{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":70497,"databundleVersionId":7713244,"sourceType":"competition"},{"sourceId":57418,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":48172},{"sourceId":57419,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":48173},{"sourceId":57420,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":48174},{"sourceId":57421,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":48175}],"dockerImageVersionId":30699,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\nfrom tensorflow import keras\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-28T12:11:37.246716Z","iopub.execute_input":"2024-05-28T12:11:37.247493Z","iopub.status.idle":"2024-05-28T12:11:39.307457Z","shell.execute_reply.started":"2024-05-28T12:11:37.247461Z","shell.execute_reply":"2024-05-28T12:11:39.306388Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Introduction**\nIn this notebook, I will give a tutorial on predicting six different classes of fruits using the double-step prediction approach. These categories consist of three types of fruit (apple, banana, and orange) in either fresh or rotten condition. Rather than using a single classifier, we can achieve the same result with multiple classifiers. The process is divided into two steps: first, predicting the type of fruit, and second, predicting its freshness.\n\n\n**Prerequisite**: for a successful run of this notebook, you will be required to upload the models for each classifier which you can find on the links below","metadata":{}},{"cell_type":"markdown","source":"![CSC532 Fruits.png](attachment:cd9253c4-697c-4f9b-bd82-365ab32fd651.png)","metadata":{},"attachments":{"cd9253c4-697c-4f9b-bd82-365ab32fd651.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAy8AAAJVCAYAAADX48ojAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAP+lSURBVHhe7N0FdBTJFgbgH9fgCRrcWdzd3R++uLuzwLK4u7vr4ra4u7u7awRLgtubW6mBJAQIxGZ6/u+kT2aqe3ymum9X3aoQbm5un2Hy8eNHPHv2DNGjR0eoUKGkyNA+fPiAT58+IWzYsLrE2N69e6c+V1v4bD9//oy3b98iXLhwCBEihC41Llv7Lks9FT58eESIEEGXGJetfZdlPyQL62XjsbXv8uvXr9XrjRYtmi4xNvkuhwwZEqFDh9YlxsV6OfiF1P+JiIiIiIgsGoMXIiIiIiKyCgxeiIiIiIjIKjB4ISIiIiIiq8DghYiIiIiIrAKDFyIiIiIisgoMXoiIiIiIyCoweCEiIiIiIqvA4IWIiIiIiKwCgxciIiIiIrIKDF6IiIiIiMgqMHghIiIiIiKrwOCFiIiIiIisAoMXIiIiIiKyCgxeiIiIiIjIKjB4ISIiIiIiq8DghYiIiIiIrAKDFyIiIiIisgoMXoiIiIiIyCoweCEiIiIiIqvA4IWIiIiIiKwCgxciIiIiIrIKDF6IiIiIiMgqMHghIiIiIiKrwOCFiIiIiIisAoMXIiIiIiKyCgxeiIiIiIjIKjB4ISIiIiIiqxDi7t27n+XC58+f8fHjR4QKFQohQoRQK40sdOjQCBkyJN69e6dLjC1s2LD49OkTPnz4oEuMS76/4cKFw9u3b9X32ujkuyy/W3m9tkC+w/LblcXo+F02NvlsZb/Letl4ZH8rr1O+z7aA32X/e/TwMVycnsDFxbQ4O+P9+w+IZR8TDg72sI8dC7Hj2KvHDWqWeLwcwsnJSb3z8kN7/fo1IkSIYDMHBcIWKlFhi6/XVl6rsKXXK/WUVKZhwoTRJcZmS58t62Vjs6Xv8vv379XBfPjw4XWJsdnSZyv883rv3X2AY0dO4vjRE7h+8xpcnjjB46Ub7GPEQSzT4hAzrvovnjx1guszJzg/eYQnpv9hwoQ1rY+D+PETIlu2LMieMwvSpEultg0sllhPhXBzc1PPRn5kz549Q/To0W3iTIGcHZCATVokbIFEzPK52sJnKz8wOSMiZyjMPzojs7XvstRTckAgJ1qMzta+y7IfkoX1svHY2ndZTrLI640WLZouMTb5LsuJbzmxZHS/+l1+cP8BDu47gkOHjuL4iSP48PEDMqTOgVyZCyFFkj9U0BIjmr3e+sfcPJ7D9elj3H98GyfPH8SJcwdUUJM1c07kypUDufPlQIqUyQP0N2aJ9TKDF+4kDYfBi7ExeDEuBi/GxeDF2Bi8fOvIoWMYP3Yirly/hPSpsqklc7rcSJ86u94iYDwwBTKnLx7GqQuH1f+IESOgTdu2KFWmWID0pGLwYkEYvBgXgxdjY/BiXAxejIvBi7ExePnKM2iZhGs3LqNSyXr4X6n6iBU9tl4b+FZsnI2Vm+eqz6Jtuzb+DmIssV7maGNERERERP5w+tRZ1K5RH81bNEOKhBkxc/hGNKvVNUgDF1G1TCNMH7IORfNUQP/+/VC2VEVsXL9VBV1GweCFiIiIiOg3SCtMn54D8WftmkgUNw1mDN2ANvV7IZ5DQr1F0LOLFBUNqnZQQUz2DIXwV7dOaNKwJZydXfQW1o3BCxERERHRL7p39z4qV6iGOzceYMaQ9ejUZCCSOKbUa4NfvNgJ0a5BHxXEvHR7g8oVq+DIwWN6rfVi8EJERERE9Au2bt6JChUqIl+2Uhj5zwKkSpZBr7E8qZKmx8T+K5AjY0E0atIAk8ZNt+puZAxeiIiIiIj8QLqJDeo3HO07tkbPtmNVXou1+KfNGNSv2h4TpoxGkwYt4WKl3cgYvBARERER/cTdu/dQpVJ1bN2+CSunHkHBnKX1GuvRqHonzB21FSfPHEMl6UZ2xPq6kTF4ISIiIiL6gTdv3qBz+66IENYOG+acQ+xY8fUa65M8UVpsW3gFjnGTon3b9nj8+LFeYx0YvBARERERfYfMpdapXTc8eHQfUwev1aXWT/JgJAhr0rAFPDw8dKnlY/BCRERERPQdg/qNwN6DOzF35FZdYhyzhm/Cmzdv0aZFRzXptTVg8EJERERE5IvZM+Zj0dI5GNRlOmJEs9elxjJpwEpcu34F/3Trq0ssG4MXIiIiIiIftm7ejpFjhqr5W/JmK65LjSdmNAcM/3sutu7YhHFjJulSy8XghYiIiIjIC8kB+bvH32hSowv+V6qBLjUumaemX8dJmDJtAo4fP6lLLRODFyIiIiIiLyaPn47UyTKiXpV2usT48mQthloVWmDMiPG6xDIxeCEiIiIi0u7fe4CFi+eZDuSb6xLb8WfFFnjs9Bh7du3XJZaHwQsRERERkTZu1CSUKVwduTIX1iW2I3rUWCpoGz5shC6xPAxeiIiIiIhMrly5hoNH9ltNq8uT585o0LkErtw4q0v8r1KJurCPHg/r1mzUJZaFwQsRERERkYnke9Qs3wzx4yTWJb/m9ZuXaNunGvJVTeBtkTJZZy0keBszdiw+fvyoSywHgxciIiIisnkuzi64fuO6Slr3r2Hd52D/ivtflgn9liNC+Eh6reXLmj4fUifNiF3b9+gSy8HghYiIiIhs3n9rNgVqnou0vPw9rDFOnNuPfuPaqhaZhWs851V5/eYVxs/piwLVE6ryXqNa4MkzZ7Xu8+fPWLNlPkrVS6vWFayRGFMXDcWHD+/VenHh2knUbl9IrZftZHu5nX9kTJMD69dt1tcsB4MXIiIiIrJ5GzdsRM5MhfS1wPHm3WuMntkTebMWxZ5ld1CnUmsVZMxYPByvTMHNtoVXsePf63CMlxTDpnbF23dvVGCyZutCzBy2QbXibJxzDhWL10bo0GHUfXq8csOuQxswtvditX5Ej3lYvG467jy4rtb/rix/5MXBI3vx/v3XIMkSMHghIiIiIpvm7OSCB4/uIXeWIrokcEhrSb7sJVA0b0WEChlKlbk+c8Llm2dR939tEC5seLUUyVMOj5zvqnWfPn7Ep8+f1LYiUkQ7xHVw1Nc81SjXFPYx46rLyROnhYPp8oPHt9X135XEMaXpfuLhyMHjusQyMHghIiIiIpu2bctO5M1WXF/zv25DG6ouXObF3D1MZEidHSFChNDXgKfPnHH15jnUaJ33y/Yygtgj53vw8HiBdCmzoFjeCqjfuTgGTuiAW/eueusSFjliFNjHiKOvBaxMaXNh+9Zd+pplYPBCRERERDZt+7YdyJ+9hL7mfz4T9qV72I8kiJMEa2ee9Hab7YuuIVWyDAgVKrSa6X/VtGOqNaTlP5UwddEQfPz4Qd868OTNVhR79u/0d/5MQGLwQkREREQ26+XLlzh26pDqzhUc7CJHw7v3b/Hg0Y+7eUW1i47alVph8sBVOH5uP567P9VrAk+uzEXUwAFnTp/TJcGPwQsRERER2awL5y8iUoTI+lrQk/wVGeVs2r/D8NDpriqTkclu3r38TeuKtIA8cLqDsGHCIXQoz4T9wCa5NIcPHtXXgh+DFyIiIiKyWc5OrohiF11fC3qS/9K8dndkTZ8XdTsWUTkvpeqnw+Y9K/Hx00ecuXQE5RtnUuX5qzli+r/D0a5BH9USExRix4wHtxfu+lrwC+Hm5qY6sckMms+ePUP06NERKpTn6AdG9uHDB3z69Alhw4bVJcb27t079bnawmcrZyXevn2LcOHCeUuIMypb+y5LPRU+fHhEiBBBlxiXrX2XZT8kC+tl47G17/Lr16/V640WLZouMTb5LocMGRKhQ4fWJZbl/v37qFu3LipXrozq1asjThzvye2zZ8zHxnVbMHXwWl1CXsmcM5GihsXIsYN1SfBiywsRERERGVaCBAlU4Lx69Wo0a9YMc+bM0Ws8ubo8CdaWF0sn3caePHHV14IfgxciIiIiMrTSpUur/+7u7pg/fz5q1aqFzZs9Z493e+GGKJFto5Xsd8SJFQ+uri76WvBj8EJEREREhiRd+JydnZE+fXpd4unx48cYNmwY2rZtC2cXZ7a8/IBqeXlmOS0vzHlh32rDYc6LsTHnxbiY82JczHkxtuDOebl69SrOnj2LM2fOwMnJSbWumI5v8erVK7Xe3t4eMWLEwJUrV9R1n6LZxUL29IXRrFZXXUJeHTm9G92HNcK5c2d1SfBi8MKdpOEweDE2Bi/GxeDFuBi8GFtQBi/y3l68eFEFIpcuXcLp06fh4eGBlClTIlWqVEiYMCFixYqFmDFjqmNaWSJFioRNmzZh+PDh+l48SeJ+y5YtsW71ZoT6GAldmg3Ra8ir9TsWY+Xm2Vi3abUuCV4MXriTNBwGL8bG4MW4GLwYF4MXYwuK4OXEiRPYt28f9u7dq1pRUqRIoQIW8/+f1RvSPez8+fP6GlCnTh00btxYXR4+aAxuXX+AAZ2nquvk3exlo3Hp1gnMWTBNlwQv5rwQERERkcWRXJUVK1aowKNv374IEyaMaj2ZOXMmunXrpoY+/uOPP34auNy7d+9L4JIjRw4sWbLkS+AiYtrHxAv3Z/oa+eT85JFqybIUDF6IiIiIyGIcOXJEBSkNGjTAypUrkTVrVsyYMQOtW7dG8uTJ9VZ+Z2dnhzx58mDQoEEqST927Nh6jSdHx/hwY/DyXS6m4MXB3l5fC34MXoiIiIgo2L148QLjx49H9+7dce3aNTRq1EgFLRLE+JxY8ldI9z0JXCSA8Y1DHHu88LCu4OXA8W1o26caXr95qUsCj7S8xHJgywsRERERkbJz50506NAB+/fvR6tWrVTQUrVqVUSOHFlvEXhixYoZ4C0vZy8dReVm2XDz7mVdYr2cnzyEY8L4+lrwY/BCRERERMHCxcUFo0aNwoABA5AlSxZMnDgR1apV02uDRvwE8RA5UhTcfXhDl/iPDFCx/cB/CBsmHHYf3qhLrdP9R7fw6rUHMmXOoEuCH4MXIiIiIgpyW7ZsQceOHfH8+XOMGzdOJeY7ODjotUFHRsArVKAo9h3dokv855HzPVy4dhK1K7fCoZM78cLtqV4D1c3r72GNTUHNBrTuVQX5qiZAuUYZscMU7EjQI548d1ZdwvYd24La7QupbeT/qQuH1HrfyG3lPuS+/LK9Xx04sR1pU2aEvQNzXoiIiIjIBsnQyhKsLFu2TOWzSKtLhgzBe2a/RKmi2H98m77mP4dO7oBjnCQomLOMan25dOOMXuPJ45Ubpi4aim4th2P/ivsY2m0Wxs/ti+Nn9+ktgIdOd7F8/SxM7L8C+5bfQ6PqnTBgQnvcuuf7RJty2/mrJmDa4LVq+7YN+mDY1K7f3d6v5H6LFCmir1kGBi9EREREFCRkcsmuXbuqYZCHDBmCYsWK6TXBK0++nKaA4Q4ePL6tS37P23dvcPjULuTLUQJR7aKjSJ5y2Lx7BT5+/KC38FSuaE0kjJdMXf4jVTaULVJTtXKYSWtQ9XJNED1qLM+WoVxlkDZ5Zpy9fExv8ZW0umzbvwa1K7ZE/DiJ1fYZ0+RE7Fjxce3WBb3Vr5PnLI9XonRRXWIZGLwQERERUaCTbmISuCRNmlSN/hUcXcS+R+aQka5j0s3LP67dvgAn14cqeBCZ0+XG1VvncefBdXXdLEmClPqSp8Txk+PWvat4/eaVuh45YhTYx/g6wlqoUKGRMH4yXLruvRVHvHn7SnVV6z++neoyJkvxOilx4tx+OD99pLf6dfJeJHZMhkSJHXWJZWDwQkRERESBatasWRg6dKia2b5du3a61LJIC8OR07v1tV8nLSBb965WI4zJSGMSRNTrVEwNBHDw5A69VeAZ1n2O6obmdalTqbVe++uk1aVoMctqdREMXoiIiIgo0IwZMwbLly9Hv379gnwksV+RJ29OdcAuyfa/Q1o/pLVj6uC13gKIvh0nYf/RrXj5yl1vCdy4c0lf8gx6zl89iSSOKREhfERV5v7yBR673FeXhXRHu3LjLNIkz6hLvpK8mpjRHEzP+5Qu8b8PH96r11KiVGFdYjlCuLm5qaENPn78iGfPniF69OgIFSqUWmlkHz58wKdPnxA2bFhdYmySHCefqy18tlIJvH37FuHChVP9Po3O1r7LUk+FDx8eESJE0CXGFRzf5SlTpuDQoT2mS56j3gQleb3ysCFCGv93Kz5/khfr2bfd8Ewv9dPnTwgZIqR6zUYndbJ8vqFCm/e5YTB//kJ92XjkGCNkyJAIHTq0Lvmqf//+OHPmjOomljp1al1quf5dsAz7dx1D/05TdInfbT+wFlv2rMTALtMRLmx4XQo8fe6CjgP+RMs6PZAxTQ50HdIALk8fY/jfc1Xey/krx/HPyGbo2WYssmcsoEYba9a9POLHToR+nSYjWpSY2HlwHSYtGIhR/ywwBTmp1CSVS9ZNV/cRIXwkHDuzFwMndkD3liORI1NBqVBV0n/UKDEQJXI0/Uz8buGaSXB1v41+g3uqGMGSjjEYvDB4MRwGL8bG4CVwSfBy9vQC5M8TRpcQkX+NneSOo0d/72y+Nfhe8NKlSxc4OTmplpdYsWLpUssmx8MVyv4Pjap2RsFcZXTpz0nLSE9TAFKyYBUUy1tRl341e9lo3H1wA12aDcbfw5sgZ+ZC2Hdki2rlkeCkY+MBKJKnvKrrJXjpPKAOiuevhI27lql8mUTxk5tuO0Tl0AifwYvsL2SggDGzeqmBB0SmtLnwT5sxiOvwazkr0oLUoX8tLFv5L6JFi8rgxVIweDEuBi/GxuAlcJmDlwUz4uoSIvKPA4dfomkbZ5sLXmT+ltevX2Pq1Km6xHrs2bkfkyfMwKQBK3VJwJF5XqTlpWb5Zsibrbgu9c4cvPzdaiRSJQv6IaTHze6DuImjo1W7Jio+sLTghTkvRERERBRgJHAR1hi4iIJF8iFi5PBYuWmOLrEd0oVt77HNaNKini6xPAxeiIiIiChAmAMX6Spmzbr/0xnLN87GsxeuusQ2LFk3A106d7Ho3hwMXoiIiIjI34wSuIhUqVMiV85cGDenry4JGJKfMqHf8u92GRMyctjcUVuDvMuYStJ//gDlK5XWJZaJwQsRERER+Ysk5wsjBC5m0vpy+8FlDJ7UWZcY13/bF2HJf9MwatxwXWK5GLwQERER0W/766+/1H8jBS4isl1kzJwzFcfP7sWoGf/oUuPZfXiDStKfOm0qEiVKqEstF4MXIiIiIvotnTt7tkqMHDlS/TeaOHHiYMbMadi6bxUWrJqgS43j5PkD6D26JYYNHYlMmYN+ZLPfweCFiIiIiH6ZTED5/PlzjBgxQpcYU6o0KTF2zDjMXDoK+45t0aXW7+T5g2jXtwa6dOyOUmWK6VLLF6p79+4qE0nmE5BxumX+BFuYG0NerywyLrktkHlA5LXawmcrZExy32b6NSJb+y7LvCfy2drK5xvU3+Xjx4/D6fFZ/K+CnS4hIv+4d/891m18iSZNmukSYxg7diz279+PJUuWqGMMOb4w8jFGwkSOiBYlFnoP6agmjEyaMJVeY53WbJmPXqNboGrlmuj0Vztd+i1LPMYI4ezsrCapJCIimjFjBi6eX8xJKokCiHmSyr17D+oS67dgwQL8+++/2LRpky6xHdu27ETPXj1Qr0pb1K/SXpdal16jWqg8l4b1mqF1u2ZWN4F5iBcvXnyZYd/NzQ1RokSxmRn2JZIMEyaMLjG29+/fq6jZVmbYl1ZEGaPcFlqabO27bKqz1Izz0kpsdMHxXZZJ5TjDPlHAMQcvR46c0CXWbdWqVZg4caJqcXFwcFBlcowhdZQttIhLvXzx/CV07NAZaZNnRbeWwxE2TDi91rK9eu2BvwbXx617VzB82HAULJJfr/k+iQ+kZc2SjjFCmpv5ZPHa7GcLi/Ct3IiLLb1WWfh6jbvIjsOWXm9Qv1YiChy+/d6sbdmxY4cKXMaPH4/YsWN/KTfK6/PrkjxlMqz+bwU83j5B297VcOXmOfUeWLKTFw6i4V+l4PH6OdasXY1CRQv4+tp8W4Rv5cG1MGGfiIiIiH7o2LFjGDRoEHr27In06dPrUtsVKVIkzJw7BSVKF0PngXWwZusCfPz4Qa+1LGu3LUS3IQ2RNUtWrFy9FPHiW3fLOoMXIiIiIvqumzdvomvXrmjatCmKFi2qS0laAZq3aoSFi+bj1JU9aNS1NJZtmInXb17qLYKXBC2Nu5XBpr1LMXXKVAwfPQjhI1h/l2sGL0RERETkq5cvX6Jx48aoWrUq/vzzT11KXiVPkQyTpo3FiFGDcfzCbtU9a+GaSXju9kRvEbTMQct/OxaiQ6c2WLF6MXLmzq7XWj8GL0RERETkq3LlyqF48eJo3bq1LqHvSftHGsyeP9UUxAzD0XO7VEvM7GWjcezMXnz89FFvFTiu3jqPpetnqKBl5ebZaNGyKdasW47CRQvoLYwjhJub25fRxp49e4bo0aPbzGhjMkCBjOJjC2TEIvlcbWW0MZkLREakkiZdo7O177LUUzLSWIQIEXSJcQXHd3nKlCkcbYwoAJlHGzt69KQusR41atRAkiRJMHToUF3iOznGkBFNbWW0Mb/Wy0cOHcWyJauw/+Aedf2PVNmQOV0u5MxUCEkc/TdPzLMXrjh98TBOnDuAE+cPwMnlAbJlyYUqVSujbPlSeiv/k/hAFks6xmDwwuDFcBi8GBuDl8DF4IUoYFlr8NKuXTs1BLLUCT/D4OXHZB995vR57Nq+B3v37sGVGxcQK0YcpEzyB+xjxoWD6bJcdzBdlv/2pkXu2+XpYzx55qT+q8tPndX/+49u4fqdi0gQNzEK5CuoRg7LkStroEwhwODFgjB4MS4GL8bG4CVwMXghCljWGLwMHDgQFy9eVBNR+gWDl1/j6uKK7dt24eqV63B67ARnF2fT4qRaT3wTM7oDHOxNwY29gxqiOnHSRChSrCASJ06ktwg8DF4sCIMX42LwYmwMXgIXgxeigGVtwcuMGTOwdu1arF+/Xpf8HIOXgPPkyRMV0MhxuUwCGjtObL0meFhi8MKEfSIiIiLC3LlzVWvLrwQuFLBixoyJtOnSIn2G9MEeuFgqBi9ERERENk4Cl3nz5mHXrl26hMgyMXghIiIismEMXMiaMHghIiIislEMXMjaMHghIiIiskEMXMgaMXghIiIisjEyqhgDF7JGDF6IiIiIbMjgwYOxZs0aBi5klRi8EBEREdmIjh074ty5c9iwYYMuIbIuDF6IiIiIbEDt2rXx6tUrLF68WJcQWR8GL0REREQGJjPClyxZEvHixcO0adN0KZF1YvBCREREZFC3b99GqVKlULBgQYwYMUKXElkvBi9EREREBnTw4EE0bNgQzZo1Q48ePXQpkXVj8EJERERkMCtXrsSUKVPUyGK1atXSpUTWj8ELERERkUFcunQJffv2xdmzZ1U3sdy5c+s1RMbA4IWIiMgPzp5/jRQZL6FD1/u6xDLI85HnJc/P0ji7fMDxU6/w8eNnXfLVvfvv0KjlXSRMfREJUl5AlrxX1LY/ug1935MnTzB16lT8888/yJgxI/r164c4ceLotUTGweCFiIgsjovrBxQqdV0tctnWvHnzGavXvUD5ajeROO0FdXAvS7psl9Gqw33sO/jS4g/uP3z4jF79H6FSjVvYs99Dl3pyffIBDZrfw8497qhVLRr69YyDYoXtYBc5JNp1uY/KNW/h4JFXemv6GZlwsk2bNnBxccHo0aNRuXJlvYbIeBi8EBGRxTl45CWu33yrllNnLK9FITBdu/EWpSrdQNvO93Hm3Bv8kTYCateIjvKloyBMmBD4b+MLzJr3BO/f6xtYqNChQyBRwrCIGSM04sQOo0s9HTj8EleuvUGfHnExbEA8NK4XE8MHxkOyJOGQPGk4ONjLbULrrel7JCG/Q4cO+Pfff9UcLr169ULixIn1WiJjYvBCREQW5e3bz1i55jlSJAuHRI5hsHb9C3UW3xZcvPwGVf68pYK2Tm0dcPlkaqxfkVQd4E8Z54jTh1Lh6J6UGNIvLsKHD6FvZbl6/BUbZw6nQtrU4XWJp/sP3qvgJl0a7+VSNrBPXJzYn0p9/vSt96aodePGjWqmfOkiFjt2bIwdOxblypXTWxAZG4MXIiIbYz7ocXV11SWW5dadtzh+8hXKl4mCiuWiYdded3Uwb3TSVWz4GGc8ffZRHfR3bGOPiBG/3U3HixsGceN4b8mwNteuv1GtSOHDWX4AZimcnZ2xcOFCNGnSBLNnz1YtLBK0/P3332rySSJbweCFiMjGfPjwQXU3qVatmjoYsjQbt7jh/QeoHIiihezw2nRQf+io7/kP5iT6Vf89x6PH79G64/0vOSJps17C4BFOcPf4qLf+SpLcy1a5iWfPP2LN+hfIVfjql7ySCtVv4vCxl/j8C409prcUy1Y999f9XLj0RuWGZM4YAXVrxUAIfx7Xy3PattMddRrf8ZY3I8/rmCk49EneJ3m/MuW+8mVbc47NmXNfu+7J65HXJXkp5mR7+V+s3HXMmPsEr1590lt+O5iAvL5aDW5jxZoXeP36E8r876a6vddtfjQAgdz3+CmuX56jPG69pndw9fq3wa35M37h9lGtl9ctt5HvxeWr1hEMSyvLhQsXMGHCBBW0yAhi8rudM2cO2rdvrxLziWwNgxciIhvjdc6HWbNmqesSzFgCaXVYv8kNObNFRMrk4VW3ory5ImHV2uemg+uvB8U+HTj00nRQfEe10NSoEl3liMSNExaTZ7iiUo3bqpuST4+d3mPEGCd07fkQmTN45pWULRlFHTRXrX0b0+c88VPgIUHCP/0eolP3B4gUMZS6n6qVouLO3fe/dD87drubDlY/438Vo6nEdf+6fvMN2v91H5euvEHNqjEwYlA80/OKpl5f9bq3ceT4S72lBC6f0LT1PfV+ZUwfXr0GWeT9X7/ZDSe95B1t3Opmuv0dPDQFi9X/F01t978KUVQguGjJM7z0Erz4FMUuFOqYArOc2SKpLmJ/d4mNaRMcMXFUAiR0DKu38p08RxmdbLjpM0uaOKz+vOxUflSJCtfV8/JJBgY4ZFpfqcZNPHho+jxMn0vJYlEQK2YovYVlefz4Mfbs2YPp06ejS5cuKvF