AIPKySjdLZjL/uRVp1c/gT1yDvqbjyDCx6vSm18mctUPkMlZyEAKbp43Ta0e3III\ndWKsfRY3qxjMwBnfrwQJ3o5LgFgoQHznfqwN609s15+E7O9Hzc3FnD4dNSMD36WXeVvsI4ejFRVh\nTBiP4vejpqURefQxnJZW9JEj/671xBYtRrgS46ILcfbtQ2SkI1ta8V9yMYGP3YCak0N0wQL08nKS\nPvNp3IEBIs88iz56lOcXm5SEmjUEoarvaFVwjrahFI5AJOcipcrAD3+EkplO4NprkLEYgSuuwDzv\nHJSUU9sF4us3YDc2IrrboK8PUTWc0J2/wJwy+QPnx5roYU2QIMH7EyEwVjyK0tlC/ILPobbuQ4S6\nwXFQYiGsqdfgmEHM1U8hnDiRy24B3cDNzMeadSN2aR1S07EmXwP+JBjoJPDoN7GHlqLvWo7Wthcn\nbzi4Dkp0AKeoBlQdbe9K4nWXQDANIv3IIYW4xZ7djAwkI4WCvnURdtU0/C/cgaubyKwi9J2vozVs\ngGjYM0nP9wzLRXsDxsrHsUvrcIvG4mYV4eZX42aXeglbhh+7fBL2mPMHX7q+8jHUzibctFwvtaus\nDtHdigykoR3eSXTeNzFXPOIdu+EF3OxS3OwSRDyECPdhbH4ZfdPLWKPOAd3EHVKIXTUNmTkMNz0X\nmToUpa8dmZqNzMjDGjEbUoZAUvqgWAXQti9G6TuGsGM4+SMSgjXBGbGPttP3vR9gzpwxKLQcW6f3\nx3eipqQQ/MQZooeDQbSCAtSsIdgHDiB8JlpuricKhUD29uK0tqFmZqCVlaGPGo0w/j4hZ9SOxW5u\nJnL/n1BSU3C7e1DKy/DPvYD+H/yQyPwXSf7qV9CrvN8Bt6sLe8NGjIkT/upQlF5RgZKSiRJ5lZ5b\n7kGtrUUrLkYvL0OvrADbxmltBd3A2rWT6PyXMMbXoRbkE3tjOUTD6OPHYUyc5LVNVFZ+4AaxEoI1\nQYIE732i/Sit+5G+ZJSORmRyJure1Wi730AZ6EJt2oHasofIRV9GIBBHD+CWjUeE+1Db6oledzsk\nZ+IWjcEtHAOAvmUhatt+7LFzATDWPOsNJyVlgKrjBtPRjh7AKR6LtmMpSlczTtkEzMV/RIQ6cSqn\n4haO9sSq64IQ6JsWojbtQm3egejpQIlHvKGoIYXo2xbhDClCPXYYu2o6yqGtyKwiAs/civSnQE87\nItLvVT5PChqQqdmDAQNvIToO4ZTWYU2/Drv2Qty8avTNL6O17sdNHYo9di72yDlIw4++YT5udjnm\nisfQ6jfgVE7GLhyN3rAZa+xcrx1BCM/1INzjfe1Lws0uRaZkYSy5F6mZ+J77EaKvHRGPITMLAFA7\nm9AObiQ2+5Moxw4h0/P+RT8QCd5viIAfJSMdtajIE5qOQ+8PbsWcNBHfRR864zZ36Ne/wz58GH3k\nCMLPPIe9bRvG1CmDx0cXvoq9fadXaU1KOkWsun19CPNUx47+n92J8PsHp/Wt3btxjx1DzcoCoO/O\nX4Gukfr97xJ99nnk4SZie/agjxqFPmoEoXvvQ2oaenkZSnIyxuRJ70I4CuyBNKxVG1FLS5CdXRg1\nY3AHQvTe/A3ibyzH6enGaWhEr6xEKy8Dx8E+UE/yzV/HmDgZxTTRyso+cGIVEkNXCRIkeK8TC2Mu\nuRfR24E9YjbatsXELr8FkEgJuA6ivRFpBJH5w3EBc8+b2K37vWjS62477Wnt4TPx71gK0QGEFcPJ\nH0584jz0ra/gpudgl01EO7gRp2w8dls9asNGAr/7GNIwUdsODp5HObwd8/X7idz4S9y0HNyMfNy0\noUhfMjLShz3uIjD8ROd9C+XwDrTdb2K+/AuEEIRrLsDJHIaTXYZ2cCNqrB+n5vzTrvdkrHM++c7b\n9OGvQXQANMNzTQC0+vXYNXNxRszEXfssihXFHnshxCPINc8grPhJIyCgr38B4drEzv8c2uon0feu\nwikcjXJktyeqVd3roX1rHWM/hDV8FtqBtaiHtuKUntnHMsEHG6EomBO9Xk/pOAz8+neo+fk4LS04\nLS2er2nbUbS3JVSJgPeBauCP9+A0HsQYP/6UxwNXnz64Qto2vbd8m+Sv3YTb309syVKSv3YTUgii\nS5ZijPPCOKyt20GAPnw40nHQK8rRxtZgbd8O6emI1FT0oiICl18Kuo4MhYk+/iRm3TjUIe/0Uv1r\nqOnZpP7k9sGv7aYm1OxsUu/8GSBx+/qJr1mLb673/wFhGCR99jPv+jofNISU8owPdnT0n/nBBAkS\nJPgHUPesAKHgZhVjzv8pItwLKVnEJlyOb9EfcP0puAUj0Pe8iWsGvGpo71FCn70X7Djm6w8ietqQ\nuo/Y1d/3Kohv4dgQ7UffsRR19wpvy71qGmrLbmKX3IxyaBuipxUhJU52OeaSe0A1sAtHox1Yg1M6\nHnv4DERkAGFFcIrHorQdOGWr/Ez4nvkhouMQ8Zk3euLO8KMe3Ii+7jlEqAeEID79ozhvDXidAWP5\nw7gpXiVVrd+Atm81sQ/99+DrM5feR2z6R1Fb9oJtIcI9aHtWoHQ1E/78A17vbKjXE58nb+NLCXYc\n31PfB8dBpmbhZuSjtuwlesnNBB66mcgNP/FiZRMk+CsYSx/Aqp6GPOl3w43H6b35GwQ+ej3aiOFE\nn3sBMTQLt7UNa+06Uu/40WCMKYDdeAjh8+G2txN+YT5qfh5Jn/zE33R9t6cHkZpKbNVq3K5ufLNn\n4kajyM4utJLiU6qvMhql764/4mzbju+qK4kvXYoyZAhJX/kSSiBAdNFinMOHCX76U7jhMEoggIzH\nEYbxD92j3u/+AHPGdHwXnPcPneeDQFZW8hnLyokxtAQJEvxbEOE+RKQP4mGUUA/CsZGOhbHmaXDi\nKJE+rOEzkGaQ2JxPooZ6iVz+TW87W1ER3S2oHQcReHZVJ6NvfBH/Y9/1Yk2zyxDhbpzSccQuuRkA\nNy0bzCSUjkbUtgMo4V6kEGht+4le/2OUUDfatsUYyx5AadkLinpWsaruXY2+/nkAYlM/QuTjv8IZ\nMRN8QfyPfRNz6f1Er/wu7pAi3OQsnLJTK0jq7uWeCF/4G/TVTwLg5JR7Q1yAm56Hk3/S9aVEtB9E\n27cKp2w8aus+zDcfJjb1IzgFI732BSnR9q4g8Pi333bjBer+NYjIAGpPG7E5n0TpOOy9Pn8K4Y//\nKiFWE/zNSDMA2qlb8tH5L6GkpWFMGI+QELzxBvSiItTsoaT+4meItw0dacVFqDnZ6GNGk/TF/4v/\n6lOT5EJ/eghrx87TXl/4fFjr1hF/YzlKMED/rT/C3rWb2MqVDPzxbgDsxkactjZcywLbwjx3DsbY\nMfiv+wgp37oFJeB9oFMrK9BGj8E+dAhrx05kNErPV76G3dr6ru9LfONGQn95HBmLkfL972Cef+67\nPkeCU0lUWBMkSPDvx4qj7lqGvmWhl2AV7sXJrUKm53p+hWYQc9mDxKZcjZNXjdp7FDc5CyK9uNXT\nvXPEImj7VmKPPg/jjYfQti/GqrsUa8rV6MsfQUT6EOEehB33emWTM3GqphKf+hG07Uswl9yDm5FP\n5LrbMV+7F7qPIpPSiF96819dvvn8HUjNwC0Yibp3BcJxcDMLsKZcg7HkbpxAOqrrnKiQnoyU+B//\nDrFZNyIVDQw/ItSDO2wEoqcN9dA27NOY+GtbF3luBGUTINKP78WfEbv4qxALY6x7FqtmLv4nv0d0\nzqcgORPRsg99xxLPL3b8ZYj+Y2j7VmGX1HktDY7lfRhIkOBdIKUk8twLmOefi5qcTHTFCqIvLSD4\nxS8gbZuB224n/Te/QgQC9P7gVtyeXgSQeudPTxsI8Hb6f/lrsCx8V12BXlr6jsfjm7cQee4FUm/9\nPoB3/pRkiMVwbQc1OYmBP9yNkpZK4LqP/E2vKfrq8UrrZz7lbecXFLzrftLISwuILnyF4Kc/iVE7\n9l0994PM2SqsiaGrBAkS/NtRmnZgrn8OgunEp1+P6OtA6AbanuW4Q4ah7V6Ok1+NNfuT+J7/MWrT\nDpS+dvAno3Q2gRVFrV+PsX4+or0Bu2wCorcN9chu1Pr1KL1tCDsOug+l9yiYAaJzPuUNKJWMw82r\nwjGT0Vp2I80k9A3zUUPHiF5/B9qeFV4PaPDEFiauA7Ew4FVznYKROHlV6DuWonQeRhnoROlqRm3d\nh1M0FilURCwEoW5ELIJMy0a07EXpOYpMHYo9+lxk8hBIykBpO4C5/CHsmgtQW/agNW7BqZj0jnvm\n5pR5Ea4AuunZaxk+RCyEcrQBe7RndeWW1WGseRr14EaUcC/xWR/DKZ+Im1MO4T6U/mNIwP/CT7Bq\n5nperwkS/BWsAwfo/Z8fogQC2Nu3I2NxogsW4HZ1I3t6CVzyYSIP/wUsC//FFwHgdHWh19bimzUD\ndejQd5zTbmwk+soi3EgEJTMDoWkInx9z9kwvaOA0qLk56CNH4DQ3Y9c3EF2wEHPyJISuo5jeVr4x\nYTxaRTmR555HG16N09CASEsbFKHR15Zg1zeAY+NGo+gjhmNM9CKWRTBI3223o5WXoaSkIKUktmw5\nauGws3ql6pUV+C/+EGpuzj90nz9oJFwCEiRI8J5GpuVgjz4X0deBUzkZZ/gMZHIm+v41ONnlaAc3\noXa3QFcL1pSrvUjTvg7c0jpETyv61ldQ2w4gk9I9B4C+Y6gDnZ5A7WlHifQRm34d9pSrcYaNRAl1\n4RTXovS00JM7irsWr+WcrQ9g112C6GtHbd2PVE2cqqloB9aCpiOHFKLufgNz8T2I7haMjS/iZJdh\nbJiPk5aNb/EfELEQIjZAfNbHUXqPIoNpCAR642aEqqG27AMzgJs/HH3rYrSDG5HJQ9BXPY5TfnxY\nJT3Xs7dyHWRmAU7FJJSmnWjbFiHTskGopwxFvR19y0LU9oPYI2eDPwm1YSPW5Ktwh5bg5lbgDJ85\neKybX41TMg7/C3cQr70I9fA2pG5CUsY/+y1P8D7HbT6CtXYdCEHyV75E+NHHUQvy8X/oQgLzLkOo\nKk7jIfSJ49EKCwk/8xxGRTnmpImD0/pvx+nowN5/gOjTT+Mc70F1u7rQy8vPupbY4iVYm7dgTp1K\nfN16nKZmIi+9jN3aipqe7onOX/wK50gL+sgRDPz0TvTh1SjpXuuL3XQEVJXI8/OJL1+BjMXQh3uW\ndAiBDIUxRo9CaBpEIoTvfxC9thYleGaLNxkOw3FbrgR/OwnBmiBBgvc+rou2fy1OwXDQDGQgBat6\nOm5pHdbo81APrEXtakHtPkJsyrUYe94kNv4y3JFzUA/vACeOXTkFa/r16JtfRm2rx82tQMTDEI+g\ndTR6QjCQilNSR+CxW4hPvBLFjdMo0hhRkI1ev4H4+Z/Dmnw19qQrwJ+Evm0xbmYR6tH9mK8/iAym\nET//cyht9egH1iJ9Seh73kSqOgKIzf44bsk4rOrpqB2NEI+g9ncQuf4O7DHn4+ZWItoOINNy0Pet\nxB4xG2WgG7dgxOCtUJp34n/ie0jHws2rwnj9AZTuVrSmHSfSsFwX5fB2T8RaMfRNL3mCP78ae/R5\noBkYK/+Ctm8N9sg5KE07MTYtwMmthKR0jNfuQd+8AGn4EKFerElXoG98CTerGJme+2/7MUjw/kAE\ng8Q3bUL4fBiTJuKbPQtjzGiU5BN2bfqokYj0dGR/P86hQ57lVY5XcZTxONa+faeIVzUzE2NcLebc\nC5BdXcQ2bSH6+BOYF17g9W0fbyGQto3b1eVVd5uaia9cSdKXvoiSnIySk4NakI9WVIS9Zw9uewf6\nqJE4hw6j5uYQe+FF9Inj8c2YPnhdrbgIrbgIc84sfHNmo48Y7qXGdXVh1zfgmz7NE6uA0HV85597\nVrEK0Hfbj5ED/YN+rgn+NhK2VgkSJHjvI6W3be7YABgLfo0S6iZ67Q/RdyxBifQTn349TlYJ+o4l\nuEmZkJEPVhQRjyCFhjzeg+nmVSMifaiNW4iPvQiZlI657EH89/9fnMIxxM//LJHr70C07id56X3c\nOOkq9G3rcIrHegMkigKui/nyL6CnHeVYIzI9h/j4Sz0xaAZwysZjJWcih5YgQj2o+9ei9B7FGXUu\n2vYlSAHanjex6i7B8iXh//NNRK69DX3jSxgbXyD0mbuJfPSnAFjjL0Vp3o1bMNxbf/4Iz19104ug\n6LhmAHvOp5BJ6XD8j7aydyW+lY8R/sRvCNz/RWRyBk5WEfqOJcSnXuut6cheonM+DdEBzNVP4CYP\nQdu7Als3INyL6DqCmzIUp2i053Pb3oDbeRi3pPbf8AOQ4P2CdF36fnYnMhzBqa8/67HRZ55D9vWS\n9KVT+7etTZuJzH8R43bPks7p6CC6dBnBa69GCQSIPD8fpayU4Je/SOie+3EOHiT1V3cihCD62lLi\nK1aQetutiJRk1Ly8QZcQoakM/PEekv/f10ke+wVvDa+8iu/ccxBZQ3hwT4DpjUd4u4yMrV1LdOGr\npP7ge4Pfi69bj71zF253F5GHHyX1l3eiJAX/pnuU9GVPQCf43yNRYU2QIMG/D9dF3bfaM6lXVbCi\nGFsW4lROQT28HaW9AX3jS8QnzEM7vA2l4zDoPpzSWm/LPKMA87W7UfqPYZfU4RaNRm07gLrzdZzC\nMbi5FdhTr0EOKfQqpb4kz8pq+EzQfRAPYQ8bgztsBDIlC3vUOYN/+LQtr6L0HyP24a/hlk/EzS4D\nVcf38i+xR5+PvnkBQtVwh5ZirHocnDjWzBsBMJfch+tLRuZVggBjy0Jc1UA9uBEZTCd27mcgKRPR\nvBMR7vWm/Nc86r4MwwAAIABJREFU48XHAgiB6G5BCXWjNm1HDXVjTb3Ws6p6a327lyPTcnFK65CK\nijViFk7tRdjVM1AbNqF0HyF28U2QlA66iVV7Efboc3GLatB2LEU7vB03Mw8nfyRu+QRk5jCs2otw\n84efahH2FvGI17urJuocCYBQCP9HrsU3ZxZuZyciEDjtEJU+ZjTG+PHEV64iuvAVjPGej69aUIB5\nzpwTfaRLlhJ78SWUnGy0ggLsQ4dQi4pw9u1HGAb6lClE57+IMWkisfUbCFx9lVdR9fm8Sq4QSNv2\nHD06jhF99jl8F3g+p+GXFuLs348xcQL9lkL1lbMwjBNrFcRQhwRRMrIHwwYA9PJyzKlTIDkZu6Ee\nLOuvGvpL10V296BmZPxNQ2UJTiXREpAgQYJ/O6K/C99LP8eumDQoevSVj6Gvehylu9XzZM0uQ/qC\niIEenJxS1KbdKH0dKOFe3KElSNdG3/U6Tlaptx0PCN0EIXDzqzBXPo6TXYp2eDvOmHOxS+oQA50Q\nTEP0tOLUXICbloP5+gPYI2bjf/pW9P2rsSbOQzuwDvXgJtzC0QCoTdsR3a2QnIlMyQIkMpiOTB2K\nzMhHOVqPm1GATMvBNZNws4ogmI7o70Rp2g7BNKxp16FveBHXlWiRXpT+Yzg5ZSi97ZhL7kFr2AiK\nhj3uIuyKyaAZiI7D+J+5lfj4S9F3vo6bU4E19kLk0OJT7qdbVINzvBLq5lZC6lBPaCoKbm4FbtGY\nt70BJ/7IuvnVnu9tqA9zzVNYE+cdP8arLL/9eABz0V1oTTtxSk+15ErwwcNav4HwI38Bx8WcPIme\nr9+C29GOUTfuHccKIbzhJEUgDAOtqPCUxwbPuWkzanERvrkXeAEEkyaiJCXh7N5D8POf9VoN4hZa\neRnW68vQqqtQUk+1xxr47e8J3/cAxnXXeYNdx+2zREoy1qYtmNOmUFwx5BSxChAQe/HpbTi5E077\nehW/HyUjg/iyN4gtfBUlOYhaUHDaY8NPP0vkkb/gu3Du33YzE5xCoiUgQYIE/16iIUTnYZz84ShN\nu1AbNmBXTUU9vB2ncDRa/TrUY4eIT7oCp2QcgUe+jpM5DKvuw0gjgG/ZA4Q/fBNEw2gPfQUQWGMu\n8MSobRGfcDkyuwzLiuBUTiVm+DHfeBgR6kZYMUJf+gvx8/4LoiFQFGRSJuqOJQgrSnzkHIxX/4Da\ntB0388QfIWviPDRVRXS1EHjl99jV04jP+eRg0pPSdQRUDWPLKzhpuVizvZx0440/I0I9WNOu807k\nWKihLmLTrsEZUoIY6ELfuwKrfJJXOZ32EZQju/E/ezvx8ZfgDClCRPqQ+cMJ/9fd+B/7FsK2TtxL\nKU9fAT2O6G7xEqvM4FmPs6Zcg7HwdzhDSwbPqW9ZiLZnBSLUQ/tAKWpZFYHpdfhe/AXx2gtxqmf8\n/T8DCd73vGWmr40ZTfAzn8EYNxanuxslPQ19+vSzPlfJzCT0wJ/Qa8agHBeS0QWvoI0cgVZUSOC6\njyBdF2vrtkEbKK1wGEn//X8BULOyUD/kicCkL5/GHg4gzYs3jr/yChYw0NZGytdvZuD2n6DVjcPt\n7ETNzn7H03p3a2Bkob/TNQtr9x5kOIxRNw41PZ3oosXo48Z51VzXPSVUQEpJfPUaAjd+9Kz3IsHf\nR0KwJkiQ4J+OdmAt2o6lyGA62q43PA/QA+tRon24wXTs4rFIMwnfwt9gF9YQH3Mh6r6VuOm5yKGl\nRLXPghWDQDIyZSi+VX8hNmEe+p7l2MNGg2qAomLXXOhdr3ELdsUkRM9RpOugb3oRa+xFBB69heg5\nn0IZ6ERf9QTWyDkY2xfjaiZioBOZW3nKuu26S72Bpq2vYudVe4NOzbtwC0d52+2OjXrwf5D+E71q\ndvUM3KQTxvuxq77nWWAZfowl90C4H6WrGSdvOE5Bnueh+updxCZcgYj0ojXvxKq9CNF3DJmahVUz\nF7vcs7Uylt6HtnMZsdmfAH8yItKLPfpEzKsY6ML/1A9A8xGb8dHT2mENHtvTita8g9i06wj87mO4\nQ0uxR8wkev7nELEwZh8oQ7PAjUA87PUH6+YZz5fgP5vY1m2E/3g3qT+9g75vf4+km29CGAbCstFH\njUTNzcXp6sbeuRNzxjvFqzBN9DGjEf4TXr92czMiJ3uw6uq0thJ+6BH0kSPOmC7l9PURW/4m9A8Q\nuO7aUx5L+uj1uPPmoQT8hJ99HvdwE0p6Gklfu4noG8uxtu84rWCNr12H8JnopWXveMw+UI/s68Oo\nG0d4/nyc+gaErjNw/wPIUJjkL32R/t/+HuvQYXyTJpJ2508TzgD/JBKCNUGCBP88pIux9D6sCVeA\nFUPdtgjpC3oDU9JBtIXATMKacCkyLQetaSdKxyGwYyhIZMoQUBSvf3XTyzjZpbjBdC+mtWEDjhGE\nQCrG6ieJXfYNtK2voDbvwS4bj5uWA4E09A0voOxbi3JoO9bIObhFNbhIlLZ61Kad3ja4LxkiA8Rn\nfuydr0E3ic+4Abd4LEr7QXxL7iF84y+8tgZVI3r1/3iBB44NVhR96ytEL77p1HMcj0aVKVmoxw4T\nHz8PNAM3p5zAn78C8SjaoU04uZXYpRPRtyxEGVqK68S93tFACuJYE9ruN8F1cPJHoLXuQxz3ggUg\n2o96cBPhj/8GEQt5A1onYSy9H4RCfM4nIB5BpucRH3shvqX3ER9zPm4wE23vKuzqGUjdRD9ueykB\np2QcMvn0VkQJ/vOxm48QuvtezLlzUZKTCX7hc4Nb4rHly7HrDyIf+Qt2Xx90dGBMn/YO0SZUlcC8\ny0/5XtJ/ffqUr7X8fNJ++fPTrkHGYvR+53vokydhbdmGMWrEO45x2o4S+u3vSfnhDwhcOQ8uuRih\nadj79uG2HUW/7lqvx3QghJLifciUrovTeIjAjTec9rpaZQWhe+9DpKSg19ZiTvLs5/yXX0b42eeQ\nto29fQdKfh7xTZsIXH3l33BHE/w9JARrggQJ/imoB9bjZuYjHBeQKD1tuCXjUNob0Y7s8gSlL4g9\nag4yswBxtB7hWlgVE1EGOolc+Z1Bx4D4lGvAtjBe+R1S9yGzS7yho4x84uMvQUQGAFDaG3EKRuBU\nT0ffMB9X1RGxMNGP3Ib//i9gl4w7vk0usMZehPniT0G6XuJV8hDUwztQBjqw6i4F7USF561KpZtT\nRvjjvx7cajeW/Qn1wDrPuL9yKtKf7PWT6r7jIQHhE+b+gFV7McqBDZirHkMGU4ledgsgcIPpEOpB\n7WhEbT2AcuwgVnGdV40+bqeDdAhffwcct5yyM7x/m6/8FjejADd5CPq2xTh51aAe92l1HW9QC7De\nSsuKRwn86StErv4+du1FuNmluEU1EI+i9nd4x4d7vdfo97Zu4+eeKiwAooteQ588cbBPMMF/Btah\nw/T/4Y/4zz0HfeQIwo88il43DnVIJkIRyGgUraQYoShEXltC5PEn0SdPIvipT9B36224kr+rwhj+\ny2No1dUYdWdwqDAM/PMuw5g0CXHlFac9RM3JxvzwxaDr3hCWadLzze8g0tNQUlNw+/qw9u0n8uTT\npP3y597vlhCInGykpmG3HUXL8Sqwd609iF9X+PiYUgIf+yixJa/jKy9FH+45eaDr0NcHtk3aH36H\nUFXkW/3fCf4pJIauEiRI8E/BWPsMANbUa0BRvf7VgS7U7iPEJ16J6DoCug+7YjL69sUYa55G2nHi\nV3wbp2oa+qaXMVY9hj1yDsbS+3HT85E5ZTgltd5wVEY+8TmfRPR14lvwC+yRc1APb8Munwj+ZNSW\nvWg7l6F2NeMMLcGpnoFbPPbElLuqYax5BjejgPjcz6O2N6A27UQ9dginuPbMMaUnDy+l5YKUxGfe\nOFgNdoaNAkXBWPk4+vbXsEfN8Z7W34m+5inUgWPELvgC1pRr0HcvR23aiYj0IoRCbPJVOEmZaIe2\notVvQHFsYld+FwDforsQtoWbX33KcqTPE8lKZ5P3vMbNiHAv2vbFmMsfwaq7BBmNQkqmF66QPAS7\ncir6hvmI3nacETMH74dTUguKgrn0fm/AqqTWq0C/Dae7h4Hbf0zsxZcxoocINLyBXX32HsYE732k\nlPR+6Sbo6sbeshU0HWvNWrS8XIxJk4ivWk1k/ks4R5oxxtfhdPdgNxzEf+nFaHl5KJmZ+C+5GBkO\ne+0CJyVBWQ0NxBYsRBs5AqJRhH5q+IV96BDWzl1oxUUowXdaRwkh0IadPV0Kx0EbVkB00WL6738A\np+0oztZtKHm5BC6+iNimzTgHG3H7+lFzclFzcxBCEF/+Jm5n53HP17kIIUjz61RkJpEWMFFzcjCn\nTD7FM1YxTcypU7w0ruNrSrQC/OMkXAISJEjwL8epmISbngeOhbHyMbTdb2BNvAJ9zdNI10Gm5SKz\nilCbdyFsC9HXgRrqxvWnorY3eBZPsRD6tsVgBnFzylFb9yGiYUDiVE6B5EzwJSEDKcisYm8gKpiG\n/89fRW3bT2z8ZSg9rVi1F2Eu+gNq42ackjovKUrTkZrpxbMOKcSpmIzUfCidTTgZeeibFyKTM0Az\nMV/6OW5WMcKOI2JhfPN/4m2fj5jleZb6TuPNGOlD7TqCfVwQqoe2obbsgdnnocsWbDIxVjyKk1UM\nmoldPQ21/SBKbAARHcD1pxCf9X+QSWmgatgltZhvPuyt3wygbVuMm5zlWYL5knBzyrFLxmKNOh+n\ncjJuRj4ymI6TW0Hv176BUlhIYPNTSFVHX/s0aBrCinrne9sfWqdkHBJB4LFvYpdNhsDxHl0pAa8f\n0dpfj+zqglG16HVTvQCDBO8r1H2rUdsP4mYVeWL15m8gTQMkJH3jZnznzEEdkoleU4MxvBqtrBSj\nZgy+WTMRmoaWl4uank748SfxnX8eak42SiBA/223A6CVnZhicju7cJuacBoPE37mWXyzZw0+pm15\nBb/TSvSYjVZUNJhA9W6w9uyl97bbiS1/E2vrNujowG0/ilZXi2/aVEL3PoAyJBNr1x4UwyB440eJ\nLl2G095O4OorMcaMxpw9C2v3HsKPPEruubNI9Z0+UU66LsRiJ3Y/EvyvkRCsCRIk+Jeg7l2Jsepx\nnKppaJsX4lvwK5Rjh70BoVAPItKP1rARpa+dgau+T9hyCRzehJs8BLWjEamo6Ac3gWPhZgzDt/xh\nrOGzsSfOA38KSusBhBXF2PoK1gSvD1RpO+BZOAkBoV7P1imzABHqwZ5wuWfmX1KLPWIW+t6VXpzq\n+udwho3Gza/CLhmHzC0HITCWPYjobMLNq0IJ96JvW+RN6FsxnKIx+J/7EUpnE3bFFM+iqnScZwN1\nOn9SKbHHXzooBmVmAU7VNKRQkWoSTmoRKBoiNgC+IOr+NcjkITjDRqE1bkbpP4Zavx61uwXpS0I9\ntBU3r+q4T6qCsfopZErWqULRnwKGz/vvYBpuXqVXmRpbg5aRhLnxRbBjaC17cFNzEPEYTtWUd7oJ\nKAoydSiirwNzzVPYw0ZBMA1zwa9QOxpxi8bgmzEN/7zLMUaMSojV9ylq826Ih730NSFwOrtQh2aR\n+v3vog4dihACNTsbNcOL6lVSUrzWgJOEmpqfh3nuOadUF80pk9EqK058wBECNSMDvWYMWnkZ+rha\nBoTKj5fvZ2JBOpqmgT8Z/cLL37VYjSxZSuihh1Fzc7B27IL2dlJ/+mO0USOxli7D7e5Br6wg6fOf\nhUgUa/ce0DT855+LtXMnINBKir116t4HWWwbrfydA1hvEV3wiifS58x+V2tN8NdJCNYECRL8S/C/\ndCduej5O2QSUw9vRDm70puYzC3FTszE3vABWBJmazcNiFM9vP8SFx1aA7sNNHYoa6SX8sTsxti1C\nbdyMRGCd/9lBQeXmlnsT+Y6DW1wDdhz/M7diF9ZAIBX/Uz/wkpyGz/KqqSNm4hTVoLTtR6bnYg+f\nAUJB27vSS4I6uAmlu8VLzkrN8SJWg2kI3Y+++w2iH/4qTkktxopHkWYyauteYud9BrdotFfh1QzM\nRb9Ha9pxqj9pLEzgye957Qkn2UsZyx5EHN6L0rAbpe0A9sTLcMrGI5MzvGtrJlrDRs9zNi0HYceJ\nXvxVlO4WlJ6j2BMuG9yit6umItNy6Ij1UR86SrYv9Yzvi5KcjG/RXbjZpeBL8kIEplyDUzX1HWJV\ndLdgrHkap7TOex8b1qOEenBKanEz8nAKRp65XeIk+n/2c9TsoSjHxU6C9x5uTpnXc30cY/QojLFj\nB79+q+qq5BegZg897TlC994PhkFsyVKs3XvQR45EHO8hHbjzlzitrYiMTEQwOOjJqvh8gKCpN0Jt\nbipKcsYpvd7vBruhHmvxEqz6g/gvuwRz1kz0ygrc7h7iK1eBouC7Yh5KSjJaUSG+c8/xUq8UBXXY\nMOzdu9GqKgcFtxLwn1WsAmglxei1NSiBs8ezJnj3JARrggQJ/iXYFVNwqqd5Rv6pQxGhLpToAGrj\nFm+bv2oKytFGhHSoHDeZ2fuewVAFTuFo5JBCXM2HWzUV5cgetKbtyGAmMikdmZE3eA3R2+75he5+\nE6X7CNHLb0E4NqK7FVdR0A+sx5p2HSLahxjoRsTD+F67G2vM+YjeDjADyEAqMpiOsf45XCOANeYC\n9L0rIdSHk1OGPfZCnLxq/PN/hj1sNNrhbTiZ+d5rKBqL2rYf9chu3OwynKIaL9L15AqrpmPVzEVp\n24/v+R9j117krb3rCEq4DzctB6dkLCQPAUAG0nFyKnBKanGKarAnzkPbtwox0I1bOAq3eKwXaCBd\nr/c3kELgnv9C27oIc+frbC0ooSzl9Ebmb+GU1g3+IzPyER2NoGjecNlJolWEe1Fb9g76zToldSde\nXyANDD+x1WuQto2annbG67mdXWgjR6CYCSus9wpuJEJ8zVq0okLsQ4eQinLW90cIAckpWFs2nzYU\nAMA50oLIzsbt6sbavAkZs9Aryr3np6WhVJTTf8u3kbaDMfLEZL+mCOrcNhRf0KtqngVp28TXrMWu\nb0DNyzslQUpJTYWsITjbdpD0uf863qPax8BTz0B3N0plObHFS1DTUr0eWFUd7Dl12tuJLXrN60U9\nW2/s2++LqibE6j+JhGBNkCDBvwbDd0L8mAGc0vFoe1egth3AKp+EXfdhnFHnoB7ejtayDz2niNiH\nvoSbPxzz1d/h5lfhFoxERPpwkrPQmnei7V+Dm5SJzCoC6Xrb3nYMJ68Ku3oa+JIxl96HvmE+WvMu\nIld+D1KHoLTsBTOAW1yLVTMX0XUE/zO34gbTMZc/hF01DfXQVqQewC2rw80owNj6CmrLHq/S2bQD\na/gM3NI67FFz0Jp2oLbuw80uRdg2xEO4eVXeRP7p4koVFdHXgX5gHda4iz0Rn1OBsfJR7Jq5yLxq\nr4Vi7XOIcI9X7Qx6MaoYPjCTsAtHo21/Ha1xM05xLUrTLvyv/NZLvsJLuNKQFNdcOugGoK97Djc9\nf9AzVV/xGEqo24uWVbwkK6V1L/6Xf4W+fTHO0BJk6knVs0DKoFj1TmC+4/XFFi0CV6KVlpzxR0Gv\nqkyI1fcYsS1biby8AP85cwjdez/Wjl1o1VVEnnjKizc9nWgTILu60UcMP/0531xB9ME/4ezdS9Ln\nP48UgsgTT2NOncLAL3+NtXotytAslPR07F270EeNHHyuuegukA5uTvlZ1+00HyH054dwDx1GKy0+\npWoffvJp3KZmzJkz0KursDZuBEXBWrIEHJfgZz5NYN7l2PX1KH7fYGgBQHzTFoypU1CHDPmb7l/v\nd76H8PvRhp39w2GCv5+EYE2QIMG/B0XBqZrq9Y/uW43asg83u8QTrM07YaAHpe0AwrE9QdbZjDT9\nOBWTkGm5qM27PJEqJeZrd6PuXYl6aAvW7E/8f/bOO7yK69rb79TTVFEXaiAJEIjeu2kuuJcUO65x\n6r1JnM/p9TqJU5zY8bUdtyRuce+AMQZsuukdhOiIIoEa6tI5Z9r+/hhZQhYCO3FsyJ33efw89pm9\n9+wZJLPO2mv9fmjbF+Jb/TLm8NnYafn4Ns5BEgKrYDQiIR2lYg9SUxXq0RLsnMEQiscsno7IKERq\nrXcbthQdpbHKDZAHTMLqNw5z8o04WQORTxzAiUtGJKSDouL0HoCQZPSd72HM+IobrJ4FkZCOOfwS\nN1AECDfjpOS51qgnj+Nb+CDmkFkIXwzatgUII0Jg0UOYw2e795Vk5MYqJMtwM6OJGZjF0/C98zBC\nUlFP7CE685tItulKaUVaCcy/Dzs9HxGfhlryHurh7Vh9RyLiU1F2Lcf33uPIdRVEPncX1oBJiOSc\nMzpinQ59xPAzBqseny71d9xJdOMmlMxMlKTOYM6qqCA87y30Ia5Nb+uTTyE5Dv7p09DHjyMybz5W\n2SGM9RvwXzSrW+c+uBlMbWARwrKwy8vdjOap1/v2wdi+AyUzg+DnrkMKhkCWUPv0QR89Gt/UyW6T\nlqIiBYOo7fqtxoaNOJOuRmQN6HbPU7GOHqP1kUeJ/+1v8M+a2a3ExK49ibVzJzG334akqtgnT+JU\nViJCIZRBAwledCGSz0f03XeR4hNQMzM65kaXL8fYsRMlL5e2l17BLi8nunQF+uiRH96G+6xpqejF\nxV0yvB6fLF7A6uHh8dnSXvMo15/At+ENjEk3IDXVILc2otRVYGf2wx48E7lyP05SFnL9CURaX5Ak\n1IMbsHtlYyfnYoz7PCKjEBGIRytZSnTyDWAZaFsXoFQdIDriSuwBE0DVkIw2hBAo1WXY/ca5QVn7\n0aOTOwS56hAiGI9avgur/yS3hs7X2e2vHNuBWnkI/+KHsfpPgkAsTlpfrP4Tu2i0npbWBnzLngDh\nWriKpCykljqCz/0AY8IXQQ8gnzyGHG7C7jOcwLw/IkVawBdD9MJvuMEnuPfMHYKdP7oz6AX8Cx9C\nrT6A1FSNdLICveQ9V1VB0RCShL51IXJjFfLhHVhDZuDkj3brfRc+hOMPYFzwZQi5x/sfN1j1OPcI\nz5mLqK7GWL4CJxRCVhTkxATs2pNEV72Pb/IkV5c0HEbJyUYrKECSJMyKCswt2/BfdzXR91cjak+i\n5p++ftNYs5bw8y/inzmj47PIoneRY2PAspCTeqEPLnZrQPu4X2Ykn8/9R1VR0lJRs7KIrFhB61//\njrFtO0pmBkpGxmnv9wFSKASBIGpe7mllo9ScbKx9+zE3bsLYuAmQkOLjsUv34Bs1omMv2uBi1Jzs\nLnP1YcMw3l+DlJSMWbIL//QLkNNST+uGBbiNaF6w+m/FC1g9PDw+e2wT+eQxnJRc9B2LMYdejFq5\nDyc+3XVfUlT0Ff9APnkMfcd7mCNm48SnI1ceQK3ch9xQidxci2/NS0jN1UQv+3+IhAz09a+hVuzG\niUtBMttQmmog2oayeyXEJGFOuQn/G7/FTsxE3zwfIi2I5ByUYzuRzAhWwVi3PlRWUA5sQNu5BDtv\nKE7OEBwhoe1bizHuOuTqwwTeuBtzxGVukCcEUv0JOMWWtQMrilq+C4GEUnsYu88I0ANYRVMg6Gao\nRHwqdr/xCD2AUrGH6Myvo+19H2vQBR3H+6dFlnFS8ohOuh714CbXtGDARPfIX5Jw0gsQ/hhEfBqS\nGUZqPom+8h84yTkYU2/BHjT99Hv2OG8JXH4pJPXC2rAJe/8BjK1bsY8eQ+nXj+iid6G1DTmrN3rx\nILQC9/jd3FlCeM48aG5CycrGWLYcNK3DyenDqDk5+C6Y2qVsILJsGVJMLIGLL0IfXIy5/wCtjz2G\n3h4gnw6rqgrR0or/wgvRR47oNq60uollh2oZlOYe3UuyjJqb4+qlbtpMy0OP4J85HYC2N+dgHT5C\n8OorUYcMBttGHzECq3Q3wrEIXHEFdn09kqZRf8edCMtEK+qa0fVNGI+5ZQv20aOIxkb0cePANOGU\nWlePTw8vYPXw8PhMkJpq0Te8gZ07BEKJSLhBnlx9BKWuHCN7KFrZZpzkbNfeFNzgMyYRdct8fKtf\nxE7KJjrtdpTqQ0it9TgJGahVZUi1RxG+ENawi7DzR2MXjMUaPAM7eyDqjiWgKMgtddgFY5AiLdg5\ng5HCDa42bGwyTkYhTkYhIjHDDRAdB/ngRjer2d45rZUsQa4pc4PLPiOQKvbhW/Z3pKrDqLtXom94\nA3PwjO5NI5rf7f73BfEt/TuOqqNvmutmQcGV+4m2giTje+dBjBlfRaRku5qtZwpW2xG9MsEXxBo4\nFf+KpyHcgl3UrmspK+4xf7QV2WhDrtiNUnsEyQi7dbL/BMq+dfhWPO0G3B7nJE7tSVd/tLUVMjKw\nt23DaWhE9uuIllbUnJwux+nmtm3IaanEfve7aEX98c+agW/KZKBnAfwPB3DaiOFEFi9G+P2oKSlI\nfj/C7Gy6+jBtL7+KvWcvMd/8Oi1/uhetf/9uR/zHGsMcaQwzIrN7Q5+cnIycmYFAIAWDhF97Hafs\nMIFLLkL2+1H75CHHxhBZvhx9zBic6hra/vYkgdmXgKJgHzqEb9xY2l55DQJ+lMREWp96Bse2Ufv0\nQVRWoQ0aSMv9DyDa2tD69+u2B49/L17A6uHh8ZkgNZ9EPbwNu2AM2CaiVyYiGENMYBeGGY96tBRi\nEjEHTUfbvRK77yis/uMxR12Fun0xVkoOSnMt6tEdCFVDbq1HDjcRnXg9khV19V1DfmKr5mA1gohN\ndjVKt87HnHQjdvE0pIZqpOaTOL0HuFnI9s58qbGGwGt3uVnJtHyUfavxbVtIdNbXO4JGJ28oIqYX\nUv1xnLyhODG9UA5sQI60tteTznCbkoI9SEoF4yHchFA0JNvEzh+F1FSL9v7zBN55CCt3MOrhrW5W\n9QxlBlJDJVJ9pWtQ8CE1AjvJzT75F/0F5ejOzqAyNhknKRsnowC1fDeRK37Q0Yj1cRGheIQv1jUp\n8DinsMrLib63FP+MaahjRmNs2kToy7eg9u2DFBtCjk8g5mtf6QgMhRCuNm9BvluP6fPR+thfMfcf\nIPreEpwPFrrXAAAgAElEQVS6OrQBZ6/PBsC2aXvqGQiH0UeNQtI11OxsnIaGbl30ZtlhUFXw+7BK\nS7GraghcdUU38f2MWP9pg1Vo785PTaH5p79Ayc4m9Pnr8M+Y3v2d7NmDU3GcwFVXdJgcaIUF+Ma5\nFsvGpk0YAR+Lj6wnNyMfxRY4R44Q8+3/Rg4G0UePQhtY5GVYPwPOFLB6Ng0eHh7/NkRyNtFLvg2A\n/617cdIKMEZfSXSLhp0xkOglPwAz3K4NeglObDIiNhnl8DYkWUGrOogxeCZSMBFaalDKS7EzCnGy\nByHiU3HSC/AtfBBLasVftgqrV28kxwI92CFmL9cdRTlWgtRyEqtgDCK1vb6u+hBS80mEGQXbwrf6\nZSIXfrNb4GgNusD9F8dG27+WyI33gqriW/AAvncfRSRkEL7lzyBJ1G5ZTmprJVp9Oca02xCxSRCI\nRS3fCVoQwk34Fj6EsG3s2CSkcDNOu1PVqfjeuhdj9NWuTWxSFmrZVjf4P7GXyJSbcU7p4nfyR+Hk\nj0IkpiMirahb3sYacSlYBvqal5Fa6zFHX9ntHh8Lf6xbB+xxziGam7FrawHQ0tNRkpORLJvAhbMw\nduxARIyOsXZDA82//BWxv/xZl8543+yLkRITEfUN2MePd3zutLQgx/T8cyNJNol/ebDLZ+H5b2Pt\n2Uvcz37S5XNz82ZEcwt6ZhDLVJB0ncjCxQSuvLzLOOvwYZz6Btqefoa4u36JVXYYbdjQLvan8ff8\nDinQsxZw4IbriS5d5pbt+P0IIXBOnkRPVoiRdsAtN9F4128Ye/Qogb89hvohFYwzPbPHZ4eXYfXw\n8PjXcWx8S57AzijsMYvnxPZCOboTu994zILpOJkD3Eai9gBRJGVBTC/XTanqEHZaX+TmGtTj+xCh\neMzJNyJXlyF0P9rGOaj71yMCsWi7VuDIITeDmZyNerKc6AW3uF3yO95FPlmBMe021L2rUUtX4iRk\n4H/3McziGQg9gG/7QqSao4i4JKwRl3Vu2AjjW/wYdu5QN6tpW6i7luHkDkXb8DpKxV5AdlUM/CGc\n1D7UzH+cylAmacZJlKqDbrMUEtbYa1FO7MO35AmkaBsiGIc15SacPsPd+tYPITdWYmcNRD2wAVQN\na8Sl2AWjsXKGINILUXe8i3JwI1K01d2bPwYntQ9ySx1S1JXb8r9xN05yLsaUm3DS8//tzVVSzRE3\nKPayUp8qSkpKF41USVVdiSpNw9i0BeP9VfimTsHct4+We/5E4LZb3ezrKT8PbX97EtHcjJKcTPiV\nV/HNmI594gRNP/sl/pkzTm9B6pgkHHiAtqYYGn//v/imTHbtWgf0xzd+HNg2WFbH3Mj8d9DHjSEp\n4xDhnYeQCofhmzgROdQ1ExtZ/B7WkSP4L7kYOSODlvsfQBs8mOiKlbTc/wD62DFdlAqMbdsIv/4G\n+pjRnVtraiL8wkvoE8YjaRp2eQXNv74bfdYloKjYJKJPnIC1ZRtSSytOU7ObvfUC1c8cryTAw8Pj\n34vjoB5Yh509pNMatBsScnUZUq8gwed/hJU5EBGTBID/1V/hhBIRCenY+aNw4lLc4+9gPHZyNtr+\n9ahlW4lc/j20vWuR2xoQsoJSfwJzxGyMabfh+EP4Ns3FiUnESe0LtomkaKgb3kTdtQIp0oJSfxxr\n2MXI0Tbs3KH4374PK28k5rhrcQLxiNgktM3zXMkq4aAe2Y6T2qc9EFPcGlA9gFx/HBHTi+h1v3Ab\np2J6oe5eSWj2N0gXrSAcjKm3QrSVwBu/dV2pVB117/sITSfy+V+d8Xjd6V0EegA7b5irP/sBgTg3\n8DTaQNFQD210a1b9MUjRNte6tV1uy+490K0dPpuiwSdE4M3fIgIxbv2sx2eGmpPTIU+l5mSjFhQg\nJ8QjJyaiFQ9y6zJNs0u3uz5hHFpREaKpicBVVyKpKnJsLL4J45Fje2jQkxQsXxr+Iwtwgr1Qhozn\nma3HONIYpig9gda/P4G5cXNnIGlaSDEx1L1VilHegJKaihwTg7F+fZdaUW3QQPQRw1Ey0pFUFf9F\nFyLHxWKdqIRQEBQVpXfvLgG3iERR8/t2ulUFAq6bVft7kOPjkFKSaXv8CcI7j+HU1aMP6I82Yjja\noIFE5s4Dzqwr7PHp4AWsHh4e/15kGbtg7BmCVcAXwu4zAiGpyEEJM28iSDJK6QrU8l1u972sguZD\nJKQhUvOw84YhCYFSuR+MMNqBDUSu+yXmyMux+45GrjuO1Wc4xCahVB3E8cehNFS6Nqe+IE5yDkrN\nYUBgTL0Jc+hFoAfcTG/fUQhFxxx9BYE5v0fbthAraxDa7hWudJXjCpoHXv5Ft8YqJ6MQJ3do+3MF\n0TbNQ9u3Bid3MDg2cvVh1APrsftPwBw+23XXiklE3TgXyWjDnHTDv/a+o2H8S/5K5Npf4qTno698\nDqViN3b+Kfaw/tBHauD6pLAGTUek5H1q9/M4O5KqIie42UhJkpDj44m8s4i2F1/CP+2CznGShCRJ\nNH7/RxiHj2CuW4/aJw8lJeWM62ttZex5fh2NB5tgwTu0VdWQNXIIafEhtEED0YYP6wye83KJLl+B\nuWIlwRtvIHjpbOxjxzjetwbHJ6Os34ccH0fjj36KPmEckr/r/0u0vFzU9DRaH/8bvskT4YNgNDYW\ns2QX0bfm45s0sce91icGeDNfZkjeUPR+/bH276clMxkpHCEwcSJaD3JeHp8uXsDq4eFx7qAGsFKL\nQXKPjkUwAZGQjr5pHmpFKXZ6oRsctmdL/Iv+gjHkIoypN6Ovew193euu4H1Culubqig4KXnoa15G\naa7GLByHOfIynN4DCcy/j/AXfu06S8WlQDAeKdyEenQndsFoNxup6ig7lyEbYbRDmwjf9CfUrQvw\nL3wIc+w1brB6lvpPyTJBAt/aVxCaH7mhCvXoDszi6R1BvLrlbZQTB7DT8rGLp33s16Yc3IxStgUn\nsx9yzWGs3GGI5CywTNfm1YriW/akm7VWVPB9ytaRXinAv0x44SLk5BRk/xm++P2LqLk5rmXuaTKn\nSr9+aMOGEF20CG3YMJQPdfCfit3URPObK1ia3p8qNY7civ0EZyeTta8WfVAxVfIeZM2HLnVqG1vl\n5WDZBL90vRskJ8SjxSShH2rCeHUOcnZv9MGDUfLyOrKlQgjsigrkuDha7n8QbeAAokuXuVatO0vQ\nR41EzuqN2rcvckLPVsF+zYf0/npShIYciqHtkcdY0S+WhL88hR610foV/gtv1eOTwgtYPTw8zhmk\nE/vdOtcPjqp1P6JXb+ycIdh5wwi+/AscwL/4UazC8SjVh7CKpxN4+z7M/pOxMwqQFI1g2StExn0F\n5fA2nIR0rCGzsAbPQG6uRd/4Jua4azGHXdJVcsoykBwHq9945AMbEcF40Hz41ryE2W88Sntjlr7l\nbcziGTi9+3eryVUObEBqbUTEt2efhEDdtwZz1OWYQy9GkmUc3efquGZ12lDqG99E9Mp0tWdT+rhO\nVu3zEU5HAN8T+qpnUQ9sxBp5GYFX/scNWoun4Vv8COqxXZjDLsGJT8W35XWU1iNYOaPPuJ7HuUfk\nzbnI6WkoqT1nNu36BlqfedYtWend+yOvHV2zBqeqGjU7u8djfiUlGftQGcbyFdjlFfinTkEYBsaG\njd3sSF+Yv4YjJQeYlFhD4aBc4m/7Gq2ZCr2GzESWFVpFLboUgy51fnFy6uqQU1PQ+vYlsvhdWh/7\nK+rJCKFZFyGiUcwdOwlcdmmX7ny7vJymX9yFkGUCn78Ou6YWY8lSfJfOxjfRLVmQfb4zBqsAsiQR\nt3kX1rYdSLJC7B3fpqBXDrGjx6MNHOApApwjnClglYQQPU6sqWnu+aKHh4fHP4H/jd9i5w3DLJqM\nvnGua4GakoMx/XZ3QFsT+IKoe1dj9ZtA8JFbcVL7EJ18IyKtL1LtUdR9qwjG19BU/G30tx9ymzts\nE2PMNa7taf3xztpP20Ip24KwLdfpKSUPOz7NddAqGEd09nfAiLiB6Vkak9Qd76JuW4RVPM3txAcQ\nDr55f8TOGIB6dAeR636Jsm8NcuVBzCk3AaCtfBZ8Qcwx16Ctfgkp3Igx6xvu+3jpZ8i1R4le8l3s\n/NNbQgJILfWIQCxSuBktfABpzXuI+AzMwrGIYDy+NS8Tvei/0Vr3opiNRJInuXvevRLA01A9zxFC\nEHnnHcKvvgFA7P/8HPvIUSJvziH2V/+DEt+DtFo74TnzkAJ+/BddeNZ72SfrEA31GNt3gCRjbt5E\n7K/uwqmqpvXRx1EH9GftuJnE+1RGZyX2uE7rM8+ipKfjv2gW5v4DhOe9hZaXh//Ky2n+7e+RcnII\nXXctUkwISZIIz52HuauUuJ/+uMs60Q0bXaes3FyEENTf8f/Qx4wh5sYbaPzVb8BxiP/V/5zxmaJr\n1uK0hfFPmYQTDhN5+x2C13+hR81Zj8+GlJTYHv9APFkrDw+PT5XI1T8Fxyb45LeIXHA7JGfjX/ks\nZvF0V3Iq6DrcWAOnIh/chIiNRa474jYpqTpyQxWSDY1DfwhA9NI78S17EqrLUEuWwOGtGFNv7rif\nXH0Ifd2rYNs4gXjC1/wM5cR+nIxCrOGz3UFnqr0FsAy36ap3ESIY75oINNa4WVZJJnrljyHS2hEk\n2/0mYPdzRfoDj30FCQhf8QOQJMxJ13dZOjrlZtTSFTgJp7eDdO9vEnj2TowhF6KVbUUf2ou2cRfC\nwQP41r1G5NI7EYE4N7iIH4wJaOteRa49hp018KzZW4/zACEwt2xDzkjHd9GFaHl5CMNE6tXr9F38\nHyJw1RUf+VZKUi9EYgJSSSlKTjbBq68ksmIl4TnzUIsG4NTXMzP/zPWtANbefR26weaWLahpaQSu\nvRrhOCj5+RilpURXriKycCEx37sTOxwmcP0XCM97C7WgAG1gEXZtLXJ8HGqu+7slSRJq376YmzYh\nvvh59FGjEEaUpt//ASWvD/6pk5HT0kCWuzZmGQbWrlIa3l6AkuqaHCCEZ018HuFlWD08PD4TpNZ6\nRDABqbUB6iqQhIO+8U2svOFuYJjah8BzPyRy8e3QqxdCcz3HtXWvYQ6eBaF491h09yqsosmoW+bj\nxCS7TlVZg7rUVEq1R1EObHKdphQVyQy7JgNmBKmlDpGY2Tm2ugz/Ow8SvvFPHX/Z+ubfh4hJwrjg\nVncP2xai7l5J+PrfnfU5tSVPgGNhzvr6R3ovvgUPYBWMwe43vvNDx0Zf/hTIOlLdMaJX/QSpvgK1\ndAXmhC92NRP44DmaTyId34daUdqZvfY4L2j+6xOY69cTuO5aApdc9Imt2/bGHOzDh4m987tnHdv4\n81/gmzUL/9QpNO0+QnhbCfqKt0l48H+RdB1j+3ai7y4h9vt3dswRjgOSiazXIszeGNt3ouRmo/Rw\nXO+OlzB3lSL5fEQWvIMkSVjHjxO49hp8o0cRnjsP6/ARYu9w9ZyNnTsJv/U2au9MgjfdiCTLCMui\n9elnsY9XEPzC52h78SX0CRMh3AY+Hf+MGTT97g8EvvJlV2v2wAGCV135ybxUj08UL8Pq4eFx7tDa\niL7uVYxpt4Ekoa96FqEHMSZej1k0BaX2KCLSjB1tI3LVj9HfeQBrzLXYfTNACFcaq7UeEYqHSAva\nxjnYib2xRrZnkIRD8OnvErnwv3B6u77hytEdyC01EIpHX/U8Ul0F0St+gL7sadTdK12Dg4lfwrfy\naSJX/4zolJu6BIHGBV9GnCIPZQ69EHPgBR/pcc0Zt7s1qmepU9WadiPZEazcoa6m6rES/G/dR3T2\nHW4JxdjrEIFYNysky2CZSEb4tMEqAFYU/5LHsdK9ZpLzDVF3Ejk9vYu2KIBdV4dobETt07P8knAc\nmv94L8EvfgE1L7fLNd+0qYi6waed986+KnyKzPT2zGnotttQ2utWm599iWhNIyn3/QFJd38PjE1b\nkLO61tC2PvIocoJK3NdGgFaFPKoXjpmAEILWx/9O4MrLUDIyOsZ/UDdqlpQgp6YRe8e3sZubkRUF\nYZqISAStqIjAlZ3ZYSU7G9+okfgvnNW5jqq6klThNrT+/QlcfQ3agH4Ym7ci6ZprHHD8BM7hI/gn\nToDiztpyj/MHL2D18PD4VJGEjWSG3cALiF74XwDoa17GGHk5kmOhHNqC793HMQdMRnIc7HZ3KiSJ\n6BU/6FwsEIddMAZ9w+tEr/xR+xiZyEXfwknv9DM/1RDAmHi9Kz1VeQD1yHbs/FHYCek4mYWYo69G\nxCS6DlVOe5CpqIiYRGhrQts0D3PUFW7gebYyAiHcUgLNh2/xowg9gDHty93GSI3ViIQ0ZKsZyY5g\ntDtrCT0IjoUwwih71+Bf9BfCn/91x3OJtL4YaX17fs91lWBGUY/vwbCMT02P1eNfx6mrw2lto/n3\n95Bw7z1E165Dzc2h7dXXcKIG8T/8fs+TJQl1wADk5KRul5TEREg8fc2pI0DQeaiq5vclvGABSmoa\nKdfMBAnkuLiO68I0kemaDAtce61rvdqWiFO9EruiGm14nrstnw6naL9iW/iWPUF04vWY27ajJCVj\nDRxA0z1/InjF5YRfegV99sVYmzajpKaiDOiPBFiHytDbLVbbdwI4+KdfQMuhQzT8/h6cI0dJfPB+\nfBPGYZTupulHP8H/za/T9te/EX1vCaFbb+4oMfA4f/AKmzw8PD5VREwvohd/uzMzqKggyUiNlUhN\nNShl23H8seDYiLhk7D7DQes5ODRGXEr0kju6fOZk9nOzkI7TERh/gL7iaeTK/Whb3iY65UacXr2x\n+08EPYBVNNndS+1RAk9/B9+CTttJqakauXxXt/V6Qt35HoHX7nL3OPY6zFHdjyClmiMEXr0LLINo\nrzFEUtzGKG31iwRevct1qeo3HmSV6IjLugThZ8PJLQY9gDlouhesnmcErroKtX9/1PZMoLllG+a+\n/Vj7DyInJCDC4R7nSpJE8KorkGNiUHx7kJTqLtetgwfdo/gPcWn/NGbkp3b5zDEtWl5/E/PoEfSR\nbkOgMAys48fxjR6FfexYl/FKRrobFAPmrnLMXYc69hS67RaU1M71hRBEj5/EqalFzc0l9P++w8lf\n3c0yPQXr0GHi7/k9oSuvIPanPwafzzU6kGSc6mqMtes6b7r+NdRdbiOaf/bFLC1OpPRLszoywVr/\nfvivuBxj/gJoaIRgECnUKbXlcf7gZVg9PDw+e2QZY9JN0NYAmoY15kqU6kPoq5513Z3MCMgq5qQb\nkGqPoK9/g+js74Ik4X/rPuzcoZjDLwH9FH9x2yLwjzux+47EmHpLx8fCH4vQA9hZA3HSCt1g9cPY\nJlKkFWPcdZ3z0guIXvWT7mN7wnGw2mWtRA8NVSI1j7ab78P39v0oJ/bRdvsjrnFCKAFj8Ezs/pPd\n13Py6Nkzuh9G1Wn7yqNdZb08zkmE4+A0NiLawqi9M/FNGId9/Diirg6AmP92FSUkXcc6eoymu35N\n/D2/77KGuWcfkbnziP1RZ/ZVCBXozGoK26blgb8Q+tZ/oRYWYG7dijZ8eI+d8u8UjKJpVyVXb96G\nc+AgwVtvJbphI9FFi0m4/16sgwexystRs7q7tvmmnd4cI7J8BcbmLYRuvZmm9ZXETNNBUyEaRYuP\nJ25AP3xThnUYHsiBADFf+0rHfCU3B6uiAnP/QbTCfMJlFlJCDP5BIAnB2JT+JI3qrP+WFAWrZBd2\nRQUA2sAilOTkHv8sPM5dPB1WDw+Pzxypcj/6u4+htJwkevG3XL/vgjFE0woRaX1Ry3dj9x2JiEt2\n3bEaq7Bz3Fo8q+8olMoD+Bc9gjny8s5FbQOlbCvGmGs7lAcAnOxBEErESc933aBOg1ayBCtrICI9\n381OhpuRGishdGatxy7P1HwS9BBOal7XC21N7pofBAmqhpOUDaof/7InsBPSsYsmIzJcAwWprRHf\nokdwEtJx8oZ95PsD7dlrrwv6XMA6epTo1m1YO0toeepprCNH8I0YjtPUTOP3fohoaMTavgN97BjA\nDaz0kSO6rOHUN+CbMB591Cjk0IeMIXQNEY2iFXZm4YXdC0TnOEmW8c2aiZKSgmhupuX+B/CNH9/N\nVeoD+iWHyJVMtNpa1JwctOJilD65GJs24xs9CnPLVpSsLJSkng0GPoyxswRj6zbkUIjQV29HTUpC\nHzECZBlr4yZyk+PwT5nc43wlqRfm6rXYRw6jDxuKVjwYZI3me/+MFBuLXrKXwJgxNP3qbuTUNMyS\nErRJk5CCARzDJPj5z/1bjRk8/jU8HVYPD49zGn3Ro6hlm2n72l87uvvL6lv59dK9/G1iIr66Y/jW\nvYrdqzfRq37cbb50fB/6hjc6rulLnwAhUKoOEL7uLtd//PC2rtalp0FqbYDGGrTSZdiF49BXv4A5\naDpq6XLkmiOEv/hbxIcD0I+DEASf+jaR6V/pEnxKrQ0Env0+Zt+RqFUHWXfFt8kNJJNcugr14EY3\nU+uPwRp6dg1Nj3OTtjfnEHnlNUhNRY6LxSk7TNx9f0RJTcXasxelf78Oi9SeqPvyV1ELC4j9/p0d\nlqcfEF2zBqephcDF//rPiLl3L+FlK3FuuYWEwCdfTiJMk6Zf303oG1/rMD+IvLeE8KJ3if+fnyPH\nnNlZrtt6hkF03Xr8UyYTWbmS8Kuvow0fTuCqK2n8zh3IRUUk/OxjnI54fGacSSXAy7B6eHh85tgF\no3ESM11dU1nB/8JPSDn0PlPTdRL3LMecciNSYxXmmKu7ZEs7iE3CHjCp4z8dfyxOWj5ayRKchDS0\nkqVou5ZiDZ51xoxj4NnvIZ/YDUJCKS8lOv5zOPmjkRqqUE4ew4lNQqku+1i1pF2QJKx+E1y91lP3\noerQVIPk2BgTv8g6s46gopOYWoiTlI1dNNnNCHucdwjTRESjyHm5mCUlEI2iZmYSvPVm1HYLUiUl\nmfCbc7GPHOmSIf0wUmws0fdXg6Z1sxK1jx5DRCM9zm999nn3Xmmd5SnhBQtRMjOR9M7gN2zaGBs2\n0rhyNcuONVE8dgiRJcuwyitQc3M+/vNbFtH3V6Pk5GBXVyPHxCApCv7p0zqauIw9e7GOHiX2v76B\nHAiccb2WBx9GiotDSek81jfWrUOKjyO6YCH+aRdglu5GSUxAHzWS6Oo1BK+7FjUj/WPv3ePT50wZ\nVq+G1cPD45xAX/YkdtYgjIv/G7P3IPSyTcQOnIh5NASS5MpgfUREhvuXeeSy7yFCiehb5hP+/G/O\n6HcvH9+LZJtYxTNRD28DNYS+cwli7xrkxkrCN/weuaESZedirGHt2piW6QaePUlLnW5vpykrUA6s\nQyvfRdst9wPQ0Z4lHJyk7vWBHucuwrKILFqM/xLXprf12ecw1q5HTk1FjotDLixEiotDHzSwyzw5\nLRXrwEGEED1mWf3Tp4FpouTkYO7bj2hrRR/mZur1USNxolEAIkuXoY0aiXJKV78cEwOnHIULIdwa\n1n4FyAWdQe79aw5SYMdwcfFAZhekYDc3E3lvCb4Z0/6p9xF+cw6RhYuJrliFc/Qo8ffeg3yKK1fT\n3b/DqatDWDbByy7FOnAQtaDnL2dKbg7yh0oQrIOHiK58HzW/L0JW0EeOdLPVqor/wgv/qUDb49zD\nC1g9PDzOCYwZX0UKNwGgGG0IzUfgrT8iN1ZhNFbj9OqNbEU76lTVkqVY/Se6lqoA2Mi04nBKvWp7\n4Br+4m/Pen8RTMCJTQI9QPQyVwxdaqgCWUGuPoR/3j2Eb/4zdu4QpKMlkJiBvvpFsE1XmqtjHx8f\nu3A8bbnd61PVkqVoOxYT/tIfTztP2b8Opby0u1yWx2eG09xMdM06rGPlBG+4Ht/0adi1tfhnzcI3\nfBiNv/oN8mmkpbTCAqJvzcc5eRI5Lq6jy/3DCCEw9+zBOnAQJT29I2Bte/V1nOPHif3h9zE2bkIK\nhZBHjuhwwQpc3VWlQpIk4k5zTP6d8X1R5Xz8qqtY4bS2oub3xX/B1NPu5wM71dgf/7BDV/UD7Joa\njO07CN5+K/axcpyUpG4KBfqMGahFRSjxsUSXLCP6/iri7+rZZjVw5eXdPgvdfBPa0GFogwchyTLq\ngH6EX3gJp7WF4DVX97iWx/mFVxLg4eFxTiASM3BS8gBQd76LiE9FsgyIhpGrDyECsci1R7ELx7mG\nA8ufwknOdTVTAT9lBKW9RMn75zbgjwEETkIGknBA8yO1NhJ48SdYBWOximdAKB5l31oCCx9EyAp2\nRj/UgxuRTKPDpOCfQpJO283vpORhF4zpWSHAjCLZ1j9fouDRI8KyugVgHwU5EEDKzSby1DOYe/cS\nuvoqfBMmIJpbaP7Dn9CHDCHm9u6nBXIohH/mDFoe/At2xXH0wcVdrkdXr0G0tiJaW3HqG7B270Yf\nMRzrWDmRhYsJ3Xwj+uhRSKqKb9JE2p56BudkLdrArplcws1dmv4+CCCdmhpa7n+QmInj0U6pj5V0\nHX3E8B7fhbF5C05NDUpuDkpiInZ1NXb5cZTkJCRVxWlpRdZ0/BddiLVxI3JKCtH3lqK1P1/ro48h\n+zS0/HzUvn3wTZlyxjre0+G0tRFZ/C6Ew6g5OdiHj2AdOow+ZhRKulcKcD5xppIAL2D18PA453Cy\nBmH3HYlyYANCkRFxqZgX3IZSdRC1bDN2wRis4ukdwSqARSJRcjibvLS24U2cxN7dM6LCActC2/wW\n/qVPIDXXIbfUolSUIpBRT+zBScpBrqvAmH47dv4o1D2rcVLzMEdfgb7yOdfgoIdMq++dB5EbTvQY\n2FZXNnPkUD0paac0nEhSZ7BqRlGO7kQkZqDsXQOqhkjO8YLVfwPNj/+V1v99EG38OOTY2I89XzQ3\nY7eFcbbvwCgtJbJ0GdGFi8CyCFx3La1//RvasKFdsqgtDz+KMAyCV12JNmSwazkqBE0//yVKVm+s\nXbs7NIDl+Hh848ej9i9Ejo9D8vtQc3ORVJXomjVYZUcIXH0F0ZWrsI+fwAmHUdsdpoLPfR87PrXD\njobxu7EAACAASURBVLjp13e76gL9++E0NKINLPpYAaM+ZDBCVWl99HH8l84msvhdInPnoQ4ciNIr\nEWv/fsL/eBYS4tHHjkHWNMydJeij3QZI38QJqIWFHfc8270jy1e4dbenmBCE35qPuWUrTksLVkUF\nzsmTxH7tK16weh7iBaweHh7