0x06-Support Vector Machines.ipynb

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  {
   "cell_type": "markdown",
   "id": "0402184c-21ec-41ef-83ad-3be749bb9dd3",
   "metadata": {},
   "source": [
    "# Support Vector Machines\n",
    "- Capable of perfomring linear or nonlinear classification, regression, and even outlier detection.\n",
    "- They are particularly well suited for classification of complex-or medium-sized datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "771ddc10-1631-4539-8fa7-0a0bf2a29483",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b14d76-44d5-4e5b-b832-3e5084324469",
   "metadata": {},
   "source": [
    "## Linear SVM Classification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4c4fb4-ddfa-453a-bba4-9470245df31f",
   "metadata": {},
   "source": [
    "- They are sensitive to feature scales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19ac831e-d2c8-40fc-9cf7-aa8d7ce7016b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.5, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.5, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.5, kernel='linear')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(0.5))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e676d75-023d-4a03-977f-ae05a6316e85",
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   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05ac88fa-b9c9-4300-819f-a1edbf4e26dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.0, 2.0, -2.0, 2.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CnZ2dYGpqKlSsWFFo06aN4Ovrm+nicYIgCOHh4cLs2bMFV1dXoUSJEkKpUqWE2rVrC+PHjxdu3ryptm90dLQwadIkwcHBQTAxMREqVaokjBkzRnj16pUgk8lynVAIQtoNcs2aNULr1q2F0qVLC6ampoKjo6PQsmVLYeHChcK9e/c0jlEqlcL69euFevXqCWZmZkK5cuWEgQMHCo8fP84yDiKioiI5OVk4ffq0MH36dKFFixZC5cqVBVNTU6FEiRJCjRo1hJEjRwrnz5/P9FilUils2bJF6NChg2BrayuYmpoKlStXFrp37662oFy6zZs3Cw0aNBBMTU0FGxsboX379qov/NokFIIgCBcvXhSGDh0qODo6CqampoKtra1Qr149YfTo0cKhQ4cyXcDuxo0bQs+ePQVra2uhZMmSgoeHh7B169aPxkGUFZEgZNNpkIiIiIiIKAscQ0FERERERFpjQkFERMXG6dOnMXr0aLi4uKBkyZKoWLEievfujWvXruk6NCKiYotdnoiIqNjw8vJCREQEvLy8UKdOHbx9+xYrVqzA1atXcezYMbRv317XIRIRFTtMKIiIqNgICwtD+fLl1bbFxcXB2dkZbm5uOHnypI4iIyIqvtjliYiIio2MyQSQtgBjnTp1EBQUpIOIiIiKPyYURERUrEVHR+P69etwdXXVdShERMWSWNcB6BOlUolXr16hVKlSEIlEug6HiKhACYKA2NhYODg4wMhIf58/TZw4EfHx8Zg7d26W+yQlJSEpKUn1XqlUIjIyEmXKlGF9T0TFXl7reyYUufDq1Ss4OjrqOgwiokIVFBSESpUq6ToMrXzzzTfYsmULVq9eDXd39yz3W7x4MRYuXFiIkRERFT3a1vcclJ0L0dHRsLGxQVBQEKysrHQdDhFRgYqJiYGjoyOioqJgbW2t63BybeHChViwYAG+//57zJkz56P7ZmyhiI6ORuXKlXH58mUcOHAAfn5+ePbsmcZxzs7OWL16NVq0aJHv8RMRFZa81vdMKHIhJiYG1tbWiI6OZkJBRMWePtd56cnEggUL4O3tnevjM167IAi4cOECZDIZtm/fjvj4eNW+T548QfXq1fMzfCKiQpXX+l5/O8USERFlYtGiRViwYAHmzZunVTKRGZFIhFatWuHPP//E69evIZfL0aZNG7Rp00YjmVi5ciUmTpyIK1eugM/siMgQsIUiF/T5aR0RUW7pY523YsUKTJs2DZ6enpkmE82aNcvR5+T02pOSkmBmZqZ6r1QqUb16dTx//hwA4OrqColEguHDh8POzi53F0NEVEjyWt8zocgFfby5EhFpSx/rvLZt2+Ls2bNZluf0lqfttT948ACNGjVCQkKC2nZjY2N07doVUqkUPXr0gKmpaY4/k4iooLHLExER0f+dOXMGgiBk+SpoLi4ueP36NTZs2ICWLVuqtqempuLgwYPo378/HBwcMHnyZISHhxd4PEREhYEJBRERUT6ysrLCp59+ivPnz+PRo0eYO3eu2jSMERERkMvlKFGihA6jJCLKP0woiIiICkiNGjXw3Xff4fnz5zh27BiGDBkCc3NzDBo0SCOh+Pnnn7Fv3z6kpKToKFoiIu1wYTsiIqICZmxsjM6dO6Nz586IiorC+/fv1cqjoqIwe/ZsJCYmoly5chg+fDikUinq1q2ro4iJiHKOLRRERESFyMbGBg4ODmrbdu/ejcTERADA27dvsXLlStSrVw/u7u749ddfERERoYtQiYhyhAkFERGRjo0cORKHDx/GwIED1WaAun79OiZNmgQHBwd4eXnh0KFDXNuCiIocJhREREQ6JhaL0bVrV2zbtg2hoaH47bff0LhxY1V5cnIydu7ciRkzZugwSiKizDGhICIiKkJsbW0xYcIEXLlyBbdv38bUqVNRvnx5AIBUKoVIJFLb39/fH+/evdNFqEREAJhQEBERFVlubm5Yvnw5goODsX//fowcOVKt/NatWxgyZAjs7e0xZMgQHDt2DKmpqTqKlogMFRMKIiKiIs7ExAQ9e/ZUtVSk8/HxAQAkJSXB398fnp6ecHJywpw5c/Do0SNdhEpEBogJBRERkZ4aNWoUJk+ejDJlyqi2hYSEYPHixahVqxY++eQT/PHHH4iJidFhlERU3Ol1QnHjxg306dMHDg4OKFGiBFxcXPDtt99qzO99/fp1dOzYEZaWlrCxsUG/fv0QGBioo6iJiIjyR7169fDzzz/j1atX2LVrF3r27AljY2NV+V9//YWxY8dizJgxOoySiIo7vU0o7t27hxYtWuD58+f4+eefcfDgQQwePBjffvsthgwZotrvwYMHaNu2LZKTk7F9+3b8+eefePToEVq1aoW3b9/q8AqIspeaCpw5A2zdmvaTXaOJKDOmpqbo168f9u/fj+DgYCxfvhyurq6q8uHDh6vtn5SUhKdPnxZ2mERUTOntStl+fn5ITEzErl27UL16dQBA+/btERoait9//x3v3r1D6dKlMX/+fJiZmeHgwYOwsrICALi7u6NGjRpYvnw5li5dqsvLIMrS7t3A5MlAcPB/2ypVAlatAvr1011cRFS02dnZYerUqfj6669x7do1+Pv7o2vXrmr7HDhwAF5eXmjdujUkEgm8vLxgaWmpo4iJSN/pbQuFiYkJAMDa2lptu42NDYyMjGBqagqFQoGDBw+if//+qmQCAJycnNCuXTvs2bOnUGMmyqndu4EBA9STCQAICUnbvnu3buIiIv0hEonQuHFjLF++XHXPTCeTyQAA586dw+jRo2FnZweJRIKzZ89CqVTqIlwi0mN6m1CMGjUKNjY2GD9+PAIDAxEbG4uDBw9i/fr1mDhxIkqWLImnT58iISEB9erV0zi+Xr16ePLkCRITE3UQPVHWUlPTWiYyWww3fduUKez+RETaa9++PWrVqqV6Hx8fDx8fH7Rt2xbOzs749ttv8fz5c90FSER6RW8TiipVquDSpUu4c+cOqlevDisrK/Ts2ROjRo3CqlWrAAAREREA0hYJysjW1haCIHx0MaCkpCTExMSovYgK2vnzmi0THxIEICgobT8iIm1MnToV9+/fx6VLlzBu3Di1Vvxnz57B29sbVatWxe+//67DKIlIX+htQvH8+XP07NkTZcqUwc6dO3H27Fn8+OOPkMvlGrNZZFxVNKdlixcvhrW1terl6OiYb/ETZSU0NH/3IyLKjEgkQrNmzbBu3Tq8fv0afn5+6NSpk9p9sU2bNmrHJCcnQ8is+ZSIDJreDsqeNWsWYmJicPPmTZQsWRIA0Lp1a5QtWxajR4/GyJEjYWdnB+C/looPRUZGQiQSwcbGJstzzJ49G19//bXqfUxMDJMKKnD29vm7HxFRdiwsLDBkyBAMGTIEQUFB8PX1xb1799S6RQHAsmXL4OPjA4lEgpEjR6JSpUo6ipiIihK9baG4efMm6tSpo0om0nl4eACAqiuUhYUFbt++rXH87du34ezsDHNz8yzPYWZmBisrK7UXUUFr1SptNqesGs9EIsDRMW0/IqL85ujoiLlz52LLli1q2wVBgFwux+PHjzF37lxUrlwZXbp0gb+/PxISEnQULREVBXqbUDg4OODu3buIi4tT237p0iUAQKVKlSAWi9GzZ0/s3r0bsbGxqn1evnyJgIAA9OPcm1QEGRunTQ0LaCYV6e9//jltPyKiwvLu3TtUrlxZ9V4QBBw/fhxDhgyBvb09xo8fj7///ptdoogMkEjQ0//y9+/fjz59+qBp06b46quvULZsWVy+fBmLFy9G5cqVcePGDZiamuLBgwfw8PBAo0aNMGvWLCQmJmL+/PmIjIzEzZs3Ua5cuRyfMyYmBtbW1oiOjmZrBRW4zNahcHRMSyaYC1NhMOQ6z5CvPTvPnz+Hr68v5HI5nj17plFeu3ZtbN++HW5ubjqIjoi0kdc6T28TCgAICAjAkiVLcOvWLURHR8PR0RE9e/bE7NmzUaZMGdV+165dw8yZM3Hp0iWIxWK0b98ey5cvVy2Il1O8wVBhS01Nm80pNDRtzESrVmyZoMJjyHWeIV97TimVSpw/fx4ymQw7duzA+/fvAQAlS5bE69evuVAekR4x6ISisPEGQ0SGxJDrPEO+dm3ExsZi586dkMvlqF69Ov7880+18ilTpkChUEAqlaJRo0YfnWGRiAofE4pCxBsMERkSQ67zDPna80qhUEAs/m8SydjYWNjZ2alaMNzc3CCVSjFs2DBUqFBBV2ES0QfyWufp7aBsIiIiKno+TCaAtG7HHz67vHPnDqZOnYpKlSqhd+/e2LNnD5KTkws7TCLKR0woiIiIqMC0bdsWoaGh+OOPP9CiRQvVdoVCgf3796Nfv36oWLEipkyZwulnifQUEwoiIiIqUNbW1hgzZgwuXryIBw8eYPbs2ahYsaKqPDw8HEeOHPno2lBFQWoqcOYMsHVr2s/UVF1HRFQ0MKEgIiKiQlOrVi388MMPePHiBY4cOYJBgwbBzMwMEolEY7D2rFmzcODAAaSkpOgo2v/s3g1UqQK0awcMHZr2s0qVtO1Eho6DsnOBg/SIyJAYcp1nyNeuC+/evYNIJIKNjY1q2/3791GnTh0AQIUKFTB8+HBIpVK4uroWeny7dwMDBgAZvzGl5z87d3J9INJvHJRNREREeq106dJqyQQA+Pv7q/795s0brFixAm5ubvDw8MCaNWsQGRlZKLGlpqYtMprZ49f0bVOmsPsTGTYmFERERFTkfPPNNzhw4AD69+8PExMT1farV69i4sSJsLe3x8CBA3H8+PECjeP8eSA4OOtyQQCCgtL2IzJUTCiIiIioyBGLxejRowd27tyJV69e4ZdffkHDhg1V5cnJydixYwd+/fXXAo0jNDR/9yMqjphQEBERUZFWtmxZTJo0CdevX8fNmzcxZcoUlC1bFgAglUrV9k1JScHGjRsRHR2dL+e2t8/f/YiKIyYUREREpDfq16+PlStXIiQkBHv37kX37t3Vyg8fPowxY8bAzs4Ow4YNw4kTJ5CahwEOrVoBlSr9NwA7I5EIcHRM24/IUDGhICIiIr1jamqK3r17w9TUVG27TCYDACQmJsLPzw+dO3dG1apVMW/ePDx58iTX5zE2BlatSvt3xqQi/f3PP6ftR2SomFAQERFRseHt7Y1JkybB1tZWtS0oKAjff/89atSogVatWuHPP/9EbGxsjj+zX7+0qWE/WIsPQFrLBaeMJeI6FLnCecmJyJAYcp1nyNdeXCQlJeHgwYOQyWQ4evSoRren6dOn48cff8zVZ6amps3mFBqaNmaiVSu2TFDxkNc6T1wAMRERERHplJmZGfr374/+/fsjNDQUmzdvhkwmw/379wEAo0aNUts/MjIS0dHRqFq1apafaWwMtG1bkFET6Sd2eSIiIsoCG/GLB3t7e0yfPh13797FP//8gx9++EFjxe0NGzagWrVqaNeuHXx8fBAfH6+jaIn0DxMKIiIqVmJjYzFjxgx07twZ5cqVg0gkwoIFC7T6LHd3dyxevBjBH1vZjPSGSCSCh4cHZs+erbZdEATI5XIAwJkzZyCRSGBnZ4fRo0fj/PnzTCyJssGEgoiIipWIiAj8/vvvSEpKQp8+ffL0WU+fPsWcOXPg5OQET09P+Pv7IzExMX8CpSJDoVBg5MiRqFmzpmpbXFwcZDIZWrdujRo1auC7777Dy5cvdRglUdHFQdm5wEF6RGRI9LXOS7+tiUQihIeHo1y5cvD29s5VK0X6tWfGxsYGgwcPhlQqhYeHB0RZLVBAekcQBFy+fBkymQzbtm1DTEyMWrlIJMK+ffvQs2dPHUVIVDDyWt+zhYKIiIoVkUiUb1/y//33XyxYsABVqlRRbYuKisK6devQtGlTuLm5YdmyZQgNDc2X85FuiUQiNG/eHL///rtqIHfHjh1Vf0/m5uZo3bq12jHx8fHsEkUGjwkFERFRFqpUqQJvb288ffoUAQEBGDlyJEqUKKEqv3fvHmbMmAFHR0f06NEDO3fuRFJSkg4jpvxSokQJ1Urbz58/x6JFizB58mSNlqsvv/wStWvXxpIlSxASEqKjaIl0i12eckFfm/+JiLRRHOq8nHZ5SkpKUksEYmJi4OjomOm1x8bGYseOHZDL5Th//rzGZ9na2mLo0KGQSCRo1KgRu0QVY/Hx8bCzs0NcXBwAwMjICJ07d4ZEIkHv3r1hbm6u4wiJcoZdnoiIiPJo8eLFsLa2Vr0cHR2z3LdUqVIYPXo0zp07h8ePH2PevHlq+0dGRuLXX39F48aNUb9+faxcuRJhYWGFcRlUyF6/fg13d3fVe6VSiaNHj2Lw4MGwt7fHhAkTcOXKFXaJomKPCQURERm82bNnIzo6WvUKCgrK0XHOzs5YtGgRnj9/jhMnTmDo0KFqT6Vv376Nr7/+GhUrVkSfPn2wd+9epKSkFNRlUCGrXr06zpw5gydPnmD+/PlwcnJSlUVFRWHt2rVo0qQJ6taty3E2VKwxoSAiIoNnZmYGKysrtVduGBkZoWPHjtiyZQtev36N9evXo3nz5qpyhUKBffv2oW/fvqhYsSK++uor3Lp1K78vg3SkevXqWLhwIQIDA3Hq1CmMGDECFhYWqvKEhATY2dnpMEKigsWEgoiIKB9ZW1tj7Nix+Ouvv3D//n3MmjULDg4OqvK3b9/i559/Rv369dGoUSOsXr0a4eHhOoyY8ouRkRHat28PX19fvH79Ghs2bMAnn3wCqVSqMZamb9++mDx5Mm7cuKGjaInyDwdl50JxGKBIRJRT+lznHTlyBPHx8YiNjcXo0aPh5eWFgQMHAgC6deumNlNTZvL72lNTU3HixAnIZDLs3bsXycnJauUmJibo1asXJBIJPD09IRaL83xOKjqUSiWMjP57hvv48WO1RfTq168PiUSCYcOGoVy5croIkQxcXus8JhS5oM83VyKi3NLnOq9KlSp48eJFpmXPnj1TW1ciMwV57ZGRkfD394dcLseVK1c0yitUqIARI0ZAIpHA1dU1X89NRcO2bdsgkUg0Vl0Xi8Xo0aMHpFIpunbtChMTEx1FSIaGCUUh0uebKxFRbhlynVdY137nzh34+Phg06ZNePPmjUa5h4cHJBIJhgwZgtKlSxdYHFT4oqKisG3bNshkMvz9998a5eXLl8eoUaOwdOlSTj1MBY4JRSEy5JsrEWVBoQBCQ4H379P+LRYDJUoA9vZp/9ZjhlznFfa1KxQKHD16FHK5HPv379eYCcrMzAy9e/eGVCpFp06dYGxsXOAxUeG5f/8+fHx84OvrqzYblKenJ44cOaLDyMhQMKEoRIZ8cyWiDGJjgadPgWfP0hKJD58gCkJaMlG1KlC9OlCqlO7izANDrvN0ee3h4eHYunUrZDJZpgN2HRwcMHLkSEgkEtSqVatQY6OCpVAoVGNt9u3bh02bNqnG/qSXjxs3Dv369UOXLl041obyDROKQmTIN1ci+j9BAO7cAR48SEsiPlaFppe7uABubupJhx4w5DqvqFz7v//+C7lcjs2bN2c6E1Tz5s0hlUoxcOBAWFtb6yBCKiiRkZEoWbIkzMzMVNsOHTqEHj16AADs7OwwYsQISKVS1K5dW1dhUjHBlbKJiAqLIABXrqQlE+nvs9sfSNv/ypXs9yfKIH2l7ZCQEOzZswe9evVS6+506dIljB07Fvb29hg+fDhOnjwJpVKpw4gpv9ja2qolEwCwa9cu1b9fv36NZcuWoU6dOmjatCnWrVuHqKioQo6SKA1bKHIhPXvr06cPLCwsYGJiArFYnOnPcePGqa2Y+eTJExw7dizL/dN/mpmZoW3btmrnffXqFWJiYj56nFgshrGxMQduERWk27f/Sya04eIC1K2bf/EUsKLylF4XivK1v3nzBlu2bIFMJsOdO3c0yh0dHTFq1CiMGjUKzs7OOoiQCkpKSgqOHj0KmUyGAwcOQKFQqJWbmZmhb9++GDdunMZ3CaKPMfguTxcuXMAPP/yAS5cuITExEZUqVcLIkSPxzTffqPa5fv06ZsyYgcuXL0MsFqN9+/ZYvnw5qlWrlqtzpf+yc+LSpUto1qyZ6v22bdswePDgbI+zsrJCdHS02rZPP/0Uf/75Z7bHDhgwADt27FDb1rBhQ4SFhWWbjMycORPdunVTHRccHIwZM2Zke5xYLMaUKVPU/vhu3bqFK1euZHuslZUVGjZsqBZvaGgokpOTszyOSRPpTGwscPRopkWpSuD8/XIIfWcO+9KJaFX7LYyzav/19NSbMRVF+Ut1QdOHaxcEAdevX4dcLoefnx8iIyM19mnVqhUkEgm8vLxQSk/+7ihn3r59qxprc/PmTbWyyZMn4+eff9ZJXKSf8lrn6fVoHj8/P4wYMQIDBw6Er68vLC0t8fTpU7x69Uq1z4MHD9C2bVs0aNAA27dvR2JiIubPn49WrVrh5s2bBbaATMa5ozM+RchKZgOsMs72kdNzAmlf0DObijAjqVSq9j4iIgJbt27N0Xk/++wztT++o0ePYubMmdke5+bmhtu3b6ttGzFiBE6dOvXR48RiMaZNm4bFixertimVSlSrVk2V5HwsmVm8eLFaIvPvv/9i1apV2R5nYmKC6dOnq8Vy5coVPHr0KNtjbW1tUaNGDbVjX79+DQCZ7s+kqQh6+jTTMRO7/66IyfIGCI74b6G0SmXeY5XkJvo1DVH/DJEICAwE6tcvjIipmBOJRHB3d4e7uzuWL1+OAwcOQC6X48iRI6puT+fPn8f58+cxadIkDBgwAFKpFK1bt1ZbZI30U7ly5fDll1/iyy+/xM2bNyGXy7FlyxaEh4dDIpGo7RsWFob9+/dj4MCBRTZBJv2mtwlFSEgIxo4di3HjxmHNmjWq7e3atVPbb/78+TAzM8PBgwdV/xG5u7ujRo0aWL58OZYuXZrrc9+5cwfm5uZQKBRISUmBQqFQ+3dKSoraCpgA0KJFC/j6+mrsl/HYjP0lgbQnTEZGRpke++HPzGb7cHBwgImJSab7f5ioaJsAAZpJUE6PzSwByknylNnnp6SkZLmIVUYZk50XL15AJpNle5xYLNZIKHx8fPDbb79le2z37t1x8OBBtW2tW7fG48ePM93fyMhIlWCsWLEC48aNU5UFBQWhY8eOaq1EWSUza9asQcWKFVXHnjt3Dv7+/tm2HpUpUwajRo1Si+nixYt48+ZNtucsV64cHBwc1I6NiIjQOE6vvtAoFGmzOWWSTAxY0RwZm3lDIiwwYEVz7Jx6ST2pEIS0hMLVVe+nlKWixczMDAMGDMCAAQMQGhqKzZs3QyaT4f79+wCA9+/fw9fXF76+vqhataqqS1R2C/yRfmjQoAF+/vln/Pjjjzhz5gwaNGigVr5582ZMnToVX375Jfr37w+JRIJ27drpVz1MRZre3tE2bNiA+Pj4jz4JVygUOHjwIEaOHKmWkTs5OaFdu3bYs2ePVgmFo6NjrjP8qlWromrVqrk+F5DWAvDZZ59pdez169c/Wq5UKpGSkqIxp7mbmxuePn2abRKjUCg0uoF169YN5cqVy3L/9H/b29trxNOhQwfY29tne85KlSqpHZeamgo7O7tM98/Yq6+wEyBtjlUqlUhKSkJSUpLGAMuEhAQ8evQoR+f96aef1N7funULa9euzfa46tWrayQUS5cuxYEDB7I9dvz48WpJPpCW2CYnJ6ttMzIy0khINm3aBE9PT9U+V69exZgxYzS62WWWzGzatEnt93zw4EEEBARk23pUqVIl9OzZUy22M2fOID4+/r/9IiMhfvgQYiMjmIjFEBsZwchIjEl/tvh/MpHeoqQE8B4CxADEmCyrj94eIerdn9LXrXB0zPZ3SaQNe3t7TJ8+HdOmTcOVK1cgk8mwdetWVXfaZ8+eYcGCBViwYAHatWsHiUSC/v37o2TJkjqOnPLK1NQUnTt3VtsmCILqoVlCQgI2b96MzZs3o3Llyhg1ahQkEkmuu4ATZaS3CcW5c+dga2uLBw8eoHfv3rhz5w5sbW3Rr18//Pjjj7CyssLTp0+RkJCAevXqaRxfr149nDhxAomJiTA3N8/0HOlf6NLFxMQU2PXoipGRUaatImZmZlpXMA0aNNB4OpJT8+fP1+q4EiVKqC0G9KH0pCk9mSlRooRaeadOnXDv3r1sk5jMhhsNGTIEbm5u2SZedTMZiNulSxe8fv0622PLli2rdpwgCLCxsVHbLzU1NdNrz5jIFFQClN2xmSVtSqUSycnJaolGxuuIiorCv//+m6Pzbt68We392bNnNRKqzLRr104joZg8eTJu3bqVg7MmAvjwocZbAHaqd8GRgPlQMUzERjAxNobY2Djtp7k5jp06BVdXV9W+hw4dwrfffpvpOKWM3edWr16tFsXWrVtx+/btbFueatasiZYtW6odGxAQAEEQMt3/w/qP9I9IJEKTJk3QpEkT/PTTT9i3bx9kMhlOnDihqs8CAgIQEBCAL774AgMHDoREIsEnn3zCbpfFjEwmg0wmg5+fn2omqJcvX2LRokVYtGgRWrduDalUigEDBsDS0lK3wZJe0tuEIiQkBO/fv4eXlxdmz56Nn3/+GVeuXIG3tzfu3LmD8+fPIyIiAkDa1GsZ2draQhAEvHv3LtMn5QCwePFiLFy4sECvgwpeetKUWeIEAKVKldJ6Du+2bdtqPZPGunXrtDquVq1aePfundo2pVKJ1NRUjYQkYzIybNgwtGnTJtskJrMnlWPGjEH79u2zTbxatGihdpwgCOjatWuOWrsyDhoVBAHm5uZISUnJMmlKl7GVLb8ToMxlrEI1z6lQKqBIBhI+3BgdrZGgvn79Gv/880+2Z7S3t9dIKPbu3Yvt27dne6xEItFIKPr27asxEQQVPxYWFhg8eDAGDx6M4OBg+Pr6Qi6Xq7pdxsbGYuPGjdi4cSOcnZ0hkUgwcuRIOLIlTe+JRCI0btwYjRs3xooVK7B//37I5XIcO3ZM1QJ+7tw5nDt3DgkJCRg/fryOIyZ9pLcJhVKpRGJiIry9vTFr1iwAaV/uTE1NMWXKFJw6dUr1JPpjT1o+VjZ79mx8/fXXqvcxMTGsXKlIMjIyUo27+Jjy5cujfPnyWp2jf//+Wh0nEok0xo/kVKdOnZCQkPZVXBCETMcrpf/M+N/ylClT4OXl9dFjFAoF7OzsNM47efJkvHnz5r/9wsKQ8vo1FKmpSFEooFAqERQhxvF/XTMcaQKgAwAF0pILBWrYv4O5SVLasampaT+NjDRaRrNLmFRnyEMClNtjxWJxrsZTkX6oVKkS5syZg9mzZ+PSpUuQyWTYtm0bYmNjAaRNcz5v3jx888036NixIyQSCfr27QsLCwsdR055ZW5ujoEDB2LgwIEICQlRjbV5+PAhzMzMNGajTB+b+OE0+ESZ0dtpY5s3b47Lly/j+vXrajP2PHr0CLVq1cLSpUvRu3dvuLi44LfffsOECRPUjp8+fTpWrFiB9+/fZ9nlKSN9mEaQiApAUBBw+bLaplQlUGVid4REWECA5oMJEQRUKpOAZ78d0pxCtlmzTMdQCIKg1kUvq8QpY3fEu3fv4s2bN1lOEvHhxA1t2rRRO3bRokVITExUTdSQ8RhfX1+DrPMMrb5///49du/eDblcnulMe1ZWVhg8eDCkUimaNm3KLlHFiCAI+Pvvv3H79m2N8Zqff/45fv/9d7Rv3x4SiQT9+vXT6DZMxYPBrkMxbtw4/P777xoJxcOHD+Hi4oJly5ap1kcYNWqUxkBUT09PBAYG5nhwK2B4Nxgi+j+FAjhwIO3nB9JneQKgllSI/j9UW2OWJyBtdqeePfVilidDrvMM+dpfvHih6hIVGBioUe7i4gKJRIIRI0ZozOhGxUdCQgLs7e3VukSWKlUKgwYNglQqRfPmzZlYFiN5rfP0dr6w9O4XR44cUdt++PBhAECzZs0gFovRs2dP7N69W9WUC6QNRAoICEC/fv0KL2Ai0l9iMVC1ato6Eh/o1zQEO6deQsUyaiMkUKlMQubJhEgEVKumF8lEQTp27Bj279+v6zAoC05OTvjmm2/w+PFjnD17FhKJRG1c1YMHDzBr1iw4OjqiW7duqjWeqHhRKBSYPn262mrrsbGx2LBhAz755BO4uLhg8eLFCA4O1mGUVFTobQsFAPTq1QvHjx/HvHnz0KxZM1y9ehULFy5Ex44dVdNbPnjwAB4eHmjUqBFmzZqlWtguMjIy1wvbGfITKyKDx5Wy8+Uzw8LC4OjoiJSUFERGRsLGxiZfPje/sb5XFxcXh507d0Iul+Ps2bMa5aVLl8aQIUMglUrh7u7OJ9fFiCAIuHjxIuRyObZt24a4uDi1ciMjI1y5cgWNGjXSUYSUHwy2yxOQ1hy3cOFC+Pn5ITQ0FA4ODhg2bBi8vb3VZvS5du0aZs6ciUuXLkEsFqN9+/ZYvnw5qlevnqvz8QZDZOBu3wYePND+eBcXIJMphIuqgqjz/P39MWTIENSuXRv37t3Ll88sCKzvs/b06VNVl6iXL19qlLu5uUEikWDYsGGZTnpA+is+Ph67d++GTCZDQEAAAKBy5cp49uyZ2iJ5UVFRsLa2ZmKpRww6oShs6b/svXv3wtraOts537P6yf/AiPSUIABXrgA5XJVdjZMT4OGh0W2qKCuIL9Xp499++uknfPXVV/nymQWBCUX2lEolzpw5A5lMhl27dqlmZEtnbGyMbt26QSKRoEePHjA1NdVRpFQQnj9/Dh8fH5QpUwZffPGFWlm7du3w5s0b1VibrKbnz5X0RUHfv0/7t1gMlCgB2NsbfDfS/MCEohCl/7LzytjYONNEQ5vkJLufuvrMD59UEBUrggDcuZPWUiESpb3PSnq5iwvg5qZXyQRQMF+qa9SogeDgYISEhGS6RlBRwYQid2JiYrB9+3bI5XJcvHhRo7xMmTIYNmwYpFKp1gufkn549uyZ2kx0RkZG8PT0hFQqRc+ePbNcEypLsbHA06fAs2dpicSH9agg/DfGrXp1velOWhQxoShE+ZVQGAIjIyO9SHzy8pkZF1IjAxMbCwQGpr2yuslVq5b20tObXH5/qQ4ODoajoyOGDRumsbJ5UcOEQnuPHj2Cj48PfHx8EBISolFev359SKVSDB06NFfjGEk/3LhxA1OmTMG5c+c0ymxtbTF06FBIJBI0atTo4z02DOjhTVHAhKIQpf+yp02bBiMjoxyt/JvTnx8rS1/JkooWkUikF4lPXn4aGRmxi1520pvhExKAlBTAxASwsCgWzfD5/aV606ZNGDlyJM6ePYvWrVvnQ4QFhwlF3qWmpuLUqVOQyWTYs2cPkpKS1MpNTEzQo0cPSCQSdO3aNduFOUm/PH36VJVYZjbWpn79+rh8+XLma4EZWPfSooAJRSHS1Q1GqVTme5JSEIlPXj4zp6sEU+HTh8QnL59pbGzMpCkLH9Z5xsbGqFatGsLCwlC1alU8fPgw0y+AiYmJ6NixIy5evAhTU1McO3YMbdu2BQBIpVJcvnwZ9+/fL+QryT0mFPkrKioK/v7+kMvl+PvvvzXKy5cvj+HDh0MqlcLNzU0HEVJBUSqVCAgIUI21SZ9iuEOHDjh58qTavoIgpNXHBjYBRlGgk4QiPj4+zzcWfcQbTMFRKpVITU0t8olPXj6DSVPRlV0iUhCJT2EnVdokTRnrvFWrVmHKlCkAgN9//11jVV1BEDBw4EDs3LkTIpEIfn5+GDx4sKq8SpUq+PLLL/H111/n6f+vwsD6vuDcu3cPPj4+8PX1xevXrzXK3d3dIZVKMWTIkCI9zoZyLzo6WjXWZuLEiRg6dKiqLDU1FR4eHmjTvDkkVaqgfpUqGscX1ym6iwKdtVDk9caij4rVDYazJRQ6QRA0EpOimPjk9TOpaMpqMoiP/QSAS5cuqeq8pKQk1KxZEy9fvoSTkxMePXqkNnPP119/jZUrVwIAli9fjqlTp6rKAgMDUadOHYSEhKBMmTKFe/FaKFb1fRGlUChw/PhxyGQy7N+/H8nJyWrlpqam6N27NyQSCTp37gwx703Fiqo14v+OHz+OLl26qN43qFIF0rZtMbRlS5S1ssLuvytisrwBgiNKqPapVOY9VkluZr6IaI0aQP36BX4dxYXOEoq83Fj0VbG4wXC2BCpAgiAgNTVVLxIfbT87JSVF17/mQvdhnbdx40aMGTMGALB27Vp8/vnnANQfMk2ZMkVV/6c7evSoqsuLPigW9b0eiYiIwNatWyGXy3Ht2jWNcnt7e4wYMQISiQS1a9fWQYRU0FavXo3p06drjrUxNkajai3x9+OvAXQF8F+PGBHSvsLunHpJM6kQi4GePfmQNId0OoZC2xuLvtLrGwxnSyDKNzlNmvQ5qfrw1vBhnZeamoo6derg0aNHcHR0xJMnT3Dw4EF4eXlBqVTCy8sL/v7+ej91tF7X93ru9u3bkMvl2Lx5M8LCwjTKmzZtCqlUikGDBhXZldZJO+9u34b/L79AFhCAK0+fZrJHBQBfAJin2iKCgEplEvDst0Oa3Z+aNQMcHQsw4uJDpwmFodxY0untDYazJRBRLimVSkRERKB8+fIadd62bdtUXVg//fRTbNmyBYmJiWjdujWOHz+e+3nmiyC9re+LkZSUFBw5cgQymQwHDx7U6FJpbm6Ovn37QiKRoEOHDpzKuzh4+DBtQLYg4G5QEORnzmDj6Qt4F//ug50mAvhV49AA7zNo6/r2vw0iEVCvHlCzZoGHXRzktc7L0zd9Y2NjfPvttwCAoKAgTJgwAcOGDYNSqUTr1q2xadOmYpNM6LU7d7RLJoC04+7cyd94iKjIMzIyyjIxGDhwoGpxso0bNyIxMRGurq7Yt29fsUgmqGgwMTFBr169sGfPHrx69QorV65EvXr1VOWJiYnYunUrunTpgipVqmDu3Ll4/PixDiOmPPsgaXR1dMSyESPwi3Q/gAMA+iOtu5Mkw0HhAIbgyI0bSM04zb4BdlHVlTx/29f2xrJ582aMGzcOjRs3hpmZGUQikd70rdUrsbEaU68lhhshNlCc5SsxPMOfxYMHaZ9DRIS0NVg+nIijfPnyOHLkSJ66n/CeQB9Trlw5TJkyBf/++y+uX7+OL7/8Um1wf3BwMH744QfUrFkTLVu2xMaNGxETE6PDiEkrmYx3qFRGAaAHgJ0AXgNwz7DHVgD++HH/16g8fjxmbdmCh69epRVxbZNCky/rUKxZswYTJ04EkHZjuXr1Khyz6bNWpUoVvHjxAmXLlkXJkiXx4sULyGQySCSSvIZTYPSyCfzmTeDJE9WYicRwI/wz2RbKlKy7MBmZCGiyKhLmZf+f6XO2BCKDlFWd9/jxY7Ro0QLh4eEAgJIlSyIwMBDly5fX+lxF7Z6gl/W9gUlKSsKhQ4cgk8lw5MgRjam5LSws0L9/f0ilUrRt25Y9JvRBUBBw+bLaplQlUGVid4REWEBAZt9dOgE4qbG1ec2akEilGDR+PKytrQsm3mJEp12egLQbi7e3t+p9fHx8jpq8N2zYgOfPn+Pt27eqwdyUzxSKtNmcPsgZU2KMPppMAIAyRYSUmA/+NAQBCAxUa4okIsMUFhYGT09PhIeHq54Qx8fH4/vvv8/T5/KeQLllZmaGfv364cCBAwgODsayZctQp04dVXlCQgI2b96MDh06oFq1avD29kZgYKAOI6ZsZTJ1vbERsEpyE8B/szqlS3t/EDN6LUbvxo0h/mAczaVHjzBu9mzY2dlh2LBhuJwhUaH8lae5tDLeWCIiIlQ3llWrVn302I4dO+bl1DpVr149mJqafnT+9pUrV8LV1VV1zJUrV7BmzZps530vUaIEvvrqK7Xz/fXXXwgMDMx23viyZcvC2dn5vwNDQ/EqLAwikQgmxsYQGxsjMdkMyUiGGGIY5SafTF+3grMlEBms+Ph4dO/eHYGBgbC0tMTx48exaNEi7N27F+vXr8fUqVNRuXJlrT5bn+8JpHt2dnaYNm0apk6diqtXr0Iul8PPzw9RUVEAgBcvXuDbb7/Ft99+izZt2kAqlaJ///6wtLTUbeCkLn3q+g96VgBAv6Yh2Dn1UibrUCTgZ8lN9GtaHcAMhEVHY8v585CdOYPbL18CSBtr4+fnh6ZNm6JZs2aFfUUGQ+uEoiBvLEXdixwMcI6Li1N7//Tp0xz1B7a2ttZIKDZs2ACZTJbtsV5eXti+fft/G96/h8fs2Xj17l2m+4sgghhiGMMYUzAFXfDfgjJPwl9h0KrvVYmIibExxKVKwaRUKY0Vef/880+ULVtWdeyJEyewe/fubBOgChUqYPjw4WoxnTlzBuHh4dkmXhUqVIC9vb3qOEEQVMd9uK+xsbFWKwQTkTqFQgEvLy9cvXoVYrEY27dvR6NGjbBw4ULs27cPSUlJWLhwITZu3KjrUMmAiUQieHh4wMPDAytWrMD+/fshk8lw/PhxKP8/YPfs2bM4e/YsJk6ciIEDB0IikaBVq1a8VxQV1asDmQyu79c0BL09Qj66UnZ5a2t81aMHpnTvjhv29pBt3w4/Pz/ExsaqrcoNpK3YfvnyZXh5eaEU193KM60SCkO/sZQpUwZKpVJtzvaMfTdNMgwEyukKwpmtBJrThbQynhMKBRQZZzz4gAABKf//n5ChGfF9chIeh4bm6LwZr+3GjRtYt25dtsfVrVtXI6FYtGgRTp8+ne2x06ZNw7Jly1TvlUpllv23MyYju3fvRps2bVTl586dwxdffJFtEmNqagp/f3+1z961axcuXLiQ7bFVq1ZFt27d1I49efIkEhMTs0287Ozs1Aa7KpVKxMfHM2miQvX555/jyJEjANLWHeratSuAtBbb/v37Y+fOnfDx8cHMmTNRk9M0UhFgbm6OgQMHYuDAgQgJCcGmTZsgl8vx8OFDAGkPRmUyGWQyGapVqwaJRIJRo0YV24eheqNUqbR1sDJMKAOkdX9Smxo2C6LatdGobl00atUKy5cvx7Vr19QefAJp439/++03TJo0CQMGDIBUKkXr1q051kZLWiUUhn5jCQwM1BiwIgiC2qJSJUqUUCvv1asX7t27l+1CVJn9IY8aNQpNmzbNdiGqRo0aqR8oFqN7o0Z4FxeHlNRUKFJTkRinxLunIqR+8D8FFLCG5oCl0iVLQqFUIuX/iYkiQ9L032nU/4y0ToCQ88Qr47EfO2f67zhdxnkIoqKicPv27VyfEwBOnz6NNWvWZHtsjx49NBKK8ePH48mTJ9ke+8svv2DSpEmq98+fP0f16tXV9snYMpP+8/z586hWrZpqv507d+LHH3/MNolxcHDA8uXL1c7h4+ODhw8fZnmu9J916tRB8+bN1Y49ceIEjIyMsk28ypcvDwsLC9VxSqUSqampEIvFTJp0aPHixaoHRN98841qQdN0CxYswO7du5GamopvvvkG27Zt00WYBSIf5i2hIqBixYqYNWsWZs6cicuXL0Mul8Pf3181E1RgYCDmz58Pb29vtG/fHlKpFH379tW4l1MhcXMDEhK0Xz/LzU311szMDC1atFDbJSkpCX5+fgCA9+/fw9fXF76+vqhatSpGjRqFkSNHomrVqnm6BEOT64RiwYIFBntj+RiRSAQTExOYmJiofSFKZ2VlpfVMIR07dtSuf3GJEvhz/Hi1TbGBYlybVTrbQ+s5VEVkhm5WQtOmSHVw0EhmbG1t1fYbM2YMunXrlu1Kvpn9PiZOnIhevXplu5JvkyZNNI7t2bNnjlYRzqzPbIkSJZCSkvLRxCSzhCIvyZO2x2aWdKVfW2Ji4kc/KzQ0FFeuXMn2nM7OzhoJxc6dO3Hw4MFsj50wYYJGQtG9e/ccXe++ffvQq1cv1ftz586hXbt2ANLWvflYMnP//n2Ympqqjl23bh38/PyyTWLq1KmDadOmqcWxZs0avH37VmP/jMc2bNhQbRBocnIy/vrrr2zPmT7mKbMWyaJoyZIlANIebqSvPfQhV1dXDBw4EP7+/tixYwdmz56tmk5c3zVt2hSffvophg8frtbNkvSTSCRC8+bN0bx5c6xcuRJ79+6FTCbDqVOnIAgCBEHAqVOncOrUKZQqVQqDBg2CVCpF8+bN+VCjMIlEaYvqWliktVSIRGpjKjLdXxDSWjbc3LJdjNfU1BRHjx6FXC7H1q1bVWNtnj17hgULFmDBggVo164dJBIJ+vfvj5IlS+bjxRVPubqbbdy4EQsXLgRgmDcWvZM+W0J+zM4kFkPk4KD6UvUxFSpUQIUKFbQ6zcCBA7U6ztzcHPv379fq2F69eiE+Pl71PjU1NctkJKPZs2dDKpVm23pUqVIljWNnzJiByMjIbI91cXHRuNaOHTtme5xCodCYcU35kS5wH8rP1qPcHPux1q7U1FSkpqYiKSkp02MzrpL75MkTnD9/Pttztm/fXiOhWLduXY5arZYuXaqWUERGRqoSoOzcvHkT9T+YitnHxwejR4/ONompUKECzp07p/ZZ33//Pc6ePZttEtOiRQuNboY//fQTFApFpue6fv26ar+OHTvijz/+yPJ6vL29sWPHDqSmpmLu3Lk4dOhQjn4PRd3Dhw8xY8YMzJ49G56enpBIJOjZsycX8CsGSpQogaFDh2Lo0KF4+fIlfH19IZfL8fTpUwBAbGwsNmzYgA0bNqBmzZqQSCQYMWJEpvU5FQCRCKhbF6hSJW2myfTZJj9MFgQh7XtOtWpprxyOhRCJRGjSpAmaNGmCn376Cfv27VONtUlvlQwICEBAQADKly8PT0/PArjA4iXHCcXhw4dVU/kZ6o1F72QyW4KJlRJGJkK261CYWH3wxVMkSvsPVU+epuaVsbExjI2Nc/SFoWrVqlo3i06YMEGr4ypXrowTJ05odezkyZMxefJkjTFAGX9m1vXuxx9/xIwZM7JNYj6c3QxI6zIyf/78HCVAFStWVDvWysoKrVq1ylHLU8aYdZ0AaXOsUqlEUlJSlkkToDnhAwD8+++/OfqbSExM1EgoFi5cmO0CYDmZl9zFxSXHvzd9lJqaikOHDuHQoUOwtbXF0KFDIZVK0bBhQz65LgYqV66MefPmYe7cubh48SJkMhm2b9+u+u/t0aNHmDNnDubNm4dOnTpBKpWid+/eMDc313HkBqBUqbR1sFxd02abTEhIWwHbxCStBSOTqWZzw9zcHIMGDcKgQYMQHBysGmvz6NEjVKxYEZ06dVLb/86dO7CysuJYmwxy/P9AeheWnCjuNxa9kmG2BPOySjRZFam+zkQGJlbK/xa1A9KSkQ/64ZP+MzIygpmZWa6estatW1erc4lEIixYsECrY5s2barxND6nfvrpJ/z4448a45Qy/sysi+KGDRsQHR2dbRLUsmVLteMsLS0xc+bMLMdIffgz40JLZcqUQePGjbM9Z2arURdU8mRiYpKrJKk4un79Onbv3g0fHx8EBQUBSGuJ+vXXX/Hrr7+ibt26WLt2LT755BMdR0r5QSQSoWXLlmjZsiV++eUX7Nq1CzKZDGfOnAGQ1sp77NgxHDt2DDY2NhgyZAgkEgk8PDyYWBY0sbjAp66vVKkSZs+ejVmzZuHy5ct4/fq1Ruv3119/jZMnT6JDhw6qsTaZ3UcMTb6slJ1XS5YswezZs7lSdkG5fTvT2RJyzMUlrdmRiIqkxMREJCcnZ5vE2NraokqVKmrHHjp0KMtj379/j6+//rrQ67y83hPi4uIwb948bN++HZGRkXBxccGsWbMwePDgHH