Labs/Lab_7.ipynb

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      "name": "Lab_7.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tirtharajdash/BITS-G513_MetaLearning/blob/main/Labs/Lab_7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlS__kkEsatO"
      },
      "source": [
        "The following notebook is adapted from NYU's Deep Learning Course."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1KS6kgcMf5e"
      },
      "source": [
        "Additional Reading:\n",
        " - Lilian Weng's Blog posts on RNNs: [1](https://lilianweng.github.io/lil-log/2017/07/08/predict-stock-prices-using-RNN-part-1.html), [2](https://lilianweng.github.io/lil-log/2017/07/22/predict-stock-prices-using-RNN-part-2.html).\n",
        " - Andrej Karpathy: [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness).\n",
        " - PyTorch Tutorials: [1](https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html), [2](https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtRy6IZxsSGI"
      },
      "source": [
        "# Preliminary: Dataset generation code.\n",
        "Run (and then collapse) the below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdfuhaF3sPBR"
      },
      "source": [
        "import numpy as np\n",
        "import six\n",
        "\n",
        "def pad_sequences(sequences, maxlen=None, dtype='int32',\n",
        "                  padding='pre', truncating='pre', value=0.):\n",
        "    if not hasattr(sequences, '__len__'):\n",
        "        raise ValueError('`sequences` must be iterable.')\n",
        "    lengths = []\n",
        "    for x in sequences:\n",
        "        if not hasattr(x, '__len__'):\n",
        "            raise ValueError('`sequences` must be a list of iterables. '\n",
        "                             'Found non-iterable: ' + str(x))\n",
        "        lengths.append(len(x))\n",
        "\n",
        "    num_samples = len(sequences)\n",
        "    if maxlen is None:\n",
        "        maxlen = np.max(lengths)\n",
        "\n",
        "    # take the sample shape from the first non empty sequence\n",
        "    # checking for consistency in the main loop below.\n",
        "    sample_shape = tuple()\n",
        "    for s in sequences:\n",
        "        if len(s) > 0:\n",
        "            sample_shape = np.asarray(s).shape[1:]\n",
        "            break\n",
        "\n",
        "    is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(dtype, np.unicode_)\n",
        "    if isinstance(value, six.string_types) and dtype != object and not is_dtype_str:\n",
        "        raise ValueError(\"`dtype` {} is not compatible with `value`'s type: {}\\n\"\n",
        "                         \"You should set `dtype=object` for variable length strings.\"\n",
        "                         .format(dtype, type(value)))\n",
        "\n",
        "    x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype)\n",
        "    for idx, s in enumerate(sequences):\n",
        "        if not len(s):\n",
        "            continue  # empty list/array was found\n",
        "        if truncating == 'pre':\n",
        "            trunc = s[-maxlen:]\n",
        "        elif truncating == 'post':\n",
        "            trunc = s[:maxlen]\n",
        "        else:\n",
        "            raise ValueError('Truncating type \"%s\" '\n",
        "                             'not understood' % truncating)\n",
        "\n",
        "        # check `trunc` has expected shape\n",
        "        trunc = np.asarray(trunc, dtype=dtype)\n",
        "        if trunc.shape[1:] != sample_shape:\n",
        "            raise ValueError('Shape of sample %s of sequence at position %s '\n",
        "                             'is different from expected shape %s' %\n",
        "                             (trunc.shape[1:], idx, sample_shape))\n",
        "\n",
        "        if padding == 'post':\n",
        "            x[idx, :len(trunc)] = trunc\n",
        "        elif padding == 'pre':\n",
        "            x[idx, -len(trunc):] = trunc\n",
        "        else:\n",
        "            raise ValueError('Padding type \"%s\" not understood' % padding)\n",
        "    return x\n",
        "\n",
        "def to_categorical(y, num_classes=None, dtype='float32'):\n",
        "    y = np.array(y, dtype='int')\n",
        "    input_shape = y.shape\n",
        "    if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:\n",
        "        input_shape = tuple(input_shape[:-1])\n",
        "    y = y.ravel()\n",
        "    if not num_classes:\n",
        "        num_classes = np.max(y) + 1\n",
        "    n = y.shape[0]\n",
        "    categorical = np.zeros((n, num_classes), dtype=dtype)\n",
        "    categorical[np.arange(n), y] = 1\n",
        "    output_shape = input_shape + (num_classes,)\n",
        "    categorical = np.reshape(categorical, output_shape)\n",
        "    return categorical\n",
        "\n",
        "class EchoData():\n",
        "\n",
        "    def __init__(self, series_length=40000, batch_size=32,\n",
        "                 echo_step=3, truncated_length=10, seed=None):\n",
        "\n",
        "        self.series_length = series_length\n",
        "        self.truncated_length = truncated_length\n",
        "        self.n_batches = series_length//truncated_length\n",
        "\n",
        "        self.echo_step = echo_step\n",
        "        self.batch_size = batch_size\n",
        "        if seed is not None:\n",
        "            np.random.seed(seed)\n",
        "        self.x_batch = None\n",
        "        self.y_batch = None\n",
        "        self.x_chunks = []\n",
        "        self.y_chunks = []\n",
        "        self.generate_new_series()\n",
        "        self.prepare_batches()\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        if index == 0:\n",
        "            self.generate_new_series()\n",
        "            self.prepare_batches()\n",
        "        return self.x_chunks[index], self.y_chunks[index]\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.n_batches\n",
        "\n",
        "    def generate_new_series(self):\n",
        "        x = np.random.choice(\n",
        "            2,\n",
        "            size=(self.batch_size, self.series_length),\n",
        "            p=[0.5, 0.5])\n",
        "        y = np.roll(x, self.echo_step, axis=1)\n",
        "        y[:, 0:self.echo_step] = 0\n",
        "        self.x_batch = x\n",
        "        self.y_batch = y\n",
        "\n",
        "    def prepare_batches(self):\n",
        "        x = np.expand_dims(self.x_batch, axis=-1)\n",
        "        y = np.expand_dims(self.y_batch, axis=-1)\n",
        "        self.x_chunks = np.split(x, self.n_batches, axis=1)\n",
        "        self.y_chunks = np.split(y, self.n_batches, axis=1)\n",
        "\n",
        "class QRSU():\n",
        "    \"\"\"\n",
        "    From Hochreiter&Schmidhuber(1997):\n",
        "\n",
        "        The goal is to classify sequences. Elements and targets are represented locally\n",
        "        (input vectors with only one non-zero bit). The sequence starts with an E, ends\n",
        "        with a B (the \"trigger symbol\") and otherwise consists of randomly chosen symbols\n",
        "        from the set {a, b, c, d} except for two elements at positions t1 and t2 that are\n",
        "        either X or Y . The sequence length is randomly chosen between 100 and 110, t1 is\n",
        "        randomly chosen between 10 and 20, and t2 is randomly chosen between 50 and 60.\n",
        "        There are 4 sequence classes Q, R, S, U which depend on the temporal order of X and Y.\n",
        "        The rules are:\n",
        "            X, X -> Q,\n",
        "            X, Y -> R,\n",
        "            Y , X -> S,\n",
        "            Y , Y -> U.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, length_range=(100, 111), t1_range=(10, 21), t2_range=(50, 61),\n",
        "                 batch_size=32, seed=None):\n",
        "\n",
        "        self.classes = ['Q', 'R', 'S', 'U']\n",
        "        self.n_classes = len(self.classes)\n",
        "\n",
        "        self.relevant_symbols = ['X', 'Y']\n",
        "        self.distraction_symbols = ['a', 'b', 'c', 'd']\n",
        "        self.start_symbol = 'B'\n",
        "        self.end_symbol = 'E'\n",
        "\n",
        "        self.length_range = length_range\n",
        "        self.t1_range = t1_range\n",
        "        self.t2_range = t2_range\n",
        "        self.batch_size = batch_size\n",
        "\n",
        "        if seed is not None:\n",
        "            np.random.seed(seed)\n",
        "\n",
        "        all_symbols = self.relevant_symbols + self.distraction_symbols + \\\n",
        "                      [self.start_symbol] + [self.end_symbol]\n",
        "        self.n_symbols = len(all_symbols)\n",
        "        self.s_to_idx = {s: n for n, s in enumerate(all_symbols)}\n",
        "        self.idx_to_s = {n: s for n, s in enumerate(all_symbols)}\n",
        "\n",
        "        self.c_to_idx = {c: n for n, c in enumerate(self.classes)}\n",
        "        self.idx_to_c = {n: c for n, c in enumerate(self.classes)}\n",
        "\n",
        "    def generate_pair(self):\n",
        "        length = np.random.randint(self.length_range[0], self.length_range[1])\n",
        "        t1 = np.random.randint(self.t1_range[0], self.t1_range[1])\n",
        "        t2 = np.random.randint(self.t2_range[0], self.t2_range[1])\n",
        "\n",
        "        x = np.random.choice(self.distraction_symbols, length)\n",
        "        x[0] = self.start_symbol\n",
        "        x[-1] = self.end_symbol\n",
        "\n",
        "        y = np.random.choice(self.classes)\n",
        "        if y == 'Q':\n",
        "            x[t1], x[t2] = self.relevant_symbols[0], self.relevant_symbols[0]\n",
        "        elif y == 'R':\n",
        "            x[t1], x[t2] = self.relevant_symbols[0], self.relevant_symbols[1]\n",
        "        elif y == 'S':\n",
        "            x[t1], x[t2] = self.relevant_symbols[1], self.relevant_symbols[0]\n",
        "        else:\n",
        "            x[t1], x[t2] = self.relevant_symbols[1], self.relevant_symbols[1]\n",
        "\n",
        "        return ''.join(x), y\n",
        "\n",
        "    # encoding/decoding single instance version\n",
        "\n",
        "    def encode_x(self, x):\n",
        "        idx_x = [self.s_to_idx[s] for s in x]\n",
        "        return to_categorical(idx_x, num_classes=self.n_symbols)\n",
        "\n",
        "    def encode_y(self, y):\n",
        "        idx_y = self.c_to_idx[y]\n",
        "        return to_categorical(idx_y, num_classes=self.n_classes)\n",
        "\n",
        "    def decode_x(self, x):\n",
        "        x = x[np.sum(x, axis=1) > 0]    # remove padding\n",
        "        return ''.join([self.idx_to_s[pos] for pos in np.argmax(x, axis=1)])\n",
        "\n",
        "    def decode_y(self, y):\n",
        "        return self.idx_to_c[np.argmax(y)]\n",
        "\n",
        "    # encoding/decoding batch versions\n",
        "\n",
        "    def encode_x_batch(self, x_batch):\n",
        "        return pad_sequences([self.encode_x(x) for x in x_batch],\n",
        "                             maxlen=self.length_range[1])\n",
        "\n",
        "    def encode_y_batch(self, y_batch):\n",
        "        return np.array([self.encode_y(y) for y in y_batch])\n",
        "\n",
        "    def decode_x_batch(self, x_batch):\n",
        "        return [self.decode_x(x) for x in x_batch]\n",
        "\n",
        "    def decode_y_batch(self, y_batch):\n",
        "        return [self.idx_to_c[pos] for pos in np.argmax(y_batch, axis=1)]\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\" Let's assume 1000 sequences as the size of data. \"\"\"\n",
        "        return int(1000. / self.batch_size)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        batch_x, batch_y = [], []\n",
        "        for _ in range(self.batch_size):\n",
        "            x, y = self.generate_pair()\n",
        "            batch_x.append(x)\n",
        "            batch_y.append(y)\n",
        "        return self.encode_x_batch(batch_x), self.encode_y_batch(batch_y)\n",
        "\n",
        "    class DifficultyLevel:\n",
        "        \"\"\" On HARD, settings are identical to the original settings from the '97 paper.\"\"\"\n",
        "        EASY, NORMAL, MODERATE, HARD, NIGHTMARE = range(5)\n",
        "\n",
        "    @staticmethod\n",
        "    def get_predefined_generator(difficulty_level, batch_size=32, seed=8382):\n",
        "        EASY = QRSU.DifficultyLevel.EASY\n",
        "        NORMAL = QRSU.DifficultyLevel.NORMAL\n",
        "        MODERATE = QRSU.DifficultyLevel.MODERATE\n",
        "        HARD = QRSU.DifficultyLevel.HARD\n",
        "\n",
        "        if difficulty_level == EASY:\n",
        "            length_range = (7, 9)\n",
        "            t1_range = (1, 3)\n",
        "            t2_range = (4, 6)\n",
        "        elif difficulty_level == NORMAL:\n",
        "            length_range = (30, 41)\n",
        "            t1_range = (2, 6)\n",
        "            t2_range = (20, 28)\n",
        "        elif difficulty_level == MODERATE:\n",
        "            length_range = (60, 81)\n",
        "            t1_range = (10, 21)\n",
        "            t2_range = (45, 55)\n",
        "        elif difficulty_level == HARD:\n",
        "            length_range = (100, 111)\n",
        "            t1_range = (10, 21)\n",
        "            t2_range = (50, 61)\n",
        "        else:\n",
        "            length_range = (300, 501)\n",
        "            t1_range = (10, 81)\n",
        "            t2_range = (250, 291)\n",
        "        return QRSU(length_range, t1_range, t2_range,\n",
        "                                          batch_size, seed)\n",
        "    "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OquDskkusBOB"
      },
      "source": [
        "# An example of Sequence classification\n",
        "\n",
        "Original experiment from [Hochreiter & Schmidhuber (1997)](www.bioinf.jku.at/publications/older/2604.pdf).\n",
        "\n",
        "The goal is to classify sequences.\n",
        "Elements and targets are represented locally (input vectors with only one non-zero bit).\n",
        "The sequence starts with an `B`, ends with a `E` (the “trigger symbol”), and otherwise consists of randomly chosen symbols from the set `{a, b, c, d}` except for two elements at positions `t1` and `t2` that are either `X` or `Y`.\n",
        "For the `DifficultyLevel.HARD` case, the sequence length is randomly chosen between `100` and `110`, `t1` is randomly chosen between `10` and `20`, and `t2` is randomly chosen between `50` and `60`.\n",
        "There are `4` sequence classes `Q`, `R`, `S`, and `U`, which depend on the temporal order of `X` and `Y`.\n",
        "\n",
        "The rules are:\n",
        "\n",
        "```\n",
        "X, X -> Q,\n",
        "X, Y -> R,\n",
        "Y, X -> S,\n",
        "Y, Y -> U.\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBi_XbJFsBOE"
      },
      "source": [
        "## [1] Dataset Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rJTk4qKsBOG",
        "outputId": "620702c3-df76-4842-ac6a-633edd7e69b1"
      },
      "source": [
        "# Create a data generator\n",
        "example_generator = QRSU.get_predefined_generator(\n",
        "    difficulty_level=QRSU.DifficultyLevel.EASY,\n",
        "    batch_size=32,\n",
        ")\n",
        "\n",
        "example_batch = example_generator[1]\n",
        "print(f'The return type is a {type(example_batch)} with length {len(example_batch)}.')\n",
        "print(f'The first item in the tuple is the batch of sequences with shape {example_batch[0].shape}.')\n",
        "print(f'The first element in the batch of sequences is:\\n {example_batch[0][0, :, :]}')\n",
        "print(f'The second item in the tuple is the corresponding batch of class labels with shape {example_batch[1].shape}.')\n",
        "print(f'The first element in the batch of class labels is:\\n {example_batch[1][0, :]}')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The return type is a <class 'tuple'> with length 2.\n",
            "The first item in the tuple is the batch of sequences with shape (32, 9, 8).\n",
            "The first element in the batch of sequences is:\n",
            " [[0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 1 0]\n",
            " [0 0 0 1 0 0 0 0]\n",
            " [1 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 1 0 0 0]\n",
            " [1 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 1 0 0 0]\n",
            " [0 0 0 1 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 1]]\n",
            "The second item in the tuple is the corresponding batch of class labels with shape (32, 4).\n",
            "The first element in the batch of class labels is:\n",
            " [1. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UViWN3O5sBOG",
        "outputId": "8a2e3906-ca86-4ea4-f7ab-17dbd2b3e973"
      },
      "source": [
        "len(example_batch), example_batch[0].shape, example_batch[1].shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, (32, 9, 8), (32, 4))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKL0uP8lsBOH",
        "outputId": "bf4ae246-b742-4813-ea06-d94781f0a4d2"
      },
      "source": [
        "example_generator.idx_to_s"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'X', 1: 'Y', 2: 'a', 3: 'b', 4: 'c', 5: 'd', 6: 'B', 7: 'E'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCZ5jHE1sBOH",
        "outputId": "79fa4ed2-ce0c-4867-f2dd-d3713b83976d"
      },
      "source": [
        "# Decoding the first sequence\n",
        "sequence_decoded = example_generator.decode_x(example_batch[0][0, :, :])\n",
        "print(f'The sequence is: {sequence_decoded}')\n",
        "\n",
        "# Decoding the class label of the first sequence\n",
        "class_label_decoded = example_generator.decode_y(example_batch[1][0])\n",
        "print(f'The class label is: {class_label_decoded}')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The sequence is: BbXcXcbE\n",
            "The class label is: Q\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVbnNNOEsBOH"
      },
      "source": [
        "## [2] Defining the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbeq69hxsBOH"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Set the random seed for reproducible results\n",
        "torch.manual_seed(1)\n",
        "\n",
        "class SimpleRNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        # This just calls the base class constructor\n",
        "        super().__init__()\n",
        "        # Neural network layers assigned as attributes of a Module subclass\n",
        "        # have their parameters registered for training automatically.\n",
        "        self.rnn = torch.nn.RNN(input_size, hidden_size, nonlinearity='relu', batch_first=True)\n",
        "        self.linear = torch.nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # The RNN also returns its hidden state but we don't use it.\n",
        "        # While the RNN can also take a hidden state as input, the RNN\n",
        "        # gets passed a hidden state initialized with zeros by default.\n",
        "        h = self.rnn(x)[0]\n",
        "        x = self.linear(h)\n",
        "        return x\n",
        "\n",
        "class SimpleLSTM(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super().__init__()\n",
        "        self.lstm = torch.nn.LSTM(input_size, hidden_size, batch_first=True)\n",
        "        self.linear = torch.nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h = self.lstm(x)[0]\n",
        "        x = self.linear(h)\n",
        "        return x\n",
        "    \n",
        "    def get_states_across_time(self, x):\n",
        "        h_c = None\n",
        "        h_list, c_list = list(), list()\n",
        "        with torch.no_grad():\n",
        "            for t in range(x.size(1)):\n",
        "                h_c = self.lstm(x[:, [t], :], h_c)[1]\n",
        "                h_list.append(h_c[0])\n",
        "                c_list.append(h_c[1])\n",
        "            h = torch.cat(h_list)\n",
        "            c = torch.cat(c_list)\n",
        "        return h, c"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZ5wTexuHRN6"
      },
      "source": [
        "## [3] Looking at the Internals"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3AY-1HqFpil"
      },
      "source": [
        "difficulty     = QRSU.DifficultyLevel.EASY  # 'EASY' 'NORMAL' 'MODERATE' 'HARD' 'NIGHTMARE' \n",
        "batch_size     = 32\n",
        "data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the LSTM\n",
        "input_size  = data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = data_gen.n_classes\n",
        "model       = SimpleLSTM(input_size, hidden_size, output_size)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pb9rMCbsFz0B"
      },
      "source": [
        "idx = 0\n",
        "data, target = data_gen[idx]\n",
        "data = torch.from_numpy(data).float()\n",
        "target = torch.from_numpy(target).long()\n",
        "\n",
        "pred = model(data)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBGW3AXaF5zm",
        "outputId": "bbcb1546-d267-43cd-bd6b-13d7e39adb90"
      },
      "source": [
        "data.shape, target.shape, pred.shape"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 9, 8]), torch.Size([32, 4]), torch.Size([32, 9, 4]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO-DzAR5F6NQ",
        "outputId": "ce9d535b-9594-4cf3-bef1-ee3b91b8ee19"
      },
      "source": [
        "data[0]"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 1., 0.],\n",
              "        [0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "        [1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 1., 0., 0., 0.],\n",
              "        [1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 1., 0., 0., 0.],\n",
              "        [0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4rZs_OaF6T3",
        "outputId": "9b4832db-844d-4090-9a70-5cb5f37be6cf"
      },
      "source": [
        "target[0]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZxmWeoXfF6qG",
        "outputId": "80b809eb-7ef8-481b-c2a5-b851a6e7d575"
      },
      "source": [
        "pred[0]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0837,  0.2619,  0.3661, -0.0025],\n",
              "        [-0.1137,  0.2903,  0.3645, -0.0167],\n",
              "        [-0.1356,  0.2641,  0.3816,  0.0362],\n",
              "        [-0.1037,  0.2601,  0.3926,  0.0089],\n",
              "        [-0.1196,  0.2848,  0.3731, -0.0103],\n",
              "        [-0.1009,  0.2663,  0.3909, -0.0031],\n",
              "        [-0.1199,  0.2883,  0.3725, -0.0162],\n",
              "        [-0.1480,  0.2715,  0.3893,  0.0344],\n",
              "        [-0.1093,  0.2694,  0.3940,  0.0246]], grad_fn=<SelectBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LXFnfDIsBOI"
      },
      "source": [
        "## [4] Defining the Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDYu-q0IsBOI"
      },
      "source": [
        "def train(model, train_data_gen, criterion, optimizer, device):\n",
        "    # Set the model to training mode. This will turn on layers that would\n",
        "    # otherwise behave differently during evaluation, such as dropout.\n",
        "    model.train()\n",
        "\n",
        "    # Store the number of sequences that were classified correctly\n",
        "    num_correct = 0\n",
        "\n",
