diff --git a/mai/lab3.ipynb b/mai/lab3.ipynb
index 049d5d0..7794416 100644
--- a/mai/lab3.ipynb
+++ b/mai/lab3.ipynb
@@ -29,14 +29,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
+      "            Date       Open       High        Low      Close  Adj Close  \\\n",
+      "0     1992-06-26   0.328125   0.347656   0.320313   0.335938   0.260703   \n",
+      "1     1992-06-29   0.339844   0.367188   0.332031   0.359375   0.278891   \n",
+      "2     1992-06-30   0.367188   0.371094   0.343750   0.347656   0.269797   \n",
+      "3     1992-07-01   0.351563   0.359375   0.339844   0.355469   0.275860   \n",
+      "4     1992-07-02   0.359375   0.359375   0.347656   0.355469   0.275860   \n",
+      "...          ...        ...        ...        ...        ...        ...   \n",
+      "8031  2024-05-17  75.269997  78.000000  74.919998  77.849998  77.849998   \n",
+      "8032  2024-05-20  77.680000  78.320000  76.709999  77.540001  77.540001   \n",
+      "8033  2024-05-21  77.559998  78.220001  77.500000  77.720001  77.720001   \n",
+      "8034  2024-05-22  77.699997  81.019997  77.440002  80.720001  80.720001   \n",
+      "8035  2024-05-23  80.099998  80.699997  79.169998  79.260002  79.260002   \n",
+      "\n",
+      "         Volume  \n",
+      "0     224358400  \n",
+      "1      58732800  \n",
+      "2      34777600  \n",
+      "3      18316800  \n",
+      "4      13996800  \n",
+      "...         ...  \n",
+      "8031   14436500  \n",
+      "8032   11183800  \n",
+      "8033    8916600  \n",
+      "8034   22063400  \n",
+      "8035    4651418  \n",
+      "\n",
+      "[8036 rows x 7 columns]\n",
       "0        8212\n",
       "1        8215\n",
       "2        8216\n",
@@ -48,119 +74,8 @@
       "8033    19864\n",
       "8034    19865\n",
       "8035    19866\n",
-      "Name: Date_numeric, Length: 8036, dtype: int64\n",
-      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
-      "       'Date_numeric', 'Close_binned'],\n",
-      "      dtype='object')\n",
-      "Обучающая выборка:  (4821, 9)\n",
-      "Close\n",
-      "0.765625     16\n",
-      "0.835938     10\n",
-      "0.750000     10\n",
-      "0.882813     10\n",
-      "0.753906      9\n",
-      "             ..\n",
-      "17.745001     1\n",
-      "4.757500      1\n",
-      "20.174999     1\n",
-      "94.080002     1\n",
-      "54.599998     1\n",
-      "Name: count, Length: 3663, dtype: int64\n",
-      "Контрольная выборка:  (1607, 9)\n",
-      "Close\n",
-      "0.750000     9\n",
-      "0.773438     6\n",
-      "0.703125     6\n",
-      "0.765625     5\n",
-      "0.898438     4\n",
-      "            ..\n",
-      "1.804688     1\n",
-      "0.656250     1\n",
-      "13.740000    1\n",
-      "27.799999    1\n",
-      "84.260002    1\n",
-      "Name: count, Length: 1421, dtype: int64\n",
-      "Тестовая выборка:  (1607, 9)\n",
-      "Close\n",
-      "0.742188      6\n",
-      "0.789063      6\n",
-      "1.367188      5\n",
-      "0.750000      5\n",
-      "0.781250      5\n",
-      "             ..\n",
-      "57.810001     1\n",
-      "111.239998    1\n",
-      "4.132813      1\n",
-      "38.915001     1\n",
-      "96.760002     1\n",
-      "Name: count, Length: 1425, dtype: int64\n",
-      "Обучающая выборка:  (4821, 9)\n",
-      "Close_binned\n",
-      "High      1639\n",
-      "Low       1591\n",
-      "Medium    1591\n",
-      "Name: count, dtype: int64\n",
-      "Контрольная выборка:  (1607, 9)\n",
-      "Close_binned\n",
-      "High      547\n",
-      "Low       530\n",
-      "Medium    530\n",
-      "Name: count, dtype: int64\n",
-      "Тестовая выборка:  (1607, 9)\n",
-      "Close_binned\n",
-      "High      546\n",
-      "Medium    531\n",
-      "Low       530\n",
-      "Name: count, dtype: int64\n",
-      "Обучающая выборка после undersampling:  (4773, 9)\n",
-      "Close\n",
-      "0.765625     16\n",
-      "0.835938     10\n",
-      "0.882813     10\n",
-      "0.750000     10\n",
-      "0.753906      9\n",
-      "             ..\n",
-      "80.669998     1\n",
-      "57.799999     1\n",
-      "57.230000     1\n",
-      "85.169998     1\n",
-      "11.795000     1\n",
-      "Name: count, Length: 3630, dtype: int64\n",
-      "Open                   float64\n",
-      "High                   float64\n",
-      "Low                    float64\n",
-      "Adj Close              float64\n",
-      "Volume                 float64\n",
-      "Date_numeric             int64\n",
-      "Close_binned_Low          bool\n",
-      "Close_binned_Medium       bool\n",
-      "Close_binned_High         bool\n",
-      "Volume_binned            int64\n",
-      "Price_change           float64\n",
-      "Volatility             float64\n",
-      "dtype: object\n",
-      "Средняя абсолютная ошибка: 0.28906502132778844\n",
-      "Среднеквадратичная ошибка: 0.2722364217628283\n",
-      "время, затраченное на обучение модели: 0.016208171844482422. Время, затраченное на предсказание: 0.0012598037719726562\n"
+      "Name: Date_numeric, Length: 8036, dtype: int64\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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wEhERVSMSiUTtMTVUt993qSRQc0B14BzOcnFInYiIiIhUij2cRERERMrikHq5mHASERERKYtD6uXikDoRERERqRR7OImIiIiUxSH1crGHk95YZGQkDA0NK3WNn58f+vXrp5L2EBERVRlBJt5RCzHhpFKVlRjGxsZCIpHg8ePHGDx4MK5fv67+xhEREVU3Mpl4Ry3EIXV6Yzo6OtDR0anqZhAREVE1xx5OemOlDakvWLAAjRs3Rv369TF69GgEBgbCycmpxLXLly+HmZkZGjZsiAkTJqCgoEA9jSYiIlIF9nCWiwkniWbbtm1YuHAhlixZgvPnz6Np06ZYt25diXonTpxAeno6Tpw4gS1btiAyMhKRkZHqbzAREZFYBEG8oxbikDqV6cCBA9DT01MoKyoqKrP+qlWr4O/vj5EjRwIA5s6diyNHjiAnJ0ehXoMGDbB69WpoamrC1tYW7733HmJiYjBmzBjxH4KIiIiqHHs4qUzdu3dHUlKSwrFx48Yy66empqJDhw4KZa9+DQCtW7eGpqam/GszMzPcv3+/3Lbk5eXhyZMnCkdefn4ln4iIiEhFOKReLiacVKZ69eqhZcuWCoe5ubnS961bt67C1xKJBLLXfIOFhYXBwMBA4Vj67Q9Kt4WIiEgUTDjLxYSTRGNjY4OzZ88qlL369ZsKCgpCdna2wjFzzMei3JuIiIhUi3M4STQTJ07EmDFj4Orqii5dumDnzp24dOkSrKyslL63VCqFVCpVKMvT0lL6vkRERKKopRu2i4UJJ4lm6NChuHnzJmbMmIHnz59j0KBB8PPzQ2JiYlU3jYiISLVq6VC4WJhwUqnK2qbIw8MDwv/fssHPzw9+fn4K5+fMmYM5c+bIv+7VqxdatmxZ7n0jIiKUbS4RERFVY0w4STS5ublYv349vLy8oKmpiR9++AHHjh3D0aNHq7ppREREqlVL988UCxNOEo1EIsGhQ4ewcOFCPH/+HDY2Nti7dy88PT2rumlERESqxSH1cjHhJNHo6Ojg2LFjVd0MIiIi9WPCWS5ui0REREREKsUeTiIiIiJlcVukcjHhJCIiIlKSIOOiofJwSJ2IiIiIVIo9nERERETK4qKhcrGHk4iIqBoRBEHth7oVSSRqPdRCkIl3VNKaNWtgaWkJbW1tdOzY8bVv+Hv8+DEmTJgAMzMzSKVSWFtb49ChQ2/65BXCHk4iIiKiGmrnzp2YNm0a1q9fj44dOyIiIgJeXl5ITU1F48aNS9TPz89Hr1690LhxY+zZswfm5ua4ffs2DA0NVdpOJpxEREREyhJx0VBeXh7y8vIUyqRSKaRSaYm6K1aswJgxYzBy5EgAwPr163Hw4EFs2rQJgYGBJepv2rQJjx49wunTp1G3bl0AgKWlpWhtLwuH1ImIiIiUJZOJdoSFhcHAwEDhCAsLKxEyPz8f58+fV3ijn4aGBjw9PZGQkFBqM3/++Wd07twZEyZMgImJCdq0aYNFixahqKhIZR8NwB5OIiIiomolKCgI06ZNUygrrXfz4cOHKCoqgomJiUK5iYkJrl27Vuq9b968iePHj2Po0KE4dOgQbty4gfHjx6OgoADz5s0T7yFewYSTiIiISFkirlIva/hcDDKZDI0bN8Y333wDTU1NtGvXDnfv3sWyZcuYcBIRERFVa1Ww2r9Ro0bQ1NTEvXv3FMrv3bsHU1PTUq8xMzND3bp1oampKS+zs7NDVlYW8vPzoaWlpZK2cg4nERERkbJEnMNZUVpaWmjXrh1iYmJeaoYMMTEx6Ny5c6nXuLm54caNG5C9FOf69eswMzNTWbIJMOGkSvDz80O/fv2quhlERET0/02bNg3ffvsttmzZgpSUFIwbNw5Pnz6Vr1r39fVFUFCQvP64cePw6NEjTJ48GdevX8fBgwexaNEiTJgwQaXt5JA6ERERkbKq6F3qgwcPxoMHDzB37lxkZWXByckJ0dHR8oVEd+7cgYbG//UvWlhY4PDhw5g6dSocHBxgbm6OyZMnY9asWSptJ3s4SRRxcXHo0KEDpFIpzMzMEBgYiMLCQgDAgQMHYGhoKN9yISkpCRKJRGF/sNGjR2PYsGFV0nYiIiKlVeGbhgICAnD79m3k5eXh999/R8eOHeXnYmNjERkZqVC/c+fOOHPmDJ4/f4709HR88cUXCnM6VYEJJynt7t276NOnD9q3b4/k5GSsW7cO3333HRYsWAAA6Nq1K/777z9cvHgRwIvktFGjRoiNjZXfIy4uDh4eHlXQeiIiIlI1JpyktLVr18LCwgKrV6+Gra0t+vXrh5CQEISHh0Mmk8HAwABOTk7yBDM2NhZTp07FxYsXkZOTg7t37+LGjRtwd3cvM0ZeXh6ePHmicOTl56vpCYmIiF5DJoh31EJMOElpKSkp6Ny5MyQSibzMzc0NOTk5+OuvvwAA7u7uiI2NhSAIOHnyJD766CPY2dnh1KlTiIuLQ5MmTdCqVasyY5T21oWl3/6g8mcjIiKqCEEmE+2ojbhoiNTCw8MDmzZtQnJyMurWrQtbW1t4eHggNjYW//77b7m9m0Dpb11AerwKW0xERERiYQ8nKc3Ozg4JCQkQXtr0Nj4+HvXr18c777wD4P/mcX711Vfy5LI44YyNjX3t/E2pVAp9fX2FQ6rC/cKIiIgqhUPq5WIPJ1VKdnY2kpKSFMrGjh2LiIgITJw4EQEBAUhNTcW8efMwbdo0+VYMDRo0gIODA7Zt24bVq1cDALp164ZBgwahoKDgtT2cRERE1dobrC5/mzDhpEqJjY2Fs7OzQpm/vz8OHTqEzz//HI6OjjAyMoK/vz9mz56tUM/d3R1JSUny3kwjIyPY29vj3r17sLGxUdcjEBERkZox4aQKi4yMLLGX18sSExPLvT4iIgIREREKZa/2lhIREdVItXQoXCxMOImIiIiUVUtXl4uFCScRERGRstjDWS6uUiciIiIilWIPJxEREZGyuEq9XEw4iYiIiJTFIfVycUidiIiIiFSKPZxERERESqqt70AXCxNOIiKit5wE6h0O1hAkao2nFhxSLxeH1ImIiIhIpdjDSURERKQs9nCWiwknERERkbK4LVK5OKRORERERCrFHk4iIiIiZXFIvVxMOImIiIiUJDDhLBeH1KlCJBIJ9u3bV9XNICIiqp5kgnhHLcSEkwAAWVlZmDhxIqysrCCVSmFhYQEfHx/ExMRUddOIiIiohuOQOiEjIwNubm4wNDTEsmXL0LZtWxQUFODw4cOYMGECrl27VtVNJCIiqt74pqFysYeTMH78eEgkEiQmJqJ///6wtrZG69atMW3aNJw5c6bUay5fvowePXpAR0cHDRs2xNixY5GTkyM/Hxsbiw4dOqBevXowNDSEm5sbbt++LT+/f/9+uLi4QFtbG1ZWVggJCUFhYaHKn5WIiEglOKReLiacb7lHjx4hOjoaEyZMQL169UqcNzQ0LFH29OlTeHl5oUGDBjh79ix2796NY8eOISAgAABQWFiIfv36wd3dHZcuXUJCQgLGjh0LieTFq8xOnjwJX19fTJ48GVevXsWGDRsQGRmJhQsXqvRZiYiIqGpwSP0td+PGDQiCAFtb2wpfs337djx//hxRUVHyJHX16tXw8fHBkiVLULduXWRnZ6Nv375o0aIFAMDOzk5+fUhICAIDAzFixAgAgJWVFUJDQzFz5kzMmzdPxKcjIiJSk1raMykWJpxvOUGo/DdISkoKHB0dFXpE3dzcIJPJkJqaim7dusHPzw9eXl7o1asXPD09MWjQIJiZmQEAkpOTER8fr9CjWVRUhOfPnyM3Nxe6urolYubl5SEvL0+xMD8fUi2tSrefiIhIbG/y7+nbhEPqb7lWrVpBIpGIvjBo8+bNSEhIQJcuXbBz505YW1vL54Pm5OQgJCQESUlJ8uPy5ctIS0uDtrZ2qfcLCwuDgYGBwrH02x9EbTMRERGpBhPOt5yRkRG8vLywZs0aPH36tMT5x48flyizs7NDcnKyQv34+HhoaGjAxsZGXubs7IygoCCcPn0abdq0wfbt2wEALi4uSE1NRcuWLUscGhql/5UMCgpCdna2wjFzzMdKPj0REZFIqnDR0Jo1a2BpaQltbW107NgRiYmJFbpux44dkEgk6NevX6VjVhYTTsKaNWtQVFSEDh06YO/evUhLS0NKSgpWrlyJzp07l6g/dOhQaGtrY8SIEfjjjz9w4sQJTJw4EcOHD4eJiQlu3bqFoKAgJCQk4Pbt2zhy5AjS0tLk8zjnzp2LqKgohISE4MqVK0hJScGOHTswe/bsMtsolUqhr6+vcHA4nYiIqo0qSjh37tyJadOmYd68ebhw4QIcHR3h5eWF+/fvl3tdRkYGZsyYga5duyrz1BXGhJNgZWWFCxcuoHv37pg+fTratGmDXr16ISYmBuvWrStRX1dXF4cPH8ajR4/Qvn17DBgwAD179sTq1avl569duybfYmns2LGYMGECPv30UwCAl5cXDhw4gCNHjqB9+/bo1KkTvvrqKzRr1kytz01ERFTTrVixAmPGjMHIkSNhb2+P9evXQ1dXF5s2bSrzmqKiIgwdOhQhISGwsrJSSzu5aIgAAGZmZli9erU8aXzVq5Oh27Zti+PHj5da18TEBD/99FO58by8vODl5fVmjSUiIqpmxHyXemkLZaVSKaRSqUJZfn4+zp8/j6