{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n\n# Hierarchical Clustering\n\nEstimated time needed: **25** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Use scikit-learn to do Hierarchical clustering\n*   Create dendograms to visualize the clustering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h1>Table of contents</h1>\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n    <ol>\n        <li><a href=\"https://#hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</a></li>\n            <ol>\n                <li><a href=\"https://#generating_data\">Generating Random Data</a></li>\n                <li><a href=\"https://#agglomerative_clustering\">Agglomerative Clustering</a></li>\n                <li><a href=\"https://#dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</a></li>\n            </ol>            \n        <li><a href=\"https://#clustering_vehicle_dataset\">Clustering on the Vehicle Dataset</a></li>\n            <ol>\n                <li><a href=\"https://#data_cleaning\">Data Cleaning</a></li>\n                <li><a href=\"https://#clustering_using_scipy\">Clustering Using Scipy</a></li>\n                <li><a href=\"https://#clustering_using_skl\">Clustering using scikit-learn</a></li>\n            </ol>\n    </ol>\n</div>\n<br>\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h1 id=\"hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</h1>\n\nWe will be looking at a clustering technique, which is <b>Agglomerative Hierarchical Clustering</b>. Remember that agglomerative is the bottom up approach. <br> <br>\nIn this lab, we will be looking at Agglomerative clustering, which is more popular than Divisive clustering. <br> <br>\nWe will also be using Complete Linkage as the Linkage Criteria. <br> <b> <i> NOTE: You can also try using Average Linkage wherever Complete Linkage would be used to see the difference! </i> </b>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['scipy'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import numpy as np \nimport pandas as pd\nfrom scipy import ndimage \nfrom scipy.cluster import hierarchy \nfrom scipy.spatial import distance_matrix \nfrom matplotlib import pyplot as plt \nfrom sklearn import manifold, datasets \nfrom sklearn.cluster import AgglomerativeClustering \nfrom sklearn.datasets import make_blobs \n%matplotlib inline",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n<h3 id=\"generating_data\">Generating Random Data</h3>\nWe will be generating a set of data using the <b>make_blobs</b> class. <br> <br>\nInput these parameters into make_blobs:\n<ul>\n    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n    <ul> <li> Choose a number from 10-1500 </li> </ul>\n    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n    <ul> <li> Choose arrays of x,y coordinates for generating the centers. Have 1-10 centers (ex. centers=[[1,1], [2,5]]) </li> </ul>\n    <li> <b>cluster_std</b>: The standard deviation of the clusters. The larger the number, the further apart the clusters</li>\n    <ul> <li> Choose a number between 0.5-1.5 </li> </ul>\n</ul> <br>\nSave the result to <b>X1</b> and <b>y1</b>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X1, y1 = make_blobs(n_samples=50, centers=[[4,4], [-2, -1], [1, 1], [10,4]], cluster_std=0.9)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Plot the scatter plot of the randomly generated data.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(X1[:, 0], X1[:, 1], marker='o') ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
        {
          "execution_count": 5,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x51a7698>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n<h3 id=\"agglomerative_clustering\">Agglomerative Clustering</h3>\n\nWe will start by clustering the random data points we just created.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The <b> Agglomerative Clustering </b> class will require two inputs:\n\n<ul>\n    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n    <ul> <li> Value will be: 4 </li> </ul>\n    <li> <b>linkage</b>: Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. </li>\n    <ul> \n        <li> Value will be: 'complete' </li> \n        <li> <b>Note</b>: It is recommended you try everything with 'average' as well </li>\n    </ul>\n</ul> <br>\nSave the result to a variable called <b> agglom </b>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "agglom = AgglomerativeClustering(n_clusters = 4, linkage = 'average')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Fit the model with <b> X2 </b> and <b> y2 </b> from the generated data above.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "agglom.fit(X1,y1)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "AgglomerativeClustering(linkage='average', n_clusters=4)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Run the following code to show the clustering! <br>\nRemember to read the code and comments to gain more understanding on how the plotting works.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Create a figure of size 6 inches by 4 inches.\nplt.figure(figsize=(6,4))\n\n# These two lines of code are used to scale the data points down,\n# Or else the data points will be scattered very far apart.\n\n# Create a minimum and maximum range of X1.\nx_min, x_max = np.min(X1, axis=0), np.max(X1, axis=0)\n\n# Get the average distance for X1.\nX1 = (X1 - x_min) / (x_max - x_min)\n\n# This loop displays all of the datapoints.\nfor i in range(X1.shape[0]):\n    # Replace the data points with their respective cluster value \n    # (ex. 0) and is color coded with a colormap (plt.cm.spectral)\n    plt.text(X1[i, 0], X1[i, 1], str(y1[i]),\n             color=plt.cm.nipy_spectral(agglom.labels_[i] / 10.),\n             fontdict={'weight': 'bold', 'size': 9})\n    \n# Remove the x ticks, y ticks, x and y axis\nplt.xticks([])\nplt.yticks([])\n#plt.axis('off')\n\n\n\n# Display the plot of the original data before clustering\nplt.scatter(X1[:, 0], X1[:, 1], marker='.')\n# Display the plot\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5d2a8f0>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 600x400 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3 id=\"dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</h3>\n\nRemember that a <b>distance matrix</b> contains the <b> distance from each point to every other point of a dataset </b>.\n\nUse the function <b> distance_matrix, </b> which requires <b>two inputs</b>. Use the Feature Matrix, <b> X1 </b> as both inputs and save the distance matrix to a variable called <b> dist_matrix </b> <br> <br>\nRemember that the distance values are symmetric, with a diagonal of 0's. This is one way of making sure your matrix is correct. <br> (print out dist_matrix to make sure it's correct)\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "dist_matrix = distance_matrix(X1,X1) \nprint(dist_matrix)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "name": "stdout",
          "text": "[[0.         0.70450561 0.71661341 ... 0.10095925 0.45597444 0.4007204 ]\n [0.70450561 0.         0.03092438 ... 0.68564387 0.24922709 0.77172553]\n [0.71661341 0.03092438 0.         ... 0.69362623 0.26067156 0.76740004]\n ...\n [0.10095925 0.68564387 0.69362623 ... 0.         0.43775359 0.30127273]\n [0.45597444 0.24922709 0.26067156 ... 0.43775359 0.         0.56292054]\n [0.4007204  0.77172553 0.76740004 ... 0.30127273 0.56292054 0.        ]]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Using the <b> linkage </b> class from hierarchy, pass in the parameters:\n\n<ul>\n    <li> The distance matrix </li>\n    <li> 'complete' for complete linkage </li>\n</ul> <br>\nSave the result to a variable called <b> Z </b>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Z = hierarchy.linkage(dist_matrix, 'complete')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-10-3814b774a052>:1: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n  Z = hierarchy.linkage(dist_matrix, 'complete')\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "A Hierarchical clustering is typically visualized as a dendrogram as shown in the following cell. Each merge is represented by a horizontal line. The y-coordinate of the horizontal line is the similarity of the two clusters that were merged, where cities are viewed as singleton clusters.\nBy moving up from the bottom layer to the top node, a dendrogram allows us to reconstruct the history of merges that resulted in the depicted clustering.\n\nNext, we will save the dendrogram to a variable called <b>dendro</b>. In doing this, the dendrogram will also be displayed.\nUsing the <b> dendrogram </b> class from hierarchy, pass in the parameter:\n\n<ul> <li> Z </li> </ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "dendro = hierarchy.dendrogram(Z)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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nX3w+n9VpuoFgAQDocFRVpi+bLpsPbHZtHpVvVFqd3sjeI2X+5fM9Hy74KgQA0OE0NTe5GircsKluk/VeEDfQYwEA6NBqrqmR/Jz8dFcjpqbmJuu9H24iWAAAOrT8nHwp6FKQ7mpkDYIFAFdl2gC5oEwZKAd4DcECgGsycYBcUKYMlAO8hmCB7KYqcvyY+/P57lj0n93UpUDE441eJg6QCwoOlKOLHHDGcbBYtWqVPP7447JhwwbZu3evLFmyRKZOnepC1YB2UhV5+TKR2jWpne8TZ6ZmPiVjRW5e7vlwEeT1AXJBmTZQDvAax8GisbFRhg8fLjfffLP86Ec/cqNOgB3Hj6U+VKRS7eqTy5h7SrprYoQBckDH4DhYTJkyRaZMmeJGXQD33LNDJDdLGrXvjqWuVwQAHHJ9jEUgEJBAIBD6vaGhwe1ZAm3lFnj7P/tkx4I4Gc+RAWMyAGQ+14PFnDlzZPbs2W7PBshc7RkL4qTnIsPGZADITK7f0nvWrFlSX18fetXW1ro9SyCzpGosSHBMBgC4yPUeC7/fL36/3+3ZANnBjbEgjMkAkELcxwLwEq+PBQGABBwHi6NHj8qOHTtCv+/atUs2b94s3bt3l/79+1utHAAAyCyOg8X69etlwoQJod9nzpwpIiI33nijzJs3z1rFOqxkrg6wcddHrhgAAFjgOFhUVlaKqrpRF9i4U2Sy36VzxQAAwALXrwqBA+m8UyRXDAAALGDwplel6k6RXDEAALCIYOFVXB0AAFlLVaWpucmobHg5088E5efkiy/FX3ETLAAASCFVlenLpsvmA5sdf9bpk3dH9h4p8y+fn9JwwRgLAABSqKm5KalQkYxNdZsc93K0Fz0WAACkSc01NZKfk299uk3NTY57N2whWAAAkCb5OflS0CUFA/VTiGABAFnKyQDBRNozgDCRdAwwhHsIFnBX6zuJxrtLKHf/BKxpzwDBRGx3sadjgCHcQ7CAexLdSbT1/TO4+ydgTSoHCLZXcIBhtn0l0FF5N1jwzIzM5/ROosG7f3L/DiTBVre/G13+6e7qd2uAYHulc4Ah3OPNYMEzM7JPvDuJcvdPtJNb3f62Gr10d/Vn4wBBeJc3g4UXnpnBf812cSdRuMjr3f509aMj8WawCMczMwA44KVuf7r60RF5P1jwny4AB+j2B9LL+8ECAACHEg3mdTJIN92DbzMNwQKZw+mVQsleJcSVQUBGczqYN9HXVekefJtpCBbIDO29UsjJ+BmuDAIymu3BvAy+dYZggcyQyiuFuDIIyBrtGczL4NvkECyQedy6Uogrg4Csw2De1CNYIPNwpRAAeFandFcAAABkD4IFAACwhmABAACsIVgAAABrCBYAAMAaggUAALCGy00BAK6K9dwOk+d18JyOthI9B0XE2bNQROyuZ4IFAMA1ps/tiHWHS57TEcnpc1BEEj8LRcTueuarEACAa9r73I7gczpwku3noATZXM8do8fC5KmYTp+EmewTMOPVxaQOtudrutw88dO7Eu3fbGN4hJPndvCcjsTa8xyUIDfWc/YHi2SeimnyvIhknoDppC6x6uDmfOMtN0/8tKd1EIjX8Cdq7J3u32xjpBHP7bDLq+sz+4OFW0/FTOYJmDbqkknzRVuJgkDrhj9RY29z/2Ybt1vrQXXxBtAlO1jOZOBeonnHwkBJ2OCNYGH6H1x7u2ptPBXT1hMwndYl0+eLk5wGASeNfbL7t0e3sWkDKuKNRjTRoLrW3c3JDJZLZuBetHnHwkBJ2JD+YOHkP7j2dtV66amY6aqLl9ZBKnlpnE1QvCCQTGOfRds22QZUJH2NqNNBdcHBck66st0auNeeOgGtpT9YOPkPjq5aJMNL42zCZVEQsM3tBlTE3UY03qA6W4PlbAzcC2KgJGxKf7AIF+s/OI921SJDeGmcDRyz2YCKpKYRTcWgOq8O3AO8FSz4Dw5u89I4GxihAQUyi7eCBeA2wisAuIo7bwIAAGsIFgAAwBq+CknE5l0SM0FHW14AgFUEi3hs3yXR6zra8gIArCNYxOPmXRK9qKMtb0fDg+iADi8Vt50nWJiyfZdEr+toy5vteBAd0OGl4rbzIgQLcx3tMsWOtrzZroM9iC7ec0ZMnivCw7g6jo60r6TitvMiBAvvYhAl3JLlD6Jz8pyRWHfgzNaHccVqRE0f4pZJjaiJjryvuHnbeYKFFzGI0luyLeRleW+UjeeMZOPDuEwb0XgNSqY2orFkwr7i1pgIN+9oS7DwIgZRegchL6M5fc5INj+MKxMa0XTy4r6SqjERthEsvI5BlM7Z7GEg5GU0njMSnRcb0XTz4r6SqjERthEsvC7Lu62tc7OHgZDnmlRcAof/8WIjivjcHBNhG8EC2cXNHgZCnisytbsXdhEu48ukMEiwQPaihyEjZGp3L+whXGaX7AsW2TaCH8mjhyE5aTyGMqm7F/YQLrNLdgULRvAD7ZPmYyiTunvhDsJl5suuYMEIfqB9OIY8pSOOOyBcZr7sChbh+H4daJ8OcAx5ueFm3IG3eHlf8ZrsDRZ8vw60T5YfQ15vuNM57oBGNJLX9xWvyd5gAQBxZNKAwVSOO6ARbSuT9hUvIFgA6PC8PmAwleMOaETj8/q+4gUECwAdHgMGo6MRbYt9JbFOyXzoD3/4g5SWlkpeXp6MGTNG1q5da7teAIA0Czai0V5OnjWCjsVxsFi4cKHMnDlTHnzwQdm4caMMHz5cLrvsMqmrq3OjfgAAIIM4DhZPPfWU3HbbbVJVVSVDhgyR559/XgoKCuTll192o34AACCDOBpj8d1338mGDRtk1qxZofc6deokkyZNko8++ijqZwKBgAQCgdDv9fX1IiLS0NDw34k2igRU/vumSG5LlBkblKFcdpfzct0oF7PcsePHpKWp5b/FGqS5S3NSZSiX3eW8XDfK/a9csN1W1aifC1EHvvzySxUR/fDDDyPe/9WvfqUVFRVRP/Pggw+qiPDixYsXL168suBVW1sbNyu4flXIrFmzZObMmaHfT5w4IYcOHZIePXpk9XXPAABkE1WVI0eOSL9+/eKWcxQsevbsKZ07d5b9+/dHvL9//37p06dP1M/4/X7x+/0R75166qlOZgsAADygqKgoYRlHgzdzc3Nl9OjRUl1dHXrvxIkTUl1dLePGjXNeQwAAkFUcfxUyc+ZMufHGG+W8886TiooKefrpp6WxsVGqqqrcqB8AAMggjoPFtddeKwcOHJDf/OY3sm/fPhkxYoQsX75cTj/9dDfqBwAAMohPE143AgAAYCapW3oDAABEQ7AAAADWECwAAIA1BAsAAGBN2oPFgQMHZOXKlbJ37950VyUr7dy5U2pqaqSmpkZ27twZs9zmzZvlr3/9qyxdujRuuY6isbFRmptP3h//0KFDUl1dLV988UWaa4Vsd/jw4XRXIa1eeOEFq9P75ptvrE4Phpw8K8SGadOm6f79+1VVtbq6Wnv27Knl5eXaq1cvXbJkSaqrk7U++eQTLS8v1z59+mhFRYVWVFRonz59tLy8XLdt2xYqt2XLFh06dKgWFhZqp06ddOjQoXraaafpT37yE62vr0/jEqTP/PnzNS8vT4uLi7W6ulr79u2r5eXl2qNHD12wYEG6q4cs1qVLF73yyiv1rbfe0paWlnRXx1Vvvvlmm9fpp58e+tmpTZs26fDhw3XkyJG6bds2veKKKzQ/P19LSkp0y5YtLizBSTt27NDKykodOHCg/vKXv9SmpqbQ38aOHet4em+88Ubo5wMHDugVV1yhhYWFevHFF+t//vMfK3V2W8qDxbBhw0I/X3TRRbpp0yZVVd25c6eOGDEiomxzc7NWV1frK6+8oq+88opWV1drc3Nz0vP+9NNPQ6Hm008/1RdffFFXr14dtWy0g/rQoUMxp71161Z96aWXdN26dRHvf/zxx0nX19QDDzzQ5r2KigpdvHhxm/cXLVqk5eXlod/HjRun77//vqqePNDvvPNODQQCev/99+v06dPbVa9Y6ySer7/+OuL3b7/9VpcsWaK/+93v9Pe//72uXLmyzWdM17FpuXPPPVd3796tW7Zs0aKiolD9t2/fHrH/OmGyHKrJ7fPRtn97y0bb102XIdzx48d148aNevjwYeM6mmq9r5hych4IF+/4dyrWsTFo0CB94okn9Oyzz9a+ffvqvffeq5999lmbz7t5LnOyzZyuk/Bt5vP5dPz48VpZWRl65eXlaWVlpU6YMMHRdFVPtidLlizRV155Rfv376+vvvqqqqouWbJEL730UsfTi+ess84K/Tx58mR99tlndf369Tpt2jQdP368NjQ0qKq2adNUE2+PkSNHhn6+9dZb9d5779W9e/fqk08+qVOnTo1aH1vHma02N+XBInyDnHfeeRF/O/fcc0M/r1q1SouLi3XMmDF6zTXX6DXXXKMVFRVaXFys7733XpvpJlohv/3tb7V3795aUlKir732mpaUlOjVV1+t/fv316effjpUbt26dVpaWqq5ubk6depUraurC/0tfINPnDgxdGAvXLhQ+/Xrp1dffbUOGDBAn3/++VA5n8+nw4YN02eeeUYPHjwYc72YNnrPPPNMm1ePHj1CPwcNGjQo5jTC/zZ8+PCIv4Vvk/BtFRTvoDBdJ/GUlJSEfl65cqX2799fhw0bpn6/XydNmqRnn322lpeX6xdffBEqZ7qOTcuFnwwGDBgQ82/RRGu4TZfDZJ833f5Oyobv/zt37tQhQ4ZoXl6elpaW6tatWx0tQ3V1tXbv3l179OihNTU1Wl5ermVlZaHfTZmEhvB9RdUs+JieB0zWSSzRGlvTYyP8HPPPf/5Tb7nlFu3WrZteeOGFOn/+fEfLYHouM91m7VknQeHb7OWXX9bx48frxo0bQ++VlpYaTSdaQxp+bLbeN1qf50yC2ZYtW2K++vTpE3W+qqqPPvqolpeX6+HDhyPWs+n2CJ/esGHDItqx4D827TnOYoVap21uPCkPFnfccYfeddddeuTIEb3vvvv0z3/+s544cUL//ve/R6TUc889N+p/umvXrtWhQ4dGvGeyQoYMGaKHDh3SPXv2aEFBge7cuVNVT3Y1nXPOOaFpXXDBBbp06VL9+uuv9YEHHtCysrLQiTN8g4eHoLFjx+ru3btVVfXgwYMRfxs6dKguXrxYp0yZoqeccopee+21+u6777ZZLtNGr3PnznrllVfqTTfdFHp17dpVb7rpJq2qqgqVGz9+vL766qsRQaClpUXnzZun48aNC7133nnn6aeffqqqqh999JFefPHFob+VlZWFfjY5KEzXSbQu0OCrV69eEdP997//raont/u0adNUVfWPf/yjXnXVVY7XsWm5UaNG6bZt2/T999/Xnj176gcffKCqJ09A4cth2nCbLofJPm+6/Z2UDT+pXXfddfrss8+qqurixYt10qRJjpahoqJCN23apP/4xz+0R48eWl1draqqa9as0QsuuKDNssUSbBhM9xXT4GN6HjBZJ6rmja3psRE+36CjR4/qiy++qOeff76jZTA9l5luM9N1YrrNVFV3796tkyZN0tmzZ2tzc7MOHDiwzfKrmjWk4eGhdW9reE+jaTDz+Xw6cOBALS0tbfPq0qVLqNzgwYPb1Pfxxx/X0aNH65lnnhl6z3R7lJWV6datW3XLli1tAlHwdyfHmWmoddLmJpLyYBEIBPTuu+/WwsJCLS0tVZ/Ppzk5OXrZZZeFDhDV6P8tx/qbyQoJPyj69+8fUS58o7ZOn6+99poOGjRI9+zZEzGNQYMGhZLkmDFjIj4TvhHCP7Nnzx59+OGH9YwzztABAwbo7NmzIz5j0uhVV1drRUWF/u1vfwu9Fy3lb9++XSdOnKhFRUVaVlamZWVlWlRUpBMmTIjoWl22bJn26NFDBw8erD179gwdpHv37tXbbrstVM7koDBdJ506ddIJEyZEdIGGd4UGtT6oRo0aFfo5vNfFdB2blnv77be1e/fu2rNnT12xYoVWVlbq4MGDtbCwMGKMhWnDbbocJvu86fZ3UjZ8vbT+qidYd9NlCD9+vve970V8pvWxZdIAme4rpsHH9Dxgsk5al4vX2JoeG4l6xJwsg+m5zHSbma4T020WdOLECX3iiSd03Lhx2q9fv6jLbNKQTp48Oeq4sK+++korKipCv5sGs9LSUv3yyy+j1qe4uDj089SpU3XZsmVtyjz55JPq8/lCv5tujwEDBkQEmtraWlXViB4QJ8eZaah10uYmkvJgEdTY2Khbt27VjRs3Ru32vPzyy3X27NmhpKWqun//fn3ooYd08uTJEWVNVsjYsWN16dKl+tprr+mAAQNCDcTKlSt19OjRofKDBg1q092/YMECPeussyIO4gcffFB//OMf6/bt2/Xxxx/XRx55RHft2qXPPfecXnnllaFy0f4DUVVdsWKF/vSnP41aLl6jp6paX1+v06ZN05tuuknr6+tjpnxV1bq6Ot2wYYNu2LAhopch3DfffKPr16+PO1jT5KAwXSeDBw/WXbt2RZ1P+AF7/vnnh7qzFy1apJdffnnob7GCRbh46zheudaam5t1/fr1EfuiqnnDbbocpvu8k+1vUnbgwIH61ltv6Ztvvqlnn312xN+CDYjpMoQ3OPfdd1/EtMJPYqpmDZDpvmIafEzPAybrRNW8sTU9NuL1VjpdBtNzmek2M10nptustW3btuncuXOj/s1JQ9ra4cOHIwY9mgazu+66KzT+rLXbb7899PO3336r3377bdRy4b1lptsjlsbGxlAIcnKcmYZaJ21uImkLFonU1dVpVVWVdu3aVfPy8tTv92vXrl21qqqqzQneZIWsWbNGR4wYoaNGjdLNmzfrddddp36/PyL9qqpWVVVFNBRBCxcujOj+Uj3ZDVpcXKxdunRRn8+nhYWFevvtt0ecHFpvyFiSafQWLVqkI0aMiPi+zy2mB4XJOvm///u/mAM6H3nkkdDPa9eu1eLiYs3Ly9OBAweGupb37t0bEbZM17FpOSdMGu7gcvj9/rjLUVdXpzfffHNon8/Ly4u5z6s62/7xyl588cURjXrwZLh///7QmBvTZaiqqooaUHfs2BHqyg8yaYBM9xXT4BPvPLBixQpH60TVvLFVNTs2TERbhry8vKTPZabbzHSdmG4zJ5w0pImYBjPbnLQtJtMyPc5MQ23rNjfR+ScezwaLcAcPHgy9onF6Qg7av3+/0SVdiUY+NzQ06KFDh2Km7daiDWRMttHbt2+fvv3220l91gmnB0VwnZiKN7gzvEfLdBCoKVsj/RcvXpywkXdyFUNwf0+0T+3bt0+fe+45o2nu27dPX3/9deM6NDc3a2NjY8R74cvgZN0dP3484jI81eQboGjzNQ2h0Xz99dfG54HW68S0sQ0XPDZsXmXy8MMPGy2D6fETXNZjx44lnF5LS0ub/cQ2Jw1pImvXrjUKl5nm+eefj7nNnIbaeO2tCc8Gi+A1ycGBdFdccYUWFBQkvCY51gqJNr3gNc7hg6w2b94c81ro8HKxrsEO/veSqFyy12rbvmbaJtO6ma4T03Kff/650XzjbVsb17nHCnmff/65TpgwIen1Er5POdmfTK/rN6mf6fFjui1MmR6PQYnCm+m2CM430fLG0voEn+z5LBrb5572nvOSPR5tiBZYk2EaLtvD5npJtl2J9w+fzbbFs8HC1jXJwTEWptMzLWd6DbaNa7Xbc810KgTrZ1o32+vOdL5ev87dZHmd7E+my2tSP5vTcrL+bGyzZLaF7fna3PdsHz+2z3mpPkc5HViYqmm1ZnO92LoHiFtti2eDhZNrkk2uNzadnmk502uwTcvZvmbaNqfrOF7dbK870/k62adM2N5mJsvr5Nr/ZPb5WPWzOa0gm8et7W3h5nzbu++5efzYOOe5cY4yXc+pnpYTNteLk/NAOtqWnHTfUjwWVQ39PGHChJh/ExEZMWKElJaWtnlfROTgwYOOpmdarqqqSiZOnCi33nqrXHjhhXL//feLz+drM3/TcibLICLS1NQU8bdf//rXkpubK5dccokcOXKkzWdtMamfad1srzvT+TrZp0zY3mYmy2u6TlovU7zlNamfzWkF2TxubW8L2/O1ue/ZPn5sn/PcOEeZrudUT8sJm+vFyXkgLW2LoxiSQqbXJKuaXW9sOj0n81U1uwbbpJzta6ZtM6mf07rZWnem83W6bRNxa5uZrBeTMqbLa1I/m9MKsnnc2t4Wtudre99TtXf82D7nuXGOMl3PqZ6WE26sF5N9IB1ti2eDRSytr0lWNb/e2HR6yZSLdw22STnb10zbZlK/ZOvW3nXX3nViug+05vY2M1kvpusuXOvlbc/6a8+0bB63qTp+kp2v6fSS0d7jJ5Zkz3lunKPau57dmpYTbp67423bdLQtPtUk+oABAACi6JTuCgAAgOxBsAAAANYQLAAAgDUECwAAYA3BAgAAWEOwAAAA1hAsAACANf8PGssKIwVFjD0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## Practice\n\nWe used **complete** linkage for our case, change it to **average** linkage to see how the dendogram changes.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# write your code here\nZ1 = hierarchy.linkage(dist_matrix, 'average')\ndendro1 = hierarchy.dendrogram(Z1)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-12-7f8383b8b602>:2: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n  Z1 = hierarchy.linkage(dist_matrix, 'average')\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nZ = hierarchy.linkage(dist_matrix, 'average')\ndendro = hierarchy.dendrogram(Z)\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n<h1 id=\"clustering_vehicle_dataset\">Clustering on Vehicle dataset</h1>\n\nImagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against.\n\nOur objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Download data\n\nTo download the data, we will use **`!wget`** to download it from IBM Object Storage.\\\n**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%204/data/cars_clus.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "await download(path, 'cars_clus.csv')\nfilename ='cars_clus.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Read data\n\nLet's read dataset to see what features the manufacturer has collected about the existing models.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "filename = 'cars_clus.csv'\n\n#Read csv\npdf = pd.read_csv(filename)\nprint (\"Shape of dataset: \", pdf.shape)\n\npdf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "name": "stdout",
          "text": "Shape of dataset:  (159, 16)\n",
          "output_type": "stream"
        },
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  manufact    model   sales  resale   type   price engine_s horsepow wheelbas  \\\n0    Acura  Integra  16.919  16.360  0.000  21.500    1.800  140.000  101.200   \n1    Acura       TL  39.384  19.875  0.000  28.400    3.200  225.000  108.100   \n2    Acura       CL  14.114  18.225  0.000  $null$    3.200  225.000  106.900   \n3    Acura       RL   8.588  29.725  0.000  42.000    3.500  210.000  114.600   \n4     Audi       A4  20.397  22.255  0.000  23.990    1.800  150.000  102.600   \n\n    width   length curb_wgt fuel_cap     mpg lnsales  partition  \n0  67.300  172.400    2.639   13.200  28.000   2.828        0.0  \n1  70.300  192.900    3.517   17.200  25.000   3.673        0.0  \n2  70.600  192.000    3.470   17.200  26.000   2.647        0.0  \n3  71.400  196.600    3.850   18.000  22.000   2.150        0.0  \n4  68.200  178.000    2.998   16.400  27.000   3.015        0.0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>manufact</th>\n      <th>model</th>\n      <th>sales</th>\n      <th>resale</th>\n      <th>type</th>\n      <th>price</th>\n      <th>engine_s</th>\n      <th>horsepow</th>\n      <th>wheelbas</th>\n      <th>width</th>\n      <th>length</th>\n      <th>curb_wgt</th>\n      <th>fuel_cap</th>\n      <th>mpg</th>\n      <th>lnsales</th>\n      <th>partition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Acura</td>\n      <td>Integra</td>\n      <td>16.919</td>\n      <td>16.360</td>\n      <td>0.000</td>\n      <td>21.500</td>\n      <td>1.800</td>\n      <td>140.000</td>\n      <td>101.200</td>\n      <td>67.300</td>\n      <td>172.400</td>\n      <td>2.639</td>\n      <td>13.200</td>\n      <td>28.000</td>\n      <td>2.828</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acura</td>\n      <td>TL</td>\n      <td>39.384</td>\n      <td>19.875</td>\n      <td>0.000</td>\n      <td>28.400</td>\n      <td>3.200</td>\n      <td>225.000</td>\n      <td>108.100</td>\n      <td>70.300</td>\n      <td>192.900</td>\n      <td>3.517</td>\n      <td>17.200</td>\n      <td>25.000</td>\n      <td>3.673</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Acura</td>\n      <td>CL</td>\n      <td>14.114</td>\n      <td>18.225</td>\n      <td>0.000</td>\n      <td>$null$</td>\n      <td>3.200</td>\n      <td>225.000</td>\n      <td>106.900</td>\n      <td>70.600</td>\n      <td>192.000</td>\n      <td>3.470</td>\n      <td>17.200</td>\n      <td>26.000</td>\n      <td>2.647</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Acura</td>\n      <td>RL</td>\n      <td>8.588</td>\n      <td>29.725</td>\n      <td>0.000</td>\n      <td>42.000</td>\n      <td>3.500</td>\n      <td>210.000</td>\n      <td>114.600</td>\n      <td>71.400</td>\n      <td>196.600</td>\n      <td>3.850</td>\n      <td>18.000</td>\n      <td>22.000</td>\n      <td>2.150</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Audi</td>\n      <td>A4</td>\n      <td>20.397</td>\n      <td>22.255</td>\n      <td>0.000</td>\n      <td>23.990</td>\n      <td>1.800</td>\n      <td>150.000</td>\n      <td>102.600</td>\n      <td>68.200</td>\n      <td>178.000</td>\n      <td>2.998</td>\n      <td>16.400</td>\n      <td>27.000</td>\n      <td>3.015</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "The feature sets include  price in thousands (price), engine size (engine_s), horsepower (horsepow), wheelbase (wheelbas), width (width), length (length), curb weight (curb_wgt), fuel capacity (fuel_cap) and fuel efficiency (mpg).\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"data_cleaning\">Data Cleaning</h2>\n\nLet's clean the dataset by dropping the rows that have null value:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print (\"Shape of dataset before cleaning: \", pdf.size)\npdf[[ 'sales', 'resale', 'type', 'price', 'engine_s',\n       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n       'mpg', 'lnsales']] = pdf[['sales', 'resale', 'type', 'price', 'engine_s',\n       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n       'mpg', 'lnsales']].apply(pd.to_numeric, errors='coerce')\npdf = pdf.dropna()\npdf = pdf.reset_index(drop=True)\nprint (\"Shape of dataset after cleaning: \", pdf.size)\npdf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "Shape of dataset before cleaning:  2544\nShape of dataset after cleaning:  1872\n",
          "output_type": "stream"
        },
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n\n   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \n0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0  \n1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0  \n2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0  \n3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0  \n4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>manufact</th>\n      <th>model</th>\n      <th>sales</th>\n      <th>resale</th>\n      <th>type</th>\n      <th>price</th>\n      <th>engine_s</th>\n      <th>horsepow</th>\n      <th>wheelbas</th>\n      <th>width</th>\n      <th>length</th>\n      <th>curb_wgt</th>\n      <th>fuel_cap</th>\n      <th>mpg</th>\n      <th>lnsales</th>\n      <th>partition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Acura</td>\n      <td>Integra</td>\n      <td>16.919</td>\n      <td>16.360</td>\n      <td>0.0</td>\n      <td>21.50</td>\n      <td>1.8</td>\n      <td>140.0</td>\n      <td>101.2</td>\n      <td>67.3</td>\n      <td>172.4</td>\n      <td>2.639</td>\n      <td>13.2</td>\n      <td>28.0</td>\n      <td>2.828</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acura</td>\n      <td>TL</td>\n      <td>39.384</td>\n      <td>19.875</td>\n      <td>0.0</td>\n      <td>28.40</td>\n      <td>3.2</td>\n      <td>225.0</td>\n      <td>108.1</td>\n      <td>70.3</td>\n      <td>192.9</td>\n      <td>3.517</td>\n      <td>17.2</td>\n      <td>25.0</td>\n      <td>3.673</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Acura</td>\n      <td>RL</td>\n      <td>8.588</td>\n      <td>29.725</td>\n      <td>0.0</td>\n      <td>42.00</td>\n      <td>3.5</td>\n      <td>210.0</td>\n      <td>114.6</td>\n      <td>71.4</td>\n      <td>196.6</td>\n      <td>3.850</td>\n      <td>18.0</td>\n      <td>22.0</td>\n      <td>2.150</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Audi</td>\n      <td>A4</td>\n      <td>20.397</td>\n      <td>22.255</td>\n      <td>0.0</td>\n      <td>23.99</td>\n      <td>1.8</td>\n      <td>150.0</td>\n      <td>102.6</td>\n      <td>68.2</td>\n      <td>178.0</td>\n      <td>2.998</td>\n      <td>16.4</td>\n      <td>27.0</td>\n      <td>3.015</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Audi</td>\n      <td>A6</td>\n      <td>18.780</td>\n      <td>23.555</td>\n      <td>0.0</td>\n      <td>33.95</td>\n      <td>2.8</td>\n      <td>200.0</td>\n      <td>108.7</td>\n      <td>76.1</td>\n      <td>192.0</td>\n      <td>3.561</td>\n      <td>18.5</td>\n      <td>22.0</td>\n      <td>2.933</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Feature selection\n\nLet's select our feature set:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "featureset = pdf[['engine_s',  'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap', 'mpg']]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Normalization\n\nNow we can normalize the feature set. **MinMaxScaler** transforms features by scaling each feature to a given range. It is by default (0, 1). That is, this estimator scales and translates each feature individually such that it is between zero and one.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.preprocessing import MinMaxScaler\nx = featureset.values #returns a numpy array\nmin_max_scaler = MinMaxScaler()\nfeature_mtx = min_max_scaler.fit_transform(x)\nfeature_mtx [0:5]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[0.11428571, 0.21518987, 0.18655098, 0.28143713, 0.30625832,\n        0.2310559 , 0.13364055, 0.43333333],\n       [0.31428571, 0.43037975, 0.3362256 , 0.46107784, 0.5792277 ,\n        0.50372671, 0.31797235, 0.33333333],\n       [0.35714286, 0.39240506, 0.47722343, 0.52694611, 0.62849534,\n        0.60714286, 0.35483871, 0.23333333],\n       [0.11428571, 0.24050633, 0.21691974, 0.33532934, 0.38082557,\n        0.34254658, 0.28110599, 0.4       ],\n       [0.25714286, 0.36708861, 0.34924078, 0.80838323, 0.56724368,\n        0.5173913 , 0.37788018, 0.23333333]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"clustering_using_scipy\">Clustering using Scipy</h2>\n\nIn this part we use Scipy package to cluster the dataset.\n\nFirst, we calculate the distance matrix.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import scipy\nleng = feature_mtx.shape[0]\nD = scipy.zeros([leng,leng])\nfor i in range(leng):\n    for j in range(leng):\n        D[i,j] = scipy.spatial.distance.euclidean(feature_mtx[i], feature_mtx[j])\nD",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-19-2630f2af58dc>:3: DeprecationWarning: scipy.zeros is deprecated and will be removed in SciPy 2.0.0, use numpy.zeros instead\n  D = scipy.zeros([leng,leng])\n",
          "output_type": "stream"
        },
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[0.        , 0.57777143, 0.75455727, ..., 0.28530295, 0.24917241,\n        0.18879995],\n       [0.57777143, 0.        , 0.22798938, ..., 0.36087756, 0.66346677,\n        0.62201282],\n       [0.75455727, 0.22798938, 0.        , ..., 0.51727787, 0.81786095,\n        0.77930119],\n       ...,\n       [0.28530295, 0.36087756, 0.51727787, ..., 0.        , 0.41797928,\n        0.35720492],\n       [0.24917241, 0.66346677, 0.81786095, ..., 0.41797928, 0.        ,\n        0.15212198],\n       [0.18879995, 0.62201282, 0.77930119, ..., 0.35720492, 0.15212198,\n        0.        ]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "In agglomerative clustering, at each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster with the remaining clusters in the forest.\nThe following methods are supported in Scipy for calculating the distance between the newly formed cluster and each:\n\\- single\n\\- complete\n\\- average\n\\- weighted\n\\- centroid\n\nWe use **complete** for our case, but feel free to change it to see how the results change.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pylab\nimport scipy.cluster.hierarchy\nZ = hierarchy.linkage(D, 'complete')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-20-8655000d21de>:3: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n  Z = hierarchy.linkage(D, 'complete')\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Essentially, Hierarchical clustering does not require a pre-specified number of clusters. However, in some applications we want a partition of disjoint clusters just as in flat clustering.\nSo you can use a cutting line:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from scipy.cluster.hierarchy import fcluster\nmax_d = 3\nclusters = fcluster(Z, max_d, criterion='distance')\nclusters",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 1,  5,  5,  6,  5,  4,  6,  5,  5,  5,  5,  5,  4,  4,  5,  1,  6,\n        5,  5,  5,  4,  2, 11,  6,  6,  5,  6,  5,  1,  6,  6, 10,  9,  8,\n        9,  3,  5,  1,  7,  6,  5,  3,  5,  3,  8,  7,  9,  2,  6,  6,  5,\n        4,  2,  1,  6,  5,  2,  7,  5,  5,  5,  4,  4,  3,  2,  6,  6,  5,\n        7,  4,  7,  6,  6,  5,  3,  5,  5,  6,  5,  4,  4,  1,  6,  5,  5,\n        5,  6,  4,  5,  4,  1,  6,  5,  6,  6,  5,  5,  5,  7,  7,  7,  2,\n        2,  1,  2,  6,  5,  1,  1,  1,  7,  8,  1,  1,  6,  1,  1],\n      dtype=int32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Also, you can determine the number of clusters directly:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from scipy.cluster.hierarchy import fcluster\nk = 5\nclusters = fcluster(Z, k, criterion='maxclust')\nclusters\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([1, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 2, 1,\n       5, 3, 3, 3, 3, 3, 1, 3, 3, 4, 4, 4, 4, 2, 3, 1, 3, 3, 3, 2, 3, 2,\n       4, 3, 4, 1, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3, 3, 3, 3, 2, 2, 2, 1, 3,\n       3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 3, 2,\n       3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1,\n       3, 4, 1, 1, 3, 1, 1], dtype=int32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now, plot the dendrogram:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "fig = pylab.figure(figsize=(18,50))\ndef llf(id):\n    return '[%s %s %s]' % (pdf['manufact'][id], pdf['model'][id], int(float(pdf['type'][id])) )\n    \ndendro = hierarchy.dendrogram(Z,  leaf_label_func=llf, leaf_rotation=0, leaf_font_size =12, orientation = 'right')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1800x5000 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"clustering_using_skl\">Clustering using scikit-learn</h2>\n\nLet's redo it again, but this time using the scikit-learn package:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics.pairwise import euclidean_distances\ndist_matrix = euclidean_distances(feature_mtx,feature_mtx) \nprint(dist_matrix)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "name": "stdout",
          "text": "[[0.         0.57777143 0.75455727 ... 0.28530295 0.24917241 0.18879995]\n [0.57777143 0.         0.22798938 ... 0.36087756 0.66346677 0.62201282]\n [0.75455727 0.22798938 0.         ... 0.51727787 0.81786095 0.77930119]\n ...\n [0.28530295 0.36087756 0.51727787 ... 0.         0.41797928 0.35720492]\n [0.24917241 0.66346677 0.81786095 ... 0.41797928 0.         0.15212198]\n [0.18879995 0.62201282 0.77930119 ... 0.35720492 0.15212198 0.        ]]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "Z_using_dist_matrix = hierarchy.linkage(dist_matrix, 'complete')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-25-bf9ca02f569b>:1: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n  Z_using_dist_matrix = hierarchy.linkage(dist_matrix, 'complete')\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "fig = pylab.figure(figsize=(18,50))\ndef llf(id):\n    return '[%s %s %s]' % (pdf['manufact'][id], pdf['model'][id], int(float(pdf['type'][id])) )\n    \ndendro = hierarchy.dendrogram(Z_using_dist_matrix,  leaf_label_func=llf, leaf_rotation=0, leaf_font_size =12, orientation = 'right')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1800x5000 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now, we can use the 'AgglomerativeClustering' function from scikit-learn library to cluster the dataset. The AgglomerativeClustering performs a hierarchical clustering using a bottom up approach. The linkage criteria determines the metric used for the merge strategy:\n\n*   Ward minimizes the sum of squared differences within all clusters. It is a variance-minimizing approach and in this sense is similar to the k-means objective function but tackled with an agglomerative hierarchical approach.\n*   Maximum or complete linkage minimizes the maximum distance between observations of pairs of clusters.\n*   Average linkage minimizes the average of the distances between all observations of pairs of clusters.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "agglom = AgglomerativeClustering(n_clusters = 6, linkage = 'complete')\nagglom.fit(dist_matrix)\n\nagglom.labels_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.10/site-packages/sklearn/cluster/_agglomerative.py:542: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n  out = hierarchy.linkage(X, method=linkage, metric=affinity)\n",
          "output_type": "stream"
        },
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([1, 2, 2, 3, 2, 4, 3, 2, 2, 2, 2, 2, 4, 4, 2, 1, 3, 2, 2, 2, 4, 1,\n       5, 3, 3, 2, 3, 2, 1, 3, 3, 0, 0, 0, 0, 4, 2, 1, 3, 3, 2, 4, 2, 4,\n       0, 3, 0, 1, 3, 3, 2, 4, 1, 1, 3, 2, 1, 3, 2, 2, 2, 4, 4, 4, 1, 3,\n       3, 2, 3, 4, 3, 3, 3, 2, 4, 2, 2, 3, 2, 4, 4, 1, 3, 2, 2, 2, 3, 4,\n       2, 4, 1, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 1, 1, 1, 1, 3, 2, 1, 1, 1,\n       3, 0, 1, 1, 3, 1, 1], dtype=int32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can add a new field to our dataframe to show the cluster of each row:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pdf['cluster_'] = agglom.labels_\npdf.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": [
        {
          "execution_count": 28,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n\n   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \\\n0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0   \n1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0   \n2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0   \n3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0   \n4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0   \n\n   cluster_  \n0         1  \n1         2  \n2         2  \n3         3  \n4         2  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>manufact</th>\n      <th>model</th>\n      <th>sales</th>\n      <th>resale</th>\n      <th>type</th>\n      <th>price</th>\n      <th>engine_s</th>\n      <th>horsepow</th>\n      <th>wheelbas</th>\n      <th>width</th>\n      <th>length</th>\n      <th>curb_wgt</th>\n      <th>fuel_cap</th>\n      <th>mpg</th>\n      <th>lnsales</th>\n      <th>partition</th>\n      <th>cluster_</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Acura</td>\n      <td>Integra</td>\n      <td>16.919</td>\n      <td>16.360</td>\n      <td>0.0</td>\n      <td>21.50</td>\n      <td>1.8</td>\n      <td>140.0</td>\n      <td>101.2</td>\n      <td>67.3</td>\n      <td>172.4</td>\n      <td>2.639</td>\n      <td>13.2</td>\n      <td>28.0</td>\n      <td>2.828</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acura</td>\n      <td>TL</td>\n      <td>39.384</td>\n      <td>19.875</td>\n      <td>0.0</td>\n      <td>28.40</td>\n      <td>3.2</td>\n      <td>225.0</td>\n      <td>108.1</td>\n      <td>70.3</td>\n      <td>192.9</td>\n      <td>3.517</td>\n      <td>17.2</td>\n      <td>25.0</td>\n      <td>3.673</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Acura</td>\n      <td>RL</td>\n      <td>8.588</td>\n      <td>29.725</td>\n      <td>0.0</td>\n      <td>42.00</td>\n      <td>3.5</td>\n      <td>210.0</td>\n      <td>114.6</td>\n      <td>71.4</td>\n      <td>196.6</td>\n      <td>3.850</td>\n      <td>18.0</td>\n      <td>22.0</td>\n      <td>2.150</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Audi</td>\n      <td>A4</td>\n      <td>20.397</td>\n      <td>22.255</td>\n      <td>0.0</td>\n      <td>23.99</td>\n      <td>1.8</td>\n      <td>150.0</td>\n      <td>102.6</td>\n      <td>68.2</td>\n      <td>178.0</td>\n      <td>2.998</td>\n      <td>16.4</td>\n      <td>27.0</td>\n      <td>3.015</td>\n      <td>0.0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Audi</td>\n      <td>A6</td>\n      <td>18.780</td>\n      <td>23.555</td>\n      <td>0.0</td>\n      <td>33.95</td>\n      <td>2.8</td>\n      <td>200.0</td>\n      <td>108.7</td>\n      <td>76.1</td>\n      <td>192.0</td>\n      <td>3.561</td>\n      <td>18.5</td>\n      <td>22.0</td>\n      <td>2.933</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.cm as cm\nn_clusters = max(agglom.labels_)+1\ncolors = cm.rainbow(np.linspace(0, 1, n_clusters))\ncluster_labels = list(range(0, n_clusters))\n\n# Create a figure of size 6 inches by 4 inches.\nplt.figure(figsize=(16,14))\n\nfor color, label in zip(colors, cluster_labels):\n    subset = pdf[pdf.cluster_ == label]\n    for i in subset.index:\n            plt.text(subset.horsepow[i], subset.mpg[i],str(subset['model'][i]), rotation=25) \n    plt.scatter(subset.horsepow, subset.mpg, s= subset.price*10, c=color, label='cluster'+str(label),alpha=0.5)\n#    plt.scatter(subset.horsepow, subset.mpg)\nplt.legend()\nplt.title('Clusters')\nplt.xlabel('horsepow')\nplt.ylabel('mpg')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "name": "stderr",
          "text": "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
          "output_type": "stream"
        },
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'mpg')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1400 with 1 Axes>",
