multiGPU_tensorflow_tutorial.ipynb

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    "# Using TensorFlow on Multiple GPUs #\n",
    "\n",
    "TensorFlow is a popular open source platform for machine learning that enables parallelism across GPU cores. This parallelism ends up amounting to considerably \n",
    "reduced computation times, as performance is scaled with data size, unlike CPU-based operations, which do not scale with the data at a constant rate (for an\n",
    "in-depth look, check out the [Introduction to Tensorflow Quickbyte](https://github.com/UNM-CARC/QuickBytes/blob/master/Tensorflow_documentation.md)).\n",
    "\n",
    "## Overview of Conda Environments ##\n",
    "\n",
    "In order to get started, you must first install the GPU-enabled version of TensorFlow. Instructions for setting up and doing that within a conda environment can be\n",
    "found in the [Install Deep Learning Packages Quickbyte](https://github.com/UNM-CARC/QuickBytes/blob/master/Install%20deep%20learning%20packages.md).\n",
    "\n",
    "Note that in addition to the GPU-enabled version of TensorFlow, you will also want to install `numpy`, `matpotlib`, and `scikit-learn`. It is\n",
    "also recommended that you use `conda install ipykernel` so that you can run your code on CARC's JupyterHub interface, like in this tutorial.\n",
    "\n",
    "## Multiple-GPU Processes ##\n",
    "\n",
    "On Xena, there are 4 dual GPU nodes available and this tutorial will cover the use of one node with 2 GPUs to run a neural network code. This tutorial will use documentation for how to do single-host, multi-device synchronous training with the Keras library."
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    "import numpy as np\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, optimizers, utils\n",
    "\n",
    "from sklearn.datasets import make_multilabel_classification\n",
    "from sklearn.model_selection import train_test_split"
   ]
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  {
   "cell_type": "markdown",
   "id": "international-relations",
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   "source": [
    "First we will need to define some parameters. We will be using a decaying learning rate with the Adam optimizer, but that is not strictly necessary, so feel free to play around with these and the other parameters like `epochs`, `batch_size` and number of `neurons_per_hidden_layer`. The variables `Nin` and `Nout` will be used to define the number of features and classes in our synthetic dataset, respectively. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "verbal-extra",
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   "source": [
    "Nin = 10\n",
    "Nout = 3\n",
    "\n",
    "init_lr = 1e-3\n",
    "epochs = 10\n",
    "batch_size = 32\n",
    "neurons_per_hidden_layer = 32\n",
    "\n",
    "opt = optimizers.Adam(lr=init_lr, decay=init_lr/epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "steady-framework",
   "metadata": {},
   "source": [
    "We will use the sklearn `make_multilabel_classification` function to generate a synthetic dataset. Also feel free to play around with the number of labels and samples."
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "focal-leather",
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   "source": [
    "xin, yin = make_multilabel_classification(n_samples=100000, n_features=Nin, n_classes=Nout, n_labels=2)"
   ]
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  {
   "cell_type": "markdown",
   "id": "young-passion",
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   "source": [
    "In order to utilize the keras library multi-GPU capabilities, the model must be compiled after allocating multiple GPUs to the problem. Also, the datasets for training, testing, and validation must be cast into tensorflow Datasets. Both of these things can be easily accomplished by first defining two functions---one which creates the model and another which formats the datasets."
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "hired-honor",
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   "source": [
    "def create_model(n_neurons):\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Dense(n_neurons, input_dim=Nin, kernel_initializer='he_uniform', activation='relu'))\n",
    "    model.add(layers.Dropout(0.2))\n",
    "    model.add(layers.Dense(n_neurons, kernel_initializer='he_uniform', activation='relu'))\n",
    "    model.add(layers.Dropout(0.2))\n",
    "    model.add(layers.Dense(Nout, activation='sigmoid'))\n",
    "    return model\n",
    "\n",
    "def get_dataset():\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(xin, yin)\n",
    "    \n",
    "    num_val_samples = 10*y_train.shape[0]//100    # set size of validation dataset to be 10% of training set\n",
    "    \n",
    "    # Reserve 'num_val_samples' for use in validation calculation\n",
    "    x_val = x_train[-num_val_samples:]\n",
    "    x_train = x_train[:-num_val_samples]\n",
    "    y_val = y_train[-num_val_samples:]\n",
    "    y_train = y_train[:-num_val_samples]\n",
    "\n",
    "    return (\n",
    "           tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size, drop_remainder=True),\n",
    "           tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(batch_size, drop_remainder=True),\n",
    "           tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size, drop_remainder=True),\n",
    "           y_test\n",
    "           )"
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   "source": [
    "Now, in order to utilize multiple GPUs, we must instantiate a MirroredStrategy from the `tf.distribute.MirroredStrategy` API. By default, the strategy will use all GPUs available, but you can configure which specific devices you want to use if you'd like. For here, we will just use the default (which is two).\n",
    "\n",
    "We will then use the strategy object to open a scope and will create & compile the model inside the distribution scope. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "stupid-disability",
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     "text": [
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1')\n",
      "Number of devices: 2\n"
     ]
    }
   ],
   "source": [
    "strategy = tf.distribute.MirroredStrategy()\n",
    "print('Number of devices: {}'.format(strategy.num_replicas_in_sync))\n",
    "\n",
    "with strategy.scope():\n",
    "    model = create_model(neurons_per_hidden_layer)\n",
    "    model.compile(optimizer=opt, loss='binary_crossentropy', metrics='binary_accuracy')"
   ]
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  {
   "cell_type": "markdown",
   "id": "chemical-parts",
   "metadata": {},
   "source": [
    "Now we can train the model via `fit()` as usual. In order to test and see if you are using all of the GPUs, you can `ssh` onto the compute node where you are running this tutorial and enter in `nvidia-smi`. You should see that there is a roughly 50/50 split between the two as the training is being performed. You can ignore all of the `INFO:tensorflow` text that gets printed out below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "conventional-carnival",
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "INFO:tensorflow:batch_all_reduce: 6 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:batch_all_reduce: 6 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "2094/2109 [============================>.] - ETA: 0s - loss: 0.6733 - binary_accuracy: 0.7030INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.6721 - binary_accuracy: 0.7033 - val_loss: 0.4448 - val_binary_accuracy: 0.7937\n",
      "Epoch 2/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4638 - binary_accuracy: 0.7734 - val_loss: 0.4137 - val_binary_accuracy: 0.8108\n",
      "Epoch 3/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4431 - binary_accuracy: 0.7870 - val_loss: 0.4047 - val_binary_accuracy: 0.8165\n",
      "Epoch 4/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4335 - binary_accuracy: 0.7948 - val_loss: 0.4006 - val_binary_accuracy: 0.8188\n",
      "Epoch 5/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4277 - binary_accuracy: 0.7978 - val_loss: 0.3987 - val_binary_accuracy: 0.8189\n",
      "Epoch 6/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4243 - binary_accuracy: 0.8003 - val_loss: 0.3976 - val_binary_accuracy: 0.8192\n",
      "Epoch 7/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4214 - binary_accuracy: 0.8015 - val_loss: 0.3963 - val_binary_accuracy: 0.8190\n",
      "Epoch 8/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4198 - binary_accuracy: 0.8035 - val_loss: 0.3960 - val_binary_accuracy: 0.8186\n",
      "Epoch 9/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4186 - binary_accuracy: 0.8048 - val_loss: 0.3949 - val_binary_accuracy: 0.8202\n",
      "Epoch 10/10\n",
      "2109/2109 [==============================] - 6s 3ms/step - loss: 0.4169 - binary_accuracy: 0.8054 - val_loss: 0.3947 - val_binary_accuracy: 0.8197\n"
     ]
    }
   ],
   "source": [
    "train_dataset, val_dataset, test_dataset, ytest = get_dataset()\n",
    "history = model.fit(train_dataset, epochs=epochs, validation_data=val_dataset, verbose=1)"
   ]
  },
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       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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BBBAAAEEEMhYgARgxkbUQAABBBBAAAEEPCOQmQRg6ncA6gpAXQmYUalRo4bZ/ENL6ncAZnTsH3/8Ie+++65ZtRda1aeP8k6YMCGjQ8Pfa/Lv66+/locfflgWL15sPtdHg9euXSulS5d2fB4qIoAAAggggAACQRUgARjUyNNvBBBAAAEEEPClQGYSgNrh5LsAjx8/Xi6++OJ0HXbu3Gk2CklrF2CniP/973/Dm4fkypVL9u7dK3ny5HF6uKm3a9cu0WTkli1bzL/r7sM33HBDVOegMgIIIIAAAgggEEQBEoBBjDp9RgABBBBAAAHfCmQ2AXjVVVeZd+xp0ffoff755+la6Dv77rrrLlOnVKlSoiv63GzCoQm/0047LXytP//8U0qWLBl1HHQzkVCbH3/8cRk0aFDU5+AABBBAAAEEEEAgaAIkAIMWcfqLAAIIIIAAAr4WyGwCcPr06dK2bduwgT5a27lz54gmGzdulLp165qVd1qGDx8uQ4YMSVF3x44djh4jXr58udSqVcscmz17drMDcWgFoK4y1HcDOimNGjWSBQsWmKpvvvmm3HTTTU4Oow4CCCCAAAIIIBBoARKAgQ4/nUcAAQQQQAABvwlkNgGo/b3wwgvNO/W0FCxYUN5++2254oorUlAsWrRIunfvLr/++qv5vGzZsrJkyRIpXLhwinpVqlSRhg0bSs+ePaV9+/bm3Xypi74/sEePHvLTTz+Zrzp06CBTpkwJVxs2bJiMGzdO+vTpY1YlRloZqCsIdQfikSNHmuNy5Mgh69atM+2iIIAAAggggAACCKQvQAKQOwQBBBBAAAEEEPCRQPIEoL7P76yzznLcet1EQ9/5t3XrVmnevLmsWbMmfKwm8po0aWISeJqw0513T548ab4vUKCATJ482RyTupQvX142bNhgPs6XL5/UqVNHKlasaB731ZWDeo2FCxeGD9M6em6tFyqaANTVhVr08eJKlSqZ1YK6QcnRo0dFHxfWXYFDm4hovcGDB5uEIAUBBBBAAAEEEEAgYwESgBkbUQMBBBBAAAEEEPCMQPIEYLSNeuedd+T66683h2kS8JprrpFp06ale5rKlSvLhx9+KProbaRSu3ZtWbZsmaOmVKhQQcaMGSPNmjVLUf+ZZ56Ru+++29E5NEGpyb/UjyI7OphKCCCAAAIIIIBAQAVIAAY08HQbAQQQQAABBPwpEKsEYKj3urJv7NixMmfOHLO7rq64011/zz33XOnWrZt5dFd37U2rHD9+3Kzo03cLzp8/X1avXm1W7Ok7/vLnz29WKNarV8+sPLzyyivT3Pl3/fr18s0335iVfkuXLhX99z179pj3Bepjx9WrVzfvLuzVq5eUK1fOn8Gj1QgggAACCCCAQIIESAAmCJ7LIoAAAggggAACCCCAAAIIIIAAAgggEA8BEoDxUOYaCCCAAAIIIIAAAggggAACCCCAAAIIJEiABGCC4LksAggggAACCCCAAAIIIIAAAggggAAC8RAgARgPZa6BAAIIIIAAAggggAACCCCAAAIIIIBAggRIACYInssigAACCCCAAAIIIIAAAggggAACCCAQDwESgPFQ5hoIIIAAAggggAACCCCAAAIIIIAAAggkSIAEYILguSwCCCCAAAIIIIAAAggggAACCCCAAALxECABGA9lroEAAggggAACCCCAAAIIIIAAAggggECCBEgAJgieyyKAAAIIIIAAAggggAACCCCAAAIIIBAPARKA8VDmGggggAACCCCAAAIIIIAAAggggAACCCRIgARgguC5LAIIIIAAAggggAACCCCAAAIIIIAAAvEQIAEYD2WugQACCCCAAAIIIIAAAggggAACCCCAQIIESAAmCJ7LIoAAAggggAACCCCAAAIIIIAAAgggEA8BEoDxUOYaCCCAAAIIIIAAAggggAACCCCAAAIIJEiABGCC4IN+2UOHDsnSpUsNQ7FixSRnzpxBJ6H/CCCAAAIIIIAAAggggAACCMRc4NixY7J9+3Zz3tq1a0vevHljfg1O6H0BEoDej5GVLfzxxx+lcePGVvaNTiGAAAIIIIAAAggggAACCCDgRYH58+dLo0aNvNg02pTFAiQAsxiY00cWIAHInYEAAggggAACCCCAAAIIIIBAfAVIAMbX20tXIwHopWgEqC3r16+XChUqmB7rAFSyZMkA9Z6uIoAAAggggAACCCCAAAIIIBAfgc2bN4efwFu3bp2UL18+PhfmKp4SIAHoqXAEpzF//PGHlC1b1nR448aNUqZMmeB0np4igAACCCCAAAIIIIAAAgggECcB5t9xgvb4ZUgAejxAtjaPAcjWyNIvBBBAAAEEEEAAAQQQQAABLwkw//ZSNBLXFhKAibMP9JUZgAIdfjqPAAIIIIAAAggggAACCCAQJwHm33GC9vhlSAB6PEC2No8ByNbI0i8EEEAAAQQQQAABBBBAAAEvCTD/9lI0EtcWEoCJsw/0lRmAAh1+Oo8AAggggAACCCCAAAIIIBAnAebfcYL2+GVIAHo8QLY2jwHI1sjSLwQQQAABBBBAAAEEEEAAAS8JMP/2UjQS1xYSgImzD/SVGYACHX46jwACCCCAAAIIIIAAAgggECcB5t9xgvb4ZUgAejxAtjaPAcjWyNIvBBBAAAEEEEAAAQQQQAABLwkw//ZSNBLXFhKAibMP9JUZgAIdfjqPAAIIIIAAAggggAACCCAQJwHm33GC9vhlSAB6PEC2No8ByNbI0i8EEEAAAQQQQAABBBBAAAEvCTD/9lI0EtcWEoCJsw/0lRmAAh1+Oo8AAggggAACCCCAAAIIIBAnAebfcYL2+GVIAHo8QLY2jwHI1sjSLwQQQAABBBBAAAG/Cpw4cUL27dsne/bskSNHjsjx48f92hXajYBVAjly5JD8+fNL4cKFJW/evFH3jfl31GRWHkAC0Mqwer9TDEDejxEtRAABBBBAAAEEEAiOwN69e2XTpk1y8uTJ4HSaniLgQ4HTTz9dSpYsKdmyZXPceubfjqmsrkgC0OrwerdzDEDejQ0tQwABBBBAAAEEEAiWQKTknyYXdNURBQEEEi9w7NixFI0oVqyYnHnmmY4bxvzbMZXVFUkAWh1e73aOAci7saFlCCCAAAIIIIAAAsER0Md+f/nll/DKv4IFC0qRIkXM44bRrDAKjhg9RSD+Avo4/t9//y3btm0LX7xSpUqSO3duR41h/u2IyfpKJACtD7E3O8gA5M240CoEEEAAAQQQQACBYAno+/700V8tmvwrU6YMib9g3QL01kcCmgDcuXOnaXGJEiVMst5JYf7tRMn+OiQA7Y+xJ3vIAOTJsNAoBBBAAAEEEEAAgYAJ6P9fro8Aazn77LOlQIECAROguwj4R+DQoUOybt26cMK+bNmyjhrP/NsRk/WVSABaH2JvdpAByJtxoVUIIIAAAggggAACwRJYu3atHD582Kz6q1q1Kqv/ghV+euszAd2kZ/Xq1eaRfX38Vx8DdlKYfztRsr8OCUD7Y+zJHjIAeTIsNAoBBBBAAAEEEEAgYAK//vqr6AYDOXPmlCpVqgSs93QXAf8JuPnNMv/2X5yzosUkALNClXNmKMAAlCERFRBAAAEEEEAAAQQQyHIBN8mELG8UF0AAgTQF3PxmmX9zQ6kACUDug4QIMAAlhJ2LIoAAAggggAACCCCQQsBNMgFCBBBInICb3yzz78TFy0tXJgHopWgEqC0MQAEKNl1FAAEEEEAAAQQQ8KyAm2SCZztDwxAIgICb3yzz7wDcGA66SALQARJVYi/AABR7U86IAAIIIIAAAggggEC0Am6SCdFeg/oIIBA7ATe/WebfsfP385lIAPo5ej5uOwOQj4NH0xFAAAEEEEAAAQSsEXCTTLCm83TkFAHdDVrL0KFDZdiwYQh5UMDNb5b5twcDmYAmkQBMADqXFGEA4i5AAAEEEEAAAQQQQCDxAm6SCYlvNS3IKgESgFklG7vzuvnNMv+Onb+fz0QC0M/R83HbGYB8HDyajgACCCCAAAIIIGCNgJtkgjWdT1BH3n33Xendu7e5+rp166R8+fIJasmplyUB6JlQpNkQN79Z5t/ej2s8WkgCMB7KXOMUAQYgbgoEEEAAAQQQQAABBBIv4CaZkPhW+7sFXk4A+ls2GK1385tl/h2MeyOjXpIAzEiI77NEgAEoS1g5KQIIIIAAAggggAACUQm4SSZEdQEqnyJAApCbIjMCbn6zzL8zI27PsSQA7Ymlr3rCAOSrcNFYBBBAAAEEEEAAAUsF3CQTLKWIW7dIAMaN2soLufnNMv+28laIulMkAKMm44BYCPhxADp09Lis2rJXlm3aLXsPHZN/t64UCwrOgQACCCCAAAIIIIBAwgTcJBMS1lifX3jGjBnSpk2bDHsxffp0ad26tVx//fUyevRoKVeunKxfv142b94szz//vEyYMEF+//132bdvn4Tq6kl37dol48aNk2+//VZ++uknU+fIkSNSpEgRqVu3rvzrX/8y58ydO3eabUjvHYCpE5dnn322vPXWW6Kfr1y50lyrUqVK0r17d7nzzjslf/78GfaVCtELuPnN+nH+Hb0MR2QkQAIwIyG+zxIBvw1A89bulB5v/SDHTpw0HgVy55ClwzpJ9uzZssSHkyKAAAIIIIAAAgggEA8BN8mEeLTLxmtkJgE4duxY6dq1q+zYsSMFTfIEoG4msmHDhnTpzj33XPnqq6/krLPOiljPaQJw2bJl0r9/f5k2bVrE8zRu3Nh8V6BAARtDmdA+ufnN+m3+nVBgiy9OAtDi4Hq5a34bgLbtOSSNH/82BenUga2kcvGCXmambQgggAACCCCAAAIIpCvgJpkAqTuB/fv3m11/x48fLw8++KA5yeTJk6VUqVIpTlihQgWTOAutACxatKjkyZNH9uzZI3fccYd06NDBrK5bunSpNG/eXKpWrWqOL1u2rJQuXVq6dOkimugrUaKEWZWn1xwzZoxMmjTJ1GvVqpVoMjJScZoAbNasmcybN0969uwpV155pUko6orDkSNHyty5c82p77//fhkxYoQ7LI5KU8DNb9Zv82/CnzUCJACzxpWzZiDgxwGo8WNTZdvew+GePd+9nnQ7tzSxRgABBBBAAAEEEEDAtwJOkgknTpyUXQeO+LaPbhp+Rv7cWfa0j9N3AIYSgNr+ggULypw5c8yjvGkVjWWVKlXS/P6dd96RG264wXw/depUadeu3Sl1nSYA9cD3339fevTokeIchw8floYNG4quENTE5ZYtWyRnzpxuQsAxaQg4+c2mPtSP829ugNgLkACMvSlndCDgxwHoxnd/lG9XbQv37sbzK8hDXWo46C1VEEAAAQQQQAABBBDwpoCTZMLOfYelwaNTvdmBLGrVwgfbS9GCebLk7G4SgA8//LA89NBDmW5P/fr1ZdGiRXLbbbfJSy+95DoBeNlll8lnn30WsT2vv/669OvXz3y3ePFiqVOnTqbbzQmSBJz8ZkkAcsdEEiAByH2REAE/JgCf/eYXefHbX8NejSsUkY/7Nk2IHxdFAAEEEEAAAQQQQCAWAk6SCSQAYyGddA43CcA1a9ZIxYoVHTfk5MmTsnXrVvPYsD4GHCr6WO7EiROlRYsWMmvWLNcJQE3+aRIwUlmwYIE0atTIfPXFF19It27dHLebihkLOPnNkgDM2DGINUgABjHqHuizHxOA36zYKje/tyCsVzBPTlkytGOWPRrggTDRBAQQQAABBBBAAAHLBZwkE0gAxvYmiDYBqI//7t2711EjNLn36quvmuReesdUr15dVqxY4ToBuHz5cqlRI/LTUGvXrjW7AWt57733zHsCKbETcPKbJQEYO2+bzkQC0KZo+qgvfkwAbtl9SM4bkXIjkGl3tZKKxdgIxEe3Hk1FAAEEEEAAAQQQSCbgJJlAAjC2t0y0CcAyZcrIxo0b022Ervi7+eabZdSoUY4aqzsG6+YgqYvTdwDqsXqOSGX9+vWiG5lo0fcO6rsMKbETcPKbJQEYO2+bzkQC0D2D21kAACAASURBVKZo+qgvfkwA6n9UGz32rezYl7QRyAtX1ZNL6rERiI9uPZqKAAIIIIAAAgggEGUCkE1AYnvLRJsALFeunGhSLb2iib+bbrrJVKlXr54MGDBAmjRpYnYF1h2Dc+TIYb7r1auX2bwjrXOSAIxtrLPibCQAs0I1GOckARiMOHuul35MACpi73fmy/TV28OeN7eoIIMvYiMQz91gNAgBBBBAAAEEEEDAkYCbZIKjE1MpTYGsSACed9558sMPP5hHb5cuXSr58uWLeP2uXbvKhAkTSAD6+P5085v16/zbx2HyZNNJAHoyLPY3yq8D0LNTVsuL034LB+i8ikVkbB82ArH/jqWHCCCAAAIIIICAnQJukgl2SsSvV6NHjw4/Fpveo7T66KzWdbICsFChQrJv3z4ZOHCgPPPMMxE7o0806ePEf/75JwnA+IU75ldy85v16/w75ngBPyEJwIDfAInqvl8HoMnLt0jf9xeG2QrlySmL2QgkUbcR10UAAQQQQAABBBDIpICbZEImLxn4wz/66CO56qqrjMOqVaukatWqEU2iSQDqir9Dhw5Jv379zCYgkcr48ePDO/LyCLB/b0M3v1m/zr/9GyVvtpwEoDfjYn2r/DoA/fn3QWn2xLQU8Zlxd2spf2YB62NGBxFAAAEEEEAAAQTsE3CTTLBPIb490h16W7VqZS6qu/ZeeOGFmU4A1qlTxzz6q+/80/8944wzUpxzzZo10rJlS7P6TwsJwPjGPJZXc/Ob9ev8O5ZunEuEBCB3QUIE/DoA6bL5ho9OlZ37j4TdXrr6XOlat1RCHLkoAggggAACCCCAAAKZEXCTTMjM9ThWZO/evVK8eHGzYq9+/foyYsQIs6Nu9uzZDY8m8XRFXzQrAJ9++mm55557zPHVqlWTe++9V2rWrGmuMW3aNHn++efl8OHDUqNGDfnpp59IAPr4RnTzm/Xr/NvHYfJk00kAejIs9jfKzwPQdW/Pl5m/JG0E0rdlRRl0YXX7g0YPEUAAAQQQQAABBKwTcJNMsA4hAR267777ZOTIkRGvPH36dGndunVUCcCjR49Kly5dZMqUKRHPqQlFfZ+grjhM772C7AKcgJshyku6+c36ef4dJQ/V0xEgAcjtkRABPw9AT09eLf+ZnrQRSLNKReXDm89LiCMXRQABBBBAAAEEEEAgMwJukgmZuR7H/iOgTxaNGjVK3nvvPVm+fLns3r1bjh8/br5zkwDU444dO2be/6fnXLFihbmGriZs37693HHHHWZlYEarCkkAev8OdfOb9fP82/sR8U8LSQD6J1ZWtdTPA9CkZVuk35ikjUBOy/vPRiCh/1haFSg6gwACCCCAAAIIIGC1gJtkgtUgdA4Bjwu4+c36ef7t8XD4qnkkAH0VLnsa6+cB6I9dB+T8J6enCMbMe1pLuaJsBGLPHUpPEEAAAQQQQACBYAi4SSYEQ4ZeIuBNATe/WT/Pv70ZBX+2igSgP+Pm+1b7eQDSpfT1H/lGdh04Go7Dy9fUl4vqlPR9XOgAAggggAACCCCAQLAE3CQTgiVEbxHwloCb36yf59/e0vd3a0gA+jt+vm293wegnqN+kNm/7gj792tVSe6/oJpv40HDEUAAAQQQQAABBIIp4CaZEEwpeo2ANwTc/Gb9Pv/2hrz/W0EC0P8x9GUP/D4AjZy0Sl6ZsSZsf37lM2XMTU18GQsajQACCCCAAAIIIBBcATfJhOBq0XMEEi/g5jfr9/l34tXtaAEJQDvi6Lte+H0A+nrpZvn3Bz+F3U/Pl0t+HtKBjUB8dyfSYAQQQAABBBBAINgCbpIJwRaj9wgkVsDNb9bv8+/EittzdRKA9sTSVz3x+wC08a8D0mJkyo1AZt/bRsoWye+rONBYBBBAAAEEEEAAgWALuEkmBFuM3iOQWAE3v1m/z78TK27P1UkA2hNLX/XE7wOQbgRS7+FvZPfBpI1AXrm2vlxYm41AfHUj0lgEEEAAAQQQQCDgAm6SCQEno/sIJFTAzW/W7/PvhIJbdHESgBYF009dsWEA6vHWDzLnt6SNQG5pXUnu7cxGIH66D2krAggggAACCCAQdAE3yYSgm9F/BBIp4OY3a8P8O5HmtlybBKAtkfRZP2wYgJ74epW8NjNpI5AWVc6U929kIxCf3Yo0FwEEEEAAAQQQCLSAm2RCoMHoPAIJFnDzm7Vh/p1gdisuTwLQijD6rxM2DEATl2yWWz9M2gjkjPy55KeH2AjEf3cjLUYAAQQQQAABBIIr4CaZEFwteo5A4gXc/GZtmH8nXt7/LSAB6P8Y+rIHNgxAv+88IC2fSrkRyJz72kiZM9gIxJc3JY1GAAEEEEAAAQQCKOAmmRBAJrqMgGcE3PxmbZh/eyYAPm4ICUAfB8/PTbdhANKNQOoOnyJ7Dh0Lh+K1HvWlcy02AvHzvUnbEUAAAQQQQACBIAm4SSYEyYe+IuA1ATe/WRvm316Lgx/bQwLQj1GzoM22DEDXvDlPvl+zMxyRW9tUkns6sRGIBbcoXUAAAQQQQAABBAIh4CaZEAgYOomARwXc/GZtmX97NCS+aRYJQN+Eyq6G2jIAjfhqpbw+a204OK3OKSajb2hsV7DoDQIIIIAAAggggIC1Am6SCdZi0DEEfCDg5jdry/zbB+HxdBNJAHo6PPY2zpYB6MvFf8rt/10UDlTRArllwYPtJVu2bPYGj54hgAACCCCAAAIIWCPgJplgTefpCAI+FHDzm7Vl/u3DcHmqySQAPRWO4DTGlgFo/Y790vrpGSkC9/39baVU4XzBCSY9RQABBBBAAAEEEPCtgJtkgm87S8MRsEDAzW/Wlvm3BeFLaBdIACaUP7gXt2UA0o1A6gyfInuTbQTyes8G0qnmWcENLj1HAAEEEEAAAQQQ8I2Am2SCbzpHQxGwUMDNb9aW+beF4Yxrl0gAxpWbi4UEbBqArn5jnsxdm7QRyO1tK8tdHasSbAQQQAABBBBAAAEEPC/gJpng+U7RQAQsFnDzm7Vp/m1xaLO8ayQAs5yYC0QSsGkAemziCnlz9rpwN9tULSbv9GYjEO58BBBAAAEEEEAAAe8LuEkmeL9XtBABewXc/GZtmn/bG9ms7xkJwKw35goRBGwagMb/vEnuGPtzuJdnFswjPw5ux0Yg3PkIIIAAAggggAACnhdwk0zwfKdoYIYC5cuXlw0bNsh1110n7777bor669evlwoVKpjP3nnnHbn++uszPF+kCnre3r17m6/WrVsnes1ElvT6nMh2RXttN79Zm+bf0XpRP0mABCB3Q0IEbBqA1m7fJ22fmZnCcd6gdnLW6XkTYstFEUAAAQQQQAABBBBwKuAmmeD03NTzrgAJwJRJT+9G6tSWufnN2jT/9lOsvNZWEoBei0hA2mPTAHTixD8bgew7fCwcvTd7NZQONUoEJJp0EwEEEEAAAQQQQMCvAm6SCX7tK+1OErAlAdi6dWuZOXOmtGrVSmbMmJFuiFkBWNb4bNy4UcqUKcPPIYACJAADGHQvdNmmBKB6dn99rvyw7q8wbf92VWRgh3O8QE0bEEAAAQQQQAABBBBIU4AEYDBvjngkw+LxCHA0CUBbIu3mN2vb/NuWWMa7HyQA4y3O9YyAbQPQoxNWyFtzkjYCaVutuLx9fSOijQACCCCAAAIIIICApwXcJBM83SEa50iABKAjJk9WcvObtW3+7cnA+KBRJAB9ECQbm2jbADRu0SYZ8FHSRiDFC+WR+YPb2xg6+oQAAggggAACCCBgkYCbZIJF3Q9sV0gA+jf0bn6zts2//Ru9xLacBGBi/QN7ddsGoN+27ZP2z6bcCGT+A+2k+GlsBBLYm5yOI4AAAggggAACPhBwk0zwQbc82cQDBw5IiRIlZN++fXLttdfKmDFj0m3n/PnzpUmTJqbOiy++KLfffrv55/3798uECRPkm2++kQULFpgddvXchQsXlho1akjXrl2lX79+UrBgwTTPH4t3AO7atUuefPJJGTdunNlRuFChQlKnTh3p27evXHHFFWZ34fR2AT5y5IhMnjzZ/N8PP/wgv/32m7E57bTTpHLlynLBBRfIbbfdJmeeeeYp/dCdiUePHp2uX7ly5UR3NA4Vp0nPL7/80px73rx5sn37duN4zjnnyCWXXGLak5Zr6v6effbZ8tZbbxmHlStXiva3UqVK0r17d7nzzjslf/78ru5TN79Z2+bfruA4SEgAchMkRMC2AUg3Aqk9bLLsP3I87DnquobSrjobgSTkBuOiCCCAAAIIIIAAAo4E3CQTHJ2YShEFevbsaRJ/BQoUkK1bt5r/TasMGDBAXnjhBcmRI4f8+eefUrx4cVM19N679IgrVKggX331lVSrVi1itcwmAFesWCHt27eXzZs3Rzz/DTfcIC1atEg3AegkiVe0aFEZP368NG/ePMV1nBwbbQLw0KFDcs0118gXX3yRJm2pUqVk4sSJUq9evVPqJE8ALlu2TPr37y/Tpk2LeK7GjRub79KLf1qNcPObtW3+zfDiToAEoDs3jsqkgI0D0JWvzZX565M2AhnQvooMaM9GIJm8VTgcAQQQQAABBBBAIAsF3CQTsrA51p960qRJZmWblg8++MAknCKV48ePm51at2zZIp07d5avv/46XO3888+XPXv2yMUXXywNGzYUTUqdPHnSrMLT5NXHH38sJ06ckKpVq8rPP/8sefOe+lRSZhKAu3fvllq1apn3umvRFW3XXXedSVD+8ssv8uyzz5qViY0aNZIff/zR1NFVinrN5KVHjx4yd+5cufTSS0UTYrpiLmfOnKYfU6dOlbffftusmitWrJhoQi2UANVzbNq0SXQFoq4w1GupwzvvvJPi/Llz5zYr90IloxWA2g+101K3bl256667pHr16vLXX3/J2LFjzUo+dS5SpIgsWbJESpcuneJ6yROAzZo1MysINeF75ZVXyllnnSW///67jBw50vRZy/333y8jRoyI+p5385u1cf4dNRwHsAKQeyAxAjYOQA9/uULe/i5pI5D21YvLW9exEUhi7jCuigACCCCAAAIIIOBEwFEy4cQJkYNJf+h2cl7f18lXRCR79ph3QxN7mrDbtm2bXHTRReZR3khFH+/t2LGj+er9998XTZaFisasSpUqabZNk2edOnUySUB9BPXGG288pW5mEoCaGNMkn5bHH39cBg0alOL8R48elS5dusiUKVPCn0dKAK5Zs0YqVqwo2bJli9iXpUuXiibS9LHgBx98UB555JFT6kWzC3B6fdZVfdpmLe3atTOrJzWBmLy8+eab0qdPH/ORJvU++uijFN8nTwBGipt+dvjwYZOs1ISmrm7UBK8mPaMpjn6zqU5o4/w7GjPq/iPACsAsuBM0s6/vaNBBRP85T5485h0GOkjccsstrp/1j9RUHdx1CfmcOXPM8msdPPS9EvruBR249C8Okd5REBr8Mup+6mXTGdV3+r2NA9AXi/6QOz9aHCYocVoe+eEBNgJxek9QDwEEEEAAAQQQQCD+Ao6SCft3iDxVKf6NS+QV71kjUuDUd8/Fokn6aOhLL71k5m46h0vvHXf6njh9VDi99/lFapOuqtN382lSS99pl7q4TQBqAqtkyZJm9Z3OORctWiTZIyRKdb6nyT1NBmqJlAB0Yqnvynv++efNikNNCKYusUoAXnjhhWaVZa5cuUQTk2XLlo3YvA4dOpjViRo7neurRagkTwBedtll8tlnn0U8x+uvv27e0ahl8eLFxjGa4ug3m+qENs6/ozGj7j8CJABjfCdo0k9f6KrLoiMVXYatf03QwTAzJbTcWd+HkF7RATnS+wlIAGZGP/Kxv27dKx2em5Xiy/mD20nxQmwEEnttzogAAggggAACCCAQCwFHyQQSgLGgDp8j+eYer7zyivz73/9OcX59F50u6tDHfK+++mr58MMP072+blTx999/m9VloaILUnTFmiayNFGVurhNAH7//ffh9/E988wzMnDgwDTbpslHnR9rcZIA1DmuPm6r/ddHbbVoEm3YsGEmyaifa4IueYlFAvDYsWNy+umnm41UNBEYanOkjukjwvqosBaNi8YnVJInALXdmgSMVEKPR+t3+sh2t27dorq/HP1mU52RBGBUxNZWJgEYw9Bq9l6XKOvAoX+h0aXQbdq0kYMHD5p3BugArEVfxKrvQoj2rzihpmpyUVf3LVy40HykS8evuuoqs8pQl5TrOxP0/J9++qn5a096CUDdyejRRx9NUyH1exNixWXjAHT8/zcCOZBsI5B3rm8kbar987JeCgIIIIAAAggggAACXhNwlEwgARjzsOm76dReN7fQp7mSl08++cQ8PaZFHxHW+V7q8t1335mnznQ1mibN0ir58uUz89PUxW0CMPnqtVmzZpmNPtIqDz/8sAwdOtR8nVYCUFf1Pffcc2b1nT4Om17RlZDJ3wOodWORANT3FupCHS3Dhw+XIUOGpNkMnWuH3mX4wAMPyGOPPRaumzwBuHz5crMjc6Sydu1asxuwlvfee888tRdNcfSbTXVCG+ff0ZhR9x8BEoAxvBM02TdjxgyzHFgHw6ZNm6Y4+1NPPSX33nuvo4ElvWb16tXLvAdCr6OP/4b+ApH6GP2riSYEI71TIKMXoMaQJeKpbB2ALn/1e1mwYVe4zwM7nCP926X9fo6sdub8CCCAAAIIIIAAAgikJ+AomUACMOY3ka5q02STvv9OE0LJN8jQFWH6pJc+Gqy7/6Ze9RY61mmjQqvpktd3mwB84oknwu/8W7VqVThxFqktyZOFkRKAo0aNMo/C6go8J2X9+vWir6hKXmKRANTNOkJz99dee0369u2bZnN0lWVoUxVt+6uvvhqumzwBmN6KR+2H7tKsRTcu0R2NoymOfrOpTmjr/DsaN+qSAIzZPaAr7nTnIi06YOjAkbroS1j13QUrV66UM844w7zLIfVgnlGD9K9Dob+y6MAf+otKRsel/p4EYLRizuoP+99yeff79eHKHWqUkDd7NXR2MLUQQAABBBBAAAEEEIizgKNkApuAxDwq6h7aoTb5Rhr6GKzuGKu73+r7419++eUU1/7222+lfft/3jOur5W6++67RXcF1h109QmzHDlymO90FVto04xYJgB111pd+aZl9erVKXbZTY2kc+LQ482pE2KaPKxdu7ZJ/umqvnvuuUfatm1rEqGFChUKz5N1J+DQJiaRkmqxTgBq0jK00UekoJMAjPlPgRPGUYAVgDHCHjx4sNkBSYv+BaFJkyYRz5z8Lya6K5K+RDSaoo/66m5DOrjrC2PdPkZMAjAaded1P1v4h9z1SdJGICVPzytzB7VzfgJqIoAAAggggAACCCAQRwFHCcA4tidIl9I5o74PsGbNmmZXWC3Jd5rVx3z1FVPJS2g+WLhwYZOAS/1IbKju7bffLv/5z3/Mv8YyARirR4Dvv/9+efLJJ03CUh8Drl69esTQ63sGNcmpJasSgFnxCDArAIP0S/ZPX0kAxihWLVu2lNmzZ0uBAgXMC1jT2sp77ty54UFc/yqjy76dFv0rkL6cVF9+esUVV4i+gFSL/tVk06ZNZvm4/rUo9Xblkc5PAtCpenT1ftm6Vzqm2ghkwYPt5cyCeaI7EbURQAABBBBAAAEEEIiDAAnAOCCncQl9h98dd9xhvg3tBht6rZTO1/TRYJ3jJS+6ak6ThentMqv19ZFWXZiiJZYJwGg2Aenatat5h6GW1Amx0Hf169cPv9s+EpNusqHv0490Dv0s5NWqVSvzOq70Slpz4Gg2AUn+fsb0NgEhAZi43xVXTluABGCM7o5ixYrJjh07pG7duvLzzz+neVZd0l2kSBHzffIknpNmJH/M+NlnnzU7DulGIzoI7d+/35xC30egg+CDDz54yl+Lkl8jNPjpuwd0FaFuda7/YdDdpvRRZj23bhCS+j84TtrppI6t7yA4dvyE1Bo2WQ4dPRFmeLd3I2ldlY1AnNwX1EEAAQQQQAABBBCIrwAJwPh6J7/atm3bpFSpUua97fqueF21p++401dH6RNmkTZr1JVy+vhs586dzcYZkYrOR88999zwV7FMAOojsCVLlhSd1+pmkz/99FPEOaMuUNFHlHURi5bUCbELLrhAJk2aZDbI1FdkRSq6KYieQzfVjHQO/Sx0nvPOO090sU16Jb1FMLr7r3rqK7p0bqy7J0cqnTp1En2ST1cubty40ViECu8ATNxviSs7EyAB6Mwp3Vq6Ik93V9KiOzSF/sqR1kGacNOEnZNBKvk5Ro8eHX5BqD5urElATTpGKrpNui6XHjBgQMTvQ4Nfeh3THan0cePSpUtHraQJvvSKPr4cemeiDpxlypSJ+hpePeCyV76Tn37/O9y8uzueI7e1ZSMQr8aLdiGAAAIIIIAAAkEWIAGY2OhrIm/y5Mkm4XTbbbfJfffdZxqU1i6yF198sXz55ZdmEYeuGtQEWfKyfft2syBEjw+VWCYA9Zx33XWXmYtq0cd4Qxtdhq6nK+q0nckTlKkTgP3795eXXnpJdN6qjzrr3Dh50Z2Lu3TpItOnTw9/HGlV3Q033GA20tBHoTVhmN4ClvQSgBMnTjTX06LvWNR/T/1kXfL3EeouzTpXTl5IACb2t8TVMxYgAZixUYY1dJANvXtBd+QNLVFO60BdZad/7dENQfR9B06Lbo8+cOBAUz1Pnjyif33RQUo3A9Fz7d69Wz777DPR9yns2bPHDH46cOlfRVIXfeGs/vWoY8eO5lh9tFgfXda/muhORpqU06J19DP9PpoSzcpB2xKAQ8cvk9FzN4S5OtUsIa/3ZCOQaO4f6iKAAAIIIIAAAgjER4AEYHyc07rKmDFjpGfPnuZrfa+fzsl09Z6urItUPv30U/MkmRZdRKEJwwYNGpinufTxXE3MaSIs+WKTWCcAdd6pc8jQog99eqxXr15mTqzv09M26NNrjRo1Mv+rJXXyLvnTbbpBpiYR9X2H+kTbwoULRee+em/qohRNEEY6h3721ltvyc0332y+18UvPXr0CM9ddTVf8l2DM3oNlib19Ok6Lbq6UROdOh/W1Y46x9cEoFrqE31Lliw5ZaEMCcDE/pa4esYCJAAzNsqwhiawdNclLTp4v/fee+keo3X1mEqVKslvv/2W4flDFXQJ+EMPPRSur+9NGDdunPmrSfKiOwXrOxBCuw7r4JQ6Iaf/YdH/wEQqe/fulcsvv9wsbdZy5513hv/C47SxQU4AfrJgo9zz6ZIwVenC+eS7+9s6paMeAggggAACCCCAAAJxEyABGDfqiBfat2+feQ2TrngLlaefftokn9IqoVVvkb7XR1P1STBNWoXeNx/rBKBeV1cY6ko5TTZGKr179xZ9T77+r5ZIq/cefvhhGTp0aJr9VANNNKZ3DvXT13Dp+xJTF03+rV+/PvxxRglAfbLvmmuukS+++CLNNukj27rIRhOEqQsJwMT+lrh6xgIkADM2yrBGvFYA6n8IdHv0UNF3P1StWjVi+/SvQvrXIS2aANSXxUZT9K86mqDcuXOn2djkr7/+crS5SOgaQX4EeNWWPdL5+dkpuH96qIMUKZA7mhBQFwEEEEAAAQQQQACBLBcgAZjlxBleQJNO//3vf009Xdzx+++/Z/gaJl05+MYbb5j3z+t79nQzSE246WPE+qolfUosKxOA2ladI+ojwJow0zYXKlTIzDt1RZ6uCnSSEPvqq6/khRdeMCsF9TVZuopQ29+vXz/p0KGDo3Ns3bpVRowYYRawbNiwIZxMjTYBGAqUPmKtbddNVPSVWzof1ifounXrZnz18etIxUl/9ThNSuq7+LXo48vXX399hvdI8gpufrO2voM/KjgqCwnAGNwE8XoHYPIt13XAiPRXjlB3ki+FHjVqlOhfiaItOri9/PLL5rBIW9BHe77k9W0egHQjkJpDJ8