diff --git a/docs/tutorials/control/mpc_demo.ipynb b/docs/tutorials/control/mpc_demo.ipynb index aae2f9e3..6b91dd37 100644 --- a/docs/tutorials/control/mpc_demo.ipynb +++ b/docs/tutorials/control/mpc_demo.ipynb @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "0fc2b864", "metadata": { "execution": { @@ -211,14 +211,14 @@ "\n", "\n", "def quadratic_loss(result): # drives the state to zero while penalizing control effort\n", - " loss = jnp.sum(result.states[0, 1:]**2) # we remove the first state (initial condition)\n", - " loss+= 0.01 * jnp.sum(result.controls[0]**2)\n", + " loss = jnp.mean(result.states**2) # states of shape (n_simulations, horizon, state_dim) by default, already exclude the initial condition\n", + " loss+= 0.01 * jnp.mean(result.controls**2)\n", " return loss" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "a603e321", "metadata": { "execution": { @@ -239,6 +239,7 @@ " loss_fn=quadratic_loss,\n", " horizon=horizon,\n", " n_samples=n_samples,\n", + " n_simulations = 1, # how many simulations to run in parallel for each control step (default 1)\n", ")\n", "\n", "key_2d = jr.PRNGKey(0)\n", @@ -265,6 +266,27 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cf44cfe6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'same_time'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nonlinear_dynamics.observation_control_alignment" + ] + }, { "cell_type": "markdown", "id": "658796bb", @@ -275,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "5dbcf7d2", "metadata": { "execution": { @@ -288,7 +310,7 @@ "outputs": [ { "data": { - "image/png": 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V2xZyTCIiInTbQUFB+l28vb2NCxcuLPY6Oe633357qceuOv9GiIiIalL2oUPGuJkzjTFvvKnXcr+6lBz35SclGRO++MJ4cPRo4+627cyXQ5deZkyYO9eYn5paq/tbXWPnkhhY2GDQVNO/CDIIl8Hs+vXr9X5sbKyxYcOGxjfffNP8mvHjx+vgPiEhQe8vWLDA6OLiYty4cWOZ273pppuMrVu3Nh44cEDvyy/U9OnTK7RNa/Zt9OjRxgceeOCs7yQDdxnwm0gwUr9+feODDz5oLCwsNObn5+s+yiBeBvXWBBYFBQVGg8FgHpBbu13ToF+Cj6ioKH1s8eLFRldXV+Pq1av1vgRdEoCZjoe8xxR4SJAmA/6XXnpJ90EsWrRIB/YSFFh+RteuXfU4mfz6668avCUnJ5sfmzNnjgYH2dnZVm9bgo0pU6boffleY8aM0c8rGVg8//zzGtCVhoEFERE5Ihnwy5js9KuvGePnfK7XcTNnVesJ4MLCQmPGv/8aT0yebNzTuYs5mNjTrbvx5FNPGTO3bdPX2Mv+2iKwYGtpG64O4BYWptcyPSaP29LYsWO1G7kIDw/XJXm3bt2q9yXVSFbTev755zW1Rlx11VUYMGAAZs6cWer2JLXpq6++0qV8pf5BSFOUhx9+uMLbLG/fyjNmzBhccMEF5vs//fSTfq7sk6T5SB+FN998U9Ot/vrrL6uOk6QVSTqQZVpRRbYr31/SiMSIESMwatQozJ49W++np6dr+pZp1ShJU3riiSf09o8//qgpUldeeaWuRiWrMUm6UuvWrfH7778X+wypcZDjZCIpSXLsv//+e/NjcuyvvvpqTWeyZtvyHSVF6tlnn9X7krJWVrd6+WxJqSIiIqotMm7Ki4mttvFTaUXWhamp+njVt52GxC+/xOHRl+DY+AlI/WUhjLm5WjMR+dyzaL1yBRq89BK8u3Sxun7RlvtrS6yxqMXVAaqTdAu3JEutnjhxQm8fPnxYc/xK1hTIKke7du0qdXuy7Kjk/Hfv3r3U5yuyzfL2rTyyZGzJfZLHTE3ahAQI9evX1+esYaoBkUF2ZbZbcilWWa5WVooS48ePxzfffKODegkGZFUqWWVJvq8M9qW5owQDpS19W973Nq3W9PXXX2PixImIjo7GP//8g6VLl+rz1mxb6jGkdsOyrqJt27alNrmTbVnbVJKIiKi62aKJXEWKrCuyn0lfz0XyTz/BeCYAcvHxQeDoixF0zTXw6tSp0guh2GJ/awIDi2pmj78Ipn4OWVlZZy2hWlavBy8vL/NrqmubFVVy0CvbLfl5Ff1MOfMvMyyWgU1Ftlve95UCawkyJLCSQf+HH36osyBbtmzRgEaCFVOBenlKG+yPGzdOZ2+k2P27777T4OXCCy/U56zZtgRNJfddgitTt3lL8hklgxsiIiJ76wtRnUXW1jIWFiJ9+XINKDJWrzY/7tGqJYJvuAGBl14KN7+qj/mqa39rGlOhqvuAnvlFkF8Ae/lFkNWUZND7999/mx+TAeWyZcvKnJFo166dDsB//fXXYo9LGlFlt1kWCWJM2y1Pjx49cOTIEb2YyKBd0psq8pkDBw7EunXrKrVd0yyBkBkby+9rmgWRWYx7771XV1aSmQLZlgQFUVFR5tkNS5azJ2Xp27evDva//fZbTYO64YYbzGdBrNm27OOBAweKBVQy61EaOTaDBw8+5z4RERFVN1umAMmsR8j4cQieMEGvKzILUiArPn7+OQ6Nuggn7rq7KKhwcYHf0KFoMucztFi4ECE33FAtQUV17G9t4YyFDcgPXiJre1keTFJppk6dqj0KJO9flnOVs+mSl//ggw+W+h6DwaA5+NLhXAawgwYN0vx9ydWXS2W2WZb27dtrnYAMaE3bKs2QIUM0KJBajmnTpmmaj9Q8SG2BBAfWuummm3DnnXdixowZ+j0qst2PP/5YZwt69uyJzz77TAf0DzzwgD4nsxOSpiQNHCMiIjB//nwEBQVp+pTMKMj2pN5EXiePSTrZrFmz8MILL5hnH8ojwYT0+pAZBal/sTwu59q2pGVJMzxJl3rppZf05/TQQw+dNUUry/yuX78ec+bMsfp4EhEROUrmh4zJKjIuyzl4EIlz5yLl51/M6U6uAQEIuuoqBN9wPTzO1F3aimsF97e2MbBwgl8EOZNdsmu11DVYptRIgCA9FyRPXwqzu3Tpome4TYXXpbn11lt1O1KM/cMPP6Br1654//33K7RNa/ZNBrhxcXE6QJeeGQsXLiz1fUKCmldeeUWLkKXXw3XXXYfHH3+82GtkxkAClPKKwp966ikNZkx1CefaruyvbFe6Usv7pJZC9k9mLEz7Ke/9/PPPNcCSnh0SMK1YscJcKC6BhhxLmXWQ4yU1DpZBhekzLGs9LEkNh/wcJKgpWetxrm2LX375RQNB+V7SY+OLL77A5MmTNT3MRArRL730Ui38JiIiqmm1nQIkMxOZmzcja9MmZG74F1nbtpmf82zdCsHjJyBwzCUONdivSS6yNBTqEDlTGxgYiJSUlGIDKpGdna3pMDKoNdUYkHMy1UAsWLCgtnfFbsjvv6zY9eWXX5ZZY8G/ESIiqgk11RguLza2KIj4dyMyN21Czv79kuv83wtcXeE3ZDBCxk+Az/nnVboY21nHziVxxoLqJKkhYB1BcRJMr1y5spZ+IkRERLbP/MiLjkbGmjXI3LhJA4m848fPeo1H06bw7t0LPj17wbdvHxhKyaCg0jGwICIiIiKnlimLsnz+BdKWLAEKC/97wsVF+0349JJAoid8evaA+5leUqZZE7lm6pN1GFgQERERkdMxFhRorUbinDnIsmjM6921K3zOPx8+vXrCu3t3uPn710gvjbqAgQUREREROY3CjAwkf/+DdsPOO7PMuovBgIAxYxBy803wKmP1SVv30qgLGFgQERERkcPLO30aSbJS5XfzUZiWpo+5BQYi6IbrtceEKcWpMr00ZIUqeZyBRfkYWBARERGRXTLm58OYlwdjbm7R5cztQsvb6RlI+eUXpP7xB5Cfby7AltmJwMsv1+DAnnppODMGFkRERERkF6SPRNK385A0b54O6IsVWltBirBDJt4Cv0GD4OLq6pC9NBwZAwsiIiIiqlXSTyLpyy81qJAaibK4eHgUXQyGYre9OnZEyI03wrtzp2rZHynUlpqKmuil4UwYWBARERFRrcg9ehQJn36GlJ9+0tQmU4fr0FtvhU+fvnDxMMD1TPAACSZqsEGdrXppOLPKzRER2bFGjRph1apV5b7m999/xyWXXGK+Hxsbi+uvvx6tWrVC7969kZSUpNvZvXt3hbZb02R/mjVrZr4vDe6k8V9hBaeOiYiIalLWzl048eBDOHTRxUhesECDCln6tdFHH6L5zz8j8LLLYKgXAffgYLj6+hbNTNTBrteOhoEF1bqbb74Zzz//fLVt7+TJk8jOzi7z+fz8fDz44IO48847zY9NnToVMTExWLRoERYuXIiCggLdTm5ubqnblecl0Fi7di1qk+zPiTNL6YkLL7wQWVlZ+PTTT2t1v4iIyLZkSdS8mFi9dhRGoxEZa9fi+MRJOHrVVUhbtEgehN/AgWg692s0+/Yb+A8eXOnaCKp9TIWiWhcfH4+goKAa+7yffvoJOTk5uPjii82P7dixA8OGDdMZCyFn/KOiolCvXr0y/3GUQEO2Y29uv/12vP7667j11lt5doeIyIGYOj2fK6ffkZq3FaSkaNfrrE2bkb5qFXL27Cl6ws0NAaMvRuikW+HVtvy+EuQ4GBI6AUnhee655/Dkk0+iV69e6N69uw4sZfBrIrffffddTfNp2bIlrrjiCmzfvv2c216wYAGGDBmC1q1b48orr8S+ffsqtM1z7dvDDz+Mv//+W8+wywyAXA4dOqTve/bZZ/Hoo4+iQ4cOuOaaa8xByN1334127drp4w899BBSU1MrdLy++uorXC7Lz505IyKpROvXr8f06dPN+9CkSRP06dMHBw4cKHUbXbp00eurr75aXz9o0CDzDILsd7du3XT/xo8fj6NHj5rfl5mZqa+fP38+LrvsMj1uptmFgwcPYsKECWjTpo0eUzluJWdeli5dqrMS7du3x3XXXVds2yby3fbv349///23QseFiIhqjwQLidKD4auv9Frun6t5m1tYmF7L6kX2MHMh/7fnnjiBlJ9/RvRzz+PwmDHYf34fnLjzLiTMnq1BhaQ0BV17LVouXoyGr7/OoMLJcMbiHH8gxqws1AYXb2+rzzbHxcXpQFXSeWTQvGfPHowbN04HzKYB+WuvvYY33ngDM2fORNu2bfH+++/rAFUGoGWdlX/vvfcwZcoUfa/k7UtQ8cwzz+hnWbvNc+2bbE+CEblvSoeKjIw0v08+/4cffkBoaKj+PMaMGaMBwRdffKEpTffcc49uU1KYrLV8+XINCExWr16t25UZi/vvv18fkxoLCR4sU6EsLV68WIOPDz/8EH379oXBYND9k2BBfm4fffSRzsJ88skn6Nevn+5jYGCgzoTITIfs94wZM3D++ecjLCxMZ0dkO3fddReeeOIJpKWl4ZFHHtHA5ptvvtHPlNsXXXQRJk+erIGX1FeY9tdSSEiIBoLLli3DeeedZ/VxISKi2lGRTs+Vad5m7UxIZXpMZO/bp7MRmVs263V+bOxZr/No1gyebVpDTikawiPgXi8Cxlz7m/GnqmNgUQ4JKvb16Ina0HbzJrhU4I9/xIgRePrpp/W2nM2WwuQlS5bo4D0vLw+vvPKKzhRcddVV+pqPP/5YB9jvvPMOpk2bdtb2ZEAtZ94lIJBBsJAz8DJwFhXZZnn7FhwcDC8vL/j5+emZfEv9+/fHiy++aL4vwcPGjRtx5MgR82u//vprdO7cWWccZJB+LikpKXqpX7+++bGGDRvCw8MDAQEB5u3KPpXH9P7w8HDze2TmRYIUKQT3OfOzk1mQP/74A999952mKJnIDM61115b7L4EZS+88IL5sTlz5mjAJsczIiJCtyXf0XRM5OchKVwSxJS2f6XNZhARkf2pSLBQ0eZt1Zk2lZ+QgKytW5G1dVvR9c6dZ5+ANRjg3aEDvHv0gE/PHlqQLd9JZmGsCZzIsTlUYPHLL79gw4YNxR6TM+P33Xcf6rpOnTqddVzkzLg4LP+opKVhwIAB5uflrL/c37ZtW6nbk9WQZAA+cuTIYo+b0ocqss3y9q08PXr0KHZfttu8efNiAYhsW874y3PWBBYyyyHc3av/V3/NmjUakMmA35TqJdcy+yJpTuV9N3mvpIDJzI3OlBmN5pWd5L0SWMh3lLQ0SxKMlBZYyPczfVciIjo3W53Vt0ZFgoWKNG+ryExISbJKU/a+/cja9l8gkRcVdfb++PlpDwlpTOdz/nnw7tz5rE7XWmRewVkWckwOFVjIEqGS/iG55Saenp42TUeSmYPaIJ9dEW5ubmc9ZhrcmgqMSx4ruV9W8bFpUFrW8a3INsvbt/KUnDWQ7Za2P+V9j9LShOT1MqtQ3aQeQtKjJAWpJH9//3K/m7xXUsRk5qIkmRUR8h1lZsVSyfsmEszIjA8REZ1bbRdDV7TTs7XN28qaCclPSoZLenrR7YQE5MfLJQ4Fcp2QgLzoaGTv2gVjyRUWXVzg2aolvLt1g3fXrlrjIalQxrR0uHh6wD0s7KygojKzLOS4HCqwEFK0a0qrsTXJla9IOpK9kgJhGdzLGW+5bbJlyxZzEXJJkqMvZ72lAFhuV8c2yyLbsSbQkLQg00yJaaAuS8RGR0drwbO1P1OpO9i8eXOxOouKktkZ2Zblfnfs2FHTj+S5Bg0aVGh78l45diXTwSzJz0FSnyyVNuMky81KTYfUvxARUfmqcla/OlW007M1zdtcfX1QkJWJzMWLkZ+UhMLkZBTm5CBBFg2x4v9d14AAeHfpUhRIaDDRBW5n/v+V4ybpTYXJKec8bhUNnMhxOdyqUMeOHdPc/g8++KDMNB4qztfXFxMnTtSaCel5IINhyd+XugRT/URJUmg8adIkPPXUUzrgFTKAl5qLym6zLFLjIAXf5woupL5D0p5kpShJOZIBtBQvS7AphdfWkoBC6h6qQoIHKTK3XCVLVs2SVC1Z2enUqVP6WGJioha5y3Epj6yOJUGcFLCbZl9k25a9NqSw++eff8Zvv/2m96XoXQrAS5JaDykcHzhwYJW+IxFRXVDaWf3C1FR9vKb7TchAW5rCVWXAXZCegdQlS3Dqqadw6KKLkDz3G2Rt2YK8o0dRkJxcVBMh/9+6uuqMg2e7dvDt3x+Bl12KkIkTEfHYY2jwxhto8duvaLNuLZp8Mhvh994Dvwv6m4OKyhw3CZxCxo9D8IQJem2vy+NSHZuxkAFdcnIydu3apYMxWW701VdfLfP1MkizTJOp6NKkzuLNN9/U4mGZXZD0GTnj/+WXX55V/2BJlpJ97LHHcMEFF+jshRQ3SyFxVbZZGhkwy6pMkqYkAYsUgJdG0od+/PFHDXhkXyQQkeVr//e//5WablVeQz5ZylUKwWUJ3MqSbcjKTRIMSHAjKVAyqL/33nu1VkK+ixy32267TWckyiNL20pjPlnx6eWXX9bibwm4ZNUsE1nSVoJqKXqX7yuF7zfddFOxn4n47LPPNCCxZZogEZGzsGWaTk2lWOUeO4b05cuRvmwZMv7dKCusmJ+TzAtfqX3o3kPTmAwNG2rKkltQEFwq8H9ndRw3a2ZZHK0+hopzMVqTg2In5Ky2ZcqL1FyMHj1aB3RlnZ2VQZ/pLLslKUyWwWnJPHdZcUjOOp9rVSB7Ir0dTKsamUjwJd2hZZlWS3KWPz09Xc/8W7ucrdRbyPEquS1rtlmRfZMlXjMyMnQmQF5T8n2W5Hn5LJlZKUlmUKQuobyBtZzpl1kL09l/qUmQn7kpxUoKp2XWQQrNZSnZsrYrr5P3CstleyWYlWNS8juaGutJMXZZ9RES/EoALStllcY0WyPfXT5HjrEEIUJm8eRvYu/evWW+vyoc9W+EiOjcAcDfesZd0n8kTaeqAYApVcgyxUoG3nK2vqqDX/m/JHvnLqT++qsGFLklVgE0NGmi3azl4nNeb7iW8f+NPR63yu2DYzQLdFQyLpExR2ljZ4cOLEojOely1lxScqydsWjcuLFTBRZUcRIQSGqXaUDuLKT+RAJBmc2wBf6NEJGzqu6z3pL+JM3uJN1IUoUKs7K0vkBSgSTdqTIK0tM1mEiaPx85u890sBbu7roqkymY8GjezOqTh448W2DL4I0qF1g4XCpUaQNEOXtbFjm7zJQQKklmBJwtqCht9SkiIrJOdafpVFeKlc5O7NihwUTqb7+b+0ZIB2v/4cP14tu/X7H6h5pkq/Qma1SmWSDZlsMEFnIWVoqIe/fubX7sp59+0rPOQ4cOrdV9IyIiIqrOlZAK0tKQsnAhkucvQM7evebHPVq0QPC11yDg0kvhbqPZaUfBZWztj8MEFjKlJ4XaUtQqhbDHjx/Hr7/+iscff7xCKwIRERER2eMSsjo7sX07kr6bj9Q//ig+OzFqJIKvuQbePXvWWJqTveMytvbHYQILWQVHmuOtWLFCZy5kNSBZDaq0HgtERERE9sCaVCHpLSFpTolff1WsdsKjZUudnQi89FJdxammONIqSxUN3si2HCawMBkwYIBeiIiIiByZFnjP+xbJ381HQWKieXYi4KJRCLr2Wnh3717jsxOOuMpSbdZ5kIMHFkRERESOLGvbNiR++RVSFy+WIlJ9zD0yEsE33ICgq6+qtdoJe+lCTo6LgQURERGRjRlzc5G6+E8kfvWV1lGYSM1EyITxOjPg4l67wzKuskRVxcCCiIiIyEby4+N1qdjkb+ch/0xDVReDAQGjRyN4wnh4d+xoN8eeqyxRVblWeQtEZfQXefjhh7VbtbXeeust/PDDD051PD/99FN88cUX5vvvvvsuVq5cWav7REREtmUsKED6ypU4cd/9ODBoMOLfe1+DCrfwMITdfx9aLVuKBq9Os6ugwnKVJVkatzJL5BJxxsIJzJkzB6tXr8aIESNwzTXXFHtu0aJF+N///odOnTrhwQcfLPZ6YTAYtBP5FVdcgfbt21v9fHx8PCZPnlzmPn3yySfYvHmzdka31u+//45evXrhyiuvhLNYunSpdnG/6aab9H6rVq1w2223YceOHXpsiYjIeeSdOoXk739A8g8/ID862vy4d9euCB4/HgEjR2hxtj3jKktUFZyxcALLly/H3LlzMWXKFF0D29LUqVP1OQkwLF8vZ8379OmDLl26YOfOnejcuTN+/PFHq5//7bffytyfgoICvPTSS9p3hIobPXq0NnucP38+Dw0RkRPVThy/7XYcHDoM8R98oEGFW2Aggm+cgOY//4xm381D4JhL7D6oMJEZCkO9CM5UUIVxxsJJ9OvXTwMA6fMxcOBAfWz37t16ZlxmMnJycoq9vn79+rj11lv19l133YX09HS88MILOjNhzfPl+eOPP5CamqqDaEuyf9999x0SExM1YLnxxhvh7e191vuXLFmiZ/olQJkwYYLOtpjIfnz11VfYu3cvGjRogOuvvx5NmjQxPy+pV19//bVet2jRQt8fHh5eLN2qWbNm8Pf31/2UbUjDRemR8vLLLxfbj2+++QZRUVHahNGabYu1a9diwYIFuv1Ro0aVenyuu+46zJ49G+PGjTvnsSQiIvuUc/gIkr//H1J++hkFCQnmx3369EHQVVfBf/gwuHp61uo+EtU0zljYSGZeJmIzY/W6JkhajQx0JaffRAavV199tQ5yz0UG7zKIruzzJQOD888/H+4Wq1vIIL5Hjx6Ijo7Wgf2MGTNwwQUXIDc3t9h7JWiQgXxISAhOnTqlqVHr1q0z121IDxMZ8Lds2RJpaWkYM2YMDh06ZB7Ud+vWDYcPH0abNm20kWKHDh30vmW61X333YfnnntOAxJJ75Lg4pVXXsGBAweK7cuzzz6rn2nttn/++WfdPwnipEP8zTffjL/++uus43PhhRdizZo1GiQREZH9kiyA/MREZG7ejOTvv0fsm28i6p57ceji0Th88cVI/PQzDSqkdiL09tvR8s/FaPr5HAReMppBBdVJnLGwgcMph/HP8X+QlpsGfw9/DGkyBC0Cbd9cZtKkSToQl0G75PXL2XVJX/r444/LfZ/MDPz999/FZgYq8nxJMpvQvHnzYv8w33///Xp588039TGpM5DXzJo1C/fee6/5tRJoyKyLn5+f3pfGQBJoSPrVkSNHdEB/+vRp1KtXT59/4oknNLVITJw4EU899VSxFCwJtiSIkIDFRGZJZHuWNQ7y3SRgkdcKCWYkaLjhhhus2rZ8x0ceeUT358UXX9Tn5b1SU1GSzHbk5eVpQNS1a1erjikREdm2g7Q0q8vauhW5R44g9+hRvc45ehSFKSmlv8HVFX4DBiDomqv1uraXiiWyB/wrqGYyQyFBRXJOMur51ENMZgyWHl+KyHaR8DHYdlUFOfsuA9Vvv/0WwcHBetZfZgVKCyz27dunqU4yKJdBdEpKSrG6iXM9Xx6ZSTAFBuLkyZM4ePCgpi2ZBAUFaaqUDPAtA4uLLrqo2HuvvfZaXHbZZbofkdI8KDhY60Zk1qFdu3bm18ogXQIamVmQfZeBvlzkcdkfS8OGDTurcFrSkj777DNzYCF1KTKz0LRpU6u2LelRcl9miEyk6F1S1Eoy7bOkixERUe12kJaAImHmTCQtWADk5ZW6LfcG9eHZrDk8mjeHR7NmMDSoD/cGDeHZtAnrEIgsMLCoZul56TpTIUGFl7uXXsdnxevjtg4sTLMWM2fO1IG73C5LQECAFmebUqhkAGxZ73Cu58sTGhqKpKQk8/3Y2Fi9DgsLK/Y6uS8rR5V8b8nXyIyJ1GVERERoIPL6669jyJAh+vz48eO1UFxWqRKSbmX5Oeedd95ZqWBybEqSwEKK3//99190795da0FMNRfWbNv0HUvb/5KSk5NLfS0REdVcB2lJcUqY/QmSvvkGxjN1iJ7t2sGzTWt4SgBhujRpAleL//9MAUvWlq3IKCdgIaqLGFhUMz+Dn6Y/yUyFacYi2DNYH68JcoZflpWVPP8vv/yyzNdZFmdX5vnyyKyJpE5ZnrkXx44d0xkAE7lvWXgtStZxyGskrctUJC2rU5nSmiQIkBoLSYuSgmhTStMll1xS4X2WfZQZCpmpkEBCZhNMsw8NGzY857ZN31H23/R60/5LobqlXbt26axF69atK7yfRERUtQ7Sxvx8JMyZg6QvvtQARHh3747wBx6Ab5/zqxywENVlLN6uZjIrITUVEkzITIVcD24yuEZmK4QMWGUpU7lI6lBtkBQnmYkwpfpIUCBFzdIczlQMvWfPHi2kHjt2bLH3SrqVpE0JqUP44IMPNBVKai2OHj2KDRs2mF/bu3dvDUwSEhK0X4ashiUrV1mmGMlz//zzj1X7LbMf8+bN04Z28h1MMxvWbFtmU/r374/33nvPvOSv9AKR4KekZcuW6Upd7GNBRGT7DtKFWVl67eLlheT583Fw2HAkfPSxBgleHTqg8ayZaPrN3HMGFWUFLIWpqfo4EXHGwiakUFtqKiT9SWYqaiqosKxTqE19+/bVJVxlkH777bfrYx9++KEOpiWdSAqaZbUkKW4uuXytvG/w4MGahiXL5UoNg6lztQzEH3jgAa23aNu2rdY7xMXF4c4779TnpVhdghB5TmYfJB1LCrCnT59u1X7LDIXUbkhQ9v333xd7zpptS1Ah9RumgGfTpk36fSxJsCRpVnJsiIjIth2kZTYh//Rp5Bw5gqwtWzQIEJ6tWyHs/vv1NXLiqjIBi2nGQrpTy+NEBLgYS3ZUc3JyxjkwMFCLkaWOwFJ2drauPCSrFUn6jaOQVZSysrIwcuTIUp+XM+SSGmV6Xl4vA/aSfSYst1eV58Wff/6pRdkSHJiWnc3MzNQz/DIol/SgkisiyZK0UpMgMwRypl8CCAlGLIu5xcaNG7F//35NgZKZEMsz//LrLMXmMushy8jKsreW7zd9hgz+S/PLL79ovYT02PAo0cjoXNs2zWJI0CS1F/K89BGR7y9F9EIK6aXPhWWqmCNx1L8RIqpb5N/r3CNHkbbkTyR+9bWmQQlD0yYIv/c+BFx8EVzc3Cq17aIai781SHENCID/sKGssaA6O3YuiYGFBQ6aqpec+ZcUItPSsAQsXLhQazUsl+N1JPwbISJ7VZCaioy165CxejUyVq1C3qlTxVZ1Cr/7bgRKam2JVQFtvYwtUV0KLFi8TTZzzTXX8OiWIMXmRES2VFcGvcaCAmTv2IH0Vas1mMjavl0aL5mflwDCu2dPBIwcgcCxY+FaYha6KuS4OvOxJaosBhZERER1rHeDI5Ii7JwDB7SJXcaatdoN21QzYSLLw/pecAH8LugPn969OfgnqmEMLIiIyCnUlTP1zrgUasmfXX5SEnL27EG2XvYie+8e5B4+ApxZWdDE1c8Xvv0vgG//fvDr3x8Gi+W+iajmMbAgIiKH58xn6qujd0NtBBbWBHrGwkJkrFmDlJ9+Rt6JEyhITUFBSioKEhJKfb2Lt7euwiQzE25BQfBs3RqhN06w+8CJqK5gYEFERA7Nkc/UVyd7Wgq1tEDPPSICOfv2IXvfPuTs3Vd0e/9+GLOySt2GrODk1a49vNrLpR3cwiOQ+ttvcA8P18BJUqNqM3AiorMxsCAiIodmb2fq7aF3g3x/CSpkKdSaPgYFGRlI/t//dLnXwpwcnYlI+OTTs+ohzNzc4B4WBkODBnALDYWrlxfC7r4bni2anxVAugUG2EXgRESlY2BBREQOzZ7O1Nc2Sf+SmRpb1ZqUTG+SlZlyjx5F9u7dyN69p+h61y4Uppfeido9MhJebdvCUy9t4NG0GdJXrURhSmqxn50hsp7dBk5EVDYGFkRE5NA44Dz7eNhisC0pTMkLFiD32HEUpKXBmJOjQUWpqUxubloDIcXUMotkaNIE4XffBUP9+qX8/LytDhZsHTgRUdUwsCCbWbx4MXr06IHw8HC9n56ejvXr12vn7aFDh2Lbtm2IiIhAhw4d9Pm1a9fC29sb3bp1s6ufyk8//YQ+ffogMjJSG8T9/vvvuPzyy+Hq6lrbu0ZElRxw1vUVpKxpNpe9dy9y9u4tWpVp1y7kHDx41qpMpoJqr3bt4NWhQ9GlYwfAxQXpy1cU605dWlBRmZ8de0gQ2S8GFk5g48aNOHr0qA7QTYN0k8OHD2Pz5s06KL7ggguKvV4YDAY0btwYXbp0gbu7u9XPZ2RkaFftsqxcuRJ33nkn9u3bp/cTEhLQvXt3NGzYUC8ScDz99NMYNmwYnn/+eX3Na6+9hkaNGmHGjBl6f/Xq1fDz80PXrl1Rm8aPH4958+bhkksugZeXF959910NjiZNmlSr+0VElRtwOvsKUsbcXOQcOSpLLgEurnBxdQHkRIjlbVdXuLgU3dbXHzxoXtY1Z89e5J08Weq2XTw9i2ohwsPh5uODkIkT4dOzB1zc3M56rUfjxgwWiOoYhw0spkyZgtdffx0PPPAApk+fjrpMBuJffPEFBgwYgOXLlxd77vHHH8f//vc/jBw5EosWLTK//rffftPAIC8vD5s2bYKvry9+/vlntGvXzqrnJfBYtmxZmfsknzt58mR4nOl0Ku+VwERmJUwGDx58ViBkadq0aWjVqhXeeecd2JMnn3wSEydOxI033qiBFxE5DmddQSo/MRHpK1YgfdlyZKxaVWaNQ0VIGpNn+3a6MpNHi+ZaP2HML4AhMtJcC+HdqWOpQYXgzAJR3eOQgcVff/2FH374AS1atEBBQUFt745d6N27NzZs2ICDBw/qYFzEx8fj119/Nc9UWOrYsaMGHCIrK0tTfe6//378+eefVj1fHpkhkWBEUobEunXr8M8//+jZMdM2hcxElBVYyKzI6dOn9bbpPRIc+fv76+2tW7fixIkT+jtQchsyWxIcHKyzH3JMZJZBgi7Td5F0rNzcXHTu3Bn1S5mal+1KmlbTpk1L3b/hw4fDaDTixx9/xDXXXHPO40FElVfdKUvOsoKU/Bsky7WmL1uG9KXLkLV9uzxoft7Vz0+/n7xO05cKC8u8LWlL0hdC05nat4OnLPHari3cAgOLfaY8z8JpInKqwCI2NlbPFn///fe47bbbant37EZISIjm/X/22Wd45ZVX9DGZxbjwwgu1jkGCjLJIXcPFF1+MTz75pFLPl7Rw4UJNewoKCjKnNMlAXdKHJKXIRGY87r33XnMqlCUZ/J88eRIpKSnm9/Tr108DgyuuuAKnTp1C+/btsWPHDg2C5PdBZlXE1KlTkZOTowFC27Ztcf7552tgsXTpUlx33XVo1qyZ7pt8xhNPPKEXEzl+d999twZqqampqCdnNPPzi+2bm5ubzubI92RgQWQ7tkhZstUKUtJTIS86WlOICjMy4RYUqMXLpossoVpV8hkZ69bprET68uXIP3PyxcSzQ3v4DRwI/0GD4NW5M1yquQ6MhdNE5FSBhZxhkfSTO+64Qwd+NfF5WfmlN+6xNW9376L81wqQgOvmm2/Giy++qIPfTz/9FM8++6zOWpyLzHRIHUZln7cksxUy2Dd55JFHNKVKZh4sZyxKm0kxueeee/DHH3+clQo1ZswYtG7dGitWrNDvKMXUQ4YMwcsvv2wOqIQEMnJp3ry5ucbjyiuvxOzZs3HVVVfpY3v37kXPnj01JUuCDwlaZVbmgw8+MNdPSJCxZMmSs/ZPZju++uorq44HEdlPypK812/wYCR9Nx9ZW7fC1d9fi41zDh+Bi8FQdPHwOHMt9z3g6mEoChxOnSq6nDxzbXEpSEws93O1Y7Q50AiEuwQzEnD4+KAwJxfG7GwU5mTDmJ1jvtbHsrPPPJejnyH1EOZtennBt18/DSb8Bg7QFCVbY3oTEdVaYCFneiVXXwaIkroiZ6wDAwPRqVMnjBo1Ss+wm3LwrfHGG29o0bDlGeZzkTPXcjGRs9DWkqDi/G/OR21Yf8N6+Bgq9p+nFEJLzr/UUkgqUExMjJ7dLy2wiIuL00G+/IxkRkHO+M+ZM8fq58sjsyMy+K9uMvCX7yK1NTJbIIGfXOSz/v7772Kvveyyy8xBhZDUOQnUpM5DUpiEvLdJkyaapiWBhWzTx8cHt9xyi/l98rv20UcflTpDJMeIiGyjOlOWZGnUrK3bkLVlMzI3b9G0IWNmpvn5lAULqmWfXc4Uj0vNgTE/T9OMZH+Rn69LsubLJTq6Sp/h3qC+zkj4DRoEn/POq5aZECIiuw4sZMAmKSWS4iKBgKSwyNlpCSrS0tJw4MABTYG57777tBBWbptWHCrvLLgEFv/++6+eqbaWFABLakxdIANnGRTLTIWk+owbNw6enp5lDtIlxUgCEalFWLNmjdZRWPt8eWQlp0yL/7Sri2mlKkmhkjQmSzLzYKlBgwbF7h85ckSvv/7662KPy8yKqc7i2LFjWldhuYysrIhV2u+m/F7L9yQi26hsypL8/yPpSFmbJYjYjKwtW5Gzf3+x+gMhMxVSnCwBgDE3D8Y8uc4tuj5zGyVq+NxCQnRFpGKXhnJpqPuW8vPPxWZY5LHgcTcUBRjJyUWXpKT/bicna9qUrLTk4uUJV08vuHh7FV3LfS8vuHh6wdW76NotwB+Gxo0rPJtNROTQgYWsGjR//nxNKZHc/NIGZoWFhVqE/fbbb+uZX0llKY+sJiTpLKbCZCGF29u3b9fPk1mJ0gIOCVwefvjhYjMWMli0Nh1JZg5qg3x2ZUhg0aZNGz3mMtNQFsvi7Mo8Xx6ZQZBlbqubqXD7qaee0mC1PCX/45X3yjEp7zvJLERycvJZAUTJGgtTkCP1G0RU+03vJE0pfeVKpP25BJnr1yO/lNlEGZD79OgO7+494N2jOzxbtTpnDYJ0lTYFGi7u7jpzUpa8mNhSZ1gK0zNgqBcBN/n3y8r/e4iIHJW7rdb9l9mI8shZ4REjRuhFUqTORXLupS+CpV69emkRrSw3W9YshpyxL+us/bnI4LSi6Ui1TVJ7HnroIZ0Zqq3+D9L87ptvvtHAryKzSyXJjIBlGpssdSszCrNmzTorsJAVpMqrAZEVpWSJYml2Jyl4JrJ9mV2R1LH+/ftrELpr1y5zjYgpbaokCdquv/76Sn83orrM2pWeyisWlm3I8qqpixdrIzbL1CYYDPDq0B4+EkR0l2CiGwwRERXeT0lp0qVUrUg3slVROBER6npgIYO06n69KT/e2sfrMkn/qk2mRnLSeVtmrCpL0pvef/99fP755xpkSHAgK1NJ/YQETqNHj9agVOp4pL5EAoeySEO+xx57TIMBKdCWZWRlVuW7777TIEh+B2VBgLFjx+p2pQeHzG7JbFjJ4Gj//v3YvXu3LiRARLZd6cmyWLgwI0NXQ0pdtFiDCilqNpF0JP+RI+E3aCC8u3Sp8dqDisywEBE5K5uMyGU1HstGaOXp1q2b1bn7VDoZEJdM4bF03nnn6UDc8vVy5r8sVX1eCvKlQZ50qDYFFjLbIDMZlko2yJNZiNDQUPN9CQCkxkOWiTXV6kgAsXPnTg02pGBbaimee+65Yl3AZWnZli1bnrVf0tlbghOZtZA0PEllkhWfLFPj5s6diw8//FB/f+U7yupTUitk2e9Cgg1ZgcvaVbKIHFV195CozEpPBenp2qchdfEiZKxcBaPFLKakNwWMHAH/kaPgJY3aarn2wJbLsVb3z4KIyBZcjNolp3rJ6kEyiLNcJUjqI6QfgvQFkJoKGShKMbecZZYzyZUhqTbyH4llse25yFlo+VzpjxAQEFDsOVm6VIp8ZTUhOeNOlSd1CbfffjteeOEFLf52FvI7Iv1TZAlcyyCoruDfSN1hix4SUoeQ9NVXcAsL0zoEqY2Qs/vBEyZoHYIw5ucja8cOZKxZg4y1a3U1J1lVycTQtAkCRo6C/8gR8OrQodaDCUf9WVhi0EJElR0710hgYUl2QlJaZNlO6bEgaUvykbJ8qeSzy5nhhg0boqYwsCCqPAYWdYMMNBO//vqsFY5Cxo+r0tny0rbrGhQEvwv66+pNEkxkbtiAwvT0Yu/zaNYM/qNGImDUKHi2bVsngglb/yxqKmghoroVWNi8OEF6BEiR9a233mp+TP5TkCZlq1at0t4BJYuyiYjIsXpIWHPW21SHkPLzL8hctw55cXE6UI5/++3irwsMhG+fPtr8zbdfX3jU4dWUqrOfR001ISSiusvmgcWpU6d0adnSyOMnT5609S4QEVEFVHSFo/LOekvH6Oxdu5G1fRuyt2/X1CbpVG1JOl179+wB37794Nu3r67opKsxkU1Xm7Jl0EJEdZPNA4sLL7xQlz/9+OOPteBVCnulNmLBggWYPXu2ruhDREQO2kPC4qy3W0QEcg8cQNx778MtIADZu3cje98+IC+v+JtcXODZrp3ORsishE+PHuX2iKjLbLnaFJfIJSKHCyy6dOmijfJk+U7pbREWFobExERd7UcKe4cMGWLrXSAiB+HMRaSO9t3OtcJRfmIicg8fRubWbdqYTpaCzTt9GsasrLO25RYaqkvAenftCu+uXeDVuTPc2Lm+2n4WlcUlcomoutVIAwhZRefqq6/WmgpJfZJlOmXp0PDwcNgjG9ezEzksW/5tOHMRqT19t4oEOC6enjAmJCBjw27kHj6CnMOH9FoCioKylrh2c4N7RAT8hwyBT88e8OrSFYaGDepUwbUtWPbzcJQlcomo7qmxznJBQUHaX0DSoCraQK+mmBqh5ebm6tK4RFScdCkXMuNYnZy5iNSevtu5ApyC9Axkbd2KzE0bkbVxky77atmErhgXFxgaNIBHixZwDw3VfhNuvr4wNGumvSWcJSh0xNkmewlaiKjuqZHAYs2aNdrTYNeuXXjkkUfw5ptvahO9t956C1988QXshSyF6+Pjo302ZOBUkf4YRM4+UyFBRWxsrJ4kKNmNvKqcuYjUXr5baQGOrM7k2bIFsnbsRNamTcjeu1dW1TirsFqWe/Vo2QKezVsUXbdooY9Z1kU46+DblrNNznrMiKjusnlgIc3xLrvsMg0oYmJizI937doVhw8f1j4Wffv2hT2QqXrpsCxN8o4dO1bbu0NkdySosEXHcUctIrVmYGgv302a0+UeOozC3Fydlcg9ehQFSUlnvc7QqJGmMHn37KlF1R7Nm1u1QpMznvW25WyTPaXHERFVlxrpYyH1FNIgb/r06YiOjjY/179/fyxZssRuAgshq1a1bt1a06GI6D8yi1fdMxWOXERq7cCwpr9bQWoqcg4dQu6hQ8g5eKioLuLgobOWeDXxbNMGPr16wadXTw0mDPXq2WS/HJGtZpvsKT2OiMihAgtJnWjQoIHeLlm8l5eXpzUX9kZSoLy8vGp7N4jqFEcqIq3owLDkd5MCZ1lBqSAxEflJSShITNLZg/ykRPNt7T7t5gYXd3e4uLsBbnLtXjR74C6PG/S2i8Edxrw85Bw5ojMS+bGxZe63W3AQXP384R4SAkPz5gi+4Xr4dOli46PluGw122Qv6XFERA4XWHTq1EmXm5UAwjKwSEpKwvz58/U5IiJHSqexZmAodSn50dHI2rlTG8Rl79pVlH6UmKiBiS25R0bCs2XLonqIlq3g2aplUZF1cDDz+ivAVrNN9pIeR0RU3VyMNbC26rBhw5Cdna09LCTFqHfv3vjkk0/QqFEjLey2VXpFaVJTUxEYGIiUlBQEBATU2OcSUe2pSJGsNa+V1yR+/bV5xkJmH2Q2wattW+QcPKhBhFxKq2Ewc3fXGQT3oGC4hYTowNI9JBhGuGhAIkXULl6e2kjOPTgExvx8oCAfxvwCvW0syAfO3JYVmjyaNCkKIFq2ZI+IamaLIuuiVLq/UZiaCteAAA1YWGNBRPaoImPnGlkVauHChXjppZfw3XffaR+L7du344orrsDLL79co0EFEdU9FSmStea1MpCXegUJJGQ2Iv/PP7VZXGmN4SR48GzVCl4dO8CrY0d4tWkD97AwDSJkMFkyPdQUsLh6eprPZLu4uSPw0jEOMZPjrGwxk+ZIqX9ERHY1Y2FPOGNBVHeUnFkwpZyEjB931kCutNe6eHvDp3s35B45iux9e5Gzd5/OSBhzckoPIlq31iDCW4KIjh3h2batBgkVWbkp6auv4BYWpilWhVlZmoITPGECDPUiquOQEBEROe6MhfSp2LJlC8aPH49evXrZ+uOIiCpWC5GXhzyphdi+A5nrN8BYUICCNWv0MUlTSSjleLr4+MCrdWtNU/Jq364oiGjTpkJBRGmYe09ERI7M5oFFy5Yt8fHHH+Pdd99Fu3btMG7cOL00b97c1h9NVK3YzKpyx6I2j5tpoJ57/LimLuUeO6apTPlvv4W86NPIO3FCA4iSTeEsudevD6/27eHVri0827bTa0PjxnCxQQNNR1x2l4iIqMZToQ4dOoRvvvkGc+fOxf79+7W3hcxiXHPNNQgJCUFNYSoUVQabWVXuWFTktRKAZP77rw78pQ5BaxEkJcjKWQD5p0xWXNLeDYcOau8G6eeQs3+fpjeVx8XTUxvDyTKsMoMhKVASPARdNbZWlmNlEEtERPaiImPnWqmx2LRpE7766it89NFHuO+++/Dmm2/W2Gc7a2DBgYh95Ok7u6rWLJR8rcwWpC1divRly5C5bj2MpTSmdA0MNAca5kt4GNxCw1CYllYUPJwJJAqSyw4gJEjxaNwYHk2bahDh0biRBg8aUISFmWcgHO1vydH2l4iIHItd1ViUtG3bNl0d6vvvv0dhYSGaNGlS07vgdHg23bbYzKpyx6K01+bHxSFjw7/I2rYV6UuXIWfv3mLvkUG+LL2aHx+Hgrh4nT0oTElBrlwOHTr3D8vFRbchPRyKll4908OheQu4+fk6VS8Nwb99IiKyJzUSWBw7dsycBrVr1y706dMHjz/+OK677jrtbUE11wGY6k5BrS3OZFfkWJheK3UMsi/ZO3boMq2Jn37634tcXeHdrRv8Bg+C/6BB8GjVyrwEq0ymSvF0fnw88uPkEld0W4KOM4+5+Hjrcq7aBK5lC3g0b65BTF3Av30iIqpzgcWnn36K2267Da1btzYXbktBN1UPnk23PXsqqLU2WLDVmeyyjoXUJEiTOOksnXvkiF7nHDmCnAMHkX/6tEQJ/23D1xe+F1ygwYTfwIHaDbo0EmC4BQbqRWYgqDj+7RMRUZ0LLNq2bYsVK1bgggsusPVH1UmOejbd0dhDMytrgwVbnskuzM3VOgj30BBkJyQgd+dOpC76A7lHj8GYmVnuykp+gwbqPvv27g0XD48q7Qfxb5+IiOpgYLFhwwacOnWKgUUdOJvu7IWktsq9t+aYVSRYqI4z2bIykyzRmrP/gDaEyzlwQC+yXCsKCkp/k7s7PKQoulkzTUkqum6m1+7h4Wd1mSbn+tsnIiKyeWARGRmpq0CRc59NFywktd0xq0iw4OrjDSOMyNq6FXBzQ2FysqYqpS1eDBeDO4zSs6HQCBgLi90uzMnRDtMaQBw6pIXTpZFVmrSuoUWL4gFEo0ZwMRgqcRTI0f/2iYiIaiSwuOSSS/Diiy9i0aJFGDVqFI+6jdT2SjYsJLXtMSuZ8pZ38qQWPmdt3oy02BjkHo9CbtRx5EWd0GLp0oKC9L/+qtD+SXdpDSBay8pKreEpnaZbt4Z7BGcf7Elt/+0TERHVWGAhy8rGxcXhoosu0kZ44eHhxZ6fOHEiHnvsMau3FxMTozUb6enp6NixI8477zwb7DXZWyGpM6ZYneuYFaRnIC/qOHKPHde0pOydu5C9e7c2gZPjIZK++KL0jRsMMNSvrylIsi2ZSXBxcwVcXDUgcXF1Ofu2uxs8GjcpCiDatIahQQObdJcmIiIi52TzwKJ79+54/vnny33eWm+88QZmzZqlwYS7uzsmT56st3/88Ud4Wtmd11ll5mUiNTUO/v5h8PWwbr1+Rykir+0UK1n2VFY6ytywAfkJCfCU9J+WLTUFyNqu0KXRY+NhQPbOnTrAz4uK0nSkjH//1VmHgoSEct/v6ucHQ5PGGgwUNXtrAo8mjfXaUD8SLm5uld43IiIiooqqlc7blbVs2TItApegQhw5ckSXrl2wYAHGjh1bJztvS+fiHb9/jb/2/YbUlFj4Gfww0LsL2rTvr/0BvDp2qNLgtyKyDxxAyo8/wZiTA7eICAQMr3oAYOrenB8bp52TC5KS4B4aatOu1/InkXfsGDLWb0Dm+vXI+HeDNms7i6srDI0bwbNFy6IeCnItzdhaSDM2P/O2ZJ+l6Dnv+H+zD3KRzyhISSl3X6RZnEeTJvBo2gQGuW7StCh4aNIEbkFBLIgmIiKiutV5WwZX0m17zZo16Nu3L66//nptmhcdHa3N8qw1aNCgYveDgoLg6sSpGmWl/+QnJWkhbsqvvyJp2yYs7umCNG8gJANI9E/Hstg18Fq0Gl6SZm8wwKtDe3h37Qqfbt002JClP6uyQo8U/OadikbOgf0WKwYdRO7hwxpUCC0W/vVXGBo3hqFRQy3sNTT677bl9zE1QpPmaXo5eRJ5J4tuy4BcLqbtKldXJMyaVZRb7u2ttQByrbe9veDq7aPHzC04pGjWJDgI7iGm20UXV4vlTjWQOHGiKIiQYEJmJmJiin1nWR7Vu3t3TS/SHg2HDxft8zEJEI4jfenSYq+XWRv5HG0Ol55e7vGUgEm2K4XQWhBtDiKawM3fHzXFGdPNiIiIyMlmLK699lqsWrUKoaGhGDFiBN58800kJydrGtO///6rUZC1JCCR1CeJnn766Sf07t0bH374IdzKSPvIycnRi4m8r3HjxnY/Y1Ey/ce3f3/kHj6C1F9/Rfrq1UB+vr4u0Q/465IGiGzVFYFtOyAjPgYxpw7g4u3u8Pl3j+bjl+QeEQHvrl3gFhQMF09PuHh66KyGi4enxX0v821JqdGlRyWAOHgQuQcOmnP8S5IBuBYOn+PXyi00FIaGDWHMztYA4lyD7+omTdo0yAgJ0U7O+aeiiz0vNQkajJ1/PnzOOw/e3boWm/mRPxvpBC3BVM6hQ8g9VHSdc/jQ2bMbLi5wrx95ZrahxOxD4+JBVm2p7XSzymAgREREVMdmLFavXq29LPbs2YPZs2frLIVptkHSmmQm4/bbb7d6e1lZWTh69Cji4+O1kNtgMKCwsLDMwGLatGmYOnUqHIkMmFKXLEF+9GkdpGf98w/i3nvfHEwIrw4dEDB6NBqOGIQ9yf8gOScZPj6eSHLxRL1GF6LNjdfD291bz5jLsqNZW7bqdfa+fciPjUXakoqtEHQWg0EHnkWrBhUV+8q1Bgv5+UWzDifkEoXcEyfMqxXlnjyJwpQUrR8oWUMgg3x5vxQNmy+yPWMhsnfv0QZsLl5e8O3bB+5h4SjMyoQxKwuFWdkWt7NQmJmFwvQ05Ccm6opLkopUkJSI/DO3pQ9DYUaGXmSfTN/Hu0sX+JzXG77nn68zO65eXmV+fZnxMURE6MW3xKybpDfJQF2uPXTGplGNpaPVlRW9HDEQIiIicnY2Dyx27Nihy8xKhFMy/aZp06YaJFREu3bt8M477+jtkydP6spQrVu3xgMPPFDq65988kk8/PDDZ81Y1BY9023Mh8HVoIW6OgCPitLlQnUQHnVCC4Vzo6KKBRJCBtmBl12GgEtGFxtEDQkAlh5fiviseAR7BmNwk8HwMRQNCGVgK5fAMWP0vgy8pVg4e88eHVAWZmfDmJOrqUaFuTnm28Xu5+bqIL9oudGiQELOvJfVs0BmLaTAWS6lKZAUIgkyTpzQ9CUNJurX19tl8evbt1rSdCSNqzAtTQMMSSmTa/lcmZ2orkG0W2AgfCqwKIGzr+hV3RwxECIiIqoLbB5Y+Pr6IjY2Vm+XDCw2b96MoUOHVnrbDRs2RLdu3bBx48YyXyOrRdnTilFffHg3firchAeWeCByf/mr/piakckZbxn4RjzyMNx8z17xqUVgC0S2i0R6XroWb5uCilK35+0Nn9699VJb3AIC4Nahg8661PRa/bJ8qgz85SKrOpFtV/SyBUcLhIiIiOoKmwcWI0eOxP33349ff/21WDrTW2+9pU3z3nvvPau2k5eXh+PHj+sqUCaSDrVz584qBSc1Ka8gD1+6b0CcVy4euTQT45e64OI9XuZceylu1hV/GsmMihFZO3fBmJEB14AA+A8bWmpQYSLBRHkBRV3KZXe0/a1tcowklUjO+ssAXYIK+X2z12PnaIEQERFRXVEjxdsLFy7E+PHjNaDw8vJCZmam1kZ88sknGDdunFXbyM3N1WLvTp06oUOHDlr8PW/ePNSvXx9//vmn1QXgtbnc7L6dK7Bg8VtY5ncCMV5FBeX96vfDixe8iAifCLscIDtaLruj7a89sYfft4r9nP/WlblMgTd/zkRERNWvImPnGutjIYGAzFBIXYSsDiV1F5GRkRXaRkFBAX7++Wds2bJFU6ykuZ6sMlWRpVNrK7BIT03Ap/Of1CLrCN8IrMndix0ecShwKUSgZyCe6/schjcdXunt5xbk4kjKEbQObg1X6aJcDUw9JCxz2eXMsC17SNSl/aW6EwgRERE5KrtaFcpEVoG67rrrzGlNsnMVJSs/XXnllXpxNKlJMUjPTUM933rwNviiP9qjfpY/DkTk42D6UTy87GFc2vJSPHnek/DzsD6l40DSAfxw4Af8evhXDVrOjzwfrw54FWHeYXUul93R9peqprrqboiIiKh61Eh3uWeffRbr16/X2wcOHECTJk0QHh6Oq6++WldJqgsCguvBz8MfMRkxyMrL0OtGhgh8NnQ2bu18q84y/HLoF1y18Cpsitmk78nMy0RsZqxeW0rLTcP8ffNx/a/X48pfrsTXe77WoEKsP70eV/1yFdZFr6vWXHZZTUquJe3EXnPZHW1/iYiIiJyJzVOhtm3bhkmTJplXbpLb6enpePDBBzFhwgS8//77uOiii1BTarvGYvG6uUjLTYW/RwBG9hmHtp0G6HObYzZjyqopOJl+Ei5wwZWtr0R93/rIzM+Ev4c/BjcejMTsRPx08Cf8efRPZBdk6/vcXdwxsPFA8+sfW/EYDiYf1G3c3uV23NX1Lri5lt7jwxlz2Su6v0ynISIiInKQGotZs2Zh69at2h1bSA8JWSGqa9euOpPh4eGBp59+GnUhsDDVWkhalM5gBIQWfy43Ha9ueBU/H/pZ74d4hWBks5HYl7gPh1MOm2clTEvMSjBxSYtLEOr933ay87N1G98f+F7v96rXC68NeK3U4nBr2WrwXdvbZaE3ERERkQPVWPj4+GjBtti+fbuuDNWlSxe9n5GRYfVqTs5CgomSAYX5OQ8/vHTBS+ge0R3TNkzTGYpv935rfl46aV/U/CJc0eoKdA3vWmrRupe7F57v9zx6R/bGC2tfwMaYjbh64dV45YJX0L9h/0oN6m2Ry27LQb01+8sma0RERETVy+aBxfDhw3HHHXfg+uuv17SoG264QQfEMlGydOlSzJkzx9a74HAkeDiRfgJ/HPlDU6NktkECiSnnTUGYj3VF2aNbjEbH0I54dPmj2Je0D3f+dScmdZqEe7vfi4Kjx2t1SVZ7GNSz0JuIiIjIwQKLevXqmQOIa665BpMnTzbPXlx88cWaEkXFSaO7MS3HwNfdFwnZCbrC0+Amg60OKkyaBTbD3NFz8ca/b+C7fd/h052fYvPpjXjyZA+EJOXX6UE9m6wRERERVa8a62NhL2q7xqIiZDWo9Lx0+Bn8qtxVe9HRRXh+zfPIyMtAQKEnpriMRj/vjrp6kgzqgydMgKFe5eswHLHfhC0L01kUTkRERM7Aroq3TX0rPv30U/z22284ceKEdsseNmwY7rrrLnh7e6MmOVJgUd2iUqPwyNKHsCd5n94fbzwfN8a0hiE4xKkG9bUdALAonIiIiJyFXQUW+fn5GDRoEHbv3o2xY8eiUaNGiImJwY8//oiwsDCsW7dOu2jXlLocWJg6dL/+z7P47tRver9fQXNMu+AVhLTpVOP74oxn9e1lNoaIiIiopsfONm+Q98svv+D48ePYt28fZs+ejeeee06XnpVGeeLLL7+09S6QBQ83Dzw9/FW8fN7z8HA1YI3bEUza8wyi0qJq/DjJQFvSr5xpwF1a/YjMysjjRERERM7M5oFFbGwsRo0apZ22Lfn5+eHyyy/X56nmXdp+LD4f9QXCvcO1od71v12PDdEb+KOoInb/JiIiorrK5oGF9KyQrtuSEmVJMrDWr1+Pzp0723oXqAydwzvj29HfolNoJ6TkpOD2Jbdj3t55PF5VILMvsnyvpD9JUbxcS/2IM83KEBEREdXYcrNRUVE4dOiQ+b70rRg8eDAmTpyIBg0aIC4uDnPnztXXMLCoXfV862HOqDl4fu3z+O3wb3h5/cs4kHQAT5z3BAxuhlpdycpRSRG6LN/rbPUjRERERDVevP3mm2+a+1WcyyOPPKKvryl1vXi7LPJrMGfXHLyz6R0YYUSver0wfdB0hHiFWL2NwymH8c/xf5CWmwZ/D38MaTIELQJrfqUnIiIiInKSVaFkkwUFBVa91tXVVS81hYFF+VacWIHHVjym/S4a+jXEu4PfRduQtlbNVHyz9xsk5ySjnk89xGTGINgzGNe3u77OzlwQERERObpaXxVKUp/c3d2tutRkUEHnNqDRAMy9eC4a+zfGyfSTmPDHBPx97O9zvk/Sn2SmQoIKL3cvvU7NTdXHiYiIiMj51cioPiEhAQ888ABatGgBDw8PNG3aFJMmTcKpU6dq4uOpgloGtdSi7vPrn4+s/Cw8uOxB3P7n7fhg6wc6o5GcnXzWe6SmQtKfZKYiOz9brwM8AvRxIiIiInJ+Nm+Ql5ubi+7du6OwsFCDicaNG+P06dPav0KWmt25c6dOr9QUpkJZX2SdX5iPNze+ibl75p71XBP/JrqqVJewLugS3gVtg9siKj0KS48v1ZkKCSoGNxnMGgsiIiIiB1brNRaWfvrpJzzxxBPYvHkzfCxWx5HlZwcMGIAbb7wRd955J2oKA4uKF1nLKlGbYzZje/x2bI/bjqOpR896jYerB9qHtkf7kPboFdkLFzS4AL4eNddRnYiIiIhqd+xsk+VmLR07dkwDCMugQj/Y3R3Dhg3TrtxUszMVElRYFlnLLENku8gyZy6kiDvQMxBjWo7R10jPi53xOzXIkGBjR/wOfWxb3Da9zNs3D80CmuHqNlfjslaX6XuJiIiIyLnZPLCQeoqZM2ciKysL3t7exWYs/v77b0yYMMHWu0DnKLKOz4rXx0sLLMqa3ejfsL9ehEx6HU87roHGxpiNWHRkkc5qvLHxDby35T2MbDYS17S9RtOmpLCfiIiIiJxPjdVYyOpPt9xyCxo2bIiYmBh8/fXXiI6OZo1FDavIsrCVXUJWlqqVZnvf7fsO+5P2mx+XOgwJMEa3GA1fA9OkiIiIiOydXdVYmFaFmjp1KhYuXIiTJ08iMjISw4cPxwsvvKCBRk1ijUXRLIQ1RdaxmbFauB3mHaazG7Lak8xujGs/DhE+Eec81vKrJalS8/fNx+Kji5FTkKOP+7j7aHAhQUa7kHY2+TkTERERkRMGFvaEgYX1q0JVZ9M7qcH45dAvGmRYFn8PbjwY0y6cxhkMIiIiIjtU64GF1E9IcbatXl8VDCxsM7thLfl1kzoMCTD+OvYX8o35OmsxY8gM1POtB0cPxoiIiIicSa0HFu+88w62bt2Kxx9/HO3bty/zdQcPHsTbb7+tO/vKK6+gJjCwsJ8B9Y64Hbj3n3uRmJ2oqVUfDv0QbUPawhmW6CUiIiJyBhUZO9uk8/btt9+ORo0aoWfPnujduzceeeQRXRlq3rx5mD17Nh577DH06dMHHTt21JkKuW+N3bt347bbbtP3de3aFffcc4822yPbkmBCBv7VfZZeGuzNvXiuDtClnuPGP27EyhMrYc9L9Eq9iVzLLI48TkREREQ1UGMhRduff/45/vjjD+zYsQNJSUka6UhgMHLkSEycOFELua1RUFCAbt264cEHH0Tfvn2RmZmpAUt8fDw2btxYbCnb8nDGwv5I/cXDyx7GhtMb4ObihinnT9HCbntR1SJ2IiIiIkdV66lQtiK7atkHQVKpWrdujaVLl2LQoEFWbYOBhX3KK8jD1LVT8fOhn/X+TR1uwsO9HoarS8Un1QqNhXpdmffauoidiIiIyJHUeiqUrZRsrpaTU7R8qYeHRy3tEVUXg5sBL/Z/Efd2u1fvf7H7Czyy7BFk5WeZB/cyc1BW+lFuQS5WnFiBp1c9jQvnXYhLf7oUh5IPVcu+SfAgNRUSTMhMhVxLETuDCiIiIiIHnbGwJLt96aWXYv/+/dpkz2AwlPo6CT5MAYgp6mrcuLFVURfVDmmu98zqZ5BXmIfOYZ3xaK9HsTl281mF09IXY83JNfjz2J9YFrVMC8wtSbH5GwPfwAUNL6iW/eKqUERERFTXpDprKpSlyZMnY9asWVi+fLnWXpTl+eef1+Z8JTGwsG+bYjbhgaUPaP2FLnPbeLCuGHUy/SRSslOQW5iLVSdXITP/vxmMcO9wDGs6DAMaDcAnOz7RbUg61GO9H8MN7W44a8aLiIiIiOp4YPH000/jvffew+LFi7WQuzycsXBcR1OO4o4ld+BUxil4unmieWBzTW+SmQwTqXkY3nQ4RjQbga7hXc11FVKz8cK6F/DTwZ/0/jVtrsET5z8Bg2vpM1tEREREVMcCi2effVb7ZCxatAj9+vWr8PtZvG0/rEktOpV2CjcvvhnRGdHmx2QGY0zLMbi4+cXoFNapzCJt+dX+YtcXeGvTWzDCiPPrn4/pA6cj0DPQZt+JiIiIyJk4XGAhvSiSk5PRrl27cl8nKU3Tp0+vdFAhGFg4XsO5vYl78fqG17WmomNoR1zb7lq0DGppdcAiPSceX/m4FoI3C2iGGUNnoGlAUxt+OyIiIiLn4HCBhfS6WLZsmV6XJTExEaGhofDy8tIvZ+mtt97CDTfcYNVnMbCofZVZvtXawumyApZ9ifu0y/fpjNM64/HWoLd0BqMiYjJiNLhp7N+Y9RpERERUJ6RWILBwr7G9ArBy5UrtwF3S4cOH0aZNm3LfGxwcjOjo/9JhLJUMNMi+SYAgA38JKqThnFzLMq7yeFlBgzx+ruVdLTtkmwIWma2IbBephd/fjv5WC8K3x23HnUvuxJQ+U3B1m6vLXL52T+IebIvdhm1x27A9frsGJUJmPaSuQy7tQtoxyCAiIiKq6cDi0KFDOHnyJO65555ij8tsRXp68aVCS5IVfazt0k32TWYdZDZBBv6WMxbyuC0DFumc/dnIz/Ds6mfx+5Hf8cLaF3A4+bAuZyuvkwDCdNmTsEdXnrIkXcHl9/Bo6lHM3jFbL438GmmAIatRydK4lVl5isvYEhERkTOo0cBCNG3aFFddddVZKzfJbAbVDaaGczKbUJ0N56wJWGR1qVcvfFXTo2ZsnYGv93yt3b4lIClJ3isrTXWN6KrXUt8hXb2Xn1iOv479pcvdnkg/gTm75ugl0jcSw5oM00CjW0Q3qzp/V6TWhIiIiMie1WiNRVZWFnJzc2s1dYk1FvbDFmfqZaAuAUtqbmpR/4smg8scqC8+ulg7dWcXZGsQ0Ca4TVEgEd4VXcK7oIl/k3JnIGT/JbhYcmyJBhumLuGmnhqDGg/SbclMRrPAZmcFGpWpNSEiIiKqs8XbX3/9NTp06IAePXrAHjCwcH4VCVjiMuNwPO042oe0r9JgPjs/G2tOrdGZDOkCnpZXfAZE9qVDaAddHlcCDbl2gYsGFpKiJalbsg2ZwRnXfhwifCIqvS9ERERETlm8Las4DRo0CN9//z2GDx9e7LmoqCjs3bv3rMeJqsKaQm+TcJ9wvVSVBAaSxiQXac63Nnot1kWvw674XVoELoHOhtMb9GIS6hWqPTUCPAPQLrgdPNw8EOIVUuVaEyIiIqLaYPPAQuop0tLScPnll2PWrFkYN24c4uPj8corr+DDDz/E5MmTGViQUzG4GTCg0QC9iPzCfO0YvjN+J3bE78CuhF04kHQACdkJehFbY7dqytbUflOZBkVEREQOqcZqLKSp3TXXXIPRo0fjt99+Q6NGjbSLtjzm6nruItfqwlQosgdSjyGN/3bE7cDWuK1aF5JvzNfajJcveBl9G/St7V0kIiIigl3VWJhWfZL+FS+99BLi4uJ0hkICjZoMKEwYWJA9kuVtpTv4kZQjev/mjjfj/u736+xHXcKld4mIiOxLRcbONh/ZL1++HK1atdKgYsqUKVi1ahW2bduG+++/H4WFhbb+eCKH0D60Pb675Dtzw77Pd32Ocb+P01WuSg68YzNj9drZyHeVYva5e+bqdcnvTkRERPbN5jMWUlchHbMffvhh+Pv7mxvljRo1Cp07d8bcuXPh7e2NmsIZC7J3fx//G8+teQ4pOSnwcvPC4+c9jrGtx+JI6hGn7XnBpXeJiIjsk92lQpVGUqKk3mLEiBE6m1FTGFiQI6TeyKzElFVTsD56vd4f1GgQ2oS0QU5Bjl30vKju4ybfV2YquPQuERGRfbGr5WbLEh4ejqVLl2LFihW1tQtUx9lz12vpYzFr+Cx8uetLvLvlXSw7sUyXqh3TcowubSvBhfS8kMF9TQcWtjhu1nRNd8YAkoiIyJnUfPW0BV9fX1x00UW1uQtUR8lAUwbH0vVazpLLtazMZE+1C9Kp++ZON2PuxXPRNKApMvMz8d2+77DoyCIcTz2uncXLG3jboh7DVsdNBvsSoEgwIQGTXEvX9OoIAli7QUREVDNqbcaCqDbJ2Ws54y5nx2t7BuBcpGP3/Evm49k1z2Lx0cU6c7Hx9EZ0r9cdod6hGNx4sDbaq4nZGFseN9m/yHaR1TqzYBkImWZCJBCSz7G3nzMREZGjY2BBdZKtU2+qmwyC3xz4JoY2HoqZ22fiUMohbIrZpBd3V3f0b9AfI5uN1CBDZjpsNZi29XGrSNd0ZwsgiYiIHB0DC6qTTKk3MuCu7tQbW7qoxUV6kX4XMnshl4PJB7H8xHK9eLh6oFdkLxhcDegW0a3aB9OVOW61Wd/gaAEkERGRI6u1VaFqC1eFImcr6j2UfEgDjEVHF5kb7Ak3FzdNLwrxCkHLoJa4p9s9CPAsfzUHayRnJ2NXwi6t8ZDt+3r42m2BvPzz9u3ebzFn5xyd2bmi9RUY1nSY3RTpExER2TuHWG62tjCwIGcNWORP+UDyAQ0yfj30K05lnCr2vI+7DzqHd0b3iO7oFt4NXcK76GC/PAlZCdidsBt7EvcUXSfsKbZdWb1KtqfbjOiGtsFtdQBf270p5FisPrUaM7bM0CDI5PHej2N8h/E2/WwiIiJnwsCimg4OUWXU9ll608B6W9w2bba3N3Evdsbv1EDHkgtc0Cq4lQYZEhS0D2mP6IxoHYibgggJBkpT37c+4jLjkG/ML/a4t7u3BiwSaDQPaI4d8TvQwK+BpmRl52dr+tS49uM0ILGVf0//qwHF5tjN5n2S/Vlzag3CvcPx25W/6WNERER0bgwsqungEDlLB+mCwgIt+N4au1UDji2xWxCVFnXO90nw0SywmQYdsjqVXNqFtNOASb6rBCyyrS1xW7A9djvS8tLOen+wVzAifSLh5uqmA/tLW16K5oHNUd+vfrUO8OV7SUCxLnqd3vd088R1ba/DxM4TdeZozI9jdLbloZ4PYWKnidX2uURERM4slalQ1XNwiJy5g7TslwzGJdiQy/6k/ToTYQog2oe21yDC11B2DYWlQmOhFpLLtmS2QK5Ppp8s9z0SdEmAIZ8rF5ndiPSN1ABEArMwnzAtRC+PzMhIQCHF60JSsca2HovbOt+Ger71zK/75dAveGrVUxoU/XHlH2ct0WvrlDdnqOchIqK6J5WBRfUcHCJnmbGozUBrQ/QGTQ9LzE5EXFacpltFp0eflZpVGtOMhxxLCczCfcL1Wu4HeQbht8O/4c9jf5qL1WU25I6ud6ChX8NSZ22uWniVBj+3dr4VD/R4oMZS3uwhPY6IiKgyGFhU08EhqgwZRMpyrKm5qbpykizHykFkKX+LuakaYJxKP6UpSqczTuttCcakfiM2Kxb5hcVrOMoKPkY1H4W7u96taVvlkcH9A0sfgJebF36/8ncNVGwdQDLYJCKiujJ2Zh8Lompmiw7SNaGmU3Uk6AoICUDbkLZlplYlZSfpLIfMfJgDDovbUqtxW5fb0Ca4jVWfKQ0Epbh8e9x2bTT4dJ+nbd54j036iIiormBgQWQD1d1B2tbsMVVHOoiHeofqRWo9qoOLiwse7PEgJi6eiO/3f4+bOtyExgGNbdp4j036iIiornCt7R0gotqfqZCgQtJ6pOhcriWVSx53Rr0je6N/g/66VO6MrTOq1IFcgolzdSCvyGsrKrcgV5fRlQUDJCgkIiKqTQ45Y7Fx40bs3bsXw4cPR716/636QkQVVxdTde7vcb820PvjyB+69GxZ6VjlaRbQDBl5GVh5YiX8PPx0FSy5Ns1QFLtt8EPH0I7wcPNA66DW8PcsvzFheeRnI58pq2CtPbUWmflFAeDPB3/GrOGzEOQVVOltExER1ZnA4s8//8STTz6JzMxMDSyWLl3KwIKoiupiqo4spzuq2SgsOroI7215Dx8M/aBC75cVpl5Y9wJ+OPBDhT9b+mtImlnr4NZaGyKBhtyW2SJJ1Sqt1kSaFUogseLEimKdxIW8L68wT7ujT/pzEmaPmI0Qr5AK7xcR1T4uS02OzqECi5ycHHz88ceoX78+GjeueF40EaHMVB1Jf6ruVB17dm/3e7Hk2BIdrG+O2Ywe9XpY9T5ZqerZ1c9i4eGFWgfycM+HtV9Gem66XqRJoN4+MxMk1/p4bpoe3+yCbA0C5GJJjrsEGHoJag1fD1+sObkGK0+u1PdZ6hTaCQMaDcCAxgO0eeGRlCOYtHiS9iKRawkuJOCoazgoI0dmj7VuRBXlYjQajXAwJ06c0MBCZiwGDRpUofdyuVmi0tXFQdnUtVPxv/3/Q/eI7vhi1BelzhhYkpmBJ1c+icVHF2vfjFcHvKozHxWZ6TiRfgIHkg4UXZKLro+nHdeZibL4uPugX4N+Gkxc2OjCUoMGCS5uXXyrLtMrq2V9MuITu2vKaEsclJEj/xvIZanJnnG52RKzHHKxPDhE5PgrWVWHO7vciYWHFmJL7BadGZCBe1nyCvIwecVk/H38b+3u/ebANzG0ydAKfZ6bqxuaBjTVy7Cmw8yPS4f2QymHNMjYeHqj7o8McJoENMEVra7AJS0u0fqM8kgwMWfUHE2HkiDjlkW34NORn2on87q0AIEpnU9m4GTZ57r2O02OGZjWxVo3ck5OvyrUtGnTtKmH6cIUKiIykRSmG9rfoLff2fxOmbMGOQU5eHDZgxpUeLh64N3B71Y4qCiPDCSkuHtE0xHa5E9S0aQ7eNfwrtrLw5pGgUICkTkj52jncZkFuXnRzTiZfhLOrrRBmTRgtKa7O5E9rIxnWesmJxrkWnr9OHOtGzknpw8spNhbOgWaLlFRUbW9S0RkRyZ1mgR/g7/OFvx+5Pezns/Kz8L9/9yvtRhSeP3+kPfLndmo7QFyI/9GGlw09m+sQYXMXESl/ffvngyWpMmgMy0nzEEZOXpgastlqR2VM/5bVRc4VPF2ZXh6euqFiJxLdeVDB3oG4pZOtxStDrXlA4xsOhIGN4P5M+775z5sOL0B3u7eunqU9MGwpxW6SjsO9f3qa3Bx65+34mjqUZ25+GzkZygwFjhlcWhdXYCAnGtlPPlblPQ9W9R5OFoNXW2nplHlOX1gQUTOp7r/0xnXfpw2mZPC6m/3fotRzYsKsicvn4zNsZvha/DFR8M+0iJvexogl3ccJM1Lai6koFvqNyS4uKjZRXB1dbW6DsEeBiPW7oMtB2Xk3OwpMK1IrZu1fxuONkhnzZRjc6jA4vDhw1izZg2SkpL0/pIlS3SFqC5duuiFiJyfLf7Tkffd0fUOvLL+FXyw9QNNHVoWtQynM09rmtTHwz9Gl/Ca+TfG2gGyNcdB8sWlgPu2JbdpqteC/QtwfbvrrSoOtYfBSEX3oS4uQEDVw9ECU2v/NhxxkM5CdsfmUDUWJ0+exKJFi7B+/XqMGzcOx44d0/sHDx6s7V0jIgfPh7642cVaLCmdrH84+IMGFV5uXpgxdEaNBRUm8h++LBVb3n/81h6HUO9QfDbiM7QNbqs9NL7Z+w12xu/E6YzTZRaH2kMxqz3sA9Ut1vzd2YOK/G3YQ/1IRbFmyrE51IzFhRdeqBciqrtslQ+dU5ijqU7S4Tq3IFd7R4xoNkKLoR39OAR5BenMhRRyS+8M6Rge4R2BW7vcqrUj9njG0B72gag6VHdKYUX+NuylfsRRU9PIyWcsiIhstXqK/Ecry7s28muEUK9QDG86HM0Cmtntf8AVPQ5SpP7lRV9iQvsJOhMjjfQk9evqhVdrB3LLpXZtfcbQmtVeeNaSnIGkLMksodRwybXcr6qK/G2Y/p3wdPXEuuh1+rfvCIN0SeuStE2pf5Nre64JISfovF0V7LxN5BxsUVisecvH/kFaXpr+Ry3/Adv7f2iVOQ7SG+Or3V/pQCcjL0MfaxXUSutMpJeGq4urHgs5YyhpE9V5LCpSN2GrfSBy9G7a1v5tyBBPltGetmEaUnJSdCb2xo43YkKHCfo+ouoeOzOwICKys5WQaooMNCTAkLOpppzrloEtcXuX2zGy2UhtDFidx6IyA6269PMg5yKzcvK3JXUQkrIkswsyuyhn4aWWozTSDDMxO7HM5yvytxGTEYOX1r2EZSeW6X1Z3c50IkGC+ls63qL7wr8rqs7AgqlQREQOWMBZHSQ96t7u92LR2EW4u+vdOtiQpWkfX/k4Lv/5cu00HuIVUm3HojKFpHXp50HOpaLpfFGpUbju1+swdMFQ3P7n7Vh9crXOOFT0b0PeI3VUV/x8hQYV7q7uuLfbvVh57Uq8NegtnZ2Uv0Pp3TPq+1H4YtcX2giUqDpwxoKIiJQMNr7Z8w2+3P2lDviFdPC+rfNtuKTFJebGgfaYGiLbljO94T7h2iGdnNeu+F1wc3VDE/8mdh9wWpuytDxqOZ5c+aSmYVpqHdwaN3W4CRc3v9iqv7+T6Sfx/JrntZ5CdArthBf6v6DbMSkoLMCio4vw0baPcCz1mD4msyq3dr4VV7e5Gh5uHtXwzeseZ55dTWUqVPUcHCKiuig9Nx3z9s3TM5kSBIj6vvUxqdMkXNH6iioNPCqSGy7/ScvnS8CQkJWgaSQJ2Ql6Wy/ZZx7LStBlgkWQZxAe6/2YBkIuLi51chDgrOR34o2Nb2j6npyFv7Dhhbi/x/16Bt6ef87l7YMM8mWAP3P7TL0vS1tP7jUZi48u1lkH0++1rOJ2ffvrdeAvM40lyeIL3+37Dm9veltnHyS4llmK8R3G67EqK+1q4aGF+tkSkIhI30hNhby81eUwuFbtREJdYg99f2yJgUU1HRwiorpMBkTSVG/Ozjk6iDcNcG7pdAvGthlbbKlaawdwEgT8e/pfHchIDYfkfEvwkJqTqtcpuSla+yGXAmNBhfbXzcXN/J7+Dfrj6T5Pl7pcsLMPAuxhMF3dZAnop1c9jT+O/lHscZlRe2/we2gV3Mrhfs7J2cmadrjm1Bq9f13b6zQoNs1MSPD9v/3/w9zdc3UVNyF/c2Nbj9WAoaFfQ33saMpRPLfmOWyO3az3e0T0wNR+U9EssJlV+5FXkIcfD/6oAYbUhQjZ9pTzp2BAowE2+e7OxJYzsfaCgUU1HRwiIoLmh39/4Ht8tvMz88BDai9u7ngzrm17rTYTLG0AJ2dRj6QcwZbYLXrZGrsVx9OOV+iQyvKY8lnS6E+WAdZr71BN3ZD7en3mvsykyCzLR1s/Qm5hrg7C7ul2jxaoms7aOvsgwN4H05UhweiDSx/E1ritcIELhjUdpkGk1ADlFebpz/nRXo/q2XzTLJW9/5wlnevhZQ/jVMYp/R1/tu+zGNNyTJkDfwmo5Hd7f9J+fUxWbpMlsWWxhU93fqpBuhyHh3o+pH+T8nxFyTYkkJm9fbb5RMJVba7SGRR7OGbOVKTvaBhYVNPBISKi4meNfzr4kwYYptQJSWfqGNpRz45KGsWehD06qJUB4I6EHTrzYEkebxnUUvPjJaVDUpfkuuTtQI+ia/mPuqLkDO4L617QmREh+ydncNuGtHXqQYC9D6YrQwbS9/19nw7A/Q3+GlQEeAbo95NmjxuiN5h/F8+vfz5e6PcCGvg1sOuf8/f7v8fL61/WoEhmXN4e9Lb+blqTCrY2eq0GGKZZDpM+9fvg+X7Pm2cxqvp79MHWDzTlzAij7uMrF7yCbhHdUFMkOJ6+cTqaBjTFwz0fLnVGyl44499dSQwsysHAgoioamRA9Nvh3/DJjk/MxZ+Sjy1pSJaN9oScje0c3hndwrtpZ/OuEV1rZP18GYRJesebG9/UQEfOcMsMy40dbsQPB39wykGAPQ+mK2PFiRWYvHyy1hnIAPP9Ie/rQNeyRmdg44FYH70e72x6B9kF2dqn4ZFej2B089H4dt+3dvVzlp+HNKWU30sxqPEgvHzBy5X6e9iXuE8XWdgWtw0TO03EFa2uKLemqDIkaHtq9VM4nXFaZ0CkxuqurndVeRGH8siJiNc2vIaFhxeaH5O/XZk5kdnHYK9g2CNn77mTyuLt6jk4RERUNlPx5zub39ECayHpGHLWVIqnz4s8D+1C29VqEagMrKetn4Y/j/2p92Wm5LYut2l6jbMNApzlzKkEhTJoljPWEkjI75Esk2oqWi6thuR46nE8s/oZc51B3/p9ManzJOyM32kXP2eZVXlo6UPYk7hHB+n3db9PA4LKpCzVJAnKX93wKn459Ivebx/SHtMunKazjtVt5YmVuqKV1JPIcRnffrweN0l5EzJjdWfXO/X3uSrBja1qkJyxtsmEgUU5GFgQEVWvg8kHMW/vPJ2xaOjbEEOa2l9ev6RWSPqJqUZkTIsxmNh5Ihr4NnCqQYCjnzmVegL5OUlNj5BC5af6PGVVcCqzZTJj897m93T2QhrC3d/9fgxtMlTrTWz1c5ZASD5PFiCQgbgce7mYbkune1llTc7GS7rfawNeQ78G/eBI/jz6p6YXynfwcPXAgz0f1Jmw6giMZBU6We1LVsESzQKa4cX+L5pTr2Tm5PV/X8e+pH16X2avpKZmYKOBFZ6lccQapLzCPGyK2aTpbrWFgUU1HRwiInKes3UygJHZFVmWU8gZcDkrekP7G2okPaumOMLPorR9lrPT0ilaZh1kwCqDR/n5VHTwKOl5MnshCwYIqf2Rmh0JNLwN3pouJRe5L8dH75+5ls+S5VolbUmuS14sH5cVzUwBhAz+zkV6SsjMS32/+nBEcZlxeHbNs1h1cpXePz/yfLx0wUt6fCtr7am1uk1Jt5L6K1ntSmZzLFecMy3LK/Vd0tTPNDsqA+3JvSejTXCbWp/Rk98B6UMSlRaFVy98Fe1D26O6/s2SIv/1p9fjnUHv6ImC2sDAopoODhEROR8ZcE5dM1W7jAsZgMvgQgY1sgKVPZJB0fb47Wgb3NZu88wrS84iy2pEMnCUAZoMKt8c+GaVljqVgajOXmx5T1c7qglSCyABqhSXS9qOXMt9OTMuZ+Gva3edwzefk9kZWYJaapckuJLvOaXPFK1pqUgAKL/Pb216yxzkN/JrpLMUvSJ7nXOgPXvHbC0sl2BOAtCrWl+Fe7rfc86/XVvVIMk27lxyp3lGxcfdR39/L2x0IaoiJiMG9/x9j263Ov4mqoKBRTUdHCIick4y8JS6i1nbZ2kql5D/vGXJUinylg7e9kLST27981bsTdyr9yV1o0e9HtqvQK4lnau6C3drigww3/j3Dfx86GcdKEqQJ83Z5Kx1dcy2yNltqb+Qz8nIzyi6zsvQgnC5bXktj8vAWX4Pil0MRdeyEIHpvitc9ZhLX5d6vvU0gJDnHPXnUFEyKzRl5RQNdoUEGLJ6VNPAplrH1CSgiV7LYzLgtzwuG09v1BmlE+kn9L4sjysrP1Xk5y0zA9IMcMmxJXpffm+k/kJmH8tKm7PFjIXsxx1L7tBrWf66WWAzTVuSIFP6gFzT9ppKbfdA0gHc/ffdOpMjx+/DoR+iY1hH1BYGFtV0cIiIyLlJXv7SqKUaYOxO2K2PSQ65dBiX4lpZurQ2SarNbX/epvsm+yX9OUqSQZIEGD0jeqJ7ve7aidpWRcHVnWb144EfMXXtVK3PkUHoZS0v00G+Pa9k5Yh5+rb4OcviDRIUyqxDec0sJeXMFGRIP5k/jvyhRfn1fevjhf4vVKl2QJaUlvoLU9AtaVHP9HmmzKVxq7MGSVbmuvOvO3XGQmZcZg2fpWlh8vssgbKQZqIP9niwQn+PssqZFPqn5aXpTNdHwz4qtdFnTWJgUU0Hh4iI6gY5U7361GoNMEy5+e4u7tq0TFYXkoJR0+vkP3wZTMRnxiMuK05vS/653JZ0CxlIyWCiqmvvS9qHnA2Vs8JyZvWzkZ9pM0DZv80xm7UWQfqG5Bvzi71PBkwSaMiZ/0GNBsHN1Q32OKCWoOL5tc9rcCeDTllSVGYY7HklK2dZeas6fs6mYyG//xL0Hk09qsGGpBpJ35Go1ChEZ0RrEFGSBJBPnPcE/Dz8qmX2UZbwlfopU9+cK1tfiYd6PIQgryCbBMcyKyH9VeTfAglmPh72sXmW02g0ahdz6QUiRjQdgVcufAWebp7n3K6ssic1J3IcZUbyvSHv6QxMbddMMbCopoNDRER1iwwKNsZs1ABjXfQ6fUzONsoymzKYlGVqZQWgc5G0Gck9l/4ClSGDHzkbKkGEFJl/OuLTUpuoyet2xO/QQGNT7CZsj9uuue8mMmCXs/+yH5UdlMgxkYZs725+V9NXpNuzFKfKwLqyA2ppsiipLEJWbZImhjJ4sveVrJytV0hVAidrjoU01ZTfGQky5HdZLjIAbxfSrtpneiQold8pqdURsgKXpFhd1uqyap3BkxmPySsma+2ODP7fH/p+qYs/LLQIEqSPjwQJpvqoksGN/I1JXyCpCRIjm43UHieyoIE9zI4xsKimg0NERHWXNB+bvX02lp9YftZzklMe5hOGcO9wnUWQwdfxtOPm/GgZkMkqN1IrcGvnWyuUey+Bwd