            "image/png": 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NS0vjuuuu49prr+WRRx5hwYIFvPPOOzz00EObtR0wYAAZGRnceOONLFiwgMcff5wpU6bUfF5WVsbll1/OjBkz+PLLL3nzzTeZOXMmPXr0AKBDhw6UlJQwffp01qxZQ2lpKXl5eYwYMYKRI0fyzDPPsGjRIt577z1uu+02pk2bts3ar7jiCl5++WXuuusu5s+fz9ixY5k1axaXX375Dn9Xu8qwUZIkSZIkSfUuq/rME76x+rjOhCEEQKNWO37P6NGjufrqqxkzZgw9evRg+PDhFBQUbNauadOmPPbYY7z44osceOCBPPHEE4wdO7bm82g0ytq1axk5ciR5eXmceeaZDB06lHHjxgEwcOBALrnkEoYPH05ubi4TJkwAYPLkyYwcOZKrr76abt26ccoppzBz5kzat2+/zboHDhzI448/zgMPPECfPn14+umnee655+jVq9eOP/wuCsJvLij/likqKiInJ4fCwkKys7MbuhxJkiRJkqR9ysaNG1m0aBEdO3YkLS2t5vpXX8K/RkNGLqRm1X0d5cVQuhqOuQUa71/34+1ttvZzgp3L15zZKEmSJEmSpHqX3RYad4SSlfUzXskKaNKpelzVHcNG7bZvbmAqSZIkSZK0PZEodDgKYht3/IToXRWvgqpy2P+o6nFVdwwbtcvi8eojozYdv15SUtKQ5UiSJEmSpL1Mq4OrZxoW5tftOIX5kNMWWh1Ut+PIsFG7IRKp/uPzl7/8haOOOoqzzjqLiRMnsmTJEuB/YaQkSZIkSdKWpOVAj2EQq6jeU7EulBdVz2rsMax6PNUtw0btsC2Fh2PGjOHSSy/lmGOOoWPHjtx7770MHz4c+F8YKUmSJEmStDXtjqh+rV8IscrE9h2rhPWLYP8jof2gxPatLUtq6AK054vH40QikZrw8Pbbb6dDhw6ccMIJPProo9x+++38+Mc/BuCDDz7gO9/5Dr///e+55JJLGrJsSZIkSZK0F4hE4cCzq0+KLvgY9usB0eTd7zdWCWs+hRYHQq+zIHBOVL3wa9ZWVVVV784aiUTYuHEjM2fO5OKLL+bpp58mIyODNWvWsGbNGg499NCaew466CAuvfRS7rnnHgDCMGyI0iVJkiRJ0l4koxkcchk07wWrP9n9JdXlRdX9NO9V3W9Gs8TUqe0zbFQtTz75JD/72c8ASEpKoqioiHg8zg9+8AMuuugi1q5dy4svvshJJ53EsmXL6NatG2+88Qbwv2XWP/jBD1i2bBkLFy4kCIIGexZJkiRJkrT3aNQSBlwBnY6F4mXVy6o3nVIdErIyGuet1EpeTK/guYwKXkyv4K3USlZG44RUT3aKV1XfV7y8up8BV1T3q/pj2KjN/O53v+Pvf/873/ve9xg6dChVVVUMGzaMBQsW0KRJE5o3bw5Uz2Js06YNb775JoWFhTXLrD/88ENat25NUpKr9CVJkiRJ0o7LaAb9L62ejZjeBFbPD3l/RYxno5U8k1nOO2lVLEiOsSwpxoLkGO+kVfFMZjnPRit5f0WMgk9D0ptU39//Umc0NgTDxn1cLBar9b5p06YAnHzyyXTs2JHJkyeTkpLCGWecQa9evVi7di3FxdVzmTMzMznttNOYO3cuV1xxBQsWLGDRokX8/e9/5zvf+Q7t27ev9+eRJEmSJEl7t0gU9v8ODBobUnhOFW91q2QhcSIFAU2XB+SsipC1KkLOqghNlwdECgIWEuetbpUU/aiKQWND9v9OdT91ZfHixQRBwOzZs+tukL2UYeM+alPIGI3W/pv39T0YH3jgAfLy8qiqqqJZs2accMIJrFq1irfeequm/Q9/+ENuu+02Xn75ZU499VR69epFZWUlN9xwQ/09jCRJkiRJ+lYJw5B/rKliVnoVeQfDYYcF5B0a0PLAgCYdofH+0KQjtDyw+vphhwXkHQyz0qv459qqve4MiSlTptC4ceOE9Tdv3jyGDRtGhw4dCIKg5myN+uA6131IRUUFKSkphGFYEzI+8MADvPbaa/Tu3ZvTTjuNs846i+9///v06NGDsWPHMnbs2Jq9GM855xz+9re/8Y9//IPvfve7JCcnk5yczNChQ/noo4/49NNPadeuHZ06dWrIx5QkSZIkSXu5DwvivLo4xn7pAU3TA8iAtMbbvieNgCCAlxfFaJsV4aAWdTi1cQ8Vi8UIgoDS0lI6derEGWecwZVXXlmvNTizcR/wxhtv8N3vfpf77rsPgCAI+OyzzzjssMO444476NixI0899RQ/+9nPeOqpp8jKyuKCCy7gvvvuo7KykpSUFKqqqujYsSNHHXUUH3zwQa3ZjQDNmzfnqKOOMmiUJEmSJEm7JQxD3lwWIx5SHTTuhKbpAbEQ3loWS8jsxng8zoQJE+jSpQupqam0b9+e8ePHb9ZuSzMTn3vuuVoH586ZM4djjjmGrKwssrOzOfjgg5k1axYzZszg/PPPp7CwkCAICIKAsWPHAlBeXs6oUaNo06YNmZmZDBgwgBkzZmw27gsvvEDPnj1JTU0lPz+fQw45hF//+tf88Ic/JDU1dbe/h51h2PgtV1ZWxtixY5kxYwZvvfUW+fn5ADz77LNkZWWxYMECfvWrX/H888+zZMkS7rjjDiorKznvvPMAmDRpEkDNYS/nnnsu//nPf3j77bcBag6FkSRJkiRJSoTFRSH/WR+jReau3d8yE/6zPsbiot0PG2+44QZuv/12Ro8ezSeffMLjjz9OixYtdqmvESNG0LZtW2bOnMn777/P9ddfT3JyMgMHDuSee+4hOzubFStWsGLFCkaNGgXA5Zdfzttvv83UqVP56KOPOOOMMzj++OP5/PPPa/otLS3ljjvuYNKkScybN6/mYN+G4jLqb7FYLEZ6ejotW7akbdu2VFVVMW3aNC699FLeeOMNrrrqKtavX8/o0aN54okn6N69O9deey3Jycm0a9eOn/zkJ1x33XX07duXDz/8kLlz5zJx4kT+/Oc/c9hhhzX040mSJEmSpG+hjwpilFZC+6xdu79RMiwtru6nY86uT5IqLi7m3nvv5b777uPcc88FoHPnzgwaNIjFixfvdH/5+flcc801dO/eHYCuXbvWfJaTk0MQBLRs2bJW+8mTJ5Ofn0/r1q0BGDVqFC+//DKTJ0/m1ltvBaCyspKJEyfSp0+fXX3UhDJs/JaoqKhg/PjxvPnmm1x//fUMHjyYMAyJx+MccMABxGIxNm7cyIwZM7jkkktYsWIFP/vZz1i3bh39+vXj8ccfZ8iQIUD1FN3U1FRGjx7NzJkzOe+88ygtLeXOO+8kCAKDRkmSJEmSVGdWlYakRqm1BHlnBEFAajRkddnuzWz89NNPKS8v59hjj92tfja56qqruPDCC3n00UcZPHgwZ5xxBp07d95q+7lz5xKLxcjLy6t1vby8nGbNmtW8T0lJoXfv3gmpMREMG78l1q5dy0MPPcTy5ctZsmQJTz75JH379gXgiy++oH///jRt2pTf/va3PPfcc/ziF7/g3HPP5bXXXuPII4+s6WfevHm88sornHzyyXTu3JmpU6fy1VdfbfMPvyRJkiRJUqJsrILoruWMNSIBlFbuXh/p6ek7Pl4kstkekZWVtQsYO3YsZ599NtOmTeOll17i5ptvZurUqZx66qlb7LOkpIRoNMr7779fc9DvJo0aNapV564Gs3XBDff2VmvXwssvw733wq230uqpp/jREUfwgyFDyM3N5c477+SRRx4BoEuXLkybNo0RI0bQvHlznn/+eXr16kWzZs144YUXapLyDz/8kP/3//4fs2fPrvkL1axZM4NGSZIkSZJUb9KSILab2y3GQ8hI3r0+unbtSnp6OtOnT99u29zcXIqLi9mwYUPNtdmzZ2/WLi8vjyuvvJJXX32V0047jcmTJwPVsxNjsVittv369SMWi1FQUECXLl1qvb6+3HpP48zGvU1ZGTz9NLzxBqxeDSkpkJwMlZWct24dV372GQf36MGAwYO55PLLOeCAA2jbti2tWrUiOTmZ4447jqeffpri4mL+9Kc/cdVVV/Hkk0/SoUMH3n33XX74wx8yfvz4mr0AJEmSJEmS6lOLjIDyWPWp1LsyYy8MQ8pjkLuTJ1l/U1paGtdddx3XXnstKSkpHHHEEaxevZp58+ZttrR6wIABZGRkcOONN/Lzn/+cd999lylTptR8XlZWxjXXXMPpp59Ox44dWbp0KTNnzmTYsGEAdOjQgZKSEqZPn06fPn3IyMggLy+PESNGMHLkSO666y769evH6tWrmT59Or179+bEE0/cau0VFRV88sknNb9ftmwZs2fPplGjRnTp0mW3vpftcWbj3qSsDH7/e3j2WQgC6NULuneHzp2he3fyDjuMDllZzPvwQ05ZtYpLL7qIu+++m9/85jfE43GCIOCkk06iUaNGPPXUU3zve9/jtdde4w9/+AM/+tGPWLp0KQ8//DBt27Zt6CeVJEmSJEn7qN7No2QkQ8kuLoMuqYTM5Op+dtfo0aO5+uqrGTNmDD169GD48OEUFBRs1q5p06Y89thjvPjiixx44IE88cQTjB07tubzaDTK2rVrGTlyJHl5eZx55pkMHTqUcePGATBw4EAuueQShg8fTm5uLhMmTABg8uTJjBw5kquvvppu3bpxyimnMHPmTNq3b7/NupcvX06/fv3o168fK1as4M4776Rfv35ceOGFu/2dbE8QfnNBeQO5/fbbueGGG7jiiiu45557ADjvvPN4+OGHa7UbMmQIL7/88g73W1RURE5ODoWFhWRnZyey5Pr36KPVQWPnzpC55fPfZ69cyU+ef56ftGrFhVddxWPAyJEjadOmDQsWLCAlJYXrr7+e5557jvvvv59jjjmmfp9BkiRJkiTtUzZu3MiiRYvo2LEjaWlp220fhiH3fVjJvDVxujTZ+dmJn68POXC/CD/tl7xH7WW4p9vWz2ln8rU9Yhn1zJkz+cMf/rDFk3OOP/74mvXrAKmpqfVZ2p5jzZrqpdPNm281aATo3aIF3Zo357m1azn173/nnN/8htQnn6R///6kpKQA8OMf/5jvfOc7Bo2SJEmSJGmPEwQBR7SJ8unaOOvKQpruxHLodWUh0QAGtokaNDaQBl9GXVJSwogRI3jwwQdp0qTJZp+npqbSsmXLmteW2uwTZs2q3qOxefNtNosEAT856CDWxmI8P3cuzJrFGWecQceOHWtORcrLy+OEE06oj6olSZIkSZJ2Wr/mEY7rEGVNWci6sh1blLuuLGRNWcjxHaP0a97gkdc+q8G/+Z/+9KeceOKJDB48eIufz5gxg+bNm9OtWzcuvfRS1q5du83+ysvLKSoqqvX6Vvjss+rDYCLb/5Ed3q4drbOz+dOyZZR99FHNdRN9SZIkSZK0NwiCgO93TuKETkmsrwj5pLyMpWlFLG28loXNV7Kg+UoWNl/J0sZrWZpWxCflZayvCGvuMQNpOA26jHrq1Kl88MEHzJw5c4ufH3/88Zx22ml07NiRBQsWcOONNzJ06FDefvttotEtb/J522231Wyu+a2yYUP1qdM7ICkS4dw+fXhn7lwqiotJr+PSJEmSJEmSEi0WhLTrUEqHZsV8sbGclZVxquIBSVUBARACVUmVJGWHZDWL0CEtlTZZWcSCLJIwbGwoDRY2LlmyhCuuuIK///3vW90c9Ic//GHN7w888EB69+5N586dmTFjxmZHjG9yww03cNVVV9W8Lyoqol27doktviFkZkLljh/D9IO8PE4KQ2jWrA6LkiRJkiRJSrwl8Y28XrmO/PhGUtMjHJGRSmlFhFUb4myogsoYJEchMwlaZEbISImzLqzi1aq1zI9v4DvJTWkX2f5hNEq8Bgsb33//fQoKCjjooINqrsViMV5//XXuu+8+ysvLN5u92KlTJ/bbbz+++OKLrYaNqamp385DZLp1g9deg3h8h5ZSB/E4VFRA9+71UJwkSZIkSdLuC8OQObFiXqtaT1kYp3WQSnJQnYOkpELj1C2vdIUoLYMolWGcJfFynqlYxVFJTegTzXJJdT1rsLDx2GOPZe7cubWunX/++XTv3p3rrrtui8ukly5dytq1a2nVqlV9lbnn6N8fnn0WCgqgZcvtt1+9GnJzq++TJEmSJEnaC8yJFTO9ch3JBLQLUnc6KEwOIrQjlbVhJdMr1wHQNym7LkrVVjRY2JiVlUWvXr1qXcvMzKRZs2b06tWLkpISxo0bx7Bhw2jZsiULFizg2muvpUuXLgwZMqSBqm5A++0HRx4JzzwDWVnVy6q3ZsOG6lDytNNcRi1JkiRJkvYKS+Ibea1qPckE7BdJ2eV+giBgvyCFNfEKXqtaT7NIikuq61GDn0a9NdFolI8++oiTTjqJvLw8LrjgAg4++GD+/e9/fzuXSe+IYcOqA8cFC2Dlyuol1V8Xj1dfX7Cgut2wYQ1TpyRJkiRJ0k4oD+O8XrmOsjBOs2DHDsjdnmZBMmX/7bc8jG//BiVEg55G/U0zZsyo+X16ejqvvPJKwxWzJ0pPh0suqV4e/e9/w8cfQ0pK9SnVlZXVezTm5lbPaBw2rLq9JEmSJEnSHu7jWAn58Y203oWl01sTBAGtSCE/vpGPYyUcnMDl1IsXL6Zjx458+OGH9O3bN2H9fhvssTMbtRXp6XDOOXDrrXDxxXD44dCjR/WvF19cff2ccwwaJUmSJEnSXqEyjPNxrJhUIjWHwSRKchAhlQgfx4qp3INnN06ZMoXGjRsnrL8HH3yQI488kiZNmtCkSRMGDx7Me++9l7D+t2WPmtmondCsGQwZUv2SJEmSJEnaSy2Jb2RVWEHzYNf3adyWpkEyBWEFS+Ib6RTNqJMx9hSxWIwgCJgxYwZnnXUWAwcOJC0tjTvuuIPjjjuOefPm0aZNmzqtwZmNkiRJkiRJajCrw0piYUhKgmc1bpISRKgKQ1aHlTt9bzweZ8KECXTp0oXU1FTat2/P+PHjN2u3pZmJzz33XK0l4XPmzOGYY44hKyuL7OxsDj74YGbNmsWMGTM4//zzKSwsJAgCgiBg7NixAJSXlzNq1CjatGlDZmYmAwYMqLUN4aZxX3jhBXr27Elqair5+fn86U9/4rLLLqNv3750796dSZMmEY/HmT59+k5/BzvLmY2SJEmSJElqMKvi5URJzD6NWxMlYFW8fKfvu+GGG3jwwQe5++67GTRoECtWrGD+/Pm7VMOIESPo168f999/P9FolNmzZ5OcnMzAgQO55557GDNmDJ999hkAjRo1AuDyyy/nk08+YerUqbRu3Zpnn32W448/nrlz59K1a1cASktLueOOO5g0aRLNmjWjefPmm41dWlpKZWUlTZs23aXad4ZhoyRJkiRJkhrM2rCStDpefJtGhHU7ObOxuLiYe++9l/vuu49zzz0XgM6dOzNo0CAWL1680zXk5+dzzTXX0L17d4CasBAgJyeHIAho2bJlrfaTJ08mPz+f1q1bAzBq1ChefvllJk+ezK233gpAZWUlEydOpE+fPlsd+7rrrqN169YMHjx4p+veWYaNkiRJkiRJajBVhHU8rxGC/46zMz799FPKy8s59thjE1LDVVddxYUXXsijjz7K4MGDOeOMM+jcufNW28+dO5dYLEZeXl6t6+Xl5TRr1qzmfUpKCr17995qP7fffjtTp05lxowZpKWl7f6DbIdhoyRJkiRJkhpMEsFOxoA7L/zvODsjPT19h9tGIhHCsPZTVFbWnkk5duxYzj77bKZNm8ZLL73EzTffzNSpUzn11FO32GdJSQnRaJT333+faDRa67NNy6w31fn1vSG/7s477+T222/nH//4xzYDyUTygBhJkiRJkiQ1mGZBMhuJ1+kYG4nTNEjeqXu6du1Kenr6Dh2qkpubS3FxMRs2bKi5Nnv27M3a5eXlceWVV/Lqq69y2mmnMXnyZKB6dmIsFqvVtl+/fsRiMQoKCujSpUut19eXW2/NhAkTuOWWW3j55Zfp37//dtsnimGjJEmSJEmSGkyLSCqxOp7bGCOkRSR1p+5JS0vjuuuu49prr+WRRx5hwYIFvPPOOzz00EObtR0wYAAZGRnceOONLFiwgMcff5wpU6bUfF5WVsbll1/OjBkz+PLLL3nzzTeZOXMmPXr0AKBDhw6UlJQwffp01qxZQ2lpKXl5eYwYMYKRI0fyzDPPsGjRIt577z1uu+02pk2bts3a77jjDkaPHs0f//hHOnTowMqVK1m5ciUlJSU79R3sCsNGSZIkSZIkNZjcIJloEFAR1s3sxoowTlIQkLuTMxsBRo8ezdVXX82YMWPo0aMHw4cPp6CgYLN2TZs25bHHHuPFF1/kwAMP5IknnmDs2LE1n0ejUdauXcvIkSPJy8vjzDPPZOjQoYwbNw6AgQMHcskllzB8+HByc3OZMGECAJMnT2bkyJFcffXVdOvWjVNOOYWZM2fSvn37bdZ9//33U1FRwemnn06rVq1qXnfeeedOfwc7Kwi/uaD8W6aoqIicnBwKCwvJzs5u6HIkSZIkSZL2KRs3bmTRokV07NhxiweUVIZxHq9Ywdp4JS13cvbhjlgZL6dZJJmzU1qRHDjvbmu29XPamXzNA2IkSZIkSZLUYJKDCL2iWbwaX0NlGN88EAxDoJIIGwnCjQT87+CVkGTCII04aUAyfOOglMowTjlxekWzDBrriWGjJEmSJEmSGlSvaCPmx0pYEi+nHanVpyuHcSIUEY2vJRoWEVBJSEhA9enS//s1ICSZWJBNLNKMONkQVJ8OvSKsoH0kjV7RRtsuQAlj2ChJkiRJkqQGlRpE+E5yU56pWMXaeAXNIxtJji8jCDcdaJJCnMzqEPG/V2p+DeMEVBIN1xKNrSUMGlEZaUNBPI30SHW/qc5qrDd+05IkSZIkSWpw7SJpHBnNIi1cRlj1GUFYQkgGYZBFGKTC1gLDIEIYpFa3I6P6vqrPSAuXc2Q0i3aRzfeJVN0xbJQkSZIkSVKDqwwryI5/SadgHTGS+Ip0YjsZXcWI/Pe+JDoFa8mOf0llWFFHFWtLXEYtSZIkSZKkBlUVVpJf+Qnr4ytpHdmPnAh8GS+jMKwiKQxIJ0L0G4e/fF0sDCkjThUhOUES+0ezyATWx1cSVAbsn3wASUFy/T3QPsywUZIkSZIkSQ0mDENWVi1ifWwlmUFjokES2UDPaCMK4hUUhOVsCGPEw+olukn8L3SsIiRO9fXMIErzIJXmkRSi/22TSWPWx1aSFmTQOqlr9cEzqlOGjZIkSZIkSWowxfF1rK5aQmqQQTT4X1QVJaBVJJUWpFAYVlEaxtgQxigjVhMwNiZKZhAlI4iSEyQRoXaYGA2SSCWDgqolZEWakR1tVr8Ptw8ybJQkSZIkSVKDiIcxlld9QTyIkRJkb7FNhIAmQTJNdnEZdEqQTgUbWV71BY0ijYkE0d0pWdvhATGSJEmSJElqEEXxdWwIC0knq07HSSeLDWEhRfF1Celv8eLFBEHA7NmzE9Lft4lhoyRJkiRJkupdGIasiy0nDMNay6frQjRI+u94KwjDsE7H2hVTpkyhcePGCevvmWeeoX///jRu3JjMzEz69u3Lo48+mrD+t8Vl1JIkSZIkSap3lZRTHF9HapBeL+OlBukUx9dRSQUppNbLmPUtFosRBAFNmzbl//2//0f37t1JSUnhb3/7G+effz7NmzdnyJAhdVqDMxslSZIkSZJU78rjpVSFlSSRUi/jJZFCVVhBeXzDDt8Tj8eZMGECXbp0ITU1lfbt2zN+/PjN2m1pZuJzzz1X6/TrOXPmcMwxx5CVlUV2djYHH3wws2bNYsaMGZx//vkUFhYSBAFBEDB27FgAysvLGTVqFG3atCEzM5MBAwYwY8aMzcZ94YUX6NmzJ6mpqeTn53P00Udz6qmn0qNHDzp37swVV1xB7969eeONN3bqO9sVzmyUJEmSJElSvdsYbiAkrLcDWyJBlDCMszEsJYumO3TPDTfcwIMPPsjdd9/NoEGDWLFiBfPnz9+l8UeMGEG/fv24//77iUajzJ49m+TkZAYOHMg999zDmDFj+OyzzwBo1KgRAJdffjmffPIJU6dOpXXr1jz77LMcf/zxzJ07l65duwJQWlrKHXfcwaRJk2jWrBnNmzevNW4Yhvzzn//ks88+44477til2neGYaMkSZIkSZLqXRUVQP3vn1hF+Q61Ky4u5t577+W+++7j3HPPBaBz584MGjSIxYsX7/S4+fn5XHPNNXTv3h2gJiwEyMnJIQgCWrZsWav95MmTyc/Pp3Xr1gCMGjWKl19+mcmTJ3PrrbcCUFlZycSJE+nTp0+t8QoLC2nTpg3l5eVEo1EmTpzI9773vZ2ue2cZNkqSJEmSJKneNdRBLTs67qeffkp5eTnHHntsQsa96qqruPDCC3n00UcZPHgwZ5xxBp07d95q+7lz5xKLxcjLy6t1vby8nGbNmtW8T0lJoXfv3pvdn5WVxezZsykpKWH69OlcddVVdOrUiaOPPjohz7M1ho2SJEmSJEmqd1/fz3BPHDc9fccProlEIpuFmJWVlbXejx07lrPPPptp06bx0ksvcfPNNzN16lROPfXULfZZUlJCNBrl/fffJxqtvdR80zLrTXVu6ZkikQhdunQBoG/fvnz66afcdtttdR42ekCMJEmSJEmS6l31wTD1Hzgm7eBJ1F27diU9PZ3p06dvt21ubi7FxcVs2PC/w2dmz569Wbu8vDyuvPJKXn31VU477TQmT54MVM9OjMVitdr269ePWCxGQUEBXbp0qfX6+nLrHRWPxykv37El5LvDmY2SJEmSJEmqd2lBJgEB8TBWL4fExMMYARHSgowdap+WlsZ1113HtddeS0pKCkcccQSrV69m3rx5my2tHjBgABkZGdx44438/Oc/591332XKlCk1n5eVlXHNNddw+umn07FjR5YuXcrMmTMZNmwYAB06dKhZ7tynTx8yMjLIy8tjxIgRjBw5krvuuot+/fqxevVqpk+fTu/evTnxxBO3Wvttt91G//796dy5M+Xl5bz44os8+uij3H///Tv/xe0kZzZKkiRJkiSp3qVGMkgKkv97UEzdq6KCpCCF1EjmDt8zevRorr76asaMGUOPHj0YPnw4BQUFm7Vr2rQpjz32GC+++CIHHnggTzzxBGPHjq35PBqNsnbtWkaOHEleXh5nnnkmQ4cOZdy4cQAMHDiQSy65hOHDh5Obm8uECRMAmDx5MiNHjuTqq6+mW7dunHLKKcycOZP27dtvs+4NGzZw2WWXccABB3DEEUfwl7/8hccee4wLL7xwh599VwVhQ+3GWU+KiorIycmhsLCQ7Ozshi5HkiRJkiRpn7Jx40YWLVpEx44dSUtLq7kehiGLKueyLraCrEjTrXcQhkQ3lpJUWkK0rIRIZQVBGCcMIsSTU4ilN6IqoxGxtAzYxn6MxfF1NI22omPygQ22X+SebGs/J9i5fM1l1JIkSZIkSap3QRDQNNqK9fGVxMIqokHtmCqoLCdtzUrSV+aTXFJIpLIc/jtnLgDC/3VEPDmVykY5lLVsz8b9WhIm196XMRZW1Yxn0Fi3DBslSZIkSZLUILIjTckMctgQFtKIJtUXYzEyly8ic+kCkkqLCaNRqlIzqEpvBJEt7AgYjxOpLCe1cA1p61ZRlZFFSbsulLbqAP89xbmMYjKDHLK3NYNSCWHYKEmSJEmSpAYRCaK0TurCgorZVFBGxoYKshbOI33NCmJJKZTn7LflgLFWJxHiqelUpKZDPE5SaTGN/zOb1HWrKO50AKWZKUTCKK2Tu9TLQTT7OsNGSZIkSZIkNZisSFNyk9pRuOo9mny+gOSyUiqymhAmJe98Z5EIVY1yiFVVkr56OUklX7Gxa2eatzh02/tCKmE8jVqSJEmSJEkNJggCWn0F7T5bSFhexMbGzXYtaPyaMCm5up/yItp9tpCWX+FejfXEsFGSJEmSJEkNp7iA6Kf/JDOeTpDThkrKiVG5W13GqKSScoKcNmTG04l++k8oLkhQwdoWw0ZJkiRJkiQ1jFglfP5vKP2KaE4bGkdb0CjShHgYp4IyQuI71V1I9X3xME6jSBMaR1sQzWkDpV9VjxPbvRBT22fYKEmSJEmSpIax9CNY/QXktIQgIBJEyIo0o2m0JSmkUUk5FWEZMSq3GjyGxIlRSUVYRiXlpJBG02hLsiLNiAQRCILq/ld/Acvm1vMD7ns8IEaSJEmSJEn1r6IM8j+A5HRISq25HASQGqSTErSmPCyjLCymPCyjMiwHwi10FBAJoqRHGpEeZJEapG++P2NSavU4X74PrXpU/151wrBRkiRJkiRJ9W/NAihdB43bbPHjIAhICzJIDTOIE6MqrKCKSuLEIAyrZ0ISJYlkkoIUIkTZ5hkwmU3hq2WweiG0PmC3Sl+8eDEdO3bkww8/pG/fvrvV17eNy6glSZIkSZJUv8I4LJ8HkSSIRLfZNAggGkRJjaSTGckmK9KErGhTsiJNyIxkkxpJJxpsJ2iE6nEiSbD84+rx9yBTpkyhcePGddL31KlTCYKAU045pU76/ybDRkmSJEmSJNWvjcXVp0OnZ9fvuOnZ1eNuLK7fcetJLBYjHv9fkLp48WJGjRrFkUceWW81GDZKkiRJkiSpfm1YV71nY33vnZicVj1u6fodah6Px5kwYQJdunQhNTWV9u3bM378+M3abWlm4nPPPVdr78g5c+ZwzDHHkJWVRXZ2NgcffDCzZs1ixowZnH/++RQWFhIEAUEQMHbsWADKy8sZNWoUbdq0ITMzkwEDBjBjxozNxn3hhRfo2bMnqamp5OfnA9XB44gRIxg3bhydOnXaue9pN7hnoyRJkiRJkupX6Xogvt0l1AkXSaoed8M6aNZhu81vuOEGHnzwQe6++24GDRrEihUrmD9//i4NPWLECPr168f9999PNBpl9uzZJCcnM3DgQO655x7GjBnDZ599BkCjRo0AuPzyy/nkk0+YOnUqrVu35tlnn+X4449n7ty5dO3aFYDS0lLuuOMOJk2aRLNmzWjevDkAv/zlL2nevDkXXHAB//73v3ep5l1h2ChJkiRJkqT6VVnWgIMHULlxu62Ki4u59957ue+++zj33HMB6Ny5M4MGDWLx4sU7PWp+fj7XXHMN3bt3B6gJCwFycnIIgoCWLVvWaj958mTy8/Np3bo1AKNGjeLll19m8uTJ3HrrrQBUVlYyceJE+vTpU3PvG2+8wUMPPcTs2bN3us7dZdgoSZIkSZKk+hWPQ9iQ48e22+TTTz+lvLycY489NiFDXnXVVVx44YU8+uijDB48mDPOOIPOnTtvtf3cuXOJxWLk5eXVul5eXk6zZs1q3qekpNC7d++a98XFxfzoRz/iwQcfZL/99ktI7TvDsFGSJEmSJEn1KxKB7Z0eXafjb3/5dnr6ju8nGYlECMPa6WllZWWt92PHjuXss89m2rRpvPTSS9x8881MnTqVU089dYt9lpSUEI1Gef/994lGa9e7aZn1pjq/vjfkggULWLx4MT/4wQ9qrm06NCYpKYnPPvtsmyHn7jJslCRJkiRJUv2q74NhagmrD4rZjq5du5Kens706dO58MILt9k2NzeX4uJiNmzYQGZmJsAWlzDn5eWRl5fHlVdeyVlnncXkyZM59dRTSUlJIRarPduyX79+xGIxCgoKduo06e7duzN37txa12666aaaZeHt2rXb4b52hWGjJEmSJEmS6ldGEyBSvZy5Pg+JiVdVj5vZdLtN09LSuO6667j22mtJSUnhiCOOYPXq1cybN2+zpdUDBgwgIyODG2+8kZ///Oe8++67TJkypebzsrIyrrnmGk4//XQ6duzI0qVLmTlzJsOGDQOgQ4cOlJSUMH36dPr06UNGRgZ5eXmMGDGCkSNHctddd9GvXz9Wr17N9OnT6d27NyeeeOJW6+7Vq1eta5tOyv7m9boQqfMRJEmSJEmSpK/LbAop6fV/UEzlxupxM5rsUPPRo0dz9dVXM2bMGHr06MHw4cMpKCjYrF3Tpk157LHHePHFFznwwAN54oknGDt2bM3n0WiUtWvXMnLkSPLy8jjzzDMZOnQo48aNA2DgwIFccsklDB8+nNzcXCZMmADA5MmTGTlyJFdffTXdunXjlFNOYebMmbRv3373v4s6EoTfXFD+LVNUVEROTg6FhYVkZ2c3dDmSJEmSJEn7lI0bN7Jo0SI6duxIWtp/ly+HcXj/z7B+KTRuXX/FfLUcmrSFg8+AwDl4X7fFn9N/7Uy+5rcqSZIkSZKk+hVEoPUB1cuad+Bk6ISIx6rHa93LoLEO+c1KkiRJkiSp/u3XGTKawoZ19TPehnXV4+V2qp/x9lGGjZIkSZIkSap/KenQ/qDqfRuryut2rKry6nH2P7iBT8L+9jNslCRJkiRJUsNo2xtyu0DhSqirY0XCsLr/3C7Q5sC6GUM1DBslSZIkSZLUMKLJ0PVIyGgMhSsSHziGYXW/GY2rx4kmJ7Z/bcawUZIkSZIkSQ0nqzn0OA6S0xIbOG4KGpPToOdx1eOozhk2SpIkSZIkqWHldoIDhkJqJqzL3/09HKvKq/tJzYReQ2E/D4WpL0kNXYAkSZIkSZJEbidIOw0+/zes/qL6IJfMphCJ7ngf8Vj1qdMVZdC8a/XSaWc01ivDRkmSJEmSJO0ZsppDn5Ng2Vz48n34ahlEkiA9G0qrYP6XsHg5bCyHtFTo0Bq6t4eMZCgrgngVZDSFLoOgTW+IGn3VN79xSZIkSZIk7TmiydD+IGjVA1YvhIUfwIt/h9mfQeEGSIpCUgSq4vDuB5CTCX27wQnfg04HVc+QTE6v0xIXL15Mx44d+fDDD+nbt2+djrW3cc9GSZIkSZIk7XmS06FJJ3ivAD5eD827QP/DoU9/6Nm3+tf+h0NuF5i3Hmaurm5fx0FjXZgyZQqNGzdOaH9BENR6paWlJaz/bXFmoyRJkiRJkvZMTz8Nb7wJ3bpDZuaW2zQBNmyAf78Buc3hnHPqtcQ9SSwWIwgCALKzs/nss89qPtt0va45s1GSJEmSJEl7njVr4I03oHnzrQeNm2RmVrf7979h7dqElRCPx5kwYQJdunQhNTWV9u3bM378+M3abWlm4nPPPVcr4JszZw7HHHMMWVlZZGdnc/DBBzNr1ixmzJjB+eefT2FhYc0sxLFjxwJQXl7OqFGjaNOmDZmZmQwYMIAZM2ZsNu4LL7xAz549SU1NJT8/H6gOF1u2bFnzatGiRcK+l20xbJQkSZIkSdKeZ9YsWL26OkTcEc2bV7efNSthJdxwww3cfvvtjB49mk8++YTHH398l0O7ESNG0LZtW2bOnMn777/P9ddfT3JyMgMHDuSee+4hOzubFStWsGLFCkaNGgXA5Zdfzttvv83UqVP56KOPOOOMMzj++OP5/PPPa/otLS3ljjvuYNKkScybN4/m//2+SkpK2H///WnXrh0nn3wy8+bN2/0vZAe4jFqSJEmSJEl7ns8+g5QUiOzgXLlIpLr9/PkwZMhuD19cXMy9997Lfffdx7nnngtA586dGTRoEIsXL97p/vLz87nmmmvo3r07AF27dq35LCcnp2Ym4tfbT548mfz8fFq3bg3AqFGjePnll5k8eTK33norAJWVlUycOJE+ffrU3NutWzf++Mc/0rt3bwoLC7nzzjsZOHAg8+bNo23btjtd+84wbJQkSZIkSdKeZ8MGSE7euXuSk6vvS4BPP/2U8vJyjj322IT0d9VVV3HhhRfy6KOPMnjwYM444ww6d+681fZz584lFouRl5dX63p5eTnNmjWreZ+SkkLv3r1rtTn88MM5/PDDa94PHDiQHj168Ic//IFbbrklIc+zNYaNkiRJkiRJ2vNkZkJl5c7dU1m5/f0dd1B6+o6fah2JRAjD8Bul1K597NixnH322UybNo2XXnqJm2++malTp3Lqqadusc+SkhKi0Sjvv/8+0Wi01meNGjWqVef2Dn9JTk6mX79+fPHFFzv8TLvKPRslSZIkSZK05+nWDSoqIB7fsfaxWHX7/y5T3l1du3YlPT2d6dOnb7dtbm4uxcXFbPjarMrZs2dv1i4vL48rr7ySV199ldNOO43JkycD1bMTY7FYrbb9+vUjFotRUFBAly5dar2+vtx6R8RiMebOnUurVq126r5dYdgoSZIkSZKkPU///pCbCwUFO9Z+9erq9v37J2T4tLQ0rrvuOq699loeeeQRFixYwDvvvMNDDz20WdsBAwaQkZHBjTfeyIIFC3j88ceZMmVKzedlZWVcfvnlzJgxgy+//JI333yTmTNn0qNHDwA6dOhASUkJ06dPZ82aNZSWlpKXl8eIESMYOXIkzzzzDIsWLeK9997jtttuY9q0adus/Ze//CWvvvoqCxcu5IMPPuCcc87hyy+/5MILL0zId7Mtho2SJEmSJEna8+y3Hxx5ZHXYuL19GDdsqG535JHwtf0Md9fo0aO5+uqrGTNmDD169GD48OEUbCH8bNq0KY899hgvvvgiBx54IE888QRjx46t+TwajbJ27VpGjhxJXl4eZ555JkOHDmXcuHFA9Z6Kl1xyCcOHDyc3N5cJEyYAMHnyZEaOHMnVV19Nt27dOOWUU5g5cybt27ffZt3r16/noosuokePHpxwwgkUFRXx1ltv0bNnz4R9N1sThN9cUP4tU1RURE5ODoWFhWRnZzd0OZIkSZIkSfuUjRs3smjRIjp27EhaWtrO3VxWBr//Pfz739C8efXr66dTx+PVIeOmoPGSS2An9lrU/2zr57Qz+ZoHxEiSJEmSJGnPlJ5eHSDm5lYHjh9/DCkp1adOV1ZW79GYmwunnQbDhhk07gEMGyVJkiRJkrTnSk+Hc86BoUNh1iyYP7962XRmZvVhMP37J3TptHaPYaMkSZIkSZL2fM2awZAh1S/tsTwgRpIkSZIkSVJCGDZKkiRJkiSpzsXj8YYuQduQqJ+Py6glSZIkSZJUZ1JSUohEIixfvpzc3FxSUlIIgqChy9J/hWFIRUUFq1evJhKJkJKSslv9GTZKkiRJkiSpzkQiETp27MiKFStYvnx5Q5ejrcjIyKB9+/ZEIru3ENqwUZIkSZIkSXUqJSWF9u3bU1VVRSwWa+hy9A3RaJSkpKSEzDg1bJQkSZIkSVKdC4KA5ORkkpOTG7oU1SEPiJEkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJsceEjbfffjtBEPCLX/yi5loYhowZM4ZWrVqRnp7O4MGD+fzzzxuuSEmSJEmSJElbtUeEjTNnzuQPf/gDvXv3rnV9woQJ/Pa3v+X3v/897777LpmZmQwZMoSNGzc2UKWSJEmSJEmStqbBw8aSkhJGjBjBgw8+SJMmTWquh2HIPffcw0033cTJJ59M7969eeSRR1i+fDnPPfdcwxUsSZIkSZIkaYsaPGz86U9/yoknnsjgwYNrXV+0aBErV66sdT0nJ4cBAwbw9ttvb7W/8vJyioqKar0kSZIkSZIk1b2khhx86tSpfPDBB8ycOXOzz1auXAlAixYtal1v0aJFzWdbcttttzFu3LjEFipJkiRJkiRpuxpsZuOSJUu44oor+NOf/kRaWlrC+r3hhhsoLCyseS1ZsiRhfUuSJEmSJEnaugYLG99//30KCgo46KCDSEpKIikpiddee43f/va3JCUl1cxoXLVqVa37Vq1aRcuWLbfab2pqKtnZ2bVekiRJkiRJkupeg4WNxx57LHPnzmX27Nk1r/79+zNixAhmz55Np06daNmyJdOnT6+5p6ioiHfffZfDDz+8ocqWJEmSJEmStBUNtmdjVlYWvXr1qnUtMzOTZs2a1Vz/xS9+wa9+9Su6du1Kx44dGT16NK1bt+aUU05pgIolSZIkSZIkbUuDHhCzPddeey0bNmzgJz/5CV999RWDBg3i5ZdfTugej5IkSZIkSZISIwjDMGzoIupSUVEROTk5FBYWun+jJEmSJEmStJN2Jl9rsD0bJUmSJEmSJH27GDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQjRo2Hj//ffTu3dvsrOzyc7O5vDDD+ell16q+fy8884jCIJar+OPP74BK5YkSZIkSZK0NUkNOXjbtm25/fbb6dq1K2EY8vDDD3PyySfz4YcfcsABBwBw/PHHM3ny5Jp7UlNTG6pcSZIkSZIkSdvQoGHjD37wg1rvx48fz/33388777xTEzampqbSsmXLHe6zvLyc8vLymvdFRUWJKVaSJEmSJEnSNu0xezbGYjGmTp3Khg0bOPzww2uuz5gxg+bNm9OtWzcuvfRS1q5du81+brvtNnJycmpe7dq1q+vSJUmSJEmSJAFBGIZhQxYwd+5cDj/8cDZu3EijRo14/PHHOeGEEwCYOnUqGRkZdOzYkQULFnDjjTfSqFEj3n77baLR6Bb729LMxnbt2lFYWEh2dna9PJMkSZIkSZL0bVFUVEROTs4O5WsNHjZWVFSQn59PYWEhTz/9NJMmTeK1116jZ8+em7VduHAhnTt35h//+AfHHnvsDvW/M1+GJEmSJEmSpNp2Jl9r8GXUKSkpdOnShYMPPpjbbruNPn36cO+9926xbadOndhvv/344osv6rlKSZIkSZIkSdvT4GHjN8Xj8VrLoL9u6dKlrF27llatWtVzVZIkSZIkSZK2p0FPo77hhhsYOnQo7du3p7i4mMcff5wZM2bwyiuvUFJSwrhx4xg2bBgtW7ZkwYIFXHvttXTp0oUhQ4Y0ZNmSJEmSJEmStqBBw8aCggJGjhzJihUryMnJoXfv3rzyyit873vfo6ysjI8++oiHH36Yr776itatW3Pcccdxyy23kJqa2pBlS5IkSZIkSdqCBj8gpq55QIwkSZIkSZK06/aqA2IkSZIkSZIkfTsYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2SJEmSJEmSEsKwUZIkSZIkSVJCGDZKkiRJkiRJSgjDRkmSJEmSJEkJYdgoSZIkSZIkKSEMGyVJkiRJkiQlhGGjJEmSJEmSpIQwbJQkSZIkSZKUEIaNkiRJkiRJkhLCsFGSJEmSJElSQhg2SpIkSZIkSUoIw0ZJkiRJkiRJCWHYKEmSJEmSJCkhDBslSZIkSZIkJYRhoyRJkiRJkqSEMGyUJEmSJEmSlBCGjZIkSZIkSZISwrBRkiRJkiRJUkIYNkqSJEmSJElKCMNGSZIkSZIkSQlh2ChJkiRJkiQpIQwbJUmSJEmSJCWEYaMkSZIkSZKkhDBslCRJkiRJkpQQho2StJvCMCQWizV0GZIkSZIkNTjDRknaBWEYAhCLxQiCgGg02sAVSZIkSZLU8AwbJWk7wjCkoqKC3/3udzz88MM11wCi0SglJSXceOONHHvssdx00028+eabtdpIkiRJkrSvMGyUpG1Yv349QRCwevVqnn76af7yl7+wceNGIpEIVVVVPPHEE/Ts2ZO3336bI488krlz53LSSSexaNEigiBo6PIlSZIkSapXho2StAVhGPLYY48xYMAAVq5cSevWrRk2bBhr1qzhr3/9KwAbNmxg+fLl3HzzzfzrX/9i7NixPPTQQxQXFzNx4kQKCwsb+CkkSZIkSapfho2StAVBEJCTk0NWVhZTpkwhCAKGDBlCs2bNeP755wHIyclh8ODB/PjHP+bzzz/ntNNOo2vXrvTo0YMHH3yQ+fPnN/BTSJIkSZJUvwwbJekbNu21eOSRR3LEEUfwl7/8hZdeeomlS5dy8MEH88UXX/DSSy8B0KdPH1asWMH5559PZmYm//rXv5gzZw4pKSk888wzbNiwoSEfRZIkSZKkemXYKElUB4yxWIyqqqqavRYbN25M7969+fTTTznttNO44IIL+Ne//kVmZibPPPNMzb0PPfQQRUVF3HTTTfTt25f58+cTBAFPPfUU8+bNa6hHkiRJkiSp3hk2StrnxWIxwjAkGo2SlJTEmjVr+Nvf/sayZcto1KgRnTp1ok+fPkyZMoUPPvgAgLlz5zJjxgygOqgsKSkhNTUVgH/+859cdtllbNiwgfXr1zfUY0mSJEmSVO+CcNN6wW+poqIicnJyKCwsJDs7u6HLkbSHiMfjBEFQM4uxtLSUG264gSeffJI1a9aQnp5OSkoK+++/PyeddBJPPfUUN954I7Nnz+bFF18kIyODgQMH8tvf/pbCwkI6dOhAmzZtKC0tZcOGDcyYMYO8vDyi0WgDP6kkSZIkSbtnZ/K1pHqqSZL2CGEYbhY0VlZWctRRR7FgwQIOOugg9ttvP95++23CMGThwoW0a9eO3r1788gjj/DQQw/x5JNPkpuby0cffcSsWbPo378/r7/+Ou+88w7xeJyLL764gZ9SkiRJkqSG4TJqSd9qFRUVQHXIWFVVBUA0GuWLL77g4IMPZvTo0cydO5f//Oc/Ne2nTp3K888/z/r162natCnz58/n2GOPJT8/n9mzZzN06FAKCwtZsWIFjzzyCGEYcuCBB3LRRRcZNEqSJEmS9mmGjdI+asWKFTz//PN88cUXVFZWNnQ5Cbd8+XIGDx7Miy++CEAQBCQlJTFv3jxuueUWjjzySD744APuvfdeFixYQGVlJQMGDKC0tJRYLEbfvn05+uijiUQivPvuu2RkZNCrVy8mTZrEL37xCz755BN69OjBueeeWzNDUpIkSZKkfZ1ho7SPmTdvHieeeCKHHHIIf/zjH7n88st59dVXG7qshArDkObNm3Paaadxwgkn1Fx/+umn6d+/P2PGjKFZs2b83//9H7169SI3N5cmTZrQsmVLotEozz77LAAjR45kzZo1bNy4kf/85z8ccsghzJ49G4Bbb72Vu+66i4MPPrghHlGSJEmSpD2SezZK+4jPP/+c999/n5tvvpkBAwbw97//nZycHABat27dwNXtmng8TiQS2exaLBYjOTmZyy67jM8//5ysrCxatmxJSUkJQ4cO5Z///CeXX34506dP54ADDqBTp0585zvf4ZNPPqFp06a8+OKLnH766cycOZO8vDy6d+/OCy+8wC233MK7775Ly5Yt6dmzZwM9tSRJkiRJey7DRulbLhaL8frrr3PSSSfRunVrBgwYwP/93//VBI17o3g8DlATNObn59O0aVMyMzOJRCJEIhHGjx/PxIkT+eqrr2jSpAl33XUX5513HgMHDuTHP/4xL7zwAosXL2bMmDG0b9+eH/3oR5x11lnk5eWxdOlSfv3rX/PCCy9w8cUXc8QRR/D2229z7LHHkp6e3pCPLkmSJEnSHs1l1NK31CWXXEK/fv34v//7P44++mjatm3LwoULOeuss8jJySEMw4YucZfEYrGaQHHixIn079+fn/zkJwwZMoRVq1ZRWVnJySefzE033cSoUaMYPnw4JSUlXHDBBcycOZMuXbpw2mmnMX/+fFasWMFhhx0GwPHHH89DDz1EZWUlX3zxBaNHj2b48OH87Gc/49BDD+WKK64waJQkSZIkaTsMG6VvkXg8zqmnnkokEmHKlCmUlpby8ccfEwQBffv2JRaLUVBQQDwe3ysONQnDkNLKkLVlIes3hmysColGo7zzzju0bduWK6+8kqKiIlq2bMnQoUN56aWXuOKKK/j3v//NmWeeyc9//nPuu+8+9ttvPxo1asRdd91FeXk53/ve94hEIqSkpNCpUyfi8ThVVVV07NiRDz74gMsuu4ynnnqKcePGkZTkBHBJkiRJknaU/xUt7YUqYyHz18X5fH2cwoqQ5Ag0Swt4dNylvPLKK/zsZz/j1ltvpbCwsGY/xrPPPpupU6fy0ksvccopp5CdnQ2wR4aOFbGQmStj/HVBFR+vjrExBgGQTgVlf/8N0x8aT05ODmPHjmXhwoX89a9/pWfPnjRq1IjXXnuN8vJyfvSjH9Xs6Th8+HAef/xxXn75ZZYvX06PHj1IT0+npKSE999/nxdeeIHbb7+dU045hUmTJnHXXXc19FcgSZIkSdJeybBR2ouUx0LeWFrFSwtjzFsbZ31ZSEgIQUDVmkV89OcnadG+GyNH/YrMzEwyMzNr7t1///1p0qQJL774InPmzGHgwIG1Zu2VlJRw55138v3vf5/+/fs3xOMB8O7yKn73YQWffxVSXhWSlgRJ/52DvXbNWub+eRJhGHLM6Kc4+4Lvsn9OlKuuuopXXnmFHj16kJGRQdu2bXniiSc47rjjSE5O5uc//zmPPvoopaWlzJ8/n/32248VK1ZQUlLCEUccQbdu3XjyySc55ZRTGuy5JUmSJEn6NnAZtbSX2FAZ8oc5FYx/u4J/L62iqDxOTho0ywholgY5LTtQVV5GcVITbv8gyoz8KsaPH8+NN97I6NGjWbVqFZdeeinFxcVMnDiRioqKmr4rKyu58847+fDDDxv0ZOq/L65i9BvlfLwmJDMppH12QMvMCPulV786tGtN4/Y9IBJlzopSRr1WzqdrKsnLy2PNmjUcfvjh9OnTh+TkZP7973+zcOFCwjCkRYsWHHjggQRBQGFhIWVlZXTq1InBgwczffp05syZY9AoSZIkSVICOLNR2gtUxEL+OLeCP8+vIhaGNM8MSI7UXv6clhTQvPfRFMyezl/OaMrTVZWkpacRhCFlZWWkpqZy1VVXccghh/DUU09RVlbGmWeeyezZs3nwwQcpLCzkF7/4RYOFjR+squLO98pZtzGkXRZEI1v+fyG5BxzBmo9fo/Kjv/JFr2P55duVJL33AW3btuWss85i1apVPPbYY+Tm5nLSSSfxs5/9jF69etUcHlNQUEDLli35+9//TpMmTer5KSVJkiRJ+nYLwr31SNodVFRURE5ODoWFhTV71El7mzeXVnHzW+VsqAhpkRkQ2cY+i//5y50se+9FUtr3Yf+Onfh/p/UnJazg3HPPpaqqiiVLltC2bVsqKyuJxWJs3LiR7t278+c//5levXrV41P9TzwM+fn0jby1LLbNoBGgZMVC/nF5XzKa70+LI05n6etPkRFU8uSjf+S4445j7ty5nHPOOfTv35+Kigo+/fRTli1bxve//30OO+wwzjjjDP8tkCRJkiRpJ+xMvubMRmkPF4uHvLS4iqLykNyMbQeNAHnDRpE3bBTryuKEQUBOz1SObp9Ebm4uixYtory8nGHDhvHkk09y0UUXMX78eLKysurpabZs/ro4H62Ok5Wy7aARIGO/tuT2PobVc2eQ/9LvaT/ybnoNPp3Dj2oEwJo1a1i+fDlvvfUWv/vd7+jRowdNmzYlNTW1Hp5EkiRJkqR9m2GjtIdb8FXInIIYSQGkRnf8vqzUgFUbQl5cVEXSkvdYsmQJgwYNomnTpvzmN79h0qRJdVf0Tpq2oIqSipB2Wds/GTuSnEKfi37DP35+MGE8RuOmTVi4YAFPvtWIr95/nqeeeophw4ZRXFxMZmYmrVq1qocnkCRJkiRJYNgo7fHyi2KsLYPMlADYfhgHsPGrAj778x3QojuT3niMuxZ8wH777cdNN90E0OAzGb/pk7VxohGIRnbs+Rq17kyPs27iP8/cxQe3DiOtTXdGVRXTIieDW265hTPPPLOOK5YkSZIkSVti2Cjt4UqrIB5CdMdyOACKl8wn/1+PEY/FiCancMX1Y/jN+JtrPg+2sxS7PoVhyIbKkB3MGWt0PeUXrP/PLDZ+tZLGx/2cow5ox+RLjqqbIiVJkiRJ0g4xbJT2cCnR6vmMO3OSU+6B3+HgX/yRkuQm5PUbyFXHptVVebstCAKSIzv3fACRaBLtv3sO6z+fSVrPY+jUp1md1CdJkiRJknacYaO0h2ucGiEtCcqqQjKTd3z6X8tDTmBlSUh2SkB2yp4zk3FLcjMizF8XEobhTs26bHXoiTQ/5ASWlgS0SN/2wTKSJEmSJKnu+V/n0h6uZ7MIHRtHKK+CeLjj8/9KK4Eg4Oj2UbJT9+yw8Xv7JxENQkqrdm5+YxAEfFUO2Slw7P47cXqOJEmSJEmqE4aN0h4uOzXguP2jRCMBheU7FsbFw5DiipAWGQFHtNnzJzAf3T5K26wI6zdW7+G4o+JhSElFwEEtonRq7D9nkiRJkiQ1NP/rXNoLfKddEgc0i1BaBUXlcba1w2E8DFldFhKNBHx3/yidG+/ZsxoBMpMDTugUJQS+2sFANQxDCkqhUQqc2iVpjzr0RpIkSZKkfZVho7QXaN0owhUHJ9O9aZTiyoCC0pCyqpCvh47xMKSwPM7KDdX7Hg7tmMSPD0whspeEcD86IIXv7Z9EcQWsLYtvc4ZjPAxZsaH66UcekMSgti6hliRJkiRpT7Dnr6+UBECv3CRuOjzCH+ZUMHd1nHUbQwJCIkEIIcRCiEQC2mYFfLd9EiMPSKbRHn4wzNelRgP+32EppEbh5UUxviwKyUwJaZwC0Uj1c1TEQ77aGLCxKiQ7NeC8A5L40QEpzmqUJEmSJGkPEYQ7s0HaXqioqIicnBwKCwvJzs5u6HKk3VYeC5m/Ns4/vqxiTkGM8hhEAshOCRjcIcqAVkm0ywr22gCuKh7yxtIYf11QxQerYhRXhMSBgOqp2M0yAo5oHeUHnZPp0zyy1z6nJEmSJEl7i53J15zZKO1lUqMBfZpH6Z0bYUMlbKwKiUQCMpOrP9vbJUUCjm6fxFHtoiz6Ks6sghglFRANoHFqwMA2UXIz3AFCkiRJkqQ9kWGjtJcKgoBGKexVS6V3RhAEdGoSpVMT92OUJEmSJGlv4fQgSZIkSZIkSQlh2Cip3lVVVTV0CZIkSZIkqQ4YNkqqc5vOoYrH4wAkJVXv4LBgwQI2btzYYHVJkiRJkqTEMmyUlFBhGBKGIb/85S+ZOnUqQM2J0ZFI9T85t956K+3bt+emm27igw8+aLBaJUmSJElSYnlAjKSE2hQsLl26lO9///u1PquoqOD8889n3rx53HrrrRx44IHk5uY2RJmSJEmSJKkOGDZKSqjKykqSk5N54IEHACgvLyc1NRWA1157jddee40pU6YwePDghixTkiRJkiTVAZdRS0qo5OTkmt/ffPPNXHzxxSxbtgyAt99+m4yMjFpB46b9HCVJkiRJ0t7PsFFSQn388cf8+te/BqBp06a8/vrrzJ07F4D999+fNWvWsGrVKqD6wJhNy64rKysbpmBJkiRJkpQwho2StmvT7MMHHniAGTNmAP87WfqbXn/9dcaOHQvAFVdcQUZGBi+99BIbN26ke/fu7L///tx7773A/w6MWbx4Ma+88gpVVVV1+yCSJEmSJKlOGTZK2qpNgeKm2Yd/+ctfKC4uBv4XFG5aIr1JdnY2Bx54IJ999hkAI0eO5NVXX+XDDz+kf//+nH322dx11108+OCDzJkzhzfffJOLLrqI119/ndLS0vp6NEmSJEmSVAc8IEbSZuLxOJFIpCZQXLhwIR07duSVV16p1e7MM89kxYoV3HffffTp0weo3rOxsLCQnJwcAH7605/ywAMP8Le//Y2+fftyzTXXsHLlSu666y7i8TirVq3irLPO4oYbbiA7O7t+H1SSJEmSJCWUMxslbWZTyDhx4kQOO+wwLrvsMgYNGkRBQQF/+MMfuOSSSwC49tprad26NWeffTaLFi0C4IgjjmDBggV89dVXAGRmZjJ8+PCa2Y0Ad955JzNnzuTBBx+koKCA3//+9zRp0qT+H1SSJEmSJCWUYaOkzcycOZMBAwbwm9/8hvPPP59f/vKXXHDBBTRv3pwwDHnggQdqlkVPnjyZ5ORkrr76aubMmUNKSgq9e/fmo48+qunv5z//Of/5z3+YNm0aFRUVBEFAVlYWRx11FKmpqQ34pJIkSZIkKZEMG6V9XGllyNqy6ldZVfVBMP/3f/9Hbm4ub7zxBhdffDGHHnooP/7xjwG45JJLaNWqFY899hglJSVkZGTw+9//HoCf/OQnFBQUsG7dOpo1awZAVVUVLVq04Prrr+e4444jJSWlYR5UkiRJkiTVuSDcdMzst1RRURE5OTkUFha6H5z0X1XxkM/WxZm1Msa8tXEqY9XXU6PQaMVMxpx9FP/81784+qijat1XWVlJcnIyd999N7feeiv/+te/6NWrF1D9d+2QQw7h8MMP55FHHuF3v/sdl156KVVVVSQluT2sJEmSJEl7q53J15zZKO1j8ovi3PN+Bf/3QQVvLIsRAFkp1a9YCH+b8S7J6VnMKGzB6tJ4zYnUUH34C8CVV15JeXk5zz33HBUVFUD1KdSTJ0+mpKQE+N++jwaNkiRJkiTtOwwbpX3Igq/iPDCnks/WxWmTFdCtaYTcjIDs1OpXi8yANk3SiMcqeX/hKv4wp5KCstp9xGLV0yCvuOIKJk2axJIlS2o+GzhwIL/97W9ZvHgxF198cX0+miRJkiRJ2gMYNkr7iDWlcR77pJKC0jhdmwSkJwVbbJc34FggpPLz1/lizUYenVdJScX/ZjdGo1Gef/55xo0bR35+PlOnTq0JIAFat25N+/bt6/pxJEmSJEnSHsiwUdpHvLcyxpdFcTo3DogEWw4aAXKat+WA7/yA9559iIyCOXy2Ls5Hq/8XNv7rX//i+uuvJxaLcc0119CqVSui0Wh9PIIkSZIkSdrDuZmatA8orQx5Z3mcnBSIRrYeNAIkJacw9LJx3HPOYTxz66UMuPQ3/LWkDa0PyeaZvzzN1KlTGTFiBMnJydxxxx319ASSJEmSJGlvYNgo7QM+XRtn5YY4HXO2HTRu0qxNJ8765RTeeOp3vHDtUN5o34OHq4rJykznlltu4cwzz6zjiiVJkiRJ0t7IsFHaB6wvDwmB5OiOhY0APY88gW6Hf491y7/kw48/ZfjBrbjg+0fUXZGSJEmSJGmvZ9go7QMq4xDuwn2RaBK57buwf6NOdO+VnPC6JEmSJEnSt4sHxEj7gNQI7Picxv8JgoAwDAlDSN2JWZGSJEmSJGnfZNgo7QNaZAZEAyir2vn5jYUV0CgloGWmYaMkSZIkSdo2w0ZpH9CtaYQOORFWbdj5sHH1hpCezSK0aWTYKEmSJEmSts2wUdoHJEUCDm8dZWMVlO/E7MYNlSEEAYe2ihIEho2SJEmSJGnbDBulfcQhLaP0bh5lYWFIRWz7gWNZZciXhSEDWkU4oJn/VEiSJEmSpO3zNGppH5GRHDCiRzJVMfh4bYzcdGiWDpEgoLBgGRk5zUhOTaMqHrKmLGT9RhjQOsrw7skkeziMJEmSJEnaAU5XkvYhzdIDLuydzJAOScRD+GxdyPMP/447zzqEZUVVLPgqzrSpk0mJBpzcJYlzD0gmM9mgUZIkSZIk7ZggDMOdPzFiL1JUVEROTg6FhYVkZ2c3dDnSHmPdxpCPCmJ8tCbOVQMasX/fQSz5+B3a7t+JadNepEfn9g1doiRJkiRJ2gPsTL7mzEZpHxKLxYjH4wA0TQs4un0SrRe+QDxWxerPZvHwHx9i4fyPDRr3YbFYjG/5/4OSJEmSJNUhw0ZpH7ApYIxGo0QiEZYtW0Z+fj4ARx55JC+99BIlJSUkJSURi8UaslQ1kE0BYzRaffL4smXLWLJkSQNXJUmSJEna2xg2SvuASKT6r/oXX3zBUUcdxSGHHML3v/99/vznP5OZmcmQIUM48cQTuffee1m7dm0DV6uGEATVe3O+9dZbHH300Rx11FGceOKJjB07loKCAgBnPEqSJEmStsuwUfoW+mYoNHPmTO6//35+97vfceihhzJt2jTy8vK4/fbb+dvf/gbA+PHjeeutt3j77bcNlfYBVVVVm117+OGHueSSS+jduzcvvPACP/3pT3nrrbcYP348YNgoSZIkSdo+w0bpWyIMw5oAadMstU2ee+45brjhBt58801GjRpFv379mDJlCi1atOBvf/sba9eupXfv3gwaNIhJkyaxevVqABYtWlTTt/Zum5bSb/pZJiUlAdVB9KaZi9nZ2Vx++eX89re/pWfPnjRp0oT58+czZcoUli1bRiQS8c+CJEmSJGmbDBulb4kgCGoCpMcff5xXXnmF5cuXA3DZZZfRrVs3UlJSaNGiBQCNGjXi+OOPZ8GCBbzyyitA9ezG1157jZ///OcMGjSIgw8+mJUrV24WXmrPtilYfOCBB7j44ouB/y2l3/SznDBhAq1atWLUqFG8/PLLAPzgBz/gJz/5CdOnT6dfv35ce+21DBs2jFatWnHbbbcBBs+SJEmSpG0zbJT2Ut8MfeLxOHfffTeNGzfm17/+Nddeey3HHnss77//Pm3atOH444+noqKC6dOn19wzbNgwsrKymDFjBmVlZRx55JE89NBDZGZmcvTRR7NkyRJatmxZ34+m3VBYWFgTLMbjcX70ox8Btf+83HTTTfzpT3/i17/+NY8++ijf+c53gOrZjitXruSOO+7g2GOPZfbs2dx9990ccMABPP3005SUlDi7UZIkSZK0TYaN0l4mFosRj8c3m234+eef88wzz/Db3/6WDz/8kDlz5tC1a1fOO+88CgsLOeeccwjDkOnTp9eERW3atOGYY47hnXfeYdq0aQCcfvrpPPTQQ/zqV78iMzOz3p9vT/Poo4/y61//GvjfjME91cyZMznssMO4//77AbjkkksYNGgQRUVFNX9eFi5cyCOPPMLFF1/MOeecQ7t27ejQoUNNH/PmzeMf//gHP/3pT2ncuDHr1q1j1apVFBQUMGbMGGDzZfqSJEmSJG1i2CjtZaLRKJFIhM8++4xp06ZRWFgIwKRJk8jKymLkyJHMnz+fiy66iH/84x/07NmTsrIyunbtyuGHH857773He++9V9PfqaeeyoABA+jSpQtgkPR1hYWFTJ06lX/961/EYrGaGYN7qhYtWtCvXz+ee+65mmtTp07luOOO48033wRg9uzZVFZWMmTIEKD65x2GYU0AnZycTNeuXfnjH/9Ifn4+d955Jz169GDy5Ml897vfrfdnkiRJkiTtXfbs/3KWVGNTGDR79myOOuooDjnkEF544QU+/PBDAHJycqiqqmLYsGEceuihrFu3jn/+8588+eSTNUuhf/SjH7Fu3TqeffbZmn67d+/Ogw8+SN++fev9mfY0y5cv56233qp5n5OTQ8uWLamqqmLdunUNWNmOad++PUOGDCE/P7/mZ9ykSROSk5NrTh0/6KCDWLVqFStXrqy5LwiCmpD5kEMO4bzzzuORRx6hb9++vPrqq1x88cWce+65fP/736//h5IkSZIk7VUMG6U90NdPlt4kCALWrl3LlVdeSdeuXfn/7N13dBTl28bx7+xueg8kpEGoCaEEQov03pEO0kFAiiC9KqBIR0FFqjRBekeqdOm9l9BbCCFAet0y8/7BL/uKWABBRO/POTmcnd2ZeWZ3s4e9cj/PfebMGcaPH094eDgAjo6OnD59mrS0NC5cuMDq1at55513SEtLY+bMmZhMJkqWLEn+/PlxdnYmPT39TVzaP9b9+/cpUqQILVq0YNu2bdbttWrV4uDBg9jb27/B0f22X75HMqd4lytXjgIFCjBv3jwAatasSVhYGIcOHSIiIoKcOXNSvnx5a8OXTI8fP2b69OkADB06lB9//JEDBw5w/PhxSpQo8TddkRBCCCGEEEKIt52EjUL8w2Sux5jZWfrMmTPW+9auXcvly5fp3bs3uXLlwsPDAwcHBwCqVatGrly58PX1JXv27NZ9tm3bxsKFCzly5AgA8+bNY9iwYf/I8Ozv9svAVVVVypQpQ/bs2enevTsZGRkABAcHo9frrRWPf/e6jb91vswq18z3yMmTJ63XkidPHqpWrcqVK1fYtWsXADVq1MBkMrF69WrgSSfqrVu38v7777Nhwwa2b99OmzZt2LZtm7XisUiRIoSEhLz26xNCCCGEEEII8e8iYaMQ/zCZ6wKOHTuWnDlz0rlzZy5evAjApUuXCAgIoHDhwtbAKfPf0NBQ3n//fXbu3ElYWBj9+/enTJkydOrUiYYNG1qr0yRkhHv37tGxY0f69Olj3ebv74/RaKRPnz7kyZOHgQMHEhUVhYeHB8HBwdZ1Lv+udRszQ8bM8925c4fU1NSnHjN58mT8/f1p3bo14eHhTJ48GXhSzZgrVy5mz54NQPXq1SlQoAB79uzh1q1bhIeHs3DhQu7fv88nn3xCq1atCAgIYO7cueTKletvuT4hhBBCCCGEEP9OEjYK8QZdunTpmW1Go5GuXbuyePFivvjiC7755htrV+hSpUpx6tQpkpKSrI09Mv9NSkqiW7durFixghYtWhAVFUXNmjWJjIxk0KBBEjICCxcuZODAgVy4cIFs2bKxaNEi1q9fbw1s8+TJw7p161i4cCEPHjxg8uTJZM+eHb1eT2xsLCaT6W8ba2bIOH36dN555x26dOlC9erViY6ORlEUFixYwLx58/jmm2/Yu3cvw4YNY8qUKXz11VfkyZOHGjVqcOrUKY4fP46dnR3VqlUjPT2dNWvWANCyZUu2bt3KsmXLuHfvHrNnzyZLlix/2/UJIYQQQgghhPh3MrzpAQjxXxQfH09YWBgPHjxg3bp11KhRw3rf4cOHWblyJWvWrKFSpUpP7Ve4cGFy587N0KFDmTp1qrWpx9GjRzl37hwdO3akVKlSlCpV6u+8nH80TdP45ptvGD16NL6+vpQsWZL4+Hj69etHXFwcvXv3xsHBgRo1atCkSRP69u2Lm5sbffr04cMPP2TlypVUqlSJ3bt3Y2Njg6qqr6y6UdU0rsdrRCappP9v+UV7AwS66nh45Ti9PvqIx48fM3DgQMLCwrh06RJOTk4kJSXxww8/0KdPH5o2bcqjR4+4cuUKd+7c4cGDB2iaRsWKFdm0aRPfffcdJUqUoFatWixfvpyVK1fSsWNH3N3dAShQoMAruRYhhBBCCCGEEAIkbBTijYiNjaVSpUoYjUbGjh1LZGQkHTt2BODKlSv4+vpSuHBh6+PNZjMGg4GQkBA+//xz2rVrR1xcHPXr1+fs2bMsWLDAus5g5hqO/yUZFo2IxyqnYiw8StMwqeBoUMjlpnBtxyIWLVzItGnTaNy4MWlpabi6ugIwdepUypUrx8SJE8mVKxdeXl4EBgZy5swZSpcuzdChQxkwYAB16tTh6tWrJCQk4ObmZq0ofVkpJo2zD1UORVm4Hmchw/Jku6KApoGDAY58PQVb16xsX72OXAG+ANYQ2WKxcOXKFVxdXenRowcLFy6kVKlSbNmyhZo1awJP1lysWrUqixcv5tKlS4SEhNC+fXvc3NysQaMQQgghhBBCCPGqSdgoxGtktGjciFdJ+t/sW1dbyO2mw9HRkT179rBr1y727dvHuHHjMBgMtGvXjoyMDJKTk7l37551WmtmIxBN03jvvfdIS0tj48aNTJgwATs7O2bNmkXdunXf1GW+MWZVY3+khf33LNxN0tA0DXsD6BUwq3DsRhy7pn5HSNFyFK7cEBsbG2xsbKz7GwwGxo4dy7Bhw+jbty8zZ84kJibGen/z5s05fPgwq1evJi4ujoMHD1K7du2/FDRGJassvmjicqyKjR6yOSo42/7/8TRNI+LUEY5tXkK9iT+xLjoLbTxVvBx11pAzJSWFUqVK0aJFC2v37NKlSwOQmJjItWvXKFasGGXLlmXZsmX8/PPPhISE0KBBg5cetxBCCCGEEEII8TwkbBTiNUgyahyPtnAwysLdRBWzBmhgo4MAZygbkJUsWb04ePAg7dq1IzExka+//hqLxULnzp0ZMmQI27Zto1ChQtYpu6dPn+bq1as0a9aMDh060KFDB2JiYvD29n6zF/uGpJs1Vl42sS/Sgr0Bcrgo2Bment6cw9WTHx/eJcFQk/kXNd5TLcSe3Ul6ejqqqlK0aFGqVKnCF198QaVKldi4cSPR0dEcP36cd955B4D+/ftjNpuJiooiLCzsL405KlllzlkTdxJV8rgr2OifDS0VReHRlePYO7oQlCuAc48szDptoWtRO7wcn1yfk5MT4eHhHDx4kFGjRpEnTx7r/vPmzePWrVsUKlSI0qVL89NPP+Hr6/uXxi2EEEIIIYQQQjwvCRuFeMUepqr8cMHMhccWHG0gwEXB3vAkVEoza0SnaMw5HI3JNYDo2CQAevbsybVr1+jSpQt58uShe/fuLFmyhFu3btGtWzcePnzI+PHjcXZ2pmHDJxV6mqb9Z4NGi6qx5qqJn+9a8HdRcLH97UpDRVEo17QrO7+fyK29q5ke/5DcObNjq6icOnWKkJAQJk6cSN26dRkxYgSzZ8/GaDSyd+9eevbsCTzpUv3ll19ia2v7l8acZNT44eKToDGfh4Je9/vVkQZbOyxmE6bERwQF5OJKnMaii2a6FrHBTqei1+tp1qwZ27dvp1atWrRt25ZChQoxa9YsIiIiGDx4MDqdDoPBIEGjEEIIIYQQQoi/lXSjFuIVSszQWHjBzPnHFvK4KwS66qxBI4CDQSHQTUdIjqzcvxfJmSRn9h85QaVKlZg7dy7FixenV69eODo6MnDgQNavX0/r1q1p0KABefLkYc6cOdZpwH9lKu/b7sxDlX13Vfycfz9ozFSlwyDaT1hGkYp1KN1tAsXeH82Cxcu5dOkSuXPn5vPPPwdg6NChVK5cmfv375OYmEh8fLz1GH81aAQ4+cDC1dgnFY1/FDQCBIVXRUPj2omfwWIij7vChUdmzj18EjQCHDt2jLVr11KjRg0OHDjAp59+Ss6cOTl+/Dgffvihdeq9EEIIIYQQQgjxd5Jvo0K8QoeinlQ05vudKbLwZE0+O71CYP7C/DCsHSvHONK6ZQumTZtGwYIFGTt2LF9//TVdu3bl9OnT3Lx5k9DQ0FcSeP0baJrGkfsWVMDV7vkC16DwagSFV8Nk0biRoJHqZkspXz0BAQFER0dbp6P37duX8uXLU7t27Vf6fJssGoeinkz3/r33xS+5eQdQsMK7HF4zh7zFK5KjUClsdDoO3bdQ0lfHz3v28Omnn1KyZEmmTZtGSkoKdnZ2EjAKIYQQQgghhHjj5JupEK9Iulnj8H0VV9s/DpQURUFVVfQ6hcCS1WnYbxITmxbE9n/7DBgwAE9PTxwdHcmSJYu1SYx44k6SRsRjC9kcX7yy00avoFfgyH0L2YyRnDt3jiZNmlino/v7++Pv7/+qh8yVOJVbCSr+Ls83ZoONLbU/HMnXbd5hxehuNBr4FQY3X049smfYlh/Z8eNy2rZtS44cOQBwdHT8T1e6CiGEEEIIIYT455CwUYhX5EqcSlSyRqDrn4c+Op0OGzsHdOYM0j3yEvHIRGi2J5V0tra2dOvW7XUP9611LU4l2QQBLi+2nzEthYv7NmHwDGDBvI2M+XkpJUsU57333ns9A/2F24kaJvXJNPrnlcU/Ny0//579K6Yx68NaeOcKITkpEQ8XRyaNG0Xz5s2tj5WgUQghhBBCCCHEP4WEjUK8IokZGqqmYat/vqVQvXIEcXrbCjLSUki1uL/ewf2LpJlB4cUDtqTYB+xfMR1TRgaKvSvjp86mQ5O6r2eQv5Ji0viTZRp/U4HydQguXZ3YqNs8vH2ZR0oWutYvS9088tEthBBCCCGEEOKfSb6xCvGKaC/4eEc3D4rVaomqWl543/8yDXiZQr4s/rlp9vEMNBsH0lxyUKmU3Ssf2+/RtJd/hXV6A1458uKVIy8Rj1V5rwghhBBCCCGE+EeTsFGIV8TZRkEBTKqGzXOUsRWp1pSCVZpyKxGcbF7/+P4t7PXwstldttwhJBk1zBlg/zd++jna6FA1y0vt+1QFpwJ2+lc0KCGEEEIIIYQQ4jV4vvmeQog/FeSpw9tRx8PU50vCFEXhQaqGj5NCPg/5VXxeudx02Bsg2fhyiePDVI0cLjq8HP6+dQ4DXBT0OoUMy8vXJaaaNGx1T44lhBBCCCGEEEL8U0nCIcQr4mSjUMpXR3w6mNU/D5XMqkaiEUr76l6occh/XW53hbweeh6kvPi+ZlXDaIF3/HToX2YRxZcUkkVHdmeFBynPFzb+1rTrB6kaudx0BEkwLYQQQgghhBDiH0y+tQrxCpX1N5DbXcf1eO0PA0ezqnEtTiPIQ0e4n6xm8CJ0ikJpXz0WTSPF9GKVgveSNbI56Sjs9ffORbbTK5T215NiAsufBNGapj3T/MakamSYobSf/m8NSYUQQgghhBBCiBclYaMQr1AWB4X2BW0IdNVxOVbjfrL6VOhoVjWiklWuxGrkdtfRtqANHvYSHr2osGw6SvnquZ2okWZ+vsDxfrKKpkH9vAZcbP/+57xYNj2Brk+C6D9qGKMoCjfPHOTgqu8wpqeiahrX4zTyeugo4i0LNgohhBBCCCGE+GeTsFGIVyy7q45uRWxokNeAnUHherzG5ViVy7EqN+I1HAw6GuYz0LWILf7O8iv4Mmz1Ci3y21DSR8+tBI3oFPV3KwbTTBrX41VMKjQNsqGUjw6L5eWatfwVnvYKrQvYkNVR4Wqc9pvjfXjnGrfPHWFe30bERt0kIyODq3Eafs462hSweSMhqRBCCCGEEEII8SIU7Y9KbP4FEhMTcXNzIyEhAVdX1zc9HPEfk2TUiIhVSTZqKICzrUJIFh1ONhIavQppZo1tN80cvm/hYaqGrf7J2pk65f/XxDTonjSVqR5ooIiXgk73/wFvamoqjo6Of+uYr8VZ+OF8BneTFVxsIZuTgkHRuHRgCwsGNCW4dA2KN/wAz2J1SDFBHncdbQvYkN1VgmkhhBBCCCGEEG/Gi+RrslicEK+Ri61CSR+Z+vq6OBgUGuSzoYRHGjfTHTkarRKbpmHRNJxtdIR6KZT01RPkocPwi7UOv/rqK5YuXUrOnDmpXLkyjRs3Jlu2bL+5XuKrZLFYyOuhp1cJB048sHAw0sTNWDOaTk+GVyF8CpfnzuWzFPcvirejjjJ+eor56HG3k3BaCCGEEEIIIcTbQcJGIcRbKykpiYIFCzJ69GjatWtHbl0UDx8+pEChUHSoKMrTlYwJCQl07dqVs2fP8tFHH/HgwQPWrl3L3r17Wbp06WsNGgH0ej3p6elMnTiBI0eOUKJUOO+EVQTPQFYu+ZZs7k48vPSYjqH2lAuxxVYvIaMQQgghhBBCiLeLzMsTQrx1LBYLZrMZFxcXwsPD+frrr6lWrRoBAQGsWbMGg+5JsKfT6TCZTNb9jh07xvHjxzlw4ADdu3dn8ODBODg4sHz5cs6cOfNKx6iq6lONYFRVZenSpQQFBbFt2zaKFy/O4YMH6NmiNv3qFcUcdQl/Fx1mk5Fvh/fEYkxHVdVXOiYhhBBCCCGEEOJ1k7BRCPHWyAzf9Ho9BoOBiIgIHj9+zOnTp7FYLERERDBy5EgURSExMZHFixfTuHFj6/4nT56kYcOGJCUl0blzZ3x9fUlMTGTdunUUKVLklYwxs/mMTqdDURRSU1OxWCykpKRw7do1PvvsMyZPnswHH3xAv379MBqN1KhRgx07drBhwwbCwsLYuHEj+/bte6oqUwghhBBCCCGEeBvINGohxFsjM3y7fPkynTp1wsbGhiZNmmCxWHBycsLX15ejR48ycuRIPD09uXnzJhcvXuTIkSOEh4eTmJjI5MmTmTdvHu+88w7r16+nYsWKwJMp2Zqm/eVGUnr9kzU6ExIS+Oqrr7h//z5ffvklLi4uVK1alTlz5tCpUycqV67MiRMnyJs3L5s3b2b+/PksWbKEixcvoqoqEyZMoFKlShgMBgkdhRBCCCGEEEK8NeQbrBDiH0nTtKemIQMcPnyYIUOGMGnSJMLDw1m8eDE9e/bkiy++YPPmzcydO5eWLVvi4+NDq1atKFy4MCaTiRkzZgDQvXt3FEVhyJAhbN682Ro0RkdH880337B3796/PO4ZM2aQN29eRo4cycKFCzl+/DiHDh2icuXKjBs3DoPBQMmSJfHy8mLnzp2cOHECOzs7BgwYQEhICFFRUQQHB/Pzzz9z7NgxCRqFEEIIIYQQQrxVpLJRCPGPomkaqqpaKwR/6fHjx8ybNw+DwcC2bdvw8/ND0zSKFy9OWFgYc+bMQdM0ZsyYgY2NDbVr18bZ2Zkff/yRU6dOERYWRuPGjfnhhx9wdHSkfv36REZGMm7cOBISEqhVq9Zzj1NV1WeCwN27d9O3b1/8/PwoVKgQ+/fv5/jx42zfvp3y5cszevRoypYtS0pKCuPHjydnzpwcO3aMpKQkbG1t6dWrF56enjRq1IgpU6awbt06SpcuLYGjEEIIIYQQQoi3hnyDFUL8oyiKgl6vJzExkWnTprFs2TIePnyIpmnUrVuXWrVqoSgKBw8e5NChQyQnJ6PX6/n000+JiIggZ86cKIqCoiikpaXRqFEj3NzcmDt3LgAzZ86kRo0ajB49mubNm1OzZk28vLxYsWIFJUqUeO5xZgaA58+fByA9PZ1du3bh6+tLcnIyPXv2JCUlhcqVK3P48GHy5s2Ll5cXzs7OxMXF4ebmhqIo/PTTT7i6umJra8vly5dJTEzk5s2b5MmTh9u3b3P//v1X/yQLIYQQQgghhBCviaL9ep7iv0xiYiJubm4kJCT85bXYhBCvnqZpKIpivW2xWBg/fjxjxoyhUKFC3L9/H19fX4YOHUq9evXo0KEDy5cvJ1u2bABUq1aNoUOHkj9/fry8vEhPT6dly5Zs3ryZbt26MWzYMCpXrsydO3fYtWsXgYGBWCwWHjx4wPXr1ylatCguLi7Ab1crZvr1fampqQwcOJCDBw9y8OBBHBwcqFOnDlevXiUxMZH8+fOzYsUKIiMjad68OW3atCE+Pp61a9eSmpqKv78/ycnJpKWlkT9/fm7evEmxYsU4ceIEderUoW/fvuTLl+81PvNCCCGEEEIIIcTzeZF8TSobhRBvhMVieSpozOziHBERwcqVK5k+fTpHjx7lxx9/JCQkhA8++ICvv/6aqKgoihYtSvny5Zk+fToxMTGMGjUKgPnz52M0Gtm5cyefffYZw4YNIyUlBbPZTGJiInPmzAGeNHHx8/OjfPnyuLi4YLFY/jBohCeVjMnJydaO2I6OjhQrVgyLxUKnTp04ffo0lSpVIiUlhUmTJpGSksKmTZsoXrw47u7ubN26ldKlS/Pw4UOGDh1Kly5d6NGjB5GRkSxYsICRI0ei1+v57LPPmD59ugSNQgghhBBCCCHeSrJmoxDijdDr9aiqyqZNmxg3bhwNGjRg0KBBHD16lOvXr9OuXTtiY2OZMmUKx44dQ1EUbt68ydChQ7l79y7ffvstX3/9NUeOHEHTNC5cuEC9evVo3749x48f5+LFi1y+fJmlS5cSGBhIiRIlOHLkCMnJyTg7Oz8zll+yWCzPbNu6dSsjRoxg6NChNGrUiLi4OK5fv86FCxe4desWBw8epHHjxiQnJ/Po0SM8PT3Zs2cPxYoVw2w2k5KSwu3bt3nvvfdYtmwZx44ds4atfn5+tGvXjnbt2r32510IIYQQQgghhHidpLJRCPG3yuwyfezYMUqVKkXr1q1RVRUfHx/gydqHfn5+DBkyhGnTpmE0Glm6dCnvvfcee/fupUaNGjx8+JBLly4RERHBpEmTCAkJYdy4cQCMGTOG999/n40bN1KhQgVWrFjBmDFjrGslmkym3x1bZtViZtCYkZFhrbjMli0bbm5ubNy4kYEDB1KvXj3i4uIYOXIkuXPnplixYmzevJmmTZsyZswYzGYzx48f55NPPqFOnTrkypWLc+fO0apVK06cOMH27dut5/2tZjhCCCGEEEIIIcTbSMJGIcRro2kaZrP5qW2KoqBpGkOGDEFVVbZs2cLevXtp0aIFDx48wM/PDycnJyZNmsSkSZNo2bIlRYoUwcHBAS8vL/bt28eyZcto1qwZPXv2pH379nh6erJp0yaio6Px8vLio48+Yt++fRw5coSLFy8SGBjItWvX8PHxwc3N7anx/DJ8zJxGvX79emrWrEmnTp2YN28eAGFhYZQtW5YLFy7w8OFDLl++jIODA82aNcPNzQ2j0UiuXLlISEigd+/exMXFce3aNQ4dOkTXrl3JkSMHO3fupGrVqnzzzTcUKlToqbUqhRBCCCGEEEKIfwMJG4UQr4XJZLJ2ltY0jb1791qDvTlz5rBnzx5Onz7Nrl27sLGxoWvXrvTv35/w8HCCgoKsDWDc3NwwmUwcOXKE0qVLk5CQQEREBF999RWffPIJUVFRGI1GEhISGDlypPX8WbJk4cGDB+zbt49mzZpx8OBB+vbtaw0UDxw4QMeOHenfvz8TJ04EwGg00rdvX7p06UKxYsUA6NOnDzly5GDs2LHkz58fBwcHjEYj3t7exMXFERwcjI2NDefOnSMoKIgjR47QoEED9uzZw9ixYwkKCqJy5cp89913eHl5AfDRRx/h6+v7t70WQgghhBBCCCHE3+WlwkYPDw88PT2f+cmSJQv+/v5UrFiR+fPnv+qxCiH+4aZOnUqfPn0AsLGxITk5mb59++Lt7c3nn3/O7t27AXByckLTNIoXL87JkyeJi4sjf/78XL9+nePHj9OzZ09y5MhBYmIiAwcOJDQ0lMePH9OkSRNcXFzImTMnvXr1YtmyZfTo0YPw8HAOHTr0VNhoMBjYtGkT7733Hjqdjh07dlClShXOnDlDpUqVqFu3Lq6uruTOnZuffvqJjz76iBUrVrB161YmTJhAs2bNOHv2LP7+/kRHR7Ny5UpGjRqFj48PN27coHTp0hw9epT9+/ejKAoGg4E7d+4QFBTEN998g16vp3LlyrRp0wY3Nzdy5szJzJkzZcq0EEIIIYQQQoh/tZcKG0eMGIFOp6Nu3bqMHDmSkSNHUrduXXQ6HT169CAoKIju3bsze/bsVz1eIcQ/jMViYdiwYbi6ujJp0iRKvFOWx2kaV+4+pEWr1vz00080adKE2bNnU7BgQcxmM/nz50en06HX60lMTGTZsmV07doVVVVZv349xYsXZ/v27RQuXJioqCiaNGnChQsXKF68OOHh4Xz++efcvXuXIUOGUKhQIT7//HPCw8Px9vbGpBp5bInilvEcdbtWYP3JHxj7w1CcAvUkGZ9UP/r5+XH69Gly5szJ6NGjiYmJIS0tzdrspXnz5mzcuJHcuXNz5coVKlasSGBgIDdv3iQ6Ohp3d3fWr19PYmIiLVq0oFSpUixatIiFCxdSuXJlFi1axO3bt9m1axcTJ04kPDyco0ePUqZMmTf9cgkhhBBCCCGEEK/VS3Wj3r9/P6NHj6Zbt25PbZ81axbbtm1j9erVhIaGMmXKFD744INXMlAhxJuVYdG4HKsSm6ZhUkGnGvlx9kSmfTGaYsWK89nXc3Et0YCzsSonDmXw8Np1tu/cjSktmcuXL+Pt7c3w4cMxGAy4u7vj4+PD3bt3KVOmDJs3b6ZDhw4UK1aMLVu2sHHjRpo1a8akSZN4//33KVCggHUcdnZ2NG/enMqVK1unJQNkqGk8tkQRq0aRrqaiKGBx0/MwTSUiJhpVu8eOhdvYsGkTE1f8yNotO/lh4UKmTZtGw4YNMRqNXL16ldmzZ7Nv3z5WrVrFlClTiI2NJTk5mZ07d1KpUiU+//xz/Pz8GD9+PFu3bkVVVQYOHIi7uzsAjRo1oly5chQsWBBvb2/69OmDjY3N3/1yCSGEEEIIIYQQb4SiaZr2ojs5Oztz+vRp8ubN+9T2a9euUbRoUZKTk7l+/TqhoaGkpKS8ssG+jMTERNzc3EhISMDV1fWNjkWIt1F8hsax+xYORVm4l6yh/u8jI/lxFJv6VyUgpDhVPl6M0fJku6eDgq0Orh3dwfapQ0hLSUKzmNBrKtmyevBOqRJMnTqV8PBwYmJi8PT0JCkpicKFC3P06FEcHBxo164dEydORFEUqlSpgoODA9OnTycwMBBN055qrGKxWEgnmTvmSySrcdhgj2Zx4Go8PEjRSLdo2OpAr4Nlw8dw++xFPlo0iakdx5I/tAwLpn6Br5sdALdv36Zv375YLBZSU1M5ffo0ZrOZwMBAYmJiOHnyJD4+PqSkpGBnZ8fOnTv54IMP6NatG0OHDpWGL0IIIYQQQggh/pVeJF97qWnUnp6ebNiw4ZntGzZswNPTE4CUlBRcXFxe5vBCiH+Ie8kqM08bWRph4nGaSg5XCPbUEeypo1hef/JUbMa9mFj2nL1BolEl0Fnj1JJxLOlTE31GIrW7DkPLSEGHhoNPbh4nG1m9Zi2DBw+mYcOGNGzYkDZt2qAoCpcuXWLz5s20a9eOM2fOcPDgQQA6deqEo6MjDg4OAM8EekYljdvmC6So8TgrnlgsTpyK0bidqGLQQVYHBTc7BWcbhdi7D3By88BJbyb54W3SHGyZfwmiklW2bdvGqVOnyJs3L9u3b8fe3h5VVVm0aBFubm60bdsWHx8fDhw4wNSpUzEajZQoUYKPP/6YJk2aSNAohBBCCCGEEELwktOohw8fTvfu3dm9ezelSpUC4NixY2zevJmZM2cCsH37dipWrPjqRiqE+Fs9TFWZf87ErQSVfB4KNrqnw7TYdHAs0wbl9AEyDi7g4H6FdTvnkMUnO+Vb9qRgxXcx2NiSo2BJln3WkcAiZfCu2IH1H5Vh3fofKVoklNq1a9O7d29cXV1Zvnw5KSkpdO7cmSpVqvDTTz9RtmxZWrduTevWrX9zjKpm4a75EqlqIs6KB0ZV4exDC4/TNTztFfS/yv8Kli/Fphk/8PBOCtXavcuGqdO5tHMz4+JjCcqVAzQLJ0+eJFeuXOzatQuAhg0bUrp0aTp06MCePXsYN24cXl5epKWlkSVLlmeWkxBCCCGEEEIIIf7LXips/OCDDyhQoABTp05lzZo1AAQHB/Pzzz9bGyD079//1Y1SCPG30jSNH6+ZuZGgEuShYPhV0KhpGtfiVfRZ8+CZsyA3NkzBNbAwQT2/p0L58uTMYm99rHu2ALLlLsCNEz/j6ZsTG2dPPHy92bZtG9myZUPTNFq1asWmTZuYPn0669evZ8yYMVSvXt16LkVRsFgsz3RyTlJjSVLjcFTcUBQdd5MsPErV8HR4NmgEKFqlLJtm/MDhDdup37M9eYrm4eTuW2S4l6NmYT/0UacwmUzkzZuXGzdu0KpVKyZNmkRUVBRt27bl4sWLfPDBB4wcOdK6RqMQQgghhBBCCCH+30uFjQBly5albNmyf+nkM2bMYMaMGdy6dQuAggULMmLECGrXrg08CRk+/fRTZs+eTXx8PGXLlmXGjBnky5fvL51XCPHH7qdonH+k4uv0bNAIEJ8BsWkaLnYKuWp2Iu7aCXJUaI5n4crcS9XI4aGh+99+lw9vx5SRQWTEKe5FnCa8zRAK1WhB6NrPrQ2kfHx8qFOnDgaDAbPZTIcOHaznypye/OugUdM0Yi33QdPQ6wyYVY3IJA07A78ZNAL4B+WmSpvG/DR3GfZOjjTp35I8YWFE3CvFmWP7SNy7jz59+nD06FFu377NgAED6NChAxEREdy9e5emTZtiZ2f3Cp5hIYQQQgghhBDi3+mlw0aLxcLatWu5dOkSAAUKFKBBgwYYDM9/yICAAMaPH0++fPnQNI0FCxbQoEEDTp06RcGCBZk4cSJTpkxhwYIF5MqVi+HDh1OzZk0uXryIvb39n59ACPFSTsdYSMjQ8HP+7dTufoqKSQVXHdgGFsIlIJgHp3fgU64Z8WQlNh3cdGkc37SImFsRhJSrTVzUTRS9nnLvtiDGLjs9v/iecgEGVFVFUZRnqqF/3Qjm19K1FBLUR9gqjgDEpGkkGzXc7f947cQmA7ryOCqajdMXsHfFBoJKBHPn0gNiYx7TqWd/KlasyLx582jatCk6nQ4vLy+8vb1f8BkUQgghhBBCCCH+m16qG/WFCxeoX78+0dHRBAcHA3DlyhW8vLzYsGEDhQoVeukBeXp68sUXX9CxY0f8/Pzo378/AwYMACAhIYFs2bLx/fff06JFi+c6nnSjFuLFTTqWwa1ElUDX3+4htT/SQrJRw9XuSbD36OIBzi8YRt76PbEv1piUDZ9zccMs7J1debf3BEKrNibqylnWTOxFwQrv4l+vLxWz62gVYvtUoJj5cfQ8zVYSLA+5ajyBs+KJoihcibNwOVYlq8Of76tpGleOneZuxHVibl8hJdEGzzIfYbi0iSObllC8eHGmTJlCrly5nufpEkIIIYQQQggh/tVeJF97qcrGzp07U7BgQY4fP46HhwcAcXFxdOjQgS5duli7yL4Ii8XCypUrSUlJoXTp0ty8eZPo6GiqVatmfYybmxvh4eEcOnTod8PGjIwMMjIyrLcTExNfeCxC/NelmMDmD3rVm1T45exqz+BwHDx9OT2zD+b0zngEBNFy5HwKlK9jfYxP3kI4uWcl4UEkAaqJVLP9M6Hii3R0VrEAinUfkwWed29FUQguFUZwqTDi719i6kdTuHKmH1k9XJkzZw516tT584MIIYQQQgghhBDiGS8VNp4+ffqpoBHAw8ODMWPGULJkyRc61rlz5yhdujTp6ek4Ozuzdu1aChQoYA0ss2XL9tTjs2XLRnR09O8eb9y4cYwcOfKFxiCEeJqNHtQ/qHnWK2D8xW2d3kCOKm2xcXbHvUIH3gkPJ6/H02mlTqejxadzcHBx51qc+odh5vN5Ei1mTrfW6+BFy7Q1TcM70JfWI0dwObYgDUvlok5+2786MCGEEEIIIYQQ4j/rpb7uBwUF8eDBg2e2x8TEkDdv3hc6VnBwMKdPn+bIkSN0796d9u3bc/HixZcZFgBDhw4lISHB+nP37t2XPpYQ/1XZHBVSTb9/v6PhSXXjL/mWrE3Yh9NwzVsSe8Nv1xg6uLijaRpGs4rnn6yt+GcMig0Kyv8qHMFWr6DxYoGjoiigaPjmKYCTdw7c7f5yAiqEEEIIIYQQQvynvdQ363HjxtGrVy9WrVpFZGQkkZGRrFq1ij59+jBhwgQSExOtP3/G1taWvHnzUrx4ccaNG0eRIkX45ptv8PHxAXgm1Hzw4IH1vt9iZ2eHq6vrUz9CiBdTPJseRVHIMP92dOfrrKBpYPnVkq+pZnC0Aa8/WDcx2QROtjoKZdX/7mOeh5PihqPOhQwtFQBvRwU7vUK6+fmPodOZ0DQ9j5I9cLFVKJRVwkYhhBBCCCGEEOKveKlp1PXq1QOgefPm1vXSMhs7vPvuu9bbiqJgsVhe6NiqqpKRkUGuXLnw8fFh586dFC1aFHiy/mJmBaQQ4vUJyaIju4vC/RSVnG7PBofejgrOtgqpJg2X/8061tBINUGQpw67P/hkeZACBbPqf/O4L0Kn6PHU+3FHvYSmaTjbPKnIvJuk4vA7lZW/pjekkpHhwZ1YF0r46PD/ne7bQgghhBBCCCGEeD4vFTbu3r37lZx86NCh1K5dmxw5cpCUlMSSJUvYs2cPP/30E4qi0KdPH0aPHk2+fPnIlSsXw4cPx8/Pj4YNG76S8wshfputXqF6oIEFF4w8TNXwcnw6hLPRK2R3gYuPwc6iYauHuHRwtVPI7vL7gV1smoZOgTJ+enQv0Azm97jrvXlgvkm6loKD4kyAi8KDVIUko4aL7R8fX1FMKIpGVGw27A0KZfz1L9SgRgghhBBCCCGEEM96qbCxYsWKpKenc/bsWWJiYlDVpxdvq1+//nMdJyYmhnbt2nH//n3c3NwIDQ3lp59+onr16gAMGjSIlJQUunTpQnx8POXKlWPr1q3Y29u/zLCFEC+glK+OuAwDP14zk2bW8HVSsNH/fxiXy01HkknlZvyTlRLd7BRCvXQ4/07I9yhNIzZNo25uA8WyvZrpynaKA76GPNw1R5ChpZHVwYFgT7j4yELi/wLH3xqNopixsU0iOi4b0fFeNMxroGAWmUIthBBCCCGEEEL8VYqmaS/awJWtW7fSrl07Hj169OwBX2Lq9OuUmJiIm5sbCQkJsn6jEC9I0zQORVn46ZaF+8kqOkXB2VZDryiYVI24NI0HqRppZgU/Z4Xc7gqutlgrBFVNIy4dHqVq2BugRi4DtXMZMOheXQWhpmncN98g2nIDnabHTnEkMlnhcqxKmlnDXg+ONgpPclINdBmoSioxCV7ExuanTm5HKgZIVaMQQgghhBBCCPF7XiRfe6mwMV++fNSoUYMRI0aQLVu2lx7o30HCRiH+ujSzxoVHKkfvW4hK1rBoGrZ6hWBPhaJeelLNGsejVS49tpBigszcTtPAzU6hqLeOEj568nvqXkuop2kajy33uG+5SYaagoIes8WBh8kKkUmQZrFga8jAxpCB2WKHKSMbue3yEuZtT4CLVDQKIYQQQgghhBB/5LWHja6urpw6dYo8efK89CD/LhI2CvFqaZqGReOZ6kRN04hM0riRoJJuBp3ypDN1Pg8d3o5/T6Bn0owkWB7yWI0i1ZKIigWLCslmBc1ih43FFxe8yefmgrOthIxCCCGEEEIIIcTzeJF87aXWbGzatCl79ux5K8JGIcSrpSgKv9XsWVEUsrsqZHd9cyGejWJLVoM/WTRf0g0pWDCjoaHDgL3igF6xeWNjE0IIIYQQQggh/gteqrIxNTWVZs2a4eXlReHChbGxefoLfK9evV7ZAP8qqWwUQgghhBBCCCGEEOLlvfbKxqVLl7Jt2zbs7e3Zs2fPU2uwKYryjwobhRDibZaeno69vf2bHoYQQgghhBBCCPFcXqqy0cfHh169ejFkyBB0un/2umdS2SiEeBsZjUZsbW2tt8eMGUPhwoWpUaMG9vb2qKr6j//8FUIIIYQQQgjx7/DaKxuNRiPvvfeefNEVQojXJDNonD9/Pp06dUJRFPR6PSEhIZw5c0Y+f4UQQgghhBBC/CO91LfV9u3bs3z58lc9FiGE+E8ym83PbPv4449xc3NjxYoVdOjQgZs3b1KxYkXOnz9P//79f3c/IYQQQgghhBDiTXqpykaLxcLEiRP56aefCA0NfaZBzOTJk1/J4IQQ4t8sMyw0GJ79KE5OTiYpKYk9e/Zw+vRpcuTIwYIFC6hQoQJTp05l0qRJv7mfEEIIIYQQQgjxJr1UZeO5c+cICwtDp9Nx/vx5Tp06Zf05ffr0Kx6iEEL8OxkMBgwGA6tWraJKlSqMHj2a48ePA9C2bVt8fHxQFIXg4GAA/Pz8+OijjzAajYwfPx4AVVXf2PiFEEIIIYQQQohfe6kGMW8TaRAjhPinunnzJtWqVePGjRu4ubmRnJyMXq9n9erV1KtXj969ezNlyhQWLVpE69atAbh9+zalS5cmOTmZxMREzGazVDgKIYQQQgghhHitXiRfkw4DQgjxGqmqitFofGpb5vTphQsXcufOHZYvX058fDynT59Gp9Px4Ycfcvz4cT788EPc3NwYNGiQ9ViBgYF06dKFpKQkFixYIEGjEEIIIYQQQoh/FAkbhRDiNVFVFZ1OZ+0sfezYMeD/12j8/vvv8fDwoF69eqSnp1OoUCH69OnDw4cPWbhwIcHBwTRs2JD79++zd+9eawfqli1bUq9ePcLCwmQatRBCCCGEEEKIfxQJG4UQ4hVav349AOnp6dZwsEaNGtjZ2VGlShUKFizIjBkzALC3tyc1NRVHR0draDhu3DgA69qN7733HnZ2drRs2dJ6juDgYDZs2EBoaKj1HEIIIYQQQgghxD+BfEsVQohXpFu3bjRs2JD4+Hjs7e1RVZUSJUqwb98+unbtSq9evVBVlZ49e5KcnEzTpk1JSUlh586dODo6kpqaCoCjoyP37t0DoHTp0jRp0oTw8PA3eWlCCCGEEEIIIcRzkbBRCCFekQYNGmAwGGjYsCEAS5Ys4dSpU2zcuJEpU6bQsWNHDAYDqqry7bff0qhRI9zc3GjevDkXLlzA0dGRjRs3kpKSQuPGjQFwd3dn0aJFrFmz5g1emRBCCCGEEEII8XwkbBRCiOdgVlUep6nEpKo8ik+kYsWK5M6dm7lz51ofU7ZsWcqXL8/PP/8MPFmz0cPDgzt37hAUFETevHmJiopi5MiRDB06lGLFivHdd9+RkJBAWFgYgYGB1K9fHxcXFzp37vymLlUIIYQQQgghhHhp0sZUCCH+wLmHZjZcM7P3nkqqSQMNUu9e4sS+faBpdOvWjWvXrjFmzBhcXV3p0aMHe/bsoWPHjpQpU4a4uDg6duyIv78/s2fPtoaIly9fJiMjg+bNm2NjY8P69es5duwYI0aM4LPPPnuzFy2EEEIIIYQQQrwkRdM07U0P4nVKTEzEzc2NhIQEXF1d3/RwhBBviZhUldGHMjgRrZJi1nAwgL3+yX0mFU6NqELGg+voUFHM6YQEB7N582b8/PwIDQ3l0qVLmEwm7O3t8fPzIyIiwtqVOjY2ljp16uDr68vatWvf4FUKIYQQQgghhBB/7kXyNZlGLYQQv3I/WaX3znT2Rlqw1WvkcgVfJx0e9k9+vB11FGn1MTrA1r8guVqP5/yFC+TPn59Dhw4xaNAgzGYz48aN4/333+fevXsEBwfz7bffMmbMGIKCgjhz5gxFixZ905cqhBBCCCGEEEK8UlLZKIQQv2A0q3Tfkc6JByp+TmBv+O2/yaiqyq4+pUiPvU/w+BO4JkdxdmxdNNVCmTJliI6O5urVq6SlpfHJJ5/w5ZdfYmtrS2pqKkFBQaxbt47g4OC/+eqEEEIIIYQQQogXJ5WNQgjxkrbftnD2oYq3w+8HjQA6nY7g5oNRLSYe/dCbFN+idJr1M1WrVmX79u3cv3+f9PR0Jk+ezJgxY7h37x7r168nISGBS5cuSdAohBBCCCGEEOJfScJGIYT4hc03zFhUcLb9849H/3caYeealdjLR1AeXuOiIYh5y9bSqVMnMjIyAJg5cyYAWbNmpUqVKjg7O7/W8QshhBBCCCGEEG+ShI1CCPE/V+NUzjxUcbN7vsfrDAbyNuiFJSOVx1u+4nGaytabFubMmcOhQ4f49NNPuXLlyusdtBBCCCGEEEII8Q8iYaMQQvzP7USVdLOGs83z7xNYrQM2jq5EH/kRU1Is91NUVFWlYMGCfPbZZ69trEIIIYQQQgghxD+RhI1CCPE/GWYNTQOd8vz7GGztyVGlLS4BwViMKWRYnqznKIQQQgghhBBC/BcZ3vQAhBDin8LRRkGnA4sG+hfYL6TlcHStP+VWoorji+z4gjRNw2KxYDDIR7cQQgghhBBCiH8mKb8RQoj/CfHU4WKrEJ/+YvvpdDrSzCo6IJ/n6/tYVRTFGjRu3ryZo0ePkpaWBjwJIoUQQgghhBBCiDdNwkYhhPgfH2cdZfx0pJhB09QX2vdxGvi76KgW+PpKGzVN46uvvsLT05OhQ4fStm1bGjZsSGRkJIryAnO/hRBCCCGEEEKI10TCRiGE+IV6eWxwMCjEZTz/PulmFZMKNXLqsTe8mo/V36pUPH36NKtXr+arr77izJkz7Nq1i9TUVNq3b4+qvlg4KoQQQgghhBBCvA4SNgohxC+U8nlSnZiYAQnGPw/wjBaVqGQIyaKjRf4XaGP9JxRF4datW1y5csW6bfr06fj5+dG+fXsiIyMZN24cx44dw9XVlcTExFd2biGEEEIIIYQQ4mVJlwEhhPgFnU7Hx+/YkmrS2H3XQrJRJYs9z1QsmlWV2HRINkKwp44x5e3J4vDq/n6jaRqdO3cmPj6e48ePYzKZcHBwQFEUWrduzY8//kjFihXZtm0bFSpUeGXn/TMWiwW9/jV2wRFCCCGEEEII8VaTykYhhPgVe4OOseXt6FTYhqwOOmJSFW4lqNxLVolKVrmdqBKZBLZ6hXp59HxTxZ5cbi/+cfrrqdIRERHW7YqiMGTIEM6fP8+JEyewsbFBr9ezcOFCEhMTOXr0KBs3bqRChQqkpaWxaNGi1zqV2mKxAEjQKIQQQgghhBDiD0nYKIQQv8HWoOPDMDuWv2vP0HdsKRegJ7ebjhyuOopl09O1qA2L6zowurwDPs7P/1H6y4Dxl01dNmzYQIECBbh//751e4UKFShevDjjxo0DoGnTpnh7e1O2bFlCQkKs+27dupUlS5Zw7ty5v3rZvyszZFy0aBHt27fniy++4M6dO6/tfEIIIYQQQggh3k6K9ltdCP5FEhMTcXNzIyEhAVdX1zc9HCHEf9ivpyDPmzcPFxcXmjZtysOHD6lQoQL16tXjyy+/tFY3rl69mhYtWnDx4kXy5cvHoEGDWLlyJXnz5qVq1aps3bqVCxcuMHDgQHr16oW9vf1rGeuuXbvo378/cXFx1KtXj59++om8efMyYMAAqlatiqqq6HTy9yshhBBCCCGE+Dd6kXxNvhkKIcQrpGka6enpfP755xw4cOCp+/R6Penp6YwcOZKtW7cyYsQIvv32W+7fv4+3tzfdunVj5syZpKSkoCgKmqZRtWpVcubMyTfffAPA6NGjmTp1KoUKFWLPnj2UKlWKa9euMWjQoFcSNP5yunRGRgabNm0iMTGR8+fPU6FCBW7dusXUqVPZuXMnUVFRzJ8/n4yMDAkahRBCCCGEEEIAEjYKIcQrpSgKGRkZnD9/Hn9//6fuO3XqFEFBQWzevJmzZ8/i7OzM8ePHraFks2bNcHNzY/LkydZjAbi7u7N69Wru3buHra0tdevW5auvvmLr1q1MnDgRNzc3LBbLM2tAvozMQPTIkSP4+PiwcOFCEhISqF+/Pp9++ilpaWl88sknlCpViocPH3Lu3Dk2btwIPLsGpRBCCCGEEEKI/x4JG4UQ4i/4dcBmNBpxc3NjxYoV5MyZE6PRaH3chg0b8PHxYdu2bQwaNIgDBw5QrFgxZs2aRVxcHP7+/nTv3p2vv/6a69evA3Djxg1y587NgwcPWLly5TPnzwwZ9Xr9U2tAvuz4d+3ahbe3N/Pnz2fChAksX76c7NmzkzNnThRFoWXLluzbt4958+axb98+jEYjGzZsAHip8wshhBBCCCGE+HeRsFEIIf4CRVGIjY1lx44dANja2gJPQsAPPviASZMmkZ6ejqIo7Nmzh9DQUNzc3DCbzWTJkoVx48axa9cuDh8+DED//v3x8vKiTp06NGjQgAYNGlCjRg3u3r1Lnz59njn/y4SMmVOlM8d/6tQpa3OZkiVLki1bNn744QdKliz51OP37NnD0aNHmT59OnXq1CF37txomsbBgwelulEIIYQQQgghBCBhoxBC/CVpaWmMHz+ed999F3jSVXr48OHo9XqSk5PZsmULV69eBSA0NJSTJ08CYDAYUFWV8uXLkzdvXpYsWUJMTAwODg6sX7+eXr164erqysKFC+nUqZN1SvarmioNsHPnTq5evUrVqlUZM2YMsbGxuLi40KVLFwwGA87Ozk89/urVq+TJk8darbl8+XJy5MhBQECAtRJTqhuFEEIIIYQQ4r9NwkYhhHhBmqZZq/0cHBxo1qwZDg4O+Pr60rx5c9zd3QEYPnw4N27cYN++fQCUK1eOxMRE67RjnU7H3bt3MRqNbNu2jYsXLwIQHBxMjx49+OGHH6hcubL1nPBqwrzLly9TokQJ3nvvPb777js0TePAgQNcunQJgI4dOwKwZcuWp85dpkwZMjIyaN26NTVr1qRPnz5069aNVatW0bt37788LiGEEEIIIYQQbz8JG4UQ4jllhoyKolir/UwmE9u2bSM+Ph5FUUhLS6N///5omkaBAgUoXbo069evJzIykkqVKlGqVCkGDBjAqVOnSE9PZ9++fbRu3RpHR0cWLlxIcnKy9Vzw/1OYXzRkVFX1qenSmdsA1q1bh8Vi4eLFi4wZM4atW7diNptZvHgxKSkpZMmShY4dOzJ16lQSExOt5y5Xrhzz5s2jUaNGFCpUiKNHj9K4cWM8PT3RNE2mUAshhBBCCCGEkLBRCCGeV2bImJaWxqeffkr9+vU5efIk3bt3Z/v27VgsFmsTl4yMDAD69OnD+fPn2blzJ1mzZuXrr7/Gzc2Nxo0bExQUROfOnWnSpAljxoxh9erVREREWM8F/z+F+UVomoZOp0Ov12M0Grlz5w7wpJISYMmSJVSuXBlvb28MBgPh4eEMGzaMJUuWcOXKFQD69u1LVFSUdS3GzOMWKlSIsWPHMmnSJHLkyIGqqmiahqIoMoVaCCGEEEIIIYSEjUII8Ud+Xa333XffERAQwK5duyhfvjw6nQ5PT09CQ0OpXr06EydOBMDOzg5N0yhbtiyFChVi06ZN3LhxA29vb7Zu3crixYsZM2YMSUlJFCtWjFatWqFpGjNmzCA9Pf0vjTOzaU2XLl0ICgqiefPm9OzZk2PHjgEQGBhoXWMxs9qxc+fOAKxZs4bU1FRy5MhB7dq1GTp0qDU4/WWYqGkaqqqi0+kkZBRCCCGEEEIIYSVhoxDiP+3Ppv4qikJUVBQPHjzAaDSydu1a+vbty759+xg4cKC1Y7OXlxfNmzfn6tWr7N69G0VRrNOYe/fuzebNm9m/fz8Anp6elClThrZt21orJQGmTJlCxYoVsbe3f66xp6enEx0dbR1npuTkZHr06MGNGzf4/vvvGTZsGGlpabRv3x6AGjVqcOHCBa5evYrBYEDTNBITE/Hw8GDjxo3cvHkTgHHjxjFhwgTs7Ox+83nJrJQUQgghhBBCCCEyyTdFIcR/Vub030y/XuMQ4Pjx49SoUYNTp04RGRnJzp07CQ0N5datW2zevJkDBw6we/duzGYzFSpUoEKFCgwbNgyAxMREdu7cSZ06dejQoQNhYWG/OQ4HBwcAOnToQLt27Z577NmzZ2fGjBkAHDhwgNWrVwNw8OBBdu/ezcaNG6lUqRK5c+fmzp07REREcP78eSpWrIivry+ffPIJJpMJRVE4c+YMZcqU4eLFi9aGNnnz5qVFixbP+WwKIYQQQgghhBASNgoh/sMUReHatWuMGjWKCxcuPLU+otlsBiBXrlxcvXqVbNmykTt3burVq8dHH33EO++8w7Rp02jTpg1t2rRhyJAhuLu707dvX86cOUPFihXJmjUry5YtIyMjg6lTp1K4cOFXMm6z2YyiKPTp04fZs2cTHBxM5cqVuXr1KgD37t2jfv367N27l6pVq1K6dGn8/f05fvw4hQoVokCBAowcOZItW7YQHh5OzZo1adKkCd27d6d58+asW7cO+P8p1kIIIYQQQgghxPMyvOkBCCHE3+HXVYwA165do1q1aty5cwedToe9vT3dunXj+++/x9/fH3jSVKVAgQIcPnyYsLAwZs+ezYULF8iaNStJSUkULFiQUaNGsXv3bu7cuUPlypXZsmULR44c4dtvvyU0NNR6PovFYg00NU3jXrLGrQSV6BSN2HQNRQEPOwU/Z4Wcbjp8nZ40Xcnsgm0wGKxjMpvNrFmzhujoaIoUKcLBgwfJkiULAEajkfnz57Nu3TpatGjB7NmzyZ07NwA3b97E1taWKlWqsGvXLg4ePMjly5eZOHEiRYoU4YsvvkBVVet6jEIIIYQQQgghxIuQsFEI8a9j1kykqAlYMAMaOvTYK87YK44A1iDN39+f5ORkihcvzoULFzh//jxms5lWrVoxfPhwqlWrRkpKCjqdDjc3NwCyZMlChQoVnjpfXFwcBQsWJEeOHACUL1+e8uXLW88FWLtDA1yOtbD7joVLjy2kmEBRwOZ/uZ5JBU0DF1uFAp5QOdCGvB56a9B47tw58uTJg6OjI4sWLeLrr7/mwIEDpKamWsPG9u3bM2jQIN5//30mTJhgHef9+/f59ttvCQsLo23btpQsWdK65iTAxo0biYiI4Msvv5SgUbx2v/UHACGEEEIIIcTbT8JGIcS/gqZppGspxFkeEKtGka6m/vJedq0+RMqDDHr37Iu7rRdmsxmDwUC1atVITU3l8ePHPHr0iI0bN9K9e3e6du3Kli1bCAoKIj09ncjISOBJdeLjx49ZuHAhTk5OzJgxg+TkZKZPn/7MmH5dHZhh0dhyw8zuO2ZSzeDjpBDgwjOBy50Lx3DMU4KjDzQuxpqomt3E1U2z+HTEcHx8fMiXLx9ffPEFBQoUYNSoUfj4+LB9+3bat29vrdDs0aMH69evJzo6mk6dOnHjxg2mTJmCi4sLbdq0sZ4rIiKCzZs3s3jxYq5evUr//v2pXr36q31xhPiFzN+9X3c3l+BRCCGEEEKIfwdF+7NWrG+5xMRE3NzcSEhIwNXV9U0PRwjxGqiaSrT5JjGW25g0IzbYYac4oFOeVBJuWLqVQR0/BaDrsLb0GtyDPI6hxD6I58MPP6R169asWLGCtLQ05syZQ9asWXn33XdJSUmhZ8+eHD58mKSkJGbNmmU9Z4cOHbh8+TKNGjVi0KBBfzrGdLPG0ggTByItZHVUyOrw28HK8U2L2DlvPI0Gf0O+klXYvmw28fHx2Mddo0/7xri5ONO5c2cqVarE559/Tvbs2WnevDm3bt3ixx9/xMfHB4C0tDTWr1/PhAkTcHBw4P79+3Tt2pUhQ4Y8db7U1FRWrlxJbGws3bt3f+5O2EI8j5iYGLy9vX/zvp9++gkbGxveeecdHB0d/+aRCSGEEEIIIV7Ei+RrEjYKId5qmqYSabrMA8sdbBV7bHF4pkIq7nE8QzqNJO5xPBaLhVJVi9BzcDcKeYZTPLQUQ4YMwcvLi6FDh9KlSxe6du1KTEwMo0aNYtOmTdjY2NCmTRuGDx9urcp69OgRWbNmtZ7jl+sxPjtGjTVXzWy+YSa7i4Kz7e9XcKXEP2LxsHb4BRWl9oef83XbcOKi7xL63kA+/WQwdXPb8MMPPzBt2jTat29P9+7duXLlCgUKFGDhwoW0atUKk8nEgwcPCAgIIDU1lZiYGHLmzPlcYxXiVQkPDydnzpzMmzcPJycna/XismXL6Nu3L/b29qiqSkBAAJMmTeKdd95500MWQgghhBBC/I4XyddkUS4hxFst2nyLGMsd7BUn7BTH35yK6ZHFnSKlCuHlk4Vq71bk7uVohvcezR3TJWrXqcXFixepVKkSAQEB7N69m0ePHuHt7c3EiRNp3LgxV69e5ezZswDWadGZQaPFYgH4w/Du4mOVPXfNZHP846ARwMk9K3mKV+Tm6QMkPoqiasch6HQ6PDw82HHLwrU4lfr165MtWzZ+/vlnYmJiCAoK4t1332XcuHE0atQIV1dXJk+eDICjo6M1aMzssC1Bo3idMt9nH3zwAdu3b+fKlSvAk+UCHj58yNdff03Xrl25du0aCxYswNfXl7Zt2xITE/Mmhy2EEEIIIYR4RSRsFEK8tTK0NGIsdzAodtgodn/42Mbt6pGUkILFojL0i35cO3ubz4eM48Tp43h7e2Nra0v16tW5c+cO27dvB8DBwYHPPvuM9u3bk56ebm0W80t/FtypmsaO2xaMFgXP35k6/Wsl6rZBp9dxYtNiCpavh1dgMEm3zhGXmMSuO2ZcXV2pVasWd+7cYdu2bQB8++23dO/eHTc3N7Zu3WoNG38ps8mMEK/LL9de7Ny5M7a2tixevJj09HQAtm7dysWLF+nRowc6nY5KlSoxb948Hjx4wPLly9/k0IUQQgghhBCviISNQoi3VrwlBpOWjh1/vt6bT0A2CpcI4fDuY9g72DFmxjAeR8exe/vPnDx5EoCmTZvi6OjIsmXLSE190mDG2dkZd3d3NE2zTgV9ETcTNK7GWfB1ev593Lz9CSxcmitHdmDKSKNwlYbcu3wa442jXHysci9Zo0mTJjg6OrJy5UpSUlIICAige/fufP/991SsWBFN06ydsIV43TLfa4qioNfrOXXqFLdv32b8+PHMnz+fa9euAeDk5IS9vT22trYoikJGRgaurq60bNmSH374AeCFf8eEEEIIIYQQ/ywSNgoh3koWzcJjSxR6bJ67i22zjg0xWyys+v5HCpcoQL/Pe1DrvSq07tQCTdPw8fGhdu3aVKtW7ZkqwMzbL9ox92aCSrqZP50+/WvhDTtiMZs4uXUpJeu1QzWbuH9mN4lpJm4lqHh7e1vHamNj89TYLBYLiqI8U4UpxOuS+V579OgREydOpEKFCixbtowOHTpgNBpZt24dAK6uruTOnZvFixcD/18ZXLhwYRITE0lOTpau1EIIIYQQQrzlZE6dEOKtlK4lk64mY684P/c+gXmyExIaxJGfT/Bui1oE5Ajg83kDyG6T3zr9s3///k/t061bN2bPns3atWtfapyRSSr6l8j8smbPg39wUS78vIFSDd4npFxtLu3fgmOBqkTlqQbwzFgzyZqM4nVTVfWpMNtsNtOvXz+ioqIA2LVrF8WLFwegR48ezJ49m44dO1KiRAkKFizI/Pnzef/993FwcABgy5YtVKxYEWdn52eOLYQQQgghhHi7yP/mhRBvJYtmRkVF9wIfY4qi8F7nRqSlpLF55ZO1DjUNzKrpqXBD0zRMJhMAXbt2JSUlhfr167/UOB+mati/RPanKArvNOqMMT2VU1uXUqJuG5w9vXB0duFx+v9PM9U0Taadir+FpmnWhki/DgMNBgOenp5s2bIFTdMoWbKktVHMiBEjuH//PqtWrcLd3Z3OnTtjMpkoVKgQQ4YMoX79+pw9e5ZGjRr95rGFEEIIIYQQbxf5H70Q4j8lqFBePLK6cz/yASaTGUV5dmq0oijWqclhYWHY29u/iaHik7cQzh5eRF09h6d/Ljp9vR7v4OL8MltUFEWmnYpX6vfW+sxcjxFg2rRpTJgwwdqgCJ50nw4ODiYjIwNVVbG1tcVoNOLo6EiHDh2YNWsWUVFRlC5dmk2bNtG+fXvOnj2Lj48Phw8fplatWn/L9QkhhBBCCCFeL5lGLYR4K+kUPTp0qKjoX+DvJjqdjvFzPsXV3QWAdFVBp7x46eHzTvX0sFe4Hv/ChweejLXFZ3NxcHG3bsswWvB4mVJJIf5E5ns683196tQp/Pz8cHd3x87ODpPJxOzZsxk9ejTu7u6EhITw6aefMm7cODp37oy/vz/Vq1fnwIED7N27l0qVKlmD8M8//5zs2bOzZcsW2rdvj7+/PyNGjMBisci0fyGEEEIIIf5lpLJRCPFWclCcsNU5YNTSXnjfzKDRZMlAQcFRcX3ufTOnLGcGMpnTrX9PDlcd5r/QFDozaFRV9cm59Xr8nOWjW7x6me/p8ePHExwcTL9+/ejRo4e1mcuDBw/46aefGDt2LBcvXmT16tWMGDGCBQsWsHHjRgBat25NRkYGO3bsAMDGxgaz2YyPjw/ly5dn06ZNT/3OSNAohBBCCCHEv49UNgoh3kp6xYYsOj8i1SvY/6+5y4syKuk46dxw1nk89z6Z57l+/Tq9e/emXbt2NG3a9HerHHO4KNjqIdWk4Wjz8tOddTodSUYNBwMEukrYKF69iIgIOnXqRHJyMp999hlhYWEkJiZSsGBBALy8vBg8eDBlypTh9u3bTJgwgQULFuDu7s6aNWto1KgRoaGhlChRguPHj3Ps2DFKlixpnZa9fv16XFxc3uQlCiGEEEIIIf4G8o1VCPHWctd7Y6PYYST9hfdVNQsqZrLo/dEpz/9RePLkSX7++WdmzZqFXq8nLCzsd9e4A8jnoSOXm477KX+9icv9FI1gDz2Brn99jUZpKiN+bcqUKdjb27NixQpatmxJ/vz5KVWqFE5OTgDY2dlRpkwZ9uzZQ7169YiMjGT//v307NmTEydOsHXrVgDatGlDREQE69evB8DW1hZAgkYhhBBCCCH+IyRsFEK8tRx0zmTR+2HUUjFrfzyd+Zc0TSVFi8dZ54m73vs3g7fMTrq/ZDQamThxIpUrV+by5cssXbqUfPnyYTD8fpG4XqdQNfDJ/YkZLx/wxaZr2Omgcg79SzeEuXr1KpUqVSIjI0OayoinXLp0iVWrVtGwYUOCg4N/8zGZvyezZ88mODiYZcuWERYWRv78+YmOjmb16tUAlC1blj59+tC+ffu/bfxCCCGEEEKIfw4JG4UQbzU/Q16y6P1I0xIxaRl/+nhVs5CsxeGocyXQpgA2iu1vBm+ZAeLhw4eJjIwEnlRo9erVCw8PD/Lnz4+jo+NzjTHMW0cZPz2RyRrp5hcPHFNNGg9SNCpmN1Agy8t/bGuaxsGDB5kxY4b19ovub7FYXvr8L0rTtN8MfcWr9+DBAx49ekSVKlWA335vKIpCdHQ0MTExZM+e3fr+37VrF4ULF+bWrVucOnUKgD59+pAvX76/7wKEEEIIIYQQ/xgSNgoh3mp6RU8OmwJ46wMxamkkqbFkaGlPhSWapmHWjKRocaRoCbjoPMllE8rB3UeoWLEiBw4ceOa406ZNw9fXl86dO1O2bFk+/fRToqKiKFOmDGXLlmXfvn0AfziFOpOiKDTKZ0Mxbz03ErQXqnCMT9e4najxjq+eunkMv1uRqKrqU0Fg5vVv2LCBixcvAhAUFET//v354osvrON6XhaLBUVRrA09/qwxzqtgNputoe++fftITk5+7ef8r7K1tcVgMHDu3Dng99/XPj4+FCxYkNWrV9OhQwfKlSvH9evX+eqrr9i8eTNhYWF/57CFEEIIIYQQ/0ASNgoh3np6xUB2m/zksy2Otz47GirJWixJ6pOfZC0Oo5aGiy4LWdNy8viUCQedM35+fowfP56yZcs+dbyNGzcyd+5cxo8fz6lTp5g+fTqHDh1i0KBBAPTs2ZPDhw9z4sQJdDrdc1UIOtsqdChkQ6Xseh6mwY14lTTT7++XYtK4FqcRl65RPVBPm4I2OBj+OBzU6/UkJyezZs0abt26RXp6Og0aNODEiRMApKWl0bRpU1JSUli0aBHwx9WNv7wv89iDBw+mcOHCtG3blnnz5v3mY1+Wqqrs3r2bAQMGWI/5ySefkCVLFrp06ULZsmUZPnz4Xz7Pf8mvQ+jf4+HhQaFChfj2228BfrPh0erVqzl69Ciff/45n376KTExMZQvX55Vq1ZRpkwZWZNRCCGEEEIIAUg3aiHEv4SiKLjoPXHRe5JNy0WyGodFM6OhoUePvc4ZS5JCr769OH78OBcuXCAkJASAx48fk5ycTGBgIEajkR9//JFKlSrRvn17EhIS2L9/Pz///DMNGjQgOTmZ0qVLU7FiRUaNGsW6deuee4zOtgptCthQMIvKjjtmbiWoGC0atnqwe1IwSLoZzOqT20Eeeqrl1FM4q+6ZKkSTyWQNhPR6PWlpaQwZMoT58+eTJ08ezGYzefLkoXLlyty+fZtOnTqxfft2atWqRWJiItOnT6dNmza/W92o/aLDt6Zp7Nixg48++ghfX18++ugjrl+/zogRI9A0jU6dOr3UGpCZ51BVFZ3uyTWeOHGCFStWULVqVUaMGMHVq1eZPn067777LidPnqRGjRr4+/vTuXPnP1wrUzx5fjPfIw8fPiQhIYHAwEBsbGysz3mmkJAQ6taty7hx41i7di2NGjWyHkNRFH7++WcWL17M4MGDcXV1pVOnTnTq1OmNXJcQQgghhBDin02+qQkh/nXsFAfs9A7P3uEGVatW5fTp06xevZomTZoQERHBBx98QHBwMHPmzMFgMLBv3z769OlD9+7dWbRoESVKlGDdunXUrl3beqiPPvqI5s2bc+XKFYKCgp57bDpFobiPniLeOq7GqdxK1LiTqBKbrqEAWRwUcrg86WCdx11Br/vtkNHGxgaAu3fvcu/ePebOncuqVato0qQJCxYs4N69ewQGBpI1a1YcHBy4ceMGixYtws7ODqPRyMKFC9m0aRN169Z96viZ4ZKiKJw5c4aVK1fy4YcfYrFY6NChA0OGDAEgJSWFFStWsHTpUsLCwihWrNhTAeXvuXHjBuPHj2fGjBno9XprIGaxWIiJiWHJkiVER0fTqFEjbG1t+eSTT2jVqhVXrlxh06ZNmEwm4uPjMRqNEjbCM8955u3Mf2/evEm/fv3Yv38/efPmxWw2s3LlSnLmzGndJzN4bNOmDWfPnqVVq1YMGTKEihUr4uTkxKZNm1i5ciXvvvsuoaGhb+AqhRBCCCGEEG8T+aYmhPhPyAxfKlSowKZNm5gzZw5NmjQhf/78FCtWjHPnznH69GmKFi1KeHg43bt357333mPr1q3WadZpaWkcOXKEYsWKUa1aNQIDA1m7di2DBw9+rjFERUWRnp5O7ty5UTSVkCx6QrI83/gz1y/MDBlnzZrFqFGjSE1NxWQyYW9vT40aNVi5ciWurq4sX74ci8WCg4MDe/bsYciQIZQrVw6dTkfu3LmJi4tj1KhR1KpVy1rhlhkynj9/HkdHR5o3b06JEiWwsbGhSJEiVKlShTt37tCvXz927txJSEgId+/eZfv27RQrVuwPg8a1a9cSGRmJg4MDgYGBpKam4ujoiF6v5+rVq+zcuZMbN25gb29Prly5MJvNODk5YW9vT6tWrdiwYQPVqlVj3759z0x7/y9TFIWYmBhiYmIoVKiQ9TVQFAWj0UifPn2wsbFhz549uLu706NHDzp37szYsWMpVaoU8P9TpoODg1myZAlDhgxh2rRprFmzhri4OLy8vJgwYQL16tV7Y9cphBBCCCGEeHvImo1CiH+tX3YyzgxhcubMSdWqVbl58yZbt24FoHbt2phMJtasWQNAs2bNUBSFXr16PRVsLV++nBUrVhAXF4erqytHjx597qDxxx9/JCAggJ49ewJYG638mcwO0JlVfCtWrCBbtmx069aNe/fuUbBgQb799lvKlCmDpmk4Ozszbdo0VFVl06ZNVKhQAScnJwwGgzVU8vT0pFWrVhw5coSRI0daQ8b09HSuXLlCaGgoTZs2pWfPnixevBgvLy98fX2xWCz06tULg8HA9u3bOXjwIFmyZGHLli1cuXLFOt5fSktLo2/fvjRp0gQHBwfatWvHJ598gouLC3q9nu3bt9O9e3eGDx/O7t27WbFiBT/88ANZs2YlPT2dfv36YWdnx6FDh1i7di1ly5YlJiaGY8eOPdfz928XFRVFx44dreuJrlq1iqVLl2KxWNixYwd3795l4cKFFCxYkIcPH3Lz5k2uXbtGYmLiM8fSNA0nJye+/vprLly4wA8//MC2bds4ceKEBI1CCCGEEEKI5yZhoxDiXyezk25mQHf27Fni4+Ot95crV46goCDmzJkDQLVq1ShYsCD79+/n1q1b1K1bl7p169K+fXvef/99lixZQu3atfn444/JlSsXPj4+AGTJ8pxlicDmzZspX7489+/ft3a//r3GHZqmoaqqtRpTr9czceJEnJ2d6dmzJzlz5sTT0xN40mE6c8rxjz/+SHx8PP7+/iQlJZGamsrVq1cBuHLlCjExMcCToDNXrlwoisKSJUuYNGkSNjY2zJ07l+zZs9OjRw/OnTtnrXzL9PPPP3Py5Ek6duxIiRIlSEtLA+DatWusWLECeLbD9cOHD1m7di0bN26kc+fO2Nracvv2bUqWLImvry8dO3YkMjKS5ORkHj16REBAACEhIVStWpWEhATy5s3Lhx9+aF1fE+D7779n9uzZf0tH7H86Pz8/6taty+nTp/Hz86NNmzaYzWb0ej0xMTFYLBZOnjxJkSJFqFGjBlWrVmXPnj1Uq1btmWNlvnZ6vR4vLy9CQ0PJnz//331JQgghhBBCiLechI1CiH+FzIAR/n9a6BdffEH27Nlp3bo11atX57vvvgOgQIECVKlShfPnz3Pw4EEMBgM1atQgPT2dZcuWATB//nx69uxJQkIC33zzDQEBAZw6dYqBAwdiZ2f3h2P56aefSEpKAsBoNAKQmJhIpUqVyJUrF7NmzQKeDeY0TSMjI8O6jmHm/W3btmXo0KGUL1+es2fPkjdvXmsIefjwYUaNGsWdO3fw9vYme/bsZM2alYkTJzJv3jyOHz9OcnIyFy5cYPPmzdYx3bt3DwcHB65fv86ECROYPn06Xbt2xcHBgWbNmgEQGxsL/H+FaFpaGs7Ozty/fx940p24YMGClCpVily5cj31GsCTMNXf3x+TycTp06f55JNPGDRoEGfPnuXmzZt4eHhw8+ZNIiIiKFWqFJGRkWzYsAEXFxcqVKiAt7c3BoOB+vXrM2LECObNm0eZMmWYOXMmZcqUee7q0H+LzCrXX95OSkpi165dREdHkzt3btLT02nbti0AgYGB3Lt3j/r169OhQwfOnDnD5MmTyZkzJ1euXOHWrVtv6EqEEEIIIYQQ/2aK9us5b/8yiYmJuLm5kZCQgKur65sejhDibxAfH8/gwYM5e/Ys3bt3p2LFiixcuJAVK1bwxRdfUKtWLY4dO8Ynn3yCn58f33//PcnJyfTu3Zvo6Ghmz56Nn58fABkZGej1+uduRnLkyBGqVq3KzJkzadOmDfBkqmvDhg3ZsmULc+fOZe7cuaxdu5YCBQo8E9DpdDqio6OZOnWqtcHH0aNHuXv3LmazmcGDBxMZGcmCBQusj/f19SVv3rzs378fnU6Hi4sL9evXZ/fu3URGRpI7d27Kli3LypUrGThwIFmyZOH777/Hzc2NiIgI3n33XWbOnGkdQ0pKCs2aNcNsNrNt2zYsFos12GvYsCGnT59G0zTi4+NZuXIl1apVswa8mc1GMqsyTSYTYWFhXL58mWzZsjFlyhTq169Pp06dOHLkCJMnT+bChQuMGzeOuLg4ihYtyqlTp4iNjWX8+PEsW7aMypUrExERQWRkJE2aNGH8+PE4Ojr+tTfJW+TXnaOTk5M5ceIEpUuXxtbWlmvXrrFmzRoWLFjA8uXLKVSoEACXLl2iX79+6PV6Nm7caN3/3r17DB06lGbNmvHuu+/+7dcjhBBCCCGEePu8SL4mlY1CiH+FtLQ0Bg0aRFpaGu7u7uTIkYO5c+fSrl07smXLRnR0NBcuXODLL78EICwsjMqVK3PkyBEuXryIs7MzpUuX5sqVK+zevdt6XFtb2xfqeuzs7IyDgwN58uSxbouPj8fBwQEnJyeqVKlCzpw5mTNnDlu3brVOc84MGb/99lt8fX2ZNm0aBw8eZNWqVVSpUoUuXbrg4eHBihUr2LRpE/b29ixbtgxXV1cURUGn02FnZ0fTpk0xGAzs2bOHnj17Mm7cOBRF4fPPPyd//vzMnDmT0aNH07x5c3bu3EnPnj05e/asteoxc92+Hj16sGPHDs6fP49er7dWN2ZOYR47diwJCQnUqFHDGi5aLJanms08fvyYmjVrcunSJVRVpXr16lSpUgWDwcDgwYPx8vKiVatWrF69mi+//JISJUpw7tw5jh07hqenJ02aNCEkJIRdu3Zx4cIFRowYwZQpU/5TQSPwVJA7ZMgQfH19adWqFVWqVLF2mS5btixZs2a1Vs3Ck4Yv77//Pjt37qRdu3YsXLiQSZMmUalSJWu3ciGEEEIIIYR41SRsFEL8K1y9epX58+ezc+dOAHr16kVISAjDhw8nZ86cXLx4kY8//pgjR46wZ88eDAaDdapuZgDZpEkTFi1aROvWra3H/b0OyykpKdbpxL8sEM+cTnzjxg3rtsOHD+Po6Ii9vT2hoaGYzWamTZtGkyZNSE1NJSEhgU6dOuHv78+IESNwdHQkPT2dU6dOUbBgQb777jvy5MlDeno6jx8/Jjk5mbCwMOrUqUOxYsXw9/dn9+7dlChRgtmzZ9OsWTNy585N+fLlcXNzIyUlhbZt2+Ln50fBggVxdna2Tsdt3rw5qqqyY8cOMjIyrNdbrlw5ypcvz6hRo4D/D7zc3d2pXr269TnKDBkzp3WfO3eOIUOGsH79ekaPHk1aWhrHjh2jT58+7Nu3zxoUBgcHk5ycjKZpfPbZZ3Ts2JGSJUtisVgYMGAAAKVKlWL16tWsXr2apKQkunbt+lfeIm+NX1e7ZmRk0LFjR6ZPn05aWhp79+5l0aJFODo6Wp+TEiVKULlyZXbt2sWdO3eAJ69Z8+bNWbhwIampqcyaNYvFixfz8ccfs3PnTkJDQ//2axNCCCGEEEL8+0nYKIR4K927d++pkE/TNLy9vXFxcQHAxcWF9evXs23bNqZOncru3btp27YtDg4OzJgxA4CiRYtSqlQp61qJHh4ehIeH/+45M8+XlJRE7ty5ef/994mMjHwqkHz48CFBQUHcvXvXuu3atWuEh4czYMAAsmTJwtWrV8mePTsNGzZk3bp19OzZk5UrVxIQEIBOp+P8+fOkp6eTmJjIyZMnyZ07N15eXjRs2JCMjAzMZjPu7u5MnDiRS5cuceTIERRFwc3NDScnJ9q2bUtcXBwzZ85k0KBBJCUlUalSJX788UdWrVpFixYtmDdvHlFRUeTJk4fy5ctz6tQpjhw5Yh2zm5sbHTp0YOXKlcTExFjDxpiYmKee98yQMT4+ns8++4zw8HC2b9/ORx99xIIFCyhfvjzFihWjcePG3L17lxUrVmAymazrDXp6elq7gNvY2ODi4sK+fftISkpCURScnZ0pVarU74a+/yaZAfAvp0zDkzUz4+Pj6dWrF25ubtaq3ClTpnDjxg2WLVuGnZ0dlSpVwsPDgylTpgBPXquIiAiaNWvGqlWrWLNmDSdPnuT999//269NCCGEEEII8d8hYaMQ4h/r1w0xMi1ZsoSQkBCWLl1q3VakSBEePHjAo0ePrNu2bNmC0WikadOmAFy/fh29Xs/KlSs5ceIETk5OjBgxgvnz5/9p05fr16+Tnp4OPAkyp02bRmJiIi1atOD27dvWx+XIkYN79+5x+vRp9uzZg6Zp3L9/n5EjR3Lo0CHmzZtH/fr1iYyMZOXKlXzzzTesWbOG8uXL8+DBA/R6PQ0bNrROjc6ePTuDBw+mYcOGhIeHW8PGHTt2MH78ePR6PeXLl6dmzZq4urqSkZFBeHg4wcHBREdHk56ejtFopEGDBsCTysR69erh6urK119/DTxpQJORkcG0adM4efIkzZs3Z9u2bbRo0YILFy7g7e0NwMiRI/Hx8WHu3LnA/1fgvf/++2TJkoXx48dTr149Zs2axXfffUdCQgLr1q2jatWqTJo0CXd3dzp37syXX35pbcoTHR3NkiVLqFOnDufOnWPlypVERERYQ+N/mz9aJjlzXcxNmzYxd+5czp49C4CjoyO9e/fG1taWYsWKWY+TP39+mjdvzsSJE4EnlaB169Zl1qxZ1K9fHx8fHzZv3ozRaETTNLJly/aar04IIYQQQgghJGwUQvwD/HraKPDU1FzgqUrBVq1a0alTJ0aNGsXq1auBJ9OaixUrxsmTJ637BwUFER0dzebNm9m9ezfz5s1j8ODBTJ8+nYCAAFRVfa5Qa8yYMeTLl49+/foRHx8PQNOmTZk/fz6RkZF0796diIgI4EmlX548edi/fz+xsbEoisK4cePYtm0b/fr1Y/369cyYMQMnJyeyZs1KQEAARqMRi8WCvb09Tk5OJCUl4eHhgaZpBAQEsGLFCn744Qe+++67p7pUL1++nLt377J+/XpOnjzJqVOn6Nq1Kxs2bGD69OmsXLmS1atXkyVLFvbs2WO9nsKFC1O7dm3WrFlDcnIyoaGhdOzYkZiYGGrVqsWDBw8ICQnBwcGBkJAQzGYzmqZx8+ZNcuTIweTJk1m1ahV3797l3r17HDp0CFtbW8LDw5k2bRqFCxemVq1atGzZkri4OIoVK0bNmjXp0qULZrOZYcOGERUVxciRI6lTpw7u7u7odDpmzZpFzZo1CQoKevE30Vsi87XL7MgN/9/t+/DhwxQoUIBOnToxZ84cihcvzuTJk0lISKBChQqUK1fuqUY+8GS5gAsXLrB9+3YcHR3p168fU6dOpXDhwpw/f55+/fpha2v7n6gMFUIIIYQQQvwzSDdqIcQb8+suu3fu3CFr1qzWdf00TWPcuHH88MMPODk5ER4eTvv27SlVqhSPHj3i66+/Zs6cOcyaNYsGDRpQpUoVatasyaBBg1AUhWvXrjF8+HCOHDlCdHQ03bp1Y9SoUTg5Ob3QOGfMmMGePXuIiYkhNDSUQYMG4e/vD8BPP/3E6NGjSUlJYcWKFeh0Otq1a8fjx4+pUqUKOXLkwGw2k5CQwBdffAFAYGAgwcHB3LlzB5PJxPXr17G1taVu3brs3bsXs9mMv78/oaGhbN26FaPRiKqqeHp6kpSURJs2bVi7di358uWjYMGCLFq0CKPRSI8ePawBYL169fj444+Jjo5mxIgRnD9/nr37fiZNSSbdkszOPTsY2X8ctRvVYOiwwdjgQGJ0Cq4ubri7u1tfn8zXITU1lTJlyjBhwgRatmxJUlISAwYMIH/+/EyePJnAwEBiYmLYsGGDNSi9ceMGYWFhLF26lIYNG6JpGsWKFePKlSscP36cggULAk+a+zg4OPyl99LbIjk5mb59+zJ37lwGDRrE+PHjrfe1adMGs9nMggULMJvNfPfdd8ycOZMPP/yQ3r17s379eho1asSFCxcICQkBnrxGmQHxmTNn3tRlCSGEEEIIIf7lpBu1EOKtkBk0Tp8+nXfeeYcuXbpQvXp1Hjx4wJ07d2jQoAFr1qxh+PDh9OvXj8ePH9OlSxcAsmbNyujRo3n33XcZMWIE+/btI0+ePJw6dQpFUVBVlbx587J06VJWrlxJXFwckydP/tOgMbPpyy+dP3+eQoUKMWPGDO7evUvHTh25/ygKo5ZBtRpVWblyJbdu3SI0NJRixYpx8OBB0tPTuXbtGl9++SVz5szBYDBYp8D27t2bMmXKcOXKFa5fv07RokXJmzcvBQsWxGKx4OPjw8WLF7l16xZ2dnakpqai0+m4cOECJpPJOoU8LCyM3bt3ExISQkJCApMnT2bWrFm4uroyffp0kpOT8fHxoWLlCsQmPGb0vCEcTd3EyYztKPkTyF7Ei5+2beVkxjZOZGzlQdaLnL19lO8Xzqdbt278/PPP6HQ6MjIyWLZsGRkZGcycOZM8efLg6enJ0qVLcXBwwGw2Ex0dzfHjx2nbti358+enbdu2JCQk8N577/HZZ59x+/ZtFi1aZG1ukxk0apr21geNmqYRpWZwypzIEXMCh83xHDfGc8OSivlXf89zdHTk4MGD+Pn5cfXqVc6dOwdAREQER48epUqVKtjZ2eHk5ETfvn2pUKECO3bsIDIyktq1a1OiRAnGjh1rPa9Op+PTTz99qqmREEIIIYQQQrxJhjc9ACHEf9exY8fo2bMnjx8/ZuDAgYSFhXHhwgWyZs3K1atXCQ0NZenSpTg5OREfH8+OHTs4e/Ysq1evpkmTJgCMHTuWyZMn07hxY0JCQjAYDM9UTBYrVoxbiRonb5u4FqeSagKdDlxtoLC3nsKeGtO/HM2qVatwc3OjVKlSdOnShQIFCmBUM/AN9mTpgkVU6x9Ky4nVGPP+t3Tq25rOA9vww+Q1bFq6HYvZAmioqkqBwiHERD9k+/btaJpGYmIihQsXpn79+pQuXZp+/fphMBjQNA1FUahQoQKnT5/mypUr1KpVy9pAJjIykoSEBHx9fRk7diyXL1+mZMmSzJw5k82bN/PNN9+QmJhIcnIyFouFIUOGMGfOHPz9/cnIyGDixIkM+LQ3rmGQvagX65duoVTL/OjQ4e7hRrthjfHy90RD5VHcIxqGdiE5Pg2DjYEsWTy5f/8+7u7uDB06lG3btuHs7EyjRo24ffs2jx8/xt7engMHDjBw4ED27t1LQkIC0dHRDBgwgI0bNzJ58mT69etHmTJlqFWrFunp6fTp04e+fftaX5u3eXpvhqZyQ03lgiWZO5Z0MlBRAA1QAMWskXL8AnVLlCHEwR17k4qtrS2lS5fm9OnTmM1mNmzYQOHChfH19bVOUwcwmUzY2NhQrVo1Ro4cye3btwkICKB37960bduWsWPHkj17dgDKli1L2bJl39jzIIQQQgghhBC/JJWNQojXRtM0bsSrrL5iYuLRDEYezGDckQy+P2/k7EML30z5Fi8vL/bv30/Xrl0pVaoU77//Pnq9nvz589OnTx80TaN3797kyZOHO3fuULlyZUaMGGE9h5eXF+PGjaNevXrs37+ffPnykZaWZj3/mRgL006ZmHw8g803zEQlqySbVBIyVK48SmfQsM/wdXdg8bqtfNBrIDVr1mTt2rUMG/EJERlHOZz2IxlZ4rB103Pn8U2cfAy0/awBe9YdpknRzuxc/zM5C/hT+/2KAJjMJi6cvUgWfzccnR1QFIWcuQKZMWMGFy9etDZbadCgAUFBQTRv2YrV6zZgtnFi245dOPvk4sSJE9bp0CVKlOCrr77i6tWrNG7cmDJlynDx4kX27NmDqqq4urri7e1N//79OXjwIIsXL+bAgQPUqVOH+QvmcTp9J4aANIJLBnLr/D0u7ruBAVv0GPDx90aPAc2oY2L7OSTHp+HgbIdXgAdZA905cHA/w4cPJ3fu3CiKQpMmTZgwYQLjxo0DID09ndmzZ5M9e3YWLFjAl19+yZUrV6hevTqappEzZ07Cw8OZM2cOc+fO5fbt208FjW+zaDWD5cZo1htjuKGm4aIYyKHYk0PnQKDOgfQj5xmb6x1GV2rIyCmTWZJxnxt6EykpKTg5OdG4cWMcHR05dOgQ169fx83NjXLlyjFv3jzgSWdugHfeeYeIiAiyZs0KQJUqVWjcuDGxsbFv7NqFEEIIIYQQ4o9IZaMQ4rW4Gqey9aaZK7EWUszgbAMGHaga3IiH9TsPsX7xIqau2vm7XXKzZs3KF198wfHjx1m5ciVVqlRh1apVNG/enF27dlGlShWMRiO2traMHz+eAQMGWKfnqprG9ltmNl43Y1QVfJwUcrg8XUmXkBbLrZ2LCKrQgLKDF/HQSUerJgbOXnzSSfrAhVIEBGUjOT4Fg0GPYjawYNh6fpy9E7PJjJ2DLUnxKXTr1YJze67hE+hFYmwyNZtX5t1mdVk0Yxm7ftzP/Qf3adK6EaNGjSI5ORmAI8eOE5+QSMKJCKLv3CTm4SM8cxfmpkthnDy9CXunPE3erc3+HVuYMGECdnZ2fPfdd9StW5e7d+8yY8YMatasiY+PD6dPn2bhwoVs3bqVChUqAHDvQST37kax4KvlNOtTh9K1SlC0bCFyhgQ88zxHXX/A2QOX0Rt0TN37ORePXWb5pM3kCcvOtRtXeffdd8mdOzebN28mJCSEiIgIDAYDZrPZOoX60aNHeHl5kTVrVmrXro3BYGDQoEHodDo6der0it9db1akms5m00NiVRN+ih0G5dm/29k5OeKbLw8OLi4cm/EDjo5OxLdsSC2P7Fy/fp2AgAC6du1K//792bp1Kz169KBv3740bNiQTZs2UblyZRwdHVmzZg0lSpTA2dkZAF9fX1atWvV3X7IQQgghhBBCPDepbBRCvHInH1j47syT6kVPB4UQT4Ucrjr8nHUEuOgI9tRhvHkMGwcXDiT7sPGGGbPF8sxx0tLSmDx5Mg0bNqRKlSoA1iYYffr0AcDW1haAbNmyPbUO4M7bZtZdteBgo5DPQ8HFVnlmyq6blx9FazTDkhKHd8YdYtM1ll64w4Vr53Fyd8DD0x2AQqWDObHjAi3z9uLsgQh6fdOeRh/VwMXDCUWn8N3gFdy8dIfUpDSKlC1A7U7lCSmdkw8Hf4BqUUlLTiPOHM27zWuRlpZGNt8AHsUlkpwQT8z1szi4epAztBQD5u8iJG9usoWEcz0qlmtZyjNy5nI2bdpEvXr1+Oyzzzhy5AifffYZR48e5dChQ9Zr8fLyIioqCoCfdmzB4KZSrU0Zom89xoAt2QKy/mbQCJA92BcbWwM6nY7Dm05Sq1UlilYMIeLUda5fu04Wb080TSMmJoZ79+4xadIkduzYgU6nQ9M0Fi5cyM6dO/nkk0/IyMgga9asLF682Pqa/Zs8Uo1sNT0iTjURoNj/ZtAIEBhakOAypchROISClcsRtf8Ym4ZNYI8plsKVy3Pw4EEqVqxIzpw52bVrF9HR0dSvX5+2bdvSvXt3GjduTPPmzRkxYgTvvfeetSGREEIIIYQQQvzTSdgohHilIh5bWHLJRJpZI+h3Qj4Aewd7NIsJQ+pDNl23sOeuar1P+19TDTs7OwoUKMD27duJi4tj165dXLlyhQULFlC2bFkyMjJ+cwyXY1U2XjfjbAvejn+8JmDJdztgMWZwbP18ri77jMn1C3Hl1HWy5/PHaMlAQU96cgZ5iwTSfWIbvt39KZValKR629LY2BlQFAVXT2dunoskITaZuIcJfFB6KOP6f41Fn0E2f2+c3ZxZN38rgcWykTckhAf3I0lPikNnMODg4k5otSbU7zORa8f3sG36MAzmNGzVNA6tm0+nvsMoUKgwq1ev5qOPPqJIkSLky5ePypUrM2PGDFJTU8mRIwfVqlWjW7dulChRgqaNmxFYLBs9Jremz9cd//Q10+l0VGtRBpPRzOKJ62me5yOunrxFenIGZpOFXr174e3tjYeHB3ny5KFatWro9Xo0TUPTNHbt2kW+fPkYMGAAJ0+e5MiRI5QoUeJPz/u20TSNw+YEYlQj/oo9uj9Zb7Jo7aqkJSXj4OpCw4/7cmbNJvbO+oHjd66R839T0+vUqUNkZCSbN28G4Ntvv2Xu3LkEBQWRJUsWjh8/Tv/+/f+OyxNCCCGEEEKIV0LRtF+1yvyXeZHW3EKIv8aianx13MjVeI287n/c/OPxvRt82aIY1Tt/QlD9j7CxtWNQSRu8nPTWxyxduhR/f39atmyJra0t0dHRDBo0iGHDhlnXtPstC84b2X/PQrDnn/89RdM01k7szbGNCwkICuL90XVITlG4fug05w9GUL1lOSo2DqfrO58wbt1AchX2w4wJNIUvOs1hz8qjZPX3oMXAuswbsZo67Svin8Of6cMWMnjKh8wdvYyY+4+wmC3Y2BpQ9Abs3HxQ05MoXKUxucPKsenbj3F09eRx1E2K125FzW6f8dPMzzi2cSGeuQpTu/tIvulcBTdHO8xmMwaDgUOHDlGxYkU2b95MtWrVyMjIYPny5dy7H0mhZr7ovc3YYIvynH9T0jSNRtm7Y7DRk6tgAOcPXsFstuATmJX4mCTsbO3J5p2NlJQUnJ2dSUxMJDExEUdHR5YvX07lypWf6zxvs0eqkcXG+9ijw0V5vlVIfhj4KdePnaLv8rlcP3GaQyvXc2jZWkpXq8KBbTt4/PgxHTt2xMbGhtmzZ+Ph4QHwTJMjIYQQQgghhHiTXiRfkzUbhRCvzNU4lRsJKv7Ov13N+Etu3gEUrPAuh9fMIXexCiT5leTMI41qTk/u37VrF2PHjmXjxo3s27ePK1euULlyZezs7P7wuNEpKmceqng5PF+XY0VReKdRZ+5eOE65JqUoXD4/yWkOlK6Vj2PrjzL5w3kYbPTEP0zkxrk75CicDVVV0esMNOxZnePbz5ORmkHu0OzU6VSe9dN30LZ/E1SzhRMHzpCUkPy/TtXgFZCNjtOG45GtAdtnf8e+ZVNxdPOk98LDxEffwTdfYQw2T6aFZ46paO33sA2uxOV4HSUdNAyGJx/bpUuXJl++fIwdO5YSJUrg7u5Ou3bteGS+x8n0bVhQXihoVBSFsIoF2P/jcU7/fInQcvn5cGJrchT2oW/lsVw9dZuU5BQKFy7MnTt30Ov17Ny5k5IlS/5nQrErlhRSNAtZld8Pun8trFZVrh87xa55i2k0tA/euQI5//NBnHNnJyMjgyxZshAeHs6+fft4+PChNWz8rzynQgghhBBCiH8fCRuFEK/M8QcWjBZwtPnzoM9gY0vtD0fydZt3WDWmO2U+nMyGeH9yaq78uGYVy5Yto0WLFgQGBgKQO3fu5xrDmRgLCRkavp7PFzYC+OQthE+eXFw8cJgqLYvh7GHAZDJRrsk7mNItbJy7C2d3R7Jm90BDRa97Un2Zt2gOCpUL4ujWs0zuOo8eX7fmTkQ0309YiaZq7F59CM2iYTDoaduzBWW6d0WzS0RLf0TFNn1xcHH/P/buOk7Kqv3j+Oee3O4uepeOJZaUXhqlkVaQEBQRJEWkBEkpaVFQuktSWFq6u5ZlqYXtmrrP74915wFFH1B8jN95++IlO3HPuc/MLLvfuc65MDg64+rli6uX7y/G5JcvgutHd+FVqQ1HH/hRPhBMJhPTpk0jODiYZcuWcfz4cTw8POz3S7UlYsOGBi0vSlGUnM7WXi54B3oQlN+fsWv64+BkJO5GPBqthloNq1OpdFUMBgPe3t706tXrhY//b2ARKhfVDJzR/tcg/WlFa1QhdN0WLsUcolKrpoQUCWfUsR3YPF1J0kMAOfuPDh069M8bvCRJkiRJkiRJ0v+QDBslSXolhBBcSRS4/3bh4TO8g/Pz5qivObByFus+aoBXniKstqbh4uTI6NGjad269UuP436GQK/57SXcP6fRaHitTTM2Tx/Bse9PU6dDRSw2BSEU6rarQmTNYvgGe6FizVlCjWK/X/M+dbl75T46g46hjafi4umEUFX0Rj3FK4YjLKBTDPQZ/T777phxNxpQjCno9AYqt+zxm2OKeqMrW2YMI/XiXm77tGTgkI9ZMPdLXF1dmTx5MiVLlqRkyZLP3C9FfYRARfOS3941Gg1GJwPu3q4kPkjmuwkbUG2CncsOUKhMGO9//hZNinR8qWP+m2RiI0PYcFJePMQF0Gi1lKpXk62XrnJ84zYaf/gu3p5e3BHZpKpWAjRGnJyc/qRRS7+HEAKbzWavIpYkSZIkSZIk6eXIn6QlSXolzCqYbQK99sVDPoCi1RoSUakuD+/e5tKly3StFETLupV/9zgyLKD9HStQC0YWxyvAg/2r91GtRRn0TjqEAI1Wg2+wFwDP2+C2SFQBwooEc27/FbQ6DY4uRt4c0ITOfdtgESY2L9jDos9WkZKSjlCMKGjQKNYXGlOeEhVx8w1k+5S+mMd2JTw8giVLltC4ceNfvY8V809/e7nnASC0UCA7lx1EATbM3UWhUnno/2VXykRH4KPxfunj/ZuYhcAmBC/58gagRO3X2L9kJSc2bafmW+1w9vRAUTVYlX/1lsn/CFarFY1G88yydUVR7EHjunXrCA4OJiIiAnd3d/uWA5IkSZIkSZIk/ToZNkqS9EroFNAoYPsd+YlGq8MrpCD5PQpQPuolSiOfw6CF39P2SqPVU+PNqlz+8Q5Wiw39C+53qNVpqduhMi4eTtTvUo3w8mEYcMrp1IzAxd2VJm/WR6NRcroXKwIhXvTYOso37oTOyYOIBm8x6+1qeP+XvSg1Lzju53H1dKZe+2p4B3pw/3YCXUe0wsndAQsmFOX/9x6C2p92wPw98aDRyYmGfXvgHuCHs6fHT5cKNL8jEJZenfv371OoUCGWL19uD/BzO6xPnjyZzz77jJCQEIQQODk5sXHjRgICAv7iUUuSJEmSJEnS358MGyVJeiW0GgVvB4VbqSovW1WnKAoZFoGjXsHV8McCGHeDgll9+ftZrQ6Uq1eGyLqV0GgsCMxofjYU5VfOK6pBKaIalAJAxYpCToMcIQT1W9XA400/LKpAn27DJqwoVvcXHlfRag1xL10fV4MGF8N/v72T4g4oqKhoXzJ4rN48ihotKj5TvaViBRSclRcf87+RUdGgUzSYETj+jvtHVImy/90qcoJGowwb/1KBgYFUqFCBefPm0bBhQzQaDYqicPr0adatW8esWbNo164dJpOJypUr061bN77++mt8fHz+6qFLkiRJkiRJ0t/a/+9SFUmSXqmoIC3ZVrCpL1//9ThTUNJXg5fDHwtginhr0GkUsqwvN4bsbH+sVle0GhNmqw6N8svl2AoaFBTEr9S3CXKCVoPigBAqCgoOmpz22nqNQpCLwCpUsrJ8n3v/5x5TCFLNUDFIg/EF1vC6a/zQoPw0lpeTGzA+vUxURUWDgofmxcf8b+SEhjCNAynixZbA/5ZkYcFToyNQ88eqeKXfx2q1oqo5748RI0awZcsWjh07Zr9+ypQphIeH065dO65du8aAAQM4c+YMXl5ecgm1JEmSJEmSJL0AGTZKkvTKlPTV4uukkJD1ckFfpkWg00L5gJdrvvE8hb005HFVeJTxcmMQQkd6WhgoKtlWLTpFi/KzwE5B+anL86+FjQIdenSKHisWdIoBg/KfOjgfZxNWqyMPU158/8Mn2eBmUCjt92Jz46bzwogzKurvChyfJlBRUTHijJvu//eejYqiUETrgkYBs/j98yqEIB0bxTQuOLxks5m/o9zQ7p9Ep9Oh0Wg4evQonp6eFCtWjIULF2I25+x36ufnR1paGu3btycyMpK7d++yb98+Fi9ejLf3/+/3gSRJkiRJkiS9CBk2SpL0yrgbFaoEaUnOhgzLi4V9VlVwO0VQ1FtLIc8//i1Jr1WoFKQlywqml6xuTEvLi8VqxNmYjVFrQKD+oooxJ2z8ZXVjbrBnUBwRIiemc1Jc0fy016FN2DDqs/HRBhKfpnuh+UkzCx5nCqqGaAl0frGKKmfFHW9dEAAqthe6z6/Jvb+XLgBnxeMPHevfIK/GAT/FQKKw/O5jpGPDWdESrnV+hSP737Nacyo8n26sIn7PZqn/Azbbs++DW7duUbVqVRo0aMDMmTO5fv06q1at4tq1awA4ODjwww8/kJqaytmzZ1m3bh2VK1cmPT2db7755q84BUmSJEmSJEn6R5FhoyRJr1S9fDoqBWm5kypIMf12+JBtFVxLFIR7aWhXRI/u55sk/k4VArWU9NNyI1lgeYmONUmZLly/F46DDnSKihYdKrZngsWc2kYdOXWMOZeLn/7TY0QvjJjJxqA44qC4ADlBY4ZIxkPrT8OQfPb5eZChYn3OknOrKriXrnIvXfBaqJYmBXQvvHxTURTy6IrigDM2bL+7ulGgYsOGA87k0RWXy0cBvaKhjNYNqyJI/R3Lqc1C5YmwUFjjjI+i/xNG+L+T26156dKljBs3DuBv9xrJrbrUanMqSGNiYgBYu3YtGRkZHD9+nGnTprF06VKcnZ2ZOXMmALVq1SJPnjwUL16cfPny2Y+3fft25s+fz6VLl/7HZyJJkiRJkiRJ/yyyQYwkSa+UQavQvqgeRz0cirdxL13g46jg6QBaBVQBaRZIyBCgKJTw09KhiO6/dll+GU56hfZF9JisgkuJKmGu4PIbjWdUVeVxFiSZ4DWP4hQ02rivXkWInKXTKlY06OwNYrQ/feu0YkH8VP2nw4gjLlgUE3rFiLvGFwUN2WoGZrLw0PqRR18Eg2KkYzGBv5PCoXs2ricL9BqBw08rarOsCjYh8HPSEJ1XQ+0wHfoX2KvxaW5aH/LpS3LVcgwLJvQYf+ql/GIEKhZMaNGRV18Cd61siJGrhNaFJGHhiDUZADflxf4ZzRYqD4SJCK0z1fWef7tg7rc83TAo18KFCxk2bBgeHh5UqVKF27dvkzdv3r9mgL8it+ryzJkztGnTBp1Ox/nz51m9ejXly5e3B4lvvPEG9+7dY9CgQQwZMoTatWvTpk0bpkyZwvHjx6lSpQq7du3i2rVrDBw4kPz58wM586Kqqj3MlCRJkiRJkiQphyL+ruueXpHU1FTc3d1JSUnBzc3trx6OJP2/oQrBjWTByQdWjj9USTULbCpolJwwsKi3hgqBWop4v1jjk98j2SRYfsnC+cc2sm3g7fCf0BMg26qSkKGSYdPiblSoHqKlYX4dOg3csVwg1noJk5qBlZwqtpw9GzUIQGD7aV9E8VOtox4NGvSKAy6KB0LJuY1RccJLG4C/Li865dl20qkmwdkEG2cSVJJNAgXwNCqU8ddSwleDs/73z4tNWLliOkac9RI2rGjR20PS37wfVmxY0KIjVFeYCGN5tP/wKrxXzSYEh6zJ/GhLQSDwQo/jr+y/aBYqScJCNipFtc7U0fvg/A/Zq1FVVYQQvwjTzp49S/fu3Wnfvj3vvfcemZmZODk5/UWjzKGq6jNLugF27drF3Llz0Wg0lC1blt69e2M0GuncuTOZmZmsW7fOvvT74cOHlChRgl69ejFq1CgAtmzZwsGDBzl79iylSpVi6NChODs7o6oqiqLYA9iMjAzOnj1LiRIlcHFx+d+euCRJkiRJkiT9j7xMvibDRkmS/nSJ2YJHGSpmFXQa8DAqBDor/5PqrtzQc9uJ6xw4exP3YtVzwkKRMxZ/Zw0FucfZrd9QIiI/kZGRFC9eHCEEFmHige0W9yw3SFOfYMNq38dQgw4HxQkHxQkNOjRo0ShadOjQavQYFAc8NQG4a33R/yxk/F+xCQs3Tee4Y72AiWyUn8atQUNOdJq792RuKxgrAoERR0J1RclvLIlOBo3PJYTgsprBWVs6d23ZmFFxRotOyal/tYmc/RkVBfwVA8W1LpTUumJQ/nm7l9y7d49FixZRvnx5oqOjWbRoEe+++y5ZWVlkZmZy//59AgICsFqtuLu7/0/HlrtU+udBI8DWrVsZPHgwjx8/5tChQ/bKyzFjxrBt2zamT59OZGQkAPHx8VSpUgVnZ2e2bdtGaGjof33srKwsJk2axJgxYwgLCyMoKIgZM2ZQsmTJV3eCkiRJkiRJkvQ38TL5mlxGLUnSn87LQcHL4a+p5tIoCnldrGya0Ic6DZrQKLI2ZlWDRgGdsPLdzLF0nzSR8uXLs33TWm7fvs2KFSuoW7cuBp0DYZoihOoKk64mYRLZP1U6WjAoDmgULVp0OGlccVRcURTluUtO/ypaRU9BYxm8dYHcsJwm2fYIC2ZsCODpMeZ8rceAu8aPAvpSeOkC/zbn8XeU2506QuPMPZ2Jy9YMbosszEJFAHqNQhHFmaJaF/JqHND/A0PGhw8f8v7777N582aqVKmC0WgkOjqaqlWr4uDgQLVq1bDZbHh5eXHs2DGKFCnCpEmTKFeu3P9sjLkh47lz51i8eDGhoaHUqlWL4sWLU79+fbZt28a33377TPOamjVrsmfPHiZMmMDy5csBuHr1KiVLlmTz5s388MMPdO7c2X77n1dNpqamMnfuXFJTU3nw4AE//PADer2ezp07M2HCBMaPH09ISMj/aAYkSZIkSZIk6e9HVjZKkvSvZrVa0el0ZGRk4Oz8bAfgGzdu0LBhQ0aPHk3r1q3JzMykZ8+eXLt2jQkTJlCtWrW/aNSvnk3YSLElkGCNI0VNwCQyEagoaDAoTrhrfPDTheGu9UH7gvsQSs8SQvxUGwp6/jeVu6+CzWZ77r6Dn3zyCUePHmXSpEkUL16c1NRU+7+ju3fvZs+ePYSHh6PVailYsCBt2rShY8eOfPrpp3/aPoY/D/7MZjP9+/fnq6++om7duly5coXMzEwmTpxI69at2bZtG8OHD6dZs2YMHToUyHmeNm3aRPv27SlWrBiBgYEcOHCAWbNmERUVRZ48eYD/fO/4uczMTKpUqcK1a9cYPXo0/fr1A2D58uV88cUXdO7cmV69ev2tPniQJEmSJEmSpD/qZfK1f16phSRJ0m84duwYaWlpQE6okBsWODs78+OPPzJx4kRSUlIA2LBhA6qqUqNGDVRVxcnJiVGjRmGxWNi7dy9W68t3HP670ipavHQBRDiUp7xjA6Kcmtj/VHBsQGGHCnjpAmTQ+AcoioJe0WBQNH/rkCl36bHNlrMlQG4w+PTr3WazsWnTJkqUKEHx4sW5c+cO2dnZJCYmAlC7dm3GjBlDp06daN++PVFRUQQFBaHX6/+UoDF3bD9fLn327Fl2797N8uXLWb9+Pbt27aJFixb06dOHu3fvUrNmTYoXL05MTAz3798Hcr4vNG3alN27d9OsWTOcnJz44osvaN26tT1ohP903N6+fTtfffUVt2/fJjs7GycnJ3r16oWzs/Mze1U2btyYoKAg9uzZQ0JCwt/6NSBJkiRJkiRJfyYZNkqS9K+wc+dO3N3d6du3L+np6QD2X/YnTJjA+fPnOXLkCAsWLCAmJgaAoKAg4uPj8fPzQ6PRYLVayZs3L8WKFePQoUPYbDb+jcXfiqJgUIwYFUcMilGGIv8PbNmyhd69ez9zWW4ouGDBAqpWrUqPHj2YP38+kPMaqV+/Pl9//TWvvfYa7777Lo0bNyZv3ryMGzeOzMxMnjx5wt69e9mzZw8NGjQgIyODJk2a/Cnjzw3+vvnmGwYPHsz58+cBOHjwIImJiTRp0gQhBMHBwUyaNAkhBN9++6196Xd6ejrr1q0DsL+nK1SowKBBg6hSpQqdO3fm0aNHzzzm4cOHKVKkCF26dGHGjBnUrl2bkSNHAtCsWTMKFSrE6dOnSU1NBcDFxYWGDRty9+5dtm/fDvwn2JUkSZIkSZKk/09k2ChJ0j/S0yGgxWKxL188dOgQgYGBJCQkcObMGT7//HNGjx7NtWvXaN26NX5+fuzZsweTyUT58uVxdHTkq6++euaYBQsW5Pr16xiNMoiT/pmeDrlUVeXGjRvMnj2b27dvo9FoUFWVM2fOUKVKFSZPnkzdunXx8PBg8ODBzJ49G1VVGTduHAMGDKBhw4a0a9eOESNGMGHCBD799FPu37/PuXPn+Oyzz+jUqRMhISHs3LmTMmXKvNLzyH1Pbt26lTx58jBq1Ch0Oh23bt0CwN3dHQcHB+Lj41EUhezsbDQaDW3atGHVqlUA1KtXj4CAAL799ltSUlJ+UXnZunVr3N3dWb16tf0yk8nEnDlziIyM5Pr162zevJl+/frx+eef8/333+Pr60u9evU4c+YMBw8etN+vWbNmODo6smzZMtLS0p7buEaSJEmSJEmS/u3kejlJkv5x7t69y+rVq+nUqROurq4IIbh//z5FixZl8eLFDB48GICkpCQqVKjA5cuXCQoKQlEUqlWrxr59+zh27BhVq1alQ4cODB48mGrVqlGgQAHS0tLYtWsX7dq1A5D7rkn/KLl7GuaGXBaLBb1eT+PGjfnuu+8YO3Ys8+fPRwhBamoqlStX5tNPP7XvZ3r48GEWL15MkSJFqFGjBoMGDXrm+IcPH8bPzw9FUahSpQqjR4+mVKlSODg4vJKxQ85S6dz3naIoZGRkMHXqVNq3b8+IESMwGo32+wQGBhIWFsbSpUv56KOP0OtzuqcbDAacnZ1JT0/Hy8uLZs2aYbPZcHV1/cXj+vj40KVLF7744gs6dOiAm5sbmZmZLF26lDVr1uDs7IyzszO9e/fm8OHDjBo1igYNGtC+fXu2bNnCnj17qF27NgaDAW9vb1q2bGm/jyRJkiRJkiT9fyQ/cpck6R/l1q1blCxZkiFDhjB27Fj0ej0Gg4Fy5coxZcoUOnfujKIoNGzYECEEt2/fxmq1YjabEULQqVMnTCYT33//PUIIxo0bR4ECBahVqxbNmjWjRIkSJCUl0bJlSwAZNEr/KLlVi5MmTaJ169Z88sknrF69mvz589O2bVtWr17NkydP0Gq1FChQgKFDh6LRaBg0aBB+fn4kJiby+PFjdu7caa8qPH36NMePH2f69Om0b9+eNm3aEBoail6vJyoq6g8HjbmPkxuSZmZmPvO+27VrF2fPnqVRo0YYjcZnqporV65M6dKlmTNnDpcuXQIgJSWFAwcO0KRJE1xcXADo0KEDnTt3Rghhv//Tx+nbty937txh48aNADx48ICIiAiSkpKAnD0sFUWhe/fu/Pjjj9y9e5f8+fPz2muvsWvXLvvWDAC9evWiU6dOsqpRkiRJkiRJ+n9L/iQsSdI/RlJSElOnTsVms5Gdnc2aNWv44IMPKF++PAsXLkRVVerUqUN8fDwff/wxvr6+xMfHs2jRIoxGI6qqUrhwYSpVqsSRI0c4fPgwTk5OrF69mjlz5lCgQAFGjx7N2bNnKVGixF99upL0Umw2GzNnzsTf35+VK1dSrVo1nJyc7JWAderUITg4mM8//xzIqQr09PSkf//+HD16lLlz53L58mVKly7Nli1bOHfuHKmpqWzZsoWOHTsyb948RowYwaRJk+wVhK9CbrB45coVmjVrRs2aNfn888+5evUqkBMeWq1WihUr9sztAVxdXXnvvfcoXLgwVapUoV69ehQsWBCDwcDrr79uv11usKjValEUhfj4eHsjKYCwsDDatWvH5MmTEULg7e1NSEgIx48fJzMz0770WlEUwsLCuH79OgDt27enSJEiBAUFvbL5kCRJkiRJkqR/OkX8G7sfPOVlWnNLkvT3dPv2bfr378/69etp3749x44d4/LlywA4OTnRqFEjihYtyqJFi4iLi6Nnz55s2LABgOTkZPz8/Pjxxx/x8PDAYDBw4sQJ3nnnHerWrWsPXiTpn+7AgQN8+OGHtG/fnvfff/8XVblZWVlMmjSJmTNnEhcXh8Fg4NSpU7Ru3ZrBgwfTtWtXIGcPw71799K1a1fGjRvHrVu3SE1NpVSpUn/KuM+fP09sbCybN2/GbDYTFBTE0qVLKVeuHCtWrCA2Npbw8HBWrVpF06ZNgZzw0GKxcO3aNYoVK0ZWVhYHDhxg3759VKpUiYYNG/7qHA0fPpybN2+SN29eoqKimDBhAgAXL16kZMmSrFmzhtdff51x48axZs0a+vbtS8eOHQGYNGkSS5cuZf/+/XKZtCRJkiRJkvT/ysvka7KyUZKkv61Fixbh7OxM/vz5URSFTZs2UbNmTdLT059p8vDNN9/w6aefsmbNGvvXn3zyCbt37yY8PJz4+HiWL1+OXq9HVVXKli1LkSJFcHV1JTs7+686PUl6ZbKyshg+fDiOjo68++67z1yXuxeio6MjDRo0wNnZmcmTJwM5PzDkLr2GnO7ORqORyMhI3NzcyM7OJl++fH84aBRCYLVaf3H53bt3GTRoEB06dMBsNrNw4UJGjx7NyJEjOX78OOvXrydPnjy0bNmSQYMGcfPmTSCnwnDfvn189dVXJCYm4ujoSN26dRk9evRzg0YhBIsXL6Zr166UKVOGVatW0adPH6ZNm8bixYuxWq0ULVqURo0aMWXKFAA6d+5MjRo16Ny5M506daJZs2aMGTOGzp074+Tk9IfmQ5IkSZIkSZL+zWTYKEnSX+7nIcSuXbsoU6YMvXr1Ijs7GxcXF44fP06XLl149OgRy5Yto2DBgiiKgtVqZeHChWRnZ5M3b168vLzIzMykZMmSFC5cmIIFC2K1Whk/fjzp6en2x1q4cCEff/zxK2lsIUm/h1WYSVOTeGy9yz3rDe5ZrnPfepMntntkqCnYhA14trP0r3n06BGnTp3irbfeQq/XP1PVmNtwBaBw4cK0aNGC2bNnA1C9enVKlCjB2LFjKV68ODVr1qR69eqsXr2aIUOGvPT743ljzW32otPl9KS7du2a/bqQkBAaN24MQNWqVe2XV69enfLlyzNt2jQAvvzyS7RaLTVq1KBr167UqVOHZs2a4ePj89wKw9zzXbduHfv27UNRFPR6PVOmTGHKlClUqFABX19fNBoNCxYssHe3HjRoEPv372ffvn0EBQUxadIkvvnmG7y9vfH29ubIkSP07dtX7uUqSZIkSZIkSb9Bho2SJP1lcoO/3BBi/vz5jB8/nm7dulGvXj327t2LEIK0tDTu3LmDqqqMHz+e9evXU7VqVZydnbFYLCxYsAC9Xo9Op6NcuXIAzJkzhwsXLpCamoqPjw+BgYFkZGRgMBgAZMgo/SWEEGSoKcRbrnLJdISrpmPctlzgnuUa96zXibdc5Zb5LFdMR7mYfZiH1ttYyKm+vXPnDpmZmcAvQz0nJyesVqv9+p8H+Lnhm4uLC6+//jpCCObOnQvAzJkzmTdvHl26dCEuLo5u3brZG6u8qKc7Sf98rLnB3Oeff05wcDBNmjShcePGbN68GYDatWtTokQJ+9cAoaGhNGzUiGvXr/Ptts0kuOiZtmYZ/T4ZhgBKlizJ9evXGTJkCAaD4RfzkfuYnTt35tSpUwDUqFGDRo0asX37dkqXLk2XLl3o378/hw4dYteuXVitVipXrkyNGjUYP348NltO2NuxY0emTJnCggULKFy48EvNiyRJkiRJkiT9fyT3bJQk6X8i2yow20CrASfds00eIiMjOX36tH1pdPPmzRk9ejTvv/8+MTExmEwmfHx8qFSpEqdPn+bevXuMGTOGuXPncuvWLZycnJg3bx7t27dnwoQJDB48GEVR0Gg0dO/enaZNm1KrVq1X2tRCkl6WVVh4aL1Fgu0uVmFCjyMGxQGNon3mdkIIVGyYRBZWzKyft53tS2Pw9vQlwjVR/gABAABJREFUPS2dtWvX4u/vn3NMqxWdTsft27dp164dFouFY8eOPffxMzMzuXfvHvnz56dz585s3bqVJ0+evNDYzULlkTDzRLWQgQ2BwIAGT0WPn8aAm5LzgcGXX37J4sWL8fDwIC0tjTVr1hAQEMDkyZOZPXs2EydORFEUVqxYwdatW9m1axfly5dnypQpzJs3j2XLlpGvVHGuqZnsvXaBJYNGkpmWRtet36IABjQ4oJBX64T+ahy1ipVB+9P3kuzsbKxWKy4uLlgsFnQ6HS1btsTf358vv/wSgISEBFq0aEGVKlX48MMP8fX1pXbt2kBOtXPevHlZt24dvXr14uzZs/j5+b308yxJkiRJkiRJ/0Yvk6/p/kdjkiTp/xkhBHFpgtOPrByJV4lNUzHZQKeAn7NCQRHPlR2LmTN5LKqqEhUVRUhICLt377bvrxYWFoaiKFSvXp29e/eSmZlJXFwckZGR7Nmzh7Jly/Lw4UOysrL49NNPCQsLIzU1FV9fX2w2G7t27aJ06dL2Mamqaq+8kqT/pSw1nVjLRdLURIyKEy6Ky68uxVUUBS06bpyIY/QHE0lKTKLDBy0oH1mBxGsmXFxcsNlszyxNzps3L9HR0YwZM4Zt27ZRv3594D9LmB8/fsyIESPInz8//fv3p0ePHkRHRz9zm+fJEDYu2tI5Z0sjUbVg4dnPJxUUXBQNtpNX+LrfMNKeJPHRRx9RpkwZLl26hIuLC0+ePGHx4sW0bduWZs2aAfDGG29QpkwZpk2bxvz586lbty4bN21i9Ozp1J4+khRhxTFvEEVeq8TJ1ZvxTMzExdsLk7CRmJJMh0rVSH+YwKBVX9OnwRvEn75Anz59iIyMZObMmej1eiwWC1qtFicnJywWC3q9nlWrVnH9+nUWLFiAr68vN2/eJD4+nqtXr7J7927eeustmjVrZh+nJEmSJEmSJEkvT4aNkiS9ck+yBGuumNl228aNFJU0E6iCnJjCZiVl42jMNw5iijsHGh1fzF1EzNbVXLlyhbFjx5KUlMSSJUuIiIjA3d3dviT08OHDVK9enRs3bnDu3DmWLl3KmTNnuHbtGtevX+e1114jODiYmTNn0qpVKwB7KKPRaGTQKP0lstR0blnOkqmm4qJ4/KKS8dd8++UKPH09mLV6Ip7+7mSKFEqU98eo16P96Rg3btygd+/edOjQga5du7Jjxw66devG0qVLqVy5MmazmbS0NGbMmMGdO3d4//33gZz9EXP3SHxe0CiE4KaaxX5rEvdUE45o8FEM6FGeub1NCNKx8tWs2Zi93Zi8egn1Q8IxKhoqVKgAgMFgIDY2looVKwI5FYgODg4MHjyY3r17c+/ePYILFyKoViV2L1tNsas3iAiPQKMo1HurA6+/18P+eI6KDn1SBiWqVcJkNvPt+CncefSAjzq9Tc+ePXnnnXcoUaIErVq1wsvLC0dHRy5dumSvai5XrhwPHjzg0KFDaLVa5s+fz4ABAzh//jwFCxaU3yMkSZIkSZIk6RWQP1VLkvRKXXpiY/C+bKaesHDyoUpSFthUQIAGgUarQ+uTF1NCLKopE9dqb7Es1oljp8+zd+9e9u7dy6hRo7h69Spbt27F29ub8+fPU758eTIyMqhduzZ3795Fq9Xi7+/PpEmTcHJywsXFhR07dnD37l170CiEQKvVygBB+stYhYVYy8WfgkbPFw4aT/94jo3LttHtw474BvigU/Q4Kx4k2R4RZ75CSkoKPXv2pGTJkhgMBkqWLEloaCiLFy/G19eXhg0bUqFCBVq1akV4eDh79uxh2LBhRERE/NfHFkJwxpbGJvMjEoSZUMUBf40Rg6L5RTCpVRQeHT3L6WUbiP6wJxd8DHxvSSBL2Oz7RKalpVG5cmWWLVsGYA/+mjVrRmZmJhdvXmeHSMIYWQSDVsv9/cfR/PQ4Di6/bP5icHLk0r7DtP10MPW6tOP7cdP57Ov5lGnXjM8//5x58+Yxfvx4ICdcjI+Pt39gUaFCBdq1a8f48eMpUaIEO3bsoGrVqnzxxRdUr179hZ4bSZIkSZIkSZJ+m6xslCTplbmaqDL6kJmjD2yYbTmfZhi0oLXnEzl/8SjbmKQ1w3IuCS3LlZOHcS3WkF5DP2PVqlVotVoURSEsLIw7d+4A0KpVK44dO8a0adMQQmAymUhNTaVhw4bcuXMHb29v4D972MHzK7Yk6X9FCMFD6y3S1ERcFA8U5cVD77PHLuDs6kRAcM7ejKqqotXocMSVJPEAB70rlStX5oMPPrA3LRFCULBgQbZv387BgweJj48nPj6eTz/9lPLly7/wY19RM9ljSUSLQrBi/K+3v3HsFA6uLuQJDsVdMXLekoYWhYZ6X7SAh4cH0dHRTJkyhcuXL9vHu337dry9vblitGG2ZVG1WlXKbVqGZ6D/rz6Wqqp4BPjh6uvNtSPHea1Da7LT0tk14yuwWJn0fl+8vb3p1q0b5cuX58mTJ+TPn5+HDx8SFBSEoij27tOqqlKsWLEXnhdJkiRJkiRJkl6MDBslSXolUk2CaSdMHHtgw2QFnSYnaPw5IQQ6V1+0Th5Ys9OwXN6NIeI14r7twwNnD6rWjOazkR8zZMgQLl68iKenJ1qtlo8//hhvb28eP34MQEREBLVr10an0+Ht7W3fjzE3aJSkv1qmSCPBdhej4vTCFY25DEYDVouVxMdJhOYPtlfn6hUD2dYMEnXxtOv4JjpFb99zMTdc9/Pz+917DqYKKwesSaiAv8bwQvfRG43YLBZSHz/BL38eArUOXLRlEKZxpLjihFarpWXLlnz//fc0bdqUfv36UbhwYSZMmECxiuWxlS+Mn2LA0ajFMfC3u8RrNBrSniTiHRpMdnoGANG93ubB9Vus+mA4Pi6uTO7Qg4cPHzJjxgyys7O5f/8+fn5+9vlxcHCgSJEi9mP+1p6VkiRJkiRJkiS9PLm2UJKkV2LHbQv779rItv160Aj/qTb0qNMbgMwzW3AMLITG2QuXuh9Q69M1VKpSlZEjR5KSkkJ6ejopKSl4enqSkpJCWFgYV65c4eLFixiN/6m6kkulpb+bZNsDrMKEgd8O0J6nSu0ohIAfY05gNlsA7MuSnbVuZKnpfLN8IfBqK3hPWFN5qJrxV14saAQoUfs1hICLMQexms04KloMQuGILZnsn96Whw8fZs2aNURFRbFw4ULatWtHeEQEr88cgxYNTi8Rxrp6e5EYfx8nd1dunTrLmLotiPlmOXnLlGTpiM8ZOX0KAwYMoGPHjpw+fZoHDx7w6NGjXz2eDBolSZIkSZIk6dWSJUCSJP1hySbB9zdtpFv+s3T6v3Gv9CaP1wxHmNJJ3jUL56i2pO6dzxp/X6q51ePbJUtQVRWdTsdXX32FRqOhVq1a9oDRZrOh1b5ctZgk/a9YhZlE2wP0OP6uMMs/xI/aTV5jxYK1RFUvS6kKxe3H0ShaTsScYepn86kaWfOF9mF8EenCygVLKu4anX3PxBfhFRJI2SbR/LDgW4pWr0LBCpH4aAzcVbO5bsskcf9xRowYQVRUFEuWLOH+/fv4+PhwR2NhjeUhvuhf+LFyqxDDihdhVufeGJwcqdTqdbpM+4yQohEsmTCV6aPHYb3/mPHjx1O8eHEiIyOf+WDi5+7cucOGDRt47733XngckiRJkiRJkiT9OlkKJEnSH3b5icrFRBtWNaeq8b8RQqB18cK72ScAZJ7cABYTGq9QLi35mAZRRfnmm28wGo2sWrWKRo0a0aBBg2cCAxk0Sn9nWSIDs8jGoLx8VSOAwaCn38h3SUlK5eOeY/gx5gS3rsVyP+4hi6Z9x8xhi6jXugb5C+V9JeNVVZU7ajbpiorHT59D5lZS/jc6g4E2I4eQnpTM/J79uRhziEfXb5J+9z5Tpkxm0KBBdOzYkZCQEAACAwPR6/XEq9nYhMD4EntZKoqCqqooikLJ6JqMPbKdd+ZMJqRoTuDa5IOeNPi0P3kiCtnvM2HCBFJTU3/1mLNmzWLp0qUvfL6SJEmSJEmSJP02WdkoSdIfdiPZxuPMnL9rXyA3yK3Q8q73AWkHv8N8/zJp+xbYr1eBmjVr8sMPP+R8/dN+jJL0T2FSMxCIl96r8Wmh+YOZ+PUolsxaSZf671KwSD7SUzMwOhp575N3qNEiChOZ6PnvTVx+TW6loEajIdFi4fK2PRw/dZmKLZvglz8vOv2LVR365c/Du1/PZPushXxWvxXBRcLJSE1D7+jA1NFjadem7S/uc081Yfwdn3lqNBoMjg5Ysk0EFiqAarOh+enDBxe9A5Fd36SBIQCAQ4cO4eXlhbOzs/1cd+zYQWBgICVKlABg165dNG/eHEVRZMW0JEmSJEmSJL0CMmyUJOkPu52iYhM5vaZfdPFl7i/+AW/P5eHKIWh0RhQnT3S++anfayQL6ztis9nsQYgk/ZNYMAF/vFKuZsNqVK1bifjYe9y8EouXjwelo0oghCBdJGIR5t913NxQ7ekl3mN69+Pw5m24e3txaMU66vbsQr13u77wMSMb1qVk3Ro8jo3j3pXrGLw98a1Qkmhj0C8fXwiShOWlqhqfFhhegEMr12PKzMTo5GS/XKcoCCFIsmSTV+tI//797depqorZbGbw4ME8fvyYxo0b07x5c5ydncmTJw8gK6YlSZIkSZIk6VWQYaMkSX9YpvXlQxV7Z9i8kRiDi2NLvodXmwlo3PyxqmAV8hd/6Z/rVS7J1em05C0YRt6CYfbLFEUBoSBeMtC0Wq3odDr7e2v+/Pn4+Pjg4+ODTbUx8PQuPLNsbJ02hy1TZlOtfSuc3N1e+PhanY6AgvkJKJgfk1BJEhZszxmj+tPIf29rFmdPD6q82QLVpj5zee68a57qSn/u3DmGDx9Ojx49aNCgAQcPHmT//v2MHj2a77//nsePH1OuXDn77WV1oyRJkiRJkiT9MbJcSJKkP8zNoPyu0ECoNgAMnoGY711C0efsb2fUgkF+d5L+wZTfWbH3/GP98t2VG6o9751ns9meuc3TdDodFouFixcvUqhQIcaMGcOAAQPo3Lkz7l6e6F2d8Qjwo3Lb5hhdnNk8+cvfPVYbAo0C+uf8qKFBQasoqL8zk63QrBEdPh+Bo6uL/bLc8xWAsNno1q0b3bp1w8/PjytXrnDw4EFUVcXR0ZHo6GhiYmJo1KgRGRkZDBw4kMWLFwPyQw5JkiRJkiRJ+qPkr/OSJP1h4Z5a9NqcX/JfJjxQNFqsaQlk3zmDQ75yCKMbGgVCXTU46X9vzZMk/fVyG8O8igpHm832i+PYsKJFi1Fx/MXtc8OyxMREIGf5cK6LFy/i6enJzJkzef/994mNjWXatGloNBrUlHR7FWJgofxENW9MzOLlqD+Fly8rW6g4KVrclF8uotAqCr6KgWxe7ti556L9qXLx3K4Y7l66ijk7G0VRSE5K4vTi1exZupo7d+7w2muv4e/vT7169Th8+DBHjx4FwGw2o9FoCAwMpG7duhQoUIA+ffrQvn17Nm/ebA9sJUmSJEmSJEl6eTJslCTpD8vrriHAKWdJpFX9rzd/Rta1wwB4NhkKioKrASoFy29N0j+bg+KMBh0q1t99jNzAK3dvxYQHj/9zHRa0ih6j4vSL+x0+fBiNRkOrVq1ITk5+Zs/TwoULU7JkSebNm0fp0qUBaNCgAb1792bHstVYM7OxCBWjkxNlGtTB6OzMtpkLfvEYLyIDK4GKEd1zKjMBAjVGzIiXCmQ1Gg2ZqWl88+HHvBNQhCUfjWBsdAsW9ByAqqocXLuJTYPGMnPseEaPHk2nTp0AeO2113jw4AE7d+7EZrNhMBgAWL9+PdWqVWPq1Kls3LgRm83GuHHjSE5O/sVjywBSkiRJkiRJkl6M/I1ekqQ/rLC3htL+OdWNqni5thguZZoQ1HMJind+NArkd9dQxl9uJyv9szkoLjhonDCJrN99jNwKxfjY+7zVoDdD3hlF3M14AMwiGzeNN9rnVA2qqkr9+vVJTU1lyJAhjB49GsjZr1Gj0dC3b180Gg0eHh72x2nRogXurq6cnPk1SSInIA2MKEjR6pVZN+6Llx67RaiAQoTW+Vdvk0fjgAMasnixTyiEEBxetYHu/oWJPXOBD5bNZ9DG76jSriUntuxg9/zFFGxen8JlS6NRFKKiogC4evUqn3/+OSaTiUOHDnHhwgUAkpOTMZvN5M+fH4AaNWqwfPlyDh48iLe3t/1x7dWUPz0fVqv1mTH9Ezw9ZkmSJEmSJEn6s8mwUZKkP8xRp/B6AR0expyg0fwSv9cqioLFBirgoIPofFrCXOUS6n+DVxnE/NPCEq2ixVsbhBXzC8/D05VzQggunbnKZwOmsGn5NsIKhDBy5hACw/yxCSsKCp7aAPvtVVW1P861a9fw9/dn3rx5nDx5ks8++4wbN26g+2np8RtvvEFwcDDfffed/f7BwcH06NGDg/O+JV21YBEqTm6u1O/TjWHbV730+T/CTKDGSF7NL5d55wpQjIRqHXgsLC80R4qiYErPwC9fHhr160mxmlXxDg2m2eC+hFcqz77vVuPm4U7jRo1JTEzk+PHjAOTNm5fq1avj7OxMbGwss2bNYs2aNcTExHDhwgUiIyN/83FzK0PXr19PdHQ0vXr1YvLkyfYx/d08/TrKDUpzn/v79+//5p6ekiRJkiRJkvQqyLBRkqRXokqIjjcK6XAx5ASHJuuLVTiabWBRwaCFKsFaWoXr/5a/wEsvL/d5FEL8ZsDx9PXPuw7+E5Zs2bKFXbt2/RnDfeU8NH44KM5ki/QXun1u5dyj+49RFAWjg4Els1bw9fSltOjclOA8gdy6Gku6NQkXrScuigcmkwn4TyCWmZlJ+fLl2bRpEyVKlKBbt24IIfj000/54YcfADAajfTt25f58+dz7949+2O3adOGx/cfcGPhKu4LE0IIQopGkLdU8Zc67xRhQSsUquo8MPxGoxyNolBR54GDopDy1HLzu5eu/mKfyNzXQfHar5G/XGl2zvnafp2zpwfZaekY/b0ponWhcbUalCpVirlz59rPrUmTJhiNRq5fv85XX33FiBEjePPNN5k6dSphYWHPPNbTe1wCZGdn07NnT/r06UNkZCT58uVj0qRJDBs2jPv37z8zvr+KEMIeyD/d4Cb3Pbho0SKKFStG06ZNadiwIdeuXZPfZyVJkiRJkqQ/jQwbJUl6JQxahR6lDDTIp30mcDTbfhk6CnL2dsy2gk2AUQflArR8WM6Aj5P8tvRvcezYMT766CMURbEHID8POGw22zPXw7PBTe7tp02bRkBAAAMHDuTIkSP25id/Z0aNEwG6/NiwYBWWX1yvqqo92BJCYDZb6NbkfWaOmU9KUir5I/JSr3kt9Hod7l5ujOgzjo/e+oS4qw8IUPKhUbQYjUYAxo0bR9++fSlbtiwmk4nw8HAOHjyIn58f+fLlIz4+nj59+hAbGwvAO++8g06ne6a6MX/+/Kxbt46P2nXBU9ETjwn1JUO0VGElRVipoHMn329UNeYK1ThQTutOirCSKWws7DOI2W/14d6V6/Z5gf+8DnzCQihWowpP4uK5dfIsAJsmzeL2uYtENa5HZa0bpUuVombNmhw8eJDr168zefJkDh48SFxcHE5OTvTr149t27bRvXt35s2bx+rVq+3PB+QEt0IIe/XijRs3OHbsGGvXrmX8+PEMHTqUWrVqMWfOHI4dO/bM+P4qiqLYxz1t2jTatWtHQkICycnJdOnShYkTJ9K9e3cmTJgAQN++ffnxxx+Bvz4olSRJkiRJkv595G/1kiS9Mr5OGgZFGWlXRIefE+g0OXs4Zlt/+ceiglYDHkaom1fLp5UNFPPR/vcHkf4xsrKymDx5Mnv37qVv374ULVqUy5cvPxNuaLVaMjIyGDx4MBUqVKBnz55s3rz5mePMnz+fefPmMXXqVI4ePcq7776Ll5eX/Tiqqv5tm3d4a4Pw1AaQKVKwiWeXgms0GlRVJSszC7PJjMGgJ6JEQa5fvMnFU5cB6NqvIwkPnnDj8m2adKhHcmIKt48/wl3vg8ViYeDAgeh0OoYPH87q1TkdmN9//31SUlIoWrQo+/bt4/bt25w6dYqMjAzee+89duzYgbOzM507d2bIkCH24Fav1/P6669T1CeQhgZfPBUdd0Q2WeK/z61NCO6pJtKFlUo6DyrpPF44gIvSuVNScSZBmCnXsSVpT5K4/uMJ4NkQL/f5LlytIoHhBZjV+V365I9k9+JlvLVgCiPf7oWn1ojBYKB27dpkZWVRqFAhvvjiCy5evEhiYiIZGRn4+/sTEhLC6NGjKVq0KFu3biUlJQWNRkN2djbXrl2jadOmjBo1igcPHvDDDz/g5+dHhQoVGDlyJEFBQRw9epTPP/+c6OjoFzrHV+3pZfO5X3/11Vf4+PiwZMkSAgMDiY2NRVVVSpcuzbJly+jbty81a9akbNmy7N+/n/Xr12O1Wv/yoFSSJEmSJEn695FhoyRJr5Sfk4aBFYxMqG6kUQEt+TzAUZ8TPGp/+mPUQpALVA/V8nElA+Nfc6Cwtwwa/+meXn6qqipFixbFycmJBg0acPz4ccaPH094ePgz4cbatWspWbIkP/74I23atMFkMtG+fXtGjBjBrVu3gJzuygULFuTNN99Eq9XaOwkLkdPJWKPRoNVqefjwISdPniQlJcV+/V9No2gI1RfBDR/SbEmY1Wz7dVtX7eTNGt1o/drbLJ65HIB2PVuRmpzKsQOnyc7KplhkYYqWiWD1N+sJjvClWImi7P/+CBs3biQqKorFixfTpk0bYmNjOX78OEWLFiUuLo4rV64QFhbGokWLMJvN7N69m+3bt+Pj40PTpk05fPgwAwcOZN68eXh5ef1i3KEaB1rqAyihdSFRWLijZpEsLJjFf0IumxCkCyv3VRN3RTaeGh1NDH68pvNE+xIBllZAHb03lXQeBJQviV+JCI5v3cnDG7eBX1Y3ajRa8kWWIj0lldJdWjHpdAyftOpEmM6J2NhYmjZtypw5c7DZbBiNRs6cOcPHH3+M2WymRIkSbN26lZs3b+Li4kLlypW5du0at27d4vLly9SrV4/WrVtTqFAhEhISCAgIIDQ0lN27d+Ph4cHmzZuZMGEC58+fp1u3bhgMBtLTX2yZ/Ktgs9lQVdVexZj7nouNjWXhwoVERkbSs2dPevfuTbly5XBycqJz586UKFGCCRMm4O/vz65du6hUqRJ79+6172v5d3ivSJIkSZIkSf8eMmyUJOmVM2gVauXR83l1B6bWdOSzqgbeKaWjbWEtbxXXMbySgam1HJlS04HWhQ24G2VlzT/Z06EfYA9Djh07RkBAAKqqEh8fz6lTp9BoNFy4cIGNGzcCkJKSwgcffMCePXto1qwZSUlJpKWl8fXXXxMTEwOAh4cHd+/e5c0336Rz58507NiRkiVLMmLECBRF4dq1a7Rs2ZKIiAi6du1K+fLlOXfu3N+iYktVVXToKeBQCn99GCaRSULqA1RhI2+hMOJuxeNgNPJjzAnMJjPBYYFUrFmeY/tPcvHUFYSi0n14e2K2HmLZ+M2UKVaOc2fPsW/fPkwmE1lZWQwYMICsrCw+++wz+xzr9XoCAgIYMmQIjRo14v79+xQuXJjZs2fTuHFjbDYbPj4+vP322786dk+NnoZ6X1oY/CmhdQXgsTBzR2QTq2ZxT5jIRsVfY6C+3oc2hgAitM4vPe8ajQajVofr2Vuweg/Fypcl9vwljvx4hEeqmTRsZAobGcLG/YxUPipdHZOHE8Eli+B4L5FWDoEoD5M4ceIEFStW5M6dOyxZsgSz2UyzZs3w9vbm5s2bQM5rKTk5ma1bt6IoCqVLl+bcuXOEhobi7e1N8eLFuXLlCjVr1rSH2gEBAZQuXZrGjRtz7NgxOnTogNFoJDU1lbFjx3Ly5Mnf+ep4ebnbDZw7d44+ffowceJEIGfLgiNHjpCcnMz48eOpV68eZ86c4ebNm3h6erJ8+XJWrFjB9OnTOXr0KFOmTOHkyZPs3r0b+OuXgUuSJEmSJEn/Lrq/egCSJP17OeoUyvhrKeMvqxb/jXJDxdygYu/evaxatYpixYrRvn17GjRowPXr1ylcuDA+Pj7MnTuXjRs3cvbsWUaOHEnTpk2pVq0a+fLl4+LFi9SsWZOkpCSKFi3K48eP7Y/zwQcf4OXlxfnz5ylbtixCCKpUqcLAgQOpWrUqn3/+OYGBgRw4cICgoCC6d+/OkCFDGDlypP32/6swJXdOcpd154ZDB2MOM2/ePG7cvoaqszJ1/UjCSvlRtmop7tyIJ+HSTUb1ncCIGYNo1qURPZp+yKGDh8kbGcCeNUewWW3Mn/61/Xg3btygRo0aLFiwgB07djBjxgyioqJYvnw5U6dO5fHjx+TJk4fBgwcTHBxsD32NRqN9j8IXoVUU8mudyK91IlvYSBQW0oUNARjQ4KnocFN0aP7A/NpsNoYNG8a0adNo27Yt6Q8fkhp3j/ub96LUrI41wJtsIRCqirOzE1/fPU9hDz+2mowsnr+AIf36M336dJo0acLIkSOpXbs2DRs2RAhBQkICJpOJgIAAgoODiYmJISQkhMWLF7N27VpiY2MJDg4mLS2NvHnz0rBhQ5YvX87169ft4ytcuDC1a9dm1qxZ7N69mwIFCtj3dDx06BCVKlX63ef+31y5coW8efNiNBqx2WwkJCTQq1cvduzYQeXKlbly5QplypTh/PnzFC5cmPv37yOEICUlhcqVK+Po6Mi4cePYtm0befPmpWXLlgAcP34cBwcHNm3aRJ06dYiKivrTzkGSJEmSJEn6f0j8y6WkpAhApKSk/NVDkSRJ+ld68OCBmDhxoggNDRWNGzcWnp6eonnz5uLWrVtCCCFmz54tNBqNAESdOnVEQkLCM/dPTEwUZcuWFe7u7mLu3LlCCCHCwsLEm2++Ke7evfvMbW02mxBCiIsXL4rQ0FCxePFisXjxYmEymYQQQpw8eVJERkYKDw8PMXLkyD/5zP8jOTlZNGnSRPTr108IIYSqqkIIIcxms3j33XeFm5ubeOedd0RISIgwGA3ijVZNxVXTCbFwx1Th7ecpvP09Rb7CYeJoxvfiZOZOUah4fhFRvJCYMG2ccHNzE0WLFhV58+YVgIiIiBBubm5ixIgRolChQsLPz08sXLhQZGdnC6vVKurXry8cHBxE9erVRfPmzQUgVq5c+cy4/goWi+W5lz948EAUK1ZMzJo1SwghRGZmppg5c6Zwc3MT69atE+mqVSTazCJFtQizahOqqorx48eLkJAQodVqhYeHh3BychIajUYcPnxYmEwmMXz4cOHh4SE0Go04cOCAyM7OFuPGjRM6nU7odDqh0WiEn5+fMBgMws3NTTRq1EhkZGSIpKQk0blzZ1G5cuVnxpiamirat28vgoODRWRkpHBzcxM1a9YUJ06c+NPmasSIEaJmzZoiPj7efvmMGTNE1apVRY8ePYS/v78oVqyYKFSokAgODha7d+8WS5YsETt37hR9+vQRjo6Oom7duqJRo0aifv364vXXXxeHDx8W165dE2+//bYYPHiwGDZsmHjw4MGfcg6SJEmSJEnSv8vL5GsybJQkSZL+K1VV7UHf0/r27StKliwpmjVrJo4fPy5UVRWHDh0S3t7eonPnzsJms4ns7Gzh4+MjKlSoICIiIsTs2bNFx44dxZdffilOnjwpFi1aJEJCQkSxYsVEo0aNRLNmzYSrq6swGAyiTJky4sCBA0IIIcaNGyeioqJE165dRYUKFUTTpk3F48ePhdVqFbdv3xaNGjUS/v7+YuDAgaJZs2aiSpUq4syZM/bx/xnWrVsnMjIyRM+ePUWFChVEcHCwEEKI2NhY8eWXX4rKlSsLHx8fMWnSJCGEEAcPHhRFihQRgDh37pxITHksIgqHizz58og8+cLEmIkjxabtG0RYnjBBTuN24eDgIG7fvi0ePHggihcvLgARGhoq2rZtK2rXri2MRqPYtm2bEEKIq1evikqVKgkXFxdRqlQpcf78eVGlShUxZ86cP+X8X8TPXzc//vijePTokf3rzZs3Cx8fH3s4vWbNGnHy5ElRpEgR0aFDB3s4nfscnj9/Xvj6+govLy8xZ84ccf36ddGtWzcBiI0bNwpVVcX58+eFu7u7KFSokKhVq5ZYs2aNOHHihAgODhZ6vV64u7uLqKgoceHCBXHx4kXh5OQkZsyYIYQQYsWKFaJIkSJi6dKlQoicwFiInADw/v37Yt26deLatWuvdI6sVqv9/7nzlZKS8otgPl++fMLR0VF07txZbNu2TcTExIhBgwYJvV4vTp06Zb9dXFycyJMnjyhevLjo2rWr2LJli6hSpYoICgoSDg4Oolu3biI9Pf2VnoMkSZIkSZL07ybDxqfIsFGSJOnl5QY7qqrag5DnOXTokHBychJVq1a132fEiBFCq9UKBwcH0bt3byGEEJ9//rkwGAwCEJ6enqJw4cKiXLlyIiQkRMyYMUOEhISIqVOnipYtW4ro6GhRrVo1ERQUJMqXLy/KlSsnVqxYIQICAuzVbO7u7uLx48f2cQwYMEA0aNBAXL16VQiREwI6OTmJyZMn/1lTJJYvXy7Cw8NFRkaG6NGjhyhfvrxwcXERiqKIypUriwIFCoj69esLRVGEl5eXOH78uBBCiOHDhwsXFxdRv359cfv2bREaGiqcnZ3t4aLRaBTt27cXPj4+AhCKoogTJ04Im80m5s6dKwBRoEAB4eHhIbp27SocHR1F4cKFxaJFi0T16tXF0KFDxZYtW+z/7gUGBopvv/1WCPHXVjZu2LBBeHl5ibCwMFGmTBmxZs0aIYQQN27cEIqiiJMnT4rHjx8Lg8Eghg8fLiZMmCA8PDzEtm3bxPfffy9iY2OFEELEx8eLzz//XOh0OrF+/Xoxe/ZsER4eLgBRtmxZIUROQNinTx8RFhYmmjdvLry9vUVERIT9Nbh9+3YhxH+qLTt37iyqVq0q0tPTRVxcnGjXrp1o0KDBM+N/Xtj+R/zwww+idOnSz1yW+34bN26cKFCggKhZs6aoXr26OHbsmBBCiE8++UQAwtHRUVStWlVERUUJo9EonJycRNeuXUVCQoJYsWKFWLx4sQgLCxMajUbs3r1bCJFTfbtp0ybx8OHDV3oekiRJkiRJ0v8PL5OvyQYxkiRJEgDZ2dmMGDGChIQEFEVBVVUURUGr1ZKWlsawYcPo1KkTU6dOtXfgrVixIjVq1ECv17NhwwZKly7Npk2bqFSpElarlTlz5jB37ly6d+9OUFAQDg4OpKSk0KRJE7766isSEhK4c+cOnp6ezJw5k2PHjnHs2DFGjhxJ27ZtSU5OxmQysWHDBiZNmsSMGTM4cOAAbm5uzJgxA4Bbt26xZ88eypYtS6FChQD44YcfMBgMrF+/nsuXL7+S+bHZbAwePJht27ahqirNmzfn7NmzODk54eLiwvHjxxFC4OzsjIODA9u2baNt27YUL16csLAwBg8eTEJCAs2aNSMgIICYmBgOHTqE0WgkIyPD/jhGo5HY2Fjatm2LRqPB29ub8ePHoygKderUISgoiBs3bpCZmYmqqkRGRhIXF8dnn31GuXLlGDFiBA0bNsTNzY3Tp0/j4eGBu7s78J9GIKqq/ikdiH9+TFVVWbZsGR9++CE7duxg+vTp7Ny5Ex8fH6ZPn87x48fJmzcvlSpVYuzYsXh6etKjRw92795NfHw8qamp1K9fn+bNm1O2bFkWLlyIg4MDAwcOpGTJknTp0oVp06bRv39/mjZtyokTJ9izZw9Dhw5l586d3LlzB0dHRxYvXsyAAQPYunUrPj4+nDlzBgCz2QxAnz59OHLkCKmpqYSEhFClShWOHDnCrl277OeS2wDp93q6WzvkvJ5u3brFokWLALBardy8eZPo6GhWrVrFkCFDaNKkCTExMXzwwQfExcUxcuRI8uTJQ/HixRk0aBBTp07l4sWLZGZmsnz5ci5cuMCWLVsYN24c9evXRwjB1atXAXB3d6dx48b4+fn9ofOQJEmSJEmSpP/qTw4+/3KyslGSJOnFnDt3Tvj7+4v3339fCJGzpNNisYgvvvhCBAYGiqpVq4oPP/xQ+Pv7i86dO4vz588LIYRYv3698PPzEwMGDBATJ04UVqtVxMfHCy8vL2E0GkVERIQ4f/682LRpk1i6dKnQ6XTi9ddfF0ajUbi4uAg3Nzdx8uRJsWzZMjFp0iSxY8cOER0dLQoVKiScnZ2Fh4eHKFOmjH3prdVqFWPHjhUeHh72qstq1aqJevXqiY0bN4pZs2aJ9u3bizlz5oiNGze+svlRVVVERUWJGjVqCKvVKjIyMkR6err48MMPxaRJk0R0dLSoVKmSCA0NFdHR0UIIIY4cOSKqVq0q3n77beHp6Sk2bdokhBAiKipKKIpi31dQo9GIunXrisqVK4syZcqIatWqiVatWomAgACh0WiEoihixYoV4vbt26JChQoCEF5eXqJBgwZi5syZYsSIESIrK+uZysUHDx4IT09P0bFjR5GWliaEyKnOe/o2T++j+EeqHp9e/vtznTt3Fl5eXqJdu3b2y44fPy5q164t3n33XWG1WsXKlSvt57h//34RHBwsChQoIPR6vciXL5+IiIiwX/bxxx+LrKwsUbp0aQGIqVOnCiGEWL16tQCEq6urqFatmli8eLGIjIwUYWFh9v07nzx5Inr16mVf7p5r48aNwsfHR5w+fVoIIcTNmzfF9u3bRbZqE2mqRaSqFpGhWn/XHD09L6qq2pdGp6WliW7duokiRYrYr7969apo3769CA8Pt98uICBAODg42M9hyZIlwsnJSRw9elQIIcTly5dFeHi48PPzE6NHjxa3b9+2PydHjhx56fFKkiRJkiRJ0vPIZdRPkWGjJEnSi8nKyhLjx48Xfn5+IjMzUwiR07xl4MCBYv78+fbbbdy4Ufj5+YlGjRqJ06dPi4kTJwpANGvWTGzcuFF88MEHolu3bkKv1wtFUYRGoxFjx44VJpNJJCUlCT8/P2E0GkWNGjWEr6+vcHZ2FsOGDRNC5Cz5dXBwEG5ubqJQoULiq6++ElOnThUuLi7i+vXr9jFcv35d+Pj4iIkTJwohhNiyZYto3ry5CAgIEAUKFLAv0X3VDhw4ILRarahWrZqoUKGCGDRokDAYDGLgwIFi586donjx4qJChQoiLCxMCJETNPXr108ULVpU+Pv7i6ZNm4r27dsLnU4n3N3d7XPk4+Mj6tSpIxYvXiwA0bZtW1G6dGnxzjvvCI1GI0qXLi1CQkJEeHi40Ol0AhD+/v4iKSnpuePMDcWSk5Ofe/3x48dFu3btRJcuXcSsWbNe2RLhK1euiF27dj2z1+CePXtERESE6NKlyzPj+/jjj0VkZKQ4d+6cECInlHRwcBAODg5Cq9UKwN6o5dKlSyI6OlqUKFFCuLm5CVdXV6HT6YSjo6OoUaOGSExMFEOHDrUvk75586YQQohr166JsmXLCm9vb/sehT/88INwcXERPXv2FPv27RNJSUmiSZMm4o033hDpqlWcs6aJH8xPxHfZ98TMrFgxLeu2mJZ1W8zIihWLsu+KbaYEccKSIhJsphcOH5OTk8VHH30kqlWrJjp27ChGjRolVFUVO3fuFN7e3mL16tVCiJz3W9u2bUXz5s3Fe++9Jzw8PESpUqWEXq8X5cqVszeL8fHxEY0bNxaDBg0S/v7+okuXLmLgwIFCr9eLxMTEP/gsSpIkSZIkSdIvyWXUkiRJ0gsRTy17dXBwoG7duri5uTFp0iQAdDodb731Ft26dePChQs0atSIVq1a8ejRI2JiYqhfvz5JSUk4OzuzefNmXn/9dbZt28adO3eYP38+RYoUoXjx4qxYsYITJ07wzTff4OnpyTvvvIOvry99+vRBURS+/vpr7t69y6xZs8jOzqZ58+ZcvXrV/tiurq5899139rHmyZOHd955hylTpgDQsGFDVqxYQUxMDNevX6d58+avZH6eXvp6/vx5kpOT8fX1JS4uzr7ctXDhwixYsIDq1asTHh6ORqPhwYMHrFu3Do1GQ40aNUhISODhw4ds3LiR7777DiEEAQEBDB06FE9PTxRFIS4ujri4OCpVqkRsbCwuLi4IIfDy8uLs2bOUL1+e9PR02rdvz4ULF7h//z4eHh7PfS4VRUEIYV8+/bQxY8YQHR2Nk5MTvr6+TJ06la5du/Lw4UP7bWw222/Oy8+vP378OOXLlycqKor+/ftTo0YNVq1aBUCVKlWoXr06t27d4ubNm/bxRUdH4+bmRv/+/WnTpg2pqalUq1aNypUrYzQaURSFokWLAlC4cGFee+014uPjMZlMdOrUiY4dO6IoCgcOHODtt98mJiaGHTt2MGHCBDQaDe3ataNs2bIEBARgtVqZOHEiAMWKFaNTp07MnTuXBQsWUK5cOW7fjaPewN4sNt1jk/kRR6zJPFBNaAAHNDigQYdCimrltC2V7ZbHfGe+zxbLY27YMlF/ZUm6qqrMmDGDQoUKceLECd5//31q165NlSpVUBSF/Pnz4+rqSo8ePTh16hSenp6kp6fz8OFDzp07x8qVK9m4cSOOjo6cOHGC7t27c+fOHaKjo9m+fTs7d+5k3rx5LFq0iGHDhrFz5048PT1/87mTJEmSJEmSpD/dn5t7/vVkZaMkSdIvPb189mlpaWli0KBBIiQk5JnLf/jhB+Hv7y98fX1F+/btxYoVK4Srq6uoWbOmCAgIEIqiCEVRRK1atYSvr6/YsGGDWLdunejevbvw8vKyNz7p0qWLCA4OFmvWrBEXLlwQo0ePFhqNRuj1erFy5UohhBAhISGie/fuz3TLHTt2rAgICHimucXp06eFoihi7969f2gufr7M1WKx/GJ+4uLiRMOGDYWHh4eoU6eO0Ol04uzZs2LJkiUiLCxMuLi4iIkTJ4pvv/1WlCpVSpQuXVrky5dPTJ06VQQFBdmbvwDCxcVFFClSRLz++usiMjJStGrVSri6uor8+fOLxo0bi4ULFwqdTidatWol8ufPL1avXi30er0oVqyYmDt37jPj+rXKuuc9vzabTcTFxYmqVauKVatW2S+fNm2aUBRFLFu27DfnSVXVXzxeUlKSiI2NFXny5BE+Pj7iypUrIiMjQ/Tv319ERUXZlyWvWLFCVKxY0V6JKoQQt27dEiEhIfZ5cXNzE6VKlRIPHjwQc+bMEUajUZQuXVpcunRJCJGzfNjBwUG4urqKhQsXirS0NPHWW28JnU4nwsPD7UuGTSaTaNeunWjatKm9arJLly4iNDTUvux+5cqVwsXFRWzfvUusv3FOTMu6LcZm3hAzMmPF4qx48V32vd/8821WvJiddUeMy7wpJmbdEltMj0SKarHPc65Tp06J8uXLi88///y51aMZGRli4MCBQqfTCScnJzF8+HDh4eFhXy6+f/9+ERERIQICAuzvoYiICBETEyMURRFDhw791feyJEmSJEmSJL1KsrJRkiRJ+kVDCvhP9ZtOpwNg7dq19qpCIQSOjo40aNAAIQSzZ88GcppobN68Gb1ej9Vqxc/PDw8PDzIyMnjy5Al58uShVq1aODo6EhUVxePHj7l79y7NmzcnLi4Og8FAeHg44eHhxMTEEB8fT9u2bWnZsiXTpk0jMjKSxo0bExkZCcCAAQNYvXq1vQoOoEePHlitVpYuXWq/rHjx4jx69Ijq1av/oXnKbfxhMplQFAWdTodOp+PatWt89913jBo1ilu3btG4cWOEELRt25aIiAhmzJhB9erVqVSpEh4eHkydOpU333wTBwcHLly4wJ07d+jXrx9PnjwBQK/XU79+fbKysrh58yZWq5XU1FTCw8MxGAxkZmYSGxvL+fPnCQ0NZf/+/URHR1O7dm3u37/P+fPn6d69+zPPY27Dl9zn++fP79WrV8nMzLSf55UrV3j8+DEtW7ZkyZIlFClShM8++4xhw4YRHR1tP87+/ft56623ePTokf34iqKgKAo2m41169ZRpEgRvvnmG0JDQ3nvvfdISEggPDycvXv3snHjRs6dO8ecOXOwWCzUrhdNWMEC7NjzA/O+XYxFqGzYsIGwsDBat26Nr68v/fr1IzY2lt69e1OjRg3KlSvHpUuXWL16NQCFChXCbDaTkZFBQkICer2eVq1asXbtWr755hsiIiIAOHToECtXrmTMmDEUL16crKwsbt++zd27d/niiy8AqFOnDj9eu0Ri1aJcDHRCg0IexQEvjR7tU3P6axRFwU3REaoYcUfHaVsq35rucdCSRJwwcVfN5m5GChOmf0FCchJVe3bitJrO+MUL6D9yOF8unMesWbNwcnLi/fffp169euTJk4dz586RkpJCamoqqamprF27FpvNRv369QkMDKRIkSIoisLcuXOZP38+PXr0sD/XkiRJkiRJkvR3oQjxJ7Sj/BtJTU3F3d2dlJQU3Nzc/urhSJIk/elUVX2mc+65c+dwcnIiNDQUg8GAEIIFCxYwYsQI3N3d0Wg0GI1GevbsSffu3UlISOD111/n/PnzzJw5kzp16tCzZ09UVeXatWtUrlyZ4OBgJk2ahJeXF61atWL16tVUqVKF06dPU6JECQ4ePEh2djYpKSlUq1YNrVbL6dOnSUlJsYd1aWlpfPTRR/z444+89957TJw4kdatWwM5nXP79evH0KFDMRgMALzzzjscPnyYEydOYDQaX8lc3blzh6tXr/LZZ5/RuHFjPvzwQ8aPH8+ECRNIS0vDaDSSP39+Hj58SP369bl16xa+vr6ULl2azz77jFOnTnH8+HEGDBjAkydPKFSoEPfu3UMIQVpaGhqNhkqVKlG3bl1GjRqFl5cXnp6eaDQazGYzkZGR3Lp1Cz8/P/bv3094eDgWi4WbN29iMBg4cOCAfSkx5Cxf1mq1v3lOQgimT5/OiBEjCAgIwMvLi7lz51KiRAn27NlDx44dMZlMuLm50aNHD7p164aXlxeqqmI2m3FwcGDDhg20aNGCK1euEBoaikWn4baaxZezvgQ3Z87s2ENIwXy89W4vyviF4KlquHbtGnXr1iUxMZE33ngDFy9PduzcwYB1i9EVCGFi7ZbcOnQMnYMDlTq0JP7kWdq+3YXOrzenVaOmXLlyBT8/PwYPHkyPHj2YPXs2gwYNwmq10qVLF8qXL8+7776LzWYjf/78WK1WgoODiYuLIyEhgbp167Ju3Tpu3rxJiRIlWLBgAW3atGHVqlXs3bsXNzc3PDw8GDZsGA9UE1ssCTxUzQQpRgzK8z97tQlBKlYyhY0MYcNMToCvswlcdQY0KJiEjWRhIdFmBo2GzMNnOThmOiHRVTm/YAUFm9QhT+ni7Bk5FdVmw83fl8fXbpGdls6+m5epmKcQq5Yvp0ePHvTp04e5c+disViAnADcarUyYcIEdDodc+fOpXPnzqxbt4527drRt29fhBDPhM6SJEmSJEmS9Gd4mXxNho2SJEn/Ul9++SXz5s3D3d0dR0dH2rdvT4cOHYiJiWHy5MkEBATg4uJCw4YNOXjwIF9++SUrV66kRYsWZGRkYDab0Wq1FC9enHz58rJnz16MjkYSHz+hQHgB2nVqy+ejJrF48WJmzpyJoigcPHiQr776ipCQEHbv3s3UqVPp2bMn69ato0KFCqSlpWGxWPj++++xWq32qqxKlSpRuHBhRo0aRWhoKJ9++ilff/0127Zto3DhwgCkp6ej1+t/d9D4dAj75ZdfsnjxYlxdXTl27BgpKSnUa/4GAYXzsfW7lfjkC8E7LAiyLJzaHsPAkSMYN+RjypUrx48//oirqyspKSkUKlSItWvXMmTIEDZv3ozNZsPDw4OJEycyceJE0tLScHJyonLlyuzevZuiRYsSHR3NnDlziIuLo1mzZpw4cYLWrVszffp0mjRpwtChQ3FycqJAgQK/eT4ip8kbGo0GVVVRVZVx48ZRqFAhlixZQqdOnfDz82PYsGHYbDZGjxmDV5ECvN3qTbSODgzbtgIAQ7qJ+yfOcefYKaqULsvt27eZMmUKDx48IKJ4MSp274ChWQ3S9ArbW/Xm7rZ96Jwd8SpZGKO3JzUmDiEyTyG2v/0R25avwsfHh3yRJbl94yaJcffIX7UCj6/fIjs1HVdvT4pH1yQhLp6rMYcIiyrDo/NXMGh1pD1JZP369dSrV4/ExEQ+/PBD1q1bR2pqKjVr1iQoKAh3d3fy5ctnD719fX0JDw/n5MmT9O7dm08//ZTevXvTp08fVqxYgdFoxGw2M2XKFDp06ADAY9XMBssjElQzIYoDmucEdTYheCTMPBRmMoUNFYECYFPRaLVYUUm3Wbk4fznpN+N47fPBOKMhKSuT7a378DDmKPka1yLlVhwuwQGYEpNJvHANJzdX3l3yJYlJScxt/Q6R7ZtRNiqKHV/M5cmjBMxmM7Vq1eKTTz5h5syZrFy5EmdnZ+7du4fRaERVVUwmE2+//TY2m40FCxbIPRolSZIkSZKk/4mXydfk2htJkqR/GCFU0tQk0kUyVtWMig2doseoOOOu9eXsiXP2Za0fDRxA7ehaxN6KpXKVyiTaHrBu13J+2PsDQXkCKFKyEGMnH+fssfMkJSbTpEkTbDYbo0aNYu/evZw7f47YuFisOhNmq4ky5Yty6ZSgSpOyNHyvErOmO7F2x3JOnTpJcnIKVapUYcKECZw6dYoKFSpw9uxZ1q1bR+PGjWnQoAGPHz+mZ8+enDlzhlKlSpGdnY2DgwO9evVi5MiRnDlzhtDQUIYOHcqoUaM4dOgQERERKIqCi4vLC82PSc3koTWWbJGBDSsaNBgUR/x1eTh/9DLvvfceT548oX///pSKjKRn3/e5G3eb2ynx/Pj1fiq2e42IasUoWqskacmZnC6zj1EDB2Gz2Dh46BAIgY+PDwUKFODMmTMYDAbKli3L9u3bsdlspKen06VLFx49esSwYcPw8/Pj8OHDFC5cmLi4OPz9/QEwGo3s2bOHokWLcvToUbZt20bVqlXt55G7LPrXKhlzlzWnpaXh4uKCTqdj2rRpJCYmMnToUNq0aQPAsmXL6N6vL0OnT6Ltijn41KjAiW9WsXntBsLKlWL7+OmcXrERBxdnvnJywqhoeKPp66xYvxavTk1Ib/kaOgUMqVnoLDmVfXmqRVGyU0u2vTec4xPnsT85ldtrtwNgM+qJeKcNSdO/QjVbiDt6iuz0DPwL5GVkzCY0Oh3ffjQCU1o6d4+epuHwD8nXpDY7+37KhC+mkpmZSdeuXSlRogQVKlRg//79HDt2jP3791O6dOnnzoWLiwt58+blypUrAEyfPp1mzZqRlJREy5Yt7bezCJU91kQeqmbCfiVoTBdWbqvZJAsLWhSc0PxnabUOMq0WDn82g4uzv8Pg6Ubk6H5koJJqM6Nx1GP09cIp2B9hMmNNSefO+X0YPVyp/uVo8lcrz6Gp33BqxQYATi5dz7XdB2gyYgDKxdt8N2M2R48exdPTk++++46iRYsyYsQIEhMTCQ4Otr8PKleuzNKlS7l58yZly5Z9ofeFJEmSJEmSJP2vyLBRkiTpH8IqLCTbHvLEdo8MNQUVG6CgoCB+6j9isDoy7ospOHk68PV3s3ALciBN3MHJL4uj1i18N2EDezce5pMvP8RoNDJ5yGxC8gcyYn4/pg/7ipuXYxnz2Wg++ugjAgp4s63VNpp0jubY7tNotVrKVixL3vz5OLLjBN37vkWn99swd/w3FIkshFboyE7P4tKlS9StW5cjR45QuHBh7ty5Q7Vq1YiOjubGjRtERkYyduxYVq5caV/+2alTJyZPnszq1aspW7YsgYGBnDx58lfDpedJtj3inuUGj9W7mNSsny7NqUgzm618OvFTti7aS1ieMPbv34/O24Pd1jjy1C+K8YIec2Y2aQnJBBQtSIFalbh46AKrP5qN1qBDCIHR1RFTWhY1WjblwYVrhIeHc+zYMSZNmkS9evUICgoiKCiIw4cP4+7uTmBgIMOHD+fUqVOcOnWK27dv4+npae+K7ObmxtixYylXrhz58uXD2dn5mfN5eik8PH8J9Zdffsnq1av57LPPqFixIvPnz6dFixaEhoYCkCVsXPJzxKV6Wc59tYy0s1eoFl2HhGNnWfFOfwLDC/AkLh6jg5GMxCSc/X3puGoeip8HCbO/xFgoD34aI8lXbxF/+gJPrt4gsFxJTKlpBJYrjlehfFz6ahUOnu4YvdzROjuRdPcBq7sOwM3bkzc/+xif0GCWDR1DVnoGy4aN5fjGbbj7+RJQMB8JsXchNYOiIXm406YxG94fzs1r1/D09GTJkiW4ublhs9nw9fXliy++YNq0ac/tsH358mVSUlLsXcg1Gg21a9f+xe1OW1O5bsskSDE+N2hMFhauq5lkCxUXtM/s32gzm9k3cQ5HP5+DZ6kIgmtXxpSUgqOHOzZUsmwW9AjcC4Tx5OhZbu86iM6gB8A10J+gfHlY+0YPbBYLTSd+gul2PKs+nYC7lxflO7fhcWwcjl99Q2JiIg4ODgCULl2akJAQxo0bx6xZs+z7sLZo0YIBAwbg5OT03PeCJEmSJEmSJP2VZIMYSZKkfwCTmskty1luWy6QoaZgVJxx1XjjqvHCReOZ83/Fk6OHj7Fu6SYavV8ZTUAWFpuJdDWZZXPW0L5UX74ZvwrfEE8qNynDirkbqNu0JjOXTsTfO4ibV2IB2Lh9PbGWC7hFaOg6sB3t3mrN44eJVKxZnlUL11MssghJCcksm7eWek1rk52RzeVT1/lg6ju06NEQ/wA/vLy8WLlyJceOHaN58+aMGTMGgLCwMNq3b8/27du5ceMGRqORx48fA9CtWzdUVX0maHkRQghumc9xKns38darWFQzDjjjrHHDWeOOk8YdcyJsWLCTxw8TiYu/Q0zsNjZbrvNEvUTijduElgknvEY59I5GYuZsRouGY9/txsXbHVBQrSqmjGwUjYaErEdkYOX+/ftotVqWL1/OrVu3SEtL4/Dhw9hsNmw2G+fPn2fUqFE0bNgQvV5P3rx5uXnzJpcuXeKdd95hy5YtdOnSheLFi+Ps7Pzchj5Pyw0azWaz/bJixYoRHx/P8ePHAWjWrBmBgYEcOHWCvU/imJl9h63qE1zLFSfjSRIbJ8/iswZtSEl4jFavp1jNany4ahH9Vn6FzWxBZGRy9NARjt69hXeZolybuYRF5RqxqkV3Dk2cQ8b9BEp0bMHdwyeJ3XOYR2cv4VOkINnJqZQe3gffSqXROhgxZWbiEuCHX94wClUsR4232vHkzl2uHjnOW9PHgQJBEQWxWSx8P2M+Y6o35cKStZgzs7h79y5v9eqBqqq0adOGAgUK0KxZMz744AN70Hj//n327t3LkSNHePfdd2nbti2NGjX6zSq/x6qZH22puKB97h6NacLKdTUTsxC4/SxoBEh5nMiF79YR2rQ20Stm8ujH09yPOcrhD8fy5OwVHAwGFK2W+/uPkRn/APeIfHgVKYh34YI8vnSd7e+PwOjshCUzk9P7DnD15BkAEmLj0D1IJOvRE9xCg3i9XRt++OEHChUqRK9evbDZbMyePZsNGzbYXwNXrlzB3d2d7Ozs//LukCRJkiRJkqT/PRk2SpIk/c2ZRRa3LOdIsT3CSXHHWeOBTtH/7FaCDJHCyR/P4OTmSGBIACY1kzQSObD9KEsnbqJ+mxqoqiA1MYNHiY84f+IiWoPCjzEnWPP1JnRaLU4ujpw4cIqT9w6Qp0AePhzxHoGhAXj6eFCqfHEexD8i+UkyPQe/Tb3mtTi6/ySqqpKRlsGpHReo264qHfu15sbN68TGxqIoCr179+bMmTPs3bsXvV5PnTp1KFKkCG3atKFFixYUKVKErVu38t5777F48eKX2oNOCMEN8yluWs6gChuOuOKocUarebYC0C/Im0Il8qHVafH0dWPOF3P5ccl0dJjxLpCHC9tPUqVLfdz9vYg/f5MHV+KIPXmV4GL5cfbM2Y/E4GgkT2QhLn7/I2Xav8bpC+coXbo02dnZXLhwgcqVKxMZGUlERARms5n58+ejqipXr15Fp9Ph4uLCmjVr2LlzJ4MHD/5FmPp0JaMQ4hfh46NHj2jatCkrV660X1a9enVKlCjBrl27+PH8GY5Zkqn2XldWL1/O9MM7uWBLJ+1xIntHTSX1/kPSVCttTm6i4davCK1clhtnzqEKlRvHTuHg4oxn4QLs6j6Y/fW68uT0RW5si6FIy0Y4+3rj7OuNztHI+e/W4uDhxve9P0YIQdKNO6CAxtWJ6jNHEd65OcJqIzU9nQsxhwCo2LIJWenpuHh6UKL2a5RtXI875y/nLAv2dKduz7cIK1mMnl9Np2DtakyZPJnSpUsTGhrKoUOHWLNmDaVLlyYtLQ2ADRs20K9fP7p06cKtW7dYv349U6dOxdXV9VdfK1dsGaQKK16/eO+AVQhi1eyfKho1v2i4IgBbgBdhLepjSUpBNVso2LYJwfVfw2Yys79JT05/NJHzw6aSePw8Rh8vKs0fg97LHY98oTh4efDw7CUenr4AFhsXvlnDzZNnCAgvgCkjk3WfTWVGdBvSnyRx/tpVunfvjhCCc+fOcePGDdzd3enQoQMffPABJ06cYOzYsTRs2JAyZcr86vlKkiRJkiRJ0l9Fho2SJEl/Y6qwccdyiTQ1CWfFC63y/P37MtVU0tREHB0dsFlspD3OxIqZbJFO5ehyrDo/m7eHtqFExcJcPH6N3jWGYzZZ+GryUkb1ncDqrzditdqoVKc8xaLC+XLIEuLv30FRYOeGPXh6u/NGh4a82aMFX362kOxsE4mPkji460cmLxlDYFggKAouigfNetSlVKVifPjhh6xYsYLSpUvz+uuvM3LkSADy5cvHd999R/ny5fH19SUmJoaGDRv+rvm5Z71GrOUSCAUHxeUXS4+fVqpyUYSq4p8/kNLRxVk88FsenrqKZ7Avrn4e6B2MlGleDY1Oy4zXhxJUNC8/zFzL41v3AbBkm+k4tz+fnl9Io77RDD3/Hf3GjkSj0XDkyBEKFizI+fPnKVCgAEIIRo0aRf/+/dm+fTvNmjVj5syZ1KxZk5o1a/7mOdlsNhRF+cW5+Pn5cePGDXbv3s2DBw/sl3d5+23OXrnEtL1b2WlNpELft1E0Gnb1+YQ7c1aQduwc94+cQkEhsGg4IXny4ODqQtEB3UjOSOOHLduwKQKLyUTsybM45wnGJSQQW5YJm8WCb7kS5K1VGYGg3rSRVHj/bTR6HapNpebYgTj6eYMqOPPJdJYWqMGjH0/jWaow6Y8ec/rQEe5evILOaMQ3Txh3zl3kysEfCS5SiOy0dMq/0ZASdWpQsVVTOnw+gqptmxMYGoLB053JyxazYMECezfuPXv28NFHHwHw9ttv88UXX7B7926+//57Klas+Jtzmi1sXFDTcUH73M7ND1UTycLyq9dnCBtZqBTu2AzVZOHKN2swpaZzb9dBrFnZhLxRh1vfbcQ1PB8lR/XF97VyeBQPx692JdIePCK8SV10DgasJjPO/j7onRyxWqx0372SCs0ac/6HfXSYNJL24z8hIf4eNpuNR48esWrVKhYtWoTNZsPb25tdu3bRuHFjfHx8GD9+/G+esyRJkiRJkiT9VWTYKEmS9DeWqj4hRX2Ms+KO5jlLPwFsWEhTk1DQEFU7ElUVnNh3FpPZAmgwq1n2veMGz3oXL193vPw8eHdMZ6KiS+Hq5Uy1epXRaDScPXqBrp+25viOc/RuMIw3otoz5eNZtOjyOqeOnGPQ+L5Ur1+FZXNX07P5h3j6eFCrcXW8fDzw8ffm4umr9Ko3hBVfr6Fk6RKMGDGCuXPn0q9fP2JiYuzLfQsUKMDMmTOZM2eOPUx6WTbVyh3LZVRsOCjOPCcjekZ029dAUbh66gaR0cWp1/U1ds3cyJ6Za4Gchisu3u6ElSqIwcnIw2t3cXB1wsEtZ188oao8unYXn9BAsswKRkMq1uLBREdHc/XqVe7evcvs2bM5fPgwWq2Wx48fExMTw6JFi5gwYYJ9H0XgN5dMa7VazGYz48aNo1+/fmzdutUeLvbv3599+/bZ5zFdWFHrlANfT87vjEF3LZ4jk+biXbggabHxPDl/hSOT5xFWtTyuQf6oFguKAo46PeEVInFwdeX2ybOkB3phs9rwrliaOmu+JKReNVAUPIqHc3rxaoq3b0Zq3H3uHz/LowtXCKtWAZ8iBXH09sCvciQAWY+eEDVuII22fU3pQT2xZZt4FHuH83sPcmTVBt6aPh5zVjZfvvUeC94diG/eMDKSU3hjcF8MDg5Yf1oeXqFxPXSORhYumM+1a9dITExk7dq1jBo1iszMTBITEzEYDFSvXp3g4OAXeq3EqtkkqhY8nlPVaBOCh5jRofxi6XSuDGFDA7gXyINnsUKcn/4Nj09dIKBOZdyLFqTMuP4U7NqKhP3HuP3dRlBVVK2W4AavofdwxZyegWtwIPnrvYZrkD9uIYF4FsrLg6w0sjLSeXQzluXDxvJVr49wD/QnrHQJAPr168fcuXOZOXMmV69eZceOHVy5coVVq1a98LlLkiRJkiRJ0v/aXxo2jhs3jvLly+Pq6oqfnx9vvPGGvZNkri5dutg7bub+qV+//l80YkmSpP8dIQSJtvs5HYmVX+/nla3mdF3WYcA/xIdqjSuw8aud3DgTixYtqkYFTU64lZ1pwsvfk1a9GtGqVyNGLhnAvIPjmb7yM4LzBpL0OAWj3oEF+yfSeWgLmrwVzeH4HYQXK8C0T2cTf/se4+Z/wpx1UzgYt51hk/uj1+sIL16QAZ2H07FOD/IWCOOrvVNZGfM1nTt3ZtKkSSxevJjGjRtz7949+7h/rcvyi3qsxpMpUjHi+F+DRgD/EB/K1CjBk/hE1n+xnfajWxNZtxh3z93ixuELXNp9ggMLt+AXHgoq1PuoLRNiVzDq/CLyVSwKAraOW0pmcjqKzhGDYiXZ00TH995FVVXWrl3L0KFDCQoKYv78nKDs5MmTREVF2Z/P3JAxt2rxxIkTJCYmPhM+bt++ndDQUNatW8edO3fo1q0bnTt3BnIq+lxcXPjmm284ffUSG00POWdLp2z9OtyIOcyn1RpxdMP36L1z9jaM//EU7b5fTMtVc/AvU4wnV26SeCOWfaOnMSUgkqRrt7GlZvDoynX8akTx5MR5LI+TKDXgHd5KPo17/lC07q5kZWbi4OFGYLmSJJy7glavp+vRTRRs0YDw3u3ROhgRqopLWCAao4F8TWrT5u4BnPKFcHjDFtaMnkxAwXw07NuDml3bs+DRZbrNnogpM4uj67bkzMlPr4eSdWvQcepYrp05T9Nmb1CxYkW6detGzZo1WbhwIV5eXi/9WnkiLAhA99ymMFayhA2HX/mRyCRUzKjoyPkZJOLtVngUKUCeNg0p+vG72NIziV25lVKjPyCgThWSz13l8eHTWLKzcS2YB6/SRbl/8hxB5UqScPYy9aaPosGXYxEmCxfXbyd6eD/0DkaK1ajCgoQr1HmnEw+u3yQrK4vJkydz+PBhOnbsiFar5dKlS9hstpc+f0mSJEmSJEn6X/pLu1HHxMTQu3dvypcvj9VqZejQoURHR3Px4sVnunLWr1+fRYsW2b82Go1/xXAlSZL+p7JFOqnqE4zKr3ecFahkiTQ0aFAU0Bv0dB/xJm9VGcDU3l/z3uSOuPu54Ghw5uD6k2z97gdKVCxChdqlAXBycMJMFvHxd3Fxd0KJV/j83dl8OKU7hUsVRO+o49s5K9i2+gcat61PcN4gAPIUeLZKT1EUqkVXYsikfuQrlIdskUmKmkDffu/j7e2NwWCgS5cur3R+7ltuoAob2udUqz2P3qCn2+TO9Cn/Ed/P3UOhcgUpVDYfXad0IC7exKbRi6nQtjalmlSiRP0KFK9fAQAt0GnOh4yKfIe4M9dZ3m8mDQe3B1sGB75dzolvY3B1daVZs2ZUrFiR7t27PzM3Qgi0Wq39AzOTycSoUaNYtWoVefLkwcHBgbfffptmzZqRnZ3NggULaNq0KXPnzgVg//79vPHGG4wcORKz2cz9+/e5fuMGp27fIKr/O9Ru3JBNJ86QnZ6Bd1gwby6bRXKAO7vafcCNrXvYN2oaNcd8RHiT2sR8MpV5JaMJLFuC+jNGU7RVI9a2e4+4HQeIGP0eh1v15UDvT4j6fBDOQf48OX0Rj1JF2NzpQ0IrRZK3ZmUAtnQfjGfBPATUqEjcrn3wU4Z3eMA4qnwxHAdvD3RuLigGHbcOH6fC6w3xzRNCu3HD7XMTVqIo/vnzcm73Pqq1b4mzpwcAD2/cJuXGHap/1IvutRqQfekWTZs2/WOvFTUbPb9WtWhFwK9WNVp+6vWu+en+XsUK4RGRn3u7DhHcIhrX8LzEb95LYL1qmB4nEtaqASWG9wadFpvNRv6Or1OoSgWcvTyIjTnMrELVaL1uAZ75Qon74RC12rWiUFQ5stLSSbr/kIeXryNsNvIVLcw777xjX9Z98uRJxo8fz8iRI6lcufIfmg9JkiRJkiRJ+jMpQgjxVw8iV0JCAn5+fsTExPDaa68BOZWNycnJrF+//ncdMzU1FXd3d1JSUnBzc3uFo5UkSfpzPbbGc9tyHhfF87n7yAGYRTZPbPHoMNiXWVuEmb1bDrBhzg+c3nuR0PBAsjNNGIwG3h7cmnpv1iArI5uDW4/hH+rL3s2H2LnsAEXKFCKqUSn2rT3K8T1nyVs4hIy0TBwdHflgRC8atKz7q2P9bMAUrpy7xjfbZ2Oz2RAaFbPIJMJYASfNq//eaxNWDmSuxSasOGh+PYx9mgDuq2msn7KedZO/JzMti9DCQWSlZaF1cKTRx50o26L6r95/++QVbB67BNViQ6PVgAKqNSdo7d69OzNmzECv/0/wKYT4xfN27NgxunfvjsFgYMiQIRQqVIisrCyKFy+Og4MDVqsVf39/vvjiCzp27Gi/X+/evZk3bx6RkZEMGDCAkZMm8OBJAg0/6EnKnXtkZ2YS8/UyGrz3DsWG9yZZWNAkpDAjX2UMbi70u3ecjEdPmF+6PlpDTlWis78P5vQM7p04h1mvQVQsxv0te7k5dwWP9h7FJTSQjPiHOIcFUm5wLyp2aGkfz44PR3H/xDkykpLRe3mQ//U6XP1mLYpOS/Llm7hH5MeanoFiNFCsbVPeHz70uXN6+cCPLBs2hlpdO1Aquibrxk0l5psVeAUF0HTOODrVqEdV/Ys3DHoemxAsMN/FIsRzm8NctKWTLCy4/kr1cJJqIR0bxqcqH+8dPM7R4VOI6NMBR39fDr81GNOTZAxe7pSZ8BGhr9fBqoBeUbA8eILt5BWurt5K0vVYHpy+QKnOLYloXp+YMdOp1KszwRojs99+H1dfbxxdXAitVp6La7+nepWq1K5dm9OnT7NlyxZat27NyJEj8fPz+0NzIkmSJEmSJEkv62Xytb+0svHnUlJSAH6xRGrv3r34+fnh6elJrVq1GDNmDN7e3s89hslkwmQy2b9OTU398wYsSZL0J7JhBfjVoBFyKhsFAuWZJaCCig1LE1W3DA9iH3PnWjwePq6UrRBpP1bio2RWz9mKOduMk5sjg798l6r1osgWGTTtEM392EfEXo3H2dtI5YpV/mtgmC88jC0rd5CVmY2jkwNCaLBhwyLMf3genscqLKjY0LzEbiA5MyVo+n5drh67RfLDFJq+H41vkDs+FSpiwuE371+nb0tuH7/MpZ0nsJgtOLo5U751Vd6fPosWDgV+cfufP29ms5kvvviC4OBg5s6d+9w9927fvk1ERASxsbFATmBpsVi4c+cOqqry0UcfUal5Y+r66TixdC27ps/H2cuTRv16cevEaXRGI9moYLHi7OdNZPf2nF60krgDxwirVoH80dW4smEnhybNRagq94+dwcnPB/cS4eSpWIywRjUJjK6KJfY+pwdOxCVPMLW3LsDxp3m2WSxo9XrqTByGOS2DB0mJaPMEoLeqPDp6FnNyCjW+mkD6nXs4+HjiVKE4Hr/xo0ahimXxDPRncf/hmDOzCC1emA+Wz6d0/drEqdlkij++ZNiGwCYEv7Zo34yK9leqHnPv//NrfaJK4RDoy4kPPsOSnoFGpwMFQpvVJaxZNADxa7dzfsQMTE+SwKYS8Xo0QghcgwM4v3Q9JTq2wNnfl5Nfr6TF96tZOmQ0geH5+WD5QtLcHHjUuCHpPxzl+++/x9vbm5iYGIoXL/6H50OSJEmSJEmS/mx/m7BRVVU++OADqlSp8swP0/Xr16d58+bky5ePGzduMHToUBo0aGDfgP/nxo0bZ+94KkmS9P+ZVqfl/9i76/Aorq+B49+ZWYsbcQIEdwnu7u5uxaG0SAulpQalVCm0eIsXabECxS24Q7Dg7nHP6tz3j5AtFErhrdH+5sPDA5mM3JlNZnfPnnNPzvxBhOT3f+J7oeFBjJ46GBc3IznCvJGdXa4lFJ1CWP4QwvKHYBGZz3UsLx8vmndu9DfOJ/f/T8qXdQq1ulbl8tGrlKxdFE8vI/HPsT9Fp1C5e0Pc/byp2qsRucrlQ4cZgcButztLpZ0j/FVmY3R0NKtWrWLJkiWEhoY+NfMxPDycXLlyER0dze3bt8mZMyfR0dFs2bIFHx8fvL29OWFLIaBCKQZUrEhqXAKe/n6oDgeHVq7lzrkL+N65jzE46zEv0q4JlzfswGG1AVDtnaHcOXSCk/OWU6hlfUr36UTM6fNEfT2flMwMio18Bb2vF3Yvd5Kv3KD8uBFIwP0DJ9j19QLKv9qLXNUrICsKJm9PPDxdyMCBrNNRoEsLYo6cxD0sGN/iBQFI49k/D4pOR40eHXHz8abOK13JV74MjxZc/BmlF7/s4+kBxRc9hlBVZJ0Oj4LhpJy9TI2VU8m8F0P0Z9+SfPYSVxf+hGq3E7PvGP5VyxATeRhFkjG4udAj8geWNetN2t0HnF60kkIdmpJwIhprRiaFq1Xi8uHjXDp0jKD6VYlo1oAObXpgNpsxmZ4dCNdoNBqNRqPRaF4mL02wcciQIZw5c4a9e/c+trxTp07O/5coUYKSJUuSL18+IiMjqVu37hP7GTNmDCNGjHB+nZKS8lgHUI1Go/m3UB7mYj0tKJVNevhHoD6S3Sg99k/2er8OtoQXybo32oQFGQUFXdZ+Hh4rO+gjPSPrK1vjdvVo0v6XMuvsrEPdc86n+KJ0kh6ZrOxJh8PxXM1mZOfVgvJNS1OhWRlkVAQq6m/mvT2uZJNKlGxS6eG1sWFXBR4Y0ekefzpVVdXZBCZbVFQUqqpSuHBh4MnMx+zzaNWqFV9++SVffPEFn332GVFRUTgcDoKDg8lfPoK1IhWfh9fV098Ph92OJEmUa96IDVNmYZs8l/IfjQCjjjuHTuAWmIOQCqUA8CuYl2pvD0UxGijUqiE6owEAl7xhnF60ktjKZQhrXof442eRH3Yw31inO0mnzlOgaR0CSz/eOVzml2BdWJNa5Gpa+4lrZviNLurZIprUJ6JJ/V9+3h5eFxWB6+9s+zx0SMhIOH4jrGhAxvyMoKiM5NzSkpCM0dcLVAeKyYBqsSIcDkKb1wEBZz+dxZkJ0xF2B3n7dyDz5j2KjeyDi9XB8SnzOL96M67+vhRu05jLG3eSq3UD6o1/k/S7SditVuJu3OLc7v141yiLm6sbQggt0KjRaDQajUaj+dd5KYKNr776Kj///DO7d+8mZ86cz1w3b9685MiRg8uXLz812Gg0GrUGMhqN5j/BRfZAQYcdG3oMT11HLxnRSXocwo78cB0ZBQn5YXl1ViBSh+mpHZuFABUVo+SKjAIia5kkZQcMFXTS04/9qF8HzuxY0UkGTM9obvNHyOhwxYt4xz30sonMdDMHtxyndusqvxmclQA9OmzZXwASKg502F/w6VCSJGTs2GUT/oo7wGOdpmVZxmq1MnXqVCpWrEjVqlUJCwvDbrc7p/fIHmd2YDI7YNqmTRtsNht9+/bl0KFD3L17F4fDQZs2bYhzU0ix2AhXXJ3nqDwMdJZv1QS7zc63g97g1tGTOFLTSb37gHqfj8Xo4e48XpF2TZB1CorB4FwW0bcThydOx5aRAcDNJT+TfOEa+177kPAuzWm/cQHeLk8+lnpJhoelzr++5uLhH69ndFLPvg5CVZ3dqFVVZdusBfjUKo9vicov9Lg8jSJJ+Ml6bjrMT01udEchMeun4qkMkkSagPhT59nUvB91vp9EUPVyhDatRezuo5yeMJ0qCz4nsG5l7myIJO5gFJkP4vAuXYT4vceJ3nUYNS0Th81GjkL56LrlexSDgUV1O7HztQ+4W70St/cfo8nwgbR99w3CihXmhppJgGx45hQKGo1Go9FoNBrNy+ofDTYKIRg6dCirV68mMjKS8PDw393m9u3bxMfHExwc/DeMUKPRaP45rpInHooPyWrcbwYbJWRcJA9SRBxCGJAksjIK0WHDSnZu4m8FDFXsyCiYJDcEAhkZFQcKCnbsuEhu6H7j2M9iE2Z8leDn7hT9ooQQhBryE5Nxh5MnzvJh78kEhPhRoW5p3Dx/O8BpRIcVBRkH4mHOWiauiBeY+xGyArR67JilYAKkrMyz7EzGpKQk0tPT6d27N/Hx8dSsWROHw4Gfnx9hYWF8/fXXLF68GLvdjl6vfywDcu7cuZQtW5bu3btTvHhxTp48yYULF1i6dCmXL1/mvmpBJ54M7EXOX0p4REmqd22HR5G8HDsVhZRuoWy/zs4gXvY2eleXJ65lyrmr2NMznBl8udrUJ0eVMpQY0AUBuPzGz4/+Yaao+vBqPsr+cJn37wQbJUlCUhRSYuPZ8/2P5K9YloUjxtL4wzdxL1QRXJ65+XMJloxcJuOp33OTsoLsqhDITwnu6ZBBVXHLE0rLfctxzxlExv1Yzn8xl/hDJwHYXLUD+ft0ILBWRRJPnsfo682DrfvI17sN+3u9RfM5n5F2N4boFRu4tf8YPnlzEVatAoHCgfXCDZqNHEKdPl0BsAkVWZKe2sxGo9FoNBqNRqP5N/hHg41DhgxhyZIlrFmzBg8PD+7fvw+Al5cXLi4upKWl8eGHH9K2bVuCgoK4cuUKo0aNIn/+/DRs2PCfHLpGo9H85SRJwlcJIdkRm5VlKD291NckuZFOEg5s6NAjSRI6DNiFDQcOdOhRnnK7FwLs2HCVPJzlzkbJlUyRhqQakSRwkTxeOLvKLmxIkoyv8ud9KPTrUmlZltm78hgfTfqCGxdvk5luJjBnDpbPWE+v0e1/cz+uko5UoQccyDhQkcnE7YXHI2HDbJcIMgQQqmQFG61WK8OHD+eHH34gMDCQOnXq8OWXX2IwZAXqChQoQPv27Zk0aRKDBg2iWrVqzu3eeOMNfH19OXXqFGXLlgWgTJkylClThvT0dOx2O1999RWhfdoQVrncY2OJ2rSdExu3kbdsVql0iTJlEKXyYxYqsqSg2u1Iv5pPEsBhtaIYDKTHxHN02gLC6lcjtG0DVAQ5W9bHgcCKwB0Fw2+U0hskGYOQsaE+sY7l4bb+zxGs3rVgGQtGjKVQlQok3nuA3tWF6FUbsHTqCwX+eDdzP1mPRFZnauVX18FL0uEiKZiFiutTyun1qsAgK9g93XD39CDuxFkuzF+JLS6RsjM+4PqcFeg9PTjz0XTKfD4aryL5iD92hsSo8+Rp3QD3nEFc276ffI1q4eLnzeEpc3ELzEFafCK1Pn6TmoVLon8k2J0o7OSQ9eSUtfJpjUaj0Wg0Gs2/0z8abJwxYwYAtWrVemz5vHnz6NWrF4qicOrUKRYsWEBSUhIhISE0aNCA8ePHa6XSGo3mf4KXnAN32YdUNQF3fJCeMoedTjLgJnuTqsbjEDKKpKCgByQEdnS4PVFCLQTYMKOT9LjJ3mTXl7rJXpjtGVikDEySOwbpxdLKhBBkihR8lGA8ZL//30k/4tclxna7HZ1Ox9GjR1m39mdibyVis9j4+Mc3Obn7At+NX0qpKkUoU/3pXXt1koSrMJKJGQVBOq7YebEMMlXYMUoWMnQ5KG8MZtOmTcybN4+IiAgWLlxIzpw5OXfuHD179nQGGgHc3Nzo27cve/fupW3btrRu3ZpLly5RtGhRZs2ahcFg4K233qJYsWKPHc/V1ZU+ffqwe99eZnQeSN0hr1CqWmUMri4c+PEnojbtoHKHVuQsWgjIKhsOkYxcUtOxCQm97imBZlVlx9ufYU5K4dLP2wiOKEH1iaMw6/TYEejJKoMGCXfpyUBlNomszMAEoTrL9gFsZJWU55SN6B9mbqoOhzPL0nktHQ5sFgsHV6ylWpd2vPLNJ6RmpJOndX2+rd+ZLZs3ky9v3ueak/NZwmQTXpKeZOz4/urx1ksyAei5jhnTU7IbFUUh9VAUF7btoeyb/dn3+jgSz1yk+d4fUMJDuL95L/fWR4IsoXM14V+9LAknz2OLT+LBzkM0mP4R+96dxLWte9C5upBy6y65qleg/MQ3KFCw0GOBRocQZOCgkuyF8U+Yr1Kj0Wg0Go1Go/kn/ONl1M/i4uLC5s2b/6bRaDQazctHkXTk0hflqu0UaWoibvggPyUI4S57o+IgXU1CiKyyUB0GdOgRkvrYPIaqULFhQYceL9kfvfTLhzd6suaAVLHjJnk+9Vi/RQhBOkmYZDdCdPleaNvf2l92sHHDhg3MmDGDwMBAypQpQ758+Vi8eDHe3t7o9Qa8At3o/UE7Lp68yoLPVlCoTD5c3ExPDZK5omJDwoKeVEy/aq7z7PEgqRikDDJxI23nPRqNKMXVq1cJDQ0lMDCQ/PnzExUVhbe3N6mpqY81PXE4HBQqVIjly5fzyiuvMGvWLHx9fTl27BgGg4GMjAzq1auHTqd77PGSJInChQsz74cl9Bv/Lpu/mM7x5etIS0wiR1govadMpEiNx+c29JcMJMo2YoUNTyE9EUCTZJmwauW5vn0fndfPJ7hsSQASVBuxWLEjUAETEqbfuTZukkKGcGBGxYSEAxUzKn7oySu5Oh/D7EDjzdPR6E1GggvkQ1YU7FYb53YfoNnIIQghiDNA3SrVyezendmzZ9OyZcs/3OjNXdJRRHFjvz0JH3SPzZUJECQbSVDtJNsseOsenyvRmpbOvjcnknD5OiXfGkDBnm05OGICl+avIuboaZIvXEXn5kLuzs3I07U5qbfucX/rflLOXSEm8hCVOrai/YqZxEZfIu78ZcLrVsNYKA8ukkyQ/PgHpw+wECwbKa5z/0Pnq9FoNH+nX8+V/KzGdhqNRqP536B9bK7RaDQvORfZnXB9Cdxlb9JEAplqKqr4dfdcCQ/JFxfJCxtWrJgxSi54y0Ho0GMmA4tqxqxmYMea9T0lEOPDBi5CCCwig1SRgI8SSJiuMHZsmNW03/1gCMAh7KSJRAyYyKUriov84sGS7OBi9v+z36gMHjyY7t27kydPHvz8/Pj444/ZsmUL9erVIzg4mIL5C3Jy3XUcqp0KjYpzZMdJTh2IfuyNjqoKrKqZDDUZBzYClTykSCVRkdGRgYqDX/oqP85utSIQqFjRk44ZN26tv8X8kR9gtVpp0qQJLVq04Pjx43Ts2JHKlSvj4+PD999/T1JS0i+NXBQFIQQLFixg3759BAYGsmLFCo4dO8aJEyeoVKkS48aNc16DX1+bsJxhdJjyEePP7Gbgd5N5d8tKPohc6ww0PrqdLEmEy654S3pScGB/ymNYqGUDGn0zzhloBPCR9Lgg4yDrBYIrv53VmE0CvCUdCmDGQQYCVxSKKe7oHzbLAbh67CRvlKzOpy26MrFJJyLnLcGcnoGi1+OfJ4wL+w9xHyvuNkF1nQ/Dhw3jzJkzREZG4nD8drfo51VYccNNUkjGnnWNHpkrUy/J5JKMGHU60lExJ6c6v2dwdyMjJh5XPx8kux238FD0nm5Ez1pK7qa1qb9rMWEdmhB3MIrYw6cw34slqFo5VJsNa2IK6fdjcQ8OILxuVcoP6Yl7oXAksrItjY+8DEsSNiQhUU3ng/vvzHOp0Wg0LxNJkrBYLJw5c8b5tUaj0Wj+t2nBRo1Go/kXcJU9yGsoTU5dIXSygQyRTJqaQLqaTIaaQrqaRKpIxCibyKUrQn5DBL5KMLIs4SJ5YMQFJBVF0uEieTwsnQaLyCBdTSZNJCAQBCp5yGcoQz5DGXLqCoEkkSYSsIgMhHg8CCaEwCHsWWMQKXgqfuQ1lMJTebHyaVXNyrwUQjgDQJIkYTab6d+/P99++y0mk4ldu3bh4eFBs2bNmDFjBlWrVuXSpUvkzp2HyJ8O0K3QSNZM34GikxnXdwp7tu0jXU0h1Z5IJimoOPCWAylirEgFYy1auJTATSmMFRMGMpDVFMzpyaio2Ow2rFYLDlR0BhUDqUStjOT0nhv4p+fh5Pyf6NSxE0FBQZQuXZq8efNy4MABDh06xOuvv861a9e4d+8eq1evZunSpURGRnLhwgXq16/P0qVLCQoKokGDBhQqVIh8+fIRHh7OsGHD2LRpExcuXECSpMeCvJKUlWFolBVMAX7kKV2CwHx5sgK0DwNxjwbPAIySTEHZlQBJTwYO0oQDx6/2+Si7EKThwAsd3ugQQAYqqvr7wWYDMi7ImIWKDomSsge+clYJedSm7ayeOJnI+Uup1asz43b/TIm6Ndg8Yy4nNmzF5OZK8Xo1ObBmA3oVmnqEEiAbnPM4r1y5ktjY2Bf6mXqaINlIhOJJsrBjEyqntkbyYZ1WHFmzAQAf2UA4Jublr8mPHQfx4PR557Y5Cucj5eZdfA2u+FeNwLd0UUAQULE0AfnDCahWFklROPn2JO6v2UFg7YoElC1BrQ9GkLtmpaydCMgUDmyohMkmcjzSACZR2EjDQRWdF/nlP6Ejjkaj0fyN0tLSGDx4MM2bNwfgwIEDzJw5k4yMpzfm0mg0Gs1/nySeJ2XlXywlJQUvLy+Sk5Px9Pzjk8xrNBrNP80hbCSr8aQ44rBjRRUOFEmPi+SBjxKASXLPKhMVKjZhfpi1Jz0ss04mwXEPqzDDwzn5DJIRXyUEb8X/iTkaM9QU4ux3SFQfYBcW5zx+WQQyCi6yBzmU0Ifdp18sI+vRUlaAOXPmcPbsWYYPH87kyZNZu3YtLi4ubN26lZEjR7J48WKCg4MZM2YMvXr1oly5cvj7+3Py5En8/f2Ji4vD28eLWzdvExCcg81XlqFIegyYCNTnwUv2fyzIZhEqR2wJrN7zM4sGvkv3aQMoWLmAc25Mm83G/iV7+fGNOej0enx9/ciXOw96nY4xY8bQv39/rl+/TlBQEG+//TbdunUjJiaGqlWr4ubmxt27d/H09MTd3R1vb2/S0tI4dOgQffr0wcfHh2nTpuHqmpVdevHiRapVq0abNm2YOXPmE9cGYLX1AZcdGYS+QPMQVQhihZV7wkK6UFER6MDZPdrxsGRaJqvcOFQyYlAlzop0LJKKRaiYkHFHQSfJv8z/KbK6UGcKlVTVBghy6lzxknRYEeSQ9LihsGrCJDZMmUVokYK89fNSXD09sFksTOncD6OrK51nfsbZU6dY0XM47Tt14qNRb5EjRw4mTpzI3bt3mT59Onv37qVy5cpPP8EXYBYOVlofcMOeycpOQ0i4fQdP/xxUaN2U2q90JSM5hQnNO3P79DncwoLpeexnjMhsGz6O04tX03HtHHwrl+HkyvUcGPohqs1OwT7tkYw6Ls5chmqzU2PZFMLqVyUHeoxyVum442EgV49ELtlEsGwEJOxC5Z6wYkSiss6b8jqvp3bE1mg0mpfdzp07adWqFUFBQVy6dIkJEyYwcuTIx+Yu1mg0Gs2/24vE17Q6HY1Go/mXUSQ9vkoQvkrQM9eTJdlZJp3NTfbCXwlDoKKiIiMjIf9myZOr7EkugydBIpxMNRWzyMCBHQkJPUZMshuuksdvdsr+PbIsExcXx1tvvUVISAiLFi0iJSWFli1bUqJECcLCwvj444/Jly+fMyhXvnx5ihUrhqurKyNGjOD1119HURRiYmJYtGgRUVFRfPzxx/j7BuIXV/CZ8/0ZJZlqhhyUrtONMgty4x0Rzj2RTHJSEt92fpMbR87gHxqMpEL9OnUZ9cab1KlTBxcXFxo2bEhgYCBhYWFMmjSJtm3bAjBu3DgSEhKIi4sDoHr16owYMYLatWsjhOD48eMIIdi9eze9evWifv369OvXj5SUFDIzM5k7dy7jxo0jICDgiXmvgmUj5xzpLzQflixJBEpGcggDScJOmrCThgPrwyYuJmTcJR0ekoInOmRJIkGyUQQ3qut82WdP5KwjjQRhRxUC6eFHlNlhZ52QyKd3o4rOmyJJDg4cO0JS3iCSw/yIw0ZE/66c3BaJougwuLthEypmg0K+hrU4sPBHjm/aTuf2Haj4ySeMGPwqezdsIiUlBZPJxN69e9myZQvbtm2jcuXKf3geMJOkUEP2YoPiwJQzEH+9gi3TwpIx47l55jw9J41nwu71fNm+N1GbtvNTy37UnPkRVvlhJq8xq51L4Wb1SL15h5MTZpBy8w5qYiolenXAv14lfOpVRhYSdgkcqgObJJAAH0lHLtkFNxQyhEqisGFHECabqK7zIZf89DlGNRqN5mUihMDhcKB72HwsO29l3bp1pKamkiNHjqdOB6LRaDSa/y1asFGj0Wj+x0iShISCzPMHCA2SCYNiwutXy9PS0pDdXyzQ+GjA6MaNG9SvX59cuXJRoUIFrl+/jsFgYO3atbz//vscP34cWZax2+2UK1eOe/fusXbtWu7evYsQgs8//xxJkrBareTJk4devXpRtGhRFi5cSLNmzfDw8HjmWGw2G4qi4C7r6FalNjdv3iR3ssw33yzGmGjBkpFJZkIy5cqW5dCBg6SkpDBo0CCWLl2KTqdj2bJlfPrpp7z33nuYzWZiYmKIjIxEVVV0Oh3BwcHYbDZCQkIYMGAAS5cupX379hQrVozMzEw2bdrE6tWrMZvN3L17lzfeeIPly5fz9ddf89FHH6Gq6mOdmHPLLrggk4GK2ws8fpDVpdpP0uP3HN23U4WdCjoviuvcKa5zJ8Zh5bKawS01k0Rhx646cJN0hChG8iqu6O/E8+47w1i9ejWFCxfm8uXLjJ8yiYjmdbkb4E+ButWI3rKLXdu2U7hedYySTPXWzbi1YSe27Ueo3Lwbnl26Ual0BPv378doNNK9e3diY2PJzMzEaMxqpPJnBOPC9G7UE4JJ5y5zZf8RyrduSnDBfGybNZ+Ya9cZuXIBg76bwtQeg4netZ893d+k3riRnJ29FFmS0UkyXoqeyrVrcWXGUkrWrEah/p1IQ8WOiicKsiSRIakIAS7IeEg6XCWFRGEjERtGZHIqJkoo7uSXXTH9P4P1Go1G81d62gc8kiQ5A4379++ncOHC+Pr68vrrr1O2bFkGDx7M0aNHKVeuHA6H47HnMI1Go9H879DmbNRoNBrN/8v8+fMpXbo0wDObyPy6uUf2G5fz58+zdu1aMjIy6NmzJ02aNCEiIoJGjRqxYMEC7t27x8aNG0lISEAIwdGjR/H398fT05Nbt25x7Ngxtm/fTr169bDb7dy5c4e8efMSHBzMt99+S+7cuRk0aBBRUVG/OSa9Xo8sy5w8eRKAqlWrMnnyZH7++Wf69e1Ljx498Pb25oMPPqBkyZJ8++239OzZk6SkJDIyMhg/fjz169fn4sWL9OjRgxEjRnD//n0aN26MyWQiMDCQ2NhY1qxZw9ixY7Hb7bz33nts2rSJdevWUbp0aWRZZvjw4URGRtKqVStKlCjBxYsXAZ54kxYkGciluJCI7f/1mD2PDOHAiEwhxc25zF/WU8KsEPfdKqpcTORV1zz0dslJQ4M//qk23ho1irt377Jv3z6OHj3KmDFj+OHbuTxYF0lvQygTuvfHV9Kh3xVFe8WfXsZQhuYsSdu6DTl+4CAbN24EoGjRorzyyit0796d+Ph4xo8fT0BAAN26dft/nYvdbn9imRAC9cY9ki9dI1/FCJrP/5I3d6+hQuumRG3czvdvfoCrlyej1nxPgUpluX3iNGsHvY2i15Gxfi8RiielTb5ULFmaj/dtov3g/uRQDBTUudLeEMRAUy4GueRitCmcUS7h9DaG0sTgTw29D7X1frQyBNLNGEIHfRDFFQ8t0KjRaF5KP//8My1btmT//v3AL03IHA4HEyZMwN/fn4EDB1KmTBm+/PJLAgICaNy4MREREYwfP/6fHLpGo9FoXgJasFGj0Wg0/y+ZmZnIskxCQsJvZpw9GoQ8cuQIV65cwWw2M3v2bJo1a8bixYtJSUlh9uzZ2Gw2hBC0a9cOi8VCly5dmDJlCkFBQRiNRmJjYzl48CCpqak8ePCAsmXLcvDgQRYuXIi7uzs1atSgfv36NGvWjEGDBtGgQQNnc5Zfjyc7iLdw4ULy5MlDmTJlOH78OH369OHAgQO4uLiQK1cuxowZw9WrV0lJSaFOnTpERUURHR2NzWZDlmW2bdvGmDFjMBqN6HQ6goKC+PLLL5k0aRJpaWnkz5+fvHnzsmvXLhwOB61bt+ann37i6tWrlC1bluHDh1OkSBG+/PJLDhw4QKlSpTh8+DBlypR54vpBVqC2lOKBLCBNPBlI+6NUIYgRVvIqLoRIxseOe+fOHUaMGMHhw4dZvXo1OXPm5MCBA3h7e6PX67l79y6FChXi2LFjbNq0iePHj/PTTz+RmppKRKGi1KxclYvHo7h79BSekg5JkmjTpg1t2rShQoUKzmPt3r2bgQMHkjdvXg4ePMjkyZMJCQl55rgvXbqUNf5Hmg0Bzuybq1evYjabAZwl7F7uHhhTzaQt38p7pWpxZO0mAI6s2cC6L6YB0H/mJCq0aUrM1es4bHZ8QgKdx7TpFTICvYgRFgor7vQ0ZAUVi+jcKai4kVdxJb/iSmmdJ1V03lTT+VBR50VhxQ1/2YCilUxrNJqXUPb909PTk6tXr7J7927glyZkixYtYsWKFXz33XccOXKEL774gqVLlzJu3Dh8fX3p2bMnO3fu5NKlSyiKopVUazQazf8oLdio0Wg0mheS/UbEbDaj0+mw2WyPfS/7+6qqIkkSq1atomjRojRt2pSWLVvStm1bPDw8GD16NEePHsVms/HgwQMuXbpEeno6a9aswdPTk5MnT1K5cmXu37+PyZTVECU1NZUaNWpQs2ZNHA4Hx44d49SpU4wbN47z58+zd+9eMjIyWLhwIRs3bkRVVapXr+58s5MdFN23bx+1a9dm4MCByLJMWFgYAwYMYMiQIaSlpREfH8/x48fJmTMnNWvWZPbs2eTPn587d+7wySef4OLiQkZGBt7e3rz77rvs2bMHf39/DAYDS5YsYeTIkXh4eHDjxg2qV69OdHQ0W7duZcyYMezZs4e33nqLKlWq0KNHD+rWrUvLli05ceIEbdq0IUeOHDRr1uyx8T4qn+xCCcWDOGF7rLv0nyFO2PCR9FTX+SDxSwaoqqoULlyY8uXL89prr9G/f3+6detGiRIlEEIwe/ZsDh48SN++fWnWrBllypThzTff5Pz58/z8888A9OzZk/j4eFasWPHLueTLx4QJEwgPD3f+3BQrVoxatWqxbds2Dh8+TLVq1X5zvOnp6fTs2ZOBAwdisViQZdlZ9jd//nzef/99cuTIQcuWLencuTNxcXHIsoy7uzsxMTGcj47mi75Dibt8ndK1q/H6jpXUGPMqP33+NYvGfYYxNIA+c7+meP2aVO/VmVLd23BPtXBDzSRJ2AhTTDQ1+NNSH0CArDVB0Gg0/37Zzzs1atSgXLly7Nmzh7NnzwJZjQFWr15Nv379aNmyJQkJCezdu5cTJ05gsViw2+1Uq1aNkiVL8v777wNZzyMnTpz4x85Ho9FoNP8MLdio0Wg0mt+0f/9+ZzZetuzAXUREBJcuXXIGArO/l/1GRZZlFixYwKuvvsqdO3cICAjAw8ODXbt28fbbb9OqVSs6duyILMvEx8fz+eefYzAY2LBhA8nJyTgcDvbv309ISAjx8fEAuLi4kDNnTgoVKkRISAgZGRmMHj2a4cOH89133xEaGsp3333H3r17CQwMpG3bthQpUsSZkaGqKu+99x7Vq1fn3LlzyLLM559/ztKlSzlz5gw1atSgUaNGuLm5sXDhQk6dOsWECROIjIzk8OHD+Pv7c+XKFTIzM5EkiR9++IFRo0ZRqlQpateuTVxcHBs3biQ+Pp45c+Zw9OhRAgMDiY+PZ+PGjYSEhPDRRx9RoEABWrZsyYMHDxg6dCijRo2iYcOGACxZsoQSJUr85mMiSRJV9d6EykZuCzPqnxRwTBI2bJJKFZ033kJBkiRnBmhcXBzR0dGkpqZiMBiYMGECr732Gi4uWd3LTSYTCxcuZM+ePaxYsYJJkybRq1cv7ty5w4YNG7DZbJQpU4ZKlSpRoECBJ8qbH/258ff3p1OnTpQvX/53x+zm5kbJkiUxm81s2LAhq0RaVRk0aBCjR48mKiqKr776io8++oidO3fyzjvvkJSURO7cufH19cVmszFixAiOHTvGkY3beLNiPYITMqnSognRm7Zz4cQpkrDT86d5tJ7+MXqTiVxKVkOXDsZg2j8shdZpWYoajeZf7tEpT7Lv0V26dOHevXts2bIFyLrXHzhwAB8fH3r06EHBggW5cuUKO3bsYNKkSeh0OvLmzcugQYNYtmwZ7du3x83NjdmzZ5OWlvaPnJdGo9Fo/hlasFGj0Wg0v0mv12O32+nTpw/79u0DfilBTk9PJ2/evJw8edKZ0agoCnPmzGHatGlcv36db775BrvdTqlSpRgxYgTJycmkp6ej0+k4d+4cU6ZMoW7duiQkJLBjxw6uX7/ubAiS3fglKSmJ119/napVq9KnTx9mzZrFpEmTWL9+PTqdjjNnznDx4kXq1atHsWLFiImJITU1lWLFijFnzhwyMjL48MMPGTt2LFFRUTRu3JigoCD8/PyArOBdlSpVqF27NpcvXyY0NNTZbbNdu3a8++67GI1Gvv32W3r37k1YWBgGg4HChQszZ84cbDYbkiTxwQcf4OLiQlhYGMWKFaN+/frkzZuX9957D39/f06ePMmOHTt45513+Prrrxk9ejSurq7kyZOHYcOGce7cOVatWkW+fPl+93Fxl3Q01vsTIBu4JczY/0DAUQhBgrCRhoMqijclFXcURSEzM5O33nqLIkWK8OGHH1KoUCFOnDhBUFAQw4cPp0WLFnTo0IGbN29iNpvZuXMnN2/e5PLlywBs3boVPz8/Dhw44MxunDFjBoMGDXKWN2fLDga/yJgtFguqqtKqVSt8fHxYtWqVs3HBd999R2JiIpUqVaJbt260bNmSGTNmsHv3bnbu3ElERATlypVDlmUqVKhA7ty5SU1NZcm0WRxavZ4Bzdpw9ehJ3qvamG7GELqbQuhhDKGvMSftDUFU0/sQJpu0UmiNRvOvl/0BYvZze3JysvMe3aBBA8LDw9mzZw+XLl3CYDBQvXp1unbtiru7O5GRkfz888/UrFmT5ORk9u3bR2ZmJl27dmXx4sXkypWLDRs2MGPGDNzd3f+xc9RoNBrN308LNmo0Go3mN5UvX55169ZRpkwZZ6ZCNm9vb2JjY/Hy8srqcC1JXLlyhaFDh7J7926EELRo0YLixYtz8OBBhg4dyr1799Dr9dy8eZNXX32Vu3fv8tVXX+Hh4YGqqqSlpSFJkrMpjKIopKen069fP+rVq8eWLVsYN24cW7Zs4csvv8TT05P09HTGjx+PEILu3bsza9Ys+vTpw+3bt+nUqRM5c+bkq6++YuHChTRv3pwNGzZQq1YthBBUrFiRtWvXAvDOO+8ghGDx4sWUL1+e2NhY4uLi2LFjB2lpaWRkZJCUlERCQoKzEc3q1au5dOkSqqqSJ08emjZtiouLCzt27KBz586UKlUKs9nMuHHjWLRoEa1atQKenIuxSpUqzuDn8wqQDbTQBxAmm7gtzCT/P+ZwtAmVW8KCTXVQS/Ghss4bSZI4fPgwZcuWJTIykg8//JC2bdty/fp12rZtC2Rlt9SoUYPdu3fz2WefIUkS4eHh+Pr6MnbsWKZOncqKFSsYPXo0s2fPpnXr1kBWUDF7XsX/D1VVnVmQRqOREydOcO3aNdzc3Dh9+jSbNm3iwoULeHp6OruBZ2dMduzYET8/P9atW4fVaqV79+54enrSo0cPWrRoQaFChfjuu+8YO3YsHTp0QJEkfGQ9AbKBQNmIn2zAIGkvmzQazb/X999/z+uvv/7YsuwPexYsWED58uVp3749n3/+ORcuXACgXbt2XLt2jV27dgHQsGFDFEVh1KhRlC1b1rmfuXPnsmrVKtLT0wHo3LkzX375JfXq1fs7Tk2j0Wg0Lxnd76+i0Wg0mv+yuLg4UlNTCQ8Pf+J7drsdnU7H7Nmz+eqrr+jbty/e3t40aNCA4sWLY7fbOXPmDKqqcvnyZU6fPo2/vz9paWkEBAQwcuRIgoKC2L17Nzabjbx583L27FkkSeL06dPUr1+f1q1boygKnp6epKSkEBERwfHjx/H19cVsNuPm5sayZcsoVqwY9+/fZ/Lkyfj4+KDT6ShcuDDt2rUjJiYGi8VCvnz5yJcvH1euXGHx4sVcuXKFFi1aUKZMGdavX49er2fChAlMmTKFH374gUqVKnHu3Dn27t1LtWrVqF69OgcPHsTNzQ2z2UzlypV5//33uXPnDn369GH69On4+/tToUIFLl26RM6cOZk5cyaTJ0/GYDAwePBgqlevzhtvvMH58+dJSEhg8uTJNG/e/LHr+lsNdV6Uv2ygrSGQQ/ZkTthTSBI2vCQdnuiQn3EMs1BJEDbsqOSSXaih98GUkEaqPgVPT082btxIQEDAY811tmzZwtmzZ5k/fz6jRo0iLi6O9u3bc+jQIUwmEwMHDiQxMZF58+YxceJEPvroI3r37v3EsV80i/Fp2yYlJTF//nyGDx+OTqcjLCyM+/fv8+WXX1K3bl26du3Kpk2bOHv2LElJSXh5eSHLMl26dGHWrFns3LmT4sWLk5SUxKxZs1BVlfz582tvijUazX+ap6cnJUuWfGL52LFjmTdvHiNHjiQmJoYFCxawePFioqKi6NSpE99//z3btm2jRYsW9OjRgx9++IGGDRvSqFEjypUrx3fffcf169cZO3bsC39wptFoNJr/Ji3YqNFoNP+jjh07xrvvvusMEFaoUIExY8aQO3duHA4HQghnKVWuXLn4+uuvuXnzJm+++SYxMTGEh4fjcDgYM2YMlStXZvny5TRq1IgaNWo4y6BdXV1Zt26ds7w1MzOTQYMGMX36dCAr0JldVl24cGGOHTuGu7s7wcHBJCUlUaRIEU6cOMGiRYuIiYkhLCyMy5cv4+Liwu3bt+ncuTNvv/02Pj4+nDx5koCAAIKDgzGbzZhMJlJSUhgxYgQ5cuRg/vz5ZGZmUrZsWbZt20bDhg05e/YsJpOJlStXEhAQwKlTpzCbzZw+fZpBgwYxceJEkpKS2L17NyaTCbPZjKurK23btmXKlCkULFiQb7/9llatWhEWFsbatWtZvXo1TZs2JSMjA1dXV2eJ+Z8VYPw1F0mhps6H/IorZ+1pXFLTuaumoMOCC1YMZM3D5UDGLOkx2xVkxY1gxYXiigeFFTe2rt/AyJEjmTBhAu3atSM+Pp779+9z/PhxDh8+jBCCDRs2kJiYiJubG6+//jqjR4/Gy8uLwYMHM3z4cD788EPmzp3LzZs3UVWV7t27A2BTVZKxkyocqAgUJDwkHd6S7nfLkB0OB5IkOYOMVquVUaNGMXfuXPz8/JwZOC4uLtSqVYv9+/ezbds2qlevTuHChdm8eTOtW7emSpUqAPTu3Ztvv/2WlStXUq1aNS5cuEDBggX/ksdFo9FoXhbZGeEtWrQAfvkgEeDatWssXLiQiRMn0qNHDwC6detGtWrV+PLLLxk5ciRt2rRh2rRpbNmyhW7durF06VKmT5/OiRMn2LdvH9WrV2fdunV4enr+Y+eo0Wg0mpeLVg+k0Wg0/2MuX75MjRo1qFSpEuHh4SxdupQePXowd+5cpk6diqqqKIrC9evX2bRpE/Pnz+ejjz5CkiQGDhxIuXLlGDhwIPfu3UNRFKxWK/Hx8fj4+BAREYGnpydXr15Fr9ezdetWoqOjgaw3O9evX3cGGkNCQvD19WXdunVUqVKFuLg49Hq9swTbYrHQt29f/P39GTBgAFFRUZw7d45Dhw4xbtw4zp49y8SJE9m7dy/lypWjTJkyjBw5ksTERE6cOOHMJnzllVcoW7Ys5cuXZ+bMmQwbNoytW7cSERHBkSNHOHv2LNOnT3c2PXn77bfR6XTUqFEDIQSvvfYaX3zxBW5ubuh0Om7cuIFOp6NUqVLcvHmTiIgIRowYQYkSJbh48SKVKlVCVVVcXV0BnCXmfyVJkgiVDFRWJOrJSVSQblFU3CJY3MFT3CPj8nF2TJyMcmYfEcodasj3qZieSBHFgFGSqVGjBm5ubixfvpyaNWuyfv16YmJiqFy5MuvWrWPdunXcu3ePhIQE+vTpw5o1a7h16xZnzpxh0KBBzJgxg7Vr16KqKh9++CGRkZH8uGsbG62xzLPdYZHlLj9a7/Gj9T4/WO+xyHqHudbbbLbGcd2e8VhX7ez5Mh0OB4qiPJYJqdfrKVGiBBaLhdu3b1OmTBlCQ0MJCAjg9ddfx2g0EhISQu3atTl69Cjnzp1j+PDhbNmyhTt37uDi4kLbtm3Jnz8/Li4uWqBRo9H8p2VPWSHLMpIkkZ6ezokTJwgODub69evOdZKTkylTpoxzu+LFizNkyBCmTp0KZE1DkSNHDiIjI4mJiSEgIIAPPviAZcuWcfToUb766ist0KjRaDSax2jBRo1Go/kfcePGDSBr8ve9e/cyefJkpk2bRrVq1Rg2bBilSpXi0KFDJCYmAtCpUyeaN2/OO++8w7Vr18iXLx+dO3dm0aJFzo6/aWlpREREkJycjKqqfP/991y4cIGjR48SHh7Oq6++itlsJn/+/BiNRmcAKTw8nHbt2tG0aVPS09MpU6YMt27dcjZHsVqtuLm5MWjQIDp06EDFihWJiori7t27RERE0LlLZ4Lz5WDshNEMGNyfUtWL4OruyqFjB+jSsyMoggULFiBJEjExMWzZsoW2bdvSrFkz7t27h06nY8qUKXh5eaEoCna7nRUrVlC9enWGDx9OvXr1mDdvHqtXr+bu3busWbOG2bNnExAQgKenJ2vWrKFq1ap069aNRYsW8e2333L//n2WL1+On5/fHyoVfl5CCGzCikVkkupI4Kr1FJetx0lz3MdXMpJbyUERXSie11U+aTaeHycs4+L6MwTLnrze/HUmfjaB0yl7SXDcA8DDw4M1a9YQFBTE3Llz6dSpEw6Hg27durFs2TKOHDmCXq+nQoUK7N+/n9mzZ7NhwwbWrVtHgwYNWLJkCffv3ydnpQiK1KjM+M8/5YQtmSSHHRsqqgAECAEWIYi3mjliSWCF7QHLbfe5rZqd2TeKktUN++uvv6Zu3bp06NCB1atXEx8fT82aNSlYsCA6nc45NxhkZS3mzZuXlJQU6tevz507d6hVqxYXL16kW7duHDx4EMgqFxw7duxjXdQ1Go3mvyj7g6579+7RqVMnBg4cSEhICEIIlixZAsDdu3cpUKAAu3fvBn4JUDZq1IiEhATOnz+Pu7s7tWvXZteuXZw6dcq5f+0+qtFoNJrfopVRazQazb+UEIJ0kUy6mkSGmopFZACgl4y4yp64SV64yz7MnDGThQsX4uXlRWJqKu9tXk7Zpg1YtHMzHh0bEOTpg2X/SW7fvk3+/PmZu2wxixYs5PqVK+iNBt6c+iGzPpyEkAVBYUGUjChL5KZtxMfFIYQgMjKSJk2acOzYMZKSkrh37x4OhwObzUZiYiL58uUjISHBOW69Xs+1a9eoW7cuiYmJ/PDjD+zYt5WIqiW4d+sBgSH+5MwTStmIcsz65lu+/fZb5syZQ5MmTdAbdDhMFhYuWkiddpXYsGk9Ayd2IU/RnBw7cgyvHJ5sXruN6k0qOo+XkJDApEmTmDlzJt7e3rz77ruYzWZCQ0OZO3cunp6e9OrViwsXLhAdHU2dOnVo2rQpo0aN4sGDB4SEhFC3bl0GDhyIv78/JpOJb7/9lh49ejgbpvy9j3kSSY5Y0tRELCITi5pOukhBRcUFd1xlT/QYQQASBIUGkJGRSbGIwpw9fh5Lup28+fNyYtcZLrS6CKUdGG1eZJoz8ff3J3/+/FSsWJGaNWty9+5dVq5cSefOnfn2228pV64ciqJQvnx5unTpgk6nQ1EUbt68yTdTpxKZdIcHPlYiBvfgp6FjuRcbC/7ezsxFAfCwQYxe0eEh6biyYTuR125xvlVjGuQuRAW8mD1zJm+//TYZGRm4uLjgcDhYv349derUYdWqVQwePJhXX32VPXv2EB8fj6+vLwaDgY4dOzJhwgTefPNNDhw4wDfffEOHDh2YPXs2lSpV+lsfK41Go/m7/NZUHdHR0Zw9e5b9+/cjyzJvvPEGgYGBDB8+nG+++YYhQ4ZQrlw5wsLC2Lt3L507d8bX1xeAo0ePEhQUhIuLCwCDBw+mTp06VKz4y/PrX521r9FoNJp/Ly2zUaPRaP6FUtVErthOcMlyjFu28yQ47mFW0zGr6SQ5Yrlju8jqfYspU7Ekn0/6nIY9OhMx9lU8ujZipZSAR5fGnD16jElffUWXyrVo1bAx9x884PSVS3w8cSKFuzckX/Vi+Ib5ceb+Ca5eukrBJsUp3bE8a1euIi4+DlVVAdAZDOzevZtChQrh6+uL1WpFCEH+/PnJzMykbt26OBwOWrRoQf78+ZFlmaCgIGbMmcZd6SJIgpt3bmD0k3lwP4bQwgFYyCDOcRNPH3d8/Lwxmox4+3nhV9KFVI973Lx5g09HTCPmbjzVW5dn3/ojnDpwnlP7z1GpcRlO7D+DJIEqVFzcXFi1ahUVKlQgMTERs9mMu7s7t2/f5sSJE7Rp04b4+Hi8vb3Jnz8/ANWrV6dChQqcO3eOjRs3Eh4ezpIlS0hJSWHdunUsX76cqlWrAjivw18tU03lii2Ki5Zj3LdfJVNNwyrMpKspWfNrCj2bV+5gztT5xNhuYiEDm82GotNRuXZ5AoL9yUjLYP2Pm3Fzd+XM8fNMemM2B34+gdk1kXenjqRFy+b8/PPPzsyVu3fvcvnyZUqXLs1bb71Fx44duXnzJgaDAaPRiKIoADyIjSXZauacjx4HgrBmdeh+dRfC3wsTMp4oeD2cp9FbMeCJwpEPp/CZX3EWdBrIhrcm8HHJ2ox99z1+irnCjocNXDp06EBycjKzZs0iIyODjRs3MnXqVGrXrk14eDjR0dGsXr3a+YZ30KBB+Pn5ER0dzdWrVylcuDAnT57UAo0ajeY/JzIyklWrVgFZczA+zY8//kjHjh3ZtWsXH3/8MaVKlQJgwIABZGRkMH/+fEwmEx06dODcuXMMHDiQM2fOcPbsWTZu3EidOnXInTs3AF5eXo8FGjUajUajeRYt2KjRaDT/IqpQuWe7yhXrCZIcseglE+6SL+6yDy6yBy6yB+6yNx6yH6tmbcTDz423Nr+HvkdRYsqEUaBnW3JgoGLLxvjkysmpT2aSeOYixd8eSGDdSghXAw67BW81nrK1ipGZZube/VTCyhVC5+/H5lmbsaSbEQ8DbJIsk56eTqEmdXj10/GMHTs2a7kkcfPmTRRF4eTJk3h5eXHp0iVq1KiBxWIhNjaWbZt28OHAL8gR4oPJZEJSdbi6ueDp4YHd7GDHioOUa1CCotXz8tnKt7mfeoMRfUeTq0QgH68chSzJJNxPor53N/asOUKeQjkpV6sU187cwuhiYNAn3WjYpQY6nYwQKjExD2jevDkmkwkhBAaDgZUrVzJx4kQ2bdpEpUqVWLx4MYmJiRQrVoy6devi6elJgwYNcHd3J2/evLz//vsULVr0sYzGv7pkWghBvOMul6zHSXI8wCi54CH7oZP0ZKopyJKMSXZl5/KDjO/1DdNGL2LBZ8uJs9whQ04iIS4Ru81O804NSUlK4aPhX3Bo11EKlSjA5XNXeXfgRM7suUJQcS9qtK7IpUuX2LJlC5BVeufp6ckrr7xCXFwcQ4cOpWPHjixbtoxx48axf/9+3n//faZ9O4uyQ3ri5e3FbdVCkrDhjoKHpEP3qzkrb+w+xNdhFTn0+Sy8cuWk0fQJlH97MLLRwO6vZnHJS6Fct7YcP36catWq8corr/Dqq6+i1+sJCwtj6tSphIaG0rt3b1JSUli/fj1msxmbzYa7uzufffYZP/30E3nz5v1LG/NoNBrNP+ntt99m3rx53Lt3D71eD2QFIE+ePOlc580336RQoULo9XoCAwOBrA/IcuTIQZ8+ffjmm2+wWCy0a9eOyZMnc/jwYbp06UKFChVwd3d3PqdrNBqNRvOitGCjRqPR/EsIoXLHfok79kvIKLhLPuglw1ODKVGHTrNu6WaqDetARqAvQSRSilj8JBlZzepOXOaVjriHBBLethFnP5nNg20H6D61P43fasOPn60gcuV+vPMEYk7KwJKczvHFO/AO8sXgZsLk4UqFVtXJVyYvqt3OwWWrWbJhLafj7yFJEkIIJk2aRNmyZdm5cyeenp6kpqZitVvxD/ZFFSqBOf0wuRpx93Lj5sU7FCtXgPkHJjFu0Ru4e7uREp9K3ZbVObz5JN6FDNQbVJG71+8z4ZXpONIFb894FU8fD3R6hc9XvEORsgWwmK08uB2Hb4A3Hu4evDmtP0Mn9aTPe53YcHI5P61ZTbdu3QAoUqQIhw8fpmfPnkRERFC7dm22bNnCjBkzkCSJxo0bs2LFCjZt2sT+/fs5e/YsXbt2/Vsfc4AE9R43redwCDvuki86yYCKgxQ1ARUVHUYAKtYrTaUGERQpm58DG06wcMIq4lIfYAwQXL98i9TkdIJyBuHr78srw7vyxYJxuLiYSE1O48GtWFwkdzZsWI+rmwvr1q1j6dKl6HQ61q1bx5AhQ5AkCbvdTufOnRkwYADr16+nZ8+erNm8ieZfj6dav67cVs04UPFE+c1O0w6LDbvFik++3PQ/sZFSXVtTe/QQcjWojsNu59KmXVhK5iMoZyjvvfceaWlprF27liVLlnD9+nWuXbvGpUuXaNasGSaTiT179rB8+XLnm+22bdtSu3ZtQCvx02g0/z0OR9Zz+IABA3jw4AHbtm1j7969BAcH07VrVxo0aMDw4cO5cuUKbm5utGzZktTUVI4fPw78cl8cNmwY9+7dY9WqVej1emrVqsXx48f59ttvuXLlCqtWrSI0NPQfO0+NRqPR/LtJQjzSAvI/KCUlBS8vL5KTk7UuaRqN5l8tzn6bG/ZoDLhgkJ49Kfv8b5YyZfwshu6dQmh4CDrVgZtsIQ5/4qQgkCTsQjCnYnNyVI0g5fgZHhyLpv7rLagzvg/Hlu9i01tzsKZbeO/YLGa0/4DYK3cAyFelODarjcSbMTgyrSQ9yJqPMV/Vcujc3Uk5f5X7N2/RsGFDEhMTOXz4MJ07d+by5cs8SLhL9fZlsaTaqVCrNEYXA0d2nqJa0/IUKZsfSZKQZZn4+4n0rjISn0AvkhNS8fB1wd3blYR7Kdy/HodfoDc/nJ7Oz/O38cWI2ZSoUIjYe4mUqFyYrT/uRlFkytUuhd1qJyk+hdSkNNKS0mnTqTXTJ83C29sbWZaZMGECtWrVwt/fn7fffps1a9ZQunRp9u/f/1hW3D+VIZehpnDJehxVqLjKHs7laWoiKWo8BkyPjWveJz9y4cQVCkfk50LUFVzdTYyaPYAF76/BpLjQsG1dvv5gFiZXI199/zHvDvqYdcs20ahtHXxy+HDrxi3av9KSon7lWbJoKTExMaxcuRJVVZFlGZvNhl6vx+FwkJqayr3EePaHGIlTbSQIG2nCgQfy716rRXU6EXfuEo2mfUSRNo0BmFOxOYlXbtLp5/mEF8rPjKqtMdgcHDhwgFy5crFs2TI6d+6Mq6srP/zwA7Vq1WLRokVcvHiR119/nTx58vwlj4FGo9G8DLKfh7L/zcjIoHnz5hQsWJDk5GQqV65M9+7dWbRoEUuWLMHX15f169dz+/ZtqlSpwpAhQxg+fDgGg8G5j06dOnHgwAEuXLigNXvRaDQaze96kfialtmo0Wg0/wJmNZ179qsoQve7gUaADKOM3ebAGpeCDhlkPRYM+JCAK2kIVSVF2Anv0ZqYyMOEl86NrJPZOXMDuyavokS76rT5bjh6kx5JlqkzpBVGd1esGRYu7j7J5T2nib9+n6SYrM7VeqOe21FnkQWkpKdhNBnZuXMndrudqlWrsmTJEqLPR5MQl0BmipVhn/alSuNylK1VkoEfdqN4hUIoiuIsSfYL8mHiD2+Ru1AoSbHJFK2cnzJ1i+Ef5oOqqjy4HcerDd+lRotKdB3Wiitnb3L/ZgzbV+zFN9AbgBO7z3Dm8EXSUzJQdAoZaWYWz1nGl199zptvvklAQADTp0+nT58+lCtXjjt37tC1a1fq16+Pw+F4LGD2TwQahVC5a7+MTVhwkdydy1UcZIgUZJQnxtW0ex3SUjJQHSqvf/oKl0/dYPbYpZw7eR5vf0+KlipEtQaVuH3tLoPajuTnHzZhMOrZuiaS/dsO0blve8rWK0aOPF5s3brVmcl54MABWrVqxf79+wFQFAVvb2+sYQHEqFYcIuvnyf05Ao0A5V7tCcCxmd+ze/wUvgwoQ1z0ZVx8vfm+Xmf2TP4Om8OBxW5j+vTpWCwWzpw5Q+HChcnIyKBYsWK4u7szaNAgvvrqKy3QqNFo/rMcDgeqqjrvrdkBR1dXV1q1asXq1as5ffo0vXv3xtvbm6FDh/LWW2+xadMmjh07Rs6cOWnYsCE///wz58+ff2zf48aNY/To0VqgUaPRaDR/Oi3YqNFoNP8CCY57WEQGpkeCTr9FIAipXQIhBNd2n8ZutQFgE3pkHHiLBBzAhXVbKTaoK2pmJvbkZCRZJqxcIfbP/Jn9X/+EcKiYvN1IT0ihQqc61BzQnNDi4ah2B9nxpNJtqvHJ4W/JX64QdpsNzJmkxyUQWiA/VquVU6dOce7cOUqXLk2lemWo2Lg0hUvmd755epai5QoyanY/3LxcObH9HEs+Xoc53cIHK4ZSqEw+7t+K4Y02H9G6X2MWHPqK+h1qMOmnd1l3ZR6fLn+HTq+14K1pg3l7xqtMXvsBM/d8hIePOzNnzuLtt98mMzOTokWLoqoqaWlpLFiwgFmzZvHhhx/+5fMwPo9UNZEURzwuksdjATyLyMAubOjQP7FNQGgOikTk52jkKYwmA29NH0zi/RSObD/FmRPRADRsXQe9Uc+5qAt8OO1tvt82i7a9WqDT6zgXdQEZhV2HtmE0ZpVnV65cmbp162IwGIiIiHAeyyEEZxypSEIiDjsGJOTnDMoWbt0Iz1yh3N5/jINfzMbg4UadiaMZcnEXtae8z4FvFxMaUZz4mFg+++wz/Pz8mDRpErdv3+abb74hPDyc/3hhhkaj+R8ihPjN58TsD+IuXLjA8uXLiY6Odj4ndO3albx58wI479kAVatWpVq1akyZMgWAESNGcP36dTZv3vzYh2kFCxZk8ODBf+WpaTQajeZ/lO6fHoBGo9Fons0hbCSo99BhfK6ssURhQwrxpkTTSuydt5ECNUoRXr4wkiRhwYgradzYvYsTH3xNrrpVqDywBf6eYFNlbhw+T+lOtYj87EdKdaxJwtX7OCTBpb2nOb5yN/2XvYct08K1Ixc4sGgzQUXz4FckjKC8ody/coe75y9T+7W+JFy/RTFkkhMTsVqtXL5ymbZj+lGlUTlMOtfnOm8VB4mJieQuGoIkSQyb3osiFcO5fv4OSfHJtBvYlC0/7ObE3rM06VqbD+YNz7peDgcV6paicoOIx/aXfiYdm9WGu7sb7u5u/Pjjj2zevJlKlSoxatQoZ2kZvBxz/SU5YlBxoJMeDypaVTPw22Ns0bs+pw6cY92C7fR4sy0Dx3VDFQ4a966BQ7XjH5SD+i1rozfoaNy2Ljq9jne+GEmvRoOJuReLQXLhp6XrOXfuHP3796dnz57s3LnzicyXRGEjRrUiSWBWHXigPPe5SZJE2UHd2fbGR3jlCqXf8Q2oD7upRvRsT+Tw8eQuX5ri9WsS//MujIqOWrVqMXToUOc4XobHSKPRaP4IIQRCCGQ5Kys8NTUVSZJwd//lg8UzZ84watQo9uzZQ8mSJTl+/DiTJ0+mQ4cO+Pr60qxZM1asWMHGjRtp0aIFAH5+fuTKlQtZlrFYLBQpUoRSpUpx//59LBYLrq7P9zys0Wg0Gs3/lxZs1Gg0mpdcpkjHqpqfK6sR4I5qQRgUWr7fk0+qDWXxkMl0+HIwXkG+GFwMnFm9nT0rjxDesQmurnqavdYIAeSsWYHJ1V7j/IbDFG9VhQsbjyCArZNXEn/xNmXb1cA/PBiAwEJh3DxxkWv7znA5+ip3Lt0mZ+FcRO87S+TUuXRaOIXBjVrhcv0BN27coFjjXFy3ncaouDz3eas48PR3J/5OIjXalScoTw52LD3E2lnbyVcyjMZda9NleCsU5Zcgl8PheOzr21fvYXQxcmznKRZPXo1Or6NBh5pYsVCvXj3q1asH/HNzMv4WIQRpItHZ/OWR72DDjPyMwF7OfMEUKBnO8d2nadCpBkFh/rw3Zzh2rNiEFQUdvV/v8th8lInxSdy4fIuS5YujQ0/tVlWoW60Bb7721m8eJ0HYsKCiqlkB2ufNasxWomsrDk36lozYeO6dOENwmeIIVeXm9n245wzCv0gBijapS4dBwwl/gZ8bjUaj+beQJAlJkjhz5gwff/wx9+7dI2/evERERDBkyBCSk5MZP348gYGBnDt3jpw5c/LNN98wb948dDodffr0oVOnTqxZs4ZFixY5g412u53jx4/TuXNnZ8bjDz/8gJub2z95uhqNRqP5H6IFGzUajeYlZxEZqKjPDDA9Kl04kAD/8BB6fTeKyJlr+LrpWwQVzoU5NQODSUfld/vg1aYDBszocJCBEd/wQNrNHsHB2T9zbOE2vHL6Y/Jy5eaRC7R4twdl29TIGk+6mdMbDxJerjCnNxziyxqvIysyiiITViQXsbcTKNGmCULnRtmyZSlbtiynzLuQ5SfnGHwWgQoI9EY9a2fu4NCGUyTHpdJ8YG3aD2uMp+yLxOMBxkcDjStmbeCHr9ei6BTi7yeCBCWrFaZZ77pZJen8ktnxMgUaAayYsQrLE1mNKg4cwoH8jFlQJEmi5SsN+HL4bLYv30vXEa1BgIqKkB3OdSwWKwaDnoTYRGZ+Oo/c+cPo1K8NkiRTt3U1cumLPnOMycKOANIlFeX/UdGs6PWU6N6Gg1/O5ujUBTSf8zm7x3/N6YUryNmgOsFVy6IiSBY2QAs2ajSa/57U1FTefvttFi1aROvWrRkzZgwPHjygVKlSALi7u9OtWzcqV65Mjhw52LBhA3PnzuXSpUv89NNPtG7dmrx589KgQQM+++wzGjZsSPPmzfn5559JS0tzfqAGaIFGjUaj0fyttGCjRqPRvOQcIqu89HkDYnahIpG1bonGFSlaryzxNx7w4OIt3HN4UaRCGNfw4hYgoyIhEA/XL9y0AgUbRJB0I4bYi7dxyeFJeIXCeD0yP2BqTCK7Zq7DarZgt1jxCPCh9ZtdaPJKI66fukCaOQRhd2BWfpl/yo6VFw3nCbLKyzz93PDK4UGrV+tRoWFJBCoCgao6UGTlsQDjo0pXKcryqT8jVEHhiHx0GdaKsg2KohMmVOwvOJq/lyocCKEiSY8HFcXDP9LvTLmcr3huvPw8eHAnDrvNjk6vQxYSKlmPiaqqTPlgJknxyWxevYMiJQvwxsdDCckV9HAP0sNg72/LCjVm/Su98KObpeyArhyb+T3nVqzn/KqN+BYIp/G0CQQ2rIYi6ZAeOY5Go9H812zYsIEDBw6wYMECWrZs+cT3FUWhefPmxMTE0LJlS06dOkX//v2RZZm5c+eyfv16unfvTrdu3Vi2bBlpaWlcunSJAgUKMGfOHEJDQ/+Bs9JoNBqNRgs2ajQazUvv9wJLvyZLEo/GZ2SdQkD+UALyhz7cX5ozuCiQHq4q4OEyWaeQI38IOfKHYBdq1jyGj8SScoQH02XqaxhcTbh4uaHzciVAMgAOchXNg81QgpsIdI9sIzmP82IkSWL0vH54+3s5l9ntdvQ6/WNNXLIn13808Ji/RB4++XEMLm5GgnIFYLPZUbFlzY31kvdH+yV49+ur9nxhPVmWGTv7dTy8f8lkEY9sLcsy5aqVYf/2Q8zfOI0S5Z6WxfjsI2V/949cSaOnB+WG9ODB8bNUeP0VQspnZfOkCvsj+3+5sk7/DL8u99doNP97kpKS+PTTT8mfP/8TgcZfT+3x/fffc//+fTZv3kzBggW5fPkyH330EevXr6d9+/YUKlSIpk2bUrt2bZo1a6bdXzQajUbzj9OCjRqNRvOSM0hGJHhqpttT10dGIHCGl36VESkBVgwIwIH8sEBbRaDLCgk+sr4kSShPCScFF84NgIrAgUDYVSSdA0WnxybpUIUNF+mXNztGyQ0eZio+b4ZmVqmwhJe/B/BLgEbRKUjISEioquqc80pRssqlUxLTCC8SBkB4kTBn0xdFJ6MK0El69NKv50J8ueglI4qkw4EdHQbnchkZSZIRQoXfKavPDjQ6rxESsvTL036dptWp07T6E9tlXS/xRAn3r7k+PL5JyKTgeM4ze1LlEf2fWKYCLkIGCVyl/96b5uxAQGpqKh4eHv/waDQazT8hISGBU6dOMXbsWODxAOOjz5MZGRkcO3aM0NBQChYsCMD69esJDg7m+PHjbN68mZYtWzJ58uS//Rw0Go1Go/ktL3dqh0aj0WgwSW4okh4b1uda3182ICE9NfwjoSKQUDFiR5CJkQxMmLABWU8K2bl02f/qn5FZZkdgQMao0wFWHJIXmUgYkckp/9K92Fv2R0J2loQ/DwnFmYknEI9laugelnVnd/C02+zM/3Q5rQr05dSBc1jMv1yr7DdtKg6QwCC5YJJe7k6ciqTDRfbAJh5/zCVk9BiyzuU5ybKMigMJ2XndnkXFgYyCSXr2/F6+sh4dEiZJdpa8/xmy96OTJAzI+P5O0PNl97TrcuvWLYoXL05UVNTfPyCNRvNS0Ov1uLm5cfPmTSDrA7VHZd87XF1dCQsL4/z587z77ruMHz+e1atXM3bsWBYvXvzU8muNRqPRaP5pWmajRqPRvOQMkgsesi9JagwGTL+7fohk5BLpZKI6s8+c+8KCFRMCdyQcqEAcXniSjoRARsrKVCQra1F+GDh8GvFwPTdJQcKOhIxV9SRBtpFbcSHkkexBPyUUk+SKhcznCngBKCgPcy6zGsWAhN1hR1Fk9NIv1+HTV6eTkZqJu7cbUzePp1SVpzc2cWBHQY+34o/yLwhgecsBJDtinsho1UsmMkU64vEk1GdyYMcouaE8x9O+TVjQS8bfDcj6SXrcJR2p2NEJCRsCw59Q8mxFoCcrE9NDVvCR/t0vVZ6Wyevv78/Vq1efCC5oNJr/huedKqF06dLMmTOHYcOGodPpnshuvHLlCmlpaYwePRo3NzdWrFiB0Wjkvffeo1mzZn/1aWg0Go1G8/+mZTZqNBrNn+CvDBpIkoSvEoyEhF38fnajXpIJlo2oD1upOPeDioKdZLzRS3oMyNgQJOGOGQNGrGTPZqg+DCQaHpYrP439YeG1CRnUDBySOzbFC4cQuF6+S0J8vHNdo+yCvy4sq5uy+nzXSpIk9JiysjQdWSOSFQkFHXrpl9LivMVys33lPhIeJFG8YqGn7ssh7CAkjJIJHyXoqeu8bLwVf4ySK2aR/thyo+T6MAj7fNdRFVnruUjuv1vCLoTARia+StDvBmRdJIXCihs2oeKJjszfaSjzPIQQmFHxQY9NEhST3dE/x9QBLxNVzboO2VlJsbGxfPPNN1y+fNm5zt27dylfvjxXrlz5R8ao0Wj+GtmvBbIDjc96bRAWFkbDhg25cOECS5cuBbKe97LvHTdv3uSDDz7g6NGj+Pj48O6777J9+3YOHz6sBRo1Go1G89L7d72C12g0mpdMdmDh0TnY4Omlk3+Et+yPjxxIhkh9OF/fs+WWXXBBIcOZpygwkUkG7iRLvkgSeDycC8+MQgze6HGgw+4spZYhK5D4FI6HvYrdUdBhQZYVrFIAP3+/jCkl6jGqfXdq1qzJggULMJvNWWPSF8NF8sQipaOqz3d99JIBBR2KIiNwsGXBPib1n8+Cz1dw7dwtAJr3rEdAzhz4BHg91jQmmxAqdqwokoKvEoKH7Ptcx/6n6SUjQbpwHNiwC9sjyw0YJRfsWPm9H7Os4KEVk+T2u2XRADYsKBjwVgKfa4yFFFdcJQVvWYcOCfNz/Gw+ixkVPRJusoKHpFBA+f0xvyyygwrZP4PZgd2DBw8yd+5cevToQWJiIgChoaFcvXoVd3d34M+/X2g0mn9G9muBOXPm0KZNG9544w327t37xHrZrx3atWtHo0aN6Nu3LwsWLODmzZvcv3+fvXv3MmLECDIyMmjYsKFzuxw5cvw9J6LRaDQazR+kBRs1Go3mD8gOLERHR1O8eHGGDh0KPL108o+QJJkQXX7cZS/SRCLq7wR13CUdpRQPjMikY8dEBnb0xElBqA+DjK4oeKHDgeA2PsTgjStmFOwPMxaVxzIjs9lUBzah4o6CJ3bARpojBzPemsyRqfMZ9uqrbFi/nrZt2zJ//nwWLlwIgEl2o5ChPHqMZJLqfLP1LLIkY8CFe9dieaPu5/zw2QaCcgayZ90hpoyaw5YfdmNyNdKkW232rj9CUlzKY9urQsWKBRkFLzkHOfUFkf9FmXJ+Sig+ShAZIvmR+S4l3GQvZBQc2H5zWyEEVizoJAMesu/v/kyqQsUs0vBTQnCVPJ9rfMGSkRKKBxahEogBCyq2/2fA0SZUrAgCJQM2BGUUT/xlw+9v+JLIDjJs3LiR119/3ZnN2Lx5c7Zs2UJCQgIDBw7k9OnTGI1G8uXLx/Hjx/+28f3000988sknbN26Fbv9+edO1Wg0v+3XmYu7d++mQoUKfPzxx855WUePHs2iRYseWz/7tUPBggVZsGABDRo0YMiQIdStW5cmTZrQuHFjcuTIwezZs8mZM+ffe1IajUaj0fwJ/j3vuDQajeYf9rTso4MHD/LRRx8xe/ZsWrZs6ewq+Vcwyq7k0RfHTfYiTSRgE5Znrh8oGykpu5IDMynIXCSQNPHIPHwSeEq6hwFHiMaPq/jghp3c2Al8WEJtRsWGigOBWbUjZPCRBL5k4sDOfSmQu7pAihYqxKSJnzJ62Ajy5MmDoigcPXqU+fPnY7FkjdVfl5MixsqYJFcyScWqZjozNVVVfeyNmxACq2rFhpmDa07h4enOsuhp9Hq/DeMWv0FibDJzJiwDoP2gppgzLOxYtc+5rV3YsGFGRsZd9iOXoSgusvuf+Ij89WRJJpe+iDPgaBGZCCEwSC64yV44sD818JwVZDWjk/R4ywHopGcH7YQQZJCMm+xFkD7vcwfLJUmist6bUMWEJEn4YyAdFcsLBhwtQiWDrIClAHLLJsrrni/g+U8QQrB7924uXbrkXPbgwQMaNGhAt27dsFqtzJ07l549e7J+/Xr8/f2ZNWsWdrudXr16cfHiRYKCglAUxdkt/K9y584dqlatyoABA9i/fz+NGjVi2LBh3Lp1y3kuGo3mxTxa1ZCWlsasWbNITU3l0qVL1KhRgytXrjBu3DgWL16Moih8+eWXzvUfJYTAx8eHJUuWsG/fPr755htGjBjBgwcPmDlzJn5+fn/7uWk0Go1G82fQgo0ajUbzO4QQOByOpwYEli5dyjfffMPBgwcZOXIk+fLl+0vH4iJ7kM9QhgAlNzZhJlWNx6ymYxc2hFCzxirsWEQGaWoCnrKV4rrcuCpFyZQ8iJFsxKhWElUbyaqNRGHDjApIGNCTTCjJ5MEqeaDDSg4y8HRkoMMMZOAmW5Du32XxyOl8+/4P7N31gEAlDy2NgXzeewhtmzRj6tSphIWFsW7dOl555RXi4+OdWR0AAbpclDbVwV8XhpAg1Z5MppqKDQsOyUZMTAy3bt8kQ6SgSjaMZk+Sz9vp1qU7crqR6aMW0afyGxjdDPR5ryM2hw1PX3ea9qjDqtkbSUpOxEomDhzoMOCtBJHPUBKf5ywNftnoJAPh+uKE6PKhYidNJGIRmbhKnrhI7tgwozofewdWYcaOFZPkjo8chEF6dlOh7ECjHiNhusIYHmns8zzcJIUGej/8FD0mSSaErMzEVOHA/juBLLsQpAgHNgShGNFLEoGKkfr6HJik32+u8HcTQjBnzhwCAwPp3bs3tWrV4o033uDq1ausXr0aSZK4fPkyM2bMYMeOHWRmZjJs2DAAatasyaJFi3A4HEyePJmtW7diNBqRZflPnfP118HDtWvXoqoqR44cYe3atSxatIgjR44wevTop67/dxJCsHr1ak6dOvWPj0WjeRGyLJOenk5kZCQVKlRgzZo1JCcn06xZM0aOHElaWhpvv/02JUuWJC4ujsuXLzN//nyAxzKLs19XuLi4UKpUKRo1akT37t1xdX12gy6NRqPRaF52kviPv7JLSUnBy8uL5ORkPD1f3iwJjUbz8jObzfzwww8ULFiQPHnyEBwczNGjR+nfvz9ubm7s2bPnbxuLEIJ0kUSi/T5JIg67asVB1hsYGQWdpMdD9sFXCcZD9kWWFB44LOyzJ3HckUK6cGBHRYeMq6RQVvGksuKFTpa54EjnhiMTq0hGL5IwkoEbAp0kkxKTyrvt38XNwwudYuDojl3s3buXSpUqAVklZCNGjGDQoEH06dOHxMREypcvT758+di8efOvzkElRY3nnv0qiY77xCfGM3HAdE7vP09QrgDqN6/Fh++Px0cJolSJUkiSxO3btyhUKj8dXm9GufpFUSUVi8WKwaDnwa04uhR7nTem9aN5j/q4SO746UII1uXHJP833rilqYnE2++RpMZgExYEKhlqClZhRpYUFKHHIJtwlTwwSW6PdbF+GoewkyFSMEou5NYXxVP5/88Hdkc1s8kWx32HBYMkEy9spAkH6sNGQsrDVkOCrOZCdgQK4CHp8JP0mIVKTsVEY30OAuUXC3j+XW7dukX79u1p0aIFkiRx8+ZNTpw4ga+vL0FBQRQoUIAxY8bw6aefMnXqVNzd3Rk0aBCDBw8GQKfTcfz4cb7//nsmT55Ms2bNWLt27Z82vkc74GZ3te3YsSMpKSls3LgRyMrIWrZsGd27dycqKooSJUr8acd/UQcPHqRdu3b079+f99577x8bh0bzLE/rLL1371569uxJsWLFqFmzJiNHjnR+z2q10rdvX65fv87bb79NREQEvXr1IiYmhqNHj/7dw9doNBqN5k/zIvE13d80Jo1Go/lXEkJgt9v5+OOP+eKLL5yZixaLhc2bN1OuXDkaNGjAxo0bOX78OBEREaiq+tRGJX8mSZJwl3xwN/gQKhxYRAa2h52qFUmPSXJFkR6/xQcqRtoogbQWAdgQ2B4GgQxIj2Vt5pD0VNP7ACEAREZGsnjxYsqVK4csy/Tt2JORI0dit9tp2bIlY8aMYdmyZQQGBrJnzx5iY2Pp3r07AJcuXUJVVXbt2sWCBQvo2bOnMwgiSTJeij9JtzP4+tMF1Kpbg0JBJfhy69csXfgDK+etpGTOigwYMICePXsyevRo1qxZQ9NmTUhVE0h0PODspdP8/OMOGnasTt48+Rj8fi8iipcn3FASbyUAN8nrLy1R/bu5y1mPebDIi1lNwyIysQkrKY44UkQ8Qqi4SJ4YJNMzzzs7+9WBHW/FnxBdflzlP/aBXKhsor0hiH22RM6q6XgKHb6yHquqkio5sAr1YQ4tuEsK7kJBL8s4hIqERAWdF1X03rhLL89Lk+zf5ex/N2/ezLFjx4iOjiZ//vz06NGDGjVq0LdvX/Lmzcu1a9eYNGkSoaGhjB8/nk6dOmEyPZ5ZGhERQYkSJTh69Cgmk4mMjIw/nMV069YtPvjgA1JSUqhQoQLt27cnT548ZGZm4uHhgd1ux2KxODMpGzRoQKVKlRg3bhzLly//W+5ZT1OpUiXq1avH0aNHOXv2LMWKFXPeHzSaf1r270V2oDE5ORkvLy8AqlWrhre3N5s2beLNN98EfglK7t69m59++onIyEgiIiKwWq0kJiZy/PhxVq9eTevWrf+x3zmNRqPRaP4u2rOcRqPRPOLXyd6SJBEVFcX69etZsmQJUVFRREVFoSgKAwYMIDk5mebNm+Pp6cmqVasA/vY3ELKk4CJ74Kn44an44SZ7PhFofJQkSRgkGTdJwSjJT7yxz/76+vXrzJw5k379+nHjxg0++eQTBgwYQK5cuYCsLK1PPvmEXbt2cfDgQQA8PDzw8vLi+++/58yZM0ybNo0OHTowbdo0Klas+Nj+s50+fZoFCxYwbOgIBvYfRKWyVRg/fjzt2rVj/PjxAPTs2RMXFxf27NnD6VNncFW9ST7nYM57y0m6bKawa0WKmaox6b1pdKzWh5z6grjL3v/ZoIVBMuGp5MBfF0aIPh+FTRUpaqxCgC43qmQnVSSQpiaSqaZhFZlYhRmLyCBdTSZVjSeTVEyyK7n1RcmrL/WHA43ZPCUdDfU5aK0PoLjOHaMko8gSXigESgaCJCOBkgFPFBRZwiTJlNR50sYQSH2930sTaDx27Bgff/wx69atIyUlxfk7fejQIXQ6HXPnzuX48eO88sorNG/enDZt2hATE8OuXbsYP348UVFR9OrVC5PJxO3bt3n//feJiYkBsrKe9Ho9jRs35sqVK7i6uv6h8uETJ05QtWpVYmNjKVasGJMmTaJLly5cu3YNFxcX/P39iYuL48yZM85tfH196d27N1u2bCE5OflvvWdln2t26Xjnzp25ffs227ZtA/785loazf9X9u/F6tWrqV27Nh07dmTcuHFERUUB0KdPH7y8vIiNjQUen4/R3d2dzMxMALZv3054eDjNmjVj//79j+1bo9FoNJr/Ku2ZTqPR/M97dL40SZI4ceIEp0+fdi6bNGkSlStXpnnz5hw5coQ+ffpw7tw5ChQoAEDlypUpUaIEBw4c4MqVKwDP1Wn5ZZA9H+Wvffrpp7Rs2ZLly5czb948tmzZwpIlS3B1deXmzZvObUqUKEH16tWZPn26M9OxcuXKvP/++1SqVAmbzcZrr71Gnz59KFy4MAArVqxg69atPHjwAIAyZcrQuXNnZFmmQoUKAHh6etKlSxcSEhJYvHgxAQEBTJ06lV27dtGkSRPq1atHxYoVcTG58MnHn1I4T3HcZE8Mksv/bLDCXfYmj744hQwVyKUvgp8SjF42IFBRyXq83BVvgnX5yKcvQyFDBfx1Ych/8tyIsiSRT3Glud6froZgWhoCqKH3pajOnUI6V4rp3Klp8KOVIZBuhhCaGvwJV16Ox81isdC/f3+qV6/O3r176dq1K+XKlWPnzp1AVumki4sLJpOJhIQEzGYzdrudV155hcTERFxcXLh+/To3btxAVVVu3rzJRx99RHR0NMnJyQAYDFnNeqxWKwEBAVgslj907osWLaJQoUL8+OOPfPDBB2zYsAGAIUOGADgDj3v37nXel2RZpmDBguTKlcv5QcFfLfuekX2u2YGZhg0bEh4ezu7du7l69Sqgzd2oyaIKB5lqGmlqEqlqImlqEhlqKg7x13RTf9rz9ocffsirr75KrVq1aNq0KSdPnqRTp04A9O3bF19fXw4ePEhqaqpzm1y5clGuXDnatWtHlSpV6NatG9WrV+eHH37g888//0vGrtFoNBrNS0f8xyUnJwtAJCcn/9ND0Wg0L7lt27aJixcvCh8fH9GxY0cRExMjhBBi8ODBonbt2qJVq1bC09NTdOvWTURFRT227fr160XFihXFO++8808M/f/F4XA89nVmZqbz/ydOnBCFChUShQsXFqqqOpe3atVKNGvWTFy6dMm5bM+ePcJgMIjNmzc7lx06dEjExcU9tv9Tp06JggULioCAAJE/f35RpkwZYbFYhBBCrF27VphMJrFt2zbn+qmpqaJnz56iaNGizvE+ePBAbNu2TcyePVvcv3//T7gK/22q6hA21SpsqkXYVfs/PZyXlt2edW0OHz4sihUrJqZMmSIqVKggvLy8hL+/v8iXL58QQojx48cLRVFEzpw5RaNGjUS9evWEu7u7GDx4sKhSpYpo3bq1KFGihMiVK5do0qSJcHV1FfXr1xcnTpx47HjfffedkCRJzJw584XGeeXKlSeW1atXT/Tu3fuxZdu3bxeSJDl/nzp37izq168v9u7d+9g6Li4uIjo6+oXG8Eft3r1b7NixQzx48MC5bOnSpaJMmTJi9uzZf+tYNC8Xh+oQyfY4cdd2RVy2HBenzLvE8Yxt4ljGFnE0Y7M4lrFFHM/YKk5mRoqLlqPitvWiSLDfF3bV9oeOm/37n23KlCliy5YtIiYmRuTNm1fs27fP+b0vvvhCSJIkli1bJoQQ4oMPPhAlS5Z87HdLCCHi4uLE5MmTxQcffCDu3r37h8an0Wg0Gs3L4kXia1pmo0aj+Z934cIFypUrR8eOHZk9ezZCCPbt28fly5cByJ07N1FRUZhMJi5dusSiRYsoVaoUSUlJLF68GJvNRu3atSlYsCCenp5/amfZv5Isy6SlpTFixAjKli1L9+7dWb58OQ6Hg9KlS1OnTh1MJhPHjh1zbjNs2DBOnDjBgQMHnMuqVauGr68vK1aswGw2o6oqFSpUwM/PD4ADBw5QoEABVqxYwZAhQ7h58ybz5s3j3r17vPvuuwBUqFCBevXqMXHiROd+3d3d6d27N+fOnWP9+vXIskxAQAB169alX79+BAb+O7tL/50kSUYn6dFJBpSXsLvzyyI7yy4yMpK4uDi2b99OyZIluXHjBhs2bODWrVvMnj2bsWPHUrduXex2O8WLF2fAgAFMmDCBjRs3cu3aNRo0aMC6deuYNm0aNWrU4ODBg2zZsoXSpUs/drz8+fOzfft2BgwY8FzjmzFjBkWKFKFt27a0aNHC2dU2NTWVvHnzcv/+faxWq3P9OnXqUKtWLb7++msARowYgc1mY8yYMZw7d47k5GS2b99OjRo1CA0N/eMX8FfEUzIT16xZQ9GiRWnXrh3Dhg2jZs2aREZGAtC+fXsCAwOJjIzk7t27f/p4Vq9ezcKFC/+SfWv+OKuwEGu/zUXrES5Zj3HHdpFkRxyqUDFIJkySB66SJy6SBwYpa37TVEci9+xXuGqN4rz1MPdt1zCrGc99TPFIVr+iKJjNZs6cOUOrVq1YuHAhiqJw//59QkNDCQ0N5fPPPycoKIi5c+fyzTff0LRpUwCGDh1Keno6O3fudJZNOxwO/Pz8eP3113n//fcJDg7+k6+YRqPRaDQvPy3YqNFo/ieIp5QLZ5dM/fTTTzgcDqKjo5kwYQKbNm3CbrezYMECICsQlj9/fvz9/QkICHBuv2bNGubMmcPly5dxcXFh9uzZjBo16omulS8Dh8PxRAAgNjaWhg0bcuDAAV577TUsFgsjRoxwBgA7d+6MJEls377duU3NmjUpVqwY69ev58aNG87lBw8eZPbs2ZhMJmRZJiYmxjlHXJEiRUhNTeWrr76iatWqGI1GqlWrxnvvvcfs2bO5fPkygYGBdO3alcOHDzvnw4KsMu1+/fpht/81ZXOa/z0Oh4OJEyfSrFkz3nzzTdatW8f58+epWbMmmZmZGAwG1q1bx5gxY/Dw8MDf35+ePXvyxRdfADBv3jwKFSrEyZMnqVq1Ki1btiQjIwNJksibNy+5c+emWbNmjB49mhIlSqCq6hP3npo1a1K7du3fHWtUVBQVKlRg2rRpvP7663z88cd4enry3nvvkZiYiIeHB2FhYaSlpTk/AMi+r/Xt25ctW7aQkZFBuXLl+Pzzz0lPT6ddu3YULVqUefPm8dprr/1uJ8EXkX2P+XVZ+KFDh5g4cSK9e/fm6tWrzmZan332GWfPnkVRFFq3bs2FCxecJet/xlgmTpxIcHAwY8eO5cCBA5w8efJP2bfmzyGESpz9Dhesh7lhO0OGmoqL5I6H7Ieb7I1JcnN+UCI//KuT9BglV9xkLzxkP1wlL6yqmVv2C1ywHuae7eozy6xVVXU2IVIUhdu3bxMbG0uJEiUYOHAggYGB7Ny5kzp16nDhwgXu379PsWLF+PHHH/nqq684fvw4Q4YMwcXFhYSEBHx9falVqxbz5893TqPyMr4G0Gg0Go3mb/cXZli+FLQyao3mf5uqqo+VAVssFnHjxo3H1ilVqpQYPny4EOKX0uKpU6cKT09PcebMGWG328Xs2bOFq6urqFGjhhg5cqQoVaqUCA4OFtOnTxc2m+03j/dP+3WpdFJSknO8s2fPFuHh4eLatWtCCCGsVquYPn26MBqNzmVdunQRTZs2fazU8scffxRubm5i69atQgjx1PONiIgQDRs2dJY6f/TRR8LX11fcunXLuY6qqsLf31+8++67Qgghbt26JerUqSPq1av355y8RiOEmDZtmnjttdfE3bt3xRdffCFCQkJERESE+PTTT0XNmjVFSEiIWL9+vZAkSUyePFlUqlRJBAcHi8qVK4uqVauKtm3bivDwcAGIb775RgghxKxZs0TJkiWFr6+v8Pb2FgaDQbRs2fKJ1xq//v17Ho/eTy5cuCDeeOMNcfPmTeeydevWicKFCzuncoiKihLlypUTo0ePfmw/Bw4cEAUKFBA7duxwLktMTBRHjhwR69evf+FxPYuqqo+VojocDjF16lTn1w8ePHCWndpsNjFz5kzh4+MjgoODxXvvvSeEECIlJUXUrl1b9OvXTyQkJPyh8dhsNjF48GBRoUIFsWzZMhETEyPu3Lnzh/ap+XNlOtLFFctJcTRjkziesU2cyzwozpsP/7//nss8JKIyd4ojGRvFBcsRkepIfObxrVar+OCDD4QkSeLYsWPi3XffFZIkibFjxzrXSUlJEcWKFRMdOnQQGRkZzuV2u11MnDhRfPnll0IIIe7cufOn/05pNBqNRvMy0sqoNRqN5iFJkpAkiYyMDF577TVy5sxJixYtmDRpEomJiQDkyZPnicYuffv2RZIkli5dit1up1+/fqxcuZIGDRpw48YNunTpwu3btxk0aBA6ne6J470ssjteRkZGUr9+fQYNGuRsCJGUlITRaCRPnjwA6PV6evXqRe7cuZk5cyYAHTt2JCYmhq1btzr32apVKyIjI6lXrx6Qdc43btx4rIzznXfe4eTJk85Mor59+2I2m9m4cSOAM7Nk+PDhzJ8/nzt37hAaGkrbtm0xGAyYzea/9sJo/vPEwyy79PR01qxZw4EDBzhy5AhvvfUWI0eOZNCgQcyePRt/f3927NhBuXLl8Pb2xmg0UqVKFSIiImjdujWNGzfmq6++wtPTk6lTpxIbG4vD4cBisaAoCgULFmT58uX89NNPT2QJvkjH2Rs3bjBy5Ei6d+/uzAouWLAgb7/9NmFhYc71IiMjqVq1KgULFgSgVKlSVKtWjT179rB582bnelevXiUxMZHw8HDnMm9vb8qVK0eTJk1e/II+Q3aW2P3795k8eTI//fQTQ4cO5ccffwTAx8eHjh07smvXLkqVKsWsWbP4/PPPqV69Ojt27ODq1at4eHjQtGlT9u7dy6ZNm154DAkJCc7/R0VFsXnzZt566y06duyIv78/ISEhf9r5av6YFEc8l23HSHDczSqRlj2RpD/2lkSSJEySG66SFymOeK5YTxBnv/NE05eMjAwGDhxI9+7dSUlJ4eDBg0RERDBo0CCCg4PJyMhwPv94eHjQrl07zp07x/vvv8+5c+e4cOECr776KkuXLiUgIABVVQkJCfnTf6c0Go1Go/m304KNGo3mPy0mJoZFixbx5ZdfOudYrF+/PpMnT2bRokUA1KtXj7Nnz3Lp0iV0Oh1CCFJSUvDx8WH9+vVcunQJgEaNGvHOO++wfPlyRo0ahSzLTy1PfpnY7XbGjBlDhw4dKFKkCL169XIGRGw2G76+vpw4cQLICrS6uLjQpEkTdu3aBUCLFi1wdXVl69atbN++nd69ezNw4ECio6OJjY0FYOXKlYSHhz/WwbtNmzb4+PiwcuVKEhISCAwMpEePHkyZMoW0tDRnQPa1117j9u3bfP/990iSRN++fVm/fj0mk+nvvEya/6CTJ0/y6aefUqtWLdzc3Ni/fz/vvPMORqOR0aNHM3ToUGw2G35+fnTq1Ilbt24RHh5OgwYNuHv3LuXLl2fkyJH06dOH8uXL43A4MBqN+Pv706ZNG7777jsePHjA+gN7KNi0LnutCWywxrLWGsN6awx7bYlEO9J4oFpQn3GPuHv3Lt26daNIkSJcv36dkJAQ7t6967yv+Pj4ALBq1SrCwsKYO3cuhw4dokqVKs7OtgMGDCAsLIwePXqwePFi9u7dy/fff0/z5s0fC1T+laZMmUJYWBg7d+5k/fr1AHz11VdA1gcZAF988QW1a9dm27Zt9OnTh+LFi3P69GnWrl0LQKdOnShbtqwzkPq8soPF2cc9c+YMQojH9nP06FGio6O5cuWKc269l/ne/V+V7Ijlmu00VpGJu+SLTtL/qftXJB3ukg82m52btnPEqbcfe5ztdjtubm78+OOPWK1WKlSogMPhIDg4mKZNm3LkyBEuXrzoXH/kyJHOD8V69+5NlSpVuHjxInPmzKFbt24v9IGCRqPRaDT/S7RnSI1G85/wW3P67d27lxEjRjB37lz69etH/fr1+fzzz52NHK5evUrjxo0JCAjgnXfewWazIUkSJ0+epEqVKkRHR7N3717n/rLftGQHGRVFeSkyGW0221OXX716leXLl/P111/z9ddf06BBA0qWLAlA5cqVkSSJdevWAVlZWEIITpw4QenSpbFYLAB07dqVW7du0aJFC2w2GxaLhUGDBvHpp58C0LZtW3x8fFi7du1j2Y0jRoxgzZo1nD17Fsh603b58mVn9pWqqri5ubFs2TK6dOkCgMFg+AuujubfRhWCeNXKJUcGR+3JHLQncciezGl7KrdVM5kiaw7EpwWL9u/fT9WqValfvz7btm1j69atNGvWjB07dpCenk7//v1ZvHgxK1eupESJEly4cIEPP/wQh8PBmTNn6NWrFwUKFOCdd97hu+++4/Dhw/Tr1w+r1Uq3bt0A8A3wx79yGVbbYliQcZNVlvvscSRxxpHGBUc6Zx3p7LEnssYawxLLPVbaHhDtSMMi1CfGO336dB48eMCxY8dYuXIlEydOpEGDBk/cV/R6PZMmTeL69eusWbOG/v37M3r0aG7fvk3hwoX57rvvqFu3LtOmTaNly5a4urry0Ucf/anzx/3Wffb+/fssXLiQjz76iDVr1jBlyhRmzpzJoUOH2LBhA5AV/L1+/ToFCxbE19cXs9nMuXPn8PHxYfny5cTGxhIaGsqiRYsoW7bsc40nO2vN39+fIkWK8P333wPQoUMHkpKSGDhwIJ06daJkyZKMHDmS+vXrU7lyZUaMGPEnXA3Ni0pxxHPDFo1D2HDF+w8/d/46azGbJEl4GLxRUFi4fA5T507m9u2soKOnpyetW7cmICCAoKAg4Jfnz2HDhnH16lX27Nnj3LeHhwe9e/fmwoULfPPNNxw7dozt27dTrly5PzR2jUaj0Wj+6yTxH/9YNyUlBS8vL5KTk//USdA1Gs3LIbscN1t0dDS+vr7ONxGQVQp84MAB9uzZQ+7cuQHYvHkz7733Hu3bt+eNN95g+/bttGrVigIFCuDv78/BgwdZv349s2bNIjY2lk2bNqGq6kuVxbBhwwY8PDyoXr26c1lGRgaurq7Or7/44gtmzZrFkSNH8Pb2BnjsPIYPH86aNWsYNmwYLVu25Pjx47zzzjt8/PHHtGrVimPHjlGnTh2KFCnCli1bnPfRtm3bEhUVxcqVKyldujTjx49n5syZ7Nixg0KFCjmP7+/vT48ePXj//ffx9PSkVatWnD17lrNnz2qBRc0TMoWDy44MzqppxFrT0GUk4ZqZit5hR0gSmQYXLK5eyEZPCuvcKaRzJ1gyOO8Be/bsYeDAgTRu3JjKlSszduxYvL298fX1JTIykjfeeIM33niDY8eOMXbsWOLi4ggJCaFMmTJMmjSJfv36MXv2bG7dukW3bt04duwYFosFh8NB//79mTZtGjHY2ONI4pojE0kCb3S4oiA/JXAihCATlURsOIQgTDbhf/4O+dx9yZMnDzExMRQoUIBFixbRunXrp16T37rvxMXFUaBAAWbMmEGnTp2cx4uNjcVgMDh/35/YnxAkCzup2FEF6CUJL0mHG1kfnPz6nvo00dHRBAQEkCNHDgBu3bpF3rx5OXDggDMIk5SUxCuvvMK9e/c4cOAAcXFxdOnShdTUVIYOHcq2bduw2e1U/T/27jo6irML4PBvZi3unkACBEgI7ho0uFtxKFCkFPciLVrcHUpxd3d3dwgWIAES4p6szffHNluofEVLZZ5zehqS3Zl3Zmdnd+7c9956tXAKyo3aPzsxxkwMAogGI55KS9xEDV6iBi9BY96/p0+fxsnJicDAQPO+MRgMzJw5k8WLF7N06VLKlCnD8ePHzc1/KleujKenJ56enty7d48ePXpw5MgRKlWq9FbbK/twWimDB9rLZEipWH9goPHX74kXzyJxdHHA0uqXjPgNP25j9thF2NhZIYgCthb2fPN1Tzp37kxsbCzDhg3jwIEDPH78+I1ltmjRgpSUFKZMmUJAQIB8fMhkMplM9pp3ia8p/+9fZTKZ7G/o9QuNrIuA1atXM3r0aAwGA4Ig0KtXL1q2bImLiwsNGjTgzp075ql7ADVq1GDVqlUcP36chg0bUrVqVQ4fPszZs2cJDQ1l0qRJFCpUiMmTJ2M0Gv9WgcYZM2Ywbtw4NBoNBw4cIDk5me+++45du3aRJ08eSpcuzdChQ1EoFHh7e/Ps2TPs7OyQJIk0YxKZpJGpT0dCouuw9mjsFEyeMpmZM2cSGxtLSEiIeYp0sWLFqF69OkqlkpiYGPOHilqtJjk5GWdnZwBzgHLHjh307dvXXMcyb968HD16lMaNG1OuXDnGjBnD3r17zdMqZTIwBckeG9M5qYsjI/E5PtHPKBAdjqU2HaVBjwQISEiCSKZKQ6yNEw89crDzQTQnJi3CxiAwdPBgli5dSlBQECNGjKB27dqULFmSyZMnc/r0aTIyMlizZg1169alUqVKnDp1inv37lGzZk2KFSuGSqXi3r17AGTLlo1du3axb98+nj9/Trdu3dBoNFzRJ3FaH08qRjwFDeo/qTMnCAJWKLBCwb6FPzHrx1VY2dviam1LlxZtqFG9Og4ODjx8+BAw1W08cOAAkiSRmZlJgwYNyJ49++8ue+vWrbi6uhIUFPTG+tzc3H53/0ZKWkINqTwyppEiGciUjEiAArAQROwkBUEqW3IrrLFD+bvnvPXr1zNixAhEUUQQBL788ks6d+5MYmIigYGB3Lhxg+LFiyNJEg4ODjRt2pQ2bdpw7tw5SpcuTb9+/Vi1ahXDhg0je2AeGkz5jpQcnsQjoTSkokFElASMAtwxpHLTkIIakWwKC4oobLGLSaFatWoMGTKEIUOGoNFo0Ol0qFQqmjdvzsaNG1m5ciVlypShYsWKBAcH/yZQlD9/fmbNmsX+/fupVKmSHEj6C0iSxAvdI9KMydgKjh+8z7OOyzULN7F99R5s7W1ITU5j9vqJuLg7c+7YJY7sPkGf77vTuF1d4jNfsWbKDr799ls6dOiAs7MzTZs2ZcuWLaxbt44WLVqg0+nQaDR88803VKpUidatWxMQECAfHzKZTCaTvSc52CiTyf4xtm/fzubNm/Hy8qJWrVpUrFgRgPHjx7NmzRq6d+9O06ZN2bZtG+vXryc1NZUhQ4bQpEkTli1bxvHjx6lXr575YrxRo0aMHj2a9evXM2zYMEqWLEnJkiXN69u1axf37t1jypQpnz3QaDAY+OGHH5g/fz6SJDFnzhwaNWrE7du3KVu2LO7u7owcOZLY2FgmTpyIRqOhd+/eFCxYEFtbW6bMnUCjrjVIMSRgQEdCTBL3rj2gdLXiNB1SjSrtSvAqLAFdnECnFt1ITEykRIkSFC5cmGbNmjFp0iTOnTtHTEwMAwcO5OTJk1SrVg0bGxvAdPHXrVs3li1bRunSpalQoQJXrlzBycmJM2fOcOXKFUqXLk2BAgUoUKDAZ92Xsr8XvSRxSh/PzbRX5Ai/QcCLR6j1mWg11mRY2WNQqCHrgt+gR63PJPLsRQaO609UbDJqKwtsnJ2oVasWrq6ulCtXjsePH/PgwQN69OhBaGgogYGBTJkyhSpVqnD06FHy58+PpaUlefPmZd68eSxYsACdTkdKSop5XLa2tjRr1sy0WoOBU5mxnCMZDSLZBYu3DkI8vnyd5f2GkxwdS93+X+NbtRzPwsLwq1oLd6UjXbt2Zfz48UyfPp309HSKFClCXFwcoaGhrFixguHDh9OgQQN0Oh2xsbEoFApWrVrF0qVLadOmzZ++n5IkPad18dwzpJKBEWsUWAkKHAUVAmDElH0ZjZ79ulgWbd1I+PZDBHn7UqdWLSpVqgSYssHHjx9P165dadeuHQcOHGDZsmU8ffqUWbNm4e3tzblz52jevLn5vJCWlgaY6jmWLl2amjVrUqJqJQ4lPOeprYhBAieUWAh/XI4iTTLwxJjOE10qhRztadWuLUePHqVRo0YULFgQlUrF6dOnGT58OFevXiU6Opr79++TJ08eDAbDGw28AC5evIher5cbevyFEoxRxBleYCnYfHAjGEmSuHX5LmP6TiYhNpGOfduQr3BeHt0Lw+LnzMYcubPTuF09qjesTEJcIosmrmHrqt0kxSUzdepUBg8eTIECBahZsybTp0+nRYsWaDQaJEmifPny7Nq1i5o1a36MTZfJZDKZ7D/r75GmI5PJZP/H8uXL8fHxoUuXLjg6OrJz5046duzI5s2bAQgICGD8+PH06dMHHx8f9Ho9V65cYffu3dy5cweNRkO9evW4e/euufEJQO3atSlUqNAb037v3bvHtGnTKFasGK1ataJVq1aEhIT85ducJSkpiY4dO2Jra8vKlStRKpXUqlWLL774AlEUSUpKonXr1hw6dIg2bdrQtWtX7O3tWb9+PefPnydXoB/NOzfk+6Fj2L9/P0mv0kiPklg1ZRuXDt5CHy9iIzjh5eVNvvI5uRJ6jhy5fcnUZXD06FEAmjVrhoeHB7169aJmzZqULFmSYcOGkStXLlq0aGF+3Pfff49Go+HLL7+kXbt21K1bly5dunDz5k169uz5UWvHyf4dDJLEEV0stxLDKHvrCIWe3saosiDRwYt0KwcMSs0vgUZAZ4Rpyw/SuN9itAaJyf0acmfrSBYdmEebuT+Qlp7O1q1bGTZsGNHR0UycOJE+ffoQGBhI9+7dCQoKYs2aNTx+/NicBV2sWDE8PDywsrIif/78b9QdzXJLSuMcKVijwEVUv1O20/65P2Lj6MCwA5uo0qkNufxyULxSMOf1CWy8eYGBAweyY8cORo8ezcGDB5k6dSoXLlzgyZMnWFpammsenj17llGjRlGkSBEWLFjA0KFDGTly5P9d91NDOhu0kVw1JGMtKMguWOAiqrEWFCgFAYUgoBJE7AQlD1ZvZ4p/OdZ+M5QEewvW79rOl506mTtKb926lZIlS9K/f39cXV159eoVp06d4tGjRygUCurUqcOlS5eYM2cOAFqtlocPH9K5c2fWr19PREQE0UYtO3SveGQj4GhU4CmpsBSVv9mfeq2WYz+t4emN21gJCrwkNXaCksuGJAK+bsPDR4+4dOkSW7ZsoVixYjRo0IDixYuzcuVKvLy8WLBgAQBKpZKEhATu3LnDjRs3GD58OD169KBWrVpvXRdS9mEMkoFIfRgAKkHzXssIvfWQb5oPBEzZu6vmrcfRxYHVRxbR4qvGFCwRRKO2dbGxtQbA3duN6g0rc2L/Gb4I7siD248Zs2gIbXo2Zfr0aUiShLu7O82aNePixYvmxkJZlaXkQKNMJpPJZB9OzmyUyWR/GwZJR6IxhgRDNDopA4A96w7R/8vhdOvelXlz5wOmRgNffPEFS5YsoUmTJtSuXRsLCwuWLl3Kd999h7u7O127duXw4cNs3bqVfPny0aZNG7Zu3cqOHTsICQnBwcEBCwsLli9f/sYYsmfPjrOzM23atKF79+6fpStyhl7iTqyRZ0lG7ty8z6FTF5i4ci+tagezbc1S+vfvz5gxY/Dy8iIgIIDSpUsTHh5Ov379OHToEEWLFuX58+ds3bkZ+2ISHb9ryuMHYYztPhMXdyeePYrAL3d2Rs4YhKOzA2C6CBQNSm6cDWXkkj6snbmdoyeOULNmTQIDA2nevDmhoaH06tWLnj17msfavXt32rVrx9ixY2nfvj3bt29nzZo1XL16lYULF1K3bt2/fP/J/jku6hO5n/SMCnfP4JgcS6KDJ5L4x0Hp+IQU1m05hlIhsmzeQHyzu2PISKZY2HWMFYLIGNyTTSN+IDo6GicnJwoUKEDbtm1xc3Ojb9++xMbG8ujRI7Zv386JEyc4ceIEe/fuJSgoiJCQEBISElCr1RgMBnNwPMao5bQ+HpUk4CC+2/T/+2cvcnrtZobt34Szj5e5/tvZhSvZN38p+kwtGTNn0KF+4zdqrwK4u7uTnJxsDn4GBARQpUoVWrZsSXBw8J+u+4khnT26aFIxkF2w+N2akllOr93Coi59qda1PR1mjCdTMnLl+jU2t+vNj8uX0bx5c86dO8fAgQMZMWIEc+fOxdfXl8WLF9OkSRMA2rRpg8FgoG/fvuzdu5dHjx7h6+vL5MmTOXXqFBt2bMO5UyMiBT3ZBAsU4h+PJyk6lt0zF1KkVlV8CwYhiiJ2qLE0Gniexwe/CiXp1q0bzs7O9OzZk3bt2uHj44NWq+XatWvs2LGDly9f4unpyahRozh//jzR0dG4uLgwd+5cOavxL5RsjCVNSsZSeP+66ZnpmRzeeYKD24/i6uHCjrX7WHFgPq4eLn/4nJSkFFbMWUeVusH0+LYT1rbW3L/zgFevolm5ciXt2rWjUKFCDB8+HG9vb4DPPoNBJpPJZLJ/E/lTVSaTfXZGyUik7gl3ted5rL1BvDGSNGMyacYUClbJTckqhUnTJPBC9wiDZCB37tzEx8dja2tLRkYGFhYW3Lx5k9mzZzN8+HDOnTvHtGnTSE9P59ChQ4SHh+Po6EjZsmUxGo3mqX3m9b/W0dLKyor27dvTt2/fvzzQmK6X2B+mY+IFLQuuadn5SM8TmyCqz77INeuS/HA+k2iXonhl82XSpEmAKSChVqsZNGgQgiBw8OBBDh8+TGC+AHbv283lC5exEZwYt3AEP+2ZQ6suTflp71w2nlpGgeL5zOvOCq6kJqbi6ZyNKk3K8SAslHOXzgLQtGlTsmfPzpUrV4iNjTU/b/78+XzzzTf07duXpk2bki1bNgYPHsy6deuoV6/eX7r/ZP8sL4yZXNbGUuzhFRyTY0my//+BRgA3VwfcXR1Rq1VoNGpEUSRNY8v0DWcY17QPtpZGchcrjFqt5ttvv2Xv3r0EBwdTpEgRypUrx/PnzzEajezevZvExETs7e3Zv38/p0+fxs/PzxxgzPq/QZI4oY8nQdLjKrx7ndGHF65gaWeLczZTMEMQBK7tO8z2ibMo16whVq7ObDy8n6T0tN88d9++fahUKho2bGjadjc3mjVr9laBxkRJxyF9LCkY8ELzfwONAAVDKhJUpQJqS9M5TyOIFMydl6SEBJJUAgZJIn/+/LRt25bz58+zfPlyLl68SJs2bVCpVJw/fx5BEOjduzfHjh2jQYMGjBo1itOnT+Pg4MCLFy+Id7MjwpiBj2CB4k/G4+TtSbG6NXhy9RYvHzwCTOdphQTegoZCPdphZW/HsGHD+Pbbb/Hx8cFoNKJWq6latSp2dnbMnTsXgIEDBzJw4EC2b9/O2bNn5UDjX0iSJGINL0AChfD+me0BBXNTv1Ut5k/4iRsXb2Nta4WHtzvwxx2pAS6fvkbB4vmwsbNBrzfw/HEUbl4uDB48GDDdXBw9ejSFCxd+77HJZDKZTCb7fXJmo0wm+6yMkoFnunvEGCJQosJasEcUFPDztaiVmy2lg0tx5sg5Tt08RPT9FOaNWUp0dDQBAQFYWFggSRLHjx8nMTGRWrVqoVQquXHjBqIoEhoayk8//cTIkSMZMWIEGs1vp3H9HbIZUrQSq+/quPjSgI0a/OwF1IpfLsj1BgMJWoE7gh+e5ZqxfOUMxo8fj5WVFSdPnuTcuXNMnDiR4sWLk5mZQVxKNNGR0RzbfIGyZcpjY2uNTV5rcub1+931KxQKbl+9R2J8Er65spE9pw971x5m5dplzJkxl5kzZ9KuXTvmzJnD8ePHady4sbmBxODBgylSpAh58uT5i/aW7J/OKEmc1Sfg9vwO3nERJNu6Ib3l+9DSUoMoCqzecBgrKw3rtxzDw92JzvVLUibImQdFAnm49zjVq1dn2bJlVKhQgTp16rB582ZsbGxYtWoVtra2lClTxlzPb+HChcyaNYuVK1e+sa4IYwaPDWm4C+82dTqL2soSg05HUnQMbjlMjV4K16zK9DtnUFtaotPruXX6HFtPH6Ne0dKsXr0aZ2dnczCvW7duVK9e/Z3WaZQkTusSeGXUvnVtSVsXZ/IFl+XWkZNE3L3P87v32TxmCmnRcVgF5uSuMYXmzZuzbt06xo0bR4kSJczP3bZtGydOnKBnz57Y29sTHBxMuXLlUCgUvHr1iqlTpxJUohiKMvlxEzR/GmjMUqlDC+6dOsf5zbtoOKQ3giAgiCIiUKBYUTyL5GfX4YPUqVOHHDlymM9HxYsXJyAggF27djF8+HC8vLz+sNO37NPKkFJINsahEaze6Xm/bk6k1qhp3rEh7ap353mFl+h1euJi4smW0/sPP78lCYJrlmVM3ym8jHjF2SMXkJBYsH8iOS0LftB2yWQymUwm+3Of/wpbJpP9Z0mSxHP9Q2IM4VgINliKtqZA4680blMXbYae9uV7M2nEdKo0qMDAgQO5cOGCuSmKh4cHDg4OrFixgrt37zJ9+nQaNmzIyJEjadmyJWDqoAymLL6/E61BYt09HedfGvC1E/CxFd8INAIoFQpcLAXye1jiULAaopUjw8dMBEz7UaVSERERQUZGBqu3rMAluwPlQ0oTWCjPW29vUnwSJSoUBeDejQeE3Y3g1OFzJKcmkStXLmrXro21tTV79uwhOTkZURTNNa6qV6+On5/fx9spsn+1SCmTFxkJ5HvxAJ3KEqPy7bMGK1coRKZWx7rNxzhz4Q6TxnRh9eKhhNSvio8hBWttKqKlBXq9nrFjx+Lj48PUqVOJiIhg2LBh1K5dmwoVKrzROMTCwoINGzbQunXrN9Z1z5CKDgnL98zKKlAlGKNR4s7x0+h/ng5tNBpR/nzTo3LbL0iMeMmGbVs5d+4cJ06cYObMmeTNm5d79+4xfvx4LC0t32mdL6VM7hlScRFUf5rR+LoKbZqhzchgRNmabBg5gRINalO3/9c8v3SD4d9/T558gbRq1Yp27drRp08f9u3bR9u2benVqxeOjo7mqagAu3fvpkWLFuTJk4e7d+9Sd/RArJ0dsX6H/eieKwe+BYO4e/Iscc9fIggCRqMRg16PnaCkdI/2XL58mQsXLgCm+oySJGFnZ8fAgQM5fvz4ZymDIftFmpSMXtKiRP1Wjzcajb/bBR0goFAeKtQow/XzN5EkOHfsElqtDvil1mKWvZsOYmtvw7CpA6haryJ7Nx3Ew8edyT+NxienF67ZHT5422QymUwmk/1/cmajTCb7bNKlZGINz9EIVqiEP74Ycfd2o2SFoqQmpTJ+6XByFcpGbk0xLhy9yqxZs6hduzaTJ0+mevXqLF26lMmTJ1OlShUmTpxo7jwNmDN8/m6NSm5GG7n4c6DRUvX/gwOiIFAkKDcPyzbkp6VLmPbDKCpWrEj58uWZN28eU6ZMITklie9/HECderXfKRvr4smrHNl9kttX7/Hg9iNqNK6Ktb0lBQsXQKPR4OTkRPHixbl79y4xMTHY2tq+V7aXTHbfkIZD7DNs0pJItvd4p+cGly3I4uV7sLDQUKVCYUoXD0Sn0yOJClJ08OrabdRO9kyYNoX7t+4QFxdHt27dGDdunDmzOat2Ypb27dv/Zj1pkoGHxjTshff/quSczYti9apzZMkq8lUsh3/Jom8EUjLT0rC2t8OlVCFK1wwhX758+Pj4/KaD8rsINaSSiRH3twzwZHHy9iSwQmnSk5L5+qfZ+BU2dbm+fuQEO+cuptWhtsyYOInAwEAOHz7MkSNHyJkzJwcPHiQoKAj4Zb+WKFGCx48fM2DAADyL5md9ZiT27/iVUxAEKndqzU+9hnJh625qftMZQRAQf943pWpU42T2BazdvInSpUvj6+trfm5AQMA7rUv2aWQYUwHeeK9lBQZ/77Mj673x6F4Y21fvxTObG0VKFySgYB4srSxo3a0ZXRr0oVy1Umz4cSulKxWnUMn8byzr3LFLzPthKXny+5MrIAcjZgxEoRDNn/upxgRSjYmfbJtlMplMJpOZyJmNMpnss0kwvEInaVHx59knTTrUx87BjjP7L2LEQLw+iipVqpinRjZu3JiSJUty5MgRHj58yNatW82Bxl9nPfydSJLE+ZcGEASs/yTQmMXCyoZClepgFFWMnTwTgOnTp7N+/Xqmz57KqVc7qV6v6jsHAvMU8MfO3oZSFYux/eIaJiz5jjpfVOf8qYvs2L0NMHWc3r17Nzly5HinZctkWSRJ4pkxHffoFyCIf1qn8ddy5/KmRZPKJCQks3XXaeITklEqFYiiyKZToTyPeIVPXj96jB7J1KlTefr0KVOmTDE3foHfD3T8WpykI00yYM3735xQqtV8MWooKfEJLO7WnzvHz/Ai9AGvHj9l55Q5zG7TjRyF8pMtuCSxkg4/P78PCjQaJImHxjRsULzXjYBKHVpi7WDPtX1HzL8rWLkCrdbOQ7JQ0aBBA/Lnz8+hQ4c4deoU27ZtIygoyJyRlrVOT09P+vTpQ/HixYk0atFifK/s0OwF8uGe04+bh0+QGp9gXn7kozBurt+Jys6GhLQU8+Plmx9/L6nGRMRfBZkFQUAQBPN78fXM+8iIKPq1HU7zCh25f/shm5btoGujfty8fAdRFClcqgBFy5imQCfGJzGs6xjOH79M2IOnvAyP4qeZq5k6fA51v6hhLhmiVqveuMGoFNSkG1MwSH+vGQ4ymUwmk/3byJmNMpnss9BLOmKNL1GjeasLRN9c2chbwJ8LJ69Su3UVVD5ROGm9sdHYsXz5ci5cuGDuSg2mCxhBEBBF8W99ARqeLBEaZ8Dd6t3G6J83ELf8FZj8wxiGD+yNvb09xYoVI3dhX+5rL2P9jjWyACrVKkeZyiWwd7QzB2jrNq3JvVv38Q8yBRetra3febky2etSMZBk1FIwIwG9yoIbtx9ja22Jj7crKtXbfS3p3a0Rt+8+5fyluzRrP4aK5Qpy5MQ1YuOSsLexoGDJIDQ+7gTnyGt+jiAI75TVHGvUoUdCyYedP9xy+vL1T7PZP/dHxtdshmdefzJT01BpNDT6ti/lWzXhqTGdOMk0JfT1TtjvKkHSkWY0YPOe2ZjuuXKQvUA+7p48S4U2zXD28cKg02GtVNNh8TS8rz0lJCTEPF05qznH/6t7+8qY+d7nYFEUqdKpDWuHjeXSzv0Uql6ZrT9M5/iydbhk96b+wgm0Ll8VX5XTey1f9ukYJAMZUirKXzVW0mp1LJz4E5ERUYxbOAKFQkFE2HN8cnhz7tglrKwt2HByKbkCTJ85rat8xczvFzB342Rs7W1o83Vz+rcbweh5Q9mz8RAdan6Nf2AOUpJS0Vhq6P1dV2o1DfnDcSlQopUy0ErpWAo2n3QfyGQymUz2XyYHG2Uy2WehkzLRG7WohberRyYIAl90bsSoXpPYv/44rfo1wKjUA5AtWzayZcv2xuP/blOl/0hsukSqDnxs3+15VrYOFGrcg4D2nd/4vVbKAEAU3j1x3cLSAoufu9Fm1Udz93Jj5IJ++Kr8zb+Xyd5VVtabIAikSAbITOfUkctMXrwXncGIlZWGHL6efNW+NoF5s//p8gRBYO6Ub/iq93Tu3nvGpu0ncXayY/bkb6ifz4UL/kVJ/sDMpXQMCHycY75oneoUrF6ZmKfhvLj/CDsXZ/xLFjX/XQCS9Jmg/P1z16+nff+RJMlAJkac33PiiiAIVOnchp96DeXM+q3U698DUalEIxjAx536uYqhfu3c8jbNtWIlPWpJ4H1jtrlLF8PR050V/UegTUsnW/4A+qxfQuGaVXluzCBO0r/fgmWflIQBCQnxV5nBoijg4u7E8tlr8c+Xk8VTVlC+WmnGLRpB7qBcFC5dAD//7BzeeZz5E5by9GE4KUmpnDlygcq1K1C6cgkCCubm5qU7LNw2nYgnzwm7/wwnFwcKlyrwp+MSEJEwYuSPu1jLZDKZTCb7cHKwUSaTfRZGjEgYEd7hCjRPfn8cXRx4GRGFTqcD9ZvTo9/2gvzvRPfz9c77jNvNvxDZ3d68kJP4eFPGXw8kfMzlyv47jhw5QunSpbGy+iXT1gi8uB3K7nXHaFa3DM2+qE5MbCJzFm1n3JQ1LJrVFyvL33aN/zW1WkXHNjU5cuIaTeqXp2BQTgB0MeEIBiOGDzxmP3YoQqFU4pbTDw//nL/5m4AAP08nnzRpErt27aJgwYIUK1aML7/88q3PD8af36kfUiMnW/5AbF2ciIt4gV6nQ6lSIUqm/WlEeufzrBEJ8QOyQxVKJcHtvsDa0YEqHVuTq0QR899EhA9+nWWfhulIlExNfTBl60qShFKpJCUpldTkNBZNXs6QiX2o3qgKKpWSoCKmWptTh8/h4PZj1G5WnVZbmjCk8yhWzdtA+ZAyODo70KxjA0b3nkzv77uTI7cvOXL7/v/BvEH4eXxysFEmk8lksk9JDjbKZLLPQkQ0Zxi89XNEkQlLvsPa3pIMKeU3nav/aYFGAM3Pm2CUpHfqHAugN4LVr5r4Cp+oFK8ol/iV/R+/10E2PDycGjVqMHXqVHr16sXIkSPx8/Ojboc2PDh9GTtbS75qUZk0pZLT525x8sxN8vh7Ex2TgG8297dab8VyBQkuW8CciSuKIiqlAqNCgfIDzwcf+4jPyuwE0GVmotL8ElA1IpEYGUnFto1JSEigQ4cOREZG0rt3b4xGI61bt36rzsoqhJ8DcO//BU8URbotmYm1g735dwYkRECB8M7nWdVHCAgWrR1C0dq/nRprQEL9gdPcZZ+GZASDwYhKoUQhKMzlAQwGA64eLlSrX5ErZ2/QoHVt4JdzyK0rdzm4/Rg9vu1MvZY10ev1aCzUnDp4nqtnb1AyuCghDSpjNErY2tu8x01GCdMnpfyZJvs83qb8hEwmk/0byGc5mUz2WWgESyxEK/O037dl52CLVspAiRoL4Z9fP9DbRsBBIxD3brsBg1HCIIGv3ZuncY1ohYCA8SMVvzdKBgQENO9RA1L235F10RQXF2f+XbZs2ejatSvff/89CoWCI0eOULRoUewEBQ/PXsEzmztzlu6iUp3+bNt9hmEDWrFkdv+3DjRmyQo0iKJoinBIkGJpi+OvasW9KztBacrN+ogNpsJv32NK43Zc2r4XgPiXUSREvgLg2p7DqFQqzp49S9++fZk4cSLu7u7MnTuXsLCwt1q+vajEQhDJ4MPe/1mBxqyL4gzJiLOoRvUe5RncRDXaT5R9aEDCRXy3rtuyTyfrvWI0GlGKKtRKNWEPnzC40/d0rP0NiyYvJyM9k4Zt6vDN8K+wsNSwaPJyAPR60zErGSWePgynaNlCADx/+hKNhQUWVhp2rtsHgIu7M627NUOlUr5z8NuIEVEQfzO9Wyb7K0iShCiKiKJIamrq5x6OTCaTfVJysFEmk30WoqDASeGFHt07XcxLkoSOTFyUPr8pPP9P5GIlUthdJCbt3S7GY9LB1UqgoOubF0wWgjUqQYOOzI8yPh2ZqAQ1FuI/P7Ar+3Tu3r1LcHAwGzduBODWrVu8evWKnTt3kpCQwLBhwzh16hSFCxfGQlBQqlYIm7ad4srNMMaN7MjaH7+lbs3SiKLI9ZuPeBoe9V7jUGkz0Ko0pFnZf3Cw0VFQoUb8qIEyezdXRIWCU2u3MKlBG3rmLMr9i1cREbhz+jwlS5bE0tKSNm3a4ODggJeXF5MmTSIwMPCtlm+LEjtBSepHutmQFUTOxIin8H5BPRdBjfTz9OuPySBJCPDBr7Ps/WVkZNCzZ0++++474JfO0qIocvTIUbrWHkjzEp05tucUOp2eH6et5LseP2A0GvHJ4U31RlVYt2gzYCqLYDQayZEnO7kC/OjSoA+DvvyO9tW7kztfDvZe38iYed9+8JgNkhaloH7retEy2cckCAL37t2jWbNmBAcH065dO3bv3v25hyWTyWSfhBxslMlkn42Dwg2NYEGGlPLWz8kkDZWgwVHh9glH9tcq7q7AUmlqFvM2tAaJ2HSJEh4K7DVvZnWoBDUOohtaKf2jjE0rpeMguqMS/ryGnuy/KzAwEHt7ezZt2kTp0qUpWLAg165d4/Lly/Tt25d169YBv2TKVS0fjI2TPRWL56ZyqUBzUOvC5Xts2n6S+IS3Pye8ziIjkSg7F5RWTrh/YMabk6DCVlSQ/AENSIyGX4J+kiShVKuIfxHJ9X2HMej1zAm7Sp66VbA0Qq5s2Zk7dy62trakpqaya9cujh8/TrVq1UhNTX2rYJ0oCASI1qRj/GjBvXTJgAYBP8X7ZTf7iBqsBAXJH5ht+WuJ6LETlHiL8rnpc9HpdBiNRpYsWQKAUmmavB8eHk6DBg24cOISJSsX5ovOjUmKTyJP/lzs3nCA88cuYW1jRUj9SugNBjb9tB0wBSlt7GyYt2kqIQ0qkZKcyvDpA+g+tBNOro4fZcx6dFgJdu/VRE0me1uJiYnAbzPjDx8+TN26dbG2tmbo0KE4OzvTr18/Vq1a9TmGKZPJZJ+U/Ekrk8k+G41giZfSHyMGMox/Pp0kU0pHjxYPRQ4sBJu/YIR/jbxOIiF+SmLTpT8NOGYaJB7FS+R3URDi+/tV2ZyUnigFNZkfGHDUSukoUOGk9Pig5cj+fQyvBdF0Oh2JiYlER0dz+PBhnJycSEhIoHr16ri4uNCqVSuePXvG1q1bzUHFqgEFqdqvG/M3naFn/5ls33OGQSMXM+T7JVhbWZDT992POYVeiyDBQ48cBCptsBI+bJqkRhAJFG1IwfDegTvx587SaYlJpk7csfGUalKPwOCyuPllx8HDjURJT16VLYH+ufH09GTUqFFs3bqV8uXLA/Dw4UMGDRrEy5cv32qduRXW2AoKEvg4XZpj0eGtsMD7PW84OAkqcolWxEvvlsX+/0iSRKKkJ1Bhg40glx//q0mShE6nw9bWlvbt2yOKIiNHjjSXUTh79qypMYzBSJEKBeg7ujtdBnbgxbNIcub1Y+W8Deh0OvIWzE1Ig0pMGjqLs0cuMKTzKH4YOA0PHzd6f9+NeZumUK1+pY87doxYi3YfdZky2et27NhBsWLFiIqKQhAEzpw5w7NnzwDYuHEjVatWZdmyZTRt2pQvv/ySBw8esHfvXmJiYj7zyGUymezjkoONMpnss3JWeOOtzIMRA8nGOLRSxhsXpJIkoZMySZHi0UuZeCpy4a70/Uc2g/kjgiBQO6eSurmUJGslQuOMxGe8Oe0wXScRlmgkLEGioJuC9vlV2Gl+fx9YC/a4KnzIlFIxvGdWlkEykCGl4abMhrXg8F7LkP37ZGUmKn4OokVERKBSqVAqlfTp04eKFSvi4+PzRjAyf/78NGnShHHjxgGmQKWTqKLbgP60GtcXBztLdu85jdFo5Mc5AxjSrwV2du84bV+SsEmO5rmTFykufgQqPs7NiDwKqw/Kyju6dDXflgphRovOHF26Ghe/bNTt9zWBwWV4dusul4+eQIVAoMKa4OBggoKCWLp0KRcvXuTFixecOnWK/v37c+/ePVJS3i7b01lUUURhR6KkRyd9WMfdREmPShIorXB45wZWWQRBoLDSFitBQeLHCoBKOhwEJfk/0ussezuS9Es3cpXKNH19z549pKSkMGnSJKpUqcL8+fO5d+8epUqVwj+3P+H3X6IlneLlC1OoZH5sHWw4vvc0d66GYm1jRZeBHSgZXJRRvSfx6kU0Lbs2Ra1Rf5LPeL2kQ0SBlWj/5w+Wyd6B0WhErzed33x9ffHx8aFdu3a4u7vTtm1b0tNNN3+PHj1Kx44dOXLkCPnz56dq1ar06dOHCRMm4OLi8jk3QSaTyT46+XawTCb7rARBwE2ZHUvRmhjDc5KMsWRKqbxeJk2BCnuFKy4Kb+xF139VoDGLUhSol0tJTgeRi5EGbkYbiUqTEH7eESoR/OxFyngpKOquwFr1x/tAEATclTlIk1JINLzCCnsU75D9Y5D0pEoJOCjccFfm+Ffub9n7ycpMnDRpEj/99BN2dnYEBATQq1cvWrRoQUxMDMuWLePQoUM0a9YMAI1GQ/fu3alcuTLbt283Ta+8cAErpUCRTp2pXzYvOZ/cQevijVH5HvX3JAmb5BjSNdZc8S1AUYUDrh+paYiboCa/aMMFQyLWKFC85Xvh9NoteAX4c2b9NoLbfsHjS9fYOn46GSmp1OrVhYIhlbh36hynt+5ieJUqeAsaFP7+TJw4ka+++oqmTZtib29PWFgYrVq1Yty4ce90IVpCaU+ElEGYIR0fLN563K9LlwzESzrKKR3wFf+8E/b/4yNaUExhx0l9PJYo0HzAFNY0yUAaBqorXT7a62w0mqadZwXRZW/6vf0zb948RowYgU6nM2czFy9enDVr1mBhYUF4eDitWrVi2vSptB/UjICcQbi4O6PT6ckdlJMVc9YxbuFw3L1cmbh0FJIkYWP7aWsDZ0ip2CocsJFvoMk+kmvXrjFnzhwmTZqEk5MTAFqtlnPnzmEwGBg9ejRDhw4FIDMzkzx58lCmTBmyZ89Ox44d6datG25uprJAUVFRODk5mQP5MplM9k8nBxtlMtlnJwgCdgoXbEVnMqQUkoxxGCQdAApBiY3ohJVg+68PegmCQH4XBfldFLxMMfIsWUJrkFCKAk4WkMtBRCm+3T5QCip8Vfl4CiQYXqHBEjWW/3cfSpKElnRTnUaFG76qoH9FEx7Z+zMYDL8JwAwYMIC9e/fy3XffYWdnx5YtW2jevDkXLlygbdu2bNiwgUOHDlGjRg3s7EzTFcuXL0/btm3p2bMn/fv3JzY2lqVLl1K+UD4O5CyGjS6D7DHPSLF2Qa9+h8CWZMQ2KRqdUsXpXEXxcPKjhMqRjIwM8zTPDyEIAmVUDkRIGbyUMvGWNOb3UFaWpyiK5mwvgAfnLvHjN4OwtLOlxdhhVGjdlNT4BPbOWsTWH2ZQq1cX/EsWxa1ofp5fuIbm8n0UZcoBkDNnTvbu3UtUVBTnz5/n3r17dOjQ4Z0zXjSCSHWlC7ukaMKNGXiieacAX5KkJ07SUVhhS1mlw0c595ZQ2hMjabljSMUDDRbvEXBMkwy8krQUUdhSUPFhr20Wo9H4SyOczEwEQUCtVv/mb/8ViYmJ3L9/nxIlSqDT6RBF0XwOSE5OZvbs2bx48YKdO3fi7e1NmTJlKF68OJMmTWLv3r1ER0cTEhJCYmIiBoMBBwcHdvx0AP/ReXhw5zH2jra079mS6d/NJzU5DQtLC6xt3q8e6Ov0er25ZuTvMUpGJAw4K7z/9d8lZH8dV1dXli5dSnBwMJcvX2bVqlXMnDmT6dOns2LFCnOWf9a5pHz58ly+fJk9e/aQL18+83J27drF+fPn6devH46OH6c+qUwmk31u/61vUDKZ7G9NEAQsRVvclb54qfzxUvnjrvTDWrT7z10ceNqIlPJUUMFHSRkvBXmdFG8daMyiFizIqSqItzI3RowkS3GkG1PQv1Y7TZIk9JKODGMKyVIcRgx4KXOTU1UQtfBh2Uyyfz6FQkFq6i/1VK9fv86WLVtYv349LVq0oHz58jg6OhIWFsbRo0ext7enVq1a3L17l2PHjgG/1HecN28e48aNY/jw4cTHx9OoUSPyilaUt/bickA57nvkwjItHuvkGATjn0xbliRUmWnYxT4nxcKaE3lLY+WVj8KxOrp1/oqAgACaNGnCoEGDPngfWAsKKimdsEIkUtKas7xEUUQURdISk0hP/mWKs3uuHFTp3JaMlFRKNKxtWoajAyUb1wVg39wlvDJqCapZGXtByaSR3zN37lz8/f05duwYWq2Wp0+fMnfuXI4ePUp8fPx7jdtJVFFP5UqAwpooKZNXRi36P6mZmCEZiJAySJcMlFU6EKJyRvWRGmloBJHqKhfy/TyeWOPb13CUJIlXRi0xPwcaq6qcUX7AZ0JWoBhMweKXL1/SuXNnihQpQosWLZg2bZr5b/8V4eHhxMfH07NnT9q1a4der0elUiEIAuHh4TRs2BB3d3e2bt3KiRMniIiIID4+nlq1avHw4UNevnxJZGQk/fr1Y9u2bfTs2ZPt27fj6uLGlh/3MO372bx49pLG7erRqG1dTjzejbOb0wePe8GEpXxRsSPf9fiBUwfPoc3UAr9tzJEuJWMp2mIvylNVZe/v9XOHVqvF29sbDw8POnXqxPnz59mwYQNt2rShY8eOlCtXjk2bNhEfH48oiqhUKqpUqYKXlxd9+/blwIEDPHv2jBkzZjB06FDi4+P/b8BcJpPJ/mn+O9+iZDKZ7D9IISjxUuUij7o43kp/lKIKrZROihRHsjGWFCnO1AhGVOKlND3OS5ULhZzR+J/zep3FLDt27CBXrlw8fvwYMF3AZ2XZdenSBW9vb65cucLOnTtp0qQJAO3atUOpVDJhwgSGDh2KhYUFW7ZswcLCgrZt29KhQwfz8gVBoIjClmrWPjwKqMjJwLKkaCyxTYzCNvElFmmJKHUZiHodCr0WdWYq1smxOCQ8R6lN44F3Ho4Wroa7ez5sTtykftXqxMbGMmvWLNq2bcv8+fOZNm3aGwHT9+GnsKSGygWNIPJC0GIAIu7eZ/oXnfi+Un1mtujM3tmLAbBzdaZQSCUko5Frew+Zl+Hhn4NyLZuwY+o8ECQ6VajOmG+Ho1QqWb58Ob169cLR0ZEqVaowbNgwypUrx5EjRyhUqNB7j9tRVFFf5UZ1lQs2ooIXUgbPjOlEG7UkSXqSJT2Jko5IYyZPjenESTp8RAsaqt2pqHT8aIHGLFaCgjoqV6oqnUCQeCplECfpMPxB0FEvScQatTyRMlALAjVVLlRXuaB+z3FlBaBeDyJu3bqV4OBgYmJi+O677yhVqhSDBw9m06ZNH62hzd9ZZGQkdevWxc/Pj9TUVKpUqYJarWb79u2cOXOGL7/8kty5c3Pw4EGsrKywtbWlXr165M6dm4iICBo3bsyVK1eYM2cO5cqV49KlSwBUqFCB7777DkmSSIhLZOuPe+nybVuCa5T9KOPW6fT0bzecHWv3UadZdV6GRzGm72Tm/7D0t4+VtCBIeCpzohQ+ztR72X/L6zeZsv6tVqs5ceIEefPmxWAwMGHCBKpWrQqYyodUrVoVURT58RDNFMEAAQAASURBVMcfzc8pUaIEy5YtIyEhgT59+lClShVmzZrFwIEDmTNnzgdn48tkMtnfiSD9y79JJSUlYW9vT2Jionk6l0wmk/1XGSUDmVIamVI6EkYERNSCJRaCFeIHdu+V/Tu8PiU4OTkZNzc3Jk2aRM+ePdm1axf9+vXjyZMn1K9fn759+1KunGkKcGxsLEqlEnt7e06dOsXatWt5/PgxHTp04IsvvvjT9cYbdZzSx/MkIx77uHByREfglBKHhS4T0WgAQUCrUJFiYUWEsxcvnLPz+HoYV+etZNXCJdy8fp2wsDC6dOliWl58PMWKFcPFxYWpU6dSoUKF99ofr0+lfWZI54g2hj1rN3Ji4jxyFSlIpS9bce/UOc6s20rt3l2p+lVbEiJfsXrwKCIfhTHm1B4kSSIFA9cuXmZJyBeMnTeLwR27AvDkyRP8/PzM62vcuDG7du1iy5Yt1K1b93ensr+PDMnAY2M64YYMXkiZpEoGJCQUCDgIKrxEDTlES3zE96vx+K6ijJncMaRy15BC0s9jERAQASMSEiBgCpjmE23Ip7DBWfw4N0GOHDnC/Pnz2bhxI2vWrEGr1ZqD4C9fvqRw4cIULFiQhQsXkjNnzo+yzr+LrPd3eno6lpaWnD59murVq9O3b1/Gjh3L/fv36du3L6dPnyYxMRE/Pz/S09NJSUlhwIABLF++nJSUFOLi4siVKxfPnj3j4sWLFChQgMaNG7N9+3YmTJhAeHg4AwYMwN7ensuXLxNYwY/n+vtYCQ4oPuCzJmv8V87eoFeLQcxeP4kipQtiNBpZOn0V8yf8xMqDC8hXOO/PwWKJFCkeF4UPvqqg/9wsCdnHdeLECVasWIGnpyc1a9akRIkSqNVqSpQoQbZs2fjxxx/N06Dj4uIYMWIEV65c4cSJE6hUKpKSkszXow8fPiQ5OZkiRYp8zk2SyWSyd/Iu8TU5V1smk8n+Q0RBgaVgiyXy3fP/ul/Xo0tPT6d27dp07dqVFi1aIEkStra2dOvWjdmzZ9O+fXtKly6Nh4cHBQoUYNOmTebnarVavv/+ewICAujRowfly5enVKlS71To3lFUUVflSqTSnlBLV+57BpCmz0SRnoRg0CEJAka1NaLGmuhTlymQrmPz5EV4eXhibWlJgQIFqFy5MqGhofTr14+zZ89So0YNDh8+zKFDhyhRogQWFm9fGiBr/7y+j7IrLGlu4cX1dInqQ3oS0KIeVihIN+g4uHAZe2YupOpXbXHwcKNkk3os/KoPZ48ew7NiKawEkZDAQrxq2JDn126zbds2Jk+eDEDhwoVp0aIFFSpUoE2bNly8eJHQ0FDq1q370abyWggK8ilMQTtJkkjHiBEJFeIHNWx5X+6iBndRQwmlHVFGLXGSjgSjHj1GVIg4iEqcBBUeogarj3Qj5MKFC6SlpTFy5Ei8vb3RarVUqFABLy8vbt26xcCBAzl//jw1atRgw4YNHD16FD8/v3/FdOqs41mv11O7dm1y5szJvHnzKFiwID169GD16tXY2dnRrl07nJ2d0el0eHt7m88B33//PdOmTUOr1aLX68mbNy/BwcEcOXKErl27EhwcTHh4OAAjRoxg9OjRuLq6YmlpSZUqVTBIepKlOJIMMVjjiPgOx1zU81eo1CqcXB3NwcLM9Ax0Oj3+gaZgsCiKNGhdmzOHLzBl2ByW7p6N0WgkXUzCUrTFU5VLDjTK3tqvb/KkpaXRo0cPtm7dSsuWLbl06RIHDx6kevXqjB49mpEjR9K4cWNu3LhBxYoVkSQJJycn6tSpw549e+jcuTMGg4GbN2+yYsUKChUqhL+//2fcQplMJvv0/vnfnmQymUwmk721rOnSvw6gWFpaYmlpyZw5c9BqteYL88GDB/PkyRM2bdqEi4sLTZo04fbt23To0IFDhw5x9OhRGjduzPHjx/Hy8jIv7306agqCgKeooZLKifYqL1qovKjtGEA1l/zUcA6iiV0uyj5OZvGXvehSvR4VypVnxYoV2Nra4uHhgUKh4Ntvv8XBwcGcXRkSEsKWLVu4ffv2n65fkqTfTLX96aefGDVqFGfPngVMU4GHfdGeqW274nw+lHnVmrG4Q0/y1qpC7PMXbFq8lGfGdDSFc+Nbqij7x80kWOlIK7UXjZz9WL1sOf7+/vTs2ZNatWrxzTffoNfrad68Oc+fP6dBgwYEBQVx9epVIiMjEQTho0/nFQQBK0GBjaD8LIHG19kISnIprCihtCdE7UwttSvV1M4UV9qTU2H1zoFGSZJ+tyTA4cOHadWqFR06dKBZs2asX78etVpNtmzZyMzMZODAgbi7u3PmzBnWrl1L1apVWbt2LS9evPhYm/pZZR3PkZGReHp64uDggEKhwNbWFi8vL54/f86QIUPo2rUrjRs3pkiRIlhYWPD06VPWrFlDRkYGycnJDBs2DGdnZ/LkycPFixdJT08nNTWVJUuWEBoaSoUKFdi1axeDBg3C0tLSvH6FoCS7MhBr0YFUKR6jZPyjoZrdvHyHlpU607LyV3Sq25OlM1YTH5MAQMyrOLx9vbh67ob58a4eLrTq1pSzRy7w7HEEGWISKkmDryofGsHyD9Yik/1WVqAxPT0dgJMnT3Lz5k0uXbrE/Pnz2bt3L4GBgYwdO5a4uDjq1atHzpw5WbVqFSkpKebPzxo1ajBr1ixevHiB0Whk1apVH1QaQyaTyf5J5GCjTCaTyWT/IQqFAkmSmDJlCgMGDGDNmjVkZmYCMHbsWM6ePcuJEycAUzaUh4cHzZo1Y968eaSnp9OtWzemTJnClStX+Pbbb2nVqhWenp4cOnSIRo0afZQxSpKEUhRxVVvip7BEf/8pnpngKWrI4ZONLl26kJycTN26dd943sGDB7l06RKtWrUiX758JCcnExERwaNHj9ixYwdarfYP12kwGBAEwXyRqNPpaN26NWPGjOHo0aOEhISwc+dOAFxcXDDGJrJp3DSqFizGxRvXWLFoMUXLlubOjxuopnAicu461NEJxN19SM7odG4dO4UgCMTHx/Pjjz8ya9Yshg8fTsuWLSlbtixRUVFs3rwZhUJB48aNCQ0NNTfZkb0do9GIIAjmQEFsbKz5b3nz5qVy5cpER0f/Zlr/5s2buXbtGt988w0BAQHmZidHjhzhyJEj/4rajUajkXr16jFjxgysrKy4fPkyVatWRRAEZsyYQd68eXF1deXo0aNUrlyZpk2bEhUVRUpKCnny5KFOnTrY2NiwceNGvvzyS86fP0/Tpk3R6/UkJCRQs2ZNbty4wbFjx6hWrdrvjsFCtMZPld8ccDRI+j8cb1pqOpOGzCJ7Tm8WbZ9OyeCibF25i7H9pgBQpHRB0lPTuXfjAXr9L8sJLJSXgEK5WfvTBlRY4qsKwkaUu/vK/tjrTV+yPHr0iAYNGrB161YAQkNDCQoKwt/fn3nz5uHv78+JEycYPXq0+XwzatQo1q5dy/r16zl06BANGjQwByJ37drFmjVrKFCgwF+6bTKZTPY5ycFGmUwmk8n+Q06ePImvry/Lli0jJiaG9u3b06VLFx4+fEjRokWpVKkS06dPx2AwmLOh6tWrx61btzh8+DAqlYq6dety7do1Vq9ezZMnT1i8eDFubm4fNK7XL/gEQSAjI4PBgwfj6upKhw4dKFasGCdPnsTe3p66detibW1tDsZlBUuzZ89OTEwMYWFhREdHs2nTJsqWLUvLli3JmzcvavVvm0NkrVehUJCUlMTkyZNZuXIlV69excLCgqtXr3Ls2DFCQkKYP38+V65cAeDAgQOcOnWKcePGEeDhg31sKskvXxF67Qb3N+0hpEQZMBgpWbIkPj4+jB49Gq1Wa74wDQwMZNq0abi7uzNhwgRmz55Nx44dAWjRogV2dnbmbtSfIrvx3yLr9ctq3pCWlkbfvn3x8/OjQYMG9OnTh+TkZHx8fKhRowYajYb9+/cDmIPP/v7+REdHExkZSXJyMtu3b6d9+/bUr18fpVL5j5p+++vAicFgIDMzE1EUSUpK4tWrV9jY2JgDqb6+vrx69YoWLVqg1WqxsLBg0qRJODo6otfrcXd3JzQ0lCVLlmBhYcHNmzc5dOgQkZGRjBs3jt69exMaGsqqVaveqD36RyxFG3KoCmKncCFNSiRDSn3j2M76+eSBs9y/9ZBe33XDPzAnQyf3o+eILuzZeJCzRy7g4+dFoZL5OXngLPduPDA/38HDBldvJxSSEj9VEPZKufv0u/gvnWey3iuvZ/ln/c7GxobDhw+TI0cOAK5fv86NGzfw8vJi1qxZ9O3bl2vXrjF8+HDs7e0B03m7adOmzJo1i+bNm+Pr64ujoyNGoxGNRvMXb51MJpN9fnKwUSaTyWSyf4HHjx9z/fp1wBRE+b1sDYCFCxdSoUIFrl27xrJly9i2bRsPHz5k7NixAAwZMoS9e/dy7tw584XnqVOn0Gq1jB07lrS0NMB0gZY7d+4PuoiSJMkcvPv1tO5Ro0Zx4sQJli9fzo4dO6hVqxZDhgxhw4YNFC1alBYtWjB//nzA1PnTYDAQEBBAq1atmDNnDgEBAQwePJjKlSvz448/0qpVq98dgyiKJCQkcOPGDQoVKsSqVasYMGAAtWrVIigoyHwh2b9/f6Kiojhw4AAAfn5+6PV6Tp48SWxsLMuXL6d27doMHjwYlUrFixcvuHXrFleuXGHLli0cO3YMtVrNgwcPUCqVFCpUiLVr1zJjxgxOnTpFjx49sLCwIDo6GltbW8qXL8+RI0c4dOgQDx8+lAOOv/Lr6e6CIKDT6WjXrh2nT59m9uzZVK9ene3bt9O6dWtSU1MJDg4mJCSE1atXA6BWq5EkiTJlyhASEkLPnj3x8/Pj+++/p2TJkmzbtu0Pj5u/o7CwMHMAXpIkjEYjCoUCjUbDjRs3iImJ4fLlywCUKVMGjUZDzpw5sbGxwcnJiRo1aqBWq5k3bx5+fn5kZmYiSRLPn0ewddUSyhYJwsXBDiktgf7tG5NyaTtDWoZgkfIC0hLgLY9PC9GKXKrCeClyAxIzxs9h5hjTezlrCrxKZSor7+Hzy02MavUrUql2eeaON3X3bd+zJQlxiexYsxeDpCdVikeHlgfXn5DTJRBbhdNH2Kv/DQaD4Y3mYPDvDTz++tyRmprKhAkTzL+TJAl3d3dy5szJmTNnAGjSpAnXr1+nS5cu3Lt3jx49emBra0tCQgLjxo3j9OnTAMydO5eNGzcSFxfHrFmzUCqV/4qarzKZTPY+5LOfTCaTyWT/cHq9ngEDBtCgQQPAFEQRRZFnz54RExNjflxMTAyhoaHkzZsXpdJ0MV+jRg3atWvH4cOHefz4MSEhIVSrVo0OHTowbdo0xowZQ0JCAqdPn2bgwIHY2Nh8lDFnZGQwdOhQKlasCMCNGzeYNWsWMTEx5ims48aNo3bt2giCwIsXL7h8+TLPnz8HoFmzZqSkpJgDR1lTKWfPns3mzZtZt24dr169onr16uZ1/l4A9siRI5QtW5YBAwbw7bffcv36dX766SdcXFy4evWq+XHly5cnMDCQkydP8vDhQ8qVK0erVq345ptvzLW6atasSUREBF5eXlSsWJEBAwaQM2fONzIqixQpgoeHB+XLl+fgwYNs2bKFH374gfT0dKZMmcKsWbMQBIHg4GCcnJwYPHgwixcvBvhHZdh9aln74v79+5QtW5bLly9z9+5dzpw5w+jRo6lXrx4jR45k69atHDt2jA0bNuDm5katWrV49uwZe/bsAX7Jbly/fj2rV69mxYoVREZGvnf38s9p2rRp1K1bl+DgYLRaLaIoEhoaSoECBShSpAjh4eG8ePGCTZs24ezsjIODAxcuXCBHjhzcv3+fJk2akJSUhCRJHDqwnzy5cpAQF0tGagq9hnxHQRcFdxf25vKMrkxpVRqeXoK7h+DyJji/Eq5thchQ0Gf+6VgVghIvdS7S7ipZNHYVC8YvI+z5AwwKLZJkRK834JPDi8M7fynpIIoiX/ZuzeXT13gc+oSAgrmp26o6R/eeoHfrwVw/ep/Vo3fh5uxO/boNPvXu/ldRKBQIgsDdu3c5fPgwqampn/98Y9BBUhRE3YeXd+DFbdPPSVGmv72n188dwcHBzJ8/n4ULF9K6dWuePXuGIAhERUXh6+trvrlWu3Zt8ubNy61bt8w33yIjIxk7duwb5S6sra3JkyfPB222TCaT/VvIwUaZTCaTyf6BwsLCaNasGTExMSiVSvr27UtkZCQnT54kKiqKChUqUKBAARo0aMDo0aMBU0ZHcnIytra25uCcUqmkSJEiuLq6cvz4ccCU/VirVi2WLFnCzp07ad++PWXKlKFJkybvPd6IiAi2bNli/reFhQXNmjVDo9EQGBhI4cKFefHiBU7OzlwPD+PR0yc8sVVQom4NfHPl5HF6Ij8c3Epwz46EGdLIXTA/derUMXd0zsqwtLCwICgoiJCQEPO60vQpJBqiiTaG80x7jyfa2zzV3uGF7iFKByO+ObJz7do1c7C2du3a1K9fn/Dw8DcCjq1bt+bVq1fs2bMHURT58ccfWbNmDXv27OH27dtYWVlx+PBhnJ2dKVmyJEOHDkUURXbv3k1qaioAVlZWNGzYkFevXtGkSRNKlChBkyZNGDBgACtWrMDd3Z2NGzfSsmVLlEolU6dOpXHjxuYss/+q1+vyAdy6dYvt27czY8YMHB0d8fDw4MWLFyQmJlKzZk3AlK1VuHBhKlasyObNmwEoV64c+fPnNwdws44bOzs7ypYtS506df66jTLoTMG56zvh/Co4uxwuroOHZyA17q0zBbMMGjQIgEuXLlG/fn1mz57N2LFjefnyJdWqVUOlUqHX6xkzZgy7du0yN8fRaDRcvnwZSZIIDg7G2d6OpYvms65ffQr4uWNjpWFEx4aM6dsZlxyB4Ohj+s8pOzhnBwcvUFpA9CO4vh0urDEFht6iCcyxfafo0KEDhYoUZM2knYBEipRAtgKuKDUKLpy+SLohFb2gJcOYhl9+L3IGZmfT6i2kSPF88XUDRk0dhj5R5Ifeszh99CyTJ08mKCjofV6R/6xr165Rrlw5KlWqRN++fSlXrhybNm366wei1/78ntgBp5eajqVrW+HGTri5y/TzhTVw6ke4th0i771VcPt1WeeOadOm4ezsTIsWLdi0aROhoaEMHDjQXD4gJibGHGwEWLBgAXq9nurVq9OwYUPy5s3LqVOnGDFiBOXKlfvYe0Imk8n+8eRgo0wmk8lk/yCvXr0CTEGrzZs3s2HDBgCKFStGpUqVGDduHHv37qVUqVIcPXqUUqVK8f3333P69GlcXV0pWLAge/bsISIiwrxMOzs77t+/b87IyJEjB7NmzeLkyZNcuHCBatWqkSLpCTdm8MCQSqghlceGNGKNOgx/EhB58OABdevWJTAwkC1btnD//n1zhuGBAweIi4sjPj6euJRk2o0bziZdFJezWSNZW9C5VGUs/LzpcXgjX6ybj65EAFte3GfGpaPstskgZ81K3L17l6tXr5oDcYsWLeLYsWMYJSOJhhieaG/xQH+Rh9orrNj0I32/7s/MaTO5dOcML/SPsArUE1Q5JylpKdx8egmtlAFAnTp1MBqNbNy4ETAFamvUqIG1tTUHDx4kKiqKS5cusWLFChwdTQ0okpOTkSQJHx8fABwcHKhevTpXr17l1KlT5n3Srl076tevz8mTJ1mzZg0NGzbk0qVLLFy4ECcnJ7788kvS0tIwGAx8++23HDlyBI1G8/kzjd5VRhK8egBh502BgiubTf/d3gdPL0PMY9BlvNWisjJxAZ48ecLgwYNp164d8fHxbN++HW9vb5ycnHBwcDA3ddDpTNlP7dq14+zZs0iSRO7cuSlRogRXrlzh3r17H3+b/4zRAI/Pw55xsOor2PU9nF9hCtLd+HkfHZ0J63rB+t5wfjWkxv/uoiRJeiMY4uPjQ5s2bXB3d+fChQv07duXhIQE4uLiWLx4McHBwRgMBlJSUqhduzZarRZHR0du3bpFZmYmt65doXm5QB4/C+dlbALxBg37540kav8iRn7V9I+3SVSAhY0pAGnvYZpSfWsP3Npr+hnTzYascxf8Ml06ISEBX19f+vbux6YV23FPy4u/ugil81SiaPHC3LnygAvHLiNhQBBMwWF3Dw9sREdyqQtRyK48Xzb4moP7DnH8+HEuX778xo0G2S+yptb/mtFoZPLkyfj7+3Pv3j3OnTtH27Ztad++vbk0xydn0EP4VbiwyvReiAoFyQBWDuCYzRTYdspu+tnKATDCq/umoOT51fDsyhvZjpIk/eYGBZiOw6xzR2JiIhs3bsTHx4dixYrxww8/cOPGDTp06EBGRgbVqlXj3Llz5udWrFiRVatWceDAAerXr8/Bgwc5d+4cwcHBn37/yGQy2T+QHGyUyWQymexv7PVstvz58zNjxgx0Oh3u7u707duXWbNmkZKSgpWVFb169eLw4cP88MMPdO3alaJFizJx4kTq16/PkCFDABg2bBg3btxg4cKFvHjxAjBNJQ4MDCRbtmxvrFvt7MBFXSLzM8OZkv6E6elPmJHxlNkZT5mR8ZRpGWFMzQhjd2Y0EcYMjL8KPMbHxzN48GBsbW05f/48P/30E/BLrawOHTqwadMmtHo9Yw9sZof2FWH6VGwEJUVDKuPk7Un3qeMoWqgwnqIGtwyJ2/NWE33sApkGA5nBBRlx/zSp+f3QYlr3qlWriIx7wWPdNR5qrxBjeM6Ns3fpWLE/k/vNRy1asGPZQSb3WcDdU0+wEZwoXroY+UvkZfa82YRmXiRGH0GF4AoUKlSIixcvcvfuXQRBQBRFJk+ezIIFC3B1dSU1NZXr168THBzMxIkTmTlzJjVq1MDGxoaMDFMQrW3btkiSxOHDh837JTk5mXHjxpGYmMjatWv58ssvefXqFU+ePGHZsmUUKVKEgIAABg0ahK+vL+PGjaNp06bmxiZ/a5IE8c/h7mE4twquboH7x+DlPYh7BnFPIeLma9NvV8GjnzP5zIuQfpPFmZV5BaZ6mXXr1jVPN88KRHp4eBAcHMysWbMAU5YrwIULF8iVKxdxcaZ1tGnThvPnzxMQEPCp98abnl2FTQPg8HSIuAbaDBCVoLEBC3uwtAcLO1BqACMkRcL1bejX9oaTi/lp8SLmzJljrskqCAJWVlYAdO/enU6dOpEzZ06ePn1KzZo1MRqN3LlzBz8/P65fv07RokURBIF169bRrl077t+/T758+UhMTCQ5MZ5p02ewa+8+JnVvwu11k6lYogCOdu9YNkGhMgUcrZ3g+Q0OLRpL9SoVqVmzJgsXLjS/LxQKBQaDgYMHD9KwYUNCQkJwcnJi5dLVOCjc8FDmYMg3I3GydGHzrAPk05QjSFOO7LqCPA2NoEyhYBwVHmgE0/YrlUo8PT0/4ov172IwGMznsF87ceIEhw4dYtmyZTg6OrJlyxZWrVqFjY0NKSkpn35wya9MAcbbByA92XT8OPqAlSOoLOD1myyCYPqdleMvwe3MZLh70LSMpChz7cms88KDB780EPLx8aFu3boA5nNH1k2JkJAQVqxYwaVLl+jRowdJSUl4e3sTH28K9kuShI2NDeXLl6dbt26ULFny0+8bmUwm+wdT/vlDZDKZTCaTfXR6LSRHmbKWMpPBaDRlCFnag7UTBitnFCr1G9ls27Ztw9/f3/zvnj17MmfOHHbu3EnLli0pXbo01apVIzExkdy5cwOgUqno168flSpV4vTp05QrV46RI0cyc+ZMdu3ahUql4v79+0yePNkcbMyUjJzVxXNCH89LKROtJCEKAkqEn784CBgkiRh0RBm1PDCmc1wfR36lLTVUzmRGROHi4sKRI0e4du0at27dYsaMGSxZsgRbW1vKli1Lp06dKFikCFLRvGQPLsmmibMYXbMqSlEEByuCO7fhzMbtjG3YhjwhFbB2ceLs/BVkJCTyxZhhOKHE2d6VRHs9J/TxPDWmk7nrJKdOnaL7hNYkGKKxEmxRCCrOHdpE8fJF6Dv6a1QqJc8eRdCv3TCWzlhFyeCi5C+cj3KVSrNj7T4e33+MNncaiYpYKlULZv/+/ezcuZOAgABmzJjB8+fPqVmzJpaWllStWpVz587x448/sm/fPvbu3WvOqsoKdOXIkYNq1aqxZcsWUlJSOHv2LI6OjnTu3JmUlBSePXtGlSpVWL9+PS9fvqR9+/YUKFCAPn360LlzZ9LS0tBqtZw9e5ajR49y69atv29QRZsOT85D+HXQpYOlHTj4wB81SDDoIT0B7h+HiOuQszR4FUBQ/PbradY+WLJkCZ07dzaXCdizZw/du3cHTN3I27ZtS8uWLenevTvNmzdHq9Wyc+dOunbtirOzMwDe3t6fag/8Pl0mnFkKj06bflZpQGH9+/tFEEDUABoytDosFAJKXRrcPcDp9XfYdPEZtWrVIleuXIBpyv/evXsBU9Zf9uzZUSgUPH/+nAIFChAeHk5GRgZ79+6lQYMGqNVq7t+/z7Fjx8iVKxedOnUi7PFDHj16TAE/Dyb364Svt