diff --git a/examples/other/2-scheduling.ipynb b/examples/other/2-scheduling.ipynb
index 2fc6856b..ffe6782b 100644
--- a/examples/other/2-scheduling.ipynb
+++ b/examples/other/2-scheduling.ipynb
@@ -17,26 +17,45 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning_utilities/core/imports.py:14: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
-      "  import pkg_resources\n",
-      "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/fabric/__init__.py:41: Deprecated call to `pkg_resources.declare_namespace('lightning.fabric')`.\n",
-      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
-      "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/pkg_resources/__init__.py:2317: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('lightning')`.\n",
-      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
-      "  declare_namespace(parent)\n",
-      "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/__init__.py:37: Deprecated call to `pkg_resources.declare_namespace('lightning.pytorch')`.\n",
-      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
-      "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/pkg_resources/__init__.py:2317: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('lightning')`.\n",
-      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
-      "  declare_namespace(parent)\n",
-      "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
+      "Requirement already satisfied: torch_geometric in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (2.5.0)\n",
+      "Requirement already satisfied: tqdm in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (4.66.1)\n",
+      "Requirement already satisfied: numpy in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (1.26.3)\n",
+      "Requirement already satisfied: scipy in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (1.11.4)\n",
+      "Requirement already satisfied: fsspec in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (2023.12.2)\n",
+      "Requirement already satisfied: jinja2 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (3.1.3)\n",
+      "Requirement already satisfied: aiohttp in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (3.9.1)\n",
+      "Requirement already satisfied: requests in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (2.31.0)\n",
+      "Requirement already satisfied: pyparsing in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (3.1.1)\n",
+      "Requirement already satisfied: scikit-learn in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (1.4.1.post1)\n",
+      "Requirement already satisfied: psutil>=5.8.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from torch_geometric) (5.9.7)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from aiohttp->torch_geometric) (23.2.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from aiohttp->torch_geometric) (6.0.4)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from aiohttp->torch_geometric) (1.9.4)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from aiohttp->torch_geometric) (1.4.1)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from aiohttp->torch_geometric) (1.3.1)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from aiohttp->torch_geometric) (4.0.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from jinja2->torch_geometric) (2.1.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from requests->torch_geometric) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from requests->torch_geometric) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from requests->torch_geometric) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from requests->torch_geometric) (2023.11.17)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from scikit-learn->torch_geometric) (1.3.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages (from scikit-learn->torch_geometric) (3.3.0)\n"
      ]
     }
    ],
+   "source": [
+    "! pip install torch_geometric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -111,7 +130,7 @@
    "outputs": [
     {
      "data": {
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",
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WrVuH/fv3Y8aMGTh69Cjq6upw/PhxldI62tjYICIiAl9++SVCQkIwY8YMREVFTWmvR1JSEvbu3Yvf//73uHXrFlavXg0LCwtUVVUhLS0Nf/3rX0dl+tKEzz77DJ9//jm2bt2KRYsWwdLSEmVlZfjkk09gbW2N1157TePXJKajrm5kuZQm/fCHwIoVhrFn44033sDJkydx69atcY/h8XiYP3++yoNDZGoo0CCEGJznn38eV65cwZIlSxAUFPTE45OSknDt2jV88cUXaG1thZOTE5YsWYLPP/8c/o/89nzvvfcgkUhw6NAhWFlZ4emnn8bbb7896gH22LFjePXVV/G3v/0NDMNg9erVOHv2rEpVmhUZj8aqqSAQCODp6Ym//vWvMDc3x+effw6RSIS4uDhkZWVhzZo1SsfPnDkThw4dwu9//3vs2bMHMpkMAoFgzEDDxsYGQqEQv/zlL3H06FH09vYiNDQUn376KXbt2jXl72M8np6euHLlCv7rv/4L7733HkQiEaKjo3H69Gls2LBB5XZTU1Px6quv4ic/+QmGh4fx29/+dsqbyg8dOoRFixbh8OHDeO2118DlcuHn54fvfve7iIuLU7lvCopZqEf3jOzduxe2tra4ePEiTp06hd7eXri7u2P16tX41a9+hQULFqh9XWK69u598l6MqZJKR9q9cEG183ft2oWjR49i7969OHTokNJrP/jBD/D3v/8dL7zwglJRVG3KyMgwyr0Q+orD6ONOPkIIIcTA9fb2wsnJCQcOHBhziRYhkyUSiVBXVwd/f/9xq8eXlgKRkdrrQ2kpME79zwnt2rULly5dQm9vL1paWtg9USKRCF5eXnB0dASfz9dIoDGZGQ19M5n/W0NGezQIIYQQLbh+/ToAqLQPhZCpOnRoJIWtNnC5wAcfqH7+woULMWfOHGRkZLBfy8jIgI+Pj9Is3rlz5xAfHw9nZ2e4urpi48aNqKmpUWrr3r17eO655zBjxgzY2dkhJiYGV69eVTrms88+g5+fH5ycnPDss8+ir6+PfY3H4+HHP/4x+7mfnx/+3//7f2yRUh8fH3z44YdK7TU1NeHpp5+Gs7MzZsyYgc2bN09YmJP8GwUahBBCiAbduXMHf/7zn/HCCy/A1dVVrSVihEzWmTOaXzalIJUCZ8+q18bu3bvx6aefsp9/8sknePHFF5WOGRgYwE9/+lMUFhbi4sWLMDMzw9atW9k03v39/UhKSsL9+/fx1Vdf4fbt2/jFL36hlOa7pqYGJ0+exNdff42vv/4a2dnZeOuttybs25/+9CfExMSgqKgI3//+97Fv3z42W55EIsGaNWvg4OCAy5cvIy8vD/b29li7di2Gh4fV+0cxAbRHgxBCCNGgjIwMvPXWW4iJicE777wzqi4KIZrW1wfU1mr3GjU1QH8/YG+v2vnf/e538atf/YpNGZ6Xl4cvvvgCQqGQPWb79u1K53zyySdwd3dHaWkpoqKicOzYMbS3t+P69euYMWMGAIzapyeXy3HkyBE2ocXOnTtx8eJFHDx4cNy+rV+/Ht///vcBAP/1X/+Fd955BwKBAKGhofjyyy8hl8uRmprKpqL99NNP4ezsDKFQiNWrV6v2D2IiKNAghBBCNOiNN97AG2+8oetuEBNSUwNoe8ctwwDV1cC3mcGnzN3dHRs2bMCRI0fAMAw2bNgANzc3pWOqqqrwm9/8BlevXkVHRwc7U9HY2IioqCjcunULCxYsYIOMsfj5+SllzfPy8hqVPfBx0dHR7N85HA5mzpzJnnP79m1UV1ePysQnEolGLesio1GgQQghhBBiwL4tB6T319m9ezd++MMfAgD+9re/jXr9qaeegq+vLz766CN4e3tDLpcjKiqKXaI0meKaj2eU4nA4SkurpnpOf38/Fi1ahM8//3zUee7u7k/sj6mjQIMQQgghxIBZWRnGdRT7Gjgczqj03J2dnaioqMBHH32EhIQEAEBubq7SMdHR0UhNTUVXV9eEsxqatHDhQnz55Zfw8PCgZZAqoM3ghBBCCCEGLCgI+Hb7gNZwOCPXUYe5uTnKyspQWlqqVF8GAFxcXODq6ooPP/wQ1dXVuHTpEn76058qHfPcc89h5syZ2LJlC/Ly8lBbW4vjx48jPz9fvY5N4Pnnn4ebmxs2b96My5cvo66uDkKhEPv378e9e/e0dl1jQYEGIYQQQogBs7cHAgK0e43AQNU3gj/K0dFxzJkBMzMzfPHFF7hx4waioqLwk5/8BG+//bbSMZaWlrhw4QI8PDywfv16zJ07F2+99daooEWTbG1tkZOTAx8fH2zbtg3h4eHYs2cPRCIRzXBMAhXsI4QQQgjRY5Mp6rZ//0itC22kuOVygX37gHff1Xzbpo4K9hFCCCGEEL32yivaraOxb5922ibGjQINQgghhBADFxEBJCdrvjo4lzvSbni4ZtslpoECDUIIIYQQI3D4sHYCjcOHNdsmMR0UaBBCCCGEGAF/f+C99yY+xg79mIdbWIKrmIdbsEP/hMe///5Iu4SogupoEEIIIYQYiZdeAlpbgQMH/v21cJTiFRzCepxBAGphhn/nAZKDg1oE4AzW4xBeQRki2NcOHgT27JnO3hNjQzMahBBCCCFG5PXXgY8+AkIt63ABq1GKSOzDBwhCjVKQAQBmYBCEGuzDByhFJC5gNUIt65CaCrz2mo6+AWI0aEaDEELI5PT3A9XVgFg8UiI4KEgzifUJIRr3ElKxm/Mq5BwpwAAWmDglleJ1PkeAUk4EzJj3ALw0DT0lxowCDUIIIeMrLQUOHQLOnAFqa4FHSy9xOCNVwtavH8mtGRExfjuEkOlz8CBw4ADMMPWlK1xGCoilwMsvj6zBev11bfSQmAhaOkUIIWS0ujpg9WogMnKkClhNjXKQAYx8XlMz8npk5MjxdXW66S8hZERqqvIGDXUcOAB8/LFm2iImiQINQgghylJTR2YnBIKRz59UBUzxukAwcl5qqnb7RwgZW10d8Oqrmm3zhz802QGEXbt2YcuWLbruhkGjQIMQQsi/HTw4smRCJJp6mWGpdOS8l18eaYcQMr327tV8eXCpdKRdNTQ1NWH37t3w9vaGpaUlfH198aMf/QidnZ0a6qR66uvrweFwcOvWLaWv//Wvf8WRI0d00idjQYEGIYSQEbTkghDDVVoKZGZqJ9DIzATKylQ6vba2FjExMaiqqsI///lPVFdX49ChQ7h48SKWLVuGrq4uzfb3EcPDw2qd7+TkBGdnZ810xkRRoEEIIYSWXBBi6A4d0nxZcAUud2Qvlgp+8IMfwNLSEhcuXEBSUhJ8fHywbt06ZGVl4f79+3j9283mfn5++J//+R8899xzsLOzw6xZs/C3v/1Nqa2enh689NJLcHd3h6OjI1asWIHbt2+zr7/xxhuYP38+UlNT4e/vD2trawDAuXPnEB8fD2dnZ7i6umLjxo2oqalhz/P/tiLhggULwOFwwOPxAIxeOiUWi7F//354eHjA2toa8fHxuH79Ovu6UCgEh8PBxYsXERMTA1tbWyxfvhwVFRUq/dsZAwo0CCGE6O2SC0LIJJ05o/mfYQWpFDh7dsqndXV14fz58/j+978PGxsbpddmzpyJ559/Hl9++SWYbxNNvP3225g3bx6Kiorwy1/+Ej/60Y+QmZnJnpOSkoK2tjacPXsWN27cwMKFC7Fy5UqlWZHq6mocP34cGRkZ7FKogYEB/PSnP0VhYSEuXrwIMzMzbN26FXK5HABw7do1AEBWVhZaWlqQkZEx5vfzi1/8AsePH8fRo0dx8+ZNBAUFYc2aNaNmZV5//XX86U9/QmFhIbhcLnbv3j3lfzujwRBCCDFtJSUMM5JDSjsfpaW6/g4JMWhDQ0NMaWkpMzQ0NPYBvb0Mw+Fo9+eYw2GYvr4p9bugoIABwJw4cWLM1//85z8zAJjW1lbG19eXWbt2rdLrzzzzDLNu3TqGYRjm8uXLjKOjIyMSiZSOCQwMZA4fPswwDMP89re/ZSwsLJi2trYJ+9Xe3s4AYO7evcswDMPU1dUxAJiioiKl41544QVm8+bNDMMwTH9/P2NhYcF8/vnn7OvDw8OMt7c388c//pFhGIYRCAQMACYrK4s95ptvvmEAjPt/98T/WwNHMxqEEGLq9HTJBSFkksZKP61pDDNSsFOlUyfXt2XLlo36vOzbvSG3b99Gf38/XF1dYW9vz37U1dUpLYPy9fWFu7u7UjtVVVV47rnnEBAQAEdHR/j5+QEAGhsbJ/091NTUQCKRIC4ujv2ahYUFlixZwvZRITo6mv27l5cXAKCtrW3S1zImVLCPEEJMnR4uuSCETIFYrJfXCQoKAofDQVlZGbZu3Trq9bKyMri4uIwKDMbS398PLy8vCIXCUa89umHbzs5u1OtPPfUUfH198dFHH8Hb2xtyuRxRUVFqbxYfj4WFBft3DocDAOwyLVNDMxqEEGLK+vpGKn5rU00N0N+v3WsQYsqsrPTyOq6urkhOTsbf//53DA0NKb324MEDfP7553jmmWfYh/GCggKlYwoKChAeHg4AWLhwIR48eAAul4ugoCClDzc3t3H70NnZiYqKChw4cAArV65EeHg4uru7lY6xtLQEAMhksnHbCQwMhKWlJfLy8tivSSQSXL9+HREREZP41zBNFGgQQogp0/MlF4SQSQgKAr59WNcaDmfkOlP0/vvvQywWY82aNcjJyUFTUxPOnTuH5ORkzJo1CwcfqbmTl5eHP/7xj6isrMTf/vY3pKWl4Uc/+hEAYNWqVVi2bBm2bNmCCxcuoL6+HleuXMHrr7+OwsLCca/v4uICV1dXfPjhh6iursalS5fw05/+VOkYDw8P2NjY4Ny5c2htbcXDhw9HtWNnZ4d9+/bh5z//Oc6dO4fS0lK8/PLLGBwcxJ49e6b872IqKNAghBBTpqdLLgghU2BvDwQEaPcagYEj15mi4OBgFBYWIiAgAE8//TQCAwPxH//xH+Dz+cjPz8eMGTPYY//zP/8ThYWFWLBgAd588038+c9/xpo1awCMLEE6c+YMEhMT8eKLLyIkJATPPvssGhoa4OnpOe71zczM8MUXX+DGjRuIiorCT37yE7z99ttKx3C5XLz77rs4fPgwvL29sXnz5jHbeuutt7B9+3bs3LkTCxcuRHV1Nc6fPw8XF5cp/7uYCg4z2R06hBBCjM+tW8CCBdq/TlERMH++9q9DiBESiUSoq6tTqg0xyv79I4kXtLHfissF9u0D3n1X821/y8/PDz/+8Y/x4x//WGvX0EeT+r81YDSjQQghpkyPl1wQQqbglVe0m9Rh3z7ttE2MGgUahBBiyvR4yQUhZAoiIoDkZM2nquZyR9r9dlM2IVNB6W0JIcTUrV+v3SUX69Zpvl1CyGiHD48EHJr8WeZyR9rVsvr6eq1fg0w/mtEghBBTR0suCDEO/v7Ae+9NeIjUGugLBHrDR/6UPmlbwPvvj7RLiApoRoMQQkydYsmFQKD5kVA+n5ZcEDKdXnoJaG0FDhxgvzTgCzRvAjqXAiIvKA8zywHrFsD1KuD9FWDX8MhrBw8ClLqVqIGyThFCCAHq6kYCDpFIc21aWwOlpTQaSoiaVMpMlJqKoTd/gMofDqM7BoAUEw8vf/u6SyEQ8r4lbH79dwoypgFlnSKEEGL8JrHkYspoyQUhOtO8Hrh+lIPuBd9mlXvSGpZvX+9ewMH1oxw0r6NxaKI+CjQIIYSMeOkl4M03NdMWLbkgRGcaGg6isvJlyBkxYD7FgMGcgZwRo7LyZTQ0HHzy8YRMgAINQggh//b668BHH40se5pqmkwud+S81FTgtde00z9CyISam1NRV3fgyQdOQl3dAbS0fKyRtohpokCDEEKIspdeGtlbweePfP6kgEPxOp8/ch7NZBCiE0NDdaiuflWjbVZV/RBDQ3Uqn79r1y5s2bJlUscKhUJwOBz09PSofD2iXyjrFCGEkNH8/YELF0YCh0OHgLNngZoa4NH8IRzOSDG+detGUthSdilCdKqyci/kcs2mqpbLpais3It58y6odP5f//pXUN4h00WBBiGEkPFFRADvvjvy9/5+oLoaEIsBKysgKIgqfhOiJwYGStHdnamFlqXo7s7EwEAZ7OymPpjg5OSkhT4RQ0FLpwghhEyOvT0wfz6wdOnInxRkEKI3mpsPQXvjx1w0N3+g0pmPLp0Si8XYv38/PDw8YG1tjfj4eFy/fn3UOXl5eYiOjoa1tTViY2NRXFzMvtbQ0ICnnnoKLi4usLOzQ2RkJM6cOaNS34j2UaBBCCGEEGLgOjvPYKQYhjZI0dl5Vu1WfvGLX+D48eM4evQobt68iaCgIKxZswZdXV1Kx/385z/Hn/70J1y/fh3u7u546qmnIJFIAAA/+MEPIBaLkZOTg7t37+IPf/gD7GnQQ29RoEEIIYQQYsCk0j6IRLVavYZIVAOptF/l8wcGBvDBBx/g7bffxrp16xAREYGPPvoINjY2+Phj5cxWv/3tb5GcnIy5c+fi6NGjaG1txYkTJwAAjY2NiIuLw9y5cxEQEICNGzciMTFRre+NaA8FGoQQQgghBmxoqAaAtjdcMxgaqlb57JqaGkgkEsTFxbFfs7CwwJIlS1BWVqZ07LJly9i/z5gxA6Ghoewx+/fvx5tvvom4uDj89re/xZ07d1TuE9E+CjQIIYQQQgwYw4iN6joTeemll1BbW4udO3fi7t27iImJwXvvvafrbpFxUKBBCCGEEGLAOBwrvb9OYGAgLC0tkZeXx35NIpHg+vXriIiIUDq2oKCA/Xt3dzcqKysR/kj67Dlz5uCVV15BRkYG/vM//xMfffSRyv0i2kXpbQkhhBBCDJiNTRAADrS7fIrz7XVUY2dnh3379uHnP/85ZsyYAR8fH/zxj3/E4OAg9jxW5PO///u/4erqCk9PT7z++utwc3NjM1f9+Mc/xrp16xASEoLu7m4IBAKlIIToFwo0CCGEEEIMGJdrD2vrAIhENVq7hrV1ILhc9bI7vfXWW5DL5di5cyf6+voQExOD8+fPw8XFZdRxP/rRj1BVVYX58+fj9OnTsLS0BADIZDL84Ac/wL179+Do6Ii1a9finXfeUatfRHs4DJVrJIQQQgjRWyKRCHV1dfD394e1tfWYx1RV7cf9+x9AOyluuZg1ax+Cg9+d8pnPPfcczM3N8Y9//EML/TJ8k/m/NWS0R4MQQgghxMB5e78CbdbR8PbeN7UzpFKUlpYiPz8fkZGRWuoX0XcUaBBCCCGEGDg7uwi4uCRD86viuXBxSYad3dT2QRQXFyMmJgaRkZF45ZVXNNwnYihojwYhhBBCiBEICTmM69cjIJdrbmbDzIyLkJDDUz5v/vz5GBwc1Fg/iGGiGQ1CCCGEECNgY+OPoCDN1pQIDn4fNjb+Gm2TmA4KNAghhBBCDMBk8vd4e78Ef/83NXI9f/+D8PLa8+QDicqMPScTBRqEEEIIIXrMwsICACa9FMnX93WEhHwEMzNrTH2VPBdmZtYIDU2Fr+9rUzyXTNXw8DAAwNzcXMc90Q5Kb0sIIYQQoudaWlrQ09MDDw8P2NragsPhPPEckagedXU/xMOHFzEScEy0d2PkdSenlfD3fx/W1n6a6TgZl1wuR3NzMywsLODj4zOp/1NDQ4EGIYQQQoieYxgGDx48QE9Pz5TPlUqrMTj4JYaHL0Mma4JyBXEOzM3nwNIyAba2z4LLDdRUl8kkmJmZwd/fny1IaGwo0CCEEEIIMRAymQwSiUSN8/shEtVALhfDzMwK1taBMDdXr+I3UZ2lpSXMzIx3JwMFGoQQQianvx+orgbEYsDKCggKAuzpAYUQQsjYqI4GIYSQ8ZWWAocOAWfOALW1wKNjUxwOEBAArF8PvPIKEBGhu34SQgjROzSjQQghZLS6OmDvXiAzE+ByAekEm0gVrycnA4cPA/6Uc58QQgiltyWEEPK41NSR2QmBYOTziYKMR18XCEbOS03Vbv8IIYQYBAo0CCGE/NvBg8DLLwMi0ZMDjMdJpSPnvfzySDuEEEJMGi2dIoQQMiI1dSRI0GR7e6iqMCGEmCoKNAghhIzsyYiIGJmR0BRr65HN5LRngxBCTBItnSKEEDKy8XuqS6WeRCodaZcQQohJohkNQggxdaWlQGSkdtsPD9de+4QQQvQSzWgQQoipO3RoJEWtNnC5wAcfaKdtQggheo1mNAghxNQFBQE1Ndptv6pKe+0TQgjRSxRoEEKIKevrA5yclCt+axqHA/T2Avb22rsGIYQQvUNLpwghxJTV1Gg3yABG2q+u1u41CCGE6B0KNAghxJSJxcZ1HUIIIXqDAg1CCDFlVlbGdR1CCCF6g/ZoEEKIKevvBxwdaY8GIYQQjaMZDUIIMWX29kBAgHavERhIQQYhhJggCjQIIcTUrV+v3Toa69