nLFs3HOOtV3Yxcnx254d6AFQdKdKCUnkAOdIMlokx/jqcxEx8K/+B\nnT/6Q1anEmf1QhECbdNcnNS+ENP1WFY+uAn/2/ejnDyKQMLKKkLb/T6SY2EOuxiltRHhC6Ae3IRV\n7NbzKRW7kesqsAvGou5ajpNV1J6pbd9RQxXalgWAwEkrwMno1+X6qWxZX86+0hoGj8jsdk1qqsE3\n53eoR3diDZmFvuENhKIh2jPS/8koe1a7BhAfQ1LsX0UKBXEiEaJvv4M2ehRyMHj2SafQ/Ou7kRyB\n1LcPSlwccnIyodtuIXjN1chJvbCPlaMPGUzrI48johHU3FxEJIqan4/cq1dHNlOSJCS/H/PgIcxt\nOwh95cuYJbsIv/IagWuuRElNRfL5sE9piDJ378FY+T76sKFIgQCioR77YBn6KFf43+o3HpHalw8U\ngtTcHPTiYiS/H61oAOHXXndF/VesRD1D8GodOYKxZStqnz7Y+w6A7eAbPw45IxNj9Rrs4xVYe/dh\nHz6CPmMGoroKUVfvynKNHoV1rBxhmW75w0cMkIXj0PbkMwhZJjJ3Hr7x4xBCoBUNwDdxAm1P/wN7\n/wHsPXtR8vJQvEar8w6v6crDw+OcZfG8PWiaTE1VMylpndmswAs/xhh5BSImCbm8BH3zXMxx16Cv\nfxPMMMruVfjWvEzbLfejai0Etz1L06Cvo5auwho8A7nyIPral4hc+8vOjnxJOq0sFoCTP4rIrG/g\nX/QXrNyhaKUrQA1g9Z+CvuNdorO+jlxfSfSS73TMEdE21P3rkZpqkVrrEcH4LmsqFaWoB9aBLOMk\nZSEUFXowEZg8NRtpmE43LcFoGKn+OEpLPVZfN+iIzr6j2/z/VJSKUiQrgvWvyIl9TPSiIqSYGMIv\nvIScmIixdStSKKZbZ/7paH32ebQJ47HLK1B6JRL64he61aOGvnQ9kaXLsBvqCbSv6ZviqlyIaBQU\npaP21DdxAs6ixUhxcSBJ+KZOgYSEDtco8+AhIu8uwTfZnS8FAshpqQjbwjdhPOCK6Dfd+2dEUxPx\nv77L3efjfwNN6yhPsCqOE37hRaTkJOz6euyyw+4XhVPtVE/BrjiOdfAgTJ+Gf+Z0/DOnAxBZsgTi\n4/DPvoTI0uVgmvinTu4mVRV+cw7m1m3IOTnE/8/PP1L5hSTLxP/mLuyTdYjmJqyK47T86V7i//A7\n5JgYYn/yQ6xDZaiFBah5eWddz+P8wsuwenh4fKakpscSCdv0KUwiGOr8i10E47H7jEBuqESuKkMS\nNlbmANTDm13L0SGzUI6VoO5fi5k6CHnzcuzUQnwb38TOG+GaDCA++pG5JCGSsjDHXoOdMxh913Ls\n/FEY07+MZEWxs4rxL/oLTkpe+9rg5A3D7l2E0ysTJykHp3dRF7kqJ7UP1tCLcLIG4lv0MHJDJU5K\nH9Rdy3Ay++Fb8ADYNiI5B3Xne+gb3sTOKkY5VoJoVwfQti1AK12BnZiJUHWc9qD1/wp235FuRvxT\nRjQ14VRWIScmYGzchGhpRevf76zzjJISCEdcp6r6BrThw7pkaKPr1hGZv4Dolq2gaTiVVWjDh3Vk\nGVvufwBj+w5Eayv28eMo6Wlo/fu5wacQNP7051hbt+KfNRNJVVFSUvBPu8Dds+PQ9re/E7zmatS8\n3C77ck7Wga6jD3ebtJTsbLSiAR17ExKE33kHffJkAhMn4Js4oSOINLZsIbrqfZS8PMKvv4GckoxW\n1N3YACDy5hzssjJ8M2agDRiAVVlF+OVXCFzk6sO2vfY6TlMz2sgRGO++5wa3U6ecMWAVQiAamzoM\nDuRgAG3AAKSYEHJGBkrv3kiShIhEiLzxJoErLu+mU+txfuAZB3h4eJzfmFF8b92H8McgElIxJ3zR\n/Vw4aBvexBw2G3zd9Rul6jL0LW+77lmfMWrpCpzYJCQBaskSorPvQNm9EgSohzZiTPwS2vvPo1Ts\ngUAs4Vv+DLaFb/59GJO+hL5pLnZmf6zBMz/rR/k/gxCCxju/jzZ+HPqggWiDBp11TssTT+HU1BD3\n4x+e9rpVXo61bz/6lMkYa9ZirF7j1sqGQvhGj6Lxt39AKBKisgo5GCJw05fQiwbQ+OOfISyDuF/8\nHCk29p9qCDsbDXfciTZmFP5ZM5FTUsC2af3Hc2Bb2CcqkVQVOTER/yUXdctgNj/8KM6J48Tf/Rua\nH34Mp74Op64OOTYWpU8eMbfeQstDD+NEo/hnzUQfOgSnra3Hcgu7pha5VyKSomCWltLy8GPEP/Bn\nInPmEbhsNpLfj3Ac6m//GlJcLIkP3P+Jvw+PT58zGQd88j/xHh4eHp80mg9j3HUIfwxyQ2Xn55KM\nOfba0wargGuNGpN0+mufMtbAqTjZxQjbRG44AYA9YBJOfCryiQNoS55APbINyTERsoJUfRhkGRGf\njrZ9EdFZ3/SC1Q+wrX/7LdremINz8iT+q6/CqazCPlZx2nHCcWh5/G/YtbUAyAlxSB9SfBCGQfOf\nH8BubETNysI/fRpOVRXhF1/G2ruX6HtLEI2Nbl1p00nksl2ImlpCt9+KXuQ26fmv/zz+L3weOT6+\nS7BqNzVhbN/RbV9OUzNOOPzRHjbSgtRYRexPfoS57wCNP/0F9uHDhBe/h7FiJXJGBnE/+RHW/v2g\nqqc9bhdCoA4Zirl3H+b2bRDwE/PNb4BtE7rheoRpYtfU4Js4kejylTTf/7/Y+w8QWbqM5vvup/mh\nh7vst+WP9xKe9xYAalER8b+/G8lxsPbtw2lqdgdJEurokfivvuqjPafHeY1XEuDh4XF+EJuEkzsE\ndf8GnLR88IfOPscfwskZ/O/f28dAxCbhxKUiEtIIPnMnqDpKRSlyUzVO1kAi029HaawCScLJKMRJ\n7I16cD12wdgzGh/8X8E35x58K56hefsxnKiBkv3JiMILIbBKdyOnJCNJEq1P/wNJlRF1dag52fgv\nmtVlfGTpMhzDovW557GPHsMxTfSBA3Fa25B0DW1Af6KrVmO8vxp1YBGRefPQx4xGCoXAtlESEtBn\nTic6Zx7qsGGErrvWLQvw+TF370ebOAn/jOkd91PT0ztsTJ22to4jb2P1GqIrVuKbNLHL/pp++wfC\nr72OkpGBktm9ke9U5JfuQ921HDH5KuTYEHZlNU5NDfqkiajZ2QQuutDtyg+G0EaPdF2v0twaWmHb\nIEn4x45BHzQQOSaEnJhIzA3XoyT1wj99GtFly2l55FEAzPIKnOYm5PQMwnPnYm7djj5zBvauUpSs\n3iipKe4zhtswtm7DP+0Ct/nM50NSFHyTJyGH3N99SZLwjR6N5tWr/sfgqQR4eHj8x6Ac34udM7ib\nfJRUfdit/Uw+x11tZAWRmOHWzLbWo+1djfAFQZKI3PhHiEvB7je+s/bWH8LuN8ELVttxknOx0/Lw\nH1mHKNuHYceg5mSffeJZEM3NNN/zJ3wTxyP5/ThNTRglpRAOE7rxhm7jIwsXI1qasffuRY6Nw1r1\nPvrMmah98pAT3LpXJTUVhHA76Q+VoRQW0PbcC0TffQ/f1CmIaBRz2za07Cy0wcW0zV9AZO5bCNtG\nSojHN3ZM9322tdH4vR+gjR6FtXcv9vHjxHz9q93G6aNHow0f6tZ6nuZnJ7JsOcb69WjFxYQrwxhJ\nhah9+oCmE35rPk5rK9b2HShJSWgD+gOg5fel+Q9/xFy1Gv+ls5EkiaZf/QYsCzW/s85YTkwk/PwL\nWIfK3OasQ4dQs7Kxtm2H6mokfwC78gREDdQhgzGWLEVKSsLcuRMRDmOVV2CsWYfaJ7ej5tbj/wZe\nDauHh8d/PGrJUuTKAxgzv/ZZb+UjIx3ehm/da9g5QxC6H2vUFZ/1ls4fHIem++7HOnwYrbCA2O9+\nNOUEIQR2dU1HhrBjuYYGjF2laEMGo7Rrr7Y89zzOiUrifvC9HtczjxwhMv8drNZWREkJvksvQc3L\nw1i7jtB/fxNJUToCxui6DYTffZeYW29Bzc6i+b4/IyengCwjJyQgDAM5LQ2nohy5IB//yNM32FlV\n1ahpqZg7d2IdPkLg8ss+0rMDNP/lEXwXzkIyDezaOvxTJ3d9P45DdM1a5LQ0aGxEG1yM5Ov8cmiV\nlSGlpaG0155aR46gpKd3jAkveAdj7Xr8F1+IXVeHc+IEMV9zA2phWYiWFqyTdRjvr0YryIdQiPDc\neSiJiTiNjehDh2Du2oVVUkrgtlsIzJzxkZ/N4/znTDWsXsDq4eFxfmFboPxnKfI5wqHK3kGqVIQa\nCSM+Rc3R8xlhWRj7D2Bt3nLaLOjpMPftp+XBh0h44H5EOEx0+Uq04kE03/MnpMwMJNMi9OVbaXnx\nJaivJ/53d3dITAnLwty5E334cACia9fR+vCjEAigFhdjbd8OKSkosbEI4YBposTEEPvD73fZQ+tz\nL+C/+EK34UhVMXfuIrpqFfroUfgmTvhkX9KHqP/mt/BNu4Dg56877fXD3/sJhqLQe1B/JFUlcP0X\nsKuqOowHzoZwHIhEkE5pphLRqPslITuL6Jq1ODU1BK68goZf3uVqyQJSfDzqwAFEXnsDu/YkSmE+\n8T/8wSfxyB7nEV7TlYeHx38EUmsDwSe/BW1Nn/VWPmEcTMLIpUvwz70HZd8asIzPelPnPJKqIrW2\nYu3Z2yGE3/b8C7Q99wLg1lfadXUAhN9+G/tkHVq/QhL++AckRaFtzjzCb81Hyc3Bf81VBD93Leg6\nxq5diHAEyR/oUoph7ttP24uvIIQg3C5NpRQVuXqlfh/akMGug1MkjDAM1MJCAl/4XJc9uxJNjYi2\nMFIwiKTrRJYsQU5Owm5uwTp69CM9u7l3H8bmrdgVFbS+8BLhBe98pHkJDz9I4IruGdmWRx8jsnAR\nUUWjRQ9gHTxI4LprMHfvofm3f6D5oYex6hvOur4ky12CVYDIkqW0PvmUe13XMbZtJ7xoMcEvfgHR\n2IT/qivxz74YY+Uq9BnT8U2fhm/qBUSWLMFpa8MqL/9Iz+bxn41Xw+rh4XH+oPmwMwcgep25ieR8\nQ5Jk4uVspJR85OP70DbORcSl4F/0CFbRlP+4jPIniZKZidPQSHTBO/gmjMduaMQu3YK8fSVtR2oJ\nP/0PArMvIfL2ApS0dJTUlI6GpbY5c10d00kTMVaucmWbYmJwqqqJ+9Z/4Z81E3Pbdtqeex7fxAnu\n2FkzicyZh7l3H0pGGmbJLoiEwTCI/+XPMdeuRRs9isDFF6EPH4aSktJlv5IkoY8ZjRzvmkyYhw9j\nH6sgcM3VWGvXfXS917XrsMuPgSRjbN6MHBOLVtxVdsvYWYKckgy2DZaFpChE3l5A69+ebHfw6lTX\ncNraaHtrPgm5WaSNGY6S2Ru1fz/U1FS04kGEX3wZrV8hylkyrcK2afrpz1H79qHpnnsx9+7Df/WV\nbvOULKNkZmKs34C5o4SYG2/AN2USclwccjCIWjSA6Ly3Cd12M2pWb9qe/gdOW5jonLn4vdKA/xN4\nTlceHh7/GbR3zn+A3rANy5+B4z+9e9R5hyxjtzeNaZvmYacWgKqfZZKHb8Y0xAi3OceuqMAsr8FW\nwN5QhtzuJx/7/77bbV7wskuJvLcEgNBttxDdspXIU8+gZGdj7ipFHzkCpU8e2gi3BEA4DvbBQ+hj\nR0NMiPDzLxL68q1o7aL+AEphIUpW1kc+Qrf3H0BWFZSEeEJfvf20Y8KLFmPt2InT2Ejg8svQx44h\ncPmlnc8/cXy3OcKyaHvyKWK+8y2iy1bgNDUR+vpX0YYMxti+A+vAfpSksYBrZhBd9T5KWjpWeQVy\nYSHOrlKkSy4CQM3JoddTf0dYFk5LK3JMzwodkqLgv/wylJwc1g5NYeC2gwQaGpEyO99H3I9Of9Qv\nBwIouTmgqkiqSvzdv3Yz55decvYX6fEfj5dh9fDwOG/xn1wNkood+GjBwfmAyOyP3XcUvuVPIcwI\n9pBZnkLAWZD9frdpSQi+f9Agt7gfmUOLsWtrSfjd3T1PDPx/9u48uqrrPPz+d+8z3FnTlYSEJCSQ\nmMFgMNgxYOMJJ2kdt8tJm/bX1dRpprZJ0+Zt0qZp0maqM7RNm2ZqmmZoWmd0bMdxY8fzEBtjMGAw\nMwiEJDQi6c73nLP3fv+4skAGbJx4SOB81vKypTMf69773H2e/TwxpJTYszsq+6mrwz92DDM+hvED\nvCc24SyYj3AjWOk0wf4D5L/4ZaI3/DYinUZIQfR1r0VaVqXsE2C3t2PV1p71ududc3AnA+IzXl99\nfWUSlAAzPnHKSOrJ8t+ulNlyFi4gcu0GrNpa7Nkd+Pv2U/7ZPQQ7n6H6Qx/Ebm2d2kb19KLHjiNm\nNKK2bsNdtxaZPLUVbenHd1D80a1E11/+/Nc0qw0hJZHmFuwNV1FT1/i865fuuZfCD35IdP3llUle\nJ/29CyGm7m3o3BeWtQqFQuckv2rRORWsPktkhrC3/RSrnIPxIfTci1/tU/q1IISgNhHhwmVzcZpm\nYDU3oycm8DZtwmpsZOIDH0S2tSEbGhBC4G95ClFdjdVUGaFXAwOUbv4uQ3FBMV1NdP9h1OAAat9+\n3ItXY6XTRDZcTfbDf0/plltxliyZKuz/yzLlMrkv/wf2okXIyPRRdRmNVuqTBj4mUJXZ9WeiFFZz\nE1Z9eqrdqy4WKd91N6KujtRfvveUMld2WyvB0zsQxhBZv57Y5etw5s2l+H93ISLuVPqC3dWFu+qi\naVUDnk91vJqUFSH/hS9hzWpDTlZfACj+9C68R3+O1dpCcOgw9rx5eBs3Pm8wHjr3PV/AGn5tD4VC\noV8xpnoGqqkyuiXEy9/V6VxySVsdjiUR0Sju0iXo8XHU0Ailhx8hcsNvk//yf6AmJ/Go/mPowaGp\nbdWxY1Bbi0glaV67nvibbiD51htJvufPptYRloW7bi3R37qeyBWXVwLNr34NUy4Dla5Wvwg1Noa/\n6UmK3/kuAEH3YUxQ+X/vK82xbAl3+XJir93wvPtxV1w4LQc2959fw+RyVH/iY1S99z1TFQ+gkuJQ\nfOABMp/9Z+ylSwkOHkK2zERlK52kij+5k9w3vnXi2m17WtB5Nowx+AcOEkx2AvP37qPww1uw583D\nXroE1duL/+RmRFUSPTb2ovYdOr+EAWso9Ap4bvk4Ywwjaj/aBPi6xJDa/SqdWehXlbf+jxDz2hBn\nMQEndGaR1auI/fb1eBs3Ubr5O8Tf9lb8vXtRfX0gJc4FS6bWzf/7lyCbYd5HPkl84SLU0BBBf/8p\n+4y97rXEr78OcjlKP70bymUwhmBwiPG/eB+6UHjR5ykbGhAd7diTI4z5r3yV7Kf/CZXJcPcdD3PT\njzdRfvxxgiNHKD/8aKUzV3f31PbBkSNTgebJTKkMGrwtW/F378GUSphSqbKsWMT76d3IGY1YXZ1E\nrryC4m23U7rjTrTnIW27cp0vggkC8t/6NsUHHsTfuw/d14fJZtGjxwl6jmLKZXSxhDp6FD02TnDg\nIHpiAsoeyXe980Xft9D5I5x0FQq9RPLf+Bb2wgVELjn18e1h9QhVogWBoEyGJnkBY7qbYb2XNnEx\nJcYxxkw9wguFTFMXudf/06t9GueE0o9/glVXQ+ztb6V03wOVGfbr1uBv3oKsrkLOmEH+O9+r1E09\n6XG7HhrGjE+cdp/lJzcT7NiJKZWmRmCF55F8//9Xqa96ljKfvInodb+BLpUwR3qmunalPvoRCv/5\nNfB9rmmv5tKDh8l/4VYi11+H6utHNM0g/6//RvVnPo1MJih+/4dYc+YQv+G3p+1fDw0R7N+HHhxC\nVFVRfvBB0OBctAK7cw7Vn/nUiWs6cBC1/wBqx078J56o5Oq6Z378b7QGoydzTCvvXcbz8LZuxWzb\njjtvLsk/eSexG9+Cd98D6KNHUUeOYs3poPSTO0FKEm+9EWfp0qluWqHQmYSNA0KhX5I2inF9hNh9\nh7A755Brt7FxSNnN5PQgRT1G3owwQyzBSI1HnpipI9BFetlMm1xN3KpjWO2lRszCkbEzHmukUKbo\nadpqzrxOKBSazigFSiHcSm7oxIc+gimXcC+9lMjl6xDRKBPv/xuM51H1qU9i19e/4D5Lj/4cc/w4\nsTecGIGc+IeP4S5fRuy3rn/+jVUZpANCUnrwIdyLVlK+/wH87sNELlp5SvOA0v0P4T30IPF3vA3Z\n1MTEe9+HvWA+yXe+fapEl9EahEAIgfF9Mh/+eyKvfx1We3tlEtTkl+FnR1dzX/oK7soV2MuXI2PR\nSn3U/QfwN27CXrkcoTXB0zuQjTOIXn3laS8j91/fIL3WxV6wiLw5MXGs9LN7KD/wENU3feLEvcrn\niV27geBoLzJd96KC+tD5I+x0FQq9xIzzDMeDASw1jwgputVDNLCABmc+B/x7AEG1bKagJygxAVRe\nSh3WZURkkoPBXVRHAo6XHAyaZrGco2YjjbtipDvXUYoVyTFCg5w/bdT161uOMFLw+MC6uac/sVAo\n9IKM1lAuI2InvvgZY8h8+B/QKkD4PtWf+sdp+Z7P3f65E5cAdC4PrlNpaCAlpf+7C2fZUqyWlmnr\nVXX/F16yi1LD9Nn23u49FP/3Zqo+/tFpr/v8d79HsHsvdtccrM5Ogu7DRH/jddg1p++IZoyh/Ohj\n+Nu2EezZS2TtGmRNDWrgGIkb/2jaupmbPo3d0UH8934XADV6HFlXe1ZPe9TEBFJlkHVpDCd1tlIK\nnclg1dZiSiXy3/o2iRvfMvWFwWhN9pM3Ef/938PunPOCxwmdP8JOV6HQSyzr24z5HhJJTNYgkHjk\nUcZjtnU5CVFLQe6n1mqjSS4lwQwEgkG1A4DZ9kXMcBuYa19Dp30FRipi1KJ+fC/eE0/Qo58gq49x\nKLh/2nFvXDGL96/tejUuORQ6ZwgppwWrAMVbfoTsnI27ejWR124g84l/PO0EKqMUE+/7K7x9+wgO\nH5m2TCYTFL7xLfL/VenqFBw5jBoeBqDwo1vJ/tu/A5BteSOl9PRRVG/zFuyuTmQqheo/Nv2ghSJq\n/36M7yPjcbwHH6L47f855dzUyB7Kj9yKEILoujXErvsNRG0t9kUrsebPxV669JRtrMZG3HVrKD78\nMMf/+B1M/MX78J7c/AJ3cHLb6mpEXRvaxCodvLSmvOUp7unfwkGnMpKLrrSoLT/wELkvfhn/8GHK\n9z9QaVzQ3HRWxwmFICxrFQq9KCWdoSfYyKjuwfRIGkwndjwKQEykGda7KZpxpIngG0WjXAwYRswe\nmuQyJJKyyRCXLfh+PZ95YiPdI5LlTbU8dEARX/taZi5dTEI0kJZzAYlPnqioAiZrEoZ5rqHQS071\n9qP7erGqqtHjExjPI7Lm0mmvN3/HTvTEBNbs2aj+YxS+8U0i6y+bGjkEsDracRYuQMbjuKsuwpps\nXCAbG7FaW7DSabCiICq1RUv33AvJBPmv/Ae6VEakUohoBFMqY9XWYLTG6upEjYxizZmNv2Ub7msu\nniqzdTKp9iPNGCK9GGMM73+8j671lxC/9Raiay7F6Ty1HJa3eQv23Ll4Dz+KKZeRtbWYfIHIqovO\n+t5NfOyTFL//A6yWFvKf+zcSgaDqwpUk7CjCcXBXr6q0a3UdyvfejykWSfzum6bSGUKhZ4V1WEOh\nl4jEQhmfPEMM/fVD5HYdJbq+mbQ1h4hIMKL3USZHnmESopGUbMaVcWrkLOKyFoFkWO8lr4epFq1E\nHZ+F6SZ0pIe8HiUpWmhMujgihhSSkhljVO8nIepRxsfCCQPWUOhlYHfOIXLJxTgLF5D/6tewFy0i\nckFlRFLncgjXJff1b+I9thFnwXzUnr1UfeiDyOeM1Mp4/LT5mTIenxZgGmPI/cvn8A91Y7e2EXvT\nDRRvuYX4tRvwn9yMHhlB1NYw8dd/O9Ui1Z0/DxDEXruB4vd+QPmxx4lccjE/3j3A4fECXc0LEOnF\nBAMDyGiUuGPR+YV/xvT2YQB36ZJTzstZugT/yS04Fy5HFArIxgYolIhcvPqs750aGUL39CDr6zFK\n0fi2d5CMV02//lQKkUrib3qS5J+8M2wGEDqt5wtYwxzWUOgslFSWvB4h7cwGwBiNCgIm6MGXRRrE\nAgbUDnIMEKBoEouJympiohopTuTBjaoDeLqAFgEzreUIUcnKGQh2YgiY6bZjx5/Gz13Gs7Nu+4Ot\n1IrZHNUbmWldSFLOoKQnUMajxARpK0wRCIVeSkFvL1ZTE8K28XfvIff5fyf+l+8l/+nPErv+DVMT\nrZzsXiLj28i1/e6LPoYxhvF3vxf36quI/9YbGHvHnxD/wz8gum7t1Dr5m7/DxM834rgO8QuWkrjx\nLVPLsl/4IiISJfn2P+aeA0NIAcrAhq5GJv7uI0Quv4zIurVk/vlfiP/xW7EbG0/Ju81/+3+QLa14\nDzxA1Yc/NG2k+BdhlKJw+48Jtj1N9cf+/pfaV+j89Hw5rGFZq1DoLPTqJyiToco04YgYCo9uHma2\nfTm2iFDSWXyRp0teiyUrIweHggepFR3UWh1T+5HGwhFx6u1KkGmMQePjmQwahVJJKCwFxGTtVsNM\nuzL7tlNehSUqj9D61BZKZADDkN7FfPs3kCIcsQiFXgonty215nZBIk7xK1/FXbFiKlj1tm4ld++d\nWBGL2J+/+GMIIUh94K8qXbaMwW5rxWqrlLTShQKZD32Y5Afez601c1mq86z7jcvwu7uxW1oQrkvq\n3SeaGazr28PuB5/g2x0ruWrOFUSvvw5n6VKC7sMI28FpaqJ0733Yc7uw29untgt27yXa3kH1xz9a\nyUH1vBMTowqFymP8F6F42+0Ez+wi+d53oyYm8Pftg7FxohuuefE3KBR6jjAlIBQ6Cwkxg1oxbisq\nxQAAIABJREFUm4hMApDVwwhjoQkQRtBvnqJRLiEiE1Pb1Ih2YvJET3FjNCP6AEYoUrKS1zauD9Ov\ntjHbuZyyyTFqDlAjKvUIB9R2+vVW4qIOR8SnBaRlnaXIKBFqMCjq5bwwVSAU+mUZg7P5NnR6FtiV\nwC3oPkz53vuJ/fm7cZYvQ8ZilZFKIfF7B1D5AKM1/tM7kDOaEILTVBdQCNlHcCSDSCZhssqAv2cv\n/rbtyPo01qw2Cv/5dSJXrke4LlZ9GntuF0s2PURbezP+1u3kv/JVZG0t5bZmbCRqaAiZTGIw1KC4\n7MBTFG+7HX/LU5V823IZbAuKRYrf/wF2Vxd264mKBerYMZwVFyKrqijfdz/5r38Tu6ODnY/uJ/LF\nfya64eqzenRf/L+7wLLAD9DHBohtuJrC/9yMt3kL3panEKkUdkf7C+4nFApzWEOhX5ItXHwKeKpA\nj3qMwBRxRYISx5HSIkotSdnAgN5JwYyQlI2nBJCjej9FM0qbfcnUsqiooUq2ILE4brqpZz4eObqD\nhxE4JKjHJckxvbXSeEBIRtUBfFPGGEGXewVpOTcMVkOhl4LRuJtuRc2cD5M5mCIaqXRjOniI0h13\nUn7kEaLXXI1MJsl/8Uvo4RHc1asgCCh+/wcEg4M4ixehjh8n98UvI9Np7EYLqbaT+dQP8LZuw9/+\nNJGLV6MHBynddRf+1m0QBETXrsF4PqWf3sUjnRfy7e29rLMLFH9yJ/bCBcSuvw53xYV8s/chanuG\nsD73JfxndmPVp4ldfWWloYBtYdXWog4fwUgJAwNE33gD9qx2IqunT6RyL7gAWVW5TqutDTU8jLdp\nE7cda2R+skCkNoV1FjP5vYcfwagAdeAgsq4Gd9kynOXLsGbOxLv3Pvwnn8S5cv0p+b6h0HOFAWso\n9Asq6nFG1F7KJsO47kELn4RoIMcAjdZC6qw5REVNpbSVkAgjiYkqHHHqG3NM1FIlW7FOymkVQkz9\nPKL3EVCkVnZQMhnKTNDuXIrExjcFolQzpHchsIhZVfz3Y3G6xwosa65+xe5HKHROE5Jg4WVTwSqA\ncBzshQuwmppxVq/CXbMGq7oanctR+tFt4DjErvtN/F27UQcPYZSi/PAjlO65D320p7J+fRuaTpyL\nVlK87XYSb/lDrNparJnNGM/HufQ1eNu2Q8RFDw1Svu3HNC7opHleB7MuugCnqxP3gqVYDQ0IIViQ\nbKGxuZ3oFesRUmAvWICMx3Dmz8N7bCM4DjUf/yiqrw9/y1PEf+eN2C0zp12q8TwyH/sk9vx56EwW\n6bqUbr+DxJ++i9dcuxCxbxdB92Eiq1e94G1zV1xI8ebvgdJErrwCq74ePTAAWmGvXIFRisjq1Qg3\nrAoQen7PF7CGdVhDobNQNONERS2BKZM3w9giRlROnwWrTUC/3ow8Q2q4EBJbnH5Sg2fylMkCAks6\ntNgrmGldyJDaRdGMUWfNQQuFT4F6q4tq2cbvLJnJZV3QE2x8qS83FDrn/Xj3AA91j5zVulY6jbN4\nIe7CBTiTrVNBgOOAEJhyCTyP6n/5LKrnKGpggNjvvgmExJ7Vir9jJ8Xvfg+rtpa6L3we/4lNTHzs\nE3j7D2AvWYQeGUHt2Uv5lltRI6MQj5NesYyDfaOMF8rYnZ3TJkQl7AhCCCY+9kkMAlldeS/ShQIm\nk8FdtRITBJR/ehf2pZegtabwwx+hslnURAZTLjN+06cR8Sh6aIjcpz+LiURwV63EmhxxjVxxxVSb\n2LOR+uAHsDpng1KVxgBf+DKlu35G9MLlVL33PchE2Nkq9MsJJ12FQqcxHhwlY/qY5VyCxKFf7eTh\ngyNcOjtKldWIJmBUHaBsMsy0VwAghU2rtRpXpF708TyTQyCoke0MBE/jiBhpay65YIgRs4+SHqOG\nDlJWEyAo6wxz6qrwtCBn/Jf46kOhc58QlX9+EbkvfAnnohXUfvO/CA4cxJk7F2fuXEy5TPJP34Wc\n1YZVVYU0pjJxyXHg5Bn4iTjq4CHy//QvGN8H14EgIPaudyIb6nGXLyPzkb/nNWMZ/HgU87l/OjEZ\nyhgIAvxndkHgU3rkUUo/uZOaz9yEVVND9cc/OrWee8VleLv2MvE3fwsDgzgXLKF02+3IllbE2Dim\nqgp3+TKcz9yEsCzKDzxEcLiH1Hv+FKerE6fr1Lqtp2OCgNznv4CorcEEPmiNla4l/qY3/mI3OBQ6\njTBgDYUmKeNzRP2cNnkxZTIUGCNmdmKjcEWEqInTLBcyyEbyZgTXuKTlAsb1UbTxqbPmEJfpFz7Q\naaSsJhZZb+BQ8ACuSZGSzRijiVDFCPtxiBKIEpauwZN5utXDzDFX4FoJ6ghbG4ZCL9Z1C158l6U4\nOzA4lJYsxm7vQEiJM6/SJjnWfxfqiQcIFtxIsP1paG2hePN3cC9aibNgPs6CymTKYGgI76mtRP/4\nRkrf/G/cq67Ee+RRsG1KP7sHs3cv8b/4c0zZo/Y3X4sVi1G6+x7cS1YzcdNnsFyX2Jt/h8J3vovd\nNRfje8Te/DunnmyxiP/o4yAl9vJlBFqjM1mSf/6eyrHq6rA72sl99WtE1q3FWbgAZ9VKRG3ttN0E\nh7rBtijdex+J33vzKR3CAJAS2dhI9PrrsFKVL+yp9/3li76/odDzCVMCQqFJEpsq0YIlIsywF+OK\nGP3ap0+ViZgkC7r2M8w2YtQzR6wn5dpUW1VkVC8Z3c8h/wEA8nqUA/69PF+N4zOZZV1Ki72ChGwg\nY44xbPZg42ITpX0kTWn3bZTVGHHSjJoDL/UtCIVCz8OnESkmqFtfQ6y5PG1ZQc+hOPc3idCNeOZe\naGzE+D5qcBAAnc+T/+9vo0ZGMMcGcOfOJXL1lTgrliMik6On2UxlX9/8Fta8LuJvuA6RTFQmQz22\nESviEv3N1+MuX0bsDdeh+vtI/sk7CXp7K2WpJt9zxooeOhqj+jM3EdlwDbI+DdqQ/9fPY5RG2Dbd\naxezaYZApuvwnnkG4/s4K1bg3fUzgv5jFO/4CV5vL8W7f0b54Ufxt+8g87nPA+Bt38HE331k6tqF\nlCT+4PengtVQ6OUQTroKhSYJIYjLNEIIRtVBlCnTIC/kqNmCR4E49SRIk+MYMVnPYHAETysQBo8C\nGoUlIiRFI85JOa7HgqexieKI6PMeP69HGFQ7cUQUmyhRkUIbTUAZnyJ1qZV4EUMk1kLa6iQpm8Lq\nAKHQKyDOTiR5PFoxRBH4uGIYj1YQZez4U2S/8XPUcJ6qZWncC5ag3CbK995P0N1NsG8/wcGDqMEh\nYq+9Fu/+B3EuWEr0ivXYDQ24qy5CJBMEW7eBlJAvIKqqEJaFv2s3ZmCA2A03UH7oYWS6HmfhAuxZ\nbThLlxD09lG65UdgWxT+6+tEr7ySv/7ZLiwJM+/+Cd4Tm7AbGnCWLCboPox70QpkdTXZoIhC07Zk\nFYXvfg89PAyxGOrIEYg4lH54C94992E1NJB859sxExOYkWHcy9Zh1dViAkWwb//UCHMo9FIIO12F\nQi9SXh1HUSIhGuhTW6gR7URkiohMMqH7GVa7SIpmoqYWjyzDPEMtHQiRA2rImH667CuR2BxTT1Mr\nO4jJGso6h0YRk6fO7B9Uu8jrIeKkKXCchEhTNGMYoEq0kLaf/9G/MYYhtYs6OQdHhuVjQqGXiksP\nmhgBDadZqrAiBwlKcwA51b0OQI2PU777ZwRHexHpNP5991P71S+jhkcIhoZwWlunlY0KDh2CaIzC\n975P5IrLKd/xf9hLl6AOHSRyzdWUf74RmUyQ+IPfJzh6lOIttyJra4n/4R8w/t6/JHLNBuLXvZ6c\nFxB3LMzAINr3Ks0GzlBPVR0fo/C/N+M//TTuhmuIXnIJ2Zs+hUkkoVQk+Wd/grtgAQCFH91G8Mwu\nnAuWYLV34G3cSPJd73gpb3XoPPd8na7CEdZQ6DR61KNMmN6p8lIz7MVTQWBUpKiVsxnRe5jgCB55\namU7bXYzZdNNwQiiVFElZnJQ3UudnIPAIqDIhD5CwYxQJU+UmCnpDMJIUlYTtVYHE7oXg2aGtZS0\nPYeIqGZcHwZjiJ4m0H1WTg+SNYPEZR32C4zmhkKhs6eoRpM4w1KJUWkS8hmiogePE4X5ZTSK1dGB\nv3Ub8f/3ewS7dqOGR4hetpbSbbeBUtidJ76IipoaRDxGsPMZ3NWr8J/cTPTaqwl6eij98EfUfOKj\nmEyW4HA3+S//B8n3/QXuxasrubTLl+FeuBwhBK4lEUIgU0ms6uqplqwmCMh87BPYc7um6q/qfB7/\nyc1g2WBASEHy7W/DmtGA7uvD27KVyPrLEVKiBgcxroPV2kLkopW4F6182e556Pz0fCOs4aSrUIhK\nFyqDwVN5hsxu2uRryOpjpORMEGKqJeqzhBAofMAQp54q2vBNiphYQ8yCmKwBoIVVTOijTJh+BIJ5\n9oZpIzAAfWozHjnazTriVi1N9gUcVRunHvcPqh2UGCMwJWqYNW1bbQLGdS91VgdDehf11vwTQa0o\nIt0edHn+y3PTQqHQlKLpQhCc8ns3maPqfWvImSoSb38bIhoBIPlnf3rqPr7/Q1RvLyKVAiGwV68i\n96WvIFpbQAp0Lkewfx+6WCZ6ww1YdXVT29rNzQAEh4+AY2O3tJyyfywLe+kSgskWrwCl224n2LOX\nxI1vgXiM4i234T32OFUf+TvUkR5Kt97O+NvfhbNiOal3/xm5r32d4vd+QOTii8OUpNArKgxYQyFg\nUO0kY47hkwcEUVNNQQyTEk24YvrIytiOgyRmzaCr+ipG1H6G9C7yehDHxIiJGuIiTYxKwBqXdURF\nDXW6i1Gzr7K9PoJLksRkRYFq0U7ODDBsdtHOGsDQbq2d+jCIUYvCo8Nad8p5F/U4o/oAtbKdTufK\nacuE8JCyiAakexhEgC53vaT3LRQKVRhinC6HTpMgMJX3gxeqaxp57QZMJsuhWA17RrK84XXXoo8e\nRWWyiNo6ZDIJQmIy4+ienhPHLhYp/PAW4r/3Zsr33gfRCPYf/D8AvCe3ULzjDqo/9g8IIbCbmyvd\nutaurZzfsQEQUN6xE8bH0SogcunllR2XPKipwe7qwlm0qLJ+qYTJ5aFchmj4JCf0ygmrBITOezk9\nhDGGGJUAMk4t9XIes6xL6VEbyei+qXWNMWz9+L+y+zs30+0/Sp2cQ6tczUwuYuZQAuvAU/Srbfiq\nRFYfA0AKScRKMNO+ECEkZTOOR3Zqn0UzAgieTSfvDh5hTHdPLRcCXJIYoU+pPCCFi8ajpCem/d4Y\nzVFvP2PZGfQFWzAqDios3B0KvdI0SUqc3RdFq7oau62V0YLHsUylCoEpFbEb6qn5xEcpPfgQzvLl\nxG54I/Hff/OJY5TL6KEh0JrE295KYjJYBbAXzMOae/LEKIGzYgX5/7kZYwypD30QOaMJNTJKcKgb\naQylO+4kGB1FDQ0iEnGCrVsJ+vop3v0z1N691Hzx84gwWA29wsIc1tB5byDYSZ7KaCoIZoiluFYc\nISRx0sRFA8f1QQTgyDgN16+icGEeT0xgmyhKlDnOfhr1bLIMk09qHJNg1OynzppDf7AViYMrKgFj\nUjYREzVoo+hRG/Eo4pFFYiGEoNFaSFykp0ZYJRYFRhjQOynqcaqt1qlzd0QEy0QYMNuJi3pG9T6S\ncgZgGNTPAAJbOkhdR16ViYqqU64/FAr9ammrjnFRS2VUtnz3PYhUiuIPfogaHMZkMhRv/g5BdzeR\nNZcClVzZyKWvmTaxKvu5fwWlQAWUbr8Da24XVl0dpYcfwXvk55W2qQsXoIeHMcUSdl0tsTe9EXvx\nItTRo+B5qKO94EYwY2OoYwMET24GY1CDgwR79uAuu+BVuT+hc1eYwxoKPS+DQDJkdhGhmj6zmblc\nQ6A9Dun7aRGr8UQOhxgxIGpXE1XVzBCXIqXFqD5I2syjPzlMKrWGpLRJyDTpyYL+tohMa9fq6RxG\nCw6bnxOjihRN5BmmxDiBLtPLZlKiibRVGZXxKVItZhE1dVO5sSersdqoZiZlk0MbBVTawDZbS7FJ\nEJNVHFeHKJoxqmXrKduHQqFfXVUf+TuMMZQfeRR/x06cRQuJv/l3MFpjPG9ay9aTWYsXk//u96n6\n8N/irliOGRiEri6s2R34Dz+CmN1B6fY7sDs60CMjSARWYwMTf/XXJN75Dvy+Pkw2C56HvW4Nwfan\nSbz/r3AWzKsEskHYYS/0ygoD1tB5bVwfRQiBMRoARYkGsRAAW7q0mtVUyRZcE2VY7SNKDVkzgKLE\noHmGuK4jb4YZp5s49dTK2ShKZPVAJUAUs2i0Fk0dL6dHOKoeR+Kg8MhRpkSWKEm65DW4VpyYriPC\niQLcJT2BMYYJekjLDadcgxCCnBolQoqZ9oWTVQYUNbKdfn8b/WqCCHEarIUv890MhUIvByEE0cvW\nEb2skseuczkyH/wQIp0mun490SvXT1u/9NDD+A89gnQcTL5I4o/eMrXMmT+PYM4cEu98OyjFxIf/\nntgbriO6bi06nwelUJkM3i0/wnndtQSPP4Ezdx6WGyH/hS+C1lT/y2exqs9csSQUejmEOayh89Zx\n1Y1RhowZoI2LEVjEacAWEcb0YY76T2IRIaCEQwKFR5/aUqmpSh0FhqgSM5ljXU6nvJoOZy2OiNKr\nNpFR/ZRNFkUJYwwjwQFGggM4Jsrtm2ZwbMzCIcZc+2qqxUyUCHBkDG0UCVmPLSsziZXxGDOHSYlm\nYtQhz/CS7dVPcExvZ0QdIDBlfF05boERSowyQS+/QOOtUOiMpNOHFd3xap/GeUlEo0TWrydy2Tr8\nnTtPXR6JoPr70bkcpZ/8ZPoyrRHxGEJKsh//R8zAAN7mLQD4d98GqSRWcxPuVVcRv/4NiHQab+tW\n1MgIFApQKGC0fkWuMxQ6WTjCGjovearAkH4Giwg2EaQlSaoGCgyT1f3UidnkGCSiE/TpTSRpwiaG\nRw6BACQuSYb0XlKykVqrHai0d22Qi6iV7RzXh9BofFNk1OwjTh0ZenjHRTmGjUITJTBlGqwFGBRD\nahclxmi3106dp8QhKWZg4UzWVhVTE69OLinTYa1DmYARs4d2ay096jFGtKHLuZqcGiJvRolayVfy\nFofOcVpFkJyaohJ6+QnbJv6mGyo/XH3VKcudC5ZidXaijx/HvfSSqd8bY/B37ca95mqK9z8ItoV9\nycWog92YUom69j6KjqbwhS9BEBB5zWoo5FEHDoDr4KxdQ2TdWuza2lfoSkOhE8KANXRemjBHsYkS\nUEITYOHiU8SgmS0vI2bV0GgWIrCo0q0MmO00iws5rB8hr4YRApTxyTFAQQ9Ra7VT0hO4IoUr4vQF\nW7GEw5g+QlrOZb7zegAyQR/Vkc0UgwRFYzGi99FmrwYs6uU8FN5zztQABkvaRKkmb4YZUE9j0Mx1\nNjCqDpIUDcRk5QMkOdmJZ4ZcjE2l0UHSaiRJ4yt0Z0PnPoWT+DlClvELF77aJxM6DRmPk7zxDyn8\n5E70yPETC4pFynf+FNk0A5FMIhsa0dlsZUR2bJyB+yV6okziz95F+ZFHwXGo/tQ/kvv2/xL7zddj\n19e/ehcVOu+FAWvovGOM5rg5RIw6Ou0ryQQD7Ff3IKk8pndFEl+XcGSlbEvUqmKmXjmZGhClz2yl\nVrZjjCJBPTVWpbZin9pMnewiKlIUOU6CeuKkcXUSJifvxkSa/mAOTdYCuoOHp02CsqSDhXPK+cZF\nPbaI4esCQkjSzCfHMYwxlMwYjohj6ShgplIJCmaMlIhM28+YPoJDjKQMg9fQL8NgjE1Q7MKo9Kt9\nMqEzCI72ogeGkMuWTf1OxONUf+amaetlv/QVjBAIDMn3vBvtB1ipJJEVK6bWSf3RH75i5x0KnUkY\nsIbOO74pogmI08C46cHHxwAaQ5ImhtUeSmKcDnni0fyYOYxncsRlPa5JUdCjKDQGnyrRwlCwi2ra\nqZGzEELQKa4ABHuCO8kzwlyuBqDEOMfNAcaDblySRM3zP1INTJnj+gBlnaPGaqXEBFr4BKbEruEh\nZqYbSIkm+tVTGDStchUAWXOMQHscVwfolFcjJJR1BiMVhKOtobMlioAEE6n8t4khrDGEDDBBMxB2\nOvpVZbW2EFm3hsglF592ufE8CrfchkylSPzpu05sd4byqiqbxQwPY8+Zc/oVQqGXWRiwhs47rkyw\nUF7HmO5hSO0EBK1iJX1mMwqPtOzimNnOsNpHRFRRJZuYcdJMf4CIThJRKbIcY29wJwoPixhpOgE4\nGmxCTbZpDChW/m3KBKaMQ5wkjeQYYoKjNHDm1qmOjBHVteTMAFr5lMlSY80iP7yUQvwhhnSUhGig\n2Vo+bbt2+1IC7ZFVfRgCDgYPovBo1IvCqZah0+qdKGJJQXPqRMRiRXcjZQa/cCFuchPGVALUoLCC\nMFj91Wa3tmK3nrmMnRocwt+2FXve3DOuczLvgQfxd+2m6m8+8FKdYij0oojnds452fBwNpxXHDqn\n9PpP4