/sHb3J0qp5vpm7nxOU7tAopRf8eQ9FbOKBSqQBT8Cs5ORlra2usra3x9PSkf//+fP/99+zevZvy5cszZswY+vbtR5cuXShTsiw1a9bkwIED5MyZ0xx4/sdl+/7FDAYDCoUChUKBTqdj0qRJvHr1iooVK9K4cWPzYwICApgwYYK5vEBW8NrJ6RM32Yl+ZAoUpiWYAofKd2w4plCBrRvPIl7gEn4Xq+RohMAQcPNn4sSJzJo1C1tbW3Lnzk23bt2oU6cOVatWfePckRWUlCSJEiVK8MMPPzB37lyOHDlCpUqVzJnr8rEmk8lk70aQ/uUFgJKSkrC3tycxMRE7O7vPPRyZTCaT/ddlJEPkXXhxy5TNZTCAwJv12ZQasPMAryBWHrrM5WvXadCgAZUqVSItLY2zZ89SoUIFNBoNbdu2NTfGUKvVrFu3jtatW/Po0SP8/PwA03TSmjVrYmtry7Zt25AkicePH3PixAmSk5Pp0qWLOTj20pjJdu0rbhiS0WLEQhKxFBQo/+BCyyhJZGLk+b0HPD9xkWdrd5LNwQUxLYOcOXIgiiLPnz/nzJkz2NnZmevGubi4MOXyUa4YkgjbfpBVXQcycOtK3MoWIUqbRpJC4unpS9xdsZmkJ89JfxVLjvpVKTm4O3bWNrgJalwEFTd2HsDe24N0V1vyeSvplb0G+YrkIbtfNvqP/QY7B1vu336EvaMdTq6O/DRjNSvnrkOlUhHzKo4fd82iRIWiXDx5hVmjF+GV3YNhM/qCtQ6N3pawE9GoBA2dO3fGw8MDDw8Pbty4QdGiRdmxY4d5P8TFxZEzZ06SkpJo2rQpLVu2pFGjRqZ9+vIlu3fvZs2aNZQpU4a6devSsmVLChYsyM6dO/n222958eIFDx48YN68eaSnp9O+fXs8PT358ssviYqKYtSoUaSlpXH48GEqV678SQ7ND5L8Cu4cgPhwsHIyBRrf0t3HEQS6akxTqt3zYshTmalzFpA/f35q164NQFpaGoMGDWLXrl08efIEgKlTp7Jo0SLWrVtHkSJFzMvbtGkT06dP59WrV8THx9OjRw9GjBjxxlTsv4w2HfZNML3nBRHUfxBkfP0pej3q18ZaefwubkXEk8/ThlMPYxnd9yuGTJpP69at2bx5MxYWFnTr1o0zZ85w9uxZatSowYEDB+jatSsHDhwgJiYGSZIoUqQImZmZqNVq7t27R40aNVixeB5Pdi/CVUjG2sv/T8f2tlbtPcW8TQdZMLgjBV1EsHGGwo1MGY8/u3btGoMGDeLAgQPs3LmTAQMG8PjxYwoWLMi+fftwdXUFTM2jtm3bxokTJyhVqhQjR44019qU/davu0onJyeTkJBA48aNMRgMZMuWjZ07d9KnTx9GjBjBrVu3aNeuHTqdjh9++IGGDRtia2sLwNGjR8mVKxfZs2f/+AONfgi39pluvNl7vJnB+JbmbTrIij0ncbCxIiUtg03DWuLh6sLUExHMX7mJSZMmIQgCGzZsYM+ePRw6dIgSJUowbdo0Fi5caD53ZGUKZ+23q1ev0rhxY7p27WqeFSCTyWSyd4uvycFGmUwmk8n+CpJk6qT5+DQkRoHGCiwdQKn+zeMkXTqzVu9kzOqDeLo6U6pseSqG1KJt27b88MMPDBs2jNu3bxMYGMjNmzcpXLgwO3fupHbt2sTExFC5cmXKlSvHggULzBeeu3fvpl69ely9epVChQr97hCf6tNYrYvksSENlSBiiwLFn1wAZiQksbhYbVKjYrB2d6Hw0G54Fwoi2/0oVGGRrF61iuzZs1OmTBm6dOnChQsX+Oqrr0hPT6fh3PHU7tiWtGcvWdp7CCmZmdTauRiD0UDSjVB8CudHFATzsiVJwgCkaDPYXq8zUReuodJoEEURo0FPuS8qYOfixKGFu0hNSmXj6WXkLxoIQGZGJkM6j+L505d07NOaPPn9GdTxO9y9XJm1biLTR85n7cJNZKRnkCOvH90Gf0nFZsVRaa2Z8PVcFIKSVatWkZGRwbVr16hQoQJTp06lR48eKBQKzpw5Q79+/ejTpw8nT55k5cqVVKxYke+//55s2bJx9OhRc63AESNGYG9vj0KhwN3dnUuXLjFv3jy6devGoEGDOHr0KE5OTowfPx57e3uWLl1Kjx49yJkzJxcuXDBn6P1tJEXBzd2QHG1qFvI7mYm/JyE5lSJtviUqLpFtk/tRvVgeSIzkpehMu0nriEtI5PLly+bHnzlzhipVqrB69WqaNGnCuXPnGDZsGLlz52bBggXmjsVgqkN369YtChYs+Ek2+a3otbDvB1OzFIUa1JZv/VStXk/p73dy9VksALYWSoyShL2FCku1kk3LFlC+eVcKFCjA1atXefr0KYsWLWLkyJEEBQWZMwYlScLS0pJnz55x+/ZtwFSjVavVsnnTJmrnUMGzy6ZaeKLio2167T4TsdSo2Tyxr6lRTNwzku1yYFu6mXk9ixYtYty4cWi1WtLT0+nUqRNJSUkcOXKER48efbSx/JclJyfj6elJkSJFKFWqFFOmTAFMAfmRI0fSoUMH+vfvT61atdBoNGzdutUclA8PD6dnz55Ur16dr7/++uMOLCnK1OwlM+29Ao0Xbj+k55TlxCamMLBNXYrk9eVu2AuaVilBRuxzqgxaSL1GTRk7eYb5OUWKFCEoKIjFixfz8OFDevXqRd68ed/4nITfBmtlMplM9ot3ia/JNRtlMplMJvvUJCM8PmtqkpEaD07ZwNbtt4FGAEFgxYFLLD9ynbkD2nN5RlemtyhG25ASAOYaiLt370an01GgQAFq1arFtGnTMBqNuLi40KNHD1asWEF8fLz5oqly5cq0bNnyd4vmA0QZM1mti+SRIQ0rFNi/RaARICM+EZWlJTYertj7emOjssCueH60rUJQBfqRnp7O7du3adSoEXPmzKFXr1745MqBQq3ixMR5WAsKND5u5P7qC56cPM/+lr1Y4l6CazOXkRFrqpVl7e4CgC41jQ212rHAqTAx1+6Qr10TcrdugNJCjVcuT06uPkr4/Qi0mVrsHG3ZvnoPGemmGnFH95zi0ulrjJk/jJpNquHm6UJGWgZHdp2kgF05Lpy4TJdB7Rk0sQ/5iwYyYfAMrAVHUqR4tm3dbs5SVKvVlC5dmkGDBjFnzhySk5MByMzMJCwsjDp16jBr1ix27NiBSqUiPT2dmTNn0qdPH6pVq8bMmTMZOHAgu3btws7ODisrK6Kiorh//z6lSpVi165dhIaGYmdnx4YNG+jUqRMDBw7E0tKSJ0+emDNQ/zYyU+D2fkiJBieftw40AsQlpVCpWCCNKhVn/LLt/LjnLDh642mMpUfdUoSFhb3RWCd//vw0bNjQ3F29aNGiVK1alZMnT3L//n1EUTTXehRF8fMGGgEurIWXd0xTPd8y0JiSoaXy+N1oOi7jZkQcfi42nBxel70DajKtVRliUrQ8ik7h9Pr5pKenky1bNiwtLfH19eXYsWMMHjyYyMhIqlevztmzZ8mfPz9Pnz5lwYIFFChQAEdHR0aOHEl6ejq1i+eG5zfBxuW9Ao1anZ4ftx/l+v2nAG80HykV5M/eM9c5e+M+209c4etFB/jmu8lUq1iOwYMHk5SURKFChfDw8GDgwIGEhoYydepUevfuTVhYmLm+q+zdPXjwgEWLFhEbG4utrS09e/bk9OnT5kx3gKZNm1KlShUOHTpEYmIivXr14vnz5xQqVIjx48fTr18/ihcvjtFo/PiZ1AYdPDz5y9Tp9wjszdlwAFcHO04t+o6ujatSMsif9nWDsbayxNbdl6eRsZT2tgSDzlwndMiQIezbt48XL14QGBhISEiI+dwhCIL53CEHGmUymezjkIONMplMJpN9ak8umeq1aaxNmV//58I+PimFRduOULFIII2rlUHtlgNbjWiqaxX9CFEU6dixIwsWLDB3lB48eDBHjx7l7NmzADRo0ABXV1e+//57wJSpYWVlxerVqylWrNhv1qmTjOzWRfP450Cjtah46wuumHsPSQh7RpUJQyjSqSWXZv7I8zW7SMPAyyAfsufOhaenJ6dOneLSpUss27COQZf2I4giceHPuXLkOA+ldNxqlKfh2tk45chOw5UzqL9sKlYub9YLi3v4hPAzl7H19qD/q6vUnTWaGlOG4l8hH3GRcVRuV4vQE9cJqFwEhUrJ1pW7ePb4OQAGvQHJaMTByR6Ay2eu45ndE4VSQc48vmw4+RNdB31J+29a0KhtXfQ6PS+eRqJLkPDN68Pl2xdMy/m5i263bt14+PAhYWFhSJLEzp07KViwILa2tkRFRXH48GHc3NwoX748HTt2JFu2bObAa82aNXFxcaFy5co8fPiQKlWqsHDhQtq1a0doaCjZsmVjyZIlXL16lfPnz5MnTx6OHz/OkydPsLa2fqvX5S8hSfD4HCQ8Bwdv0zThd2Cl0XDs8l3GdmtOx3oVmbJ6Nyv2nQV7D8p6qShZMJDx48f/vCoJOzs7QkJCuHnzJhcuXECtVlO8eHEUCgXHjh0D/kaBgshQCD1i+llt9dZPux+ZyKn7kbjaWWCQJJZ3qUj5PB6Uy+NBl8oBDKlbEAuVgrHbLuHtbMvGjRuxsbGhXLlyHD58mNGjR5OWlsbJkyfJzMwkNDQUURS5c+cOZ86cIS4ujv79+5sCPo9/7rSreccmMD+Ljk9i6po9rNpnCgi/3nzkq4ZVqFmmEE2GzKD9qAXEJKVjodHgbS2ycuUKOnbsSPHixTlx4gT9+vXD3d1UJzJPnjxMmjSJoKCg9xqTDA4fPkz37t25desWAF26dEGtVpuDaVnnsBo1avD48WMeP35M3bp12bhxI9WqVePSpUvcu3ePVatWsWPHDgIDAz/uACOuw6tH7x1oPHfzAav2nWZQ27p4uDiYf5+1fclpGZQtmIe12/dC+DVzndBGjRqRlpZGWFgYSqWSkiVL/j3PHTKZTPYvIQcbZTKZTCb7lOLDIeycKdBoaf+nD3e0s+FZZAwuDraolEoQBA7cjmLHyStsXTydl2H3GTNmDE+ePOHAgQMYDAYqVKhAuXLlzF14PTw86Ny5s7mL5p9dRF3WJXJZn4RSELB+h4CRZDQS//gZgiiS8vIVBVo3pEjnVlydv5KwHzdjzJOdXE1rcffuXSZMmEDDhg1xDC7O5QuXUP1cc23FgO9IxYgdCvLUrkKVHwbjX+v3M2k8CgfhWawAurR0nhw3BVbt06OIe/wcjYMNFdrVIHtQTtLjkomPTkCv07N/i6kLdPZcPji7OdMupBsd63zDiK/HE1K/Eq27NcPVw4XnT1+a17N+yVZKVSyGTqfD1saefEXycPrsSeIS4lAoTIHiyMhIPD09ef7cFMx8/vw5Go2GSpUqUb9+fSZPnszatWtZuHAhLi4u5hqbarWaq1evAtCyZUsiIiJwd3fnwYMHxMTE4ObmhsFgoGPHjoSGhlKuXDn27NlDiRIl8PLyeuvX5i8REwYRN8D23TPjjEYjHi4OuDracubmfdrVCaZH0xBmrd/Pgh2ncHN1pk35PJw8eZLQ0FDzMZxVrzGrKUxwsKmJT5cuXT7qpn2wcytBmwbqdwvkFfVzpXgOV1IzdFiplfg4WaPTGxi19TIlvttGoezOFPV1JjIpEzcrEYUo0rRpU0JDQ/nuu+9YuXIlSqWS8PBwhgwZgoODA7t27aJLly7Y2Lw2ltinpgZVNi7vvYnebk40CC7GlXtPuP/z+8dgMGU3erk6smZMD04sHEnMgYUsGNKJed92Y3mfBvTr3JqnT59y586d39ReVKvVDBgwQO70+xaygmv79+/n6dOn5t9369aNnDlzsnPnTpKSksiRIwdNmjRhxYoVxMX9cg4LDAzk8ePH5o7muXLlYubMmaxevZo9e/aYm11lBSc/Cl0mhF8DtcW7N4P52fnbD7G1ssDH3XQzKiujNusc4WBrTfXShTh55yn3Tu1FYdQCpv3k7OxsvmHztz13yGQy2b+EHGyUyWQymexTMejgwUlT4wtLh7d+2tdNQ5iwYgcFWw3Go2Z3hi/cyKg1x2gyfB6Vq1bl6dOn9O/fn9mzZxMZGQnAgAED2LhxI9evX0cQBL799ltWrFjxp+vKkAwcN8STKRmxRflO2R2CKCLpTQ1uUl+ZassV/7otPmWKcXzQeMK3HcS+eQ0atG1Famoqc+fNY9WWjezqPRILaytaLJyEa5kiWGQa3nq9FYb3wqjXc+aHuZwcPYPx/vV5di0Mt9w+KL0c8SvoT1xENEXqlcXa3oZNy3bw9FE4kRFRgMTTR+HY2tmwbN88mndqSMuuTdBmatn003ZmjVpIKa9q3Lp8h5SkVGoXbM6iScvJ6Z+DhPgERo//Dq1WiyRJbN26FR8fH4KDgxEEgapVq3LlyhWio6N58eIFefLkITU1laFDh9K7d2/atm2Lra0toihy8uRJjEYjFhYWqNVqLl68aK4B+cMPP7By5UpmzpzJ8ePHWb58uTnrKytI8LcgSfDiJhj175UZJ4oisQnJZPdwISUtE4BvmtegfOG89Jm+ks0XHhOcx40KxQrQrFkzLl26xIkTJwgLC2PhwoWUKFECnU6HRqP5+3XnfvUIYsNAqXyvhiujGxfFYJRI1+oZt/0anr3WsP3KM3qGBFG7UDaK53TFWqMkJUOHo40Fc+fOxcnJialTp/LVV1+RnJxMtmzZGDFiBBs2bMDX1/fNFUiSaXq3JP1+KYd30LFeRTK0OjYePg+AKP7yPtaoVfhn80CpVOBkb4OoVIJCQdKLMKKiovDw8Pigdf+XSJL0m6Bf1tTfWrVqsXz58jdKZHTp0oXNmzdz7949AAYOHMiVK1eYNWsWYWFhAKxcuZLSpUubG/FksbS0fGN9H/W8E/PY1BjN6v27XFuo1ej0BqLjTSUsXs+oNRiMKBQiTauUJDCHD/WHzGb+lPEcPXqUSZMmUaZMGUqXLg2YAtt/u3OHTCaT/YvIwUaZTCaTyT6V2KemKaZ27u80XWxohwZsmdiXeuWLMr1vWyZ+05INP/Tm7oqR5HC1pV/vnnz77bfcu3ePI0eOYDQaqV+/PtOmTcPf3x94+wvEh4Y0wo0ZqAXxDztO/z956ocgGSWSnr3g+YVrrK7ZluvLN+JROIiLo2Zyc+123KuVoXnz5rx48YLFrb/mxY07lPmiEd7Na1Fl5ndYWLx9hotv5TLY+XgSfvoSj/cepte6QXy9+Xtc/TyZ/8UoYiNjSIpJoHafZsS9iiPq+StaVf6KPq2GUqhkAY4/3sXMtRPImccUgPHNlY08+f1ZOmM1pw6dY8aq8ey6uo4fd83m+9lDWLtoEyqViubd6zNn2nzKly+Pg4MDU6ZMwc3Nja5du7J//34OHz6Mr68vCQkJbNu2jVOnTtGkSROcnJxYvnw56enpVKlSBYDt27dz7tw51q9fT8mSJcmbNy+tW7cmOTmZHDly4OnpidFoNDdqED9Sh+CPKiXGdHxbOb73IpwdbIl4FYe9jSVX7oVRqdsYftxxjKJ5/Ri2YCObzoayuG8TBKB58+bUqFGDPHny0KFDBxYsWIBKpfp7Tn28vQ8MWlC+/fTp11UN8iK7szVGCfbejGBVt0qcGVGPFqVyolSIlM7lhsEo8TA6helfFMXPNztPnz4lIyMDLy8vzp07x7Fjx7C0/IM6kRnJEPcMrBzefxt/5p/Ng0K5s3P86l2ev4pDEITfZJoB6PR6tDo9my885sDpy/Tu2vE3QS7Z78tqWKJQKNDpdNy4cYPMTFOAXhAE+vTpw9q1awkPDzc/p2/fvqSkpLBnzx7S0tIoXLgwzZo1Y/To0QwaNIjy5cszdepUOnTo8LuvQ9b6PrqsruzvUNv110JK5UdC4ujlO2h1pgBrVpanQmE6V569+YDNE/tRKiA7P65cQ6tWrQgICGDRokV/z3OGTCaT/Qu9/5leJpPJZDLZ/xd5772zh0JKFSCkVIE3fym54ediy7knz7G3t6dUqVJs2rSJpk2bYmlpSZ8+fd55PTcMyWRgxP49vhJIkoSttzsOfj7c3bSb+7sOEfRFPapPG4lrvtycnbKQM2Nmc1Oj4fSFc5Tv1Zkz4Q+oUq0aKWqRu4ZUrN7xvqcoipQd0oO93b/F2dedwApBpBityBdSknObjrLmq6lorDS4ujhRslEw5zcfp0CxfMxc+wOa3wlqCoLAF50bcePSbep+UYMyVUqaM4S+6NyI0b0nodfpqdOsAX45fYm6nM6ypctISEjgxIkTJCUl4eDgQFJSEj4+Pjx8+NA8BbRx48ZcuXKF3LlzM2PGDEaPHs2jR4/Yv38/devWxdbWls2bN1O4cGGUSuUb0/n+lgHG1yVEmKYJv+c03KwASkH/bLQaMRcrCzUtQsowd+CXBOXy4Ydl2xm3ahcpqekc3bqah7HpFCpU6DfTbv+WXt0HhPfKagTTa/9dw2K0X3ycxLRMHK01KEURhUJAEAQuhUVjZ6kiIVVi3uG7XNqwjGiHQLy8vP44wPi6tDjTa2f152Ud/owgCHRpVIXuE5ay6ch5ereo9UYwJyYhiUkrdpGeqeXo5TtExSXSv1E5+nRo9sHr/q8QBIHU1FSGDBnCypUryZ49Ow4ODvTp04fGjRszcuRIZs2axf79+/nqq68QBAGlUknr1q1ZtWoVjRo1olChQvTs2ZMNGzbQpk0bDAYD9evXN9/Q+EvoMiApEizer0ZoFh83ZxoEF2PhlsNULpaPUvn93zjmjl66zXeLN1Mqvz8rR3bhZXQcLjV7oLL+/11TZTKZTPZx/c2/ycpkMplM9g9l0JkCMh94YfW6Z1Gx3Hz6iiaVigOwY8cOtm/f/nYBht8boiQRbswASUDFu2d7CIKAqFTiWawgglKBnbcHQS3qo1ApSYp4iaBQYOFoj0fZolhl88KyoD9FaoWg0mhIlQwYkd6q4/WvBTSsjpWrM0/O3eDloyhEUcSgN2Dv5oi9twsKlZJdMzbQfn4/zqSfZsHWab8baMySJ78/ufLm4PSh88THJKBUKjEYDBzfdxp3b1dyBeZALWoILO6PZzY3bt68SXh4OG5ubty7d4/58+djMBh49uwZ3t7eHDp0CIBy5cqRnp4OwIULF8idOzdbtmyhZMmSuLm5cfjwYYoXL45SqXyjk+8/QkqMKUPpPbOEsjLgBEGgZpmCXFkxniXDuxCUyweAAW3qMK77F/i62eOkMVKyZMl/RqAxM8UUzHuP7s6v+6JUTjwdrEjXGmi74BinHkTyIDKJVafus+R4KEZJolvlADqU9cUuPZKcOXO+/XkgNQ4kI4gfJ9BU0D87/tk8OHj+FvFJKebAz/NXccxYu4+7T56jVinp06IW0QcWMqR5RRQZiR9l3f8mGRkZTJ06lW+++YaFCxeaG7wYjUamT5/O6dOn2bBhA/PmzcPDw4OePXty9OhRHBwc+OKLL1i4cCGRkZHmGxUKhYKIiAj27t1LRkYG5cqVo1SpUqxZs4bGjRujVCrR6XTmrMBPLi0etOmgsvigxahVSsZ//QVxSSl0GruIY5fvcP/pS8KjYpm2ejeD56ylba3y+Lg5gcoCTzs1Kl3yR9oImUwmk70tObNRJpPJZLJPITXu51qN7589lJqewc6TV8ju4cL245dZufckxXL70Do4Hxh0ODm9f90rgARJT5KkRxTevxOnIIpYuznjkjcX1u6urK3dHpcAf7TJKSgtNZQf0x/vJjV4bswkSTJg8XMDmhRJ/953PEWlklI9O3D8uynsn7OL5tN7s3P0cq5sPUlg9eLU+6oh/kE5SZD0ZEgGEFS/u5ysi2xRFGneqSHjB07j7NEL1Goawpwxi9m0fAclyhehaJmCnNx3jtMnzjBi8Hf4+/vz8uVLsmfPjlZraj7w9ddf8+WXX+Lt7c3evXupVq0a8fHxqNVqIiIiyJs3LykpKdjZ2bFw4UIEQcDf3x+DwYBCofj7ZzL+WuJLUH1Y8E8URSw1ajIydeTx9TTXWwNQKZV0a1LNNN03JfZjjPiv8eoR6HWg+P1j7m0plSKD6xRk4NrzZOoNVJmwBwuVgnStAUGArpUDmNmuLKQlQPzzd3v/psW/d5D494iiSJeGVRg0ew3bT1ymZulCjP5xC8t2ncDL1ZGFQzpRtWT+X56gUEFy9Edb/z9deno648ePZ8aMGRQvXpyKFSty//59ChQoYP777NmzGThwINWrVwdMXbu7dOnCt99+y9mzZxk3bhwFChRgyZIlfPPNN2RmZmI0GilWrBj37983Z2sPGzaMpk2bcvXqVYoUKWLu1PyXSEsw1XhVfFidUICc3u6sHt2DWRv2U+XrceTL4U1SajqWGjVjujajeYipLiOCyrTOtARw8P7g9cpkMpns7cnBRplMJpP960iShCRJnzeAo8sAvfaDgg5RcYnM3nCADK0We2srfhzehVpF/UGfCfpMBM2HXShqMaKVjIgSvEdio5lTnhzc2bib1gdXkxYdR+z9x1i5OOFdsjCZkpEkSU+KpMeAhMXPIcZMJMQPWGmRDo24MHUeJ5cd5NSKQ3gGZKfF9B7krFYUN+GXi9n/10c16xhJT8tgzcJNvHoZw6jekxjcaRRuXq7YOdhy5ex1KvvXR6lWUSakKDeu32Lo0KF0796diIgIdu/eTYECBWjYsCELFizg1q1bLF++nNTUVOLj4ylWrBhHjx4lIyMDOzvTNL78+fO/VmPs/TLgPusxLkmmDKWPkBmXN7snaw+cIS0jE6vfyz4VFaBL++D1/GUykk375x26uv+RzhXz8sPO6zyLTQVAb5DoXDEviztV+OVBggD69HdbsF77Ucb3ujIFcuPl6kivqStIy8ikQK5sbJrQm9rlivz2waJoqmkpA2DVqlXs3buXpUuX0qxZM3OJgSyvXr3Cx8cHF5dfShY4Ozvz1Vdf0axZM+7cuUO+fPno06cPK1asYP369Tx58oT+/ftz8OBBc7dpgDp16uDq6sqRI0coUuR3XptPyaAz/f8jBbrrVihKjTIFefIimntPX+DqYEfpArnffFDWuoz63y5AJpPJZJ+UHGyUyWQy2b9G1kVa1n+//v1fPJoPXkJOb3cWf9sZa0sNvp4/F/HPTPng5Wb5WJPnLB0dCGpRHwQBJ38/nPz9Ptm6sigtLCjXpirX916k0fguBFQuAkhk/mZNf7zm6MhYvu85gZSkVKYsH8Oda/c4sOUIGisLTh04S9W6wVy/cJv42HiiI2Op07oa5SuVwV3ty6BBg1CpVBw/fpwGDRoQGBjI/PnzmTt3LqtWrWLRokVYW1uj1+spW7YsL1++ZM+ePdSuXduczfg+jEYjoiiaj/HPc2x/PE72NrSpWR6D4R82jfwvYKFW0qliXtade8jguoXpVDHv7zzqL5oC+yeUSgVf1q2Ik50NXzWsTMkg////hL9q6u7fXFhYGEOGDKFbt26/CTRmnSfs7e0RBIGwsDCSk5OxtbVFoVDg4eFBjhw5CAsLI1++fAwbNowWLVqwb98+qlatSuHChc3ryVqWIAhcu3YNZ2fnz7C1H/81VyoU5M7uSe7sf9JVWj7eZDKZ7C8nBxtlMplM9q+RdZGm1+v5+uuvSUhIYMOGDZ8nGKOyMGU1GvQfVL8tX06fN39h0Jk6eSo/vH6dGgGVICL93/y/PxfQpBaBTWv/7t+MSAiAtaBAlMDw8wWnGoHUD7j4NCJSf0RrGoxsgw5TJmNWgubrr7aIYA7Q/drR3Sc5uvskltaWXL9wi/IhpUmIS8KoN1C9QWXsnexYPGUFgyb0ZtKQmRzacoLyQdVwyu5F//79mTx5MgD79+8nMDCQHDlyMHLkSHr06EFycjLR0dGULl2aiIgIunTpwoIFC6hdu/YHdXnN2g6DwUCvXr148uQJ27dv/2sbPQiCaQp1euYHL6p5tdJ8EVLmjx9gNILyw2q8/aUsrE375yMFN0Y3KcboJsX++AES775/FCpTzcaPrG6FotStUPTPH2g0gvIvnL77N3bhwgWMRiNt27YF3ixnoVAoMBqNODk5UblyZQ4fPkz58uUJCQkB4MmTJ0RFRREUFASARqMhKCjI/O+smqi/7iz9eQKN/JIJLUkfLbvxrT7bJT6o+7VMJpPJ3o985pXJZDLZv0ZSUhKbN29GEASePn1K27ZtP2nm1x8FsQCwdDQFHHXp71Xb7g/HrcsAJ98PrgkHYC8osRWUvJK0H7Sf/t/zdJKEWhDxFjWESxkkGvXYCmAjKImVdO87dCRBRCdYYEmqOdhoxBRcVCGYYz0WgmhOqNFqdajVKnQ6PQqFiIOTHSqVEmsbK3as2YPRYODbr0az5uhiipQuyITBM1AoFayYs46aTatyet9FQhs8pEiuUgwdOpQJEyagVCpZsmQJT58+ZenSpXTv3p0JEyYApoCgKIrkzZuXcePGERgY+N7bmyU5OZmFCxdSoEABHj9+TMeOHf+6Bg+vs/OApA+vu/fnx5wE1p8pOPI+XHKZ3ptGHfCJG9pIkimybu/xbs+zcvi8mV4GHVi7fr71/41oNBoSExNxcHAAfnvez/qM6dmzJ3fu3KFTp04MHDgQGxsbpk+fTrNmzfD0NGX1/d7z/lYsHUwBR4MOlB9et/GtGHSmm30fUDtZJpPJZO/nb/YpJJPJZDLZnzMYfsnEez3Qsnz5cgYMGMDYsWMZPXo0bdq0+eiBxqSkJL766isqVKhAZGTkHz9QpTEFZN5x2rMkSeaMlN/5o6le40cqdK/6OQgoSaD/RNMxdYKEnaDEWVTjKWjI+DmL0goFAqaO2O8rA0tEfsnQMiChQkBEQI+EEgFLFBzYeoQ6hb9gzpjFGAwGlEpTQxYfPy9s7G2wsNQQ+fwVgiiSr0heNi/bwb0b9zm6+yTWNlZ07teGiStGoM3UcXT/ccLDw4mIiKBZs2YIgkDTpk25ffs2EyZMMAcaAfO0RYDy5cu/U0bRHx3jJ06cYMiQIbRr145evXrRrFmzv7bJQxYbV5AMnzZoZdCbagtaO366dXxslnamwMZf0V3coAcEcM31bs+zcjLtV+OHZTS/l6zjxebDmlv9E7xNh3mVSoW1tTUrVqwA3nzfAyiVSuLi4vDw8GD9+vXUq1ePbdu2MXr0aJo2bcrcuXN/t0v73y7QCKb3scrCdMPsr6LLALUlWP2DziEymUz2LyFnNspkMpnsHyMrWyNrStiLFy9wdnY2X2xVqlSJkiVL8vDhQ0qVKvVR1/3y5UtGjRrFkiVLKFOmDCNGjMDLy+v/P8kzEF49MAUF3mIa1+s1Jx+GR7Jm/xk61a+Et9vPF+aZqaC2fvfgwv9RQLTlnJBIumRE9ZGbRugkIwZJwldpiZOgwkPUYDSYAoz2ghIrQUGGZMSa95tWnCFYggQCRowISICVoEAQIFMyYiGIaBNTWTFnHY/uhfHsUTi+uXyoWKscDs72vAiPwsHJHoPBiJOLI4d3HqflV00Y1WsiSpUSB2d7suXw5vCuk2BhQBRE9u87gJenN0lJScyZMwcLi487xTfrGMg6xp8+fYqjo6O5uUyhQoXo0KEDu3btolatWh913e/E4X/s3WV4FGcXh/F7VuKuEEdCcHcpElwKFCjuL+4VpFCKFSgUd4fiWhyKuwcLGpxgEeK+Mu+HabakQIsE7fPrxdU2uzszu9lMmP+e5xyPvyp3zaz+/f5vIjlGqYayc383239XXLNDfJgSOL7L0MeQonyo4Vno9R5n7QRaS+V7Z27zbo7tZfSpSguIzzj8Sa9o/qfAL/3nvGDBgpQsWZJZs2bRv39/UzuE9NsvX77MmDFjaNWqFTVr1mTGjBlER0fj6PjX6/dRVjG+iNYSbFwg+oESyr8PqQlgl1X5vSkIgiC8V5/AbyZBEAThv+zZqq70C6otW7ZQsmRJ6tatS8OGDVm3bh0ABQoUoFq1akRHR3PmzBng1apL/sn58+epUaMGnp6ezJ07l8WLF3P48GGqV6/+7w92zqZUN8aHv9K+JEnicWQ0Ww4H8eV3E7j3OBKd/s9KF9kIiZFK0Gjj8s8beg25NNZ4qCxIRX6rKsMXScKIuaSitMoetSSRQ22JvaQhFj0aScINM3Rvsd8kbEjFAjPSSEhIQouEJSpTpaabZIaDgx3VGlTGysYScwtzdqzfwy8DpgCQt3AAj+4/IT42gRIVinDj8i3snezJ6p2FqxdCiAqPpnSl4kiSzOieU8mTLzdGo5GJEyeSlJSETqcsA/97NdLbSK+E3Lp1K8WLF6dGjRp8+eWXTJgwAQAvLy9q165NZGQkhw4dyvT9vzK7LODoBYlR72b7sqyE61nzKCHFpyRfDVBpX39K9OswGpXKRPfcYOf2eo+1tFfC4qSYN969Xv+G77mkaLB1VSpjP1PpFc1BQUFMnjyZ/fv3Ex8fD/z1+yz959zb25uWLVsSHh5Oly5diIj4qzXBo0ePmDVrFtbW1lSsWNH09fSgUa/Xf7iJ9G9CkiBrXmVp8/uoqjUalMnrHnkzrUekIAiC8Oo+kd9OgiAIwn/NlStXuHv3boblxElJSfTv35/vv/+e6tWrM23aNPz8/Bg9ejT79u0DoFy5chQsWJD58+cDr9hA/gVSUlJo3rw5RYsWJUeOHCxbtoyqVaty48YN4BVDTK055CyrXOikxGe46UXTd/efuUyB5gOYsHw7vb+uwYIfO+Pn8edFeXyEsvwxe5lMvXCyltSUU9ujlSTiMWRa/79U2UiqbMRfbUUujVJVYiNpyKO2IU7WY5RlsqjMcZS0JLzhfo2Smqg0a6Y3+5m9k9ZimWpELUmkYMQcCcc/F3DUblKdijXLodaouXzuGsf2nWLBxGXExybg5ZcVOwcbchXIib2jHYd3HaNZ50bcuHIblyzOrJq3gaBjF8ji7YavVzYqV67M0aNHmTVrFra2tgBvPPBFlmX0en2GryUnJzNu3Dj69+9P7dq1WblyJXXq1GHSpEls2rQJgGLFilG1alXTku0PEjZIEngUACRIewehWlK0UnWX5UUTmD9yWfIqQaxe9+6WU+uTlf53eaq+/mMlCTzyKYGuQf/v939G+nlPo1He85duhb7Og/8Mf/J/VgM7/v4zHBQUROXKlQkMDGT79u00b96cPn36kJCQYJogD38Fj82bN2fixImsWrWKPHny0KJFCxo2bEiuXLm4c+cOffv2xdLy+cBdo9F8epPoXXMoVa1vEXS/suRYpT9pJq4EEARBEF6dCBsFQRCEj87x48dp0aIFK1euBGDcuHGcOHECKysrzM3NmTdvHqNGjaJcuXLkyJGDS5cuMXHiRACKFClC5cqVOXToELdv30aSpNeqbjx69Cj79u3jxo0bfPfdd9y/f5+ZM2fSokUL/P39OXjwICEhIahUqlfbrksO8CkGidGQmmh6jFqt/Ap+HBltumvl4vnI7efBiUs3KRLgB/x5cZ/wVKlszFleWQKZycpoHcivtiFNNpKSCVNqDbJMPAacJC11NK5YSX+FcQXVtjhKWiJlHWpJwldlgYWkIh7jaweOstFIjJkLWjtb7h2+SPSNRxhkmRTZgLvKHMs/p5+6e7hSpc4X2Dna4prFGTsHWw7sOMKAjsOwsLTgyYMwipUtTNEyhbh+6SY5cvthZ29DyQpFGDn3B5Yemcb5G6dYvmw5S5YsyZRBL+l9OdOXTZ4+fRoAS0tLNBoNM2bMYMSIERQpUgQ7OzsePXrEmDFjAKW6sVWrVhw5coQrV64gSdKHqW50ywlZcyuVu5k53VifpoTzfiU/zQo4SYISzZTlwmmJmb99g16pDsuSB3xLvNk2nLMpFdKJka/1sPRg+5clm8n5VT/+9/M87jx8tcptkqI+q/Dnr+BV+RkODg4GYMaMGRQsWJCQkBB27drFihUrTB9QPPu49KDQ0tKSLl26sG/fPr755hs8PDzw8vLiyJEjbNu2LVPONx8NMyvwKqAsbza8+YCwf2XQQ0qcEmy/71YBgiAIAiDCRkEQBOEjYTQaTRdhZcqUoUiRIsydOxcbGxtmzJhhqh7r27cvFSpUYMGCBeTMmZM5c+bQpk0bDhw4wLFjxzAzM6NixYq4uroyefLkV97/vHnzyJUrF3379mXx4sUMHDiQfPny4eXlZapcqVOnDjqdjrVr1wKvWDUpSZCjHPgWU5acxkeALDN55Q4KNB9A44FT6DV+MUcvXAegXZ2K2Flbcv9JJBgNqGIfg1EPAZWVJWjvgIWk5kszNzxV5sRLBpLfYombQZaJQY8ZEpW1TvirM/bzc1ZpKatxIBUjybIBW0mDv8oKC0lFLAb0rxg4Go1G4tNSiUdF4KDeRN4L58rhs8QZ0nBQafFQKX080wPMImUKkr9oHhydHclTKIAbl24RHRnNlfPXsXOw5frFG5SsWIyE2EROHz5L9YZV2LXxAPnK56BovpI4SH9N/H3TSsZnqVQqkpOT6d+/P1myZOGHH37g+PHjALRr147KlSuzbNkyAgICmDBhAt999x1nzpxh8+bNqNVqSpcuTbFixfjhhx+AN6/gfbsnoYYc5ZXQKuZx5gyLMRog9pHS99CnyNtv70PxKQo5yipDdDJzIIbRqASYFvbwRZc37wmpNYdsJZVQ5iWVqc+ek9PFxCfS8PuJ/Lb9MKO6NmFi31ZYmL/CgCJ9GqQmKR+8WNi+2TF/ZNKD1/3795MjRw6GDh2KwWCgYcOGDB48GDc3N1asWEH37t158OABU6ZMwWAwoFarX/jBSvrP87hx45g+fTqFCxfGaDR+mA8S3iXvYuDkA7FP3s2AKVmGuCfKPnyLZ/72BUEQhFciwkZBEAThg0i/2Eq/oE1vqK/T6bh9+zZBQUE8evSINm3acO/ePUqUUCp4nJ2dOXz4MHPmzKFXr16EhIQwaNAg7O3tmTlzJqBUNxYoUICQkBBSUlL+cZlpeHg4X3/9Nb/88gt9+vRh+/btjBgxgk2bNmFhYYEsy6ZwKTAwkDx58nDw4EEePnyYYUncyxgMBmITEjkRbQl5q6PSmjNg/Bxmr9vFN81r0q95LZJS0mg6eBoAHb6siIu9DceDLhIbegNsXZEL1AWvwu+075S3yoKWZlnxkMyIx2Ba6vw6UmQj0ejQIlFV40w1rfMLQ7D8ahvyqW0Ik9NIlY3YS1pyq6xxkbQkYSBeNqCTX1zpaJRlkmUDsbIerbkZ3ikSnja+eObOzt7fdnBu3X58JAvUsmQasgDg5edB2SolCX8cQfWGlSlathAu7s5Uql0en+xe6PUGipUrzKDx/egxuBM9h3Ri7fk5WKvsyKrOgUatyZSQMV1iYiJdu3blwIEDLFy4kClTpuDj4wOAk5MTly5dYvr06XTo0IHLly/z448/EhAQYHqP+/j4UK9ePW7dukViYuKH69tm5QB5a4CFDcQ8fLtlwwadMjzC0RvyVAf1B5iynZnKtlcqCPUpoEt9++0ZjZAaDxozKNXy7QfnZMmj/IkLy1CZKsuyqRegSqXiaUw8oWFPATh1+RZB1+6wa9ogmlUvS5kC/mR1+ZdhL7IMsY/BLQd4F367Y/5AjMbnz0erVq2iV69eLF++nO7du7Ns2TLUajX16tXD0tKS+vXrM2rUKLp27cqaNWtIS0sz/fy+rCo+/XWXZfm5gWifDa05+FdQpkQnvF5l7StJiASNBeSsoAyxEgRBED4IETYKgiAI783t27eZPHmyaQnpsxe0ly5don379owePRpvb29Wr15NixYtCA0NNTXNT6/wWL9+PfHx8fTp0wdQGumnpqayYsUKgoODsbW1ZciQIezcufOl04LTLxx/++03rl69yuLFi+nRoweurq74+fmh0WjYt28f0dHRpmWq5ubm1KhRg/j4eNNQmhcxGP7qQRgXF0ePHj3o1qMneBdm1rkE5vwRxNLB7WhfPge1cjvjZG7kUUQ06zZtQxXzgLZVCrHrbAjnde5QtDGSW8730uA+QGNDezNP8qit0SMThZ4ko+EfQ0dZlkmVjUTLeuJkPc5oaah1o66Z60unW6sliapaJ/KqrXksp5IoG7CW1ASorPFXWWMnaUhFJg4lVHz2T5ysRwI8jVpujZ7Lj1kLMalpR4L3nebB1VAW9JrK8l8XYzAYTEFj+veieIUiZM/ly451uxk5azCFSxXg1KGzBB27gKWVBVqthvLVSmNmqQXbVCxUNvhq82GpzvxleKdPn2bTpk1MnDiR2rVrkzdvXjw9PU23b9q0iXv37jFgwAC0Wi23b98mLi6OXbt2sX//fszMzPjf//5HcHAw1tYfeNKqkzfkr6Ms8Y+6D2lJr/d4WVZ6uMU8Amc/KFBXCTE/dWaWUPMHcPJVeiymJrx5GGvQKctCNeZQooVS6fy2VGrIWU4ZMBPzyFRlJkmSMgn51gMafj+Rom0GM3X1TgA8XZ14EB7F0QshTFqxndGLNtFnwhLGLtlMQtILKjhlWalgs3JQwp9PLEB+9sOwv39wotVqmTFjBgcOHKBVq1YZfg6XLl3K/fv3Wbt2LX379iUgIABZlpk7dy7w8uro9H1IkvTpDH55E47eyntYNpqq/TNFfIRSHR1QSalsFARBED6Yz/i3mCAIgvCxWbFiBTNnzmT79u2AEgJFR0fTrFkzSpcuTVpaGjY2NkRHR5MnTx4qV65MREQEGzduBDD1SfTx8SEpKYkzZ85w9+5dli9fTs+ePRkwYIBpu1myZHnZYQDKxVxERASLFy+mYsWKlC9fPsNxBgQEULVqVYYOHWraJkD16tXJnj07e/bsMQWRf694ST/O2NhYTp48ydatW4mIiMDb25te3w0iq6c3UoHa9Fh1kaytx3LqThQbJg6iUfPWkDuQHkN+QaexZu/VJyTo3sEys3+QXWNNVwtv6mhccUVDkmTkqawjStYRZ9STJBtIkg0kGg3EyHqeogSA5kgU1djRxdybQK3zS4PGdBaSmlpaV4qqbYlBz2OjUvnlpjIjn8qaAmobcqms8ZLN8JDM8ZLMya6yIr/GlsJqO1RX77J3+gI88gVgm9OXllN+JkfuAFxdnflj/T42rtuMwajPEDpm8/elTGBJzhw5z/1bofQf24dajQLxz5cDByf7P4PTJBLlGOw0LmTTFsRaZf/ar+Gr9PI8efIk/v7+5M3719L4Z6unAgICCA8P548//iAkJIQ5c+bQs2dPfvzxR+zs7ABM//4oOHlD0cbgXUgJDqMfKOHaP4UIshGSY5SA0qBTepIWbvh5BI3prB2g7k/K0lFQAkN96quHK+nVjGmJYOOkLJ0uUDvzjs/KETlPDbC0UypT/6xw3Hr4LLX6/oK1pTnrxvah/hfF0OsN5MnmwQ/t6vPtlGVsPXKOWw/DeBAexa/LtjJ55Y6MU6rTKxq15kql6ttWYn4A6R+GXb9+nYEDBzJu3DhTf9VGjRpRuXJl7OzsnptM//DhQ1JTU8mXLx8A586dI0+ePFy+fPkfP6j6T8maD3IHKh+kxTx87WFFGRj0yjkHlG165M+cYxQEQRDemCRn1tjJj1RcXBz29vbExsZ+XH8pFwRB+A/R6/VoNBquXbtmaoCfPi165cqVTJ8+nblz55IvXz5TFQkoS5x79OiBXq9n+fLlWFkp/f8uXbrEt99+y7Vr13jy5Am1a9dm6tSpeHt7v3D/586dY/Hixfj6+hIYGEihQoUAePz4Mb6+vixatIiWLVsiyzI3b95k/Pjx+Pv7AzBixAgePnyInZ2d6dh+++03pk2bRtu2benZs6dpuW760reLFy/Sv39/kpOT8fDwYO3atUiSRN68eRk6dCjff/894eHhVK1ale+++46KFSsCEBMTQ0pKClmyZKFbt27s2bOHTZs2ZQik3hdZlgmX07ikT+CMIZZoo44EDOhQ/tqgQsIKFXaShpxqK0qo7cimtvrXkPHvjLLMdWMiR/UxhBvTsESFo6RFi1JdZTQYUKnVJEbHYO3oQMjx00TcDeXO9RAOL1qJbNBTtGIFtq1dz5Gdu2nevDn+uXNi62LJuLVDUEkqzLFCJWtQqVRcPneNzSu207B1XXIXzKWEkSqJNDkJHaloJXNc1T64a3xRS683MffZ9+6/3WfBggUMGDCAoKAgvL29n3tcUlIS7du3JygoiNDQUKpUqcLMmTPJli3bax3TeycbIeI2PLoMT++CLlkJEzTmSiUdKMGAPg2Qlf597rmUfqQOnv+05U+bLMO1vXB2LSTGAEalyk+lVaZKp3//ZVnp82jQgyFNeT3VZuBVEMp3UcLLTJJ+XgaUoObyTmUJqp07LYbPx0yrYfFPXV/wVGT0BgOyDInJKTja2VC91xic7W1YOaqXcj406JSg0cpBCRpds2facb9L6T0V0+l0Ovr378/cuXOpXbs2kZGRhIWF0bVrV3r37s3SpUsZPHgw06dP58svvzQ9buXKlfTs2ZOvvvoKrVbL4cOHGThwIKVLlyZHjs9jQE6miboPNw8r/7awA0uHV+9FKhuVqdPJsUq1ZM7ySnW0IAiC8E68Tr72en+LFgRBEITXZDQaTRe0uXPnpnTp0hw4cIC9e/cSGBjI+fPnuX//Pvny5ePcuXMkJSVhZmaGh4cHnp6eVK9enQULFrBq1So6dOjA1atXcXV1ZfPmzezbtw9/f39y5sz5wn3LssyIESOYMGEC1atXZ9++ffz6669MnDiRZs2akZCQgKenJ/v376dly5ZIkoS/vz+zZs1CrVbz8OFD5s2bx6+//sqIESMwGAyoVCqqVatmqraEjEvfIiMj6dy5M8nJyfj4+JAnTx58fX1JS0tDlmUsLS2xt7enbNmy/Pbbb2i1yrLC1NRUJkyYgIODA99++y2jRo2ic+fOHyRoTH8u7pI57mbmfCE7EiPriZJ1pMhGjMhoJQkHSYujpMWK55cYviqVJJFHbYOXyoLrhkQuGRIIl9MwyDIqWeL+wRNsHzYegHxfVufmsdOkRMdSoFYVzFRqsuXIiWV8CjaoqVWrFl5eXuhS9ITfi+bqzkeUrJ2fBH00ssoIRgm/QlnpU+h/AMQbo0GSkZAwU1niofLGQe2Oler1BlikBxTpgeGiRYu4c+cO1apVo0KFCgCmQDr9Pl9//TXffPMNK1asMFXkgjLR9ty5c7Rp04YlS5Zw8eJFXFxcyJ790whrkFTKlGrXHMqU6rgnSoAVF6ZU9AGYWYN9FrB2BkcvsHz96tFPjiRBnqrKsKjr+yDkoFLNpUtWqhf//uOj0ihTdL2LQr5a4PZ23//jx49TpkyZDAFj+r/Xr1+PhYUF+fyK4Wd7H+Ojq1ipjZy+foc9p4I5EXwTg9HIw4go2tWtSNmCudBqNETHJeBoZ8PBs1dJTE6lRY2yYDQiJUVBaqLyPshZ4Y0qGmXZSKqcQqqchBE9RoxIqFChxlyyxFyyRCVlXi/D9J/h9KDx+PHj5M+fnzt37nDy5El2795N2bJlAejatSt9+/bl66+/pnXr1vz666/s2rWL8uXL4+TkBMCXX36JXq9nxYoV6PV6pk+fbvpQSfgbJx8o0gjunYEHFyE6VOlLamGr9FxU/e1y1ahXhi6lJCjnFAs75efKrwRoLT/McxAEQRCeIyobBUEQhPdi+PDhXLlyBXNzc44cOULDhg2ZMGEC58+fp0aNGqhUKnx8fDA3N+fUqVMULVqUWbNm4e3tTd++fTlw4ADe3t4cP36cXbt2UbVq1X/dZ3h4ODVq1KBLly507dqVJ0+eMHnyZObMmcO+ffvInz8/DRs25Pbt22zfvh0/Pz90Op0pAATo1q0bixcvNg3hSO/hpdfrM/SDTP912qlTJxYsWECxYsWoU6cODRo0IDw8nLp161K4cGH8/PwoWrQoK1asoFixYnTu3BmdTsekSZO4ffs2I0eONFXIfJAJwx9YqkHPI9LYd/QIV2/fZOOsBSQnJBBQoTQXtu4md+GCXDlxmtVbNlOvUhVatGjB9evX+fnnn6lcuTLt2rVjz549eHp6kiNHDpYvX06SHEeKnEiKMZFkOR6jrFRlmasssVTZYiFZY62yQyOZvfJxvqiK8fLly4waNYqjR4/i7+/PiRMnmDVrFm3atHnhY8eOHcuMGTOoX78+7dq1IzQ0lClTppA9e3amTp2KjU3m94oUPjB9Kjy9p/QxjLgFT+8oU+oNOkBWwlpzG7DPClkCwC4rOPsqwewbnA+MRiPTp0+nb9++xMXFZXhPzZ07lxEjRqBWq3F1deXBgwds3LCe0j42XN2/kQ6jF/A4OoEvCucmWWfgydNY7oc9ZeeUASQkp7B46yHOh9zjfMg9ujaoyKhWVbBUy0p47FsMvIu8Vo9Gvawj1hhBvOEpiXIcOjkVg5xeS/0XNRq0KjMsJTvsVE7Yq10xk15/EEh6JfqzlYwhVy8rvyvCwli7ZB6HT5zi9227+GPXbn7//Xd+/vlnQkNDadmyJT/88AMuLi5MmzaN2bNnM23aNKpUqZJhH0lJSaaK/PR9/hfP668sJR4ibiqV0UnRSqgoG/ir85cMSEo/VCtHZSm2Ww4lcBQEQRDeudfJ10TYKAiCIGSaFwUwer2eLl26cOjQIQYPHszDhw+ZPXs2dnZ2LF++nMKFC3Py5EkiIiJwcXFBq9Xi5eVFrly5GDduHF26dCEiIoKVK1ei0+no2rXrSwdi/H3/27dvp3Xr1pw/fz7DEmt/f38qVarEvHnzWLp0Kf369aN58+ZMmzYtw/YePHhAx44dyZ07NxMmTECtVpuGxahUKlauXIlKpaJ8+fJ4eXlx//59ateuzZMnTwgJCTFVucTFxdGqVSsuXLiAhYUF8+fP5+nTp/Tv3x9nZ2du3bpFrVq1GD16dIYhIZ+6Z5eXv+gCO71H4d+HJVy8eJGOHTvy6NEjnJycuHz5Mp06dcLDw4PVq1eTmprKtGnTmDlzJrdv38bV1ZXChQszePBgWrRoweHDhylcuDChoaGsXr3aVF34rjx69IjWrVtjZmZGnjx5GDlyJNbW1rRt25YHDx4wcuRIypYt+8Kfjzlz5jB16lQ0Gg0PHz7kf//7H8OHD8fc3PydHrPwniVGQfgNJURJfKpUM2rMwMxCmZybvsRclpXgUZf8V9WjmRW4ZIesucHJD9QvX5j0ovfYmTNnaN26NS1atODHH3/EYDBw6tQpfvjhB1q1akXHjh0BaNiwITqdjtGjR1Mwd06ib5zDMfE+0Q9v42ipJiE5FbtGwzk+tQ/ZszozZ+tRLDUqutUpjZWNNdi4gmd+cM2pVKW9omRjAtGGJ0QZH5NiTESSJNSyGRpJiwYt0jOtGWRZxoAOvaxDjw4ZA2aSBY4qdxw1WbGW7P81zMvwGhkNEB3K1aDjfDfyV3ycbLAyU/Fjy2o42Fjyw8LtHAq+TVhsEkmpejq2bEL3fgPI4u1r2l5aWhr+/v7UqlWL8ePHY2ub8bm/KNQU/oUsK8FjUpSyRNqg9MRErVXCbCtH5T32mm07BEEQhLcjllELgiAI75Usy6bJ0n8XHh7Orl27GDFiBO3atQOgWLFiDBo0iJUrV1K4cGFKlSqV4TFPnjzB19eXrFmzAuDq6krv3r1fuG+j0cioUaPYvHkzBQoUoHz58qaLZ39/f2JiYoiNjcXb25uUlBQsLCzo168fY8eOJSgoiEaNGnH+/HkmTZqERqOhbNmyeHh4cOzYMUaNGoUsy/zvf/8zLTk0GAxMnjyZX375BXt7e9OyxHnz5mFjY8OVK1fInz8/y5Yto2DBgjg5OaHX6ylRogRbtmyhZMmSzJ49m+XLl1O2bFmOHz9OlSpVTBeo6ZO6P/XqF71eT0pKCjY2Nv8YNKpUKnQ6HdHR0bi6urJq1SrWrFlDlSpVmDVrFqVKlcLJyYmEhASuX7+Ol5cXFy5c4OnTp0ycOJFKlSrx9OlTbt++zc6dO2nZsiUjRozAwsKCsLCwTAsa0wPm9OdiNBoZNGgQ1atXJzAwEA8PD9asWUP9+vVNYXjPnj355ptv2Lx5M2XLls3w85EewHbp0oVmzZpx7949ChYsmCnHKnxEDDoIPQ93TynDYcyslGXF/1TxpzED8z8/UJFlZdDOkyvw5KoSOuYs/9zS5PQALb36evPmzdjY2JAnTx6KFy9OgwYNmD17Nj/++KOpknHo0KFUrlyZ8PBwZs6cyY4dO3B2dmbnzp0ULNgfx3zlMOhKYJ09jNSEKEb/8ivVyxTCzz8Prs72DOmdD2xclCnk1k5K2PgPQejfGWUDkYaHPNHfIU1ORoM51pIjKkn1/LLyP0mShAYzUxWyLBtJI4Uwwz2eGh/jqvbCXZMNjfTy11elUkFqInL4DQb/OJS1u4+zb2xHrty4w86IGH4f3RMHdw8AiuT1Z9aWo3xdsRAzu3+pBIb3dpMUl4MJq3bxZeNmFCpUiK5du+Lk5PTCD8IkSRJB4+uSJGVokaUoFBEEQfhUiY+DBEEQhNcSGhoKYFpODJh60QUHBzNq1CiOHDlCbGwsAFeuXMHMzIz8+f+aDlm1alWKFSvGkSNHuHHjBgAnTpxg2bJl/PjjjxQqVIjcuXObemS9TFpaGm3atGHDhg20b9+etLQ0unXrxoQJE0hNTcXW1pbixYszZ84c4K8eZd27dychIYGjR49iZWXFzz//zOjRo9m2bRvdunWjadOmLF68mE6dOpE7d27u379v2ufOnTvZuHEjtra2xMfH4+joSEREBN27d6do0aKULVuWGzduMGjQIMaOHUuNGjX48ssv2b59O5IkkZyczNatW6lZsyZZsmThxIkTmJmZYTAYTIHBpx40xsTEULhwYX766ScAzp49y4YNG0zvl/SQMSoqiq5du5I7d26++uorRo8eTXJyMnfu3CE4OJjcuXNjNBp58OAB48ePZ8eOHfj6+pqmfOfKlYuNGzdSv359YmJicHR05MfBP1C2cB6K+jpRs2gOCAuByDvKkjyj4Z8O+x+lV7Xq9crEVJVKxZo1a9i5cycA7dq1w9vbm7CwMNNjSpQoQaFChThz5gznzp0DyPAzk87e3l4EjZ+juDC4sAmu7VNCQydfsHV7raXFSJJSweXoDbauSnXk2XVwL+ivai+U92NCQgLff/89bm5uTJ06lZkzZzJt2jSMRiP169dHrVYzZcoUALy8vKhcuTIbN26kfPnyHDp0iO3bt1OkSBF27NjBzZs3SUlJYcGixbTvM5Ds5Wrz+4Ez9Bs6FvcanaF4UyjSEPwrgEc+Zdn3awSNycZ4busucF93FaNsxEZywlJlowSNr0GSVJhLVtiqnFGh4bH+NjfTzhFviHrxA2SZlPuXGNa9BZvnT+T+g4cMaV8f79yF6NuyLnbWlhgkjTLQSGNOg8Ay5PDKQmSSgZtJZhhs3EmMjWbmpPHs3bKOsAuHwaBj0KBBdOnS5V8HRAmCIAjCf4X4jSgIgiD8q/SOGzNmzKBMmTIApioagNjYWObNm0epUqX4/fffadmyJf369QOgVKlSPHjwgJs3b5q2pdFoKF++POfOnWP79u0AREVFMXfuXI4dO8a8efNYs2YNLi4u/3hcN27c4MCBA4wcOZIePXqwfPlyxowZw/Tp09m+fTtZsmShWrVq7N69m/DwcDQaDampypCKKlWqsH//ftPxDxw4kKVLl+Lh4UHRokW5fPky48ePJ1u2bBw6dIirV68C4OjoSFpaGtWqVWP//v0UKVKExMRErl69yqRJk9i8eTNdu3bFw8OD8PBwpk2bhr29Pbdv32bmzJncunWL6dOnU7x4cc6cOcOYMWMwNzfPMGDkU5UexDk4ONCtWzcWLlxIoUKFKF26NA8ePDDdT5Ik9u/fb3q/eHh4YG9vz+7duzl16hTTp09n//792NnZ8fjxY2xtbdHr9QwbNoyYmBgkSeL3338HoGTJksyZNZNRg75h5netkU4sQT7+G5xZg3T+dzj/OwSthRNL4fhiuLJLmZL8L8FjeiiYLjo6miZNmtChQweePn0KQPPmzQkKCgIwTTm/ePEit27dMj2ucePGpKWlsWXLFoBP/nssvKLwG3BuA4TfVIbh2LxZz8UMNObKMA3ZCNf2wJU/lJ52QGRkJC1atODIkSMsX76cLVu2MG3aNMaMGYNKpSJPnjw0aNCA6dOnA2BhYUFKSgqzZs2iXr16bN26lSpVqlC6dGmCg4PZvXs3FhYW+Pn54e7uzsKFC7l69So1atR421eGeEMUt3TniTGEYyXZYal6cfXz6zKXLLGWHEiUY7itu0B4WmjGn+PURLi2h5Adi1i28yiNf15BqcIFaFs/EID2dSviaGvN2et3SExWXletRsPIrk1ISEqhZLsfafTDNPzbjWbe7vP0aliR6m6JSqAcH47RaCQmJua5c4cgCIIg/BeJv/EKgiAI/0qSJK5evUqePHm4e/eu6eupqakMGjSIIkWKmKbonjp1ikGDBrFhwwb++OMPbG1tqVevHlOnTiUiIsJ0Ufn06VMsLCxYvHixqdfh6tWr2bt3r2lAyrPu3btnqpZMFxwcjCRJGZZhf/vtt/j5+bFmzRpiYmKoX78+ZmZm/PzzzwCYm5uTkpJCTEwMzs7OjBw5ksaNG2MwGChVqhRNmzbl6dOn7NmzB0mSaN68OSEhIezatQuAkJAQUlNTKV++PHXr1uXChQu4ublhZ2fH/PnzsbOzY+LEiaxcuRI7OzsGDBjAw4cPqVChAr6+vjg7O+Pj48OoUaMoWrToZ3FhqtfrM/RePHLkCPPmzSM2NhZXV1eSk5Pp3bs3Z86coUOHDpw9e5YdO3bw+PFjmjRpQuXKlTl69Cjnz5/n999/R6/X06tXL6Kjo3n48CHW1tZcuHCBzp07U6FCBVJSUpSl07duQVgIUtAa+pSwo5BlLHJqIpK5NTh4KMGMk4/y32ZWkJYE988plWFnVsOT60rF2Qukh4LR0dGAUq15+vRpDh48yNSpU9HpdHh7e2NhYUFISAgAzZo1486dO+zdu9e0nUqVKuHi4sL+/ft59OjRu/w2CB+LJ9fh8k6l56KTj7IsOrNIkrJ02dYVHgb/GTimMmfOHC5dusS0adOoUaMG1tbWeHp6ms639vb2NGzYkLi4OJYvXw4oFcd3796lUKFCWFlZkZCQwKlTp9BqtezcuZOIiAiqV6/OxIkTMyVkBIgzPOWu7hKpcjI2khNqKXM7OqkkNVayPUZZ5qEhhCjjY27cuMHtKxfg4la4e4bs2fzo2rgGkgS1yxUGQKfXY2djReMqpdh5/CJX7jw0bbNW2cKsGdOHNaN7U7qAP/N+6MT1dRNoVDsQ7N0h/CYX1k3nq7q1qFChAo0bNza9xoIgCILwXyXCRkEQBCGDF4VfaWlptG7d2tTXcN26dQBotVpy585NbGwskZGRBAQEoFaradasGZUrV+bHH38E4JdffuH69ev07t2bvXv3cuDgfk6cPEGHDh3o0KED9vb2AKYejc86e/YspUuXpkSJEtSsWZPu3btjMChVaSVKlODhw4dERkaajhOgWYvmXLgUzM7jR/ArVojuvXsxbdo0pk6dyq1btzh+/Dj379+ncuXK2NjYsH//ftRqNQaDgZo1a2JpaWmqnDt69Cj37t1j+/bt3L9/H41GgyzLjBgxgoiICGJiYvj222/53//+x40bN9izZw+PHz/mwoULtGrVCisrK9zd3enTpw8nTpzA3d2dYsWKAby0z+WnJD1klGWZ4OBgAgICGD58ON988w3ff/89V65cISgoiMOHD9O8eXNTeLht2zYGDx7Mt99+y/nz5zP0qpw9ezb9+vWjVq1aREVFcfHiRUJDQ7G0tKRnz560bNmSDm1bkSXmMlzcArGPwdoJtVs2JFtXpd+d6pkeaSq18jUbF3D2USb7xj6C4C1KpWNKPDqdLsPzio+Pp2PHjvTp0we9Xk+2bNnw9vamaNGiPHjwgFmzZlGyZElCQkJMgU6jRo3w9PTk8OHDPHz4V1gxbNgwli9fjoeHx3v5nggf0NO7cHU3GPRgl+Xtqxn5q7I8A62lMqn68RViTm1k86ZNVKtWjeLFi7/08QULFqR27dqMGzcOUKrO7ezsmD17NhMnTqRZs2b4+fnx22+/MXv2bFxdXd/62J+VZIzjnv4KaaRgjUOmtovQ6fSm/1apVFipbDm++wwVSwVSrXogX9arS88Rk4kzc8LG0YVKRfPi5mTP0u2HATAaldeoT7OaRMTEcejcNdL+3KYsy9jbWFGjTCEGtv2SOuWLAKDXG0BjzqozD6j9/RS8LdL4sV83LCws6NSpE+Hh4Zn2/ARBEAThU/NpX+EIgiAIr0+WlaV3qQlK5c3fLmTTw6/03nPpw1/KlClDQkICQUFBfP311yxduhSVSkWFChXInz8/UVFKjyyj0YiDgwOdOnXizJkznDp1Cq/sHvw6ewyPo0Jp27E11apWwymHFa1+rEf9LlXR2ySil9OeO9SYmBi+//57ChUqxM6dO2nbti2LFy/mu+++Izw8nKxZs1KgQAEmT55MomzgoiGBdalPiGtckUex0Sy5dIJZKaEktaxG/f69mTZvDtVr1qRWrVo0atSIdevWsWrVKjQaDXv37kWtVpMzZ07y58/PhQsXOHHiBD179kSlUnHt2jX27NmDtbU1YWFhpr6M586do2vXrjx69Ai1Ws2IESMwGAzs3r2bmTNnUqZMGaZMmcLUqVOZN28eXbp0wcbGBuDT6s2oS4bwGxhvHkW+eUQZepEYhWw0cvr0afLkycPXX3+NLMv4+/uTP39+2rdvT3x8PKVLl6ZOnTrExMTQvXt3EhMTkWWZgIAAhgwZgrW1Nfv372fIkCEkJiZy6NAhwsPD6devH4UKFSIhIYH169cDyvtz6cyJzGxfEYsnwcpkUgcP0Fq8+nPRWoCDJzsuhHJ4+3o49zvaFKWCMSEhAQBbW1vKlStHcHAwI0aMAKBevXrodDo6depk6kcZGhpq+llRqVTUq1ePgwcPcujQIdPuChYsKILG/4LUBLi+Xzm/2rm/ddBoMGTs75n+/yZac7Bzxyr6JpcvXyIgIADgueA8nbOzM40bNyY0NJStW7eiVqsZPXo0xYoVY968eWTJkoVhw4ZRrVq1F37w81bPRdbzQB9CqjExU4PGs8cu8EPnkfwyYDLzJ/ym7MtgYOOybcwYtohSlQuxbkobfmpWntO3wxk6fyMA/j5ZaFWzHAs2HwDA3EyLXm/A082JKsXzsWDzAa7dVSqR/36s6R/IaTRqEpJSWLDlIM1rlGda19p8ncuMyT8PxczMjMuXL2fKcxQEQRCET5GYRi0IgvBfYDRAzEOIuA0xDyA5Tun7JamQLWyRHL2UKaeOXjyNjqF48eLY2dlx+vRpzMzMMDc3x97enrS0NLRaLZ06dWLixIm0bt0aHx8f2rZtS+/evbl16xY5cuQAlF56VapWof+P3zJl00jy1/Hml9qDuXH2DrkL+WNmboZBNhBuuA8GGXOVNS4qT1xUnmjV5gBcvHiRs2fPMnr0aIoWLUrRokXRaDTMnj2b7Nmz06tXL5q3bMnw4cPJNqIn0XbmxKWkgFaDfZ4c3Aw6j69R6b2V5cfOePZtRVrQVeoWL8OxaYs4d+4cWq2W9u3bs23bNkaMGEH37t1NE1vXr1/P+PHjqVWrFnv27GHHjh18//33FChQgNTUVNNSud27d+Po6Ei5cuVo37493t7eTJgwAU9PTxYuXMjQoUPx9fVl//79pkDgk2DQw4OLcOc4xschoEtGhVJVKiMhaS0IjtAzcPlx01Jif39/duzYQe/evcmRIweOjo4kJiYSHx/PtGnTqF+/Prdu3SImJoYbN26wdetWli9fTrFixQgLC0OlUhEeHs6oUaNYu3YtixcvpkWLFjRp0kQ5poRIpEvbIC4MlYtvxgrGVzR55Q5+XrQRjVrNll/7kfDkLkMmtWTTqRACcuehZMmS/PTTT3To0AGNRkOnTp0oV64c3t7eODk5Ubp0adq1a8e4ceOwtLRk165dlC9fHoDWrVtjZ2dH48aNM+d7IHwaZBluHYO4J8owl0wI09Rq5YOfpdsPc/baXepXLEalYnkz3klrSXQq5PF25Y9tW/jmm29Mg7DSPRuWlS9fnipVqjB06FDq1q1LtWrVqFatGnq9/rnHZaYIfShxhkisJPtMCRqvXQzh528ncu1iCA1b18XTNyt3Qu6RkpyChaUFltaWdP6+HY2KemB18xwnjUZuPQzn0u2HDG7fAFdHO+qWL8KynUeZvX4PXRtVRW8woNGo+b5VXdbsOYG/d5bn9nv1zkPyZPM0/b+NlQUXb96ndtnCyoceUaHsWDaLqoGBlC5d+q2fpyAIgiB8qkRloyAIwucu5hGcW68MybhzAhKeKgGN1hxUaqTEKLhzEs6uQw5ai7M6BRcXF4KDgxk+fLhpiXLVqlU5c+YMVlZWtGvXjosXL7J79240Gg1ly5Ylf/78jB49GlAqP1QOegKbl+HQ7qOEhUZip3bBQetCiVIlsLVwwFyywkpli63KCQujHXPGLObL6l/Rrmcr1m1ZDSh9Gt3c3PDx8TE9nVatWpE7d262bt1KyJOHODSuipWnG0u+/4k4ox6txgxNXBJpT2Nw8fLEQdKSeDEEjVEmzVKpNvk6TzHW79uNvYsz0dHRLFmyBIBt27ZRu3Ztzp49S0BAAKdPn+bSpUsMHTqUpKQkgoKCuHfvHu3atUOtVhMTE8P06dOJiYnh8OHDuLi4UK5cOWRZNlWyNWnShH379rF27dpPK2iMeQT7p8Lh2XDvDCo5DZWlDbcS1Hy/4RLTDtzh/N1I+s7axIXL17CzMkOr0fDDoEEMGzaMLFmykJyczIgRI0zLOPV6PTqdDkmSyJcvHzt37iR37twcOHCAlJQUtm7dSmJiIgMGDKBatWoYDAb8/f0JCgqic+fOSsXYlV3KlF9H79cKGg0GI6MW/I5nnR6MW7qF6d+348HW6RQKyEaryZs5c/kGozvWpVTxosyaNYuOHTsSExNDmzZt+Oqrr5g/fz779+/HzMyMiIgIBg4ciJ+fH3FxcVy7do2UFCXUtrS0pFmzZu80uBE+QhG3lB6KNi5vFID/nSzLTFm1A5dqnRm3dCvxSck8CI8iPjEZUN7Per0S/Dtn9aFsbi8OHT3GpYsXMkxNT7d+/XrGjh2Lvb09DRo0QK/XExERYbr9Xb5fE41xhBnuopUsMqVHo8FgYMbP83HL6sLGU8v54ddvaNurOcOmDcTCUqlwrly7PPWqFuHwmu2U67mcicv30qNpIM72NoxdshmAPH6eNKxYnFGLNgJgYa701syTzZOfOjXC0uKvXpsx8Ylkq9+HYm0Gs+vERdPXZVmmU/0qDJyxir6TllGu/wL+N3w6ly+eo1ChQsyePdt0bnjhcnhBEARB+EyJsFEQBOFzJctw74wSNEbeBWsncPYFOzewtANzG/Raa0avP8bWK0/B2gkp6h5hexZTrWR+2rVty9mzZ1m4cCEpKSkUKVIES0tLTp06RZkyZahZsyYjR44EwM/Pj+bNm7NlyxYiIyOJMj7inu4yJasWZtGO6fj6+L7wEA0GAyvnrqNKzi85svMUFaqU5fbNO3Tu0IUDJ/ZQrlw5bty4Yep/ZzAYsLCwoHbt2kRGRzFx9yauuFtSetwgbq3fyYHW3xK26whBE+eTEh1L3q/rcmbmEtbWaUfIgjXINx9yY8VmSo38huq7F+Pfsj4W1lYsXLiQ1NRUDh06RK1atdi2bRu+vr7o9XrWrl1LgQIFKFGiBDExMaxevZpatWqxZs0aatSoQd68eXF3d2fVqlWsXr2a7Nmzm3oPgrIcN33Z9Ccj9ALsmQgPgkFrBfZZ0ZnZ89O60+Tvt4jg+5H8dugKgaM3cfDaE3J7uXBqaD1k2UDCpX3kyOZHx44d6dGjB1WqVMHFxQVJkhg5ciRZs2Zl/vz5DB06lDt37mA0Glm6dCl+fn4sXLiQcuXK8eOPP9K5c+cME7pVKhXcPQVR98DBE16x12VcQhIdRs7BtlIHlu44gkatplaZwjStVga1WsWpy7c4eO4qE75pR/MSXgxvU5Px48Zx9epVFi1aBCg9F93d3dmzZw+bN28mKSkJd3d3vvvuOzp37kynTp2wsHiNZdzC50U2QuhZ5d/mmfOz/tu2wyzZdpgZ/dsT9NvPTOrXmla1ymNrbQkoVY8ajZqrdx5y61E4Hb+qgYutJf2/6UNqamqG8PDSpUusXLmSXLlyAfDVV19x8eLFTO/J+DJP9Q/RyamYY5Up21u7cCMHdx6jba/mZPV2N3392TDPzExL0umTzFl/gmrl8/PHom70bl+B0vlzsuKPYyQmp+DsYEvNMoVwdbDlZuiT5/bzbP/iqLgEKhXLw1eVSzB68SYWbNoPKFWjo7p9zdF5wwi5/xgHOxtClgxhx6iOdOnQjkGDBplaKnxSbTMEQRAE4S2JsFEQBOFzJMtw7zSEHAAkcPR6YU+7VJ2OE5du0n7kHC7eDQdHb9xd7Hl88xJF/Zxp17YtR48eZd68edjb21O0aFHThVOfPn04cuQIQUFBWFhYUKlSJdRqNT/8NIAH+hAkJLzcfChdqQTw4gutpIRkbl+/R9/h3Vm5fz5d+rdn0ZaZJMYncezyfrL6uVG4cGFmzZqV4XFValQnLCGO4NA7pOh15K1WkdqzRoME+weP486eI9ScOgK3/AEUbv81JXu1Y/+QX9naqT8YjJRq8zU6vZ4H8dFoXJ0oU60KnTt3Ji4ujh07dlCqVCkuXLhAzpw5OXnyJHfu3KFcuXKkpaURFhZGSkoKVapUYcGCBfz+++9s3bqVypUrI0nSJz9dWh8aDMcXQ2IU2Lkjm1kBEjcfR7PqyBWW9f2SnUObcWBkKxxtLJGBZhXyERCQixZlczFs7loCAwO5f/8+ly5d4ujRo0ybNg2VSkVUVBSdO3dm9OjRVKhQgfHjx5OcnIytrS3x8fEUKlSIadOmYWFhYeoVahL9AELPgZUDqF+9OurOowhOX7nN7umDuL5uAkM7NmT9/lM8ilB6NJ65eptsHm74eLiBlSOEnqNm6YLkz5+f/fv3k5aWRkBAAD179iRHjhxERkaydetWAFxdXZk9ezbVqlXLtNdf+ATFPIboh8rgoUwQHZfA3I37qFhECbfMtBpTyJju4o371Og1hnzN+vPbtsPkD8jB2A412bnvEIGBgQwbNowVK1bQpUsXqlWrhpubG1988QUAVlaZE/q9ijQ5mRhjGGaSZaaFbZeCrpItlw8FS+TLMGDr2e1r46IIOX+VQxfv06tVZeysbHn89Cmh4ZGERcXy85/VjFVL5ufcsjHkfMGS6We3bWVuzoGgq4zs0oQO9Sry6/JtLNtxxHS7pbkZN0KfMLpbU/xy+ONnr+bblrUwMzPj5s2bgKhsFARBEP5bRNgoCILwOYq6B7eOKxNLbZxf2j/M2tKCdWP74uJgy09z13Eu5B5YO5PdKwtrN2ykadVStGrVisGDB3Pr1i1sbGxITk4mPj6ecuXKUblyZYYNGwZAjhw5mDR1InU6VMaIHkuV7b8epq29DV93bEDdptVNF3aPQ8Mo+UUxAkpm55H+Fl26dmHZsmXcunXLNPX4jp0ao40lsfceYq8xRyNJ5G1Sh4bLptJs62LaH/sdv8plANBaWVJuYA/yfl2X8MvXAVCr1ThqzLH18+Lh9Rtcs1fRslUrHBwcOH78OJ07d2bbtm24ublx9+5dFi5cyP3790lNTSV79uzY2dmZnoONjU2Gi9JPcbq0KdhLSUBzdi0kx3I/xUJZZv/ne+fG4yjik9Mokk2pJLKxNEP/51TwjSdDAIkRraoSnZjK1FZlOLBoHPb29hw4cIBq1arh6emJj48PJ0+eJCIiAlmWadq0KQcPHmTu3LkcPXqUkydPkjev0pPu2epQAB5dhrRksLB/redWKJcvwSt/oVwhZQl7qfw58c3iwpjFmwDI7evBlTsPeRqbgNHcFvQpZDEow4cSExNNSyBz587NmDFjaNKkCbVr137j11r4DIVdB30qmFn++31fgaOdDfefROLiYIv2zwrFXScusvlQEFsPnyXo6m2mrNqJb1YXLq38hZ+7N0WlUtGyTmW2/tyZrK7O7Nq1i3HjxvHgwQM2bNjAzJkzcXFxyZTjex0xhgjS5BTMyJzXBuDB3UfYOdjxNDzqudvSAz2L8IdksbPAxsqcOasPcfVmGJOX7KdIXm+Wj+hB1RIFAGUwDGBakv4iRqORLC4OuDraciw4hDZ1vqBH42pMWbWTGWt3AZCYkkJ0XCI+WZyR1GrQmHPt2G7Mzc0xM1OWY4vKRkEQBOG/5NO7IhIEQRD+mS4Vbh4Bg05ZOv0vzLQaJvdrTURMHD/MVHoltqgbyO1HkTw4tpkmDepRp04d5s6dS2pqKlevXsXCwgIrKyvTYJWIiAhsbGz4okFJPPK6YMmrB0L+eXNgbmFOWpqOkf3GU6fw19y4cpth7SewasVKytcsScmSJfn222+5e/cuMbKebXevkJqYRJYCudE8cwEnyzI2WV68NLDyz/0xt7Ml8upNjo6diQS4+nihtbJk+4UgNL5ZyZ8/P7Isc/ToUSpWrMiqVatwcHDg999/JyAggLNnzzJz5kwsLTNeOD8XjH1CZFn+6/gvbmbexr34freOwOGraDlpE0G3HgPwODoBJxsLElP/mnRbOJtSDXQ3PJawmAR83eypUSQ7E3deJPH07zSoW5uTJ0+yb98+Bg8eTGxsLPv37yc0NNS0TxcXFwIDAylcuDDwkt5xSTEQfkOpanzD1zm94jSnlzvNa5Rl5a5jJCSlUKNMIbI42zNnw150egNYOkDELZ4+eUhUVFSGYLl48eKsXr2abNmyvdExCJ8hfSqEh4DFv3+48jq6N67G2N82U7DFALLU7MaQ2WsYPn89X343gS5jFlCjdEHm/tApw7ASLGypXcSPtVOHs2vXLvbu3cu2bdsoU6ZMph7b64gxhKFCk6nnx3JVS3HtYgiPH4Q9d5skSUi6VKKuXCFPHj/6tQ1k876L1Os2k2u3wmlarzBNq5emSol8GR6n0by8z6ZKpeJpTDw+WVxISEoFoOfXNShXKBffTV3Ohv2nUKtU5M3mxVcDJnPo7FUO34ig97j5+Pt506BBg0x77oIgCILwqRBhoyAIwucm8rYy3MPO/d/v+6caZQrxbYs6HDx7lYnLtxGflEyFInk4euIkRNxi1KhRpKWlcf36dc6cOcOTJ0+QJImGDRsSHR2Nq6srellHlPERWsxRSa//6+VpWBSxUXGMmf8TczdOouQXxfht8jp+37qe+fPnc/36dQIDAxk4chjzm3dDpdWQu2blDNv4pwtaC3tb8jWth9bSgrNzl3Nq6iKMcYmY29sRbUgl1ErC3NwcFxcXDh06xNChQxk8eDDOzs60atWKYcOGUaBAAaytrT/J6sVn/fHHH/z222+A8prJssz0Sb+ybvUK9l8PZ1izLxj0VVkeRcXT6JcNhEbG0ahMbkKfxnPqxiMMBiW461K9MAARcUmk6pTKoF/bBtKkbB6ciMfHLIkHDx7g4OBgWqbeu3dvChUq9HoH/PQOpCa8VaCT/j2zMDejRumCONpZM36Zshx6eKfGrNp9nN4TlnAxNJq1e45z4MAB+vXr98b7E/4jkqKVilsz60zd7KB29dnwSz/qlS/KpH6t+aVnc9aM7sPVNePJ6uLApJXbgb+d8yQJJBUkRGJjY4Ozc+Ys635TellHipyIRjL79zu/hkq1lcnv29bsIi4mHsi4RHnzwt9pPWQFRnMLerWqzLLxHTi09Ft+n96FXNlc0KN77SXNzg62PAiPwt7GkrPX7lCp60gWbD5AYX9ffpy9lj2nLvFrnxaERcXSe8JvNBkyk5xZnVk7beQHqSgVBEEQhA9NjEoUBEH43Dy+olxwvkZfO4CGlUswvHMjJq/aSapOT6regEqlRn58hRzFmtCjRw+OHTvGw4cPiYyMxNvbG2vrvy6w441RpBqTsJIc3uiws3q78+uSkab/7z/Gn8u1rnL1ylV8unqyfv161q9fz4ZTh/GoXIoyg3pia/HqS/NkWSZvkzqcW7AK7yL5ODpmOvmb1yfmxh2wNOfAlQtcvXaN/3XsSN++fXF0dKR06dJ06dLFVBWXoQrwEzR16lTGjx+Pg4MDrq6uFC1alPz583Pv3j2WLVnIqYtX6V27GO0DlTCwQ9VC+HSazq8bTzDlf9VpXj4vs3aexd7KnMZl81AsZ1ZsLLTEJ6cxau1R5navjY+rPUOaVmT7sbP8vE1ZYmhvr1S6Hjt2jGLFir3+6xcfobyn3yDEfpFcPlloEliK+Zv2M7xzY9rW/YJUnY45v++l2Y/TeRIRRZ82jWjZsmWm7E/4jCVGKdWNmswN1ACqlSpAtVIFnvu6l5sTT57GEhkTh4uDXcYbteYQ+0jp2/uBz1MpciJ6WYeFlLkDsvzz5qB558YsnroCaxsreg7pREpyKlozDYd3HWfnpgP8r3Zh5D9/B2b3UsI+o9GA3qjHoNZhpjJ/5f2ln/cL5vSmxY8zsLIwo1m1Msz4vj35cngxZvEmfl2+jcSUVE4uHMG9J5Hk8smKWfwT0KRl6nMXBEEQhE+FCBsFQRA+J7pkiAsDize7uPu+dT3OXr/L/qArHL0QQukAb6T4CPRJ8eTMmZMtW7bg5ub2wsemyInIyG9U1Qh/XdCl/3dMVCz3bzwgb3F/UuRE8ubNi1eeXGhTWnLXkIy19Hq/wiRJwqNEIYp0bMa5+SvJWjQ/t3Ypw24eHztLyNkrePj50Lx5cxwdHU0B48sGEHxK9Ho9ffv25eDBg4waNYo6deoQHR2Nv78/AF5eXnSpU5bzl0OoXiQHAKk6PeZaDb1qF2fGjiCGN/+CoU3LM3TlIVpO2szSA5c4dfMRHo423A6LYd7u8/x+8jrFsmfhbkQsd8NicLa1oE/vbylevDgApUuXfv2Dl2UlPHnBgKM3ZW1pQb3yRVm16zjTVv9Br6Y16NwwkGbVynDzQRhFvWyVqcKqT/P7LbxHiVF/VhS+n/fK/SeRBN8KpXGVks8HjaD06U2Oe+tK4MyQKidhxICKly9RflP9RnTjcegT5vyyiHWLNlGkTEFuXr1DxJNIurcOpHmV7Px9VJdKpUYtqdHLrxcApg/+kiSJmmUKMrlfG3L5ZjXd/l2rOjjaWmNpboattSX5c3grN5hZQOxjZUp5Jn1QIgiCIAifCvGbTxAE4XOSFAO6FOWC8zWlh2s/tKtPTi93klPTWHPgHOiS0aQpS9VeFjQCJBvjkd7iolKSJNLSlH6AURHRzBy9AN+c3nz1v9qkykkARBvSSJCV5bpq3uzivuLwbyjSsRmPzlxEl5CEc67snBg8nruHT/LgfiitWrViw4YNqFQqU9D4qS+bPnnyJJs2bWLatGm0bdsWFxcXU9AISp/Ecn62BGS1Z+XhKxke265KAR48jeNOWAweTrZM71SDjQMbU8I/K9M71eD6zG5sGNCIYjmyEBmXzJmbjwmPScLX1Z5JLcvwY7dWbxzSyrKMPjkBUhOViq1MlDe7F18UycPw+etNX7OzsaJo7mygMYe0JGV5rCD8k7SkdxokJSansGrXMY5dDGHAtJWU7jAUR1trvq76kuBerQWjHvQp7+yYXpVe1gHvphJckiTGLRrOwu3T+d93bXDzcKVFl0aceLibPi0roTZ7eaWpkZcPg3kZlUqFpbkZKak6cvlmNbWSANBqNHRtVJW2db/I+CC1VvkA0KBDEARBEP5rRGWjIAjC50SfBrIBVK8f+qUHagVy+vBjx4bEJ6XQ6ctKYDSA4d8rQQyyDtVbfIZlNBqZ/NMsYp7G8sfv+8hT0J/vRvcii7cbxj9rVPQS6JGVmPENr1+1lhZUHT+YAq0bEf/wMU+v38a9ZgU8AnJSI1qN+vp9cubM+cbP42OUnJxMcnIyefLkMX3t4sWL2NraotFo8Pb2xs/RgrYV/Bm45jRjW1fG01mpijp27SFu9tbo/7y4tjDTUKtYDmoVy2HaVu1iOalZNAd3w2O59vAprnZWlM7pij4+Cjk16Y3ChvRKV41aAlnmzPX7ZPP1xdnBNsPtb8rexoq+zWvSuWGV52+UVEo1kvH1QwnhP8ag541PRq8gLCqWaWt2kZKWhr21FQuGdKZW2cIvf8BH9N6VMQKv1xvxdUiSRIkKRSlRoWiGrxvS0v5xr8Y3PKYAn6ys3HWMpJRUrCxe4cMPSaV8H4x/r7EUBEEQhM+fCBsFQRA+J+kVeK/Z/P7vsro4snR4d6VCJvbJK1XuSJIK+S0uLFUqFcXLF+HY3pMs3jGDAsXzAkovyPRLeYnMu6x3L5gb94K5yVGzErGyHtlgwDWLB0W8/P/9wZ8YW1tbPD09ady4Mfnz5ycoKAhra2tu3rxJZGQko0ePprOnTIOifszYe52GY9fRPrAgJXJ6MH7jCYrnzErxnFmf267RKKNSSajVKlSyTM6sjvh7OGE0ymBMQ6NWgfrNql0lSUKn0zH8pxHMmjWDbFldSUzV0aepEhBmRrVp4Vx+L7lFBqS/fp4E4WVUKt5loJbd0515P/wPa0tzfLO6/vsD5D/fux/Bsl0pU8/YL5f+wYPRaESlUqHWav7c9z8d1+tzsrehVc3yGaoa/+XIUL4Xoh2DIAiC8N8jwkZBEITPibmtsgRUn/raA2JeSJeqbM/833tAmktWGIl4q91VqVOBKnUqmP5fmRgqo0WpIrGRNFiiIprMm38gSRLIYKbRYvOafSA/Bi/qLfl3xYsXZ8GCBUyaNInr169Tr149vLy88PPzY8+ePUyePBmf9tVolM2KjlULMWz1YYLvRbDs4GW8nG2Z0rHaC6sIVc/0NHz2dpVKgrQ0ZRmh7b9PRU8PCf5u3rx57Dt4mAX921C9eACL911k1vo9pKTp6Nu81ltXN76UQQcqDagzf+iH8JlRmymVhO9Q3uxer35n2aAEoKoPfy57F70aXyT9HJB+DpE1WqR/qOx807Dx66qlaVqtzKs/wPjnKoN/+V4YDAYkSfrk23UIgiAIwrM+/N9EBEEQhMxjaacMBUiJB3Prf7//v0lLUrZjaf/vu5ZsADlTA6D04QLmKuW5OEoaHFRaHhlT0WHELBNaDxtkZbGfjaTG6RMKG2VZxmg0on6FykGVSkXx4sVZsmQJGk3G51iyZEnmzZtHHBZAIlXy+7BkvwOeTrbM7FIzw/5e6/uqSwEbJ7B1eeldDAZDht6Yz0pOTua3336jc+fONCjiwJOQC5y8dJPgW6HcfayE2u9sYI8uBZx83smEYeEzY+X4LgsbX58uRRmm9IGHwwCYSRZIgCwbkd5jpaXOxh5L+d5LbpXRSNo32u5rn2/SzyP/8sFf+jn81q1bPHjwgDJlymD2Dz0nBUEQBOFTID5CEwRB+JxIKnDPBWmJb72UGllWtuMe8Eo9IK1U9mgkM/S83qTPf5ImJ2OmssRKUi6ctZKKnCortJKKlOdmjb6ZFIyoJQlvyQKHN7wI/RAkSUKtVhMREcE333xDmzZtWLZsGdHR0S+8L/DCYPLIkSNYW1vjna8kaCzI62bFlyX8Wbj3guk+eoPx9S60jUYw6iBr/n9czqlWq5EkiSNHjtC7d2+WLFnChQvKfu/du4fRaMTW1pZGg6bh334ssQlJHJ33E5O/afPqx/Im9Glg9/yycUF4jrWjUklo1H/oI1HoUpRqYvWHP5dZSNaoJS163u+AFIOlDSA99ztQ+VhJQsN7CvL0ulc6j5w+fZqKFStSokQJfv/9d65fv/4eDk4QBEEQ3i0RNgqCIHxu3HMpy56TY99uOymxYGajbO8VWEo22KmcSZET326/f5JlGR1pOKs8UD9TcZhHbY2DpMEgyxjeMlCVZUiTjVhLagppbFF/xL21ZFn+c1n5X9atW0fevHm5cuUKBoOBgQMH0qRJE54+ffrCbUiSRGpqKpcvX+bOnTuMHTuWHj16UK9ePcrVaQwu2bBTp1K/ZE6exCQyZcspANSq13xdUuKUieg5y2b4sl6fMZCJiIigUaNGfPnll4SFhbFo0SJq1KjBmTNnyJ07NzExMTRt2hR7Z1dOTO7FxrE9KVMwFzHxiew9den1julV6VOVcN1ehI3CK7ByUioJ0z789GdAaQFgl+VDHwUAZpIlWsn8z6nU74/eygZZq0WlS83wdSNGVJL6jSsbX0v60nob57+OS/98IB0UFESbNm0oXLgwJ0+e5IcffvjsBpQJgiAI/00ibBQEQfjc2LiCT1ElbDS84UWeQQdJseBTBGzdXukhkiThovZCjYY0OfnN9vuMZDkeS5U1TuqMF84+Kkvyq23QSiriZf1bLWFMQI9KkvBXWeGvzoRl5+9AesAoSRKSJLFv3z527twJwNKlS6lTpw47d+5k6dKlHDlyhJMnTzJv3jySk5//HsTFxTFx4kT69etHtWrVWL58Ob/88gtTp07F0soKuUAdMLMir7OWgV+VIZeHs2nfr8yQBrpktj8yZ9Cvc03BpyzLpiXcUVFRABw8eJDIyEiCgoJYvXo1Bw4cwNLSku+++46YmBj+97//4erqypDhP5OvUBFIjCJNp+e37Yf548RFouMS3vh1fanEaCWscfLJ/G0Lnx8LW+UcmRL3oY/kr169dq92zn7XJEnCVnJCT+q/3zkT6a1sSbNzQpOS8YMvg6xHK5mheh9dpJJjlfeGg4fpHJ5+/jt48CA3b94E4Pjx41hZWTFlyhT8/f2xtbXF0tLStJm/f8AkCIIgCJ8KETYKgiB8jnyKg0t2iHn0+oGjQQ/RD8E5G/gWf62H2qqccNV4kyInYZDffFmhTk7FKBnIqsmBucoqw21qSaKi1gkflQWSBPG8WeCYJBswyDJukhmVtE5YS+9nmMHrSg/67t27x6pVq6hatSqHDx8mMTGRffv20axZM0Dpf+jn50fPnj1ZtWoVYWFhz23Lzs6Oxo0b06lTJ9asWUNwcDCNGjUClCEtkkc+yPkFTpZqfvyyELWK5Xi9gzXqICESHDw5HWPJlq1bOXr0qOnm8PBwsmfPbgpLFy5cSO3atcmWLRuTJk0if/78JCcn06xZM2xsbOjVqxe2trY0a9GSn1YdYsnOE1TpNpLJK3dQJMAPexurlx3JmzHolMDGM3/mDFgSPn+SBB75lGXU/zCU5L1IjFaWUDt+PEG5o8YdFer3W90oSSRn8UEyGk3fExkZMGIp2b6f4dDJcUoLEgs70zn8119/xdPTk169enHy5EkAkpKSMDMzo1u3bnTp0oVvv/2WL774goEDB/75VD7eantBEARB+CcibBQEQfgcac0hX00lMIx5qAyMeRUpCRD9AFz8IH9NZXnga5AkiSyabDiq3UmUY97oAjNNTiFFTsRN7YOT6sXLAbOozKmudcFdMscgQyx6jK9YASLLEC/rSZONOKi0VNI64a/K5NDqDcmyjMGQMbDQ6XT88ssvVKhQgWXLlnHixAl+/vln0tLS8PHxMV20pj+uV69eXLx40dS78e+VMf7+/jRp0oSiRYtmeJxpSEvRRuBXUhkOlPj01Sft6pIgPgLZ1h3K/49uvfvi5OTE7t27iY+PR5IktmzZgp+fHy1atADA19eXhQsX4ufnx+zZs+nWrRs3btyga9euqFQqLC0t2bJlC4GBgZy7+ZBfN56gjH9WLq38heY1ymbu9FZZhtgn4OQLWfJm3naFz59LDmVQTFLMhzsGo1EJyj0+rqDcRnLAWu2Qae01XlWKcxb0ljZokpTqZ4OsQy2ZYSG923O90WhUzp0ac8gSYPr6Dz/8wIIFC5g0aRI7duygSpUqALRv354WLVoQFBSElZUVLi4uFC1alKVLlzJx4kRAVDcKgiAIn6aP528jgiAIQuaytIOCdeH2CXh4EZKiwdIBzK1A9czp32iA1ERl2ZdaC34lIHuZN55mrZG0+GrzIaEi2vAYjWyOhWT9rxUaRtlIMnEgg7vGD09Nzn+cYFpQbYPBTGanLoKHxlRiZD3mqLBEheoF+5JlZRhMimxAJUm4qs2ponGigsbxo6geMRqNqFSq54a4aLVavLy8sLS0JCkpiZIlS5q+XrNmTTZu3MiQIUOwsFCC4YiICJycnAgPDwdeXhmTPl36uaExag2U6wDWThByQAngzK2VPqDPDQqSlYEUKXEYDUZUWfMglWyJ0T4rbioVVapUYc+ePZw8eZKqVauyfv16GjRoYHp0tmzZSExMpGvXrgwZMsT09fQqzh49epAnTx7GjBlDQkICNvpYOLcBjInA6wXh/yopWgnX/SsoYb0gvCozS6W6MeQQWNlnPL++L4mRyrAat9esRn7HJEmFs8qDeEMUBlmfof/uuyRrzUj0yoF9yHn0+jQMaj12khOqd1TBbjQqH8qoJCA+Qgl9HTwAePjwIceOHaNDhw58/fXXpKWlmaZNu7q60qtXL3r16mU6J6empnLy5Eni4pSl+R/D7ydBEARBeF0ibBQEQficmVtD7irglhMeX4GIWxDzGDCCLIEEIIGZlbJ0NGtepbLrLS9utJIZ2bT5sVU58kR/h3g5Co1shplkjhqt6eLJKBsxoCNNTsGIHkuVHR7aHDio3P71AkuSJIqobXGStOzXRXHFmECibCBW1iPJoJIkJCTSZ5AaZRkJsFKpyaGyorLWidwq6xcGk+9a+kXls1QqFampqYwcOZIdO3ZQuHBhihUrRvfu3alRowabNm0iKCjIdH8bGxvq1q3Lhg0b6N+/P3379sXb25sVK1aQP39+KlWq9I/H8I+vr1oDxRqDZ0G4vB3Cb0B8OCCDpFbeH0YjymurQrJ3R+VfAXJXxSipTBWHrVu3Zvv27Rw4cAALCwuuXLnC9OnTTbspU6YMW7Zs4dKlSxgMBmRZ5unTp8yYMYOgoCCaNGmCjY2N6fmCjRKEhxxQqsisHF75Nf9HyTFKNVKuSuDolTnbFP5bvIso59e4MHDwfL/71qUoE9RzVQILu/e771fgpM5CjDGMGEM4Nji9t/As0SMb5lHhaCLvYeaQBSuVfaZs99KtUMKexhJYMr/pa+nnvMsXz7PiYDBZC6sI1OYgT548uLu7ExkZycWLF+nTpw86nY6YmBgSExPp0KED9evXJyEhgZSUFHQ6HTNnziQpKYmmTZtmyvEKgiAIwocgyZ95bX5cXBz29vbExsZiZ/fx/QVMEAThvUqJg8SoP4fH6JVQycIOrJ2VSsh3sUtjEjGGMKKMj0k1JmNAB3+GgCChkbRYSDa4qD2wV7uilV6/qixZNnDNkMh5fTz3jMkkYSBVNmL8c09mklLx6KmyoLDalrwaG+zeU4XNixgMhucqCu/du0fLli0xGo20a9eOsLAwZs6cSf/+/enTpw/r16/nhx9+YPTo0TRp0gRQqmk2btxI165dcXFxISUlhaSkJKZPn07jxo0z74CjQuHRZWVJfvrSajMrJVRxzgaeBThw5CgLFy7E3d2dhg0bUqxYMczNzenVqxeXL18mOjoaX19fFi1ahKOjI6AsEf/jjz9o3749Tk5O5MyZk6NHj5I3b17Gjx9PuXLlnj8WWYY