Assignment+2.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.5** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
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   "source": [
    "# Assignment 2\n",
    "\n",
    "In this assignment you'll explore the relationship between model complexity and generalization performance, by adjusting key parameters of various supervised learning models. Part 1 of this assignment will look at regression and Part 2 will look at classification.\n",
    "\n",
    "## Part 1 - Regression"
   ]
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   "source": [
    "First, run the following block to set up the variables needed for later sections."
   ]
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    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "np.random.seed(0)\n",
    "n = 15\n",
    "x = np.linspace(0,10,n) + np.random.randn(n)/5\n",
    "y = np.sin(x)+x/6 + np.random.randn(n)/10\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)\n",
    "\n",
    "# You can use this function to help you visualize the dataset by\n",
    "# plotting a scatterplot of the data points\n",
    "# in the training and test sets.\n",
    "def part1_scatter():\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib notebook\n",
    "    plt.figure()\n",
    "    plt.scatter(X_train, y_train, label='training data')\n",
    "    plt.scatter(X_test, y_test, label='test data')\n",
    "    plt.legend(loc=4);\n",
    "    \n",
    "    \n",
    "# NOTE: Uncomment the function below to visualize the data, but be sure \n",
    "# to **re-comment it before submitting this assignment to the autograder**.   \n",
    "#part1_scatter()"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Question 1\n",
    "\n",
    "Write a function that fits a polynomial LinearRegression model on the *training data* `X_train` for degrees 1, 3, 6, and 9. (Use PolynomialFeatures in sklearn.preprocessing to create the polynomial features and then fit a linear regression model) For each model, find 100 predicted values over the interval x = 0 to 10 (e.g. `np.linspace(0,10,100)`) and store this in a numpy array. The first row of this array should correspond to the output from the model trained on degree 1, the second row degree 3, the third row degree 6, and the fourth row degree 9.\n",
    "\n",
    "<img src=\"readonly/polynomialreg1.png\" style=\"width: 1000px;\"/>\n",
    "\n",
    "The figure above shows the fitted models plotted on top of the original data (using `plot_one()`).\n",
    "\n",
    "<br>\n",
    "*This function should return a numpy array with shape `(4, 100)`*"
   ]
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   "source": [
    "def answer_one():\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    \n",
    "    ans = []\n",
    "    degrees = [1,3,6,9]\n",
    "    \n",
    "    for degree in degrees:\n",
    "        # transform the original input data to add polynomial features\n",
    "        poly = PolynomialFeatures(degree)\n",
    "        # reshape to order the array as needed \n",
    "        trans_X = poly.fit_transform(X_train.reshape(11,1))\n",
    "        clf = LinearRegression().fit(trans_X,y_train)\n",
    "        \n",
    "        test_data = np.linspace(0,10,100)\n",
    "        trans_data = poly.transform(test_data.reshape(100,1))\n",
    "        ans.append(clf.predict(trans_data))\n",
    "        \n",
    "    return np.array(ans)\n",
    "answer_one()"
   ]
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       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 overriden (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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // 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",
       "\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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6fJ6fCreVhEIGEoP1IQ9yIzcFBUOzFHPohytqqrWN1/bFHbd0xV8/NYJun5iv8DLdccrtX/jOu3+bJXKDxWHVPH5/PK6ffK4vPJ6pNSs4UrLHi+bI7vNPphzHfqPztfAcQUq6JsVYtFmZQoggECHChA0dCgvjbciQNDAY4FAowBBQwRPwrPf/XrIsttpt9ypwRMnR9ACRRFAAAEEEEAAAQQQQACB2BHwetxa/8FCbf74gzOewdB35JjACoZTz6irrXRq19pj2rPumJx1bR8SnZZpV4+inGC4kNktJeRN+pfXFutbL6yX2+uPGMlhs+jRm8YHQ4aTGzArGg5t36oNH7yj0gP7Ttu23yd1LxqnlIxxKj9sOeN2T82NZOWlatjkQg0+pwerHCKeNSog0P4CBA3tb0qLZxYgaOAJQYCgIeJn4LkfflselzNYb8r1t2jY+dMibocKCCCAAAIIIIAAAggggEBXC5QfOqhlC55VZcmhVrtiViwMGHeOxsy+SPl9+gbLmDftS3Yd187Pjga+n+nsBVPJHKw8+Jzu6jMsTznd09r89P/2I9X65oJ1Wl98PGyi8f1y9djNEzS88MyrEUzfD+/YGghXzrR6w4x90MSpyu09RQe3VqvyaH2bfTGrM8bM6KOicd1bHELdZmUKIIBAuwkQNLQbJQ2FKUDQECYUxRJegBUNEUzxgke+J2ddbbDGeVddr5HTZ0fQAkURQAABBBBAAAEEEEAAga4VMNvCbvr4A214f6HMz6deVqtNg8+drNGzL1RO954hL5cWV+vz9w+o/NCJfxe1NprAeQZDcjXk3J7qPThXFmtkb8N4fX59sOWInvl0n5bsLG01zDD3mDG0u+6+oEjzRhXKFsE9GsOS7Vr//kId27f7tBOSkZOrydfepOyCIdq7oVT7N5WpofbMKzey89M0ZlYf9R+ZH/G4u/bJ4O4IJIYAQUNizGM8jSKy/8LF08joKwKRCRA0ROD10k8fCuzx2XxNvOxqjZfDyuUAACAASURBVJk9L4IWKIoAAggggAACCCCAAAIIdJ1A1bGjgVUMp9s+qN+oMZp8zU3K7JYX0smaigatX1QcOIfhTFdqhj2whZD5MisZ2uOqdXq06VCVdh2rUYPbqzSHTUN7Zml07xxlptrP6hYmcDiya4fWL1oYOAj7dNeAsRN03tU3KC0rR0d2H9feDWU6tKMicMD06a5uhRkaO6uveg/NbXMVx1kNgsoIIBAiQNDAA9HZAgQNnS3O/WJVgKAhgpl59bEfqbrsWLDG+HmXafxFl0XQAkURQAABBBBAAAEEEEAAgc4X8Pt82rp8sda+87p8npafyHekpgXeSDcrGU4+5NlV79HmpYe0c81R+c5wbkKP/lmB1Qt9R+TF7TkFR3bv1PoP3pH53trlSEvTuZdfo6HnXSCL1SpXgyewymHrssNnXOVQ0DdT4+b0Cxx4zYUAAh0vQNDQ8cbcIVSAoIEnAoFGAYKGCJ6EN371aMj+pWY1g1nVwIUAAggggAACCCCAAAIIxKpATUW5lr8w/7RvoPcaMlxTb7o9ZBWD1+vTrs+OavOSQ3LVt9xeyYzVZrdq0ITuGnJuD+X2yIjV4Ufcr+Ktm7TylQUhq9lPbqTnoCG64PpbldOjcVspj8urHauPatunh+VqaN3KlCscmKOxs/uqoG9WxH2iAgIIhC9A0BC+FSXbR4CgoX0caSX+BQgaIpjDd377eMgS45HTZgU+9cOFAAIIIIAAAggggAACCMSagNkWaNfqFVr9xsvyuJwtumdzpAQ+oT98yrTAJ/TNZeoc3F6p9YsOqKaiZR1TxpyNUDS2QGNn91NGTkqsDbtd+uN2NujzhW9q2/IlRqVFm1a7XeMuvESjZ14om71x+yazwmH7ihJtX3VEHpfvtP3oPypfEy8ZoLRMR7v0lUYQQCBUgKCBJ6KzBQgaOluc+8WqAEFDBDPz/pP/p5JdO4I1hpw3RVNvvD2CFiiKAAIIIIAAAggggAACCHS8gNfj1rIF87Vv/dpWb9Z9wEBNu/mOkMOezQHP6xYd0LH91aftYI+ibJ0zr7/yemV2/CBi4A7H9u/Vpy89r+NHDrfam9zC3pp6w60yns1XQ61bW5cfDqwIOd0ZDuYsi3MvLZIJHbgQQKB9BQga2teT1toWIGho24gSySFA0BDBPH/09JMq3rIxWKNo/ETNvP2eCFqgKAIIIIAAAggggAACCCDQsQLOulqZf7sc27enxY2sNpsmXHyFRs+cG1zF4HF7Awc9m3MYTndl56dpwrx+6j20W9IdbOz1eLT5k0XasGihfN7WtkayaNSM2Trn0quCqxuMY12VK3C+xZ7Pj8l/mjOj+4/K08RLiljd0LF/JGg9yQQIGpJswmNguAQNMTAJdCEmBAgaIpiGpc8/oz2frwnW6DNilC6894EIWqAoAggggAACCCCAAAIIINBxAjXlZVr01BOqKm0ZGuT17qvpt9ylbr16BztgVjGseH23qssaWu1USrpdY2b20ZCJPWS1NW6vlKxX1bGj+vTl53V0z65WCQr69tfMO+5VVn5ByOvV5Q2Bsy72byprNXAIrG64rEj9R7K6IVmfLcbdvgIEDe3rSWttCxA0tG1EieQQIGiIYJ5XvPx37Vi5LFjDHAJ2yRf/NYIWKIoAAggggAACCCCAAAIIdIxAWfF+ffjXP6qhpuXWR2PnXKzxF10ms6LBXH6fX1uWH9amxYcCP596WW0WDTuvUKOm91ZKWuMZBFzGzaedqz/VZ2+/JndDy3DGkZamqTfeoQFjx7fgOnagWqve2HPasy84u4EnDIH2ESBoaB9HWglfgKAhfCtKJrYAQUME8/vZW69q8+IPgzXMJ4Ku/OqDEbRAUQQQQAABBBBAAAEEEECg/QXMFq+L//a0vG5XSONWq00X3HSbBk+cHPz92kqnVry2W6XFNa12xGznM35uf2V2S23/jiZIi3VVx7XqtRd1YNP6Vkc0YtosnXv51bLZQw98NttUbfz4oHasOnLa1Q2TLitSP1Y3JMiTwjC6QoCgoSvUk/ueBA3JPf+M/oQAQUMET8P699/R+g/eCdbILuiha7/5vQhaoCgCCCCAAAIIIIAAAggg0L4C21cs1apXX5D/lIMAzKfrZ991v3oNGRa4oXl97/pSrX1vvzwuX4tOmG2SzrucN7kjmZ1da1Zq5asvtAh4TBv5ga2U7lF2fvcWTba5umF0vs69ZIBSM0KDikj6RlkEklWAoCFZZ77rxk3Q0HX23Dm2BAgaIpiPLYs/1Jq3Xg3WSM/O0Y3f/a8IWqAoAggggAACCCCAAAIIINA+AmYbn8/ffVObPv6gRYMZud0C58k1n8fgrHNrzdv7VLytotWb9xqcq8lXDVR6Vkr7dC6JWqk8UqLF8/+i40dLWozakZqmqTfdrgFjJ7R4zaxu2PDRQe1cffrVDeddMVB9h+clkSZDReDsBQgazt6QFiITIGiIzIvSiStA0BDB3O5YsUwrXvl7sIY9JVW3Pfw/EbRAUQQQQAABBBBAAAEEEEDg7AW8HreWv/Cc9q5b06Ixs8Xr3Hv+SSZsMFfJ7uNa+cYeNdS4W5S12S0af2F/DZ3UUxYLb5VEOzMelyuwldKuNStabWL4BTM06cprW2ylZAof21+tVW+e/uyGUdN6a+ysvrJYmZ9o54d6ySVA0JBc8x0Lo+Vv51iYBfoQCwIEDRHMwt7P12jJ88+cVMOiO3/0c1ms1ghaoSgCCCCAAAIIIIAAAgggEL2Aq75OHz/zZx3Zs7NFI72GjtDsO++T2TbJ6/Zp3YcHtHP10VZv1q0wQxdcO1g53dOj7ww1QwR2r12lla8skAkeTr0CWyndfo+yC1pupdTW6obeQ3M15ZrBHMzN84ZAGAIEDWEgUaRdBQga2pWTxuJYgKAhgskr3rxRHz3zZEiNWx/6qcxyWC4EEEAAAQQQQAABBBBAoKMFaisrtOipJ1rdpmfIpCmacv0tstpsqqtyacmCHao8UteiS2bhwsipvTV6Zh/ZbHxoqr3n7PjRI/rEbKV05HCLps+0lZIpbFY3mNUn5sDuU6/s/DRNv2kowVB7TxjtJZwAQUPCTWnMD4igIeaniA52kgBBQwTQJbt26P0n/y+khjmjwZzVwIUAAggggAACCCCAAAIIdKSACRnefeJ/Zb6feo2fd5nGzbs0sP1R2aEaLX1hZ6tbJWXmpuj8qwerx4Dsjuxq0rdtVjSsfv0l7Vz9aasW4y68RGbOWlsd73F5tfrtvdq/qbxFXXuKNbAKpc8wzm1I+ocMgNMKEDTwcHS2AEFDZ4tzv1gVIGiIYGbKivfr7f/7RUiNa77xXeV07xlBKxRFAAEEEEAAAQQQQAABBCITqK+p1ru//19Vlx0LqWjeqJ5y/a0aet6UwO/v31ymVW/skdfjb3GDonEFmnjxALbfiYz+rErvWbs6cM5fa1sp9R8zXtNuvqPVFfJ+v1/bVx7R+kUH5D9lKs2KlDGz+mrU1N6c23BWs0PlRBUgaEjUmY3dcRE0xO7c0LPOFSBoiMDbLIF9/fGfhNS44l+/qfw+/SJohaIIIIAAAggggAACCCCAQPgCzrq6wMrqisMHQyrZU1I168771Gf4SJk3pjcvOaRNiw+1aNjusOq8KwdqwOiC8G9KyXYTqDp2VJ88+5QqW9lKqVuvPprzhX9UVl5+q/cr2XNcn76yS656b4vX+43I0+SrB8mRYmu3vtIQAokgQNCQCLMYX2MgaIiv+aK3HSdA0BCBbd3xSr3004dCalz8xX9V4aAhEbRCUQQQQAABBBBAAAEEEEAgPAG3s0Ef/Pn3Kt2/t0XIcNE/fkXd+xcFDn02+/of2NJyq5307BTNuHmo8nplhndDSnWIgMft1qcvPqe969a0aD81M0uz7/oH9Rw4uNV711Q4tfSFHTp+rL7F6+YgbzO/WXmcG9ghE0ejcSlA0BCX0xbXnSZoiOvpo/PtKEDQEAGmq6Fef3/4P0JqzL3ni+o7cnQErVAUAQQQQAABBBBAAAEEEGhbwOtx68O//FElu7aHFLba7Zp335dUOHio6mtcWrpgp8oP17ZoML9PZuDw4PSslLZvRokOFzCrTjZ9/IE+X/impND9kKxWm86/7mYNnXxBSD9qnR5tOlSlHYeqVLOmTP4jDUp1WJXusMlq9lCSlJJu0wXXDlGvwbkdPgZugEA8CBA0xMMsJVYfCRoSaz4ZTfQCBA0R2Pl9Pj37vX8PqTHj9ns0cPzECFqhKAIIIIAAAggggAACCCBwZgGf1xvYbqd4y8aQguZMhjl3/2Pgw04VJbVasmCn6qtdLRrrPzpf5185SDaHFeoYEyjevFFLnn9GHpezRc9GTpulCZdfo0XbSvXM8n1auqv0xBkNfmmsy6ZzXHaZN7WyUu0qyEpRTppDJnMYf2F/DT+/MHAgOBcCySxA0JDMs981Y+dv3a5x566xJ0DQEOGc/O37D8p8sqj5uuCG21p86iTCJimOAAIIIIAAAggggAACCAQFzAeclr0wX+Yg4ZMv8wby9Nu+EPigU/HWcq18fY88bl8LubHmoODpvXnDOYafqcqSw/rw6T+qtiJ0u6sGt1ebPXl6PWWi3NbWV6L09Vg1s94uRyBuUGB1Q7/8dKXZbRoxpZfGX9iPuY/huadrHS9A0NDxxtwhVICggScCgUYBgoYIn4QF//2fctbWBGtNuup6jZo+O8JWKI4AAggggAACCCCAAAIItBQw2+usfPUF7VixtMWL5kNOQ86boi3LDmvjx6EHQ5vCNrtV5189SP1HtX6wMN6xJdBQWxNYtXJ0z65AxyrqXCquqA+sYKixZ2lx/nRVO7Jb7XSOz6I59Q7l+hrf3jL/2y8vXd0yUjRoQnedd/lAWay89RVbM05vOkuAoKGzpLlPswB/2/IsINAoQNAQ4ZPwys8eUU15WbDWhIuv0LgLL4mwFYojgAACCCCAAAIIIIAAAi0F1r7zemAf/1OvSVdepxHTZmvN23u1Z11pi9fTsx2afuMwmXMZuOJHwOvxaPXrL2nlRx/pQHnoYc9uq0PL86aoJK1XqwNy+KUZDQ7185zYHqt/U9jQb2SeplwzOBA+cSGQbAIEDck2410/XoKGrp8DehAbAgQNEc7DG796VJUlh4K1Rs+ap3MvvzrCViiOAAIIIIAAAggggAACCIQKbPzwPX3+rjkoOPQaP+8yjZ17iVa8tkcHtoRutWNK5vXOCIQMGTkc+hyPz9TWw1X62k/+ovEVn7c4JNqsVVjT7RztyhzS+tD80hSnXcPdtsDr5s2uoYVZgW2Ueg3K0bQbh8qe0vgaFwLJIkDQkCwzHTvjJGiInbmgJ10rQNAQof/C3/9Kx/btCdYafsEMnX/tTRG2QnEEEEAAAQQQQAABBBBA4ITAtuWLteq1F1uQjJw+W+dceo0+fWW3Du2obPG6+eS62S7J7uDN5Hh8nrw+v67/7VKtLz6uQucRTS3/VCm+E2cCNo9pW9YwrcsZr8Cpz6defmmiy6axLnvgFXNmw9CeWYGfC/plaebNw5SS3vgaFwLJIEDQkAyzHFtjJGiIrfmgN10nQNAQof0Hf/69Du/YGqw1aOJkTb/lzghboTgCCCCAAAIIIIAAAggg0Ciwe+0qLfv7sy04hp53gSZdfZOWv7hLJXuqWrw+alpvmYOf2Ys/fp+khZtK9MAza4IDyPLUaGbZUmV7qlsMqji9r1Z0myyvtfXQYIzLpnOdja8VFWQoJ80R+Llbz3TNum2E0rIaf82FQKILEDQk+gzH3vgIGmJvTuhR1wgQNETobg7r2r9xXbBW/zHjNfuuf4iwFYojgAACCCCAAAIIIIAAAtKRPbv0/pP/J7/PF8JRNH6iplx/h5a+uEvH9rV803n83H4aObU3hHEucNeTK7RkZ+iZGw6fW9PKl6vQebTF6MpT8gKHRDttaa2OfJjLqilOh7JT7RrU/cR5HVl5qZp9+whldkuNczG6j0DbAgQNbRtRon0FCBra15PW4leAoCHCuVu2YL52f7YyWKvX0BG66P4vR9gKxRFAAAEEEEAAAQQQQCDZBWoqyvX2//1CztqaEIq+I0brgpu+oGUv7VFZcehrpuDEiwdo2OTCZOeL+/HXOj0a+9BC+f0th2Lx+zTp+FoNrj2xbW9zqVp7hhbnz1CVI6dVgyK3NXBI9Lg+ObKetNVSenaKZt8+XDnd0+PejgEgcCYBggaej84WIGjobHHuF6sCBA0RzszKV1/Q9k+XBGt1HzBQl335axG2QnEEEEAAAQQQQAABBBBIZgGPyyVz/lvF4YMhDD0HDdGM2/5RS1/crYrDdSGvmfeMz72sSEMm9kxmuoQZ+8o95brlieWnH4/fr5E12zS+amOLMm6rQ0vzp+poauvPQh+PVQ/k5ivdZg2pa85qmHXrcOX3ObHaIWFAGQgCTQIEDTwKnS1A0NDZ4twvVgUIGiKcmbUL39Cmj94P1sot7K2rv/btCFuhOAIIIIAAAggggAACCCSrgN/v15Lnnta+9WtDCDLz8nXhfV/Vp68W6/jR+hYhw+SrBmnguO7JypZw456/Yr+++/KGNsfVv75YUypWyuoP3V7Lb7FoVbdJ2psxsNU2Hpk9XFmbq+V2ekNet6dYNePmYepZ1PqKiDY7RAEEYlyAoCHGJygBu0fQkICTypCiEiBoiJBtw4fvat27bwVrmX8MXP+tH0TYCsURQAABBBBAAAEEEEAgWQVO/TeFcbCnpGj23f+szz+oUnVZQ2jIYLXogmsHq/+o/GQlS8hxP7V0jx5+fXNYYytwlQUOiU7xuVqU35w9ShuzR0snbZNkCv3w6tG6bkhPffLcdjnrPCH1bHaLZt46nLAhLH0KxZsAQUO8zVj895egIf7nkBG0jwBBQ4SOW5d9otWvvxSslZqRqZu//6MIW6E4AggggAACCCCAAAIIJKNA8eaN+uiZP0kK3Zj//Ovu0o41KaqtdIawWG0WTbthiPoMy0tGroQec7grGpoRMj01mlW2RNmelud27E/vr5V558lnsQXNfnLDON1+/gBVldXrk79tV11VaEhhVjbMum2EuvfLSmhnBpd8AgQNyTfnXT1igoaungHuHysCBA0RzsTO1Sv06Yt/C9ay2u2645HHImyF4ggggAACCCCAAAIIIJBsApVHSvTObx+XxxUaJgy/YJ6OHhjQ4o1gm92q6TcNVa/BuclGlRTjbfOMhlYUHD6XZpQvUw9naYtXS1O6a0nBVLmsqYHXFnxpqiYPbFwFU3vcGQgbqstDV8s4Um2ac+cI5fXizIakeOiSZJAEDUky0TE0TIKGGJoMutKlAgQNEfLv2/C5Fs//S0itO/7757LaTnxyJMImKY4AAggggAACCCCAAAIJLuCsq9M7v/2FqstC3yDuNXS06monq6EmdGsb82nzmbcMV48B2Qkuk7zDq3V6NPahhfKHLm5pE8Tq92py5RoV1e1vUbbGnqVPCmao1pGljQ9dqsxUe7BMQ61bH/6/rS225krNsGvuXSOV0z29zXtTAIF4ECBoiIdZSqw+EjQk1nwymugFCBoitDu4bYs+/MsTIbVu/v6PlZqREWFLFEcAAQQQQAABBBBAAIFkEPD5vFr01B9UsnNbyHCzu/eSPe1i1R4PPeTXfMrc7J/PljaJ/3Tc9eQKLdnZcnVCmyP3+zWmeovGVLc848FlTVHdOVfoD1+/tkUzZvskEzacukVXepZDc+8eqay8tDZvTQEEYl2AoCHWZyjx+kfQkHhz2pUjckiaLenKpu/DJJm1iiWSFkt6XNLqKDpoPk7wbUm3ShpoVjtKWibpp03fo2iyRRWChggVj+7drXef+N+QWtd/+4fK7MaeqRFSUhwBBBBAAAEEEEAAgaQQWPPmK9qy5KOQsTpS05XV4xrVHj/xiXNTICXdFtg3P783W9kkw8OxcFOJHnhmTdRDLarbp/MrV8tyyrKIgT2ydcU992rghHNbtG1ChkXPbFV9deiZDZm5KZp79yhl5KRE3R8qIhALAgQNsTALydUHgobkmu+OHu1Fkt5ruskhSeb/JZhNN8dLGi7JK+mfJYV+DP7MvTLrY83/EzX/r+CwpCWSCiXNbDo17G5J89thYAQNESKWHzqot379s5BaV33tO+pW2CvCliiOAAIIIIAAAggggAACiS6w+7NVWrbg2VOGaVG3vperrir0w0rsl5/oT0PL8Xl9fl3/26VaX3w86sH3cB4LnNvg8LkDbWSk2DSkR5YsFmnCJVdo7JyLZTG/OOmqKq0PrGxw1oVu2ZWdnxbYRikty3yekguB+BQgaIjPeYvnXhM0xPPsxV7fL5T05aaVC2bFQfNlnrOvSvqlJPNf77GSQtfKnn4sv5P0JUkfSLpGUl1T0SskvSbJfPTArJw4eJYcBA0RApo9VV997L9Dal32la+re/+iCFuiOAIIIIAAAggggAACCCSyQOn+vXr3j7+Rz3PizVzzwfOcntPkbBgUMnSb3RJYycCZDIn8RLQ+tu1HqnXl/y6W2xvhYQ0nNZftrtKs8qXK8tZqWM8spTlOnCE4ZNIUnX/dzbLZQ1fPVB6p00fzt8pVbz4beeLK7ZGuOXeNVGp6aPnkmxlGHK8CBA3xOnPx22+Chvidu3jsuVntYFY9/EDSI2EMoKBp2yXznA6RtO+UOk9Kul/SY5IeDKO9MxUhaIgQsL6mWi/+6PshtS66/yvqNdQsXuFCAAEEEEAAAQQQQAABBKS6quN6+zc/V3111QkOv5SaM0I+/6SQT5ibD5tPv2mo+gxjO9ZkfXZeXlusrz+/7qyGn+p16t9ztyqz9miLdnoNGaZZd96nlPTQswXLDtXo4/nb5HGFnhNitu6afccImVU2XAjEmwBBQ7zNWPz3l6Ah/ucwnkbwaFMg8AdJD4TRcbMt0tOSPmk68+HUKmYFhVnpsLNpVUMYTZ62CEFDhHoel0vP/fBbIbVm332/+o8eF2FLFEcAAQQQQAABBBBAAIFEFPB6PHrvD79R6YG9IcOz2nvIljpPFmvom7fnXz1IA8d1T0QKxhSBgAkbvvXC+qhWNjhsFj1603hdPbYwsFXX/g2ft7hzbo9Czb33i8rKN59tPHEd21+tT57bLq8nNGzo3j9Ls24dLnsKYUME00jRGBAgaIiBSUiyLhA0JNmEd/FwX5F0raSHJT0URl9+Ienrksz3b7RSPkdS8waO5ufqMNo8XRGChgjx/H6/5n/v32W+N1/Tb7lLgyaeF2FLFEcAAQQQQAABBBBAAIFEFPjs7de0+ZNFIUPzedPkyLpCNnt6yO+fc3F/DZ/MeW+J+BxEMyazjdI3F6yL6MyG8f1y9djNEzS80Bz1KPl9Pn3+7pva9LH5fGLolZaVrTl336/uAwaGvFCy+7iWLNgh3ynbNxUOytGMm4fJZrdGMxzqINAlAgQNXcKe1DclaEjq6e/UwZuPuX8myXwEYKKkcNZCviTpeklfk/Sr0/TWBA0mZDDtbzyLERE0RIH3/EPfkdvZEKxp9rscPmV6FC1RBQEEEEAAAQQQQAABBBJJ4ND2rVr01O9DhuRq8Cs1+1I50nqE/P7oGX00dlbfRBo+Y2kHAXNA9AdbjuiZT/dpyc5SnfQZt2DrZrutGUO76+4LijRvVKFs1pZvc+1YtVwrX/57yIfkTANWu10zbr1bA8ZOCOntwW0VWvbSzhb36zOsm6bdOFTWVu7RDsOlCQTaXYCgod1JabANAYIGHpHOEDCbHy6XNF7SXyTdF+ZN35V0saR/kmTOY2jtModA95E0rekeZ2p69xle7D9o0CD77t1nKhJmr5Oo2Is/+aHqq5oXlUjnXn6NRs8yO1pxIYAAAggggAACCCCAQLIKmPPc3vzVo2qoObHo3FXvkS3tAqXnjAhhGTqppyZeMiDkrIZkdWPcpxeodXq06VCVdh2rUYPbGzjkeWjPLI3unaPM1LYPaz68Y5s+efapkA/KNd9twsVXaOzci0Oewf2byrTitd0twobBE3to0mVFPK88rHEhQNAQF9OUUJ0kaEio6YzJwZj/4r/QtGXShqZAoCbMnhI0hAnVVcVe+/mPVVV64oCtcRdeqgkXX95V3eG+CCCAAAIIIIAAAggg0MUCZruaD//6Rx3aviXYE7fTK4+nr3IKLwx5g7b/6HxdcM1gWfiEeBfPWnLcvrLksD786x9UW1nRYsBF487R1JvukD0lJfja7s+PafVboeeLmBfHzemnUdN6Jwcao4xrAYKGuJ6+uOw8QUNcTlvcdNpsXmgOc75T0g5JsySVRNB7tk6KAKsrir71m5+r/OCB4K1HzZijSVde1xVd4Z4IIIAAAggggAACCCAQAwJbFn+oNW+9GuyJx+VTXbVD+f1vkNV24k3cXkNyNf2mobLZ2PM+BqYtabpQX12lj/76R5Wd9O/Y5sHn9e6rOV/4R2V2ywt6bF9Zos/fP/Fv3uYXplw7WEVjQg+TThpEBho3AgQNcTNVCdNRgoaEmcqYG4h5tsx2R/8gaV9TyLA/wl5yGHSEYJ1d/L0//EZH9uwM3nbY+dM05fpbOrsb3A8BBBBAAAEEEEAAAQRiQKCs+IAW/u6X8vm8gd54PT5VlznVre81SknvGexhQb8szb59uOwOc4QfFwKdK+B2ObX8hb9p/4bPW9zYHBI9+65/UI+iQcHX1n1wQNtWhH5m0mqzaPbtI9RjQOPB01wIxKIAQUMszkpi94mgIbHntytH91tJX5ZkzlAwKxmiOfzg7qYVEZ9Imt3KYMxhAB9IMu90DzvLwXIYdBSAZkn0wa2bgjUHTjhXM277QhQtUQUBBBBAAAEEEEAAAQTiWcDtbNBbv35M1WWlgWH4fX5VlTUoo9t5ysw/Jzi03J7pmnvnSKWkt72vfjx70PfYFvD7/dqw6F2tf//tFh212myBD9ANmTQl+Cwvf2WXireGbrmUkmbThV8YpZzu6bE9nfNGPQAAIABJREFUWHqXtAIEDUk79V02cIKGLqNP6Bs/LulrTdskmYBge5SjNesQzccGzHM6WNKpKyLMion7JT0m6cEo79FcjaAhCsAlzz2tves+C9bsN2qM5nzBnN3NhQACCCCAAAIIIIAAAskksPTvz2rP2lWNb8z6pZryBlnsvZTX94rguQyZ3VJ14RdGKj3rxBZKyWTEWGNPYP/GdTLPrtftatG5kdNn69wrrpHVapPX7dNHf9umsuLQIyfNMz3vnlFKy3TE3uDoUdILEDQk/SPQ6QAEDZ1OnvA3/Imk70gyJwTPlbQ5jBGbcxzOl/Sbpq+Tq/xO0pckvS/pGkn1TS+aE4dfl2T+38BwScVh3OdMRQgaogD89KXntXPV8mDNwkFDdfEX/yWKlqiCAAIIIIAAAggggAAC8Sqwe+0qLfv7s8Hu1x53yu20q6DoJtnsGYHfD3z6+55Rying09/xOs+J2u/yQwf18TNPtnpIdO9hIzTjtnuUmpEhZ51bH/x1i2oqnCEU+b0zNefOEbKnsBVYoj4j8TougoZ4nbn47TdBQ/zOXSz23AQBzad+mXefT7eSYaukn540gI+atkZ6WNJDpwzMbHj4saSJkg5LWizJbO5pVkr4JZntlea3AwZBQxSIq994WVuXmulpvAr69tfl//KNKFqiCgIIIIAAAggggAACCMSjQFXpscCWSR5X45uvDbVu1VW5lNf3MqVmDgj8nsUizbpthAoH5cTjEOlzEgjU11Rr8bNP6ejelrs+Zxf00Nx7/kk5PXqqurxBi57eImedJ0Slz7Bumn7jUFmsvM2WBI9L3AyRoCFupiphOsrfgAkzlTExkHslPRVGT8w703NOKnemoMEUMx95+bak2yQNlFQraZkks3rCfG+Pi6AhCsV1772tDYsWBmvmdO+pa77x3ShaogoCCCCAAAIIIIAAAgjEm4DX49G7v/+Vyg4eCHTd7fQGtkzKyBun7B5Tg8OZdFmRhpx74jDoeBsn/U0OAfM8r3rtxZBV+80jd6SlBVY29B0xSqXFNfp4/lZ5PeazjyeuoZN6auIlA4JbhSWHGqOMZQGChliencTsG0FDYs4ro4pcgKAhcjNt+vgDrX3H7GDVeGXk5OqG/zALU7gQQAABBBBAAAEEEEAg0QU+e/s1bf5kUWCYZg97c/izPaVA+f2vlcXauI3MsMmFmnhx48oGLgRiXcAcEr390yVa/fpLMj+HXhaNu/BijZ93mQ5ur9Tyl3cFziM5+Zowr79GTOkV68Okf0kiQNCQJBMdQ8MkaIihyaArXSpA0BAF//YVS7XylQXBmuZTHrf+8ORdsaJolCoIIIAAAggggAACCCAQ8wKHtm/RoqeeCPTT5/WruqxBPr9NBQNukD0lN/D7vYbkaubNw9hOJuZnkw6eKnB45zYtnv9XuerrWuCYswmn33a3irfW6/P3G1fznHxNvX6I+o/KBxWBLhcgaOjyKUi6DhA0JN2UM+DTCBA0RPFonHrom8Vi0R0/+gVLRaOwpAoCCCCAAAIIIIAAAvEiYPazf/NXj6qhpjrwiW6zb73H5VVu4Wyl544IDCOne7rm3TNKjlQOyI2XeaWfoQLm/JGPn35Sx48daUGTlpWtabfcpZI9adq5+mjI6za7RbPvGKHu/cyRk1wIdJ0AQUPX2SfrnQkaknXmGfepAgQNUTwTBzZv0MfP/Cmk5m3/9TPZHY4oWqMKAggggAACCCCAAAIIxLqA2U7GrGQ4vGNroKu1lU456z1Kzx6qnF5zAx86Ss2w66J7RyuzW2qsD4f+IXBGAXdDg5a9MF8HNq1vpZxF4+ZerKrjQ3R4R1XI6ynptsCfgay8NIQR6DIBgoYuo0/aGxM0JO3UM/BTBAgaongkzHLSD/70u5CaN37vEaVn8cmNKDipggACCCCAAAIIIIBAzAvsWLFMK175e6Cf9TVu1Ve7ZHNkq2DAjbLaUmS1WTTnTj7NHfMTSQfDFmg+t2HNm6/I5/W2qNdj4BD5LReoqjT0pcCqnntHyZHCqp6wsSnYrgIEDe3KSWNhCBA0hIFEkaQQIGiIYppL9+/VO7/7ZUjNax/8T2Xnd4+iNaoggAACCCCAAAIIIIBALAvUlJfpjV89Ko/LKVeDRzUVTlksVuX3v0aOtJ6Brp9/9SANHMe/B2J5HulbdAJlxfv1yfy/qLaivEUDKemZsqZMk8/X+Oeg+eozvJum3zCUc0qiI6fWWQoQNJwlINUjFiBoiJiMCgkqQNAQxcRWHinRG78MPfz5in99UPl9+kbRGlUQQAABBBBAAAEEEEAgVgXMp7rNauaSXdvlcfsChz+b38sqOE9ZBecGuj1qWm+Nm9MvVodAvxA4awFzOPTyF59rdSslr8cvWccoLeeckHMLR0/vrbGz+XNx1vg0ELEAQUPEZFQ4SwGChrMEpHrCCBA0RDGVtZUVevl/Hg6peckDX1XPgYOjaI0qCCCAAAIIIIAAAgggEKsCzVsm+bx+VZXVy3x3pHZX/oDrAqsa+o3M09TrhvDJ7VidQPrVbgLBrZTeeEU+X+hWSu4Gr1zOfOX0ulA2e0bwnlOvH6L+o/LbrQ80hEA4AgQN4ShRpj0FCBraU5O24lmAoCGK2XPW1WnBI98NqXnhfQ+oz/BRUbRGFQQQQAABBBBAAAEEEIhFgeYtk9xOp2rKG+R2eRu3TBpwgxyp+crrnaG5d42U3cFe9LE4f/SpYwROt5VSQ41bDXU25fScptSswYHVDXaHVRd+YZS6FZ4IHzqmV7SKwAkBggaehs4WIGjobHHuF6sCBA1RzIw5CGv+f34jpObMO+5V0bhzomiNKggggAACCCCAAAIIIBBrAidvmVRX7ZJ5E9VcWQWTlVUwUenZDs27Z7QyclJirev0B4EOFzjdVko1lU656j1Kyxqo7J4zAqsbMnJTdNG9o5WW6ejwfnEDBIwAQQPPQWcLEDR0tjj3i1UBgoYoZ2b+978pn8cTrD31pts1ZNKUKFujGgIIIIAAAggggAACCHS2QK3To02HqrTzaI2cHq9S7TYN7ZmlMX1ydOjzlVrxyt/lavCqpqIh0LXmLZNsdltgJUNB36zO7jL3QyBmBEwYt235Yn325qvBrZT8fqm6rD5wnonVlqrsHlOVlj1MPYtyNOv24bLZrDHTfzqSuAIEDYk7t7E6MoKGWJ0Z+tXZAgQNUYqbrZPMFkrN13lX36CR02ZF2RrVEEAAAQQQQAABBBBAoDMEvD6/3t9yRM8s36elu0pl3hg99cry1urmqo9UkCL5azyBw59P3jLp3EuLNHRSz87oLvdAIOYFzFZKS557WtVlpYG+Bs4zKa2Xz9f4hys1s79yes7U8CmDNOmygRGN50xhYGaqPaK2KJw8AgQNyTPXsTJSgoZYmQn60dUCBA1RzsDLj/6XaivKg7XPueRKjZ17cZStUQ0BBBBAAAEEEEAAAQQ6WmD7kWp9c8E6rS8+fvpb+f2aXbZYvZxHleuzyGGxKMVmVXb3xi2TisYW6PyrBwX2n+dCoC0BE1IF0iyPR35v4wHKFptNsttlsSbOp/s9LpfWv/+2tiz5KBDMeVxeVZc3BIM8q9WhrO4XaMatl2ropMIzsoUTBpo/ftOHdNfdU4t00ahC2az8eWzrWUym1wkakmm2Y2Os/A0UG/NAL7pegKAhyjl4/Zf/o+NHDgdrj5lzkSZeelWUrVENAQQQQAABBBBAAAEEOlLg5bXF+tYL6+X2trKE4aQbD6ndpUmVa5Xlsyi16fd9tnzl979WAwflad49ozj8uSMnqovaNm+O++vq5D1+PPDlq6mRr65Ovvp6+eob5KuvC7ze+LP5vRO/9jud8vt8kteECT75vR4p8N0rNYULrQ7Lam0KHWyy2Oyy2O2S/cTPFodD1oyMxq/M5u+ZTb9u+h54LTPwZcvLkzU9vYsEG29bun+vlr/4nI4fLZGzzqPa486Q/qRm9tVlX7pPReOKWu1nWGHgKTXH98vVYzdP0PDC7C4dOzePHQGChtiZi2TpCUFDssw042xLgKChLaHTvP7Ob3+p0gN7g6+OmDpTk6+5McrWqIYAAggggAACCCCAAAIdJWBChq8/v67N5jM9tbr06HvK8nmU6W9+28CquuzL5LTlatKtQ3XzrEFttkOB2BEwAYC3rEyeY8fkKa+Qt7JS3uOVge++qqrGX1c2hgt+d+OB3/F8WdLTZM/Lly0/X7a8brIHvpuvPNnz8wLfzWvW1OYYrf1H6/W4tWHRe9r00XuBoKGhNtTVZndoxq03avy8uSGrOsINA1vrscNm0aM3jdf1E/u1/4BoMe4ECBribsrivsMEDXE/hQygnQQIGqKEfP9Pv1PJzm3B2uYgaHMgNBcCCCCAAAIIIIAAAgjEjsC2kmpd9evFba5kMHu8mC2T+jZtmdQ8AlfaBLnSxujDdLeOpPr15ldn8snp2Jle+T0eeUyQcPSYPMeOynOsVJ6j5rv59bHAa2dcVRBDY+nMrpjAwdG7t+y9CuXo1VuOPubnXnL06tVuqyLKDxVr2YL5OrBpt9yuxm2jmi+73aqBE0bovKuvU8+BgxVuGNiW0eO3TkjqsKHeU69t5du0qWyTNpdt1v1j79fgboPbYku41wkaEm5KY35ABA0xP0V0sJMECBqihP74//1ZBzatD9YeMHaCZt15X5StUQ0BBBBAAAEEEEAAAQTaW8Ds9X79b5ee+UyGppuaLZMmV64NnMvQvHO+2TKpLusSbUz1aW1q4xulZpuWl78ynT3h23uyztCe2dbIrDxwHzwk98GDJ74OHZKntFQy2xZxtZuArVs32Xub0KG3HL17KWXAAKUMGiRbQUHEZ5P4vF6te/89LVvwqrweT0gfU9LsyspLVebAkfqvXXmqsGad9RjMyoZkCgMbPA3aVrFNm0o3BcKF3ZW75dOJPw93jrpT1wy55qxd460BgoZ4m7H47y9BQ/zPISNoHwGChigdl/79We1ZuypYu8/wUbrwvgeibI1qCCCAAAIIIIAAAggg0N4CCzeV6IFn1rTZbPOWSflerxzB0o1bJh1KydH76W4Fd1KS9Ie7J+mSMb3abJcCkQmYbY48JSVyHSgODRQOHgycl9Dpl8US+HS/JSNd1vSMwM+Br4x0WQI/m3MT0mVJTZPFbpOaz1ywmXMWGr9ktTW9Zn5tlSwW+T3m7AZPYDWG+TlwpsPJP3u9jb/vdDaeE2G+amtb/Gxe78zLmpWllIEDm76KGr/36ydzlkRb14Ete/TWr/8kZ92RkKLp2Sk6WOdUrdunXRmDtSl7tJy2s9vWKZHDQKfXqe3l2wOhgvnaVblLXn/oapGTgSf2nKjvnP+dtqYn4V4naEi4KY35ARE0xPwU0cFOEiBoiBJ65asvaPunS4K1exQN0qVf+rcoW6MaAggggAACCCCAAAIItLfAXU+u0JKdpWdu1u/XnLJPNLDhmNJPShPMlkmV6aP1ZoZLDc1LHJpamjmsu565f0p7dzep2vM5nXLv3y/nnj1y7d0n1969cu3bJ39DQ4c6mDfLbbm5jV/dusnWrfFna26u7N26Bb7bcrvJlpMtS1paxJ/g79DOn9K4CSqaQwhvVbW8FRXyVpTLE/heIa85k8L8urxCvurqjumazRYIG1IGFgVWPaQOG6bUwYNlSUlpcb/irWV670+vqKZ0tfz+xtUNPp9fx3xeuZvepfNY7dqSNVLbsobJZ7FF3edECQNdXpe2V2wPrFjYXL5ZOyt2ytNkFw5Omi1Nf770z7JZo7cM5z6xVoagIdZmJPH7Q9CQ+HPMCMMTIGgIz6lFqc/efl2bP/kg+Pt5vfvqyq8+GGVrVEMAAQQQQAABBBBAAIH2FKh1ejT2oYXm6IUzXmbLpKkVa5V9UshgtkyqybpE72Z6dMzWsgGLRdr40KXKTLW3Z5cTti1vVZVce/Y0hQp75dqzV+5Dh9p/yyOrVfaCAtl79Gj86tlT9p4n/ZyfH9an7xNxIvwulzzm4OuKisB5Fu6Sw/IcLpG7pESeksPyHq9qt2Fb7HalDBmitJEjlTpyhNJGjAiEOebavOSQ1n2wTTVlq1VftUMuj1cev1+VVv9JG/5I9bZ0bcgZq73pAwKrQCK94jUMdHvd2l7ZFCyUbdaOyh3y+EK3nArHItORqVH5ozS6YLQuKrpIqWe5SiSce8ZSGYKGWJqN5OhL5H9LJYcLo0w+AYKGKOd8w6J3te69t4K1s/ILdN2D34+yNaohgAACCCCAAAIIIIBAewqs3FOuW55YfsYm0711uurIuyrwenXiTYLGLZM+zcjStpTTb0my4EtTNXlgfnt2OSHa8rvdgZUJzh071LB9u5zbdwS2Q2q3y26To3cfOfqar76NBxqbQMEEC+YMAbNdEVfEAmZrJhM6uA+XyHOkRO5Dh+U+fFju4uLAtk1ne5nDplNHjFTqiBFaeyBfR0o8cjaU6cC+xbJ5SmTeSq+y+nVqrFfpyNW63PE6kloYURfiJQw0wYIJE5oPb95RsUNunzuisZrC6fb0QLAwpvsYjSkYo6KcIlktpyzFirjV+K1A0BC/cxevPSdoiNeZo9/tLUDQEKXoliUfac2brwRrp2Vl66bvPRJla1RDAAEEEEAAAQQQQACB9hSYv2K/vvvyhjM2ObNsmUbVHdbJb02bLZO2ZY3UkjSPTkofWrTzkxvG6fbzB7Rnl+OyLU9ZmZyBQGF7IFhw7dotEzac7WW2OAoECU2BQkrg576yFxYSJpwtbgT1Awdxl5YGtrZymu2tAl/7zio88lgcWpd3qWpT81XSIDlVLodznVz+46q1tL4E6UhqT23NGiHzPdwVDrEYBpoQwZyrENgKqWxz4CDnaIIFsyXSqIJRgVDBrFoYlDvo/7N3HtBRXVfbfudOn1GX6EUIhIQkik23AVONAReMjbtJ4pI4cZzqdCd/nC9fepzE+VJtJ+4FbLCNsU0xvRgMmKoGSAgQIIS6Rpo+869zhUa6ICFNk0aj93hpCY/O3mef51yVue/de/dqYeHyS5pCgx/f5JwaEgIUGkKCkU6igACFhgAP8cTe3di96i2ftVqrw33/8/sAvdGMBEiABEiABEiABEiABEgglARe3HkSv/ggr12Xg6xnMb9yNwyt7muKkknn4ubhY7Mbrg7uGvz81mw8NC0tlCFHvC/RrFn0VbDl5cGamytnK7irqoKLW5JkAUGXNgx60Vw4LQ26oUPl/gkckUtA9IZwnD4tl8QSGSzy55JTcoPrzgyLOh6fm2bD7mn6RnOqJNhwAfXOI7BJbnjaKZckMhyE4HDGOBjeDp7YjwQxUJQ9EsKCEBVE1kJhVSEcHkdnECnmCGFhVNIoWVQQ4oIQFnpb3wV/oFFo8IcW54aCAIWGUFCkj2ggQKEhwFMsOfQ5drz1isL6gV/9CSqp96YnBoiSZiRAAiRAAiRAAiRAAiQQcgJXy2jQeJxYcmE9UlytGw+rUBdzE1bHx6Be6qCxA4BIuIkZcmiXORTCgriJbDuaK4sL4sNjsQS8rGiurEsVTYOHQTdsGPRpadAOHQqpjcbBAS9Cw24jIPpA2IuLYSsogL2gELbCAnjq2m9CfcI9ECfjLmuq7rZDW/khqsxu2DQ6WDU6OCUNvJelFzWqTTgWk45iUxpckrbNPXeHGCiEheLa4iZhoSJXzliwu+1+n4noqZCZmCmXQhLiwvD44dBI7AnTWZAUGjpLivNCRYBCQ6hI0k9PJ0ChIcATPFuQh80vP6ewvufnv4XWYAjQI81IgARIgARIgARIgARIgARCReBqPRom1xzC+PoTiluXTv0ofJI4BiVaT6dCiMSyLJ0K/CqTZGHh5MkmUUGIC/n5gdfnl6QmMSFjJAwZGdCnp0MzYAAfzAr2kHqQvSi75Dp/HraCQtgLC+TPoudD86hqcCBPNw7WhFGKXamd9Yg/8wHqTW40mCR4VJIsODRqDLL40Fp0cEhaFJlH4Lg5HTa18r14V4iBbo8bJ2tPIq+qSVgoqCqAzd1awOzcgekkHTKTMn2lkEYkjKCw0Dl0bc6i0BAEPJoGRIBCQ0DYaBSFBCg0BHioF4pPYMPzf1NY3/Gjp2GKZ4pvgEhpRgIkQAIkQAIkQAIkQAIhI9Bgd2H00+vgvSw5IdFejTvLN0Pbqu2sV2XCkeQF2Gns3K2CntJotjMwnRfKYT10ENZDh2A7fASiJE4gQ52YCH1mBvQjm4QF3YgRkPT6QFzRJooJuOvrYS8shPXIUZzdtRfFR0+geshCOA19FLvWW84g/txGuNRAvVmNRqMErwrwqlSwqvWwavXy5+YSS0KMKDGlotCcgXptrOwrHGKgx+tpEhYulULKr8wPSFjQSlpFxoIQFsRrHKEhQKEhNBzppfMEOvfXQ+f9cSYJ9FQCFBoCPLmqc6X46P/+qLC+7bs/QVyfvgF6pBkJkAAJkAAJkAAJkAAJkEAoCTz4wh7sOFHhc6nyenDXhc1IdtYolqmIuQHvJvTpsC9Ds9GMkSl49ZHLSr6EMvAw+vJYrbDl5sJ6sElccJ477/9ql7IVDNnZMAhxISMD6uRkqNqpq+//ArToDQSEGDjtx+9gdOU5TJAGwOgBJG9LRlFMxecwVx2WUbglwGKSYDGrcamtgyw62NSivJIejRohOjSVMT5v6I9i83Cs+80XEWPUBYVSCAsldSW+Ukj5Vfmwuqx++xQiwsjEkb6MhZEJI6FVU1jwG2QnDSg0dBIUp4WMAIWGkKGkox5OgEJDgAdYV1GO1c/8WmG98OvfRfLgoQF6pBkJkAAJkAAJkAAJkAAJkEAoCazLLcNjr+73uZxYewKT6g4plnBqh2BlnymoVnfcl6HZ8LllEzA/p38oQw2bL7kcUkkJrAcPyeKCqJsPl9u/9SQJ+uFpMOTkNH2MGgXJbPbPB2eTQBsEmsXA/i4V5lm10LtdMLgcMLjt0LudSChdD33jOZ+lEBkajBLqzRLcauWtvaaeDkJ0MMBk1CFn+ECkT7oO6ZOmdrrygBAWTtedlhs3iw9RCqnB2eD32Yl+CkJMaO6xkJGQQWHBb4qBG1BoCJwdLQMjQKEhMG60ij4CFBoCPNPGulqs+s3PFdY3fvkJ9BueHqBHmpEACZAACZAACZAACZAACYSSgNvjxZJ/7MTh0lrEuxpxT9kGqL2uVktosSPlJhwxdr7Ez9jB8Xj38WlQS5F7W0FkLVgPHUbj/n2w7v8c7tpa/7AKYWHEiFbCQiYkk8k/H5xNAp0g0FoMzHGoMd7e0vBYZCCpXTY4K3YgpzwfplbN24UsaDWo5LJKDu2V34vaGDMMifFQm81yX5BBo7IxcvI0DMwYpegTIoSFM/VnFBkLFqf/Dc81Kg3SE9ORnZQtiwsZiRnQqYPLpugEPk5phwCFBl4aXU0gcv8i6GoSXK+3E6DQEOAV4LTbsPzpHymsZ33hUQzOGh2gR5qRAAmQAAmQAAmQAAmQAAmEmsCxC/W4+dntuPP8p0h0nFW4P28ej/cS06DoCn2VALRqFT785gxk9GuqAR9Jw1VZicZ9+9G4dy9sR4/C63T6FZ52yBAYx42TPwxZoyAZjX7ZczIJBEKgtRgo2qbMsmkwRDRmaDUqJQ/WG+1Iqz2LcRUnMPbiCcQ7mrIMhOBg1wnBQYJN31Q6SVKpoNc2/VuIDFJMDKTYWEgGg5zZkDxmFOpT9Thma+q1UO+s9zt0tUoN0VchJzlH/hBlkQwaZTNqv53SIGQEKDSEDCUddZIAhYZOguK0qCdAoSHAIxYpyK8/9V2F9fR7lmHYNRMC9EgzEiABEiABEiABEiABEiCBcBB48Z9rUL5jlcK1Q52E1/rNgv2y8itXW//P94zDkmsHhyNEv316vV44iot94oLj5Em/fIibr7KwcE2TuKBJTvbLnpNJIFQEZDHwr9vhdHuh9QKLGnWIa27EcGmRY1o39hiaspFEpkNq3QWMrTiBay6eQLKtKWPHqRF9HNTwxmt9fRzE6+J7RWQuuDUqNOpVsOpV8r/r+0moGqpBQ4oEdNBfRIIkCwvZyS0ZC0YNxbhQXQOh9kOhIdRE6a8jAhQaOiLEr/cWAhQagjjpt37+Q7gcdp+HKUvuxsjJ1wfhkaYkQAIkQAIkQAIkQAIkQAKhJFB5tgbLn34adrsFDndzo1kVNqTMxgljYqeWEpkMv186tttFBq/LJTdybti9Ry6L5K6s6lT88iS1Wm7c3CQuXAPd8OGKEjKdd8SZJBB6Au8eKMV3ljf1T0lwq7CwUQvNZalGOw1OFGtbmkXLk71eDGyowLiLQnQ4jkl6KwwG4JzXjnMqLxokFbxy3oNyOLUSbEY1HHoJthgJVaka1AxWw3OpDJMQFtLi0+QySCJjITMpExQWQn/u4fJIoSFcZOm3PQIUGnhtkEATAQoNQVwJ7/zqZ7BZWtIsJyxajKwZs4PwSFMSIAESIAESIAESIAESIIFQEXA7PXjrF/9GZennzfckZbGhxJiOD5PHdmoZ0ZPhj3eN67ZySaIEkvXIUTR8uguNn+2Fx9L5+vHq+HgYJ4yHacJEGMeOYZ+FTp04J3UXASE2/OCdw3JmwzCnhBk2rSIUN7z4yOREjaJxuxcqTS105rOYO86B2OpCDDh4FumF9TA2umGT1KjVGVGn08PTRtaCV6WC3SDBatRAbTYiMWsEcq6fg/GjpsGkZV+S7roWgl2XQkOwBGnvLwEKDf4S4/xoJUChIYiTff+Pv0J95UWfh7HzFmDs3AVBeKQpCZAACZAACZAACZAACZBAqAhsX74HBz/+j+KJZkNMHIY8/A28ceACdpyoEA9EXzHE/cjp6SlYNjUVc7P6dXnjZ6/DAeuhQ2j4dLfcc8HT2NhpJNqhQ2CaOBGmiZOgH5nOrIVOk+PESCAgyih97+1DcgP3STYNRjmV/RrqJA8+irsIr/EsJGMp1IazMBltGJJo8vVlEPuQ3F4MPt2IjLw0zomPAAAgAElEQVQ6pBY3QOUG6rR61OoNsKmbGk6roIKkkpo+xH8mI9Rx8ZDMJvRNG4GMqdMxdPQ4SGplDJHAiTFcnQCFBl4hXU2AQkNXE+d6kUqAQkMQJ/PhX/+A6vMtDeWyZ8zG+EWLg/BIUxIgARIgARIgARIgARIggVAQOJNfiTXPPgOnreXBIJVKhQVf+ypGTh4vL9FgdyH3XB2KLlpgc7ph0KqR3jcG2QPiYNY33YzsquGx22E9cAANu3ejcd8+eK22zi0tSiLlZPvEBW2/vp2z4ywSiFACokH0xvwLeG1XCcxH6pDi8QIqB1SSEyrJgTOxRdg9YDtijBokm3WIM2iv2tBdb3Nj+HELrj3uwaALTrg0BlRqdbgoeeBug4FKo4EUFwd1bBxMiYlyeeT0ydfBFBcfocQY1uUEKDTwmuhqAhQaupo414tUAhQagjiZ9f/+K8pLin0exB8gok8DBwmQAAmQAAmQAAmQAAmQQPcRaKxzYOXv3kT12R2KINKuuQa3fvvr3RfYZSuLngsic8GyfTsa9+6D19Y5cUFlNMjlkEyTJsF07TWQzOaI2RMDIYFgCZQ3liOvMg+5lbnIP3MCfXddC8mlhdxqQQVIUKE0/SAqB129AfrQ2KFNzZuTc5CVnIVYXSyc587Bsm0bLFu2wnqxHGUqD86q3GhUtZ3aJL63RJaD2mTCsGvGY9y8hYhJYuP0YM843PYUGsJNmP4vJ0ChgdcECTQRoNAQxJWw6aV/41xhvs9D2rUTMe3uB4PwSFMSIAESIAESIAESIAESIIFgCHg9Xmx4cR+O7XwRHo/T58oUa8J9v3wa5oTONYAOJoar2Xo9HtgLC2HZvgMNu3bBU9/S8+1qduKGpxAWzFOnyA2dVTpduEKkXxLoUgIV1oomYaEiV/5cbi1XrB9X1Q8jcqcpXvOqPDg2bgsaY2t8rw+OGexr3jwqaRTi9e1nIIjvQ5FBVPfxWjQePIBqrwelkhsVqsuaTV/yLr7fRM8TbUIiRk2fidGz5kFnZA+HLr1Q/FiMQoMfsDg1JAQoNIQEI51EAQEKDUEc4vY3XsKpIwdb/rDJGo1ZX3g0CI80JQESIAESIAESIAESIAESCIZAwe7z2Pbai7BZWp52VmskzFx2D0bPnBOM66BsHadOwbJtOxp27ICroqJTvqSYGJimTIZ56nUwjhkNlVbZHLdTTjiJBCKMQKW1Us5WaM5aEBkMHY0BJdnof2aUYppkdiPxZitGD8iWMxeuJixczb+zrAz169ahftNmNFjqcU7lxjmVB842shxUag3UCfEw9uuPsfMXyWWV1JquLbPWESt+HaDQwKugqwlQaOhq4lwvUglQaAjiZD5d+SaK9u3xeeg/IgPzHn08CI80JQESIAESIAESIAESIAESCJRAzYVGrPnbWlSd+djnQjR2HpI9Arc9+X1IUtc2dXVeKJeFBcuO7XCePtOpbanj42CaPAXm66+DITsbol48Bwn0ZALVtuoWYaEiF2WNZf5vx6vCuPybkFDfDwaNEUaNEWqVGgNHJmDa0nSI/ivBDtEnpWHnTjnLwVZchAuXyirVtSU4SBKk+HgkpA3H+FvuwJCcMSGJIdg90L6JAIUGXgldTSD4n0BdHTHXI4HwEKDQEATXfR+sQsGubT4PyYOHYuHXvxuER5qSAAmQAAmQAAmQAAmQAAkEQsDt8mD9fw/j5N7X4HLW+lyY4/W4/fs/RPLgIYG49dvGY7Wi4dPdsGzeDFteXqfsRVkk83VTYZ4+HYacHKgkqVN2nEQCkUigxlbjy1YQWQvnGs4FFOYA8wA5U6H5w+iKwYb/5MLW4FL4GztnMEZNHRDQGm0Zeb1e2I8dR/26tWjYuQvVLgeKJDdq2yqrpFLJTaP75YzBpDvvQcrQYSGLg44CJ0ChIXB2tAyMAIWGwLjRKvoIUGgI4kwPrv8IRzev93mI79sft37nR0F4pCkJkAAJkAAJkAAJkAAJkEAgBA5vPoN9az6GpXKvz1yrU2PConmYfPvSQFx22kbUe7fl5cviQsPu3Z1q6izKIJkmTYR5+gy5oTN7LnQaNydGGIFae61CWDhrORtQhP1M/XzNm4W4kGy8suly+ak6bH2jEN5WvZtFMsOsB0ahz9DYgNa9mpG7pgb1Gzeids2HKKuvRpHKDWtbjaOhgjomBsMmT8XEux9gw+iQn4R/Dik0+MeLs4MnQKEheIb0EB0EKDQEcY65WzfiwNoPfB5M8Qm440dPB+GRpiRAAiRAAiRAAiRAAiRAAv4SuHimHhtf3IeLJ1fA62162lmUUumbmoIlP/xp2Jq2itJIli1b5A9Xecd15iFJMI4dC/OM6TBPngzJxGay/p4153c/gTpHHfIr8+VySKKBc6mlNKCg+hr7IjslGznJObLAkGJM6ZSfvJ3ncHSrUswwxmpx48M5MJjD08fE43DAsnkLqt97D6cunkeJyt1mDwexAY3ZjKzZN2LiA1+CpO7acm2dAtgLJlFo6AWHHGFbpNAQYQfCcLqNAIWGINAXfrode1ev9HnQGU24+//9OgiPNCUBEiABEiABEiABEiABEvCHgNPhxvoXcnE270PYLMU+U3OCHjMfXIb0iVP8cdfh3EBKI+kzMhAjxIXrr4c6IaHDNTiBBCKJgMVhkTMWmps3n64/HVB4QkgQokKzsNDH1CcgP16PF9vfPo6yopYSacJRv2FxuOHeDKik8N3y87rdcmm0ipXv4PiZkyiV3PC0s4s+Kf0w+xvfQ2yWsol1QJumkV8EKDT4hYuTQ0AgfD91QhAcXZBAFxKg0BAE7OLP92LX26/7PIhaqvf/7zNsAhUEU5qSAAmQAAmQAAmQAAmQgD8E9n1UgvxdB1Fd+qHPTGfQIHXMSCz42rdD0u9A1Gx3nDiBug0b5JrtXputwxA1KSmImT0LMTNnQjsgdPXjO1yYE0ggSAJCWMivym8RFupOw4tWtYo66T/JkOQTFnJSctDH2Cdk75XtVpfcr6GxzqGIJmfGQOTMGNTJCAOfJn4mWA8cxPm3lyP/RL7cOLqtYfaqcN34KRiw7IvQDR4c+IK09IsAhQa/cHFyCAhQaAgBRLqICgIUGoI4xtNHD2Hb6y8qPNz3yz9ArQlPumYQodKUBEiABEiABEiABEiABKKOwLnjNdi+ogCVp1bC5aiR9ydJKsT3MWHRN76L5MFDg9qz29KAhu3bUL/hEzhOnerQl+izIJo6x8yezabOHdLihEgh0OhsVGQsnKo7FZCwkGhI9GUriKwF0XNBlDAL16g8a8Hm1wrgcbeIIGK5GfdkoP/w+HAte4VfW2EhTr3xOo7mHUJNG4KDzqvCOOgwYMZMJNxzD7T9+nZZbL11IQoNvfXku2/f4ftJ13174sokEAgBCg2BULtkc/54ITb+958KD3f97FfQm8xBeKUpCZAACZAACZAACZAACZBARwRsDU6sf+EoKks/R/3F3b7psYkGZN8wA1OW3N2Riza/Lp5Uth87hvr1G9Cwaxe8DuUT020ZGbKzZHHBPHUq+y4ERJ1GXUlACAsFVQW+jIWS2hJ42i0A1H5kCfoEhbDQ39w/rMJCW5Ec21uGgxvOKL6kN2nkfg2mOF1XYoX99GkceeFfOFxw9AqhRnRqyHFrkaLRInbuPCQsvROapKQuja83LUahoTeddmTslUJDZJwDo+h+AhQagjiDi6dOYt2/nlV4uP37P0NMUnIQXmlKAiRAAiRAAiRAAiRAAiRwNQJCDPh0VRFO5ZaismQFPB6nPF3cYEzsn4jbnvwJDOYYvyC6LRZYtmxF/SefwHlGeeOyLUfqlGTEzpqFmFmzWBrJL9Kc3NUErC4rCqsKfc2bT9aeDEhYiNfFQ5RAEo2bRcbCAPOALhcWLmfX/LOgtLBa8aWUwTGY9UAmJLXU1bhxavtWbHvpOTjq6xRrixuRIz0aDPaqodJqEbdwAeKXLIE6Lq7LY4z2BSk0RPsJR97+KDRE3pkwou4hQKEhCO41Zeex5tnfKTzc8q0fIqE/a7AGgZWmJEACJEACJEACJEACJHBVAiVHKvDZBydRe34TrPUn5LlqtYS4PgZMuf0uZEyd3imCcvZCYSHq169Hw65P4XU2CRbtDo0a5smTETtvHgxjxoSk/0OnAuUkEvCDQLOw0Ny8ubimOCBhIU4Xh6zkLF+fhUExg7pdWGgLg8Pmwicv5sFSbVd8OXNqf4ybM8QPcqGbWnXuLDb981nUnzkFT2OjwvEQjxrpQmwQ/xkNiL/1NsQvvg2SwRC6AHq5JwoNvfwC6IbtU2joBuhcMiIJUGgI4lgsVZV47w+/VHi46avfQp/UtCC80pQESIAESIAESIAESIAESKA9Ag21dqx/IRcN1aWoKv1Anibe4McmG9AndSgWPfFkhwKAx2aDZft21K9dB0dJSYewNQP6y+KCyGBQJyR0OJ8TSKArCdjddjljoVlYKKopgtvr9juEWG2snK2QndKUsTA4ZnBECgttbazmQiM2vpwHt0vZtHr6XekYODLRbxahMGisrcHml59H5ckiuCur4LFZfW77eCVkezQQcoMY6uQkJD3wAMwzZnT48ysUsUW7DwoN0X7Ckbc/Cg2RdyaMqHsIUGgIgrutwYJ3/venCg9zH/4aBozMDMIrTUmABEiABEiABEiABEiABNoi4PV4sfWtY7hwsgaVp1fBZa+SpxlitDDF6tDRQz/Oc+dQt249LJs2XfGU8eXrqTQamKZOReyNN8KQk91jbrjyyol+Ag634wphweV1+b3xGG1Mk7BwqRTS4NjBkFRdX2rI78DbMSg+cBH7PlYKhzqDWu7XYE7Qh2oZv/w47TZsf+NlnDuWL//McVdVwWNvyryI86ow1qOF7pLYIF7TjxyJpIcegiEzw691OFlJgEIDr4iuJkChoauJc71IJUChIYiTcbucePNn31d4uOGBhzB09LggvNKUBEiABEiABEiABEiABEigLQLNjV8bq4+i7uIueYpGIyE2xYgREybh+rseuMLM6/Ggcd8+1K9dC+uhwx2C1Q4aJIsLMTNvYO30DmlxQlcQcLqdOFZ9TO6xILIWjtcch8vjv7Bg1pqRlZTlExaGxg3t0cLC5exFKTRRUu3U0UrFl5IGmDF72SioNd0jong8bux9fyWOf9b0M8vT0ABXVZXcaN7gVWGcRwMzlLGZZ0xH0oMPQpOS0hWXWNStQaEh6o404jdEoSHij4gBdhEBCg1BgBZ/yLzx0ych3rw0D/HmZvj4SUF4pSkJkAAJkAAJkAAJkAAJkMDlBOoqrNjw31w4bI2oLFkOj8cBlQqISzbCEGPCbd97CsaYWJ+Zu7YW9Z9slPsvuCoqrgpUzl64biri5s+HPiuL2Qu8/LqVgBAWhJjgExaqj8N5qeG5P4GZNCa5x0JzxkJqXGpUCQttsXA53PjkpXyInxetR/qEvhh/U6o/+EI6V9w7yNu2CQfWNpV7gxdwW+rlDAe1043xHg1iLhMbVDod4m+/HfG3L4ak756MjJBC6EJnFBq6EDaXkglQaOCFQAJNBCg0BHklrPifn8BhbWnuNOm2pci8rnPN54JcmuYkQAIkQAIkQAIkQAIk0CsIeNwebHqlAFXnG1BbthXWukJ536JckiibNOGWJciaNlN+zV58EnUffoiGHTvgdV39qW91SrIsLsTOncveC73iSorMTQoRQfRVyK1oylgorC4MSFgwqA2KUkjD4odFvbDQ1onWXrRi40t5cDlbHggU86bePhxDs5O79SI4deQgdq54DZ5LP5vEQ4vumloYamox0aX29WxoHaTcv2HZMpinT6cI2snTo9DQSVCcFjICFBpChpKOejgBCg1BHuCq3z4N0eSpeVy74FbkzJwbpFeakwAJkAAJkAAJkAAJkAAJNBPI23kOR7eehcN6AVVn3pdf1ujUiEsyIL7/ACx64ruw7T+AujVrYMvL6xCcYewYxC1YCNPECVCp1R3O5wQSCCUBISwU1xT7mjeLRs4Oj8PvJYSwMCppFHJScpCdlI20+DSoJV7PAmTJkQq5jFLrodFJcr+G2CSD36xDaXDx1ElseeUF2BsbfG6FKDpEpUVq0Zl2l9JnZCD54YfkPg4cVydAoYFXSFcToNDQ1cS5XqQSoNAQ5Ml88Offora8zOdl9Oz5uGb+oiC90pwESIAESIAESIAESIAESEAQEE8ni5JJbpcHVWfeg9N2UX6qNy7FCEny4vqsa6Hbsxeu8vKrApNMJsTMno24m+ZD9GHgIIGuIiD6KRTXFisyFuzupobA/gwhLGQmZfpKIQlhQSNp/HHRq+aKxtCiQXTrEd/XiHlfzIZa2z39Gppjqau4iLV//xMcNmWJp+lzF0G3YSPsJ4raPiuVCrHz5yPpgfshmc296jz92SyFBn9ocW4oCFBoCAVF+ogGAhQagjzFj//+J1SWnvZ5GXX9DZh46x1BeqU5CZAACZAACZAACZAACZCAx+PFppfz5ZJJjTX5qCvfLkMxmdXQOCxIsViR47j623tdaipiF9yEmBtugGTo3ieZeaK9g4Db48bJ2pO+HgsFVQWwuW1+b14n6WRhISc5RxYXRiSMoLDgB0UhTm58OR81F1pKHQvztHEpmHRzmh+ewjO15NDn2PHWKwrnhphY3PzN78O1/3NUv/Y63NXVbS6uTkxE8iMPwzR1KssptUGIQkN4rll6bZ8AhQZeHSTQRIBCQ5BXwicv/B1lRcd9XkZMnILr7rwvSK80JwESIAESIAESIAESIAESKNh9Hoc3lcLjtqOi5C14nFaoPU4YHNUQBWKmuHUwtNWCUZJgmjwJcYsWwZCdzRtxvJTCSkAICyV1JT5hIb8yPyBhQStp5VJIzc2bhycMh3iNI3AC9VU2fPJiHpx2t8LJpFvSkDY2JXDHIbIU/RpOHtin8DY4ewxmPvgwvDYbat97D7Xvr4bX6WxzReOE8Uh+9FFo+/YNUUTR4YZCQ3ScY0/aBYWGnnRajDWcBCg0BElX1FYszT/q85I65hrMuP9LQXqlOQmQAAmQAAmQAAmQAAn0bgJ1FZdKJjk9qC3djMbafKi8bhjddZDgwQiPBqleZT16yWhEzLy5iFu4CNp+vPHWu6+g8O3e4/U0CQuXmjfnV+XD6lKWwOnM6kJEyEjM8AkL6Qnp0KopLHSGnT9zSguqsGuVshSRWiNh3kNZiO9j8sdVyOeK0kkfPvt7NNQoMxem3nEv0idNlddzlpej+pVX0PDp7jbXV+n1SLzvXllYZc+ZJkQUGkJ+qdJhBwQoNPASIYEmAhQagrwSdi5/FScP7vd5GZiZhTlfeixIrzQnARIgARIgARIgARIggd5LwOvxYuNLR3GxsAz2ylLUNmwB4IXe0wit1w6TV4XJHi2kS9kMmgH9EX/zzYiZNQtCbOAggVASEMLC6brTcsaC+BClkBqcLY18O7uW6KcwMmFkU/Pm5GxkJGRQWOgsvCDnHdhwGsf3XlB4iU02YN5D2dDqureBdnlJMTY893/wer2++DQ6HRZ943uIS2kRTBv370fl8y/AdVHZd6LZSJeWhpSvPgZ9enqQtHq+OYWGnn+GPW0HFBp62okx3nARoNAQJNk9767A8c92+bz0TRuB+V/5RpBeaU4CJEACJEACJEACJEACvZOAq7ISh1/ajLwCDzxuNyyOvXC6K6D2umD01MtQxrm1SIYEw9gxssBgHD8eKql7m7v2ztOKzl0LYeFM/RlFxoLFafF7sxqVBumJ6b4eCyJ7QafW+e2HBsETcLs92PJqASrPKQWiIdlJmLp4eLeXVzu47kMc3bJBsdHkwUNx01e/BUndIoR4bDbUrFiB2g/WAB7PlWAkCXELFiDx/vt6tehKoSH47xl68I8AhQb/eHF29BKg0BDk2X7+0fvI277Z5yVxwCC5eRMHCZAACZAACZAACZAACXQ7AbsFKDsMXCwEXHZAowf6ZAL9xwL6mG4Pr3UAjtOn5VrkF3cewAHzXHggweG+AIt9v5y30FQyyY1klQbTZt4oCwyi0TMHCQRLQAgLZ+vP+nos5FXmod7ZJGr5M9QqtdywWTRvFh8jE0fCoGEDcn8YhnNuQ60dG/6bC4dV2a9h/E2pSJ/QvaXWhKi67p9/QeXZMwoEY+bchHE3LrwCi/3kSVT+61+wn1CWhGqeqE5OQvIjj8I8ZXI4kUasbwoNEXs0URsYhYaoPVpuzE8CFBr8BHb59MMb1+LwJ2t9L8cm98Hi7z0VpFeakwAJkAAJkAAJkAAJkECABDxuoPBjYO/zQPFWueTQlUMFDJ8JTPoykLkQkLqndIgoFWI7elQWGKwHDsiRHo2ZhVpNCrxeD2pt2+HxNkDvsUKnckKdEI9bv/dTJKWPDBAOzUgAcomaUkupUlhw+C8sSJAgGjY3N2/OTMqEUcPSXZF8jZ07UYMdK44rQpTUKsx+cBSSB3Wv+FpXUY4P//pHuJ0OX3wqlQrzH/sm+qSmXYHV6/Ggfu1aVL3xBrxWW5vYTZMnI/nRR6BJTo7kYwl5bBQaQo6UDjsgQKGBlwgJNBGg0BDklZC/fTP2f/S+z4shJhZLn/plkF5pTgIkQAIkQAIkQAIkQAIBECjPB977GnDuQOeNB14L3P5PoG9W522CnClukDV+9hlqVq2Co6jY5+2cPh3Fxmvk/7c6i2F1FkCt8iA2QQspNg5ZM2Zh4i1Lglyd5r2NgBAWzjWc85VCEhkLtY5avzEIYSEtPk3usSAyFigs+I0wIgyObClF/q7ziliMsTrc+HA2DObubcZ9fM8u7HlvhSI2c2ISbvnWD6DVt50dI8rNVf7nv2jcs+dKvpKEgb//HfRpVwoVEXEYYQqCQkOYwNJtuwQoNPDiIIEmAhQagrwSLv9DQKPT495f/C5IrzQnARIgARIgARIgARIgAT8JHFoOrH4CcLc8DdtpD6Ju/G1/A8bd02mTQCZ6nU5Ytm9H7bvvwXnunMKFTTLjQNx8uKGGx2tHrX07oPYivq8Zaq0EvcmE2578qfyZgwSuRkAIC2UNZb7mzUJYqLHX+A1NCAvD4of5eiyMShoFk5bXn98gI8zA4/Fi25uFKD+lzGLpNywON9ybAZXUfbcMxbW79dUXUJqfq6A2fPxkXH/X/Vcl2fDZZ6h84QW4K6t88+IXL0bSF5ZF2AmEPxwKDeFnzBWUBLrvpwZPggQiiwCFhiDPo+TgfuxY/qrCywO/+hOb0QXJleYkQAIkQAIkQAIkQAJ+EBAiw7tf8cOgnalLnguL2CAamNZv+AS1H6xW3ARrjqKpZNJM1Gr6QG02o8GdB2vDCZhidTDEND1hPPn2u5AxZVrwe6SHqCMgbs5eaLwgZyzkVuYiryoP1bZqv/epggqpcak+YSErOQtmrdlvPzSIfAK2Bic2/DcP1nqlMJt1/QCMmTW4Wzdgs1iw5tnfwWZRCiEz7v8SUsc0ZXy1NzxWK6rfWo66jz6SyyUN+sufIRl6X58QCg3degn3ysUpNPTKY+em2yBAoSHIy6I07yi2vPqCwss9T/+23bTGIJejOQmQAAmQAAmQAAmQAAkoCVzIA56bGVgmw+UsRWbDY9tCVkbJbbHIN7zqPvoYnvr2a+CXmTJwMmW63IPB5a5D5el3odGqEJdshOgEndBvABZ983uQuqmXBC+5yCIghIXyxnKITAUhLIiPKlvLU9z+RKsQFpKyEKPr3jr9/sTOucERqCi1YMvrBfC4lX1spt+VjoEjE4NzHqT12YI8bH75OYUXndEkl1AyxSd06N1eVAQh8BpzcjqcG40TKDRE46lG9p4oNET2+TC6riNAoSFI1heKT2DD839TeLnzJ/8DY2xckJ5pTgIkQAIkQAIkQAIkQAIdEBCNn1+Y619Pho6gip4Nj24MqkG0qBle+8EHchaD19Z2k1IRhspogHb2Iuy+OAJujyQ36a0uXQOn7bwsMoiSSWLMe+Rx9E/P6Chyfj2KCbQWFoTAUGGtCGi3Q2OHKjIWYnWxAfmhUXQQOL7vAg6sP63YjFavxryHshGb1L2ZAJ+9/w6O7d6hiK3/iAzMffirrKDQweVHoSE6vj970i4oNPSk02Ks4SRAoSFIupWlp/Hx3/+k8HLbkz9BXErfID3TnARIgARIgARIgARIgAQ6IJC/Blj+QOgx3fsGMOpmv/06L5Sj9t13Ydm8GV6Xq117dXwc4m6+GTHzb8LONWdx4WSdPNdWX4ya859ANGY1XiqZNCRnLGY++LDfsdCgZxMQQkJzxkJeRR7KreUBbWhwzGBf82ZRCilOxwfCAgIZpUZC3NzzfjFO5ykzYhL6GjHni1nQaNXdtnOXw4GP//YMai9eUMQw5fa7MXLK9d0WV09YmEJDTzil6IqRQkN0nSd3EzgBCg2Bs5Mt6y6WY/Wffq3wsugb30PSwO6t6xjktmhOAiRAAiRAAiRAAiTQEwi8shgo3hL6SIfPBr7wXqf9isbONStXwbJtG+DxtGunSUlB/O2LETNnDiS9HsUHL2LfRyXyfK/HhYpTb0OFBsQmG6FSiaQKNW797o8Rm5TS6Vg4sWcSqLRWKkohiQyGQMagmEG+jIXs5GzE6+MDcUObXkTA5XDjk5fyUVdhVew6dUwyJt+SBpX4YdRNo+pcKdb+/c/wiOy1SyM2OQW3ffcnzGq4yplQaOimC7YXL9t9PyV6MXRuPSIJUGgI8lgaa2uw6rdPK7zc+OUn0G94epCeaU4CJEACJEACJEACJEACVyFgtwC/EQ+3KOuLh4aZCvhxKaC/er16x+nTssDQsGvXVQUG7ZAhSFhyO8zTpkGl0cghNtY5sO75o3Dam26gWSo/R0PVPkXJpJyZc3HtgltDsyV6iSgColmzL2OhMg/nG84HFN9A80A5YyE7KVv+TGEhIIy93qi+yoYN/82Fy6EUSicsTMWIa7u3WsGRTetxaMNHijOa9YVHMThrdK8/t/YAUGjgpd/fYU4AACAASURBVNHVBCg0dDXx6F8vE8BNACZe+hD/LwqKPgTgpQC2Lx5LmnkVu3UAFgTg93ITCg1BQnTYrFjxix8rvMz+4lcwaFR2kJ5pTgIkQAIkQAIkQAIkQAJXIXBqF/DiwvAhemgtkHpdm/7txSdRs/IdNO7ec9X19SNHIv6OJTBNnKh4+laUK9n5zgmcO14j27udDagoWQ6DWSWXTRLDEBOLxd97Clp999ZJDx/g3uW5xlajEBbONZwLCMAA8wBFxkKioXub9ga0CRpFJIHSgirsWlWkiE1SqzBnWRaSBpq7LWZxz2HVb56Gy2H3xSB6Ncx79PFuiynSF6bQEOknFH3xUWiIvjPt7h39BcC32ggiWKFBCAplbfg9AuCZEGyaQkOQEL0eD15/6rsKL9Pv/QKGjRsfpGeakwAJkAAJkAAJkAAJkMBVCOx7EVjz7fAhuvVZYMKXFP5tx46hduVKNO7bf9V1DTk5SLhrKQyjR7dZduRMQRU+bXVDr7ZsM5zWIl/JJOH8uqX3YcSEKeHbHz2HlUCtvVYhLJy1nA1ovX6mfgphIdmYHJAfGpFAZwgc2nQGhbuVt2BMcTrc+HA29CZtZ1yEZc7e1StR+Ol2he9bvv0jJPTrH5b1erpTCg09/QR7XvwUGnremUV6xI8CyADwOQDxV/cfAdwWgoyG2QDCUHTVh5NCQwiurDd/9n24XU6fp6l33Iv0SVND4JkuSIAESIAESIAESIAESKAdArv/Baz9YfjwLPgdMPWrsn9bfj5q3n4b1kOHr7qe8ZprkLD0Thiystqd57C6sPa5I7A1NDWLdlgvoPrM+4hNMUKjFUnhQPKgIVjw+HdYgzx8pxtyz3WOOuRX5iO3Mhe5FbkotZQGtEZfY19kp2T7xIUUI/tzBASSRgER8Hi82PpGIS6erlfY90uLww33ZEAldc/txLqKcqx+5jeKUnnpk67D1DvuCWif0W5EoSHaTzjy9tc9PxkijwMjCh8B0TltMYWG8AGOJM/v/OpnsFla/hCZcMsSZE27WuWrSIqesZAACZAACZAACZAACfRIAl2Q0WAzT0X18uWwHRYJ1e0PURpJCAyiVFJHY99HJ1F8sEKeJkooVZ15H2p1NcRTw81j/mPfRN9hwztyxa93I4F6R71PWBC9Fk7Xnw4oGiEk5CTn+ISFPqY+AfmhEQmEioDN4pT7NVgtLQ8TCt/Z0wdi9A2DQrWM3342v/w8zhbk+uwkjQZ3/vgX0Ju6r6yT35voIgMKDV0Emsv4CFBo4MUQbgIUGsJNOIL8v/eHX8JSVemLaNyNizBmzvwIipChkAAJkAAJkAAJkAAJRB2BMPZosNVoUKO9B9bi8vaxqVQwT52C+DvvhD4trVN4y0/VYcvrhb651rrjsFzcirg+Bl+JJVGCVJQi5YgsAhaHBflV+b5ySKfrTsMbQCPyJEOST1gQzZv7mrq30W5kUWY0kUKgorQem18rhNfjVYQ0/a50DBzZPX1Bzp8oxMb//FMRzzXzb8bo2TdGCraIiYNCQ8QcRa8JhEJDrznqbttoqISGv17agR6A6Ja1GYCyMF9wW2TppOD4ydYf/vUPqD7fUnM0+4a5GL/w1hB4pgsSIAESIAESIAESIAESaIeA3QL8ZrCilEawrGSBocgMa4UWGDAOUDWVMlIMSYJ52vVIuPNO6IYM6fSSbqcH6/+Ti/oqm2zj8ThRWbICpjg3tHq1/Jpao8VtT/4E5oTuuZHX6c30gokNzgZFxsKpulMBCQuiWbPIWMhObiqHJHouqFS8JdMLLqEev8Vje8twcMMZxT40Oglzv5iN+D7GLt+fyABb8+zvUXvhvG9tU1w8bv/B/4OkbvoZytFEgEIDr4SuJsDfal1NvPetFyqhoS1yOwHcB0D5Gy8wxhQaAuOmsFr3r2dx8dRJ32sZU6dj8uKlIfBMFyRAAiRAAiRAAiRAAiRwFQKvLAaKg2/p1iQwmGCtuFS+SB8LJKcrF5YkxNxwAxLuvAPagQP9PpYjW0qRv6vlBpmlch+cjYdhThDPVDWNsXMXYOy8BX77pkHwBBqdjSioKpB7LIhSSCW1JfDA47fjRH2iT1QQGQsUFvxGSIMIISBu7O9+vxhn8qoUEYmfWfMeyobeqOnySI9/tgt73l2hWFdkgIlMMI4WAhQaeDV0NQEKDV1NvPetF6zQ8EsAxQB2ABBdtEShyusB/BqAyEs+BkD8JmnoBFrhp70xJC0tTVNcfLUpnVihl0/Z+N9/4fzxAh+FtGsnYdrdD/RyKtw+CZAACZAACZAACZBA2AnkrwGWB/535xUCQ3PAScMBQ3zT/zULDEvvhHbAgIC2VHOhUa557r1UhcTtbEDVmRWITdZCutRcVTyZe9uTT0Gja+nVENBiNOoUAavLisKqQl/z5pO1JwMSFuJ18RCCQnOfhf7m/sxY6NQJcFJPIOByuLHplXzUlFsV4fYdJppDj4SkbiPrK4wbczmdePe3P4e9sdG3SsqQYVjw+LfDuGrPc02hoeedWU+PmEJDTz/ByI8/WKGhvR2Kv/Y/F5lgAL4P4I+dQEGhoROQgpmy7fUXcfroIZ+LITljMfPBh4NxSVsSIAESIAESIAESIAES6JiAxw28MBc4d6Djua1m2Gs1qBYZDBfbuKmvMwEpGYCkRsyMGXKT50AyGJqXEzXON76cj6rzLc9I1ZZthlo6DV2rJ4Kn3bMMaddM8GsfnNx5Aq2FBZGxUFxTHJCwEKeL82UsiHJIg2IGUVjo/DFwZg8k0FBjxycv5cHe6FJEnz6xL8bPT+3yHR1Ytwa5Wz5RrLvga99GytBhXR5LpC5IoSFSTyZ646LQEL1nGyk7C5fQIPb3TQDPXurXMCfIDbN0UpAAhfmut99A8eef+Tz1T8/EvEe+FgLPdEECJEACJEACJEACJEACHRAozwf+fQPgdnSIylGvRnWRGY0X2skaELXz+2YhZs6NSFi6NCiBoTmYY5+V4eAnLVVfnbaLqLuwGrFJBl+8yYOHQtwoU0ld+3Rwh8B68ASby4Zj1cd8pZCKaorg9rr93lGsNlYWFrJTmnosDI4ZTGHBb4o06OkELp6ux5Y3rmwOPXHRMAy/RhSg6LrRWFuDd3//P/B6WkqbidJJooQSRxMBCg28ErqaAIWGribe+9YLp9AwH8C6S+WTMoNES6EhSIDCfO/qlSj8tKVHt3iSQLxR4iABEiABEiABEiABEiCBLiFwaDnw7lfaXcrZIAQGExrK9MCl8kVXTFYBMbPnIeHx/wftoEEhCVs8Cbzu+aNwOZtuiIma5zXnPoDRXAdJ3fK2/Kavfgt9UkWFWI5ACdjddhyrUgoLLq/yCezO+I7RxigyFgbHDobUVlPwzjjjHBKIIgJFB8qx/+NTih2Jn2Mz789EnyGxXbrT7W++jFOHWzLZhEi75Af/D6b4hC6NI1IXo9AQqScTvXFRaIjes42UnYVTaBCNoN+4VEIp2NxiCg0huGIuT12M7zcAt377hyHwTBckQAIkQAIkQAIkQAIk0EkCQmxY/YQis8FpleQmz5ZzhqsKDOaBLiQ89mPobvxqJxfreJoQFbYvP46y4lrfZFt9MRwN22Awa32vpY69FjPu+2LHDjlDQcDhduB49XFfxsLxmuNwefwXFsxaM7KSsuRsBZG5MDRuKIUFXmsk0A6B/WtLUPT5RcVXDWYN5n4pG+b4lsb24QZYcboEa//5F8Uyo2fdiGtuujncS/cI/xQaesQxRVWQFBqi6jgjcjPhFBreBXA7gH8DCPadAIWGEFw+RzdvwMH1H/o8mROT5KcJOEiABEiABEiABEiABEigSwmIMkrvfQ2u4kOoKTah/qwBaKmucUUo5v52JFw3DLovPS+XTArlKDlSgc8+OOlz6fW4UXdhJYxmJ3DpHbmk0eC27/wYMUnJoVw6Kn053U4IMSG3Mhe5Fbk4UXMCTo/T772aNCZkJbcIC6lxqRQW/KZIg95KwOP2YOtbx3DxVL0CQUI/E+YsGwWNTt1laNb+48+oONOSYaE3mbDkR7+ARtsi5HZZMBG2EIWGCDuQXhAOhYZecMjdvMXOCA2vAJgM4G+XPppDniWyigFsu/S5+XUTgKcvNYEWj6pcAyA3yH1SaAgSoDAv2LUN+z5Y5fMkfsHf9bNfh8AzXZAACZAACZAACZAACZBA5wm4a2pQs2oV6t9fAW9tGWBX3gzzvbHo60DCrDHQL/w6kLFAbvwcymFrcGLtc0fhsLY8Yd9QfQiS9zDU2pY+DDkz5+LaBbeGcumo8SWEBSEmiMbNQlwQ/RYCERYMakNTj4Xkph4Lw+KHUViImquEG+kOAvZGJz55KR+iNFzrMSQrEVNvH9FlPUxKDu7HjuWvKmKYese9SJ80tTuwRNSaFBoi6jh6RTAUGnrFMXfpJscD+EerFTMAJAIoBtCcV3cewJJWc7YAmAngF5cEhOYvieL+fwZQBuAggCoA/S4JC+JRH/Hb7BEAr4dghxQaQgDxxL492L3yTZ8n8WTW/b/8Ywg80wUJdB8Bh82K8uIiOOxWSGoN1BqN/Fk8ISOp1RDXuVrT9G/xNfFvvdncZX9Ydx8ZrkwCJEACJEACkUfA09CA2tWrUbvmQ3httpYAvR7A2Qi47IDXA+PIwUhcugT6qQsAfUzYNrJndTFOHa30+Xe7rLBWvQudsaVBhCEmFouffApaQ0tT6LAF1AMcCxGhuKbYJywUVhXC4em4wfflWxPCwqikUchJyWkSFuKGQR1iIakH4GSIJBBWAjXljdj0Sj5cDmXK2OiZg5A9bWBY12527nG75abQ1rqW8nSijPMt3/pBr39PRqGhSy5BLtKKAIUGXg6hJiCyEDZ34FTktA1rNac9oeFaAKKT20QAQwAkARA5sSUANgH4v0uNoEOxBwoNIaB46shBbH/jJYWn+//3GfkGLAcJ9DQCTpsN+Tu3IH/HFoh/+zPEDYPM62dg1PU3QKvnTQN/2HEuCZAACZAACQRCwONwoO6jj1D77nvwWCztujBkZyHh3nthzMkJZBm/bERPhm1vHVPY2Ot3A94iqFq9E59y+90YOeV6v3xH02TRT6GopkiRsSAaOvs7hLCQmZTp67EwPH44hQV/IXI+CQRAoLSwGrtWnrjCctrSdAzKEM+dhn8c2bweh9Z/pFho3iOPo3+6ePa19w4KDb337Ltr5xQauos81400AhQaQnAiZwvzsfkl0TKjZYjSSaKEEgcJ9BQCTrsNhbu2I2/7ZjisjUGFLa79rBlzkHnddAoOQZGkMQmQAAmQAAm0TcDrcqF+0ybUvP0O3FUiAbrtoU9PR+L998EwdmyXPOHqcrix7vmjaKhteRLfZa+Go+FDaLQtb8MT+g3Azd/8PlRSSxmlaD9rt8eN4lplxoLN7d9DHYKRXq1HRmKGT1gYkTACGkkT7fi4PxKISAK5288id/s5RWwanYS5X8xCfJ/w3w+wNViw6rdPw+NqKVM3OCsHs77w5Yjk1VVBUWjoKtJcp5kAhQZeCyTQRIBCQwiuhPKSYqz/918Vnpb88OcwJ3TNUwwh2AJd9GICLocDx/bsRO7WjbA3tP8kZCCIhOCQfcNcZEydRsEhEIC0IQESIAESIIHLCHg9HjTs3IXq5W/BdV5UWm176FJTZYHBOGFClwgMzVEc2ngGhXuUcbntm+F2KG/EzX34axgwMjOqz1cICyV1Jb7mzQVVBQhEWNBKWrkUUnOPheEJwyFe4yABEuh+Al6PF5++V4TSgmpFMOYEPeZ9KQt6U/i/V3evfAsn9u1utb4Ki7/3FGKTU7ofUDdFQKGhm8D34mUpNPTiw+fWFQQoNITggqg6dxYf/d8fFJ5u+faPkNCvfwi80wUJhIeA2+XE8T27cHTLJ7BZ2m4UKVZWqVTwelvqKQcSjd5kRvYNc5AhMhx0+kBc0IYESIAESIAEejUB8bvY+vnnqH7jTThKREXVtodmQH8k3nsfzNdf1+XZAjUXGrHhv7lQ/NngOQ97/UaopJa34IMyszH7S6JSbHQNj9eDktomYUE0cM6vyofVZfV7k0JEGJk40pexMDJhJLTq8N+s9DtQGpAACcgERCbXplcLIH4Gth7JA82YeX8mNLrwllSuKTuPNc/+TrG2KGU78dY7eu0JUWjotUffbRun0NBt6LlwhBGg0BCCA6mvqsD7f/hfhacFj38HKUNSQ+CdLkggtATcLheK9u/B0U3r0diqcdjlq8QkpWDsvJuQNm6C/CW32w2Pyyl/djud8LhdEL5Emq74XFteJosWDdVXKd9gjkGOEBymTodGpwvtxuiNBEiABEiABKKUgC0/H1WvvQZ7QWG7O1QnJSHhrqWInTMHKk3Xl9ERT/WKxqiV5xp8MXpFI2r3OjitLX8biAcYbvn2DxHft+c/kCOEhVN1p3w9FvIr89Ho8r/8pCh7JMQE0bxZZC1kJGRQWIjS72VuK3oJNNTa8cmLebA3tpQwErsdkB6PaUtHQmoltoaDwicv/ANlRS29cTQ6Pe788S+gNfTOvnkUGsJxldHn1QhQaOD1QQJNBCg0hOBKsFrqsfJXP1N4mvvI1zAgPbrTwUOAji66mMCZ3MPYt+ZdNNQoU3tbhyFKfo2ZuwDDx0+EJPn39I3H7Ubx53shmpJdTXAQTaNzZs5F5nUz2DS9i68BLkcCJEACJNBzCDhOn0b166+jcd/+doOWYmKQcMcSxC5cCKkbRfwT+8vx+bpTijj1+pOor9ileE08bDB58dKecwitIhXCwum60z5hQZRCsjj9LzupUWmQnpjuy1gQ/RZ0aj6A0SMvCgZNAq0IXDxdj61vFsLjVmaDp41LwcRFw8Jaxq407yi2vPqC4jwm3LIEWdNm9sozotDQK4+9WzdNoaFb8XPxCCJAoSEEhyFq3L/18x8oPM1c9giGZI8JgXe6IIHQECg+sBe7VrzerjNTXDxGz5mPEROmQB3kk5Aiw0GsJ7ImriZq9B8xEjMffKTXPmkTmpOlFxIgARIggWgj4Lp4EdVvLYdl61Yo6xC17FRlMCD+1lsQf+utkMzmbkVgtTiw9t9H4bS7fXGoVE64bR/A5WgpHaQzGLH4+z+FKKnYE4YQFkrrS33CgiiHFIiwoFapIRo25yTnyB8ZSRlyQ2cOEiCB6CNwJr8Ku98ruuJHd/a0ARg9c3DYNiz697z/zK9hqarwrRGTlIzFTz7V5WX0wrZJPxxTaPADFqeGhACFhpBgpJMoIEChIQSHKGrmvvHUdxV17K+/+wEMv3ZSCLzTBQkET+BsYT62vPI8xB+glw+RXTB69o0YOfk6qDWhrf8rCw77P8PRLRvaFRySBg7G7IcegzEmNviN0gMJkAAJkAAJ9GAC7vp61K5ahbqP18LrdLa5E1EWKXbBAiQsuR3qhISI2K1ohHomT1k6MTahAJVnlJkYExYtRtaM2RERc1tBiL/pSy2lTT0WKvKQV5WHekf7faza24gESRYW5ObNKTnITMyEQdM7y5dE7GEzMBIII4Hj+y7gwPrTV6ww/qZUpE/oG7aVC3ZulbPXW49Zyx7F4OzRYVszUh1TaIjUk4neuCg0RO/Zcmf+EaDQ4B+vdmcvf/pHcNptvq9Pvv0uZEyZFiLvdEMCgROoOF2CDS/8A26nQ+FEb47B6JlzMXLKtLD3SxCNp4uE4NBOXwjRD2LuI19FbFJK4BulJQmQAAmQAAn0UAIeux11az5E7bvvwmNtp3mwJCFm1kwk3n03NH36RMxOzxfVYvvylrrgIjBTnAN151fB42nJcIhNTsEt3/5R0FmTody4EBbOWs4qMhbqHHV+LyGEheEJw5uEheQcZCZlwqgx+u2HBiRAAtFD4PDmUhR8el6xIZUKuG7JCAwelRSWjYr7Eat+87TivoTIIJ/36NfDsl4kO6XQEMmnE52xUWiIznPlrvwnQKHBf2ZtWqz6zc8VjXXHL7wN2TfMCZF3uiGBwAjUll/A+n8/C3ujsjFh32HDMftLX4FW37VP1wnBofDTHTjw8WpFBpDYncismPPQV5E0cFBgm6UVCZAACZAACfQwAl63G/UbN6JmxdtwV7ffP8k0cSISH3wAuiFDImqHLqcb657PRUON3ReXuJEWm/A5Kk7nK2Kd+eDDGJIztlvjF8JCWUOZnLEgZy1U5qHGXuN3TEJYGBY/rKkU0qWMBZPW5LcfGpAACUQvAfHz5rM1J3HqSKVik2qNCjfcm4k+Q8OTzS0yGkRmQ+tx87d+gMT+A6MXdhs7o9DQq447IjZLoSEijoFBRAABCg0hOoTVz/wadRXlPm9j5tyEcTcuDJF3uiEB/wk01tZg7T//AvG59UjoNwDzH/sGdMbue0MsmpVte/MleFwuRWwanR6zvvAoxJM3HCRAAiRAAiQQrQTEDajGPXtQ/fobcJ471+429aMykbRsGQyjRkUkirae2O2bakPp0ZWKePumjcCNX34irI1Q2wIkOF9ovNAkLFTkyqWQqm3tCzrtQVZBJQsL2UlNpZBGJY2CWdsz+kxE5IXDoEiglxDwuD3Y8c4JlBXVKnasM6gxe9koxPcJ/fux+qoKvP+HXwHwImXoMIyaNhNDc8ZCUqt7CfWmbVJo6FXHHRGbpdAQEcfAICKAAIWGEB3Cx397BpVnz/i8ZU2fhQk33x4i73RDAv4REBkM6//9V9SWlykMzYlJuOmr34Jo/Nzd48LJImx95QU4bMoSEeKP4Gn3LEPqmGu6O0SuTwIkQAIkQAIhJ2ArKEDVK6/CXljYrm/tkCFIeuB+GCdO7PKb853dcO3FRqz/Tx68Hq/PRG+SoFZtRk1ZaSs3Kix84rtIHhT+bAwhLJQ3lvtKIQmBocqm7B3Rmf0JYWFo3FBf82YhLMToYjpjyjkkQAIkoCDgdLix9fVCVJ1vULxujNVh7hezYIrThZxY/o4t6JOahpQhqSH33VMcUmjoKScVPXFSaIies+ROgiNAoSE4fj7rDc/9DRdOnvD9f/qk6zD1jntC5J1uSKDzBFxOJzb+5x+4eOqkwkhvMuOmr30LcSnha0DW+SibZtaUncfGF/8Fa53yKR9Ahcm3L2WfE3+Bcj4JkAAJkEDEEnCePYuq19+QMxnaG+rkJCTeey9iZs2CSpIidi9CXNj0WgEqSy2KGFNz6nF892rFa8PHT8b1d90ftr1cbLzoK4MkhIUKa0VAa6XGpvqaN2clZVFYCIgijUiABNoiYGtwYtMr+bBUt5SZE/PiUoyYs2wUdEYNwYWYAIWGEAOluw4JUGjoEBEn9BICFBpCdNCbX34eZwtyfd6GjRuP6fd+IUTe6YYEOkdANF3c9tp/UZrfci0KS41OJ5csSB48tHOOunCWpboKm/77L0Xpseblx85dgDFzb4rYpzm7EBOXIgESIAES6KEE3DU1qF7xNuo3bAA8njZ3IcXEIOGOJYhduBCSLvRPt4YaXdGBcuz/+JTCbd9UEypPr1A8PKDW6rD4yZ/AFJ8QshCEkCB6K8g9FiryUG5tKV3qzyJDYof4mjdnJWchThfnjznnkgAJkIBfBITIsOmVPNgalKVjU4bEYOa9mVBrI1dc9mujETKZQkOEHEQvCoNCQy86bG71qgQoNIToAtnx1isoOfS5z9ugUTmY/cUvh8g73ZBAxwREuYDdK99C0X7lk5KSpJYbPw8Ymdmxk26aYWuwYPNLz6Gy9PQVEYycfD0mL14a0U92dhM2LksCJEACJBDBBDxWK2o/+AC176+G12ZrM1KVVou4RYsQf8cdUMf0jJr/4snctf8+AofN7duTaG6aml2Ogp3rFfsUDwyMnbcgqFOqtFYqMhZEaaRAxuCYwYqMhXh995eRDGQftCEBEui5BET5pC2vF8DlUIrOgzIScN0d6ZAk3qoM1elSaAgVSfrpLAF+93aWFOdFOwEKDSE64d2rluPE3k993vqlpePGrzwRIu90QwIdEzi47kMc3bLhionT71mGYddM6NhBN89wOuzY/vpLOHcs/4pIhuSMxfR7l0Gt0XZzlFyeBEiABEiABK5OwOt2o37jRtQsXwGRzdDmUKkQM3MmEu+9B5o+fXoU0t3vF+F0rrLvQeaUeORv+w9cjpayIMa4eNz25E+g1en92p/oqSAyFuSshYpclDUq+0111tmgmEGysNDcwJnCQmfJcR4JkEA4CZSdrMX25ccV/W3EekOykjDltjRIamY2hII/hYZQUKQPfwhQaPCHFudGMwEKDSE63f0fvgfRdKl5iIZ3C594MkTe6YYErk6gYOdW7Fvz7hWTJt6yBKOmzewx+DxuNz5d+SZOHth3RcyDs3Iw88FHmNnQY06TgZIACZBA7yIgMgut+/ah6tXXIPoxtDeM11yDxAcfgD4trccBEjfItr15TBG3qDEen3wURft2K16/bul9GDFhSod7rLHVKJo3n28436FNWxMGmAf4mjeLUkiJhsSA/NCIBEiABMJN4NTRSuxZXXzFMgNFZsPtI6DWUGwI9gwoNARLkPb+EqDQ4C8xzo9WAhQaQnSyhzZ8jCOb1vm8iYa74ikuDhIINwFRskuU7rp85Myah2tvuiXcy4fcv9fjwecff4D8HZuv8D1mznyMu3FRyNekQxIgARIgARIIhoC9qAhVL78CW66yR1Jrn7phw5D0hWUwjhsXzFLdZut2erDuhaNXNDOduCgBn674B4TQ0jwSBwzCoieebPPhgFp7ra8UkshaOGtpX5S52mb7m/orSiElG5O7jQ0XJgESIAF/CRTuPo9Dm0qvMOs/PB7X3zkCGq3aX5ec34oAhQZeDl1NgEJDVxPnepFKgEJDiE4mb9smfP7xap83U1w87vjxL0LknW5IoG0C1efP4uO//QmiCXTrIZ4gnHrnvT26ifLl31PN+7vhgYcwdHTPvEnD65gESIAESCC6CLguXkT1m2/CsnVbuxtTpyQj6f77YZ4xo0dn5R3ZUor8Xcpsg7Rx6Wu7aAAAIABJREFUKagtW4uyE4WK/c975HH0T8+QXxPCQn5Vvq8UUqnlyhtrnbkq+pr6+po3i5JIKcaUzphxDgmQAAlELIG8nedwdOuVYmuf1FhMv2sktDqKDYEeHoWGQMnRLlACFBoCJUe7aCNAoSFEJ3psz0589t7bPm9avQH3PP3bEHmnGxK4koB4cnDD839D+ckixRdFiaEbHnwYogl0Tx+5WzfiwNoPFNvQ6HRY8LXvIKH/gJ6+PcZPAiRAAiTQQwl4GhpQ8+57qFuzBl6ns81dSCYT4u+8A/GLFkGl0/XQnTaFXVdhxfr/5MLjbslaMJg1GDdbi+1vvqDYW9+MDMQvmOArh3Sm/kxAe+9rvCQspOTIAgOFhYAw0ogESCDCCRz7rAwHP7ny52Ty4BjMuHskdAZNhO8gMsOj0BCZ5xLNUVFoiObT5d78IUChwR9aV5lbfGAvdq143TdDpVLh/l/9qUc/UR4iNHQTJgKnjhzE9jdeUnhPGToM4ilCcTM+GoYQU3YufxWiPFTrEZOUjIVffxJ6kykatsk9kAAJkAAJ9BACXpcL9Rs2oHrFCnjq6tuOWqNG3IKFSFh6J9SxsT1kZ+2HKX4Xb3mjEBdPKfc7+ZZhOPLJC6gqPw+bywqbywar24rcaSo4YvyvL55sSEZOSo7cZ0EICyKDgYMESIAEegOBogPl2P/xqSu2mjjAhBvuzYTeSLHB3+uAQoO/xDg/WAIUGoIlSPtoIUChIUQneSbvCLa++h+Ft3t/8fuoueEbIkx0EyICLqcTH/zp12ioqfZ5lNRq3PqdHyM2ObpKCbgcDqz717MQZaJaj/7pmZjz0FeiInMjRJcF3ZAACZAACYSJgLjZ3rh3L6pffRXOc+03KzZffx0SH3gA2v79wxRJ17stOVKBzz446VvY4/VA3c+BqvhtqN56AA63w/e1ymFqlOV07mGHJEOSr3lzs7AgHtThIAESIIHeSKDkcAX2fngSrdrdyBji+xox875MGMza3ogl4D1TaAgYHQ0DJMC/YAIER7OoI0ChIURHev5EITb+558Kb3c+9UsYY3r+k2whQtSr3YgbFE6bG7ZGJ9QaCQaTFmqt/0/7NUM8smk9Dm34SME0Z+ZcXLvg1qjkbKmuwsd/ewb2xgbF/rJnzMb4RYujcs/cFAmQAAmQQGQQsJ840dToOS+v3YD0mZlI+uIXYMjMjIygQxSFw+rCh/86hNp6C6xy1oIVNo8NhePWY+juSmhaNAa4tcDxWQa4dW2/1U7UJ/qaN4ushX6mfsz8DdE50Q0JkEB0EDiTV4Xdq4vh9bSUqRM7i002yGKDKa5zQm500AhuFxQaguNHa/8JUGjwnxktopMAhYYQnWvFmVNY+48/K7wt/v5PEZsUXU+XhwhXVLkRb8Ib6x2wWZzyh9XigFX8u94Ja0PTazaLA26X8g9GjVaC3qSBzqSB3qSVU2Ll/7/0WbwWm2RAXIpB8Ua8sbYG7z/za7idLe/uDTGxWPy9pyB6g0TruFB8Ap+88HcI0ab1mHb3g0i7dmK0bpv7IgESIAES6CYCrooKVL/xxlUbPWv69UPSsgdhmjo1am6aC0GhoKpA7rFwaksDpJJ4xQmUDc2Hx3oYKUUu5etZWlQObynvEa+L95VCEsJCf3P/qGHUTZcklyUBEugFBM4eq8an7xYpeuKIbZsT9Jh1f6b8maNjAhQaOmbEGaElQKEhtDzprecSoNAQorOruVCGNX9RNn9e9I3vI2ngoBCtQDeRQsDt9qDijAVlRbU4X1QrN0gM5xDiQ58hseiTGos+Q2NxdNNKlBzar1jyuqX3Y8SEyeEMIyJ8F366HXtXr1TEImk0uOmxbyF58JCIiJFBkAAJkAAJ9GwCHqsVte+/j9r3V8PraPXIfqttSTExSFi6FHE3ze/xjZ6FsFBYVYjcylxZXCiuKYYHHpjqE5F5cBaAlrfOdoMFJ7LWY8QOKyR3CxCHSYXyeX2R3bepv4LotTDQPJDCQs/+VmD0JEAC3USgrLgWO985fsWDaiKjYeb9mfLDaBxXJ0ChgVdIVxOg0NDVxLlepBKg0BCikxG18t/93S8U3uY/9k30HTY8RCvQTXcSaKxzNAkLxbW4cLIWLocnrOF4vF5YnW7YnR75CX5Rs1ivlaBxVaD23AfQ6NTQ6CRodWr0GZaKhY9/Byop8FJMYd1MCJ0LFrtXvoWi/XsUXk3xCVj4xJMsVRZC1nRFAiRAAr2NgNfjgWXzZlS/8SbcNTVtbl+l0SBu0ULE33kn1DExPRKRaNpcWF0oiwq5Fbkori2G29tKNRC78gKjDsyBsSFBsccTo3civuQM4s+5IUkSjGojDBoDptx7PyZMmkdhoUdeEQyaBEggEgmUn6rDjrePX/G+0xCjxfV3pCNlcM/8HdRVrCk0dBVprtNMgEIDrwUSaCJAoSFEV4K9sRFv//InCm+zv/QYBmVmhWgFuulKAh6PF5WlFjljoayoBjXl4c1aaN5bnc2JSosDFruyHIH8da8XJst6aD1V0EgqSFLTr7J+6UswKHMk+g+Px9DsJLn0UjQPt8uJDc/9HRVnShTb7Js2AvMeeRyiKTYHCZAACZAACfhDwHr4MKpeehmOU6faNZMbPT/4ILT9+vnjutvn2t12/H/2zgM8yirr4//pk94rCQmpJKGD9N6RJk1FBcSyuG75bGt31bWXXXXd1dV1bSiIgCBFeu+9JiEhDQjpPZMy/XvuhMzkJZNkZjKTmcyc+zw86My95577uzdk5v3fc05mRaYuYoH9ya7Kbi0s3OZlUEEsIrL7c16tCy6Bb3IlpLty4SZ0g0gg1sU6BEXFYOqKP5HIYPedJgeIABFwNgJl+TIcWpMJpZwrBvP4PPSfGIH4O6jWTVt7TkKDs/00OP56SGhw/D0iD7uGAAkNVuKsUaux6uWnOdbG3PcgovoOsNIMZKYrCNRVyXHleCGup1a0+kBnyvw8HsBumbh5inV/N/13899iSD1EUKs0kNcrIW9QQV6vAqvxUFBSh/2Xi1FXq4BEy4NECwhbpCpgcwvl2ZA2GG7y83k8ePrGIyB8kt41Vmg6MtkfsQOD4B/u4bRf+utrqnXFoRtqazjbkjB8NIbOXWjKVlEfIkAEiAARIAJQ5N9E5crvUX+am5KwJRpJXBz8H1rebQo9K9QKXK28qk+FxP5bpTVygaGN/RcqpEg+PQUijQRuQimkAjd4urtj1oqBOLL6C5Rd5wr90x9/EoGRUXSaiAARIAJEwAYEKgrrcPCnDCgabos8AxCR6IchM6Mhljr3RTNLsJLQYAk1GtMZAiQ0dIYejXUmAiQ0WHE3V73yDDQqwxe54QsWI27IMCvOQKZsRaC2ohFXjhUi71I5tBpuseG25mSpi4KjvREW6wP/MA+duMDqKbAbJua0Defy8ey6i1CqufOKtUCwmo9QFfujQkTVVvC0jS1MC1HvPQs9AgLg6y5uNaVvsBtiBgYjqk8ARBLnu+Vfej0Pu778FEzka9mGzb8H8XeMMGcLqC8RIAJEgAi4GAF1TQ2qfv4ZNTt2Ahrj6RCFgYG6CAaPUSMdOj2hUq3E1aomYYGlQmL/rdKYLiw0b7270B1JAUkIT+0P3PCAWMA+WzR9phkwORJiaSEOr/6Oc1Ki+w/G6HuXuNjpoeUSASJABLqWQFVJPQ6tuYqG2tZ1gzz9JLpUSr4h7l3rlIPPRkKDg2+QE7pn3lMgJwRASyICtwiQ0GDFo8BSJ7EUSs1tyOz56D1yrBVnIFPWJlBd2oD0owW4kVbBMhN12LwDpAiN9UFYrC8CIz3BIgg605jI8OSaCx2a6Fd9EUmyTIi0gIhFN2h5UEv7QylN0Y2N9HMzKjaw94QiPnqm+CNmQLAuysGZWtbpEzi+fjVnSXy+AFN+90cERfVypqXSWogAESACRMAKBLRKJWq2b0fVz2uhafGZraVpnpsUvvMXwHvWTPDFrYV8K7jRKRNMWMiqytJHLGRWZkKpUZptUyqQ6go364o3B6Qg2icaJXm1OLg6k2OLPbyasCQeWz5+F3WVFfr3+EIh5j79Ejx8/cyemwYQASJABIiAeQQa65Q48WsOivO4Ed3MikDIw8ApUeg1INBpI9rNowWQ0GAuMerfWQIkNHSWII13FgIkNFhxJze8/zfOF7ABU2eiz4QpVpyBTFmLQGVRHdKPFCI/o7Jdk0xICI720gkLLHLBw1diLReQUVSLWZ8eahXJcPsEnqpazCjZCV4LJaRO6I7dQdMQoBEhVM1DlFqAgYFekArbj1zwC3VHzMAg9EwJ0BWSdoZ28td1yDx+mLMUVhx69hPPQySVOsMSaQ1EgAgQASLQSQJarRb1p06h4vvvoSosMm6Nz4fXpEnwu/ceCHy5RZA7OX2nhjMRIacqRy8sZFRkQKFpfau1o0mYsNDbvzdSAlOahAXvaAj4hs8CLLXjzq9SwaI8W7aJS5NQnH0C57Zv5rzeZ/wUDJg2s6Np6X0iQASIABGwEgEWeZ92pABphwuMXpJjkeyDp0dB6CTf8zqDjYSGztCjsZYQIKHBEmo0xhkJkNBgxV3d/PF7qC4u1FtMGT8ZA6fNsuIMZKqzBMpvynQCQ0FWVbumgnp6IXFYCEKifSAQdS5qwdhEao0W8z47gov51R0uaXT5EYQ3Gs4VG3DUfzjy3SIMY7XA6EAvPB4XjoLMKmhuS8N0+yQslVLi8FAkDA2BUNS9BQeWOmn3/z5DSW42Z5lxd4zA8Pn3dMiXOhABIkAEiIBzE5Dn5uoKPTdevtzmQt3694f/g8sg7tnT7jBY2iNWsDmtPE0nLrCIBVbQ2dzGhIVE/0R9xEKMTwxHWLjdXuqhm0g9VMB5OWZAEFLGBmDTh29BKTcIEFJPL8x95iWIJCTom7sv1J8IEAEi0FkCRTnVOLEpR1fv7/bmHeiGkfNjwf5uq9XJVUgtqEFWiQxylRoSoQBxwZ5ICfeGh8Q56j2Q0NDZU0bjzSVAQoO5xKi/sxIgocGKO7v98485BfISR4zBHXMWWHEGMmUpASYwXD5w02ioaUubob28kTQqHExosGXbkVqEFSvbLjzZPHdIYxHGlXNv65dIgrA/YCzAKk/f1r5cMhhjowOQd6kMOedKIats/8GEm5cIfcb2QHTfQLNrS9iSj7m2G2S12Pbph2BFolu2icsfQ3hCb3PNUX8iQASIABFwAgLqqipUrl6N2j170VZ+RFFEBPyXLYP7oIF2W7Fao0ZONTdioVHNjSowxTkxX6wTFli0AkuHFOsbCyHftAdGLIph51eXoVYZ8kiyulMzVvTFuR0bcPXEEY4Lw+66G/HDRpriFvUhAkSACBABGxCor1Hg+MZslOXLWllntQSHzIjWRbE3N3bRbXd6MVYeu4Yj2WVGfy2yr5ejYgOxZEQUJieFQGBm7UEbLNNikyQ0WIyOBlpIgIQGC8HRMKcjQEKDFbd09/8+R1FWht5izKChGLnoPivOQKbMJcDSADCBIfNkUbs1GMLjfZE0MgwBPTzNncKi/g98dQKHs8raHcvTajCtdDe8lS3zcPKwI3gSqkXGUzqMiQ/EyoebCpCz0NqSa7XIPluCm1er2i1y7RPkhn4TInT1J3hGBAyLFtnFg25mpGPft19wZmUplGY98RzE0rZv9HSxmzQdESACRIAI2JiAVqFA9ZatqPplPbQNxh/Y8728dCmSvKZMAU/QtZF9TFjIq8nTF2++UnEFlggLIr5IlwqpucZCjG8M2GvmNpZWihUZZTdkW7Y7ZvWCX4gKWz95D6xPc/MNCcPMP//FoQtkm8uA+hMBIkAEuiMBjVqDS/tvIuOE8ZSAsYOCMGByT2SX1+GZtRdMiqZv5tAvwgcfLuqPhBDbXsCzFXcSGmxFluy2RYCEBjobRKCJAAkNVjwJB374GjdSL+ot9uzTH2PvX27FGciUOQSqiut1IaWs4LOxxp6n90j0Q/KocLBCh13VWKhqn9d2dFh8Ol6WhYHV5zluZXvE4IzvoDZdZWu6/Nq0ViGvjTIlci6UIvdCGeqq2o5yCI7yQr8Jkd22aPSxdauRfeYEh0/ckOEYvuDertpemocIEAEiQATsREBXh+HYMVSs/AGqkhLjXggF8J4xA74LF0Hg6dElnmq0GuRVNwkLLB1SekU6GlTGP5u05xATEeL94nURC+xPnG8cRALzhYXb57iRXoFjG7jpB4MiPTH+gd7Y9+2XKMhM5wyZ9NDvERaf2CXsaBIiQASIABHomMDNjEqc3JILpVzdqrNMAvyzrBRVLQTjji029RAJeHh/YT/MG9giZa+pg+3cj4QGO2+AC05PQoMLbjot2SgBEhqseDCO/Pwjcs+d0lsMi++NSQ89ZsUZyJQpBNhNfnar4/LBm0ZrFbCH8SyMtPeIMLCb/F3dTuZW4O4vjrU7rVgtx8yS7RBplPp+Sr4IW4OnQyFovyD12sdG4I5of6P2GRuWVunywQI01LZdSDIy2R99x0XA0896xa+7grOioR5bPn7PSAqlFQhPSOoKF2gOIkAEiAARsAMBeXY2yr/5BvL0K23O7j50KPyXPABReLhNPWTCwrWaawZhoTwd9ap6s+dkaY/ifeP1xZvZf1tDWGjpiFKhxvYvLqGh1vB5g8fnYerDKZBV5LWKFAxPTMLEB1eYvRYaQASIABEgArYlwFLmHtuQhcoiw++bqnoFrlU2YJu7AhUCQ2SauZ58dE//bic2kNBg7i5T/84SIKGhswRpvLMQcGqhQS2TQeDZNalw2IE4+es6ZB435NMPiuqFaY/9n7OclW6xDvYB6+SWHJTdaJ2rki0gItEPfSdEwMvffsULV524jhc3XGqX5+Cqs4ity+H0OefTH1c94zvch3fm98Xioe0Xs1Qp1bh6qgRXjhUavfnCJuELeIgbHKyrWSFxMy3Hc4fOdUEHoymUvH0w68nnKYVSF/CnKYgAESACXUlAVVmJyh9XQbZ/f5t1GMTR0fB/8EG49e1jE9eYsHC95rpeWGCpkGRK459D2nNAyBPqIhaaUyGx/xYLxDbxudno+d3XkXmymDNH4vBQ9B0fjq2ffIDqEkM6DpZakaUj9AkOtalPZJwIEAEiQAQsI8DSBrN/17PPlqJRqdYVez4uUSJTrLHM4K1RLLJh65/HdKs0SiQ0dGrLabAFBEhosAAaDXFKAk4rNNQdP4Gyf/0LQU89CfdBbaeaseaunt22GWkH9+hN+oX10OWwpWZ7AixdAksLxD5YqRStP0iJpQIMnBaFnsn+dq9B8M2RXLy+Oa1NKKwmw/SSXazKgr5PjdALO4KnQMvjdwjz1dnJWD6qV4f9WAd5vRJpRwp1dRw0auO3XEQSAfpPikSv/oF2Z2fSogAYS6EUO2QYRixYbKoJ6kcEiAARIAIOTEBXh2HzZlT9sgHaRuN1GAQ+PvC7bzE8J060aj0BJizk1+br0iA1p0OyRFgQ8AS6gs3NqZAS/BMg6SBq0ZpbwlJM7vo6lZPK0d1bjGm/64O8cydwYuPPnOkSho/G0LkLrekC2SICRIAIEAEbEMi9VIYfv7mEK2oFDktVgBWegLKaDRseH9VtCkST0GCDg0Um2yVghR8zIkwEnIKA0wkNWo0GVevWoWpN05cjvpsbwt59F+KIHjbfsEt7d+LCrt/083j6B+Cuv7xi83ldfYLGOiVO/5aHgqtVRlGERHuDFTRkX54doXUU0TCk8gxi6nM5rh4MGI0iqWk3CE2JaLidg6yyEZcO3MSNtIo2EYXF+mDIndFw83IMju3tpaKxAVs+erdVCqUJD65Aj0RKoeQIPwfkAxEgAkTAEgL6Ogzfr4SqtNSoCZ5IBO/Zs+A7f77uc2BnG5szX5avFxWYwFCrqDXbLB/8JmEhMEUXtZDolwip0D4RliyV4t4frqA8nxt5MWpBHIKipPj1w7cgrzO8J5JKMffplyHtwkhhswHTACJABIgAEdAR2JFahL98exb1fC1UVnz6+eWSwZiaYtp3UntvBQkN9t4B15vfij9qrgePVuxUBJxOaKjetAkV333P2SRReBjC3nnH5mmU0o8cwJktG/RzSz29sPClN5zqwDjaYljhq9Pb8iCvV7VyTSDk6Qobs/Q/LN+wo7T2ajSINArMKdoKgdZQyIsJDExoMLW1V6OhIxsVBXW4sPcGSq8bf4AidhNg4JQo9Eyxf2RIR2thxSv3fvMFp5s7pVDqCBu9TwSIABFwWALynBxUfPMNGtO4xYlbOuwxcgT8HlgCUUiwxetgwkJBXQFSy5qKN7M/1Ypqs+0xYSHGN0afCinRPxFuws4LH2Y7YmRA7oVSnNqax3knPM4XoxbF4dz2LZwIXdZp0Iw5SB470RpTkw0iQASIABGwMYEHvjqBw1llVp9lTHwgVj48zOp2bWGQhAZbUCWb7RFwnCdOtE9EwL4EnE5o0DQ0oOCFF6G8cYND1m3AAIS89KJVQ+dv37qsU8dx/Jef9C8LhCIsfuMD++6wk87O8k+e3XFNly7JWPMLc8ew2THwDnSML/QtfayTq9DntR2cVAXN7yfWZqB/Dbd+w/7AsSiRmPbAhBW6vvzaNHhILK+pwB6wFGZX4+LefNSUNRjl2yPRD4OnR0HqIXLoE3Zs/Wpknz7B8TF28DCMWEgplBx648g5IkAEiEALAro6DKtWQbavnToMvXrBf/mDcEtJMZsd+71XVFeki1hoToVUJTceJdmecSYs9PLpxYlYcBe5m+2PrQfIG1TY9p9LUDQYLmmwyxnTf9cXWo0Mm/7xNjRqw4UHFqE7+8nnwT7XUiMCRIAIEAHHJtDed83Oem6N75qd9cHU8SQ0mEqK+lmLAAkN1iJJdro7AacTGtiGKIuKUPDc89DIuOHgPnNmw3/ZMpvtWd6Fszj8Ezea4v63/mFTccNmi3Fgw0q5GkfWXUXJtda37tmHH1a8OHlUGPiCjusZ2GuZRm+ZaLWYWbINHqp6vVusNsP24KkAW5gJzZq3TDRqDdKPFupqOLAUC7c3ibsQg6ZHIbK3vwme2acLpVCyD3ealQgQASJgDQK6OgxbtqLql/XQNrRTh+GB++E5frzJn7eYsFBcX9wkLLCohYo0VDZWmu0yDzxE+0TraiywVEhJ/klwRGHh9oWxSNCcc9y0U33G9UDyqHAcWvUtrl06zxky9v7l6Nmnv9l8aAARIAJEgAh0PYH2ouet4U1nouetMb+pNkhoMJUU9bMWAdOe2FhrNrJDBByXgFMKDQx3w8WLKHrjTUDDLQwc+Kc/wmv8eJvsyM0radj33Zcc23e/+g7EUse7VW8TAF1gtFGmxME1mWAFDG9vnn4SDJsTg4Aenl3gSeemYHkzV6w8wzES3liA0eVHOa+d9R2ILI9YkyezRd7MisI6nNyc22Z0AyuwzQptS9wsj6IweYEWdGwzhdITz0Hs5ng3TS1YIg0hAkSACDgVAV0dhhMndKkwVSUlRtfGEwrhPXs2fBd0XIeB2SupL9FHKzCBoaKx7ZpEbcFkwkJP75764s1JAUnwEHl0K/blN2XY+306J6rSy1+KqY+koOLmNez4zyec9QRFxWDqij+BZ+KFh24Fg5wlAkSACDghgY7qAXZ2yZbUA+zsnJaMJ6HBEmo0pjMESGjoDD0a60wEnFZoYJtU89tvKP/f15z9YgUCQ//2OqQJCVbfx+KcLOz67784duc//xrcfXytPpcrGqytaMTBnzJRVyVvtfzYQUHoPzESQrGgW6BRa7SY99kRXMw35HweW34IoY3Fev9VPCE2hc6Eim9aqoJ+ET7Y8PgoCGxQj4Klqko9dBMZx4uMpnySeoowZEY0wuMd86wfX/8Tsk4f55wNSqHULX5UyEkiQARcjIA8NxcV33yLxtTUNlfuPnwY/JcuhSgkpM0+TFhgtRWaUyGVNViWqzrKK0qfColFLHiKHf8yQ1tQWHTi7m/TUFnEvawxdnECQqK8sP3zj1Gef50zfMYfnkJARE8XO4W0XCJABIhA9yXwzZFcvL45zWYLeHV2MpaP6mUz+9YyTEKDtUiSHVMJkNBgKinq5+wEnFpoYDfYyj7/HLI9ezn7KPDzQ/j770Hob92ULxUF+fjt0w85c8156kV4B5mWX9/ZD1tn1ldZVIdDazLRWMct+swu2A25Mxq9+gd1xrxdxmYW12LmPw9BqdbCS1mLGSU7OH5c9YjFOd+BJvkmEvCw9c9jkBDiZVJ/SzuV5ctwaksumOhjrEX3C8TAKT0hkjiW4KNsbMTmj99FfTU35/aEZb9Dj97JluKgcUSACBABImAlAurqalSu/gm1e/a0ikZtnkIcHQ3/h5YbrcPAhASdsHCrgHNJg/FIiI7cjfSK1KdCYumQvMS2/b3akT/WfP/q6WKc28kVEiKT/DFiXizyzp/B4TUrOdP1GjgEo+5+wJoukC0iQASIABGwMQGKaGgCTEKDjQ8amW9FgIQGOhREoImAUwsNbIEsv2/ha69DnpHB2XNJXBzC3vgbeGKx1c5CTVkJNv39bY49ugnWebzFuTU4sv4qVApuGiyBkK/7cuyot+hNWfmGc/l4cs0FDKw+j3hZFmfItuBpqBWZ9oDjo3v6Y97ACFOm7HQflVKNS/tv4uopQ/RFS6PeAVKMXBDncIW4jaVQcvP2wWxKodTpM0EGiAARIAKWEtAqlajZvgNVP/8MTX3rtIjMrsDHB373LYbnxIn6OgzlDeWc4s0sgsGSFuEZwYlY8JH4WGLG4cc01il1BaBZnavmJhTzdQWgxW48bPr7WxwxnhV+nvP0i/Dw9XP4tZGDRIAIEAEiYCBANRqaWJDQQD8VXU2AhIauJk7zOSoBpxcaGHh1VRVuPvss1OXcfLye48Yi8E/WyzvbUFuD9W//lbPXUx79I0Ji4hx1/x3er+tp5br6ABo1txix2E2A0YsSEBjRfVMYNMNffzIHR//9LgQapX4/iiUhOBA4psP9YZGmbfRPAAAgAElEQVQM7y/s12UiQ0uHSq7V4NTWPKOprNjDC1Yvo0eCYz2goBRKHR4p6kAEiAAR6DIC9WfPouKbb6AsKDQ+p1AAn5mz4LtwAar4jfpoBRa5UFRfZJGfPTx76Ao3sz+siLOzCgu3wzmxKQfXLpdzXu4/KRKJw0Jxad9OXNj5G+e9vhOnov+UOy1iTIOIABEgAkTAfgTq5Cr0eW2H0XS3nfWKZRO4/No0eEgcszZfy/WR0NDZ3abx5hIgocFcYtTfWQm4hNDANk+ek4PCl17WRTi0bP5Ll8Bn7lyr7K9S3og1rz3PsTV+ySOISO5jFfuuZoSF+J/fdb3VhyQ3LzHG3psAnyDnKLKdefwwDq5bg/zKBtQrmm4aHvYfiQK38Ha3nNVk+HBRf5unS2rPCaVCjQt7biDnXKnRbsmjwpAypgd4NqgbYcnPQ1splKau+DOCo2MsMUljiAARIAJEwEwCivybqPj2WzScO9fmSP6gvii4czAuCwp1kQuFdW2IER3MHeYRpi/ezMQFX6lj1hIyE6FZ3Uuv12LfD1c4Y9hnqCkPJUNeL8OvH74FlcJQ/8rNy1sXzSCSSM2ahzoTASJABIiAYxB44KsTOJxlWW2i9lYwJj4QKx8e5hiL7MALEhq6xTY5lZMkNDjVdtJiOkHAZYQGxkh2+AhKP/qIi4vPR8gLL8B9kGm58NtjrdVo8ONLT3G6jL5nCaIHDO7EFrneUFZb4/KBm0g/2vqhgnegFGPvTYS7t/VSXtmTMFvrlo/fQ3VJU5Hl2kYlilRirPScBC1a/6pit0hGxwViyfAoTEoKsUnhZ0t4FGRV4eSmHCgaDSkZmu2Exvpg+JwYiN0c4+ZL4dUM7Pn6c84y/cJ64M4/Pq1PyWEJAxpDBIgAESAC7RNQy2So+nktarZvB9Tc3xdqrRoNqgZUBUhweGwAzgfVWYQz1D20KVohMEX3t7/UuvW4LHLKjoM0ag12/i8NNWUNHC8mLOmNoEgvHP9lDbJOHeO8N3zBYsQN6R4PkuyIlqYmAkSACDgsgR2pRVix8ozV/ftyyWBMTQm1ul1bGCShwRZUyWZ7BEhooPNBBJoIuJTQwBZc8eOPqP5lA2f/+e7uCH/3HYh69Oj0ufjp1ec4t8KG3XU34oeN7LRdVzGg0WhxZlseci+0voEREOGJMYviHeaBtTX2pCgrE7v/9xnH1MDpsxE9fBxSC2qQXSpDo1INqUiAuGBPJId5O2yoqqyyEUfWZ6G6hPswgy3O00+CUQvj4BPkbg1snbZxZM1K5J7nfvgeetciJAwb1WnbZIAIEAEiQAS4BNhFjNpdu1G5ejU0tbW6N5uEhUY0qhp0AoNMpMbpEQFI6+cDrRlRcMHuwfrizSwVUoBbAOFvQeDK8UJc3JvPYRLdNwBDZ8egsvAmfvv0Q7BLD82NhHc6PkSACBCB7k9ArdFi3mdHcDG/2mqLYdH0Gx4f5TAX3TpaGAkNHRGi961NgIQGaxMle92VgMsJDezLbsl776P+9GnOnonCwxD2zrsQeHp0ai/XvfUKGmVNX6JZG3znXCSNmdApm64yWK3S4NiGbBRcrWq15PA4XwyfFwOhSOBUOA788DVupF7Ur4kvFGLBC3+DxN0xHsibC1ulUOPUb3m4kcath8LsCEV83DGzFyKT7X+7tL6mWle4vWWqCLGbO+Y+8xIk7p37N8BcZtSfCBABIuDMBBouXdbVYWjIy0GjqlEnKjCBQalpSmWp5QGp/X1xZrg/5NKOf8cHuwUjObCpvgKLWAh0C3RmfJ1aW32NAtu/uASVUqO3I5YKMH1FX0jchdj91b9RnJPFmWPyI39AaGx8p+alwUSACBABImB/ApnFtZj5z0NQ3lbr0BLPWF3ArX8eY9eUveb6TUKDucSof2cJkNDQWYI03lkIuJzQwDZOU1+PghdfgvLGDc4+ug0ciJAXX+hU+hSW57a23JCvvt/k6eg3abqznBebrUOr0eL4phyjD6h79Q/E4BnR4Jtxw9FmjlrRcF1VJTa+/zfOTcLYIcMwYsFiK87S9abYzcjMk8W4uPeG0SJkicND0Xd8hN33M+3gXpzdtokDKH7oSAybd3fXQ6MZiQARIAJORqA6Pxd5X/0LipNndVELilvCQstl5vd0x7FxgagMkLS5+gBpQFMaJP+mdEgsgoGaaQSOrs9CfkYlp/OgaVGIGxysu+TALju0bBFJKRi/9FHTjFMvIkAEiAARcHgCG87l48k1Fzrt50f39Me8gRGdttOVBkho6EraNBcjQEIDnQMi0ETAJYUGtnBlYSEKnn8BGpmMcxa875yBgIcftvh8bP3nB7pQ9OaWPGYCBt1pnWLTFjvVDQae331d93D69pY0Mgx9xvUAjxUncLJ2bscWpO7fzVnVnX96Bv7h3etDXFvbUpxXg+MbsyGvV7XqEhLtjeF3xUDiLrLbrqpVKmz95/uoKS1p4QMPM/7wFAIiIu3mF01MBIgAEeiOBGQKGa5UXEF6wXkot+xExJFsCNq4RVntK8KxcUG4Hu0O3Pb7ndVUYNEKzRELTFhwxs8Att7jwuxqHFqTyZnGP8wDk5YlQaNRYfNH70FWYUhTyecLMOvJ5+AdSEKOrfeG7BMBIkAEupIAExueXXfRosgGFsnw/sJ+3U5kYHxJaOjKU0ZzMQLO98SK9pUIWEbAZYUGhqvhwgUUvfkWoDGElLPX/Zcvh8+smRYR3fnlpyjJzdaPpRvSHWPMPFmE87u50SVs1IDJkUgY2j2KTXW8Sm4PtUqJX955DfJ6Q7HLoKhemPbY/5lryqH711XLcfSXLFQW1rfy08NHjFEL4+EbYr80UcYKQwdGRmPaY3/uVGSTQ28KOUcEiAARsAKBemU90ivSkVqWirSKNORV5iLuSg2GHSmDex230HPzdAoxH2eH+ePyAF9oBE1fx/wkfvrizUxcCHEPIWGhk/vDUlHu+O9lyCrlektMz5m0LBn+4R5IPbAH57Zv5sySNHoCBs+kizGdRE/DiQARIAIOSYClUXpm7QWzajawmgwfLurfrdIltYRPQoNDHkWndoqEBqfeXlqcGQRcWmhgnKq3bNXlDuY0Hg/Bz/4FHkOHmoGyqeveb79AQUa6flyvAYMx6p4lZttxlQHX08pxfGNOq+WyKIbkUeFOiyH7zEkcW7eKs77R9y5FdP9BTrdmtVKDMzuuIe9i6wLfQjFfJzawCAd7tdvrZDA/Riy8D7GDzf/5t9caaF4iQASIgK0JMGEhozKjSVgoT0NudS40aLqoEVzYiJH7SxFc3GjUDVZqOKOPN06NDIDEL7BJWLgVtRDqEUrCgpU3L/XQTaQeKuBYjR0UhMHTo9Egq8WvH7zJqVEk8fDU1SgSS92s7AmZIwJEgAgQAUchwApE70kvxsrj13A4q8xoilsmSo+OC8SS4VGYlBTSbQo/G2NMQoOjnDzX8YOEBtfZa1pp+wRcXmhg+eQr/vc/1GzbziHFk0gQ9rfXIYmLM+sMHVr1La5dOq8fE5HUB+OXPmKWDVfpXHKtBgd/yoTmttQK7MswyyHsrKkS2Jnb9u9/oOKmIYrDzcsbdz37VwiEQqfcfrbm7LOlOLfrOlg9jpaNL+Bh6Oxe6JkcYJe1yyorsOkfb0OjMqR4knp6Yc7TL9JDF7vsCE1KBIiAIxBgRZszKjKQWt4kLORU5eiFhWb/3GUqDDtchvgrtW26XB7pg6r549Cr3yidwBDuEe60v98dYd9klY26aAa1yvC7lhV+nrGiL8RuQpzY8DOunjzKcXXYXXcjfthIR3CffCACRIAIEIEuIFAnVyG1oAbZpTI0KtWQigSIC/ZEcpg3PCTO8X2UhIYuOEg0BfcZIvEgAkRAR8DlhQYGQatWo/i999Bw5iznWAh8fBD27jsQBZuer/bY+tXIPn1Cbyc0Nh6TH/kDHbfbCFSX1mPv91eglHPTK4Qn+GLU/DjwnKzwc8vll13Pw/bPP+YQYQXDWeFwZ2+lN2pxbEM2GmXKVku1Z6qsi3t24OLubRyfeo8ahyGz5jn7ltD6iAARIAI6Ao2qRk7EQnZVdithoRmVQKVBv7NVGHCqAiLlbeIxjw83oRvEgcHwW7YEURPngM/nE+UuIMBE/cM/XwWrz9CyDZ3VC9H9AlFRcBPb/vUhWL/m5hsajpl/eobSBXbB/tAURIAIEAEi0HUESGjoOtY0UxMBimigk0AEmgiQ0HDrJGgaGlD4yl+hyM3lnA1RZCTC33oTfA8Pk87M6c2/4MrRg/q+ARE9dcVlqRkI1NcosOe7dDTUKjhYAiI8MW5xAoQigVPjOvLzD8g9d1q/Rh6fj3nPvQp3bx+nXnfz4ljdhkM/ZaKmvHWKjcThoeg3PqLLhSZWM2PzR+9CVlFu2BceDzP//Cx8Q8NcYl9okUSACLgWAblajsyKTF3EAvvDhAW11nhtBT0ZrRbR2XUYcbAUXjVNUWD8W8KCVCiFm8ANYjcP+M6bB5+5c8GXSFwLqp1Xm59RiaPrszheBEZ6YsIDvXWv7f7vv1Gcy31/8sOPIzQuwc6e0/REgAgQASJABKxLgIQG6/Ikax0TIKGhY0bUwzUIkNDQYp9VFRUoeP55qMsrOLsv7dcXoS++CJ5I1OGpOL/zN1zet1Pfzyc4FLOffL7Dca7SQdGowr6VV1Bd2sBZspe/FBOXJUHi5hyhmm3tJ8uNvOHd16BRGx7mRPUbiDGLl7nKEdCtU96gwuG1V1GeL2u17qg+ARgyMxoCQdfegM1Pu4z9K7/i+BPSKw6TH/0DpflwqdNJiyUCzklAoVYgszJTX7w5qzILKq0hZVxHq/Yvk2PEgVJE5sshFbrBTSDVRS6IBGL9DS6P0aPhv+QBCAMDOzJH71uZgEqhxvYvL4Nd5mhuLNf2lIdT4BvsjuuXL+Lgj19zZqX0nlbeBDJHBIgAESACDkOAhAaH2QqXcYSEBpfZalpoBwRIaLgNkCIvDwUvvwxtA/e2tefECQh8/PEOHzimHtiDc9s36626+/hi/vOv0UEEoFZpdDUZSq9zczlLPYSYtCwZHr7Of/Px0t6duLDrN855mLrizwiOjnG5M6JSqnWFwAuuVrVae2gvb4xYEAeRuOuiW1gqiX3ffomCTEMxd+bY6MXLEN1voMvtDy2YCBCB7k1AqVYis+qWsFCehqtVV6HSmC4sNK/eVynG1PNaxJwrgRtfArFA0io0XBwTg4CHlkOalNS9oXVj7y/uy8eVY4WcFSQMDcGAyT1hLGqPzxdg1pPPwzswqBuvmlwnAkSACBABImCcAAkNdDK6mgAJDV1NnOZzVAIkNBjZmYbz51H01tuARsN51+++xfBdsKDdvcw4dhinNq3T9xG7uePuv77tqPvfZX6xAsDHf83BjXRutIhQzNeF9PuFmpaaqssctsFELIph4/t/Q32NIXeyX1gP3MlyI7Nrhy7YNBotzm6/hpzzpa1W7xfmjjF3J0Dq0XEkkbXQ1ZSVYMvH73EiTlhKq9lPvQCRRGqtacgOESACRMDqBJiwwMSE5uLNVyuvQqlpXQ+no4mlAqmuaHOybyJ6X6iCeMs+aGR1RoexWlZ+D9wPz/HjKcd/R2Bt+H5NWQN2/i8VGrWh9oKblwjTf9cXIokAt1+CYa4kj5mAQXfOtaFXZJoIEAEiQASIgP0IkNBgP/auOrNrPtFx1d2mdbdHgISGNujU7NqF8v980erdoCeegOeY0W0yzTl7CkfX/qh/n+Xfv+/Nv7vsg+RmEOd3X0fmyWION1bweczd8QiNcY3aBNcuncehVd9yGAxfsBhxQ4a59L9SLJIg7XABUg8VtOLAolzG3psAllqrq9q5HVuQun83Z7qU8ZMxcNqsrnKB5iECRIAIdEiAiQisrkJqWSrSytN0hZwtFRZ6+/dGSmAKUgJSEO0dDcWlyyj/5lsob9ww7odQAJ+Zs+C7cAH47u4d+kodbEeA/Q49sDoTJXk13M8Xd8WgZ3IAWMrGXz94EyqFXP++xMMTc595CWKpm+0cI8tEgAgQASJABOxIgIQGO8J30alJaHDRjadltyJAQkM7h6Ji5Q+o3riR04MnFCL01b9CmpxsdOT1yxdw8MdvOO8tfuMDCIRddyvb0c555skinN/d+mHF0Fm9EN3PdfI47/zyU5TkZuu3h0W7zH/hdQhNqP3haHtqC3+yz5Xoohu0hguZumkk7kJdZIN/eNdEvSgVcmz++9ucyJOmFBPPwTsw2BZLJ5tEgAgQgQ4JsLRHTFhgogKLWsioyIBCY8jH36GBWx1YxEKif6IuaoEJCzE+MRDwm9LUKQsLUfHtd6g/fbpNc+5DhsB/2VKIwsNNnZL62ZDA9bRyXRrCli0k2htjFyfoLrkc/2UNsk4d47w/7K67ET9spA29ItNEgAgQASJABOxLgIQG+/J3xdlJaHDFXac1GyNAQkM750Kr0aD0o49Rd/Qopxff0xPh77xt9Et24dUM7Pn6c07/hS+/CamHp0uewIKrlTi8NqvV2vuM64HkUa7zkKKyqABbP3mfwyF57EQMmjHHJc9FW4u+mVmJ4xuzoVZx1QaWYmvkgjiE9uqa6Je8C2dx+KfvOW6GJyRhwoO/c/noJDqwRIAIdA0BJizkVOc0CQtlqbqIBbnacCvdVC8kAgkS/BJ0ogITF2J9YyHkCznDNfX1qFq3HtVbtwAqtVHToogI+D+4DO4DqWaNqext3U8pV2P7F5fQIDOkyOILeJj6SAq8A9xQUXATv336IQDD71Tf0HDMZCkb+Xxbu0f2iQARIAJEgAjYjQAJDXZD77ITk9DgsltPC7+NAAkNHRwJjUKBotdehzwjg9NTGBqK8LffAstP3LKVXsvFjv98wnntrr+8Ak//AJc7fHXVcuz6OhWKBu5Di9hBQRg0LcqlHtie2for0g/va3EGeLjrLy+75Lno6AehLL8Wh9debXVuBEIeRi6IR1is7cUGlopi93//jeJcrkg2fskjiEju09ES6H0iQASIgNkE1Bo1cqtzkVbRJCxcqbiCRnWj2XZEfBF0qZBuCQsxvjFgrxlr7EKFbP9+VP7wI9TVhvpBLfvyPTzge/fd8J4+DSyqk5rjEDCWljJpZBj6jo8A+z2267//4kRSMs8nP/IHhMbGO84iyBMiQASIABEgAjYgQEKDDaCSyXYJkNBAB4QINBEgocGEk6CuqUHBCy9CVVTE6S2OjUHY66+D72bIcVtVVIgtn7zH6Tfr/56Db2iYCTM5TxeNWoN9P2agPF/GWVR4gi9GzY8Dq8/gKo192WdFoOuqKvVLZrfjJy5f4SoIzF5ndWkDDq3JRH0NNy0Iu6nJIhvC43zNtmnuAPazvPWf7+se1jQ3JhjOfvJ5l06FZi5H6k8EiIBxAhqtBnnVefrizekV6WhQNZiNi4kILGKhORVSnG8cRIKO0zU2XrmC8q+/hiKbm3ZH7wCfD68pU+B37z0QeHub7RcNsC2BquJ63WWOlukGPXzEmPa7PhCKBDCWyjMiuS/GL3nYto6RdSJABIgAESACDkCAhAYH2AQXc8F1nnC52MbScs0mQEKDiciUBQU6sUEj4z44l/btg9AXXwRPLNZZklWUY+MHb3CsTnvs/xAU1cvEmZyj26X9+Ug/WshZjHeAFJOXJ0MobsoF7Sqt7Hoetn/+MWe5I+++HzED73AVBBatk4kMB3/KRE0Z98GbTmyYH4vweD+L7Joz6PSWDbhy5ABnyOCZdyFp9HhzzFBfIkAEiACYsHCt5pq+xkJ6eTrqVfVmk2Fpj+J94/XFm9l/myIsNE+kKisDq0FVd/hwm3NL+/RBwPIHIY6ONts/GmB7AlqNFvt+uIKy2y5zjFoYhx4JflCrlNj0j3dQV1mhd4bVGpr91AvwCnCd2li23wmagQgQASJABByVAAkNjrozzusXCQ3Ou7e0MvMIkNBgBq/G9HQUvf43aJWGXLhsuMeI4Qh66ildvlt5fR3WvvESx+qkh36PsPhEM2bq3l2Lcqp1D4hbNpb2ZtKDyfANdu/ei7PA+zNbNyL98H7Dl32BAKxuh1hqiISxwKxLDJHXK3FgdSbYzc2WjYkNI+6KRY9E24oNisYG/PrhW5DXGQRGVsR77jMvQ+LuemfZJQ4dLZIIWIkAExau11zXCwssFZJMyb2sYMpUQp4Q8X7x+ogF9t9iQdPlBnOaRi5H9cZfUb1xI7QK40WkhcHBukLP7sOGuVR6Q3M4OkLf3ItlOLUll+NKWJwPRi+K1+1b6oE9OLd9M+d9qgvlCDtHPhABIkAEiEBXESChoatI0zzNBEhooLNABJoIkNBg5kmoO3kSJR98CGg0nJEsvUDAit9Bo1Zh9St/4bw39v7l6Nmnv5kzOWB3uQwougiUZgAqOSCUAEGJQGg/QNJU7LpBpsDOr1Ihr1dxFjB4RhRiBwY74KJs6xLLf73h/b+hvrpKP1FEUgrGL33UthM7kXV5gwoHVmW0EhtY+q0Rd8Ugore/TVebcewQTm1az5kjafQEDJ4516bzknEiQAS6FwEmLOTX5uuFBVbE2RJhQcATgKU/ak6FlOCfAFbQ2dLG0r/VHTmKipXfQ11WbtQMTyqF7/z58Jk9Sx+hael8NM62BBQNKmz74hLncxa7zDHt0b7w9JOgQVaLXz94EyqFoXC41NMLc59+CSKp1LbOkXUiQASIABEgAg5CgIQGB9kIF3KDhAYX2mxaarsESGiw4IDU7t2Lsn9/1mqk76KF8L3nHqx6+WmwB8zNbeSi+xEzqJumydGogYxtwKn/AjkshYwhX70BAA+IGQftkEdx4GwsSq5zb2xGJvtj+NwYl7wdaaw4+Ki7H0CvgUMsOHmuO4Q9WDnwUwYqC7mRDTweMHxuLNgZs1XTqNXY/NG7qC0v1U/BFwgw56kXqZi3raCTXSLQDQiwB/j5svymGgtlaboizrWKWrM9Z8JCrG+sQVjwS4BUaJ0HwvKsLJR/8w3kVzLa9Mtz3Dj4PXA/hP62+3fUbCg0oE0CZ7bnIfus4fcR65gyJhwpY3roxhxbtxrZZ05wxg+bdzfih44kqkSACBABIkAEXIYACQ0us9UOs1ASGhxmK8gROxMgocHCDajauBGVK39oNTrg4Yew/fRhKBoMD0TvmLMAiSPGWDiTHYeVpAMbfw8UnDPJibT6ybismgf4RgG3HpKw23VTHkqBSOJadRmagd2e458vFGLRS2/SrUKTThS3k6JRhYOrM1FRWMd5g4kNw+bEoGdKgAVWTRtyI/UiDvzwNadzeMoAeI6ag6wSGeQqNSRCAeKCPZES7g0PidA0w9SLCBCBbkOACQs3ZTc5EQs1ihqz/eeDjxjfGL2wkOifCDehdVPpqSorUblqFWT79oNTLbiFt5L4ePg/tBzShASz10AD7EOgoqAOe75L42wp+5w17dE+EAj5KLtxDds/+4jjnF9YD9z5x6d16T2pEQEiQASIABFwFQIkNLjKTjvOOklocJy9cBZPWAL+aQDYNWX2h/0/+0S/HMC3Fi6Sfet8DsA9AFg1PvZ07SiAd2/9baFZzjASGiykyB44VH7/Pao3cXPgMnOnY3tALjD8MzNw+mykjJtk4Ux2GnZhDbDpj4DaeB7n270qVcZgf81j0Gp5AHvy69sTfM8ATFyaBP8wDzstwr7T6tImvfc66muq9Y5EJPXB+KWP2Nexbjw7ExsO/ZSJ8oLWYsMds3ohuq9tilyyn/ddX36K4twc1DQqUSFTQCZXYWfQRFSKubeA2fEfFRuIJSOiMDkpBAI+feToxkeOXHdhAuznvrCuUC8spJalolph+PfcVDRMWOjl00tfvDnBLwHuItvUeGG1F6q3/oaq9eugbWg06qLA3x/+D9wPjzFj6OGzqZvoAP1YAeg936W3EtvH3puA0BgfXSTt9s8/Rnn+dY63Ux79I0Ji4hxgBeQCESACRIAIEIGuI0BCQ9exppmaCNC3fjoJ1ibwMYD/M2LUUqHBCwCrHjsIQCGAwwBCALBr8Sx3zRIAq6ywCBIaOgGRfaljKZRk+w2Ffpm5EwIlFGEh4N8qFttnwlQMmHpnJ2bq4qFMZNjwO5MnbdR4YFfVU2jQeHPGDBwfjPiFd5lsx9k6Gk2bdM8S9Bow2NmW2qXrUcrVOLgmE+X53BRd7AG/LcWGM+fTsPnTf6Beodavt1QSiH0B45rENSOtX4QPPlzUHwkh7J90akSACDgyASYsFNUVcSIWKuWVZrvMhIVon2h9xEJv/942ExaanWO+1584iYrvv4equNiozzyRCD5z58DnrrvAd7NuBIXZkGiA2QSyz5bgzPZrnHERiX4YuaBJRGDpkljapJYtqt9AjFm8zOy5aAARIAJEgAgQge5OgISG7r6D3c9/Ehq63545usfsijKLPT8L4AyADwHM6UREw+cAHgOw55ad5jw87Gn1JgDsmnk8gJudBENCQycBalUqlHzwAepPs21vaqf5CtTweRCFh4EvlaL3yLEYMnt+J2fqouHFacCX40yOZGARDIdqH0KRojfHwXBxKkb5rgLvsYNAcFIXOe9Y05ze/AuuHD2od0qXNunlNyGSWCf3tmOttmu9USrUOLQmE2U3WosNQ+6MRq/+QVZ1aMO5fDy77iKGlB5DZEM+x/Zh/5EocAtvcz6RgIf3F/bDvIERVvWJjBEBItA5AuzhfHF9MUdYqGisMNsoDzz09O6JlIAU3Z+kgCR4iLoukk+Rl4fyb79F46XLbfruMXIE/B5YAlFIsNnrowH2J9BYp8T2Ly9B0WAQuoViPqb/ri/cvcVQNDZg09/fRqPMUCNEIBJjzlMvwMPXz/4LIA+IABEgAkSACHQxARIauhg4TUcRDXQGbE5gI4C5FgoNLNF40a3Im1gA3OtLwFcAHr4lZvylkyshoaGTANlwjVyOojfegDz9is7aOb4SlTwNeHwBRD3CETdyDEYsWGyFmWxsghV+/mqSyf1w9qgAACAASURBVDUZmDdXGsbjYt1MjmPugipM9fkHxPwGIHwg8MgegO9aNRpYtMsv772OhhZpkyJT+mHcAw/ZeBNdxzwTGw7/fBWl11sXX71jpvXEBiYyPLnmgg6sh6oOM0p2gK81FHuvFXpie/BUaHnt57/+6J7+JDa4zvGklToogZL6ErAUSLoCzuVpKG8st8jTKO8onaiQHJCMJP8keIo9LbLTmUHq6mpUrv4JtXv2ABrDv0ktbYqjo3V1GNxSUjozFY21M4FTW3ORe6GM40W/iRHoPTxM99qZrb8i/fA+zvv9p96JvhOm2tlzmp4IEAEiQASIgH0IkNBgH+6uPCtFNLjy7nfN2jsjNLC0SN8DYFehxxlxd+KtSIesW1ENnVkRCQ2doddirFpWh6K/vgLFteu4yFeijNf0pZ8nFCJ24hSMe+RxK81kQzPpW4A195s8QZkyCvtqHodWa3jAyuNpMcH73wgUtdDH7l0F9OaKESZP0k07luTlYOcX/+R4P/qeJYimtElW3VEVExvWXkXJNa7YwDIZDZ8bi8hkbv0EcyfPKKrFrE8PQalmGeua2oDqC0iQXeWYOus7EFkeTBduu7HIhq1/HkNplMzdBOpPBDpBoLS+lBOxUNpQapG1nl499amQWMSCl9h+6dC0SiVqtm1D1c9roWloMLoegY83fBcvhtekSVSHwaIdd5xBpTdqsW9l00WW5uYdKMWUh1MgEPBRXVKMLZ+8p6vR0Nw8/Px10QwCochxFkKeEAEiQASIABHoQgIkNHQhbJpKR4CEBjoItibQGaHhHwCeBMD+ftqIoywRfnM1Qvbfra/zmr46EhpMZ9VhT1VlJQpffAkXym6i+JbQwAYFuXth5iefQ+Dj06ENu3b4fi6Qw6030ZY/Co0bdlY/hXq1L6dLP4+t6O12m42YCcBS9iPhOu3U5l+QQWmTumTDVUo1jqzNQnFeDWc+Hp+H0QvjEBbHPaOmOqXWaDHvsyO4mM8t/irSKDCzeBvEGqXelJwvwdaQ6VDx23+ow2o2bHh8FBWINnUTqB8RMJNAWUOZQVgoS0NJQ4mZFpq6R3hG6Is3sxoLPhL7//5mqZ4aTp/W1WFQFrDyXUaaUACfO++E78KF4Ht0XfomiyDToA4JaNQa7Po6DdWlXEFpwgO9EdTTC+xM7P3mPyi8msGxNW7Jw4hM7tuhfepABIgAESACRMBZCZDQ4Kw767jrIqHBcffGWTzrjNDwC4B5AJ4A8EkbQNiTLyYysG8RbSfl7ZgmCQ0dMzKrh7KgAHuffQr5ijr9OF8tH8Mj4xD6+msQeNnvFmS7C5HLgHdYDnnDze22+mu1wNHaZbip6MPpEiq+gjFeX4NFNXAbD3ghH5B0fWoJszbPSp11aZPefQ0NtYYH35Q2yUpw2zCjVmp0kQ23iw0CIQ9j703UPZAxt+1ILcKKlYbaKy3HJ8gyMaD6IsdkuldvXPLm/kwYm/PLJYMxNSXUXHeoPxEgAkYIsJoKLBUSS4PE0iGxmguWtB6ePfQRCywdkiMICy3Xobh+HRXffouGC9x/d1r2cR8yBP7LlkIU3nbNGEvY0Bj7Ecg4UYQLe25wHIjqE4Bhc2J0r+WnXcb+lSyjqqGFxSdi4vLHwGOhfdSIABEgAkSACLgoARIaXHTj7bhs+uRlR/guMnVnhIadAKYAeBRN9RiMNVYEmn2THAngWAdMc9p5P7JXr17CnJz2urjIjllxmSdXfoNLWzfqcyZ7aXm4QyOGODYGoX99FQJPB7xleO0o8M0MkyhkNw7HGdkCTl83fg2m+P4DUr5BYOF0WL4diBphkv3u3qk4Nxu7vvyUs4zR9y5FdP9B3X1pDu0/S6N04KdMlOdzC0Szgpnj7+sN/3Dzfu4e+OoEDmdxc2I3A+Br1bpaDR6qej0TDY+vi2poELi3y2lMfCBWPjzMoVmSc0TAUQlUNlbqRIVmYaGwro2b/R0sINwjnCMs+Eoti3yyNSd1TQ0q16xB7c5dbdZhEEVGImD5g3Dr39/W7pD9LiRQX6PQFYBWKQwpkUQSAWY81hdSDxHUKiU2f/QuZBWGOiM8Ph+z/u9Z+ASTmN2FW0VTEQEiQASIgAMSIKHBATfFyV0iocHJN9gBlkdCgwNsgr1cuLhnO85v3gBlYSGg1cJdy8NwjVjnjiQhAaF/fQV8Nzd7uWd83tPfAFtYEE37rUHjje2Vz0Kpleg7sgiGcd7/QbCoHcFq9ifA4Ac7Mu8U75/8dR0yjx/Wr4XlSF748hsQSaROsT5HXoSiUYX9P2agqtggADB/xW4CsFQTPkHtiwDNa6uTq9DntR3sx7fNFll/AyMqT3Dez3OPwkm/O9pFxC6ZXn5tGjwkQkdGSb4RAYcgUC2vbopYqEjT/V1QV2CRX2EeYTphofmPv7Rz9VsscsKMQVqVCjXbtqNq7Vpo6owL+HwvL/jdew+8pkwBTyAwwzp17Q4Ejm3Iwo30So6rg6ZFIW5wsO61S/t24sLO3zjvJ40ej8Ez7+oOyyMfiQARIAJEgAjYlAAJDTbFS8aNECChgY6FrQl0Rmig1Em23h0b208/tA9nfvsVmvp6KIuKINYAo28JDWxqSVJvhL78MvhSB3rwfPw/wPbnOiRztHYp8uXcvL/J7rvQx50F4rTTpr8HDH+sQ/vdvQOlTbL/DjbWKbHvhyuoLW/kOCP1FGHikt7w9Ov45+5kbgXu/qKDYDGtFpPL9sJfwX0QtDN4MqpE7d+OXvvYCNwR7dgPOu2/k+SBKxJgwkLLiIWbMhbAaX4LcQ/hRCwEuAWYb8QOI3R1GM6cQcV337Vdh0EggPeMGfBdtBACT9dISWiHrbDrlEU51Tj4UybHB78wd0xelgxWf6i+ugq//v1tqJUKfR+ppxfmPP0ixFIHu8hiV5I0OREgAkSACLgqARIaXHXn7bduEhrsx95VZu6M0EDFoLv5Kbl68ihObPhZtwp2E1FTVIxx6qaIhuYm7dsHIS++CL6Y+7rdlm5CRMNNeQqO1HKjEnyFhZjs8zH4LYpfG12Di0Q0FOdkYdd//8VBMHrxMkT3G2i3rXXFiVnKib0r01FfbXgIwzh4+Ep0kQ3u3u3/3K06cR0vbrjUIbpAeSkmlh3g9CuWBONAwBignfzY78zvi8VDe3ZonzoQAWcnUKOoQXp5uq6+AotYyJflW7TkYLdgJAcmIyUgRScwBLoFWmTHnoMU166h/Ntv0Xix7X973AYNgv+yZRBH9LCnqzS3DQmoVRrs+O9lyCrl+lnYr5NJy5L1KQAPrf4O1y6e43gxfMFixA2htHw23BoyTQSIABEgAt2IAAkN3WiznMRVEhqcZCMdeBmdERqWAPgewEEA44yscSKAPQCyAMR3kgEVg+4kQGPD886fweE1K/VvaWR1GF1UBZ6Gm4fFbcAAhDz3LHiOIDZ0UKNBqZFge9WzYKmTmhsPWkz0+RQBIm6hQqNIXaRGg9G0Sa+8CZHYkGrKBkeOTBohUFvRiH0r09FYp+K86x0gxfgHeutyXLfVvjmSi9c3p5nEdVT5UfRo5KZzORgwGkXStnNkvzo7GctH9TLJPnUiAs5EQKaQcSIWrtdet2h5TEhgokKzsBDkHmSRHUcYpK6uRuVPa1C7e3fbdRgiInQCg/sgEq0dYc9s6UPqoZtIPcT9nRI7KAiDp0frpjV2oSGgRySmP/4kWI0GakSACBABIkAEiABAQgOdgq4mQEJDVxN3vfk6IzSw+P4iAOycxgC4/Vs4KxD9MIAPAfylk2hJaOgkQGPD89MuY/9Kbh3vmVPmourzz1s9RHAfMhjBzzwDnqjth542cLG1SbkMeCcCgPGk9Gdl85DVyGqPG1q82yEM9Nhkgns84IV8QOLcKR5Y2qT177yKRlmtnknPvgMw9j7XqE1hwkHo8i6sVsP+H69A0ajmzO0b4o7x9ydCLDVeJ8HUiAZm1FNVixklO8FrUdChRuSN7UFT2oxqoIiGLj8KNKGdCDBhIb0iXV+8+XrNdWjb+D3TnouspkKzsJASmIIgtyDw2okastNyzZpWq1CgZts2VK1dB01Dg9GxfE9P+N1zN7ymTgVPSHVdzALcDTvLKht10QxqleGzmMRdiBkr+kLsJoRGo8Zvn/4dVUVcIWL6759AYM8mIYIaESACRIAIEAEiQEIDnYGuJ0BCQ9czd7UZTREaWNTCUAAszwo31wrwOQCW0H43gDkAmr+BzgCwGQDLB5IAwLIcA4bdIKHBBifT2G2z+S+8DvWZsyj7179bzeg+fBiCn3zS/g8Rvp8L5Oxv5V+ZMgr7qv8ArU77amrugipM8/0AIh43NY1RnDETgKXsR8K5m7F9H3Pfg4jqO8C5F+7gqyu/KcOB1RlQKTQcTwMjPTH23gQIRa2LqJpUo6GFtUFV5xBXl82xf8p3MHI9jEctUI0GBz805J7FBOqV9ZyIhWs11ywSFvykfvpoBSYwsJoL3V1YaIbK6jDUnziJipUroSpi90qMNFaHYfp0+N69iOowWHwau9dAdi4O/XwVRdnVHMeHzu6F6L5NqcAyjx8Gi5xs2WIGDcXIRfd1r8WSt0SACBABIkAEbEyAIhpsDJjMtyJAQgMdCmsTGATgsxZGmQjgByAHQOmt1wsBzGvRhz3RZamRXgfw2m0OeQFgib9ZjDwbdwhA8K3+7JoTS6+0ygqLIKHBChBvN1Gefx3b/s1KbRgaK9DnHRiMmp07Uf7Fl61m9Rg1CkFP/J99w97TtwBr7uf4ptYKsav6CdSoQjivj/b+GuHidNPo3bsK6D3TtL7duNeJjWtx9cQR/QoEIjEWvfwmhI6QGqsbc7WG68V5NTj8cybnliizGxrjg1EL4yAQctNN1MlV6PPaDrQIUmjXDYlajpnF2yDUGtI0NQqk2Bo8HWo+9xYyu4R9+bVp8JDQ7WRr7C3ZsC8BJixcqbiij1jIq86DBlxRzxQPfSW+HGEh1CPUaYSFluuX5+ai4ptv0Zia2iYWFunov3QpRD2oDoMpZ8dZ+uRfqcDRX7iCdVCkpy7VHxPZ5PV1+PXDt6BoqNcvWSiWYO4zL8HNy5DW0ll40DqIABEgAkSACHSGAAkNnaFHYy0hQEKDJdRoTHsExgPY1wGiawBaxjW3JzQwU24AngNw761xdQCOAnjn1t/W2BESGqxB8TYbNaUl2PSPtzmvzvjj02A5dFljqRLKv/pfq5k9x41D4B//YD+xQaMGvpoEFBgKDKbWT0Fq/VSOr5GS8xjh9aNp5MIHAo/sAfitb42bZqB79DKWNolFMrCIBmqOQaDgaiWOrMtqJR5EJvlh+NxY8PjcjwYPfHUCh7PKTHY+qfYK+tZc5vS/6N0HV7x6c14bEx+IlQ9TwU6TwVJHhyLQoGpARkWGvnhzbnWuRcKCj9hHV7SZpUFif4d7hDulsNC8earKSlSuWgXZvv1oS8EU9YxEwIMPwq1/f4fac3LG9gSUCjW2f3EZDbWGKFH2O2nqw8nwCXLXOXB7DSj22qA75yJ5zATbO0gzEAEiQASIABHoZgRIaOhmG+YE7pLQ4ASbSEuwCgESGqyCkWukvroKv7zLDVKZ8ugfERITp+9YvWkTKr5j2bO4zWvyJASsWGE/saEkHfhiLKBWoEYVhJ3VT0OjNYgEYn4Dpvt+ACnfUIegTYQCMbDiIBCcZAPKjmWyKPsqdn/FTYs19v7l6NmHHhg50k5dSy3HyU05rZ7zxd8RggGTIzkPOnekFmHFyjMmu8/XqjGzeDvc1IZc60q+CFtCZkDJF+vtfLlkMKamtF0o2uQJqSMR6AICzcJCWnmaTlzIqcqxSFjwEnsh2T8ZyYHJusiFCM8IpxYWmrdGI5eD/b6v3vgrtI2NRneM7+0Fv3sXg/3+5wmcW5TvgiPbLae4sOcGMk5w02glDg9F/4lNF1QqCm5i278+BEuv1NxYlOzM/3sWAqrd0S33nJwmAkSACBAB2xIgocG2fMl6awIkNNCpIAJNBEhosMFJUDY2Ys3rz3MsT1j2O/Toncx5rWr9elSuWt3KA8+JExD4+9/bT2y4sAbaX1Zgf83vUark5pgf4rkWMdKTplGb9yXQ/x7T+nbzXic2/IyrJ1nAUVNj6ZIWvkRpkxxxW7PPleDMNhZgxm39J0UicZhBAFBrtJj32RFczOfmy25vTTF1uRhSxRUnrngm4KJPP92wfhE+2PD4KAhui55wRE7kk2sSkKvluoiFZmEhuyobai23mLopZDxFnpxUSD28eoDP46YoM8VOd+3DotzqDh1CxY8/Ql1eYXwZQgF8Zs6Ez/wFEHh6dNelkt+dJFBdWo+dX6VyBHA3LzGmr+gDkVgAdpZ2fPFPlF3P436ufHAFeiQ6/0WOTuKl4USACBABIuCiBEhocNGNt+OySWiwI3ya2qEIkNBgg+1gXwp/fOkpjuXR9y5FdH9WyoPbKn9ag6q1a1u97jFmNIL+9Ce73W7M3rABZ/aWcVI8BImyMd77P2A55tttLJJhzr9cRmTQaNRY//arkNfJ9Fii+g3EmMXLbHC6yKQ1CKQdKcDlAzdbmRo+NwY9UwL0r2cW12LmPw9BqTbcIm1vfp5Wg+klu+ClMkT8aHh8bA2ZDpXYA1v/PAYJIawEDzUi4BgEFGpFK2FB1aLWiKleMmEhyT9Jnw4p0ivSpYSFlpwa09JQ8d13kGdx8+237OM+dCj8ly6BKCzMVMTUzwkJsAiFfT9cQdkNw+cHtsyR82MR0dtft+Ks0ydwfD33UkpEUgrGL33UCYnQkogAESACRIAIWIcACQ3W4UhWTCfQ0WMy0y1RTyLQvQmQ0GCj/Vv9yl+gVin11ofPvxdxdwxvNRv7klm58gdU//prq/fchw9D8BNPgCcS2chL42YbZApdrmBlXR1QdQ1Q1EPAU2Gq79/hJeggZz2ryXDX5y6RLqmZXmFWBvb873MOTEqb1KVH1uzJ2M/d2R3XkH22lDOWL+BhzD0JCIk2FNbccC4fT665YPIcEQ35GFlxnNM/x6MX7l/xEOYNjDDZDnUkArYgoFQrkVmZqUuDxKIWrlZdhUpjKGJu6pzuQnckBSTpohbYn57ePV1WWGhmpiwqQsXKlag/fqJNjOKYGPg/uAxuKSmmoqZ+Tkwg72IZTm7J5awwNNYHY+6Ov1UAuh6b/v6WrhB0c+MLhZjz5Avw9DeI4k6MiJZGBIgAESACRMAiAiQ0WISNBnWCAAkNnYBHQ52KAAkNNtrOdW+9gkaZ4Vbz4Jl3IWk0qxneuunEhlWrUP3LhlZvug8ZguCnnwJPbMjxbiOX9WaPrs9Cfkblrf/XAo016OO1F8l1nwEwdrObB8SMB4Y+CiRMd/rCz7fzN5o26eW3IOxigcjW58LZ7Gs1Whz9JQs3M6s4SxNJBJjwQG/4hjQV4GSNiQ3PrrtoWmSDVovJZXvhr2j6GWIRQJH+7lj60qvwDgp2Noy0HgcnwIQFJibohYXKq1BqDCK4qe67Cd04EQvR3tEuLyw0s1PL6lD9y3rUbP0NWpVx0Ubg7w+/+xbDc9w4+6VFNHWzqV+XEFA0qLDti0uQ1xvOjEDIw7RH+8DTT6rz4fbPF+y1/lPuRN+JU7vER5qECBABIkAEiEB3JUBCQ3fdue7rNwkN3XfvyHPrEiChwbo89dY2fvAGZBXl+v/v6IshExuq1q1D1U9rWnnk1r8/gp97FnyJxEbeGszezKzEkXVZnHl8gtww+aFkCFT1QNFFoCwTUDYCIikQmAiE9gUknjb3zREn0KVNeuuvnNuGLEUWS5VFzfEJqJUa7F+dgfJ8btoKNy8RJi5NgoeP4WeOpVF6Zu0Fk2o2BMtLML7sINzFAkT4uUEqEugKg7NIF2pEwJYEmIjA6iqkljVFLGRUZlgkLEgFUl3EQnJAU/FmJiwI+FSouOXeMVGhdtcuVK75GZpaw8WCln14Egl85s6Fz9w54EubHh5TIwKMwOltecg5x42qSxkTjpQxPXSAyvOvY9u/P+Jc8PAKCMKsJ1gB6K6NdKUdIwJEgAgQASLQ3QiQ0NDddqz7+0tCQ/ffQ1qBdQiQ0GAdjq2sbP3nB6gsNOSATx47EYNmzOlwtqqNG3WplG5v0pQUhLzwPPhubh3asLSDUq7G9i8vo6FWoTfBbmOzB64BPVxTSOiIpbG0SeMeeAiRKU3Ff6k5PgF5gwp7v09HbXkjx1nvQCkmLkmC2E2of50ViN6TXoyVx6/hcFYZp3hncyf2MzM6LhATKw9DWHaDU9Nkxh+eQkBET8eHQh52GwIs7RETFpqLN7NCzgqN4d9wUxfChIXe/r11wgL7E+MTQ8JCG/DYxYD6k6dQ+cNKKAsK20TsOX68LopBGEApbkw9h67Sryxfpvu907J5+kl00QwCIV9XAHr7Zx+h/OYNTp+Jy1cgPIEKQLvKOaF1EgEiQASIgOUESGiwnB2NtIwACQ2WcaNRzkeAhAYb7emO/3yC0muGvLvxw0Zh2F2LTJqt5rffUP6/r1v1lSQmIvSlF8H38DDJjrmdWM76rDMlnGFxQ4IxaGqUuaZcpv/xX9Yg69Qx/XqFYgkWvfIm3TbsZiegrkqOPd+no1HGTSkTFOmJsYsTdQ9+bm91chVSC2qQXSpDo1Kti1qIC/ZEcpg3PCRCVBTk47dPP+QMC41NwORHHu9mdMhdRyKg1qiRU52jS4XEohZYvYVGNVckM8VfiUCCRL9EffFmJiwI+QZRzRQbrthHnpWFiu++Byv43FaTJifr6jBIYmNdERGtuQMCGo0Wu79ORVVJA6fn2HsTEBrjo3st88QRnNy4lvM+u8DALjJQIwJEgAgQASJABDomQEJDx4yoh3UJkNBgXZ5krfsSIKHBRnu377v/4uaVVL31qH4DMWbxMpNnq9m1C+VffInbr0xL4mIR8sorEHhaN8Kg/GbT7TptixIMbl5iTF/RByIxpcswtnEatRrr334F8vp6/dvR/Qdj9L1LTN5n6ug4BKqK67F3ZTpUCg3HqYjefhhxVyx4fPM/Ohxa/R2uXTzHsTfpod8jLD7RcRZOnjg0ASYs5NXk6YWFKxVXLBIWxHwxEv0TdWmQdBELvjEQ8Sn9iqmbryotRcWqVag7eKjNIcLQUPgvWQL3YUN1hXypEQFjBDJOFOHCHm6kQmSyv+73DGuNMhk2/eNtKBoMny2EYjFmP/kCPHz9CCoRIAJEgAgQASJgAgESGkyARF2sSoA+/VsVJxnrxgRIaLDR5h1d+yNyzp7SW7fkJrPswAGU/uvfgIb74FMcFYXQv74Cga+vVbxnRXHZbe6KgjqOvdGL4hAeT19q24JceDUDe77+nPP2uCUPIzK5r1X2hYx0PYGi3GocWnMV7GeiZWORPQOn9DT74WFNWSk2f/SOLg1Gc/PvEYkZjz9JBWG7fnu7xYwarQZ51U3CAkuHlF6RjgYV9+azKQthIkLLiIVYn1iIBCQsmMKuZR9NfT2qftmAmi1boFUaL6LN9/SE76JF8J42FTwRMTaXsSv1r69RYPuXlziCtkgi0F3qcPMU61AcW7ca2WdOcLAMnD4bKeMmuRIqWisRIAJEgAgQgU4RIKGhU/hosAUESGiwABoNcUoCJDTYaFvPbN2I9MP79db9wnpg5p//YvZssiNHUPrJPwG1mjNWFBGB0Ff/n73zAGvzuvr4XxMtJDYe2AbbGAPee++9nThOnNGk2btJmtF+GU2bJm0TJ02apGn2jjO8R+K9916AjW2wMcZmI0AItPie+8pIvGZJQhIa5z6Pn7Rw77nn/O6LjN//Pee8AnFEhNM2b1yQk16M/SuzeF9mt7hH3NS91bYD2cDBVUuRuX+3LUQqmxQYp33xVBEOrrGXPauLqs+EOPQc1t7pIA+s/AXnDuzhrRu16G7E9+nvtC1aEHgEmLBwqfySXVgozkCVyX6T2dGImbCQGJ5oa96cGJZIwoKj8BqZxzV63rwZZT//DLO2vFFLArEY6hnTobn5ZrdnGbbCdVrqwwT2LDuPK2dLeR72n9IZiYNiua8VXMzCxo//w/u+JjoWM558DiIxlTbz4aMl14gAESACRMDHCJDQ4GMHEgTukNAQBIdMITpEgIQGhzA5P+n0tk04vnGdbSFLd5//wl+cNwRAd/AgCt9+B+zFR/0hbt8O7V5+BZLYGJfsskVmkwXrPz4FndbePFQkFmD6w32gUFtv19FonMDKt/6OypIi2zedLY9FXH2XwJl9V3FyW24DB4fO7Youqc41dq0q12LV4tdhNtp/xkIjozH76T9BKKKyZL77FHjGMyYs5JTn8DIWdEZ+NpkjO4sFYr6wEJ4IqYg+sx1h19wcrtHzIdbo+XsYr1xpcqpyxAiE33kHJLHWF8Q0iEBLBPLOlWL3L+d50yLaKzHx7mSuNJ/FYsav77+Nsmt5vDmTH3gcsV3p4kdLfOn7RIAIEAEiQATqEyChgZ4HbxMgocHbxGk/XyVAQoOHTubcgb04sPJnm3WRRIpFf3vT5d2qjh5DwZtvNijdIAoPR7uXXoQ0Pt4l22f3X8WJrfwXqskj2qP3uDiX7AXLIlYSZ/Xbr/PCHb7gdnQbOCRYEAR0nOxl47GNOQ2aowtFAoxdlITozqFOxX98wzqc3r6Jt2bovIVIHDrCKTs02f8IMGEhtyLXJiywckiVxkqnA2HCQrewbrYeCz0ieoA1dKbhPgLVmZko/fZbVKdnNGk0JCkJEXf/DrIk6rPiPvKBb8lkMGPDp6d5lzpYG4+J96SAiQ1sZOzZgSNrV/BgUN+nwH82KEIiQASIABHwDAESGjzDlaw2TYCEBno6iICVAAkNHnoSck6fwM7vv+RZX/TaWxCJXa/frD91Cvn/+Cdqa2p4doUKBWL+9ALkqalORVNTZcSvH52CscZelkmmFGP6I32oAXQLJM/u24VDq5fxZt38f3+DPFTt1BnQZN8lwPo07F1xoUGZC6lczN1ADY2QOey8qTeMsgAAIABJREFUoVqPlW++xmvuyZ6Vuc++BNbkk0bgEGAiVW7ldWGhKB3pJemoMFQ4HaBIIOILC+E9IBM7/sw5vWEQLzBeu4bS77+Hbu++JimIY2MRcdedUAwb5nSvliBGS6FfJ3By22Wc2XeNx4P1/hkwpQv3NZb5tvrtN2Ay2H+/k4TIMPuZP0Oh1hBHIkAEiAARIAJEwEkCJDQ4CYymt5oACQ2tRkgGAoQACQ0eOsj8rPPY9OkHPOs3/elVKDSta+BcnZGB/NffgEXPbw7KGlBGP/UHKIcNczgidmP73OF83vyB07ugW3/XSzE5vLmfT9z61cfIO2u/9epqDw4/xxDw7puNFmxfchbFufwb6ExkYGIDEx0cHek7t+Lob6t50/tNnYVe4yY5aoLm+SABJixcqbzCy1goNzRe078594UQomtYV1uPhaSIJMjFch+MOHBcMpeXo+yXpSjfuAEw8fsg1UXJNXpesMDa6JlEwcA5fC9Goi2swsbP08HE67ohV0kw9cFekMqsf4fs/vFbXDxxhP/72Kz5SB451oue0lZEgAgQASJABAKHAAkNgXOW/hIJCQ3+clLkp6cJkNDgIcKl1/Kw7j1+qSTWDJq9kG7tqMnORv7fX4e5rIxvSihE5P33cy9EWhoVJdVY/8lp3j981VEyTLm/F4RC+ohsjp/ZZMTPf/0/sP/WjdRxk9B/6qyWsNP3/ZBAtc6ILV9nQFfGzyRi5ZPGLOoBkUjoUFQmoxGrF/+du7laN6QyOeY+9zJCFAqHbNCktifAhIWruqs2YSGtKA1ag/1MHfWQCQsJmgSkRqVy5ZBIWHCUXOvnWQwGlK9dB+3y5Q1E+zrrrNFz6PRpnMggUqlavylZCEoCTFzY9v0ZFF3mi9XD53VDp5QIjsm1C+ew+bMPeXzY74rTH38GQiH18QnKB4eCJgJEgAgQgVYTIKGh1QjJgJME6C2ak8BoesASIKHBQ0fLXiYu/we/+fOk+x9Du26JbtnRmF+A/L+/BmPe1Qb2whYuRNjCW5ot77B32Xnkni3lrR21MBEdurcu48Itwfm4kavnzmLLFx/xvKRmjT5+aK10r7xIj63fZMBQzb/1HN87EoNnJThcSuX8of3Yv/xHnjcpYyZgwPQ5rfSQlnuKABMWrumu8TIWSmv4n52O7M2EhXhNvK3HQs+InlBISGByhJ275tRaLKjcsQOlS5bAXFzSpFnl6FEIX3Q7JLGU3ecu9sFqJ/tEIQ6tu8gLv11XDUbfmsj9vWE2mfDrf96CtpCfXTr14T8guktCsGKjuIkAESACRIAItJoACQ2tRkgGnCRAQoOTwGh6wBIgocFDR8tuuy95+Tme9dGL7kGXPv3ctqNZq0X+G2+g5vyFBjZDJ09G5IMPQCBseNu6MKcC2747w1sTE6/G2EU9HH5h6rYg/NDQ4bUrcGbPDpvnrI7yLS+/DqGIbh764XE67HL+xXLs/DGTlwXEFvca2xEpIzs4ZMdiMWPtu/9CeWGBbb5QLMa8Z19qdVk1hxygSS0SYMJCflU+T1goqW76pXRTBgUQoIu6i01YSI5MhlJibfpKw7sE2Jnqjx3n+jAYLvJf+tb3RJaaiojf3YWQ7t296yDtFpAEWB+s3z4+BYPeLlCLxAJMfaAXVOHWfitpO7bg2Po1vPi7DRyK4QsWBSQTCooIEAEiQASIgLcIkNDgLdK0Tx0BEhroWSACVgIkNHjwSfjxLy/wGvsNmXcLegwd6dYdWa+GgsVvQ3/8eAO7imFDEfOHP/DqSrM0flYGpuSqzjZfIAAm35uKsFi6XevI4ax55x+824edUvtg7J33OrKU5vg5gcZup7KQhs3ris4pkQ5Fl3P6JHZ+/wVvbvdBwzDs5tscWk+T3E+goKoArARSWnEa0ovTUVxd7NImPGEhIhkqKZXccQmkGxfVnD+Pkm+/Q/Xp001alXTqxAkM8v79SWx3I/tgN3VoXTayTxTxMNQXpnVlpVjz73/AZDDY5kjlCsx55v8go3Jdwf74UPxEgAgQASLQSgIkNLQSIC13mgAJDU4jowUBSoCEBg8e7Ip//RXsH5J1o++UGeg9vuX+Cc66VGs0ouijj1C5Y2eDpbKUFMS88AJEKutN2pz0YuxfmcWbx8q/DJnd1dltg3J+ZWkJVr75N17sQ+cvROKQEUHJIxiDPrktF2f28UuWsVuqY2/viai4ll8ss9vVGz56F0WXL9nwsRIas5/+M9TRVKrFG89UYVUhJyjUCQuF+kKXtu0c2pmXsRAqDXXJDi1yPwFjXh5XIkm3d1+TxkXh4QhfdBtU48ZBQBlp7j+EILbYWOZoaKQMU+5LhUgsBPt7YMe3nyE3I41HyRMXUoL4GCh0IkAEiAARCGICJDQE8eG3UegkNLQReNrW5wiQ0ODBI/n1/cUoycu17dBz5FgMmjXfIzuy2tOl334L7Wp+Cj7bTNqlM2JfegkCdRjWf3wKOq399hz7B+/0h3tDoZZ6xK9AM3ruwF4cWPkzL6z5L/wFyrDwQAuV4mmCAMsK2rfyAnLP8Ov0hyjEmHRPCpRhIS2yy886j02ffsCb17l3P4y5/Z4W19IE5wkU6YvswkJROgr09tJVzliLU8XZmjezUkhqqdqZ5TTXCwRMpaUoW7oUFZs2A2Z+T5W67QVyGcLmzYN61iwIZdYSNjSIgLsImM0WbPo8Hay3T/0x7o4kxHSxfmbknD6Bnd9/yft+ZMdOmPbo042WvHSXb2SHCBABIkAEiECwECChIVhO2nfiJKHBd86CPGlbAiQ0eJD/5s8/wrXzZ207JPQfjJEL7/DgjoB21SqUfPNtgz3E0dHQzn4c6SeqeN9LGdkevcbGedSnQDK+/dvPkZt+yhaSJqYdZj/9p0AKkWJxgIDZaMG278+gJM9egowtU0fJMOF3yZDKxC1a2fLF/3D1HL9XyvTH/ojIuE4trqUJzRMo1hfzMhZYzwVXRkdVR1vGQkpkCjQhGlfM0BovEGBlBLWrVkO7Zg1qq6sb31EkgnrKZIQtWABRWJgXvKItgpFAxt6rOLXdfsmEMejSKxJD51gzRw3VerASjPqKchseltU27bFnwMQGGkSACBABIkAEiEDrCZDQ0HqGZME5AiQ0OMeLZgcuARIaPHi2u3/8BhdPHLXt0CEpGRPueciDO1pNV+7YgcIP/8u7zWkUSHEkfCYEsXEQKqy9GGRKMaY/0gcSKTUxduRQzCYTfnntRV7fjeRR4zFw5lxHltOcACNQrTNiy1fpvAwhFmJsvBqjb02EUNSwEXt9BMVXLuO3D97mUWmfmISJ9z4SYKQ8H05pdSlPWLiq45e2ctSDDsoOXMZCSkQK918SFhwl13bzWOnAik2buCwGs9b+4vZGj5QjRyL89kWQtGvXds7SzgFPQFdWg/WfnIbZZLHFKpWJMO2h3pApJdzXDqz8BecO7OGxoN8lAv7RoACJABEgAkTAywRIaPAycNoOJDTQQ0AErARIaPDgk3Bw1VJk7t9t2yGqUxcuLd4bo+roMRQsXozamhrrQcv7IS+kO9innzgqCiK1BgOnd0G3/lQT3tHzaKzczcT7HkH77kmOmqB5AUZAW1iFrd+cgbGGX6Kla79o7ueL3VJtbuz64StcOsVv5D7pvkfRrnuPACPl3nDKqst4wkKeLs+lDdor2/MyFsJlVALNJZBtsIiVC9Tt3o3SH3+CKb/pjBVZn96IuPNOhHTr1gZe0pbBRID1Xdj18zlcu6DlhV3/d63CS9nY8L//AKi1zWGlF2c9/SdIpC2X3QsmnhQrESACRIAIEIHWECChoTX0aK0rBEhocIUarQlEAiQ0ePBUT25ej5Nb1tt2CI2MwtxnX/LgjnzTNefO4dobb0BXWYuj6qmoraexamIUmPm3GRBJWi7x4jWHfXyjo7+tQfrOLTYvRRIpFr7yOkRi6y1FGsFJ4FqWFrt+ykSt/b0RB6LPhDj0HNa+WSjlRQVcCQ32gqpuRMZ1ttbpbkGkCCba2hotT1i4UnnFpfBjFbE8YSFSHumSHVrUdgTYz4r+yBGU/vADDJdymnREmpCAiLvuhLxv37ZzlnYOKgI5acXYvyqLF3NkRyUm3JUMgVAAlhXJendpC67x5oy/+0F07JkSVKwoWCJABIgAESACniZAQoOnCZP9GwmQ0EDPBBGwEiChwYNPwpm9O3F4zXLbDlK5AgtfecODOzY0bcwvwNa/rURBNb9paUrlbnTs3R4xzzxtK6XkVcf8cLN1/3kLpVftLzg79kzF+Lsf8MNIyGV3E7hwtABH1l/imWU6wcgF3dEhsflb8gdW/IxzB/fy1o698150Su3jbjf9xl65oRwZxRlIK05DWlEaciv59c4dDSRGHoOUqBSbuBAlj3J0Kc3zQQL6tDSUfv8Das7aex/d6KY4JoYrkcRKJQmEzZcv88EQySU/JWDQm7D+k1Oo1plsETBxYfK9KQiLsZarPLVtI05s/JUXYZc+/TF60d1+GjW5TQSIABEgAkTAdwmQ0OC7ZxOonpHQEKgnS3E5S4CEBmeJOTE/+/gR7PmpfmNmAW5/fTGEQu/1RCjMqcC2b9JhzM+HpcraCDrMVIDUyp1cfoOkUyfE/vnPkMRSCaXmjraqXIvl//gLb8rgOTcjafhoJ54ImhrIBI5vzkHmQX4JF7FUiIl3J0MTbX3R1Nio0pZh5eK/w2Kyv6DSRMdi1lMvBM2L0gpDhU1YSC9OR05F0zfVm3uGmJCQGplqExaiFdGB/MgFTWw12dmcwKA/dqzJmIXqUK7Js3ryZAik0qBhQ4H6BoHDv2Yj63gRz5mew9ujz/g47msse23te2/yPuelMjlm//H/IFeF+kYQ5AURIAJEgAgQgQAiQEJDAB2mn4RCQoOfHBS56XECJDR4EHFe5hls/fJ/vB0WvPh3yFQqD+5qN11rqcWWrzNQclXH1QM2FRXDotWib8UmqMz2GsIijRoxL7wAWRL1GmjqYM4fPoD9y5bwvj33uZcQGkE3pL3yMPvBJuznbc+y88g7V8bzVhkWwokNdY1AGwvl6G+rkb5zK+9bwxcsQreBQ/0gcuddrDRUIqMkw1YOKac8B7X1apY7ajFCFmETFljz5mh5NJWcchSeH8wzXr2K0iU/QreH3zi3vusCuQya2XOgmT2LsvP84EwD0UXuQsd3Zxp87k97oBdEEiFXGm/zZx+C9XmqP4bedCsSBw8PRCQUExEgAkSACBCBNidAQkObH0HQOUBCQ9AdOQXcBAESGjz4aBTnXsZvH77N22H203+GJibWg7vaTeekF2P/Sn694PbyUsTt/QywWHg+CCQSRD32KFSj6YZ+Y4dzY9Pe0MhozH32Ra+cI23iPwSMBjO2fpMBbYGe53R0JxXG3J4EkajxUi41VVVY+dbfYKyutq1jDULn/PH/AqIHiM6o42UsXCq/5JKwEB4SjpTIFDBRgWUusJ4L1MvCf34+HPXUVFKCsp9/QcXWrYCZ32i9zoZALEbotGkIu2k+RBqNo6ZpHhFwKwGzyYJNn6ehvNj+2c02GLOoB9olWJ/LC0cOYt/SH3j7xsR3xeQHHg+arDW3QidjRIAIEAEiQAQcIEBCgwOQaIpbCZDQ4FacZMyPCZDQ4MHDqywpxsq3XuPtMOWhJ8H+genpwW5Xr//0NCrq/eNXJBZi+sO9gfNpKHjnbdTq+f8wZj6FLVyIsIW30Mu7egdksZix9O8vw6C3lp5ig5VMYqWTaBCBGwnoymqw+at01FTZSyGxOQl9ozBoRnyTP1uN1e8eOGs+kkeO9TvIVcYqnCk5w/VYYKWQLmovwgK+uOlIUGEhYbYySExYaKdsR59NjoDz0zlmrRbalStR/tt61BqNjUchFEI1bizCFy6EOJpKY/npUQeM22m7riBtVx4vni69IjF0jvX3vOrKSqx+5w3e7w9CkQgzn3wOmph2AcOBAiECRIAIEAEi4GsESGjwtRMJfH9IaAj8M6YIHSNAQoNjnFyaZaypxk+v/om3dtxd9yMupZdL9pxZ1Fg2Q/KI9ug9zlov2JCTg/x//BOmgoIGZlkTTZbdIAwJcWbLgJ1beCkbG/73Hi++8Xc/iI49UwI2ZgqsdQQKL1dgxw9nYTHX8gz1m9QJPYY0/nKJfV6sWvw6qisrbGtCFErMe/5lSEJkrXPIw6v1Jj3Olpy1NW/O1ma7JCxopBouW4HLWohMRXtlexIWPHx2vmDeXKlD+ZrV0K5b16gAXuejYthQhC9aBGmc9e8xGkSgLQmUF+ux8bM03ue8VC7CtAd720rl7fn5O2QfO8xzs8/EaegzaVpbuk57EwEiQASIABEIeAIkNAT8EftcgCQ0+NyRkENtRICEBg+CZ3V5l7z0LNiN+Lox7OZF6D7Is3XXWTbDhs/SUF5kL98iCRFh5qN9IJWLbb6Yy8qQ/+ZbqDl7tgEFaXw8Yp5/DpJY75R58uAxtNr0iU2/4tTWjTY7QrEYC19+A2JqONpqtoFsIPtEIQ6tu8gLUSAARi3sgfbdGi/1cnbfLhxavYz/UmrSNLAXU7406gsLLGMhqyzLJWEhVBpqExWYuBCniiNhwZcO2sO+WPR6lP/6K7SrVsOiY72EGh+yPr0RcccdCOne3cMekXki4BgB9vvd9u/PgvVnqD8Gz4xHQl9rps3Vc2ex5YuPeN9XR8Vg5h+eC4iSeI6RollEgAgQASJABNqGAAkNbcM9mHcloSGYT59ir0+AhAYPPw9LX3+Zd0N5wPQ5SBkzwaO7Xs4owb4VF3h7pIxsj15jG94CrTUYUPjf/0K3a3cDn4RKJaKfegqKAf096q+vG//tg7dRfOWyzc32iUmYeO8jvu42+ecDBE5suYyzB67xPGGiH2sOrY6SN/DQbDJxZTZ0pSW274mlIZj33MteayLfGLZqUzUySzNtpZAulF2Aubbx2vnNYQ+VWIWFlKgUpEakIi6UhAUfeEy97oLFYEDFho3QrlgOs7a8yf1DundD+B13QN6nj9d9pA2JQHMEGhOSo7uEYtztSZxYajIYsPa9f4GV0Kw/WF+G2K4kmNHTRQSIABEgAkTA0wRIaPA0YbJ/IwESGuiZIAJWAiQ0ePhJWPPuv6DNv2rbJWXMRAyYPttju7Jsho2fp0FbaM9mEEuFmPVYX142Q30H2M28sl9+QdlPPzf0SyDgejaELVgQlE0LWX1lJhYB9hI4A2fOQ/KocR47QzIcOATYz+OuX87h2gUtLyhVeAgm3ZPS6M9k1rFD2Pvz97z5PUeOxaBZ870GxmA2cKWQWLYC67PAhAVTLb/nhCPOqCQqJEck28ohdQrtBKGg8YbYjtijOf5NgPVdqNi6DWXLlsJcbBfTboxK2qULwhfdBvmgQZTh4t9HHpDeV+uMWP/JKRj0drFVKBJgyv2pUEdaBeRj69cgbccWXvzdBw3DsJtvC0gmFBQRIAJEgAgQAV8jQEKDr51I4PtDQkPgnzFF6BgBEhoc4+TyrI2fvI+CbHt2gaf/oZl7pgR7l/OzGer3ZmguEN3+/Sj84INGa2QrBg1E1BNPQqRSuszCHxey2sqsxnL9MfvpP0MTQyWl/PE828JnQ7UJW7/JQHkRv/l6TLwaY25NhFDEf/Fea7Fg3X/eQlk9gZI1D5377EtQhoV7JASj2YjMskykFVmbN58rOweTxXlhQSlRomdET66/AvvTWd2ZhAWPnJh/Ga01m1G5cyfKfv6l0b5AddFIOrRH2K23QTlieFAK2/51qsHr7YHVWbh0mp+pkDq6A1JHd+SglF69gl8/eBvss7xuyFShYL87hCgUwQuOIicCRIAIEAEi4EUCJDR4ETZtxREgoYEeBCJgJUBCg4efhB3ffYHLaSdtu3RK7YOxd97rkV3Z7elNX6ajLL/KZp9lM7DeDCEKiUN7GnKvoODNN2G8cqXBfHG7doh97lmw/g3BMm5s5Mhe9M57/hW6ZRssD4Cb4qwsrcbmr9J5N2CZ6W4DojFwWsOfp9z009j+7We83bsNHIrhCxa5xSMmLDAxgWUrcMJC6TkYLUanbcvFcl7GQrw6noQFpykG7gL2olW3axfKli6FMc+e2XdjxOKYGITdcgtUY8dAIBIFLhCKzO8JXMvWYueSTF4c6igZJt+bCpFYyIkLG/73HoouX+LNGXXb7xDfd4Dfx08BEAEiQASIABHwFwIkNPjLSQWOnyQ0BM5ZUiStI0BCQ+v4tbh6//KfcP7QPtu8mIRumPLgEy2uc2XClcxS7Fl6nrc0aVg79J3QySlzrEEny2yo2n+gwTqBVIqoRx+BavRop2z642T2wmDpG6+gRldpcz9xyAgMnb/QH8Mhn9uYQMGlcuxYkgkmCNYfA6Z2QfeBMbyvsXJm3MuqHHszaVb3e9ZTL0AT087pSJiIwMofcaWQitJwtvSsS8KCTCRDcmQyl63Aei0kaBJIWHD6NAJ/AScw7NmDsp9/blZgEIWHI2zBzQidOBECiWNieODTowh9lYDJaMaGT9OgK6vhuTj+rp6I7hTKfS1jzw4cWbuC9/0OPZIx/p4H6YKCrx4s+UUEiAARIAIBSYCEhoA8Vp8OioQGnz4ecs6LBEho8DDsG+v0speEs5/+k9t3ZS8mN31xQzaDRIgZj/aBTOn8CxxmT7tqFUq//wGol/5f57h65kxE/O4uCMRit8fiKwaLc3Pw24fv8Nxh2SgsK4UGEXCFwIVjBTjyG/+mq0AAjLktCbEJap7J/OwL2PTJ+7yvOZoRxcoe2YSF4jSu34LBYnDaZSYscKWQolKREmEVFkRCunHuNMggWcAJDHv3cT1/jLm5TUYtVIcibP58hE6bBqFUGiR0KEx/J3ByWy7O7ONn5nTtF41BM6xZaeVFhVj33pswm+zZYSKJFLOfegGqiEh/D5/8JwJEgAgQASLgVwRIaPCr4woIZ0loCIhjpCDcQICEBjdAbM5E+q5tOPrrKtsUVqd3wYuvuX3XvHNl2P3LOZ7dpKHt0Heic9kMNzqmP3kSBf/+NyzlFQ18lqUkI/qZZyAO90zdeLdDctLgqa0bcWLTr7ZVAqEQC195A5IQmZOWaDoRsBM4uvESzh8u4CGRykWYeHcKQiP4z9bWLz9GXmYGb+70x55BZFxn3teYsJClzbJlLGSWZqLazO8J4cgZhIhCkBSeZGve3FXTFWJh4IqJjjChOS0T4ASGfUxgWArj5ctNLhAqldDMmQ0mVAvl1qa5NIiAPxBgJSlZacr6GWkypRjTHuwNqVzMlUza9NmHvJ5cLK4BM+YiZfR4fwiRfCQCRIAIEAEiEFAESGgIqOP0i2BIaPCLYyInvUCAhAYPQ75w5AD2LV1i24W9rL7972+7NYWeZR9s+SoDJVd1tn1EYgFmPtoXMpXz2Qw3IjEVFqJg8WLUnOc3mWbzWOmL6D/8AfLevTxM0vvmWemawkvZto1jE7pj8oOPe98R2jGgCFgstdj1Uybys8t5camj5Jh4dzIkIfaMgZK8K/j1/bd489p1S8T4ex9GtjYb6SXWUkhnSs64JCxIhVIkRSTZSiF1DesKibD1nxkBdWAUTJME2MvVqgMHUMpKJOU0IzAoFFDPmgXNrJlgYgMNIuBPBJi4sPWbDBTn2X/HYv4Pm9cVnVOsmQpn9+3CodXLeGFFdYrH1IefpMbm/nTY5CsRIAJEgAgEDAESGgLmKP0mEBIa/OaoyFEPEyChwcOAG2vqeutf/gmJzH234q9e0HIvLuuPHkNi0W8S/9Zza0KtNRhQ/MUXqNi0uaEZgQCam+Yj/NZbA6aRZ01VFZb+/UUwEadu9J82G6ljJ7YGI60lAhwBg96ELV9noKKEn3XQITEMI2/uDoHQ/mvK7h+/QfaJozCYaqA366E3VePCICFKI8xO02QiQv2MhW5h3UhYcJoiLagTGFgGg+ESvxRYfTosa4EJDOyPSEUCAz05/kng3OF8HNuYw3O+XTcNRi9M5C6NVJYUY+17/4LJYC9PJxSLMfOJ56CJifXPoMlrIkAEiAARIAJ+ToCEBj8/QD90n4QGPzw0ctkjBEho8AhWu1F2I57djK8/5j33stvq9bIX4Vu/5t+0Y9kMMx7pA3mo+2tfV2zejOJPP0OtydSAXEhSEpfdIInlN7b1MGKPmL908jh2LfmKZ3vmk88hvH1Hj+xHRoOPQHmxnstEMtbwBYPkEe2ROrYDLpZftJZCyjoKw4qjXGmOuqFXC5A1KgRgDR6aGUxYSAxPtGUsJIYlQiKijIXge9rcE7G1yfNelC1b1myJJIFcBs3MmVDPng2RSuWezckKEWgDAjptDTZ8ehomg/3zVywRYuoDvaAMC+E+lzd//l/kZ53neUcXE9rgsGhLIkAEiAARIAL1CJDQQI+DtwmQ0OBt4rSfrxIgocHDJ1NeWIDV77zB26WxGuuuunEtS4udP/KzGboPisGAKV1cNdniuprz51Hw1mKYiooazBUqFIh65GEoR4xo0Y4vT2DlrljZq7ohD1Xjpj//1a0lr3w5fvLNOwRYNtLunzO5zBmD2cBlK7CshQtJ+3At3F6qrP1pAyIu8QWJ3H4SaDvy+yewfgpMTKhr3twjvAcJC945yoDepdZsRuWuXdAygSGP3wy3fuBMYFBPnw7NnDkQhYYGNBMKLvAJsM/lXT+dA/s9q/7oOyEOScPac1/KPLAHB1f+wvt+ZMdOmProUxAK7WXwAp8WRUgEiAARIAJEwLcIkNDgW+cRDN6Q0BAMp0wxOkKAhAZHKLViTk2VDr+89iLPwvh7HkLHpORWWLUu5bIZvj2D4txKmy2hyJrNoFC7P5uhvsPmykoUffQRqvbbX8bX/37o5EmI+P3vIQwJaXWc3jbAuC7/56vQl9tfLnQbOBTDFyzytiu0X4ASsNRacLniMpexcGbfVRhPhoJ9rW5YhGZk9t0Ovcr6DIprapG4vRrCeolEBoUAF8cq0S3KnrHAhAWpyLOhkQSHAAAgAElEQVQ/+wF6JBRWIwRqjUZU7tyJsmXLYcrPb5KRQCaDeto0aObOgUitJpZEICAIXDxVhINr7H2aWFAR7ZWYcHcyhEIBKktLsPZdVjKppt7vYCLMeOI5hMW2CwgGFAQRIAJEgAgQAX8lQEKDv56c//pNQoP/nh157l4CJDS4l2cDayyt/vsX/8hkAdv3Ri68Ewn9B7V6Z9ZMdseSszw73QfGYMBUz2Uz1N+MvZCv2LQJJV98CfZC6sYhiYtDzNNPQRof3+pYvWmg9OoVrPsPvwHvqEV3I75Pf2+6QXsFEAH2s5JbkYu0kjSkF6VzAkOFscIaYS3QJXMQIgr4PVUMIVU4228bTFLrS6zoTCNizpsQIgqBXCyHXCTHsLkL0Xs09Q0JoEfFJ0JhPXkqtm2HdvnyRjPX6pzkMhimTYdm9iyINBqf8J2cIALuIFCtM2L9J6dg0NszyVjvnMm/T0FYrIK76LHli//h2nn+72B9p8xA7/FT3OEC2SACRIAIEAEiQARaQYCEhlbAo6UuESChwSVstCgACZDQ4IVD/flv/weDvsq206BZ89Fz5NhW77ztuzMozLn+shIAy2aY/nBvKDXezSIwXL6MgnfegTHncoOYBBIJIu7+HUKnTfObskNpO7bg2Po1tlhYs8cFL72OEIWi1WdGBoKDACcsVOZaeywUpyGjOAPlhvImgxeYhehxciwUleG8OTp1MWpHX0VKdDJ6hibi3JfLYazS2+aEKJRgPV/c2Vw+OE6IomyMgKWmBpVbtqBs5UqYi0uahGRt8jwT6pkzqUQSPUoBSWDfigu4nMH/GUgZ2R69xsZx8Z47tA8Hlv/Eiz2iYydMo5JJAfk8UFBEgAgQASLgfwRIaPC/M/N3j0lo8PcTJP/dRYCEBneRbMbOqsWvo6K40Daj94Sp6Dt5eqt2LrhUju3f82/Sde0fjUHT2yZ7wGIwoOSrr1GxYUOjcSkGD0bUY4/6xUupTZ9+wGvsGNU5HtMeeapV50WLA5sAExbydHlWYaEojfuv1sCv690SAUmNDD2PT4TSrIZcLINMLIdMJEP3AbEYOK0LJ9Sd2bsTh9cs55nqPWEK+k6e0ZJ5+j4RaJKARadD+foNKF+3FmZt04KYUKWyCgzTZ0CkUhJRIhCQBK5klmLPUn5zZ3WkDJPvS4VILISurBRr/v1PfskkoQjTn/gjwtt1CEgmFBQRIAJEgAgQAX8jQEKDv52Y//tLQoP/nyFF4B4CJDS4h2OzVtb/910UXb5om9Nj2CgMmbugVTtv//4MCi7ZsxlYSv8Mls0Q5t1shhuD0O0/wPVusFTa+0bUzRFFRiD60Uch79evVbF7crGxphq//O1FWCz2cgl9Jk1Hn4lTPbkt2fYzAkxYuKa7xmUrsD9MWCirKXM6CiGEiNfEIzUylWvgHK3vhP0/XoTFbC+1xoyycmisLJrZZMKaf/8DlSXFtr1EEinmPfcSWMNyGkTAGQLmsjJo161DxW/rYdHbM2VutCFUh0Izew7U06ZCSJldziCmuX5GwFBtwoZPT0NfYS8HKRAA4+9KRlSciiuZtO2rT5CXmcGLjH5P8LODJneJABEgAkQg4AmQ0BDwR+xzAZLQ4HNHQg61EQESGrwAnv2j9MrZdNtOXfr0x+hFd7u8MyuXxMom1R9d+0Vh0IwEl226c6GpsBAF772Hmgy+j3V7hE6ezJVTYuU3fG1cTj+FHd9+znNr+mPPIDKOXz/f1/wmfzxLgL1cyq/KtwoLLGOhJB2l1aVObyqAgBMWUiJSOGGhZ0RPKCX8m+HZJ4twaC2/ASkTEscu6oGYLmpcPHEUu3/8hrd34tCRGDrvFqf9oQXBScBYUIDy1atRsXlLo/116qiwvguaeXMROmUKhDJZcMKiqIOKwOFfLyLruD0DlQXffVAMBkyx9r46f/gA9i9bwmMS3r4j2O8JQpEoqFhRsESACBABIkAEfJkACQ2+fDqB6RsJDYF5rhSV8wRIaHCemdMr9v7yPbKOHrKta9etBybd/6jTduoW7PjhLPIv2stbsNt20x/uA1V422Yz1A+o1mxG2dJlKFu6FLBYGsQqjo5G1KOPQN6nj8scPLHw4KqlyNy/22aa1cBf8OJrEAiFntiObPooASYsFFQV2HosMIGhpLrpmvVNhcGEhc7qztaMhUirsKCSqlqM+vjmHGQezOfNk8rFXCNShVqC3z58ByV5ubbvs7JKs5/5M9RRMS3apgnBS8CQmwvtipWo3LULMNuztm4kwrLPNHOYwDAZQqk0eIFR5EFFoLGSlAqNFFMf6AWJVIQqbRnWvPtPGKur7Z+9QiGmP/ZHRHToGFSsKFgiQASIABEgAr5OgIQGXz+hwPOPhIbAO1OKyDUCJDS4xs2pVUfWrUTG7u22Nez228wnn3PKRt3kotxKbP2Gn7Kf0DcKg2f6RjbDjUHp09JQ+N57TTYWDZ02FRF33ukz2Q2s7rK24JotjNZmn7h0yLSoTQgUVhXayiAxYaFIX+SSH11CuyAl0pqxkByR7JCwcONGFkstdv2Uifxsfr18TYwcE36XjKKc89jy+Ue8ZZ179cWYO37vks+0KLAJ1Fy4gLLly1F14CBQyy/LVT9ySYf20MyfD9Xo0RBIJIENhaIjAvUImIxmbPwsDZWlNTwuY27rgXZdNVzJpO1ff8rLTmUT3dFziw6CCBABIkAEiAARcD8BEhrcz5QsNk+AhAZ6QoiAlQAJDV54Ek5t24gTG3+17aQMC8f8F/7i0s67fzmHvHP2WvAsm2HaQ70RGuG7ZS3MlTqUfP0VKrduazRmcUwMoh5/DPLUVJeYuGuRvrICy15/mWdu6LyFSBw6wl1bkB0fIsCEBK55M+uxUJSOAn2BS951Cu1kFRYiU5EcmQy11D29Egx6EzZ/ld7gxVdcUjiGz++GLV/+D9fO8xvCs6blrHk5DSLAXozqjx2HdvUqVJ863SwQaXw8NDfNh3L4cMreokcnKAmc2HoZZ/fbLxkwCF16R2Lo7K7WX5aPHcLen7/nsQmLbY/pj/8RIrE4KJlR0ESACBABIkAEfJkACQ2+fDqB6RsJDYF5rhSV8wRIaHCemdMrzh3YiwMrf7atE0uluO2vbzptp6KkGr/97xRvXZdekRg6x/oPYV8fVUeOoOij/8Fc2nhte/XMmQi/43YIQ9qmBNSlU8ex64eveBjn/PH/qByNrz9YDvpXrC/mZSyw0kiujDhVHC9jQROiccWMQ2u0hXps+TodJgO//Fjq6A5o19WC3z54m2cnJqEbJj/wOFgpJRrBSaDWZIJuzx5oV62C4VJOsxBkKcnQzL8J8v796JkJzseFogZQkqfjPmfrJ/vIlGJMfbA3QuRiVJVrsfbf/4Sh2t4wnX3GTmP9mzp2IoZEgAgQASJABIiADxIgocEHDyXAXaJ/gQf4AVN4DhMgocFhVK5PbOwF9qLX3oJI7FxpiiPrL+LCUX6TQlY7WBPte02Vm6JlrqxEyRdfoHLHzkaniNu3Q/Tjj0PWs6frwF1ceWN/BnmoGjf9+a/0As5Fnm29jPVUYBkLXNZCURquVfFvqzrqXwdlB64MUl0DZ08KC435lHeuFHuWnm9Q8Wbkzd2RfWwN1xy6/hh/z0PomJTsaHg0L0AIWPR6VGzeDO2aNU2WqqsLVT5gAMJumg9ZMj0nAXL8FIaLBCxmCzZ/mY6yAruIwEwNn9cNnVIiuJJJW7/8GFfPneHt0GvcZPSbOtPFXWkZESACRIAIEAEi4GkCJDR4mjDZv5EACQ30TBABKwESGrzwJORnncemTz/g7XTTn16FQhPm8O41ehPWvn8CZpP9ZjOrG8zqB/vj0B08iOL/fQyzVtvQfYEA6lkzEX7bbRDKvFcS6sb+DPF9B2LUbXf5I96g9LmsuozXvPmq7qpLHNor23NlkFg5JPYnXBbukh13Lkrfk4fTO67wTIqlQgybG4vtX/0bFou9sS8r58F6wFADc3eegO/aMpWUoHzdr6jYuBGWqqqmHRUKoRw2jCuRFJLgmz19fJcyeRaoBBr7bO3QIwxMyGVZC2f37cKh1ct44Wti2mHGE6xkknOXRQKVIcVFBIgAESACRMAXCZDQ4IunEtg+kdAQ2OdL0TlOgIQGx1m5PLP0Wh7WvccvlcReBLKm0I6Oxv4xXNek0FEbvjbPXFGB4s8+h2737kZdE0dFIfL++6AYPNjjrjfan2H+QiQOof4MHofv4gbaGq2tFBLLWrhSyX8R76jZWEWsrccCExYi5ZGOLvXaPHardv/KC7icwS87pgoPQURsJs4f4v8MjVh4B7r29/zPjdcA0EYNCBhyc7nySJU7dwImu9B040SBVIrQiROgnjULknbtiCQRIALXCZQX6bHx8zRYzPYG6ZIQEVimqEIthbbgGta9vxgWk8nGjAm40x55GpFxVDKJHiQiQASIABEgAr5MgIQGXz6dwPSNhIbAPFeKynkCJDQ4z8zpFay+7/J/8Js/T7r/MbTrluiQLZbFsO6/J1FdabTNV0fJMfWB1IAo66Pbvx/Fn3wCs7a8UR6KIUMQee/vIY6OdoiXK5MunTyOXUuoP4Mr7Ly1hgkLGSUZtlJIuZW5Lm0dI49BSpS1eTMTFqLkUS7Z8fYik8GMrd+eQVk+/9Z6ZEcxCrN+hMlQY3OJNZxn/UXoxq23T8mz+zHBqfrECWjXroP+2LFmNxOGhkI9fTrU06dBpHZPg3LPRkfWiYD3CNRaarH1uzMozq3kbTpoejy69o+G2WTCho/eRUke/++ZvlNmoPf4Kd5zlHYiAkSACBABIkAEXCJAQoNL2GhRKwiQ0NAKeLQ0oAiQ0OCF4zSbjFjy8nO8nUbffg+69O7n0O4XTxbh4Nps3tzBM+OR0NdzL94dcsyNk1gJpeJPP4Vu3/5GrQpkMoTfuhDqGTMgEIvduLPV1IGVv+DcgT02u3K1Bqy8FTXVdTtqhw1WGCrsPRaK03C54rLDa+tPZEICExXqhIVohf/+3OjKarD5q3TUVNlv2LJYlerzKL1ygMdn4Mx5SB41ziVmtMi3CFgMBuh27uQEBuPl5n8OxLGx0MyZDdX48RCGhPhWIOQNEfARApmHruH4Jv7PUkyXUIy9PYn7e//YhrVI276Z5210lwRMefAJKkvnI2dIbhABIkAEiAARaI4ACQ30fHibAAkN3iZO+/kqARIavHQyP/7leZgMBttuQ+ctROLQlsvysBusmz5P4zUqlCnFmPlYX4jEQi95771tdPv2ofjzL2Au5ZeIqfNA2qULIh96ELKkJLc6teadf0BbmG+zmdBvIEbeSv0Z3Aq5BWOVhkpexsKliksubR8hi7AJC6yJc4wixiU7vrqo4FI5dvxwltcc2mIxwli5CgKBPatBKldg3nMvgf2Xhn8SMJWWomL9epSz/gvlFc0GEdK9GzRz50IxbBi9CPXP4yavvUSgoqQaGz9L4/W8EokFmHJ/L4RGyFBwMQubPnmfawRdN8TSEMz6w/NQRfheaT0vYaNtiAARIAJEgAj4FQESGvzquALCWRIaAuIYKQg3ECChwQ0QHTGx4l9/ha7M/vLc0fT7/Oxy7FhylrdF6ugOSB3teH8HR/zzpTmsoWnpkh9Rvn49YLE3v67vY+jkSQi/806IVKpWu95of4abbkXi4OGttk0GmiagM+qQUZzB9Vlgf3LKc1AL+4sdR9mxZs31MxZYz4VAz0Q5dzgfxzbm8BBVV6TDYjgC1iS6bqSOm4T+U2c5ipLm+QiBmqxslK9di8o9u5vtv8DclffvD828eZClpgT8c+8jx0Nu+DEBVjJp23dnUHRDyaS+E+KQNKw9jNXVWPufN6ErLeFFOezmReg+aKgfR06uEwEiQASIABEILgIkNATXeftCtCQ0+MIpkA++QICEBi+dwq/vL+bV+u05ciwGzZrf4u67fsrE1Qta2zx2645lM8iUkhbX+vuEmgsXuN4NNecvNBqKSKNG+O9+B9XYsa16wXbxxFHs/vEb3h5z/vgi1FH+W2LHF8++yliFMyVnbA2cL2ovwoLGhaTm/A8LCeMJC+2U7Vp1/r7IqiWf2E3bI79dRNbxItvU2loLyq4sg1xVA6HI+msO69Ew99kXodCEtWSSvt/GBGotFlQdPozyNWtRnZ7erDeswTP73FPPmglpXFwbe07bEwH/IZB58BqOb+aXTIqMU2HCnT0hEAqw95cfkHX0IC+gTql9MOaO3wfd3zP+c6rkKREgAkSACBCBhgRIaKCnwtsESGjwNnHaz1cJkNDgpZPZ/PlHuHbenpmQ0H8wRi68o9ndy4v0WP/Jad6crv2iMWhGvJe8bvtt2Mu3ig0bUPr9D7Do9Y06JEtJQcS9v0dIQoJLDh9Y8TPOHdxrW6tQazCf+jO4xLL+Ir1Jj7MlZ60ZC0VpyNZmuyQsaKQarmkzK4PEMhfaK9vTCx+AK/ux/YezvGam1RVZqCzexpX/EFz/TceRz5pWHzYZcJmAuaICFVu2oGL9BpgKC5u1IwoP55o7h06ZAlFoqMt70kIiEIwEyov1XClKs8meOccub0y+LxXqSDlyTp/Azu+/5KGRqUIx66kXIFO2PnsyGJlTzESACBABIkAE2ooACQ1tRT549yWhIXjPniLnEyChwUtPxK4lX+PSyWO23TompWD8PQ82u/thdmP5GP/F09QHekETLfeS176zDatVXvLlV9DtsTds5nknEEA1YTzCFy2CODzcKcdXv/MGygsLbGsS+g/CyIV3OmWDJgN1wkJ6cTonLmSVZbkkLKilak5Y4MSFyFR0VHUkYaGJB0xfacDmLzOgr7D2f2GZDiWXV0EoLIFSY28EPP2xZxAZ15keUx8iwDK2WHk43a7dqDUam/VM2q0rNLNmQTl8OASSwM9m86FjIlcChAArmbT1uzM8YZaF1ndiJyQNbYeqci3WvvsvGPRVvIjH3/MQOiYlBwgFCoMIEAEiQASIQPAQIKEheM7aVyIlocFXToL8aGsCJDR46QQOrlqKzP27bbtFdYrHtEefanL3ap0R6z48wbt5166bBmNu7eElj31zG/3x4yj69DOYrl1r1EGBTIaw+fOgnj0bwhD7i9amotFXlGPZG6/wvj3sptvQffAw3wTgQ15Vm6qRWZqJOmHhQtkFmGvNTnsYKgm1CgtRVmEhThVHwoITFEvydNj2XYbts8Kgz+fEBoVaaiuxFpPQDZMfeJy4OsHVE1NrDQbo9u9H+W/rUZOZ2fwWAgEUQ4ZAM2smQpKT6ew8cSBkM2gInN1/FSe25vLijYpTYfydPQEBsPXLj3H13Bne93sMG4UhcxcEDSMKlAgQASJABIhAIBEgoSGQTtM/YiGhwT/Oibz0PAESGjzPmNvhxKbfcGrrBttuoZFRmPvsS03unr47D6d3XuF9f+yiJMQmqL3kse9uYzEYoF2+AtoVK1BrMjXqqCgyAhF33gnlqFEQCO3NcW+c3Fh/BnYu7Hxo8AnUmGuQWZJp67HAhAVTbeP8m2Onkqh4GQtxoXEQCpo+IzqHlglcPFWEg2uybRPL8jajpjILqggZJCEi7utj77wXrNY4De8TMBUXo2LjRlRs2gyz1t5zpzFPhHI5VBMnQj1jOiSxsd53lnYkAgFGgJWh3PTFjSWThJhyfypXZu7svl04tHoZL2p1dAxmPP4sxFJpgNGgcIgAESACRIAIBAcBEhqC45x9KUoSGnzpNMiXtiRAQoOX6J/ZuxOH1yy37SaVK7DwlTca3Z3VXmfZDNU6+0tcTYwcU+5LpVut9YgZ8/JQ8u13qDrIb9xYH2pI926IuOceyJIbL33QoD+DJgzzX/gLcQZgMBtwrvScTVg4V3YOJovzwoJSokRyRDKXrcAyFzqrO5Ow4IHPneObc5B5MJ+zbDKWo/jiLxAILFztcaFYwIlns576E0RisQd2J5M3EmD9ZapPn+YEBt2Bg4Cl+cbnkk6doJ4+Haoxo8HEBhpEgAi0ngBXMumbDBTn6XjG+k3uhB6D20FbcA3r3l8MS71LC+xywrRHnkZkXKfWO0AWiAARIAJEgAgQgTYhQEJDm2AP6k1JaAjq46fg6xEgocFLj0P28SPY89O39XYT4I7X3270tn32iUIcWneR59ngWQlI6EO37Bs7Lv3pNJR89RUM2fYb3TfOUw4fhvC77mpwQ3j122+gvIj6MzBeRrMRTEyoa958vuw8jJbma8c3dh4KsQLJkcm2Hgtd1F1IWPDC54zFUotdP2UiP7uc262icD90pSchEguhjmLNoQUYOHMekkeN84I3wbuFuawMFdu2o2Lz5iZLvNnoCIVQDBkM9fQZkKWmkMAZvI8NRe4hAmf2X8XJG0omRXdSYdwdPWGxmLHho3dRkscvqdR3ygz0Hj/FQx6RWSJABIgAESACRMAbBEho8AZl2qM+ARIa6HkgAlYCJDR46UnIy8zgagDXHwte+jtkShXva6yZ68bP0qAt1Nu+LlOKMfOxvtwLQxqNE2C3hyu370DpDz/AXFra6CSBWAz1zBnQzJsHkVrNNX9c/o+/8OYOu3kRug8aGhSYmbDAxIS6Hgus34IrwoJMJOOEBZaxwP7Ea+JJWGijJ8igN2HzV+moLK2BxVyDoos/wWKuhlQmhiosBFKFgivZFqJQtJGHgbmtLXth02boDh4ATM33KhFpNAidPAmhU6ZAHBkZmFAoKiLQxgTY71GsZJLFXGvzRCwRYvJ91pJJxzesw+ntm3heRndJwJQHn2i25GIbh0XbEwEiQASIABEgAg4QIKHBAUg0xa0ESGhwK04y5scESGjw0uEV5+bgtw/f4e0255n/A6sDXH9cy9Zi5xJ+k9BeYzsiZWQHL3nq39tYqquhXbUa2pUrwRqvNjYEchk0M2eiJKEz9q36hTdl7nMvITQiMDNHmIiQVZZlK4V0tuQsDJbGGTX3FDBhoWdET6RGpSIlIgUJmgSIhNY+ADTangB7ubbl63SYDBZUlaWhvGAP55RcJYE8VIqeI8di0Kz5be9oAHjAZS9sv569cLXxBvX1wwzp0QPq6dOgHDYMAqr9HgBPAIXgqwRYhtfWrzNQcpVfMqn/lM5IHBSL/Kzz2PzZh2CXO+qGWBqCWX94HqoIEv989VzJLyJABIgAESACjhIgocFRUjTPXQRIaHAXSbLj7wRIaPDSCVaWFGPlW6/xdpv68B/Abs/VHzt/ysS1C/ZmoSKxALMe74sQhcRLngbGNqz5aukPS1C5fXuTAZ0NEeBaqAKiMA13e1EZFo55z78SMOVLWD8F1rC5fsYCa+js7GDCQlJEkq2Bc1dNV4iFVOffWY7enJ93rhR7lp6HxWJB8aWlMBnKuO1V4SEIUUgx++k/QR3FFzm96Z8/78VlL6SlcY2ddQf2t5i9IAgJgXLUSKinTEFI9+7+HDr5TgT8hkDG3qs4tZ1fEim6cyjG3Z6Emiod1r3/FvTl/MbswZTR6DcHSY4SASJABIgAEXCRAAkNLoKjZS4TIKHBZXS0sBkC7ErvEwDuBtCD9VIFcBQAu8a+zkly7O3o2GbWbAAwzUmbjU0nocENEB0xYaypxk+v/ok3ddxd9yMupZfta+wm8oZPT/PmdO0fjUHT4x3ZguY0QqDmwgWUfPU1qtPTG3x3v9CAKkEtBEIRRGFh6DZmHEbdfo/fcjRbzMjSZtmEBZaxUG2udjoeqVDKCQt1zZu7hXUjYcFpim2/IH1PHk7vuIKayksozWN/ZYAT0dSRMsT364exd97b9k76kQfG/AJOuGR/TAX2vi5NhSCNj0fo5MlQjR4FoVLpR5GSq0TAvwloC6uw6Yt0fskkqRBT7usFpUaCbV9/ClbOsv7olNoHY+74fcBcNPDvEyTviQARIAJEgAi0ngAJDa1nSBacI0BCg3O8aHbLBNj13tUApgNgV0e3ssujACYAYN9jb5j/1bIZ24w6oYG9HWqsHsMpAG87Ya+pqSQ0uAGiIyZYev6Sl57lmg/WjRtvzx3+NRtZx4t45qY92AvqKLkjW9CcJggw9lUHD6H0xyUw5lzmZtWgFntE/LJBqTI1khfcitCpUyEMCfF5npZaC7K12bZSSBnFGS4JCxKhhCuFlBKZwokLXcO6gn2Nhn8TYM/9/pVZyEkvRumVX2GousIFJBIJERolw9SHnkBsV7ph39wpW2pqUHXgACq2bkX1Kb4I3Ng6Lnth5AhOYAhJTKSXlv79I0Te+yEBi9mCLd9koPRqFc/7upJJaTu24Nj6NbzvKTRhmPnk89S7xg/Pm1wmAkSACBABItAUARIa6NnwNgESGrxNPPD3ewHAPwEwAYCJC3VviwcCYKIBu844GMARB1HUCQ3jr693cJnT00hocBqZ6wuWvv4yqisrbAYGTJ+DlDHscQGqdUas+/AEzCZ7veD23TUYvZAlx9BwBwFW8kS3dx/KfvoJl69eRrrQxDM73CyFHAIuuyHs5psQOmmST9VRZ8LCxfKLSCtK47IWMkoyoDfZm4Y7yoiJCD3Ce9hKISWGJUIiImHBUX7+NM9kMGPrt2dQmHMZxZeWA7B+vkikInTu1Q0zHv8jNT294UCZQFOTeQ6V27ZBt3s3LPqWf8akXTpbsxfGjKHsBX/6ASFfA45AXSZX/cBiuoRi7KIkFF2+iI0f/4fXl4FleU156MkGZSwDDgwFRASIABEgAkQgyAiQ0BBkB+4D4ZLQ4AOHEEAusJJJLOuAdZAdBcDaedM+XgLAivMvBXCLg3GT0OAgKH+atubf/4S2wJ6gkjp2IvpPm82FkLbrCtJ25fHCGXt7EmLj1f4Uol/4Wms2Y9e7b+H80YOoNRo5n2W1AoywSHn+iyIioJk1k3uBKFQovB4bExZyynO4jAX250zJGeiM/MaWjjjF+ikwMYE1b2YZCyQsOEItcObotDXY/GU6CrK2QF9+1haYTCHBpAd+j679mQZOw1RaisodO1C5bTuMuZ+9/fwAACAASURBVPza7o3RYc2clSNHWrMXelD2Aj1BRKCtCZTlV2HzVw1LJk29vxfEUjPW/edNVGmt/WrqBvsdjP0uRoMIEAEiQASIABEILAIkNATWefpDNCQ0+MMp+Y+PowHsBJADoEsjbncFcAEAy+MOv967oaXoSGhoiZAffn/jJ++jIJs9CtbRffBwDLvpVphNFqz94ARqquw37MNiFZh8bwqV3vDQOa9a/DoqigpgrqiAubQUsUYLUiyN3+oXyuUInTIF6lkzIY6I8JBHABMWLldctvZYKErjMhYqjZVO7ycWiJEYnmgrhcT+t1TEF1GcNkoL/JpAYU4Ftn5zFAVZP6HWYhXX2IhoH4Xb/vpXiKXB+XywbIWqgwdRuXMX9CdPAhZLi+cckpSE0AnjoRwxok0EyBYdpAlEIAgJsN+jmMigLeBnIA2c1gWs19WObz9DbkYaj0yHHskYf/cDlNUVhM8LhUwEiAARIAKBT4CEhsA/Y1+LkIQGXzsR//bnSQDvAWB1KW5uIpRi9k4HQG8ALRd6tpZbYs2g/3PdHisYz667bwOwy424qHSSG2G2ZGrHd1/gctpJ2zTWfJA1ZM06XojDv17kLR8yOwHxvVmSDA13E2A3Gpf/81W72dpa9O2WDM2hozAXlzS9nVjElUbRzJkDaadOrXaLCQtXKq4grSQN6UXpnMBQYbSX1nJ0A5FABNawmWUrsD89InogROT7PSYcjY/muYfAhWMF2PHdclQW2yv4CQTAwJmzMGLBXPds4gdWak0m6E+cQOWOnag6dAi1Bn6vlsZCEIWHQzVuHELHj4OkY0c/iJJcJALBReDElss4e4Df0oxlhI5Z1ANn9uzAkXUreUDkag1mPvEcZCrWTo0GESACRIAIEAEiEGgESGgItBP1/XhIaPD9M/InD98B8DSAd6//tzHfTwDoA4DVyVnrQHB1QkNjU1lppkUArF1tWzdIaGgdP6dW71/+E84f2mdbE5PQDZMfeBybvkgHS/mvG3KVBDMe68M1baXhfgLZxw5jz8/f8QzPe+5lKFWhqNi8GWXLlsNcxi+vcKMXikEDoZk7FyHJyQ5nnbDa77mVubbmzZywYHBeWBBCaBUWolK5rIWk8CTIxDL3gyKLAUfg0LpzOLjiQ5hN9hJcIrEUt776KqLiogMu3rqArH0XMlG5cyd0e/fCUu7Az51YBOXgwVBNmAB5374QiFiVRBpEgAj4GoGCS+XY8cNZ1NpbXEESIsKU+1NRXZGPDR+9B4vFbHOb9WWYdP9jiO3a3ddCIX+IABEgAkSACBABNxEgocFNIMmMwwRIaHAYFU10gMAnAB4A8DoA1o+hscHEgREAbgewxAGbrKdDFoDdAFixaPYGiK1/A0ACgEwAAwA4UrCd2WlqdEpISBBnZTU3xQFvaYpDBA6sXYXjWzehxmSBpbYWkrBopM5+CNc2XYGQXS2+PnqN7YiUkR0cskmTnCewf9mPOH94v22hMjwC859/xfb/LQYDV6ddu3o1TNf4NyRv3C0kMRGaeXOhGDKkQfkF9nIzT5dna97MhAWtQeu0w0xY6BrW1VYKKSkiCXKx3Gk7tIAIWMwWrH5vJXJO/saDEd6hFxb99XGIJYH1Mt2QewW6XTtRuWs3TPn5Dj0A0vh4hE6cAOXo0RCFhjq0hiYRASLQNgQMehM2fJYGfQU/M2nonK5o302Ode8vhq6Un6nYd/IM9J4wpW0cpl2JABEgAkSACBABrxAgocErmGmTegRIaKDHwZ0EPCE0NOWfBsBRAKzvw3MAFjsQCAkNDkDy1BSzpRabM/Lx7b5LKDq+B320p2xbVYtkKAqbh25GEVQhYkSqpNAoJJj9RD/IlI33C/CUn8Fkd9Xiv6OiuMgWctcBQzDiFqYB8ketxcLVb9euWIGa8/beGo2xErdrxzWFrRqWgvSaS7ashbKa5jMjGrPFhIUETQIvY0Eh8X4z6mB6JoIp1urKGnz759egr6j/4l2AXuN/j/G/G+Zwho6vMuPEhX17UbVvHwyXWOuklocoMgKqUaOhGjMaTGigQQSIgH8Q2L/yAnLS+UJCp5QIDJ2TgF0/fMUrV8kiatetBybe+zD1ZfCP4yUviQARIAJEgAi4TICEBpfR0UIXCZDQ4CI4WtYoAU+UTmoOdV1PCNavYUIrz4RKJ7USYHPLM/Mr8OwvJ3Ay13qLPUGXjcFl9vrogADi0NsgEthLJFWFi/HQEwPRI5Zu0nriaBr0Z2CpQrfcga4DBje5HctMqE5Ph3bVKuiPMJ3PPowWI/QmPapN1dx/DUILshJVSO+jQX57GVAvU6WpDQQQIF4Tz/VXYKWQkiOSQcKCJ06fbNYROH/oNH7773/Anu26IVV0xOjbH0DqKP/rQWDIzeVKIun27YMxx7GqgkKlEsrhw6AcMwYyVgJNSKXq6CeECPgTgZy0Yuxfxb9LIw+VYur9qcg+vg+HVi/jhSNThWLmH56HXEW/X/nTOZOvRIAIEAEiQARcIUBCgyvUaE1rCJDQ0Bp6tPZGAp5oBt0cZZbvveF6+aSkVh4HCQ2tBNjU8hXHcvH80pMwmu0v8jro8zCqZK9tibwWsKgXAgJ79sJGuQElIcCbC/pgfv84D3kXvGazjh3C3p+/5wGY9/wrUIWzXu3ND/ZS9mrmceQu/Q6WfUdQXaODudZe9/nG1cXRIUjvrcH5nqEwSu0vMZmw0Fnd2da8OTkyGUqJsqXt6ftEwK0E1r73IbKOHefZDOswGRN+NwlxPVv+eXCrMy4YM1y+DN3efVZx4bJj4oJALIZ80ECoRo+BYkB/CKRSF3amJUSACLQ1gapyAzZ8ehrGGv7fwWMXJUEs1WLDR+/CYq7/PQEm3vcw2ndv7a/NbR057U8EiAARIAJEgAg4QoCEBkco0Rx3EiChwZ00ydZoADsBsBoNXRrBwcocsborrNtvOAB+IVnn+bFG0D9cL6E00PnlvBUkNLQSYGPLmcjw9E+s/zd/RBqKMbGQJaJYR5hFgOrQOagVqbj/Xy6sxSqFgSU6cOPft/YlscHN57Nv2RJcOHzAZvXG/gw3bldQVQDWWyGtOI37b5HeWnJJWWFCr+NlSDmphcRoadZLo0SIwn6dIJ4wBt17j+IyFlRS65nTIAJtRaC8qAA//+1vqCqvsbkgkoQitvtCTLy7N8Lb+Zb4xYQ+w8WLqDp4yClxgWUVyVJTubJIiqHDIFL5Vlxtdf60LxHwVwK1llrsWHIWBZf4Td17DIlF6qgY/PrBYl55RBZn7wlT0XfydH8NmfwmAkSACBABIkAEnCRAQoOTwGh6qwmQ0NBqhGSgHgHWPZN1jI0CMAoAa/xcf7AG0ay581IAt7iB3AoA8wB8DODhVtojoaGVAG9cfvZaBWa9v4uXyVA3J9RYgekFLBkFkNQC6loBqlRTYRFHcl87HGJChtR+A08iEmDdk6OpjJIbz2jlW6+hsqTYZrHbwKEYvoBpd9bBhAROWCiyCgsF+oJmd5fWmJGYUcEJDuEldg1RIpRCLpZxTZtlYhlEAmuT3ZCeSVwvB+XQoRDKqaGzG4+WTLlA4Mi6VTi0Zj0M1SbbalXEAMR0HYFJ96RApmrbXjG1RiOqMzK4XilVhw7DVGTvrdJsuExcSO4J5fDhUAwbBnGE72douHB8tIQIBCWBsweu4cQWfhaTJlqOifckY9/S73Dp5DEel5iEbph8/2NUHi0onxYKmggQASJABIKVAAkNwXrybRc3CQ1txz5Qd34BwD8BnLzeN6HuTeYAADvYBWgAQ9i75HoAvrn+tQ8AsD91YxwAVm+HZUnY6+4ArBvsq9ebQLO3Qv0ApLUSKAkNrQRYfzlr/Dz/v3tsPRluNC0112DetTXcl0MtArCiHXrlOJglHWBGLZaqDDDc8OnUJ06DFY+OhEhIH1utPSpdWSlW/OuvPDO9586BNk5sy1hgGQyujI7KDhhSHo2kEyUIPXURouaTHCCQyaAcOgTK0aMh79MHApFViKBBBLxJwFhdjVWLX0fBpQKYrmfmCAQiRHa5BbEJ7THujp4Qib3bu8BcqYP++HFOXNAfOwZLFUsGdGBw4kIylCOGQzF0KIkLDiCjKUTA3wiUFVRh85fpsNQrSykUCThhND/rSIO+DCFKFWY++RwUao2/hUr+EgEiQASIABEgAq0gQEJDK+DRUpcI0Bs7l7DRomYIiAGwN8jTAJQC2HpdXJjILq8D+PN1IaK+ie0AxgJgbz6ZgFA3nmJVc65nSbAC2iUAYq8LC+zqO6tzcR8AfqF5146HhAbXuDW6akPaNTz0bf1mzzdMq63FwrxlYK/twi3Wj6FqxQiYpPHIEpuxR26/VVx/5Sd3DcSU1HZu9DQ4TbH+DDt//Bp6UzWqTXruv+njhDDKnX+R2kHZAalRqUiJSOH+qwmxv8Qwl5WhYus2VGzaBFNBy8KFSKPhBAfV2DGQJiRA4EAD6eA8QYraEwSyjx3GriXforxID4vFqm2HKLsgvONUdOkdiSGzPP9MGgsKoD9yBLqDB1Gdlg7waqs3E3V9cYFlLoSz6oQ0iAARCEQCZpMFm79Kh7ZAzwuvz4Q4hMdWYfNnH6LWwlf5J/z+IXTokRyIOCgmIkAEiAARIAJEoBkCJDTQ4+FtAiQ0eJt4cOzHriQ/AeAeAD2u92I4CuAdAGsbQdCU0NAfwIMABgHoBIDVfDACuHhdwHj/eiNod1AlocEdFK/buPOzA9h9vvnSHvOuroLGbISi1voxVCMfCGNIEtYrDCgU1U9gsTs2OjEK39431I2eBo+psuoyW4+Fyxt2QZxVZgveoBDg3HiZQzDaK9tzzZtTIlO4P+Gyll9oshce7GZ2xYYNqDpyFKht/HzrOyCJi+NqyTPhQRIT45BvNIkItIYA632w6ZP3kZd5HhUlettjGt5hGkJUncFe4vUc1r41WzRYW2swoDo9HVXHj0N//ITDzZw5Q0IhZCkpUA4bai2LROKCW8+GjBEBXyXAyiWxskn1R3TnUAyZGYvf/vsOqiv5PRtSx05E/2mzfTUc8osIEAEiQASIABHwIAESGjwIl0w3SoCEBnowiICVAAkNbnoSdDUm9Hp1Q4vvkmfkr0cng47LamDDENIL+cpeWKsw2ppA3+gSu+B++tWpUIawxBkazRHQ1mhtZZBYj4UrlVds0xO3VUNaZX/ZX9pJhLw+rIBVwxGriOUEhTpxIVJu7aPh6mA3tis2b0bl9u0wF7MkpZYH9zJ19CgoBg+ml6kt46IZrSBQevUKfn1/MWqqjKgsszaHFkvUXAkloUiEkQu6o0Niy+JaUy4wMcN09Sqqjh2D/thxVKelgYkNjg6BXAZF//7cz4J8wACIVNRM3VF2NI8IBAKBgkvl2PHDWd7vWJIQESbe0wN7f/oURZcv8cJs160HJtz7EIRCKksYCOdPMRABIkAEiAARcJYACQ3OEqP5rSVAQkNrCdL6QCFAQoObTvJgdgkWfryvRWuz87chzmB/0WyU9sDO8L7IlDZf1P+Xh4djcDw1NL0RMBMWMkoybA2ccytzGz0DSZUFPbZZX6DWjdx+Emg7WsWbGEUMT1iIkrPe7u4fLMuBlYap3LkDun37UKuvbnkTgQAhPXpAMWQwlEOGQNKhQ8traAYRcJLAodXLcHbfLugrDNBXsiQ6QBU5GKrI/hBLhZh4dzI00axVkGPDotdDf+oUJyywzB5HyojVtyyKjIBi0GAoBg+CPDUVAmnjoqBj3tAsIkAE/JUAa1a/8bM0VJXzxckhsxNw7dwWnD/E/91LGR6BGY//ESEK1h6NBhEgAkSACBABIhCMBEhoCMZTb9uYSWhoW/60u+8QIKHBTWfxw4Ec/N+KUy1aW5S/F2GGq7Z5BklnfBM7GMYWPpX+cVNvLPp/9s4DPMoq6+P/6Zn0RhJCQoAQOqH3XgQELIhiZVcsa3fX1fVbdV27rruW1bWsrmUVhRVRiiCCgPSOQAiENEoSSO9l+sz3nDfJTN7UmWSSTDn3efIkzNxyzu++M8zc/3vOGd+7zfk9vUOlvlIQFejnTPEZZFdm2+VyUI4RMadqD0+pyaVyBN82HUN7jxIEhh6+Peyax5mdzHo9ao4eRfWevcKd3vbmpaf0SvWigzI+HhKp4zUmnOkHz+UZBPSaGmx481XoqqpQVaaFXmuCRCJHeJ+bIFMEwC9YJYgNPn5UdqhpM+t00J07B01ysiCm6TIy7L6m62dT9ukjRC2QuKDs14/rlXjGpcVeMIEOETi88TwuJReL5ogdHIqw6DwcWf+t6HGZXIH5D/wBodG9OrQmD2YCTIAJMAEmwATcmwALDe69f+5oPQsN7rhrbHNnEGChwUlUP99/AS/8cLbV2XzNwG0Fv0JhuGDtV6yMxJrIqW1a8dw1Q7BiSt82+3lahyp9lShi4VKlOD2Cvf72PytH2GUL1HI1fOQ+CAnviev/9Bd7h3d6P1NFBar370fVnr3QpaXZvZ4sNFQ4mPUbPw4+dNe3ovlDYLsn5I5eTSDj2GEc+m41KNVRZZEWRqMZPv59ERx9lcCF8qFPv3UAZDIpSCjTpaZCm5wMzZkz0KWnA0aTQ/ykajV8RiRCPXIkfEeOhLxH1wt+DhnMnZkAE+hSAllninFow3nRmuoAJcbM98OuLz6E2Sx+z5ly83L0HTmmS23kxZgAE2ACTIAJMAHXI8BCg+vtiadbxEKDp+8w+2cvARYa7CXVRj97IhoSdTKML0+CQnfOOlu2MgibIue2aYW3RDRUG6qRUpwiRCvQT1ZFFixou4hyY4ChPqHW+gpUZ+HA+x+hutSWsqr/2ImYuPSWNrl3RwdDbq4gOJDwYLhsqzHRli2Ux57qOqiH08HtCFDkg4QKfHBjAnYSoNReW//9jpDv3GyyoKJIA7PZgpBeC6HyjYZZq0Mv/3LEVx6FPj0NFqOxycxmiwUagwk6gxn0t5RSfymkUCtkwt/K+H61wsKoUVAlJEAi59ozdm4Pd2MCXkWgskSLnz87A6NenFpywrU9cWzjx9BUVoh4DJoyA2MXL/EqRuwsE2ACTIAJMAEm0DwBFhr4yuhqAnzy0tXEeT1XJcBCg5N2pq0aDRILcEO1EsGas1BqTwmr0hFdrkKNTVEL27TCU2s01BhqbBELxWdwsfxiu4SFEFWItcbC0PChoGLO9YfsVSXFWP+Pl0SMpyy7A31HjW2Te3d3IKGh+uhR1Bw5WhvpYLFfdJEFB0M9IhE+w4dDnZgIeVjHClp3Nwtev2sIFOdkYcv7b8FiMMBQrUVVpRlSiR8ClZMhqStj309zEtG6DKtBdFVWaA0oqdKjSmcUSYPVCjVSQuJwLiwOQaNH4abZQzF3cCRkUv4o1jU7yqswAfcjYDKasfPLFJTm1YiM7z82HAWZG1CUdVH0eGTf/phzzwNc/Nn9tpotZgJMgAkwASbQKQRYaOgUrDxpKwT42y1fHkyglgALDU66Eqp1Rgx7fmuL58AxRilmaRRQ6DKg0hwRVq2WWFAtleG76NbvwKOb0pOfnw8/lfvf+asxanCu5Jy1ePOF8gswo/VC2M1tUZAyCCQoULQC/UT5RbV4937m8SM4uHaVaJob/vw8fIOCnbT7XTONsbRUqOlAooP29Olm7yZvzRJFr15QJw6HesQIqAYPgcyfC2V2zc65/iqUBkmfmQltaip0aek4mXQcOfpqwXCDRAWd1BdqxSCoFf2ExySwYHDVfoQa86A1mpBTokGNoTaFiU6mQGZQL6QHxyI9JAY5/j1gkYjriCTGBOGNm0ZgQGSA68NhC5kAE+hyAie3ZyHtSL5o3eBIXwSGnEHG0QOix+n/8oUPPwEff/8ut5MXZAJMgAkwASbABFyTAAsNrrkvnmwVCw2evLvsmyMEWGhwhFYbfe/45DD2ZRQ122t2jQK9TFLI9Vnwqdkn3PFbKq1NCrQ2egnMElmLs09LCMfKuyc40dKum4qEhdSSVCENEhVwPl92vt3CAhVtph8SGKL9ou1OC3Rw7WpkHj9sdTogLBzXPeE69RnasxvmmhqhgDSJDprjx2HWaBybRiIRUiupBiTAZ+BAqAYMAAkRXFjaMYzu2NtCokJWFnTnz0N//gL0F85Dd/GiqL6CARYckhpgoFAsEg+kahgl/gjymQ6p1Ed4TGYxIq5wG3JLipAR1AsZwb2QFhyLnIAImBsJC81xUsgk+PuNiVgyKsYdMbLNTIAJdBKBK+ml2PetLWKKlpErpUgYU41T274XrSqVyzH/vt8jLCa2k6zhaZkAE2ACTIAJMAF3JMBCgzvumnvbzEKDe+8fW+88Aiw0OI8ltp7Jw30rjzeZ0d8MXF+tAr3xyAz5UFfvgFZSG9FAbWPUImhl6hYt+Xj5GMwbGuVESztvKq1Ri7TSNKuwkFmWCZPFsQKxZF2AMkAQFQRhIWwoYvzbX29gwxuvoLK40Op0/JgJmHTjrZ0HoYtnphQ32nPnoElKgjYpCbrM8w6lWKo3lwrzkvCgGlArPNDfMr5DtIt307nLkQClv3ixgahwAfqcHMDU9mvyssSEVKmtBoNG6g+ZPA4BPqNA9UC0UiUu6IxY529BtbxlobQtj96+eQSLDW1B4ueZgJcQqKnQY9unydBrxO9RgyYqkbzzS5gbvXdNvul29Bs9zkvosJtMgAkwASbABJiAvQRYaLCXFPdzFgEWGpxFkudxdwIsNDhxB01mC5Z8sB9JOeWiWUfpZBimr017JDWVwrdyC8qkFtR/jd4acRXKFUHNWkIpRtY9OMVl85nrTDqklYiFBaOlaYHYtjD7K/xFxZt7BfSC1I67otuaV1NVie9eeVbUjUQGEhs8tZmqqqBNTobmVBI0p5NgzM1rt6sU5aDqHw9F795QxsUJP7KQELujSdq9MA90iABFKRjy8mDIyYE+O6f298ULMNDeO1DXo+GiFG91TGpAlUIGiY8PJCofVOvkCOq5GGZlJDIKqoSIrGKpGVt9DTC185MVRTZsfnQap1FyaMe5MxPwPAJUeH731+dQmF0lcq7XAB/kp69FTYX4s9XASdMw7tqlngeCPWICTIAJMAEmwAQ6TICFhg4j5AkcJNDOr8MOrsLdmYDrE2Chwcl7lJZfiUXv7oXBVButILUAS6uV8LHUvu1IzDVQVaxHOT1R13aFT0eBKqKJJa54AKc36ZFemm6NWEgvS4fR7Liw4Kfww+DQwbU1FsKHIjYg1inCQmOIWcmnsOfrz0UPX/v4MwgM7+HknXfd6Qz5BdAmn7YKD+aKyg4ZKw0IgFIQHmrFB0GEiI0FRURw61wCZq0WVCBcEBJycmAgUeFyDgx5+YDZ8Vonja2VKBRQ9usHn4EDoBo4EBV+auxYZXv9mE0WaKrUKFZcBY3R9h6WJTdht4+Rije0q7m6oNoup3gQE2ACDhFI3nMZZ/ddEY3xD1FAit0oyrogejyiTz/MvechSGXtj6ZyyDjuzASYABNgAkyACbgVARYa3Gq7PMLYdn4V9gjf2Qkm0JAACw2dcD2sO5GDx745JczcxyDFNK3CtgqlEar4H3QN3oUOhE5EjrppnnJXSCliMBlAYgLVWDhTdAYZZRkwmA0OU/OV+2JwWK2wQOmQ4gLjOkVYaGzYsU3rcG7/buvDPv4BWPr0i157R77FbIYhOxvatDTo6Cc1TTi47nCTSCCPjIQyphfkkVGQR0ZAEUW/I6GIiIBEqezwEt4wAe2PqbQUxoIC4ceQnw9jQSGM+fkwFOTDVFTsPAxSqVCXQ9WvL5R9+9Wmy+rbp8leHfh2Fc7/WlvAnlpZpQ5F2qEwqQaJbElWGnFC1XZKppYccKcUcc7bBJ6JCTAB6KpQcOIkdm+qhIUisCQSQO4DqcoXYdGpuHzuVxEk38AgXP3IE1D7czF5vnqYABNgAkyACTCB5gmw0MBXRlcTYKGhq4nzeq5KgIWGTtoZEhueXJuEWZVyRJqk1lW0EgvMFd9A1qBuwfHg0cj062ft051FUklYIDGhvngz1Vtoj7DgI/MR1VjoE9SnS4SFxtu55b03UXw52/pw72EjMP32FZ206+45LaVa0qWlQ5eeDl1qqvCbik07s8nCQqEQBIhIKKIihd/y8HDIgoOFH2+IhiARwVxZKQgJprIyGMvKYCotg7GwsFZYyM+DsbAIVHPD2U0ilwuRJ/WigrJvXyj7xEGqUrW5FKUf2/jmKzBotULfC4XVqNFJoPBfDIvUVzT+gI8BmYr2RVa4c9H7NiFyBybABMQEzCYgdQtw9D/QZhzDz2WPQWMOFPUJt6zHFYMU8O0B+AQKAgRFMMy771GEx8YxUSbABJgAE2ACTIAJtEiAhQa+OLqaAAsNXU2c13NVAiw0dOLOnDpXhM3/Pg2NwXaXL93126v0B/iabAe5pwOHIiVgsGAJpRB546YRXZavnESE82Xncbb4rCAupJakQm/WO0yFhIVBoYOENEgUtdAnsA9k0u5NaWDQabHmhadq75Csa2MWL8HgKTMc9s+bBghRD5cv10Y8pGdAf+kS9NlZsGhqD5o7o1ENAFlQUK3wEFIrPtT/yEmICAgQxAiJjxpSP19IqWaAvLbuSXc0QTSoqYG5ugbmmmrhb0tNDUzV1bW/y8trxYQ6UYH+NpWV21WEuaP+EDdFTAyUsTFCGiRBVIiJAaVFam+jqCCKDqIc6meuVAi1GXSKXgj0mVF793FdI4lhu1qPfLntNWfvmjRN8vPz4afqvn2111buxwSYQAcIFKQA6x8ArpyAxSLB3sq7kKcXR0j5W46gpCrdtojCFwiOw8RbVqD/WM+tsdQBqjyUCTABJsAEmAATaECAhQa+HLqaAAsNXU2c13NVAiw0dOLOnNyehbQj+ajQGlBcpUeVzoh1fjpMLtqOYEOZdeU0/wQEjJ2D5RPjMGdwZKcWfqZ6CpllmVZhgSIWqKCzo42EhYGhA4WoBRIW+gX163ZhobEPuRmp2PHph6KH2I0bewAAIABJREFUr37ocYTFxDrqrtf3p4N1uvNeEB0uXYIhKwv6S1kw5OY6pTZAewDTwbnUVw2JulZ4kPr6QqL2EQ7UJTI5IJNCQgXF6Tf9W0q/pYBUZv1NdQ0sRmPdjwEQ/jaJ/22ofd6s1cBcXScqdKLoYi8LeY8egqCgiOklCAmK2FghFZLM39/eKezuZzab8OO/3sTlS1nILKy2jivzn4JIaR/RPHqJBVt8DahoUIfG3oW+vX8SxvUJtbc792MCTMDdCJz6Btj4MGCqvaEhVTMDp6oXi7yQmXOgrd4DKkjfsA0JKcboFc8CI252N6/ZXibABJgAE2ACTKCLCbDQ0MXAebn2litkckzA4wiw0NBJW2oymPHDv05Cr7VFM4TFBSBgcgSOr/4PdLkXIZVIoJJLMXDcBMy8dXmnWGIym3C+XByxoDU5fme6SqbCgJAB1hoL8cHxkEtd+87jpO0/IWnHT1aucqUKy557FdJujrTolI3upknNen1tYeI68YGKEgv1BPLzYalLtdNNprn/shIJZKEhUFCaqR4R1rRTgrgQHd3l6aaKc7Lw9d9fR06pLRpLJ/WBLmAheljEhcArBbFBD50ta5xd+/HaDcNx6/jedvXlTkyACbgZARIZ1v3OanSxIRY7Kx6GxWJ7ozCZNbDUbITZYhQ5F+tbhukRF2sDqJZ8zGKDm209m8sEmAATYAJMoKsJsNDQ1cR5PY5o4GuACdQSYKGhk66Ei0lFOLLpgmj2KUv7o9fAEOxd/QUuJZ2wPtdr4BDMutP25bsjJpGwcLHiorV487mSc2iPsKCQKmpTIdUVb+4X3A/0mDu17Z98gLzMNKvJUf0HYu7dD7iTC25rK6WrMpeX1xYzJuEhLw/GfCpwTH/nw1RS4ra+Oc1wqVRIDyUPDYE8IgLyCKpdESEUzxb+HR7uckW0P3zvU1w4vE+EIMu3DyKVkxFoFn+0ypeZsV1tQKOHW8X33DVDsGJKX6ch5omYABNwEQL5Z4GPZ1gjGfRmH/xc/hiqTbYIJrPZCJNmCyzmCpHRYaoaXBWVDnl9lJRMCdy3B4ioTTnJjQkwASbABJgAE2ACjQmw0MDXRFcTYKGhq4nzeq5KgIWGTtqZnV+moCinyjq7OkCBRQ+NgFQqwZENa5F2yHZYFx7bBwse/EO7LDFbzLhYXissUJ2FlJIUaIwah+ciEaFhxEL/4P5QyNxLWGjotNlkwpoXn4JRb6s3kTh3ARLnLHCYDQ9wPgGLXg9jKdUtKIOpvO53/b+Fegb1j5V2SnFk53tUN6NUKqRwkgUGQBYcUldzou53g/oT8pAQSAMDIZE6eMt/pxlu38Rf78vAgU/fhp9RXCz8SOh0jDfFQGURf7w6Lzdhv48R9saRckSDffvAvZiAWxGgws+fzBFqMlCjskmHqpYjW5dodcNiMUOn2QepKVvkmp9cjwXRaVDLxBEOiB4F3LNDSIXHjQkwASbABJgAE2ACjQmw0MDXRFcTYKGhq4nzeq5KgIWGTtiZyhIttvz7tGjmIVOjMWx6L+GxUz9vwemdW63PB4T1wHVPPGOXJSQsXKq4ZK2xkFKcgppGh372TERpjxKCE6zFm+lvdxYWGvtMaV62vP+W6OG5dz+IqP4D7MHDfVyEAEVGWDR1tRG02toCzFR4mf6mx2s0MGtqavsIf2tgMRoAkxmwmGEx0Y8RMFsAs0n4N0wmUM0J+i3UbZDLIVHIAfotk4v/Xf+YQg6JqrYOhNTPz/abClPTY3U/VNRa0qA4sotgdJoZRy6U4JF31mNG8V7RnNVyPxwPnYc5Wl80lk5OKo04rbKlkGvNGK7R4LSt4omYgOsQSNkEfHO71Z4M7WT8WrXE+m96n6/RJENmPAOpxPZeoZCaMb9nGoKVLaR7vGUVMGiR6/jJljABJsAEmAATYAIuQ4CFBpfZCq8xhIUGr9lqdrQNAiw0dMIlcnpXDlIO5IpmXvRgIvyCVcJj5/bvxrFN66zPK9W+WPbXV5u1hISFrIosq7BAqZCqDLZICXvNl0vkSAhJsBZvpr+VlH7AQ1vKvl04vnm91Tu6c3zZc69BoazdA25MgAk4TqBaZ8Sw57diXMlR9Km5JJrgnP8AVPmOxBRt00io/T4GnFeYW12Q9Jnk5+fDT+XatV8cp8YjmICXE/jyOuD8LgFCkSEOuyoegNlii0So1mYB+qNQSGyCggQWzI7KRE91K593+s0CfmP7f97LKbP7TIAJMAEmwASYQAMCLDTw5dDVBFho6GrivJ6rEmChwck7YzFbsPmDJNRU2FL29IgLwKzbB1lXunDiGPav+arByhLc/sqbQhoVEhZyKnOswgKlQ2qPsCCTyEDpj4aEDRHqLAwIHQAq6OwtbfdXnyH7TJLV3bCY3rj6oT96i/vsJxPoNAJ3fHIYR9Iu4+r8bVCZdaL3sZ97zEacJRzD9WKxgCSGnWo9cuWWFu2alhCOlXdP6DS7eWImwAS6gYCuCngthhImQWv2x89lj0FjDrQaotUXQqc5DLW0XGTchPBsJAQUt2GwBHgqB1D5d4NjvCQTYAJMgAkwASbgygRYaHDl3fFM21ho8Mx9Za8cJ8BCg+PMWh2Rf6ECu1enivqMX9wXfRLDrY9dSUvBzs8/sv5bb9IjcsV8pFZn4mzJWVTqKx22ioSF+OB4m7AQMgA+ch+H5/GEAZSG4btX/wptlY3j4KkzMWbR9Z7gHvvABLqVwNYzebhv5XHE1mRjUulhkS1liiD8HD4HU3VK9DGKc6cbYMFWXwNKZc2LDR8vH4N5Q6O61TdenAkwAScTuHQA+PxqmC1S7K64D4WGfrbPPsYKVFUdhp+sEBTBUN+GBBVgdOgV+wxZ8RMQN8m+vtyLCTABJsAEmAAT8BoCLDR4zVa7jKMsNLjMVrAh3UyAhQYnb8DhjedxKdl2F55cKcU1j46EQll76EaH4GfTjmL3R/8WijZrTFqYzSakz1BB729/YVgppOgX3M8qLAwMHQi1XO1kb9xzuoqiAmx8U5yKavrtd6H3MFvhSff0jK1mAt1PwGS2YMkH+5GUXYapJQcQrRWniUsKHIY0/0GYq1Eg0iR+T6uRWPCTrx7Vjd7qEmOCsO7BKZBJ+eNZ9+8wW8AEnEjg2OfApj/gVPVipGpmWCc2mbQoqzoCP8kVUV2G3n5lmNbjIuwudXPNO8CYO51oME/FBJgAE2ACTIAJeAIBFho8YRfdywf+Jute+8XWdh4BFhqcyNagM2HjOydhMtpykfcZHoaYWT7WVEhnis6gpqwUA35pmHIEOD9ZBU1Iy0IDCQt9g/paizcPCBkAX4WvE633nKkyjh3Goe9Wixy68ZmX4ePP6RU8Z5fZk+4kkJZfiUXv7oVcXy2kUJJbjFZzzBIpfoq4CnpZABbUKBFkFn/kKpOahcgGfd3DCpkEmx+dhgGRAd3pEq/NBJhAZxA49G9kb1iFg5V32N4jzAaUVp2AGpcgl9jSTIarajA3Kh1yacsp1pqYuOB1YOL9nWE5z8kEmAATYAJMgAm4MQEWGtx489zUdBYa3HTj2GynE2ChwYlIz58sxLEfL8JgNkBL0QpGLTIS9yLX94JoFanBgsHbbEUP6cmssUpURtpSjUggQZ+gPkJ9BaqzMDh0MAsLdu7VwbWrkXncltIlMDwC1z7+tJ2juRsTYAL2EFh3IgePfXMK8dWZGFN2QjSkUBWOX8JmwM8iwdU1Sqgt4o9d+TIztqsNIA3i7ZtHYMkoyuHOjQkwAU8jUL5zJXasK4fRohRcM1tMKKtKgsJ8CSpptdVdP7keC6LToJbZREu7WHBEg12YuBMTYAJMgAkwAW8jwEKDt+149/vLQkP37wFb4BoEWGjo4D5QKqT8mnwhYuHs96XQ5ltgNJuEWfU+1TgzdivQ+B3HYsGQLVpIGty0dzlRiaCh/QRhgX4Ghw2Gn8Kvg9Z55/ANb7yCyuJCq/PxYydg0tJbvRMGe80EOpEAiQ1PfnsK0/J/QbheXLj1WPBonPfrhxCTBPNrFFA0eiPMUppx3fLBuGF0bCdayFMzASbQXQQoynP7+3tReT5NMMFiMaOsKhkwXYafrMRqlkJqwvye6QhWim/AsMturtFgFybuxASYABNgAkzA2wiw0OBtO979/rLQ0P17wBa4BgEWGtqxDwU1BaAUSGeKzwgCQ7G2GCqNH4Ycmy+aLbd3CvLiUppdYeB2DXyNSvjI1VDLfTBu4RKMnr2wHdbwkIYENFWV+O6VZ0VQJt14G+LHjGdQTIAJdAIBSqP0zMrdiEtaB6nFljbOIFVgS8Q8aGVq9DRKMFujRH1yOLVChphQNUZM7YURs1lo6IRt4SmZQLcSoJswDnyficvnioDcJKE+VXl1CozGKwiQFViLP0thwayoTPRUV7XDXgnwVA6g4rSI7YDHQ5gAE2ACTIAJeDQBFho8entd0jkWGlxyW9iobiDAQoMd0AtrCq01FkhYKNTY7pavH97z4hBEZQ8SzXZm7E/Qq2usj8UGxFqLN19etQ01RbY7gIfOmINRC66xwxru0hqBrORT2PP156Iu1z3xFwSEhTM4JsAEOokAFYj+dtV3SN+9FVVaI+qDtS77RGN/6CRQZdd4gxTzLGqE+SsR6KOwWjJqXm8kjI3sJMt4WibABLqDwLmDuUj6JUdY2lKUjsrSX6HVX0agrABSiS090rSIi4jzK2ufif1mAb9Z376xPIoJMAEmwASYABPwaAIsNHj09rqkcyw0uOS2sFHdQICFhmagF2mKbMJC0VkUaApa3xoLMPToAih1tuLMVUFF0Ew+XysshA8VaiwEqYKs82z7+F8ouJBp/Xf/cZMw8Yabu+ES8Kwlj21ah3P7d1ud8vEPwNKnX4REwm/7nrXT7I2rETAZjdjy3psoyb0CjcEEndEMs8WC4Bk3YOjYMRjSMxCXjhYgec9lken00px0Q3/EDAxxNZfYHibABNpBIP9CBfb8LxWWOsWxMnc3akpOwF9WBIXElh5pQlg2EgLFKdccWu6WVcCgRQ4N4c5MgAkwASbABJiAdxBgocE79tmVvOQTJ1faDbalOwmw0ACgRFsipEKiaAVKh0Q1FxxpAaU90D95GhRSBdRyNXzkPpiwuB+GjolrcZrdX32G7DNJ1udjhyZixh13ObIs922GAB10Fl/Otj7Te9gITL99BbNiAkygCwgU52Thpw/eFtKk1DcS+xb//v/g4+8vPH58yyWcPymOCpPJJZhx2yCEx3AKlC7YJl6CCXQagepyHbZ/fha6mtqoheqSU6gsOgw1SqC2FFnXHRlyBcOC27iJozUro0cB9+wApLJO84UnZgJMgAkwASbABNyXAAsN7rt37mo5Cw3uunNst7MJeKXQUKotrS3eXCcs5FbntotrT7+eQuHm0NMDoL+ohEwiF+aRK6W45tGRUChb/gJ86Lv/IePYIeu6EX3jMe93j7TLDh5US8Cg02LNC0+JDjnHLl6CQVNmMCImwAS6iMDxzeuRsm+XaLWYwcMwY/ndQmSR2WzB/m/TkZtZLuqjVMsx57eDERDq00WW8jJMgAk4k4DJaMYvK8+hJLdamFZTfg7l+Xug9JHB398CFKZSIiUMCSrAqJArlFGtfU2mBO7bA0QMbt94HsUEmAATYAJMgAl4PAEWGjx+i13OwfZ+tHU5R9ggJtBBAl4hNJTrymsjFkrOCr+vVF9pF7Yo3yghFVL9T5g6DAadCRvfOQn6gl3f+gwPw/hr+rW6xq9bfsDZPTusfYIionDNY39ul108qJZAbkYqdnz6oQjH1Q8/jrBeXGyWrxEm0FUEDHodNv3zdVSXloiWnLBkGRLGTxYeM+hN2P11qvVAsr6jX7AKs5cPgjpA2VXm8jpMgAk4icCxLRdx/kRttJK28gLKc7dDKpMgMNwHEqkEqClBvPFXTAzPbr/IQJMv+RgYwakmnbRtPA0TYAJMgAkwAY8kwEKDR26rSzvFQoNLbw8b14UEPFJoIGGhYcTC5SpxTnB7+Ub4RliLN5O4EK5uWlD4wqlCHN18UTTlzNsHIiIusNVlzu7ZiV+3bLT2ofQiNz7zkr2mcb9mCCRt/wlJO36yPiNXqrDsuVch5dQKfL0wgS4lUHDxPH7++F+i6CKZQolFjz6BwPCI2oPIagN2fJGC6jKdyLagHmrMumMQKMKBGxNgAu5BIPNEgZAWjZqu5jLKLtP/xWYEhvlAppAKj1OKyOnDfCH54RHApHfcMYpkuPY9FhkcJ8cjmAATYAJMgAl4HQEWGrxuy7vdYRYaun0L2AAXIeBxQkN2ZTae2P1Eu/BGqOuEhfChgsDQnLDQeOJfVqagMLvK+jDdkbvw/uG1d++10jKOHcah71Zbe9Bh+K0vv8FFi9u1c7WDtn/yAfIy06wzRPUfiLl3P9CBGXkoE2AC7SVwctuPSP5lm2g4RRfNf+APkMpq08pVlmgFsUGvqc3nXt/Cevlhxq0DIW8l/Vx77eJxTIAJOJdAw+LPek0BSi9vhsVsgH+wyioYRsUPwKw774VMrgAKUoD1DwBXTthvCNVkuP5DTpdkPzHuyQSYABNgAkzAqwmw0ODV298tzrPQ0C3YeVEXJOBxQoPZYsa92+5FlcF2+N8S9zCfMAwNHyrUWSBhgSIYHGl0SLbl36dFQ4ZMjcaw6b3anCbnbDJ2rfxE1O/m5/8GhYrzk7cJr5kOZpMJa158Cka97S7JxLlXI3HO/PZMx2OYABPoIAF6TW799zugAtEN27CZV2Hk/EXWh4ovV2H36lQY9bb0c/RkVN9ATLkpATJ57d3Q3JgAE3A9AhVFGkEspDSSRl0pSnI2wmzSwcdPAd/A2hRo4bFxmHP3A+LPN2YTkPYTcOQ/wHmq6WIrIG/zUgL0mwmMvxcYsIALP7ve9rNFTIAJMAEmwARclgALDS67NR5rGAsNHru17JiDBDxOaCD/3zj6Bo7mH22CItQn1CoqkLhAwgIVJ21vS95zGWf3ies9LHxgOPxD2hYLKLXIto/eFS19/Z+ehX9oWHvN8epxdJi55f23RAzm3vMQouITvJoLO88EupNARVEBfvzXGyIBkN5z5977MCL7xltNy7tQjn1r0mE2iQ8bYweHYOJ18W1GiHWnj7w2E/BWAjqNETv+exZVpToY9RUozfkBJmM1FEoZ/EN9hDoMVH9q3n2PQOXr1zImXRWQlwQUpQEGLaDwAcIHAlHDAZW/t+Jlv5kAE2ACTIAJMIEOEGChoQPweGi7CLT/ZLFdy/EgJuCyBDxSaPjx/I/44uwXCFGFWGssUORCpG9kh4SFhrtoMVuw+cMk1JTb7qDv0TtAyC1uTysvyMcPb78m6nr1Q39EWExve4Zzn0YEUvbtwvHN662PSqRSLHvuNSiUKmbFBJhANxJIP3oQh7//RmSBX3AIFv3+SSh91NbHs1NKcGh9JiyNbmzuN6oHxiyIc9p7dzei4KWZgMcQMJnM2LM6DYVZlTDqy1Gas0kQGSgCieoyUPpIv5BQzL/vUfgGBXuM3+wIE2ACTIAJMAEm4B4EWGhwj33yJCtZaPCk3WRfOkLAI4UGKgZdY6hBlF9Upx1O5V+swO5VqSL24xb1Qd8RPezaD211Fda+/BdR39kr7kP0gMF2jedOYgK7v/oM2WeSrA+SYEPCDTcmwAS6l4DFYsGerz8XvT7Jor4jx2DKzctFxp0/UYhjWy42MXjw5J4YPjOmex3h1ZkAExAI0Gv62I8XceFUkUhkkEolCKDiz3IpfPwDMO++RxEYbt9nIkbLBJgAE2ACTIAJMAFnEmChwZk0eS57CLDQYA8l7uMNBDxSaOiKjTu88TwuJRdbl5IrpLjm9yOFlAH2NIvZjK+feVyUl5gO3ejwjZtjBOjQ47tX/wptVaV14OCpszBm0XWOTcS9mQAT6BQCuppqbPrn69BUVojmn3rzcvRp9J537mAukn7JaWLHiNkxGDixZ6fYx5MyASZgP4Fzh3KRtDMHRn1ZXSRDjZAmKSDURyjgrlT7glIXhka3Xa/K/lW5JxNgAkyACTABJsAE7CfAQoP9rLincwiw0OAcjjyL+xNgoaEde2jQm/DDOydhNNiKl8YND8OEa/o5NNuaF5+GXlNjHTP2mhswaPJ0h+bgzgDlgd/45qsiFDPuuAuxQxMZDxNgAi5CIDc9FTs++1BkjcLHB4sefRL+IaGix0/tzEbqobwmljsSNeYibrMZTMCjCFxOK8WB7zJg0NlEBnLQL0gFla+8TmR4EKHRHIHkURvPzjABJsAEmAATcDMCLDS42YZ5gLksNHjAJrILTiHAQkM7MF44VYijm8XpPWbePhARcYEOzbbhjZdRWVxkHZM4ZwES5y5waA7uDGQcO4xD360WobjxmZfh489FJPn6YAKuRODYpnU4t3+3yKSIvvG46p6HQHVV6lvD1CwNO9Nd05OWxCNmkFiYcCUf2RYm4KkESvOq8cvKc9BWFaM0ZzNMptobJXz8FPANVELl64s5d3Mkg6fuP/vFBJgAE2ACTMCdCLDQ4E675Rm2stDgGfvIXnScAAsN7WD4y1fnhAKI9c0vSImFDyQKxQ8daT998DaKsi9ZhwyYOBXjr7vRkSm4L4CDa1cj8/hhK4vA8Ahc+/jTzIYJMAEXI2AyGrDlvbdQlp8rsmzk/MUYNnOu6DGz2YJD6zKRk1oqelwqk2DasgGI7OuYsOtiKNgcJuBWBDRVemz/PAWVRfkoydkEs0kj2K/0kcE/2AcqPz/MvedBhPTkdElutbFsLBNgAkyACTABDyXAQoOHbqwLu+XYaaALO8KmMYEOEmChwUGAVaVa/PjhadGoIVOjMWy641+ud/73I1xJTbHOFZc4CtNu/a2DFnH3DW+8gsriQiuI/mMnYuLSWxgME2ACLkigNO8Ktrz/FsxGo9U6imZY8MBjCIuJFVlsMpqxb0068i+KazvIlVLMvG0QQqP9XNBDNokJeBYBo8GEXV+louBijkhkoNpUVPzZx88fc+99CCFR0Z7lOHvDBJgAE2ACTIAJuC0BFhrcduvc1nAWGtx269hwJxNgocFBoMl7LuPsviuiUQsfGA7/EB8HZwL2r/kaF04ctY6L6j8Qc+9+wOF5vHmApqoS373yrAjBpBtvQ/yY8d6MhX1nAi5NIGXfLhzfvF5kI0UiLXzkCciVStHjVBNn96pUlFypFj2uVMsEsSE40telfWXjmIA7E7CYLTi4PhMXTmUK6ZLqIxmkUgkCw9VQBwYIqc+Co7hQuzvvM9vOBJgAE2ACTMDTCLDQ4Gk76vr+sNDg+nvEFnYNARYaHOBMX7h//DAJ1eV666gesf6YtXywA7PYujbOV07FE+mgjZv9BLKST2HP15+LBlz3xF8QEBZu/yTckwkwgS4lYDGbQRFdVCC6YUsYPxkTlixrYotOYxRyw1cU1aZrqW9KtRxUHyc4gsWGLt1AXsxrCCTvzsGpncl1IoNW8JtqpQSEquEfGoS5JDJERnkND3aUCTABJsAEmAATcA8CLDS4xz55kpUsNHjSbrIvHSHAQoMD9AouVWDX1+KDsXGL+qDviB4OzGLrevqXbTi17UfrA37BIVjyf8+1ay5vHdRYrPHxD