9GxvpeqVTi9OnTkMvl2LVrl9p4pUePHqFGjRq5jpP0x7Nnz+Dj4wMfHx88f/5co7xOnTqqLlF2dnaFHyAViqCgIDg5OalN1mBlZYXBgwdDIpGgWbNmeptY5vU7rs4Sig0bNuDChQsAgNu3b+P69ev45JNPVAuz9enTB3369NFFaFnS24RCEIArV7SfLcHDI9sBTkRU/BRmnZef94TOnTvjypUrWLJkCWrWrAk/Pz9s2LABW7Zs0ZiLPisfu/bo6Ghs374dMpkMRkZGqrjTrV69GoGBgZBIJGrjZEj/KZVKnD17FnK5HDt37sT79+/Vyo2NjTnWphgLDw/H2rVrIZfLM111vVatWqrEMmMX3qJObxMKiUQCHx+fLMu9vb217iZRUPQ2oQDSkoo7dwpktgQiKp4Ks87Lr3vC4cOH0b17d/j5+WHIkCGq7Z07d8bdu3fx8uVLjS4Mmcnptb9//15talGlUokaNWqovmw0bNgQEokEQ4cO1ZgHP9cUirQ+5O/fp/1bLAZKlMhzH3LSTmxsLHbs2AGZTKaRVALgWJtiTBAEnD9/HnK5HNu3b1eb2AVISyxDQkK0nqBGF/Q2odBHep1QpIuNzffZEoioeNLHOu+zzz6Dv78/3r17pzbwfevWrRg6dCguXryoMSd9ZrS99vv376Nhw4YaA+tNTEzQs2dPSKVSeHp65m7K4NhY4OlT4NmzrOvtqlXTxsyx3taJJ0+eqLpEpY+1+VDdunUhlUoxbNiwLBdhJf0UFxeHnTt3Qi6X4+zZswCANm3aqMbdpHvz5g3Kly9fZBNLJhSFSB9vrllKf9KVz7MlEFHxoY91XvPmzZGamop//vlHbfvdu3fh5uaG9evXY+zYsdl+Tl6u/d27d/D394dcLteIA0ibWnvEiBGYNWsWypQpk/UHsWVZ76SmpiIgIAAymQy7d+/WWBtILBaje/fukEgk6N69e6YTJZD+CgwMhI+PDxo0aIC+ffuqtiuVSlSrVg2WlpaQSCQYPnx4kRtrw4SiEOnjzZWISFv6WOfVrFkT1apVw9GjR9W2h4aGwsHBAT/88ANmz56tcVzG6XpjYmLg6OiY52u/e/cu5HI5Nm3ahDdv3qi2W1pa4vXr11kvmMWxb3ovOjoa27Ztg1wux6VLlzTKy5Urh2HDhkEqlaJevXo6iJAKy+nTp9GhQwfVe2NjY3Tt2lU11ubDRVl1Ja/1fdZzhxIREemhj3UpyKps8eLFsLa2Vr0c82l6SldXVyxbtgzBwcE4cOAA+vfvDxMTEwwcOFAjmfjtt99w5MgRpKamprVMaJNMAGnH3bmTD9FTXlhbW2Ps2LH466+/cP/+fcyaNQsODg6q8rdv3+Lnn39G/fr14e7ujtWrVyMiIkKHEVNBMTY2VptaOjU1FQcPHsSAAQPg4OCAL7/8Ejdu3IA+P+NnC0Uu6OPTOiIibeljnadtl6eCaqHITEREBBISEtRWXI6Ojoa9vT0SEhLgYG+PEU2aQNK2LVz+P1NMYrhR7tYPAgBPT46pKGIUCgVOnDgBuVyOvXv3Ijk5Wa3cxMQEvXr1glQqRZcuXXI31oaKvMePH0Mul8PX1xfBwcEa5a1bt8aZM2d0Ms6CXZ4KkT7eXImItKWPdd7YsWOxdetWjUHZ/v7+GDJkSIEPytbWn3/+iU8//VRje7MaNTDcoz2qbu+OEoqskwMjEwFNVkX+l1SIRECNGmkrDFORFBkZCX9/f8hkMly9elWj3M7ODiNGjIBEIkGdOnV0ECEVlNTUVJw6dQpyuRy7d+9WPcwYO3Ys1q9fr7avIAiFkmCwyxMREdH/9e3bF3Fxcdi1a5fadh8fHzg4OKBp06Y6iuzjhg8fjj179qBXz54wNvrv1nz58WN84bcefRUD8B2+w1VchQDN54DKFJF6C4Yg/DebHxVJtra2mDBhAq5cuYLbt29j2rRpatOMvn79GsuWLYOrqyuaNGmCtWvX4t27dzqMmPKLsbExOnfuDD8/P7x+/Rrr1q1D06ZNNRbyfPfuHapWrYqpU6fi9u3bugk2h9hCkQv6+LSOiEhb+lrnde7cGVevXsXSpUvh7OyMrVu34o8//sDmzZsxbNiwHH2Gzq49KAhvjh6F34ULkJ05g9svX6oVV0ZlyCGHCJpPLN2XvEOpahkSiGbNgHwaD0IFLyUlBceOHYNMJsOBAweQkpKiVm5mZoY+ffpAIpGgU6dOOVpThfTXunXrMH78eNV7d3d31bo2tra2+XoutlAQERF9YPfu3RgxYgTmz58PT09P/P3339i6dWuOkwmdev8eFUqXxlc9euDfZctwbckSjG3eFVZIu8F7wlMjmTiP83iP95qfJRKlTQ1OesPExAQ9evTArl278OrVK6xatQoNGzZUlSclJWHbtm3o2rUrKleujNmzZ+Phw4c6jJgK0qtXr9RmgLp27RomTZoEe3t7eHl54fDhw1AUkVbIYtVCsWHDBnz22WcoWbIk4uLi1MquX7+OGTNm4PLlyxCLxWjfvj2WL1+OatWq5fjz9fVpHRGRNgy5ztPZtd+9C9y/r7bmRGygGJdmlcQlXEJd1IUt/nsy+QzPMBqjYQ5z9GvUHJ/1bI3WtWvDyMgoLaGoXRtwdS28+KlA/Pvvv5DL5di8eTPCw8M1yps3bw6pVIqBAwfC2tpaBxFSQYmMjMTWrVshl8szHWtjb2+P2bNnY9KkSXk6D1so/i8kJATTpk1Tm5It3YMHD9C2bVskJydj+/bt+PPPP/Ho0SO0atUKb9++1UG0REREmchiVh9TmKIN2qglEwBwDMcAAIlIhN/1ALRbuBDOX36JhTt24HlYWNqipaT36tevj5UrVyIkJCRtrE2vXmrdnS5duoSxY8fC3t4ew4cPx6lTp6BUKj/yiaQvbG1tMXHiRFy5cgW3bt3C1KlT1VZbDw0N1ZgtTBeKTQtFz549IRKJYGtri507d6q1UAwcOBABAQF4+vSpKut68eIFatSoga+++gpLly7N0TkM+WkdERkeQ67zdDmGApcvq22KDRTj2qzSme7+CI9wCIdwCqcQj3iN8nYtWkAybhz69++f9SJ6pJfevHmDLVu2QCaT4U4m645UrlwZI0eOhEQiQfXq1XUQIRWUlJQUHD16FDKZDEePHsWzZ8/UBvTfunULixcvhlQqRYcOHXI01obTxgLYvHkzJkyYgHv37mHevHlqCYVCoYCVlRVGjhyJdevWqR3XpUsXPHv2DI8ePcrReQz55kpEhseQ6zydXbtCARw4oDY708cSinTJSMarIcfgf+8Ujt+6pbFAlpeXF7Zv314gIZNuCYKA69evQy6Xw8/PD5GRkRr7tGrVClKpFAMGDEAprk1SrMTFxcHS0lJt21dffYWff/4ZAFCpUiWMHDkSo0aNQs2aNbP8HIPv8hQWFoYpU6ZgyZIlaosEpXv69CkSEhIyXda+Xr16ePLkCRITEwsjVCIioo8Ti4GqVdPGP/yfiZUSRiYff/ZnbmKCwa1a4OjcuXi5Zg1+GDoUNZ2cVOVDhw5V2z85ORlBQUH5GzvphEgkUq20/erVK+zYsQPdunVLG0fzf+fPn8fo0aNhb28PiUSCM2fOsEtUMZExmRAEAcePH1e9Dw4Oxg8//IBatWqhZcuW2LBhA2JiYvI9Dr1PKCZMmIBatWqpTav1ofRl7DObXsvW1haCIGQ5r3NSUhJiYmLUXkRERAWqenW1QdnmZZVosioS7kveZfn6cFG7SmXKYHafPnhw6xYuXryISZMmoVu3bmqnOHjwIJycnFRz4SdwNqhiwczMDAMGDMChQ4cQHByMpUuXwsXFRVUeHx8PHx8ftGvXDs7Ozli4cCGeP3+uu4Ap34lEIty4cQO7d+/WGGtz8eJFfPbZZ6pFE//99998O69eJxS7du3CgQMH8Mcff2S7iuDHyrMqW7x4MaytrVUvR87lTUREBa1UKeCDL4FAWlJRqpoiy5dqhex0Li4QWVmhRYsW+OWXX9SmngQAuVwOQRBw4sQJDBs2DHZ2dhg3bhwuX76s0V2K9JO9vT1mzJiBe/fu4fLly/j888/VZoB69uwZFixYgKpVq6J9+/bYtGkT4uM1x+GQ/jE1NUXfvn2xb98+hISEYMWKFXBzc1OVJyQkYPPmzQgODs63c+ptQhEXF4eJEydi0qRJcHBwQFRUFKKiolQj3aOiohAfH48yZcoA+K+l4kORkZEQiUSwsbHJ9ByzZ89GdHS06sXmYSIiKhRubsAHXZZyxckp7fiPaN68udq06TExMfj999/RvHlz1KlTB0uXLsWrV6+0Oz8VKSKRCE2bNsXatWsRGhqKrVu3onPnzmoPUwMCAjBy5EjY29tjzJgxuHDhAhPLYqJChQr4+uuvcevWLVy9ehUTJ05E6dKlUaFCBXTp0iXfzqO3g7KfP3+OqlWrfnSf3r17Y+fOnbCyssKoUaOwdu1atXJPT08EBgZyUDYRUSYMuc4rEtcuCMCdO8CDB2ljKj52u04vd3FJSyayabVP+3gB58+fh1wux/bt2zWeThsZGWH9+vUYM2ZMXq+EiqDg4GD4+vpCLpfj8ePHGuXOzs6QSCQYOXIke2gUM0lJSXj48KHa+GKDneUpMTERlzNMrQcAS5YswdmzZ3HkyBGULVsWbm5uGDRoEM6cOYMnT56oZjd4+fKlatrYJUuW5OicReIGQ0RUSAy5zitS1x4bCwQGpr0UCvVkQRDSBnJXq5b20nIGn7i4OOzatQsymQxnz55Vbb937x5q166tep+SkgKxWJxtN2PSH4Ig4K+//oJcLse2bdsQGxurVi4SidCxY0dIpVL06dMHFhYWOoqUCtKTJ09Qo0YNw0sosiKRSDTWoXjw4AE8PDzQqFEjzJo1C4mJiZg/fz4iIyNx8+ZNlCtXLkefXaRuMEREBcyQ67wiee0KBRAaCiQkACkpaYvWWVgA9vZZLoinjcDAQPj4+OD+/fsaU80uWbIEmzdvhkQiwfDhw2FnZ5dv5yXdi4+Px549eyCTyXD69GmNcmtrawwePBgSiQRNmzZlYlmMREdHw8bGhglFuswSCgC4du0aZs6ciUuXLkEsFqN9+/ZYvnx5rhZ7KZI3GCKiAmLIdZ4hX3tWBEGAi4uLqpuwsbExunbtCqlUih49emgM/Cb99uLFC/j4+EAul+PZs2ca5S4uLpBIJBgxYgQcHBx0ECHlJ4Pt8qQLvMEQkSEx5DrPkK89KxEREejduzcuXryoUVamTBkMGzYMEokEDRs21EF0VFCUSqVqrM2OHTsyHWvTpUsXSKVS9OrVC2ZmZjqKlPKCCUUh4g2GiAyJIdd5hnzt2Xn06BF8fHzg4+ODkJAQjfL69etjy5YtcHV11UF0VJDi4uKwc+dOyGQynDt3TqO8dOnSGDp0KCQSCdzd3dklSo8woShEvMEQkSEx5DrPkK89p1JTU3Hq1CnIZDLs2bMHSUlJAIASJUrg9evXqklQqHh6+vSpKrF8+fKlRrmbm5tqrE2FChV0ECHlBhOKQsQbDBEZEkOu8wz52rURFRUFf39/yOVy1KxZE76+vmrl06dPR2pqKqRSKerWraujKKkgKJVKBAQEQC6XY9euXRqrrhsbG6Nbt26QSqXo3r07x9oUUUwoChFvMERkSAy5zjPka8+r5ORktS+NcXFxsLOzU/W9b9SoEaRSKYYMGaJafJaKh+joaOzYsQMymQx//fWXRnnZsmVVY20aNGhQ+AFSlvJa5+ntStlERERU9GR8An3lyhWkpKSo3l+/fh2TJk2Cg4MDvLy8cOjQISgUisIOkwqAtbU1xowZg4sXL+Lhw4eYPXs2KlasqCoPDw/HqlWr0LBhQzRs2BCrVq1CeHi4DiOm/MIWilzgEysiMiSGXOcZ8rUXhIiICPj7+0Mmk+HatWsa5XZ2dhgxYgQWLlzIhdOKmdTUVJw8eRJyuVxtrE06ExMT9OjRA1KpFJ6enjAxMdFRpIaNXZ4KEW8wRGRIDLnOM+RrL2i3b9+GXC7H5s2bERYWptru7OyMR48ecWagYuzdu3fYtm0bZDIZ/vnnH43yChUqYPjw4ZBIJHBzc9NBhIaLXZ6IiIhIb9StWxcrVqxAcHAw9u3bh759+0IsFkMikWgkE97e3jh+/DhSU1N1FC3lp9KlS+Pzzz/H33//jbt372L69Olqq62/efMGK1asQN26deHh4YHffvsNkZGROoyYcootFLnAJ1ZEZEgMuc4z5GvXhbdv38LY2Bi2traqbQ8ePEDt2rUBAJUqVcLIkSMxatQo1KxZU1dhUgFQKBQ4duwY5HI59u3bpzbeBkgbk9O7d29IJBJ07twZYrFYR5EWb2yhICIiIr1Wrlw5tWQCAPz8/FT/Dg4Oxg8//IBatWqhZcuW2LBhA2JiYgo7TCoAYrEY3bt3x44dOxAaGorVq1fD3d1dVZ6cnIwdO3age/fuqFy5MmbNmoUHDx7oMGLKDFsocoFPrIjIkBhynWfI115UJCcn4+DBg5DL5Th8+LBGtycLCwv0798fn376Kdq2baubIKnA3Lp1SzXW5u3btxrlzZo1g0QiwaBBg2BjY1P4ARYzHJRdiHiDISJDYsh1niFfe1H0+vVrbNmyBTKZDHfv3lUr6969Ow4ePKijyKigpaSk4PDhw5DL5Th48KDGFMPm5ubo27cvpFIp2rdvD2NjYx1Fqt/Y5YmIiIiKNTs7O0ydOhW3b9/GlStXMGHCBNVTaYlEorZvSkoKNm3ahLi4uMIPlPKdiYkJevfujT179iAkJAQrV65EvXr1VOWJiYnYunUrOnfujCpVqmDevHl4/PixDiM2TGyhyAU+sSIiQ2LIdZ4hX7u+SExMxIEDB9CrVy+YmZmptqdvK1myJLy8vCCRSNC6dWtOR1uMCIKAmzdvQiaTYcuWLZnOBNWyZUtIJBJ4eXnxv+EcYJenQsQbDBEZEkOu8wz52vVdv379sGfPHrVt1apVw6hRozBq1Cg4OTnpKDIqCElJSaqxNkeOHNEYa1OiRAn0798fEokEbdu2hZERO+dkhglFIeINhogMiSHXeYZ87frun3/+wZ9//gl/f39ER0drlLdv3x5SqRT9+vVDiRIldBAhFZTQ0FDVWJt79+5plDs5OWHUqFGQSCSoWrWqDiIsuphQFCLeYIjIkBhynWfI115cJCQkYO/evZDJZDh58iQyft2ZNm0ali1bpqPoqCAJgoCrV69CLpfDz88PUVFRGvu0adMGUqkUAwYMQMmSJQs/yCKGCUUh4g2GiAyJIdd5hnztxVFQUBB8fX0hl8vx5MkTAMC///6rNrj33bt3iIuLg6Ojo67CpAKQmJiI/fv3QyaT4fjx41AqlWrllpaW8PLyglQqRcuWLQ12rA1neSIiIiL6CEdHR8ydOxePHj3C+fPn4e3trZZMAMCff/4JJycndOnSBVu3bkVCQoKOoqX8ZG5ujoEDB+LIkSN4+fIlFi9erLbaelxcHGQyGVq3bo0aNWrgu+++w8uXL3UYsX5iC0Uu8IkVERkSQ67zDPnaDZEgCKhbt67aGhfW1tYYPHgwpFIpmjRpYrBProsjQRBw+fJlyGQybNu2TWPVdZFIhA4dOkAikaBv374GMdaGLRREREREeaBQKODl5YUqVaqotkVHR2P9+vVo1qwZXF1d8eOPPyI0NFR3QVK+EYlEaN68OX7//XeEhoZi8+bN6NixoyppFAQBJ0+exPDhw2Fvb4+xY8fir7/+0hiHQ/9hC0Uu8IkVERkSQ67zDPnaDZlSqcS5c+cgk8mwc+dOvH//Xq3cyMgI+/btQ48ePXQUIRWkly9fqsbaPH36VKO8Zs2akEgkGDlyJCpWrKiDCAsOB2UXIt5giMiQGHKdZ8jXTmliY2OxY8cOyOVynD9/HkBaf/zQ0FDVKt1A2qBfMzMzdokqRgRBwIULFyCXy7F9+3aNVdeNjIzQqVMnSKVS9O7dG+bm5jqKNP8woShEvMEQkSEx5DrPkK+dND158gQ+Pj54//49VqxYoVb22Wef4e+//4ZUKsWwYcNQvnx5HUVJBSEuLg67d++GTCbDmTNnNMptbGwwZMgQSCQSeHh46G1iyYSiEPEGQ0SGxJDrPEO+dsq59+/fw87ODrGxsQAAsViM7t27QyKRoHv37jAxMdFxhJSfnj17Bh8fH8jlcrx48UKjvE6dOpBIJBgxYgTs7Ox0EKH2OCibiIiISAdevXoFNzc31XuFQoF9+/ahb9++qFixIr766ivcunVLhxFSfqpatSoWLFiAwMBAnD59GiNGjICFhYWq/N69e5gxYwYqVaqEnj17YteuXUhOTtZhxIWHLRS5wCdWRGRIDLnOM+Rrp9x78OABfHx84Ovri1evXmmUN2zYEIcOHYK9vb0OoqOCFBMToxprc+HCBY3yMmXKYOjQoZBIJGjYsGGR7RLFFgoiIqL/i42NxYwZM9C5c2eUK1cOIpEICxYs0HVYVMy5uLhg8eLFePnyJY4cOYJBgwbBzMxMVf7u3TtUqFBBhxFSQbGyssKnn36K8+fP49GjR5g7dy4qVaqkKo+IiMDq1avh7u6OBg0aYOXKlQgLC9NhxAWDCQURERUbERER+P3335GUlIQ+ffroOhwyMMbGxvD09IS/vz9CQ0OxZs0aeHh4QCKRwMhI/SvXoEGDMH36dLXF9Ei/pa+0/fz5cxw/fhxDhw5VmwHq1q1b+Prrr1GxYkX06dMH+/btQ0pKig4jzj/s8pQLbAInIkOij3Ve+i1NJBIhPDwc5cqVg7e3d65bKfTx2qnoSk1NhbGxser906dP4ezsrHqfnnQMGTIEpUuX1kWIVECioqKwfft2yGQyXL58WaO8XLlyGD58OCQSCerVq6eDCNOwyxMREdH/iUSiIttHmQzXh8kEAPzzzz9qM0BduXIFEydOhL29PQYNGoSjR48iNTW1sMOkAmBjY4OxY8fi0qVLuH//PmbOnKk2lubt27dYuXIl6tevD3d3d6xevRoRERE6jFg7TCiIiIiICtGQIUPw6tUr/PLLL2jYsKFqe1JSErZv346uXbuicuXKmDt3LtiRpPhwcXHBkiVL8PLlSxw+fBgDBw6Eqampqvz69ev48ssvYW9vjwEDBuDQoUNQKBQ6jDjnmFAQEZHBS0pKQkxMjNqLqCCVLVsWkyZNwvXr13Hz5k189dVXKFeunKr81atXuHr1KlvciiGxWIyuXbti27ZtCA0NxW+//YbGjRurylNSUrBr1y706NEDjo6OmDFjBu7du6fDiLOntwnF6dOnMXr0aLi4uKBkyZKoWLEievfujWvXrmnse/36dXTs2BGWlpawsbFBv379EBgYqIOoiYgop86cOaPqwpTd6+bNm3k61+LFi2Ftba16OTo65s9FEOVA/fr18dNPPyE4OBh79+5F7969IRaLIZFI1PZLTU3F+PHjceLECXaJKiZsbW0xYcIEXLlyBbdv38bUqVPVVlt//fo1li1bBldXVzRt2hRr167Fu3fvdBhx5vR2ULaXlxciIiLg5eWFOnXq4O3bt1ixYgWuXr2KY8eOoX379gDS5oZu0qQJGjRogFmzZiExMRHz58/Hu3fvcPPmTbWnAdnhID0i+lDiy0SkhGc9Q4dJWROYVzbPsryo03WdFxoaikOHDuVo3379+sHW1lZtW24GZSclJSEpKUn1PiYmBo6OjqzvSWfCwsJgZWWlNkvQ0aNH0bVrVwCAo6MjRo4cCYlEojbAm/RfSkoKjh49CrlcjgMHDmjMBGVmZoY+ffpAKpWiY8eOGmN0tJHX+l5vE4qwsDC1DA4A4uLi4OzsDDc3N5w8eRIAMHDgQAQEBODp06eqX9CLFy9Qo0YNfPXVV1i6dGmOz6nrmysRFR2JLxPxT61/oExUZrmPkbkRmjxsordJhb7XeZzliYobqVQKuVyusb1ly5aQSqXw8vJCqVKlCj8wKjDh4eHw8/ODTCbLtCW2YsWKqsSyZs2aWp/HYGd5yphMAIClpSXq1KmDoKAgAIBCocDBgwfRv39/tV+Ok5MT2rVrhz179hRavERUvKSEp3w0mQAAZaLyoy0YRES5sW7dOuzYsQPdu3dXW9fiwoUL+PTTT2FnZ4dRo0bh4sWLOoyS8lPZsmXx5Zdf4saNG7hx4wYmT56MsmXLqspDQkKwePFi1KpVC5988gn++OMPnYwB09uEIjPR0dG4fv06XF1dAaTN85yQkJDpvL716tXDkydPkJiYWNhhEhFRATpy5Ah27tyJAwcOAADu3buHnTt3YufOnXj//r2OoyPSnpmZGQYMGICDBw8iODgYP/74I2rXrq0qf//+PXx9feHv76/DKKmgNGjQAD///DNCQkKwe/du9OrVS627019//YWxY8fCzs4Ow4cPx6lTp6BUfvzBV34pVgnFxIkTER8fj7lz5wKAah7fjP1q07cJgvDRgS2c9YOISP+MHz8eXl5eGD16NABgx44d8PLygpeXF8LCwnQcHVH+sLe3V620/ffff+Pzzz+HtbU1gLSuUR8KDw/Hpk2bEB8fr4tQKZ+Zmpqib9++2LdvH0JCQrBixQq4ubmpyhMSErBlyxZ07NgRVatWxfz58/H06dMCjanYJBTffPMNtmzZgpUrV8Ld3V2t7GNTrn2sjLN+EBHpn+fPn0MQhExfVapU0XV4RPlKJBKhSZMmWLt2LUJDQ7F//361tS0AYMuWLRg5ciTs7e0xZswYXLhwgetbFBMVKlTA119/jVu3buHq1auYOHGi2mrrL1++xKJFi+Ds7Iw2bdpAJpMhLi4u3+MoFgnFwoUL8d133+H777/HF198odpepkwZAMh0xcHIyEiIRCLY2Nhk+bmzZ89GdHS06pU+NoOIiIioqLGwsEDPnj01HpbKZDIAQGxsLDZu3IhWrVqhZs2a+P777/ndppgQiURwd3fHr7/+itDQUGzfvh3dunVTG2tz7tw5jB49GnZ2dpBIJDh79my+dYnS+4Ri4cKFWLBgARYsWIA5c+aolVWvXh0WFha4ffu2xnG3b9+Gs7Oz2nRsGZmZmcHKykrtRURERKQvBEHAmjVrMGbMGLUZoJ48eYJ58+bByckJnTt3xtatW5GQkKDDSCm/mJmZwcvLC4cOHUJQUBCWLl0KFxcXVXl8fDx8fHzQtm1bODs749tvv8WLFy/ydE69TigWLVqEBQsWYN68efD29tYoF4vF6NmzJ3bv3o3Y2FjV9pcvXyIgIAD9+vUrzHCJiIiICpVIJEKLFi3wxx9/4PXr19i0aRM6dOigKhcEASdOnMDQoUNVLRlUfDg4OKhW2r58+TLGjRunGmsDAM+ePYO3t3emExjlht6uQ7FixQpMmzYNnp6emSYTzZo1A5C2sJ2HhwcaNWqktrBdZGQkF7YjIq1xHYrizZCvnQzDixcv4OvrC7lcjsDAQJiamiI0NFRtIpvg4GAYGRnBwcFBh5FSfktISMDevXshl8tx4sQJtfE0BrewXdu2bXH27Nksyz+8rGvXrmHmzJm4dOkSxGIx2rdvj+XLl6N69eq5OidvMET0Ia6UXXwZ8rWTYVEqlbhw4QLu3LmDCRMmqJVNmDAB69evR5cuXSCRSNCrV6+PdhUn/RMUFIRNmzZh48aNCAwMNLyEQhd4gyEiQ2LIdZ4hXzsRACQmJsLe3h5RUVGqbaVLl8aQIUMglUrh7u7+0ZkySb9ER0fDxsbG8FbKJiIiIqKCkZycjC+//BJOTk6qbe/evcOaNWvg4eGBunXrYsWKFXj9+rUOo6T8ktfkkAkFEREREamxsrLCwoULERgYiFOnTmHEiBGwsLBQld+9exfTpk1DpUqVcOPGDR1GSkUBEwoiIiIiypSRkRHat28PX19fvH79Ghs2bMAnn3yiKndwcNCYIejDmTXJMIh1HQARERERFX1WVlb49NNP8emnn+Lx48eQy+UoV64cjI2N1fbr27cvwsPDIZFIMGzYsFzNqJklhQIIDQXev0/7t1gMlCgB2Nun/Zt0ioOyc4GD9IjIkBhynWfI106UFy9evEDVqlVVs22KxWL06NEDUqkUXbt2hYmJSe4+MDYWePoUePYsLZH4sK+/IKQlE1WrAtWrAx8s3Ee5k9c6j12eiIiIiChfhIeHo0mTJqr3CoUCe/fuRe/evVGpUiVMnToVt2/fzv6DBAG4fRs4ehR48iQtmUjfnv5KO0Fa+dGjafvzOblOMKEgIiIionzh7u6Oy5cv4+7du5gxYwbs7OxUZWFhYfjpp59Qr149eHh4IDExMfMPEQTgyhXgwYP/3n9MevmDB2nHMakodEwoiIiIiChf1alTB0uXLkVQUBAOHTqEAQMGwNTUVFVeqlSprBfJu3MHePFCuxO/eJF2PBUqjmIhIiIiogIhFovRrVs3dOvWDREREdi6dSvkcjkkEonafkqlEp988glaN2sGqZMTXCpWVJUlhhshJSbrZ+AmVkqYl1X+t+HBA6BKFY6pKEQclJ0LHKRHRIbEkOs8Q752osIgCILaYmqnTp1Cx44dVe+b1agBSdu26F2rJR7NdoQyJeuF14xMBDRZFflfUiESATVqAPXrF1j8xU1e6zy2UBARERFRocq4MvOdO3cgFouh+P/g68uPH+Py48eYIvZBC0VLdEVXNERDGMNY47OUKSKkxBj9l1AIAhAYCLi6ckrZQsIxFERERESkU5MnT8arK1fws0SC+k5Oqu2JimScxmlMx3QMwRBsxdacfWD6uhVUKJi2EREREZHOlbOwwOTu3TG5WzfcfP4csoAAbD57AZHv01befou3eI3XOfswkQhISCjAaOlDbKEgIiIiIt1LX2sCQIMqVbBKKsWjORuwEAvRAi1gBCN0QRe1Q2IQgyVYgrNPbkOpVKp/XkpKYURNYAsFERERERUFmYx3MBWboPX///cO72ADG7XyUziFYziGYxuOwWlfOYxq0waj2rRBNTs7ILercpPW2EJBRERERLpXosRHF6UrjdIQQX0w93mcV/37xdu3+HbnTlSfNAltvb3hc/Ag4uLiCixc+g8TCiIiIiLSPXv7XM/KtARLMB/z0alWIxh9MHPU2Xv3IJk6FXZ2dhg9ejSuXr2a39HSB5hQEBEREZHuicVA1appA6r/z8RKCSOTrFstTGGKDiZtsX/yHLxcuxZLhg5FLQcHVXl8fDxkMhkuXLhQoKEbOi5slwtc6IiIDIkh13mGfO1EOhUbCxw9qrYptytlC4KAv8uUgWz7dvj7++P9+/d49eoVypUrp9rnwYMHuHr1Kvr164cSJUrk/3XombzWeWyhICIiIqKioVQpwMVFbZN5WSVKVVNk+fowmQAAUe3aaNahA9avX4/Xr1/j5MmTaskEAKxduxYjRoyAnZ0dPvvsM/z111/gM3btMaEgIiIioqLDzQ34YHG7XHFySjv+/ywsLNCmTRu1XZKTk7FlyxYAQGxsLDZs2IBPPvkELi4uWLx4MYKDg7UO3VAxoSAiIiKiokMkAjw8/mupEImy3x9I29/DI9v9TUxMsHfvXowePRqWlpaq7Y8ePcKcOXPg5OQET09P+Pv7I4GL4+UIx1DkAvvUEpEhMeQ6z5CvnahIiY0FAgPTXgqFerIgCGkDuatVS3uVKpXrj4+Pj8euXbsgk8lw5swZjfJDhw6hW7duebgA/ZDXOo8JRS7wBkNEhsSQ6zxDvnaiIkmhAEJDgYSEtBWwTUwACwutpprNyrNnz+Dj4wMfHx88f/4c9vb2ePnyJcQffP79+/dhY2MDe3v7fDlnUcGEohDxBkNEhsSQ6zxDvnYiQ6dUKnHu3DmEhYVh4MCBamWenp44ceIEPD09IZVK0bNnT5iZmeko0vzDhKIQ8QZDRIbEkOs8Q752IspcSEgIKleuDKXyv1mlbG1tMXToUEgkEjRq1Aii7MZ7FFGcNpaIiIiIqICZmJhgzpw5cHR0VG2LjIzEr7/+isaNG6N+/fr46aefEBYWpsModYMtFLnAJ1ZEZEgMuc4z5Gsnoo9TKpU4ffo0ZDIZdu/ejcTERLVyExMTBAUFoUKFCjqKMPfYQkFEREREVEiMjIzQsWNHbNmyBa9fv8b69evRvHlzVXmzZs00konIyMjCDrNQMaEgIiIiItKCtbU1xo4di7/++gsPHjzArFmz8OWXX6rto1Qq0bhxY7i7u2P16tUIDw/XUbQFh12ecoFN4ERkSAy5zjPkayei/HXmzBm0a9dO9d7ExAS9evWCRCKBp6en2rS0usIuT0RERERERZRSqYSHh4fqfUpKCnbt2oWePXuiUqVKmD59Ou7du6fDCPOOCQURERERUQFp3749/vnnH9y5cwfTpk1TG1/x5s0bLF++HK6urujYsSP0teOQwSQUcXFxmDJlChwcHGBubo4GDRrA399f12EREVE+On36NEaPHg0XFxeULFkSFStWRO/evXHt2jVdh0ZEBs7V1RXLli1DcHAwDhw4gP79+8PExERVXqVKFY11LPQlwTCYhKJfv37w8fGBt7c3jhw5Ag8PDwwZMgR+fn66Do2IiPLJ2rVr8fz5c0yePBmHDx/GqlWrEBYWhmbNmuH06dO6Do+ICGKxGD169MDOnTvx6tUr/PLLL2jYsCGkUqnaftHR0ahZsyZmz56Nhw8f6ijanDGIQdmHDx9G9+7d4efnhyFDhqi2d+7cGXfv3sXLly9hbGyc7edwkB4RGRJ9rPPCwsJQvnx5tW1xcXFwdnaGm5sbTp48maPP0cdrJyL9JgiCWgvF77//jnHjxqneN2/eHBKJBIMGDYK1tXW+npuDsnNgz549sLS0hJeXl9p2qVSKV69e4e+//9ZRZERElJ8yJhMAYGlpiTp16iAoKEgHERER5UzG7k7Pnz9Xe+B96dIljBs3DnZ2dhg2bBhOnjwJpVJZ2GFmyiASijt37qB27doa03LVq1dPVU5ERMVTdHQ0rl+/DldXV12HQkSUYz/88ANCQkKwYsUKuLm5qbYnJibCz88PnTp1QpUqVbB27VodRplG9xPfFoKIiAhUq1ZNY7utra2qPDNJSUlISkpSvY+OjgaQ1ixERFTcpdd1+t4zduLEiYiPj8fcuXOz3If1PREVRRYWFhgzZgw+/fRT3Lx5E35+ftixYwfevXsHAAgKCkJ4eHie66o81/eCAahRo4bg6empsf3Vq1cCAGHx4sWZHuft7S0A4Isvvvgy6NfTp08LuprOVEBAQI5jvHHjRqafMW/ePAGAsHr16o+ei/U9X3zxxZf29b1BDMpu3rw5UlNT8c8//6htv3v3Ltzc3LB+/XqMHTtW47iMT6yioqLg5OSEly9f5vtgGF2JiYmBo6MjgoKCis3Aw+J2TcXtegBek76Ijo5G5cqV8e7dO9jY2BT6+UNDQ3Ho0KEc7duvXz9Vq3O6hQsXYsGCBfj+++8xZ86cjx5vCPV9YSqO/z0UFv7utMffnfbyWt8bRJenunXrYuvWrVAoFGrjKG7fvg0Aav3SPmRmZgYzMzON7dbW1sXuD9XKyorXVMQVt+sBeE36wshIN8Pt7O3tMWbMGK2OTU8mFixYkG0yARhWfV+YiuN/D4WFvzvt8XenPW3re4MYlN23b1/ExcVh165datt9fHzg4OCApk2b6igyIiLKb4sWLcKCBQswb948eHt76zocIqJizyBaKLp27YpOnTph/PjxiImJgbOzM7Zu3YqjR49i8+bNOVqDgoiIir4VK1Zg/vz58PT0RPfu3XH58mW18mbNmukoMiKi4ssgEgoA2L17N+bOnYv58+cjMjISLi4u2Lp1KwYPHpzjzzAzM4O3t3emzeL6itdU9BW36wF4TfpCH6/pwIEDAICjR4/i6NGjGuU5HTaoj9delPD3pz3+7rTH35328vq7M4hB2UREREREVDAMYgwFEREREREVDCYURERERESkNSYURERERESkNSYUORAXF4cpU6bAwcEB5ubmaNCgAfz9/XUdltZiY2MxY8YMdO7cGeXKlYNIJMKCBQt0HVaenD59GqNHj4aLiwtKliyJihUronfv3rh27ZquQ9PKzZs30b17d1SuXBkWFhawtbVF8+bNsXnzZl2Hlq82bNgAkUgES0tLXYeilTNnzkAkEmX6yji7kL65cOECunXrhtKlS8PCwgI1atTAokWLdB2WzhS3OqYwFcd7TkEobt81ChP/xrSTn/UaE4oc6NevH3x8fODt7Y0jR47Aw8MDQ4YMgZ+fn65D00pERAR+//13JCUloU+fProOJ1+sXbsWz58/x+TJk3H48GGsWrUKYWFhaNasGU6fPq3r8HItKioKjo6O+OGHH3D48GH4+vqiSpUqGDFiBL777jtdh5cvQkJCMG3aNDg4OOg6lDz74YcfcOnSJbVXVgtm6gM/Pz+0adMG1tbW8PX1xeHDhzFz5swcz5BUHBW3OqYwFcd7TkEobt81ChP/xrSTr/WaQB916NAhAYDg5+entr1Tp06Cg4ODoFAodBSZ9pRKpaBUKgVBEIS3b98KAARvb2/dBpVHb9680dgWGxsrVKhQQejQoYMOIioYTZs2FRwdHXUdRr7o0aOH0LNnT2HUqFFCyZIldR2OVgICAgQAwo4dO3QdSr4JDg4WSpYsKYwfP17XoRQphlLHFITieM/Jb8Xxu0Zh4t+YdvKzXmMLRTb27NkDS0tLeHl5qW2XSqV49eoV/v77bx1Fpr30LhnFSfny5TW2WVpaok6dOggKCtJBRAWjbNmyEIv1f/mYzZs34+zZs1izZo2uQ6EMNmzYgPj4eMycOVPXoRQphlLHFITieM/Jb8Xxu0Zh4t+YdvKzXmNCkY07d+6gdu3aGl/i6tWrpyqnoik6OhrXr1+Hq6urrkPRmlKphEKhwNu3b7FmzRocO3ZM77/ohYWFYcqUKViyZAkqVaqk63DyxcSJEyEWi2FlZYUuXbrgwoULug5Ja+fOnYOtrS0ePHiABg0aQCwWo3z58vj8888RExOj6/CKlOJQx1DRwO8aVFRoW68xochGREQEbG1tNbanb4uIiCjskCiHJk6ciPj4eMydO1fXoWhtwoQJMDExQfny5fHVV1/hl19+wbhx43QdVp5MmDABtWrVwvjx43UdSp5ZW1tj8uTJWL9+PQICArBq1SoEBQWhbdu2OHbsmK7D00pISAjev38PLy8vDBo0CCdPnsT06dPh6+uLbt26GfQ4ioyKQx1DRQO/a1BRoW29pv99JwrBx5rR2MRWNH3zzTfYsmULVq9eDXd3d12Ho7U5c+ZgzJgxCAsLw4EDB/DFF18gPj4e06ZN03VoWtm1axcOHDiAGzduFIv/dho2bIiGDRuq3rdq1Qp9+/ZF3bp1MWPGDHTp0kWH0WlHqVQiMTER3t7emDVrFgCgbdu2MDU1xZQpU3Dq1Cl07NhRx1HmzZkzZ9CuXbsc7Xvjxg00aNBAY3txqWNyKz9+d5Q5ftcgXctLvcaEIhtlypTJ9MlAZGQkAGT6RIF0a+HChfjuu+/w/fff44svvtB1OHlSuXJlVK5cGQDQrVs3AMDs2bMxatQolCtXTpeh5VpcXBwmTpyISZMmwcHBAVFRUQCA5ORkAGkzW5mYmKBkyZI6jDLvbGxs0KNHD6xbtw4JCQmwsLDQdUi5UqZMGTx+/FgjGeratSumTJmC69ev631CUatWLfzxxx852jf9v78PFac6Jrfy+rujzPG7BulaXus1JhTZqFu3LrZu3QqFQqHWt/H27dsAoNdTQxZHCxcuxIIFC7BgwQLMmTNH1+HkuyZNmmDdunUIDAzUu4QiPDwcb968wYoVK7BixQqN8tKlS6N3797Yu3dv4QeXz9K7BenjU8V69epluoZG+jUZGel/T1l7e3uMGTNGq2OLex2Tnbz87ihr/K5BupQf9Zr+3xkKWN++fREXF4ddu3apbffx8YGDgwOaNm2qo8goo0WLFmHBggWYN28evL29dR1OgQgICICRkRGqVaum61Byzc7ODgEBARqvLl26wNzcHAEBAcVijY13797h4MGDaNCgAczNzXUdTq71798fAHDkyBG17YcPHwYANGvWrNBjKioMoY4h3eB3DdKV/KrX2EKRja5du6JTp04YP348YmJi4OzsjK1bt+Lo0aPYvHkzjI2NdR2iVo4cOYL4+HjExsYCAO7du4edO3cCSOtaU6JECV2Gl2srVqzA/Pnz4enpie7du2s8YdW3L0Fjx46FlZUVmjRpggoVKiA8PBw7duzAtm3bMH36dL1rnQAAc3NztG3bVmO7XC6HsbFxpmVF3dChQ1G5cmU0btwYZcuWxePHj7FixQq8efMGcrlc1+FppXPnzujZsye+/fZbKJVKNGvWDFevXsXChQvRo0cPtGzZUtch6kRxq2MKW3G75+S34vpdozDxbyz38rVey5eVMYq52NhY4csvvxTs7OwEU1NToV69esLWrVt1HVaeODk5CQAyfT179kzX4eVamzZtsrweffwz//PPP4VWrVoJZcuWFcRisWBjYyO0adNG2LRpk65Dy3f6vLDd4sWLhQYNGgjW1taCsbGxUK5cOaFv377CP//8o+vQ8uT9+/fCzJkzBUdHR0EsFguVK1cWZs+eLSQmJuo6NJ0pbnVMYStu95yCUBy/axQm/o3lXn7WayJB4ByARERERESkHY6hICIiIiIirTGhICIiIiIirTGhICIiIiIirTGhICIiIiIirTGhICIiIiIirTGhICIiIiIirTGhICIiIiIirTGhICIiIiIirTGhICIiIiqijh07hv379+s6DKKP4krZREREREVQWFgYHB0dkZKSgsjISNjY2Og6JKJMsYWCiIiIqAg6ffo0kpOT4eLiwmSCijQmFERERERFUEBAAADgs88+03EkRB/HLk9ERERERVCNGjUQHByMkJAQ2Nra6jocoiyxhYJIB+Lj41GhQgWIRCJUq1YNKSkpme6XmJiIli1bQiQSwczMDGfOnCncQImISCeCg4Px5MkT9O/fn8kEFXlMKIh0oGTJkpgzZw4A4NmzZ5DL5Rr7CIKAESNG4OLFixCJRPDx8UHbtm0LN1AiItKJ9O5OY8eO1XEkRNljQkGkI59//jkqV64MAPj++++RnJysVj516lTs3LkTALBs2TIMHjy40GMkIqLcy49W6NOnT8PFxQWtW7cupKiJtMeEgkhHzMzMMH/+fADAixcv8Oeff6rKVq1ahZUrVwIApkyZgqlTp+okRiIiyr38aIUOCAjgYGzSGxyUTaRDqampqFOnDh49egRHR0c8efIEBw8ehJeXF5RKJby8vODv7w8jI+b+RET6JCkpCTVr1sTLly/h5OSER48ewdTUVFX+9ddfqx4cLV++XO3BUWBgIOrUqYOQkBCUKVOm0GMnyi1+SyHSIWNjY3z77bcAgKCgIEyYMAHDhg2DUqlE69atsWnTJiYTRER6KC+t0I8ePcLgwYOZTJDeYAsFkY4JgoBGjRrh5s2bqm2urq64cOFCtgsZXblyBd7e3rh06RKSk5Ph6uqKKVOmYOjQoQUbNBERZYut0GQo+BdMpGMikUitn2z58uVx5MiRbJOJM2fOoGXLljh//jwGDBiA8ePHIzw8HMOGDcMPP/xQwFETEVF22ApNhoItFEQ69vjxY7Ro0QLh4eEA0gbzBQYGonz58lkeo1Ao4OLiguDgYFy6dAkNGzYEAMTGxqJ58+Z4+PAh7t27hxo1ahTKNRARUeby0gpNpC+YFhPpUFhYGDw9PREeHq7qKxsfH4/vv//+o8edPn0aT58+xdChQ1XJBACUKlUK33zzDRQKBWQyWYHGTkRE2dO2FTo7V65cQbdu3VC6dGmULFkSTZo0gZ+fXx6jJdIOEwoiHYmPj0f37t0RGBgIS0tLHD9+HH369AEArF+/Hi9fvszy2PS5yjt37qxRlr7t7Nmz+R4zERHlzuPHj+Ht7a16Hx8fDzMzszx9Jru8UlHDhIJIBxQKBby8vHD16lWIxWJs374djRo1wsKFCyESiZCUlISFCxdmefzjx48BINMuTaVLl0bZsmVV+xARkW5o2wr9MQqFAmPGjIFIJMK5c+fwxx9/YPny5fj333/h6uoKb29v1v9U6JhQEOnA559/jiNHjgAA1q5di65duwIA6tWrh/79+wMAfHx88OjRo0yPj46OBgBYW1tnWm5lZaXah4iICl9eWqE/hl1eqShiQkFUyBYsWICNGzcCAL755huMGTNGo9zIyAipqan45ptvdBEiERHlQV5boT+GXV6pKGJCQVSINm7cqLqJjBo1SjWd4IdcXV0xcOBAAMCOHTvUZgZJl94ykVUrRExMTJatF0REVLDy2gr9MezySkUREwqiQnL48GF8/vnnAICOHTvijz/+yHJfb29vGBsbQxAEzJ07V6M8/UaS2U3j3bt3CA8P55SxREQ6UNCt0OzySkUREwqiQtKtWzekpKRAEAScOHECJiYmWe7r4uIChUIBQRBw6NAhjfI2bdoAAI4fP65Rlr4tfR8iIioc+dUKTaRvmFAQ6aEOHTqgWrVq8PPzU7sZxcbGYtGiRRCLxZBIJDqLj4jI0ORnK/THsMsrFUViXQdARLknFouxYcMGdOnSBa1atcKQIUNgZWWF3bt349mzZ/juu+9Qs2ZNXYdJRGQw0luhcyK9FVobH3Z5dXd3VytL7/LaokULrT6bSFtsoSDSU+3atcOFCxfQsmVLbN++HWvWrEGZMmWwefPmXD/xIiIi/cAur1QUiQRBEHQdBBERERFlT6FQoFatWggJCcHly5fRoEEDAGldXps3b46HDx/i7t27bKWmQsWEgoiIiEiPBAQEoEuXLjAzM8u0yytbqamwMaEgIiIi0jP//PMPvL29cenSJSQnJ8PV1RVTpkzBsGHDdB0aGSAmFEREREREpDUOyiYiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq0xoSAiIiIiIq39D8Nl49Y9kx+FAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(9,2.7))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$    \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x'_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x'_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d5b44c-641f-4fc4-8a07-3a0992b1d213",