vhY0kbgYy+obG0OqdYZsg4FgEEEEAAAQQQQACBmAu4SSbEvBGcEAEEHAu4+c3aPP92DEdFEoCxugfisQuw/nVENxnR0qJFC5k1a1aazdcXyeoLZbXoX0P0vYDRlldeeUVuvfVWc9jHH38cftdEtOeJVN/2Aajby9/JzxuTNgK5p1NVubVN5VjQcQ4EEEAAAQQQQAABBGIm4CaZELOLcyIEEIhawM1v1vb5d9SIAT2AFYAxCrwut549e7ZZHqzv18uZM2fEM+uGGvpyUy1DhgwJL8t20gxdzqzvLdCi5wi9DDXSscmThU899ZTcfffdTi6Roo6u/tNVgFpIAEbH99C4ZfL+vKSNQC6odZa82qNBdCehNgIIIIAAAggggAACWSzgJpmQxU3i9AggkI6Am98sCUBuKRUgARij++CBBx4wK+206LsCmjRpEvHMTzzxhAwaNMh8p6v0dBfeaIq+x0Dfr6Avik3rhat6Pt1SXbdW1/Lhhx+adzBEW3T1n64C1KIbi+gOTLEqtg9AH/+4Ue79LGkjkDJn5JM597ERSKzuH86DAAIIIIAAAgggEBsBN8mE2FyZsyCAgBsBN79Z2+ffbhyDeAwJwBhFff78+eGkX9++feW111475cyhXXlXrlxpduDdtm2b6Nbk0ZSBAweaLdG1pPdevjZt2siMGTNMPd1xWLeIj6Yk3wQkf/785gWvefLkieYU6da1fQBa8eceufDFlBuBLHqog5zBRiAxu4c4EQIIIIAAAggggEDmBdwkEzJ/Vc6AAAJuBdz8Zm2ff7u1DNpxJABjGPHQY8D6+K++n69p06Ypzq6P4t57773mM93uXHdmSl6S7xoU6Xutq6v/dOdf3XikQYMGMnPmTPPYcfKiO0L17NnTfKTvDJwwYUKK7ydNmiStWrWSfPnyRez93r175fLLLze7KGm5/fbb5cUXX4yhlIjtA9DR/98I5EiyjUDev7GxtKjCRiAxvZE4GQIIIIAAAggggECmBNwkEzJ1QQ5GAIFMCbj5zdo+/84UaIAOJgEYw2AvWrTIda4CfwAAIABJREFUPCZ78OBBszW4PhasK/H038eOHWu2adeiW4gvWLDAbJOevDhJAGr95InEGjVqmKRirVq1RFftff7552b14fHjx+W0004z16lSpUqK67Ru3VqWLl0ql112mZx//vlSqVIl0159d6G+o/DVV181qwa1aLLx+++/lyJFisRQyv4EoGJd8vJ3sjjZRiD3dq4qt7RmI5CY3kicDAEEEEAAAQQQQCBTAm6SCZm6IAcjgECmBNz8ZkkAZorcmoNJAMY4lF9++aX06NFD9uzZE/HMmvybOHGiVK58aiLIaQJQT6zvEXzyySfl5MmTEa9TvHhxGTdu3CmrELWyJgB15WBGRVc06vsDS5cunVHVqL8PwgA0+Iul8sEPv4dtLqpdUl6+tn7UVhyAAAIIIIAAAggggEBWCbhJJmRVWzgvAghkLODmNxuE+XfGctQgAZgF94Du1vvCCy+YRJ/+0HLnzm0SfldccYXZVVffqRepRJMA1ONDq/V09+HNmzdL3rx5zerCiy++2Dy2e/rpp0e8jq4K/Pbbb83xq1evlh07dpjVf9quUqVKmXcZ6qYhukFJtmzZskAoGCsAx87/Xe7/fGnY7+wi+WXWvW2yxJOTIoAAAggggAACCCDgRsBNMsHNdTgGAQRiI+DmN0sCMDb2fj8LCUC/R9Cn7Q/CALRs027p8tKcFBFaPKSjnJ4/uo1ffBpimo0AAggggAACCCDgAwE3yQQfdIsmImCtgJvfbBDm39YGPIYdIwEYQ0xO5VwgCAOQbgBSa+hkOXL8RBjmg5uaSPPKZzqHoiYCCCCAAAIIIIAAAlko4CaZkIXN4dQIIJCBgJvfbBDm39w4GQuQAMzYiBpZIBCUAeji/8yRJX/sDgvef0E16deqUhaIckoEEEAAAQQQQAABBKIXcJNMiP4qHIEAArEScPObDcr8O1bGtp6HBKCtkfV4v4IyAD3wxVL5MNlGIF3qlJT/XMNGIB6/PWkeAggggAACCCAQGAE3yYTA4NBRBDwo4OY3G5T5twfD5akmkQD0VDiC05igDECa/NMkYKiUL5pfZtzDRiDBudPpKQIIIIAAAggg4G2BtWvXyuHDh00jdUPBHDlyeLvBtA6BAAucOHFCfvnlFzl58qTkyZNHKlas6EgjKPNvRxgBrkQCMMDBT2TXgzIALf1jt3T9T8qNQJYM6yin5WUjkETef1wbAQQQQAABBBBA4B+BLVu2yK5du8w/Fy9eXIoWLQoNAgh4VGDPnj2yadMm07rChQtLyZIlHbU0KPNvRxgBrkQCMMDBT2TXgzIAHT523GwEcvT4yTD3hzc3kWaV2Agkkfcf10YAAQQQQAABBBD4R+DQoUOybt26MIcmAE877TSzuihbtmwwIYCABwR05d++fftMwv748eOmRWXLlpWCBQs6al1Q5t+OMAJciQRggIOfyK4HaQDq8tJsWbZpT5j7gQurSZ+WbASSyPuPayOAAAIIIIAAAggkCfz555+ye3fSxnX6jSb/eByYuwQBbwho0k8f+w2VfPnySbly5Rwn6YM0//ZGxLzZChKA3oyL9a0K0gA06PMl8t/5G8Mx7Vq3lLx09bnWx5gOIoAAAggggAACCPhDQBMLO3fulO3bt/ujwbQSgQALaPLv7LPPluzZsztWCNL82zFKACuSAAxg0L3Q5SANQB/8sEEGf7EszF7xzAIy7e7WXggDbUAAAQQQQAABBBBAICxw5MgR85jh/v37Rf9ZHzukIIBA4gV0Na4m/goVKiQFChRwvPIv1PIgzb8THy3vtoAEoHdjY3XLgjQALd74t1zy8ncp4rl0WEcpxEYgVt/jdA4BBBBAAAEEEEAAAQQQ8IJAkObfXvD2ahtIAHo1Mpa3K0gD0KGj/2wEcuxE0jsbxvY5T86ryA5rlt/mdA8BBBBAAAEEEEAAAQQQSLhAkObfCcf2cANIAHo4ODY3LWgD0IUvzJYVm5M2AnnwoupyU4uKNoeYviGAAAIIIIAAAggggAACCHhAIGjzbw+Qe7IJJAA9GRb7GxW0Aei+T5fIRwuSNgK5pF4peeEqNgKx/06nhwgggAACCCCAAAIIIIBAYgWCNv9OrLZ3r04C0LuxsbplQRuA3p+3QR4al2wjkGIFZNpdbARi9U1O5xBAAAEEEEAAAQQQQAABDwgEbf7tAXJPNoEEoCfDYn+jgjYALfp9l1z6yvfhwGbLJrJ0WCcpmCen/cGmhwgggAACCCCAAAIIIIAAAgkTCNr8O2HQHr8wCUCPB8jW5gVtANKNQGoOnSzHk20E8nHfptK4QhFbQ0y/EEAAAQQQQAABBBBAAAEEPCAQtPm3B8g92QQSgJ4Mi/2NCuIA1Pn5WbJqy95wcB/qUkNuPL+C/cGmhwgggAACCCCAAAIIIIAAAgkTCOL8O2HYHr4wCUAPB8fmpgVxALrnk8XyycI/wmG99NzS8lz3ejaHmb4hgAACCCCAAAIIIIAAAggkWCCI8+8Ek3vy8iQAPRkW+xsVxAHovbnrZcj45eHgVi5eUKYObGV/sOkhAggggAACCCCAAAIIIIBAwgSCOP9OGLaHL0wC0MPBsblpQRyAfvp9l1yWaiOQZcM6SQE2ArH5VqdvCCCAAAIIIIAAAggggEBCBYI4/04ouEcvTgLQo4GxvVlBHIAOHtGNQCZJsn1A5NN+TaVheTYCsf1+p38IIIAAAggggAACCCCAQKIEgjj/TpS1l69LAtDL0bG4bUEdgDo9N0tWb03aCGRo1xrSuzkbgVh8q9M1BBBAAAEEEEAAAQQQQCChAkGdfycU3YMXJwHowaAEoUlBHYDu+nixfPZT0kYgl9UvLc9eyUYgQbjn6SMCCCCAAAIIIIAAAgggkAiBoM6/E2Ht5WuSAPRydCxuW1AHoHe/WyfDvlwRjuw5JQrKlDvZCMTiW52uIYAAAggggAACCCCAAAIJFQjq/Duh6B68OAlADwYlCE0K6gC0cMNf8q9X54ZDnD2byLLhnSR/7pxBCDt9RAABBBBAAAEEEEAAAQQQiLNAUOffcWb2/OVIAHo+RHY2MKgD0IEjx6TW0MkpNgL57N9NpUE5NgKx806nVwgggAACCCCAAAIIIIBAYgWCOv9OrLr3rk4C0HsxCUSLgjwAdXh2pvy6bV84zsMvrinXNSsfiLjTSQQQQAABBBBAAAEEEEAAgfgKBHn+HV9pb1+NBKC342Nt64I8AA386Gf5fNGmcGwvb1BGnr6irrWxpmMIIIAAAggggAACCCCAAAKJEwjy/Dtx6t67MglA78UkEC0K8gD09px18vCEpI1Aqp1VSCYNaBmIuNNJBBBAAAEEEEAAAQQQQACB+AoEef4dX2lvX40EoLfjY23rgjwA/bj+L7nitaSNQHJkzybLh3eSvLlyWBtvOoYAAggggAACCCCAAAIIIJAYgSDPvxMj7s2rkgD0Zlysb1WQB6D9h49JrWGT5eTJpDB/fkszqX/2GdbHnQ4igAACCCCAAAIIIIAAAgjEVyDI8+/4Snv7aiQAvR0fa1sX9AGo/bMz5bdkG4E8fElN6dWUjUCsveHpGAIIIIAAAggggAACCCCQIIGgz78TxO65y5IA9FxIgtGgoA9AA8YuknE//xkO9pUNy8jIy9kIJBh3P71EAAEEEEAAAQQQQAABBOInEPT5d/ykvX0lEoDejo+1rQv6APTW7LXy6MSV4fhWL3mafH1HC2vjTccQQAABBBBAAAEEEEAAAQQSIxD0+Xdi1L13VRKA3otJIFoU9AHoh7U7pfsb88Kxzpk9myxjI5BA3Pt0EgEEEEAAAQQQQAABBBCIp0DQ59/xtPbytUgAejk6Frct6APQvsPHpHaqjUDG3dpc6pUtbHHU6RoCCCCAAAIIIIAAAggggEC8BYI+/463t1evRwLQq5GxvF0MQCJtn5kha7fvD0f6kW61pOd55SyPPN1DAAEEEEAAAQQQQAABBBCIpwDz73hqe/daJAC9GxurW8YAJHLH2EUyPtlGIN0blpUnL69jddzpHAIIIIAAAggggAACCCCAQHwFmH/H19urVyMB6NXIWN4uBiCRN2etlce+StoIpGap02RifzYCsfzWp3sIIIAAAggggAACCCCAQFwFmH/HlduzFyMB6NnQ2N0wBiCRuWt2ytVvJm0EkivHPxuB5MmZw+7g0zsEEEAAAQQQQAABBBBAAIG4CTD/jhu1py9EAtDT4bG3cQxAInsOHZU6w6akCPL/bmsudcqwEYi9dz49QwABBBBAAAEEEEAAAQTiK8D8O77eXr0aCUCvRsbydjEA/RPgNk/PkHU7kjYCeezSWnJtEzYCsfz2p3sIIIAAAggggAACCCCAQNwEmH/HjdrTFyIB6Onw2Ns4BqB/Ynv7fxfJl4v/DAf66sZlZcRlbARi751PzxBAAAEEEEAAAQQQQACB+Aow/46vt1evRgLQq5GxvF0MQP8E+I1Za+Txr1aFo1279Ony5e3nWx59uocAAggggAACCCCAAAIIIBAvAebf8ZL29nVIAHo7Pta2jgHon9B+/9sOueatH8Jxzp0ju9kIJHfO7NbGno4hgAACCCCAAAIIIIAAAgjET4D5d/ysvXwlEoBejo7FbWMA+ie4uw8elbrDU24EMuH286VW6dMtjj5dQwABBBBAAAEEEEAAAQQQiJcA8+94SXv7OiQAvR0fa1vHAJQU2lZPTZcNOw+EPxhxWW25uvHZ1saejiGAAAIIIIAAAggggAACCMRPgPl3/Ky9fCUSgF6OjsVtYwBKCu6tH/4kE5dsDn9wTZOz5fFLa1scfbqGAAIIIIAAAggggAACCCAQLwHm3/GS9vZ1SAB6Oz7Wto4BKCm0r81cI098nbQRSJ0yp8v/bmMjEGtvfjqGAAIIIIAAAggggAACCMRRgPl3HLE9fCkSgB4Ojs1NYwBKiu53v+2Qa1NtBLL84U6SKwcbgdj8G6BvCCCAAAIIIIAAAggggEA8BJh/x0PZ+9cgAej9GFnZQgagpLD+feCI1Hv4mxRxntj/fKlZio1ArLz56RQCCCCAAAIIIIAAAgggEEcB5t9xxPbwpUgAejg4NjeNAShldFuMnCYb/zoY/vDJf9WW7o3YCMTm3wB9QwABBBBAAAEEEEAAAQTiIcD8Ox7K3r8GCUDvx8jKFjIApQzrLR8slK+Wbgl/2OO8s+XRbmwEYuXNT6cQQAABBBBAAAEEEEAAgTgKMP+OI7aHL0UC0MPBsblpDEApo/vKjN9k5KTV4Q/rli0s429tbvMtQN8QQAABBBBAAAEEEEAAAQTiIMD8Ow7IPrgECUAfBMnGJjIApYzq7F+3S89R88Mf5s6ZXZYPZyMQG+99+oQAAggggAACCCCAAAIIxFOA+Xc8tb17LRKA3o2N1S1jAEoZ3l37j8i5j6TcCOTrO1pI9ZKnWX0f0DkEEEAAAQQQQAABBBBAAIGsFWD+nbW+fjk7CUC/RMqydjIAnRrQ5k9Mk01/J20EMvLyOnJlw7KWRZ7uIIAAAggggAACCCCAAAIIxFOA+Xc8tb17LRKA3o2N1S1jADo1vP3eXyiTlidtBNKraTl5+JJaVt8HdA4BBBBAAAEEEEAAAQQQQCBrBZh/Z62vX85OAtAvkbKsnQxApwb05em/yVOTkzYCOffswvLFLWwEYtmtT3cQQAABBBBAAAEEEEAAgbgKMP+OK7dnL0YC0LOhsbthDECnxnfmL9vlureTNgLJmyu7LBvWSXLmyG73zUDvEEAAAQQQQAABBBBAAAEEskyA+XeW0frqxCQAfRUuexrLAHRqLP/af0Tqp9oIZNKAFlLtLDYCsefOpycIIIAAAggggAACCCCAQHwFmH/H19urVyMB6NXIWN4uBqDIAU69EcjTV9SVyxuUsfxuoHsIIIAAAggggAACCCCAAAJZJcD8O6tk/XVeEoD+ipc1rWUAihzKPu8tkCkrtoa/vL5ZeRl2cU1r4k5HEEAAAQQQQAABBBBAAAEE4ivA/Du+3l69GglAr0bG8nYxAEUO8Evf/irPfPNL+MsG5c6Qz/7dzPK7ge4hgAACCCCAAAIIIIAAAghklQDz76yS9dd5SQD6K17WtJYBKHIop6/eJr3f+TH8Zb5cOWTZ8E6SI3s2a2JPRxBAAAEEEEAAAQQQQAABBOInwPw7ftZevhIJQC9Hx+K2MQBFDu6OfYel4aNTU3w55c6Wck6JQhbfDXQNAQQQQAABBBBAAAEEEEAgqwSYf2eVrL/OSwLQX/GyprUMQGmHsumIb2Xz7kPhCs9cUVf+xUYg1tz7dAQBBBBAAAEEEEAAAQQQiKcA8+94anv3WiQAvRsbq1vGAJR2eG9+b4F8k2wjkN7Ny8vQrmwEYvUPgs4hgAACCCCAAAIIIIAAAlkkwPw7i2B9dloSgD4LmC3NZQBKO5IvTP1VnpuatBFIo/JnyCf92AjElnuffiCAAAIIIIAAAggggAAC8RRg/h1Pbe9eiwSgd2NjdcsYgNIO77RVW+WGdxeEK+TPnUOWDmMjEKt/EHQOAQQQQAABBBBAAAEEEMgiAebfWQTrs9OSAPRZwGxpLgNQ2pHctveQNH7s2xQVpg5sKZWLsxGILfc//UAAAQQQQAABBBBAAAEE4iXA/Dte0t6+DglAb8fH2tYxAKUf2iaPT5Wtew6HKz3Xva5cem4Za+8HOoYAAggggAACCCCAAAIIIJA1Asy/s8bVb2clAei3iFnSXgag9AN50+gfZerKbeFKNzSvIEO61rAk+nQDAQQQQAABBBBAAAEEEEAgXgLMv+Ml7e3rkAD0dnysbR0DUPqhfX7qL/L81F/DlRpXKCIf921q7f1AxxBAAAEEEEAAAQQQQAABBLJGgPl31rj67awkAP0WMUvaywCUfiCnrtgqN72XtBFIwTw5ZcnQjpI9ezZL7gC6gQACCCCAAAIIIIAAAgggEA8B5t/xUPb+NUgAej9GVraQASj9sG7dc0iaPJ5yI5Bv72ollYoVtPJ+oFMIIIAAAggggAACCCCAAAJZI8D8O2tc/XZWEoB+i5gl7WUAyjiQjR6bKtv3Jm0E8sJV9eSSeqUzPpAaCCCAAAIIIIAAAggggAACCPy/APNvbgUVIAHIfZAQAQagjNlvePdHmbYqaSOQm86vIA92YSOQjOWogQACCCCAAAIIIIAAAgggEBJg/s29QAKQeyBhAgxAGdM/+80v8uK3SRuBnFexiIztw0YgGctRAwEEEEAAAQQQQAABBBBAgAQg90ByAVYAcj8kRIAEYMbs36zYKjcn2wikUJ6cspiNQDKGowYCCCCAAAIIIIAAAggggEBYgPk3N4MKkADkPkiIAANQxuybdx+UpiOmpag4/e7WUuHMAhkfTA0EEEAAAQQQQAABBBBAAAEERIT5N7cBCUDugYQJMABlTH/y5EnRjUB27DsSrvzi1efKxXVLZXwwNRBAAAEEEEAAAQQQQAABBBAgAcg98P8CrADkVkiIAAlAZ+zXvzNfZqzeHq7cp2VFeeDC6s4OphYCCCCAAAIIIIAAAggggEDgBZh/B/4WMAAkALkPEiLAAOSM/Zkpq+Wlab+FKzetWFT+2+c8ZwdTCwEEEEAAAQQQQAABBBBAIPACzL8DfwuQAOQWSJwAA5Az+8nLt0jf9xeGKxfKm1OWDO0o2bJlc3YCaiGAAAIIIIAAAggggAACCARagPl3oMMf7jwrALkPEiLAAOSM/c+/D0qzJ1JuBDLzntZSrigbgTgTpBYCCCCAAAIIIIAAAgggEGwB5t/Bjn+o9yQAuQ8SIsAA5IxdNwJp8OhU+Wt/0kYg/7nmXOlSh41AnAlSCwEEEEAAAQQQQAABBBAItgDz72DHnwQg8U+oAAOQc/5eb8+XWb8kbQTSt1VFGXQBG4E4F6QmAggggAACCCCAAAIIIBBcAebfwY198p6zApD7ICECDEDO2Z+avEpenr4mfEDzykXlg5vYCMS5IDURQAABBBBAAAEEEEAAgeAKMP8ObuxJABL7hAswADkPwaRlm6XfmJ/CB5yeL5f8PKQDG4E4J6QmAggggAACCCCAAAIIIBBYAebfgQ19io6zApD7ICECDEDO2f/YdUDOf3J6igNm39tGyhbJ7/wk1EQAAQQQQAABBBBAAAEEEAikAPPvQIb9lE6TAOQ+SIgAA5Bzdt0IpP4j38iuA0fDB71ybX25sHZJ5yehJgIIIIAAAggggAACCCCAQCAFmH8HMuwkAAm7NwQYgKKLQ89RP8jsX3eED/p360pyX+dq0Z2E2ggggAACCCCAAAIIIIAAAoETYP4duJBH7DArALkPEiLAABQd+5OTVsmrM5I2AmlR5Ux5/8Ym0Z2E2ggggAACCCCAAAIIIIAAAoETYP4duJCTACTk3hFgAIouFl8t3Sy3fJC0EUjh/Llk0UNsBBKdIrURQAABBBBAAAEEEEAAgeAJMP8OXswj9ZgVgNwHCRFgAIqOfeNfB6TFSDYCiU6N2ggggAACCCCAAAIIIIAAAsy/uQdUgAQg90FCBBiAomPXjUDqPfyN7D6YtBHIaz3qS+dabAQSnSS1EUAAAQQQQAABBBBAAIFgCTD/Dla80+otCUDug4QIMABFz37tW/Pku992hg+8tU0luacTG4FEL8kRCCCAAAIIIIAAAggggEBwBJh/ByfW6fWUBCD3QUIEGICiZx/x9Up5feba8IEtzykm793QOPoTcQQCCCCAAAIIIIAAAggggEBgBJh/BybU6XaUBGAW3Ae///67vPjiizJx4kTRf86TJ49UrlxZrrzySrnlllskf/78Mbvq1KlTZcyYMTJnzhzZvHmz5MyZU0qUKCF16tSRdu3aSc+ePaVgwYJpXm/nzp2mrePGjZP169eLPmpaoUIF6datm/Tv31+KFi0as7YmPxEDUPSsE5b8Kbd9uCh8YJECuWXhg+0lW7Zs0Z+MIxBAAAEEEEAAAQQQQAABBAIhwPw7EGHOsJMkADMkiq6CJv2uvfZa2b17d8QDq1atKl999ZVUrFgxuhOnqr1r1y7p3bu3jB8/Pt3zLFq0SOrVqxexzo8//iiXXHKJSRxGKqVKlTLnb9iwYabaGulgBqDoSTfs3C+tnpqR4sDv7m8rpQvni/5kHIEAAggggAACCCCAAAIIIBAIAebfgQhzhp0kAZghkfMKixcvlmbNmsmBAwfMqrtBgwZJmzZt5ODBgzJ27Fh58803zcmqVasmmnxLb2VeelfV5KKu7lu4cKGpdtFFF8lVV11lVhkeP35cNmzYYM7/6aefypdffhkxAbhp0yZp0KCBbN261awaHDhwoHTp0sWcb8KECfLss8/KsWPHzGpCvU7p0qWdQzioyQDkAClVFV2dWXf4FNlz6Fj4m9d6NJDOtc6K/mQcgQACCCCAAAIIIIAAAgggEAgB5t+BCHOGnSQBmCGR8wqa7JsxY4ZJqM2aNUuaNm2a4uCnnnpK7r33XvPZ8OHDZciQIc5Pnqxmr1695P333zfX0cd/u3fvHvE8mjDShKDWS12uv/56GT16tPn4448/liuuuCJFlU8++cQ8sqxFVxq+/fbbrtqa1kEMQO44r3lznny/JmkjkNvbVpa7OlZ1dzKOQgABBBBAAAEEEEAAAQQQsF6A+bf1IXbUQRKAjpgyrqQr7ho3/mdDhr59+8prr712ykEnTpyQWrVqycqVK+WMM84wq+9y5cqV8cmT1dB3/bVo0cJ8MmzYMBk6dGhUx2tlva6u6NPkYKdOnWTSpEkRz9G5c2eZPHmy5MiRQ3TFoK4GjFVhAHIn+fhXK+WNWUkbgbSuWkze7c1GIO40OQoBBBBAAAEEEEAAAQQQsF+A+bf9MXbSQxKATpQc1Bk8eLA8/vjjpua8efOkSZMmEY964oknzKPBWqZMmSIdOnRwcPakKvqo70cffWQeH9Z397l5jFgfRe7Tp485qT6anNYKQv3u6quvNvXeeOMNufnmm6Nqa3qVGYDcUf5v8Z/S/79JG4GcWTC3/DiYjUDcaXIUAggggAACCCCAAAIIIGC/APNv+2PspIckAJ0oOajTsmVLmT17thQoUED+/vvviI/d6mnmzp1r3hOoRR8B1keBnZYjR47I6aefLocOHTKP7Oqju1r0XX26Qk93gz3rrLMkd+7c6Z4y9AixVtIkoh4Tqeh3uhGIFj0m9Miw0/amV48ByJ3iuh37pc3TKTcCmTuorZQ8nY1A3IlyFAIIIIAAAggggAACCCBgtwDzb7vj67R3JACdSmVQr1ixYrJjxw6pW7eu/Pzzz2nW1t17ixQpYr5PnsRz0ozkjxnrJh26Ok9XE+r7+vbv329OkTdvXrPxyIMPPhhONKY+d6NGjWTBggUmmajJyvSK1tmzZ4/oMfPnz3fSTEd1GIAcMZ1S6cSJfzYC2Xs4aSOQN3o2kI412QjEnShHIYAAAggggAACCCCAAAJ2CzD/tju+TntHAtCpVDr1dEVevnz/rMDSHXl1F930ij62qwm78847z6wIdFp0BZ5u3qFFHzfWJKAmHSOV7NmzyzPPPCMDBgw45Wtd8afvAaxZs6YsW7Ys3cvrOwuXL19uVgnqikCnRQeY9IqeK/TOxI0bN0qZMmWcnjrw9a56Y67MW/tX2KF/28oykI1AAn9fAIAAAggggAACCCCAAAIIRBIgAch9oQIkAGNwH2zfvl2KFy9uzqTv09N356VXdDONbdu2mQ1Bli5d6rgFzz33nAwcONDUz5Mnjxw+fFi6dOliNgPRc+3evVs+++wzuf/++82qPX0keOLEiXLBBRekuIY+pnzgwAHznkJ9X2F6Revoyj9NWu7du9dxW/XaTgsJQKdS/9R7bOIKeXP2uvBBbasVl7evbxTdSaiNAAIIIIAAAggggAACCCAQCAESgIEIc4adJAGYIVHGFTSBdfbZZ5uKPXv2lPfeey/dg7SuHlOpUiX57bffMr7A/9d49NFH5aGHHgrX79q1q4zxPvLMAAAgAElEQVQbN050tV/yojsFt2rVSkK7Di9ZssQkA0NFd/XV73Q34VmzZqV7/dC7DfUYfdeg00IC0KlU9PXG/7xJ7hib9Jh5sUJ5zEYgFAQQQAABBBBAAAEEEEAAAQRSC5AA5J5QARKAMbgP4rUC8Omnn5Z77rkn3OJVq1ZJ1apVI/ZA3y/46aefmu80AVi7du1wvXisAOQR4BjcWGmcYs32fdLumZkpvv3hgXZS4rS8WXdRzowAAggggAACCCCAAAIIIOBLARKAvgxbzBtNAjAGpPF6B+Drr78u/fr1My2uUKGCrF27Ns3Wv/XWW3LzzTeb70eNGiU33HBDuG483gGYESsDUEZCaX+vG4HUGT5F9iXbCOStXg2lfY0S7k/KkQgggAACCCCAAAIIIIAAAlYKMP+2MqxRd4oEYNRkkQ+Ixy7AX331ldlkREtGj+9OnjxZOnfubOqOGDHCvBcwVBo2bCgLFy5kF+AYxT4Rp7ny9bkyf13SRiB3tKsid3Y4JxFN4ZoIIIAAAggggAACCCCAAAIeFiAB6OHgxLFpJABjhB16V54+Xvv3339Lzpw5I55Zd/1t1qyZ+W7IkCEyfPhwxy3YsGGDlC9f3tTXc3z33XdpHps8WfjUU0/J3XffHa7bq1cvef/9982/6268uiIwUtHvSpUqZb7SY3QX4lgVBqDMST4yYYWMmpO0EUi7asVlFBuBZA6VoxFAAAEEEEAAAQQQQAABCwWYf1sYVBddIgHoAi3SIQ888IBZaadFd9bV3XMjlSeeeEIGDRpkvtJVeh07doyqBeXKlZPff/9ddCfhLVu2pHnsSy+9JP379zfff/jhh3L11VeH677xxhvSt29f8++6Y7HuXByp6Heh4/Tx4z59+kTV1vQqMwBljnLcok0y4KOkjUBKnJZHfniAjUAyp8rRCCCAAAIIIIAAAggggIB9Asy/7Yupmx6RAHSjFuGY+fPnh5N+mlx77bXXTqkV2pV35cqVUrhwYdm2bZvkypUrqhYMHDhQnnvuOXOMrgAMrSZMfZI2bdrIjBkzzMe643CZMmXCVTRxWLp0abMTcKdOnWTSpEkR26CPEGuSUncZ3rRpU5orBaPqwP9XZgByo5Z0zG/b9kr7Z1Pu4Dx/cDspXoiNQDIny9EIIIAAAggggAACCCCAgF0CzL/tiqfb3pAAdCsX4bjQY8D6+O+sWbOkadOmKWrpo7j33nuv+Wzo0KEybNiwFN+/++670rt37zS/1y909Z/u/KsbjzRo0EBmzpwp+thx8jJmzBjp2bOn+UjfGThhwoRTWpv8MeBPPvlELr/88hR19LMrr7zSfHbdddeJti2WhQEoc5rHT5yU2sMmy4Ejx8Mnevv6htK2GhuBZE6WoxFAAAEEEEAAAQQQQAABuwSYf9sVT7e9IQHoVi7CcYsWLZLmzZvLwYMHpWDBgqKPBetKPP13fZxWH73Vcs4558iCBQukUKFCKc7iJAGoByRPJNaoUcMkFWvVqiW7d+/+P/buO8qKYlvg8CZHiRIERMlZRHKQIFkQUAFFBPUa4JojT0RhUK/i5YoKKigGFFRERRRQMkjOaUBAUYkiSZIEJb1VjZwzIxNO6FDV/Tv/PB/TXbXr231rrdqrz9kyfvx46+3D06dPS548eax5ypUrd0G06q1AVUDcu3ev9XuFjz32mLRv3966ThUMX375ZTl16pSo5iYrV65M9gahHWRsQPErdhmxUJZtORAa6JEW5eWhFhfmOv6ZGAEBBBBAAAEEEEAAAQQQQMBUAc7fpmbO3rgpANrrKRMnTpRbb71VDh8+nOLIqvg3efJkKVu27AV/j7QAqG5UvyP40ksvydmzZ1Ocp3DhwjJhwoQL3kJMevGSJUukU6dOqf6WoGoOosZI7fcM46FjA4pH79y9Ayeul/cXbAkN1KJSEXnntlrxD8wICCCAAAIIIIAAAggggAACvhHg/O2bVMa1EAqAcfGlfLPq1vvaa69ZhT71P7SsWbNaBb8uXbrI/fffLzlz5kzxxmgKgGoA1VF4+PDhMm/ePKubb/bs2a23Czt06CAPPPCA5M2bN93V7du3z4pVFfq2bDlXTCpVqpR07NhRHn74YSlYsGC6Y8RyARtQLGrJ7xm/coc8Om5N6B8vyZtdFvVtHv/AjIAAAggggAACCCCAAAIIIOAbAc7fvkllXAuhABgXHzfHKsAGFKtc+L4fdx+Rlq8kbwSy/OkWcnHubPEPzggIIIAAAggggAACCCCAAAK+EOD87Ys0xr0ICoBxEzJALAJsQLGoJb9HNQKpOmCqHD8ZbgTy/h21pVmFwvEPzggIIIAAAggggAACCCCAAAK+EOD87Ys0xr0ICoBxEzJALAJsQLGoXXjPjcMXyoqt4UYgj7UsLw80pxGIPbqMggACCCCAAAIIIIAAAgiYL8D52/wc2rECCoB2KDJG1AJsQFGTpXhDwtfrZdTCcCOQVpWLyNs9aQRijy6jIIAAAggggAACCCCAAALmC3D+Nj+HdqyAAqAdiowRtQAbUNRkKd7w+Yod8vhn4UYgxfJml4U0ArEHl1EQQAABBBBAAAEEEEAAAR8IcP72QRJtWAIFQBsQGSJ6ATag6M1SumPTb0ek9avJG4GseLqFFKQRiD3AjIIAAggggAACCCCAAAIIGC7A+dvwBNoUPgVAmyAZJjoBNqDovFK7+tTpM1I1YaqcOHkmdMkH/6ojTcoXsmcCRkEAAQQQQAABBBBAAAEEEDBagPO30emzLXgKgLZRMlA0AmxA0Wilfe31by6QVdsOhi56onUFua9ZWfsmYCQEEEAAAQQQQAABBBBAAAFjBTh/G5s6WwOnAGgrJ4NFKsAGFKlU+tf1/2qdfLhoa+jCNlWKyogeNdO/kSsQQAABBBBAAAEEEEAAAQR8L8D52/cpjmiBFAAjYuIiuwXYgOwTHbd8u/T5fG1owOL5csiCJ6+xbwJGQgABBBBAAAEEEEAAAQQQMFaA87exqbM1cAqAtnIyWKQCbECRSqV/3YZdh6Xta/OSXbjqmZaSP1fW9G/mCgQQQAABBBBAAAEEEEAAAV8LcP72dXojXhwFwIipuNBOATYg+zRVI5AqA6bKn6fCjUBG31lHri5HIxD7lBkJAQQQQAABBBBAAAEEEDBTgPO3mXmzO2oKgHaLMl5EAmxAETFFfFGnNxbI6u3hRiB92lSQe5vSCCRiQC5EAAEEEEAAAQQQQAABBHwqwPnbp4mNclkUAKME43J7BNiA7HE8P8rTExJlzOJtoUGvrVZU3uxOIxB7lRkNAQQQQAABBBBAAAEEEDBPgPO3eTlzImIKgE6oMma6AmxA6RJFdcGny7bJ/32RGLrn0gI5ZF4fGoFEhcjFCCCAAAIIIIAAAggggIAPBTh/+zCpMSyJAmAMaNwSvwAbUPyGSUdY/+shaTd0frJBV/dvKfly0gjEXmlGQwABBBBAAAEEEEAAAQTMEuD8bVa+nIqWAqBTsoybpgAbkL0PyMm/G4H8laQRyJg760qjchfbOxGjIYAAAggggAACCCCAAAIIGCXA+duodDkWLAVAx2gZOC0BNiD7n4+Or8+XNTsOhQZ+sm1F6d2kjP0TMSICCCCAAAIIIIAAAggggIAxApy/jUmVo4FSAHSUl8FTE2ADsv/Z6Pdlony0JNwIpN0Vl8gbt1xl/0SMiAACCCCAAAIIIIAAAgggYIwA529jUuVooBQAHeVlcAqA7j0DnyzdJn3HhxuBXFYwp3z3RDP3AmAmBBBAAAEEEEAAAQQQQAAB7QQoAGqXEk8CogDoCTuTsgHZ/wys23lI2g9L3ghkzYBWkjdHFvsnY0QEEEAAAQQQQAABBBBAAAEjBDh/G5Emx4OkAOg4MROkJMAGZP9zoRqAVB0wVf46fSY0+Md31ZUGZWkEYr82IyKAAAIIIIAAAggggAACZghw/jYjT05HSQHQaWHGT1GADciZB+O6YfMlcWe4EUjfthWlF41AnMFmVAQQQAABBBBAAAEEEEDAAAHO3wYkyYUQKQC6gMwUFwqwATnzVKjfAFS/BXj+c131YjKsWw1nJmNUBBBAAAEEEEAAAQQQQAAB7QU4f2ufIlcCpADoCjOT/FOADciZZ+LjJdvkqS/DjUBKXZxLZj/e1JnJGBUBBBBAAAEEEEAAAQQQQEB7Ac7f2qfIlQApALrCzCQUAN15BtbuOCgdXl+QbLK1Ca0kT3YagbiTAWZBAAEEEEAAAQQQQAABBPQSoACoVz68ioYCoFfyAZ+XDciZB+DPU6etRiAnT58NTfDJ3fWkfpmCzkzIqAgggAACCCCAAAIIIIAAAloLcP7WOj2uBUcB0DVqJkoqwAbk3PPQbug8Wf/r4dAE/a6tJHc3Lu3chIyMAAIIIIAAAggggAACCCCgrQDnb21T42pgFABd5Way8wJsQM49C09+sVbGLtsemqBD9WIylEYgzoEzMgIIIIAAAggggAACCCCgsQDnb42T42JoFABdxGaqsAAbkHNPw5jFW+XpCetCE5QulEtmPUYjEOfEGRkBBBBAAAEEEEAAAQQQ0FeA87e+uXEzMgqAbmozV0iADci5h2HN9oPS8Y1wI5AMGUQSE1pL7myZnZuUkRFAAAEEEEAAAQQQQAABBLQU4PytZVpcD4oCoOvkTKgE2ICcew5OnDzXCOTUmXAjkE/vqSd1S9MIxDl1RkYAAQQQQAABBBBAAAEE9BTg/K1nXtyOigKg2+LMZwmwATn7ILR9bZ5s2BVuBPJ0u0py19U0AnFWndERQAABBBBAAAEEEEAAAf0EOH/rlxMvIqIA6IU6c1IAdPgZ6PP5Ghm3fEdolk5XFpNXb67h8KwMjwACCCCAAAIIIIAAAgggoJsABUDdMuJNPBQAvXEP/KxsQM4+AqMXbZFnvlofmqRMoVwyk0YgzqIzOgIIIIAAAggggAACCCCgoQDnbw2T4kFIFAA9QGdKvgLs9DOwatsBuf7NhaFpVCOQdQmtJReNQJymZ3wEEEAAAQQQQAABBBBAQCsBCoBapcOzYCgAekYf7InZgJzNv2oEUmXAVDmdpBHIZ73rS+3LCzg7MaMjgAACCCCAAAIIIIAAAghoJcD5W6t0eBYMBUDP6IM9MRuQ8/lv8+pc2fjbkdBE/dtXln81KuX8xMyAAAIIIIAAAggggAACCCCgjQDnb21S4WkgFAA95Q/u5GxAzuf+8c/WyOcrwo1AbqhRXIbcdKXzEzMDAggggAACCCCAAAIIIICANgKcv7VJhaeBUAD0lD+4k7MBOZ/7DxZukQFfhxuBlCucW6Y/2sT5iZkBAQQQQAABBBBAAAEEEEBAGwHO39qkwtNAKAB6yh/cydmAnM/9iq0H5Mbh4UYgGVUjkIGtJWfWzM5PzgwIIIAAAggggAACCCCAAAJaCHD+1iINngdBAdDzFAQzADYg5/N+/C/VCGSKJOkDIl/8u77UvIxGIM7rMwMCCCCAAAIIIIAAAgggoIcA52898uB1FBQAvc5AQOdnA3In8a1fmSubdocbgSRcV1lub0gjEHf0mQUBBBBAAAEEEEAAAQQQ8F6A87f3OdAhAgqAOmQhgDGwAbmT9EfHrZbxK3eGJrvxqhLyctfq7kzOLAgggAACCCCAAAIIIIAAAp4LcP72PAVaBEABUIs0BC8INiB3cv7+gl9k4MTvQ5NVKHKRTH2ksTuTMwsCCCCAAAIIIIAAAggggIDnApy/PU+BFgFQANQiDcELgg3InZwv3/K7dB6xKDSZagSyfmAbyZE1kzsBMAsCCCCAAAIIIIAAAggggICnApy/PeXXZnIKgNqkIliBsAG5k+9jf52SqgOmJmsEMv7eBnJVyfzuBMAsCCCAAAIIIIAAAggggAACngpw/vaUX5vJKQBqk4pgBcIG5F6+Ww75Tn7c80dowmc7VpGe9S93LwBmQgABBBBAAAEEEEAAAQQQ8EyA87dn9FpNTAFQq3QEJxg2IPdy/einq2X8qnAjkC41S8jgLjQCcS8DzIQAAggggAACCCCAAAIIeCfA+ds7e51mpgCoUzYCFAsbkHvJfnf+L/LcpHAjkIpFL5IpD9MIxL0MMBMCCCCAAAIIIIAAAggg4J0A52/v7HWamQKgTtkIUCxsQO4le+kvv0vXt8KNQDJlzCDrB7aW7FloBOJeFpgJAQQQQAABBBBAAAEEEPBGgPO3N+66zUoBULeMBCQeNiD3En30z1NSNWGqnD0bnvPLextIDRqBuJcEZkIAAQQQQAABBBBAAAEEPBLg/O0RvGbTUgDULCFBCYcNyN1MN395jvy092ho0uc6VZUe9S5zNwhmQwABBBBAAAEEEEAAAQQQcF2A87fr5FpOSAFQy7T4Pyg2IHdz/PDYVTJh9a+hSW+qdam81PkKd4NgNgQQQAABBBBAAAEEEEAAAdcFOH+7Tq7lhBQAtUyL/4NiA3I3x+/M+1men7whNGnlS/LINw9d7W4QzIYAAggggAACCCCAAAIIIOC6AOdv18m1nJACoJZp8X9QbEDu5njxz/vl5rcXhybNnDGDrKMRiLtJYDYEEEAAAQQQQAABBBBAwAMBzt8eoGs4JQVADZMShJDYgNzN8pETJ6VawrRkk351X0Opfmk+dwNhNgQQQAABBBBAAAEEEEAAAVcFOH+7yq3tZBQAtU2NvwNjA3I/v9f8b478vC/cCOT5TlXlVhqBuJ8IZkQAAQQQQAABBBBAAAEEXBTg/O0itsZTUQDUODl+Do0NyP3sPvjJKvl6TbgRSLc6l8qLN9AIxP1MMCMCCCCAAAIIIIAAAggg4J4A52/3rHWeiQKgztnxcWxsQO4nd+Tcn+U/34QbgVQtnkcmPUAjEPczwYwIIIAAAggggAACCCCAgHsCnL/ds9Z5JgqAOmfHx7GxAbmf3EU/7ZduI8ONQLJkOtcIJFvmTO4Hw4wIIIAAAggggAACCCCAAAKuCHD+doVZ+0koAGqfIn8GyAbkfl4PnzgpV/yjEcjE+xtJtRJ53Q+GGRFAAAEEEEAAAQQQQAABBFwR4PztCrP2k1AA1D5F/gyQDcibvDYdPFu27D8WmvyF66vJLXVLehMMsyKAAAIIIIAAAggggAACCDguwPnbcWIjJqAAaESa/BckG5A3Ob3/45Uyae2u0OTd6pSUF2+o5k0wzIoAAggggAACCCCAAAIIIOC4AOdvx4mNmIACoBFp8l+QbEDe5PSt736SF7/dGJr8ihJ55ev7G3kTDLMigAACCCCAAAIIIIAAAgg4LsD523FiIyagAGhEmvwXJBuQNzlduHmf3PLOktDkWTNltBqBZM2c0ZuAmBUBBBBAAAEEEEAAAQQQQMBRAc7fjvIaMzgFQGNS5a9A2YC8yeehYyel+rPTkk0+6YFGUrU4jUC8yQizIoAAAggggAACCCCAAALOCnD+dtbXlNEpAJqSKZ/FyQbkXUIb/3e2bPs93Ahk0A3V5OY6NALxLiPMjAACCCCAAAIIIIAAAgg4J8D52zlbk0amAGhStnwUKxuQd8m876OVMjkx3Aike92S8p/raQTiXUaYGQEEEEAAAQQQQAABBBBwToDzt3O2Jo1MAdCkbPkoVjYg75I5fM5P8tKUcCOQ6iXyylc0AvEuIcyMAAIIIIAAAggggAACCDgowPnbQVyDhqYAaFCy/BQqG5B32Zz/4z659d0kjUAyZ5T1A1tLlkw0AvEuK8yMAAIIIIAAAggggAACCDgjwPnbGVfTRqUAaFrGfBIvG5B3iTx47C+58tnpyQL45sGrpXKxPN4FxcwIIIAAAggggAACCCCAAAKOCHD+doTVuEEpABqXMn8EzAbkbR4bvTRLdhw4HgrivzdeIV1rX+ptUMyOAAIIIIAAAggggAACCCBguwDnb9tJjRyQAqCRaTM/aDYgb3P47zEr5Nt1v4WC6FHvMnmuU1Vvg2J2BBBAAAEEEEAAAQQQQAAB2wU4f9tOauSAFACNTJv5QbMBeZvDN2ZvlsFTN4WCuPLSfDLhvobeBsXsCCCAAAIIIIAAAggggAACtgtw/rad1MgBKQAamTbzg2YD8jaHc3/YKz3fWxoKItvfjUAy0wjE28QwOwIIIIAAAggggAACCCBgswDnb5tBDR2OAqChiTM9bDYgbzN44OhfUuO55I1Apjx8tVQsSiMQbzPD7AgggAACCCCAAAIIIICAvQKcv+31NHU0CoCmZs7wuNmAvE9gw0GzZOfBcCOQwZ2vkC61aATifWaIAAEEEEAAAQQQQAABBBCwT4Dzt32WJo9EAdDk7BkcOxuQ98nrNXq5TF2/OxTIbfUvk4EdaQTifWaIAAEEEEAAAQQQQAABBBCwT4Dzt32WJo9EAdDk7BkcOxuQ98l7fdaP8r9pP4QCuapkPhl/L41AvM8MESCAAAIIIIAAAggggAAC9glw/rbP0uSRKACanD2DY2cD8j55czbtkdvfXxYKJHuWjLIuobXQCMT73BABAggggAACCCCAAAIIIGCXAOdvuyTNHocCoNn5MzZ6NiDvU7f/jz+l5vMzkgUy7ZHGUr7IRd4HRwQIIIAAAggggAACCCCAAAK2CHD+toXR+EEoABqfQjMXwAakR94avDhTfj10IhTMy12qy401S+gRHFEggAACCCCAAAIIIIAAAgjELcD5O25CXwxAAdAXaTRvEWxAeuTsng+Xy7Tvw41Abm9wuSR0qKJHcESBAAIIIIAAAggggAACCCAQtwDn77gJfTEABUBfpNG8RbAB6ZGzoTN/lCHTw41Aal2WXz7/dwM9giMKBBBAAAEEEEAAAQQQQACBuAU4f8dN6IsBKAD6Io3mLYINSI+czd64R+4YFW4EkiNLJlk3sLVkyphBjwCJAgEEEEAAAQQQQAABBBBAIC4Bzt9x8fnmZgqAvkmlWQthA9IjX3uP/Cm1/5O8Ecj0RxpLORqB6JEgokAAAQQQQAABBBBAAAEE4hTg/B0noE9upwDok0Satgw2IH0yVu+FmfLb4XAjkFduqi7X16ARiD4ZIhIEEEAAAQQQQAABBBBAIHYBzt+x2/npTgqAfsqmQWthA9InWXd9sFxmbAg3AvlXw1LS/7rK+gRIJAgggAACCCCAAAIIIIAAAjELcP6Omc5XN1IA9FU6zVkMG5A+uXptxo/yyoxwI5A6lxeQcb3r6xMgkSCAAAIIIIAAAggggAACCMQswPk7Zjpf3UgB0FfpNGcxbED65Grmht1y5wfLQwHlyppJEhNaS0YageiTJCJBAAEEEEAAAQQQQAABBGIU4PwdI5zPbqMA6LOEmrIcNiB9MrXn8Amp88LMZAHNeLSJlC2cW58giQQBBBBAAAEEEEAAAQQQQCAmAc7fMbH57iYKgL5LqRkLYgPSK091/jND9hz5MxTUqzddKZ1qFNcrSKJBAAEEEEAAAQQQQAABBBCIWoDzd9RkvryBAqAv06r/otiA9MrRnaOWycyNe0JB3dWolDzdnkYgemWJaBBAAAEEEEAAAQQQQACB6AU4f0dv5sc7KAD6MasGrIkNSK8kvTL9B3lt5o+hoOqWKiCf9qIRiF5ZIhoEEEAAAQQQQAABBBBAIHoBzt/Rm/nxDgqAfsyqAWtiA9IrSTO+3y13fRhuBJI7W2ZZO6AVjUD0ShPRIIAAAggggAACCCCAAAJRC3D+jprMlzdQAPRlWvVfFBuQXjn67dAJqfdi8kYgsx5rIqUL0QhEr0wRDQIIIIAAAggggAACCCAQnQDn7+i8/Ho1BUC/ZlbzdbEB6ZWgs2fPSu3/zJR9f4Qbgbx285XS8UoageiVKaJBAAEEEEAAAQQQQAABBKIT4PwdnZdfr6YA6NfMar4uNiD9EnTH+0tl9qa9ocDuvrqU9GtHIxD9MkVECCCAAAIIIIAAAggggEDkApy/I7fy85UUAP2cXY3XxgakX3KGTNskQ2dtDgVWv3RB+eSeevoFSkQIIIAAAggggAACCCCAAAIRC3D+jpjK1xdSAPR1evVdHBuQfrmZtv43uWf0ilBgF2U/1wgkQ4YM+gVLRAgggAACCCCAAAIIIIAAAhEJcP6OiMn3F1EAdCDF27Ztk6FDh8rkyZNF/Xe2bNmkbNmy0rVrV7n33nslZ86cMc+akJAgAwcOjOj+2bNnS9OmTVO89vLLL5etW7emO85ll10mW7ZsSfe6aC9gA4pWzPnrdx06LvVfnJVsojmPN5XLL87l/OTMgAACCCCAAAIIIIAAAggg4IgA529HWI0blAKgzSlTRb/u3bvLoUOHUhy5QoUK8s0330jp0qVjmpkCYExs3BSBgGoEUuv5GbL/6F+hq4d1qyHXVS8Wwd1cggACCCCAAAIIIIAAAgggoKMABUAds+J+TBQAbTRfs2aNNGjQQI4dOya5c+eWvn37SrNmzeT48eMyduxYGTlypDVbxYoVZdmyZdY10X6SFgATExPTvL1UqVKSK1fKb2+dfwOwY8eO8vzzz6c6TtasWaV8+fLRhpnu9WxA6RJ5csFt7y2V734INwLp1bi09L22kiexMCkCCCCAAAIIIIAAAggggED8Apy/4zf0wwgUAG3Moir2zZkzRzJnzixz586V+vXrJxt98ODB0qdPH+vf1Nd4+/fvH/XsSQuA6o2tWD/nC4C33XabjBo1KtZhYr6PDShmOkdv/N/UTfL67HAjkAZlCsrHd9MIxFF0BkcAAQQQQAABBBBAAAEEHBTg/O0grkFDUwC0KVnqjb46depYo/Xq1UtGjBhxwchnzpyRqlWryoYNGyR//vyye/duyZIlS1QRUACMiouLoxSYsu436T0m3AgkT/bMsoZGIFEqcjkCCCCAAAIIIIAAAgggoI8ABUB9cuFlJBQAbdLv16+fvPDCC9Zoixcvlrp166Y48qBBg6yvBqvPtGnTpGXLllFFQAEwKi4ujlJg58Hj0nBQ8kYgc59oJiULxt64JsoQuBwBBBBAAAEEEEAAAQQQQMBGAQqANjqDrkkAACAASURBVGIaPBQFQJuS17hxY5k3b571m3sHDx60vgac0mfRokXW7wSqj/oKcKQdfc+PRQHQpoQxTIoC6mvlNZ+fIb8naQTyxi1XSbsrLkEMAQQQQAABBBBAAAEEEEDAQAEKgAYmzYGQKQDahFqoUCHZt2+fVK9eXVavXp3qqAcOHJACBQpYf+/SpYuMGzcuqgiSFgBbtGghK1eulCNHjki+fPmkcuXK0qZNG+sryOorxml9zv8GoGoUopqR/PTTT6KKP0WKFLG+ytytWzdRDUIyZMgQVXyRXswGFKmU+9f1eHeJzPtxX2ji3k3KyJNtK7ofCDMigAACCCCAAAIIIIAAAgjELcD5O25CXwxAAdCGNJ44cUJy5MhhjdSuXTuZNGlSmqOqgtvRo0elXr16ot4IjOaTtACY2n2qGKgae6gCXmqf8wXAtOZu2LChfPrpp1K8ePFoQrSuVRtMWp9du3aFfjNx+/btUqJEiajn4AZnBP47ZaO8Oeen0OCNyl4sY+5K+SvtzkTAqAgggAACCCCAAAIIIIAAAnYJUAC0S9LscSgA2pC/vXv3SuHCha2RbrrpJhk7dmyao6q37Pbs2WM1BElMTIwqAlUAHD9+vHTq1MkqoBUrVkxOnjwpmzZtko8++sj6XUH1yZQpk0ycOFHatm2b4vjly5eXSpUqSatWraw48ubNa311WRUkhw8fLqoopz7qGvVv6u/RfKJ5c5ACYDSyzl/7beIu+fdHK0MT5c2RRVb3b+nY26DOr4gZEEAAAQQQQAABBBBAAIHgClAADG7uk66cAqANz4EqYJUsWdIaqUePHvLhhx+mOaq6Vt1TpkwZ2bx5c1QRqCKdesMvtc9bb70lvXv3tv6sioNq/PNvJya9J61x1FeKO3fuHComPvLIIzJkyJCo4qQAGBWXVhdv//2YXP3f2climtenmVxagEYgWiWKYBBAAAEEEEAAAQQQQACBCAQoAEaAFIBLKADakGQ33wCMJNy7775b3nnnHevSMWPGSPfu3SO5Ldk1hw4dsgqU+/fvtxqb/P7775I1a9aIx+ErwBFTaXeh+i3IGs9Nl4PHToZiG979KmlbjUYg2iWLgBBAAAEEEEAAAQQQQACBdAQoAPKIKAEKgDY8B27+BmAk4S5fvlxq165tXaqKgW+//XYkt11wzf333y9vvPGG9e8LFiwIdS+OabB/3MQGZIeic2P8sxHIvU3LSJ82NAJxTpyREUAAAQQQQAABBBBAAAFnBDh/O+Nq2qgUAG3KmFtdgCMJ99ixY9Zbe+pz7bXXyuTJkyO57YJr3nzzTbnvvvusf1fdilXXYrs+bEB2STozzqBvN8qI78KNQK4ud7GMvpNGIM5oMyoCCCCAAAIIIIAAAggg4JwA52/nbE0amQKgTdlq3LixzJs3zyq8qd/Xy5w5c4ojq4YaDRo0sP7Wv39/GThwoE0RhIdRHYZVp+F4C4Dq7T/1FiAFQNtTpP2Ak9fukvs+DjcCyZ8zi6x8hkYg2ieOABFAAAEEEEAAAQQQQACBfwhQAOSRUAIUAG16Dp566il58cUXrdEWL14sdeum/LbUoEGDpG/fvtZ1U6dOtbrw2v1ZtmyZ1SFYfe666y4ZOXJkTFOot//UW4DqM3/+fGnYsGFM46R0ExuQbZSODLRt/zFpPDh5I5AFT14jxfPlcGQ+BkUAAQQQQAABBBBAAAEEEHBGgPO3M66mjUoB0KaMLV26NFT069Wrl4wYMeKCkc+cOSNVq1aVDRs2WJ189+zZI1myZLEpgvAwquj37rvvWv8wevRoufXWW6OeI2kTkJw5c1pNQLJlyxb1OKndwAZkG6UjA6lGIFc+O10OHQ83Ahlxa01pU7WoI/MxKAIIIIAAAggggAACCCCAgDMCnL+dcTVtVAqANmbs/NeA1dd/586dK/Xr1082+uDBg6VPnz7Wvw0YMEASEhKS/X3UqFFyxx13pPr3xMREyZEjh5QtWzbVqN966y3p3bu39feiRYvK5s2bQ78HeP6mKVOmSJMmTayxUvocOXJEOnfuLNOmTbP+/MADD8jQoUNtlBJhA7KV05HBur+zWBZs3h8a+/5mZeXx1hUcmYtBEUAAAQQQQAABBBBAAAEEnBHg/O2Mq2mjUgC0MWOrVq2yviZ7/Phx6zf41NeCmzVrZv3/Y8eODXXjLV++vKhOvRdddFFUBUBVIFRv96kx27ZtK9WqVZOCBQvKqVOnZOPGjTJmzBiZPn26NWamTJlk/Pjx0qFDhwtW2LRpU1HFxBtuuEEaNWokZcqUseJVv12ofqNw+PDhsn37duu+ChUqyMKFC6VAgQI2SlEAtBXTocFe/HaDvPXdz6HRm5QvJB/869xXy/kggAACCCCAAAIIIIAAAgiYIUAB0Iw8OR0lBUCbhSdOnGh95fbw4cMpjqyKf6orb0pv8aX3BmDSv6cVtioKqq8Ad+zYMcXLVAHwu+++S