CgIHmZhoYGPD09kZCQUOb958+fj8aNG8Pf3x8nT54Urd3lYQ8nERERkbJEHFIvbaFsWFhYiZAPHz5EUVERTExMFMpNTEyQlZVVajNPnTqF7777Dt9++61KPoaysIeTiIiISFkivtkyKCgI06ZNUyh7tXfzTfz3338YPnw4vv32WzRq1Ejp+1UGE04iIiKiaqS04fPSNGrUCJqamrh3755C+b1792Bqalqifnp6OjIyMuDj4yMvk/3/d8DXqVMHqamp8he2iI1D6kRERERKEmSCaEdFaWlpoV27doiJiZGXyWQyxMTElLrLjK2tLS5fvqywD/b777+P7t27IykpCRYWFqJ8FqVhDycRERGRsqro1ZbTpk3DiBEj4Orqig4dOiAiIgJPnz7FyJEjAQC+vr4wNzdHWFgYtLW10aZNG4XrDQ0NAaBEudiYcBIRERHVUIMHD8aDBw8wd+5cZGVlwcnJCdHR0fKFRHfu3CnzpSrqxISTiIiISFkiLhqqrICAAAQEBJR6LjY2ttxrIyMjxW9QKZhwEhERESlJzH04ayMmnFRjCYV5r69EVMXUPZCl7u8LLYn6/xkRoOZ/2IsK1RpOIpGoNV5VkKj7/yFVOSacRERERMqqwiH1moAJJxEREZGSOKRevqpftkREREREtRp7OImIiIiUxSH1cjHhJCIiIlKSwISzXEw4iYiIiJTFhLNcnMNZywQHB8PJyanMr8W6LxEREVFFMeGs5hISEqCpqYn33nvvja6fMWMGYmJiXltv79698PDwgIGBAfT09ODg4ID58+fj0aNHbxSXiIjobSLIxDtqIyac1dx3332HiRMn4rfffsPff/9d6ev19PTQsGHDcut8+eWXGDx4MNq3b49ff/0Vf/zxB8LDw5GcnIytW7e+adOJiIjeHjIRj1qICWc1lpOTg507d2LcuHF47733Sn3f6eLFi2FiYoL69evD398fz58/Vzj/uqHwxMRELFq0COHh4Vi2bBm6dOkCS0tL9OrVC3v37sWIESNKvU4mk2H+/Pl45513IJVK4eTkhOjoaPn5/Px8BAQEwMzMDNra2mjWrBnCwsLk5x8/fozRo0fD2NgY+vr66NGjB5KTkyv3AREREVGNwISzGtu1axdsbW1hY2ODYcOGYdOmTRAEQeF8cHAwFi1ahHPnzsHMzAxr166tVIxt27ZBT08P48ePL/W8oaFhqeVff/01wsPDsXz5cly6dAleXl54//33kZaWBgBYuXIlfv75Z+zatQupqanYtm0bLC0t5dcPHDgQ9+/fx6+//orz58/DxcUFPXv25BA+ERHVSBxSLx9XqVdj3333HYYNGwYA8Pb2RnZ2NuLi4uDh4QEAiIiIgL+/P/z9/QEACxYswLFjx0r0cpYnLS0NVlZWqFu3bqXatnz5csyaNQtDhgwBACxZsgQnTpxAREQE1qxZgzt37qBVq1Z49913IZFI0KxZM/m1p06dQmJiIu7fvw+pVCq/3759+7Bnzx6MHTu2RLy8vDzk5Sm+I1rIL4BUq3LtJiIiUoXamiiKhT2c1VRqaioSExPx8ccfAwDq1KmDwYMH47vvvpPXSUlJQceOHRWu69y5c6XivNxjWlFPnjzB33//DTc3N4VyNzc3pKSkAAD8/PyQlJQEGxsbTJo0CUeOHJHXS05ORk5ODho2bAg9PT35cevWLaSnp5caMywsDAYGBgrHsu92VbrtREREpH7s4aymvvvuOxQWFqJJkybyMkEQIJVKsXr1ahgYGIgSx9raGqdOnUJBQUGleznL4+Liglu3buHXX3/FsWPHMGjQIHh6emLPnj3IycmBmZkZYmNjS1xX1hB+UFAQpk2bplAmpJ4Qrb1ERETKYA9n+djDWQ0VFhYiKioK4eHhSEpKkh/Jyclo0qQJfvjhBwCAnZ0dfv/9d4Vrz5w5U6lYn3zyCXJycsqc+/n48eMSZfr6+mjSpAni4+MVyuPj42Fvb69Qb/Dgwfj222+xc+dO7N27F48ePYKLiwuysrJQp04dtGzZUuFo1KhRqe2QSqXQ19dXODicTkRE1YYgEe+ohdjDWQ0dOHAA//77L/z9/Uv0ZPbv3x/fffcdPvvsM0yePBl+fn5wdXWFm5sbtm3bhitXrsDKyqrCsTp27IiZM2di+vTpuHv3Lj788EM0adIEN27cwPr16/Huu+9i8uTJJa77/PPPMW/ePLRo0QJOTk7YvHkzkpKSsG3bNgDAihUrYGZmBmdnZ2hoaGD37t0wNTWFoaEhPD090blzZ/Tr1w9Lly6FtbU1/v77bxw8eBAffvghXF1dlfsAiYiIqFphwlkNfffdd/D09Cx12Lx///5YunQpLl26hMGDByM9PR0zZ87E8+fP0b9/f4wbNw6HDx+uVLwlS5agXbt2WLNmDdavXw+ZTIYWLVpgwIABZW6LNGnSJGRnZ2P69Om4f/8+7O3t8fPPP6NVq1YAgPr162Pp0qVIS0uDpqYm2rdvj0OHDkFD40Wn+qFDh/Dll19i5MiRePDgAUxNTdGtWzeYmJhU8tMiIiKqehxSL59EeJNVI1RjBAUF4eTJkzh16lRVN0V0z5MPqTWeprmtWuNR7TDdNUit8ZbuL/2XRFV51zvs9ZVE5iRV7y+ma34eo9Z4Hr0XqzUeAMwprN2/7Pe5t0PlMTLf7S7avcxO1b41CuzhrKUEQcDNmzcRExMDZ2fnqm4OERFRrcYezvJx0VAtlZ2dDXt7e2hpaeGLL76o6uYQERHRW4w9nLWUoaFhiY3SiYiISDWEWrq6XCxMOImIiIiUxCH18nFInYiIiIhUij2cREREREoSZBxSLw8TTiIiIiIlcZPJ8jHhpBor9IPv1RovG0VqjUfiq4o5ROHn1LtPZbDrbLXG22agp9Z4AJCbW6DWeNPf36TWeDFTW6g1HgCcWPZUrfFaNnis1nhU9ZhwEhERESmJQ+rlY8JJREREpCQmnOXjKnUiIiKiGmzNmjWwtLSEtrY2OnbsiMTExDLrfvvtt+jatSsaNGiABg0awNPTs9z6YmHCSURERKQkQRDvqIydO3di2rRpmDdvHi5cuABHR0d4eXnh/v37pdaPjY3Fxx9/jBMnTiAhIQEWFhb43//+h7t374rwKZSNCScRERGRkgSZRLSjMlasWIExY8Zg5MiRsLe3x/r166Grq4tNm0pf7LZt2zaMHz8eTk5OsLW1xcaNGyGTyRATEyPGx1AmzuEkIiIiUpKYr7bMy8sr8XpqqVQKqVSqUJafn4/z588jKChIXqahoQFPT08kJCRUKFZubi4KCgpgZGSkfMPLwR5OAgBYWloiIiKiqptBRET01gsLC4OBgYHCERZWcou1hw8foqioCCYmJgrlJiYmyMrKqlCsWbNmoUmTJvD09BSl7WVhwlkL+Pj4wNvbu9RzJ0+ehEQiwaVLl9TcKiIioreHIBPvCAoKQnZ2tsLxci+mWBYvXowdO3bgp59+gra2tuj3fxmH1GsBf39/9O/fH3/99RfeeecdhXObN2+Gq6srHBwcqqh1REREtZ9MxCH10obPS9OoUSNoamri3r17CuX37t2DqalpudcuX74cixcvxrFjx9SSI7CHsxbo27cvjI2NERkZqVCek5OD3bt3w9/fH3v37kXr1q0hlUphaWmJ8PDwMu+XkZEBiUSCpKQkednjx48hkUgQGxsL4MUqN4lEgsOHD8PZ2Rk6Ojro0aMH7t+/j19//RV2dnbQ19fHJ598gtzcXPl9ZDIZwsLC0Lx5c+jo6MDR0RF79uwR8+MgIiJ6K2hpaaFdu3YKC36KFwB17ty5zOuWLl2K0NBQREdHw9XVVR1NZcJZG9SpUwe+vr6IjIyE8NJ+Crt370ZRURHs7OwwaNAgDBkyBJcvX0ZwcDDmzJlTIkF9E8HBwVi9ejVOnz6NP//8E4MGDUJERAS2b9+OgwcP4siRI1i1apW8flhYGKKiorB+/XpcuXIFU6dOxbBhwxAXF6d0W4iIiKqKIEhEOypj2rRp+Pbbb7FlyxakpKRg3LhxePr0KUaOHAkA8PX1VRiOX7JkCebMmYNNmzbB0tISWVlZyMrKQk5Ojqifx6s4pF5LjBo1CsuWLUNcXBw8PDwAvBhO79+/P7755hv07NkTc+bMAQBYW1vj6tWrWLZsGfz8/JSKu2DBAri5uQF4MbQfFBSE9PR0WFlZAQAGDBiAEydOYNasWcjLy8OiRYtw7Ngx+W9eVlZWOHXqFDZs2AB3d/cy45S2Yq9QKEIdiaZS7SciIhJDVb1paPDgwXjw4AHmzp2LrKwsODk5ITo6Wr6Q6M6dO9DQ+L/+xXXr1iE/Px8DBgxQuM+8efMQHByssnayh7OWsLW1RZcuXeT7bt24cQMnT56Ev78/UlJS5ElhMTc3N6SlpaGoqEipuC/P+zAxMYGurq482SwuK9589saNG8jNzUWvXr2gp6cnP6KiopCenl5unNJW7J3OvqpU24mIiGqDgIAA3L59G3l5efj999/RsWNH+bnY2FiFEc2MjAwIglDiUGWyCbCHs1bx9/fHxIkTsWbNGmzevBktWrQot9ewLMW/Cb08PF9QUFBq3bp168r/LJFIFL4uLpPJZAAg764/ePAgzM3NFeq9bnJ0UFAQpk2bplC2sO2Ycq8hIiJSl8q+Iehtw4SzFhk0aBAmT56M7du3IyoqCuPGjYNEIoGdnR3i4+MV6sbHx8Pa2hqamiWHpI2NjQEAmZmZcHZ2BgCFBURvyt7eHlKpFHfu3Kl0Ilzaij0OpxMRUXVRVUPqNQUTzlpET08PgwcPRlBQEJ48eSKfnzl9+nS0b98eoaGhGDx4MBISErB69WqsXbu21Pvo6OigU6dOWLx4MZo3b4779+9j9uzZSrevfv36mDFjBqZOnQqZTIZ3330X2dnZiI+Ph76+PkaMGKF0DCIioqog5rZItRHncNYy/v7++Pfff+Hl5YUmTZoAAFxcXLBr1y7s2LEDbdq0wdy5czF//vxyFwxt2rQJhYWFaNeuHaZMmYIFCxaI0r7Q0FDMmTMHYWFhsLOzg7e3Nw4ePIjmzZuLcn8iIiKqfiSCwFkHVDN9afmJWuNlQ7kFVlT1quI37PBzJV9Hp0rBrsqPRlSGr/a/ao0HALm5WmqN952Gat/A8qqlAfXVGg8ATix7qtZ4LRs8Vms8m2u/qjzG5eY+ot2r7a1fRLtXdcEhdSIiIiIlsfuufBxSJyIiIiKVYg8nERERkZK4aKh8TDiJiIiIlFTZV1K+bTikTkREREQqxR5OIiIiIiVx0VD5mHASERERKYlzOMvHIXUiIiIiUin2cFKNFXxOnLcfVZTs3i21xiPxCYV5ao+p7o3Y1f19odOkq1rjAcCOhh5qjbf8x75qjTdiyE61xgOAYRIdtca786++WuPZqCEGFw2VjwknERERkZI4pF4+JpxERERESuKaofJxDicRERERqRR7OImIiIiUxCH18jHhJCIiIlISFw2Vj0PqRERERKRSTDhJ5TIyMiCRSJCUlFTVTSEiIlIJmYhHbcSEsxR+fn6QSCSQSCSoW7cuTExM0KtXL2zatAkyWcX/KkRGRsLQ0FB1Da0hLCwskJmZiTZt2lR1U4iIiFRCgES0ozZiwlkGb29vZGZmIiMjA7/++iu6d++OyZMno2/fvigsLKzq5tUY+fn50NTUhKmpKerU4ZRhIiKitxETzjJIpVKYmprC3NwcLi4u+OKLL7B//378+uuviIyMBACsWLECbdu2Rb169WBhYYHx48cjJycHABAbG4uRI0ciOztb3lsaHBwMAMjLy8OMGTNgbm6OevXqoWPHjoiNja1Qu4p7TQ8fPgw7Ozvo6enJk+NiHh4emDJlisJ1/fr1g5+fn/xrS0tLLFiwAL6+vtDT00OzZs3w888/48GDB/jggw+gp6cHBwcHnDt3TuE+p06dQteuXaGjowMLCwtMmjQJT58+VbhvaGgofH19oa+vj7Fjx5Y6pH7lyhX07dsX+vr6qF+/Prp27Yr09PQKfQZERETVjUwQ76iNmHBWQo8ePeDo6Igff/wRAKChoYGVK1fiypUr2LJlC44fP46ZM2cCALp06YKIiAjo6+sjMzMTmZmZmDFjBgAgICAACQkJ2LFjBy5duoSBAwfC29sbaWlpFWpHbm4uli9fjq1bt+K3337DnTt35PeujK+++gpubm64ePEi3nvvPQwfPhy+vr4YNmwYLly4gBYtWsDX1xeC8OJvf3p6Ory9vdG/f39cunQJO3fuxKlTpxAQEKBw3+XLl8PR0REXL17EnDlzSsS9e/cuunXrBqlUiuPHj+P8+fMYNWoUe46JiKjGkkEi2lEbcYyzkmxtbXHp0iUAUOhFLO4x/Oyzz7B27VpoaWnBwMAAEokEpqam8np37tzB5s2bcefOHTRp0gQAMGPGDERHR2Pz5s1YtGjRa9tQUFCA9evXo0WLFgBeJLDz58+v9LP06dMHn376KQBg7ty5WLduHdq3b4+BAwcCAGbNmoXOnTvj3r17MDU1RVhYGIYOHSp/7latWmHlypVwd3fHunXroK2tDeBFYj59+nR5nIyMDIW4a9asgYGBAXbs2IG6desCAKytrctta15eHvLyFN+DrZGXB6lUWunnJiIiIvViD2clCYIAieTFbx/Hjh1Dz549YW5ujvr162P48OH4559/kJubW+b1ly9fRlFREaytraGnpyc/4uLiKjykrKurK082AcDMzAz379+v9LM4ODjI/2xiYgIAaNu2bYmy4nsnJycjMjJSod1eXl6QyWS4deuW/DpXV9dy4yYlJaFr167yZLMiwsLCYGBgoHAs+Xp9ha8nIiJSJS4aKh97OCspJSUFzZs3R0ZGBvr27Ytx48Zh4cKFMDIywqlTp+Dv74/8/Hzo6uqWen1OTg40NTVx/vx5aGpqKpzT09OrUBteTdQkEol82Bt4MdT/8tfAi17R8u5TnESXVla8Mj8nJweffvopJk2aVOJeTZs2lf+5Xr165bZfR0en3POlCQoKwrRp0xTKNP67W+n7EBERqUJt3c5ILEw4K+H48eO4fPkypk6divPnz0MmkyE8PBwaGi86inft2qVQX0tLC0VFRQplzs7OKCoqwv3799G1a1eVtNPY2FhhEVFRURH++OMPdO/eXan7uri44OrVq2jZsqVS93FwcMCWLVtQUFBQ4V5OqVRaYvi8IP+hUu0gIiISS23tmRQLh9TLkJeXh6ysLNy9excXLlzAokWL8MEHH6Bv377w9fVFy5YtUVBQgFWrVuHmzZvYunUr1q9XHOK1tLRETk4OYmJi8PDhQ+Tm5sLa2hpDhw6Fr68vfvzxR9y6dQuJiYkICwvDwYMHRWl7jx49cPDgQRw8eBDXrl3DuHHj8PjxY6XvO2vWLJw+fRoBAQFISkpCWloa9u/fX2LR0OsEBATgyZMnGDJkCM6dO4e0tDRs3boVqampSreRiIiIqh8mnGWIjo6GmZkZLC0t4e3tjRMnTmDlypXYv38/NDU14ejoiBUrVmDJkiVo06YNtm3bhrCwMIV7dOnSBZ999hkGDx4MY2NjLF26FACwefNm+Pr6Yvr06bCxsUG/fv1w9uxZhWFpZYwaNQojRoyAr68v3N3dYWVlpXTvJvCiZzIuLg7Xr19H165d4ezsjLlz58oXP1VUw4YNcfz4ceTk5MDd3R3t2rXDt99+W6k5nURERNVJVb5paM2aNbC0tIS2tjY6duyIxMTEcuvv3r0btra20NbWRtu2bXHo0KE3iFo5EuHVyX5ENUTBw5tqjSe7d+v1lahaEwrzXl9JZKEffK/WeMHnFqg1nk4T1UwNKs+Ohh5qjdf3x75qjTdiyE61xgOAYXmVn1uvDG1BvTMee91T/Wd6yGSIaPfqc29Hhevu3LkTvr6+WL9+PTp27IiIiAjs3r0bqampaNy4cYn6p0+fRrdu3RAWFoa+ffti+/btWLJkCS5cuKDSNwKyh5OIiIiohlqxYgXGjBmDkSNHwt7eHuvXr4euri42bdpUav2vv/4a3t7e+Pzzz2FnZ4fQ0FC4uLhg9erVKm0nE85qpnfv3grbDr18VGSPTiIiIlI/MbdFysvLw5MnTxSOV/eiBl68Pvr8+fPw9PSUl2loaMDT0xMJCQmltjMhIUGhPgB4eXmVWV8sXKVezWzcuBHPnj0r9ZyRkZGaW0NEREQVIRNxkXpYWBhCQkIUyubNmyd/RXaxhw8foqioSL5vdjETExNcu3at1HtnZWWVWj8rK0v5hpeDCWc1Y25uXtVNICIioipU2t7TNf3Nekw4iYiIiJQk5jvQS9t7ujSNGjWCpqYm7t27p1Be/Erq0piamlaqvlg4h5OIiIhISYKIR0VpaWmhXbt2iImJkZfJZDLExMSgc+fOpV7TuXNnhfoAcPTo0TLri4U9nEREREQ11LRp0zBixAi4urqiQ4cOiIiIwNOnTzFy5EgAgK+vL8zNzeV7hU+ePBnu7u4IDw/He++9hx07duDcuXP45ptvVNpOJpxUY6l7/z97I3E25qeqoyVR/4+8bQZ6ao2n7u+LZ3+fVGs8AOjlNFat8VYO2KjWeNsq9y4NUST9qd59OPNr4QBrVb1LffDgwXjw4AHmzp2LrKwsODk5ITo6Wr4w6M6dO/JXcAMvXkqzfft2zJ49G1988QVatWqFffv2qXQPToAJJxEREZHSZJKqe5d6QEBAma+Zjo2NLVE2cOBADBw4UMWtUsSEk4iIiEhJfG1j+WpfnzYRERERVSvs4SQiIiJSUlXN4awpmHASERERKUnMNw3VRhxSJyIiIiKVqhUJp0Qiwb59+6q6GQBe35aMjAxIJBIkJSWprU1liY2NhUQiwePHj6u6KURERDWaDBLRjtqoRiScWVlZmDhxIqysrCCVSmFhYQEfH58SO+XXBBYWFsjMzFT5fldiY3JKRERUtqp401BNUu3ncGZkZMDNzQ2GhoZYtmwZ2rZti4KCAhw+fBgTJkzAtWvXqrqJlaKpqany95USERERVSfVvodz/PjxkEgkSExMRP/+/WFtbY3WrVtj2rRpOHPmTKnXXL58GT169ICOjg4aNmyIsWPHIicnR34+NjYWHTp0QL169WBoaAg3Nzfcvn1bfn7//v1wcXGBtrY2rKysEBISgsLCwgq3OTMzE71794aOjg6srKywZ88e+blXh9SLew5jYmLg6uoKXV1ddOnSBampqfJrgoOD4eTkhK1bt8LS0hIGBgYYMmQI/vvvP3kdmUyGsLAwNG/eHDo6OnB0dFSICwCHDh2CtbU1dHR00L17d2RkZFT4mV7n33//ha+vLxo0aABdXV307t0baWlpAABBEGBsbKzQHicnJ5iZmcm/PnXqFKRSKXJzc0VrExERkbrIJOIdtVG1TjgfPXqE6OhoTJgwAfXq1Stx3tDQsETZ06dP4eXlhQYNGuDs2bPYvXs3jh07Jt+Bv7CwEP369YO7uzsuXbqEhIQEjB07FpL//4aAkydPwtfXF5MnT8bVq1exYcMGREZGYuHChRVu95w5c9C/f38kJydj6NChGDJkCFJSUsq95ssvv0R4eDjOnTuHOnXqYNSoUQrn09PTsW/fPhw4cAAHDhxAXFwcFi9eLD8fFhaGqKgorF+/HleuXMHUqVMxbNgwxMXFAQD+/PNPfPTRR/Dx8UFSUhJGjx6NwMDACj/T6/j5+eHcuXP4+eefkZCQAEEQ0KdPHxQUFEAikaBbt27ytx38+++/SElJwbNnz+Q91HFxcWjfvj10dXVFaxMREZG6yEQ8aqNqPaR+48YNCIIAW1vbCl+zfft2PH/+HFFRUfIkdfXq1fDx8cGSJUtQt25dZGdno2/fvmjRogUAwM7OTn59SEgIAgMDMWLECACAlZUVQkNDMXPmTMybN69CbRg4cCBGjx4NAAgNDcXRo0exatUqrF27tsxrFi5cCHd3dwBAYGAg3nvvPTx//hza2toAXvRgRkZGon79+gCA4cOHIyYmBgsXLkReXh4WLVqEY8eOoXPnzvJ2nzp1Chs2bIC7uzvWrVuHFi1aIDw8HABgY2ODy5cvY8mSJRX7YMuRlpaGn3/+GfHx8ejSpQsAYNu2bbCwsMC+ffswcOBAeHh4YMOGDQCA3377Dc7OzjA1NUVsbCxsbW0RGxsrf/7S5OXlIS8vT6FMEAT5LwpERERUfVXrHk5BqPzU2ZSUFDg6Oir0iLq5uUEmkyE1NRVGRkbw8/ODl5cXfHx88PXXXyMzM1NeNzk5GfPnz4eenp78GDNmDDIzMys83Fuc9L389et6OB0cHOR/Lh5qvn//vrzM0tJSnmwW1yk+f+PGDeTm5qJXr14K7Y6KikJ6err8c+nYsWO57XxTKSkpqFOnjsL9GzZsCBsbG/lzu7u74+rVq3jw4AHi4uLg4eEBDw8PxMbGoqCgAKdPn4aHh0eZMcLCwmBgYKBwCLL/yqxPRESkTlw0VL5q3cPZqlUrSCQS0RcGbd68GZMmTUJ0dDR27tyJ2bNn4+jRo+jUqRNycnIQEhKCjz76qMR1xb2NqlC3bl35n4t77WQyWanni+sUny+en3rw4EGYm5sr1JNKpSppb2W1bdsWRkZGiIuLQ1xcHBYuXAhTU1MsWbIEZ8+eRUFBgbx3tDRBQUGYNm2aQlmDhhXv+SYiIlKl2jr3UizVuofTyMgIXl5eWLNmDZ4+fVrifGlb9NjZ2SE5OVmhfnx8PDQ0NGBjYyMvc3Z2RlBQEE6fPo02bdpg+/btAAAXFxekpqaiZcuWJQ4NjYp9XK8uZjpz5ozCsL3Y7O3tIZVKcefOnRJttrCwAPDic0lMTCy3nW/Kzs4OhYWF+P333+Vl//zzD1JTU2Fvbw/gRYLctWtX7N+/H1euXMG7774LBwcH5OXlYcOGDXB1dS11nm4xqVQKfX19hYPD6UREVF1wDmf5qnXCCQBr1qxBUVEROnTogL179yItLQ0pKSlYuXJlqUPCQ4cOhba2NkaMGIE//vgDJ06cwMSJEzF8+HCYmJjg1q1bCAoKQkJCAm7fvo0jR44gLS1NnhDOnTsXUVFRCAkJwZUrV5CSkoIdO3Zg9uzZFW7z7t27sWnTJly/fh3z5s1DYmKifNGSKtSvXx8zZszA1KlTsWXLFqSnp+PChQtYtWoVtmzZAgD47LPPkJaWhs8//xypqanYvn07IiMjKx3r8uXLSEpKkh/Jyclo1aoVPvjgA4wZMwanTp1CcnIyhg0bBnNzc3zwwQfyaz08PPDDDz/AyckJenp60NDQQLdu3bBt27Zy528SERFRzVath9SBF4tfLly4gIULF2L69OnIzMyEsbEx2rVrh3Xr1pWor6uri8OHD2Py5MnyVc/9+/fHihUr5OevXbuGLVu24J9//oGZmRkmTJiATz/9FADg5eWFAwcOYP78+fJFRra2tvJFQBUREhKCHTt2YPz48TAzM8MPP/wg7+lTldDQUBgbGyMsLAw3b96EoaEhXFxc8MUXXwAAmjZtir1792Lq1KlYtWoVOnTogEWLFpVYDf863bp1U/haU1MThYWF2Lx5MyZPnoy+ffsiPz8f3bp1w6FDhxSmAri7u6OoqEhhrqaHhwf2799f7vxNIiKi6q629kyKRSK8ycocomqgjpb56yuJyN6oqVrjkfi0JOr/HXubgZ5a47W+eUmt8Z79fVKt8QCgl9NYtcaTqfmfyW1N1BoOAJD0Z2O1xtNQc+bx3r0fVB5jvcUw0e712Z/fi3av6qLaD6kTERERUc3GhLMStm3bprDt0MtH69atq7p5Sundu3eZz7Zo0aKqbh4REVG1xkVD5av2czirk/fff7/EXpbFXt22qKbZuHEjnj17Vuo5IyMjNbeGiIioZqmtiaJYmHBWQv369RU2X69NXt2/k4iIiEgsTDiJiIiIlMQV2OVjwklERESkJL5pqHxcNEREREREKsUeTqqxdjT0UGu8w9J8tcYj8QlVMOiVm1ug1njq/r5Q956YAHA06Ru1xuvpOEat8aT6hWqNB6h/X8y6tXAAmouGyseEk4iIiEhJTDjLx4STiIiISEm1r89WXJzDSUREREQqxR5OIiIiIiVxlXr5mHASERERKYlzOMvHIXUiIiIiUimVJJwSiQT79u1Txa0r7XVtycjIgEQiQVJSktraVJbY2FhIJBI8fvy4qpsiZ2lpiYiICPnX1en/LRERUXUhiHioyqNHjzB06FDo6+vD0NAQ/v7+yMnJKbf+xIkTYWNjAx0dHTRt2hSTJk1CdnZ2pWO/UcKZlZWFiRMnwsrKClKpFBYWFvDx8UFMTMyb3K5KWVhYIDMzE23atKnqplRKcXLaoEEDPH/+XOHc2bNnIZFIIJGIP6EkMzMTvXv3Fv2+RERENZkMgmiHqgwdOhRXrlzB0aNHceDAAfz2228YO7bsvXT//vtv/P3331i+fDn++OMPREZGIjo6Gv7+/pWOXek5nBkZGXBzc4OhoSGWLVuGtm3boqCgAIcPH8aECRNw7dq1SjeiKmlqasLU1LSqm/HG6tevj59++gkff/yxvOy7775D06ZNcefOHdHj1eTPioiIqCbIy8tDXl6eQplUKoVUKn3je6akpCA6Ohpnz56Fq6srAGDVqlXo06cPli9fjiZNmpS4pk2bNti7d6/86xYtWmDhwoUYNmwYCgsLUadOxdPISvdwjh8/HhKJBImJiejfvz+sra3RunVrTJs2DWfOnCn1msuXL6NHjx7Q0dFBw4YNMXbsWIUu3NjYWHTo0AH16tWDoaEh3NzccPv2bfn5/fv3w8XFBdra2rCyskJISAgKCyv+JobiXjkdHR1YWVlhz5498nOvDqkX9xzGxMTA1dUVurq66NKlC1JTU+XXBAcHw8nJCVu3boWlpSUMDAwwZMgQ/Pfff/I6MpkMYWFhaN68OXR0dODo6KgQFwAOHToEa2tr6OjooHv37sjIyKjwMxUbMWIENm3aJP/62bNn2LFjB0aMGFGi7qlTp9C1a1fo6OjAwsICkyZNwtOnT+Xn79+/Dx8fH+jo6KB58+bYtm1biXu8PKRe2hSApKQkSCQS+bNERkbC0NAQBw4cgI2NDXR1dTFgwADk5uZiy5YtsLS0RIMGDTBp0iQUFRVV+vmJiIiqA5mIR1hYGAwMDBSOsLAwpdqXkJAAQ0NDebIJAJ6entDQ0MDvv/9e4ftkZ2dDX1+/UskmUMmE89GjR4iOjsaECRNQr169EucNDQ1LlD19+hReXl5o0KABzp49i927d+PYsWMICAgAABQWFqJfv35wd3fHpUuXkJCQgLFjx8qHg0+ePAlfX19MnjwZV69exYYNGxAZGYmFCxdWuN1z5sxB//79kZycjKFDh2LIkCFISUkp95ovv/wS4eHhOHfuHOrUqYNRo0YpnE9PT8e+fftw4MABHDhwAHFxcVi8eLH8fFhYGKKiorB+/XpcuXIFU6dOxbBhwxAXFwcA+PPPP/HRRx/Bx8cHSUlJGD16NAIDAyv8TMWGDx+OkydPynsz9+7dC0tLS7i4uJRor7e3N/r3749Lly5h586dOHXqlPz/AwD4+fnhzz//xIkTJ7Bnzx6sXbsW9+/fr3SbXpWbm4uVK1dix44diI6ORmxsLD788EMcOnQIhw4dwtatW7Fhw4YSCTkREVFNIeYczqCgIGRnZyscQUFBSrUvKysLjRs3ViirU6cOjIyMkJWVVaF7PHz4EKGhoeUOw5elUunpjRs3IAgCbG1tK3zN9u3b8fz5c0RFRcmT1NWrV8PHxwdLlixB3bp1kZ2djb59+6JFixYAADs7O/n1ISEhCAwMlPfYWVlZITQ0FDNnzsS8efMq1IaBAwdi9OjRAIDQ0FAcPXoUq1atwtq1a8u8ZuHChXB3dwcABAYG4r333sPz58+hra0N4EUPZmRkJOrXrw/gReIXExODhQsXIi8vD4sWLcKxY8fQuXNnebtPnTqFDRs2wN3dHevWrUOLFi0QHh4OALCxscHly5exZMmSin2w/1/jxo3Ru3dvREZGYu7cudi0aVOJ5Bh4kQAPHToUU6ZMAQC0atUKK1eulLflzp07+PXXX5GYmIj27dsDeDE0//L/izdVUFAgf14AGDBgALZu3Yp79+5BT08P9vb26N69O06cOIHBgwcrHY+IiKgmq8zweWBg4Gtzh9d1slXEkydP8N5778He3h7BwcGVvr5SCacgVH4ia0pKChwdHRV6RN3c3CCTyZCamopu3brBz88PXl5e6NWrFzw9PTFo0CCYmZkBAJKTkxEfH6/Qo1lUVITnz58jNzcXurq6r21DcdL38tevW5Xu4OAg/3NxW+7fv4+mTZsCeLF6uzjZLK5T3Bt448YN5ObmolevXgr3zM/Ph7Ozs/xz6dixY7ntrKhRo0Zh8uTJGDZsGBISErB7926cPHlSoU5ycjIuXbqkMEwuCAJkMhlu3bqF69evo06dOmjXrp38vK2tbam91pWlq6srTzYBwMTEBJaWltDT01MoK683tbT5LAVCEepKNJVuHxERkbKqah/O6dOnw8/Pr9w6VlZWMDU1LfHvbGFhIR49evTa9Rn//fcfvL295etG6tatW+l2VirhbNWqFSQSiegLgzZv3oxJkyYhOjoaO3fuxOzZs3H06FF06tQJOTk5CAkJwUcffVTiuuLeRlV4+cMsHt6XyWSlni+uU3y+eH7qwYMHYW5urlBPmQm/ZenduzfGjh0Lf39/+Pj4oGHDhiXq5OTk4NNPP8WkSZNKnGvatCmuX79e6bgaGi9mZLz8i0hBQUGJeqV9VuV9fqUJCwtDSEiIQtnAem0wqL5DGVcQERGpT1W9acjY2BjGxsavrde5c2c8fvwY58+fl3cuHT9+HDKZrEQH2MuePHkCLy8vSKVS/Pzzz2+ce1VqDqeRkRG8vLywZs0ahcUmxUrbP9LOzg7JyckK9ePj46GhoQEbGxt5mbOzM4KCgnD69Gm0adMG27dvBwC4uLggNTUVLVu2LHEUJzyv8+pipjNnzogyVFwWe3t7SKVS3Llzp0SbLSwsALz4XBITE8ttZ0XVqVMHvr6+iI2NLXU4HXjxOV69erXUz1FLSwu2trYoLCzE+fPn5dekpqaWuydo8V/wzMxMeZmq9jMtbT7Lh3qtVRKLiIiosqr7tkh2dnbw9vbGmDFjkJiYiPj4eAQEBGDIkCHyFep3796Fra2tPD958uQJ/ve//+Hp06f47rvv8OTJE2RlZSErK6vSC30rvUp9zZo1KCoqQocOHbB3716kpaUhJSUFK1euLHVIeOjQodDW1saIESPwxx9/4MSJE5g4cSKGDx8OExMT3Lp1C0FBQUhISMDt27dx5MgRpKWlyRPCuXPnIioqCiEhIbhy5QpSUlKwY8cOzJ49u8Jt3r17NzZt2oTr169j3rx5SExMVFgsI7b69etjxowZmDp1KrZs2YL09HRcuHABq1atwpYtWwAAn332GdLS0vD5558jNTUV27dvR2Rk5BvHDA0NxYMHD+Dl5VXq+VmzZuH06dMICAhAUlIS0tLSsH//fvnnYGNjA29vb3z66af4/fffcf78eYwePRo6OjplxixOoIODg5GWloaDBw/K56SKTSqVQl9fX+HgcDoREVHFbdu2Dba2tujZsyf69OmDd999F9988438fEFBAVJTU5GbmwsAuHDhAn7//XdcvnwZLVu2hJmZmfz4888/KxW70gmnlZUVLly4gO7du2P69Olo06YNevXqhZiYGKxbt65EfV1dXRw+fBiPHj1C+/btMWDAAPTs2ROrV6+Wn7927Zp8i6WxY8diwoQJ+PTTTwEAXl5eOHDgAI4cOYL27dujU6dO+Oqrr9CsWbMKtzkkJAQ7duyAg4MDoqKi8MMPP8De3r6yj14poaGhmDNnDsLCwuS/VRw8eBDNmzcH8GIYe+/evdi3bx8cHR2xfv16LFq06I3jaWlpoVGjRmVu9u7g4IC4uDhcv34dXbt2hbOzM+bOnauw79bmzZvRpEkTuLu746OPPsLYsWNLrGh7Wd26dfHDDz/g2rVrcHBwwJIlS7BgwYI3fgYiIqKaqia8acjIyAjbt2/Hf//9h+zsbGzatElhPYWlpSUEQYCHhwcAwMPDA4IglHpYWlpWKrZEeJOVQETVwB6zoWqNd1iar9Z4JD5BpT/KSzdBVnJesyqlF+i9vpKIVmveU2s8ADia9M3rK4mop+MYtcbbY1vxfabFcvaPkpt+q1JdNX8v/u/eDpXHCLL8RLR7hWVsF+1e1YVK3qVORERERFSsRiec27Ztg56eXqlH69Y1e0FJ7969y3w2ZYbeiYiISHzVfdFQVav0u9Srk/fff7/MpfxvskdUdbJx40Y8e/as1HNGRkZqbg0RERGVp3amieKp0Qln/fr1FTZfr01e3b+TiIiIqKaq0QknERERUXVQVW8aqimYcBIREREpqbbOvRRLjV40RERERETVH3s4iYiIiJTE/s3yMeGkGqvvj33VGs9Hv+y3LlENUaT+DbWnv79JrfGWq/n7YuWAjWqNB6h/I/aY5G/VGq+rwyi1xgOAL0t/SZ3K5NTCAVbO4SwfE04iIiIiJVXFm8xqktr3KwYRERERVSvs4SQiIiJSEofUy8eEk4iIiEhJ3BapfBxSJyIiIiKVYg8nERERkZLYv1m+GtfDKZFIsG/fvqpuBoDXtyUjIwMSiQRJSUlqa1NZYmNjIZFI8PjxY1HvW9n/H6pqBxERUVWSQRDtqI2qXcKZlZWFiRMnwsrKClKpFBYWFvDx8UFMTExVN63SLCwskJmZiTZt2lR1UyqlvKTQ0tISERER8q8zMzPRu3dv9TWOiIiIapxqNaSekZEBNzc3GBoaYtmyZWjbti0KCgpw+PBhTJgwAdeuXavqJlaKpqYmTE1Nq7oZKlXbn4+IiKgiuEq9fNWqh3P8+PGQSCRITExE//79YW1tjdatW2PatGk4c+ZMqddcvnwZPXr0gI6ODho2bIixY8ciJydHfj42NhYdOnRAvXr1YGhoCDc3N9y+fVt+fv/+/XBxcYG2tjasrKwQEhKCwsKKv42kuIdPR0cHVlZW2LNnj/zcq0PqxT2HMTExcHV1ha6uLrp06YLU1FT5NcHBwXBycsLWrVthaWkJAwMDDBkyBP/995+8jkwmQ1hYGJo3bw4dHR04OjoqxAWAQ4cOwdraGjo6OujevTsyMjIq/EyV8eqQ+unTp+Hk5ARtbW24urpi3759pU4rOH/+fJmfARERUU0jiPhfbVRtEs5Hjx4hOjoaEyZMQL169UqcNzQ0LFH29OlTeHl5oUGDBjh79ix2796NY8eOISAgAABQWFiIfv36wd3dHZcuXUJCQgLGjh0LieTFO7xOnjwJX19fTJ48GVevXsWGDRsQGRmJhQsXVrjdc+bMQf/+/ZGcnIyhQ4diyJAhSElJKfeaL7/8EuHh4Th37hzq1KmDUaMUX2OWnp6Offv24cCBAzhw4ADi4uKwePFi+fmwsDBERUVh/fr1uHLlCqZOnYphw4YhLi4OAPDnn3/io48+go+PD5KSkjB69GgEBgZW+Jne1JMnT+Dj44O2bdviwoULCA0NxaxZs0qt+7rPgIiIiGqPajOkfuPGDQiCAFtb2wpfs337djx//hxRUVHyJHX16tXw8fHBkiVLULduXWRnZ6Nv375o0aIFAMDOzk5+fUhICAIDAzFixAgAgJWVFUJDQzFz5kzMmzevQm0YOHAgRo8eDQAIDQ3F0aNHsWrVKqxdu7bMaxYuXAh3d3cAQGBgIN577z08f/4c2traAF70YEZGRqJ+/foAgOHDhyMmJgYLFy5EXl4eFi1ahGPHjqFz587ydp86dQobNmyAu7s71q1bhxYtWiA8PBwAYGNjg8uXL2PJkiUV+2D/v3feeadEWW5ubpn1t2/fDolEgm+//Rba2tqwt7fH3bt3MWZMyfcev+4zeFVeXh7y8vIUyoT8Aki16lbmkYiIiFSCQ+rlqzYJpyBUvgs5JSUFjo6OCj2ibm5ukMlkSE1NRbdu3eDn5wcvLy/06tULnp6eGDRoEMzMzAAAycnJiI+PV+jRLCoqwvPnz5GbmwtdXd3XtqE46Xv569etSndwcJD/ubgt9+/fR9OmTQG8WJhTnGwW17l//z6AF4l5bm4uevXqpXDP/Px8ODs7yz+Xjh07ltvOijh58qRCOwDAw8OjzPqpqalwcHBQSBo7dOhQat3XfQavCgsLQ0hIiELZl6M+wuzRA8p9BiIiInWorUPhYqk2CWerVq0gkUhEXxi0efNmTJo0CdHR0di5cydmz56No0ePolOnTsjJyUFISAg++uijEteV1dMmhrp1/69Xrnh4XyaTlXq+uE7x+eL5qQcPHoS5ublCPalUKmo7mzdvXmIqQ5064vyVed1n8KqgoCBMmzZNoUy4uE+UthARESmLPZzlqzZzOI2MjODl5YU1a9bg6dOnJc6XtkWPnZ0dkpOTFerHx8dDQ0MDNjY28jJnZ2cEBQXh9OnTaNOmDbZv3w4AcHFxQWpqKlq2bFni0NCo2Efz6mKmM2fOKAzbi83e3h5SqRR37twp0WYLCwsALz6XxMTEctupCsVD9y8PfZ89e1aUe0ulUujr6yscHE4nIiKqGapNwgkAa9asQVFRETp06IC9e/ciLS0NKSkpWLlyZalDwkOHDoW2tjZGjBiBP/74AydOnMDEiRMxfPhwmJiY4NatWwgKCkJCQgJu376NI0eOIC0tTZ4Qzp07F1FRUQgJCcGVK1eQkpKCHTt2YPbs2RVu8+7du7Fp0yZcv34d8+bNQ2JionzRkirUr18fM2bMwNSpU7Flyxakp6fjwoULWLVqFbZs2QIA+Oyzz5CWlobPP/8cqamp2L59OyIjI1XWpmKffPIJZDIZxo4di5SUFBw+fBjLly8H8H+9mERERLWRTBBEO2qjapVwWllZ4cKFC+jevTumT5+ONm3aoFevXoiJicG6detK1NfV1cXhw4fx6NEjtG/fHgMGDEDPnj2xevVq+flr167Jt1gaO3YsJkyYgE8//RQA4OXlhQMHDuDIkSNo3749OnXqhK+++grNmjWrcJtDQkKwY8cOODg4ICoqCj/88APs7e3F+UDKEBoaijlz5iAsLAx2dnbw9vbGwYMH0bx5cwBA06ZNsXfvXuzbtw+Ojo5Yv349Fi1apNI2AYC+vj5++eUXJCUlwcnJCV9++SXmzp0LQLVTFIiIiKqaIOJRG0mEN1mtQ1RB27Ztw8iRI5GdnQ0dHR1R7/084QdR7/c6Ev3Gao1HKlBU8T12xTL9/U1qjbf8h35qjfe/ARvVGq8qxCR/q9Z4XR3Uv03clzIztcYrgHpHvT7K2q7yGMOalVwP8qa+v/2jaPeqLqrNoiGqHaKiomBlZQVzc3MkJydj1qxZGDRokOjJJhERUXVSW9+BLpZqNaRenWzbtg16enqlHq1bt67q5imld+/eZT6bskPvWVlZGDZsGOzs7DB16lQMHDgQ33zzjUgtJyIiqp74pqHysYezDO+//36JvSyLvbptUU2zceNGPHv2rNRzRkZGSt175syZmDlzplL3ICIiIvE9evQIEydOxC+//AINDQ30798fX3/9NfT09F57rSAI6NOnD6Kjo/HTTz+hX79+lYrNhLMM9evXL7HpeW3x6v6dREREpJyasA/n0KFDkZmZiaNHj6KgoAAjR47E2LFj5dtFliciIkKpHWeYcBIREREpqbrP4UxJSUF0dDTOnj0LV1dXAMCqVavQp08fLF++HE2aNCnz2qSkJISHh+PcuXPytwNWFudwEhEREVUjeXl5ePLkicLx8ktV3kRCQgIMDQ3lySYAeHp6QkNDA7///nuZ1