8DSp1+EhE5CuDEBJuCyBGoqyrH5ndehq6ktKFvfJi+7Hf1GjWtid02FHr+sTBEJvdRJ5UtiwyAE9VC7rK9sGBNwRwIXThXiwPfHRSID+eEfrEJgjxAWGdxxU9lmJsAEmAATYAJeQoCFBi/ZaBdyk0+gXGgz2JRuJcBCgwP4j/xwHhdPF1tHUH7ia34/EgqlzIFZbF3TDu/HkfXf2uZTqnDLC6+3ay5vHbTlvTdRfDnb6n7v4SMx/bY7vRUH+80E3IpA9pkk7P7qM5HNUrkcCx74AyjCq3GjGjm/fJUKTaUtqoz6+PjVig2UyoUbE2ACHSdwOa0Uu78+iJKcH2E26awTqgOUCIkKxVX3PoygiMiOL8QzMAEmwAQ8kIDFYoHZbAb95sYEmEDHCNANhFKp1OEbCVlo6Bh3Hu04ARYaHGfGIzyTAAsNdu6ryWDGhndOwKg3W0fEDQvDhGv72TlD026Xkk5i7+r/ip649aV/QCZXtHtObxpo0Gmx5oWnRB/ixy5egkFTZngTBvaVCbg1gcPr1iD9yAGRD34hoVj48ONQ+TYt9lxZosWur85BU2UQjfHxVwhplALDWGxw6wuCje92AoVZldj2yXaU5PwMi9n2OlOp5QiL7YF59z6MwB4R3W4nG8AEmAATcDUCRqMRxcXFqKioAP3NjQkwAecQkMvlCAwMRFhYGOhvexoLDfZQ4j7OJMBCgzNp8lzuTICFBjt3L/tcCQ5+nynqPf2WAYjqF2TnDE275WWmY/sn74ueuOGpF+Ab2P45222MGw7MzUjFjk8/FFl+9cOPI6xXrBt6wyYzAe8kYDIa8PPH76Mo+6IIQFT/gZiz4j5IpNImYCqKNYLYoK0Wf4lXB5DYMAgBoT7eCZO9ZgIdJFCWX4PN7/2AkpydsFhsN1ZQ5GaPuAjMu+9hUOF2bkyACTABJiAmQMJCVlYWdDpbFBgzYgJMwLkEVCoVevfubZfYwEKDc9nzbG0TYKGhbUbcwzsIsNBg5z4f+C4DOaml1t6UqmPxIyMhlbb/7aQ09zI2v/sPkQWLfv8kQqKi7bTKu7slbf8JSTt+skKQK1VY9tyrkErbl8rKu2my90yg+wjUlJfhx/fehLaqUmTEkOlzMPrqa5o1rLxQg92rmhMblJh1x0D4h7DY0H07yiu7IwFKTbbhzW9QnLNPZD6liYzsG4WrfvcQiwzuuLFsMxNgAl1CID8/HyUlJUKKl4iICPj5+Ql/c2MCTKBjBCgNWXV1NQoKCoSUZKGhoYiMbDt9IwsNHePOox0n0P6TQcfX4hFMwJUJsNBgx+7otUb88O5JmIy2PJv9x0Rg9Pw4O0a33IUO177/2/OiDpT3OLJf/w5MBYvqAAAgAElEQVTN6y2Dt3/yAfIy06zu9kwYiDl3PeAt7rOfTMCjCORfyBQivCxm213U5OC02+5E3PCRzfpaXliDXV+nQlcjjmzwDSSxYRD8glUexYidYQKdRaCmUofv//ZflFw+JlpCJpei18A4zPvdg/ANCu6s5XleJsAEmIBbE6BaDBkZGUK6pKioKISEhLi1P2w8E3BFAqWlpcjLyxOiGfr3799mzQYWGlxxFz3bJhYaPHt/2Tv7CbDQYAerC0lFOLrpgqjn7N8MRniMvx2jW+5CKUNWP/snUYfpt69A72EjOjSvNww2m0xY8+JTMOptRWET516NxDnzvcF99pEJeCSBcwf24NgP34t8kyuVWPDgHxEcGdWsz5TqZdeqc9BrTKLnSWSgmg1+QSw2eOTFwk45jYC2RodvX/oQpblnRHNKZRL0HTEIc++5HypfX6etxxMxASbABDyNgMlkQlpa7c1P8fHxUCqVnuYi+8MEup2AXq9HZmZtKusBAwZAJms9iwELDd2+ZV5nAAsNXrfl7HALBFhosOPS2LM6FXkXKqw96QBr4QPD21TR7Zgaq//6JEwG22H5hOuXIWHCZHuGenWf4pwsbHn/LRGDufc8hKj4BK/mws4zAXcmQHcEHvh2FS6cOCpyIyAsHFc/9Eco1c0fdpbmVWP36tRmxQaKbKAIB25MgAk0JaCr1mLNS++gNC9D9CSlhUwYPwpz7robJPZxYwJMgAkwgZYJUCRDenq60CEhIcGu/PHMkwkwAccIOPo6Y6HBMb7cu+MEWGjoOEOewTMIsNDQxj5qqw1C2iSLLWsSBk3qicRZMU65Ata9/gKqy2y1H0bMW4jhs+Y5ZW5PniRl3y4c37ze6iIVjL35ub/xgYgnbzr75hUEKqo0WP+vt1GWmwOzxQKpRAKVXIq+w4bjqjvvbbY4NIEpya3G7lWpMOjEkQ3+IRTZwGKDV1w87KRDBDRV1fj2pbdQlp8lGieRSDBk2hTMuvMOrnnkEFHuzASYgLcScPQA1BmcqnVGnLlSgYyCKuiMJqjkMvSP8MfQ6ED4qeTOWILnYAIuRcDR1xkLDS61fV5hDAsNXrHN7KQdBFhoaANS+rF8nNgm/hI+7+6hCI50ThoBKgZNRaHr2+CpMzFm0fV2bJ13d9n91WfIPpNkhRAW01u445kbE2AC7kfAZLZge0o+Vh68hP2ZRVAbqjGvcAeUZlu0F31w08aPx8KblmDu4EjIpE0/yhVfqcKe1WnNig0zbuM0Su53ZbDFnUWAbnBY+8qbKC/MF4sMABLnzsP02290StRmZ9nP8zIBJsAEXImAoweg7bW98eelhjfC1c8pkQBT4sOxfFJci5+X2rs+j2MC3UnA0dcZCw3duVveuTYLDd657+x1UwIsNLRxVez4MgXFOVXWXoHhasy/d6jTvoA3Lmjcb/Q4TL7pdr5WWyFA6VW+e/Wv0FZVWnsNnjoLYxZdx9yYABNwMwJp+ZV44ttTSMopF1keqcvHjKJ9ABqEkwHYFzoZ4QmD8cZNIzAgMqCJt8WXq4Q0Ska9uKg0pU8isSEg1MfNCLG5TMC5BMoL8rDu7/9ERVFxk4lHLbgO025Z7NwFeTYmwASYgIcTcPQAtD04Wvq81NpciTFBLX5eao8NPIYJdCcBR19nLDR0525559osNHjnvrPXTQmw0NDKVVFdpsPmD2x3zVPXYTN6YciUaKddS3tXf4FLSSes8/UaNBSzfnuv0+b3xIkqigqw8c1XRa7NuOMuxA5N9ER32Scm4LEE1p3IwZNrk2AwicWEeocHVaYiseK0yH+jVI5tPeZApwrE329MxJJRTdPYFeVUYs//0pqIDT5+cky/ZaDTItI8dmPYMY8lkH8+A1s++BAVRba6U+SsRCLF6IXLMOWmOR7rOzvGBJgAE+gsAo4egDpqR1ufl1qbTyGTtPh5yVE73LH/xYsX0bdvX8TFxYH+7mjbtWsXZs2ahRkzZoD+dsc2c+ZM7N69G7/88gvob3dpjr7OWGhwl531HDtZaPCcvWRPOkaAhYZW+KUcyMXpXTmiHlQE2j/EeXfEHtmwFmmH6K7d2hYe2wcLHvxDx3bVw0dnHDuMQ9+tFnl54zMvw8ff38M9Z/eYgOcQoC/Nj31zqnWHLBZMKj2MWI34fbhCEYjt4bNglCrw9s0jmhUbCrMrsfebpmKD0keGqcsGIDyG3y8852piT+whkH7kAPasWo3qMq2ou0SqwKgFt2Lqsmn2TMN9mAATYAJMoBEBRw9AHQFo1+clOyZs6fOSHUOd0oXq/1CjyPSubCw0NKXtTKHhv//9L1asWIHf/va3oL87szn6OmOhoTN3g+dujgALDXxdMIFaAiw0tHIlbPskGWUFGmuPsGg/zLlziFOvnVM//4jTO7dZ5wwI64HrnnjGqWt42mQH165G5vHDVrcCwyNw7eNPe5qb7A8T8FgCqXmVWPyvvS1GMjR0XGY24qqinQg0iO/AzlH3woGQiVDIpdj86LRm0yhRgWiKbNBrjCKWcqUUU5YmILJvoMcyZseYQD0Bs9mE45s34NTPO1BTYat7Qs9LZT4YPvc2TL9lotNSQjJ5JsAEmIC3EXD0ANRePo58XmprTopsaOnzUltjnfF8dwkNBoMBmZmZUCgUiI+P77ArNTU1yMrKgq+vL3r37t3h+bpjAhYauoM6r+kNBFho8IZdZh/tIcBCQwuUygs12PqfZNGzI6+KxYBxUfZwtbvPuf27cWzTOmt/pdoXy/4qTgtk92Re0nHDG6+gsrjQ6m382AmYtPRWL/Ge3WQC7k2AChku+WB/k5oMrXnlb6zCVYU7oDAbRN3S/eJxImgkEmODse7BKc0WiKb38j3/S4WmUjxWKpNg0pJ49BoQ4t5A2Xom0AoBXU0NKEXjxVPJTUQGmSIQQ6bfgpm3j4WkmeLqDJYJMAEmwATsI9AZQkN7Pi+1ZS3VbGjp81JbYzv6fHcJDR212xPHs9DgibvKPrkCARYaXGEX2AZXIMBCQwu7QCmTKHVSfaNoz8WPjIDaX+nUfbtw4hj2r/mqwZwS3P7Km5BIpU5dx1Mm01RV4rtXnhW5M+nG2xA/ZrynuMh+MAGPJrD1TB7uW3ncYR97anMxrXh/k3GnA4ciJWAwPl4+BvOGNi8EU72dXatSQb8bNnpfH7e4L/oMD3fYHh7ABFydQEVhAX754j8ovHQFNZXiSAaluicGTl2K6TcPh0zOnzdcfS/ZPibABFybQGcIDe39vNQWqdY+L7U1tj3P16fWaWlsfSql559/Hi+88AKee+45LF++XPi9Y8cOFBQU4M0338Qf/vAHFBYWYtWqVdiyZQtSU1ORm5sLlUqFwYMHC2Puv/9+yGQy0VItpU5q+PiFCxfw/vvv4+OPP0Z6eroQrXDVVVfhb3/7G/r06SOar6UaDQ0f37ZtG15//XWsXLkSly5dQmhoKBYvXozXXnsN4eFNP3OazWZ8+OGH+Oijj5CWloagoCBh/ZdffllIR1TPhRjZ2yorK/HSSy9hzZo1yMvLQ8+ePXHjjTeC5li0aFGzNRrOnj2L//3vf9i+fbtQz6KoqEiwZdy4cXj00UexYMEC0fL1gkVzNjVMpXT48GGsXbtWqAmRnZ2N0tJSgcPkyZPxxBNPYOLEiXa55ejrjFMn2YWVOzmRAAsNToTJU7k1ARYamtk++sDz44enRYdSkX0CMeO2gU7f7MupKfjlvx+J5r3p2Veg8vVz+lqeMGFW8ins+fpzkSvXPfEXBITxQaEn7C/74PkE7vjkMPZlFLXL0UGV55BYIY40o4mOBY9Br5HjsPLuCS3OSyljKI1SRZEtHV5959Hz49B/TES7bOJBTMAVCVxJO4e9q/+LyqLKJiKDb9AQDJx8NSYvHcAigytuHtvEBJiA2xFw9ADUHgc78nmptfmnJYS3+nnJHtsc6bNv3z588skn+OKLL4RhdADdsNXn9a8XGm699VZBSAgICMCkSZNQXV2Na6+9Fr/73e/w1VdfCYJCTEwMEhISEBkZKRyiHzx4EDqdDtdddx3WrVsnSgVoj9Awbdo04SCcCjz7+fkJ85GI0atXLyQlJQlCQX1rS2igw3MSP44fPy7MR5EcxKCkpAQjRozAkSNHoFSKb1ykGgfEgR6nw/uQkBDBBvKdBApiR8KLvUIDiQy09okTJxAcHCwUrzaZTNi5cyeGDBkiiDE0f+Ni0Pfccw8+/fRTQbih4tmBgYE4f/48jh07JrhPgs8f//hHKwsSYjZt2oT9+/cLaammTp1qfY7+pvmozZ07VyicPXToUCHdFPEhoSg5OVmwZfXq1bjpppvavKwcfZ2x0NAmUu7gZAIsNDgZKE/ntgRYaGhm64ovV2HHFymiZ8Yu7IN+I3s4faOLc7Kw5f23RPNSvQGqO8CtKQFKM0Xppuqbj38Alj79IueW5ouFCbgBgWqdEcOe34p21wG0WDCq4hQSqjIaeSvB/rBJ2Pb6Cvip5C2S0NUYsOebNJTm1jTpM3xmDAZP7ukGFNlEJtAyAbpR4tyBPfh183pBYNA0iGSQQIKAiMlIGD8FE5fEQybjSAa+lpgAE2ACziDg6AFoW2t2+PNSKwtQNGfy8/Nb/bzUln3teb6t1En1QgPNTQfUH3zwgVBXoWFLSUlBRUUFJkwQ31hCosDChQtx8uRJ4Y78m2++2TqsLaGBOtIhOUVP0OE6NVqDDsePHj2KF198Ec8+a4umb0tooPHjx4/Hjz/+iLCwMGE+EkNINCFbvvzyS0EsqW/ff/89li5dKvSluYcNGyY8RbUl7rrrLkFcoeaI0EDRH++88w7GjBkDiq6oF0quXLkiiA4UNUGtsdCwe/dugUHjKA6KSJg3bx40Go0gPJDQU9/sKQb9008/YdSoUYIw1LD98MMPgu8kKlGkA0WStNYcfZ2x0NCeVyqP6QgBFho6Qo/HehIBFhqa2c0TP2ch/Wi+9RnK5X3toyOhVLd8gNXei6KypAgb/vGyaPj8+3+PHnF92zulR4/b8t6bKL6cbfWx97ARmH77Co/2mZ1jAp5C4MiFEiz76GDH3LFYMKn0MGI1OaJ5zBIpFt33EGZNGd3q/AadCfu+TUdhVmWTfoMm9cTwmb1YuOzYDvHobiJgMhpxZMO3yDx2GJoqg0hkkMpUCOo5F/GjhmHi9f0gZZGhm3aJl2UCTMATCTh6ANoWA6d8XmplkW/vn4RxfWx36bdljzOet1dooAN3OpD39/d3aNmff/5ZOAyn9EDffvutdaw9QsPmzZsFoaJhozmWLVsmRBjQgXx9a0toID8pCqJeMKgf949//ANPPvkk7rzzTnz+uS06nw7+ac6///3v+NOf/iSygaIgKAKAIhvsFRqoWHVERIQwhiINKMKiYaPDfYoQodZYaGgN+DPPPINXX30V7733Hh566CFrV3uEhtbmvf3224V0WBQZQSmdWmuOvs5YaHDoJcSdnUCAhQYnQOQpPIIACw2NttFituCH905BW2UrHBo9IBhTb0zolA03aLX45oU/i+aeufwexAypvZuBm42AQa/Dmuf/jPpcnvTMmMVLMHjKDMbEBJiAGxBYdTgLT6873WFLpRaTUK8hUlcgmis2Ihh3/OlPCImKbv2LisGEA99nIi+zvEm/+NE9MGpeHKRcHLfD+8QTdB0BbVUV9nz9GQounhcEBhIa6ptcGYzg6PnoO6IPJlzLIkPX7QqvxASYgLcQcPQAtC0uzvq81NI6r90wHLeO792WGU593l6hgaIRKCqhpUasKQUQpf6hSAGtVit8N6R0QRQdkJiYiFOnTlmHtyU0yOVy4U59+t2wnT59WphrwIABQpqf+taW0EARAbRm41Z/wE9iyNatW4WnyRcSVCjtE9VyIFGhcbvhhhuEdFD2Cg179+7F9OnThagEqj3RXKPUTGVlZc0KDcSRhBeKDiGhQ6+vrfFEtSsOHDgg1GqgaIn6Zq/QQPUeSEygdEm0NvlOjdI7kTDz1ltv4bHHHmv1mnP0dcZCg1NfwjyZHQRYaLADEnfxCgIsNDTa5vwLFdi92vZhgp6mu/96D6kNfXR2ow9Gq/7yOCxms3XqSTfeivgxLecad7YN7jJfXkYatn/6gcjcqx96HGExse7iAtvJBLyawOf7L+CFH846hYHcbMCsoj0IMZRa54sO9kFszx6gqDD/0Nbfs00mM45sPI/sFNv4+ol6xgcJqWUUSnFBQacYzpMwAScToBSMVLuouqy0icig8otFUNQcxA2PqhUZWEBzMn2ejgkwASZQe2BMB7HUqHZA40NrRxk58/NSc2s/d80QrJjStdHz9goNdNc/FVJurlHKn+uvvx6UQqml1viAvS2hITY2FllZWU2ma2lcW0ID1XvYs2dPk/maG1dfpJmuFxJMGheypknq0yDZKzSQSEN1LhpHYjQ0aOTIkYIY0ziiYcOGDUK6JhIYWmqNIzLsERqoyDXVdqBoi5Yapc4iH1trjr7OWGhw9J2H+3eUAAsNHSXI4z2FAAsNjXby6OYLuHDKVqhUrpTi2t+PhFzReQdOa195FtoqWxqP0Quvw5BpszzlGnOaH0k7fkLS9p+s88mVSix77jVIpZ23N04znidiAkwAzr5DT2XSYk7RLvgbqwS6MSFqhPopERBWKzb4tBF2TxFsx7ZcFL3n129TcKQvpt6UAN9AccE+3kYm4CoE6usxnPhxI8xmUxORwS8kEf7hExA3LAzjr2GRwVX2je1gAkzA8wg4egDaFgFnf15qvJ4rRzS0dqBOEQYUaUCpf0iQoKLFQUFBwgE9iRADBw4Uagw0jChoS2hoKQKhvUIDFWEmUaFxa0tooKgGqbRp7SQ6oH/77bftjmhor9CQk5MjRG9QdMdTTz0liBUk2lBxbLLr448/xn333ScU864v4E0+tiU0UJ0LqqlBYgqlXrrmmmuEGg9Uj4HEp6effhqvvfaaXf45+jpjoaGtdxp+3tkEWGhwNlGez10JsNDQYOdMRjN+ePck9FqT9VH6gk53AXZm++Htv6G8IM+6xNCZczFq/uLOXNIt597+yQfIy6wtXkUtKn4A5t7zoFv6wkYzAW8k0Bk5h/2M1ZhT9At8TFrE9/CzFjcM6xWLufc+BIXKp1XUdFh7akc20o7Y6vLUD1AHKASxISTKzxu3i312YQK6mhoc/G41cs7WpiKrqdBDW12bLkkikSEwYirUQQNrRYbFfSHhSAYX3k02jQkwAXcn4OgBaFv+dsbnpYZrunKNhpaEhnPnzgnCAtUfoKLGje/+p+LLlOPfnYQGKvhMqZMoPREVQ25YZLl+v6hYMqWEsjeioT51Ut++fYXCzc215lIn/fvf/8YDDzwgFGdeu3Ztk2Ek7FCdCUeFhj//+c9ChMrjjz+ON954o8m8VAeD6mHY45+jrzMWGtp6p+HnnU2AhQZnE+X53JUACw0Ndu5yWin2r80Q7eXUZQmI7h/cqfu77aN3hbzK9S1+7ARMWnprp67pbpPT3ZprXngKxro8kWR/4pwFSJy7wN1cYXuZgNcSqNYZMez5rbBYnIsg2FCG2UW7MDLKV5QapmfCQMz8zb2QNcq729zq6UfzcXJ7VhPbKKpt4nXxiE7o3P8HnEuEZ/NkAkXZl7B31X+FVEn0Wqqp0EFXU5vrWCpTIzj6KijVUeiTGI5xC/uwyODJFwP7xgSYgEsQcPQAtC2jO+vzEq0rkQDJz8+33pjRli3Oel6pVIIO1umnudRSlDrnhRdeaPHAmeoDTJkyBZT2h/L6N271RYXdSWggHygCglIt0SE8HcY3bKWlpYJwQnUT7DmIp7ENi0EfOnRIiCZo2Kj+wuLFtTc0NkydRNEGVPCZUjVRBEXDRtEWQ4YMEYSLxkIDFXIm9vTz1VdfNdmX3/3ud/jPf/6Df/7zn/j9738ver6wsFCIoqCaDfb45+jrjIUGZ716eR57CbDQYC8p7ufpBFhoaLDDB9dnIvusLSehUi3DNY+OhEzWNIzRmRfG3tVf4FKS7QMT36nflG5xTja2vP+m6Ik5dz+Anv0HOnMreC4mwAQ6mcAdnxzGvgxbejpnLTcv0oCZJftgrisuVz9vnxFjMGXZ7ZA0E47eeO0r6aU4tP48jAZbzRzqQ1/KR17VGwljI51lLs/DBBwmIKRK2rcLv/70g1DXif5dVaqDQVcbhan0jUZQ1GzI5L7oOyIcY69mkcFhyDyACTABJtAOAo4egNqzRGd9XpqWEI6Vd3d9LUC6w57SEVHh3+HDhzdB0JbQQIfSUVFRQrqd7du3CzUI6tvnn3+Ou+++W/h/0d2EBrqbn+7q79Gjh5ByiQ70qZEgc++99+KLL74Q/m3PQXw9DyrY/K9//Qvjxo0TCk9TBAO13NxczJ49GxQdQq2h0EBRExTNQFEVR44cQc+ePYU+FG3x8MMPC2IBtcZCw+7du4W9GD16NI4fP95kX6nIMwkoo0aNAvUNCAgQ+pB4cttttwkFou31z9HXGQsN9rzTcB9nEmChwZk0ea56ApQo/hF6/wUwgN6XAfwK4C0Am9uBSQ3g/wDcDKAPgGoABwD8re53O6ZsMoSFhjokBr0JG/95EpQ+qb71G9VD+KLe2e3E1k04s2u7dRn/0HBc/6e/dPaybjV/yv7dOL5pndVm+pC57Pm/QaFUuZUfbCwT8HYCW8/k4b6VTb+IdJTLx8vHYJAlX7jTm75oNmwDJ0/H2MVLhC+nbbXSvGrsW5MOTVVtGpqGLWFcJEbOieU7xNuCyM87nYCQKmnt18hJOSPMbTaRyKCtE8Uk8A8bDb/Q0cI1Tp9dxsyP4+vU6bvAEzIBJsAEmifg6AGoPRw78/PSvKFR9pjg1D71tQboQJ0OuyllELVPPvlE+N2W0EB9HnnkEbz33ntCzQCKBCDhgWo2JCcnC3UFKNe/uwkN5NdvfvMbrFy5EhT1MWvWLAQHB4OiESoqKoToA3rulVdeEeoZ2NPoEJ+KUlPBZxIZaE6TyYSdO3di0KBBQkTJwYMHRUIDXcPjx48XokVIDCDxwMfHB/v370d5ebkg5Lz77rtNhAaKdqBaDlTYesyYMRg6dCgUCoUQfbJixQpQVMaIESOE1FDh4eGCXfQ5naI4yN+FCxfis88+s0tIcfR1xkKDPVcL93Emgba/aTpzNZ7LGwjIAWwEcDWAMgA7AdD/nrMB0HN/BvC6AyBI6qUqQqNJfAawDwDdSjkNAJ2gLAewyoH5WurKQkMdmUvJxTi8UZzHcNYdg9Cjd63q3pkt/cgBHF63xroEFTe+9aV/2HUHbmfa5Upz7/n6c2Qln7KaRPnXr35YHF7qSvayLUyACTRPwGS2YMkH+5GUU+40RIkxQVj34BTIpBKkHz6Aw+tt76f1iySMn4zx191o1/sq5bvftyYNZQWaJjZSCqUJ1/WDQslF6J22gTxRqwQKL13Avv99KaRKokY3RFSVaGEyWSCT+SKo52whmoHawIlRSJwZwyIDX1NMgAkwgS4k4OgBqD2mdfbnJXtscGYfKjL8l7/8BevWrQMVHqY79qnV3xxij9BgNpvx6aef4sMPPxSKP9OBNh1uP/HEE8IBOkVNuKPQQH69//77+Oijj5CRkYHAwEDMmTNHEBcopRH5TM9RGiJ7G4kUL774ItasWYP8/HxBlKGIBXqMxAuKLmgY0UDzkkDx8ssvY/369bh06ZIgUpDgQHtDwgQJB40jGmgcCRqUdon6UBok8qdhv4KCAjz77LP4+eefcfnyZaHOxoIFCwRbyK/WUmY19NfR1xkLDfZeLdzPWQRYaHAWSZ6nngBFHlCkAVXlI3GhPi/EmDrBgCpJjgNg722cHwK4H8AOANdSur26hRbWCRoULZEA4HIHt4CFhjqAe9ekITfDdvClDlBi8UOJXfJlPTcjFTs+pS23tSX/9xz8gmvDHL290QfQ7179K7RVlVYUg6bMEO5Q5sYEmID7EUjLr8Sid/fCYOp4sQaFTILNj07DgEibKJy0YyuStm9pAqbPiNGYfNPtkMraFgkoyu3gukzkZTYVREKifIUi0fT/BDcm0FkEKD1Syr5doKhH+puaUW8S0iWZzRYofXvVpUpSc3qvztoEnpcJMAEmYAcBRw9A7ZhS6NLZn5fstYP7dQ8Buq4ozRSlOjp69CjGjh3bPYa4yKqOvs5YaHCRjfMiM1ho8KLN7gJX6cQiD0A4gKkA9jdak3LgvARgLYCb7LAnrG4+uk7jAVxqNIbiC+8G8AaAP9kxX2tdWGgAoNMYsfGdk7CYbYdeAydEYcSc2A7itW94ZXERNrzxsqjzVfc+jMh+/e2bwMN7Ncdn+u0r0HvYCA/3nN1jAp5LYN2JHDz2jS1Kqb2evn3zCCwZFSMaTuLksR++R+rBvU2m7TVoKKbddifkCkWbS9Jh7oltl5D5a2GTviQyTFuWgOBI3zbn4Q5MwFECmqpKHFq7GpdTz1qH6jVGVJfrAIsEfmFj4Bc6SkiVJJNLMfH6fug1gG9OcJQz92cCTIAJOIOAowegjqzZmZ+XHLGD+3YeAUr91L9/fyFVUX2jCJAnn3xSSBVF6Yioj7c3R19nLDR4+xXT9f6z0ND1zD15RUpntAdAFoC4ZhztByCzLiqBvgVSNEJrjdIifVk354xmOlLEBEU6ZNRFNXSELQsNtDknCnB8i1jPmbtiCEJ7UiBK5zezyYTVzz4hyis+6cZbET+m6wt1db63jq+QefwIDq4VZwpb+sxLUPt3florx63lEUyACdhLgL48P7k2qV2RDRTJ8PcbE5uIDPVrk9hw4qcfcHYPZTIUNxJxZ/7mHihUti90LdlM86QfzcepHdloVPoBcoUUY67ug7hhdH8ANybQcQJ0vV1KOoGjG9eC6jLUN22VATWVeqHQc1DUHCh9a4s0qnzlQnRNWK/aXNfcmAATYAJMoOsJOHoA6qiFnfl5yVFbuL/zCdxyyy3YvHmzUDA5Ojoaxb1/Qm8AACAASURBVMXFQjoiKoBNaZSo+DUVdvb25ujrjIUGb79iut5/Fhq6nrknr/gogHcAfA9gaQuOFgMIBTAcQFtyNBWPfqyuiHRzSegDAdTncqC/bflkHKfMQgOAX746h8IsG8aAUB8suG+YXYVDHUfe/Ih1r79gzb9MPYbPno8RV1HJD24Hv1uNzGOHrSACwnrguieeYTBMgAl4AAFKC/DEt6ccqtlANRneuGmEKF1Scyjo0DZ51884te3HJk+HxfTG7BX3Q+VrX0TC5dRSHNpwXsiP37jFj+6BkXN7C3eWc/NOAtU6I85cqUBGQRV0RhNUchn6R/hjaHQg/FRUqqvtpq2qwpEN34rqEVFVLqoZoq0xQOUbg8CoWZDJ1cJk/iEqTLt5AOgzCzcmwASYABPoPgKOHoC2x9LO/LzUHnt4jPMIbNy4USiKTYWYSWSgGgcxMTGYO3euENVAB+bcAEdfZyw08FXT1QRYaOhq4p69Xr0w8M86gaA5byk/RCKAawBsagMHCRaUfP4PdQJGc91JaCCRwR7horXlvF5ooC/wm98/JbpTdei0aAyd1qtLr9qfP34P+RcoSKW29R01FlOW3dGlNrjqYhvffBUVRQVW8yjSgyI+uDEBJuAZBKjg4Y6UfKw8dAn7MoqaRA6QlxIJMLV/OJZPjMOcwZFC4Wd7G6VQOrrxuybdgyJ7Ys5d98M3MMiuqUquVGPf2nTQ3eWNG0XATbohHn5BKrvm4k7uT4Cu2+103R68hP2ZLV+3U+LDsXxSHOa2ct1mJZ/C4fXfQlddZQVDQll1mQ4GrRl+YWPhFzrSegNEWC8/TLkxAT5+bacAc3/S7AETYAJMwLUJOHoA2l5vOvvzUnvt4nFMoCsIOPo6Y6GhK3aF12hIwP5vp8yNCbRN4GMA9wJ4BQDVY2iuUd2GyQBuA7C6jSm3Abiqbk6qx9BcoyLQ0XVzHmxjvvOtPB/bt29f+fnzrXVpG4A790g9nCekxGjYKJohMKz2jsGuagfXrkbmcdtd+z3i+mH+/RQs492N7vBc+4r4ZTVx6a3oP5bTSnn3lcHeeyqB+jvDMwuroDWY4KOovTN8SE/77wxvjs35X48KKdjo8LZh8w8Nx9y7H4B/qH3pjyhH/sHvM1GSW91kGaVahgnX9EPP/sGeuj3sVx0BZ91Zqq2uEkQwSpfUsJlNFlSVagFJIAIjZ0CpjrQ+3WtAMCZc1w9yRdtFzXnDmAATYAJMoPMJOHoA6gyLOuvzkjNs4zmYQGcQcPR1xkJDZ+wCz9kaARYa+PpwJgEWGpxJs4vn2v75WdGBERX2nHf30C62AkjasRVJ27dY16U7bG946oUut8PVFsw+k4TdX30mMuvax59GYHiEq5nK9jABJuDiBLKSk7Dvf1+A6uI0bPR+O+fuBxEUYTvMbc0VSp90cntWs0WiadyQKT2FqDiJA1EXLo6OzWtAwFm5sun/N4pi0FaJM2AadCZUl+mhDhoG/7CxkEhtqZf6j43AqLm9+driK5IJMAEm4EIEHD0AdSHT2RQm4DYEHH2dsdDgNlvrMYay0OAxW+kSjnDqJJfYBseNoDtTN7+fJBqYODsGgybWFlnsynb+xFEcWPN1gyUluPWlv0Mm9+60CMc3b0DKvl+sXHz8A7D06Re7tH5GV14HvBYTYAKdSyA3PRW7Vn4Kk0EvWkjl64fZd92PsF6xdhtwKbkYx7dchNHQtG5DZJ9A4a5zTm1jN0636Egiw2PfUDbM9jeFWY8/98qFKi9NPIkF0FQbYNCp66IYokTPj5gTiwHjI/n/v/aj55FMgAkwgU4h4OgBaKcYwZMyAQ8n4OjrjIUGD78gXNA9FhpccFPc2CQuBu2mm9dc2qRFDybCL7jrc2wXXDyPbR+9KyLJd+4DP33wNoqyL1m5xA5NxIw77nLTK47NZgJMwBUI0PvtL198DINWKzJHofLBzN/ei8i+8XabWV6owYHvM1BZLJ6LJlAHKDFpSTzCY/ztno87ui6B1LxKLP7XXhhM4vRbjlgcrbmCseW/Qm3WIiHCX0gNRs1stqCmTA+F72D4h40TRTHI5BKMX9wPsUNCHVmK+zIBJsAEmEAXEXD0ALSLzOJlmIBHEXD0dcZCg0dtv1s4w0KDW2yT2xg5DcAeAFkA4pqxuh+ATAA1AEIAiG+jbDpgOYAv6+ac0cx8swHsAECVgxM6SMmri0Hv+DIFxTm2woshPX1x1YquT5tEe1hTUY7vX3tOtJ2z7rwPvQYO7uAWu+9wo16Pb174Myxm293CYxZdj8FTZ7qvU2w5E2ACLkGg5Mpl7PjsQ1HxXTJMKpNh3DVL0X/8JLvvHDfoTTi2+SKyU0qa+Ebpk0bMjkHCOL4T3SU2vp1GUAHOJR/sR1JOebtm8DNWY1T5KURrr1jH+ypliO/hD5PBDJ1GCb/Q6VD6iiMq6cYHEquo2Dg3JsAEmAATcE0Cjh6AuqYXbBUTcG0Cjr7OWGhw7f30ROtYaPDEXe0+n+h2tDwA4QCmAqDCzw0bVbJ9CcBaADfZYSZVpKT56DolkYIEjIaNCkTfDeANAH+yY77Wunit0FBTocem98TpD4bPjMHgyf/P3nmAR1V0b/xND0kIvYSE3iH0XqRKk94ERVAUCxYsgJ90URA+lb+CfCKKgmKnN0Gl995b6DVA6CSQvvk/Z2CXbOreZHdzd/ed58lDyJ05c+Y396bMe8859k+bJBskh+m/jXsfhoQE037V79oLFRrKLeWa7erpk1g9639mi+/wxnsoEFLCNYFw1SRAAlYlcO96hPoeI0Jvylamdn3U79Ybnl6Wpa+TItOndkdg/5qLSDKkfuNdCvjW6VCKqZSsuoP2M/b3kat4de4ezRO6JyWicmQYKkcdh3tS6hRbxQN84OdbCf4F6sPd3fxeK1Y+L+p3Kg3vXI9rNGh2gANIgARIgARsTkDrAajNHeIEJOCEBLQ+ZxQanPAm0PmSKDTofIMc0L3/AJgMQBL+S8TBzUdrqA1gAwB5Fa0+gN3J1iZRC/K16Y8+ki97BoDXAKwG0AVA9KOLHQAsexQVUQHApWyyclmh4eSua9j3r7mG0+G1asid3zebSLM+fOmUT3DvRoTJQJVmrVC7g2y/a7aUBbI9vLzRZ9wk9cYxGwmQAAlYg0DU7VtY8/3XiLx5I5W5fEHBKlVbQH7R/y1rNy5FYdui04iOTB286OPnidrtSqJ4ZabAsYymfno9N2sHNp9KfY+k62FSEorFXEGte/vhnyABreZN/hDxcQuAf2ATlC5mnqrLzQ2o1jIEFRsUtTiqRj+k6AkJkAAJuB4BrQegrkeIKyaB7BPQ+pxRaMg+c1rQRoBCgzZe7J05AXndTASA9gBuA1j7SFxoDUBeURvxSIhIbmk9AEmNNB7AhymmyP1IoKgF4AqATQAKP+ovr0pKeqVfM3cr0x4uKzSsm3sM1y8+TpuUt4gf2r6UM2mTjLu0dvZMhJ84Ztq0EqE10KzfwEw30Vk7SFoTKdxqbEXLlseTg95w1uVyXSRAAjlEIDoqEpt+nYOIs5Ll0Lx55/JDkz79NaWxi7kfjx1Lz+Da2Xtprqh45Xyo1bYkoxtyaL+1Tns/NgGhH/6NJAtLMwQkRKHW3f0IipHg1NTNA27w8CoPN9+agJsXqhYLhLuoCwB8A7zQqFtZFCohvwaykQAJkAAJOAIBrQegVllTbBRw9SBwPQxIiAU8fYBCFYGi1QEf1oayCmMa0RUBrc8ZhQZdbZ9LOEOhwSW22e6LlNes3wLwAgCJNpDXGfcC+D8Ay9PwJiOhQbrnAiCREn0BlAJwH8BWAJMe/WuNBbqk0BATFY9lX+03OzQIbR6MKk2KWYNplm3sWDwPJ3c8zryVP7g4nnpzaJbtOfJAgyERf44fiYS4WNMyqrVqhxptJKiHjQRIgASsS0C+5+xftRxHN61Lw7Abqj/ZDtVatoWbu7tFE0v6pCObw3FsS3iaB9QS3VCnfUmEVGJ0g0VAc7DTzrO38PTMbZl64GFIQJWo46gYdSLNNEliINazAHJ714eb5+N9L1PIH/7enihcMjcadi2rxAY2EiABEiABxyGg9QA0yyszJAJhK4Fd3wFnJGlCWgq4G1CmOVDvZaBiByk+leXpOJAE9ERA63NGoUFPu+cavlBocI195iozJ+CSQsOpPRHY+/d5MzrtXwlFYEHRdnKuHdmwBvtWSWDMwyZv0j499pOccygHZ74Vfgl/fSVlSB631i8ORlD5ijnoFacmARJwdgLnD+3Htvm/mYmcxjUHV6yCxk8/Bx8/P4sxSCqlXcvPIvJWTJpjSlTJj1rtSsKHefgtZmrvjr/uuICRiw6lP21SEkJiLqPm3YPwS0ydJkkGxrv7IN63JvJ5lAUeRS+Y7qu8udCkTUlUbRYMd3f+iWLv/eV8JEACJJBdAloPQLM0X8QxYPFgIHyf5cOL1QK6zQAKV7Z8DHuSgE4JaH3OKDTodCOd2C3+Fu/Em8ulaSLgkkLD+l/DEHHucUoLERhEaMjpJgdckr4jeeszbjK8fHOubkROMTm+dSN2L1tomt7NzQ1Pj5sELx/XY5FTe8B5ScBVCdyNuIoNc38wq5ljZCH1Gpr1exH5iwVbjCchPhGHN1yG1AZKK/2Or7+nKhQdXCGfxTbZ0X4EZm85i/HLjqY5Yb6426h+7xCKxD6ur2Te0Q23cpVHQa/q8IJPKhuxbkmo0DoEg7rzEMh+O8qZSIAESMC6BLQegGqe/cAfwNI3gcTU9Z8yteXhDXSZDtTok2lXdsg6gQ8//BDjx4/HuHHjIJ+zWZ+A1ueMQoP194AWMyZAoYF3CAk8JOByQoPkzl42zTxtUtUniqHqE5YfGtnq5rl5+SJWTp9iZv6pt4ZrOtCylW/2tiuCiwgvxpa/WAieemuYvd3gfCRAAi5KID4mBlvn/4qLRw6mIuDu6YmGPfqgTK16muhcvxCJXSvOIur245RwyQ2UDC2AWm1KwJvRDZq42rpzWhENgfH3EBp5BCHRl9Od/o53QXj41ENBt7TTY910N2BDrniM7lUNz9QvYetl0D4JkAAJkICNCGg9ANXkhogMi17RNCTNzt2/zVGxQV4ak5ZkacGj7K84lYUWLVpgw4YNWLduHeRzazZrCg3nzp1D6dKlUbJkScjnbA8JaH3OKDTwzrE3AQoN9ibO+fRKwOWEhjP7rmP3SvMf2O1eroo8hSxPhWGrzYx98ADzPh5pZr75cy+ieNXqtppSl3blF9CFk8YhOvJx1Emlxs1Qt3MPXfpLp0iABJyTgHwvOrZpnUppl9YfxmXrNkCdp7qqNHeWtoS4RBxafxknd19Lc4jk56/dtgSCK+aD8Y9yS22zn20IJK/R4J8QhdDIoyj54EK6k8W4++Kmf02UQGl4uqWu6SEZtY97JWKvTwIMbsC81xqhXinW6rDN7tEqCZAACdiegNYDUIs9unYU+LZ51iIZUk4ikQ2vbsyxNEoUGizedSUuUGhIzUvrc0ahwfJ7jj2tQ4BCg3U40orjE3A5oWHjb2G4evbxAXbu/L5o/2qobg50/hj/AeRNWmOr07EbKje17hsXer9tI2/dwJLPJpi5+cSzL6BktZp6d53+kQAJOCGBq6dPYtNvPyL2flSq1fkG5EbdTt1RsnotTT9HJLph5/KzuH8n7eiGQiVzo2br4shX1N8JiTrWku7HJqDemEWofO84yjw4C7d03sZMcnPDRb8KyO9ZDQWSUqdJklXfcTdgm28Cbng8LOApL3ge/rAd/H08HQsKvSUBEiABEjAR0HoAahE6Kfw8q7W2mgyZGZaaDYPW5EiBaAoNmW3O4+sUGtJmpfU5o9Bg+T3HntYhQKHBOhxpxfEJuJTQEBudgKVf7jPLkV25cRCqtQjRzU6umPYZbl95nIqhYqMnUK9LT934Zw9Hzuzdha3zfjGbqseI8fALzGOP6TkHCZAACaQi8ODuHWz8ZTZuXDyfJp1iFSujfpdekBoOlrZ4iW5Ydwmn9qSd318OoUtVL4jQ5sHIFeBtqVn2syKBmPtROLJ+DZYvWYn70WmLQjLdpVwhiPephgqG/EgdwwAYABzyTsBh70QVxWBsT5QviLkvNbCixzRFAiRAAiRgbwJaD0At8u/YcuCPfhZ11dSp769ApY6ahmSn85w5czBw4MB0TaSMGN20aROmTp2KLVu24ObNm8ifPz+aNWuGkSNHombN1C+d7d+/H59//jm2b9+Oy5cvw8fHB4UKFULDhg0xePBgNG7c2BQhkJ4TlqZSEl9nzpyJGTNmICwsDIGBgWjdujUmTpyIn376Kc0aDdevX8evv/6KlStXqjFXrlxRPlauXBn9+/fHa6+9Bg8PD5NrxhRMafmaPJXS+fPnld1//vkHp06dQkREBPz9/VGjRg28/PLLePbZZ7Ozbbocq/U5o9Cgy210aqcoNDj19nJxGgi4lNBw9sB17FphnjapzYtVdPXG6IaffzDLCR5cqSpaPv+yhi11/K7bF/6BU7u2mRYSkL8gug0f7fgL4wpIgAQcmkBiQjz2rFiCE9s3p7kODy9v1GjTAZWaNIO7++M/GjNbdMT5e+pnU3rRDZ7e7qjUKAgV6xeFh1dax9iZzcDrWgnExUTj2Kb1OLZ5PRLiYnE3Oh7nbz5IZeaKb1Fc8q+G0MSCyGtIe29uuBuw1TcBdx9FMSQ38m3/OmhbtahW99ifBEiABEhARwS0HoBa5PpPXYEz6y3qqqlTmZbAgMWahmSn8+bNmzFr1iz8+OOPyszzzz9vZk6ECGOTA/vRo0fD3d0ddevWVTUKzpw5gz179sDb2xvz589H586dTf3lkL1jx44qd3+tWrVQvnx5xMbG4uLFizhw4ACGDRuGyZMn48aNG+rzVatW4dq1a2jXrh2KFn38s/eDDz5ApUqVMl3mK6+8gu+++w5eXl6qxoOIILK+Bw8eoFOnTpg7d26qYtA///yzEhRCQkKUf0WKFMHVq1exbds25WvXrl2xaNEiU1Ts4sWLIWMWLFighINevXqZ/CpYsKASVaRNmDABY8aMQdmyZVWapQIFCuDSpUtKcElMTMRbb72FadOmZbomR+qg9Tmj0OBIu+scvlJocI595CqyT8ClhIZNf5zAldN3TdT88/rgqcHVNKW7yD7yjC3sWbFYHWwYW57CRdH53Q9sPa2u7C/7v0m4e/1x/vIyteuhcW8bvNGjq1XTGRIgAUchcDnsGHYumYf7t2+l6XK+oGBVLLpAiOUFfiW64eimcFW7wZD4MK1OyuYX6K0i8EpUyQ83d/4qa4v7RSJXjm/dhFM7t0LEBmOTbEmnr0fhQVyi+tJ1n0I4HRCKkklFUCohbVEpAUnY75OIY16JQBrbVT0kDxa93gQe3EtbbCVtkgAJkIDdCGg9AM3UsdgoYJJE3Kf9+0Cm4zPs4AaMuAT4BGTPjMbRmaVOWrZsGbp06YISJUqog/fatWubZpBrPXr0UAfvIjzIAb+0li1bYv369erN/meeecbMIxEU5EBf3vA3tuwUgxYBoHv37siXLx/Wrl1riq4QsaBfv35KGJA2btw4SFSCsR07dgz37t1Dgwbm0YsS2fDUU09BIjJ+//139OnTxzTGktRJu3btgp+fH6pWrWq27pMnT6ooCxFbRHRIOa/GbdNVd63PGYUGXW2fSzjDv85cYpu5SAsIuIzQEBeTgKVT95sd4FRsWBQ1WhW3AJP9uoRt24RdSx/+oiJN3pDtO/6/uhJDbElD0lTMn2AevdCgRx+Ur9fIltPSNgmQAAloIhAfF4tDa/5WxaLTKhQtf1BL6rsabZ+Cl4+vxbajbsfg4NpLuBR2O90x+Yv5o+aTJVAwxL6HBBYvIqOOcnhy9SBwPQxIiAU8fYBCFYGi1e1+6JHczZuXL+L45vU4d3AfkgyS6Ch1i4lPxM47XgjzD0UxFEPZBM+09AM18IqH1GKIx/10AlC8PNywYsgTqFAkt1Ww0ggJkAAJkEDOEdB6AJqpp+e3ArM7ZNotyx0GrgJK2vdvq8yEBolgkMiFNWvWoFWrVqmWNmTIEHz11VcqrZJ8Lk0O2Y8ePYrbt28jb968meLIjtAgPkmKJYm6kDROyZukLSpVqhSio6NTCQ0ZOfXvv/+ibdu2Kmph3rx5pq6WCA0Z2ZWoC4m+kCiOzz77LFMujtJB63NGocFRdtZ5/KTQ4Dx7yZVkj4DLCA3nDt3AzmVnzWg9+UIVyIGNntql40ew/sfvzFzqOepj5ApwjcOIS0cPY/3cWWbr7/zuCOQpXERP20RfSIAESEARuBV+GTsW/YGbly6kSURqy9Tv2hshVUI1EZNi0ftXX8Dtq6nT9RgNFa+SH9WaByMgn+VChiYnrNVZClqGrQR2fQec2ZDOG5puQJnmQL2XgYod7FKoUgSFy8ePqijCa2dPZbjaPEWCUKVZW2w56IGwXRFp1mEQA3FuSdjtk4DTnoY0oxiMk3zRpwa619JPfShrbTXtkAAJkIArEtB6AJopo92zgeXvZNotyx06TwXqvJDl4VkZmJHQIHUMChcurCIVJM2RsW/yeSRtUu/evVXkgkQwSJM0TFIbQdInjRo1CvXr1zerd5DSz6wKDbK/AQEBKtWRiACS0ill69atG5YsWZKm0CDjJQpC0iVJlEVMTIx6SSUyMhILFy5E9erVVaonY7NUaBA7f//9NyS6QRiKf9IkWkLSSkmEiPjkLE3rc0ahwVl23nHWQaHBcfaKntqWgMsIDZvnnUT4yTsmmn55vNHx9eq6ixS4c+0qln852WzX2w1+B4VKlLLtnaAT63tXLsXRjWtN3vj4+aHX6Im62yed4KIbJEACOiAgB9YndmzBvlXLVT7/tFpQ+Uqo1qotCpcqY7HHSYYknD98E4c2XEJ0ZHya46RgdHDFfJAIvQLFdBjhEHEMWDwYCN9n8bpRrBbQbQZQuLLlYzT0TIiLw5m9O3Fs8wZE3rye4UipEVS1eVvERAfh5K4IJMQZcOdBHC7djk6V0OKcZyJ2+SQgJoMyGhLJ8Gmv6hQZNOwXu5IACZCA3gloPQDNdD3bvwFW/SfTblnu0P6/QMPXsjw8KwMzEhrkoFxEAkvak08+CYkEkCYH6h06dDAd0ktqpXr16qnUQQMGDFBpmJK3rAoNIg4EBQXB09NTiQTJizcb7b/zzjsq2iJl6qQTJ05ARAhJoZRek2iIs2cfvxBpidAgosXTTz+t6jKk12S9EoXhLE3rc0ahwVl23nHWQaHBcfaKntqWgEsIDfGxiVg6dR8SEx7nuaxQv4hKPaG3Jgcgv49738ytpn36o1TNOnpz1Sb+rPr6S9y4+Lhgd0jlULQYMMgmc9EoCZAACViTgOT337VsIS4eOZiu2cKlyyrBoWjZChYLqAlxiTi+/SrCtl9FYkLaaX1kwkIlcqNigyIIKptXHzUcDvwBLH0TSIzTjtnDG+gyHajxOGexdiPmIx7cu4sT2zYrUSguOv1IERlVsHhJVGzUHDHRRRC24xrioh/WZjC2mIREXLoVjej4RIR7GLDPJwG30ij2nHyM1GT4vHcNpkvK7kZyPAmQAAnojIDWA9BM3XexiAapJdCoUSOV/kiKI2fUpGizFG82NoPBoKIFVq9erQozi2gRFxenikdLUWWJgjC2nBAaJFrh0KFDKrrg/fffR+XKlZEnTx4lVogIUbFiRRUhIeKCsWUmNEjxaTlElzoUL730EgYPHoxy5cohd+7cqpC2RDNIwevmzZurGhbO0rQ+ZxQanGXnHWcdFBocZ6/oqW0JuITQcOHITWxfcsaMZKsBlXWb33r+xDGIiYo0+Ss5vqu1bGvbO0EH1kVk+XP8CBgkzcajVrtDF1RpljpPpw7cpQskQAIkkCaBi0cPYdeS+ZCD7fSaFIqW7+vBlataLDg8uBeHQ+svqSiHjFpgAV9UaFAUJUMLwMMzg9frbbl/IjIseiX7M3T/NltiQ0J8PC4dPYQz+3bhyonjadbTMDopb1sWr1od5Rs0Q9SdABzfdiXdSBIZkz/YHw9K5MK8MxHYfOoGpGB0yiYRJ03LFUT/hiXRunIRFn7O/h1BCyRAAiSgOwJaD0AzXYCL1Wi4fPkyQkJCULRoURWlkJ0m6YimTJmC8ePHIzAwEDdv3lSRCNKyKjQkT510/vz5VJESYjut1EnHjx9XwoKkhQoPD08VCfHXX3+ptE9ahYZVq1apSI46depg9+7dqXB9/fXXeOONNyg0lCkjkSISKmJ5OHF2bj6OdXkCFBpc/hYggEcEXEJo2LrglFlhzVy5vdHpjer6eOMzjVsx5Vv95eo2RMOefZ3+pr125hT+/W662TrbD34HBV0kbZTTbzAXSAIuRCA+NgYH/vkLYds2ZXi4nS8oGFVbPImSoTXg5m6ZKHAr/L4SHK6du5chUV9/T5SrWwTlaheGd66Hf2TbpV07CnzbPGuRDCkdlMiGVzdqSqMkeY+vnz+LM3t34fyhfYiPiclw2Z7ePihXtwGKlq+Hq2cSceHoTZUiKb2Wt3AuhDYPQVC5PCaR6H5sAo6E38Pp61GQotG+Xh4oVzgAVYIC4e9jR/Z22WBOQgIkQAIkkJyA1YWG2ChgktTxSUPBzjZ6N2DEJcDHvukWJcIgPj5efRgP/pMvpVq1ajh8+LCKSmjSpEm2V5kvXz7cuXNHRQ2UL19e2ZPCy5J2ST4kBZOW1rJlSxUdMGnSJLOICrEh9REk/ZFEGiRPnbR161a1lpo1a2LfvtQpJPv166fqTaQUGkSUCA4OVh9ppUaSMTJWxI1FixalWkbTpk2xZcsWCg0UGrTc4uxrBQIUGqwAkSacgoDTCw2ScmLJl/vN0k2Ur1sEtdrqL22S8Y7a/PtcnDuwx3SDSYqNJwe97hQ3XEaLOLTuH3UwZ2wenl54etwkeDx6C8XpAXCBJEACTkfg3vUIHF6/Gmf37cpQcAgsWFgJDqVr1oG7PbT+FwAAIABJREFUh4dFHG5duY+wHVdx6ditNN+mNxrx9HZH6eqFUKZWIeQplMsi21nuJBFps1prq8mQ2WRSs2HQmkwLREfeuoGze3er6IWoWxlHfciUUqi7XIMn4ONXAReOReL2lYzTKQXk80Fos2AUr5xfty8qZIaS10mABEiABKxPwOpCg7j4U1fgjA3S3pRpCQxYbH0ImVgsXbq0Sg908OBBiKiQssmBeY8ePdSh+5w5c1T0QfIm6ZCk8LGIBpI+SZpELvTp00dFQyRvmzZtQrNmzeDj46NEAEkpJG3gwIHK9rRp0/DWW29pYiBFm3v27KkKVkvdA0mJJE0KMEs9iD///FP9P7nQIHNLlIZETEpqp+Rrmj17tkp7JC9HpBQaRIyR4tOSFioiIgIimiRve/fuVdEMsi5JO1WlShV1WfpPmDBB+SCNqZMY0aDpJmfnbBOg0JBthDTgJAScXmi4ePwWti08bbZdLZ+rpHJZ67Xt/3sFDq9/WORKWkD+Aug2fIxe3bWaX6tnfY2rp0+Y7Eku87avaPsl0GrO0BAJkAAJWJGAHHwf2bAGp/fsgCHRPN9/8mn88+VH+fqNUap6LfW935J2/04sTuy6hrMHrmf4Jr7YEqGheJX86rA8d35fS8xr63NsOfBHP21jLOnd91egUsdUPaXWwvnDB3B27y5EnDNPkZie2QLBxVE8tBGi7xfBxWN3MmUmUZBVmgahdPWCcPewLOrEkiWxDwmQAAmQgHMQsInQYOefp7beiffeew9ffPEFChUqhFatWqmDdGmzZs0yTf3pp59ixAhJo2tQh+cVKlSAr68vJLWSRARERUVh5cqVaN++vRojNR3u3bun0hOJ+CDCgqQ2ksN3sTF58mT85z+Pi2ovXbpU1YCQfhLdICmNpA0fPlzVSsisvfjiixCBwMvLCxLhIAKARA7cv38fnTp1wty5c1MVgxZBY/r06ap2ghz8i/AgNRskekPWKhESKYUG8UNEFxFf5JpEReTKlQsFCxZUa5LWuXNnLF++XNWiEF+EhdSnuHDhAoS1sKTQQKEhs3ua161LgEKDdXnSmuMScHqhYdvi07h49JZph3wDvND5TUlRod9vA6d2bcf2hb+bfJZ0Gs9+/LnFaTUc8XZMTIjHH1KfISHB5H711u1R/cmHv0iykQAJkIAzEJCC0Uc3rcPJHVsh3/cyagWLl0LJGrVQslpN9fZ9Zi0uOgGn90Xg5O4IxERlbFts5Svq91B0qJQf/nl9MjNv2XUbv4Epb/7dvXYVl8OOqg9JkZRkSD/NkdFp34DcKBFaCz4B5XH9ggckGiSzJkzK1SmsUk95eFFgyIwXr5MACZCAqxKwidCQgxGCttjH6OhojB49Wh2eSzogeWtfmvxcT97kbX2JONiwYYOq1yCiQFBQkIogEJFA0gX5+/urIb/88otKgyR1CiTdkMxRrFgx1KpVS0UsyEF7yjZjxgzMnDlTpVSS/tIkQiFlBEVaDES8SD5eIgrkkP+TTz5RhaelLkTyiAaxIWO+//57NU7mFJFCohGGDRumxBGJ9EhLaJDaEiKSSBTH1atXIfdY8n4S4SHCjYgbp0+fVsKNFNQeNWqUWpf4RaGBQoMtnmXaTJ+Afk8YuWskYF8CTi00JMYbsGTqPrO3FeXQoHa7kvalrHG2q6dOYPX3X5uN6vb+WATky6/RkuN0T6s+Q5uX30SRMuUcZxH0lARIgAQsJBATFYXjWzbg+NaNSIiLzWSUG4qUKYtSNWqjRGgN+Pg9/AM7vZaYYMCFIzdVWqV7NzKuT2C0UaCYvxIdQirlh1+gt4WrSNHNRjml4w3uuBqdG+H1J+LyqVMZFtlO7pG7hycKlaqEgHwVERtTEDcv30diQsb5rqV4c7EK+VC2ViEUKRWo65cSsrZJHEUCJEACJGBtAjYRGsTJiGPAzGY5VvPI2pxojwSyQ0Drc1aGNRqyg5tjs0CAQkMWoHGIUxJwaqHhcthtbFlwymzjWvSriMIlA3W9mZJiY/FnH5v56OyH7gdXr8LBNatMa3b39EQfVZ/BS9d7RedIgARIIDsEYh88UAWjRXSQNECZNYlwK1a+kop0KF6lGrx80k9/lGRIwpXTd3Fy1zVEnL+XYR0H47xy0F4wJACFSwWiQHAA8hfzh7evhcWMz28FZnfIbAmZXpeXG+/F+yA8OhCXHwQiIiYABrgBBcpnWrzSYEiCf95g5Aosj/jYYoiNsSwSwT+PN0rXLITSNQoiV0AWhZZMV8YOJEACJEACzkhA6wGoJgYH/gAWvaJpSJqdu38L1OiTfTu0QAI5REDrc0ahIYc2yoWnpdDgwpvPpZsRcGqhYcfSMzh/+HFBSB8/T3QeUhPuOk6bJLtjMCTit9HDzEJJG/Z8BuXqNnDa2/efb79CxNnHtTRcpQC2024oF0YCJKCJQHxsDE7v3oFzB/bixsXzFo0VQbZgSAkULFEKBYuXVP+ml2IpOioOl47fVqkEb1yKssi+dBLhIXeBXCgQ7K+EB/kILOCb9pv+u2cDy9+x2LaxY3SCJ27G+eFWrB9uxeXCzVg/RCemITLnKQH4m9etEFEiMT4R7h654R1QDklJJeHhZdnLBLK2oPJ5UbZWYRQtzegFzRvHASRAAiRAAoqA1gNQzdhEbFj6ZtYiGzy8gS7TKTJohs4BeiOg9Tmj0KC3HXR+fyg0OP8ec4WWEXBaoUFSRyz50jxtUpmahVD3qVKWkcnhXos+/Qj3bz+uLRHasi1qtn0qh72yzfQJ8fH4c/wHZgVSa7R9CtVatrXNhLRKAiRAAjomEHnrBs4f2IdzB/fhztVwTZ6K0KCEh0fiQ/7g4vD0Mj+0f3AvDheP3VIft8Izr1WQ0gEvHw8V6WCMePDP44NcAV7w2j8Lbn8/LrqYluPRiZ64FZsLt+L8lKAg4sKDtESFFIMNSR5I9A9GolcgDIlJ6sPTR2onFIO3Xwl4+hSAmygHFjRJDSW/D5SqXjDraaIsmIddSIAESIAEXIOA1gPQLFGRNEqLBwPh+ywfXqwW0G0GULiy5WPYkwR0SkDrc0ahQacb6cRuWfaXiBMD4NJI4BEBpxUawk/eweZ5J802ulnfCihaJvOCmnq4O/79bjqkboGxla5ZB0369NeDa1b34cqpMKz5foaZ3XavvY1CJUtbfS4aJAESIAFHInDn2lWcP7hXRTpE3ryh2XVJtZQvKBgFgovDP28+5ArMg1y5A1Xkg3weF+P+MNLh2C3cuZZ56qaMHHCPjoD3nX3wxB14JN0FDJFA0gMkGmKRaIjHgwQgOtFD4iSQJKmQVBFI+Vf+L834dfnXHYlJnjDAU/0r/d29csMnsAy8/YrDxz8E7h7pp41K7qfoD/mDA1TUQpHSeSD1KNx0HtmoeaM5gARIgARIIMcIaD0AzbKjUiD6xCpg53fAmfXyUzQNU25AmRZA/ZeBCu0Bd/m5y0YCjk9A63NGocHx99zRVkChwdF2jP7aioDTCg07l53BuUOP0yZ55/JAF0mb5GFZvmZbAbfU7rb5v+H0nh2m7vJ2avvB2lNSWDpfTvbb//cKHF7/r8kFT28fPD32E7h78BfjnNwXzk0CJKAfAklJSbh1+ZJJdHhw765VnJP0SyI8yIeHpx9ioz0RE+WOqDuxMCQYkKQOMZJEEXh0oJH0KK3fw/8nGRKQmHAfhoT7SIy7h6R4y9MyZb4AN3h5BMDHK7/68CxSG24+uTMfBqhIhaJlAlGkTB4UKRkI71wW1pmwyDo7kQAJkAAJkMBjAloPQK3CLjYKuHoQuHECiI8BvHyBghWBotUyrWdklflphATsTEDrc0ahwc4bxOkevUZFECRAAk4pNCQmGrBs6n7ExSSadlgKPNbr6DhvyB9a+w8O/PuXyX85BOo58iOnvGNXzfgSNy6cM62tWIXKaDXwVadcKxdFAiRAAtklkGQwqDoO186eVt875fOYqMjsmjUfL7UPEgxIiJePRCTEGdT/M23xWY+KcHfzhKdHbiUueHnKR154uCcrzBxUHXBLW4D28HRHoZK5lbhQtHQe5JY6EhamUsp0TexAAiRAAiRAAhkQ0HoASpgkQALaCWh9zig0aGfMEdkjwIiG7PHjaOch4JRCw5XTd7HpjxNmu/TE0+URVC6vw+zc2X27seXPn8387fvRZ6lybTvMgtJxVAqg/jl+hFnh61rtO6Nq89aOvjT6TwIkQAJ2ISDRDlLTRwQH9XHhPG6FXzSre2MNR5IMSQ+Fh7jER/8azL53qzkSYoGkxyJ/evM+FBUC4CXCgmeAEhg83DMQBySSoUA5uHu4ISCfrxIScud//G++on4QsYGNBEiABEiABOxNQOsBqL3943wk4AwEtD5nFBqcYdcdaw0UGhxrv+it7Qg4pdCwa8VZnD3wOJe1FK7s8nZNhzqEuH7+LP7+ZqrZznd+dwTyFC5iu7shByxfDjuGdXNmms3c4Y2hKBBSPAe84ZQkQAIk4BwEEhPicftKuCni4d6N64iOvIeYyHupxYGsLjkJMBiS1EeSFGg2GGCIeQDD/dtIgof6cHPPBbj5w8PdB+7uPo/EhQB4uOcyizhwczPAAwlwd0uEOxLg4RaPAPebyO1xXX0EtHsTgbVbqZRIrK+Q1Q3jOBIgARIgAVsQ0HoAagsfaJMEnJ2A1ueMQoOz3xH6Wx+FBv3tCT3KGQJOJzTIgcdSSZsUnWAiWjK0ABp0KZMzhLM4qxwILfhkrNnoli+8iuCKlbNoUZ/D9q5ciqMb15qc8/bNhd5jJkIKmLKRAAmQAAlYl4CkXYq5H6VEB6nzIP9G35MP4+d3ERcTo0QA9X3YzQ3uj74fy//V193c1WG/fO7u4amKSvvnyQu/PHngJ4Wm/3kbfrcOwsc9QYargs+xSf6IS8qlxAQ3GODulpBMWEiEm1taBS0frb1YLWDQGha0tO6tQGskQAIkQAJWIqD1ANRK09IMCbgUAa3PGYUGl7o9dLFYCg262AY6oQMCTic0XL8YiXVzj5uhbdq7HIqVz6cD3Ja7IKkwfh/7PuStVGOr16UXKjZqarkRB+j51/QpuHX5osnTkMqhaDFgkAN4ThdJgARIgATSJBBxDJjZDEiMyz4gD2/g1Y1AYecS2bMPhhZIgARIgAT0QkDrAahe/KYfJOBIBLQ+ZxQaHGl3ncNXCg3OsY9cRfYJOJ3QIEjuXo/G5bDbuBR2G1G3Y9D1nVoOlTbJuK3L/m8S7l6/ZtrlKk+0RO2numZ/13ViIS76AeZ9PMosjUedTt1RuUlznXhIN0iABEiABLJE4MAfwKJXsjTUbFD3b4EafbJvhxZIgARIgARIwEYEtB6A2sgNmiUBpyag9Tmj0ODUt4MuF0ehQZfbQqdygIBTCg3JOcbcj4evv1cOoM3+lGvnzER42DGToeJVq6P5cy9m37BOLFw6ehjr584y86bjkOHIFxSsEw/pBgmQAAmQQJYJiNiw9M2sRTZIJEOX6RQZsgyfA0mABEiABOxFQOsBqL384jwk4EwEtD5nFBqcafcdYy0UGhxjn+il7Qk4vdBge4S2m2Hnkvk4sX2zaYL8xULw1FvDbDehnS3vXrYQx7duNM3q4+ePXqM+Zn0GO+8DpyMBEiABmxGQNEqLBwPh+yyfQmoydJvBdEmWE2NPEiABEiCBHCSg9QA0B13l1CTgsAS0PmcUGhx2qx3WcQoNDrt1dNzKBCg0WBmoNc0d3bQOe/9aYjLpncsPT4/9xJpT5Kit5VM/xZ2r4SYfSoTWQLN+A3PUJ05OAiRAAiRgZQKGRODEKmDnd8CZ9QDSKvzsBpRpAdR/GajQnoWfrbwFNEcCJEACJGA7AloPQG3nCS2TgPMS0PqcUWhw3ntBryuj0KDXnaFf9iZAocHexDXMd+HwAWz8ZbbZCBEaRHBw9BZzPwrzJ4w2W0b9rr1QoaFzFbt29H2i/yRAAiRgVQKxUcDVg8CNE0B8DODlCxSsCBStBvgEWHUqGiMBEiABEiABexDQegBqD58caY5z586hdOnSKFmyJORzNusRCAsLw99//43du3erD/m/wWDA7Nmz8cILL1hvIjtY0vqcUWiww6ZwCjMCFBp4Q5DAQwIUGnR8J9wKv4S/vvrczENJnSQplBy9nT+0H5t+nWO2jM7vjkCewkUcfWn0nwRIgARIgARIgARIgARIwEUIaD0AdREsFi+TQoPFqDR3fOeddzB16tRU4yg0aEbJASSQKQEKDZkiYgcXIUChQccbHRf9AH9+NNLMQ0ktJCmGHL2lrD/hG5AbPUd+BDc3fnt29L2l/yRAAiRAAiRAAiRAAiTgKgQoNGRvpyk0ZI9fRqNnzZqFEydOoHbt2qhTpw6GDRuGpUuXMqLBdshp2YUJ8CTLhTefSzcjQKFB5zfEn+NHIC4m2uRl7ae6osoTLXXudebuLfu/Sbh7/ZqpY6kaddC0b//MB7IHCZAACZAACZAACZAACZAACeiEAIWG7G0EhYbs8dMyulu3bliyZAmFBi3Q2JcELCRAocFCUOzm9AQoNOh8i1dM+wy3r1w2eSk1DKSWgSO3B/fuYuGkcWZLaNCjD8rXa+TIy6LvJEACJEACJEACJEACJEACLkaAQoNlG75v3z6MHTsWmzZtgjALDQ3F0KFDUa9evXRrNERERGDKlClYtmyZqt/g4eGBqlWrYtCgQXjppZfSjIa/cuWKmmf58uW4ffs2SpUqhYEDB6q3+cuWLYvz58/j7Nmz6uvGJp8bv75z505MmzYNhw4dwr1795SNvHnzqq6xsbH49ttv8euvv+Lo0aPq/1JfolevXnj//feRO3fuNGHImiWF0ZYtW3Dz5k3kz58fzZo1w8iRI1GzZk3LAFqhF4UGK0CkCRJIhwCFBt4aJPCQAIUGnd8JG37+ARePHDR5WaxiZbR64VWde52xe+f278HmP+aadeo6bDRyFyjo0Oui8yRAAiRAAiRAAiRAAiRAAq5FICOhIT4xHlfvX3VoIEX9i8LLwytba1izZg06duyoDuZFKKhevboSDrZt24a3335bHcKnLAYtwkSHDh1w7do1da1GjRqIjo7G9u3bERkZiWeffRa//PKLmV+XLl1C48aNcfHiRQQFBeGJJ55QfdetW6fml4LIGQkNgwcPxowZM9CoUSMlREjaIfE9T548SnAQf3bs2KGEAklHFBAQgF27duHy5ctqXRs3blTXkreJEydi9OjRcHd3R926ddVazpw5gz179sDb2xvz589H586ds8XX0sEUGiwlxX4koJ0AhQbtzDjCOQlQaND5vu79awmOblpn8jJPoSLo/N4InXudsXvbF/6BU7u2mTr5582Hbu+PZX0Gh95VOk8CJEACJEACJEACJEACrkcgI6Hh4r2LGLZxmEND+bzZ5ygeWDzLa3jw4AHKlSsHiTQYP368ijYwtnnz5qFv374wGAxmQsP9+/dRpUoVXLhwQUU0SFFjOaiXFh4erg7m9+7di++//x4vvviiyV6XLl1U9IMcqP/222/w9fVV106dOoUWLVooQUBaehENXl5eKhKibdu2qdYrUQsLFixQAoeIEYGBgapPTEwMXnvtNfz444/o378/fvrpJ9NY8UV8KlGiBBYtWqTECWOTaz169IC/v78SHpILFMYICy3QmzdvjvXr12c4hEKDFqLsSwLaCFBo0MaLvZ2XAIUGne9t2LbN2LV0vslLD08v9P3oU4c+lF/82QRE3bphWlOZ2vXQuHc/ne8E3SMBEiABEiABEiABEiABEiABcwIUGjK+I+Tg/fnnn0elSpVw5MgRk2BgHNWzZ08sXLjQTGiYPn063nrrrVQH98YxIjJIceNatWopwUGaREiUKVMGIhbIwX1wcLCZY5Ly6NVXH2YGSE9okOvffPNNqgUdPnwY1apVU4KJfO7j42PWR8QUmVvSIkkEhlE0kAgGiVyQqIhWrVqlsjtkyBB89dVXKqJDPjc2SfN048bjv5cteeaE7wcffJBhVwoNlpBkHxLIGgEKDVnjxlHOR4BCg8739HLYMaybM9PMy54jP0Ku3A/foHC0dv/ObSz673gzt0VkELGBjQRIgARIgARIgARIgARIgAQciQCFhox3S2op/PDDD/jwww8xbpx5nT4ZuXjxYnTv3t1MaOjUqRNWrFihrnXt2jXVBElJSSqiQA74JfpBIhfmzp2LAQMGoGXLlli7dm2qMVJvQVIgSUtPaFi5ciXat2+fauznn3+O4cOHq8iKL774Is0FG33++++/VUTE9evXUbhwYSU6iGjg5pb6GFLSJvXu3RvPPPOMqvtg60ahwdaEad+VCVBocOXd59qTE6DQoPP74W7ENSz7YpKZl+1eexuFSpbWuedpu3dm7y5snWeeS7P7f8ZB0iexkQAJkAAJkAAJkAAJkAAJkIAjEaDQkPFuycG9HL7PmTNHRTakbPv371eRCclrNEi9Aym2bEmTugwSvTB58mSMGDECL7zwAmbPnp3mUCnqfPfu3XSFBpmzcuXKqca+8cYb+Prrry1xBz///DP69eunajfUr1/fojFPPvkk/v33X4v6ZqcThYbs0ONYEsiYAIUG3iEk8JAAhQad3wmJCfH4bcxwMy+b9OmP0jXr6NzztN0TkUHEBmMLyF8Q3YaPdsi10GkSIAESIAESIAESIAESIAHXJsBi0Bnvf1aEBkkDFBYWBokSKFCgQIYTSLRBwYIFTULDwIEDVQRFWk2iC6Soc3oRDSm/brQhNRhmzpypijmLCJJRGzRoEJo2baqKVktRaRE30orKSG4jZdojpk5KTTij5yyt/ZBUVmdlQ4Eyrv0diqu3FwEKDfYizXn0ToBCg953CMCCT8YiOvKeydMabZ9CtZapC1TpfSkS4rr4048g6ZOMrVy9RmjYo4/eXad/JEACJEACJEACJEACJEACJJCKgNYDUFdDaEydlLIQtJFDWqmT2rRpg9WrV2PVqlVo166dRciMtSBat26txqZskZGRpgLOWoWGiRMnYvTo0aoGwqRJ5tkG0nNOCk+HhISgaNGiqhC2lsZi0KlpaX3OKDRouePY1xoEKDRYgyJtOAMBCg0OsIurZnyJGxfOmTwtW6cBGvV6xgE8N3cx8uYNLPl8gtkXm/bpj1IOGp3hcBtAh0mABEiABEiABEiABEiABKxKQOsBqFUndwBjP/74o0pnJCmJpBh0yloFUqNAahUkT500ZcoUyFv9Eh3w3XffWbRKKQBdtmxZVahZhISgoCCzcbNmzcLLL7+svqZVaJCCzhLNIJEHUgzaw8PDIp+kgLT037x5M5o0aWLRGFt2YuokW9KlbVcnQKHB1e8Art9IgEKDA9wLW/6Yi7P795g8LVKmHNq8/KYDeG7u4sld27Bj4R9mX+wxYjz8Ah8W5WIjARIgARIgARIgARIgARIgAUciQKEh492SYs3lypXD1atXMWHCBIwaNco0YOHChaoYssFgMBMaJPqgSpUqkKgAGTN06FAlICRvUk/h+PHj6NGjh+nLHTt2xF9//aW+JsWVjWNEhGjevDmknoM0rUKDjJH0R0uXLsXTTz+NqVOnqkiF5O3atWvqulHMkGuLFi1SvoiIIjUqWrRoYTYmLi5O1a8oX768EjFs3Sg02Jow7bsyAQoNrrz7XHtyAhQaHOB+2P/PXzi87h+Tp/758qP7+2MdwHNzFzf/PhfnDjwWTPIUKoLO741wuHXQYRIgARIgARIgARIgARIgARIQAhQaMr8PpNBx586dERsbi9DQUMib/ufPn8fWrVsxZMgQTJs2zUxoEIsHDhxQNRpEHChUqBCqV6+uDvfv3LmjogRkfJ8+ffD777+bHLhw4QIaN26sBIpixYqpWglRUVFYt24dpFaERCZIH+N140BjqqL0ajRIP5lX1iDRCX5+fqhZs6byWdZ04sQJFa1RuHBhJagkb59++qkqUi1iiognFSpUgK+vr/Jh3759yr+VK1cq/6zd9u7di9dff91kVvyUGhWSVkiYSpPIDxFE9N60PmdMnaT3HXU+/yg0ON+eckVZI0ChIWvc7Drq1O4d2L7gN9OcEm76zITP4e5uWcimXZ1NZzKpzyC1JmKiIk09KjRsivpde+nBPfpAAiRAAiRAAiRAAiRAAiRAApoJaD0A1TyBkwyQQ/6xY8eqg3phJkWV3333XVUwuXTp0qmEBlm2HIr/73//w5IlS1RxaIkAKFKkCEQYkIN5iS6QdEnJW3h4OMaMGYMVK1ao8dJ3wIABKhVTYGCgmluiLOSw39gsERqkr4z9+eef1cf+/ftx9+5dVaxaajFIxETPnj2V0JGyyYG/iCkbNmxQ9Rok0kIO+EU8kUgJiTTw9/e3+k6vX78eLVu2zNBu8pRVVnfAiga1PmcUGqwIn6YsIkChwSJM7OQCBCg0OMAmXz19Eqtn/c/M027DxyAgfwEH8P6hi3cjrmHZF+aFs5r1G4gSoTUcZg10lARIgARIgARIgARIgARIgASSE9B6AEp6OUNgy5YtKsJBBA6JiGBzLAJanzMKDY61v87gLYUGZ9hFrsEaBCg0WIOijW1E3bqJxZ99bDbLky+9jqLlKth4ZuuZP7F9M3YumW9msNfoCfD1D7DeJLREAiRAAiRAAiRAAiRAAiRAAnYkoPUA1I6uudxUkp5IIg1q165ttnaJhpBaCVLXQVIZDR8+3OXYOPqCtT5nFBocfccdz38KDY63Z/TYNgQoNNiGq1WtGgyJ+G3McCQZDCa7DXv0Rbl6Da06jy2NbfxlNi4cPmCaIm/RYuj09vu2nJK2SYAESIAESIAESIAESIAESMCmBLQegNrUGRc3HhMTg1y5cql0SVJcWVIlSS0HSdsk+9SsWTOsXr0aXl5eLk7K8Zav9Tmj0OB4e+zoHlNocPQdpP/WIkChwVokbWxHIhokssHYQlu0Qc12HW08q3XMi0Ayf+IYxD64bzJYqUlz1O3U3ToT0AoJkADrDUZwAAAgAElEQVQJkAAJkAAJkAAJkAAJ5AABrQegOeCiy0yZmJio6jOsWbMGUthZajRI4ebKlSujb9++qjCyt7e3y/BwpoVqfc4oNDjT7jvGWig0OMY+0UvbE6DQYHvGVplh9ayvcfX0CZOtUjXqoGnf/laxbWsjt6+GY8XUT82madF/EEKqhNp6atonARIgARIgARIgARIgARIgAZsR0HoAajNHaJgEnJiA1ueMQoMT3ww6XRqFBp1uDN2yOwEKDXZHnrUJty/4Had2bzcNLliiFNoPfidrxuw86tiWDdizfJFpVjc3N/QeMxHeufzs7AmnIwESIAESIAESIAESIAESIAHrEdB6AGq9mWmJBFyHgNbnjEKD69wbelkphQa97AT9yGkCFBpyegcsnP/Qun9w4J+/TL19A3Kj1yjzAtEWmrJ7t/U/zcKlY4dN8xYILo4Obw61ux+ckARIgARIgARIgARIgARIgASsSUDrAag156YtEnAVAlqfMwoNrnJn6GedFBr0sxf0JGcJUGjIWf4Wz35u/x5s/mOuWf++4z+Fp85zTEp9hnkfj0JcTLTJ9yrNWqF2hy4Wr50dSYAESIAESIAESIAESIAESECPBLQegOpxDfSJBPROQOtzRqFB7zvqfP5RaHC+PeWKskaAQkPWuNl91PUL5/D3jC/N5u30zgfIW6So3X3RMuHNSxex8n9TzIa0fOFVBFesrMUM+5IACZAACZAACZAACZAACZCA7ghoPQDV3QLoEAk4AAGtzxmFBgfYVCdzkUKDk20ol5NlAhQasozOvgOjoyKxYOIYs0lbPP8yQipVta8jGmc7unEt9q5cahrl5u6Op8d+Ai8fX42W2J0ESIAESIAESIAESIAESIAE9EVA6wGovrynNyTgGAS0PmcUGhxjX53JSwoNzrSbXEt2CFBoyA49O45NSkrC7+P+g8T4ONOs9br0RMVGT9jRC+1T/fPtV4g4e9o00JGKWGtfLUeQAAmQAAmQAAmQAAmQAAm4EgGtB6CuxIZrJQFrEdD6nFFosBZ52rGUAIUGS0mxn7MToNDgQDu87IvJuBtx1eRx5aYtUKdjN92u4GEUxlgASSYfqz/ZAdVbt9Otz3SMBEiABEiABEiABEiABEiABCwloPUA1FK77EcCJPCYgNbnjEID7x57E6DQYG/inE+vBCg06HVn0vBr3ZxvcTnsqOlK8arV0fy5F3W7gpM7t2LHoj/N/HOEuhK6BUrHSIAESIAESIAESIAESIAEdEVA6wGorpynMyTgIAS0PmcUGhxkY53ITQoNTrSZXEq2CFBoyBY++w7euWQ+TmzfbJo0X1AwOg4Zbl8nNMy2dvZMhJ84ZhoRWLAwOr83Am5u/BasASO7kgAJkAAJkAAJkAAJkAAJ6JSA1gNQnS6DbpGArglofc4oNOh6O53SOZ5yOeW2clFZIEChIQvQcmrIsU3rsOevJabpvXx90Wfc5JxyJ8N546IfYP6EMTAYEk39Qlu0Qc12HXXpL50iARIgARIgARIgARIgARIgAa0EtB6AarXP/iRAAoDW54xCA+8aexOg0GBv4pxPrwQoNOh1Z9Lw6+KRg9jw8w9mV3qP+QQ+fn66W8WZfbuw9c9fzPzq8MZQFAgprjtf6RAJkAAJkAAJkAAJkAAJkAAJZIWA1gPQrMzhzGPOnTuH0qVLo2TJkpDP2axHYNasWVizZg0OHDiAa9eu4d69e8iTJw9q1qyJF154Af369XOYbANanzMKDda7j2jJMgIUGizjxF7OT4BCgwPt8a3wy/jrq8/MD+/fHIoCwfo7vBdBRIQRY/PPmw/d3h/rML/IONBtQVdJgARIgARIgARIgARIgARyiIDWA9AcclO301JosN3WhIaGIiwsDFWrVkVISAgCAgJw4cIF7NixAwaDAT169MC8efPg7u5uOyesZFnrc0ahwUrgacZiAhQaLEbFjk5OgEKDA21wfEwM/hj/gZnHzfoNRInQGrpaRXxcLOZ/PBqJCfEmvyo1aY66nbrryk86QwIkQAIkQAIkQAIkQAIkQALZIaD1ADQ7cznjWAoNttvVbdu2QcSG3Llzm01y5MgRtG7dWkU5/PTTT+jfv7/tnLCSZa3PGYUGK4GnGYsJUGiwGBU7OjkBCg0OtsF/fjQSUv/A2Gp36IIqzVrpahUXDh/Axl9mm/nU5pW3UKR0WV35SWdIgARIgARIgARIgARIgARIIDsEtB6AZmcuZxxLoSFndvXjjz/G2LFjlcggYoPem9bnjEKD3nfU+fyj0OB8e8oVZY0AhYasccuxUX9Nn4Jbly+a5i9fvzEadH86x/xJa+LNv8/FuQN7TJd8A3Kj54jxcHOAkExdgaQzJEACJEACJEACJEACJEACuiag9QBU14uxoXP79u1TB9ubNm1ShX3lTfuhQ4eiXr166dZoiIiIwJQpU7Bs2TJVv8HDw0OlARo0aBBeeumlNNPyXrlyRc2zfPly3L59G6VKlcLAgQMxbNgwlC1bFufPn8fZs2fV141NPjd+fefOnZg2bRoOHTqkahqIjbx586qusbGx+Pbbb/Hrr7/i6NGj6v9SX6JXr154//33U0UOGO3LmqdOnYotW7bg5s2byJ8/P5o1a4aRI0eqegk50SZNmqTmF5bfffddTrigaU6tzxmFBk142dkKBCg0WAEiTTgFAQoNDraNKQ/xcxcohK7DRulmFZIuaf6EMYiPjTH5pEcxRDfA6AgJkAAJkAAJkAAJkAAJkIDDEsjoADQpLg7xV6867NrEca+iReHm7Z2tNUhB4o4dO6qDeREKqlevroQDSe3z9ttvq0P4lMWgRZjo0KGDSu8j12rUqIHo6Ghs374dkZGRePbZZ/HLL7+Y+XXp0iU0btwYFy9eRFBQEJ544gnVd926dWr+3bt3Zyg0DB48GDNmzECjRo2UEHHixAlVTFkKKIvgIP5IfQMRCmrXrq1qHuzatQuXL19W69q4caO6lrxNnDgRo0ePVnUQ6tatq9Zy5swZ7NmzB97e3pg/fz46d+6cLb5aB4vQ0qJFC1WvYenSpXafX6u/0p9CQ1aocYw9CVBosCdtzqVnAhQa9Lw7afh2avcObF/wm9mVrsNGI3eBgrpYyeWwY1g3Z6aZL60GvoZiFSrpwj86QQIkQAIkQAIkQAIkQAIkQALWIpDRAWjchQu4/O571poqR+wEf/F/8C5RIstzP3jwAOXKlYNEGowfP15FGxibFCLu27evKkycXGi4f/8+qlSpog7CJaLhnXfeMRUsDg8PVwfje/fuxffff48XX3zRZK9Lly4q+qFbt2747bff4Ovrq66dOnVKHayLICAtvYgGLy8vFQnRtm3bVOuVqIUFCxYogUPEiMDAQNUnJiYGr732Gn788cdUaYjEF/GpRIkSWLRokRInjE2uSTFmf39/JTwkFyiMERZaoDdv3hzr169Pc4hEYPzzzz+Ij4+HiDFbt25VzEeNGoWPPvpIyzQ51pdCQ46h58QWEqDQYCEodrOYQCcA7wKQnxwi958AMAfAdACJFlt52PFDAOMyGBML4OFPzOw3Cg3ZZ2hXCw/u3cXCSea3R70uPVGx0RN29SO9ybYv+B2ndm83Xfb2zYWeoz6Gh6enLvyjEyRAAiRAAiRAAiRAAiRAAiRgLQIUGjImKfn/n3/+eVSqVAlShFje7E/eevbsiYULF5oJDdOnT8dbb72Vbv0AERnq1KmDWrVqKcFBmkRISLocEQvk4D44ONhsHkl59Oqrr6qvpSc0yPVvvvkm1YIOHz6MatWqKcFEPvfx8THrI2KKzC1pkSQCwygaSASDRC5IVESrVqnrKg4ZMgRfffWViuiQz41N0jzduHFD0y0qfD/44IM0x4hQI3MYm6enpxJ93nvvPZMYo2myHOhMoSEHoHNKTQQoNGjCxc6ZEJC8NRMkmgvAWgBRAOSniCTyWw6gm0axwSg0HACwP4254wG8bKVdodBgJZD2NLNi2me4feXh2xjSgitWQcsXXrGnC2nOZTAkYsHEsYh9cN90vXStumjy9HM57hsdIAESIAESIAESIAESIAESIAFrE6DQkDFRqaXwww8/4MMPP8S4canfp1y8eDG6d+9uJjR06tQJK1asgFzr2rVrqgmSkpJURIEc8Ev0g0QuzJ07FwMGDEDLli2xdq0cy5g3qbcgKZCkpSc0rFy5Eu3bt0819vPPP8fw4cNVZMUXX3yR5oKNPv/9998qIuL69esoXLiwEh1ENHBzS30MKWmTevfujWeeeUbVfbB1k+gLWfucOXPUOiRq5K+//kKxYsVsPXW27VNoyDZCGrAxAQoNNgbsQuYbAJDXt0VcaA7goZwOSB4b+elWDcBQAP+ngYlRaBj/KLpBw1DNXSk0aEaW8wP2rVqGIxvWmBzx8PTC0+M+gfybk+3amVP49zsJ4nncmvd/CcWryGPARgIkQAIkQAIkQAIkQAIkQALORYBCQ8b7KQf3cvguh9sS2ZCy7d+/X0UmJE+dJPUOpNiyJU1SAUn0wuTJkzFixAi88MILmD17dppDpajz3bt30xUaZM7KlSunGvvGG2/g66+/tsQd/Pzzz+jXr5+q3VC/fn2Lxjz55JP4999/LeprrU4S4SDCiYg8ElGi90ahQe87RP8oNPAesBYB+Y7cHcBoABNTGG0KYBOAawBEIjZYOCmFBgtBuWq3tA709VAHYdfSBQjbJrf8w+bh5Y3eYybC0ytnBRBXvU+4bhIgARIgARIgARIgARIgAdsSYDHojPlmRWiQNEBhYWGQKIECBQpkOIFEGxQsWNAkNAwcOFBFUKTVJLpAijqnF9GQ8utGG1KDYebMmaqYs4ggGbVBgwahadOmqmi1FJUWcSOtqIzkNlKmPbJ26qS0/BXBRXyTNEoSGSIpp/TcKDToeXfomxCg0MD7wBoEJDHfbQC5AJSRCLw0jF4AUByAiA5bLJyUQoOFoFy1myExEfM+HoX42BgTgkpNmqNuJ9G8cqYlGQxY9N/xkBoSxlYitAaa9RuYMw5xVhIgARIgARIgARIgARIgARKwMQGtB6A2dkd35o2pk1IWgjY6mlbqpDZt2mD16tVYtWoV2rVrZ9GajLUgWrdurcambJGRkaYCzlqFhokTJ2L06NGqBsKkSZMs8kcKT4eEhKBo0aKqELaWZu1i0OnNLSmnYmNjcfXqVRQpUkSLi3bvq/U5k5oZZ2WjH57VsZGAzQlQaLA5YpeYoDoAqaNwC0B6Mrsx4uGtR4WhLQFjFBqWATgGIN+jOXYAWAEgzhIjFvZh6iQLQemt24aff8DFIwdNbgUWKowu743MMTdvXDiHVTO+NJu/aZ/+KFWzTo75xIlJgARIgARIgARIgARIgARIwJYEtB6A2tIXPdr+8ccfVTojSUkkxaBT1iqQGgVSqyB56qQpU6ZA3uqX6IDvvvvOomVJAeiyZcuqQs1yvhwUFGQ2btasWXj55YelLrUKDVLQWaIZJPJAikF7eHhY5JMUkJb+mzdvRpMmTSwaY69OW7ZsUZEXEtUgNSQsXZO9/Es5j9bnjEJDTu2U685LocF1996aK+8CYMmjgs210jE8FcAQAFMADLNwcqPQkFb3SwCksu4GC21l1o1CQ2aEdHr95K5t2LHwDzPvug0fg4D8GYeW2mo5e1cuxdGNj4tuuXt4oPfoifDy9bXVlLRLAiRAAiRAAiRAAiRAAiRAAjlKQOsBaI46mwOTS7HmcuXKqbfmJ0yYgFGjRpm8kNoAIjQYDAYzoUGiD6RQsUQFyJihQ4cqASF5k3oKx48fR48ePUxf7tixoypuLF+T4srGMSJCNG/eHFLPQZpWoUHGSPqjpUuX4umnn4bUN5BIheTt2rVr6rpRzJBrixYtUr6IiCI1Klq0aGE2Ji4uTtWvKF++vBIxrNlEHDl37hw6d+4Mb29vM9M7duxA//79cfLkScVW0k/pvWl9zig06H1Hnc8/Cg3Ot6c5saJnAfzyKCWSpEZKq0ndBnnN/FsAr1roZP9HNR1WAjgHQH4qSDXdcY8KTj8A0AjA49fZMzZ8JoPLxUuXLu0pP3jZHIvAg7t3sHCyaFKPW/2uvVChYXq3ou3Wl5SUhKVTJiLy5g3TJMEVq6DlC6/YblJaJgESIAESIAESIAESIAESIIEcJqD1ADSH3c2R6aXQsRx4S5qe0NBQyJv+58+fx9atWzFkyBBMmzbNTGgQJw8cOKBqNIg4UKhQIVSvXl0d7t+5c0dFCcj4Pn364Pfffzet6cKFC2jcuLESKIoVK6be2I+KisK6desgtSLk8F36GK8bBxpTFaVXo0H6ybyyBolO8PPzQ82aNZXPsqYTJ06oaI3ChQsrQSV5+/TTT1WRahFTRDypUKECJGWR+LBv3z7l38qVK5V/1mzGlFSBgYGoXbu2ivAQ0UfOfoSfNBFm5s2bh1y5JBu4vpvW54xCg7730xm9o9DgjLuqfU1SIaixxmGXAbR+NMZWQkNGLs0H0PNRCqVOFvpOocFCUI7WbfmX/8Wda4/zPYZUrooWAx6Gg9qz3b4ajhVTPzWbsmHPZ1CubgN7usG5SIAESIAESIAESIAESIAESMCuBLQegNrVOR1NJof8Y8eOVQf1wkyKKr/77ruqYHLp0qVTCQ3iuhRu/t///oclS5ao4tASASC1BEQYkIN5iS6QdEnJW3h4OMaMGYMVK1ao8dJ3wIABKhWTHLrL3HLgLof9xmaJ0CB9ZezPP/+sPvbv3w8pqCzFqqUWg0RM9OzZUwkdKdvevXuVmLJhwwZVr0EiLeTgX8QTiZTo1q0b/P39rbpbwkHSTm3cuFFFLly/fh3ygqDwq1Onjopo6N4952o8al2s1ueMQoNWwuyfXQIUGrJL0DnGS4Ugo2hg6YrOAyj1qLOtUidl5IuxLoTUaQgAEG+p4+n0Y+qkbALMyeEp0xV5enuj95iJ8PD0sqtbB1evwsE1q0xzSt7NnqM+hq+/3KJsJEACJEACJEACJEACJEACJOCcBLQegDonBf2vyliTQAQO4xv9+veaHhoJaH3OKDTw3rE3AQoN9ibunPMZD/1vAiiYzhKzUgw6I1qSRin2UYdiAB6/zp41xhQassZNF6Ounj6J1bP+Z+ZL65cGI6hcRbv6lzKyokiZcmjz8pt29YGTkQAJkAAJkAAJkAAJkAAJkIC9CWg9ALW3f640n6QnkkgDSRWUvEk0hNRKkLoOkspo+PDhroTFKdaq9Tmj0OAU2+5Qi6DQ4FDbpVtnpRrRbQCS0K6M1BRKw9MLAIoDkMT5W6ywkiAA4Y/sBAKIzKZNCg3ZBJiTwxMTEjDv41FIiDNqT0Dlpi1Rp2NXu7l170YElk75xGy+el16omKjJ+zmAyciARIgARIgARIgARIgARIggZwgoPUANCd8dJU5Y2JiVL0BSYUkxZUlVZLUcpC0TbJPzZo1w+rVq+HlZd8MAK7C35br1PqcUWiw5W7QdloEKDTwvrAWgUUAugEYDUAKPydvIi5sAhABQAQCgxUmfRvAlwDCAFSygj0KDVaAmJMm1s/9HpeOHjK5kKdwUXR+9wO7uXRkwxrsW7XMbL4eH3wIvzx57eYDJyIBEiABEiABEiABEiABEiCBnCCg9QA0J3x0lTkTExNVfYY1a9ZACjtLjQYp3Fy5cmX07dsXr7/+Ory9JUkEm6MR0PqcUWhwtB12fH8pNDj+HuplBQ0BbAMQBaA5gL2PHCsAYB2AagCGAZiSwmHJKyMfOwEMSHatBAB5FVyKPj9+TR2Qe/Y5ADMfRVC89ujz7HKg0JBdgjk8/uSOrdix+E8zL7r/Zxz88+azi2ervv4CNy5K6ZKHrWDxUmj/+jt2mZuTkAAJkAAJkAAJkAAJkAAJkEBOEtB6AJqTvnJuEnBUAlqfMwoNjrrTjus3hQbH3Ts9ej4KwIRHhZnXALj/qMi0vNK9AoDksUlM4fiHAMYB2ACgRbJrNQHseyRc7HmUJik3gKoASj/qNx3AW1YCQaHBSiBzysz9O7ex6L/jzaZv0O1plG/Q2OYuPbh7Bwsny638uNVq3xlVm2utsW5zVzkBCZAACZAACZAACZAACZAACVidgNYDUKs7QIMk4AIEtD5nFBpc4KbQ2RIpNOhsQ5zAnc4A3gUgVYckFu8EgDkAvkpDZJDlpic0SCTEfwDUA1AOgPzfHcBVANsBfAtgrRV5UWiwIsycMrXsi8m4GyG3yMMWUqUaWvR/yebuHN+6EbuXSb3zx63rsNHIXSC92ug2d4kTkAAJkAAJkAAJkAAJkAAJkIDdCGg9ALWbY5yIBJyIgNbnjEKDE22+gyyFQoODbBTdtDkBCg02R2z7CfasWIJjmyVT18Pm6e2D3mMmwsPT06aT//vtdFw7e8o0R76gYHQcMtymc9I4CZAACZAACZAACZAACZAACeiFgNYDUL34TT9IwJEIaH3OKDQ40u46h68UGpxjH7mK7BOg0JB9hjlu4cqpMKz5foaZH21efhNFykhQjG1aTFQUFnwyBklJSaYJqj/ZHtVbt7fNhLRKAiRAAiRAAiRAAiRAAiRAAjojoPUAVGfu0x0ScAgCWp8zCg0Osa1O5SSFBqfaTi4mGwQoNGQDnl6GJibEY97Ho5AQF2dyqUqzVqjdoYvNXDy5axt2LPzDzH6nt/+DvEWDbDYnDZMACZAACZAACZAACZAACZCAnghoPQDVk+/0hQQchYDW54xCg6PsrPP4SaHBefaSK8keAQoN2eOnm9Hrf/oOl44dMfmTt0gQOr0j5T5s09bOnonwE8dMxnMXKIQuQ0fCzY3fXm1DnFZJgARIgARIgARIgARIgAT0RkDrAaje/Kc/JOAIBLQ+ZxQaHGFXnctHnoQ5135yNVknQKEh6+x0NfLE9s3YuWS+mU89PvgQfnnyWt3PuJhozP94NAyGRJPtKs1ao3YHqYnORgIkQAIkQAIkQAIkQAIkQAKuQUDrAahrUOEqScC6BLQ+ZxQarMuf1jInQKEhc0bs4RoEKDQ4yT5H3bqJxZ99bLaahj36oly9hlZf4dl9u7Hlz5/N7HZ44z0UCClh9blokARIgARIgARIgARIgARIgAT0SkDrAahe10G/SEDPBLQ+ZxQa9LybzukbhQbn3FeuSjsBCg3amel2xNIpn+DejQiTf8WrVkfz5160qr8SxbBi6me4G3HVZNcvMA+6f/Ah0yZZlTSNkQAJkAAJkAAJkAAJkAAJ6J2A1gNQva+H/pGAHglofc4oNOhxF53bJwoNzr2/XJ3lBCg0WM5K9z13L1+E41s2mPz08vFF7zET4e7hYTXfj2/diN3LFprZq9y0Jep07Gq1OWiIBEiABEiABEiABEiABEiABByBgNYDUEdYE30kAb0R0PqcUWjQ2w46vz8UGpx/j7lCywhQaLCMk0P0Cj9xHGtnf2Pma5tX3kKR0mWt4n9MVBSWTpkIqdFgbF6+vug6dDR8AwKsMgeNkAAJkAAJkAAJkAAJkAAJkICjENB6AOoo67KXn+fOnUPp0qVRsmRJyOds1icQFRWFL7/8EgsWLMDp06eRmJiIoKAgNGzYECNHjkSVKlWsP6mVLWp9zig0WHkDaC5TAhQaMkXEDi5CgEKDE210YkI8/hw/EvKvsVVt8SRqtetklVXuWPQnTu7camarTqfuqNykuVXs0wgJkAAJkAAJkAAJkAAJkAAJOBIBrQegjrQ2e/hKocG2lE+ePIk2bdrg/PnzCA4ORv369VXK47Nnz+LAgQP48ccf8dxzz9nWCStY1/qcUWiwAnSa0ESAQoMmXOzsxAQoNDjZ5q6dMxPhYcdMq8oXFIyOQ4Zne5W3wi/hr6+mAEgy2cpTuKiybc3UTNl2lAZIgARIgARIgARIgARIgARIwE4EtB6A2skth5mGQoPtturevXuoXr06Lly4gM8//xzvvPMO3N3dTROGh4dD7t8SJUrYzgkrWdb6nFFosBJ4mrGYAIUGi1Gxo5MToNDgZBsctm0Tdi1dYLaqHiPGQwo2Z7UlJSXhn5lf4fr5M2YmWr84GEHlK2bVLMeRAAmQAAmQAAmQAAmQAAmQgEMT0HoA6tCLtYHzFBpsAPWRyeHDhyuB4e2331apkxy5aX3OKDQ48m47pu8UGhxz3+i19QlQaLA+0xy1GHnzBpZ8PsHMh0a9nkHZOg2y7Ne5A3ux+fefzMaHVKmGFv1fyrJNDiQBEiABEiABEiABEiABEiABRyeg9QDU0debVf/37duHsWPHYtOmTeot+tDQUAwdOhT16tVLt0ZDREQEpkyZgmXLlqn6DR4eHqhatSoGDRqEl156SaUAStmuXLmi5lm+fDlu376NUqVKYeDAgRg2bBjKli2rUghJ2iD5urHJ58av79y5E9OmTcOhQ4cgEQFiI2/evKprbGwsvv32W/z66684evSo+r/Ul+jVqxfef/995M6dO008suapU6diy5YtuHnzJvLnz49mzZqp+gg1a9bMKtIMx4lvUodB/E+5XptMaGOjWp8zCg023hCaT0WAQgNvChJ4SIBCgxPeCSI0iOBgbCWr1cQTz76QpZXGx8Vi2ZRP8ODeXdN4d09PdH73A+TOXzBLNjmIBEiABEiABEiABEiABEiABJyBQEYHoIkJBkTdjnHoZQbk84WH5+N0O1lZzJo1a9CxY0d1MC9CgaTzEeFg27Zt6m17OYRPWQxahIkOHTrg2rVr6lqNGjUQHR2N7du3IzIyEs8++yx++eUXM3cuXbqExo0b4+LFi+qQ/YknnlB9161bp+bfvXt3hkLD4MGDMWPGDDRq1EgJESdOnID4nidPHnVgL/7s2LFDCQW1a9dGQEAAdu3ahcuXL6t1bdy4UV1L3iZOnIjRo0erlEV169ZVazlz5gz27NkDb29vzJ8/H507d84K1gzHbN26FU2aNEFISIjiIQLKkiVLcOPGDRQrVgydOnVCnTp1rD6vrQxSaLAVWdq1FsV73qgAACAASURBVAEKDdYiSTuOToBCg6PvYBr+S+okSaFkbN6+udBrzAS4u3toXu3+f/7C4XX/mI0LbdEGNdt11GyLA0iABEiABEiABEiABEiABEjAmQhkdAB69/oD/P3dEYdebruXqyJPIb8sr+HBgwcoV64cJNJg/PjxKtrA2ObNm4e+ffvCYDCYCQ33799HlSpVVG0BiWhIXltA6grIwfzevXvx/fff48UXXzTZ69Kli4p+6NatG3777Tf4+vqqa6dOnUKLFi2UICAtvYgGLy8vFQnRtm3bVOuVqIUFCxYogUPEiMDAQNUnJiYGr732miqq3L9/f/z00+NMAOKL+CQ1EBYtWqTECWOTaz169IC/v78SHpILFMYICy3QmzdvjvXr15uGSOTFq6++igYNGqBhw4ZKzEnZJCrkm2++gaenp5apcqQvhYYcwc5JNRCg0KABFrs6NQEKDU64vZfDjmHdnJlmK2v76hAULlVG02ojb93Asi8mw5CQYBontR46Dx0JL28fTbbYmQRIgARIgARIgARIgARIgAScjQCFhox3VA7en3/+eVSqVAlHjhwxK0YsI3v27ImFCxeaCQ3Tp0/HW2+9lerg3jiTiAzyNn6tWrWU4CBNIiQkXY6IBXJwHxwcbOaY8eBdvpie0CAH83LwnrIdPnwY1apVU4KJfO7jY/63sIgpMrekRZIIDKNoIBEMErkgURGtWrVKZXfIkCH46quvlAggnxubpHmSyAMtTfh+8MEHpiGTJ0/GiBEjFA+5RyW1k6xPojNWrVqFN998U0VpSLTFxx9/rGWqHOlLoSFHsHNSDQQoNGiAxa5OTYBCgxNub0J8PP78aISZQBDasi1qtn1K02o3/PwDLh45aDamSZ/+KF3TcUIsNS2YnUmABEiABEiABEiABEiABEhAAwEKDRnDkrfmf/jhB3z44YcYN25cqs6LFy9G9+7dzYQGSeuzYsUKyLWuXbumGpOUlKQiCuSAX6IfJHJh7ty5GDBgAFq2bIm1a9emGiP1FuSQXVp6QsPKlSvRvn37/2/vTuC9mvb/j781aVAoKhm7DURSmRIqynQTEQpXROax2+1eM/dxkamff12ETBG3v6EUyVASZbpUfhnSNRUqQ1cpGqj+j8+yv+d/5vPd372/37OH1348PMo5e+291nPt/Tin9f6utcqUtQ2VbWNlm1lxxx13lNvgTJ1ffPFFNyPi+++/V9OmTV3oYKFBeftJ2LJJJ554ok4++WS370OYx0033aSrrrrKXdJmfdjsj+JHxr1+/fouHLFloKJ8EDREuXeomwkQNPAcIPC7AEFDQp+E6Q/eo6X/WVDUusbb76g/XjQ069Yu/fQTTX9gdInzt925pWxmRHm/JGV9YU5EAAEEEEAAAQQQQAABBBIiQNBQeUfawL0Nvj/88MNuZkPpY968eW5mQvE9Gmy/A9tsOZvD9mWw2QuZT/CfccYZeuihh8otaps6r1y5ssKgwe7Zrl27MmUvvPBC3X333dlUR+PGjdOpp57q9m7Yb7/9sirTq1cvvfzyy1mdm+1JNlMiM0vC9muwfSdKHxaEWCBS0YyLbO9ViPMIGgqhzD2CCBA0BNGjbJIECBqS1JvF2vLx7Jl677mJJVrX76p/qN4WDats8cYNGzRl1G1a+d2yYudupj9ePFSNW+xQZXlOQAABBBBAAAEEEEAAAQTSIMBm0JX3ci5Bgy0D9Mknn7gNi5s0aVLpDWy2wTbbbFMUNAwaNMjNoCjvsNkFtlxQRTMaSn89cw3bg+Hee+91mzlbCFLZMXjwYB100EFu02ob3Ldwo7xZGcWvUXrZozCWTsrsD2H3sX0tbHPs0ocFIRaI2KbatvdElA+Chij3DnUzAYIGngMEfhcgaEjok/DTD99p8oibSrSuRdt2an/IYbKZCZXNSlgwe6beLRVStNmvq/Y/7qSEatEsBBBAAAEEEEAAAQQQQMC/gN8BUP93iHeJzNJJpTeCzrSqvKWTDjvsME2bNs3tJXDEEUdkBZDZC6Jnz56ubOlj1apVRRs4+w0abrzxRreXge2BMHz48KzqYxtP77DDDmrevLnbCNvPEcZm0DbTY8cdd3S3nT9/vtq3b1+mCq1atXL7WUyaNMltWh3lw+97ZntmfGEdLfnbqDLKCNQt0gIEDZHuHipXQAGChgJiF/JWtm7lpNtv0Or/Li9zW1tGabcDu2nnPTupZq1aJb6/dvVqTRpxg35du7bo67Xr1tWxQ69W3Yiv21hIX+6FAAIIIIAAAggggAACCPgdAE2b2NixY2XLGdmSRLYZdOkPvNkeBbZXQfGlk0aMGCH7VL/NDhgzZkxWZDZgbgPntlGzjS+X/gT//fffr7PPPttdy2/QYBs622wGm3lgm0HXrFkzqzrZBtJ2/qxZs3TggQdmVSbMkzIzFkaNGuU21y5+fPbZZ9p11121YcMGZZafCvPeYV/L73tG0BB2D3C9qgQIGqoS4vtpESBoSHBPz5k6WR+9VnYjrEyT627RUG27HKQ2+3ctWlLp7YlP6D/vvFFCZe+jj1O7A7snWIqmIYAAAggggAACCCCAAAL+BfwOgPq/Q7xL2GbNrVu31rJly3TDDTcUbVBsrZowYYLbDHnjxo0lggabfbD77rvLZgVYmaFDh7oAofhh+yksWLBAxx9/fNGXe/fureeff959zTZXzpSxEKJ79+5uQN0Ov0GDlbHljyZPnqyTTjpJI0eOdDMVih+2obJ9PxNm2PcmTpzo6mIhiu1R0aNHjxJl1q9f7/avaNOmjQsxwj4ys0VsL4aXXnpJe+21l7uF7VPRr18/tzdD3759XT2jfvh9zwgaot6jyasfQUPy+pQW5SZA0JCbWyxKrV/zi2Y/8Zi+WfBhpfWtUauWWu61t7Zrs6tmjX9U0qai87ds2ly9LxmmGll+aiMWMFQSAQQQQAABBBBAAAEEEAhBwO8AaAi3jN0lbKPjPn36aN26dW4JH/uk/6JFi2SbFNuGxfaJ++IzGqyB77//vtujwcKBbbfdVh06dHCD+ytWrHCzBKx8//79NX78+CKPxYsXq2vXri6gaNGihdsrYfXq1ZoxY4ZsrwibmWDnZL6fKZhZqqiiPRrsPLuvtcFmJ9SvX18dO3Z0dbY2LVy40M3WsAF9C1SKH7feequuuOIKF6ZYeNK2bVvVrVvX1WHu3LmuflOnTnX1y8dx6aWXOl8LXbp06aItt9xSb775ptsE2sKNmTNnunpH/fD7nhE0RL1Hk1c/gobk9Sktyk2AoCE3t1iVWv7NV1ow+zUten+ONm7c4KvuPc86X9u13tVXGU5GAAEEEEAAAQQQQAABBNIg4HcANA0m5bXRBvmvvfZaN1BvZrap8pAhQ9yGyS1btiwTNNg1bOPmu+66y+0hYJtD2wyAZs2ayYIBG5i32QW2XFLxwzY+vuaaazRlyhRX3s4dOHCgW4qpUaNG7t42y8IG+zNHNkGDnWtlx40b5/6bN2+emxlgm1XbXgw2Y8JmCVjQUfqYM2eOG+y3QX3br8EG/W1pJwtPbKaEzSpo0KBB3h6NJ554wjlanS0YsUH4E044ocgkbzcO8cJ+3zOChhDxuVRWAgQNWTFxUgoECBpS0MmZJq5Z9ZMWvjVbC9+erXU/r66y5Tvu0UHd/3RmledxAgIIIIAAAggggAACCCCQRgG/A6BpNIpCm2fPnu1mOFjAYTMiOOIl4Pc9I2iIV/8mobYEDUnoRdoQhgBBQxiKMbvGht9+1Zfvz3GzHH5c+k25tbfllI4ZcoW2aNwkZq2juggggAACCCCAAAIIIIBAYQT8DoAWplbpvIstT2Sf2u/cuXMJAJsNYXsl2L4OtpTRsGHD0gkU41b7fc8IGmLc2TGtOkFDTDuOaocuQNAQOml8Lrhp0yZ998Vn+njWq/r6Y9vH4f/vzdCh55Hq0Cs/60TGR4iaIoAAAggggAACCCCAAAIVC/gdAMUyfwJr165VvXr13HJJtv+ALZVkeznYsk3WT926ddO0adNUu3bt/FWCK+dFwO97RtCQl27gopUIEDTweCDwuwBBA0+CE1i1/Af95+3Zsv0cmrdqq/Y9emmzGjXQQQABBBBAAAEEEEAAAQQQqEDA7wAokPkT2LBhg9ufYfr06bKNnW2PBtu4uV27dhowYIAuuOAC1alTJ38V4Mp5E/D7nhE05K0ruHAFAgQNPBoIEDTwDCCAAAIIIIAAAggggAACCCCQs4DfAdCcb0RBBFIs4Pc9I2hI8cNSTU0naKgmeG4bOQFmNESuS6gQAggggAACCCCAAAIIIIBAHAT8DoDGoU3UEYGoCfh9zwgaotaDya8PQUPy+5gWZidA0JCdE2chgAACCCCAAAIIIIAAAgggUELA7wAofAgg4F/A73tG0ODfmBLBBAgagvlROjkCBA3J6UtaggACCCCAAAIIIIAAAgggUEABvwOgBawat0IgMQJ+3zOChsR0fWwaQtAQm66ionkWIGjIMzCXRwABBBBAAAEEEEAAAQQQSKaA3wHQZCrQKgTyK+D3PSNoyG9/cPWyAgQNPBUI/C5A0MCTgAACCCCAAAIIIIAAAggggEAOAn4HQHO4BUUQSL2A3/eMoCH1j0zBAQgaCk7ODSMqQNAQ0Y6hWggggAACCCCAAAIIIIAAAtEW8DsAGu3WUDsEoing9z0jaIhmPya5VgQNSe5d2uZHgKDBjxbnIoAAAggggAACCCCAAAIIIOAJ+B0ABQ4BBPwL+H3PCBr8G1MimABBQzA/SidHgKAhOX1JSxBAAAEEEEAAAQQQQAABBAoo4HcAtIBV41YIJEbA73tG0JCYro9NQwgaYtNVVDTPAgQNeQbm8ggggAACCCCAAAIIIIAAAskU8DsAmkwFWoVAfgX8vmcEDfntD65eVoCggacCgd8FCBp4EhBAAAEEEEAAAQQQQAABBBDIQcDvAGgOt6AIAqkX8PueETSk/pEpOABBQ8HJuWFEBQgaItoxVAsBBBBAAAEEEEAAAQQQQCDaAn4HQKPdmsLX7ssvv1TLli218847y/7OEa7AokWLNHz4cL344otasmSJGjVqpC5duugvf/mLunfvHu7N8ng1v+8ZQUMeO4NLlytA0MCDgcDvAgQNPAkIIIAAAggggAACCCCAAAII5CDgdwA0h1skughBQ/669+2339ZRRx2lH3/80YU5nTp10tdff61///vf7qajRo3SRRddlL8KhHhlv+8ZQUOI+FwqKwGChqyYOCkFAgQNKehkmogAAggggAACCCCAAAIIIBC+gN8B0PBrEO8rEjTkp//Wrl2rNm3auGBhyJAhuu2221SzZk13sxdeeEF9+/bVr7/+qrlz56pDhw75qUSIV/X7nhE0hIjPpbISIGjIiomTUiBA0JCCTqaJCCCAAAIIIIAAAggggAAC4Qv4HQANvwbxviJBQ37671//+pdOOeUUtW7dWh9//LFq1apV4kZ//vOfdccdd2jAgAGyc6N++H3PCBqi3qPJqx9BQ/L6lBblJkDQkJsbpRBAAAEEEEAAAQQQQAABBFIu4HcANK1c9sn5a6+9Vq+//rrMrH379ho6dKj23XffCvdo+O677zRixAg9++yzbv8G+0T+HnvsocGDB+uss87SZpuVHdpbunSpu89zzz3nlgzaZZddNGjQILcnQatWrWR7FnzxxRfu65nD/p75+jvvvOOWFJo/f75++uknd42tttrKnbpu3Trdd999evzxx/XRRx+5/7cliU444QT99a9/VcOGDcvtXmvzyJEjNXv2bC1fvlyNGzdWt27ddOWVV6pjx455eSQyQYI53X///WXu8fzzz6t3795q0KCBa2Pt2rXzUo+wLur3PSNoCEue62QrQNCQrRTnJV2AoCHpPUz7EEAAAQQQQAABBBBAAAEE8iJQ2QDoht9+1aoffsjLfQt10YbbbKOatYINQk+fPt0NatvAvAUFtlSPBQdvvvmmLr30UjcIX3ozaAsmbH+Bb7/91n1vr7320po1a/TWW29p1apV7tP6jz32WAkGWyaoa9eu+uqrr7Tddtvp4IMPdufOmDHD3f/dd9+tNGg4//zzNXr0aB1wwAEuiFi4cKGs7ltuuaUbjLf62L4HFhR07txZW2yxhdvv4JtvvnHteu2119z3ih833nijrr76atWoUUP77LOPa8vnn3+u9957T3Xq1NFTTz2lPn36hN6d55xzjsaMGeMCkFtuuaXM9S1Q2X///d3XP/zwQ+2+++6h1yHMCxI0hKnJtfIhQNCQD1WuGUcBgoY49hp1RgABBBBAAAEEEEAAAQQQqHaBygZAVyxbqudGlh3krfZK+6jA0Zf+TVs1385HiZKn/vLLL275Hptp8Pe//93NNsgcTz75pFu6Z+PGjSWChp9//tkNfC9evNjNaLjsssvcQL0dS5YscQPzc+bM0QMPPKAzzzyz6HrHHHOMm/1g+w/YckB169Z13/v000/Vo0cPFwjYUdGMBvtUv82EOPzww8u012YtPP300y7gsDCiUaNG7hzbC+G8887T2LFjddppp+mRRx4pKmt1sTrttNNOmjhxogsnMod97/jjj3czCix4KB5QZGZY+EHv3r27Xn311aIiFm5YyGH1NufSx/jx43XyySe7L1ubLYiJ8kHQEOXeoW4mQNDAc4DA7wIEDTwJCCCAAAIIIIAAAggggAACCOQgQNBQOZoNvJ9++unabbfd3CfnM4FBplS/fv00YcKEEkHDnXfeqYsvvrjMwH2mjIUMe++9tzp16uQCBztshoQtl2NhgQ3cb7/99iUqZksenXvuue5rFQUN9v177rmnTIM++OAD7bnnni4wsb9vvvnmJc6xMMXubcsi2QyMTGhgMxhs5oLNijj00EPLXPeSSy7RP//5Tzejw/6eOWyZpx98zoQx38svv7zoGnbPXr16qV69ei5oadGiRYn7H3LIIUXBhC0FlQkdcngFClKEoKEgzNwkgABBQwA8iiZKgKAhUd1JYxBAAAEEEEAAAQQQQAABBAolQNBQubTtEfDggw/q+uuv13XXXVfm5GeeeUbHHXdciaDh6KOP1pQpU2TfO/bYY8uU2bRpk5tRYAP8NvvBZi48+uijGjhwoGwA/ZVXXilTxvZbsCWQ7KgoaJg6daqOPPLIMmVvv/12DRs2zM2ssA2UyzsydX7xxRfdjIjvv/9eTZs2daGDhQbl7SdhyyadeOKJbpDfBvvDPg466CC3L4Qt62ThjQUftrzUTTfd5Lxsg2h7fm12Q//+/cO+fajXI2gIlZOL5UGAoCEPqFwylgIEDbHsNiqNAAIIIIAAAggggAACCCBQ3QIEDZX3gA3c2+D7ww8/7GY2lD7mzZvnZiYU36PBBsZts+VsDhs4t9kLN998s6644gqdccYZeuihh8otaps6r1y5ssKgwe7Zrl27MmUvvPBC3X333dlUR+PGjdOpp57q9m7Yb7/9sipjMw9efvnlrM71c5LNrrCgxvaVKH7YptrDhw/Xrbfe6kKQl156SYcddpifSxf8XIKGgpNzQ58CBA0+wTg9sQIEDYntWhqGAAIIIIAAAggggAACCCCQTwE2g65cN5egwZYB+uSTT2SzBJo0aVLpDWy2wTbbbFMUNAwaNMjNoCjvsNkFtqlzRTMaSn89cw3bg+Hee+91MwIsBKnsGDx4sGwmgW1abZtKW7hR3qyM4tcovexRGEsnZa5vsz9eeOEFt0yStd1CGdu3wZZ6atiwoTZs2OD2z2jevHk+X5PA1yZoCEzIBfIsQNCQZ2AuHxsBgobYdBUVRQABBBBAAAEEEEAAAQQQiJKA3wHQKNW9EHXJLJ1UeiPozL3LWzrJPl0/bdo0N0B+xBFHZFXNzF4QPXv2dGVLH6tWrSrawNlv0GCbKtvmyrYHgs0EyOawjad32GEHN4BvA/l+jjA2g67qfra8lFm1b99e8+fPr+r0av++3/fMgpQvrKOlP1R75alAKgQIGlLRzTQyCwGChiyQOAUBBBBAAAEEEEAAAQQQQACB0gJ+B0DTJjh27Fi3nJEtSWSbQZfeq8D2KLC9CoovnTRixAjZp/ptdsCYMWOyIrMNoFu1auU2arbx5e22265Eufvvv19nn322+5rfoME2dLbZDDbzwDaDtqWHsjlsA2k7f9asWTrwwAOzKVKwczJ7SowePVo2YyPqh9/3jKAh6j2avPoRNCSvT2lRbgIEDbm5UQoBBBBAAAEEEEAAAQQQQCDlAn4HQNPGZZs1t27dWsuWLdMNN9ygq666qohgwoQJbjPkjRs3lggabPbB7rvvLpsVYGWGDh3qAoTih+2nsGDBAh1//PFFX+7du7eef/559zXbXDlTxkKI7t27u42Q7fAbNFgZW/5o8uTJOumkkzRy5MgySw3Zfgj2/UyYYWUmTpzo6mIhiu1R0aNHjxJtWL9+vdu/ok2bNi7ECPuwmQo24N6gQYOiS69Zs8btZWFt6NKliwtBsg1Owq6fn+v5fc8IGvzocm4YAgQNYShyjSQIEDQkoRdpAwIIIIAAAggggAACCCCAQMEF/A6AFryCEbihbXTcp08frVu3zi3VY5/0X7Rokd544w1dcsklGjVqVImgwar8/vvvuz0aLBzYdttt1aFDBze4v2LFCjdLwMr3799f48ePL2rh4sWL1bVrVxdQtGjRwu2VsHr1as2YMUO2V4TNTLBzMt/PFMwsVVTRHg12nt3X2mAD8/Xr11fHjh1dna1NCxcudLM1mjZt6gKV4odtuGwD+xamWHjStm1b1a1b19Vh7ty5rn5Tp0519Qv7sJkkTz75pPbee2+3N4Pda/bs2W6vBvuaLU1l+1vE4fD7nhE0xKFXk1VHgoZk9SetyV2AoCF3O0oigAACCCCAAAIIIIAAAgikWMDvAGhaqWyQ/9prr3UD9WZmmyoPGTLEbZjcsmXLMkGDOdmA+F133aVJkya5zaFtBkCzZs1kwYANzNvsAlsuqfixZMkSXXPNNZoyZYorb+cOHDjQLcXUqFEjd2+bZWGD/Zkjm6DBzrWy48aNc//NmzdPK1eudJtV214MNmOiX79+LugofcyZM8eFKTNnznT7NdhMC1vaycITmynRt2/fErMOwnpGbP8LW3rKQpvvv//eBSTmfsopp+icc85RrVq1wrpV3q/j9z0jaMh7l3CDUgIEDTwSCPwuQNDAk4AAAggggAACCCCAAAIIIIBADgJ+B0BzuAVFQhCwT/LbDAcbaLcZERzxEvD7nhE0xKt/k1BbgoYk9CJtCEOAoCEMRa6BAAIIIIAAAggggAACCCCQOgG/A6CpAypgg215Iptp0Llz5xJ3tdkQtleC7etgSxkNGzasgLXiVmEI+H3PCBrCUOcafgQIGvxocW6SBQgakty7tA0BBBBAAAEEEEAAAQQQQCBvAn4HQPNWES6stWvXql69em65JNtc2ZZKsr0cbNkm66du3bpp2rRpql27NloxE/D7nhE0xKyDE1BdgoYEdCJNCEWAoCEURi6CAAIIIIAAAggggAACCCCQNgG/A6Bp8ylkezds2OD2Z5g+fbpsY2fbo8H2JWjXrp0GDBigCy64QHXq1ClklbhXSAJ+3zOChpDguUzWAgQNWVNxYsIFCBoS3sE0DwEEEEAAAQQQQAABBBBAID8CfgdA81MLropAsgX8vmcEDcl+HqLYOoKGKPYKdaoOAYKG6lDnnggggAACCCCAAAIIIIAAArEX8DsAGvsG0wAEqkHA73tG0FANnZTyWxI0pPwBoPlFAgQNPAwIIIAAAggggAACCCCAAAII5CDgdwA0h1tQBIHUC/h9zwgaUv/IFByAoKHg5NwwogIEDRHtGKqFAAIIIIAAAggggAACCCAQbQG/A6DRbg21QyCaAn7fM4KGaPZjkmtF0JDk3qVtfgQIGvxocS4CCCCAAAIIIIAAAggggAACnoBtQLxw4UL3f61atWKzYZ4MBPIgsH79en322Wfuym3btlXNmjUrvQtBQx46gUtWKkDQwAOCwO8CBA08CQgggAACCCCAAAIIIIAAAgjkILBp0yZ9+umnsk9cN2/eXFtvvXUOV6EIAghUJvDjjz9q2bJlqlWrllq3bq3NNqt8WJeggeep0AIEDYUW535RFSBoiGrPUC8EEEAAAQQQQAABBBBAAIHIC3z77bf673//qxo1aqhp06Zq0KCB+zsHAggEE9i4caN+/vlnfffdd7K/N27cWM2aNavyogQNVRJxQsgCBA0hg3K52AoQNMS266g4AggggAACCCCAAAIIIIBAdQvYbIbFixdr3bp11V0V7o9AYgU233xz7bTTTm5WQ1UHQUNVQnw/bAGChrBFuV5cBQga4tpz1BsBBBBAAAEEEEAAAQQQQCASAhY2LF++XD/99JNbRokDAQTCEbBgoVGjRmrSpElWIYPdlaAhHHuukr0AQUP2VpyZbAGChmT3L61DAAEEEEAAAQQQQAABBBAokIDt2WBLvNifHAggEEzA9mKwZciq2pOh9F0IGoK5U9q/AEGDfzNKJFOAoCGZ/UqrEEAAAQQQQAABBBBAAAEEEEAAgdQJEDSkrsurvcEEDdXeBYmpwDaSjpG0t6R9JO0laXNJYyWdEbCVp0s6V1J77zofSBot6dGA1y1enKAhREwuhQACCCCAAAIIIIAAAggggAACCCBQfQIEDdVnn9Y7EzSktefDb3dfSRPLuWzQoOE+SWdL+kXSdO/6vSTV88KGC0JqCkFDSJBcBgEEEEAAAQQQQAABBBBAAAEEEECgegUIGqrXP413J2hIY6/np80HSBoo6T1JcyRZGHBLwBkN/SWNl/SNpIMlfeFVvaWkWZJaSOonaUIITSJoCAGRSyCAAAIIIIAAAggggAACCCCAAAIIVL8AQUP190HaakDQkLYeL1x7L5N0R8CgwQKLTpL+JOmxUlW3r9nSSe9K2jeEZhE0hIDIJRBAAAEEEEAAAQQQQAABOaXUOgAAFnhJREFUBBBAAAEEql+AoKH6+yBtNSBoSFuPF669QYOGHSUtlrRO0laS1paqui2dtEJSHUl27tcBm0bQEBCQ4ggggAACCCCAAAIIIIAAAggggAAC0RAgaIhGP6SpFgQNaertwrY1aNBgG0tP8pZhsg2myzsyMx76SHouYPMIGgICUhwBBBBAAAEEEEAAAQQQQAABBBBAIBoCBA3R6Ic01YKgIU29Xdi2Bg0aLpE0UtIzko6roOoWRFggcbGkOwM2j6AhICDFEUAAAQQQQAABBBBAAAEEEEAAAQSiIUDQEI1+SFMtCBrS1NuFbWvQoOFKSTd6ezPYfgzlHbZvwymS7NzhWTTv80rO2aVWrVqb7bijrcLEgQACCCCAAAIIIIAAAggggAACCCCAQHwFvvrqK/3222+2FLktP86BQN4FCBryThyLGzwoqavPmn4jqWclZeIWNOwsab2kpT4d4nB6Jj35Kg6VpY4IxFiAdy3GnUfVYyPAexabrqKiMRbgPYtx51H12AjwnsWmq6hozAXS/q5tV2zv05h3JdWPgwBBQxx6Kf91nFZFaFBeDRZJ2iWPQUOhl07Kv3L13SEzk+MP1VcF7oxAKgR411LRzTSymgV4z6q5A7h9KgR4z1LRzTSymgV4z6q5A7h9agR411LT1TQ0CgIEDVHohWTWIeiMhkJvBp3MXvi9VfxgTXLv0rYoCfCuRak3qEtSBXjPktqztCtKArxnUeoN6pJUAd6zpPYs7YqaAO9a1HqE+iRagKAh0d1brY0LGjTY9LbFxaZ42ZpyxQ9bX26FpDqS7Nyvq7W10b45P1ij3T/ULjkCvGvJ6UtaEl0B3rPo9g01S44A71ly+pKWRFeA9yy6fUPNkiXAu5as/qQ1ERcgaIh4B8W4ekGDBmv6XEkdJdlm0Lbxc/HDvvaopHcl7Rtjp0JUnR+shVDmHggwe4hnAIFCCPAzrRDK3CPtArxnaX8CaH8hBHjPCqHMPRDg32g8AwgUVICgoaDcqbpZtkHDRZLsv3ckDSwl1F/SeEm28fTBkr7wvt9S0uuStpd0gqSnUyXrv7H8EuvfjBII5CLAu5aLGmUQ8CfAe+bPi7MRyEWA9ywXNcog4E+A98yfF2cjkKsA71qucpRDIAcBgoYc0ChSocBbxb7TXNLOkn6Q9Fmxrx8naWmx/79e0nWSZkrqUc6Vx0gaLOkXSbZptR29JNWXdI+k8+mPKgX4wVolEScgEIoA71oojFwEgUoFeM94QBDIvwDvWf6NuQMCvGc8AwgURoB3rTDO3AUBJ0DQwIMQpsCmLC5msxG+LHZeVUGDnXq6Fyjs4ZX7QNJoSY9kcT9OYaogzwAChRLgl9hCSXOfNAvwnqW592l7oQR4zwolzX3SLMB7lubep+2FFOBdK6Q290q9AEFD6h8BABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyF2AoCF3O0oigAACCCCAAAIIIIAAAggggAACCCCAAAIIIJB6AYKG1D8CACCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkLsAQUPudpREAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCD1AgQNqX8EAEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIHcBgobc7SiJAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDqBQgaUv8IAIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQO4CBA2521ESgagL1JR0saTTJbWVtF7SHEn/I2lK1CtP/RCIuEBtSd0l9fb+bCNpc0nLJL0u6Q5J70a8DVQPgbgKPCbpFK/yx0l6Jq4Nod4IRFBgC0mXSeonqZUk+31yqaS3JN0k6aMI1pkqIRAngR0kXS7pcEk7SdokabGklyTdIunrODWGuiJQTQK7SjpC0j7ef/b/NSQNkvRwJXWqJ+lvkvpL2kXSz5LekHSz92c1NYfbIpAcAYKG5PQlLUGguEAtSZMlHSVphaRXJNk/HA+VZN+zX27tF1kOBBDITaCXpJe9okskvSdpnaQOXrC3QdKFku7N7fKUQgCBCgSO9YIFG5ix32MJGnhUEAhPwEJz+9m2s6RvJL3jDYK2lLSX9+GVceHdjishkDoBe49elbSVpK+83x8tzNtXUnNJP0rqIel/UydDgxHwJ/B/JF1aTpHKgoaG3vvX2QvQZ0lqJulg72fdaZIe91cNzkYAgdICBA08EwgkU8BSekvl53vhwg9eM/f2frg28H6htcFRDgQQ8C9god353swF+xRM5rCfq5dIsl9+f5PUXtIn/i9PCQQQKEegsaQPJX0rabWkAwkaeE4QCE2gkTe4aZ+w/ov3c2xjsau38D6sYp+85kAAgdwEbNbrQZLGeB9I+dW7TF1Jj0g6UdIM799vud2BUgikQ2Cw9+EuW7HBxjRul3RMFTMaRks6T9J079xfPKo/eh/StBUgLHC3oJ0DAQRyFCBoyBGOYghEWMA+FWPLt2zj/SI7u1Rdr5b0D0lPeb/MRrgpVA2B2ArYJ0Jt1sO13vsW24ZQcQQiJGCfpB4gaX9JI7xly5jREKEOoiqxFrjNCxhGeksnxboxVB6BCArYsps2+9XGYCy4syXJih/tvKXJ1kiqH8H6UyUEoixgy2jarNeKZjQ08cZI7P2zZQEXlWrM/ZLO8gKLYVFuKHVDIOoCBA1R7yHqh4B/AZv695q31qdNfS99/EHSZ5Iswd/a27vB/10ogQAClQncKsl+Sb1P0rlQIYBAYIE+3qfN7N2yWXu29ITtk0LQEJiWCyDg9hiyQU/7vdCWSfoSEwQQCF3APgxm68Hb+1ZZ0GBLKtnMIg4EEMheoKqgwZZFsllDNk5ivz+WPmy2us10+NSb1ZD9nTkTAQRKCBA08EAgkDwBW7bFPo02wdvIr7wWLpdkS1DsKemD5BHQIgSqXSDzy+7fJV1f7bWhAgjEW8AGP23JJFsuyfZBWUvQEO8OpfaRE+gqyWbA2ia0O0raz/tkqM2OtX2InvOWpohcxakQAjETsHepdxVLJzEbNmadSnUjIVBV0PA/koZIsj+HllNjWz5wpfd1+/uqSLSKSiAQQwGChhh2GlVGoAqBzA9RWyPefpiWd7zvDdbYJ0TtF14OBBAIT8ACPFsv1D651kmSvW8cCCCQu4B9Au1Pkg6RNNO7DDMacvekJAKlBc6RdK+ktyW9VcEGmw94a1vb/kMcCCCQm4DNGHrBW1s+sxl0DS/csz307MNi10kqvj9KbneiFALpEqgqaLAPYdos2Mu896w8HQsaLGTgw5jpenZobcgCBA0hg3I5BCIgYEu1nC3pRkm2H0N5h31qzT69doqkf0WgzlQBgaQI2Jq6b3pB3sPeOqFJaRvtQKA6BI6W9Kw3CGob+GUOgobq6A3umVSByyUNl2Qb09aSZEuUWfBggy5HSrrTW1bpBknXJBWBdiFQIAGbKWR7Dh1R6n6zvH29XipQPbgNAkkSqCposPfqMG+cxPZjKO+wTaBtWTMbJ7F/z3EggEAOAgQNOaBRBIGICxA0RLyDqF5iBWxwxjZZt43I5nu/pNpSLxwIIJCbwFbekkn2yc49JP1U7DIEDbmZUgqB8gSu9D6gYt970NsQs/h5fSVN9Pb3auYtY4YkAgj4F+jivUsW4tnyLW9IqiOpp6QRkuz9shlGFQ2E+r8jJRBIhwBBQzr6mVbGQICgIQadRBUR8CnA0kk+wTgdgRAEbNq7Le9yqqT/SOomaVkI1+USCKRZwGYFnS6pvGX+CBrS/GTQ9rAFLpY0yrtoRZ/k/E7Stt6A6CthV4DrIZACgS0lLZS0hReel9503d49m9Vga8PvIunHFJjQRATCEqgqaGDppLCkuQ4CVQgQNPCIIJA8ATaDTl6f0qJoC9jPUvvk2ZmSFnkhw+JoV5naIRALgRWS6lUwfb2jJBu0sU2if/DWvL45Fq2ikghET8DCvMletWzZ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width=\"999.68\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feel free to use the function plot_one() to replicate the figure \n",
    "# from the prompt once you have completed question one\n",
    "def plot_one(degree_predictions):\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib notebook\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.plot(X_train, y_train, 'o', label='training data', markersize=10)\n",
    "    plt.plot(X_test, y_test, 'o', label='test data', markersize=10)\n",
    "    for i,degree in enumerate([1,3,6,9]):\n",
    "        plt.plot(np.linspace(0,10,100), degree_predictions[i], alpha=0.8, lw=2, label='degree={}'.format(degree))\n",
    "    plt.ylim(-1,2.5)\n",
    "    plt.legend(loc=4)\n",
    "\n",
    "plot_one(answer_one())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Write a function that fits a polynomial LinearRegression model on the training data `X_train` for degrees 0 through 9. For each model compute the $R^2$ (coefficient of determination) regression score on the training data as well as the the test data, and return both of these arrays in a tuple.\n",
    "\n",
    "*This function should return one tuple of numpy arrays `(r2_train, r2_test)`. Both arrays should have shape `(10,)`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.        ,  0.42924578,  0.4510998 ,  0.58719954,  0.91941945,\n",
       "         0.97578641,  0.99018233,  0.99352509,  0.99637545,  0.99803706]),\n",
       " array([-0.47808642, -0.45237104, -0.06856984,  0.00533105,  0.73004943,\n",
       "         0.87708301,  0.9214094 ,  0.92021504,  0.63247944, -0.64525447]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn.metrics.regression import r2_score\n",
    "\n",
    "    r2_train = np.zeros((10,))\n",
    "    r2_test = np.zeros((10,))\n",
    "\n",
    "    for degree in range(0,10):\n",
    "        poly = PolynomialFeatures(degree)\n",
    "        #  newaxis is used to increase the dimension of the existing array by one more dimension.\n",
    "        trans_X = poly.fit_transform(X_train[:,np.newaxis])\n",
    "        clf = LinearRegression().fit(trans_X,y_train)\n",
    "                \n",
    "        r2_train[degree] = clf.score(trans_X,y_train)\n",
    "        r2_test[degree] = clf.score(poly.transform(X_test[:,np.newaxis]),y_test)\n",
    "    return (r2_train,r2_test)\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "Based on the $R^2$ scores from question 2 (degree levels 0 through 9), what degree level corresponds to a model that is underfitting? What degree level corresponds to a model that is overfitting? What choice of degree level would provide a model with good generalization performance on this dataset? \n",
    "\n",
    "Hint: Try plotting the $R^2$ scores from question 2 to visualize the relationship between degree level and $R^2$. Remember to comment out the import matplotlib line before submission.\n",
    "\n",
    "*This function should return one tuple with the degree values in this order: `(Underfitting, Overfitting, Good_Generalization)`. There might be multiple correct solutions, however, you only need to return one possible solution, for example, (1,2,3).* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 9, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    \n",
    "   \n",
    "    return (0,9,6)\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Training models on high degree polynomial features can result in overly complex models that overfit, so we often use regularized versions of the model to constrain model complexity, as we saw with Ridge and Lasso linear regression.\n",
    "\n",
    "For this question, train two models: a non-regularized LinearRegression model (default parameters) and a regularized Lasso Regression model (with parameters `alpha=0.01`, `max_iter=10000`) both on polynomial features of degree 12. Return the $R^2$ score for both the LinearRegression and Lasso model's test sets.\n",
    "\n",
    "*This function should return one tuple `(LinearRegression_R2_test_score, Lasso_R2_test_score)`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-4.3119904517942373, 0.8406625614750235)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_four():\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn.linear_model import Lasso, LinearRegression\n",
    "    from sklearn.metrics.regression import r2_score\n",
    "\n",
    "    poly = PolynomialFeatures(12)\n",
    "    poly_X_train = poly.fit_transform(X_train[:,np.newaxis])\n",
    "    poly_X_test = poly.fit_transform(X_test[:,np.newaxis])\n",
    "    LR_clf = LinearRegression().fit(poly_X_train,y_train)\n",
    "    Lasso_clf = Lasso(alpha=0.01,max_iter=10000).fit(poly_X_train,y_train)\n",
    "        \n",
    "    LinearRegression_R2_test_score = LR_clf.score(poly_X_test,y_test)\n",
    "    Lasso_R2_test_score = Lasso_clf.score(poly_X_test,y_test)\n",
    "    \n",
    "    return (LinearRegression_R2_test_score,Lasso_R2_test_score)\n",
    "answer_four()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Classification\n",
    "\n",
    "Here's an application of machine learning that could save your life! For this section of the assignment we will be working with the [UCI Mushroom Data Set](http://archive.ics.uci.edu/ml/datasets/Mushroom?ref=datanews.io) stored in `mushrooms.csv`. The data will be used to train a model to predict whether or not a mushroom is poisonous. The following attributes are provided:\n",
    "\n",
    "*Attribute Information:*\n",
    "\n",
    "1. cap-shape: bell=b, conical=c, convex=x, flat=f, knobbed=k, sunken=s \n",
    "2. cap-surface: fibrous=f, grooves=g, scaly=y, smooth=s \n",
    "3. cap-color: brown=n, buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y \n",
    "4. bruises?: bruises=t, no=f \n",
    "5. odor: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, none=n, pungent=p, spicy=s \n",
    "6. gill-attachment: attached=a, descending=d, free=f, notched=n \n",
    "7. gill-spacing: close=c, crowded=w, distant=d \n",
    "8. gill-size: broad=b, narrow=n \n",
    "9. gill-color: black=k, brown=n, buff=b, chocolate=h, gray=g, green=r, orange=o, pink=p, purple=u, red=e, white=w, yellow=y \n",
    "10. stalk-shape: enlarging=e, tapering=t \n",
    "11. stalk-root: bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z, rooted=r, missing=? \n",
    "12. stalk-surface-above-ring: fibrous=f, scaly=y, silky=k, smooth=s \n",
    "13. stalk-surface-below-ring: fibrous=f, scaly=y, silky=k, smooth=s \n",
    "14. stalk-color-above-ring: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y \n",
    "15. stalk-color-below-ring: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y \n",
    "16. veil-type: partial=p, universal=u \n",
    "17. veil-color: brown=n, orange=o, white=w, yellow=y \n",
    "18. ring-number: none=n, one=o, two=t \n",
    "19. ring-type: cobwebby=c, evanescent=e, flaring=f, large=l, none=n, pendant=p, sheathing=s, zone=z \n",
    "20. spore-print-color: black=k, brown=n, buff=b, chocolate=h, green=r, orange=o, purple=u, white=w, yellow=y \n",
    "21. population: abundant=a, clustered=c, numerous=n, scattered=s, several=v, solitary=y \n",
    "22. habitat: grasses=g, leaves=l, meadows=m, paths=p, urban=u, waste=w, woods=d\n",
    "\n",
    "<br>\n",
    "\n",
    "The data in the mushrooms dataset is currently encoded with strings. These values will need to be encoded to numeric to work with sklearn. We'll use pd.get_dummies to convert the categorical variables into indicator variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "mush_df = pd.read_csv('mushrooms.csv')\n",
    "mush_df2 = pd.get_dummies(mush_df)\n",
    "X_mush = mush_df2.iloc[:,2:]\n",
    "y_mush = mush_df2.iloc[:,1]\n",
    "# use the variables X_train2, y_train2 for Question 5\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X_mush, y_mush, random_state=0)\n",
    "\n",
    "# For performance reasons in Questions 6 and 7, we will create a smaller version of the\n",
    "# entire mushroom dataset for use in those questions.  For simplicity we'll just re-use\n",
    "# the 25% test split created above as the representative subset.\n",
    "#\n",
    "# Use the variables X_subset, y_subset for Questions 6 and 7.\n",
    "X_subset = X_test2\n",
    "y_subset = y_test2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "Using `X_train2` and `y_train2` from the preceeding cell, train a DecisionTreeClassifier with default parameters and random_state=0. What are the 5 most important features found by the decision tree?\n",
    "\n",
    "As a reminder, the feature names are available in the `X_train2.columns` property, and the order of the features in `X_train2.columns` matches the order of the feature importance values in the classifier's `feature_importances_` property. \n",
    "\n",
    "*This function should return a list of length 5 containing the feature names in descending order of importance.*\n",
    "\n",
    "*Note: remember that you also need to set random_state in the DecisionTreeClassifier.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['odor_n', 'stalk-root_c', 'stalk-root_r', 'spore-print-color_r', 'odor_l']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_five():\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "    clf = DecisionTreeClassifier(random_state=0).fit(X_train2,y_train2)\n",
    "    dic = { key:value for key,value in zip(X_train2.columns,clf.feature_importances_)}\n",
    "    ans =[key for key,_ in sorted(dic.items(),key = lambda item:item[1],reverse=True)[:5]]\n",
    "    return ans\n",
    "answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "For this question, we're going to use the `validation_curve` function in `sklearn.model_selection` to determine training and test scores for a Support Vector Classifier (`SVC`) with varying parameter values.  Recall that the validation_curve function, in addition to taking an initialized unfitted classifier object, takes a dataset as input and does its own internal train-test splits to compute results.\n",
    "\n",
    "**Because creating a validation curve requires fitting multiple models, for performance reasons this question will use just a subset of the original mushroom dataset: please use the variables X_subset and y_subset as input to the validation curve function (instead of X_mush and y_mush) to reduce computation time.**\n",
    "\n",
    "The initialized unfitted classifier object we'll be using is a Support Vector Classifier with radial basis kernel.  So your first step is to create an `SVC` object with default parameters (i.e. `kernel='rbf', C=1`) and `random_state=0`. Recall that the kernel width of the RBF kernel is controlled using the `gamma` parameter.  \n",
    "\n",
    "With this classifier, and the dataset in X_subset, y_subset, explore the effect of `gamma` on classifier accuracy by using the `validation_curve` function to find the training and test scores for 6 values of `gamma` from `0.0001` to `10` (i.e. `np.logspace(-4,1,6)`). Recall that you can specify what scoring metric you want validation_curve to use by setting the \"scoring\" parameter.  In this case, we want to use \"accuracy\" as the scoring metric.\n",
    "\n",
    "For each level of `gamma`, `validation_curve` will fit 3 models on different subsets of the data, returning two 6x3 (6 levels of gamma x 3 fits per level) arrays of the scores for the training and test sets.\n",
    "\n",
    "Find the mean score across the three models for each level of `gamma` for both arrays, creating two arrays of length 6, and return a tuple with the two arrays.\n",
    "\n",
    "e.g.\n",
    "\n",
    "if one of your array of scores is\n",
    "\n",
    "    array([[ 0.5,  0.4,  0.6],\n",
    "           [ 0.7,  0.8,  0.7],\n",
    "           [ 0.9,  0.8,  0.8],\n",
    "           [ 0.8,  0.7,  0.8],\n",
    "           [ 0.7,  0.6,  0.6],\n",
    "           [ 0.4,  0.6,  0.5]])\n",
    "       \n",
    "it should then become\n",
    "\n",
    "    array([ 0.5,  0.73333333,  0.83333333,  0.76666667,  0.63333333, 0.5])\n",
    "\n",
    "*This function should return one tuple of numpy arrays `(training_scores, test_scores)` where each array in the tuple has shape `(6,)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.56647847,  0.93155951,  0.99039881,  1.        ,  1.        ,  1.        ]),\n",
       " array([ 0.56768547,  0.92959558,  0.98965952,  1.        ,  0.99507994,\n",
       "         0.52240279]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.model_selection import validation_curve\n",
    "\n",
    "    clf = SVC(random_state = 0)\n",
    "    val_score = validation_curve(clf,X_subset,y_subset,'gamma',np.logspace(-4,1,6),n_jobs=-1,scoring='accuracy')\n",
    "    training_scores, test_scores = val_score[0].mean(axis=1),val_score[1].mean(axis=1)\n",
    "\n",
    "    return (training_scores,test_scores)\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "\n",
    "Based on the scores from question 6, what gamma value corresponds to a model that is underfitting (and has the worst test set accuracy)? What gamma value corresponds to a model that is overfitting (and has the worst test set accuracy)? What choice of gamma would be the best choice for a model with good generalization performance on this dataset (high accuracy on both training and test set)? \n",
    "\n",
    "Hint: Try plotting the scores from question 6 to visualize the relationship between gamma and accuracy. Remember to comment out the import matplotlib line before submission.\n",
    "\n",
    "*This function should return one tuple with the degree values in this order: `(Underfitting, Overfitting, Good_Generalization)` Please note there is only one correct solution.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0001, 10.0, 0.10000000000000001)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_seven():\n",
    "    \n",
    "    return (np.logspace(-4,1,6)[0],np.logspace(-4,1,6)[5],np.logspace(-4,1,6)[3])\n",
    "answer_seven()"
   ]
  }
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