1/3a0BjgwmPhnxCTqEdrDqvVKnICkafx37S4tsJeXDtL9j24zFDe1usHpVouj0aE3p+Pngz+Z8eBMZEEmna8kjr8iAWmYnZPD3+a7PzakicmZZvnNtn5W1hrPPWFQkcKrIsajJ4yZB9ovrXjTXTkljzqf7PG316lHlkaBFem1I6pc0OHxjwBt6nMqyIXoDHlz6oM5syKyn1ErIDKNlsCDL5sqxkQUMhNR4PdTzIT329vK7xsCimg4OERGRBAqS5iGDLdOl5HKYlgMyKdyUs5U7E3bqcxc1vwhT+0096z2lkcHEff/cp7MlklI1e/hs7VxemfQNeY/sx1d7vjJ3SJd9G950OCZ0mIAu4V1K/fy/jv+lwYTkepvSWGSlm+aBzfWM6674XUjKSdLHhzQegmf6PqPf25qgRwZlpmZrj/R8BDd3uhmOxtF7hZSnogGAtceipmd65Hdt7u65+HDbhxq0yu+9/M5L5/DKDsrn7JyjK1mZUrme7/e81oycy6HkQ3qiQNLDZPZRUqNkH+T4nkg7obOB8u+LnIiQ9DApQLe32TEGFtV0cIiIiCozIJPVXGSlmBUnVujZTTlb+s7gdzQ1qSySNnL/0vux+uRqHcjPHD5Ti1CrWtsgswTS1ViW6JT18E26hnfFjR1u1O3tStilZ2MXHVmkAYyJdL2WWg1JVzqdeVoHkZJyIrUdknol25Z9kp4Tkm5V1syMDO4mL5+ssz8yyJNAS1JUHJUj9gqxVeBkzbGorZke+TuUzuGm7t1SXP1E7yf0d97aWURJU5KAwjzL1vEWnVGoyCxkfFY87v37Xv07k9//0S1G6yILEjjIsZAaldcHvq5/Z/Y4O8bAopoODhERUVUGZNJ1WRpcyWBcHpPUiX4N+5XacVpet+zEMp3ZkJQJWdO/ugcXkib15e4v8fuR3zX3W8jnWdZlyPK1Mui/tOWlZ61GYzmIlMBJcshNq2lJEPJc3+d0qVHL10rw8fiKx7XYXApYZT1+SSOhuhU41eZMjwT4r6x/RWsWhNQJSZAhaYxyCfMKK7r2DtNlY+VaZhfkpIDMsplWeZKaDUnfq+wxfXT5o1h5cqXel5MHsgKa/P29P+R9TS2019kxBhbVdHCIiIiqOiCTs6YSNEiRtRSR3t/9fl3K1nTGU9I25Gy+nFWVgfcHQz8wDzJs2dRr3t55mL9vvqY1SbG5pEjJ7IQENNYWu0pwIvs3Y8sMLWqX/b+72924sOGFOjshTb6k10BCdoLWecwYOkNXrKK6qTZneiR4lpqpObvmmIPq8siqcL4evrrSlMwySNAsy1BXdQWrp1Y/hd8O/2YuMJdO3f0b9rfr2TEGFtV0cIiIiKqDpDm9vP5l/HDgB70vedbSaVg6MT+58kksOrpIl5WcMWRGsRkNW6dDSKAiPQAkLaMqS3/K7MULa18wr6QlRe3S0GtLzBak5KZovcjMYTPRNaJrlfeZqCok2N2ftF+DXQmwZaU3vZy5LxfTogdCgmWZaZTZguoyb+88XRzi3m73oqF/Q9g7BhbVdHCIiIiqi+Rqy0pN0zZM0zOmMpiXgmg5oy9FoO8OfhcDGg2w23QIa76fFGa/tuE1XQXHRM7KymyIdEZ2hiVZqW6cCJD0RQk4pEmmaZnYuiq1AmNnF6P8S1CHMLAgIqLatDV2Kx5a9pCeGTWlXEwfNF0LSu05HcJa0rPgwWUP6llhCSSknkLqNpxlSVaqOxzp786WnDqw+Ouvv/D+++8jJiYGnTt3xrPPPovGjcteZaMkBhZERFTbpHZC6iqk8PmlC17ShljORGZZfjrwky5XKzMW9jrLQlRTneYdmdMGFkuWLMHFF1+M5557Dn379sW7776LrVu3YseOHfqFrcHAgoiI7IH89yvde8trsOXIeLaXHJU9LfVqDyoydq6+Huc1QGYnrr32Wjz99NMYOnQo5s+fj7S0NMycObO2d42IiKhCZFUoZw0qhAzAJBWqLg7EqOYDAZkFlOvqIOlPMlMhQYX8jcq11DhZ9nghBw8s0tPTsX79ep2xMPHy8tIA4++/ixqfEBEREVHdSlmS2QVZ9liu5X5VSU2FpD/JTIWsnCbXsnCCPE7lO3cvcjtx8uRJnTauX79+sccbNGigdRdlycnJ0YvldI7ltTAYDPD29kZWVhby8vLMj3t6euolIyMDBQUFxQIaDw8PDXYKCwvNj/v4+MDd3b3YtoWvry9cXV11dsWSv7+/vl+2b0mmmfLz85GZ+V/kLe/38/NDbm4usrOzzY+7ubnp9kt+T34n/pz4u8e/J/4bUT3/lstZ0Iy8DEQERcDP04//lvP/J/6faydjI+md8s+JfxCfHo8QtxCcTjiN39N/x7Xtr0V4UHiVfk69g3pjXdw6XWTBu8AbvYJ6IT8rH6lZqXVuvGesSNWE0UHs2LFDvpVx9erVxR5/9NFHja1atSrzfc8995y+r7zLpEmT9LVybfm4vFeMGDGi2OOzZ8/Wxzt06FDs8UWLFunj/v7+xR7fuXOnMSUl5azPlcfkOcvH5L1CtmX5uHyWkM+2fFz2rbTvye/EnxN/9/j3xH8jqvff8ud/eN649fhWp/u3PDo+2um+E//PrRs/p7bt2xrf2viWceLUicUeHzR0ULV8pw8+/sAYkxFjbN++fZ3+3YuKijJ/j3NxmOLt6OhonZ345ZdfMGbMGPPjEydOxO7du7FuXVFTHmtmLGQVqaioKHMBCs/u8+y+M59p4Hfiz4m/e5X7e5Kzn9J3QjrvSq1AsjEZId4huKThJcXqBhz534iY/BgsO7EM8Unx2iBP+mhIbw1H/k78d69uzVj8cuIX84yF1FnIKmTVMWPB373/fk7yMw0KCnK+VaEaNmyogcSLL75ofqxDhw4YPHgwPvjgA6u2wVWhiIjIGjJIkbztMO8wLeCUXGtJixjXfpxTNHrjyjfkDBylgaQjq8jY2WFqLMRtt92mK0BNmjQJzZo1wzfffIO9e/fi66+/ru1dIyIiJ2NZwGm55KSzFHCWtvKNBE7yOFdyIkchQURku0g2srMTDhVYPPXUUzh8+DDatm2LevXqITk5GbNnz0aPHj1qe9eIiMjJyOBammLJ2VAZcEtQIWdDnWXQ7eyBE9Ud8jfpLH+Xjs6hUqFM4uPjERcXp7MWkitXEUyFIiKiinDmRm9MIyGiOpsKZRIWFqYXIiIiW3Pms6FMIyGi6uSQgQURERFVD2cOnIioZjlM520iIiJnS7GSlafkmojIGXDGgoiIqBZqG/45/o+uyiQF1FIkziUyicjRccaCiIioBskMhQQVyTnJ2iNDrmXlKc5cEJGjY2BBRERUy/0jpLmXPE5E5MgYWBAREdVS/wjp5i3X0jGY/SOIyNExsCAiIqqFxnvSjM4ZG+8RUd3F4m0iIqIaxv4RROSMGFgQERHVAvaPICJnw1QoIiIiIiKqMgYWRERERERUZQwsiIiIiIioyhhYEBERERFRlTGwICIiIiKiKqtzq0IZjUa9Tk1Nre1dISIiIiKya6Yxs2kMXZ46F1ikpaXpdePGjWt7V4iIiIiIHGYMHRgYWO5rXIzWhB9OpLCwEKdOnYK/vz9cXFxqJeqToCYqKgoBAQE1/vlUefzZOS7+7BwXf3aOiz87x8WfnWNKtdEYU0IFCSoaNGgAV9fyqyjq3IyFHJBGjRrV9m7oD5yBhWPiz85x8WfnuPizc1z82Tku/uwcU4ANxpjnmqkwYfE2ERERERFVGQMLIiIiIiKqMgYWNczT0xPPPfecXpNj4c/OcfFn57j4s3Nc/Nk5Lv7sHJOnHYwx61zxNhERERERVT/OWBARERERUZUxsCAiIiIioipjYEFERERERFXGwIKIiIiIiKqMgQUREREREVUZAwsiIiIiIqoyBhZERERERFRlDCyIiIiIiKjKGFgQEREREVGVMbAgIiIiIqIqY2BBRERERERVxsCCiIiIiIiqzB11TGFhIU6dOgV/f3+4uLjU9u4QEREREdkto9GItLQ0NGjQAK6u5c9J1LnAQoKKxo0b1/ZuEBERERE5jKioKDRq1Kjc19S5wEJmKkwHJyAgoLZ3h4iIiIjIbqWmpupJedMYujx1LrAwpT9JUMHAgoiIiIjo3KwpIWDxNhERERERVRkDCyIiIiIiYmBBRERERES1jzMWRERERERUZQwsiIiIiIioyhhYEBERERFRlTGwICIiIiKiKqtzfSyIiIiIiGqLsbAQBQkJgKsrXNzcADe3s6+t6BlhjxhYEBERERHZWGFGBpJ/+BGJX3yBvBMnyn+xKehwd4eLqyvqv/wyAkaNtPufEQMLIiIiIiIbyY+LQ+LXc5E0bx4KU1Kse1Nhoc5sIC8PRrlvLHSInw8DCyIiIiKiapZz6BAS5sxB6s+/wJiXp48ZmjZB6C23IPCyy+Di5VUUQBQUAAUFRdf5+RpQGPPzi57Ll+fy4RYa5hA/HwYWRERERETVwGg0IvPff5H42RykL1tmfty7WzeETJoI/yFDilKcTEx1FU6CgQURERERURXIDEPakiVI+GwOsnfsKHrQxQX+w4Yi5JaJ8OnRvU4cXwYWRERERESVlL58OWJefQ25R47ofRdPTwRecTlCbroJns2b16njysCCiIiIiKgSNRQSUGSsXKn33YKCEDxuHIJvuB7uoaF18ngysCAiIiIislJBSgriPvgASd98q8XWMBgQMmECwu66E27+/nX6ODKwICIiIiKyoo4iaf58xL/3PgqSk/Uxv8GDUe/xx+DRrBmPHwMLIiIiIqLyZaxdi5hXpiHnwAG979GqJeo9+ST8+vfnobPAGQsiIiIiolLkHjuGmNffQPrff+t9t8BAhN1/H4KvvRYu7hxGl+SwR2TKlCl4/fXX8cADD2D69Om1vTtEREREZOcKMzKQe+KkXhdmZqIwMwOFGWVcp6YifdnyouZ2bm4IvuEGhN9ztxZpkxMFFn/99Rd++OEHtGjRAgXSpZCIiIiIqAzSzTr5u+8QO/0tFKanV+g4+V5wAeo98Tg8W7Xi8XW2wCI2NhYTJ07E999/j9tuu622d4eIiIiI7FjOwYOIfvY5ZG3erPddAwLgFhAAV19fuPr4lHvt2aYNfM7rDRcXl9r+Gg7B3dHapN94442444470Lt379reHSIiIiKyU4W5uUiYNRvxM2cCeXlw8fFBxIMPInjcDXBxc6vt3XNKDhVYvPHGG8jIyMATTzxh9XtycnL0YpKammqjvSMiIiKi6ljWVbpZu3h4wOf88+Hq4VHhbWRu3ozoZ55F7qFDet9v4EBEPvcsDA0a8AdkQw4TWGzatEkDi3///RduFYgyp02bhqlTp9p034iIiIio6rJ27kL0M88gZ88evS8pSVLjIP0i/AYNhHtwcLnvL0hPR+z06Uj+dp7edwsNReRTU+B/0UVMZ6oBLkbJL3IAM2bMwP333w9XV1fzY1K4LTlv8pjMSpQWcJQ2Y9G4cWOkpKQgICCgxvafiIiIiEonKzTFvfc+Er/8EigshGtgIFy9vJAfE/Pfi1xd4d29O/yHDIbf4CHwbNG82DbS/v4bp1940fyewLFXot7kyVzFqYpk7BwYGGjV2NlhAgvZzZIrQPXq1QsDBw7U5WbdrVxLuCIHh4iIiIhsK33lKpx+/nnknTyp9wNGj0a9KU/CLSQE2bt2I/2ff5C2dKl5FsNEul3LTIZvv35IXrAAaX/+qY8bmjRB/RemwrdPH/7oqkFFxs4OkwolMxOlBQ9lPU5ERERE9is/MVG7Waf++qved29QH/Wffx5+AwaYX+PdqaNewu+/D3mnTiFt2TKk/7MUGevXI/foUSTOmaMX5eaG0IkTEXbP3TrbQTWPI3IiIiIiqtEslJSffkbsq6+iICVFU5xCJoxHuKS8+/qW+T4pvA654Qa9SC1FxqrVSF/6DzLWrIWhaRNEPv00vNq140+yFjlMKlRpLGssrMVUKCIiIqLakXv8uKY9STAgPNu2Rf2XXoR35878kdgpp0yFKk1FVociIiIiotopzM45dAgZq1YhfuYsGLOz4eLpibB770HozTfDxWDgj8VJOHRgQURERET205Au9/Bh5Bw4gJwDB89cH0DeiRPFXufTpw/qT30eHk2b1tq+km0wsCAiIiIiqxnz8pB77BhyDh78L4A4eFAfQ4kVPE3cwsPg2aoVAi+7TC+Syk7Oh4EFERERkROSMtqi5Vr/Rta27XALDIB7eATcI8LhHhEB9/D/rl39/M4a7GsAcfx4UfBw0HQ5gNyjx4D8/FI/U/pPeLZuBc/WrYsurYpun6uxHTkHBhZERERETkKCgcx//0XaX38j7Z9/kH/6tFXvc/H2PhNohMMtIBB5UceRIwFEXl6pr5fVmzxatTQHDqaLbIOzEXUXAwsiIiIiB6ZLr65cqcFE+ooVKExLKxYw+F1wAXz790Nhdjby4+KQHxt35jpWL4Xp6TBmZSHv+HG9WHL18YGHBA+mi8xGtGoF98hIBhB0FgYWRERERA6mIDUVqb//gbS//0bmunU6U2HiFhoKv8GD4D90KHz79j1nszhZtUkDDVOwkZwMj4YNiwKI+vXhUoFl/aluY2BBRERE5CDyk5KQ+MUXSPp6rs40mMgKS37DhsJ/6DB4d+0Clwosya+zEk2bcpUmqjIGFkRERER2Lj8hAYlz5iDpm291hkFIjUPgmEvhP2woPFq0YGoS1ToGFkRERER2Ki82FomfzUHSvHnaWE54tm+PsLvuhP+wYUxTIrvCwIKIiIjIzuSdPo2ETz5F8oIFMObk6GNenTsj7K67tH6CKy+RPWJgQURERGQn8k6eRPwnnyDlf9+bC7K9u3VD2D13w/eCCxhQkF1jYEFERERkBylP8R9+iOT/fW9uPufTuzfC7r4LPn36MKAgh8DAgoiIiKgWe1AkfPopEj//QntJCJ++fTTlyfe88/hzIYfCwIKIiIiohhXm5iJ53jzEf/QxCpKS9DHvrl0R8egjOlNB5IgYWBARERHVEGNhIVJ/+w1x77yr9RRCloqNePgh+A0dypQncmgMLIiIiIhszGg0ImPVasS+9RZy9uwpGoSFhyPsvnsRdOWVcHHnkIwcH3+LiYiIiGwoa8dOxE6fjsx16/S+q58fQm+7DSE3ToCrtzePPTkNBhZERERENpC1YwcSZs1G2pIlet/FYEDwuHEIveN2uAcH85iT02FgQURERFSNKU+Za9cifvZsZK4tmqGAiwsCL70U4fffB0PDhjzW5LQYWBARERFVQ1F22pK/kDB7NrJ37jwzynJH4OjRCL3tVni2asVjTE6PgQURERFRJRlzc5GycCESPvkUuUeO6GMuXl4IuuoqhN5yM2coqE5hYEFERERUQYUZGUhasACJcz5HfkyMPuYaEIDgcTcgZMIEuIeE8JhSncPAgoiIiKgUkt4kzeskcMiLifnvOvo00pcuRUFKStFgKjwcITffjKBrr4Gbnx+PJdVZDCyIiIgIdb3gOv3vv5G5cRPyYyV4iEX+6dPIj42FMS+vzPcZmjZB6KRJCLz8crh6eNToPhPZIwYWREREVGdl79uPmBdfRObGjWW+xi00FIZ69eAul8h6etuzTRv4DRwIFze3Gt1fInvGwIKIiIjqnILUVMS9PwNJ33wDFBTAxdMTgVdeAY8mTWGILAoiNJgID4cLZyOIrMLAgoiIiOpU3UTKTz9rJ+yChAR9zH/4cEQ8/jg8GrHHBFFVMLAgIiKiOiFr5y5Ne8ratk3vezRvjnpPPwW//v1re9eInAIDCyIiInJq+UlJiHvnXSTPny+V2nD18UHYPXfrsrBMcyKqPgwsiIiIyCkZCwqQvGAB4t5+x7w0bMCYMYh49FEY6kXU9u4ROR0GFkREROQ0CrOydIWnjFWrkb58OXKPHtXHPdu2ReTTT8Gnd+/a3kUip8XAgoiIiBy6GDt79x5krFmDjNWrkbV5c7HeE67+/gh/4AEEX3ctXNw57CGyJf6FERERkUPJi442BxIZa9dpd2xL7g3qa0G2b79+8O3fH24BAbW2r0R1CQMLIiIiskvG/HzkHj+OnP0HkLN/P3IO7NeGdnnHjxd7nRRj+/TpcyaQ6AePZs3g4uJSa/tNVFcxsCAiIqJaZTQakR8bWxQ86OUAsg/sR+7BQzDm5p79BldXeHfurEGEzEh4d+kCF4OhNnadiBw1sDhw4ABefPFFLF68GOnp6ejYsSOeeeYZjBkzprZ3jYiIiKwIIKQpXc7Bg8g5cLDo+syl8MyqTSW5eHvDs1UreLZpDa82beDZpg28OnSAW2AgjzeRnXGowOLDDz/EpZdeinfeeQcGg0HvX3HFFdi4cSO6detW27tHREREFr0jcg4c0KAh1yKQKFkPYebmBo+mTTVwsAwiDI0awcXVlceVyAG4GOX0gYMqLCyEp6cnPv74Y0yaNMmq96SmpiIwMBApKSkIYDEXERFRlcgwIu/ECWTv2YOcvXt1habsvXuRf/p06W9wcYGhceOiWQi5tC669mjRAq6envxpENmZioydHWrGwjyNWlCgX3LmzJn6BYcPH17bu0VEROT0pN4h59Ahc/CQs6foujA9vdTXGxo2NAcPHnrdGp4SQHh71/i+E5HtuddEILBt2zZs3boVSUlJGvF06tQJPXv2hJubW4W3t2TJElx88cUaXAQFBWHevHlo0qRJma/PycnRi4kEJERERFS2wuxs5B45gpxDh5Fz6CBy9foQco8dA/Lzz3q9FE5r0NC+Hbzad4BX+3bakM7Nz4+HmagOsVlgkZycjHfffRezZs3CqVOn4Ovrq0FFWlqaXsLCwjR96aGHHkK9evWs3u6IESOQn5+v25cZC6m5WLZsGfr27Vvq66dNm4apU6dW4zcjIiJyvCZyMttQ8lIol4wM5B49htxDUgNxCDmHDyMvKkrODJa6LdfAQHi1a6cXUyDh2aI5V2UiItvUWMhA/7rrrkOPHj0wbtw4DBo0CA0bNjQ/HxMTgxUrVuDbb7/V69mzZ2sRdmX07t0b3bt31wDG2hmLxo0bs8aCiIicRn5cHLJ27NBL9vYdOrtgzMpCYV5e0XKtpcwynIusuqTpSy1awLNVS3i0bAXPli3gHhnJHhFEdUiqPdRY/Pnnn+jSpUupz8kMxdVXX62XgwcPYv/+/ZX+HAkaXMtZLUKKu+VCRETkDArS05G9cxeydmzXICJr507kR0dXaBsuHh5FF09PLZg2NGkCz5YSPLSApwQQrVrCLSSEAQQRVYhNAguZobBWq1at9HIuGRkZGD9+PB5//HF06NBBU6FmzJiBPXv26KpQREREzkJmGfJiY5F36pQGDXnR0cg9clSDiNzDh89OU3Jx0WDAq3MXeHfuBK/27eHq7/9fACEXgwdcPQyAwcCAgYhswmFWhZIajTvuuANTpkzBli1b9L6kQC1fvhz9+vWr7d0jIqI6TjKLC9PSUCCN3goKYDxzgdQ35Mv1mfv6eKHeL8zMRF70aQ0g8qJPIf9UURAhqU1l1TgIQ4MG8OpyJojo3BleHTrCzc+3Rr8vEVGN1Fi8+eabmDx5slWvfeSRR/T1NYV9LIiIqDKrJMlgPz8mBvkykxATq9fm+3I7NhbG7OxqO7gyy2CoXx/uDerDUL8BDA0bwKtjR3h37gz30FD+EImobtRYXHvttejVq5f5/qOPPgoPDw9dBapRo0ZavP3VV1/hyJEjuPPOO22xC0RERErOn8mgXzs/ayfoA3pbO0DL7EFhYdnXMtsg13l5Vh9NF29vuLi56UW6ScPNFS5u7kXdoy0e19d4ecEQGakBhKFBfbjXPxNENKjPGgcicjg2CSxk1SW5iDVr1miHbElZMhgM5tdMmDBBl47dvn27VTUWRERE55KfmIic/QfOBBCmQOIgCquhh5EEAe71ImAIj4B7RATc69XTa0O9M/fPXFy9vPiDIqI6yeY1FhI4yOyFZVAhXFxc0KdPH+zYsQNXXnmlrXeDiIiciMwiSK+FbOn8LF2g5XrPHhTEx5f+Bjc3eDRteqYLdGu9SJCgsweuMnvgWsq1q3mWQTpFuwYEsOiZiKg2A4vw8HAsXrwY8fHx2hTPcpWnn3/+GXfddZetd4GIiByYMT9fO0Bn79mN7N27kSOBxN69KExPP/vFLi4wNG78XwAh121aw6N5c7h6eNTG7hMR1Rk2Kd62lJeXp8vP7tu3D2PHjkWDBg0QFxeHH3/8ESEhIVi7di38/PxQU1i8TURknwpzcoo6QB85rN2fZXnV3EOHNJVJm7yVUtzs2aaNLq3q1UEuHTSYcPXxqZX9JyJyRhUZO9s8sBC5ubn47LPP8Ouvv+LkyZOIjIzE8OHDdbbC29sbNYmBBRFR7ZH/cgoSEooCh8NHioKII0f0dt7Jk2Uuserq5wevdu3gKQFE+w5FQUSL5nApkWZLREROHljYEwYWREQ1Iz8pqah4+kwBda6synTwIAqSk8t8j9QxeDZvDo8WLeDRornellkISW/SegciIqpby82Wlg41c+ZMXSFqyJAhuPXWW3Hw4EEcOnQII0eOrIldICIiGwYQ0g1al3OVlZjOrMYkMxOlMtVBSAChQURzeEog0bw5l1glInJgNg8sZEJk9OjROH78OIKDg7F37159vH79+vr4qlWrtMCbiIjsl3SMllSlYilMcn34cFE/iDIYGjWyWImpld6W2QguyUpE5HxsHlgsW7ZMg4pt27ZhxowZiI6O1sd9fX0xYMAAzJ8/H/fcc4+td4OIiKwgRdI5R44WNZGT9KUzwUPu0aMwltMkTrpDe7a0WIlJgggJIHx9edyJiOoImwcWe/bs0fQnT0/Ps9b/btiwoRZzExFRzS/hmns8qlgnarmWVZmQn1/qe1w8PeHRrNmZ2gepgWihBdTyGFdiIiIimwcWQUFBiIqK0tslAwupubj88sv5UyAispGC9PSiJVyPHkXusaO6hGvRTMThUpdwNa3AZJp18GjZsqj+oUULGOrXL2ooR0REVBuBxUUXXYT7778fn3/+uS47KyQd6vXXX8fq1avxxRdf2HoXiIicWmFWls4+FAUPx4pdl1lALSd7vL3hKYGDRSM5uXaPjGSHaSIisr/AQgq2f/jhB1x33XUaULi7u2P69On6uNRXSBE3EVFdVJidjfy4uKJLbCzyY+X2meukRBhz83RWoeSlMC+32HMoKCj3c9zCwuDRtKn5ovUPsoRrw4ZcwpWIiKpNjSw3K0XaR44cwcqVK7WmIjQ0FAMHDoS/v39NfDwRUe3NJBw9al5JKe9EFPIkgNBAIg6FqanV9lnS/0HrH5qZAohm5vtufn7V9jlERES1Flhs3boVXl5eaNeuHYYNG2brjyMiqvlO0vHxRUuv6hKsZ5ZjPXwYeadOnfP9UhDtHhEB9/DwoovpdmgIXDy94OJhgIuHB1w9PPRaOk3rtcXF1dsbrv7+TF8iIiLnDiykjuLee+9Fz549MX78eFx//fWoV6+erT+WiKhaSbfo3OPHz9QuHCu6PlPHUJiWVub73AIDtQBaVlLyaNIUhnoR/wUPEREMCIiIyGm4GOV0m41JD4u5c+fi22+/1ToLmbmQIOOKK67Qfhb22paciOrYzENiojaB00JoWUHpTPCQd/QYClJSyn6zq2tRIzjtIt3iv07SLVrAPTi4Jr8GERFRrY2daySwMJGPWr58uQYZ//vf/5CXl4e3334bt912W03tAgMLojpK/v2RmobcEyc0eMg7cfLM9QnknpTHTsGYlVXuNmSGQesXztQxGCwKol09PWvsuxAREdljYFEjxdsm0sdi0KBBaNy4saZDvfHGG9i3b19N7gIR1YWUpTPLrebI9ZnbecejUJieXv6bXVw0eDA0bvRfAbReN4FHkyZsAkdERGQPgUVsbKwuLyuzFevWrUOnTp0wdepUTJgwoaZ2gYjsfEYhPyYGBbJSkkykyqWwEMbCM7eNhf/dh1GXWJXVlcx9G840gSs3Zcm09GrDhrrUqqQvFV031MfcGzTQImkiIiKyw8Bi48aNePbZZ7FkyRJERkZqP4uPP/4YXbt2tfVHE5EdK8zMRPauXcjatq3osnWbBgrVQRq8FaUsnVlyVW43aaxBhKygRERERA4YWGzZskWb4C1evFjToFxdXW39kURkh7MRuUeOFgUQ24sCiZx9+89u7ObuDjfJ33R1LVo6Vf690GsXuLiYbp95zsUFbsHBxYOH5s2KUpYYPBARETlfYCFBxZAhQ/RCRM5N0pi0GDrqhDaDy42K0tqGrF27UFhKipJ7vXrw7tYN3l27wrtbV3h16ABXL69a2XciIiKy88AiKioKO3fuxLhx42z9UURkY4UZGcg7fRp50ac1gNDgQVZXipLrE6UGD5aN4Lw6diwKIs4EEobISP7MiIiInITNA4sLL7wQ77//PrKzs7UDNxHZZ6qSLLWadzoG+aej9TrvdDTyJYCIOV10ffp0uY3gTNxCQ+EhRdGNGxcVRUt/h7bt4NW2jXaJJiIiIudk88AiNzdXA4pu3bph7NixCA8PL/a8dOSW4IOIqrFfQ0qKFkLnJySgIDlFV0qSZVj1OqXoutD0+JnnjLm5Vm3f1d9fZxp0NaXGjeHRuFHR6kqNGunKSq413PSSiIiI6khgsX37dg0u3N3d8fPPP5/1fEFBAQMLIivIwD8/Ph75sbFF1xI4xMUX3TbdP3MbeXmVOqYSFLjXj4ShXmTRdWR9GCLrwV2u60fCvV4k3PwYOBAREVEtd952tO6BRDVB/gRlxkB6OEjQkCfXMbFFAURMDPLi5DoWBQkJFdquW2Cg9mxwCw6CW6BcAosuQWduB525f+biKq9h0EBERET23nk7Ly8PBw4cgI+PD5o1a1ZTH0tUuylJaWlFAYIEDHodZ75vvsTFwWjtDIPBAPfwMLiHhxddwsLgHnbmtjyu98M0oGCjNyIiIqpJNRJYSArUbbfdhri4ODzyyCN48803sW3bNjz88MP4+++/a2IXiKo3YEhNLUo9OhMYaNCgqUnymOk6FsbsbKu3K0XP7hERMERE6LUsxeoeEQ6DXhfdl9kGF/aCISIioroYWJw8eRI333yzrgx16NAhpJ1ZVUY6b0vdxV9//YVhw4bZejeISlWYnY2CpCTtv6AFzWeuC1JSUZBqun/mMbmdmFg0w5CTY/URdQ0MhCFCZhTOBAzmS/h/QURYGFdMIiIiIodm88Bi2bJlGDFiBMaPH4/p06ebAwvRu3dvrFy5koEFVd+SqZmZKEhLK6pZkBWRJBDQ6yTkJyagICGx6DoxSWsWCjMzK/15EjBI+pEGB6bUJNNti2s2fCMiIqK6wOaBRXJyshZ8CBcXl7OKQcLCwmy9C+SoqUYSECQk6LXe1hmFNBSkpZ65TiuaUZBrCSYkaC0oqPgHGgxwCwgoKmIOCIBrYADcAopuFxU2B8BV7svjQcE60yAzDAwYiIiIiGowsJBZiddffx2ZmZnFAotjx45h7ty5+O6772y9C1TbswiSbmTql3DmIn0WJMXIPIugwUP8mRmFxEovl6rc3TUgcA8NhVtoCNyDQ4rqF0JD4BYSUvS4XIcUPS5LrJYMeomIiIjIzgKL8847D/3790ePHj0QGRmpfSsmTZqE+fPna/+KytRX5Ofnw83NjYPB2qhHkCZrejnTcM10MTVgk4vWJfzXhM3qFY9KcA0IKBr8h4XCPSQUbsHBcPP3g6u/zCj4F12XvB/gDxcvL/5uEBERETnjqlBff/01Zs6cqbMTp0+f1qVnn3rqKV0VqqKrS8mKUlu2bEFhYSH69u2Lt99+G126dLHZvjt9wzUpXLZMNzLVIJyZRdDnEhOLOjNXYIWjsmYRzJczKUcaMMisQmhY0YyCXIcVzShwuVQiIiIix+EwDfJkpmPs2LGYPHkyevXqhZycHNx99926XO3evXvNdRx1rUGesaCgKCiQhmpxcShMS0VBejoK09JRmJ5mvl2QnobC9AytRShMTzfXJVSYm1tRgzVzk7Uzt0s+JvUIFoGEi48PZxGIiIiIHExFxs41ElhIzwpfX1+0atUKubm5eOGFFzQYuPXWWzFq1KgqLWXbqFEjLF68WFeecqbAwrTCUVHQEIM8CRz0+nRRECEzP2d6JyA/v/If5OZmrjUwX5tqE0yzCfK4pCEFBcHVz48BAhEREVEdYVedtxMTE3H99ddjxYoVen/GjBn4/PPPMXToUFxxxRXYvXs3mjdvXqltHz9+XK8daWWpmMO7sOHYKgwqbFWUYiTLnsqSqEmWt4uure6V4Opa1HE5IqIoxchP6g784Obn/99tf3+4+poel2v/omBBZhPYcI2IiIiIqsjmgYU0wOvevbt58C91FhJcXH755fD09MQvv/yCBx54oMLblVQoeV+/fv10++W9Ti6WUVdtyS/Mx8M/3Yrtoem4+N9CjP+nEO6F5b9HCpHd60k35nraedkQKV2Y68E9sl5RR2a5SHM19xoplyEiIiIiqr0ZC0mDEjKFsn37dvNKUJLGlJSUVKlVoW644QbExMRg9erV5abmTJs2DVOnToU9cIELOhTWw3ak4/ferjjWNghPx/ZBRGB9uMmSqJpyFFSUeiTLoQYHszaBiIiIiByCzWss1q5dqylPMlOxcOFCbNq0CUuXLtXnrrrqKk2TkqLsihRxjxs3DmvWrMHy5cvPmUZV2oxF48aNa7XG4p/j/2DKqinIyMtAhHcEpg+ajm4R3WplX4iIiIiIHKZ4+95778WsWbMQGhqqS8ZKb4uDBw/isssu08JudyvTeCSoGD9+vM5SLFu2DC1atKjwvthL8faRlCN4aOlDOJRyCO6u7ni89+O4tu21LIwmIiIiIrthd4GFKX3JMoDIyMjQfhZBQUFWvV/6Vtx4441YsmQJ/vjjj2JBhY+PDzw8PBwqsBCZeZl4ZvUz+PPYn3r/0paX4pk+z8DL3atW94uIiIiIyG4Di+TkZJ1pOHHiBOrXr6/N7cLDwytUq1HWDMX777+PCRMmOFxgIeTwf7n7S7y96W0UGAvQLqQd3h70Nhr5N6rtXSMiIiKiOi7V3gKLTz75BI8++qjOUsjqUAkJCTrD8Pzzz+vjNcneAguTDdEbMHnFZCRmJyLAIwCvDXgNFzS8oLZ3i4iIiIjqsNQKjJ1dbb0zsgqUdMh+6aWXkJ6ejujoaGRlZeHjjz/GM888g3/++cfWu+AQzqt/Hr675Dt0DuuM1NxU3P3X3Zi5bSYKjedYj5aIiIiIyA7YfMbigw8+0NWb5s+ff9Zz999/v0ZAL774Iur6jIVJbkEuXt3wKhbsX6D3BzQagEtaXIKmAU3RLKAZfAw+tb2LRERERFRHpNpT5+0GDRrAtYzOzvJ4w4YNbb0LDsXDzQPP9n1WZy5eWvcSVpxYoRcTWZ62aWBTc6AhF7nd0L8hDK6GWt13IiIiIqq7bD5jIdFNz5498fTTT2uBtZubmxYsy7KzMmMhfS5qMriw9xkLS3sS9mDevnm6NO2x1GNaf1EWdxd3LfhuHdwabYLb6KVtSFs08G3AJWyJiIiIyDGLt+fMmYPXXnvNfD8+Pl4LtmVZ2IiICL0v9Rayk1OmTMFjjz2GmuJIgUVJKTkpOJ56HEdTj+pFgg3TJSs/q9T3+Bv8NdiQIKNtcFu9bhnUEt7u3jW+/0RERETkWGo9sNi6dStWrVpl1Wu7d++O/v37o6Y4cmBRFvkRxmTG4HDKYRxIOoD9SfuxL3GfNt/LL8w/6/WuLq5oHtAcD/d6WGs4iIiIiIjsMrCwZ84YWJQlryBPgw1ToLEvaZ/eNqVUebh6YNaIWehZr2e1FZ7/dPAndI/orrMkREREROTY7DKw2LlzJ37//Xdzg7yhQ4fivPPOQ02rS4FFWeKz4vHC2hewNGop/D388eWoL9EquFWVg4qHlz2M5SeWw9fgizkj56B9aPtq22ciIiIiquN9LMRzzz2Hrl27YtasWdixYwe+/PJL9OnTR/tbUM0L8w7D6wNeR7fwbkjLTcOdf92J0xmnqyWoEBl5GbpNqf0gIiIiorqhRhrkvfHGG1i4cCEOHjyIpUuXYs+ePVqDMW/ePCxZssTWu0Cl8HL3woyhM9A8sLnWZ9z1113amK8yQcUjyx7RoMLTzRPvDHoH7UPaa7rVHUvuQGxmLI8/ERERUR1g88BCAoixY8fi4osvLvZ4v379MHHiRKxcudLWu0BlCPQMxMfDPtbeGAeTD+L+f+5HTkFOhYOKZSeWaVAhgcrQpkPx4bAP0di/MU6mn9SZi8oELPamoLAAm2M2Y83JNVosT0REREQ1HFh4eHjo0rKlkcc9PT1tvQtUjgZ+DTQQ8DP4YVPMJjy58kkdRFtTGG4ZVLw/5H30qd/HnGo1c/hMvZZVqu77+z5k52c73M8hMy8Tfx37C0+tegqD5g/CTYtuwh1/3YEnVz2pzxERERFRDRZvHzt2DG3btsVLL72Eu+66C76+vsjOztY6i3vvvRfr1q1Djx49UFNYvF269dHrdXZBlqe9od0NeOK8J8psrCdBxcPLH8ayqKKg4r0h76Ffg35nvU5Worpl0S1Iy0vDoEaD8Pbgt+HuavNm71USlxmnwdLS40v1mOQW5pqfC/AI0PqRAmOBppC9NfD/7d0HfFRV9sDxk15IAQKhNxEEBJGioID0JgiIUv66YsGy6urC2kVlbatrQVEWCygiNhAUQQULUqRIBwEb0pt0UknP/3NunJhAykymvpnf9/OZT5I3Jfe9N/PmnXfvPWeC05PeAQAAfJnPZYWaNWuW3HnnnaZInq1hsbGx8txzz8ltt90mnkRgUboFuxbI/csKihWOaTtGRrcaXeGgwkZ7QXSuhQ6xGtx4sDzZ6UmfqgSub38dBqYZsnSdthzbUuz+ujF1pXv97tK9XneTRvfHoz/KfUvvkyOnj0hkSKQ80vERGXzuYK+1HwAAIKACC1ujVq1aVZhutkOHDpKQkOCJf31WOwI93WxZ3t32rjy/7nnz+386/0euaHxF8eFPS+8xJ+FaA0OHP11ap/Sgwkav/o9dMtZc6b+x5Y3yr3b/El9wOO2w3Lv0Xtl0dFOx5RdUu8AEE9rLolXKzwyEjp8+boaMrTq0yvx95blXykMdHqKaOQAA8Ds+GVj4CgKL8r2w9gWZ/tN0CQ0KNROyO9XpVOGgwubT7Z/KYysfM7/f0+4euaHlDeJNW49tNZPVj54+atbnktqXSLd63aRr3a5SPbp6uc/XeShTtkyRyZsmS77km4KAL3Z90QyRAgAA8Bc+F1hs375dXn/9ddm1a5dkZf01Zl0NHTrUZIfyFAKL8uXl55kr8l/u+tJchZ/SZ4q8veVt+W7fdxUKKmymbZ0mE9ZPML8/1ekprw0h0iFfj6541AzPOrfyuWZ96sbWrdBr/XDoB3lg2QMmvW50aLT8+9J/S/9G/V3eZgAAAAn0wOLgwYPSokULad68uZmkHRYWVuz+Hj16yKBBg8RTCCzsoz0Uty+63UxgDpIgc1VegwqdU6E9GBX14roX5Z1t70hIUIi83P1l00vgyYDpf5v+J2/++Kb5W3snnu3yrMSExzg94Vvnpqw7vM78PeK8EXLfRfeZOShWoamDfz7xs7RIaCFhwcU/owAAIHAl+1Jg8c4775gMUN999534AgIL+6VmpcqNX90ov5z4xQQVE3tMlM51Ojt9cq+9BfN2zDMn3i90fUEuq3uZBAe5N/OxpofVtLHf7v3W/H3j+TfKP9v+U0KCQ1zy+ppNS4dF6fAopUUCX+z2oqnn4ct2nNohc7bPkfk75supzFMy4JwBJtgCAADwucDivffeM0XydCiULyCwcMyx08fkna3vSI/6PaRtDdekBc7Oy5axi8eaat2qZqWa0r9hfzOEqFnVZi7PGnUo9ZDc9d1d8uvJX83V+PGXjHfbMKzlB5abYWR6kq61QcZ1HCcDGg3wqUxYGmR9tfsr+WT7J2dNXFev9XrN6QASAAD4B58KLLSORf/+/WX16tUmxay3EVj4htM5p80kcZ3HkZr9VwHFhnEN5fJGl5sgo2F8Q6f/z6Yjm2TM4jFyPOO4VI2sKhO7T5QLEy8Ud/oj7Q+TktZ20t6vYT+TllYrnTsrKTNJNh7ZKFUiq0idmDqSEJlgV9CiH/Ofjv9keid0m2s9DqVD0rTH6OqmV8vKgyvl/Z/fN6/76eBPyXIFAADEpwKLpUuXmkJ42hgNMM4MLrp16yYDBw702G4jsPAtOoH6+/3fm5PdZfuXmb9tdDiRBhn9GvUzvRqO0uE941eONz0k51U5z8wP0UrjnqBDo6ZumSqvb37dpNlNjEqUJzs/WWbNj7Lodvnol4/M/JDkrOTC5VpLo1ZMLRMM6E3Xr+jvGjjottXeCR3SZqNDtIY2GWpqi9iyYGlPxuDPBpvA6KaWN8nYdmNdsCUAAICV+VRgMWfOHJkypWDceUmuvvpqufnmm8VTCCx8e06HprPVE+FVB1eZE3KbtoltpV2Nduaqv1bAjouIk/jw+GI/9SRbr95rKthXNr4ib2992zy3R70e8kyXZyQ6LNoraW11aNTu5N3mb61qPqbdGLt7A3ROim6PVze8KgfTDppltSvVljzJM3U4dFK9vXSeTK8GveSqJldJ+5rtS5zXojVH7l58twlIZg6cKedVPc/u1wcAAP7HpwILX0NgYQ0nM07KN3u+MSfVWr3b3hNnDTBCg0PNVXd1S6tb5B9t/uH2yeHlDfuasG6CfPTrR+ZvrXWhgc75CeeXm8pWn6fZmpT2eui6DGo8yEw618xdup4H0g7IgZQDciC14HYw9aC5aXVwpSl1dajTwHMG2jUcS+e/6CR3LRQ44/IZXt12AADAuwgsXLRx4Bv05FmDjP0p+yUpK0mSM5MLf+qwIP2Zk59zVpDxRKcnTJYjX6ETux9b8ZgpyqfFB//e+u8yutVoEwgV9euJX+WlDS/JigMrzN+VwirJza1ulmubX+vQvAcdPqXbplpUNYcmj2tPiA6J0nkY4zqMk5HNRjqwlgAAwJ/4VGChqWYnTCgoilaS66+/XsaO9dxYbgIL/6Nv4fSc9GIBR4O4BlKjUg3xNacyTskTPzxhAiXVunpr+U/n/0j9uPomgJq0cZJJxatDnDT4GNFshNx6wa1m4rknffDzB/LMmmdMZqvPhnwmidGJHv3/AADAN/hUYLFu3TpZsmRJsWVpaWnyxRdfyMmTJ2Xq1KnStWtX8RQCC3ibfuQ+3/m5/Gf1f0xGLO2F6N2gt0kBa5u83rdhX7m7zd0m4PAGnacyasEo+fHYj6ZtE7qVfnEAAAD4L58KLEqTl5cnnTp1khdffFEuvbRimXIqgsACvkLra4xbMU7W/rG2cJlOUL+n3T3Sqnor8TYdkjXi8xFmEv2kHpOkaz3PXQAAAAC+wZFzZ6/NygwODpa+ffvKsmXLvNUEwKs0TezUPlPlgYsekE61O5mT92l9p/lEUKE0I9SoFqPM70+vftqkowUAAPC5wEI7SnSYVFhYmLeaAHidZlz6W4u/yeu9Xzc9Ar5UoVvpBHOtiXEo7ZBM3jTZ280BAAA+zO1DoT7//HN57733ii3Lzc2VrVu3ysGDB+XHH3+UBg0aiKcwFApwjBYwvGPRHaa2xYcDPpTmCc3ZhAAABIhkXxoKFR4eLjExMcVuCQkJct1118m2bds8GlQAcFyXul2kX8N+Zq7F46seNxO7AQAAzkSBPADlOnb6mAz6dJCkZKfIgxc/aGpqlEXnY2j19CX7l5jJ6TWia0iP+j1MFfR6cfXY4gAAWITXs0LpUKeQkBC3Pd4ZDIUCKmbWr7PkyR+elOjQaFPbomalmmdluVq6f6kJJtYcWiPZedklvo5WAjdBRv0e0qJqC5+bVwIAAHwosHj55ZfNHIr7779fmjZtWurjdu3aZR6rw6Oefvpp8QQCC6Bi8vLz5PoF18umo5tMz8NL3V+Srce2ypJ9S0xA8dvJ34o9vm5MXelWr5t0qtNJ9ibvle/2fSfr/lhnhlTZaHDSvV53E2Roqt2wYJI5AADgS7weWKSmpspTTz0lEydOlAsvvFAuu+wyE2Boo1JSUmT79u0mzeyaNWvklltukSeffFKqVrW/snBmZqYcPXpUqlevLhEREQ61jcACqLjtJ7fL8PnDJSc/RypHVJZTmaeKZbi6sPqFJrtVt7rdpFF8o7N6I5Iyk2TZ/mWyeN9iWX5guZzOOV14X2x4rHSt21Wua3GdtEhowW4CAMAHeD2wsNGT/7ffflsWLFggW7ZsMZW2tUHnn3++qWExevRoqVOnjt2vpz0c//vf/2TGjBly5MgRWbx4sXTr1s2hNhFYAM55ZcMrMmXLFPN7pbBKpgaH9kx0rtNZqkRWsft1MnIyZPWh1aYnQ3s9TmScMMtjwmJkzqA5UjumNrsKAAAv85nAwtUmTJhg5mP07t1b2rRpQ2ABeEFOXo58vvNzMyG7fY32Ehbi/PAlzTS1+ehmeX7t87L1+Fbzum/1fcv0ggAAAO/x28DCZv/+/VKvXj0CC8DP7EveJ1fNv8oMkbq3/b1y/fnXe7tJAAAEtGRfqmMBAPbSVLQPXPSA+X3iholnTQgHAAC+y+8DC53orZFW0RsA3zW0yVAz+VvT1T70/UOSlZvl7SYBAAA7+H1g8cwzz5juG9tNh1AB8F2aSWr8peOlamRV02MxadMkbzcJAADYwe8Di4ceesiMCbPd9u3b5+0mAShHtahqMv6S8eb3d7a+Y+pfAAAA3+aVwELT0BZ16tQpOXDggFv+l9a50IkmRW8AfJ8WzdNhUfmSL+OWj5PUrFRvNwkAAPhKYJGXlye9evUyxfLatm0ru3fvNsvnzp0r48aNK/f56enpJiPUH3/8URig6N/MmwD80/0X3S91YurIwbSD8uyaZ73dHAAA4CuBhVbb1qBAb++++66MGjVKDh06ZPfzv/jiC+nYsaMMGTLEFNYbO3as+Xvq1KlubTcA79ACfM90ecbUs/hsx2fy7Z5vfWZX6KTyB79/UPrP6S//Wf0fWfvHWlOPAwCAQOXROhafffaZTJs2zfRQqG3btsltt90mV155panM/c4777i9DVTeBqxHU89O3TJVKkdUlk8Hf2rmYHiTVg0fs2SMrDiwothynXDes35P6dWgl1xU8yIJC3a+eCAAAN7kswXytEEdOnQwPReJiYlm2ZIlS0wPhN4ILACUJDs3W6758hr55cQv0qVOF/lfz/+Z7FHekJ6dLnd/d7es/mO1RIVGyT/b/lN+Pv6zLN63WJKz/kpnHR8RL93rdZfeDXpLx1odJTwk3CvtBQDALwMLtXbtWgkJCTFzLGwWLVok2dnZ0q9fP7f/f3osAGv6/eTvMuLzEZKVlyWPdnxUhp833ONt0Ankdy66UzYc2WCGaU3uOVna1ig4lmndjbWH1srXe742QcaJjBOFz4sJi5Fu9bpJ/0b9TWDkraAIAABLBxYffPCBnH/++dK6dWvxBQQWgHXN+GmGPLf2OdNTMGvgLGkY39Bj/zspM0lu//Z22XJsi8SGx8rrvV6XC6pfUOJjc/JyZMPhDfLNnm/k273fyrHTxwrvu67FdXJf+/sILgAAluDIubPbJ2+HhobKZZddJosXLz7rPk0x+91337m7CQD8xLXNr5UOtTrI6ZzT8vDyh80JvCeczDgpt3x9iwkqdJ7H1D5TSw0qVGhwqFxc62IZ13GcLBq2SN7t/66MOG9EYXD071X/ZqI3AMDvuD2wGD58uLzwwgsycOBAmTlzpll24sQJuf/++6VJkyYEFgDsptmhnur0lOkx0JP8p354SrYd22aGIbmL9jbc9NVN8vOJn83k7Lf6viUtElo41OY2iW3kkY6PyJOdnjR/f7L9E7l/2f1m7ggAAP7CY3MsNFXsyJEjzSTt+fPnm8nbjz32mFxzzTUSHOy5rLcMhQKs78udX8oD3z9Q+LcOjWpVrZU5gdeb9iZo8OGsw2mH5eavb5bdybslMSpRpvSdIufEn+PUa+rwKA0qtLelc53OMqHbBNN+AAB8kU/NsVBZWVmm1sTjjz8uR44ckd69e8uCBQvMJG5PI7AA/MO8HfPkq91fycYjGyUlK6XYfUESJE2rNJULEy80gUbbxLZSK6aWQ69/MPWgjP5qtOxP3S+1KtWSt/q8JfXi6rmk7ZqmdsziMZKRm2HaplmuYsJjXPLaAAD4bWChqWWvu+46SUtLkwcffFAuuugiGTZsmOmpmDBhgkd7KxSBBeBf8vLzZOepnbLx6EbZdGSTmTStwcCZdBiT9jY0im9U7GeNSjXM8KSi9iXvk9Ffj5ZDaYekbkxdM/ypdkxtl7Zb26kZplKzU83QKp0MXiWyikv/BwAAfhVYvPHGG7J//3657777Chuzfft2k1pWU86+9957EhERIZ5CYAH4v6PpR2XT0U2mN2Pj4Y2m/kVOfskTvXUYUsO4hnJO5XOkUVwjE0C8vP5lOXL6iFmuE7U1+HAHrX9x2ze3ycnMk9I4vrG80fsNt/0vAAAsH1iU5vDhwzJgwAC5/PLL5YknnvDY/yWwAAKPZpHambTT9GzsStpVeNuTsqfUzFLnVj5XpvSZ4vYq39ouzTh1JP2I1ImpY/5nvVjXDLkCACAgAguVmpoqS5cuNQGGpxBYALDRbFIHUg6Yk3sNNGw/9QT/4Q4Pe2xo0oHUAya42JeyT6pHVZc3e78p51Y5lx0FAPA6ywQW3kBgAcBXh2/d+s2t8vup3yU+It7MuWhZraW3mwUACHDJBBau2TgA4ElFq3vr3A9Nm6sTzHX+hc4BaVy5sZmEHigycjLkt5O/mUCrfmx9qpUDgBcQWLho4wCAp6Vlp8k/F/9TVh9aXeL9WvnbBBuVG5ufGnDoJPO48DiJDos+K8OVlbJ77UneY4KqH4/+aG7bT24vnHSvKX8vqX2JdKzV0VRf90SAlZmbaeblaHCjw+aaVW0mTao0kYgQzyUcAQBvI7Bw0cYBAG+dZGtF8R1JO8yJre2nzsXIl7JHr2pPR6WwSuYWHRr91+9h0ebvuIg46VS7k7Sv0V5CgkNc0quweN9i+W7vd+Zv7V3Q4Efnp9h+15vt95iwGNPzcCrjlAkibIGE/kzOSj7r9TWA0OVnTrLXk/xLahUEGm1rtJXI0MgKr4OOCD6YdtAEMhpE2H5qoJObn1vssSFBISaoa161uTRPaG5+alt0+5ZFq6xr+uL9KftNOmTbT90nd7W5i2xgAHwWgYWLNg4A+Fp2q91JuwsDDZ1svuPUDnOSWlo63dJoJfH+jfrLgHMGmBNjPdl35ERc0/l+9vtnpkih1uKwV2hQqCkGeCrz1Fn3aU+A1vTQKuqtqreS1tVaS81KNc16bziyQX44+IOsOrTKnPQXFR4cbgohdqzd0QyZ0sBMt0duXq4JDDQo0Z9n/q2V1fW1dF5LaeugAVGTyk0kLDjMpC3W1MBn0oKMDeIamECjRdUWkhCVYAosauCgwaDun8Pph027SqI9TtP6TXN7BjIAqAgCCxdtHACwAj3R12E7OowqPTtd0nLSzO+Ff//5uy7XE95FexcVq1auczg0wLj8nMtNRqzS6HO14vn8HfNlb8rewuW1K9U2z9deCg0YdK6I/jS3jFOFy7TSeFF6Mn5BtQtMEKHzSbRaup7Al+fY6WNmqNiqg6tMoKGpep0VGhxqhpZpG3S4k/lZuYkkRicWBl26nTVA0PojP5/4ufCnLrOH9ibp9tWii3Vj65rhXTN+niF/pP1h0hu/3fdtiiQC8DkEFi7aOADgj7Jys+T7A9/LFzu/kKX7lkpWXlbhfXrlf+A5A6VPgz5SObKyCUy+3vO1CSjW/rG22EmyPmbwuYOlXY12ds3t0GFTtiCjRnQN8/rO0pP9Xcm7TG+GBhv6+hok6JAlHeqlPST6U9tn+13v08fo0CxbINEwvqFdQU1Jjp8+bnozNMj46fhPZv200KIGEHVi/wokEiITzuoZ2pu8V25YeIMcPX3UDKua2neqmS8DAL6CwMJFGwcA/J32XHy751sTZKz5Y03hHA498dbeBD1Z1qFItiE/F9e6WAY3Hiw96/csd14B7KND2m5ceKOcyDhhtrlWYNfhYgDgCwgsXLRxACCQ6JyDBbsWyBe7vjBX4IvOARjUeJDpyagVU8urbfRXOtfjpq9uMr0dbRPbymu9XiNwA+ATCCxctHEAIFDppHDtwdDJ1HoV3ZHJ3agYHUZ181c3S0p2inSo2UEm9ZzkVLYrAHAFAgsXbRwAADxJU+/e8vUtkp6TLp3rdJaJ3SdKeEg4OwGAJc6drVlJCQAAP6TZsSb3mmwmxy8/sFzuXXqvKc4HAFZAYAEAgA/RLFuv9njV1PXQ4oMPLnvwrAKBAOCLCCwAAPAxHWp1kJe7v2yyc2m638dWPFZqgT0A8BWh3m4AAAA4m86xeLHri3LPkntk/s75phaH1g2JDY+VSmGVJCYsxqSlrWj9DQBwtaB8rS4UQJi8DQCwkoW7F8oDyx4otcdCh0zZggwNOGLDYk0F734N+0m3et1MUUBX0Oru6w+vN69fP66+xEfEu+R1AfjPuTM9FgAA+DANEMKCwuSdbe9IUlaSpGalSmp2amHhQj3h19vxjONnBSRa9fva5tfKkHOHVLjo3p7kPfLxrx/L3B1zTZ0NG60QXj+2vtSLq2d+arBh/o6tJ1Ujq5KiGAhA9FgAAGBBOqE7LTvNBBm2YEP/1mrqWnBvzvY5hYGA9mhc2eRKuabZNVI3tm65r62ZqJbsWyIzf50pqw+tLlxePaq6qcB+5PSRMp+vPScaYCREJUh8eLzp3dCbBiPm9z+XxUXEmd/1J0O6AN9EHQsXbRwAAKxKezTm75gv7/38nuxK2mWW6TyNHvV6yHUtrpM2iW3O6lU4lHpIZm+fLZ9s/0SOnT5mlmkgcVndy2T4ecOlU+1OZmhVena67E/dL/uS98nelL3mZvv9j7Q/JF8cH2WtbdNbSFBI4U/9X7a/C5cFhUivBr3knvb3uGhLASgLgYWLNg4AAFanczNWHlwpM36aYX7anJ9wvvytxd+kd4PepldChzstO7CscC5HQmSCDG0yVK5uerXUjqlt9//TYVkHUg7IvpR9cjLzpOk10VtyVnLh7zqkKzkz2fzUHpaK0JS8OocEgHsRWLho4wAA4E9+P/m76cH4fOfnJgBQmtK2aJ2MDjU7mN6J7vW7e2R4kv5vHcqVlZdlgprc/FzJy/vzZ/7ZPz/7/TP56NePpEZ0DZk7eG6F544AsA+BhYs2DgAA/uhExgmZ/dts+eiXj+To6aNm7oOmsh3WdJg0im8kviwjJ0OGzhtqekRGnjdSxnUc5+0mAX4t2YFzZyZvAwAQoLJzs+X3U7+bYCIyNFKs4odDP8gtX99i5n+82/9duTDxQm83CfBbjgQWVN4GACBAhYWESfOE5pYKKlTHWh1lcOPBZpL446seNwESAO+zZGCRlpYm+/fvl9zcXG83BQAAeMG97e819TK0x+WtrW+xDwAfYKnAQgOJO++8U6pWrSoXXHCB1KxZUz766CNvNwsAAHhY5cjK8sBFD5jf3/zxTdmZtJN9AHiZpQKL5557TmbNmiWbNm2SEydOyNNPPy1/+9vfZOvWrd5uGgAA8LD+jfpL5zqdTUG/x1c+XpgqF4B3WCqweO211+Tmm2+W5s2bm79vvfVWOeecc+TNN9/0dtMAAICHaYG/Rzs+KlGhUbLhyAaT6QqA91gmsDh8+LDs27dPLrnkkmLLO3XqJOvWrfNauwAAgPdo8b6729xtfn9p/UtyJP0IuwPwklCxiGPHjpmfCQkJxZZXq1ZNVqxYUerzMjMzza1oyqyiP1VYWJhERUXJ6dOnJTv7r8wSERER