+Xm5uKTTz7BmjVrYGpq+sbxmXASERERKUnMRUNhYWEwMDBQOMLCwpRqX1ZWFho3Vtzer06dOjAyMkJWVlaZ102dOhVdunTBBx98oFR8DqlTjTViyE61xrtd8Fit8ari7Uy1fVveqvhMY6a2UGs8dX9fbCt7FE5lpPrq3U9V3ftinryk3r1bAeBQm4q/YU8MbnZ31RpPHcScwxkUFIRp06YplEml0lLrBgYGYsmSJeXeLyUl5Y3a8fPPP+P48eO4ePHiG13/MiacREREREoS8xd2qVRaZoL5qunTp8PPz6/cOlZWVjA1NcX9+/cVygsLC/Ho0aMyh8qPHz+O9PR0GBoaKpT3798fXbt2RWxsbIXaCDDhJCIiIqqxjI2NYWxs/Np6nTt3xuPHj3H+/Hm0a9cOwIuEUiaTlbkNZGBgIEaPHq1Q1rZtW3z11Vfw8fGpVDuZcBIREREpqbqvUrezs4O3tzfGjBmD9evXo6CgAAEBARgyZIh8hfrdu3fRs2dPREVFoUOHDjA1NS2197Np06Zo3rx5peJz0RARERGRkmQiHqqybds22NraomfPnujTpw/effddhbcBFhQUIDU1Fbm5uaLHZg8nERER0VvAyMio3E3eLS0tXzsX9U3nqjLhJCIiIlJSbX0Hulg4pF5JlpaWiIiIqOpmlCs2NhYSiQSPHz8us05kZGSJVWdVJTg4GE5OTlXdDCIiojcmgyDaURu9VQmnj48PvL29Sz138uRJSCQSXLp0Sc2tqhqDBw/G9evXq7oZRERE9BZ4qxJOf39/HD16FH/99VeJc5s3b4arqyscHByqoGXqp6OjU+KNA0RERPRmBEEQ7aiN3qqEs2/fvjA2NkZkZKRCeU5ODnbv3g1/f3/s3bsXrVu3hlQqhaWlJcLDw8u8X0ZGBiQSCZKSkuRljx8/hkQikW+GWjy8ffjwYTg7O0NHRwc9evTA/fv38euvv8LOzg76+vr45JNPFFaFyWQyhIWFoXnz5tDR0YGjoyP27NlTqeeNj4+Hg4MDtLW10alTJ/zxxx/yc68OqRcPa2/duhWWlpYwMDDAkCFD8N9//8nreHh4YNKkSZg5cyaMjIxgamqK4OBghZiPHz/G6NGjYWxsDH19ffTo0QPJyckKdRYvXgwTExPUr18f/v7+eP78eaWei4iIqLqpCavUq9JblXDWqVMHvr6+iIyMVPgNYvfu3SgqKoKdnR0GDRqEIUOG4PLlywgODsacOXNKJKhvIjg4GKtXr8bp06fx559/YtCgQYiIiMD27dtx8OBBHDlyBKtWrZLXDwsLQ1RUFNavX48rV65g6tSpGDZsGOLi4ioc8/PPP0d4eDjOnj0LY2Nj+Pj4oKCgoMz66enp2LdvHw4cOIADBw4gLi4OixcvVqizZcsW1KtXD7///juWLl2K+fPn4+jRo/LzAwcOlCfT58+fh4uLC3r27IlHjx4BAHbt2oXg4GAsWrQI586dg5mZGdauXVvhZyIiIqKa561KOAFg1KhRSE9PV0jcNm/ejP79++Obb75Bz549MWfOHFhbW8PPzw8BAQFYtmyZ0nEXLFgANzc3ODs7w9/fH3FxcVi3bh2cnZ3RtWtXDBgwACdOnAAA5OXlYdGiRdi0aRO8vLxgZWUFPz8/DBs2DBs2bKhwzHnz5qFXr15o27YttmzZgnv37uGnn34qs75MJkNkZCTatGmDrl27Yvjw4YiJiVGo4+DggHnz5qFVq1bw9fWFq6urvM6pU6eQmJiI3bt3w9XVFa1atcLy5cthaGgo752NiIiAv78//P39YWNjgwULFsDe3v61z5KXl4cnT54oHEVCUYU/CyIiIlUSRPyvNnrrEk5bW1t06dIFmzZtAgDcuHEDJ0+ehL+/P1JSUuDm5qZQ383NDWlpaSgqUi65eXluqImJCXR1dWFlZaVQVvyO0xs3biA3Nxe9evWCnp6e/IiKikJ6enqFY3bu3Fn+ZyMjI9jY2CAlJaXM+paWlqhfv778azMzsxLvXX11juvLdZKTk5GTk4OGDRsqtPvWrVvydqekpJR4hdbL7SxLWFgYDAwMFI6U7LTXXkdERKQOXKVevrdyH05/f39MnDgRa9aswebNm9GiRQu4u7tX+j4aGi/y9ZeH58sasq5bt678zxKJROHr4jKZ7MXMjZycHADAwYMHYW5urlBPKpVWup0VVV6bKlInJycHZmZm8vmrL1N2C6agoCBMmzZNoWxUm6FK3ZOIiEgstXWxj1jeyoRz0KBBmDx5MrZv346oqCiMGzcOEokEdnZ2iI+PV6gbHx8Pa2traGpqlriPsbExACAzMxPOzs4AoLCA6E3Z29tDKpXizp07b5QIFztz5gyaNm0KAPj3339x/fp12NnZKd2+sri4uCArKwt16tSBpaVlqXXs7Ozw+++/w9fXV6GdryOVSksk25qSkv9PiIiIqPp5KxNOPT09DB48GEFBQXjy5An8/PwAANOnT0f79u0RGhqKwYMHIyEhAatXry5zUYuOjg46deqExYsXo3nz5rh//z5mz56tdPvq16+PGTNmYOrUqZDJZHj33XeRnZ2N+Ph46OvrY8SIERW6z/z589GwYUOYmJjgyy+/RKNGjdCvXz+l21cWT09PdO7cGf369cPSpUthbW2Nv//+GwcPHsSHH34IV1dXTJ48GX5+fnB1dYWbmxu2bduGK1euKEwvICIiqmlq61C4WN66OZzF/P398e+//8LLywtNmjQB8KKHbteuXdixYwfatGmDuXPnYv78+fKEtDSbNm1CYWEh2rVrhylTpmDBggWitC80NBRz5sxBWFgY7Ozs4O3tjYMHD6J58+YVvsfixYsxefJktGvXDllZWfjll1+gpaUlSvtKI5FIcOjQIXTr1g0jR46EtbU1hgwZgtu3b8PExATAiw3n58yZg5kzZ6Jdu3a4ffs2xo0bp7I2ERERqQMXDZVPInDSAdVQg5v1U2u82wWP1RpPIpGoNR5Q++cgVcVnGjO1hVrjjVz1UK3xljf+7/WVRCbVL1RrvPdT1Pv35uSlTWqNBwCH2ig/OlcZnezuqjWeyYmKbyn4pjze8RTtXrF/HRPtXtXFWzmkTkRERCQmWS3/hV1Zb+2Qek322WefKWw79PLx2WefVXXziIiI3jqCiEdtxB7OGmj+/PmYMWNGqef09fXV3BoiIiKi8jHhrIEaN26Mxo0bV3UziIiI6P/jKvXyMeEkIiIiUhITzvJxDicRERERqRR7OImIiIiUVNu3lVMWE04iIiIiJXFIvXzc+J1qrF9MP67qJqiUxlvwnSl5C35AC1DvpuEy9e9tr3bq/t5Q92cqU/PfGQDo84c4b8mrqH1t56g13sDMbSqP0b5JN9Hudfbv30S7V3XBOZxEREREpFIcUiciIiJSEgeMy8eEk4iIiEhJnMNZPg6pExEREZFKsYeTiIiISEkcUi8fE04iIiIiJXFIvXwcUlchS0tLREREVHUzEBsbC4lEgsePH1d1U4iIiOgtxISzgvz8/CCRSCCRSKClpYWWLVti/vz5KCwsLPOas2fPYuzYsWpsJREREVUFQcT/aiMmnJXg7e2NzMxMpKWlYfr06QgODsayZctK1MvPzwcAGBsbQ1dXV93NJCIiIjWTCYJoh6o8evQIQ4cOhb6+PgwNDeHv74+cnJzXXpeQkIAePXqgXr160NfXR7du3fDs2bNKxWbCWQlSqRSmpqZo1qwZxo0bB09PT/z888/w8/NDv379sHDhQjRp0gQ2NjYASg6pP378GJ9++ilMTEygra2NNm3a4MCBA/Lzp06dQteuXaGjowMLCwtMmjQJT58+rVDb8vLyMGvWLFhYWEAqlaJly5b47rvvFOqcP38erq6u0NXVRZcuXZCamio/l56ejg8++AAmJibQ09ND+/btcezYMYXrLS0tsWjRIowaNQr169dH06ZN8c033yjUOX36NJycnKCtrQ1XV1fs27cPEokESUlJ8jp//PEHevfuDT09PZiYmGD48OF4+PBhhZ6TiIiI3szQoUNx5coVHD16FAcOHMBvv/322pHYhIQEeHt743//+x8SExNx9uxZBAQEQEOjcikkE04l6OjoyHszY2JikJqaKv+f+CqZTIbevXsjPj4e33//Pa5evYrFixdDU1MTwIuEz9vbG/3798elS5ewc+dOnDp1CgEBARVqi6+vL3744QesXLkSKSkp2LBhA/T09BTqfPnllwgPD8e5c+dQp04djBo1Sn4uJycHffr0QUxMDC5evAhvb2/4+Pjgzp07CvcIDw+Hq6srLl68iPHjx2PcuHHyxPXJkyfw8fFB27ZtceHCBYSGhmLWrFkK1z9+/Bg9evSAs7Mzzp07h+joaNy7dw+DBg2q0HMSERFVR9V9SD0lJQXR0dHYuHEjOnbsiHfffRerVq3Cjh078Pfff5d53dSpUzFp0iQEBgaidevWsLGxwaBBgyCVSisVn6vU34AgCIiJicHhw4cxceJEPHjwAPXq1cPGjRuhpaVV6jXHjh1DYmIiUlJSYG1tDQCwsrKSnw8LC8PQoUMxZcoUAECrVq2wcuVKuLu7Y926ddDW1i6zPdevX8euXbtw9OhReHp6lrh3sYULF8Ld3R0AEBgYiPfeew/Pnz+HtrY2HB0d4ejoKK8bGhqKn376CT///LNC0tunTx+MHz8eADBr1ix89dVXOHHiBGxsbLB9+3ZIJBJ8++230NbWhr29Pe7evYsxY8bIr1+9ejWcnZ2xaNEiedmmTZtgYWGB69evyz+bV+Xl5SEvL0+hrEAoQl2JZpmfCxERkbqIORRe2r95Uqm00kneyxISEmBoaAhXV1d5maenJzQ0NPD777/jww8/LHHN/fv38fvvv2Po0KHo0qUL0tPTYWtri4ULF+Ldd9+tVHz2cFbCgQMHoKenB21tbfTu3RuDBw9GcHAwAKBt27ZlJpsAkJSUhHfeeafMhCo5ORmRkZHQ09OTH15eXpDJZLh161a57UpKSoKmpqY8mSyLg4OD/M9mZmYAXvxlAl70cM6YMQN2dnYwNDSEnp4eUlJSSvRwvnwPiUQCU1NT+T1SU1Ph4OCgkBx36NChxHOeOHFC4TltbW0BvOjlLUtYWBgMDAwUjt1Pr5b7vEREROoiZg9naf/mhYWFKdW+rKwsNG7cWKGsTp06MDIyQlZWVqnX3Lx5EwAQHByMMWPGIDo6Gi4uLujZsyfS0tIqFZ89nJXQvXt3rFu3DlpaWmjSpAnq1Pm/j69evXrlXqujo1Pu+ZycHHz66aeYNGlSiXNNmzZV6t7F6tatK/+zRCIB8GKoHwBmzJiBo0ePYvny5WjZsiV0dHQwYMAA+ZSB0u5RfJ/ie1RETk4OfHx8sGTJkhLnipPg0gQFBWHatGkKZcdaja5wXCIiopqitH/zyurdDAwMLPXf1JelpKS8UTuK/33/9NNPMXLkSACAs7MzYmJisGnTpkolwUw4K6FevXpo2bLlG13r4OCAv/76q8xhYxcXF1y9evWN7t+2bVvIZDLExcXJh9QrKz4+Hn5+fvIu9ZycHGRkZFTqHjY2Nvj++++Rl5cn/8Y4e/asQh0XFxfs3bsXlpaWCgn765Q2lMDhdCIiqi7EHFKvzPD59OnT4efnV24dKysrhRHJYoWFhXj06BFMTU1Lva64I8je3l6h3M7OrsQI6OtwSF1N3N3d0a1bN/Tv3x9Hjx7FrVu38OuvvyI6OhrAi/mQp0+fRkBAAJKSkpCWlob9+/dXaNGQpaUlRowYgVGjRmHfvn24desWYmNjsWvXrgq3r1WrVvjxxx+RlJSE5ORkfPLJJ5XquQQgv2bs2LFISUnB4cOHsXz5cgD/16M6YcIEPHr0CB9//DHOnj2L9PR0HD58GCNHjkRRUVGl4hEREVUXVbVoyNjYGLa2tuUeWlpa6Ny5Mx4/fozz58/Lrz1+/DhkMhk6duxY6r0tLS3RpEkThV1tgBdrR5o1a1apdjLhVKO9e/eiffv2+Pjjj2Fvb4+ZM2fKkywHBwfExcXh+vXr6Nq1K5ydnTF37lw0adKkQvdet24dBgwYgPHjx8PW1hZjxoyp8JZKALBixQo0aNAAXbp0gY+PD7y8vODi4lKp59PX18cvv/yCpKQkODk54csvv8TcuXMBQD6vs0mTJoiPj0dRURH+97//oW3btpgyZQoMDQ0rvcUCERERVYydnR28vb0xZswYJCYmIj4+HgEBARgyZIg817h79y5sbW2RmJgI4EVn0eeff46VK1diz549uHHjBubMmYNr167B39+/UvElAt82Tyq0bds2jBw5EtnZ2RWea1pRv5h+LOr9qhuNt+A7U1JL36jxMgEStcaTqTdclVD394a6P1OZmv/OAECfPxaoNd6+tnPUGm9g5jaVx2jRqHKdNOVJf3hBtHu97NGjRwgICMAvv/wCDQ0N9O/fHytXrpRvo5iRkYHmzZvjxIkT8PDwkF+3ePFirFmzBo8ePYKjoyOWLl1a6VXqnMNJooqKioKVlRXMzc2RnJyMWbNmYdCgQaInm0RERNVJTXglpZGREbZv317meUtLS5TWDxkYGIjAwEClYjPhrAFOnjyJ3r17l3m+Iq+lUpesrCzMnTsXWVlZMDMzw8CBA7Fw4cKqbhYRERFVISacNYCrq6vCqyGrs5kzZ2LmzJlV3QwiIiK1EoTKLbR92zDhrAF0dHTeeDsmIiIiUj1ZDRhSr0pcFkxEREREKsUeTiIiIiIlcdOf8jHhJCIiIlISh9TLx4STaixtNU/QzlfzDJSq2KOySKLe/f80BPXGq4rPtGWDx2qNd+dffbXGU/f3BQDUVfP/xxw1P6Ob3V21xgPUvy9mv8uhao2nDuzhLB/ncBIRERGRSrGHk4iIiEhJMvZwlosJJxEREZGSasKbhqoSh9SJiIiISKXYw0lERESkJC4aKh8TTiIiIiIlcVuk8nFI/S1haWmJiIgI0e8jkUiwb98+AEBGRgYkEkmNee87ERERqQcTzhrAx8cH3t7epZ47efIkJBIJLl26JGrMyMhIGBoalig/e/Ysxo4dW+o1FhYWyMzMRJs2bQAAsbGxkEgkePz4sahtIyIiqm4EQRDtqI2YcNYA/v7+OHr0KP76668S5zZv3gxXV1c4ODiopS3GxsbQ1dUt9ZympiZMTU1Rpw5nahAR0dtFJgiiHbURE84aoG/fvjA2NkZkZKRCeU5ODnbv3g1/f3/s3bsXrVu3hlQqhaWlJcLDw8u954oVK9C2bVvUq1cPFhYWGD9+PHJycgC86JkcOXIksrOzIZFIIJFIEBwcDKD8ofmXh9QzMjLQvXt3AECDBg0gkUjg5+eHqKgoNGzYEHl5eQrX9uvXD8OHD6/8h0NERETVHhPOGqBOnTrw9fVFZGSkQlf77t27UVRUBDs7OwwaNAhDhgzB5cuXERwcjDlz5pRIUF+moaGBlStX4sqVK9iyZQuOHz+OmTNnAgC6dOmCiIgI6OvrIzMzE5mZmZgxY0al2mxhYYG9e/cCAFJTU5GZmYmvv/4aAwcORFFREX7++Wd53fv37+PgwYMYNWpUpWIQERFVFxxSLx8Tzhpi1KhRSE9PR1xcnLxs8+bN6N+/P7755hv07NkTc+bMgbW1Nfz8/BAQEIBly5aVeb8pU6age/fusLS0RI8ePbBgwQLs2rULAKClpQUDAwNIJBKYmprC1NQUenp6lWqvpqYmjIyMAACNGzeGqakpDAwMoKOjg08++QSbN2+W1/3+++/RtGlTeHh4lHm/vLw8PHnyROHIF4oq1SYiIiJVkUEQ7aiNmHDWELa2tujSpQs2bdoEALhx4wZOnjwJf39/pKSkwM3NTaG+m5sb0tLSUFRUelJ27Ngx9OzZE+bm5qhfvz6GDx+Of/75B7m5uSp/ljFjxuDIkSO4e/cugBcLlPz8/CCRSMq8JiwsDAYGBgrHjqcpKm8rERFRRbCHs3xMOGuQ4rma//33HzZv3owWLVrA3d290vfJyMhA37594eDggL179+L8+fNYs2YNACA/P1/sZpfg7OwMR0dHREVF4fz587hy5Qr8/PzKvSYoKAjZ2dkKx5B6dipvKxERESmPy4lrkEGDBmHy5MnYvn07oqKiMG7cOEgkEtjZ2SE+Pl6hbnx8PKytraGpqVniPufPn4dMJkN4eDg0NF78zlE8nF5MS0urzN7RitLS0gKAUu8zevRoRERE4O7du/D09ISFhUW595JKpZBKpYr3l5R8NiIioqpQW1eXi4U9nDWInp4eBg8ejKCgIGRmZsp7BadPn46YmBiEhobi+vXr2LJlC1avXl3mQp+WLVuioKAAq1atws2bN7F161asX79eoY6lpSVycnIQExODhw8fvtFQe7NmzSCRSHDgwAE8ePBAvgoeAD755BP89ddf+Pbbb7lYiIiIajxBxP9qIyacNYy/vz/+/fdfeHl5oUmTJgAAFxcX7Nq1Czt27ECbNm0wd+5czJ8/v8xhakdHR6xYsQJLlixBmzZtsG3bNoSFhSnU6dKlCz777DMMHjwYxsbGWLp0aaXbam5ujpCQEAQGBsLExAQBAQHycwYGBujfvz/09PTQr1+/St+biIiIag6JUFtnp1K117NnT7Ru3RorV658o+uPmgwWuUXly1fz72eSKvgtt6ichVuqoKHmR6yKz7RFg2y1xrvzr75a46n7+wIA6qr5/2OORL3P6GZ3V63xAOC3a++oNV6/y6FqjVe3kZXKY+joNBPtXs+e3RbtXtUF53CS2v3777+IjY1FbGws1q5dW9XNISIiUhr778rHhJPUztnZGf/++y+WLFkCGxubqm4OERERqRgTTlK7jIyMqm4CERGRqGrrYh+xMOEkIiIiUhKH1MvHVepEREREpFLs4SQiIiJSEns4y8eEk4iIiEhJTDdfQyB6izx//lyYN2+e8Pz5c8aroTFre7yqiMlnrPnxqiJmbY9H4uLG7/RWefLkCQwMDJCdnQ19fdVvkF3b41VFzNoerypi8hlrfryqiFnb45G4uGiIiIiIiFSKCScRERERqRQTTiIiIiJSKSac9FaRSqWYN28epFIp49XQmLU9XlXE5DPW/HhVEbO2xyNxcdEQEREREakUeziJiIiISKWYcBIRERGRSjHhJCIiIiKVYsJJRERERCrFhJOIiIiIVIoJJxHRKx4/foyNGzciKCgIjx49AgBcuHABd+/ereKWERHVTEw4iahGef78uUrvf+nSJVhbW2PJkiVYvnw5Hj9+DAD48ccfERQUpLK4J0+exLBhw9C5c2d5Yrt161acOnVKZTHVSdX/36qT/Px8/PXXX7hz547CQZUzYsQI/Pbbb1XdDBJJnapuAJE6pKenY/PmzUhPT8fXX3+Nxo0b49dff0XTpk3RunVrUWM9ffoUixcvRkxMDO7fvw+ZTKZw/ubNm6LGqwq+vr7o3r07unXrhhYtWqg8nkwmw8KFC7F+/Xrcu3cP169fh5WVFebMmQNLS0v4+/uLFmvatGnw8/PD0qVLUb9+fXl5nz598Mknn4gW52V79+7F8OHDMXToUFy8eBF5eXkAgOzsbCxatAiHDh1SSdxbt26hsLAQrVq1UihPS0tD3bp1YWlpKVosQ0NDdOjQAe7u7vDw8ECXLl2go6Mj2v1LIwgC9uzZgxMnTpT6vfjjjz+KGi8tLQ2jRo3C6dOnS7RDIpGgqKhI6RjOzs6QSCQVqnvhwgWl45Xlxo0bSE9PR7du3aCjoyN/RjFlZ2fD09MTzZo1w8iRIzFixAiYm5uLGoPUhwkn1XpxcXHo3bs33Nzc8Ntvv2HhwoVo3LgxkpOT8d1332HPnj2ixhs9ejTi4uIwfPhwmJmZif5DuDRFRUWIjIwsM8k9fvy4qPG0tLQQFhYGf39/mJuby5MId3f3EsmLGBYsWIAtW7Zg6dKlGDNmjLy8TZs2iIiIEDXhPHv2LDZs2FCi3NzcHFlZWaLFedmCBQuwfv16+Pr6YseOHfJyNzc3LFiwQCUxAcDPzw+jRo0q8f/s999/x8aNGxEbGytarGPHjuG3335DbGwsvvrqKxQWFsLV1VX+d6dXr16ixSo2ZcoUbNiwAd27d4eJiYnKvxf9/PxQp04dHDhwQGXf+/369ZP/+fnz51i7di3s7e3RuXNnAMCZM2dw5coVjB8/XvTYAPDPP/9g8ODBOH78OCQSCdLS0mBlZQV/f380aNAA4eHhosXat28fHjx4gK1bt2LLli2YN28ePD094e/vjw8++AB169YVLRapgUBUy3Xq1EkIDw8XBEEQ9PT0hPT0dEEQBOH3338XzM3NRY9nYGAgnDp1SvT7lmfChAlCvXr1hEGDBgmTJ08WpkyZonCoyl9//SVs375d+PTTTwVbW1tBQ0NDJZ9pixYthGPHjgmCoPj/MCUlRTA0NBQ1lrGxsXDhwoUSsY4cOSK88847osYqpqOjI9y6datEzPT0dEEqlaokpiAIQv369YW0tLQS5WlpaYKBgYHK4hYUFAinT58WRowYIdSpU0fQ0NBQSZwGDRoIBw8eVMm9S6OrqyukpKSoLZ6/v78we/bsEuVz584VRo4cqZKYw4cPF7y8vIQ///xT4e9qdHS0YG9vr5KYxc6fPy8EBAQI2traQqNGjYQpU6YI169fV2lMEg97OKnWu3z5MrZv316ivHHjxnj48KHo8Ro0aAAjIyPR71ueHTt2YNeuXejTp49a4zZo0AANGzZEgwYNYGhoiDp16sDY2Fj0OHfv3kXLli1LlMtkMhQUFIga6/3338f8+fOxa9cuAIBEIsGdO3cwa9Ys9O/fX9RYxUxNTXHjxo0SQ9inTp2ClZWVSmICL57tv//+K1GenZ0tyvDvq65fv47Y2Fj5kZeXh759+8LDw0P0WABgYGCg0s/vVfb29ir5mVKW3bt349y5cyXKhw0bBldXV2zatEn0mEeOHMHhw4fxzjvvKJS3atUKt2/fFj1esczMTBw9ehRHjx6FpqYm+vTpg8uXL8Pe3h5Lly7F1KlTVRabxMFFQ1TrGRoaIjMzs0T5xYsXVTIfKDQ0FHPnzkVubq7o9y6LlpZWqQmZqnzxxRfo0qULGjZsiMDAQDx//hyBgYHIysrCxYsXRY9nb2+PkydPlijfs2cPnJ2dRY0VHh6OnJwcNG7cGM+ePYO7uztatmyJ+vXrY+HChaLGKjZmzBhMnjwZv//+OyQSCf7++29s27YNM2bMwLhx41QSEwC6deuGsLAwheSyqKgIYWFhePfdd0WNZW5ujk6dOiE6OhqdOnXCr7/+iocPH+Knn37C5MmTRY1VLDg4GCEhIXj27JlK7g8AT548kR9LlizBzJkzERsbi3/++Ufh3JMnT0SPraOjg/j4+BLl8fHx0NbWFj0e8GKOuq6ubonyR48eQSqVihqroKAAe/fuRd++fdGsWTPs3r0bU6ZMwd9//40tW7bg2LFj2LVrF+bPny9qXFIN9nBSrTdkyBDMmjULu3fvhkQigUwmQ3x8PGbMmAFfX1/R44WHhyM9PR0mJiawtLQsMc9IFRP5p0+fjq+//hqrV69Wy5zRxYsXw9jYGPPmzcNHH30Ea2trlcabO3cuRowYgbt370Imk+HHH39EamoqoqKicODAAVFjGRgY4OjRo4iPj0dycjJycnLg4uICT09PUeO8LDAwEDKZDD179kRubi66desGqVSKGTNmYOLEiSqLu2TJEnTr1g02Njbo2rUrgBer5Z88eSL6vF9jY2Ncu3YNWVlZyMrKwr179/Ds2bNSkxexDBo0CD/88AMaN26ssu9FQ0NDhe85QRDQs2dPhTqCiIuGXjZlyhSMGzcOFy5cQIcOHQC8mH+7adMmzJkzR9RYxbp27YqoqCiEhoYCgPxn6tKlS9G9e3dRY5mZmUEmk+Hjjz9GYmIinJycStTp3r07DA0NRY1LqiERBEGo6kYQqVJ+fj4mTJiAyMhIFBUVoU6dOigqKsInn3yCyMhIaGpqihovJCSk3PPz5s0TJc5HH32k8PXx48dhZGSE1q1bl/iHVezVuMnJyYiLi0NsbCxOnjwJLS0t+eIPDw8PlSSgJ0+exPz58xWSwLlz5+J///uf6LFe9fjxY7X8o5afn48bN24gJycH9vb20NPTU3nMv//+G6tXr0ZycjJ0dHTg4OCAgIAAlUwLefz4MX777TfExcUhLi4OV69ehZOTE7p3766S3uNBgwbhxIkTGDBgQKmLhsT4XoyLi6twXXd3d6XjvWrXrl34+uuvkZKSAgCws7PD5MmTMWjQINFjAcAff/yBnj17wsXFBcePH8f777+PK1eu4NGjR4iPjxd114qtW7di4MCBKuutJfViwklvjT///BOXL19GTk4OnJ2dVbKaWp1GjhxZ4bqbN29WYUteJKBfffUVtm3bBplMppL5f+qyZMkSWFpaYvDgwQBeJC179+6FqakpDh06BEdHxypuYc33zz//IDY2Fvv378cPP/ygsr8z9erVw+HDh0WfHvC2y87Olv+SUvzL34QJE2BmZiZqnFGjRuHrr79W2J4MeDGsP3HiRJXMUSXVYcJJtd78+fMxY8aMEkN3z549w7JlyzB37lzRYz5+/Bh79uxBeno6Pv/8cxgZGeHChQswMTGpFfvICYKAixcvyhd/nDp1Ck+ePIGDgwPc3d3x1VdfiRrvzz//hEQikS9USExMxPbt22Fvb4+xY8eKGqt58+bYtm0bunTpgqNHj2LQoEHYuXMndu3ahTt37uDIkSOixgNebG+zatWqMveLFHMaxqVLl9CmTRtoaGjg0qVL5dZ1cHAQLe6PP/4o//ty9epVGBkZ4d1335Vvp6WKRN7W1ha7du0S9TnKU9bnKZFIoK2tjaZNm4o+z7H4Z83NmzcxY8aMWvWzRlNTE5mZmWjcuLFC+cOHD2FqaorCwsIqahm9CSacVOuV9UPrn3/+QePGjUXvWbl06RI8PT1hYGCAjIwMpKamwsrKCrNnz8adO3cQFRUlaryq0KBBA+Tk5MDR0VE+lN61a1eVDTt37doVY8eOxfDhw5GVlQVra2u0adMGaWlpmDhxoqi/NOjo6OD69euwsLDA5MmT8fz5c2zYsAHXr19Hx44d8e+//4oWq9jQoUNx5MgRlQ79FtPQ0EBWVhYaN24MDQ0NSCQSlPbPgNhzDhs3boxu3brJE8y2bduKdu+yHDx4EKtWrcL69etF3cS+LMWfZ1nq1q2LwYMHY8OGDaIME1fFzxp1JNVPnjyBIAho0KAB0tLSFHa+KCoqwi+//ILAwED8/fffSsUhNauSzZiI1EgikQj3798vUR4TEyM0atRI9Hg9e/YUPv/8c0EQFPdUjI+PF5o1ayZ6PEEQBCcnJ8HZ2bnE4eLiInTp0kXw9fUVjh8/Llq8AwcOCNnZ2aLd73UMDQ2Fa9euCYIgCF9//bXQpUsXQRAE4fDhw0Lz5s1FjWVmZibEx8cLgiAI1tbWwq5duwRBEIRr164J9evXFzVWMX19fbXt3ZqRkSHIZDL5n8s7ajpDQ0NBS0tL0NDQEPT09IQGDRooHGLbt2+fYGNjI2zcuFG4dOmScOnSJWHjxo2CnZ2dsGPHDuH7778X3nnnHWH69OmixKuKnzUSiUTQ0NAQNDQ0BIlEovC1hoaGIJVKBV9fX+HZs2eixCjt0NTUFBYsWCDiU5E6cJU61VoNGjSARCKBRCKBtbW1Qs9DUVERcnJy8Nlnn4ketyreVOPt7Y1169ahbdu28tWqZ8+exaVLl+Dn54erV6/C09MTP/74Iz744AOl47333nvyP//1118AUGJfPjEVFBTIe02OHTuG999/H8CLIdPStrxSxkcffYRPPvkErVq1wj///IPevXsDeLGNlqq2njI3Ny8xT01VmjVrBuDFZxoSEoI5c+agefPmaoldVFSEffv2yRe42Nvb44MPPhB94V6xiIgIldy3LAsXLsTXX38NLy8veVnbtm3xzjvvYM6cOUhMTES9evUwffp0LF++XOl4VfGz5qeffsKsWbPw+eefy3/WJCYmIjw8HPPmzUNhYSECAwMxe/bsN37GEydOQBAE9OjRA3v37lVYwKalpYVmzZqhSZMmojwPqVFVZ7xEqhIZGSls3rxZkEgkwtdffy1ERkbKj+3btwunT59WSdyqeFPN6NGjhfnz55coDw0NFUaPHi0Iwou3j7Rr106UeEVFRUJISIigr68v73UwMDAQ5s+fLxQVFYkS42UdOnQQZs2aJfz222+Ctra2kJSUJAiCICQkJIj+ZqP8/Hxh2bJlwqRJk+T/HwVBEFasWCF8++23osYqdujQIcHb21vtvYr6+vrCzZs31RIrLS1NaNWqlaCrqyvvgdfV1RVsbGyEGzduqKUNqqatrV3qm4ZSUlIEbW1tQRAE4datW4KOjo4o8ariZ0379u2F6OjoEuXR0dFC+/btBUEQhJ9++kmwsrJSOtbLvfFU8zHhpFovNjZWyM/PV1s8f39/oV+/fkJ+fr6gp6cn3Lx5U7h9+7bg7OwsTJ48WSUx9fX1y3xFob6+viAIL/7R09PTEyVeYGCgYGxsLKxdu1ZITk4WkpOThTVr1gjGxsbCF198IUqMl504cUIwNDQUNDQ0FF7ZFxQUJHz44Yeix1O3+/fvCx4eHmob+i3m6+srrFixQmX3f1nv3r0Fb29v4Z9//pGXPXz4UPD29hb69Omjkpi3b98u9xCbk5OTMGLECCEvL09elp+fL4wYMUJwcnISBEEQTp06JVhaWooSryp+1qg6qU5OTpb/0lr8s6Wsg2oWLhqit8rz58+Rn5+vUKavry9qjOzsbAwYMADnzp3Df//9hyZNmiArKwudO3fGoUOHUK9ePVHjAYCJiQmWLVtWYiP7qKgofP7557h37x6uXr0Kd3d3PHjwQOl4TZo0wfr16+VD28X279+P8ePH4+7du0rHeFVRURGePHmCBg0ayMsyMjKgq6tbYkGYstLT0xEREaEw9DtlyhSVvSbR09MTd+7cgb+/f6mLhkaMGKGSuAsWLEB4eDh69uyJdu3alfi7OWnSJNFi1atXD2fOnCmxWCg5ORlubm7IyckRLVax1y3iEXvB4OnTp/H+++9DQ0NDvjL+8uXLKCoqwoEDB9CpUyds3boVWVlZ+Pzzz5WOVxU/a5ydneHo6IhvvvkGWlpaAF5MzxgzZgySk5Nx8eJFxMfHY9iwYbh161al719Vi9pI9TiHk2q93NxczJw5E7t27cI///xT4rzYP7SK31Rz6tQpXLp0SS1vqpk4cSI+++wznD9/Hu3btwfwYn7Xxo0b8cUXXwAADh8+XOqbOt7Eo0ePYGtrW6Lc1tYWjx49EiXGqzQ1NVFYWIhTp04BAGxsbFSy8vjw4cN4//334eTkBDc3NwAvXhVob2+PX375Bb169RI95unTp5GQkKD2PT6/++47GBoa4vz58zh//rzCOYlEImrCKZVKS31ve05OjjxxEdurr1ktKCjAxYsXsWLFCpVsNN+lSxfcunUL27Ztw/Xr1wEAAwcOxCeffCKfozt8+HDR4lXFz5o1a9bg/fffxzvvvFNqUg0AN2/exPjx49/o/rdu3ZKvSn+ThJWqsSruYSVSufHjxwt2dnbCnj17BB0dHWHTpk1CaGio8M477wjff/99VTdPNN9//73QqVMn+TBsp06dhG3btsnP5+bmKrVy9GUdOnQQJk6cWKI8ICBA6NChgygxXpaTkyOMHDlS0NTUlK+MrVOnjjBq1Cjh6dOnosZycnISZs2aVaJ81qxZgrOzs6ixijk7OwsJCQkquXd1MXz4cKF169bCmTNnBJlMJshkMiEhIUFo06aNMGLECLW25cCBA4K7u7taY9YmT548EdatWydMnTpVmDp1qrB+/XrhyZMnVd0squY4pE61XtOmTREVFQUPDw/o6+vjwoULaNmyJbZu3YoffvgBhw4dEj1mTEwMvvrqK4XXzU2ZMkWlPQ/qFBcXh/feew9NmzZF586dAQAJCQn4888/cejQIfl7ucXy6aef4tixY1i9erW81/HUqVOYNGkSevXqhXXr1okWS1tbG5cvXy7xJqrr16/DwcEBz58/Fy1WsSNHjiAkJAQLFy5E27ZtS7yaVOxpH6/Kz8/HrVu30KJFC9Spo5qBr8ePH2PEiBH45Zdf5M9XWFiI999/H5s3b1br+7Bv3LgBR0dHPH36VOl7/fzzz+jduzfq1q2Ln3/+udy6r05BEUNMTAxiYmJKfWFATXwTz+s+w5ep4vMk1WHCSbWenp4erl69iqZNm+Kdd97Bjz/+iA4dOuDWrVto27at6HPH1q5di8mTJ2PAgAHyZOzMmTPYs2cPvvrqK0yYMEHUeFXl77//xpo1a3Dt2jUAL5LqsWPHYsGCBfjmm29EjdWoUSPs2bMHHh4eCuUnTpzAoEGDRJmXWszCwgIrVqzAwIEDFcp37dqFGTNm4M6dO6LFKqahoQEAJeYbCoKg0rlqubm5mDhxIrZs2QLgRVJtZWWFiRMnwtzcHIGBgaLHvHHjhsIvYqraagp4sYH4ywRBQGZmJoKDg3Ht2jUkJSUpHePVOYdlUcX/x5CQEMyfPx+urq4wMzMr8ffnp59+EjVesbS0tDLfiqXsSxjK+wxfxjmcNQ/ncFKtZ2VlhVu3bqFp06byV9116NABv/zyi0p6VRYtWoSvvvoKAQEB8rJJkybBzc0NixYtEi3hNDIywvXr19GoUSP5nqNlUcW8yiZNmpSYB5ecnIzvvvtO9IQzNzcXJiYmJcobN26M3NxcUWONGTMGY8eOxc2bN9GlSxcAL+ZwLlmyBNOmTRM1VrETJ06o5L6vExQUhOTkZMTGxsLb21te7unpieDgYJUknC1btlRIMi/9v/buPS7n+/8f+OOq6KDQUJJ01Dmp0Sf51JBJRrPZ+pq0Rg7ZNF9CYWVlkm1Ijlk6OWwlzWEyh7IODl9TXOlDOlBO1WzEN51U798fvl2/LlcRvd7XpXreb7duN73e1+39fO1auZ5e7+fr9czNxYgRIyQ287HQt2/fVpN4HR0d/PLLL0xitEy4Xky++LZz507ExsYyrQt9lZ9++gkLFixA//79MXDgQLH3VyAQdDjhlPZ7SKSHVjhJl7dp0ybIy8vj66+/xunTpzFlyhRwHIdnz55h48aNWLRoEdN4qqqquHLlisTKTWFhIWxsbJitqMbFxWH69OlQVFREbGzsSxNOvnY5v0goFMLW1pb5yoOzszP69euH+Ph4UUvAmpoaeHl54eHDhzh9+jSzWBzHITw8HBs2bBC1zhs0aBCWLVuGr7/++qXvc2ejq6uLhIQE2NvbQ01NDUKhEAYGBigqKoKtra3ECiEf+PqZAZ6XfrQkJyeHAQMGwMjIiLfSAWnq168fLl68CENDQ6nF1NXVxZdffgl/f3+pxSRdAyWcpNspLS1FdnY2jIyMRLssWZoxYwZsbGwkjj358ccfcenSJWYrK4DkI8O28F0D2Iyv5CEvLw8uLi6oq6sT7eQWCoVQUlLCiRMnYGFhwTRes+Zd1Xx3AcrIyHjpdScnJ17iqqioIC8vDwYGBmIJp1AohJOTEx4/fsxL3Jb4TDilJSIiol2vY7nrHwD8/f2hqqqKwMBApvd9md69e+PKlSu8HREWERGBefPmQUlJ6ZXvK+v3k/CLEk7SpTU1NSE2NhbJyckoKSmBQCCAvr4+PvnkE3h6evKyWvXdd9/hxx9/xOjRo8VqOM+ePQs/Pz+x5K+jf2G+6pzBZtL6MOczeaiursa+ffvEakY9PDygrKzMPJa0tVa39mIrVj44OTnh008/ha+vL9TU1JCbmwt9fX34+vqisLAQv//+Oy9xW2L9MyOLTTwvtga9c+cOtLS0xFZRBQIBbt68ySRes0WLFiE+Ph7Dhg3DsGHDJDabbdy4kWk8APD29sbIkSN5aQsMPH8vL126hH79+r205Sof7yfhFyWcpMviOA5TpkxBSkoKrK2tYWpqCo7jcP36dVy9ehVubm44dOgQ87jt7UvN4i/Mlo8MOY7DpEmTEBUVBW1tbbHXvffeex2K0+zjjz9+6fXKykqkp6d3utUqGxubdv/jIycnh3n8F1cSm8+LDAwMxNq1a+Hs7Mw8JvB8p7+rqytmzpyJ2NhYzJ8/H9euXcO5c+eQnp6Od999t8MxXrUKn5ubi/fee4/Zz4wsN/E0a7lazKexY8e2eU0gECAtLY15zHXr1mHjxo344IMPWj1RgVYdSVso4SRdVkxMDBYtWoTDhw9L/MWclpaGqVOnYuvWrRLdeVj5+++/ATzfYS0tfH/QzZo1q12vi4mJ6XAsaR6PEhwc3O7Xrl69ukOxXkd6ejqWLFkicSg7S8XFxQgLC4NQKBQdHO7v7y/REehNvWoVnu+d+LIgrYRTFqS56hgSEoKlS5dCRUVFbLympgY//PBDhzcoEemihJN0WRMmTMC4cePa3GkbGhqK9PR0nDhxglnMyspKrFq1CgkJCXj06BEAQF1dHdOnT8d3333H+1mDXemDjo5HAfLz8zFixAhe2j5Ky4sbd9rCahX+bdCVfg9lSV5eHmVlZRKta//55x9oaGh02d/7rqrzb9MjpA25ubn4/vvv27zu6ura7mL/9nj48CFGjRqFe/fuwcPDA2ZmZgCAa9euITY2FqmpqTh37pxYL3DSNmkfj/Lo0SPs3bsXXl5eEpusHj9+jPj4+FavsZCbmyv2ffN5kWFhYczakb7MX3/91eqZiiw21b1uIhkWFgYfH583/sfZ6/xOd/bHv0+fPkVYWFibB7939hrH5tXvFwmFQrzzzjsymBHpCEo4SZf18OHDVs9ubKapqSlahWQhJCQEPXv2RHFxsUTckJAQTJgwASEhIdi0aROzmK3pSsf2pKWlYeHChbhw4UKrSaCDgwN27tzJpLPR1q1bkZubC19fX4lrffr0QWZmJp48eYJVq1Z1ONaLhg8fDoFAgBcfONnb2/PaLSY7OxteXl64fv26RGxZrRyHhobC3d39jRPOF3+/Hjx4gOrqatH9KisroaKiAg0NDWYJ54t1qgKBAFVVVRLjrP+xMmfOHKSnp8PT07PVg9/5cvfuXRw5cgS3b9+WOD+VxUal5nOFBQIBjI2NJTbQVVVV8bZpifCHHqmTLkteXh7l5eUYMGBAq9crKiowaNAgZh+qenp6iIyMhIuLS6vXf//9d/j4+KCkpIRJPEByE8/Ro0cxbtw49OrVS2w8OTmZWUxpcnNzw9ixY7F48eJWr0dERODMmTNMOqoMHz4cGzZsaHODTmpqKpYuXYrLly93ONaLSktLxb5vPi+y+cxRvlhbW8PQ0BD+/v7Q1NSUSFh0dXV5jd8alo+j9+/fj+3bt2P37t0wMTEBANy4cQNz587F/Pnz4eHh0eEYgGSd6osrc3zVqfbt2xfHjh0TtXuVhtTUVLi5ucHAwAD5+fmwtLRESUkJOI6Dra0tk41KcXFx4DgOs2fPRnh4OPr06SO61rNnT+jp6YlOACGdByWcpMuSk5ODq6srFBUVW71eV1eH33//ndmHgKKiIoqLizF48OBWr9+9exdGRkZMe3FLcxOPLOjq6uL3338XlSe8KD8/HxMmTGDSblJNTQ3/+c9/MGTIkFav3759G5aWllI5DF1a1NTUcPnyZV7bS74ulgmnoaEhkpKSYGNjIzaenZ2NTz75BLdu3epwDEB2dar6+vpISUlp8/eDD3Z2dnB1dUVwcLDo/5WGhgY8PDwwceJELFiwgFms9PR0ODg4SOyEJ50TPVInXVZ7uuuw3KHev39/lJSUtJlw3rp1i3ndUWdNJNuroqLipR82CgoKzPqoy8vL4/79+20mnPfv32/3Rqb2eBtqDZ2dnSEUCt+qhJOlsrIyNDQ0SIw3NjaioqKCWRxp16k2W7NmDYKCghAXFyexk5sv169fx88//wzg+e9fTU0NVFVVERISgg8//JBpwtnyfa2trZV4fC+thhaEDUo4SZcl7WTMxcUFq1atwqlTp9CzZ0+xa3V1dQgMDBTrV01eTVtbG3l5eW0mRLm5udDS0mISy8bGBocOHYK9vX2r13/99VeJlbKOaG8tr0Ag4C3hjIqKgpeXF/Ly8mBpaSmR3LM6GF1WnJ2dMX/+fERFRcHW1hbA89XNBQsWYPz48TKbV0frVJtt2LBBVDOup6cn8f+PjzNje/XqJUr8tLS0UFxcLOr01XwUHCvV1dVYvnw5EhMT8c8//0hcp13qnQslnIQwEhISghEjRmDo0KH46quvxA6a3759O+rq6rBnzx5ZT7NTmTRpkihRf7GesaamBqtXr8bkyZOZxFq4cCGmT5+OwYMHY8GCBZCXlwfw/ENt+/bt2LRpE/bv388kFgBmj3M74vz58zh79iyOHz8uca0rHDcVHR0NLy8vjBgxQpSMNTQ0wMXFBVFRUTKbF6tKtqlTpzK5z+uwt7dHVlYWzMzMMGnSJPj5+eHq1atITk5u8x9rb2rZsmU4c+YMduzYAU9PT2zbtg337t1DZGQkwsLCmMYi/KMaTkIYunXrFr788kucPHlS9KEiEAjw/vvvY+vWrV320SVfKioqYGtrC3l5eSxcuFC08SM/Px/btm1DY2MjcnJyXnoawetYtWoV1q1bBzU1NVEN4c2bN1FVVYVly5ZJ5UOu5c8N3/T09DB58mQEBgYyew87atKkSdi9ezezlWsAKCgoELVENTU1hbGxMbN7v4nOfE5n8+/DsGHD8PTpU/j5+eHcuXMYOnQoNm7cyHSj2ZAhQxAfH48xY8agd+/eyMnJgZGREfbs2YOff/4ZKSkpzGIR/lHCSQgPHj16hMLCQgCAkZERnRnXAaWlpViwYAFOnDghloy5uLhg27Zt7W4l2l4XL17Evn37UFRUBI7jYGxsjBkzZsDOzo5pnBfFx8fjhx9+EP3cGBsbY9myZfD09OQtppqaGq5cuQJDQ0Ne7v86G6z4rMerr6/HrVu3YGhoKNbfXFY6c8IpTaqqqrh27RqGDBmCwYMHIzk5GXZ2drh16xasrKw6dUOE7kj2v3mEdEHq6uq8Jyjdha6uLlJSUvDo0SNREjh06FDeDtC3s7OT+v+7jRs3IjAwEAsXLhQdcZOVlQUfHx/8/fffbR4L1VEff/wxzpw5w1vC2bdv33av1PLx+L66uhq+vr6Ii4sD8Hyl08DAAL6+vtDW1m6zC9nb7J133kFBQQH69+8vOq+yLQ8fPuRtHvX19a0eNt/Wprs3YWBggFu3bmHIkCEwNTVFYmIi7OzscPToUd67thH2KOEkhHQK6urqGDlypKynwYstW7Zgx44dYqcmuLm5wcLCAt9++y1vCaexsTFWrFiBrKwsWFlZSWw66ehmpTNnzoj+XFJSgoCAAHzxxReiMxTPnz+PuLg4rFu3rkNx2rJixQoIhUL88ccfYhv2xo8fj2+//bZTJpybNm2CmpoaACA8PFzq8QsKCuDt7Y1z586JjfNx1uisWbMgFArx3nvvISAgAFOmTMHWrVvx7NkzJgfME+miR+qEECJjSkpKre7GLywshJWVFdOzW1t6WTmCQCBg2hrR2dkZc+bMwWeffSY2vn//fuzatQt//PEHs1jNdHV1kZCQAHt7e7HH2EVFRbC1tZXZmap81KlKy+jRo6GgoICAgIBWuxtZW1vzFru0tBTZ2dkwMjJi0naVSBetcBJCiIwZGRkhMTERK1euFBtPSEjA0KFDeYsrzZ3y58+fx86dOyXGR4wYgTlz5vAS88GDB9DQ0JAYf/r0KbNNWW9Sp8pys0tjYyN+/fVXXL9+HQBgbm6ODz/8kLda1StXriA7Oxumpqa83P9ldHV1ZdL9irBBCSchhMhI8/mXISEhcHd3R0ZGhqiG8+zZs0hNTUViYqKMZ8mGjo4OfvrpJ3z//fdi41FRUdDR0eEl5ogRI3Ds2DH4+voC+P87/6Oiopi1RpRlnep//vMfuLm5oby8XHSCw/r16zFgwAAcPXoUlpaWTOMBzxNa1udttvQ2NEQg/KBH6oQQIiNycnIYOXIkvL29YWpqil27dolWqszMzODn58f0sPkXzZ49+6XXo6OjmcVKSUnBtGnTYGRkhH/9618Anp8IUFhYiIMHD2LSpEnMYjXLysqCq6srZs6cidjYWMyfPx/Xrl3DuXPnkJ6ejnfffbfDMVq2tXxVnWp7up+9jlGjRmHAgAGIi4sTbaJ79OgRvvjiCzx48ECizvJNtVzFvXTpEr755huEhoa2Wvfb0dMG2nvqBOuSD8I/SjgJId2era0tUlNToa6uDhsbm5euWKmqqsLCwgIrV67s8MpcZmYmYmJikJSUhKamJkybNg3e3t5wcnLq0H3b66OPPhL7/tmzZ8jLy0NlZSXGjRuH5ORkpvHu3LmDHTt2iM7ENDMzg4+PD28rnABQXFyMsLAwCIVCVFVVwdbWFv7+/rCysmIeS9p1qsrKyrh06ZKo00+zvLw8jBw5EjU1NUziyMnJif1ONG8QaomPTUOka6GEkxDS7QUHB2PZsmVQUVFBcHDwS19bV1eH1NRUKCkpia1udcTTp0+RmJiI2NhYZGVlwdDQEN7e3vDy8sLAgQOZxGivpqYmLFiwAIaGhli+fLlUY3d2KioqEAqFEnW3BQUFGD58OKqrq5nGs7a2xqZNmzBu3Dix8bS0NCxatAhXr15lEud1fs5ft698e0mzIQLhCUcIIeS1FBUVcYqKirzcu7CwkFu5ciWno6PD9ejRg5syZQovcV4mPz+fGzhwIPP7ZmRkcB4eHtyoUaO4u3fvchzHcfHx8VxmZibzWLJgbGzMLVu2TGJ82bJlnLGxMZMYjx8/Fn0dO3aMs7Cw4A4cOMDduXOHu3PnDnfgwAHOysqKO3bsGJN4shYXF8dZWlpyioqKnKKiImdlZcXFx8fLelrkDdCmIUIIeU2GhoaoqKjg5d5GRkZYuXIldHV1sWLFChw7doyXOC9TXFyMhoYGpvc8ePAgPD094eHhgZycHNTV1QEAHj9+jNDQUKY7t+Xl5dv1OtaPfzdt2oRp06bh+PHjrdapsvDiJiWO4+Du7i4a4/5vJXDKlCm8PN6OiYmBqqoqPv30U7HxAwcOoLq6mmmdqqwaIhB+0CN1Qki396q6zZZycnJ4m0dGRgaio6Nx8OBByMnJwd3dHd7e3rC3t+cl3pIlS8S+5zgOZWVlOHbsGLy8vLB161ZmsWxsbLB48WJ8/vnnYmdiXr58Ga6urigvL2cWS05ODrq6uvDy8nrppqsPP/yQWcxmfNepyvrxtrGxMSIjIzF27Fix8fT0dMybNw83btxgFktfXx/BwcFiDREAIC4uDt9++61Uj/UiHUcrnISQbm/q1KmiP9fW1mL79u0wNzcX7TS+cOEC/vOf/+DLL79kHvv+/fuIjY1FbGwsioqK4ODggIiICLi7u6NXr17M47V0+fJlse/l5OQwYMAAbNiw4ZU72F/XjRs3Wt0M1adPH1RWVjKNdfHiRezevRubN2+Gvr4+Zs+eDQ8PD97aobako6OD0NBQ3u7/Jknkl19+iZCQEPTv37/D8W/fvt3qTnJdXV3cvn27w/dvqaysDA4ODhLjDg4OKCsrYxqLSIFMH+gTQshbxtvbm/vmm28kxoOCgrhZs2YxjTVx4kROQUGBGzhwILd8+XIuPz+f6f3fJvr6+typU6c4juM4VVVVrri4mOO45zV6ZmZmvMSsqanh9uzZw40bN45TUVHh/uu//os7efIkL7GavY11qmpqaqL3u6N0dHS4w4cPS4wfOnSI09bWZhKjmYWFBbd27VqJ8TVr1nCWlpZMYxH+UcJJCCEt9O7dmysoKJAYLygo4Hr37s001pQpU7hDhw5xDQ0NTO/7NgoNDeXMzc25CxcucGpqalxmZia3d+9ebsCAAVxERATv8W/evMmNHTuWk5OT4/755x9eYiQlJXHKysrcnDlzOEVFRVGSt2XLFs7V1ZWXmO3RMsHvqOXLl3O6urpcWloa19DQwDU0NHCpqamcrq4u5+fnxyTG1atXOY7juIMHD3Ly8vKci4sLFxISwoWEhHAuLi6cgoICl5yczCQWkR5KOAkhpAVNTU0uJiZGYjwmJobT0NCQ/oR4VF5ezs2cOZPT0tLi5OXlOTk5ObEvlpqamrjvvvuO69WrFycQCDiBQMApKSm1uprM0p07d7g1a9ZwhoaGnJaWFufv7889e/aMl1jDhw/n4uLiOI4TT/JycnI4TU1NXmK2B8uEs66ujnN3d+cEAgHXo0cPrkePHpy8vDw3a9Ysrq6ujkkMgUDA2dnZcZGRkVx6ejrn4eHB2dracra2tpyHhweXk5PDJA6RLto0RAghLYSFhSE4OBhz586FnZ0dAOB//ud/EB0djcDAQAQEBMh4huy4urri9u3bWLhwIbS0tCQ2TvGxqaa+vh5FRUWoqqqCubk5VFVVeYnx66+/Yvfu3cjMzISrqytmz54NV1fXdu9gfxMqKiq4du0a9PT0xDZG3bx5E+bm5qitreUt9su0nAsrhYWFuHLlCpSVlWFlZcW0x7msGyIQflDCSQghL0hMTMTmzZvF2kwuWrQI7u7uMp4ZW2pqasjMzMTw4cNlPRWm+vXrBzU1NXh5ecHT0xMaGhqtvq6jbRhfZGBggF27dmH8+PFiSV58fDzCwsJw7do1pvHai4+E81V69+6NK1eudCjm29QQgXQcJZyEENJOeXl5sLS0lPU0mDE3N8e+fft47dfe7OnTpwgLC0Nqair++usvNDU1iV1n2RdbTk5O9OfWjrvieGrDuG7dOuzduxfR0dF4//33kZKSgtLSUixevBiBgYHw9fVlGq+9ZJFwso5ZVFSEmJgY7NmzB+Xl5Zg4cSKOHDnC5N5EOuhYJEIIeYn//d//xc8//4yoqChkZ2d3qV7R4eHhCAgIQGRkJPT09HiNNWfOHKSnp8PT07PVx/csnTlzhrd7v0xAQACamprg7OyM6upqODk5QVFREUuXLpVZsgkAM2fOZL6aK21vQ0ME0jG0wkkIIa3IyMhAVFQUkpOTMWjQIHz88ceYNm0aRo4cKeupMaOuro7q6mo0NDRARUUFPXr0ELv+8OFDZrH69u2LY8eOiTrGvE3CwsLg4+ODvn37MrmfNOpUm2VmZiIyMhLFxcVISkqCtrY29uzZA319ffz73//mLe6rsFzhlHZDBMIPWuEkhJD/U15ejtjYWOzevRtPnjyBu7s76urqcOjQIZibm8t6esyFh4dLLZa6ujreeecdqcV7HaGhoXB3d2eWcPbs2VMqPy8t24VevnyZ13ah0ibLhgiEH7TCSQgheN57OiMjAx988AE8PDwwceJEyMvLo0ePHhAKhV0y4WwvFiuAe/fuxeHDhxEXFwcVFRV2k2OA1WqcNOtUAem2C31dHdk05OrqitOnT6N///74/PPPMXv2bJiYmPAwSyJNtMJJCCEAjh8/jq+//hoLFizA0KFDZT2dtwqLFcANGzaguLgYmpqa0NPTk3h8z2ePemmRZp0qIN12oa+rI2tZPXr0QFJSEiZPnszrMVZEuijhJIQQAFlZWdi9ezfeffddmJmZwdPTE9OnT5f1tN4KLB6EtexX31UdP35cqnWqAwcORFFRkcSGr6ysLN53pNfX1+PWrVswNDSEgoJkKnH8+HFoa2u/0b1p93nXRAknIYQAsLe3h729PcLDw5GQkIDo6GgsWbIETU1NOHXqFHR0dKCmpibraXZaq1evlvUUeCftOtW5c+di0aJFiI6OhkAgwP3793H+/HksXboUgYGBvMSsrq6Gr68v4uLiAAAFBQUwMDCAr68vtLW1RY0RZLlhibyd5F79EkII6T569eqF2bNnIysrC1evXoWfnx/CwsKgoaEBNzc3WU+v06uvr8fdu3dx+/Ztsa+uYM2aNQgKCkJ1dbVU4gUEBGDGjBlwdnZGVVUVnJycMGfOHMyfP5+3Y5hWrFgBoVCIP/74A0pKSqLx8ePHIyEhgZeYpGugTUOEEPIKjY2NOHr0KKKjo7vl4z4Wm2oKCgrg7e2Nc+fOiY3zdQj765g0aRJ2794NLS2tDt3HxsYGxcXF4DhOqnWq0jyGSVdXFwkJCbC3txf7uSgqKoKtrS2ePHnCW2zSudEjdUIIeQV5eXlMnTq1W9Qh8mXWrFlQUFDAb7/9xuuGmtdJeJoPQ2d1fJCsfj6kdQwTADx48KDVVqFPnz7lfZMU6dwo4SSEEPJSjo6OUFZW7tA9rly5guzsbJiamjKaVev69u3b7sSH9aqqtOtUa2trsWXLFpw5c6bVY5j4WFEdMWIEjh07Jnpk3/xeR0VFYdSoUczjka6DEk5CCOnGiouLERMTg+LiYmzevBkaGho4fvw4hgwZAgsLCwBsVgDNzc3x999/d/g+r9KyrWVJSQkCAgLwxRdfiJKh8+fPIy4uDuvWreNtDvX19a0mgEOGDGEax9vbGydPnsQnn3wCOzs7qawwhoaGwtXVFdeuXUNDQwM2b96Ma9eu4dy5c0hPT+c9Pum8qIaTEEK6qfT0dLi6umL06NHIyMjA9evXYWBggLCwMFy6dAlJSUnMYqWlpeGbb75BaGgorKysJOob+ej17ezsjDlz5uCzzz4TG9+/fz927dqFP/74g2k8adep9unTBykpKVJvF1pcXIywsDAIhUJUVVXB1tYW/v7+sLKykuo8SOdCCSchhHRTo0aNwqeffoolS5aIbQC5ePEiPv74Y9y9e5dZLDm554eivLgKx+emIRUVFQiFQomD/AsKCjB8+HDmu8lHjx4NBQUFBAQEtFqnam1tzTSeubk5fvnlFwwbNozpfQnhAz1SJ4SQburq1avYv3+/xLiGhgbzx98tH3VLi46ODn766Sd8//33YuNRUVHQ0dFhHk9adarNNmzYAH9/f+zcuRO6urpSiZmSkgJ5eXm4uLiIjZ84cQJNTU1wdXWVyjxI50MJJyGEdFN9+/ZFWVkZ9PX1xcYvX778xl1i2vLee+8xvV97bNq0CdOmTcPx48fxr3/9CwBw8eJFFBYW4uDBg8zjSatOtdmIESNQW1sLAwMDqKioSJQpPHz4kHnMgIAAhIWFSYxzHIeAgABKOEmbKOEkhJBuavr06fD398eBAwcgEAjQ1NSEs2fPYunSpfj88887fP/c3FxYWlpCTk4Oubm5L30tH4+FJ02ahIKCAuzYsQP5+fkAgClTpsDHx4eXFc7169dj+fLlUqtT/eyzz3Dv3j2EhoZCU1NTKpuGCgsLWz2CydTUFEVFRbzHJ50X1XASQkg3VV9fj6+++gqxsbFobGyEgoICGhsbMWPGDMTGxkJeXr5D95eTk0N5eTk0NDQgJycHgUDQal92WR/8zoq061RVVFRw/vx55rWhLzNw4EDs378f48aNExs/ffo0ZsyYgb/++ktqcyGdC61wEkJIN9WzZ0/89NNPCAoKwtWrV1FVVQUbGxuJTTZv6tatWxgwYIDoz7KQmZmJyMhI3Lx5EwcOHIC2tjb27NkDfX195v2+pV2nampqipqaGqnG/PDDD/Hf//3f+PXXX2FoaAgAKCoqgp+fH7V+JS9FK5yEENJNhYSEYOnSpVBRUREbr6mpwQ8//ICgoCAZzYyNgwcPwtPTEx4eHtizZw+uXbsGAwMDbN26FSkpKcw6DMnKyZMnERwcjLVr10rtqKnHjx9j4sSJuHTpEgYPHgwAuHv3LhwdHZGcnIy+ffsyj0m6Bko4CSGkm5KXl0dZWZlEq8J//vkHGhoazB8B37hxA1u2bMH169cBAGZmZvD19YWJiQnTOM1sbGywePFifP7552LHPl2+fBmurq4oLy/vcAxZ1qnK4qip5vufOnUKQqEQysrKGDZsGJycnHiJRboOeqROCCHdVHNi8iKhUIh33nmHaayDBw9i+vTpGDFihKjrz4ULF2BpaYlffvkF06ZNYxoPeJ7gtpYI9enTB5WVlUxiDB8+XFSnOnz4cKnWqcriqCng+X/LhAkTMGHCBJnEJ50TJZyEENLNqKurQyAQQCAQwNjYWCzpbGxsRFVVFXx8fJjGXL58OVasWIGQkBCx8dWrV2P58uW8JJwDBw5EUVER9PT0xMazsrJgYGDAJIYs61SlddRUREQE5s2bByUlJURERLz0tV9//bVU5kQ6H3qkTggh3UxcXBw4jsPs2bMRHh6OPn36iK717NkTenp6olVIVlRUVJCbmwsjIyOx8cLCQlhbWzPv+gMA69atw969exEdHY33338fKSkpKC0txeLFixEYGAhfX1/mMaUpIyPjpddZPebW19fHpUuX0K9fP4kzW1sSCAS4efMmk5ik66EVTkII6Wa8vLwAPE8kHBwcJDab8GHMmDHIzMyUSDizsrLg6OjIS8yAgAA0NTXB2dkZ1dXVcHJygqKiIpYuXcpbsinNOtUxY8ZIjL24Ws1Cy5VbWZ02QDo/WuEkhBCC2tpa1NfXi411dJfzkSNHRH++f/8+goKC4O7uDnt7ewDPazgPHDiA4OBg5o/wW6qvr0dRURGqqqpgbm4OVVVVXuK0Vaf6559/8lKn+vjxY7Hvnz17hsuXLyMwMBBr166Fs7Mz03jPnj2DqakpfvvtN5iZmTG9N+n6KOEkhJBuqrq6GsuXL0diYiL++ecfiesdXSFr3kX9Kl3l4HdDQ0N4eHi0Wqe6d+9eFBcXS2Ue6enpWLJkCbKzs5nfW1tbG6dPn6aEk7y29v1tQAghpMtZtmwZ0tLSsGPHDigqKiIqKgrBwcEYNGgQ4uPjO3z/pqamdn3xlWw+ffoUgYGBcHBwgJGREQwMDMS+WCsrK2u1JejMmTNRVlbGPF5bNDU1cePGDV7u/dVXX2H9+vVoaGjg5f6k66IaTkII6aaOHj2K+Ph4jBkzBrNmzYKjoyOMjIygq6uLffv2wcPDQ+pzsrKyQkpKCpNe53PmzEF6ejo8PT2hpaXFe69xadepvnjuJ8dxKCsrQ1hYGIYPH848HgD8+eefSE1NxcmTJ2FlZYVevXqJXU9OTuYlLun8KOEkhJBu6uHDh6KVvt69e+Phw4cAgH//+99YsGCBTOZUUlKCZ8+eMbnX8ePHcezYMYwePZrJ/VrTsk7Vzc0N/v7+yM7ObrVOlbW2zv20t7dHdHQ083gA0LdvX16OsCJdHyWchBDSTRkYGODWrVsYMmQITE1NkZiYCDs7Oxw9erRLtChUV1dnfoD9i6ZOnSoxtn37dmzfvl1s7KuvvmK+MerFHeNycnIYMGAAlJSUmMYBnpdH/PDDDygoKEB9fT3GjRuHb7/9FsrKysxjka6JajgJIaSbmjVrFoRCIYDnRwht27YNSkpKWLx4MZYtWybj2XXcmjVrEBQUxMsZn81kWaeqq6sr9qWjo8NLsgkAa9euxcqVK6GqqgptbW1ERETgq6++4iUW6ZpolzohhBAAQGlpKbKzs2FkZMS873d7tex53lE2NjYoLi4Gx3HQ09OTOG80JyenwzHeREfqVF/V6aclll1/hg4diqVLl2L+/PkAgNOnT+ODDz5ATU1Nu08jIN0bPVInhJBuqKmpCbGxsUhOTkZJSQkEAgH09fXxySefwMrKStbTY6K1x91vg47UqW7atKldrxMIBEwTztu3b2PSpEmi78ePHw+BQID79+9j8ODBzOKQrosSTkII6WY4joObmxtSUlJgbW0NKysrcByH69ev44svvkBycjIOHTok62l22OrVq2U9BeZk1emnoaFB4nF9jx49mG3wIl0fJZyEENLNxMbGIiMjA6mpqRg7dqzYtbS0NEydOhXx8fGtninJQm1tbZu1hpGRkdDU1GQar76+Hn/99ReamprExocMGcI0jiw1V8fxdfQTx3H44osvoKioKBqrra2Fj4+P2NFIdCwSaQsVXhBCSDfz888/Y+XKlRLJJgCMGzcOAQEB2LdvH9OYTU1NWLNmDbS1taGqqoqbN28CAAIDA7F7927R62bMmCFxtuObKigogKOjI5SVlaGrqwt9fX3o6+tDT08P+vr6TGLIWnx8PKysrKCsrAxlZWUMGzYMe/bsYR7Hy8sLGhoa6NOnj+hr5syZGDRokNgYIW2hFU5CCOlmcnNz8f3337d53dXV9bU2p7THd999h7i4OHz//feYO3euaNzS0hLh4eHw9vZmGg94vgtfQUEBv/32m1QOfpe2jRs3IjAwEAsXLhSdNZqVlQUfHx/8/fffWLx4MbNYMTExzO5FuifapU4IId1Mz549UVpaCi0trVav379/H/r6+qirq2MW08jICJGRkXB2dhbbiZ6fn49Ro0bh0aNHzGI169WrF7Kzs2Fqasr83h3Baie+vr4+goODJUof4uLi8O2338qs3pOQ1tAjdUII6WYaGxuhoND2Ay55eXnmvbLv3bsn0fIReP6ona+NJ+bm5vj77795ufer1NbWtnmNVZ1qWVkZHBwcJMYdHByk2rudkPagR+qEENLNtLYBpCWWK5vNzM3NkZmZCV1dXbHxpKQk2NjYMI8HAOvXr8fy5csRGhoKKysriXM4e/fuzTReU1MT1q5di507d6KiogIFBQUwMDBAYGAg9PT0RGUDM2bMYBLPyMgIiYmJWLlypdh4QkIChg4dyiQGIaxQwkkIId2Ml5fXK1/Deod6UFAQvLy8cO/ePTQ1NSE5ORk3btxAfHw8fvvtN6axmo0fPx4A4OzsLDbOcRwEAgHz7j/SqlPNy8uDpaUlQkJC4O7ujoyMDFEN59mzZ5GamorExEQmsQhhhWo4CSGESEVmZiZCQkIgFApRVVUFW1tbBAUFYcKECbzES09Pf+n19957j2k8adWpysnJYeTIkfD29oapqSl27dqF69evAwDMzMzg5+fH26oxIW+KEk5CCCGEAWVlZeTn50NXV1cs4bx27Rrs7OxQVVXFJE5mZiZiYmKQlJSEpqYmTJs2Dd7e3nBycmJyf0L4QI/UCSGE8O7OnTsQCASiNogXL17E/v37YW5ujnnz5jGLk5ubC0tLS8jJySE3N/elr2XdL15adaqOjo5wdHTEli1bkJiYiNjYWIwdOxaGhobw9vaGl5cXBg4cyCweISzQCichhBDeOTo6Yt68efD09ER5eTmMjY1haWmJwsJC+Pr6IigoiEkcOTk5lJeXQ0NDA3JychAIBGjtY46PGs7Dhw/Dy8sLK1asQEhICIKDg8XqVN9//32m8VoqKipCTEwM9uzZg/LyckycOBFHjhzhLR4hr4sSTkIIIbxTV1fHhQsXYGJigoiICCQkJODs2bM4efIkfHx8RJ2HOqq0tBRDhgyBQCBAaWnpS1/74kokC9KuU23p6dOn2LdvH1asWIHKykrmCTUhHUGP1AkhhPDu2bNnomOYTp8+DTc3NwCAqakp0zMjWyaRfCSUr+Lo6IhTp05JNWZGRgaio6Nx8OBByMnJwd3dnZfOTYR0BCWchBBCeGdhYYGdO3figw8+wKlTp7BmzRoAz7sa9evXj7e4N27cwJYtW8R2cfv6+sLExIR5LGnVqQLP37fY2FjExsaiqKgIDg4OiIiIgLu7O7Ne9ISwRJ2GCCGE8G79+vWIjIzEmDFj8Nlnn8Ha2hoAcOTIEdjZ2fES8+DBg7C0tER2djasra1hbW2NnJwcWFpa4uDBg8zjzZgxA2fOnAEAlJeXY/z48bh48SJWrVqFkJAQZnFcXV2hq6uLLVu24KOPPsL169eRlZWFWbNmUbJJ3lpUw0kIIUQqGhsb8eTJE6irq4vGSkpKoKKiAg0NDebxDA0N4eHhIZHsrV69Gnv37kVxcTHTeNKqU3Vzc4O3tzcmT54MeXl5JvckhG+UcBJCCJGaBw8e4MaNGwAAExMTDBgwgLdYKioqyM3NlejhXlhYCGtra1RXVzONp6qqiry8POjp6cHNzQ2jR4+Gv78/bt++DRMTE9TU1DCNR0hnQo/UCSGE8O7p06eYPXs2tLS04OTkBCcnJwwaNAje3t7ME79mY8aMQWZmpsR4VlYWHB0dmcdrrlPNzMzEqVOnMHHiRAD816kS0hnQpiFCCCG8W7JkCdLT03H06FFR3++srCx8/fXX8PPzw44dO5jEaXn2pJubG/z9/ZGdnQ17e3sAwIULF3DgwAEEBwczidfS+vXr8dFHH+GHH36Al5eXVOpUCeks6JE6IYQQ3vXv3x9JSUkYM2aM2PiZM2fg7u6OBw8eMIkjJ9e+B3d8HPwOSL9OlZDOglY4CSGE8K66uhqampoS4xoaGkwfqTc1NTG715uQl5dHQ0MDsrKyADyvU9XT05PpnAh5G1ANJyGEEN6NGjUKq1evRm1trWispqYGwcHBGDVqlAxnBlhZWeHOnTsdvo8s6lQJ6Swo4SSEEMK7zZs34+zZsxg8eDCcnZ3h7OwMHR0dnDt3Dps3b5bp3EpKSvDs2bMO36dlnWplZSUqKytx+PBhpKenw8/Pj8FMCem8qIaTEEKIVFRXV2Pfvn3Iz88H8Lzrj4eHB5SVlWU6LzU1NQiFQhgYGHToPtKqUyWkM6IaTkIIIVKhoqKCuXPnynoavJFWnSohnRGtcBJCCOFFyyOKXsXNzY3HmbwcqxVOZ2dn9OvXD/Hx8VBSUgLwvE7Vy8sLDx8+xOnTp1lMl5BOiRJOQgghvJD1EUXtxSrhzMvLg4uLC+rq6kRncAqFQigpKeHEiROwsLBgMV1COiVKOAkhhHRrrBJO4O2tUyVE1ijhJIQQwpu0tDQsXLgQFy5cQO/evcWuPX78GA4ODti5cycvrSZbqq2tFT3mftH+/fvx4YcfolevXrzOgZDujBJOQgghvHFzc8PYsWOxePHiVq9HRETgzJkz+PXXX5nHbmpqwtq1a7Fz505UVFSgoKAABgYGCAwMhJ6eHry9vTsco7PUqRIia5RwEkII4Y2uri5+//13mJmZtXo9Pz8fEyZMwO3bt5nHDgkJQVxcHEJCQjB37lzk5eXBwMAACQkJCA8Px/nz5zsco7PUqRIia3TwOyGEEN5UVFSgR48ebV5XUFDg7XzK+Ph47Nq1Cx4eHpCXlxeNW1tbi2osO6qpqaldX5Rsku6OEk5CCCG80dbWRl5eXpvXc3NzoaWlxUvse/fuwcjISGK8qamJSWehZmlpaTA3N8eTJ08krj1+/BgWFhbIzMxkFo+QzogSTkIIIbyZNGkSAgMDxXqoN6upqcHq1asxefJkXmKbm5u3muglJSXBxsaGWZzw8HDMnTtXYlMUAPTp0wfz58/Hxo0bmcUjpDOiTkOEEEJ488033yA5ORnGxsZYuHAhTExMADyv3dy2bRsaGxuxatUqXmIHBQXBy8sL9+7dQ1NTE5KTk3Hjxg3Ex8fjt99+YxZHKBRi/fr1bV6fMGECfvzxR2bxCOmMaNMQIYQQXpWWlmLBggU4ceIEmj9yBAIBXFxcsG3bNujr6/MWOzMzEyEhIRAKhaiqqoKtrS2CgoIwYcIEZjGUlJSQl5fX6uN7ACgqKoKVlRVqamqYxSSks6EVTkIIIbzS1dVFSkoKHj16hKKiInAch6FDh0JdXZ332I6Ojjh16hSvMZrrVNtKOPmsUyWks6AVTkIIIV3SnTt3IBAIMHjwYADAxYsXsX//fpibm2PevHnM4vj6+uKPP/7An3/+KXG4fE1NDezs7DB27FhEREQwi0lIZ0MJJyGEkC7J0dER8+bNg6enJ8rLy2FsbAxLS0sUFhbC19cXQUFBTOJUVFTA1tYW8vLybdap5uTkQFNTk0k8QjojSjgJIYR0Serq6rhw4QJMTEwQERGBhIQEnD17FidPnoSPjw9u3rzJLJYs61QJ6QyohpMQQkiX9OzZMygqKgIATp8+LWotaWpqirKyMqaxZFmnSkhnQAknIYSQLsnCwgI7d+7EBx98gFOnTmHNmjUAgPv376Nfv368xFRXV8fIkSN5uTchnRkd/E4IIaRLWr9+PSIjIzFmzBh89tlnsLa2BgAcOXIEdnZ2Mp4dId0L1XASQgjpshobG/HkyROxR9slJSVQUVGBhoaGDGdGSPdCCSchhJAu7cGDB7hx4wYAwMTEBAMGDJDxjAjpfuiROiGEkC7p6dOnmD17NrS0tODk5AQnJycMGjQI3t7eqK6ulvX0COlWKOEkhBDSJS1ZsgTp6ek4evQoKisrUVlZicOHDyM9PR1+fn6ynh4h3Qo9UieEENIl9e/fH0lJSRgzZozY+JkzZ+Du7o4HDx7IZmKEdEO0wkkIIaRLqq6ubrW7j4aGBj1SJ0TKaIWTEEJIl+Ts7Ix+/fohPj5e1OO8pqYGXl5eePjwIU6fPi3jGRLSfVDCSQghpEvKy8uDi4sL6urqRGdwCoVCKCkp4cSJE7CwsJDxDAnpPijhJIQQ0mVVV1dj3759yM/PBwCYmZnBw8MDysrKMp4ZId0LJZyEEEIIIYRX1EudEEJIl3HkyJF2v9bNzY3HmRBCWqIVTkIIIV2GnFz7Dl8RCARobGzkeTaEkGaUcBJCCCGEEF7ROZyEEEK6lLS0NJibm+PJkycS1x4/fgwLCwtkZmbKYGaEdF+UcBJCCOlSwsPDMXfuXPTu3VviWp8+fTB//nxs3LhRBjMjpPuihJMQQkiXIhQKMXHixDavT5gwAdnZ2VKcESGEEk5CCCFdSkVFBXr06NHmdQUFBeqjToiUUcJJCCGkS9HW1kZeXl6b13Nzc6GlpSXFGRFCKOEkhBDSpUyaNAmBgYGora2VuFZTU4PVq1dj8uTJMpgZId0XHYtECCGkS6moqICtrS3k5eWxcOFCmJiYAADy8/Oxbds2NDY2IicnB5qamjKeKSHdByWchBBCupzS0lIsWLAAJ06cQPPHnEAggIuLC7Zt2wZ9fX0Zz5CQ7oUSTkIIIV3Wo0ePUFRUBI7jMHToUKirq8t6SoR0S5RwEkIIIYQQXtGmIUIIIYQQwitKOAkhhBBCCK8o4SSEEEIIIbyihJMQQgghhPCKEk5CCCGEEMIrSjgJIYQQQgivKOEkhBBCCCG8+n+oZvNjR5sVKwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -169,12 +84,89 @@
     "from imblearn.under_sampling import RandomUnderSampler\n",
     "\n",
     "df = pd.read_csv(\"data/Coffe.csv\")\n",
-    "print(df.columns)\n",
+    "print(df)\n",
     "\n",
     "df['Date'] = pd.to_datetime(df['Date'])\n",
     "df['Date_numeric'] = (df['Date'] - pd.Timestamp('1970-01-01')).dt.days\n",
-    "print(df['Date_numeric'])\n",
-    "\n",
+    "print(df['Date_numeric'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
+      "       'Date_numeric', 'Close_binned'],\n",
+      "      dtype='object')\n",
+      "Обучающая выборка:  (4821, 9)\n",
+      "Close\n",
+      "0.750000      17\n",
+      "0.765625      15\n",
+      "0.882813      11\n",
+      "0.753906       9\n",
+      "0.773438       9\n",
+      "              ..\n",
+      "7.760000       1\n",
+      "88.459999      1\n",
+      "104.330002     1\n",
+      "10.850000      1\n",
+      "100.930000     1\n",
+      "Name: count, Length: 3690, dtype: int64\n",
+      "Контрольная выборка:  (1607, 9)\n",
+      "Close\n",
+      "0.835938      6\n",
+      "0.781250      5\n",
+      "0.757813      5\n",
+      "1.851563      4\n",
+      "0.738281      4\n",
+      "             ..\n",
+      "100.620003    1\n",
+      "6.020000      1\n",
+      "85.959999     1\n",
+      "91.529999     1\n",
+      "111.000000    1\n",
+      "Name: count, Length: 1436, dtype: int64\n",
+      "Тестовая выборка:  (1607, 9)\n",
+      "Close\n",
+      "0.703125     6\n",
+      "0.851563     6\n",
+      "0.750000     6\n",
+      "0.742188     5\n",
+      "0.781250     5\n",
+      "            ..\n",
+      "47.275002    1\n",
+      "31.760000    1\n",
+      "75.500000    1\n",
+      "2.406250     1\n",
+      "8.107500     1\n",
+      "Name: count, Length: 1427, dtype: int64\n",
+      "Обучающая выборка:  (4821, 9)\n",
+      "Close_binned\n",
+      "High      1639\n",
+      "Low       1591\n",
+      "Medium    1591\n",
+      "Name: count, dtype: int64\n",
+      "Контрольная выборка:  (1607, 9)\n",
+      "Close_binned\n",
+      "High      546\n",
+      "Medium    531\n",
+      "Low       530\n",
+      "Name: count, dtype: int64\n",
+      "Тестовая выборка:  (1607, 9)\n",
+      "Close_binned\n",
+      "High      547\n",
+      "Low       530\n",
+      "Medium    530\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
     "def split_stratified_into_train_val_test(\n",
     "    df_input,\n",
     "    stratify_colname=\"y\",\n",
@@ -241,41 +233,246 @@
     "print(df_val['Close_binned'].value_counts())\n",
     "\n",
     "print(\"Тестовая выборка: \", df_test.shape)\n",
-    "print(df_test['Close_binned'].value_counts())\n",
-    "\n",
+    "print(df_test['Close_binned'].value_counts())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка после undersampling:  (4773, 9)\n",
+      "Close\n",
+      "0.750000     17\n",
+      "0.765625     15\n",
+      "0.882813     11\n",
+      "0.773438      9\n",
+      "0.753906      9\n",
+      "             ..\n",
+      "58.810001     1\n",
+      "40.535000     1\n",
+      "91.860001     1\n",
+      "90.779999     1\n",
+      "96.970001     1\n",
+      "Name: count, Length: 3651, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
     "rus = RandomUnderSampler(random_state=42)\n",
     "X_resampled, y_resampled = rus.fit_resample(df_train, df_train[\"Close_binned\"])\n",
     "df_train_rus = pd.DataFrame(X_resampled)\n",
     "print(\"Обучающая выборка после undersampling: \", df_train_rus.shape)\n",
-    "print(df_train_rus.Close.value_counts())\n",
-    "\n",
+    "print(df_train_rus.Close.value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Date        Open        High         Low       Close  Adj Close  \\\n",
+      "75   1992-10-13    0.464844    0.472656    0.457031    0.472656   0.366803   \n",
+      "7819 2023-07-17  100.830002  101.809998  100.040001  100.930000  98.501541   \n",
+      "6447 2018-01-31   57.230000   57.450001   56.700001   56.810001  49.579262   \n",
+      "706  1995-04-12    0.769531    0.789063    0.769531    0.785156   0.609317   \n",
+      "4437 2010-02-05   10.895000   11.020000   10.630000   10.850000   8.420099   \n",
+      "...         ...         ...         ...         ...         ...        ...   \n",
+      "4113 2008-10-22    5.120000    5.245000    4.880000    4.995000   3.876349   \n",
+      "4544 2010-07-12   12.635000   12.760000   12.490000   12.635000   9.845955   \n",
+      "6517 2018-05-11   57.720001   57.860001   57.070000   57.270000  50.514595   \n",
+      "3336 2005-09-21   11.642500   11.775000   11.530000   11.667500   9.054512   \n",