+1KhRCBs2LIYNG4bhw4er7mF2dnb6FkS2J4Sbm5uqUj9+/Ihnz54hevToCBXKMn/UAUnOPH769ElVCLbg3bt36nO1hc/2s+ko4e3btwgXLhxC+PfUpRWwte+y1FPhw4dHhAgRdIlxBeZ3uV69erh3756+5qlAgQKIHDkybl5fjQUz4urSoLVrrwfqN7uLCaPio2LZqKpszsKn6D3gEebPSITCBSKrMjM5EK9iChDkLH63Tg5o1TSWqZ7zfK8kqBgz0RnjJrugSf2Y6NMjzpfWDDkrL2f/kyQKiwWzEiFxwq+/H0kkr9f0rrrP5QsTI1UKz7wM82PJAfPY4QlUmViw+Bn+7vMQf3VwQJvmXx9fDrYbmF7LtRtvvN2PbySnR1qNtuxwx7IFiZEja0S95ufktWzY4o6VixIjwx9ffxdyn27unxA9WihvrTjyHktrRc2q0VVAI2TUL3nNA3rHRcM6MVSZmTnfSIINafmQAOKx0wf1muxjeU+ql3ylcF66gn3vuX2vXPi2ToK/foMfY9b8J6bvRgL13TC/JhlyuaYpcA0fLiSWzk9kCkw8n5Pcz6r/3FRuUNsWsbx9N4KSDE7QtI0z9u49qOovr4t03ZQRwmS5ceMGnj59igQJEiBLlixIly4d0qRJA0dHR31PlkuOMUKGDGn6jhh/kAVbO8aQ+EAWSzrGYMsLEZFBvHjxAidOnMDOnTvV0Klz587FuHHjMGDAAHUWt1mzZqhRowZKlSqFGDG8H6CKvXv34vbt2/pa0JOD5BWrnyNe3NDqzLxZ0YKREdshNHbscv9uC4aMUCUH414PTuU4qoHpQFzW7T3goVoGfGpkCmq8Bi5CgowWTWKpVqBjJ77tuuSVBCjLVj1Tj/Fnde+PL60ntWtGU/ezfZf3oYJ9evvus9ouIPNAJNiIEd174CKkNUXyZm7feffDVhIzuR9Z/MJr4BKQJEBZv/kFCuaLjFLFonh7TY4JwqpgRoLOk6e9f16fPn1GEVPA27KJfbAELl51794df/75J9q2bavmYJE5WdatW4fTp0+roCV58uSoUqWKGj2sadOmKFGihFUELkRBjcELEZGVkpaTTZs2qa4k9evXR6VKlVQOi3Q7uX79ujo7KCMRyVncChUqoFWrVhg8eDAWLFigJrTzKV++fME6zOot08G05EQUK2SnghUzOdCWYGbzdjd1EOubVCnCqRYGn+QsfPp04XHvwXvcf+D9tnJA7nO0KzPpRiZn7I+f/Nq1yjfyfK5df6cew3zG3ysJaiJECKkS1C1ZmlTh1XOVeVmkFenhI9/HYZYBBAoXtFPd86Qr1uZtbqqFK7DJ40lrT45sEX0dZU3yhIQMM+2TdMOzhAYB6fa1dOlSFbTI71CCFAlmihYtqlpZpDVZWl969OiB8uXLqwBGTj5s3rxZ/Z6JyBODFyIiK3Hp0iWsXLlStaTIQY90/dqwYQNixoyJdu3aYdu2bRgzZow6qystLc2bN0fNmjVRtmxZ1SUsU6ZMSJYsGa5du4b797/OEu/g4IA+ffqo+5VuY8Fl/6GXeP7iI+YuegrHVF8TzBOnvYi1G16og9e9B3wPJiJE8L11QM7Qy5l56QIm+Q9e/aiVQwIhyc+Qx/SagO6Tx8tPePP2k5pQ0utzNi+SkC6P/TPhwnq2kkjOy5u3fkiQ8QOZxFJmqu/Y7QHyFrv2JWlfZrKX/A+vZPSyxXMToXSJKKobXI6CV1Gu6k3s2O3xzWSYTerHwMTRCfD02Sc0aX0PqbNcRP8hj9XM+IFFvhdCRmPz+R7L0rDFXbXeJwkcLWW+GOlmJL+1FClSIFu2bChWrJhqaWnUqJEaAbBv377q97tlyxaV8yK/25cvX6qTDRLISKK+jBIoLaryGyZjevLkKS5dvISzZ86rHCj6FoMXIiIL5u7ujhUrVqiDF2k5kQMbyU1s0aIFVq1ahYkTJ6Jx48bImjWr6eDdbwdpixcv1peAOnXqqLPBhQoV0iXBQ7pfSVK+TE4o3b/MCePmRRLD5eBezvTLkMKWQnI85OC+SEE7lXj+vaV+7Zj6Fr6TwCtZ0nCq69zVa/4fCUveTxklTGa3P3v+jXoPJ41xxMxJjli1OIm3li0zCWBmTHRU88g0axgTl6++Qf1md1Ch+i01kpuZdL+qVC4qzh1NjTlTEyJ1yvBqUIJs+a+q/KTvde3zj1evPO+0Ts3ovr6/5qVEUWMkskuAIy2p0gqzaNEiFcDIyYi4cePiyJEjqguonKBYvXo1nJyc9K3IWrg4u2LF0tXo33sIWjfviGqV/0TB/EWROnVq5M2bB5X/VxnVa1RV9bIqy10AlStUR4vGbdGrxwDMnjEfd257z1m0JQxeiIgs0IMHD9RwqG3atFFBirScyBlXOSMrZXJdgpjfIUFO7ty5sWzZMhX4WILTZ1+rpW1Le4wcHE9Nyuh1GT00vjpgluRnGVLYJ8kXkUDCJzmQlq5dcgbeZ7euN28+4YWb760ikh/j5v5RnbX3ba4Vs5gxQqkRxqJGCYkyJaKo0cp8W8zdmn4ke9aIqqUoIAI0mdhzwxY3NYDA1v+SqmR1eR6likdROT4/ymGRbnq9/46D80fToHvn2Dh34Q16DXj8zfsrsXLxInZqEs2dG5MjTapw6DPwMQ4f831Ya/+QPCjP/2G+eW+9Lkad2FKS+KWlRn77U6ZMUSctJJlfJquUFtj+/ftj+/bteP36xzlaFDwk4f3k8TMYNXQCKparipIlS2HX1kOIGjY+sqUpjJplW+Cf1mMwb9Q2rJ5+HDv+va6WNTNOYP7o7ejXcTLqVW6PPJlKIY5dMty89BgN6zc2/f5KY0DvYdi7+4Dps7fsrqkBicELEZEFuXLlCsaPH69aWg4cOKDOvs6YMQN169ZFokSJ9Fb+I11TpM+9vb29LgleEmBsMR2wS4J77hzfH2VLJqwUa9c/V/+9knk8fEvIl65icvDtGD8MEsT3npgvj+vbXCLilCmQkgAiW5avAwf4Jn68MEiWNCxOnXntrXXid2TKEAEZ00fA7n0e+G/jC3+1YEiujuSGSLDiM1FdhoiWYO9n5PZNG8REofyRTO/hazVfyvekTB4OPbvFMT3nzzh5OuCDl2RJwqlWuX0HXvppkAGjkxwZOfEgdYPk0khum7TQSCAjv++jR4/qLSk4HTl0DJ3adUfOHLnR9a/ucHnohsbVumDrgssqIKldqRXKF/sTBXOVQZY/8iBZojSwjxEH4cKGV0us6LGRNGFqZEqbC/mzl0SZwjVQo3wzdGjUH8smHUT/TlNhFzYOJo2bbnqMnGhcvwXW/7dZP7pxMXghIrIAx44dUzkn0h3s6tWraN26tWplkXkeIkX68QG0tZOWEUnGz58nskoa/56UKcLhj7ThVd6Li6v3/AqZx2Xpymfe8jMkkXzkOGeV7C25HL4l9P+77JkaKMAraQGS+V/kYDlfnh+/91GjhFLd3G7ffYdho519zY+RyR9/lDdjJs+vXUt7hAwZAl16PMSk6a6+tsDIa/xZHk28uGHVbW/c8t4FTbqTyUSUfsnDEa9M20kui7Ra/WwkMQneJOCKG8dv3Rd/hcwBU6FMVBw6+hLTZrn6OkiABGRBMXiApZFcNulSJi213bp1Uy2rEtD07t1bjT5IQe/IoeP4s3oD/PVXN8SJlhRDu83Gkgn70KXZEOTJWkxv5X8pEqdDnUqtMWnASswZuQXZ0hXGjGmzTfVdBWxYu0VNo2BEDF6IiIKRm5ubmoSua9eu6nKvXr1UlxBJ1pV5E2yBBCOSGF+mVJQfdmeSlhlpSZBgRCYm9Cp7lojqoLZAiWvqwL9br4coWfEGFi19hny5I6mhj30OGWwXORRimwKUgiWvq7lL5DbN295DpZo34fHys5rzxOcwyr6pVS066taKjpVrnyNDrsto2f6eui+ZZ0Rm3M+e/5p6zn4h3bAkL0UChaGjnFQyfPlqN7/cX75i15Ak3SXMXvBU38J3ZUpGQRS7kOj89wPT7R5g8fJn6N77kbr9k6cfkMgxjN7S0849Hshb9Kp6DJmoctV/z9X2hUpdx8XLb9GqWSwVqEmrR+NWd1Gz/m01945sJ9tXq3NbTdIp73XxIlH0vQYc+ew6t3dQ8/yMnuCCrPmuqOcq74u83zLjfunKN0wBjA1GL17IQAAyFLN0LUuYMKEaiGPgwIE4f/683oIC0+GDx1TQ8k+PXsiRoTBmD9+ERtU7qZaTwJYofnJUK9MYs4ZtRNXSjTB9+iyULVXJkEEMgxciomAiXTs6d+6sRhGTVpcRI0agSJEieq1tkNYByfGQ3BIZnvhnihaKrA7KN26WIXq/tkokShgGm9Ykxx9pI2DFmmcqaHn0+J3K9ZhhCgZ8m7H+w8fP+KdrHJVjc/HyG3UbyRORiRGXLUikclj8QnI/BpoCnXnTEyG96fFlgkW5L5kEU/JhOre3/2GLkldykC5J55I037GNPeLHDaO6pJnvT1pOShe3Q56cP24RkhaqeTMSqdeyYs1z/PXPQ/U+16waDWuXJkWeXN5HlUuWJCySJwuHbTs9VMtMuy4PsGTFUzUEtNf3QkZFK5jfTrV0zV30TG0n29978E493++91wFB7nfGxIRqglBpCZL3w/Mzc0eC+GHUbP0xogd8q481klHNmjRpooZlltw4qWekfuEoZYHjyOFjqF2jPvr3HYR8WUthzojNqkUkWpQfD9QRWCoWr+MjiKmIjeuNE8Rwhn3OsG84nGHf2KSeMsIM+zJ60OzZs1W3sIYNG8LO7ttRkoLjuyxnbM+eXhBsM+z/qu/Nev8zctb+ezO8EwUk8wz7R4+e1CVBT+aPWbt2LbZu3Yrq1auryWoDsjuqrc6wL/veIQNHYt36NWhaqyvKF61lOs6yvPdg7baFmDhvALJlyYnR44ab9jd+HxKfM+wTEdk4mcFe5mqQkYGkT7rMz+Jb4EJEFFBkfqdOnTqpgTpkUBAZtUwmuKXf9+zZczSo0wSnjp/B6F7/olKJuhYZuAhpiRn29xzcvHkTVStXx80bt/Ua68TghYgoiMgcLTI3Q4wYMTB69GgULlxYryEiCnxZsmRRyfxVq1ZV8z3JiZQzZ87oteRXVy5fw/8qVUMMu3iY0H85UiVNr9dYrizp8qjubNHs7NWEpzu379FrrA+DFyKiQCZdCyQpX7qJyahA0v9cZsUnIgoOMiCIDAySJEkSdUJFLjs7O+u19CMb1m1GvXp1VRexXu3GIWwY65lbKGKEyGpkshwZC6J12xYYN2qS6gZnbRi8EBEFolevXqkRxDw8PNT8LcWLF9driIiCT5QoUVRSvwQukksoXVhl0kv6vpXL16JL147o0Xo06ldpr0utz4DOU9G5ySBMmTEB3f/qpUutB4MXIqJA8uTJE5XXIomOMmSpTCRHAUuS7a+dSfNLyfpCtpfbMVmfbF2qVKnUCRaZ9FK6ksnIh5KbR94d2HcYvfv+gyHdZqFo3gqehVasUsl6GN93GdauX4EpE2fqUuvA4IWIKBA8ePBAHRBIMr4ELkRElkxahceOHavqrA4dOmDVqlV6DV25fBXt2rdFkxqd1Uz3RiGz+o/vuxQTJo/GpvXbdKnlY/BCRBTArl+/jp49e6qWFgYuRGQtJBdPAhcZmUxGI5OE/suXL+u1tunxYyc0adQMJfL/D3X/11aXGkeWP/Kif6cp6N7jL5w8YR2DNzB4ISIKQDKTtQQuiRMnZuBCRFapQIECGDduHBwdHVUws3DhQr3GtkiuYuMGzZA9Y0F0bjpIlxpPoVxl0b5RP7Rq2RK3b93RpZaLwQsRUQA5duwYevTogTRp0jBwISKrFjFiRLRo0ULlwBw9elS1xpw4cUKvtQ1DB45CkgRp0KP1KF1iXBWK1UbNCs3RuWM3XWK5GLwQEQWAvXv3qi4WWbNmZeBCRIaRPXt2lQuTIUMGNazy1KlT4e7urtca19nT53D4yGG0b9hXlxhfnUqtYR8tPlYsXaNLLBODFyIif5KReSZMmIC8efMycCEiwwkZMiQaNGig5qu6ePEi2rdvj507d+q1xtS3z0BUK9MI0aPG0iW2oWb5phg5egReerzUJZaHwQsRkT/NmDED0aNHV/MkEBEZlczQP2bMGHWiRrqTDRkyBPfu3dNrjWPdmk0IEzI8qpRuqEtsxx+psqF43kqYOG66LrE8DF6IiPxh/vz5qj94y5YtVQBDRGRkoUKFUnPCDB8+XAUu0gqzevVqvdb6eXi8xMiRI1C9bGNdYnuql2uCjZvX49rVG7rEsjB4ISL6TRK0zJkzB61atULmzJl1KRGR8UkujLTClChRQuXByGAlZ8+e1Wut1+QJ05EuZVYUzFVGl/zcwAkdkK9qgi9L+caZsGTdNHz8+EFv8XPuL1+g58jmePLcWZf4XhYU4jo4onLJehg8cJgusSwMXoiIfsPz589Vd7Hy5cujcuXKupSIyHaECxdOjUjWr18/PHv2TLXCTJ8+3WoT+h89eoQFi+agRvmmusTvWtT5G/tX3MeeZXcwosc8rNo8H+t2LNZrf+6xy308cvHeBc+3sqBSs1wzPHV9hn179usSy8HghYjoN0jgEiFCBNXqQkRky3LlyoWRI0eiVq1aWLp0qcr/27p1q15rPU4cPY1sGfIhXYosuuTXhQoZCqmTZUS5IjVw/srXoaWfPHNG79EtUaB6QtU602tUC1Umzl46im5DG+LqzXNo1KUU6nUqhs27V3xTdvOu54Shpy4cQu32hdT9lGuUETsO/IfPnz+rdQeOb0PfsW2wess8lKqXVm1Tt2NRXLt9Qa33q9ChwyBr+nw4csjyhscO8fTpU/VqP336hNevX6udsYwqYXTyemUJHTq0LjG2Dx8+qM/VFj5b+QHL65XPNkSIELrUuGztu/zq1SuECRNGLcFl/fr1mDVrFoYOHYpUqVLp0oAXHN/ladOm4fzZRVgwI64uISL/OHD4JZq2ccbBg0d1ifF4Pca4cuUKli1bpoaPz58/P2rUqBGo9WRA6ti2G9IlyYmqZRrpEr+RbmOJHVOooYbN5q8cj7sPb6Jn27F4++4N+oxphRSJ06FO5dYIHSo0Fq2ZjGNn92Fot9mIFNEOV26cxZDJXTCq10LEjOag7sO3slv3rqjAp3vLESq5/vqdi6prWecmg5A9YwEVvHQf1ggNq3VE7UqtVBCycPVEnLt8AoO7zkDYMOHU/fjF/mNbMXnhQKxdvzJY97k+hTQf+MhOUp6Y/DeXGXkxR6i+rTPiImzls5VFDvR8KzfiYmvfZfOBvG/rgmK5fPmyClyaNWuGFClS+LpNQC5B/VrN3yciCli+/d6MsgjzMYbUizLnleTAPH78GB07dsS8efPUCXKft7OkRbq6HTi8B7mzFFGv53fJ+yCtHFv3rUYhnTcj1x863UWF4rURLmx4hDIFL5VL1cf79+9w6fpptY1fHTixHQVyllaBi0ieKC0ypc2FUxcPq+sidqz4KF/sT8/HChkKuTIVhuvTxyqH5lfIe+H61Bnnzlzw9T0LriWEm5ub2lN9/PhR9VeU0XJkJAmjk7ME8gaEDRtWlxjbu3fv1OdqC5+tVBxv375VfXFtoeXF1r7LUk+FDx9etRIHB5mkLWnSpEHSXSw4vstTpkzB2dML2PJCFEDMLS9Hj57UJcYjxxjS6uKzB4CUSyuMLHHixFE5grJYovVrN2HenH8xZeCvj5wmLS+b96zQ14D4sROhe6uRKqiQultaQ5asm47hf89FhPCR9FbeW2z82vLi87HMShWsqlp5fHss3+7Hr/4aXA+p0iZD956ddUnwY84LEZEfLV++HG/evGGeCxGRH8hJtTp16qhJfFOnTo3Ro0ejefPmWLdund7CcmzdsgP5shXX136dOWF/6cT9qmVFgrnAOulkfiyviwQugSFbhvzYuXuHvmYZGLwQEfmBs7OzCl6qVaumS4iIyC8SJUqETp06qZw6yX+xtCDm/fv32H9oN/LnKKlLfl/8OInVPCkzl4zEy1eeo67Fih4bHi/d4KGvC+nCdefBdSRLmEaX+E282Alx+frZXxqG2T/yZi2Gu/dv4uaNm7ok+DF4ISLyA+n2kD59ehQsWFCXEBHRr0iZMqWvQcy///6L+/fv662C3vVr11UwkDBeMl3iPyUL/E/9X7V5rur+m9gxJexjxsXqLfNV8r481urN81TLTNoUnnOEhQsXAa/fvoKTywN1XfhWVjh3WVy8fgobdi1V9/Px00c8dLrzZeSygJYgbhI1oMCpk5Yzhw+DFyKinzh37pyaQZqtLkRE/uc1iEmbNi3WrFmD+vXrq/lidu7cqVpCgpKzkyui2EXX1/xPck2a1OyC1VsXqGR9SZyXbl2PnO+heJ2UKFgjMc5fPYlBXaYjqn5cR1OQUL5oTbT4pyJK10+nRhHzrSyJYyo1Qtm67YvV/RSsnghdBtVTCfmBxSFmPPUeWQom7DNh33CYsG9swZGw37t3b8SPH1+dIQxKTNgnsn62nLDvVzIS2f79+78sktwvwyzLki5dOr3V75NRz4YNG4Zy5cqhaNGiuvSrFcvWYO6s+Zg7yvrmpgkKnQfWQfJUidGrX3ddErzY8kJE9AM7duzAtWvX2OpCRBRI5GRU8eLFVcuLDKssI5KdP38ebdq0QefOnVW33VOnTqkBU36HnOQ7ffo0Bg4ciD59+uD69et6jSeXAG55MRrp8ubiYjktLwxeiIi+Q7ouyE5TJlmLESOGLiUiosCSIEEC1KxZExMnTsS4ceOQLFkyNVu/dDOrVKkS2rZti/Hjx2P79u2qRcUv5D6zZs2qLsvkmU2bNsX06dPVdeHq6vql+xZ9y8EUvMh7ZCkYvBARfYeMLiZnBGWHSUREQStDhgxqaPqZM2eqvMNBgwYhX7588PDwwJIlS1CrVi1Ur14dffv2xYIFC9Q2EuhI17MTJ06oSYXv3LmjRossVaqUvldPixcvVrffs2cPXFxcGLz8gAQvT5666GvBjzkvzHkxHOa8GFtQ5bw8ffpU5bjI7NB58uTRpUGLOS9E1o85L4FHHvfGjRuqG9jDhw/V/kGW58+ff1lkm59xiBEXGVPnQ7NaXXUJeXXk9G70Ht0SJ04e0yXBiy0vRES+2LBhg+quEFyBCxER/ZictEuTJo3KkZGTTd27d1eJ+TKK2dKlS7Flyxbs2rVLtcjIwCu+kS5q0aPHxJu3r3UJ+fTqtQfsIkfV14IfgxciIh/c3d2xceNGlC1bVpcQEZG1ihYtGo4ePaqveZITU7NmzVJBT7z48eHm/kyvIZ9cnjxCzOix9LXgx+CFiMgHaXWRoZFlmE4iIrJuMkrZ5s2b1WVHR0f07NlT5c8kTZpUldnHigU3j+fqMn3rsetD03tkr68FPwYvREReyHwDEryUKVNGlxARkTV78uSJ+v/nn3+qvD6fc704xLZny8sPSMuLtE5ZCgYvREReSOASM2ZMFClSRJcQEZE1k5Z0yX2RIZIjRYqkS7+KZR8DLzysK3g5cHwb2vaphtdvXuqSwCPBi72D5XQb42hjHG3McDjamLEF5mhj8juR/s9ydk4mTAtuwTnaWPYsQTtqEJGRTZ3lwdHGLNili5fRoH5DbJhzTpf439lLR9FnTCuM6rkQSROm1qUBR4KXJeumY/jfcxEh/LcBWUCq3Dw7OnRojyrVLGPaAAYvDF4Mh8GLsQVm8LJmzRo18ZlMjmYJgit4OXXyiGnvoHYNQcr0ctVrDhnS+L9b8enTZ/W52kA1ZfpgTa/38yeEDBHS9N3SZQb26eMn9XrNB/OfP4fGjBmz1GUjsvbgRQZpyZ49O/avuK9L/EfqsTGzeuHIqV0oWbAKGlXvpNcEnKAMXvJVTYB5cxcgZ67suiR4MXhh8GI4DF6MLbCCF3kPpdXlf//7H0qXLq1Lg5etfZdlPyQL62XjsbXvsuTOyeuVUa5sgbUHL6JksTJoVrM7Cubyf77jQ6e76DW6BSoWr4112xdjZI/5iBolhlon3bz6j2tnCmr+h+UbZuPMpSOIFiUmOjYegCJ5yqvfx5Pnzug7pjWql2uCqQuH4M6D60gUPzm6NBuCzOlyq/vxGbzIb2znwXUqaHru9uSb7X/X/mNb8c/IZjhy5AgiRw7cIMmvmPNCRGQiuS5yEGkpgQsREQWdMmXKYu/RLfqa/xw6uQOOcZKgYM4yCBsmHC7dOKPXePJ45Yapi4aiW8vhqrVnaLdZGD+3L46f3ae38AyAlq+fhYn9V2Df8nuq9WbAhPa4de+K3sI7ue38VRMwbfBatX3bBn0wbGrX727vVwdObEe+XIUsJnARDF6IiEx27tyJUqVK6WtERGRLylcsgz1HNuLtuze65PfI7Q+f2oV8OUogql10FMlTDpt3r8DHjx/0Fp7KFa2JhPGSqct/pMqGskVqqkDBTFpgpOUletRY6nKhXGWQNnlmnL387Sz30uqybf8a1K7YEvHjJFbbZ0yTE7Fjxce1Wxf0Vr/n5PmDKF2mpL5mGRi8EJHNO336NG7fvo1ChQrpEiIisiVJkyWBY/zEOOglgPgd125fgJPrQxU8COm2dfXWedX1y6skCVLqS54Sx0+OW/eu4vWbV+p65IhRYB8jjrosQoUKjYTxk+HSde+tOOLN21d45HwP/ce3U/kpshSvkxInzu2H89NHeqtfd/HaKTx74YqCRfLpEsvA4IWIbN6ePXtU4GIr/dOJiOhb0nXswPHfD16kBWTr3tW4efcyKjfLpoKIep2K4e7DGzh4cofeKvAM6z5HdUPzutSp1Fqv/XUS/OTNVdDX4aWDE4MXIrJpz58/x+7du1GwYEFdQkREtqh0ueI4dGqnmtfkd0jrhxzwTx281lsA0bfjJOw/uhUvX7nrLYEbdy7pS55Bz/mrJ5HEMSUihI+oytxfvsBjl6+jn0l3tCs3ziJN8oy65CvJq4kZzQEXrp3SJQHjzKWjKFfB8rpTM3ghIpsmgUvixImRKVMmXUJERLYoceJEyJktN5aun6lLfs3F66cQL3ZCpEicTpd4ypIuD16/fYVzV47rEmDj7uWqRUZcuHpC5dvkzVpMXTdbuWmu6rYlwY2M+nXr/lVkSP3tcMXSpaxskRrYsHMJDp3ciY+fZOTGD7j38CbcPJ7rrX7N1r2r4PTkPgoXtbwTewxeiMimSZcxtroQEZHo+ncnrNk6H5eun9YlfiMtI1v2rFTzuoQLG16XeooRzR4Fc5b2lrgvCfuDJnRUXcu6D2uMdg36IluG/GqdsIsUFTkzF0Kb3lWRv5ojZi8bjV5txyGJYyq9hXdy2+4tR6qhkgtWT4SCNRKr0ca8tvb4lTzHZRtmoXv3bhY5lDvneeF8AoYjZyg4z4txBeQ8L5Ko369fP8yZM8ci811s7bvMeV6My9a+y5znxboNHzQGd285oW+HgJ+wWOZ56TqkAWqWb4a82YrrUu9knpfOA+rg71YjkSpZBl0adP5dOwUnL+3D3IXTLbJeZssLEdksJuoTEZFPrdo1Vbkre49u1iW2w8n1AVZsmoN/enfTJZaHwQsR2SQm6hMRkW8i20VG29btsHzDLF1iO5atn4n8eQsiRUrPOWgsEYMXIrJJTNQnIqLvqVG7CtxePsX8leN1ScCIED4SJvRb/t0uY0JGDps7amuQdxmTOW7W7ViMzl3b6hLLxOCFiGwSE/XJSDw8PFCnTh3kz58fN254jmBERL9PctFGjx2BRWunYNXmubrUuGQY5n7j2mL4sBGIETOGLrVMDF6IyOacO3dOHeBxRn3yjYuLCwoXLqxa5S5cuKBLLce9e/cwcuRIPHjwQJd89ebNG5UoHlCcnJxQpEgRZM+eHVevXtWlnnbu3InUqVOjSZMmcHV1Vc8pb968qkyWGjVq4OLFi3prIuuTKnVKjBszHuPm9MWB49t0qfHIAAEyiEDrFu1QrERhXWq5GLwQkc3Zt28fcubMyUR9skpr167FwoULVd6WWeTIkVXZsWPHkDZtWl3qfw4ODuq34u7ujlOnvk6AJ6OHbdvmeTAngZ6MArhy5Uq8evUKcePGRZQoUXDmzBm0adNGBUBE1ipfwdzo22sg/hnZDE+fu+hSY2ndqwpKliiLRs3q6hLLxuCFiGyKDOm5f/9+dUBG5Fe7du1CmjRpVODQoUMH1bIgQYK0Nkhrh5l03xo8eDD++OMPtY20WMybN0+tk+FGN2zYgAIFCqh10koxe/bsL7c3t/i0a9cOCxYsUC0/sl2XLl3U/cqw6HPnzsXUqVPVbapUqaLWy3OToKFevXrftBYdPXoU5cuX//J8O3bsqB7HbMaMGeq3sHXrVtVSItvJfSxatEg9XxnWuEyZMur/jh07vrTqODs748iRI4gaNSpy5cqFFClS4L///lPBkzwfuRw7dmy8ePFCBTZE1qxazUpo0qAlGnQpoUuMo3HX0kiWJBl69v1Ll1g+Bi9EZFMkcJEDQQYv9KuktaFbt24qIMiTJ4+aX2nmzJlYv369Wi8BhQQa8+fPV/MQ5ciRQ20TK1YstV4Cl86dO6u+9BKgxIsXD6NGjVL34ZW0aIwePRrZsmVTcxrJ/Y8fP14FL/IYdnZ2KpgoVqwYqlevjjhx4uhbenf8+HE0btwY165dU8GFPI9Nmzahffv23lptJMCQ5/P06VNkzJhRPcagQYNw4sQJtV6CtiRJkqiWlPv376sy6UL26NEjtX2CBAlUmb29vZrnQ4KeK1euqPuV9QkTJlTriaxZhy6tkD9PQbToUVGXWD+ZADNUmJAYP2WUmqfHWjB4ISKbcuDAARW4yAEg0a9KmjSpalWQFpMWLVqoMpnsVEge1eHDh9VBvHShkiBGAohSpUqplpEVK1aog3tprWnVqhXGjBmjAgoZ+c7NzU3dh5DgRlpXJKiZMGGCClQOHjyoJj6U1hUJRGSyx5YtW6J///4quPBJAi15Du/fv1ddt9atW4fNmzergOrkyZNq8SpfvnxqG2lxKVmypAq6zPkqMWPGRO7cuVUwImVy3/Kc5X/RokXVcxHmlqN06dKp51axYkWMGzcOESNGVOuJrN2g4X0ROUoElG2YXs2HYq2u37mI4nVSweXZQ8yaO+3Lb9haMHghIpshZ4rZZYz8Q7peycG8iBQpkvovAYKQrlTSaiGtFObWFgk8JBh5+fKlSrSX1pPatWur7lnSHUsS3Z88eeItyV6CHwmShAQqkncit5fb+pUEOuaEfgkmhAQR5lYQnyOSmVt5JLiS/8L8uuQ1yOAW8n/79u3qOZu7jMntzOR25cqVU0GLvP6lS5eiU6dOqqWTyAjk9zFnwQx06tAFf7YriD1HNuk11mP2stFo2KUkatesj63bNyF6dOvL/WTwQkQ2Qw64pDsPgxf6XRKIyEH875ID/B49eqgWCekyJq0v/fr1C/aWQHldP2LuOiYj9Um3udu3b6tcl0SJEuktoF6DdIsbNmyY6p6WNWtWNTiG5MAQGUnFKmWxdMkyjJ/bF9MXD9ellm/QxI5Ysm46Jk+Yhi7d2/30d2+pGLwQkc2Qgy5JkmaXMQoMMsKWnJm9deuWap0Qkv8hrTHSSuPo6KhaWKRVQrpmFS9eHCVKlFD5M+bWDiHdr27evKkuS76KtOjI7eW+zcz5L98jQXr8+PHVZXMCv3Rdu3v3rros+Ta/Qp6zPE9pvZQWFXl8ybkxPyfJoZHXaiatNtL6I+RxiYwmVZoUWLtuFW7cP4cug+qqeVIs1ZWb51R+y9VbZ7Fq5SoULlZAr7FODF6IyCbIAaCMhCTBC1FgkNYJySmR4ENGApP8FMkVkZHDpMuWdBeTVpuuXbuqpHlpcZHtzKORmUlgIIn2kowvuSOSWyJ5M9GjR0fYsGFVtzLZRgYHaN68OZYsWaJv+ZU8zp9//qkC9YkTJ6rHlu5cEsBLVzRpNflVMt+LkPuQ5yCjkpktX75cJefLc27durXKfZH8GNlO3gMiI4oWLSpmzp2M3Pmzo1mPChg9sydu3fM+H1Jweuh0F+Pn9kOzv8sjYeL4WLl2KRIntf4BNBi8EJFNkDPDkiQtZ4+JAoMEKNJlqnTp0ipHRQ7ypUXFnHMirSzSTUxaYLZs2aIS6iVR32vXKyHzpDRq1EiN6CWJ8xIESTAjpKVDJoWUnBhpBZEBKKSVxTfp06fHpEmTVLAiI4dJDow8NxlswJy38yskOEuWLJm6nCVLFvU8zeSx5DmdP39eDaksLS8FCxZUgRtHGyMjkxMFzVs1UgG80/PbaNq9LCbOG4CHzp6tnMHB/eULzF0xVgUtZ68cwsTxkzF89CBvLbzWLISp4vwsF6S5V8ZilzM71toH7lfIWSvZKchZLFsgc1vI52oLn62cpZSuGTJ6hn/6plsLW/suSz0lFfD3DtiMxNa+y7IfksVW62VpsZGWC7Fs2TLVamEUtvZdli5z8nptZSJc+S7LULteuzYa1c++y6dOncXEsZNx7sJpVCpZD/8rVR+xosfWawPfio2zsXLzXESKGBHtO7ZDoSL5/fWbs8R6mcELgxfDYfBibAxejIvBC4MXo2DwYlx+/S6fNgUxI4aNwpVrl5A+VTa1ZE6XG+lTZ9dbBIwHj2/j9MXDOHXhsPofKVJEtG3XBiVLFw+Q3xqDFwvC4MW4GLwYG4MX42LwwuDFKBi8GNevfpfv3LmLndt2Y/fuvTh5+ijsIkdFhtQ5kCtzISRNmBpR7aKbyqIhimn5kZev3PHC/RncPZ7D6clDnDx/ECfOHcCdB9eQJmUGFCxYQLWypM/wR4D+xhi8WBAGL8bF4MXYAjp4kTk2ZOhaGeI1ZcqUutQyMHgxNtbLxsXgxbj8812W78X+vYewe9de7Nu/G86uj/UaTxLASCAjAY1wMwUqkr8iAYvs580kAMqdMx8KF5aApQCix/DcPjAweLEgDF6Mi8GLsci8EoMHD1aLzDPB4MW4GLwYF4MXY2Pw8nvkeyKDybx47obnz16o5cUL02VT2aePnxA1WlREjRoF0aJHVYtMDCv/I0eOrO8h8DF4sSAMXoyLwYtxyGc5fvx4HD9+XM0pUb9+fQYvBsbgxbgYvBgbgxfjssR6mUMlE5HFcnJywuXLl1GjRg01R8uLFy/0Gk8yv8XcuXMxffp0NQ+GLDI0rBw4CPnfu3dv7NmzBx07dkTRokVVTsHOnTvVDsg3Ui7rZTuZq0KGrD1z5oxeS0RERMGJwQsRWaxDhw6pWcLz58+PMGHC4MqVK3rNVxLAJE+eHBs3blTj7Mvs5jKPhTk4kfk2JMDp1KmTmn+iT58+mDZtGk6ePKnW+yTlcp8yH4cEMS1atMDYsWNx+/ZtvQUREREFFwYvRGSRpFleJvmTSSWln2+BAgWwdetW1XztlUwAKDN4S5cFmU1cupbJzN5ubm56C6BkyZJqYkAh28tkgQcPHlTXvZKARwIcaXWRoEm6BGTIkEFN6Hf9+nW9FREREQUXBi9EZJEkWJB8FJm5W2TKlAk3btxQs4R7JcPJes1/iRUrlgp8ZNhZM58zfMuM5nfu3MGbN290iSe5Ll3VBg0apLqMySIzksvs5K6urnorIiIiCi4MXojI4kgLyPbt23Ht2jVvuSfSdet73b0CkgQvu3bt8rbUrFlTryUiIqLgwuCFiCyOtH5IkvzEiRO9BRC9evVSI49JHovZ48ePvyToi3v37qkRb7xO8Hfz5k19yTMwunDhgmp9kVHLvJLRVGTExUuXLukSIiIisiQMXojI4kjOSuzYsVUivleZM2dWXbu8BhcyGpkk9stw0e7u7mpmculqFiVKFL0FVB6LBDVC7lvyXSSXxicZsla6iUlujeTbyH3KUNT3799X901ERETBi8ELEVkUyVfZtm0bihcvrsbR90paRbJnz65GATMn7ufMmVMFM2XKlEG1atXURJbSxczr+PuSsD98+HA1VHK/fv3QvHlzZMmSRa/1TsplzhcZclm2l+GXR48e7a21h4iIiIIHJ6nkZGiGI92CbGkCKVv7LnudpFKGNJY8mO7du+u13kl3sp49e6JKlSq+trRYOlv7LlviZGiBifWycXGSSuNivRz82PJCRERERERWgcELERERERFZBXYbY/cEw2G3MWPz2m3M6Ng9wdhYLxsXu40ZF+vl4BfC1dVVBS/yYchBkHzxbOHDkNcrbOG1Clt7vXIwLxWpLbC1z1bqKflsv/f5Llq0CPv27cPUqVN1iXXjd9m4WC8bl7xWWWzhYF7wu2xclvjZhnj69Kl6VvLk5EyBnM20hS+ffPHkNdvCGS8hUbP80GylYjEH4rbA1r7LUk+FCRPmu5+vjBQmQyy3bNlSl1g3W/ouy/dYvs+sl43Jlr7L8lpl8TmXlFHJd1m+x7ZyQM96OXix2xi7JxiO/NDYbcy4ftRtTOZjqVu3LoYOHaqGULZ2tvZdlv2QLKyXjcfWvsvsNmZcrJeDn22EyERkE06ePAkHBwdkzZpVlxAREZGRMHghIsOQ4EUCF1tpziciIrI1DF6IyBDc3d1V8JItWzZdQkREREbD4IWIDOHEiRMq/ydz5sy6hIiIiIyGwQsRGYIEL1myZFGDjhAREZExMXghIqsno78cP36cifpEREQGx+CFiKyetLo8fvyYXcaIiIgMjsELEVk9CV7SpUuHxIkT6xIiIiIyIgYvRGT1JHhhlzEiIiLj4wz7nMnZcDjDvrH5nGH/8uXLaNmyJcaOHYuMGTOqMqPgTM7GxnrZuDjDvnEF1Hf5zu17uHr5Gh4/csHjx05wdnKCk7MzXJ+YLrs+Nu3b3yNWjNhwsI9jWkz/HRwQO44D4saLjUSJEyBd+rT6ngKXJdbLDF64kzQcBi/G5jN4WbhwIbZs2YIFCxao60bC4MXYWC8bF4MX4/rd7/K9u/dx6MAxHD50BMdPHIEcfCdPlAZ2kaMhio9FyoTHyxdw83gOd9N/dw/Py7I4uTyA85OHyJopJ3LlNi15syNFymTqMwhoDF4sCIMX42LwYmw+g5eOHTsiYcKE6r/RMHgxNtbLxsXgxbh+5bt89PAxrFi2GoeOHDS9R2/xR6psyJwuF3JmKoQkjqn0Vr/n2QtXnL54GCfOHcCJ8wfw/MUTZMuaE2XLl0K58mX0Vv7H4MWCMHgxLgYvxuY1eLl//z7q1q2Lvn37omDBgnoL42DwYmysl42LwYtx+eW7fOTQMYwfOxE3b99A5ZL1kN4UtGRJnxehQgbeb/3qrfM4deEQNu5ahk+fP6BN2zYoXba4v1tjLLFeZsI+EVktmdvFzs5OTU5JREQUnCRoqV2jPlq1boU0SbJh5rANaFS9E7JnLBCogYtImeQP1CjXFDOGrkfpgtUxbNgwlC1VERvWbVYnOI2EwQsRWa2jR48iZ86cKoAhIiIKDkePmoKWmvXRuGkjJEuQHjOHbkCLOn8jdqz4eougEzZMOPxZsaUKYvJlK4VevXuiXOmK2LjBOEEMgxciskpOTk5fghciIqKgJl3IRg4fi3r16iKBfQrV0tKuYV84xkuqtwg+MaM5oPmf3TBt8H/IkDo3OnXugLatOuLNmzd6C+vF4IWIrJIELhEjRmTwQkREQU6CgJZN22Hu/Fno22Ei/mo+FMkTBc3wxb8iiWNKdGk6GF2aDcHufTtQq3pdNTSzNWPwQkRWSfJd2GWMiIiCmrOzC2pWqwNX56dYMHoHiuWrpNdYrkol6mJC32Vwd/dAtf/VwLmzF/Qa68PghYisztOnT3HkyBG2uhARUZC6eOES/qxZB3+kyIGpg9daRBcxv8qQJgeWTNinhmmuW7cONm/YptdYFwYvRGR1Tpw4oYZtZPBCRERBZfvWnahduzaa1eqGjo0H6FLrM7rXIjSt+Rc6dmmHqZNm6lLrweCFiKyOBC/sMkZEREHlzu176NmzJ3q2HYsyhavrUutVo3wzjOn1LyZMHov9ew7pUuvA4IWIrIq7uztOnjzJVhciIgoST58+Q+OGTU1BSw0UylVWl1q/bBnyo3+nKWjfsR0uX7yqSy0fgxcisiqnTp1S/xm8EBFRYHv9+g2aNWqFlEnSo1Xdf3SpcRTMWVq9rmZNm+Pxo8e61LIxeCEiqyKtLlmzZmWXMSIiCnSd2nVDhHB2qoXCqCoWr4NyRWuhSaMW8PDw0KWWi8ELEVkNc5exLFmy6BIiIqLAMW70ZDx6+NjQgYtZo+qdkC55NrRp0cHiZ+Jn8EJEVkOGR/7w4YNqeSEiIgosJ0+cxozZU9Tkk3aRoupSY5PX6v7iFebOXqhLLBODFyKyGhK8ZMyYETFixNAlREREAW/ooBGoWa4ZUiVNr0tsQ43yTTFz5gyL7j7G4IWIrIJ0GZPgJXPmzLqEiIgo4K1fuwkurs6oVraxLrEdRfKUR/rU2TF5/AxdYnkYvBCRVZDA5dWrV8iUKZMuISIiClgfP37EqFGjUb1cE8SM7qBLLdeT585o0LkErtw4q0v8r7opaFu4eC5cnF11iWVh8EJEVkGCl2zZssHBwfJ3JkREZJ2W/rvSFLTERrUyv9fq8vrNS7TtUw35qibwtkiZrLMGGdPkRPlif2LsqIm6xLIweCEii/fy5UscO3aMc7sQEVGgkTyPCZPGo2b5prrk9w3rPgf7V9z/skzotxwRwkfSay1fjXJNsHvvTly7el2XWA4GL0Rk8Y4ePYp3794hX758uoSIiChgnTh2CvEdEgXaLPrS8vL3sMY4cW4/+o1rq1pkFq6ZpNe9wvg5fVGgekJV3mtUCzx55qzWff78GWu2zEepemnVuoI1EmPqoqH48OG9Wi8uXDuJ2u0LqfWynWwvt/td8WInQv7sJbFz215dYjkYvBCRxTt+/Djy588Pe3t7XUJERBSwtm7eiTxZi+prgePNu9cYPbMn8poeZ8+yO6hTqbUKMmYsHo5XpuBm28Kr2PHvdTjGS4phU7vi7bs3KjBZs3UhZg7boFpxNs45h4rFayN06DDqPj1euWHXoQ0Y23uxWj+ixzwsXjcddx74r9UkU9qc2LFjh75mORi8EJFFkxYXCV7Y6kJERIFFAojde3cgT7ZiuiRwSGtJvuwlUDRvRYQKGUqVuT5zwuWbZ1H3f20QLmx4tRTJUw6PnO+qdZ8+fsSnz18njowU0Q5xHRz1NU81yjWFfcy46nLyxGnhYLr84PFtdf135cxcGJevn4eryxNdYhkYvBCRRZPAJXz48KrlhYiIKDAcPXwcsWLEQfJEaXWJ/3Qb2lB14TIv5u5hIkPq7AgRIoS+Bjx95oyrN8+hRuu8X7aXEcQeOd+Dh8cLpEuZBcXyVkD9zsUxcEIH3Lp31VuXsMgRo8De9NwDmkzOmSltLmz4b4susQwMXojIokmiPgMXIiIKTOvWbETBHKX0Nf/zmbAv3cN+JEGcJFg786S322xfdA2pkmVAqFChUa9KO6yadgxJHFOi5T+VMHXREHz8+EHfOvDkylwImzdv0tcsA4MXIrJYr1+/xuHDhxm8EBFRoJFWjF17d6BAztK6JGjZRY6Gd+/f4sGjH3fzimoXHbUrtcLkgatw/Nx+PHd/qtcEnrzZiuPi1bNwdnbRJcGPwQsRWazdu3cjUaJESJUqlS4hIiIKWC4urvB46YbECVLokqAl+Su5MhfGtH+H4aHTXVUmI5PdvHv5m9YVCbQeON1B2DDhEDqUZ8J+YEoYLxkiRoiM0ycDbhJM/2LwQkQWa9euXShUqJC+RkREFPCeuDxBFLvo+lrQk/yX5rW7I2v6vKjbsYjKeSlVPx0271mJj58+4sylIyjfOJMqz1/NEdP/HY52Dfqolpig4BAzHlydLSdpP4Sbm5vK+Pn48SOePXuG6NGjI1Qoz9EPjOzDhw/49OkTwoYNq0uMTUZsks/VFj5bOSvx9u1bhAsXzltCnFEZ9bt87tw5DB48GHPmzFEJ+2ZST8n1CBEi6BLjsrXvsuyHZGG9bDy29l2WLq/yeqNFi6ZLjE2+yyFDhkTo0KF1ieWZO3cuChcurFrzfdq6eQfGj5mEuaO26hLyqvPAOsicLQM6dGmlS4IXW16IyCKZW128Bi5ERES/6t69e5g3bx7atGmDhQsX6tKv7t99EKwtL5ZOhmC+f/++vhb8GLwQkcWR1hXJd2GXMSIi8i9HR0f88ccf8PDwwKxZs9CsWTMcOHBAr4WaxySoumBZI5kz5skTV30t+DF4ISKLs2fPHqRMmZKJ+kREFCDKlCmjLwHXrl1Dz549MWDAALi6usLZ1YXByw9I8OJqeo8sBYMXIrI4O3fuZKsLERH52YsXL3D37l2VL7lv3z6sX78eixYtwuTJkzFkyBB1Uswn2ddUq1bNdLvb7Db2A9Jt7KETu40REflKZtR3cXFh8EJERN/16NEjrF69Gt27d1eJ+JUqVUL9+vXRrl07DB8+HEuXLsWhQ4fw8OFDNZhAlChR9C29S5cuHaJFjY43b1/rEvLp1WsP2EWOqq8FP442xlFtDIejjVm3ESNGqJ1M8+bNdYl3HG3MuDjamHFxtDFjC6rRxk6cOKFOcMl/6foVJ04cZM2aFTly5EC8ePEQNWpUtf+Q75lP48aNw5o1a/Q1qHqmRYsWqFy5Mvr3GYqnjz3Qs+1YvZa8WrZ+BnYcWouVa5fokuDFlhcishhOTk6c24WIiL6Qrl3SklKzZk307t0bt27dQpEiRTBz5kwsXrwYXbp0QYECBZA8eXLY29v7GriITZs26UtA6dKlsW7dOhW4CPtYseDm8Vxdpm89dn1oeo/s9bXgx+CFiCzG5s2bkSlTJibqExHZOAk2WrZsqbp/SYuKdAdbtWoVhg4dqgKZZMmS6S1/7vbt26olLEWKFJgyZQq6du3qrYXX3sEebu7P9DXyyeXJI8RPkEBfC34MXojIIjx//hwbN25E2bJldQkREdma7du3o23btlixYgVKliyJqVOnqm7EefLk+W6rys8kTpwY06ZNw/Tp05E6dWpd+pW9Q0y88GDw8j0SvMSKFVNfC34BlvPy+PFj1d3DWkiOgPTBtYW+xkI+X+mPagt9jYXkgVjyTL+/Inbs2KqJ/HuMkvMio8KcOXNGdQ/4Eea8BK4dO3boS0FPvseyGOW3+zPy25V6WRajk++yuV62hf2Q5IC8f/8ekSJF0iVA0aJF9SXjCYicF5nb67///lNDF1eoUEEtQbVfu3TxMhrUb4gNc87pkh97/eYlug5pgFMXDukSIEL4iCiWrxKa1OiCmNEddKkxVG6eHe3btUfVGpV0SfAK0OBF+qlnzmQlyWnqVZvYxrG8bb1e82sVVv56Hzt9RK9ewwwfvMjEYTJpmJxdK1iwoC71HYOXwCVdKpYvm6mvBa3P8uM1/dnKSRb5fKWOCmEjOyJ5vTb12ZqYX6/Hy884evSkumxE/gle9u/fr4IWmQW/YsWKagnq+t3d3R3Zs2fH/hV+Gw7YHLzULN8MebMVV2UPne5i0MSOSJYoDTo2HmCo73q+qgkwf+4C5MiVXZcErwAPXk4fSoVYMW3jrBlRYMte8LYpeBlh+OBF+jQfPXoUo0aN0iXfx+AlcEnwcvb0AiyYEVeXEJF/HDj8Ek3bODN48eHq1auYP3+++m9uafnecMZBoWSxMmhZuyfyZS+hS77Pt+BFLFwzCcfO7MPQbrNVS8yVG2cxemZPXLjm+dkXzl0OHRr1Vy0zch99xrRGmcLVsHLTXNWKEypUaNSu1AqNq3dSl8WhkzswYW5/3H14Q73HVUs3QtNaXdX9P3nujN6jWqJWheaY9u8w3Lp3BZEjRkGL2t1RsURdtd94/+Ed5q0YjxUbZ8PjlRuiRYmpgqsiecr7ab9y+NROdBvayPT9PeKtJTE4MeeFiILVmzdvsGHDBua6EBHZCBklrH379qpbtJwwqVOnTrAGLqJokeLYd2yrvvbrXJ85qcClcO6yKrAQMUxBigy/vG/5Pez497o6IbVpz3K1TkgAM2PxCBXQSKvPorG7sOfwRm/d0RzjJsXY3ovV+v9mnsK5K8dx4tx+vRZwcn2ApetnYHTPhWqbET3mYfG66bjz4LpaHzJESOQ3BWSrpx9X6/t3mox5K8er5+sXB47vQPYseSwmcBEMXogoWEmSvsyF8KPWJSIisn6S1yhDG8tIYjLilyTmx4xpGYng5SuVxv7jvxa8dBvaUHWpkqVK8xzIl704yhetpdcC9jHiwDFeUtXCES5seKROngG3713Taz2VLlxNdTUTCeImQZrkmXD55ll1XUiZQ6x46rK0miSKnxy3H3i/jxrlmqpZ8EXyxGnhYLr84PFtdV1acFIly/AloEqUIIUKaJ4+c1bXf+b0xcMoVqywvmYZGLwQUbCRLm9sdSEiMj7pHtyhQwc4ODioLsIyK74lSZM2NaLaRcehkzt1yc8N6z5HtWaoVpFZp3Hx2mmMmvEPPn78oNbfuncVfw2uj7KNMqBys2yYt2KcKvcqSYKU+tK3pKVGgodWvf6H0vXT4X8tcmDb/q8TbQrpJiZB0ve8ffdGdRmr3jovyjXKiEZdSuGRyz299sfk+Ut3tSLFLWvuNQYvRBRsJHCRfA4ZDpOIiIxH8hQHDx6MefPmoWPHjqrFRSaTtETFihbDkdO79bVfI4FPzXJNceL8Adx7dEvln/w9vDH+V6qe6u4l3bbqV22vt/ab42f3YeiUv9C+YT9snHseq6YeRfF8fh/xS4KfaYuG4sylo5gxdD3Wzz6D2SM3I669o97ix06aXkuOLHksaphkEVJemCzCnLhjLvuVhYgCh2+/N6+LmW/rLH2R4KVMmTK+rvve4p96yhqXoH6tRBQ4fPu9GWUx81l+7Ngx1U3s4cOHahj88uXLf7ONJS3FSxf5pZYXnz59/iRvgroso49Ji0imtLkQKqTnQFiuT/2WZ2J25dY5/JEyK1IlTa/2fe/ev8Vzt6d67c+9efsK125fQKmCVVRwJdw9XuC1qdwvJOgpVtyzS7dv71dwLSFlJBtZzOORy39z2a8uRBTATD/Sn/0mzaON+bbOkhdJ2JSRDYsVK+br+u8tMtKYVOK+rTPaIiP4CPnv2/rAWOT7REQBz7ffm1EW2QdJ3eG17N9//1WtLDIp5IABA5A8eXJv6y1xSZ06pemA/6VKgP9VL9yfYdmGWSqnJK6DI+wiRYXzk0dqkQNuadHZvn+t3tpvHGLExe3710wByxN8/PQRW/eu8pas/zNhw4QzBS0xcO7KCXX7l6/csWDVRLzwQwAkgwbsObIJhYrmt7hjjJDSZUMWGd7u1atX6r+57FcXIgpgpoP0MGHC+Pp7My/ym5XhE31bZ6nLixcvsGbNGlSrVs3X9T9aZHQy2RH4ts5oi3n4a/nv2/rAWH5nngYi+jnffm9GWczDJJuvL1y48Es3MWl5kUFZvG5vqYucHBs8aDCWrZ/pp9G4vCbsl2+cUeW6dGsxXCXnp0uZRQ2NXLdjEeSv5oj1O5agTf1epvcpjL71z8kwzJLwL7kqhWsmwc27V9Dsz6567c9Jsn79qu2w7+hmFKyeCDXa5EO2DPmQK/PP842WmwKxNi09R4SztGMMzvNCZMGMOs/LxIkTVZ0xcOBAXeJ3nOclcHGeF6KAZWvzvEyePBl79uxBt27dkCVLFr2FdWlYtxmSxEuHVnX/0SW2Zfv+NZiyaAi2bt9k+kxDqRjBko4xmLBPREHq3LlzWLlyJapWrapLiIjICCQx//z58xg3bpzVBi6ie8+/VMvD5RtndIntkJOhS9fPRJfOXRAunGWeFGXwQkRBavny5SppM1OmTLqEiIisXbt27VT6gbS8xInz/aF7rUGqVClQsdz/VA6LrVm2YQZChvac98ZSMXghoiCzY8cOnDx5ElWqVNElRERk7SR/MWnSpL/VFdhSdfyrLQ4c34bdhzfoEuOTWflXbJyD3n0tu7scgxciChIyatqyZctUd7FEiRLpUiIislbPnz9Xk01WqFBBTUBpJDLz/7ix49B/XDscPLFdlxrXsxeuGDKpMypXroyMmdLrUsvE4IWIgoR0F5MuBWx1ISKyfrdv31YHuu3bt0fdunV1qbHkK5AHHdp1Rr9xbXHz7mVdajwySlrv0S2RJFkitO/UWpdaLgYvRBToLly4oIbOlMDFzs5OlxIRkTU6ffo0GjZsiD59+qBSJb/P+G6NGjWth0rlq6LjgD/VXClG1KF/LYQJFwpDRvTXJZaNwQsRBSoZ6nfatGnIly+f4XdyRERGt3v3bjV/y5gxY9QUGbbgnz5/IVOGrKjZJp8uMY62farBzeMpJk0bazXzfDF4IaJANXXqVHh4eKB58+a6hIiIrJFMLtyvXz/MmTPHpkaMlDlsRo0bglix7NGyZ2Vdav0kcLl17wpmzZsOO7vIutTyMXghokCzYcMGtbNr0aKFSn4kIiLrNHfuXDV/y+rVq5E4cWJdajtkcuTpsybByfU+qrbMBfeXL/Qa6+P6zAllG2XAxWunMHXaVMSJE1uvsQ4MXogoUFy5ckV1F2vSpAly5MihS4mIyNpIa8vSpUuxa9cuRIsWTZfanujRo2PLto3IljU7StdPh3OXj+k11mPL3pWo1DQr4sd1xLbt25Apcwa9xnoweCGiAPfx40cVuGTLlg21a9fWpUREZG3atGmDy5cvY9OmTbrEtkkLzMChfTCg71C07Vsd67b/q9dYvplLRmLA+PaoXaMhlq5YCAcHe73GujB4IaIAJ3kurq6uzHMhIrJSModLo0aN8OHDByxevFiXklnVGhWxaOFizF05DuPn9tOllmv41G5YtmEmxoycgF79uiFMmDB6jfVh8EJEAWrr1q1YsWKFynOJHdu6+tESEZHnUMhy8snBwUGdjCLfZcz0B9auW4Ub98+i0V+lsGrzXLx7/1avtQxrty1E425lcOrSAaxatQqlyxXXa6wXgxciCjA3btxQO7oGDRogT548upSIiKzFwYMH0bt3b2TJkgVDhw7VpfQ90aJFxYJ/56BV2+ZYt3ORCmKWrJsGj1dueovgYQ5a5q0cj6rVKmH9xrVInCShXmvdGLwQUYC4fv26SuqU4TPr16+vS4mIyFps2bIFvXr1QtmyZdGtWzddSj8jQymXKlMc6zauRtfuXbDz0H8qiJlvChyePHfWWwUNc9Dy79op+LN2DezcvRV1G/yJcOHC6S2sH4MXIvI3SeaUHV7q1KnVGTsiIrIuktciLS3NmjVjvqI/FClWEKv+W4r+A/vh+IU9qN2+EP4aXE8FMoExOtmDx7exYecSDJzQQQ3hvHzjDNRvWAdbd2xEjT+rWM3Ek7+CwQsR+cvZs2dVwJI5c2b06NFDlxIRkbWYOHEipk+frurwGjVq6FLyj3z5c+PfZfOwYuUK5CuUC2euHkS7vjVQoUlm9BzZHOt3LMaVm+fw9LmLvsXPuXk8x827l7H36GaMnd0bdTsWRa12BfDfjoVImio+xk8cg01b1+N/VSsgVKhQ+lbGE8LNze2zXJChTZ89e6bGsP6dF/z48WMUKlQIpw+lQqyYxovyiIJD9oK30avXCBQpUkSXfEtGgvn06RPChg2rS4LOiRMnMHDgQBQoUAAdO3bUpYFL6ikZqjJChAi6xLg+f/6Mt2/fqub+ECFC6NLANWXKFJw9vQALZsTVJUTkHwcOv0TTNs44evSkLrEcr169wqhRo3Do0CH0799fDW//O969e6e6ThnxLL9P/qmXX79+jf17D2H3jr3Yd3APnF0fqfLQocPAPkYcxDItDjHjqv/iyVMnNaGk85NHeGL6//bdG