diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4ab32c69..607d30c0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,9 @@
 ## Changelog
 
 ### Development
+- renames ``game_fun`` parameter in ``shapiq.ExactComputer`` to ``game`` [#297](https://github.com/mmschlk/shapiq/issues/297)
+- adds a TabPFN example notebook to the documentation
+- removes warning when class_index is not provided in explainers [#298](https://github.com/mmschlk/shapiq/issues/298)
 - adds the `sentence_plot` function to the `plot` module to visualize the contributions of words to a language model prediction in a sentence-like format
 - makes abbreviations in the `plot` module optional [#281](https://github.com/mmschlk/shapiq/issues/281)
 - adds the `upset_plot` function to the `plot` module to visualize the interactions of higher-order [#290](https://github.com/mmschlk/shapiq/issues/290)
diff --git a/docs/source/notebooks/basics_notebooks/custom_games.ipynb b/docs/source/notebooks/basics_notebooks/custom_games.ipynb
index a0ce982b..14f66d49 100644
--- a/docs/source/notebooks/basics_notebooks/custom_games.ipynb
+++ b/docs/source/notebooks/basics_notebooks/custom_games.ipynb
@@ -12,14 +12,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:19.696179Z",
-     "start_time": "2024-12-17T14:23:18.268301Z"
+     "end_time": "2025-01-10T12:14:05.982266Z",
+     "start_time": "2025-01-10T12:14:04.426262Z"
     }
    },
    "cell_type": "code",
    "source": [
     "import shapiq\n",
     "import numpy as np\n",
+    "import os\n",
     "\n",
     "shapiq.__version__"
    ],
@@ -27,7 +28,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.1.1'"
+       "'1.1.1.dev'"
       ]
      },
      "execution_count": 1,
@@ -86,8 +87,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:19.711170Z",
-     "start_time": "2024-12-17T14:23:19.698170Z"
+     "end_time": "2025-01-10T12:14:05.997215Z",
+     "start_time": "2025-01-10T12:14:05.985240Z"
     }
    },
    "cell_type": "code",
@@ -147,8 +148,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:19.727173Z",
-     "start_time": "2024-12-17T14:23:19.713181Z"
+     "end_time": "2025-01-10T12:14:06.013212Z",
+     "start_time": "2025-01-10T12:14:06.000205Z"
     }
    },
    "cell_type": "code",
@@ -174,8 +175,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:19.742179Z",
-     "start_time": "2024-12-17T14:23:19.730173Z"
+     "end_time": "2025-01-10T12:14:06.029218Z",
+     "start_time": "2025-01-10T12:14:06.014204Z"
     }
    },
    "cell_type": "code",
@@ -207,8 +208,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:19.758170Z",
-     "start_time": "2024-12-17T14:23:19.745174Z"
+     "end_time": "2025-01-10T12:14:06.045214Z",
+     "start_time": "2025-01-10T12:14:06.033206Z"
     }
    },
    "cell_type": "code",
@@ -245,8 +246,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:19.789577Z",
-     "start_time": "2024-12-17T14:23:19.760172Z"
+     "end_time": "2025-01-10T12:14:06.061209Z",
+     "start_time": "2025-01-10T12:14:06.046217Z"
     }
    },
    "cell_type": "code",
@@ -280,7 +281,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "e6e0bc19180b4969bae2cbcabef70fdf"
+       "model_id": "218e4aac6918408d8a38f1c9646509fb"
       }
      },
      "metadata": {},
@@ -308,19 +309,20 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-17T14:23:20.357939Z",
-     "start_time": "2024-12-17T14:23:19.792499Z"
+     "end_time": "2025-01-10T12:14:06.076763Z",
+     "start_time": "2025-01-10T12:14:06.063214Z"
     }
    },
    "cell_type": "code",
    "source": [
     "# save the precomputed values to a file\n",
-    "cooking_game.save_values(\"data/cooking_game_values.npz\")\n",
+    "save_path = os.path.join(\"..\", \"data\", \"cooking_game_values.npz\")\n",
+    "cooking_game.save_values(save_path)\n",
     "\n",
     "# load the precomputed values from the file\n",
     "empty_cooking_game = CookingGame()\n",
     "print(\"Values stored before loading: \", empty_cooking_game.value_storage)\n",
-    "empty_cooking_game.load_values(\"cooking_game_values.npz\")\n",
+    "empty_cooking_game.load_values(save_path)\n",
     "print(\"Values stored after loading:  \", empty_cooking_game.value_storage)"
    ],
    "outputs": [
@@ -328,20 +330,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Values stored before loading:  []\n"
-     ]
-    },
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'cooking_game_values.npz'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[7], line 7\u001B[0m\n\u001B[0;32m      5\u001B[0m empty_cooking_game \u001B[38;5;241m=\u001B[39m CookingGame()\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mValues stored before loading: \u001B[39m\u001B[38;5;124m\"\u001B[39m, empty_cooking_game\u001B[38;5;241m.\u001B[39mvalue_storage)\n\u001B[1;32m----> 7\u001B[0m \u001B[43mempty_cooking_game\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mload_values\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcooking_game_values.npz\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m      8\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mValues stored after loading:  \u001B[39m\u001B[38;5;124m\"\u001B[39m, empty_cooking_game\u001B[38;5;241m.\u001B[39mvalue_storage)\n",
-      "File \u001B[1;32mC:\\1_Workspaces\\1_Phd_Projects\\shapiq\\shapiq\\games\\base.py:426\u001B[0m, in \u001B[0;36mGame.load_values\u001B[1;34m(self, path, precomputed)\u001B[0m\n\u001B[0;32m    423\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m path\u001B[38;5;241m.\u001B[39mendswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.npz\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    424\u001B[0m     path \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.npz\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m--> 426\u001B[0m data \u001B[38;5;241m=\u001B[39m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mload\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpath\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    427\u001B[0m n_players \u001B[38;5;241m=\u001B[39m data[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mn_players\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m    428\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_players \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m n_players \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_players:\n",
-      "File \u001B[1;32mC:\\1_Workspaces\\1_Phd_Projects\\shapiq\\venv\\lib\\site-packages\\numpy\\lib\\npyio.py:427\u001B[0m, in \u001B[0;36mload\u001B[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001B[0m\n\u001B[0;32m    425\u001B[0m     own_fid \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m    426\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 427\u001B[0m     fid \u001B[38;5;241m=\u001B[39m stack\u001B[38;5;241m.\u001B[39menter_context(\u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mos_fspath\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mrb\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m)\n\u001B[0;32m    428\u001B[0m     own_fid \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[0;32m    430\u001B[0m \u001B[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001B[39;00m\n",
-      "\u001B[1;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'cooking_game_values.npz'"
+      "Values stored before loading:  []\n",
+      "Values stored after loading:   [ 0.  4.  3.  2.  9.  8.  7. 15.]\n"
      ]
     }
    ],
@@ -356,19 +346,42 @@
    ]
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T12:14:06.092763Z",
+     "start_time": "2025-01-10T12:14:06.077767Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "# initialize a game object directly from precomputed values\n",
-    "game = shapiq.Game(path_to_values=\"data/cooking_game_values.npz\")\n",
+    "game = shapiq.Game(path_to_values=save_path)\n",
     "print(game)\n",
     "\n",
     "# query the value function of the game for the same coalitions as before\n",
     "coals = np.array([[0, 0, 0], [1, 1, 0], [1, 0, 1], [0, 1, 1], [1, 1, 1]])\n",
     "game(coals)"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Game(3 players, normalize=False, normalization_value=0.0, precomputed=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  9.,  8.,  7., 15.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
   },
   {
    "metadata": {},
@@ -379,7 +392,12 @@
    ]
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T12:14:06.108755Z",
+     "start_time": "2025-01-10T12:14:06.095753Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "print(cooking_game.characteristic_function)\n",
@@ -388,8 +406,17 @@
     "except AttributeError as e:\n",
     "    print(\"AttributeError:\", e)"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{(): 0, (0,): 4, (1,): 3, (2,): 2, (0, 1): 9, (0, 2): 8, (1, 2): 7, (0, 1, 2): 15}\n",
+      "AttributeError: 'Game' object has no attribute 'characteristic_function'\n"
+     ]
+    }
+   ],
+   "execution_count": 9
   }
  ],
  "metadata": {
diff --git a/docs/source/notebooks/basics_notebooks/data_valuation.ipynb b/docs/source/notebooks/basics_notebooks/data_valuation.ipynb
index 6f4fa23a..cc9b75c1 100644
--- a/docs/source/notebooks/basics_notebooks/data_valuation.ipynb
+++ b/docs/source/notebooks/basics_notebooks/data_valuation.ipynb
@@ -32,8 +32,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:12:05.353388Z",
-     "start_time": "2024-11-07T15:12:03.635224Z"
+     "end_time": "2025-01-10T12:10:49.879006Z",
+     "start_time": "2025-01-10T12:10:48.286843Z"
     }
    },
    "outputs": [
@@ -41,7 +41,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Shapiq version: 1.1.0\n"
+      "Shapiq version: 1.1.1.dev\n"
      ]
     }
    ],
@@ -62,24 +62,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:37.540707Z",
-     "start_time": "2024-10-22T16:04:37.438398Z"
+     "end_time": "2025-01-10T12:10:50.054578Z",
+     "start_time": "2025-01-10T12:10:49.882009Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"406.940312pt\" height=\"325.986375pt\" viewBox=\"0 0 406.940312 325.986375\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2024-10-22T18:04:37.516569</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.8.0, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "def plot_synthetic_data(ax, X_train, y_train, X_test, y_test, title):\n",
     "    ax.set_title(title)\n",
@@ -155,7 +144,20 @@
     "fig, ax = plt.subplots()\n",
     "\n",
     "plot_synthetic_data(ax, X_train, y_train, X_test, y_test, \"Synthetic Classification Data\")"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -169,8 +171,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
    "source": [
     "class SyntheticDataValuation(shapiq.Game):\n",
     "    \"\"\"The synthetic data valuation tasked modeled as a cooperative game.\n",
@@ -224,10 +224,12 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:37.551682Z",
-     "start_time": "2024-10-22T16:04:37.549258Z"
+     "end_time": "2025-01-10T12:10:50.070091Z",
+     "start_time": "2025-01-10T12:10:50.055577Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -241,17 +243,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Full coalition value:  1.0\n",
-      "Empty coalition value:  0.0\n"
-     ]
-    }
-   ],
    "source": [
     "from sklearn.svm import LinearSVC\n",
     "\n",
@@ -274,10 +265,21 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:37.577065Z",
-     "start_time": "2024-10-22T16:04:37.554955Z"
+     "end_time": "2025-01-10T12:10:50.085090Z",
+     "start_time": "2025-01-10T12:10:50.072088Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Full coalition value:  1.0\n",
+      "Empty coalition value:  0.0\n"
+     ]
+    }
+   ],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -292,17 +294,6 @@
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   {
    "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
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    "source": [
     "fig, ax = plt.subplots()\n",
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@@ -327,10 +318,23 @@
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     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:37.657117Z",
-     "start_time": "2024-10-22T16:04:37.567430Z"
+     "end_time": "2025-01-10T12:10:50.241642Z",
+     "start_time": "2025-01-10T12:10:50.087089Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "# Compute Shapley values with the ShapIQ approximator for the game function\n",
-    "exactComputer = shapiq.ExactComputer(n_players=n_players, game_fun=data_valuation_game)\n",
+    "exactComputer = shapiq.ExactComputer(n_players=n_players, game=data_valuation_game)\n",
     "sv_values = exactComputer(\"SV\")\n",
     "sv_values.plot_stacked_bar(\n",
     "    title=\"Shapley Values for Synthetic (Training) Data\",\n",
@@ -370,10 +363,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:39.005886Z",
-     "start_time": "2024-10-22T16:04:37.657523Z"
+     "end_time": "2025-01-10T12:10:53.578653Z",
+     "start_time": "2025-01-10T12:10:50.242637Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
@@ -424,10 +419,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:39.066692Z",
-     "start_time": "2024-10-22T16:04:39.013827Z"
+     "end_time": "2025-01-10T12:10:53.829762Z",
+     "start_time": "2025-01-10T12:10:53.580649Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "from matplotlib import patches\n",
     "\n",
@@ -478,10 +475,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:39.216073Z",
-     "start_time": "2024-10-22T16:04:39.071637Z"
+     "end_time": "2025-01-10T12:10:54.146354Z",
+     "start_time": "2025-01-10T12:10:53.831753Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ],
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"719.420312pt\" height=\"503.394375pt\" viewBox=\"0 0 719.420312 503.394375\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2025-01-10T13:10:54.046830</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.9.2, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: 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@@ -495,17 +505,6 @@
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "data_valuation_game = SyntheticDataValuation(\n",
     "    classifier=classifier,\n",
@@ -517,7 +516,7 @@
     ")\n",
     "\n",
     "# Compute Shapley values with the shapiq ExactComputer for the game function\n",
-    "exactComputer = shapiq.ExactComputer(n_players=n_players, game_fun=data_valuation_game)\n",
+    "exactComputer = shapiq.ExactComputer(n_players=n_players, game=data_valuation_game)\n",
     "sv_values = exactComputer(\"SV\")\n",
     "sv_values.plot_stacked_bar()\n",
     "plt.show()"
@@ -525,10 +524,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:40.635663Z",
-     "start_time": "2024-10-22T16:04:39.216847Z"
+     "end_time": "2025-01-10T12:10:57.601216Z",
+     "start_time": "2025-01-10T12:10:54.148388Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
@@ -541,17 +553,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy on test data before removing corrupted samples:  0.5\n",
-      "Accuracy on test data after removing corrupted samples:  1.0\n"
-     ]
-    }
-   ],
    "source": [
     "classifier.fit(corrupted_X_train, corruped_y_train)\n",
     "print(\"Accuracy on test data before removing corrupted samples: \", classifier.score(X_test, y_test))\n",
@@ -564,10 +565,21 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:40.640022Z",
-     "start_time": "2024-10-22T16:04:40.636571Z"
+     "end_time": "2025-01-10T12:10:57.617287Z",
+     "start_time": "2025-01-10T12:10:57.604217Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on test data before removing corrupted samples:  0.5\n",
+      "Accuracy on test data after removing corrupted samples:  1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 10
   },
   {
    "cell_type": "markdown",
@@ -580,17 +592,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [
-    {
-     "data": {
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "def plot_decision_boundary(ax, classifier, X_train, y_train, X_test, y_test):\n",
     "    classifier.fit(X_train, y_train)\n",
@@ -621,10 +622,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:40.881221Z",
-     "start_time": "2024-10-22T16:04:40.641575Z"
+     "end_time": "2025-01-10T12:10:57.851328Z",
+     "start_time": "2025-01-10T12:10:57.621215Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x700 with 2 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 11
   },
   {
    "cell_type": "markdown",
@@ -640,16 +654,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Players:  160\n"
-     ]
-    }
-   ],
    "source": [
     "from sklearn.tree import DecisionTreeClassifier\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -665,15 +669,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:41.055176Z",
-     "start_time": "2024-10-22T16:04:40.879435Z"