   "metadata": {},
   "source": [
    "## Soft Margin Classification\n",
    "- Hard margin classification is when we strictly impose that all instances must be off the street. problem is that it works only if the data is linearly separabel and is very sensitive to outliers.\n",
    "- Soft margin classification creates a good balance between keeping the street as large as possible and limiting the `margin violations`.\n",
    "- We can specify a number of hyperparameters when using SVM e.g C, a low value craates many margin-violations while a high creates less - still the one with the more violations will probably generalize better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38824548-f948-4adc-adc8-34a8dde06e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caf58330-1b02-41f2-9abc-f734e7d240aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, LinearSVC(C=1, loss=&#x27;hinge&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, LinearSVC(C=1, loss=&#x27;hinge&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=1, loss=&#x27;hinge&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge'))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the SVC model on the iris dataset.\n",
    "\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length and petal width.\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) #iris virginica.\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\")),\n",
    "])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3465b92d-0c0d-4beb-ade3-1f4ead1e9afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the model to make predictions.\n",
    "# Unlike Logostic Regression classifiers, SVM classifiers do not output probabilities for each class.\n",
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e417193b-5ca3-4b8a-9192-7970cdbf37ec",
   "metadata": {},
   "source": [
    "- Instead of using the LinearSVC class we could use the SVC class with the linear kernel SVC(kernel=\"linear\", C=1) or\n",
    "- Use the SGDClassifier class with SGDClassifier(loss=\"hinge\", alpha=1/(m*C)) this will apply a stochastic gradient descent to train a linear SVM classifier, slower then the LinearSVC but very good for online classification tasks.\n",
    "\n",
    "- The LinearSVC class regularizes the bias term, so you should center\r\n",
    "the training set first by subtracting its mean. This is automatic if\r\n",
    "you scale the data using the StandardScaler. Also make sure you\r\n",
    "set the loss hyperparameter to \"hinge\", as it is not the default\r\n",
    "value. Finally, for better performance, you should set the dual\r\n",
    "hyperparameter to False, unless there are more features than\r\n",
    "training instances (we will discuss duality later in the chapter)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5b93db-d329-43e3-abae-fdea8773db23",
   "metadata": {},
   "source": [
    "## Nonlinear SVM Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3910022-85f0-415e-ad37-1dc9cd6eaa13",
   "metadata": {},
   "source": [
    "- One approach to handling non-linear datasets is to add more features, such as polynomial features which allow for classification.\n",
    "- To implement this ides we can create a Pipeline containing PolynomialFeatures transformer followed by a standard scaler and a LinearSVC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7032e4ae-2281-4dd5-ba29-1bee6a26d306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c59808a-28f8-4d25-9fd3-10f0323aa874",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arnol\\anaconda3\\Lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=10, loss=&#x27;hinge&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge'))])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the moons dataset in which the data points are shaped as two interleaving half circles.\n",
    "\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "X, y = make_moons(n_samples=100, noise=0.15)\n",
    "polynomial_svm_clf = Pipeline([\n",
    "    (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\"))\n",
    "])\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2999856-7e4e-4b60-80dd-00c5f0b01a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76ccdf1-70c1-4e52-9047-789c1a4de25d",
   "metadata": {},
   "source": [
    "## Polynomial Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a446751-a35f-4c27-b209-e939406bf6f0",
   "metadata": {},
   "source": [
    "- At a low polynomial degree, this model can deal with very complex datasets, and with very high polynomial degree it creates a huge number of features, making the model too slow.\n",
    "- Using the math trick `kernel trick` it is possible to get the same result as if we added many polynomial features.\n",
    "- There is no combinatory explosion of number of features because you don't actually add any features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28c70e63-e2b1-40a3-a068-5e09daa7e21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "372b73af-cab9-4d8e-a2ea-2992d16c5c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# poly100_kernel_svm_clf = Pipeline([\n",
    "#         (\"scaler\", StandardScaler()),\n",
    "#         (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "#     ])\n",
    "# poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83b9b8c-dc87-41b9-a751-aa6b773037c1",
   "metadata": {},
   "source": [
    "- If the model is overfitting we might want to reduce the polynomial degree and increase it for vice-versa.\n",
    "- The hyperparameter coef0 controls how much the model is influenced by high degree polynomials vs low-degree polynomials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ce8d05d-a3f4-4b67-8ada-01a6b4edd226",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'poly100_kernel_svm_clf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m$d=3, r=1, C=5$\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m18\u001b[39m)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39msca(axes[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m----> 9\u001b[0m plot_predictions(poly100_kernel_svm_clf, [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.5\u001b[39m, \u001b[38;5;241m2.45\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1.5\u001b[39m])\n\u001b[0;32m     10\u001b[0m plot_dataset(X, y, [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.5\u001b[39m, \u001b[38;5;241m2.4\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1.5\u001b[39m])\n\u001b[0;32m     11\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m$d=10, r=100, C=5$\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m18\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'poly100_kernel_svm_clf' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10.5, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa39b2b-9f9b-4b4d-b336-1abd1f7fbca1",
   "metadata": {},
   "source": [
    "## Similarity Features\n",
    "- Adding features computed using a `similarity function` which measures how much each instance resembles a particular `landmark`\n",
    "- The simplest approach is to create a\r\n",
    "landmark at the location of each and every instance in the dataset. Doing that creates\r\n",
    "many dimensions and thus increases the chances that the transformed training set\r\n",
    "will be linearly separable. The downside is that a training set with m instances and n\r\n",
    "features gets transformed into a training set with m instances and m features (assum‐\r\n",
    "ing you drop the original features). If your training set is very large, you end up with\r\n",
    "an equally large number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8233348e-576b-4764-9355-14e6068d6437",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X1D' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m x2s \u001b[38;5;241m=\u001b[39m gaussian_rbf(x1s, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m, gamma)\n\u001b[0;32m      8\u001b[0m x3s \u001b[38;5;241m=\u001b[39m gaussian_rbf(x1s, \u001b[38;5;241m1\u001b[39m, gamma)\n\u001b[1;32m---> 10\u001b[0m XK \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mc_[gaussian_rbf(X1D, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m, gamma), gaussian_rbf(X1D, \u001b[38;5;241m1\u001b[39m, gamma)]\n\u001b[0;32m     11\u001b[0m yk \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     13\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10.5\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X1D' is not defined"
     ]
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(10.5, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3185b4-c30d-44a3-acab-e5baf40f1b5c",
   "metadata": {},
   "source": [
    "## Gaussian RBF Kernel\n",
    "- The similarity feature can be computationaly expensive but the kernel trick can help us obtain a similar result if we added many similarity features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ddc7ec4f-b573-4e2b-bbe4-d0fd4db1ebc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=0.001, gamma=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=0.001, gamma=5))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.001, gamma=5)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=0.001, gamma=5))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets try the SVC class with the Gaussian RBF kernel.\n",
    "\n",
    "rbf_kernel_svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "])\n",
    "\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbb8937-b42b-4813-a318-707d6896787c",
   "metadata": {},
   "source": [
    "- Increasing gamma makes the bell-shaped curve narrower hence each instance's range of influence is smaller: the decision boundary ends up being more irregular, wiggling around individual instances. A small gamma value makes the bell-shaped curve wider: instances that have a large range of insfluence, and the decision boundary ends up smoother. Gamma acts like a regularization hyperparameter: if model is overfitting, we should reduce it, if it's underfitting we should increase it ( Similar to the C hyperparameter )."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "312093d0-c4a6-4f8c-9019-78255f15c1a7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'save_fig' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m):\n\u001b[0;32m     27\u001b[0m         plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 29\u001b[0m save_fig(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmoons_rbf_svc_plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     30\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'save_fig' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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a0bBx6XB2Gzi6SESIplCASAghCaBhaIkxFecr3QRCBKHftXg4Wzs4+IA8/zGAllsgRFsoQCSEECI6zm4DaE1rQjJKyDDS4nzApWBjCCFJowCREEIEomFownC2drD4DyNpQutznOl3nbpI8wt9jNbhJUSrKEAkhJAE0DA0YSh7kBnSZY4z/a6TQ4VnCElPFCASkgHSrRw9US/O1g6T1ULZA6IJlD1MDgWGhKQ3ChAJIUQAKk4TH613SLSIftfCUWBISGagAJEQQkjK+BNHOmkkJP1QYEhIZqEAkZAM0EfDS1NC2cPYKDgkWkTDS+OjwJCQzEQBIiEq1trGocvJRb2/wMJQVkr1IolyKDgkWkYXfqKT+7fd0qaHwxn9QmaRxYuKUprbTIgcKEAkRKVa2zj86JYcuNzRA0STkeGlF/opSJQQZQ+jo+CQkPSjxO+6pU2Py24ZCZdbF/UxJqMP773QTEEiITKI/kskhCiqy8nFDA4BwOWOnWEkqaEhaPFRcEi0iC78DMfZ2hW76ONw6mMGhwDgcutiZhgJIeKhDCIhhMRAJ5GR8ctZEEK0jeYZEkLCUQaRkDSXDotYK4Gyh9FRcEiC+ddZ1c6wP/ptDwnOGNJvmhDCowwiIRmAKpgmhj+BpOzhcBQcknSQ6b9tyhoSknlc3XbBj6UAkRBCIsj0E8hIKDgkRPuouBQhJB4KEAlJY7T+YeJo+FlkFBySdJDJxWkoa0gIEYoCREIIOYOGlkZGwSEh2kZZQ0JIIihAJESlCiwMJiOLuw5igSXyGohUnCY5FBwOoZNKkk48did0yKwRFVrJGhZZvDAZfXHXQSyyaKcYEiFq4nLawKzFgh9PASIhKlVWyvDSC/0x1zkssDCUlUYOEAEqTpOITB56FgkFhyQdZdJvXEu/4YpSL957oTnmOodFFi8qSilAJEQOFCASomJlpbEDwGho7mFiaN5hKC2dWBJChtPib7iilAJAQtSCAkRC0gwNLU0MzTsMpcUTS0KEMBdnKd0EydHvlxAihuiDvQkhmkXZQ2EoOAxFJ5eEaBf9fgkhkbicNrAR1oSeQwEiIWmEhpYKR8FhKDq5JOnKY3cq3QTJ0e+XECImGmJKSJqgoaXCUXAYik4uCdEmrVQpJYRoCwWIhMigtY1Dl5MDYzr0uAuQZ9SB44YS+PGqkcbDB4eUPYyPgsNQFBwSok1q/e22tOnhcOrhY16cchdgwGiCjhvqm6gaKSHqRwEiIRJrbePwo1tygtYzXDrsMSajf0mLVIJECg6Fo+BQvSeXhIgpXSsUq/X329Kmx2W3jAxaz3DUsMeYjD6890IzBYmEyCCZ+YcAzUEkRHJdTi7mYvcA4HJzMdc7jIXmHQrnsTspOIR6Ty4JkUK6VS9V8+/X4dTHXOweAFxuXcz1DgkhyqMAkRANo+AwMel2opgMNZ9cEiKmdMwe0u+XECIHGmJKiIrxcxcjGegcgCVfh6LERw5kFI+tCz54gTylW6I8OrkkmcZszYaPeZRuhijS/ffLz12MhuYuEiIfChAJUanhcxfD5cBkZHj9hS6UpzB3MZ0FCtIUZ8HhUrgxCkr3E0tC0l26/4aHz10cjuYuEpIYl9OW9HMpQCREpYTOXfzqoAFdTh8KLD4KFIMEVytNlwxCMtL9xJIoT41D3dNpeClna0/736/QuYt7DmZhvNND2URCBEqmQA1AASIhmnf/o/6xk5RNHEJLWfhRcEgyWTr8/jMhOEzEbx4tAUDZREKkRkVqCEkT/kqo9JOm4NB/UsmfWNLJJSHaRMFhdFQJlRBpUQaREIkVWBhMRhZzuKjJyFBgYSFFaRqaklv2IpNRcEhZQ0LSYXipVoPDIosXJqMv7lzCIos3pCjN8SY6HSVETTT7i+zu7sbq1auxb98+1NbWoqOjA6tWrcL9998v6PltbW1YuXIl3n33XfT19WHmzJl46KGHsHz5cmkbTjJOWSnDSy/0o8vJgTEPetx7kGc8Bxw39PMrsPiHhcYuSkNioeCQgkNCeFo+Dmg1OASAilIv3nuhGQ6nHj7mxSn3UVQaJ0LHDWX7iiz+YaHxitIQQpLnctqSnn8IaDhAtNlseO655zBz5kxceeWVeOGFFwQ/d3BwEMuXL0dnZyfWrl2L0tJS/OlPf8LFF1+Mjz/+GEuWLJGw5SQTlZUylJUyMOZDl6sLBSYfOM4X8phvj+ooOExSpgeHFBgmz36sU+kmEBKg5eCQV1HqLyDjY15ku7ow3uQKCRAB4NBREwWHhKiYZgPEqqoqOBwOcByHjo6OhALEF198EQcOHMC2bduwYMECAMCyZcswc+ZMrFy5Ejt37pSq2YQQEWV6YAhQcJgKR+0RmHd/onQziIg8ti7NHg/SITgkhKQHzV6+4TgOHJdctuWtt97C5MmTA8EhABgMBvzwhz/Erl27cOrUKbGaSQiRSKYHh3whGoCCw0Q5ao/A/u6nMO3+O5rnf610cwgJ/JYJIUQNNJtBTMWBAwdw/vnnD7t9xowZAICDBw+isrJS7maRDNLaxsHZHXp9pu6QG0COMg3SGAoOKTAUylbngHn3pwAAbtD/vckB0F9Zh/bv65A/dgmA9xVrHyHp/Htub8/BQL9p2BBTKkpDiHRcTlvK28jIX6jNZkNxcfGw2/nbbLboO3ZwcBCDg4OBfzudTgAAgwdMQ4tx823VUpvTAWMetLfn4D9vz48w3zC14NBkZMjPd8PHfPEfrFEeuzPw/+biLPgEfn99zBvyX63i7P5jEwNgKs5X9ftRwz7v+Owgsk5+gYGCk+g5y/978xRlAQC40lHIGTEKOboKxdqnZon0dQwe+Jjy66967M6IxwU1fBej4ew2Tfyek3GqlcPtty+H2y3+chQmow8F+a6022diUPP3XYsY54NXz8HL6cF8HHze4cc6/rZI9ynB5wNYcTHgCW2PL4FT/owMEAHEHJ4a676HH34YDzzwwLDbna4t8BjMorRNTk73FqWbkHGczoKUitH84hdfYtSonmG3Wywu6Av70e5KpXUqlzf0v44k3meLe59oTVFE0PuHRj7nE+5Dyr34AgA4L/J9vUB3L9Da1ydnizQjob4uD+o47uTFPi6o8vfP/6bVsP9EdsxeALd7XErbiNXf9Rf243ga7jexKHrsTSejgc7Ro/3/0wB0x/ixnjjolq9dMeUB9cPb2dcn/AeTkQGi1WqNmCW02+0AEDG7yLvrrrvwy1/+MvBvp9OJ0aNHw2JahFyTdoaHMOaB070FFuOikOUWiLT8V973p7SN6eMmYPLEzMr88plDc3FWUs/3MS9a3PtQYZw1bKiT2vFZQ8CfZdAKH/PihPsQxhrPknWfd3x2EDn1H8IzwoGesziYzluAkoIJUR/f051ZvyWhEunr+uz9KCxW/nfFZxDDqfH3z/+utfSbTlS/IfVzi3njynHWRIoCE6HUsTdd2Y91YtTJbegr/RbtF0yK2J/4vAwnDroxdpoROr3y1ehd3XZ/BjFMIv1dRkYG06dPx/79w0/S+dtqamqiPjcrKwtZWcM7IA4GTQZaHKfNdmcqk5GhqICDLkM+M36uoQ56UeYb6ji9pvadf26STtNzk/z7XPqTFFudA7lb3kZW4QHYanrBLZyGqur469rq6PwpokT6Og4GxU9EPbYu5FlzYz5GLb//dPhdC8FxqdVBNBl9sBZA8e+WVsl17E13HNNB72XQMy84HYsZAOr0nCoCRJ0OYIbh7dAlcPhT/kipgKuuugq33347du7ciXnz5gEAPB4PXn75ZcybNw8jR45UuIVEqNY2Dl3O6D/GAot//UE16LP1g0H41Zv7V/Zg7OjQ+YQFFh/KVfJ+ojndxqHLGf3EQOh7yORCNMEVDdP9JDIZwYVnAH/xmWx3M+onHkPOrJEw1yxGaWG1cg0kJI5kftctbXo4nNFP+Iss/vUHtWbNynaMHz28b9TC+0nXz4RolxgFagCNB4gffPABent70d3dDQA4dOgQ3njjDQDApZdeCrPZjJtvvhkbNmzAsWPHUFVVBQC46aab8Kc//QnXXHMN1qxZg9LSUjzzzDM4cuQIPv74Y8XeD0lMaxuHH92SE3M+n8nI8NIL/YoHiX22fgDwD8NqFfacsaN9mDxRWx3L6TYO194Se46lycjw+gtdMYNECg4pMIzEVueAr/Eksps+wal5J5Bt9HdhnjPDCs1lswVlDQlRSrLLWbS06XHZLSNjLi5vMvrw3gvNmgtIxo/2aHIYaTp/JkTb2AhrytvQdIB42223oaGhIfDvTZs2YdOmTQCA+vp6jB07Fl6vF16vFyyowlpWVhY2b96MlStX4o477kBfXx9mzZqFDz74AEuWLJH9fZDkdDm5uMVeXG4OXx/Uoco59PnzWUU5so98YFhk9V9hFFp1U6uOn9AL+ky6nDqUR+gwKTD0o+BwuPbN+5HXehSunK3oWqRD0dRzYK0+W+lmZbw+W3/g+KYUj61LE8eMVC7+OJz6mIEIALjcOuw5mIXxzqF+hs9gUaZLfHUnjII+E4dTT/uWaI6mA8QTJ07Efcz69euxfv36YbeXlZVhw4YN4jeKqM5vHw09cTAZGZ54ZAC/+HW2ZNlHPjAEoPjJk1xOt3H4zeq8+A+MgoJDCgwj4bOG5mPvoHV2E4yTx8E0ZQqsNISUaIhcv/HfPFoS8m+T0Yd1j7TiJ78uo0yXiFra9Piv1aVKN4MQyWg6QCQkGS43h+bTwrKPXU4uoQBRSGBosbhgMrK4wWmBxT//UKw5fVLrcurg9iQ+OZsCQwoMo2nfvB/5jXswWHgAjgt0MC++hOYWEs1S4nfucuvQdNqgSKaryOKF0eiNuQ6iyehDkWXoNbWS6XQ49Un1d4RoBQWIhIggkYxhSUk/Xn3eju5uY9TH8EGfWHP61CpTg0MaThqbrc4B7shXMDdvw+nZTYIrkpLMo4XhpcnOO9S6ilIvnnlmM/L7oy+3EBzw0Zw+QlLjctpEmX8IUIBISEqSHUpaXurDyLL4HVyXU5fSnD618nT2wGNzqf7ETgqUNYzN/u6nyG0/gcHCA2j/fg7MNZQ1JNqV6b/3kpJ+jDe5BC23IHSeJc3pI0R6FCCSmLS0jIScMnGOodgyLTikrOFw7ZuH1qPV99tgbj+BLHczTi5rhfGsaoylrCGRSUubDp0xhvIXWnyoKPVFvT8W+r0Tom2sv0vpJsiOAkQSlZaWkZALBYbiyC40AQmsCallFBgOx69jaCiqRZ7ZnwlgI7OBkcDpsS5YapZT1pDExQ9RT1VLmw5X31ISt69784X2hIJEztZOv3lC0kWhWekWyIoCRBKV0GUkEi3kIpYCC4tb7EUsFBiKx2BgKLQkdyVeazJ9eFkk7Zv3ByqSGory4V68KOT+sRQYkgSIMRKhU+BQ/k6nTnCAKPa8wyKLFyajL+4QTKIuBkNoER5CpOJy2kTdHgWIRLPKSv3Zy+AhsA1N3LBlLVIx0DmAPpv/4E6BoTgev9eR9FAtraDAcDhbnQO5W96GqfAA2mf3gls4DaNoCClJY2L+/itKvXjvheaQCp/HmwzDlrUg6vLUve00X5LIRqwCNQAFiERhqc5xLCsNvb/AwglaQmJkefzso8HAYO/k0OYwnVlyIjOG0SarwOITtO8njk3foaU0nDQy+7ufIvtMRVJMGAFzzWIaQkrSVrShpaku4VBRGnq/kKyiyejD6HJP3McZDQwddh1a2qgAjBBC9331WLeMrSJEPBQgEsVIMccxUlYxHB908o+z2YF7V2fDE7amkcfD4f7fFwbaoYWlJJRcM7G8lGHjYyfR2e0/AfLPMwyVSqEHtaOs4dCi9gDA9AxYAAy8/Ar0eV+jZZkbxrNouQoiDrUubxFtaKkUSzhEyiqG44PO915oRt0JI/7f6hJ4PMPb4PZwuH1VmaaWkVByzcRE9j0hWkQBIlGMVHMcw7OK8R739ZcueDw5Atoh/1ISQrNyBRaf4msmemxdKBsBjJvMr++YvpnCYBQY+rVv3o+81qMYOFN4xqc3wImr0LzgaxiKc2ChrGHaCZ6bTYZEOhZItYRDeFYx1uMcTn3E4DDVNohFaFauyOJVxZqJQvc9IVpEASLJSMEnNpYIma5Y5MzSlZf6Azohr3fkqF5QwH3shB5dzujtS7T9tNh9ZgeHfEVSc89htM5ugnHyOLinTAHzcUADkL9oCcqKJyrdTCIRmps9ROzCNGogZ5YukazcoaMmQQF33QkjZflI2nM5baLOPwQoQCQZJPxqN39i0+YQvg0lsnTlpUzUzOVvVucNG04bTGj7g0vMZ1JwSIHhkPbN+5HfuAeDhQfQPVcH8+KhRe19XoZuuFBSMEHhVpJ0I9byFlJIp2OCElk6sbNy0YbU8rQ0pJYQOVGAqFFyLGAvZBkJ//BGdc/LE3OJii6B5dCVGI4qVKzgEIjffgoM0+skMBl8RVKzuxmnZzeBW0hzC4l0wkdteDpN/jnOZy7upTq3uVDgUP5Yy/Ok45qHUg2LlZOah9QSomYUIGqQXAvYJ1LwRW1o3cLUnGga3ql6OnsAAIX5+qB5hpkhk+cZBheeAQB9vw3ZzdswWGKHY0kOzDWX0NxCIpnIozZCf4fJLGIfrKLUhzdfaEdnjKH8sYLQdBxamkmON0U/FaYhqETX06Z0ExRBAaIGybmAvdCCL2pAQaF47n80L8Kt/pOyVE/GtCSTA0PAP4Q0u+kTZE3rAdfX678xH2hY1gPjWdUYS1lDIjGhozYSWcQ+korS1LKQmXqMSAex1pKkIagEAJCfB0CdQ9tdTpsk26UAkWgaBYXyE+NkTO0yPTDkC8+Ycraia5EOXOkIGKfMC9xvAShrSAgoe5juaAgq0QKxC9QAFCASBbnd/sXo4xVMCZ7jGK3QTCoSWUoiVjVRIq2WNl3SQ8CEonmG/qyh+dg7aJ3dBENRPkyLF1EwSEgMQo4V/v7OF7dgSpFFukAkkWUkiHKUXN+REB4FiEQRrW0cfvHr4YvTBzMYGJ54ZAD5+j70BWXQxc4UJrKURJdT1JcWlZBA12hgcMcpUqNGLW06XH1LSdwgPtmhrxQYDhWeMRUegOMCHbip0zCKhpASEpXQ7GFLmx4/+XVZzODQaGBY90irpCf+6bS4u5BgV4v9nRrWdyQEoACRKETIPEqPh4O3d0CWoaNiLyWhBCGBrs3O4c5V+TK2ShydEs1DytTAsH3z/pB/84Vn+IqkOSVjKGtIiABCjhtCqoG6PRyMMtT+SpfF3YUEux12HW5fVSZjq1KXDpVjiXykmn8IUIBIJCBkCQ6hLIV6AOo5CCYyHFWI8PLtkV4vkfUU4wW6p9u4uO3PBJkaGPJzC7NdTcge1xq4nY3MRvNCF7gyWq6CECH4Y4iQ4YBaJfaQVLGHTsYLdlva9HHbT4jWSTH/EKAAkYhMyBIcRiPDPf/PCSBHvoaJJJHhqPFELt8eKtai9bu6PsUTJ36DX4xdg7kFSxNqv62xL3BbdqEp8P/1TXrc+2iRoG1pEWe3gYP/s8ukwBAYqkjqKmhE91wdXFNnwlsylE02gwrPEOHC54NnIpu3SNBwwCf/R5uFbMQckprq0MntXVvw8In7cdfY+7GgYJEo7T/eZIhZwZQQR+0R5OzbjoEyJ+pz2mHGGKWbJBsKEDVIzQvYCxk66nZzgF67FUfFGo4qtHx7pEXrGWP4c9NqnBj4Fn9uWo05liXguPhZQY+tCyP0wIhxwYvcexJue32TXpSiMHLh7DbgzModmRYY8llDt/sL9E7rh3HyOIyav0LpZpE0IMfwf6GjNmItYi82PnsodDigs1e7GSyxhqSmMnSSMYYnmx7F8YGjeLLpUcy3nCeovwPEaf/xJoNm5mYS8fAF2/rH9uL0uTqYa+Zk1EVUChBF0m8fADcwNIHAbJUuO6bWBez7bP1oPmGAFjODWrOz6/9wuLcWAHC4txY7u/4P8wujDw302Pzr9wwFhZEVCjgZA4B7Hy3SxHqI/Ikc/0swFWtv/mUq+A7OVWKHb64B5sW0qD3RluBRG57OnpARDzwlLlaZrBZ0HNNu4Kcl27o+x8HerwEAB3u/xrauz3Fe4ZKUtytkCC3gXyeRCsNkjuCpGO1n5uWrcc1fKecfAhQgiqawWIfcXP/VVIfNG3H4jZhBo1IL2McaVjToNeC3awtkbE1mYozhuZO/gw56+OCFDno8d/J3mFdwwbCrqkIDQ15FqQ9vvtCO2oPGuENNEykKI8cSFcHC5xj6mBdwibZ51bHVOeBrPBn4t77fBnP7Cbh1X6NlmRfGs6ppbiHRrPJShhF6O1AEmK3qCMpa2vT4r9WlSjcj7THG8NTJ30MHHXzwQQcdnjr5eywsWCw4ixgNPwR1z8GsuENNEykMQ8tUaN+IMj0sZ03Al7kDqFRx3ynV/EOAAkRJRBp2Ey1oBKTNNiZKyLySaMOKjhzVaa6ktBYFZw8BwAdvSBaRDwoB4YFhsIpSHzqd4nVeUi9REYzrdICzDfi3mSFDSfmrnQNFtcgzn/nc8v2FZ1xjc2FR2bCY7c21eHjHs7hr/m1YMHK20s0hGpLM8UxsnK0dJqsFjqN66u9kEJw9BAAffKJmEStKvRjvTHyaRTS0TAUJpuX+jgJEmcSaq+FQYLI/gwfIA/rs/eDCvgZyzCvJzxU6j1K9wxeVEJ495Omgx/+eWI1zRp0DjuNUcSLFE2OJCiFDX01GHwrzvRkVGPoaTyK76RO0TjsM4+Rx8FkrQwrPjFVRYAicmUv05Toc72rCk1+uw/wVs1LOAhCidpZcHy1Qn4Tw7CFPzCyi2FJdpkLsyrEkeb7uTiA3+edrvb+jAFEF5AjIwvkYQ7sLKCzWQ8fJ//rWYvGqgWaS8Owhzwcvvhn8Gl8ZdmBh4TIFWiYtfuhr+DBVrtMR+P/SMTmoKFVPNl5K/KL2g4UH0LVIB2PpOFRqoPDMtua9OGirAwActNVhW/NenFd5jsKtIkSY4KHriRhR7EubBerlFJ495ImdRVQTMSvHktR5rOaknytlfyf1/EOAAkRCNIPPHnLgwDA8cObA4dmTj2BBwdKEr1KFzxGsb1JfldmK0qF5ioETtaLgoaTp32Ha6hzgjnylyUXtGWN4as8G6DgdfMwHHafDU3s2YOHIszV1VZVktkwZoaA0PnsYq79LNosYPkfweJO6ToXFqhxLlCNHfyfl/EOAAkQiIptd2JfeYGBwuVlKawCmA6Hl2/lhtm7mwumBpoidJQAwMLS6muFmLpi4LMHtEDJHUC0yaYF7fm4hjxvsQi6AwcIDaP9+Dsw12qpIGnw1FQB8zEdZRCJI8LxqpYRnDzvsworlGAw+uN3I+HlpiQ6ddDMXTg82x+nvWpLo7+LPESQkVenQ31GASERxuo3Db1bnCXrsmnt7YDLGXy8x2hqA6SK4fHs0/DBbj60LOgDPj3obA/k9UR9fbBgBk054ZwkImyOotEwKDIGhRe0HyjpQPGbod1Vf0Q2ubCzMGska8sKvpvIoi0iEUsO8av7Yk0gF06fubYfRiJTmpaWDRIdOmnRZeK3mn3B47FEfX2ywJtzfCZkjSEgq0qW/owCRiKLLKbyC6Yji9MwIJqO8lMUMgD22LnjODDU3W7MxDuNlall8cixOnWmBIZ81NOVshW1aL4yTx8E9ZUrgfjOgqcCQF341laeGq6qu7ugnoIRE4nAKr2A6ojjzCq3Z6hywVg9fJinRoZMVWSNRkTVSzKYljQrDZBbW3wVdUQEAR9zHhpO6v3M5bZIPLwUoQCQyMxj8QyZjZc0yXfhwKjVcOQeA1SsdGDd6qIOUcnHqTAsMgaFF7U9MPIb8Mda0WdSev5oacy6RjFdV5ZjcT9JLssVpDAZ/UBEra5ZOAhe4XE3o3z0avXMXA6OVblVy1qxsx/jRQ8tfUGEYIoTa+rtUUIBIZLXm3h6UlzJ0OZVuifqkun6h1MaN9mLqxMTXixK2RIU/G5mJgSFfkdRUeADts3thXjgbo1S8MG+i3D4PTve2x55L1NcBt88Nk94k+utHCgiDr76yLPX91tSuz9avSPVtJSVzPHrq3nZUlGZGgMhf4HKV2NE9VweP4zCsX9QBN3wH6GoDBx1YYZnSzRRs/GgPzproSvh5tExFZlO6vxMTBYhEVjS8dDi1B4apirZEBY/rdMDl4dDVyNBXOGLojqCRHely9bZ9837ktR4F4C86AwDZ7mZ8O7sRlgklMNcsTousYTCT3ojXVjwFx0D0QiPFOQWidpbhQaEcw3GINNRQoCZZ6TK8NPi4BQwdu4KZ3c1oWdYK41nVqKpejoa6zeiY4P8d9m/eBp2HgesbPn+eZRWgp2wiSpZPl+4NyEjIXEu32z9MOdpj0qW/0ypdT1vSz5W6v5NzBAwFiASn2zhaj1BmHrsTOvg7h3QMCsMFL1HB47OFLV4jrvrVpLSu8Be8qL1hXCtMIwr8dxSaUZ/Tj4KyGlSlUdYwXEVeCSrySiTbPgWE6U3MY2T4kj7hwofOJzu8NB3wQ0azXU2hxy0AKAxdH64+px+WmuWBC1xV1cvRaj2K7gagbdkBjO+P8pvsbIPr260YWFeD/lkLUDR7slRvRzax5loKqaKq9f4uLeQLK7oYidT9nVz9GwWIGe50mw7X35rZy03IyWN3AmeOO5kQGEYSPoy012FK6wp//NV3V85WdC3SwTV1Jrwl+YH7zdBWRVKhdjTX4sHDz+K+Ebdh4eizRd02BYQkGafb9PjerbGX9DEZGd58oT0kSMyU4e7B+ErKroJGdM8dftwKN7Zw+AWukoIJ6IYL5rPORqsu+vlD/ywz3IdqUfFJM+ynFoKrrIAvb6hKbKSCN1olpIqqlvs7rbPVOZC7bzsGypw4pG8HB2HDoqXs75Si2QCxp6cH//M//4O//e1vsNvtmDJlCn7zm9/guuuui/m89evX4yc/+UnE+1paWlBeXi5Fc1WrS8ASB1IsN5HoGoBaFjKEtDgLDpf/v2qRyBzBVGTa/EJ+UXtz8za0nlnUPp2zhMEYY1hbux4nB09ibe16LBg1O+UJ+RQUklQJWdLH5ebQ6dSJWoBLS/PS+Kyhuecw2kU6bpUUTIBOH2O/F1ajAUC7tRUFX36EEXVD/UNnXw76d49G35ylogSKWvosiLz4i7mDhQdwen4OzDVzBF28laK/i0TuAmuaDRCvvvpq7N69G2vWrMGkSZOwceNGXH/99fD5fLjhhhviPn/dunWYElQ+HgCsVjrhSFaiAV8iawBqUbRKpD6WeJEXqcWbIwikVrE00wJDALC/+yly209odlH7VAWX+U62rDcFhERp0YaXJhpkJLoGoFKCC804Lpb3uFVVvRxtJXVwljWCr2FnsPWBtZ1E/v5GmLY2ob1xWcpzFbXyWRB58Rnz1mmHwS2chrEJXBQRo78TSs5+UJMB4vvvv4+PPvooEBQCwLJly9DQ0IBf/epXuPbaa6HXx64aVlNTg3PPPVeO5maEZAK+eGsAapFWC85EmiOYisD8wg4jevVDhWc6jung7B36jlhyfSH/1qJIhWey3M04eaZgQyIdTToIXyRY6OLA8aqNEqKESBe1kgkyEl0DUE58JWWzuzmQNVTiuFVaWA0EB6Rn/vdU6TtwHzkM06ET6N94fsrZRKk+i5a20MIzHfb06+/S2Zhp+XBOHofKBL77yfZ3WqDJAPGtt95CXl4errnmmpDbf/KTn+CGG27Azp07sXDhQoVal7nSMeATQq3rFioh+Iq7zVuEq34VezK+lgUvat9a3QtDUT48QUOHgws2ZJLwRYJjLQ5MWUISi5ormKo54EsEnzUcLLHDsUSdox0q569A25Q6uIq2wLV/q2jZRDEJKT5D0k8i/V0qlFi/V5MB4oEDBzB16lQYDKHNnzFjRuD+eAHi5Zdfjvb2dhQUFGDp0qV48MEHUVNTI1mb1cpm1/YVDqVQUBgq0jBSx9H4k/G1ij+pap3dBENRftosap+q8KupvOCrqu5ue+hzKCgkMYh5bO2wCz8epXv1Uj5r6NZ9jfbZ/YplDYUqLawGrqgOZBP1x9rQv/GoaHMTUyWk+AxRN9bXDY/VHP+B/OMF9HdiZhHl7is1GSDabDaMHz9+2O3FxcWB+6MpLy/HPffcg/nz58NisWD//v1Ys2YN5s+fj61bt2LmzJkxX3twcBCDg4OBfzud/tHyPnhVOb8sGh/zoL09B3c9VBD/wQB8TFvvTyoeuzPw/8GFZoTuGx/zhvxX6zi7/7fGAJiK/RXupHqP/u9gctsUq032Y50wb38PhsLDaDunF9y8GRg5YYl/215tzpcV09ZTe0KupvL4q6qffvsp5k9dGnqnR9n95qPDWkTR+joGDwAOPib95yZWv+pjXrS352DlQyPiPxgAY1744IOpOD9tjtXBOj47iJz6D9E/woHB8wpgPmsZSgomiH4M47cn5nYr5lyO9knH4Nq6HQOHdsGwsxltp87HiCXTRHuNZKRjf5dJmJ7Bq9eDMZ3g72u8/m5L0x7Rsog+H8BE6CsT6e80GSACiBmVx7rv4osvxsUXXxz49+LFi3HZZZdh+vTpuO+++/D222/HfN2HH34YDzzwwLDbO1w70WcQfuVBDZzOAng8wq5uONxfod2l3uE+sglaGsfhSn4zLe59KTdFFYKXCgrbH6fcBQBGifIyRqMX3TmHcNzVn9J2TrgPpdaQ0UDn6BkA/KMV0Asc/zqFL0IaYYzh999uAAcODMM7Mg4c1ta+iVG9C1Q1N6Ovjz6/SKL1dU7XFpjzzGiXY7flpXacDeZ0FsAtsL9rcx9Gfl7XsGNa2lgAOBd8J/DP7gagW8I3e+KgW+QtjgZGjcZgoHvxwenaL/JrJCYt+7tMsgCoxXmC+3Qh/d3vt21A+aQakfq7PKA+9d9oIv2dJgNEq9UaMUtot/uHLvGZRKHGjh2LRYsWYceOHXEfe9ddd+GXv/xl4N9OpxOjR4/GCNM85JqirxGkNj7mwTEcEfRYg4FhrHU6SkzaX3JCqGiZwlT5mBct7n2oMM6CjotdSElt+GwhMJQtjGXAaErqdR7+VSvGjQ49ofAXfJiY1PYA/34/4T6Escaz4u53+7FO5Oz9AgDADQ59D/pHHoOjqheGKRMw5kzWkAzp6WyF/Zv2iJ0lADAwOGBD6WwdTHqjzK2LrqebUoiRROvrLKZF4HqMKCyW/vjlsTtFOf76mBfHUC/osQYDwyTrZFT0tAk6zmlFx2cHkXXyC/gKTqLnLA6m8xagpGCCpK/p8zKcOOjG2GnG2MtcpKB59weY+nU2jnmUzSJqtb8jfo4Pv8C4iuP4+txBQf27y+uG40hH7P6O2TC6JvX+ztVtB0swrokmkf4uqQCxt7cX48ePR1tbG8aNG4cjR47AaBy+AwYGBnDhhRdi69atMJlM+Ne//oWlS5cm85Ihpk+fjldffRUejydkHuL+/f4rSMnMJWSMQaeLP348KysLWVnDOywd9NBxmoy341pzbw9GlukApPf4+uB5hTroJZ1XqOO0830ZmoujS2iZimQ7pgljfDhrYqShMal3dP79Hn077Zv3I79xDwYLD6B7uv/7Hlp4ZjHNNQwSPHE+22jES99/Go5+/++IeRnaDrtROtUI7szJYXFOAbKzkjuRkopOGz9D2UXr6wbsHlSOzovwDPEp0a8+fq8DlYYOALq0OLnmi2kZi2rhmNwO4+RxGD1/haxt0Ok5yQJErzULeq8Xhh67op+XFvs7MoTzAnqfDxznFfRdzdab8NqKp+AY8Pd3Ph/DqTo3KquN0OmC+jtT6v2dTgcwgzi/n0T6u6SOvLm5ubj77rvx85//HPX19Vi/fj1uvfXWkMcwxvCjH/0IW7duBcdx2LBhgyjBIQBcddVVeP755/H3v/8d1157beD2DRs2YOTIkZg3b15C26uvr8fWrVtx4YUXitK+dNPTy+HIUf9BRstrE0ZCxWYiU3LtwkQKSaSifXPokKT8xj0wu5txOsMWtU9EvKqj5QDK80oAAD4PQ77ZhcoRJuhE6twIkVp3L4fD9dkwFuahyKv9SqW+xpMYUaZHX3kefLMmw1p9ttJNEt2XI4+h4pMe2N8F2OSZqihaQ7TBVucAd+QrZDVvw+7JrTBC+AXgirwSVPD9nZch+5QL460mUS+GKFG9lJf0pbmf/exn+MMf/oDGxkb89re/xY033ghTUKR855134o033gAAPPbYY7juuutSb+0Zl1xyCS666CLcdtttcDqdmDhxIl599VV8+OGHePnllwNrIN58883YsGEDjh07hqqqKgDAhRdeiMWLF2PGjBmBIjWPPvooOI7D6tWrRWtjOrn/0aGrxSajf71DLQeJFBRGp4ZF7aVeJ4q/ol6e1wJLRQ5YXzcAoH18K05P16myzLuSaCkKogSPrUuRY/O9jxYB8AcYJqMP773QrPkgsaDYg/6RlfCWpM+QWV5V9XK0lYxBu3U3ij57F1lbTqC98RxVLYFB1Ik/F3DlbEX793NUuzSVUn1u0gFiVlYW7rvvPtxyyy1oaGjAX/7yF/zsZz8DAKxduxZPPPEEAODnP/857rzzTnFaG+TNN9/EPffcg/vuuw92ux1TpkzBq6++GhKIer1eeL1esKBqa9OnT8frr7+Oxx9/HP39/SgtLcUFF1yAe++9F5MmTRK9nenG5ebQ5dRpbr1DCgpjkyIwLLJ4YTL6Ei793enU4dDR4cMywheeTkbHZweRc+JTtE47DFtRPrjSEUFlrStUXeZdThQUEgK43Do4nHrNBoi2OgcAoM95EvWlOuQg/QJE4MwSGIuq0VC2GWzbTpTUNqMdgG7MKNmyicn0dwY9Q4dduv6ORNe+eT/yWo/CUPkl7LNLYZ4yRZXBoZJSGtz/4x//GI8++ii+/fZb/O53v8NNN92Ed999NzCx/ZprrsHvf/97URoaLi8vD2vXrsXatWujPmb9+vVYv359yG184ErSHwWFsUmdLawo9eK9F5rhcPoz+sebDPjNoyVxn7fmz5GDkVSu5tuPdQKjAWP3q+ha5IWxdBwqZZ6Ho3YUFBKSPvgTYFfOVuxbkgNzyZy0PwHms4mOoi3I378Bpt3nob1xoizZxOD+Tmhf5/FyuH1VWcT70iV7rWbVcwpxylUAo0qDQyWHlwIpBoh6vR4PPvggrrvuOjQ1NeH222/HK6+8Ap/Ph8WLF+Oll14SVPiFKMNiccFkZHC502N+EAWEwsg5jLSiVLyroMlezW/fvB/ZjR8AN3wHPWdxyDp/UcKdQcupbDjs0SebFxW7UFE5kNA21SDRoHDnyVo8tvVZ/Oq82zBv1OykX1es7RAiVLr1d9HY6hzwNZ5EdtMnaJ12GMbJ4zA2gy6GlRZWA1dU41TpO3Af+TesB5vQv/Eo+uYslTybqIb+TgwtbfrAhd1I0iW76etxADFqyGxvrsXDO57FXfNvw4KRyfdTqWxHyQu1KZcH+8EPfoA1a9Zg3759ePHFFwEA06ZNw9tvvx2xAhoAnDp1Cps2bcL777+Pb775BqdPn0ZxcTHOO+88rFy5MuEiMyQ5JSX9ePV5O7q7/RVoTzTpQuYbagEFhcIEB4WAcvMLpcSfGPH0/TaY20/AVHgAHd/1D62qvPSHCU8gbzmVjcuWnQ/XYPQO05TlxXuffKGJIDHZTCFjDH/ctQ71nU344651mFs5K6n1ncTaDiGJKCnpx9+fP42uM/1dfZP+zHzD9DMW9eibMwDXrEVpWZRGiMr5K2Cz7oXFfArm03o0Kt0gjWhp0+OyW0bGHCqbVtnNwsjrlzPG8OSX63C8qwlPfrkO81ck39+JsR0lpBwgchyHW2+9Ff/5n/8JACgtLcUHH3yAwsLCqM95+umn8cgjj2DChAm46KKLUFpairq6OvzjH//AP/7xD7z66qv4wQ9+kGrTiADlpT6MLNPWj5yCQuHUUHRGDu2b9yO76RN4yjqQZz7zfc4HGib3wGC1wHzW2ehuSG7bDrspZnAIAK5BPRx2k2oDRDGGj24/uReH2usAAIfa67D95F4sHH2OYtshJFHlpV6MLNPGyZkY0rEoTSL498/6u+I8kvAcTn3ceZRan5srxLbmvTho8/dTB2112Na8F+dVJt5PJbsdl9Om+DSPlAPEuro6rFq1KvDv3t7eqJlD3ty5c/H555/j/PPPD7n9iy++wPLly3Hbbbfhu9/9btztkMxAAWFiMiFbyOOrkJl7DqN9dhOMk8fBPWVK4H4L/EOOfF6GbriUa6gCxJxTyBjDs7s3QMfp4GM+6Dgdnt29AQtGnZ3Q1VCxtkMIiY4CoiH1Fd2YcJCBO/IVUL1U6eYQFbDVOZDfuAf9RR7U57TCjDEh9zPG8NSe0H7qqT0bsHBk4v2dGNtRSkoTBNva2nDxxRejo6MDVqv/5KO3txe//e1vYz7v6quvHhYcAsD555+PZcuWwW63Bxa9J5nJY+sK/AH+oJD/I5FxtvZAcGiyWgJ/asFXeRNL++b9yP7kWbhytsJxQSfM370ElfNXoLSwOuQvk7ictsAf4A8K+b9U8Fk/H/N/fj7mC2T/lNgOISQyXU+b/3+iDJ3LJKWF1eDKynDswh7o3e+if+NbgcquUhK7ryPiad+8H+bdn2Kw8ABqxzbAXDO8eBOf9Qvup/jsXyKS3Y7SxWl4SWcQe3t7cdlll+H48ePIy8vDv//9b6xevRr/+Mc/8L//+7+48847MWbMmPgbCmM0+ucHGAwpJzdJggosvriT+E1GhgKL+Ac+yhImTyvDSMOrmgYTWvUN8F/9y93yNszuZrTTovaSVx8Nz/rxEs3+ibUdkv7kWAOxUFB/50ORRYPD6PLzAFAWEeArm9bBgd3AsX/DurUJ7Y3LJK1sGquvAxLr74g4+NFG2a4mtE47DG7htIjLWoVn/XiJZv9S3Y7Sw0uBJANEj8eDa665Bl9++SUMBgP+9re/4eyzz8YDDzyAt99+G4ODg3jggQcCRWuEamxsxMcff4zy8nJMn06LnMqtvJTh9Re60OWMnlgusPhQXsqi3i9UeEAIUFCYCK0EheFSrfLW9cWXqHK/i/6xveg6N3MXtZdzSYrgOYPBgrN/QuYQirUdQsRQUerDmy+0ozOov+M6HTAWDhVq01q1Rvu7nyKreRtOje1FV45u2NC5TMWvk3jK8A4mFljR01SPxjpp10gUs6IpEceIMj36ymMXbwqeMxgsOPsnZA5hsttRS/YQSDJA/NnPfoYPPvgAAPDss8/ikksuAQDMmDED3/ve9/DGG29gw4YN+PWvfy148Xm3240f/ehHGBwcxKOPPgq9PnZRCCKN8lKGcgkOahQQpi6d5xYKWWTYpHdhZNUXaBndCuNZ1Rm5qH1w5yHHFUbGGB7f+mzU+zlwgrJ/O5r24s5/PZDydggRU0WpDxWlQ1f3OdsATNYYde9Vis+OZPUcRsuyzD0+xuOxmoHTys/RFNTfaTV7rQHRijcxxvDwjtj9nZDs37ZTe3HHx7H7u1jbUUP2EEgiQLz//vsDmcF7770Xt9xyy7D733zzTXi9Xtx77714/fXX427T5/Phpptuwueff45bb70VP/rRjxJtFlGZTAkId3Z9jsdO/A9+NfYhzCtYLMlraDVbKJStzgETgJfv6UJXr/+QlLPvU7jyj6F3og6+ceXIKipHQWE/cipGIwejMyprKHdQGPLaXjdOOk9HvZ+BobWnA26fGyZ95BNrflkLl9ed0nYIIcPxwWGe4Vs0XNAJy+Llkh0fxVoXTkltuQ7o+nr9SyJJvC5iNPGGoALay15rQbwLAy6vG03dcfq7vvj93ZN71sHli9PfxdmOGiQUIL744ot44AF/VHzjjTfiwQcfHPaYadOm4Qc/+AFee+01bNq0CXfddRdmzZoVdZuMMdx66614+eWX8cMf/hB//vOfE3sHRBUyJSAMxhjDH5t+h/qBOvz8yH/giUkbML9wiSjbTudsYbD2zfuR13oUI8r0GAP/AXywrxnHzj8BbuE05JRUBp3sFCrSxqJiF0xZ3rjrIBYVi1clVcmgMNielv3wsqGTlJXn3Y4ZZVNDHlOcUxCzk9txai8OdxyNuQ0h2yHqUFicUm07IoERZXrkjR6F41NzJQsOg9dz+8+PV+GPy+/HwkptrbFYVb0cJ7AZ7r5mmLetQ//G89E3Z6mkQ02jUesQ1LTPbsYo3vRla2h/d8/82zGzJLH+blvzXhyyDfV3d8+7HbNKhfV3ahpeCiQQIL7//vv42c9+BgC48MIL8fzzz0d97KpVq7Bp0yZ4vV7cc889eO+99yI+zufz4ZZbbsG6detw/fXXY/369dDpqPPRgkwMCMNt7/oUh3q/AgC42CAeOXEX3py5NaUhcumeLeTxV70HdHvQW22DrWhoyIenOAtcmXoKz1RUDuC9T76Awx69UygqdqW8BqKc8wqFiLQkxbvffoQfTLtc8HecMYY/7/lrStsgRA7hF+VIqOA5VS6vG7/d/ie8+70XNPc79hetGQMXtwWu/VthkqFojZaka3YzUN03ikhLUvyj7iNcNyWx/u6P+0L7u7ePfoTrpyawDZUMLwUSCBAvvfRSuN3RU6bBpkyZAo/HE/MxwcHhtddei5deeonmHaoUBYPDMcbw7MlHwEEHBv8clsbBemzr/ATnFV2Q0LYyJSjk8YvauwoawU3XIX9q9AnjalFROZByABiJ2oLCYOFFZZIpJrOvex8OdaS2DULkotXjL+vvgq6oAIA0SzjwJ88cODD4i9Q1dDdj66k9WDTqXEleU0qlhdXAFdU4VfoOptUCdbFPVzOOWrObyRJSvCm8qEyiRWkAf3+X7DbUlj0EUljmIhU+nw8333wz1q9fj2uuuQYvv/wyBYcqESkYBCggDBecPQz2eMP/YGGhsCxipgWGfNbQ7f4CvdP6YZw8DqPmr1C6WbJTc1DIE2NJCsYYXml5hZa1IETjolVkfHjHs5rMIvI8VjMAWq8wXQVGKrm/gH2ZN2rxJjGWtojV3wnehsrOBRQJEB988EGsX78eeXl5mDRpEh566KFhj7nyyitjzl0k4vDYndAhNDinYDC2SNlDXrwsIme3AXn+/5qshTK0VhnhixFzR75CdvM2uErscF2cC3PN0owqNAOoZ16hEPGWpLjslRuxaukvMG9U9EIVO07txdH+o8NupywiIeLhjnyFwb5mnHZJU5kzUvaQp+UsIq8drchv3AObxMteEHkFF2+yzTXAsjj6OUe8JSku2nQjVi/6RczCTNuao/d38bKIasweAgoFiCdOnAAA9PT04Le//W3Ex4wdO5YCRJFEygr64AXyAHNxFnScIl8DzYqWPeSFZxGDM4V892oqjlxmWetsdQ74Gk8ir/Uo8vJagIFuAIDd2I6WZe6MK7+upaCQx2cPI50Q8lp72/HHXeswt3JW5DLdZ+YeRtsGLWtBSGr4E2B9zlYcme2GcWy1JPO2o50887ScRcwpGYPmCwG2bSdKPmlGe+MK6MZQoJgurJYe5E+KXbwp1gUQ3unedjz55TrMXxG9v/vjvtj9XbwsohrPDxSJDNavX4/169cr8dJpLdrwUGB4VtDHPHCIV3QxYwxlD6MfTPgs4iLf0KR3fgipj3mBNN3v/AmLK2crbHN1sJeOgMeae+beXFhKxmRE1lALQ0hjcfvcON3THvX7zYuVBXT73DjdG30btKwFicZj66JRLHHw87jrphxB8YhcyZa2EHLyrOUsYmlhNVBYjbaSMXAUbUFu3SZk756N9saJVLQmTeiK8hFrbm68vooXKwsoqL+LsqyFWrOHgEIBIklNIoGgGFradOh0Rq8uW2gJXWw4nbmZC6cHT8U9mDxz4iEsm/W+ZFdVt3dtwcMn7sddY+/HgoJFkryGUHzW0HzsHbTOboJx8jiYpkyJe8LScipb8sqgctJitjASk96El65+Co6wNaMYY7jvk8fR0NUEH2Mx5xKa9CZsuGItvq3tQOlUIzj98N8BLWtB1KilTa+JCo5jpuXDObkKo8aeD1ZYKMlrCD15XrtnPc6rPEeS/k6OtRdLC6vRthgoHvwGlRMLUdso/mto5XuVDto370d+4x60FB7AIX0OzCVzoj7WpDfhtRVPwTEwvL+7+4vHUd/VBB9YzLmEJr0Jr162Fvv3d6Cy2gidLrH+Tq3nCxQgqlCsABCQd45gS5sOV99SApc7+oHfZGR484X2jAgSTbos/OWsf+I/Dl4Kp7cz6uM6WAfczAUTlyV6GxhjeLLpURwfOIonmx7FfMt5ig3vcdQeQe6+7RgsPID27+fAXHOJoCvZLaeycdmy8+OuLfjeJ1+oOkhMl6AwXHleCcrzSkJu29a0B/WdQ2dO8eYSlueVwGsuQOUIE3QG7Q0/I5mnpU2Py24ZGXcNuPdeaFbNyTwrL5Rs2ya9CS9d9jiuf+fn6HL1RH1ce79dktEAwWsvxhriJyZfjwPACFG3qcXvlRYFX6w+PbsJ3MJpMAsYuVSRV4KKsP5u66k9ONYV2t/FyiKW55agz1yA8VYTdBEuiEai5uwhQAGirOIFfjw1Da/pdOpiBocA4HJz6HTqRAkQtZCtHJ0zDq+N+hscXnvgNmNhXshjig1WmHTiB4cAsK3rcxzs/RoAcLD3a2zr+hznFS6R5LWCBRee0fW0gZ1qQVbzNpxc1grjWdUxD8Th2cLjR3NjBocA4BrUw2E3qS5A1PoQ0mSIUdVULXoGQj+/3oE+hVpC1IKfJ+5w6mOexAOAy62Dw6kX5UReC1mlMZZKvPHdZ4ZlWIJJNRogeP5joksOJKO+ohuu/XuQ18qlVLQm/HM93mSQ9XuVqXyNJ1He+wUaLuiEebGwi9WRiFHVVPBrqfj8gQJEkXjsTngGYg/DUFPgp0ZayFbyJxLlxnKMKZ8k++szxvDUyd9DBx188EEHHZ46+XssLFgs2Qk6P7ew1PBtyO0dhU60f18HS03s+S9CsoVakK7ZQiHiVTVVU0XS8AAwElPB0Ofn5ui4TM7ME5dmGcGItJRVipRhkVqkhcvFPjkPVlpYjbYa4BR2A+Z/w7q1Ce2NyxKeiyjkcyXSyS/wwDs1tXoH8aqainGhQu3ZQ4ACRNHkFGfDnEsnGqmQO1splJrWKwzOHgKADz5Js4jtm/fDfOwdnJh4DPljrPBODV5gNlfQ8A2H3aSq4DCRuY+ZHBTy4lU1VaIiaawgMDj4I0StUs1Wsr5uqZqmCmIsXJ6o0sJqYFE1ThnegQ2HUdDRgf6NR9E3Z6ngbKKQz1VOWshSi0XfbwNSLBAfrzCTkIqkgl9LgXOK3gF7/AedQQEiIREEB4WA8oEhMDx7yBMji8iP3Q+W37gHpsIDaJ/dC/PC2RiVBstTtJzKxooL48x9NHnx1rv/REWFf+hhpgaGvHhVTaWsSBotEKQgkBB+off0I+cQv0gq56+AzboXxftOwWzSQ4KaNbJoadNjxa3ayFKnwlbnQO6Wt6EvPIAvR7phRPLZw1QqkgqlhewhQAEiIQFKBoVCqpKGZw95qWYR2zfvR17rUQwU1SLPPNRJNCzqgcFqgblmcdosT+FwxM9mulx6OFgZykc4ZWqVukWrahos1TlIFAgSkjniVSaVY4hfPN4S7a9VLPecWiXwo5xOz24CJoyAJcXzlWhVTYOJMedWiQvPQqZfBKMAkWQ0NWQKhVQlZYzh4Yb7o26DA5dwFpHPGmY3fYLWaYdhnDwOPmtloGO0AGkTGJLURKpqmiwKBgnJXPEqk8o5xC+epqwOVNkOw9zqhQ3Ch5kSebRv3o+q/n3omtwLbuE0VIk0yknKObdKZw9NlmLBj6UAkWQktc4rjJYJdPkG0TTQFHUbDAytrhbBS2vw6wQNFh5A1yJdShW/iLbsPFmLx7Y+i1+ddxvmjZJmXbFgvYN2cJ6hf1MgSEjmileZVI4hfkLwayM2YAvy92+FKcmiNUQ6+n4bCitz0V9agZySMREfI8c6monSQvYQoACRIPbSEh12HYxGBnecyqKFFvWvgaimoJAntCrpl9074cXQWfY9VQ9gZn7oEBshS2vY6hzgjnwFc/O2wDpBYl11E5spy4uiYpeo2/T0CVtqJl0xxvDHXetQ39mEP+5ah7mV4q0rFt4BMS8A5MFoKaZ1EIlqROrvuM5sGB0mgf2dD0UWbQ7HU5qQyqQmvQn/PfcW3PX544Hn3TP/dswsmRr4t1TLaoQrLawGrqjGqdJ34D5yGNaDQPtmQDcm+SUwoqHvVXK4AguA5oj3KbGOZiyKZw8LrHA7ewU/ngLEDHe6TY/v3Rp7aQmjgeHJB+wYURw5CFTD2oTRJBoUCpkLKCYhVUkjBZH/6Pg7riv7j5gHO34IaV7rUXCD/sAoF0B/ZR3av68TvKi9XNY8+RXGTxw6eAVXE00Wf0D2+QAgD8xSkNL2tC54uYpUl6eIdEUyODvo8zAA4gb4hKQien83tDC60eDDMw+0Re3v1FD1Ud9vAzfSDCC146Pc2RUhlUkZY3j54NshQeQ/6j7CdVMuV+zkvnL+CpzCO5g0agKch+rRiFEpb3PNynaMHz100VcN3yut4IvSGA0OfOY9AePYalRFOJeRex1NIbSSPQQoQMx4QpaWcHs4jCj2YepET8zHparQ4oPJyOKugxgvW5lsplDIXEAxCa1KmszSFu2b9yO76RNkj2uFaeaZoKjQjPqcVnBlFRgrY9awqNgFU5Y3duXQLC/OmetIOSDkhS9PwTwMqM/sYCV8sftEF7mPFxASonbC+jsdRhT7cNZEaY8XRRYvTEZf3AqTUmWV5M6uCK1MqsTyFkL5ujsRbx0FoZ/rOdMGKSBMAl+Upn9sL7rOjb4Os9zraMbjctoUrYieTF9NASIRTayhqkD8TGNFqQ9vvtCe1DbEKDYjZC6gmIRUJV1YsDhmEDm5bfqwg13ulrdhKjwA27ReGCePg3HKlMB9ZqS2gGwyKioH8N4nXwheezBZtGZhbOGL3cdb5D48IKRgkJAhqa4vV1HqxXsvNCu2Rp3c2RUhlUkXjjxb0eUtYvFYzdjT9hWqbFb4MA6IMsRU6c81XdnqHDDv/hTmnsNoWdYK41nVMS90q+lCg5JDS5PNHgIUIBKRtLTpcPUtsYeqmowMb77QHjdIFDpcVcwKpELnAoqFf72YldpO/h6MsZhB5Nd1/4uL9GcyhAP+hZOP1ZwAJoxQ1fIUFZUDomUHwyUSGBYKzGYWijz3UWnh2UNecBaxdzB0AV0KCAmJrKVNj8tuSX19uYpSZQIFubMrQiuTMsYUX94imqrq5WjAZjSgEfn7N6B/43nomxO5sqlSn2s4pbPUYuFHRLkKGuG4OCdq1pCn9DqaEduksewhQAEiEai+afgJdXA2T8jQHZebQ6dTl9J8RamWpUhmGGcq3MyF04PNMSu1nXa1YO3Jx6N3qgx4euRrmDTlhjMHOw4eqxkc1Ft4RizJZgvLKwfw5pYv0Bkjm1lY7ELFKGmCWaWEZw95fBbxk+OfYtFZyxRoGSFDPLYumK3ZSjcDx5uGnxoFZ320vr6c3NkVt88TtzLp6d4OrN2zXhXLW0RTVb0cbSV16Cn9Bu4j/4ZV5ZVNtZ7NDM4atp8pqidkeowa1tHkaTV7CFCASAS699HhV8mEZATFIPVahULnAorJpMvCazX/hMNjj/qYfH0+fnjwe9E7VQ6wGb0onXuxLBXdEiVFAQQxhpFWjBpIuwAwFj57GOvE6y9fv4Xzpi5VtMIbIWrxm0eHr4EmJCOoBUpkV0x6Y9zFx/NNZvzwvTsVX94intLCamB+NU7hHYxBPr51+m+Xu8CdUGrJZiYqOGvYPVf4UlxqWkcz0CYNZg8BChBJCsTICEYj5wL2QuYCSpFFrMgaiYqskTEfwweRXY1O5Oz7FF5PBxyTT8NQVYnS6YtlK/edKDELINDcwuT1DNjg8rrR0t0W+8SrpwNurxsmg/q+S4SogZozgolQKrsiZPHxeEGkmvo7j9UM1uef1iF3gbt0xmcNB3R70DvNBuPkcRg1f4Xg56tlHU1A29lDgAJEoiJKrFModC6gVHMRI7HVOQL/b0IOyo90YHz7CQwW2uFYkovRNdepZm5hNGIUQKDAMHGRCsuYAKy7/ml09kc/8SrKKaDgkJA0p8bsSjAhQaSa7DMfRkltD97X78XBHPkK3KUzX+NJ5OW1wHaWC/lTF8FafXZCzzfpTaq40MCfv2g1ewhQgCirVKt8SkHI0hJSkTNLGI2QuYCtrha4mQsmLvYi9GLgh1UUFhsCRWcA4Mii5rhVu9QilQIIFBQmTki10bL8EpTla+fEi2jf6TYOXTH6uwKLD+WlkY+7UlGyv1MDNWVXtM5ftAZoYwewbnATdIyDj2OSF7jLBPkFHnClIxIODnlqudCg1DmMGNlDgAJE2YhV5VNs5aXeiEtL1DfpI847TBXX6QBnG5r/pURQGEzIXMBigxUmnbTBIT+swpSzFbZpvXBOHgePNTdwv6UkdtUuNUmmAAIFhsLR8hNE7U63cbj2loK4/d3rL3TJGiRG6u+4TgeaugsjzjtMN2rJrqSLqurleM/Xi2+2fRi4TeqpKenMUXsE+n4buJFmeKyxi0CpmZJDS3linBdoNkDs6enB//zP/+Bvf/sb7HY7pkyZgt/85je47rrr4j63ra0NK1euxLvvvou+vj7MnDkTDz30EJYvly47I1eVz2QksrSEGJQOCsMJmQsoJX7h19bZTTAU5QuejB2JFIVhEpFIAQQKCoWjoJBoSZfA/q7LqUO5zHP6wvs7zjYAo8MjaxuUpJbsihjU0N/99cjH0HEcfGzoQgdlERPnqD2CnH3b0V9Zh9qxOphL5ijdpKS4uu3Q6bSfPQQ0HCBeffXV2L17N9asWYNJkyZh48aNuP766+Hz+XDDDTdEfd7g4CCWL1+Ozs5OrF27FqWlpfjTn/6Eiy++GB9//DGWLKErPskQMnTHZPShdEwOAG1P8hcLf0A0FR5A+/dzwJVNw6gUhpCKWRgmWUIKIMzJHxu4nQLD6CgoJESd0mV9OS1TdX9HWUTBHLVHwE61IKt5G04ua4XxrGqYS8ZoZsRUJEqf14h1rqDJAPH999/HRx99FAgKAWDZsmVoaGjAr371K1x77bXQ6yOv+/Liiy/iwIED2LZtGxYsWBB47syZM7Fy5Urs3LlTtveRTipKfXjrsSPo7Pbvd2Nh3rDHqHm9HanxhWcY5wNGA44Pv0B2k/+AaLBaYK6Zk/IBUYzCMKkQUgBh7e4X8dKyB4GSEbK1S0soKCRE/bS+vlw60EJ/R1nE2Ozvfgpz+wn0V9ah/fs6WGq0M5VGjXoGbKKeM2gyQHzrrbeQl5eHa665JuT2n/zkJ7jhhhuwc+dOLFy4MOpzJ0+eHAgOAcBgMOCHP/wh7r77bpw6dQqVlZWStl8LhGYEi7wd4GxuAEDVZAuqAve6JG+jFvBzC7N7DqNych68ej1qR5+H3IrtOD2vH8ayalEWtU+lMIxYBBVAGOiEq9gCmuEyhIJCQpSVTEZQq+vLpQOt9HctvU2yFbjTknTMGrq67QDywIqLocTlADGHlvI0GSAeOHAAU6dOhcEQ2vwZM2YE7o8WIB44cADnn3/+sNv55x48eJACRPgzgpGK13CdQ0swFOZ7UTU5B0COzK3TBn5uoavEju65OnRMtYAxHdALnD63EObSaaIdEJMpDCO24AII7t7OwO2ssDDw/+lYAKHlZDY67dHfU2GxCxWjBkJuCz6YU0BIiLIoI6gtauvvgtl7GjFw9CDyDjFUtExBT/t2FF++VJY2yaGlTZ/S74SyhtIR+1xCkwGizWbD+PHjh91eXFwcuD/Wc/nHJfpcwD+HcXBwMPBvp9MJAPDBAx+LPtGdMWHXFBjzxtyOWHzMG/LfSMpKgHJ9WHZjQn7Qv/Qxn59JOj47iNy24wAAbtD/ncj2nEbLBXYYpkzAmAn+eQg+L8OJg26MGrsYOj0Hnzf1Cn7xCsPML5sty1VVV7cdRdChyFAENm5C1Mf5PPKWtgcQ2M9i7O9gp09l43tLzodrMHqHacry4u+ffY78Ec2B24yWoWOQEvsjFbubavHklj/j54t+hjmjoxeG4N+X2t+fjw5hEUXv6+L3UT6BH7kvrL8Tsu1kxOrvOPjgY16UlXhRFqd2i9D3JTWmB7w6HRjTi35ME5MUx1219HcAUJYzAmU5YdMliiYAo5ehccxncH9zDMYvBtH3UiP6FlyG4gmFsrRLyPldMlra9Fhx68i4mfZ3nm9CRak34nmR0XMaJ89vG3ZepEY7mmuxZvef8Zs5P8P8GEWQXN12+M58FZV4L72D/ir8QvraRPo7TQaIAGIeAOIdHFJ57sMPP4wHHnhg2O0trn0wG8xRn9fmLgBQFnPb/scdRr4reglqsbW498V+QPhUQho5GtkCwImxYTf6s9KeXuD416E77sRBt2gvXeusjVkY5s2tuzDbIkeFt6AvS706vygt+8Tb7wBw7FhOzOAQAFyDeny7lcOECcE/JnXun3gYY/jjt+twor8Jf/x4HR6bdFbcY6Ztl7j7XGx9fdr8LKQWra/rcO1EX4y+DgAc7gIAS+O+hsP9FdqD+7s8wCHhxxGxv8uD9n6OFxTCgbOBCH2LGqVnfxfPAmD0ArQGaiY2odPVJGsLTrgPibq9Y7YCuNxVMR/jcutwwFaP/sKuhM+L1IQxhke/XYfj/U14dGu8vm6obxf7HEMY/vXj789E+jtNBohWqzVips9u90fRkTKEYjwXAO666y788pe/DPzb6XRi9OjRqDDNQp4pP+rz9Fa9gDl9DJOsk1Fukv6Sto950eLehwrjLOgdncPbUhz9vRC/js8OIqf+Q3TMOAlDYR5yJk9H8YSZMZ/DZxDHTjNCp0/9KidjDPe8vzHmRPk3ujbi6vPmin5V1T/m/kw74vxulObzMrTsc6Niljj7nddtMgp6XNFMI0pmaH9o7c7GPTj61VEAwNH+o6gffQDzxkQe0uXzMNh2uWGda4TOoN4iDb3dmbPEQSKi9XUjTPOQG6OvAwCvVSeovxtrnY4S01AWyGN3wlws/nyt4P5Ox4Ve0OHsNs31d44Pv8C4iuP4+tzBQBZGjdKpv0tF47HP4PnmGMq+KIXeUC55NtHHvDjhPoSxxrOGfd9TMWAU1ocVffwV3O0fIm9UsaDzIjXaeiq0rzs94kDE4cuubjtYcbFk5xjx8NnD4FFJMR+fQH+nyQBx+vTpePXVV+HxeELmIe7fvx8AUFNTE/O5/OOCCXkuAGRlZSEra3gHpoMBOi767hxZhohz+oIVWnyoKOUgx8fC2W1AHqB3dEIHnerWJlQzW50DuVveRlbhAXSdr4Nu6iSMTrDQjE7PiXIQcXkFFIbp64CX84gy9y947UKudGi8u3q64th0ek7UYIUT+BmK/bpKYIzh+V1/DSkM8fyuv2L+uHNinozpDOp+7zrxzp/SSvS+Th+zrwP8/d3rL3ShK0Z/V2DxobxUB2DoMUK2nQodN3z7HHSinkTLgfMCep8PHOeV9WQ0WVrt78QydtJStJVWwla8G0WftSHvCx86vxkr+dxE//ddvO+20G05q46icvZ4VM5fIdpry4kxhj/uC+3r/rjvr1g0OrSvczlt4EqtIec/cvf1nCexuYeJ9HeaDBCvuuoqPP/88/j73/+Oa6+9NnD7hg0bMHLkSMybNy/mc2+//Xbs3Lkz8DiPx4OXX34Z8+bNw8iR0i2YLveC9ME4W3vIv43F+YDLnynUWueoFK6zFbYth5HdvA2nZzeBWzgNOQpX3oo2UT6YGIVhaFH74XoGbOh3KfN7VsLOxr043BZaGOJwWx12Nu7F/Cr5yssTbSgvZSinoi5ERHL1d1IoLawGFlWjoWwz3IdqUfFJMwbWnUD/rAUomj1Z6eaJKnvpeaicX6h0M5ImpAhS8DmRUsRe1iKcJgPESy65BBdddBFuu+02OJ1OTJw4Ea+++io+/PBDvPzyy4E1EG+++WZs2LABx44dQ1WVf9z0TTfdhD/96U+45pprsGbNGpSWluKZZ57BkSNH8PHHHyv5tkQVHhACCMkSUnGZ2PjlKQCAGxzqjLgzlbe4smmiLE8hhoq8ElTkxamukCQKDIcLrkBqzCtUriEyYozhue2RC0M8t30D5o2Rr7w8ISRzSdnfyaGqejnaSsag3bobBV/WwbS7GQP7/IkJllUAAOibsxTW6iIlmxlRV6MTQEXcxxXnjQHglLw9UohXBGnhyLPhPjO9RslzIimWtQinyQARAN58803cc889uO+++2C32zFlyhS8+uqruO666wKP8Xq98Hq9YGxoOEJWVhY2b96MlStX4o477kBfXx9mzZqFDz74AEuWqHcsfzzxAkIiXMjyFNP9w588Z+bFcGUVqlivZ3tzLR7e8Szumn8bFsSorpUMCgqHy/RlKcKzhzzKIhJCpCZlf6eE4Gwia20FcAwGu79isMfRDevWJrQ3LkPJ8unKNjRI++b9yD7UBSC9sp3hwrOHvOAs4pz8sao4N5L6XESzAWJeXh7Wrl2LtWvXRn3M+vXrsX79+mG3l5WVYcOGDRK2Th7hQSEFhIlr3xw6HzW/cQ/cuq/RPrsf3EL1ZAmDMcbw5JfrcLyrCU9+uQ7zV8wSJXtDgWEoWsDej88exioMQVlEolXMWgKXrZ36T5WSqr9Tg6rq5UDYteZTO95BV9FJ5O/fgIF1NegOKwKmGzNK8uxipPMis7sZtjEmAP+fpK+tJD57GKuvW7v7Rfz1mj8pWndBjuwhoOEAMRNRQCgefghpeV4LLBU5YH3dAID28a1wTc+FuWap5FnCZK+KBl/hEmNxYC0FhsksSp+oRLKFBVYXTFneuOsgFljVUc57V2MtnvjsWfxiyW2YO0bYd87tdaO1J05hiJ4OuL1umAzqm/tDCFGeWvo7taucvwJtnXVwFO9G257jqOgYqrDb26NHz1ff4pvyc1E0tzruovSJCpwXGb5FfllO4Pb28a04PV2HMut8mNbF7++KitXR3yX6nXP7hBRBssPtcys+z1WOC9YUIKoYBYTSaN+8H9lNn6B12mHYJ48D0AOPlV/XqwJjZcgaJntVNHx8fPC4+ESvqiYaGO48WYvHtj6LX513G+aNkn+YT8vJbFy9KP6i9G9u+SLhIDHZIaTlowbw+u4v0GWL3lkUWF0oTzFoFQNjDH/etg4nHE3487Z1mDNa2HfOZDDhL9c+hc7+6IUhinIKKDgkhESkhv4uUUoOay0trEbLuOn42U8WweWKcpr+EmAyePHeE/tRMTG5jCLX2Rr4f74AX+vsJtiK8uGdOibokUPnRe998gUcMS7SFhW7UFGpjv4u0e9crCJI7t5OAEBRRZWiwaHUhWmCUYCoEjSHUHr81TFTzlZ0LdLBWDpOsTLMyV4VFVJdK5Zks4WMMfxx1zrUdzbhj7vWYW6l/MN8Ou0mQYvSd9pNggNEMeYWlo8aUEUAGE/wPMJI8wZjZRfL8ktQlq/dwhCEEOUo1d8lSw3DWh12U/Tg8AyXR4/Wf+5EUdHpkNt9Bh3wgxkYePkV6DyxK20zs3+Rda6oFi3LemA8axpGxbhIXlE5oIoAMJ5437loFwAiFUFyOW2AqUjxEVZyDS3lUYCoEAoI5WOrc4A78lXg6pjSy1NEWmNHyFVRIdW1oj0/1WGk20/uxaF2/8H2UHsdtp/ci4WjtTnMR+qCM8kM45RaeBXS8OqjyWYXwwW/93NHzhL/jRBCNEWJ/i5VWhrW2rHkJE6Pawi5jcEAYAZaL6sHB6ELo+tgqVme8HmRGgsIxcs8J3MBINJ5U/CoqjnlsyR6N6HkrIVAAaJMKCCUh63OAV/jycC/9f025LafwGDhAbR/P0cVy1Ps696X1FVRIdW1wp8vxvxCxhie3R16sH129wYsGKW9oiS9g3ZweukOsmIFWmKLt4ZhvOyiEOHv/fnvPSnmWyCEnOHNsYJ1fw1Ap3RT4pKzvxODksNakzFy1iUYOz10SQmfl+H41y5ULbgGOr10bVZDpjWSeJnnRC4AuJy2iOdO4aOq1q94UpL3wpM7ewho4eiiQZytfdifyWoZ9kfE1b55P0xbX4LJuQGFhtdQaHgN+fkf4eSiWnT+YCzMNXMUDw4ZY3il5RXouNCfHt8JBS/JEv48vrpWJBy4kOe7nLZAcMhGWFMaGsFnD/mruD7mC2QR1a5nwIaeARt6B/3rFhktxZJegYsUaCktOHsYjM8i+ny+kPv526N9F6MJf++7mpR/70Q7PLYumK3ZSjeDiEiu/k5MfPAQ3N/xQQQJFSnQUlpwgB+M/875fL6Q+2N9F6MFh8DwUVU7Tkn33vngUO5K6hQgioSzd1AwqBBbnQP9G9+C++Q62KYdRs/yUjiunxf4syxZjqrqxIdOSGFb814c7T8aMmQGiN8JCauu1YHeztbAQS3VwBAIzR4G47OIUnTQYuADQ8B/UDVaiiV/zV0Ne/Hrdx8InNQkG2iJjQ/cIn3nDrfV4a97/hZyf3B2UajwIFTH6fDczr8q/t4JIcqRur9z+9yitjdecEHHsyHbTu3FHR+H9ndq2EfhAT6P/869sP9vgi4ABI++Chd+XqTjdPjzHmn7OyWW2aIhpiIxFefDlJsf/4FEVPZ3P0V28za4SuzwzTXAvPgSVQSCkfBzMWKtsRNtKIuQ6lrFWRYYy8qjdKnJCb5KFiw4i6i2uYj9rk4A8h5QGWN4/LNn4PIOnbCoYRF5IWsYbtj92rD7w+coxhNpCOs37XXYl78P38E8cd4MIUQzpOrveMU5BaJXk1RqWKvWMMbw2x3PwOUL7e+U3kdC1jF87qvI/V3wdzF49FUk4edFPubDoY467LPswyiR+zslhpbyKININMlRewQD69ZD734XLcta0fmDsRh1xU9UGxwCqV8VrcgrwVkjJgb+JpqKMNFUhKlF4zCl+lyUjpkU8/V3nqzF91//KXaerBV0H3+VLNYwHzVmEY15hbJfbdvRsAdNnaeG3a50FlHIGoYDnsFh9yeSRYw1hPWVlldU9/0ghEhP7P4u/K88N3ZV5e3NtbjizZ9ie/Pw/i7SfUoOa9Waraf2oMEZub9Tch8J+c4NeCP3d3xwGy84jDWqSqr+TonsIUAZRKIBwYVn9P3+H29W8zZ8O7sRlgklsNQsVnVgyDPpTXj1srXYv78DldVG6HTDOyIhV0WTKTwTa5mKaPe5fW6cFrJAukyLxhYWq3NResYYnvj8zxHvUzqLGGsNQ8YYHvj3YzjhaIr4XA6coCxiePaQ52M+HO0/il1Ne7Fg/LnJvwlCiOaI1d8lI1bxlGj3JRLQytHfFQns7+RelJ4xhod3Ru/vlMwixso8MzDc9fljON4Vvb9bu/tFvLTsQaBkRNTXiDWq6mj/Uew4tRfnjRWnv5NzzcNIKEAkqsUHhuZj78A0uRcAwEb6ixg0Tu5AwVk1ihedSVR5bgn6zAUYbzUlXF2MDwx3th7Aowde8S9YD2EHj1jLVES7z6Q34aWrn4IjxgLpUnXwkVSMGsCbW75Ay+m+wG3GvMKQxyixKH207CFPaKAllWhrGLo8LjgHu6M+L3ABwOuGyRD5MxYyhPW5nX/F/HHnqKK6HSFEPqn0d7xkllGIVaUy2n1KDWuNpqJyQJWL