        "    # Iterate over every batch of sequences. Note that the length of a data generator\n",
        "    # is defined as the number of batches required to produce a total of roughly 1000\n",
        "    # sequences given a batch size.\n",
        "    for batch_idx in range(len(train_data_gen)):\n",
        "\n",
        "        # Request a batch of sequences and class labels, convert them into tensors\n",
        "        # of the correct type, and then send them to the appropriate device.\n",
        "        data, target = train_data_gen[batch_idx]\n",
        "        data, target = torch.from_numpy(data).float().to(device), torch.from_numpy(target).long().to(device)\n",
        "\n",
        "        # Perform the forward pass of the model\n",
        "        output = model(data)  # Step ①\n",
        "\n",
        "        # Pick only the output corresponding to last sequence element (input is pre padded)\n",
        "        output = output[:, -1, :]\n",
        "\n",
        "        # Compute the value of the loss for this batch. For loss functions like CrossEntropyLoss,\n",
        "        # the second argument is actually expected to be a tensor of class indices rather than\n",
        "        # one-hot encoded class labels. One approach is to take advantage of the one-hot encoding\n",
        "        # of the target and call argmax along its second dimension to create a tensor of shape\n",
        "        # (batch_size) containing the index of the class label that was hot for each sequence.\n",
        "        target = target.argmax(dim=1)\n",
        "\n",
        "        loss = criterion(output, target)  # Step ②\n",
        "\n",
        "        # Clear the gradient buffers of the optimized parameters.\n",
        "        # Otherwise, gradients from the previous batch would be accumulated.\n",
        "        optimizer.zero_grad()  # Step ③\n",
        "\n",
        "        loss.backward()  # Step ④\n",
        "\n",
        "        optimizer.step()  # Step ⑤\n",
        "\n",
        "        y_pred = output.argmax(dim=1)\n",
        "        num_correct += (y_pred == target).sum().item()\n",
        "\n",
        "    return num_correct, loss.item()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3rJDW6L1p2N"
      },
      "source": [
        ""
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "My8ZZikGsBOI"
      },
      "source": [
        "## [5] Defining the Testing Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEgzImcJsBOI"
      },
      "source": [
        "def test(model, test_data_gen, criterion, device):\n",
        "    # Set the model to evaluation mode. This will turn off layers that would\n",
        "    # otherwise behave differently during training, such as dropout.\n",
        "    model.eval()\n",
        "\n",
        "    # Store the number of sequences that were classified correctly\n",
        "    num_correct = 0\n",
        "\n",
        "    # A context manager is used to disable gradient calculations during inference\n",
        "    # to reduce memory usage, as we typically don't need the gradients at this point.\n",
        "    with torch.no_grad():\n",
        "        for batch_idx in range(len(test_data_gen)):\n",
        "            data, target = test_data_gen[batch_idx]\n",
        "            data, target = torch.from_numpy(data).float().to(device), torch.from_numpy(target).long().to(device)\n",
        "\n",
        "            output = model(data)\n",
        "            # Pick only the output corresponding to last sequence element (input is pre padded)\n",
        "            output = output[:, -1, :]\n",
        "\n",
        "            target = target.argmax(dim=1)\n",
        "            loss = criterion(output, target)\n",
        "\n",
        "            y_pred = output.argmax(dim=1)\n",
        "            num_correct += (y_pred == target).sum().item()\n",
        "\n",
        "    return num_correct, loss.item()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83yZjjnNsBOJ"
      },
      "source": [
        "## [6] Putting it All Together"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0jneB2FtxaH"
      },
      "source": [
        "### Graphing Utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfQ-U7zYsBOJ"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import HTML, display\n",
        "plt.rc('figure', figsize=(10, 10), dpi=100)\n",
        "\n",
        "def _cstr(s, color='black'):\n",
        "    if s == ' ':\n",
        "        return f'<text style=color:#000;padding-left:10px;background-color:{color}> </text>'\n",
        "    else:\n",
        "        return f'<text style=color:#000;background-color:{color}>{s} </text>'\n",
        "\n",
        "# print html\n",
        "def _print_color(t):\n",
        "    display(HTML(''.join([_cstr(ti, color=ci) for ti, ci in t])))\n",
        "\n",
        "# get appropriate color for value\n",
        "def _get_clr(value):\n",
        "    colors = ('#85c2e1', '#89c4e2', '#95cae5', '#99cce6', '#a1d0e8',\n",
        "              '#b2d9ec', '#baddee', '#c2e1f0', '#eff7fb', '#f9e8e8',\n",
        "              '#f9e8e8', '#f9d4d4', '#f9bdbd', '#f8a8a8', '#f68f8f',\n",
        "              '#f47676', '#f45f5f', '#f34343', '#f33b3b', '#f42e2e')\n",
        "    value = int((value * 100) / 5)\n",
        "    if value == len(colors): value -= 1  # fixing bugs...\n",
        "    return colors[value]\n",
        "\n",
        "def _visualise_values(output_values, result_list):\n",
        "    text_colours = []\n",
        "    for i in range(len(output_values)):\n",
        "        text = (result_list[i], _get_clr(output_values[i]))\n",
        "        text_colours.append(text)\n",
        "    _print_color(text_colours)\n",
        "\n",
        "def print_colourbar():\n",
        "    color_range = torch.linspace(-2.5, 2.5, 20)\n",
        "    to_print = [(f'{x:.2f}', _get_clr((x+2.5)/5)) for x in color_range]\n",
        "    _print_color(to_print)\n",
        "\n",
        "\n",
        "# Let's only focus on the last time step for now\n",
        "# First, the cell state (Long term memory)\n",
        "def plot_state(data, state, b, decoder):\n",
        "    actual_data = decoder(data[b, :, :].numpy())\n",
        "    seq_len = len(actual_data)\n",
        "    seq_len_w_pad = len(state)\n",
        "    for s in range(state.size(2)):\n",
        "        states = torch.sigmoid(state[:, b, s])\n",
        "        _visualise_values(states[seq_len_w_pad - seq_len:], list(actual_data))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5Zp0iOkHbgc"
      },
      "source": [
        "### Main Train-Test Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2c47xn9sBOJ"
      },
      "source": [
        "def train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=True):\n",
        "    # Automatically determine the device that PyTorch should use for computation\n",
        "    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "    # Move model to the device which will be used for train and test\n",
        "    model.to(device)\n",
        "\n",
        "    # Track the value of the loss function and model accuracy across epochs\n",
        "    history_train = {'loss': [], 'acc': []}\n",
        "    history_test = {'loss': [], 'acc': []}\n",
        "\n",
        "    for epoch in range(max_epochs):\n",
        "        # Run the training loop and calculate the accuracy.\n",
        "        # Remember that the length of a data generator is the number of batches,\n",
        "        # so we multiply it by the batch size to recover the total number of sequences.\n",
        "        num_correct, loss = train(model, train_data_gen, criterion, optimizer, device)\n",
        "        accuracy = float(num_correct) / (len(train_data_gen) * train_data_gen.batch_size) * 100\n",
        "        history_train['loss'].append(loss)\n",
        "        history_train['acc'].append(accuracy)\n",
        "\n",
        "        # Do the same for the testing loop\n",
        "        num_correct, loss = test(model, test_data_gen, criterion, device)\n",
        "        accuracy = float(num_correct) / (len(test_data_gen) * test_data_gen.batch_size) * 100\n",
        "        history_test['loss'].append(loss)\n",
        "        history_test['acc'].append(accuracy)\n",
        "\n",
        "        if verbose or epoch + 1 == max_epochs:\n",
        "            print(f'[Epoch {epoch + 1}/{max_epochs}]'\n",
        "                  f\" loss: {history_train['loss'][-1]:.4f}, acc: {history_train['acc'][-1]:2.2f}%\"\n",
        "                  f\" - test_loss: {history_test['loss'][-1]:.4f}, test_acc: {history_test['acc'][-1]:2.2f}%\")\n",
        "\n",
        "    # Generate diagnostic plots for the loss and accuracy\n",
        "    fig, axes = plt.subplots(ncols=2, figsize=(9, 4.5))\n",
        "    for ax, metric in zip(axes, ['loss', 'acc']):\n",
        "        ax.plot(history_train[metric])\n",
        "        ax.plot(history_test[metric])\n",
        "        ax.set_xlabel('epoch', fontsize=12)\n",
        "        ax.set_ylabel(metric, fontsize=12)\n",
        "        ax.legend(['Train', 'Test'], loc='best')\n",
        "        ax.grid()\n",
        "    plt.show()\n",
        "\n",
        "    return model"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2co8qpQsBOK"
      },
      "source": [
        "## [7] Simple RNN: 10 Epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "znKmUPYAsBOK",
        "outputId": "ca5e6fa7-ad09-4bf4-8004-c5e5371f1ab4"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.EASY\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the RNN and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleRNN(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 10\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 1/10] loss: 1.4213, acc: 24.29% - test_loss: 1.3710, test_acc: 31.35%\n",
            "[Epoch 2/10] loss: 1.3603, acc: 30.75% - test_loss: 1.3846, test_acc: 37.50%\n",
            "[Epoch 3/10] loss: 1.3673, acc: 40.22% - test_loss: 1.3910, test_acc: 40.12%\n",
            "[Epoch 4/10] loss: 1.3636, acc: 39.82% - test_loss: 1.3435, test_acc: 43.85%\n",
            "[Epoch 5/10] loss: 1.2752, acc: 44.35% - test_loss: 1.3282, test_acc: 36.59%\n",
            "[Epoch 6/10] loss: 1.2631, acc: 40.22% - test_loss: 1.2412, test_acc: 41.03%\n",
            "[Epoch 7/10] loss: 1.2340, acc: 44.35% - test_loss: 1.2670, test_acc: 47.58%\n",
            "[Epoch 8/10] loss: 1.1193, acc: 50.40% - test_loss: 1.2446, test_acc: 48.69%\n",
            "[Epoch 9/10] loss: 1.2194, acc: 48.89% - test_loss: 1.1098, test_acc: 50.81%\n",
            "[Epoch 10/10] loss: 1.0805, acc: 48.69% - test_loss: 1.0649, test_acc: 52.72%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "338JAwk0sBOK",
        "outputId": "a1c7d5f7-1982-4ee2-d73e-3de90ff3c1ce"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.MODERATE\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the RNN and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleRNN(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 10\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 1/10] loss: 1.4182, acc: 25.50% - test_loss: 1.3808, test_acc: 24.70%\n",
            "[Epoch 2/10] loss: 1.3436, acc: 24.90% - test_loss: 1.3828, test_acc: 25.40%\n",
            "[Epoch 3/10] loss: 1.3512, acc: 24.19% - test_loss: 1.3897, test_acc: 23.69%\n",
            "[Epoch 4/10] loss: 1.4417, acc: 25.10% - test_loss: 1.4170, test_acc: 25.71%\n",
            "[Epoch 5/10] loss: 1.4003, acc: 22.98% - test_loss: 1.3831, test_acc: 24.09%\n",
            "[Epoch 6/10] loss: 1.3793, acc: 24.90% - test_loss: 1.3703, test_acc: 25.10%\n",
            "[Epoch 7/10] loss: 1.4177, acc: 24.40% - test_loss: 1.3830, test_acc: 24.29%\n",
            "[Epoch 8/10] loss: 1.3700, acc: 23.19% - test_loss: 1.3737, test_acc: 23.29%\n",
            "[Epoch 9/10] loss: 1.3777, acc: 27.52% - test_loss: 1.3965, test_acc: 25.71%\n",
            "[Epoch 10/10] loss: 1.3759, acc: 25.00% - test_loss: 1.4012, test_acc: 22.18%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4kHQw5vsBOK"
      },
      "source": [
        "## [8] Simple LSTM: 10 Epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "UmRePSfAsBOK",
        "outputId": "675b476f-cc63-40e6-9f25-17a5c0ad4405"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.EASY\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the LSTM and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleLSTM(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 10\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 1/10] loss: 1.3471, acc: 25.10% - test_loss: 1.4155, test_acc: 23.49%\n",
            "[Epoch 2/10] loss: 1.3951, acc: 37.90% - test_loss: 1.3406, test_acc: 46.88%\n",
            "[Epoch 3/10] loss: 1.2989, acc: 38.51% - test_loss: 1.2762, test_acc: 37.60%\n",
            "[Epoch 4/10] loss: 1.2094, acc: 41.23% - test_loss: 1.2093, test_acc: 39.92%\n",
            "[Epoch 5/10] loss: 1.1916, acc: 41.13% - test_loss: 1.1248, test_acc: 47.48%\n",
            "[Epoch 6/10] loss: 1.1318, acc: 44.56% - test_loss: 1.1188, test_acc: 47.38%\n",
            "[Epoch 7/10] loss: 1.1173, acc: 54.03% - test_loss: 1.0288, test_acc: 68.45%\n",
            "[Epoch 8/10] loss: 1.0983, acc: 71.77% - test_loss: 1.0890, test_acc: 71.88%\n",
            "[Epoch 9/10] loss: 0.9827, acc: 70.56% - test_loss: 0.9912, test_acc: 73.39%\n",
            "[Epoch 10/10] loss: 0.9984, acc: 76.61% - test_loss: 0.9550, test_acc: 78.33%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D0U8ZHxLXIKewjFtm/8zh3BISm4fw4eQLiAhy7XgvSQhcYfBD0DQRCo6cyhbrIC2niAmz1pGZX0bHmDA+njKApiHGgmf3Dk3CpGDFriy2HJJWAiGEEEIIt8g7DO9eDinLwRIMN34MA/507lNKrPzhnfWk5hQRHxHIh1MG0Dw04JznOIMkBK5gCYIrXzYeb5gNB9fV+tS0nCJumrWOo/mldIgJ5eOpA2hW7UZp2zyEq3sZy1xLK4EQrtWkSRPMZjNKqbN+PfbYY/UuWynF119/7bhghU+q6d6Te1AIFzu6FWYPh8ytEBINkxZApzHnPKW4vII/ztnAjox8mof689GUAbSIDHJRwKfz5mlHvUviYOh1C2z6CL6dDnf+CH7nzgAPHDO6CR3NLyUpOpRPpg48a9Z4z9D2fLPpMMt2ZrHtcB7dWkQ4qxZCiGp27dpFWFgYJpOJzz77jBkzZrB79+6T+0NDQ90YnWgMMjIyTj6We1AIN9m3DD6fBOUF0Lwj3PyFMdvkOZRV2PjTh7/y64EThAf68eHkASRGue/3VVoIXGnkvyAkCnJ2w08vnfPQg8eKmTBrHRl5pbQ/RzIA0C4qlLE9jbEEMuOQEK4TExNDbGwssbGxREREoJQ6uR0bG8unn35K586dCQwMpFOnTrz++usnzy0vL+eee+4hLi6OwMBAWrduzdNPPw1AmzZtABg3bhxKqZPbQpyp+v3mzHswMTHRHdUTwvP9+j58fIORDLS5FCYvOW8yUGGzM33uRn7cm0Owv5n3br+AznF1WxzX0aSFwJWCm8Llz8KXf4RVz0OXayC60+8OSz9ezIS313Ekr5R2USF8MnUAUWHnbk24d2h7vt18hOQdmWw/kkfXeGklEMKdPv74Y2bMmMGrr75K79692bhxI1OnTiUkJITbbruNmTNn8u233/L555+TkJBAeno66enpAGzYsIHo6Gjee+89Ro8ejdnsmmnnhG9x5D3oiatxC+FWdjt8/yT8+IKx3eMmY2ZJP//znKZ56H9bWbI9E3+ziVm39qNv6yYuCPjcJCFwta7jYfNnsHcJzJ8Oty+Gaqucph8v5qZZ6zicW0K7qBDm3jGQ6LDA8xbbPjqMK3vEM3/zEWYu38tbt/ZzZi2EcA2twVrs+utagqGBb4AeffRRXnjhBcaPHw9A27Zt2bFjB2+99Ra33XYbBw8eJCkpiUsuuQSlFK1bn/pEKSrKmKc6MjKS2NjYBsUhGqD6/We3G4/Lzaf9zXYaD7sH7XY7+fn1myVPCJ9TUQZf3w3bvjS2Bz8EQx4+7++s1ponvtvB/347hNmkeGViby5Jau6CgM9PEgJXUwqueAFeXw3pP8Ov70L/KcDpyUBi8xDmTq1dMlBl+tD2fLflCEu2Z7IzI9/tzU9CNJi1GJ6Kd/11HzkC/iH1Pr2oqIiUlBQmT57M1KlTTz5fUVFBRITRejdp0iRGjBhBx44dGT16NFdeeSUjR45scOjCgardfyYg0pXXlntQCM9UfBw+vRkOrgGTH4ydCb1vrtWpLybvYc6aNAD+c10PRnX1nA98JCFwh8hWMGwGLPobJD8GHcdwyBbJhLerJQN3DCQ6vPbJAEBSTBhjusexYEsGM5fv5Y1b+jonfiHEORUWFgLw9ttvM2DAgNP2VXX/6dOnD/v372fRokUsW7aMG264geHDh/Pll1+6PF7he+QeFMIJTqTBZxPg2F4ICIcbP4TEIbU6ddaqFF5ZsQ+Af13dlfF9WjotzPqQhMBd+k+BLZ/D4V8o+fovTMi4g0MnSmlbmQzE1DEZqDJ9aBILtmSwaNtRdh3Np1OstBIIL2YJNj4pdcd1GyAmJob4+HhSU1O5+eaaPzkKDw/nxhtv5MYbb+S6665j9OjRHD9+nKZNm2KxWLDZbA2KQzRQtfvPbreTX1BAeOWsUi65dgPIPSiEYzUpSsFvzv1QnAPhLY2ZhGK61OrcuesP8tTCXQA8OKojt17YxomR1o8kBO5iMsNVM9FvDSIodTFdyzthbjaEuVPrnwwAdIwNY0z3WBZuPcory/fx2s19HBi0EC6mVIO6TbjT448/zvTp04mIiGD06NGUlZXxyy+/cOLECe6//35efPFF4uLi6N27NyaTiS+++ILY2FgiI42OKW3atGH58uVcfPHFBAQE0KSJ+wedNTrV7z+7HSw2Y9sVCYEDOPIetFgsMrhdND4lJ2D/j5j2reCivR+jdDnE9YQJn0F4XK2K+HbzER75aisAfxqcyN1D2jkz4nrzjr9qPupIQCIfmMYB8O+A9/n0D52Jjah/MlBl+rAkABZuy2BPZkGDyxNC1N2UKVOYPXs27733Ht27d2fw4MHMmTOHtm3bAhAWFsZzzz1Hv3796N+/P2lpaSxcuPDkp88vvPACycnJtGrVit69e7uzKsJLOfIe7NtXuqCKRsBaCqk/wLLHYdZl8FwifH4r5t/ew0+XY28/AiYtrHUysGJXJvd/tgmt4eYBCfx9dCePnbFLWgjcJCOvxFh0rPAKhgStobU+DOufhrH/bXDZnWLDGd01lsXbjzJz+V5enSitBEI426RJk5g0adJpz02cOJGJEyee9fipU6eeNtjzTGPHjmXs2LGODFH4OGfegzLLkPBJdruxsnDqSkj5Hg6uhYrS049p3hFb28FsOBZC3+v/jimgdh/crks9xl0f/UaFXXN1r3j+dXU3j00GQBICtziaV8qEWes4cKyYhKaRhIx5Fb4cB7/Oge7XQ5tLGnyN6cOSWLz9KAu2ZnBfZgFJMWEND1wIIYQQwpudSDMSgNSVRmtAyfHT94fGGgOFE4dA4mAIj8dutZK5cKHR3bsWNqfnMnnOBsoq7AzvHM3z1/fEZPLcZAAkIXC5o3mlTHh7HWnHimnVNIi5dwykeWQQpN4Gv70P8++DO1eDpWFdh7rEhzOqawxLtmfyyop9zJwgXQ6EEEII0cgUH4f9P5xKAk6knb7fP8z4IDZxiPEV1bFBa4DsySzgtvfWU1Ru48LEZrw6sQ8Ws+f30JeEwIUy841kYH9OES2bBDF36kBaRAYZO0c8AXsWw7F98OPzMPT/Gny96cOSWLI9k/lbjjB9WBLto0MbXKYQQgghhMeylhhdf6oSgIwtgD613+QHLftD4mVGAtCiD5gtDrn0wWPF3DL7Z3KLrfRsFcnbt/Uj0OIdg/ElIXCRrHyjm9D+nCJaRBrJQMsm1aaVC4qEMf+Bz/8AP70EXcdBTNcGXbNrfAQjusSQvCOTV1fs5eWbpJVACCGEED7EboOMzacSgIPrwFZ2+jHRXU61ALS+CAIc3436aF4pE2evI6ugjI4xYbx/e39CA7znbbb3ROrFsvJLuentdaRWJgOf3jGQVk3PMsd056ug4xWwewF8Ox0mL611f7Wa3DcsieQdmXy72WglSIySVgIhhBBCeCmt4XjqqQRg/yoozT39mLB4aFfZAtB2EIQ5d0Xg40Xl3PLOzxw6UULrZsF8OPkCIoP9nXpNR5OEwMmyCoxuQqnZ50kGwOizdsXzkPYjHP4FNsyGAX9q0PW7tYhgeOdolu3M4tUV+3jxxl4NKk8IZ9Nan/8gcV7yc6w/+dk1nPwMhUNZS2D3QmMmoNQfIO/g6fsDwqHNpaeSgGbtGzQOoC7yS63c9u569mUVEhseyEeTBxDdgPWk3EUSAifKLihj4ts/k5JdRHxEIHOnniMZqBIeD8MfhQV/heVPQMcxENmqQXHcN6wDy3Zm8fWmw9w7LIm2zb1zoSfh2ywWow9ncXExQUFBbo7G+xUXFwOnfq7i/OQedJzy8nIAWcxMNNzuxZR/9wD+Beknn9ImC6rVgFPdgOJ7g9n1b2lLym1MmfMLWw/n0TTEn4+mDDj/+zwPJQmBkxjJwDr2ZRUSFxHI3DsGktCsljdJ3z/Cli8gfZ2RGEz8rEGZbveWEQztFM2KXUYrwQs39Kx3WUI4i9lsJjIykqysLACCg4M9es5mu91OeXk5paWlJxdy8gRaa4qLi8nKyiIyMlLekNXBue5BT329XaUu9bfb7WRnZxMcHIyfn7zNEPV0Ig0W/R32LMIfOKqb8K3tIlbbu7He3pHIo5H0skTSk0h6WfPo3iKCEBf22S+vsHPXx7+yPu04YQF+fPDHC7x68hb5TXWCnEIjGdhb2Xw0d+pAWjerw6fyJpOxQNmbl8DeJbB9HnS7tkEx3TcsiRW7jFaC6cPa1y0eIVwkNtbo51n1hsyTaa0pKSkhKCjIIxOXyMjIkz9PUXs13YOe/no7W13rbzKZSEhIaJQ/K9FA1lJY/V/46UWoKEWb/HjbejkvW8cxvGcimZkFlGUWkJFXSkbeURZtOwqASUGHmDB6toykV0IkPVtG0iEmFD8nTPlps2v+8tkmVu7OJtBi4t3b+9OtRYTDr+NKkhA42LFqyUBMeACf3jGQNvXpohPdCQY9ACufhkUPGdNjBTetd1w9W0UypGMUK3dn8+qKffznemklEJ5HKUVcXBzR0dFYrVZ3h3NOVquVVatWMWjQII/rlmOxWKRloJ5qugc9+fV2hbrW39/fv1G2pIgG2psMCx+EE/uN7TaXsqLd33hqQQHto0NPrqlUWFbBtsN5bErPZXN6LpvSc8nIK2XX0QJ2HS3gs1+M7kVBFjPdW0ScTBB6JUQSHxHYoERVa80j87ayYGsGFrPirVv70b9N/d+feQpJCBzISAZ+Zk9mVTJwYf2SgSqX/AW2zYOc3ZD8T7j6tQbFd9+wJFbuzmbexsPcOzSp9l2YhHAxs9ns8W9ozWYzFRUVBAYGNso3iL7uzHuwsb/ejb3+wslyD8Lih2HXd8Z2aCyM+jd0u5YvP/4NKGB011MtnqEBfgxMbMbAxGYnn8vMLz0tQdhyKI/CsgrWpx1nfdqp1YibhwbQq1UkvVpF0LNVJD1aRhIRVLt7WmvNvxfs5LNf0jEpmHlTbwZ3iHLIj8DdJCFwkONF5dw8+2d2ZxYQHRbA3KkDGz541y8ArpoJ746CjR9B9xuMZbTrqXdCEwZ1iGLVnmxe+34fz17Xo2HxCSGEEELUV0UZrHkFVj0PFSWgzDDwLhjydwgIo6Tcxsrd2QCM7nbuLpAx4YGM6hrLqMrEwW7XpGQXGknCISNJ2JVRQE5hGct2ZrJsZ+bJcxOjQiqTBOOrU2w4/n6/b+F6dWUqs38yWi+eubYHl3ePc9RPwu0kIXCA40Xl3DbnV3YdrUwG7hjouPn+EwZC/ynGFKTz74O714Kl/rNf3DcsiVV7svnfb4e4Z2h7rx0NL4QQQggvtm85LPobHNtnbLe+GMY8DzFdTh6yam82JVYbLSKD6BofXqfiTSZFUkwYSTFhXN/PmK2x1Gpj+5E8NqWf6m508HgxqdlFpGYXMe+3wwD4+5noGh9Oz5aR9E6IpGtsKCszFF+lpQAw48ou3NCvYTNAehpJCBqoyMrJZCAqLIBPpg6knaMX/xr2KOxaaPSp++FZGP5YvYvq27oJlyY158e9Obz2/T6euVZaCYQQnkEplQa0Psuu17XW05RSgcALwE1AALAEuFtrnXmWc4QQnijvECx5BHZ8Y2yHRBvdg7pf/7sZFRdXDhge3S3WIQPUAy1m+rZuSt/Wp/r8HyssY8uhPDZWJgibD+WSW2xl48FcNh7MZc6aqiONLoR/Gd6BP17StsGxeBpJCBogt9jKazvMHC4uoHmo0U3IKVNOBYYbC5Z9OhFWzzRmHIrtXu/i7huWxI97c/jy10NMu0xaCYQQHqM/Vf91Dd2AZOCLyu2XgCuA64E84FVgHnCxC2MUQtRHRTmsew1+eA6sxaBMcMGf4LKHIfD3M/SUV9hPdus5X3ehhmgWGsBlnaK5rFM0YIwTOHCsmE2VYxE2peey/UgeVptm8sWtmT6svdNicSdJCOopt7ic2+b8wuFiRbMQfz69Y4Bz55/tdAV0vgp2fgvf3gtTloOpfoMu+7VpysXtm7F63zFeX5nC0+Prn1wIIYSjaK2zq28rpf4OpAA/KKUigMnARK31isr9twM7lVIDtdbrXB6wEKJ2Un+AhQ9Azh5ju9VA44POc3y4uTb1GAWlFTQPDaBPQhMXBWrMNNameQhtmodwTe8WABSVlPH5/MXcMrqjz06lKysfqq4AACAASURBVAlBPS3bmcWOjAJCLZoP/9iP9tFhzr/omP8Yv1RHNsLPb8KF0+pd1H3DOrB631q+/DWde4a2p0WkrMophPAcSil/4BbgRa21Vkr1BSzAsqpjtNa7lFIHgQuBsyYESqkAjO5FVcLAmEazLlPbVh3r6dPhOovUX+pf/Xut5WdgXj4D046vANDBzbENewzd/QajheAc5S3ccgSA4Z2jsNsqsNvqF7sjKG2jaYB3vv61jVkSgnq6rm9L8ovLKD+0jSRXrUwXFgsjnzAGF694EjpdCU3O1t32/C5o25QLE5uxNvUYr3+/j3+Pk1YCIYRHuQaIBOZUbscC5Vrr3DOOy6zcV5OHgUfPfHLp0qUEB9e9u2RycnKdz/ElUn+pf20oXUFiVjKdjn6FyV6KRrG/+TB2xl1LxaEQOLT4nOfbNSzYbAYUTQoPsHBhWsODdwBvfP2Li4trdZwkBA1w68AEFi7c5tqL9v4DbPkcDqyG7/4Ct/zvd4Nwauu+4UmsnXWMz39JZ9pl7YmXVgIhhOeYDCzSWh9pYDlPAy9W2w4DDo0cOZLw8NrPWmK1WklOTmbEiBGNch5+qb/Uv7b1VwdWY17yECp7FwD2Fv2wjX6OVrE9qO28PBvSTlC4bgPhgX7cc8Pws04B6kre/Prn5+fX6jhJCLyNyQRj/wtvXAwpy2HrF9DjhnoVNTCxGQPaNuXn/cd5Y2UK/7qmm4ODFUKIulNKtQaGA+OrPX0U8FdKRZ7RShBTue+stNZlQFm1sgFjNef6/GOv73m+Quov9a+x/gVHYek/YevnxnZwMxj+OKZeN9d51eplu3IAGN45hpCggPMc7Tre+PrXNl5ZV9wbNU+CwQ8ajxc+CLnp9S7qvuFJAHy2IZ2MvBJHRCeEEA11O5AFLKj23K+AFRhW9YRSqiOQAKx1aXRCiFNsFbDuDXi1f2UyoKDfH+GeX6DPrcYHmXWgtWbJdiPHH+XE2YXE6SQh8FYX3QfxfaA0F/43xfiFrIcLE5txQZumlNvsvLkyxcFBCiFE3SilTBgJwfta65N/2LTWecA7wItKqcsqBxm/B6yVGYaEcJMDa2HWYFj8dyjLN96XTF0BV74EwU3Pf/5ZbDucz+HcEoIsZgYlRTk4YFETSQi8lZ8/XPcuBIRD+jpY+VS9ilFKnWwlmLshncz8UkdGKYQQdTUc41P/d8+y7y/Ad8D/gFUYXYXGn+U4IYQzFWbBV3fBe6MhcxsENTG6M09ZDi36NKjoxdszABjSMYog//pNry7qThICb9a0rfELCPDji8Yy4PVwUbtm9GvdhPIKO29IK4EQwo201ku11kprvecs+0q11tO01k211iFa6/Fa6xrHDwghHMxug59nwSv9YPMngII+t8E9v0LfSXXuHnQ21VcnFq4jCYG36zYe+t4OaPjqT1CQWecilFL8eXgHAOauP0iWtBIIIYQQopomRXvxe3c4LHoQyvIgridMWQZXzYSQZg65xr6sAlKyi7CY1cmVg4VrSELgC0Y/DdFdoSgb5k2hPqt3XNy+GX1bN6Gsws6bP6Q6IUghhBBCeCO19XMG7fkXKnMrBEbAFS/A1O+hZT+HXqeqdeDi9s0JD/Su2Xy8nSQEvsASBNfPAUsw7F9ldB+qI6UU9w0zxhJ8/PMBsgqklUAIIYQQYPplNgD2zlfDvb9B/ylgcnz//sWVswuN7irdhVxNEgJfEdXByNjBGGB8YE2di7g0qTm9EyIpq7AzS1oJhBBCCFF8HJWxCQDb8CchpLlTLpN+vJhth/MxKRjRJcYp1xA1k4TAl/SaCD0ngLbDl5Oh6FidTq/eSvDRzwfILig7zxlCCCGE8Gn7V6G0nYLAeAiPc9plqtYeuKBtU5qFes5iZI2FJAS+Zszz0CwJCo7A13eB1nU6fXCHKHq2iqTUauftH6WVQAghhGjUUr8HICusm1Mvs0S6C7mVJAS+JiAUrn8PzAGwdwmsfa1Opyul+HNlK8GHaw+QUyitBEIIIUSjlWIkBNlhXZ12iayCUn45cAKAkZIQuIUkBL4otrsx8xDAskfh0K91On1Ixyh6tIygxGqTVgIhhBCisTqeCrkH0CYLOaGdnXaZ5B2ZaA09W0USHxnktOuImklC4Kv6/RG6XA32CvjydijJrfWp1ccSfLj2AMeLyp0VpRBCCCE8VWXrgG7ZD5s50GmXObkYmbQOuI0kBL5KKRg7EyJbQ+4BmD+9TuMJhnaKpnuLCIrLpZVACCGEaJQqxw/otkOcdom8YitrU4xJUEZ1ldmF3EUSAl8WFAnXvQcmP9jxDfzyTq1PVUoxvbKV4IM1aZyQVgIhhBCi8bDbjLWNcG5CsHxXJhV2TceYMBKjQp12HXFukhD4upZ9YfjjxuPFj8DRrbU+dXjnaLrGh1NUbmP2T9JKIIQQQjQaRzZCaR4ERqDjejntMosquwuN6ibdhdxJEoLG4MJpkDQKbGXwxSQoK6zVadVbCd5fc4DcYmklEEIIIRqFyvEDtB3klFWJAYrKKli1JxuQ8QPuJglBY6AUXPMGhMXDsX2w8IFanzqySwyd48IpLKvgnZ/2OzFIIYQQQniMyvEDJF7mtEv8sCebsgo7CU2D6RwX5rTriPOThKCxCGkG170DygSb58KmT2p1mjHjUHsA5qxOI6/Y6swohRBCCOFuZYWQvt543M55CcHJ2YW6xaKUctp1xPlJQtCYtL4IhjxiPF7wV8jeXavTRnaJpVNsGAVlFbyzWloJhBBCCJ92YDXYrcZMhU0TnXKJsgobK3ZlATBKugu5nSQEjc2l90PbwWAthi9uB2vJeU8xmU6NJXhv9X7ySqSVQAghhPBZKSuM705sHViz7xiFZRVEhwXQu1Wk064jakcSgsbGZIbxb0NIFGRth8UP1+q00V1j6RgTRkFpBe9JK4EQQgjhu1KcP36gqrvQqK6xmEzSXcjdJCFojMJiYPwsQMGv78G2eec9xWRS3Fs5luDdn/aTXyqtBEIIIYTPyTsMObsBZcww5AQVNjvJOzMBY/yAcD+PSgiUUoOUUvOVUkeUUlopdc15jr9EKbVaKXVMKVWilNqllPqLq+L1au2GGt2HAObfB8fPv87AmG5xJEWHkl9awZzVac6NTwghhBCul7rS+N6iDwQ3dcolNqSd4HhROZHBFi5o65xriLrxqIQACAE2A9NqeXwR8CowCOgMPAk8qZS6wznh+Zghj0CrgVCWD1/+ESrOvc6A0UpgjCWY/WMqBdJKIIQQQvgWF0w3umS70V1oeOcYLGZPeyvaOHnUq6C1XqS1/j+t9Ve1PH6j1nqu1nq71jpNa/0RsAS41LmR+giznzEVaVATY0XCZY+d95QrusfRLiqE/NIKPliX7vwYhRBCCOEadvupFgInDSi22/Wp6UZldiGP4VEJQUMppXoDFwE/uDsWrxHREq5+3Xi87jXYtfCch5urzzi0Jo3SCmcHKIQQQgiXyNoORdlgCYGWFzjlElsO53E0v5RgfzOXJDV3yjVE3fm5OwBHUEodAqIw6vOY1nr2OY4NAAKqPRUGYLVasVrr1gWm6vi6nudx2o3AdMGfMK9/C/3N3VRErYTwFjUePqpzFInNg0nNKWbVUcVYb69/PfnM619PUn/vrL+3xSuEcKGq2YXaXAx+/k65RFXrwGWdogm0mJ1yDVF3PpEQYHQRCgUGAs8opfZprefWcOzDwKNnPrl06VKCg4PrdfHk5OR6nedJlH0AlwYvpUnxfvLfvY7VSY+gVc2/qBdHKlJzzHx/xMQ3i5IJ9pU7qR584fVvCKm/d9W/uLjY3SEIITyVk8cPaK1ZvC0DgMtldiGP4hNv47TWVRPjb1VKxQCPATUlBE8DL1bbDgMOjRw5kvDw8Dpd12q1kpyczIgRI7BYLHWM2gOd6I6efRnNivZyRfAW7Jf9o8ZDR9k1a15dTUp2MWkB7XhgVEcXBuoZfO71ryOpv3fWPz8/390hCCE8kbUUDqwxHjtp/MCezELSjhXj72diSMdop1xD1I9PJARnMHF6l6DTaK3LgLKqbaWMxTAsFku9/6k35FyPEt0BrnoFvrwd85qXMbcbZExPehYW4K/DO3D33E188HM6kwe1Jzo80LXxegifef3rServXfX3pliFEC6Uvg4qSiEsDqI6OeUSVd2FBiU1JzTAF9+Cei+PGlSslApVSvVSSvWqfKpt5XZC5f6nlVIfVDt+mlJqrFIqqfJrMvAA8JEbwvcN3cZD30mAhnl3QEFmjYcO7xxFm1BNidXOzBV7XRaiEEIIIRwsZYXxPXEIKOesHLx4+6nViYVn8aiEAOgHbKz8AqNrz0bgicrtOCCh2vEmjC5Am4BfMNYveAiY4YpgfdboZyC6izHTwLypYLed9TClFGMTjH2frk8nLafIlVEKIYQQwlFSnDt+4MCxInZm5GM2KYZ3jnHKNUT9eVRCoLVeqbVWZ/maVLl/ktZ6SLXjX9Fad9Nah2itI7TWfbTWb2it7e6qg0+wBMH1c8ASDPt/gJ9erPHQ9hEwOKk5FXbNC8l7XBejEEIIIRyjKAeObjEeJw5xyiWqFiMbmNiUJiHOmcFI1J9HJQTCg0R1hDHPG4+/f+rUQKOzuH9EewDmbz7CtsN5rohOCCGEEI5StRhZdFcIc86n97IYmWeThEDUrNdE6HETaDt8ORmKjp31sC5x4VzdKx6A55bsdmWEQgghhGioqulGnTS7UGZ+Kb8dzAVgpCQEHkkSAlEzpeCKF6BZeyg4Al/fBVqf9dC/juiIn0mxak82a1JyXByoEEIIIepFa0hZaTx2UkJQ1V2oT0IkMY10RkJPJwmBOLeAULjuPTAHwN4lsPa1sx6W0CyYiQOM8d7PLt6NriFxEEIIIYQHObYP8g+B2R8SLnLKJU52F5LFyDyWJATi/OJ6wKh/G4+XPQaHfj3rYfcOTSLY38zm9FyWbK95ulIhhBBCeIiq2YUSBoJ/sMOLP15Uzs/7jwMy3agnk4RA1E7/KdD5KrBb4cvboST3d4dEhQUw+ZK2APxnyS4qbDLZkxBCCOHRUp073eiynZnY7JrOceG0bhbilGuIhpOEQNSOUsYqxpEJkHsA5k8/63iCqYMSaRJsISW7iHm/HXZDoEIIIYSoFZsV9v9oPHbW+AGZXcgrSEIgai8oEq6bAyY/2PENpt/m/O6Q8EAL0y4zpiF9adkeSq1nX9RMCCGEEG52+FcoL4CgphDb0+HFF5ZV8ONeY6IRGT/g2SQhEHXTsi8MfwwAU/L/EV588HeH3DKwNfERgWTklfLh2gOujU8IIYQQtZOywvieOBhMjn9L+P2uLMptdto2D6FDTKjDyxeOIwmBqLuB0yBpJMpWRr+016C86LTdgRYzfx7RAYDXVu4jv9TqjiiFEEIIcS4pzh0/sLhyutFRXWNRSjnlGsIxJCEQdWcywTVvokNjCSvLwLRsxu8OubZPS5KiQ8kttjLrh1Q3BCmEEEKIGpXmGV2GwCnjB0qtNr7flQVIdyFvIAmBqJ+QZtiuMtYkMG98H3Z+d9pus0nxwKiOALzz036y8ktdHqIQQggharD/R9A2aNrOmDDEwX7am0NxuY24iEB6tIhwePnCsSQhEPWm2w5mb/Tlxsa390J+xmn7R3aJoXdCJCVWGzNX7HVDhEIIIYQ4q6rpRp00u1D17kImk3QX8nSSEIgG2RV3HTqmO5Qch6/vBPuptQeUUjw0uhMAn65PJy2nqKZihBBCCOFKVeMH2g11eNFWm51lO40FSmUxMu8gCYFoELvJQsU1s8AvCFJXwrrXTts/MLEZQzpGUWHXvJC8xz1BCiGEEOKU3INwPAWUGdpc4vDi1+8/Tm6xlaYh/vRv08Th5QvHk4RANFzzJBj9lPF42eOQsfm03Q9WjiWYv/kI2w7nuTo6IYQXUUq1UEp9pJQ6ppQqUUptVUr1q7ZfKaWeUEplVO5fppRKcmfMQnidqtaBlv0g0PH9+xdXLkY2sksMfmZ5q+kN5FUSjtH3duh4Bdit8L8pUF58clfX+Aiu7hUPwHNLdrsrQiGEh1NKNQFWA1bgcqAL8FfgRLXD/gZMB+4EBgBFwBKlVKBroxXCi6U6b7pRu12zpGr8gMwu5DUkIRCOoRRc9QqExkLOHlj6j9N2/3VER/xMilV7slmTkuOmIIUQHu4hIF1rfbvWer3Wer/WeqnWOgWM1gHgz8CTWutvtNZbgD8A8cA17gtbCC9it0PqD8ZjJwwo3pieS1ZBGWEBflzUrpnDyxfOIQmBcJyQZjDuDePxL+/CrgUndyU0C2biAGNas2cX70Zr7Y4IhRCe7SrgF6XUF0qpLKXURqXU1Gr72wKxwLKqJ7TWecDPwIWuDVUIL3V0szERiH8YtOjr8OKrWgeGdo4mwM/s8PKFc/i5OwDhY9oNhQvvgbWvwjf3GH9swowmw3uHJvHlr4fYnJ7Lku2ZslCJEOJMicBdwIvAU0B/YKZSqlxr/T5GMgCQecZ5mdX2nUYpFQAEVHsqDMBqtWK11n4V9apj63KOL5H6+079TXuXYwbsrS/GZsfo6nseta2/1ppFW40pyId3ivKJnxd49+tf25glIRCON2wG7P8Bjm6Fr+6EW+aByURUWACTL2nLKyv28Z8luxjeOVoGGwkhqjMBv2itH6nc3qiU6oYxXuD9epb5MPDomU8uXbqU4ODgOheWnJxczzB8g9Tf++t/0d4viQK2lUSxf+HCOp17vvofLoL0E35YlKYk9VcWHmhAoB7IG1//4uLi8x+EJATCGfwC4Np34K3BxsCln9+AC6cBMHVQIh+tO0BKdhHzfjvMDf1buTlYIYQHyQB2nPHcTuDaysdHK7/HVB5Lte1NNZT5NEaLQ5Uw4NDIkSMJDw+vdWBWq5Xk5GRGjBiBxWKp9Xm+QurvI/W3FuO3ZQoAna+8m87NajdBV23r/9/l+4BUhnSKYdzYXo6I2CN48+ufn59fq+MkIRDOEdURRv0bFtwPyx6DNpdCXA/CAy1Mu6w9Ty7YyUvL9nBVr3gCLdLHUAgBGDMMdTzjuQ5A1eeM+zGSgmFUJgBKqXCM2YbeOFuBWusyoKxq2xiXDBaLpV7/2Ot7nq+Q+nt5/dM2gK0cwltiielsTAhSB+erf/LObAAu7x7n3T+nGnjj61/beKW/hnCefn+EjmOMPz7VpiK9ZWBr4iMCycgr5cO1PtaeKIRoiJeAgUqpR5RS7ZVSE4E7gNcAtDEbwcvA/ymlrlJKdQc+AI4AX7sraCG8RtV0o+2G1DkZOG/R2YXszizAz6QY1inGoWUL55OEQDjPyalIYyBnNyT/E4BAi5k/j+gAwGsr95Ff6n2DdIQQjqe13gCMAyYA24B/An/WWn9c7bDngFeAWcAGIBQYrbUudXG4QnifFOetP7C4cnahC9s1IyLYuz5FF5IQCGcLaQ7j3jQeb5gNuxcBcG2fliRFh5JbbOWtH1LcGKAQwpNorb/TWnfXWgdqrTtrrd8+Y7/WWs/QWsdWHjNca73HXfEK4TUKMiFrO6CckhAsqVydWGYQ9E6SEAjnq5qKFOCbaVBwFLNJ8cAoo6vwuz+lkZUvH+4JIYQQTpO60vge18NYN8iBDueWsPlQHkrBiC7SXcgbSUIgXGPYDIjpDsXH4Ou7wG5nZJcYeidEUmK1MXPFXndHKIQQQviuVOd1F1pa2V2oX+smRIcFOrx84XySEAjX8AuAa2eDXyCkrICf30QpxUOjOwHw6fp00nKK3BykEEII4YO0PjV+oJ0Txg9Udhca1VW6C3krSQiE60R3MqYiBVj2KBzdysDEZgzpGEWFXfNCsnQDFkIIIRwuexcUHjU+lGs10KFF5xSWsSHtOCAJgTeThEC4Vr/J0OHyU1ORWkt4sHIswfzNR9h2OM/NAQohhBA+pqp1oPVFYHFsl55lOzKxa+jWIpxWTeu++rfwDJIQCNdSCq5+1ZiKNHsXLP0nXeMjuLpXPADPLdnt5gCFEEIIH5OywvjuxOlGR0vrgFeThEC4XkhzuOZ14/GGt2H3Yv46oiN+JsWqPdmsSclxb3xCCCGEr6gogwOrjccOHj+QX2pl9T7jf7ZMN+rdJCEQ7tF+OAy823j8zTQS/AuYOCABgGcX78ZYkFQIIYQQDZK+HqzFEBIF0V0dWvT3u7Kw2jTtokJoHx3m0LKFa0lCINxn2KMQ0w2Kc+Cbu7n3snYE+5vZnJ7Lku2Z7o5OCCGE8H4npxsdAibHvu1bLIuR+QxJCIT7WAJPTUW6bxlRO+Yw+ZK2APxnyS4qbHY3ByiEEEJ4uRTnrD9QUm5j5e5sAEZ3jXNo2cL1JCEQ7hXdGUY+aTxOnsGfOpXQJNhCSnYR83477N7YhBBCCG9WfByObDQeO3j8wKq92ZRYbbSIDKJbi3CHli1cTxIC4X79p0CH0WArJ3T+nUwf1BKAl5btodRqc3NwQgghhJfavwrQENUJwuMdWvSSat2FlFIOLVu4niQEwv2UgqtehZBoyN7JrYXvER8RSEZeKR+sTXN3dEIIIYR3SnVOd6HyCjvLdhpj/WT8gG+QhEB4htAouOYNAPx+mcUzPYxPHl77PoW8Eqs7IxNCCCG8U9X4AQd3F1qXeoz80gqahwbQJ6GJQ8sW7iEJgfAcScNhwF0AXLrjUfo3t5JXYmXWqhQ3ByaEEEJ4meOpkHsATBZofbFDi65ajGxk1xjMJuku5AskIRCeZfhjEN0VVZTN66HvAJp3f0ojK7/UzYEJIYQQXqSqdaDVBRAQ6rBibXbN0sqpwWV1Yt8hCYHwLFVTkZoDiDq6in80/5ESq42ZK/a6OzIhhBDCezhp/MBvB0+QU1hGeKAfAxObObRs4T6SEAjPE9Pl5FSkk4vfpaM6yKfr00nLKXJzYEIIIYQXsFVA6irjsYPHD1QtRja8cwz+fvI20lfIKyk80wVTIWkkJns5s0PfxGwv44XkPe6OSgghhPB8RzZCWR4ERkB8b4cVq7U+mRCMktmFfIokBMIzKQVXvw4hUbSypvF3v7nM33yEbYfz3B2ZEEII4dmqugu1HQQms8OK3X6kgMO5JQRZzAxKinJYucL9JCEQnqvaVKS3+y1hiGkTzy3Z7eaghBBCCA+X4pzxA0t3GIOJh3SMIsjfcYmGcD9JCIRnSxoBA+4E4HnLm2zfs481KTluDkoIIYTwUGUFcGi98djB4weW7MgCZDEyXyQJgfB8wx+H6C40V/n8x/IWzy7ahdba3VEJIYQQnidtNdgroEkbaJrosGKPFkNqThEWs+KyTtEOK1d4BkkIhOezBMK176DNAQw1b6JnxucsqZwDWQghhBDVOGm60S3HjQXILm7fnPBAi0PLFu4nCYHwDjFdUCP/BcA//D7h84VLqbDZ3RyUEEII4WGqxg84uLvQ5uPGW0ZZjMw3SUIgvMcFd2BNHE6AsvJg4XN8vSHV3REJIYQQniPvMOTsBmUyZhhykEMnSjhUpDApGN4lxmHlCs8hCYHwHkphGf8GJZamdDalU5H8KKVWm7ujEkIIITxD6krje3xvCGrisGKrZhfq17oJzUMDHFau8BySEAjvEhqNebwxFelNtu9Y8vVHMsBYCCGEAKeMHygotTL7pzQAxnST1gFfJQmB8Dr+nUezt81EAAZum8GY/yzileV7OZxb4ubIhBBCCDex250yfuDlZXvJLiwnKlBzXd+WDitXeBZJCIRXSpzwAicCWhCjchmd/zkvJO/hkmdXMPHtdcz77RDF5RXuDlEIIYRwncxtUJwDlhBoeYFDitx9tIA5a9IAuLatnQA/edvoqzzqlVVKDVJKzVdKHVFKaaXUNec5frxSKlkpla2UyldKrVVKjXJVvMJ9zAHBNLn6GQCm+S/iytY2tIY1Kce4//PN9H9yGQ9+sZl1qcew26VLkRBCCB9X1V2ozcXg59/g4rTW/PObbdjsmpFdoukcKf9LfZlHJQRACLAZmFbL4wcBycAYoC/wPTBfKdXbOeEJj9J5LLS+GD97Ka9Gz+enhy7jryM60LpZMEXlNr749RA3zVrH4Oe/56XkPRw8VuzuiIUQQgjnSHHs+IFvNh1h/f7jBFpM/OPyjg4pU3guP0cXqJRSwGVAAPCT1rqgtudqrRcBiyrLqc3xfz7jqUeUUlcDY4GNtb2u8FJKwah/w6zLYOvntBxwJ/cO68s9Q9vzy4ET/O/XQ3y3JYP04yX8d/le/rt8Lxe0bcp1fVsypnscoQEOv/2FEEII17OWwsG1xmMHjB8oKLXy74U7Abh3aBLxkUFsanCpwpM1qIVAKfVvpdT31bYVsBTjU/sFwFalVLuGhVineExAGHDcVdcUbhbfG3pOMB4veQS0RilF/zZNeebaHmz4x3D+e1MvLk1qjlKwfv9x/vblFvo/uYz7P9vEmn050qVIiHpQSrVVSo09x/6xSqk2rotIiEbs4FqoKIWwOIjq1ODiXl62l+yCMto2D2HKpW0dEKDwdA39iPRa4Jtq29cBw4B/YHT9eQt4DLi1gdeprQeAUODzmg5QSgVgtF5UCQOwWq1YrdY6Xazq+Lqe5ys8pv6DHsZvx9eo9HVUbPkS3eXU0BM/BWO6RjOmazQZeaV8s+kI8zYeYf+xYuZtPMy8jYeJjwjkml7xjO8dT+tmwbW+rMfU302k/t5ZfwfG+zwQDsyvYf80IBe4yVEXFELUoPp0o7XoYXEu1QcSP3ZVVwL8zFit9gYGKDxdQxOCFsC+atvjgR1a66cBlFJvAHc18Bq1opSaCDwKXK21zjrHoQ9XHneapUuXEhxc+zeD1SUnJ9frPF/hCfXv2GwUnY5+RdmCv7MiFeymsw+oSgDuS4IDcfBztomNOYojeaW8/kMqr/+QStswzYAoO72aaYJq+dvhCfV3J6m/d9W/uNhhY2kuBF4+x/7lwJndOoUQzuCg6UarDyQe3TWWwR2iHBCc8AYNTQgqqPy0vbK70DDgrH3GzwAAIABJREFUg2r7M4HmDbzGeSmlbgJmA9drrZed5/CngRerbYcBh0aOHEl4eHidrmu1WklOTmbEiBFYLJY6nesLPKr+5YPRb6wlpPAoY5oexH7R9POecjdQarWxfFc28zYe5qd9x9hfoNhfYOargyZGdolmXO94Lkpshtn0+09cPKr+biD198765+fnO6qoJsC5xogVAs0cdTEhRA2KcuDoFuNx4pAGFVV9IPE/x3ZpcGjCezQ0IdgG3KKU+hgYh/HHf0G1/a2BnAZe45yUUhOAd4GbtNYLzne81roMKKt2PgAWi6Xe/9Qbcq4v8Ij6WyJh+GPw9Z2YV7/0/+zddXhUV/rA8e+ZmSgxggQIJMFdg1uRAsXqQktpaUu93f6qW9m2u9S7la3Lbl2gbhQL7lCguGsghBAkAiE65/fHmUBIkxAZTd7P88yTO3Pv3PveZZvMO+e858UaPwFC6p//bX5+XNqtCZd2a0JKRjY//ZnE92sOsuvISX7bcJjfNhymQVggl3WL5opujWlRP6TEc3j8/j1I7t+37t+JsSYC/YD3Stk/ADjorIsJIUqxZ4H5GdWhXH/3SlO8kDg6IsgJwQlfUdVlRycDXTAf+v8LLNVazy+yfzTwR3lPppQKUUp1UUp1cbzU1PE8xrH/BaXU50WOvw4zIvEgsFIp1cDxCK/abQmf1OkaaNgFcjNh/vMVfntUWCB3XNCchPsH8svd/ZjQO5bwID8OZ2Tz3oLdXPjaQi59ZylfrthPepZvzRsXwgWmANcqpf7mWNABAKWUVSl1H3AN8LXHohOipjhTPzCoSqeRQuKarUojBFrrBKVUN2AYpnjsm8J9SqnawCLOLTo+n+6YXgKFCqf2fAZMBBpipoEXug1zD+84HhQ7XtQkFgtc9AJ8MhLWfgY9b4Wo9hU+jVKKzk0i6Nwkgn+MacvcrUf4Yc1BFuxIZd2BNNYdSGPytC1c2KYeVV/LQQif9QLQH1NH8IRSarvj9dZAPWAB8JxnQhOihtDaKfUDJRUSi5qlyguxa623AFtKeP0EcH8Fz7UAKLU8Xms9sdjzQRU5v6gBYvtC24th668w6wmY8FOVVlwIsFkZ1bEhozo25EhmNr+uO8R3qw+yPSWT6ZtSSFBWBh7KoEusTJUWNYvWOkcpNRy4EbOgROES06uAH4DPtdayNIkQrnR0J2QkgdUfYvpW6hRSSCyg6n0IQpVSTYq91kgpNVkp9ZJSqkfVwhOiEob9y/xy3DMfdjpvBZj6oYFMGtCMmf83gGn39qdPs0jytOKur9dx/FSu064jhK/QWtu11p9orcdqrds5HmO11p9KMiCEGxROF4rpDf6VWylRCokFVL2G4EPgu8InSqkwYAXwD8y8/sVKqUFVvIYQFRPZDHrdbrZnPwEFzp3vr5SiQ3Q4b4/rTN1AzaH0bO75ei35BfL5R9QcSqlIpVSnMvZ3dEwdFUK4yu4i/QcqQQqJRaGqJgT9gWlFnl8PNAL6Ypak24BJDoRwr4EPQ3AdOLoD1nzqkkuEBfkxqXUBtfytLNt9jBdnbHPJdYTwUq9jvhQqzQeY5mVCCFcoyIN9S8x2JesHpJBYFKpqQlAXSCry/GJgidZ6hdY6E7MCUOcqXkOIigsMh8GPm+35z8PpEy65TMNgeOnyDgD8b8leflmXdJ53CFFtDAF+LWP/b8CFbopFiJrn4Gqzql5QJDSo+EctKSQWRVU1IUgDGgAopYIw607PLrI/H6jcpDYhqqrbRKjXBk4fh0Wu+6JyRPso7h5s6in//sMGNiWlu+xaQniRepTdZ+YYUKFF0ZVS/1RK6WKPbUX2Byql3lFKHVNKnVRK/aCUiqpk/EL4tjPLjV5gVtmrACkkFsVVNSFYBtyllLoMs/RcIOcuM9qKc0cQhHAfqw2GO1Y9XPkBHNvtsks9MKw1g1rXIzvPzu1frJEiY1ETJANdy9gfD6RW4rybMUtMFz76F9n3OjAWuAq4ADNF9cdKXEMI33dmudEhFX6rFBKL4qqaEPwdyMMsMXcr8JrWejOY5jSYX9oLq3gNISqv5YXQ4kKw50HCUy67jNWieOOarsTWCSYp7TT3TpEiY1Ht/QzcopS6uPgOpdQlwE3AT5U4b77W+nCRx1HHOcOBW4AHtNbztNZrHNfoq5TqXfnbEMIHZadD0hqzXcGCYikkFiWpUkKgtd6FaULTFWimtX64yO5g4B6kMY3wtOHPgrLCtmmwd7HLLhMe7MeHE7oT7G9l6a5jvDxr+/nfJITv+iewHfhJKbVWKfW547EW8639DuDpSpy3pVLqkFJqj1Lqq8JO9ZgRBz9gTuGBWuttQCLQpyo3IoTP2bsYdAHUaQERTc5/fBFSSCxK4ozGZHnA+hJez6RiXYqFcI36bSF+Iqz+CGY9DrctrPB8y/Jq3SCUV67qzF1freXDRXto3yiMS7pEu+RaQniS1jrd8c38I5jGZFc6du0GngH+rbU+VcHTrsR0md+OmS70NGb56g6YerVcrXVasfekOPaVSCkVAAQUeSkUIC8vj7y88i9JXHhsRd5Tncj9e9f9W3bNxQoUxF2AvQIx7Ug5W0j85KjWWLSdvLzzj2Z72/27my/ff3ljrnJC4JgadD0wGoh1vLwfsxzpV1rrgqpeQ4gqG/w4bPwODm+A9VOg63iXXWpUx4bcOag57y3Yzd9/2EDL+qG0axTmsusJ4SmOD/xPU7mRgJLON6PI0w1KqZWYvydXA6credrHKCG+2bNnExxc8TUvEhKc1+zQF8n9e8f9D93yOyHA6uMhHJ4+vVzv0Rre2mylwK7oFGknc+cqpu+s2HW95f49xRfvPysrq1zHVSkhcMzpnAX0ADKBPY5dw4ArgDuVUiO01hlVuY4QVVarLgx8yNQRzJ0M7S6BgBCXXe6h4a3ZfCiDRTtSue2L1fx2T39q1/J32fWEqI601mlKqR1ACyAB8FdKRRQbJYgCDpdxmheA14o8DwUODh8+nLCw8ifqeXl5JCQkMGzYMPz8/Mp/E9WE3L8X3X9aIn5/pqCVlW6X3weB5fv/8S/rk9m9YiOBfhbeumkAjSpQO+BV9+8Bvnz/GRnl+whe1RGC5zDzOu8F/uuYPoRSyg+YBLzpOObeKl5HiKrrdQes/hhO7INlb57tU+ACVovizXFduPjtpSQez+LeKX/y6U09sFldM1VJCE9QSgVivvzpBoTz17o0rbW+pQrnDwGaA18AazCLWAzFLGSBUqo1EAMsL+0cWuscIKfIOQHw8/Or1B/2yr6vupD794L7TzS1cKpxd/xC65TrLZnZebw0awdgColj61Vu1Nor7t+DfPH+yxtvVT+dXAa8q7V+tzAZAFNXoLV+D3gP88dCCM+zBcCwyWZ76ZuQ7toVcSOC/fnwhniC/Kws2XWUf0uRsahGlFKxwBbMh/WbHI+xwARMHcAYoELLnyilXlFKXaCUilNK9cWsUlQATNFapwMfAa8ppQYrpeKBT4DlWusVTrotIbxf4XKjFVhdSAqJxflUNSGogyn+Ks02ILKK1xDCedpeDDF9If80zP2Xyy/XpkEY/76qEwAfLNrDb+sPufyaQrjJvzGjAr0xPWcUcA0QglmS+jQwooLnbAxMwfxd+RbT3Ky31rqwn8H9mPq0H4BFmKlCl1fpLoTwJfYC2OtYzb15+RIC6UgsyqOqCcEu4C9rUBdxMWbFCSG8g1IwwrES7oZvzq7j7EJjOjXi9guaAfDI9xvYmiwlNaJaGIIZIV4FFC5TorTWOVrrfwNzMQ0ry01rPU5r3UhrHaC1bux4vrvI/myt9d1a60itdS2t9eVa67LqB4SoXpLXw+kT4B8K0fHnPVw6EovyqmpC8C4wXCk1XSk13DHMG6eUGqGU+h1TXPx21cMUwomiu0Hna832zMfN0gsu9siINgxoWZfTeQXc9sVq0rKkk7HwecHAPsd2BqAxIwaFlnNul2EhRFXtcUwXajoArOefGy4diUV5VbUx2bvAZMw3RTMwowG7HdtDgcmOWgIhvMuQJ8EWBAdWwJafXX45q0Xx1rVdaRIZxIHjp7l3yp8U2F2fiAjhQomYKT5orfOBJMz0oULtgGwPxCVE9VWB+gHpSCwqospLnmit/4n5ozAeeNzxuA5orLV2/SRtISojPBr63We2E56GPNd/bokI9ufDCd0J8rOyeKcUGQufNw+4pMjzT4H7lVL/VUp9BNwN/OaJwISolnKz4MBKs918yHkPl0JiUREVSgiUUjElPTBDx8swxWBTHNvBRfYL4X36/Q1CG0Laflj5vlsu2bZhGC9daYqM31+4m2kbpMhY+KwXgeccnYABngc+w3QsvgT4GnjAQ7EJUf3sXwYFuRDeBOo0L/NQKSQWFVXRPgT7MPNEK0r+nyi8j38tGPoU/HwnLH4VuoyHENcXXF3cuRGbk9L5YNEeHv5uAy3qh9CmgXQyFr5Fa52ImTZU+Dwb039mkseCEqI6K6wfaDbILJBRCikkFpVR0YTgZiqXEAjhnTqNg5UfQPI6WPA8jHndLZd9eITpZLxk11Fu+3wNv97Tj4hg6WQshBCiFIX1A+dZblQKiUVlVCgh0Fp/6qI4hPAMiwVGPA+fjoI1n0KPWyHK9b9AbVYLb13blbFvLyHxeBb3TV3HxxN7YLWU/q2PEEKIGirzMBzZDChoOqj0w6SQWFRSRUcIhKh+4vpB27Gw9TeY/Q+Y8KNbLlu7lj8fTIjniveWsXBHKq/O3s4jF7Vxy7WFEEKUn/rzc4Ztfh7bnifMcp8WG1j8wGJ1PHe8ZrWVsM/x/Jx9xY8t9rz4vsMbTCANO0GtOqXGKYXEorIkIRACYNhk2DELds+FnQnQcphbLtu+UTgvXdGJ+6au490Fu+kQHc6ojg3dcm0hhBDlcGAV1pmPEGzPB0+3kCljuVEpJBZVIQmBEACRzaDX7bDsLZj1hCnaKkfTF2e4pEs0m5LS+e/ivTz03Xqa1wuhdYNQt1xbCCFEGbKOw3c3oez5JEX0JOrSZ7BZgII8sOeffZT4PA/sBUX2FX9ewnvLOta/FvS8tcQwpZBYVJUkBEIUGvAQrPsajm439QSl/OJ1hb9f1IYtyRks3XWM275Yza939yc82D0JiRDCB5zYBwdWQYcrzFQU4Xp2O/x0B2QcREc2Y13jWxgeHQ9+3ve7WQqJRVVVuTGZENVGUAQMesxsz38eTqe57dKmyLgb0RFB7D+WxX3fSCdjIUQRv9wDP94KS17zdCQ1x/K3YOcssAaQf9lH5Fu9s0BXComFM0hCIERR8TdB3dZw+jgs+rdbLx3pKDIOsFlYsD2V1xN2uPX6QggvlZsFiSvM9qJX4Phez8ZTEySugDn/MtsjX4IGHT0bTxmkkFg4gyQEQhRltcGI58z2yg/g+B63Xr5DtCkyBnh7/i5mbkp26/WFEF4oabWZUw6Qnw0zHgEtI4guc+oYfH8z6ALocCXET/R0RKWSQmLhLJIQCFFcy2HQfKj5A5zwlNsvf2nXaG7pb77lefDb9exMyXR7DEIIL7J/mfkZ3d0sQ7lzNmyb5tmYqiu7HX66HTKSoE4LGPufMrsCe5IUEgtnkoRAiJKMeA6UxfQm2LfE7Zd/bGQb+jSrw6ncAm77Yg3pp/PcHoMQwkvsX2p+drkO+t1ntmf8HXJOei6m6mrZG7ArAWyBcNVnEOC9K75JIbFwJkkIhChJ/bZnh4lnPW6+NXIjm9XC29d1JToiiL1HT3H/N+uwS5GxEDVPfi4c+MNsx/aDgQ9BRKz5Bnvhi56NrbrZvxzmPmO2R74MDTp4Np4ySCGxcDZJCIQozeAnICAMktfDhqluv3ydkIAzRcbzth3hP3OkyFiIGufQn5B/GoLrQL3W4BcEo14x+5a/CymbPRtfdXHq6Nm6gU7XQLcbPB1RmaSQWDibJARClKZWXfNtHMDcyZB7yu0hdIgO54XLzeoWb87bxcxNh90egxDCgwqnC8X2PTuXvdVwaDvWfHid9oDbRzCrHbsdfrwNMg9B3VYw+jWvrRsAKSQWriEJgRBl6XWHGZ7PTIalb3okhMu7NeamfnEAPPjtOnYdkSJjIWqMwoLi2H7nvn7Ri+BXCw6sgHVfuT+u6mTp67B7LtiCHHUDIZ6OqFRSSCxcRRICIcpiC4Bhk8320jcgPckjYTw+qi29mkaaIuPP15CRLUXGQlR79oKz/Qdi+567L7wxDHY0Ukx4CrKOuze26mLfUpj3rNke9W+I8u7iXCkkFq4iCYEQ59PuEojpY+bxznvGIyH4WS28M74bjcID2XP0FPdPlSJjIaq9wxsgNxMCwiGqhALXXndA/famkaIHlkj2eSdT4YdbQNuh87XQ9XpPR1QmKSQWriQJgRDno9TZZmXrp0DSWo+EUTckgPcnxONvszB32xHemLvTI3EIIdykcLpQTG+wlDBP3OoHY14z239+cXY0QZyf3Q4/3Wamg9ZtDaNf9eq6AZBCYuFakhAIUR7R8dBpnNme9bjHuoR2ahzBC5eZIuM35u7kl3VJMlIgRHV1pn6gb+nHxPSGrhPM9rQHoECmE5bLkldh9zxTN3D1Z+Bfy9MRlWnLoQwpJBYuJQmBEOU19CnzxyNxOWz91WNhXBHfmIl94wC4b+o6er8wl8d+3Mi8bSlk5xV4LC4hhBPZ7aUXFBc3bDIERcKRzbDyfdfH5uv2Lob5z5vt0a+avjNeKjffzvsLd3Pl+8ukkFi4lCQEQpRXeDT0+5vZTngK8nM8FsoTo9tyXa8YavlbOZKZw5RVidz86Wq6Tk7g1s9X8+0fB0jN9Fx8QogqSt1magP8gqFRl7KPDY48u/jB/Bcg/aDr4/NVJ4+crRvoMh66jvd0RKVatusoI99YxIsztpGVW0CPuNpMvrS9p8MS1ZTN0wEI4VP63QdrP4cT+7D88SHQ3CNh+FktPH9ZR54e244Ve44zd2sKc7akcCg9m4QtKSRsSUEp6NokgqFtoxjWLoqW9UNQXj5HVgjhUNh/oElPUytwPl3Gw59fmmVIZz4K13zp2vh8kb0AfrwVTqZAvTZmVSEvdDg9m+emb+W39YcAqBviz2Mj23J5t2j5HS5cRhICISrCv5aZOvTznViWvoZ/yxc8Gk6AzcoFrepxQat6/Ovi9mxJzmDOliPM3ZbChoPprE1MY21iGv+etZ2YyGAubBvFhW3r06NpJH5WGSAUwmuVd7pQIYvFTH/5YCBs/Q12zDYNzMRZi16BPQvMqMtV3lc3kFdg57Nl+3g9YQencguwKJjQO5YHhrcmPKgcSaEQVSAJgRAV1WkcrHwflbye+P3vYfkjE+o0hYgY8wgI9UhYSinaNwqnfaNw7ruwJYfTs5m7zYwcLN19jMTjWXy8dC8fL91LaKCNwa3rc2G7KC5oVU/+2AjhTbQuX0FxcQ06QO87YfnbMP0hiFsB/sGuidHX7FkICxxf4Ix+Deq38Ww8xazYc4ynftnEjpSTAHSLiWDyJR3oEB3u4chETSEJgRAVZbHAiBfg01HUz9wMsx87d39QbUdyEFvsZ2HC4J4umA3CAxnfK5bxvWI5lZPPkl1HmbMlhXnbjnDsVC6/rj/Er+sPYbMoejaNdIweRBFTRz5ACOFRx/fAycNg9TcrnFXEoMdg80+Qth8WvwpDn3RNjL4kMwV+mARo02ugy7WejuiMI5nZPP/7Vn5eZ6YHRdby59GL2nBlfGMsFpkeJNxHEgIhKiOuH/njf2Rfwn9pFmnDkn4A0hJNEeDpE+aRvL7k9wbXOTdBKJ4wuGAYu1aAjRHtGzCifQMK7Jp1B04wZ+sR5mxJYeeRkyzbfYxlu48xedoWWkWFmOSgXRRdGkfIHyUh3K2wfiC6O/hVsPlUQAhc9CJ8O8F0V+90DdRr5fwYfYW9AH6cBKeOQP12MNI76gbyC+x8vnw/ryfsIDMnH6VgfK8YHhremohgf0+HJ2ogSQiEqCQdN5DN0SeJHTUKi59jyk1OpkkMznnsNz9P7IfsNMg6Zh6H/iz5xMF1iyUMjqShdiyEN6nyFACrRREfG0l8bCR/v6gN+46eYs7WFOZsTeGPfSfYkXKSHSkneXfBbuqG+DOkTX0ubBtF/5Z1CfaXXxlCuFxlpgsV1XYstBwOO2fD7w/Ajb95fdMtl1n4MuxdBH61HHUDnh8B/WPfcZ78eRPbDmcC0LlxOM9c2oFOjSM8HJmoyeSvuxDOFBAKUe3NoyTZ6ZB2oISEwZE0ZKdD1lHzOFRKR+Ra9c4mCm3GQMcrqxRyXN1aTBrQjEkDmpGelceCHUdI2JLCwu2pHD2Zy7erD/Lt6oME2Cz0b1GXoW2jGNq2PpFB0hhHCJcoHCGobEKgFIx0fBDetxg2fgedrnZefL5izwJY+JLZHvO6x0dKUjNzeHHGNn5Ya5aFjQj24+8XteGa7k1kJFZ4nCQEQrhTYDg0CDfFfyU5nQbpB8xoQkkjDTkZcCrVPJLWwJZfoEFHqNfaKeGFB/txSZdoLukSTW6+nT/2HSdhixk9OHjiNHO3HWHutiPwE3SKDiPWquiRmUOjSClKFsIpCr8wUFaz5GhlRTaFgQ/BvGdNd/WWwyGoBn0DnXn4bN1Atxug8zUeC6XArvlyxX5emb2dzGwzPWhcjyY8PKINkbVkepDwDpIQCOFNgiLMo0HHkvefPnE2QVjxPuxfAvOeccma4/42C/1a1KVfi7o8PbYd21MymbvVjB6sO5DGhqQMNmBlxiuLGNYuimt7xtC/RV35pkuIqiicLtSwc9VXLOv7N1j/DRzbaX5PjH616vH5AnuBSQZOpUL99ma0xEPW7D/BU79sYvOhDAA6RIfxzCUd6BpT22MxCVESSQiE8CVBtc2jYWeo0wLe7WPWHE9aU/HVSCpAKUWbBmG0aRDG3YNbcCQzm1kbk/lo3mb2nYQZmw4zY9NhGtcO4tqeMVwV35j6YYEui0eIaqtwulBcOfsPlMUWYJKAzy+GPz4yzcuiu1X9vN5uwYtmqpRfLbj6s4oXZjvBsZM5vDRzG9+uNtODwgJtPHxRG67rGYNVvjQRXkg6Ewnhq+q3hc6O5fPm/Mu9lw4NZFyPxtzfsYBpd/dhYt84QgNtHDxxmn/P2k6fF+dx2+ermb/9CAV27dbYhPBpFW1Idj7NLoCOVwMapt1vvj2vznbPg0WOlYTGvgF1W7r18oXTg4a8uvBMMnB198bMf2gQE3rHSjIgvJaMEAjhywY9agoG9y6E3fOh+WC3h9C6QSj/vNisWDR9YzJTViWyev8JZm9JYfaWFKIjgrimRxOu7t6EBuEyaiBEqU4eMdN7UBDT23nnHfEc7JgFyetg9cfQ81bnndubZCTDD7cCGuInQqer3Hr59QfSePKXTWw4mA5A24ZhPHtpe+JjI90ahxCVISMEQviy2rHQ4xazPeefpsOphwT5W7kivjHf39mX2fcP5KZ+cYQH+ZGUdprXEnbQ98W5TPpsNfO2pciogRAlKZwuFNXeTA10lpD6ZxuUzZ1sGnVVNwX5pm4g6yhEdTS9GNzkxKlcHvtxI5e+u5QNB9MJDbDxz7Ht+O2efpIMCJ8hCYEQvm7AQ2aubPI6s+qQF2gVFcrTY9uz8vGhvH5NZ3rGRWLXMGdrCjd/upoBL83jP3N2cCjttKdDFcJ7OHu6UFHdb4ZGXc1KZbOfcP75PW3BC2aRBf8QuOpTt9QN2O2aqasSGfLqAqasSkRruLxbNPMeGsTEfk2xWeUjlvAd8v9WIXxdSD3oe4/Znves+abMSwT6Wbmsa2O+vaMPcx4YyC39mxIR7Meh9Gz+M2cn/V+axy2f/sGcLSnkF9g9Ha4QnlXVhmRlsVjNWvzKYqYZ7lng/Gt4yq45sNixgtLYN6BuC5dfcuPBdC5/bxmP/riRE1l5tI4K5dvb+/Da1V2oFxrg8usL4WySEAhRHfS5B4Iizfzj9V97OpoStagfypNj2rHisaG8Ma4LvZuZUYO5244w6fPV9H9pPq8l7CBJRg1ETZR1HFI2m21XJARgRgh6TDLbvz8I+TmuuY47ZRyCH28DtBkFqWKjxvNJP53Hkz9v4uJ3lrDuQBohATaeHNOOaX/rT8+mMj1I+C6vKipWSg0EHgbigYbAZVrrn8s4viHwKtAdaAG8qbX+P3fEKoRXCQyDAQ+aqQALXoSOV3lkqb3yCPSznml+tjv1JN/8cYDv1xzkcEY2b87dyVvzdjKoVT2u7RnDkDb1Zdhd1AyJKwANdVqaOf+uMuQfZmrhsV2w9E244GHXXctJcvIL+GFNEot3pgJgs1qwWRT+qoA79t9P06xjJAe15DN9A2rmNmwWhc1iwWZV2CwKq0XhZ7U4fhbdd/a14sdYLeYahcehC1hxRPHP/yzhRFYeAJd0acQTo9rKEsuiWvCqhACoBawHPgZ+LMfxAUAq8CxwvwvjEsL79ZgEK96FjCT443/Q915PR3RezeuF8Piotjw4vBWzN6cwZVUiy3YfY/72VOZvT6V+aABXd2/CNT2a0CQy2NPhCuE6zuw/UJbAcBjxPPxwCyx+xXyjHtnUtdespKzcfL5emch/F+8hJeOvoxkP26bS1LaeTB3EuLQ72b/0kIsjsgJ5tKwfwuRLOtCneR0XX08I9/GqhEBrPQOYAaYRUjmO3wfc5zj+ZlfGJoTX8wuEQY/Br/fA4teg2w3mj78PCLBZGdu5EWM7N2Lv0VNM/SOR71cf5EhmDm/P38U7C3YxoGU9ruvZhKFto/CTUQNR3biyoLi4DlfA2s/NcsXTH4bx30E5/ua6S3pWHp8t38cnS/ee+Ta+YXgg1/eOJSzQRl6BpmHqEkau/xWAhW2eZExkP/ILNPl2TX6B3fHT8dxuP/N6gV2TV+B47cz+ovvOHlNQ5L15BXas9nzuHNKaSQOby+8gUe14VULgDkqpAMzIQqFQgLy8PPLy8ip0rsLjK/q+6kLu3wvvv/2V2Ja+gTq2k4Ilb2D+kkMFAAAgAElEQVS/4DGXXcpV99843J+HLmzB3wY1Y+62I0xdfZBlu4+zaEcqi3akUjfEnyu7RXNVfDQxHhw18Mp//3LwtXhrhJxMSF5vtl1VP1CUUjD6NXivD+xKgK2/QrtLXH/d80jNzOGjJXv5csV+TuaYxRHi6gRz56DmXNa1Mf42x4fw9CR4/ymz3WMSY0bfzRgXx5aXl8f06dMZ1T9OkgFRLdW4hAB4DHi6+IuzZ88mOLhyHy4SEhKqGpNPk/v3rvtvGHYRPY/tRC97m7lpceT4uXaUwNX3f019GBoGy1MsrExVHD2Zy/uL9vL+or30rm9nXDO7R7/c9LZ///PJysrydAjlopR6FHgBeKOwNkwpFYipGxuH+WJnFnCX1tq3F9Y/sBJ0AUTEQHhj91yzbgvo93+w6GWY8Sg0HwIBoe65djFJaaf5cOFupv5xgJx8s9pYmwah3DW4BaM6NDi3jqggH76/GU4fh4adYfhzHolZiOqmJiYELwCvFXkeChwcPnw4YWFhFTpRXl4eCQkJDBs2DD8/P2fG6BPk/r30/vVI7J8swZb8J8MCN2Af8YJLLuPu+78ByCuwM29bKt+sPsiS3cdYccTC38b2okecE5s4lZPX/vufR0ZGhqdDOC+lVA/gdmBDsV2vA6OBq4B04G1MvZkb5tm40JnpQv3de90BD8DGb+HEPrMYwQj3frjek3qS9xfu5se1SeQ7mhV2aRLBPYNbMKRNfSyWEjL9ec/AgRUQEOboNyAFvUI4Q41LCLTWOcCZ6qTCWgU/P79K/1GvynurA7l/L7z/Yf+Ezy/BuvZTrP3ugdpxLruUO+/fzw/GdGnMmC6NeezHDUxZdYApq5Po29KFq7KcNyYv/Pcvg7fHqpQKAb4CbgX+UeT1cOAW4Dqt9TzHazcBW5VSvbXWKzwRr1O4sv9AWfyCYNQr8NWVsOI96HwtNOjg8stuOZTBuwt2MX1jMoVNy/s2r8Pdg1vQt3md0msId8yGpf8x2xe/BZHNXB6rEDVFjUsIhKgRmg0yjz0LzDd/l73v0XBcYXyvWKasOsDMTckcyWxL/VD5prCaeAf4XWs9Ryn1jyKvxwN+wJzCF7TW25RSiUAfoMSEwFl1Yy6rGck7jS1pDQrIi+4J7q7xiBuEtc1YLNt+w/7b/1Fw4++meVnxMJ1w/38mpvHeoj3M3370zGtDWtfjjgua0rVJBAD5+aU0VkxLxPbT7SigoPsk7K1Gu/V/K1+tGXIWuX/fvf/yxuxVCYHjm6GiLQabKqW6AMe11olKqReAaK31DUXe08WxGQLUczzP1VpvcVvgQnijoU+ZhGD9VOj7N4hq5+mInKpDdDhdYyL4MzGNb/84wD1DWno6JFFFSqlxQDegRwm7G2B+t6cVez3Fsa80Tq0bc3bNSJ3MrfQvyCXbFsGs5VtBbXPq+csj0DqUoZYEbEl/sP6LR0msO6jUYyt6/1rDjnRFQpJiZ4ZJNBSarnU0F0bbia6VTPLGZJI3lvzmyFM7iDs6j0Zpf6B0PmlBcSzO64N9+vQKxeEsvlYz5Gxy/753/+WtG/OqhADTYGx+keeFc/0/AyZimpXFFHvPn0W244HrgP1AnEsiFMJXRMeblUO2/GLm3V47xdMROd2E3rH8mZjG1ysTuXNQC6wlzTkWPkEp1QR4Aximtc524qmdUjfmqpoRy+LNsAv8Ww1m1OjRTjtvRamGGTDnKboc/YkOV/4dgs9dY7+i92+3a+ZtT+W9RXvYcNDUrfhZFZd2acRtA+KIq1Or9DdnZ2DZ9B2WtZ+iUreePWejeGpd9l8uiij+McD1fLVmyFnk/n33/stbN+ZVCYHWegFQ6l90rfXEEl6TTwBClGbwP2Drb7B9OiSuhJheno7IObSGle8zVvnzTHATDqVnM2/bEYa1i/J0ZKLy4oH6wNoic8itwECl1D3ACMBfKRVRbJQgCjhc2kmdXTfm9JqRA2amk6Vpfyye/KDR527Y+C0qZRN+85+BS98p8bDz3X9+gZ3fNybz7vzdbE/JBCDQz8K4HjHcNrAZjSLK6KB+aB2s/gg2fg95jm81bUGmeVr3m7FEd8PTC376Ws2Qs8n9+979lzder0oIhBBOVq8VdBkPf34Bc/8FE3/3qgZElbbyA5j5KH7A39p9wr/WBvDFiv2SEPi2uUDHYq99AmwDXgIOAHnAUOAHAKVUa8yo8XL3helE+blwYJXZdkdDsrJYbaY3wcfDYd2X0PV6iO1T7rfn5Bfw49ok3l+4m/3HzIf5kAAbE/rEckv/ptQNCSj5jblZsOkHWP0xHFp79vV6baD7zdDpGgiKqMqdCSHKQRICIaq7QY/Chm9h/1LYNRdaXujpiKpm31KY9fiZp9ecnspkdSOLdqSy/9gpYsuaiiC8ltY6E9hU9DWl1CngmNZ6k+P5R8BrSqnjQAbwFrDcZ1cYSl4P+achKBLqtvZ0NGYEsdsNpovx7w/A7YvAWva3i1m5+UxZdYD/LtrD4Qwz06t2sB8392vKDX3jCA8q5f2p200SsG4K5KSb1yx+Zppjj1sgpk/1+PJCCB8hCYEQ1V14Y+h5Kyx/G+b+0zQgsnh64L2SMg7BdzeaJk7NBsGehQTvncX4uMv5cm8oX61M5PFRbT0dpXCd+wE7ZoTgTGMyj0ZUFfuXmJ+xfb3nv8kL/wXbfocjW2DFu9DvvhIPSz+dxxfL9/Hx0n0cP5ULQFRYALcOaMZ1vWII9i/h40V+rumKvPqTs/cOEBEL3W+CLtdDSD0X3JQQ4nwkIRCiJhjwoPnW7/BG2PyjmZPra/Jz4JsJcCoVojrAuCnwy12w+Sfusf3Ml0zg29UHeGBYKwL9rJ6OVjiB1npQsefZwN2Oh+8703/Ai/qqBUfCsMnwy91myeIOV5zTPfnYqVy+WLmbz5ftJzPHLBEaExnMHRc054r4aAJsJfy3d2IfrPkU/vzS/PcLZmnTViOhx83QzIe/pBCimpCEQIiaIDgS+t4L85+Dec+aYfnzTAXwOjMegaTVEBgB13wJ/sEw8GHY/BNRB2bSN2wkyzLqMm1DMlfGNz7/+YTwJHsBJDpmOrm7Idn5dL7OfHhPXE7utEfYM/R9ko6f4se9Fv6+ehHZeXYAWkWFcNegFozp1BCbtdgHensB7JhlpgXtmgM4OpCFNoRuN5qpSeHR7r0vIUSpJCEQoqbofZcpxj2x1xQZd7/Z0xGV35rPzDeMKLjiI4hsal6Pag9txqC2TePpiJmMyLieL1fsl4RAeL+UTZCTAf6h0KB4LbXr5RfYOZKZw+GMbFLSszmckc3hIj+D08bxoV6F/87feWnzm8y3dwUsgJ1OjcO5e3ALhrWNwlJ8qd+MZPP7Zc1nkHHw7OvNBpvagFYX+d6XEULUAJIQCFFTBITABY+Yb9oXvASdxplv2b3dwTUw/SGzPeSJvxZFD3wItk2jVepMmluHse4AbDyYTsfG4e6PVYjy2rfU/IzpDRbnTnE7lZN/9gO+40N+SkY2yenm5+H0bI6ezMGuyzpLPT6yjeQO2zSe9f+Uu8J6ovNzuH9sDwa1iUIVLfi122HvQjMasO13U+MDpli66/UQPxHqNHfqPQohnEsSAiFqkviJprg4LRFWfQD97/d0RGU7mQrfToCCXGgzBvo/+NdjGnWFlsNRO2fzbN0Erk0xowQvXdnJ/fEKUV77HQlBXPnrB+x2zbFTuWc+1CcX+Xb/zAf+9Owzc/vPx2ZRRIUFEhUWQIPwQKLCAmkQFkiDcPOzYVAP9JQ/ic5I4scOy5h2uiv9W9Q5mwxkHYd1X5ki4eO7z564SW8zGtD2YvALLPf9CSE8RxICIWoSWwAMfgJ+uh2WvG4ShKDano6qZAV58N1EyEiCuq3g0vdKLzwc+AjsnE2vjNk0ViP4Zb2Fx0e3LX3JQyE8SesKFxS/M38Xb8zZSW6BvVzHhwTYiAoLoGF4kPmgHx7g+LAfRIOwQKLCA6hbK+CvU36KG/kyfDMey4q3CWk92cSeuNI0ENv8MxQ4er75h0LncWa1oKj25YpRCOE9JCEQoqbpeBUs+Q+kboWlb8CF//R0RCVLeMosTegfCtd8BYFhpR/bpAc0G4RlzwIeDZ3JPRkT+GHNQW7u39Rt4QpRbqnb4fRx04W3YZfzHn7iVC5vzTPJgFJQN8R8uI8KC6RheOBfv90PDyQkwEl/3tuMhlYXoXbMpPu+97D97wuzJGmhBp3MaECHK820RCGET5KEQIiaxmKFoU/B1GthxfvQ6w4IbeDpqM614TuzBjrAZe+ZjsvnM/Bh2LOAkXlziGI0X67Yz0394s6d6yyENyhcg79JD7D5n/fwr1clkp1np32jMH6+ux9+xVf0cSWlYOTL6D0LCT+dCKcBW6BJALrfDNHdpIGYENWALPwrRE3UeiQ07mm6pC582dPRnOvwRvj1XrM94EFoO7Z874vrDzF9seo87gn4nT1HT7Fs9zHXxSlEZZ2ZLtT/vIfm5Bfw6bJ9AEwa0NS9yUCh2rEUjH6NoyFtKBj2HDy4DS59BxrHSzIgRDUhCYEQNZFSZ6cKrf0Mju0u62j3yToOU8ebRKX5UFPvUBEXPAzAOMtc6pLOlyv2uyBIIargnPqB8/cfmLY+mdTMHOqHBjC6YyMXB1c63eEqlrZ8HHvP27237kgIUWmSEAhRU8X1gxbDwJ4P85/3dDSmkdEPkyBtP9SOgyv+V/HlGJsNhuju+OlcJtl+Z/aWFA6nZ7skXCEq5cReyEwGix807l7moVprPlqyF4Ab+8bhb5M/2UII15DfLkLUZEOfND83fQ/JGzwby/znYfdcU2h5zZemu3JFKWV6LQAT/eYQZk9nyqpEJwcqRBUU9h+Ijge/oDIPXb7nGFuSMwjyszK+V4wbghNC1FSSEAhRkzXsDB2uMNvznvFcHFunweJXzPbFb1Wtc2vL4dCwM4E6m5ttM5n6RyJ55VyqUQiXK5wuVI7+Ax8tNqMDV8Y3JiL4/MXHQghRWZIQCFHTDX4CLDbYOfvst5fulLoDfrrDbPe+CzpdVbXzKWVWHAJuss3mdMZx5mxJqWKQQjhJYUOy89QP7E49ydxtRwC4qV+ci4MSQtR0khAIUdPVaQ7dbjDbc/9lih7dJTsDpl4HuZlmxZVhk51z3tajoX47QshionUWX0hxsfAG6QdNjYyyQJNeZR76yVIzOnBh2/o0qyfr+wshXEsSAiGE6fRrC4IDK2HHTPdc026Hn++EYzshLBqu+hSsTuosbLGYJUuBm20zWL/7ILuOnHTOuYWorMLpQg07Q0BoqYedOJXL92sOAnBL/2buiEwIUcNJQiCEgLCG0Ot2sz13slnxx9WWvAbbpoHVH67+AkLqOff87S+DOi2JUKeYYE3gq5UySiA87Mx0obLrBwobkbVrGEbvZpUorhdCiAqShEAIYfT/PwgMhyNbYOP3rr3Wzjkw71mzPeoV0+DI2SzWM6MEk2zT+X3NLrJy851/HSHK60z/gdITgtx8O58VaUQmnbaFEO4gCYEQwgiqDf3+z2zPfxbyc11zneN74YdbAA3xEyH+RtdcB6DjVejacdRVGYzNm82v6w657lpClOVkKhzdYbZjepd62LQNhzjiaEQ2ppPnGpEJIWoWSQiEEGf1ugNCGkBaIqz51Pnnzz0F31wP2WnQuAeMfNn51yjKakP1fwCA22zTmLp8J9qdRdNCFEp0jA7Ub19qjw2tNf9bLI3IhBDuJ79thBBn+QefaezFopchx4mFuFrDb/dByiaoVQ+u/hxsAc47f2k6X4s9NJoolUbHI7+y7kCa668pRHGFS/qW0X9gxZ7jbEnOINDPwnU9pRGZEMJ9JCEQQpyr2w1QuymcSoWV7znvvCveg43fmZ4HV30GYW6aDmHzxzLgfgDusP3G18t3uee6QhR1pn6g9P4DHy3ZA5hGZLVrSSMyIYT7SEIghDiX1Q+G/MNsL30Tso5X/Zx7F8NsxzmHP1euLq1O1XUCeUH1iVbHCNj0DSdOuag+QoiSnD5hRsYAYkpOCPaknmTOVtOI7OZ+Td0VmRBCAJIQCCFK0v5yiOoIORlmedCqSD8I300EXQCdrjm7vKk7+QViG3AfALepn/n+j73uj0HUXIkrAQ11WkBoVImHfLJ0HwBD20gjMiGE+0lCIIT4K4sFLnzabK/8ENKTKneevGz4ZgJkHYUGHWHMf8BDyyiq7jeT7R9JjCWVI8u+wm6X4mLhJvuXmJ+lLDealpXLd2sOAHDLABkdEEK4nyQEQoiStbjQfIApyIGFL1XuHDMehkNrzZKm13xpipY9xT8YS797ABiX/S2Lth/2XCyiZjlP/4GvVp5tRNanWR03BiaEEIYkBEKIkikFQx2jBH9+CUd3Vuz9qz+BtZ+DssAVH0HtOKeHWFH+vW8jyxpGc0sy2+d94elwRE2QcxIOrTPbJRQU5+bb+Xz5PgBu6S+NyIQQniEJgRCidDG9oNVIM/+/sLNweRz4A6Y/bLaHPAkthromvooKCCU7/jYABqV8xsHjTlxWVYiSHFxl/vsJj4GIJn/Z/fvGQ6RkmEZkYztLIzIhhGdIQiCEKNvQJwEFW36GpLXnP/7kEfh2AtjzoO3F0P9+l4dYEZGD7yVLBdPacpDVM2WUQLhYGf0HpBGZEMJbyG8fIUTZotqb1YEA5k4u+9iCPPj2RshMhrqt4dJ3PVZEXKqgCA61vgGANjs+IDevwMMBiWqtjP4DK/YcZ/MhaUQmhPA8SQiEEOc3+DGw+MGe+bBnYenHzf4HJC6DgDAY9xUEhLovxgqIHf0QWQTShr38OXeqp8MR1VVeNiStNtslFBR/tMSMDlzRTRqRCSE8SxICIcT51Y6D7jeZ7bn/Al3Ckp3rv4GV75vtyz6Aui3dFl5F+YXWY0v0VQDUXvNmyfcjRFUlrYGCXAiJgshm5+zak3qSudtSALi5vyw1KoTwLEkIhBDlM/Bh8KtlPuRsm3buvuT18NvfHMc9Am1GuT++CooZ/QintT+t8raRuPp3T4cjqqP9jvqB2L5/mTr3ydJ9aG0akTWXRmRCCA+ThEAIUT4h9aHPXWZ77jNgd8y9zzoO31wP+dnQcjgMesxzMVZA/UYxLI8YA4B9wcsejkZUS2cSgnOnC6Vl5fL9moOAWWpUCCE8TRICIUT59b3XNBk7uh218VvQdqy/3A5piVC7KVz+oely7CPChj5IjrYRd2o9WTvLqI0QoqIK8uDAKrNdLCH4elUip/MKaNswjD7NpRGZEMLzfOcvtxDC8wLDof8DAFgXvUj7pKlY9swHv2BTRBxU28MBVkx8x/bM9B8GQPrM5zwcjahWktdDXpb5b6JemzMv5+bb+WzZPgAmSSMyIYSXkIRACFExPW+F0EaojCRapM40r138llme1McopcjueS952krDYyvRiSs9HZKoLgqnC8X0PWfUbPrGZFIycqgnjciEEF5EEgIhRMX4BcGgR888Leh1F3S80oMBVc1F/Xvyix4AQPqs5z0cjag2SmhIprXmf0v2AHBjn1hpRCaE8Bry20gIUXFdxmNvdxmJkQOwD3nK09FUSXiQH3vb3E6BVkQkLShfN2YhymIvgMQVZrtIQ7KVe4+zKcnRiKxXrIeCE0KIv5KEQAhRcVYbBZf9lz9jbwWLzdPRVNnIC/rxi918k5szX1YcElWUshly0sE/FKI6nnm5sBHZ5d0aEymNyIQQXkQSAiFEjdchOpy5dSdg14qAXTPg8CZPhyR82f5l5mdML7CahHnv0VPM2epoRNZPlhoVQngXSQiEEAIY0r8/0+29ALAvesXD0Qiftn+J+VlkudFPlu5FaxjSpj4t6ksjMiGEd5GEQAghgNGdGvKZzRRHqy0/Q+p2D0ckfJLWZ0cIHAlBWlYu3602jcgmSSMyIYQXkoRACCGAQD8rXXv0Z3ZBPAoNi1/1dEjCFx3dAVnHwBYIjboCMGXVAU7nFdCmQag0IhNCeCVJCIQQwmF8rxjezL8MAL3xOzi228MRCZ9T2H+gcQ+w+ZObb+fTZaaYeNKAZtKITAjhlSQhEEIIh9g6tajTshfzCzqjtB2WvObpkISvOdN/oD9QvBFZQw8GJoQQpZOEQAghiri+dyxvFY4SrJ8KJ/Z7OKKaQyl1p1Jqg1Iqw/FYrpQaWWR/oFLqHaXUMaXUSaXUD0qpKE/GfI5z6gf6ntOI7IbesQTYrB4MTgghSicJgRBCFDGkTX1SwjuzpKA9yp4PS9/wdEg1yUHgUSAe6A7MA35RSrV37H8dGAtcBVwANAJ+9ECcJTuxDzIPgcUPoruzytGILMBmYXxvaUQmhPBekhAIIUQRVoviul4xvJV/uXnhzy8g45Bng6ohtNa/aa2na613aq13aK2fAE4CvZVS4cAtwANa63la6zXATUBfpVRvT8Z9RuHoQHQ38A/mf45GZFfESyMyIYR3k4RACCGKubp7E9Za2rHS3gYKcmHpm54OqcZRSlmVUuOAWsByzKiBHzCn8Bit9TYgEejjkSCLKywoju3HPmlEJoTwITZPByCEEN6mXmgAF3VoyNsbL6WX/4uw5hMY8ACE1Pd0aNWeUqojJgEIxIwOXKa13qKU6gLkaq3Tir0lBWhQxvkCgIAiL4UC5OXlkZeXV+64Co8t6z22fUtRQH7jXny0eDdawwWt6hJbO6BC1/JG5bn/6kzuX+6/6E9fUt6YJSEQQogSTOgdy9XrO7Jet6Bz/i5Y9hYMf8bTYdUE24EuQDhwJfCZUuqCKpzvMeDp4i/Onj2b4ODgCp8sISGhxNcDc48zIm0fGsVPG9L4Zl0+oGhvS2H69OkVvo63Ku3+awq5f7l/X5OVlVWu4yQhEEKIEvSIq03rqDDeSL2Uj/1fgT8+gn7/B7WksZQraa1zgV2Op2uUUj2A+4BvAH+lVESxUYIo4HAZp3wBKLp+bChwcPjw4YSFhZU7rry8PBISEhg2bBh+fn5/2a82/wCbQTfoxJG63ci176RNVAj/N65Pteg9cL77r+7k/uX+ffX+MzIyynWcVyUESqmBwMOYuaINMUPFP5/nPYMwv+zbAweAZ7XWn7o2UiFEdaeU4vreMTz5S1d2WprRMm8PrHgXhj7p6dBqGgtmys8aIA8YCvwAoJRqDcRgphiVSGudA+QUPi/8cO7n51epP+ylvu/ACnO9uP58seIAALcMaIa/f/UqJq7s/27Vhdy/3L+v3X954/W2ouJawHrg7vIcrJRqCvwOzMcMMf8H+J9SaoTLIhRC1BiXdo2mlr+NV7IvNi+s+hBOF5/CLpxFKfWCUmqgUipOKdVRKfUCMAj4SmudDnwEvKaUGqyUigc+AZZrrVd4MGzDscLQat2GwxnZ1A0J4OIujTwclBBClI9XjRBorWcAM4DyDrHeAezVWj/oeL5VKdUfuB+Y5ZIghRA1RmigH5d1i+arFd1J8m9KdM5ekxRc8IinQ6uu6gOfY0aI04ENwAitdeHE3fsBO2aEIADze/4uD8R5rpOpcHQ7AG/urAtobuwjjciEEL7DqxKCSuhDkSXoHGZhRgpK5KwVJwrfU/RnTSP3L/df9Gd1NS4+mi9XJPJy1hjesL2FXvEu+fGTyLMEAp69/+y8AgL9Kvah05v/vbTWt5xnfzZmBLlco8huk2hmLGVFtGbpIS2NyIQQPsfXE4IGmCXnikoBwpRSQVrr0yW8x6krToBvVp07k9y/3H911yzUym+ZvXg84DuiTh9mx9d/Z1fUGMD995+RC+uPK9YdU6RmK/7ZrQBLBWpWy7vihKgAx3ShlfbWAFzeTRqRCSF8i68nBJXhlBUnwLerzp1B7l/uv6bcf0HjZB74biPvcyVP8zbt0ubR9OoXSFi41C33n5KRzawtR5i5OYXV+0+g9dl9cV360yG6/L+7yrvihKiA/UsA+OFYHAC39I/zXCxCCFEJvp4QHMYsOVdUFJBRyuiA01ecqOp7qwO5f7n/6n7/oztH8/yM7XxxsicP12lM8KmDBGz6Goh12f0np59mxsbDTN+YzJrEc5OALk0iGN2xIRd1aECTyIqNbFb3fyu3O50GhzcBsLKgNYNa16NF/VAPByWEEBXj6wnBcmBUsdeGUcYSdEIIUVEBNitXd2/Cuwt287XfFUziDSwr3sbS/DmnXicp7TQzNiYzfWMyaxPPXc0oPrY2Izs0YGTHhkRHBDn1uqIKDqwENPt0Q1KpzaT+zTwdkRBCVJhXJQRKqRCgRZGXmjra1R/XWic6lqCL1lrf4Nj/PnCPUupl4GNgCHA1MNqdcQshqr/resXw3sLdvHQ4nhvrNsLv5CFijy0CLq3SeQ8cz2L6xmSmbzrM+gNnkwCloEdsJCM7NmBkh4Y0CA+s4h0Il9i/FIAVBa1p0yCUfi2kcZ0Qwvd4VUIAdMf0FChUONf/M2AiZim6mMKdWuu9SqnRwOuYTpYHgUlaa1lyVAjhVI1rBzO0TX3mbD1CQu1rGXXyVVod/gWOTILozhU61/5jp5jumA60MSn9zOtKQc+4SEZ3asiI9g2ICpMkwNvZ9y3FAqy0t+Xm/k2rRVdiIUTN41UJgdZ6AVDqb1Ot9cRS3tPVZUEJIYTD9b1jmbP1CE8d6MqIiKYEpu1FfzICRr8CXa8v8717j54yIwEbk9l86Gxhr0VB72Z1GNmxISPaR1E/VJIAn5FzEg6tA2BnUCdelEZkQggf5VUJgRBCeLOBLesRExlM4vEsfu72Mf3/+BtRmRvhl7vN0pOjXgH/s0W+u46cZMbGZH7fmMy2w5lnXrdaFH2a1WFUx4YMbx9F3ZCAki4nvJw++AcWnc9BXZfhfXtIIzIhhM+ShEAIIcrJYlGM7xXDCzO28fG6k1ibPcjYiB1YF70I676CQ3+yb8i7/HywFjM2HmZ7ytkkwGZR9G1Rl1EdGjC8fQNZp74aOLRhLtHAGt2G8b1iznu8EEJ4K0kIhBCiAq7q3oRXE8mB6i4AAA4iSURBVHaw+VAmiZEWCkbfT1JYZyJn3kXIkS3UmzKCPXmT2G7vh59V0a9FXTMS0C6KiGBJAqqTU9sXApDfpC91ZJRHCOHDJCEQQogKiKzlz5hODflxbRLf7LHy85tL2XO0gHo8wxt+b9PXuoU3/d/hvthU6l7xGuFhsiZ9dbQ/5Rixp7eCgviBYzwdjhBCVInF0wEIIYSvub53LABJWYo9R7Pwt1no3LY1KZdNJafvg4Ci+f5vCf96JBzb7dlghUvMnTuTAJVHuqU2ca06eTocIYSoEhkhEEKICuraJII7BjZl5ebdXD+4M8M7NCQ0sLAD8FPQrB/8eCsc3ggfDoJL3oZ2l3gyZOFE6afzyNy2ECyQ27i3WS9WCCF8mIwQCCFEBSmleHBYSya2snNx56LJgEOLoXDHEojpAzkZ8O0NMOPvkJ/rmYCFU01dlUhXvQWAuu2HeDgaIYSoOkkIhBDCFcIawY2/Qb/7zPOV78MnF8GJ/Z6NS1RJXoGdL5buIt6yAwAV18+zAQkhhBNIQiCEEK5i9YNhk+HabyAwApLWwAcDYNt0T0cmKmnW5hTqZG6jlspBB0ZAvbaeDkkIIapMEgIhhHC11hfBHYshOh6y02HqtTD7SSjI83RkogK0ho+X7aenZRsAKrYvWOTPqBDC98lvMiGEcIeIGLhpJvS+yzxf9iZ8OhrSkzwblyi3PZmwMSmDPlaTEBAr04WEENWDJARCCOEuNn+46AW4+nMICIMDK80Uop1zPB2ZKIcFyRYs2OljM/UDxPb1bEBCCOEk/9/evQfpVdd3HH9/ciEBCSjkAgbQoYVoCjSRADooUtFpaWmxhtSW2k4VAaVImXZaQHrRMlK0HUxbUq0pNwtDJ9W2U8sgbQAjNykRLwgFL1wCjYSESBKSoCH59Y9zVrdLLmTZfc7u/t6vmTPPc86effbze56d75nvuT02BJLUa7NPhbOXwQFHwaZn4PrT4JZLYOsLXSfTDjy+dhP3rw2z8gR7bnsO9ti7+fwkaQywIZCkLux3KJzxXzDvDKDA7X8F//hO2PBU18m0HZ+9ewWFcPqMJ5oFBx8H4/0qH0ljgw2BJHVl4mQ45XKYf2Wzx/mx2+HTb4FHlnWdTP28sHUbNz+4CoCf37v95mlvNyppDLEhkKSuHXkanPUlmP4zsPHp5kjBsk/Atm1dJxMwYfw4bvrQ8fzGoS8wbe19zUIvKJY0htgQSNJIMPUweP9SmPseKNvgto/B9fNh45qukwmYMnkCb993Jdm0GiZMhlfP7TqSJA0ZGwJJGin22AtOXQTv/BRM2BO+d2tzCtHjd3edTMD+zz3cPDnoGJgwqdswkjSEbAgkaaSZczqceStMPRw2rGy+r+COhZ5C1LEfNwSeLiRpjLEhkKSRaMZsOPM2OHIBlK2w9M+abzjetLbrZHUqhanP9X0hmd8/IGlssSGQpJFq0t7wrsVwykIYPwm+/UX4+xPgyeVdJ6vPuhXsuWUtZdyE5pQhSRpDbAgkaSRLYN57mwuO9zsU1j0BV/0CfOVTUErX6aqRFc11HOXAuc21HpI0htgQSNJocOBRza1JZ58K27bAFy+EJb8Fz6/rOlkVxvU1BIe8qeMkkjT0bAgkabSYvC8suBZO/gSMmwjfvhnWPtJ1qipkxV0AlEO8fkDS2OP3rkvSaJLAcWfDQfNgzXe8H36PvLDgOh68aTGzDz6u6yiSNORsCCRpNJp5dDOpN6bN4rGpb2P2pCldJ5GkIecpQ5IkSVLFbAgkSZKkitkQSJIkSRWzIZAkSZIqZkMgSZIkVcyGQJIkSaqYDYEkSZJUMRsCSZIkqWI2BJIkSVLFbAgkSZKkitkQSJIkSRWzIZAkSZIqNqHrACPF+vXrd/t3tmzZwqZNm1i/fj0TJ04chlQjm+N3/I5/9I1/MLVuLNrd92G0ft5DxfE7fsc/Osf/UmtdSinDHGVkSzITeLLrHJLUYweVUv636xC9Zs2XVKmd1nwbgiTAq4ENg/j1KTQbloMG+fujneN3/I5/dI5/CrCyVLgBeBk1fzR/3kPB8Tt+xz96x7/Lml/9KUPtmzOovWTNdgWADaWU6o7DO37H33L8o2/8oy3vkBlszR/ln/fL5vgdf8vxj87x7zKzFxVLkiRJFbMhkCRJkipmQ/Dy/BD4aPtYI8fv+B1/veOvTe2ft+N3/I5/DI+/+ouKJUmSpJp5hECSJEmqmA2BJEmSVDEbAkmSJKliNgSDlOR3kzyW5Pkk9yQ5tutMvZDkoiT3JtmQ5Okk/5ZkVte5upLkwiQlycKus/RSkplJrkvyTJLNSe5PMq/rXL2QZHySS5I82o79e0n+JP1uVK2xx5pvzYc6a771vo56b0MwCEneDVxOc8X5G4BvADcnmd5psN54K7AIeCPwDmAi8J9JXtFpqg4kOQY4G/hm11l6KcmrgDuBLcDJwGzgD4AfdJmrhy4APgicC7y+nf8j4ENdhtLwseZb86HOmm+9r6fee5ehQUhyD3BvKeXcdn4c8ATwt6WUyzoN12NJpgFPA28tpXy56zy9kmRv4D7gHOCPga+XUs7vNlVvJLkMOL6U8paus3QhyX8Aq0opZ/Rb9nlgcynlPd0l03Cx5v+ENb+umm+9r6fee4RgNyXZAzgaWNq3rJSyrZ1/U1e5OrRv+7i20xS9twi4sZSydJdrjj2/AixP8s/tKQRfS3Jm16F66C7gpCSHAyT5WeDNwE2dptKwsOa/iDW/Ltb7Sur9hK4DjEJTgfHAqgHLVwGv632c7rR7yRYCd5ZSvtV1nl5J8us0pw0c03WWjhxKcwj1cuBSmvfhb5L8qJRybafJeuMyYB/goSRbaerBxaWU67uNpWFizW9Z86us+db7Suq9DYFejkXAETTdchWSHAz8NfCOUsrzXefpyDhgeSnlw+3815IcAXwAqGED8WvAbwKnAw8Ac4CFSVZWsoFUvaz59bHeV1LvbQh23xpgKzBjwPIZwFO9j9ONJFcApwAnlFKe7DpPDx0NTAfu63eTgfHACUnOBSaVUrZ2Fa5Hvg88OGDZ/wDzO8jShb8ELiul/FM7f3+S1wAXUccGsjbWfKz51FvzrfeV1HuvIdhNpZQfAV8FTupb1h5GPQm4u6tcvZLGFcCvAm8rpTzadaYeuwU4kmYvQd+0HLgemDPGNwx97gQG3nbwcODxDrJ0YS9g24BlW7GejknWfGs+ddd8630l9d4jBINzOXBtkuXAfwPnA68Aru40VW8sojl0diqwIckB7fJ1pZTN3cXqjVLKBuD/nTubZCPwTEXn1H4SuCvJh4ElwLHAWe1Ugy8AFydZQXMIeS7w+8BVnabScLLmW/N/rLKab72vpN5729FBag8V/iFwAPB14LxSyj3dphp+SXb0D/PeUso1vcwyUiT5EpXcgq5PklOAvwAOAx4FLi+lLO42VW8kmQJcQrPHdDqwErgB+PN2b7LGIGv+i1jzK6n51vs66r0NgSRJklSxMXcOlCRJkqSXzoZAkiRJqpgNgSRJklQxGwJJkiSpYjYEkiRJUsVsCCRJkqSK2RBIkiRJFbMhkCRJkipmQyCNYkk+kqQkmdp1FknS8LHeazjZEEiSJEkVsyGQJEmSKmZDIEmSJFXMhkB6CZLMTHJVklVJfpjkgSTv6/fzE9tzO9+d5NIkTyXZmOTfkxy8nddbkOSrSTYnWZPkuiQzt7Pe65IsSbK6XffhJB/bTsRXJrkmybNJ1iW5OsleQ/w2SNKYZ71XjSZ0HUAa6ZLMAL4CFOAKYDVwMnBlkn1KKQv7rX5xu97HgenA+cDSJHNKKZvb1/sd4GrgXuAiYAbwe8DxSeaWUp5t1zsKuB3YAnwGeAz4KeCX27/T3xLg0fb13gC8H3gauGCo3gdJGuus96pWKcXJyWknE/APwEpg/wHLbwCeBfYETqTZMDwJTOm3zoJ2+Xnt/ERgFXA/MLnfer/UrvfRfsuWAeuBQwb83fR7/pH2964csM6/AGu6fu+cnJycRtNkvXeqdfKUIWknkgSYD3yhnZ3aNwE3A/vS7KHp89lSyoZ+858Dvg/8Yjs/j2ZP0t+VUp7vW6mUciPwEM2GgiTTgBOAq0opK/pnKqWU7UT99ID524H9k+yzO+OVpFpZ71UzTxmSdm4a8ErgrHbanunAD9rn3+n/g1JKSfJd4LXtote0jw9v53UeAt7cPj+0ffzWS8y5YsB8X55X0ex1kiTtnPVe1bIhkHau7yjadcC1O1jnm8Ds3sTZoa07WJ6eppCk0ct6r2rZEEg7txrYAIwvpSzd0UpJ+jYQhw1YHuCnaTYiAI+3j7OAWwe8zKx+P3+kfTxicLElSbvJeq9qeQ2BtBOllK3A54H5SV5UrNtzP/v77SRT+s2fBhwI3NTOL6e5G8QHkkzq9zonA68Hbmz/7mrgy8D7khwy4G+6F0iShpj1XjXzCIG0axcCPwfck2Qx8CCwH83FZW9vn/dZC9yR5Gqa28udD3wXWAxQStmS5AKa29AtS3IDP7kN3WPAJ/u91nnAHcB9ST5Dc5u519JciDZnOAYqSZWz3qtKNgTSLpRSViU5FvhT4F3AOcAzwAO8+L7PlwJH0dwfegpwC3BOKWVTv9e7Jskmmg3Px4GNwL8CF5T2ntTtet9I8kbgEuCDwGSaQ8xLhmOcklQ7671qle3f0UrS7khyInAbsKCU8rmO40iShon1XmOR1xBIkiRJFbMhkCRJkipmQyBJkiRVzGsIJEmSpIp5hECSJEmqmA2BJEmSVDEbAkmSJKliNgSSJElSxWwIJEmSpIrZEEiSJEkVsyGQJEmSKmZDIEmSJFXMhkCSJEmq2P8BEgCz2R0kfDkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "113LXCawsBOL"
      },
      "source": [
        "## [9] RNN: Increasing Epoch to 100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "DBAVGTm9sBOL",
        "outputId": "e87e3214-e8c6-44ca-ff92-55bfd268e62f"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.EASY\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the RNN and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleRNN(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 100\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 100/100] loss: 0.0000, acc: 100.00% - test_loss: 0.0000, test_acc: 100.00%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "6HqtnTBt2-bj",
        "outputId": "cb59336e-942d-4794-d029-ae9868e4628e"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.NORMAL\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the RNN and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleRNN(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 100\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 100/100] loss: 0.5357, acc: 74.90% - test_loss: 0.4036, test_acc: 76.61%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "LadvbqEa2_Ds",
        "outputId": "66bf3ac7-d0b9-4592-8cad-0c45b8673b8b"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.MODERATE\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the RNN and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleRNN(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 100\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 100/100] loss: 1.3884, acc: 23.99% - test_loss: 1.3915, test_acc: 24.70%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vypEVetpsBOL"
      },
      "source": [
        "## [10] LSTM: Increasing Epoch to 100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "kiN0O1hLsBOL",
        "outputId": "70018e58-efd6-4e3d-cbc2-6ed2dacef90f"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.EASY\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the LSTM and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleLSTM(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 100\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 100/100] loss: 0.0011, acc: 100.00% - test_loss: 0.0012, test_acc: 100.00%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "QE16R2K-3xfh",
        "outputId": "a2856d20-3473-45a3-9fd0-8a1bed6b7e85"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.MODERATE\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the LSTM and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleLSTM(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 100\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 100/100] loss: 0.4207, acc: 92.44% - test_loss: 0.3275, test_acc: 92.24%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "c15fMtlO3zhY",
        "outputId": "527867cb-540a-4b16-b01d-a340315f84a8"
      },
      "source": [
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.HARD\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the LSTM and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 4\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleLSTM(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.SGD(model.parameters(), lr=0.0001)\n",
        "max_epochs  = 500\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 500/500] loss: 1.3537, acc: 25.30% - test_loss: 1.4038, test_acc: 24.60%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XX8bZZ5+Nyy67DJMmTcIpp5wCAOjduzeefPJJXHDBBbjwwguxa9cuTJs2LakdGTduHGbNmoUTTjgB5557bj6rkhMIIQ8QQlYQQloIIfWEkPcJIWeY0gwihLxGCDlACGkjhKwmhHw/X2UueY7uNv7uWpTfcmSA0jQUMexg22bmPjDzAePvnDFZLpAXim9MpTQNJQTr06D7qfWaerfx90vfBk7+lo8ZK4qKvSvzXYKiZ/To0Rg9ejS37+abb8bNN98sTH/nnXdyTtJmrrvuOlx33XV+FrHQuQzAcwBWwPh+PQ5gJiHky5TStniaV2H4uX8HwCEANwN4mxByAaV0TR7KrChwSsqnoXSq4g5W06DHAK2IQk+zQk6RTMoqTUMJwfYVvmoaEnQe9T9PhWe6XxxuhcKAUjqSUjqJUrqRUroOwGgAJwI4n0n2TQB/pZQup5TuoJQ+BuCoKY0iQ/q1bUdg6n8ye4q3X8qbpkGPAR/dB6x/Jz/XzyPhqI4HpqzH9A117k6gFJj1ILD8RX4/Z55UBD4CTfuAKT8D9q0yCQ1FUHYoTUP+2LsS6HsS0GuAb1myMwyxbAws1WC1IMjfU+huU1iKIqBP/O8RZt8SADcRQj6GISzcCKACwDxRBoSQcgDlzK7eABCJRBCJRDwVJpHe63nFRiQSwbCtj3D7orEYaJHWWyPWZ5aLZ0k2vovgyonAyomInPk9X/JkJ5XclD2deoYE53vljWV78ObyWry5vBbbHnURRrpuHUKLnzauee7o5G5NBxK6hUi4E6lf/pfZDwLv3gFtzxJg/WRE7t6QLFckGgF8KFe67dZteiU05IM9S4GXrjEk5IcafcuWHdPryhFa4TfdTu+tKGQIIRqAvwBYTCn9nDl0I4C3ABwGEAXQDuB6Sul2SVYPAHjIvHPmzJmorEzP8X/WrFlpnVdMfNf0e/WqVajbUUSmIQASQ6DW5mZMmzZNmCKbz/LU+gU4O74tu75XWloCSEzweMnTSz3ZZ59uuZfs0ZAwdnGTx7Etm5AwjmbTn1FXg6Hx7ZkzPkE00EN4vh9l9oOR+zckZyg+/XQOrolvL1q4EM2Ve3y7jtd2297ubpFUJTTkgx3zjb9ZVEcpmaF0UQofhQKA4dtwFoBLTPsfheHTcDUMn4bvwfBpuJRSukGQz1gA45nfvQHsHTFiBKqqvIURjUQimDVrFoYPH45QKOR8QpESiUQAk3fIeeedB3rmqPwUKE3u+WwmAKB//74YNYpfKyUXz1JbXgvsM7ZHjZLcOz0KcmA96KBzAM15yPZczRIc6Gi1z5MhrXoyz97NNcwcbO7EzOlbgX0HXOdBdvUCtluvqS3YABjZYMTwq0HLq7DlQCtO6N8DvcqZ+5Vhmf0iWF1mTGUAuPKKy4GNxvYll3wLGHSOp7wopdi4vwVf0mpR1vcLQI++abfb5uZmd+X3VEKFP2QpxCM7lsyKeZKiIFCLuym6O4SQZwFcC2AYpXQvs/80AHcBOItSGv8cYx0h5FIAvwTwc3NelNIuAF1MHgCAUCiU9mAxk3OLlWBAA4q0zkFNkz6vrD7LQEozI73GR/8NrJwIXPQL4NtPOGbJBkTxUu5065nOOZc8NdN7HoHUcJVLH0hthwIa5u9qwu0vLccX+/XAov++UphVXt9NZmwWCmj8tsdyvb2iFq9M+QAfl/8eCPYA/vdAKj+Pz9NtWuUInQ9czBakBdMYlXlS6ZI/ebBwzJOUM3jmFOM9JAbPArgewJWU0p2mJAl7IrMaNwb1vcsuRdieEhR0yNWVE42/y/7mKnm3syI1hVz9eL2x2Ofexo48FcgJKt5O4/15Y9luXKatN35Ec1Nf1YnmgywJDWyTy0r0JEVB0J2fbCA+MxcOh/NckuInYcNaZDPizwG4FUYY1Zb4mgyDCCEJQ+YtMIwYXiCEfJ0Qchoh5NcAhgN4Pz9FVhQ6BS00KOK4+PIVSQSiJFx5vX/Z82FRosyT8kG2hAbWEbo7jywVJUswGERlZSUaGhoQCoWSi5x5Rdd1hMNhdHZ2pp1HMSCqJ6UU7e3tqK+vR9++fZOCWJHwi/jfeab9PwIwiVIaIYSMAvAEgA8B9IIhRNxOKc2f92O3oLg+OqymrTSFBgq8Mxqo+gJwzR/zXZgkaWs4ZedxA59YennnEirRLni8Lav3NOLzfc24NMermSuhIR+Q7H6kNeiIxYpM4s42cx8HNn0A/GQmUNHHOX0BkzclUgHovQkhGDx4MHbu3Indu3ennQ+lFB0dHejRowdnA1xq2NWzb9++GDRoUJ5Klh6UUseHRSndBkCtAK2wJcrMrAVLUGg4m+wENr5n/CggocH/CU124K2DFJAZrRi+vMJtF3z/b0t8Ko83lNCQD7K0YiGlFOUIY2bZ/Thm+rnA6Lezcp2iZP7/GX+XvwgM+01+y5IxxTWj5zdlZWUYMmRIRiZKkUgECxYswLBhw4rNPMcTsnqGQqFi0zAoFL4S00tb01COwjThTN+XyoWmgRaDpoHdlvg3uMkmT8MAJTTkA9Y8iVLfZnApgG9oG3GSVg/smuFLniVHMagvHVDuKoCmaaioqEj7/EAggGg0ioqKipIWGrpLPYuK+s1A8z7g9KvzWw5KgerpwMChQP9T/cmviIgw2nithLSNCY2iVqCTS/6XSjJznwfqWzqxfOcRXPOVQVxkJB6ZpoG/M59uOYiTjumJ0wb08r+gGaCEhnzACg2xCBAs8yVbSgHqt2/7EXNwkiKnBD4OhfkpUCgUrnj+YuPvzxcDg87KXzm2zwEm/9DYHtOUv3LkiWiMMU/KsV14LtCIyXa+QL59vgdpKSCfhmufWYT6li789poz8MsrTnc+QWKetGr3Efx40koAwK4n/p/fxcyI0vUALGRY8yTdv+XMKYCYn4+0swl45mv+5VcQFEbHqVAoujkNW/J7/dpl/uZXbJoGPTVIK3w7eO+QDMN5Zgv/i1JImgZjuZfZmw/KE1FZeVP7P9/nbqE1/qzcoISGfMBpGvy1O9T97Pya9jqnKTZc3J4jbeGCjmGft7IVyEyVQtGtKeC+Ke94uDespqEUF8zU2KVKcmnr7/oZeLznbqInFUXIVed1GkSf2kIZkyihIR+wLSIW9S1bSikoOyrOtJEVSCP1F/uB78fr63Deo7PwyEebclQe75TiU1Eouh3pCOFNe4HxXwYWPOV/eYqd1gbgL2cDM//gKjnrCF2on7pIGlEQE61Ky9UM/JrXU9tzHgX+fJbxLCTolOJMshsryv8T/x741MOFZA+JN08qhLkt2yK40DSYz++MxDD8zwvw23fW+VC6zFBCQz5gG43fmgbKPNJM7Pua64CdCzIvUKHh0KM8Pm0zAODlxbvc59nZBOxekrMvT6F+4BQKRZaZNxZo2Q98+pgPmfndkeS5Y9r0PtBUCyx5xlVyVmgoiMVQBWVYsfNI2tnlTGj44Jep7YV/Apr3AkuelianFPhT6O8YQJrwRGhCeteUrnVQDJoGBlnIVdM4Zdamg9he34p3VuXf+kM5QucbP30aKBBjZVQ9CgTSfMTjh/pTqIIjC9MQL1wGNO4Evvd34Gs/9D9/E/lTUxbAFI5CUTKk8T4VwNhWSr4H3r0Hp7bbDgM9j7FNLo18mS+oblnDKZMVfwlrnpRrB2GbclMAAWQ4uOccu80hV/P/nbJf+0cm8KS2zRGA7YRamuP6Kk1DrpBJwzE/HaEpdPaR6v6ZPpUM2dBdNsYjTH3+rv95FxKFoPdVKEqFdN6ngn4H/Rt5v72iFvM37gIWjgMatro7KcSEYG7Y7JicnXwpBJkhHIni7/NrsHF/E5o7I3hu7nbsPtyedn450zTEmbulPvWDyIeWOqW8k7ZbqORHsWkaZFoSzjyJf89j/q+IlzZK05ADtBkPADvmAD9bAJT3zprQAJikzgIUGnSdoiuqo0dZvhaWyuZHN0fmSTm5ikKhyC7pCA2lP8+3vb4F97+7Hg8FX8FlwRnAnEfchYRlO8YjO4GTL3GdvBA0Da8srsETM3fiienA9ed+Ae+t2QcAuNXjcjQJuTLXQsOPJq3ArkRZbdqpL/datiiargMo9EUrnddpMM8NFJDMoDQNuSCw8kXgyA5g7T+NHZzQ4J9PA6Wm6EkFuJDZzROW4usPTkF9U0d+CuAwU5fRRF6uvjwF1IEoFAoPsH1EWpoGHz/ZhTBSFlDfbIStvECr9ngmO3j0NmFWCJFpNu47mtxeuO1QxvlxJkC5noG3a9u+3GrvmoZcmvGk5QjNbHsxT8o1SmjIJYkHT9Pv3Gyzh+nFKMAl1Q/vXI8NFXeg87Ub81QCpWkoWDZNBV65znDCVyhKEW5QI+iLOh3is/uqaUi/J2npFHy3/BrYiGbKBcR0irauVDl03dsgmRu7eSqgldauKPRMp4OZMqcTNSlBYrzemzCmTTkXGmw0DUjTPMlN9CS7MU8BmPa1dUW5WnRG2PdIbp6UcdvyESU05IMsahq0DGZbXFwBeONG41+aH4jbArMAACceylNkpmxqGnJEyTpCv32bEbFr+v3ZvY5CkS/sBm/LXgCeOAFY+ZI8TQGYJy3Zfgjn/dFLqExvaPFO2Elo+P7fluArD81AQ3xBradmsJoJN30kG3I1/T5139EOnPXQDPzwxaVp5wEAlPleZyI0AMBJ5AD+FHqBzTyj/Lwj/1akPf6VRkxya4aVQ02D4FKHW7vwlYdmIBpLCTa3TWDajI0SsoBkBiU05JaEpiF7jtAkm0JD+2Fg2wzjX/vhtLLIf9vPYsdRQCrEoqY9/TCDCkVBw5qMmkcGCWH5o3vl5xeA0DB2umwla3/6P5L8a5/f2lrDnCex+u6WA4yWxkVfzCn8Myj6B2sN34NlGYRHBQDKFCJToeGmwDxz5hnl5xlbnwZfnBrEu+3WacjhjKBohfG51cbaFWz9ZX4n5uhLIvOkhAlTrkcd+e+BuiNZdITOqtDgpFp3k0W+w6FlteMocfOkYlDDKBSFDGc+UZyO0OGoZADq06SJpiU0De4GutH4YNuryYvEldYzvlllMW0jEsss04jZGbiAfBoofJi6K3BNgx1sKTQiXtzN7NMgErS0PH2P898DdUfYhu3zOg280JDFjiLNBuuL0FC3DnjtBuOvZxzMkwqkY7FDKTQUiiLFTtPA8vGvxRNKBeAIHc5wFtyJxIDJyTwpQSxeHiKZtXVD3kw+mevqPvkgEhBEqUloiLe7vY3t+O0763itjB2dTcDUu4FdizyXQoaTU+/f5tVgwsIdgiMyMc+lT0MusXm1pcKtx+hJCaFBrdNQyiQbBdMCfF4ROruahgIZrU66FqiZA7w00vu52ZTOc7UidN50DYUvUJUim+ua8daKPQUR4UWRIW41DSsmAKsmWfcXgLavKyIbmPnVPkn8f5dCA2XPShTFm3lSJmT2Xspi9mdG1BxNPy5E/eL11Xhn1V5859nF7jKa/TCw+lVg0v/zVoA0oycdbu3C/32yBY99vBkdYRsBQKZpsJsoLYB3xwyvTbNxhBa0jXxVR63TkA/YBuCnTwOl2XeEzjgHH1p6V3yWJOJy4RvdvVlVZiFXc6MCVmPH7sW3n14IAOhVHsL/O2ewQ2pFQcP2RU6dTfN+675CME/KmabB3XViukDT4OJbxU6+5C2kJaN5Ij6FSCdEbp60uc74dkpNzMwcEc34uymEXfQkuUDItq2o7ZoLMk1DYZgn2V2Jrbvcp4HZTalwcTdlntSdyJYjNDU11gLUNORalfbc3O34aN3e1I4CnG1QmFFSkYjEB19RpByuATpTsfj3HXVaq0bwHhRAyNWuLPs0EJfRkxJERbYbssHjkR1AR6ORxKdJ/swUDaly6j6uqxT1zach3cqlZ57EzrDbOqdLfRpyFHK1uS7t0OCE2xbXg3WEjulU2MbMfg+5QgkNOUUQPclHnwbA5Fjjt30f9TBLJsvCp6K4Yf3eo3hqRjXue2s1s9d9ucdO3+x/oXyg5IfUSpUiRMvXV0KROfWbgb+eZ/yLM+aDDfh0y0H5OaL3oBA0DdJZap8coT37NAgcoUX3rnEX8My5wP+dbDmUkdCQ/qmQLe6VKb4JDeneGDtHaJssuS7OnE56ImtdEYP8G+9T/xntAsYPNf5lOOmrSa0WvCYAACAASURBVDQmbEl1KouepDQNIIQMI4R8SAjZTwihhJDvOaS/PJ7O/G+QKd0vCSG7CCGdhJBlhJCvZ7cmDrhdpyEWMRqo22zNO/xeEVqy5LmnLHKoaTjabrzQ3MfEaZ0GZvuF+R5Vs6W+IrTS0uQVdfeLmO1zLLs0ULy7ep+3fApBaMiyeVICQtz6NCSEBhbBubs/41OwMkYGnWpmmgbmG+1T4BICICLxacgWFr8Ox+hJkpvGnGYdKMtMklyapfn1/YprqgAA4VbPl9IIKxzINA2p3TqlyTbu9hrZJP89EE9PAOsA/NLjeWcAGMz8q08cIITcBGA8gIcBnBfPfwYhZKAfBU4LapaMJWn+cjbw5KlA1J2ztLG4G6vFyGbI1TSzyOHQJxiftgi4tI0tFpQjdPckXzNLCh8Q9J0E1PsEgCaz8U6DdCd+pBO+/vRLiWzc+jSkVst1sG037eN9GryU0JRtJv2x63Ch3rBET8q60GDaYSPcul3dWDRQFl/QrZ1ZFvpPD21eGDYVYk0T29dTKr5MvjTPBSU0UEqnU0r/l1L6nsdT6ymlB5h/7BtyH4AXKaUvU0o3Afg5gHYAP/ar3K5JPHnOPEkiNMQiQEudIck21brL3jwkz6bQkLamIXcEA0bz5l5MxxWhM/KEzuBcD1fJ0U383w824Z7Ja3JzMYUjgYLqrRUyDncCt0xcYTI9sr60F2ubcM/eXwEHN0lyEr3opS84JmaYvfo0OEZPMgsN3vymXXHjC59hxS4Pi7yx33+fzIkHxg5gXNnf5dfxhLsbY0mVrkaMC4Rk69TAbLoUvHx7dfx7B2XaMXZ/jFLbdRpy7SdaKtGT1hJCygF8DmAMpXQxABBCygCcD2BsIiGlVCeEzAbwDVlm8bzKmV29ASASiSAScW/DZk4b02PQIxFosVRsg1i0C7ooz2gYoUQ+0Sjg4rp6TOcGyNFwF6iH8rKEBPti0VS5I5FwskwR01872Abu5V7KymabR7yjZO9JLKaL73eifKaXk81fVs9EeXQ9hliadUqXdO+hU55RHXhrpeFA/nSFsV+nNKv1S95Hqmf9Pnpps4UC1XXP5U23nsV0XwqNN2s0bGtuxPJJK7HriXioSsFgZnRwJtABYPIPgXsEa84UqE9DtknU2rVPg3BxN9G58vz8Mk9avvMIfvD3z1LP3fHkVLugPmkD7mn5s+11soF1UJueTwN7yKJpkHquu5X+fBpc86GNJFdydy1e02BjniR4fPlycSt2oaEOhuZgJYxB/h0A5hFCLqKUrgZwLIyYXWZvs4MAhtrk+wCAh8w7Z86cicrKyrQLu3nzZtQcnoYhBzbjy/F91Zs2YtuRaZa0mh7BdfHtefPno718i2P+W2s1rqmuWL4U9Vucw5JqehjHH12J/b3Owv5oFY7rAYicSao3b0qWe/bs2QiHqrjjs2bNcrwW++JOm2attxu+y2zb5bG7FQCC3Mdnw8aN2H1Qfk5bW8CxjOZ6JsrTeOQIFqVZJy/oeqqM6d7DdKhvaMCyLF4v1/cRcNtm843RTW/bWo1pbc79gAiv9WxvdxnOWGGhJSL4mtuNlFobJAeENglplSk3+GWeJBIC5AhXhBaaJ8nzy8g8KSMvav8doftTgaYjy47QXjQNdgIa68dg/0zS0TT4Ncr2U9MgE3gY8yRdtk5DQtOQW4paaKCUVgOoZnYtIYScBuBeALdlkPVYGH4QCXoD2DtixAhUVVVJTrESiUS4j/WZQ4fijItHQVu81RB3AJzxpdMx5JJR1pOjnYb3BYDLL78c6HeK4/W2zdmOz/enZqwuPP9c0CHXyE/QYwhM/QW0jVMAAIMCX8TdbU/izz84G1hrTX7Gl4Yky331VVcCvQZy9Rw+fDhCIZGOIsWEFe8kt0eNEtTbBXStBhLvHOzyWLe3CeM3LON8Gs4+6yx85Tz5OX/ZuggNnakBE5s/W0+dBFAejHeMcQuefv36pV0nL9y7dGayp8jG9SKRCKbPsA4yBw4YkN365fA+emmz+eaez2YCAIaeORSjLnHuB1jSrWdzswrvmi7CIYXdYMaTn4L7AUtNQysONnfim6cdK0nh83DDZ58Gs9Cwvb4Fh1rDuPjUY7j9ohj2Xs2T8rciNOsI7d2EaNG2Q/hCvx445dieyX2aC38OvzFun7uAI3bCAL9OW3o+DYXg+uW2DJpE+NFMmgZR+8xXNYtaaJCwHMAl8e1DAGIAjjOlOQ7AAVkGlNIuAMmwRQmJLhQKZTTACAQCCIRCnCdJANTYZylEyjwgFAwBLq6rabymIUhgf171bCAuMADAF2KGOcqbK/fhelH5mcxDwaAlbzf3hzVPSvtekkCyE7TLg8Q/xuyLGdA08f1OnGN624kWSPpGJHhk+jZMXrEXs+8bhtMH9k7u1wig5WAAynYf2RrwirprTdNyUj+NkJxcB8j8nc4loUAw7bJ6rWex3JOCRCw12KSXzMpmaJ501bj5AIBZ9w7DkON6O6T2A38G3omxojmAxdXjFwAA5v7mcm6QHBP5NLgwT2JnvDMpeUa1zsBP8PN9Tbh14jIA4MyhhA7k2RYaYFpY1tY8iUq1SOyMulUYlNwf1/ctG8PszNq8q3UaJNGTVMhV//ga4vPhlNIwgFUArkocJIRo8d+fCc/OBZw4LXNW9t4YKUyN0MkRukscLmz5TokjF+eolUdHaJczcwnpXBahQIjpPRQtZDR5hSFcPT+vxrkQtcuB+U8BMf+c0nMxKSa+RgFM4XRj1DoNRYzdO6t5mLtLI3rS1oPifr5Q0UX9NkNNPV+fmMicSSgz2GkavJfTj3O5VYA9OkJv2Nck3O+r0ODWPImahDyHkKtuLmfr0yDLMRfmST4O1mXrNLCaBZ1SoXYmkKfvQUFpGgghvQCczuw6hRDyNQBHKKV7CCFjAXyBUvof8fS/ArATwEYAFTB8Gq4EMILJYzyAVwghK2FoIX4FI7Try9mujwVh9CTJYDLNhdR4dZfP6zSw6tPPpwDf+E9/83eLy9m2xEyFW4c6EV1RHT3Lxccs6lNRpzZxuPG3sj9w4U/SLkeuEcsMueqk8mQqUOAomaE48G6e5OUzzNot6K58HKSvbYEuoigzT5IhXtzN2aeBG65lcC8yCrnKrM3g1RFa9lj91TS4rxsfpdDGp4HK4/2kZZ5k2pZ3k7kLuZqeeRLTFkyXsFtFO9cUmqbhAhiWzYk4j+Pj24/Efw8GcCKTvgzAOAAbAMwH8FUAV1NKkyvpUErfAvCbeB5rYWgiRlJKbZbizDKuhIY0NA3UrGnwe3E3Jr8ZDwB7lnKHSfU0YPcS/pz2lNaCUktQ2PQg7mbbkmpu4kHTYKIzIr+HMS+P6LALrYRCYUO+ZpYU3hA/JZvOQiY0OJknOUwK/TE4EdPKHkCAXUA0FgH+cTnwfhYmfHzzabCf7DEPyIQhVz0O5D2lnvUg8OgAYK4RlNEvTQP7PF8M/QlvhP7ovmTNdcBfzgEWjgcRFcivsUDTPuM6AlihYdfhdlz8+BzM3Gi1AreMU9hisuZJtjfWOLZ0x2F8uG4/s9ulpuHpr9kEIHDA11i9YuGHE54olazTkOGl06SghAZK6TxKKRH8Gx0/PppSejmT/klK6emU0h6U0mMopVdQSucK8n2WUnoSpbScUnoRpXRZ7molwNXibmzjdzdYsNgV+r1Og7msDalILj3ChxD8138AL387dXz1a8CTpwDznjDKR+GP0ODybRGquR16eHPpROZJyfzd2lwCRbeacgFNbHRr2BnQzNYQUeQVW02DrD8TjRSYCROHCZBbgnPwZW03BhxgPom7FgL71wBr37A9Nz18Ehrif92HXE3cBwd7I4t5kniQ5sjip4FYGJj/hPmq3mEEhURwjzJEMDywGt8KbMSJpF52Jv9JWfAUcHQ3MOdhaBCMKfwyT5r7uHEdQTL2eb3y2R4caO7ET19bZU1rdzlm29anIV6uWyYsQ0c4Kk4DswaJuWGNO4FFgtC0rnC2a3MfclUmgPC+HSKtS0D5NHQHBOZJ0tmi9DQNyKbQYO54Ei9Maz1GbLzPmv6jXxl/5xkzMjr1aS1jz+ZJ7jUN5oFZV1Q+Q2NRGfoWISX/yLpCRW5hvxVK0VAcCL/l6ZgnCQckrHmSu9ljXvucRYdYn2YanHwazAg1vq5WhGa3MzBP8knTQOPPhq13CPJvOD8wTRUiq+ZJunj9FgrK+TREbEyLdBuLAy7kqrnIXB2MdDGd2pql8TKD6Zrpjo/chnh1gWxC02x9JdK6pMYqzutG+IkSGvKBV/MklxIlhUly9ds8SZKftvxvpoIkymBVGGekaVj2AvDGja6dihMdEB+Fw5umoTMi7xSEof6kGRfXq6YUDfmjqT2C659fjFeW7OLaWL6iZSh8wNZswnlCYd/RDrwwvwYd7AhZMuFUe6Qd/1jAmENyfY94NrM97PMEUwYkmryTT0MAMfw4MB2DOrYJ0rvRNDDXTHfst32OvcChx4Clf8ORmlX4+/waHGkL88cF6zSw36syk9Cw81Cb+DqBsuSmndDAdiETF+3E9A11+GSjB0ttyaDUbHKkm4aWMzcewNJ5HwMrXwI1D/S5fJg8XC7uxnWLpnN0mabBSCwsAwBg3WSgxmK0IiCzL6VGxAIIm6vZPOkfC2pQe6Q9WW+aY6GhoByhuw2efRrcDxY8RU/y2uDdOlbrMSAQtAg7xgvM7Jvxe6ClDvj+RHeC0fT73ZcVqQ7D0UHOhi4bnwbbTs2MSz+MQiGv5knd3Dbqb/NrsGbPUazZcxQ3XnBCcr/SNBQHYkdomzbtwhH6+88vwYHmTvQ78SBuTOYp7stGPbMQLZ1R/DS+kjuVzQ0yZRo7bQse/d5ZjuXIBU6LuyU+FbcEZuPB0GvAntcA3Mjfd6F5kvwZpN3jvH4DcNFS+fGVLwGf/A79ATzR+U8s2nYIr99xUeo4MxGnCYSGoMnU6Kpx87BjbDy8KlvhQCpEslhosH7HHv1oU3J77IXyKpgyku5ly82m6ozE8NPXVmFXxc0AgMpRx0l1SLbRk6T6bxtNA5fQZQd6cBPw3s+M7TGiCFXO4wm3l5IJurxPAz/WeHzaFjz76XYcV1VhX7YsUVzTn8WOMHpS/GU+tA2Y9RDQdtiaxkP2+dA0WD6TSWHFpGmgpib92bPA5+8CBzf6VECexNLrAQ8+DWbsfBpcx5EGis48SYiKnpQT2rpSwj774VSahiImLdPF1LM/0NwJANjawMw0S/rjlk7zZJHzTOTcarntfK5JFNHJp+EcbSf323lySN5fZ7aqs80xU7CQRdsPmc5ly5kQGlLP1WyexH5yuN6A1TR4FJgAwEah7gpKqWVdjQRdpsxDTTuF6QC+fvbRCeXbbDdpq2mQ3ZPD26Xls5znod2IjQ3FebHaq5hOLWON5s6oxAxSaRpKFIE24IVhQKTdEB5++E/IXwq7XKlHTYNHXGsa4tcVaBooFbR0iZ0kx9T/cndt0/UAeVgzEeYX0bfoScWmaRDuVYPWXMB9MJhGpoSG4sB79CRJ3yAYAMTY/jPel3WEY6gIaXJHeZmKKtKR3GwPC/q5cDsQ6iFz0hDn6QOJ9u81VHYm5kmZYJtNtNPhZOsEYmVqXVmUEXffcKqFku0uHUfogKxrcbtOA/j7zz07Qd7Sy7GCXFcbQPul2p9kITw2r5ZOfixhX3zJwbDYBKwjHEOPsgDSGZvJkK/TwOyVrNMg/h4oTUNpQgUD+0i78bc2HtgpnZUiqVlyzZGmwaWDkU5lPg0OgyFdB1a/YpukqT2CX76xGnM2p+wz9TQcoc14ip5ka4JQXK9a957rN4jpFO+v2YfaI+05vS7brDhNQ3E1IQVLhuZJTEbMpo49h9tx5oOf4Bevr7Y5R+wwi5UTk5utXaY++8DnwOODgQ/uEuYoHND75Qgd73I5e28XeDVPMtuNp4utliLi0HeYfBqq0IrFFfckd1UgLDjJgBUSdc3JPCm76zRQymv07fxR7HJMPPshZC8ufPMsaftjc2GvVd/cwadK57GGrYshvrdmL8588BO8+tku04g+MxWNG9NpnYrbmFBoUI7QpYbIPInvrDvCEYx+eTkiMe+rL5ulfUdNg9cG5vYFSQoXZvMkjzEqWg4Cz10MLH3eMem4WdX4eEMdfvLKSkRjOqIxPTn4koc1s2IOlWarabAIDXbOjsX1qnVztwIAwNsra/Grt9bi0ifn5vS6nF2vF2d7RUHgW/Qkp95Sj+H1ZUb4y08E8fBTBWLXdhDnGTZHiUuEo1z7Ol8i28hG/rRVp1wSfbR5YEqIQz+fjxWhIx02B81lorhMW88dLocLLTwAyrQhoZmQ36bKlgLIo12ZB7y2QkM87c+CHxk72PYnc4RmcuwMm825WI2HS/MkgdBw71vrAAAPfrARvOAu0cm71AprkrzMAq1IqBVfQgkNpYnIpyFOOBLBZ9X7MG8LE9HAQ4/GtSM275YDQMNWb+U049WnwWKeJNE0yF6wuY8BDZuBmb93LNqBppQa+Ipx8zDsybmI6lbHMtnHu6UjjINv3YNrwjO5/Xa33n7xGRNFZp6UVwpEYlm643Cerszbsyb3FsZtUfiNrG8Qzpaz224Hgs4DGMvMsCRv8URM4hyfNA0u8nH0QXDl0+DmiDO25zppGphvKqExxExDsnI7TQObjVYmTQfAccJPWgfX5kkUGhFrGkQKeafIWG203KGUYvMk3TRGSSt6ksQ8KXWas3bArSGp3BGa/wa4Nk9SmoYSxUZo6EPaUV0xGmet+gN7QnIrEtPxwvwafFZjHdBQSjlpf8v+1GrMGHcG8NyFxsqR6WLRXEgaKLXTNHiwy4442INKqD3Sgf1NnTgYdx5k74lMc/DSqy/huM2TcF/HX7n9dh8w64fL5oUtNk2DaGc3s6nPV23Z8I+sYKqEhuJAPAHo1xourCbZTmiQzbCKG5FFCJCU1z6ykUMDXfAnYJW9mSl7Dfs01n0yp1LZPtvwnl7Qo/h98HUM11Zajzl9w5j73IN2GNGgGCqInXkSUwTGPMnpOsLDcqnBVUJzEBZeaDBrGuT3WqcUg3AYtwdnCS4i82lgrqVTBPUuPBR8Bd/SNqQXPYkVGoT19a8jZscm0zfs58yr2SKI2qfQVelfPzb8YrNIcY1kip1k9CRnE6LBDYus5wH4nykbMHb6Fvz2X+uE2bPtaPbG/YaQsPbN1M5MIhV5dYQ276ay1y07w7NE9CT2xXxpUY0wbc3uWuF+mXVIADGBeZJNYYosepIaoHpfgZlSamvO5jofiSO0eiRFjK1Pg3P0pARuw0dz2lUXExZWTYNEaBDl76I8OLQd+PRR4EPngBbi8b7IxMXjG2FnJpPBy3Vm4zzcGZyGF8vGWw96ME/6GX0Xg0gjd9iteVLWNA0e4H0aUohWM5av0wA8W/ZX4TFZ9CQ2L6rruKT+LfwoOANvlI01Vdut0MCYJzkJnxmHXE3xWc0h/OSVlZZL6FS8InTi+8Qdqf4Y2upJ7i6eJkpoyAdu1mmQ8M6qvQCAvY3WzogCVhXhC5cC7/+cSZTBoMazeRLfvCzrNGQZUfSkLlGUEADsq3extgl3Bj4CYNgSdkVj2H+0A/uPduDtHRr+EHwNm8tHY2Bkn0MBmGsVqXnSQDQ6J/Kd+LM4XAMsHA90teShDO5ZuuMwLn58Ds5/bDaG/uETbD3osry7FgH/vAlo3MXtlsUqzygspCJnZHNFaH7cJM+T1xw4h1y1FNlmRtnIXyQ02LTPLlG8ezGiiRqzlYnoXXAUqEwz1dzYz3XprJTHbN53R0fo1DfiVGL9ntg7Qqe2dafvarqahjSjJzmZJ8nQKcVZZCe/M5YQnJzbrq7r6Bvez+WXSujWp4HVNDj4cWbYJ2sSQYty909mnpRIa6pXrMua2EdUyNV8wDbEw9uBvasc0rt8cU3JNFCgrYHfmUkYVq+O0MRsniRo4PYX9JDWSjQ+SxtgBClZRA52/+SyxwAAu+lxoPQsXPvMImyrT8w+aHijYjoA4Aftk4HUUkvW8rL3usg0DYk7trzil/krxPMXA7Ew0FQLXPvnnF/ebUu9beIyRBiNwN/n1WD8TV9zPnFSfJGmjkbgJylfGi56EmOrpESGYsbOdFHcN7SHo+hqC6NfT3YW2Z15Ej+oT8enwWQKcmgb0P9U6PGQr+JwqDZ19NB4ReYr5j2iQZRj9CST+RKJhXEq2Y8d9PiMBPJ2rSq5XYEudIKxx3cMuWp/XTtNg64bz/kUUgdd90/TUNPQihP7VyIU0OD2wZnXabA3TzKHGzHOJ4SINftdLUBlf64ODS2dGNDXmlSnOtiWwGfn0qehi9U0OPnGZCo0SDQmZk2DaAJBlmmGEZ2cUJqGbCNSqbEPtakWmHClUyauL8c3QgGZCA3mj1SibtKQqy6jJ2XJVj4xS+sYv1uy+2RyADpFUmA4Bk2oQqpDIeaX0/xis/e6yHwahOTKpyFxH2PxWbbdS3JzXTMuqxvxtGCHgKa93E9+YR9mv9I0FC9paBqmrN6Lcx8123a7M0/ihAZ+NC1MbxUamN+rJhn+cFPukKd3KI80bwFiTYN54Olk8uJUPoqhn/4En5b/BiO15VIzVDdM35rSohyDZv6gk3mSQ1QjO58GnVI8GfoH5pT/Fj1W/8P+Oi6Fhimr9+KqcfPx89dkE5kSDRTkYw9h5B/z+ckkgvy7ms2JcP3zi9HUbtVAUJ2vJ3vtZsuihxKcNA1ZcoTWpEKD+B7K2qxlXOIzJTCSKXQ8zshkdCVqUhFaG88hLo6xt3Lsa7SGIktcicPzOg3ZIWEHyM6AyBYMEn0ECVIvayU6sariF1hf8dNUAsvLWTpCgxqfZoDnJm5Wm6c2VfSk4kP4ptv6NNj3DdyAmRsXx6SCJDcAcWWeZKNpWBS31d/4XvrmSbK8RYcdBkgkfinb18zFYK/vgcUAgNHBGbbOuU6w97o/MZkqOZkDO9wL1jxJNy2MqlOKfwssAACEGhx8FR2Ek8StmbjIMA2as6WeP+AApfJ1GnTL3Jp81lyqaeBSGflvb2ixXss0MckLDZJJT0thmHROmoYMO2WZSRdXB51a7qHttYWJ/aO4RjKlgldJ0MOLq0kk1wSPTN1gm8e12mcIQjzo337AZJcqm3lO+jSYdkvrIcknwxcyoWmQqQD5Elj3a0jZEp5MrHHQLR9Oi6aB6XyKzDyp0ManIkewbGNVomfrQtbBQAJOaDCdtmjbISzadiibJVP4hl37FbezxF6+W2GnIOUDQXYA52jvDgehgTORs/apwnPsSMO+3s2g3jl6kjj6ThCxjD41QWYF5mOIe98NS5kE2JknxbwMI1w+G+sn3eXYA5SbpHQyT7KcH08jfA6dVk2DMaGX2k7mo8f49m5bfBd1Ewl9LjQNbi8nXxGavX/isZO89EpoKGqEHZnnHsp9epnkmiAgWmKe4dmyv+LHgenCY7LFWyxIFnczzwJkG9GK0HJNg2gfTb68IZEg5UXTkLcAnsXP/qYOXDV+vi+RibyQuwiz8gvJQq62h6O4deIy3DpxGTqkzv2KgsFucCE1cTAeuE4lfTrVpRG+zPbRj320CWOmmhamYmD7xTFTN6KmnjGzYcqnU+A4HMHrZWNFFRHmbZRBvlZOTKe465+r8Y8FNUBnMy5a+gv8IDDPdL45P+s1TMawDuVLbYcQBaXAvOp63DZxGfYd5U2KmjqMBVcXzPkIO8dfZcmVvXfHIKVp2HnIId4/4KiJYDUN5hp5ChPrQrsT+OAXuKl9skM+stl5G02DWWigMC3EF6/buskYMvtHqCAmQemje4GP7uPqQJCKKGQOucq2BHauKa2xh+C+rdnDBgdhcg23YULoKfwgMM/yXh5pC+P+d/mF+wDghsDC5LYsMHLCEfo8shWvhR7HELI3ud9tmf1ECQ1ZJ4MZmWR6t5oG3jxJNEAOEucBxlWBNcL9ljB7UvWYxHaw46j/5kk2qrioQGiQahqEDtIpB6QygdBAnFTP7H3w8MyfmL4FL8yvwZYDzXhlya68rAosfrT5EXxaO6PYeagt54ut5UvMY299TDDLCwCtXam2lWthSmEPF9Um8e7a9eEenFR5pYPcPIntqzsiFBMW7cSkJbtwtF08c52YIDnU2oVJS3ZhfyNr151qX5RSPBKahDO1PYKCyuu4lxmI6zG+vc7dUo+P1tfh8WlbgHWTMfjgfDwVsrfRN5viJuqQSuAcPSlBGaKglGL0yyuwcNsh/M40uHt+3g7Mq27AsIW34JRm61oMrKahiqTu291vrratg7ScDGVEbofvp9AwoH07tM/fwW0dr9umk2YPu3UaRKlNeyiA936GfvvmWTM/VA2snGgEjWHyF2oaqM6NUth7ZBl7uLF8ENy3+95eKz6+9G+4OrBG2HafmrFFeKUztJQ/GzcpyyoUqfFuTikfg0sDn+Plsifti59loUFFT8o2wrGo14eanqZBJDTIZtrdwEYhMl2URxQ9af5T+OLcx3BD4ETL6bpuXgczgYuy6lFAEqM6JvBpEAkNZmErAWueVGae/RDlZX6LY+w57u77rkNt+Pt8Yy2JsdONjiYYILjlopNcne8XYpkhV8Noir2N7fhi8pdx3bJAic5xWMz4UtusCUIeZEdFWqRmO6M6RZlG0tI0JN5CbnDIeUi6C7mqM9thiU1LwtwjEXGO+04wZlAUwEByVFJceQMNMH1HeziCXkyAoXZW6C2rFJ7PaVsIkWgaJPdJuM+kaWCO1DXxEY+OdtivlcAO9thvzYEmF4uTOowFhOthxPE0mRQX/AyTS+t5hLNAYGbrLfdRfE3DNFqyToML8yRXtPAmwqKFBnVd58YS/BN3GT2Jv4hll7SddTQyaXgOt8od2kXn8CFX+Xs4GIdlRYufrDQNRY0wooNX8yS3mgaYfBoEs+dBB/MkQD4bLzdP4l+Rmvr4R4V1/p1rhDEVzVD9eVa17VfQ4QAAIABJREFUY5mk2ESD0gWdiqhusug3hNE0iMyTNC/mSS5f5K6oNd3G/c2ClNklr+NTSvHuKmvM8rKgpLui1CSg+UPaMpLnm2eNMpYgqotnR4VMux+Y8tM0zB8VvhFuw8X6GpTHzUqSH/u0zJPih6l1n1OerH05ZQaXsZi4/yegqG/pxIJtRohuTmigumhTgHFO7ZF2LN1xGO3hKGZvOojOSAxBRuBv7eQHUFydKo8R50yNOl2urUWw45CLV0ysOU5tpiqSME9KYPafCgqX3k3BDuyT39fdS3C8bl3dl0OPATsXOOQt/157Exrs00ZJakXphFa9MxJzHXGobOcshBgrBu4729WCq7RUNCbxk3FRFyZ8PCv68KZ4OijjMK7b+ITJSaXcsNe6ThF7vfawf98du5CroiGKTik06LhcMy30q4SGYkc045G+psFuIGN0rE6aBudBiExosJv1YBnz/np8vq8JnVF3r+ncLUbnOmHhDjwwZUNq0ORm8GMjNEQFmgbRPQnHdGn0pEQRykXmSV4coV0O5EST6QE/Z/jbDgFT7wb2WtXshYToIyIVGqbcCYw9AWitz+yiegyIpGYHc+YIbYKtOScz2J5EgeUvAOvfAuo3Z6lkCicCH/wcj0XHYUzwFQCpPsg21LUHnwbuvbAxj2T7PNbETWbSQkDxjbGf4v5/GaY57CKhnHmS3fAuXo9Ln5yLf//HUnz76YW449WVePCDz7k6tnWYhAZizcOSNYAbA/MxqexJXPDJd6ALtMNpO0KTGB98wHRuwEloYO5VADqGkj3Ay9/GVN1hjZsN/7I/DiDICn+WyQXH05nE9t/uGFKBOhLO1394/3PsPiyLmMjT//1b8aPAJ8nfbEkHz74bE8vGsYWxFs9FXWKtrNBAmUlBJh9d53LnA4/Jo9TJCvOjl5bhUKt8sbTHPnKIWuUBqU+DTiEzQ/xxYDouD5iFhuxOGimhIR9k4NNg132Z7TxFA2Q3A3/ZNayahnj+pkFtADFc+9dFnN21m+s99vFmvLl8D1bs8rAKcfxjLBpXxwSqdpFw0BWJiQUsoidf1jJBFAtrPGRvmoZF2w7h+XnbuQ4hIAi/6PTR8sTHvwZWvwpMsDr0seTbp0F0/aAsNOWGd4BoB7Dmtcwu+uIVwBMn8ov75ALLIohiTYPsW0AI+PbVriIq5QttqxFE4ofBuQBSfZBtyEsv0YS4KUi7xd14k40EMal5krsBuO14xHRw92FjNeS3V+7lDrV28X0pJ5zLhAZKMUIzJjrKO+qdx0VOIVeZ+pWZzJPMM/heNQ1nazscChdn53zHJHbBR8zCjS1ObYxZYDDhfP3Oqr2eevxrAqmJKLb99N4zm0uXrpklbUv1a6xPA/sszcFW2F+uzaI4h2vd1sxs4z5xtCzZPJ8oDL7oGK9pkERPosB3A4utGTmE180UJTRkGb7zTfxNX9Og2cw6G3aFbFqR0JC+eZJbTUNidsRt38B2jKeRfaja9E/3Dd/mXoocob1qGhIdk9A8yVHTYC803TpxGZ78pBqzN6dmyEVaBV+FhoPuZka89utLth/C4u3+DVZF13dUuGQ6w1K3Doh1AbVL3V3PN+QziNwss1tn2g6Jzbki5yQHdnZBExzMk3TZpJHgvGTEOGb2mx1cxiR+EOb+z86nQY67989insQtIyEun05NoWMFl3Je3E0sCDmZJzlqGlifBhJzPzg99kuOSVhzYnO+nsyTHL6nbBsrt1lQzj12ZSOW9uam6w508N8XkU8DdJ27T/a3SHKQaYMB6BYNt9PErN2V7NLLfRqo8FnrlHKaqNTJyjypuBG1kUw0DY79kX2DFjYyE3KhwaWJTbKjc9d5akip3+aU/xZDV/weWPsGXH2EbAbmiQ+k1BFa13F07xY0NHdKSspETxJEsXBceZHTNMjrsrexPbktmkzPVGiIxHQ8N3c71tYeBXR3Npheht8d4RhunrAMt0xYhjaX2iXnAog7SYeT/Ll2vDXkTGiwaBpS29yK0FwiUx4ShzxFfklqiuwmECQDupR5kuS8V7+DL7bwpgmR+PW4xd1YTYNUaODhzVhT5TPMgsTI/CUA4J7JqUhC7WbzJGZbly0+QPmU4hWhzenNeejCBMbAnBGsvJonMQP7EGKcTb0tFX0ck0g1DY27ccvy691dB3Aec7BCA6NVl40FnLC3iBDtc74Oofx3PFFkzhSqbTMubXwvlW860ZMoP8lo1jQRblssiI6tvQ2omWvJ2k5o+G3obVShDQ9MWY/fv/c5Vwc+dCyJ/5VMAiuhodgRvyLpIovLncjVaXE3f82TxGekNA3uOk8Cao3q4dbmPiq3N4wkNQ3sTB1j+vHBXeg74SJM/vujjtGTXGkaLOZJTitLxvNhnqlIk5Tp4PWNpbvx1IxqfO+5xUDM3aBe2J9KCtLBRD9pC/shNIg/IbmTGfK7poYumRmWDR4phUnToISGQiH5+OxWaXVo2JSK+y8A+Pet93G/U9GPxIKCbGBv/lZIHaFtirr7sHxdgkiU6SO65JqGmESAMvtSGP57PI4hVyXHQ4hy75b5PfOkafCysJaLwR3v08Aw43/Qt0MQ9jbNa7H3jl0bwhJvyKU2l2t/oV6OeXhVEhtWAFZNw9CWJVw60WDbEVZoINZvkTywSmp7QLQOeO17lqydLD1uC8zCm8truX0xXdwHUErF7U0JDaVEQlXtdXQjUU+bU1GT6kxgnsQPdMXlkM0uuIm8BLgzgTJfzxzBaNXuI+5OfvocYOp/CQ8lfRoIP3OQILjuDQDAvcF3JOZJKQ2IK58GW/MkG7WkQ1+WqSN09cHUgkNOJlP2uCiHTwN3UXxt59fGL6kh18iXpeI0DZIwOsa9UpqGQsSVpoHqwhXPxZoG0yw45fslUfAHNu9o1F6rIfyt847QMqjLELDtXWbzF3G0GxbDPCk1XBHOaTiaJ4kdoctJlHu3MjFPCiLmvhdyMbgLyNZViroI58pdy8hH/vxSZbFbhdrt2hDsHYuWmTQqokfjKlfTOUlNg02bTMdk1eTToOuU+0Y7RWO0w2kMJRICdJOmIVlMKslPCQ3FjXhF6PTNk/jdFhsFR02Dm8XdZK+wVNNgcYT25tNAQBE2hRrdfrDFvXC1+pXk5tfJZpxIjGhMUQdNQwKZyRYbocFV9CQzrCmQbXhEJpnguJMjnhPcbXRpntQcdnHNWATobJaLEuF2oHGXq+txSJ674wfLt6gRxPTXG5mWgq1GVDYQo6ZNtn11Kp+GQiH5+Bx8GiI2A267gU+MCZUJANG4lMn2c7ymQSa8mDUNYvMku8YtXiAzkV/qWGtn2AjGsHdV/DxgABrxo8B00HbxZBE1O7ia78nuJSbzJGr8W/0qsG9Vap8ETlA3pXN2hI5x204z2tTDOIAdRJaRGG4NzErVzQNb6sQOu0yhkpsVhNU0UFkyWziNfnk/Pg/h5b3VRxY9yZKvzXWPtEmsFJiyaKCW9sBeTxON72xw0kTpgtoYQoNAOwPJ2lkqelIJkqYj9Nrao8k4/pdp66DvXMSnMqlsRQNkN+pTmd2da0fouGCiu2xemkBoSMeW8kukFm+XP4oF5fcCSA24nEKuBhFzNE8SL+7mJXqSnaaBsdUVpNN8FRqcNQ0Hmzvx9y0Bx3T46/nAEydA60zNbEd0isenbcbk5XuM409/FThg2GdGYzoemLIe763ZK8tRXObEPueznMvsBpJvnwZm1lMSuUY3f6u6iXkSIeQBQsgKQkgLIaSeEPI+IeQMQbpvEEI+JYS0EUKaCSELCCE9cl1et5oG0VoxiWbBPmtzk9QJ/56KJkoooymQRU+yNU9ir2cnNEgcNgFe2zvw8Aoj7POEK43zALxa9n94KPQaesy6X3i+oUvjHVy5Pvvlb5tO0IHts43rvHhlal/yuHwwbK6DKKIdd5zzj3D+RibD8LoyT+KFzcdCL8eFIG993cfr9mFHQyukYaRZoQGsJoi/jlvfa1aAjJZVcce2N5jN2Fyt0sDnD3ZFaPnZditCz62WraNhEhp0Kh1XyXXEYpw0DSKhgVLzehMJzTuVaBpU9KQiRyDrepUE4+m/95wRXuuHgTl4pez/EHj1WoutLDuQFUdPSl91ZZFqk/VwoWnQ+BkxFgLDWZff530AOJTwNp4xneJngQ9xrfaZbb4hRG2iJxmfqzKBpqE32oFOZuE180fApU8DHz3EejxT8ySuS3bh07Bsp8tB59HdAIDA3qXJXW8u24N/LNiB303ZALTsN3Zu+RgA8OH6/XhzeS3ufWudJStrmVMM1WpxKtnvQtOQgVqWyzvXPg3yT080xn4sbLa5UY//C90VEJcBeA7AxQCGAwgBmEkI6ZlIQAj5BoBPAMwE8HUAFwJ4Fsig80uT5ADUIeRqRLCoY/KwjU8Dq2mglCb7Uc48iTk/arO4G4tMaLAb3hlmpjKhJLW/qrOOO6ZTKlz0M3VyyJhcdyiHxTzJHClO4gidKEMCi9Dg0B3wK0I7axqS9yjRJnoOkKYNiQaFXc2exxAaqMMK1an87MyTZNGtzHDac8IPM+ubO9OKQGTNP6FpsBEauEASafg0QLe4I3FCg412TYST0EAFQ/KYTlOCJoNOlXlSaSKcMvX2UClN2dZ/U/scY0MTk8eikS788o3VmLBwh0XTcDWWW/Kyi/ucwPuK0DzChhwsl6bXiG5ZCdkQeNy/kNcdmYRnyp7j9p3QsRUPhN7ENwObkvvE5kkxafSkG7b+Nz4q+z16wKrKHEL2AU+cIC+US58GuzC6QOaaBg4X5knSwbmknGxn/M6qWmEaAGhsM659NtmB59+cIk3X3BGxvDaTyx7LrtZVkHnORIfD24CP7kt+4WSaBqmzplnTUMJQSkdSSidRSjdSStcBGA3gRADnM8n+DOAZSukT8XTVlNK3KaXyqAlZImna4CQ0CAbbjtGTAMS0slQ2lHWEZgY20S4k+h/5itDia1uKavsOijUN5QjzPg0BZuY53C4cEHG5Bsri5knMcEV4ikn9ZvE508XbplPN/V/AQWrgfBqI7vjVSmqVEmXodZw0rSgAh3GeR6HBVC6Lj55U0yBN5oBYQyqCgHruvggoZIG2WGJ6SpA1F0P6VE3Rk2KUctYAMq2DG4EqHU2DruvQYvwzKUMElOpKaChFhA3T40P96Wsr0BBflfAcsoM71vDqf2Dahn147OPNMLrVVN6iga5w5sJNmeFeS5HSNDA5BeSaBgAW8ySvXNvIL+wVRBSVMattt1BoIOJ1GjRQnNE4H2dpu3ChVu1cCCaLpo4I5m9hZtRses4+rTuA5v3S4776NLiYhfYU/xt8Z9zcIddkEAL0Qjs+LP9f/Gf1j9DZJR7DHWkLW8y0BpKjznavmUgV7DuZa/MkAFg5EaiZA4AfJPKO0Oy2SZiwMb0ocRJelkcAgBAyEMBFAOoJIUsIIQcJIfMJIZfIMiCElBNCqhL/APQGgEgk4vmfmc4uY79uY55E9SjaOkUDNeM5hiPyEJgxxjypMxxGZ9hIy34HflDzACaFnoRRJ/GA0KppEPfHYUEdk6XVY+gwOTmfT6qxvvwO3B1MhcHsRErQiTQfQDhir/1sCgO7DrVwA6pwJCJw3k6h61HOfyMSiUDnfvPlZIfUMZ1yz9POwRvgnZXdaBo6OrsQiUQQi/fFuq2mwXpvopGIa4fkBBooItEoKCjOIHuwteJ2PBRM+QIuYSx1ygnb3lJEIhFpyF4zdoNp8+dFA8UNfxMsUuaQfyQatVzLzHXPLsI3xs5Be2cXzCMbAip8h1lzPg06wmFzm6fCPESmf6k84xpAkQ8Cg0houGTpnfhX078nfweJjq0Vt+OBrr8K39NE+f3ov0QEXaVSZIBp9gPwLDQ0NHfh9aV74rnxjWrwvpn4lnY+FulnC8PQmQnC2c7es6bBNLpKSL+80FAGGRpEM22ZDXwq0YUwtcrEMlWoLHpSArvZlxSp9E/P3oYDy3biskS1Jc98ABoxasHNwAIAY5rE5kmZCg02v0R4/SBxQX1sikoA9COpSE5EjwAQaKCIuJTOsoxPQkPOzZPidBrOimwteJtc8eydd4vg0oAQogH4C4DFlNJEYPNT43/HAPgNgLUA/gPAHELIWZTSbYKsHgDwkHnnzJkzUVlZ6alM3zX9XrhoEXb3Ai49chj9Jec0NR3FlBlz8WvT/kQrnPPpp0h8qs0tsyOSevbTp3+C+g4ACFr66ssD64AIsG+/eHJCI7z9j6yf/PTTuThXUo/6gweweOYssMOKe4JTUE6i+DpJTbocOdyQ3F4y8wOsbj8NN0jyBIAoAnhyylLcxtR+zpxPLfeT7a/r9u9Hc2MAZ8Z/T5s2DefW7sGJyfPnYCR7jUgUibsbi+mYNm1a8tjWrVsByH28zNGTnJg5ew76lAFDDmzGlwHsa+yCTF8tClyyYsUKnN7QALmoYUWDjmXLlkPXNdwTNLS8PwrOwMPR2wEAdW0k2RXLvnXTpk3DOfX1OMXF9dh22tzUxD2r1tYWkzOxjpqGNqDCdXVAAKxZswZAwNGU+VBrGK++9wmGCYSGjz+eZvlmXdXagkSQWA0Ui5YsAaUBiL4LGmiyrXyldjdONx1PHDt4UAOgiTVHDCKhYfDhpYKUwCh9LjpgHVc1HjkCHAvMmjXL9lpm2tvbnRNBCQ05IHPzJAKajIohoic6kldyeoFCiIJWfwIEQtK0mWsaBB1nQG6eRADLOg0EyGjGtAe6EBEIDYkwqub1LkR15oQGj6tkLtregKGS8LaUUtS3GLPsp2vMR/zDe0C/8UdLXhohQGsD0PPYtKa/vd5GeVOTmCdJVLeWs4nJDc9mJVxxLG8nTYND+3z3TmPF5x+8Yj0mOFfqNOhApiIHvyARu99m28Zeu4R5DsBZAFgtQuKlf4FS+nJ8ew0h5CoAP4YhIJgZC2A887s3gL0jRoxAVVWVILkNa/ifF33jmzj3hL4I1I0DJN/kPr1745hTzgJqxMcvv/wKPLx6ofBYsLwyme+Ia64xBl/rl0oH/QMHDABaREfcaRouv+JyNG18RHjsuIEDcfwVVwIrFyT3HaT9LOmO7dcH8U8WvnXuGahr/jJgExuBgOK4wcdDb0q9WVdceSXWbXjeki7B4MGDMejY04C4snfUqFEITP0oro8CrrryCiC1fha0QCBpHqiDYNSoUYhEIpg1axZOP30IsJvX8LMELUKDfQ9wxZVXYlBVBbRFW4A64AsnnQY0LhGmFfnSXXjBedCWLQNabS/DoYHiwgsvxISta4Rtg3VU59dpSKUdNWoUOv75pqT98LDnVfXuCTDuFD179QZ8iNVwzle/Bmzb4Kq/HTZsGOhm6/6rR1yD8hAvEAZ3PYSEkYYGiq9fdDG0zauE60IQUIwaNcr4MX0+wC9anTz2YeMabGhscBxDiXwa7BCNtfr1Nfqs4cOHIxSyt/BgaW5udk4EJTRkHZkVuLc8UpECnM4UOT+z9CetIG/eZOQ1apywfF/WdgvPlTd4s6ZBkC4gb2oadIt5klv/CRmVpAtdunV2SCPGvTSbqcrMkxKIOm8LzEju4lOPQUuD1RG6vqUTt05Yhq0HjR4/xgo2qyYhdMbtlmy/VDcVmP0gcOmvgaseTO4fjMPA6teAc2609RnxOhNtDjGXROKxza6AKl58kFpOB8CpgS1nCNTgHq2meDqbgQ1vG9utB4GKY8wXTG1naJ6U6ZCdyiQF7homwaKbmScRQp4FcC2AYZRSdsiZsAncZDplM5CcaOaI+zokbeUSbTgUCnn66ArLqQXiedgEQoh14fR1T1n3x59xgOk77RyhA8EgKNFwJtmNh0OThNeS2V0b+VL8OvgO1uunSoWOoM1kU0ADYIrmtA/HWtJxa+V0NoI4RCciyemw1AsZDFq/J+x3RyOJ/wyMZ5C6bsh0vrmPDO39DNqW6dD08wGi4XwiN0/VOKFB7tPwHW0JpurfRCAQNMoTL55m820MCr47QU3z3Dlp0KEFAiAgwu84+0xlK0KHQiF0uLyuBooe6MSvg++g3yF+oVbzNyK9bz2FFtAsZZRRFgqi06JpAGIICN5xVijQQbRA8nZfrG3Cr4LvMmWnyfN3N3XhJFNOofr1wBfOT7bxdHwa7BCNtRI5eO2/3KZVPg3Zhh8BCPa5w43JiJHEPl0fpMKdRTs9TFXAJsqBZZ2GuHkSM5i0M0+SahoyoBJd0IX2l+KYx6L7xkd4cNOxMR9DTeNUy/uPdkDXKYaPX5AUGAAgZnoFRYPli7c8bmwsHMftn1b+ADD1Lst+m2K5wpVPA7tKrMunRWDq4CVCAyGwLpwHF8KP3TtiCkPXGYnh7R0afvfe52jrimL1bnaKiDD/p3m9DGDryYdWlZgnmeO2l7BTNDF4FsD1AK6klO40JdkFYD8AcxjWLwEQz4ZkEVfRkw5vxyUNb1p2pxyh2UGMKX8mKp1OjetNL38A52jm25IohtwReqS2AncH38eLZeOl/Z3dd0gUclWowaXMQLjzqKNDK4ERutZstmfOm5t1pbonR+iAOUzlK9chsOx5nNowEzoF3i1/WFo+fkVouU/DM2XPJsvOlYHIh2FiH0Tquu/R499gjelVROsLsd/2vkQ+LnC7ngIBxd3B93FHcLqlLzfMqNlvq/d+1LAYSGy7O0OUR5dosUOzIzTTpieXPYaLtZTKgq3HPFEI10S43zh+Cw3CSeIsh1xVmoZ8kMZHXbZSJgD8OPgJ2qI9QPEFxxeQVUO6XegrgesVoYkgWoGt0GDVNCRmvtKlBzoB3WokqUmEBtGrytofuhIaTFFv2Pv15rLd6NVnB5o6+HseM9nKUsGsk8Y4UbIdWL9E5751BnDF/ziXzyWuhAZulVh3GOZJzMDXJvwjN7CA8fGzPLZD24HlL6R+m9+rfasNB/xBZ5scLzS8sbwWiw9qwMH9+Gj9AZRFW7Eh0VwIQUc4hqnr5M7pOLgJePU7wGX/DeB4ebo0kMWM56MnmQQIu8gwpcVzAG6G4T7QQggZFN/fRCntoJRSQshTAB4mhPx/9r473o6qXPt5Z/be5+SkAQFiIIRQpTcRGyhSJSper13xs9+rcr32jp/YPlQUUezlil3vlYsKBAWlSZFeAqEEEggJCQlJSD/n7D3zfn9MW3VmTdmHEznP73eSvaesWXtmzVrrXc/zvu+diHwa3gJgPwCvHuvKOhkNFiR9Ut5EPaCsvwhCe56E7Bh7SNS5tDL7bmGsi951NRmhUcMdym01CEOMcFtywBWRRMoRpRumW9ISxzZT9CQpBLZcQIt7gEEfPnlklZ15jeFp8qR8pMUl9fPs/hJ1oyeFIHiIk77mTLLF37A3LU8/q8e6Gw3AnpTTf0rHVjEasnfLJeypjSAZ7hreB+E3+gij6EmRaNZQD7e6J0UWGw0NrONPRE/attFERmhClpXQtIrxHO8+/LJzFsDFjVhqtN1yqehdaURz9KQ8R+hmkruJmESjxoRKBNbiLkd10DeK98ptNUScyLGWVO7H1+qrfz3lFQxJpwg9YQJ9wa0G8W9Bwrayd9LJEZqrGA3yqhyzud4EaEZDF74+UfjJCcBNPxQrlX3c+iTwoxcD3z86miwojs7Ln8za/kgv1FbfPn/xQqzbkmNUX/R+YPNqYP5H7MdUhJzEzcIuqMc/fYyG9yCKmHQVIilS8ve65ABmPheRn8I3ANwJ4HgAJzKzxWugfwhCxqaRHno1cmeIz9ouT2LQ2sW50W3m0OMILfWYjGHMpsxB2R5yNe9t15PUmSa9xDIjEISMEeTl8WEEYSjb/d0tUHseVcqj5aSQVmBZGp/m4jHjGNAONmNgND/DujhO+AgLmdeMaTAEDFEwaDCkVm/Ymlu+iKRsD2HKNZh+pzi+7eslYwxjd8GQBAB21oi6jzpVmQaTj4ENK9cPG48b7gXoBiGWrRMcjoT+M5ov5LBrkpSpGMU+DfWDcJhY+iYxYTT0HSZ5UnmjIWm4eY2KUfwCivGZuVcubLlzRuiS0ZNMSYESh+WqGMIIyLC6V4ppINFoKPfM1MQrRPbERyLURDgqPnbBXfrGgglJ0X1U99tXK4W7JDINlpVx9QBNT5uTaE59dl209OeWl/l40yq5rhpFLpclvzdUnLVamICc6N2CYzzDc6kIWZ7Exu169KSnhzyJmcnyd75y3JeZeTdmnszMz2fma8eynoksJAgZB5/5FyxfU04KCmQTErmtqkZDxDR8sHUBpv3oKOx257nW8q4Z+CDmrfiucd8lA5/Gaa2/pd/tyd1y6sssJSMEzEaDx/KKf8DASI7oIZInsbQKO+v852h1lPoWDnHhrUrCOInhYIj92R/bn8J32t/Urr3rkzfh9FtOttYtum72e1oU5N8jhBljGL+n1z5Uziv4//7xbqze6Lbgl9wzD5nRZXq24uR3Jj2JadiM9/p/REeJ3pTnh2YrT6sTh8pku5rR4JKnIcGbfnyjNsYTGCPdEG/80T9w9FeuxPUPxhJVJblbHntX9neYomGJKCtPMqLPPm0TRsNYImmMJR9qpPkvPoeZCxvuJDGcWq8c09AyxBh+dO0WrNksRxbKjAZxo91oaHv18zSomIQRgPXJtN1o0LfJ8iSX1Xd5sicOJmRgUwCTIRaV8cXWT/C/nf9rdITTYJOZpeyUHaf/6jac+I1rIm0nM/D4PWCXVVGB3RCNw7wO1iPZkLI6ZRLJEwtYjAYVNl0/h4o8hLVXsMpqV4Ifdc7BLzpfRh05nQjZCDNv1/wbnj5MwzaB5On0wqitFcVnz0NucreYaUjCaO624NuVryOiCtNACNFVmA5jAAllxT8IQ4zmMA2I+2xxQuVvfUJ7ZyX9PzPWbVYWxZQIYyq7eIp/c04d7PCFehQtrLURZP1Y3Cfd+/jmnDN0RNmd3cJjJvfME/o8M9Mgb5uMYXys/d/aceyol8+b+qp+e5XlSSWYBlsZw70ANz8cGW2/ufnJ1aDvAAAgAElEQVTRpILpMbb5gri/DP4Z5EkTPg19RlPypK2jUeeb10QDB3nSJBI60pJGgwnHfPVKvMd/FB8X+vyEzRAnk+tHsyxMKtpkcoRmp7wTNkTRk0wTbjYOwqbrqLRzMUSjQY5sYDMa1E4noX+TVb+jvQXFlzVN8kc3A997ATD3BWB+p/XUSxZEwWauvG8VTlr7G3hXfA6zwmMB/Jt+sBQ9SXSEzqBqmsUjNHmSRVZl8mkYRau4e5YmzoH8WVndVAcC+TlUG4Q8cCMrReLVZabBfAwDym/v70rTBNyRGNGuLK0IJ0dog5yxCViZhoKmpS4amOVJclsNQmCE29bOnhBlulYvrd5TOfwk65NjsQ+o4GNig2gQFk0K2+hlhld8H0o7vyIEOb7jLBgNySTbGHIVan9oaa8lkrvZVBFBKO+pLE9KfBpyz88YJdNxI4JPg/pcgMi3JwiR0zbL1d0Yjl5AM0zDhDxp24YxelJZowHYMlrcyQWhObOxiCkQ9JAlfRpUmKMTySHbEtz2iJ2CPc//OmjrWmnbQfQwvPsvrly3SRg2UqmeIHsSpUBmpqGkT4O0+iszDR4YPYO1onfODit1KkxGw70XAeuWALf/Mq25KWpGgnf/8jYEV0Q5Il7lXWU8ZsOwcB1FnvTd9rn4Xvsb+dFVQPKgmuOLQQamoZRcTXzHwkBxggw1w1G6N6wzES5InmUdWZ16vjgJk7NAK5+fJtGTthX4xHiBtwCfujAy+usYDR/+7zu1bQl61J91P9ukMbdlsy7B7JDJaBC2/eN7OG7hGejlJE+LpCiynAgAhiCPXy0lepLWZ4f9MRpe7Wd5KYoyQr/R/xtm/vnfo7E3fmdDeOiy/fer0KLQ5SCZhA5QFwff8AG8zvur8VzNaLAwYzZ2WK+jvX4cBtL+qixv6gjtXA/1OozepjX4UfvrONm7Kdur+DTkOcKXNRrM0bDEGkW/poUevtU+D6f55RK0RYX0N3rShNEwlkjlSSWNBmJs7SYNwf6KdAMufAGH0BzTkKz9qFdMomBIcbULXpb9HpFDDu7urbIc6YZJGNUkLkDiQBV9Zk9cpSuSJ5VlGljqIKyxzbXVsLwVcAsco2CpZXWVcHNF2SrvXSFk9RHuLW1di3n+TTjFvxk7wpQgJst7IBpSbBGlEnSfhlFuFU/kJQ9ihWkQDRRD9BFVD23EZWcA5x0Z5XwwoI7ESYTNR0SWJymfJ+RJ4w6/6pyFtbF0s2iF0YSkPd3zmD3pUkDuk81y17ZNGu3nEMLyPg2bV+GZq+ZjX2+5dlxWl2jipk7Gp5Ms62kp8iSdaRAXEhwWYyqgVeAI/en2rzF18SXAreenfWgIwtm91zpfg0qsRydyl1f712CXZZfiC/6PjZGx1PHJauQ6+zTYoS5uVvVpcHGE9qTr6GXMXfANnOjfih90BF8goZ34CBGG9idK0ufi31G0eJCU8TLvHzjVvwFfbP+0sEwNE/KkbR3NyJOcmIagmGkQnXtR0hFahU1ra2IajAnfBIx0m7WOO9QFuKf1FJJGURhwzfKkOiFXAZ9Eo8EMTevMjp23CJNDMYnhCc209CvOuwZl0PaFNQZLyFXz5CiRJ5HsF5OTp8GDGj2p5ZDcTXzXlPCKip5ZLctJnnT9edH/t/8SpidaN+KX6eo3LlkjbOcoKtT8j2DyrJcAmAQAuOC2ZVhy/134fnrghNEw3lCFaShyWAUakjM4XhsoyNMAdgq5alrMyUMiRVGZUjHnEKD63Cm5dcNQkSf1y2hw/G1b16bvaQAPPwxehid4Os7pfL/gxHKLE6YjzUxDqHy3XKMBpiEMZYOuar8ZOMiTPDCy5VbDwuAWw+JkCUdo+XcUo6h9JHWcSm4+K0ZMyJO2bZjpsfIviYvRwGGv3BDScw/dZob5agMGnwa/IGrA3SscctOXQAc9kC3kaiJP8tvSdhWigeU7xIIWEeVpkB2qTFA76yLdrvliJqYhu/dJmeq1Hnx8fUHBcm0G28KqpuLMmCCvUyQoTENeyFVD9KRC2Y/ENBTJk1Q6Xpb3aER2TgSbtIyaWcwTiHX7x+JMtscMYNFlwIL/wZzL3oFdEYXI/N5VD+GBlcKznDAaxh2qGQ36OVqP24ydari2ueCoadomlKyFXC12hC5GIhHxlevqTIPMJor3/NG1m7Bxq7BIZjEaDqCHMc/7R6n6iYjkSQ4QFjI4Fhzdz3OcrlEmh5HJsdbUFl19GlTZqA35RkMgXa9Kv0mC3DdvzpNnnHQQ4Bmr/p5tSKdnsnQqYLbmeXCebzHj+d7d2Inyw/cm96WWQ3SffdommIaxRMo0lHuosiN0TjMNRsu9gDWZBpY6vQwDcYQmyWgoqNeWUT1Wfh0MoGvs4MRMkiwxDfozcV41SiFLSVTHPBM0R2iNacjKCNjy7INRfZvktBxv0ijoACiIjy5ioG1hGoTPRGyQjsZMh6cwToFoQGStJXKENkVPslY1KUWon2BI3T8f2G434bBinwatZBYHCPNz6Ic86T/8CzGT1uEzvbdFpQuTnQO9h7E83CmukWz0TGB8oY5PQy6UZx2SX3ol3/3ahLys7JnvQYZCR2inukSO0CorO01ZjW0p77D4G47/2hX4RWc9npN0YRaWc/5AvSSZ0ZjhMpXktA4he8kWJ3hUHCUxgYmJKsrTAOS018aYBrtsyLV8F0fovDwKJ/q3qi6EEVSfhgZCrj5/02V4e+er1v0JMqOhBoP4dGIaiOiFRHQRET1GRExE/1Li3BcQUY+I7lC2nxmXJf7d13ztLTAJkfskT+KcuPfGcmv6NNhe2synIYNL1IBBvzkLuY2e0elIZBrgiUyDjtJGg+Kc6sI06D4N8jXFgbJrs/FNq2YkMg1meVKRZCy32xIHXWHybxpsVmwYxtI7r8KsFVcoTINgEEkdM5mjJ4nvUk83lELxvRKdwy/6T+AXr8y+q47DKJYnubTMxowG4fNH2v+DN7f+igPokdwwsRNGw/iGqf+7Odw39xwXeZLaKLghHwejf5NnSLAoVUV3hG4bHKHLGzUcR9zJb9ctZZFGfT+kyXKf5EmlQusK8iSgYEFQAKGe0WC6inpvm5An2Wqo5mmoxjRAcFCuP6mXYJAnUc2Fohds+rPTccm9qMc0PI2MBgCTEWXwPL3MSUS0HYCfA/ib5ZB7AMwS/o6uUUdnrFg/jJWSAihuYBUiNyQhSXObaNgryTTUdIROHZFkmORJRRNUBsFrUJ47h1bhS+3/0rZ7wsoBe9kAa4oW4RS5yIJQYRpsHZburGeniUdLEYNiIraojPwQhcV1k8L8sXnybzIa/vuWRzHnwlfguTe9D3uT4PAoDNxSZCkCPJMjtLRBT5b1mxsfwd3LY5lOXq4JA9PgqZNuZb8kZ7Ik4GuKKTPJsKZga7Rd2KdmHM8KmDAaxhvU9+KM7tvw8a4hrLEAozxJk0n2x2gwLpiQl0+Ss+4IbepDXSUu6fGpPKnIaBDlSbLR4KvT55pGw73hHNwb7qZtj+6bC0PECEM5Cao4Xt4e7m09tYzRYPa9KmYabAtm7vIkO8IgkGVD5C63ysoXmQY7rAsreSiRp8G13lMCezADEWmY5QmmwQ3MfCkzn8HMF5Y89fsAfg3gBsv+HjOvFP6eqFdTN5x35UP40X3CLU4epmO0mwQEoNszS4FEhCV9GqgheZLGNMRGA0mddnGosXaNBEgqXuib8xsk8qQwZKzdmtXPNLjVkSdFTENxyFZ1INTlSdn+vLCEGgSmwY9/mzpYFIV/U9uStHZkCV9YNLDPoczxTJQ1iSuUBH1w0pK7GYyGXhDi+7+fDyz9B3p5bduYp0GUNuinyEYDwSRyTZ5xXb7BNEhlmWbNK3Sy0dAcYzeBJsBaYswAXrrCbIPLKqbaXzRmNJj6YvIx9Nh1OMxbbKuM5ght6lfLO0IDi1dvdjAa5Ahi4qq/j1B6XzZsqefPF8bchYqZ9KQciccKRi9maDOmIUMvp214zpyEefJpen5qW5tGhoRzy2/DDuvucrpufkZoEwtUvs9ySe7mgfE+/39xcedTmEL5z/xtKz4P/Pr10nj2o845mLvsIqtPgwfGiedcjcee3Iq8nn9y6GY0fKF9Pvai5alkrRIaDCdswrgyGqqAiN4GYE8An8s5bJ9Y8rSYiH5FRG4eRzXhkTLxSjr4sjIi6A5mxuOCbjmmIahrNJjpwcHYp0Fd6cktC4RWSWfjKkhWDi69eyW2Cold3tv6k3ZsUQhSDRziwVWbMH/BCoShvDJmZxry5QZiGVZ5kgliDoq4E9F1q+bOJWRLMhxJwWOORGJ23rS0A6FzU1cofdajJ0m3pqsPAATg2+veDfzXyfjab/Lo4IKM0IaVGvn4/vo0mCLRthFoTIPVyW+CaRhXMLWLaAqbP/Uz9Rl62Ejdp6FvIA97zX+DfT+HWi4ao09DSUYuuQ9FY5van8nSFPlu/+n2R0vVQUWolVgSzAiCLOQqIC8I9tje17v0M1t4AABwQfBCp+qoDNYUGFQIv3+7U1lAwaq+kkPDU6VjTuVn/WS+T0OID7d/j4O8hzGzwAn5yE1XAg9cqi3qvvCeM3LKZyxatQlfv+wBe8HMzkwDAJzd/sG4Zhq2aUdoItoHwJcBHMPMPTKbgzcCeCuA+xFJkz4L4O9EdBAzG0P2ENEAgAFh01QA6Ha76HZLsASKM1YQBgi7XbSCkXKMAFjLmGxEUJZpqLfa0usmjIKMxKdB/O3F6dPJvLrVMAiM0W4XK9dvwQEFx7YKIj6pYAAnnHM1AMZXBn6K17WulK5rgtpZ9npy+3I1GtR2SUGYHR13ghoFTboMB8hkUGqdmcPsOqMjqQt1KPgXFK8GCoZCdyQtb+vIKLbPrqRli+7CR7fXQ7fbxc0Pr8OaxUvxcqVssb57hEutSyLd7igCZUVUfA69Xldbyxsd7WIw/hyEUUZWtfiUaQjZqZ9QXdB7QQ/c7SJI33Xx/ekhCEL0gl76XG3yJOYgfTe7yv+uKHv8BMxowdwn21aqRZgnUmPj02CE34IhmrZ4dac8DVXkSQC06EkqVEdo8f75kANtBF1D8IgSCOBht+kDQOWgf4ygF90bk9GQ90td5EmnjX4Sk2YfgjmPXeJUGz2ykOFBjxrYBwumDrYAy5pkFCtK7rvKLrgQiXka7GhqIcd2jaT8KPeEBWFPls4VYDo2F7KQ+ejv4us2azQQkY9IkvRZZraaecx8qfD1LiK6EcAjAF4L4CeW0z6JyLiQcNlll2FoaMi5jsuXedLL+NDiJbh3/ny8ZHiLZJEUQWyMecTklo3rS63i8Gg9o2HRokUA9tMGt0SeJG4vMhoYAHrdvGA+jYDAuPKqq3H/k4QXF5hYZSOeJBO1A+kRvI7+ql3X5Rq3334bIuJM35+XNXT+/PnS91lP3oaj4s9rHl8BYLoz02DznVi/fn16ne02L8aL4u1LHnoQsy2/R4UoGbjjtluw+eFo8F43Eln2ANAdGcHqVY8rZxJuv/0O+Mtux/tvaGE/WoqXD6hHZL9vcg4V/ferr8KKlXMgWhXivbnllpsRhkciefN2o8dxxSUXIInKcPc9C7HbunXYQath9NuWP7Yc8+cXr2S+Qvl+x+13YPkjk7B+ow+ApDq1EGDxkiW4e/WdOCzeJt5L8b3ftHEDrlDaw+WXl8ssumVLjTjhE0jxDFqHl3g3adsZlDJ6Nhzt34PnBffghvBAnOpdh8U8Sztm1+EH8a9elm8l7FOGaABAkUHSJ0foJNx14YKEeC0l5Koq6dl188JSdVDB8OD59ZxVM6ZBlyfljfMuIVcDeGhNmursUKuODUZ/vpb7rGU6bcGx/s3GfaocqZyPRnaOa56GfiJhaAZavr0eef51BrTRKyFAM2CCabBiKoAjARxORN+Ot3kAiIh6AE5i5ivUk5j5SSJ6AIDd0wg4C8A5yrWWnXTSSZg2bZpzBW/80z1YLkx+gkk7YN68eWgtBMooX1xfqMmTOqASKx8trrfastdeewGP2B2hJQqyQHoUwpPzAPQJBMYxx7wQ/uK14MfyX8yyPg2tVlT/IQO1mzzD7Sa18eTWrBNRDa7DDjscuH+9cX8e0zBv3jzpe++eHrAk+jxzxx2AJwPNoLT9vuQ6at2mT5uKQ+Lr0LKbgdhU32P3OcAT+WWarnnYIQdj+iFReUvXbgHicbzd6WDnHWcA0rybceihh2LeYbvg/TdcZpxAiE9zsolej3HMMcfgt38dBtasTreJbfXIZx0Buo8ABmZhDf4+8MG0bgBw0EEHgRbcAyjz6qSMXXfZFfPmHWy9forb5a+HHX4YDj1wHs594Fpg6xbpvW8jwDPmzsVBzzgovS+2sIVTJg+l7aHb7eLyyy/HiSeeiHbb3SLfsMGdTp+AHT9qfx37e0u17SG7yVt+0/kSXjvyGXyr8x0AwLd6ckDBXUaXSAnB+mo0eEVGg54Iy5zcrdqkpkjC0laiJ8k+ZbJPwwlP/LxSHRIQeYVMUS6YEQQJ06BHTypiGoomwwE8+J67hEq9tx0yTHT9jlNZAHBs9+/WfZ5iJGiRrRwRlHSErgMiMi7IJr9jsJ3TFkwh0XPQpmBcR0/alo2GDQDUkfm9AI4D8GqkUyYZRDQFwF4AfmErmJlHIJBrieyp3W6XGnjbivVJFJVR1vIUu5C8V+BjI9/CAuxasuzqIM+sfY/yNMgdgwvTYJPLNAkPDL/Vgu8Vv5TldZbR/TCF3Us6r52nDShGgyIZ8j1AmSwmyDMa1Hb51csfwKfjz2cvewO63ntxU7ifdIxt5W40pntMcTfS6wihrmQZgP6cfct+3xPLk7Nzq0YmgUGenx6f14EDwBDZ/XXavqc5MovltRb8Dje1r8I7Rz+CWbRWPR1+q4WtPY7zMWdInqXnEeD5+PHfl+AFe8/AIbO3s9ZFRMtvAe12WhOVaSDy4vaR7A+Nn4lDrT2U7rtKHDsBO0wGA5D4NLhNDA4RHFeLpoB99WkoBGs+DQMNOEInZReymGLfYwhE0WTu7P12mY4tw3UW3RhBKDtCy3vttVUn3SaE8OAROf/qppmGPBBYSpZaSZ4EUZ6U79PQFMwLVYnR4OfKk8qghV49edLTKXoSEU0hosOIKGHg94i/z4n3n0VEPwcAZg6Z+W7xD8AqAMPx983xOV8johcR0Vwiej6ACwEEAH7T79/jEclGQ/IwSxoNrp3dAeEizKCiLL/N4Z5l6wDoTlQ+RR22OPErjp7kjYkjNMEcncaEqnkazA6M0TbN4VeNnqQkvhkUJr/dEtGTVq7Logv5CHFe59vataxMQyyD0n+H6GwbGD+3DX4gvsUIsjlCMwBSfA5IvrplApEdMQU50jsO8x2h77sYO9AmfLfzTUsBhIUr9JV4ceL+ixsewVf+fB9O/fZ19npYkNwLiWmIpRerNmQMykTI1XGGElGrQrhP6KaSu1Ssr0ZDQbuioItQlScZHaHLGw0uq9Fq