+     "end_time": "2025-01-10T12:10:58.247552Z",
+     "start_time": "2025-01-10T12:10:57.854324Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Players:  160\n"
+     ]
+    }
+   ],
+   "execution_count": 12
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "outputs": [],
    "source": [
     "data_valuation_game = SyntheticDataValuation(\n",
     "    classifier=classifier,\n",
@@ -687,10 +699,12 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:04:41.056814Z",
-     "start_time": "2024-10-22T16:04:41.055761Z"
+     "end_time": "2025-01-10T12:10:58.263501Z",
+     "start_time": "2025-01-10T12:10:58.249487Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 13
   },
   {
    "cell_type": "markdown",
@@ -703,17 +717,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "outputs": [
-    {
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-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "budgets = [10, 100, 1000, 5000]\n",
     "erg = {}\n",
@@ -761,10 +764,23 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-10-22T16:05:15.069797Z",
-     "start_time": "2024-10-22T16:04:41.062860Z"
+     "end_time": "2025-01-10T12:12:43.099030Z",
+     "start_time": "2025-01-10T12:10:58.266031Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
   },
   {
    "cell_type": "markdown",
diff --git a/docs/source/notebooks/basics_notebooks/sv_calculation.ipynb b/docs/source/notebooks/basics_notebooks/sv_calculation.ipynb
index 1b692e39..8c011492 100644
--- a/docs/source/notebooks/basics_notebooks/sv_calculation.ipynb
+++ b/docs/source/notebooks/basics_notebooks/sv_calculation.ipynb
@@ -19,8 +19,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.096233Z",
-     "start_time": "2024-11-07T15:16:43.959504Z"
+     "end_time": "2025-01-10T12:14:20.955825Z",
+     "start_time": "2025-01-10T12:14:19.361009Z"
     }
    },
    "cell_type": "code",
@@ -33,7 +33,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.1.0'"
+       "'1.1.1.dev'"
       ]
      },
      "execution_count": 1,
@@ -92,8 +92,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.126230Z",
-     "start_time": "2024-11-07T15:16:46.099232Z"
+     "end_time": "2025-01-10T12:14:20.971829Z",
+     "start_time": "2025-01-10T12:14:20.956833Z"
     }
    },
    "cell_type": "code",
@@ -152,8 +152,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.142231Z",
-     "start_time": "2024-11-07T15:16:46.128230Z"
+     "end_time": "2025-01-10T12:14:20.987376Z",
+     "start_time": "2025-01-10T12:14:20.972822Z"
     }
    },
    "cell_type": "code",
@@ -179,8 +179,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.158233Z",
-     "start_time": "2024-11-07T15:16:46.147238Z"
+     "end_time": "2025-01-10T12:14:21.003371Z",
+     "start_time": "2025-01-10T12:14:20.988363Z"
     }
    },
    "cell_type": "code",
@@ -212,8 +212,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.174244Z",
-     "start_time": "2024-11-07T15:16:46.159234Z"
+     "end_time": "2025-01-10T12:14:21.018371Z",
+     "start_time": "2025-01-10T12:14:21.005377Z"
     }
    },
    "cell_type": "code",
@@ -281,7 +281,7 @@
     "from shapiq import ExactComputer\n",
     "\n",
     "# create an ExactComputer object for the cooking game\n",
-    "exact_computer = ExactComputer(n_players=cooking_game.n_players, game_fun=cooking_game)\n",
+    "exact_computer = ExactComputer(n_players=cooking_game.n_players, game=cooking_game)\n",
     "\n",
     "# compute the Shapley Values for the game\n",
     "sv_exact = exact_computer(index=\"SV\")\n",
@@ -290,8 +290,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.189782Z",
-     "start_time": "2024-11-07T15:16:46.180241Z"
+     "end_time": "2025-01-10T12:14:21.033370Z",
+     "start_time": "2025-01-10T12:14:21.019366Z"
     }
    },
    "outputs": [
@@ -328,8 +328,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.365289Z",
-     "start_time": "2024-11-07T15:16:46.191764Z"
+     "end_time": "2025-01-10T12:14:21.142986Z",
+     "start_time": "2025-01-10T12:14:21.034373Z"
     }
    },
    "cell_type": "code",
@@ -382,8 +382,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.381306Z",
-     "start_time": "2024-11-07T15:16:46.367293Z"
+     "end_time": "2025-01-10T12:14:21.157908Z",
+     "start_time": "2025-01-10T12:14:21.144932Z"
     }
    },
    "cell_type": "code",
@@ -442,8 +442,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:46.600919Z",
-     "start_time": "2024-11-07T15:16:46.384829Z"
+     "end_time": "2025-01-10T12:14:21.314210Z",
+     "start_time": "2025-01-10T12:14:21.158901Z"
     }
    },
    "cell_type": "code",
@@ -547,8 +547,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:51.516931Z",
-     "start_time": "2024-11-07T15:16:46.605924Z"
+     "end_time": "2025-01-10T12:14:25.120874Z",
+     "start_time": "2025-01-10T12:14:21.317206Z"
     }
    },
    "cell_type": "code",
@@ -598,8 +598,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:51.595448Z",
-     "start_time": "2024-11-07T15:16:51.518933Z"
+     "end_time": "2025-01-10T12:14:25.167909Z",
+     "start_time": "2025-01-10T12:14:25.122873Z"
     }
    },
    "cell_type": "code",
@@ -638,8 +638,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:52.080746Z",
-     "start_time": "2024-11-07T15:16:51.597451Z"
+     "end_time": "2025-01-10T12:14:25.563052Z",
+     "start_time": "2025-01-10T12:14:25.170918Z"
     }
    },
    "cell_type": "code",
@@ -700,8 +700,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:53.051974Z",
-     "start_time": "2024-11-07T15:16:52.082742Z"
+     "end_time": "2025-01-10T12:14:26.360311Z",
+     "start_time": "2025-01-10T12:14:25.565980Z"
     }
    },
    "cell_type": "code",
@@ -739,8 +739,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:18:25.286249Z",
-     "start_time": "2024-11-07T15:16:53.053975Z"
+     "end_time": "2025-01-10T12:15:58.308779Z",
+     "start_time": "2025-01-10T12:14:26.364241Z"
     }
    },
    "cell_type": "code",
@@ -782,8 +782,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:18:25.771060Z",
-     "start_time": "2024-11-07T15:18:25.288247Z"
+     "end_time": "2025-01-10T12:15:58.668948Z",
+     "start_time": "2025-01-10T12:15:58.311776Z"
     }
    },
    "cell_type": "code",
diff --git a/docs/source/notebooks/game_theory_notebooks/core.ipynb b/docs/source/notebooks/game_theory_notebooks/core.ipynb
index 53b0b6c5..f2ab83d5 100644
--- a/docs/source/notebooks/game_theory_notebooks/core.ipynb
+++ b/docs/source/notebooks/game_theory_notebooks/core.ipynb
@@ -155,8 +155,8 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from shapiq.exact import ExactComputer\n",
-    "from shapiq.games.base import Game\n",
+    "from shapiq import ExactComputer\n",
+    "from shapiq import Game\n",
     "\n",
     "\n",
     "# Define the PaperGame as described above\n",
@@ -185,7 +185,7 @@
     "paper_game = PaperGame()\n",
     "\n",
     "# Initialize the ExactComputer with the PaperGame\n",
-    "exact_computer = ExactComputer(n_players=3, game_fun=paper_game)\n",
+    "exact_computer = ExactComputer(n_players=3, game=paper_game)\n",
     "# Compute the egalitarian least core abbreviated to \"ELC\"\n",
     "egalitarian_least_core = exact_computer(\"ELC\")"
    ],
diff --git a/docs/source/notebooks/language_notebooks/language_model_game.ipynb b/docs/source/notebooks/language_notebooks/language_model_game.ipynb
index 77b46c0e..9349fbcf 100644
--- a/docs/source/notebooks/language_notebooks/language_model_game.ipynb
+++ b/docs/source/notebooks/language_notebooks/language_model_game.ipynb
@@ -642,7 +642,7 @@
    },
    "source": [
     "# Compute Shapley interactions with the ShapIQ approximator for the game function\n",
-    "approximator = shapiq.SHAPIQ(n=n_players, max_order=2, index=\"k-SII\")\n",
+    "approximator = shapiq.KernelSHAPIQ(n=n_players, max_order=2, index=\"k-SII\")\n",
     "sii_values = approximator.approximate(budget=2**n_players, game=game_fun)\n",
     "sii_values.dict_values"
    ],
@@ -687,7 +687,7 @@
    },
    "source": [
     "# Compute Shapley interactions with the ShapIQ approximator for the game object\n",
-    "approximator = shapiq.SHAPIQ(n=game_class.n_players, max_order=2, index=\"k-SII\")\n",
+    "approximator = shapiq.KernelSHAPIQ(n=game_class.n_players, max_order=2, index=\"k-SII\")\n",
     "sii_values = approximator.approximate(budget=2**game_class.n_players, game=game_class)\n",
     "sii_values.dict_values"
    ],
diff --git a/docs/source/notebooks/tabular.rst b/docs/source/notebooks/tabular.rst
index 362b7527..38ef02bc 100644
--- a/docs/source/notebooks/tabular.rst
+++ b/docs/source/notebooks/tabular.rst
@@ -8,3 +8,4 @@ The following notebooks provide basic examples of how to use the ``shapiq`` pack
    :maxdepth: 1
 
    tabular_notebooks/*
+   tree_notebooks/treeshapiq_lightgbm.ipynb
diff --git a/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb b/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb
new file mode 100644
index 00000000..11a4343c
--- /dev/null
+++ b/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb
@@ -0,0 +1,1248 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Explaining TabPFN\n",
+    "\n",
+    "TabPFN is a foundation model for tabular data, which uses in-context learning to do solve classification and regression tasks.\n",
+    "TabPFN outperforms traditional models like Random Forest, Gradient Boosting for small datasets and raises the state-of-the-art for tabular data!\n",
+    "Recently, a major update was released, which includes a new architecture and an updated API.\n",
+    "\n",
+    "For more information about TabPFN, check the [github repository](https://github.com/PriorLabs/TabPFN) and the associated papers ([TabPFN](https://openreview.net/forum?id=eu9fVjVasr4), [TabPFNv2](https://www.nature.com/articles/s41586-024-08328-6)).\n",
+    "\n",
+    "In this tutorial, we see how we can **use shapiq to explain the predictions of TabPFNv2**. \n",
+    "We will use the California housing dataset and train a TabPFN model to predict the house prices.\n",
+    "Many explanation methods show that models tend to learn interactions between the latitude and longitude features, containing information about the exact location of a house.\n",
+    "We want to see if TabPFN also learns the interactions between latitude and longitude.\n",
+    "\n",
+    "First, lets import the libraries (tabpfn and shapiq) and check their versions.\n",
+    "Note that this tutorial uses the latest version of TabPFN (> 2.0.0) and will not necessarily work with older versions."
+   ],
+   "id": "af7fe5c630d43e1d"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:55:35.932354Z",
+     "start_time": "2025-01-10T13:55:31.928667Z"
+    }
+   },
+   "source": [
+    "from importlib.metadata import version\n",
+    "\n",
+    "import shapiq\n",
+    "import tabpfn\n",
+    "import torch\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "print(\"shapiq version: \", shapiq.__version__)\n",
+    "print(\"tabpfn version: \", version(\"tabpfn\"))\n",
+    "print(\"Device: \", device)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shapiq version:  1.1.1.dev\n",
+      "tabpfn version:  2.0.1\n",
+      "Device:  cpu\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Get the California Housing Data\n",
+    "Now let's load the California housing dataset and inspect the data."
+   ],
+   "id": "229e7c0478fc1c96"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:55:35.978513Z",
+     "start_time": "2025-01-10T13:55:35.933357Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x_data, y_data = shapiq.datasets.load_california_housing()\n",
+    "feature_names = x_data.columns\n",
+    "\n",
+    "# copy the data to make sure we don't modify the original data\n",
+    "dataset = x_data.copy()\n",
+    "dataset[\"HousePrice\"] = y_data\n",
+    "display(dataset.head())\n",
+    "display(dataset[\"HousePrice\"].describe())"
+   ],
+   "id": "af75a5d50fbb096e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
+       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
+       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
+       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
+       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
+       "\n",
+       "   Longitude  HousePrice  \n",
+       "0    -122.23       4.526  \n",
+       "1    -122.22       3.585  \n",
+       "2    -122.24       3.521  \n",
+       "3    -122.25       3.413  \n",
+       "4    -122.25       3.422  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MedInc</th>\n",
+       "      <th>HouseAge</th>\n",
+       "      <th>AveRooms</th>\n",
+       "      <th>AveBedrms</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>AveOccup</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>HousePrice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>6.984127</td>\n",
+       "      <td>1.023810</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>2.555556</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>4.526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>6.238137</td>\n",
+       "      <td>0.971880</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>2.109842</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>3.585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>8.288136</td>\n",
+       "      <td>1.073446</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>2.802260</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>3.521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>5.817352</td>\n",
+       "      <td>1.073059</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>2.547945</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>3.413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>6.281853</td>\n",
+       "      <td>1.081081</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.181467</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>3.422</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    20640.000000\n",
+       "mean         2.068558\n",
+       "std          1.153956\n",
+       "min          0.149990\n",
+       "25%          1.196000\n",
+       "50%          1.797000\n",
+       "75%          2.647250\n",
+       "max          5.000010\n",
+       "Name: HousePrice, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "Now we have loaded the data.\n",
+    "**HousePrice** is the target variable we want to predict.\n",
+    "The target ranges from 0.15 to 5.0.\n",
+    "\n",
+    "In order to use TabPFN, we need to split the data into a training and testing set.\n",
+    "Note, that TabPFN works best for **small sized datasets** (less than 10k samples).\n",
+    "On CPU, we can only use a very small number of training data points to fit the model. If you have a GPU, feel free to increase the number of samples."
+   ],
+   "id": "2d3e6649c1ae8450"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:55:35.994521Z",
+     "start_time": "2025-01-10T13:55:35.979512Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# split the data into training and testing sets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(\n",
+    "    x_data.values, y_data.values, train_size=500, random_state=42\n",
+    ")\n",
+    "print(\"Train data shape: \", x_train.shape, y_train.shape)\n",
+    "print(\"Test data shape: \", x_test.shape, y_test.shape)"
+   ],
+   "id": "e77933887d0a119f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train data shape:  (500, 8) (500,)\n",
+      "Test data shape:  (20140, 8) (20140,)\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Fit TabPFN\n",
+    "Now that we have the data, we can fit TabPFN to the training data and make it ready for predictions. \n",
+    "\n",
+    "**Note** that TabPFN at the end of the day is still quite a big transformer model, which needs a GPU to run efficiently.\n",
+    "If you are on GPU, feel free to increase the number of samples in the training or testing sets in the following:"
+   ],
+   "id": "8be176b5890b9eaf"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:55:36.326775Z",
+     "start_time": "2025-01-10T13:55:35.995512Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "model = tabpfn.TabPFNRegressor(n_jobs=7, device=device)\n",
+    "model.fit(x_train, y_train)"
+   ],
+   "id": "a1100c73d7b0867e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TabPFNRegressor(device=device(type='cpu'), n_jobs=7)"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TabPFNRegressor(device=device(type=&#x27;cpu&#x27;), n_jobs=7)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;TabPFNRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TabPFNRegressor(device=device(type=&#x27;cpu&#x27;), n_jobs=7)</pre></div> </div></div></div></div>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "When we have the \"trained\" model, we can use it to predict the house prices.\n",
+    "These predictions are very competitive with the state-of-the-art models."