0kfLHvJiDR2WQ6R1DAHA5XWhK15/12eHq9iCaHs8eFRVtP7uz3v+ioVVqfd3Sg4t5VGASFTBVucI+bev8STyG/dgsPAAHBfoYFq8KOR+C6CJrKEYgjOGPmsxnv787wktWB9rmQoAMZewKM8rQXmEg63c+INl/gjAOiE4KHZGfoJMGGP43+0bYj9GQKClhFjZRV5RTkHMNgsZwtrWq773TojWceZ8GGztQBp3g0mtVxdjmQoAMZewiBZcKEVti9IzxvDUXgH9nYyZVqGE1HEoqqiK2WZBo6p6xXvvSmYPAQoQiQq0b96PvNajGFGmBztzsjrY14wji5phPKta0UXtlRRpKOn2pj0JL1gfcUJ10DIV0e5TQ/EZqRe1T9XOxr040n406v13Lrkd0yumxg20lBItuyhUrCDT52VwfOXG+PklqnzvhBB1S2bB+lhr4PHbiXQfFZ+Jb1vzXhyyRe/v7p53O2aVTpU105qISBcAXE4bYCoSNF0n1qgq5mVoO+zG5LNLUn7vSg8t5VGASBRjq3PAvPtTGIpq0VrdDluRvwqspzgLAGCpUcfSFHKLNkk6mQXrw5/DC16mItp9sbYrNbUHhoCw4ZXvH/4I35txeVoPryzLL0GD4ySe+OxZ/GLJbZg7xj8MzOdhaDe7UJKnvhMFQoi6JbNgffhzeMHFU6Ldp9SQSK0QUiH07aMf4fqp2unv4hWkiaQ8rwQNnSfx2NZn/dN8Rg31d/lmF8pyUw8O1YICRKKI9s37YT72Dk5MPIb8MVbkT10Ea/XZSjdLUUmVVo6T7Yu3TEUkSmYRtRAY8oQMr1Tj0FKxMcbw523rcMLRhD9vW4c5o+MPAyOEkFhiZQKjZfviLVMRCWURhVFbER+xJLpWdKyCf2JRy7kPBYhEVrY6B3K3vA1T4QG0z+6FeeFsjNJYoRkxCa1IGi8TGCnbF29CdSz8EhZyZRG1FBjyxJjDlw6Cq5gKqdhq94l3hbRYp43vCiFEuHiZwEjZvngZrliULqyiBWor4pMql9OWcHAIxC74lyq1DC3lUYBIJBNeeIY78hWym7ehfuIx5MwaCbNGlqeQgqvbDt2ZZXSEHKSSWbA+3oTqWORawoIPDNV0UExEqnP4tC54DUR+GNgz219E9agqwMcByIPDZwc3dI6H3GxxPuveAZugYJOCSJKOnC39YEUdOLXjHVTOX6F0c0SVzIL18TJcsWg1+yU3tRXxSVaywWG8gn+pUNPQUh4FiER0/PIU2U2foNDcH7i9o9CJlmU9MJ81W3PLU4jF1W0HkAcgsTUM45VWjpTti7dMha3fATDAai6KeL9UVwO1mC0kkTN/exq+DlkD0cd8qGurx6HTDZgz+mx0wYXc7GJwBvGvygsJNKMFkRQ0Ei0rWT4dpzcDBVs8sE07jAbr5rQp5iZkrlukbF+8DFdHvwMcAGuOvP0dUZdkg0Mg9jSf+RXJB4lqvVBOASIRFV+R1JWzFV2LdHCWjoDHaj5zby4sadKJJSp4KCkAsOJiBJ8y7zxZO2zSMy+VBevVskwFkHmB4a7G2mGFW7QgVlYuOChjjOGVXW9FHAb24pYNOPe65N/zlw21eHrzs7hj+W04tyr57UQLIu1B38U+1hfxMYSoWcny6bDVjUL5lrfR76hD17mtaChr1MTF11hrG6Yy1y1dMlxalMx6lXJKpiBNsHjTfOatSKG/az6Ap798RXXnChQgElHY6hzgjnwFc/M2tM5uArdwmiY6KqmFzzFkHgbUu0IeE2/Ss9oWrE+UGIGh1oKteIVb5Hg/Ql4jWjAoJDu3+8ReHGmNPAzsSGsddjfsxWRMT6zR8O+75z9fhwZ7E57/fB3O+aH4RQBC3p8rW9RtEyIXa3URHD0LUOkswFlePXagUekmxRVvbcN0m+uWDLUHW+HifaZyvJ9Yr5FqcAjEn+az49RejEmiv+vu78Bze19XZZE3ndININpnf/dT5G55G3r3u2j/fj/M370k44NDl9MWclCKdWCKNOk5XHleCaaWTIz6V6bCq6Y9A7aQoRPJBofhwRZjic8xkVukwi08Od5PtNew+2whf4A/WAr/E7L9F7f4h4FFwoHDX7b/Nan3Fhx4Hmmtw+4Tw38PhBBtirS2YbiKvBKcNWJi1L/yXPX1d2IJD7a00N/F+kzleD+xXkOM4DB4mk8kHDj8eU/i/V3PgA27m/fjiK0ewPBzBaVRgEgSYqtzBP4ctUcwsG49spq34eSiWnT+YCzGLrohI4eQ8hIJDIHQYQtA6PqEWiVWYMiLFWypUXDhFsD/mT63fegzleP9hL/Gxyc+jRoQJsPtdaOtO/YwsPbuDniYJ6Ht8oFn8L57cYu2fw+EEL/g6qRA6PqExE9IAK0m8T5TOd5PtNcQIzgEBE7z6U2uv/vL129FPVdQGg0xJYLwi9qXGr4N3MbycmCrbEXXubqMXdSeJ3S5inDJrG2oZmJPto5UJfO57Rswb4x6y5EHB2eA/zPlA8F5Y86W9P3YfTYwxvDM9hdDXuOVXW/h/ElLRdtnJoMJ//vD2Et8FJoKYNxjTGi74cNWA8NVT+zF3HHa+z0QbWDWErhs7TBZLUo3JWG+vFLg1C4ABUo3Ja5k1jbMJOHLe8Ra1kMtYn2mC0eeLfn7ibbPzs2rAsdxKQeHgLBpPoWmAni+Ed7f9QzYUNvVEPVcIdZSUXKhAJHExS9q3zq7CbaifHinjgm6twJjaTgpgOQWXE10bUO1EhIYJjPvLlawpYYDaLjwgJYXfGVQ7PcTPo/w0OkG1LXVh7yGFEFWqaUEpZboQ72Yh6ELrqj3D3t8UPYwUtGbOWO183sghIRKZm1DLUtm3p3WAuh4nyljTPL3E22fbW/djwU1y0R5DSB+wT+fh+GUwP6uZ8AW91xBDRfBaYgpCbDVOdC+eX/gz/7upxhYtx4m5wa0LGsFt3AaRl3xE1RVLw/5y1T8cFIhQ0kj4bOHwQcHIDSLqHb8cNJ4Q0mTmXcXPlSTp7ZhGMH4gDbSZ3q4rQ5PfPZsyu8nfB5h8HBRc1ZxyBDN4NdQ+1BNPnsYad/RXERCtI0/kY/0+9bCUMpEJDPvLnyoJk/Nw3DjfaYP74jc34n1fmLts2e+fUuV+4y/mM5nD6OdK0g1lcbB7IIfSwEiAXAmS7j7Uxhcm1BoeA2FhteQn/8RTi6qhWNJDixLMjsYDJZqYAgIm/Ss5rmIQgNDXjLz7uIFW2qbi8gHtLE+06au5qTeT6zCMsG0GmQJKXqj9gCXEBJZ8NqGkfBrG6bL7zuZeXdaC6CFfKYN3ZH7O7HeT6x9puaL7EZLcdxzBTVcBKcAMcPZj3Wif+NbyG76BK3V/0bfeXlwXngWHNfPg+P6ebAsWZ7xhWd4YgSGPLfPI3htQzUJL0AjBGMMT3z2bODfQjJmQoItNRxAg7m9brTG+UyjifR+hAaFge1rOMgSWvTG7VXX74GIw2OLPreHCONzdCvdhKjcPo/gtQ21jjGG320P7e/iBb9aDKCFrFcZjRjvR8g+U9tFdv7cSci5QmuP+P2d3WeDOatY8ONpDmKGM29/D668r9E9Vwfz4ksoEIwg2QI0sZj0Rk2tbZjKWoY7GvagsfNU4N9C5t0lcgA1GdSxj0wGE/5ybeTCLW6vG3e+cx+cAz0Rn8u/nzZPK4z6oYnuiVQZTSTIUss+4wkqemMuUF27iXjMVlqPMllcTgEA9QaIJr0xY9Y23HpqDxq6Q/u7ePPuhARbfACtln0Ua71Kt8+N2z+6D12uGP1diu9H0D7rUc8+C7+wHu1cgVeUI25/F23N41goQMxAtjoHcna8A/xgBlrP+hK6+ZNo+GgEUgSGweJNelaDVBe5D88e8mJNxOaL2fy/RT/F2OLRUbct9gFUDGX5JSjLj/yZ/vX6Z6J2CF2+ThSaLTDqjUkvPcEHWVuO7sBT//fMsPv/3wW3Y1H1fNXtM168ojeEEO2qyCtBhcr7u1QxxvDwjsj9XbRCPHwxm5Vzf4pxBdH7OzUG0LE+0ze++4ykFwRMehNeWvYAPmveg0e++uuw+1eedzuWjp2vin0WadRVrHMFqeRmW9Hr6hX8eAoQ0xy/PAUAcIP+H2u2uxmDFd0AZsB82UUoK56oXANVSOrAUCvEWLJiR8MeNHU1D7s9WhYxuJjN6/vewovXrlW8kpdYwjuE4Ct6Y7PF+Q2W5I/Avw5+FLEy2r8OfoSrZl8uyusQQggJ5c8eRu7vImURg4vZ/PXgW3htRfr0d1JfEHA5bSjLKcY7zdsj9nfvfvsRfjBNPf2dWEt/JSOZ7CFAcxDTWvvm/TBtfQmGyi9hnlmHnLltyPlOD9q/34+uq/xLVZQUTFC4leqR6CL36SrRAjTRRMse8iLNu5NjEXmlRZpXKBatFqkhhBAti5Y95EWadyfHIvLpiD9P2zbQoPpK8Py5lNKSOc+gADEN2eoc6N/4FkzODehadBJtswvQetEU/9+cMTDXzMGYCUuUbqaqUGCYXAGaWFweF5qdrVHvD5+IHb6shZqWs9jVWIvrX/opdjXWJvX8aMtSiEnLRWoIIakz2PqUbkLGcnldONkTp78LKsQTvkSDWpaz2N5ciyve/Cm2NyfX10mNP1fzWYtVXwk+eIqOUpLNHgI0xDTt2N/9FNnN29A6uwmYMALmmjkRC8/4vHSSCCS/yH26ETMw5NU2H4CXeaPef+eS27F4/NCcuODsISDOIvJiCF/Dcc7oWYKHAQUfnMUOCMNpuUgNISQ1uvxCAKfiPYxI5MvW2P3d3fNuxwVVQ3Pioi3wLuYi8okKX79x/grhfZ0cgs/X3F6X4ErwSsxDlOKcKlnJnntQgJgmHLVHkLNvO/SFB9D+/RxwZdOo8EwMNM/QL/wgxheI+cWS2zB3zOyktxu8TEWkAzgHDu8f/ghjCivxX/+4B79Y/LNA9jB8LkG0YjZyiTTsNV7AKmdgyItXCfRQyzfY9OVb+PrUQZxblfxnSwgh6YAvEHPX/NuwYGRq/R2/5EK0/u7tox9hbEElfvqve/CbeT8LZA/D+7toxWzkEGnIq1LBarBIF/JNelPMSvAH2r7BK1+/hdqWg5g3Spn+TungkB+xlCwKEDWoffN+5LUeBTBUeMbkbsbJZa0wWC1Rs4YkcwPDnSdr8djWZ/Gr827DvFGzI1YnTSVTFk7IMhWnu9vxzJnXe/yzZ9DUOfzqt9JZxOBhrz7mixmwKhEUhotWCZQxht//+ymc6mzG85+vwzk/VNeVYUIIEYuQwE/MbJmQJRdO97bjyT3+1/vtjmfQ4Izc3ykVmAUPeeX7OiWDVV6sUV7RKsEzxvC7L55Ck7MZf9y1DnMr5e3v1DDvMJWhpTwKEDWEr0hq7jkMw+RemEYUAIVmAEB9Tj+MZdWUNYwhU4eTMsbwx13rUN/ZhD/uWoezrKPBcdywA1gymbJoYq0JyKu3N+CBfz8OABGDQx5fzEaJLKKQYa9qCAzj4YvXAAgUq5k7Tvkrw4QQIiahgZ+Y2bJYawLyjnU14K7P/f1dpOCQxxezkTswU+OQ12TP2baf3ItD7f73wherWThanvfQO2gHp5flpeJK9XxEs0Vqenp68POf/xwjR45EdnY2Zs2ahddee03Qc9evXw+O4yL+nT59WuKWJ6d9835kf/IsXDlb4bigE47r5wWKzvCFZyg4jIyvTpqpBWjCD5a1XQ3DgkMpCsSU5ZdgcunEiH+TSibgb/veDrxeLOHFbOQSvk94/L6xeTskq0YqJr54TfBnS8VqCEkfHa1etO1uAGvrwKkd7yjdHEUJqQwqRYGYirwSnDViYsS/qdYJePlgAv1dUDEbOYTvD55ShXPCK8ongjGGZ3eHfrZyF6tJh+whoOEM4tVXX43du3djzZo1mDRpEjZu3Ijrr78ePp8PN9xwg6BtrFu3DlOmTAm5zWpV10merc6B3C1vw+xuRvvsJnALaW6hUJk6nDRY8MEy1hBJuQvEhL8e784lt2N6xdRhtxflFMheWCVaG/l9s7dxPxZPXiZrm5IRnD0EQpe8oCwiIdpmrS4Cqq/C6c37UbDFA9u0wzjh2ZiRU02EDpOUO1sW/nq8u+fdjlmlw/u7VBeRT1S09imRRUx1pFfwBXEgdMkLKbOIvYN2AHkwWoole41EiHHBWpMB4vvvv4+PPvooEBQCwLJly9DQ0IBf/epXuPbaa6HXx8/x1tTU4Nxzz5W6uUnhOlth23IY2c3b0D+2F13n6mCuuSTjDvjJytThpOE+Of7psINleOAXPs+OJ1WBmFiv9/7hj/C9GZcrPj9OSJGdV3a9hfMnLVW8rbEEZw/D9/WLWzZgzlhl55cQ4rFFH5ZHhCtZPh22ulEo3/I2+h116MJutNUgo84ZhAR+4UEkT6o5d7Fe7+2jH+H6qcr2d0IK7Mg15DXV87bwC+I8Pou4YFT693diZQ8BjQ4xfeutt5CXl4drrrkm5Paf/OQnaG5uxs6dOxVqWfLaN+9H/8a3MLBuPQbWrUf/Wx+gt/9DtCxrhfOyCoxddENGHeiTlenDSYN193fghdpNUYdI8kMu+ExZpMVmpVisXu7XS4aQIjv8khFqxmcPI+1rPotIiNLM1mylm5AWrNVF6J+1AJVZ52Jcf5nSzZGV0GGSfBAZ6ZgoxWL1cr9eooQU2JF6yGsqQ0qD8dnDSPuazyJKoWfApprMISBeLQRNZhAPHDiAqVOnwmAIbf6MGTMC9y9cuDDudi6//HK0t7ejoKAAS5cuxYMPPoiamhpJ2hwNX3jGlLMVtrn+A5unOOvMvUZYahZSYCgADScdwlcore1qwBFb/bD7gwOxeWPOjpspEzOLKCQzp/SyFsBQkZ2G3gYAQE5W4bDHFJrlH/YazZcNtXh687O4Y/ltgSUs+OxhrH1NWURCSDoQMkxy4cizZc2WqSk7F42QAjtSDnlNJjAMr8oODGUPY+1rKbKIwRXhlZbqshbhNBkg2mw2jB8/ftjtxcXFgftjKS8vxz333IP58+fDYrFg//79WLNmDebPn4+tW7di5syZUZ87ODiIwcHBwL+dTicAwAcvfDEWSQ1nP9YJrm4/TKd34fSMk+DmTUXOiFEoKZgw7LFSLGrPb1OKbcvN1W0HALAznz886n1PUu93/zh4wJBfhP99/4GYB8v/3bYBM8tqcDrO4uqtPR0YdLlh0htTbp/L65b19Xi+M98Jn8DvhsNnhyFLhwlZ45CbHf3KIFPBd40xhuc/X4cGexOe/3wdzr5uJjiOg8vjRlucfd3e3QHXoBsmg3j7OrD9M/tGDfsoFuZRugXqFLuvE2+nib29iK9xpm+O1Udz8CXUh6uVT8fg7euDl9OD+ThF+3i5zjOEBmJnj6gRsBxFBwbdIvZ3Mr4eL9H9XpYzAmU5IwRtU0wh524C+wnGGP6480xV9p3rcG7Zmf7O68bpOKN+xD634M+3jJbihM8xxObwndmXcV4/kUMtxxQuZffpp59i2TJhhR5qa2sxa9YsTJo0CRMmTMAHH3wQcn9LSwtGjhyJhx9+GL/5zW8SaseJEycwffp0XHDBBXj77bejPu7+++/HAw88MOz2jRs3wmw2J/SahEjF7XPj1kO3otPTGfUxhYZCPH/W8+j0dMLpcUZ9XIGhACNMsTuPRLS72mV9vXRX66zFA8eHjkmrxq/CbIv/qirt6/j6+vpwww03oKurCxaLRenmqAb1dUQrqL/LHNTfpSaR/k7xDOLkyZPx/PPPC3rsmDFjAPgrjUbKEtrt/giazyQmYuzYsVi0aBF27NgR83F33XUXfvnLXwb+7XQ6MXr0aIw1nYU8U37M5zq+rgOaT8N0ehdaz2+DYcqEqFlDqfm8DCcOujF2mhE6vbaGl/FXnYCgrKFG+LwMLfvcqJgl3n4Pvoo1xIS/zFyLzhjDRopyClGal4uRyAVQKUpbhChBpayvB/iv6tl2uWGda4TOEHm/81fgYmUM1YYxhtde2xhSte+13o1YsmIuOI5DgQL7OtA2D4NzixuWRUZwUfa5GhiclEKMJFpfN8I0D7lx+rpEeOxOmAPTKqThY160uPehwjgLuhiLlHF2G0zF4r03JdiPdaKy7kP0z3Si/Wxlzi948p1nmPC36rWwxxommV2I8txcQOb+brwS/Z2Kz++GjfhKAGMMd/8ztL/b5NyIy5b6+7tKGfd176A95JxLyDmGVBI5d0mkv1M8QKyoqMAtt9yS0HOmT5+OV199FR6PJ2Qe4v79+wEg6XmEjDHodLHr9mRlZSEra3hnpoM+pOPh5xYCADfoP2jlAuivrIPtuzpYaharYm6hTs+p7gASi8tpg043NF5dOy0PpdNzKR9E+LHvnD7yujsVRaWoQGlKr5FudIbh+52v+sXp1LvQfTS76yMvYfHlyVrVLGHBGThVB4ic4r2gOsXu68TbaWJvL+ZrcbFfi4MuZgCpBRzTQe/1Qc+84HRMFf27HOcZIy2lGGmh/i6Yms7v+LmG/PlbMq3a1rQXhzrClrDoqMPO07WSLmERrmfAhqziyOcKkc4xpMb5hJ+7JHKo1WQV06uuugo9PT34+9//HnL7hg0bMHLkSMybNy/hbdbX12Pr1q2YP39+yu0LXtTeNnc7Tn/3WODPeVlFRq5PlCqxqlylCz44NBVYFV+UVY12Ndbi+pd+il2NtTEfp4WF7qMJXsIiGL+EhcKzBwghhMhge3Mtrnjzp9jeHLu/U0rwuZsYS1gE45ewkKu/U1NRGkDcZS3CafLa6SWXXIKLLroIt912G5xOJyZOnIhXX30VH374IV5++eWQNRBvvvlmbNiwAceOHUNVVRUA4MILL8TixYsxY8aMQJGaRx99FBzHYfXq1Um1qeOzQ+jPMiO/cQ9MhQfQPruXFrUXCQWGoYKDQzIcYwx/3rYOJxxN+PO2dZgzetawxwQfVLUWGPL4JSzCBS9hoZYsIiFEZp19SreAyIAxhie/XIfjXU148st1mL9iltJNChCzujy/hEW44CUspM4iqvXcS6pzGE0GiADw5ptv4p577sF9990Hu92OKVOm4NVXX8V1110X8jiv1wuv1xtydWH69Ol4/fXX8fjjj6O/vx+lpaW44IILcO+992LSpElJtWeC4XPkG7Kwr+YwMGEEzCoZQqpltHRFKLUenNSGX2cRQGA5j7mVZwfuD84aahUtYUHSgcfWRWsgSoDLKQDQonQziAyCl/fgl/NYUH52nGdJT8wL+0otYRFMjedfYi9rEU6zAWJeXh7Wrl2LtWvXxnzc+vXrsX79+pDbnnjiCdHbc2CpDuZcHThQ1lAMlDUMpcaDkxrx6ywGT2J/bvsGzPmev8qZw2fX5FzDcG6vsCUs3F63atZqJIQQIh5+eY/g/u6pPRsw/9LZirZL7PM3t0/YEhZun1uS9RrVeP4l5dBSnmYDRLUZPWEJ8vJzlW6G5lHWcDg1HpzktquxFk989ix+seQ2zB0TvfMLzh4C/uEnh9vqsLnxM8zEQuRmF4tSMCXSwvRyMhlM+N8fPoXO/uhV+wrNBRQcEkKIxmxvrsXDO57FXfNvw4KR0fuX4Owh4O/v+CxiBabL3h6pLuyb9Ca8dPVTcMTo74pzCiQJDgNtUOH5l9QXuilAJKpBWcPhMj043NVYiz98+gx8YGjqPBWYUxhpGEl49pCn43R4edcmzBi5QJQ2hS9Mf84PI7dHaqWWEpRaSmR/XUIIIeLb3lyL321/BgwMJ5ynAnMKo/V3wdlDno7T4Y/7/oqHRj0qSpsizXGM1B6pz9/K80pQnid/f9czYFPd+Zcc2UNAo1VMSXqhCqXD9QzYAgcmtR2c5MIXm2noPImmzlMAhuYURsJnD4M7S8B/VbWuvR77uveJ0q7g4jB8MRhCCCEkWXwgVu88iRNOf3/HZwMj4bOHkfq7g7Y60fq7SHMcg/Hnb6lUKFUrtVUsDSbHNBkKEImixCh/nG4yPWvICx8uCiAwpzC8pDWfPeSirK7EgcMrLa+kXAo7fGkJWlKCEKJK+fnQt3cr3QoiUPhwUQCBOYWR+run9sjT3/FZykjtSecL+2o9D5MrewhQgEgUlM4Hl2Sp9aAkt2gBHz+nMDyL6Pa60RpnEnuHuwNuryeldvHZQ/6qbfCSEoQQ4aiCKSF+0QK+4DmFwdw+N073CujvfKn1d+FZSr49n9V9mrZZQ0D952FyFdmjOYhEdhQYRqb2g5KcImUPeXwWcd6YoZLWJoMJf7nWX7Sly9eJnKzCkOcwL4N+nxkmgzHpNgVnD8PnfNCSEoQQQpIRKXvI47N2C0cG9Xd6E15b8RQcA5GLtvh8DD31Zpj0qfV30eY4PnNoE+ZPWxolf6ltaj4Pk3pZi3AUIBJZUXAYmZoPSnILzh5GukIanEWcXzW0MG5ZfgmMuTqMzZ44fJsehi6TK6V20cL0hBBCxBScPYzW3/FZxPMqh/qXirwSVEQp2uLzMhw/lVp/Fy1o9TEfDjnqZVmYXilqPA+Tc2gpj4aYEtlQcBgZBYeh+OxhtOEzgH+ORfhcRCmvrgUvTB+tPTQXkRCiBh2tXtgPtqD/8FdoqNusdHNIDHwgFq+/izQXUSpC5jg+uzv9+js1ViwNJvf6zZRBJJKjwDA6Cg5D8dnDuI/jF8Y9sxC81EMvaGF6QogWWKuLgOqr0LN5Ior+7x20sDqcaG2FuWYOSgurlW4eCcIHYnEfB4bWPukWgg8nZI6jlAvTK0HNFUuVyB4CFCASiVFwGB0Fh8PxxWZisWTn4/crHkBp3ghZgkOAFqYnREweW/TfERFHyfLpcBSbULl3P/TVh9FS1og2gIJEFeEDsVgKs/LxpwsfQFnuCPmCsd5uvLTsATgGnWCFhREfIvXC9HLSwrmY3NlDgAJEIiEKDqPrHbSD06v7gKSE4GIz0RTlFKA03z/3Qs4ra7QwPSHioQqm0iuaPRn2Uy2YXrgAWd4+tCrdIBIiXrEZwB+IlefK1+/w521lYyahTLZXVY7ag0OlsocABYhEAhQYCqPWA5LSyvJLUJYfv0PkD5xKXFkjhBBCUhWr2IycMvG8Te3BIU+pcxwqUkNElYkHmUT0DtoBAEZLscIt0TYKDgkhhJDUZfJ5m5qDQyWzhwBlEImIMvkgI4SaJ0FrEQWHhGgPzT8kRB0y+ZxN7RVLeUqe51AGkYgikw80QvDBIWUOU6f0VTVCSGpo/iEhysrkczYtXKxXw3kOZRBJyjL5QCNE8Dh3nye91g2SGw0tJYQQQpKT6edrWpl3CCh/nkMBIklaph9ohNDSwUgrlD5oEkIIIVqT6edsWjkfU0P2EKAAkSQp0w80QmjlYKQVajloEkKSo+b5h5wt9np0hGgVna9p73xMDRfCaQ4iSRgdbITTysFIK9Rw0CSEJE/N8w9NVovSTRCdN8cK1uVUuhlEIXS+NkQL52NquhBOGUSSEDrYCKOFSdBaoqaDJiGEEKJ2rm47uFI6V9NKxVKeWi6EUwaRCEbBoTBaG8qgFWo5aBJCCCFq5er2r7fMiqlqupYu1qvtQjgFiEQQCg6FoeCQEEKG89i6VD28lBCtczltcDltFBieocXzMTVdCKcAkcRFwWFitHQwIoQQQoi20XlaKK0Fh2rLHgIUIJI46KAjnJaGMmiJ3WdT1VU1QgghRC3oPC0yrQSHPLWd51CRGhIVHXQSp7UDEiGESE3Ny1sQolV0jhaZ1orSqDF7CFAGkURBB57EaO2ARAghcqL5h4SIh87RItPqSC61ZQ8BChBJBHTgSYxWD0iEEEII0RY6R4tMa/MOAfVmDwEaYkrC0IEnOVo6IBFCiFxoeCkh4qDzs/i0eC6mxuwhQBlEEoQOPomj7CEhhMRGw0sJSQ2dn8VG03zERwEiAUAHn1TQQYkQQobTSvaQs7Ur3QRCoqLzs9i0eqFe7RXaKUAkdPBJklYPSoQQIhetZA9NVovSTSBkGDo/i02L8w61ggJEAoAOPsmigxIhhBBCxEbBoTBaPA9Tc3EaHgWIGc7VbaeDTxIoeyivXtrfhGiKVoaXEqJGLqcNbISVzs9i0Pq8QzUPLwUoQCQkaVo+MGlJsY72MyFapJXhpYSohctpCwSHJDq6SC89ChAzlKvbDgBgxcUKt4QQQkg6oeyhuujyisDZupVuBomDhpQKo/V5h2ovTsOjADED8Qchkhy6cqUMGmZKiHZQ9pAQ4Sg4TIxWg0Mt0WSA2N3djZUrV+I73/kOSkpKwHEc7r///oS20dbWhh//+McYMWIEzGYzFixYgM2bN0vTYBWizGFq6OAkLxpmSqTSN2hXuglphbKHhCSGgkPh6AK9fDQZINpsNjz33HMYHBzElVdemfDzBwcHsXz5cmzevBlr167F22+/jbKyMlx88cX47LPPxG+wilD2kGgZZRGJmOj7JA0tZQ85WzstcUEUQ8GhcFofWgpoZ3gpABiUbkAyqqqq4HA4wHEcOjo68MILLyT0/BdffBEHDhzAtm3bsGDBAgDAsmXLMHPmTKxcuRI7d+6UotmKCzkQeZjCrSEkMcU6qyZKQxP1Cw4MizgaTSEWyh4SkjgKDoXTcnCoNZrMIHIcB47jkn7+W2+9hcmTJweCQwAwGAz44Q9/iF27duHUqVNiNFOV6ECUGhreoKxinZWyPiRpvQO2wPenWGelocsS0FL2kBAlUbVS4ejcS36aDBBTdeDAAcyYMWPY7fxtBw8elLtJkqOhpeKhK1jKoyCRJIICQ+lR9pAQ4eicTLh0GFoKQHMjoDQ5xDRVNpsNxRGKtPC32WzRP8TBwUEMDg4G/t3V5e8Uu7v64POK3FARuXr6wIqKAUcvAMDnZejrc8PZ6YZOn3w2NtP0DfbB6Ev+CrnP49/v3Q43dAba78kwIhudPjt60QdzlrDhgezM913vcIOj77ss1LLP+SI0hTr/d6UbvSH393b3AQAYo2H3waL2dX2d8GF4Z+fpcyKnOAvO3n7Z2iiUj3nR5+6D090JHacPuY/rc8KU7VOoZdLr8fTCMdCPrj4XepwDyA77/kuJzjMic/WcWWYs6JxMTOm23/sG+2C0FGNQgn0lFiHndr0+/zlLT59y76Mvgf5O8QDx008/xbJlywQ9tra2FrNmzRLldWMNUY1138MPP4wHHnhg2O0XnfcforSLEEKI/Lq7u1FQUKB0M1QjWl931X/Mkr8xhBBCRCOkv1M8QJw8eTKef/55QY8dM2aMKK9ptVojZgntdv9VnUjZRd5dd92FX/7yl4F/+3w+2O12WK3WlOZFys3pdGL06NFoamqCxUIV3ORC+10ZtN/lp5V9zhhDd3c3Ro4cqXRTVCVd+jpAO9/FdEP7XRm03+WnlX2eSH+neIBYUVGBW265RdbXnD59Ovbv3z/sdv62mpqaqM/NyspCVlZWyG2FhYWitk9OFotF1V/mdEX7XRm03+WnhX1OmcPh0q2vA7TxXUxHtN+VQftdflrY50L7u4wsUnPVVVfhm2++CVnOwuPx4OWXX8a8efPoSjIhhBBCCCEkIymeQUzWBx98gN7eXnR3dwMADh06hDfeeAMAcOmll8JsNgMAbr75ZmzYsAHHjh1DVVUVAOCmm27Cn/70J1xzzTVYs2YNSktL8cwzz+DIkSP4+OOPlXlDhBBCCCGEEKIwzQaIt912GxoaGgL/3rRpEzZt2gQAqK+vx9ixYwEAXq8XXq83pGJPVlYWNm/ejJUrV+KOO+5AX18fZs2ahQ8++ABLliyR9X0oJSsrC6tWrRo2hIhIi/a7Mmi/y4/2OVEL+i4qg/a7Mmi/yy8d9znHqLY3IYQQQgghhBBk6BxEQgghhBBCCCHDUYBICCGEEEIIIQQABYiEEEIIIYQQQs6gAJEQQgghhBBCCAAKEAkhhBBCCCGEnEEBIiGEEEIIIYQQABQgEkIIIYQQQgg5gwJEQgghhBBCCCEAKEAkhBBCCCGEEHIGBYiEEEIIIYQQQgBQgEgIIYQQQggh5AwKEAkhhBBCCCGEAKAAkRBCCCGEEELIGRQgEkIIIYQQQggBQAEiIYQQQgghhJAzKEAkhBBCCCGEEAKAAkRCCCGEEEIIIWdQgEiIAn784x+D47iYfwMDA6K+5uHDh3Hvvfdizpw5qKiogMlkwogRI7B06VI89thj6OjoEPX1kvH+++/jwgsvRHFxMXJzc3H22Wfj6aefhs/nU2SbiT63vr4ezz//PG699VbMnDkTBoMBHMfhoYceSrr9hBCSLqjvC5VqnyFn/ybGaxINYYQQ2d14440MAKuurmbnnXdexL/BwUFRXquvr4/94he/YHq9ngFgBoOBjRs3jp1zzjmsrKyMAWAAWGFhIfviiy9Eec1kPPzww4G2jB8/ns2YMYPpdDoGgF1xxRXM6/XKus1knvtf//VfgecE/61evTrhthNCSLqhvi9UKn2G3P1bqq9JtIUCREIUwHeS69atk/R1+vv72UUXXcQAsIKCAvb444+zrq6ukMfs2bOHrVixggFgmzdvlrQ90Wzbto1xHMd0Oh3buHFj4PZ9+/YFOvLHHntMtm0m+9zVq1ezyy+/nD344IPsgw8+YN/73vcoQCSEkDOo7wuVbJ+hRP8mRT9N1IsCREIUIFcned111zEAbNSoUezAgQMxH/uHP/yB2Ww2SdsTzaWXXsoAsJ/+9KfD7nvllVcYAGa1WpnL5ZJlm2K1h/+cKUAkhBDq++IR2mco0b9J0U8T9aI5iCTtNDQ0QKfTYezYsfjb3/4W9XH/+te/wHEcampq4PV6ZWyhPDZt2oTXXnsNer0er7/+OqZNmxbz8b/4xS9QXFwsU+uGOJ1OfPzxxwCAm2++edj911xzDSwWC2w2Gz755BPJtylFewghRGrU9/lppe9LlhL9G/WLmYcCRJJ2WltbMWHCBDQ0NODHP/4x+vr6hj3G6/Xiv//7vwEAjz/+OPR6vdzNBAC88cYbuPLKK3HBBRfguuuuw9NPP42urq6Ut+vz+XDXXXcBAO644w4sXLgw5W1Kpba2Fi6XC9nZ2Tj77LOH3W80GjFnzhwAwM6dOyXfphTtIYQQqVHfp62+L1lK9G/UL2Yeg9INIERsc+fOxZEjRzB37lzs2bMHe/bswfnnnx/ymBdffBEHDhzARRddhIsvvjjidn73u9/h/fffT/j1n376acyePVvQY997772Qf7/++utYtWoVNm7cGLVdQvzzn//EsWPHYDQasXLlyqS3E0yq/VFXVwcAGDNmDAyGyIek8ePHY/PmzYHHxpPKNqVoDyGESI36Pm31fclSon+jfjHzUIBI0pJOp8NFF12EPXv24Ntvvw3pJHt6enDfffdBp9Ph97//fdRtfPvtt9i6dWvCry3kKuiECRPwu9/9DpdddhnGjRsHjuOwfft23Hvvvdi5cyeuvPJKbNmyBeeee27Crw/4O0kAWLZsGSoqKpLaRjip9ofD4QAAFBUVRX0Mfx//2HhS2aYU7SGEEDlQ36edvi9ZSvRv1C9mHgoQSdqqrq4GABw5ciTk9jVr1qC1tRW33HILpk+fHvX569evx/r16yVp27333jvstosuughLlizB+eefj127duHXv/41Nm/enNT2P//8cwDA0qVLU2lmCKn2B7/mlclkivqYrKwsAEB/f7/k25SiPYQQIhfq+7TR9yVLif6N+sXMQ3MQSdriO8lvv/02cNvJkyfxhz/8Abm5uVi9erVSTYvKZDIF2vXpp58mfSWuqakJADB58mTR2iaV7OxsAIDL5Yr6mMHBQQBATk6O5NuUoj2EECIX6vu00fclS4n+jfrFzEMBIklbkTrJu+++G/39/fj1r3+N8vJypZoW04IFCwD4J9sfP3484ef39fUFDuIFBQWitk0KQoalCBneItY2pWgPIYTIhfo+bfR9yVKif6N+MfPQEFOStsrLy5Gfn49jx47B6/Vi3759ePnll1FZWYk777wz7vOVmphuNBoD/+/xeBJ+fk5ODgwGAzweD7q7u5NuRzip9gd/MtPY2AiPxxNxAjx/ssA/Np5UtilFewghRC7U92mj70uWEv0b9YuZhwJEktYmTpyI2tpanDhxAnfeeScYY/jtb38Ls9kc97lKTUw/ePBg4P9HjRqV8PM5jsPUqVOxf/9+7Nq1C1deeWVK7eFJtT9mz54No9GIgYEB7N27F3Pnzg253+12Y/fu3QCAefPmCXrNVLYpRXsIIURO1Pepv+9LlhL9G/WLmYeGmJK0xl/JeuSRR/DZZ59h9uzZ+NGPfiTouevXrwdjLOG/VCfH89XlpkyZgsrKyqS28f3vfx+Av6S5zWaL+dgdO3YIKkst1f6wWCy48MILA+0Nt2nTJjidTlitVsH7NpVtStEeQgiRE/V96u/7kqVE/0b9YgZihKSxu+++mwEI/G3evFnpJrF///vf7De/+Q07fvx4yO2dnZ3sjjvuCLR148aNw567bt06BoBVVVXFfA2Hw8GqqqoYADZ79mz25ZdfhtzvdrvZF198wa677jqm0+lYR0dHyu8rFVu2bGEcxzGdThfyvvft28fKysoYAPbII48Me96dd97Jqqqq2J133inaNlN9brAbb7yRAWCrV6+O+1hCCBEL9X3a6PvCCe0zlOjfxOoXiTZQgEjSGt+pAGArVqxQujmMMcbeeuutQJsqKyvZnDlz2KxZs5jJZGIAGMdxbNWqVRGfK7STZIyxgwcPsvHjx4e81ty5c1lNTQ3Ly8sL3D5hwgRx32CSHnrooUCbxo8fz2bMmMF0Oh0DwC677DLm8XiGPYfvTG+88UbRtpnKc7ds2cKsVmvgLysriwFgZrM55PbGxsak9xMhhMRDfZ82+r5U+gy5+7dUX5NoCwWIJK1t2bKFAWAGg4EdPnxY6eYwxhhrbGxk99xzD7vgggvYmDFjWE5ODsvOzmbjxo1j//Ef/8F27NgR9bkPP/wwA8C++93vCnqtnp4e9sQTT7DFixczq9XKDAYDy8/PZ9OmTWM33XQTe+edd9jAwIBI7yx177zzDrvgggtYQUEBM5vNbObMmezJJ5+M2unECxCT2WYqz/3kk09CrtpH+6uvrxeyOwghJCnU92mj70u1z5CzfxPjNYl2cIwxlsiQVEK05PPPPw8swMsvoKtlV1xxBd555x189tlnWLx4sdLNIYQQokLU9xFCUkFFakha+/rrrwEAM2bMULgl4ti+fTvOPfdc6iAJIYRERX0fISQVmg0Qu7u7sXLlSnznO99BSUkJOI7D/fffL+i569evB8dxEf9Onz4tbcOJrNKpkzxy5Ag6Ojrwy1/+UummEEIIUTHq+wghqdDsOog2mw3PPfccZs6ciSuvvBIvvPBCwttYt24dpkyZEnKb1WoVq4lEBdKpk5w8eTJoRDghhJB4qO8jhKRCswFiVVUVHA4HOI5DR0dHUgFiTU0Nzj33XAlaR9SAMYaDBw+C4zjU1NQo3RxCCCFEctT3EUJSpdkAkeM4pZtAVI7jOHR3dyvdDEIIIUQ21PcRQlKl2TmIYrj88suh1+tRXFyMq6++GgcOHFC6SYQQQgghhBCiGM1mEFNRXl6Oe+65B/Pnz4fFYsH+/fuxZs0azJ8/H1u3bsXMmTOjPndwcBCDg4OBf/t8PtjtdlitVspqEkKIxjDG0N3djZEjR0Kny+hrpiGoryOEkPSSUH+n3BKM4mlvb2cA2KpVq5LeRn19PcvLy2NXXHFFzMetWrVK0KKm9Ed/9Ed/9Kedv6ampqT7j3REfR390R/90V96/gnp7zjGtF8aqqOjAyUlJVi1apXgpS4iueSSS7B37160trZGfUz4VdWuri6MGTMGH239K3LzzEm/ttx8XobGb9wYM8UInV79V4M7nMfhPnoMnmONKPq2GoMjF0I3sgJF4wuUblpCfMyHRvc3GGOcAh1H2YpobFu+wTjDNhyZNoiKsy9KeXta+76nA63s896ePlx03n+gs7MTBQXaOp5IKWpf9/LnyDXnKdiyxPmYF42DdRiTVQ0dp1e6ORmD9rsyaL/LTyv7vLevBxf9cLGg/i4jh5hGwxiLm3LNyspCVlbWsNvzC8zIy8+Vqmmi83kZzGYXLIUmVZ+8AUBbZx1M+76Edb8Ppv7voGfMRIxcPl3pZiXFx7wwu8ywmApUfRBR2qAhF91NBmRZ6tBt/j9Uzl+R0va09H1PF1rZ57ozP0MaNhkqal+Xa0Febr4CLUqej3lh1pthyaHjrpxovyuD9rv8tLLP+cSEkP6OAsQz6uvrsXXrVlx44YVKN4Wc0dZZB/c338B9pB7Wg1MxMHoZPDWjUFJdpHTTiMRKlk9H+2agrG40BjpqcbJtHXKmzoS1+mylm0YIIYQQktY0HSB+8MEH6O3tDZRzPnToEN544w0AwKWXXgqz2Yybb74ZGzZswLFjx1BVVQUAuPDCC7F48WLMmDEjUKTm0UcfBcdxWL16tWLvhwyx1e2F6/BXyD+TNew7bykFhhmmZPl02OpGwdc4EQVbPoHNsQUDtlMpZxMJIYQQQkh0mg4Qb7vtNjQ0NAT+vWnTJmzatAmAPyM4duxYeL1eeL1eBE+1nD59Ol5//XU8/vjj6O/vR2lpKS644ALce++9mDRpkuzvgwyJljW0UnCYkazVRUB1USCb6Dq5FSfb1sG0eBFKC6uVbh4hhBBCSNrRdIB44sSJuI9Zv3491q9fH3LbE088IU2DSEpO7XgHrK2DsoZkmOHZxA9wavI3ME6ZQoEiIYQQQoiINB0gkvRAWUMiRKRsYndbB9oWg4JEQgghhBCRUIBIFMVnDXPrspHtoKwhiS84m1j0f++g3fEBGhY2IqdkDAWKhBBCCCEpogCRyKatsy7w//r2bgzYToVkDfvmUNaQCMNnE21jRqF8y9sYPHkAjiWtaKuhbCIhhBBCSCooQCSSa+usQ9+B3Rh5wgSueyBwu67v/2fvzePbqO/8/9fosi3bkizZ8hU7zmFCDifhCAQICSGwCwt8u9BSCt92d9vS/RV2+/32+Jbdli9tKdstpd32C6WEQmiSAqEshZTl6hYCCSSExOQA5yAoia/4lEeXL1nX/P6QRx5JM6OZ0YwO+/N8PPRILM3xkTSa9+f1eV964jUkZIWjtQo0PoNYz0Wo+eMrcJ8h3kQCgUAgEAiEbCACkaAp3a5dCJ9wwfy+HrHYIkzUtCBa5oi/aALK7mhDWX6HSChyhLyJ3bU9mN+6Kd/DIxAIBAKBQCgqiEAkaALrNazaMwmDuxZTDZej9MarUJrvgRFmLaw3kTrVgpo/vo+BjS50AyiraUZ15eJ8D49AIBAIBAKhKCACkaA6fR+8gvCpTtQcaYKu4kJMbLyK5BYSckLcm3gVaNcqzNv7MqaOxb2JPc4+AJfle3gEAoFAIBAIBQ8RiATFcIvOAPHCM5MnP0JlRww6/1JMLNqImk0khJSQe9K8iVefAZoug9t/BrV24k0kEAgEAoFAEIIIRIIiul27wAwNobEjlniOMZfDdHweDKYmUniGkHe43sSG/a+gvwmYOHEY3bXdJDeRQCAQCAQCQQAiEAmyGPa5EHp3LxjvKGqONMG36KaZFwMAmkC8hoSCwtFahdji/wmEOlD9chnoFccTuYmk0imBQCAQCARCMkQgEhIM+1yYdPekPW+gJxL/n2lqfxkmNhIvoRqE6IDgawMjRvhG9YKv2yqjmL+EyHGpTF35VdS99wqpdEogEAgFxsCwHl6/sL2rskZR74zmcEQEwtyFCEQCgJmQUeuHMVT7LOkblFYCAHyeUgSbNpL2FAoJeUaBivi/OugSzzOOmrRtB4Z1uOW7NQiFKcHjmYwMdv78FOqrw8nPO3i+QwLsi2zQLf4HjO7qiPdNvOA4uoaGYF6xhngTCQQCIU8MDOtxw5ebEArrBLcxGWN4bWsvEYkEQg4gAnEOMuxzgYlRAJrg9p9BeO97iZDRqYbLMdJaj1iFU3B/4jWUTqp3kLE7gFAXGLsDDCX+8/MFdKLiEABCYQpefTXqHJHEcxTtTjsvEYzJ1GxqS+qbOOrZi+6lxJtIIBAI+cDr14uKQwAIhXXw+vVEIBIIOYAIxDkE25uwsSOGmN6A0TVNsP3xQwTdMZgm/4q0o1CJNFHI9Q4yEWhNqjeSKxiJUJwhXsQm7k2sevsVuL3Hce5kD0zr1xFvIoFAIBAIhDkLEYhzBG5vwqixAYEFFwKIwR/5LCgLBevNJGQ0G0RFYZ7hjiVEuxP/J2IxDutNrG3fjVDZPoxiL/qcn6Bx7U2ZdyYQCAQCgUCYZRCBOAtg8wdZDJ6ptG3KXaUwDM30JjQxUQRCHajesBw6SjgpnCAOVxgWkigUgh0j8SomE/cm3oyxXYth3fsO6OUncW54K/EmEggEAoFAmHMQgVjEsCGjVXsmYQ/XoMTckHiNKrMm/j8yFI/XJ70J1aHYRCEfRCjyU7OpDbQr7k0MVh3BOPEmEggEAoFAmGMQgVgkDPtcSX+HP/kkETKqNy7C8LrPCO/cHPeQkBDS7JgNwjAVIhTTEfImli1dhWhNZWI74lkkEAgEdaD8PgDzJG5n1ng0BAKBCMQCh/US4swIGifsiefHJvRJIaOleRzjbGc2CsNUiFBMJ9WbWNF3IvEaU1mKrpZ20h6DQCAQsoSiRyBH9FH0CBhHtXYDIhAIRCAWMn0fvAJmeARVHTGU+C6Fr/mimRdNQOyKeSRkVEPyLQxtlhhMRiZjH0SbJabaOVOF4lwXiVxvom+6AK1+kob5bBccx70Yondhchlpj0EgEAjZYGu0wWSMZeyDaGu0AZjI2bgIhLkKEYh5IrWwDJBeXCYYjqD2QAuCTRthvZl4CXNFvoUhS70zhpe2uOELCBtMmyWGeqd6ApGFcdQQbyKHmk1tSX9TviFM7D2JeXurMNnpQtfFO4g3kUAgEBRS74zitS398AaEi+ZVWaKod0bBoBYUPUS8iASChhCBmGPYkNEIHcB5rkUojSV7AFOLy5DCMrmjEIThBB1M+tuqB6ycr9/syN0yAfEmCsPYamG/sRbeI6dQdhQwDBFvIoFAIMiFokfA2GoBxEVivTMqb18iEgkETSACMYewXsOqPZMo8V0AevVlAIBYhZN3e8cmUlgmF+RDGE54pqBDJO15g8PKszVnP9rP+7yWwpH9TNgeikQozlB1wRLggiWYeHU38SYSCARCjmBstaB8Q0QkEggaQQRiDhj2uRB6dy8Y7yhqjjRhqmETSr98FQkZzTO5FIasZzCGKFABGOwW6Cj5Pz8hAZkqHLUQjNywUyISk7HfeBW8R06B6WuB47UjxJtIIBAIGkNEIoGgHUQgagxbaKayIwbT5F9hYuNV8cIXhLyRK2HIDRdlhV2MiQAh9c/FFY4R2p84t9pCkXgTham6YAmoBTbQe0G8iQQCgZCBePXS7CAikUDQBiIQVYQNIeUWm0ktNENCRvNHLoQhnyjMNdzzcj2LaopFUsSGH25uYmOfHvZJP4Yy70YgEAhzEjb/MNtjsCIRABGKBIIKEIGoEn2vP4u603rUG1fB446CKeFM0kmhmbxSCMJwcJiCP6BDjAG8YSs8RgN01Ey1Nqslhjono/q42LFo4VUkRWyESeQV+0g5dgKBMLcYGNbD69cjxsTQN2VFsKQEOmqmGneVNYoG/ZAq4pCFPRYRigSCOhCBqBK2d60wVa1C74qr4LgxWQwSr2F+0FoYSvUWDg5TuO1OK6ef4VVp25iMDJ7f4tdEJHLHp5VQJN7EdOhABYI9Ywi9uxf00lE4Wi/M95AIBAJBUwaG9bjhy02cfobNaduYjDG89ksv6m3qn58IRQJBHYhAVInJBdej9m8uJWIwzxSCtzAVf0An2uweAELhuIexTkaJbyWkCkXiTdQGR2sVaFwFczsA1xH4vXsRpPvQuPamfA+NQCAQNMPr14s2uweAUFgHL2pRr0VC/jREKBII2UEEokpUb1iW7yHMWbiiECgcYVjIsONncxTVFoqkiE1cJKL1ZoztWgzH8XcQO9eDc8NbYVq/jhStIRAIhBzADWOl6JmMcCIWCQRxiEAkFCW5EoVAfoUhm7soRLa5iwaHlYSdakzNpjbQrnkwt++Gbt97mPC+ge7LSQsMAoFA4DIwrIc3oBd8vcoSRX0WUTZ8XkWAiEUCgQ8iEAlFRT56FwK5E4Zeesb4DY/o8LXv2hAWCU9VI3eRhJ1qD9ebaD3yCiLnutC1gbTAIBAIBGA6d/HOBtHwVJMxhte29GclEoEUryIRiwQCL0QgEgqaXHoKWfLlMQz4onBWAWZHPJM15NWJikMgnrv40XED/IGYKt5EgISdaknNpjbQzfNQvvdlNHa40F/bAxCBSCAQ5jjegLTcxUPHS7AwEMnam8hCxCKBwA8RiISCYy6JQi6ltlKYHTHZ+/3ooQoA6lVC5Yadkt6J6uNorYK75yK0IoBhegIg+pBAIBQ5rLCi/GYA8zQ7z78+FJ8PqOVN5CIkFolQJMxFiEAkFAT5EIWA9sLQS0cR8Kl+WF5CYQq9PQzqnNkfS2tvIhGKAGWuBBAE7TpMWmAQCISiJMnjZqsFU2nKyXlDYR28Ab2qApELKW5DmOsQgUjIC6mCEMiPKATUF4bcPEIAqG0ugcnIiLa6MBkZWC0MhoYp+APx7bp7xcNLM52/yiGc7C8VLYvYAHNXKOqa56G//TTMI50Yb/0IfaQFBoFAKCJShSFLlSUKkzGWMZewyhJNKkpztrdwp6NELBLmIoX7i8zA6OgoHnjgARw9ehRHjhzByMgIfvjDH+JHP/qRpP2Hh4dxzz334NVXX8XExARWrVqFf/u3f8OmTaSyoFbky0vIkgtvIQubRwgAZjB4essk/AEKDBPBWPgQKowXgaJmfn5WSzws9Et3lmXsmSgGG6Y6QU+qJhS1KmIDzN1CNklFa/a+A3r5SdICg0AgFDRCopBLvTOK17b0wxvQI8ZE0Rc+jUbjYuioGTtUZYnbpkxFaQoRUgmVMFcoWoFI0zSeeOIJrFq1Cn/7t3+LLVu2SN53amoKmzZtgs/nw8MPPwyn04nf/OY3uO666/DWW29hw4YNGo58bjHbRSEgLAy51DoZ1DoZMEwM/pAfVlMMFJWcb/jpaV1W4pALOw61haIW3kRg7hayYVtgLO95DxPmT9DlJkVrCARC4cAVQYCwMORS74wXkIkxUZSG/FhoCiUJRAA4cdpUdOKQC8lXJMx2ilYgzp8/H16vFxRFYWRkRJZAfOqpp3Ds2DG8//77uOyyywAAGzduxKpVq3DPPffgwIEDWg171pNvQQhoH0LKIkUY5ptUoVjI3kRgbheycRobcY7uI0VrCARC3pHiLSTEISGohNlI0QpEilLuadm5cyeWLFmSEIcAYDAY8MUvfhHf//730dfXh8bGRjWGOScgorDwMTvKNAk7VbuIDTA3w05HhqIwWUJAXb5HQiAQ5ipEFGYPXwgqEYqEYqRoBWI2HDt2DFdeeWXa8ytXrgQAHD9+nAjEDBSaKNS6NYXawnBomEJgNDm8RmlRGqmo7U0ESNipGsTbXixGvytetIbkIhIIhFyhJIRUDm53GYKTprQQ00IuSqMGRCgSip3Z/QsVgKZp2O32tOfZ52iaFtx3amoKU1NTib8DgbhQiiEeb18ssGOVM+aQZzTpb8bu4PwRUWVcYkx4ppL+NthnBENMo/P7PBxhaI8LLCaLczFMBG53Gf7p7krV8g1ZTEYGlsowGEa8D2KZ3YgJzyQ8dPx92OzZCUWdvRwAEPEEMEaPw2wvyep4adirQHloBGkfAMBkr5R9CCXXey5xXL0MnjMNKDtMwfDepxjxv4WuS/vQvKh486FjUSbp30IlVpiXRN4RtHVMrGB/R0IU+u8/53hS5jhWTm8kFT+jviEKd9+9CeFw9ouRqZiMMVgrQ4X/nVqnRaF/GKCH4//nzp00gFzvuadYPvMYI73X9pwUiIB4iKrYaz/96U9x//33pz3fFToBs8GsythySVf4hPSNK1L+DnWpORQF58/tOf0qnS8QsGYlDr/1rQ8xb95Y2vMWSwiltklp4+S8L7dan+P0Mb1afC/c7z6L48u63nNNE+BragHQEv97HDj7cS4ucm3pOh7O9xBEmZgo/s9YCwRtXfATmHXFZ+sAoCt4Kt9DKAxSg2BCQ7ybZcsZjxXh8IKsjiFm7yZtkzhbLD9f7mc+qc3nnQq53nNPoX/mE8EJydvOSYHocDh4vYQejwcAeL2LLN/73vfw7W9/O/F3IBBAU1MTWkzLUGGS79nIFzEmiq7wCbQYl6WFfoh6CnOAmKdQS/i8hWoT9z52ZHWM8xcswXmLpa8CZWLCM5n4f7beRJaIZyYEWW2PIjW9+i3Hkyh2vRcanjM+NLr+jMlVAbgvnIca66J8D0kRsSiDruNhtCw3QqfXNnw6G8ZGtY9+KEYEbV3p+agoS12tK2xiTBRdwVNoKV1S8L9/1eF6C7mewhwwach+inmR3YLza40AlM1FqFRvKQ9KolKyxq+dN3FOX+95olg+87FY+mKLEHNSILa1taGjI32Szj63YsUKwX1LSkpQUpI+4dVBX9AXhRA6Kj5ubk6hDrqknEKtp3apBWZMDmGBrjbc3MJyR+ELfJORgc2qB0WpVx6cfd8T9CT8nuwL2AAz32GE9iPoiahb7dRRO31s+bmJ7PVeyFCMDvpoDHrvKCgdU9DiSgo6PVXQ70FX2JdD3hC0dZSu4H9DQhTD718tZnILdTkvOMPOJ6KB7BYHTUYGtmYbqOl7vKK7iCPze2dtSdK5tc55t9UDAChP3JuoRX7iXLreC4VC/8x1MuaOc1Ig3nzzzbj77rtx4MABXHrppQCASCSCZ555BpdeeikaGhryPMLcEfKMQof4BZPLQjNqVR0dHKbgDwhf8FZLDHXO5BworigECrca6b33BDG/KXnsVku8n6IWqFXpNPk7mRaKnfFVq1KbCTZLDPXO7D2g3JYYs62ADVVmBTCQ72EQCIQCYmBYD69f+L5cFRtBQ3U85jIXojC1WB0XxlEDxiNN0j1wjxcLmtJzt9SyFZngm/uEUkSjkI0ZGNbDGxD5TizxnpCC57bVkkI2hIKkqAXiG2+8gfHxcYyOxkMiT5w4gT/+8Y8AgL/5m7+B2WzGV7/6VWzfvh1nzpzB/PnzAQBf+cpX8Jvf/Aa33norHnzwQTidTjz22GM4deoU3nrrrby9n1wRogOIIZbI68qVMFS7FcXgMIXb7hTP5zMZGTy/xY8SfXIYWaGKQi7zmxhVQ0mlkG2lU+HvZMa4mowxvLRlhIhEAoFAkMjAsB43fLlJtLm8ydiA17b0iwqSbOAThGrMHxY0RbF0cWGFeidFUXF68wIzYnFgWI8b7mzI8J3EMn4nqRVPiUgkFAJFLRDvuusudHd3J/5+4YUX8MILLwAAOjs70dLSgmg0img0mlTZsaSkBLt27cI999yDb3zjG5iYmMDq1avxxhtvYMOG4q0amAnuDY6xO4BQFxi7Q9MQUi37E/oDuozFXkJhCu9/oEPrsvLEc1YLAzMYDA1T8AeE99fSW1foKPUmnu3SS/hOdBjsiaDeqU6YLBGJBAJhtuP160WFCBC/tx46XoKFgRmxxXqwlHi6CqGdVSEgJBZPfFwh6TvxBvSSRDvXm0hEIiHfFLVA7OrqyrjNtm3bsG3btrTna2trsX37dvUHVYAkCUP2RqdhW4pcNa2Xys83J4sGk5HBr34WxLf+pTSj9/HpLZNzWiQC0r2Jg8MU/vUB6cUr1OydyO2ZSEQigUCYq/zrQ8kizmSMYevPhvDlf6nN6Ona+fNPUV89U3V4rgpCMdjPZGBYh//zsPoiLuFNpLXLTSQQpFDUApEgDq8w1JBcNq7PhlCYQv8gJcn76A9QqgtEiyUEk5HJKE6tlvh58+3plOpN9Ad0CEek+aMNtgoYHFFEaD8m6KBqRWwYR00id4QIRQKBMFug/D4A82TvFwrr0DtokOTp8o3qUbfEpmh8fNgsMRiNUdE+iCYjA5tlJt1gYFgHn0hdgVzlJWbCF9AhHFGvWFwqxJtIyDdEIM5CcikMcy0KuQVmAj7NT6cJNTWT+P2TowiMGgW3YUXf0DCFL91ZlndPZ7a5iUIYHFZNRCIJOSUQCLOBmWqk2veeZGxVANSLLqpzRvHYY7tQOrkKlEBlR67gGxjW4ZY7azLau5e2uAtCJGoNEYn5hXZ5YG7fk/Z8GP6052IGHXDjBQi88Cx0EeXXphHJ8+iJNRvgaM1dZX8uRCDOInIlDHMdQipUdbTUq93qndbUOhnU1Wa+ifgD+fN08sF+9l463jtRDaHIXj8TtH/6HOqEnBKRSCAQipUZYRgXCkylKY+jUU5NzSQaTWHoqMz2ySexroAvoJsTAhEgIjFfuN/ugL7nFfRe1At9VXILtGgl32KNATpcgOG/7kE2iyz60ZlG9paPYtDvO4mBritQf23u66MQgThLYMWh2sKQG+4R9IWmnzXBYIvnmlktMdRBfWFSLK0o5ipqtcTgorY3kRWJBAKBkImMLSSs4u0K1CJVGKpBZHRSleMQ8gMRidpBuzyI9fYBACJRDwCg3N0JWI/Be40O1OKVmNe6KeNxYhEGfYdCaFp7K3QGdUo/9izehdAJF8oPvIzAjk6M1yxIvGbQp3sVa65uU+W8iXOoejRCztFKGALSwz2e3+JP6zWoBK4oJIKw8EkNOwUKUySGaDcM9vLMGxMIhDmJtBYSMby2tVczkaiFMFSL1KghqcQQTbTTmsuEfWMI0cGsolmISFQf99sdKBk8hCnrMZjtJQAAprwc3YtGYHRYYG5bgxpra97G19y6CW5nMyYc7dB196MS/QAAanw8aTujoRxTxyswueO0qiGpRCAWMVqKQwAY7IlIDG/UoU6h0cxGFFotTMZiLwTtYb2JAZ+0a8BgYGC1CIcHaSMSh8hEhUAg8CK1hYTXL61dgRykCsMqSxQmYyzjOPlgKiszb8SBTxAqSSWJMREgBEx4pqBDRLU882LCYGBgbbYCCGad8kBEonJolyfx/0jIjbKP9kMf6sfgRb2g1qzElLM58boVyKsw5FJjbQXWtcLd5uJ9XTedmjNZfxSWj3pQuq8X7t6NqngTiUAsUrQUhzPGQZucB7U8hbXOeHEWboXP7l4KP3lo7hmhfGN2lEnOCX3wvrGMHmduXuJcnFQQCAQV8dBAWfxfCun3KcpvhpIKodkg12NY74zitS39Sb0Mz/Ya0tpaZEPQF8IEHU8lUbO2gMFuQcwzrmoxsmLhF/d5Ue+MgYE6efFEJMqDLTTTVKfHVDQupqYm+jFSN47ARTqY224sGDEohuAY2Z/pwovRV/0yvK6TMB3rQsWOK7P2JhKBWGTkRhjGjYPBq06lSkBYFGbbwqHWmfy61UJJaiHRUJfZ+2gwMKA9FD49rdO8lcRsQIpH12RksKhF+gq8wWFVpXgNY3cAoS6EPKModdgUH4dAIBQ+XPGVhNUJhqeaZq4KwAwM6+HrGZ3+ywym0hH/7/Rw+ZrVc6l3Jr8uxatoMjJorItKsnf+WCVGolFVUkbSjj8dGaIWUvPLtYqwsllikuzd4paZgiXcfr2A8lZMRCRKw/12B0p73kHvRSfRP11ohi0wQ9XWY76E3MJionHNZ+A+z4WJ5naM7d0H/b6TcPfepNibSARiEZELcajmqmGmQjNatHDg8yqmwoo9djvaA9z3QCkiKT38IhEK3/thqaJx5It89kxM/eyDvvg1ZbHNTMislpjsyYfaIaeksimBMDthhWGaR46JAqGhPIxohoFTAdzw7ZWZ8xy39EsOY+V6FcO+MQBsq4oZ2DYSL21x43SXAf/ngao0WwfE7d13flipal0BraB8XqAKoKNVSR7VVMqjftQjWUiqNX9iP1MlPRvVqLJNRKIwifYU1n0YvkYHQ/VCNK75TL6HlRPYkNSe2niBG+fJ7QjsWIHwmv8h25tIBGKRoJU4ZHMDtBKGYiGkWrVwSPUqZtru09M6XoOZ7TjUQKpXzmopjJ6JyZ+9CRP0JIDs+yaqJRIZuwPweIlIJBBmEYLCUO3z+H2g6HgZeimTcq4n0zdqlpbnGJCX5+jQe+GoApjF7NyAv8R+vTMGXyAm0dYpryuQDdK8cjE4m8tAR8tww50NGQR3bZLgDtGBJM9jtvOpeie/AJQCEYnakNqeouTyDUURQqo2bIEbr6Md/rPtWLSvHwNdV6Dy8gslH4MIxCJA62I0aonDuVKFNJdeOjke0U9PS+shdbYrd+NXs2+iaiKR9EgkEGYFuRKGLEylA4ytMjEpl7TP9Ni0CGOVOzeYaVUlncHhuGAUQklUiBBcrxzl8wIAjLbk6mJsGO6J0yZJgtvVZeR4GWdEVNg3BnSOwlYZRd0Smyrjl4uaInGuw/UaymlPMZvhehMHrC44T76C0Rc+kbw/EYhFgpricIIOJspPG+zZT5ADvmhCHM5mYQhoExabCakeUanwhdNy0cLLyO2bWEgikUAgFCe5FodcCqENhVxxGE8jkSdSB4cp3HanNWetrgCgQT+EhirAtJidm8gXtVz+1wM1iETEw3p3/vxT1FeHNVuEF0M1kUgPzVkvIvEaisP1Jg6eOCx5PyIQC5wQHeC9aXEb2PMhFPueyDW0WzLed60Swj2MRgaWSmbWC0MWrcJic0m+wmnVFolqUKheRGZSvWIOBEKxk9rEnvL7kCjyMpK5uEsmpBV7iaHKkvuwSz7kiENufQG5hed6ezK3kZIakhqRUJGayrJ4C+95RcQhEPcyjuutAEZA0e6iFYkA5lyoKfEaSof1Jk5WvA7gLUn7EIFYwLBGIBWpDexf2uJOEolcQxFj+PMUuNQ54yuDfOElbM+72uYS1DpLMh6LUFx091IATzl4ILsQVDVFYrYtMAo+1NRmzvcICIS8w9/EPrklhdziLqnwtZBIJVsRqjZyxaEc2KigUlu5orHJRY4wZMMpqVEzgHpVzn+21wCgOhF2yi30I7TYrjbZ2qO5GGoa6+1D4ypgyFSLqcuXEa+hBOYt2gDg15K2JQKxwOEzAr6AtFwzX0CXuLEpNRR1TiZpZZANJW1ZzHoMC9NLRsgOsV6S2YagqpWXOCdEIoEwx5HcxF5mcZdUUltIFCpCUUVCKKkxUGorhdkRA7yyd+Ul4glAB/77vBRxyBU+rIeM8aiX0ynWS5JvsV0rVBGJ9BBgr8q88SwgEvUAABhLRYYtCUqQ1tmakHPkGgEx1GphMVfyDAnisCGo2TIjFLOblHH7dyohHyFFBAKBIBehqCI+JuigqtXJs4VvIS+TOKR8QwlxyDiq8xI+yS625wpV7JGHzv4YBc7Q8VMod3eiJ3oSXaVzy3OaK4gHcY5QiOIwHI43581UMMVq0dZLKaeVhFRhFPQFMcEjfBhEALLYlUBOyCl/NT07Ir4xoDOGumZDVqu8xItIIBC0Im7vYhkLpmTKc5QTWpqKlLoCubC5gLg45PMYpuJtP43UUOPZQn+0Fv4P/WkVXFnEwp0ZWy3gG9ByeHln4M09KOnbh4nmIALzdTC3rSHhpRpABGIBorb3MBtxqJXXcGiYwrf+RbyapsHA4Fc/C2pe7EVOK4mhnikAmT8Li02PKkf68zGGgTsETHgmQYn8/JR83lKEbiZBng+kiETxanrxCYbJGMNLW0YUiUQSakogELRiYFiPL/9Lrag4NBoYbP3ZkODEX473EOBfFE6tK8DWEii1zXj31GxzFPEEgArAbOevUyAmDoWEIVuYxEqdgtFwOcIR4VBTo4FBuMDsXSZmakw4BbfJNue2WKFdHhjb/wsG6zEMrI/BtKwV80lRGs0gAnEWE/SFABVC0bUIKZVSDTQSoWA0qn5qXvhaSUxM58jN/A1YbOrkPdjseugoYY+ZN+XcQObvQYrQpT0UvvdD5Tl7WpFJJPol5d3qMNgTQb1TWTgQaX1BIBC0wBvInEcZlmDvsvEesnDrCnjp6LRd0T6/jgtFuwXFIVcYut/uSHo9EvWgpG8fPl18BhUtDjz1pWehCy9J2sZ9YjciPX2wnayFP7wA/2fXl7R5ExohrcZEhpxbqxMIzc6wy6oaPSZXLsLUmmbiNdQYIhALDLmrhJnI1nuoRBxKaSRfiKQKQj6hMhWVGqIjzeAKNyGOn5ttQuylo2nj4/tuMvVMHBqmMo4/X6hV4TRbiBeRQCBIZWBYn7H6aa5RK/dQevpFur3ja0U0MKyDv8cPoBRG78xiKzUaz5mzNdeiHlHQLg9ivX0o7XkHuiWccNPycnQvGoF52cWY17ppOsA0ec60dOWFcPsrMdHRjsrXz8Gk/wJC0RytNBcaHhpwCHsii41IiCzg5hIiEAsQtcJLDbYKAMqMUzbiUEoj+fv/b3aFRdRCiijkItb6g4UVdZmQ14Q4eVxeOop9vX/G4+7v438v+g9cbN2Y8XxAZi9jdy8lWsFUa7IViQZbBSZoj+LKpoUQaqobGwYqKwCQXogEQiEzMKzHDXc2ZOyf+P/+b+FNbKUUBxOzF0FfEBabXtTeme0l8E73W46HTlYLhE7G21WYjDG8trUXht4+1I6/C9c151C2ZDViDisOD57C5sN/xF3nfw5/nSGskO351lO7C79qvQumd5thMNYgsmwDrM0z9/WzPUb8689qM34OlM8LoDLjdgTtoF0exPoHMRULoKvUDTOa8z2kWQ8RiEWITVKieUyyFyuVbKpKSm0kPzaePw+WXFGYSmrrD6VIC5vkb0Jss+vwzMBP0Bv+FL/tvA9L5/0FFEVJEvXiXkZp4ZndvRSsFkqT/FA1PIkTdDBrkUggEPJPlbVwm9hLCR0NhXUIjBdewfgqh16ySORNv6gCb549EPcemh2lSf2W/T1+0bw6YDp00q9HDYCS6iio6ho4Fl4MhmHwzK5fozcwhGeO/wV/dd4NoKjMc4jm1k0oc7ow0dqOqr1Hoe89hCh1E2qubsu4L5fO/hJYh3U5aXWhKlYnKHokL9Vf1cT9dgdKBg8hZj2Gs+sqSFGaHEEEYhFS74zhpS1uwdLLQV8Itsoo6pzKy2UqzTukPYUXusjCFYb5DGFUgwP+t3Fy/AgAwDV1FJ8a9mBJ9KrEe1T6/UkJKQLifRKz7YcoRjYi0eCwIkL7sxKJAAk1JRAKgXpnFK9t7YXXn3wfoPw+AABT6chbE/sRT+EJP62ZyNC/li+0VA7hoAeRqAdUeSWitnho6P5zh3HC7QIAnHC7sP/cYVzedJGk43G9iUz7x6g+9Aomd5zGxJoNqLLWZFx8AID/u7kZpi3y+yFy80GzsUVzFW6o8cBFJ0GtWUmK0uQQIhCLlHpnTPBGNUGHFOcgKA0tBeLhpfc9wF+tLF/MJlHIwjAMnjj379BBjxii0EGPJ879O55afjUoikrKV5T7XbIhRR8f12UMNWX7IUoRiFLyUlOPw4pEJbAiUSnEi6geoVEPSm0CrgYCQQL8TezNce+ILT+hfwPDevzvB4o/v2uCnpRsJ6SKw1QxRHloAGZJ5wjt3gdq+T60V4zBhFYwDIPN7duho3SIMTHoKB0ePfAU2qrn83oRK0r57zXNrZvQA2ChxYrQGT0mMLP4cKijNGOoKdsPMZNAnKCDGBrRwzeqB2CCwVaRaMUEAKU2E2wW4fmb2jC2WlD0UNF6EeubDKisrIS7dSEaiTjMKUQgFhBqF6jJNf4AVVAtFDIZs2KF6z0EgBiiODl+BAf8b2OtbVPi/bJCUYlInB9QzysoNS+VzxtpdpRNV3TNfagpQLyIUggF+Jsyx2IAUAHGbkfIN7PNgaFjeOij3+OeVX+HS2tXJJ43WYiIJMiDcVTHJ7+2zHlkauMN6Au2hUKE9ktaJGbDTDPZCTm2VOh+K9TTL5XBNhesF+hgbduEGmsr3u89lPAeAkCMieEU3Ykj/m6snZ/sRQz5aYwFaUGRyEe9M4qFzWHJ24vBisM7vjsvxd4l25BsWjLNRYKR0XwPQTEHzh3Bz/dtxnevuAuXzrsg38ORxdyLjyhwsi1Qk6nEtRjZ5B7KpaI8LgrEUNqwd4KexAQ9iSqHPm/iMEL7Ew814XoPubBeRIaZ+bzY989+HvlCal6qmIcR0YjE62XG4LITJKW/CbWKRc02QgE66QEATLUj/WG3J/Zhn4s57Pj1qRfROdqPX596ETGHPfEa33EJBClwG6sXIpbyGExGcTGgVh6l3AWxVDvB92C3E0PM1pns0r28xgXNmL/uDtRYk72HXHSUDk/s355k7wDAZI0Lw7Gg8P1DV2nFVFT9AmCsnRnTixeeA+K5lkIpQmyNCTGUXCsUPSJr+0IjKnGBoZBgGAaPHtyKTl8vHj24Ne16LXSIB3EWkk2Jay16HvLhsEtvTi+VXHgM5Qg+1lBPcPaJIQpUxBsIR3xlSF1ZzESq95B7XK4XkYvUVeJCxewogxOTeOLnXkAvfMviq6anRqhpSKBn11wiVbAx1cq8fWK5RNxjUiPJIpF4FwlCsKFz+fIkSqHaHm9snqkdRqY8Sop2S164kupFTJw/C7spGloqc15fYq9L/J97v+ASY2I4OezCgZ7DaV5Ek9WBkD8/C0wGhxXwSts26AuBz0cjVmMiXk0VcDaXycq5ZWy1Bb+IMhvJJne2ECACkVD0aJlnyCcu5K7QcrePMRF4Q/Hy36WcPlBSYL2HFCgwSBfOFCg8ce7fcan16rTcDK5IjI8pWSim5gh29xZW6BQrEoGQou94wjMle6LCZa6GmnJFmlJRmNifJ5doc/t2XDbvwrTrVUgsEqFIECKf4aa5wOSwSE5DMTtKs4omkoOgOKTdPFZKOgzD4NEDT4nbu/3bcWlz+v0DgGioaX/AgrM9ZlgRt8Fne7LrkzhBBxUtzAulQAjXmKgERbthcsibOxByjxx7V6gQgUhQDakVTA0GBuEwFOelsWghDFMFoZaVx6S2K6mI+hGho4hVlWJo6hyvsQQABgyGQn0IMyGYqPRiQUK5iVJyBAsBpUVrDA4rQrRH8XlzVbCGdnkR6zkH85lX8PHGIRhbWpEvf6+awpAl1RsQY2KSVlXZ87NCkYhEghCsSASguVCUWsHUYIghHIakfomvbelXzYsYF4nyvIhyERKHLCZ7JTDdB7HKkrldidEUxoLmOgBBhGNhDI97xO3d2AjC0TBMBumCqbeLwfVb/hmhaHaikEWpEI/3qfZknScvlWIvVlMsjE95QEWAg30f89q7d87uxtWLpPWtzjdEIBJUQU4F0wfuC8JohOS8ND6BqGY4aS5FIZdM7UoATFc7M2KCjkLnDeKJxj9hrDIkuH2VoRomnfj3kBpyKiVHsJDIpj9iNmjpRaRdXpjbdyNYdQTeq4OwrN8Epy33fZ60EIZA+moqi5xVVXY8oRHiTSQIExeJI6B82nkT5VQwfeQ+97S9y9wv0RvQiwpEOV5EYEYkAtmlnvAhJg6p6bD8GDPzXhpM/XjtlyPw6uICxdt+GiWDH2KqahCB1WUoXbwcC5rrUD8vLrhMehOeuPHHGDcIf25VZVZBcSjkPfSMmVUThyxKP1ulKRBzNaKlUBkL0ohf6hUwWuyg9MDv3tjJa++2HHkBaxraQFGUrGJK+YAIRIIqyKlg6rBn3kYINb2G3BtzvnoUibUr4cKObwEWJq1YKjVMySGn5YqOobSIUDYo9iLaLYmVbCVo6UVkxWFDqw/HllTAdP7FOReHWglDFrFcIrm5GUy1g3gTCaJoLRLlVDCttqtbqTIuEqXnIrLhpnJzEoXI5DUUu0/WL7GgHiHQLg8W4BBilx1C/8Z5uLj1iuktZmzbWJDGvIbzsh6vWpiMDGyW5O8yV2G8XLK1RRQ9QryIKsIWRDJa7GAnGQd6DuPkML+9YyvwXmhrkV1xN9cQgUjIKQZDXFSIVqzkQS1hWAiiMFtmit8EE+9HieFnP8fTnUFAQjDjvfcEMb9pRhDKLSKkFmzrC6W5iBUO5bc9rVZuq2v1KJ3nBODOmzjUQhgCM95DsVwiubkZRCQSMsEVibDmZ0JsMMSrTYoVp1GKnII1fAXT5NqMTMKQHROA9HukfxiMI9nj6qjTw19tQ5mzWdY4MhHyC0+69b4xycd58B43mip9YGxVACDYuzBb0W1wWDFB+3MXZkqK1agGKw5NVgdikbhtYxgGT+wXt3dP7N+Op257GOGAp6BFIhGIhJzywH1B1DrlCUQ1wklngzBMJVUoKjVUFpu0z3V+E4PzFstfDbdaGAm5lrn3RsolV7mIuURrcQgA4VgEg2PuzLlEsTBMeum5RCTklJAJViTCPyxlDSwj7OSaGjUDqM+4/SP3uVHvVF8gsqGmckQiwL+4yMK1H0qKswmKQwG0aDUhpXop5ZEWStJU6cPSBUEwjgjv60KFaaySagskt2QiFB9cccglHItgSIq9i4bzWnFXCkQgEnKK3PDSoC8IVCkXh7NRGKaSujqsZVECJdQ6M7c0CYfjYcpC26R6K7PyIuaoKEAhkwthyGLSG/H0LY/AOyk8IbSXWWWJQy7Em0gQg3FUA0wUmMzOc8KKQ8ZRDcYj7VpVO7yUC1ckxsclXyhymcjCVmYUh3b+3yVjES8tHfLTaRNwsW0B/txDt9+Fqff3wPJRDN1nF0k6HiD8mYqFltY5GTy/xQ+/SG2BUJiBP6CDfzqdNOIzodQ7Mx0X8laqBQkzVQe+a9OkN+J3tz0Cn4i9S82dLVQvIhGIBAwOU9M3Mz0CnSGU2pKNQ75CCYG4d6tKwe9Ga2Go1JNEIQZUxPtDUdCp2oydm2sCFJZQrHUKX0NSqqhmqmgrFYPdgphnXPH+s6EvYi7FIUtdRQ3qKtS71lNhRSKBIIp/GAOheYr7ERbapJq9DynxJqaixE5y7SDvPTELz21FqUO04T2LmDAEgB7XLjDtH8N0rAwVzJUIr7oe+LOyMXERs691TgZ1AtfQ4DCF2+60pti75M/OZGTw0ha3JiKRhJlmz1hQfOGitrIGtZXSfouF7EUkArGAkJt8rgaDwzrc/rXUm1XKuFSanOcKKbkSchASg0pEQoyJAqF4+e+IZzzt2Nl+92qFneYSKVVUhSraKq1oOle9iLkUhwf6juDBk5vxr3V34bL5F2p+PqbagdAI8SIShBkYMeGmb6vTbkIK1CgNoDLr42QiG2+iUjJ5DdUSIezkOXVCzp1UC3kNJzraUbV3DHp3E6LNN6Hs6jZYXdK8v/Hcw/Tw0mwL0/gDOkn2zhfQaepFJKhLe+8R/OLkZvyfprtw6QLt7V0ukNbIpwAZGxvDN7/5TTQ0NKC0tBSrV6/GH/7wh4z7bdu2DRRF8T4GBwdzMPLCQurNSm5RmUyweWliyI3Tj9B+RKaTvbOd/FO0O/EA4kYw9ZEtfMfknjebfDf2/bOfiRhszoToWAswR5Dt4ygXNUSznFLzhUIuxSHDMHjsw204N3UOj324DQyTu2uHW5GVQODipaolt5tQC8o3lOgBKIbJGC9ooxSuXVLDhvDBZxdFEQgtjYTcoMoy34crSh0J8Rfy00kP9jU+cdjj2gX/7l0wvR5AiX8FmCu+jpqr2wAAVVZp30VqxVIuhbDwmq0NougRlUYyt+DzHjIMg8c/iNu7xz/Irb3TkqL1IN5yyy1ob2/Hgw8+iPPOOw87duzA7bffjlgshjvuuCPj/lu3bsX555+f9JzDQVaelSK3EEmtk8ETP/ciMBrfnq9QitUSQ50Er6Va4aSpxjQfYYTcc2a7IizVmyiUMxHwxScrpbbSvIYZFxrFWKwm12Gl+88dxomReJnvEyPy2lhkA8lHJOQCVmRk8kTamisBTMz0AIRwyw2x0FY5CNkQQJkdUWIXKd/QTO4nD7H+QVmhp1Lzs9x+FyaHe1B/0Iup4y0IVa+C5doNSdvUO6N47ZcfwzdqAFOZftywbwzWZmtBe++ytUEkzFRdDvQcxifTbZw+cbtwoOcw1s7X3t5pTVEKxNdffx1vvvlmQhQCwMaNG9Hd3Y3vfve7uO2226DXi68ArlixAhdffHEuhptz8hFaKKUQCSsy2KqkzmpgyRL2e5JvGNUQhoUgCoVINvTKjbyUPlhCORNeOgpgXLG3bmg42ftMeyiMcVIAK8oZjI2r652WSrZhpsXSrDjX4pBta8E2CdZROtltLLI6P8lHJGgEW9yj3hnFa1t74fWL5DJa44KPQTxvsQEjaEAvAGjSm5EPMbGo9DhicAv58EG7PIj19qGkbx8+Wj8C0/xWNYrLJtESa4ZlPIyz1atQnyIOWRqqQ6hfXIXU5rghOgBmcQ0AfnGYKbx0pp5DHNpDYZRj3/Jp7/ggxWqyh21rwbV3T+zfjkubc2PvtKQoBeLOnTtRUVGBW2+9Nen5L3/5y7jjjjtw4MABXH755XkaXX5hxUA+ECtEAsTbVUxMz9vUanKvhjAshkl+ajECQFnVOrmVTqscenjpKCboSdkiUUrxGTVQkodocFgzht6KUSxexHwUpHnn7G6ccM80CY4xMZxw586LyEK8iAQ1SfW61DvlefzYiTjbmzFXIpFFazuXSRy63+5AxeBpjFn3wXuNDtbLN6HGqkHPV9+0jdPzl0yn6JGsPnsh28lffKZwIV5EdTjQcxgnh5Pt3cnh2eFFLMocxGPHjmHp0qUwGJL17cqVKxOvZ+LGG2+EXq+H3W7HLbfcImmf2QjtkXYzC/qUic64KIw/qhz6xEMp3Jw6pVXXUvMKiwm+PBM5cHMTpcJ+X6znVypSis9ki1LP5lwgF+JwLEgnPUYnR/C7j3dCRyWbFh2lw6MHnsLo5IikyoTZkktBTCgeRjzZ5xZmm7vFOKrj/Rl9Q4lHMcN9D0Li0HPGCwBoaPVhdFMt5l1/p+rikG1lEfroOIZPCYxV5LsL0YGsivtIqeegFpSKlbRJLqJ0Um0X13vIhfUiSslFLNQKpkCRehBpmsbChQvTnrfb7YnXhairq8O9996LtWvXwmKxoKOjAw8++CDWrl2Lffv2YdWqVaLnnpqawtTUVOLvQCCeKBxDNF6hMktiiAH0EBiB5G5px4giRHtgsAvfQGJMBG53Gb73b9I8SRVWYJweTXvebI9P0Cc8/OLBZp8xyDGGv+GsVCKewPQ5S2Qfj/JMT5YRryAa3z/770su7DmzPbfBXg4ACHlGZV8vpXYDJjxTGa8RLtbpxVjf9DXAfu9iMIy6608MEwHDpIf+MIggluFGzF4r3Gsm/ptVfk1SiCFI+xLXk1IYKoaonkIEFBjoEYuqk+sZiwGM3Q5E1M0dHZ/yJP5vtCSv0h/oOZS0mpoYCxPDKboThz1duLT5IoxyCsmUl8hsjioVmx1BDw1TpfjxY7m/DRQFgraOieXl3pkNMSYKt7sM3/px5ub27Pa879FaHW/dQA8LFmCRjL0q/q+HBnwDnHM4szturvAPz/yf/SxSPjP2M2SoGJzNekT9OjCMHjGV70nnzu4BdaYblmN6ILAGU+vWwbGoKvk7nJ4DwOrkzY2MIQYmgz0QsxkxlVP0GUb4XBTEf4OS5xns9Vygv2eGiiEKBjqdHtDgupE9nmjc5rHjELN3J4dd+KDzEC5tFvciMtG4DczVe4vJmPIUpUAEIBrbK/baddddh+uuuy7x9/r163HDDTegra0NP/jBD/Dyyy+LnvenP/0p7r///rTnu0InYDaYJYw8A2zf2FCXCscQ3ywQsCISkbbi5Q1/BHtFutfJz55DoN+tO8MYZDF9Dq+SY3LHp+aYFNIVPqHOgZReLxKvEaH9/BL2GwtbAVwl8wT8GI1R6Mr2wx/iWYiokH6d0eGDSfspupY4+wPI/npqAnxNTdN/NODsx2pdoBVApxYXO/+PiWEYPPbpdlCgwCDd2FGg8Niu7Vhw3gpQ1MwxfJr+ICuQ6QuamCiAG0IBImjrgp/ArFPB1uWYQMCKcETaolVf+DRKQwJRFuza2KRKnr/UtbZQkXgUuePO8Fn464fgB4CGCwEG6Duk7m+OwmWA/TIE1gPxZYwB+CYHkjdixyv0+VYgsx0VuZ14VbZ3wbKP0Mdn7zKMg4ukeUYZ1LuW1aYB8AFAw3pQXvWvG/nMfPDy7J3YPLtCYxuYjBx7V5QC0eFw8HoJPZ74yjbrSZRKS0sL1q1bhw8++CDjtt/73vfw7W9/O/F3IBBAU1MTWkzLUGFSp99RyDOalQcRACY88ZVfIQ9RjIngDATiMFIwGBi0ONpQY8p9Va9Ur6EcWK9hth4eAPCc8aHs8Hvx406Jl5dmzOXJ45iYqcoSM+gweMsK1L10DLpI/PNkSuLf0eSFV8K+yKZ4jCHPjIdX6vWT6ToRwueJrziKeRIrjMo8iPd+dxzNTcnXWrzA0aVp27Kea66nmo8YEwEdPgiH8RLoqJnbXsQTUHRtsVAeOuvry3PGh3nn3kd54xgOLfWheRF/YQWphEbj90FG5n1QCNZjmOotTDtvNAz60xFeYwkADBh4KBq2tTqY9Mak18KB+DnU9iZS0zZBzIs4NppdZMNsRdDWlZ6PijKBFcECJcZEcQbS2lgZDDGscCxAvUmCV4X1omXrTRTCkz7PAZBbLyPXUwjIeq8xJoqu4ClYB2pRNfUpzKMf4fh6I+YtzO4eB8S9hszwMGz7x2EKLEXkwr+BfVEV/8Zc7yEPUudcE54pQVvpMSqbTj/wXRotTcn3IJslhjrnMsF9MtmdGBNFV/gEWozLoKMkhFX7VfCIa4DnjBc1GIaRfke16yYbxqc8CTuYjb1jCQc82kXQCCDH3im6osfHx7Fw4UIMDw9jwYIFOHXqFIzG9A8gGAzimmuuwb59+2AymfDf//3fuOqqq5ScMom2tjY899xziEQiSXmIHR0dAOIVSuXCMAx0uswT2pKSEpSUpE8oddBL+yFKQAcdGCo77V7hMGCCDiZNhpXy4H1jaKjVIZcpq2yOXIWjPMOW6czk5elUidP3vLobZf3vw31BLwxVlYhkISgYGACsgPuvz4BCBIZpgRbxjqLm7RPwfXI5mCWr4GgVMHQilDpsAKb7I3m8kvIpKhzx62OCjotYqcVr7A4DvHQUk56wYB4gRSm7XuY363DeYmnbUjDIymnVUYak30T8d6v8N0JBl/XvnmJ00EcZGJgYKCoKnT67PBadbrqSZ1ZHmcm3KJE4cSg1mLD1C4/ANxn/7caiDLwfhVG1yph4T1VlVpSWpDeqLrE7EPLTmIh4JJe0l4QzXtFU7DPVqdfyblYhaOuo7K/5QuaR+9xorAUACe/RVh/Pv/N4takG6UgXNBQ9Avil5Y1lKsYiJf+R4RmDXKJTHugYwFBmBqggdAbldye334WJjnaE6QDq363GVON1sH1eWDjEc+x0op+F1DmXDhFBe6H0N7GgmcHSxakig4LY9Fyq3dFREueltnpQnqGCq2hKMTroQUEfiwKULqvrRpXxRJAYQzb2LnE8PXL+nnQypjuKZkbl5eX4/ve/j29+85vo7OzEtm3b8LWvfS1pG4Zh8KUvfQn79u0DRVHYvn27KuIQAG6++WY8+eSTePHFF3Hbbbclnt++fTsaGhpw6aXpngYxOjs7sW/fPlxzzTWqjE8NKNqdVcI0ixotL8bGKZw6Hb/JSO1NmA3ZFqEBlFVso11exHrOAQD0k/HJsdndBb3tGNyfKwNVuxzzWjfJPi6XWJTB2Y9DmH/ZrUmT1mGfC+5F7aja8ypK9nbBc6oFABAti0+Wdc3zJItGk8Miu9qplFYYqbDCzDtdvEatgjG0J/M2hUQhtbsIBWhVCrSw4jC1IXAmaitrUFsZv95iEQZucwg1NSZJRtBkjYvEsSCtrkgkEGQQGNfhxOn4pE5Kb0K2GiRb7EPrSbac41O0uADUaqzut+OL9YwewBqg9NhBxKyjaG89AxOyL07TEqxF2ZlFGL7iBtS3ZvbAiIlDuQ3n89FGLFeQthfyyMbehfyFb+cUL51//etfxy9/+Uv09PTgJz/5Cf7+7/8eJtOMUv7Od76DP/7xjwCAn//85/jCF76Q/Winuf7663HttdfirrvuQiAQwOLFi/Hcc8/hz3/+M5555plED8SvfvWr2L59O86cOYP58+cDAK655hqsX78eK1euTBSpeeihh0BRFB544AHVxpgN7AQ/W9RqefGjh2bCiUzGeFN1LUSiWq0rlEzW3bs6UNr7DmzmSVTWlgGVAFVpRvuSDhgcFphXrIHTpkFJ7mmctlYMrwB8tT0InziCNaMRMKMTAIDRoUkEzkTh7rkJNZvaJB0vtS2GViIRyK4VBh9S+0TJraqqBYXU7iIUEAhHk8lYkJYtDNVCK5FIWl4QpPKvD83cK03GGF7b0i9JJAJICMVCmWTnehy0ywNz+x7UVvajpDqKqE6PE1gP3dJBnG2egLVNndYWzqAVIb16Yc5SF+Pz2UZMa7gLHYVy/QJAKETDVF4JYPZ87oVcuZSLYoFYUlKCH/zgB7jzzjvR3d2N3/3ud/j6178OAHj44Yfxq1/9CgDwzW9+E9/5znfUGS2Hl156Cffeey9+8IMfwOPx4Pzzz8dzzz2XJESj0Sii0WhSqdm2tjY8//zz+MUvfoHJyUk4nU5cffXVuO+++3DeeeepPs5CQM0Vr1A43giWr6l6NuTTa2hu3w3z2Em4L+iF5/LlnFeDMKIV87P0GkrFaWsFbK0YrmnGUXcPZkJ6y8G8fxxV57YjuHUFxtd9RpE3MVciMX6cMlgtDExGRnbpb38A+PR0enhqPA8xeWEi236as41svIdKvYZqo7ZIZKrjYaYEglxCYR28Ab3kfoe59iYWEu63O1Da8w56LzoJfVUlqOoaRK1WUF5g6GIb5p/3t6qcJ/TpCVDD1aAHy4Am8W2z7XkoBJ99tFpisu2dQc9gxKPDydPpU3GbJYZ6Z27rPpDeiMKo3Z6p0L2HQJZFav7hH/4BDz30ED799FP8+7//O77yla/g1VdfTSS233rrrfiP//gPVQaaSkVFBR5++GE8/PDDgtts27YN27ZtS3qOFa5zhUJf8eL248u1OHTv6oD5zCsI1Xjgva4M5hXXa+ollAorFLkM1zTDe6wdk0ffQ/M7/fCcuhz2G6+SdLwZb6K0kFP2e5iY/m7kh5zGhWKlHnh6S7wfIgB091L4yUOZv+NHH+ffxmRk8PSWSdQ6mYLwHhYS2XoPC0UcsrAikUAoNgrVm6gWrJeQSxh+wHoMw9foQC1emUjFiEUY9B0KqVJchO1zaPkohn7/YozVLUaNhPBSteEuogIz9rHOGY+u8gd06OrVJUVeCRGJUvjmD/nfg8nI4KUt7vyIRLrw8hELATXsYzHZtawEol6vx49//GN84QtfQG9vL+6++248++yziMViWL9+PZ5++mlJhV8I6cQ9P+rkIcZvaOkrXhZLSJGHRy3yFVJKu7wo3/syzOF+uC/oBXX5crTkyEuoFKetFVjXiu7aXRhocqHm2KsIbu3C5OrLUHXBEknHyKU3EYgLxUr9BCqr2PzE7O4Fce81hUr9RNJ5csHAsA6+AP/4KV8pnFHpXgatyDb3UIrxO9hzBL/asxnf2nAXLmm+QPG5pB6H5CMS1CJu72IIhXMzJynUkL1sYL2EYzU9CKya+RyjlWYAFTC3rVElhDSVHtcuhE64UP9uNaLNN2HivEZVxaHcmg8zi6jJQrHOyagWXRUKU/AFdDkXiADQP2KCr3MCjNXG+3qVNXNu7mzgwLkj+Pm+zfjuFXdheXWz4uOw9u4bF/9PXNywomhsWtYlLj//+c/jwQcfxNGjR/HUU08BAJYvX46XX36ZtwIaAPT19eGFF17A66+/jk8++QSDg4Ow2+244oorcM8998guMkOQRupEv6ZmEs896cHoaLwCrdRVL7XGAuReHLJew6kaD7wbCsdrKJX5rZvi3kRHOwJnPsCi9n54+i6HY91SSaE0uRKJQLpHMejTI73plzyCviBQlXtxeMudNSILKdWSc5WEYCb9yGWVYC5Scw4ZhsHj729Fl7cXj7+/FWuaVmfo75TdcYgXkaAmNTWTeOV33fAH4vau80QQ//IbieWSFVJMIadsYZlUItHpFjTuTuhD/Ri4qBfUmpVoztGiqtvvwvxuwNx/FXobrai/WloevlSyqfnAJxQBIOIzAdC2cJlWxdEGhvW44dsrRRdSTMYYXtvaq6lIpF0eGNv/C8N1Z3GyZUyV4kZyYBgGjx7cik5fLx7+4En89oYfKz4Oa++eOPw8NixYr/JItSNrgUhRFL72ta/hn/7pnwAATqcTb7zxBmw2m+A+v/71r/Gzn/0MixYtwrXXXgun0wmXy4U//elP+NOf/oTnnnsOn//857MdGoGD0ES/zhlDQ21uV4LyIQ7ZXMNw+D24L5jU3GvIF/KnVpGMJG+izQVH55uY3Cndm6gk5FSpSAQ4Yq5T9q5pWGx6VOV48c0X0GX0ssvNVeJDV1UJwCt7v2wql8rJqzjQcxgnh10AgJPDLhzoOYy18y+SfU61jkMgyKXeGUFjbTyPmfLnJvWi0ENO2ZDR0lAvSpaPIRwZT9uGKS8HaoGR+SGY227UxEsoBV1DXV7Om4nUuUypV9sW41oWR/MG9Bm97KGwDl6/dlEzrKc6WNODwEUVqhU3ksP+c4dxwh23U6foThzxd2OtTf5vl2vvTtGd2H/uMC5vKg57l/VV7HK58MMf/jDx9/j4uKDnkOWSSy7Bu+++iyuvvDLp+ffeew+bNm3CXXfdhc985jMZjzMXUKvdBZD/fMRshSGgTBxycw1D15XDvOIq1byGYrlfqZP20AiNWAwAKhAa9YAbfa1EPLLeRL+jHfSCI6h/RztvotK8RC4WGykoU0jIyTtkGAZP7N8OHaVDjIlBR+nwxP7tuLT5QlleRCXHIWGmhNlAIYSc0q7kHkKx3j7oe17BWI0HgVU6lC1ZHX9e4P4+P0/CsKt0CAvHY4hNWoHl0lIq5KBmSg8hO9xvd6B2/F18urYLupZ6zF/zmZyPgWEYbG7Xxt5tbt+Oy+bJO06+yEogDg8P47rrrsPIyAgcDgdomsb4+Dh+8pOfiBaPueWWW3ifv/LKK7Fx40b85S9/QUdHBy6++OJshlf0qNXuggubj6izy29Anw35EIes1zCoO4TxC2hVvIapglCO54apdoCJMEBnCIzdDobTKyeUUmVRqmBMeBNduzAAZd7EXIWcKqnypiUR2p/V9VgIZFucRmrSPXcVFABiTEyR90/ucUiYKWE2kU+R6H67AxWDp+Go02MqGrfHUxP9OJvjkFG51Fhb0VPbg7Nrh1C19xVM7vBgYs0GOCTmIFK+IcmVTNVakLcVmK0rNqx1Zuha6tGYB3EIJHsPAXXt3Qm3q2i8iIoF4vj4OG644QacPXsWFRUV+Mtf/oIHHngAf/rTn/Db3/4W3/nOd9DcLD+p02iM5wcYDNq66Oc6EU8ASEk3lDKBNxkZWC3ykqbVEIeJ80sUh2xfw2DtCKjWEMzrs8s15E7E1WhEngr3mNQInXQ+KWIx1ZtY+4EXnr4BSd5ErkgE5IWcAtK9idwqb6nkMv91tqHkepQTWpq6Csoid1VVreMQCGpgq4xkLFpjMsZQZVE3jC7XIpENITVUHUH/+iD6p5+PF5ZBXkNGpdLcuglupwujJSfgdf0F9ft64e7dCF1To6hQZBzVifzPTMhdLBWj3hnDS1vcgoXNOnv1uO8haa2qCLkl1XvIoqa9KxYvoiIVFolEcOutt+LDDz+EwWDAf/7nf+LCCy/E/fffj5dffhlTU1O4//77E0VrpNLT04O33noLdXV1aGtTNxGZMIPZUYoxOj3PQGwCz2K1xFCX0otODLXEIUW7ZXkOAaB5eSWOLQli3trbFZ9Xa2HIh5BYzCQUud7EIbjg6ByR7E2cyUuUG3Iqz5uoZpU3QnYo9R6yyF1VVes4BIIaNFSH8NqWfngDwqHvVRZtqjXmst9crLcPi9fY0IVKTC45H46FxRmZVWNtBda0og8v47yaRfCf6EQvGiXtK9WLqLZI1LoCqVaFavJNMDKat3Oneg9Z1LR3xeJFVFQ27+tf/zreeOMNAMDmzZtx/fXXAwBWrlyJz372swCA7du349NPP5V8zHA4jC996UuYmprCQw89BL2e5CtpidnOn99Z52SwZHFU8CFHHCbOlYcwvvpmA5iJ7G4yrDBjqh05E4epsOdmqh0IBWhJIYXzWzfBsmET/BfrcG7dEZjaX4Tn1d2SJiSssZGaAM9+txHan1TFTS6s91p0bAq810LMhvDSXMAwDH65Z7Pg6xQoPLF/OxhG/Ls72H0Y//Lq/Vkfh0BQk3pnFMsWhwQfWlZpjPebk+bdyoZI1IPJkA9Yvkwwt7CYKHU0YnJKuq2R66WVawOVYJNo72wi9m6250tGbbmPKGIYBg/tfVTwdTXt3eb2wrd3sj2IP/rRjxKewfvuuw933nln2usvvfQSotEo7rvvPjz//PMZjxmLxfCVr3wF7777Lr72ta/hS1/6ktxhzWrULFSTSsQTgMmhTbNZtSbhcryHaWNwmBXtxxWHYnD75Fw6T3lfOCmwY2HzFcU8itnkJirJSwTkexO5aOG9FiIbIasFurFhoFK+MVSafygnvDQUDaPfPyj4OgMGQ2MjCEfDMBlM/NswDB57fytC0bDs44T8pEANQX0oekRyXprWaBlqOnT8FMrdnQjWMujSBVEG5b3cgNzaOyFiDivcoROgRmOI9S4AJOQixkNNpeciyk27kEumEFQgLiLz0QNxLuMZH0T/6LDg66rbu1gYJj3/cQoBWQLxqaeewv33x1Xx3//93+PHP07vC7J8+XJ8/vOfxx/+8Ae88MIL+N73vofVq1cLHpNhGHzta1/DM888gy9+8Yt4/PHH5b2DWY4WhWpyQd4n4WM0qEozAPlVW6W2DeD2yfnmn3+IX/31j7C26UIFg5WHHKEoVOnUfuNVoueQ2woDUJ6byJLL8FM5CxdSCg5okaskBaWebanhpUf6OhBlZt7XtzfcjZX1S5O2qSqzChpLADjYexin3KcTf39nw91oSzmGlOMQCLMNLfMRB97cg5K+fZhoDuLofB3Mzuya2OfL3qVSY21FV1sPmODHqDn3AiZ3nJZctEZOwRq5aRdyyUUIqhKqLFFpubnW2ZUqMhak8dHQJ4hy8gXVsHf3XHE3Vtam2zt7mbWgxSEgQyC+/vrr+PrXvw4AuOaaa/Dkk08KbvvDH/4QL7zwAqLRKO6991689tprvNvFYjHceeed2Lp1K26//XZs27YNOl1+mkXPZZR6fTKhlvcwl8jpKceNVQ9Fw/jZvt/gpdu25CzxWKpQ5HoT3Y4hWD98E8GtufEmAspaYmhFxBOADvLC18VWeylfPN/V2VymaThaPuAr0f3GyTfxuZU3Sr7GGYbBEwd+n3SM10++ic/KOEa2UCOkCiqhcFE7H5EtSjNFvQfPesC0rBXzVahQKmTvxqc8gvvE15bUDxWMF61phuf9PRg/9xYWTBetqblauHYFK8DleBIB7b2J2aJ2HmK9Myqam0uN0rA1V2pm7yJRD6jySihZ3FfKWJCG0WLH797Yqbq9e/XTN/H55bmzd2oiWSD+zd/8DcJhYZcpl/PPPx+RSER0G644vO222/D000+TvMM8YLaXIOgR/67yjZybH+3yonzvy6Btx/DJijCMkL5iKkccspWuKFBgEA977PH34/3eQ7iiObdFAJhqR6KYTWZvogt+yPcmyl1FLWShqGThQmi1l6KD09enMmNJu7woP7offY0unNDrQKEwwt4AdVpbHB09ik8UlgtXs72Fkj6jBEIuUdOLWFEzBkNLPYauWqZKhVIhe/f2mXdw5fKrBfeLRRgAIYxPeUBNTzPUChmvsbYC17eir/1lNOsr8anEkgNsuCkAxd7E+HHyLxQZR40mi+j1TrHiTJVxTyzy08tTTbi9gD/oPqSJvSuWgjR85MVdF4vF8NWvfhVbt27FrbfeimeeeYaIw1lEvsJL3bs6YNr3NAZXHIB3QxksGzZJXjmVm8/FrqayxpLlF+9vzkvisdRCNk5bK1rW3QHjsla4PzcJffhVBLduS1R+FYKbuC/HIJkdpaoVssmGiCcepi1UnCnX0C5v0vUauKEe5hVrVFnpVwOu95ALW+pbyjXOMAyeHXg2q2OQ/EOC2uSiMIxctMiHZCzqee7eObub1979+sNnJf2OjRY7TFYHTFYHxoJ04qEGUVsFgpFRRKLCnsxUEt5EmZ5bk8OSeLC2MNdRToVCIf6O5MAVh1rbu2IoSMNHXpoN/vjHP8a2bdtQUVGB8847D//2b/+Wts3f/u3fiuYuzhVCdKAgVqrkkusKke5dHagbfw+Ty8dgXLIAjWtvkn2MbLyHLFwvIp8BZENuuCuqLGpMhuV6E71oB84cQM07/XD33ISaTcIhOtnkZOTTo5j3fFgeYj3nUDf+HrrXnYPRuQCNMoWh0gI1UsnUkuLmrX+P71/zLVzSLFyo4mDvYZyePJ32vJSVWbW8hyS8lMBHoRSo0YJISD3BMhakwTAMthx5gdfe9fr78UH3IVzWIj1qhs1/DvlnRGK2ts9dPobyzk4MvAnUX7tB0j5sj0RWJMq9Jvi8ivHjFt98TS65bNOiBVxxCGhv74rVi5gXgdjV1QUAGBsbw09+8hPebVpaWohALGIm6GDORaKlvgzMxfNRavLJ2k+p91CIh/Y+imdu/gUoikorBsKG3BgtdugMMzHpXGMJZGcwZecm1u6Ce9EQqvZsR3DrCoyv+wwcrcJNfLPpFcUnFAFtxCK3B2eMicAbUvf42a4cW+rLQDmrFS1mANr15WRXU/kmhCxDY248/v5WrGlazZtbweZiCB2DLRfO13SYFYdqeQ9JeCmhWFArzJQqswIYy+oYrD064u/GKbpTcLtf7dmMtfPl596nCkWlv/fm1k3owS6EKj5G/bv9GHgTMLW0SSpaM5OXmL1QBHIvFvPtvdSyAq9WpIrDXNm7ze3bcdm8dHtXyOQlxHTbtm1gGEb08Q//8A/5GBpBBQol10wOSryHQpwbHcJhX5fkSpEAEuE3aobhJIRiBgE8v3UTzCvWwLuhDIMrDqD0nc1w7+oQH29KmI1c2NDT1PBTNbx97HG4x9eK2dikOBwNY2jMLWgsWVgvoJJjcMuF86GGOCTeQ0IxUUheTdbuGC32xORZCNaLqJRUm6eE5tZNsF61CSM3h2AIvQJj+39h4M09kvdnHNVJYadKvWPcENTUMFStxFy+bBB7vRZTqGmqOARybO9i0uq4FAp58SASCocJz1TGqo6Dw1RO+tMVA+FYGIMSbia/3b8Na+dfpHi1KFMYjtR+VFJDTrneRG9VDyo7tmNyxxWYWHOVZt7EgWHddGXQmVyZoC8EdE4CAGyVUdRWzyTK8y088AnKXHuuZxsmgwm/u+0R+CaTP1uGYfDjN3+BLm8vGIZJ5GfweQFNBhOe+tzD6DwwgqpVRuj06b8DvnLhavc9JN5DQhIeGpnWxQeG9YIVHIF4G4BCrVjMtrbYv6oP1tIamBX0PRwL0jP2JxLC0Kj29g6I2zw+b6JUW1djjduwntpdCJ2IexMDOzoRXvM/JHkTAXU8iknviSPeBob1GP6Qv5IOY6uS1fcw355DFi3btKgNnzgE1Ld3lYsmYC6zpZ2/GNpapEIEYgGTq/6HYh6/wWEKt91pzdADLt7kPFUk5iPMFD4fossrNTu8SW/C7z7zC/zdzm8iMCUcwuMe94g2U5V8Ps7NbGw69K68xJ7oR/Xowa24pJE/7IFFft9EF8acnyB86i9w7OuFu2djxtxEudXdBoZ1uOXOmozX1Utb3AmjOVFAYpCi3bPSe8hSW1mD2srk7/GD7kPo9PQk/s6US1hbWQOd2YqaGlNSOLUQalYtpUbEF0QIcxexCf/AsB433NmQsQfca1v6C0oksq0tDNZ9GFgfg23ZCjQrKHiV6sEzGUz47ed+ga++8E0Egrmzd0ptHTDTAmPE0Q7LobMwt/8XBroWSM5NBNQXilKvq50//xT11Zm9TIVke4opH1EoqktNe2dzjKGyfHbYHiIQC5x8Jzz7AzrRSTwAhMJxDyO3ybnBYVUULjjjVYpD+Uph9M4YHbVXb+W0tmBpsjbiuc89Bu+kH5MhH4wVtrRttGj6za6wshXlAMhKfpblTVzbij68An/VOZS72GbEwt7ETAVsUr/Xzl69pOvKF9AlBCLxDKpDRakDY35aVgh0ak9EFrFVVTmomXdIQksJSvEG9KKTeAAIhXXwBvSq2KFUbyU1agbj4dg7a2Z7x4rDhlYfPqzXwXr5RkWtLYQ8LPOqGvH72x9L87BwUdveZWvruN5EmuNNnFx1GQymGduUybPI9YqxrTGAzGIx9Xs922uQdF2N660wOVROls8RhexF5HrFpaC1vSsWiEAkFAz8XqXkG06hrN7WVdSgwqADUCXrxpMtfM1c5SQ/SxWJANC49iYM+1yYcH4CP8ebqGueJyoUU72JUryFxYRa4T3MxCgiDrMqx9KaTFXe5PSKSkXtojQACS0lFD78XqX6pG1Mxhhe29qb0d456vQobXKCso1k1fdQjodFa4wWO7a8+oJiWwekexOrz40DHE/oZPt5mFizQXFBGyBdLErxFs42iinUVApa2rtiggjEAiVX7S0Mdm1DFeSEmfokeSszrN7abNC7fYBN3jiVkktxCPA3L5dbQlmOSOTzJpa2XwAa0r2JvkB9QYnDVG9mKlJyQQopxEdrMlV5E6tImgm1xSEJLSWIYnXmewQJJHsr/dK8lZNTygt8qdWTUE0O9BxOqp6qtF0A15sYzwCM37t1pzsR9f4F1ftOwt17E2quFk6j4JLsVUwXi1K+11ySq5zaQhWJcq/tbO1deYm0fNdigAhEgmYoDTMl8CMW9vDogadkrazKEYlA3JtIuw7DPtUHs0mPnox7zIgoqtOLVE9wvhgc1uOzX5OX+8hlNuUehiSGmcqpSCo1zEwLryEJLSXMdaK2iswbCZDrxU4xMtm6tur5qCyTZ1PScjJbgR7XLnirOmH5aDsCO1ZgvGYBDPqZCb6uqVHUu5gqhCh6CNSoGame4HwxMKzHTV/LXU6tGiLRoLeDGf8IgDHr8bDIuba1sHeFxMDh1yVvSwRiAZKL4jQTnilu8UiCTNRecT3YcwS/2rMZ39pwl2AzVrGwh1N0J945uxuXNK6UPOmWKxKjNfHiP8ykH7qxYQDC1U25GLOYtKiNNC91cu4jS6FUjlODilKH5GtYqMobFzk5SFqKQ+I9JBDkkQ/vYSZ7l8nWtfd34JLGlQCyu4/EQ1Bd8Fa2I0x/jJahmXNOeKZgbr8C7t7FsryL3BzSfJPrnFqguIrW8KG2vSsU3H4XJjraUbHbK3kfIhALlHwXpyFkRq0VV4Zh8Pj7W9Hl7RVsxsowDH65Z7PgMShQ+N3HO7GmoU1W02E5FU4BoLN+FNY+P8qORuEFUHXBEknnKXZYcaiW91A/SQPaFduVjFQvolo5SFqIQxYiDgkEZeTSe5jJ3kkJ8fvdxztxxdKrEA54eNtAyYENQXX7XeBOnSeHe+Bt/wuqD51EYMchhNf8j6T9pLbOmIswtlpQ9FBWoaZ6n3DVXK3JR86tlrj9LkwO92DVCTvOBuoBvCppv8IJlCYAyF1rC0LhwF0tFWrGGoqG0e8fFDwGG/ZAVcQFjOy4e1YoBoT3c9paYV6xBv6Ldfh0xQcwtb8Iz6u7ZZ2nGFFbHCaOW5mfAjUHzh3B557/RxwfkRIorA4hP62ZOCShpYS5zlS0eFI5Mtm7cDQs2nsxKcTP6kg8xoJ04qGEGmtr0qO5dRPMN9yIkZtDmKg7C+uJJ5Mekzt2gnZ5FJ1rrkDRI4r2KzWot3r6Yf8x3P70P+JgzxHVjslHOFDY10JLLN4Tdap2teR9iAexANHaezhBB5P+HhyOt6ngg/ZQMBoZhDPkbFkt0hq8EpJJzbUQKqN8pK8DUWYmBOTbG+7GyvqlScdKhD0INBzOOBYJIadOW3y1tbt2FwZsLtQcexXBrV2YXH2Z6t5Ek5GBLc/XlVbiMF8wDJPUV+yx638g2YuolHDAA0qvjdeQhJYS5CJWtGPEo5Ng72KoshROD0QWXaUVQGELRSn2zmQw4Z+vvBM//ssvEvul2ju+ED++nsEs2XoXZwrcxOEWuBnoukJWj0UhCvW6Uooa+YhuvyurqrwMw+CJw8+LRmfNGXzy7w1EIBYQufQeGuwWIAQMDutw+9esonlZRgODX9w/imo7/4qe1RJDnZP/tVyha56H/vbTCI4cwGRrEN3wYr7ERsHUyEwvxAPnjuDn+zbju1fchUvn8ecCqglfVdLUMsp8RvWNk2/icytvFLzZmTQUiQAwv3UThmua4XW0I3DmAyxq74en73LYb7xK8rlS+be7erCgYSo+DluVpGqiWqKFOKRdXpTvfRl62zEcaSkDBeXNl5Ww/9zhpL5iHSPdWFnTovl5tRCHLEQcEqQipWiH0RDDY/cPo9rOf+9RuxdvPtl9dg8eOfh7fHvjPwvmvquJVHv3wtGXZdm7VFIXvFIFIyDvniRU4GakeQhVe19BYMdMtdUpuhXAlzIe88F/GcLC5jAovw8AYKuMoN4pLa+/WFAiEnVNjehvP43K4eMIjLjhvhyKReL+czOVcOdSewoh5HpmiUAsMHKde+iXULQjHKFQbWewZLG2RtFmicFkZDJUmORfZXO0VgGtN2Ns12JUvf0KBhgXuoaGYF6xJu71EjqexZEIq0z1rlzSKL7alK3nRWozVilGlfe9aSwShbyJ4+s+k9QCo8oShckYy1hJ7dK1OtQ7TdMLJQPxsSA/eQCUzwtUqSsO3bs6UNr7DgZXnAR1+XKYa5pFr00hxMKAxWAYBpvbt6f1FdPCixjy04g7vCs0K/tNWloQ5CKlaEc4okO1PYZli7VtWC71vlhl1cbust6Vbn9/TrwrWts7MVLvbaxdTEWuaHQ7XfCiHUAXWoK1oAJjcHgiML0eQigqXMTEZIzhorbg9EJDPNWAokeA6eIuqb0Vixm5RWscrXbQ2AD0LkbVW69gILQLk8t6UOZsliUU+ezdXGpyz0WnsJsAEYhzlIgnoHoVU75Q1YjPhFJv/DLL5BGqd8bw0hZ3Wo86yudNVMLMtHpbs6kNXrsJK/tPwDd6DAO1PYDESXiqd0Ws35KcKpBCSGnGemnzhZKMqhCsYRyTmf8lp8Ip15sYoY+g/p1+eE7N3mLZ0wAAWu9JREFUeBPrnVG8tqVfci8mrigLcSqH5nLxxGirgMmhXpUy964OtKATE2uCCDQtQKNE77YQrMdbDtzrG5jpK6a2F5HNNSwvscMHbSbZJO+QkG+y7S9X74zitV9+DN+oAYzVxn8Mq/gxaJcHoa4OTE304wP9GVkRCbn2ruTC3kmFbzGMTzRmspfcAjfD7FgBbG74ETzH/LCdrEWpcQGCyy6CtXnGrvF9r6yHje2tmEuRGPaNIUQHNUulkFu0xtFqB1rtGDpuwsozB+Ef68XAJQBkCEQ+e6fVdR7i8VAXEkG6D8zxMHqOV0BO0BIRiAVCLsNLzY5SjNHjqh5zcJjCbXfyharO3HDE+sux1DvTRSRFB2VP1kNNraj10RhAMPPGEPauyOktKAepzVgZhsloVKXc7JR4E+VUOE14E127MIB0b2K9U1lYlpBYBJQJxmy81NlS1VyJiRBgPP981Y+didTrm4XrRVQDbiGaWETbsHPiPSTIZswDYF7WhxkY1uOGO7PrL0f5htBQDdQvsQAKFlJolwfm9j2IWffh7FUVoGpr00MhBci1dyXX9k4JmbyMYnYz1bNV8zetcF/hwtT7e2D56Cj0vXsQpW6S1C6DcVSrJhKleakZWJutYByVaTZW9cJsMvMRDaYahBsWwBmNSJ7LAeL2TqvrXMsFUaWwrS2q9o4h6r8CwebFqL60BfiVtP2JQCwgCrm1RVdv+g2Gm3soJVRVqL9cISDkXRHzImZDOBaR1Iz1t/u3ZTSqUm92WoecApm9idmQLBYDvH0JM/2G6pxR7Pz5KfhGk1f+ub0atcoxio15gTy1Tkq9vlnY67y9vwOXUCuzCjPVsoUFF+I9JGjN2d70qRH3vpBtfzk25E5x8Y63O1Da8w761nZB11IP83nLZIXf5dK7AuTH3mUL914oRyyy1Fhbgetb0bN4F5j2j1F96JVEu4xMLTLUEonc6J3wdNsIxpac58iN7OLaT4p2I0QHVBOJueyPKGbv5kouYo9rF0InXFjiWoQJfwsm1mxATasdY+OjmXeehgjEOYrZXgJvCIj6xyCl4fmPHkqPRzUZGTy/xa9pgZpcNCfP5F3Rwoto0hszNmMtN5nx//3xO9LKfUts2poLkch6E//U/jv88wU78H+GT2IdT25iNvAZLSHRCAAUYkAFQHlo1FfrMH9JWereqoxLCP3k9OTCll1ri1CAlh1eyl7fYhOvLUdewJoGac2gxdBaHLIQ7yFBERabpM3+9aH0haZMHkEpcCfI2fSIA4Dm5ZVwt9Rj3oJ1YGQs7OTDu5Ive6cWQmKRe78TKnAXz1VsxkhzO6r2HoN+Xz/cvZm9iWqKxHpnFCE6OC0AI5L2Y8ViqAAqeTPjowCM0raVYO/UvM4LObx0zVgbPi2/GM4rnbCY5dcCIAKRoJhQOJ5zWKdxRTetb0z7hzpEvStCXsSKUgfGsijuIaUZayajylfuOxO5EIkMw+C5gaM4FxvDbxpPYH5rFRpU9CbyIXadxJgoEAJM9kroKOG8IS2hrBYA/Tk/bzgWxmCG1Xv3uCfRQ1MJuTKSxHtIyBdiHkEpZOs1FELuglG+vCv5sndqw9p7rlAsL7GLFrjjtstgvYmTO05jYs0GUW8iKxKzJUQHFEeoMY4a1byJcnMRAcBkknd9S7F3ai805CKlIh8QgTjHKbWq8wOJ+MbAzTcsFhiGweZPdoquNmmZi5gJKUZVCdmIRCBzXuL7/YdxnI5PQlxTNDoWG0FtHNK0b2Kh4nl1N8zuLnT6etHfEoIZzYqOo7R6qUlvwtO3PAKvyMSrRBfL2lgS7yGBkAw1SoPyTQBQXxgqIdfeFbloZe+0gCsU3zm7W1KBO9ab6K3aA8uhTxHrXQxkCDctBFiRWAyI2bvJkA8A4KyZn/eFhlyhn/QAcCralwjEHCJW9SzsK4U1mvv8PClFO8SI+MYQoUPQIrkqFzekcCyMwXEJq02xMEz62XVDUVrhFBD3JjIMg0cOJRdAeH7wY1yx/p/hdXw4k5uYZd/EQod2eUGd+ggl/e/j3MYhGJe1Zt3aQkn1UgCoq6hBXQX/xGssqG6LCwIBmLZ3fpEqnxkqdGpBlTVz0Q610UoYBiPSc4lY8uFdme0YLXb87o2dkgvc1Vhb0bO4B5W9YXgnMx8/7kXMbVXTYiejvatUx94VcngpS7RM+QIEEYg5QlrVs8xVPtWmzhnlbS1x6gSDBzZnXskrtZlgdugSrSzURmlIg6nXhaHKAWTKrzTpTfjDTY9gmO4GY7PxbmMvs4qKQ7V7yOUatUNOud5DIB66dJx24fTEKK5Ydwe6Xbvgdgyhas/s9Sa6d3WgsucQJhtdcH9OB8uKTYqEIRel4lCMbFu1sJis8XDrXHkRCYXNwLAeN3y5KXOVz629ORWJ9c4IXtvamyZcz/YY8a8/yzwBZz2C1KgZQH3G7ePtK7TLb47a5PWq4vOusF4VY4UNQGGEcRYTfH0bpRa4i0Q9Wg8vq/BSvmPlMxdRLdScsxW6zWtBJ/o6TahdLn+BIXfLaCozNjaGb37zm2hoaEBpaSlWr16NP/zhD5L2HR4exj/8wz+guroaZrMZl112GXbt2qXpeKVVPaPShFouqHfGsHRxJOmxZFl+G4kq9R66d3XA1P4i+qY+RH9LCGU1mcP56itqsLRqAZbWLOZ91AqsRAGFd3M42HMEtz/9jzjYc0TWfglvokzBkAg5DcSFItd7yEVH6fDIoXgZ8/mtm2BesQbeDWU4t+4ITO0vwvPq7pxVONMS2uWNh5SeeQXn1h1B4IZ6tKy7IytxqKQwjRTY77qYFzcIhYnXL7HKp4iHUSvqnVEsaw0lPRY2hyXty1htYBzVgn0Li4G6ipok+3Zh48U4z7EAS5yLscS5GM4iCfEElNs7tWDbd/DZu83tcXsnBFVeqfXwVGc2iEO15myhAl8QLXM24+j8bpwLfQhj+4twv90h+xhFKxBvueUWbN++HT/84Q/xxhtvYM2aNbj99tuxY8cO0f2mpqawadMm7Nq1Cw8//DBefvll1NbW4rrrrsOePXtyNPrZBxuqKobJyMBmke4dlXMzol1eTO7YiaD7ebgv6FVlYl5sMAyDx9/fii5vLx5/f6uoceIjG5HICpg9rt04TruSquMBM17E9/sPA4hXOm1ZdweMy1rh/twkKNObmNz5BrxHTsk6dyHh3tWB8r0vQx9+Fe7PTcKyYRPmS+xLJoTSvEMxxoJ0IqxUTXFosjpU80gS5gaU3xev1KhCIY5cwoaqimEyxlBl1cY7qoXnqRjC5bhka+/UgPUe8tk71os4G1Ar3YfyyStQoyXZXO/F8FupsbZi/ro7MPo39Ri5qBf6nlcwuWMnPGe8ko9RlCGmr7/+Ot58803s2LEDt99+OwBg48aN6O7uxne/+13cdttt0Ov5VyafeuopHDt2DO+//z4uu+yyxL6rVq3CPffcgwMHDuTsffBB+bwAim9lqd4Z4w1V5cLttyOGkptRrOccKioGYF7diJFFS+FovVDW/iaLA6ER5Z6aQggz5Ya6KK1Gl01eYsxhx2NvvyBaAOGRQ9txecNMbka8b6ILfrSDXnAEtR944ekbANVYj1jFTGK1Wu0xtIDNNTRzcg1bshSGQPZ5h3zkwmsoN1RZLlKq6BKKA6bSAWAi38OQTb0zyhuqykXrHMu4B0p683AxKkrjizuFYMekooa9ywbWe1ioBe6SxkK7FYeZUiq1uVAaIRQK0RIbXEhHjeu9kL2HXNjCSJWlJ1DyST+G+hok71uUAnHnzp2oqKjArbfemvT8l7/8Zdxxxx04cOAALr/8csF9lyxZkhCHAGAwGPDFL34R3//+99HX14fGxkZNx5+JbH7MaiGleE2qR7DeKU0AipHNzchSXwZ/Ze5zJ9ibTT7hhrqwifLZVKNTkpcYjoUxGPSKF0CYSC/4w/ZN7HbtAu0YQkPXe3AGrEAg/npvJzDZ3oSJNVcVnFD0vLob5e4uTNmOwf25MlVyDbUUhoC24pB73ZgN6lfnY/NeCbMDapQGqvNf4VNK8ZpUjyDbX262UEwiUW17p4RwNIwhKQV/8lzgzuSwIEQHZO/HXahXSxwq/Z2ruRjConTeVgzeQ14aG1EyMgL4pO9SlALx2LFjWLp0KQyG5OGvXLky8bqQQDx27BiuvPLKtOfZfY8fP55XgWi0VQAI5l0kqukRlIpaK1XRmuLzwErFE+O/OX3a25WWKJ/tqqpckZhaAIHy+QAAxnJbYhuxgj+sN3GgtgcD088Z6AkwjSOo7OiBaV8v3D0bUbMp+4bu2eI9cgplR/ejJNyPI6uimKi9AvXWSzDRC4z0Jm9bZQ+hvlGacStWryEX9roZn/IAkFdEgzC3iOf0mfM9jILwCEpl6PgplLs7AQ2KWhaLSOQrDJNrL6LJYOLt2xge86HMZAOQucBdLpEyp0yN3uKbi4lV4weAKkvy7yQbcRgJuWHs78RwbeaCg0qQ28s6pCCyqlDosvuxUuY+RSkQaZrGwoUL05632+2J18X2ZbeTuy8Qz2GcmppK/B0IxFdmYojGm3ELIPZa6nYGezlCnlGAHgJj1+ZCZMcjNq7amvhD/DjqjIfy0GAQb2Qu9bPiwugZRHU6RKEDE6MQi8ofWCwGMAqbnTJRSGqUym4jp6mqNzaTb1Jemn7tjk3SeOz9pxKrqSw6Soffvr8daxouULyqaii3IxzwYHScRnlJZo+Qs7QaztJpQ1AFUJ742E2VM/uKfTfVlYuBysUzT0z/zPudbyDsOo3qj4MY/88zmLzwStgX2SS/DynXuxCeMz6UHX4P1NTMKqzJXI7x5k6cbqrGP333QYRCwrdSU0kUr7z1bkaRGBqNf1aM3Q6o0HQ3LtDiGC121Rr5tvcewf/b+zi+ue7rWNN0Ae82hnI7Qt74+ZX8FsWgYkDQRyddU9kQy/+cvyARtHVMLOPvKDUnS2w7Jb9JuUizd9Gc2TuljLx7AobeNzDeFMTR+TqUOS5UvUG32WDH+JQHUx4aRkt2vzEl9i4TDMPgt+9v18TeyaWmrBo1ZcnCJ2zwJNlK3vfO6BGZnACjl2CTPNNzUhm/k9TrnTunFIOdg6Ueh2VgWI+bvpapGn8Mrzw5XZ3YPxx/0u6QNX4AGHxnL0r6D+DjK2gY7ZUoc8zXpBm92NyNa+tWW+cDAMpL+G0pa+fUtndq4ChfjBEHhY6mfdAPvyB5v6IUiABEbwCZbg7Z7PvTn/4U999/f9rzXaETMBuEV0L7wlYA80SPHd/uNEpD/pmF91BXxn2yYSB8VNPjSybxfhXufxlwGKvi/+8GRhUdqALoVDqACsgZPH1QWtW8mWPH8fOc40jgNFzuzrTnY0wMn7hdePO/D+ICC/9EXs75fUo/UwBZl3ov2QSsAIZWsE/0whfqFduDl67wCfnnbgJ8TS08LyyC74xVVBwCQGhKj2MHKUwuyvQZTH9Wiq9BgePFR6HKERmGwaOfbkXXZC8efWsrfn7eMpF7Zvz8A0flXOtSUOmammZiQrs2BMWMoK0LfgKzTtzr1zdlBZC5gnTf1FmU8jS01oquYPEWwQIArAGw5noA8QqDUy6gT5M2Gur+xuTZO3GOBI7gE7cr7Xn17F22VGS0lRQuw4npQJizkxlsUtn0vyH5OXxJ9k5qMIfI0M/QVoTC88V3D+twjO7EpM0/M/ZJBfmHa+2YwPUomf5T22s9/bjptu7noCgq43ervr1TiybA/gWMXjEB/GanpD2KUiA6HA5eT59n2lvB5yFUY18A+N73vodvf/vbib8DgQCamprQYlqGCpNwaGOZQy8px2GFYwHqTckrLSHPTENctTyKMSaKgfBR1BtXQ0flvtQ4EPcaAskrVkoZ2XMciwzvIbDcjuGlFaixLpJ9jNCoJ+69UQDrrcm04hqLMKAPhuG4xAidIfMqpzfm4fUasjAMgz/8YYdoovyO0d/j2r++RJVV1XAg/j6leBOTxuGZ8WZl6/npObMHkU/OoOyAHlWx5Zisjk9ExRrCMnoGo5cwqDxIgYoKfw76yZlxlo30YLLhDLzzx2E4fxGaF21I2z6ok5Y+39hqxMIVyaFGrMcQgOLrLhWp16FSDvQcwumPTgMATk+eRmfTMVzazB/SxV7r1qVjoPTyrxkx+DzTShkbjWR9jNmIoK0rPR8VZeKzzTKnQZq9c85HfZn2n3+MiaIreAotpUvyZu+yYeTdE2hCFwKNLoy0NcDekjsBlBqJIAe59i4TDMPg+T+K27vnR3eoZu/kEg54JN3n+g+/hqUfl+BccD2q1y8T39hDA1an+DYpxJgousIn0GJcpur1HjRKC5dtnLJjYZn8VC3Wa0ivPgc0OVG27EJUW+TP5eQyPuVJu7ZTbV1v3TFcNk84fDkWZTBwNIz61Ubo9PktSiSGHHtXlAKxra0Nzz33HCKRSFIeYkdHvM/HihUrhHZFW1tbYjsuUvYFgJKSEpSUlKQ9r4Ne9IfYWAu8tqVfQuw2ACRvU+qwAYg3KYUnXqJWrfxEHaWHjsrtZTAT565Tra8OFaWgp6LQOcpB6RhFP1CdDmAUGrFKQzx3Q6oR1BmojNt6YjQqzOILAuFIGMOj4onyI+MejGAYRp0Rdl12Cwwl9nh+2UTEIy8O3xnflhqhERn3ZFWFsuW8qzDsbMSEvR2Grm4snl7RY0aFqyFGKT2O4AospvZCTwmHulC1M56R9sUdMDgssKxYL1h4RqeT+H3rqMQ1yeYZ6nQzuYbZmhM2z5DSa5dryDAMnjz4+6TCEE8e/D3WLrhIdDJmqrIjMu6Rf82I4YwXq1HDEOuKTy/kBEFbR+kyTjobaxmJOX0MUu2dlsTtXfF94VQUqG6qwBgTBVNTqYrYkkqlYab9UWR8emFG5j1Gir2TQigSxvC4uL0bHh9BlIrAZMht7l/IT6OyXNrnQlFRlOvMoKIQvR7j7V90YBRes2pf71KPpbPaoaOke/uGjp9C2Uf7YbQeA31TBajapWhWoRK4VKgIkq5PPlv3+OHf4/L54rYOAHR6da51rdDJmO4XpUC8+eab8eSTT+LFF1/Ebbfdlnh++/btaGhowKWXXiq67913340DBw4ktotEInjmmWdw6aWXoqFBeglYuWRb9YwVUyE6kJRMnO+Kp1JRsyqWFmTb6kJNhIrRpGIymPDbL6YnynOxma2wlddgPEgnjpuNUFRS4ZSFrUKZbauCROXT2l0YSFQ3E/ZWMIwOGAc6rtKBosRyBGbyBI1ozbqPIRduT0O1rrFcVScFsisMkc01IwZpeVG4zLYqn3Md9nfLFrEBclf4ikWoMAyXqjJrXsShVOizH4IZccM/aAbKM2/P2DSoRlQghCaGQO/7BCV9+9C7fgRGhwXmtjWosea3fzWfrWP7Wl7elLtWKvmmKAXi9ddfj2uvvRZ33XUXAoEAFi9ejOeeew5//vOf8cwzzyR6IH71q1/F9u3bcebMGcyfH4+b/spXvoLf/OY3uPXWW/Hggw/C6XTisccew6lTp/DWW2/l821JhiuuUsUiUFiCUUpVLDXQT9LF2D5SlHKJE2mnpQZOS+bvnD0eKxQLQSQCyGqCL1XAxaIMzn4cQvOiDXkJ/wiP+wAUpzAE0svKs8gpL6+2SCQtLwiE3MP97Y75c3sfAoDayhrUVhbOHEdOZcse1y4w7R+j/tBSjJsaoDs/vy3V5EKN0gDqFe1Luzwwt+9BGMninqo7i5GbdTDVtubUawjwV/gWs3WF0tcyVxSlQASAl156Cffeey9+8IMfwOPx4Pzzz8dzzz2HL3zhC4ltotEootEoGGbGY1BSUoJdu3bhnnvuwTe+8Q1MTExg9erVeOONN7BhQ3qOUaGTKrjyLRj5mtznyltIVea/XLoaSPEefth9BL/etRnf2HQXLp4vLx+lvNShijcxW5EIQBVvYjHA2GxgqoU9nFLJtTBkSV1RZZFbXl4LT+JcuH4IhEIkV17Fgz1H8Ks9m/GtDXfhkuZ8FqBJRqo4dPtdmOhoR9XeMZS6lyLYvBE1V7clargUMtwG94xVWYTdwJt7UNK3D2M1HgRW6RBNmqvp8uI1FGr/JGbr5poXsWgFYkVFBR5++GE8/PDDgtts27YN27ZtS3u+trYW27dv13B0+YNPjIV4RBuFGFAxXSjGIT+EgU8Iio1BSzyv7kZJ//toXzIEo74VZRKq5xU6Yt5DhmHw5Ltb0e3pxZPvbsVFX1wte0VLLW8ie3MdU9gfSE1v4mwmX8IQmFlRFSsMIadJNSsSVRkb8SISCHlHS6HIMAwef38rury9ePz9rVjTJN/eqY3cfniTwz1YdcKOkGkRhq+4ATWt2hQRU4skUcjtX+jh2ZgHb/tpDHTFK6uXuztREurHwPoRmJbl3kvIhWtHgfRrVIqtm0texKIViATp8Am2GBMFQvEKohERsSfnmLmGdnlhbt8Nfdk+uD9XBmOtujljWqN0VbS96zBODcVXuE4NudDedRiXLFC2olVs3sSBvlJ4PcL5JXKa0qtBlT0EkymKUEg4ed9UEoXNrqw8t9rCUMk1F46GMTQmXhhiaGwE4WhYVu6PmqGmoRHiRSTMbiZDPjDVUnsV5IdUoRhvfRcfs1J7x/XoyIlW0IpsmqVTLRfL2r5/xATviIi9s6iX5ysoCrnns0YzVicuMYSxrGo3Gip8oMorMVw7gJH5IVjbNuXUSzgWpPFh/zE8cvD3+F+X/B0ubliR0YZKtnWxMEz63Oa55gMiEAkFIfbkwopDQ+OHoBt1MK9YI1hpshARWhXNFF7KMAye2rs9qbrWU3u3Y02L8hUtqd7ETAY+2/DBTN7Egb5S3LDxSoSmxMXYa++8p7lIZMfoqAR2vvpf8DLCXnibPYT6efLGo4XHUOlKvBaFIbQqWkMgEPIP+5seHY/fx0J+WtG9JzUfTE7Oc7bw2Tsl4tDtd0F3uhPAPAz0RqBrkrbfwLAeN3x7ZcZWMa9t6VcsEqWIQi71zihe29qLs291wkz3YqLiI4yfR4GyVQEAohVmWGzjGKkdxwiMiBd9q9Js4T7VI8iFYRhs+ehFdPv7seWjF3HZ+eszHk/I1oXHfCgz2QAA9jLrnBCHABGIhCKmulaPiWorTOvPLwhxWFHqwJifljShP9grvCoqFl7K9R4C8bj4bL2ILFxvYqpIlCou1BCJAHiFotdjEhWHQLwpvddj0kQgcquQcsdaVw3UIaDKObQMJc20Ei+2AKBFYQg1RSLxIhIIhUd5iR0+hNA+0KHIC5hN5eRsSLV3qyxNoChK9n2qx7ULoRMuOE/q4PE3QbemEQ6J4aVev15UHALxpvTegF6yQOQKQkCaKHS/3YGKwdOJv8vhx6XlQzjb1gVr60I0rvmMpHPLQUz4cRGzkR90H1Jk73htnTO+ODDXFjOzr5pAIOQJRsSjUcgwDIMnDsR77AAzlSC5xZSE9mO9h1xYL2Km/aVQXupAeakDnhid5M3kExdCmKwOmKzxvpBSb/SpMNWOxCMUiItFthJoLmHPzYpD7riEOHDuCD73/D/iwLkjks7Bfk5jQTrx2aktDrkr8UD6NZc6IVJ6LR3sOYLbn/5HHOyR9t7Vfp+pIp5AIOQXhmGw7eOdiuwd957FInX/bEi1d+39HbK9ht17dyB0woX6d6uBsr9H2R03SxaHakL5hhIPIC4K2YcYtMuDyR07YZh6Af3r9yce7vUncPaqSZhvuBGNaz4j296xcO1e6gNAki0Uegihlb1Lnc8ofe/FAhGIhKLDvasDpn1PYyD8ETrrR/M9HNkcHT2KT9yuRAll7qqoGKz3kFt6md2f9SKqBevF9MTojDdbIRIFbBSKRJZMgkwtuGKQfXAFoZQxMAyDRw9uRaevF48eFDc8fMZQK9gJT+o191bXbnhiNN7q2p00IWKfT10oECMbkZntNQKo10KEQCCox9HRozgxIt/epd6zWKTurxQ+e7f1o52yBWlLsBar+q9CeM1nUXN1mxZD5YXyDQH+4fgf0/9KFYUs7rc7QO17HL1tf4GnLYiyJasx7/o7E4/56+5AjbVVkr0TEoFKxZ8UhOwde83IWfBmSZ3PyLH1xQoRiISigXZ5MbljJ4Lu50EvPwnf51vQsu6OgggvlQrDMHh24FnZq6Ks95ACf94FBUo1LyILKxJZ8SDXwAPqiUQg3ipCDfiEIJ+HUIng2H/uME6444aHLYnNJRfewlTo6Age2/8U7zX37MGdKDNV4dmDySv8zx7cCXOJPc2jLCYWU43uwV5pE7hcV2UlEAi5IRt7x1aT5IOtnKzFpJxPXPDdy6WQiygnapRO8hIyjmrAPn1PtTski0IAGDp+CoEd24DJ7fBe4wO1ZiXmXX8nHAv5i+vw2Tup3kCtyOR5jsViiha8geToqHfO7k567x/0abNgkU+IQCQUBWxRmoqKAVAXh1B5+TrNEp+1LJt/sPcwTk+eFlwVPdzTwbtfOBrG8Kh4dS33aLySpJqYS+x4+sALWYX5qBFyKofwuC9dAI7G63OHRj1pIjAbQcjlg97D+M5/35+Y1LCNdRmGyam3EECSoDvc0wHXcKeg5/nZA/+Z5Jnm80izQpE9diq8XuYDv8+LF5GEmRJmK1RgLN9DkMUHfeL2TmiRUU7lZDWZ8o3g8b38i2nsvVwuBpO2fagZq022hxCIh5Cyj6HjpzDw5h4Y219E79qP4V1XgZLLN4i2pOCzd48eeApGiz1nYpCPTJ7n3x/6T8UL3ixGix1bjryQZO8ePyTP3hUDpEgNoWiortWjosmKHmc1HK0XanIOk0W7CSabeyjWY+fpAy/gyvOuSisAYzKY8NsvileStJnlVZKUQnvXYbiGO9OeV1IsgFuUBFBWJlwKfE3pmQgDdIbA2O0Ca9JZnpNh8LN9jyHEmbCwK8/vnN2Ndcs2anDWZFKFW3mpI76Cf3Cn6DX3zIE/pL0uVB03IRKDyYWM+IpJfOJ24WjlUfwVLs04djV7IxIIsxG2imKxwDAMHj8kbu+EKpJqUTk5EyE/jfb+Dpyi+e3dbGmSTrs8MLfvgRF+VNXMFH3r07swcrMOptrMvQpHJ0fw0/d+nWbvTtGdeW1DIqWP4fZ2fnsnpzrugZ7DSddJjInhxIgLRy1HMU+CvSsWiEAkFB0RhznfQxAlJFDJVMqq6MiYR7CfnNNSA6dF+Wrkh91H8Otdm/GNTXfh4vkXZHyNG9aqRoN0YCacMBdCMZfs7z2EHn9f2vM6SofffbwTVyxNF/1qwCcKuUjxPE9FptKel1Idl612m1qKnkVH6fDswLO4lrkE0ESWC5OpnyaBQNCWcCyMwXHl/VOzrZwsVpWZ+9pqa3N8PAyDrR+JL6YpaZIeCbkB5L44DR/utzug73kFYzUeBFbpMFg5M5eiauthdjYL9irkRngc7PsY50YH07bJZRsSPqTMsYIC9k7qgncme3dDHuydVhCBSCh42PDS/rJ98FaXwVyzJt9DEqSi1CEYKmcymPDU5x5G54ERVK0yQqdPv4lUlVkRjozCZFC/iuWT725Ft6cXT767FRd9caZNhdBrUsSFkgbpgDKhaLOHYCqJZuyDqLQpvVJGJ0fws72/4X1Nq7LsXGEo1hZFzPPMMAz+/fWfo9vTy7svm9fK12OT2xLl096uJO8hS4yJ4fTkaRzsPYzLBHJYksZqjbeJUaPlhZZh4gQCITMmvQnb/8fD+PTICJxLjaBS7N1kyAdnzXzVo14A8bZM3Ncee+9J/PaGH6OyrBqhaAiDKjZJ7yodgkU/APNHUbiHQrIL1UhpSm8yxlBlFW5xMfLuCVDTL5cMHoI+1I+Ri3pBrVmZ0UvIktp6iWEY/PrQDt5tc9WGRAgxzzPDMLj/Lz9Hl1fY3kkRt6nRMiysvdvdtQebWrWPGMoFRCASChba5UWs5xxKe9/B0PKTMC5ZAPP5hdHzUCm1lTXQma2oqTFBZ+C/CWWqGinmCRSC2z8x1TMk9JqYuJic8gEAbGZLVgZejlCsnxfES3vfg88jfD4lTemVIGU1lUWJp5UPqaIwFSHPcygSQiAoXAWYm9fK9x2XlzowNjmSMaTniQO/x9oFF+VlRZlAIOSPuooaRM1WNFan27uxIA1TpYSewSKeQCHEer5yXztFd6JjpBuXN1XDpDfh6VsegVckrFVqk/QaayuwrhU9tbsQaD+A6kP9mNxxGhNrNkhudcE2pff6hRdEq6zRtB6ItMuD0iPvAjcuQlnpH6GLRQAAzIpyjMwPwdx2o6CXkItQT94Pug+h15ceLcOilr1TipDnORQJITAlbu8yLXhLCWH9XcdOXDKvLfHeizlCighEQsES6zmHuvH30L3uHCqXrtMs71ALhMJMs0XMEyi2D9s/McbEkvLLAAi+RlFUxrBW1ovEzUdTAp9QBNJvrvXzgjkRgHykeobZ1dSnPt4pul82ntZMIaTZoEZeayQWwcDosOiq+/C4svdOIBBm8PaOIlTmQ+jTE8Ca4l0klYOYJ1BsH24IIDfsEUDaa9yw0bqKGtRVqFdUprl1E9zOZnir9iD60T6UtPvh7r0IuqbGpO2ERGO9M1kA0i5P8gZ+gObcvmO9fdD3vIK+iwehwyIErloKpqYy8fr8DMJQSBSyMAyD3+7fLnqMbOydlqiR1yolhHV4fARURXzhvNhTaYhAJBQ0ldYIKA2L0vBhsjgQGqEVV7UUCzOVgl3ngCdI84oBMU+gENx9gPQqlUKvZToukBxqyI49G7hGKZNYzAV8opDLgZ7DOOU+Lbj/dzbcjbb6pbILKij1Fsol27xWW3kd/t/nfwxMpYdBxaIMvB+FsXBtTUFNFAiEYqPm6jZ0vw2UnurCaPQjdE+Nw9y2RpInqJgR8wRK2QdIr1KZ+prWxWdqrK3A9a3oWbwLTPsBLDw7hJKuBpTorQAAejAKd+/ijCGo7rc7UDF4Go66ZI/iVHRG8AyGPoL3Gh2oRSsAL2BvuUAwUikVbpVtIeTau0zRUNnOF+SSbV6rmMjks3fFXnOBCERCwaKfpIHKwi9KoxXjKSJRzBMotKqaug8Luy/DMIKviR2XCztGtbyJLKmGaoynyqWaN1s+US9mLKWEm7x+8k18duWNkkNtciUM1aSm0oHT/p60MLBYhIHbHEJNBRGHBEK21FzdBtrViAXtFkyEzoKmd2FyWY/kXLJiQ8wTKGbvhAqIsG2Z+F5TUnxGLqw38WxHO/SjnHZW5wGWj/ZhcscVvCGobNVRQ9UR9K8Pop/n2NHpYjNU7SKUOZvhKF+MvkPScvGlCENAmr37r5NvYOOKy0BRFDwx/kVuLh6Ozc21WFRKbWUNur3nZNm7YhWKRCASCg7a5UX53pehtx3Dhw1hGFGcq6TZhJnadY601TcxT6CQty91n9R9+ZDrRWThtkBg34OapH6WqR5GMZgoAFRgfMoDKiL9HGLI6dUl5kErRlHIMh6kFYWBEQgE+Tha7Qg1Xo/A4WGsGfsQHyE/4fa5QMwTKORFFCsgwvc8+1quWliwuYlc3H4XvJXt8Pb8BdX7TmKg64qk10v69qH3ol4YWheizHG+YNN6LrFI5n58UoUhixR7R4/5YDJKr0vAtXeprZMKlWzsXapQLHSRSAQioaBw7+qA+cwrGLygF1hUDcuK9XkrSkPlMcyUhfUiZvIE8nn7MrWpEEOsgmUmtBaKLHLEXNxghmC02CWH3GQ8f5Y5DcUsDLm4znXLDgMjEAjKMJlroZ/8BLDkeyTKyWQbM3kC+byImTxcYihtYaEGiYI2rl0YaR5CQ/e+pNf7LwmBql2JRpU8xZnyDIVItXf+mC/xWlmJDUD2vZjVjEDSCiVhz6nkqid0thCBSCgI2FYWprJ9cF8wDury5Zifx9AZk8WBUCA7gVdRGi/bn60XcTxI4/hAl6gnkM/bl6lNhRiZKlhKIVUoAsUTRiIVJTkNs0UYApkLQsghxBNCTCAQZieZct3EPIF8k/JMHi4x5Law0IJ4CKoLvpRURDOgSq6pUmHIhbV3nhiNGlSpar/YegaFjJr2jutNLFSIQCTkHfeujkQrC0NVJczrry+YVhbZeBFZsg01paMjePK9p0Rj//m8fZmqVHrGvaAAVJVX8b6e7WogS2qOIjD7hKIUZpMwBOLf5+GeDsEwsEsa5RvNQl1JJRAIuUFKrhufFzFTRAc97gVFAXbzjL0Lj/kAAGUmm+QWFlqiRdEhNYQhF9aOzQYbJhexsGcl9g5Qr/+vFhCBSMgbrNfQUHUE9HI3jEsWoHHtTfkeVgLVvIgSVsXEej1VMha4Rz2isf9C3r5sq1SqSWq+ActsFYvsd3rn+v+JC5pWAJhdRpVhGDx3YKdgGNiaz0rrWcbHgXNH8PN9m/HdK+7CpfOUH4dAmK0w46MAjPkehiI+7D+GX//Xs7z2LpvcbtkRHc4ZD04hTtCzZXzKA0qvjjAExMWhkv7MXIrNe8iihr37sP8YHjn4e/zLun8uKHtHBCIhr1TX6jFRV4HY6iUF2+dQay9ipqRnk8GEbV/4NbrHuwHMxPtzUcvblyuExCLALxiVNEvOJwzD4NH3n0SXtxfb9z+PKxavT/pOszWmUtDyHONBOin3kAu7qnqw9zAWQrx0O0uIs4LKMAwePbgVnb5ePHpwKy5pJEVvCAQuBr20ZuuFCMMweOLw86L2Ltt+dXJg7TK3Sna+xaLSBTJ2MZotyqZmzj0LnzjM1J85ky0aV6FegdZzhExhz3LsHReGYbDloxfR7e8vOHuX3ryKQMgRurFhMJN+wGZGlNPMtZAwWbI3FKyxEYo150t6TqW2sgaX1F2Mxc4FaLRW4bzaxUkPZxa9ffJNeakj8QDiq5TsA0gX0AwjP8ck17zVtRuu4U4AgGu4M9FzEkg3plq8Hy3PwVYuZcPA+KBA4YkDv5d03tTfxf5zh3HCHf89sNUF5UCNFPZKNIEwl9l/7jBO0fF7o5i9W+JcLPjQwt6ZrI4ZsRikE49ck7pAlukemjpWk9UBo0X9BQSxnoZ8/ZlZMtkiNcSh1nMENe1dKtz5nxJ7pyVEIBLygvfIKZQd3Y+gzovOsqF8Dycj2U46WZEYDniSnueGLQDJ/Zr4YG+ihR6OoRQ+sfhW1+6MArpQ8MRo0NERPHtwZ9J3yvacBMSNqVpodQ72uqtkLBnDwIbHRxBhRHqKcOB6Dze3J/8eNrcL/x6EUGNhh0AodPS+sXwPQRZ8v28xe5cPWKHIJxZzIRgzLZDxjSd1zLmEW2EdkGfv1BCHgLRF9myQEvYsx94l9uOZ/ymxd1pBQkwJOcfz6m6U9L+PyZZxDF6sg3nFmoIpSsOHGrmIQHwSPDqefBwlvZ641U1nU05bKmx7D1ZssVXDHtv/FFrnzQdFUQWTv8hdXT0x2C3Yr3JNy4VJ7UrE2pQoJbUlilrnSDLmOmQMA7OarKA+Fs+REvMeAvJ7lBHvIWGuUGqoBDCV72HIgu/3XcitcfgE15iEqpNKQ1QZhsGjB55Kunc/euAptFXPT9y78yECAeF5h1h/ZiF7t6xu5v1ka8PFKouqZVOlhD1LsXep8M3/ctWTUwpEIBJyBus1NBq8GNg4BOOyVrTksZWFHEwWB0Iq5CKWl9jhQwjhgAemKrvsXk8s7E2Vzd8rBqGoJCeOz/i4hjtxYrAby+tb0sJeci0YUyuTxsXZ/YL9KhmGETSmqW1KlCJmsJWcQ2iVN1NBiFiEgRshwddTi0NwvQupn52cHmXEe0ggFB5iv2+1J/Rakkmgsf3t2MIj/+uSv8PFDSskHftg38eJ8Ftg+t5Nd+KIv7sgBHSqSMzUn1nI3rnOqfd+lCyyKyFbe5cKwzB4fO9TWds7LSEhpgRNoF1euHd1wL2rA55Xd8Pz6m6Y2l/EZKML9A0eWDZsymufQ6Wo6aHYdzIeOsm9OQDJN7hMFEvIqZKcuNTQFRbW+JhL7EkhqeWljqT8RW4eo9pwj80NiWXFGd93emrIhV+/vVnw/agRVpLpM5N7DrVCgFLhqxzIehf4PjspuRnEe0iYS/S7bKh6ZxTn3tgCtz+9eEahIfb7LvTUATmwOYBs4ZEtH70Io8WeFAbK9zBa7Pjdxzt5792FFIbLnXO89+luUXv38K5HNX0/qSGaWpxDbUJ+GiE/jX0nd+MU3anY3uUCIhAJquPe1QHTvqdhM/wBNsMfUFn5Jioa3sPAxiEEbqhHy7o7CjqkVAjWM6HGRNRsqsKWIy+IJz1LvMEVg0hUkhOXSWzxHSNVMAJxMeeNxXM/vTGPYuGYKjq55wBmxJnYd3rO1y/r/chFyWfGx3iQzqk4ZL0LYp+dWG4G+5sk3kPCXKDm6jZMrNmAUu8FqHrLBv/uXehx7cr3sASR8vsu1Am9EpTkxLH7FIOAtuscqKLseO7ATtHvtN8/pOn7KeTPjBWCqQ8gHkm29SPxz64QchFJiClBMbTLm/acuX03TGX74F+nQ8BZDeP55ydeswBFKQy5sPmI2ba+CMcicE94FfV64qOQQ04ZhsEjuzYn/paSE8cVW0LNkqXk1bGfAxNh4EcI5aV2UAYK40FlIlHscw1HwxgeFU9kF0Lq+xFDrc9MK2EICPccC8fCGJTS+ywWFmxmTcQhYS7haLUDrTfD96YdK8/0YLJ8DN3YheYCjMwJxyLSft8S7V0hwzAMfrUn2d5lCqHlVskUuncXWhiulMItQqjxfrT+zISqzguOZ7q1SDgQ7z0JCOeihqKhrO1dLiACkaAI964OlPa+A5t9+hIKjgIAzrR2gbp8OcpqmoteDAqhhkg06Y14+pZH4J30YzLkAwAYK2xJ2yjp9VSIBWwOdh7COV9f4m8pOXFSxJZ7VPmEQovPxmQw4bdf5E9kD0fD+NeXfoDRIH/VwWzfD3uObD4zrgc6F15DLia9KfF7EMJeZuU1ltQITcQhYc5iamkDunpQZ2xGN3ryPRxeuPYuFdb+OWvmF704BIAPug+hJ8XeZcqJkyK2Ck1AixVuCUfD+M4rP0BAxN5l+360+MxCWfTCjEUY+BBCeUnm3pPZ2LtcQgQiQRa0y4vyvS/DHO6H+4JeBJYsAABEHOUAAArLizK3UC5qiMS6ihrUVcSTnrnlqrOlkLyJDMPg129vTntezIvIFrP5p43/iGZ7k+CxbWb1miWrhdNSA6eFP5H9qb97TLQKWrbvhxWoe09/gEfefizt9f919d1Y17o27RxaCkMAiRXVTAaX+3uQCsk7JBDixEaF7y2FgNjveyxIAzHel4qKVO8hi5gXkW3w/r/W/SNaROydkgVjrREr3PL728XtXbbvhxWo7579AL/ck27vvr3hbmxYmG7v+MhGGCpFib3LNUQgqkTw2WdhMJamPc+UWPMwmszEDBTwmRZMvvAKdBGBvJ6p9B93abgfUzUeeDeUwbzi+lnrJZSCWuGmwMxNiS2hrZZQzLc3Me497E97XsiLyC1m88KHO/H4Fx8umJCabBETj2pRU1mN/z7+Jm9ltP8+/iZuvuDGxHNaC0MuWhhdkndIIMQp0VsBFFdPRC4VpQ6MBeM5Wvlq46AGH3QfQq+f397xeRG5Dd6fP7oTT902e+xdpqqfauCsqMYbJ/nt3Rsn38TnVt4osnd+hGExQQSiSpRdPY6yMp4mmb7h3A9GAlFKD6AFZSvOQB8PnubHZk76s7NsElRtS9G0p9AaNUUioL6h5IpEILfeRCHvIQtfThxfMRu12j/MBVJbXLCwgvy9T3fjovkrE89rJQxDfjqRk1FeYlf12EQYEgjplJVYoeeENhYbWiyS5hIh7yELX04cXzGbQmhnUSyktrhgkRLWmyntgUAEomoMXzgP4xVl+R6GZJgYBXQD7qvPA6WTXinJjNmbW6iUYhCJAHLuTQxFQhjwDwm+npoTp1WDdzVQ0sMx10gpUvP0gRdwTctVmn6e3EptPhl9oaRAxCGBkA49GEVwvBfMBW7QZz+EY+HF+R6SYrj2DygeoRiKhNAfELd33Jy4XDR4VwIb8vqtDXfhkubCtHWA8iI1RBhKhwhElaixLkJFZXm+hyGZWJTBKEKosS6CTj87QhryiRYiEdAu5BTQ3pv40bljiIp4p//3prtxxeKZHAG1G7yrRWoPx4u+uDrvgpUPKUVq6DGfZoUOUg1vTCB0XSlEHBII6Tha7aCxAWO9fXC+9Q4GvO9hPOpHmbMZNdbiXMxN2L8iEopH+sXt3Xc23I31nJy4XDV4lwM35PXx97diTVNh2jpAWZEaIg7lQQQigaASaotEIPfeRLU8ZVK8WX8+9iaaqhrx3RfuxTeu/nqS95ClELyIhR72ys0l/NWt98M/GQAAWHW2pO2OD36C547sxNH+46quDGudx8EtREPEIYGQTrzlhR20qxFN7Xsw1rMP3nVDcLehaEUioL1QVMtbJsWb9frJN9Fsa8T//tO9+Nb6ryd5D1ny7UUsppBXsSqqQLq9I+JQPkQgEggqopVIBLLzJqYaQj5vopqeMinerOGAG0+8Fz/f/9v1GM550/Nn8u1FVBL2yhVsuYIV/narA+Cpi8UwDH729iM45+9XdWVYa6NLvIYEgnRYb2Jd1xhsw4MQDngsLuQKRSnCT01vmRRv1uCoG49Nn+8Xex5DL0++aD69iIUa8iqGUCGcVHu3ytIEiqKIOJQJEYgEgspwRSKAvHsThQxhqjfx+ECXap4ysZ6ALF10N/799V8AAK84ZFGjibxSMoW9ColBrauCykXtleFcCUOAiEMCQS4leiuCGISO9vMuGBUr3PvNGCdygWsPpQo/Ne+JmbxZANDp6cb9f4nbOz5xyKJGE3klFGLIq1JSv9v2/g5cvWhjnkdVfOjyPQCljI2N4Zvf/CYaGhpQWlqK1atX4w9/+IOkfbdt2waKongfg4ODGo+cMBcwWRyJia2avdoqSh2oKHUg5KeTQvvE4DOEXOy6ae/he09BR8VvCaynjGGU55E5LTU4r3Yx76PVuQgvHno5cT4xuMVscgnXe8hFR+nw5HtPYWxyBED880t9FBLclWFgJoxJyXfLve5y4TUk4pBAkA89GMXY6VFMnjoK+uyH+R6OJrC2EJi5L4X8dEZ7B6h7T2SprazBEudi3sd5NYvwn0el2zs2dy5XpH4eLGp8LrmG77vd+tHOonoPhULRehBvueUWtLe348EHH8R5552HHTt24Pbbb0csFsMdd9wh6Rhbt27F+eefn/Scw0EmJAT10CLkFJBe6U1q2IjrXDdcw52Jv7UO7RRqx/C/N92N5Q1L057Ptom8EsRaRriGO+E6110UK6tqrAznol8U8RoSCNnDhpma2wHnW70Y8L6HIN2HxjWfyffQNIF7P2IYBo/vvS+jvcu1t0yoHcN3NtyNtvp0e5dtE3m5ZNMuotDg+25PuF3Yf+4wLm8qjvdQKBSlQHz99dfx5ptvJkQhAGzcuBHd3d347ne/i9tuuw16vT7jcVasWIGLLy7ectCE4oArEqkYAFSoctzUvAw+kSjFEKaKSBatCsSk5vVxz/fnY2/ib1ffmPecBylFdgo9PwMQ/26ljJ8IQwKh+IgXrbkZ7rc7UH8ICPb04NzIFpRcvqGoi9ZkYv+5wzhFJy90nhx2Yd/J3bikcSVMVkfW90S5iJ3v9ZNv4rMr82vvlLaLKETEPuvN7dtx2bzCfw+FRFGGmO7cuRMVFRW49dZbk57/8pe/jP7+fhw4cCBPIyMQ+NEyXC41zIZFatgIKyK5N1Qgubm6mrCeOaHztXelhwTlGilFdnIdBqQEse9WKPwKSA8lJeGkBELxUXN1G0JXfBEV/itQ9ZYNE6+9ih7XrnwPSxMYhsHmdn57x4YYhvw09p3creieqBSl9+BcIaddRKEj9lmzXkSCdIrSg3js2DEsXboUBkPy8FeuXJl4/fLLL894nBtvvBFutxtWqxVXXXUVfvzjH2PFihWajJlAAABTpR1ACJTHA0qnbgEbILnK22FfV8awkUubL8y4evj0gRdwYXMbKIrKuneiFM9cvttaAEA4MppoGZHaLoIl12FAYvBV7ZO7MpwLb2Hi3MRrSCDkBNabOPb2YlQfegXRnjPoXjcEc9uaWeVN3H/uME64+e3dCbcLHSPduGzehdj60c6cecuKwTsnpcBOIdk6gN/eTflG8Pjep0Q/a+JFlEdRCkSaprFw4cK05+12e+J1Merq6nDvvfdi7dq1sFgs6OjowIMPPoi1a9di3759WLVqleC+U1NTmJqaSvwdCMR7jsWi8ebzxQI71mIa82yA/bwN5ipEJrzAMA1m+rpVA7MhfqyxIJ3xZvnb97djVe0KDEporl4esWCcGsXYGNsWQ9mYQ5HMnjn36AhCU2GYDEZF5+A97nTTdkZC8/bxoAcA0FreCpSLb6t2M3glMAyDzfviVfs279uKi+pXxQVfNJzxux0KDGNsZAgmffyzLi+Jf69qvC++ewzl8Uyfm10syf89KCbc23pOI2jrmBhiIg3BCxF2vMU2bjVxbFwGz5l6GA+/Dscrgxj0vIvJ8/swb+EGzc6Zq3kGwzDYfFBciG0+uB0X1KzAoARv2VQonLgnZoOke7CK52Nh799S7+M1ZdWoKauWdMx8w2fvIqNehKIRuCe8Of+sWYplTh2LSN827wJx9+7d2LhRWvnZI0eOYPXq1QAgugKQaXXguuuuw3XXXZf4e/369bjhhhvQ1taGH/zgB3j55ZcF9/3pT3+K+++/P+35ruMhmM15/zhl03W88MMGZiPxz306F7EzpPrxw7FSDPnFb5aDXjfoD8J4aMHPEYgEBI9lNVjhP8CAmzvph/IxP9SS+XyTexlMZnEOIQJ7pVzv8ffp1uD8WnAkcASfTK+cf+J24c3/PogLLPFVVSnf7cTJKkxM/+3T4D0PHOV+5tz828L4fCcmCmMchYagrQt+ArPOnIcRZU9X8FS+h5BfGgA0xO8NJQDgBfoOaX/9J98D1CccC6PfJy7E+n1uDBwJ42eS7Z06n4t0+6r+90AfnH3zOzF7J+W7dR/V5rNm0fpazxY59i7vimbJkiV48sknJW3b3NwMIF5plM9L6JlenbYr8Mi0tLRg3bp1+OCDD0S3+973vodvf/vbib8DgQCamprQstyEisrCccFnIhZl0HU8jJblRuj0xN2eK4Q+99DotGdFNW+iCU8vfRi+oB+ToZnQEUPFTFOsqjIbnBXlaEA5gEbZZ/DGPIn/y/EoWtGo6HzZwEQYBPaGYVlnBGVIv95Zr2GVTj1vbi5gGAbP/3FHUtW+50d34Nq/vgQURaFm+rMOBzxJ+7GeQi2JRRkMHA2jYf4YdLoZj2GhMTYqY0l1DiFo60rPR0WZOoW2ckWMiaIreAotpUugozIXsJsLeM54YTj8OqKhQQxeQcN0/iLVvYnsPaB+tdbzjBl7J0RVmQ215eWARHs3PjVzzzRalN+7avJg72IRBvTBMByXGKHjsXfFCp+9eyGwAzdcFbd3jXn4rFlyd63LZyRwJvH/yNik5P3yLhDr6+tx5513ytqnra0Nzz33HCKRSFIeYkdHBwAoziNkGAY6nXjdnpKSEpSUlKQ9r9Oj4C4KKej0VFGOu9hJ/dxLbfFKp/BNC0UVchMbbE40wJn4eyzI31hYKQ7MHMMTmTl2tnmKWkIZqCSByDa6pwyF19xeCh90H06spgLx8L9P3C7sd+3BJY0rE89Teu3zClOJh5NWoNRqL+h7jI7oBV4EbR2lK1qRpaP0RTt2taleXA0s/jsMvLkHTfs7MdF5Fr0XPadJbqJOT2kuVFLtXbZUGmbul2Pj6trOXKEzaP+55xI+e3dixIUDg0cKpoVFLq51OfS4doEZGkJLsBYAMD7kybDHDEVZxfTmm2/G2NgYXnzxxaTnt2/fjoaGBlx66aWyj9nZ2Yl9+/Zh7dq1ag2TQJAFt5IjNUInFfJQA77GwmrBbRA/HqQTwitffNh9BH//u3/Eh91HeF/njrEQm9tLQaxK7ZYjL6C8xJ74znMpDtlrt1A9hgQCYYb6azdgctVlMA8uRPVOE/y7d83aSqdK4bOdatrPbDnYcwS3P/2PONjDb+9mA2wRGj57t7l9pio7IY7b70L33h2ofH0AzrdWIbirLP44LJ5ryiXvHkQlXH/99bj22mtx1113IRAIYPHixXjuuefw5z//Gc8880xSD8SvfvWr2L59O86cOYP58+cDAK655hqsX78eK1euTBSpeeihh0BRFB544IF8vS0CAcBMVUe2d6JalU5Z+CqeqrUqyhVanmB+vIoMw+DJd7ei29OLJ9/diou+uDrx2njQA2r6rldsojB1QnKw72PBKrWn6M6cNwbmtqwACj9Zn0AgxKldvgRYviTuTfygExNdZ9F90Y5ZV+k0W7gLbVrYTyUwDIPH348XbXn8/a1Y07Q6b2NRG67Na+/vSOpxycJtYVEoXsR80+PahdAJF+rfrYbB1IDJVcviv3EAuvFRYMdDko5TlAIRAF566SXce++9+MEPfgCPx4Pzzz8fzz33HL7whS8kbReNRhGNRpNWF9ra2vD888/jF7/4BSYnJ+F0OnH11Vfjvvvuw3nnnZfrt0Ig8JIQitMT72ISioCwWAS0FYxsn0UAiT6OFzauBFsgpdCFodjKNPudMQyTsVx7rkp6pwpDAoFQnNRfuwFDx+vA9C9A9c59GKB3YXJZD5pbN+V7aAWH1vZTKmzvPwCJ9lWXNF6Y83GoRar9qyh1FJS9K2Tcfhem3t8D20cxlPhXYrxxAezXboBF4fGKViBWVFTg4YcfxsMPPyy63bZt27Bt27ak5371q19pODICQV2KXSgCKWIxlh6CqpZgZPsscpPYnz7wAq7+7AaMIFxwRWiExGCmkNBwLCypXHs4FoZJr37xLNLHkECYncx4E0G8iRJI8ir6c5uryE0zYO3dE/u3Y81nL9D83GrBZwNT7V++7V0h4vYnRw+FPj2BiOssqg81ocy0FBNrNqC+Nbv5TtEKRAJhrmGyOBJhp4B2QhHQ1tClevD4BGMqR3qP4cn3nsU3Nt2Fi+dfILj9oe6PE95DIB5+4hruxMHew1iItuwHPw1fo95MSDGEUjHpTXj6lkfgFWlubC+zqm4siTAkEOYG9dduAO1qA9PVQbyJElFrsVWqfeF6D4G4vTs57CoIe5eKlMgYIfJl7woRt9+FiY52AMCC4ZlGzWOnR1Hq/SuMNS9G2dVtKFPhXEQgEghFRGp+IqC+UARyGz4jFvJ5sOcIfrn7McTAoNfXh9/ueRLn3/pjOPTpidYMw+C5AzsTq6ksOkqHJw78Hj9tkBZ3nwm+nA++sBY1BSEfdRU1qKuoUe14YhBhSCDMPRytdqB1Q8KbOHXyGLrXDRFvYgaULram2jsx+5LqPWTJl71jyUYIipFLe1eo9LW/jIjrLKp6ylHiX4EIZtqWReoWY2JNI2qy9BpyIQKRQChC8ikUgdyFzzz+/lZ0+84lnnMNd8J1rhuO+ekCMXU1lYVt/XC08ij+CvIrHIudh835WDs/nhzPlz9RrBBRSCAQgBlvYqQrnps40vMqetasRJmzmQjFDEi1oXz2LtW+cMmnvbvQ1iK6TzHbvUKBrSSs940BAJgRN6LeUdQfWopg80ZYbkj2EKvhMUyFCEQCoYjhTty1ylEE8lO9jc8AsjkWlzYnJ6Ozq6liSezPDjyLa5lLAChPYufL+Xh871O4wDo/MZ5iNo6prVWIMCQQCMCMN9H9th11HYcw1XMMgVWdcF8OIhIlIGRDWQ77ugrK3oX8dFy0TreW4Nq7Zz/7mzldDEZLuIVmqiJxj6lJVwFgPsZHGzBxxQZVvYRiEIFIIMwSUovZAMXrVRQygGyOReqqajgaxlCGJPaR8AjCsQj0UJ6nwJfzcYruRMdId9GW2CaikEAgSKXm6jbQrkag9yJUvfUKRryvoq91IUznLUvejohGQVIXEUcn4z3+8mHvxEJCO0a6k1pL5KuF0mxFrNBMtPkm+Jsak153tNo18RQKQQQigTDLyHX4KaC+WBQKnwH4V1VNBhN+d9sj8AkksceiDGLHzTDpjYrHxG3Um5rzUUwltlMFIUBEIYFAkE7cm2gH7WpEU/sejPXsg/mIDwDAlJeju2IEE452lC27BEBTXsdaDKQKMS58USoARO1dyO+Drrse1PgoQkHxcwtFvDAMg83t9xe9vStE2EIzYTqAlqEZyRfy6ROFZmquVq/IkFKIQCQQZim5EoqAumIxU/iM0KpqbWUNaiv5k9hjEQZuU0jWOFJXVoWMeKE36iWCkEAgaIGj1Q4aG4DexZiYjD8XGfOg6VgnpqzHMOzZg5LaL+Z3kAVOXIiJ2zveKJUgjaqSKv5jOqrgG6hAeYkJOoMyEbf/3GGccPPnOBayvSt02Cb2zpM6lPhXIlCzAAb9dMhoCVQvNJMNRCASCLMcvjxFID9iEcgsGMW8hywUKN7cjGwRKjLDrqYWQ6NeIggJBEKuYL2JyWzAwJt7ULfvALy3AL0fvABz2wUk7JQHISHGhc++iOW6xyIMfJC3IMolk2gtJHtXLLC5hRNdNJpOL0K0+SZYbmhT3MQ+FxCBSCDMIXLpVQTSjVgmwch6DzORaIwbDcNkUJ5TKLUVRaE26uUTgwARhAQCIb/UX7sBI6eXAxiA8S+jmOiJ5yo2rvlMvodWMLBCLON2ObYvhWrvihVue4oG/405LTSTDUQgEghzkFx7FVkyCcZQNIzBwLDoMSyllfiPm+6Hs6JasjgMBzwAKhAOeEDphccjRKE06iXeQQKBUCzYF1XBNzmAcucXUXboHQR7enBuZAvKlqwGAMQcM33c5qJ3kRViYlhLKvHw9ffDWV6dMzFWKPauGBkJnAGljwtrHe3H5KmjGOuisfD0KgSbN6LsBnWa2OcCIhAJhDlOrr2KXPgE2jOf/TX6/d2C+9hKLXCaHUBMvAIbH+UldsU5Gblu1EvEIIFAmA1Ur18Gb0MjKtr34P9v7/6Doqr/PY6/DhCrSGoCIvgVuMU3TAUd/TrqtUktc/xx7w1raPRO3UxtRqeaHJthxp9A5dQfNY5/VJM/Kmb8ldo43W8jjaNeKWiy0izQ8ieCP74JLAa7i66w7P0D2FgBA2H3sLvPx0x/cM5ZeHc6w7sXn3Pe51bRj4r91yU1NDo8+6+Ouk8V8RVK+vsTJlbpf10NYvEmvCCeF9N3T3XdBUkjNOirUoXb//zv2f+Pfkq8ETirhm0REAFIMm9V8U6+aEw9fSbD17hVFEAwa35Wcb76HUlVrc17X+z+f6p6wi+6+odd/WKGK+bBf5hTpAkIYoGtqvacblZW6Pavl2SJf072qkkyXPIaPNP/vwNn1bAtAiKAdjpaVZT8HxaDFauDAEJRR+P7W1+XcevKT6pJP6mr1quefa7B0eo/NCkkb0FF39b6uooHiuy6z5Gh6v+Qhs14VGFtn2MJYAREAJ3yWlU04RbUYEEgBICOta4u2o+kauih/9OgmOb/wb7dZNeNiAu6kfQLA27QZ7SuGrp/+KXlpfYvqDEtQdK/zC6tVxEQAXRJX7kFNRAQCAGge+IeT5f13HB5jW25fFUJDLiBSapqvV9BEmatlfPMSQ3+uUnRtbM8zxY2uV364yYBEUCIIyx6IxACQM/F3DnI4+9DZD3X+YCb30c0qCI19AbcwPdaX2qfbI+V4fjzmrv/zN90K2mG6h8e3v56DSIERAA90llYlII3MBIIAcA/7jbg5oFD/1T1jdAccAPfaH22cODxJkVWxMo2fKqkPwfPhE0dHnATSe8FARFAr+nsmcVWgRoYCYQAYK6uDLi5cuakJMl1f5QkKSp9IregolOtYdBLxXVFlvZXlDtDDVP/SwkhEAY7QkAE4BN3BqhACIy8bgIAAkfbATeJX59XzLDmATdOV61+v/2zblR8qYqJGdyCCi+e11OcPqe0cw/JEjbQs+9GVYqcwyZoYAd/kAglBEQAftFRyLrzltS2fBUeOwuBrQiDABBYWgfc1LfdeHlcy4CbC7oytkz908Z5DbeRGHATCu4cNHOzskLu69f1QJFdltoMWcdOkSRFRLa8jzJFIXEL6V8hIAIwTWdhrKPVxp4wmiQpWkZNjRRGCASAYHO3ATf9Dl1W2OVTXruvjrpPFfEMuAlmbQfNSJLhcOgBSc4zA+Qc/p8Km5iueMJghwiIAPqc3g5wTS63pNuKvH+IwsKNXv3eAIC+qfUW1KojJdLNP7c3umoUu79Y1RN+UYWk/kOTWE0MIm1fYh9e9eegGal52EzY1OEh+2xhVxEQAQAAELQ6HnCTrmE//K+cFaWqG1umKyr0DLeRGHDTV3U4WKZFuK35JmPXDZsiS/vLEuKDZnqCgAgAAICQ0ry6uEhVR0qU+PV5RcfZPfuc9dd0xnpYN0dxC2pf0nrLaMylaP0t8h9y36xtd4wlfJCsv7tkH5Ya8oNmeoKACAAAgJDU0YCb25dKlPB1sVy/Ng+4sfz7tPafY3WxV905TOZOzm8LNfjnJoVXxco5fKquPjBMEfFxHR88gkEzPUVABAAAQMhqP+Bmmqzn0hX9Q6HCD/0qy+VvPbvcAwboWvJtBtz0ktZbRhPLIz3bDIej3XHOM4PVP/IR1U+dxi2jfkBABAAAANpo+47F+pYBN42uGskuxZ5oHnBTfv06zyreI6PaqitlRWo8d1GRpf3V5M6QI+7fJDUPkmknSer/eLr6+7nOUEVABAAAADrQ2YCbET8Uyl5RrDpboa6oUJK8hty0MuLjg3JK6t2GxbTVOjimI0NK+ini+iO6lTRDAx9P18BOj4S/ERABAACALmq7upj49XlJUoPaD0yRpPphF1U34XpQ3ZLadlhM1O8P/uXx92lQp/vqp07jecE+iIAIAAAAdFPz6mLzCmNHtz7err8uR/FvnncuXjlfJiO2ebCKa3C0b4pyh8vQFF25WCgZrh59q/A/7O22uaurvIbF9Mscqcio+Hv+Gdwy2jcREAEAAIBeFhkVr4Qn4z23pA64/5oGDWu+DfVWo61b38sYcH+XjnMZYTr1kDTmR4vC7B2vanZVv4jYdtsqTlkUETmCYTFBjoAIAAAA+EjrLanXj5ToevdyoUejvaZLx7nDJT0klVWl6D75IMAlNa+csvIX3AiIAAAAgI91NPCmtzW5Xbp487SGzXhUYUa4z38eglOY2QUAAAAAAPoGAiIAAAAAQBIBEQAAAADQIiADos1mU3Z2tmbNmqW4uDgZhqHc3NxufY/KykotWrRIsbGxioqK0pQpU3T48GHfFAwAAAAAASAgA6LVatXmzZvldDqVmZnZ7c87nU498cQTOnz4sDZt2qQvvvhC8fHxmj17tgoLC3u/YAAAAAAIAAE5xTQ5OVk3btyQYRiqrq7W1q1bu/X5bdu2qbS0VN9++62mTJkiSZoxY4bGjh2r7OxsHTt2zBdlAwAAAECfFpAriIZhyDCMe/78/v37lZaW5gmHkhQREaHnnntO33//va5evdobZQIAAABAQAnIgNhTpaWlysjIaLe9ddupU6f8XRIAAAAAmC4gbzHtKavVqiFDhrTb3rrNarV2+lmn0ymn0+n5ura2VpJkq61Xk6uXC/WhJpdb9fUNqvujQWHh974ai+7hvJuD8+5/gXLOHfZ6SZLb7Ta5kr6l017nqFOTu8mssu5Jk9uleme96ly1vDjcjzjv5uC8+1+gnHNHvV1S1/qd6QHx6NGjmjFjRpeO/emnnzRu3Lhe+bl3u0X1bvvefvtt5eXltdv+5NT/6ZW6AAD+Z7PZNGjQILPL6DM67XXPPWZCNQCA3tKVfmd6QExLS9OWLVu6dGxSUlKv/MyYmJgOVwlramokqcPVxVarVq3SypUrPV83NTWppqZGMTExPXou0t/q6uo0YsQIXb58WQMHDjS7nJDBeTcH593/AuWcu91u2Ww2JSYmml1KnxIsvU4KnGsx2HDezcF5979AOefd6XemB8SEhAQtXbrUrz8zPT1dJSUl7ba3bhszZkynn7VYLLJYLF7bBg8e3Kv1+dPAgQP79MUcrDjv5uC8+18gnHNWDtsLtl4nBca1GIw47+bgvPtfIJzzrva7kBxSM3/+fP32229er7NobGzU9u3bNWnSJP6SDAAAACAkmb6CeK8KCgrkcDhks9kkSadPn9a+ffskSXPnzlVUVJQkacmSJcrPz9eFCxeUnJwsSVq8eLHef/99ZWVl6Z133tHQoUP1wQcf6MyZMzp06JA5/0IAAAAAYLKADYjLly9XeXm55+u9e/dq7969kqSysjKlpKRIklwul1wul9fEHovFosOHDys7O1uvvvqq6uvrNW7cOBUUFGjatGl+/fcwi8ViUU5OTrtbiOBbnHdzcN79j3OOvoJr0Rycd3Nw3v0vGM+54Wa2NwAAAABAIfoMIgAAAACgPQIiAAAAAEASAREAAAAA0IKACEmSzWZTdna2Zs2apbi4OBmGodzcXLPLChp2u10rVqxQYmKi+vXrp3Hjxmn37t1mlxXUuKb978iRI1q8eLFGjhypAQMGaPjw4Xrqqad0/Phxs0sDPPjd4Fv0O//jmva/YO93BERIkqxWqzZv3iyn06nMzEyzywk6Tz/9tPLz85WTk6OCggJNnDhRCxcu1M6dO80uLWhxTfvfhx9+qEuXLum1117TgQMHtGnTJlVWVmry5Mk6cuSI2eUBkvjd4Gv0O//jmva/YO93TDGFJHleA2IYhqqrqxUXF6ecnBz+AtULDhw4oHnz5mnnzp1auHChZ/usWbN06tQpVVRUKDw83MQKgxPXtP9VVlZq6NChXtvsdrtSU1M1ZswY3jOLPoHfDb5DvzMH17T/BXu/YwURkpp/qRiGYXYZQWn//v2Kjo5WVlaW1/YXX3xR165d07Fjx0yqLLhxTfvfnc1SkqKjozVq1ChdvnzZhIqA9vjd4Dv0O3NwTftfsPc7AiLgY6WlpXrkkUcUERHhtT0jI8OzHwhWtbW1OnHihEaPHm12KQB8jH6HUBZM/Y6ACPiY1WrVkCFD2m1v3Wa1Wv1dEuA3L7/8shwOh9asWWN2KQB8jH6HUBZM/Y6AGISOHj3qud3gr/45efKk2eWGhLvd+sFtIQhW69at044dO7Rx40ZNmDDB7HIQhOh3fQ/9DqEo2PpdxF8fgkCTlpamLVu2dOnYpKQkH1eDmJiYDv9qWlNTI0kd/rUVCHR5eXl66623tGHDBr3yyitml4MgRb/rW+h3CEXB2O8IiEEoISFBS5cuNbsMtEhPT9euXbvU2Njo9VxGSUmJJGnMmDFmlQb4RF5ennJzc5Wbm6vVq1ebXQ6CGP2ub6HfIdQEa7/jFlPAx+bPny+73a7PP//ca3t+fr4SExM1adIkkyoDet+bb76p3NxcrV27Vjk5OWaXA8CP6HcIJcHc71hBhEdBQYEcDodsNpsk6fTp09q3b58kae7cuYqKijKzvIA1Z84cPfnkk1q+fLnq6uqUmpqqXbt26auvvtL27dt5J5QPcU3713vvvaf169dr9uzZmjdvnr777juv/ZMnTzapMsAbvxt8g35nHq5p/wr2fme4W9+uiZCXkpKi8vLyDveVlZUpJSXFvwUFEbvdrjVr1mjPnj2qqanRyJEjtWrVKi1YsMDs0oIa17R/TZ8+XYWFhZ3up92gr+B3g+/Q78zBNe1fwd7vCIgAAAAAAEk8gwgAAAAAaEFABAAAAABIIiACAAAAAFoQEAEAAAAAkgiIAAAAAIAWBEQAAAAAgCQCIgAAAACgBQERAAAAACCJgAgAAAAAaEFABAAAAABIIiACAAAAAFoQEAEAAAAAkgiIAAAAAIAWBEQgBDkcDsXHx8swDD344INqaGjo8Lhbt27p0UcflWEYslgsOnr0qH8LBQCgB+h3QPcREIEQNGDAAK1evVqSVFZWpk8//bTdMW63W88//7yKi4tlGIby8/M1ffp0/xYKAEAP0O+A7jPcbrfb7CIA+J/T6dTDDz+siooKJScn6+zZs4qMjPTsX7lypTZu3ChJevfdd/X666+bVSoAAPeMfgd0DyuIQIiyWCxav369JKm8vFwff/yxZ9+mTZs8zXLFihU0SwBAwKLfAd3DCiIQwlwul0aNGqWzZ89qxIgROn/+vL788ktlZWWpqalJWVlZ2r17t8LC+FsSACBw0e+AriMgAiHus88+04IFCyRJS5Ys0Y4dO3Tr1i099thjOnjwoCwWi8kVAgDQc/Q7oGsIiECIc7vdGj9+vE6ePOnZNnr0aBUVFWnw4MGdfm779u365ptvdPz4cZWUlOj27dv65JNPtGjRIp/XDABAd9HvgK5hHR0IcYZh6KWXXvJ8PXToUBUUFNy1WUrS2rVrtXnzZpWXlyshIcHHVQIA0DP0O6BrCIhAiDt37pxycnI8Xzscji7dZrN161ZdunRJVVVVWrZsmS9LBACgx+h3QNcQEIEQVllZqdmzZ6u6uloxMTGSmhvmhg0b/vKzM2fOVHJysq9LBACgx+h3QNcREIEQ5XA4NG/ePF28eFHR0dE6ePCgMjMzJUkfffSRKioqzC0QAIBeQL8DuoeACISgxsZGZWVl6ccff1RERIT27Nmj8ePHKy8vT4ZhyOl0Ki8vz+wyAQDoEfod0H0ERCAELVu2TAUFBZKkDz/8UHPmzJEkZWRk6JlnnpEk5efn6+zZs6bVCABAT9HvgO4jIAIhJjc3V9u2bZMkrVu3TkuXLm23PywsTC6XS+vWrTOjRAAAeox+B9wbAiIQQrZt2+a5leaFF17QG2+80e6Y0aNH69lnn5Uk7d271+t9UQAABAL6HXDvCIhAiDhw4IBnPPfMmTO1ZcuWTo/NyclReHi43G631qxZ468SAQDoMfod0DMRZhcAwD/mzp2rhoaGLh07cuRINTY2+rgiAAB6H/0O6BlWEAEAAAAAkiTD7Xa7zS4CQODZunWrioqKJEklJSU6ceKEpk6dqtTUVElSZmam5z1TAAAEKvodQg23mAK4J0VFRcrPz/faVlxcrOLiYklSSkoKDRMAEPDodwg1rCACAAAAACTxDCIAAAAAoAUBEQAAAAAgiYAIAAAAAGhBQAQAAAAASCIgAgAAAABaEBABAAAAAJIIiAAAAACAFgREAAAAAIAkAiIAAAAAoAUBEQAAAAAgiYAIAAAAAGjx/9wEmZjebpboAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1050x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10.5, 7), sharex=True, sharey=True)\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.sca(axes[i // 2, i % 2])\n",
    "    plot_predictions(svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.45, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "    if i in (0, 1):\n",
    "        plt.xlabel(\"\")\n",
    "    if i in (1, 3):\n",
    "        plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d01f11e-42ae-46d3-89d9-bc1a772f0852",
   "metadata": {},
   "source": [
    "- Some kernels are specialized for specific data structures e.g string kernles when classifying text documents or DNA sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99ec02b-027d-4115-bbdd-1721816be038",
   "metadata": {},
   "source": [
    "# SVG Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09fbe78-8761-4e3f-a394-3e16a88d831d",
   "metadata": {},
   "source": [
    "- The SVM algorithm not only supports linear and non-linear classification but also linear and nonlinear regression.\n",
    "- To use the regression instead of the classification we reverse the objective, instead of trying to fit the largest possible street between two classes while limiting margin violations, SVM tries to fit as many instances as possible on the street while limiting margin violations.\n",
    "- Width of the street is controlled by a hyperparameter.\n",
    "- epsilon determines the width of the margin. Adding more training instances within the margin does not affect the model's predictions; thus model is epsilon insensitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fcf7ffa0-23e5-487a-80db-5d4f51e845af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(epsilon=1.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(epsilon=1.5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVR(epsilon=1.5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2a8e0f-4d86-4bbc-a639-e918aac2fae4",
   "metadata": {},
   "source": [
    "SVM Regression on a random quadratic training set, using a\n",
    "second-degree polynomial kernel. There is little regularization in the left plot (i.e., a\n",
    "large C value), and much more regularization in the right plot (i.e., a small C value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21ec0189-2c38-452b-b1d9-5f3004f9085c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "0d0a5881-83f0-4297-95f2-4efd89bab0e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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