pFntn0ZQkj6g21U0UrSrp+azKSMz6jeh8AiIlNoFAU9zAxrKbaI0Os/iRAW9WIeffpJioxT\nQwdRWY1vCiAgp4dJ0khMVhMTdRTNGMf0VhrlQuqsSlBrjEEIgTYBw2ovjdaiaROpjqltRKiiVnYA\n0KeeomBGmGtfixCCksoTkfGz6uc9HvTQb7aRoIl2Z/VLf2NDv/b+feMhoha8c41GWFlUaQFQwE1u\nwsuuBzROciMGA6qGoHghiBLggQkn4ZwPnn3PCoVeLg0NqTP+gYUjrKHzSkRUUTZ5JnQ/NVYrBkUg\nKyOgz74RH1UbaZALpnI9SzqDbwrsDf6PDrmOEhkCU6ROdOFTYJ71WhwZ47juRukyeTNKFS1ofLJm\ngAG9g7Took7MoVaeeJz27PGU8SkyRmDK9KunaLVWYQmnkv8qEghRGRJttS8i0GWOBI9VumFZdc97\nrb4uoFFYuICgTnRSL8+uRWTo/PPui2eDKCGdpxB4KFEG42KMg5N8DKMtjI6DLCGsHDKyB8sZRshy\nJaA1kRc8RujXWxishl5NYcAaOq802PNBCTKmjxpaaZCL6NWbSFCPIyuz6qtFG9GTyvVEZRURkcTR\nceIyTbNYisQhJqvoEJdPbVcnZzOqDROmjwIjeOSoNq24JKgezBMb6CFYueiUcwooYxNhWO/GMi57\ngzuppo0cg8yxrpxar6yzHFQPIJFk1SDa+CStGUBl5AOtp1ozjqh9ZPUgjojgiiqKHKfdvvRlu6+h\nX29CjoKuAaIY7SDsHNLKoP1ZBMXF2LEdCOmjgzjam4MJZoLwMX4Lwh7AcvehyosJ801CodDLJXx3\nCZ0XyjrHUX8jY8FhlPEIKKCMzyF1PzFTiyVOtDf0TJZ+9RQDwQ6Oq4MADAQ7GNUHKakcMVFHVFQC\n2oASvi5ObZu259AlN5CimQjVBJRJykbsUhmZrZSXEdYw2n2SQJcnt9IYY5DCotFaiEMShxQgOaQe\nONFuFQuXBE0sY4xDjOnDJ67vgQfJ/MPHpn62RIRa2UGLtYpGawGzrNe8DHc19OvOim1HOkexY88g\n7CGggLTHEAh00ACijLAyGF1LUFqAkEyO+Dtg4hhdg5AlLLcfRHn6zmX21bikUCh0jgpzWEPnvJwe\nol9tI6BIhCokNrOdSotDT+cRQuKI2NT6fcEWIqYKjxwlJojJWop6giIjWETQ+EREFXViDlkzQFzU\nkjej1MrZVMlmxtQRRvQ+olRRZIy59gZKJktUpCof9iJPr3oCS9fRbJ+YLNUfbGfMHKRLXsO47mWU\n/ThEScu5GDRj+jAKjzrRSZ3swJIngmzjeai+fkZn5XFFnFo5+5W7waFfW1bkGRAe0p4gKC3CqDhO\n4nGE0ChvNsIew6goCA8hFdprRvvtJ+3BYCceRwdxTNCC0VEwcRAKN/kIXu5SMLEzHj8UCoVOFuaw\nhs5rUaqpEbNwRIwyGRziU8ss4bA/uIc2cQk5BojIFC32SsbUEco6R72YR4kJ5jiXkVfHyeg+fIpE\nSODpEjFZQ701H6m6iU2mEdTIWSREPYHxMQQIIekNnmCGtZAq0QImQYtYD9aJ1+W4PoJv8kSpQwoH\nhMYYBcjJES2DJiBGLVn6mVBHsVWEFusiHBlFuC727A4cfZBIbDejpQnqWEbWDFAlm1/R+x36NSAK\nYFx00IKQGQJ/JkbbYJIYBMaA8upw7EGC8gWTQefpxy+MSmDZWbTsR5BHOlm8zJV42csR1hgwgVEN\ngDVZGstlqjBxKBQKnaUwYA2d82wZoUHMB8zUBKZnWcKly76avBkmo/pJmSYAaq12ND5D+hlmykoB\n7aP6cSxc5jrX0OM/jkESMykA6qwTI5oZ3c+QfgaAGdYSADrtq3ATT2FUAVWeP+08tFF4ukRSziA9\nWUHAMhGazBKqrXakkASmjLQqTQXipoGj5glKZCmbLJVxVonBpU52UvQgalJ45BhQT5MUDUgRvtTP\nJ9LpQToDBIXTVYTwcZMPYXQSP78O6fRgRQcRaLzcGlAxhJ3BckdQXjuYCMIexKiqE6Oloowd3UlQ\nXA7GBlFClVYi5AjKL1VGbZ0BpD2IkB7Ky6P9Zpzko2hvFqrcSSVoDbPSQqHQ2Qk/xULnhWG9h6IZ\npd0+0QzguDqINBEiIoUrEpUarMybWl4nO/FNkTF9mLhME6GalKhUDihwHIWHb6po5MREqkB7uCRp\nlsvp00+REpUAWAoLv7AEjIMQcFx1UyZDhBQjei8WLhYuST2DiEyi8YhYKWzpMhTspsAIHfaJNAYL\nC0mCXv0ES2UtrkxyXM0BDDHZORUHzJPXvsx3NvSrRjpH0EEKoycrUogcdmwPqjQHY2IIaxxjImhV\nNbncRwcuxkSxIkcwppogtxw3uREvuw6QWO4RtF+P9if3aazKvuwBpNOP0THs2DaElUUHMzBWHyao\nRqkkRk228xQTk+d3DCvSjdEOQWEVRle/sjcoFAr9WgoD1tB5IW4aSE4Gj88KKJM1PXh33LyJAAAg\nAElEQVQmS6fcgIVDj36cTms9UCnhUiM7wGiO+dtJyzlUWTOBZx+OShRlDgePooxP2urENwXyZoR2\naw0t4sJpI6mHgydJiSbqrXlERdVkK9gEAodGsZQCwxSzj1KXmaCx7XcZV0cZUftpsBYwrnsYVnup\nl/OwRZQOZy19wRZiJo0vFuAbwah+GjDMlGF/9/OPBlGujGg6QwgjsSO9BEWB0SmMjmDHt2NUFUbH\n8HMnqk9obxZOfBuIEl5mJWCDKGCMZKo5gBGVx/u+mfydjSotqqyn6tCqGmNsHCsHOoYuzzvp3AzS\nPYTlDOFnr0K6uxGWjwliWNFdlXqu5gztlUKhUGhSGLCGznkZ3U+/eYpGsYg4JwqcN1qL0FqjKNOv\nN+OSwBVV+LpIn3qSJrmMw/oxFGUsHIzRVFEJWOfZG8ioASxskFBUY+TVEDPtldQzDyEECRoITBlb\nVOpTNsvluKKSPxuXaeJU+rB3sIajeiMtYhUD9gEirkLrYQb008RJU2/NxRhD0Rxn2OyhwCgd9lpA\nUomHK/mAM+3p3a5C5w87vgVjHIQoIYQPztDkKGs1leByKWqyATEEkzmslb9Foxrwcq9BWBmmPhJM\nHD93BaAQ1iDGOFjOIDoYwgQzThzYxNF+SyVwDWbgF1KVFIGTSPcQ0hnCL6wEUcKODOLlVuKmNgIG\nTJgWEAqFXlgYsIbOaVk9SK96igiJqVapWmuO6EeoZyFZBjBAhGos4dBoL6Css5TJ45GnRrTgmTxi\ncjw0r4YZNQeJizqMCMjrAhOqnzo6MMJQ1GNMmB6a7eUc14cY1z10OpXRrKisoqQniJCaNvJqiwhJ\nMRNLRLDcFvrqxqkjR5d9FRaVYNeVcdCCtDWXGjMLqEzukpMv4bweIUIKS7hhce/zUFBcWMk1tcaQ\nzgBGJysTnKa9xQvAwoocQFgT0/JbrcgRpDWCcQZQXlulQYCJ4yQfQqsERqXxsosA96T9aezEEwTF\nJVjuEJZ1CFVafMq5aa8D7TdPTrZy8XLrwDj4hQswQd1z9hkKhUKnFwasoXPWqDoAWiIRlJjA0g4l\nPU6eYerEbAwanzwGjUMcjwwFfZyYqGG+8zoyuh+lfdrsS8iZITKql+OmG0tEsHGwYwcZz1vY2MSt\nNNWyhaIeR0wmkEpcaumstGmVDQD0qCdIi7mk7ROTtISQlM0Eg2YHKauJFDOolm1k9QATuo+kbKBG\nzprqvGVNTqBKyMoIbU/wOGWTJcAjTppAFJljrT9lglno3GBFdqG8diy3F0QZVboATBIAoxpRqhEh\nM9ixp9FlzXMnNqlyJ27qQYTMYnRl0qAqLYLIHhA+wh7CsnME+YtRXhvSyqK8LpgaoZU8O0qrvSbQ\ncVRpKdLpATmKtPJof9bJZzw1mosoIJ1+tNdVaT4QCoVCZykMWEPnrLwZpUwGRZlWcTFjppssx2hk\nMWmrEyEEth8jRTNpew7H1HaOqJ/Taq0mJWbgECcqqxFCkBIzSMkZHFfdDOinUabELP9y5lgOILBk\n5aUUFdX0qF6KegKJTY5BhJJ0ciURmaRatJClF2WGiVpQpSujXC3WCgLtMaL30mwto6SzHFPbiZCi\noEeokbPOeJ0xUUeTWM6g3kGtmI0n8kzlHobOPSIAFIgixjinXcXoKvz82tMuA/s59VE1VmQf2m/A\nju/Az13Gsz0ttDcX3B6ENYywJpD2cYLCCtzUg2gdI8ivqZySNYoVOYTwa0DY4D/3nCspCFZkL0KW\n0F7YIjgUCr044RBM6Jw0pnsQRuJTxMLFlXEkDhKHtF0JVo0xzJIX0+wsJa9GyJoBWsVqXBIM+DvI\nmD7S1vQP1pRoIskMEqIJTJxjehuDeidFPc6Q2gNAlDo8chg0MWpxiOFTAKBsshgjCHQJeVLvdSls\nhvQuyiYHgCviRKkmIRqZaa847TUG2iOvR2iw5pMXQyAgaTdQZ3Vg0BT08Zfj1oZ+YYYz1TJ9UXsx\nMYT0McadfKReIaxhEPmz3EmcypeayqipsHIIaxwMSHuAqVxXQIgcdnQH2mslKC4CLPziYoL8JVO7\nsyL7MSqJtIuVDlknE0Xc5ONAGcsZRvvTJz+GQqHQ2QhHWEPnpChJsDRKFbGIk9fDWLjUiFkEpsyQ\n2kWVaOWYfoousYEe8xgxaimaMYbUM9hEqRUdp+zXkTFmyRMf1I1yMQJJmQyBKSKEoN2pLDfGMKCe\nxhVVDOs9REU1SdHEkHmGkjaUPUPSgUCX6VGPERNp0rILISQCiIpaxsz/z96bR9lx3Pe9n6rq7rsv\ns+8z2HcQBAmK4iKapLWZshJbPooj2y+x86yc9579LCexJNvJO17iOCeOnWdLliV5jW29kyiWbFG2\nREmkJO47QYAAsQ5mgNn37e63u6reH30xgyEGXExShMD+4OAAc7u6um7dO/f++rd8f0PETW41peBS\nVuwYy3aMzfIOkrQg5dr9Z8FMMGfOsFX+4Bu9tRH/SKR3FqmKBJWNb0BeLQKDxSKlD94kyHPoyi6k\ndx6rWzH1V9/lTMVfBBGg690obxQTNCGcGdzYEAhNUBtAqCK6vh2I4cROgDAElUPr5tH1TVidRsWG\nsEHr+ovYBPXCHYBLvfgDjVzWiIiIiNdGZLBGXHPM6tO4IkGT3EST3MSkPspfH57jhu4sP9CzD99U\nsWjSspVt8t1IIem2N5AQTQzpB1F4bHFueVXFS54Mc/Nc4qs5phAaoSt2HFekKJgJ4uSYCl6kx7me\nZrGJ6eBFAupAo8sVFTarfWjrs6THWDYjpGjHp8KCHlo1WGumQNFO06K20ay20MwWrLUU7dS6dqw5\n1UdW9r6BuxrxejF+Pyaov+55dG0n0h3FmnjYZSp+EumNgklf2VgV5Ub1/npjUYgSQpYRno81aUzQ\nhNVdaFFFxQaRsowJWkHUAYMxCZDBujlk7BSON0K9cDcIc4VVuwi5jDVZonSViIiIfwxRSkDENYfE\nwRpYMqMAdKkDdMb66fAGMFYzZp6hXe5BCEndlhn1nyElWonJNB1yLz3yhtdUaW+tZV6fw9i1L/Iq\ny6zYCZQw9GaLNMtNIDRVU2Ben6PD2UuPcxBjA/Kiv3FNScFOsWiGCKhSZhaJIi/W8lfrFKnYxfXX\nR7NsRwlsJRxjSowEoWSQsXrdut72WAvlJURQfsOmFHK5UZH/SteOg8m+rmtJ90IoYWUSWJPEBD0Y\nvx9d24Ou7Ua6Ywg1d9l5TvwkKjZ02eNGN6GDToLyIYSawkke56JBaYImTNCB8kaR7hSIGqa+q5GG\nsGaYmno+NFRlFV25no39IBYneQShojSViIiIfxzC2ivnVM3OFl5/wlVExFvAiplg3gyy2blj3ePG\nGObtGVrkNqRwKJhJJvTzeOToVQdxGx7T14K1hmH9MN3yeuIyv/q4tj4jwSM0J3xywXsAyYqZYMWM\n0yp3MKqfJiGaqNsSBp9t7rvXr9XqMKdQXt533dpQ40AJb914KRSBqTFrTvHfH4vzT29cJBtX9Do3\nvebndS0ix0+TOvp5xK7drGz56Bsyp5N4HmOSmNrON2S+l0VUw+p+3UqYf/qSVsOxM1iTwPi9hEbl\nxfeOITREN74RU7ETKG8ErRNgWkEWkKqAX3hP2JY1aG+cq3HTj+OXr4eGwkDY7WocXbnhivOvrSHy\nkURERFyZtrbMFT9EopSAiGsOYzWz+iQ9Ksyzq5hFYiKLQHBOP0CPOoRsSENlZBc7RCfj+lkMl4cz\njdEM6gfoF7cQd9Z7x0b8p8jSw7I4zyb1LqRQWGvxbYkqK0zrF0nRRq0aoyhnSMtOsrKbjOhiPHiO\nJrGFrOhk2D6ExMVau86zOxg8QDObKZpp+tWtSKGY1sfxbZWkaGbRDLPV/cHV53wy+AdiZNjq3EWX\nc4BNTWO0yV6aVZQzeBHTvYNi9lcgmX7D5gwq36vOYhYhaljdhoqdRMjKaj6sip1E+z3oRoepi2L9\na4VR6w1FGTsJVmJNw+sraviVbShvttFCtYI1LogaNuhoeEYtWIGu9yKEbpSPWUC/CmP18jVERERE\nvBaiT5CIaw6BpFluwxMZfFPlvH6EuWAQISSd6gAxMoz6zzAcPByOF4Je5yZi8nIjpm5L+JSo2iUA\njA3wTQVrLWVmqbFCnCYEkropcza4n7P6fgJbISt66HT2g7DU7Fr1trEBUjjEyVA3ZQJqxMlTsBNc\njHgEtoqmTkZ2omyc+eAcxmryYhMtcitNchP9zm3r5oyRpcYSc/osAB+5rpe2ZAolNpY+elsiBDbT\nhlWJVx57lSHUPE7yCGDRta0E1V1rB2UZN/kcF1UITH0AXe3ncn0pAIMTO49UsyhvDJV4EeEsY3Uv\npr4JIX2kU0cIgxN/MZzemcVJHMFNHUbIElAFKripx3DigyBqb+pzj4iIiIg8rBHXHEIImtQAABVT\nQqBIqlD+JyM7mA/OUWGBbnHlau2ymWfKHGezuoPt4n2s2LCJwKC+H4lLnBzb1HtxZOi9POt/ixTt\nJGimjT3ESZOVCcp2DouhRW0FoKTnuGAeY7O4m2mOkpKtDIjbUCbOiH6UKV4kLrLkZR950YcjEqww\nygqjxEyarOomMDVqpswF8zA50Uens58pcwzQuCSJGl1dm1jdil98F6En01tXba8rBzHOPJd6OZ3E\nGXStjvE3vWQmgal3YHQa5c2gdRohDNg41gosEhtkkbJCUOtHxY9hTRxjJKbeiw22oGInkPGzaL8N\nU+8D4i+7dumMg7CNVIWIiIiI105ksEZck8wH51ixE3SpAwgESdG0eqxOgYRoJiVbr3i+b6qrMlVL\nepQqyyybUWJkqFKiwARGBzgmTofch0Bi8NHUmeBppInTIjeRFG1h6LUR7vdEGoth2r5AXDTRKnci\nhGgYnCL8YyUFPYMr4pTsLAKXXm5C2zpLZpSqWaRmSyhiuI0OR93qINYaZvQJ6vaNKyh62xBUcSoj\nBJkdb/VKNsCGRV02yZWDYhJ7qf6pqGNtrGEgWtaH6wXGNCMbnbKwTdggzL0ONV5rYC1+ZQ/WNCHk\nWaQzD8JHxs8T1GJIdx6/dANO4kSoEFD8QdbyZTdA6JdREIiIiIh4ZSKDNeKaZNmO4JIiLrNsE+9e\nzVk1VmOtpcICRT1DXRTIy02cC75Nv7yVuAoLSTKqA2s02gQs2iEG1G2s6AkMBockWdGNsZpJewSj\nA1rVDiyGLD2M6WdIiGbaZFiEM2kPsxyM0q72kJd95EwvgfUx1AlMlTpFSnYeRQxNnRXGUcToltez\nrMewBJTFDCU7T9xk6VY3AJYpcwzZcKeG+a2nWWCImM28JXv+/YK7eJjU5Ncod9xDveVGALziaWKL\nz1K4Cg1W4czixE/gF+8ELNIdDsX5X+51tgmC0i1IdxTpja/lsoo6WIV0pwjK1yG9CUxtG+AgvWGE\nXMIv3ImKnUFIDSwjZJmgsq9R6AWg8P1+EGW034vWGV7WWIWXtGqNiIiIeO1EBmvENYdu5HN2qgNM\n6WMYq+l2rgfAYvBFha3qLkp2nqpZQliJps6oeYpN3M6SHaFVbSev+pkPzqEJGNXPUKdAKJrlYW1A\nk9pMQufocW5ACmfVizogbw2r+K1uhOcVTfSRpgOADrkfX1cYs0/h2xoCcPAoMoUihsIjQ1igtWxG\nydhOhFBsUXcyrp+lbot4Io0xAVqu6XoW7AQKj2554/d8z79fSE7+Pao4jrAaqQtkhv6EemYntbY7\nqOcPvNXLuwzpDSKdZYJKQ4FA1FHxIWSwSFA5iHAmsUE3Vyp4Mn4nVqcASI98Ad0l8HPbwFZQ3jmk\nU8bUtiHUHNKZxFoFKHRtT3g5NYUJupDuFFYtYnUbVqdBaKSzFBZ2+V1c7sWNiIiIeGOJDNaIa4qK\nWWJYPwwYOrmOwNTJ0L16vG5LCCQSl6zsIiu7ANjGe1k05yjaKWbsizTRj0OcKos4xIiLDH3cjE+Z\nBQbpENehqdGtbqRuiwjjcME8yoB6FwqHYf0IPkUG1LvYfolclbUWi0FIRdw0AZo2sYu4aqKgp1mw\ng8TI0+XuA6DPuZmCnkI3imc0AXP6DM1qCwUmyV7y3NrEXqbtUeLy9Wl9XhNYS3zuUWpNh7DOWoGV\nn9qOLI4i0HgrJwCDcV67lNn3CuP3ABoVGyMod4GN4Rca3cvkHE78NH6xgyt/lLtYE+Zv+5ldkB7F\nWIEULkL6GBM+dyd+EotAl2/lohf37OIkZyc28/4du3HT38YaD+lNYq0EYfCLd2H8btzUQ4DABJ3o\n2lZeydsaERER8Y8hMlgjrilcEihiODgIISkzS0CJHF2c9x9B4hKT2csaAzjCIylbGtqnlsVghDZ3\nBz3OIbTxmdIvMsyD7FQfIC3aKdoZFvUwNYoA5GQPXep6PJFEW58EeVrYQVI0UzUFxvRTxMmTkq3M\nmTNYDJvUu5iTjxJ3x6jVHKbtUVwylJjC2r1YLCPBE5SZwwJp0U6L2EpFLJCQTewU9yDEWk5jzuki\nRxdDwUO0yK3k3tadrixu6Rz1zLb1Bmt2F36yF3f5FBJNrfkdXNVVajaBqe/ErGuQJUBUUO40Vjdz\n8WPcTT1OUNuMDbo2nKre2ouUGut3EQR9IKoobxAv8xD10g2r/lEn+QTSWWaTdGhPTaFii1iTJqjs\nANsEonpRjAAAv7IPN3kE6U6i6/1gFeGAS3VgIyIiIl4fUeOAiGuGwNRZsiMYfFKilZRsIzB1DAGe\nTHLBf5yEaKJVbV/NaQXQtk5NFxm2D9HDTYzzLGnaaVHbScs2AlvjXPBdPJJ0ixuYt0MscR7VMBQ8\nMsRFji7nunXrWTGTVO0ibXIXi+Y8i2aUBDna1R6qLCKthxYFMrIHa8MuVxc9vhd/L2f0i1RtAYc4\nCpciU5c1GAAY9w8TF3lanC2smAlStKFkJGf1mjEBqfEvU+r+EVCxt3o1SGcC4c5hagNI7wK6Gr7H\npDveyENdS/8Qag6rm7jUSBRyKQzz2wzSHUeoWYSqEZRubowIcJJHCMoHABdECS/9KNa6+MWbgRgI\nHxU/jnSWCEo3rnps1yFKYSevxrVDHdhpdGU3Kj7YWOdVfGMQERFxVRA1Doh4W1CzKxTMBAnRxIqZ\nIClaG7JTofzPgBvmlp4Nvkm3OkhadmCt5VzwHbrVQWI6xzhPs0Pdw4odZ1q/yKT22eLcxU73/QBc\n8J+iyDhZ+hp6r5uYNSdJi47VdfimjMBBIhAohJA0qy3ERI6imeK8foQYufC4EGRkP0JAVoTG6oIe\nZsleoEseJC276JD7mNbHMcZSo8CKniCrutc99zLz2IbbKyvXH4u4HFmZxrrZVe+rqC+BdMEGxAqn\nqNQXMYnOt3iVgCiDBRU/AzasspfeeYRcbBiZa6wVRa0hvTGwDrq2CxO0gN9FqDRQR7rTGL8bax2c\nxAtY62KCNurFO5DODJAERHh+5SDGmcWa/GXXCC+eeslaUliVwNoEJmgiMlYjIiJeL5HBGnFNsGIm\nmTWn8Cnh2wogaKOOQ4yimSVOjvP6YWJkGFC3hfJSDS9mXgywZEaxaBySSOvQorbSLDezbMaQDa+R\ntZYOsZuUbWaRYXrkIWIyg2MT6ySyJswRYmTodPaTZs3oSckWkqKZpWAEiSSnevCpUjBTZGTn6jU8\nUnTK61ixYwS2yoR+lgF1O56bouIvo60GwDcVZsyLdKsb2e6+57I9kd5gmANpv/9E8t9MZH2J3NBn\nqWX2Uws2obfcSGr6GxgnS7nrHhZ2fByumrzWGNY4GD+JNWnCUHsJ6SxhGkVQL4eu7kM44zipJxFY\ntN+BqW9BqGWkO4nx+9CVPaj42UaKwHmC8i0YfwAIcFLPEpSvDzVar5BqcMnVwraxJh+O11mwbviv\nKF1m1EZERES8FqJOVxHXBBWzgE+ZFJ34lGgSA8ybs8z6Z7igH2XRnCcl2kiKVmIygxCCSX2ECX2Y\njOxBWEWKdnrljZwz38ZagxCSvOpfzXct2RlG7OM0q60kRDN1UcZYjStSCAQVs4Cxhj51Mx1qHwv6\nHCUzT2BrDPnfJbB1SnaWTeo2etwbSMsOfFvCpwKAtYaKXWTCHCZRU3RNSbrVDXSofbgiNKAysg0p\n17xV2mp8U6Vilnhpeo9UBYSofo9egasfMXuB+N/9NsbNsrTtY1TFDrzHvwjWUuz9MOXO9yNrM+TP\n/SHpC3+N0JW3eskYvw9T34S1iTDkLqoobwprY69grFqkdw7Q2KAdXd2GX96D8Rs3RrqNoPwOhDNN\n6LeoARpd3X3JHArjt4ENU0uEmkfFjwLhDZOQ8zjJp7iY0CrdcZzESaQ3jIoNYepbcNMP4iSO4iaf\nRrojb+zmREREvK2IDNaIa4J2tZtmsRWHOJvFXbSq7RirMWgydNGqttPlHKDF2bp6TovcTqvcSUJm\nqVOgzCxJ2UqPOrRazGStZT4YxhhDWnYwIG9nrCFxlZe91G2JspnnbPAAQ/ohTgdfo2bKzOqTVG2B\nOX2aBT1ETvZhjGZeD7JiJ1bnbld7aJabqZglzgbfJC5ybHfei3vqcbyn/xYhBFnZs2o0x0WOil0A\nwJUJhBDMmTOM6ieoNNrHXiSoHMSaJiJCbKYF3bMbENhYE6ZvP5Wf+p2w6EooEBLjtVHo/QmM14wV\nV0/BkJs8gnCmwCbwi3ev6aq+BCf5HELNAxrpzoa6q7hY3YqKXUDFhi4ZrXHipxFyEbBgMutC/ip+\nEqEWwr8y/Fc6cyBqCLmMk3oOIepcDPcbvx+/dDPWEqYyiBpC+AS1vrDLVdD+Ju1ORETE24Go6Cri\nmmFOn2HaHCdDD/1uWFQyERymYKfpV+8kIZsYDw7jijjtas+6c401gEVeYqSUzQLWwHn7IH3yZrKq\nh6KZYk6fo1sdwBFJRoMniZEloMYK41g0rexgmVG2OHdTsYtIFItmGGMtNZbIiwEEiiV7nm3uu5nS\nx6iYJfKinyZngBUzQdUsk5O9xGQoDr9oLuDbEglamDEn2OzcgRSKJT1KYKs0qy3MmdMYo2lVO1db\nxkZcK2iEWkDFzhOUb7riqLCpQFejAGoN4UygvLHGuQLwcVPPYnQa6U4B4BfWp5UIuYR0R7E2i3Qn\n0PU+7GprVY1Q043w/8X0CYtwh3FiF9C1bRi/NzSGhW6s+xBRLmtERMTL8XJFV5GH9fuJ5RnUA3++\n+uMXjozy8Pm5t3BBVxctcjvgoFir7o6JLM1sJi5yAORlPxnRs3q8YpY453+XUf3EOmMVYN6coySm\n2KU+SFaF56RlJ5vc2/Bkmgl9GBBkRDeeSNImdtImdxOTWdrVbhzhkZEdpGQrCZGnTexC4jJrT+Hg\n0aXCZgYp0YZPhTl7hrHgOQSSgCoX9KPYRqGNSxxHJEjKZjyRZF4Pct5/DIQFEXa6iokcF4qz/PzX\njlLx9Zu401c3Qi5zMWx9xTF+geT4V8CujUtOfg1Zm3mTV/dKGCDY4HGF1TmM3xGOEWEIXzgT68/2\nNzeMVYt0zuOmngAsNmhF1/sRcgU3/Qgg0H4HujaAX7yxoQiwHmvy6Np+TH0gVCmQYYrEXLnGXx17\nutE165JcX1nEjZ3HL70D4/cBAmtyWJNCqMWG57eBKCNU9NkVERHx6omKrq5STv72FyieusDMA8/R\ntK+bW7/1hyw+/jz269+m6Qd/BiEE+bhLNhZJF11ECIEAysxwPniEPvVOmuQmhFQIIfBNFWstCRUa\nr+f9xwBLnBx5eXnryD4n9GRZaxkKvkunPEBSNjMRHCYmsnTIfQgEZeYp6TlcEadXhecYq6mbMp4M\nv9Bb1HYCU6PP3kKZaTKyCyXC1y4u8mTpocYKTbKflGwjIzvRNlhNTajZFTL0oKRLn3Mzs8HZRlrC\n2rpzsocbs52U9iwQd96+96IqcQJT724UDoEIKiRm7qfceQ9OeRRZHUf5JYR9iVHb0OB9K1Gxswi1\nTFB+x0uOWMDD+P1I7zzKHSeo7MKJnccPugi1WWtgY4BFxk7iuFP41c0IZwIb9GCDTsAQVMMbO1Pf\nsja9qCKcaWzQwUZY66KcKYxxKNU7mFrOYfwehKyueXNNhnrxB1iT1bK46UexxkUA1sYJjW0fJ3Ec\nrEtQuVzZICIiImIj1K//+q9f8WC5XL/ywYg3DeMHHP3FT+F2NhPMzCGlpTxT5Mznvgn7b6f19v0I\npWi7ME573MXLpZh77BjP/PR/JtHTSrKvHaHefgbLshklZ/tRxFlmhJhJM2QexhqNEnHO6e+wZM/j\nEichm6iYZSrM44kULWrrhnPO6lMU7BRp0UFatiOExGKIizwxmUYKh5jIkBTNBNSIiyxYwYI9x6w9\nTZPctDrXhD5MmQXKdo5FM0yT3MSMPsG0eREHDwePEvPkZOjNlZc0BRjTzzBvB9G2Tlp2kJItNMkt\nlzVAkFKyuTl12eNvJ4zfizU5Loafha4RK5ykntmOt3wEpzSKW7lAYdPPhLmrDfzMTqyTfotWHWJ1\nUyPXc82XIOQSXvoRrNVY04TVeUzQCTbT8GSypp+KxuoWVOwCfuU6hNA4iZOYej9hQE2Aufw5SncK\n5Y0j1HxY1Y+LcCdw4mcajQIcUHNIp0xWbeG2/jaks4iXfAZdH2AtWHfx37BVqw2akc4CJmjCiY2g\nvDGsBeksY20MrBepWERERKySSsV+40rHIoP1KkQoydb/45/S+6N3MHHfM3hdnez6xEewFib//gkS\nvW1kdvRx5Of/gHOf/jLWWtyWHLMPH2H8f3yHppt2kuzf2FNyLTMRHKHKAh4pyszRyi7KzNIid1AX\nBcDSKnaSlC04wmPBniNOloA6RTNFRnavM/RGg6eo2SI52UdMpAGBEi4xkWVaH0Nal1lzipopMmfO\nUrTTLJhh5u1Z2sRuWtW2dZ2o0qKTjOzEN1WqLFMzRVYYpVfeRLPagieyLJohpHBWUxgu0iQ3sWhG\nEEgWzTDLZowmNXDFvTCNVIK3p+Eqwr/W4hZOIYNlqu134i0eJjH3KKWuDxIrnkUYlZ0AACAASURB\nVKGW2Q/qasv1laj4aYQsNZoAgHQnEM5cKGXl9wIulzYHUIkjSFlB13sQAqzOI2QBLEh3EavzDY3W\nK78XrMli/G6kM4PVLeE1rIMFnNgFhJpFORWszmCMh/IuAAqhCiB0o+NWiHDHcBMnQmPaxrAmhjUJ\ndH0bWIUNWlHeJNbEGsb51fYaREREvFVEBuv3MV0/dDO9H74LrylD6637GPiZ91OdXCA50MHic6fR\nNZ/Zbz/HzDeeRkhJrC3P5o9+ACeVwBrDsU98luy+zbiZq0VX8s0jK3so23k6nH2UzDRLXGCH+z48\nmSQucuRkL3GZwxHhF2RO9JJV3Vg08/YcKVpXQ/gAS/oCDgmEgBlzCiN80jKsdK7ZAgmaqbKIIkaF\nOWJkidNElWUS5EmqZib1ESyaBT2EJ9K4Ms6KnaDILA4uLWIHnsziSA9HeEihiNOEI2JMBkcp2znS\nsp2aKVC001SZJ6BGns0kZG6dQXwpo/pJFoZPsvzVM+Sv3/7mb/5VSGLiGySnvo5xswSZ7eh4J05p\nEGGqOLVZnNo0IihhhcK6b61ndR3WgM00ZKwKSHcOIWro2rYwnB4/FerrNhByBemshAauiaFiwwhV\nxeocCBHKS6Wewug80p1EiArWZDa4sGikBLiEHlIHTB7pjCNUHb9wO8Ipo9wRpLMMeFjrYoP29bms\nJobVGbAx3NRT6HovmGZANTzfHqY+0EhRiIzViIiINV7OYH37xY2/Rwz/2dcZ/NxXLtPGfCXO/8V9\nPP6j/x6/UObs7/8ND97xC9Tnl8ODfhXn6b/n2Mc/Q2VsFjeXQsZc9vynjxLvacVrzmKqNRafOcPM\nd5/n9O/+T8qjs1ht3oRnePWhhEuvcxNSKDrEflrFzpcdf9H72KQ2kaGTRTO87viAexu97o04IkGL\n2E7NrlA3JQDa1C7iKkO3c5A2Zzsdah8WwzLnUXgkRUu4JhMP7Y9L5u1yrsPBxRCwYseYN2ew1lLW\nC7g23fDmhgZ4VoRV2VVW0NTIs4kMHVTELFPBUcpmYd2afVNlLHiGDrEf57iDv1wksDWGg4fR1l8d\nd2RymcBco+8Lq3EKZynWtjD9uKDc/m685RdASGott2G9PCtbPkq15VZS098iN/z5t3rFhIVW4etj\ndSdW53FTj6O8MRAmNFZRWN2CrvetnSbKCLmCrrfjxE9jjYM1CaxOYK3E1LtQ8ePoejeYJFAHNYeT\nfBwVf6Fx/gKIGtI7jVCzADiJw7jph8KVld8JVoAqoqt70PVNGL8LXd0D1kOo0rpn4qafBlkHJNq/\nXLEgIiIi4h9D5GF9k5i49xGGPvO3jHW1k97URcrbuL7NmrXQrbUWIWHsSw8z+/BRYm05Ej1tdP+T\n2xFSsvztB5GP/A1b//LzePkMwUqZwokLLD51Aozl9q//Fzb9q3uY/Oqj1GaXMFWf3g//APn9G+dn\nXoucDx5FosioTjwyXAgeo2TnXrFdaWBrVFi8LMxeMJMkaCIlW6jaRVKiHSnWXktjAwaDB8AKuuVB\nlu0YA+oW4iqLqkxSrp2k4NZpkgMU7AQuKebNGfrVrVTsMgjoKXYz4hxlxU6yZM+zYiZoUpvwRBJX\nxKmYJeIiQ0p2khBZFuwQrXIH2gZM2MMY5nHJ4Yg41mpKdo686iO/ewst79wLCAJbaeTghu+z//jg\nGfrzCTrT154xoSrjpCfvpWx3ULz3YfI3eCQWHqfWfBMm3oFO9oH0sG4WP9FPpfk2cF+5C1Ny6j60\n24x9E7pgqfhJlDsRSlIBiDLWJELRfgHggAjC0L4VCGchzEUVAU7iLELWMX4XVvcgnGmUO4lyFsOp\nZB0hNTZoRTjzKHcOIYsIVUY60zjxCxi/HRUbQqoyxu/GmBggsLoN6Y4hnWVMfSvaOAibw+ow0mB1\nK9K7gNVpLnpLhZpFeRNhwwPdROQXiYiIeLW8nIc1Ugl4gwhKFU78x79i7sHn6f3wnYx84X6y+7bw\nrcBh6dgIH7pl47Ds8z/3+3itOVpv3cvM/YfxCyXueuTTnP+zr5Hc1sOFP/s6NtCMfeURUjs3M9r5\nIbao8GVrfudujB/Q+UM3Iy8xiHf80j/n3OfuJdWao+1dBza87rVKXvQzr88hkMzrc/iUiZNdN6Zm\nVtD4JEQzFhPqmdrzNMnQWC2aKXxbwxBQtDNkRDvNciudznUABKYOWBwZQ6DIiC60DXCUy071/tXr\nlM0smVoVncgzo0+Rkq0Y6vi2ghCCLnUdVgdMTP8JpnsTA6kfpGTmKNrpS+aYY1Q/TYp2ahTR1OmV\nN5GQTaRlB5VggbqYZcYcpVe8C0dKeuRBLs1XlELR7qzpzgoh+MwP779m81t1so/lbR9DAZn/8CtI\neZxy53tBemTO/wW13HXUm24EIDV9H9WW26gnXr7FKRB2vrIbSU5tjLd0FCvj+NmX9/QD6OouQKPi\nL6Kr23FTzxFUt2Cth+OewOoWgkq4ZumEAv5B0Ak2gdVZhCggnSqmvgnpTqL9dkxtN6DA1ziJozjJ\nZxBymaC6G4REuefBxtA2hjU5pAzwS43PKdOCqYVRAhO0Y02CxbLDv73vCJ/+MZ+s27qmMmA8Ls2p\nNfV+cGa5Us6sip1BOrP45QNgr6JUjIiIiKuayMP6BuEXykx8+SFM3afjA7fQ+q7r2P/b/5o9k9MU\nPvkZNn/0hzc8b+XMCG46wey3DyPiDktHBnGb0ox9+SE2/+8foDg4RvtdBznzX/8nia5W+n/i3avn\nOsk42d0DDP7Bl5CuQ6Jn7Uu3eGaM6W89g5NOkN7azbFf/jyxjibiHc0bLWMVEZRBOGH3n+8zrLXE\nRKbxf1hhjK3qbnKqZ924eTPIihmnapaZMaeo2wIdci81UyQhm5jQRyjbOWoU6JIHQAgqZhGHBPPm\nHCt2nIKdINfoQJWWHWTV5X3Wx+UgC3FJjRXa5S5a1BZckSDbUAEQQoAQLGSqJLxOsqqbopmiLooE\npsqYfoYYGQQuSdmMsT5JkSctujAEONIj77STTkyxXHfJyX68zKNYkwTz8h7Da9VYXUUIhBDITIYg\nvRUTD4sQjZMhSG0BGUqK1ZpuRCc6wdTBBCCvfA/vZ3e/JhUBb+UkWI30F3EqE+jE5e+RNUIvpHIn\nMLoV6c5gdRaru7A6rKS/2IXKmtyaJxYwQStCLqPcZaQzgdVZHG8OS4B0ZrG6AxN0Ir0hpPLD9qsm\njRMfIqj1I1UVRICud2HNRjJTDtgUcUeyrSVFb7q5UZiluOiFDfNeG9jUqgd2TQ9XEKY9SIQzi5Tl\nhnLB1dNNLCIi4q0n8rC+QVitwy9CeXmIK9aS46a//FUGP/23DH/2Xnb98k8C0P7eQ+QOXDkkP/rX\n36L5nXupjM+x/998GKstE199jJZ37sVrybLn134GgJv+8levOIdfqKCrNXS1joqHYTl/scDMd5+n\n+ZZ9dLwHhKNe2Qi1ltzQH1Hs/hGC9LZX2o6rBmM1YJnRJ6jZAn3OzQgkMZvasONTh9rLWXM/FRbI\n0k/ZzpEUeeY5g6/LCAQOHgPO7QCU9AxFM8UkxzDU6Be3kFJtrJhxpPGoiWVaVLhfga2h8KjbCoH1\naWYzFbFIyc6hTY00nQzq+9ks72TaHAch6HFvwqBZ0hco2XlqrOCRIk6OOmWKTCEM9Do3IYRgMjiK\nb8tsTjSh6wOo6rsZaPwm+6VDREYAYA25wU9hZJJS1w+TGfsiK5t/9rL3tazPE59/HBBIXaHY9+Ng\n/FWDdhXjI+sLq4ZveA1LqCu6wX7rGrGl51kZ+Bc4pSGEeTWeWUVQORieXt2O1TmkO4IJmhsh93UL\nYjXUbmNY04T2TagkUOvE1LeBMKjEEYzOY4MubNCBFSOo5FMoVSWodYXGrC8QsgIixstl3Ash2NeR\nvSQh20fGBsN59cY3wipxDKxCV/fhph8lqOzF1AeQaoUoVSAiIuK1EHlYXwPP/9zvM/KFb9L+7kOo\nRGzDMXOPHEXEXLr+ya246SRCCJz0lXUGnXya6twSia5Whj77VXb96k8hhWD6G08x8FPvfVXrar/z\nelIDnTx0x/9Nsq+d9NYexv/uESrjc2z7hR/jhX/3R1SnFqhPL9J+9w1XnkgIatn9mHjnyxq34/c+\nivjGn5NZPoHeduU2kd8rBoMHWDETdKmDrNgJqnaJjOrCFVfe92a5hbToQAmFtnVW7Ck6xCZycjst\nzlZyspcR/QQJ8kjhUbdFaiyTpZdWZztSSGaD09QpUbbzLNtRUrQzrB/CE0mm9BF8KnSrg+RVPwEV\nXNJ4pCjYaeI2T40VXJGgYhdYMRPM2bMIBC1yGz5VSsySEHnSdFJgkrzsw1qDQNIkB3Bi09igiUsr\nraU7hRM/ifEvb4TwdkJVJ4kvPI30V7AqhlOfp57ahnXXV8cLXcGpjFPufD/1zC6c8gWyF/6KavM7\nw98BazBW88WH76e7cpi8q9GJ0EPuLT5Laupr1JoOXb4A6RDE2tCJbnSyN8ybXb1oBRU/EVbXI0BU\nLq46PKzmVz2YMnY+1Cpdp50a4KUfQvvtIMBJPYuu7kSqKkFlN5gWwEcIH+P3YXUzQhYwugVrsihv\nBuO3IaRBuVNY62DqOxHOAtKdwAZXTo9wEodBVrE6hZM8jHRC2SwVP4HVifVqAYS6skLUwgIwvw9M\nFuldQMgyKjaEqfcSGa4REREXiWSt3gAGP/VlRv/mQfylItndA6S39W44ruXWfXS+7x246SsXZvjL\nRRAC6ShO/sZfIpRAxlxKw5MEKyV2fPwjdN5zC04yLIgZ+uO/J97ZjJtdH+ade+wY5z7zFTrec2j1\n2s037URIyex3D5PdM8DIX3+T0ugMBz/zi3S+7yaOfeJz5K7bctlcAGJ5Bjl+Ftuy8XMDmPja4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87LmXo/OOlnk6VYIekzfPKsXn68X070Zlq7GCa9FuEPSxdYNfQcgEaBOtJpHoUuGxhi2NtFox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gRsRwTbY5OPLsHS6rFVL6avsUhWF6Bhm/MVfiEXCbqcB9Si96lei2XEsC03lhHD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\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9T+AceiVfd2rm0GIH8s+h4Bx+DjUzkq95BSS1H8U/TMZ++VEnqJIJLEFOj+IY34aUi2LL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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QirN-RKu87yi",
        "colab_type": "code",
        "outputId": "85d40e9f-4386-43ee-e708-b00c98a59b2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "cell_type": "code",
      "source": [
        "result = base_network.predict(X_test, batch_size=32)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 10))\n",
        "plt.scatter(result[:,0], result[:,1],\n",
        "            c=y_test[:20000],\n",