1VuFykqcufMj8JFCqJg4XyIETO6Kg8MEh/IEhzHGN/D4IXIglly8HL06FEMGDAAxYsXR7t27XRp4GPwErgYvBAFLEsNXu7fv4+RI0fixYsX6NmzJ5IlS6bX/DoGL7/Hw8MDjx464fEjU4Di5AKnx6bLTs6my054/+G9GrEztoNpieOAOHFliQ2H2PaIESOGvofAx+CFiH6JpQYvBw4cUIFLxYoV0bJlS10aNBi8BC4GL0QByxKDl8OHD6uuYo6Ojujatas69vMPBi/GZYnBC3NeiOiX7N69G3369EHVqlWDPHAhIiL/kXm4/v77b+TLlw9Dhgzxd+BCFNQYvBCRn0jrzowZM9RwyHXq1EGTJk30GiIisnTSOjJ69Gj8+++/KjFfJhImskYMXojopy5evIguXbqoycskeJFJKImIyDrIPFzdu3fHo0ePMGLECJWrSGStGLwQ0Q+tXLkSnTt3Rvz48dWoNDKyGBERWYelS5eiffv2SJkypQpc/JOYT2QJGLwQka9kEA4ZBnnZsmXo0KGDCmCCcoQTIiL6fefPn1fJ+JKnKKOJsZsYGQWDlwD05s1nHD72Eu4eH3WJZevQ9T5SZLyEs+df6xLLcfvuO1y8/Aaf1Vh43t27/w6NWt5FwtQXkSDlBWTJewXHT72Cs8sH9f/jR19uRL9k586dKliRUVWkj3TJkiX1GiKyRi9ffUK1OreRs9BVVVdaEqmzzXW4b85ffIMK1W+q+l6WomVvqH3Ej/YTtkxGZJw7d65qbUmVKhXGjRuH3Llz67VE1s8QwYtUeFIhmys23xaptKXyDkwbt75A1dq30X+Iky4JevJeDB/jhLxFvb8fcn3wCCdV2Vu6F24f0brjffyv1m1cuuI5IZOZ65MPaND8HnbucUetatHQr2ccFCtsB7vIIdGuy31UrnkLB4+80lvTr5JJJ4cPH46xY8eq0cR69eqluosR+cek6a7e6iPzIicgipW7jmmzn1jNSR9Lc+PWW3Tv/RBZ81355n2dPMPV4gIV35w4/VrtO6UOf+VjP33l2hv82fA2rl1/h1ZNY6HHX7GRL3ckFfDUbnQH1evexvWbb/XWts3d3R0LFy5ULSxXrlxRc7g0btzYooa4JQoIhmp5iRE9FKr/Lxpq14j+zVKkUGSEDhW443HHjRMG4cOHQMoU4XSJJzkYL1vlJrbtdNclAU/OPK1Y8wI5Cl7B+CmucHnyESWL2anXLhX9g0fv1Y5s34GX+haWK3y4kEgQLwwcE4RG9Gje5xyS8fJlZ9anR1wMGxAPjevFxPCB8ZAsSTgkTxoODvahESc25xn6FTICzaZNm1RCvgQrMna9tLZUrlxZb0EUMArkjeytXi5eJLI68Bww9DHKVL6J+w/e6y3pZz6YYpJxk11QsOR1LFzyzHQwD5QvHUW9rxn+CI+rpoP9oaOccfmq9xNAlkjmhosVM5Sqw8OG9b6f3rDZDc+ef8TksQlU4CIBjJy0ihM7jKr3E8QP881+wtbIrO2S1yJBy5kzZ9CmTRs1BHLmzJn1FkTGYohJKuXMUvlqN5EwQVjMnZ4QkSJaVkwm3bKq1L6NyWMSmHbWdro0YM2Y+wT9Bj9WAdy4EQlMBwmRTJ/j152AnKWSgwTH+GERUb8/0m1swxZ3rFyU2LSzs44J/+QM7oixzli2IDFyZI2oS40rMCeplNmVt2/fjh07duD9+/coVaqU6h4WN65lT07ISSoDV2BMUim/2yEjnTBnasJv6kDpbttrwCMsXv4MjerGQP9enBzzZz58+Iy+gx5j7qKn6oB/kmnfkjZ1eNN3RG9gIsHN1etvkDJ5eMi8gdLzoEGzu7h7/x3WLU+qTvRYA9lP7T3wEqsXJ0GihNbZghBYk1RKvb1u3Tr8999/6titfPnyP9xXBCZOUmlcnKTSBknQcPjYK7x+HXhd1qQlYsIUF0SxC4n5MxKhcIHI3gIXIddTpQj/JXCxVtdMO+MwYUIgfDjjVxiBxdw1TLoTyNDHlSpVwsyZM9Xwx5YeuPzI1atXUaFCBRQuXFgtRYsWVfPRrF69WgV49JX0hz9+/Li+FryktbpZo5iIFjUULl15G+jde43g4JGXmL/4GZIkCouFsxIhXRrvgYuQY0gJaKz5WFK+Cw8eflB1foQIPFzxSgIWmSRY8hNr166NMWPGBFvgQhTUbK42MCctNm97T52ZkjMihUpdV/2E8xe/pnIqzNt8L0/GvN5n4qN0C5P7kbOM4s699yhV6Qb6D3msrjdscfdLn2Q5m2T26PF7dO35ECkzXfqyXvovSz9muY+fkWb1p88+okmDmMiUwf9no6WJXvqgS1Kk+fkkTntBPUff+k/79flLILduk9s39yutZmvWv1Cfh/Dt/d2z3wO1GtxWXeMkECzzP8/kTa8DDvxoAAJ5HvKZy+PJ7dJmvaRygHz2s/fL98PaSEvFnj17MH78eDRt2lR1D3N2dlb/p0+fjipVqiBy5Mh6a+smwZfMHr1r1y5s27YN7dq1w+LFi3HyZMCe8bR28+bNw19//aW6B7q6etZXwSlypJAqiPHJnMjdqsN9b/WLdJXasMXtm0RtqYMdU13AvoMvcfX6W9RreufLwB5y+579Hqm60qegehypU+S2dRrf+VIXySLJ6MdO+i1XT1pdlq54jk+fPqNLBwfVbcq/Hj56r3IlcxX+mispdaS0lvnMQZH3QgamkfxC82s259hIDwCv20v9KvVsptxf83HSZbus3ucz577W01JnS91t3i/Ka/xv4wv8r9YtHDr60hTAvFcDs8jtzfuF7+2HzeT+JY/G/BxlnzR7wVPV0ueVtdT50rtl8+bNGDZsGGrVqoWNGzfif//7HyZOnMg5W8jm2OypDNmxrFz7XCX8SWVcs2p01WIRMQDP7kSNEhJd2sdG5fJR1XXpqzttgqNa6teOqcqkj7ckHC5b9QyF83/tDy79eRcvf46bt36ciCj5NDt2e6hWlzIlo+hS/5FAQvqgy8FExzb2GNQ3LrJkjIh/lz1To3xJcGPm1+cv77EERC3b31PXzduVLh4FN26+xYrVz9UO63ui2IVCnVoxkDNbJIQOHQJ/d4mt3seJoxIgoeOPmzJPn32N0pWuY8sON5Qs6tknPF2aCCoHqFKN2772sw+K70dgkiEyFyxYgI4dO6odnMzP8vDhQ+TLl0+1ukgip9F3eNKFIX369HB0dMTdu3d1qenA6/BhNGzYULXMSFe5SZMmqT7j4smTJ+o9kxYpCfRkG3n/1q5da/oeeH4/pXvEnDlzVLm08FSvXl2d/TSvl9sOGjQIa9asUdvIfch9Xbt2Ta0Xbm5u6kxpmTJl1H00atRI9VUPKvXr11f/pcuJPLfly5er68Hl8tW3cHL+gDSpwnnr9iu/wy5/P8Tufe6m364dhvb3zHOTg1U52Fz133O95VfyMWzc8kIdXMt9ysAe8puXLrPSzapVh3umg2rvB+RB9TjXb75B+7/uq4FIalaNgRGD4qFqpWjq4F3q0SPHf56TKAfyckAv3cXy5IykS3+fvI7J010xcdoT1fVa8kpkiRsnrDoR16n7A3VQb7Zxq5vpud7Bw8emul/nmP6vQhS1X1i05JkKBoS89qat76l6NmP68Go7z7o3PNZvdsPJMz8e5dI+VhjVIid5LbJ/kxxHqfOH9Iur9gc/IgFn+Wq3cOHSG/Xc5HGlO3vvAY/QtM3dbz4XYYl1/unTpzF79my0atXqS8ASO3ZsdOvWDVOnTlX1B5EtsrmcF6lYpd/vidOv1EHwyMHxUL50VG9N7uZthG/3Z17vs++wnFGT1hU5sG7dLJYqEz/q7z19zhMVKJi7e3klO4wQIT5/0wXMqzt336FyrVtqsIB/5yQyBUx+/+y+l/MiZ84+fvoMu8hf70sq8179H6md8vwZCVGkoOfr8Ovzd3H9gEqmnbx0c5g5KaG3s6xy3xK4SNcA8b33V3zvOQvf1slZsxr17uDN20+qe4U8vpmc2ZNRzeQgpU+POOo74JfvR1DyLefl1atX6uBXRpaR//K7lf9SfunSJdUtTFpS0qRJg3Tp0uGPP/5Qi1F8L+dFuo1JkDZ48GDEjBlT5QGdO3dOtTjJQASJEydW20muj/TddXBwwPPnz/HPP/+obhd58uRRwYsMLxonThw1P4JsI/ch99uvXz8kSpRI1ZU3btxQQZE8B2nVkQMJCVjs7e1V8NK3b1/8+eef6oAjTJgwWLRokfpspFweW/pL3759W00WJ/Xtli1bVP7RgAEDvL2uwOxbLUGTVzKkqnRDkecfVDkvb99+Vl2guvV6qOqbeab61mdLgtQdksvntR6UJHQ5Ky+/89lTEn7pDmuug+WtmjAqASqW/frblfqoa88HWLbqubpNCVOQ4lVQPI7Uc27un1SCudePc9deD9V6IwfMEtD8yNETr1SgI0GW5LpIPeUXP6pXpYUkVMgQ3roVSwtFk9Z3cfzka6xanFh1QZN9g5zAeuz0AcsXJjYFGN739fJ5htNdemVEyHpN72JA77hoWMf7/FDmE1Xm527OCy1b0g5jhydQZeJHz/l766QbtbSkSHA32/R9k+6IQvYzE6e5YNhoZ1MAFA91a0VX5UFR5/uW8yJdWV+8eKHqbq//Zbl48aKqx6WukXopZ86cKvk+XrwffzeCE3NejMsSc14MFbzIGanvMe80zRWVnLlq38oeXdo7fFNJmbcRwRm8+IW50s+UPsIvD1bwo0DAN769Pr8+/x8FLz597/0Vvxq8rN3gGaD4tgOVVqs/G97Bk6cfsGZJUjVKmV++H0FJgpfs2YuqHZlUklJpykG5kOuyo5CDY/nNSkUaMWJERIoUSV02KklSzZIli2qt8EqCF+kKJ0GdWYIECdCjRw8VyH3P0KFDVWBTs2bNL8GLnOmUgwYhrTIywZt0rzOXeSW3kceQeXFkBms5+J88ebJqWZFgRvgMrHz63nrZQUoQJZ91QO8k5bvkW2tP9uzZ4f5if6AEL76RA+ZunRxQy3Tg7tecvJ/VwblzRPK1PjQfUPuso78nqB7nV07AmR+7aqWo3g70f+ZH9er3+Nx3/Sx48epHwYtPARm8jJ7gjDETXXzdJ5lP9klrjvl9Nt9PYNb55uAlT54C6sSHHG/Jwb78N5MTMlGjRkW0aNFUPZI3b15Vz0n9bg0YvBiXJQYvfj/StQI/Gio5XlzvFYAk/8lQwsH9vZNmf5mjpGGLO6rC9dolyxr49fnLmUYZAW33Pg9UrXMryCaT3H/wpQqUMvuSCyStVCmShTUFLx9NBw/eA19L+X6YSQuAHAjHihVLHdxGiRJFVSRy9k5aXOSAXXIXnj59qg7upeUlRYoUyJgxo+EWaU1KmDChfme885rzIl25evfurbrI7d27V603H7B36NBBdemS1hHzOjMJ/uR9/h7ZaUmXMOl6JV3GJNCRHCKvpGvHj/KI5DOTfCN5/Bo1aqjg6OXLb7sMmbu++fY++Hfx7fHkuxWYo7j5HCpZWhrixg6jRs6S7klyEB8QfHY/M5MTawGZ+B1Uj2MpJLgsXNBOjVxZqcZNbN7mplqafJMmVXjV+iEt9n/3eajyagKbBFdHj79CbIfQSJ3y2xM4sUzBlgQuEvC8fOm961hQ1fnJkydXwYksctJE6hqpy9+8eQMnJyc1P4uczLhw4YJqgTGfrCKir2y229iPzj6ZtxG+3d/37uN3Wl6EJHzKjltyM4ScLerU1kEl3/+sIg2sbmObTDul5aueq+fm84DC5+vz6/OXbgizFzxRiaGyw4sZIzSaN46pmu+9dlH70Wf0Ky0v0jVBWl2k//OPyE7LfBu/fD+C0s+GSpbgRbo+ySI7P8mrkG5OZ8+eVS0CEsBIl7EMGTKobgdyZs/a+bXbmNmSJUvU+yFdtuS9kQRXaSmRgwg5a+az5cVrK4rw2vIis1RLq4oEidJCI0Gkby0vK1euxMCBA788R6/PTYIa6R4mjynd1WSb7z33wDrDJ/W1dGnzSkZmkxHnJKgKyqGSTS8R6za9QJtOD9ScVDMmOX6pc2XdtRtvVS6FnPh45PRe1U9m8eOF8VMdbGY+w9+htb239UH1OHLC5pSpnpTHke5fDx6+83bw/72WHK8Co9uYnHSSfI+161/g7r33qjXaK6+fm3nglb97P1LdzeTkUL1aMdCiSaxv6ksZzKX3wMfYtNWzDpZ9Qsc2DiiU3/tQ/gHV8mJuTfc6GIBvvH6eQVHn+9ZtzCdzN2CpyyVokdEAJXdRghdpDc2UKZM6kSF1uiViy4txseWFfJUyeThVae7cmFx1Bdiz/6UKxlq0+zbh06do0UKpwEWCmIA4aykJjoVKX0f7vx6YDrJC4a8ODipJctHsRKovsG/8+vxlJyeDFlw8ngajh8ZH5Egh1Eg0WfJeVYMOBDTTbw2v33xSj9v3nzhfBkvwufgl6d9SyY5CuhlIS4QEKTKxpLQ2SOuDJJXLMMhSyUrSZ7Vq1VS3KhkmVw5ebYXXYZIluJPcDjkAkJ2O7HDlgMGvJEC8efOmSsKXwEV4eHio99ivZHsXFxcUKFDgS3AjBy6/ch/+JaONmcnzkIBFhs7+nRNX/iX7/jIloqB0cTvsO+iBk6c9DzzlYxs47DGKlLmOzdvdULZUFIwZFl/9Zv9blhRZMnkPXn9XUD2O1IWtOtxHpRq3TAfrb1TL06QxjphpCtZWLU6iWgv8Irbp4Fp6GUiwJfkz/iV1b46CV9V7IK0SfXp4DoayfGES1Y3KJwk6KpWLinNHU6ugJnXK8Kr7cLb8VzFn4VMVCJrJvmnGREcc3ZMSzRrGVDlE9ZvdQYXqt1RgE9BUnf/6k3ovRw3x/Ax9W/yS9B/U7OzsED9+fNWyLHW1jCq2YcMGdXIladKkaqARaTGuV6+eGnRl//79+pZEtofBy2+QpESfZ6b8S3bgEgTIWacLx1OhXq3oqsVgzATv3VF8kgpYukQ9f/ER+w/5b/Z8eV3DRjup/JSl8xOrnWqtatFRtmQUFMwXWbWUfM+vPH/peiDd+/ZvT6keR7qUdf77AW78ZGS1XyWJo7EdPLsLZsscUb0O3xbpKmBO6jQSObMvo9HIkLjz589XB6jSgiN9rps0aYIJEyZ4GwHLaOTsmLw+GTI5V65c6qyRdNGQkcfk7Kac0ZQkeWmV8Su5DwlazN05pPuXJOP/SgAk9yFBpwRB8hyly5kM5xyUc9HIKEYy+MDff/+tBiII7rO50noQIYLkdEmA6HlALjkIclAsv9E9m1Oga0eHL7/ZVCnDIVzYgNl9BdXjSKuG1IltmsfC1v+SqhM58hilikdB4oTynfDbGVyp0yR5XoKXn7Uw/IyMsDVo+GPV9Xf7+uSm+js+/lchmnpeuXNE/GEOkpxgl9aY9Ss8T1xJF7o+Ax+rec18km7bvf+Og/NH06B759g4d+ENeg14rPY5AUnm/5KWE9kfSTdl8+foc5EBZ36Ud2lJpIuntIhKwLJq1Sq0bdtWdSOWURIlmNm0aRO7lpHNYfDyAzIUpNeuA2b3H7zDPV+G1w0o0oWqS4fYKhiQSl6atb9HKunyZaOokbpkokrpi/y7pMn9yrW3KhiSoS19ku5hfuHX5y/PPW+uSGjX0l6NCnbz1ju9JuDkzhlRdVeTZntbZw5mpMuSjIwl3aFatGihuirJgABG8OjRI1StWlWNpCWBmgwpWq5cuS9Dikq3L0nil22k7NatW+pMpl9Jy4TkqUjXMGl9kYOKrFmzqsRav5IzrJIvI61h8hy7d++unuP38ngCg7wvMgpbiRIldEnwki6er19/VnVC+PCeuyXpSiTBTMXyUb850HR3/2SqgwOmvgiqxzl+0jP/ToIVr12mxGOn9yqQ8Au5j8oVoqrnPHKcs79OpJn3ZVIPS6uLV3L/N/y4P5G6vme3OKbbfMbJ09+fr0aee9MGMVW3sXMXXqt9TkCSYCtLpohq6Grpnmc00k1JupBJvSMTC0urqUzCKyejli5dqk7KENkCBi++kHHdHROEwd1779RZOa/kQHjOgqeqadqv4sbxbLGQnBDZIfiFVOqSSC7dA8KF9b6j8ymrqbKuWTWa6jYm/X3lQN23x3n//rPqIvE90lIh+Uq3774zBRPedyry3CdNc9HXfs6vz1+ep7zPctYxaiC0fkiCcKoU4dVzlwnlfJL+29LSZGvkbJ6MYiU7wBgxYqj8DDmIlkR3ayX5JjLrtLwG8yJnKqULhrkftiTjS36KtLjIhG+tW7dWSfeS7yIk32TatGlf8l2EdO2SfBTzSGPSSiGBh9z/smXLULJkSZUTY76NbCfbe83JkXVyv+Z8lhw5cqjbyn3IfUm+oM98l8Ak/efN3d6Cm9QBMnfIpm3upoPg8OoMvjDXmxcuvvFWn0kdNmais6/zM/2OoHqceHElKfvzNy3M0p1Mus/+yj5FAiCp26TlRer8751Ykvv80cAokiMprS637rxTrfdeyWcikwL7lXQDk/fP/H5+zyvTc5J9lexrzMMqB6SK5aKo1vxBw51U65RP8rn6NVC0ZFKXyaAj0qIu+WvHjh1Ds2bNMGvWLNy75zmfGpFRMXjxhZz9q/dnDFWpSx9l6aMr8xBIQrjM5iv9lTNn9Hs/aOmyJMPwTpjqqu5n4ZJnaiZiaTKX/zIzcZceDzHv36dYvPwZ2na+j+Llr5uCjU9o2TTWT7sTyPo+f8dVZ7RkRJca9W7jj+yX0bjVXfV48j9jritIn+MKLl7+/tko2ZFVKBtV7VjkPoaPcVbPSYbGrFjjljqj5fO5+PX5y32WrXJTPRfZqcgQxqPGO6NUpRtqErMaVaKpiTADmuwgRw+Np56DzNBfsuIN9Z7IUrP+bSRLf1F1m7BVSZIkUa0vsgOUnBnJk5EDepl3hCigyYkf8+9PFqlTs+W/oupZOZCVyRHNA75I8rqMVjVusguatrmn6s1+gx+jYMmrqousdJ0KCEH1ODKJsEy2KF1kO3R9oOrK7r0fIV+xa6r1JJGj34fElYT+yWMTqFwhyVOUfB2ZQV4mATbXbTKTveQvykmk75HEdek2KyenZDZ6qYtlkXlSWne8h8IFvE+CKa3oUofL/cuEjzJ5p3l7GbRFBlwoXsQzKN65xwN5i15Vn7FsI9vK65XZ6y9efotWzWL90gAzfiUnq4YOiKcGQyhsev3y3Mzft3JVbyLpHxfVe28kMuGwjKoog4Y8ePBATTwrQ7XfuXNHb0FkLAxevkNGRZk3IxGyZ4mIXXtfYtHSZ9i6w10dZK9ZmtjXoXe/R5LBZW6TFMnCqVnqu/d+iPMX3qh1EthI3opMmPhP30f465+H2GJ6nBKmHcCapUnV8/ALaY6XPsXb1iVHhTKeI0pt2e6unve2nR4qEKtdI5raWf2ITNgo4/JLN47xU1zUc7pz973aUUqSvc/b+/X5yzaSN3P+4htMmemqRgGToZXlLJjM4N/vn7imAENtGuAypo+g+nNLwqh0J5D3RBaZ4blMiahoWDdoznZbMhmGWUabmjFjhmpJkLN30nogo2oRBZS9Bzy+/P5kkTP7kmRd/88Y2LslOYoW+jq8tCR7yySDRQpGVnWv1JuLTPVn3tyRsXpxElStHE1v6T9B9Th/pA2v9ikyquGKNc9VXSlDDUur+VpTXZkn16/N8yV5elPGJVB5g4XyR1YtODIKmLyvh4+9VHW1DArwo8R0OanTq1scNSLao8fvVAvQ0FHOqv6XhP1unWIjcqSvt5dW9IL57VRr1NxFz9CuywN1G+la17GNvRopTvY1IlmSsEhu2ufJ/ke2kW2XrHiK9OnCY9mCRCrwCixy3zs2JFe5lWfOv/7yfZPn/Wf1aChX2vpHXfSNTGYpA7aMHj3a9Bl+UPkxCxcuVMPnExmJIYZKJjKqnw2VLGQnJQmbATmMoXQ7kC5lktzesGFD1bfaUnxvqGQjCo4hOadMmRLgQyUT2TK/DJUcGGSEMsmFkfxG6R77o/2If3GoZOPiUMlEZBUcHR3VKFTSCiP/ZWQb2QESEZF1kFEWpfuYDMwhOXcjRoxgazoZAlteiCxYcLW8eCXD6korjDyOzAUio90EJ7a8BC62vBAFrOBqefFKjtGkHpeh8qUez5cvn14TMNjy8uvu33uAy5eu4NEjZzjJ4uSkhs53cTX9d31s2ue+R6wYseFgHwexHWLD3sEeseM4IG68OEiSLCHSpEmt7ylwWWLLC4MXIgtmCcGLkIpadnwy+aUMLSxdyYILg5fAxeCFKGBZQvBiJkMrS11eoUIFFcQEVLDB4OXnJPfo+NFT2Ll9D/bt3ws39+dIlTQ97CJHQxQfi5QJj5cv4ObxHO6m/+4enpdleeR8Dy/cnyJv7gIoXLgA8hXKAzu7X8ub8ysGL0T0SywleDHbuXOnSupPnTo1+vTpo0uDFoOXwMXghShgWVLwIq5cuQIZlEXqFhmZTEaa9C8GL75zd3fHxnVbsGP7bhw7eRDxYydGtgz5UTBnKaRP7b9eDA+d7uLI6V04eGIHDp3ciSwZcpmOwwuifOVSiBs34OpvSwxemPNCRH4mQdSQIUNw//59lQtDRETWJVWqVGrG/syZM6NLly6qNYYClpubO8aNmozChYpi07od+CNZTkweuBpzR21Fm/q9/B24iHixE6JyyfoY0WM+1s44idIFauDcqasoXrwEenbvj0cPH+ktjYfBCxH9Epmpv2/fvmo2ewYwRETWSWbq79atGxYvXqyS+aWVgPzHM2iZgiKFi+LSuRvo22ESRv6zAH9WbIkUidPprQJezOgOKF2oGvq0n4Bpg9bi/cuQKF7CFMR0M2YQw+CFiH5Z/Pjx0b9/f7i6ujKAISKyUoULF8awYcNUsri0wpw4cUKvoV8hgd+40ZNV0HLr6gMM/msmBnaZhlyZC+stgk6qZBnwV/OhnkHMKx3EdO+PhwYKYhi8ENFvcXBwUAHM8+fPGcAQEVmpJEmSqG5k6dOnR9euXbFs2TK9hvzi7OnzKF+mEh7cfIJRPReiZ9uxyPJHHr02+HgLYl6GRLmy5bFpwza91roxeCGi3yYDfEgA4+HhwQCGiMhKSbJ9mzZt0KFDB8yePRujRo1SSfj0Y/NmLUL9BvXRovbf6NpiGNKlyKLXWA5zENOq7j/o0rUjBvQZpkY+s2YMXojIX+zs7DBgwAC1o2MAQ0RkvcqXL68GZblw4QL+/vtv3L59W68hr16+fIk2LTph3dqNmD18E4rlq6TXWK5KJepiQt9l2LN3N2pUqYOHD6y3GxmDFyLyNxm6WAIXGbJ58ODBupSIiKyNjEI2dOhQNRx99+7dcfDgQb2GxO1bd1CzWm1EixgHUwevhWO8pHqN5cuQJgeWTNiHyBGioVLFyjhy8JheY10YvBBRgJDx/WVH9/DhQyxatEiXEhGRtZGcxoEDB6JAgQL4559/1ATFBGzetBVVqlRF3crt0LHxAF1qfUb3WoT6VdqhUZMGmDppppq7xpoweCGiACNn6po3b44FCxbgwIEDupSIiKxRq1at0K5dOzV57bhx43Spbbpz+y769umLHq1HoUzh6rrUetUo30wNMDBh8lgc2H9Yl1qHEM7Ozircku4er169QsSIEVXi1q+SYfZKlizJGfaJAlD2ArfQrftgFCpUSJdYh3Xr1mHVqlXqzJ0MqxyQpJ6SVh5Lmu3XSGbMmIGL5xdzhn2iAGKeYX/vXuvtfnX8+HFMnTpV1eeS2G9vb6/X2IZnz56j7p/1USBHGZX4biR7jmzC4Emd1EANqVKn0KWWLcTdu3dV8CJNRjL9f6hQoRAiRAi18leYgiBUqFCBwQtRAJLgpVPnfsifP78u+ZYcyMvv9u3bt7rEMkyePNlU4T9TXQ4C0ocPH9QJlt85yWJtpC4OFy6c+myDqll/zpw5uHppORbMZPBCFBBU8NLaCVu37dIl1kkmJp45c6aq15s1a4bUqVPrNVD1lBxDSv1sNFL/tmrWHrFjOqJ/pym61FjWbluIuSvGYdac6XBw8B6YyjGG7G8tafS5EG5ubmqPKF86+ULK0KdyIPSrHj9+rM4OM3ghCjjZC95Gr14jUKRIEV3yLdlZSMuppbVEyKRdf/31l0r+lK5kAUXqKRkgQLqoGZ0ELLLjlAOD3zmp9Duke8jZ0wvY8kIUQMwtL0ePntQl1m38+PHYvn07OnbsqCa5FHJgKwe4cqBrNC2btYPHizcY0+tfXWJMs5eNxt5jG7Fk+UJEjhxZl3rGB7JY0jEGc16IKFDIEMoStCxfvhxbtmzRpUREZM0kB6Zhw4Zqji+jT2g5bvQkPHrgZNgWF68aVe+EdMmzoU3LDuqEqCVj8EJEgUZaXVq0aIFJkybhypUrupSIiKxZ5cqVMWzYMJXfOHHiRF1qLCdPnMaM2VPVBI92kaLqUmOT1+r+/BXmzl6gSywTgxciClRVq1ZF7ty5VQDz5s0bXUpERNYsR44caj6YO3fuoFevXnjw4IFeYwxDB49AzXLNkCppel1iG2qUb6pym9zdPXSJ5WHwQkSBTlpfZJQwGUKZiIiMQUYfGzFiBOLFi6fm+dq9e7deY93W/7cJLi7OqFa2sS6xHUXylEf61NkxZcIMXWJ5GLwQUaCTgUBq1qyp8l+uXr2qS4mIyAhatmyJGjVqYMiQIZg1a5YutU6SnD5q1ChUL9cEMaM76FLbUt0UtC1cPNcUwLnqEsvC4IWIgkSxYsWQM2dOwyd4EhHZonLlyqk8mMOHD6Nbt264deuWXmNdlv67AjGjxUG1MrbX6mKWMU1OlC/2J8aNssx8JgYvRBRkqlWrhh07dmDfvn26hIiIjCJTpkwqgJGhdrt06YLNmzfrNdbBw8MDEyZNQM3yTXWJ3wyc0AH5qib4spRvnAlL1k3Dx49+n/fG/eUL9BzZHE+eO+sS38uCSo1yTbBrz05cvXJNl1gOBi9EFGQyZMiA//3vf6r7GBERGU+MGDFUAn/58uVVPsygQYNw//59vdaynTh+CnHtHVEoV1ld4nct6vyN/SvuY8+yOxjRYx5WbZ6PdTsW67U/99jlPh653NPXPPlWFlTixU6EfNlKYNcOyzvZyOCFiIJU9erV1UzNK1eu1CVERGQ0DRo0wNixY/H8+XO0b98ea9as0Wss17ZNO5Ers+fEm78rVMhQSJ0sI8oVqYHzV07oUuDJM2f0Ht0SBaonVK0zvUa1UGXi7KWj6Da0Ia7ePIdGXUqhXqdi2Lx7xTdlN+9eVtufunAItdsXUvdTrlFG7Djwn5rUWBw4vg19x7bB6i3zUKpeWrVN3Y5Fce32BbX+V6RPnQ07tu/Q1ywHgxciClKxY8dW3cck98XFxUWXEhGR0aRPn161vkidP336dPTp0wfXrlleNyQhB/+79u7wd/Dim7fv3mDE9O5IFD85ti28ij1LbyNF4rSmIKM1Xr5yR4Y0OTDkr5lIljANZo/cjPmjt6NUoarflCVNmBq37l3BqBk98HerkaqlZ2yfxZixZASOn/3aQrLjwFo8e/EEa2eeVC1BxfJVwLRFw/Du/Vu9hd9kz1AAF6+egavrE11iGRi8EFGQkx1ZrFix2H2MiMgGyGiT0gojI3lJK8yiRYv0Gstx9PBxRIpgh7QpMuuS3yNBkLRybN23GoVylVFlcv2h011UKF4b4cKGR6hQoVG5VH28f/8Ol66fVtv41YET21EgZ2n8kSqbup48UVpkSpsLpy4eVtdF7FjxVcK9eqyQoZArU2G4Pn2scmh+hYy2li5lVmz8b4susQwMXogoyIUIEUIFMBK8WOpZOCIiCjgpU6bEwIED0axZM9VtWIKYo0eP6rXBb93ajcifo6S+9uumLhyiumjlr+aIniOaoXPTwcibrbha98LtKaLaRUfkiHbqupBZ+xPETYLLN8/qEr+5fe8a5q8crx7LvKzfsRjOrg/1FkBcB0dvj+Uf2dLnw6ZNm/Q1y8DghYiCRaFChZAnTx5s2LBBlxARkdFVqlRJtcI4ODioIZWlW9nZs792AB/QVJexPTuQTwcbv8OcsL904n7VshIyZEh1oi4wmB/L69Kz7Vi9NmDlzloEF6+etahu3gxeiCjYyLwAErzcvHlTlxARkdElTJgQ//zzjwpe7t27p1phJB/mwIEDeoug5erqihfuz9TM8v4VP05iNcHlzCUjVT6LiBU9NjxeusFDXxfShevOg+sqp+VXxIudEJevn/2lYZj9I12KLAgdKgxOnwzeANMrBi9EFGxy586NHDlysPWFiMgGlSpVCuPHj1cz84cJEwY9e/ZEx44d1fww5tGzgoKryxPVrSuglCzwP/V/1ea56nUkdkwJ+5hxsXrLfJW8L4HH6s3zVMuMOccmXLgIeP32FZxcHqjrwreywrnL4uL1U9iwa6m6n4+fPuKh050vI5cFBnnuLs6Wk7TP4IWIgpW0vmzcuFGdfSMiItuTK1cuFbjMmjULadOmxZw5c9C0aVOVF+nm5qa3+n0yXPPcuXNx584dXeLdvbsPECVywAUvEcJHQpOaXbB66wKVrC+J89Kt65HzPRSvkxIFayTG+asnMajL9C9Bk2PcJChftCZa/FMRpeunw/U7F30tS+KYCkO7zca67YvV/RSsnghdBtVTCfmBJXaseHB2ctXXgl8I05dChbYyAsSzZ88QPXp0hAoVSq38FY8fP1Z92E8fSoVYMUPrUiLyj+wFb6NXrxEoUqSILvnWhw8f8OnTJ4QNG1aXWB/pPuDo6IgWLVroku+Teip8+PCIECGCLjEuOWP39u1bhAsXLtD6Tvs0ZcoUnD29AAtmxNUlROQfBw6/RNM2zjh69KQuMZ53796pHI/QoQPm+O/ly5fYvn07duzYoY4vs2XLpoKadOnSIUmSJHorvzt9+rRq0ZGZ/2vUqIE6deroNZ7+nb8Ma1dvwKQBnH/MNwPGt0fk6GExaFgfXRK8Ajx4ObA9hS4hIv+qWvehTQQvhw4dwuDBg9U8AHHj/vigmcFL4DIHLzMnxtElROQfx0+9YvDiD8eOHcP58+dx7tw5nDp1CgkSJECaNGnUHDJZsmRB/Pjx9ZY/1rBhQ9y+fVtdTpEiBerXr4+8efOq62NHTsbFs1cxuOtMdZ28m7poKO45X8G0WRN0SfAK8OCFiALW5MmTDR+8CGl9kTNqTZo00SW+Y/ASuCR4GTdunL5GRAEhSpSIDF4CwNOnT1Ugc+bMGRXUSHdjmfg4U6ZMSJw4sTqGlcXOzs70nkdRi1wWMsO/z7pN9q0tW7bEsEGj8fldOHRvOUKvIa9ktv7/ti/Euk2rdUnwCrDgxcPDQ1+yDkY54PMrqVjkc/2dz9baBMcBX2CTpu7vMcp3WVpfhg8fjmnTpqkhNL+HwUvgun79ur4U9OR7LPsiSdy1Be/fv1d1shz0GZ18l2U/JPWUUerlH5Hfrbxe84GzSJ48ub5kPEEVvPj04MEDnDx5UgUyjx49wosXL9Q+QvaLZpEiRUK0aNFUIHPp0iVd6l2CuImRMmFmtKzTQ5eQV/uPbcXwad1w8PDXWfyDU4AFL9aGwYtxGTF4+REjfZel9UWa8xs0aKBLvsXgxbhkPyQL62XjsbXv8uvXr9XrlYNmWxBcwcv3yHsvgYwskqwv/+/fv4958+bpLb76448/4PbsNZInyMDg5TssLXjhaGNEZDFk5LEtW7bgzZs3uoSIiOjXSJAsLfhyMix79uwoVqyYOjHiVdSoUdG1a1dMmDABDvYOap4X8p3L00eIGT2Wvhb8GLwQkcWQeV9kh7J7925dQkRE5H+S82ImI47Nnz8fpUuXVtfjx08ANwYv3+Xk+gjx4iXQ14Ifgxcisigy8AeDFyIiCig3btxQudnSCjNmzBg1LL/kwJjZO8Rky8sPuDx5hFix2PJCROQrCV5kJJkrV67oEiIiot8nwytL0CKDwsjIZD45OMSCm8dzfY18cn7yEPb2DF6IiHwVJ04ctr4QEVGAkRwY34IWswQJ45uCF7+3vLx+8xJt+1RDvqoJviwyc/6wqV3x5Jmz3so4nFwfqvfIUjB4ISKLYw5emLhPRESBLWas3+s2Nqz7HOxfcV8t80Ztx90HNzBv5Xg1up6RSLcx6VpnKRi8EJHFyZkzJxP3iYgoSEiXqLgO8XHm0hFd8uvixU6I3FmL4M6D63jz9rUqu3LjLJr/XeFL60yvUS2+tMxI603XIQ2w+/CGL604BWskxvTFw/Hx49d5ag6d3IE/2xVU6wtUT4jxc/qabvtKrXvy3Bmte1VRQxnX7VhUbVOqXlqs2TL/SwD1/sM7zFwyUpXL+nKNMmLHgf/8HGCdv3Icnz5/Qro/0uiS4MfghYgsEruOERFRUJC5h4oXK4m9Rzbrkl/n+swJx87sQ+HcZREhfERVFiO6A3q2HYt9y+9hx7/XVcCwac9ytU5IADNj8Qh0aNRftd4sGrsLew5vxKkLh/QWgGPcpBjbe7Fa/9/MUzhnCiZOnNuv10qXrgdYun4GRvdcqLYZ0WMeFq+broIoETJESOTPXgKrpx9X6/t3mqxah+T5+sX+Y9uQPXMeJuwTEf0ME/eJfo+MqlSnTh3kz59fjbJERD9XvFQR7DmySV/zm25DG6rWDFmqNM+BfNmLo3zRWnotYB8jDhzjJVXBUbiw4ZE6eQbcvndNr/VUunA1JEvk2aqRIG4SpEmeCZdvnlXXhZQ5xIqnLkeLEhOJ4ifH7Qfe76NGuaawjxlXXU6eOC0cTJcfPL6trocKFRqpkmX4ElAlSpBCBTRP/Zibc+zcPhQpWlhfswwMXojIIjFxnyyBi4sLChcurJJ9L1y4oEstx7179zBy5Eg8ePBAl3wlOWMy03hAcXJyQpEiRdRws1evXtWlnnbu3InUqVOjSZMmcHV1Vc8pb968qkyWmjVr4vLly3prIsuTKXMGhAkTBifPH9AlP+c15+W/Wadx8dppjJrxz5duX7fuXcVfg+ujbKMMqNwsG+atGKfKvUqSIKW+9C1pqTl98TBa9fofStdPh/+1yIFt+7/OVyMiR4yigqTvefvuDVZsnI3qrfOqLmONupTCI5d7eu2PSQB0/fZFFCtRSJdYBgYvRGSxmLhP9GNr167FwoUL8fz512FeI0eOrMqOHTuGtGnT6lL/kxnLJR/N3d0dp06d0qWeB1jbtm1TlyXQe/bsGVauXIlXr14hbty4aj4NaUXt2LGjCoCILJFn17ES2H98uy75NVHtoqNmuaY4YQp+7j26ZQpcruDv4Y3xv1L1VHcv6bZVv2p7vbXfHD+7D0On/IX2Dfth49zzWDX1KIrnq6TX/pz8NqctGoozl45ixtD1WD/7DGaP3Iy49o56ix87dmavZ5cxe8tJ1hcMXojIYpkT9/ft26dLiILfrl27kCZNGhU4dOjQQbUsSJAgrQ1eA23pvjV48GD88ccfahtpsZg3b55a9/HjR2zatEkF6OZ1s2bN+nJ7c4tPu3btsGDBAtXyI9t16dJF3e+HDx8wd+5cTJ06Vd2mSpUqar08Nwka6tWr901r0dGjR1G+fPkvz1eCCXkcsxkzZqjf3NatW9UM5LKd3MeiRYvU85WDuzJlyqj/O3bs+NKq4+zsjCNHjqjfaq5cuZAiRQr8999/KniS5yOXY8eODTc3N29BFpGlKVG6qEqQ/12S2G6KGNTlh053VYtIprS5ECpkKFXm+vTXgvcrt87hj5RZkSppevW7e/f+LZ67PdVrf+7N21e4dvsCShWsooIr4e7xAq9N5X5x6uJhFCtRRF+zHAxebAz7QpO1kYOhAwf83oxPFBTkjGa3bt1UQJAnTx58+vQJM2fOxPr169V6CSgk0Jg/fz4iRIiAHDlyqG3MSa8bNmxQtw8dOrQKUGQSvREjRqj78EpaNEaPHo1s2bIhfPjw6v7Hjx+vghd5DDs7O3VQU6xYMVSvXl11t/TN8ePH0bhxY1y7dk0FF/I8JHhq3769t4DixYsX6vk8ffoUGTNmVI8xaNAgnDhxQq2XoC1JkiSqJeX+/fuqTLqQPXr0SG0vr0PY29ur1yZBj+Styf2mT58ejo5+O+NLFBwyZkoPhPikEuB/lQy1vGzDLJVTEtfBEXaRosL5ySO1SH1x5PRubN+/Vm/tNw4x4uL2/WumgOUJPn76iK17V3lL1v+ZsGHCmYKWGDh35YS6/ctX7liwaiJe+CEAkkEDdh/eiOIlLSvfRTB48Qf2hf7KP32h5QzfxYsX9dZE3smZ4IMHD+Lx48e6hMgyJE2aVLUqzJ49Gy1atFBlp0+fVv/l5NDhw4fVQbx0oZIgRgKIUqVKqZaRFStWqIN7mfG7VatWKiCRbaWbpLRQmIUKFUq1rkhQM2HCBBWoyO/h9evXqnVFAhGZgK9ly5bo37+/Ci58kgMneQ7v379HmzZtsG7dOmzevFkFVCdPnlSLV/ny5VPbSItLyZIlVdBlrqNjxoyJ3Llzq2BEyuS+5TnL/6JFi6rnIsz7x3Tp0qnnVqFCBRWcRYzomTRMZInk99Wz5z9Ytn6mn0bj8pqwX75xRpXr0q3FcJWcny5lFhTOXQ51OxZB/mqOWL9jCdrU72X63YfRt/65vNmKq4R/yVUpXDMJbt69gmZ/dtVrf06S9etXbYd9RzejYPVEqNEmH7JlyIdcmX8ekCw3BWJtWnawqFHGzBi8GJg19YWWHSr7QpNv5GAsefLk6oCNyJJIHSoH8yJSpEjqvwQIQrpSyUkiaaUw7/zlwEiCkZcvX6qTS9J6UrduXXUSp3jx4uqA/8mTJ95OLElAI0GSkEBF6lq5vdzWryTQMZ/EkmBCSBCRMGFCddlnK7y5lUeCK/kvzK9LXoN0dZP/27dvVyekzF3G5HZmcrty5cqhYsWK6vUvW7YM3bt3V63/RJasYOH8SJokmQpgvidC+EiY0G/5l2R9WfYuu4v+naYgZnQHtY0EDi1qd1flsn5A56koVbAqurccodab70MCFK9kaOU6lVqry5Ei2qFP+wlf7r9dw76oVaHFl/Uxozlg7qitajQxM5/3myJxOiwat1vdh+S8lC5UDX07TvJ2G5+271+juqw1blZPl1gWBi8BLKD6QkuXggIFCnxZ59++0BkyZFBnxyy1L7ScxZPAhsg3coaYwQtZGglEpM77XdJKIQf048aN+7L069dPdQULTvK6fsTcdezcuXNq33H79m1VvydKlEhvAfUaOnfujGHDhqnuaVmzZlXdP6XeJ7J03Xv+pVoeLt84o0tsh7S0LjUFbl06d0HYsGF1qWVh8BIIpLXBv32hpdKXHUhA9YWWAMaS+0LLevNZQCKfJHCW75l8R4msgbQqSz1369Yt1TohpM6TelNaaST34927d6q+la5Z5kX2GebWDiEnkW7evKkuy/dfTgjJ7eW+zcx1/vfIfiZ+/PjqsvmklZzIunv3rrocL57nHBJ+Jc9ZnqfU7UuXLlWPL/sZ83OS/Ya8VjNptZHHE9IKRGTpUqVKgYplq6gcFluzbMMMhDT9lMtXKq1LLA+Dl0ASEH2hpbUmoPpC9+nTR7WW+GQJfaGlW4GccWRfaPoeCYxlYeI+WQv5vko9KvWdnDySOlnqR2ktl7qudu3aqs6Wlhc5UdS7d29Urlz5Swu8mQQGcnJJkvGlvpT6VPYV0aNHV2dFZd8g28gJsebNm2PJkiX6ll/J4/z555/qhNbEiRPVY0u9KyfYZD8hrSa/SnIchdyHPAdpiTdbvny5OiElz7l169aqvr906ZIKen7nsYiCQ4e/2uDA8W3YfXiDLjG+ew9vYsXGOejd5x9dYpkYvASSgOgLLTsYCTiM3hdaztx16tSJfaHph+RAUA6UiKyB1KPSZap06dKqXpbvrtR95nq2RIkSqkVdWmC2bNmickLkxI/XrldCcgMbNWqkWrHlZJEEQRLMCKmHZSAU2Q9IK4gE99LK4hsZ6WvSpElqXyGtmFLvy3OTE2zmfdX/27sLuCjSPwzgj92CAYrd3f1XEfTsPrv17O5u7+zAOhMT27Pr7O7A7m4pEURs77+/l1lFBEUF2Z19vn7mA/vO7LIxzs4zb30PCWfp06dXv+fLl089TyP5W/KcLly4oJoRy3eEg4ODav7MGnYyF4kTJ8KkSZMxanrP7xrhy1z5v/TD2Fl98Hv1GsidN6dWapoYXsLJz7aFli+5/v37W0xbaJnHg22h6Wuk75dcvQ06mh1ReJKLRHJskppzY/CQmgSZLV6OYUatWrX6okzuO3HiRFUTLev279+vmlsJ+X6QC1PS3FfWySIjM0rzq6CkU7/8fXkc+V4IHFDkmLtlyxZ1fwkLclFIgpPU6Ad+zkIuAEjNuWwrjyXPTZ6jkfE1yE8jeT1By4TU/Bifu3w/BW7GJsd8aXlgfN3yvKSVQNBgRmTq7EsURYe2nTFiWvdQDS9szsbM7I3kKe3QpUd7rcR0MbxEgNC0hZYaFktpC21sA21sE00UHDkJk1HHgjZlJCIiCi/NWzdGmVLl0HlYXa1EfyS4PH/5FKPH/6mVmDaGlwgQ2rbQvXv3DrO20NLuWJolBCV/J6LbQsvVOdlO3gOir5HaF2PfMSIiol9hwNBeSJMmDToNqa2V6MfUBcNw6fopTJs16bOL36aM4SUChKYttFTnh2Vb6CNHjnxWaxOYKbSFluDGttD0LdJ3SvZ3GaWOSM/kgo40V5MlcNMuIvr1IkeOjPGTRuG5vzemLjSP2onQcF42Dtv2r8bchbMRL15crdT0RfL19f1PfpGmPDLPhly1/1a/Bj2QGgk54TfVMay/RmpspOZCSLAJzRebDMkpn6slfLZSAyXN7mR0s5/pd2QuzHlf/hHS/r9EiRJftMHXI0vbl+V7SBZL2Zd5XNYvaQ4tr9fa2lor0TfZl+UE31yu3P+oJ0+eoE6t+siVpTCGdv1bKzVPXYbVw4WrJ7FwoQvy5A15wkpTPC6z5oWIzErevHk/zi1ERET0q8h8edt2bEHMOJFRv3MJPHS7q60xH5eun0a1lvnw8vVz7Ny546vBxVQxvBCRWZE+VNJsjE3HiIjoV5Mm+MPHDEbrVq3RsIsj9hwJmIDcHCzfOAut+1VB+bKVsGL1YtjYmmeTVIYXM8S20GTJpOZFRsGTOYSIiIgiQq161bB40TL87fIXZi4epZWaLqc5AzFv5URMHD8Vg/7sg2jRomlrzA/DCxGZHem4f+LECe0WERHRr5cnbw5s2LgGl2+fQvNe5bFm6wK8eftpMnFTsH7HYrToUxEnzu3DmjVrUKFyGW2N+WJ4ISKzI+FFRqy7ceOGVkJERPTrWVlbYdGyeWjTvhVW/TtPhRhpnuXn76ttETGMoWXG4pEoX74MNm5ZizRp9TGqK8MLEZkdCS8yuSprX4iIKKLJSGuVqpTDlm0b0LRpE0N4ma1CjMvqKfB65q5t9WsYQ8vfC/+Cg0MJ7NmzC+06tQpxugxzxKGSOSSn7nCoZH2T45QchJ2dnXHnzh04OTlpa/SHQyXrG4/L+sWhkvUrNPvyq1evMH/uYsydN9twByB/ruLIn6MoiuYvjaQ2KbStwsbrN69w/Ow+HD+zH8fO7IW71yPUrlEfnbq2Q8KECbStfpwpHpcZXvglqTsML/pmDC/nz59Hnz59MH/+fDXzsR4xvOgbj8v6xfCiX9+zLz9//hxbNm3D7l17cfzkYbx85Y8cmQugaL5SKFG4AtKkyKht+X18nnvjiOsuHDq5E4dP7VQBJn/uwnAs6YDK1Sp8Njn4z2J4MSEML/rF8KJvxvASK1YsNGrUCNWrV0etWrW0tfrC8KJvPC7rF8OLfv3ovvz27VucOuFqCDL7ceDAPty+dwMxosdE/LjWiB8vwec/DUskwz9fv2cfl+eyvPBRv/u/9INtYjvYF3OAYyl7/K9YYcSNGz4z5DO8mBCGF/1ieNG3wOFlypQpePDgAcaOHaut1ReGF33jcVm/GF70K6z25fv37uPGzdt49vQZnnn74NkzQzDx8YWPWp6pY2H8+FawsgpYEiS0gnUCw+/W8ZEiZTJkyZJFe6TwZYrHZYYXfknqDsOLvgUOL8ePH1dNx5YuXRqm1eSmguFF33hc1i+GF/3icTnicbQxIjJb+fLlUxO1njx5UishIiIiPYvk6empal4kScoVXEnNlpAk5fXKIlcKLIFcmZfP1RI+WyGv11I+W0vbl+U4Ja/V+HrHjx+vRnYZOHCguq033Jf1i8dl/ZLXKosl1EQI7sv6ZYrH5UhPnz79GF6kmlOaYljCzic7nrxmS6iuF1LlJzuepRxYjEHcEljavixBRT5b4+e7c+dOTJs2DS4uLogXL54q0xNL2pdlP5b9mcdlfbKkfVleqyzSxNUSyL4s+7GlnNDzuByx2OeFbat1R/6jsc+LfgXu8yI8PT3RsGFD1felVKlSqkwvLG1flu8hWXhc1h9L25fZ50W/eFyOeJYRkYlItxInTqz6vri6umolREREpFcML0Rk9vLnz49Tp06pGigiIiLSL4YXIjJ7efPmxZMnT1SAISIiIv1ieCEis5c+fXo1YRfDCxERkb4xvBCRLhibjhEREZF+MbwQkS5I07EbN27g8uXLWgkRERHpDcMLEemC1LzY2dmx9oWIiEjHGF6ISDcKFCiA06dPa7eIiIhIbxheiEg3pPZF5nt59OiRVkJERER6wvBCRLoh4SVevHhsOkZERKRTDC9EpBtx48ZFwYIF2XSMiIhIpxheiEhX8uXLp2pe/P39tRIiIiLSC4YXItIVaTrm5+fHpmNEREQ6xPBCRLqSNGlSFWDYdIyIiEh/GF6ISHeaNm2KVKlSabeIiIhILxheiEh3smfPjurVq2u3iIiISC8YXoiIiIiIyCwwvBARERERkVlgeCEiIiIiIrPA8EJEJuH169dYt24d6tevj5IlS6J06dLo1KkTzp8/r21BRESkb0+fPsXlS5dx7ux5PHnyRCulwCL5+vr+J7+8f/8e3t7eSJAgAaJEiaJW6tm7d+/w4cMHRI8eXSvRtzdv3qjP1RI+2//++0+dCMeIEQORIkXSSvVLD/vyy5cv8eeff8JwPELXrl2RPn169br27t2L2bNno3PnzihRooTaVo5TMWPGRKxYsdRtPbO0fVm+h2ThcVl/LG1flmOavF5ra2utRN9kX44cOTKiRo2qlehXWOzLHh6e2LvnAC5fuAJ3d3e4ubnB3dMNbh4PtS0+lyiBLWxtkiKJbRLY2togbbo0KFXaEanThP+omqZ4XGZ44Zek7jC8mJ/du3dj0aJFGDNmjOHAbKuVBtixYwdWrFiBcePGqePT9u3bceLECZQpUwbjx49HtGjRMGXKFPVz7ty5ans5cUibNi26dOmC3Llzq8dZvny52i/k/Vq9erX6P5EnTx706dMHNjY2apuzZ89i2rRp8PHxUYtsmyhRIvTu3VvNHSNXxGT9vn371Jd0lSpV0Lx583ALUgwv+sbjsn4xvOjXj+zLclw7e+YC9uzcjwMH9uHqzYtInDApMqXNAZtEdrA1/C63bQ2/y08bwyKP7fH0Cby83dRP9ftTd/XzwePbuHH3ElImSwv74o4o+Zs9ChbOpy7shTVTPC4zvPBLUncYXsyPhBA59rRo0UIr+cTDw0OFh44dO6oAIeHF2dkZ2bJlU7U0cj8hn/mdO3dUrY3s59u2bcPOnTvx119/qXAh4cXFxUU1RStXrhzevn2LSZMmIXbs2KpMwsqAAQNUGMmXLx+ePXuGIUOGoEmTJurvyuMPHz4cWbNmRb169fDq1StMmDABCRMmRPv27cNlX2N40Tcel/WL4UW/vmdfPnb0BFYsW4X9B/cgSuQoyJG5APJmL4LCeRyRNmVmbasf4+3jiTOXjuLU+UM4deGQqrUpmP9/+L1GdVSuWl7b6ueZ4nGZfV6IKELJl/zjx49hZWWllXwubty4KiDIl0VgrVq1+hhchHyRZM6cWX15yhdKunTp8Pz5c/j7+2tbQAUPR0dH9SUr29vb26vAI89Bqu0l0KROnVptKycddnZ2uH79urp99+5dFViktkXuL6FHQtClS5fg5+entiEiIjp25AQa1m2GXj37IKlVevzVfSY2zD2DMX3no16VNj8dXEQCq8Qo+b/K6Nl6FJZN2Y+ZI9cjXxYHOM+ahwplq2Dzhm3qwqYeMbwQkdlJnjy5as4VmIQU6R/ToEED1K1bFwMHDsSLFy+0tQGkeVhITbxknQSfhw8D2hxL/xsJNFmyZFG3PT09cfLkSVStWlUNKCCLNDmTGhq56khERJbtqBZaBvQfjII5S2Le2H/RvE53FMxdQtW8hCdpgla3civMHbMFtSq0wOxZc1GpfHVdhhiGFyKKUBImpIZDmm0FR2o1pK+J1JSERGplRo4cqWpEpN+L9JGRJl5x4sTRtvg2qcWR4NO/f38Vftq2batqaYx9ZoQ0J9uyZQv27NnzcVmyZMkXQYqIiCzH8aMSWprizyEjUCxfecwftxWNqneAdfyI+W6oVqaRFmKaqxBTuUI1bNmknxDD8EJEEU5CwZEjR1S/u6BkqGQJLtIMLCQScKRvjIxIZqxZkSZjQZuafcvx48fRrVs3FX6WLl2KatWqfWzTLM3avLy8VE0LERGR9AUZMWwMOnXqjJKFq6vQUqdSS8SKGfoLZ+HJGGJqlm+BAQP7o03Ljrpo5szwQkQRrlixYkiRIoXqXC99UKRDpDTFks7506dPVzUigfu3BCUdCaXJ161bt9R9ZejJZcuWqcEMQkvulyZNGlVjY2wWJv1b1q9fr9ZlyJBBPcf58+erJmVCaovk+RIRkWV55v0MzRq1hOvJs3AatBTVyzZGlCimOWCBhJgx/ear78ia1evg9i3z/t5ieCGiCCc1K/369VM1J/KzVKlSqFixIjZu3KhG/DLO8RKSePHioWnTppg3b566b9++fVG5cmWkShX6MfBluGZXV1ds3rz5Y5MwGYJZbt+8eVM9R+njIkNR1qlTR4WbP/74Q/WDISIiy3HtynX8Xr02EsZLhql//oPM6XJqa0xXvuxFVc2QdTwb1KpVG7t37tPWmB8OlcwhOXXne4Yx1ANL25fDa5LK0aNHo2jRop8FJRmFTGpiJBhlypRJK/11LG1fNsUhOcMTj8v6xaGS9WvThn8xYGA/NKnZCU1rdtFKzcugCW2x9+hmtG3VCZ27tfvq/0lTPC6z5oWIyED6tEifF2M/GQmEUvsiYck4iSUREVmu1f+sR8/e3dC/g5PZBhfxV4+Z6NFyBGbMnoJB/YdppeaD4YWIyEAmo5Qr4LVq1VKjjUlnfWlGNnTo0K/2tyEiIv07eOAIBg8dgFF95uK3YlUDCs1Y9XJNMGXoSqxauxzznF20UvPAZmNsnqA7bDambz/TbOzBgweqOUfGjBm1EtPGZmP6xuOyfrHZmL5cvXJdDRzTqHp7NK7RSSvVB9cLh9D1z/qYMG4yKlQqo5V+wmZjREQR6NGjR2jdujWuXr2qlRAREYXsyRM3tGzeCmXta+guuIh8OYrhz+4z0LdfL7ieOquVmjaGFyKyGIUKFVJDHp84cUIrISIiCp7MidKiWWsUzO2AHq1GaKX641ikEro0H4b27drhzu27WqnpYnghIotSvHhxDm9MRETfNHr4BKRNkRX9O0zQSvSraumGqFe1DXr37K+VmC6GFyKyKDIh5tmzZ3H79m2thIiI6HPXr93E0WNH0eWPoVqJ/jWq3gEJ4yXFxnVbtBLTxPBCRBZFmo0VLlyYtS9ERBSiEX+OQe2KzZHAKrFWYhnqVWkFJ6eJePUqYNoAU8TwQkQWx97env1eiIgoWPt2H8TLF69Rs8IfWonlyJG5AIoXKIv5zou0EtPD8EJEFkf6vVy/fh137tzRSoiIiAKGBh49eizqVGqhlVieOpVbYtXqVfDy9NJKTAvDCxFZHJlN39HREXv37tVKiIiIgOVLViNtisxwKFJRK/m24VO7onitFB+XKi3yYPnGWYYg9E7b4tuev/DBwPFt4PXMXSsJvuxXsLNNiSql68Np3N9aiWlheCEii+Tg4IDdu3erST6JiIj8nvth6rTJqFullVYSem0b9cPBVQ+wb+VdjOu/EGu2umDjrmXa2m974vEAjz3ua7cCBFf2q9Sr3BoXL17C9Ws3tBLTwfBCRBYpT548SJQoEWtfiIhIcT11BlnT50b2jPm0ku8XJXIUZDE8RuVSdXHh6imtFPDydsdgp3YoUSeVqp0ZNKGtKhPnLh9Hn9F/4Nqt82jeszyadC+NrXtXfVF2694Vtf3pi0fQsIujepzKzXNj16EN+O+//9S6Qyd3YOikjli7bSHKN8mmtmnc7Tdcv3NRrQ+tqFGjIV/2oti9Y79WYjoYXojIYknty549e7RbRERkybb9uwuF8zhqt8LO6zevMG52X6ROngE7Fl/DvhV3kDFNNkPI6IAX/s+RK2shjOo1B+lTZcW88Vvh4rQT5R1rfVGWLlUW3L5/FROc+6Nf+/GqpmfSkGVwXj4OJ88d0P4aDGFmPbx9vLB+jquqCSpdvCpmLRmDN2+/bwSxXFkKYteuXdot08HwQkQWS/q9XLp0CVevXtVKiIjIEknNxd79u/C/fKW0kh8jjyO1HNsPrIWj1m9Gbj9yu4eqZRoiRvSYiBIlKn4v3xRv377B5Rtn1DahdejUTpQoXEGNCiYypM6GPNmK4PSlo+q2SJI4OaqUbhDwtyJHQZE8JeH59InqQ/M95L24c/8mPD1Mq+M+wwsRWSxra2sVYFj7QkRk2Y4dOYlkSVIjedI0Wsn3mbl4lGqiZV87JQaOa40erUaiWIEyap2P71NYxUuAuLHjqdsiXhwrpLBLiyu3zmkloXPn/nW4rJ6i/pZx2bRrGdw9H2lbBHS4D/y3fpSELKl92bxhm1ZiGhheiMiiGZuO+fv7ayVERGRpNq3fguJa2PgRxg77K/4+qE76I0eOjEiRImlrw5bxbwVeBnaapK0NWwVy2WPr1q3aLdPA8EJEFk067idLlkyNPEZERJZHmnrt2b8L9oXKaSU/TmpuZJ6UOcvHq/4sInGCJPB74Qs/7baQJlx3H95QfVq+R7IkqXDlxrnvGob5ZxTLXxqXrp2Fh7uHVhLxGF6IyOJJ7QtHHSMiskyeHp4qXKRKll4r+TnlStRQP9dsXaCCUZqUmWCTyA5rt7mozvsSPNZuXahqZrJlzKu2jREjFl6+9oebx0N1WwRXVvJ/lXDpxmls3rNCPc77D+/xyO3ux5HLwpo0bYsdKy7OnP6+5m3hieGFiCye9Hu5efMmzp49q5UQEZGl8PDwQvx4CbRbPy9WzDhoWa8n1m5fpDrrS8d5adb12P0+yjTKBIe6aXDhmitG9Jyt+sKIlIaQUOW3emg7oBoqNM2OG3cvBVuWNmVmjO4zDxt3LlOP41AnNXqOaKI65IcX20TJ4OFmOp32I/n6+qqBod+/fw9vb28kSJAAUaJEUSv1TCam+/DhA6JHj66V6NubN2/U52oJn61c5Xj9+jVixIgRbu1NTYml7ctynIoZMyZixYqllYSNyZMnq+Ng9+7dtZKIZ2n7srz/svC4rD+Wti+/fPlSvV4ZFMQSyL4sfTyiRo2qlZgWLy8vLF68GIULF0aRIkW00k+2/7sLUyZNw4IJ27USCqzH8EbIWyAXuvZsr5VELNa8EBEZlC9fHps3b8aNG6Y3mzAREf04GZBl3bp16NevHwYNGgQPj8/7b9y//zBMa170Rpq8PXjwQLsV8Vjzwit8usOaF30Lr5oXMXbsWLXfdOnSRSuJWBGxL9+6dUv77deT/Vi+i6JFi6aV6Nvbt2/VMVmuWOud7MvyPSTHKUs4Lr969Up9vvHifRquNl26dNpv+mPqNS+iU6dOuHDhgnYLaNGiBRo1aqR+HztiIm7feIi/esxUt+lz81Y64fLtk5i/aLZWErEYXhhedIfhRd/CM7xcvHhRNRubNWsW0qT5sbH+w1JE7MszZsxQTeiIKOzEjx8bx4+7arf0xxzCy7///qsuUAWWPHlytG3bFutXb0GU93HQs/UobQ0FJvPIrN46Dxv/XauVRCyGF4YX3WF40bfwDC9i5MiRsLKyQocOHbSSiBNR4eXcmUWYPSWJVkJEP8P17Eu06ujO8BKOpLmv9Gt58eLFx0WaigVepOzkyZPaPT5nm9AOubMUR+v6vbUSCuzYmb0Y7NQOp1xPaCURi+GF4UV3GF70LbzDi4w4Ju2iZ8+ejRQpUmilESMiw8siZzuthIh+xqGjLxhewtj169dVTfmZM2fg6uqK58+fq+8FGSBBzmNlkYtQxkWa7/n5+WHmzC+bhdWrVw+nT1yArXUadG42RCulwPYc2YRpi4Zj7/6dWknEiuTj46PCi5z8PHv2TH3wltD+VsKavGa2rdYfOeGTA6mczFtCeLG0fdlwzFIn8/JFFV7++usvJEmSBK1bt9ZKIkZE7Mvy5c7wQhR2jOHl2LFTWon+yDmGnF+E1wXSy5cv49KlS+rnuXPn4OnpCTs7O2TPnh158+ZVS9KkSbWtgzdlyhTVad+oaNGiaN68ueqLNHzoWHg9eR5us9Sbu5WbnLHryHqsWrdMK4lYkdzd3VV4ISKiAHI1T5qPTZ06VYUYS+Ls7IxLF5YxvBCFEWN42b//sFZCoWE4P1WTB+/Zswd37txBypQpkTlzZmTNmhU5c+ZUt79H9erVVU221Kg3aNAAJUuW1NYAc2YuhOuJ8xjbb4FWQoFNWTAMT57expTp47WSiPVZzYtc0ZTqNda86A9rXvTL0vZlX19f9dmGZ82LGDJkCFKnTq2uzEUU1rwQmT/WvHyf8+fPY9euXWpJliyZChkyN8vPDKIi4UeO5fXr11fBJU6cONqaAKtXrsfyJf9g5sj1WgkFNmhCW9ilTIxBw/poJRErsnwhGr8U5QRIGMv0vBgFt06Pi1Fw6/S4WNprFcGt0+Pyq45TlSpVwpYtW1Qn0ODW/6rlV7zWwAsRhY/g/r/pZTEKbl1ol927d6N///5qqHrpn9KnTx/V91ACR9q0aYO9T2gXub/U4EhT4Lhx436x3sY2MXz8vLVXQUF5eD1W71HQ9y2iFv1fhici+gEyE3OWLFmwdq1pDA1JRKQ30td66dKlas6VBQsWIGPGjJg3b56aSNLe3l7bKvzZJk0M3+ffH15ev3mFVVvmoXqr/CheK4Va2vSrivNXTGNUrrDi5vVIhRdTwfBCRBSCmjVr4p9//sG1a9e0EiIiCgsbN25UE0dKMzEZ8UtCizTtklqSXy1FiuTw+c7w8vLVCwwc3xrb9q/BuAEu2LfyLnYvu4nfyzdBn9F/YO/RzdqW5k9qXlKlSq7dingML0REIciXLx/KlSuHlStXaiVERPQzZK6VXr16YcWKFSq0jBo1CmXKlInQfpsylHLqFOlw8MR2reTbDp3aifuPb2NEz9nImCY7okSOgujRYqC8Qy10aT4Mc1dMwNNnHgHbntyB4VO74ojrblRunhs12xaG1zN3FZjGzeoLh7ppVK1Nwy6OOH3xiLqPWLxumprdfvaysR+36TSktgoTRrJ9k+6lUa1VPpSok0ptU61lPpw8d0Ctf/nKH1PmD/24TvqveHm7q3WhcfT0bkSJEhXZc2bTSiIewwsR0VdI7cvhw4dx4EDAFwEREX2/Bw8ewMnJSQUX6XwvozlK30JTUbFiZRz4jvBy8txBlCpaBbaJk2kln+TLUQxv373FrXtXtBIJAXuwcedSLJq4C6tnHkMia1vEjBELlX+rh91Lb+DAP/fRsHp7FVakVsdo4eopSJ4klarV2WXYLqlNCixZP0Ot8/F9iumLRqCrISytd3bFhjmnkSNTfgzqPBkFctmrQV+cDcHH3/B4OxZfU/dPmSwdxszsrZq8hcahk7tQvIjjF4McRCSGFyKir5Av2dq1a6vmY0RE9P3WrFmjmog9fvwYEyZMQIcOHdREkqakctUKOHgydOFFwsXDJ3dgFddaK/lc3NjxYJMw6RcBoX3jAUhg9anvSIzoMZE1Qx5VsyEd0dOnyoLnfj4qbBjlylJQhSSp2ZHtHYtUxM27l9VzeOLxAG/fvkHqFBnVttbxEyGFXVpcuXVO3fb0dlO/N67RUd1XllJFK+Ox+z21LjTOXDqKChXLa7dMA8MLEdE3SO2LzOy/evVqrYSIiL5FhiiWYeelM36jRo0wbtw41RzXFKXPkBa2iZOopl3hIW3KTEiUwFa7FeCF/3PMWDwSNdoWUs2+eoxoDD9/X21tgCSJkyNWzOBrPWwS2SFatOh48Pi2ui3N0B6730f2jAHv8VNvd1y7dR51OxRTTcZkadajrNrGzxCSvuX2/Wtw83wIh1LFtBLTwPBCRPQN8ePHR40aNVTfFw+PgDbMREQUsg0bNqB79+6qRmHSpEnqIpCpq1ixEo6d2avdCpmEieRJ08DH75lW8jk/QyjxePpE1XSERGplhk3uZHh/ImPJpL2q2deEAYsQN3Z8bYtvS2htgyY1O6HrsPqqn8sfvcqhTPFqyJOtiLYFkCJpWqyf44qDqx58XHYuuY7M6XNpW4TM9cIhFCviYFJNxgTDCxFRKPz+++9qtn02HyMiCtnDhw8xfPhwuLi4oFmzZhg6dCjSpUunrTVtFSqVUX1TQqNAruI4cHzrx075gZ27fByxDQEnfeqsWsmXpIbF3fMRShapaAhDsVXZ8xc+ePnaX/0eWkdd96B/hwkqoKyZeRzVyzVRgVHEi2uNN29f4+HjO+r29zpreB2Vq5pWkzHB8EJEFErGoZNPndLvTNlERD/q33///ay2pWrVqtoa85AmbWpky5oNyzbM1EpCZl+wHFIlz4DBTu1U8yrpHC9BYeu+VZjg3F/ViATu3xKUjEwmTb5u3rui7isjiEnn/Pfv32lbfJvcL12qzKoGx9gs7LcGGbBum4taZ2ebEkXylsSspWPwyO2euo/0lZGBBL71d06dP4jbD66i5G8OWonpYHghIgolBwcH9WU8c+ZMNbkaEREBb9++xZQpU7Bw4UJV2zJgwACkSJFCW2te+vTvgVX/zlcd8r9GmoQN7jxFdaDv9md92NdOiVL102PtVheM6TvfUP71kdTixbFC8zrd1Whhct+uhseoUb4p0qTIpG3xbTsPrsOJcwewzeXyxyZhzqM3YY0hvNy8d1mFyDYN+yJ/zmJo3K2UCjflm2Y3BKzVeP/hvfYowVu2YRb69u2NKFGiaCWmI5Kvr+9/8sv79+9Vh1QZ/cEUn2hYe/fuHT58+IDo0aNrJfr25s0b9blawmcrVxtev36NGDFifKw61TNL25flOBUzZkzEihVLK/m1Xr58iZ49eyJVqlTo06ePVho+ImJfnjFjBs6dWYRFznZaCRH9jENHX6BVR3ccP+6qlejLlStXMG3aNFhZWaF9+/ZIluzLoYPNzZgRE+Ht5o+erUdpJaZJ5o6xL1gWDoYAZeT/0g9DJnZAy7o9QtWvJTjrti/CIdetmL94lsoHspjSOQZrXoiIvoOEpjZt2mD37t1YtWqVVkpEZHm2bNmiLubkzp1b9W3RQ3AR7Tu3wr5j/4a6/0tEsYqfEEcMz9E4JLPUpuw+vBHePp7Bzj8TGnLfFZuc0X9wb63E9DC8EBF9p1y5cqFt27aYNWsWzpw5o5USEVmOv//+G/Pnz1fhpUmTJlqpPsSLFxcdO3RWTadMWfPa3dT8LxWb5VBDLZdtlFlNnjmq95yv9rf5mqXrZ6JQwcLImCm9VmJ6GF6IiH6AjD5WpkwZ1f/Fz89PKyUi0jeZu6VHjx64d+8exo8fD0dHR22NvtRrWBPPnnvAZfUUrcT0xIkdD73ajFYz58tQy/JzaLdpav6XH3H41E6s37EY3Xt30kpME8MLEdEPktoX6W8kNTBERHonzWW7deuGjBkzYuzYsUidOrW2Rn+kj/B4pzFqBLA1Wxdopfp19eY51VdmxPBRSJQooVZqmhheiIh+kExeKQFG2n2vW7dOKyUi0p+5c+diwoQJqlO+HPcsQdZsWTBqxFhMmjcYh07u0Er1x+uZO3qPaob6tRupuW5MHcMLEdFPyJcv38f+LxcvXtRKiYj0QZqH9e/fH2fPnsXkyZNVc1lLUrFKWXRq1x0DxrcOdkJKPegwqCby5M6Pnv26aCWmjeGFiOgn1a5dG/b29qoZxY0bN7RSIiLzJs3EevXqBVtbWzXpZIYMGbQ1lqVdp5ZwLF4GzXqW1Ur0o0XvCogZMwYmTB6FyJHNIxYwvBARhQG5MintwAcPHoyrV69qpURE5kfmmJLaZGkm1qBBA3Tt2tVsTmzDy/hJI5EyRWq07V9NKzF/HQfXgrevJ+bMn6nmTzMXDC9ERGFk4MCBar4DCTAXLlzQSomIzMe1a9dUbcv58+cxZswYVKumn5P1nyEn9zNmT4GH9yNU+iMn3DwfamvMz427l1CmUWZcvnEGznNnImnSpNoa88DwQkQUhmTW/cKFC6sAc/r0aa2UiMj0bd68Wc3bYmdnp5rB5siRQ1tDImHChNi6fQvsizmiZtvCaiJLczNvpROa9SiLtGkyYOfOHcicObO2xnwwvBARhbHu3bvDwcEBQ4YMwfHjx7VSIiLT9OLFC9UZ38nJCU2bNlXzuMSOHVtbS4FJDczw0UMwfNhoDJnYHrOXjdXWmL4Rf3dT4aVh3WZY8c9i2NjaaGvMC8MLEVE46NKlC8qXL68CzOHDh7VSIiLTcuTIETV3izR1ldqWmjVramvoa2rWqYali5dh+/7V6DmisZonxVRdvXVe9W+RmqKJE6Zi0LC+iBYtmrbW/DC8EBGFE5kPQWbiHzp0KPbv36+VEhFFvDdv3qhO+TLYSJ48edRoYvnz59fWUmjkypMD6zetwVv4o1W/ynCaMxC371/T1ka8R273MGXBMLTuVwVePk+wZs1qs5jH5VsYXoiIwlHr1q1Rv359VQOzZMkSrZSIKOKcPHlS1bbI3C2jRo1SF1rixImjraXvYWVlhUVL5sNp/CScv3YMrfpWwt8L/8Ij93vaFr/e8xc+WLBqkgot+45tRt9e/bFpy3qkSZta28K8MbwQEYWzP/74A71798b69evViGR37tzR1hAR/TofPnzAvHnz1GhiefPmVbUtRYoU0dbSj5JhpMtXLIuNW9bCacIknLt2FC17V1T9YTy93bStfo1VW+ap0PLvvpXo1rUbdu3ZjsbNGiBGjBjaFuYvkq+v73/yy/v37+Ht7Y0ECRIgSpQoaqWevXv3Tv0njh49ulaib1I9LJ+rJXy2Mj7969ev1X/USJEiaaX6ZWn7shynpMNkrFixtBLzcf/+fcyfP19d7WzZsiUqVKigrQleROzLM2bMwLkzi7DI2U4rIaKfcejoC7Tq6I7jx121kohx6tQpLFq0SB1XmjRpEqZNxOQcQ07go0aNqpXoV2iPy4cOHsG0qTNw7cYV5MxcQC15s/8PObMU1LYIGw+f3MGZS0dx+uJR9VMmnGzTpjWq/l45TM75JB/IYkrnGAwvDC+6w/Cib+YcXozWrFmjrn6WKlUKrVq1Qrx48bQ1n2N4ITJ/ER1evLy8sGzZMmzatAn16tVTwSWsJ5xkeAnZ3Tv3sGvHXuzbux+uZ48jXlwr5MpSCEXyOiJj2hywSZgUCa1DN+qXr98zeD59ggeGwOJ64TBOnT+Euw+vI2umXHB0cIBjqeLIkStHmH5fMLyYEIYX/WJ40Tc9hBdx/fp1zJ07F0+fPlW1MIUKFdLWfMLwQmHhhf8HNGt9D/cevMHGf9LB1sa0TjDv3HsDf8NzzJo5pmE/1wo1Fy69Qv+hj+B65qW6nTljTMydkVL9HtJ9TE1EhpeNGzeq4CJzeUjfu0yZMmlrwhbDS+j4+7/E4YNHsGfXfhw4tA/uno9VedSo0VSISWxYbBPZqZ/C66mbanbm7vUYXoafr9+8UuUSgIoWtodjyRJwKGWPhAkTqPLwwPBiQhhe9IvhRd/0El6MFi9erJqSNW7cGGXLlkWyZMm0NfoML+/f/4ejJ/wxw9kTx0/5qxNQYRU/CsqVjocObRIjfVr9tM0OT+4e77BgsRfWb/LB3ftvtVIgdcpoqFTeCg3qJkCaVNFNOrz4+L5Hgz/u4uatN1izLA2yZYmprQGuXn+F2o3u4K3hpTWunwDW1lHg5vYOTRomQJNW9+Dj8x5rl6dFxvSmvb9ERHiRYY8ltDx69AgNGjRAmTLhO8IUw8uP8ff3N+zHPvDx9sGzZ7545m1Ynj0zlPnig+G8PL5VfMN+bw3rBFawsjb8bvhpbW2FuHHjao8Q/hheTAjDi34xvOib3sKLkD4wq1atUiMAyUmGLDlz5tRdeHlmONnsPeARtmz3VbflKnre3LEM+/AHFWgePAw4Ae/V1RYdWtsYToTUTQrCsFtg9Xof9Oz/wPDeAbFjR4Z90ThInCgq7t57Y3gvX6jyUcOSqZN+Uw4vr1//h849H+DWnddwcU4Nu6Sf5p5wmuqOiX97qPKSJT6drMnradflgSG8vcXiuanV6zZlvzK87N27F/v27VM/paZFlpCapYYlhhf9YngxIQwv+sXwom96DC9GEmK2bdumFhkBSGpiChcurIvwIiecrTrcx/5Dfij7WzyMGGL32YmquHbjNTr1eICLl1+hf68kaNcyseF1ayvpI+cFXhg28gkSJoiCyeNSoESxOIZj+6c3Smq3btx6jZTJo6tgY+rNxkLStfcDw/7yAmuXpUXqVOZ7fAvv8CKjFxpDi3zXS186WdKmTattEf4YXvTLFMMLh0omIjIRuXPnVkMqz5kzBylTplTDmHbt2hVr167F8+fPta1+3pkzZ1TThF9p6w5fFVyK/y+OOuEOGlxEpgwxMHd6KqRNHV1dcT9/MaCfA30iTammzvBA/HiRP9ZIBA4uQm5LrZYEF3Mlgevho3eIFi0SYsXiqUpwJLAMGzZMDcV+8eJF1KlTRw0E0qJFi18aXIh+NR4RiIhMjJx4tG3bVs1+LTUw69atU536nZycsGPHDjx58kTb8sfICU+nTp1UZ95f4dWr/7B2g486Ee3VNQnixQ35qydF8mjo1M4GL19+wKq1nwKWnMxK/4c2ne6rJlFyNdux/A2kyHQR9mWuw9PLUGgg63bsfo5GLe4iTbaLar0sVevcwglXf7VNYLJtyswXceDwC1Xz06TVXaTKckndJ1Oeyxg47DGeer/Xtv5EXtO8RU9RpOS1j38j6CLPV563kfTvmTLDE3n+d1Wtl78jf0/+bmht3uqrnk/LZomQJ9fP1z56P3uPWfO88Fulmx+ft7xvvQc+Un1qgnr85K1aJ++Ncfv8xa+i7+BHn/W7kdqfjf/6fvG4VWrfwrpNPupzEsbPtbDjNfX33r37Dxu2+KBG/ds4cvyFIcC8Rb5iAe+XcZug9wlKnofsJ8bPP1v+yxg5zg3P/T7/HEO7T5mKly9f4vjx41iwYIEKLHI8kP4QcpFj3Lhxauh1qQ0g0juGFyIiE2VjY6OGNp09ezYaNmyoOnZOnz5dtWNv166dKj927Jjq9Pk9JBA9ePBAnfzIZHVy1TY83X/4BmfOvVSdsTOk/3bTA+kHY20VRY005ffi8xNOOXFfvf4ZGja/q/p+1KuVQNU+xNauzt+49Qpdej3A5auvDOsSYtyIZKhV3RrnLrxEncZ3cOzkC7VdYPI4W7b54Pd6t+Hm/g71a1ujYd0EqtnVgiVP0b7rfcOJ76cQIie6Q0Y8xuC/HiN/ntiYMy2lWv5olBCRI0dCEtuoWL4wDWb/nfLj85L7N293D2MnuiFdmujq8SuVi4fDx16gbNUbH/sBfY10bt+110/VulQsF18r/TkSJP4a/QQxY0ZCt442GDHUDvlyx8bSld7q+Uq4MZI+SfIerlzjjZL2cdVrkCVpkmhY9s8z3LodEMLk/ZRA1K7LfXXbuF2FMvFx89ZrFUolpITEJnE0tG6eSA3cIK91zF/JMGtqSowaZme4/fWmz7KfVah+A9t2+aLcb/HV382eNRamO3uiet07H/tVBfatfSqiSJPgEydOqJrYHj16oEaNGujTp4/qG1exYkVVy9KlSxdVY0tkSdjnhX1edId9XvRNz31eggpuX5bP+vTp06p/zPnz53Hu3DlVJh388+XLhzx58qgOuvHjx1dLcFdipeZGAlBgcmLUvn17FYjCus+LjComJ71VKsTHlPEpvtmPxTgClVz5NvbRkKvk0m/j1Bl/RI0aCeNHJjM8ntUXjyUnxb7PPyCBdZTP1u3Z76dqOeTEVAKNkdS8/NH2ntp26oQUqFbp02NKSOk98KHhZP0Z5s1IpfrqCAlCNRveQfXKVuqEWp6PkbGT+dL5aVQneiEnxNJHZa6L1xd/4/6DN6jX7C5ixoiMFS5f73wunfF/r39bNblbOj+1GqEtNIzvXXB9XqQ26P2H/xAv7qfHkuc76M/HKri5OKdCKYeA1z17vpcKOkE70At5ryJF+k81WfPwfIfqhiAozf/mTEulgpGRPLZ8RlILJ0J6bl97ziGtk/2lbpO7ePX6g+rIL3/fSGpzOnR7gBZNEmFI/6Tq/Q/tPvWzvtXnRf7/yoUJw/mYOr5Js05XV1dcvnxZHd+zZcuGXLlyqZ9Zs2ZF4sSJtXuaDvZ50S/2eSEiop8iJwgyM3bz5s0xceJEbNmyBaNGjUKWLFlw+PBhdOvWTTUxk/bv5cuXR5UqVdRQqdIMTa7aDh8+HCtXrkSGDBm0RwwgE2fWrl0b9+8HXC0PSzKkrZywyrWT0HzXy4mkXPWWK+JP3D6/Uv7mzX9o1SxRiCeZcl/pyB50XfasMZHMLhru3H2jTlqDKlIwDsqUivfZ/eQ8rLIhcInrNz817ZLaGWnWlsZwchw4uIic2WOpE3QJOEYSUDZt9YFD8bgoXzr+Z38jZYroKsxIXxbjXCYhkVAnNTjy3gT9uz9K+sUEDi5Cnp+DfUA4uXw1dE3a5L0K2vcmOPLYxuAS1iQkyPsoTeoCBxch732uHLGwdaev+vwC+9Y+FVak/5rUoMj/UWm2Kf9P5f+cDMwhFw+aNWum1sn/Ywkpf/31l2oyOm3aNLRp0wb29vYmGVyIfjWGFyIiMyZX/6QZmLHWRPrErF69Ws0dI23hZQAAaXJWokQJ1ZdGrp65ubkhTpyAWoHA5KqvqZPO2zIfTFifZGbNHANxgungLjUhQTuMJ0wQVZ2AP378VgWVwB48CghbgecekZG/nri9Q6ECsT+rhTCSZnIicEAyRUULx1H9lf5oe1fVLgVuUhaY1HrJCGh7D/ihVqPbOHnaX/WBCW8HD79Q72/eYPoCSS1VxvTR4fX0vRpiObDw2qeCc+3aNbVcuXIFN2/ehKenp5qzI3369Pjf//6nQkzdunXRqFEj9f/ayspKuycRGTG8EBHpiDTbSJgwIdKkSaPawsvV2kqVKn2sfZEwM2LECNXsLDBpbibDJMsoZ2HNyiqKqil4bzjXDXqyHxyppfF/+UHVoEh/isCCKwtMTpLlZLlbn4coVvr6x07b0ulbOn+HJMlXHjOo3DljoVK5+KpZVZ9Bj3Dl2ivcvvsGU2Z4qI7hMqLa/wwn+kYyv40YO9FdPZegizRbCw05AZfwIO/N1/qMfA9pNib9Peo1vfOxY3xIzylHtphYtyKd4WcsTJjijpyFrqBxy7s4ffblZ5+rfNZD+tmp4a4vXHqJ6nVvGx77mup3ErTTfFhRzQV936uBFCrXuvXZ+2tcVq3z0bb+3Lf2qbAiFxNkkIzNmzer4dBlFEEXFxeMHDkSrVq1UhcYpJ+b1LxIgJGamuXLl+Pq1avaIxCRYHghIrIwMlqRUaJEiVSgkSZo0vQsPCSxMQQqwwnirTtv4Pv82yevMoKU9O9IbhcNsWOH/nK4NKlq3/WBOlk+d+GV6qw9bWJAZ/o1y9KqjvRhQZpIjR2eTPWfkY7tpSvfVKNTLTSEme6dbFT/mMAjqvn7B5zZN6qXQHU8D2kx9qkJicwwL/1d5L0JbpSt7yXz6ThWuIEuvR4ibtwoanJQeR5L5qVW/T+CI8NZSz+T3VsyoFZ1K+w7+EKNINa28+eDGkgNSPtWiXHpZFY4jU6OuHEiqWAnIUYGHQhrEoxfvvqg/u7QAUm/eG+Ny98TUiBVyohvuy/NP2WkMLlYIH1ZZD4naeYpA3QMGjRINQeVCw4yQ740H5PfJ0+ejD179phFDSlReGJ4ISKyMAsXLlQ/a9WqBWdnZzXEanhKYhtNjTR26cor3Lj5RisNmVzJl9qKIoXiGE56Qz/IyPpNPti8zRcd2yTG9g3p1Mmz1JCULxMfaVJ92T/lZ8jwxjv3PMffTilw/2p2PLiWHacOZlYTawadXyWZXUBokj438nxCWgI3NQuOjLQlTaLkvTl45MtR076HzGw/xslNda5f4ZJGBbz6tWUEtPiqf0iihCEHPWleJSFm0tgUuHgyM5rUT6De94lT3bUtPpH3ok4NaxzcmUn9HWlS1qPfQ9zURiYLKzFiyChvAbUnBfLG/uK9NS7SPExGsjMHhQoVQvfu3bF48WIMGDBANSGT2hoJOHJbRhokskQML0REFqZp06YYP348OnTooEaYDG9yNby24QT27dv/MHm6e7Ad5o1kKFvjJIxVK33fcMAnXQP6PEhYCdp5