+      "3122 2004-11-15   13.797500   13.860000   13.687500   13.790000  10.701671   \n",
+      "\n",
+      "        Volume  Date_numeric  Close_binned_Low  Close_binned_Medium  \\\n",
+      "75     4390400          8321              True                False   \n",
+      "7819   5244500         19555             False                False   \n",
+      "6447  13118400         17562             False                False   \n",
+      "706   10294400          9232              True                False   \n",
+      "4437  22069800         14645             False                 True   \n",
+      "...        ...           ...               ...                  ...   \n",
+      "4113  29681400         14174              True                False   \n",
+      "4544  12906200         14802             False                 True   \n",
+      "6517   5843400         17662             False                False   \n",
+      "3336  16207600         13047             False                 True   \n",
+      "3122  10700400         12737             False                 True   \n",
+      "\n",
+      "      Close_binned_High  Volume_binned  Price_change  \n",
+      "75                False              0      0.007812  \n",
+      "7819               True              0      0.099998  \n",
+      "6447               True              2     -0.419999  \n",
+      "706               False              1      0.015625  \n",
+      "4437              False              3     -0.045000  \n",
+      "...                 ...            ...           ...  \n",
+      "4113              False              3     -0.125000  \n",
+      "4544              False              2      0.000000  \n",
+      "6517               True              0     -0.450001  \n",
+      "3336              False              2      0.025000  \n",
+      "3122              False              1     -0.007500  \n",
+      "\n",
+      "[4821 rows x 13 columns]\n"
+     ]
+    }
+   ],
+   "source": [
     "df_train = pd.get_dummies(df_train, columns=['Close_binned'])\n",
-    "\n",
     "df_train['Volume_binned'] = pd.qcut(df_train['Volume'], q=4, labels=False)\n",
-    "\n",
     "df_train['Price_change'] = df_train['Close'] - df_train['Open']\n",
-    "\n",
+    "print(df_train) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Date      Open      High       Low     Close  Adj Close    Volume  \\\n",
+      "75   1992-10-13 -0.881061 -0.882121 -0.880250 -0.881109   0.366803 -0.731159   \n",
+      "7819 2023-07-17  2.093371  2.095750  2.098759  2.096893  98.501541 -0.670368   \n",
+      "6447 2018-01-31  0.801237  0.792199  0.802249  0.788980  49.579262 -0.109940   \n",
+      "706  1995-04-12 -0.872031 -0.872824 -0.870902 -0.871845   0.609317 -0.310940   \n",
+      "4437 2010-02-05 -0.571952 -0.572180 -0.575927 -0.573479   8.420099  0.527179   \n",
+      "...         ...       ...       ...       ...       ...        ...       ...   \n",
+      "4113 2008-10-22 -0.743100 -0.741883 -0.747938 -0.747047   3.876349  1.068937   \n",
+      "4544 2010-07-12 -0.520385 -0.521049 -0.520286 -0.520563   9.845955 -0.125044   \n",
+      "6517 2018-05-11  0.815758  0.804247  0.813318  0.802616  50.514595 -0.627741   \n",
+      "3336 2005-09-21 -0.549799 -0.549994 -0.549004 -0.549244   9.054512  0.109935   \n",
+      "3122 2004-11-15 -0.485933 -0.488725 -0.484463 -0.486324  10.701671 -0.282042   \n",
+      "\n",
+      "      Date_numeric  Close_binned_Low  Close_binned_Medium  Close_binned_High  \\\n",
+      "75            8321              True                False              False   \n",
+      "7819         19555             False                False               True   \n",
+      "6447         17562             False                False               True   \n",
+      "706           9232              True                False              False   \n",
+      "4437         14645             False                 True              False   \n",
+      "...            ...               ...                  ...                ...   \n",
+      "4113         14174              True                False              False   \n",
+      "4544         14802             False                 True              False   \n",
+      "6517         17662             False                False               True   \n",
+      "3336         13047             False                 True              False   \n",
+      "3122         12737             False                 True              False   \n",
+      "\n",
+      "      Volume_binned  Price_change  Volatility  \n",
+      "75                0      0.007812   -0.001871  \n",
+      "7819              0      0.099998   -0.003009  \n",
+      "6447              2     -0.419999   -0.010050  \n",
+      "706               1      0.015625   -0.001922  \n",
+      "4437              3     -0.045000    0.003747  \n",
+      "...             ...           ...         ...  \n",
+      "4113              3     -0.125000    0.006055  \n",
+      "4544              2      0.000000   -0.000763  \n",
+      "6517              0     -0.450001   -0.009070  \n",
+      "3336              2      0.025000   -0.000990  \n",
+      "3122              1     -0.007500   -0.004262  \n",
+      "\n",
+      "[4821 rows x 14 columns]\n"
+     ]
+    }
+   ],
+   "source": [
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
     "scaler = StandardScaler()\n",
-    "df_train[['Open', 'Close', 'High', 'Low', 'Volume']] = scaler.fit_transform(df_train[['Open', 'Close', 'High', 'Low', 'Volume']])\n",
-    "\n",
+    "df_train[['Open', 'Close', 'High', 'Low', 'Volume']] = scaler.fit_transform(\n",
+    "    df_train[['Open', 'Close', 'High', 'Low', 'Volume']])\n",
     "df_train['Volatility'] = df_train['High'] - df_train['Low']\n",
-    "\n",
+    "print(df_train) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<Feature: Open>,\n",
+       " <Feature: High>,\n",
+       " <Feature: Low>,\n",
+       " <Feature: Close>,\n",
+       " <Feature: Adj Close>,\n",
+       " <Feature: Volume>,\n",
+       " <Feature: Date_numeric>,\n",
+       " <Feature: Close_binned_Low>,\n",
+       " <Feature: Close_binned_Medium>,\n",
+       " <Feature: Close_binned_High>,\n",
+       " <Feature: Volume_binned>,\n",
+       " <Feature: Price_change>,\n",
+       " <Feature: Volatility>,\n",
+       " <Feature: DAY(Date)>,\n",
+       " <Feature: MONTH(Date)>,\n",
+       " <Feature: WEEKDAY(Date)>,\n",
+       " <Feature: YEAR(Date)>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "import featuretools as ft\n",
     "\n",
     "es = ft.EntitySet(id=\"stocks\")\n",
     "es = es.add_dataframe(\n",
     "    dataframe_name=\"stock_data\", \n",
-    "    dataframe=df_train, \n",
+    "    dataframe=df_train,                            \n",
     "    index=\"Date\")\n",
     "\n",
     "feature_matrix, feature_defs = ft.dfs(\n",
     "    entityset=es, \n",
     "    target_dataframe_name=\"stock_data\")\n",
     "\n",
-    "feature_defs\n",
-    "\n",
+    "feature_defs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Open                   float64\n",
+      "High                   float64\n",
+      "Low                    float64\n",
+      "Adj Close              float64\n",
+      "Volume                 float64\n",
+      "Date_numeric             int64\n",
+      "Close_binned_Low          bool\n",
+      "Close_binned_Medium       bool\n",
+      "Close_binned_High         bool\n",
+      "Volume_binned            int64\n",
+      "Price_change           float64\n",
+      "Volatility             float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
     "# Оценка предсказательной способности\n",
     "from sklearn.linear_model import LinearRegression\n",
     "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
@@ -283,7 +480,6 @@
     "\n",
     "X_train = df_train_regression.drop(['Close', 'Date'], axis=1)\n",
     "y_train = df_train_regression['Close']\n",
-    "\n",
     "X_test = df_test.drop(['Close', 'Date'], axis=1)\n",
     "y_test = df_test['Close']\n",
     "\n",
@@ -292,8 +488,24 @@
     "\n",
     "X_test_encoded = X_test_encoded.reindex(columns=X_train_encoded.columns, fill_value=0)\n",
     "\n",
-    "print(X_train_encoded.dtypes)\n",
-    "\n",
+    "print(X_train_encoded.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Средняя абсолютная ошибка: 0.28573230577357767\n",
+      "Среднеквадратичная ошибка: 0.2813734754209575\n"
+     ]
+    }
+   ],
+   "source": [
     "model = LinearRegression()\n",
     "model.fit(X_train_encoded, y_train)\n",
     "\n",
@@ -302,8 +514,23 @@
     "mae = mean_absolute_error(y_test, predictions)\n",
     "mse = mean_squared_error(y_test, predictions)\n",
     "print(\"Средняя абсолютная ошибка:\", mae)\n",
-    "print(\"Среднеквадратичная ошибка:\", mse)\n",
-    "\n",
+    "print(\"Среднеквадратичная ошибка:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "время, затраченное на обучение модели: 0.025032997131347656. Время, затраченное на предсказание: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
     "# Оценка скорости вычисления\n",
     "import time\n",
     "start_time = time.time()\n",
@@ -314,15 +541,33 @@
     "predictions = model.predict(X_test_encoded)\n",
     "prediction_time = time.time() - start_time\n",
     "\n",
-    "print(f'время, затраченное на обучение модели: {training_time}. Время, затраченное на предсказание: {prediction_time}')\n",
-    "\n",
+    "print(f'время, затраченное на обучение модели: {training_time}. Время, затраченное на предсказание: {prediction_time}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "# Оценка корреляции\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "corr_matrix = df_train_regression.corr()\n",
     "sns.heatmap(corr_matrix, annot=False)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],