Zpp21CCCF6jZZOEUKIqaPK4IQQQrSAZjQIIcTURUQAycman9XgckfapSCDEEJMEs1oEEIIAerqRgIOkUhzbVpbj8xm+Ptrrk1CCCEGg2Y0CCGEjAQD772n2Tbff5+CDEIIMWEUaBBCCBnx0kvAm29qpq2DB4E9ezTTFiGEEINES6cIIYQoS00FXn0VkEpHPiaLyx35eP99CjIIIYTQjAYhhJDHvPTSyN4KPn/k8ydtEle8zuePnEdBBiGEENCMBiGEkImUlgKHDgFnzwI1NcoVxDmckWJ869YB+/ZRdilCCCFKKNAghBAyOf39QHU1IBYDVlZAUBBV/CaEEDIuCjQIIYQQQgghGkd7NAghhBBCCCEaR4EGIYQQQgghROMo0CCEEEIIIYRoHAUahBBCCCGEEI2jQIMQQgghhBCicRRoEEIIIYQQQjSOAg1CCCGEEEKIxlGgQQghhBBCCNE4CjQIIYQQQgghGkeBBiGEEEIIIUTjKNAghBBCCCGEaBwFGoQQQgghhBCNo0CDEEIIIYQQonEUaBBCCCGEEEI0jgINQgghhBBCiMZRoEEIIYQQQgjROAo0CCGEEEIIIRrH1XUHCCGEGIj+fqC6GhCLASsrICgIsLfXda8IIYToKQo0CCGEjK+0FDh0CDhzBqitBRjm369xOEBAALB+PfDKK0BEhO76SQghRO9wGObR3xqEEEIIgLo6YO9eIDMT4HIBqXT8YxWvJycDhw8D/v7T109CCCF6i/ZoEEIIUZaaOjI7IRCMfD5RkPHo6wLByHmpqdrtHyGEEINAgQYhhJB/O3gQePllQCR6coDxOKl05LyXXx5phxBCiEmjpVOEEEJGpKaOBAmabG/PHs21RwghxKBQoEEIIWRkT0ZExMiMhKZYW49sJqc9G4QQYpJo6RQhhJCRjd9TXSr1JFLpSLuEEEJMEs1oEEKIqSstBSIjtdt+eLj22ieEEKKXaEaDEEJM3aFDIylqtYHLBT74QDttE0II0Ws0o0EIIaYuKAioqdFu+1VV2mufEEKIXqJAgxBCTFlfH+DkpFzxW9M4HKC3F7C31941CCGE6B1aOkUIIaaspka7QQYw0n51tXavQQghRO9QoEEIIaZMLDau6xBCCNEbFGgQQoiJkkgkyL1+fXouZmU1PdchhBCiN7SUZoQQQoi+YhgG169fx4kTJzDQ2oo4ABxtXpDDGdkQTgghxKRQoEEIISakuroaaWlpqK+vH/mChQXaHR3h0durvYsGBtJGcEIIMUEUaBBCiAlob29HRkYGbt68Oeq14jlzkFRaCnMtbAqXcTi4HxUFt8FB2Nraarx9Qggh+osCDUIIMWKDg4M4c+YMLl26BJlMNuYxORERWFFSopXrmzMMjlhbo+0Xv8DSpUvB5/Mxe/ZsrVyLEEKIfqE6GoQQYoRkMhlycnJw+vRpDAwMPPH4/d98g7DmZs3OanC5AJ+P3vR05ObmIjs7Gz09PQgKCgKfz8f8+fPB1VZFckIIITpHgQYhhBgRhmFw584dHD9+HK2trZM+z7W3F2+kpcFCJtPcxnBra6C0FPD3BzAS/Ny+fRsCgQCVlZVwdHREYmIiEhIS4OzsrKmrEkII0RMUaBBCiJFoampCWloaKioqVDo/rrwc38vJ0VyHUlOBPXvGfKm5uRkCgQBXr16FRCLBwoULwePxEBQUBA5HqzmwCCGETBMKNAghxMD19PTg1KlTyM/Phzq3dGdnZ/ygpwc+H36ofqcOHgRee+2Jhw0NDSE/Px9CoRCtra2YPXs2eDwelixZAiuqvUEIIQaNAg1CCDFQYrEYFy5cwIULFzA8PKxyO5aWllizZg2Sk5NHHu5TU4FXXwWk0pGPyeJyRz7ef3/cmYzxMAyDsrIyCIVC3LlzB9bW1oiLi0NSUhI8PDym+B0RQgjRBxRoEEKIgWEYBvn5+Th16hR6enpUbofD4WD58uXYtGnT6D0SdXXA3r1AZuZI8DBRwKF4PTkZOHyY3ZOhqo6ODuTk5CA3NxcDAwOIiooCj8dDVFQULasihBADQoEGIYQYkIqKCqSlpaGpqUmtdsLCwrBjxw7MmTNn4gNLS4FDh4CzZ4GaGuDRXxkczkgxvnXrgH37gPBwtfr0OIlEguvXr0MgEKCxsRFubm7g8XhYvnw57OzsNHotQgghmkeBBiGEGIDW1lYcP34ct2/fVqsdT09P7NixA3Pnzp367EB/P1BdDYjFgJUVEBQ0LRW/GYZBXV0dhEIhbty4AQ6HgyVLloDP5z85UCKEEKIzFGgQQogeGxgYwOnTp5GdnQ25XK5yO3Z2dti0aRMSEhJgbm6uwR5Or97eXuTm5iInJwfd3d0IDAwEn8/HggULqCYHIYToGQo0CCFED0mlUggEApw5cwaDg4Mqt8PlcrFixQqsW7cOtra2GuyhbsnlcrYmR0VFBdXkIIQQPUSBBiGE6BGGYVBUVITjx4+jo6NDrbYWLVqEbdu2wc3NTUO900/Nzc3Izs5Gfn4+JBIJFixYAD6fTzU5CCFExyjQIIQQPVFfX4+0tDRUV1er1Y6fnx+efvppBAYGaqhnhmFoaAgFBQUQCARobW3FrFmzwOfzqSYHIYToCAUahBCiY11dXThx4gSuXbumVjszZszAtm3bEBMTY9Ij+QzDoLy8HAKBgK3JsXz5cvB4PKrJQQgh04gCDUII0RGRSIRz584hKysLEolE5Xasra2xbt06rFy5EhYWFhrsoeHr7OxETk4OLl++jIGBAURGRrI1OczMzHTdPUIIMWoUaBBCyDSTy+XIy8vDqVOn0NfXp3I7HA4HCQkJ2LRpExwcHDTYQ+MjkUhQWFgIgUCAhoYGuLm5ISkpCXFxcVSTgxBCtIQCDUIImUYlJSVIT09Hc3OzWu1ERkZix44d8Pb21lDPTEd9fT0EAgEKCwvZmhw8Hg8+Pj667hohhBgVCjQIIWQaNDc3Iz09HSUlJWq14+3tjR07diAyMlJDPTNdfX19yM3NRXZ2Nrq7uxEQEAA+n4+FCxdSTQ5CCNEACjQIIUSLent7cfr0aVy+fBnq3G4dHBywefNmxMXF0d4CDVPU5BAKhSgvL4ejoyPi4+ORmJgIFxcXXXePEEIMFgUahBCiBRKJBFlZWTh37hxEIpHK7VhYWGDVqlVYu3YtrK2tNdhDMpaWlhYIhUK2Jsf8+fPB5/MRHBxs0pm8CCFEFRRoEEKIBjEMg8LCQmRkZKCrq0uttpYuXYotW7ZgxowZGuodmSyRSMTW5Hjw4AG8vb3B5/OxdOlSqslBCCGTRIEGIYRoSE1NDf71r3+hvr5erXaCgoKQkpICPz8/jfSLqI5hGFRUVEAgEOD27duwsrJCXFwckpKS4OnpqevuEUKIXqNAgxBC1NTR0YGMjAzcuHFDrXbc3Nywfft2LFiwgJbp6KGuri62Jkd/fz8iIiLA5/OpJgchhIyDAg1CCFHR4OAgzp49i0uXLkEqlarcjo2NDTZu3Agej0fZjgyARCLBjRs3IBAIUF9fD1dXVyQlJSE+Pp5qchBCyCMo0CCEkCmSyWTIycnB6dOnMTAwoHI7ZmZm4PF42LhxIz2gGqjHa3IsXrwYPB4Pvr6+uu4aIYToHAUahBAySQzD4O7du0hPT0dra6tabc2bNw/bt2+ndf5Goq+vD3l5ecjOzkZXVxcCAgLA4/GwaNEimqUihJgsCjQIIWQSmpqakJ6ejvLycrXamTNnDlJSUhAaGqqhnhF9IpfLcefOHQiFQpSVlcHBwQEJCQlUk4MQYpIo0CCEkAn09PTg1KlTyM/PV6vgnrOzMzZv3ozY2FjD3Tjc3w9UVwNiMWBlBQQFAfb2uu6V3mppaUF2djby8/MxPDyM+fPng8fjISQkhDb7E0JMAgUahBAyBrFYjMzMTJw/fx7Dw8Mqt2NpaYk1a9YgOTnZMOsvlJYChw4BZ84AtbXAo78yOBwgIABYvx545RUgIkJ3/dRjipocQqEQLS0t8PLyAp/PR2xsrGG+JwghZJIo0CCEkEcwDIOCggKcPHkSPT09KrfD4XCwbNkybN68Gc7Ozhrr37SpqwP27gUyMwEuF5goq5bi9eRk4PBhwN9/+vppQBQ1OYRCIW7dugUrKyssW7YMfD6f9uoQQowSBRqEEPKtiooKpKWloampSa12QkNDkZKSgjlz5mioZ9MsNRV49dWR4GEqaXu53JGP994DXnpJe/0zAoqaHLm5uejr60N4eDj4fD7mzp1ruEvrCCHkMRRoEEJMXmtrK44fP47bt2+r1Y6npyd27NiBuXPnGu4a/IMHgQMH1G/nzTeB119Xvx0jJ5VK2ZocdXV1bE2OuLg42NP+F0KIgaNAgxBisgYGBvD1119DKBRCLper3I6dnR2eeuopJCYmwtzcXIM9nGapqcDLL2u2vT17NNeekWtoaIBQKMS1a9cAAIsXLwafz6eaHIQQg0WBBiHE5EilUgiFQnzzzTcYHBxUuR1zc3OsXLkS69atg62trQZ7qAN1dSObuUUizbVpbT2ymZz2bExJf38/8vLyIBQK0dXVBX9/f/D5fKrJQQgxOBRoEEJMBsMwKCoqQkZGBtrb29Vqa+HChdi2bRvc3d011DsdW70aEAimtifjSbhcgM8HLlzQXJsmRC6X4+7duxAIBGxNjvj4eCQlJVFNDkKIQaBAgxBiEurr65GWlobq6mq12vHz80NKSgqCgoI01DM9UFoKREZqt/3wcO21bwIePHgAoVCI/Px8iMVitiZHaGio4e4HIoQYPQo0CCFGrbu7GydOnMDVq1fVamfGjBnYtm0bYmJijO/Bbv9+4IMPNDubocDlAvv2Ae++q/m2TZBIJMLVq1chEAjYmhw8Hg+xsbGwtrbWdfcIIUQJBRqEEKMkEolw/vx5ZGZmQiKRqNyOlZUV1q1bh1WrVsHCwkKDPdQjQUFATY1226+q0l77JohhGFRVVUEgEODWrVuwtLTEsmXLwOPxMHPmTF13jxBCAFCgQQgxMnK5HHl5efjqq6/Q29urcjscDgcJCQl46qmn4OjoqMEe6pm+PsDJSbnit6ZxOEBvL0DpWrWiu7sbOTk5uHz5Mvr6+hAWFgY+n4/o6GiqyUEI0SkKNAghRqO0tBTp6em4f/++Wu1ERkZix44d8Pb21lDP9NitW8CCBdq/TlERMH++9q9jwhQ1OYRCIWprazFjxgy2JoeDg4Ouu0cIMUEUaBBCDF5LSwvS09NRXFysVjve3t7YsWMHIrW5MVrfXL0KxMZq/zoFBcDSpdq/DgEwuiZHTEwM+Hw+/Pz8dNsxQohJoUCDEGKw+vr68NVXXyE3N1etgnsODg7YvHkz4uLiTG+pCc1oGLWBgQHk5uYiOzsbnZ2d8PPzY2tyGO2eI0KI3qBAgxBicCQSCS5evIizZ89CpEaBOS6Xi+TkZKxdu9Z0M/b09wOOjrRHw8jJ5XIUFxdDIBCgtLQU9vb2SEhIQGJiImbMmKHr7hFCjBQFGoQQg8EwDAoLC3HixAl0dnaq1daSJUuwZcsWuLq6aqh3BoyyTpmU1tZWZGdnIy8vD2KxGPPmzQOfz6eaHIQQjaNAgxBiEGpra5GWloba2lq12gkMDMTTTz9Na9UfpcU6GjIOB/Xr18Pl//6PRs71jFgsZmtyNDc3Y+bMmeDxeFi2bJnpzvARQjSKAg1CiF7r6OjAiRMnUFhYqFY7bm5u2L59OxYsWECjto/TcmXw//fd76LRzo6qWeupx2tyWFhYsDU5vLy8dN09QogBo0CDEKKXBgcHcfbsWVy6dAlSNUbabWxssGHDBvD5fHC5XA320HjIZDJ0L1kC19u3wZHJNNcwlwvw+RCfPq00ck7VrPVXd3c3Ll++jJycHLYmB4/Hw7x580wvUQIhRG0UaBBC9IpMJsPly5dx+vRp9Pf3q9yOmZkZkpKSsHHjRtjTJuRxtba2Ijc3FysDAuC8fDmgxub6UaytR2ZL/P0BjIycV1dXQyAQoKioiEbO9ZhUKsXNmzchEAhQW1sLFxcXJCUlIT4+nmpyEEImjQINQoheYBgGd+/exfHjx/HgwQO12oqOjsb27dsxc+ZMDfXO+DAMgytXrkAikSAxMXFktDo1FXj5Zc1dJDUV2LNnzJd6enrYata9vb00cq7HGhsb2ZocDMNQTQ5CyKRRoEEI0bl79+4hLS0N5eXlarUzZ84c7NixA2FhYRrqmXHq7e1FVlYWFi9ejDlz5ii/ePAgcOCA+hc5eBB47bUnHiaVSlFUVASBQICamhoaOddjAwMDyMvLQ3Z2Njo6OuDr6ws+n4+YmBiqyUEIGRMFGoQQnXn48CFOnTqFK1euQJ1bkZOTE7Zs2YLY2FgaDX+CO3fu4P79+1i1atX4D4epqcCrr45koZrK/hgud+Tj/ffHncmYSFNTEwQCAY2c6zlFTQ6hUIiSkhLY2dkhPj4eSUlJlC6aEKKEAg1CyLQbHh5GZmYmzp8/D7FYrHI7lpaWWL16NVavXg0rKysN9tD4iMViXLhwAUFBQQgPD3/yCXV1wN69QGbmSPAwUcCheD05GTh8mN2ToarHR879/PzA4/Fo5FwPKWpyXLlyBSKRCNHR0eDz+QgLC6PMYoQQCjQIIdOHYRhcvXoVJ06cQE9Pj8rtcDgcLFu2DJs3b4azs7PG+mesamtrcefOHSQnJ8POzm5qJ5eWAocOAWfPjhT1e/RXBocDBAYC69YB+/YBkwlgpkAul6OkpAQCgYAdOVdUs6aRc/0iFotx7do1CAQC3L9/H56enuDz+VSTgxATR4EGIWRaVFZWIi0tDY2NjWq1ExoaipSUlNF7C8goMpkMAoEAjo6OWLx4sfojzP39QHU1IBYDVlYjFb+nKaNXW1sbW82aRs7111iZxWJjY8Hj8eDt7a3r7hFCphkFGoQQrWpra8Px48dx69Yttdrx9PTE9u3bER0dTQ+Wk9DW1obLly+Dx+MZ1ej/4yPnVM1af/X09LA1OXp7exEaGgo+n0+ZxQgxIRRoEEK0YmBgAN988w0EAgHkcrnK7djZ2eGpp55CYmIizM3NNdhD48QwDPLz8yEWi5GUlGS0D3SKkXOhUIibN29STQ49NlZmscTERCQkJFBmMUKMHAUahBCNkkqlEAqF+OabbzA4OKhyO+bm5lixYgXWr18PW1tbDfbQePX19SEzMxMxMTHw8fHRdXemDY2cG47HM4stWrQIPB4P/v7+NFNJiBGiQIMQohEMw+DWrVvIyMhAW1ubWm0tXLgQ27Ztg7u7u4Z6Z/zu3r2LpqYmrFq1CpaWlrrujk7QyLnhGBgYwJUrVyAUCtmaHDweD4sXLzaozGI5OTl4++23cePGDbS0tODEiRPYsmWLrrtFiN6gQIMQoraGhgakpaWhqqpKrXb8/PyQkpKCoKAgDfXM+InFYmRmZsLf3x+RkZG67o7eaGpqglAoxNWrV9mRc0VNDho51x+KzGJCoRDFxcUGV5Pj7NmzyMvLw6JFi7Bt2zYKNAh5DAUahBCVdXd348SJE7h69apa7bi4uGDr1q1YsmQJPQROQX19PYqKipCcnAz7acr+ZGiMZeTcFCgyi125cgVDQ0OIjo4Gj8dDeHi4QdwXOBwOBRqEPIYCDULIlIlEIpw/fx6ZmZmQSCQqt2NlZYV169ZNXKWajCKXyyEQCGBnZ4elS5caxEOYrjEMw9bkMMSRc1OiyCwmFApx7949eHp6spnFbGxsdN29cVGgQchoFGgQQiZNLpfjypUrOHXqFHp7e1Vuh8PhID4+Hps2bYKjo6MGe2j82tvbkZOTg6SkJLi5uem6OwZprJFzqsmhf8bKLLZ06VLw+Xy9rMlBgQYho1GgQQiZlLKyMqSlpeH+/ftqtRMREYGUlBS9fFDQZ4qq6oODg+DxeJRNSQMMdeTcFD2eWSwkJITNLKYvaa8p0CBkNAo0CCETamlpQXp6OoqLi9Vqx9vbGzt27KANyyro7+9HZmYmFi5cCF9fX113x+gwDIOamhoIBAJ25JyqWesnqVSKW7duQSAQoLq6Gs7OzkhKSkJ8fLzOZ0cp0CBkNAo0CCFj6uvrw+nTp3H58mW1Cu45ODhg06ZNiI+Pp1F4FZSUlKC+vh6rVq2ClZWVrrtj9B4+fIjLly8jOztbb0fOyYh79+5BKBSioKAAcrmczSymq5ocFGgQMhoFGoQQJRKJBJcuXcKZM2cgEolUbofL5SI5ORlr166FtbW1BntoGoaHh5GZmQlfX19ERUXpujsmRzFyLhQKUVVVpVcj50TZ4OAgrly5AoFAgI6ODvj4+IDP509LZrH+/n5UV1cDABYsWIA///nP4PP5mDFjhkkVzSRkPBRoEEIAjCwfuXHjBjIyMtDZ2alWW4sXL8bWrVspm4+KGhsbcePGDaxatYoKzemBx0fOY2JiqJq1Hno0s1hJSQlsbW0RFxen1cQJQqEQfD5/1NdfeOEFHDlyRCvXJMSQUKBBCEFtbS3S0tJQW1urVjuBgYFISUmBv7+/hnpmWuRyObKzs2FtbY3Y2Fh6iNUzipFzoVCI9vb2aR05J1PT3t6O7Oxs5OXlYWhoCHPnzgWPx0NERAT9XBEyjSjQIMSEdXR04MSJEygsLFSrHTc3N2zfvh0LFiygX+Iq6ujoQHZ2NhISEuDh4aHr7pAJKEbOFdWsp2PknKhmeHgY165dg0AgwL179+Dh4cFmFrO1tdV19wgxehRoEGKChoaGcPbsWVy8eBFSqVTldmxsbLB+/XqsWLECXC5Xgz00HQzD4Nq1a+jr68OKFStow7yBGWvknM/nG0w1a1PBMAxqa2shEAhw48YNcLlcNrPYrFmzdN09QowWBRqEmBC5XI6cnBycPn0a/f39KrdjZmaGpKQkbNy4Efb29hrsoWkZGBhAZmYm5s2bR8vNDNzw8DCuX7+OS5cu0ci5nlNkFsvJycHDhw8RHBwMPp+P+fPnU2YxQjSMAg1CTADDMCguLsbx48fR0tKiVlvR0dHYvn07Zs6cqaHemaaysjLU1NQgOTmZ0tYaERo5NxwymQxFRUVKmcUSExORkJBAmcUI0RAKNAgxcvfu3UN6ejrKysrUamf27NlISUlBWFiYhnpmmiQSCTIzMzF79mxER0frujtT098PVFcDYjFgZQUEBQE0ozWux0fOQ0JCwOPxaORcDykyi129ehUymQwLFy4En89HQEAALYEjRA0UaBBipB4+fIhTp07hypUrUOfH3MnJCVu2bEFsbCztH1BTU1MTrl+/jlWrVhnOiGlpKXDoEHDmDFBbCzz6XuJwgIAAYP164JVXgIgI3fVTj9HIueEYHBxEfn4+hEIh2traMGfOHDazmKWlpa67R4jBoUCDECOjKPR2/vx5iMVilduxsLDAmjVrsHr1alraoybF3hhLS0ssW7bMMEZI6+qAvXuBzEyAywUmShqgeD05GTh8GKD9JuOikXPDwDAMSktLIRAIUFxcDBsbG8THx1NmMUKmiAINQowEwzC4evUqTpw4gZ6eHpXb4XA4iI2NxZYtW+Ds7Kyx/pmqzs5OCIVCxMfHw9PTU9fdmZzUVODVV0eCh6lkJeNyRz7eew946SXt9c8I0Mi54VCkns7NzcXQ0BCioqLA5/OpJgchk0CBBiFGoLKyEunp6WhoaFCrnZCQEKSkpMDHx0dDPTNdDMOgsLAQPT09WLFiheGsyT94EDhwQP123nwTeP119dsxcoqRc6FQiLt379LIuR5TZBYTCARoamqCh4cHkpKSsHz5csosRsg4KNAgxIC1tbUhIyMDRUVFarXj4eGBHTt2IDo6mkboNGBwcBAXLlzA3LlzERgYqOvuTF5qKvDyy5ptb88ezbVn5Gjk3DA8mlns5s2bMDc3x9KlS8Hj8TB79mxdd48QvUKBBiEGaGBgAN988w2EQiFkMpnK7djZ2WHjxo1ITEykgnsaUl5ejqqqKiQnJ8Pa2lrX3Zm8urqRzdwikebatLYe2UxOezamhEbODUdvby+bWaynpwfBwcHg8XhYsGCB4cxiEqJFFGgQYkCkUimys7Px9ddfY3BwUOV2zM3NwefzsWHDBnpw0RCJRIKsrCx4e3tj3rx5uu7O1K1eDQgEU9uT8SRcLsDnAxcuaK5NE6IYORcKhbhx4waNnOsxmUyGW7duQSgUorKyEk5OTmxmMScnJ113jxCdoUCDEAPAMAxu376N48ePo62tTa22FixYgG3btsHDw0NDvSP379/H1atXsXLlSsN8qCgtBSIjtdt+eLj22jcBNHJuOO7fvw+hUIiCggJIpVIsWrQIPB4PgYGBtASOmBwKNAjRcw0NDUhLS0NVVZVa7fj6+iIlJQXBwcEa6hlhGAY5OTkwNzdHXFyc4T5E7N8PfPCBZmczFLhcYN8+4N13Nd+2CZLJZLh9+zYEAgEqKyvh7OyMhIQEGjnXQ49nFps9ezb4fD6WLFlCmcWIyaBAgxA91d3djZMnT6KgoECtdlxcXLB161YsWbLEcB+E9VB3dzcuXbqE5cuXw8vLS9fdUYlMJkNnZyecYmJg1dSkvQsFBQFqBspktLFGzqkmh/4ZK7NYXFwckpKS4O7uruvuEaJVFGgQomfEYjHOnz+PCxcuQCKRqNyOlZUV1q5di1WrVtHomYbduHEDnZ2dWLFihd5vopdIJOjo6EBbWxva29vR3t6OtrY2tLW1oaurCxYiEf565Ai0+ljK4QC9vYC9vTavYrJo5NxwKDKL5eXlYXBwEJGRkeDz+YiMjKTgkBglCjQI0RNyuRz5+fk4efIkent7VW6Hw+EgPj4emzZtgqOjowZ7SAYHB5GZmYmIiAi9WoImFouVgohHg4menh5MdJuf3dGBX2dkaL+TRUXA/Pnav44JYxgGZWVlEAgENHKu5yQSCa5fv45Lly6hqakJ7u7u4PF4lFmMGB0KNAjRA2VlZUhPT8e9e/fUaic8PBwpKSmYNWuWhnpGFCorK1FeXo7Vq1frJG3t0NCQUhDx6J8PHz5UuV2/tjb86uRJzXV0PAUFwNKl2r8OAUAj54aCYRjU1dVBKBSisLAQZmZmWLp0Kfh8PmUWI0aBAg1CdKilpQXHjx/H3bt31WrHy8sLO3bsoIcILZBKpbh48SI8PDywYMECrV2HYRgMDAyMWuKk+LO/v18r16UZDeOmGDkXCARobGykkXM91tvbi9zcXGRnZ6OnpwdBQUHg8/mYP3++3i/RJGQ8FGgQogN9fX34+uuvkZOTA7lcrnI7Dg4O2LRpE+Lj42FmZqbBHhIAaG5uRn5+PlauXAlnZ2e122MYBn19feyypseDiaGhIfU7PUWDbW04evIktPnuYTgcdNfXY4aPjxavQiYy1sh5bGws1eTQQ49nFnN0dGRrcmjiPkTIdKJAg5BpJJFIIBAI8M0330CkRgVmLpeLVatWYe3atbCxsdFgDwkw8lCWm5sLAIiPj5/SLBHDMOjp6RkzmGhvb4dYLNZWt6dEJpOhrq4Ozc3NOFdTA181Eg88SZujI3797LPw8vJCZGQkIiMjERwcDAsLC61dk4xvvJFzqsmhf5qbm9nMYhKJBAsXLgSPx0NQUBDNXhODQIEGIdOAYRjcuHEDGRkZ6OzsVKutxYsXY+vWrXB1ddVQ78ijenp6cPHiRSxbtgze3t5jHiOXy9HV1TXungmpNupRaFB/fz/Ky8sxNDQEKysrvPbgAbY+eACtLM7gciF5+WXcfeklFBcXo7S0FN3d3bCwsEBoaCgiIiIQFRUFDw8PenCaZnK5HLdu3WJHzp2cnNiaHDRyrl+GhobYzGKtra2YPXs2eDwelixZAisrK113j5BxUaBBiJbV1tYiLS0NtbW1arUTEBCAlJQUBAQEaKhn5HFFRUVoa2vDypUrAQCdnZ1KGZwUf+/o6FBrydt0Mjc3h5ubG9zd3eHm5oaGhgYUFRXBysoKVlZWMDMzg1d3N95IS9NeJx6pDM4wDFpaWlBSUoKSkhJUVVVBKpXC1dUVkZGRiIqKQmhoqE423JuysUbO+Xw+VbPWMwzDoLy8HAKBAHfu3IGNjQ2WL1+OpKQkeHh46Lp7hIxCgQYhWtLZ2YkTJ07g+vXrarXj6uqKbdu2YdGiRfQLX8MkEgna29tx7949nDt3Dra2tuByuWhvb0dnZ+eEaWH1iYWFBdzd3dkPDw8PeHh4wN3dHTNmzICZmRk6Ojrw6aeforq6esw29n/zDcKam2Guye+ZywX4fODChXEPEYvFqKqqQnFxMUpKStDW1gYzMzMEBQWxy6xmz55N7/1pQiPnhqOzsxPZ2dnIzc3FwMAAoqKiwOPxEBUVRT8vRG9QoEGIhg0NDeHs2bO4ePGiWktorK2tsWHDBvD5fFrLrgaRSMTuj3h8dqK7uxsPHz5Ed3c3Zs+erdeZXaysrNgg4vFgwtnZedwHC4ZhUFBQgC+++GLCfUGuvb14Iy0NFjKZ5or3WVuPzGb4+0/6lPb2dpSUlKC0tBTl5eUQi8VwdHRkg47w8HDYU+E/raORc8PxeGYxNzc3NrOYnZ2drrtHTBwFGoRoiFwux+XLl/HVV1+plYrUzMwMiYmJ2LhxIxwcHDTYQ+M1ODg4ZrG69vb2cYsfyuVy3L9/H9bW1npTzMzGxkYpgHj07w4ODlMepRwYGMA//vEP3Lx5c1LHx5WX43s5Oap0fWypqcCePSqfLpVKUVNTwy6zunfvHjgcDnx9fREVFYXIyEj4+flRxjUtG2vknGpy6J9HM4vduHEDHA4HS5YsAZ/Px5w5c3TdPWKiKNAgRE0Mw6CkpATp6eloaWlRq63o6Ghs27YNXl5eGuqdcWAYBv39/eMGEwMDA1Nqb3BwEK2trfD29p725SAODg5KS5weDShsbW019uBWWlqKI0eOTLmY37qbN7GlsBAMoN7MxsGDwGuvqdPCKD09PSgtLWVnPAYHB2Fra4vw8HB2xoM2MWuPYuRcKBSioaGBRs71mCKzWE5ODrq7uxEYGMhmFtPnmVtifCjQIEQN9+/fR1paGsrKytRqZ/bs2dixYwfCv90wa4oYhsHDhw/HLFbX1tamVjrgR6/x4MEDAMDMmTO1Nhrr5OSkFEQ8+qe20xFLJBJkZGTg0qVLKp0fExOD7w0Pw+pnPwOk0pGPyeJyRz7ef1+tmYzJkMvlqK+vZ4OOuro6MAyDWbNmsUFHUFAQPVRpAcMwqK+vh0AgoJFzPSeXy9maHBUVFVSTg0w7CjQIUUFvby9OnTqFvLw8tTYMOzo6YsuWLVi2bJlJLP9gGAbd3d3jpoUdHh7W2rWHh4dx//59eHh4qD36yuFw4OLiMiqIUHzoatNsU1MTPv74Y5Vm1qytrfGd73wHS5YsGQnA6uqAvXuBzMyR4GGigEPxenIycPjwlPZkaMrAwADKyspQUlKC4uJi9Pb2wtLSEmFhYWzgoS9L5IxJX18fW5ODRs71W3NzM7Kzs5Gfnw+JRIIFCxaAz+dTTQ6iVRRoEDIFw8PDyMrKwrlz59QqvGZhYYE1a9Zg9erVRpfJRS6Xo7Ozc8xgoqOjQyc1Jjo7OzE4OIhZs2ZNOqAzMzODq6vrmLMSbm5uerVBXy6XIzMzE6dOnYJMJpvy+cHBwXjxxRfHrs1SWgocOgScPQvU1ACP/srgcIDAQGDdOmDfPjaFra4xDIP79++zezuqq6shk8ng7u7OBh2hoaFG97OnSzRybjiGhoZQUFAAgUCA1tZWzJo1C3w+nzKLEa2gQIOQSWAYBteuXcOJEyfQ3d2tVluxsbHYsmULXFxcNNS76SeVStHR0aFU8Vrxd32qMSGTyXDv3j04OTmN+bCjqDHx+AZsd3d3uLq6GkSV5M7OThw5cgSVlZVTPtfc3BybN29GcnLy5AKw/n6guhoQiwErKyAoCDCADFAikQgVFRVs4NHR0QEul6uUQtfb25tGdTXk8ZFzqmatnx7PLGZtbY3ly5eDx+NRZjGiMRRoEPIEVVVVSEtLQ0NDg1rthISEYMeOHfD19dVQz7RreHiYDSYen53o6urS+xoTvb296Orqgr+/P7y8vJSCCUVA4eLiYrBL1hTB77Fjx1Tav+Ll5YU9e/aY3Jp6hmHYFLrFxcWoqKiARCKBs7OzUgpdW1tbXXfV4IlEIuTn59PIuQHo7OxETk4OLl++jIGBAURGRrI1OQz1Hkn0AwUahIyjra0NGRkZKCoqUqsdDw8P7NixA9HR0Xo3mqeoMTFWMNHT06Pr7k2alZWV0rKmxsZGzJkzBytXrpywxoShGhwcxOeff47CwkKVzufz+di+fbteLf/SFYlEgurqana2o7m5GRwOB/7+/mylch8fH3rYUgONnBsOiUSCwsJCCAQCNrNYUlIS4uLiKLMYUQkFGoQ8ZnBwEN988w0EAoFK690VbG1tsXHjRiQlJel0U+TAwMCYG6/b2trQ19ens35Nla2t7Zj1JTw8PGBvbw8Oh4PW1lbk5uaCx+ONvd/ACJSXl+PTTz9VKRB0cnLCCy+8gMjISM13zEh0d3ezQUdZWRmGhoZgZ2eHiIgIdsbD0dFR1900WMY2cv773/8eGRkZKC8vZ4sa/uEPf0BoaKiuu6Y2RWaxwsJCNrMYj8eDj4+PrrtGDAgFGoR8SyqVIjs7G998882U6zI8ytzcHHw+H+vXr5+WESBFjYmx6ku0tbVhcHBQ633QFAcHh1HLmxSfT/RvyTAMrly5AolEgsTERIN8YHkSiUSCkydPIisrS6XzFyxYgJ07d9Ko5BTI5XLU1dWhuLgYJSUl7PLJOXPmsEFHQEAAZVdSgUQiwY0bN3Dp0iWDHjlfu3Ytnn32WSxevBhSqRSvvfYaiouLUVpaalDfx0QezywWEBAAPp+PhQsX0nufPBEFGsTkMQyD27dv4/jx42hra1OrrQULFmDbtm0aXw6gqDExXjChTgas6ebs7DxmsTp3d3dYW1tPub3e3l5kZWVh8eLFRrvf4N69e/j444/R3Nw85XOtrKzw7LPPYtmyZUa3hGy69fX1sSl0S0pK0NfXB2tra4SGhrKVyo11Jk2bxho5N9SaHO3t7fDw8EB2djYSExN13R2NUmQWEwqFKC8vh6OjIxISEpCYmEiZxci4KNAgJq2xsRFpaWkqZex5lK+vL1JSUhAcHKxyG3K5XKnGxOMBhUQiUauP04XD4WDGjBljBhNubm6wtLTU2LXu3LmD+/fvY9WqVUa534BhGGRlZeHkyZMqpQUODAzE7t274ebmpoXemTaGYdDU1MQGHTU1NZDL5fD09GSDjpCQEKN8X2qLMdTkqK6uRnBwMO7evYuoqChdd0drWlpaIBQK2cxi8+fPB5/PR3BwMA1oECUUaBCT1N3djZMnT+Lq1atqZU9ycXHB1q1b/13k7AlkMhk6OzvHrHzd0dGh1p6Q6WRmZgY3NzelJU6KP93c3LT+UCAWi5GZmYnAwECjrabe3d2NTz/9FBUVFVM+18zMDJs2bcKaNWuMchmZPhoaGkJ5eTkbeHR1dYHL5SIkJIRdZqXNavTGRC6X486dOxAIBAY1ci6Xy7Fp0yb09PQgNzdX192ZFiKRiK3J8eDBA3h7e4PP52Pp0qWUWYwAoECDmBixWIwLFy7g/Pnzas0QWFlZYe3atVi1atWoEXqJRIKOjo4xg4muri69qTHxJFwul60x8XgwMWPGDJ3VmKitrcWdO3eQnJxsNGugH3f9+nUcO3ZMpf01np6e2LNnj8GkUTZGDMPgwYMHKC0tRUlJCSoqKiCVSjFjxgw26AgLC4ONjY2uu6r3DGnkfN++fTh79ixyc3Mxe/ZsXXdnWjEMg4qKCggEAty+fRtWVlaIi4tDUlISPD09dd09okMUaBCTIJfLkZ+fj1OnTuHhw4cqt8PhcBAXF4e1a9dCLBYrBROKv3d3d+t9jQkFCwuLMYvVubu7612NCblcjkuXLsHR0RGLFy/Wu4cMTRgcHMQ///lPXLt2TaXzeTwetm/frtHlaUR9w8PDqKqqYmt3tLa2wszMDIGBgYiMjERERAR8fHyM8j2tKfo+cv7DH/4Qp06dQk5ODvz9/XXdHZ3q6upiM4v19/cjIiICfD7fYDOLEfVQoEGMXnl5OdLS0nDv3r0pnSeVSiESiTA0NIShoSG4uLggICAAw8PDagUr083a2nrMWQl3d3c4OTkZxMNNW1sbLl++bNRpaysrK/HJJ5+oVHne0dERL7zwglGvCTcmnZ2dSil0xWIxHBwc2BS6ERERcHBw0HU39ZJi5FwoFOLWrVs6HzlnGAavvvoqTpw4AaFQqNY+PWOjyCwmEAhQX18PV1dXJCUlIT4+3mhno8loFGgQo/XgwQMcP34cd+7cGfN1hmEglUoxNDSkFFAo/q5YWmVra4uAgAC4uLjo7UO5nZ3dmPUl3N3d2RoThohhGOTn50MsFiMpKckoR8OkUilOnTqFzMxMlWbC5s2bh507d9KDqYGSSqWora1FaWkpiouL0dTUBA6HAx8fH6UUusb43leXPoycf//738exY8dw6tQppdoZTk5OtDTuEfX19RAKhbh+/To4HA4WL14MHo9HSzxNAAUaxOj09/fj9OnTyMnJgUwmg0QiUQoiHg0qJsrkY2FhAT8/P73ZwOno6DgqmFB8GOPoUF9fH7KysrBo0SKjLRDV3NyMjz/+eMqzbcDIPqFnnnkGy5cv14v3J9GM3t5eNugoLS3FwMAAbGxsEB4ezgYeLi4uuu6mXtHlyPl4P3uffvopdu3apdVrG6K+vj7k5eUhOzsbXV1dCAgIAI/Hw6JFiwwmsxiZGgo0iMFjGAY9PT1oaWnBuXPncOnSJTx8+JANKKaaycnMzAyzZs3CnDlzpv3G5+LiMmaxOlVrTBiq4uJiNDY2jrnZ3hgwDINLly4hIyNDpbS1AQEBePHFFzVer4XoF7lcjsbGRnaZVW1tLRiGgZeXFyIjIxEVFYWgoCBKofuIsUbO+Xy+0Q5WGCpFZjGhUIiysjI4ODiwmcUokDYuFGgQgyCXy9HV1TVmsbr29na0tLSgrq4OIpFIreu4u7vD399faw/1ihoTYxWrc3d3N/kHBrFYjKysLPj5+SEyMlLX3dGKnp4eHDlyBGVlZVM+18zMDBs2bMD69etpKY0JGhwcVCoY2NPTAwsLC4SGhrKzHR4eHjTDBRo5NyQtLS3Izs5Gfn4+hoeHMX/+fPB4PISEhNB72QhQoEH0hlQqHbPGRHt7+7g1Jvr6+lBTU4Pe3l61ru3o6IiAgAA4Ojqq1Q7w7xoTjwYQioDC1dWVfsmNo76+HkVFRUhOToa9vb2uu6MVN27cwD/+8Q+V0tZ6eHhg9+7dJp/RhoxgGAYtLS0oLi5GSUkJqqurIZVK4ebmxgYdoaGhJjUTOha5XI67d+9CIBDQyLmeU2QWEwqFaGlpgZeXF/h8PmJjY/UisxhRDQUaZFopakw8Xl+ivb0dnZ2dk94MKxKJUF9fj7a2NrX6Y21tDX9/f7i5uU1p5ITL5Y5ZrE5RY4JGmydPLpdDIBDA3t5+0oUPDc3Q0BC++OILFBQUqHR+QkICUlJS6JctGZdYLEZlZSU729HW1gZzc3MEBgaylcpnzZpllD9fkzXWyLm+1uQwZWNlFlu+fDl4PB7V5DBAFGgQjXu8vsSjwURPT49aNSZkMhkaGxtx//59tQrfcblczJkzB7NmzRo3KLC0tBwzLayHhwecnZ3pF5MGtLe3IycnB0lJSXBzc9N1d7SiqqoKn376KTo7O6d8roODA773ve8hOjpaCz0jxqytrU2pYKBYLIajoyOioqIQERGBiIgIo0wiMRmPj5x7e3uDx+PRyLkeUmQWy83NRV9fH8LDw8Hn8zF37lwa0DMQFGgQlQwODo5ZrK6trU3tZUxjUVTabWhowPDwsMrtcDgceHl5wdfXFxYWFrC2toanp+eYsxOOjo4UTGgJwzC4evUqBgYGwOfzjfIXhlQqxenTp3H+/HmVguvo6Gjs3LlTI8v5iGmTSqWorq5mZzvu378PDofD7oWKjIyEn5+fUf4cToRhGFRWVkIgENDIuZ6TSqVsZrG6ujo2s1hcXJzRLrU1FhRoTEF/P1BdDYjFgJUVEBQEGOv7m2EYDAwMjNoroQgmBgYGpq0v3d3dqKmpUWldOzCSptbGxgZBQUFYvXo1wsLC2GDCzs6Ogolp1t/fj8zMTCxYsAB+fn667o5WtLS04OOPP0ZTU9OUz7W0tERKSgoSEhL0771pSjdBI9bT08POdpSWlmJwcBC2trZKBQOdnZ113c1pRSPnhqOhoQFCoRDXrl0DADazmLZrckil/RgaqgbDiMHhWMHGJghcLt3/noQCjScoLQUOHQLOnAFqa4FH/7U4HCAgAFi/HnjlFSAiQnf9VAXDMOjt7R03mFA3g5O6BgYGUFtbO6lKyZaWlrCxsYGNjQ2sra2V/vTz88OOHTsQHh4+Db0mEykpKUF9fT1WrVpllEsUGIaBUCjE8ePH2YKPU+Hn54fdu3fr12iqMd8ECeRyOerr69nZjvr6ejAMg9mzZ7NBR1BQkMkksaCRc8PR39+PvLw8CIVCdHV1wd/fH3w+X6OZxQYGStHcfAidnWcgEtUCePSRmQNr6wC4uq6Ht/crsLOj+99YKNAYR10dsHcvkJkJcLnARKnuFa8nJwOHDwP6lBSGYRh0d3ePWuKk+FOdZUjaMjw8jIaGBjx48EBpyYmVlZVSAPHo383NzUe14+joiC1btmDZsmU0IqVjw8PDyMrKgo+PD6KionTdHa14+PAhjh49ipKSkimfy+Fw2LS1Y72XdcJYboJkSgYGBtjZjpKSEvT29sLKyopNoRsVFWW0+6ke9/jI+ZIlS6iatR4aK7NYfHw8kpKSVM4sNjRUh8rKvejuzgTABTBRvaOR111ckhESchg2NnT/exQFGmNITQVefXXk9+ZUamlxuSMf770HvPSS9vr3OEWNibHqS7S3t6tUEEwXFDMszc3N7P6JRwOKyQYLFhYWWL16NdasWWOUo+aGprGxEYWFhUhOToaDg4Ouu6MVRUVF+Oyzz1RaUuju7o7du3cjICBACz1TkaHdBIlWMAyDe/fusZXKq6urIZfL4eHhwe7tCAkJMfr77HSMnBPNaG1thVAoxJUrVyAWi9maHKGhoZNeitrcnIrq6lchl0sxcYDxOC7MzLgICnoP3t50/1OgQOMxBw8CBw6o386bbwKvv65+OwqKGhNjBRMdHR1qZWCaTmZmZqNqS7i5uaGpqQlCoRAPHz5Uq/3Y2Fhs2bKF8qPrAblcjuzsbFhbWyM2Nlb/9htogEgkwpdffokrV66odH58fDyefvpp/XpQ09ebINE5kUiEiooKtnZHZ2cnuFwugoODERERgaioKHh5eRnlzzqgnZFzoh0ikQhXr16FQCBga3IoMotNVFumoeEg6urUv//5+78JX1+6/wEUaChJTQVeflmz7e3ZM/njh4eH2RoTjy916urqUist7HSysLBgg4nHq18/XmOiqqoK6enpqK+vV+uawcHBSElJoSltPdHZ2QmhUIiEhAR4eHjoujtaUVNTg08++QQdHR1TPtfe3h47d+7E/PnzNd8xdej6JkgMBsMwaGtrY5dYVVRUQCKRwNnZmV1iFRYWBltbW113VSvGGjnn8/lUzVrPMAyDqqoqNrOYpaUlli1bBh6Ph5kzZyod29ycispKzd3/QkNT4eVF9z8KNL5VVzeyj1GT+5+trUf2UT66XFkkEo1KB6v4e09Pj+YurmVWVlZj1pdwd3efVI2J9vZ2ZGRk4ObNm2r1w8PDA9u3b8e8efPo5q4HGIbB9evX0dvbixUrVhjl3hiZTIavv/4aZ8+eVSn4j4qKwgsvvKB/aWun6yZIjJJEIkF1dTU729HS0gIzMzP4+/uzy6x8fX2N7j6tGDkXCoVobm6e9Mg5mX7d3d3IycnB5cuX2cxiPB4P0dHREIsbcP16BORyzd3/zMyssXhxqcnv2aBA41urVwMCwdSWIz+JuTmDuXM7sH//12ww0dfXp7kLaJmNjY1SAPHonw4ODir9whgcHMQ333wDgUAAmUymct9sbW2xceNGJCUl0RpZPTEwMIDMzEzMmzcP/kb6YNna2oqPP/4YDQ0NUz7XwsICO3bsQFJSkn4+bGnjJsjlAnw+cOGC5tokBqGrq4tNn1taWgqRSAR7e3ulFLp6F2yrICcnB2+//TYKCwvx4MEDvPLKKwAw4cg50S1FZjGhUIja2lrMmDEDq1adh7n5HQCqP5eMxoWLCx/z5pn2/Y8CDYwMuEVGaq/9lJTfwsXlgfYuoAZ7e/txq1/b2tpq7IFIJpMhOzsbX3/9tVo1OMzMzMDn87FhwwaTrWqrj8rKylBTU4Pk5GT92m+gIQzDICcnB2lpaSqlrfX19cXu3bv194FD2zfB0lKA0kubLJlMhrq6OnaZlSJQnzNnDqKiohAZGYmAgAD9ybg2BWfPnkVeXh4WLVqEbdu24cSJE0hKSho1ck41OfRTQ0MD8vI+h7e39vZTLF5cCjs7073/UaABYP9+4IMPNDuQp8DhyBARkY24uC813/gkOTk5jRlMuLu7w8bGRqvXZhgGd+7cwfHjx9Ha2qpWWwsWLMC2bduMds2/IZJIJMjKysKsWbMQHR2t6+5oRW9vL/7v//4Pd+/enfK5HA4H69atw4YNG/R75k2bN0EuF9i3D3j3Xc23TQxSX1+fUsHAvr4+WFtbIywsjF1m5erqqutuThmHw8GJEyewZcsWAGOPnCtqchhrBj5DVFW1H/fv/x2anc1Q4GLWrH0IDjbd+x8FGhgpbltTo732HR3b8Oyzv9Za+xwOBy4uLmzw8Ggg4e7urrMR5sbGRqSlpaGyslKtdnx8fJCSkoKQkBAN9Yxowr1793Dt2jWsWrXKKJZAjOX27dv47LPPVFry6Obmht27dyMwMFALPdMwbd8Eg4KAqirttU8MFsMwaGxsZIOOmpoayOVyzJw5UymFroWFha67+kSPBxqPerwmR0xMDPh8Pvz8/Ka3k2SUgoIgiETau/9ZWwchNtZ0738mH2j09QFOTsrFbjWPwYsv/ggWFmKVWzAzM8OMGTPG3DPh5uamVzfhnp4enDx5EgUFBWplynJ2dsbWrVuxdOlS/VzTbqLkcjlycnJgYWGB5cuXG+X/jVgsRlpaGi5fvqzS+cuXL8czzzxjGJtBp+MmyOEAvb0AVVYmTzA0NITy8nKUlJSguLgY3d3dsLCwQEhICBt4eHp66uV9Z6JAQ2FgYAC5ubnIzs5GZ2cn/Pz82Joc+vR73FRIpX3IzXWCcsVvTeMgPr4XXK5p3v9MPtC4dQtYsED719m27X/g5nZvwmPMzc3h5uY2ZjDh6uqq9+tXxWIxLly4gAsXLqhVcdzKygpr167FqlWrYGlpqcEeEnV1dXVBIBAgPj4enp6euu6OVtTW1uLTTz9FW1vblM+1s7PDd7/7XSxcuFALPdOS6boJFhUB+pbOl+g1hmHw4MEDdm9HZWUlpFIpXF1d2U3l4eHhehPQTybQUJDL5SguLoZAIEBpaSns7e2RkJCAxMREzJgxQ/udJQCAvr5buHFD+/e/RYuK4OAwX+vX0Ud6vGh4eohVn2SYErl85J9aUWNirD0TLi4uBrlRTC6Xo6CgACdPnlSr4B6Hw8Hy5cuxefNmODk5abCHRBMKCwvR3d2NLVu26H3Qqwq5XI5vvvkGZ86cUakAZkREBF544QU4OztrvnPaNF03wem6DjEaHA4HXl5e8PLywqpVqzA8PIzKyko28Lh8+TLMzMwQGBjI1u6YPXu2Xs52PM7MzAzR0dGIjo5Ga2srsrOzIRAIcO7cOZWqWRPVMMz03Jem6zr6iGY0bk3PYN7Jkw1ISnKCk5OTUd04ysvLkZ6ejqamJrXaCQsLQ0pKCmbPnq2hnhFNGRwcRGZmJqKiogxjv4EK2tra8Mknn6Curm7K51pYWGD79u3g8XiG+bNNMxrEQHV0dKC0tBTFxcUoLy+HWCyGg4MDu8QqPDx8WjddT2VGYyxisZitZk01OaYHzWhon8kHGv39gKMjLU+eqtbWVqSnp+POnTtqtTNz5kzs2LEDUVFRhvmQZuQqKipQWVmJ5ORko/xFxzAMcnNz8a9//Uul5X5z5szBnj174OXlpYXeTZNpuAkyAJpKSjAnPJx+zolWSKVS1NbWsrMdTU1N4HA48PX1RUREBKKiouDv76/xVQP9/f2orq4GMJIZ8c9//jP4fD5mzJgBHx8fldp8vJq1hYUFW5PDoO81ekgq7UduriNoj4b2mHygAWg/4cqsWYNoarIxil+w/f39+Prrr5Gdna3S8hIFe3t7PPXUU0hISDDKZTiGTiqVIisrCzNnzsR8Ix2F7uvrw2effYbbt29P+VwOh4PVq1dj06ZN+p22drK0fBNsd3LCgWeegYODg1LBNkrxSbTl4cOHSil0BwYGYGNjg/DwcERFRSEiIgIuLi5qX0coFILP54/6+gsvvIAjR46o3X53dzcuX76MnJwc9PX1ISwsDDweD/PmzTPIpdb6iLJOaRcFGtBuCnlACheXL5CYmI6XX34Zq1atMsiCZlKpFJcuXcKZM2cwNDSkcjtcLhcrV67EunXrtF7Dg6jm/v37uHr1KlauXGm0e2Xu3r2Lo0ePqpS2dsaMGdi9ezeCg4O10DMd2b8fzN//Do5MC3nkuVzI9+5F9f797DIXxWizj48Pu8wlICCAHpyIVsjlcjQ2NqK4uBglJSWoq6sDwzDw9vZm339BQUF6nfVJKpXi5s2bEAqFqKmpgYuLC5KSkhAfH08Bu5pG6mh8AEAbD4FUR4MCDWi/KG509LMYHr4Nc3NzBAQEICUlBatXrzaIrD0Mw6CoqAjHjx9HR0eHWm3FxMRg69atcHNz01DviCYxDMNuroyLizOKGbjHicVipKenIycnR6XzY2Nj8eyzzxpVkCwWi3HzH//Aspde0t5FHqsM3tvbywYdj482Kx78NDHaTMhYBgYG2BS6JSUl6OnpgaWlJUJDQ9n3n7u7u97eAxsbG9maHAzDUE0ONQ0MlOL6de09BFJlcAo0AACrVwMCgWZnNTgcGby9y7Fhw7vo7+9HS0sL2traYG5uDn9/fyQlJWHFihWYO3euXo7k1dXVIS0tDTVqLqlQBFcBAQEa6hnRtO7ubly6dAnLly832jXA9fX1+OSTT1SqUG9ra4vnn38eMTExWuiZbjAMg6tXr6KzsxMJCQlw3LFD8zdBLhfg84ELF8Y9RDHarHjoq62tBcMw8PLyYjMJ6ftoMzFcDMOgubmZff9VVVVBJpPBzc0NUVFRiIyMRGhoqF6uRBgYGEBeXh6ys7PR0dFBNTnUcPv2anR3C6DZWQ0uXFz4mDdv/PufKaBA41t1dUBEBCASaapFBubmEqSkvAFHx072q1KpFA8ePEBLSws7wxEYGIikpCTExcXBXg92jHd2duLEiRO4fv26Wu24urpi27ZtWLRokd6ODBHg5s2b6OjowIoVK4xjv8Fj5HI5zp07h9OnT6u0rygsLAy7du0yqhH2iooKlJWVYcmSJfD29h75ouZvgoC19chshr//pE8ZHBxEWVmZ0mizhYWF0mizh4cH3VOIVojFYlRUVLAFAzs6OmBubo6goCD2/Tdr1iy9ev8panIIhUKUlJTA3t4e8fHxSExMhKurq667ZxCGhupw/XoE5HLN3f/MzKyxeHEpbGwmf/8zRhRoPCI1FXj5Zc2197e/ieHmdgoCgWDUAw7DMOju7kZzczPMzMzg7+8PBwcHLF68GHw+H76+vprryCSJRCKcO3cOmZmZkKoxqmltbY3169djxYoVNKqix4aGhnDhwgVEREQY136DR7S3t+OTTz5BbW3tlM/lcrnYunUrVq5cqVcPFepobW1Ffn4+goODETnWelFN3wRTU4E9e1Q+nWEYtLS0sGvrq6urIZVK4ebmxj70hYaGGmVGNKIf2tra2A3l5eXlGB4ehpOTk1IKXTs7O113k9XW1gahUIgrV65AJBIhOjoafD4fYWFhRnMf05bm5lRUVmru/hcamgovL9Xvf8aCAo3HHDwIHDigmXZee23k762trcjIyMCtW7fGPFYkEuHBgwcwNzfHzJkzYWFhAX9/f3YKVNujzHK5HLm5ufjqq69U2hyrYGZmhoSEBDz11FO0OU3PVVZWory8HMnJyUa130CBYRhcuXIFX375JcQqFIqbNWsW9uzZg1mzZmmhd9NvYGAA2dnZcHZ2Rmxs7MRLNbVxE9QQsVisVLBNsRQ1MDCQXeaib6PNxHhIpVJUV1ezsx3Nzc3gcDjw9/dnAw9fX1+9WAotFotx7do1CAQC3L9/HzNnzgSPx8OyZcsoMJ9AQ8NB1NWpf//z9z8IX1/N3v8MFQUaY0hNBV59dWSp8lQG9rnckY/33x97EK+yshJpaWlobGwc83yZTIbe3l7Y2trCwsICZmZmcHBwQHx8PJKSkrSydKOkpATp6elobm5Wq52oqCjs2LHDaNf3GwupVIqLFy/C3d0dCxcu1HV3tKK/vx+fffbZuIH9RDgcDpKTk7F582ajWEYmlUqRm5uL4eFhJCYmTv4BQ1s3QQ1ra2tjU5hWVFRALBbD0dGRTV8aERGhV6PNxLh0d3ez77+ysjIMDg7Czs5OKamBrjP3MQyD6upqCAQCFBUVUU2OSWhuTkV19auQy6WY2p4NLszMuAgOfp9mMh5BgcY46uqAvXuBzMyR35sT/a5VvJ6cDBw+PPFyZMUGzBMnTqCnp2fcY7hcLlxdXdHe3g6GYcDhcDB//nzweDyEhoaqPWLX3NyM9PR0lJSUqNXOrFmzkJKSgvBw082oYCiam5uRn5+PlStXwtnZWdfd0Yri4mIcPXoUvb29Uz7XxcUFL774IkJDQ7XQs+nFMAxu3bqFe/fuYfny5aqt09bWTVBLHh1tLikpwf3798HhcODn58c+9Pn5+enFaDMxPnK5HHV1dez7r6GhAQzDYPbs2ez7LzAwUKcDGD09PWxNjt7eXoSGhoLP51NNjjEMDdWhsnIvurszAXAxccAx8rqLSzJCQg6b/J6Mx1Gg8QSlpcChQ8DZsyP1rB791+JwgMBAYN06YN8+peyNTzQ8PIzMzEycO3duworEs2bNwuzZs1FRUcEGJl5eXuDxeIiNjZ3yFGhvby+++uor5ObmQp3/ekdHR2zevBnLly+nG5SeU6StBYCEhASjXFYyPDyM48ePQygUqnT+4sWL8Z3vfAe