5qaTxYtOFI+MjJTw8HBJTU01RXxsoqOjJTQ0tNhrq0qVKklwcLCkpBSvLhobG2uer69flKbyysnJkfT09MJl+vyYmBjJysqSjIyMwuUhISHm9c9cT9aJ/cR7j88TxwiO5YHy/TSg9gD5LPoz2XZ8mzy98ml5uefLPrFOwWHBMnvHbNl6cKsMbzpcmlVtFtD7iXVKs+R+ys/PF7vlW8RPP/2ka5W/ePHiYsvvvvvu/GbNmpX6vPHjx5vnlXUbPXq0eaz+LLpcn6v69OlTbPmUKVPM8hYtWhRbvnDhQrM8Nja22PKtW7fmJyUlnfV/dZneV3SZPlfpaxVdrv9L6f8uulzbVtJ6sk7sJ957fJ44RnAsD8Tvp3OfPjf/s62f+cQ6tRjZIr/lOy3zY1rGcB4RAO+9rX66Tvv27Stcj/JYpkCeRuka2X344YcycuTIwuUjRowwE7m/+eYbu3ss6tWrZ4ZV2Yp8EJVzpUFxRcg6V0+4csd+4r3H56mkY8Rrm16TGTtmSGJ0oszoOUNiw2M9fnU/PSNdpm2bJu9ue1dyQ3KlcqXK0jqutSzdt9TU3dCifgObDpR/tP+HVAmuwnGPXhjx9e9cfWzlypX9r/J2+/btpVWrVjJt2jTzt270WrVqyb/+9S8ZN26cy6sHAgAA68jIyZCr5l0le1P2yojzRsgjHR/x6P/femyrPLriUVNbQ/Ws39O0oVpUNdl+crtM3jRZvt37rbkvNChUhjQZIrddcJvUrFTTo+0EHOHIubOlAov58+fL0KFD5YUXXjCTuCdMmCBLly416WbPnHtRGgILAAD815pDa2T016PN7+/2f1faJLbxSEDz2ubX5J1t75iUt1UiqsjDHR+Wvg36msxVRek8kFc3viorDhTMDw0PDpfh5w2Xm1vdLAlRpZ/LpGeny66kXSZo2ZG0Q3ac2iFJmUnSq34vubLJlRIfEe/29URgSvbXwELNnTtXXnnlFZMlSnsvnnzySWnSpIndzyewAADAv41fOV4+2f6JNIpvJLOvmC3hIeFu+1+bjmwyvRS7k3cX1tZ48OIHTVXwsmw4vEFe2fiKrD+83vytKXOvbX6tjDxvpBzLOGYCBxNEnCoIIg6kHij1tSJCIqRfw37yf83+T86vdr54Ulp2mlQKq+TR/wnP8uvAwlkEFgAA+De9kj947mA5nnFcrmpylVzR+AoTZGhPwpk9CBWlPQja8/D+z++buRM63ElravSo38Pu19BTsFWHVsmrG16VrcfLL/arwcq5lc+Vc+LPMT/V7O2z5ZcTvxQ+plW1VmYYWN+GfSUyNFLcaeqWqTJp4yS54fwbZEy7MW79X/AeAgsXbRwAAGBNC3cvlPuW3ldsmQ4XahTXSM6pfI75qcGG3urE1JGQ4JCzXkMreidnJktKVookZ/3182TGSZnx0wzZn7rfPG5Q40Fy/0X3V3g4kgYYS/YtkUmbJslvJ38zAUTjyo2lcXzjgp+VG5tAokpklRKfu/noZvno14/k691fmzbb1nXouUNl2HnDpF5svQq1q6z2vr75dZm8eXLhfJF5Q+ZJvTjX/h/4BgILF20cAABgTXry+/FvH8t3+76T3Um7yxxKFBYcJg3iGkhceJwJHGxBxOmc02X+jxrRNWT8JeOlS90uLmtzek56hYcWHT99XD79/VOZ9essOZR2yCzTLFSd63SWkc1GSpc6XZzusdE2ak/NlC1TCrfB4fTDMvCcgfJMl2ecem34JgILF20cAADgHzRI2JO8x0yALnrTuRGZuX+l7yxJTFiMSV2rgYftp/Yi3NjyxmIpbX1Fbl6uLNu/zPRirDy4snC5BhhPdnrSDNuqaFDx0oaXZNrWguyc97a/V9rXbC8jPx9pAphPBn0i51YpGKIF/0Fg4aKNAwAA/JtmcTqYetAEGdpboEFDXEScxIUV/NTeg9DgULEqDaZm/jpTZv4yU7LysswwKw0uLqt7mcNBxfPrnjdDwJROUNfJ5mrs4rEmja5mqHqp+0tuWQ94D4GFizYOAACAP9A6Gvcvu7+wxoYGBWPbjTUZpewJKp5d86x88MsH5m+dpK4pcm1+P/m7DJ031Exi/2jgR3J+gmczUzkyqV+HcWkP1N1t75bgoGBvN8nvzp3ZogAAAH6uSZUm5qTf1sug2az+74v/MwFHeT06T/3wlAkqdLjTvy/5d7GgQunwpwHnDDC/64m7s/5I+0OuX3C9SeN7KuOUuCot8LD5w0zvzVtb35KJGya65HVRHIEFAABAANDeCR3C9L+e/zNDojSo0ODig58/ML0SJQUVT6x6Qmb9NssEFTqE6qqmV5X42ne0vsNkh9LCf1qjo6Jy8nLkgWUPyIYjG2Tu73Nl8GeD5ds9BdXKK0LX4a0tb8kNC28wE9oToxLN8re3vi2fbv+0wq+LkhFYAAAABBCdXzFn0BwzmVsnrj+z5hm567u7TFapohPAtcdgzvY5ZsjQf7r8RwafO7jU19RUs0OaDDG/a+G/ipZJe+PHN0xQoXNbNN3uiYwTMnbJWJM6WH93hK7PHd/eIS9veFly83NN8cJ5V86T2y64zdyvQZNWaofrEFgAAAAEGM0MNbnnZNODER4cLkv3L5Wr5l1lehy012DcinEyb8c8CQkKkf92+a9JJ1sePWHX19Jq4qsOrnK4TXqS/8bmN8zvj3V8TGZdMUtuaXWLaYPWJbnysyvlq91f2f1aOvRpxcEVEhkSKY9f+rhZDw1Y7rzwTlOpPCc/xwQtmo4YrkHlbQAAgACmRfl0+JFtYnfTKk3NMh3a9FzX56R3g952v9Z/1/xX3vv5PTOB+8MBH9pdN0N7I4bNGyZHTh+RK8+9Up7o9EThfduObzO9J7b5INqecR3GSUJUwlmvoz0t2uuhBfx0Mrn2ejzf9Xkzx6SojJwMGf31aPnx6I9SP7a+vH/5+1I5srLd6xlIkpm8DQAAAHtoIKFBwP81+z/ztwkqgkPlxW4vOhRUqJtb3SxRoVEmGNDihPbQYVOPLH/EBBVaCV17UYrSIGXmgJmmR0SDnW/2fCNDPhsiC3ctLDbk6kj6Ebnlm1vktc2vmaBiaJOh8uHAD88KKlRkaKRM7D5RaleqLXtT9sqYJWMkO7egajkqjh4LAAAAGEv3LTWZkzR7VKc6nSq0VV7Z8IqpzH1u5XNl9hWzJSQ4pMzHv7vtXVMjQ4dRfTDgAzmv6nmlPvbn4z+b3otfT/5q/u5Zv6c80vER+eXEL/Lw9w/LycyTEh0aLY9e8qhdw7e0F+S6BddJWnaaDGo8SJ7q9JTT1cn9DXUsXLRxAAAA4Hi9iP5z+ktKdoo82+XZwlS0Jdl2bJv8bcHfzLyORzo8IiOajSj39bVnYeqWqfLmj2+aeRJalyI1O9Xc16xqM3n+suelYXxDu9ur80ruXHSnmeD9z7b/NL0u+AtDoQAAAOAV8RHxckPLG8zvkzdNluy8kocYpWalyn3L7jNBhQ65OrM+RmnCQsLk9gtvN3U5NJCwBRU6lOu9y99zKKhQ2jNjG36l9S2+3v21uMvpnNPy5KonZdDcQSbTldYT0WFjug38AUOhAAAA4FLp2enS/5P+ZlK2ZmTS+Q5F6dwInTC+YPcCM89BM0BpQOIoDVo++/0z8xqX1rnUqTbbJp5rvY9pfadJq+qtxJX2peyTsYvHFg7jKkrnpbSq1kouTLxQLqx+obRObC1x4b4xsoahUC7aOAAAAKgY29yJWpVqyedXfi7hIeGF92lxusdWPmZSyb7T7x1zQu1tmlHq7sV3y7L9yyQhMsFMaK8VU8slr738wHITSCVnJZvihPe0v8dUGN94ZKNsPrpZUrJSznqOzlGxBRoda3WUGpVqiDcQWLho4wAAAKBitPje5Z9cbrI16VAjnRCudp7aKSM+HyEZuRk+N6dBJ3GPWjDKZMbSbFLv9ntXYsJjnKr8PXXLVJm0cZLJVHVBtQtMtq2alWoWe4xuk01HNxUGGnuS9xR7nUc7Pmr3UDFXI7Bw0cYBAABAxc36dZY8+cOTpgfgy6Ffmire13x5jcnGpFfh3+j9hlnmSw6lHjJtPHb6mLSv0V7+1e5f0rJaS4ezRaVmpcq45eMK0+4OazqsoCBhkZ6bsqqGa4Cx6cgmE3BoYFFS2lxPILBw0cYBAABAxekciEGfDpL9qftlTNsxcijtkElnq8OB5gyaYyqA+6Ktx7bKjQtvNL0qqnnV5jKy2Ujp36i/mQ9Rnp2ndso/F/9TdifvlrDgMJMS98x5JlZBYOGijQMAAADnzN8xXx5e/rBEhkQWnqi/0esNpydbu9uvJ36V6dumy8LdCwszW8WGxcrgcwebYUlazK8ki/YsMuubnpMuNaJryEvdXnL5RHBPIrBw0cYBAACA85Oir5p3lexI2mH+vqnlTTK23VjLbNaTGSdl7u9zTU/LgdQDhcs71Oxg6m50q9fN9Erk5uXKpE2TzJwKdVHNi0xNjYSoBLEyAgsXbRwAAAA4b/HexSbjkmY4ervf2+ZE3Gp0kvXKgytl5i8zZen+pWYytkqMSpSrml5l5kTo/WpUi1EmeAoNDhWrI7Bw0cYBAACAa+w4tUPqxNSRyNBIy2/Sg6kHZfZvs2XO9jmmVodNVGiUqduhczH8BYGFizYOAAAAUJrs3Gz5du+3JvtVek66PHHpE3Je1fP8aoMRWLho4wAAAACBLNmBc2ffShwMAAAAwJIILAAAAAA4jcACAAAAgNMILAAAAAA4jcACAAAAgNMILAAAAAA4jcACAAAAgNMILAAAAAA4LVQCTH5+fmGxDwAAAACls50z286hyxJwgUVKSor5Wa9ePW83BQAAALDMObRW4C5LUL494YcfycvLk4MHD0psbKwEBQV5JerToGbfvn3llkWHb2HfWRf7zrrYd9bFvrMu9p01JbvpHFNDBQ0qateuLcHBZc+iCLgeC90gdevW9XYzzA4nsLAm9p11se+si31nXew762LfWVOcG84xy+upsGHyNgAAAACnEVgAAAAAcBqBhYdFRETI+PHjzU9YC/vOuth31sW+sy72nXWx76wpwgfOMQNu8jYAAAAA16PHAgAAAIDTCCwAAAAAOI3AAgAAAIDTCCwAAAAAOI3AAgAAAIDTCCwAAAAAOI3AAgAAAIDTCCwAAAAAOI3AAgAAAIDTCCwAAAAAOI3AAgAAAIDTCCwAAAAAOC1UAkxeXp4cPHhQYmNjJSgoyNvNAQAAAHxWfn6+pKSkSO3atSU4uOw+iYALLDSoqFevnrebAQAAAFjGvn37pG7dumU+JuACC+2psG2cuLg4bzcHAAAA8FnJycnmorztHLosARdY2IY/aVBBYAEAAACUz54pBEzeBgAAAOA0AgsAAAAATiOwAAAAAOC0gJtjYW9K2qysLAlEYWFhEhIS4u1mAAAAwGIILM6gAcWuXbtMcBGoKleuLDVr1qTOBwAAAOxGYHFGAZBDhw6ZK/aaVqu8IiD+uP7p6ely5MgR83etWrW83SQAAABYBIFFETk5OebEWisLRkdHSyCKiooyPzW4SExMZFgUAAAA7EJgUURubq75GR4eLoHMFlRlZ2cTWACAG3uJT+ectvvxUaFRDFEF4NMILCpYAMSfBfr6A4AngopRC0bJpqOb7H5Om8Q2Mr3fdI7RAHxWYE0iAADAB2hPhSNBhdp4ZKNDPRwA4Gn0WAAA4EVLhi8xw5xKo8FEt1ndPNomAKgIAgsAALxIg4rosMBMGALAvzAUyg/s3r1bxo4dW6yon2a3GjNmjOzfv9+rbQMAAEBgILDwA0uXLpWPP/64WDarH3/8UV555RWJi4vzatsAAAAQGBgK5cJUgK7kSFrBTZs2yYUXXlhs2YYNG6RRo0YEFgAAAPAIAosyaFDR4YMO4g2rr1lt95jbzZs3y6WXXlps2caNG6V169bm97S0NHn11VfN7y1btpSBAwe6ocUAAABlX7DNy7P/gm1wMLVbrIbAwg9oYHHHHXecFVgMGjSo8IN86tQp+fXXX2Xr1q0EFgAAwKP0XGT9huGSlLTB7ufEx7eTdm1nUrvFQggsyhmOpD0H3lBW6sGidHL2iRMnpFWrVoXLDh8+bAKLRx991PwdExMjzz77rMyePVvmzp3rtjYDAACURHsqHAkqVFLSevO8kBCyplkFgUUZdI6Dr6cATE5OLvZ3Tk6O3HXXXZKXl3fWvAsAAABv69J5dZnBQm5uuny/3DtD0eEcAguLa9KkiTRt2lSGDBkiXbp0MZO269evL5UrV5YGDRp4u3kAAADFaFBBL4R/IrCwuLCwMFm9erV89tlnpoflySefNMOjrr76am83DQAAAKVkHHUkA6hVEFj4Ae2duP766wv/rlGjhrRr167YYyZMmGB6M7Zt22bmW1xzzTWmZwMAAACezzi62oEMoFZBYBEgkpKSpG7duuamGaJ0LgYAAADgKgQWAeLxxx/3dhMAAADgx4K93QAAAAAA1kePBQAAcOskVXv440RWINAQWABAEZwMAa75HI1aMEo2Hd1k93PaJLaR6f2mE1wAFkZgAQB/4mQIcA3tqXAkqFAbj2w0z/O3LDlAICGwAIA/cTIEuN6S4UvMMKeyPnfdZnVj0wN+gMACAErAyRDgGhpU0AsBBAYCCwAoASdDgOfZO9mbid6AbyKwAAAAPsHeIVFM9AZ8E4EFAADwGu190EBBJ2/bi4ne8ASyBDqOwMIPJCcny8qVK6VXr14SGlqwS3Nzc+Wbb76Rjh07SuXKlb3dRAAASqS1KzTNrD3DoJjoDU8hS2DFEFj4gW+//VZuvPFGOXXqVOGyX375Rfr37y8HDhwgsAAA+HxwwQRv+BKyBFYMgUU50Wpenv1VQ10pONj+CqSbN2+W1q1bF3v8xo0bpXr16lK7dm03thIAAAT6+Y8j5yxWRJZA+xFYlEE/VEuWthJv6NZ1i4SE2FckaNOmTdKmTZtiyzSw0GCjqD179khsbKxUrVrVobZwgAEAIDDod/76DcMlKWmD3c+Jj28n7drO9NvggiyB9iOw8AMaWAwZMuSswKJt27bm9127dsk111wjhw4dkhMnTsjNN98sEyZMsOu1OcAg8ORLeJDOU0qX3ODSH6X3hwflS1a+J9sGAO6/qOpIUKGSktab59l7QRT+i8CinK497Tnw1v+2h86r2Lt3b7HeiYyMDFmzZo3cdNNN5u/t27fLxIkT5eKLLzbBRdOmTWXcuHGSkJBQ7utzgEEg0UD67sRMOSciT9as7FDu45+rK7IzM9g8DwD8TZfOq8sMFvQCy/fLyz9WInAQWJRBu/R8Pfret2+f+ZmYmFi4bNKkSZKWllYYbPTp06fwPs0aVa1atQpN6OYAA3+ngbQGFY7QxxeMRa7ktnYBgDfoOZCvnwfBt1gqsDh58qS8+eabJrWqniB37txZbr/9domMjJRA1aBBA4mJiZGHH37YDIdasmSJLFq0SCIiIqR58+bFHqvDoEaOHCnvvfeehISEOPy/OMAgkLTvsERiIkvv1UvNOC7rVttXzAsAgEBQxghi36J1GXSCsg79GT16tAwbNswEGX379pWcnBwJVHFxcfL5559Ldna2zJ492wRbTz/9tFx//fWFNS1sE7cHDRokzzzzjHTq1MmrbQasIDgkqjCYLumm9wMAAAv2WOgV9i1btpisRjbNmjUzwcbatWvlkksukUDVtWtXcyuq6GTurVu3mqDiySeflPDwcDPZW7ddIPf0AAAAIEADC1U0qFDx8fGFk5VRug0bNpiejeeff75w2Zw5c6Rx48ZsNgAAAAReYHEmHdZTo0YN6dCh9IwEmZmZ5maTnJwsgWbUqFHmBgAAAEigz7E40+TJk+Wdd96RGTNmSHR0dJnBh/Zs2G716tXzaDsBAACAQGDJwOKtt96SsWPHyocffii9e/cu87EPPfSQJCUlFd5s6VkBAAAABPBQqGnTpskdd9xhUqZeddVV5T5e067qDQAAAID7WCqwePfdd+Xvf/+7CSo03ay7BHoV3UBffwAArPBdfTpHi3PaJyo0yhT+hdv2iIQHFVQjzy1jPJDeHx6UL1l+eqplmcBC61fceOONpmL0xIkTzc3mwQcflIEDBzr9P2xF47KysiQqKnBz1Kenp5ufYWFhOv3d280BAMCy3BEA6GuOWjBKNh3dZPfrtklsI9P7TSe4cAPdH3cnZso5EXmyZmXpCYVsnqsrsjMz2C8v5FomsNDq0kuXLi3xviZNmrjkf2hBOZ0IfvToUXNSHRxsySkoFaZvcA0qjhw5YgI4DbRyc8WS65GXZ/9BPDiYqzgAAPd8H7kjANBAxZHXVBuPbDTPiw4rPeENKkbPOTSocIQ+vuBcpZL4E8sEFnrSr1Wl3Uk/xLVq1ZJdu3aZStWBSoOKmjVrilUP4us3DJekpA12Pyc+vp20azuTqzgAAJfyRACwZPgS08tRVhu6zermUBtQce07LJGYyIRS70/NOC7rVvvv/rBMYOEpWplae0B0OFQg0p4a25AwK9Lo35GgQiUlrTfPCwnhKg4AwHU940XH0y8ZvtQtAYC+ZqD3QvjSSIXgkKgyzyeCQwqGm/srAosS6BCoyMhIz+8NuFSXzqvL/HDrAf/75eWPhQQAoKI947bx9JEhkQEfALgDIxV8C4EF/JYGFfRCAAC83TPur+PpfQEjFXwLgQUAAIAbesb9fTx9RaVnp0tIGXOddURBRTBSwfsILAAAANzQM+7v4+kdUTS1qs4lycovfY6DzkvRIWRnPq88jFTwvsDKpwoAAACPc6SWhyueB++gxwIAAAAes2DoQomJqFrq/amZJ2TLmq7sEQsisAAAAIDHRIWWnSGronMs4H0EFgAsScfdOtJFrrne3ZW3HAAAEFgAsGhQMWrBKIcq2rZJbCPT+00nuAAAwE3osQC8zJcqhlqF9lQ4ElSojUc2mucFeoVaAADchcACfjXsxWrjMqkY6rwlw5eYYU6l0feNpja0EoZ5AQCsiMACfjXspaK5r72FiqHO06DCn3ohGOYFALAqAgv45bAX2/NiQyuJVVAxFIphXgAAqyKwgF8Ne7Fy7msqhiIQhnkBAPwXgQX8atiL1eZYAIE0zAu+iTk9AFyFwAIAgADFnB4ArhTs0lcDAAABMacHAM5EjwUAAAEtX8KDRBYMXShRoZGlPup0Tob0/6SfZPl+wj0AXkJgAQBAAA+FujsxU86JyLMr8YWm896ZGWyJdN4API+hUAAABCitpaNBhSP08fo8ADgTPRaAn9Irio5++QcHR0lQUJDb2gTAd7XvsERiIhNKvT8147isW016YwClI7AA/DSoWL9huCQlbXDoefHx7aRd25kEF6hQcEpgam3BIVGmnk7p95POG0DZCCwAB+pfWOXESU8GHQ0qVFLSevPcsk4uEBgqEpwSmAJAYCOwAETk++Ud/PbEqUvn1eUGChpY2bsNEBgqEpwSmAJAYCOwQMDS3gcNFPRkyJ9PnLStrm4vQ2QCS3nBKYEpAEARWCBgaa+D9j7YM4acE6e/MEQm8LgjOAUA+B8CC0igBxecMDmGITLwB/S6AYDrEVgAqDCGyMCK6HUDAPcgsABQYQyRgRXR6wYA7kFgAQBwmfTsdAnJK7/HICK4YCiit9M80+sGAK5DYAEAcIoGCjbdZnWTrPyygoB8uTsxU86JKCf68FCaZ3rdAMB1gl34WgCAAHQ6x/7q3OFB4lBQUTTNMwDAt9FjAcChoSz2VilHYFowdKHERFQt9f7UzBOyZU1X83v7DkskJjLBMmme7RnmpaJC3Td0C/4u3wTf+t7PLePSr94fHpQvWX91FgI+gcACgENDWfTL7Lm6Zz8PUFGhkRIdVnYxPZvgkCifT/fs2DCvAm0S28j0ftMJLixG97U9vW/uurii/982THDNyvIDaj0O78wM5jgMn0JgAcChoSxnPi82tBJbEH6rIp+NjUc2mueVFWDBt+hJ/agFo2TT0U3lPtZdF1d0uJ+jwwT18QXDBDkOwzcQWACo8FAWb10xrGggBLjzs6Hvy26zujKUxYJ039kTVHjq4kp5wwRTM47LutXdXP5/AWcRWACo8FAWb10xBHzxs8FQFv+wZPgSM0/GGxdX7B0mGBzCXDf4JgILH2bv1VsbJgy6bxszYdk3rxjqWPayTgAAT2Ioi39MnNahTlpnpTTZQcwtA9waWKxatUouvPBCiYpy/xd8SkqKzJ07Vw4fPiytWrWSvn37ij+qyNVbJgy6bxszYdm3rhjaEEzDVzGUxVoc7W0C4MY6FiEhIfLggw8W/p2UlCRTpkwRV9u/f78JJl599VXZsWOH3HjjjTJs2DC/zIhQkau3tgmDcN82tj0P7qUBgw45Ke9GSk/4KttQltJuej+s3dukGZm0KjwAF/dYXHzxxTJr1iz56quvpH379jJ48GB55JFHxNUeeOABqV69uqxYsULCwsJkzJgxcv7558ucOXPk6quvlkC9elswYZBJXFYfUwsA8P3eJq1nUpB6WORa6pUArgkstm3bJj/99JM5sW/SpIk89dRTcsUVV5ihSk8//bT07NlTXCk3N9cMgXr22WdNUKHOO+886dKli8yePduvAwvb1Vt4bxszxwIAAkN5E6e1SKI99UyAQORUj8WSJUtk4sSJ8ssvv0jNmjXNkKjmzZtLRESEpKenS3S0606G9+7da16zadOmxZbr36tXry71eZmZmeZmk5ycXOyn0kBF54ecPn1asrOzC5freugtLS3NBDY2kZGREh4eLqmpqZKX91fXqa5vaGhosddWlSpVkuDgYBN0FRUbG2uer69fVFxcnOTk5Eju6YL/qa+XF5EnMTExkpWVJRkZGYWP1W0eFB4kedl5kp+Tbx6bE5bjsnWKiCj4mZaWZ147JCSn1HXSk28dlqZPL/rYouuk+9BGn1/aOunr2/adXh3SbREUUnAgL2udktNSTFuVvq5ESpn7qeg2jqgcUep+0m1le93k5BTJzwp3ap3+enzBemRmFt++Je0n3b7Z2fkSFhZk1ikoKKfU954+VtsbGRlk9smZ78kz33spGQXbrVKlYLNORR9/5jrZHhv850DKM9ep6HsvIyO5cLvpY7SdZb33iu6P8Phwl3yeQqNCJT83X/Ky8go/H6Xtp/TsgudmZeUX7ucz18n23tPtoI8LDw8y/9P2WGePEbb3u9J9V/DeK/kYUfQ9rP8rukq03e89e44Rf73nk6VSpdBS10md+fkobT9JmK5TvmRkFN/GJX2e8vML1kPf90U/H86sk25P2+dD951t25W2n2zvd6XP1X1S2ntP16fodoiNrFbmMaLoY4Nzo0pdJ/2m1vevvo9t72FXfT/lhRSsU3p68f1R0udJt0XBdij+2JI+T/o+zsvMk+CIYLM+Oaf/Ol458/2UmvnXcUK3rYQV/z535ju36P4Iyol0yXduVn7Bvjx9Oq/YNitpP+n21W0bEhJU7LElrZPtfRkdHWS21ZnboOjnqfCY8mcsVN4xwvb5CP3zDLGs/ZSVVfw7Nyoquoz3XvHPR3RYFZd850ZFBZtjRMFxu+A44YrvXPMezsmT4NBgs045Rc5pzvw82fZHpB3fuUWPKcrZ8whPnMM6NOUg3wXy8vLyd+zYkf/JJ5/kjx8/Pn/IkCH5Dz/8cL4r/fjjj7pW+atWrSq2/P77789v3Lhxqc/T9phUD2XcRo8ebR6rP4su1+eqPn36FFs+ZcoUs7xFixbFli9cuNAsj42NLbZ869at+UlJSWf9X12m9xVdps9Vcz+fW2y5/i+l/7vocm1bWlZafvXB1d2yTl98MTf/20Xn5EdHB9m1Tp/Na5g/9a26Ja6Tbh9716mkfVflsipmXe1dp1cnT3DZflq9/nu3rNNNN11vtm///rF2rdO//lXNPL5Fi+Z2rZPuixMnDtn13tN9rK/9ybyZdq1Tu/ZR+cmnj561TqW99x599OEy33vNmzd3y+dJ3zMN7mlg1zr16NXNbIPrRlW2a530cfp4fZ49nydH1+nQsUOlHiPOfO/p9nPkvVfeMaJ37552r5O+D+w9Ruw/vNOBY0Rzs331fW/f5+mm/JycNPO5OvO9p8vPXCd9XV1u737S44D9772YMo8Renyydz/pe7jKZZXtWqc33vifw+ukx21710n3xzPP1rTr8xTTMia/5Tst8x9+5GG3fD/p96SrvnPtPe45+p374Lj7zDbT46U966TbVh+v29qeddJ9Z+/3U0TtCNN2e48R+r2k7yV795O+98o+7sW45TtX22j/McKx79zaN9Y272F7v5+mvlXXHOPsee/pMVOPnc6eR3jiHHbfvn2F61GeoHyHwhCREydOmMjIExmgitq5c6c0btzYzOPo06dP4fK///3vJivV5s2b7e6xqFevnuzbt89Ev77aY5F8Olk6Tu9o/v5u2HcSExFT5tWTi6ZfZK6e6GN1SI8reyyWr2hjousunVcVdg+X1mOxfsOlpsfiovYri3UlO9tj0ePjHqbHYu0NayUoJ6jUdTqVdkTWr+5hlnfqukIS4mqXup/+OP6HeV3bNq5euXqp+ykp/Ygs+65gf7Tr8J3ERia4rMdi9ZqLzdWTSzr+tX1L67FYveZS02PRvt0qCQqKLLPH4vvll5irJz26b5H09NxyeiyOm+2mPRZtL14hIXnRZfRYFDxWeyy69lwr4UGxZfRYJJl2qF49N0h0dJVS33tHTh6RbjO7Fe6PavHVXNZjcfGMi80VX9vno/Qei5OybXMPc+XrgraLzH4uvcfiuPy4oae56n1+a33dKi7rsbC9L9fetFYiQyJLvcJ6LOlY4WOXjFgiiVUSXXaVKzn5qCxZerFZpp/9SpWqlrpOp3NOyeJvLir2+Si9xyJTfljewfRY2B5bVo/FuvWXmKuRHS7+65hS0jrpV9mWrTdKZuZm83nSi9l/rWuQ2U961bhI082