        "            cmap=\"Spectral\", \n",
        "            s=1.0)\n",
        "plt.setp(ax, xticks=[], yticks=[])\n",
        "plt.title(\"MNIST data embedded into two dimensions by UMAP\", fontsize=18)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qx8A3lFJ/HGz78EvkBCDN9/1HU5SXUEr9G5ubNwMY7ZA0XcH39BTruh/VK6XysF8Ed/u+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zy6+WdLUtgN/upeXm1DqazPE13obdIs8NH2VVpBFD1Sh5NiIIOJBrxx/JxWmyErSXh2Z03ahq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\n/wez2qVN4lRJWAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wePbQDmIQE0e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Multi-input and multi-output models and the `Embedding` layer\n",
        "\n",
        "Here's a good use case for the functional API: models with multiple inputs and outputs. The functional API makes it easy to manipulate a large number of intertwined datastreams.\n",
        "\n",
        "Let's consider the following model. We seek to predict how many retweets and likes a news headline will receive on Twitter. The main input to the model will be the headline itself, as a sequence of words, but to spice things up, our model will also have an auxiliary input, receiving extra data such as the time of day when the headline was posted, etc. \n",
        "\n",
        "The model will also be supervised via two loss functions. Using the main loss function earlier in a model is a good regularization mechanism for deep models.\n",
        "\n",
        "We will also use an `embedding` layer. This layer turns positive integers (indexes) into dense vectors of fixed size. eg. `[[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]`. The parameters of this layer are the vectors.\n",
        "\n",
        "Here's what our model looks like:\n",
        "\n",
        "![alt text](https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/multi.png?raw=1)"
      ]
    },
    {
      "metadata": {
        "id": "H2AXa03MQE0g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's implement it with the functional API.\n",
        "\n",
        "The main input will receive the headline, as a sequence of integers (each integer encodes a word). The integers will be between 1 and 10,000 (a vocabulary of 10,000 words) and the sequences will be 100 words long."
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:42.969916Z",
          "start_time": "2019-03-05T12:12:42.792604Z"
        },
        "id": "O-oqItqTQE0h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Embedding, LSTM, Dense, concatenate\n",
        "from keras.models import Model\n",
        "\n",
        "# headline input: meant to receive sequences of 100 integers, between 1 and 10000.\n",
        "# note that we can name any layer by passing it a \"name\" argument.\n",
        "main_input = Input(shape=(100,), dtype='int32', name='main_input')\n",
        "\n",
        "# this embedding layer will encode the input sequence\n",
        "# into a sequence of dense 512-dimensional vectors.\n",
        "x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)\n",
        "\n",
        "# a LSTM will transform the vector sequence into a single vector,\n",
        "# containing information about the entire sequence\n",
        "lstm_out = LSTM(32)(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f6M9kcSHQE0j",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we insert the auxiliary loss, allowing the LSTM and Embedding layer to be trained smoothly even though the main loss will be much higher in the model."
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:44.549821Z",
          "start_time": "2019-03-05T12:12:44.536473Z"
        },
        "id": "M1qQjL_aQE0n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KEXrfVYIQE0p",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "At this point, we feed into the model our auxiliary input data by concatenating it with the LSTM output:"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:46.193047Z",
          "start_time": "2019-03-05T12:12:46.146507Z"
        },
        "id": "jRvBLeQIQE0q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "auxiliary_input = Input(shape=(5,), name='aux_input')\n",
        "x = concatenate([lstm_out, auxiliary_input])\n",
        "\n",
        "# we stack a deep fully-connected network on top\n",
        "x = Dense(64, activation='relu')(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "\n",
        "# and finally we add the main logistic regression layer\n",
        "main_output = Dense(1, activation='sigmoid', name='main_output')(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BMdGUKqKQE0t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This defines a model with two inputs and two outputs:"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:47.070464Z",
          "start_time": "2019-03-05T12:12:47.067696Z"
        },
        "id": "eqt6hoSGQE0u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cDwUwM-2QE0z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We compile the model and assign a weight of 0.2 to the auxiliary loss. To specify different loss_weights or loss for each different output, you can use a list or a dictionary. Here we pass a single loss as the loss argument, so the same loss will be used on all outputs."
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:48.060647Z",
          "start_time": "2019-03-05T12:12:48.012672Z"
        },
        "id": "40m66hYDQE00",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='rmsprop', loss='binary_crossentropy',\n",
        "              loss_weights=[1., 0.2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rUnLWYuUQE03",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can train the model by passing it lists of input arrays and target arrays:\n",
        "\n",
        "```python\n",
        "model.fit([headline_data, additional_data], [labels, labels],\n",
        "          nb_epoch=50, batch_size=32)\n",
        "```\n",
        "Since our inputs and outputs are named (we passed them a \"name\" argument), We could also have compiled the model via:\n",
        "\n",
        "```python\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss={'main_output': 'binary_crossentropy', 'aux_output': 'binary_crossentropy'},\n",
        "              loss_weights={'main_output': 1., 'aux_output': 0.2})\n",
        "\n",
        "# and trained it via:\n",
        "model.fit({'main_input': headline_data, 'aux_input': additional_data},\n",
        "          {'main_output': labels, 'aux_output': labels},\n",
        "          nb_epoch=50, batch_size=32)\n",
        "```"
      ]
    },
    {
      "metadata": {
        "id": "n8PVcBwPQE04",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Shared layers\n",
        "\n",
        "Another good use for the functional API are models that use shared layers. Let's take a look at shared layers.\n",
        "\n",
        "Let's consider a dataset of tweets. We want to build a model that can tell whether two tweets are from the same person or not (this can allow us to compare users by the similarity of their tweets, for instance).\n",
        "\n",
        "One way to achieve this is to build a model that encodes two tweets into two vectors, concatenates the vectors and adds a logistic regression of top, outputting a probability that the two tweets share the same author. The model would then be trained on positive tweet pairs and negative tweet pairs.\n",
        "\n",
        "Because the problem is symmetric, the mechanism that encodes the first tweet should be reused (weights and all) to encode the second tweet. Here we use a shared LSTM layer to encode the tweets.\n",
        "\n",
        "Let's build this with the functional API. We will take as input for a tweet a binary matrix of shape (140, 256), i.e. a sequence of 140 vectors of size 256, where each dimension in the 256-dimensional vector encodes the presence/absence of a character (out of an alphabet of 256 frequent characters)."
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:50.156057Z",
          "start_time": "2019-03-05T12:12:50.150288Z"
        },
        "id": "8ceF9p0YQE06",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, LSTM, Dense, concatenate\n",
        "from keras.models import Model\n",
        "\n",
        "tweet_a = Input(shape=(140, 256))\n",
        "tweet_b = Input(shape=(140, 256))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cICM7c8JQE0-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To share a layer across different inputs, simply instantiate the layer once, then call it on as many inputs as you want:"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2019-03-05T12:12:51.729970Z",
          "start_time": "2019-03-05T12:12:51.421564Z"
        },
        "id": "ooh5XHXhQE0_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# this layer can take as input a matrix\n",
        "# and will return a vector of size 64\n",
        "shared_lstm = LSTM(64)\n",
        "\n",
        "# when we reuse the same layer instance\n",
        "# multiple times, the weights of the layer\n",
        "# are also being reused\n",
        "# (it is effectively *the same* layer)\n",
        "encoded_a = shared_lstm(tweet_a)\n",
        "encoded_b = shared_lstm(tweet_b)\n",
        "\n",
        "# we can then concatenate the two vectors:\n",
        "merged_vector = concatenate([encoded_a, encoded_b],axis=-1)\n",
        "\n",
        "# and add a logistic regression on top\n",
        "predictions = Dense(1, activation='sigmoid')(merged_vector)\n",
        "\n",
        "# we define a trainable model linking the\n",
        "# tweet inputs to the predictions\n",
        "model = Model(inputs=[tweet_a, tweet_b], outputs=predictions)\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}