2traa7ZgO1NfXo6ioCAsWLNBMuktt3QS1rKenR6lg2+DgIGxtbZUKBhprwE10r7+/H2VlZWwK597eXlhZWSEsLIwNPHSV6l0qlaKoqAgCgYCtyZGYmIiEhARa9vyYgYFSNDcfQmfn2W+L+j363MSBtXUgXF3Xwdt7n0mnsJ0IBRpT0N8PVFcDYjFgZTVSTFfdJFE9PT346quvcOXKlQkf/GNiYhAYGIiioiJUVlYCGNl0rZgCnTlz5oTXkUgkyMrKwtmzZ1Vas65gYWGB5ORkrFmzhtZ5GoCenh5cvHgRy5Yt+3d2ISPT2NiIjz/+GA8ePJjyuTY2NvjOd76DJUuWaKFn06urqwu5ubmYPXs2FixYoJ2AUhs3wWkgl8tRX1/PjjbX19crjTZHREQgKCjIKJbLEf3DMAzu3bvHvv+qq6shl8vh4eHBpnAOCQmBpaXltPetqakJAoGArcmxaNEitiaHMQ5KqUMq7cfQUDUYRgwOxwo2NkHgcvX//qdrFGjoiaamJqSnp6O8vHzcYywsLLBq1SrMnTsXBQUFKCgoYGdDwsLCwOfzER0drTTDwDAMrl+/jhMnTqCrq0utPsbGxmLLli1GlebTmBUVFaGtrQ0rV640ygcouVyO8+fP46uvvlIpbW1ISAhefPFFzJgxQwu9mz4ikQg5OTmwtLREfHy8Uf5fa9rAwAA721FSUsKONitS6EZFRVFhUaI1IpFIqWBgZ2cnuFwumw0uMjISXl5e0/qgPzAwgCtXrkAoFKKjowO+vr7g8XhYvHgxZY8kaqFAQ48wDIPi4mKkp6dPODrr4OCAzZs3Y8GCBbh69SqEQiHa2toAADNmzGBrcrS2tiItLQ319fVq9Ss4OBgpKSk6SblLpk4kEuHChQsICwtDSEiIrrujFR0dHfj0009RXV095XPNzc2xZcsWJCcnG/SInVwuR0FBAXp6epCYmKgXNXgMkWK0WVGp/PHR5sjISISEhOhlwTZi+BiGQVtbG5vCubKyEhKJBC4uLuz7LywsbNqWdcrlcpSUlEAoFKK4uBh2dnZsQhqqyUFUQYGGHpLJZLh8+TK++uorDAwMjHuct7c3duzYgYiICJSWlkIoFOLu3bsYHBxkM1t5e3urvObS3d0d27dvx/z58w36gcyUKDbDJicnG8V+g8cxDIOCggJ88cUXEKlQWM7b2xt79uzB7NmztdC76aOoYBwbG/vEZZNkakQiESoqKtgHP30YbSamQyKRoKqqip3taGlpYWttKVI4+/j4TMv7r62tDdnZ2bhy5QqGhoaoJgdRCQUaemxwcBBnz57FpUuXJiygFxkZiR07dsDZ2RlffPEFvvzyS7S0tEAikQAYmQHx9vaGu7v7pDZu29raYuPGjUhKSqJlGAZCkbbW1dUVMTExuu6OVgwMDOAf//gHbt68qdL5K1euxNatWw16GUBzczOuXbuGsLAwhIWF6bo7Rk8x2qx46KuoqIBEIoGzszO7xGo6R5uJ6ens7FRKoSsSiWBvb8/uLYqIiICjo6NW+6CoySEUCnHv3j14enqyNTmMsQ4T0SwKNAxAR0cHTpw4gcLCwjFfZxgGDx48gEQiwcyZM2FpaQm5XI62tjY0Nzejv78fwMgeDy8vL8ycOXPMjdxmZmbg8/nYsGED5Z43IC0tLbhy5QpWrFhhtPtnSktLceTIETx8+HDK5zo7O2PXrl0GnYK5t7cXly9fhqurK5YsWUKZ3nREIpGgurqane14dLT50YJtNNpLtEEmk6G2tpYNfBUrF3x8fNj3X0BAAMzNzbVyfUVNDqFQiJs3b8LCwgKxsbHg8XhGm2yEqI8CDQNSU1ODtLQ01NXVARj5oe/q6kJdXR0GBwcBAFwuF7Nnz8bs2bNhZmYGhmHQ19eH5uZmdHR0QC6Xg8PhwNXVFd7e3nBycmJrdGzbtg2enp66/BbJFDAMg7y8PMhkMiQmJhrlw41EIkFGRgYuXbqk0vmLFi3C888/b7CBs0QiQW5uLmQyGRISEmifgJ7p6upiH/oeHW1+NIWutkebienq7e1FWVkZ+x7s7++HtbU1WzAwIiJCa/sqHq/JERISAj6fj/nz59NACFFCgYaBYRgGhYWFOHLkCG7evDlu0T9ra2v4+fnB3d2dfQAdHh7GgwcP0NLSwqa49fLywq5du/D0009TuloD8vDhQ2RlZWHp0qUGv99gPE1NTfj444/R0tIy5XOtra3x3HPPYenSpQYZgDEMgxs3bqClpQUJCQlU78EAyGSyUQXbAGDOnDns2nptjjYT08YwDBobG1FSUoLi4mLU1dVBLpfDy8uLne0IDg7W+NJRqVSKW7duQSAQoLq6mq3JER8fT0E2AUCBhsHp6enBqVOnkJubi/v376OpqWnC/RsODg4IDAxU+oGXy+UYHh6Gk5MTRCIROBwOrKys2JocXl5e0/GtEBXdvn0bzc3NWLVqlUHvNxiPXC5HZmYmTp06BZlMNuXzg4OD8eKLLxpshpSamhrcvn0bMTEx8PHx0XV3iIr6+vqUCgb29fXB2tpaqWCbob5Hif4bHBxUSqHb3d0NCwsLhISEsPuLPDw8NDoQc+/ePQiFQhQUFEAulyMmJgY8Hg/+/v4GOeBDNIMCDQMhFotx4cIFXLhwQamS+PDwMBoaGvDgwYMJC/65ubkhICAAjo6OWLt2LZKTk2FpaYnm5mYIBAJcvXqVneUICwsDj8fDvHnzaApUj4hEImRmZiI4ONhoNwJ3dnbiyJEjbFHKqTA3N8emTZuwevVqg3zfdnR0IC8vD35+foiOjqZfzEbk0dHm0tJS1NTUQC6XY+bMmUopdI1x4IDoHsMwaGlpYVM4V1VVQSqVwtXVVSmFrqZWNQwODuLKlSsQCATo6OiAj48P+Hw+1eQwURRo6DmGYZCfn49Tp06Nu0wKGMnIU1dXN25RPg6HA29vb+zcuRMpKSmjsqQMDQ0hPz8fQqEQra2tAAAXFxckJSUhPj5e5RS5RDNqampw9+5dJCcnG+x+g4kwDINr167h2LFjKqWt9fLywu7duw1yBmBwcBA5OTmws7PD8uXLaWmNCRgaGmJHm4uLi0eNNkdGRsLT05OCTaIVYrEYVVVVKC4uRmlpKVpbW2FmZoagoCD2/Td79my1338Mw6CkpAQCgQAlJSWwtbVFXFwckpKSqCCmCaFAQ49VVFQgLS0NTU1Nkz5HsTn80fobzs7OCAwMZB9Q7ezs8NRTTyExMXHUQw3DMCgrK4NQKMSdO3fAMAy4XC5iYmLA5/Ph5+enke+NTI5MJsOlS5fg4uKCRYsWGeWDx+DgID7//PNxs6o9CZ/Px/bt2w1upEwmk+HKlSvo7+9HUlISpUg1UYqsgYolLpWVlexos2JTeXh4OO2hI1rT0dHBvv/Ky8shFovh6OiIiIgIREVFITw8XO2CoI/X5Jg7dy74fD7Cw8ON8vca+TcKNPRQa2srjh8/jtu3b6t0vuIXV3t7O2bPng0XF5cxf5A9PT2xY8cOzJ07d8zXOzo6kJOTg9zcXDZw8fX1BZ/PR0xMjME92Bma1tZW5ObmgsfjGe1a7vLychw5cgTd3d1TPtfR0RG7du1CZGSkFnqmPQzD4O7du6irq8Py5cvh7u6u6y4RPTI8PIzKykr2wU8x2hwYGMiurdfEaDMhY5FKpaipqWHff/fu3QOHw4Gvry872+Hv76/y8tTh4WFcu3YNAoEA9+7dg4eHB3g8HpYvX041OYwUBRp6ZGBgAKdPn0Z2djbkcrnK7djZ2WHTpk1YvHgxsrKykJmZyRbvG0toaChSUlIwZ86cMV+XSCS4fv06BAIBm7fbzs4O8fHxSEpKMtqHYF1RLJcbHh5GYmKiQe43eBKJRIKTJ08iKytLpfMXLFiA7373u2qPsk23pqYmFBYWIioqCsHBwbruDjEAHR0d7Np6xWizg4MD+9AXHh5OS1uJ1vT09CgVDBwYGICtrS2bQjcyMlKlrHgMw6C2thYCgQA3btyAhYUFli5dCh6Ph1mzZmn+GyE6Q4GGHpBKpRAIBDhz5gxbD0MVXC4XK1aswLp165SWYXR1deHkyZO4evXquOdyOBwsW7YMmzdvHvemwTAM6urqIBQKUVhYCJlMBg6Hg+joaPD5fISFhdEom5p6e3uRlZWFxYsXjxv4Gbr79+/j448/xv3796d8rpWVFZ599lksW7bMoN5ripzzM2fORExMjEH1negPqVSqVLCtqamJHW1WLHNRZ7SZkInI5XI0NDSw77+6ujowDANvb282hXNQUBC4XO6U2n348CFbk+Phw4dsTY558+bRnjUjQIGGDjEMg6KiIhw/fhwdHR1qtRUTE4OtW7dOuMGqvr4eaWlpqK6uHvcYS0tLrFmzBsnJyRMWB+vt7UVubi5ycnLYZS+enp7g8/lYtmwZrSdWwZ07d3D//n2jTVvLMAwuXryIEydOTJiSeTyBgYHYvXu3QW0iFIvFuHz5MjgcDhITE43y/5XozsOHD5VS6A4MDMDGxgbh4eGIiopCREQEXFxcdN1NYqQGBgaUCgY+fPgQlpaWCA0NZWc7PDw8Jt2eTCZDUVERhEIhqqqq4OzsjMTERCQkJFBNDgNGgYaO1NfX41//+hdqamrUasff3x8pKSkIDAyc1PEMw+DWrVs4fvw42tvbxz3O2dkZmzdvfuLIsVwux+3btyEQCFBRUQFgZNQ5NjYWPB4P3t7eU/uGTJBYLEZmZiYCAwMRHh6u6+5oRXd3N44cOYLy8vIpn2tmZoannnoKa9euNZiRWkUWrfb2diQmJtIvSaJ1crkcjY2NKC4uHjXarHjoCwoKomCXaAXDMLh//z4bdFRXV0Mmk8Hd3Z19/4WGhk44gPkoRU2Oq1evQiaTYeHCheDz+QgICKAZYQNDgcY06+rqwokTJ3Dt2jW12nF1dcXWrVtVXoYhlUohFArxzTffTLhca86cOUhJSUFoaOgT22xubmaL9ShqcoSGhrJToIbykDid6urqcPv2baNNWwsA169fx7Fjx1RaFujp6Yk9e/bA19dXCz3TjsrKSpSWlmLJkiUUaBOdUYw2K2Y8enp6Ro02u7u700Mb0QqxWIyKigo2hXNHRwfMzc0RHBzMvv+8vb2f+P4bHBxkU++3tbVhzpw54PP5WLJkCQXNBoICjWkiEolw7tw5ZGVlTbgx+0msra2xfv16rFixQiM/ZAMDA/j6668hFAon3IA+b948bN++HZ6enk9sc2hoCAUFBRAIBEo1ORRToLRxcWT08dKlS3B0dMTixYuN8pf94OAgvvjiiwn3Bk0kKSkJ27dvn/QImK61trYiPz+f/UVKiL5gGAbNzc3saHNVVRVkMhnc3NzYtfVTGW0mZCoYhkF7e7tSCl2JRAJnZ2dERkYiIiIC4eHhEw62MQyD0tJSCAQCFBcXw8bGhk1IY0jLaU0RBRpT0N8PVFcDYjFgZQUEBQFPSnojl8uRl5eHU6dOoa+vT+VrczgcJCQkYNOmTVp5UJ9MSl0zMzPweDxs3LhxUqPvDMOgvLwcAoFAqSbHokWLwOPx4O/vb5QP2E/S1taGy5cvG/UNsrKyEp988olKaWsdHBzwwgsvYO7cuVromeYNDAwgOzsbTk5OWLZsmXHP3KlyEyR6Z7zR5kcLts2aNcsk789E+yQSCaqrq9nAo7m5GRwOB/7+/mwKZx8fn3HvpR0dHcjOzkZubi6GhoYQFRUFPp+PiIgIrb5npdJ+DA1Vg2HE4HCsYGMTBC6X7n9PQoHGE5SWAocOAWfOALW1wKP/WhwOEBAArF8PvPIKEBGhfG5JSQnS09PR3NysVh+ioqKwffv2aVmGUVlZiX/9618TFgm0tbXF+vXrwefzJ51dorOzEzk5Obh8+bJSTQ4ej4fFixebxBQowzAoKCiASCRCUlKSUT6QSqVSfPXVV7hw4QJUubXMmzcPO3fuNIhZL6lUiry8PIjFYiQmJhpvAgR1boLEILS1tbEbysvLyzE8PAwnJyelFLrGurST6F53dzebwrmsrAxDQ0Ows7NjC1ZGRkaOuc9teHiYTb3f1NTE1uRYtmyZxgqgDgyUorn5EDo7z0AkqgXw6O81DqytA+Dquh7e3q/Azo7uf2OhQGMcdXXA3r1AZibA5QITJclRvJ6cDBw+DFhZNSM9PR0lJSVq9cHb2xspKSmImOZf3ooH4pMnT6Knp2fc49zc3LB9+3YsWLBg0qMIEokEhYWFEAgEaGhoAGAaNTn6+vqQlZWFhQsXGtR+g6lobm7Gxx9/jHv37k35XCsrKzz99NOIi4vT+1FURUKFpqYmxMXFGe17Vq2boL//9PWTaJRUKmVHm4uLi0eNNkdGRsLX19coB0qI7snlctTV1aG4uBilpaVoaGgAwzCYM2cO+/4LCAhQGuR8vCYHl8tla3LMnj1bpX4MDdWhsnIvurszAXABTJQpceR1F5dkhIQcho0N3f8eRYHGGFJTgVdfHfm9OZUsnObmDMzMZFi27J8IC8tV+fqOjo7YtGkT4uLidHozV2RDOn/+PIaHh8c9LigoCCkpKfDz85tS+/X19RAIBCgsLIRUKmVrcvB4PISHh+v9A+dkFRcXo7GxEatWrYKlpaWuu6NxDMPg0qVLyMjIUCltbUBAAF588cUppUHUlfr6ehQVFWH+/PnwN+aHaVVvglzuyMd77wEvvaS9/pFpoxhtVhRsGxwchJ2dnVLBNicnJ113kxipvr4+pRS6fX19sLKyQlhYGJvC+dElyL29vWxNjp6eHgQHB4PH42HBggWTrsnR3JyK6upXIZdLMXGA8TguzMy4CAp6D97edP9ToEDjMQcPAgcOqNMCA4CDmJiTWLjw7JTOtLCwQHJyMtasWaNXyzB6enpw6tQp5OfnT7gcZunSpdiyZQtmzJgxpfb7+vqQm5uL7OxspZociilQGxsbtfqvK8PDw8jMzISfn5/Rbg7u6enBkSNHUFZWNuVzzczMsGHDBqxfv17vR0e7u7uRm5uLWbNmTWkGzyCpfxMc8eabwOuvq98O0RuK0WbFQ59itHn27Nls0BEYGDjlgm2ETAbDMGhqamLffzU1NZDL5fD09GTffyEhIbC0tIRMJsOtW7cgFApRWVkJZ2dnJCQkICEhYcLAuKHhIOrq1L//+fu/CV9fuv8BFGgoSU0FXn5Zc+0lJv4fwsLyJnWsqg/p06mpqQnp6ekT1kKwsLDAqlWrsHbt2ikHS4qaHEKhkL2GlZUVli5dCj6fb1CpQhsaGnDz5k0kJyfD3kg3y968eROfffaZSmlrPTw8sHv3br2fFRCJRMjJyYGlpSXi4+ON/wFK0zfB1FRgzx7NtUf0Sn9/P8rKythlLr29vexos+LBz1gTXhDdGxoaQnl5Obu/o6urC1wuFyEhIez7b+bMmUqp96VSKRYtWjRmTY7m5lRUVmru/hcamgovL7r/UaDxrbq6kX2MIpGmWmRgbi5BSsobcHTsHPeo4OBg7NixY8rLjnSFYRjcvXsX6enpbOrasTg4OGDz5s0qL/9qaWmBUChEfn4+W5MjJCSErckx2SnQ6SaXyyEUCmFra4ulS5ca5ci3SCTCP//5TxQUFKh0fkJCAlJSUvQ6laZcLkdBQQF6enqQmJhotMGiEs3fBAFr65HN5HoeUBL1MQyDe/fuKRVsk8vl8PDwYDMJKUabCdE0hmHQ2trKvv8qKyshkUjg4uLCpnD28fFhZzkUNTl4PB6WLFkCmew+rl+PgFyuufufmZk1Fi8uNfk9GxRofGv1akAgmNpy5CfhcGTw9i7Hhg3vjnrN3d0d27dvx/z58w3yYVQmkyEnJwenT59ms0iNRd0N7SKRiC3W8+DBAwAjVcuTkpIQHx+vVxWXFSn3EhMT4e7uruvuaEVVVRU+/fRTdHaOHzyPx8HBAd/73vcQHR2thZ5pTmlpKSorKxEbG4uZM2fqujvTRxs3QS4X4POBCxc01yYxCCKRCOXl5eyDX2dnJ7hcrlLBNi8vL4P8/feo3//+98jIyEB5eTlsbGywfPly/OEPf5hUkVuiPRKJBJWVlez778GDBzAzM0NAQAAiIiJgaWmJiooKtibHunWXYGlZAkCmwV5w4eLCx7x5pn3/o0ADIwNu2lxCn5LyW7i4jDwk29raYsOGDeDxeEaxDGNwcBBnz57FpUuXJtwIHBkZiR07dqi8/IlhGFRUVEAgEOD27dtgGAbm5ubsFKgua3IwDINr166hv78ffD5f7/cbqEIqleLrr7/GuXPnVEpbGx0djZ07d+pVYPi4lpYWXL16FWFhYQgLC9N1d6aXtm+CpaVAeLj22id6jWEYtLW1obi4eNRosyLoCAsL01hK0um0du1aPPvss1i8eDGkUilee+01dikZpQTWH52dnWwK57KyMohEIjg4OGDOnDlgmDrMn/9XrV178eJS2NmZ7v2PAg0A+/cDH3yg2YE8BQ5HhoiIbCQkpE2p2J2h6ejoQEZGBm7cuDHuMYqig0899ZRaD5xdXV1sTY7+/n4AgI+PD/h8/rTX5Ojv70dWVhbmz59vMMvfpqqlpQWffPIJGhsbp3yupaUlUlJSkJCQoLcjl319fcjJyYGrqyuWLFlilIHiE2nzJsjlAvv2Ae+Ontklpkkx2qzIZtXS0gIzMzP4+/srLXPR13vGRNrb2+Hh4cHObhP9I5PJUFtby6Zwdnf/AsHBpTAz08bjMBezZu1DcLDp3v8o0MBIcduaGu217+7eg7t3xfD09NTeRfRETU0N/vWvf6G+vn7cY6ytrbFu3TqsXLlSraBAIpHgxo0bEAgE7PXs7OwQFxc3LVW3S0pKUFdXh+TkZL3eb6AqhmGQnZ2N9PR0SCSSKZ/v5+eH3bt36+37XiKRIDc3FzKZDAkJCUb5fzhp2r4JBgUBVVXaa58YtM7OTqUUuiKRCPb29oiMjERERAQiIiL0ejb0UdXV1QgODsbdu3cRFRWl6+6QSbhyJQDDw3Vaa9/aOgixsaZ7/zP5QKOvD3ByUi52q2kcDtDbC5jCflJg5AG1sLAQGRkZ6OrqGvc4V1dXbN26FTExMWqPXI1Vk2Pu3Lng8XiIiIjQ6MiYRCJBZmYm5syZg7lz52qsXX3y8OFDHD16VKWikxwOh01bq4+b9hmGwY0bN9DS0oL4+Hi4uLjouku6RTdBokceHW0uKSlhZ1J9fHyUCrbp471FLpdj06ZN6OnpQW6u6rW0yPSRSvuQm+sE5YrfmsZBfHwvuFzTvP+ZfKBx6xawYIH2r1NUBMyfr/3r6BOJRIKLFy/i7NmzEE2Qycbf3x8pKSkIDAxU+5p9fX3Iy8tDdnY2G+R4eHiwNTnUXQPc2NiIwsJCJCcnw8HBQe3+6qOioiJ89tlnE27yH4+bmxv27NmDgIAALfRMfbW1tbhz5w4WLlwIHx8fXXdHP9BNkOix3t5epYJt/f39sLa2ZgsGRkREwNXVVdfdBADs27cPZ8+eRW5ursoVqcn06uu7hRs3tH//W7SoCA4O87V+HX1k8oHG1atAbKz2r/PMM+/Ay2vqa9yNgVgsRl1dHe7fvz/hRmJPT08EBQVppEAfwzBob2/HvXv32IDD3NwcXl5emD179pTTlSpSN3K5XMycOdMg1w4/iVQqRWVlJZqbm1U639vbGyEhIXqZ5GBoaAjNzc1wdHSEm5ubUf7//f/27jyorTPdE/9XQoDYEQJhsA0GsxnjeMU2GDCyjRPbiVfoSnfSiWN3Tzq32j2/mZqaP7r73pmq6dTUVNf0H7dT08kt7Did3m7Aa7zFxBarN7ybzRiDsY0wWhCbkITEOb8/iM5FIGEE2vV8qlzgcM7RC8GPznPe932euUrq7cV/+fd/d/rr9Jw4gajt2xEWFkY/fzInLMvi+fPn3Nr6rq4uMAyDhIQEbrYjPT3dpfv0zH75y1/izJkzqK2t9fj+QOQ/DA3dxN27zr8JXLPmBiIjNzj9dTyR3yca9DDPdeRyOSorK2dcjiMQCLBlyxbs2LHDYRVIent7UVNTg2vXrnE9OdLT0yGVSrFq1arXTsGr1WpUV1ejsLAQEonEIWPyNE+fPsWxY8egUqnsPjcsLAwffPABVnngL/jo6Chqa2sRGhqK/Px8j0yC3M5FQfB/7d+Pl7GxCAkJQVxcHCQSybSPkZGRlISQWRsdHbUooavRaBAYGMg1bMvJyYFEInHq7xTLsjhy5AhOnTqF6upqpKenO+21iOPRjIbz+X2iMTICREbS8mRXam5uRmVl5YxPzsPCwvDOO++gqKjIYWtx9Xo9bty4gerqavT29gKY6MlRWFiIoqKiaZsNzXtNBgcHIZVKPXJN8HyNj4/j3LlzuHjx4pzK1i5fvhwffvghoqKinDC6uRsfH8e1a9cwMjKCoqIin6z05jAuCIIsjwd5ayv6tFoolUooFAruo0aj4Y4LCgqaloCYPxeJRJSEEJtYlkVvby/XJfrJkycwmUwQi8UWJXSFQqFDX/ef/umf8Le//Q1nzpyx6J0RFRXlkNl54lwm0wjq6yNBezScx+8TDYAKrrgDwzBoaGjAmTNnMDw8bPO4+Ph4lJaWYsWKFQ67yTD35Kiursb9+/e5nhxr1qyBVCpFamoqRkdHUVVVhTfeeMNj9xvMV19fH44ePYru7m67zw0MDERpaSk2b97sUTd/LMuiqakJnZ2dyMvL89kZKIdzYxA0Go1QqVQWyYf5c7VazSXAAoHAIvEwf5RIJIiJifHPssTEJoPBgCdPnnA9Lfr6+sDn85GWlsYlHosWLZp3/LJ1/pdffomDBw/O69rENW7cSINe77z4R1WnKNGgEvJupNfrcenSJVRVVc3Y8C8rKwulpaVYvHixQ1/fWk8OoVCIhQsX4pNPPvHJDd8sy6K2thYVFRVzKlublJSEw4cPe1zX7JcvX6KxsRE5OTm0fMFeHhoETSYT1Go1lErltCREpVJhfHyiiy+fz0dsbKzVJEQsFtOSOQKVSsUtsWpra4PBYEBkZCSys7ORk5ODZcuW2b13j/iGJ09+hZ6ePwFwQvyjPhqUaADUFNcT9Pf34/Tp07h586bNY3g8HvLy8rBnzx5ER0c79PWNRiNu3ryJo0ePYnh4GGKxGKGhoSgoKHBJTw5XGRoawp///Gc8evTI7nN5PB7eeustvP322x514zY4OIi6ujrEx8c7pFSyX/LCIMgwDPr7+6clIOY/5iSax+MhJibG6p6Q2NhYBAUFOXRcxPOZTCY8ffqUSzxevnwJHo+H5ORkbrYjJSWFZsn8hFbbgsZG58U/6gxOiQYAYPt2QCZz7AM9gQCQSoHLlx13TV/37NkzfPPNN3g6wzKOoKAgvPnmmw5tlPfy5UvcunUL27ZtQ39/P6qrq9HY2Mj15MjJyYFUKnV4Tw5XevDgAb7++usZl6rZIhaLcejQIaSlpTlhZHMzNjaGuro6AEBhYSHdMM6XDwVBlmUxMDAwLQkxfzQXhQAm9mlNnQUxL9Fy9Hp+4pkGBgYsGgZqtVqEhoZyJXSXL1/u8IdbxLM8eLAdGo0Mjp3VEEAkkmLlSv++CaRE4wddXUB2NjBDuwe7CYUTD/Ko0p19WJbFvXv3cOLEiRmrIEVHR2Pv3r3YuHHjnG/+zd2vAwMDkZ+fb3GdkZERNDQ0oLq62qInx+bNm5Gfn++wqljOZjAYUFFRwd2U2ysvLw/vvvuux9x0sSyLW7duQalUWt3ET+bIT4Igy7IYHh62moQoFArodDru2MjISIt9IZMTEm/590/swzAMuru7udmOrq4usCyLxMRE5OTkYPny5UhLS/OoWV0yfzpdFxobs8Ewjot/fL4QubktCAnxnPjnDpRoTFJeDvz854693uHDjruevzGZTJDJZDh//rzFm/9USUlJKCsrQ0ZGhl3X7+/vh0wmQ0FBAeLj420exzAMHj16BJlMhtbWVgATsyobNmxAcXGxRzdm6uzsxJdffgmFQmH3uWFhYXj//fexZs0aJ4xsbtrb29HS0oLc3FwsXLjQ3cPxPX4eBFmWxejoqNVZEKVSaTEbGBYWZnUWRCKRIDw83GtnPoklrVZr0TBwcHAQQUFByMzM5GY7qOiEb5DLy9He7rj4l5lZjoQE74l/zkKJxhSffgr89reOuc6vfz3/65CJmYVz586hpqYGDMPYPG7lypU4cODAjEmD2e3bt6HRaLBlyxa7yta+evUK1dXVuH79OtftPD09HcXFxVi9erXHlMBlGAbnz5/HhQsXZvyZ2bJs2TIcPHjQY5YLKBQKXLt2jasYQzdxTkRB0CadTjetPK/54+DgIHecUCi0OgsSFxeHqKgo+v31UizLoqenh0s6Ojo6MD4+jri4OC7pyMzMdNiSXuJ63d2foqtr/vEvJeVTJCf7VvybK0o0rCgvB44cmViqbM9yZYFg4s9nn3nVQzyv0dfXhxMnTuDBgwc2j+Hz+SguLsbbb79ttXfC6OgoLl++jBUrVmDp0qVzHoter8fNmzchk8m4nhxRUVEoKipCYWGhW/tKKBQKHDt2DF1dXXafKxAIcODAAUilUo+4GdJqtaitrUVkZCTy8vJoc6arUBC0m8FgmFamd3KvEPNbbWBgoM0kRCQS0e+4FzEYDHj8+DHXqVylUiEgIADp6elc4pGYmOgRsZTMnlxejo6OI2AYE+zbsyEAny9AevpnNJMxCSUaNnR1AR9/DFRVTbxvzvRea/56SQnwxRcetRzZJz1+/BgVFRV48eKFzWNCQ0Oxa9cuFBcXc2tpHz9+jPb2dpSUlDhsvwHLsnjy5AlkMhnu378PhmHA5/Oxdu1aFBcXY+nSpS57k2FZFvX19fjmm28wNjZm9/mLFy/GoUOHkJiY6ITR2cdkMqGhoQF6vR5FRUXU+ModKAg6jLlXyNSlWAqFAmq1mpt1FAgEiI2Ntdq0UCwWe8yMKZmOZVkolUqLErpGoxHR0dFYvnw5srOzsWzZMmoe6iV0ui60t38MjaYKgAAzJxwTXxeJSpCR8YXf78mYihKN12hpAT7/HLh4caKf1eSfFo8HLF0K7NgxUSaeSti6DsMwuHHjBk6fPm2xZGGquLg47N69GyqVComJiVi1apXTxqTRaLieHOa13IsWLYJUKsX69eudWhVpeHgYX3/99YyzPbbweDxs374du3fvdvsGR5Zlcf/+fbx48QKbNm2CWCx263gIKAg62fj4ONcrZOrG9Km9QsRisc0yve7+t0ssGY1GdHR0cImHXC4Hj8dDSkoKli9fjpycHCQlJdEMlofTalsgl38OtfriD039Jt8y8yAULoVYvAOJiZ/4dQnbmVCiYYeREaCjAzAYgODgiWa31N/HvQwGAy5fvozLly9bfYqv1WqhUChQUFCADz74AEuWLHH6mEwmE+7cuYPq6mp0dnYCmJhhyc/PR3FxMeLi4hz6eo8ePcJXX301p7K1MTEx+Oijj+zeSO8M3d3duHv3LlauXOmz3di9HgVBl2IYBhqNxmoSYq1XiLXO6XFxcVT62QNoNBq0tLSgqakJra2t0Ol0CAsLQ3Z2NrfMiiroeTaTaQQ6XQdY1gAeLxghIWkQCCj+vQ4lGsQnDAwM4MyZM7h+/TpYlgXLsnj16hUAYMGCBdzypQ0bNmDfvn0QiUQuGVd3dzeqq6tx69YtrifH8uXLIZVK572p2WAwoLKyErW1tXM6f8OGDfjxj3/s9mVJGo0G9fX1WLhwIVavXk3rmQmZBZZlMTg4aHVPiEKhmNYrZOpSLPMeEU8pW+1PGIZBV1cXmpqa0NLSgu7ubrAsi8WLF3NJR2pqKs1SEZ9AiQbxKS9evMBXX32F2tpaxMfHW611HxgYiJKSErz55psue5M19+SoqamBWq0GMLGsq7i4eE49OZ49e4Zjx46hr6/P7rGEhobivffew7p16+w+15H0ej1qa2sRGBiIgoICBAYGunU8hPgKlmUxMjJidRZEoVBgdHSUOzYiImLaLIj5c9pP4BrDw8MWJXSHh4cRHByMrKws5OTkIDs7G7Gxse4eJiFzQokG8Sl37tyBUqlEXFwczpw5M+ONeGRkJHbv3o1Nmza5bJ0swzBoamqCTCZDS0sLgInEZ8OGDZBKpa/tycEwDC5duoRvv/12TmVrs7KycPDgQZfN6Fhj3l8zMDCAwsJCREREuG0shPgjrVZrsSF9chIyeQlmaGiozSQkIiKCZh8B1NbW4ve//z3u3LmD3t5enDp1Cnv37p3z9ViWxYsXL7ik4+nTp2AYBvHx8dxsR0ZGBi2HI16DEg3iE3Q6HS5fvozs7Gykp6cDmNhkWVtbi2+//RZardbmuQsXLkRpaSmys7NdNVwAE+V6a2pquOpKAJCWlgapVIpVq1ZNmzZXKpU4duwYt+/DHgKBAPv27cPWrVvdenPQ2tqKx48fY8OGDUhISHDbOAgh1un1+mkJiPnvAwMD3HHBwcFWE5C4uDhER0f7TRJy8eJFNDQ0YO3atdi/f/+8E42pdDod2trauP0d/f39EAgEyMjI4BKPycuDCfE0lGgQr9fe3o7W1lZs377d6n6D0dFRXLhwAVevXuUquFiTk5OD0tJSl98AGwwGrieHXC4HMDHbMrknx7Vr1/Dv//7vFuuuZ2vhwoU4fPiwWztp9/b24ubNm8jMzMQyqkxEiFcaGxvjeoVM3RvS398/rVeItX4hvtwrhMfjOTzRmIxlWfT19XGzHe3t7TAajRCJRMjJycHy5cuRlZXl9n13hExGiQbxWiaTCVeuXEFcXBzWrFnz2uOVSiVOnjyJu3fv2jyGz+ejoKAAu3fvdvmSHms9OcbHx6HX62EymRAZGWn3U6uSkhLs2bPHbfsfhoeHUVdXh5iYGKxfv95nbzAI8Xcmk2larxDzR5VKxS31DAgIQGxsrNUkxNt7hTg70ZjKaDSivb2dSzxevXoFPp+P1NRUbrYjKSmJZjuIW1GiQbySXC7H9evXsXXrVkRHR9t1bkdHByoqKvDs2TObxwiFQuzYsQNbt251y026RqPBX/7yF/z5z3/GyMgIACA8PBwJCQmQSCSvfTMWiUQ4ePAgsrKyXDHcaYxGI+rr62EymVBUVITg4GC3jIMQ4n7j4+Po7++fthTL/Lnph2aQfD4fMTEx05ZimXuFeHrBCFcnGlOp1Wo0NzejpaUFra2t0Ov1iIiI4EroZmdn05444nKUaBCvYu5+zbIsCgsL5/ykhmVZNDY24uTJk9BoNDaPE4vF2LdvH9atW+eyp0JjY2M4efIkZDIZGIaBSqWCXC7H0NAQgIn9FgsWLEBCQoLVKfLc3Fz85Cc/sbuSlSOwLMttiiwoKHDrpnNCiOdjGAYDAwM2y/RO7hUSHR09bRbEnIx4wsMMdycak42Pj6OzsxPNzc1oamrCixcvAADJyckWJXRplpk4GyUaxGsMDAzgypUryMvLQ2JiokOuaTQa8f333+PixYsz7n9ITU1FWVmZ0xvJPX/+HEePHuV6gEw2MjICuVwOhULBLUOIiYlBYmIiRCIRQkND8ZOf/ATr16936hht6ezsxIMHD7BmzRokJye7ZQyEEN/BsiyGhoZsJiHmIhoAEBUVZbVZoUQicdmeBU9KNKYaGhpCS0sLt8xKq9UiJCQEWVlZXOIRExPj7mESH0SJBvEK9+7dQ19fH7Zt2+aUJkZDQ0M4e/YsN1tiy7p167Bv3z6H1zRnGAbfffcdzp49+9qytUajEX19fZDL5dwb7cKFC/HJJ5/grbfecnnte5VKhYaGBiQnJ2PlypW0HpgQ4nTmXiHW9oQoFAqLSoPh4eFWZ0EkEgnCwsIcFrM8OdGYjGEYPH/+nEs6Ojs7wbIsEhISuKQjPT3d45eqEe9AiQbxaHq9HpcvX0ZmZiYyMzOd/npyuRyVlZVobm62eYxAIMCWLVuwY8cOhyxPUqvVOHbsGDo6Ouw6z9wZWCwWc13HAwMDsX79ekilUixevHjeY5uJTqdDbW0tQkJCkJ+fT11sCSEeY3R01OosiFKp5JahAkBISIjVWRCJRDKrXiEjIyNc7F69ejX+8Ic/QCqVIiYmBklJSU79Hh1ldHQUbW1taGpqQnNzMwYGBhAYGIjMzEwu8ZBIJPQQicwJJRrEY3V0dKC5uRklJSUu32/Q3NyMyspKrtysNeHh4XjnnXdQWFg4p0opLMvi5s2b+Pvf/26xBGC2EhMTcejQISxevJjryXHt2jXodDoAwNKlSyGVSrF69WqHJgEMw+DatWsYHh5GUVERdQ8mhHgVc68Qa00LJ+/ZCw4Otpj9mJyEmHuFVFdXQyqVTnuNDz/8EMePH3fhd+UYLMuit7eXm+148uQJTCYTYmNjkZ2djZycHGRmZkIoFLp7qMRLUKJBPI7JZMLVq1cRExODdevWuW0cDMOgvr4eZ8+eteiWO9WCBQtw4MABrFixYtZPfLRaLf7617/izp07cxrb1q1bsW/fvmlT2+aeHNXV1ejp6QFg2ZPD3gpdk7Esi6amJnR2diIvLw8SiWTO1yKEEE9kNBqnVcYyf1Sr1dzSWoFAYHNPSExMjM9ssjYYDBYldBUKBfh8PtLS0pCTk4Ps7GwsWrSIZjuITZRoEI/y6tUrNDQ0YMuWLR5TsUiv1+PSpUuoqqriyjBak5WVhbKyMixatGjG67W2tuL48eMWXXZnKzo6GgcPHnxt0zuWZdHR0QGZTIZ79+6BYRjw+XysXr0aUqkUaWlpdr0xvHz5Eo2NjcjJyeE6rxNCiD8xmUxQq9XTZkEUCoVFrxA+n4/Y2FirndPFYrFXLzNVKpVc0vH48WMYDAZERkZyS6yys7NplptYoESDeASWZdHQ0IDx8XEUFRV55NMRtVqN06dP49atWzaP4fF4yM/Px549exAVFWXxNaPRiFOnTuHKlStzev01a9bg/ffftzuIDwwMoK6uDrW1tdza5IULF0IqlWL9+vUzloUcHBxEXV0dJBIJcnNzPfL/CyGEuBvDMOjv77eahEzuFcLj8SAWi612To+Li/OqDdgmkwkdHR1cNauXL1+Cx+MhOTmZ61S+ZMkSn5ndIXNDiQZxu6GhIXz//fdYv379a2cDPEFXVxcqKirw9OlTm8cEBwfjzTffRElJCYKCgvDy5UscPXp0xj0ftgiFQvz4xz/Ghg0b5nWjbzKZcO/ePchkMm7s5o3cxcXFFkuhxsbGUFdXBwAoLCxEUFDQnF+XEEL8GcuyXK+QqQmIUqm0KK0uEomsJiBxcXEevy9iYGCASzpaWlowOjqK0NBQi4aB81m+S7wTJRrErR48eAC5XI5t27Z51ZMclmVx7949nDhxAiqVyuZxUVFRWLhwIdra2l5bttaatLQ0HDp0CGKxeD7DnebFixeQyWS4desW1xBr+fLl2Lx5M0ZHR6FWq1FYWDhtVoYQQojjsCyL4eFhq0mIQqHginsAE/vtpiYh5o/uaNA6E4Zh8OzZM26Z1bNnz8CyLBYuXMgts0pLS/PqZWRkdijRsMPICNDRARgMQHAwkJYGhIe7e1TeSa/Xo6qqCunp6cjKynL3cObMvHH9woULFm8IwMQmusePH2NgYADh4eFITU2d9dMcPp+PPXv2YPv27U6ddtZqtbh27Rqqq6vx9OlT9Pf3IzMzE++88w42bdpEa22JJQqChLgMy7IYHR21mYSMjIxwx4aFhdlMQsLDw92+7FWr1aK1tZXrVD40NITg4GCLErpxcXFuHePrmEwj0Ok6wLIG8HjBCAlJg0BA8e91KNF4jZYW4PPPgQsXgM5OYPJPi8cDUlOBnTuBX/wCyM523zi9SWdnJx4+fIiSkhKfuZEdGRnBuXPnUFNTA4ZhoFAo0NHRMW3zeGxsLFJSUmbsVLtgwQIcPnzYZTXYFQoFGhoawLIsXr58yfUQMffkKC4u9pp68MQJKAgS4pHMvUKmLsVSKBQYHBzkjhMKhVYTkLi4OERFRbk8CWFZFj09PVzS8fTpU4yPj0MikXBJR0ZGxoz7B11Fq22BXP451OoL0Os7AUy+ZeZBKEyFWLwTiYm/QFgYxT9rKNGwoasL+PhjoKoKEAiAGYoNcV8vKQG++AJISXHdOL3J+Pg4ZDIZoqKisG7dOrc/YXGGzs5O/PM//zPu3r1r8xgej4fExEQkJSVNWy4mlUqxf/9+l+yJ0Gq1qK2tRWRkJPLy8riZE4VCgZqaGjQ0NHCzNKmpqZBKpVizZg1NdfsLCoKEeC2DwWC1TK9CobDoFRIUFGSzTK9IJHLJ+7Rer8fjx4+5xEOtVkMgECAtLQ3Lly9HTk4OEhISXHrPoNN1ob39Y2g0VQAEAGaIfz98XSQqQUbGFwgJofg3GSUaVpSXA0eOTLxvzvTeOpVAMPHnj38EfvYz543PG/X19aG+vh7FxcUO32/gKdra2nD8+HFoNBoMDAygs7PTYmp7KoFAgKSkJCQmJnJla5cvX+70cZpMJjQ0NECv16OoqMjm7IrBYMCtW7dQXV2Nly9fAphYI1xQUICioiKPKT9MnICCICE+y2g0QqVSWU1CrPUKsda0UCwWO2VZL8uyUCgUFiV0jUYjoqOjudmOZcuWOXVPilxejo6OI2AYE2ZOMKYSgM8XIC3tj0hMpPhnRonGFJ9+Cvz2t/O/zu9+B/zmN/O/jrdjWRbXr1/H2NgYioqKfLLMnclkwunTp1FVVWXx31mWRV9fH549e4axsTGb56ekpOA3v/kN8vPznfrEhmVZPHjwAM+fP0d+fj5iY2NnfV5HRweqq6tx9+5drifHqlWrIJVKkZ6e7pOzU36LgiAhfstkMqG/v9/qnhCVSoXx8XEA/9ErxNpsSGxsrMNmvo1GIzo6OtDU1ITm5mb09vaCx+MhNTWVSzySk5Md9h7U3f0purrmH/9SUn6H5GSKfwAlGhbKy4Gf/9yx1zt82HHX8zbmsrW5ublYvHixu4fjFD09PTh69CjXhdua8fFxvHz5Ei9evLCoPBUQEIC0tDRIJBLweDykp6ejrKwMycnJDh9nd3c37t69i5UrVyI1NXXO17HWkyMxMRFSqRQbNmzwiDW1ZB4oCBJCbGAYBhqNZtosiHmJlrmCIY/HQ0xMzLQkRCKRIDY2dl5LgzUaDTfb0dLSAr1ej7CwMGRnZ3OdyiMjI+d0bbm8HO3tjot/mZnlSEig+EeJxg+6uib2Mer1jrumUDixj9Iflys/fPgQPT09Xle2drZYlsWVK1dw6tSpGbuFT2YwGPDs2TMoFApEREQgKyvLal30DRs2YN++fQ5ZmqTRaFBfX4/ExESsWbPGYU99zD05qqur0dHRAWBiw6G5J0d8fLxDXoe4EAVBQsgcsSyLwcFBq0mIQqGw6BUSHR1tNQmxt1fI+Pg4urq6uMSju7sbALB48WJutmPp0qUICAh47bV0ui40NmaDYRwX//h8IXJzW/x+zwYlGj/Yvh2Qyexbjvw6AgEglQKXLzvump7OYDCgqqoKqampyPbRCjQajQbHjx9HW1ub3efy+Xxs2LABKpUKT548sXlcYGAgSkpK8Oabb86pSZPBYEBtbS0EAgEKCgqcmuy9ePEC1dXVuHnzJvdEKzs7G1KpFDk5OT65XM4nURAkhDiBuVeItVkQhUKB0dFR7tiIiAirCUhcXNxrq1QODw9bNAwcHh6GUChEVlYWl3jY2iP64MF2aDQy2Lcn43UEEImkWLnSv+MfJRqYeODmzD24LS3AsmXOu76nePbsGe7fv+9TZWunun37Nv76179aBMbZio+Px+HDh5GcnAyWZfHw4UNUVlZCoVDYPCcyMhJ79uxBfn7+rG7YGYbBzZs3odFoUFhYiIiICLvHOVejo6O4du0aZDIZ18RQLBZj8+bNKCgo8NnfCZ9AQZAQ4iZardbqLIhSqcTw8DB3XGhoqNUkRCKRTOsVwrIsXrx4wc12PH36FAzDID4+Hjk5OVwJ3cDAQGi1LWhsdF78y81tQViY/8Y/SjQA/OpXwJ/+5NgHeWYCAfDJJ8C//qvjr+0pGIaBTCZDeHg41q9f75Mbg0dHR/GPf/wDN2/enNP5mzdvxoEDB6btYTCZTKitrcW5c+eg1Wptnr9w4UKUlZVh2Qw3a62trXj8+DE2bNiAhISEOY3TEViWRXNzM2QyGZqbm8GyLAIDA5Gbm4vi4mKn7EEh80RBkBDigfR6vc0kZGBggDtOKBRaVMeanIRERUVBr9ejra2NK6Gr0WgQGBiIjIwM5OTIwOd/C2DcCd+BAAsXfoL0dP+Nf5RoYKK57dOnzr3+DKtkvJpCoUBdXR02b9486ypG3qa9vR3Hjh2zqD0+WxEREfjwww+xYsWKGY8bHR3F+fPnIZPJuKoe1uTk5KC0tNQikejt7cXNmzeRmZk5YyLiDuaeHNeuXeNmgVJTU1FcXIy1a9dSTw5PQUGQEOJlDAYDVCrVtKVYSqUS/f39XJnewMDAaRvSeTweVCoVnj9/jrS0/4Hw8CGnjVMoTMPGjf4b//w+0RgeBqKiLJvdOhqPx6Krqx/hPtSpnmVZ3LlzBzqdDps2bfLJdfgmkwmXLl2CTCbDXP6ZLF++HD/60Y/sWr6kVCpx/vx5PHz40OYxfD4fGzduxKZNm/Do0SNER0dj3bp1Hv3/YGxsDHfv3kV9fT3kcjkAIDw8HBs3bkR+fj6io6PdO0B/NjyMmNRU8JwbBIGhIfhUECSEeCyTyWSRhEz+qFaruQqQQiGDvXvL4dyFGDwUFAxBIPDP+Of3icb9+8Dq1c5/nf37/xdiY186/4VcwGg04uXLl4iNjXXpHgBX0mq1ePz48YwN92wJCAjA0qVLER8fP+dlZIODg+js7LRYnzrZ6Ogo+Hw+MjMzsXjxYo9OMiYzbwqUy+VQKpVgWRY8Hg9isRiJiYmIioryyaV3nmyRSoV/PnnS+S907x6wapXzX4cQQmYwPj4+qVfIdQAOLOltw9q19xARscrpr+OJ/H7dwqSKa07FML7xo+7v78fIyAiSkpJmVTLO27AsC7lcjq6uLoueF7MVGRmJzMxMm922ZysqKgqrVq2CUqlEV1cXVxpQr9fDZDIhNDQUfD4f3d3d6Ovrw5IlSxAXF+fxN+k8Hg+RkZGIjIxEamoqent70dvbC5VKBZVKhdDQUCQmJkIikdCyKhcRzOH3fE5cFWwJIWQGAQEB3H6OoaER3L3r/NdkWf+Nf37/Tu6q/mJ8vhM2WbrQ+Pg4enp6EB4ejqSkJHcPxykMBgPa29vntBeDx+MhKSkJSUlJDrvZ5/F4kEgkEIvFePLkCbq6uhAUFDSt3K15k5tcLkdqauqcmxW5WlBQEJKTk7F48WKo1WrI5XIMDg6io6MDz549g0QiQWJiIkJDQ909VJ9mctFsWHt3NyKSkhAbG+uTvXUIId6Hx3PNTaCrXscT+f3SqZERIDKS9mjM5MWLF3j48CGKiop8dqnUgwcP8M0330Cn09l9bmxsLN577z2nVFPq7+/HzZs3sWjRIiQnJ+PSpUu4efPmjHtGVq1ahV27dtmsF+7J5HI56uvrcffuXYyNjQEAMjIyUFBQgOzsbK9ZIuZVRkYQk5Li1D0aLID//NFHMAQGgsfjITo6elplGPMTRuouTwhxFZNpBPX1kZiIUs5CezT8OtEAqOCKLQzDoLq6GqGhodiwYYPHL8uZC71ej7///e+4cePGnM4vLCxEWVmZw2+OdDodamtrERISgvz8fItlRD09PaioqEBra6vN8wUCAbZu3YodO3bMexmXO5h7ctTU1HB9Rsw9OTZt2oRwb83aPZWTgyCblobBxsZpmzLNH/WTupFHRUVZTUAkEolX/i4TQjzbjRtp0OudF/+o6hQlGlRC3gqVSoWamhoUFRUhLi7O3cNxiidPnuDLL7+EWq22+9yIiAj89Kc/xcqVKx06JoZhcP36dQwNDaGoqMhmkztzr4rKykr09vbavF54eDjeeecdFBUVeeVsAMuyaGlpgUwmQ1NTE1iWhUAgQG5uLqRSKfXkcBQ3BkGWZTEyMjIt+TB/Prm/THh4+LQkxDwzEhoa6pMPQwghzvXkya/Q0/MnOLYruBn10aBEA9QUdzKWZXHr1i2MjIxAKpV65c3p65hMJpw7dw6XLl2aU9naFStW4IMPPnDoXghz4tDR0YG8vDzEx8fP6jyGYVBfX4+zZ8/arFAFAAkJCThw4ABycnK89mbMnPzW19dzPTlSUlIglUqpJ8d8eXAQHB0dtZmEDA39R+370NBQi9mPyUlIRESE1/7eE0KcizqDOxclGj/Yvh2QyRz7QE8gAKRS4PJlx13TmbRaLaqqqrBq1SosWbLE3cNxit7eXhw7dgzPnz+3+9zAwED86Ec/QmFhoUNvWnp6etDY2Ijs7GxkZGTM6Rp6vR4XL17E999/D9MMv8TLli1DaWkpFi1aNNfhut3Y2BgaGxshk8nw4sULABMzTAUFBdi8eTNEIpGbR+ilvDAImrsGT01AlEqlRVGH4ODgaQmI+WN0dDQlIYT4uQcPtkOjkcGxsxoCiERSrFzpJTeBTkKJxg+6uoDsbGDSUuF5EwonHuSlpDjums7S0tKCzs5OlJSU+ORmTJZlUVNTg8rKShiNRrvPT05OxuHDh2c90zAbQ0NDqK2thUQiQW5urkNudtRqNU6dOoXGxkabx/B4POTn52PPnj2Iioqa92u6C8uy6OzshEwmw507d8AwDHg8HlatWoXi4mJkZmbSDaQ9fCwIGo1Gq0mIQqGw6BosEAiszoLExcUhJibGJ2d1CSGWdLouNDZmg2EcF//4fCFyc1sQEuIFN4FORInGJOXlwM8d2LelvBw4fNhx13MGo9GIqqoqLF68GCtWrHD3cJxicHAQX331FZqbm+0+l8fjYefOndi1a5fD+oaMjY2hrq4OwMRm8qCgIIdcd7LOzk5UVFSgs7PT5jHBwcF48803UVJS4pQxuNLg4CDq6upQV1eHgYEBABPLxYqLi7Fx48ZpJYGJDX4SBE0mE9RqtdUkRKVScT10AgICEBsba7Eky5yEiMViWq5HiA+Ry8vR3u64+JeZWY6EBM+Lf65GicYUn34K/Pa3jrnOr389/+s404sXL9DY2Iht27Z5Te8Fe927dw9ff/21xYbS2YqNjcWhQ4ewdOlSh4yFZVk0NjZCoVCgsLDQ6bMJLMvizp07OHXqFFQqlc3joqOjsW/fPp+oLDY+Po779+9DJpPhyQ+l3oRCIfLy8lBcXIwFCxa4eYRewJ+CoBUMw3Bdg6dWyFIqldzSRHNH+6kJiEQioV4hhHip7u5P0dU1//iXkvIpkpO9L/45AyUaVpSXA0eOTCxVtme5skAw8eezzzzyIR6HYRjU1NQgODgYeXl5Xn9zaY1er8c333yDhoaGOZ2/adMm/OhHP3LYk/AnT56gubkZ69atc/n+CJPJhKtXr+L8+fMWZUSnSk5ORmlp6Zz3iXianp4eVFdX48aNG1xPjqysLEilUrzxxhu0JGYmvh4E54hlWQwMDFhNQhQKBfd7BgAikchqEkK9QgjxbHJ5OTo6joBhTLBvz4YAfL4A6emf0UzGJJRo2NDVBXz8MVBVNfG+OdN7rfnrJSXAF1949p4MtVqN6upqFBQUOHS/gSd5+vQpjh07NuNTfFvCwsLw05/+FKtXr3bIWBQKBa5fv47U1FS3V3waHh7GuXPnUFtbyy0NsWbVqlU4cOAAJBKJC0fnPKOjo7h+/Tqqq6u5nhwxMTFcTw5fbUI5b74aBJ2EZVkMDQ1ZzH6YP+/r67NI8iMjIy0SkMlJSGhoqBu/C0IIMLFno739Y2g0VQAEmDnhmPi6SFSCjIwv/H5PxlSUaLxGSwvw+efAxYsT/awm/7R4PGDpUmDHjoky8Z5cwpZlWdy+fRuDg4OQSqUO22/gScbHx3H+/HlcuHBhTmVrly9fjg8//NAhS5pGR0dRU1ODiIgI5OXledTPu7e3FydPnsTDhw9tHsPn8yGVSrFr1y6bvTy8jbknR3V1NR49esT15Fi3bh2kUqnPVlqbN18Jgm7Esiy0Wq3VWRClUomRkRHu2LCwMKuzIBKJBGFhYT45A02Ip9JqWyCXfw61+uIPTf0m31vwIBQuhVi8A4mJn/h1CduZUKJhh5ERoKMDMBiA4OCJZrre0KDYXLb2jTfeQGpqqruH4xR9fX04duwYnj17Zve5gYGBKC0txebNm+f9Jm4ymXDt2jXodDoUFhZ69NPJtrY2VFRU4OXLlzaPCQ0Nxdtvv43Nmzf71MZXaz05lixZwvXkoPX1NnhrEPRw5l4h1vqFTO4VIhQKbSYhkZGRlIQQ4kQm0wh0ug6wrAE8XjBCQtIgEFD8ex1KNHxca2srOjo6sH37dp9cF8yyLOrq6lBRUWGxPnq2kpKScOjQISQkJMx7HA8fPkR3dzfy8/MRGxs7r+u5irkT+enTpy1uaKaSSCTYv38/Vq1a5VM3M9Z6coSHh6OwsBBFRUWIiYlx8wiJvzMYDNM2pJuTkMm9QoKCgqYlIObPRSKRT/27JYR4D0o0fJTRaMT333+PxMRErFy50t3DcYqhoSH8+c9/xqNHj+w+l8fj4a233sLbb7897yf1z58/x507d/DGG284rEKVqxkMBly+fBnffffdjH1G0tPTUVZWhuTkZBeOzvlYlkVXVxfXk2N8fBw8Hg8rV66EVCqlnhzEIxmNRqhUKqtd09Vq9bReIdb6hVCvEEKIM1Gi4YNevnyJW7du+XTZ2gcPHuDrr7/G8PCw3eeKxWJ89NFHSE9Pn9cYNBoN6uvrkZCQgLVr1/rEjahGo8Hp06dx48aNGY/buHEj9u7d65NduIeGhlBfX4+amhquJ8eCBQtQXFyMvLw86slBvILJZOLK9E5NQlQqFcbHxwFM7Mcy9wqZmoRQrxBCyHxRouFDzN2vAwMDkZ+f7xM3vlMZDAZUVFRwDe/slZeXh3fffXdeN4sGgwG1tbUQCAQoKCjwyfX83d3dqKysRHt7u81jAgMDUVJSgrfeessnl+WNj4/jwYMHkMlk3M/BXBK6uLh43svtCHEXc6+QqQmI+Y95VpPH4yEmJsbqnpDY2Fivb/TpCLW1tfj973+PO3fuoLe3F6dOncLevXvdPSxCPAYlGj6iv78fMpkMmzZt8tmmZF1dXTh27BhXotQeoaGh+OlPf4o1a9bM+fUZhsGtW7fQ39+PwsJCny+Lat53UllZOePPPDIyEnv37kVeXp7PLsGQy+WQyWS4efMmDAYDgImeHMXFxVi5cqXPft/E/5h7hVjrmq5UKrnff2Ci2efUWRDzEi1/