3fl6o3Gjz/+WIoXL57utdFewAYUrZj7109c86s88Mmq0MQFc2WV5U+3kAwZMrgfDDMigAACCCCAAAIIIIAAAgjEJMD5OyY2391EAdCBlG7dulVee+01q9Cn/oeWNWtWq+DXpUsXq6uu+k29lD7pFQDVbwZOmjTJektPvW24e/du2b9/v6jfa1Nv6FWvXl3atGkjt99+u+TJkyfVlam3D2fOnGmNs2nTJtm3b5/19p+Kq1ixYtZvGXbr1s1qUOJUsYcNyIEHz+Yht+w7Kk3/NyfZqAufvEaK0QjEZmmGQwABBBBAAAEEEEAAAQScE+D87ZytSSNTADQpWz6KlQ1I/2SqwvIVA6fJkROnQsG+3aOmtKpCIxD9s0eECCCAAAIIIIAAAggggMA5Ac7fPAlKgAIgz4EnAmxAnrBHPWm3txfLop/DjUAevKasPNqKRiBRQ3IDAggggAACCCCAAAIIIOCRAOdvj+A1m5YCoGYJCUo4bEBmZPqFbzbI23PDjUCaVSgk799BIxAzskeUCCCAAAIIIIAAAggggABvAPIMnBOgAMiT4IkABUBP2KOe9Os1v8qDSRqBXJw7myzr19yx34aMOkBuQAABBBBAAAEEEEAAAQQQSFOA8zcPCAVAngHPBNiAPKOPauKf9/4h17ycvGP04r7NpWje7FGNw8UIIIAAAggggAACCCCAAALeCHD+9sZdt1l5A1C3jAQkHjYgMxJ95sy5RiB//BluBDKyZy1pWbmIGQsgSgQQQAABBBBAAAEEEEAg4AKcvwP+APy9fAqAPAeeCLABecIe06Q3vbVIlvzye+jeh5qXk0dalo9pLG5CAAEEEEAAAQQQQAABBBBwV4Dzt7veus5GAVDXzPg8LjYgcxL8/KTv5Z35v4QCbl6xsLx7e21zFkCkCCCAAAIIIIAAAggggECABTh/Bzj5SZZOAZDnwBMBNiBP2GOa9KvVO+WhsatD9xa+KJss7dciprG4CQEEEEAAAQQQQAABBBBAwF0Bzt/ueus6GwVAXTPj87jYgMxJ8E97/5Dm/2gEsvSp5lI4D41AzMkikSKAAAIIIIAAAggggEBQBTh/BzXzyddNAZDnwBMBNiBP2GOaVDUCqZYwVY7+dTp0/7u31ZLmlWgEEhMoNyGAAAIIIIAAAggggAACLgpw/nYRW+OpKABqnBw/h8YGZFZ2u45YJEu3hBuBPNyinDzcgkYgZmWRaBFAAAEEEEAAAQQQQCCIApy/g5j1C9dMAZDnwBMBNiBP2GOe9NmJ38t7C8KNQFpUKiLv3FYr5vG4EQEEEEAAAQQQQAABBBBAwB0Bzt/uOOs+CwVA3TPk0/jYgMxK7Jerdsgjn64JBV00T3ZZ/FRzsxZBtAgggAACCCCAAAIIIIBAAAU4fwcw6SksmQIgz4EnAmxAnrDHPOnmPUekxZC5ye5f1q+FFLooW8xjciMCCCCAAAIIIIAAAggggIDzApy/nTc2YQYKgCZkyYcxsgGZldTTfzcCOZakEcj7t9eWZhULm7UQokUAAQQQQAABBBBAAAEEAibA+TtgCU9luRQAeQ48EWAD8oQ9rkk7D18oy7ceCI3xaMvy8mDzcnGNyc0IIIAAAggggAACCCCAAALOCnD+dtbXlNEpAJqSKZ/FyQZkXkITvl4voxZuCQXesnIRGdmTRiDmZZKIEUAAAQQQQAABBBBAIEgCnL+DlO3U10oBkOfAEwE2IE/Y45r0ixU75LHPwo1AiuXNLgv70ggkLlRuRgABBBBAAAEEEEAAAQQcFuD87TCwIcNTADQkUX4Lkw3IvIz+sPuItHoleSOQFU+3kIK5aQRiXjaJGAEEEEAAAQQQQAABBIIiwPk7KJlOe50UAHkOPBFgA/KEPa5JVSOQqgOmyvGTp0PjjLqjtjStQCOQuGC5GQEEEEAAAQQQQAABBBBwUIDzt4O4Bg1NAdCgZPkpVDYgM7N54/CFsiJJI5DHW5WX+6+hEYiZ2SRqBBBAAAEEEEAAAQQQCIIA5+8gZDn9NVIATN+IKxwQYANyANWFIQd8tU4+WLQ1NFPrKkXkrR40AnGBnikQQAABBBBAAAEEEEAAgZgEOH/HxOa7mygA+i6lZiyIDciMPP0zys+Wb5cnPl8b+ufi+XLIgievMXMxRI0AAggggAACCCCAAAIIBECA83cAkhzBEikARoDEJfYLsAHZb+rGiBt/OyxtXp2XbKqVz7SUArmyujE9cyCAAAIIIIAAAggggAACCEQpwPk7SjCfXk4B0KeJ1X1ZbEC6Zyjl+E6dPiNVE6bKiZNnQhd8+K860rh8ITMXRNQIIIAAAggggAACCCCAgM8FOH/7PMERLo8CYIRQXGavABuQvZ5ujnb9mwtk1baDoSmfaF1B7mtW1s0QmAsBBBBAAAEEEEAAAQQQQCBCAc7fEUL5/DIKgD5PsK7LYwPSNTPpx/XMhHUyenG4EUjbqkVl+K0107+RKxBAAAEEEEAAAQQQQAABBFwX4PztOrmWE1IA1DIt/g+KDcjcHI9btl36fBFuBFIifw6Z/380AjE3o0SOAAIIIIAAAggggAACfhbg/O3n7Ea+NgqAkVtxpY0CbEA2Yro81Pe/HpZrhyZvBLK6f0vJl5NGIC6ngukQQAABBBBAAAEEEEAAgXQFOH+nSxSICygABiLN+i2SDUi/nEQa0cnTZ6TKgKny16lwI5Axd9aVRuUujnQIrkMAAQQQQAABBBBAAAEEEHBJgPO3S9CaT0MBUPME+TU8NiCzM9vxjQWyZnu4Ecj/tako/25axuxFET0CCCCAAAIIIIAAAggg4EMBzt8+TGoMS6IAGAMat8QvwAYUv6GXIzw9IVHGLN4WCqFdtUvkje5XeRkScyOAAAIIIIAAAggggAACCKQgwPmbx0IJUADkOfBEgA3IE3bbJh27dJs8OT4xNF7JAjllbp9mto3PQAgggAACCCCAAAIIIIAAAvYIcP62x9H0USgAmp5BQ+NnAzI0cX+HvW7nIWk/bH6yRazp30ry5sxi9sKIHgEEEEAAAQQQQAABBBDwmQDnb58lNMblUACMEY7b4hNgA4rPz+u7VQOQqqoRyOlwI5CP76orDcrSCMTr3DA/AggggAACCCCAAAIIIJBUgPM3z4MSoADIc+CJABuQJ+y2Ttrh9fmydseh0Jh921aUXk1oBGIrMoMhgAACCCCAAAIIIIAAAnEKcP6OE9Ant1MA9EkiTVsGG5BpGbsw3qe+TJSPl4QbgbS/4hJ5/RYagZifWVaAAAIIIIAAAggggAACfhLg/O2nbMa+FgqAsdtxZxwCbEBx4Gly6ydLt0nfJI1ALi+YU+Y8QSMQTdJDGAgggAACCCCAAAIIIICAJcD5mwdBCVAA5DnwRIANyBN2WydN3HFIrns9eSOQtQmtJE92GoHYCs1gCCCAAAIIIIAAAggggEAcApy/48Dz0a0UAH2UTJOWwgZkUrZSjvXPU6etRiAnT58NXfDx3XWlQRkagZifXVaAAAIIIIAAAggggAACfhHg/O2XTMa3DgqA8flxd4wCbEAxwml2W/th82TdzsOhqPpdW0nublxasygJBwEEEEAAAQQQQAABBBAIrgDn7+DmPunKKQDyHHgiwAbkCbvtk/Ydv1Y+Wbo9NG6H6sVkaLcats/DgAgggAACCCCAAAIIIIAAArEJcP6Ozc1vd1EA9FtGDVkPG5AhiUonzI+WbJV+X64LXVX64lwy6/Gm/lgcq0AAAQQQQAABBBBAAAEEfCDA+dsHSbRhCRQAbUBkiOgF2ICiN9PxjrU7DkqH1xckCy0xoZVcRCMQHdNFTAgggAACCCCAAAIIIBBAAc7fAUx6CkumAMhz4IkAG5An7LZPeuLkuUYgp86EG4GMvaee1Ctd0Pa5GBABBBBAAAEEEEAAAQQQQCB6Ac7f0Zv58Q4KgH7MqgFrYgMyIEkRhnjta/Pk+13hRiBPt6skd11NI5AI+bgMAQQQQAABBBBAAAEEEHBUgPO3o7zGDE4B0JhU+StQNiD/5PP/Pl8rny4PNwLpdGUxefVmGoH4J8OsBAEEEEAAAQQQQAABBEwW4Pxtcvbsi50CoH2WjBSFABtQFFiaXzp68VZ5ZkK4EUiZQrlk5mM0AtE8bYSHAAIIIIAAAggggAACARHg/B2QRKezTAqAPAeeCLABecLuyKSrtx+UTm+EG4FkyCCSmNBacmfL7Mh8DIoAAggggAACCCCAAAIIIBC5AOfvyK38fCUFQD9nV+O1sQFpnJwoQ1ONQKoMmCqnkzQCGdervtQpVSDKkbgcAQQQQAABBBBAAAEEEEDAbgHO33aLmjkeBUAz82Z81GxAxqcw2QLavDpXNv52JPRvz7SvLHc2KuWvRbIaBBBAAAEEEEAAAQQQQMBAAc7fBibNgZApADqAypDpC7ABpW9k0hVPfLZGPluxIxTy9TWKyys3XWnSEogVAQQQQAABBBBAAAEEEPClAOdvX6Y16kVRAIyajBvsEGADskNRnzE+XLRF+n+1PhRQucK5ZfqjTfQJkEgQQAABBBBAAAEEEEAAgYAKcP4OaOL/sWwKgDwHngiwAXnC7tikK7cdkBveXBgaP2MGkXUDW0vOrDQCcQydgRFAAAEEEEAAAQQQQACBCAQ4f0eAFIBLKAAGIMk6LpENSMesxB5TSo1APu9dX2pdTiOQ2FW5EwEEEEAAAQQQQAABBBCIX4Dzd/yGfhiBAqAfsmjgGtiADExaOiH/sxHIgOsqyx0NaQTiv0yzIgQQQAABBBBAAAEEEDBJgPO3SdlyLlYKgM7ZMnIaAmxA/ns8Hhu3Rr5YGW4EcsNVxWVIVxqB+C/TrAgBBBBAAAEEEEAAAQRMEuD8bVK2nIuVAqBztoxMATBQz8CoBb9IwsTvQ2suXyS3THuERiCBeghYLAIIIIAAAggggAACCGgnQAFQu5R4EhAFQE/YmZQNyH/PwIqtv8uNwxeFFqYagawf2EZyZM3kv8WyIgQQQAABBBBAAAEEEEDAEAHO34YkyuEwKQA6DMzwKQuwAfnvyTj+12mpMmCKnDkbXtsX/24gNS/L77/FsiIEEEAAAQQQQAABBBBAwBABzt+GJMrhMCkAOgzM8BQAg/QMtHrlO/lh9x+hJQ/sUEVua3B5kAhYKwIIIIAAAggggAACCCCglQAFQK3S4VkwFAA9ow/2xGxA/sz/o+NWy/iVO0OL61yzhPyvS3V/LpZVIYAAAggggAACCCCAAAIGCHD+NiBJLoRIAdAFZKa4UIANyJ9PxXvzf5FnJ4UbgVQsepFMebixPxfLqhBAAAEEEEAAAQQQQAABAwQ4fxuQJBdCpADoAjJTUAAMyjOwbMvv0mVEuBFIpowZZP3A1pI9C41AgvIMsE4EEEAAAQQQQAABBBDQS4ACoF758CoaCoBeyQd8XjYgfz4AR/88JVUTpsrZJI1Avry3gdQoSSMQf2acVSGAAAIIIIAAAggggIDuApy/dc+QO/FRAHTHmVn+IcAG5N9HosWQ72TznnAjkOc6VpEe9WkE4t+MszIEEEAAAQQQQAABBBDQWYDzt87ZcS82CoDuWTNTEgE2IP8+Do98ulq+XBVuBNK1Vgn5b2cagfg346wMAQQQQAABBBBAAAEEdBbg/K1zdtyLjQKge9bMRAEwEM/Au/N/keeSNAKpdEke+fahqwOxdhaJAAIIIIAAAggggAACCOgmQAFQt4x4Ew8FQG/cAz8rG5B/H4ElP++Xm95eHFpg5owZZB2NQPybcFaGAAIIIIAAAggggAACWgtw/tY6Pa4FRwHQNWomSirABuTf5+GPP09JtX80AvnqvoZS/dJ8/l00K0MAAQQQQAABBBBAAAEENBXg/K1pYlwOiwKgy+BMd06ADcjfT8I1L8+Rn/ceDS3y+U5V5dZ6l/l70awOAQQQQAABBBBAAAEEENBQgPO3hknxICQKgB6gMyUFQL8/Aw+NXSVfrf41tMyba18qg268wu/LZn0IIIAAAggggAACCCCAgHYCFAC1S4knAVEA9ISdSdmA/P0MvDPvZ3l+8obQIqsUyyOTH6QRiL+zzuoQQAABBBBAAAEEEEBARwHO3zpmxf2YKAC6b86MfAXY98/A4p/3y81JGoFkyXSuEUi2zJl8v3YWiAACCCCAAAIIIIAAAgjoJEABUKdseBcLBUDv7AM9MxuQv9N/+MRJuSJhWrJFfn1/Q7miBI1A/J15VocAAggggAACCCCAAAK6CXD+1i0j3sRDAdAb98DPygbk/0eg2f/myC/7wo1AXri+mtxSt6T/F84KEUAAAQQQQAABBBBAAAGNBDh/a5QMD0OhAOghfpCnZgPyf/Yf+GSVTFwTbgTSrU5JefGGav5fOCtEAAEEEEAAAQQQQAABBDQS4PytUTI8DIUCoIf4QZ6aDcj/2X977k/ywjcbQwutVjyvTHygkf8XzgoRQAABBBBAAAEEEEAAAY0EOH9rlAwPQ6EA6CF+kKdmA/J/9hf+tE9uGbkktNCsmTJajUCyZs7o/8WzQgQQQAABBBBAAAEEEEBAEwHO35okwuMwKAB6nICgTs8G5P/MHzp+UqoPTN4IZNIDjaRq8bz+XzwrRAABBBBAAAEEEEAAAQQ0EeD8rUkiPA6DAqDHCQjq9GxAwch8k8GzZev+Y6HFqt8AVL8FyAcBBBBAAAEEEEAAAQQQQMAdAc7f7jjrPgsFQN0z5NP42IB8mth/LOu+j1fK5LW7Qv/avW5J+c/1NAIJRvZZJQIIIIAAAggggAACCOggwPlbhyx4HwMFQO9zEMgI2ICCkfYR3/0kg74NNwKpXiKvfHU/jUCCkX1WiQACCCCAAAIIIIAAAjoIcP7WIQvex0AB0PscBDICNqBgpH3B5n3S/Z0kjUAyZ5T1A1tLlkw0AgnGE8AqEUAAAQQQQAABBBBAwGsBzt9eZ0CP+SkA6pGHwEXBBhSMlB86dlKqP5u8EcjkBxtJlWI0AgnGE8AqEUAAAQQQQAABBBBAwGsBzt9eZ0CP+SkA6pGHwEXBBhSclDf+72zZ9nu4EchLN1aTm2rTCCQ4TwArRQABBBBAAAEEEEAAAS8FOH97qa/P3BQA9clFoCJhAwpOuu/9aIV8k/hbaMG31ispz3eiEUhwngBWigACCCCAAAIIIIAAAl4KcP72Ul+fuSkA6pOLQEXCBhScdL85Z7P8d8qm0IKvvDSfTLivYXAAWCkCCCCAAAIIIIAAAggg4KEA528P8TWamgKgRskIUihsQMHJ9rwf90qPd5eGFpzt70YgmWkEEpyHgJUigAACCCCAAAIIIICAZwKcvz2j12piCoBapSM4wbABBSfXB47+JTWem55swd8+dLVUuiRPcBBYKQIIIIAAAggggAACCCDgkQDnb4/gNZuWAqBmCQlKOGxAQcn0uXU2emmW7DhwPLTo/3a+QrrWujRYCKwWAQQQQAABBBBAAAEEEPBAgPO3B+gaTkkBUMOkBCEkNqAgZDm8xn+PWSHfrgs3AulZ/zJ5tmPVYCGwWgQQQAABBBBAAAEEEEDAAwHO3x6gazglBUANkxKEkNiAgpDl8BrfmL1ZBk8NNwKpUTKffHkvjUCC9RSwWgQQQAABBBBAAAEEEPBCgPO3F+r6zUkBUL+cBCIiNqBApDm0yO9+2Cu3vRduBJI9S0ZZl9BaaAQSrOeA1SKAAAIIIIAAAggggID7Apy/3TfXcUYKgDpmJQAxsQEFIMlJlvj70b/kqn80Apn6cGOpUPSiYEGwWgQQQAABBBBAAAEEEEDAZQHO3y6DazodBUBNE+P3sNiA/J7hC9fXcNAs2Xkw3Ajkf12qS+eaJYIHwYoRQAABBBBAAAEEEEAAARcFOH+7iK3xVBQANU6On0NjA/JzdlNeW6/Ry2Xq+t2hP97e4HJJ6FAleBCsGAEEEEAAAQQQQAABBBBwUYDzt4vYGk9FAVDj5Pg5NDYgP2c35bW9PutH+d+0H0J/rHlZfvni3w2CB8GKEUAAAQQQQAABBBBAAAEXBTh/u4it8VQUADVOjp9DYwPyc3ZTXtvsTXvkjveXhf6YI0smWTewtWTKmCF4GKwYAQQQQAABBBBAAAEEEHBJgPO3S9CaT0MB0IEEbdu2TYYOHSqTJ08W9d/ZsmWTsmXLSteuXeXee++VnDlzxjxrQkKCDBw4MKL7Z8+eLU2bNk3z2v3791uxTpgwQbZs2SJnz56VUqVKSadOneTBBx+UggULRjRXtBexAUUrZv71+/74U2o9PyPZQqY/0ljKFaERiPnZZQUIIIAAAggggAACCCCgqwDnb10z425cFABt9lZFv+7du8uhQ4dSHLlChQryzTffSOnSpWOa2c4C4LJly6Rjx46ya9euFGMpVqyYfPXVV1KrVq2YYk3rJjYg20mNGLD+izNl16EToViHdK0uN1xFIxAjkkeQCCCAAAIIIIAAAgggYKQA528j02Z70BQAbSRds2aNNGjQQI4dOya5c+eWvn37SrNm/igIAgAAIABJREFUzeT48eMyduxYGTlypDVbxYoVRRXf1DXRfpIWABMTE9O8Xb3JlytXrhSv2blzp9SsWVN2794tmTNnlkcffVTat29vXTtp0iQZMmSInDp1SooUKSIrVqyQ4sWLRxtqmtezAdnKacxgd3+4XKZ/H24EckfDy2XAdTQCMSaBBIoAAggggAACCCCAAALGCXD+Ni5ljgRMAdBGVlXsmzNnjlVQmzt3rtSvXz/Z6IMHD5Y+ffpY/6a+xtu/f/+oZ09aAFRf1431c/vtt8sHH3xg3T5u3Djp0qVLsqE+++wz6yvL6nPHHXfIe++9F+tUKd7HBmQrpzGDDZ35owyZHm4EUvvy/PJZbxqBGJNAAkUAAQQQQAABBBBAAAHjBDh/G5cyRwKmAGgTq3qjr06dOtZovXr1khEjRlww8pkzZ6Rq1aqyYcMGyZ8/v/X2XZYsWaKKwI4CoJpXvdF3+vRpad26tUyZMiXFGNq0aSNTp06VTJkyiXpjUL0NaNeHDcguSbPGmb1xj9wxKtwIJGfWTJKYQCMQs7JItAgggAACCCCAAAIIIGCSAOdvk7LlXKwUAG2y7devn7zwwgvWaIsXL5a6deumOPKgQYOsrwarz7Rp06Rly5ZRRWBHAVB9Ffmee+6x5lVfTb7ppptSjEH9rVu3btbf3n77bbn77rujijWti9mAbKM0aqA9R05Inf/MTBbzjEcbS9nCNAIxKpEEiwACCCCAAAIIIIAAAsYIcP42JlWOBkoB0Cbexo0by7x586zf3Dt48KD1NeCUPosWLbJ+J1B91FeAI+3oe34sOwqAPXv2lNGjR1tDqgYgRYsWTTFW9TfVCER91D3nvzJsBxkbkB2KZo5R94UZsvvwn6HgX73pSulUw97fmDRThqgRQAABBBBAAAEEEEAAAfsFOH/bb2riiBQAbcpaoUKFZN++fVK9enVZvXp1qqMeOHBAChQoYP1d/e6e+v29aD5JC4AtWrSQlStXypEjRyRfvnxSuXJlUV/bVV9BVl8xTu1Tu3ZtWb58ueTNm9cqVqb1UdccPnxY1D1Lly6NJtQ0r2UDso3SuIHu+mCZzNiwJxT3nY1KyTPtKxu3DgJGAAEEEEAAAQQQQAABBEwQ4PxtQpacj5ECoA3GJ06ckBw5clgjtWvXzuqim9ZHdf89evSo1KtXT9QbgdF8khYAU7tPFQNHjRolHTt2TPES9caf+h3AKlWqyLp169KcXv1m4fr16623BNUbgZF+1AaT1keNdf43E7dv3y4lSpSIdGiuM1zg1Rk/yKszfgytok6pAjKuV/KGOYYvkfARQAABBBBAAAEEEEAAAW0EKABqkwpPA6EAaAP/3r17pXDhwtZI6vf01G/npfVRzTT27NljNQRJTEyMKgJVABw/frx06tTJKqCpr+iePHlSNm3aJB999JH1u4Lqoxp3TJw4Udq2bXvB+OpryseOHbN+p1D9XmFaH3WNevNPFS3Vm4aRfjJkyBDppUIBMGIqX1w4c8NuufOD5aG15M6WWdYOaCUZM0b+zPgCgkUggAACCCCAAAIIIIAAAi4IUAB0AdmAKSgA2pAkVcAqWbKkNVKPHj3kww8/THNUda26p0yZMrJ58+aoIlBf2VVv+KX2eeutt6R3797Wn1VxUI1//u3E8/eo4qDqSHz11VfL3Llz05z//G8bqntOnToVcawUACOmCtyFuw+fkLovJG8EMvOxJlKmUO7AWbBgBBBAAAEEEEAAAQQQQMBpAQqATgubMT4FQBvy5OYbgJGEq7r1vvPOO9alY8aMke7duye7zY03APkKcCSZCu41tf8zQ/YeCTcCee3mK6XjlTQCCe4TwcoRQAABBBBAAAEEEEDAKQEKgE7JmjUuBUAb8uXmbwBGEq5q8KGadqiPKga+/fbbyW5z4zcA04uTDSg9IX///V+jlsmsjeFGIHdfXUr6taMRiL+zzuoQQAABBBBAAAEEEEDACwHO316o6zcnBUCbcuJWF+BIwlW/76fe8lOfa6+9ViZPnpzstlq1asmKFSvoAhwJJtc4IjBk+g8ydGa4EUi90gVk7D00AnEEm0ERQAABBBBAAAEEEEAg0AIUAAOd/tDiKQDa9Byc/608VXhTv9OXOXPmFEdWXX8bNGhg/a1///4ycOBAmyIID6M6DKumHakVAHv27CmjR4+2/q668ao3AlP6qL+p3xFUH3XPBx98YFusbEC2URo50PTvd8vdH4YbgVyULbOsoRGIkbkkaAQQQAABBBBAAAEEENBbgPO33vlxKzoKgDZJP/XUU/Liiy9ao6nOuqp7bkqfQYMGSd++fa0/TZ06VVq1amVTBOFhli1bZnUIVp+77rpLRo4cmWwO9ZXgXr16Wf+mOharzsUpfdTfunXrZv1JNRe55557bIuVDcg2SiMH+u3QCan3YvJGILMfbyqlLj735iofBBBAAAEEEEAAAQQQQAABewQ4f9vjaPooFABtyuDSpUtDRT9VXBsxYsQFI6vOu1WrVpUNGzZYnXz37NkjWbJksSmC8DCq6Pfuu+9a/6De9Lv11luTzfHbb79J8eLFrU7ArVu3lilTpqQYQ5s2bawiZcaMGWXnzp2pvikYywLYgGJR8889Z8+eFdUIZN8ff4UWNbRbDelQ/dwbp3wQQAABBBBAAAEEEEAAAQTsEeD8bY+j6aNQALQxg+e/Bqy+/jt37lypXz/5b5oNHjxY+vTpY804YMAASUhISDb7qFGj5I477kj174mJiZIjRw4pW7ZsqlGrN/V69+5t/V19tXfz5s2h3wNMelPSrwF/9tln0rlz52Rjqn/r2rWr9W+33XabqNjs/LAB2alp5li3v79U5mzaGwq+V+PS0vfaSmYuhqgRQAABBBBAAAEEEEAAAU0FOH9rmhiXw6IAaCP4qlWrpGHDhnL8+HHrN/jU14KbNWtm/f/q67Tnu/GWL19eVKfeiy66KKoCoCrCqbf71Jht27aVatWqScGCBeXUqVOyceNGGTNmjEyfPt0aM1OmTDJ+/Hjp0KFDiivcvn271KxZU/bu3Wv9XuFjjz0m7du3t66dNGmSvPzyy9a4qrnJypUrpUSJEjZKibAB2cpp5GAvT9skw2ZtDsXeoExB+fjuekauhaARQAABBBBAAAEEEEAAAV0FOH/rmhl346IAaLP3xIkTra/cHj58OMWRVfFPdeVN6S2+9N4ATPr3tMJWRUH1FeCOHTumubolS5ZIp06dRH0lOKWPeoNwwoQJqf6eYTx0bEDx6Pnj3qnrf5Neo1eEFpMn+7lGIBkyZPDHAlkFAggggAACCCCAAAIIIKCBAOdvDZKgQQgUAB1IwtatW+W1116zCn3qf2hZs2a1Cn5dunSR+++/X3LmzJnirOkVANVvBqq381QnYfW24e7du2X//v2ifk+tQIECUr16dVG/23f77bdLnjx5IlrZvn37rFhVoW/Lli3WPaVKlbKKhw8//LD1hqETHzYgJ1TNGvPXg8elwaBZyYL+7ommcllBGoGYlUmiRQABBBBAAAEEEEAAAZ0FOH/rnB33YqMA6J41MyURYAPicVCF61rPz5D9R8ONQF6/pYa0v4JGIDwdCCCAAAIIIIAAAggggIBdApy/7ZI0exwKgGbnz9jo2YCMTZ2tgfd8b6nM/SFJI5AmpaVvWxqB2IrMYAgggAACCCCAAAIIIBBoAc7fgU5/aPEUAHkOPBFgA/KEXbtJB0/dKG/M/ikUV6OyF8uYu+pqFycBIYAAAggggAACCCCAAAKmCnD+NjVz9sZNAdBeT0aLUIANKEIon182Zd0u6T1mZWiVeXNkkdX9W9IIxOd5Z3kIIIAAAggggAACCCDgngDnb/esdZ6JAqDO2fFxbGxAPk5uFEvbceCYNHppdrI75vVpJpcWSLlRThRDcykCCCCAAAIIIIAAAggggICI1Zz00ksvtSy2b98uJUqUwCWAAhQAA5h0HZbMBqRDFryPQTUCueq56XLg2MlQMG92v0qurXaJ98ERAQIIIIAAAggggAACCCDgAwHO3z5Iog1LoABoAyJDRC/ABhS9mV/v6PHuEpn3477Q8v7dtIz8X5uKfl0u60IAAQQQQAABBBBAAAEEXBXg/O0qt7aTUQDUNjX+DowNyN/5jWZ1L03ZKMPnhBuBXF3uYhl9J41AojHkWgQQQAABBBBAAAEEEEAgNQHO3zwbSoACIM+BJwJsQJ6waznpN4m75N6Pwo1A8ufMIiufoRGIlskiKAQQQAABBBBAAAEEEDBOgPO3cSlzJGAKgI6wMmh6AmxA6QkF5+/bfz8mV/83eSOQ+f/XTErkpxFIcJ4CVooAAggggAACCCCAAAJOCXD+dkrWrHEpAJqVL99Eywbkm1TGvRDVCOTKZ6fLoePhRiAjbr1K2lSlEUjcuAyAAAIIIIAAAggggAACgRfg/B34R8ACoADIc+CJABuQJ+zaTnrrO0tk/uZwI5D7mpWRJ1rTCETbhBEYAggggAACCCCAAAIIGCPA+duYVDkaKAVAR3kZPDUBNiCejaQCg77dKCO+CzcCaVy+kHz4rzogIYAAAggggAACCCCAAAIIxCnA+TtOQJ/cTgHQJ4k0bRlsQKZlzNl4J639Ve7/eFVokgK5ssqKp1tIhgwZnJ2Y0RFAAAEEEEAAAQQQQAABnwtw/vZ5giNcHgXACKG4zF4BNiB7PU0fbev+o9Jk8Jxky1j45DVSLF8O05dG/AgggAACCCCAAAIIIICApwKcvz3l12ZyCoDapCJYgbABBSvf6a1WNQKpPnCaHD5xKnTpWz1qSusqRdO7lb8jgAACCCCAAAIIIIAAAgikIcD5m8dDCVAA5DnwRIANyBN2rSe9ZeRiWfjT/lCMD1xTVh5rVUHrmAkOAQQQQAABBBBAAAEEENBdgPO37hlyJz4KgO44M8s/BNiAeCT+KfDiNxvkrbk/h/65aYVCMuoOGoHwpCCAAAIIIIAAAggggAAC8Qhw/o5Hzz/3UgD0Ty6NWgkbkFHpciXYiWt+lQc+CTcCuTh3VlnWj0YgruAzCQIIIIAAAggggAACCPhWgPO3b1Mb1cIoAEbFxcV2CbAB2SXpn3F+2XdUmv0veSOQRX2vkUvy0gjEP1lmJQgggAACCCCAAAIIIOC2AOdvt8X1nI8CoJ558X1UbEC+T3HUCzxz5lwjkCN/hhuBjOxZS1pWLhL1WNyAAAIIIIAAAggggAACCCBwToDzN0+CEqAAyHPgiQAbkCfs2k9689uLZPHPv4fifLB5OXm0ZXnt4yZABBBAAAEEEEAAAQQQQEBXAc7fumbG3bgoALrrzWx/C7AB8SikJPCfyd/LyHm/hP50TcXC8t7ttcFCAAEEEEAAAQQQQAABBBCIUYDzd4xwPruNAqDPEmrKctiATMmUu3F+tXqnPDR2dWjSQhdlsxqB8EEAAQQQQAABBBBAAAEEEIhNgPN3bG5+u4sCoN8yash62IAMSZTLYf689w+55uXvks265KnmUiRPdpcjYToEEEAAAQQQQAABBBBAwB8CnL/9kcd4V0EBMF5B7o9JgA0oJjbf36QagVwxcJr8kaQRyLu31ZLmlWgE4vvks0AEEEAAAQQQQAABBBBwRIDztyOsxg1KAdC4lPkjYDYgf+TRiVV0fWuRLP0l3Ajk4Rbl5OEWNAJxwpoxEUAAAQQQQAABBBBAwP8CnL/9n+NIVkgBMBIlrrFdgA3IdlLfDPjcpO/l3fnhRiAtKhWWd26jEYhvEsxCEEAAAQQQQAABBBBAwFUBzt+ucms7GQVAbVPj78DYgPyd33hWN2HVTnn403AjkCJ5ssmSp2gEEo8p9yKAAAIIIIAAAggggEBwBTh/Bzf3SVdOAZDnwBMBNiBP2I2YdPOeP6TFkOSNQJb2ay6FL6IRiBEJJEgEEEAAAQQQQAABBBDQSoDzt1bp8CwYCoCe0Qd7YjagYOc/rdWrRiDVEqbK0b9Ohy577/Zack1FGoHw1CCAAAIIIIAAAggggAAC0Qpw/o5WzJ/XUwD0Z161XxUbkPYp8jTALiMWyrItB0IxPNqyvDzYvJynMTE5AggggAACCCCAAAIIIGCiAOdvE7Nmf8wUAO03ZcQIBNiAIkAK8CUDJ66X9xdsCQm0rFxERvasFWARlo4AAggggAACCCCAAAIIxCbA+Ts2N7/dRQHQbxk1ZD1sQIYkyqMwx6/cIY+OWxOa/ZK82WVR3+YeRcO0CCCAAAIIIIAAAggggIC5Apy/zc2dnZFTALRTk7EiFmADipgqkBf+uPuItHxlbrK1L3+6hVycO1sgPVg0AggggAACCCCAAAIIIBCrAOfvWOX8dR8FQH/l05jVsAEZkypPAj39dyOQY0kagbx/R21pVqGwJ/EwKQIIIIAAAggggAACCCBgqgDnb1MzZ2/cFADt9WS0CAXYgCKECvBlnYcvlOVbw41AHmtZXh6gEUiAnwiWjgACCCCAAAIIIIAAArEIcP6ORc1/91AA9F9OjVgRG5ARafI0yISv18uoheFGIK2rFJG3etAIxNOkMDkCCCCAAAIIIIAAAggYJ8D527iUORIwBUBHWBk0PQE2oPSE+PvnK3bI45+FG4EUz5dDFjx5DTAIIIAAAggggAACCCCAAAJRCHD+jgLLx5dSAPRxcnVeGhuQztnRI7ZNvx2R1q8mbwSy8pmWUiBXVj0CJAoEEEAAAQQQQAABBBBAwAABzt8GJMmFECkAuoDMFBcKsAHxVKQncOr0GamaMFVOnDwTuvSDf9WRJuULpXcrf0cAAQQQQAABBBBAAAEEEPhbgPM3j4ISoADIc+CJABuQJ+zGTXrDmwtk5baDobifaF1B7mtW1rh1EDACCCCAAAIIIIAAAggg4JUA52+v5PWalwKgXvkITDRsQIFJdVwLHfDVOvlg0dbQGG2qFJURPWrGNSY3I4AAAggggAACCCCAAAJBEuD8HaRsp75WCoA8B54IsAF5wm7cpOOWb5c+n68NxV0ifw6Z/380AjEukQSMAAIIIIAAAggggAACnglw/vaMXquJKQBqlY7gBMMGFJxcx7PSDbsOS9vX5iUbYtUzLSU/jUDiYeVeBBBAAAEEEEAAAQQQCJAA5+8AJTuNpVIA5DnwRIANyBN24yZVjUCqDJgqf54KNwIZfWcdubocjUCMSyYBI4AAAggggAACCCCAgCcCnL89YdduUgqA2qUkGAGxAQUjz3asstMbC2T19nAjkD5tKsi9TWkEYoctYyCAAAIIIIAAAggggID/BTh/+z/HkayQAmAkSlxjuwAbkO2kvh3wmQnrZPTicCOQa6sVlTe70wjEtwlnYQgggAACCCCAAAIIIGCrAOdvWzmNHYwCoLGpMztwNiCz8+dm9OOWbZc+X4QbgVxaIIfM60MjEDdzwFwIIIAAAggggAACCCBgrgDnb3NzZ2fkFADt1GSsiAXYgCKmCvyF6389JO2Gzk/msKZ/K8mbM0vgbQBAAAEEEEAAAQQQQAABBNIT4PydnlAw/k4BMBh51m6VbEDapUTbgE7+3QjkrySNQD66q640LHuxtjETGAIIIIAAAggggAACCCCgiwDnb10y4W0cFAC99Q/s7GxAgU19TAvv+Pp8WbPjUOjeJ9tWlN5NysQ0FjchgAACCCCAAAIIIIAAAkES4PwdpGynvlYKgDwHngiwAXnCbuyk/b5MlI+WbAvF3+6KS+SNW64ydj0EjgACCCCAAAIIIIAAAgi4JcD52y1pveehAKh3fnwbHRuQb1PryMLGLt0mT45PDI19WcGc8t0TzRyZi0ERQAABBBBAAAEEEEAAAT8JcP72UzZjXwsFwNjtuDMOATagOPACeOu6nYek/bB/NAIZ0Ery5qARSAAfB5aMAAIIIIAAAggggAACUQhw/o4Cy8eXUgD0cXJ1XhobkM7Z0S821QCk6oCp8tfpM6HgPr67rjQoQyMQ/bJFRAgggAACCCCAAAIIIKCTAOdvnbLhXSwUAL2zD/TMbECBTn9Mi79u2HxJ3BluBPLUtRXlnsY0AokJk5sQQAABBBBAAAEEEEAgMAKcvwOT6jQXSgGQ58ATATYgT9iNnrTv+ET5ZGm4Ech11YvJsG41jF4TwSOAAAIIIIAAAggggAACTgtw/nZa2IzxKQCakSffRckG5LuUOr6gj5dsk6e+DDcCKXVxLpn9eFPH52UCBBBAAAEEEEAAAQQQQMBkAc7fJmfPvtgpANpnyUhRCLABRYHFpZZA4o5Dct3ryRuBrE1oJXmy0wiERwQBBBBAAAEEEEAAAQQQSE2A8zfPhhKgAMhz4IkAG5An7EZP+uep01YjkJOnz4bW8cnd9aR+mYJGr4vgEUAAAQQQQAABBBBAAAEnBTh/O6lrztgUAM3Jla8iZQPyVTpdW0y7ofNk/a+HQ/M93a6S3HV1adfmZyIEEEAAAQQQQAABBBBAwDQBzt+mZcyZeCkAOuPKqOkIsAHxiMQi8OQXa2Xssu2hWzteWUxeu5lGILFYcg8CCCCAAAIIIIAAAggEQ4DzdzDynN4qKQCmJ8TfHRFgA3KE1feDjlm8VZ6esC60ztKFcsmsx2gE4vvEs0AEEEAAAQQQQAABBBCIWYDzd8x0vrqRAqCv0mnOYtiAzMmVTpGu2X5QOr6xIBRShgwiiQmtJXe2zDqFSSwIIIAAAggggAACCCCAgDYCnL+1SYWngVAA9JQ/uJOzAQU39/Gs/MTJc41ATp0JNwL59J56Urc0jUDiceVeBBBAAAEEEEAAAQQQ8K8A52//5jaalVEAjEaLa20TYAOyjTJwA1372jz5fle4Ecgz7SvLnY1KBc6BBSOAAAIIIIAAAggggAACkQhw/o5Eyf/XUAD0f461XCEbkJZpMSKoPp+vkXHLd4Rivb5GcXnlpiuNiJ0gEUAAAQQQQAABBBBAAAG3BTh/uy2u53wUAPXMi++jYgPyfYodW+DoRVvkma/Wh8YvWzi3zHi0iWPzMTACCCCAAAIIIIAAAgggYLIA52+Ts2df7BQA7bNkpCgE2ICiwOLSZAKrth2Q699cGPo31QhkXUJryUUjEJ4UBBBAAAEEEEAAAQQQQOACAc7fPBRKgAIgz4EnAmxAnrD7YlLVCKTKgKlyOkkjkM9615falxfwxfpYBAIIIIAAAggggAACCCBgpwDnbzs1zR2LAqC5uTM6cjYgo9PnefBtXp0rG387Eoqjf/vK8i8agXieFwJAAAEEEEAAAQQQQAAB/QQ4f+uXEy8iogDohTpzChsQD0E8Ao9/tkY+XxFuBHLDVcVlSFcagcRjyr0IIIAAAggggAACCCDgTwHO3/7Ma7SrogAYrRjX2yLABmQLY2AH+WDhFhnwdbgRSPkiuWXaIzQCCewDwcIRQAABBBBAAAEEEEAgVQHO3zwcSoACIM+BJwJsQJ6w+2bSFVsPyI3Dw41AMqpGIANbS86smX2zRhaCAAIIIIAAAggggAACCNghwPnbDkXzx6AAaH4OjVwBG5CRadMm6ON/qUYgUyRJHxCpUTKfPNKivFxd7mLJoFoD80EAAQQQQAABBBBAAAEEEOAnuHgGLAEKgDwInghQAPSE3VeT/rMRyPnFXVUynzxMIdBXuWYxCCCAAAIIIIAAAgggELsA5+/Y7fx0JwVAP2XToLWwARmULE1DfW/+L/LspO9TjU69EagKgY15I1DTDBIWAggggAACCCCAAAIIuCHA+dsNZf3noACof458GSEbkC/T6uqizp49K+NX7pRXZvwgOw4cpxDoqj6TIYAAAggggAACCCCAgCkCnL9NyZSzcVIAdNaX0VMRYAPi0bBL4OTpMzJ+5Q4ZNmszhUC7UBkHAQQQQAABBBBAAAEEfCPA+ds3qYxrIRQA4+Lj5lgF2IBileO+1AQiLQReean6anA5aVK+EM1CeJwQQAABBBBAAAEEEEDA9wKcv32f4ogWSAEwIiYusluADchuUcY7L6AKgV+u3CnDZv8o239P/avBFAJ5ZhBAAAEEEEAAAQQQQCAIApy/g5Dl9NdIATB9I65wQIANyAFUhkwmQCGQBwIBBBBAAAEEEEAAAQQQEOH8zVOgBCgA8hx4IsAG5Al7ICeNtBBY/e+vBjflq8GBfE5YNAIIIIAAAggggAACfhXg/O3XzEa3LgqA0XlxtU0CbEA2QTJMxAJWIXDVTnl91mbZ9vuxVO+jEBgxKRcigAACCCCAAAIIIICAAQKcvw1IkgshUgB0AZkpLhRgA+Kp8EqAQqBX8syLAAIIIIAAAggggAACXghw/vZCXb85KQDql5NARMQGFIg0a73IiAuBJfLKwy3KS9MKdA3WOqEEhwACCCCAAAIIIIAAAikKcP7mwVACFAB5DjwRYAPyhJ1JUxBQhcAJq3bKsPS+GkwhkOcHAQQQQAABBBBAAAEEDBTg/G1g0hwImQKgA6gMmb4AG1D6RlzhrsD5QuDrszfL1v1p/EYghUB3E8NsCCCAAAIIIIAAAgggEJcA5++4+HxzMwVA36TSrIWwAZmVryBFe+p8s5B0CoFXWIXActKsQmHJkCFDkIhYKwIIIIAAAggggAACCBgkwPnboGQ5GCoFQAdxGTp1ATYgng7dBVQhcMLqX2XYrB/TfCOQQqDumSQ+BBBAAAEEEEAAAQSCLcD5O9j5P796CoA8B54IsAF5ws6kMQhQCIwBjVsQQAABBBBAAAEEEEBAGwHO39qkwtNAKAB6yh/cydmAgpt7U1ceTSHwoebl5JqKfDXY1FwTNwIIIIAAAggggAACfhLg/O2nbMa+FgqAsdtxZxwCbEBx4HGrpwKqEPjV318N3pJGs5Bqxc/9RiCFQE/TxeQIIIAAAggggAACCARegPN34B8BC4ACIM+BJwJsQJ6wM6mNAhQCbcRkKAQQQAABBBBAAAEEEHBMgPO3Y7RGDUwB0Kh0+SdYNiD/5DLoK4mmEKi+Gty8El8NDvozw/oRQAABBBBAAAEEEHDHRmFWAAAgAElEQVRTgPO3m9r6zkUBUN/c+DoyNiBfpzeQi1OFwK/XqK7Bm+WXfUdTNahaPI883Lw8hcBAPiUsGgEEEEAAAQQQQAAB9wU4f7tvruOMFAB1zEoAYmIDCkCSA7pECoEBTTzLRgABBBBAAAEEEEBAUwHO35omxuWwKAC6DM505wTYgHgS/C4QTSHwoeblpQVfDfb7I8H6EEAAAQQQQAABBBDwRIDztyfs2k1KAVC7lAQjIDagYOSZVYqoQuDEtb/K0JlpfzW4SrE88nALCoE8MwgggAACCCCAAAIIIGCvAOdvez1NHY0CoAOZ27ZtmwwdOlQmT54s6r+zZcsmZcuWla5du8q9994rOXPmtH3WXbt2SaVKleTQoUPW2E2aNJE5c+akOs/ll18uW7duTTeOyy67TLZs2ZLuddFewAYUrRjXmy5AIdD0DBI/AggggAACCCCAAAJmCnD+NjNvdkdNAdBmUVX06969e6gQ98/hK1SoIN98842ULl3a1pk7d+4sX3zxRWhMCoC28jIYArYJnC8EDpu5WX5Oo1mIeiNQdQ1uWbmIZMiQwbb5GQgBBBBAAAEEEEAAAQSCJUABMFj5Tm21FABtfA7WrFkjDRo0kGPHjknu3Lmlb9++0qxZMzl+/LiMHTtWRo4cac1WsWJFWbZsmXWNHZ+JEydKhw4dpHDhwrJnzx5ryEgLgB07dpTnn38+1TCyZs0q5cuXtyPMZGOwAdlOyoCGCZw+c1YmrlFfDf6RQqBhuSNcBBBAAAEEEEAAAQRMEuD8bVK2nIuVAqCNtqrYp752mzlzZpk7d67Ur18/2eiDBw+WPn36WP82cOBA6d+/f9yz//HHH1K5cmXZvn27fPjhh9KzZ8+oCoC33XabjBo1Ku44oh2ADShaMa73q0CkhcDKl6jfCOSNQL8+B6wLAQQQQAABBBBAAAGnBDh/OyVr1rgUAG3Kl3qjr06dOtZovXr1khEjRlww8pkzZ6Rq1aqyYcMGyZ8/v+zevVuyZMkSVwQPPvigDBs2zHrTcNasWaGvCkb6BiAFwLj4uRkB2wSiKQQ+1KKctOKrwbbZMxACCCCAAAIIIIAAAn4WoADo5+xGvjYKgJFbpXllv3795IUXXrCuWbx4sdStWzfF6wcNGmR9NVh9pk2bJi1btow5gqVLl1pvGao3DteuXSvq9wXP/1YYBcCYWbkRAU8FVCFw0tpf5TX11eC9R1ONRb0RSCHQ01QxOQIIIIAAAggggAACRghQADQiTY4HSQHQJuLGjRvLvHnzJFeuXHLw4EGrKJfSZ9GiRdbvBKqP+gqw+ipwLJ9Tp05JrVq1RP3u4DPPPCPPPvusNQwFwFg0uQcB/QQoBOqXEyJCAAEEEEAAAQQQQMBEAQqAJmbN/pgpANpkWqhQIdm3b59Ur15dVq9eneqoBw4ckAIFClh/79Kli4wbNy6mCM6/SVimTBlZt26dZM+ePaYCYKlSpaxmJD/99JOcPXtWihQpYn2VuVu3bqIahDjVfZQNKKa0c1MABSItBFZSbwQ2P/fV4IwZ6RocwEeFJSOAAAIIIIAAAgggkKIA528eDCVAAdCG5+DEiROSI0cOa6R27drJpEmT0hxVFdyOHj0q9erVE/VGYLSfn3/+2fotQdVdeOrUqdKqVavQENG+AZjW3A0bNpRPP/1UihcvHm2IojaYtD67du0K/WaiamBSokSJqOfgBgSCJHC+EKi6Bv+UxleDKQQG6algrQgggAACCCCAAAIIpC9AATB9oyBcQQHQhizv3btXChcubI100003ydixY9McVb1lt2fPHquIl5iYGHUE6ncDZ8yYkeJckRYAy5cvL5UqVbKKhyqOvHnzWl9dVgXJ4cOHW12F1Uddo/5N/T2aTzRvDlIAjEaWa4MuEGkhsGLRi6yuwa0qF+WNwKA/NKwfAQQQQAABBBBAINACFAADnf7Q4ikA2vAcqAJWyZIlrZF69OghH374YZqjqmvVPerru5s3b44qAjW26tybJ08e2bhxo1xyySXJ7o+0AKiKffny5Utx7iNHjkjnzp2tJiXq88gjj8iQIUOiipMCYFRcXIxA1AIUAqMm4wYEEEAAAQQQQAABBAIpQAEwkGm/YNEUAG14Dtx6A1D9xqB6I0/932HDhsn9999/YUIznPvtr/S6AKe37EOHDlkFyv3791uNTX7//XfJmjVrereF/s5XgCOm4kIE4hJQhcDJibtEfTV4854/Uh2LNwLjYuZmBBBAAAEEEEAAAQSMFaAAaGzqbA2cAqANnG79BmDPnj1l9OjRVvffJUuWSMaMGR0rAKqBVYHxjTfesOZYsGBBqHuxDWTWbwReeuml1lB8BdgOUcYIukA0hUDVLKR1Fb4aHPRnhvUjgAACCCCAAAIIBEOA83cw8pzeKikApicU4d+d7gL866+/hppx9OnTR2rUqJFiZKp7r/qoNwX79+9v/bfq9Fu3bt0IVxK+7M0335T77rvP+gfVrVh1LbbrwwZklyTjIPD/7d0HmF1Vvffx3/Sa3gshQOihJqFKUylKFRK8XBCNBRRRLFe8wEu9Kly5FrAhchHwfZUHBMEAAheuGFAgJAEMGJqQQkL6TNr08j7/dcqcmXNm5pwzp+zy3c8zz0zO2XvttT7/fXZm/2eV3gIkArkiEEAAAQQQQAABBBBAIFGA52+uBxMgAZij6+DYY4/Vs88+64bL2vx65eXlKUu2BTWOOuoo954l6K6//vq0arBixQqXyMtmszkD77rrrowPtd5/sWHGJAAz5uMABIoqYInAx5Z9oFvSGBpMj8CihoqTI4AAAggggAACCCCQVwESgHnl9U3hJABzFKorr7xSN954oyvthRde6LfH3U033aQrrrjC7ffEE0+4VXjT2YqRALTef9YL0LbnnntORx99dDpVTWsfbkBpMbETAkMWSDcRuPeEYbrso3vqFIYGD9mcAhBAAAEEEEAAAQQQ8JIAz99eikbx6kICMEf2ixYtiif9Lr74Yt12221JJXd1dWnmzJlavny5W4F3w4YNqqioyFENIsWkuwrwYCdNXASktrbWLQJSVVU12GFpv88NKG0qdkQgJwKxRKAtFvL2AIuFkAjMCTeFIIAAAggggAACCCDgGQGevz0TiqJWhARgDvljw4Bt+O/ChQt15JFH9ir95ptvls3fZ9u1116r6667rtf7Nkx3/vz5/b6fTlXTSQA+/vjjbpXgmpqalEVu375dc+fO1ZNPPune/8pXvqJbb701ndOnvQ83oLSp2BGBnAp02dDg1z7QLU+RCMwpLIUhgAACCCCAAAIIIOBRAZ6/PRqYAleLBGAOwV9++WU3TLa5uVn19fWyYcEnnHCC+/e9996r22+/3Z1tr7320uLFizVs2LCiJACPP/54LVu2TGeffbY+9KEPaY899nD1tbkLbY7CX/ziF25lXtv23ntv/e1vf9Po0aNzKCVWAc6pJoUhkLlAJonAr35kT31sJqsGZ67MEQgggAACCCCAAAIIFF+ABGDxY+CFGpAAzHEUFixYoAsuuEDbtm1LWbIl/x599FHNmDEj6f1C9QC0BOBf/vKXQVtuPRp/+9vfxlcfHvSADHbw7Q1o7cvSuH2kitS9JzMgYFcEPCGQbiJwrwn1uuwje5EI9ETUqAQCCCCAAAIIIIAAAukL+Pb5O/0msmcaAiQA00DKdJeVK1fqlltucYk++6BVVla6hN+8efPcqro2p16qrVAJQOt9+PTTT7vefm+++aY2bdrkev9ZvSZPnuzmMjzvvPPcAiWxIcWZGgy2vy9vQA0rpF8eKw2fKp17tzR2z8GayfsI+EbAEoF/em2dbnn6Lb21fke/9SYR6JuQUlEEEEAAAQQQQAABBJyAL5+/iV3OBUgA5pyUAtMR8N0NqKNNuvNkae3SSPMq6qTTb5EOnJdOc9kHAd8IZJIItKHBH585SaWlJb5pHxVFAAEEEEAAAQQQQCBsAr57/g5bgArUXhKABYLmNL0FfHcDevJq6W8pFkI59NPSx/6TIcFc4IETIBEYuJDSIAQQQAABBBBAAIGQCvju+Tukccp3s0kA5luY8lMK+O4GtOlt6b5PSxteT27PhAOkeXdJY5PndST8CPhdwBKBj7++zq0a/Ob67f02x4YG0yPQ79Gm/ggggAACCCCAAAJBFPDd83cQg+CBNpEA9EAQwlgFX96A2pqkP10uvfyb5JBV1ktn3CrNPCeM4aTNIRBINxG45/h6nX/4NM3ZbbT2mThcZQwPDsHVQRMRQAABBBBAAAEEvCzgy+dvL4P6tG4kAH0aOL9X29c3oFfvlR75utTelByG2Z+VTr5Rqqj2e4ioPwIpBdJNBNrBw6rKdciuozTbvqaP0sG7jFRtZTmyCCCAAAIIIIAAAgggUEABXz9/F9Ap6KciARj0CHu0fb6/AW18MzIkeOPyZOGJB0aGBI/Zw6P6VAuBoQtYIvAJGxr89Nt6Y13/Q4MTz1ReWqL9Jw/X7OmjXVJw1vRRGj+MZPnQo0EJCCCAAAIIIIAAAgj0L+D752+CmxMBEoA5YaSQTAUCcQNq2yk99i3plf+X3PzKYdKZP5H2/0SmNOyPgK8EskkEJjZw1zG1mr3raNdDcM70UdpjXL1KSlhV2FcXAZVFAAEEEEAAAQQQ8LRAIJ6/PS3sj8qRAPRHnAJXy0DdgF7+f9Kj35Q6mpPjNOcL0snflcqrAhdDGoRAooAlAv9n+Xo98do6LV7ZoFVbUgyRT4NsVG2FZrkhw5FeggdMHaGq8rI0jmQXBBBAAAEEEEAAAQQQSCUQqOdvQpy1AAnArOk4cCgCgbsBbVgeGRK86c1klkkHRYYEj959KGQci4CvBDZsa3GJwJdWbNGSlQ16fe02dXZ1Z9yGyvJSHThlhEsIWg9BSw6OrK3MuBwOQAABBBBAAAEEEEAgrAKBe/4OayCH2G4SgEME5PDsBAJ5A7IhwdYT8NXfJaNUDZfO/Km035nZgXEUAj4X2NnaoVdXN+qlFQ1avHKLlq5s0M62zqxaZSsN25BhGzo8Z/po7TK6hmHDWUlyEAIIIIAAAggggEAYBAL5/B2GwOW4jSQAcwxKcekJBPYG1N0tvfx/pcf+TepoScY4/IvSiTcwJDi9y4S9AizQ0dnlFg+x3oHWS3Dxigat25biM5OGwbhhVdHegZFegvtOGq6KstI0jmQXBBBAAAEEEEAAAQSCLxDY5+/ghy6nLSQBmFNOCktXIPA3oPWvR4YEb347mWTyodK8X0ujpqfLxX4IBF6gu7tbaxqbeyUE31y/XZZTz3SrqSjTIdNGujkEbeiw/TysuiLTYtgfAQQQQAABBBBAAIFACAT++TsQUcp/I0gA5t+YM6QQCMUNqHWH9MjXpWX3JQtUj5DO/Lm072lcHwgg0I/A1uZ2LV3VoCUrIr0EX1ndqNaOroy9SkukfSYOj/QSjM4lOGlETcblcAACCCCAAAIIIIAAAn4UCMXztx8DU+A6kwAsMDiniwiE5gZk3ZeW3i09drnU2Zoc/iO+LH30OqmcRQ34bCAwmEBbR5deX7vVDRe2eQTt++adbYMdlvL9KSNrIvMIRlcb3mvCMJVZppANAQQQQAABBBBAAIGACYTm+Ttgcct1c0gA5lqU8tISCN0NaN0y6f7PSJvfSfaZMjsyJHjktLTs2AkBBCICNmx4xeam6ByCW9yqw+9u3JkVz7Dqch06bVR8LsGDdxmpmsqyrMriIAQQQAABBBBAAAEEvCQQuudvL+F7qC4kAD0UjDBVJZQ3oNbt0oLLpNceSA61DQk+6zZpn4+H6TKgrQjkXGDzjlY3j6AlAxev2KJla7aqvTPziQTLS0s0c8qI+DyC1ltwbH1VzutLgQgggAACCCCAAAII5FsglM/f+Ub1YfkkAH0YtCBUObQ3IBsSvOTX0p/+PfWQ4CMvjQwJLmPBgiBc57Sh+AIt7Z16dXVjPCFoycFtLR1ZVWy3sXXRhGBk6PDuY+tUUsKw