y3r22/rm8tB6Lgve7uqzHDxIfnVjqe++P43sLH6vrViexUanHiOPJB2XF0k6Fj61cKbHU/ZQXkifvfHmB1NMxOHask94XFtZaWl8wrTDBQek9Fqdl3Q+XmB6Lovuj5B6L4/LL1l7mqvr5rf/6fJTWY9FzTk/TY7HsqmUSkhdS5ntP919YWJ4dPRZpMuDTy838hh+uXy1xUXEu+c49mfqHfL+44Fil2yE+urpLvnOz8lNk8/rLzH5qc9Ff27fkHovjsm1zT9Nj0azlt4WPLWmdbO9L7bFo33GV6fEqq8fCHCeCRNbdtE5C80PL7bHQ47b2WPTts02ysoLK7LGwHeN799ooUVGVSz3uHTiyq9jno2ZCfZd852qPxTfftjTHbdt5ij3fua3bfSchfyZRKOk7NyMnQwZ8PsD0WCwatMgci0vvsSjYH5GRQdKx82qR7IhS33tFjynde6+VqNDKPt9joY+tXLmySc5kO3d22VCot99+22SA0iFIrVu3NmlmbT91OJS71K9f32y433//vVhgsX37djPXojS2DXsm3TBnbpzSAibdqCXRHV+S0jZ6Scv1jVLSct25IVEFB2K93zb+X98MeitKDxrBYcGmS7joY12xTra5BXrCqa995rjTom3PzQ01X2IhIVLiY3WdSlrXktap6L4Lzf5rW5S3TnkhsaatttctaZ2Ktr3oNtZ9ceY6Ff9AF7xuXFysxEbGObVOZ27fiIiSt2/R/aTbV08YbOtU0hhgW1v0sbb26j4p770XFJ5V+HhdJ9v6lbRORR9b0jrZ6D4KD88vfKztMWW994ruD21H0XUqbV1LW6einw8NSvW1z/x8nLmfgjKy/lweVGw/l/Te0+1gG0qj61TSNqvIMaLo+73g81T6MaLgPRxscu/rl6u+n/RtrMOWitLlujlt21Qz2dhOOMv6PP31ntf3Zhmfpxwp8tji2+3MtqdkHDMnTpUqBUlYdJiERoQW7iezXlF/tT03t+A19X1f0uej6HtP11GDioLlwYXDOIvSbVRUfHw7sy1iYkoeL6//s+j73ZxUlvl5+uv4o7+Xd4wo+thKkZVK/TzpNmsWV3IixzPXySY7e7PExISVecwueO0ss066P87cd2e+93RbFCwv+fNR9POk7+PgiCCTqjs0NFeiw4qukw6HSxd9aHh4qAkq1m8YKampP4k9XmxSkJGprGO2bXnB8NyCTH62nyV9PoKDsovtj5jIGJd85+r2LVgeXOI2K/p50u2r29bWhpKOKSUds3U7xFYq/Rhx5ndoed9PRb8/SlqnoscIPZG2/zu3+OfDVd+5uv8KgumzjxNnfp5yctIKjxGDFl5R7lwZDSps61TSPMyS9kdQUJDElvH9dOZ3qLPnEZ44h3UkA6PDgYX2EFx00UWyceNGc3v//ffloYceMh/exMREeeyxx+TOO+909GXLb2hoqJkcPmPGDLn11lvN35pydtmyZWYZAASaiube1xPqdm1nej1db/9P+pX5xV7R+jFdOq8uc/KtTdEAy18yFhVcbe7g93Uh9HULAoVKZbZh/YbhkpS0weX/H9ZUkXTxFGJ1jMOBhUY4Xbt2NTebQ4cOmVSwOsH6zMnVrvTf//5XOnXqJN27dzfBjWaD6tevnwwfPtxt/xMA/Cn3vkpKWm+ea8/Jt8Ntyj1dZha1Pzt5KnRCEBta+klkUbZaEYGYscjK782YmBbSru1HZQZ7qRnHZd3qbna3wdGgQntCOlKbIiAsGLpQYiKqlvs4CrF6oY5FrVq1ZPr06dKtWzczZMldGjZsKFu3bpWPP/7YVN5+5ZVXZNCgQWY8Gvxdvjkh0ROWP0dHlHpSAwTi+728K9meuppt70mfrX5MWSfJ1I/xD/a8N+3tQQoOKT1odaYXq6K1Kcq7An46568x8vAtUaGRpPL3hcBCuxZL+uDrMu1J0FoT2nvhLlWqVDFDoRA43N2lDvjD+92bV7L1hFCv9DpyhVqHY8VEJJR5Ilm050NP0EL+nIdR3mPhW3yhl6W8XqyK1qYorzht0eF8QCBwOLB46aWXZNKkSdKmTRszYVtvF1xwgZlNrvMdevQomOkOeLNL3d+7s8s7ieIky7qs+H7X4OCVIxGml0V7Iewp6Ono/AZ3zccAHKVDY3Tc/cYjGx1+nj/TXp8iicvOQu9NYHA4sNAK1zrPQtO7Lly4UJ577jmThkolJCTIm2++6Y52AnZ1qVe0O9tqfGFyprVYcyidtd7vQaYdrpzfUNETMUfmY8A9c2l87bPkShoQT+833a6JwLqNbD2PVksUYI+iQXzBsci+4B/+y+HAQudQFB2KpG8qrTFx9OhRadGiRbn5bQF3dqlXtDvbEeUNyXDXhC+9wqvDR3Tirb1s6TQDmSeG0tn7nrD3tXzp/e5NRT8/zMfwLY7MpfHX96Y9PXNlXcgI1CxLgdB7E8hCXfHh0sJ1egMCQXlDMmy0q1yvarkquNDX0RShtlzs9pxQZ+bZd+D35y5qTwwtsvc9YQ+u6pVMT+LKCrI8MfwvkK/SV3QujbeH6cF3siz5e+8NXJgVCvB3Fbm6ouNv9aTenqta9ioomBZtV1AxasEo2XR0k12vGygns64cWuSJK25c1fMtXKW3fy6Nbw3Tgy9kWfL33hsUILAIsDHkVi8OZYUhGRpMlJcpxN20DfYGFYF0MuvKoUWOvCccwVU938JV+orNpfH3YXoASkZg4QfBgt5/b40MqRueb8nqu/42JMPX6ElvecECJ7O+857gqp5vcSTjFVfpAQQ6Ags/mXBaN1x8qvoufIcGFeUNx+JkFigLV+kBwGWBxb59+2THjh12vWC9evWYyO2lCaf7s4JkaLcfyj+J9ED1XcATwbc9E9n9fUItAMBawyvv318wimCZHyY2sCuwmDlzptx33312veA999wjL7zwgrPtQgUnnA5zYQ55wJeDivUbhktS0gZvNwUAAIeGV2b9Of/IH4eih9obLIwZM8auFwwOZtq/q1kxl315FTjdVesBgUF7KhwNKkh7iUBjz3HYn1NNA/DRwEJP/EJDmY4B11XgdFetB/iu8uoM6P2a9lZ73hzRpfPqMgNvJtQikDh6HA6UVNMAPKNC0UJKSoo8++yzMn/+fNm/f7/UqlVLevXqJY8++qhUq1bN9a2E31bgdEetB/gme+b16AmO9iwUPTkqD2kvURH6HrOveKS15ug4015/TjUNwEcDi5ycHOnataucPHlSrr/+ejNZ+48//pD3339fPv/8c9m8ebPExMS4p7XwmwqcvlDrwRe+5D1RLdiddBhFSHZ6mSdvsXFtJCV5o92vqUkLCiZlVxKrsHc/UjvGNzhaQNJfj8OKVNMAvBpYLFiwQJKTk00AERcXV7j83nvvlc6dO8uHH34ot9xyi0sbCf+rwBkoygueig5DcOQqva/o/0k/O4a85UvLqq3lzd5vlvmotKyTsm1dP7Eie7Os+VLtGHuGpvmS8trjSPavihSQ1GGbVruib89xmFTTALwaWOzatUt69OhRLKhQERER0r9/f3M/4MoCgFZLF6onH3oSosO8HKEnO7Ghvn+V3vGTqyDZemK7XDqzu1+N9dbeBw0UtB6MFWvHWC3ltLvaa08BSUWiCQBwQ2BRp04dmTp1qmRlZUl4+F9V2fLy8mTZsmUyfPhwR18SAcjRAoBWolejdUK6PWOdUzNPyJY1XcVKil5t15Oy8k6Sr194vfxy4heH/ocVrgzrdtDeB3tqafhK7ZiKBEP6eH2eVdrraPYvewpIAgDcFFgMGDBAHnzwQenSpYuMHj3aBBqHDx+Wd999V3755RcZMWKEoy+JAFSRAoBWSheqJ532nKz42nATR+k6lhdYzBo4y64gy4pjvbWdvtD74I5gyBfmhTjSXrJ/wZnUu1abpH9mr35Z3yUVzbgHeCSwiIyMND0T48aNM7djx45JlSpVpHfv3vL2229LQkLphdzg7wfmDLcXALzWIiecqECQRQkcj7BiMGRPe32xno+vn3D6w8UNV6dAt6J1q7u5JeOeVZKE+PN7OCACi0OHDkl2drYJIlRmZqaZXwH/5MiBuaJj5K1YABAAfPEkq+gFHntOOP1ZRXohrDJJX3sSNVBwpOffihn37EkS4s4kKPp69vSYWm0uqE8FFjNnzjS1K1544QXzN0GFf6to97AVDswA4H+Z2HT+mmMnnN6eS+MrqXfdPUnflZnNtI2vHIkwCVB0rltZvcKpGcctF2RW9BzClUlQNKhYv2G4JCVtcMnrBQqHA4v69evLkiVL3NMaWPrAbMUx8lbjr0W9ALjqJKvghPOixIIUz/Yei/29xoovpEB3fQKHIDNEuLwiocEh6Xb3ePnK0CJHkoS4KwmK9lQ4GlTstNBcUJ8JLK644gr573//Ky+//LJce+21Ur16dfe0DJY7MDNG3r0CpagXAOcysSnS4/oGT2Q2c3XtIV+rr1RekhBPBEJdOq8usw3MBXUisJg4caKsWbPG3MaOHXvW/ffcc0/hMCkArhMoRb0AOJeJDb7DFzKbOfMdYJX6Su5WXq8Qc0GdCCy0TsWFF15Y6v0NGjRw9CUBOIiiXgBgDd7ObOZoj5cV6yvBwoFFpUqVpGXLllKzZs2z7jt+/LjJGAXAvSjqBQBwR4+XL8yxgHU5nDl+2rRppQ51Kus+AAAAAP7L4R6LsqSkpEhMTIwrXxIAAAABypVpeuFDgcWiRYvk008/lU2bNklaWpr84x//KHa/Lvvss89k+vTp7mgnAAAAAozr0/TCJwKL1NRUUxjv1KlTptq2/l5UXFycPP/88yYdLQDA+qibAsAbfC1NL9wQWAwePNjcvv76a0lKSpJhw4Y58G/gKfYURmNiFoDyUDcF8D/2fP/7wtAiX0jTCw/NsejTp08F/xU8QT9c5fG14jcAfA91UwD/Y6VhRd5O0wsPTt6eP3++yf60a9cuycrKKnbfHXfcIY899lgFm4OKph7VQmgbj2x0+LkUvwFQHuqmAIE1rEgxtAgeCSx++uknufrqq+XWW2+VG264QcLCword36JFiwo1BM5F9dP7TbdrGJSi+A0AR1A3xXHlHY9P52TwJoTPDStSDC2CRwOL5cuXm7kWr776qlP/GK4/cGjhG3swxwIAvDssteiQVMBXhhUphhbBo4FFQkICtSqAcujcFXuuDvnCJDkEFjI9+d6wVH0eAARkYNG9e3d55JFH5Oeff5bmzZu7p1WAxU/c1m8YLklJG7zdFKAYMj35zrBU7Tles7JD4fMAx4bScVEKfhJYfPPNN6YYXuvWraVVq1YSGxtb7P4RI0bI7bff7so2ApaiPRWOBhVMkoMnkOnJd4al5gZ7oDHw6wyPgF8EFnXr1pVRo0aVen+jRo2cbRPgN7p0Xl3muFYmycFbyPQEWH8onT6eoXSwdGDRqVMnc/NmV/7u3bslNDRU6tSpI8HBXPaB79KgoqzAgkly8BYyPQHWzvBo+xwzlA6Wr2OhDhw4IIsWLZL9+/dLrVq1pGvXrnLOOeeIO7344osyYcIEiYiIkIyMDImMjJTXX3+don0AACCgMjwCfhNYTJ48We69915Tw6J27dpy+PBhSU1NNZO63VUcLzc3Vw4dOiTr16+XmjVrmp6Lhx9+WK666irZsWOHJCYmuuX/AgAAACifw+OIfvvtNxk7dqxMmjRJTpw4YbJDHT9+XGbOnCnPPvusrFixQtwhJCTEVPvWoMIW1f/zn/80AY0GGwAAwDN0uI7OESvtRtaiQNnPFHqEkz0WixcvliFDhshNN91UuExP8q+88kq55ZZb5Ntvv/XYHIxNmzaZnw0bNhR/qm2gqG8AAPBVZC0KDBR6hNsDCz05dtVEIZ2EfezYsTIfoyltdU7FmU6ePCn/+Mc/TEBTVj2NzMxMc7NJTk4Wb9GgYsnSVl77/wAAVBRZiwIDhR7h0cCiR48eMmbMGHnvvffkmmuuKczKNG/ePJk6dap8/fXXdr/W9OnTZf78+WU+Zu7cuSbFbVEpKSkyYMAAqVy5skybNq3M5z/zzDPy+OOPi1VR36DiKDAEBCY+++5B1qLAQKFHeDSwaNq0qcnOdOutt5oeA80IdeTIEXOyP27cOIeGQY0fP97cHKFzKi6//HKTFUqzUsXHx5f5+Iceekj+9a9/FeuxqFevnnhDcHCUdOu6xa7HUt/Ad7vqtWKuM/cDcC+G6bgPWYsCA4Ue4dGsUHfeeaeZZ6HzKWzpZrt16+b2dLNa8VuDCv2p/7tKlSrlPkeHUZU0lMpbH9SyahoURX0D3+2q/355hwq2DiAwdReG6QQenTgckl36hRwmFgMWqmOhxemuv/568ZScnBwz/OnXX3+VGTNmyM6dO83NNnm7WrVqHmuLP/G3A7O7uuq1tyk+vp0kJdmfgUwfr88DiiIwdQ+G6QSe/p/0k6z80o/d4UH58lzxkdSQQB8Gaa1zmoCZvD106FB58MEHpUOHv67cbt++Xe644w758ssvTX0LV9NeCh0GpcOYtH5FUY8++qgMHjzY5f8zEPjjgdkdXfX6mu3azrQ7q5fSoIKKqLC9FwhM3Y9hOv7Pkd5lVzwP1kIWKwsGFjoEKT09vVhQoZo0aWKGRH388cdmUrer6VyKdevWufx1AxEHZvcPZQPOfO8QmALOK3qxZsnwJWUek3W+25qVBecqXOTxX2SxsnhgoUORGjRoUOJ9ulwL5sG3cWD2TWSy8W8EpoBrac90mYGFwyWAYUVksbJ4YKETtCdOnGiyMkVGRhYuz83Nla+++spki4J1cGD2HWSyAQDAcWSx8h0Ox/N9+vQxcyh69eolc+bMMcOTtIaFZtYLhzkAABELSURBVGs6cOCADB8+3D0tBfy4C9cRjmaxAgAA8Mkei9DQUNMzcdttt5kgIi8vz0SKXbp0MXUl4uLi3NNSP6AT3+3NVuRIViNYF5lsAABAQKeb1cxMmv1Ji+JpcbyEhARTBRvlBwsdPqAGAoojkw0Q2Ci6CUACvY6Fio2NNTe4D8NeAMC/UdsEgL9wKrCAY3Rc/OprVjv8HNLkAYB/obYJAH9EYOFBDHkBANi+D6htAsDfEFgAAOAF1DYB4G8ILOCVyYh5uWS9AgAA8CcEFgHOXdlImIwIAAAC7fzndDnlAvy9nACBRYBzZQBQkcmIOzODpXWeSEh2esB+CAEAgGe56wJot1ndJJARWAQgd2UjcWQyYnp2uvnwZeWLvPJxd7vbAQAA4EvnP5rBU8sDbDyyUQK9nACBRQByZzYSeycjxgRHyfnV2/IhBAAAlj7/0fun95vu0AgLfy0nQGARoLydjYQPIQAA8JfzH0oKFCCwgNfwIQQAAPAfwd5uAAAAAADro8cCAPyAPWN7ybAGAHAnAgsA8AOBnuIQAOB9DIUCAIuypTh0lL+mOQQAeBc9FgBgURXJrubPaQ4BAN5FYAEAFkZ2NQCAr2AoFAAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnUXkbgM/JzU136n4AAOB5BBYAfM73yzt4uwkAAMBBDIUC4BOCg6MkPr6dQ8/Rx+vzAACA99FjAcAnBAUFSbu2MyUv77Tdz9GgQp8HAAC8j8ACgM/QICEkJNrbzQBgAczFAnwPgQUAALAc5mIBvofAAkAxXAUE4OtzsZKS1tv9HOZiAZ5DYAGgGK4CAvBVzMUCfBuBBQCuAgKwDOZiAb7LsoHFs88+Ky+88IL8/e9/l6eeesrbzQEsjauAAAAgIAOLlStXypQpUyQ+Pl5SU1O93RzAL3AVEAAABFSBvJMnT8rf/vY3efvttyU2NtbbzQEAAABgxcDi5ptvlpEjR0rXrl293RQAAAAAVhwKNXnyZNmzZ4989NFHdj8nMzPT3GySk5Pd1DoAAAAgcHm1x+Kuu+6SatWqlXnbtWuXeezWrVvl0Ucflffff1/CwsLs/h/PPPOMmYthu9WrV8+NawQAAAAEJq/2WOhJ//jx48t8TNWqVc3PJUuWSEpKinTq1KnwvlOnTsmvv/4q7733nhw+fFhCQkLOev5DDz0k//rXv4r1WBBcAAAAAH4UWMTExJibI3MriurevbsJNDTdbElBhYqIiDA3AAAAAO5jmTkWkZGR5laUBhO6TIdMAQAAAPAey2WFAgAAAOB7LNNjURKdd+HIRG4AAAAA7mHpwKJy5crebgIAAAAAhkIBAAAAcAXmWAAAAABwGoEFAAAAAKcRWAAAAAAI7MnbAAAA8L7c3HSn7od/ILAAAACAU75f3oEtCIZCAQAAwHHBwVESH9/Ooefo4/V58E/0WAAAAMBhQUFB0q7tTMnLO233czSo0OfBPxFYAAAAoEI0SAgJiWbrwSArFAAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcBqBBQAAAACnEVgAAAAAcFqo8y8BANZwOue0U/cDAIDSEVgACBjdZnXzdhMAAPBbDIUC4NeiQqOkTWIbh56jj9fnAQAA+9FjAcCvBQUFyfR+0x0a5qRBhT4PAADYj8ACgN/TICE6LNrbzQAAwK8xFAoAAABA4PVY5OXlyezZs+W7776T6OhoueGGG+SCCy7wdrMAAACAgGapHousrCzp37+/PPTQQ3LeeeeZ26233iqbN2/2dtMAAACAgGapHovnn39e1qxZIz///LPUrFnTLLvpppskLS3N200DAAAAApqleiymTJki1113XWFQocLCwqRy5cpebRcAAAAQ6CzTY3Hq1CnZs2ePXHTRRTJp0iRZv3691K5dW6699lpp0aJFqc/LzMw0N5vk5GQPtRgAAAAIHF4NLF577TVZunRpmY+ZOHGi1KhRQ1JTU83fjzzyiPTs2VO6dOkiq1evlgsvvFA+//xz6dOnT4nPf+aZZ+Txxx93S/sBAAAAFAjKz8/PFy/RwGDXrl1lPmbgwIESExMjKSkpEhcXZ/6eP39+4f3Dhg2TQ4cOyfLly+3qsUhKSpL69evLvn37zOsBAAAAKJmO9qlXr54ZPRQfHy8+22PRoUMHc7NHbGysNGrUSJo1a1Zsuf6tw6JKExERYW5nDoXSDQQAAACgfHqRv7zAwqs9Fo7SIU0ff/yxrF27VqKioiQjI0MuvfRSE1x88MEHdtfBOHjwoAlUtBqvt6I+ekysh31nXew762LfWRf7zrrYd9aU7KZzTA0VNKjQuc3BwcH+MXlbPfDAAyaoaNq0qbRp08bUr6hevbpMmDDB7tfQDVK3bl3xNt3hDMWyJvaddbHvrIt9Z13sO+ti31lTnBvOMcvrqbBkYBEZGWkmam/cuNFkiNK5EhpgeKPnAQAAAIBFAwsbDSb0BgAAAMA3WKpAnj/QieTjx48vNqEc1sC+sy72nXWx76yLfWdd7DtrivCBc0xLTd4GAAAA4JvosQAAAADgNAILAAAAAE4jsAAAAAAQmFmhrGzXrl1y4sQJU9SvUqVK3m4OHPDrr7/K0aNHpXPnzmw3C9GiPr///rsp7FOjRg1vNwcO7rvt27dLYmKiT9QfguNWrVol4eHh0q5dOzafj9PiwTt37iy2TNP5d+rUyWttgmNyc3Pl559/lujoaDnnnHPEGwgsPFgN8aqrrpIffvjBnODoB3jy5Mly3XXXeaoJqKA5c+aYIoz6YT158qRkZ2dLaCgfHV+nX5D333+/LFq0SBo2bCg7duwwX5AzZsyQatWqebt5KIMG8Pfee6988cUX0qBBA7MvmzRpIh999JHXvizhOP2Ou+uuu6RRo0YmuIdvmzVrlowbN65YOn/9rluyZIlX2wX7fPLJJ3LnnXeaoEJvNWvWlA8//NDj33cMhfKQe+65x5RY37t3r7ny/dJLL8lNN90kv/32m6eagAr66aef5L///a/5koR1aCDxf//3f6aHUItq7t69Ww4cOCB33HGHt5uGcuzfv18GDx5sAoz169ebCzF61Zt9Zx0//vijPPPMM3LDDTd4uylwgAaBy5cvL7wRVFjDsmXLZNiwYfL000+b774tW7aYIPHw4cMebwuBhQdkZmbKBx98YK7cVKlSxSwbPXq06d5/9913PdEEOOHRRx9l+JMF9e7d2/QSale+qlq1qowYMcJ8WcK36RXToUOHFu67qKgo6dChgwkM4fvS09Nl5MiR8uqrr0qtWrW83Rw4OJRGg0LtodfeeVjDv//9b+nVq5e5YG3TrVs3Of/88z3eFgILD9AeCj3QFh1jql+Y7du3N1dSAXjG2rVr5dxzz2VzW4QeH/WK6aRJk8wQNi38BN+nF9F0LtqQIUO83RRU4HxFg8K+fftK9erVZcqUKWxDC1y8Xr58uVxxxRVmXpr28h46dMhr7WGguAfoUAyVkJBQbLn+rVcFALifjs+fN2+eLFy4kM1tETpkVI+ROmS0Z8+e0qVLF283CXZ8zvQkZ8OGDWwri7ngggvMZ8128UWDittuu00aN24sPXr08HbzUIpjx46Z3iXtaTrvvPPM3Aqd03TJJZeY0TJnnnu6Gz0WHhAWFmZ+ZmRkFFt++vRpM24YgHt9/fXXZqz3iy++KH369GFzW4QOFdVeJh0Cpb2+AwcO9HaTUIZTp06ZE9Hbb7/d9DZpgKFzC/W7T3+3XWSDb9LgoWiP7i233CJt27aVmTNnerVdsO8c85tvvpFNmzaZoF4zkOpNk2B4GoGFB2hWE3Xm+GD9u379+p5oAhCw9GCrQzKeeuopGTt2rLebgwqIiYkxJ6vr1q0zV+fgmzSAaNWqlcyePVsefPBBc1u8eLEcP37c/L5t2zZvNxEO0hTdzG3ybdWqVTPlC3Rems7dVdpLoZO5v//+e4+3h8DCAzT/utat0GEYNkeOHDH5vXWCKQD30FSzml1IJ7Z548oNKiYtLe2sZdq1HxERYYIM+CYdglE0o5DeRo0aJXXq1DG/M5TNWp+7pKQkWbNmjbRs2dJrbUL5goODzbnkmQGgZtfTeTKexhwLD9G0e1dffbWpYaEf0ueee87M1td0mPBtekLzxx9/mEltasWKFRISEmKuzMXHx3u7eSiF1owZNGiQXH755XLppZcWywZFkUPfpr1LepVbs5zoZ0z3pR4z9ap3ZGSkt5sH+CU9VupcJk0so8PadOio1kMYM2aMt5uGcjzxxBOmTpMmuNCfq1evNvMrtDaJpwXl5+fne/y/BvCQDJ0MpeNM9YP7wAMPFKafhe/SGhbz588/a/nLL79s9iN80/vvvy+vvfZaifeRcta36deSTgLWz50eL3U46TXXXCNdu3b1dtPgIP3O055D3Z/w/UK+//vf/2TlypWmd1AzWf7jH/+Q2NhYbzcNdti6datJeLFnzx4zzP7mm282F9U8jcACAAAAgNOYYwEAAADAaQQWAAAAAJxGYAEAAADAaQQWAAAAAJxGYAEAAADAaQQWAAAAAJxGYAEAAADAaQQWAAC32rVrl8ybN4+tDAB+jsACAOAyO3fuPKtS/dKlS+WOO+5gKwOAnyOwAAC4zHfffSd33XVXsWWNGjWSwYMHs5UBwM+FersBAAD/cODAAVm7dq2kpaXJRx99ZJa1bt1a6tevL3379i183Pbt282td+/e8uOPP5rntW/fXmrXri3Z2dmycuVK8xodO3aUqlWrnvV/9uzZI5s2bZKEhARp27atREdHe3Q9AQAlI7AAALjEoUOHZOPGjZKeni5z5841y/Sk/8SJE/LII4/IoEGDzLKvvvpKnnrqKalWrZrUqFFDUlNTTYAxadIkmTBhggkwTp48KXv37pUVK1ZIkyZNzPPy8/NlzJgx8t5775mg49ixY+Z/zpkzRy666CL2IgB4GYEFAMAltNfh1ltvNUGDrcdCvfPOO2c99vDhw/L666/LkCFDzN9XXHGF3HzzzbJgwQLp16+fWda9e3d56aWXZPLkyebvqVOnyhdffCG//fab6a1QTz/9tIwaNUp+/vln9iIAeBmBBQDA4xITEwuDCqU9ENprYQsqbMt0aJXNtGnT5IILLpDFixeb3gu9xcTEyC+//GJ6LmrVquXx9QAA/IXAAgDgcVWqVCn2d0RERInLMjIyCv/evXu3ZGVlyezZs4s9bsSIEWY5AMC7CCwAAJYQFxcnPXr0kOeee87bTQEAlIB0swAAl9GhSUV7GVxJh0m9//77kpKSUmy5ZpUCAHgfPRYAAJfR9K9Hjx41E7jPPfdck27WVR577DFZtGiRyQB1yy23mIxTq1evNkX5li1b5rL/AwCoGAILAIDLNG3aVL788kuZN2+ebNu2zZz8n1kgTx8zYMCAYs9r1qxZsYnbqmXLlsX+1poWa9asMelmNaCIjIyUXr16yciRI9mDAOADgvI1rQYAAAAAOIE5FgAAAACcRmABAAAAwGkEFgAAAACcRmABAAAAwGkEFgAAAACcRmABAAAAwGkEFgAAAACcRmABAAAAwGkEFgAAAACcRmABAAAAwGkEFgAAAACcRmABAAAAQJz1/1NqGh2bb7oTAAAAAElFTkSuQmCC", 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" ] diff --git a/dynestyx/control/mppi.py b/dynestyx/control/mppi.py index e2253b28..fc964ef8 100644 --- a/dynestyx/control/mppi.py +++ b/dynestyx/control/mppi.py @@ -21,13 +21,13 @@ from numpyro.distributions import Distribution import dynestyx as dsx -from dynestyx.control.discrete_controller_simulators import ControlledSimulatedResult from dynestyx.models import