XDr+ywAAYWXJCm9Vm1SiWNxv9pWQIX9lRKwDh1589bV/i/ydKob3RPrbyHskfWl+lHT+v3r9tQpDuXPG1Eo/Ce3cM9LZv2fXJCrMnL/4KsTXJ8+9WJE46NzORo0Kduv2t0Ps9/pf4diq2Zh03Neb7Nmzqw79U6ZMUUMny4Ab0vxzyJAhOHXqlLYVkWVgeCEisjByEiQjlv1KMudG6ZLxsHufH7r1fhjsBIAyAaI0YZL+Ix3b2qhZ4r9HMrvo6uQ16FV9ac4kTZZkhLCwdN8QtEJTgyDNlKpWtFKTLs6a6wkZVjgoCVbBlQcl88rUq2Wtmo3JcNJyoh5cPyIJil97PKmpkMEI7tx7Y/gsPg91MlfKtFke2q1vkyAkHeElzMWIHnKSk+d57/4bVQMm/aDCmgRJ2Wfkucuko0FJfyipaTJ30tRMJqmUPmq2trbo16+faloWtB8bkV4xvBARUbiTGhGZ40Sa7siEjNL3QTpWS4f3nv0fqZnYC5a4piaRlL4Xrf9I/N2jP8nEjVJjI82SuhoC0rJ/vNF38GMUL33dcHL9DqlThl2nbOm/EsvwmmRiy6Adw8tVu/lZqJDX0aOLrZobxWmqh5qRvmvvB+q1y0SOMuN+hd9vGgLMt0+sjZ3hZWjfR4/fom6TO8hR8ApatL+nHk9+5i5yFTkLXcWlKyEPvSyd/6tWslIhSB5DBhNYuPSpmpiymnSwN4SkoM3snBd4oXTlG+rzkm3l/e3U4wHKVLlhCEsf0K5VYnUfecxKNW+p5zJirBvWb/ZRHfzLV7+pOu3XrWmtJsIMaxLGnEYnU8+hfrM76nOQ90QWGZAgfc5LhufzRNva/CVPnlzVnsrEtbFjx1Yd/GXY9PCedJYoojG8EBHRLyGd2KdPSqH6PshQutI0SZpfLV/lrcKFBIJ92zKgS3sbwwmodqfvIKNhLXROrebzWLXuGXoNeIStO3xVTcX6FelQtMjnEyv+KBl4YNBfj1UncZl40ziDvCzSp0LmGqnf7K6aANNIXrvz36lUPxE5yZaRr+S1b972HCmSR0O/nkkgQzCHhgTBwf2SYsfGDKpGR2zb+Vw93o7dfupvNaxrjeTJvh7WZMLGvwbbqZG6ZKS0AUMf4+69t+ozkk72Qe8vfUmk341M+Cjbyvu7bddzlC0VX41Clkcboli2kX4zFy69wow5nmpySBlaWWqCZAb/YQNkYk+1aZiTkeB2bspgCL+J1Hwu8p7I4nr2JSqWtcIfjRNpW+qHNCGT+WOmTp1qeI/foWPHjqpZ6I0bN7QtiPSFM+xzhn3d4Qz7+mbpM+yHN2mKEtYz7OuJcRZ36fS9YPbno4oZydDJNerfVqOHSVghy/atGfbD2okTJ9TklhcuXFATYcpEteGNM+zrF2fYJyIiMmPSVOvegzco5Rg32OAipElW0BHHiH6VggULqs78MiqZzC0zduxYeHl5aWuJzB+PrkRERKEk/SmiRI6Em7deq2ZQQUmncGmyJjPq584V9v06iEKrfv36mDBhAu7du4eePXvi4MGD2hoi88bwQkREFEqZMsREtcrxsXLNM9Rvdlt1Wpc5TmSRzuhlq97CGCd3NTBBrerW2r2IIkaePHng5OSkamNk8svVq1dra4jMF8MLERFRKEmTfulwLn1ZvJ+9R59Bj9Gm0321TJvlCbukUbFwdmrV6T2kZmVEv5L0VWjfvj26dOmCv//+GzNnztTWEJknHlmJiIi+g4z2Vau6lRrV6t6VbGp2fVkunsyCxXNT4zfHuF9MkkkU0apXr676wuzatUvNC/P8+afR8IjMCcMLERERkQUoWrQoRo4ciUePHqF///64deuWtobIfDC8EBEREVmIjBkzqsksEyZMqGbnP3LkiLaGyDwwvBARERFZEGtrawwbNgwODg6qBkbmhSEyFwwvRERERBZIOvJ36tQJkydPxuzZs7VSikhenk9x+dIVnDtzAU8eP9FKKTDOsM8Z9nWHM+zrG2fYD1+cYZ8obP3qGfZ/xOHDhzFgwACUL18effr00UpDjzPsf5+7d+7jyKHjuHblOtzc3ODubli83ODm8RAJrW1gkzCp4Qw9EjyfPoGXtzsSJbCFbeKksLUxLLY2SJ02FQoXKYCcubNrjxh+THGG/UiGEwEVXuTkx9/fH7Fjx1Y7oN7JByE7oCX8RxNygiufqyV8tvK5yuuVz9YSwoul7ctynJLXaglhLSL25VmzZuH82cUML0RhxBhejhw5oZWYpps3b6JNmzZqThjpE/M95DglxyhLuUD6vcflhw8eGcLKCRw7chwnXY+p7+0CueyRKW12JDYEFdtEduqnhJaoUaNp9wog5+cSYjy93eAhPw3Lwyd3cfL8QfV7vjyFUbhwQRQpVhAZM6UP8+8K+fuymNI5RiRPT08VXuTDkDdTdjxLOOGT1yss4bUKS3u98h/NEoKasLTPVo5T8lot5fP91fuyNB25eH4pwwtRGDGGl4MHj2olpsvPzw81a9ZErly5MG7cOK3023iO8SVf3+dYvHAZ1m9YZ3hfnyNH5gLIlaUACuV2QOb0ubStfo675yOcvnQEZy8ZQpEhzPi/8kPpUmXRqk0z2CULm2O4KX62bDbGZmO6I//R2GxMv9hsLHyx2RhR2DKHZmNBlSxZUs3OP3HiRK3k69hs7BOZP2ees+EYumQBchoCS/WyjZE/Z3HEjBH+31kXr7ti/fbF2H5gLapXro0OnVv9dIiRfCCLKZ1jWMalSyIiIiIKlT179uDMmTPo1q2bVkLfIqFlstN0lHT8DccOnUSftmMxrr8LihUo80uCi8ieMR/6d3DChAGL4PbQC2XKlsXAfn/i8aPH2hb6wPBCRERERJ9hgAm9E8dcUblCNRzYewhdm/+JKcNWouT/Kmtrfz2p6RnVZy6Gdv0bN6/dRsWKlbF21UZtrfljeCEiIiKiLzDAfJ00p5o0fjq6d++BprW6YObI9SjvUEtbG/Eci1TC1GH/oHOzIRgx8i/06NwXL174a2vNF8MLEREREQWLASZ4jx89QcO6zXD+zGU4DVyCqqUbamtMT5XSDTB+gAuuX7+BqpV+x/mzF7U15onhhYiIiIhCZAwwnTt31kos2+4d+1CtanXky2aPMX3nI23KTNoa0yWjnc0btxUZ0mRHvQZ1MWfWwo8jiZkbhhciIiIi+ioJMPfu3UPr1q21Essjo4wNHvAnuvfqgkGdJ6N5ne7aGvMxoudstKzbA+MnjkLzJq3h6emprTEfDC9ERERE9E3r1q1TwyI3adJEK7Esg/oPwz9rlmH9HFcUzV9aKzU/jWt0wpLJe3HkxAG0a9MJr1690taYB4YXIiIiIgqVBQsWqDkB69atq5VYBhkGecPmNVg7+yQSWdtqpeYrdfIM2L7oCs5fPI2eXfup+eLMBcMLEREREYXa5MmTkSVLFlStWlUr0bfV/6zHjNlTMKKXM2wSJtVKzV/sWHGxbMp+7Nz7L0YMG6uVmj6GFyIiIiL6LsOGDYOjoyPKlSunlejToQNHMHjoADSr1RUOhSsEFOpIymTpMGXoSixZsQDznF20UtPG8EJERERE36179+6oU6eOCjC+vr5aqX7cvXMPnbt0xu/lmqBlvZ5aqf7ky1FUTWg5fuJoQ1g7rJWaLoYXIiIiIvohLVq0UEvNmjVx+/ZtrVQfenbrg+IFyqpZ8/WudPHqaN94gCGsdYGfn59WapoYXoiIiIjoh0ntS8eOHdG8eXOcOnVKKzVvG9dvwTMfHzU7vaWoV6UNiuQtielTnLUS08TwQkREREQ/pVq1aujbty969uyJHTt2aKXmSYaDnjDBCfWrtoFV/IRaqWVoULUtVq5ehgcPHmklpofhhYiIiIh+mvR9+fPPPzFt2jQsX75cKzU/c2e7IE3yTKhauqFWYjkyp8+FGuWbYexIJ63E9DC8EBEREVGYsLe3x4gRI7Bz505Mnz5dKzUfz5/7Yc68WWhQva1WYnkaVGuL69ev4+qV61qJaWF4ISIiIqIwkz17dowaNQpubm6qJsacRiKbMdUZFR3rIF/2olrJtw2f2hXFa6X4uFRpkQfLN87C+/fvtC2+7fkLHwwc3wZez9y1kuDLfoW4seOrJnOjR4zXSkwLwwsRERERhSkbGxs1F0zSpEnRv39/3LhxQ1tjuh4+fIT1G9eqE/fv1bZRPxxc9QD7Vt7FuP4LsWarCzbuWqat/bYnHg/w2OO+ditAcGW/SsWSdfDhbSTs33dQKzEdDC9EREREFC5at26t+sIMGDAAR48e1UpN0+mTZ5E/Z3HYJk6mlXy/KJGjIEv63Khcqi4uXP008pqXtzsGO7VDiTqpVO3MoAltVZk4d/k4+oz+A9dunUfznuXRpHtpbN276ouyW/euqO1PXzyChl0c1eNUbp4buw5twH///afWHTq5A0MndcTabQtRvkk2tU3jbr/h+p2Lav33yJ21EI4fMb3R4xheiIiIiCjcVKlSBb169UK/fv0wf/58vH37VltjWrZt3YUieRy1W2Hn9ZtXGDe7L1Inz4Adi69h34o7yJgmmyFkdMAL/+fIZQgJo3rNQfpUWTFv/Fa4OO1EecdaX5SlS5UFt+9fxQTn/ujXfryq6Zk0ZBmcl4/DyXMHtL8GQ5hZD28fL6yf46pqgkoXr4pZS8bgzdvX2hahkztbYezavetjMDIVDC9EREREFK4KFCiANWvW4O7du+jatSuOHTumrTENfn4vcOjoXhTJV0or+TFyoi+1HNsPrIVjkYqqTG4/cruHqmUaIkb0mIgSJSp+L9/UEOLe4PKNM2qb0Dp0aidKFK6AHJkLqNsZUmdDnmxFcPrSp1qtJImTo0rpBgF/K3IUQyArCc+nT1Qfmu+RO2thvHzlj3Nnv7/WJjxF8vX1VXHq/fv38Pb2RoIECQxvahS1Us/evXuHDx8+IHr06FqJvsmY5fK5WsJnKweO169fI0aMGIgUKZJWql+Wti/LcSpmzJiIFSuWVqJfEbEvz5gxA6tXzdFu/VrGi3sW8N9WscTXa6mfre/z/3D8uGvADR2Sc4zIkSMjatSoWsnXrVixAjNnzkStWrVQr149JEqUSFsTcTat+xfrVv+L0X3maSWhJx32t+5bpd0CkidJjb7tx6tQIcduacq1fONsjO23ALFixtG2CrhfmpQZ0ah6B1y9eQ6jpvfEhEGLkcjaVq0Prizo3zIq71ALAztNCvZvBfc4oSVN0FKksUHfgT20kojH8MLwojsML/rG8BK+InJyOdmPZQntCZC5k/+7csIni97JviyvVz5bSzguy3euNI2KE+fTiWqZMmW03/Tne8OLkJn4Z82ahWfPnqFq1apqiR8/vrb21+vcvifyZ3FEpVL1tJLQCxxCHj65g14jm6JPu7Gq5kKEdXgx3ic4YR1eJCi5rJmC7bu2aCURj+GF4UV3GF70jeFFv+R7SBYel/XH0vblly9fqtdrbW2tlejbj4QXId9d69evx9q1a1XYk1n6ZfnVx3f524UL/Q//TDv8QzPqBw0U67YvUp3opRYnTux4KjyMntELY/svhE3CpGobacLV/a+GaF6nO/6Xr1Sow8u8lU64de8qhnWbZjh2fPl+h3V48XnujUp/5MTmTVuQPkM6rTRisc8LEREREf1yEnh+//131YRMOvVLn5g2bdpg1apVKhD9Kjdu3FSh+keCS3DKlaihfq7ZukAF9zQpM8EmkR3WbnNRnfdl/pe1Wxeqv5ktY161bYwYsfDytT/cPB6q2yK4spL/q4RLN05j854V6nHef3iPR253P45cFtas4iWAdfxEOHP6nFYS8RheiIiIiCjCxI4dGw0aNFB97hwcHODi4oJ27dph3bp16uQ/vLk/8VAn6WFFajxa1uuJtdsXqc760nFe+qM8dr+PMo0ywaFuGly45ooRPWd//Lsp7dKiym/10HZANVRomh037l4KtixtysyqRmfjzmXqcRzqpEbPEU1Uh/zwkiRxMri7eWq3Ih6bjbF5gu6w2Zi+sdmYfrHZmH6x2Zi+/WizsZA8fPhQBRdpUpY2bVoUKlQIefLkQf78+bUtvo98HjJRZuXKlfHbb79ppZ+sWrEOi12WYc6YzVoJBdZ3THOkTpccg4b10UoiFmteiIiIiMhkJE+eHB06dMD06dORM2dONbllz549Ub9+fYwdOxZ79uxRF+5CSy7wnTlzBsOHD8eQIUO+mO3fw8MzTGte9MYmoR083D20WxGP4YWIiIiITE6GDBnQsWNHODs7Y/bs2ahRowY8PT3x559/olKlSmrSy82bN8PH5+vzl8iobyVKlFC/79+/H61atVKPZ+RpCC/xGV5ClCSxHTy92GwswrHZmH6x2Zi+sdmYfrHZmH6x2Zi+hXWzsW+R4ZWlJuXw4cM4dOgQ/P391XttY2OjFltbWzV3TMKECdUi57Z37tzB6NGjtUcIkDRpUrRt2xarV2xA/JhJ0LX5n9oaCmzb/tVquORtO02jWR3DC78kdYfhRd8YXvSL4UW/GF707VeHl6AkyEiNjIeHh1rkd3d3d3h5eanfvyVxgiQomKOUGraYvuR64TD6jW2BU64ntJKIxfDCL0ndYXjRN4YX/WJ40S+GF32L6PDyLfJZ7Nq1C+PGjdNKPqlTpw4un7/BmpevkJoX5+XjsHf/Tq0kYrHPCxERkY74+fmhUaNGsLe3x82bN7VSIsslofnYsWParQB58+ZVfWlkSGYbG1s1GSMFz8PrMZIlTaHdingML0RERCGQJiglS5ZUw7RevHhRKzUd9+/fx/jx49XQskG9evVKXXEOK25ubihVqhQKFiyIa9euaaUBdu/ejSxZsqBly5aqmY48p2LFiqkyWerVq4crV65oWxP9Wi9evFAd9YV03peO/k5OTmpAAJE8eTL4MryEyM3zMRInSqzdingML0RERGZK5sFYvHix6sBsFDduXFV24sQJZMuWTSv9edIJunDhwnj+/DlOnz6tlQY0CduxY4f6XYKeNO1cvXq16kRtZ2eH+PHj4+zZs+jWrZsKQES/muyz0jm/Vq1a2LRpE8qWLautCWBjm5g1L1/h8fSx4T2y0W5FPIYXIiKi7yBzTGTNmlUFh65du6qaBQkJUtsgtR1G0nxr5MiRyJEjh9pGaiwWLlyo1knfnn///ReOjo4f182dO/fj/Y01Pp07d8aiRYtUzY9sJ3NdyONKX7cFCxZg5syZ6j41a9ZU6+W5SWho0qTJF7VFx48fR5UqVT4+XwkT8neMpAmNhJPt27ejbt26ajt5jCVLlqjnK31VKlasqH5K/wFjrY50jJYmOVZWVihSpAgyZsyIDRs2qPAkz0d+T5IkCXx9fT8LWUS/igSXZcuWqbljgmObJDF8/RheQiLNxpInt9NuRTyGFyIiou8ktQ19+vRRgaBo0aJq0Iw5c+aoq7pCAoUEDRcXFzW4hMwQLtskThzQ9ELmppD7SwdnCSgpUqRQnYnlMQKTGg1p3lKgQAE1UIU8/pQpU1R4kb8RL148FSZKly6tOh7LSVpwTp48iRYtWuD69esqXMjzkPDUpUuXzwKFzJchz+fp06fInTu3+hsjRozAqVOn1HoJbTLjudSkPHjwQJVJE7LHjx+r7eV1CBmuVl6bhJ6rV6+qx5XJBlOmTKnWE5mSxDaJfqjm5fWbV1i1ZR6qt8qP4rVSqKVNv6o4f8U0RuUKK26ej2CblDUvREREZi1dunSqVmHevHlqrgghQ7YK6Sgvs4LLSbw0oZIQIwGifPnyqmZk1apV6uReZgtv3769CiSy7d69e1UNhZGMRCa1KxJqpk6dqoKKzG0ho1lJ7YoEEemMLJ2OZeI+CRdBSdCS5/D27Vs14d/GjRuxdetWFahcXV3VEljx4sXVNlLjUq5cORW6Ll26pNbJ3Bn/+9//VBiRMnlsec7y87ffflPPRRhrjrJnz66eW9WqVVU4ix07tlpPZErSpU+rfvr4PlU/Q+PlqxcYOL41tu1fg3EDXLBv5V3sXnYTv5dvgj6j/8Deo6YxJ8rPklD3zNcLWbNl1koiHsMLERHRD5CmV3IyL6QTsJCAIKQpldRaSC2FsbZFgoeEEek8LB3tpfakcePGqnlWmTJl1Am/zEsRuJO9BBoJSUKCivQ7kfvLfUNLgo6xQ7+ECSEhIlWqVOr3oCOSGWt5JFzJT2F8XfIapKmb/Ny5c6fqnG9sMib3M5L7Va5cGdWqVVOvf+XKlejbt69q8kZkamR/LlbEAQdPbtdKvu3QqZ24//g2RvScjYxpsiNK5CiIHi0GyjvUQpfmwzB3xQQ8fRbQLPPQyR0YPrUrjrjuRuXmuVGzbWF4PXNXwWDcrL5wqJtG1do07OKI0xePqPuIxeumYd5KJ8xeNvbjNp2G1FbNuIxk+ybdS6Naq3woUSeV2qZay3w4ee6AWv/ylT+mzB/6cd2gCW3h5e2u1oXGEdddSJU8HdJnCDgOmQKGFyIioh8gQUROen6U1FLICf3kyZM/LsOGDVNNwSLSt+adMTYdO3/+vGo2JzOXS1+X1KlTa1tAvYYePXpgzJgxqnla/vz51Uzo0geGyBSVKfsbDpwIfXg5ee4gShWtAtvEybSST/LlKIa3797i1r1PI+wdPb0HG3cuxaKJu7B65jEksrZFzBixUPm3eti99AYO/HMfDau3V2FFanWMFq6eguRJUqlanV2G7ZLapMCS9TPUOqkpmr5oBLoawtJ6Z1dsmHMaOTLlx6DOk1Egl72qEXU2BB9/w+PtWHxN3T9lsnQYM7O3avIWGkdP70VJx9+0W6aB4YWIiCiMyQhbUnNx+/btjzN8S/8PqY2RWhrp+yET+0mthDTNMi7Sf8ZY2yGkNubWrVvqd+mvIjU6cn95bCNj/5eQSJ+b5MmTq9+NHfil6dq9e/fU78mSfXny9TXynOV5Sj+XFStWqL8vfW6Mz0n60MhrNZJaG/l7QmqBiEyR42/2KmD4+n17UAkJFw+f3IFV3OAnIY0bOx5sEib9IiC0bzwACaw+DTkcI3pMZM2QB1GiRFUXQtKnyoLnfj4qbBjlylJQhSSp2ZHtHYtUxM27l9VzeOLxwPD/6w1Sp8iotrWOnwgp7NLiyq1z6rant5v6vXGNjuq+spQqWhmP3e+pdaFx9vIxlC5XUrtlGhheiIiIwpjUTkifEgkfMhKY9E+RviIycpg02WrYsKE6WZGaF+k0P3jwYPz+++8fRyMzkmAgHe2lM770HZErqdJvJkGCBIgePbpqVibbyOAAbdq0wfLly7V7fiJ/p0GDBqo25O+//1Z/W5pzSa2JNEWTWpPvJfO9CHkMeQ4yKpnRP//8ozrvy3OW0Z2k78vly5dV6PmRv0X0K8SLFxcF8xbFUdfdWknYSpsyExIlsNVuBXjh/xwzFo9EjbaFVLOvHiMaw8//U583kSRxcsSKGdAsNSibRHaIFi06Hjy+rW5LM7TH7veRPWM+dfuptzuu3TqPuh2KqSZjsjTrUVZt42cISd8iweXDh/fIlTuguampYHghIiIKYxJQpMlUhQoVVB8VOcmXGhVjnxOZZ0I6sEsNzLZt21SfEOkEH7jplZB5Upo3b65G9JKO8xKCJMwIqemQSSGlT4zUgkizLKllCY6M9DVt2jQVVmTkMOkDI89NBhsw9tv5HhLO0qdPr37Ply+fep5G8rfkOV24cEENqSw1Lw4ODmooaGM/GyJTVKZsKRw9s1e7FTIJE8mTpoFPCLU0foZQ4vH0iarpCInUygyb3AmRIkXGkkl7VbOvCQMWIW7s+NoW35bQ2gZNanZC12H1VT+XP3qVQ5ni1ZAn26eLBCmSpsX6Oa44uOrBx2XnkuvInD6XtkXIzl46BscSpdQFEFMSydfX9z/5Rap4ZWIpuZrzrfaueiBXquSLQK5cWQJpniCfqyV8tnJlUjq8SntyU/sPFx4sbV+W45ScBIZ0kqYnlrYvy/eQLDwuBzQXk5oLIcFGajfMmaXty9I8Tl6vtXXwzYr0RvblyJEjf9ac0Ry5ubmjauVqWDB+u6rV+Jqdh9Zj4arJmDxkhQoRge06tAFL1k3HhIGLVTMx6bC/fONsjO234GMtinTY7/FXI/RrP/5jkJBO9uNm98P04WtUnxjpsH/n/nUM7DRJrRdBH0s6/OfJVhhl7H/Xtvjkkds99BzRGH3bjUOurIW00tDrMbwR/mjdAEWLFzGp4zJrXoiIiIjI4iVJYosihYphxabP51sKjn3BckiVPAMGO7XD7fvXVEB/8/Y1tu5bhQnO/VWNSOD+LUHJyGTS5OvmvSvqvjKCmHTOf/8+9CMJyv3SpcqsanCMzcJ+a5AB67a5qHV2tilRJG9JzFo6RgUZIX1lZCCBb/2d7fvXwMP7oQoupobhhYiIiIjIoFffbli33QWXbwTM2RQSaRI2uPMU1YG+25/1YV87JUrVT4+1W10wpu98Q3klbcvgxYtjheZ1uqvRwuS+XQ2PUaN8U6RJkUnb4tt2HlyHE+cOYJvL5Y9NwpxHb8IaQ3i5ee+yquVs07Av8ucshsbdSqlwU75pdkPAWo33Hz4NqhGUBJuVm+eiT9/eJtlih83G2DxBdyyteQKbjemXpe3LbDamX5a2L7PZmHkbO2Ii7t12w9Cuf2slpknmjrEvWBYOhgBl5P/SD0MmdkDLuj1C1a8lOEvXz8DpKwcwf9Fskzwus+aFiIiIiEjTvksrnDp/EPuPb9VKTJNV/IQ4cnrPxyGZpTZl9+GN8PbxDHb+mdBw83yIVf/OR/9BfbQS08PwQkRERESkiRs3Ljp36ox/Ns/VSkxT89rd1PwvFZvlUEMtl22UWU2eOar3nK/2t/malZvmoERxB2TMFDCaoCliszE2T9AdNhvTNzYb0y82G9MvNhvTN701GxNyLKpWuQYcC1dBs1pdtVJ9O3BiG/6a0gU7d21HwoQJVZkpHpdZ80JEREREFIhcVJgwcSyWbZil+oDo3ZlLRzFgXCuMHTvuY3AxVQwvRERERERBZM6SGZMmTVZDDa/fsVgr1R8ZOrnj4Fro2a0vSpcpqZWaLoYXIiIiIqJg2JcoimGDh6vJIKV2Qm88vd3QpHtpNKz7B5q3aqKVmjaGFyIiIiKiENSqWx3tWnVWtRPPfL20UvMno5RVb5UfDsVKY8CQXlqp6WN4ISIiIiL6ii492qNa5Vqo3Dy3LgKMzLgvs/Fnz5IbU6ZPUAMumAuGFyIiIiKibxgxeiiKFCyuAszpi0e0UvOzefdy1OlQFCmSpcLcBbPUKIDmhOGFiIiIiOgbZCjo+S7Oqg9MzxGNsW6bi7bGfIx37o/RM3qh5R/tsW3Hv2Y5nHckT09PNc+LjMEu80XIB2MJY7DL65XFnKrJfobMAyKfqyV8tkJer6V8tpa2L8txSl6rJf3f5b6sTzwu65e8Vln0NO/J11jivnzz+i20b98J9gXLoVPTwYgSxbQ/67fv3mDoxA44ce4Axo0ZD3vHotqarzPF43IkLy+vj+FFJlWSid8sYeeTHU9esyVMDiZkgiHZ8SzlwGIM4pbA0vblV69eqc/WUj5fS9qXZT+W/ZnHZX2ypH1ZXqssMqGuJZB9WfZjSwmnxn3Z55kP+vQcgFf+79G+UX9kTp9L28K0XL15DhPnDsLLN36Y8rcTUqVOpa35NlM8LnOGfc7krDvyH40z7OsXZ9jXL/kekoXHZf2xtH2ZM+zrV9B9WW47z5yPyVMnoJxDLVQuVRe5shbSto5YElo27Fyq5qipWqkm/hwx6LsDtSkely0jIhMRERERhTEJMK3bNceatWsRKfordBpSWzXPOnnugLbFryehRealadGnIh573cJc53kYO2GEbmoCGV6IiIiIiH5C5syZMGHyGGzdug0Jk8ZD71HN0H9sSxw+tROvXr/UtgpfF6+7qtDSql9l+L3xxJpVa+GydB6K2Yeuf4u5YLMxNk/QHTYb0zc2G9MvNhvTLzYb0zdLbjYWEg8PTyxasAzr1q8x7A/+yJG5AHJlKYBCuR3CrG+Mu+cjnL50BGcvHcfJ8wfx3O8ZSpcqhzYdWiBNmtTaVj/HFI/LDC/8ktQdhhd9Y3jRL4YX/WJ40TeGl6+7cukqdu/cj/379+P8JVdYx0+EArnskSltdiROmBS2iezUTxvDEjVqNO1eAeT73fPpE3h6u6XmG7gAAAGWSURBVMFDfhqWh0/uqrBy694VZEiTBSVKOMCxlD3yF8wb5scThhcTwvCiXwwv+sbwol8ML/rF8KJvDC+h5+Pji327D2Df3gO4fuMa3D3d8Mzn04z9Ca1tVIgxPLgKKl7e7toaIG6c+LBNnBSpU6VGCQd7lCztgKRJk2hrwwfDiwlheNEvhhd9Y3jRL4YX/WJ40TeGl5/34P5DPHFzh/sTd7g9ccPbd2+RJEkSFU6SJLVFUrskEdLhnuHFhDC86BfDi74xvOgXw4t+MbzoG8OLfpnicZmjjRERERERkVlgeCEiIiIiIrPA8EJERERERGaB4YWIiIiIiMwCwwsREREREZkFhhciIiIiIjILDC9ERERERGQWGF6IiIiIiMgsMLwQEREREZFZYHghIiIiIiKzwPBCRERERERmgeGFiIiIiIjMAsMLERERERGZBYYXIiIiIiIyCwwvRERERERkFhheiIiIiIjILDC8EBERERGRWWB4ISIiIiIis8DwQkREREREZoHhhYiIiIiIzALDCxERERERmQHg/yJ00s3gGoDAAAAAAElFTkSuQmCC"}}},{"cell_type":"markdown","source":"# **Load the models**\nFirst, we need to load all the pre-trained models including apple