9CSxr4miJzU3vgx2OmBL6GUnMKf3yhRyNa9ttC1+MiIipgEIHKPwqH39kMkhoQTTkAf1OVSJniQnXbOfO2CQx1UBWYzW5DkM5KkkSs73BtCtlAwyAYd6SPEmMa6MBkRyo9uREffnxJ8/H3+fBaBs5KPZiAyE+wH8N4A1AJ7LzKtzz2oAet4BjmLmV3QEi0ooirgxdrh20RPWaw6gqzANRdGTAH8MKu9RmKQtcIjGUe7FTUKSms5LHabUUJ8aKyGvIg0IDmm5dHh3GPjtm4AbfxBfz1AH1bgrZBpyjIbCkKviSlJo2A8lT4NqJKjf5TCp5sRXGfLkSVH0pOJ7YdPa2ij/xHhmAPetrC7vGekF6fUTtOLoSfMXPJZum4ieNM5Q4r6HDiFXE0wVDGBK+xFzG6w1kS1Cwe+bueIKnLLwo9K2puRJhLLRk1j6XiaLsluFPNQdbcPA7gidhzPav8Ie4SP5ZdeUJ328/Vv9oBV36NsqQH0OLsyJCS6O0NcOvL90uSZMwoh5TI23tfOSTJWUJ02jrfhO51ulzhGxdWQUq+q5q+ZiXBkNzHwVM5Ph763x/rcy87E555/JzIcp217PzLsw8wAzz46/P9TfXxJBYxrApXM0pOcZPptQPrdAhrvDuaWOz+qi12kAXWliNw35kRdCeGMWPSnpbIpWN0pb+5znsOfGNEDJlik+T7W20sRhydXAfRcDl34MCHqWSbCdaRD3JTIotRsMQ8Yv/vEIHl27xco0JGV6FqNB+j3WkKusRVihaLPwXX924m+eTDlGgyG5W5lByxb+WI5gZDn5yaXAplXmA+Jtw3EoYM1oABAKxlVben6i0dB/xm4CBpSIj14mZKdoAGfJn8xD91PJNADA3muvkr6b+nSvItMgMparWJf8tRR5ksh6eggbNhrMuVqcwbo86VlzZ2S76xokoFieVM0Rup9Qx6Yqz4ZgZmT7hSm0xbjwmTylbphj+JSWo9eDB240Ua5e/gT6hshoEMBc2urUyix4QUrnFhDwAM8uPkhA8pKYXpZBjMorzAXSo8inYSzkSWWMhmrJ3cy5CiKo0ihTcjdVy66WYfzemZx+HF12W65fRXbtrGyR0RhJjQa5jEWPb8Rn/nA3XnLuNWmW6ehHiUyDvkpu82kQJyES08DpP0LdQ+neGdkcof2YnNGz8guSuxVg1HJoYRlb1wHnHgx8bR/zBDO+HwnTID6vKE9DRD0n+Ez7l3gO3asdO8E0PEUowSC75GlIMEWQJ/nxRNV2bgi/MCpTZZTuni3GdSWmgVNW9j2j78cjvLN2TFvpW1RH6MaZhlqsDiOI3/PD5+yAJWfNw9dec6iwt94zTByhXdmsMTUalLHJhUVSkWQJTz73G1OwNZdp6OWFxO/pY9E3e6/EY6zG32sGE0bDNowouZvCElSwOsXnX2g01NDw3RHuhd/0Xux8fJZ0x7DqS0GpjiiEV0vH54oouVv0WfXFUFGVaTB3LrGsQFniNkmAXB3IJbmRMAm+5qq/GO+9uk1ciRONhp6FaUgmAZtHA2mCNDqaGcKFTIM4YAisW1djGvSBRbx1xjYn3KvJOY7Q4ILkblktjANSN2DjoF7Y3tcIBKdx4hTpdLsmnwb0EDJr8rbvxtlnJ+RJ4wAlHB4Z7qvAojyp7edLWQJq5WYTroWS7cqam6aCIzQh649DeBhmXV/vS/5ULMXG/2r7h9iDVpS+rr1Cfn2fhnjhYKDdjoOtuEVPckEQO0K7GKbHerfjg63f17yiO9TxkSr4m/gIcdLj/4VjvLvGRJI9BVtz5wO9PKbBkIg0YL9ehKQcEMK+Gg3bcvSkcQ/PUycdmdEQMhWGIU2gZrbMQx2mgUH4ZO9deJV/DToOic3ymAa/wspOERvRBFyjLlSpT3J0XpSFQqZB0fa3bBIU7eLZeYseWwtTLAe9s87KS7J4A9kqpno9qURhgrTyyc2Ym9ZXl9bYmIa7lq7Fz+6/Cd/Z9w7svEbO1qkaDZHHiGCIGJiUtiszxKFUVlSe+7MetciTCtu7KGcwTTA5xHBXjviSwDfIk4DsWckG5ITR8JSgT/KkKaQYDUG+PCnaV12makXJdmWbCFZ1hE76kQAehqEbDW1FniTKSY7y7i99zfwKeai35pplhB7oxNMwEo2GmvIkdvdpOL9zdq1rlYU+DpVnOl7uX4+XrLkWL+kAD4a7NFg7MyZj2OKrGG0b7eW8G11dmt1FK2IE+zC5rxkMuBATRkMf4SltgjhM5UldtKTV3TyIk5Gil6tTY7AwRXHIr5f8v4iyqeFbCPACvtX5+KrwBFqzcUo2xxhJJnVqSEK9I1KYBlc/D2FA73jmaBTqNvH7AOlhYHWjQdDsh1kED7GOPgK82b8MazjLZyLLrbJjL13wGO4KHsTQIx8V0tnF19J8GlhhGvT74ionG+n2NAmHbcJvatsjDj4NZohGg+HdV4wGNU8DR1aDscwJpmEcoJQjdAmjQWAafC8xGuzypB58FKRuroammIYKjLIYPSmAZ0xAKb7/jIYdn7UK6WGbS0FkGjpJeGPZaDhl5CxcOvDJSsUH8OA7hVwde/8nNbN3FenY7rQq/TwW8qQOusbxJbm7vTC0NwcD07ARk2pmfbaj3/KkCaOhjyAyyBviyUIXfgmjIUM/fRrMV7QjWe01TpKdh8QIZ7R+iVZ3LCY72eSzyuBVVDaQzzSo8iS9DvKEX40IYr90Vs6Ab4vgpDugJRCZBi9lC+wY7fbSrObitQ71FuMdrUulY2VH6J7wOcDBQgz67Lcw1N9K6Xb9mll5bs/zK3++F2F7f2sdi2BjGsQyjEeIo4pJpsghhoUVK9l4DBC1XYWZmjAaxg9KyJPK5GnYm7KIWS1KzrcYDeQelak8XCdnDICsY5VfSZ7EkjzJNHaK7//waK+/On3Pr+3TEAaZPAmAwjTAmAHcFQE8eJ45B4SI/Chz/YE+DpVP7rYLPZF+Fpm4fqFDtuAi8WJgkCdP0kMmb+JJfZMneVQ+hG2p8vtW8gTga0ZDJk/KS9Slw8w0mEiouvIk+Wr5aJRpGIPISUDs0xD2l2kwT15jeVKB0cChHjUnQa4RJkyoO16YG/bV9F0chNMkZTnypNFudrx4rVm0Jve6YtIgDyEOooe14wFd9ywyDQMYxRmtX2rntBzb/ozHrsQH1nweOyALi2q6X9H7oLcRu9EQbbcHLyo2GkYKmAa1/aTypAmj4alHKXmSu9Eg5buJT7GuIDOXHFtKwLFd+emig+09qZqnIe5DQZhiiI4m9pVLVm9CX1fRG2Aakuhxg4k8SWEa6hh/SXK3ojY2g8Y+87tJJlt2LN5FSLo5k57MObIZtNEzSmKTeo/mOUKP6vKkTeif0QD0V+o9YTT0EUXyJFdIZQgvV2hItlLHEdomT7ojVMUj2Rny/xmq+DSMBcTJZ/P1S2Q99s5FlyepdZDvmziZdfVp6Hj5ztjZdzPTYPNVEb+LRgMJK6xG/+dmZg8AACAASURBVBahsxWNWh8h5gg0c/pTlN+TlJvkVvg3/2Ls6y3Xr+NopJ7e+hOOGr4en25nhkeZQWu0V1F2ITENhihqzGm4VbVOrdgRWvV5MfqfTBgNTw1KRU8idOFjA00rPliA7yXRk8xDN3GIDTxUqkxnOLarorDfdfM0BPAw2ZC80Vf8v/rtIFsvJwan/kmDHZ1pQG2jgeB7xXdhBsobDaNcL6yv2k/b8uGMJ7TQs4RcjY2GPJ8GMQBGjI081EdG0Ozz11jZfSt5AvA8daVSZBrcXzxbnobA040GY/p3R2QmgNzR3BAemFsvszypvxRZVXiIJl7EQfPyJI4idnyhfb62y+YIXSRPcneEFrIye7bslTotnED2aUiOs1+v283aGRfIMsTf2CHRaAgwZIlylBc96ZneMuM5zo7QMV7lX4udEWc1t/o0GJiGXmg8upRPg1WeZGMaelHEZqX9JO+qzDSMv/fuaYFS8qQoWs5Sf06pSxTJk8RFh+U8w3JMRTi2q2RhwNa/Vs/TkMmTTLIaNW9J8/JTvVaVITANA51kHJflSXXcWV2jJ1VhGkxO6GVgkicVRTJ8qtFGz9ienIyGlQu0TSrTYF+YrQa/j2PAhNHQR3hEeKv/l/Q7MTKjgcswDaI8KWucodfWjm1CnqR2NCans6gudplPlTBqZXFp8OzS5xCA7f/0ZrzyuldgsA/Ogq/zr8I00jWMKbWu3BJtch+GgMVoyL2fwoSlQ2ajwZVpyByhZYjtsNvNjucCWYZYF1EGdZJ/K17m/8NwBhuZhsTgsrVx3yHil4qbBk+P22oZnwbzdcQypnafwMEU+2ssvgq4/1JIRpiRabBHT2qjZ2RgMp8GYfsE0/DUoKQ8CUBpo+Hlmy+IzzdPBreOZEk1A254eC8tTzKjcp6GhGlgzyhPEmVcfWcamGv7NCT95qR+RE+CW56GHSoYDSO1jQadwR6PC4wiOsj3aYjkSZbf8NDftE0bMAmB8IyDEovILigTDbB02X0reQLYeeNCvKZ1TfqdUF+eJL5cIfXHaFA7rJ6FjiwKudrvlZ5v9f619DmEEPtvvAHTti6zrnJXB2NnWme5bjyQqz4NGo0o331Xo0GcuLc98yqbqbNOIPs0hNL/2fEZRKZBzgid74Atts/nePdpxwIAsSlPgzyBNqFqNvSTvVuM95Zhnvh0ezajISpj0apN+OwD/4qLBs7AM2kp8PNXAL95PbDmwexgizxpRFqxEpmgXuzToBgNnBgN4sYJo+EpQYn7nrzlK7xnVLuUZejeNDyaGQ2ND++uTIOe4FFE+aSZUfsWQ65+v/fyguP7LHlpdWoaDQBzIk8yMQ3NJHcr0s1Phb7AVYQR6POOMlAn3z71f65QF22L0ZD6NPTKBX5RHaF7DRsNuuy5OUwYDX3EwY/9TtnCafSkHnw8EupZLU2QmAbBggwaZxrMsBk4RY7Q/dYpVulY+7miQcyFBpZeH3USKG+TO6ocuVAvm/Tb5EnqYO2XZBrE64s+DeIK6wD0ybB4HWf5nDIBO8x7CIcu/iEQdBs3Gub5N5ZqFzajIXnG967IVu+e5S3KDnjoiuyzMckjS47QsgN5FwzWHKE5PXaCaXjKMXUWHn7RuU6HJiF/q04WbFMUUerRT0fLPLRSeVJF3x8DVHnST4OT8dKRL+Hg4R/jToO0w2X8GeE2ftw7pXRdAAAz9q7lCB3EElkAGBowR08SsZxn4PLgCOfyE0foojGyyni4lQeKD8q9Zn1H6LFGZDSYQq5G9e7mOUIbsAlD2G3GlPR706ygmnW7SUwYDX3E6umHyhs482kYRQtv7X4cfwqeV6pMSYZgWOlwScpmgy16ks3/It+nof/yJJfSl4Qzpe/9zTrNaTZlFQTGM2kpjvXuwCRBj6vVhxWmgdyYhtFRIZQphUZ6UvRbUMsbJJPRYL/e1hHBcVqQRqnXAOTf6GLURjIcuR1Poy149pLvATf9UPKLEFHWpyFBB72SPg3F8iQjHr0p+2zN02B2hO6gF6m2LPKkiehJ4wADU7B+9vFOhyaT/sc2GBinEuerEGPeuxgNi8JdK10/D7vQGhxMi50XStzAKSsbxB5z9/Ae2Igh3BPO1Y52YRru4j2wCZMq1AXAjvvWYhoeW7clfWcHLXkaRKzm6VjK8liWh4hpKJ6MVlnY29qwPMlHiNm0ulaZ/YZNniQzDW73cpjbUZ6uTrbo2zTT0M8xYMJo6CMemP0q6TuBpZCrS3gW/rP7Pqzm6bnl2JK79c/XRZEn5UyExf9FjBemQWVJOv1IehSDwDkGFvCXgU/g/M5Xce/g23FgHGpUpRE7m5ZJ901cPZ/RGoHNVBoVmAabk/egwgLYQ66aQyaK34dHhbI4n2mQ5EmORq2a3C3FqnutbEUV2QMQ1c8eclVHt9sz7jMn1BPKfXJp9tkgT+IwwAd+d0f6XfVlCjmLIJVgInrS+ELPcUhNJvTlRA36+SrEABQu8iSbv1od/O/Ambho4AzsR0uN+6vlaYDENIiw/c6i8WeU2wirrvDOPAh1HKH/vmh1+px8Px4zcpgG2zYbkuRuRe2ryi/YinpMg7oK7iPEzztfqVVmv2GTJyVPZaTnvkiaGKpE2VyhaSlhP+deE0ZDH+F58u2NjIZosiBalkUdd5nkbnVgc4S2WcFRw7ToVqn/TIPLgKsaDZOo2sqeK2z3Sr0X72n9Kd4ud0R7Xf8xHOQ9nH4XV+Z3DFfjm+3vAACe6y2UzuuOio7J5kmwajSIrJUx5Kpye8WvW0fM8iSTc3klBiCeHPe0QZ2t+RiqskhiOEf5Sub2NWJhGjrUww/a5+Df/IvMFxIdZXt6O+wpDtYiW9RCAIbejiaSu40vqIEOrMfFQ29V7TqzjWnI/JlcmIbRmvr0PBzpPWDcXoVpEJlrtd83TbhckouOolXaaFvLUzB89CeAmQfUYhqkPEZpOfk+DWXkZgwPnkPI1SqTyy19kCeNd9iMhuTujga6X8YmHjSWlcz3yMvmCraF2aqYMBq2UXjqrEuUJwnRk4ojKclRIUzbm4Ta0diMmlO8m3DHwL/hba2/aPs8mCUyTcJlwFUHRdcs3FWRb2BlsDkbA8CnhcRlqpznFf71AIDfdr4obe8KPgYcmsPDmZiGt/p/xpmt8zFglCfZO/eRUbM8Sb0GUIEBYE6ZBlNUCRvTUNWfh2Cnlk3b9954Iw6mJdr2ed6NONm/BZ9q/8Z8IXGF1cA0BFr0nezaPsIoYIvFaNDkSRsfB64+G9i40lyXCfQFriuGyWS1qt+BbbIrRq0LHPrHfsaKty4oVXSEFvM0iDDdQ9tCgIhRtEsbbSeNnI3u0R+JL1L93kX1i++PpzMNpth1ZQ0c3yG5WxXUbTOaI/Q4d4IGYqPB4CdwxJwoz0o30MeQL/XeZCwrfSZe/5iGfobd7lPqyAkAUXI3EYTMEVpcAS/Ss32r8x1cOXw49qOl8qS34YZhW72yOffO8ew6xKpOqU1DNXhM8pkmYXMaN+k4xf9FiInLXB2Hez0xb0JgZhoUfwOfQpzZ/jkA4K5wD6Gu+SETAWBkVJZDJTD5NFTK9h2vlvfgyWQ454VcrcM02IwGHc/efI1xx1RDqF0rA2DwaQiFTNNzZwwhXCvLk9hQHyPTAAC/fQOw/Fb4914EzPqw4VdMoB8obzRUlSeZz9vfe1Q4prguVa/vAlvJO1RIKHa4twg7xuFB1XtsuudzvccLyxxFq7TREIKyxcBaRkM+02A7qww8h5Crn2j/tlSZQH0He9WY2xaMhtP4Irym/Wdte5I3xeTTMMJmFi/xMyGh/bjKGl1hSjDbFCaMhj7CU60GNsuTXMKvLhh8p7aN66SxN4CV/xOUCQ+bYCw6AiemQWFxBmpkzHaBzcBS72pRAqQErsZXKCaWshkNmjwpq9O+lCVMSxiiPJ+GQIz+I0yGG2EakIVc1ZkGtjpCVzVUvZzwwGWSDplYCStNbIieFApMQ8v30BV9WygEs+4nZGQaAGD5rdH2lXcCs5yqP4EGEDJhDU/FDNqYe1wSMrVpn4ayx/Qzm0GTEonjvdvSzyt5B2lf1Xs4gk7pc0WjoU5GaNloMDEN5muXgUtytyqouyqeF/p7vGElb49nxGHUzQti0f+jBp8GWz6LZO5HfjY3aTpPwzYlT6IIxxHRKUQ0tenytyWoNkMUcjWaGIiWZZns0HJxDTMNFsq8Sv3qhH51RRVH6H7Ddq/UDuU4/w4c491VaFy1HY2cgfWCXCa0OULLeSnk6EkujtAZeoKuf/NwVq7JaKjm02CWIgB29qWq0UBoJhlOKUPZIE9KcjC0fYJH8vPxEnkSqUZDsn/8DrxPJ4TMeN7It3FjuF/+cTDnxHGFy3n9lR65I7Qw2GWQ5H/5be9YbFYiHlVd+R7m8vKkEJTO7anGoh2J77ej8VHWAGg5+DRUQV1DZFuQJz3B03DI8A/x095Lco/z40fXDfSwsTZZd9pe/cyoaJppGLfRk4joS0R0pfCdAFwG4HIAlwBYQER71avitgvdpyFMjQbRsmzayqwKe8jV8co0FKOfjn4m2Dppk+X/w/Y5hZM91wn3rvf/LKsDm5kGlWWxPaO8jNCTsRUAEAZCiFeI0ZNMIVcraJhZN64BYNWGYasjdNWQq2KYSq0eJSbj5szoFuQwDS0virGuRtFi6ExDNmEan0YDEe1BRNZMXET0ciKaO3Y16i8CZoyijc0WJ8gEqTypYvQel4mbi9HQ57zJ8TXqI5EebsSQtq+qcVSFaQAIfrIaWJNpSOWUSTlSeTqjWPZZeQ7J3arg6SBPYhA2YErh3KflRc9p00jPYDRY5Enx/ZOYBqtCoRrGM9PwKgBC8HG8GsDxAM4A8DIAPoAza15jm4VmNICBWEYidnTVLff+yJPUcqsYDXNoVe36FMGl86rM4lSEVeZieIlDUKE8qRJjY5EnqZN3W9lJ57c7ybrg53oLcc/gO/CJ1q8lKY14LdOKvSlLdD4yeZL6jK9ZtLpxR2igmYHL9CzLyZOi81u+7qeQMg3aZCLZPz6NBgBfA/CfOftPB/DlMapL35Ek3yvq05P+v5/yJJdJZhMsgA1l8kUUIYsIZYoqVO03DKNdT55Ua/wVFgCSKIs5zAWj/O9s+8U+DVVQ93mqfW0/9fdVkdzrosiWLeGZqb/Dxh6EqdHQP6aB+ugIXbemuwJ4UPj+rwAWMvNZzDwfwPcAHFvzGtssNHkScxpBRXzxqr/YjJ8c8mt8tvsWXBi8oGIZYmnmzrBK4pFP2iLI1IAaftONaRhbeVLR6r2IAF7hZLWS5CY0a/TVybttkk2xxv/8zlel7QlT8e7WxQiF8KBFhk/56ElZB6y2vRO8WzGFhk1nVQ6na0tEaAsMYC9HL8M6mc+VJ+lMQxY9Salj3HdUS5g1JngeIubZhr8BOGaM6tJ3JCFXpw7mM5y28NaucJInObAYY8U0HD9ydq0ykn6SDWNldXlSx1heHqJMy9Fn8uowDcJY4egI7fKs7g7n4i2jHwcAbD9UhUmRIQbISOrwdIielLSpogXTSe3sXtjCYetlR9tbHdFoaHqeMk7lSQB6QBTcJJYmHQ9AdDF/HMCONa+xzcIsT4omXqLDbJ2Oe/WkvfCz4GQMc70sjRHM9eg2TJ1VhdpZOfk0FIazbRa2CD5mpsFLs5zaYJrYF07CLfIkVdZjS3TngQuT4AWC0XCIp4cfla9b3adBlW9sR5vLl1UAl/CMLjAbambDLDTkacjkSQTPkwehFkKjPCn51v+pX2VsDyDPK3gTgBljVJe+I2EaTE6yI0JflOVpeKrlSf1DUkOGh4d4V9wX7la5rKTPM4WRdTGOTBhBu/Tvj3wakjrU8GmAoIEnF6aBnBYxfhmcgKvDQwEAfgM+DX8LjpC+97xObaZB7cOqRr1zxUNh+UgQyftVNPcZbGX319VoSPtsL1tYaDy52zhmGu4GcBoRbQ/gbYg6/0uE/bsDeKLmNbZZaNGTwECYOHhm+6p2elFHksTkrt/okmamJ3cbH0G2dN8Ph+hJY+zTYNPVm4yGAF4ln4ZJikOzBg6MxohqSNiiEHngwol+sirugipGAwkhV/uNvOzlZbShJkPN1h7O/cs92rY8piHKCJ2Tp6HPA28NLAWQR4MeA2BZzv5tCgErE0EBazAt/Zz0sVX77aaMBjUjvYitjSxEZePKZJgZQhck9TRNWKvew2GUnwBLE8GaPg2pnCXNDFw/epJ4DKH+vEBleke8odqStrFmGqrIq7NQ1vkQp3jqopHdaIi3+300GsaxT8PnARyGyDD4EYDrmPlKYf9LAdxc8xrbLIzypNSnIXsZq1OIjDAepJqIEmSTJ411BKKbp59k3K52YC7G9FjX3cYC2JiGog7T5CNQNPiSJSO0ylpY8x0gdDAa3A2BFpUzGhiw+jT0A0R65It0X4lyTEyDzTAzRcXihGnwKWY/ZHlSEOr17Gec/YbwGwBvIKL/JCEwORH5RPR+AK8D8OunrHYNI8hJCb2GdaOhnz4NLhORmbTWum8L6mX+TZCMJ0MWWaELkn7VdL+qsjXDFRyhmzMaDPKk3ORu5fxYfvb2o0ANhFxVx9zf7XlWfXmSssDR75xOVeTViWFkCrMqIo+htjFDmdHQ0bc1hnEqT2LmywEcAeBDAN4OIJ3txezDNQC+Veca2zKM8qQ0063oCF3xMXCmoW2GabBFT2penvT74IVYxdvpO07+f9gwsIvxHLUe4zHkqq0DNK3oRUxD+Zd7Cm3NP8AiT9KYBqtPAxdGIqISId3KOkKLq3BVOvyykGKmC2DUj55kj/Skbw+D2BHaiyQQsk9DgF6ot3hrnobxg7MAXAngXAAriOgaIroGwGMAvgHgagBfegrr1yiShQzTRHYNT8+Oq+kI7dL3uUymRf+gucO/wjqekn6v23dmjtBRXaswDcnkLfNpMMiTKt7DkYohV1PUztOQOEIXMw12LlTHiQfMxIv23QkeVTeoEkhM765HYunkg2tPcMc6elIloyH+3cXJYGU2WC4jX54Er1V4bFms4ak4dfsLsckyh2oCtWeazLyQmb/JzD9j5mFh+zpm/iAzX1X3GtsqjMndDCFX66ymJkxDExIiTv+X613HIDm7+1rj9hvD/bAo3FXfMXln+L75t2hMAwj3FuhkR8c4epKtA/QNq+0hqFKHWcg0sDlPQ1upg92nISyMRFTG2KmT3G1MmAbYmYYyk3ETK2QzvkwSs5BFeRKgOkJHenlb9KTxKU9i5hFEi0nvQBRpb8f47yZEC00nxMf8U8BZnsT1IvA0JU9KQihHkPsjMedKUEGSkuQUSX6jmpHeBcmk9Tj/jrgezcqTyhsNwrVqOUIbMkIX5H1wMQAYBD8uh4hqS4nEecqG4R4YzSd3sy2sNIUqwVCS98sUQlxE0kef6l2Ho31ZcmobOdLnKDENzYxzI2hHsqeGE/+KqJunYSoR7aZs24WIPk9EXyGiZ9er3rYNPbkbGg65mq1sNaH9tsmT6kzc8l5Y49qJ56UhJ1WoK18hCG8c/XT+9S2p3PsFG9Ng2u4iTzJhchHNz6GR2SgTcrVIUlRmBb40/czZJHisfBrMTANhT3rMuRzTPbHd4yGD0cBCyFWNaaAQvVDPJ5EMNuNZpMTMITP/lJlfzswHxH8vZ+bzmfuYhegpQJ4j9J3hnunnLORqVZ8GF3lScauYTHI7FNurGHSgymqtyg78MXh+6TLUxTDT76464RqpGHI1Qx1HaDG5W7FjtWvI1ZAptWU8atanYdHqzQiZG4+eVIaJHq0QlKVX4ZykzS7gPXKP8wAct892+FbnO9Yy9O3Aa4+cLfk0NMkT+300GID6TMMPAfxP8oWIpgH4B6I8DR8G8HciOrbmNbZZaA+PQ5gy3VZ9CRki01D8YhStFhWFCKsCG8XNIHOp5KPVsjANrDMN64TVuwTi6srY+zRYmAbD9pCp1OQ7wVRsyd1PbPZpUCfvHYth4IELmYYyxk6d6EljkdXWxjTs7q3CO1uXOpfj4keSwKTvTvM0eJ6WEdrm0zDemQYi2oGIDsnZf3AsZf2nQKjOAwF8qvsOvHn0E3iQM2a1vk+Di1Ns+XfH1o6qMCLJe5+c+6nuO3B/OLtUGervNMuT6iR3k88NC8dRwdG4hjwJYrCJJIpOzmRv9xmTnUYKFqI7eQ34NIxAnNgSQu5Hcjfz+PCz3onp5yd5Mt41+iHcEB5Y+np15EnXhQdhYbi7/UAOce7LzGoH2zuz+4wp+NypBwkO8M3JkxhC8sE+oe6IfDSAi4XvpwHYBcDzEYXauwuRAfG0hJpmvh0OG5mGOhrBlGlwsKZdJUyqA0+d+tmiFzFTSl9LIA8t39wsdXmS7ZrZ79yKZiKAuMLk4AqYJ5QueRpMKJYn2fI0uMmTCGGhT0MZ2U755G7ZwDIW2dK9HHlSGZSJnmRmGqJj2yamAWHs02COntTPaBk18Q1Ei0s2/ABRArh/CiTyJHH1e0G4B/4eHiKFfx4Ln4Yqk2lTO1rN0ypNFDOmIcJmTMJvgxeXKkNnvZszGqI8DUpZXolFphpGw060Pusv0mvan+nOUwcxc/qkwnIZWfI5asCnQQzlzojmG/XlSWq+IHMfeXl4ZPr5Id5F+l4GVRYOs3ZGuCR4TrpdZRDbI2sxLXzSWIbYdh/lndLPM6YMYlLHF3xZmnOEZhBa49xo2BHAcuH7qQCuZeZ/MPNGAD8HcGjNa2yzUJ/drM33Ao/dDkCeDNV5CcswDUUOzaz8n16jFtNgvmYIyyq756Nt8WnQyzLXS1wd2dpQBBBX2JkGvWPcw3sce9Nyw9H5KJInEWx5GlSjIS/kaj7TUCaLZxWfhrFmGprISmr2I7ExDSafhuh9aHmeFj1pJ1qPV276HaZAdoIPx7/RcByAP+XsvwjACWNUl74jTKkGXX5qkqRWztPgEKa7GtOgt6M7wn0qta6EZRPrsQWDpcrQJvVGeVJFR2jojtABlVhkqmE0PMe7L/MxSyaPRbISh+uFICTq3iaiJw1DjvDDzLXLVKWzJn8/oDkJdzWmIbuetMDry/OJGYv/CKxaWFiGFOkxec59MBoAU6j/ZlF3RH4SwDMAgIgmIYq5fZmwvwdgqOY1tlkYaaJHbwQgO3TVeSGSMWoTilchii1um3Ne9fqNWHwKrC8J+SWYBpvRkHV0W3lsjQZ79CTzpHSOt7r0NdSJo4pn03041r+zsG426UyLQqtBAUSZufPiu5vKK4uUaRiTxILNMA1Go8FyH02O0KJPg5qnAQD+z5af4fWtq6RtyYRsHEdP2gn5uXrWANh5jOrSd6QRV4UJYNJPif3XWMiTyrSIJCeD2OY+0v133B7ujU91316plirTAJRLFPr93sugjj2NMg3oaMZXz8v3gXvfcXunn9lrqG9yYBoSL4hiZExDEz4NujyJG5cn2ZjoriEBbpXFkWp5GqLf+JpnzZbnHS2D0XvPHyxlFLw1nsg8NofxzjRcD+C9RPRKRCH1BgH8Udi/L2Qm4mmFvIWDnmRFV30MnCZ3E2OA26/pyjQoHXWNCAx2nwZLB0AeWi1zPVV5le1FExmJpmKNu6KMT0NVFDpCW6AaDcd4d1mP/ffWxdZ9GzDUdw09pQzaWDlCNyFPco+elC9P8uB59cO9jhOsAHB4zv5nAShvOY9TmORJSX9qWjntp9FQZrFnc8wA+IJk9PfBi/DK0c9jNbavFOFG9WkA3Cexjxx7Hr7ce6PBp6F89KQPjb7buH2Y9TwNIbUB3z5mfPikZ6afyW8oyIYj08AODq4MSD4NdVewZXkSWeVJy3hH5zJdfRrE8T4LLV1+3KkSMj55C95xzB7odATDydQ2QvO7Yb/3CRUkGkUVx7lJujuYFuq/YdQdkT8OoAvgAgDvAnAOM98DRMl7ALwGURzupyXyHl5oGFTKQnSEfkKIAW5DsTwpYRrM26vAFj2JrfIkD23fXE+1/i4D51jLk8oyDVVQmBHaAnVFx+YIDQAn+7dY963jqcbwoo1BiJ40Fj4NBNYSDlVBGabB6AjNQp4G2/uhYBvwafgDgHcQ0anqDiJ6BYC3AbhwzGvVJ4QGT+jMQBD7/HrRk1z65DKLPZs4n6nO6ytseGPriqgeEO+F2++lOASQS/jvIqmWzagwypO8NtCZ7FRHLmAlnGFgGkyLxaqPpLFOoPRcQn0HW4lpYML/3LrMzPY4yOUSqGOhaWElpJbxuVVZHKkSPSmTfRKYhDlMyzCfePDy3DI0pCF2xf6gIgyStXHNNDDzgwCeiWglaU9m/qiwewjAf+CfKHFPWeSFvjJR1SIeCmcVX0BI7vYEHIwGzqfp7CFXm2caotfRtNlH28Y0GKInmUvOXkFxpWQsYFs1adJoGCxMOGOGTTtaFh700J/NIsvTMBY+DV4/fRosRoPJmT2RJ7V9D0RuA2TyHMax0XAmgPsBXEhEtxHRz+O/2wD8L4AHAHz2qaxgkzDlaUgmyqaIedXzNLjF7C/CHQPPAgD8JDilUj3cUJ5pSIwuF/+6ovHJtvBgCrk6ODAIdKYYj9fQGNMQj5HCfCExEO4I94o2HH6as09D5ghNtaVEsk9Dco16uTJcmIaQfNknIJ3El0cVpiGVfRJk3wOTPMkKwpVB5NJ7fvASYbPu0+BXlbopbYKZxr1PA5i5y8x3MvPDyvaNzPxHdfvTCXkLA0VMw8e778IyKjYcyjANleVJfTAacuVJFqbB1adB3GqL3tQv2PSZTU3YAWASVTMaKoU+NZZjDunaFCL17lg6Qjcj7zGVUSa5W5KyIPNpKEbyDo3XkKvMvB7AcwF8EUAbwKvjvzaALwB4DjObw49sg0gdHg1Mg1GeVFH6ud8uxf29i9Gw3/v/hNcGX8Avg/75otucSnNBZqOqSshV2/5RA9MwaWDAmWmAwjQsOfB0PPGKX7qdK5WjGw3JvO8No5/GN/b4PnDY8UMaGQAAIABJREFUG52MBkazeRpUR2jAzOzkMcLn9f4FG2lq+l1P7mbIkUO+9GyetfsOuPT9x8CrwAirc5C3j34EX+u+Jvec1EghknwPjEyDBSEI7+5+EP8y8nn8NDhZP0AwFN71wr2cy5Whvh/9ZxpqB7GPZUinAXgpgCSg7SOIQrH+ipmbmy1tY8iTJ/UKjIYuWljk74nZvRX5F4nfv42YhFH2c2nkIocgG9PQL0doa/Sktk2e5ObTIKKp+MeuGAumoTi1vRlNGQ0ehfD6mJOrG4YYDXuAP1ZGQ1gpLKyKMtGTTO8pC3kaiOA0QCbtamxbeTkw82ZEbMI/DaNgQ5B6QuevxqYTsIpPbvqk4smLS9mDQ1NwF/YB99HoFMcTZ3mSxWgwnV9Upq0P6cLXxzrPA3zH1WSFadhjx6nAfkfLXp0uMBgDydxhKwaxfGhvgMgpya8YctXz6vs0iON30rJN9zOvn747nJvEfwUQ5b8RYRoz2WtLz7Xte9h/1jTcXKGdqgbNXeFemOs/nntO8u54BNn3IMffRQWDMIIO7uC95R0p05DNZwbbFafiBoZiXOdpIKLpAK4D8F8ATkK0etQGcCKAnwK4Nk749rRE3sOTmQbb/vzHc8Wu706ZBoCwHvm0qrtPw1gwDTnyJEemwWWqNNZGgy1SULNGQ35qexuKci+4wm9YntRTVq4I2f0aG58GVHL0VGEOc+tebiZPImdntmTVbhw7Qj+tEBhDrkaft3A2Gc2MhmpDcKL5z0NRiwinlUu0VhVVHKHJM8uTjD4NBX28jWHnWGgpX9gHdt7fqY66TwOXDsMaUssoSRDf/+STS8ksnNuET4ORaShpNGzEEPLGauOY6cnypOS+VunndGd6h3cjlScRWDAajNGTLLBfQ3eErvyYVHkSaNxnhP4SougX7wOwEzMfwcxHIAqh9x8AjsTT2KchP3pSvud8UaP+cvf1WLj98VmIP9idjhMs5l1y99vyNNRZragiT+pYMkKrRo+LT0NdTWdZjGufhgaNhiZ/jzrgEFgwGsbCpyFshIUxDWidEuXK8iQ3yVHii9GET0a/QESDRPQmIvo6Ef2YiP5L+fvJU13HpsCpT4MuTzru2Yfg3n3fiwt3eGcqm6zat7oYDXkzkT8Fz8Pw638f17lSFZwhy5McFwHSCVXxAlZRH5E3Bmj7PB848Qtuz0X1aeDyRoM0IRUgzh2Sz6UdoYnK95+nfFX6ajYa3J/BT3sn4/rwwNzJkHFc8lqK0RCv/FtmRh+2RMiK6qtPrIvmBakjNAFcWZ5kuYbBp6EyTEaD31+joa486ZUAvsvM3xU3MnMXwPeIaH9E+tX31bzONom81UKJqja8Bwwv1+h4AtMxhVlgGmKtYc45xWFZzSfXTe4WMmnRdqIOyCxPsoVcVQccW708yWgYY6bBajT88/g0eAgbXdlWO9eIVB+7kKv7esuxr1c/MrQpw7nNEdoEUZ4EkNM9Hu/yJCLaHcCVAOYiyuszHcBaANsB8BHlcNj0VNWvaQQ5Pg1fftUhAA7BN39xK/DYSmlfaTjq2234z+77cN9O+1a7dklUYRqSsdPFp6FoApjny2eU4g7tgCsP+BKOW/ip3HLJV4NsREthZWDL9SDO+xJO3iViHYOEkKvm+7WCd8BkDGMabdEL2G534OXfBC56PwA5elICM9ujb1s9tA8+t/Yt8W+ww8SAq/KkpATT4siScCbu5rnW8vVF0OJF2TTEK5E8ubcwDcPcxiDJCgDrNdLoSUK5VYdTwyRxvDMNMxBFxrDhPgA71LzGNou8hyc6E5lebOtKfHp+3KkKhxRNsBiEv5/wRzyQup7o+031KbMadnHwXOl7AM+axdMsT/LgW1bRdJ8GF3nSWDMN5hXfJjTzCaoyDU0ZDb6D0bC5RFI9dVAX5UkumW/HC2rLk+KXuR0zDS4SsEyeNG6ZhrMRGQrPRZS3hwC8DsAURCG7twIweAmaQUSfJKKbiWgjEa0ioj8Q0TMtxxIRXUpETET/UvuXOMCUEVrtp8RhoWr/5DmsUha1nmxi3l8wlzcaksmhFj3J4DheHD3Jfk3ttyfPzYXJ8ZQ110pMg3nd1sQ0uEjVJZ8GIpim67mT5s4QEHSFY/V27OyMbvgNJpgW1Mjz5TILmIb8Z6yvxhfNHZJxx5VpMOWDKszTILaxqj6CGtMwzn0aADwIQIu/LeBUAA/VvMY2C/fkbuaXMNdogIfzr38Ydy7LAo+IyVA2sJ6IOwRh0/Rn4mzvbcYyWThO3k74Qe+lhRPBT3TfiTU8VdrWi6eYpmvZHKE9m9GghVw1Q5Ynje0a7H601Lh9XMiTGshFACQhV+1l/Tl4dqmoVeozImTJ1ooiftXBY9zsesYsWqtta5eImpXEjGj5Hg7Yehu+3T6v8JwDvUfwm/YXMUDV/FzGAMchYqNvAtJGQ8w8wsxnA/gbosSgrngRgO8gMkJORORDdxkRmULefAD9nxNLCA0hVzknQlLlRQ0nn4b8vq/PcwuhHhlcFwGSkMsueRoKoyflXFO7/4kh5cLkaEwDGpMnST4NidzIYSyLfBrk81REvg6WerYnFyYrM91vUz8tTtb/NucD1jobF9R8RZ6EfKPB9ntGps01zmcKjQaJaRCMhrY5n8kW6AyE9RoGR+jqMDAN49xo+C6Ak4hoPhGdRERz47+TiegSRJ36t+tXc9tEneRukdFgR9Kol63bKp2T4MiR7xnPISJrmL+sHvpLdlbvTThk5Md4IkfiZNIKvvl5e1kGOEv0JPKsvZ3aWbkxDWNrNOzprTRuL5InlQm9OFhRntSkI3SeEVRWUqQzDZyunPeTKfp697V9KzvBbHrC/eB4onTIE5fiAys+apYPGPA8fyH+xb+uSvXGAkMAHo4/b0A0rxHjhd4A4GjXwpj5Jcx8PjPfw8x3AngrgDmIfOtSENFhAD4M4O2Va14BeXkaTKjaP3klJ6cm9HtykUCWJ7ldk2ID2iUjdNXoSabyk+dGDvdXzwjN+SuFBrBl4igX4y5PCuGlz9U2/4jyN1jq2Z4EBObxJTd6kskwE67/4KyXAiecaSzXxMaS15YNzNS5uwTTcNIX8fDr/mqUJ71w352N5SQQjQaJDbLk8DDlgyqTEbry2kZO5K1+oZapw8zfJaKdAXwCMsVMAEYBfJ6Z9dnr0wR5nbLsCK2jqCMsoghH0dZ0domfRNFAZYueFMDPZT/YUO/TXrAnOne3gdER7VgjyLeu1rjLkxQ/j3EAW1SlBAE8eI6T+urypPoRgqJy8uVJAfxSXaDJpyExGvrp0zAWTtZl8Pj6aAHglAfPLDz2quBQHOvfmX6fjs39qlZdLAUwGwCYuUdEyxGxBP8b7z8AMGS6c0digKQ0DxENAfg1gNOZeWWRAykRDQCSvmAqAHS7XXS75Ricbs+gz072xWWFYdYXVDUaXN6vvL6aCOj1ov6A++0JLcB1EYB70b1yieRXbDTY2Uq1vJAZQbdrdQ4X24NqAAVBgLAXlMoMxORJZSbnSiVziG63C3J4TowooEK320UQmPt7gn3s7HodeN0R4x0rK08Sj+t4QG9wB+OE80XeXfq5ijwpZCDodq0yTCP70Z4CJtU3IsJgxy38fK/XlRY9w5au4ADM8iTbu520MQo5vR9BUI1TV71oGBSxdITyfZfj8bX5EWY+k4i+DeAEyHka/srMJZbZ/vngmtzNRDFFmR3z5Ukq1GanRw2IqclCpsFtu+k47eX1WlYjwFiq59v2OEtVxLPH2qehKspMHqobDU3Kk+xtswevlB+MOXpSVH4/n994axvdrrtRpxpTQznz7pnrbwcwr2q16uIKAK8A8Ln4+/kAPklE2yNiut8M4OdVCqZoOfhcANcx893Crm8AuJ6ZXSPmfxKGHBKXXXYZhobMkwQbljziAfCwfkPm2520s/nz5wMAHl8ZHSPuK4sVK1Zit4Jj8t5AAqf1CUN7n9sExL7C1VC/7967AbzIyWgoKjN/4UEub+26J3Hd/PlYu2yZ8ejkngHAwNqFEPL84qGHHsK9W/6CV+TWRsZIN8BVQpnJud1u1scvXboU8+c/jI0bNziUSFi8eDHmz38QyzcDpile5NNgft5/vfoG7LRpHY407DMlKcz26duGh7OFwocW3Ye7OgtwhKFc0zxnw6YtUpmrVq/GjfPnYz+bPMmwOLhgwQLctngHQ5shrFiRn/8qufaVV1whvcv3P7wchxiPN/mOmO9x8lumb3kYx8bbFi1ahP1ya2TGps1bIArCb+e98ejSpThiLnD55ZeXKmvLFjdmu5TRQERzcnZfH/8lGEqOZ2az0Fsv/4UAPoqIap4F4JXM/AfHc18A4GoAdzPzYcq+0+NynwHgTgDvizW2fYVrcrdqPg3FnafmRJbIkyxl2nwaykxoNX26xWggMH4bvBhf9H6q7PCsRkaRUSSWbavP+IV7PavmaWjSETpPnhSwIWlSDnSjIUts1k+fhvHGNHAJfkZdPfVzFhjmPnEFgE9XrVZdfBnAs4logJlHAPw/ALsgiqoXIGIEPlSx7O8AOAiCvImITkXkR3F4iXLOAnCO8H0qgGUnnXQSpk0rl2bo9kvuBR57FNOnTwfiHFbJuzBvXmS4XbrhTmBtlFyqav+06+zdgHWVTgUA+J6HefMiccBHbrocQZDf9t44+il8pvUL7O89aj3mG91X4YPtC7Tt4i90fecO2G9fYLG+3ThhLWCTy8jDdpixI+bNm4erL15jvL/JMwSANQuHouXRGHvttSf2OHYecEdudSR0BoekMnF79N/gwAAQz+Hm7r475s3bH3949MYo1lgOQhD22XsvzDthH9y/ciO+etcNeNfoh/CvexNOWfp1ALHkxrJweMIpLwfar0Vw/fbg2UfhzMfnAPHcs6xPw+DQUBoX7fBDDsIhAxsi3tEB06Zvj/DJ7Do77zwT8+bNw8O3ftJ4vKlOBx9yKGbMPQ4XLpCnkAzCrF12jcSSFiTt4sQTjsfq9euA2Ibc9+AjgBW/0443LcjZ3u2dZz4jfebBzR3w9F2xz4o7AbOyORdTpkwBYtssYMLnum/Ba/eYC/BinHjiiWi33XmvDRtcjNLyPg0PA1hS4c8VkxFN6k8vUyki2g7RatXfDPteh2hA+ByAI+Ly/xLLqvqKPHmSKTuoiJDzfRpMk7aiSXUID16OPCmjH83bi2DyafB8H2SI9EEAfhUcj3tDxQ7N8WnQ8zSY8YvgRADA34ODpN/62pHP4K2jH8VFQoSn5TwDX+ieZilp7FBGIFDGuVZEU2FfPeJcp+peQWwlNUu4OuiL8qR+Jncbb0ZDmTC2ZeoeeO6RrJoGMy9l5gtigwHMPMzM72Tm7Zl5R2Z+KzO7jVYCYnb7ZQBezMzisvBxAPYC8CQR9YjSlNwXENFVljqOMPOG5A/ARgBot9ul/9IoL0Kfl/RByTGeMC5UzdPg+cXrfXmLTr5HaX1c1EnXhwfhOz17AKplvCO+GbzKXA8SF3Hc2m0rcUhWJrdmaUz+Pcz3aVDGK8+PnpHl/orP2u/ITrG+56HdNjhH58Frye0n2SyMgb7vRddzaCoMQtuPfkOnE5V3eXgk7tol89/K82loT5qGdmcA/rEfQ2vvY3HKwWJup+gc1+Ruol/I5IEOWpb8SyZQqyM9V8+L7oFNnmSqU8tvYXCgrSkrGMXRx5L70+l0MHUoc3JuTTIvIpgX0cz3OGlj7XYb/vPfg9aBp1ojRhZBlF5+o/dqbMDkNGR9pf7LAWXlSW9HH6NRMPOlAC4F5JvhgO8jWrEKAKg924cA/IiZfxqX+24AL0X0W75cs8q5yPsJgeTTUN4RumNYbQ606EL6y0IUd8SGwuuGXA2ZEKo/OodpYHi4LdwH+3vC8oNn92nosZtPw3m9V+Ke1oEY2Os5eON2lK78rMNU3BTuj5f7N0hl/Dw4CQTGGe1fOfzK/qBOAj1XNMU0FJUVtW377xlBW2JLVPmAJ0RPcnWcrILxxkKVMRrKyFqeSqOhaVA0MJyHKEfQscysLkp9GcCPlW0LAHwQwEX9rl+WpyHbpoVcFb73M09D3lBdJZZ73uTbNUN87ZCrFRyh8/M0qBeOyhKjJ7HXBoX6eKvlaeAmHaGzcijd5lAe5DwNCcTPhJx256kLOGLZEUzyZnPI1WzbQDuT5DnBa8llcpKPplzI1Y6vZ/1mUOH7kzjcewRMnpQZDdSZajzetCBnN8ZN975yogZty7hyhGbm8/tUj8ogorcB2BPAaQDOUPZ1EEmdzkq2MXNIRH8F8Lx+1y2vY5acfKzOXfaGZAqxqE6wdMbAAyEnckKMH/Zehm93snCPdXwayG8pUQLi7XHtzJ22m0+DrV4BfNwzcDiuf+sxWPjgktRo4PQ8wWGcW+iihR8HL8Vr/KvxTM+sZa2De8Pdcmn9qE79n8AmEpYu+5XZigR5TtU95MuT1IRB+qCfZYR+OsmTyuRaKOMg3vsnMhoQSZLeiEj+vZGInhFvX8/MW5l5JRSiP55ELTUYGI2jG0TPUAwbndffVvarcZgYzJw2mEpcRHy3dyo6nfLXzfsdrgavs9EQR09yCblaL3qSsi+9rwIb1J4EGjEZDer0qcLEz7Livf+sacCapEoU18jNEdoz/AaPCJ/ovhOfav0a5834NP5t1RecqicaL3nyJPM9zs6dMtACRkqMcV5LjpTFST6ackZDy9f96yKjIb8uaUZoEKYNZf0nDZiNBlOgk8KM0CKe827g1p8Bm1fl1uuG4ABMpq04xLN3ZX6fh7XxNWqWBBHtg2hl6TRmNs1idkSUdfRxZfvjiPwbbOUOENG05A9KNA3XP1v0AkBmBWxZLvM6CZOuvUjKETIhCHoILcUm9bg4fB5u2/VNQl3cjQatkw8BNrwkJJwjohsweqF58uQqTwKiCAXdbheBoSxxpWSzEF+5XxP328O9C48Zy1XvJibLh3kGwXGM0BJ7PIFqNOgrsdkA6Rr9qlchStZ4MxrKtIAysq3AGyjdd5WNvDGGeA+iiElXAVgh/L3uKaxTiq2j0WS3LWhJ8o2G/jENx+2nK3DfNPpJfLX3egy08qP3idhzxyQFRp7REPWzI6yvQ8o+Zm7vnGcxGpwTiznu18ozMA1oW5zhTUxDWViYhsmd7PmUWTiOQq6mFRLKIPw2OA6HjvwQjwwd6DzWiddOznFme4STd5wyUOqHkN82Mg1l5ElA9B7qTIPM9pnLi40GD5gyKZOh0YC5LZjkSXaiwXDtyTsCH87LkxzhDd1P49TRLxrLSt6zccU0jCcQkY9IkvRZZn6g4eIbiaaxqQu8ybKvWIdJ6OUM3CajQZ9Uy9cIQbjlllui1TDDOybqRxet99NIB+5GgzzJC5nw579chpcMj0K9a8nLr9bxb1dehZ02LpSDrsdwZRoAYHh4GPPnz8eqDZtxcLwtOVr8PZuRdQj90t0NG8KxqRgLpiFBFy0MVnSmdkHA+WtiW3igQL5RnmnYiCFsj03FB4r17COLUQV5CfNU5CWs0o712n2LpDHW4LxMaQ2eUxVbEqNB8lvImbRWrVqBYQ6YV6aTyVWnJchvCia7bT+J9FTMNIyijQGFhRTr4cwcsk2eZFpgKxhL2X5NXWqTSHSF+2MxGrgJpsGSEVquUbLq7VhkPGkMhO4kaY4MDy3Ps0qUVXgS0xDBJXJjXJH044wpHWBdOabBaDQYAj4YozbGaHue9v4xqDA5opinYdpkUZ5kyiFpNmZKj+lOfg3m9iqi3/lXtlmjAdHq/5EADo+d4oBoKkyx89tJAK5F5OcwUzl3JvJ91RuJpvHEhi3A3eZ9RT4NITx02i3YomuafBr0EKv6Ks1RRz0bNz1oluCIr+PMnXdOIx9UlSf14GHeKadgYMkZwPo10rEZ0yDj+ONPAD0yIEWlyMpTOyZ7vQYHBzFv3otw/9IVaU7ydPVaOG8Ti5kc+/OyDaPYOc403PS8AbTCEcOeCBcER+NV/rWl69NPyQ8AwG/nG3TK/VAHfdmnwW1yvJEnYXsqazSML6ahjE9DGXlSSO2+RdKYgIyt3TirtzABUJ/qc/eagUsWRCEf+ylPMiF5Lwda7tdt+clE2n7NpG/9U/B8vKmlxSNJ4fp7g52j9B2mYB5ly8z3aTAzDeL9tRkNnso07GoKVJoPLnDIFaviJk+idKIfCJKCQ2dvl35+4b47InzIkWlQygaAB3kX7TijIRFmZ+8wuYMy4yt5LeXZcFwf/R4Q2NKXMzxPP4NjLjsPok/DbjOEOZ8lI/Tl4VF4u3+JtM1qzDaQmDEra5z7NIwzbADSReQE70UUPePVAJYw8ygR3QrgeAB/AJDE9j4eOZmq40gf6WwtsdzLeJgDwEDHfqyLT0Peozf5NOgr8WqZhHZLTc8uHi9oICVr1bWDYamMAD4G2m1j9KSkdmqn3e4MAi3zfcsL82mqTbvdRssQzUJcedgyBvKkrYZskXqdqly7Wn3FCecy3hHbYyMmk904KYuQfG0FVUw0qBoNZqYhiOvqzjSUruc4MxpaCPBMcotJWM4Rulx0DACljp1AhkSe1JLkSfKzeuNRczBlwMeRu++AV39VD1HqBMPE40e9eXiutxAHew9HGwwMQrKy3iljNHiJ0WBHstL6hd5pOMhbgkMF+eJ2gy0hLGTBdd/2Z2DTSvR2OxrAX7Xd7nr6DKUycidRm8RzrPKk7B1ZdtDpmH1AmQwN8eUs8iQRyVBs7e1bk4BelBiSmdLjQ+H5H7PPjvjum47A3jtPwV47TcETf2tDJIQ2DTwDA2//o5aYzsQ03M9zcNNzv4OjDtwX+MmJ8XX1avUEo2Wg5ZczdFsWpqGkTwNgmbwXTNxFpmGnaYKh0DYzDefya7GwOxsfaF2A2ZTExbX93v7MM5JS+800jKtRk4imENFhRJTkWdgj/j4n3n8WEf0ciByamflu8Q9RZOzh+HuSIvUcAO8iorcQ0f4AvocotKuSIKAvv8e6zyl6Us6zvy48SPre8b3CzjNxhPZ980SsbpZRT7H4e/Cj32AwGjyL0RBRdOYf3q6Q0ZgML5CNaeifPKk/RoPpnLO6b8CVwaG550ltj+3UblUwtbR7uVGQgQ0XGFEeGEPxLEM06vJgMxq0kL4Cykh8+oGuIpt4ob8Afxn4hNO5ZZ5Z6CCBmEAzSJiGPJ8G3yO88vDZ2G2HoRo+DXqfenu4N64IxfQUZikHoMiTCi7VSuVJ9jaX9OfDGMDvghdL+yYJ2XcL2+3O+wMHvlKQ0xT7NBTdwzxWzso0iHfFYjSI0ZPWzH1pNfbHwWhIHaFtxXey+oUWpsH3CPMOnoV9Z06F7xEmD8rX/f/sfXecHMWZ9lM9M5t3lVZa5SyUJZRACEkIhAILIiMLTJAJNj6CTTLGHCBwDoc5sI/DxtmG487pwyAbCTAYMCZZIAMGAybJBCEEKO7uhPr+6Onpquqq7uqenrSq5/eTdrq6UneFft9605v9FgD9x0kad3+y7+q9oUuBIf7fmR7xjC/ECTvxqCc5htByNaBQxu5A4Fhx8yLH0B118rnQjXr8MnsItlJXoqOWNMRJ1JdfPamqmAbY6kabUAhxguvzv6/LXw8B4BdgzgNK6R0ALs3X8TSA/QGspJSKxtGxozjvSURJoF+fPhGP5qZyafVJK1Dn3/asAFx3DM9wiPnPP3S8Vsh6EQSUexab8SHSzULJfPgEdwvDNDiBsgjzcXXaZN89b9NQIklDiWwaZEP0IVqkIe1ZsPMkB29sjWJBJeO3i7rvuTuQiaJoJns95UQ8lHXnMa9mlm+HJnFkz1eU5SutnrRbkyGSIYw9Rs4yUoNyoSBp4NST1Gvbz97BF5Jvi8wRhQhnfx7Uqu9RK6ElaXDvetdVCEPo/N5BCif+/PPI3H0Ge0/ysWnwlLXrT2SZCOsKQpGwEnEaRgrOQCvehvNXMQLM6bfjVh3gpQTeA0xNg3SuGBHSWSmEbN4F69+rYEtgJKo3infgazfkuSba3pMs20e9e0PFQEqqU6/FUkkaHJqnJNUXUFVMA6X0AUopkfxbm7+/llK6xKf8OjEadD79O5TSUZTSekrpgZTSx0r3FC78GD729COs96THqTfg+KC2es/mKDOEBgGmDusnrZeC4KQ5w3HpionqjvuAANhvcJ/CtUucep/PdbkqnvSoxZj1JLykgZVyOLWybfLqSaWB6C1IBhnjGKTDGuXUDeBP2CkIx0RcmT4zsHwQLMvrcpVlzrqE9yE+pwWK5rykYZcPYX1Z+lPS+h/NTsFV6bX4eM8XkIMlJTSAyqsn7YKaIQqCSm1rO/W6BMwRwzSUC3vSXvUkP0I+Tu9JORDe25hk+3D6cvUq99Ap6HzIVU9SrxfeQ5LwTJRlKAKeV4gxoGMIXZz3JHn7CdaWLCnfg4jlHn4QqfNGDYSRNKi+BwxTQ0EKTN7UoW04fPIgnH7QKE8R73dX0T2JepLdJ3DfaQqCH2VW4APaUkgbMaAFM4b3wddPmM4U0kRC2LMC4jQAfl77vNIqoq2eBKB9AjDpKGDOJ5Quch1jdfa9tjUq9t04qXqOMbTfTZQYLGFg5NYlhJ/eKE+wKNSTFGVZEZiDGcP74o3tg8DSErLAOCTACMjdJCQGR4H7PUXf5saCf+nCZi011lGpJ6mDu8mMv4Mg2xzYd8+rJ5VI0kCjSRqC+qMqE/QUfpKGPRp9DULC8p547gqhnmTHkLCJr90+kga237uZcbRIDj/LLi9cqz4zlZY07Ipgh+FAxvC8T1vxIJ2F48mfuPSskTSUDa7LVX9Jss49ALYKyNvPeNMl+xr1kFTymb9oQjuG9dVnWHUkDbr2ZsFrziGQFZKGCDYNUeI0JLPdTJKiPCNpsCJKGoiOF6zCK1DMlRTPNDjfcMsiuPWMeUGV+tYtc7lqp/N0BAVwbeYM/LnpMHy/53IAth3DnecvZCoLp57EIcDlqt0HC853ww+OSbV/Hsf4ioipAAAgAElEQVQQOi+VWOMf+FVmrH77pxZg0Y1Pc7YdTq74IKGt9jH1pF4Ff5sGf53SrELS8KfsdPxT8F4wtE8D2lvqcEv2KNyWORT3zvqu0wMun/1R8Y9a6ccYJGWTkdHrJKAcJ14gTn3Uk7wnPepowmFO5ZzDLdkYsPWUxRA6ovekKGVUp+osMoI9DSv1ClJt0oElcR23k7KShuD34cBP0kC5fG794odFNa7llDT0TPQaSeqorQHA33Kj8YvMUi5NdmK7kzZhb87LIBibhvLB9Z7kjk8oQ1wRKjGAdF/zBrKS5fH7LsngPIu/TYO75hrBO1UIFafBUU/KZ9NhGoLc1sqYhp68PZHXENrOm/DxWudmZaU6ESUNcagnMW5AE8j5ajg48KqQyguxkgb2Xak89GTYsdBsQ96wimlQfympkgETWWkN9aT8c+guFVk2i1hyAj5WSYM3qdSSBsM0lBh+UYv988hPjH+ePdyTNm5QC1IJC92owxcy5+CtQQul9RauFRw/G4JeBqmBDXOKSUC5UxlXDUZdp9QQTdG/H2Q6lfUowdT1EbU312o0hPZT4VJBpdYWBFG1hS2jS8j6IeH4AWfA6u9nkEBWg7nppkn0+Kh2UYWkISkwDSrCLIzb0mJBLAt3Zw/g0j5AG57JjcX7EpUiFu/SfvhrbgKXJlNP6kIdeiTCY6OeVB6kszmks/aaTXHqSWpEt2mQSRqEPUHCcNiBrcIhkSeO/Z6D3auaoSa4AyUNBVUc5NsUmYZ41JO+nz3SaVBo3877Xt8ZYpc8YAnn7lavCpAO5J4FAfZtF06xJTEKAHBMQwPp0WQKRdogGJykge0YA25fF+9ruJctwKOe5BhC+zENwjg7ZYRu2EyDnnqSLoMty2clLI6A30ra7R8RvGxp9SH/10SE7qVg9cpVRI2MaJQRin2b6jhVKNeIjIfbjrw9m2lQ91l6usAsbkuQNBQIUMkCVXtPUts0bEcbxnX9DE/m9sOvsguleUQQAnyy5yJcmv4U3saAfJvsyXp0Y1Rd6KgnRdFtjoNpyMHi5mIc6klWwru191D+I6CjGrQHDQEElwuWMdOVNJRTPYlYCVyQvhDXpk8rpGWQxDE9X8S87pvxgI/HKwqCzpnDuDRZwKoupLA5N7Zwva15AmhdC7Y3T/DkNYgfjpQB4CUNfqf/7BwUpUm+kJyq5mDhKGGeyPLo0EEs8efaNPhIqJnV2Ei6+Js0hKShoEvuSDf4NsU1e/ysYbjlDJ4ZFyEyZqu7r8L1mZOk9Tvfqo9axuPI7i9jXtd/+fZ0QdeNWNb9DaQbB/r2QQkdm4aA7zb7DW5Et5af/iiSBt4lO1/GuceNj9hGMsS3RWQwNGwaVJKGzy7l9z8q65uAsN9jlaSBPWg9r+07wFkbbfuI2CBhVoxNQ23D1jH3TvT9R/XH26+7eWTQZRpaG5KcDq1KPFlYCIpJ5XhXUrUtBbPpEVBYzGJ8l/bDRPuGB2r1JP9TgCwSOLFnnV7f8k1vyPF6neyGwJ+sl2axBXsLiqYaFdUQmldP4q/TMWwJCYl6klzNwCayVHNtNxq0jUjZE3ZRv7oa1JNAEsjBwht0UCEpk1fFy+X/qUBBMLgv7x9cxvB0ow6/zR2MPundeDK3HzoXL8c5Bw1DdoM62JZBfHDsGQioIJXVm8Mv0uGSHPrqSRQEA9v8D0HsPT54j2DXRkIjuBt7TNDkkTT4eVYSUPCeJM8v9qG9tR4zR/T3r1PA43Ry4bd33bnXz9ExnjQuJwHeQntRImqio54U+E12+9eAtJZ6ku63jrdpYNN5FR/nXoZzYy20kQzh+MHDTKldrhZyKOiG5jqvg5igJRBaAihTwrB4Br0r2QqM8Gdw48C+5nK190ExO1lDnzBcrSxva0MSs0a4xtEOA6HUB1UyDa4RFZWItgkka4M5EbBAQRjmZSvyXpo0JQ3vDDiQaSkeyMSG7Ieoh7rjUFmbhihMgxd+rnodiJIGlkCI4x0kJN6TUoLnK52gbbslblRZsP1OR2AayitpsDxtZqG3B1AQT+RYKdNAU6Cw8OPsSjxLx9rvJ8zpnkFR2JNnGuoS0NZbpoqT3ECovCc1trO1e9vzcbAh1uXAkTTs9HFKwDINHzIedEToqiepJA0io9+QtKRSF114DaElBx6tg6VlWQXiqLsmUTop8Na9N9lHnpVBF1Kakga9HvN1+akn2b99bRpSIaT6HpsGHfUkPZsGgATOmbgkDd2ZiK54tRsuv6TBMA0lhupDwBuLqiQNevW1NaSwYHw7vnHCDKxdMBpThrSp26bwt2kotO1dnH0aU950IQ5CJuXqZ7/reHnyjdPg9jE971xP/rSGu1I/yN4huyGw7lBLZtOgERFavkmVxqaBd7nK6/ZHdgHJIJHwek+qE2Js6PRzT4CkgX07rKShWtWTAP65swLzpoLNCPqraQBeKVGEUCsGRSBBCOaO6osxLfovnj3RXDNvhH5jEqKHgmD3pBOBmacAx96saE+Xn3EzOSeXz9Ix+ENW7o3HYvTtf5g9Aph2ItOoey/4BJfvnJhf3J/qUwlHV8bF4ssC2mDrF5uX7KkHXwxMOwFYc5syq3TExx0WrIqiCLTKt2M3tKl/p1It91+Lvo47Mktwb26Olvcc8b2qmAg2lVNP0jKEFiUNYZgGuctVP6ZBaR8iOwDVjNOgC1l9JEHQwzANXi9KcYAfIcDf1X8cMExDyRHMNPjwzpIUuaQBAFbPG4F1R08trFWZy1W/PtkfFKLMU5e0vL6HBYbg/TbX/7djeCz7wE0dYjMX7AY3vF+Tp+nEcDbCqT4KLInwGC31Se4dsqpD1Sdp8O9PFDetgKieVApJg3drTzKu8Aa11msR7LtoENPASozceSlKGlQfgFyJI0KzonrHQQAnaSCspEHdF1vSwDMEOsHdSvKNMlBi5IAm3H72Afj0FP3TRXZuTh0qOUlWaqTIDgwIrGQKOO5mYP9TFIbQ/s4uZP1y7TMIbm76lLwAgz1oAE78AdeqNgjj6hJe5li8rk9KHGcMnBQYrdjtmfAuZAdq9a3AiT8EJh3JZw16jX1GBLvq1IrTYP/NWXW4JP1v0gz9F52DyzOfRA4WPtjdE1in99uiVsFywE4nkWmQ2zQUwzSobBrCqydJ6ahApiHct0Fq7ik8QyZbAqkDG6ch/5xGPamXguWKVS7jdCUNrUJIeOdUUpz4rk2Dn/ckpw7vQrMI8U4YhvkhoOhqct3BFk4+JScAJ84eiu+fPhefOHiMW97Jx/TPmhTOY9LjOT4wHXtC+40TZqClPskRi+wJNfdu554Vql0/6Ng0RIFaPckfae6ZRfe/cTENoqTBjbGRTBAtz0VdqPclN1jChldP4n11V0rSwKlg5ee0WtKgfu85EN69I/Q8P9EwxJpBRRB5vSlcrvLEnHf8dfYHJ58DlgixAgxIpQgj8nK8JzmEskfyIJE0xKmeFGI8YlFP0nK5yr8TGRoZvf2Xtu4MrFNXPYllMPk4DUJ9+b9pjpYphmlQ2TT4qScp3qVs/gXM47DrUqWexCJbckmD065hGmoaqsXJSxpUXL580xdx2MQOoVy+XqrYcJk+iYZLoYO7sQQ+KBLJBO4Z/Tk8nJ2KX2YPyefxbup1yQSWTelAihXPyqQcoxfj2sT5OLnnSknjXpySz+fGaeCrp6C8ehJzQj2hg3F9OXiaVns6kLnBFCGPrxBePUmHEGbziMHdbjtnfmD5ICQSXqYhxagniXYUqi0uE+B3nr2XZghwfUPo0m6uLNPgMMSsaliW6DENtg4uP4ek/uqFayNpqAEQn5NZ33KKyLQBVegYgTr5HLBMg8i86iE801CwrfPYNPDXDUnLeyglBB4L1TNiydPVXfXJH1wLqfd3t8y24zS3Ln26mAMAsGyKTQecfMDIwDq9amDB78ufachLGqjPfA5j0xCHy1WmdyKCWOccCNpbNA/7xi6RJlsJvo10TkPSsPxLylubUvv7FnVaM5KGmof8FVuJYIKBKMTLLK47Zir6NPELTHVKkyvIDximQfCk40u6yL42gk1DghA8M/gEnJq+EjudiLcyXUNngcsMrbgPKbA+cSgezU2FDjI+BLpFCCjl3yEbB6Ctkd0k4lt4Oka/cXpPCmMITUE4JqJvc/EuaC3JeKdIFt/NHI299e24q/UkLebGnq/+J/AOOJsGgdlWvY3IPvI1IXM/yL77rErKJSAH4tHb1pKSGKOGGkDQug8R3I0KkgbJ+Oe4gyE1ePUk5mAoEtMQAImKjtN6VlAh9BhCpxKSU2P9vdRrCK1dNJYTXTJwUnAe52++vR9nV+LAru948t388dl47AtLMWtkv8A6dV2ucmWY36pnz/oZQovek5oHYm6X3PbG48DBsWmQHKQ6e6fXEDqfV2rT4D+P1xw4Cg997jDfPBg6G7jkH8Cpv5a+D/E7mMlq7McLLsBvDr3Xm24lcU3rtd50abuGaahpqKaJnqQhuL7GlJdAc7honeBuonqOM+Hk4jZZJ3lJQ11S4gecWTzfyxwJDJ4BzDy50FtPXdyC1vvIqcAWdV45exLNGkITWV9iQWkXMQsdQliUNPAEKEHmhB/j7T7RbEkAucvVFDL4ZmYN1i97AB9YA6RxBkTY9ha6kgZ3Hic11ZNKTVJzhskS9aQcJ2nwt2kQt2oZI+pd7wbVjsg7g8J7UpB6UrGShpKoJ0nUVtQHXzKbBpmkQQ+qOA06Nfgo4tjQYNqtjsmBeWSP8y68bmaTCQsdAS53fSsNgCe4m+Ser8tVifRgGxQeoRIi0+BjAJ2f52pJg6yQ//M31KU4lS85KNDaAVgJaXXiWknrMA0Auhs7vIn1bWr1KwGJEpMbhmkoOVTqSUERoeWSBnHTlBHUyg3XOQUgcmKL9Z4kc7kqJd4tXtJQl7S8oj/mJOmbmY8B5z4E1DlGz4L+kCdNz0WgF3mZCmE/ePb2kmTcf7JMg7QvAro03N5FQVzB3bKweH/vJ98BjF7E5WHHvLGONwwHIaCTjsLjYy8CbYj4rIRgXfZMLunBfPAyK2FLe3QlDX6fb05iFMEQuj7Jp3c3+wfGCguOsHfUkzhDaH2bBo+kQWLELUqYckbSUGOQnsoossqCu5FAzyn2Hh9OFYUNVFcSlb6EVw3E2bc9dnmCGmdDKiGRZOv30WtP6By4BSMW1fEBwYEXnXehbK9IBsCuI7jMK9S1V1SdZqcV9pl2G15WQwmPq+jgEdF3uYrAQ0FLx06G21+9B7WiVC6ro54ExXBmuhTDzDJyeRWuEksaTHC3EiNumwZvPZJyinplkgbRKNYlsjXdlHGLj6I+aXk/XIRXgZL3lqlL0PPVDeUuA3calFdPSjEn0by9QeDZEXY2DEXDro8i90cFHfWkd/vuj1fe78GCxPMAgNXzRgKb+Dyr50/A5/68HxLIYcRBJ+HoiSuBD18HXnuokIclOFPJJLJsLCbic1KkCYsQbErOxMSuH6MvdmGh9Sx+lzuocI+CajMNYwe2ADu89y5Lf1JpCO3ncvXN3ECMsN4DAFw+Iws8z/Q7p+N1RB+BhtCc9yR/5kj0sqLz/gzPUP0I3tpU6kkymxZhr1R6Twrul8oQ+mMHjARCxwoMkjSoY4konXnkYasnEfy3dTLOzd1uJ4b4Xmh5T1KgmO/SYxM/h0TrQMzV0PN3bRriIwbFU2u/788J3ddgSeIZ/CS7wu2TWF/+L78vBYy73wYlMpJUg+BWjR3TzAU959tZA16lnu2OW7FT3w7apKxDSz0JinmV3qvK7ElKGEPoWoeCaUjwxLouRDeRfpIGtRGZmy6qOfgxqXL1JFbSYLtl9UxkLpCdqEMqYRo0iHddcOpJBAAoZ5TLqfNoSBpKpWokj6nhzozcoCm458Cf4GXqnoa31HtjWEwZORBdqMc3M2vwft/pdqIoJuXG3Kue5P709omKYmMZCEFjXQLdqMO76I9f5RYXbEccxo03hJavgBws/PTMAz3pv8wuxv9ll0RST2K9DrFR1AGgp8+Y4GcLAdEo8L5LDsFpC8YWknKs9yQf968UxDOGFx7u1YU26km1h8iEoIJp4PdoGdOgR+xyanRMNcumDAnRSW83UjLdCV+mgc8vdbkK4G7rkPD9ktQfr1qqegUeePKVmHvUJ7VqCfaeFH4O5UJ4nHqKTsR/ZFZze6wYc8BVT2KZ1iBCP4SkQYNp0JE0/C63ICCvjSCbBxW2o015TzdOg3w0lUfLnhTjPamXghUh6U8H7zKTfwC8ojL72nuSn6a8mlSQy1VvU4w4DjnUJyXqRL4xKSQMAidpkNhIaKDgPYkVF+YJVpGolKO0C0+EbA5wb4ZSm+DmMkjKMPrBhfESXb8Jxu+8epLXeJdDSh0Vli3XpNAHtYjdpp7LUCJ9RmfOsh/8j+Ce8IgeNtjn42NS8HhvzkX4UWYFPtvj+kLfQtuxoy1YhUAGXtKQwLiBLVhz4Gi3L5pxGgB4jEXHDQr2umLUk3oxtGwavMhpRoRm18xHe113yW1NxbmPbqmXKDf4uOIUmQTRZqshb9NHRQmp5kdDFRE6lt0/puUnek+KA16iOVzt6axcmsvZNBSz/3gkDX7RrPL0jlLSoEnLsNBhGph6ndo+8ImGntFWT4o20q56UqTi2jBMQ4mhYggSVrCkQUa4iycjstN/P+9JXAaIBKSr7yrrtXNSzyfykoZ6mSE08bHfkEkahBP/YhhnXtJgX4jRiX37UibI5wnzrok9MoFqTMwJjRuTSW1ES4nlE5BHdioYLE5/u22m1EDf7pPNNegEz7H1l719cMpSWPhi+uO4IXM8ttBBhfuiTQMLmcqQA1rfB9dmzsCTdD83jRKQnA6T6YXMEJpdC7rqSXVIe+ejZH4+mBMCWhmeofoRIBlQEksy70kQ9l6pepK/NPkL6bPQTZO4IH1BIW0HwzRE857k9qNZxjRIbBoAYMG4AWioE10N8513JA05bl3zeY6f7bVV6mirz/dM/j0Ku3SKUVUKrLvQhrLx0HXSIuxAAHCRjllwkgYN9aQrOxWG4OJ3JiZJQwFxMA0S9aTtVH2Yo2sIPX2YwpYw4NDW+WXUk2odigFMMKoRKgJKxjSIm5wfxywLjEMp36e0cOrsflDC2zRYoKhLJLx9snh1GKECtgFPndA03FOBLemccqdUTEOAak4pIdtOiLBR2vS2fx8JoyNLpO9TVEkjaqZB9g5EDxgM9tI6HNn9FXzUMj5A0qBr0yBRdQM/r3+QPRI3ZE7ku+gjSfKLpOy4QWaNI7OwYFHVfPGHzOUquxZyfsw0gzpkvMdHwns5v+cC3JZdyqX95NHX8MGeeO00DOJF5F1Gqp4UTLxSWL55bssuxZTuH+EvuSmFtA/3MnOoSO9JYSQNvzj7QCycMIhLk9o0QFw/fJ7rV3v92//+M4sl5RD5sEjmOCQuuP5B4vsmUWEf9LNflkHNNISRNFCcs3is/FYk9SRh7PzaDxpnLUkDkz0/j/zUk3QxcbCC8dCcY8blao1D6uGGEs4jRZjtxss0ePOQQl6VEZmPpMFnYwq2aaCoT8nUk3zs7YNsGkiwRxAZCu9UkDRQSpEkCqIy6JS9hJAxjiLTaBsRB0kC6rn8djY/9SQizJOA+hWnggCwjfbBc3Q0LIugqU415o73pGCdWpX3pCAPLl5Jg/ses2DXHf9enLnPqzMRkIhMA2c74jBbzFiEkzSI75PPf1fuIM8c6krncP7tz4TrtEHlECpysoxpkDihkDQRtLOJa5NVTyp2X5QzDXKbBkJIISiiA3GO1+UlDXyfg9+jY9ztXXfl3fd14Cf9j4owNg0y9CjUk7j3GTSf/W57vjMaa0P1TNI4DaWRNHzgI2koDbzPYSQNNQ+5GJm3aVBJGrwQmQbZ5HeJH7HdwpFFIU3ULXedJ+naNPAGrXUJf/UkSQXeukSXq0V5T3LLWpYjadBQN6kCSQMLh3xW2h84ScypXUGYJQaZEZgE6Yk4EFrS4Hj7IgRKH9fOtGfbVBlCq2waglSbkkSQ0DC/OeLCc8joqDqwTIMVWT0pK2MaWEkDawjt80x1yHi9eWjOz8df+0Arn0FlEMp7UoC9kU6chlyApEGGg8e3AwAGtzVEPIl3+9EgU1v0OYgQ2xOJfEcNkls/NAfP4hYOrpx9SGUIvV9HOOJvaF+ZrVc80ofJQ/Kn18phC/+tam4UpTvh6hg1oJm7LkSEDvKeRDSZO4+kQcflqmpuhne5qmf4435nnOycu/M4MWQm9xTUCZQ3dgnTBztHqSNCG5erJcZLHUdi+r9uQ27wTFjv2Kd+OehKGqKpJxFF3sId9rRTOHX2U3dSxWn417CVGLhlI/43ewhOTUk+SrKI0GJnuf7xp93FLAFe2ybIELpykobA9igtxJlwi0jGPhlOPcm2aVCpJIVjGgpZCPFRT7KfoZg4DTr2ECqw7XrXkpcAsSUN0ZgGTg3MIYzY2AyWpqSBpIvyRW9QveB2N0qBT/wB6PoQuH2NNLf7U+U9yf+kV9BO1cJnl+6H0QOacdikQZHVd3zhZydF5JKGW06bg6a6ROFwgnNtLlNlSdQBOVdi6BBWKpuG+WMH4NsnTcdbLwo+rQX88tyD8MGeNEb0b/LNFwXrL1yEl7buxMIJNtMWp8vVfi0ik6Nf900nz8L+I/oq7gaMw4V/Bf4zb3sVyuVqEXEaSiVpYA2h8/W9RocAa24HGoOjcvvi/CeBNx8HJq8Cnv0lMGkV6I9eLNzedc6f0brlIWDmGuDBr3FFS+09yTANJcY/B67ApM5zQeqaYX13DgB7o0poeE8SvcAAXuIiIZnbfobQ1P5sFNIyHkNo98pbsaSTxMJTc76JS17ZhDSSckmDH9MQZNNASCT6yNExlZHASpsGLZerpYFOnAYQkWD2lrEYol6lnpSmvBE+VUoaJJIMv1NBpl0l02DZYyMLTiZCx6ZBB+w6Eu05uHwJx8DaTc/CgpVLIwoyVMI0sIyCpvck26ZBjHprhMS9DxQYdZD6dt6JgP1bLmkI2ra8blnlsIjrarWxLoGTDxhpX2QjrIXAiNA+bpwVkobDJ3dw31CvpEGAxR92OHujn2OOo2YMwfot/kzD3NHeyMzxgGLK0DZMGerqyMdpCC1KXnRlIn0aU1g1c6gnXVpeNu79Ruu16mEki2AapIevMdg0qPo0qVOjbADaJ9j/AGDumQCAHH2hcJv0HQl0nCEtKqMJ44T58pQahACDpiCRcomtHCxO70ztcjVY0iBVT4J8Q3TVk9xhz3AuV10jmrfbZnnq7c7kgFmnCY1ZyFDXU0x9KuE9EdG2aXB+C+pJ6tKBYN8Pyds0FGMIrQrWVyx0Nm0i5pONPfMhVXlPEtWRwsRpgE+cBme+EgI0puRj7riNLU7SEG4MOPUkqpY0yGwaKKx4JQ0WL+VxEGTTQPzimxjULAKHkSW8YlBP0o0IrTytLFI9icp2Ol9Jg8g02NeiCgZHBMqIVUFCqjpYi1WCF7NxdKwr3jOOxdUe6P1PWqhMcRpk7cRi06BfXRxgH0PG+LOxkEoJwzSUCURQRUgwQW7epvqnFV6XqxLCMZ/kZRosj/ekMYPckww2TsNbfWfj5J4rcVDXTYX7z7z5IbD0GuCIb7IPxvlsrkv4R4T2QiJyFxZs0CJ4ZuCq/MMcwjyLp/YC0f2L7OEAgD9np4BDBQ2hgyUNTpwG/z5aHNOgoZ7kZ9Mg61OySEkD8QZ3UyGqTYMItgq1e1nXexJv0xC3ehLDtBB9m4ZiXSQa1AACiUyBoZ95Cl9clCIo6tOhKdQeWIqbd7kc8JcJl2APGPUYvz3FYwgtbz8bJGkQmAbn0E4Vp6EaESst6JFcloBp0PCepIQqIvQZdwEDJwFr13trC2HToM6bR2j1pODsxYJ9Co7uW3YdugdOxw8yRwAwhtC9B8KpImvTsCE3FzdmjvUWkVfEXfmJmsWFPKRPI+aPHcD1ZWj/Vi4/ewr1aG4q3sYAHD65A0Det3WyDph4BNOBBOd/OJWQiMh9bRokRLCgnhQkTr93zKXAST8BPvYz3+qdGAH35Obh0O7/wBnpz4u5Ff1S5IkR+qfn/n1kNwxXcKOWNFAQ/sPJSsCGz/U276OeVAguQ/wNoSmlnkjkMtj9jEPS4M7PnPDsfN+8TAOAyN6TOEJGImlgmWG/Z6on6UCXqwa1icBRlHqXy5c87mbg0CsLKUE2aW6VwXmUhEeUeUd5ScP8j1+NpgseYRrzU0/yHnzJ84WTNLjqSf7tVRPUEqIIfS4HcxQkHYgiaRizCDjvMWD0wd7qwkgaghDWe1IZDnGoikk5+DP41+o/YAds4/RSG0IbpqFcILwutSUQy9dnVmMLbRcK6QR3U0saxLxfPn5G3kUdS0nz+u2Fw2mm3ptOnoVPLxmHW07LE5EsEyBIGgghOHKGrfNYMJbyXYAySQOfFrQgs1YDMPVYoMEbFIUty6oEv0qH8MG3xHZBgMWfA8BHzS4VAiUN+Q2DH1OJpEFkkgAP08YHHSP5IGrONWMkv/KbwMKL8Y+F33bv69g0WMGG0FrB3ZQ2DWG3LbcOP5sGonDDaGl4T/pp8yc8aXKXq6yUR83AsKhHGqJf9Wo+ETXQB0/ABwR3k3o2E6Ri3DSSGICC6kkalPRpFIKEYRpkNhl+Ng0qFVsBgTYNwr7lfiNLKWmIWT0pTlpQILCL7anK/11kiCrNOoS/Z+yo8JfNG/Ayq1HSwKknCd8u5tpEhO4tYNUSICf2LYiuInVsGiRNKYy8iEz9h2Ma3FN9lqttrEvg8pWT5EwASXgiHQ7r24jN65bjV59e4GlD0lnmt0w9Kdi4z/c+kf7UyGwBh34B/7PgLjwy/KzAkhxEuw8N6BhCE2jYNDBphROHAEmD0qahsR9w+DWoH8Kocfm5XM3/tQhBSmGNRQjycRrc+y318rxqAkGe/lZezU9U97OIXNLgUU+SxAmx3QYE65Gxb3wAACAASURBVNP+pvkkjO66DX/KTi+kZVjmTCJpYJdNkE2D9xStek9EDSJi5Hx5unMYwkp4HTDTwONOdfIx9t9Uk6oIAOCUA0d6qg0bIGp99gD1TU7S4HSCqX+ET1nPHqchAdFiGuTfSIw7TN0XXTQNsP/KxqsIKEckCsUa0aYhVFOBdgg+jAAhhajdenX52Bvmx5QqDmzk7YeTNKyeOwIAMGeU2mvSEdMGa9Tp15rbnkg/ssu11OpJxntSucCwfwT+QdlcyF3mcdVKDaFtqHxQ8zo77BTQE29zpxTEkkaHbGtgiEtOJUPVW/amvwqOtwZ1HlF4wDJDz1y9HDOv2yDPTAhACNYsXwTsmQrc/DtkJx4JvPiXwP7giK8Dm7yqUn7QsWnw5vOXNBR+CuJ//rTd4uuUbJacT24r2OVqW2MKe3vkKj22pIHi77mRWJl4AgAwsLUB2O7Nq7RpUHheum3iTbi08S6c+vhsLr0hSYC80xdZlPSCWlXepoF9PwnN0zJ3zTFOBgJcrnZnFcyMgDqZ4b7hGXoFCIADur6LYWQbfjPU63wCAHDBJuC9F4Bd79ruFwsl2b+Sr8X0E4G2IUDbMODG/fO5qWePX7dqKjqnDcGL7+7EF+96HoC+ikN27tnApKNw0a0f+uSikp9M/e0TgXPuB5r56M9ivm+P+i/gRUkWT3My70lyUofbD474JjB6oUYDATj/SWDr88AorwpNUYhTZTaiREXVUiSbhoD793x2MfANt4VgKJ5pzGLgzHvwYd0Q4IZnAYgSPglCShrOPWQc9h/RFzOVrmjlkcnDIMdJGvh7fDwqwzT0DggnhbJN2SNZkKyTHCwkLYJMztEfV08QpTs5haQBAI6cMURZn1sPf1qbzgacAjBteHkGHUmD/yLwu82T2Pwb7tOUwvB+jdjywV5p7gKa+gMXPY9cNgu8uETZ1rXp0/Bq/ST8uK7Ze3PUQuD1h5VltbwnEeEjJ5NWyV7GyPn2B3n3VgCCxywizJOgzdRHPalPQwJHTxqKzmmD8dun3yqkpxKkII0isPfa/86uQpJkcV92Nn6Nn0rr0/We1N5Sh4c+d1jejuIovPLY3dz9JOeaUbImHPe8eea2C+4zJpWetsRqiKd+3uWqN7hbd0aXaUgj0zhAbFGrXwZVDgJsRT9spaoTSgo0DwCaDwae+y1TjvB/ITk9JcQmgve6BD2Bd4nXJS0snNCO17fvLqRp+3pPpJAYfyi6cXdwXjCnpdzBVQIYNkdegMm3xRoGYLc8H9dIzvuQin2L2/v8JB5h0NQ/HuZDQKwrXiCKtVx++1foTSpG0gCgbxMzZkUFdwMwcj7ozr16eQFNkYrbp4RFCkEQZRjWt1Fp56cL3qaB7x97aQyhewu4SUrlBJ+wiGRDf+sZc/HjT7ibm1RikU/znMgGSBo2XHQIhvSRRbYUO8obnYnqSd78fryphFD32DT4Q8ZUUOq9x9CHqorkvwEtRcEfZY/AZjJRfvPEH/qW1d20gyUNknrqW4Ch7ikHH9BPmHXSDYdJYzydnNR9NZerKZXAjSfPQjJhcYR6IxMF1rJs70ndqMN/ZFbjaToeqo+HbdPgfe/sO/jF2Qfiz59fKt2QT5g9HE/9++HcW/Iy0m79iYJNg5smuue9sOd8aV/dPrv1Sw2hmfZ6GFMJzq5EQIJQW8Jz+Dqm34ZpqGo0xuS/v22Y+1tK6DAMsWoOMftvBgmtnSbWAFGsepLMpkHT5q1Ps4+7Z26RB3tPchDWqUIlEa9Ng+JAMSJSSXsM2xpCnEOHMVDWyJtMCN+A+jbuMtQjhpQ0BGF4Pw26Kqg5zXylljQYpqFcEIxR5epJItPgnSajBrTmjZnz9UglFnaa1zOEZLgT7iJvqtNc8ILUZHAfP0M2vl0PgS+VNPDEexC97rdERBJb6idcmduLIOKeqjaS1g7cS9SBm3QNoYNUt5TviiEc0oIxcBAjwrUz3GVYn6CTsGvhld78AJKMS+Hmerdti+hvfjmFetII8h4A4PefWYSDx7dz64HFwNZ6DGip5zZ3P7/sluTl1RGeabgztwCocz2OndR9NXDx3wvX7Gkv954dZot5ni5GrU+LeKlrYbpttu5qRm7OmcDU43BRz6d98ylH/fQ7gYlHAkffxGSWjDmT9skl4+V11bdg14GfxX9njsJ76KuU3LIqDr4BopZew5UKA9bfjPtTby6fv3QiVkztwK2nS7y6KVopQME0UOFIoZoRq4eemPePY/YfhsMnD8Idn2K+cVFcrk4/CTjqBklWf6nF6rnD0T50jJswsROYcgyXh3t7cbhc1Zgvt58zH4dP7sD1HytONQnQ51FKzDMY9aSygSWcIRcheZkGyUIhRO4hh88CQDNwjcV70tGCsKBOnD0M9z/xHE5bptjMWZsG33qJJFdwMCJf9SSOxg54PhkDExKl/Oyw3p/yKXyGBReoTwhZA1zWEJoE2zRwGH84njzgBlz+kG0k0D3/M2h5+MtObYVsnKShjrVpIV7GSrEbUoV6UjPpAgBMHtLmuceXl9WrljQQiWtgqT0BU+8TdBLQNhTAa8IdXqIjU4/QtWm4In0WPgYIhE/tnJDuk0g1Aif9GL95yl9tR7knjT3E/sdllhjxM+XXLhgDFXYffAW+9uB9YhGhL+5vX0nDoouB+67NX+jseAHeazRdcvdvqnM9+Pk2F2wIzTQQXF+VQG3SEOEZ/N55BLS31OPWY+cJqRFsGk64VZXZt6pvnDgTePo5N+H473mekbOVDxr3mCQNB40bgIPGiaql0aA8kASQY+4ZQ+jeAiJKGoKZBnk9lrC5S7Lk//qpYrgV8IbQWhAWYyph4bjROSyeoNDpIyzR6LkJz01BdB1I68tMyB09deaeTD1JuQ4jLjy/faS4pZx3ucqqnIkVLv8SrB6Fe1BBRYFFkJ2E6PbxvREr8Qr9az47keZLMqf2nHqSj+G+iBz1ulx9PDcRN2SOV5RQwUfSwHmbkkgaHAvqADi15FSSBgnRwg6V7CM2p+tmpJHEDjTnmQamDqOetO8hQD3Jj9ARj2GC8pROPcnRG+U98KkR4SDHjmDKp2m4iq52qEckwljFHBFaWr5Im4ZwdQEYqFANzoOnBcojaYgTfq1xRtJGPamXgJM0yH1lazEN4Ak16eaeTwrNNESUNASCM4QOr54U1CtdSUOgaozoaikC/E4DOHzs53zTmmMfZPWifBcqpoEE20lwrVoWZ8TP70/MaQejnsTGbLAkjJvqVYuG0NtpC1b3XIOX6XB5AZ9u83XKG7ckp28pSJgwvxMf1hBaFqeBwQs519WlTD3pffQpBOyxO2gkDbWGS5btBwA4/1CF6lAYyKK160pHAw6axKpOnT8KAHDA6CDbDLvQvNFqV5MsQqsn+dmZKbJJCcwDPgkAeCg7Td3WgBjGSIbxh8dSzRHTi3PZySFuQ+igw6ZioVNXR35sU80eN8OAIGlgL9ptZuMxi/FepiVpCM4SJ3I+74CWUdJgmIZyQTjFlHtPEq5lk0QgouU8g51YMkmD78mQBL6iUBnBKqQFeU+SpMmXVxDXwGaNyDToVjl5FdDPVScIZhokY6rrPQkQbBoYJs5DwEvmiCBpYOeuSr0ixcx31lbGcbmqA9GmIfKHjfMTr65DatMglTR4+y9TCZS6XAWAy14BLnwa2+AGI9SyaUiwDL7ZumsB5x82Hg997lBcsnw/6f3IxpnSjd9P0sBLXIPyzB/bH3/+/GH4xTkHanXttnPcGBPe+iWG0Cx0vw/akgYJ0zDuUPxh2UasTV/uuXXeqDuBy/4J1Ld6y8WBU/4X+OyzQKuGZ0IfjB/UitvPUcTyCIuw33CnWJgJW0xEaG/m4CzJeuBzrwIXPSudU0R1dcadwEXP4SlrOnO7CiUNPs2VU9Jg1JMqAJmvbCedh4xpsLiyoWwaZAs+iqQhrD6kxZ9sK/skNfQLljT4LRLxoKpYQ+hYT3hDvUc7okBQRGilf3VmnLOCVy2/gGd208zmT/wkDfJ+NNXx6mm63wrbqDj6+6aSX37EueyDmCQaYnHIGfUsVTANze32PzzP9EvjI8VKGox6Uk2AEIIR/b2nnoX7oSqLniGUbRfs78rQvvoeX9hgjn61y9akNjOgyRSpiNU9jUORxXue9G6ryXZrWypYCaDviFiqGtga4HREF+VQTwokqmNWTwJsd7cqKA/UUkDr4HC2fUC8khQN+DXHSxpK2w9zXFUBECjodw3vSQAfIdk/uJvEb7enUbmkgQvo5Wkg5KwkfobQQUxDsE2Db9PsCVtgZj1ReGyIdNrj/76U/JNCPYkQgY2SShrUTIPKpiHF7FyNdbxNg2dWK3ZDlfckXchVxcT69ImXtMME+ImJVbPMJ5I2oMk0cHrZhmnY5yBTT+KkgD5Mg+K3KlNRe65YmO2j85tqrrsoKqMKArOjrUGevWy0X/Frtrle8s2IMliielKx37tSqSc15IOlqeJ4hIB63tt3slGkWmWEn+pzSwMvzS8ljKShAlBJGrQ4b4+kQZIlf18ZEZqFQtJw5PQhePODPZg9MkhXVaPPrE2Dr6SBSDMFLQK/V+n3/bKv2QSNr2bQegxjCB1mY8r3M0g9SXmKyEoaGAKVUBrOpqFISYMl856kgGjTEId6kl88BNV4PDbv25jw5Dr8W88FhZ6owK65qUNbUTjYTPifEAapJ1FKBfUk/XeRK++BmEEIhFL3kKon6RHfXDsaW5s24aHl2EAmX9BjdqKpJ8kn/IJxA3Dxsv0wcTCvhlRLy2NIn0Zcs2oKWuqTwF1OahSmIW7CMiI9E4Sz7wOe/AGw4MKiq1IugfyNHoSV5JZ35vjt4+y8MOpJvRR6XmQCXNUh6KPjodAlHUlI71sWwb+p/H6Hha4aTkT1JL8cHDksO+UW2pKX1Id//eK1nki/kIcEG0IrwUka+GXvEct6OCtw91lVBH4ey70nNXM2DfpbbfGSBlmdfqea8nv/GrIclzeOxmt790rvq+pfPnkgwzT4e2/RsmmIaAhtmIZegkB7I705ofSexJQv1WFlobuC9FKJqIbQigOVC5dO0OtoKRDTS/3EwWPsH3f55/PvSxlO0nVVivzQPh5Y+dXi64GcUWDv8ExDFaonBXw5nXmRTut5/IuK6pPB7AMgoNLgOVelPwEA+M+8S0npFqMjacj/DS1pKBUmHoEsSeDR7BR89fjp/D0poc6nSbWqFN8Sx9vHCXOG5/MxH0IEeTcqXj4vq/+2sx1jQqFOjSjTTM0ANNyjqsAwbhlW0iBuRUESMGH+8R9rhmlQqCeJ1dl9kEO0aYi8RWdcYl+faXTRUp/k37vUQYFTPzvfWOs0f8Y5SD2JEMKrOIX46GcM01C1CLXLyNSTlPfDtyPsurq98tbjU7Sw23AEpa6kQbNPcRCrtYQoQxW3TUOpvSfFAOUBaz69G6xLax2mobzzrFoOfwzTUCHIJvBvcwsxo+t7+HbmRDtBoZPN0ppSL0wSAsa+EcQ0lGhWNvZD4gtvYcoVD+C4WaK7zGDxs+wDxunVM+m3rp2LW06bgys6J9n3hG9OsZKGD5rGSdPfpjazIqt/wfh2eY2CG14dBKsSKaCyaQD1Sho8jfKngklOPUklaZCrJ3Vn9DfaoiUN4jVJ+J/oK+bf0skd+h6fWPWngFPgE2a7a0Er0ngimiF0iFduUGaUzXuSBu3N2coFUAa5GacgBwu5ued465HM5YvzrmevPXqqndA2DOg32nZ3WedjPxcCv8vORzdNAft/PFQ5bTfZRUNjsOefZ/9dckVw3nln238Pvih8V4RDDG2ngmHaqDLmTdn3/LoJzTRUkfekcsIwDRUAATBuYIv03g60MPlULldVRJtTzk7TkzQwm0dOERgsDqQa0KcpwPODdKFSuVaVwoNUW0MKK6YORn3Sfi5O5B6oABbMwLzUcSSyh13jSV/R/XW7vjALW2EI/UjW/rB+0OIVpQfZNCihZBo06vRzuapoLsmI0hoZ9aSebE4yq30MoaMySRLQcYf516EY84RFsGyy7SN9eL9G+Ns0sHX4T4ZvnDgD911yCE6YPbxo9aT9OuT7CQBkq+RjY1AkVPuj7/38rZBrJyh/dtWNuGvmrUAfvZgpFy6dgH986QjMGZX3bmMlgPOfAv7tUf99LMQed0H6Akzr/gHQ2qFdBqgym4aVXwH+/T1g0OTgvEf+h523PYIacVV4Tyov1FNJImnQeR9l955UHe/TMA0VQkPKwqarluFXnz7IJ5fK5SpzKZvbBUmDmC6jvhlJAy0h06ADxaqWpSpVZAKq940IrXEcl7PqkDvoAk86F4QLAFbaTASWfdGtUizEnfa4HbkgfQHu6HMmnlp0K9dR2yajeElDVlj2wQbG/Il5QiVpoHJJQ4r53ZPJhTCEFiNCh9s0O9ryTOpR3waG7A+66iY8kJsJAMiSlKfP7PvMCgbTn1s5Ed84YQZ+/ekF0o+Fk5uLzTB2CdBnJDBhhbR/CYsUDg/0vCexkgb+1s/OUvvT35sJrtqgUgglaggo50d8a9TOShp0aCaVaquibF1SmOOJpIa9m977Ifn/3Rg0xRLBJYDuhyoZInp1mLxcXzQ8K4aqr/bVk7rC2jRUUUTocsIYQlcAFgGG9W0EIQTNO9VDoCKhA12uFpgGcWOQuzQtoJSSBiWCDflkz6iMR+CDZKAD4+JPtQtqLPPPBaafxPv/FqtUbEzb0Ya72tbgx/NmAr9n6hY9HUWUNOQorxYVuBkJYuaxA5uRShD0aUwJXZDbNFgWQSpBkM5STBrc6t/eoCnA1ufztUVTT/reaXNw39+34vSDRtsJc88E5p4JkqPYkJuLU3uuwIpDDkFb11a+IDMeD+T2x9LEJqCP7V+9IZXA6nmOr3UJ05DvZg+7paaagM88HfgBoqC+koakRTBlaBvw4ftsi1yejrYGjGlvxqvbdnvKv7arCgkoAwAxqCdpui7VcJ4EPgBc9DkT62zTNe4usZvJXgcSTT0pFKqMaWDBzZb8upk2qgN4hU+Tos9I4KM3gAnLS9U9KapF0mCYhgoglXCJIT8x8B/7nYSTtv4nn0gsboP088LklTRIFkKKCeDTMsin1+VAvuf1jKpFol6uvsvRzv4fjDXzRuC9nd2Y2BEQ8VPTU4c2ggIG+XhPolRkjCjzv6qUDxh3nSKBGihpEJiG+mQCf1u3AhYh/LtPunNJlEY8ffVy7E1n0bepLuBb4pbzEtJ6z7t86mAsnzrYWzOx63g4Nx2L6tt9mYZL0ufi6aWvAzPXaLXpgJM0EKLnOYzyjJyIJ6483Fa3i2gI/coOQ1D1ChSlnsT89j9w9c2jA0O/q1BFLybEIG28aDGWfftPQRVK0qqDyNXFmYdM1mMazroHeOHu0N+GYmEMoSUghCwmhPyOEPIWIYQSQo4NyL+QEPIIIeR9QsheQsgLhJCLhDzr8nWx/14o7ZMEgKGa/NbuQ32PBc59BGhiT6t5NsM/ToOOpCFhh16/9CWegSgXZCdlDX2ANbcBJ/8PUNckfUc6evUOvnbCDPxg7TyN06gYJA2h4jSEC+7mUU+KKmkQ9O4D9eklD9WQSrjqBqt/BvQdBZx8W+F+irGkpJSiuT6J9pb6fIt+L8ktN3loH/9+hQTvUtKfE/0QrcCSy4F+o0K1kWbPYUIQ9twYTDsBDy+7s3CZct5zgCG0ahR3G/WkqkWoXUbmqEE3SBpXMrjVYgJEFeN5qRKoJjvosiGETcOEoMM2oCbUk1jIgtk1NjGHlX5rqW0ocMA5QL3Ge4kRRtIgRzOAZwD8EMCvNfLvBvAdAJvzvxcCuIUQsptS+j0m33MADmeuq+Yz6rePEIsAg6fxPt4JEfT51cSDlnHlyAP9Q6+XE+yzTDrSTZZ5T4rJpzhVqUdF9GPtv6yFjoZyuWojF4Kx4aJ6M0zDLafPA/7XrSFQ0pAMMGCfcrT9j0GCUU/ytSMRE5iunDp/DJ+t1F9dbZeO6lGOwjRQCOO68uvY9VoWwFN2NU665R9VWoZ1qyaj37a/hS5nUB7E6z1JXZmO+g4vwQ7Rr1JC8wU1psIdwLCoDlKszIhoCO1xn+1XPsh7UkWjLkvaZr9zVSguq5Z5WlVMA6X098hrcetscpTSTQA2MUmvEUKOB7AIAMs0ZCil78TY1SLBShrUzymnTfj8ckmD/dfXe9LnXgX2fmC7vSsGcXK/KperPtIUIM6w6THI5/0O0T3aNupNU3Ya7yHwFX288/yD8daHe209+EJed7OfNcqVXNkeugKedfg8YOpxQL8x/vkYsIbQ4rP4h8pgAsfpBgUsATxGmxxkY2M/b5qy6kn6H0XR7S0hrn1RYZhZSYPE/kj2Wvs3parmY2NQJALVk3yYhrBNFcGgx0tv6VV2w5r9ce7PnsJnDq9g8LZagjCXgvaI/z51Nr7+hxdx08mzQjSiqPXUXwHrLwOO+W6IunwQYcJ1Nw+1HVTUNbkaFpXQtAiBKhE0VBfTUCwIIbMALADw78KtCYSQtwB0AXgUwBWU0jd86qkHwB6vtgJ2pL0w0facvM5f55NPKUUmn5bNqIUeuVwO6bR9duksi3QmizRTJJvNIJ3mN4BM2iEo+MWUzuYAp/+pVvtfxOiBzrPkaM7znGFgZVwt8HQmCxBvHTKxHGvTnMtlI7WdTqe5fS1HXdPxTDYLKtTJPqfqzJeCqvvCPEc6nUYCVqE90b1uLke5digFstksR1xmszlWg77Q7uSOZkzuaOb6YVHKvWe3/zxJz85x7jmO/b6TQf5s4qNm3VOmnjQ/PuJ4smxLDsQdgxwF9XnXxUS+zGadNcKPiYOmVEJZf5LyMo90Og2aP1VjJQ3pTFbrfWWz/IlcOptFNuMyBZl0BmlCgZy77jLpbm7ztvvgXSe5XBYE4d9VqaOKGkSALLibJiGhFadBkT8s4uUZ9Grbr6MV91+6JHS5fRYhvSetnDYEK6cN8alPkqaSNIw/HLhwk/xeBEQZaUIs4OP/yyeykoZs9e1/Rj0pRhBCtgAYCPt51lFKGV+VeAzAWgAvAhgC4BoADxFCplFKdyqqvCKfj8OGDRvQ1NQUun8bN24EAByTv6aUYv369QCArXsB1TC89dZbWL9+C5Z3dcPhgTds3Ii3ehoLZe679140CcV7snadojrHHx94AHvr2kP3XwbnWd59dysezz+f85xhMOa95zAj//sP99yDnOV1IbftPQuiOLG7uwvOdvHcc89h/fvParbovqz169ejqytRqOfNLVvgaLD/+c+P4oPmrd7isJ/zGOkdm9FzxlZEK8Mgrl+/HvO3bYPKo/i2bduwfv36Qjt79uzGpk2buDF99rnnMJMpo2oXAEZtex77O/2/9z505n/v3bMHlHEvytYRZTwd2EZb9rve/Le/oe29zYV7e/a67xwA9uzZU3BY+9FHO9Av//uZzZuxZUuru26ENvyeVw27Ty+++CKGDrXHy2Gm7Pry8yPbo6z/GKEn69evx/vv23OUZRoeevgR7Gx8PbBH/3rLwjDmesPGe7FpRwuQ79k999yDugRAchk4SmCPPvIwFoIUmM3169dj927+vQLA5mc2Y+aA8GO5Z8+eUPkNoiHUiX6QelKR7cRnCB0nwV564r+qgruVC+WQ4pYpuFtso8c48kC2J65aY0N1sAy9hGmArY7UAmA+gK8RQl6mlN4OFFSeHGwmhDwG4HUAqwH8QFHfVwFcz1y3AtiyfPlytLW1KYp4kU6nsXHjRixbtgypVKqgSEUI0Nlpk22vvb8bX376EWn5IUOHorNzBpKvXAHkGd/lK1bg1R2kUGbF8uVobeCHsSudxWWP38dHpwVw6GFL7UiccSD/LB0dg7Bs2TL+OUPAeuJfwBb798ojOnn7jTx+ve2veOGjbVxac1MjPuzpAgBMmzYNnQeM8JST4TOPbij87uzsxJeffRBIdwMARgwfAWy37y1YcDDosNlcWXY8oTgoIcRCZ6fcL/9fnr2lMI6dnZ1I3PFzYIdNTIuShvb2dnR2zi2009TUhNmzZuGxfz5QyDNt2rTCu3PqVIFs2ga8af9etnwFkFdzb2psAN3rzpPOzk7vvI2Ii/5iv+upU6ei88CRhfSvPvcg0NNduG5qbATye3Sfvv2APL06c/9ZmDGtU/mu/Z5XBWf8J02cCOx8AZZlAVm3Pud+/7YWdHYeLK9E6E9nZyfuePdJvLRjO+dyddHiQ4CBEwP7dO//bS7MOwBYvmIlEv/ci1tffBoAcMTKFahPJWxx0zN2noMOnAu8xPfhhn88jPe6eGJ///1ngr75dOix3LFjh3Zeg+hYPXc4brz/Zcwe2Tc4czHGyRpFg4KGBuHg8QPwyMvv42RnL550FPDCXcDsM0LXZVBiDA2jZqQDZr4MnQ289Vdg5skxtyFHbPY3jIfBamQackbSEB8opa/mf/6NENIBYB2A2xV5PySE/AOAMowipbQbQIGqcU5OUqlUJCJKLEfyaQCQSqrrsywrn89dFalUPRJJl4Ovr0shleKH0QneJUoaUqk6oAgiUNpHYrnPEuX9JNwTj1SqjtfbdtqQ7ApsWjKRiDwu7DK0mCjGyVRK+a782qI+99nvcCqVkj6rm5d45kwimeD6m0jy4+77Dph5lqpzGTNCCGf7wtYRdb6LIJY4Pvx4cnFHmBOwZJIfA+98jt43K+E9aWPra65PatefSqVspwXgXa7qrjdCLPBrvA7JpCseT9WlkEry/U0SgI1WmEqlpCe8dckkuhF+LOMYd4NgXLB0AmaN6oe5o/oFZ5apJ0U4f1TRWEHxf4Jwy2lz8cRr23HwuLw0+/jvA68/AoxeFLouaadqHdX0LIMmA4suAR76j3xCkX1jn+303wJvPg6MPbS4OiuJTHdwnjKjSniG6nK5GhMs8PYIHAghLQDGAXi7bD0SwYy+1ubsIzf2C+7mawhdLdBwGSgjhuLyniS0xDYaqYZQlBxNzAAAIABJREFUom72GTz1eGrO/88bzOq3ZUl/28HdSvsxE09IfE9MYvBgFQqKvqi9hPgjTaO5XOUglJOqluSyEGeN7ElI1bjBiQeEkCsIIU8QQnYSQrYSQn5LCJnI3O9PCLmJEPJi3g33G4SQGwkh8frvjQmphIVDJw5Ca4MGkxYU3E0Xir2tWJuGlvokDp04yHUiUNcETFgGpBrCVybtVWlQLcRY2THCjSJf/DeAKd/Qxx73RLnOpEswR6pQ0lAt07SqqEhCSAshZH9CiKN+PSZ/PTJ//6uEkJ8y+c8jhKwihEzI/zsLwKUAfs7k+RYh5BBCyGhCyAIAv4GtkCCVRJQbepuzmpCSehbK55f736k2aDANkjRW0hDbpi/zgx4Svo6BPAnhvCcB4VyuKtsqRzRQBlkhKo1/bDc5c2OXK9/8bYrKNHAuV/X66wnbR6xg/XJNd729jGcAgEMAfBe2Kuoy2LbhGwghjlnM0Py/SwFMg23PthJqVdTaQRFMdFgmoGoOxcvQkfYWr0psaVAtL9VBjP1pKIInb8y7fA8Zt8jBNhLeZXxjXcBaqkJPSsYQWo65AP7IXDt2BT+BvfkPATCSuW/Btj8YAzv2wisALgdwC5NnOGwGYQCA9wA8DGA+pfS9+LuvC73BL0wSbm0Tjkj2kzRQneBu1QTVCZgkmZU0xLeUYrIE1AWjiiPaNHhAkTd7jdhHJTFOvfMkZgTGaWDhwzTEiaBXF5ZpKLhcFSNCa4AK3phgJXimgb238CLgnWeBMYfk1ZMU+Zyqqob6iweU0pXsNSFkLYCtAOYA+BOl9FkAJzBZXiGEXAng54SQJKW0amL0hIb0UENv92OlVaVSTyoNovYjuNz3T5+L2x57HVceOSViGzWOOCzfj7oBeOU+YNap0ftxxu+AjVcDS68KVezjPVfgnMR63Nj4aa2gXgBw5IgsGgaOwOyRCnXAzm8Brz0ETDtBfr+CqBKeobqYBkrpA/BZ7ZTStcL1TQBuCqizvLG+QyK8pEGtDy7mLo96UpEzWWslSGwa2AePazXFIWnwP0YXLosdj+KZBoLSSxq86kgiF8FelFk9SbEAG1PRtsaowd34PonSRKaPh68L1Z+qof1KB+eIc3tAnh0qhiEuF9tOGfZvrMjmXJe7eZfQVpZxWe3TZoZx66tyUe26IQYyGa8rbxYlfU4GFuPdLExbCcZ9tqrckgn9sWRCf2WeuJ+Rc51eBS6NSTZb2K0oSLQ+zTzV/kcR2X07BkwE1txh/w5RxyO56XgkNx3DWxq1+p5Op7F8OMWyZRORUbm6n7XW/pcDkKv8GLFgv6N+zxt13urmryqmYV9EaPd0xOK8JcklDXaal2moTQpCpmLBqifliqB6OXq2xKfc3uBu7qn0ZelP4ba6L+PbmRO9/bJTAAA5GtHtoq+kodQ2Dfy1v6RBzTSUhLlJ1nP6q9OGteHZf+3A6rnDQ1XjdDsK0+CtzBI82Sgzcley91M9J8bxg9gW5DcAeCQvYZDlaQdwFfhgnyJidbENFOeuWIXGnm1Ynv/9+BOP470XuzDx7ZcwKZ/m54I4y7hAfumll7G+6x+ePM+8T+C4+b3/3nvRrGFmUYrnZDHprZfhGKyEcbG88IPtcEJYRnPN7CKuZ1y6Zw9a8r+L7VMcGLhjMxYw16Uey/hhz+fuvXtCvc/ae04bOcZeTud5S+Vm2zANFYaeoIE/Ae9oa8DXjp+OxroEEhKKwhVc1wLTEEwKyrrNPnZsun6su9cyqyc9T0djVvctBVUhqVErEdPDMA0KaRUtv6TBXxhTZklDoh6AG67ll+cuwDsfdWF0e7O6jA96IjANC8YNwGMsyeuRJkafi73QpoHFd2HbLSyU3SSEtAG4G8DzsD3qqRCLi21A4mY7Tny0BXjO/nnAAQeCjj0U1oObgXfsND8XxNkcxcV/sYmI/SZMQOdh4zx5rOfeBf5h+/RdvnwZ+jSq+1/S52T79OAzwLv27zAulhPvfRfYHb4ci7ifMfn6uoJPxqh9ihPkn422QncepR7LuLFh52bc/ew7uPiIaeicHexKvlxztlQQ3cWrEPU5dd1sG6ahwvA7CZQSV/n8aw4YKbvLZpEwDVVo06BB8Mu8xyRikjRwbzmhdgtaEnhO0oPHJ7JNg6JcObwniXPcl8nzkfb0hyoWYxFI8o7WGlKJyAwDAGQoawuh917XzBuJie+NBp4M2ZjIXMiyVJ3xZTwghHwHwFEAFlNKt0jutwL4A2yO8DhKqVL2HreL7WLLqit1DzWSyaTtzpcxiPdrL8FsklbCkuZNcO6v9fpfkudkYfF90i+n9150ENszkvj6FAs4l92k9GMZM248ZTYu3b4HY0Lu17X2nDKUYm3q5q1CKnLfghbdFzISqPPRqwmmQQNSu41SGEKXWNLg9Z4Uoo28sSw/phHVk7h6cyVjGi48bDz262jBx+fzDG5U70kNJD4d08ITJ5XemSMhinpSwiKYO0rwABLW1kmVo5fxDMTGdwAcB+AwJkYPm6cNwAbYIQOPppR2lbmbpUFM3pNUjCTvYCNyU/Git03gqgL7Da2995ywSGiGwaB41CYV2Yugt1SjLWiPTUNVbgzlVU+aOtRWNxjRvzFflrnJRaMuh6QhnIeemSP68mMa5oNqqdsq1Qfj4uUTseGiQ9Am+KBX2WvYkDzflGNL0T0bicozDTLENSK90KbhuwBOBXAKgJ2EkMH5f40AxzA0AzgLQBuTJ5pPx2qBNLhbhGo0ihajEhcvIvZDIxJ7+VEt7zSPqhljg1qCUU+qNEq4bnuNpCHAe1IYk4Zbz5iLHz3yGk6bP8p7k5M0hHtXHW31eHdHN2aO6KvO5OHhfNqQENZD+zbiqqOm2CSRtEIf+MaEqKKPh0zScPRN+L/N2/HrXBGRZRWgibpYn553uVri9SaqfUmyVM2JcXz4dP7vA0L6JwD8GMBsAE7UqpeFPGMAvFaifpUe0vmk6XJVg0BkY8NUzbyJStgefq19IDBjdbz96a0wDISBJgzTUGFo6RxHXNAldbk642PA5juAgz9bXD06Ng0y9SS2ihAKSkP6NOILnZPlN6OqJ33qT/jfuvH46aOv45xFY5XZPDWOWgA89SNpXtUzDWxloqtG9Z6k1VLpENqmoaENl2XOLU1nksVEq3XhEGWZmJgGvZPe4Dy9TdJAKfV9oCC33TUN2XyK0Xl7UPyfyiBiPxr7Ap3fiLcrxaJq3qmDEqj4GvR6GKahwvDdR4pYyQ9ffiiaH3wIeJptLEam4bhbgCO+DjT2i+6fGYCeepL/ZlucITSDKOpJTQOAITMxCrClAGEw/SQAwN5BM4EbxENREfn+RFVR8Bn7vYhXRScI1eA9qTCnYrJpcHqdiRDcDYCH+ItCXkgNoauNTjGIDsIflfB/Q1QTS2cMah5mczCIgNrUV+lF0DvRCb+4h/drQr9mgSCKc5MgxGYYyoCgXnuDh0UE6z2pJIbQouSH2OLz/uM1SjvRwSPaNLQOlveJUvwmuxB/yU1GdsmV+vUVgyqICF0AxygWD857Uqk/yhr1V8+JsUHRKEVASwZsjVUzb5xIwxOPrGw/YkGVvNMCIjrVMNinYSQNFYa/oCG/je98O2Lllv91NSCielLIKtRl2YuYCUgPFM8hez71M0Xc3IfMBFZ+Heg7QmwJ3ajDmp6r8Mri8vgO95prMCllYhri9p7kjGE2pnOY0JHiFaga4s+geLDrwVkzsaonVaFNQ59hwJXvxKZGaMCAGKbBIDyqkIrct6D1Te/ZFa3y2acLjdXmcJdyO+P061kCshQRoVXpOg9Ii5Q0AMD8c4FJ6hO7chEK2t6uSjhfCz2I2eVqllVPyuX0C3piLsQzGIZn6EUQIrnzf0NUU1PekwCkGnvHRK66Z6i2/hjUAmqTiuxF8CMOPPfGLA5Xef8xwNq7mQqrcZOIZtPAlsrFZdRQYvUkFUIRiDG5XSyAIeDLRSh4R6v8koYCEvGeYO5Eo3uRalRnjANaMVtK2wWDMoKTNOQZ0giSBtU6r8o4DQalA+uB0GwUBpow6kmVhs9aLagnnfor4IkfAqtuCF9/qilav6oIQdtZSYK76RLkMagHSNWTlClxGwqX32+G7ysrlyG005dkvCpp3agDztxgE3V1IdbesNncZUKLauPzDO7TgH9u282lGfWkXgSZelKpmjLzpgSotndq1JMMwsMwDRWGFm0w/nD7XyRUuTM1LZuGIO9JtS1pkBF2ShWeYtSTpKgA0+BpU8EolGMMYvaeBAAYeaAqmxr9xwL/9hfbGxeAA8b0x0FjB2DsQP2Ip988aSau+u2zmDS4Ff/1wCsAzIlxr4JUPSk+lN/5soGBQa3BMA0Vhh9BHAst3Hd0DJWUEtVqCF0C70kqQ+hQlcSsnlQB+EaELpchtPPqYooIHcvJ7CA3fkjCIrj9k/ODGuUuh/VtxA/XzsOfX95WYBrMiXEvglTSEB+hH8YExyACYrafKhpmbzCIAGPTUGGUfNk2D7BPMD+zudQtlQyB6kmxSRpKawitepJQhtCIV9JASqzmIIN/i+VRT6pP2nXTCcvthJpU4wvmQo16Ui9CTJIGMyUqhGNvBvqOtP9WBdjgbmZSGOjBMA0VRlk28EGTgX6jytBQBMQSETomRFJP0m9d7T0pjCF07euhDu8rGAgvutj+O+2EkksaLlsxEQePH4CjZw4FANAR84Fz7gc++2zsbVUKLKNg1JN6EyRxGuJ0uRpbTQZSdEwBPvs3YP9TKt0TG73gW2JQfhimocKIy7Vib0bQO4rtu1li9aQw0DOErk2bhu+dPpdPmHsWcN4TwPHfLznTcN6h4/GLs+cXJA0AgGFzbIlcERg9QN/2IDaMW2L/bR3CJbOzwkgaehG44G6OLlGUiNAq70mGbdi3QKQ/DQz8YJiGCsN804u3aSjGEJoryjINFR4Y5SPFbNNQCfWk8YNasGhCO9MJAgzcD7ASSu9JJ8weXsYehsfFy/fDxw8cif8JskOIE6v+E1h6DXDWBi6Zk1zt8/tLLwKRyFdHh3TDLVZjsO+CdblqNgoDTRhD6ArDbwPfJw5+NJ4xKE5DMa+JO11j1ZPKaAgdrpK4K6wMlCpZCknDt06agS8fNw2TrvpDiXsWDS31SXz5uOnlbbSxn6vaxYBVSTLqSb0Uzr41YZntknvgpOKrLLoGg9qC2RwMwsNIGioMo54UjFJKGjiEiQg9/ST778KLtKt/o35ChE7lITOEjmXuVBmpoJA0EELQkEpIChiIIBzTYPaXXglHPYkQ2x13nxgkcVW2FRiUGGZrMIgAI2moMMw3XUM9qfgq9GClgvM4OPa/gYUXc24yg3Bf35Pwj3d348HcDKwX7t178SHY05PB0d95BIDPI8Uex6AylIJfHHT35z6/OCKCNYQ277B3ogjvScoaDdew78LsEwZ6MExDOZGoA7I9QPOgQpJZqsFIJfxP/ePznsQsh1wmOG/HlFDVjxjUDzc/f7T03vhBLXyCTnC3GpY0KGnZMsVp6M2wDN/V+1EC3dV9Qh3WgIGxaTAID8M0lBNn3wv88Su28WIevsHd9oWTH40v1eQhrb73R/Rr9L3v2zx7wUoasj2R61ThgsPGY29PFiunDS6ilpgowgkrgJfuwUP9TgQ+LKI7cUOhnmSgD3ZPMZIGAwMDKYzDBIMIMExDOTFkJnDKHVzSPm+oqEHUzB3dX5p+29kH4qGXt2HNASPj6UuKYT6a5G0Wg6a6JNYdPTVi6Tx7E5ekYc0vgO3/xOP37QHwVvR64oaRNBQNYwi9D6AIsYBqy90HjqgMOJg4DQbhYZiGCiNUYK/eiHlnA3/9KTDlGGWWse3NWDi+HTu70nhmy0cA7C1uwfh2LBjfriwXGlYCuOCvQDYN1PtLN8qOAePtvxxRDWDsocA//wiMOSRcfYkUMHAiKDbF1sUwUM56wzQUDda5wj6/v/RaGPUkgyJhgrsZRIBhGgwqi6b+dpRMH+KGEIKfn30gKKUYc4VoQlwkxA/lgHHx1h8RhW6dcz/wl5sZlTZhoz/pR8BzvwWmHltcO9WCTJf7O9lQuX7UMFi1RkMK9FIUgruFhzK4W/XtBgYlBWvTYGCgB8M0GFQemqeh7KnpPrPJDZsDnHCre83poRLbV//cT0SuPldtL/K9F93fRUZpNjA2Db0WpVBPqra9wKC0MHuDQQQY+X8Vw2zipUe1vuJyRYSmFZpkSrWZd58vb0eKxC2nzal0Fzxgh9TYNPRWVOvOZVA7MOpJBuFhmAYDg5pCvN6Fqo70GL3Q/jtkf+ntajscWzG1GE9YpQE7psamoZeiCPUkZZWx12hQ1TDekwwiwKgnVTHMJr7vQqlfLKonFd9QdeHoG4FNPwNmr610T3oFjKTBQBtGtL3PwsRpMNCFYRqqCMundCCdtU+QNr35Ib7QqR9t2GBfQe8QKSt73joYWHxZObvS68CqnBmbhl6KUgR3i71Gg+pG7/iWGJQXhmmoIoxpb8YVeUYhl6OwzDGhgQjOJWkMNg2GVAiPj/0C+M2ngOO/V+meBMJsIb0Mw+cBW54A9lsZuQqVytrKaYNx9f97DvNG94tct0ENwRwoGESAYRqqFIZhKA8qZQgcBLUhtPIiEnLxq0aXFARVcCI6+Shg4puAVf0mYcamoZfhzA1AZi9Q1xy5CtWMGNTagOevW4GGZCJy3Qa1BLM3GISHYRoMahK9fbtT8zLx2jRUStIQteuEkOrQva5ihoE3hK5YNwxKAcsqimEIQlOdIQn2GXAuzM1GYaAHs0NUEaqAFKoZxPWuau6dx+5ytegqDKoYxqbBQISZEgY2asumIZvNIp1ORy6fTqeRTCbR1dWFbDYbY8/KgwNGtuJfH+zBAWP6o6urS5lP9ZypVAqJRPFSRMM0GBjUEmL2nlQ5nqH6P1K1ChOnwcDAIBA14nKVUop33nkHH374YdH1DB48GG+++WZNqm1efUg79vRk0FyXxKuvvqrM5/ecffv2xeDBg4t6fsM0VBGqVb++N6P2Xnm8p0OVev7I6knxdqOXwh3UWvw4GhgYlAO1oZ7kMAyDBg1CU1NT5D0tl8th165daGlpgVXF6qXFQvaclFLs2bMHW7duBQAMGTIkcv2GaTAwqEKoTRpijtNQewpaBgYGRaB6yUODyqE6Z0U2my0wDAMGDCiqrlwuh56eHjQ0NPR6pkH2nI2NjQCArVu3YtCgQZFVlXrvmzMwqGEopU4x2zTkDM/Q61B70jMDA4OyowakkI4NQ1NTU4V70jvgvMdibEMM01BFMB97g2DEbNNQoUkXtec18J2rOIy7ZgM/GJU1g1qDmbPxII73aJgGA4NaAonZpqHoGsoLUqVi9GrCzOF9cdDYAVg9d3ilu2JQhTD0lwGA2L8lBqXH6NGjccMNN1S0D8amoYpQawRcb0DNRUTmIkIXX52RbvU+JCyC2z85H0BxYmiD3olpw/pUugsGVQHGENrwDLEi6ET/mmuuwbp160LX+8QTT6C5uXRxWnRgmAaDmsLVR03BTfe/hC8fNy2W+qqVaNYK7lbDp0PmtNPAoLz446VL8Pr7uzF7ZL9Kd8WgGmAkDSXD22+/Xfh9xx134Oqrr8aLL75YSGtpaSn8ppQim80imQwmxwcOHBhvRyOgqtSTCCGLCSG/I4S8RQihhJBjA/IvJIQ8Qgh5nxCylxDyAiHkIkm+8wghrxFCugghjxFCDijdUxiUEmcuHIO/XrUMkwa3VborlUGvidNgYGBQToxpb8aSiYMq3Q2DqoFhGkqFwYMHF/716dMHhJDC9QsvvIDW1lb8/ve/x5w5c1BfX4+HH34Yr7zyCo455hh0dHSgpaUF8+bNw7333svVK6onEUJw66234rjjjkNTUxMmTJiAO++8s6TPVlVMA4BmAM8AOE8z/24A3wGwGMBkAF8C8CVCyCedDISQjwG4HsC1AGbn67+HEFJ1u2e1nnpXG+I0ipo0uDW2uuLE+EEtijss01D88q252CDm22ZgYGBQPJjvaC19BSil2NOTifRvb082ctm4v5Wf//zn8bWvfQ1///vfMWPGDOzatQudnZ247777sGnTJqxcuRKrVq3CG2+84VvPtddei9WrV2Pz5s3o7OzEaaedhg8++CDWvrKoKvUkSunvAfwe0CMMKaWbAGxikl4jhBwPYBGA7+XTLgbwfUrpj/L1ngvgSABnAvhabJ03qEn816lz8O2N/8CZB4+pdFcAAP/vvINxx5Nv4pJl+8kzxG0IXangbhH7bngGAwMDg30Xe9NZTLn6nrK3+/x1K9BUFx/JfN1112HZsmWF6/79+2PmzJmF6y9+8Yv4zW9+gzvvvBPnn3++sp61a9fi5JNPBgB85StfwY033oinnnoKo0aNiq2vLKqKaSgWhJBZABYA+Pf8dR2AOQC+6uShlOYIIfcCOMinnnoA9UxSK2AbFYYxLHTy6pbJ5rI1abgY9jmrCYOak/jqsVMABPe/HM85ZXAzrj1qkrqdbBYppz/ZLFBkX3K5XOG3+HylfM4c9bYbFsX0r5bnbBhEfc7e/l4MDAyMelIlMXfuXO56165dWLduHe6++268/fbbyGQy2Lt3b6CkYcaMGYXfzc3NaGtrw7Zt20rSZ6CXMA2EkC0ABsJ+nnWU0lvzt9oBJAC8KxR5F8AknyqvAHCNmLhhw4ZIQUY2btwYkMMehtdefQ3r1/8zdP3VguDn7B2o5HM2dW+Fczbx8MMPY0ej/4YShMxOC46W4vr167l7pXzOd95Rt+uHXDYB5wMXppwKZs7KsWfPnhL1xMDAoCoQs31cudCYSuD561aELpfL5bBzx060trVGigjdmIoWQVkF0QvSpZdeio0bN+Jb3/oWxo8fj8bGRpx44ono6enxrSeVSnHXhBDuMDBu9AqmAbY6UguA+QC+Rgh5mVJ6exH1fRW2HYSDVgBbli9fjrY2fQPcdDqNjRs3YtmyZZ6BZfGZRzcAAEaNHo3OTj9epjqh+5y1jqp4zg/fAJ63fy5ctBgYNKWo6ubv7sF1d72AE+YMxaLx7QDK85zrP3oam7dvBQB0dnZql7v8yXuRTudClxNRFWNZBkR9zh07dpSwVwYGBpVHbdo0EEIiqQnlcjlk6hJoqktGYhpKjUceeQRr167FcccdB8CWPLz22muV7ZQEvYJpoJS+mv/5N0JIB4B1AG4HsA1AFkCHUKQDwDs+9XUD6HauHfuKVCoVicDQLWdZVk0TMFHfT62hos/JtJtKprjrKOjom8J3T52jaKp0z8lu2lHbiKNvZs6q8xsYGPRiGJerVYUJEybg17/+NVatWgVCCK666qqSSgyiovrYreJhIW+PQCntAfAUgKXOTUKIlb9+tCK9MzAoCrUpUhZx+cpJaGtI4jNLJ4QqZyJCGxgYGMQBwzRUE66//nr069cPCxYswKpVq7BixQrMnj270t3yoKokDYSQFgDjmaQxhJD9AWynlL5BCPkqgGGU0tPz+c8D8AaAF/L5FwO4FMCNTB3XA/gJIeRJAI8D+Cxs164/KunDGBiUAr3kdGh0ezOevno5LKt2n8HAwMCgZlGjLldrDWvXrsXatWsL10uWLJG6bx09ejTuv/9+Lu288/joA6K6kqye7du3l1S9tKqYBgBzAfyRuXbsCn4CYC2AIQBGMvct2PYHYwBkALwC4HIAtzgZKKV3EEIGArgOwGAATwNYSSkVjaMNDKofbGyGGpY0ADAMg4GBgUHFYPZfg/CoKqaBUvoAfGYypXStcH0TgJs06v0O7CBwVY2aC7RlUAH0DklDVNQ4n2RgYGBQHahR70kGlUVvtGkwMOi9MBu9gYGBgYGBQQVgmAYDg1oCq560D0oaDAwMDAziAGvTYL4lBnowTIOBQU1h35Y07HtPbGBgYFAC9BKnGgblhWEaqgjGosEgEPsgo8CC7OPPb2BgYBAP2L3UUB8GejBMQxXB2EEbBMMQzQYGBgYGRcIcwBhEgGEaDAxqCWajNzAwMDAw+P/t3X2QHHWdx/H3ZydLdpPsA0E2u0GS3CkGFDwiIAgSgRgJp7kjSk5IHRKRHIpUROo8oK4qBK8O1AhyXsJd5DB4JYqgyGEBBUk8jQ9IAXoi4YgoBJIDEjCE3WR3s5Ps7/7onqV3sjvJPszOTM/nVdU1092/7v19e3a/vd9+GisBFw1mlaTKi4am+tpSd8HMrPJV+b7EhsdFg1lFqe5Ef+snTuSdbY2s+eRJpe6KmVkFq+59iQ2Pi4YyEnwzkh2IqvtP9p1TG3ngc6dz5syWUnfFzMxsP5IKDsuXLx/Ruu+9997R6+wQldU3Qlc73whtB+RTylbFJF0DfBQ4GugCfglcFULYlGhTB9wInA+MBx4CLgshbBv7HpuVKe9Liubll1/ue/+9732PZcuWsWlTX4pi0qRJpejWqKjuw5ZmFceJ3qraB4BVwCnAXKAWeFjSxESbrwHzgYVx+6nAPWPcT7My531JsbS2tvYNTU1NSOo37c477+SYY46hrq6Oo48+mltuuaVv2Z6eHi6//HLa2tqoq6tj+vTp3HDDDQDMmDEDgAULFiCpb3ws+UyDWSXx0SGrYiGEeclxSYuB7cAJwAZJTcCngEUhhB/HbT4J/K+kU0IIvxrjLpuVp0rdl4QA2c6hL9fbGy3Xk4GaYRwvr50wKtvsjjvuYNmyZaxcuZJZs2bxm9/8hiVLljBx4kQuuugivv71r3Pfffdx1113MW3aNLZs2cKWLVsAeOyxx2hpaWHNmjXMmzePTCYz4v4MlYsGs0qSvKfB17OZNcWvO+LXE4jOPqzLNQghPCPpReB9wH5Fg6TxRJcx5TQAZLNZstnskDqTaz/U5SpNNcSZ+hizWWr7jZZfnNlslhACvb299Pb2RhN7dlPzpbcOeV01QPMI+tJ79VY4ZOKBG+YvF/c793rttdeyYsUKzj33XACmT5/Oxo0bWb0rH31FAAAQHElEQVR6NRdeeCEvvPACRx11FKeeeiqSOPLII/uWP+ywwwBobGykpaWl33pzQvx/QW675fclhEA2m92v4DjYz99FQxnxv4B2YBV6dMhslEmqAW4GfhFCeCqe3Ar0hBB25jXfFs8byDXAtfkTH374YSZMmDCsvq1du3ZYy1WaaogzrTGO29fFhxPj5RjnuHHjaG1tZdeuXfT09EQTs50j+ud/uNo7OqB235CX6+7uJoRAe3s7u3fv5o9//CNLlizh0ksv7Wuzd+9eGhsbaW9v57zzzmPBggXMnDmTOXPmcPbZZ3PWWWf1W2dXVxft7e0Ff25HR8d+03p6eujq6mLDhg3s3bu337zOzoM7e+OioYz4wLEdUKWeUjYbfauAY4H3j3A9NwA3JcYbgK0f+tCHaGxsHNKKstksa9euZe7cudTWpvc7RaohztTHuKcDnnxztBzj7O7uZsuWLUyaNIm6urpoYmiIjvoPUQiBjl27aJg0CQ1jP9o4zMuT6urqkERjYyNdXV0ArF69mpNPPrlfu0wmQ2NjI6effjrPPfccDz74IOvXr+fiiy9mzpw53H333X1t6+vrB81NIQQ6OjpoaGjYL87u7m7q6+uZPXv2m9szdqAiJMdFg1kl6ffIVVeZVp0krQQ+AswOIST/g3gFOERSc97ZhinxvP2EEPYAexLrBqC2tnbY/0SNZNlKUg1xpjbGff3//SvHOPft24ckampqqEneh5BpGPK6ent7YU8vGj+p/7qKLPezampqaGtrY+rUqWzevJkLL7xw0GWam5u54IILuOCCC1i4cCHz5s1j586dTJ48mdraWkIIg8aQuyQpt93y+yJpwM/6YD97Fw1l5B1TKvcxXDZWfKbBqpei/+j/FVgAnBFCeD6vyRNAFpgD/CBeZiYwDXhkDLtqZraf6667jqVLl9LU1MS8efPYs2cPjz/+OK+//jpXXnklN910E21tbcyaNYuamhruvvtuWltbaW6OLsqaMWMG69ev57TTTmP8+PEceuihY9p/Fw1l4IeXncrPn32Nvz1leqm7YuXOlydZdVsFLAL+GuiQlLtP4Y0QQlcI4Q1JtwE3SdoBtBMVGY/4yUlmCd6XlMQll1zChAkTWLFiBV/4wheYOHEixx13HFdccQUADQ0NfOUrX+HZZ58lk8lw0kkn8cADD/SdNbjxxhu58sorufXWWzniiCPYvHnzmPbfRUMZmDXtUGZNG9tq0SpU8vKkhsHu6zRLrc/Erz/Jm/5J4Pb4/eeBXqIzDX1f7jYGfTOrHOPq+95mx/kqh2JZvHgxixcv7jdt0aJFLFq0aMD2S5YsYcmSJYOub/78+cyfP380uzgkLhrMKokEn98I+3pg/NCv6zSrZCGEAx4eDSF0A5+NBzMbSGYcXPEU2Z5u9j3ydKl7YxXCRYNZpWka+jOqzczM+mk+ErJZwEWDHZyxu4XczMzMzMwqkosGMzMzMzMryEWDmZmZmZkV5KLBzMzMzMpSCP4i09EwGtvRRYOZmZmZlZXctxR3dnaWuCfpkNuOI/nmbz89yczMzMzKSiaTobm5me3btwMwYcIENMwvpevt7aWnp4fu7u6+L0pLo4HiDCHQ2dnJ9u3baW5uJpPJDHv9LhrMzMzMrOy0tkZfYporHIYrhEBXVxf19fXDLjwqQaE4m5ub+7bncLloMDMzM7OyI4m2tjZaWlrIZrPDXk82m2XDhg3Mnj17RJfnlLvB4qytrR3RGYYcFw1mZmZmVrYymcyI/unNZDLs3buXurq6VBcNxY4zvRd2mZmZmZnZqHDRYGZmZmZmBbloMDMzMzOzgnxPwxC0t7cPqX02m6Wzs5P29vZUX0PnONOlGuKshhhh+HEONdel0XC2gX+v0qMaYgTHmTbFzvnyN+0dmKQjgK2l7oeZ2Rh7awjh/0rdibHkfG9mVaxgznfRcBAUPex2KtAxxEUbiHY+bx3GspXEcaZLNcRZDTHCyOJsAF4KVbaTGEG+B/9epUk1xAiOM22KmvN9edJBiDfgkI+2Jb5YoyOEkNrz/Y4zXaohzmqIEUYcZ2q3SyHDzffg36s0qYYYwXGmTbFzvm+ENjMzMzOzglw0mJmZmZlZQS4aimsPcF38mmaOM12qIc5qiBGqJ85yUS3buxrirIYYwXGmTVHj9I3QZmZmZmZWkM80mJmZmZlZQS4azMzMzMysIBcNZmZmZmZWkIuGIpL0WUmbJXVLelTSe0vdp6GQNFvSjyS9JClIOjdvviR9UdLLkrokrZN0VF6byZLukNQuaaek2yRNGttIBifpGkmPSeqQtF3SvZJm5rWpk7RK0p8k7ZL0A0lT8tpMk3S/pM54PSsklc33oEj6jKQn48+hXdIjks5JzK/4GPNJujr+vb05Ma3i45S0PI4rOTyTmF/xMVYi53vn+7GNZnDO933TUhFnOeV8Fw1FIunjwE1Ed7G/B/gt8JCklpJ2bGgmEvX7s4PM/wdgKfBp4GRgN1GMdYk2dwDvAuYCHwFmA98oVoeH4QPAKuAUoj7WAg9Lmpho8zVgPrAwbj8VuCc3U1IGuB84BDgVuAhYDHyx+N0/aFuBq4ETgBOBHwP/Jeld8fw0xNhH0knApcCTebPSEudGoC0xvD8xLy0xVgzn+z7O9+XB+T6SpjjLI+eHEDwUYQAeBVYmxmuIvmX06lL3bZjxBODcxLiAl4G/T0xrArqB8+PxY+LlTky0mQf0AlNLHdMgcR4e93l2IqYe4LxEm6PjNqfE4+cA+4ApiTafBt4ADil1TAVi3QF8Km0xApOA3wMfBH4C3JymzxJYDvzPIPNSEWOlDc73zvfl/vfjfF+5cZZTzveZhiKQdAhRhb8uNy2E0BuPv69U/Rplfwa00j/GN4h2nrkY3wfsDCE8nlhuHdFO5OQx6udQNcWvO+LXE4iORiXjfAZ4kf5x/i6EsC2xnoeARqKjbmVFUkbS+URHFh8hfTGuAu4PIazLm56mOI+KLyN5Lr4cZFo8PU0xVgTne+d7yvjvx/k+NXGWRc4vq+u2UuQtQAbYljd9G1EFmAat8etAMbYm2mxPzgwh7JW0I9GmbEiqAW4GfhFCeCqe3Ar0hBB25jXPj3Og7QBlFKek44h2GnXALmBBCOFpSceTnhjPJ7o85KQBZqfls3yU6NTyJqLT1NcCP5N0LOmJsZI437/Zxvm+TDjfp+ezpIxyvosGszetAo6l/7WCabIJOJ7o6Np5wLckfaC0XRo9ko4E/gWYG0LoLnV/iiWE8GBi9ElJjwIvAH8DdJWmV2YVx/m+glVLvofyyvm+PKk4XiO+fixv+hTglbHvTlHk4igU4ytAvxsB47v1J1Nm20HSSqIb984MIWxNzHoFOERSc94i+XEOtB2gjOIMIfSEEP4QQngihHAN0U2PnyM9MZ5A9Pv2a0l7Je0luilsafx+G+mIs5/4CNPvgbeTns+ykjjfv9nG+b5MON+nM99DaXO+i4YiCCH0AE8Ac3LT4lOhc4hOF6bB80S/bMkYG4muXc3F+AjQLOmExHJnEf3ePTpG/SxIkZXAAuCsEMLzeU2eALL0j3MmMI3+cR6X96SUuUA78HSx+j4KaoDxpCfG9cBxREfXcsPjRE90yb1PQ5z9KHqk5duIblRNy2dZMZzvne+pjL8f5/vKjHM/Jc35pb4rPK0D8HGiJ0tcRPRUidXA6yTuXi/3geipBLk/xgB8Pn4/LZ5/VRzTXxH98d4LPAfUJdbxIPBr4L3AaUTV8XdKHVuif7cAO4mOULQmhvpEm38jOhV4JtHRjV8Cv0zMzwC/I7qx6C+As4mu7b2+1PEl+ngD0eMPZ8Sf1Q1ENyjOTUuMg8T9E+KnaaQlTuCr8e/rDKLH560FXgUOT0uMlTY43/etw/m+DAbn+/R8lnE/yybnl3xjpHkALo8/yD1ER1pOLnWfhtj/M+KdR/5wezxfRM/5fYVoh7kOeEfeOiYD3wE6iB7v9U1gUqljS/RvoPgCsDjRpo7o+tcdRM8mvwdozVvPdOABoDP+Y/4qMK7U8SX6dxuwOf5d3B5/VnPTFOMgcefvRCo+TuBO4KX4s9waj78tTTFW4uB873xf6vgS/XO+T1Gc5ZTzFa/MzMzMzMxsQL6nwczMzMzMCnLRYGZmZmZmBbloMDMzMzOzglw0mJmZmZlZQS4azMzMzMysIBcNZmZmZmZWkIsGMzMzMzMryEWDmZmZmZkV5KLBLMUkLZcUJL2l1H0xM7Pics63YnLRYGZmZmZmBbloMDMzMzOzglw0mJmZmZlZQS4azEaBpCMkfVPSNkl7JG2UdHFi/hnxdaYfl3S9pFck7ZZ0n6QjB1jfQklPSOqS9Jqkb0s6YoB2R0u6S9KrcdtNkv55gC42S7pd0k5Jb0haI2nCKG8GM7Oq4Jxv1WhcqTtgVukkTQF+BQRgJfAqcA5wm6TGEMLNieb/GLf7MtACXAGsk3R8CKErXt9iYA3wGHANMAX4HHCapFkhhJ1xu3cDPwOywDeAzcDbgPnxz0m6C3g+Xt97gEuA7cBVo7UdzMyqgXO+Va0QggcPHkYwAP8BvAQcljf9u8BOoB44g2jHsRVoSLRZGE9fGo/XAtuA3wF1iXYfjttdl5j2U6AdmJb3c5V4vzxe7ra8NvcAr5V623nw4MFDpQ3O+R6qdfDlSWYjIEnAx4AfxaNvyQ3AQ0AT0VGenP8MIXQkxr8PvAz8ZTx+ItHRqFtCCN25RiGE+4FniHYkSDocmA18M4TwYrJPIYQwQFf/PW/8Z8BhkhqHEq+ZWTVzzrdq5suTzEbmcKAZ+Lt4GEgL8Hr8/tnkjBBCkPQHYEY8aXr8ummA9TwDvD9+/+fx61MH2c8X88Zz/TmU6MiVmZkdmHO+VS0XDWYjkztb923gW4O0eRJ459h0Z1D7BpmuMe2FmVllc863quWiwWxkXgU6gEwIYd1gjSTldiBH5U0X8HainQzAC/HrTODHeauZmZj/XPx67PC6bWZmw+Ccb1XL9zSYjUAIYR/wA+BjkvZL5vF1qEmfkNSQGD8PaAMejMcfJ3rCxacljU+s5xzgGOD++Oe+CmwALpY0Le9n+kiSmVkROOdbNfOZBrORuxo4E3hU0q3A08BkopvhPhi/z9kB/FzSGqLH6l0B/AG4FSCEkJV0FdHj934q6bu8+fi9zcDXEutaCvwc+LWkbxA9Xm8G0Y1zxxcjUDMzc8636uSiwWyEQgjbJL0XWAZ8FLgM+BOwkf2fiX098G6iZ2c3AOuBy0IInYn13S6pk2jH9GVgN/BD4KoQP687bvdbSacA/wR8BqgjOpV9VzHiNDMz53yrXhr4SV1mNpoknQH8N7AwhPD9EnfHzMyKyDnf0sj3NJiZmZmZWUEuGszMzMzMrCAXDWZmZmZmVpDvaTAzMzMzs4J8psHMzMzMzApy0WBmZmZmZgW5aDAzMzMzs4JcNJiZmZmZWUEuGszMzMzMrCAXDWZmZmZmVpCLBjMzMzMzK8hFg5mZmZmZFeSiwczMzMzMCvp/We6uyLEiixwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTrYc4FksBOL"
      },
      "source": [
        "## [11] Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lo6ePz7esBOL"
      },
      "source": [
        "import collections\n",
        "import random\n",
        "\n",
        "def evaluate_model(model, difficulty, seed=9001, verbose=False):\n",
        "    # Define a dictionary that maps class indices to labels\n",
        "    class_idx_to_label = {0: 'Q', 1: 'R', 2: 'S', 3: 'U'}\n",
        "\n",
        "    # Create a new data generator\n",
        "    data_generator = QRSU.get_predefined_generator(difficulty, seed=seed)\n",
        "\n",
        "    # Track the number of times a class appears\n",
        "    count_classes = collections.Counter()\n",
        "\n",
        "    # Keep correctly classified and misclassified sequences, and their\n",
        "    # true and predicted class labels, for diagnostic information.\n",
        "    correct = []\n",
        "    incorrect = []\n",
        "\n",
        "    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch_idx in range(len(data_generator)):\n",
        "            data, target = test_data_gen[batch_idx]\n",
        "            data, target = torch.from_numpy(data).float().to(device), torch.from_numpy(target).long().to(device)\n",
        "\n",
        "            data_decoded = data_generator.decode_x_batch(data.cpu().numpy())\n",
        "            target_decoded = data_generator.decode_y_batch(target.cpu().numpy())\n",
        "\n",
        "            output = model(data)\n",
        "            sequence_end = torch.tensor([len(sequence) for sequence in data_decoded]) - 1\n",
        "            output = output[torch.arange(data.shape[0]).long(), sequence_end, :]\n",
        "\n",
        "            target = target.argmax(dim=1)\n",
        "            y_pred = output.argmax(dim=1)\n",
        "            y_pred_decoded = [class_idx_to_label[y.item()] for y in y_pred]\n",
        "\n",
        "            count_classes.update(target_decoded)\n",
        "            for i, (truth, prediction) in enumerate(zip(target_decoded, y_pred_decoded)):\n",
        "                if truth == prediction:\n",
        "                    correct.append((data_decoded[i], truth, prediction))\n",
        "                else:\n",
        "                    incorrect.append((data_decoded[i], truth, prediction))\n",
        "\n",
        "    num_sequences = sum(count_classes.values())\n",
        "    accuracy = float(len(correct)) / num_sequences * 100\n",
        "    print(f'The accuracy of the model is measured to be {accuracy:.2f}%.\\n')\n",
        "\n",
        "    # Report the accuracy by class\n",
        "    for label in sorted(count_classes):\n",
        "        num_correct = sum(1 for _, truth, _ in correct if truth == label)\n",
        "        print(f'{label}: {num_correct} / {count_classes[label]} correct')\n",
        "\n",
        "    # Report some random sequences for examination\n",
        "    print('\\nHere are some example sequences:')\n",
        "    for i in range(10):\n",
        "        sequence, truth, prediction = correct[random.randrange(0, 10)]\n",
        "        print(f'{sequence} -> {truth} was labelled {prediction}')\n",
        "\n",
        "    # Report misclassified sequences for investigation\n",
        "    if verbose:\n",
        "        if incorrect:\n",
        "            print('\\nThe following sequences were misclassified:')\n",
        "            for sequence, truth, prediction in incorrect:\n",
        "                print(f'{sequence} -> {truth} was labelled {prediction}')\n",
        "        else:\n",
        "            print('\\nThere were no misclassified sequences.')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5WYb8Slv9ol",
        "outputId": "d42c8a2a-fe96-4076-eaea-3e6fd30bfc95"
      },
      "source": [
        "difficulty = QRSU.DifficultyLevel.EASY\n",
        "evaluate_model(model, difficulty, verbose=False)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the model is measured to be 88.81%.\n",
            "\n",
            "Q: 258 / 258 correct\n",
            "R: 198 / 249 correct\n",
            "S: 172 / 232 correct\n",
            "U: 253 / 253 correct\n",
            "\n",
            "Here are some example sequences:\n",
            "BbXcXbcE -> Q was labelled Q\n",
            "BbXaXdE -> Q was labelled Q\n",
            "BbXaXdE -> Q was labelled Q\n",
            "BYbdYabE -> U was labelled U\n",
            "BbYaYdbE -> U was labelled U\n",
            "BYbdYabE -> U was labelled U\n",
            "BYdcaYbE -> U was labelled U\n",
            "BbXcXbcE -> Q was labelled Q\n",
            "BYbdYabE -> U was labelled U\n",
            "BdYdXbbE -> S was labelled S\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxyKfNMDsBOL"
      },
      "source": [
        "## [12] Visualize LSTM\n",
        "Setting difficulty to `MODERATE` and `hidden_size` to 12."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "cwStPfJcsBOM",
        "outputId": "dfc82fed-ef0f-45d0-c717-6979eb5af901"
      },
      "source": [
        "# For reproducibility\n",
        "torch.manual_seed(1)\n",
        "\n",
        "# Setup the training and test data generators\n",
        "difficulty     = QRSU.DifficultyLevel.MODERATE\n",
        "batch_size     = 32\n",
        "train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "test_data_gen  = QRSU.get_predefined_generator(difficulty, batch_size)\n",
        "\n",
        "# Setup the RNN and training settings\n",
        "input_size  = train_data_gen.n_symbols\n",
        "hidden_size = 10\n",
        "output_size = train_data_gen.n_classes\n",
        "model       = SimpleLSTM(input_size, hidden_size, output_size)\n",
        "criterion   = torch.nn.CrossEntropyLoss()\n",
        "optimizer   = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
        "max_epochs  = 100\n",
        "\n",
        "# Train the model\n",
        "model = train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=False)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 100/100] loss: 0.0150, acc: 100.00% - test_loss: 0.0144, test_acc: 99.80%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x450 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVIdxjB4sBOM"
      },
      "source": [
        "# Get hidden (H) and cell (C) batch state given a batch input (X)\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    data = test_data_gen[0][0]\n",
        "    X = torch.from_numpy(data).float().to(device)\n",
        "    H_t, C_t = model.get_states_across_time(X)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "LV67gxLmsBOM",
        "outputId": "d67943fa-afd2-4374-ac1d-04f73c8c183c"
      },
      "source": [
        "print(\"Color range is as follows:\")\n",
        "print_colourbar()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Color range is as follows:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<text style=color:#000;background-color:#85c2e1>-2.50 </text><text style=color:#000;background-color:#89c4e2>-2.24 </text><text style=color:#000;background-color:#95cae5>-1.97 </text><text style=color:#000;background-color:#99cce6>-1.71 </text><text style=color:#000;background-color:#a1d0e8>-1.45 </text><text style=color:#000;background-color:#b2d9ec>-1.18 </text><text style=color:#000;background-color:#baddee>-0.92 </text><text style=color:#000;background-color:#c2e1f0>-0.66 </text><text style=color:#000;background-color:#eff7fb>-0.39 </text><text style=color:#000;background-color:#f9e8e8>-0.13 </text><text style=color:#000;background-color:#f9e8e8>0.13 </text><text style=color:#000;background-color:#f9d4d4>0.39 </text><text style=color:#000;background-color:#f9bdbd>0.66 </text><text style=color:#000;background-color:#f8a8a8>0.92 </text><text style=color:#000;background-color:#f68f8f>1.18 </text><text style=color:#000;background-color:#f47676>1.45 </text><text style=color:#000;background-color:#f45f5f>1.71 </text><text style=color:#000;background-color:#f34343>1.97 </text><text style=color:#000;background-color:#f33b3b>2.24 </text><text style=color:#000;background-color:#f42e2e>2.50 </text>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "57kQEsMosBOM",
        "outputId": "98b12ade-4896-4b1c-cef0-7efe23e6586a"
      },
      "source": [
        "plot_state(X.cpu(), C_t, b=9, decoder=test_data_gen.decode_x) "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
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