+   ],
+   "id": "25603c1d4540f2c5"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Evaluate TabPFN",
+   "id": "db580ea3627edae2"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:57:53.128517Z",
+     "start_time": "2025-01-10T13:55:36.333769Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# we downsample the test data for more efficient inference\n",
+    "x_test, y_test = x_test[:2000], y_test[:2000]\n",
+    "predictions = model.predict(x_test)"
+   ],
+   "id": "d36110af9fa1b058",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:57:53.144447Z",
+     "start_time": "2025-01-10T13:57:53.129439Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import numpy as np\n",
+    "\n",
+    "mse = mean_squared_error(y_test, predictions)\n",
+    "r2 = r2_score(y_test, predictions)\n",
+    "print(\"MSE: \", mse, \"R2: \", r2)\n",
+    "\n",
+    "average_prediction = np.mean(predictions)\n",
+    "print(\"Average prediction: \", average_prediction)"
+   ],
+   "id": "fdd1896b91cfbd4a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE:  0.27149947144257525 R2:  0.796390135236755\n",
+      "Average prediction:  2.0852828\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:57:53.331718Z",
+     "start_time": "2025-01-10T13:57:53.145436Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# we will reset the model to less training data because we are on CPU\n",
+    "if device == torch.device(\"cpu\"):\n",
+    "    print(\"Resetting the model to less training data:\", x_train.shape[0])\n",
+    "    x_train, y_train = x_train[:50], y_train[:50]\n",
+    "    model.fit(x_train, y_train)"
+   ],
+   "id": "7f6253cf223e9136",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resetting the model to less training data: 500\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Explain TabPFN with shapiq\n",
+    "Now that we see how TabPFN performs, we can use shapiq to explain the predictions.\n",
+    "First, we will use the KernelSHAP method to explain the predictions."
+   ],
+   "id": "85a7dadbec463d65"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T13:57:54.328409Z",
+     "start_time": "2025-01-10T13:57:53.334623Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x_explain = x_data.values[1000]\n",
+    "y_explain = y_data.values[1000]\n",
+    "\n",
+    "prediction = model.predict(x_explain.reshape(1, -1))[0]\n",
+    "print(\"Prediction: \", prediction)\n",
+    "print(\"True value: \", y_explain)\n",
+    "print(\"Average prediction: \", average_prediction)"
+   ],
+   "id": "15e30787bb74905",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction:  1.8186865\n",
+      "True value:  1.844\n",
+      "Average prediction:  2.0852828\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Traditional Explanation with Baseline Imputation\n",
+    "The traditional way to explain any black-box model trained on tabular data is by using imputation strategies for feature removal (excellent [paper by Covert et al.](https://jmlr.csail.mit.edu/papers/volume22/20-1316/20-1316.pdf)).\n",
+    "During explanations, the model is queried multiple times with different subsets of features removed.\n",
+    "Removed features are imputed using different strategies, such as the baseline imputation.\n",
+    "Baseline imputation replaces the removed features with the mean/mode of the training data.\n",
+    "\n",
+    "We can natively use the ``shapiq.Explainer`` (specifically ``shapiq.TabularExplainer``) to explain the TabPFN model:"
+   ],
+   "id": "b225c897c1181eee"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:02:39.961668Z",
+     "start_time": "2025-01-10T13:57:54.329359Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "explainer = shapiq.Explainer(model, data=x_test[:50], index=\"SV\", max_order=1, imputer=\"baseline\")\n",
+    "explainer._imputer.verbose = True  # see the explanation progress\n",
+    "\n",
+    "shapley_values = explainer.explain(x_explain)\n",
+    "shapley_values.plot_force(feature_names=feature_names)"
+   ],
+   "id": "41314e231db2e986",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Evaluating game:   0%|          | 0/256 [00:00<?, ? coalition/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "58adb18d135f41429ff10942996b0c2a"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x300 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Explaining TabPFN with Remove-and-\"Retrain\"\n",
+    "\n",
+    "Since TabPFN is a foundation model, it uses in-context learning to solve the classification and regression tasks.\n",
+    "This means that \"retraining\" the model is quite inexpensive, because we only need to provide the new data points with whatever features we want to remove.\n",
+    "A recent paper by [Rundel et al.](https://arxiv.org/pdf/2403.10923) shows that this strategy is very effective for explaining models like TabPFN.\n",
+    "\n",
+    "Because of ``shapiq``'s notion of cooperative games, we can easily implement the remove-and-\"retrain\" strategy for TabPFN as game.\n",
+    "The game takes the model, the training data, the explanation data, and the average prediction as input.\n",
+    "The value function of the game performs the remove-and-\"retrain\" strategy for TabPFN and returns the predictions for the coalitions."
+   ],
+   "id": "cdba7867ce6fbbb0"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:02:39.977683Z",
+     "start_time": "2025-01-10T14:02:39.963673Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "class TabPFNGame(shapiq.Game):\n",
+    "    \"\"\"The TabPFN Game class implementation a remove-and-\"retrain\" strategy to explain the predictions of TabPFN.\n",
+    "\n",
+    "    Args:\n",
+    "        model: The TabPFN model.\n",
+    "        x_train: The training data.\n",
+    "        y_train: The training labels.\n",
+    "        x_explain: The data point to explain.\n",
+    "        average_prediction: The average prediction of the model.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, model, x_train, y_train, x_explain, average_prediction):\n",
+    "        self.model = model\n",
+    "        self.x_train = x_train\n",
+    "        self.y_train = y_train\n",
+    "        self.x_explain = x_explain\n",
+    "        self.average_prediction = average_prediction\n",
+    "\n",
+    "        print(\"Initializing TabPFN Game\")\n",
+    "        print(\"Train data shape: \", x_train.shape, y_train.shape)\n",
+    "        print(\"Explain data shape: \", x_explain.shape)\n",
+    "\n",
+    "        super().__init__(n_players=x_train.shape[1], normalization_value=self.average_prediction)\n",
+    "\n",
+    "    def value_function(self, coalitions: np.ndarray) -> np.ndarray:\n",
+    "        \"\"\"The value function performs the remove-and-\"retrain\" strategy for TabPFN.\"\"\"\n",
+    "        output = np.zeros(len(coalitions), dtype=float)\n",
+    "        for i, coalition in enumerate(coalitions):\n",
+    "            if sum(coalition) == 0:\n",
+    "                output[i] = self.average_prediction\n",
+    "                continue\n",
+    "            x_train_coal = self.x_train[:, coalition]\n",
+    "            x_explain_coal = self.x_explain[coalition].reshape(1, -1)\n",
+    "            self.model.fit(x_train_coal, self.y_train)\n",
+    "            pred = float(self.model.predict(x_explain_coal)[0])\n",
+    "            output[i] = pred\n",
+    "        return output"
+   ],
+   "id": "37a977c5f4a88aee",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "With this game implementation we can now use helper functions from ``shapiq.Game`` like ``precompute`` to precompute the values of the game to speed up the explanation process.\n",
+    "For reproducibility, this notebook loads a precomputed game from the file ``tabpfn_values.npz`` if it exists."
+   ],
+   "id": "c8b473a6c67a54a2"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:02:39.993671Z",
+     "start_time": "2025-01-10T14:02:39.980669Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "\n",
+    "if not os.path.exists(\"tabpfn_values.npz\"):\n",
+    "    tabpfn_game = TabPFNGame(model, x_train, y_train, x_explain, average_prediction)\n",
+    "    tabpfn_game.verbose = True  # see the pre-computation progress\n",
+    "    tabpfn_game.precompute()\n",
+    "    tabpfn_game.save_values(\"tabpfn_values.npz\")\n",
+    "\n",
+    "tabpfn_game = shapiq.Game(path_to_values=\"tabpfn_values.npz\", normalize=False)"
+   ],
+   "id": "7b2606969b5bab0",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "Now that we have evaluated all $2^d$ coalitions, we can use ``shapiq.ExactComputer`` to compute any kind of game-theoretic explanation.\n",
+    "\n",
+    "With a precomputed ``shapiq.Game``, we can quickly check different explanation methods like Shapley values or Faithful Shapley Interaction values.\n",
+    "We can also query the game for values from specific coalitions like what TabPFN predicts when we provide all features or only a subset of features: "
+   ],
+   "id": "1d7f391c3f67721e"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:02:40.009674Z",
+     "start_time": "2025-01-10T14:02:39.994665Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(\"No features: \", tabpfn_game[()])\n",
+    "print(\"All features: \", tabpfn_game[tuple(range(tabpfn_game.n_players))])\n",
+    "print(\"Latitude and Longitude: \", tabpfn_game[(6, 7)])  # lat. and lon. are at index 6 and 7"
+   ],
+   "id": "a96e3795ea1df8a0",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No features:  2.0861093997955322\n",
+      "All features:  1.8420544862747192\n",
+      "Latitude and Longitude:  1.6669323444366455\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "Only providing the latitude and longitude features results in a prediction of 1.66, which is less than the average prediction of around 2.0 and the prediction with all features, which would be 1.84. \n",
+    "This suggests that the latitude and longitude may reduce the house price.\n",
+    "Let's compute some explanation values for the TabPFN model with ``shapiq.ExactComputer`` and check this out:"
+   ],
+   "id": "704e9c58dd3273d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:03:00.196100Z",
+     "start_time": "2025-01-10T14:03:00.168899Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "exact_computer = shapiq.ExactComputer(n_players=tabpfn_game.n_players, game=tabpfn_game)\n",
+    "sv = exact_computer(index=\"SV\", order=1)  # compute the Shapley values\n",
+    "fsii = exact_computer(index=\"FSII\", order=2)  # compute Faithful Shapley Interaction values"
+   ],
+   "id": "1887b05e6bd7cda8",
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:03:02.672221Z",
+     "start_time": "2025-01-10T14:03:02.238547Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "display(sv.dict_values)\n",
+    "sv.plot_force(feature_names=feature_names)"
+   ],
+   "id": "7bfdd3a9e1ff6b1d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{(): 2.0861093997955322,\n",
+       " (0,): -0.16847709885665363,\n",
+       " (1,): 0.030854925797099225,\n",
+       " (2,): -0.04534098236333772,\n",
+       " (3,): 0.06734204618703749,\n",
+       " (4,): 0.010694948690278039,\n",
+       " (5,): 0.023461689409755293,\n",
+       " (6,): -0.09278867258912055,\n",
+       " (7,): -0.06980176979587177}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x300 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "From the Shapley values, we can see that both latitude and longitude have a negative impact on the house price.\n",
+    "When we compute second order Shapley interactions (``index=FSII``, ``order=2``) we can see that the interaction between latitude and longitude together actually has a very negative impact on the house price."
+   ],
+   "id": "fceea72f0e13feb1"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:03:05.650284Z",
+     "start_time": "2025-01-10T14:03:05.111943Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "fsii.plot_force(feature_names=feature_names)",
+   "id": "7df6eae3201659ab",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x300 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Explain TabPFN with Approximation Methods for Shapley Values and Interactions\n",
+    "When we have a large number of features, the exact computation of Shapley values and interactions can be computationally expensive.\n",
+    "For this reason, we can use approximation methods like KernelSHAP, KernelSHAP-IQ or Faithful Regression to approximate the Shapley values and interactions with a computational budget.\n",
+    "\n",
+    "To illustrate the approximation methods, we use the same TabPFN game (which has only 8 features) but reduce the computational budget to 50 model evaluations.\n",
+    "First, we approximate the Shapley values with KernelSHAP:"
+   ],
+   "id": "e9b187c6a678a8a8"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:03:19.687073Z",
+     "start_time": "2025-01-10T14:03:19.327787Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "approximator = shapiq.KernelSHAP(n=tabpfn_game.n_players, random_state=42)\n",
+    "sv = approximator.approximate(budget=50, game=tabpfn_game)\n",
+    "sv.plot_force(feature_names=feature_names)"
+   ],
+   "id": "7203ae35139cc10a",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x300 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-01-10T14:03:21.717660Z",
+     "start_time": "2025-01-10T14:03:21.298376Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "approximator = shapiq.RegressionFSII(n=tabpfn_game.n_players, random_state=42, max_order=2)\n",
+    "fsii = approximator.approximate(budget=50, game=tabpfn_game)\n",
+    "fsii.plot_force(feature_names=feature_names)"
+   ],
+   "id": "c0baa11868d4769e",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\1_Workspaces\\1_Phd_Projects\\shapiq\\shapiq\\approximator\\regression\\_base.py:342: UserWarning: Linear regression equation is singular, a least squares solutions is used instead.\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x300 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 18
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/tabular_notebooks/shapiq_scikit_learn.ipynb b/docs/source/notebooks/tabular_notebooks/shapiq_scikit_learn.ipynb
index a81c5d9f..f994c5ee 100644
--- a/docs/source/notebooks/tabular_notebooks/shapiq_scikit_learn.ipynb
+++ b/docs/source/notebooks/tabular_notebooks/shapiq_scikit_learn.ipynb
@@ -16,9 +16,7 @@
    "cell_type": "markdown",
    "id": "080de90c",
    "metadata": {},
-   "source": [
-    "### import packages"
-   ]
+   "source": "### Import Packages"
   },
   {
    "cell_type": "code",
@@ -26,8 +24,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:15:48.519797Z",
-     "start_time": "2024-11-07T15:15:48.503808Z"
+     "end_time": "2025-01-10T13:18:13.686187Z",
+     "start_time": "2025-01-10T13:18:11.584918Z"
     }
    },
    "source": [
@@ -43,22 +41,23 @@
     {
      "data": {
       "text/plain": [
-       "'1.1.0'"
+       "'1.1.1.dev'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 2
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
    "id": "9eb96897",
    "metadata": {},
    "source": [
-    "### load data"
+    "### Load Data\n",
+    "Let's load the California housing dataset and split it into training and test sets."
    ]
   },
   {
@@ -66,8 +65,8 @@
    "id": "7fca3f5a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:15:48.676852Z",
-     "start_time": "2024-11-07T15:15:48.618317Z"
+     "end_time": "2025-01-10T13:18:13.733714Z",
+     "start_time": "2025-01-10T13:18:13.688188Z"
     }
    },
    "source": [
@@ -78,7 +77,7 @@
     "n_features = X_train.shape[1]"
    ],
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -87,7 +86,9 @@
     "collapsed": false
    },
    "source": [
-    "### train a model"
+    "### Train a Model with Scikit-learn\n",
+    "Here we train a random forest regressor with 500 trees.\n",
+    "The model achieves a relatively high R2 score on the test set."