classifier, banana classifier, orange classifier, and fruits classifier. All of these models were trained using the InceptionV3 architecture. The models can be found from the following notebooks:\n\n* apple_classifier: https://www.kaggle.com/code/dylanyves/apple-classifier/notebook\n* banana_classifier: https://www.kaggle.com/code/dylanyves/banana-classifier/notebook\n* orange_classifier: https://www.kaggle.com/code/dylanyves/oranges-classifier/notebook\n* fruits_classifier: https://www.kaggle.com/code/dylanyves/fruits-classifier/notebook","metadata":{}},{"cell_type":"code","source":"# Load the entire model\napple_classifier = keras.models.load_model('/kaggle/input/apple_classifier/keras/apple-cls/1/apple_classifier.keras')  \nbanana_classifier = keras.models.load_model('/kaggle/input/banana_classifier/keras/banana-cls/1/banana_classifier.keras')  \norange_classifier = keras.models.load_model('/kaggle/input/orange_classifier/keras/orange-cls/1/orange_classifier.keras')  \nfruits_classifier = keras.models.load_model('/kaggle/input/fruits_classifier/keras/fruits-cls/1/fruits_classifier.keras')  ","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:12:33.041086Z","iopub.execute_input":"2024-05-28T12:12:33.041427Z","iopub.status.idle":"2024-05-28T12:13:25.842294Z","shell.execute_reply.started":"2024-05-28T12:12:33.041392Z","shell.execute_reply":"2024-05-28T12:13:25.841415Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **First-step Prediction**\nIn the first-step prediction, the goal is to classify the image into three fruit categories: apple, banana, or orange. The data for fresh and rotten are merged together in the training since we want to focus on getting the fruit.","metadata":{}},{"cell_type":"markdown","source":"### Define a function to predict the fruit","metadata":{}},{"cell_type":"code","source":"def predict_fruit(file) -> int:\n    image = load_img(file, target_size=(224, 224))\n    image = img_to_array(image)\n    image = image.reshape(1, 224, 224, 3)\n    image = image / 255\n    pred = np.argmax(fruits_classifier.predict(image, verbose=False)) + 1\n    return pred","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:13:49.856149Z","iopub.execute_input":"2024-05-28T12:13:49.856768Z","iopub.status.idle":"2024-05-28T12:13:49.862300Z","shell.execute_reply.started":"2024-05-28T12:13:49.856735Z","shell.execute_reply":"2024-05-28T12:13:49.861315Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Prepare and predict the data","metadata":{}},{"cell_type":"code","source":"relative_path = os.listdir('/kaggle/input/csc532-fruits-categorical-classification-2024/csc532_2024_validation/validation')\nabsolute_path = [\"/kaggle/input/csc532-fruits-categorical-classification-2024/csc532_2024_validation/validation/\"+ i for i in relative_path]\n\nresult_dict = {\n    'ID': [], # ID to store the relative path of all images\n    'abs_ID': [], # abs_ID to store the absolute path of all images\n    'fruit_code': [], # fruit_code is the output from the fruit predictor\n}\n\nfor i in range(len(absolute_path)):\n    pred = predict_fruit(absolute_path[i]) # predict the fruit \n    result_dict['ID'].append(relative_path[i])\n    result_dict['abs_ID'].append(absolute_path[i])\n    result_dict['fruit_code'].append(pred)\n    \n    \n# Take a look on our dataframe\ndf = pd.DataFrame.from_dict(result_dict)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:13:54.849027Z","iopub.execute_input":"2024-05-28T12:13:54.849674Z","iopub.status.idle":"2024-05-28T12:14:08.592850Z","shell.execute_reply.started":"2024-05-28T12:13:54.849643Z","shell.execute_reply":"2024-05-28T12:14:08.591955Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Add a new feature 'fruit_name' for intepretability\n\nfruit_codes = {1: \"apple\", 2: \"banana\", 3: \"orange\"}\ndf['fruit_name'] = df['fruit_code'].map(fruit_codes)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:15:04.486982Z","iopub.execute_input":"2024-05-28T12:15:04.487377Z","iopub.status.idle":"2024-05-28T12:15:04.500013Z","shell.execute_reply.started":"2024-05-28T12:15:04.487334Z","shell.execute_reply":"2024-05-28T12:15:04.499124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Visualize First-step prediction**","metadata":{}},{"cell_type":"markdown","source":"### Visualize Fruits Distribution","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plot_pie_chart(data, labels, title=\"\", colors=None, autopct=\"%1.1f%%\"):\n    plt.figure(figsize=(4, 6))  # Adjust figure size as needed\n\n    if colors is None:\n        colors = plt.cm.Paired(range(len(data)))  # Use default colormap\n        plt.pie(data, labels=labels, autopct=autopct, startangle=90, colors=colors)\n        plt.axis('equal')  # Equal aspect ratio ensures a circular pie chart\n\n    if title:\n        plt.title(title)\n        plt.legend(labels, loc=\"best\")\n        plt.show()\n\ndata = df.fruit_name.value_counts().values\nlabels = df.fruit_name.value_counts().index\ntitle = \"Fruit Distribution\"\nplot_pie_chart(data, labels, title)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:15:06.888885Z","iopub.execute_input":"2024-05-28T12:15:06.889633Z","iopub.status.idle":"2024-05-28T12:15:07.058021Z","shell.execute_reply.started":"2024-05-28T12:15:06.889601Z","shell.execute_reply":"2024-05-28T12:15:07.056664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Visualize Sample\nVisualizing the entire validation dataset would be time-consuming. Therefore, we will sample 20 data points to get a general idea of how well the model performs to classify the fruit type.","metadata":{}},{"cell_type":"code","source":"np.random.seed(99) # Seed for reproducibility\nsample_df = df.sample(20).sort_values(by='fruit_code')\nsample_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:18:28.976171Z","iopub.execute_input":"2024-05-28T12:18:28.977151Z","iopub.status.idle":"2024-05-28T12:18:28.990130Z","shell.execute_reply.started":"2024-05-28T12:18:28.977116Z","shell.execute_reply":"2024-05-28T12:18:28.989044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Define a function to plot multiple images","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom matplotlib.image import imread\n\ndef plot_multiple_images(image_paths, fruit_names, figsize=(10, 6)):\n    n_images = len(image_paths)\n    if n_images == 0:\n        print(\"Error: No image paths provided.\")\n        return\n\n    if len(fruit_names) != n_images:\n        print(\"Warning: Number of fruit names does not match the number of images.\")\n\n    # Create a figure with 2 rows and 5 columns\n    rows, cols = 4, 5\n    fig, axes = plt.subplots(rows, cols, figsize=figsize)\n\n    image_count = 0\n    for i in range(rows):\n        for j in range(cols):\n            if image_count >= n_images:\n                axes[i, j].axis('off')  # Hide empty subplots\n            else:\n                # Read the image\n                img = imread(image_paths[image_count])\n                \n                # Plot the image\n                axes[i, j].imshow(img)\n                axes[i, j].set_title(fruit_names[image_count])\n                axes[i, j].set_xticks([])\n                axes[i, j].set_yticks([])  # Hide tick labels\n                image_count += 1\n\n    fig.tight_layout()\n\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:18:56.113193Z","iopub.execute_input":"2024-05-28T12:18:56.113592Z","iopub.status.idle":"2024-05-28T12:18:56.122984Z","shell.execute_reply.started":"2024-05-28T12:18:56.113560Z","shell.execute_reply":"2024-05-28T12:18:56.122096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_paths = sample_df.abs_ID.tolist()\nfruit_names = sample_df.fruit_name.tolist()\nplot_multiple_images(image_paths, fruit_names)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:18:57.119051Z","iopub.execute_input":"2024-05-28T12:18:57.119780Z","iopub.status.idle":"2024-05-28T12:19:04.934297Z","shell.execute_reply.started":"2024-05-28T12:18:57.119750Z","shell.execute_reply":"2024-05-28T12:19:04.933403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The model was able to classify the fruit correctly on the 20 data points from the sample","metadata":{}},{"cell_type":"markdown","source":"# **Second-step Prediction**\nIn this step, we will focus on predicting whether the fruit is fresh or rotten. Each fruit class has their own freshness classifier which was trained only using the data corresponding to the class. ","metadata":{}},{"cell_type":"code","source":"def predict_freshness(file, model) -> int:\n    image = load_img(file, target_size=(224, 224))\n    image = img_to_array(image)\n    image = image.reshape(1, 224, 224, 3)\n    image = image / 255\n    pred = np.argmax(model.predict(image, verbose=False)) + 1\n    return pred\n\ndef categorize_fruit_freshness(df):\n    for index, row in df.iterrows():\n        if row.fruit_name == 'apple':\n            freshness = predict_freshness(row.abs_ID, apple_classifier)\n            df.loc[index, 'freshness'] = freshness\n        elif row.fruit_name == 'banana':\n            freshness = predict_freshness(row.abs_ID, banana_classifier)\n            df.loc[index, 'freshness'] = freshness\n        else:\n            freshness = predict_freshness(row.abs_ID, orange_classifier)\n            df.loc[index, 'freshness'] = freshness\n    return df\n\nnew_df = categorize_fruit_freshness(df.copy())\nnew_df","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:20:20.924707Z","iopub.execute_input":"2024-05-28T12:20:20.925563Z","iopub.status.idle":"2024-05-28T12:20:39.031097Z","shell.execute_reply.started":"2024-05-28T12:20:20.925531Z","shell.execute_reply":"2024-05-28T12:20:39.030160Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Add a new feature 'freshness_type' for intepretability\nfreshness_codes = {1: \"fresh\", 2: \"rotten\"}\nnew_df['freshness_type'] = new_df['freshness'].map(freshness_codes)\n\n# Add a new feature 'class' which concatenates freshness_type and fruit_name\nnew_df['class'] = new_df['freshness_type'] + new_df['fruit_name']\nnew_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:22:50.124595Z","iopub.execute_input":"2024-05-28T12:22:50.124959Z","iopub.status.idle":"2024-05-28T12:22:50.140719Z","shell.execute_reply.started":"2024-05-28T12:22:50.124929Z","shell.execute_reply":"2024-05-28T12:22:50.139691Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Visualize Second-step Prediction**\nSimilar to the previous visualization, we only use 20 data points.","metadata":{}},{"cell_type":"code","source":"sample_df_2 = new_df.sample(20).sort_values(by='class')\nsample_df_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:24:49.311679Z","iopub.execute_input":"2024-05-28T12:24:49.312586Z","iopub.status.idle":"2024-05-28T12:24:49.326672Z","shell.execute_reply.started":"2024-05-28T12:24:49.312550Z","shell.execute_reply":"2024-05-28T12:24:49.325694Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_paths = sample_df_2.abs_ID.tolist()\nfruit_names = sample_df_2['class'].tolist()\nplot_multiple_images(image_paths, fruit_names)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T12:24:50.107620Z","iopub.execute_input":"2024-05-28T12:24:50.108582Z","iopub.status.idle":"2024-05-28T12:24:54.291337Z","shell.execute_reply.started":"2024-05-28T12:24:50.108538Z","shell.execute_reply":"2024-05-28T12:24:54.290416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It seems like the model struggles a lot more on predicting the freshness of the fruit.","metadata":{}},{"cell_type":"markdown","source":"# **Submission**","metadata":{}},{"cell_type":"code","source":"class_code = {'freshapple':1, \"freshbanana\":2, \"freshorange\":3, \"rottenapple\":4, \"rottenbanana\":5, \"rottenorange\":6}\nnew_df['Code'] = new_df['class'].map(class_code)\nnew_df","metadata":{"execution":{"iopub.status.busy":"2024-05-28T10:52:28.123614Z","iopub.execute_input":"2024-05-28T10:52:28.123958Z","iopub.status.idle":"2024-05-28T10:52:28.133364Z","shell.execute_reply.started":"2024-05-28T10:52:28.123927Z","shell.execute_reply":"2024-05-28T10:52:28.132495Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_df = new_df[['ID', 'Code']]\nsubmission_df","metadata":{"execution":{"iopub.status.busy":"2024-05-28T10:52:28.158190Z","iopub.execute_input":"2024-05-28T10:52:28.158775Z","iopub.status.idle":"2024-05-28T10:52:28.171416Z","shell.execute_reply.started":"2024-05-28T10:52:28.158744Z","shell.execute_reply":"2024-05-28T10:52:28.170287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_df.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T10:52:28.172899Z","iopub.execute_input":"2024-05-28T10:52:28.173252Z","iopub.status.idle":"2024-05-28T10:52:28.180711Z","shell.execute_reply.started":"2024-05-28T10:52:28.173222Z","shell.execute_reply":"2024-05-28T10:52:28.179778Z"},"trusted":true},"execution_count":null,"outputs":[]}]}