DynamicalModel +from dynestyx.types import SimulatedResult -# (result: ControlledSimulatedResult) -> scalar, called once per sampled rollout +# (result: SimulatedResult) -> scalar, called once per sampled rollout # (vmapped across all n_samples candidates) on that candidate's full rollout result. # See MPPI.loss_fn for the full shape contract. -type MPPILossFn = Callable[[ControlledSimulatedResult], Real[Array, ""]] +type MPPILossFn = Callable[[SimulatedResult], Real[Array, ""]] class MPPI(eqx.Module): @@ -49,23 +49,28 @@ class MPPI(eqx.Module): step (receding horizon); the remainder becomes next step's nominal sequence, shifted left by one with the last entry repeated. + Each rollout is run under the `"previous_transition"` observation/ + control convention, so a candidate's $u_k$ influences $x_{k+1}$ and + $y_{k+1}$. `dynamics` is copied (via `equinox.tree_at`) rather than modified, so the caller's + model keeps whatever `observation_control_alignment` it was built with. + See [Issue #312](https://github.com/BasisResearch/dynestyx/issues/312). + Attributes: dynamics: a `DynamicalModel` (the same model used for the real simulation or some approximate). Each candidate rollout is computed by calling `dsx.simulate`. If `dynamics` holds trainable parameters you're also fitting via the outer simulation, they remain in the differentiable pytree so gradients through planning are tracked too. - loss_fn: `MPPILossFn`, i.e. `(result: ControlledSimulatedResult) -> scalar`, + loss_fn: `MPPILossFn`, i.e. `(result: SimulatedResult) -> scalar`, called once per sample (vmapped) on that candidate's full rollout. Every - field carries a leading `n_simulations=1` axis -- e.g. - `result.states.shape == (1, horizon + 1, state_dim)` -- matching how - `dsx.simulate` never drops that axis, even for one trajectory; - `jnp.sum(result.states**2)`-style reductions don't need to care, but - explicit indexing does (`result.controls[0, 0]` is the whole first control - vector, not a scalar). `times`/`states`/`observations` have length - `horizon + 1` (including the starting state) and `controls` has length - `horizon`, matching `ControlledSimulatedResult`'s own - `control_time = time - 1` convention. + field carries a leading `n_simulations` axis -- e.g. + `result.states.shape == (n_simulations, horizon, state_dim)`, so + `(1, horizon, state_dim)` by default. `times`/`states`/`observations`/`controls` all + have length `horizon` and are index-aligned: at index `k`, + `states[k]` is $x_{k+1}$, `observations[k]` is $y_{k+1}$, and + `controls[k]` is $u_k$ -- the control that produced that state. The + starting state $x_0$ is not in `states` (no control produced it); it + is available separately as `result.x_0`, shape `(1, state_dim)`. horizon: Planning horizon length `H` -- the number of internal one-step `dynamics` calls per rollout. Defaults to `10`. noise_std: Standard deviation of the Gaussian perturbations added to @@ -73,6 +78,8 @@ class MPPI(eqx.Module): to `1.0`. n_samples: Number of sampled control sequences per call. Defaults to `20`. + n_simulations: Number of independent rollouts drawn per candidate + control sequence, forwarded to `dsx.simulate`. Defaults to `1`. dt: Fixed planning step size. Defaults to `1.0`. temperature: MPPI's $\\lambda$; higher values flatten the weights toward a uniform average, lower values concentrate weight on the @@ -95,6 +102,7 @@ class MPPI(eqx.Module): default_factory=lambda: jnp.array(1.0) ) n_samples: int = eqx.field(static=True, default=20) + n_simulations: int = eqx.field(static=True, default=1) dt: float = eqx.field(static=True, default=1.0) temperature: float = 1.0 batched: bool = eqx.field(static=True, default=True) @@ -118,31 +126,23 @@ def _rollout_and_score_one( u_seq: Real[Array, "horizon control_dim"], key: PRNGKeyArray, t_now: Real[Array, ""], - ) -> tuple[ - Real[Array, ""], - Real[Array, "horizon+1 state_dim"], - Real[Array, "horizon+1 observation_dim"], - ]: + ) -> tuple[Real[Array, ""], SimulatedResult]: """Roll out one candidate control sequence by calling `dsx.simulate` on a copy of `dynamics` pinned to start at `x0`, then score it with - `loss_fn`. Returns `(loss, states, observations)` -- plain arrays - only, since `ControlledSimulatedResult` isn't JAX-pytree-registered - and so can never itself cross a `vmap` boundary; it's built and fully - consumed here, inside the per-candidate function that gets vmapped.""" + `loss_fn`. + + Returns `(loss, result)`.""" times = t_now + jnp.arange(self.horizon + 1) * self.dt # (horizon+1,) + # Pin the rollout to start at x0, and plan under the "previous_transition" + # convention so y_{k+1} is paired with u_k (the + # control that produced x_{k+1}) rather than with u_k at the same + # index. pinned_dynamics = eqx.tree_at( - lambda m: m.initial_condition, + lambda m: (m.initial_condition, m.observation_control_alignment), self.dynamics, - dist.Delta(x0, event_dim=1), + (dist.Delta(x0, event_dim=1), "previous_transition"), ) - # dsx.simulate's same-index convention pairs ctrl_values[t] with both - # the transition from t and the observation at t, so it needs - # horizon+1 entries; u_seq only has horizon (one per transition). - # Pad with a repeat of the last control, used only to drive the final - # (never-transitioned-from) observation -- purely internal plumbing, - # never seen by loss_fn (which gets the real, unpadded u_seq below). - ctrl_padded = jnp.concatenate([u_seq, u_seq[-1:]], axis=0) # Relies on dsx.simulate's internals (Simulator/DiscreteTimeSimulator) # staying plain JAX array ops with no data-dependent Python branching, @@ -151,27 +151,26 @@ def _rollout_and_score_one( pinned_dynamics, rng_key=key, predict_times=times, - ctrl_times=times, - ctrl_values=ctrl_padded, + ctrl_times=times[:-1], + ctrl_values=u_seq, + n_simulations=self.n_simulations, ) + assert res.times is not None assert res.states is not None - assert res.observations is not None - states = res.states[0] # squeezed, for plan_step's own batching below - observations = res.observations[0] - - # ControlledSimulatedResult's fields all require a leading - # n_simulations axis (matching how dsx.simulate never drops it, even - # for one trajectory) -- so loss_fn sees the same n_simulations=1 - # shape a real single dsx.simulate() call would produce, not a - # squeezed one. - result = ControlledSimulatedResult( - times=times[None], - x_0=x0[None], - states=states[None], - observations=observations[None], - controls=u_seq[None], + # Under previous_transition, dsx.simulate returns times/states of + # length horizon+1 (t_0..t_H, x_0..x_H) but observations/controls of + # length horizon. Drop t_0/x_0 so loss_fn sees four index-aligned + # length-horizon arrays: states[k]=x_{k+1}, observations[k]=y_{k+1}, + # controls[k]=u_k. Everything else is passed through from `res` + # unchanged, so each field keeps its leading n_simulations axis. + result = SimulatedResult( + times=res.times[:, 1:], + x_0=res.x_0, + states=res.states[:, 1:], # drop x_0 + observations=res.observations, + controls=res.controls, ) - return self.loss_fn(result), states, observations + return self.loss_fn(result), result def plan_step( self, @@ -181,20 +180,19 @@ def plan_step( ) -> tuple[ Real[Array, " control_dim"], tuple[Real[Array, "horizon control_dim"], PRNGKeyArray], - ControlledSimulatedResult, + SimulatedResult, ]: """Do MPPI's full planning step and also return the batch of every - candidate rollout considered (`n_samples`-wide `ControlledSimulatedResult`) + candidate rollout considered (`n_samples`-wide `SimulatedResult`). -- useful for debugging/plotting what MPPI weighed, or diagnosing a - `loss_fn`. `__call__` (used by `DiscreteControlLoopSimulator`) is a + `loss_fn`. + + `__call__` (used by `DiscreteControlLoopSimulator`) is a thin wrapper around this that drops the rollout batch, since `PolicyCallable`'s return signature can't carry a third value. - Note: the returned result's leading axis indexes *candidates*, not - independent draws from the true generative process -- it's not a - real simulated trajectory. `filtered_states_mean`/`policy_states`/ - `predicted_*` are always `None` (not meaningful for a planning - rollout). + Every field is shaped `(n_samples, n_simulations, horizon, ...)`. + `predicted_*` are always `None` (not meaningful for a planning rollout). """ x0 = x_hat.mean nominal, key = s @@ -210,11 +208,11 @@ def plan_step( rollout_keys = jr.split(rollout_key, self.n_samples) if self.batched: - losses, states_batch, obs_batch = jax.vmap( + losses, rollouts = jax.vmap( self._rollout_and_score_one, in_axes=(None, 0, 0, None) )(x0, control_candidates, rollout_keys, t_now) else: - losses, states_batch, obs_batch = jax.lax.map( + losses, rollouts = jax.lax.map( lambda args: self._rollout_and_score_one(x0, args[0], args[1], t_now), (control_candidates, rollout_keys), ) @@ -229,15 +227,7 @@ def plan_step( u0 = weighted_seq[0] next_nominal = jnp.concatenate([weighted_seq[1:], weighted_seq[-1:]], axis=0) - times = t_now + jnp.arange(self.horizon + 1) * self.dt - result = ControlledSimulatedResult( - times=jnp.broadcast_to(times, (self.n_samples, self.horizon + 1)), - x_0=jnp.broadcast_to(x0, (self.n_samples,) + x0.shape), - states=states_batch, - observations=obs_batch, - controls=control_candidates, - ) - return u0, (next_nominal, key), result + return u0, (next_nominal, key), rollouts def __call__( self, diff --git a/tests/test_discrete_control.py b/tests/test_discrete_control.py index bbc473ff..cd238d88 100644 --- a/tests/test_discrete_control.py +++ b/tests/test_discrete_control.py @@ -1110,3 +1110,89 @@ def flaky_loss(result): ) assert jnp.all(jnp.isfinite(u0)) assert jnp.all(jnp.isfinite(next_nominal)) + + +def test_mppi_rollout_arrays_are_horizon_length_and_causally_aligned(): + """MPPI plans under "previous_transition" (#312), so every rollout array + handed to loss_fn has length `horizon` and shares one index: states[k] is + x_{k+1}, observations[k] is y_{k+1}, controls[k] is u_k -- the control + that produced that state. x_0 is excluded from states (no control + produced it) and carried separately. The observation model here leaks + 100*u into the mean so the pairing can be read straight off the output. + """ + horizon = 3 + + def transition(x, u, t_now, t_next): + del t_now, t_next + u = jnp.zeros_like(x) if u is None else u + return dist.Delta(x + u).to_event(1) + + def observation(x, u, t): + del t + u = jnp.zeros_like(x) if u is None else u + return dist.Delta(x + 100.0 * u).to_event(1) + + dynamics = DynamicalModel( + initial_condition=dist.Delta(jnp.zeros(1)).to_event(1), + state_evolution=transition, + observation_model=observation, + control_dim=1, + ) + mppi = MPPI( + dynamics=dynamics, + loss_fn=lambda result: jnp.sum(result.states**2), + horizon=horizon, + n_samples=1, + noise_std=jnp.array(0.0), # candidate == nominal, so u is exactly known + ) + + nominal = jnp.array([[1.0], [2.0], [3.0]]) + _, _, result = mppi.plan_step( + dist.Delta(jnp.zeros(1)).to_event(1), + jnp.array(0.0), + (nominal, jr.PRNGKey(0)), + ) + + # Plain SimulatedResult now carries the controls; no ControlledSimulatedResult. + assert result.times is not None + assert result.states is not None + assert result.observations is not None + assert result.controls is not None + assert result.x_0 is not None + # (n_samples, n_simulations, horizon, ...) -- n_simulations defaults to 1. + for arr in (result.times, result.states, result.observations, result.controls): + assert arr.shape[:3] == (1, 1, horizon) + + states, observations = result.states[0, 0], result.observations[0, 0] + controls = result.controls[0, 0] + # x_0 = 0 is excluded: states start at x_1 = u_0 = 1. + assert jnp.allclose(states, jnp.array([[1.0], [3.0], [6.0]])) + assert jnp.allclose(result.x_0[0, 0], jnp.zeros(1)) + # Each observation reveals the control that produced its state: u_k, not u_{k+1}. + assert jnp.allclose((observations - states) / 100.0, controls) + + +def test_mppi_n_simulations_draws_independent_rollouts_per_candidate(): + """n_simulations>1 runs several rollouts per candidate, so plan_step's + batch is (n_samples, n_simulations, horizon, ...). The candidate's control + sequence is shared across its draws; only the sampled dynamics differ.""" + n_samples, n_simulations, horizon = 5, 4, 3 + dynamics = _lti_1d(A=1.05, B=1.0, Q=0.25) + mppi = MPPI( + dynamics=dynamics, + loss_fn=lambda result: jnp.mean(jnp.sum(result.states**2, axis=(-2, -1))), + horizon=horizon, + n_samples=n_samples, + n_simulations=n_simulations, + ) + + _, _, result = mppi.plan_step( + dist.MultivariateNormal(jnp.array([2.0]), jnp.eye(1)), + jnp.array(0.0), + mppi.initial_state(), + ) + + assert result.states is not None + assert result.controls is not None + assert result.states.shape[:3] == (n_samples, n_simulations, horizon) + assert result.controls.shape[:3] == (n_samples, n_simulations, horizon)