mfm7ePEiGhoasHbtWuzfv58SDUKmoETDB9y+fRv9/f3YsmWLT05zMwyDCxcu4Pz58zP2f7Bl2bJlOHjwIKKjo+c8hra2NrS1tWH9+vVITEyc83W8kclkQk1NDc6dO8dVaLJm0aJFKC0txTIfLnGq0+lw/fp1yGQyLvkSiUTYvHkzCgoKfD75JP6NZVkMDw9bTUIUCgV0Oh13bGRkpMW+kMkJiSdX45sPHo9HiQYhU1Ci4cVGR0dRVVWFnJwcr92E/DoKhQLHjh1DV1eX3ecKBALs378fW7ZsmfMyslevXuHGjRvIyMhAdnb2nK7hK7RaLS5cuACZTMat77YmJycHpaWlPr20iGVZtLa2QiaTUU8OQjDxb2J0dNTqLIhSqbTYTxcWFmaza3p4eLjXLvulRIOQ6SjR8FKPHz9Ge3s7SkpKfHKKmmVZNDQ04JtvvrGYqp+tRYsW4fDhw3OefRgZGUFtbS2io6OxceNGWhoziUKhwMmTJ3Hv3j2bx/D5fBQVFeHtt9/2+af8KpUKtbW1qK+vh1arBQAkJydDKpVi3bp1PrmHhxB76XQ6q80KFQoFBgcHueOEQqHNJCQqKsqjkxBKNAiZjhINL2MymfD9998jPj4eq1evdvdwnGJ4eBhff/01Hjx4YPe5PB4P27dvx+7du+e0jMxkMqG+vh5jY2MoKiryySTOUZ48eYKKigp0d3fbPEYoFGLnzp3YsmWLz99wG41GrifH8+fPAUw8uS0oKMDmzZshFovdPEJCPJO5V4i1poUajYYr0xsYGDhtL4j5c5FI5PYHQpRoEDIdJRpepKenBzdu3MDWrVvntd/AkzU1NeGrr76asXmcLTExMfjoo4+QkZFh97ksy+LevXvo6elBQUGBT5ZtdQaWZXHr1i2cOnXKonnYVGKxGPv37/eZMsAzMffkqK6uxu3bt7meHG+88QakUimysrJ8/mdAiKOYe4VYmw1Rq9Xcvj2BQIDY2FirFbJiYmIQEBDg9LFSokHIdJRoeAFz92sej4eCggKfvEkZGxtDZWUlampq5nT+hg0b8O67785pk2FXVxfu37+P1atX09r6ORobG8P333+PS5cuzbjULTU1FWVlZUhNTXXh6NzHWk+O+Ph4SKVS6slByDyNj49DrVZPS0IUCsW0XiFisdhmmV5HFVGhRIOQ6SjR8HAajQZXr15Ffn6+z26u7e7uxtGjR9HX12f3uaGhofjJT36C3Nxcu89Vq9VoaGjA4sWLsWrVKp9M4FxtaGgIZ86cQUNDA2YKLevWrcP+/fv9ZjkRwzC4f/8+qqur8fjxYwATPTk2btyI4uJiv6tkRoizMQwDjUZjsSF98kb1qb1CJicgkz9/Xa+QkZERdHR0AABWr16NP/zhD5BKpYiJiUFSUpLTv09CPB0lGh7s7t27UCqV2Lp1q8+Wrb106RK+/fbbOZWtzcrKwsGDB+1e5qTX61FTUwOhUIhNmzb55M/W3V6+fInKykq0trbaPEYgEGDr1q3YsWMHQkJCXDg695LL5aiursaNGze42Z/MzExIpVLqyUGIC7Asi8HBQatd0xUKxbReIda6ppt7hVRXV0MqlU57jQ8//BDHjx934XdFiGeiRMMD6XQ6XL58GdnZ2UhPT3f3cJxCpVLh2LFjePr0qd3nCgQC7Nu3D1u3brVrFoJhGFy/fh1DQ0MoKipCWFiY3a9NZo9lWTQ3N6OyshK9vb02jwsPD8c777yDoqIiv7rJ1ul0uHHjBmQyGTebJxKJUFRU5BcNIQnxRCzLYmRkxGYSMrmXUEREhM0khN5fCJlAiYaHefLkCVpbW1FSUuKTT3lZlsX169fxj3/8Y05laxcuXIhDhw5h0aJFdp3X3NyMjo4ObNy4EfHx8Xa/Lpk7hmFQV1eHb7/91qKW/lQJCQkoLS3F8uXL/WoZG8uyaGtrg0wmw8OHD7meHGvXrkVxcTFSUlL86udBiCfTarXTEhDz3yfHt9DQUKt7QuLi4hAREUH/ponfoETDQ5hMJly5cgVxcXFYs2aNu4fjFCMjI/jLX/4yY/+FmWzbtg179+61q0xqT08PGhsbkZ2dPadqVMRxdDodLl68iCtXrsBkMtk8btmyZSgtLbU7mfQFarUaNTU103pyFBcXIzc31+dLBBPizfR6vdVZEKVSyRWDACb2Z1mbBZFIJB7fK4QQe1Gi4QF6e3tx7do1ny5b29zcjOPHj8+pbG10dDQ++ugjZGVlzfqcoaEh1NbWIi4uDuvXr6fA7UFUKhVOnTqF27dv2zyGx+Nh06ZN2LNnDyIjI104Os9gNBpx+/ZtyGQyrk8J9eQgxHuNjY1BpVJZTUL6+/steoVM3pA+OQnxhF4hhNiLEg03YlkW9fX1YFkWhYWFPnkzbDQaceLECchksjmdv27dOrz33nuzLltrNBpRW1vL/UyDg4Pn9LrE+To7O1FRUYHOzk6bxwQHB+Ott97Ctm3bXlv9xVc9e/YMMpkMt2/fhslk4npyFBcXY9myZT4ZNwjxJyaTiUtCJicgSqUSKpWKK5YSEBDA9QqZWiFLLBa7pFcIIfaiRMMOIyNARwdgMADBwUBaGhAePrdrDQwM4OrVq9i4caPPlrZ8/vw5jh07NuNGYFuEQiF+8pOfzHo2gmVZ3L59G319fSgoKPDZmSFfw7Is7ty5g5MnT0KtVts8TiQSYe/evdiwYYPf3lgPDw9zPTnMzRHj4+NRXFyMvLw81+zpcmQQJIS81vj4OPr7+6clIeZeIeZlqHw+HzExMdNmQeLi4hAbG0vLLh3AZBqBTtcBljWAxwtGSEgaBAKKf69DicZrtLQAn38OXLgAdHYCk39aPB6Qmgrs3An84hdAdvbsrnnv3j0oFAqfLlt7+fJlnD17lmuYZI+MjAwcPHhw1stDnjx5gqamJqxbtw6LFy+2+/WI+xmNRly9ehUXLlyAXq+3eVxycjLKysp8thrbbDAMgwcPHqC6uhptbW0AJmZ+NmzYAKlU6vgHF84IgoSQeTP3Cpm8IX3yHpHJvUJEIpHVPiFxcXE08z8DrbYFcvnnUKsvQK/vBDD5lpkHoTAVYvFOJCb+AmFhFP+soUTDhq4u4OOPgaoqQCAAZti7yn29pAT44gsgJcX6cXq9HlVVVcjIyEBmZqZzBu5marUaX375JZ48eWL3uQEBAdizZw9KSkpmtQ5VqVTi2rVrSElJwYoVK/z2SbcvGR4exrlz51BbWztjb5XVq1dj//79kEgkLhyd5+nt7UV1dTWuX7/OVXHLyMjgenLMaymFM4IgIcQlzL1Cpi7FMichkx/oREVF2ayQ5YvVL2dDp+tCe/vH0GiqAAgAzBD/fvi6SFSCjIwvEBJC8W8ySjSsKC8HjhyZeN+c6b11KoFg4s8f/wj87GeWX+vo6EBzczNKSkpmvd/Am7Asi5s3b+Lvf//7jE+kbUlISMDhw4dnNSMxOjqKmpoahIeHIz8/n9al+qDe3l6cOHECjx49snlMQEAAiouLsWvXLr+vWa/X63H9+nWLnhzR0dHYvHkzCgoK7N9Q74wgSAjxCOZeIdZmQZRKJVfxDpjoc2QtCZFIJAgNDfXJB3xyeTk6Oo6AYUyYOcGYSgA+X4C0tD8iMZHinxklGlN8+inw29/O/zq/+x3wm99MbPK6evUqYmJisG7duvlf2ANptVr89a9/xZ07d+Z0/tatW7Fv377XriEdHx9HQ0MDdDodCgsLfTJhI5ZaW1tRWVmJly9f2jwmNDQUb7/9NjZv3uyTSxHtwbIsHj9+DJlMhgcPHoBlWQQEBGDt2rWQSqWz68nh6CBICPEqo6OjNpOQyZUjQ0NDpy3DMich3torpLv7U3R1zT/+paT8DsnJFP8ASjQslJcDP/+54673f//vIJKTv4dUKkVMTIzjLuxBWltbcfz4cYsa4bMVHR2NgwcPYtmyZTMex7IsHj58iGfPnmHTpk2IjY2d42iJNzJ3dD99+vSM5ZElEgkOHDiAlStXeuUbnKP19/ejtrYWdXV1GBkZAQAkJSWhuLgY69evt57YOzoIlpcDhw877nqEELcy9wqx1rTQXKQCmNg3ZqtrenR0tEfGaLm8HO3tjot/mZnlSEig+EeJxg+6uib2Mc5h1Y8NLIKCGLS28pGa6nn/oObLaDTi1KlTuHLlypzOX7NmDd5///3XLnl5/vw57ty5gzfeeANLly6d02sR32AwGPDdd9/h8uXL3CZHazIyMlBaWork5GQXjs5zGY1G3LlzBzKZDM+ePQMw0ZNj06ZN2Lx5838k7o4PgoBQOLGZnPZsEOLzjEaj1SREoVBM6xViLtM7NQmJiYlxS68Qna4LjY3ZYBjHxT8+X4jc3Ba/37NBicYPtm8HZDL7liO/jkAASKXA5cuOu6YnePnyJY4ePQq5XG73uUKhED/+8Y9fW6Z0YGAAdXV1SEhIwNq1az3y6QdxD41Gg9OnT+PGjRszHrdx40bs3bsXIpHIRSPzfNZ6cqxYsQLFxcXI/i//BTwKgoQQJzCZTFCr1VaTEGu9QqxtTBeLxU5bHvvgwXZoNDLYtyfjdQQQiaRYudK/4x8lGph44LZ8uXOv/5rVQV6BYRh8//33OH369JzK1qalpeHQoUMzlq01GAyoq6tDQEAACgoKqPY3sam7uxsVFRUzVjgLDAzE9u3b8eabb1IJx0mGh4fR0NCA6upqaDQaJGg0+J8VFc57QV8JgoQQh2MYhusVYq1poblXCI/Hg1gstto1fT69QrTaFjQ2Ou8mMDe3BWFh/hv/KNEA8KtfAX/6k2Mf5JkJBMAnnwD/+q+Ov7Yr9ff348svv0R7e7vd5/L5fOzZswfbt2+3OSXKMAxu3boFtVqNoqIiREREzHfIxA+wLIsHDx7gxIkTUCgUNo+LjIzE3r17kZeX55ZpeU/FMAwePnwI9sgRvNHQgABnvB34ShAkhLgcy7IYGBiwmoQoFAqMjY0BmEhCoqOjrSYhr+sV8uTJr9DT8yc4djbDTICFCz9Berr/xj9KNDDR3PbpU+defw5tJTzGrVu38Le//Q06nc7ucxcsWIDDhw8jKSnJ5jFtbW1oa2vD+vXrfbZLOnEuk8mEmpoanDt3DqOjozaPW7RoEUpLS19bgMDvUBAkhHgZlmUxNDQ0rU+IUqlEX1+fRan9yMhIiwRkchLy8OEb0OudF/+EwjRs3Oi/8c/vE43hYSAqyrLZraPxeMDQEBDuZZ3qR0dH8be//Q2NjY1zOl8qlWL//v0ICgqy+vW+vj5cv34d6enpWO7MtWvEb2i1Wpw/fx4ymWzGhn8rVqzAgQMHkJCQ4MLReSgKgoQQH8OyLLRardVZEKVSyVXiEwjGUFp6HM7dBspDQcEQBAL/jH9+n2jcvw+sXu3817l3D1i1yvmv4yhtbW04fvy4Rbm62YqMjMTBgwdtJg8jIyOora1FdHQ0Nm7cSEtZiMMpFAqcPHkS9+7ds3kMn89HUVER3n77bf9eqkdBkBDiZ8y9Ql69qodO977TX2/t2nuIiFjl9NfxRP7d3QqAweBbrzNfJpMJp0+fRlVV1ZzOX716Nd5//32EW3lyaTKZUF9fj7GxMWzZsgVCoXC+wyXEKolEgl/84hdob29HRUUFnj9/Pu0YhmFQXV2NGzduYNeuXZBKpf5ZfICCICHEz4SGhiI5ORki0Svcvev812NZ/41/fp9ouKoQjTcUvOnp6cHRo0fR09Nj97nBwcF49913kZeXN60ULcuyuHfvHnp6erBp0yafbV5IPE9GRgZ+/etf4+bNmzh16pTVxpJ6vR4nTpxAdXU19u/f73/llF0UnO63tiIsJgZxcXGIioryr58xIcQj8XiuiX+ueh1P5PdLp0ZGgMhI/16ezLIsrly5glOnTnFl5OyxdOlSHDp0yGrH7mfPnuHevXtYvXo1lixZ4oDREjI3Y2Nj+P7773Hp0iUYZni6npqairKyMqSmprpwdG7kgiDIAvjPH30Eww8zRkFBQVbr5EskEohEIkpCCCEuYTKNoL4+EhNRylloj4ZfJxqAfxdc0Wg0OH78ONra2uw+l8/n45133sFbb701bZ+FWq1GQ0MDFi9ejFWrVtGNA/EYg4ODOHPmDK5du4aZwl9ubi727ds3Y98Xn+GCIGhsaYFKpZpWIUahUECtVnP/LwQCAVcVZmoSIhaLaU8XIcShbtxIo6pTTuT3S6cAYOdO5/bR2LHD8dd1hNu3b+Ovf/3rjOVAbYmPj8ehQ4emzVLo9XrU1NQgODgYO3fudFoXT0LmKioqCh988AG2bNmCyspKtLa2Wj2usbER9+7dw9atW7Fjxw6EhIS4eKQu5IIgGBgYiISEBKuVvkwmE9ewa3IC0tTUBJVKxTUI5fP5VrsGm5MQijeEEHuJxTud2kdDLPbQm0AXoRkN+F9ncJ1Oh7///e+4efPmnM4vKipCaWmpRQMchmFw/fp1DA0NoaioCGFhYY4aLiFOw7IsmpubUVFRgVevXtk8LiIiArt370ZBQYFvPlH34CDIMAw0Gs20JMQ8K2I0GgFMNOyK+WEPyNQkJDY21maZbUKIf6PO4M5FicYPtm8HZDLHPtATCACpFLh82XHXnK/29nZ8+eWX6O/vt/vciIgIfPjhh1ixYoXFf29ubkZHRwc2btyI+Ph4Rw2VEJcZHx9HXV0dvv32W66+ujUJCQkoLS3F8uXLfW85oBcGQXPXYGt18hUKhcVenOjo6GnNusx/pwp4hPi3Bw+2Q6ORwbGzGgKIRFKsXOlBN4FuQInGD7q6gOxsYFIjyXkTCice5KWkOO6ac2UymXD27Flcvnx5xnXptqxcuRI//elPLfoNyOVy3Lp1C8uWLUNmZqYjh0uIW+h0Oly8eBFXrlyZsTDCsmXLUFZWhoULF7pwdE7mY0GQZVkMDw9PmwUxf5y8ZDQiImLaLIg5IaHZWUJ8n07XhcbGbDCM4+Ifny9Ebm4LQkI84CbQjSjRmKS8HPj5zx17vcOHHXe9uZLL5Th27BhevHhh97lBQUH40Y9+hIKCAu4J7tDQEGpraxEXF4fc3FzfXEpC/JpKpcKpU6dw+/Ztm8fweDwUFBRg9+7diIyMdOHonMhXg6AVWq3WZhIyPDzMHRcaGmo1CZFIJAgPD/e9mS1C/JRcXo72dsfFv8zMciQkeGb8cyVKNKb49FPgt791zHV+/ev5X2c+WJaFTCbDiRMn5lS2NiUlBYcOHYJEIgEAGI1G1NXVgWEYFBYWWuzRIMQXdXZ2oqKiAp2dnTaPCQ4OxltvvYWSkhLfaPjnS0FwjnQ6nUXiMfnzyb1YhEKhzTK91CuEEO/T3f0purrmH/9SUj5FcrJ3xj9Ho0TDivJy4MiRiaXK9tyfCwQTfz77zP0P8QYGBnD8+HGbFXVmwufzsWvXLuzcuRN8Ph8sy+L27dvo6+tDQUEBoqOjHT9gQjwUy7K4c+cOTp48CbVabfM4kUiEffv2Yf369d5/g+kLQdBJDAaDRZneyR81Gg23NDUwMNDqLEhcXBxEIhHNBBPioeTycnR0HAHDmGDfng0B+HwB0tM/o5mMSSjRsKGrC/j4Y6CqauJ9c6b3WvPXS0qAL75w/56Mu3fv4i9/+Qu0Wq3d50okEhw6dAgpP3wTHR0dePToEdatW4fFixc7eqiEeA2j0YirV6/iwoUL0M+wjyE5ORllZWVIT0934eicwJuDoJsYjUao1WqrSYharQbDMAAmeoWIxWKrSYhYLEZAQICbvxNC/JtO14X29o+h0VRhohPETAnHxNdFohJkZHzh93sypqJE4zVaWoDPPwcuXpzoZzX5p8XjAUuXTvTJ+OQT95ew1ev1+Mc//oHr16/P6fzCwkKUlZUhODgYSqUS165dQ0pKClasWOH9T2gJcZDh4WF8++233DJCW1avXo39+/dzSw+9ljcFQQ82Pj4OtVptdUmWSqXilrfy+XwuCZnatDA2NtY3lucR4iW02hbI5Z9Drb74Q1O/ybfMPAiFSyEW70Bi4id+XcJ2JpRo2GFkBOjoAAwGIDh4opluuId0lO/o6MCxY8dmXNphS3h4OD744AOsXLkSo6OjqK2tRVhYGPLz8+nJGiE29Pb2orKyEk1NTTaPCQgIgFQqxa5duxAaGurC0TmJJwdBL2buFTJ1FsT8+eReISKRyOqekNjYWNo3R4gTmUwj0Ok6wLIG8HjBCAlJg0BA8e91KNHwciaTCefOncOlS5fmVLY2JycHH374IcLCwnDt2jWMjo6isLDQN26KCHGB1tZWVFRUoKenx+YxYWFh2LVrFzZv3kzdq4ldWJbF4OCgzSRk8jK+qKgoq0lIXFycb3e2J4R4LEo0vNirV69w9OhRPH/+3O5zAwMDubK1TU1N6OrqQn5+PuLi4pwwUkJ8G8MwuHbtGs6cOYOhoSGbx0kkEpSWluKNN96g5Yhk3liWxcjIyLSlWOa/T96nFxERYZF4TN6oHhoaSr+PhBCnoETDC7Esi5qaGlRWVnJT6vZITk7G4cOHMTY2htu3b2PFihVIS0tzwkgJ8S96vR7fffcdqqqqZvy3mZGRgbKyMiQlJblwdMTfmHuFWOsXMrVXyNRZEPPnERERlIQQQuaMEg0vMzQ0hK+++mrGdeG28Hg87NixAwUFBbh+/ToWLFiAtWvX0psIIQ6m0Whw+vRp3Lhxw+YxPB4PGzduxN69e6lkNHE5vV5vMwmZ3CskODjYZhISHR3t9+8f//t//2+cPHkSbW1tCAkJQX5+Pv7P//k/yMzMdPfQCPEIlGh4kXv37uHrr7+eU9na2NhYvPfee5DL5eDz+SgsLKTqJYQ4WXd3NyoqKvDkyRObxwQGBuLNN9/E9u3baTMv8QhjY2Ncr5Cpy7L6+/un9QqxtickJibGL3qFvPXWW3j33XeRm5sLk8mEX//612hqakJLSwvCwsLcPTxC3I4SDS+g1+vxzTffoKGhYU7n5+XlISUlBSMjIygsLERkZKSDR0gIsYVlWdy/fx8nTpyAUqm0eVxUVBT27NmDvLw8v7hBI97JZDJxvUKm9gtRqVRcyeeAgADExsZabVoYExPjs0URlEolJBIJampqUFRU5O7hEOJ2lGh4uM7OThw9ehQqlcruc8PCwlBYWAgej4f169cjMTHRCSMkhMyGyWRCTU0Nzp07h9HRUZvHLVq0CGVlZcjKynLh6AiZP4Zh0N/fbzUJUSqVXK8QHo8HsVhsNQnx9l4hHR0dSE9Px6NHj5CTk+Pu4RDidpRoeKjx8XGcP38eFy5cmFPZ2qSkJCxZsgSrVq3C8uXLnTBCQshcaLVanD9/HjKZbMaGf2+88QYOHDiABQsWuHB0hDgHwzAYGBiYthTL/HFsbAzARBISHR1tkYBMXpLlycsLGYbB7t27MTAwgPr6encPhxCPQImGB+rr68OxY8fw7NmzOZ2/ZMkSFBUV0RIMQjxYX18fTp48ifv379s8hs/no6ioCO+88w7CqTEe8VEsy2JoaGjaLIh5ZmRyr5DIyMhpsyDmZMTd/Z8++eQTXLx4EfX19Vi0aJFbx0KIp6BEw4OwLIu6ujpUVFRwT3dmi2EYMAyDbdu2Yd++fRAKhU4aJSHEkdrb21FRUTFjP5yQkBDs3LkTW7Zs8dm17YRYw7IstFqtzSRkcnGUsLAwq0mIRCJBWFiYUytk/fKXv8SZM2dQW1uLlJQUp70OId6GEg0PMTQ0hK+//hoPHz606zyWZdHf34/MzEz86le/Qnx8vJNGSAhxFpZlcfPmTZw6dcqitOhUsbGx2L9/P9asWeP3ZUUJAYDR0dFpe0HMScjk5pkhISFWl2JJJBJERkbO+d8Ty7I4cuQITp06herqaqSnpzvqWyPEJ1Ci4QEePnyIP//5zxYNlGZjeHgYBoMBv/zlLyGVSp00OkKIq4yNjaGqqgqXLl2acVZz6dKlKCsroyenhMzAYDBYnQVRKpXQaDTccUFBQVZnQSQSyWt7hfzTP/0T/va3v+HMmTMWvTOioqIQEhLi1O+PEG9AiYYbGQwGVFRUoK6uzu7zXr16hY0bN+K///f/7vZ1qYQQxxoYGMDZs2dx7dq1GYtB5ObmYt++fRCLxS4cHSHez2g0cr1CpiYharWa+3cnEAiszoKYy/QGBARYvf6XX36JgwcPuvA7IsQzUaLhJs+ePcPRo0ehUChmfc74+Dh6e3sRGhqK//bf/hs2bNjgxBESQtzt5cuXqKioQFtbm81jBAIBtm3bhh07dtDeLEIcwNwrZOpsiHlplrlaHJ/PR2xs7LQlWRKJBGKxmPZTEQJKNFyOYRhcuHAB58+fn7G05WQsy0KhUMBgMKC4uBgff/wxoqOjnTtQQohHYFkWTU1NqKysxKtXr2weFxERgd27d6OgoICqzRHiJOZeIVNnQcwfJ/cKiYmJmTYLYk5IvLlXCCH2oETDhRQKBY4dO4aurq5Zn6PRaDA4OIjExES8//772LJlC20CJcQPjY+Po66uDmfPnrWotDNVQkICysrKqH8OIS7GsiwGBgZsJiEGg4E71twrxFoSQjOTxJdQouECLMuioaEB33zzjUWgmYlWq4VSqUR0dDRycnJw+PBh6uxNCMHo6CguXryIq1evck9PrcnOzkZpaSkWLlzowtERQqxhWRbDw8M2y/TqdDru2MjIyGl7QswfaU8m8TaUaDjZ8PAwvv76azx48GBWx4+NjeHVq1cIDg5GfHw8tm/fjj179tBaT0KIBZVKhVOnTuH27ds2j+HxeCgoKMDu3bsRGRnpwtERQmaLZVmMjo5anQVRKBQYGRnhjg0LC5s2C2L+GB4eTiseiMehRMMOIyNARwdgMADBwUBaGjBTs96mpiZ89dVXFrW8bWEYBr29vQCABQsWIDY2FocOHUJGRoajhk8I8UFPnz5FRUXFjEsyg4ODsWPHDmzbtm1+a8PtDYKEkHnT6XRWN6YrFAoMDg5yxwmFQptJSFRUFCUh82QyjUCn6wDLGsDjBSMkJA0CAcW/16FE4zVaWoDPPwcuXAA6O4HJPy0eD0hNBXbuBH7xCyA7e+K/j42NobKyEjU1Na+9PsuyUKlUGB0dxYIFCxAcHIz169fjxz/+MU2REkJmhWVZ3L59GydPnkR/f7/N40QiEfbt24f169fP/qZjLkGQEOIS5l4h1poWajQarkxvUFCQReIx+XORSEQFJGzQalsgl38OtfoC9PpOAJNvmXkQClMhFu9EYuIvEBZG8c8aSjRs6OoCPv4YqKoCBAJghqXQ3NdLSoB/+ZceXL78Bfr6+l77GkNDQ1Cr1dyUZ0hICN577z3k5uY68DshhPgLo9GIK1eu4OLFi9Dr9TaPW7JkCUpLS2fuYjzXIPjFFwA1EiTE7cy9QqztCenv7+cqXwoEAosyvZM/isViv0xCdLoutLd/DI2mCoAAwAzx74evi0QlyMj4AiEhFP8mo0TDivJy4MiRiffNmd5bp+LzGfB4Jmza9A9kZTXYPE6v1+PVq1eIiIhATEwMeDweMjMz8dFHH0EkEjngOyCE+LPh4WGcPXsWdXV1Mzb8W7NmDfbv34+4uDjLL8w1CAoEE3/++EfgZz+b4+gJIc42Pj5utVeIQqGASqXC+Pg4gIleIWKx2GoSEhsb65P7R+XycnR0HAHDmDBzgjGVAHy+AGlpf0RiIsU/M0o0pvj0U+C3v53PFVgAPKxbdxpr1ly0+IrJZEJvby8EAgEWLFgAHo8HgUCAvXv3Ytu2bbR+khDiUHK5HCdOnEBTU5PNYwICAiCVSrFr166J5ZrzD4ITfvc74De/mf91CCEuxTAMNBrNtKVY5r8bjUYA/9ErxNaSrKCgIDd/J/br7v4UXV3zj38pKb9DcjLFP4ASDQvl5cDPf+646xUV/RlZWQ1gWRavXr2CyWRCQkIC9wQgMTERhw8fxqJFixz3ooQQMkVLSwsqKiogl8ttHhMWFoaf83hY9oc/OO6Fy8uBw4cddz1CiFuxLIvBwcFpsyDmz6f2Cpk6C2L+3BN7hcjl5Whvd9xNYGZmORISKP5RovGDrq6JfYwzLGu2E4uAACNKSv4/MMxTxMfHIyQkhPvqtm3bsHfvXuoOSghxCYZh0NDQgLNnz1qthCceGsL/rKhA4Pg4HDa3KhRObCanPRuE+DyWZTEyMmIzCRkdHeWOjYiIsJmEhIWFuXzsOl0XGhuzwTAOuwkEny9Ebm6L3+/ZoETjB9u3AzKZfcuRX4fHM0EiacaePf+P+2/R0dH46KOPkJWV5bgXIoSQWdLr9fjuu+9QVVXFLYEAgF+dP48suRwBjnxLEAgAqRS4fNlx1ySEeCVzI2JrZXqHh4e540JDQ63uCYmLi0NERIRTlpk/eLAdGo0M9u3JeB0BRCIpVq707/hHiQYmHrgtX+6865eV/Q+IRK+wbt06vPfee1S2lhDidv39/Th9+jRu3ryJBI0G/7Oiwnkv1tICLFvmvOsT4ia1tbX4/e9/jzt37qC3txenTp3C3r173T0sr6PX663OgiiVSgwMDHDHBQcHW50FkUgkc+4VotW2oLHReTeBubktCAvz3/jne+UC5uDzz19fvXGueLxxtLdvwRdfCO2rXU8IIU4UExODQ4cOYcuWLVC//z7GeTzHzmaYCQTAn