7J+HWUaUS18blzcNxWVZ6TBrSIEdZyFYa/qGa9l16dhCQ8ImKvA3nNykf8ljYvp99ykbl5zNrHihQ9wXVxx+HJGMcN9LOIssyliqb97ZfOS4M54vHsJftMLP3fbNt/O3DocrdRmE0Glk0tAvZPd0BuP8kku8mLWHHiUtUrViOmw/CUavVjB8/nmrVqrFr1y4WLVpEfHw8hQoVIi0tjfv373Pu3DlOnTrF+vXr2bFjB4cOHcLLy4vx48cTGBiYKa+BIAiCIGSW18nXRNgoCIIgvBdG2UiqnESqnIQBZXiMBqWHlplkmSkhi16WCZfTiJJ1xMo69LKMGglbSYOTSoubZIb5BwqTAIKDg+nSpQvffPMNjRs35ubNmxgMBgICApg/fz7Lli1j7969piDS19cXJycn1q9fj1ar5bvvviM+Pp7t27cDfy29vnfvHidPnkSSJFMQ+a6k7zOdTqejX79+LF26lKZNm3L58mUePXpE8+bNGT16NOfPn6dNmzakpKQQGxuLm5sbFSpUoFmzZlSoUAFJkrh9+zYXL17k/Pnz1KhRgzJlyvzzQcgyPAxWAsfkWLBzf+3+ouhSlCo0S3slaPQs+NYVvW9ClmWMRuNz4fOLql+Fj5wsQ8hBuHNCmQittfz3x7zt/mIeKe/hoo2U1gcfsXD9PUJ11zCXrN/oQ6V0kl6PKi0ZyWhEkmVkSYWs0WAwt8zwM2yQ9STKsfhEGHELCUFSq5UPJ17R3891er0BjUbN7pPBdB49n5+7fU2LmsoHIrNXb+PMpesM/KYvOas2IyEpma+++orExESOHj2a4cOE5ORkLC0tWbBgAVOmTGHXrl0kJiayZ88eihcvTrFixd74tREEQRCEd+l18jWxjFoQBEF4L1SSCkvJBkveXVWLRpLwkMzx4OPsuefu7k727NmZNGkSU6ZM4ejRo2zcuJGAgAB27txJzZo1SUxMpFWrVhw8eJBy5coxZMgQsmfPDkDVqlWZMmUKu3fvplq1aqaLYV9fX3x9/6ra0ev1aDSZ+ys+vRoz/eJ76dKllChRAo1Gwx9//MHy5cupW7cuMTExrF69mm7dutGyZUsKFy5M6dKlCQ0NpX///sTHx7N8+XKaNm2Kra0t7du3Z9CgQWTPnj1DP8d/JEngVVDpiXbzCITfBNmgLKs2t3l5daJsVPqTJkUrFWDuAeBfHmxcM+U1ehPpfTNTUlKYO3cukiRRpUoVcubMibm5uQgdPyWSBDnLQVqi0ifXzv3dBY6yDLGPlSrKvNU/+qARwFXtTaqcTJj+LsArB46qtFTMYiLQJMahjY9BmxCLSq9TXgNZBklCVqkxmluQZuuI3saeFBsb4mxVOGm9cPHKg6TyhOv7Ie4J2Lq9UgVz+rnu2MUQ9p+5gp+HC/W/KE61UgXwzerCliNnKV84AB97LaWzO/NFxa7kDGzKxs1bGDFiBPfu3SM6Otp0bgd48OABTk5OHD9+nKVLl1K+fHmyZFEGoeXI8XH12xQEQRCEtyEqGwVBEAThHfl76BceHk6VKlW4cuUK9evXZ/ny5VhZKdNRhwwZwpgxY7CxsaFevXp89913FC5cGICoqCicnJy4ceMG7dq1w8nJiS1btjy3v/cRTAUHBzNixAjOnz/PwoULSUhIoHHjxsTFxZmq83Q6HeXLlydfvnwsXLiQ/fv38/3339OiRQu++eYb03Navnw5LVu2xMnpLYISowGi7sGTa0rPvLQkZYW+Wq30lwQliDQY/lxTbwUu2SFLHnD2/SiWnc6aNYsBAwaQL18+zM3NiYuLo169egwfPvyFS+6Fj5wuFa7tUapvrRwzv0WFQQ+xf1Y05q0BLtkyd/vvkFE28EAXQrjhPmaSJebSS8JYWUabEItFxEMsw0LRJCnTrI1qNUatGbJGi2wKDGUkgxGVPg2VPg3ZqEevksHBC3uvimjcApSf+8dX4cZBpd/rK1RD7z4ZzHdTlhMVl0D9isU4HnyDH9o1oFGVkqzfd4pvJy9jYqdafFWxKPgUgxxl6T9wEBs3bqRdu3Y0bdqUHj16kJKSwoEDB4iJiaFHjx6EhoZy7tw5WrVqxZgxY3BwcMi011cQBEEQ3iVR2SgIgiAIH8izgV960BgdHY2joyNGo5Hu3buzbds2zMzMsLKyMt2/YMGC+Pj4MHDgQLp06WLa3tWrV1mwYAHdunXD39+fn376CX9//xfuO7OCRtNk1WeWED548ID27dubKimDgoKws7NjyZIl5MyZk+PHj1O+fHkMBgNarZbmzZszbtw4ACpXroyPjw/r1q2jTp06BAQE4OTkRK9evd7+YFVqJTx0ya5ULMZHQFKU8m99qhI8as3B1hWsnJRllB+gCuxloeH9+/dZuXIlc+bMoXnz5gBMnjyZb775hjZt2ohqp0+R1lypNjS3htDzkBIP9u6ZM6U6OVZ5nzt6Q67K4Pieh9G8JZWkxksbgEbSEqa/R4KcgpVkl2FKtDopAdu7V7GIfIwqLRWDuRWp9s6genk1oqwBo7k5OjkVJLDSm2Ebp0N16Q+wPg2+JcCzgFLVeOsIPLmu9Hy1dgKN2XPbO3zuGgOmr6RG6YJ837ouro52JKekYWlhBkYDjUrmYKytOTsuhFK+RW/cchbjxMmTrFu3jokTJ9KgQQPTz/yhQ4c4ceIEpUuX5quvviI2NpY//vgDS8t3vMxeEARBED4gETYKgiAIQiZ6NvCbNGkSM2bMwNvbm5o1azJgwAC6d++OwWBg+vTpHD161DQEpUSJEpQqVYrx48eTI0cOcuXKxZUrV/j111/RaDSmnl/Vq1cH3k0VY/o+0kPGpKQkU+Wll5cXWbJkYeXKlUycONH0aWb+/Pmxt7dn9+7dlC9f3hSo3b17l1y5chEREYGrqyu9e/cmKiqKgICATD3mDKwclT8fkfTvU/rrkh48p7t27RqJiYk0b96cK1euMHToUPbu3UuNGjXE8ulPmVoL/hXByVdZ6h/9QFnib+mgDOZ6HbKsLM1OjAKNOWQvB9lKvPuekO+ISlKRVZMDG5Ujj/S3SDBGoZXNMZMtsA5/iO3dq2iS4tFZ2aOztn+FXqoyBvQYZB0ayRxblSMW5jZIFiiVz4lRcHU3PL0LOcsrg3RcskPoOYgLV9orWNqC1hJZpSUlTc+Q2Wtwtrehf5u6uDjYgUGPJanI0REY9GlobF3o3LYl4xavJ+hOBLVyS0iSxN27dylevDgA169fx9raGg8PD2bOnEnp0qVp1KjRu355BUEQBOGjIMJGQRAEQchEBoOBefPmUaJECbZu3cqgQYM4d+4cw4cPx9HRkc6dO1OhQgW2bt3KnDlzKFeuHEajkWzZsjF16lRatWpF165dsbKy4v79+3Tq1Ilhw4ZhbW2dYT/vIohK3+alS5cYMWIEYWFhlCxZkho1alC1alU6duzIoUOHePTokekxxYoVo2TJkqxbt45cuXLRqFEj4uLiOHfuHJUqVcLVVemHWKlSpUw/3k9B+mu6Y8cORo8eDYCPjw9DhgwhT548BAcHY2VlRaVKlTh79ixffvklBw4coFChQh/ysIXMIEnKEme7LPDgAjy6pPRZlFTK0mqthRJKvohsVJZjpyVCaoKyDNg9N3gXBiefDzLMKDNJkoSd2hkrlR3h+vtEJ9/B+sYRbMKfgNqCVAfXf+mrKGPEiEHWI2NAJWmwVjlgo3JALT1zeaNSK1XNulQIu670bMxZHjzyQ5bcEHVfWV4dfR8SopAMaURHxXPy0k02/9QaF2M0cmS0MlxGa4nk5I06a15wzUmnCpZMXrGVnTt3Ur58efz8/ChcuDCBgYGULl2aAwcO0KZNG2bOnImLy6sPphEEQRCEz4EIGwVBEAThDRgMBiRJyrDUGJRKte7du+Pu7s6kSZNo1qwZqampODo6MmzYMNq2bUvBggUJDAxkyZIlBAcHU6BAAQwGA25ubvzxxx88evSI69evU7lyZVNY9ffJqO/Kxo0b6dGjB9WrV6dVq1acPHmSZs2acfz4cSpVqkSpUqW4fv06t27dMi3x7d27N1qtlg4dOjBjxgyCg4MpUaIEbdu2fefH+zF4+vQpzs7OGI1GJEl6LgiePn0648ePp127dhQtWpTffvuNjh07MnXqVOrVq8f3339PgwYNOHfunOk11ev1rF69Gjc3N6pVqyYGxXzKzCwhe2klKIy8A48vK5PQk+OUfqKy/Eyw9kwrdY2FshTbuwi4+f852OTzeg9oJC0eBjfcb5xGHxZPgo0zqRqQSVVeF1RISEikvzIyMjIgI6FCI5lhqXLCQrJGI/3DZY3WXAlpEyLgym5lGr1vcXDNofzRpSjL0xOjuLjzD7RmWp5os0DOCmBmrSy3tnZS/hvQGwxogHbt2rF48WKaNGlC+fLlWbFiBQsXLuTGjRtMmzaNL7/88l2/hIIgCILwURIDYgRBEAThLYSGhqLX68maNSsWFhYkJCQwcuRI5syZw/Xr13F3dwfg3r17FCpUiLFjx9K1a1fOnj1Lnz59UKlU1KxZkwULFjBx4sTnLk71ej1qtfqNg6b0kGrdunV4eHhQpkwZZFl+LriUZZmUlBQaNGhA1apV+f777wE4deoUpUuXpn///owdO5b169fz888/07VrVzp37pxhG2fPnuX06dMUKVKEkiVLvtHxfkoePXpEjx49yJIlC7Nmzcpw27Zt2yhSpAiOjo7Uq1ePJk2amHpxzp8/n86dO/Pjjz8yfPhwypQpg7OzM6NGjaJw4cLIssyaNWtYsmQJbdq0oVmzZh/i6QnvSvqy6D/DLZLjwJCmVDOqNEoVo7XTn8NlHF5/2fWnJC0JLm2H8Btg74Gs1qJHh17WYUBHmpyKUTagRI0SKkmFFjM0khkaSYsGs9c/NyZGKeFirkrgU9QU4KZ/oBMZGYmHhwcDBgxg8ODBWFhYvDTsT0xMJF++fHz55ZeMGjUKOzu75waDCYIgCMLn4nXytXdfIiEIgiAIn7iEhATTf6cPT7ly5QqVKlWiYMGCNGzYkFq1avHo0SNsbGxo0qQJSUlJnDhxAlCCPF9fX9q1a8fEiRMBKFy4MBMnTsTW1pbdu3czfPjwF1bBaDSaNwoa048z/bHfffcdI0eOJCEhwRQ0HjhwgNu3b5vuZ2ZmxpMnT6hUqRI7d+40XUT369ePnj17AtCwYUM8PT05fPgwDx8+BJQqT4CiRYvSpUuX/0TQCODh4YG7uzshISFcu3bN9PXhw4czYsQIXF1defz4MVeuXKFVq1ZMmTIFDw8Pxo4dy+TJk+nbty8AEydOxGg0UqZMGTp06EChQoXo3r07FStW5KuvvvpAz054ZyRJ6d/o6A1ehcC/AuQOhDzVIKAyZCulVDLauHzeQaPRAFf3KEGjgydolOBQK5lhqbLGRuWAk9odF40HLhpPXDQeOKmzYKt2wlJlg1Yyf+m58e+1FDfuPyYqVjmPy1aOyhL2G4cgLARZltHr9abzoouLC3Xr1mXNmjVcvnz5uW0nJCRQv359du3ahbW1NU2bNsXNzQ1zc3MAETQKgiAIAiJsFARBEISXio+Pp1u3bpQoUYL9+/cDyvCUhIQE+vTpg7e3N0eOHGHMmDE8efKE//3vfwQHB1O8eHHq1avH2LFjM2yvW7du3L17lyVLlqBSqShRogSrV69m3759tGzZMlOO+e+TpG/dugXAokWL2Lt3L+fPn2fjxo14enrSpEkTAgMDmTRpEgA3b97E3t6eqlWr0rlzZ1q2bElwcDATJkzAy8uLyMhIVCoVtWvX5uDBgxw6dAjghVOWP3fpr/NXX31Famoqa9asAZQBMIsXL2bw4MFotVrMzMywt7fH1taW5cuX8/PPPxMcHEzv3r1xcHDgyZMnlClThpUrV7Jw4UJ8fHzo2LEjkZGRDBgwADOz5yflCsJn4dEleHJN6Wn5st6Vr8lgyPghC8D+M5cp1nYIe05d+us2aydAhltHkFLiTQHhkiVL2LdvH8OGDePGjRtMnjyZ1NTUDO0sNm3ahE6nw9NTmQQ+duxYhgwZYgobBUEQBEEQPRsFQRCE/xidnEqSMY4UORGdnIqMjEYyw1yywlKywUKyNl1YXr9+nXnz5pEtWzZ27txJ5cqVAThz5gzBwcEMHjyYfPnykS9fPmxsbBgyZAhr1qyhQIEC9OrViypVqnDixAlKly4NQEBAAE2aNOHOnTum40kf/GIwGF45tDPIelLkRFLlZIyyHklSYYYF5iprzFTKBe/MmTP57bffsLe3Jz4+nkOHDuHv78+KFSswMzNj/PjxVKhQgYULF/LDDz9QtGhRKlasiLe3N8nJySxZsoS8efOaQrVly5Zx5swZJk+eTNu2bXF0dKRx48aZ8035RBgMBlQqFZIkmSqnKleuzNq1azl48CCPHj3i0qVLODs7ExgYCIClpSVffPEFBoOBU6dOZdje9OnTkWWZLl26YG9vT/Pmzd/7cxKEDyLhKdw+rlQYai0ybbNqtfIhy9Lthwm6doevKpWgcvF85PHzYH/QZSoXz4uro52yLNrWXRkMc/sYg5cfYsHChTg4ODB79mwKFizIsGHDmDx5MoUKFaJChQr4+vqyfPlydDodP/30E/ny5QPezbAuQRAEQfjUicpGQRAE4T8hxZjIQ10I11JPcjPtLKG6azzR3yVMf4+HuhvcTrvAqSd7uKsLJjotHFmWKVSoEB4eHvj4+HD9+nWOHz8OwP3795EkicKFC5u2X6FCBXLmzMn58+cJDw+nTJky1KhRg8GDBwN/LetbsmQJw4YNe+74XiVoTDYm8Eh3k6tpx7meeprbaee5p7vMnbRgQtLOcDX1OBuPraR4yWJMnDiR9u3bM3LkSNq3b49Go2Hw4MHMmTOHc+fO0aBBA7y9vfnpp58ICAhg/vz5ADRp0gSdTseoUaO4cOECjx8/5tdff2XChAlYWFiQnJyMlZUVzZo1+88tF0zvnfnw4UPT91Or1VK9enVSUlJYu3Ytf/zxB1999RXW1tYYDAacnZ2pW7cusbGx9OzZk71793Ljxg2++eYbpk+fDohll8J/jGyEW0cgKUZZKp5Zm5VlpqzagUu1zoxbupWEpBRuPQwDoEO9Suw9fZmz15QPeiRJApUKbFzRh14kMvQWEyZM4OLFi1SqVAmAQYMGsX//fsqVK0d0dDSHDh2ic+fO3Lx5k9atW2facQuCIAjC50gMiBEEQRA+a7Is89TwiMf626TIiZhhgZlkiUr66/O204fPMrLfr9g72/LzkgG4uLngovEi9YGKET+NxNnZmfPnz1O+fHlGjhzJw4cP8fHxYffu3VSpUsVUlThz5kwmT55MSEgIRqORdevW0bVrV27cuIGzs7Npf39f6vxvjLKBCMMDnujvoJNT0GCOmWSBir8GxxhlA3rSGNhxBHFRCUycM5ZC3qUwkywzbMvPz49ixYqxfPlyLCyUiqIVK1bQu3dvNm7cSLly5dizZw+dOnXCwcGBp0+fYmlpyfDhw/9zlXd/nwD+xx9/MHDgQJKSksiZMyf16tWja9eupiDx9OnThISE0LlzZ7p27UqBAgVMIfKGDRsYPnw4RqORqKgo/Pz8GDduHOXKlftQT08QPozoBxC0BiztlWE4mWTJ1kNMWb2TAW3q0bBSCVLTdNhaK+c/o9FI/uYDqFm6ID91aoS9jdVfQ1+iH4KzDxRt/MxUcDIMhUlNTRXLpAVBEIT/PDEgRhAEQRBQLhYf629xT38ZvZyGreSEhco6Q9AIcPHMFSKeRHLm0HmmDljE7UuhPNHfIcr8PucvnKdt27bkzZuXEydOcOXKFTw9Palbty4//vgjSUlJpkApPVRMSUlBpVLRoEEDoqKiMgSNoISMrxo0GmQ993RXCNVdA1nGRlKGI6iljINjVJKaK6dusmPlPjp+2xrJNYVbaedJMsYDoNPpAOjXrx8HDx409XIEaNGiBc7Ozqxdu5aYmBiqVavGuXPnWLZsGRs2bCAkJOQ/FTSmfw777PfowIEDdOrUibp16zJjxgzc3Nzo27cvq1evxt7enlq1auHs7EzJkiVJS0sjMDCQypUrM336dEJDQ/nqq6+4cOECy5cv5+jRoxw9elQEjcJ/05ProE/L1KAxOi6BuRv3UbFIHr6qXAIzrcYUNILys9y6Vnn+OHGRCyH3gGeWP1s7KQFozKMM23z2/CqCRkEQBEF4PSJsFARBED5b4fr7PDbcxgwLrFR2L+2tVe3LSpSqWIzKdSqg1+uZMXwhj68+RXJMA63BFDjGxMSwdetWACZMmMDly5epV68eixYtYuTIkaxatYpevXqZKgbTh3vo9fo3On5ZNhKqu8ZTw0Oln6TK5h/7g108fRlrWyu8PD2xkRyJ10dzT3eJVDkZrVYZwNCnTx/S0tLYsGGDKYAE6NGjB7NnzzZNX3V0dCR//vyUKFHijY79U5b+Go8bN44aNWoAMGfOHPLly8dPP/1E1apVWbRoEf/73/8YMmQIUVFRVK9eHU9PT7JmzcrChQsJCgqiVKlSLFq0iC+++IKDBw8CULBgQfz8/D7UUxOEDyslDsKug2XmrjZytLPh/pNIXBxs0f7ZlmDXiYtsPhTE7/tP8zA8ir7NapGSpmP3qWCSUlJNj9WrzDh37S53gw5m6jEJgiAIwn+ZCBsFQRCEz1KSMY4nhjtoZO1zS4n/zieHF/55c5CSlELl2hXwzubJTz1+4capUPKUyElY3ENKlixJQEAAhw8f5ubNm+TMmZM1a9aQI0cOpk+fzsaNG5k9ezYtWrR4bvtv2pMvyviEp8ZHWEi2aKR/n0psZm6GXqcnKjIaSVJhp3YmwRjDI91NZFnGYDAASuC4YMEC7t+/b3ps79696dOnj2nowX+VTqdj69atzJw5k927d1O/fn1AmTLt6emJRqMxhccjRozgzp07HD9+HBcXFypVqsSTJ09YtGgRfn5+jB8/nsOHD3P58mUqVqz4IZ+WIHwcIu9CSryyhDqTdW9cjbG/baZgiwFkqdmNIbPXMHz+ehoNnExgj5+5fv8Rvb6uweZDZ7l65yEAv+8/TYXOwynWezpXzx6HtKRMPy5BEARB+C8SYaMgCILw2ZFl2dTf0FyyfqXHBNb7AlmWuXzuGv3H9iZHgB8Lfl3O7vWHSFJFkyanUqdOHaKjo9m+fTsA1atXZ+7cuezcuZOgoCBTMJUZ7ZB1chqP9bdRyWq0rxA0ApQLLIUsw8mDQaSl6ZAkFRbYEm18QqwxErVazaZNmxg+fDj3799n1apVpgASlEo+R0fHtz72T4HBYDD1znzWokWL6N27NxMnTqRPnz50796dlJQU8uTJw40bN4iNjUWj0WAwGHBycqJ48eLs378fgJo1a6LRaAgKCjK9BywtLbGyyrzlooLwSUuIAIkMvREzy6B29dnwSz/qlS/KpH6t+aVnc9aM7sPVNePJ7unG91NX0KVhIHGJyYxc8Dv5mn5P08FTKejvw6Mtk6lVxA8SozL9uARBEAThv0iEjYIgCMJnJ1lOIM74FAvpn5cdPyt3wVwULJGPC6cucevaXYZO6U+O3NmIiYzl4YNHxBoiaNCgAVqtlv379xMXFwcowaKrqyuAKbh71X3+k1hDBCnGBCwkm1d+jLuXG4H1vmD1/A1cPnsVWZYxU5ljlI1EGR6xb98+Bg4ciMFg4Pvvvydr1qyvNAX7c6RWq1GpVNy9e5fg4GBSUlIAJTDMnz8/sbGx1KlTBwALCwuKFi1KWloaK1euND3+zp07hIWFkTdvXgCyZ8/O/PnzmT59uuk9kBnvBUH4LMiy0hdRa/HOdlGtVAF+7t6U5jXKUrl4PnJ4uRPg64FvFheexiZgaWFGYIl87Au6wleVSxC3fwFzBv2PLK4uYDCIsFEQBEEQMskHDRvHjBlDiRIlsLW1xc3NjQYNGnD9+vUM95FlmaFDh5I1a1YsLS2pWrUqN27c+EBHLAiCIHwK4o1R6OU0NLxaRWC6SrXLY2Zuxu9Lt2JuYc63o3qw+tBCug9tT4wxDEtLS3755RcWLFhgmsD2bJiUmcFdjDEcCfVzw2z+iZmZlt4/dSE2Oo6h3Udz6tBZdm3cz/kDV5k5aQ4DBvanZcuWaLVafvnlFzp06JBpx/upuXTpEoGBgZQqVYq2bdtSrlw5Ll++jI+PD7Vr18bGxoZ169aZ7l+jRg1KlCjBgAEDmDFjBqdOnWL8+PE4OjoSGBhoul9AQMCHeDqC8PFLS4TkWND+c1uLzHb/SSTBt0L5qpLSf3Zi31bE7V/AyK5fY2H+5+8ISVIqLhOfvtdjEwRBEITP1QcNGw8ePEiPHj04ceIEu3fvRqfTUb16dRITE033GTduHFOnTmX27NmcPHkSa2tratSoYapAEARBEIS/SzbGI6F67aqyIqULkrdwLq6ev871SzcBKFgiHxrJjER9HAZZR8mSJXFycsqUpdIvY5D1JBnjXnn59LP8cvowfvEInN2caFujG31aDKR/6+Gsnb+ZHt90ZciQIe/giD8u6cujjUbjC5dK63Q6Bg0ahIeHB+fOnWPXrl3kyZOHli1bcuvWLerUqUPu3LnZtm2b6TFubm5MnDiRVq1asXTpUr788ksuXLjA3Llz8fX1fW/PTRA+WclxoE8Fzbud7JyYnMKqXcc4djGEAdNWUrrDUBxtrWlZU5n+/uyU6gw0ZhAf/k6PTRAEQRD+K96sY30m2blzZ4b/X7x4MW5ubgQFBfHFF0rvrMmTJzNkyBBTH6zffvsNd3d3Nm7cSLNmzZ7bZmpqKqmpf02YS1/mJgiCIPx3JMvxqN/wV1ylWuU5eTCIDb9tYdC4fgCo0WKQkkmTU7CUlKnO73J5bJqcgkHWYyb983JDWZYxGo2o1WpkWUaSJPR6PQsmLMXZ3Ym6TatTqmJx/PPlIEcJT3y1n/fwl6CgIEaMGMF3331HhQoVUKle/Jnq+vXrTcunAXbv3s358+dJSkoiPDycMmXKUL58ebZu3crevXsJDAxEp9Oh1WqZPn06sbGxJCQk4OXl9T6fniB82ox6kI2geretG8KiYpm2ZhcpaWnYW1uxYEhnapUtbLr9peduSQ0G3Ts9NkEQBEH4r/ioejbGxsYC4OTkBMCdO3d48uQJVatWNd3H3t6eUqVKcfz48RduY8yYMdjb25v+eHt7v/sDFwRBED4qRoxIvFkYWOKLotRoWIXAun9ND5aQkP/8531Q9iP/43MwGAxIkmRaup1+Aa3RaChVqTh/bNiHk6sTTTo0oHCpAn9u9/kqv0/Z36sWs2TJwuHDhzl27JjpaxMnTuSHH37gjz/+yHBfc3Nz1qxZQ65cuejQoQNNmjTh2LFjlClTBoB69ephb29v6tGo1f4VMjs4OIigURBel2xU+ja+Y9k93Zn3w//Y8Es/9s0akiFo/EeSBEbDv99PEARBEIR/9dGEjUajkb59+1KuXDny588PwJMnTwBwd3fPcF93d3fTbX83aNAgYmNjTX9CQ0Pf7YELgiAIHx01GoxvGAxqNBq6DepIyS+Kmr4m/xleSu/p16YKNZKkwohMclIKq+atf+4+arWapMRkJgyZQe/mA5n80yxCLt8CoHnnRtjYWWPvaPfcdj8H6SFjeuViVFQUaWlpeHp60qxZM7Zu3cquXbuoW7cuc+fO5fz589SqVYvVq1cDYGVlxePHj+nVqxcDBgzgwoUL/PTTT2TJkoUzZ84QERFBwYIFyZkzJw8ePODRo0cvPZZnp3kLgvAPJLUS6L2HD23yZvfCN6vr6z1INoLqgy76EgRBEITPxkcTNvbo0YNLly6xatWqt9qOubk5dnZ2Gf4IgiAI/y1Wkh1G3m453LNVc3p0aFRmmEvvZ7CBmWSBRjLDIOvYu+UgU4bPYdPy7cqx6PUAXDh1iYYlW3HqUBDFyxXm1KGzjO0/mQM7juDi7kztJtXZvnY3iQlJGGUDEhLmqvc7mOF1vKi34sukh4zjx4+nbNmydOzYkW+//RaA3r17c+/ePRYsWECBAgUIDg5m+/bttGvXjsmTJ3Px4kWKFi1K3rx5KVOmDB07djStqLh37x6jRo3iyJEjAAwZMoSdO3fi4eGRYf/79u1jx44dQMahQK/yHEQ4KfxnabTKEuqPtXrQaADtu+0nKQiCIAj/FR9F2NizZ0+2bt3K/v37MyxLypIlCwBhYWEZ7h8WFma6TRAEQRD+zkplqyx6fosle8/2+9OThrVkh0p6P5WBKkmFlWyLjlSKlC5I+aql2bJK6XOs0SiVN+sWb6Z4+cKsPrSQNj2b0X9sb66cv8bCScsB+N83rbl/K5TDu46jIxWtZI6FZP1ejv91GAwGZFl+aX/FZ6WHeRs2bMDPz4/ly//P3l2HSVW+fxx/n6ntLnaXpZZcurtzaenuEAQBRUBEFAm/CCgIAuIPKVG6QZAWlu7uzmWD7d2p8/tj3BEEJWUR7td1cSkzJ54zszvMfOZ+nns+vXr1ol27dhQuXBij0UjevHmpU6cOixcvJlu2bPbpz0OGDCEuLo41a9aQOXNmOnTowK5du2jYsCHTp0/ns88+o0KFCpjNZvLmzQvwyHuN9PN/9dVXfPLJJ1y9epVBgwbRvHlzgH+8hvR9tVotSUlJzJo1C+BfbTQkxGvFyRP0jmB6TZs8mo3gJp8vhBBCiJchQ8NGVVXp06cPy5cvZ8uWLWTPnv2h+7Nnz06mTJnYvHmz/bb4+Hj27t1rX1NJCCGE+Ct3rQ96xREjKS98LKtqAVXFSxvw5I1fEqvVirc+EEVRyJTFjzJVSxJ1N5ota3cAEB0Zw707UTRqW48Lpy/Rq+mHdKnXl7JVSzLgi14AZAnNTO0m1Zk3ZQGJqXF4afzRKxlbtXPo0CHOnj0L/BmyabVaFEVhz549fPnll2zZsoWoqCjg0UpBjUZDbGws48ePp1WrVuzcuZP27dvTtGlTunXrZg8We/fuja+vL8nJyfZ9c+fOTYUKFdi4cSMnTpygffv2zJs3D3d3dxYsWMCWLVuYPHkya9asIV++fA+dV1XVhwLR4cOHc+LECUJDQ9m+fTtdu3Z94rVrNBoSExM5ffo0xYoVY/To0cTFxf2rjYaEeK3oHcHZB4wv/rr80qmqbYq3q3dGj0QIIYR4I2TowiTvvfceP//8MytXrsTNzc2+DqOHhwdOTk4oikL//v0ZNWoUuXLlInv27Hz66acEBQXRuHHjjBy6EEKI15hBccJbm4k75ssYcERRnv+7tRTicda4467xfYkj/GcajQY31Yedyw/y25rN+Hj6kJKcypoFG6hWryLefl6cOnKWT3qOJP5+AtXqV+KXbf9HngI5AYiLjcfDy53O/dvSrFxHDm0/SaEGlV7Z+B/n3r17VKxYkY8++oihQ4diMBgAWwfp/v37c/r0aYoWLcoPP/xAWFgYa9aseWyl4DfffMOFCxdYtGgRrq6u9tvTu3EDFClShFKlSrF9+3YaN25s/zLz3XffpUOHDqxfv54CBQpQu3ZtateuTWxsLF5eXo8d94PHvXjxIjNmzODXX3/Fx8eHbNmysWXLFpycnB7azmKxPDS9GuD333+nT58++Pv78+677zJgwIAXfESF+A/yDISYKxk9ikeZjaA1gPPjXweEEEII8WwytLJx2rRpxMXFUaVKFQIDA+1/0hdwBxg0aBB9+/alR48elCxZksTERNavX4+jo2MGjlwIIR6mqqqsxfaaCdBlw1njTpIa/9xTVY1qCqgaAnU50Sr/zvdzfze2r8d/zYg+4wjKEoh3oCcWs4U92/ZzYOdhFEUhvFkNou5G8+O6KXz5w3B70Hhs/0nmTF5AzL1Y8hbJSb8vu1GuSCVcNZ7/yvifhslkws/Pj/bt27N9+3bOnDkDwP3795kzZw6lS5fm3LlzbNy4kXHjxrFjxw6mT58O/Pn4qKqK1Wrl7Nmz5MiR45Fu0A8GfQD9+vXj4MGD7N69275NsWLFCA4OZvPmzdy+fdt++98FjenHvXnzJmvWrKFdu3bExMSwevVq1q1bx759+9ixY4c9aHxwqjRAWlqa/Thubm5kyZKFAwcO0KBBg+d7IIX4r3PzBxSwmDN6JA9LjQdHN3DxyeiRCCGEEG8ERX3DFwuKj4/Hw8ODuLg4aRYjhHjp0gOQv1YxidfDfcs9rpiOo6oqTorbM01ZNaqppKnJBOqyE6TL9a9Md31cBRzYQqoSJUrQuHFj3hvehZuW8xyPOMs3H39P1tAQxs3+gktnr9CoRBu6DGhHrXeqkS1XFvZuO8DMr+cRVjQvPT/uiMEbvLSZyK4vgFbRv/TxP40Hr/H06dPUrl2bwYMH07NnT3Q6HcuWLaN69ep4eHhQpUoV9uzZg9lsJjAwkCtXrjzy+FSvXh2LxcL8+fMJDg5+qKLwwf8HqFq1KiEhIYwZM8YeTl64cAFvb297U5gHmc1m+5qY6fbv30/79u1xdnamcePGDB8+3H5f0aJFyZMnD99//z0eHh72869bt46JEyfi4+NDzpw5GTlyJABTpkxhwIABREREUKpUqb99/oV4Y5nSYO88MKWA66urFv9HqgoxVyF3FcghyzQJIYQQf+dZ8rXXokGMEEK87v6uy6yiKGi1WhITExk6dCjVq1dn2LBhREREANL8IaN5av3Iog9Do2hJVGOxqE+uplFVlWRrPEZSCNBlI1AX+sJBY0JCgv3/rVbrQxVwCQkJfP/99+zZs4f4+HgALl++jMFgIHfu3AToshGszUVYydyUq1uCo/tPcObYOXLkycYnXw9k56Y9DGg7lFaVuzKoy2eUrlKCPqM64+Ct4K0NJKs+f4YFjenXmJiYyCeffMLx48fx9PRk06ZNnD9/HoAmTZoAUL9+fW7cuIGzszMDBw4kNjb2oSYq6RWL4eHhHDhwgJMnTwI89NwoioLJZGLHDtvall27dmXx4sUcPXrUvk3OnDkfCRrTn4/0oDF9zUiAkiVLUrx4cU6ePEmZMmWAPysWP/vsM5YvX87p06ft4xwyZAjdu3enSJEilC1blgULFtCtWzdu3bpF3bp1KVeunL1q82ma4gjxRtE7QFB+SE20hXyvg7REMLiAf66MHokQQgjxxpB3uUII8RTSQwGLxfJQ8KiqKvPnzycsLIzdu3dTsWJFjh8/TsOGDbl8+bI0f3gNeGszEWoogrvWhxQ1nkQ1FpOahlV9+Hk0q0aSrfEkqjHoNQay6QuQWZf7hTpQx8bGEhERQVBQEPfu3QNsP0sajQaTycS6desICgriq6++onXr1nTu3BmAzJkzEx0dzd27d0lLSyNAl428LiUoWqww0ZExLFuwkjQ1mcadwpmxaiKjfxhG7087sf3OCjoPa4aToyNZ9GFk1xdErxhe7AF8QQcOHCAsLIzt27dz6dIlLBYL69atY+/evYAt6Js7dy63b99m+fLllC9fnoMHD6LX65k+fbq9WjC9ArBDhw5oNBrmz5/PrVu3HjnfuHHj+PzzzwFo164dP/30E/Xq1fvHMab/fn/zzTeEhYXRpEkTRowYwalTpwBo1qwZQUFB9tAyPZRs3LgxWbNmpXfv3pw4cYLffvuNhQsXMnPmTMaOHcv7779P48aNmTt3rr0RXu3atdmzZw/nzp17aOq1EG8N/5zg4AqpCU/e9t+mqpAcA745ZAq1EEII8RJJ2CiEEE/hzp07BAcH8/PPP6PRaDh58iQ7d+5EURRu3brFZ599xtatW/n888+ZOXMmCQkJTJ06lbi4uIweugBcNZ6E6ouQ3VAId40vZkwkqfdJsMaQYI0hUY3FqKbioHEmRJ+P3IYS+GiDnjssPnnyJNWqVaNy5crkyZOHgwcP4ufnZ79/+vTpFCpUiN27d7N48WJOnz7N6NGjWbVqFQsWLMDV1ZXatWuzcOFCLl68iKIouGt9CFByoKBhzbxN3Lh0C6vWhKO3hrCyoVRtVAEXjTtZ9PnI7VAKf12WFwpKn8Xj1itND9E2btyIr68vGzZsYMiQIZw8eZJChQqxZMkSLl++jEaj4fLly0RHR3Ps2DFKly7NsWPHCA4O5tChQ2zYsMF+TFVV8ff3Z/DgwSxZsoSPP/6YmJgYbt++zZUrVxg5ciRr1qyhX79+9n2aNm360LgeV22sqioffvghkydP5oMPPqBatWqsWLGCFi1aALZQMSwsjEOHDnHz5k20Wi1JSUmcPXuWzJkzc+TIEWrWrEmjRo1ITU2lTp06fPXVV4SEhLB06VK++eYbGjVqhKIoVKlSBX9/f3744YeX8tgL8Z/j4gvBBSApBqwZvHZjyn3Qu0CWorZu1EIIIYR4KWTNRiGEeEr9+vVjzZo1+Pr6cvToUSZPnkz37t05duwYBQsW5MKFCwwePJitW7eSJUsWrl69yoYNGyhdunRGD108QFVVjKSSZk3ChBFVVdEqOhwVFxwUpxcO6MxmM82bNyclJYWFCxfi4eEB2Lou586dGzc3N7Zs2ULDhg0JCwtj/fr19mm93bp1Y8+ePZw4cYIbN25QrVo1ChQoQL9+/cicOTNffvklLi4uODk50alLJ7LmDMaCBQUNBsURHfpXWk371zUSL126hK+v70P/3larVo28efMydepUjEYjBoOBtWvX0q5dO77//nuaNm1K69atWbx4MTlz5iQlJYWbN29Srlw5xo8fT9myZe3nSf+vxWJhwoQJfPnll5hMJooVK8aJEycIDQ1lzJgx1KxZ85Gxpoefj5u6fP78eSpUqMCUKVNo3rw5AGfOnKFcuXIMGTKEjz76iFmzZvHdd9/Rv39/KleuTK9evbhw4QIVK1bk3Xff5datW/j4+FCrVi00Gg25cuWiT58+tG7dGicnJ6xWK6mpqej1ej7++GPmzZvHsWPHCAgIeNlPixCvP2MyHFoC8ZHgFZwxY7CY4P4tyF0JcpTLmDEIIYQQ/yGyZqMQQrwAVVUxmx+utkhKSuK3337j8uXLZM6cmdjYWLp37w5AoUKFuH37Np07d8bFxYWtW7dy9OhRDAYDy5YtIykpKSMuQ/wNRVFwUJxw1/riow3CVxeMlzYAJ43rS6kEPH/+PDdu3KB58+Z4eHhw6tQpoqOjKVmyJFOnTgVs6wCGh4ej0Wjw9va2V9v17duXs2fPsmrVKjJnzsx3331HQkICXbp0IU+ePMTFxTFo0CD+97//kTd3Xpw0brhqPHHRuKNXDK982n76+dasWUPx4sWpXr061apVY8qUKaSmpgKQL18+IiIiMJvN9qCvXr16uLm52dc7PHXqFO3ataNIkSLkz5+fDz74gBMnThAZGfnQedIDR61Wy6BBgzhw4ADr1q2jXbt2bNy4kf379z8SNKZXXaZPXz9//jyzZs2yr7MItiDSaDSSP39+wPYakCdPHt59912mTZuGoig0atQIX19ftm3bBkCRIkW4efMmJUuWpESJEjRs2BAvLy/KlClDmTJlOHToEF26dMHJyYm0tDS++uorNm7ciF6vp3nz5sydO5eAgABZ11W8nQzOEFoBNFpIiX/151dViLsNPtkhS/FXf34hhBDiDSdhoxBCPMBqtaIoin1NtvTGHi4uLvzvf//jnXfe4datWzg5OT00dfTHH38kPj6eYcOGUaRIEc6cOYOiKCxatMjeyEK8HfLly4e3tzfz5s3Dw8ODoUOHotfr+fDDD5kzZw4JCQm4ubnRtWtX9u3bx5EjR+xhWqFChWjSpAkTJkwAoGbNmqxZs4Y5c+Zw9uxZFi9eTHBwBlUB/Y2vv/6aDz/8kHfeeYdVq1bRqVMnJk+ezLx58wBb45ezZ8+yZ88edDod+/bt4+TJkzg4OLBnzx7mzJnD3bt3GTVqFPPmzWPDhg1MmDCB4OBgVqxYYW/Wkh7KPRiohoaGUqlSJXr06EHx4o8PDNLXekxNTaV3794ULlyYsWPHUrJkSUaPHk1SUhKHDx/GxcWFFStW2M+lKAq1atUiOjqaatWq0a5dOxRFYdu2bVy6dIm6devi5+dnX9cxfTz16tVjx44dLFiwgDNnznDt2jU+/vhj5s+fj9FoBKB06dLUrl37kesR4q3i+0fQl3If0l7hl3LpQaOTp62qUefw6s4thBBCvCUkbBRCvHX+Wkn04N/TK69Gjx5Nnjx5aNGiBQMGDEBVVRo1akT//v3Zu3cvO3bsQKvVYjKZAFtgkJiYiIOD7UPLli1b6N27N0lJScTGxr6iKxOvgtFo5OLFi4AtnH4wdLZarezcuZONGzdy6NAhmjdvzooVK3B3d6dv375cunTJHmiVK1eO8uXLM3LkSODPgKtPnz7s2LGDXbt2AeDg4ECFChUIDQ19tRf6D5YsWcKvv/4KQLZs2ZgwYQLDhg2jYMGC+Pn5ceXKFZYtW8aFCxeoWbMmdevWpU2bNri7u9OsWTPq1q1r7wqdI0cOoqKiiI6OxtHRkeTkZMDWSXrFihUcPnwYePpQ7q9VyRaLhfr169O/f3/S0tI4fPgwe/bsYdy4cfzf//0f3333HSkpKdy9e5d169YRGxtrfx2YNGkSycnJZM2alfz583PhwgUcHR0pUKAAJUqUoEmTJmzdutX+O+7g4MB7771H3759GTRoEK1ataJo0aLs27ePGTNm2KdoCyGwrZGYszxkKQaJUa8mcEwPGvWOEFYLPAL//XMKIYQQbyEJG4UQb5WYmBh7aKGq6iNrzqWlpdGpUycWLFjAwIEDqVSpEjNnzqRDhw5cvnyZihUrUqNGjYcCIoA+ffpw//596tevT44cORgxYgQtWrTg9u3b9gom8WaoUKECw4cP5969e2g0GrRaLWlpady/fx+NRkOePHmYN28eYWFhuLi4EB0dDUCWLFlo3bo1EydOJC0tzR5ArlmzhitXrth/DsuVK8f8+fMpWbJkRl4m8Oc6hw92TL5+/Tr9+/e3B2xVq1alfv36LFq0iNDQUL744gs6d+7MhQsXWL9+PYsXLyYgIACr1YqDgwNms5mbN29y584dpk6dSs+ePSlXrhz/+9//AHBycgJsFYkpKSmsXr3a/hg+zVjTq5KPHj1KZGQkWq2WsLAwZsyYAUCePHnw9PSkV69eNGjQgPXr11OrVi2yZ8/O6dOnadWqFfv372fTpk1s3bqVQoUKMWvWLMaPH8/+/fu5c+cO3377LQ4ODoSHh6PT6fi///s/wBZ0Ojg4MG7cOI4dO8bYsWPZvXs3O3fupGzZssDjG9QI8dbSaCF3Fcha0tYwJinGFgj+GywmiL0ODi5QoK6tslIIIYQQ/woJG4UQb43Lly9TuHBhe1BoNptRFIW7d+/y3XffYTQauXbtGps2beKzzz6je/fufPzxx/z888+cP3+eBQsWAPD++++zZcsWjhw5gsFgwGKx4ObmxtGjR+nTpw+DBw/m7t275MuXzz6FU/w3PRiypVcwtm7dmqNHj3Ly5EkSExPp1KkT+fLlo02bNmzZsgVvb2/atm1L06ZN2bFjB3v37rUfY+DAgRw9epSNGzcCUL16dQIDAxk8eLB9G61WS+vWrdHr9a/oKv+eRqMhJibGXumnqiohISG4uLjYpzd7eXlx6tQpxo4dS9++fdm7dy/Tp0/HbDbz22+/sXjxYhYtWkTZsmVZuXIl33zzDXv37sXHx4e5c+ei0Wjo378/S5YsYdGiRURHR5OSksLZs2cpXbo0ly5dsk8/ftJYASZMmECWLFkYMGCAvTq0X79+uLm5ERQUBNh+961WKw0bNuT27dvs37+fAQMG4OLiwqlTp+jatSuNGzcmISGBMWPGALaKVi8vL/r06cPixYu5e/cuJUqUoHz58ixfvpy0tDR0Oh2qqmK1WvH09KR27drkzp37oQpYmTYtxF9o9ZCnii10VFVbIGh+8u/8U1NV21Tt+7fAKwQKNZCgUQghhPiXSdgohHhreHt707RpU3766ScAe5jTp08fdu3ahcFg4NChQwCUKVPGvl+dOnXInz8/e/fuJTIykvDwcCpVqkS7du3o06cPOXLk4OuvvyYkJIR3332Xnj17vvqLE/8KjUZjb3SSPj23T58+mEwmduzYwciRIzGZTIwePRqTyUTPnj3tDUS6deuGqqps3rzZvvZn/vz5ady4MV9//TUWiwUfHx++/vprWrVqlSHX96AHp4On2717N3nz5uXrr7+2VwEnJiZSrFixhxqsrFu3jtTUVMLCwti7dy/bt28H4PDhwwQGBuLn54erqyvlypWjZcuWFCtWjPLly9s7bzdv3pz333+fDz/8kGrVqhEYGEhUVBRLly5lzZo1BAY+PNXxr9PXwRYGtm/fntmzZ/PFF1/www8/2NdxDA4OpnXr1vY1IHU6HRqNhho1anDt2jV0Oh09evTAxcWFd955h6lTp3L27FnCwsKIiIiwB4gAvXr14uzZs9y5cwdPT0/Kly/PpUuX7F9GKIryUMdrVVXtFbBCiL+h0UL2UlCsqa1pS9xtSIgEi/nJ+/4dVbV1vU4PL3NWgKJNwPP1WvdWCCGEeBNJ2CiEeGt4eHjwzjvvEB8fz+zZswE4efIke/fupU+fPgCUL1+eW7duce3aNcAWMOl0OsqUKcOZM2dwdHREq9Uye/ZsKleuzLVr1/jmm28YOHBgRl2W+Bdt3rzZ3pDFwcGBAwcOoNPp6NatG5MmTWLHjh3873//o3Xr1mzcuNHeZOTevXt4eXlRv359IiIiOHDggP2Y77//Ptu2bbM3DmrSpAnvvPNOhlwf/Fm9mR6GRUdH29ciLVasGMOHD2fSpElMnDgRAFdXV+Lj43Fzc7PvGx0dzZUrV2jcuDF9+/blnXfeoVSpUnTo0IEePXpQuXJlLl++zKVLlwBbIFejRg3c3d2ZM2cOAOPGjWPDhg20bduWX375hcWLF+Pr6/vY8aaHdzExMZw5cwaLxcL169c5ceIEo0aNolOnTmTJkoWQkBD7fv369ePMmTPMnTuXc+fOMXDgQFq2bImbmxsGg8FeUbplyxZ0Oh0BAQHUqlWLZcuWYTKZcHR0BOD06dP4+vpy//59AKpVq8b8+fPp2LHjYx9fqWQU4hl4BEKRxpC3Bji42kLH2Bu29RxV6xN3B2wBZVIMxFyD1ATwywlFmtjCRr3jvzp8IYQQQthI2CiEeCOpqvpQ5VP6OmkFCxakXr16jB8/HoAVK1ZQsGBB+3pqmTJlolq1anz55ZfAn+u/xcXFkZCQYP97lixZ+Oabb1i1ahVNmjR5ZdclXq3Q0FAyZcpE06ZNCQ0NpXnz5pw6dYqePXvi6uqKg4MDAQEB9u3btGnDrl272LdvHwA9evQgNTWVzZs32yskK1WqxNmzZylUqFCGXBM8vinSsmXLKFmyJI0aNaJFixacO3cOBwcH+vTpQ48ePZgyZQrffvstAGFhYezcuZP4+HiuXbvGli1byJYtG/7+/ly/fh1XV1fOnz9PixYtKFy4MDVq1MBoNLJs2TL7ecuWLUuxYsVYtmwZFy9eRK/XExYWxuDBgwkPD7dvZ1VVzKqK5Y81VjUaDefPn6dZs2bkyZOHuXPnEhMTQ1xcHKqqsm3bNsaPH89nn31G165d+fDDDzlz5gz58uWjYcOGDBw4kFKlSrFo0SKWLVtGfHw8a9euBaBv374kJyezbt06NBoNLVu2JDU1lR49enD48GESEhKYPXs2pUuXplKlSgD4+/tTvXr1f/05E+KtoTNAthJQuh0Ubgi+OcCYZAsdo6/ZpkMnRkNyLCTftwWLCfcg5rotYIy/Y6uUzFEWSrS0VTN6hzzxtEIIIYR4eXQZPQAhhHjZHqx8AltDi/QKp/Sp1EuXLmXt2rWcPHmSFi1aALbmMA4ODowaNYpy5crRt29f2rZti8lkYu7cufTv3x9nZ2f7eQwGw6u/OPGPVFUlCQuxqpk41YxFVdEArooOH40eN7R/W2mmPhBmWSwWtFot0dHRnD17lnPnzvHZZ58xePBg+/T75s2bs2nTJo4ePWpv5tKtWzfmz5/Pjh07qFixIiEhIRQqVIjt27fTpUsXcuTIAUCuXLme6/qsqsodNY3b1jQirUYiVSNGbNfopejJpHHATzGQVeOIQXn894np06Gtf1xvSnIy/fv3Z/v27bRp04ZSpUoxduxYhgwZwocffkj58uX54IMP8PT05PPPP8fBwYH4+HiOHDnCtGnTGDJkCG3atKFv374cP36cw4cPM2bMGM6ePcv333/P1KlTqV27NqtXr2bbtm107NgRPz8/dDodNWvWxM/PDx8fH/v4LKrKTWsqt9Q0bplTiVZMmP8IR10VHbF7jzKuc28qlCvHihUr8Pf3x9XVlWLFijFgwAAmTJhAQEAAYWFhJCQkcOrUKfbu3cunn37KyZMnUVWVMmXKULRoUQoWLMj9+/f56KOPaNSoEdWqVaNRo0asXbvWXp05Y8YM3n33XXbt2kV0dDQhISF8//33UrEoxL9N5wCZ8kJAHkiKtoWKyTEQd8fWvdpqAVRQ9ODoZquKdPUFZ29w8wOD8xNPIYQQQoh/h4SNQoj/pPQw6HG3aTQaVFXlyy+/ZN68ebi4uFC6dGk6depEyZIl7dWNjRo1wsvLi6SkJIoXL07+/PkB23qN06dPZ968eWzYsIHbt2/TsWNHevXqlRGXKp6CSbVy2ZrCSUsiN62pJKtWrNgCKhXQoOCkaPDXGAhTnMmlc8VJsf38WK1WVFVFq/0ziEz/2fLx8WHMmDFMnz4dNzc39Ho9KSkpODk50adPHxYsWMDvv/9OkSJF0Ov1eHt707BhQ9asWUPlypUJDw9n3LhxODg44Onp+dzXZ1VVzlmTOW5J4LollTSsaFBwRIMGBRWVWEyctSShURS8rRqKGDwpoP3zOuOsJi5bU7hrNXLbmsrysd/i6uNF5RbvYMnsx2ezptGkfFWcFS179uxhypQpeHh4ULJkSZycnHjvvfdYtWoVX3/9Nbdu3SJnzpy0adMGRVHo168fFy5coE+fPty6dYsOHToQExPDml/Xsez0IRxDQ3CsXJxj387g/VlT6NC/D/4aB4rWqkrt2rVRFAWLqnLWmsRRcwLXjIlYdVp0KDiqWrQKWKxW7pHG+s2bcMwWTPnvR5Omc8NX64aT1hb8t2/fnpYtW+Lo6EhycjIXL16kffv2KIpCvnz5CAoK4ty5c6Slpdmrl2NjY9mwYQMjRoygWrVqDBgwgOXLl7Nlyxby5s1L9erV2b9/P0eOHMHV1ZUSJUo89/MohHgOimILEV3/sqyCagWr1VbFKOG/EEII8VqRsFEI8Z+SHiimh0E7d+7EycmJgIAAMmfODMD58+f54IMPuH37Np9++ikAq1atonv37hw5coTMmTPTuHFjtm7dSp06ddDr9VSrVo1cuXJRq1YtWrRoQbdu3ejUqROHDx+mSJEiODg4ZNg1i392w5rK7+ZYrltSARUPRU+AokP/QGWfWVVJthi5QSpX1BQCrImE3TdTyj+LfRrx/fv3+eqrrzh37hxly5alZs2aFCpUiPfee4+oqCgmTJhAv379cHJywmw2kzVrVmrWrMnq1avtTYQAunTpwtatW3F3dwds02xfpAouTjWx03SfU9ZEVMBb0eOE5rHHtFosWBSFWMXMJlM0Gw7upVaegsQ56zlrTeZeXCzx125xavl6ji1fR9NxnxGjWAjp3oLr3h50mfQl676YgDE5BWdnZ5YvX062bNkYMmQIDg4OKIrCuXPnAEhMTMTf399+7nHjxpEpUybmzp0LWQIY/+MMrn1/je/mzabWpwMIrlqO4B27ccoSxDFrIlZLAk5oyKp1IpfGmSvWVE5ZE1FU8NM74aho2b9iHbG371CoZhV8QoLROzhivHoLrdHClskzWRYfR+qNO1ivR9K7c1c6tG+Po6MjsbGxuLu787///Y8rV64wdOhQsmTJQsuWLTly5Ih9Hc3ixYvj5eVF8+bNGTx4MNevXydLlixUqVKFmTNnUqNGDQoXLoynpydVqlQBsDeL+S80fHnclzJCvDEUDWhlRSghhBDidSRhoxDi9WQxQ+w121Qps9H2oUJnQOsRhMXVn5GjRjF37lwyZcrE3bt3iYqKom3btgwbNgwXFxfy58/PggULcHFx4f79+2zatIljx46xePFimjdvTvHixSlSpAhxcXEsWbKEQYMGsWTJEpYuXcqyZctYsWIF2bNnp3Tp0hn9SIi/YVVVDprj2GWJI0W1kkkx/O3UYZ2i4K5zwNViYemXE9m7ah1OHu6UKVeOIR27cerYcfr06UP+/PkpXbo0y5YtY+7cuRw8eBAXFxfq16/P/PnzmTFjBj169CAtLQ2dTseAAQOoV68eGzduJF++fGg0GrJnz87OnTvt536RoPGONY31pihuWtMIUAw4K/8cHGm0WrBY+H3URLbPWwRODkx0NFD2gx7UadmUXZ9NZPOMORSsWYUhK3/GL2tm+74rvprMyv9NROfsROuvv6CUXwgfde6O2Wy2h+0zZswgW7ZstGjRwt59WVVVoqOjOXLkCKXLleVKZg8OGyM5efYM7v5+HPxxAU17diUkODO5vv/Gfj4V25T3RStXcGzdJjwyB1K1Tm3ylyjG7kUr+PnjkegdHXFyd2XzD/Mo9U49mn46kPof9mbDlJlsHDcVi8lEWkoK/rlz8OmoL9i2bRtly5Rhz549XLhwgYiICPLnz2/vEN+kSRPmzZvH0aNHmTBhAr/88gtga3Dj5eVl7zg+ePBgFi1aRO7cuR96fNOnoL/uAd5fv5S5cOECwcHBODk5ZfDIhBBCCCHE20BRH1wl/g0UHx+Ph4cHcXFx9ioTIcRrLC0R7p6HWycg4a4tdFQAVcFoMvHlkp2MnL+REoUL8H7/DyheqgxRUVGcPHmS999/n4YNGzJ//nzS0tKwWCwMHz6cn376iaJFi6KqKrdu3eL06dNYLBYWLFhA79692bRpk33Nvfj4eHmt+A9QVZXd5vvstNzHWdXipej+MdQzG42s/GoyK76cSI4SRajQpinnjxzj8OrfKFCyONWKlKBokSL29Tu3b99O1apVGTNmDEOGDCEqKophw4axY8cOexfp1NRUHB0dqVu3Ljly5GDs2LG4uLi8tGuMshpZaYrkntVIsOKI9gmh5YPXmL1YIcoP6E6ijyvH5yzl5uYIRl3eD5dv82mFuuQpV5J+C/4PnV6P1WrFajYzsFAlUhISGblnA8bMPpxd8RtL3/uY4EyBjBw5kho1auDm5kadOnVITk5m6dKl9srG+Ph43uv3Ptv27KLSZwOIOXKaO8dOU6FNU3xDgslVpoQ9qEv3+7xFLPzsfxhNJoJrlOf6xp24+npRoWlD4u9GEZwvF3Xe6wbAph/m8tPAz5h4di/HNm5j4fAvMRuN1OjRkfof9CYqMZ4J4S3x9/AmOTIKgIoVK7Jnzx7Gjh1L48aNMZlM6PV6Ro8ezZQpU3j33XcZOHAgDg4OdOvWjZiYGFatWvXSnr/XweXLl+nYsSNms5kZM2ZQoECBjB6SEEIIIYT4j3qWfE0qG4UQr4/7N+HUbxB/17YwvKuv7b9/iIqMYfbGgzQpV4BFn7QFPxfIEkiePHkoX748t27dYvr06Xz//ff06dOHL7/8kgMHDrB48WKqVavGkiVLaNGiBZs2baJGjRoUL16cHDlysHHjRnvYKEHjf8NpaxK7LHG4osVTo3/i9glRMeyYt4iSjevy/vzvAagJzHIdztGN27DWD6dFixYcPXqUESNGsGXLFnLlysXYsWPp378/vr6+tG3blp9++ol+/fqRmprKgQMH+Omnn1i8ePFLDRnBtgblFnMMd61GsiiOaJ6iOvLBa2z107dcsCTjhQYHK1xfv51jly5QNjQPRcNrcO34KYzJKeg89LbuzgeOcO/KNXKVKUFqXDwuisKJ9VvwDcvF7dMX7Y1XmjVrxogRIyhXrhyHDh2iTp06ADi5uVKwa0uOpcSy8eP/4ezhRvtxX5CvUln7+B4MGiN+WcaMHgMo2r01pb7+BA90XN93mK3DxrH/ty00G/g+pRqFkxwXz6pxU9g+dwFmo5GpnftyattOavTsSIevR2E2GlGtVo7+sgInZ2ca/jiecJdAJvT7iFOnTnH+/HlbMxyr1d7Yp0OHDixatIivvvqK48ePc/78eW7fvs38+fMfeUzTm029zh5sbJTu1KlTfPzxx5QsWZJixYrx8ccf4+fnl4GjFEIIIYQQb5PX+x20EOLtEXsDjq2GhHvglRk8Mj0UNAIE+XvTqlY5YtOsnI21QuQFrEdXYk6IAaBjx46EhYWxYMECkpOT+e6772jYsCHVqlUD4OjRowD0798fgJw5c7Jp0yaGDh366q5TvLA41cQu830UFTyVJweNAF5BmSjTvBFJsfe5dfa8/fYsuXMRf+M2pgLZ2XHmOF26dMHNzY1du3axbNkyTCYTM2bMAKBcuXL83//9HydOnODq1at8++235MuX76UHjQCHzfFctCQTpDg8VdD44DXGx8Ry4vJFe1OcIzMXkrVyGZITE7lmTaX2+925fvIMF/YdAmyB2sX9hzE4O2FKTWNco3Z8mL88iVducOfcJSr36khAYCZ27dpFUlKSvZPzjz/+SFxcHADHLAlYi+fl/R+nMHzzcsbs3fhQ0PhXhWpWJme18lhdHHFDi2oxk6V0UdwD/UlNScW7VEEu7DvE51UacunAEbp+9xVt/jecs7v2kqdiGQxOjqz9Zho/9PyQgQUrsm7S9/iHZMaqWjgf4MRPy5fQtm1bVFVl8ODBjB49mpSUFABCQkKoW7cuJUqUoEKFCnz33XdERkZSs2bNR8b5OgeNqqpisVhQFOWRcebKlYvVq1czYsQIGjRoQEBAgHTPFkIIIYQQr8zr+y5aCPH2SL5vq2hMTbAFjZq/Xw+tS4PKJKcaWbL9MHgFo8ReR3d+C1hMZM+enaJFi3Lz5k0OHz5M4cKF2bx5M7GxsWzZsoVz584xZ84cKlasaF9zz8fH59Vdp3gpDpsTuGs1EqAYnmm/Kp1akZacwv6V6+23XTl6gqDcoXgVzMPY7ybj6OjI5MmTCQsLIzY2ltTUVMaPH2/vVt2qVStWrVrF+vXrKV++/Mu+NAASVTMHLQm4oP3bNSj/TpVOLUlKS+Xw7MUcHDmFiSGluX3oOMb4RBaVbcLKAZ+h+HtR6p16rP1mGlaLBY1Gg8HZCQVoNfoT2nz5KWMPbeWTDUuYeGYPJYe+R1i1ihw+fNi+FuWoUaNYsmQJ+/btI8ZqYo85Dmc0OGq0eAcHPnGcjr5e+Fcowd3dh7l35CQanW2ihSUlFc/QrMT4uLDq66nkLFWMvvOnU6JBHVw8PbAYTeh0Oi4fOo5f1hCMqakoGg2mNCM5SxUjV1AW7qhGzliSuHHjBu+++y6dO3dm7ty5VKtWjS+++AKA1q1bk5KSQkxMDBUqVADAZDI902Od0dLXjkxKSmLs2LGMGjWKiIgIoqOj0ev1DB48GL1ej4eHh317IYQQQgghXgUJG4UQGe/2Sdv6jB5B8IQPxDlDMlE4Vxa2Hz7Nzah4FM9grHcvYLpt65BbokQJoqKi8PPzo3///hw/fpzixYtTr1498ubNS6tWrZg2bZp0l/6PSlTNnLIk4aHonrriL11AaHayFsrPqe0RLB05nv55y3Dk1020/OJjMjm5EW81cS82Bnd3d6xWKxEREfTq1Quz2cy+ffvsx/k3KhkfdNGSQqxqwuspqzYf5JYjKx5hOTn67Wwub95J47kT6XZgLS1XzSR8ykhOz1nKvk1bCX+/B2d27uX83oMAFKxWCYvFyuVDRynZuC4BodmwWq04OtoqD3O2a8SVq1f59ddfsVgshIeHU6BAAeLi4jhrSSRONeOj6J860IqxmsjRtiE6nZ6Lv/3O7YPHmFu1Jdd37qdIu3dIxcqF/YfJlDM7bj7eANw8c47gfLk5uS2CtOQUvu/enxsnz1K7dxe+ObWLuv164uTsjMFs5YQlkRMnT2AwGBg8eDDbtm2jYcOG7N69m7i4OAoVKkSpUqXYvXs3u3btAkCn+++tLDNr1iyCg4NZtWoV+/fvp3fv3gwePBiADz/8kLS0NA4cOIDFYsngkQohhBBCiLeJhI1CiIxlSoVbp8DBFZ5iyqKiKPR4pxoJSSks2bIXdAZQFPRR50FVOXDgAF5eXnh7e1OzZk1+//13pk6dyv379xkxYoR93Tbx33TFmkq8asLjOZYcVhSFat3aEXX1Br9Nn0Xzzwcx8exeCtaojDMa8jUN5/rVa5QrV47Q0FDmzJlDly5duHHjxivtSn7GmoQB5YkNYR4nFjN5ujbHL19OwlrUJ1vVsmi0tt+rwp2aYzVbiI6MJHOpIvhkDmLHT4sB8AkJokTD2mz5v5+4cuQEYJtCrNFo8FT03EtMwNnLg1KlStk7HB89epRGTZtw0pqEM9pnqpyLUk24BwUQVLIQ+6fMYUH9zmSpUJJep7eSq241NCYLORvXYskX41nxv0mMb9KB83sO0m/BD3xzajdhlcvimzWEvvOnE96vJ86eHrZO3ICv3onbxmROnjlDlSpVAAgODubjjz/m119/tVf6NWvWjJs3b3L37l3g9a38+7ugMDIykjlz5jBp0iQiIiJYuXIlH374IT/++CNbt27F19eXli1bMnPmTCIjI1/xqIUQQgghxNvsv/c1vhDizXLvIiTFgFfQU+9SKGcWcoZkYuPeE7SrUwEfVx+IvsrWtcvYvn07w4YNw9fXF7CtXZYrV65/a/TiFbtnNaLCcwVxACEF8hFasiiJMbGUaFgHrU6H2WhEZzCQu2wphm1YjLrtED4+PvTq1evlDv4ppKgWoqxGXJS/X0rgn8SrZvzz58EnTyhXNkeQv2UDnH1tlYEXN2zHLTgAj7yhpKhWPv51EX5ZMwOgMxhoOeJjhpapxQ/vfkjHb0bjmckPnd7A3mWr2TxvISXKlaFGjRr2cymKQozVSLzVjKfy9G8nLKgkqxb0aCjSpSVXt+0mW9VyVBk5EACrxYKTgwMlPnkPP4MTxzdtJyBHVrpNHY9nJlv360odWnI2Yh9Hft1MtsIFsFqt9rDwwu97uHj3Js7u7gQFPfq6Yjab0el0VKlShe3bt7+2jVPSu3enh7uRkZH27t8A169f5/79+7Rq1YqTJ0/y6aefsm3bNurWrWvfbtSoUeTMmZOIiAiaNm362gaqQgghhBDizSKVjUKIjHX/pm3qtObpwwqNRkOPxtWIjktg8/6TYHBm6A8r6PpeP8qUKUOzZs3+xQGLjHTbmobDC/zTpdFoqNqlLUn349izZLXttj/CHCdFg1eJAnz0ydAMCRoBYlUzqaoFJ549bLT+EeIZNFqKdGlJSmwcV7fvAeD3Lybya+9PyFalLIHlipOCxR40pvPPkZXesybjmcmfMXWa803Lboyq3YxtsxdQc9B7tJ027qGwK328RqzP9JykqBbMqOhR8MweQmCJQtw5coqEW39WGGpV0Lq7UHfUYIauX0SPGd/gmckfq9UKQGCuULIUDOP0jt0kx8Wj0WjYtXA5g4pW4evmnTEmJDEhYv1jK1LTp0urqvraBo3wZ6XlmjVrKFOmDHXq1KFhw4Zs3rwZgJMnT9qqdatVo1y5cri5ubF9+3bWrFlD/vz5MRqN5MiRg2rVqjFq1CiioqIy8nKEEEIIIcRbRMJGIUTGMib/Y0OYv1O2YC6C/LzoNfZHHCp05NcDZ5n2xSCmTJmCt7f3vzBQ8TpIwoz+Bf/pyl22BF6BAUT8vBRjSoo9bNShYEYlDevLGOpzSVMtmFDR8ewVaGZULKhogODSRXAN9GdD/xGM8yrA+TWbCf9uNHWnjcHg7ooF9bHHKFavFh+t/Inxx3fQesww3p//PRNO7KRsm6YkqmZU9eH9UlTbFN9nqZgzo2JFRYOCoigU6dIKU3IyJxes4o+DodVoUAGLoqDV6bBarVitVnvXZUVRqNq1LWlJyXzX8T0+CCvHrH5DKduiMf8XeZaynVtjMmj/ca3C16nK76+Pa7q5c+fy/vvvU7duXb7++mtCQ0Pp168fa9eupUGDBpw9exYPDw8OHjzInDlzKFiwIFarlUWLFrFo0SIAvvzyS4oUKfKvrzUqhBBCCCFEOplGLYTIYOoTm8I8jk6npXP9yni5udC9cTVKBzlA7jL/wvjEm0ar01GxXXMu7D2E2WjC4OQEgPIcAd/rSqPTUah9Exw93SnSuQVBJQs/dP/joy0brU5Hppw5yJQzx1Pv8yL8C+TGyceL+Jt3sJhMaB+zrqrmMeu5ZikYhpuPF5GXr1H/g95U69bOfp/FbAYt9inIr4v0qdEPslgsj4zTbDZjNBpZtGgRvXv3ZuBA2xTz27dvM2nSJDZt2kS9evWoV68eN27c4OrVq+TMmROLxcLSpUuZOXMmLVq0wGKxULJkSWbPnv2qLlEIIYQQQggJG4UQGUzvDBbzc+1av2Ix6lcsZvtLzDVbsxjxRnNCSyLGFz5OsXq1KF6/9kO3mbGiRXmhadovyqBo7BWWhmcMP7UoaFDsdZk5w6uSM7zq3277dx5X8WdWVTw0ukfuc1BsFYiPC9H+aZwKtmnfWhQUjYb6M8bi6Olu3ya90k/3D8fUaDT0+nEyzh5/7mcxm22VqjotTq/Z5I3ExERcXV0B7GtMpq/JmJiYyPLlywkJCaFKlSrodDqSk5M5dOgQEydOZPLkyYwePRp3d3e+/fZb2rdvD8DYsWMZNmwYtWvX5p133uH06dPcvn2bjz76iA4dOrx2YasQQgghhHg7vF7vxIUQbx+PTKBawfr30x2fyJgMWgO4vr7rr4mXI1BxIO0l1Ng9LhhLUa14K3oclIz7p9FL0eOoaEl9jqncWhScFQ3mf3h80kO8Zw3iUrEQqDg8cru3oseABuMzPCeOigY9D48zPWhU/1iT0fzHVPInjTM9aLSYbV9YaHW2QNSKiq/m9fnyYe7cudSpU4ezZ88CtqBUURRSU1NZtGgRQUFBjB07lvr16zNq1ChiYmIwGAxkyZKF3LlzM3/+fMaNG8exY8fo06cPbm5uXL16lRw5cjB79mxWr15N+fLl6dOnD9HR0QwZMgT9YypEhRBCCCGEeBWkslEIkbH8QsHZE1LiwOU511pMigWfLOAZ+FKHJl4/vhoDqkXFqqpoXvKae2lYCdY8Gqi9Sk5o8FL03LGm4f4MHZ7TuSs6YlSzbc7zYx6e9BDP8Rm6XVtVFRXwf0x456XocdNoSbCacVCeLtzTo8FR0ZCoWvjro638MV3aiIqDonnqKlOt7s/Hyqxa0aDgrTw+bLNYLPaw79+WXvFZsmRJKlasSPbs2e23jR8/nhMnTmCxWFiwYAF169Zl5MiRLF26lMDAQLp27UqlSpW4desWmzZtsldFAsycOZO7d+/ywQcf4OzsTHh4OOHh4Q9do1Q1CiGEEEKIjCKVjUKIjOXgCpnyQko8/E2ThH9kMYNqgcD8kIEVaeLVyKZ1xE3REc/zTb3/OymqBQMK2bXOL/W4z0pRFPJqnUnDivU5fh+8FD16FEx/U2mYioqbosP5Gf75j8OMu6Iju9bpkfscFA35NK4kYPnbJieP46sYMKM+9ldeVVVMqPgrhucKlGNVMz4aPcEax8fer9VqURSFc+fOce/ePfvt6Z2uX6b0QDNfvnxkz56dM2fOcO3aNft9y5cv5/r169StWxeATz75hNDQUNasWcP9+/dp1aoVBoOBtm3bsmzZMk6cOMH777/P2LFj0el09s7a6dIb4qQHjXFxcc/0vAghhBBCCPEyyCdzIUTGC8xvq2qMu/NsgaPVCvdvgleIrUJSvPE8FD25Nc7cf0xn5BcRjYlgrSPBj5kq/Krl1DrjruiIe45A1VXR4qHoSMbySEcXi2rrAu2nGJ66qs+qqsSpZvJqXXD7m0rLPFoX3BQt959hvN4aPQ5oHtv5OwUrDmj+tjLxSeNNwkJ+jYt9Onx6AJf+87J582YKFChAlSpVqF27Nj179kRV1cc2oXlej+uCvW/fPlq0aMGUKVMA6NatG0WLFkWn05GSkgLYplc3aNCAmzdvsnLlSooVK8b8+fOJiYnhiy++oF69ehw5coS5c+cyZMgQDIaHq0nTQ8YVK1ZQqVIlhgwZwvXr11/adQkhhBBCCPE0JGwUQmQ8Nz/IV93W4CXu9tMFjlYzxF4H9wAIqw36x1cxiTdPcZ07noqee6rppRwvXjWjUxVKaz1e+tTs5+Gh6CmkdSNONWNSn7XaTiFE44gBhRT+DLxUVSURCx6K7plCvLuqEX+NgZI6j7/dxl9joLjW/ZnG64CGAI2B1L9UcJr/qGoMVhxweoap3ukiVSO+Gj35tC72cDE9gFMUhbNnz9K3b1/q16/P5s2b6dy5M0uWLOGDDz4gLi4O4IVC7L9WFqaHiABFihShTJkyHD9+nMuXL+Ph4UGdOnVITExk06ZN9u0aN25MYGAgmzZtIjo6mtKlS7Njxw5WrFhBREQEv//+O+XKlXvsOO/du0fdunXp3bs3FStWpEGDBo9UPwohhBBCCPFvk7BRCPF68MsJBcLB0RVirkJi1OObxpiNEH8XYv+oaCxUH1x9Xv14RYbx0Rgop/PAiJVE9cWmU6epVmJUE8V07mTTPDpNOKOU0rmTRePIbdX4zOGXq6Ijs8YREyppf4R/qVjRoZBF4/SPHZ4fFG01oQAVdF5PXD+yhM6DHFonbqppmJ9yvMEaBzwUHQl/VGFa/ghEfRQ9mZ5j7cwk1YJZUSmv88JT82f15vjx4/Hz88NoNLJ06VJUVeWDDz4gX7589O3bl0mTJrFw4UL27t0LPL550IP++nw8+Pf0kHHs2LFUqFCBzp07s2zZMuLj4zEYDNSoUYO4uDiWLl0KQNu2bdFoNGzatInU1FQAPDw8qFKlCrt372bLli32Y2fLlo3MmTOjqioWi+Wx49y9ezexsbFs2bKF0aNHU7duXYKCgp71oRRCCCGEEOKFSNgohHh9+IVC0aaQsyLoHGxTpKOvQuwN25/oq5AQCc5ekK8mFGkkHajfUgW1bhTXuROjmoh/zsAxRbVwW00jTOtCWZ3nK2kY8rQcFS3V9N54KjpukfbMgWOgxoFgjSNpWImzmjChEqI44vEUTWesqsodaxpGrFTWe5NH8+R1LB0UDeF6X7JqHLmhppKsPrm7vA4N2TVOOKIhVjWRgAVvRU+oxhntMz4XiVYz91QjRTRu5NO4YDabmTlzJrNmzeLgwYMMHToUg8FAXFwczs7O+Pv729dobNeuHc7OzuzYseOpzpX+c3L58uWH/g6wdetWihUrxoIFC2jZsiWOjo5MmDCBSZMmAVC7dm1y5szJli1biIyMJCQkhCpVqnD06FF27dplP06bNm0YNWoUVapUITIy8pHza7VaEhMTmTFjBjt37uTWrVsAnDhxguvXr5OcnMz69ev55Zdf+PHHH9m1axcm08upBBZCCCGEEOJJJGwUQrxeXH0hZwUo3R4K1oPspSGoAAQXghzloOg7UKoNZC1uay4j3kpaRaGyzpsyOk+SVDO31FQsTxnIWVWVSKuRKNVEIa0btfW+9vX9XidBGkfC9b64Kzquqan2KsWnoaAQotgqB82Krcu1u6L9x9BSVVXiVTPX1FRcNVrCDb4U07o9dQjroehpYPCnkNaNGEzctqZhfMKYDWhsVZMKGFDIpjhieIbnwmJHTKPfAACI0ElEQVQ2c8+cSgxmimvdqar3RqMo7N27l8mTJ9O/f39KlSrFgAEDAChUqBB37tzh7NmzaDQa0tLSAKhevTq7d++2Pw7/JDU1lWbNmjF8+HAA7ty5w/r16wGIj4+nXLlyHD58mL59+zJ+/HgUReHnn3/m4sWLeHh4UK1aNWJiYli2bBkA7du3586dOyxdutQegAYEBFCgQAGKFi3K0aNH7edWVZW0tDQ+++wzMmXKxPfff0/fvn2pWbMmly5don///uTMmZMKFSowYsQI5s+fz9ChQ3n//ff58ccfn+r6hBBCCCGEeFGv36crIYQAMDjZQsY8VW3Tq/PXhtyVbNOttc/eOEK8eXSKQmWdFw0N/vgoBq6rqdyyppGiWh7p5KyqKqmqlbvWNK6pqTgqGurofQnX+z7X2oCvSlatE+/oA8ipdeaummarOHxCgGdVVWJVE9fVNHJoneloCKKczpNkrFz94zGKVU0kqmYSVDMxqomb1lSuqqkYsVJU60ZzfSbCtK7PXO3prugI1/tST++Hn0ZPJEauWlO488c541QzsaotiLxiTSFKNZJT60yHP8YYhYlIq/GJaz+qqkqCauaGxoRWqyHnxSjMm/cTfddWBVi0aFEaNWpEUlIS9erVs++XL18+cubMydixYwFwcHAgKSmJEydOULhwYeCfp1FbrVYcHR2pWLEix48fp2zZsgQFBbF8+XIAwsPDGTFiBNHR0bz77rvkzp2b5ORkFEVh+vTp9m2yZ8/Oxo0bSUhIIF++fHTq1InmzZs/1KSmQIEC7Ny5k5o1a9pvUxSFI0eOsGrVKlasWMHBgwc5fPgwDg4O9OzZk4SEBJYvX87hw4dZsmQJM2bM4Pz58xgMBs6dO4fVan2tKniFEEIIIcSbSVYNF0II8Z+lKAq5tC4Eaxw5Z0nihCWRe1ajrcuxmh6qqKjYqui8NDoqaN3IpXHGU/PfCK39NQYa6/05oUnkqCWBSNWIxarigAZHRYMGBRXb+pOpWFABD0VHWZ0nxXXuuCs6VFXlrmrkkiWZW2ran48RYFA0BGucyaxxJIfGGZ8XfFy0ikJ+rSt5NC5ctaZww5rK7T/CRisqGiCz1oEgxYHMGieyaBzRKgom1coRcwLHrAncsdrWqnREi6OiQYuCikqK2YRRq2BUrbhqdHhevMuC/sM4dvAQefLk4eLFi3z77be0bt2a+vXr8/PPPzN79mzGjBkDQMGCBenevTudOnVCp9NRr149tmzZwu3bt2nRosVjr8dsNtubrFgsFjQaDWfOnOHYsWPkzZuX48ePkz9/fttjaTDg6upKt27diIqKYtWqVZQrV47mzZvz66+/MmjQIDJlykTRokWZPXs2e/bsoWbNmgwZMsR+PqvVag8ds2XLxqFDh0hNTaVcuXIATJ06lfDwcGrUqMGuXbuYPn06x44do1+/fmg0Gry8vPDy8kJVVRRF4fz586SkpFClSpWX2nFbCCGEEEKIvyNhoxBCiP88Z0VLEZ07BbVuxKgmolUTCaoZMyoaFNwULd6KHh9Fj/41nDL9JHpFQ1GdOwW0rlyxpnLb+meFogUVBfDU6AhUXMikcSBU64TrA+szKopCJsXB3njFpFrtYaMj2qduGvMsdIpCqNaZUK1tzUezqmJBRYvy2PPpFQ0l9R4UUt24bE3huiWV22oql69c4cr+IxRtVh9HvZ4sioEgjSM5NE4M+eYL/Dy9OHv2LP7+/kyaNIkhQ4ag0Who2rQp9evX59dff2XIkCG4u7uj1+tp164dqamprFmzho8++oigoCAWLFhAqVKlHhpPeuiXHjSeO3eO0NBQALp06YKjoyNHjhwhKirKdn1/hJLHjh1j+fLlrFmzhgoVKgC26dVnzpzhm2++YcyYMXTs2JGmTZvajwe2IFOr1doDwbVr11KvXj06depErly5yJo1K8HBwVgsFnbs2EHDhg35/fffadKkCUePHrUHngCbNm1i27ZtXL58mRUrVtCyZUvKly//sp5aIYQQQggh/pGEjUIIId4YWkXBTzHghyGjh/Kv0CsacmmdyfVHgGdSrX+EjQoGlKeeIqtXNOhf8UoqOkVBx5PH56BoyKt1Ia/WhaSkJAZPW8SWlav4qkkHFs6ey4Gbt/joo4+4evU0e/bsYfv27fj6+rJixQrmzZuH2WzGYDCg0+moUaMG27dv58cff6R///6YTCb0ej3dunWzVx/6+vo+dhwPhn7vvfceVquVEiVK0LNnT2rXro27uzvt27dn3bp1VKhQwR5KxsbGkjdvXs6dO0eVKlVYsWIFXl5e9OrVCwcHB1RVJSAggICAgIfOl97J+tKlSwwdOpSVK1dy6tQpBg0axKhRozh27BjBwcFUqFCBFStW0KZNGy5cuGAff3oX6ipVqpAtWzYOHz5MUFAQ+/fvJyws7LmfNyGEEEIIIZ6VhI1CCCHEf5QtNHyzpE//VVUVFxcX6tWuw5IFC8kVFIKTkxNjx47F09OT69evc+vWLRYtWsTEiRNJTU2lR48e9OzZEz8/W5f6smXLUrZsWWbPnk2fPn3Q6/UPnefBoDG9sjD9/MuWLePUqVPcv3+fTz75hJCQEMaMGcOIESMoVaoUefLkoXTp0uzbt499+/ZRtmxZwNaEplixYgwZMoTJkydz5coVpkyZQuvWrTEYbCF4eiOYB6c1X7hwgeHDhxMTE0OOHDm4desWXl5eZM+enbFjx7J27VqqVKlC0aJFCQ0NxcnJ6aHxL168mOXLlxMWFka+fPlYunQpjo6O/94TJYQQQgghxN+QsFEIIYQQGU5VVaxWq73CT1EU0tLS2LlzJ0lJSQQGBnLq1Cl7BaGDgwO5cuXi448/ZurUqdStWxcvLy8Ajh07RlpaGiVLlqRs2bIcPXqU06dPU7BgQfv5/loFqtVqSUtLw8HBgcjISJYvX86yZcto1qwZX331FRqNBkdHR/r378+kSZP4/PPPad++PX369GH9+vX2sNHBwYGpU6dSs2ZNoqKi6NixI87OzvZrVFXVHjKmB5wAPj4+pKWlsWPHDtq3b4+XlxdGoxGDwUDnzp2ZMWMGBw8epEKFCnz00Ud0796d/fv3U65cOdavX09sbCyfffYZefLkAZCgUQghhBBCZJj/3sJVQgghhHhjWK1WezWhVqvl5s2bzJw5k4sXL2IwGBgxYgRz587FxcWFH3/80b6fn58fZcqUwdvbm7Zt29qDxrt37zJu3Dh27twJQJMmTdi5c+dDQWN6ZeGD9u7di5ubG3fv3sXf358mTZrg4eFB7ty57eFg0aJFqVmzJgsWLMBkMlGqVCnKli3L4sWL6dy5M56envZGLc2bN6dXr172oBFsAadGo+H69ev07t2bFi1aMGHCBK5evYqXlxctW7bE2dmZu3fv2rcH6NWrFxaLhbVr15KQkEC7du1YsWIFjRs35s6dO3Tr1o2bN2/So0cPaQIjhBBCCCEynLwjFUIIIUSG0Wg0KIqCxWLhyy+/JFeuXIwaNYpatWoxcuRIdDodJUqUoHDhwvz000/2/by8vOjatStubm4UKFCADz/8kMGDB1OoUCFu3bpF5cqVAR4K+x4851/5+/uTNWtWRo4cCUCpUqWoXr06a9assW/j4eFB7dq1URSFadOmAfDBBx8wcOBA7t+/z/jx45k7dy4ajYbExES2bt1KbGzsQ+dZuHAhJUuW5M6dOxQoUIAVK1bQvHlzbty4Qe3atalYsSK///47ycnJ6PV6TCYTTk5OtGvXjg0bNnDw4EEAateuzaBBg/jpp5/o37//iz0JQgghhBBCvEQSNgohhBDilflrVaHZbKZXr140a9aM+/fvc+DAAXbv3k3Hjh0ZMWIE165dIyQkhBo1ahAVFcWSJUvs+xYuXJg1a9bQrFkzbty4wYkTJ/jhhx/YvHkzxYoV+9tzJicn06NHD4YPH26/LSQkhD59+vDDDz+QlpZGcHAwtWrVIj4+nkWLFtm3K1q0KFWrVmXcuHEAZM2alS5durB8+XK6devG9evX6dChA2FhYXz22Wds374do9GIqqqkpqaycOFC+vTpw7JlyxgxYgQTJ07kwIEDzJ49Gw8PD2rWrMmdO3ceCjnBVt146dIlbty4gaqqL/5ECCGEEEII8S+RsFEIIYQQT2YxQUIkRF+F6Ctw/yakJcJTBl8WiwV4tKpQURTy5MnDypUruXfvHrlz5yZTpkwMHDiQAgUK8MUXXwBQoUIFihYtyqRJkwBbQ5W1a9fi7+/P559/zqxZs1i7di0NGzZ84jnNZjMODg7MmjXLfptOp6Nu3boEBATYqxvLlClDwYIFmT17tn07Hx8fatWqRf369R+pWly8eDGVK1cmJSWFX375hbFjx1K2bFkMBgOKopCQkMC+ffvo168fERERlClThpo1a9K1a1dat24NQHh4OAEBAaxZswZVVdHr9ZjNZvz9/Tlx4gTt2rV76q7jQgghhBBCZAQJG4UQQgjxeOY0uHMGjqyEiB9h389wcBEcWAT7F8DuObbbLu2BpJhHdjcajYCtMUp6I5SZM2cyYMAAlixZgtFoRKvV0rRpU0JDQ/H09ESn09kaxTg50vn99/hp/ny2XD9HUogvVZu/w9Vr1yhSpAi5c+cmIiLC3rk6fbr04875448/PnROd3d32rZti9VqtU+HBggOzELLxh2Y/O0U7hwFL0suKpWtwZkzZ1i1apV9u4YNGzJt2jT7OpGqqpKYmMicOXNo3LgxCxcupHz58pQtW5aAgAD7ftHR0fj5+REaGkrTpk0pX748R44c4YcffiA0NJTk5GSyZctGmTJliIiI4NdffwWwN8XJnDnzy3lehRBCCCGE+BdJ2CiEEEKIh1ktcOsE7J0PR1dB5HlQLeDkAR6B4BkErr6g1UHiPTi3FfbNh9ObIC2RnTt3Uq1aNaZMmQLYqhcPHjxIgQIFGDNmDNeuXaNz5840a9aMEydOEBISQrt27ZgzZw5XTUlstcQyK+0miXXL4BocwBfffsNqYyTRtYvTevF0SvZoy5br5xk2eiQODg4oivKP5xw9evRD5zxz5gylSpWiQYMGTJo0ibvH4eAM2P6xA+rRQiQkxPNFt9lsGwHGbcXJ7FCMM9vukxRpe3geV52ZkpLCb7/9RpMmTez3x8fHExUVRUpKCgC+vr7kypWLTJkycfz4cSZMmECWLFkAmDNnjr3SslGjRgwdOtS+7qQQQgghhBD/JRI2CiGEEOJPqQlw4lfbn9Q4W7joFQzOXqB3BI3W9kdnAEd3cA8A76ygNcDV/aTsms/nQwezbds2du3axbVr1wBYsGABvr6+nD59mqVLl7Jnzx4uX77MlClTMBqNNO/UHqNqpf/kcewzx2FSrQR7+VCjU1t+n/A9PvFGAhUHshcII3fnZuz2VlhgvM05SxLJycl8/vnnjz2nn58fZ86ceeicEydOxGq10rBqW25fu8fYdxdx8TdbIec98zl0Wj0zDnXHMxuEBhWiX4lFZL3SgS3DYGz/n4iJjH/kYfP09KRw4cIMGDCA//u//+Pdd9+lTZs2NGvWjFy5cvHll1/i5uZGw4YNiY+P54cffuDatWskJCTw448/MmXKFCIjIzGbzYSFhdG1a1dcXFxe5TMvhBBCCCHES6HL6AEIIYQQ4jWREgcn1tnWZHQLAIPT0+2nKODkjkXnhFPSPTIZjGQODMBsNrNu3Tp69OjBtm3bqFu3LgaDAVVVyZ8/Px07dmTVqlUs+30LSRULUKhdE3ZOmUXz93raD121c2u8gwNx8fQAwF1rwB2wqCp31TRWpNymuIMX/pkCyJw582PPqdfrHzrnypUrWTxpF86nK1I2sDVT9nZArR1N2tUUzkRGMLr1au4nRaJzAFdfDVYfK3O2DWfZ2m/RoCdEKUuDQe64BdrGmL624syZMxk1ahSffvopRYoUoUyZMri6unLlyhWmTZuGxWJh2LBhJCYmMmTIEBYuXEhiYiLJyckMHz6cXr16vdznUwghhBBCiAwgYaMQQgghwJQKJzdA1BXwymybIv0PjCYzo2etIOLoWYZ0bEiNUgVRFQ1WtwDyZw3AYrWSakpm69atdOvWDaPRSGpqqu1UJhMGg4HGjRszZfo0frtxnrxqHup1aMPuKbPYPmcBlTu2AsDN14cKbZoCYDYaWfG/SZzbvZ+GA/tQoHol7pPKHmMsDnmzU85iJS019R/P2ahRYyZ/PZ29S29QNZ/CwDYT0a5NZd3hmSSnxdOt+hhKhtay7WMx8vOOL5n7+0hyBxVnUOMfKZe9CdHnYO8kKNHHgkewxt6wpVChQsyZMwdHR0cURSE1NRVHR0fAtl7j4cOHSUtL491336VBgwacPHkSq9VKnTp1XvrT+SaxWCz29TeFEEIIIcTrT6ZRCyGEEAKuHoCoS7Yp008IGgGi4xKYuWobWw6c4r2vZnPk3BV0Oi0arZYL9xIpkTOIhkWycP7cWVavXk23bt346aefSE1NxWAwAOCbLYQ7d+6QqlgJxoGgXDloPWYYmfPnfew5E6Jj2TZnAae2RTC7/1CuHD2Bp94Rf60jJ86fI7BYfho2bMj58+f/9pyOMdmJvHcHvZMGjxDQaXV8UH86Y9v+yry+56gc1sx+vrjkKNYfnU3FfE2Y1m0vlfI1QecIfmEQfQ6O/qjFkqawZ88ehg0bxt27d3FycrKHjw8GjUePHiVfvnw4ODhgsVgIDg6mVq1aEjT+A6vVCmAPGtP/rj5lB3QhhBBCCJExJGwUQggh3naxN+HaIXD2AK3+qXYJ9PWifXgFGlQshp+XG+N/Wsvctb8DkDMkE2sPXaRtmaz4uehZuXIltWrVwmw2M2zYMC5cuICqqoycPQPv0GwUKVoURVEwODpSb0AvQksUeew5vQIDqNC6KUXr1cTN14d130xnx0+LcVG0ZArNxtq166jRuhl+fn6PPWfqfZg0Yh6ZXHNRKKyY/bhajQ5PF79HzufrFkS1/K1ISInhzv2rAFisFpYdmMSBtNmc3nObQmHFqFOnDnfv3n1ojcWoqCiSkpI4dOgQ7733Hh4eHjRv3tx2PqnS+0fpoWJ6o5158+bRqFEjPv74Y06dOmUPc4UQQgghxOtJwkYhhBDibXf7JJhSwNHjmXbrVL8SJrOZ4nmzU7dcEfqMn8PB05fI7O9NoK8XejdvahUK4cypk9y7d485c+awatUq6tSpQ7kqlfi270eUaFSHoJw5nvqclTq0wGIyk71YIQrXqcbsAZ9w+dAxMmfOjHMmP04oKdSqVYszZ848cs6KFSrz7YZeVC70DkFeoU91vvCiXTBZ0lh36P/4cetw3hnnx2/H5hLom4VozRlK+DZnz9Zj/PDDD7i6ugKwceNGWrVqRdOmTalSpQqKovDzzz9TuHDhZ3p83yaqqtorFtNDxhMnTjBq1ChGjhxJtmzZWLFiBW3btmXOnDnAn6GkEEIIIYR4vciajUIIIcTbLPk+RJ4HJw9bo5dnkDtLINkC/Th56QZfvteKo+ev8c0vv3Ly0g3yZgtCcfakYfEcrD9+kwULFjB58mS2bt3K6tWrORh1k6pzJpA/U+ZnqlQLzBWKX9YQbpw6S8uRH3Pt+Cl+/XYGN06fxS93KBfUFCrXD2f9+vUPnXPF0tUcWBzLe80XkS1vwFOfL9g7J9n9C7Jw93hCAwrzWfPFFMxSAYPOATUr5DxRFe1toOif+xQtWpTGjRujqio//fQTvr6+z/Covp3++jPQt29ftm7dioeHBz/99BOlSpXi+vXrTJgwgUGDBtGxY0d7KCmEEEIIIV4v8i5NCCGEeJvF3YbURHB0e+ZdFUWhxzvViE9K4ef1uxjbtzV1yhbm6Plr7DxyFqPZSq4sgRQPDWTTpk1s27aN4OBguvbsQfGPehAYkOmZp8QqikLVrm1JSUhk14LltB49jEK1qnLt2Cku7drP/bQU9KEhFC9e/KFzNq38LnWDPiYkx9MHjenna1C8J9n88lO9YBuK56iO7o+p5ooGDK5wfRc8uIygr68vffr0oW/fvhI0/o2/rrtoNBr57rvvWLhwIQDvvPMOCQkJxMfHU6pUKQBCQkJ49913AZg6dSog1Y1CCCGEEK8jqWwUQggh3mZJMYBqS86eQ6GcWciTNYgV2w/wTpUStAuvgINeR4l8OTDodWBwokuNolRq2pmqVasCEKOaSLZa8FSe721IloJhBOYK5cDqDZRoFE6FNk3ROxjIXqwwqQYHoqxGunTpQqVKleznTLgJVgtoDc9+vhwBBcnql48DF3+jZqF2uDt5Y1WtaBQNDh6QeBfS4sDR87ku563015D55s2bLFmyBBcXF1q2bEm1atWoVKkSR48e5cSJExQoUACArFmzUrduXdavX0/v3r2lulEIIYQQ4jUk79CEECKDmc3mjB6CeJslRj51U5jH0Wg09Ghcjei4BFb+fhCA5jXKkD3Y31a9pncid6AHdauWs+9zXzWThhWH53wbotFoqNa1HYkxsRxcvQGA0k0b4J89CwYV7qhGcufOTd26de37xN+A582lNIqG+sV6EJ8Sza6zqx66z+AKxkRIvPN8x36TGY1G+///tZLx0qVLzJw50/737Nmz07RpU+7cucOaNWsAaNq0KVqtlvXr19u3c3Jy4vLly4SEhEhVoxBCCCHEa0rCRiGEyCAWiwUAnc5W3RUdHZ2RwxFvK7MRNC/WHblswVwE+Xkxf30EKal/BkyKotgqJq0WsPwZqptRUXi0uu1Z5CpTHK/AAHYtWI4xJcV+u07RkIblke1NSaB5gfkc+UPK4uMWxG/H5pFmSkHzRyWoVg9WM5jTnv/YbxqTyUSHDh3o1KkTYJvqnP5c79q1i7i4OKZNm8b48ePZt2+ffb9q1aqRKVMmfvnlFwAaN25M1qxZmTZtGuvWrSMuLo6jR49y+/ZtSpYsKVWNQgghhBCvKXmXJoQQGUSrtQU8e/fuJVOmTIwfP56UB0ITIV4JRfvwgoPPQafT0rFeJUoXCMX4SKXuH1O0H5imrdhufSFanY6K7ZoTWrIoZqPpwbOh4dEQ80UvU6vRUbtwR/IFl8ZkeaBiz2rrq/OCee0bRa/XU7JkSVasWEFMTIw9FJw3bx6tW7fGbDbTsGFDgoODWbJkiX2/sLAwKlSowKlTp9ixYwcAbdq04d69e/Tt25du3bpRtmxZihcvTsOGDTPk2oQQQgghxJNJ2CiEEK/AX6cQAqxevZp+/fqxY8cO3n33XQYNGoSDg0MGjE681ZzcwWJ68nZP0KBiMcb0boWHq/PDd5iNtvI/g5P9Jle0aFEwqy82DbZYvVq0HPkxzh7u9tuMWPFSHp0W7prpxS+zXO4GdK8+BldHD/ttphTQOcl6jWB7nUt/rWvWrBmBgYF89dVX9vsWLVpE8+bN8fHxoWLFihQoUIC9e/dy8uRJ+zGqVatGbGwsS5cuBWzVjWXKlKFy5cq0atWKGzdu8PPPP+Pt7f3qL1AIIYQQQjwVCRuFEOJfZLVasVgsj0wXVVWVu3fvMnnyZObOncu7776Ll5eXTAsUr56bHy9eZ/gPU6JNqeDoDg5/drv20uhxUjSk8GJh4+POaUYlQPNoFxi3IEB9serGx53PmACOHuDybE2u3ygPvs6lP0aBgYH07NmTqVOnkpqayrVr1zh9+rS9mzRAvXr17CFkOhcXF5KTk1m5ciX79u3DYDBQv359Tp48iaIoeHt7YzKZZL1GIYQQQojXmHyqFUKIlyQqKop79+4B2D8IazQatFot9+7dY9KkSaxdu5Y7d+6gKAoNGzakTJkyhISEkClTpowcunibufraFjM0pf47xzelgGewba5x+inREqBxIJ6X2xwpTbWiQyFAebRC2CuHrfowJealnpKUGPAv8EI9dv5TIiMjiY2Nfei2B1/nRo0axZw5czCbzbRp0wYHBwfmz5/P8uXLKVasGDlz5iQ11fazVrVqVYoXL87SpUtZv349KSkprFy5kvDwcKpWrUpSUhIAHTt2xMXFhZUrVxITE4Ner5cvZoQQQgghXmPyTk0IIV6CI0eOEB4ezjfffAP8WQGVkpLC4MGDyZo1K8uWLeOjjz6iVatWrFu3Dn9/f1q2bMnWrVsf+fAuxCvjEQQegZD8L/wMmlJtQWZAroduVhSF/FpXLKqK+QXXi3xQjGoiUONAFo3jI/e5BUFgUUi8/dJOhzEJtAYIKffkbd8E69evp27duhw9etR+m6qqJCUlMXDgQLJkycLWrVuJi4vjxo0bZM6cmZ49e9K7d2+mTp3KvXv3uH79un29Wp1OR48ePQgNDaVr165kyZKF5cuX8+GHH/Ljjz9StWpVVFXFw8OD8uXLEx8fT2JiYkZdvhBCCCGEeEqK+riFxN4g8fHxeHh4EBcXh7u7+5N3EEKI55Camkrv3r25c+cOP/zwA8HBwQBs376dAQMGMHnyZMqXL4/RaKRNmzYcO3aMffv2YTQaKVGiBF26dOHzzz/HarVKxY549W4chRPrwTPw5Zboxd4Ar8xQvMUjHVRSVQsLjHe4pxoJVh4NB59VqmohUjVSV+9HIZ3bY7eJPAERX4GDOzj7vtj5VNV2vOCSUO6jN69BTPr6iw++HqWlpREbG/tIJfbq1asZOnQokydPpkqVKphMJvR628/RyZMnadCgAYGBgWTOnJm9e/eSI0cOmjRpQpcuXezH37hxI46OjtSsWdN+XKvViqqqaLVaUlNTcXR88Z8TIYQQQgjxfJ4lX5NPtEII8QIsFguqquLo6Eh4ePhDjQ3MZjMbNmygSpUqlC9fnt9//52mTZuyYcMGateujaqq+Pr60rNnT7777jvMZrMEjSJjZMoHPtkg7vYLd6a2S4m3BZc5yj42iXNUtFTQeaFRFeLVF5tObVVV7qpG8mhdyK91/dvt/PJDaC1IuAXmtBc6JfHXwckb8rd8s4LG9JBRURQ0Gg1Xr15l586dxMbGYjAYyJQpE/v27WPnzp2ALYCcMWMGoaGhlC1bFrB1o07/Ljtnzpw0bNiQmJgYFi5cyLJly6hYsSLTpk0jODiYdevW4ejoSIMGDexBo8ViAf6cng1I0CiEEEII8R8in2qFEOIZqapqX5NRq9Xap0zXqFGDHDly8NtvvxETE4NOp+PAgQMcP36cSpUq0bhxY7Jly8aRI0eYPHmyvSFMmzZtSE1NZfz48fbjC/FK6QyQqyIYXCDx3osfz5Rqm5YdUhS8s/7tZqEaJ4rr3Lmvmkh8zsDRqqrcUNPw1xiopPNG+3eNarAtG5mnEQQWh6gzzx84JtwCcyoUaAle2Z/vGK+r9CYvERERVKpUiSJFitCtWzcqVqzI2rVrSUtLo3PnzsyYMYP4+HgcHBy4ffs22bJlw8HBAZPJZD8OgIODAx07duTatWssXLiQYsWKMWLECCIiIjh06BBNmjSxnzv9tS89YBRCCCGEEP9NEjYKIcQzsFqt9oofgFOnTlG3bl3Onz+Pl5cXVatWJSoqyl7d2LFjRzZv3kz58uU5ffo0kydPJjQ0lPv37/PLL79w+vRpsmfPTs+ePUlJSQH+oauvEP8mzyDIU8VW2ZgQ+fwVjsYUiL8DgWGQo8xDjWH+SlEUKui8KKH1IFY1c89qfKawPVW1cF1NxU+jp67eDx/Nk6eAO7hBiXchqAREn4XkqKc+HVYzRJ21BY0F20L26k+/739FVFQUjRs3pmLFipQuXZpdu3Yxbdo0zGYzn332GcnJyXTv3p2LFy+ya9cuAKpUqWJ/zUufPg22ZjJ3796laNGiVKxYkQkTJtjv8/DwIHv27A91lZbXPiGEEEKIN4OEjUII8Qw0Gg1Go5EhQ4bQrFkzxowZw6ZNm1i8eDEA4eHhBAcHs2HDBtLS0qhatSr+/v6oqoqDg61Drtls5scff2TRokX2KqBx48YxYsSIDLsuIQAIzA/5atqausRcB7Px6fdNDykToyC4EITVAt2jXaH/SqcoVNV7U0vvg15RuKqmcl81Yf2H0DFNtXLLmsY91UQ+rQuN9QEEap58rnTOPlD6fcjXBNLibWsvJkf9fb5qMcL9qxB5EjyyQOl+kKvuP+ao/1keHh7cvXuXli1bMnLkSPLly0fVqlUZMWIEV69eJSIighYtWmA2m9m8eTMWi4VWrVqRkJDAV199hdlsq1BNTk5m7ty5/P777wD88MMPbN682X6e9GBRlo4QQgghhHjzyDs8IYT4Gw9W3KSLj4+ndevWrF69miZNmlCgQAH8/Pz45ZdfiIqKIjg4mEqVKnHt2jVWr15NUFAQEyZM4JdffqFUqVK8//775M6dm2+//ZZ33nmHQoUKAbYP3unNEITIMIoCQfmhWFPwywHxdyH2JhiT/yGJM9sCxpirtnAxf23IXwf0T7/GnlZRKKpzp7khE8W17liB62oa16wp3LGmcc9qJNJq5IY1lavWFKJUI4EaA3X1vjTQ+z9VReNfObjZqhPLD4ZsVcCUAvdOwt0TturF6HNw75QtiIy5YFufsUgnqPgxBBV/M4NGi8WCXq+nc+fOXLx4kSVLltjvy5EjB/fv38dgMBAUFETFihXZu3cve/bsoUSJEnzxxReMGzeO8uXLM3jwYAoVKsTMmTPJnDkzACEhIbi5uT32dVUIIYQQQrxZpBu1EEL8RfpU6Qen9KU3TDh58iRly5Zl+fLlVK9um0M5f/58vvzyS9q0acPQoUM5f/48H3zwAR4eHsybNw9FUTh48CC7du3i9OnTFCtWjG7dumXU5QnxdCwmuHsObp+E+7ds6zCi2LqhKIDVClgBDTh7Q1AYZMoLLt4vfOo41cQ1SypRqok71jTSsKJFwUvR4a9xIEAxkFnj+I/rMz6rhFsQexkSbkLSXbBaQO8M7iHgFgS+eUD3hvcoSX+di4uLo1GjRhQoUIApU6YQGxtL48aNSUxMZPny5WTJkoUTJ07QqVMnwsPDGTlyJAAbNmxg27ZtnDt3jlq1atGzZ88MviIhhBBCCPGyPEu+pntFYxJCiNee1WpFo9HYp/Xt3buXTZs2UadOHQoUKICDgwNnzpwhe/bs+Pr62verWbMmW7ZsYfny5QwYMIBcuXJRoUIFVqxYwcqVK2ncuDHFixenePHiD53PbDaj08nLsHhNafW2KsfAfH9Mj46GpGhITQSstuTNxRucvcAj8JkqGZ/EQ9FTUPfs1Yovwi3I9udN90+vO+kV1h4eHoSHh7NgwQLKlCnDqVOnqF69OosWLSIgIABVVSlQoABlypRhz549REREUL58eWrXrk3t2rUfOqbFYpGGL0IIIYQQbxmZRi2EEH9IDxlTU1P5+OOPqV27NgsXLqRRo0YMGzYMgODgYG7evMm1a9ewWCwA+Pv7kytXLk6dOsWiRYsAqF69OjqdjuvXr9uPn15Inj6NUIJG8Z+gaMA9ky14zFUJCtaFgvUhXw3IUgx8s7/UoFH8u9Jfd5KTk+23PTjJJb2iu2PHjuj1euLj4/ntt99Yvnw5AQEBWK1W+2tY8+bNuXbtGrdu3XrkWOmvjxI0CiGEEEK8fSRsFEK8lVRVfWTtMJPJRMuWLalevTppaWmcPn2aHTt2MGjQICZMmMCZM2coU6YMoaGh/Pzzz0RGRtr3TUlJQaPRMHv2bABKlCjBsmXL6Nu3r30baYgghHiVVFW1h37pIeDChQspWLAgbdq04bvvvgMe7gKtKAqqqpIpUybCw8Px9fUlJSUFsAWIGo3GHiBWrlyZbdu20bx584f2BwkZhRBCCCHeZvKJVwjxVkn/8K0oyiOhn1arpXr16uzevRuTyUSmTJnw8PCgR48e9gYIAJ999hkHDx6kf//+HDlyhDVr1nD8+HH69+9PSkqKveOqn58fqqpK0xchxCuXvv5ieuinKArXrl1jxowZtGzZkpCQEPr27cvq1asfuy9Au3btMJlMbNy48W+nQwcGBsrrnBBCCCGEeIjM4RNCvFXSP3ynpKQwdepUYmNjqV27NmXLlkWn01GnTh3y5MljbxBjtVpxdHSkf//+dOrUiS+//JK6deuSlpbG6NGjadKkCTdv3mTKlCkULlyYH3744aEP5Mqb2LJWCPHaSg8ZFUXBZDIxfPhwNmzYQOnSpdFqtbRr147OnTsDEBcXx+eff06xYsUIDg62HyP9i5hcuXJRpEgR1qxZQ+PGjSlVqtRjzymvc0IIIYQQ4kFS2SiEeKP8dWr0g+uLAaSlpfG///2P4OBgli1bxu7du+nQoQOffvopYFuTsXPnzsyZM4e0tDT7h+4GDRqQO3duvvnmGwDeeecdtmzZwqpVq0hJSaF79+7cuHEDq9VKQEDAK7paIYR4WHo36U2bNvHFF19w9OhRGjZsyO7du5k6depDza1GjhzJ4cOH+f333x+pTEx/3XzvvfcYNmzY3waNQgghhBBC/JWEjUKIN4pGoyE1NZXU1FRUVbV3lz5x4gQ3btzgxo0bnDp1ipkzZxIREcHmzZsZNGgQEydO5PTp02i1Wlq0aIGjoyOTJ08GbJVCbm5udOnShW+//ZbY2FgA3N3dcXNz48iRI0ycOJF+/frRrVs38uTJk5EPgRDiLWGxWB47fXns2LG0a9eOTZs28e233/L555+zfPlyvL29OXPmDEajEYCsWbNSr149vv/+e6Kioh46RvoXLQUKFKBFixb//sUIIYQQQog3hoSNQoj/rMd9yL516xY+Pj5ERESgKAp3795l7NixFCpUiLVr1+Ln50fXrl155513OHPmDM2aNWPo0KEYjUYmTpwIQJYsWejWrRvffvst8OcUwa5du7J161a8vLzs57t06RItWrRg5syZjB49mjFjxkgDGCHEvyq96lCr1aIoCpGRkaSlpdnv7969O4GBgRiNRnLmzAlA9uzZCQ8PZ8OGDVy4cMG+7ZgxY/j999/Zvn37q70IIYQQQgjxxpJPxEKI/6z0EPDB0NFqtZI7d25WrFjB+PHj6dixI0ePHuXYsWP07NkTd3d3KleuzNq1a2nYsCEuLi7s3LmT6dOnM3/+fM6fP49Go6Fdu3bcuHGD77//3n5sDw8PKleu/NAYqlatyooVKzh+/DgdOnR4NRcuhHjj/VPDlfQvNNauXUvp0qWpW7cu9erVY//+/ZjNZrJnz0716tXRaDTs2LHDvt+AAQM4ffo0ERER9tsKFixIkSJFOH36tL1ztRBCCCGEEC9CwkYhxH+SqqpcuXKFRo0a2ac7A3h6ehIUFITVauXMmTMcOHAAd3d3ChQogNlsBiAxMZFJkyZRo0YNpkyZQv78+bFYLCQnJ/PTTz8BtsYIs2bNolatWk8cS4ECBf6dixRCvHXSqxbTv0y5ePEiN27csN+vqiopKSl8+OGH9OvXj5o1azJ+/Hjc3Nz49NNPWbduHQCtWrVCVVW2bNli37dYsWKULl2a5cuXc+7cOfvtERERfPrpp4/tNi2EEEIIIcSzkm7UQoj/nPRuq3fv3mX16tX89ttvVK9enbx58+Lq6oqXlxdXrlxh8ODBREREEBcXB2D/IO3q6sqxY8cIDw/Hzc0NVVU5e/Ys5cuXZ/r06Xz44Ye4u7vTsWPHjLxMIcQbzGqBqNNwfTfEXQVjEmi04OCuIaAwHLi3lAnfjSIpKQmDwUD9+vUZPnw4zs7O6PV6goODmTZtGjVr1gTg8OHDjBw5En9/f+rWrUuJEiUoXLgwu3bt4siRIxQpUgSwTbHu3bs3CQkJ9rE4OTnZKymls7QQQgghhHhRUtkohHgtWVWVi/etbL1mZt0lM79eNvO/2au4nWi1fxh2dHSkc+fO5MuXj6+//ppt27YBts7R27dvp0SJEtStW5dr165x6tQpFEWxTxPs0qULAwcOpGPHjhQtWpRDhw6xfPlyLl++jLu7u30c/zSVUQghnpU5DS5vge0jYMcYuLgB4m+AMRFSYmFHxA6qt8tLr/e6UzZLc34cv4IKFSowf/58pk+fbj9O586dqVmzJrNmzSJHjhxMmzaNihUrcvz4cXt1Y/v27YmJiWHNmjX2/cLDwzl9+jTFixd/aFyKokjQKIQQQgghXgoJG4UQr5U0i8re2xYmHzLxzYE05p8yseyciemrd/Fxl8bU7/IB3x+I50SUBa1Oh9FopH///nh7ezNt2jSioqLsFY779++nZcuWWK1Wli5dCvy51tno0aOZOHEiqamptGrViu3bt+Pr64uzs/ND65bJh28hxMuSlgAHv4cD0yDmIrgFg38B8MwG7sHgEQLXLbswa5LpU/V7quiHEr0ijI5VhmMymdizZw+qqqLT6fDy8mLz5s1MnjyZAQMGcO7cOb799luuXbtmDxurVKmCj48Ply9f5v79+4DtCxRHR0dZn1EIIYQQQvxrJGwUQrw24tNUZp8wMfOYkTPRVnycFPL6aMjro6Fi2dK0+HQGJ3+dzby5s5l2xMRZfW5OnzkDwCeffEJKSgpDhgzB3d2d4OBgTpw4QfHixSlYsCARERFcv34dRVEwm80oikLfvn1ZuHAhQ4YMAR7u8CqEEC+TKRkOTIfLW8EjK/jkAoPLo9tVCmtGzkxFOHRnDX75AQUWfLuVyMhI8ubNa38Ns1qtrF27FkVR6NGjBwBXrlxBr9ezceNGezXjzJkzmTlzJp6ensCfX6DI65wQQgghhPi3SNgohHgtJJlU5pw0sfeWhRA3hVAvBTfDw1WFJeu3p0DlBlz5bRY3dyxk/SUzWYpXZ/acOXh6evLtt9+yadMm9u3bR1RUFLdu3UKr1VKzZk2uXLlib/6i0z28XK3FYkFVVXvVoxBCvEyqCkfnwY3d4JMbDK5/v22wdyj5gktzM/o80zcOZMCagkzf1w13Bz9SInUkJCSg0+nQaDSYTCa8vLzYtWsXUVFRLFiwgObNm9OuXTty5MhhO15wMPDnlylCCCGEEEL82+STtRDitbDqgonDkRZyeCo46f9+6nKNrh+TKWcBNk8ZjIsxkuSgkiRZdFy9epXQ0FD69+/P1q1buXv3Lnv37gWgZs2a9OzZk2bNmj32mFqtVqZLCyH+Nfcvw/Vd4J4Z9E5P3r5cnoZoNTpWHZhG3aLdmN/vIp9U+o0dW/bQsGFDfvnlFwDatm2Loii0bduWrFmzcuvWLYYOHcqIESMICwt76JjyZYoQQgghhHhVpBu1ECLD3Uu2cvCOFX9nBUfdP4d+vplDqfveKK4c3cXOGcNwDCmIxTWAW7fvkjVrVrp168bixYuZMWMGBw8eJCYmBm9vbwYMGPCKrkYIIR52Yy8YE2xrMz6NHAEFKZS1EmariUJZK+LrFoRbgSB83WZzLcdcevXqxeXLl/nwww9Zt24da9euJV++fOTJk8d+DKvVKgHjP0iv9JTHSAghhBDi5ZN3WEKIDHck0kpsmoq345O3VVUVd99MNBkymbsXTxF5dCsXjuzmjskBAFdXVzp37syCBQs4deoU3t7e9o7SMo1QCPGqpcXDtZ3g5APPUkBdJlc9DDpHNh2bD4CDGzhZ/WgUNpBBgwbx/fffs3XrVvR6PY0bNyZPnjxYrVZ74xcJ0R5PVVX7shkajYaUlBSMRmNGD0sIIYQQ4o0i70SFEBnKYlXZc9uCix40T/FJPH26c+5S1SnZsBOp8dHE3brIktUbgD8DxRYtWhAYGIjVarXvIx++hRCv2r3TkBQJrgHPtl/+kLLkDCjChTuHuRx5AgCDt4mb+6Dfex9y4cIF6tSpY98+PUCTxi//TFEUFEXh9u3bNG/enLJly9KlSxfOnz+f0UMTQgghhHhjyCdvIUSGSjHbulD/tRnMP0mvVCxRvx3Fw9tgcHbHKTA38HCgKE1fhBAZzZRk+6/mORauKZO7PvEpMaw7/CMATi4GzGmgsTig1+vtVYyArDv7lC5dusTq1asZM2YMer2eDz74gG3btjF48GCOHz8O/PlvjBBCCCGEeD6yZqMQIkOZrGBVQfMMn5PTP1QbHJ0p36IXWcN7EuDyaKgoH76F+JPVakVVVal8e8Uspufft0i2ylQOa0bBLBUB0GhBtYLlj1m/8lz+PbPZjE736Nvcfv36cfDgQUqXLs2cOXNwd3cne/bsDBo0iCVLllCwYEH5t0MIIYQQ4gVJyY8QIkM5aEGrgPkFllO0qOCA5ckbCvEWenCNOq1Wi8lkIiUlxX6f+HfpHJ5/X61GR/tKwyiSrTIAVrMtcNQ9xfq2b7v0oPHEiRPEx8fbb3/vvfdQFAVXV1fc3d0BqFixIoUKFWLPnj2cOGGbsi6/G0IIIYQQz0/CRiFEhnLUQSYXDXFpz/fBTlVVkk2QzUv/kkcmxJshfY26mJgYOnfuTNGiRencuTOnTp2SCq5XwMEDFA2Y057/GFbV9m2MMREc3EHv8pIG9x93+vRpvL29H7ve4vfff09wcDBNmzalRo0aTJo0CYA6depQsmRJYmJiuHLlin37Jk2aEB8fz9q1awGpjBdCCCGEeBESNgohMpRGUSgTpMFoAbP12QPHRBM466FogEwnFOJxrly5wvbt2/n8889JTEykX79+HD9+nK5du3L48OGMHt4bzy8MPLJAwq3nP4ZG0aCqkHofslQA7Vv63cpfqw3z5s3Lzz//TK5cuR66fd26dXz11VcMHz6cxYsXU7p0aYYPH860adMAaNasGXfu3GHr1q32fWrXrk2ePHn47bffOHfu3L9/MUIIIYQQbzAJG4UQGa6QnxZ/F4XI5GcPG28nQh4vLdncpQpFvN3MZvNjbx8+fDh16tTh7NmzTJs2je7du7Nlyxaio6NZsWIFaWkvUHInnkjnANkqgzHBtt7i80qJAUdPCC710ob2n/NgtaHJZEJRFOrUqUNMTAyxsbH2+yZOnEiRIkXo2bMnhQoVYvLkyfTp04dRo0ZhNptp3rw5Pj4+bN26lcjISPt+NWrUwMnJyb7MgBBCCCGEeD4SNgohMpyrQaFKiJZEI880nfpWohVnPVTJopUpb+KtlL4eI/y5Rt3t27cf6lI8fPhw3NzccHFxwdfXF4CAgADq1avH1q1bpbrxFQgqCU4+kHjn+fZXrZBwEzIVBbfglzu21939+/cBW7j4888/89lnnwGg1+tJSkrCaDRSuHBh++0Ajo6O9p91sP2e9O7dm9jYWFauXImDgwONGjXi7NmzbNiwwb5dmzZtWLNmDYULF341FyeEEEII8YaSsFEI8VqonkVHtSxa7iSqRKWo/7g4v1VVuRZvxWiBJrl1FPCVKdTi7ZKamkrLli355ptv7EH7rFmzyJ8/Pw0bNqRu3bqcP38eq9VKzpw5qV69OjExMZw+fdp+jJ49e3Lv3j22b9+O1foCJXfiiVwDIHcD2zTolJhn21dVIfo8uGeG3PXhbfheRVVVkpOTGTRoEO3btwds4WJERARbtmxh/vz5FCtWjPfeew+DwcD777/PkiVLuHfvHhaLBV9fX+7du8f169cBW0Wkp6cnRYsW5dChQ4AtWDQajdy6dcsezms0trfFD4b1QgghhBDi2UnYKIR4LWg1Cs3y6GmQU0eqGc7EqNxOtGKyqPbqrVSzLWQ8G6PipFNoH2agYrAEjeLtExcXx+XLl7l37x6pqal07NiRcePG0aNHD7766isA+vXrx+7duwHo06cPFy5cYM+ePfZj5M2blwoVKrB27VoOHjyYIdfxNsldzxY4JtyGxLu2EPFJrBaIOmObPl2sG3hl/9eHmeG2bNmCoig4OzvTqFEjfvrpJ/t9+fLlY+/evXTv3p3y5cszatQoALp3705aWhqzZs1Cq9VSpUoVbt++zfLly+373rx5k/Pnz1OmTBkAvLy8WLt2LYMHD0arffjfkb/+XQghhBBCPBsJG4UQrw2dRqFBqI6BJQ00yqnDUafharzKuRiVs7EqtxJV/J01tM2nZ1ApB0oHyfRp8fZ4sNo3ICCApKQknJycMJlMFCpUiF9++f/27js8qmrr4/h3ZtJJhTRCDUnoAST0YkCKVBFEgSsv2BULKKBSREBAERuCCIiCjSuCiiBFRHoJVSIgiJTQQi8JSSCkzHn/OGaukQ5DAuT3eZ55ksycsk/cbk7W2Xutb+jVqxeNGzcmJiaGFStWMGfOHDIzM6lfvz6VK1fmp59+ylWB95FHHlHOxjxitUH0fyC6C2Sdg2NbzMCj/SKT6DLPwandcPwP8C0GtXtCSJW8b3NeW79+Pc2aNXMUcqlfvz5+fn6sW7cOgOPHj1OlShVq1qzJkCFDKF68OFlZWfj7+/P0008zbtw4zp07xwMPPECNGjUYPHgwr7/+OrNmzaJPnz5UqFCBmJgYx/nCwsKACwvPiIiIiMiNsRh3+B3WmTNn8PPzIzk5GV9f3/xujohcg9QMg4MpdtKzwWoBTxcLpXwtuNkUYJSC4bPPPiMxMZF+/frh5uYGmEs8bTYb7du3x83NjW+//ZYTJ05QuHBh3n33Xd577z1KlSqFv78/KSkpvPPOOzRo0IB58+bx7LPP8sYbb9CtW7d8vrKCyzDg1C44uAYOrIa0Y+bSaIvV/MzIBpsbBJSBUrFQrCZ4Fs7vVueN48ePM3DgQNavX+/IJTp58mT69OnDggULqFWrFnPmzOGtt97i/vvv5+WXX8Zut2O1Wjl06BCRkZGMGTOGJ554gqysLN566y1WrFjBn3/+yd13382oUaMcAUYRERERuTbXEl9zyaM2iYhcM283C+WLaDmbFFybN29m2rRpHDt2jA8++ABXV1dsNhsZGRl4eXmRlZXFuXPnCAwMZOrUqXz77beMGTOGTp06sXXrVmJiYli6dCkNGjSgVatWeHl5sWPHDtLT0/Hw8MjvyyuQLBYoEmW+yraBw7/B2eNwPgVsruDmDQEREFzZ/LkgyAmgBwUF0aFDB2bPns0PP/xAhw4dqFChArVq1eKTTz6hVq1aNGzYkBkzZrB48WK6detGSEgIWVlZhIWF8cgjjzBu3Dg6deqEj48PgwYNIi0tDavViqenJ4AjOCkiIiJyp7mV7nNujVaIiIiIQ07BlnfffZdJkyYxefJk+vbtS0pKCgBubm5YLBZSU1Px9PQkOTmZH374gdKlS9OxY0cANmzYgIeHB3PmzGHlypWAmQ9vxIgRCjTeIjwDoEwTqNwZYp6Eao9AxY5Q9K6CE2gEM0fi4cOHeeONN/j9998pXrw4H3/8MQA1a9YkNjaWVatWsW3bNvz8/GjSpAnJycnMmjUL+F8l9l69evH777+zYsUKx7ELFSqEp6cn2dnZt9QNuIiIiIgzGYbhuM+5FRYw645LRETkFpNzo+Di4sJ9993HqFGjWLBgAS+88AJpaWkAVK5cmd27dwPg5+dHRkYGdruddevWsWvXLlasWMGzzz5L06ZNiYyMBCA0NDR/LkjkMhYuXEj58uVZs2YNZ8+e5fjx4yxevJiVK1fi4uJCw4YNCQwMZOLEiQC0aNGCiIgIJk2axNatWxk5ciSjR4+mXLlyrFy5klatWl1wDpvNpkCjiIiI3HFyAosWi4UdO3bQu3dv9uzZk8+tUrBRREQkXxiGQVZW1lVt+8wzz/Dhhx/y/fff06tXL9LT03FzcyMoKIj9+/cDZsXp48eP07FjR6Kjo7Farbz22msMHz5cQUbJd5fr65MmTaJFixbMmzePoUOH8u233xITE8OIESMAqF69OrGxsSxatIiDBw8SHBxM165dCQ4OplmzZowbN45y5coBUK9evTy5HhEREZFbgcViYevWrezfv5/u3buze/du3N3d8312owrEiIiI5LP4+HgiIiLw8fG56FLPnPemTJnC22+/TcOGDYmOjuadd95h//79jqrsycnJrFixglq1ahEcHJwflyJyWXFxcfj4+FCyZEl8fX3JzMykefPm1KhRg3feeQcwczh+8cUX9OrVi9WrVxMdHc3ChQsZMmQI99xzD8OGDQPM/n7kyBFHoFFEREQkL9izIXkfpBwyX6d2Q/ppyM4Eqw1cvcC/NPiVBO+i5vduhW78vFlZWY70MTkSExOJioqiaNGidOvWjcGDB9/4iS5BBWJERERuMXa7HcMwsNn+V/Too48+YsCAAQQGBlK0aFG+++47ihYtesG+OcHE7t27U6FCBdq2bUtcXBxHjx4lISGBMmXKYBgGfn5+tGnTJs+uSeRKDMPAYrEwf/58XnzxRVJSUggLC8Nut/PVV19Rrlw5rFYrmZmZpKam4u3tjc1m46677sLb25sPP/yQTz/9lBo1alChQgVmzpzJgAED8PT0xM/PDz8/P+DiN98iIiIizpSeDIc3wt6lcDoBMs8CFnD1AJs7WKxg2CHtOBzfDoYBNhcoFAylGkJYLTMA+fet/VXLuZ/KudfZv38/ISEhuLu7ExgYyIABA3j99depWbOm06/5emkZtYiIyE2UU+zFarVis9k4fvw4+/fv5/z582zdupX//ve/TJ48maSkJJ555hmSkpIuOIblH3ckderUYdKkSWRnZ1OpUiXHTYflWu9aRJzMbrc7+nsOi8VCYmIib7zxBl27dmXHjh2sXLmS8PBwnnrqKZKTk2nUqBEbN27kt99+c+zn5ubGuXPnmDx5MgkJCQQEBPDiiy+ydOlSR2Xpf1KgUURERG6WzHOw/XtY1B/WjzNnMnqHQHBlCKkMhSPBrwT4FjO/BoRDcCXzM/9wMyi55RtYOgTWjYUzidd2/pz7/A8++IDSpUvTuXNnGjduzKpVq3B3d+eBBx7A29vbkV7p3/dj+UHBRhERkZsoZ0n0iRMn6NatG8WKFaNDhw7Url2b4OBg2rRpQ6NGjfjmm2/46aefclXS/becG4377ruPRYsWsWnTJkqWLJkn1yFyKYZhOJb6/zMFQE6mnrlz5+Ln58egQYPw8PDgq6++YuHChVitVs6cOUP37t3JyMjgvffeY/PmzZw7d4758+fTsWNHmjdvztatWwGzKFJgYOAtcQMtIiIiBcOJP2HV27B5qrlMOrAiFIkCN++rm6Ho4g6+xSE4Gtx9zFmRK0bAnl/BfpGU1pfKdPj222/z+eef89prr/HFF18QHR3NwIED+f7776lQoQIPPvggY8aMAbgliuLlfwtERETuYPv27WPUqFGMHDkSPz8/li5dSufOndm8eTPp6emO7apUqUK9evX45JNPOH369EWPlRNstFqthIWF5Un7RS4nZ1mP1WrlyJEj9O3bl1deeYW4uDjOnTsHwNatW8nKyuLdd98lNDSUcePGMXbsWH799VfCw8MpWbIko0aN4ujRo7Rr147w8HA++eQTunbtys8//0zbtm1znfNWuIEWERGRO5thhx0/wapRcPwPKFLOnLlotV1534uxWMCzsDkbMvMcbPwE1n0E58/8fb6/H95ebLVSUlISP/zwAwMGDOCJJ54gMjISm83GihUriI+PB8yCkrt27WLevHmO4+UnrTkRERFxgkvljDt69CgffvghmZmZTJ8+nXr16lGvXj1WrFjBtm3b2L17NxEREQAMHz6c5s2b8/vvv9OoUaM8vgK5k9mz4fRuc9nOmUQ4e8x8mm77+2m7T5iZQ8gn7MpP6f9ZxMhisXDq1CnWrl1Lv3798Pf3xzAMvv32Wx566CHeeecdWrRowbhx40hLS+Obb76hcePGuLq6YrfbiYuLw9/fn9jYWObNm8fatWtJS0ujY8eOjvNlZ2fnynUqIiIicjPZs2Hbd/DnD+DuZ85mdFbGIosV7EWO4lckhD1LszmfaqHWc1Y8AyxYLBZ27NjBokWLqF+/PuXLl8fd3Z3t27dz9uxZoqOjeeqpp5g2bRpVq1Zl9uzZtGrVCoDo6Gg6dOjAiBEjHO/lJ1WjFhERuQE5M7tyLF68mNDQUMqXL+8IyPTq1YsZM2bw448/UqtWLQAWLVrEI488wltvvUXXrl0d+5ctW5ayZcvyzTff4OPjk7cXI3ecjFRIXPd3IvM9kHUOsPydxNxi3kzbM81t3X0hpCqUbACh1a7uyf2CBQvo378/Xl5etGrVigEDBpCWlsYXX3xB3759+fXXX4mMjKRx48bUq1ePSZMmOfZdtWoVY8aM4dlnn+Xuu+++4Em+gowiIiKS1wzDzM/4x3SzsItXoLOOa/7N8NXy4azfvYAPH1lOdoaFk39CsdoQ3vkEn0+dyODBg6lUqRJHjx6lVatWTJ48GYDChQuTlJRE+/bt6dmzJ7GxsQCkpqayZ88eqlSpwi+//EKLFi3YsmULlSpVck7D/+Fa4mtahyIiInIdcp7V5QRIRo8eTWhoKE8//TTNmjWjV69e7N69G4AOHToQFBTE0qVLHfs3adKEyMhI5s+fz8GDBx3vv/3221StWhUvL6+8uxi54xgGHPkdVrz5j0TmRSGosrl8p0iUmcw8sJz5c1AlM/fQ3hV2Vr1jsH4cpB4xj/XvHIlbtmzhkUceAaB+/fr4+/uzfv16mjdvDkChQoXo0qULzZo1Y8iQIQQHBzN48GC+/vprWrZsydixY+natSv33Xcf/v7+VKxYMVegMef/rasJNP6zKM0d/vxcRERE8kDiWtg+0wwyOivQCObfDMfPHMTTzZsPui/BYrFgczPwKH2Gdyb356G23di0aRPx8fGsWbOGUaNGMWvWLIYNGwZAp06diIyM5LvvvnMEGjMyMvjss8+YP38+6enpNGrUiG3btt2UQOO1UrBRRETkH+zZkJFm5lL5d+xi5syZfPvttxw5csQRHDEMgzFjxjB16lRGjx7Ntm3b+O9//8vmzZv56KOPAIiNjaVixYqsXLmSP//803G8Xr168fPPP7Ns2TLHe+3bt2fEiBGa0SXXzZ4Ff8yAuPfM2YxFyv+dyLzQpZcAGdjxLAyhla34FrPwy/dxfNRjKYc2XJgjccqUKRw7dozMzEy8vb3p1KkThQsXdhRyMQyDgIAAHnzwQf744w8SExN56KGH+Prrr4mKimLevHlkZmayZMkSJk6cSFBQUK7jX01l9X9WebdarezevVsV2UVEROSGnD1p3kOBOavR2T76+UUWb52GzerCbwmLsVgs+Pn5YnimsPmvNbhk+FG5cmU8PT3p1q0bAwcOZMyYMaSkpNCrVy/2799P586d+eKLL/j111958MEHGTt2LCVKlMDNzQ03NzfKly9/SzyAVbBRREQKvKx0OLgW4t6H+T1hQW9Y8BL8/CJsnJxN7x6vERAQwGuvvcbAgQNp3rw5s2bNcuxfs2ZNhg8fTufOnTlz5gy//PILcXFxLFmyhEWLFgHm08iDBw+yePFix373338/d999NyVKlMjrS75h2dnZt8SNjORmz4Yt38C2GeZMxcDyYHO98n5Wi3lLuGzb9zz5RXlGrrmXxCP7WT8ehvcdx+zZs0lJSQHMmY1RUVG4upoH7tChAxEREcTFxZGWluYI+p08eRJ/f3+yssxSiw888ABjxoxxBO2rVKmSa2bitcgJgJ48eZLHH3+c8uXLs3Hjxms+joiIiAiYBWG2fWc+qA0o4/zjZ9uz8fEIINivBD/Hf8Gw7zqzeOs0AO6t8TB+HiEk/pnMuVPmg1vDMOjSpQvp6enMnz+f8uXLM2PGDAzDYMKECTz22GMEBASwfPly/vOf/+R6OHwrPIBVsFFERAqsrPPw11xY/Jo5C+zgmr+LZriCxQab96zi3ufDmfL5Z7ze/jtmT9jIZ59NxtPTk1GjRgHmP+YxMTHce++9fPTRR0RHR7NhwwbeffddbDabIyh5//33U7JkSWbPns2ePXscbZg5cyZ33313vlz/jbDZbFgsFs6ePZvfTZF/2Dkf/voJfIpe2xP5n+O/4MH3i/P+nKdpGv0wM3on0qF5N1LTUlkyez29nn+Jli1b8s0335CYmEjjxo0BszBSUFAQLVu25Oeff2b8+PGkpaWRlZXFpk2biIiIoFSpUo7z2O12PDw8HEHGnJmJV/LvgOSBAwd45ZVXePXVV3F1dWXbtm1Uq1bt6i9YRERE5B+Ob4f9K8C/1PVXnM6x8/CmXD/bDTs2qw2Lxcq5jFSqh99DdKmGLNr6DQCVStSlVrnmHD15iPmfbcZisTheERER7Nu3D4C2bdsyffp0pk+fzo4dO/j8888JCwvDbrffcpMAFGwUEZEC6XwKbJwI8ZPh7AkIiICgimaQxisQCgVB4TAvwoJKEVvhIUqcbcLvn3gQknw3Xl5eJCUlOXIturm5sWPHDiZOnMjIkSOZO3cuPXv2xGq1snz5cjZs2ABAmzZtKF269B2Rj3HJkiXUq1ePBx54gBEjRuR3cwTzSfyOH82qiZ6Fr36/hZunMmrWozQofz+zXjlBt9hBeLn7YLFAWDlvnqn6OaM6LqNwQCDvv/8+f/75p6Pyes7Xbt264e/vz+DBg+nevTsxMTHMmzePF154AfhfPsWcwOLVBhmzs7Nz7Zfj3LlzxMXFMX36dOrXr09UVJRSD4iIiMh1OxgHmeng4X9jx/luzYf0/vIeflz/seM9wzAfmpYJiWbf8W0E+RanVkQLDpz4k417zFVQNSObk21JZ8KUD8lINffbu3cvR44cceRozHn4WqJECTw9PcnOznY8vL0VZjP+k4KNIiJS4GSlw2+fQsIS8A8H/9Lg4n7hdlFF76JqqVj2nPyNjJA9uBWC4YPeZtmyZYSHh1O8eHHHtqtWrSI9PZ3q1avj4uLCH3/8QXZ2NidPnuSLL74A4IknnmDChAmEhobm0ZU6T06waMmSJaxYsYJhw4ZRt25d7rrrLgYNGsS7777L+fPn87mVBZc9G7ZOg/Qk8Am7tn1rRd5LTJmmuLt45np/1Y7ZTFrUj7NFdmHfW5xRT/7oqJzet29fhg0bxrlz5wDzprdVq1bUqFGDBg0aMG7cOI4dO0azZs2A61/OkxNA/Pjjj+nfvz+//PILmZmZlC1blgcffBC73e4I3ucEJkVERESuRdpxSFwH3k7I09gm5kkerNObiQtfYeY6M3+7zWo+nE3PSCOgUAhnzp2iSqmGhAVEMPe3SQDUiGhGhRI1+HX7FO5p1JRXXnmFli1bUrduXSIjI4ELH77abLarenibH1zyuwEiIiJ5bfsP5jKJwpFm0YzLqRPVmi37VzJkekeOnTmAr1sQUUVqY80oxIwZM7j77rsJCQnBz88Pb29v3n//fTp16sTHH39M69atqVatGvXr18+bC3MSwzCw2+25ZopZLBYMw6BJkyYUL16cF198kd69ewPg5+fHRx99RGxsLDVr1syvZhdoJ7bDsa1m8Pxa43p+XoFULd2I3/b8yr7j29l7fBtTlr7OyZRD/KdBf0KLFCM9A/Yugn0c4Omnn6Z06dJ8+umnzJs3j5YtW/L666/TpUsXFi1axKlTp2jQoAEAmZmZjtyOV2IYxgVByTlz5tCjRw+KFClC2bJlmTp1KrGxsUyZMoW2bdsyc+ZM5s6dywMPPKCZjSIiInJdjmwyVzoFV77xY3m4etEtdhCn047y7ep3SM88S5f6rwAQGhDO/hN/4uXug59XEaqHN2HOb5PYcWgD5cJqUKtsC/7cv4lC2UUpXbo0kyZNomPHjjfeqHxwa4ZARUREbpJzp2DfcvAKunKgEcwcKuXCanAi5RD/d/cgvuy1jTcaraRmyAOMHj2aTp06kZGRwQMPPMDjjz/Oli1beOSRRwgODuaVV17hgQceuK1mMuYEfGw2G1lZWSQkJDgKfFgsFoYOHcrBgwcpV66cY5+XXnqJ9PR05s2b55jpJnnrwGrIzri6Pn0x91btzvmsczz9SQyfLupPy2qPMrXnbrrUfwV3V088Q7I4cwh+W/sHbm5uvPrqqyxdupT77ruPuLg4kpOTqVKlCrVq1SIuLo7Vq1cD/1tmfTk5S4D+HWjMzs7mm2++oUuXLsTHxzN9+nRmzJjB1KlTmThxIuHh4cTGxvLnn3+ycuVK4MLcjiIiIiJXcmoXWF3B4sQI2eP3jKBjnd58uWwo01a9g2EYhPiVwt3Vi4RjWwGoWjqWEP9SzN4wAYDq4fdQMrA8SafP0Kl9Nzp27Ijdbr8tV28o2CgiIgXKoQ3mUolrKZ5RK7IlxYtEkZqeBIBvmI1o20NMHv09x48fp06dOkybNo3nn3+eWbNmsX//fiZNmoSPj8/NuYibyGKxkJKSwvPPP09wcDAPPvgg999/v6PSb04uyt27d5OVlYVhGLi5ufHoo4/y7bffsmvXrny+goIn8xwc3mTmGr1eQb7FqFoqltCAcAY/OJ1O9fri7eHvWD7v5u5Ctj2Lnbv+pFGjRgAUK1aM/v37M3/+fPz8/ADo2LEjiYmJHD16FLhw+fSGDRtISkrK9V7OEqA///yTjz/+mM2bNwOwe/dulixZwqhRo0hMTKR79+60bNmSxo0bU6dOHQBat26Np6cnM2fOBC5cXiQiIiJyOYYdTu8GN2/nHtfbw4+OdXrRoVZPZq4by4y490k7n4y3hz92uxk8jAipSkRIVTbvW86p1CN4e/hTM6o5yWkn+GLSf832GcZtuXpDd2QiIlJg2LNg7zJw9by2KnPVSjeidFAlft+3jMOnE/Dwh7SkDLL2hDJ9+nTHklGAsLCwq142mt8Mw7igcl1WVhavvfYaGzZsYOrUqQwZMoSTJ0/y9NNPs379evz8/OjcuTNTpkzh6NGjjmDSq6++yrFjx5g9e7ZyN+ax1MOQkQLuvjd2nFZ3PY6PRwBr/prreC/nv2/83qX8dup7PKx+FA29MClkzuzXRo0asWzZMtq3b3/BNklJSdx777389ttvud7ftm0bzZs3p0aNGixfvpzdu3eTnZ1NVlYWvr6+1KtXj4oVK5KUlMRPP/3EokWLiImJAaBmzZpUr16dBQsWOAoxiYiIiFyttONw7jS4O3mOQM49dte7X6Nb7Ot8ufwNVv05i4Mn/8Ld1cw37WJz5f6az/LxE2sp7G2uhKpd/l5cLB78/Ms8MjIybstAIyhno4iIFCBpxyAl8dpmNQLYrDZqRbZkX9w2lm//nk71+uLh68LRLXBPp0qMGTPm5jT4JsnKysLFxcURSNq1a5cj8fTx48f56quveO+992jZsiUApUqVom/fvrz99tt89913DBs2jIiICJYvX07nzp0B8Pf3p0WLFuzatYusrCzc3S9ScUduitQjZtGjf9V3uWbFCkdSJqQKv+9bxv3pz+Pt4ceiLd/w1YrhnDhzkCcavMfbrdYRHel2wb45y6UNwyAoKOiix/f39ycxMREPDw/He3a7nYEDB1K4cGF27txJUFAQmZmZ2Gw2XFxcKF26NAkJCWzYsIGoqCjHft999x1Wq5UOHTrQvHlzXFxcKFq06I39AkRERKTAOXcKMs+Ct5OzHuXcZ3u6FaJ19Sc4nXqU+fGTAdh3fBvhwZUACPUvDYDdsGPBQmHvYDpXGkHzxyri5nbhPdftQjMbRUSkwMg8B9mZYLuOf7drRDTD16sIK//8kdT0JFzdrWSkwr8mBt4WcgJDW7duZcmSJZQtW9aR827nzp0UK1aMkiVLOrYvW7Ys9913H/Hx8ezZs4fw8HDatm3L+PHjOXbsmONmavLkyUyZMoVCha4zcaBcl8yz5tfrLPjsYLFYaBvzFOcy0xj+w394eEwko+c9S5PKXZjTL5lWdz2BNduN86mXzht0sarTObMeATw8PJg+fTpz5swBYMuWLezatYv27dtTtGhRjhw5QnZ2NmlpaZQtW5b69euTkZFBQkICmZmZACxfvpzx48c7lls3a9aMt956i2LFit3YL0BEREQKHHuWuZTacpMmENoNM590p/ov83yLMfRs9RGNKj14wXZWi9VxHxVVuA5ebje4ZCWfaWajiIgUHDcQGPRw9eKhun0o7B2Kt4c/Z7g9A40Aixcv5qmnnsLX15fSpUsDMGHCBBo0aEBkZCRHjx4lISGBjIwM3NzccHd3p2TJknh5eZGSkgLAoEGDqFWrFocOHSIkJATA8fTVbrcrd95tqkxIFfw8i3Do9B4613+FtjFPOT6zG1mAC9ZryUHA/4LbOf3pv//9L1u2bKFNmzYEBgYSFBTE6NGjmTRpEgEBAWzfvh1XV1eGDBnCgAEDOHToEPfddx9169YlIyODTZs20aNHD1566SVnXrqIiIgURDf5ft76d9UZV5sbdcu2vqp9LBZuertuNgUbRUSkwHDxAKuLObvRxePK2/9bdMn/5Wa0Z5qJpG90NtnNkpOP8d9Bv5MnT/Lmm2/SpEkT3njjDQ4cOIC7uzs//PADW7ZsITo6mtjYWL788kvq1q1LpUrmEo9Dhw5x5MgRihcvDkCNGjU4evToRZfMKtCYt3L6smHceH+0Wqz0b/8V3h5+jvey7VlYLTbIdsHqcvn/d3KqSv9zhuOGDRt49NFH+eijj4iNjeXNN9+kevXqzJo1i3bt2jF27FimTJlCYGAggYGBtG7dmg0bNtCjRw/atWvH+PHj6dy5M5s3byYzM5MFCxbg7e3kLO4iIiJSIFldzCrUhh3Ig/SIObkcL7YaJFe7bo8U8JekYKOIiBQYXkFmvsa0YzeWBNow4PwZCL/HeW1zFrvd7qha98+bGMMwsFgsHD9+nKVLlzJkyBBCQkIICQlhwIABJCQkMGLECKZNm8bQoUPp0qULjz76KC+99BJ2u50PP/yQZ555hoCAAMcxg4KCyM7Ovm0TV98pCoWAzd3M2+h6g3kbAUegMduehc3qgs3698zEFPDwu3jV65x+kBNo/ufs1ho1apCYmOgo7FKxYkXuvfdePvjgA1q3bk2lSpV49913cx1v165dVK1alVOnTuHv709sbCyxsbE3fnEiIiIi/+Dhbz5IzToHtjwI8F0pyJjDw//mtuNm09QDEREpMFzcoXSsGSj8O33KdclIAddCULyO89p2o3Iq8VqtVmw2G8ePH2fQoEH07t2bRYsWkZqaCkBCQgLR0dGkp6c79o2KiqJNmzbMmzePxMREKlasyOTJk4mKimL06NG8+uqrdOnShSFDhlwwa1GBxvznU9QMnmekOPe4OUHGHOdTICAidyX3nKfz/+wHTz75JGPHjs2Vq7FXr15MmzaNnTt3Amb18uXLlxMXFweYS6zj4+PZsWMHw4cPZ+rUqdx3330ULlxYM2VFRETkpikUbAb2zjv5Pup6ZWeYsy19bvO6d7p7ExGRAiWspjkz6+yJ6z9GyiEIqgCFI53XruuVnZ3N66+/TuPGjQE4e/YsgwYNIjIykrVr17Jjxw569erFq6++CpjFXnICO+fPnwfMoh0hISGkpqYyfvx4AGJiYpg6dSo//PADBw8eZNCgQVitVkdwSW4dbt4QVAnOHr85xzcMg+xMA3sWBFfO/VnO0/n4+Hhq167Ntm3bsFqtjB8/niNHjji269OnD6dOnWLBggWkp6dTr149GjZsyAcffADA3r17GTlyJK1bt+aHH35g7Nix9OjR4+ZckIiIiMjfrC4QUMb5D22v1/kUcPMBn7D8bsmNUbBRREQKFO8QKF4bUg5D1vlr3//sCTOvS+nG5tf88M9ZiTabDT8/PypUqMDOnTtJSEhgz549/Pjjj/zyyy/MnTuXJ598kgkTJrBz504iIiKoV68eP/30E6tWrXIcJzExkWrVqjF58mTS0tIAM8iUU+E3KyvLsRRbbj0l65tVFDPPOe+Y/8wplHbMQqrnHvacX8GJEyfIzjYrUv/222989dVXvP/++1StWpXChQvzxhtvsHPnThYtWuRY1u/t7U2nTp348ssvSUhIAKB///78+OOPrFu3jrJly/Lcc88xa9YsfvvtN9q0aeO8CxERERG5jIAIc0bhrfBMPf00+BYDd78rb3srU7BRREQKnEqdIKwGnPzTzHN3tc6dgtSjULatGbDMa4Zh8NJLL1GnTh3HsmmA8uXLs2/fPk6fPk1UVBRPPfUUjRs3Zs2aNbRo0YKhQ4fi6urKwIEDAejduzfBwcF06NCBN954g65du7Jw4UL69OlDcHAwc+bMueDcLi4uCjTewoKjoUg5SNrrvBtlx6zFPSvpN7MRQxY2Y+R7b9CnTx8SExM5cuQIffr04ZVXXsFmszFhwgSCgoIICQmhffv2TJw4kWPHjjmO8+STT7Jz506WL19ORkYGLVq0oFKlSmzevBmAhg0bOgoSiYiIiOSV0GrgEWDe6+cne7b54Lhkg1u3COXVUrBRREQKHHcfqPG0uaT65A5zlqM9+9LbZ52H03sg7SiUuw8qdsz7WY05VX6PHz/O5s2b6du3L3a7mXiyadOmpKWlsWfPHtzc3IiNjWXmzJn83//9H5GRkcTFxTF27Fh++OEHDhw4QIUKFfjkk0/o2bMnq1ev5uTJk3z99dfExMSwd+9evLy8gKtPYC35z+YKlR4yC8Q4czn1d2s+ZMT3DxNVqjK//rqIcePG8fbbb1O8eHFCQ0Np3749Z8+epWrVqlitVkeexuHDh7NmzRpWrVrlmAU5b948DMNgwoQJ7N+/HzBzjT7xxBPOa7CIiIjINfItBkXvgtTD+duOtGNmDsmiMfnbDmdQsFFERAokr0Co3RMqPGAGDo9vg1O7zCea51MgPRnSjpvvn94DhUKh+pNQ5eG8qVQHsHbtWiZOnAjgCOSUKVOG1157jYMHD/LWW2+RmJiIu7s71atXZ+7cuQAkJyfzzjvv0KFDB959913KlStHamoqdrvdkSMvICCAN954g59//pn58+cTHh7OvHnziIqKonr16nlzgeJUwZUgooUZFHdGkvP9J/7ku9Uf0qZiT8ZNHE256NKULVuW0NBQxxLrVq1aUa1aNceSfHd3d7KzsylfvjwPPvgggwcP5oMPPmD69Ols27aNmTNn0rVrV8qUKePYXkRERCS/lWwAVlfISM2f8xt2OHsMStQDz4D8aYMzKdgoIiIFlps3VOkKTd6Emj3AvzRknoVzJyE9CeyZ5j/49frCPcOgTNO8m9F47tw5vv76a3r06MHatWsBcymzzWbjxIkTTJgwgS1btjBhwgTAXEp9+vRpkpKS8PPz4/Dhw3h7e+Ph4UFaWhoJCQm0bduWr7/+OlfOx0WLFjFixAgaNGjAiBEj6NGjB2FhYSoEc5uq0B7Cm0BSgtmHr8bF/lsbBvy04iusuPB8v0cpFpO7MnVO9enIyEjuueceEhMTWbZsGYBjJuOoUaNo2rQpn3zyCS+//DK1atWibdu29OnTRxWmRURE5JYSHA3FapmTDAx73p8/eT/4FDP/3rgTuFx5ExERkTubZ4D5D3vpxnD+jJnH0WI1l6S6+eR9zhTDMPD09OTNN9/k0KFDPPHEE3z88cc0bNiQjh07UrduXcaMGYPNZuOZZ56hSZMmREVFsXHjRs6ePYuXlxePPvoogwcPZvv27axbt46IiAg+//xzQkNDsVgsjmIvRYsWZfXq1TRu3JgFCxZQqFChvL1YcSoXd7jrMfPJfMIiOHsSAsIvDJIfOr2H1Ttm06F2T6z/+jDznMHp3RaS049RKMCF6h0KYxh2LP/aLjs7G5vNRps2bVi+fDnff/89sbGxuLm5AVCqVClGjx7Niy++SOnSpW/mZYuIiIjcEKsNKneC07vMwJ9/6bw7d0aqmauxanfwKZp3572Z9FhZRETkb1abGXj0KWpWrXb3zZ/kzDm5En18fJgwYQKurq6MHDmSxMREKlWqROXKlZk1axaNGzfmmWeeoW/fvnh5ebF161bsdjtubm7069ePCRMm4OHhQd++fVmwYAFFixbFYrGQnZ3tOEfFihWZO3cuw4YNU6DxDuHiAdUfh5rPgWdhOLbVvGnOzvjfNou2/JdZ6z9m7c55ANgNOxmpcPIvOL3HQmg1KFbdht3lPH/+uR2r1XrBDEibzYbdbicmJoY6deqwceNGxyzcf26rQKOIiIjcDnzCzBRLmefg3Om8OWd2Jpzeba6mKnV33pwzLyjYKCIikk9yimlcit1uJygoiCFDhnDo0CH69+/PmTNnqFSpEomJiQC89NJLVK5cmdmzZ5OZmelYyurm5sZTTz3F5MmTeeaZZ4D/LW/NWQIrdy6LFUo1hLtfgyr/B+5+Zk7SQ79ncWIH3OXXkWCvSBZv+JFjf8CJP6ykHIYiZSHmGTv1+kLsvXVITk5m9uzZAI6CRDn27dvH+PHjycjIoEmTJmRmZrJz507z/CouJCIiIrehUrEQ1RLOHDRzuN9M9iw4sd1cwl3lYXPiw51Cy6hFRETyiYuL+c9wWlqaY1ZhzvJm+F/AplWrVlgsFtq1a0ejRo04dOiQo8AGwIABAxgxYgQAKSkXVgbJWe6qIGPBUyjIzOMYeS8c3Won9bALSQkQcKw8lY/XYfPBpZwuuZQmzRvhU9xOUHkrFqsFiwUefvhhxo0bx+eff07r1q2pXLmy47iGYTBx4kROnDiBm5sb99xzD/PmzSMwMDAfr1ZERETkxlhtEP0fMxC4cz7Yw8CriPPPk3UeTu6AwApQo4dZvPJOYjHu8AzwZ86cwc/Pj+TkZHx9ffO7OSIiUkAZhoHdbsdmszkCit9++y3Dhw8nIiKC5s2b8+yzz172GC+99BLbt2/n8OHDFClShMWLFzs+O3LkCGlpaURERNzsS5Hb3NChQ9m2bRvu7u6sXLmS9u3b89577+XaJidA/eOPP9K/f38AnnrqKaKjozl8+DCfffYZSUlJjB49mkaNGuUKkouIiIjc7rIzYes02PWz+XNAGefMPDQMSDsKaccg9C6o8TQUCr7x4+aFa4mvKdgoIiJyk10sELN//34effRRGjduzJEjR/j444+ZNWsWbdu2vWB/u92O1Wrl9OnTvPbaa0ycOBGr1cquXbsoWbLkBdtaLBYFfgq4nD7zT1lZWTz99NMsX76cgQMHkpiYyIQJE/Dz82Pq1KlUrVrVsV9OnzUMg61btzJw4EA2bdpE0aJFOXr0KF26dGHw4MF4enrm0xWKiIiI3FyGAYfWwx8zzHQ0vsXAs8j153TPSIPkvWYByqhWENXaLEh5u7iW+JqWUYuIiNwkOQEbi8VCZmYmr732Gr/88gt16tTBZrPRtWtXHn30UcD8x3vIkCFUr16dYsWK5TpOTvAnICCAl19+mZSUFMqWLUtYWNgF5/x3gEluPYZhPs1OOWS+zp6A7POABdy8wTvULFLkUwzcfa712AaGYVy0Hxw7doxffvmFN954g0ceeQSAmJgY+vfvz7Rp06hatapjv5xgtWEYREdHM3PmTNLS0ti/f3+u5dQiIiIidyqLBYrVgsJRsP0HOLAKziSaBSW9Q8HmduVjGHY4e9KczWhzM/MzVuoEgeVufvvzk4KNIiIiN4nFYiE5OZn169ezbNkytmzZQrt27fjxxx/ZvHkzs2bNcmw7bNgwwsPDWb58OZ07d75gZmLOz6VLl+bLL7/M0+sQ58g8C4c3wf4VZtXnjBQz8AjmshzDALsdLAZY3cDDH4rXhuJ1oUiUWfTlUnJmJOYEt7ds2cKsWbNo1KgR0dHR+Pn5sW3bNtzc3HIFC5s2bcp3333H8uXL2bVrF5GRkblmReZ8tdls+Pr6KtAoIiIiBY5nAFR/HCJbQOI62LfcnOlo2M37MzcfcHE3vzfsZr7HjFQzL6PFAh4BENHcrDgdWOHOKgRzKVpGLSIi4gQ5Oe7+rX///kyZMoXw8HC++uorIiMjSUhIoGbNmrz66qv06tULNzfzsWjbtm1JSUlhxowZBAUFXfZ8Wi59+7BnmwHGHbMheT9YXaBQiDlr0XqRx76GAdkZcO6UOevR1fPvp+APQUD45c+VnJzM9OnT6dWrFxUqVODEiRM0adKEyZMnk5KSQmBgIJ9//jldunRxzLz9/PPPefbZZxkxYgQvvfSS8i+KiIiIXEbmWfPBccphSN5nBh4z0v4OPlrA6gr+JSEgAnzCzHyPt0texsvRMmoREZE8kjMLLCfQeOzYMfz8/HB3dwfMoho///wzGRkZREZGAhAeHk7Lli1ZsGABrVu3pmLFigC8+eabVK1alWXLltGxY8fLnlfLpW8PqUfhj2/NZTc2dyhS9spLbiwW8+m4T1HzdT7FfIp+aheUaweRzcHikjsnY0ZGBkOGDGHatGm0adOGTZs2ERkZyaRJk+jXrx+dOnXi3nvvpW3btowZM4amTZs6AtonT57Ew8ODr776ig4dOlCqVKmb+SsRERERua25ekFoNfMFf69OyTRnNFqsZrCxIMxevBz9pSIiInIDcgI+c+fOpXbt2rRq1YrWrVuzfv16srKyCA8Pp0mTJlitVlasWOHYL6ey9KpVqxzvRUdHU61aNbZv3052dnaeX4s418mdsOptSFgCPsXNp9pXk9vn39x9ILgyGHaDTVOy2fgJZKfnvoVzdXWlXLlyJCcnc+LECcqVK4fNZqNz5840btyYQYMGAfD222+zY8cOevbsyeLFi1m+fDlr167l8ccf59FHH8Xf398JVy4iIiJScFgs5j2eqxe4eCjQCAo2ioiI5JKdac4ky0g1l0L80+7duzl48KDjZ8MwOHfuHL1796ZXr140a9aMd999Fx8fHwYNGsS8efMA6Ny5M4ZhsHjxYse+1atXp3bt2sycOZO//vrL8f6qVasYNGjQRZdky+3j1C5Y9xEkH4CQ6Gsv9PJvFgv4lbBQONzG7l/t9Pu/j3i5bz+WLFnCqVOnsFgsNGjQgMqVK3Pq1CnAnHXr7+/Pk08+yYYNG1i3bh0RERFMmjSJpKQkHn/8cZo0aUK5cuUYNmwYL7zwAn5+fk64ehEREREpyLSMWkRECry0Y3BoAxzfDkl7/1cZ2N3HrD634cT3TJg+nLPn0nBzc6NNmza8/vrreHl54erqSvHixRk/fjzNmjUDYNOmTQwbNozg4GBatWpFjRo1qFq1KqtXryY+Pp5q1aoB8OSTT/Lss8+SkpLiaIunpyc56ZSVN+/2lHYcNkyA1MMQVOHyhV3+yW7YsV5m42x7NvO3f8KUJYMp7F6CsBKhzJ03mxo1avDll19SsmRJunfvTs+ePdm9ezcREREA1KpVi6ZNmzJ48GDmz59P+/btuf/++9m4cSNVq1Z1LPkXEREREXEGzWwUEZEC63wKbP4vLBoIv31qBhyzz5u59WyusGH7Ctq9XJ6+Q5/kLu8H+aj/TOrVq8/UqVOZOHGi4ziPPvoozZo1Y8qUKZQpU4bx48fToEEDtmzZwvz58wHo2rUrp06dYu7cuY79WrZsyfbt24mJicnVLhV+uX0Zdtg2A07thiLlrj7QCGC1WLlc3b7EU7vYemAVPVuNYVy3jbxUeS49uw7l66+/Zu3atbi6ulKvXj0qV67Mm2++abbHMChSpAjdunVjwYIFHDhwwJFjtFatWgo0ioiIiIjTKdgoIiIF0ukEWDkStn9nVgQOrgyB5cyKcV5FwCsQ9p1fTZblLL3bTOS+iAEk/1yJdqUGk5mRSVxcHIZh4OLiQkBAAIsWLWLs2LG89NJL/PXXX4wdO5b9+/c7gouNGzemSJEi7Nmzh6SkJMAMBHl4eCg/4x3kQBzsWwH+pa89X8/v+5bT56umxP1l9hn7v9bxlwwsR9Poh7mncmeSLXsZt/YxXh72NC4uLgwcOBCA0qVL06VLF3766SdOnDjhCFy3aNGCRYsWUaJEicsGNEVEREREbpSCjSIiUuAk7YV1Y+DkDgisAN4hF5+BdnfFjkSGVmPtnjkElAHf4jB/5hKOHT9GVJnyWCwWsrKysNvtzJ07F4vFwlNPPQXA3r17cXV1ZeHChcyZMweAzz77jM8++8xRhCNn9qLyM94ZsjNgx2yzL3lcR+pDD9dCZNuzWLfLnA17sSXVtaNa8seBOPpNbYXdPZWXa81lVK//snjxYuLj4/Hw8KBRo0bYbDaGDBni2C8wMJDGjRsDWp4vIiIiIjeXcjaKiEiBknkW4j83C3cEVbz8MtdihSOoUKw2a3bOZfwvfVm/ewGJp3bh6xbE6V0unDmTgq+vWfkjMzOTgIAAVq9eTXR0NNOmTePBBx+kcOHClClTxjxesWKAWbgjp4q13DmObjYD2QHh17d/ubAYqpRsyKa9S9h2cA0Vi9e5II9jtj2Lb+PepWxYDD1bfkTWUT/W/jENgA8++IAvvviCiIgIxo4dS7ly5ZxwVSIiIiIi10Z/6YiISIGya74ZFCocdXX59OqVuw+b1YXZG8bT6q4nmNpzN8Pa/cLaNWto2fQ+vvnmGwAefvhhLBYLDz/8MKVKleLQoUMMGDCAoUOHUrFixVzHVKDxzmMYsH+l+dXF4/qPUyeqNa42N5b+MQPIPbvRMAxsVhdOnEnEzcUTbw8/fIrCjoSt3FOvNV999RXHjx/H29ubjh07Eh0dfaOXJSIiIiJyzTSzUURECozzZyBhMXgWBperrItRJiSaKqXuJsueSZVSDQn0CSOwQhjPpH3OFtcv6dGjBwkJCfTp04d58+Yxd+5cKlSokGtWmWYy3vkyUuD4NigUdGPHqVSiLpEh1dhxaD0Jx7YSHlyZbHs2NqsNi8VCtj2LptEP89HPvbDbs9lzbDOZ5+D9bv9l5vz/4uPj45wLEhERERG5Tvn6l8/y5ctp27YtYWFhWCwWfvzxx1yfP/LII47E5v9McC4iInI9jsRD6lHwDr22/epEtcbNxYNfN091vBdWOogmhfvSq8crTJw4kSVLluDq6sr9999PuXLlsNvtjsIvCjTe+VIOQ0YquDsh1lenbGsMDJb8MR0A2z8qzaRnnqV51f+j3/1fkJ6ZRq2IFoxss5JCZ6Lw9lagUURERETyX77+9ZOWlkbVqlUZN27cJbdp0aIFhw8fdrxylquJiIhcq9MJgMWsPn0tcmab7TqyiYRjWwFw9cvkfDJ0b92HXbt25XoYZhgGVqtVhV8KkJRDkH0ebFc5Y/ZyqpVuTOmgSmw9sIojSXsBOHw6gU9+7cezn9Zmzc55NK/6f7zecRqP3TOMQn7unD0O6Uk3fm4RERERkRuVr8uoW7ZsScuWLS+7jbu7O6Gh1zgFRURE5CJO7QJXr+vbt07ZNmzau4R5mybz3L3v4+bqBgZknHTH1RWys7MdwUVV+y140k8DFnDGf3qb1UatyJbsi9vGzHUfYcHCrA3jCSgUTLfY12ka/Z9c27t6wblTZhs8A278/CIiIiIiN+KWz9m4dOlSgoODCQgI4J577mH48OEUKVLkktufP3+e8+fPO34+c+ZMXjRTRERuAxmpYHO7vn2rlY4ltmJHoks2dLxnAFnp5veaxViw2bOde7waEc1Y8PvnzIh7n4jQqrze8Vvqlm190W0tVjDsYM9ybhtERERERK7HLR1sbNGiBR06dCA8PJzdu3czYMAAWrZsSVxc3CX/qHvrrbcYOnRoHrdURERuB1YXMyhzPWxWF/7v7tcueP9qKlrLnc/Z/cDD1Ysu9V/liXvepFRQhctvbJgzKtUXRURERORWcEsHGzt37uz4Pjo6mipVqhAREcHSpUtp0qTJRffp378/vXv3dvx85swZSpQocdPbKiIitz7fYnDm4I0dw27YsVqsGIb5s9elJ9tLAeLi4fxjVixe56q2y84Eq+vNaYOIiIiIyLW6rZ6BlylThsDAQHbt2nXJbdzd3fH19c31EhERAQgoYxbxyAkUXg/r39PHstLBxR18wpzUOLmt5VQ4d/Zy6quRkQLuvlAoJO/PLSIiIiLyb7dVsPHgwYOcPHmSokWL5ndTRETkNhRUCdy84bwT0vmmHga/kmYAU8QnDNx8zLygeS0jFfxLg801788tIiIiIvJv+RpsTE1NJT4+nvj4eAASEhKIj49n//79pKam8vLLL7NmzRr27t3LokWLaNeuHZGRkdx777352WwREblNBZSB4Mpw5sCNzW7MOg8ZaVC60fUXnJE7S6FgKBRkVoXOS4ZhzrItUjZvzysiIiIicin5GmzcsGEDd911F3fddRcAvXv35q677uL111/HZrOxefNm7rvvPsqWLcvjjz9OTEwMK1aswN3dPT+bLSIitymLBSp0AHc/c2bi9TAMOLXLDFqWutu57ZPbl9UGpWLNWbPXW4Toepw7BR7+UPSuvDuniIiIiMjl5GuBmEaNGmFcZmrJggUL8rA1IiJSEBSOhPLtYfOXZlGNQkFXv29OoNGrCET/B1y9bl475fYTVgP+/BHOnjBnOuaF1CNm0NunWN6cT0RERETkSm6rnI0iIiLOULYVVHwA0k/Dqd1XNxMtKx2O/2EW4oh5CgLL3fx2yu3FOwRK1IOUw3lTKObsSbMCdelYc9auiIiIiMitIF9nNoqIiOQHixUqPmjOBts2A45uMZeiFgoCF8//BW4MO5xPgdSjYGRC6F1QuTMUjsjX5sstrHw7Myh9eg8Uibp557FnwZmD5vmCo2/eeURERERErpWCjSIiUiBZrFCyAQSWh/2rYN9ySDlkzmDEAhjmV7dCEFTBXKpavLY5k0zkUjwLm4HstWMg7fi1LdO/WoYBJ/8yi8KUb69ZjSIiIiJya1GwUURECjSvQHN2WGQLSEk0l8BmpgEW8AwA76LgU9QMTopcjWK1oPx98Md3Zr/xKuK8YxsGnNxh9tu7HgUPP+cdW0RERETEGRRsFBERAVzcIaCM+RK5ERYLVOgI2VmwY5Y5W9Yn7MZnIGZnwsmd5mzJGk+bs3JFRERERG41mqchIiIi4mRWm5nfs0o3wDDzOGalX//x0o7DiW1mHsjaPSGkitOaKiIiIiLiVJrZKCIiInITWG1Qro2Z83PrNDgaDxYXc5aju8+V9zfscPYEpB4zc4dW6Ajl7ru6fUVERERE8ouCjSIiIiI3UeEIqNcHEtfB3mVmzsWkBLC5m4FD10L/ywlqz4SMVLMKumE3C85EtTCLGRUpp2IwIiIiInLrU7BRRERE5CZz8TArmpdsACd2mAHHU7shaS+cTzYDiwBWF7P4S4l64FcKQqvdnIrWIiIiIiI3i4KNIiIiInnEYjWXVQdVMH/OzoT002DPMj+zuYOHv2YwioiIiMjtS8FGERERkXxic4VCwfndChERERER51E1ahEREREREREREXEKBRtFRERERERERETEKRRsFBEREREREREREadQsFFEREREREREREScQsFGERERERERERERcQoFG0VERERERERERMQpFGwUERERERERERERp1CwUURERERERERERJxCwUYRERERERERERFxCgUbRURERERERERExCkUbBQRERERERERERGnULBRREREREREREREnELBRhEREREREREREXEKBRtFRERERERERETEKRRsFBEREREREREREadQsFFEREREREREREScQsFGERERERERERERcQoFG0VERERERERERMQpFGwUERERERERERERp1CwUURERERERERERJxCwUYRERERERERERFxCgUbRURERERERERExCkUbBQRERERERERERGnULBRREREREREREREnELBRhEREREREREREXEKBRtFRERERERERETEKRRsFBEREREREREREadQsFFEREREREREREScQsFGERERERERERERcQoFG0VERERERERERMQpXPK7ATebYRgAnDlzJp9bIiIiIiIiIiIicvvJiavlxNku544PNqakpABQokSJfG6JiIiIiIiIiIjI7SslJQU/P7/LbmMxriYkeRuz2+0cOnQIHx8fLBZLfjfnks6cOUOJEiU4cOAAvr6++d0cuY2o78j1Ut+R66F+I9dLfUeul/qOXA/1G7le6jtyve70vmMYBikpKYSFhWG1Xj4r4x0/s9FqtVK8ePH8bsZV8/X1vSM7pdx86jtyvdR35Hqo38j1Ut+R66W+I9dD/Uaul/qOXK87ue9caUZjDhWIEREREREREREREadQsFFEREREREREREScQsHGW4S7uzuDBw/G3d09v5sitxn1Hble6jtyPdRv5Hqp78j1Ut+R66F+I9dLfUeul/rO/9zxBWJEREREREREREQkb2hmo4iIiIiIiIiIiDiFgo0iIiIiIiIiIiLiFAo2ioiIiIiIiIiIiFMo2CgiIiIiIiIiIiJOoWBjHhoyZAgWiyXXq3z58o7PDcPg9ddfp2jRonh6etK0aVN27tyZjy2W/LJ8+XLatm1LWFgYFouFH3/8MdfnV9NX0tPTee655yhSpAje3t488MADHD16NA+vQvLDlfrOI488csE41KJFi1zbqO8UPG+99RY1a9bEx8eH4OBg7r//fnbs2JFrG407cjFX03c07si/jR8/nipVquDr64uvry9169Zl/vz5js813silXKnvaLyRqzFy5EgsFgsvvvii4z2NO3I1LtZ3NO5cnIKNeaxSpUocPnzY8Vq5cqXjs1GjRjFmzBgmTJjA2rVrKVSoEPfeey/p6en52GLJD2lpaVStWpVx48Zd9POr6SsvvfQSP/30EzNmzGDZsmUcOnSIDh065NUlSD65Ut8BaNGiRa5x6Jtvvsn1ufpOwbNs2TKee+451qxZw8KFC8nMzKR58+akpaU5ttG4IxdzNX0HNO5IbsWLF2fkyJFs3LiRDRs2cM8999CuXTv++OMPQOONXNqV+g5ovJHLW79+PRMnTqRKlSq53te4I1dyqb4DGncuypA8M3jwYKNq1aoX/cxutxuhoaHGO++843gvKSnJcHd3N7755ps8aqHcigBj5syZjp+vpq8kJSUZrq6uxowZMxzbbN++3QCMuLi4PGu75K9/9x3DMIzu3bsb7dq1u+Q+6jtiGIZx7NgxAzCWLVtmGIbGHbl6/+47hqFxR65OQECA8emnn2q8kWuW03cMQ+ONXF5KSooRFRVlLFy40IiNjTV69eplGIbuc+TKLtV3DEPjzqVoZmMe27lzJ2FhYZQpU4aHH36Y/fv3A5CQkMCRI0do2rSpY1s/Pz9q165NXFxcfjVXbkFX01c2btxIZmZmrm3Kly9PyZIl1Z+EpUuXEhwcTLly5ejRowcnT550fKa+IwDJyckAFC5cGNC4I1fv330nh8YduZTs7GymTZtGWloadevW1XgjV+3ffSeHxhu5lOeee47WrVvn+u8Pus+RK7tU38mhcedCLvndgIKkdu3afP7555QrV47Dhw8zdOhQGjZsyNatWzly5AgAISEhufYJCQlxfCYCXFVfOXLkCG5ubvj7+19yGymYWrRoQYcOHQgPD2f37t0MGDCAli1bEhcXh81mU98R7HY7L774IvXr16dy5cqAxh25OhfrO6BxRy5uy5Yt1K1bl/T0dLy9vZk5cyYVK1Zk9erVgMYbubRL9R3QeCOXNm3aNH777TfWr19/wWe6z5HLuVzfAY07l6JgYx5q2bKl4/sqVapQu3ZtSpUqxfTp06lQoUI+tkxECorOnTs7vo+OjqZKlSpERESwdOlSmjRpko8tk1vFc889x9atW3PlFBa5GpfqOxp35GLKlStHfHw8ycnJfPfdd3Tv3p1ly5bld7PkNnCpvlOxYkWNN3JRBw4coFevXixcuBAPD4/8bo7cRq6m72jcuTgto85H/v7+lC1bll27dhEaGgpwQUWio0ePOj4TAa6qr4SGhpKRkUFSUtIltxEBKFOmDIGBgezatQtQ3ynonn/+eebMmcOSJUsoXry4432NO3Ill+o7F6NxRwDc3NyIjIwkJiaGt956i6pVq/Lhhx9qvJErulTfuRiNNwLmMtZjx45RvXp1XFxccHFxYdmyZYwZMwYXFxfHjEaNO/JvV+o72dnZF+yjccekYGM+Sk1NZffu3RQtWpTw8HBCQ0NZtGiR4/MzZ86wdu3aXDlIRK6mr8TExODq6pprmx07drB//371J8nl4MGDnDx5kqJFiwLqOwWVYRg8//zzzJw5k8WLFxMeHp7rc407cilX6jsXo3FHLsZut3P+/HmNN3LNcvrOxWi8EYAmTZqwZcsW4uPjHa8aNWrw8MMPEx8fT5kyZTTuyEVdqe/YbLYL9tG487f8rlBTkPTp08dYunSpkZCQYKxatcpo2rSpERgYaBw7dswwDMMYOXKk4e/vb8yaNcvYvHmz0a5dOyM8PNw4d+5cPrdc8lpKSoqxadMmY9OmTQZgvP/++8amTZuMffv2GYZxdX3lmWeeMUqWLGksXrzY2LBhg1G3bl2jbt26+XVJkkcu13dSUlKMvn37GnFxcUZCQoLx66+/GtWrVzeioqKM9PR0xzHUdwqeHj16GH5+fsbSpUuNw4cPO15nz551bKNxRy7mSn1H445cTL9+/Yxly5YZCQkJxubNm41+/foZFovF+OWXXwzD0Hgjl3a5vqPxRq7FvysKa9yRq/XPvqNx59IUbMxDnTp1MooWLWq4ubkZxYoVMzp16mTs2rXL8bndbjcGDRpkhISEGO7u7kaTJk2MHTt25GOLJb8sWbLEAC54de/e3TCMq+sr586dM5599lkjICDA8PLyMtq3b28cPnw4H65G8tLl+s7Zs2eN5s2bG0FBQYarq6tRqlQp48knnzSOHDmS6xjqOwXPxfoMYEyZMsWxjcYduZgr9R2NO3Ixjz32mFGqVCnDzc3NCAoKMpo0aeIINBqGxhu5tMv1HY03ci3+HWzUuCNX6599R+POpVkMwzDybh6liIiIiIiIiIiI3KmUs1FEREREREREREScQsFGERERERERERERcQoFG0VERERERERERMQpFGwUERERERERERERp1CwUURERERERERERJxCwUYRERERERERERFxCgUbRURERERERERExCkUbBQRERERERERERGnULBRRERERGjUqBEvvvhifjdDRERERG5zCjaKiIiIiIiIiIiIUyjYKCIiIiJOl5GRkd9NEBEREZF8oGCjiIiIiABgt9t55ZVXKFy4MKGhoQwZMsTx2f79+2nXrh3e3t74+vry0EMPcfToUcfnQ4YMoVq1anz66aeEh4fj4eEBwHfffUd0dDSenp4UKVKEpk2bkpaW5tjv008/pUKFCnh4eFC+fHk+/vhjx2d79+7FYrEwbdo06tWrh4eHB5UrV2bZsmW52r1s2TJq1aqFu7s7RYsWpV+/fmRlZQEwZ84c/P39yc7OBiA+Ph6LxUK/fv0c+z/xxBN07drVeb9IERERkQJMwUYRERERAeCLL76gUKFCrF27llGjRvHGG2+wcOFC7HY77dq149SpUyxbtoyFCxeyZ88eOnXqlGv/Xbt28f333/PDDz8QHx/P4cOH6dKlC4899hjbt29n6dKldOjQAcMwAJg6dSqvv/46I0aMYPv27bz55psMGjSIL774ItdxX375Zfr06cOmTZuoW7cubdu25eTJkwAkJibSqlUratasye+//8748eP57LPPGD58OAANGzYkJSWFTZs2AWZgMjAwkKVLlzqOv2zZMho1anSTfqsiIiIiBYvFyLnbExEREZECq1GjRmRnZ7NixQrHe7Vq1eKee+6hSZMmtGzZkoSEBEqUKAHAtm3bqFSpEuvWraNmzZoMGTKEN998k8TERIKCggD47bffiImJYe/evZQqVeqCc0ZGRjJs2DC6dOnieG/48OHMmzeP1atXs3fvXsLDwxk5ciSvvvoqAFlZWYSHh/PCCy/wyiuvMHDgQL7//nu2b9+OxWIB4OOPP+bVV18lOTkZq9VKTEwMXbp0oW/fvrRv356aNWsydOhQTp48SXJyMsWLF+evv/4iKirqpv1+RURERAoKzWwUEREREQCqVKmS6+eiRYty7Ngxtm/fTokSJRyBRoCKFSvi7+/P9u3bHe+VKlXKEWgEqFq1Kk2aNCE6OpoHH3yQSZMmcfr0aQDS0tLYvXs3jz/+ON7e3o7X8OHD2b17d6521K1b1/G9i4sLNWrUcJx3+/bt1K1b1xFoBKhfvz6pqakcPHgQgNjYWJYuXYphGKxYsYIOHTpQoUIFVq5cybJlywgLC1OgUURERMRJXPK7ASIiIiJya3B1dc31s8ViwW63X/X+hQoVyvWzzWZj4cKFrF69ml9++YWxY8cycOBA1q5di5eXFwCTJk2idu3aF+znTI0aNWLy5Mn8/vvvuLq6Ur58eRo1asTSpUs5ffo0sbGxTj2fiIiISEGmmY0iIiIiclkVKlTgwIEDHDhwwPHetm3bSEpKomLFipfd12KxUL9+fYYOHcqmTZtwc3Nj5syZhISEEBYWxp49e4iMjMz1Cg8Pz3WMNWvWOL7Pyspi48aNVKhQwdG2uLg4/pkZaNWqVfj4+FC8eHHgf3kbP/jgA0dgMSfYuHTpUuVrFBEREXEizWwUERERkctq2rQp0dHRPPzww4wePZqsrCyeffZZYmNjqVGjxiX3W7t2LYsWLaJ58+YEBwezdu1ajh8/7ggUDh06lJ49e+Ln50eLFi04f/48GzZs4PTp0/Tu3dtxnHHjxhEVFUWFChX44IMPOH36NI899hgAzz77LKNHj+aFF17g+eefZ8eOHQwePJjevXtjtZrP1QMCAqhSpQpTp07lo48+AuDuu+/moYceIjMzUzMbRURERJxIwUYRERERuSyLxcKsWbN44YUXuPvuu7FarbRo0YKxY8dedj9fX1+WL1/O6NGjOXPmDKVKleK9996jZcuWADzxxBN4eXnxzjvv8PLLL1OoUCGio6N58cUXcx1n5MiRjBw5kvj4eCIjI5k9ezaBgYEAFCtWjHnz5vHyyy9TtWpVChcuzOOPP85rr72W6xixsbHEx8c7ZjEWLlyYihUrcvToUcqVK+ecX5SIiIiIqBq1iIiIiNyacqpRb9q0iWrVquV3c0RERETkKihno4iIiIiIiIiIiDiFgo0iIiIiIiIiIiLiFFpGLSIiIiIiIiIiIk6hmY0iIiIiIiIiIiLiFAo2ioiIiIiIiIiIiFMo2CgiIiIiIiIiIiJOoWCjiIiIiIiIiIiIOIWCjSIiIiIiIiIiIuIUCjaKiIiIiIiIiIiIUyjYKCIiIiIiIiIiIk6hYKOIiIiIiIiIiIg4xf8DnNR0hCUc+2wAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "As you can see, we are seeing the distribution of each cluster using the scatter plot, but it is not very clear where is the centroid of each cluster. Moreover, there are 2 types of vehicles in our dataset, \"truck\" (value of 1 in the type column) and \"car\" (value of 0 in the type column). So, we use them to distinguish the classes, and summarize the cluster. First we count the number of cases in each group:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pdf.groupby(['cluster_','type'])['cluster_'].count()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "cluster_  type\n0         0.0      1\n          1.0      6\n1         0.0     20\n          1.0      3\n2         0.0     26\n          1.0     10\n3         0.0     28\n          1.0      5\n4         0.0     12\n          1.0      5\n5         0.0      1\nName: cluster_, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now we can look at the characteristics of each cluster:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "agg_cars = pdf.groupby(['cluster_','type'])['horsepow','engine_s','mpg','price'].mean()\nagg_cars",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-31-fb63ecec89ff>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n  agg_cars = pdf.groupby(['cluster_','type'])['horsepow','engine_s','mpg','price'].mean()\n",
          "output_type": "stream"
        },
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                 horsepow  engine_s        mpg      price\ncluster_ type                                            \n0        0.0   450.000000  8.000000  16.000000  69.725000\n         1.0   211.666667  4.483333  16.166667  29.024667\n1        0.0   118.500000  1.890000  29.550000  14.226100\n         1.0   129.666667  2.300000  22.333333  14.292000\n2        0.0   203.615385  3.284615  24.223077  27.988692\n         1.0   182.000000  3.420000  20.300000  26.120600\n3        0.0   168.107143  2.557143  25.107143  24.693786\n         1.0   155.600000  2.840000  22.000000  19.807000\n4        0.0   267.666667  4.566667  21.416667  46.417417\n         1.0   173.000000  3.180000  20.600000  24.308400\n5        0.0    55.000000  1.000000  45.000000   9.235000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>horsepow</th>\n      <th>engine_s</th>\n      <th>mpg</th>\n      <th>price</th>\n    </tr>\n    <tr>\n      <th>cluster_</th>\n      <th>type</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">0</th>\n      <th>0.0</th>\n      <td>450.000000</td>\n      <td>8.000000</td>\n      <td>16.000000</td>\n      <td>69.725000</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>211.666667</td>\n      <td>4.483333</td>\n      <td>16.166667</td>\n      <td>29.024667</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">1</th>\n      <th>0.0</th>\n      <td>118.500000</td>\n      <td>1.890000</td>\n      <td>29.550000</td>\n      <td>14.226100</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>129.666667</td>\n      <td>2.300000</td>\n      <td>22.333333</td>\n      <td>14.292000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">2</th>\n      <th>0.0</th>\n      <td>203.615385</td>\n      <td>3.284615</td>\n      <td>24.223077</td>\n      <td>27.988692</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>182.000000</td>\n      <td>3.420000</td>\n      <td>20.300000</td>\n      <td>26.120600</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">3</th>\n      <th>0.0</th>\n      <td>168.107143</td>\n      <td>2.557143</td>\n      <td>25.107143</td>\n      <td>24.693786</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>155.600000</td>\n      <td>2.840000</td>\n      <td>22.000000</td>\n      <td>19.807000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">4</th>\n      <th>0.0</th>\n      <td>267.666667</td>\n      <td>4.566667</td>\n      <td>21.416667</td>\n      <td>46.417417</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>173.000000</td>\n      <td>3.180000</td>\n      <td>20.600000</td>\n      <td>24.308400</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <th>0.0</th>\n      <td>55.000000</td>\n      <td>1.000000</td>\n      <td>45.000000</td>\n      <td>9.235000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "It is obvious that we have 3 main clusters with the majority of vehicles in those.\n\n**Cars**:\n\n*   Cluster 1: with almost high mpg, and low in horsepower.\n\n*   Cluster 2: with good mpg and horsepower, but higher price than average.\n\n*   Cluster 3: with low mpg, high horsepower, highest price.\n\n**Trucks**:\n\n*   Cluster 1: with almost highest mpg among trucks, and lowest in horsepower and price.\n*   Cluster 2: with almost low mpg and medium horsepower, but higher price than average.\n*   Cluster 3: with good mpg and horsepower, low price.\n\nPlease notice that we did not use **type**  and **price** of cars in the clustering process, but Hierarchical clustering could forge the clusters and discriminate them with quite a high accuracy.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(16,10))\nfor color, label in zip(colors, cluster_labels):\n    subset = agg_cars.loc[(label,),]\n    for i in subset.index:\n        plt.text(subset.loc[i][0]+5, subset.loc[i][2], 'type='+str(int(i)) + ', price='+str(int(subset.loc[i][3]))+'k')\n    plt.scatter(subset.horsepow, subset.mpg, s=subset.price*20, c=color, label='cluster'+str(label))\nplt.legend()\nplt.title('Clusters')\nplt.xlabel('horsepow')\nplt.ylabel('mpg')\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": [
        {
          "name": "stderr",
          "text": "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
          "output_type": "stream"
        },
        {
          "execution_count": 32,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'mpg')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 1 Axes>",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\n\nSaeed Aghabozorgi\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                  |\n| ----------------- | ------- | ---------- | --------------------------------------------------- |\n| 2021-01-11        | 2.2     | Lakshmi    | Changed distance matrix in agglomerative clustering |\n| 2020-11-03        | 2.1     | Lakshmi    | Updated URL                                         |\n| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab                  |\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}