4awwOQgBBBBAAAEEEECgYAKhff4umLA/TkQC0B9xClwtQ38D+uDVyJDgLe8mx3bqHGmuDQneJXBxp0EIFFugq6tbb2/Y4YYNWzLQvr/f0JxVtUbXVWqWrTQcXW145pThqipn2HBWmByEAAIIIIAAAgggkDeB0D9/503WXwWTAPRXvAJTW25Aklq2SX/8ivSPh5LjWjNK+sQvpb1ODkzMaQgCXhVYt7UlvqiILS7yj7Xb1JX5qGFVlZfqoKkjo4uLjNKsaaM1opbevF6NO/VCAAEEEEAAAQTCIsDzd1giPXA7SQByHRRFgBtQlN2GBL90h/TElVJnitVMj75M+vDVDAkuylXKScMqsKO1Q6+saoz3Ely6qkFNbZ1Zcew1od4NF55jKw7vOlpTR9UwbDgrSQ5CAAEEEEAAAQQQyFaA5+9s5YJ1HAnAYMXTN63hBtQnVGtfjgwJbliRHMNdjpDm3imNmOKb+FJRBIIk0NHZpTfWbY+uNhwZNrxhe2tWTZwwvMolAm1REfu+76RhKi8rzaosDkIAAQQQQAABBBBAIB0Bnr/TUQr+PiQAgx9jT7aQG1CKsLRslR6+VFr+x+Q3a0ZLZ98u7XmiJ+NJpRAIk0B3d7ebN9CGC7+0okFLVjTozfXbsyKorSzTodNGubkE50wfrYOnjVR9VXlWZXEQAggggAACCCCAAAKpBHj+5rowARKAXAdFEeAG1A+7DQle9KvIkOCu9uSdPvR16YT/I5WRICjKhctJEehHoLGpTTZUePGKyNcr7zeqraMrY6/SEmm/ycN79RKcOKI643I4AAEEEEAAAQQQQACBmADP31wLJAC5BoomwA1oEPo1SyNDghtXJu847Shp7n9LwycXLX6cGAEEBhZo7ejUa2u2afGKLVq80pKCW9TQlCKpnwakzRtovQNjvQT3HF+vUssUsiGAAAIIIIAAAgggkIYAz99pIIVgF3oAhiDIXmwiN6A0otLcKD38ZemNR5J3rh0TGRI846NpFMQuCCBQbAEbNvzupp2RhKD1ElzZoPc27cyqWsOry10y0BYXmb3rKB20y0hVV5RlVRYHIYAAAggggAACCARfgOfv4Mc4nRaSAExHiX1yLsANKE1SGxL84m3Sk1enGBJcIh3zTen4KxgSnCYnuyHgJYGN21u1JNo70BKCr63Zqo6u7oyrWFFWoplTRrhegpYQtOTgmPqqjMvhAAQQQAABBBBAAIFgCvD8Hcy4ZtoqEoCZirF/TgS4AWXI+P6SyJDgrauSD9z1Q5EhwcMmZlgouyOAgJcEmts69er7ja6XoC0usnRlg7a3dmRVxd3H1blkoPUSnDl5hHYdU6s6FhfJypKDEEAAAQQQQAABvwvw/O33COam/iQAc+NIKRkKcAPKEMx2b9oSGRL85mPJB9eNiwwJ3uPDWRTMIQgg4EWBzq5uvbV+e3wOQRs6vKaxOeuqjhtWpeljarXrmLqE73XadWythldXZF0uByKAAAIIIIAAAgh4W4Dnb2/Hp1C1IwFYKGnO00uAG1CWF4QNCX7+Z9JT10pdfXsGlUjHfks6/t+lUuYDy1KYwxDwtMDaxmaXEFwS7SX4xrptymLUcFIbR9dVul6C011ysE7Tx/YkCkfWVnrahMohgAACCCCAAAIIDCzA8zdXiAmQAOQ6KIoAN6Ahsq9eJN0/X9r2fnJB04+RzrEhwROGeBIORwABrwtsb2nXy6siw4YtMWg/N7d35rTaI2oqknsORhOEY+oqVVLCisQ5BacwBBBAAAEEEEAgxwI8f+cY1KfFkQD0aeD8Xm1uQDmIoA0JfuhL0luPJxdWN1465w5p9+NycCKKQAABvwi0d3Zp+Qfb3ByCS1Zu0SurGrV2a0veql9fVR7vORjrQWjfdxtbJxtyTHIwb/QUjAACCCCAAAIIpC3A83faVIHekQRgoMPr3cZxA8pRbLq6pOd/Ij11vdTdt9dPSWQ4sA0LZkhwjsApBgH/CbS0d2r1liat2NyklZt3asXmnVq52f69U2samnMyhDiVSk1FWU9ycGxkeHEsSThxeLVKS+k56L+riRojgAACCCCAgB8FeP72Y9RyX2cSgLk3pcQ0BLgBpYGUyS6rXpR+b0OC1yQftfvx0tm/kurHZ1Ii+yKAQAgE2jq6tLohmhjcFEsQRr6vbmiWLUSSj62yvFS7jk5YkGRsZGESSxJOHlmjMpKD+WCnTAQQQAABBBAIqQDP3yENfJ9mkwDkOiiKADegPLDv3Cz94WLpnf9JLrx+QmRewN2OycOJKRIBBIIoYMOJbdGReM/BeIJwp1ZvaVZbZ1deml1RVqJdRtVq+tieHoOxnoNTRtWooqw0L+elUAQQQAABBBBAIKgCPH8HNbKZtYsEYGZe7J0jAW5AOYLsW4wNCf7rj6X//U7ykOCSUun4K6VjvimV8gCdpwhQLAKhELCegR9sbY4PJbYhxe9tsqHFkeHFrR35SQ5az8Cpo2q0q1utOKEH4Zg67TK6RlXlrIAeiguQRiKAAAIIIIBARgI8f2fEFdidSQAGNrTebhg3oDzHZ+XfpN9/Vtr+QfKJdj8hOiR4XJ4rQfEIIBBGga6ubq3f3qIVfYYUx3oSNrXldpXimLEtRjx5RI2mR+cbjM85OLZO00bXqrqC5GAYr0fajAACCCCAAAISz99cBSZAApDroCgC3IAKwL5zk/TgRdI/n04+2bBJkSHB048uQEU4BQIIIBAR6O7u1sYdrZGeg67HYGQxkti/t7d25I1q0ojqhBWLe3oQ2vDiuqryvJ2XghFAAAEEEEAAgWIL8Pxd7Ah44/wkAL0Rh9DVghtQgUJuQ4Kf+6H05+9K3X2G5NmQ4A//H+norzMkuEDh4DQIINC/gCUHG5raXULQkoM9qxZHFiVpbGrPG9+4YVW9hhTb/IPWg3DamFoNr67I23kpGAEEEEAAAQQQKIQAz9+FUPb+OUgAej9GgawhN6ACh3XFc9LvPyftWJd84hkflT5xu1Q3psCV4nQIIIBA+gKNTW29ewxGew5acnDTjrb0C8pwzzF1lb17Do7tmXtwZG1lhqWxOwIIIIAAAgggUHgBnr8Lb+7FM5IA9GJUQlAnbkBFCPKOjdKDX5De/XPyyYdNlub9Wpp2RBEqxikRQACBoQlsb2l3ycGeIcU9PQjXb2sdWuEDHD2ipiJpMRKbg9AWKbHEYYlNTMiGAAIIIIAAAggUWYDn7yIHwCOnJwHokUCErRrcgIoU8a5O6dkfSM/cmGJIcJn0kWuko77KkOAihYfTIoBA7gWa2jqiycGEYcXRBUrWbm3J/QmjJQ6rKteuCb0FIysX12nyyGqNqatSTSWLkuQNn4IRQAABBBBAoJcAz99cECZAApDroCgC3ICKwt5z0vcWRoYE79yQXJE9T5Y+cZtUO7rIleT0CCCAQH4FWto7tXqLLUQSmWcwviDJ5p1a09Csru78nb+mokyj6yo1pr7SfXc/u+9VGl1XEf0efa2+UpZQpEdh/uJByQgggAACCARZgOfvIEc3/baRAEzfij1zKMANKIeY2Ra1fb304OclSwb23YZPkebakODDsy2d4xBAAAFfC7R1dOn9hsiw4vfcisU9PQhXNzSrM5/ZwRRylWWlGhVNDEYShQlJw/rE5GHkZxueXFrKEGRfX4RUHgEEEEAAgRwJ8PydI0ifF0MC0OcB9Gv1uQF5JHI2JHjhzdIzN0nq09WltFz6yLXSUV+RmMfKIwGjGggg4AWB9s4urW1s7uk5GB1SbD0IV29pVltnn1XXi1DpstISjaq1noSxRKH1LIz+3KvXYVUksVhbqfKy0iLUlFMigAACCCCAQL4FeP7Ot7A/yicB6I84Ba6W3IA8FtJ3n5Ee+Ly0c2Nyxfb6mHTWzxkS7LGQUR0EEPCmgPUM/GBrc+8Vi10PQhtqvFOtHcVPDvYnZ70Ge/UujCcKq1K+XlXOPIbevAqpFQIIIIAAAr0FeP7mijABEoBcB0UR4AZUFPaBT7p9XSQJuOLZ5P1G7BIZErzLHA9WnCohgAAC/hDo7u7W9tYObdnRps0727TFfbVGft4R+XfP6/Zzq1ravZswrK8q7zN/YaVGJwxH7ptMrK0s90egqCUCCCCAAAIBE+D5O2ABzbI5JACzhOOwoQlwAxqaX96OtiHBNhzYhgWnGhJ84g3SEZcwJDhvAaBgBBBAoLeArWK8OZoc7EkQ9p803NHa4VnC6opSN9TYkoS22EnSXIbxRVEiw5WHV7PwiWeDScUQQAABBHwlwPO3r8KVt8qSAMwbLQUPJMANyOPXxztPSw9eJDVtSq7o3qdKZ/1Mqhnl8UZQPQQQQCB8AraycUNTW8qkoUsgJiQTtzS1qbGp3bNIFWU2j2HivIUDJw1HsvCJZ2NJxRBAAAEEiivA83dx/b1ydhKAXolEyOrBDcgHAd/2gfTA56SVf02u7Mhp0ty7pKmzfNAQqogAAggg0J9AR2eXGprao8OPW6PDkvsmEHtetyRigRdATjt4tuhxLGEYW/DEvsd7Gtb3JBDttVF1lapg4ZO0fdkRAQQQQMC/Ajx/+zd2uaw5CcBcalJW2gLcgNKmKu6OnR3SM9+Tnv1Bcj1KK6ST/kM6/IsMCS5ulDg7AgggUDCBrq5ubW1uTzmHYe/5CyPzG1rCsL2zzyrzBavt4CeyYcYjais0sqZSI2srZAuh2Jf9bK/Ze+7f7rWefaorWABlcF32QAABBBDwigDP316JRHHrQQKwuP6hPTs3IJ+F/u2npD/YkODNyRXf93TpjJ9KNSN91iiqiwACCCCQb4F0Fz5JHLbc3N6Z72oNufyq8tKeJKElDV3CMJI4dElESxYm/DuWTBxWVa5S66rIhgACCCCAQAEFeP4uILaHT0UC0MPBCXLVuAH5MLpb10SGBK96PrnyI3eV5t0lTTnUhw2jyggggAACXhJobut0KyDHFz3ptUJywhc4IMIAACAASURBVOvR1ZNtZWW/bJb7Gx7tURhLEvb0OIwkDnt6HEYSipH9K1VZXuqXZlJPBBBAAAGPCfD87bGAFKk6JACLBB/203ID8ukVYEOC//wd6bkfJTegrFI66bvSYV9gSLBPw0u1EUAAAT8KtHZ0qmGnDUtOnsMwMiy5d9LQywufDORfW1nmehVGkoTlPcOW48OUI0OUbR+XNLSfaytVV1mmkhJ6Hfrx2qbOCCCAQK4EeP7OlaS/yyEB6O/4+bb23IB8G7pIxd96MjIkuLkhuSH7nSWdcatUPcLnjaT6CCCAAAJBFEhc+KTRVkJubtfWpnY1Nre5+Q0tQRh7zf27ObJa8vYW//Q0TIxbeWlJdFhyz1yGvec6jA1hjsx56JKM0a9yFkkJ4keANiGAQAgFeP4OYdBTNJkEINdBUQS4ARWFPbcn3fq+dP986f1FyeWO2i0yJHjywbk9J6UhgAACCCBQJAFLHFoS0JKDscThtljCMCGBGEkmRvbZ2tyhrc3eXghlIE6bszC+EErKhVEsWRgdtux6HEaGK1dXlNLrsEjXKadFAAEEUgnw/M11YQIkALkOiiLADago7Lk/aWe79PQN0t9uTS7bhgSfcqM0+3MMCc69PCUigAACCPhEwBZCaWrr7N3TMNbLMJpAtCSh9TKM9UCMfG/TzjbvL4iSKgyVZaXJC6NEV1qODGOOrbYcWSwlNg/isOoKlbFIik+ubKqJAAJ+EuD520/Ryl9dSQDmz5aSBxDgBhSwy+PNP0l/+KLU0pjcsP3Plk6/RaoeHrBG0xwEEEAAAQTyK9De2ZWQFEwYohxNIEZ6IEaGMVsC0f07+lpXd37rlq/SbYVlm++wpqJM1dHv9nNNZZmq7XtFmXvf/Zzwfmzf+LFJ75e6Y+2Loc35ih7lIoCAVwV4/vZqZApbLxKAhfXmbFEBbkABvBQaV0WGBK9ZnNy40XtEhgRPOjCADadJCCCAAAIIeEugq6tbO9o63NyGPfMa9iQQ7bXYvIexnoex/Zrb/dnrMJMIWA9FG6YcSyDGkom9kocJCUR7372XkIRMlZRMfL+irIRh0JkEhX0RQCCvAjx/55XXN4WTAPRNqIJVUW5AwYpnvDUdbdLT10vP/zS5gWVV0sdukmbNZ0hwQMNPsxBAAAEE/C/Q0t7pehJGFkCJLori5jNMTCZGft6a2PuwpV3dPu11mI+o2VBm14sxljyM92iM9kTs06Oxvx6P/fVotGSj9ZZkhed8RI8yEQieAM/fwYtpNi0iAZiNGscMWYAb0JAJvV3AG49KD31JatmaXM+Zc6XTfyxVDfN2G6gdAggggAACCKQtYL0OI4ukJMxnGEsSxnoiJsx5GO+Z2NSuts6utM/Djj0CJSWKD2tONSS6ts/Q6ZrKSPIx3R6Ptp8lGRkyzVWHgP8FeP72fwxz0QISgLlQpIyMBbgBZUzmvwMaVkq/tyHBS5LrPmaGNO9uaeJM/7WLGiOAAAIIIIBAzgRskZSW9shch9ta2tXc1ikbhmxfLdGfbREV65nY672Ef8ffj75m5bkyosfnrLIhLcjWZaksL1VVeZn7bkOoLTEYeS3yPf5+WeK/E362YyrK3LGx/V05FZHyEsuIl5nifduXXo8hvRBp9pAEeP4eEl9gDiYBGJhQ+qsh3ID8Fa+sa2tDgv/nGunFXyQXUV4tfez70qEXMiQ4a2AORAABBBBAAIGBBKxnYmtHNCEYTxBGEoyWOLQkoUsuJiQMY8nGpoQkZOz92L59k5J+XXTFj1ePSzZGk4aJCciBkpBuvxRJyMRkY+z43knNaHIyfr5IEjRWXimrVvvxEgplnXn+DmXYkxpNApDroCgC3ICKwl68ky5fID30Zak1xZDgAz8pnfpDqaq+ePXjzAgggAACCCCAQJYC1ovRhjG3tPUkGpvaOqK9FnteS+zR6Ho49u3V2BZNSsbe65OUbO9kksUsQ5S3w8pLS3r1guwvCdmrV2OsJ2XfXpTRnpXxnpbxpGVPL8nEla9jPzNEO2/hDVTBPH8HKpxZN4YEYNZ0HDgUAW5AQ9Hz6bFb3pPu/4z0wSvJDRi7V2RI8IT9fNo4qo0AAggggAACCORXoL0zkkyMJRL79lpM7tEY3d/1duxQsw2N7tPjMbFHo5XX1sF8jPmNYu5LtxWnY6tSR76Xqya+ynV5dLXrUtVWlkfmf4wuTFNtK1snLlKTsNp1Ynl2jC1qw+ZvAZ6//R2/XNWeBGCuJCknIwFuQBlxBWfnjlbpyaulRb9MblN5jXTqf0mHXBCc9tISBBBAAAEEEEDARwKdXTYnY8+QaBs+bUlB6+Fo31s7IknC2Gut7V1qjb7X3/s9xyYcHy8vWlafc9Db0VsXjfVsjK1I3TvZGEkixnsmRpOK8dcSkow9yUdLSpZGV8i2ZGVksRmGU+c35jx/59fXL6WTAPRLpAJWT25AAQtops15/SHpj1+RWrclH3nQv0YSgZV1mZbK/ggggAACCCCAAAIBELC5G13isLNLlmSMJRH7Jhkt+TjQ++74aMKy53s0idlPErLVekL2OS4ApJ5vwoCJxIQVreOJyFiycZDvtn9sReswLyDD87fnPwIFqSAJwIIwc5K+AtyAuCa05d3okOBXkzHG7RMZEjx+H6AQQAABBBBAAAEEECiagM3xaD0SU/WCtKRi316SvZKUCUnG+H6JPSlTJCEjSc1IEjLW2zJ2bEtHp7qZCjKra8FGMceSjJYQjCUS4z9HE4W9ezr2DKeO7BfpsWg9GGsqYsOryzR+WJXnezDy/J3VZRO4g0gABi6k/mgQNyB/xCnvtWxvkZ68SnrpjuRTVdRGFgc5+Ly8V4MTIIAAAggggAACCCDgdQFLRrpVrW3BmIRVrd38j/FFYzrUHFuQxs39aKtdRxejif3bzQcZey+yGrbbzy1AwzyQmV4Hr11/suqryjM9rKD78/xdUG7PnowEoGdDE+yKcQMKdnwzbt1rD0h/vExq2558qM0J+LGbpcrajIvlAAQQQAABBBBAAAEEEEhfwIZfW09Dl2RMWDQmlmS0RWjiP7sFZhKSjwmJxHhSsVdyMlKu9W4M0vbP733c8wul8PwdpCsu+7aQAMzejiOHIMANaAh4QT108z+l+z8trVuW3MJx+0rn3i2N2zuoraddCCCAAAIIIIAAAgiEQqCjs0stHV1udeqWaO/EyErV1gMx0mPRvRftlZhW8jHeAzKSZOzoKsxYaVsg5a3vfMzzceP52/MhKkgFSQAWhJmT9BXgBsQ1kVLAhgQ//u/Skl8nv11RJ532I+mgT4KHAAIIIIAAAggggAACCPQr0N4ZG/bcM8Q53qMxOoTaejNa0rF3j8bIEGpLPsaSks29hkz3DJ+2HOOImgq9eu1Jno8Ez9+eD1FBKkgCsCDMnIQEINdARgLLfi8tsCHBO5IPO/RC6WPflypqMiqSnRFAAAEEEEAAAQQQQACBXAjYfIw2lNnmTLQkoNc3EoBej1Bh6kcCsDDOnKWPADcgLolBBTa9HVkleP1rybuO3z8yJHjsnoMWww4IIIAAAggggAACCCCAQJgFeP4Oc/R72k4CkOugKALcgIrC7r+TtjdLf/q2tPTu5LpX1kun3yIdMNd/7aLGCCCAAAIIIIAAAggggECBBHj+LhC0x09DAtDjAQpq9bgBBTWyeWrX3++TFnxNat+ZfIJZ86WTvytV1uXp5BSLAAIIIIAAAggggAACCPhXgOdv/8YulzUnAZhLTcpKW4AbUNpU7BgT2PhWZJXgDf9INiktlybsL02ZLU2dI02dLY3eQyotxQ8BBBBAAAEEEEAAAQQQCLUAz9+hDn+88SQAuQ6KIsANqCjs/j9pW5P0p29JL//fwdtSPUKaMiuaFJwd+V43ZvDj2AMBBBBAAAEEEEAAAQQQCJAAz98BCuYQmkICcAh4HJq9ADeg7O04UtIrv5Me/YbU3pQZx6jdIr0DXU/B2dLEA6TyqszKYG8EEEAAAQQQQAABBBBAwEcCPH/7KFh5rCoJwDziUnT/AtyAuDqGLLDhDenxb0vvLZS6u7IrrqxSmnhgQlJwlmRJwpKS7MrjKAQQQAABBBBAAAEEEEDAYwI8f3ssIEWqDgnAIsGH/bTcgMJ+BeSw/a07pLUvS2sWS+9Hv3asy/4EtWMShg3bEOJZUs3I7MvjSAQQQAABBBBAAAEEEECgiAI8fxcR30OnJgHooWCEqSrcgMIU7QK3tbtb2rYmkgx0ScElkQRhR3P2FRmzZ3RxkeicgrbgSFlF9uVxJAIIIIAAAggggAACCCBQIAGevwsE7fHTkAD0eICCWj1uQEGNrEfb1dkRWT34/ZekNUsiycFNb2Zf2fIaadJBkaHDsTkFR0xl6HD2ohyJAAIIIIAAAggggAACeRLg+TtPsD4rlgSgzwIWlOpyAwpKJH3cjpat0pqlCT0FF0tNm7JvUP2E6NDhWZHegpMPkaqGZV8eRyKAAAIIIIAAAggggAACORDg+TsHiAEoggRgAILoxyZwA/Jj1AJeZxs63LiyZx5BGz78wd+lztYsG14ijd83Moeg6yk4Rxq3j1RalmV5HIYAAggggAACCCCAAAIIZC7A83fmZkE8ggRgEKPqgzZxA/JBkKii1NEmrV8WmUfQzSf4krTl3exlKusjPQNjScEps6Xhk7IvjyMRQAABBBBAAAEEEEAAgUEEeP7mEjEBEoBcB0UR4AZUFHZOmguBpi098wjGVh5uacy+5OFTpanRxUWsp+Ckg6XK2uzL40gEEEAAAQQQQAABBBBAIEGA528uBxKAXANFE+AGVDR6TpxrARs6vPmf0R6C0ZWH1y2TujqyO1NJmWSrDMcWF7HvtgpxaWl25XEUAggggAACCCCAAAIIhFqA5+9Qhz/eeHoAch0URYAbUFHYOWmhBNqbI/MHxnoI2vfGVdmfvWqENOXQ3knBurHZl8eRCCCAAAIIIIAAAgggEBoBnr9DE+oBG0oCkOugKALcgIrCzkmLKbBjQ+8Vh20F4rbt2ddo5K6RhUViPQUnHSiVV2VfHkcigAACCCCAAAIIIIBAIAV4/g5kWDNuFAnAjMk4IBcC3IByoUgZvhbo6pQ2vZWQFFwibXhd6u7KrlmlFdLEAxKSgrOk0btLJSXZlcdRCCCAAAIIIIAAAgggEAgBnr8DEcYhN4IE4JAJKSAbAW5A2ahxTOAF2nZKa1+JrDbshg8vkbavzb7ZNaN7Vhx2PQVnSTWjsi+PIxFAAAEEEEAAAQQQQMB3Ajx/+y5keakwCcC8sFLoYALcgAYT4n0EogLb1kZ6Cbqk4BJp7ctSe1P2PGNmSFNmR4YO29eEmVJZRfblcSQCCCCAAAIIIIAAAgh4WoDnb0+Hp2CVIwFYMGpOlCjADYjrAYEsBTo7pI3Lo0nB6KrDG9+U1J1dgeXV0qSDoknBWZHvI6cxdDg7TY5CAAEEEEAAAQQQQMBzAjx/ey4kRakQCcCisHNSbkBcAwjkUKBla6RnoPUUtF6C1ltw58bsT1A3Prq4yKzI98mHStXDsy+PIxFAAAEEEEAAAQQQQKBoAjx/F43eUycmAeipcISnMtyAwhNrWloEge5uqXFVzzyCNp+gzS3Y2ZplZUqkcftIU6M9BC0pOG5fqaw8y/I4DAEEEEAAAQQQQAABBAolwPN3oaS9fR4SgN6OT2Brxw0osKGlYV4V6GiT1r8W7SEYHTq8+Z3sa1tRJ00+pHdScPjk7MvjSAQQQAABBBBAAAEEEMiLAM/feWH1XaEkAH0XsmBUmBtQMOJIK3wu0LRFWrM02lMwmhRsbsi+UcMmRxKCo3eXhk2S6idEvg+bGPmqqMm+bI5EAAEEEEAAAQQQQACBrAR4/s6KLXAHkQAMXEj90SBuQP6IE7UMmYANHd7ybnQuQVt5eLG0bpnU1Z4biOoRCQnBvglCSxROkOotUVidm/NRCgIIIIAAAggggAACCIjnby4CEyAByHVQFAFuQEVh56QIZC7Q3iKt+3vvpGDjyszLyeSImlEpehBGE4SxHoXWu7C8KpNS2RcBBBBAAAEEEEAAgVAK8PwdyrAnNZoEINdBUQS4ARWFnZMikBuBHRt7Vhu2BUZsGHHrttyUnUkpNaN79yiMDTV236NDjy1RWFaRSansiwACCCCAAAIIIIBAoAR4/g5UOLNuDAnArOn6P3DVqlW69dZb9eijj8p+rqqq0owZM3TuuefqkksuUW1tbc7P+sEHH2jffffV1q1bXdnHHXecnnnmmUHPs3nzZlfXhx56SCtWrFB3d7d22203nXXWWfrqV7+qMWPGDFpGNjtwA8pGjWMQ8KhAV5e0+e1IL8EN/5C2fyBtXxf9+kBqbypuxWvHJiQKE+cltGHI0fkJ68eTKCxulDg7AggggAACCCCAQJ4EeP7OE6zPiiUBmOOAWdLv/PPPjyfi+ha/995767HHHtPuu++e0zPPnTtXDzzwQLzMdBKAL730ks4880xZ8jDVNnnyZD388MOaPXt2TutqhXEDyjkpBSLgTQGbV7B1e08ycMf6hAShJQoT/t3RXMQ2lEh14yLzEMYXLkkxT6HtU1ZexHpyagQQQAABBBBAAAEEMhPg+Tszr6DuTQIwh5F99dVXddRRR6mpqUn19fW64oordMIJJ6i5uVn33nuvfvWrX7mz7bPPPrLkm+2Ti23BggU644wzNH78eG3YsMEVOVgCcM2aNZo1a5bWr1+v8vJyfeMb39Bpp53mjn3kkUf0wx/+UB0dHZowYYKWLFmiKVOm5KKq8TK4AeWUk8IQ8L+AJQpbtkoDJQitZ6G939FSvPaWlEYThdFhxr1WOk6Yp9AShaVlxasnZ0YAAQQQQAABBBBAICrA8zeXggmQAMzhdWDJPht2awm1hQsX6sgjj+xV+s0336zLL7/cvXb99dfrmmuuGfLZd+zYof3220+rV6/WPffcowsvvDCtBOBnPvMZ3X333W7f++67T/PmzetVl/vvv98NWbZt/vz5uvPOO4dc18QCuAHllJPCEAiPgEsUNvb0KEzsQRhLEMaGIHe2Fc/FEoWWHOwvQRibp9CGJ5eWFq+enBkBBBBAAAEEEEAg8AI8fwc+xGk1kARgWkyD72Q9+g477DC348UXX6zbbrst6aCuri7NnDlTy5cv16hRo1zvu4qKoU1Ob/P0/eQnP3E9Df/3f/9XJSUlgyYA7bzWo6+zs1Mnn3yyHn/88ZQNPOWUU/TEE0+orKxM1mPQegPmauMGlCtJykEAgZQClihsbkhIFNqchNEehL3mKFwndbUXD7GkLJokjC1c0s8Q5NoxJAqLFyXOjAACCCCAAAII+FqA529fhy9nlScBmCPKq666St/73vdcaS+88IIOP/zwlCXfdNNNbmiwbU8++aROPPHErGuwaNEi18vQehz+/e9/l80vmE4C0IYiX3TRRe68NjT5k5/8ZMo62HvnnXeee+/222/XF77whazr2vdAbkA5o6QgBBAYioAtYOIShdGFS3ZEE4XxRUyii5nY610dQznT0I4tLY8uWJKYIJwYfS264rHNXVg7Wor+IWhoJ+RoBBBAAAEEEEAAgaAI8PwdlEgOrR0kAIfmFz/62GOP1bPPPqu6ujo1Nja6pFyq7fnnn3fzBNpmQ4BtKHA2m83PZ4tz2LyDV199tW644QZXTDoJQBsm/Jvf/MbtbwuATJw4MWUV7D1bCMQ2OyY2ZDib+vY9hhtQLhQpAwEECiZgicKmzZJLEKZKEsYSiOul7s6CVSvpRKUVkhteHFvdeKJUM1KqrJMq6iLf3Ve9VFmb8LO9H/03cxcWL36cGQEEEEAAAQQQyIMAz995QPVhkSQAcxS0cePGadOmTTrooIP0yiuv9FtqQ0ODRo8e7d63efds/r1stlhPwj322EOvvfaaqqurXTHpJADnzJmjxYsXa8SIES5ZOdBm+2zbtk12jPU4zNXGDShXkpSDAAKeEujqjCQK+w4z7tXDcF1kMZPuLk9VPV6Z8pp+koOWNExMIkZ/donDft6z1+19+2KuQ2/Gm1ohgAACCCCAQOAFeP4OfIjTaiAJwLSYBt6ppaVFNTU1bqdTTz3VraI70Gar/+7cuVNHHHGErEdgptu7777r5hK01YVtjr6TTjopXkQ6CUDr8WfzAO6///4ueTjQZud5/fXXXS9B6xGY7mY3mIE2Kys2Z6ItYDJ16tR0i2Y/BBBAwP8ClijcuTHamzChR2HfHoa2j1cThZlGwfVA7KfXYb89EvtLOsaSjzUMec40DuyPAAIIIIAAAqETIAEYupCnbDAJwBxcBxs3btT48eNdSTafns2dN9Bmi2ls2LDBJfGWLVuWcQ1s3sCnnnoq5bnSSQDaMOWmpiY3T6HNVzjQZvtYzz9LWm7fvj3tusbqkc4BJADTUWIfBBAIpUBnRzRRmDhHYYohyJYoVHcIiUr6DGeODm+ODWfONrFYXkViMYRXE01GAAEEEEAgqAIkAIMa2czaRQIwM6+Ue1sCa9q0ae69T33qU7rnnnsGLNX2tWNs+O4777yTUQ2s7E9/+tMaPny43njjDU2aNKnX8ekkAG1VX1uR+JhjjtHChQsHPH9sbkM7xuYdTHcjAZiuFPshgAACORDobJd2bEg9R2HbDqltp9TWJMV+bo/+3LqjuHMW5qDpeSnCVmfulTwcKLGYQdKxvDIv1aVQBBBAAAEEEEBgIAESgFwfJkACMAfXQaF6ANocg/vuu6+ba/AnP/mJLr300qTap5MALEQPQIYA5+DCoggEEEAg3wLd3VJnWzRBaEnC6Fd7ws+x19z3HZJLHkZ/TplYjL4XlKHLuYyBLdJSUSOVVUhlVZIlBO17WWXPz+616Jf1RHTvV0juZ9sv+r2/nzN931aYZuXoXEaZshBAAAEEEPCcAAlAz4WkKBUiAZgD9kLNARhbvddW/33xxRdVmmJC9XQSgIWYA3AwVm5AgwnxPgIIIOBjAUssdrT07nVoycKME4spko4+ZvFm1Ut6JxZdAjGdBGViAjOWoIy91l8Cc7D3ExKcJCa9eblQKwQQQAABXwrw/O3LsOW80iQAc0Sa71WA165dqylTprjaXn755TrkkENS1vy8885zr1tPwWuuucb9vNtuu7n5/mKbJRCXLFnCKsA5ij3FIIAAAggUSKCrS+poHiSxaEOeE3opxnssxnot9unBaO/bF5vHBEoG6O0Y7TmZk2SlJSXtqzr6Ff25os+/SUh67PqgOggggAACmQiQAMxEK7j7kgDMUWxjc+XZ8NrGxkaVl5enLNlW/T3qqKPce5agu/7669OqwYoVK1wiL5vN5gy866674ofGehLaC7Yar/UITLXZe5MnT3Zv2TF33313NqdPeQw3oJxRUhACCCCAwFAFbFVmlyhMmCdxwB6LfXszJiYdE96zXpBswRAoKe2dIIwnDWPJw/6+xxKJ9n2wffu+n3BMWerfK4OBSysQQAABBPItwPN3voX9UT4JwBzF6corr9SNN97oSrOVdRN73CWe4qabbtIVV1zhXnriiSd00kknpVWDXCYAb7/9dl188cXuvLZisa1cnGqz92I9Cn/5y1/qoosuSquu6ezEDSgdJfZBAAEEEPC1gK3i7IY9J/Q67GiVOluljrbId5uDMfaze699gPftmOhx8ePbpV5lRvdxryX8HMpVon199fSuvPVAdL0UM00ipkoq9undOGiZ1VKKaWcCpEtTEEAAgcAL8Pwd+BCn1UASgGkxDb7TokWL4kk/S67ddtttSQfZyrszZ87U8uXLNXLkSG3YsEEVFRWDF57BHunMAbhu3To3nNjqc/LJJ+vxxx9PeYZTTjnFJSltrsE1a9b021Mwg+rFd+UGlI0axyCAAAIIIJCFgM3J2NURTRT2kyB0icjEBGTfn2PHxRKQQ0lWxs7VmkVjOKQoAraATWICsu8Q6UGTiP31bqzpndS0xW1KyyTrcWmL07jv9hV7Lfbv6PvxfRNf77NPUcA4KQIIIOAtAZ6/vRWPYtWGBGAO5WPDgG3478KFC3XkkUf2Kv3mm2928/fZdu211+q6667r9b4N050/f36/76dT1XQSgFZO4jDg+++/X3Pnzu1VvL127rnnutf6DiFOpx6D7cMNaDAh3kcAAQQQQCDgAgMlJvv2YEzszRhPVmaSzOyTwIz1vLRh2u7nlkgCtL1ZordkgC68xCRiQmLQJQ77eS+ebEzxfsrkZKokZZ9jkxKVsfdTJTbTTW72k/S03prxxGnfnwdod686JtQ/tip5fOVxWyQotoJ5wgrlscWD7DsriwfoM0RTgiLA83dQIjm0dpAAHJpfr6NffvllHX300WpublZ9fb1sWPAJJ5zg/m3DaW3orW177bWXFi9erGHDhhUtAbh69WrNmjVLGzdudPMVfvOb39Rpp53m6vPII4/oBz/4gTo6OmSLmyxdulRTp07NoZTEDSinnBSGAAIIIIAAArkQsKSkDcOOJQRTfo8mCwfcx4Z5x/ZrjiQXe+0/QBm5aAdlIFBMgXjSMLqiuEseRlcHjycKB3sttn/ifomvxVYs7++16PtusSDbJ1YXEpTFvDQ4d/EEeP4unr2XzkwCMMfRWLBggS644AJt27YtZcmW/Hv00Uc1Y8aMpPcL2QPQTv7iiy/qrLPOkg0JTrXZ4iAPPfRQv/MZDoWOG9BQ9DgWAQQQQAABBAIp4BKQCT0S+yYZrYdiPJmYaSKy7/4pEpN2bjYEgi6QboLSkoaJq43HEonxpGJCUjPptf4Sl7FjEhKYvc5BgjLol1+x2sfzd7HkvXVeEoB5iMfKlSt1yy23uESffdAqKytdwm/evHm69NJLVVtbm/KshU4AWiU2bdrk6mqJPltoxDZbbfjMM8/U1772NY0ZMyYPQvQAzAsqhSKAAAIIIIAAAkMR6OqKLkKTSXKxRWq3/TM5pm+PyOi/u9qHUnuORSAYAm7OzViCMKEHnDHJYQAAHrNJREFUo70Wmx9TNkw7OlR7wJ+jQ8pNptexsddjw71LbIeE+Tdj/073HO4EeTxHtL15P0dp5BrKxMrqNOMjnh/6TgIwGLeHobaCBOBQBTk+KwFuQFmxcRACCCCAAAIIIBBcAUtA2hyM3V1SV2fke9KXvd/Pe+6Y6PG9jkvcv8/7SeeJvT/A+futW3/nT2xP3/r1PU8+2m+O/dStV1v62nRIlpR1q47bV3SVcnqKBvczSMuyE7hua3bHFfAonr8LiO3hU5EA9HBwglw1bkBBji5tQwABBBBAAAEEEAisQGyuzNjiPPFFemKJwj7f4wnEhNfjr8VWFO+zErlLNia+lnhswjGd0Z/jq5hHVyoPLD4N855AiXRdo/eq1adGPH97PkQFqSAJwIIwc5K+AtyAuCYQQAABBBBAAAEEEEAg5wKxFcZ7JRD7SzRGezbGVx6P9XK0no92TJ+ej6l6Q6ZKcLrFhGLHJ/SgdK+15rzJFFhEARsufG1DESuQ3ql5/k7PKeh7kQAMeoQ92j5uQB4NDNVCAAEEEEAAAQQQQACB/AkkJihdkjJFz8mUr7VGhrjHhsn3/dmGvbv3okO54z/Hjkl83YaF91eWFdNfWYOdI0W5Kcvq7xz91StVuwaqY7T8tL1SGQ3kZZdH9P2SMunry/J3veSoZJ6/cwTp82JIAPo8gH6tPjcgv0aOeiOAAAIIIIAAAggggAACCPhJgOdvP0Urf3UlAZg/W0oeQIAbEJcHAggggAACCCCAAAIIIIAAAvkX4Pk7/8Z+OAMJQD9EKYB15AYUwKDSJAQQQAABBBBAAAEEEEAAAc8J8PztuZAUpUIkAIvCzkm5AXENIIAAAggggAACCCCAAAIIIJB/AZ6/82/shzOQAPRDlAJYR25AAQwqTUIAAQQQQAABBBBAAAEEEPCcAM/fngtJUSpEArAo7JyUGxDXAAIIIIAAAggggAACCCCAAAL5F+D5O//GfjgDCUA/RCmAdeQGFMCg0iQEEEAAAQQQQAABBBBAAAHPCfD87bmQFKVCJACLws5JuQFxDSCAAAIIIIAAAggggAACCCCQfwGev/Nv7IczkAD0Q5QCWEduQAEMKk1CAAEEEEAAAQQQQAABBBDwnADP354LSVEqRAKwKOyclBsQ1wACCCCAAAIIIIAAAggggAAC+Rfg+Tv/xn44AwlAP0QpgHXkBhTAoNIkBBBAAAEEEEAAAQQQQAABzwnw/O25kBSlQiQAi8LOSbkBcQ0ggAACCCCAAAIIIIAAAgggkH8Bnr/zb+yHM5AA9EOUAlhHbkABDCpNQgABBBBAAAEEEEAAAQQQ8JwAz9+eC0lRKkQCsCjsnJQbENcAAggggAACCCCAAAIIIIAAAvkX4Pk7/8Z+OAMJQD9EKYB15AYUwKDSJAQQQAABBBBAAAEEEEAAAc8J8PztuZAUpUIkAIvCzkm5AXENIIAAAggggAACCCCAAAIIIJB/AZ6/82/shzOQAPRDlAJYR25AAQwqTUIAAQQQQAABBBBAAAEEEPCcAM/fngtJUSpEArAo7JyUGxDXAAIIIIAAAggggAACCCCAAAL5F+D5O//GfjgDCUA/RCmAdeQGFMCg0iQEEEAAAQQQQAABBBBAAAHPCfD87bmQFKVCJACLws5JuQFxDSCAAAIIIIAAAggggAACCCCQfwGev/Nv7IczkAD0Q5QCWEduQAEMKk1CAAEEEEAAAQQQQAABBBDwnADP354LSVEqRAKwKOyclBsQ1wACCCCAAAIIIIAAAggggAAC+Rfg+Tv/xn44AwlAP0QpgHXkBhTAoNIkBBBAAAEEEEAAAQQQQAABzwnw/O25kBSlQiQAi8LOSbkBcQ0ggAACCCCAAAIIIIAAAgggkH8Bnr/zb+yHM5AA9EOUAlhHbkABDCpNQgABBBBAAAEEEEAAAQQQ8JwAz9+eC0lRKkQCsCjsnJQbENcAAggggAACCCCAAAIIIIAAAvkX4Pk7/8Z+OAMJQD9EKYB15AYUwKDSJAQQQAABBBBAAAEEEEAAAc8J8PztuZAUpUIkAIvCzkm5AXENIIAAAggggAACCCCAAAIIIJB/AZ6/82/shzOQAPRDlAJYR25AAQwqTUIAAQQQQAABBBBAAAEEEPCcAM/fngtJUSpEArAo7Jx0xYoV2m233RzEokWLNGnSJFAQQAABBBBAAAEEEEAAAQQQQCDHAh988IEOO+wwV+p7772n6dOn5/gMFOcHARKAfohSAOv40ksvxW9AAWweTUIAAQQQQAABBBBAAAEEEEDAcwLWAWfOnDmeqxcVyr8ACcD8G3OGFAIkALksEEAAAQQQQAABBBBAAAEEECisAAnAwnp76WwkAL0UjRDVpaWlRcuWLXMtHjdunMrLyz3f+sRu0wxb9ny4qGCBBfh8FBic0/lOgM+I70JGhQsowOejgNicypcCfEZ8GTZPVbqjo0MbN250dTrggANUXV3tqfpRmcIIkAAsjDNnCYAAE6cGIIg0IW8CfD7yRkvBARHgMxKQQNKMvAjw+cgLK4UGSIDPSICCSVMQKKIACcAi4nNqfwnwH6+/4kVtCyvA56Ow3pzNfwJ8RvwXM2pcOAE+H4Wz5kz+FOAz4s+4UWsEvCZAAtBrEaE+nhXgP17PhoaKeUCAz4cHgkAVPC3AZ8TT4aFyRRbg81HkAHB6zwvwGfF8iKggAr4QIAHoizBRSS8I8B+vF6JAHbwqwOfDq5GhXl4R4DPilUhQDy8K8PnwYlSok5cE+Ix4KRrUBQH/CpAA9G/sqHmBBfiPt8DgnM5XAnw+fBUuKlsEAT4jRUDnlL4R4PPhm1BR0SIJ8BkpEjynRSBgAiQAAxZQmpM/Af7jzZ8tJftfgM+H/2NIC/IrwGckv76U7m8BPh/+jh+1z78An5H8G3MGBMIgQAIwDFGmjTkR4D/enDBSSEAF+HwENLA0K2cCfEZyRklBARTg8xHAoNKknArwGckpJ4UhEFoBEoChDT0Nz1SA/3gzFWP/MAnw+QhTtGlrNgJ8RrJR45iwCPD5CEukaWe2AnxGspXjOAQQSBQgAcj1gAACCCCAAAIIIIAAAggggAACCCCAQIAFSAAGOLg0DQEEEEAAAQQQQAABBBBAAAEEEEAAARKAXAMIIIAAAggggAACCCCAAAIIIIAAAggEWIAEYICDS9MQQAABBBBAAAEEEEAAAQQQQAABBBAgAcg1gAACCCCAAAIIIIAAAggggAACCCCAQIAFSAAGOLg0DQEEEEAAAQQQQAABBBBAAAEEEEAAARKAXAMIIIAAAggggAACCCCAAAIIIIAAAggEWIAEYICDS9MQQAABBBBAAAEEEEAAAQQQQAABBBAgAcg1gAACCCCAAAIIIIAAAggggAACCCCAQIAFSAAGOLg0DQEEEEAAAQQQQAABBBBAAAEEEEAAARKAXAMIIIAAAggggAACCCCAAAIIIIAAAggEWIAEYICDS9NyJ7Bq1SrdeuutevTRR2U/V1VVacaMGTr33HN1ySWXqLa2NncnoyQEfCKwdOlSPf7443r22Wf12muvacOGDaqoqNDkyZN11FFH6XOf+5yOOeYYn7SGaiJQGIHLL79cN998c/xkf/7zn3X88ccX5uScBQEPCmzatEl33nmnHn74Yf3zn/9UQ0ODxowZo1122UXHHnuszj77bB155JEerDlVQiD/Am1tbfrNb36j+++/X6+++qq2bNnifteaMmWKjj76aF100UU64ogj8l8RzoAAAoEQIAEYiDDSiHwKWNLv/PPP19atW1OeZu+999Zjjz2m3XffPZ/VoGwEPCVw3HHHaeHChYPW6VOf+pTuuOMOVVZWDrovOyAQdAF7eJs9e7Y6OjpIAAY92LQvLQFLanzpS1/S5s2b+93/zDPP1EMPPZRWeeyEQJAEVq9erVNPPVXLli0bsFlf//rX9YMf/EAlJSVBaj5tQQCBPAiQAMwDKkUGR8Ae1qwnU1NTk+rr63XFFVfohBNOUHNzs+6991796le/co3dZ5999NJLL7l92BAIg4D1gLWeGtbbb968ea6n37Rp09TZ2annn3/e/SK6Zs0aR3Heeefpt7/9bRhYaCMC/Qp0dXW5Xhr2f8X48eNdj1nb6AHIRRNWgXvuuUfz58+XfTbsM2GJwA996EMaPXq01q1b5/6PWbBggUaMGOF6P7EhECYB+0PRoYceGk/+HXjggfrGN74h63iwfft2Pffcc+53rZ07dzqW73//+/rWt74VJiLaigACWQiQAMwCjUPCI2DJvmeeeUbl5eWut1PfISg2jMuGc9l2/fXX65prrgkPDi0NtcBpp52mCy+8UOecc47KysqSLGxIlw1Neeutt9x79vlhOHCoL5nQN/7HP/6xrJeG/cHoE5/4hG688UYSgKG/KsILsHz5ch1yyCFqbW11/zfEEn2pRGwIJL3Iw3uthLXlDzzwgObOneuab88fNt1K39+3lixZ4t5rb2/XqFGj3B+W7JmFDQEEEOhPgAQg1wYC/QhYL43DDjvMvXvxxRfrtttuS9rT/mo9c+ZM2S+y9h/v+vXr3bwcbAggID3yyCM6/fTTHcVXv/pV3XLLLbAgEEoBG8a13377aceOHa7Hn/1hyf5oZBs9AEN5SYS+0R/96Ef19NNPa+zYse53KPvOhgACPQLW2+9HP/qRe+GPf/xj/PepvkY2R+Yf/vAH97INFbbnEjYEEECgPwESgFwbCPQjcNVVV+l73/uee/eFF17Q4YcfnnLPm266yQ0Ntu3JJ5/UiSeeiCkCCEgu2TFs2DBnYXPYWEKQDYEwClgi3K7/T3/607rrrrt03XXXkQAM44VAm53AG2+8oX333df9bJ+Fa6+9FhkEEOgjcOmll+pnP/uZe9UWWtt///1TGtmw3//6r/9y7y1evFizZs3CEgEEEOhXgAQgFwcC/QjYynPW3b6urk6NjY39dqm3+c5snkDbbAhwrFcHsAiEXcBWqrOVHG2zBIj9BZsNgbAJ3HffffrkJz/p5jWzxMe4ceNIAIbtIqC9vQT+4z/+Iz5lyuuvv+56x9pmq//a9BH2WYn93wEdAmEVuPXWW3XZZZe55qfTA9AWALHnleHDh4eVjHYjgEAaAiQA00Bil3AK2EOa/SJ60EEH6ZVXXukXwX5htV9WbbPFEOxhjw0BBOSGpNjQFNvsL9Q2QTUbAmESsIcx6+lkCxrYolGf//znXfPpARimq4C29hWwHuGPPfaYW9zDfoeyRaLs/4e///3v8V13220312P2m9/8JguscQmFUmDjxo2yBde2bdvm5lT+y1/+kjQH4Msvv+wWl7J5MllwLZSXCY1GIGMBEoAZk3FAGARaWlpUU1PjmprO0EVb/ddW4bL/hK1HIBsCYRew+TFtYupFixY5CptTc/bs2WFnof0hE7joootc4s96iduKjdZDgwRgyC4CmpskYMm9FStWuD+w2qq/sWGOqahsPrMnnnjCrTjPhkDYBOwPqeeff76am5vdojlf+9rXtNdee7kpVv7617+6VYBtReCDDz5Yf/rTnzRx4sSwEdFeBBDIUIAEYIZg7B4OAfur2/jx411jbejWvffeO2DDJ0yY4Fbesl9UbQJeNgTCLmC/lP7bv/2bY7AVTx988MGwk9D+kAlYws+mkrBVG5cuXaoDDjggLkAPwJBdDDS3l4D1/LNeTVVVVW4V4JEjR8rmU7Ye4zZ80X6PsilVLKFhmyXQbUqW0tJSJBEIncA//vEP/fCHP9Sdd96p7u7uXu23549vf/vbsj822ZRFbAgggMBgAiQABxPi/VAK2IqN06ZNc23/1Kc+pXvuuWdAB9vXjtljjz30zjvvhNKMRiMQE7BhKrbCY0dHh0uk27Au+yWVDYGwCNhwLOuRYaubphr+TgIwLFcC7UwlUF5ers7OTveWJcgtWW4jKBI360V+2mmnxZOA999/v+bOnQsoAqESaG9vd3OLW09y62iQapszZ45bSMdGLLEhgAACgwmQABxMiPdDKUAPwFCGnUbnQMAmdD/mmGPcvE7Wu8OGbh133HE5KJkiEPCPQCzBZ38cst4bfXtmkAD0Tyypae4FYtOmWMn/8i//ot/97ncpT2L/n9jICtusd+ADDzyQ+8pQIgIeFbCphT7+8Y9r4cKFLlFu82HOnz9fu+++u2yqohdffFE33HBDfHqJH/3oR/FFQzzaJKqFAAIeECAB6IEgUAXvCTAHoPdiQo28L/Dee++5+ZzWrl3rflm1Hhs2/JcNgTAJ2Eq/NreZ9QJ8+OGHdcYZZyQ1nwRgmK4I2tpXYNKkSW5hHNvuvvtuXXjhhf0iTZ06VWvWrNEuu+yiVatWgYlAaARsGhWbTsW2u+66yy2K03ezkRYnnXSS/vznP7sh8rYoyIEHHhgaIxqKAAKZC5AAzNyMI0IiwCrAIQk0zcyJgCX9rOffu+++6xY6sF9WB3qoy8lJKQQBDwpcfPHFuv32210vje9+97spa/j73/8+3pvp6quv1n777ef2O/3005nHyYMxpUq5FTjssMPcwlC2Pf300/rwhz/c7wlsMakXXnjB9Si3P86yIRAGAZvrb+zYsdqyZYtb9OPNN9/st9m2GIj98dU2WyTEegKyIYAAAv0JkADk2kCgHwGbvN0mnbahW42NjbI5a1JttuqvTVBtm01abXN1sCEQJoFNmza5Yb421NG2n/70p/ryl78cJgLaikBc4DOf+Yzr1ZTNZr1op0+fns2hHIOAbwRsGKP9kci2J598UieeeGK/dY8lC+13MVv5lA2BMAhYD1nrKWvbYIsRJo5aOuWUU+LzZobBiTYigEDmAiQAMzfjiJAIXHnllbrxxhtda+2vz4cffnjKltvKdVdccYV7z+Y7s674bAiERWDr1q2u94atcmqbfR5sRTo2BMIqQAIwrJGn3ekK/PrXv9ZnP/tZt/svfvELffGLX+z3UOsFtXnz5kF7QaV7bvZDwA8C9odVG4lk2znnnCPrNd7ftn37drd6tm22cM6CBQv80ETqiAACRRIgAVgkeE7rfYFFixbFk342pOu2225LqrStUmcTVNtKjyNHjnQrdFVUVHi/cdQQgRwINDU1uYS3DT+x7aqrrtJ3vvOdHJRMEQgEW4A5AIMdX1o3sIAl9Kx3k61war3/rBdgqs1WlD/++OPdW5/73Od0xx13QItAKATs+WLUqFHatm2bJk+erJUrV/Y7EumRRx5x00fY9pWvfEW33nprKIxoJAIIZCdAAjA7N44KiUBsGLAN/7VVuGwumsTt5ptv1uWXX+5euvbaa2UPdWwIhEHAFjiwXzhjD26XXXaZfvzjH4eh6bQRgSELkAAcMiEF+Fzgkksucb3/bLNVgG014MTNejXZ72CvvPKKe9n+KDtnzhyft5rqI5C+wL/+67/GV8i2/zPsOaPv1tDQ4Ob/i03Bwkik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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Loss')\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='validation')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exceptional-situation",
   "metadata": {},
   "source": [
    "Finally, you can run some testing diagnostics to see how well your neural network has performed. For this, I optionally allow for `ytest` as an output of my `get_dataset()` functions. This is just a `numpy` array, which I can plot and compare with the `numpy` array that gets produced when I run `model.predict()` on my test dataset. If you would rather just look at the `model.evaluate()` results, this step is unnecessary, but I have included it here for illustrative purposes. As you can see, our neural network does a pretty good job, but probably could be better. Feel free to play around with the parameters and the network architecture to get more desireable results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "peaceful-airfare",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "781/781 [==============================] - 1s 2ms/step - loss: 0.3909 - binary_accuracy: 0.8188\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(test_dataset)\n",
    "ypred = model.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "brazilian-priest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
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       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
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       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randind = np.random.randint(ytest.shape[0])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(ytest[randind], 'o')\n",
    "plt.plot(ypred[randind], '.')\n",
    "plt.ylim([-0.05,1.05])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "silver-mailing",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-tfpt_combo_env]",
   "language": "python",
   "name": "conda-env-.conda-tfpt_combo_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}