/4E/Ou/Ov7ahLiZVqvFypUrcejQIezfv9/dw/FaQqEQixcvxuLFi6d9bWxsDCqValoS0tjYiP7+fq6yXmBgoMWG9MlJyEy9QuTyz/H6ErZzJYBc/iekp/tv/KMZDUw0t3361HnXT0kZR2dngPNegBBC5oFNSwPPmUEwLQ148sR51yfEA/B4PJrRcDGTycQlIVMbF6pUKq5XSEBAANcrZGqFrM7OvB+a8TmHUJiGjRv9N/75/YzG8PBEs1tnevYsACMjQDh1qieEeJrhYfCcHQSfPgUFQUKIo5nbBSxYsGDa18bHx9Hf3z9tKVZzczNUKhVMJhMEgjGUlnbCmYtN9PqnMJlGIBD4Z/zz+0Tj6VPA2XM6LAt0dACrVjn3dQghxG4uCoK9dXUwOnMzHCEeQKlU4vnz5+4eBpkkIiICERERSE1N5f4bwzAYGhqCSnUDPN5xJ4+AhU7XgYiIVU5+Hc/k94nGpJLPPvE6hBBiFxcFp+P/9m94JpG45LUIcZfKykrcvn3b3cMgsyQWK7B9u/Nfh2X99ybQ7xON4GDfeh1CCLGLi4LTwf/0n2hGg/i0f/u3f0NpaSnefPNNdw+FzJLB0IyentNOfx0ez39vAv0+0UhLA3g8564c4PEmXocQQjyOi4JgQmEh7dEgPi8uLg5JSUnuHgaZJZMpBj09PADOXD7KQ0iI/94EWq/15UfCw4FJy/acYulSen8lhHgoCoKEzNnIyAju37+P+/fvAwC6urpw//592qfhJQSCcAiFzo1/QuFSv90IDlCiAQDYuXOi1LszCATAjh3OuTYhhDgEBUFC5uT27dtYvXo1Vq9eDQD4r//1v2L16tX4l3/5FzePjMyWWLwTzlvgI4BY7N/xj/powPmdwakpLiHEo1EQJIT4KeoM7lw0owEgOxsoKXH8Az2BYOK69P5KCPFoFAQJIX4qLCwbIlEJHD+rIYBIVOLXSQZAMxqcrq6J91q93nHXFAonHuSlpDjumoQQ4hQUBAkhfkqn60JjYzYYxnHxj88XIje3BSEh/h3/aEbjBykpwB//6NhrfvYZvb8SQrwEBUFCiJ8KCUlBWppj4196+md+n2QAlGhY+NnPgN/9zjHX+vRT4PBhx1yLEEJcgoIgIcRPJSb+DCkpjol/KSmfIiGB4h9AS6esKi8HjhwBTKaJP7MlEEz8+ewzen8lhHgxCoKEED8ll5ejo+MIGMYEwI74BwH4fAHS0z+jJGMSSjRs6OoCPv4YqKqaeN+c6b3W/PWSEuCLL2ilACHEB1AQJIT4KZ2uC+3tH0OjqcLEJvGZEo6Jr4tEJcjI+IKWS01BicZrtLQAn38OXLwIPH1q2TyXx5voQ7VjB/DJJ1RYhRDigygIEkL8lFbbArn8c6jVF6HXP4VlB3EehMKlEIt3IDHxE7+vLmULJRp2GBkBOjoAgwEIDgbS0qjZLSHEj1AQJIT4KZNpBDpdB1jWAB4vGCEhaX7d8Xu2KNEghBBCCCGEOBxVnSKEEEIIIYQ4HCUahBBCCCGEEIejRIMQQgghhBDicJRoEEIIIYQQQhyOEg1CCCGEEEKIw1GiQQghhBBCCHE4SjQIIYQQQgghDkeJBiGEEEIIIcThKNEghBBCCCGEOBwlGoQQQgghhBCHo0SDEEIIIYQQ4nCUaBBCCCGEEEIcjhINQgghhBBCiMNRokEIIYQQQghxOEo0CCGEEEIIIQ5HiQYhhBBCCCHE4SjRIIQQQgghhDgcJRqEEEIIIYQQh6NEgxBCCCGEEOJwlGgQQgghhBBCHI4SDUIIIYQQQojDUaJBCCGEEEIIcThKNAghhBBCCCEOR4kGIYQQQgghxOEo0SCEEEIIIYQ4HCUahBBCCCGEEIejRIMQQgghhBDicJRoEEIIIYQQQhyOEg1CCCGEEEKIw1GiQQghhBBCCHE4SjQIIYQQQgghDkeJBiGEEEIIIcTh/n8xIBfNp6uZCwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -251,7 +270,7 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[ 0,  4,  9, 15, 21, 27, 31, 37, 41, 45]])"
+       "tensor([[ 0,  4, 10, 15, 21, 27, 33, 39, 45, 49]])"
       ]
      },
      "execution_count": 8,
@@ -315,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -329,7 +348,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -374,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,51 +413,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using 16bit Automatic Mixed Precision (AMP)\n",
-      "GPU available: True (cuda), used: True\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'env' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['env'])`.\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'policy' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['policy'])`.\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:551: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.\n",
+      "Using bfloat16 Automatic Mixed Precision (AMP)\n",
+      "GPU available: False, used: False\n",
       "TPU available: False, using: 0 TPU cores\n",
       "IPU available: False, using: 0 IPUs\n",
       "HPU available: False, using: 0 HPUs\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
       "val_file not set. Generating dataset instead\n",
-      "test_file not set. Generating dataset instead\n"
+      "test_file not set. Generating dataset instead\n",
+      "\n",
+      "  | Name     | Type           | Params\n",
+      "--------------------------------------------\n",
+      "0 | env      | FJSPEnv        | 0     \n",
+      "1 | policy   | L2DPolicy      | 81.2 K\n",
+      "2 | baseline | WarmupBaseline | 81.2 K\n",
+      "--------------------------------------------\n",
+      "162 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "162 K     Total params\n",
+      "0.649     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "812cd22f33dd469cb501b27936cc1105",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (32) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
      ]
     },
     {
-     "ename": "IndexError",
-     "evalue": "The shape of the mask [256, 11] at index 1 does not match the shape of the indexed tensor [256, 101] at index 1",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[21], line 20\u001b[0m\n\u001b[1;32m      5\u001b[0m model \u001b[38;5;241m=\u001b[39m L2DModel(env,\n\u001b[1;32m      6\u001b[0m                  policy\u001b[38;5;241m=\u001b[39mpolicy, \n\u001b[1;32m      7\u001b[0m                  baseline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrollout\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m                  val_data_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1_000\u001b[39m,\n\u001b[1;32m     11\u001b[0m                  optimizer_kwargs\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1e-4\u001b[39m})\n\u001b[1;32m     13\u001b[0m trainer \u001b[38;5;241m=\u001b[39m RL4COTrainer(\n\u001b[1;32m     14\u001b[0m     max_epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m     15\u001b[0m     accelerator\u001b[38;5;241m=\u001b[39maccelerator,\n\u001b[1;32m     16\u001b[0m     devices\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m     17\u001b[0m     logger\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     18\u001b[0m )\n\u001b[0;32m---> 20\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/utils/trainer.py:146\u001b[0m, in \u001b[0;36mRL4COTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    141\u001b[0m         log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m    142\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOverriding gradient_clip_val to None for \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautomatic_optimization=False\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m models\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    143\u001b[0m         )\n\u001b[1;32m    144\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_clip_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    147\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    150\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[43m    \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    152\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    545\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m    546\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m     47\u001b[0m     _call_teardown_hook(trainer)\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m    575\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m    576\u001b[0m     ckpt_path,\n\u001b[1;32m    577\u001b[0m     model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    578\u001b[0m     model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m    583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py:949\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m    946\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: preparing data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    947\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_connector\u001b[38;5;241m.\u001b[39mprepare_data()\n\u001b[0;32m--> 949\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_setup_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# allow user to set up LightningModule in accelerator environment\u001b[39;00m\n\u001b[1;32m    950\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: configuring model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    951\u001b[0m call\u001b[38;5;241m.\u001b[39m_call_configure_model(\u001b[38;5;28mself\u001b[39m)\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:94\u001b[0m, in \u001b[0;36m_call_setup_hook\u001b[0;34m(trainer)\u001b[0m\n\u001b[1;32m     92\u001b[0m     _call_lightning_datamodule_hook(trainer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msetup\u001b[39m\u001b[38;5;124m\"\u001b[39m, stage\u001b[38;5;241m=\u001b[39mfn)\n\u001b[1;32m     93\u001b[0m _call_callback_hooks(trainer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msetup\u001b[39m\u001b[38;5;124m\"\u001b[39m, stage\u001b[38;5;241m=\u001b[39mfn)\n\u001b[0;32m---> 94\u001b[0m \u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msetup\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mbarrier(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost_setup\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m    160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/common/base.py:155\u001b[0m, in \u001b[0;36mRL4COLitModule.setup\u001b[0;34m(self, stage)\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataloader_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msetup_loggers()\n\u001b[0;32m--> 155\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost_setup_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/reinforce.py:119\u001b[0m, in \u001b[0;36mREINFORCE.post_setup_hook\u001b[0;34m(self, stage)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost_setup_hook\u001b[39m(\u001b[38;5;28mself\u001b[39m, stage\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    118\u001b[0m     \u001b[38;5;66;03m# Make baseline taking model itself and train_dataloader from model as input\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbaseline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolicy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    121\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    122\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mval_batch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    123\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mget_lightning_device\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    124\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdataset_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_cfg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval_data_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/baselines.py:117\u001b[0m, in \u001b[0;36mWarmupBaseline.setup\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msetup\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbaseline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/baselines.py:174\u001b[0m, in \u001b[0;36mRolloutBaseline.setup\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msetup\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[0;32m--> 174\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_policy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/baselines.py:187\u001b[0m, in \u001b[0;36mRolloutBaseline._update_policy\u001b[0;34m(self, policy, env, batch_size, device, dataset_size, dataset)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mdataset(batch_size\u001b[38;5;241m=\u001b[39m[dataset_size])\n\u001b[1;32m    185\u001b[0m log\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEvaluating baseline policy on evaluation dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbl_vals \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 187\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrollout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolicy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m    188\u001b[0m )\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmean \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbl_vals\u001b[38;5;241m.\u001b[39mmean()\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/baselines.py:242\u001b[0m, in \u001b[0;36mRolloutBaseline.rollout\u001b[0;34m(self, policy, env, batch_size, device, dataset)\u001b[0m\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m policy(batch, env, decode_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreedy\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreward\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    240\u001b[0m dl \u001b[38;5;241m=\u001b[39m DataLoader(dataset, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, collate_fn\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39mcollate_fn)\n\u001b[0;32m--> 242\u001b[0m rewards \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat([eval_policy(batch) \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m dl], \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rewards\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/baselines.py:242\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m policy(batch, env, decode_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreedy\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreward\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    240\u001b[0m dl \u001b[38;5;241m=\u001b[39m DataLoader(dataset, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, collate_fn\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39mcollate_fn)\n\u001b[0;32m--> 242\u001b[0m rewards \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat([\u001b[43meval_policy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m dl], \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rewards\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/baselines.py:238\u001b[0m, in \u001b[0;36mRolloutBaseline.rollout.<locals>.eval_policy\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39minference_mode():\n\u001b[1;32m    237\u001b[0m     batch \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mreset(batch\u001b[38;5;241m.\u001b[39mto(device))\n\u001b[0;32m--> 238\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgreedy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreward\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/cuda1203/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/common/constructive/base.py:231\u001b[0m, in \u001b[0;36mConstructivePolicy.forward\u001b[0;34m(self, td, env, phase, calc_reward, return_actions, return_entropy, return_hidden, return_init_embeds, return_sum_log_likelihood, actions, max_steps, **decoding_kwargs)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m td[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m    230\u001b[0m     logits, mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder(td, hidden, num_starts)\n\u001b[0;32m--> 231\u001b[0m     td \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    232\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mactions\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mactions\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    237\u001b[0m     td \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mstep(td)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnext\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    238\u001b[0m     step \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/utils/decoding.py:343\u001b[0m, in \u001b[0;36mDecodingStrategy.step\u001b[0;34m(self, logits, mask, td, action, **kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmask_logits:  \u001b[38;5;66;03m# set mask_logit to None if mask_logits is False\u001b[39;00m\n\u001b[1;32m    341\u001b[0m     mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 343\u001b[0m logprobs \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_logits\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlogits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    345\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    346\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtanh_clipping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtanh_clipping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask_logits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmask_logits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    351\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    352\u001b[0m logprobs, selected_action, td \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_step(\n\u001b[1;32m    353\u001b[0m     logprobs, mask, td, action\u001b[38;5;241m=\u001b[39maction, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    354\u001b[0m )\n\u001b[1;32m    356\u001b[0m \u001b[38;5;66;03m# directly return for improvement methods, since the action for improvement methods is finalized in its own policy\u001b[39;00m\n",
-      "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/utils/decoding.py:177\u001b[0m, in \u001b[0;36mprocess_logits\u001b[0;34m(logits, mask, temperature, top_p, top_k, tanh_clipping, mask_logits)\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask_logits:\n\u001b[1;32m    176\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmask must be provided if mask_logits is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 177\u001b[0m     \u001b[43mlogits\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-inf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    179\u001b[0m logits \u001b[38;5;241m=\u001b[39m logits \u001b[38;5;241m/\u001b[39m temperature  \u001b[38;5;66;03m# temperature scaling\u001b[39;00m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m top_k \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
-      "\u001b[0;31mIndexError\u001b[0m: The shape of the mask [256, 11] at index 1 does not match the shape of the indexed tensor [256, 101] at index 1"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcda6b7e273945b3880e38863d6c53ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n"
      ]
     }
    ],
@@ -474,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,89 +563,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Policy: neural network, in this case with encoder-decoder architecture\n",
+    "policy = L2DPolicy4PPO(\n",
+    "    embed_dim=embed_dim, \n",
+    "    num_encoder_layers=num_encoder_layers, \n",
+    "    env_name=\"jssp\",\n",
+    "    het_emb=False\n",
+    ")\n",
+    "\n",
+    "model = L2DPPOModel(\n",
+    "    env=env,\n",
+    "    policy=policy,\n",
+    "    batch_size=batch_size,\n",
+    "    train_data_size=train_data_size,\n",
+    "    val_data_size=1_000,\n",
+    "    optimizer_kwargs={\"lr\": 1e-4}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using 16bit Automatic Mixed Precision (AMP)\n",
-      "GPU available: True (cuda), used: True\n",
+      "Using bfloat16 Automatic Mixed Precision (AMP)\n",
+      "GPU available: False, used: False\n",
       "TPU available: False, using: 0 TPU cores\n",
       "IPU available: False, using: 0 IPUs\n",
       "HPU available: False, using: 0 HPUs\n",
       "Overriding gradient_clip_val to None for 'automatic_optimization=False' models\n",
+      "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:43: attribute 'policy' removed from hparams because it cannot be pickled\n",
       "val_file not set. Generating dataset instead\n",
-      "Provided file name data/../../data/jssp/taillard/15j_15m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4]\n",
+      "test_file not set. Generating dataset instead\n",
       "\n",
       "  | Name       | Type          | Params\n",
       "---------------------------------------------\n",
       "0 | env        | JSSPEnv       | 0     \n",
-      "1 | policy     | L2DPolicy4PPO | 133 K \n",
-      "2 | policy_old | L2DPolicy4PPO | 133 K \n",
+      "1 | policy     | L2DPolicy4PPO | 25.5 K\n",
+      "2 | policy_old | L2DPolicy4PPO | 25.5 K\n",
       "---------------------------------------------\n",
-      "266 K     Trainable params\n",
+      "51.1 K    Trainable params\n",
       "0         Non-trainable params\n",
-      "266 K     Total params\n",
-      "1.066     Total estimated model params size (MB)\n"
+      "51.1 K    Total params\n",
+      "0.204     Total estimated model params size (MB)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█| 8/8 [03:40<00:00,  0.04it/s, v_num=9, train/loss=1.45e+3, train\n",
-      "Validation: |                                             | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                         | 0/4 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                            | 0/4 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  25%|█████               | 1/4 [00:04<00:13,  0.22it/s]\u001b[A\n",
-      "Validation DataLoader 0:  50%|██████████          | 2/4 [00:09<00:09,  0.22it/s]\u001b[A\n",
-      "Validation DataLoader 0:  75%|███████████████     | 3/4 [00:13<00:04,  0.21it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|████████████████████| 4/4 [00:18<00:00,  0.22it/s]\u001b[A\n",
-      "Epoch 0: 100%|█| 8/8 [03:58<00:00,  0.03it/s, v_num=9, train/loss=1.45e+3, train\u001b[A"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=1` reached.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b3c9b45d4fc4eb2a60a3bf80ac42a6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█| 8/8 [03:58<00:00,  0.03it/s, v_num=9, train/loss=1.45e+3, train\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0bdcd678f8140b7b5b6b123fa5084c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "source": [
-    "# Policy: neural network, in this case with encoder-decoder architecture\n",
-    "policy = L2DPolicy4PPO(\n",
-    "    embed_dim=embed_dim, \n",
-    "    num_encoder_layers=num_encoder_layers, \n",
-    "    env_name=\"jssp\",\n",
-    "    het_emb=False\n",
-    ")\n",
-    "\n",
-    "model = L2DPPOModel(\n",
-    "    env=env,\n",
-    "    policy=policy,\n",
-    "    batch_size=batch_size,\n",
-    "    train_data_size=train_data_size,\n",
-    "    val_data_size=1_000,\n",
-    "    optimizer_kwargs={\"lr\": 1e-4}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "CHECKPOINT_PATH = \"last.ckpt\"\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
@@ -627,20 +673,117 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Done evaluating instance type 30j_20m with reward -2357.900146484375\n"
+      "Folder does not exist. Creating folder and downloading taillard instances...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "bash: line 11: wget: command not found\n",
+      "bash: line 12: wget: command not found\n",
+      "bash: line 13: wget: command not found\n",
+      "bash: line 14: wget: command not found\n",
+      "bash: line 15: wget: command not found\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "# Define the folder path\n",
+    "DATA_PATH=\"./taillard\"\n",
+    "\n",
+    "# Check if the folder exists\n",
+    "if [ -d \"$DATA_PATH\" ]; then\n",
+    "    echo \"Folder already exists.\"\n",
+    "else\n",
+    "    echo \"Folder does not exist. Creating folder and downloading taillard instances...\"\n",
+    "    mkdir -p \"$DATA_PATH\"\n",
+    "    ! wget http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/tai20_15.txt -O taillard/tai20_15.txt\n",
+    "    ! wget http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/tai15_15.txt -O taillard/tai15_15.txt\n",
+    "    ! wget http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/tai20_20.txt -O taillard/tai20_20.txt\n",
+    "    ! wget http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/tai30_15.txt -O taillard/tai30_15.txt\n",
+    "    ! wget http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/jobshop.dir/tai30_20.txt -O taillard/tai30_20.txt\n",
+    "fi\n",
+    "\n",
+    "exit 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Provided file name ../../ai4co/rl4co/data/jssp/taillard/15j_15m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done evaluating instance type 15j_15m with reward -1408.0999755859375\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Provided file name ../../ai4co/rl4co/data/jssp/taillard/20j_15m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n",
+      "Provided file name ../../ai4co/rl4co/data/jssp/taillard/20j_20m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done evaluating instance type 20j_15m with reward -1380.699951171875\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Provided file name ../../ai4co/rl4co/data/jssp/taillard/30j_15m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done evaluating instance type 20j_20m with reward -1349.9000244140625\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Provided file name ../../ai4co/rl4co/data/jssp/taillard/30j_20m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done evaluating instance type 30j_15m with reward -1374.0999755859375\n",
+      "Done evaluating instance type 30j_20m with reward -1371.699951171875\n"
      ]
     }
    ],
    "source": [
     "# path to taillard instances\n",
-    "DATA_PATH = \"../../ai4co/rl4co/data/jssp/taillard/{instance_type}\"\n",
+    "FILE_PATH = \"./taillard/tai{instance_type}.txt\"\n",
     "\n",
     "results = {}\n",
-    "instance_types = [\"15j_15m\", \"20j_15m\", \"20j_20m\", \"30j_15m\", \"30j_20m\"]\n",
+    "instance_types = [\"15_15\", \"20_15\", \"20_20\", \"30_15\", \"30_20\"]\n",
     "\n",
     "for instance_type in instance_types:\n",
     "    \n",
-    "    dataset = env.dataset(batch_size=[10], phase=\"test\", filename=DATA_PATH.format(instance_type=instance_type))\n",
+    "    dataset = env.dataset(batch_size=[10], phase=\"test\", filename=FILE_PATH.format(instance_type=instance_type))\n",
     "    dl = DataLoader(dataset, batch_size=5, collate_fn=dataset.collate_fn)\n",
     "    rewards = []\n",
     "    \n",
@@ -678,7 +821,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,