    ]
   },
   {
@@ -96,8 +97,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:05.998479Z",
-     "start_time": "2024-11-07T15:15:48.680848Z"
+     "end_time": "2025-01-10T13:18:28.078021Z",
+     "start_time": "2025-01-10T13:18:13.735711Z"
     }
    },
    "source": [
@@ -118,7 +119,7 @@
      ]
     }
    ],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -127,7 +128,7 @@
     "collapsed": false
    },
    "source": [
-    "### model-agnostic explainer\n",
+    "### Model-Agnostic Explainer\n",
     "\n",
     "We use `shapiq.TabularExplainer` to explain any machine learning model for tabular data. \n",
     "\n",
@@ -143,15 +144,15 @@
    "id": "e6435098",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:07.310454Z",
-     "start_time": "2024-11-07T15:16:06.001479Z"
+     "end_time": "2025-01-10T13:18:29.026747Z",
+     "start_time": "2025-01-10T13:18:28.079935Z"
     }
    },
    "source": [
     "explainer_tabular = shapiq.TabularExplainer(model=model, data=X_train, index=\"SII\", max_order=2)"
    ],
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -166,15 +167,15 @@
    "id": "9764e3c2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:07.325455Z",
-     "start_time": "2024-11-07T15:16:07.311456Z"
+     "end_time": "2025-01-10T13:18:29.042755Z",
+     "start_time": "2025-01-10T13:18:29.028747Z"
     }
    },
    "source": [
     "x = X_test[24]"
    ],
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -190,8 +191,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:09.562414Z",
-     "start_time": "2024-11-07T15:16:07.328455Z"
+     "end_time": "2025-01-10T13:18:30.843907Z",
+     "start_time": "2025-01-10T13:18:29.044750Z"
     }
    },
    "source": [
@@ -208,12 +209,12 @@
        ")"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 7
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -228,8 +229,8 @@
    "id": "79e54c1e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:09.577928Z",
-     "start_time": "2024-11-07T15:16:09.564414Z"
+     "end_time": "2025-01-10T13:18:30.859898Z",
+     "start_time": "2025-01-10T13:18:30.845897Z"
     }
    },
    "source": [
@@ -240,50 +241,50 @@
      "data": {
       "text/plain": [
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+       " (3, 7): -0.012188341974186959,\n",
+       " (4, 5): -0.020107243385105573,\n",
+       " (4, 6): -0.011409873190119286,\n",
+       " (4, 7): -0.011490783339750722,\n",
+       " (5, 6): -0.02718532205687375,\n",
+       " (5, 7): 0.006640562391589585,\n",
+       " (6, 7): -0.05674171687511046}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
+   "execution_count": 7
   },
   {
    "cell_type": "markdown",
@@ -298,8 +299,8 @@
    "id": "d7b29c92",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:09.593930Z",
-     "start_time": "2024-11-07T15:16:09.579929Z"
+     "end_time": "2025-01-10T13:18:30.875899Z",
+     "start_time": "2025-01-10T13:18:30.860899Z"
     }
    },
    "source": [
@@ -309,50 +310,50 @@
     {
      "data": {
       "text/plain": [
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       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 9
+   "execution_count": 8
   },
   {
    "cell_type": "code",
    "id": "f0eb589b",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:09.609934Z",
-     "start_time": "2024-11-07T15:16:09.595932Z"
+     "end_time": "2025-01-10T13:18:30.891898Z",
+     "start_time": "2025-01-10T13:18:30.876904Z"
     }
    },
    "source": [
@@ -362,30 +363,30 @@
     {
      "data": {
       "text/plain": [
-       "array([[-0.01221295, -0.0304303 ,  0.04097339, -0.00673529, -0.00584265,\n",
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-       "       [-0.0304303 , -0.0680555 , -0.00652152, -0.00415446, -0.00560701,\n",
-       "         0.07471407, -0.00719869, -0.00521439],\n",
-       "       [ 0.04097339, -0.00652152, -0.04995963, -0.0085883 , -0.00376414,\n",
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-       "        -0.00030335, -0.07016564, -0.27761004]])"
+       "array([[ 0.03969378, -0.04258529,  0.02410734, -0.01456443, -0.01704401,\n",
+       "        -0.09701444, -0.05864804,  0.03137725],\n",
+       "       [-0.04258529, -0.08787131, -0.01196787, -0.01107462, -0.01241836,\n",
+       "         0.11602599, -0.01412188, -0.01177378],\n",
+       "       [ 0.02410734, -0.01196787, -0.03018256, -0.01317262, -0.01135468,\n",
+       "        -0.01644876, -0.00350493, -0.01694232],\n",
+       "       [-0.01456443, -0.01107462, -0.01317262,  0.0103145 , -0.01150326,\n",
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+       "       [-0.01704401, -0.01241836, -0.01135468, -0.01150326,  0.01640401,\n",
+       "        -0.02010724, -0.01140987, -0.01149078],\n",
+       "       [-0.09701444,  0.11602599, -0.01644876, -0.00925532, -0.02010724,\n",
+       "        -0.16357904, -0.02718532,  0.00664056],\n",
+       "       [-0.05864804, -0.01412188, -0.00350493, -0.01289569, -0.01140987,\n",
+       "        -0.02718532,  0.1734638 , -0.05674172],\n",
+       "       [ 0.03137725, -0.01177378, -0.01694232, -0.01218834, -0.01149078,\n",
+       "         0.00664056, -0.05674172, -0.26577439]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 10
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
@@ -394,7 +395,7 @@
     "collapsed": false
    },
    "source": [
-    "### visualization of Shapley interactions\n",
+    "### Visualization of Shapley interactions\n",
     "\n",
     "`shapiq` includes the following plotting functions:\n",
     "\n",
@@ -415,8 +416,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:10.034177Z",
-     "start_time": "2024-11-07T15:16:09.612930Z"
+     "end_time": "2025-01-10T13:18:31.218180Z",
+     "start_time": "2025-01-10T13:18:30.892897Z"
     }
    },
    "source": [
@@ -433,7 +434,7 @@
        "(<Figure size 600x600 with 1 Axes>, <Axes: >)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -442,21 +443,21 @@
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ],
-      "image/png": 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"
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3Qjrpngal2NGtY0e3jjIXExHV3O7J4SSFiwcOx4rN0qwyZGmztEomwgEA1jSpCZsHYx7vETCmKrOKScK8i2T0FRCdPvbzHAoIk0zsnpu53ZPDSQoXD5zBS55tllbZ0qHhkyTCocYrYFoc4UApxSqL9sypw5WsrqxdEuBTKQxK++ddJKKAAgKItXt2BFi1gds9OZzM4OKBM7gokM3SKpkKBwD4tpWV5lMhCwSThmb3u7kkAp9KEdQoPFZ6H0wKLCAA5hqp9BBUcrsnh5MxXDxwBgcFtFlaZXO7hk+3ZyYcAOv2zL3rZWvNjkkQCIFLJAhogMfamIioB4fjvHe0s79LAQSECbd7cjiZwd/5DmXjxo0ghGD16tU52T4hBM8880xOtu0YDIMtS6zfDny7nfU1RAsHQtiSxOghwNhh7OTlUOFQWxK/x8Gk3W9g/S5rjZLTMmyU7ItLIjAoqz6kDSGsAuEJ70unD9jVact+ZYJp99xriIx96mUMKRUTLseYds8vmlV8uj2E5m4duoU+Ew5nIMHFQwLOPPNMzJ8/v2DP39jYiKamJuyzzz4AgCVLloAQgvb29oLtU9EQVIHtbcDX24Btrf3nM8giMKQCGDcMGFGbs/kMVrEiHKYNV5LOIvhoW8hyjkV9mT3W0ui8i4zoJyD8BRUQJl5FwOgqCVOGyRhVJSVdlvGrFBvbNHy8TcWmNg1+NfWykVNIdIxz6rHG7/ejuroatbW1CAaDhd6dQQ9ftnAooihi6NChhd6N4oFSdvJpc4bN0iqphENdiYD9UggHzaBYY3OOhVUs5V0kwxQQkSUMP/t5Tbmdu5kRokBQXyqivpTbPZ3Ak08+ib333huUUjzzzDP4wQ9+UOhdGtTwykMGLF26FDNmzIDL5UJDQwN+97vfQdN6m+8OO+wwXHDBBfjtb3+L6upqDB06FFdffXXMNr788kvMmjULbrcbe+21F954442YpYToZYuNGzdi9uzZAICqqioQQnDmmWcCAEaPHo2bb745ZtuTJ0+Oeb5vvvkGhxxySOS5Xn/99X6/05YtW3DqqaeisrIS1dXVOOGEE7Bx48ZsX6rco2pAczvwzTbgu539hYMkArXlbFli1BCgPL/OiWRsasteOADA2mbV9hwLq7hENqkyo6ULE4dWIKIpdwnYo0bGpAYFIyokKEkaJruCBtbv0rC6ScWWDi2718YBmCdtl8uF0aNH44Ybboi5Pd4SaGVlJe677z4AQCgUwnnnnYeGhga43W6MGjUKixcvjty3vb0dZ599Nurq6lBeXo7DDz8ca9as6bcfd999NxYsWIAFCxbg7rvv7nd7qmMqUMTHOQfCxUOabN26Fd/73vcwffp0rFmzBrfffjvuvvtu/PnPf4653/3334+SkhKsWLEC1113Ha655prISVvXdcyfPx9erxcrVqzAv/71L1xxxRUJn7OxsRFPPvkkAOCrr75CU1MT/u///s/S/hqGgZNOOgmKomDFihW44447cOmll8bcR1VVzJ07F2VlZVi2bBmWL1+O0tJSzJs3D6GQtSvavNMdADa3AN80AS2d/eczeBRgeA1bmqivzNt8BqtsatPw2Y7shQMArPzO/hwLq0TnXdBEYzCtbcjxAgLotXtOapAxrlZGZRL7pqpTNHXqWNMUwtc71YS2UCfz0Ucf4dRTT8Vpp52GTz/9FFdffTX+8Ic/RISBFW655RY899xzePzxx/HVV1/h4YcfxujRoyO3n3LKKWhubsbLL7+Mjz76CFOnTsWcOXPQ2toauc/69evx3nvv4dRTT8Wpp56KZcuWYdOmTZHbrRxTi/I452CcdUQtAv75z3+isbERt912Gwgh2HPPPbFt2zZceumluPLKKyEI7GCy77774qqrrgIAjB07FrfddhvefPNNHHnkkXj99dexfv16LFmyJLI0ce211+LII4+M+5yiKKK6uhoAMGTIEFRWVlre3zfeeANffvklXn31VQwbNgwA8Je//AVHH3105D6PPfYYDMPAXXfdFSmz3nvvvaisrMSSJUtw1FFHpfci5QqH2yytsrFNw+dJhMOQUgFTh1kTDk2dOrZ15SbHwipp5V0kI94SBgWrHDmMgWL3fOGFF1BaGjsqXNd730833ngj5syZgz/84Q8AgHHjxuGLL77A9ddfH6l+pmLz5s0YO3YsZs2aBUIIRo0aFbntnXfewQcffIDm5ma4XC4AwN///nc888wzeOKJJ3DOOecAAO655x4cffTRqKqqAgDMnTsX9957b6TCauWYWjTHuSKBVx7SZO3atTjwwANj1jJnzpyJ7u5ufPfdd5Gf7bvvvjGPa2hoQHNzMwBWPWhsbIzpaZgxY0bO9rexsTEiHADgwAMPjLnPmjVrsG7dOpSVlaG0tBSlpaWorq5GIBDA+vXrc7JfaeEPAVt3AV9tZSmNfYWDS2bWv/HDgWHVg0Y4ALnNsbBKdN5F1pgCwstOJOjyAzudV4GIxrR7Th4mY7dqKal907R7rm4KYf0uDV0JbKH5Yvbs2Vi9enXM11133RW5fe3atZg5c2bMY2bOnIlvvvkmRmQk48wzz8Tq1asxfvx4XHDBBXjttdcit61Zswbd3d2oqamJHHtKS0uxYcOGyLFH13Xcf//9WLBgQeRxCxYswH333QfDYK+flWOq449zRQavPOQIWY69BCOERN7odiIIQr9ysapas+yZdHd3Y7/99sPDDz/c77a6urqs9i9jrKRZlntYA2SB3RJW2dCq4YskuRNDStlShdWJjfnIsbBK2nkXySCEuWGaOwBfkAkIwJEViGj6pns29xjY2ZM63dMjs8bMQqR7lpSUYI899oj5WfRFkBUIIUmPQVOnTsWGDRvw8ssv44033sCpp56KI444Ak888QS6u7vR0NCAJUuW9NuuWWF99dVXsXXr1n4NkrquR6q5VnDkca6I4eIhTSZMmIAnn3wSlNJI9WH58uUoKyvDiBEjLG1j/Pjx2LJlC3bs2IH6+noAwIcffpj0MYrCDv591X5dXR2ampoi33d2dmLDhg0x+7tlyxY0NTWhoaEBAPD+++/HbGPq1Kl47LHHMGTIEJSXF/gAbSXN0hzmlMM0S7uxWzgA+cmxsEpGeRfJKFIBYeJVBIxWBDRWUOz0MadGonRP0+7pxHTPCRMmYPny5TE/W758OcaNGwdRZO+pvsegb775Bj6fL+Yx5eXl+MEPfoAf/OAHOPnkkzFv3jy0trZi6tSp2L59OyRJiumDiObuu+/Gaaed1q+H4dprr8Xdd9+NI4880tIx1VHHuQGAM96hDqWjo6NfSe+cc87Bli1bcP755+PLL7/Es88+i6uuugoXX3xxpN8hFUceeSR23313LFy4EJ988gmWL1+O3//+9wCQ0No1atQoEELwwgsvoKWlBd3d3QCAww8/HA8++CCWLVuGTz/9FAsXLox8qAHgiCOOwLhx47Bw4UKsWbMGy5Yt6/ch/NGPfoTa2lqccMIJWLZsGTZs2IAlS5bgggsuSPsqJCMoZVWGjc3AuiZgV1d/4VDqBkbWMddEbXlRCYdvUwiH+lIxbeGQTo7FflnmWFjFJQEhnca90s4IU0AU0RJGX0y758ShCiYMkVHjFROafUy756fbVaxtVrHLp2fXhGoDv/71r/Hmm2/iT3/6E77++mvcf//9uO2223DJJZdE7nP44Yfjtttuw8cff4yVK1fi3HPPjam83njjjXj00Ufx5Zdf4uuvv8Z///tfDB06FJWVlTjiiCNw4IEHYv78+XjttdewceNGvPvuu7jiiiuwcuVKtLS04Pnnn8fChQuxzz77xHydccYZeOaZZ9Da2mrpmFrw49wAg4uHJCxZsgRTpkyJ+frTn/6El156CR988AEmTZqEc889F4sWLYq8Ua0giiKeeeYZdHd3Y/r06Tj77LMjJ3S3O34Jfvjw4fjjH/+I3/3ud6ivr8d5550HALjssstw6KGH4thjj8UxxxyD+fPnY/fdd488ThAEPP300/D7/ZgxYwbOPvtsXHvttTHb9nq9ePvttzFy5EicdNJJmDBhAhYtWoRAIJBbhW7aLL+2aLN00HwGq3zbqmFtCuEwdbiclnAAgPWtmuUci32zzLGwiksiIAACCa6wMyKegGjpsG/7eaTMJWD3GgmTw3bPZGmdpt3z422FtXtOnToVjz/+OP7zn/9gn332wZVXXolrrrkmplnyhhtuQGNjIw4++GD88Ic/xCWXXAKv1xu5vaysDNdddx2mTZuG6dOnY+PGjXjppZcgCGwOxksvvYRDDjkEZ511FsaNG4fTTjsNmzZtQn19PR544AGUlJRgzpw5/fZtzpw58Hg8eOihhywdUwt2nBugEFpoacsBwEqBs2bNwrp162JO/gOS7gDLmegOOCrN0m7W79LwZYv9wgEAHvvEh3UWxlFPblBwzJ4eS9ts9bElsWpv5lWd7qAB1QCqPDZfl1DKRENPeLJgqRuoq7D3OfIMpRQdAebUaLdg46x0C6gv4+meVhlUx9QCwHseCsTTTz+N0tJSjB07FuvWrcOvfvUrzJw5c+C+yQeIzdIqqYTD0DIRU4ZlJhwKkWNhFZdEEAwaCGo06ZV12hASFgthAdEdrlLVlhetwOxr92zp0dHSncTuGTDQHnCe3dMpDLpjaoHh4qFAdHV14dJLL8XmzZtRW1uLI444ot/ktgGBL8gaIDt88asMLpk5JqpKWNriAGDdLhVftcQRSGGyEQ6A9RyLERX25VhYJTrvwlbxAAxIAWHilggaKyQML08/3XNIqYAynu45eI6pDoEvW3DsxzCAdh8TDQPEZmmVVMKhoUzE5CyEg2ZQ3LK8y9I46hP28mKfeutTm+xYtgCAgEbREzJQ4RZsn2gJIP4SxgAQEH1JZfeMxiMTDCkVUVsAuydncMIrDxz7CKqsl6HDN6Bsllb5ZqeKr3fmTjgAwBc7rOdY7FlXmI+3SwR8YFfIkpKDE1m8CgSl7GcDSECka/fc1KZhSztBbYmAISUCvAqvRnByBxcPnOww0yxbu9gSRTxK3Uw0lLoH1ME9mlTCYVi5iMkNctaWSasTJacMsz/HwirReRdemebGJtpXQPQE2b8HmIAAYtM9u4IGmrsNtPr1hOmezd06mqPSPas8QlaClcOJBxcPg4SOgIHtXTp8KkWpwsJ9SrK5MlE1tizR1gNoccbUSiKrMFSVOi6Uym6+3qnimzwIh21p5FhMGVbYplPb8i6SYQoI0smqDwNYQJiUuVh/w0hdxM4eA809ekIbZ1fQQFfQgCQQ1JUKqCsR4ba7D4UzaBnYR/VBDqUUK7aEcP+qHrz6dQB61DHGJQIn7ePFj6eUYO801sUjNktz4l9fvC4mGIrcZmmVVMJheDiB0Y6rb6tVh3E5zLGwSnTehVvO4fuAkN6pk4NEQACsMbWhXMTQMiGl3VMzWLpnU6eOSreAIaUiKtwkL4PDOAMX3jA5QNnUpuGnT7Xiq50aRIIY4WBi/vygkQr+cUJV4kY5KzZLs8owAGyWVsmncPCrBm55t9vSOOrTJ5Vgt+r0rwvsapg0CekUXUED5a48WAopZdMnTQdGiWvAC4i+WLF7mnC7JydbuHgYgKxtVvGDR3aiO0Tjioa+iISV1p/8UW2stW8Q2iyt8nWLim92JRYOIypE7DvUHuEAAO9vDuLN9YGU96vxCvjZjNKMntdu8QCwmRSigPxYCbmAAMD6Htr8rDciVWonIdzuyckMLh4GGNu7dBxzfwvafIYl4WAiEmCPGglP/6gaJb5AaptldVnvyOBBxlctKtblUThQSnH7im5L46iP3MONGY2Z/V1yIR78qgGfSvPbtNfS0SsgvC423nqQCQgTO+yeukHxv/VBvLc5CNWg2K1awkl7ewu+NMYpLFw8DDB+/1o7HlntS0s4mBAAv9/HwNmj45ykFIlVGQaozdIqqYRDY4WIiTYKB4DNjnjsE1/K+8kCwQUHlWXcY5AL8WBQina/AbdE8msd3NnZ25czyAUEwARAKrunCYsWZ3bPT5pC+NUL7djebUASzG0x1/V5B5bhgoMyq3Jxih/eMDmA6Aoa+O+nmQkHBsV96wl+Moq1MQAYFDZLq3zZomJ9noUDAKz8zlqj5N71cm6bEzNAIASKSBDUAW/qu9uH2UTZ5WfLb80dg1pAZGL3/OjFL/HbrZUwCFMNWtQ1RUgHbnynC36V4neH8VCpwQivOw0gnv7cj2Dic1tKKAi2+AmWtxV/mqXdpBIOIytzIxza/Aa+bbX2R813joVV3DKBQWn+kyFry9l7F+gVELzQGpPu2Zgg3bP0/bW44xsCgwLJei9vX9GNTW1ZHHQ4RQsXDwOI/1loqEuFRID/BUuA+soBP5/BKmubUwuHfertFw4AsMrBORZWkYTevIu8wwVEQky756QGBePrZFSGexjK3v8CO19eg6+9VZGqQyJEAjy8uicfu8txGPzsMIDY6TMsnWiSQQG0Byh4KwxjbbOKDUmurBorJOw9hH2M7H7NVJ1i9bYQrPxRpw6Ts35+8+G5+NsrItATMqDqyP/ky5oy9st1+YGeALCDDuoljHiUuwjKXRLUNz+F+tzbeLtiN/aapXiNdAqsbrKW8MoZWHDxwOlHSKfoDHLx8FWLik3tiSc6jqgQMbJSRFcIsHSGT5PPd6joSDD4JxqvLGBYuZj136wrxB4vifb/LpRSdIcAVTcKk7lQVgaEAHT7gY4gEGznAqIP4tJPoTy6FLJI4OJnBk4K+FtkAFHrFUCQ3WmMEKCuREC5a3AfVL9oVtHSo8ObYPjmyEpWcchlp/mXLSpcFgb47N8oo9qT/QlZ09lz5eZvTyALBgIaUKagMB36w8qBXWBZLHoIaO9gy3NcQAD/+wR4bGlkIXtisN3S6yISYN+huZo/znEyXDwMIGbv7saSbxOEU1lEM4BDx7gHtf3q8x0qNrbpYObV/oyqktKKus6EbZ06mrr0RLsQgQCYOtxly9/L3ESu/vZuWUBANxDSSeFcIbXhakOnHwiorAdisAuIN9cADy+FAcAwWIPk3r5W7Blox9euChhJXhudAgumlORvXzmOgTdMDiBO2tuTdY9jQ5mACUMktPsNBLTB1/vAhEPiHofReRAOQHHlWFglOu+ioNSUs0FnAOAPATvaB20TJX19NfSHlkI1WI+NQdmwWJdI8Kfm1RBAIdDES2fnTPdidBW/Bh2MFMdRh2OJMpeAU/bxItNR9QTAmfuVwKsIUA2KzoCBXT4DPSEDuoVMhWLnMwvCIa0QsQzxqwa+aLbWhLafQ+2ZiXBLBDqlUDMfRmIPg1xA6AZF4OWPoT70diQHQxIIFJE5rgiAGf6deOC7ZajRWTVTogYkaoBQCokauEBqwmUHlxbwt+AUEj5hcoDR1MnGU7f70x9PvVu1hGfPqEWJIsCgFAGVwq9R6OELD5dE4JZIXF94MUMpxefNWlK/+phqCXsNyc/a7nubg5Zst9nkWMQjFxMm45HXvItU7OoCOsPTOz3KgF/CCOnheRuvfQzXE8shEFZpSPaXaBNkLCupx0eeGvQQCeNCnTh2OMXQ84+CIPOqw2CFi4cByOc7VJz6yE74VJp0wIuJSIAhpQIeOrUGu9f0bwIMahQBrXfIjygAHomtW+ctryBHUErx2Q4Vm5O4KvIpHAxKcfv73QnjlaM5aqwb00fYly+SL/FQkLyLZEQLCLcMDK0aUALCoBQhjaVuGpTC9b/VcD/JhEOq31IDQbcgodxQQQB0CjLKJ41Ez6K5kGTRcRNNOfnDAdKfYzd718tYPLcSQ0rZnzeRrd5c3thvuILnz2CJmj2h/n0OLomgwi2gxiugRCGgFOgOUezyGegMGoUvQWeIFeGwWx6FAwCs36VZEg6yQDCxvriWLExcEgEBEEiRsZA3asqA8vDw7IAKbG9jnYNFjqpTdAcNtPsN+DUKWQQqlq5GyVPLIVoQDgAQIgIkSntPFJN3A34+D4pLRGgQLGVyEsNrTgMQX8iAIgFXzynHVzs1LFkfxOomNcbCKYvAcXu68aPJpZg2gp2EmBefoidEURLHTicKBCUKgVemCOrs4G9+SQJL5XNLpCicGlaFw4Q8CgfAeqPkPkOdl2NhlYLlXSSjpoz9v9PHBMSOdraEUWRR85Syz2ZQo9ANClEg8MoCXBJAXlkF/He59W0BUIkArxFezttvD+DMOYAoQCGs8TWkUyiZNllxihouHgYgWzrYCZEQgj3rZOxZJ6M7aKDcJaBKpigvlTGsXIRHJvCprNJACDvplypIKiDM7bol1vymGRR+lS1rdAWZ+HBLBB6Z5H+SoEWsCIfdayTsWZdf4ZBOjsV+w4qz6mDilgmCAQNBjTqnh6aIBYRmsGXFkM7e34pI4HUJkM0T+8sfpSUcACAYHk0tgwLT9gDOmQtovX8rRSAIaVw8DFa4eBhg6AbFd539T4rlbgGHjHHDbegxmRUC2AHHnChnVUCYSAJBmYugVGECIqABfpUJCkVkfREusUBDgeJAKcWn29WIwIpHIYQDAKzaWvw5FlaJzrtwjHgAmIAgBOjocbyAoJR9doM6haZTCGFR75L69JK8tBJ44t20tx8kIlxUB2aMA356FFv/jLLZKhIQDCJS4eAMLpz3ieBkRVOXHrcHob5UhDvOQVqRSL/7MwHB7huvByIehBB4ZAFVHvbllkmM3bM7WHi7pxXhsEeBhIOqU6xpsrZkUWz2zES4wpUrzWlr59WlQEV48JEpIBzUA6EbFL6QgY4ARU+I7VepS0ClR4BH7iMcXsxMOKjhjgjXtD2YcBD7nyoEQiAJzMHBGXxw8TDASHRiHFkZv8gkC4AB9DuAE8L6GwBWhUjHlCOLBOUu1mBZqhAQAvhU1mDZES5V5xtKKT6xIBzGF0A4ACyAy2/hdSlVBEyoGxgFQ5fImvYK8X5ISV8Bsb294AIipFN0BcOfIZ2FjVW4BZS7hPhLBy98CDyZvnAAWNVBnr47yDnxhYOJSyKRpRLO4GJgHIU4AICOgBE3SKnMxaoB8SCEQBGAkEYhKaTfbSUKqz50hyhK08wkEAiBVyHwKrF2z6BG82r3NIXDd0mEw9gaCeMKJBwAYKXFRsnJDfKAKRGz3hm2dOGVqWOWtiJUhwcgdfQAwbCAGFqZ1yUMg5qfGfZvUSDwKkLqpcDnPwCefj+z5wSgTR+H0p/MTiocALb8JJDYpU/O4IBXHgYQm9vjN9uNrEy+Pq5IgGqwg1NfzAoEQfoViGgKZfeklGJNk7OFQyTHIgUEwOQib5Tsi0sioACC1vpE8090BSKYvwpEtM0yoDF3VLlbQIVbSO1oei5z4QAAwQMnQFx4GCTZWl+NIhK+dDEI4VpxgBDSadwTkCQQNKRormNrl+zqwR3nHZFtBSKafNo9TeGwNU4DqcnYWgnjagubCjgQcyysEp134VjraXUpU27tua1AJLVZWv08PLuCfWW6DwfuidCPZsNtUTgAbPkkoLGlT6c6rDj2M7CORIOYrR163GmSw8tFSx9oRezfOBmNXRWI6O25JYJKj4BqrwCPTKBToCtIsdNnoCtoZNVIRynF6iIQDr7QwM2xsIpj8i6SUVUKVOamAqEZrPGxPcAaIUXClhor3Kzx2JJwoBR45v2shANmTkBo4RxAEKCkYeYhhGViOLJ3hZMzuHgYAFBKM16yMDH94PkUECbM7img1iugzMUsfH6VotVnZJTuaQqHbQ4XDgCwZrtqSSTVeAWMripue2YiFJFAJA5I20yFjQKChnsZOgIGOgMG1HDVr9IjoDR6PoO1jQHPrGDLFZkyay/grDkIUSYE0q38KSKBlmDpkzMw4csWA4CWHiNupz7rL7CuD+Xw2mWyA5edSxjxtu2RCTwyEzH+cHNlKEAhEESGTyVrGKSU4uNtatIegnG1EsY6QDgYlFpesthvuOK8hkIbcUkIZ7FQZ+RdJKIq3ESZ4RKGHh7mFAw7FCSRoDSRW8IKlLL+hhc+zOzxAHDwXsCZc6BRtn8lSvr7IgoEImEZGu7Cf7Q4eYBXHgYAiSYljqxITxu6REAzkHIeQ64qENFkYvc0LAiH8XXOEA4Ay7GI547piywSTBw6MJcsTByXd5GMDCoQfW2WrlQ2SytQCjz1XnbC4ZC92chpwqZFSgIyFm+KRBB08tITx1Z45aHI8YUM7PT1P1m6JRIJxrIKW7ukUPWUDq2cViCisWr3BICPt6nYnkI47FHjDOEApJFjUS/HHfA1kHBk3kUyqkrZJMq2biYgmtpYGmfUBydjm6UVKAWefI9Nj8yUQ/cBzpgNEAJKKVQD8GbRtKqIPO9iMMHFQ5GTaOhRY0X/aG0ryCLLu3DR1Ae4fAkIE5dE4JIIdIOJCL/KnrcrZOCrFg1tfiNhc+iedTJ2r3HO2z2tHIsB2ijZF0fmXSTDrD60dQMhjaVxDq2CChLOmaCRZkKXJNjnRKCUTY18+aPMt3HYPsCPZ0eix4Phw0havRZx4HkXgwe+bFHE6AaNO79AIMCIisya6ySBQAAQSj12AED/JYx8NEwxu6eA2hIRpS6CL5s1bO3Q4QtRdIeMsOe8dz+cJhwA6zkWjRUS6ksHZqNkX6LzLoqGyhKgqhSUUgT8Kjo2taLLp0OngFcWUOlm71VbhcN/l2cnHGZPjBEOAGwTbIoE6DT10ien+OHioYhp6tKhxvmQ1peKWR0I4uVdJCNaQPTkSUAArBT8xQ6NVT1cBIpEYFC2bt4ZZNWJcbWS44SDqlOsHmQ5FlZxbN5FAjSDosfrQbu3BD4NEDUNZe3tqJBh3WZpFUqBx5cDr6zKfBuH7wssOCxGOJifdZcNGpXnXQweuHgoYhI2SibIsbBKoryLZJgCQiD5ERAGpVi1VcWObvYaCOG5EeWusCODEIysFFHhFjKye+aSL5pVS1fXpYqAPQdIjoVVzLwLJzdOxrVZ1pSgcmgZSmUCWdPZEoZu4yRKSoHH3gFezUI4zNkX+NGhMcIBYFUHWbBvyZHnXQwOBteRaQDREWBjnfuSLMfCKsnyLlI9ziszV4QZ550L251pcWzujndwJpBFYFKDjBEVYkZ2z1xC07BnDqQcC6tE511Q6qy8i5Q2y8oSdmJu7YrpgUjZfZwKSoFHlwFvrM58G3MmAT88pJ9wMCiFZgClLvteZ553MTjgf9oiJduhUKlQJKAriLR997kWEMmFA2OvITLGVLO3tiwSGAobf+3XKHwq+3JJ7CSV78a8pi7DUo6FQAimDLIlCxOXRMKizxkzA0I6Ew1quAHSFW6AjCvsKsJeEbsEhB3C4YjJwOkH9xMOACKuJbvHSpt5F0XR+MrJCC4eipBEORayQDAsRY6FVVLlXSQjWkCYLgw7BIRuUKzallw47F0vY3RV7A47Jd0TAFZ+ZzXHQkK5a3CuKjoh7yKezbJEEaxNX+wrIJpagYbq9AUEpcAjS4E3P8nslwCAIycDp8UXDpSan2/7X2Mz70JNMXSOU7wMzqNTkfNdghyLYeWirWXuVHkXyWACgkAk9rgwMhUOfUmU7rmzhw3wyWWjly9kYG3L4M6xsIqZd5HvxrtkaZaudALbKrxAdZm5USYg0umBsEM4HDUloXAAeh1V6eRYWMW0qPLGyYELrzwUGZRSbMnxkoWJefWX6dWDXRUI3WBLFS09iQ+++9TLGJVCOERjpnuWKEBAY8saZjXC7nRPk9VNVnMsRIyy+W9ZbJh5F8E8zAxgDZBAUM8izTIeFV7W/bmrq1dAWKlAUAo8tAR469PMn3vuVODUmQmFA4DwMKfczWZRRBK5cHD0yHFORvDKQ5FhV46FVcy8i0xhLgwh4wpELoRDX3KZ7mliUFY5scJ+w2VHNQoWCpfETnC5cu7EpFmqGaZZpqLcC9SkUYGwQzjMSy0cNINCN5DTngSWd8FWbjgDDy4eioxc2TMTYTXvIhWZCAgrwmHi0OyEQzTR6Z7lLqF/uqeaud2T51ikTy7yLmxNs7RKPAGhxfkcUwo88FZ2wuHo/YBTkgsHAFnnWFiF510MXPiyRRGRNMeiJDc6MJ28i1SUKAJ6QoalJQzdoFi5NYSdKYRDLkQTIQTu8JCfmHTPIIUQyszuuZLnWKSNnXkXfW2WskjgySaUKl3Kw7+BuYRhujCk8PKUKRyWfpb5c3xvGvD9A1MKB7NRMpP0zHTheRcDFy4eiojNNudYWCWdvItUWBEQhRQOfZFFkrXds9Wn8xyLDMk27yKkM3eNZsVmmWsSCQhRAO57E1j2RebbPmYacFJq4QCwHAtCss+xsArPuxiYcPFQJOgGxVabcyyswvIu7Bv6kkxAaAbFyu9C2OUrvHCIJtruGdJZKJdVu+fH26w5LAZTjoVVovMurIqHrGyWuabcy87cOzt7lzBeXQ28uzbzbR47HTjxAEvCAbAvx8IqigQEg4g0o3IGBlw8FAmJciyGZpljYRVFYlcPdj1XPAFhRTjs2yCjsaKwb1tFZOX0vume3SH2+nhkErnKSifHYtoIXnWIh0si6AmxxtVkw4zU8DCnnKVZ2kWZh/2/uR145B1g9beAW7F88o/huBnA/P0tP9bOHAurCIRAFtjfxeO0vwUnY7h4KBLy3SjZF1kAAkDKA3g6lCgCfGEB4ZZYVoXThUM0VuyeX7VYz7EYX+uc381JuETATwgCKu03RjmuzVIRWEZGoasMyShxAa98DKxaz773hwBPmgLihP3ZVxrYnWNhFUUi6Al/zh39d+FYhh+tioB2f+Ici8oscyyskmneRSq8ioDOgI5lG0LwqYn94CyrwrlvV3e4/0EzensjOgMGlm4IIqTT8Lz/xI8fjDkWVmG9CjQm70IzzCoDExCKSODNlVvCbgwDuOcN4KN1gEsGgiprmExHQGQgHHKRY2EVnncx8OB/xiJgS0d+hkKlItO8i2RoBsXnzRo6AhQGBVxS/207XThEIwkEpS6CEoViQ6uOnT4DhgFoOoUgAFI4shhRv+JgzrGwiksi8KkGOgMUIKzKwJJU2dJE0QwhMgzg7jeA975k35tui3QExPwDgONnpP3UucqxsArPuxhY8DkPDieo5T7HwipC+MQXir+CkjaaQfHhdyG0+gy4JHZlbja5Aez4OXlY8QiHaAgh+GyHGqlISCI7N4TC1k9VpzBHRgzmHAsrmDZLnwq0+nUIBCgNV908chEJB90A7nq9VziYSCKrQAC9AiLRPJETMxMOpj2zkI4HRQQMioxH3nOcRfEdlQcZWzvzk2NhFdO3ne0sAlVnwqHN37sc45JIpEveLVNMblAwvLw436K+kIEvmpnLQhAABQRUYCdC1WCN9qrOrgQn1jsgOtKB9LVZlrtIuOxNis/2pxvAXa8BK76Of7skAiBAMJS4AnHSgcxZkQG5zLGwijkzJsTDsgYE/HLHweQzx8IqcpSLIFPiCQcTl0QgCsC4GhlDi9i2uLpJhd7n6pEQQBKZG8P8PStcAircBLt8OnwhI2ejmIsFg1L4VTbRsztogFLWWFvpJqj0iFBEAUELDaiOQjeAfycRDiaSALjCy1d9KxDfPyhj4QDkPsfCKopIoBkY9O/zgUBxXtYNEvKdY2EVM+8ik6sHVaf44LsQ2uMIB4CdYPdvdKHSTdCjUpTIKLpGQis5FqLAfq9Zo10odQlJ7Z6DAas2S7dE0KMaxRO2pBvAv14FPvzG2v0lAYASW4H44SFsCFSGmDkW+ZgomQqWd0ER0gA3L7gVNVw8OJhC2zMT4RKBTi39oS9WhMPkBgXDylnFwRcyilJArEsjx2LSMAVuKb/pnk4hE5ulSwJ8Ksu78DrgZJgU3QDufAVYuS69x0ULiLlTgSm7A6oGyJl97vOVY2EVRWKZMW7ZGfvDyQwuHhxKTwFyLKySSd6FFeEwpUFBQ3nvUoU3PAei2ATERxZzLCb2ybHoa/cMaBRdQRqeg8FuGwhrxdnYLElU3oWHOnhmQKbCwUQSgBMPASaPYSFaTW1AQ1XaAiKfORZW4XkXAwPe8+BQthQox8IqskgQMqylTKo6xYot6QkHE68iQCJAj0qzTvbMB3bkWDC7p4CaPumebX4DbVmmexYKSpkYsiPN0i0TGOGToiPRDeD2lzMXDgBw6izmrBhS0bvNpjZWgUiDoM4adp0mOs28C07xwsWDAylkjoVVWN5FattmKCwcEpXxCQGmDosvHExMAdEdcr6AWGUxx2JkpYQhKRpCWbonQZVHQJVHgFtmVYnOoIFdPtZQ6PTXQzcofCED7QH2fztsltF5F45D05lwMCdHZsIPDgbmTWX/LnFnJSCCDg2kUiRAp3D8+5eTGC4eHEihcyysokgkqesipFN8YEE4DLUwr8IbDjZysoBQdYo1FnMs0k3PlEWC8nA1olRhMzF8KsUun4GOcOqkkwjpTOR0BAxmrxSBCreAMptisF3h5R3NSe8FTQfueCU74XDawcDcKbE/MwUEIWkJiELkWFiF5V2w9wmnOOE9Dw7EqY2SfUmWd2FFOOw3TEF9GoOuPLIAoDdMy2k9EF805z7HIlW6pzvs1ChEc1zfNEtJzF2aZbK8i4JgVhw+/jbzbZx+CHDk5Pi3lbjZ/1s6ewVEih6IQuVYWIXnXRQ3zjobcRyRY2GVRHkXqYSDEK44pCMcTJwqIChliaBWmDLMnhyLeOmePSH2lU+7ZyHSLOPlXRQMVWPCYfWGzLfxo0OBOZOS3yeegBhaxdYA+lDIHAur8LyL4ob/yRzGZocNhUpF37wLS8JhuIL6LAZAOVFAbO3Usb07dQefQAgmD7M3x8JKuqfddk8npFm6JAJ/ZCJpXp6yP6oG/PNlYE2OhYNJXwGxPb6AKHSOhVWYc4bnXRQjXDw4iKBG456ACpFjYRW2dsmuHggoPtgSRGcwfuneDuFg4jQBYdWemesci1zbPZ2UZikK7HcJaAWaGWCHcFhwGHD4vuk9psTN1v2aO+IKCNOe6SmCOQqKCAQ0Vr1ymiOEkxwuHhxEohyL4RWFybGwiiwSdAQMrG1W8yIcTJwiIHwhA2ubrXXAT8tTemZ0umdQA/zhZQ2/yg7SHonAJVmrElBKEdSZuDXTLD0SgSIVprciGrdE0BU08j8zQNWAf7wEfLIx8238eDYwe2Jmj/W6WBNlHAHRm2Ph3GOGCc+7KF64eHAIyXIsGh1iz0yEQdmVNyuV9j8ACIS5C1JZEzPBCQIiXo5FPGq8Yt6Xn5jdk81GUMOJnkGNojNIIYR6GyzjvW5mmmUwXGWQRQKPTW4Ju1BEApGQ/FoSVQ247UXg002Zb2Ph4cCh+2S3HwkERIgKBQ3AShdFJOgO0eIZOc4BwMWDY2h2aI5FKoIaxYotQfhVCp32nziZS+FgUkgBYSXHwmS/4XJBG/tkkZX5DYWJAr9K4Qt/sURT1rjGBANLsxQIgSvcAOnU6lde8y5UDbj1ReCzAgsHkz4CQtvWCr26EiVlxRMcwfMuihPnnpUGGYkbJZ2r7wJh4dAVZI15BqUxaXkCAaaNyK1wMPHIhZkDsW6ntRwLRSSYODQ/SxapEAiBRxZQ7RVR6RFY46FqoKVHx6Z2DS09OgxKUaKwxE+v4lzhADDBQ8DyLnJKKEvhQAhw1hz7hIOJKSAIQUg1IDe3QVCdOn4zPorEGic5xQMXDw6gJ8QmBvbF44Aci0QENNYc2RXucSCEQCIEWviYJQpMONSV5K9+Gi0g8jU86COLVYd9+uRYOAUCJvIkgUAkgETY9yGN/Y3V1Lqo4ETnXeRsbHdQBW55PjvhcObhwMF727tfJl4X6JAKhAwChVCgqZXtc5GgiASE8KFRxYRzL2sHEQlzLCqdkWPRl77CwUQSgaAGCIRi2nAXavMoHEw8sgACNuugRMmtVc2OHItCEM9mWeoSQMDOcV5FKLp0T7dMEAwYCOnE/pkBQRW49QXgiy2ZPd6sOMzay9796kNQUSDUV0Bu72SNSGYTpas41gLMvAsn9dRwEuPMy9pBRLIci+FJ8h4KRUCjWLG5v3AAWDlcEoBJDUpBhIOJWyZwiWx6XS4rEHbmWOQDzaDoMXMmVAMiYcPHKtxCWBiw+7klgkoPG4XtlQkMCnQFKXb62ACzZCPJC0XO8i6CKnBLlsLhJ0fkXDgA4RyLUhdQX8me1xQQRVKB4HkXxQWvPBSYbUWSYwH0CofuUPwPtyiwyZFlOZxjYBXT95+rCkQucyzspK/NUhSs2yzFOHbPgMq+0rV75gOXRNATMuKOS8+IoAr83/PAl99l9nhCgEVHAAdNyH5fUhCTYyEpTEDsaC+qCkTvzBgKj4N7bDiMwh/lBznFkmMRUFMLh+kjWMiVATgisCiXFYjPd+Q+xyIbEqVZVrhZOmc6roRiSfd0iewEZEvjpB3C4ewj8yIcgDg5Fh6lKCsQikQiA8g4zsZZZ6hBRrvfQFecHItyh+VYBFSK97cE0ZNCONR4WWk+Xt5FochFBYJSanmipF05FlbJtc0yld3TFZ5iWQhsy7sIhICbnwe+3prpjgA/PQo4YHxmj08T3WA5FmV9cyw8xVeBMPMugjrg5mcnR+OcM9QgJJE9s9FBORaphIMkADOihAPA1i5VAzG2zUJidwWikDkW8TAohV810O430BNiYjTXNst4ds+gRtEZMLDLp6MnZBTk7++SCChY425GFJlwAJhDQRQSzDcpwgqEIhLuuigCuHgoEMWQY+FXjZTCYfoIBdXe2P1la5eIjMl1AnYKCKtVh/E5zrFQdYquIBMNAQ2QRaDcLaDcxU7m+epFUESCCreA2hIBJUpvpWdnj4GOgJHXE0F03kXaBELATc9lJxzOmZtX4WDmWCTtj/IowNDKWAERsPYeLgSKCFAKRzbmcnrh4qFAOD3Hwq8aeH9zKG3hYCI78OrBDgHRk0aORS4aJSllDYsdAbbkZVBmrax0E5QouY3BToVA2D7UeEWUu3urEe1+A60+HX41P9UIt0SgU5re+y8QAm58FvhmW2ZPKghMOOw/LrPHZ4jlHAt3HwGxo92xAsKMdXfa8YMTCxcPBcDpORamcPAlaDyTBGBGoyuhcACYeCBw3gEgWwFhNcei1uYcC82g6A4ms1kWXnBG45ZYNaKv3XNXHuye0XkXlvCHhcO6psyeUBCAn+VfOADs8+WyOhfBFBCC8wWEIhJoDlr65PSHi4cC4OQcC6vCocpCQ6ciEkeWHt0yCdv60hMQBqVYZXHJYr/hStYndEpZ819HwEBnwIBO2dTRSo+A0gLFYKeLOYCqxsuWUySBuSHa/Aba/AYCKs1JZ71bYpWvlCcfu4TD9LGZPT4LNINCNyJJ3NZwh3sgHC4gxPDE01CmvSucnMPFQwFwao6FL2SfcADY2qVmOHPoi1tKX0Cs26mhM447pi+KSLDP0Mw72u20WTqFfNs9LeVd+ILADc8A6zMUDqIAnDuvIMIBYI4mOWxPTYu+AmJ7uyMFBM+7cDZcPOQZp+ZY+EIG3t+SWDjIIrB/GsIBcP7aZboCwmqj5MShmeVYsKhss8mQzS2ocAsoc1gMdrbIIkF5uBpR5iIQCOBT2ZIGa/7M/v2SMu/CFA7fbs/sCUQBOPdoYNoeWe1nppiNkhm/L6IFBHWmgOB5F86Gi4c848QcC1M4+JMIhxkjXBnNnlBEAtVw7tAXqwJil0/Ht232N0oWwmbpFOLZPUO6fXZPt0xghE+yMZjCYcOOzDYsCsDPjwb22z3jfcuWoM5WTLJaunIrbO6DkwVEOO+C4zz4GI484sQci1wKB4CtXQow7WTZ7GnuMKsEPSEKrxz/gLxqq/UcCytJoqrO+hlUnUYqNG4bhzkVG4rIKgVMTLHXpifEvlwSgUcmaV9lR+ddRKyMPQHghmeBjVkIh198D5iyW2aPt4lg9O+UDS6ZCYjtbb1LGPWVzN7pABQJCAYRGa3OcQ4OPZwPTJyWY9ETFg6J1oXNpYoKd3YFKiVs2XNaVkc0poDwqRRexAqIUBo5FtOSVB3ipVl6FQEu0Tn5EIWG2T0JShR2ggxo/dM9XRZyOUxi8i784YrDxubMdk4UgF8eA0wek9njbSImx8IO+gqIHe2OERA878K58GWLPOKkHIt8CQcAkAWAwhl5F8kwlzB8Ko1xiXy+Q7XUuFWqCBgXJ8ei2GyWTsFlg90zknfRHshOOEgicF7hhQMQJ8fCDkwBYS5h7GhnThQHwPMunAmvPOQJJ+VYpBIOikgwo1GxRTgA4eY1B+VdJKNvBUISrDdKRudYZJNmyYklm3RPQghcgQACt74AuqkZGb3ykgj88nvApMILh4Q5Fnbg0AoEz7twJvxPkScS2zPz2+vQHTSwolnLm3CIbFcCuoKsQdDpJ89oAdHm07HDYo7FlGEKdCNcZg9fKckigWeAuSUKBbN7skZIs2ckoFF0BimEEPu7uWUSO2WzOwDX/z0D/9YOBIkAN01ttY3BrDjsO9rW3yVTkuZY2IFDBQRzztCCha5x+sOXLfJAshyLhjzmWHQHDXzwXf4qDtE4Me8iGeYSxnubgzAsLLfsVi2CAhGbpVsCKj0Dz2bpFGSRoMwloLaP3bM12u7Z5Qf+/jTEzS2QqYEASfOzJkvA+cc6RjhYyrGwAwcuYfC8C+fBxUMe+K6j8DkW3UEDK7aEEnroFZFg/5G5EQ4mTsy7SIZuUHzbqrOE0Dh/QEoBTWeVhgl1rIhXorBlKI8sOL7CMhAgCeyeXTt74Fv8FLSNLaAA3IYOnRCErC5cyBJw/jHAxFE53f90sJxjYQfxBIQvmPvnTYDTZ8YMRrh4yDGUUmzpKGyOhVXhkMsESMC5eReJ+HibChDW9xAtIHSD/Q5BjUKjQF2JiL2GyJE0S05hiKR76gFU3PosxK07oYWXkWCwr4Bg4TNnCod9nCMcAObSsZxjYQcuGWio7hUQzR2Av3ACguddOAve85BjmnviT8yr9Yp5ybHoChr4IIlwcElsqSLXwsHEzLtweinfoBQfb2OlWjF88PSrgCAYIGCT7ySRQCI6DhxK4OvpLvpucF+A9QOIahFfU/gCwH+XA4IKMrICQlcIYnsAhkEhUg09ogSXASgC4tcgZAm44Fhg75H53vOkaAaFYQBKvlsPFIkJiO1tgKYDLZ2AQoASd553xMy7oAhpgDvz6e8cm+DiIccUslGyK1xxSJQu6JII9m9UUJYn4QCwtcuA5vyhL2aOhUHZvrJiCQsicktMOJQigImlO+ENCNi2zbm/i1VM7dNdrL8KpSxJ6ZDekdFUN0C2t2HEknXwtvjhA9BDBVBdh0hY+FLkbahIwAXHAXs1FmT3kxHMNMfCDhSJLWE0tbISXHMHUE8Ar6sAu0LgV2lCdw0nf3DxkEOS5VjU5TjHwonCATDXLp099IVSivfDrx2lYFUGQuBS2AREzQBkQ8OMqmYMqSxFbU01ZFku+oOZWQ4uyl4Ng7LpkUbv541SQDV0tJaVYlOFF3vevwJlBkVQkkA0FsSlg/2+okuCcMGxIA4UDpRSqDpQUkibsykgtrQBNCwghlTkXUAoIpsYqhrsQoRTOLh4yCGJhkLlOsfCqcLBRBEJelQKN6WOOuHqBrP+be8y8M1OFYLAPOZi1MskEgKAwkuC8CoShgwZgrLSkoLts52YqZZOrgjFxTCADh8gSHG7uCRRwJYeH3qGlMHb1I2QKIBKAhSDxZzrkoiuc48BHTUc7qDR3+5ZYGzJsbADRQKGVAJt7UysFVBAhDTnL30OdIp4cdPZ6AbFts7851h0phAO7gILB8DMu3CObTM6zVLVgS9bQnBJLEtBjPMyiQIbmSsQQBT4R6igmMJBTzy/QQABCEAFAgkUEqUIEhECAWSPDNevj4d3YmN8u6cDCDrpRGlWIESht4myJ5DfXRDBRJ/DJ9YOdHjlIUckyrFoKMtdjkVn0MCKzaGEbga3RDC9vrDCwaTQeRcGNTMT2L8lkaBEEUAIsLZZQ6qCiCAQCAIBBSsrO6mCMmiwIBzi4aY6ugUJmkuBdOFxIOOHwwPAIzMXjV9l742QTtFNWJ6GWyIFqciE7M6xsANTQGxvY699Syf7eZ6aKHnehTPg4iFHJFyyqMjNS25FOOzfqKCUpDlhL0fIAhAA6yLPZ4nYtFgmSrP8eFvIUo6FWxQgsgtasF5KLiDyim4AnekLBwBQqAFBkRD4xdEoHT889raodM+ASuG3Id0zG0K5yLGwA0UCGqqApsIICEUi6AlRuCX+uSsUXDzkgLY851h0BJgdM6VwcAlAyBniIZ95FwZl9q5UaZaUUss5FnvUikC4mZILiDyThXAAAOJW4DrvewiMqk9YNRIIgVch8MZJ9xQF1vTslnObU5LTHAs7kPsIiOYOYAjyIiB43kXhKXz9egCST3tmSuEgRwkHh6FI4eFLOZqPYKZZdgTYFaRIgHJ34jTL7zqt51iMrek1mhNCIgKi2Gc9OJ4shQPcCnDR8XCNbwAFEIz/UY2hb7onpUB3KL10z0zIeY6FHZgCwmwOymMPhFJkE2sHGs47oxQ5QY3GPQHlIsfCinA4wKHCAchN3gWl7CqxI2CgM8C66T0SQYWboNQlJF0isVp1GF8nwSPHbqcYBcTYsWNBCMHYsWMLvSvWyFY4KDJw0fHA2GGs6TVs+7MKS/dkIqI8/F4KqBRtfgNtfgMBldr2t89bjoUdFEhA8LyLwuLMs0oRk68ci46AgRVbgimFQz6mWGaDXXkXukHREzLQHqDwhQwIBCh1hasMFsrL3UEDXzZbuAwFMG14/DF/uRIQGzduxPz581FfXw9ZluFyuTB8+HAsWLAAra2ttj1Prjj33HMxduxYlJeXQ5IkVFVV4ZBDDsHHH39sfSPZCgcA+P5BwNhhkW/dEoFOadrvP5buSVDlEVDtFeCRCTSDOXZ2+diSpZalEyCvORZ2YAoIKXyB1NwBdOdWQPC8i8Li7DNLkZEsx2KkjTkWpnBQE1yxF4twALLPu+hrs8w0zXJ1kwrdwgm/1ismzSSxW0CsWbMGU6ZMwbPPPovm5maUlZXB7XZj27ZtePjhhzFx4kTs2LEj4eN7enqy3gcrJHuexx9/HOvWrYPH40F5eTna29uxbNkyHHrooWhpaUm9cT0zV0UvhFUdhlfH/FQRCUSSXvWhL5LQP93Tb4PdM+85FnYgS8DQyl4B0ZJ7AcHzLgqH888uRURzd+IcC69NJ/JUwsFTRMLBxMy7sIpBKfwqOzj3hBtAs0mzjM6xSMW0EUrKpkg7BcQZZ5yB9vZ2AMDVV1+N1tZWdHR04IwzzgAAbNu2DQsXLgQAVFVVgRCC448/Hocccgg8Hg+mTJkCANi8eTOmTZsGRVFQWVmJX/3qV3GfT9d1nHvuuRg6dChkWYbH48G0adPwwQcfRO6T7HnicdJJJ2HNmjXYtGkTnnrqKRxxxBEAgK6uLvznP/9J/gKYwsHIUDgQApS4omZQx+KW2Hsv25kBidI9OwMGdvbo6AkZlp8jkmNRjI2AeRYQLO+CTSXn5JdifHs6ls2Jqg42NUq2+w188F1q4WCXUMkXVvMu+tosXSLgirJZZso34RyL1PtJsE+9tUQeQliYVjYujM2bN+PTTz8FwPoTrrrqqshtd999N1544QW0trbi7bffhhF1cn3ppZcgCAJqa2uhhJOU5s+fH1kmUBQFd9xxR9zn/N7R8/Dmm28CAOrr69HV1YWPPvoIRxxxBFauXIlx48YlfZ543HXXXQCAQICdRCZNmoQ33ngDAOB2J+nMt0M4lHsBLbEwdEmAT2UVLK9Nrp9s7Z4FzbGwA1NAbG8Ph2l1sJ+X5saFwfMuCkNxnWUcTK5zLAaqcACSr12aB+COgIHucFCVVxFQ6WZ2Szv6SKw2Sk4cKqfVwJZtBeKDDz6IPG6PPfaIuU2SJDQ2shwGv9+PdevWRW5zuVxYsWIFtm3bhlWrVuHdd9+NCIcTTjgBzc3NeO2116BpsWL3ww8/iAiHyy67DNu3b8fGjRtRUVGBrq4uXHHFFTH37/s8VtA0DS+++CIAoKamBj/84Q/j39Eu4SAnF+7svUcQ1O1vdGV2TwE1XhEVblaNCGoU7X4Du3w6fCGjX7ndCOdYFE2vQyLyWIFQRJZyqzrDhT5oKL4zjUPJZY5Fm9/aUkUxCgcTRSRQjd4DuKozm2W737Bks8yUXT4dG9qs1Tz3S9AomQy7ljDiziJIMBr7gAMOiCwjKIqC9957L3LbggULAACHHnoohg0bFvO4N15/PfLvxYsXgxCCIUOGoKODXTl+9tlnSZ8nFW1tbbjooovw5ZdfoqysDM888wxKSuLkguRJOJi4ZVYlCOZwXLpVu2dIc0iOhR30baJs6QC6/Dl5KjPvgpM/+LKFDegGxdYEORYjsmyUbPMb+GBLEFqC46hXJjhgpAKPXLzCAWBrl4Qa6AoCFFHDnGQhp+XIVVtVS/cbVSmhriSzv2WmSxjTpk0DIQSUUnzzzTcxt2mahk2bNgEAPB5PTGWitrY2o/2MZtiwYZDl2CWaIUOGxHyfzvOsX78ehx9+ODZv3oza2lo88sgjmDVrVv872iEcKry9JywLSAILwgpqFO4cWyOZ3ZOgRGGj0QMaq6wFVApZJFA1ijL3ABAOJpLYO0hK04Gd4UmUZR5bn0YR2cyOVEufHPso7jOOQ9jWpce1ZjWUiVmVHweLcDBtln4NaA/oEAhQFmWzzJVwCOkUa5qsLVlkUnWIJpMKxOjRozFx4kQAwDfffIM//vGPkdsWLVoUsWkecsghMVWIvq/XAQccEPn3I488AgB4++23sW3btpj7HT7niMi/jzvuOGzcuBEbN27Et99+i2uuuQaXXnppv9/JCm+//TamT5+OzZs3Y7fddsMdd9yBvffeu/8ddQPo6MmrcDBxS8xuma3F0iqm3bMyyu7pV1kVoitoj93TMZgCwvy77Oy0vQLROzNmgLxmRUBxn3UcQqIli5GVmRd2Wn36gBYONBxMFW2zLFXY0oRHJnkp236+Q7WUY1GqCBhXm32RLhMBcd9996GyshIAc1tUV1ejoqICDzzwAABWIbj33nuTbmPmzJmYPHkyAODpp59GfX09jjzyyH7LHvvvvz9mz54NALjzzjtRU1ODYcOGwev1YuHChXj77bct/67RnHzyyWhrawPAmiYvvvhiHHjggRg9ejTOP/98dqeIcMjw4J+FcADYlatA2NCnfGPaPcsUgiovq4LYYfd0FHkQEIpEEMpB7wonPsV55nEQyXIsKtyZvbytPh0ffhdKKBxKlOIVDrrBhjh1BGg/m6VXEeEWhbysXaaTYzF1mGxbKTRdATFlyhSsWrUKxx9/POrq6tDV1QW/34+Ghgb86Ec/wqeffoqGhoaU23n66acxdepUSJIEv9+PM844A6NHj+53v1defQ3nnHMOGhoa0NnZidbWVlRXV+OYY47B97///fR/YSCmMXPbtm3YvHkzNm/ejE2bNqGpqangwoFtgrl3Qrp9UyLTQTcodEpQ6RZtsXs6ElNAyLkRECzvAjntXeH0QiiXaVmxpimEpq7+79Z96mWMyCBB05JwaHTBLWd4MgtpBTGQx7dZ9o85NihFV5CGB+7krvqwpUPDA6tSD1ASCMF5B5b2izHv6OhAU1MTRo0aBY8n/fVbGu6BIHCOvcw8MeV1zTgPwsHv92PTpk1oaGhARUVFwvsZlI2aLpGFzD9fGeJXjXAIVuz7LNruac7IckksIjzfo6sppegMUpS7slxK1HQW5212gNeUsQZXGwhqFEGdotyhI/kHErxhMgvszrHYFRYOiQbpZS0c8ozVNMto2Nolm+ufy7S8dHIs+h7Q7cCOORBFj66HmyMLV3GIRgjbNgMazetnzMyx6JuXYu5TodM9bUcSgaFVvQJiVxf7uQ0CwpwZo+p0YDhWHAwXD1mQKMdiRAY5FqmEQ6lCsH+RCAc1XGUIhasMsgCUuJOHUkUjiwT+HHa+25FjYQeDWkA4TDiYuCSCrqCBkE7zNmvBao6FK1xt0A0Kv8qERHeIokcND5+S8tMrZAs5EhBsbgc79hTNa1GkcPGQIclyLJJlH8RjZ4+OlVuLWzjQsE8+qGVvszTTDnN1ALeaY1FXkjzHwg4GpYDQdBZylalwEMJzHGwWDkBs3kW+xEO6ORYxdk8dEaunaff0SKQ4pi3mSEAoIkF3iMKgtLgqMkUGFw8ZYleOhSXhMNKVc/95pmiGWWVgAkIWCbwuIWvVb+Zd2H0ANyjFKos5FvsNT51jYQeDSkA4WDiYuCWCHtXIy8yASI5FBgUuQgjcUq/N1KxGdAYpSIj93CMTyxW/giCJQEM10NRqm4BgeRdsydRtbZo8JwO4eMgQO3IsilU4mGu0QZ1C05m6d0ssZ8Iupa+IrGva7gP4Nzu1uO6Y/s9vPcfCDgaFgCgC4QDkJu8iEXblWDC7J0GpwgSEX+39UkTWF5Gs16igiEJ/AUHBlqUyhOdd5B4uHjKgO5h9jkVLj46PkgiHMhfBjEZnCQc9qspgUApJJChRhJx0fZNI4ySFx0bxYLVRct80cyzsYEALiCIRDkBs3oUnh38HM8eixEaBwtI9CTwycziZDZahAIVAWFOmO47LqeD0FRCt4QpEhgLCbHxVDXYhwrEfLh4yYEtH4qFQVg40LT06Vn4XSngcdZpwyFWaZSoUkaBHpXDbdADPdY6FHQxIAVFEwsHELRMEAwaCOsmZ6yfXORaRdE8lfrpnIeyeScmBgAjlsXdlsMHFQ5poSXIshltormvuZhWHZMJh/0ZXwT/Umdgs7UYUCASwSofLhneq1arDqEoJtRnmWNjBgBIQdgiHCi8g5vfvkY+8i6BO8/I5Lyq7pykgtrcxdZWFgOB5F7mFi4c02daZeY5FMQiHvjZLRWQTIAvVdKWEY4yzfU1COsUnTdZCsKaNKEzVIZoBISC0sB0z0zl0BRIOJm6JoDvEMibsfv+bGQyuPP9qRWH3FIVeF0YWAsKcGZOP3pXBCB/DlSaZ5likEg7lBRQOlLKDSEeAjdrWKeCVBVS6SUGFAwDIAuudyjYk6LPt1nIsylz25FgkxTCAHe3A2i3AJxvZ/3e09wuESneU9dixY3H00UcnvQ8hBNdff73lXb333ntBCMF3331n+TEAbBIOJQUTDkBu8y5CGoUsFK6Zj9k9WUR4uVuALLDfs81voM1vwK8ahc2IMAWEOQ23tQtoTz0Rti+KRKAaPO8iF/DKQxq0+Q10h9LPsdjRrWNVCuEwowDCIVc2SzshhEAR2MFWyvDqIb0cCyW35dtdXcDXWwHNQEQZEAA7u4BvtwPjhrNxvWHsrkB88cUXGDFiRFbbSIltwqGw1zasv4cJa2pj9Uc3aHgUdeE/Z4nsnl1BoDtEC2v37FuBaOtmP68ssbwJlnfB5mHkcmLtYIRXHtJgc3v69kwrwmH/kfkTDtFplp3hNEu3BFR6BJQ5TDiYKBKgGqwPIxO2dOho7kmdliMQgkkNObRn7uoCvtiCSHCJ+euY/9cMdrvpdQ+TSRpnIiZMmICysrLUd8yUASIcTNwyAQVbO7eLkM56DZy2Dm+me9Z6BZS5CESCfumeeb+C71uBaOtOuwKhiIRHdecAZ3xCi4BAohwLMXGOxY6u5MKhwi1g/5GuvHQDJ0uz9Mj2zWfIBUJ4xHUo9fk/LlaHQmWdY2EY7OAW72tXF/DxeqAnkPrr4/Xs/lGPJ+09IO3doG3doEbiORWUUpxwwgnwer0oKyvDaaedFnN732WLRx55BMOHD4csyxg9aiT+/vfrQQjBCy+8EPO4l156CY2NjVAUBWPGjMHSpUv7P3lYOHy4+mOQugr88ebrMWb6JMgNNRi6zzjc+99HY+5+738fxaipEyENrUb5biMwf9GPEXRLEeFw7bXXoqGhAbIsw+v1Ys8998TOnTvT/atkRXTehR2YM1IK3RCdDGb3FFDtFVHlYSFhqsHSPdv8Rv7TPbMUEIrItKzKBYSt8EKORb7r0OLnWJTHz7HY0aVj1bbkwmFGo5Jz4RDXZunOvc3SbjLNu8hrjkWHD7jsgfi3hTTAH7S+Lc/b/dJPzd+c/uXHQFVp3DL6kiVLcOKJJ+LVV1/Fyy+/jMWLF+Owww7Dueee2+++TU1NOPvsszF58mTcf//9+OKLL/CnP/0p7u785S9/wTXXXIPGxkace+65OOuss/Dtt9/23iFOxeGm227FpRddjBlTpuFPN/wNP7/oVzjsgJkY0zgSn365Fj/71fk49JBDcN8/7sDKTz7G1X+5Fgt/chb+85//4PPPP8eVV16JRYsW4ac//Sl27dqFF198sSBr13bmXVjNsXAKssgaKA2Fwh8yInZPn2rk1+4ZbwmDUqCqNOVDed5FbuDiwQKUUnyXYLZDvOyD7WHhkOg4l2vhYNosAxqb7x5js1QFVhouMjLNu3BMjoWaZtlE1eNGp5PwfxL1QAwfPhyPPsqu8A8++GA8/PDDeOWVV+KKh7/+9a8ghOCVV15BeXk5Zh8+B5s2bcKNN97Y776XX345zjrrLADA+eefjwsvvBCdnZ0oLy9n+9rZf6li/nHH47JfXggAmLXfdNTvPRbX3HQ97r3xVlx9w99QWVmJVx95AoIkYfb35mLz9ib861//gqZpWLduHQzDwDnnnINp06YBAObNm5fea2gTduZdpJtj4RSY3VNAFWUXIGaOTV7tnqIANFQB29uBoNpbfbAgIHjehf3wZQsLpJNjUUjhoOoU3UG2PukPj70td7NmTrdEitPuF4WZd2EVR+VYpHvFnOT+BIl7IHbbbbeY76uqqtDa2hp3O9988w1GjBjBBECYw+fMiXvfww8/PPLvMWPGAADWrVuXUDgAwOyZB0f+7XK5MGb0aKz7dj0AYMPGDRg/bhwESQrbMQXMmzcPoVAIn3zyCY4++miMHz8es2bNwrRp0/DrX/8amzdvTvia5Bq3xN572ZTrIzkWRX7J5pIIKtzMqeGVCShlzZW7fAY6g0ZulwcEARhaCbjCvUntPb2NlElgeResaMGxBy4eLGA1x8KKcNjfZuHgZJul3SgioFNYPoBbzbFw5SPHIl1hkuL+iZooJan/mclI0iNhFbfbHfm3ILDDhh5SEwoHy4SFQ18URcEXX3yBu+66C7vvvjseffRR7LXXXvjggw8yf64scEms6hPMovfBrhwLp5DM7tnq03Nn98xQQLgkgqBegKbPAUqRa+DcYzXHoqlTx8dNiYVDpUfAjBGKbWtufW2WigNtlnaTbt6FVXvmRLtyLCq8wOIz4t+2sxNYv936tnYfCtSWx78tPCynr40zXcaOHYulS5eiq6sr4sB463//s74BXzDp8y559x0sPPkHAIBgMIgNGzfixONPAACMGT0Gy5a/A4P0XsG88sorcLlc2HfffQEwkbJgwQIsWLAAoVAIdXV1+Pe//40ZM2ak/btmS7Z5F7nIsXAKie2eNHd2T1NApLGEIYsEhOdd2AYXDymwkmORL+GQjzRLp2M172JnTwFyLAQh8cGrwssEhGahCiAJTDwIqQuDMQIiTX73u9/h3//+N+bNm4drrrkGa9d+iQcffLB3u4nQ+1hNE/DM889hz7HjMGPyVPzpxuvg9/vxhwsvAQQBV199FfabPh3z5s3DZZddhpUrV+Kee+7B/PnzIUkSHn/8cTz//PM48cQTMXr0aLz88svo6emJCIu//e1vuOGGG9Dc3JzBb54Z2eRd5DrHwinkNd0zAwHB8y7sg4uHJFjJsdjWqWN1joVDvDTLUpcwKD8AVvMurPY6jK7KU46FILABUF9sSX3fccMtCQcTU0CkS0NDA+666y5ccsklmDt3LhoaGnDuz3+Oa/74R5SWJjgAqxoQsDbm+1c//yX+ede/cMXWraitrcU/b7wZu40ZA5R7MbF6Eu68805cffXVOPLII+H1enHkkUfi/vvvBwDU1NRg1apVePrppxEMBlFZWYmf/exnOP/88wEAbW1taGlpSft3zoZs8i7ylWPhFKLTPVWdhXJFp3ua1YisXV/xBAQFUB3//cvzLuyDUL4AlJDN7Rq+aO5/oBxeLmLiUCWlcKjyCJiehXCIn2aZ5QcupBV9x5b5uiSayRDUKG59t8vSOOrv7+PFnnXp9zt0dHSgqakJo0aNgsfjsf7ARBMmKVjFoc+EyXSg4QoEQWZXdrpBcfVVV2Lx4sXYuXMnKisrY++gakCnP6VQ+XDNasw44lA8/5//4tg5R/XeIAosHTPPA6D8fj82bdqEhoYGVFRUZLWtoEbRHTJQ4bbeTxTS2ZV3uau4m5YppegMZv57GLQ33dMsXtlm9zSMXgEBsEFjCQSELzznpm+zOyc9ivsskmOS5Vhs7dSwpkm1XTgY1Ey8Q3+bZREfeOxEFoAAkDCw6PMdDsqx6EtNGbD/OLaEsauLuRVkkf28tjytikNfMhllffnll2PChAkYP3483lqyBLfccgumT5+esXBISIGEg91E512UWhwvXegcC6eQ03RPswKxo51VxjrCSxhxBIQiEfSE7B05Phjh4iEBiXIsKtwCukNGSuEwo1FJq0EoXpqlSxpYbgm7SJZ34agci0QIAjCkkn3ZTLSAoBY87U1NTbj99tvR3d2NsrIyzJw5Ew899FDsnVQt7KrIcKcGiHAAYvMurMwMcFKOhZOITvc0eyOyTvcUBKC+MqWA4HkX9sBfugQkyrGQBSQVDtVeVnGwctKnlEaGrZhrcF5ZYLYwroiTokhAVxD9DuBWcyxEQjA5lzkWBSRaQKQ6wd1777249957AfRaYGOWxTIQDtMnTQZt6UB4YwNGOJi4ZTbtNKQB7hRvIafmWDgFUSAoUQi8MjsWBlQa+ZIEwCOT9GbUxBMQlPZbCmTOmfR7Vzi9cPEQh4BGsb2r/wmIBUklPhhbFQ6D0WZpN0LEthl79WC16jC+TkJpNjkWDicdAZEQOyoOFd6slmKcSHTehVtO/Lqa7ihPkvtwGLbaPfsKiE4f+3mUgFBEIKCxii8/7mYGFw9x+K5D63e83Okz4A9RlCRosqnxCpiWRDiYB5JAuMowGG2WdtM376I7aOCrljzbMx1MVgKCC4ekWMm7KLYcC6fQ1+4Z0JC+3TOFgOB5F9kzMD/ZWRAvx2Knz8CmVg013vSFg5lm2R5OsyQEKHUVR5ql05FFNmXRjNu1mmMxJNc5Fg7CnEQJpBFpzoVDSqLzLhJRrDkWTsFM96zyCP3SPXf5DHQHU6R7mgLCXFvq9MXE3SsigWak8bngxMArD33om2Oxs8fApjYN5W4hrp0okXCIb7MsvjRLp2PmXUiC9dkOOc+xcBhpVSA0HejiwsEKbomgRzXizgyI5FgM/AJXXohO9zTtnj6VfSW1ewrhNM4d7YA/FFOBYHkX1npXOP3h4gEAWruBJ5YD324H2lXsXl6G7UdMx6a6emwKTymMV3WoLREwbbgSOXDEs1mWKAIUbrPMGUo44W9ts2o5x2LvXOdYOBBLAkLVgW4/Fw4WcUmAT2UNz94+rp+BlmPhFDKyexLSu4QRERAUqCmHSyJhAcKP0elScPEwevRoXHjhhbjwwgtt3/Zhhx2GyZMn4+abb45/h627gGsfBx5fDqg6qCSgjgJ1lGLsHU9j5N5jQU87Dtun7InyPlaraOHAbZaFw8y7+OC7POdYFCFJBURI48IhTRLlXQzkHAsnkczuqYiswTLSb9JPQPgBAHJNOc+7yJC0Pulnnnkm5s+fn9ET3Xffff0HzwD48MMPcc4550S+J4TgmWeeyeg50uLL74BDLgMeXcYOnJSCqDoETYcQHn/WuHYdfnTlTTjg7fdiVGldiYD9hslQdQyKNEun0xkwsLFVs5SWN5AbJceOHYujjz466X3i9kCEeI9DprhlwiqOUW1SgyXHwimYFd7odM+gRtHeN93TFBCe8DGg0w/s6sSqD96HR5FwzDHH2LpfGzduZJ+38Fd1dTUOPfRQLFu2zNbnKRQF/7TX1dXB6/Xm90mb24Hj/syWK/TEpW7BoCCUYtb196Hu3U8AMDvmnnUSukKATzUgEjapsMLNGnp46Sv/rG5SAQKkSuoek68cC4cTIyBM4ZApg1g4ALF5FybBJA4MTu5gdk+CSo+Aaq8Aj0ygU6ArSLHTxy7yNIp+AuLBO+/AOT8/D2+//Ta2bdtm+3698cYbaGpqwttvv41hw4bh2GOPxY4dO2x/nnxj2yf+xhtvxMSJE1FSUoLGxkb84he/QHc3y1hfsmQJzjrrLHR0dERU2NVXXw2ALVuYywqjR48GAJx44okghES+j1fxuPDCC3HYYYdFvu/p6cEZZ5yB0tJSNDQ04IYbbui3j8FgEJdccgmG77EbStbdhf07X8USLXkqn3kImHDTf+CRgD2qRRiU2SwrPQJK+XyGghLUKD7drkIkQKqJ1AO56pAuhBAQVQe6/LCQ8xkfaXALBxNzNoFm0Ijzx8U1akFhdk8BtV4BZS4m8PwqRauPOd8CNRWgbhndPT14/Jmn8KOTfoQ9DzgKZ/zhDvxnTQ9OPe10/OAHP4jZpqqqqK2txQMPPAAAMAwDixcvxpgxY+DxeDBp0iQ88cQT/falpqYGQ4cOxT777IPLL78cnZ2dWLFiReT2pUuXYsaMGXC5XGhoaMDvfvc7aFqv5TwYDOKCCy7AkCFD4Ha7MWvWLHz44YeR25csWQJCCF599VVMmTIFHo8Hhx9+OJqbm/Hyyy9jwoQJKC8vxw9/+EP4fL0XCk888QQmTpwIj8eDmpoaHHHEEejp6bH8Gtv2qRcEAbfccgs+//xz3H///fjf//6H3/72twCAgw46CDfffDPKy8vR1NSEpqYmXHLJJf22Yb4g9957L5qammJeoFT85je/wdKlS/Hss8/itddew5IlS7Bq1aqY+5x33nl4b/m7+I/7QHxSMg+nyI2Y51+Kb4yuBFtlEEpRtqkJ07d8iwqPyG2WDuKzcI6FEA6XSmS7KnMJGJvvHIsCs3nzZsycORMejweKomDvvfeOlEyNQAilVRVYfMuN7HsAIyftiardhkce/+BT/4U0tBqtHW39Ny4JmHXsPEyeOhWnn346SkpK4Ha7MWfOnJgDUFdXF+bNm4fS0lLIsozddtst5gC7efNmHHDAASgpKYEsy6itrcWll16amxckR7gkEsm74DkWziKh3TNIsausHA+88iJGj9oDv1wzApuGH4+lzz6I377cjk+qj8Fzzz8fuQAGgFdffRU+nw8nnngiAGDx4sV44IEHcMcdd+Dzzz/HRRddhAULFmDp0qVx98Xv90eEhxK24WzduhXf+973MH36dKxZswa333477r77bvz5z3+OPO63v/0tnnzySdx///1YtWoV9thjD8ydOxetra0x27/66qtx22234d1338WWLVtw6qmn4uabb8YjjzyCF198Ea+99hpuvfVWAGws/emnn46f/OQnWLt2LZYsWYKTTjrJ0tKviW3i4cILL8Ts2bMxevRoHH744fjzn/+Mxx9/HAB7oSoqKkAIwdChQzF06NC4kb91dXUAgMrKSgwdOjTyfSq6u7tx99134+9//zvmzJmDiRMn4v77749Rb5s3b8a9996L//7iDzg4UIbdhVJcouyJWWId7lU3pHwOKgkoe2o5L0c6CEopVoUnShJCIAiJV6EKlmORDFVjS2eqtcFW6XL88cdj3bp1uPPOO/H0008DYFU9f0cXhO4Axo8fj6XvvgMCYEvTVuzYsQOqquKt95YDAF5d8iZGjhyJ6oqq2A1LAlBeAgBYu3Yt1q9fj2eeeQZ/+9vfsGLFCpxxxhmRu55yyil47733sHjxYrz88ssYNmwYzjjjDGzcuBEAcPbZZ2PLli148MEH8e677+Laa69FfX19Tl6PXOIS2SCj0CCL3i4mZJGg3MV6I0oVAiIQ3PfEozAmfh+dKuAeNxtGoBPBb99DsPEQQPZGPjcA8Mgjj+D4449HWVkZgsEg/vKXv+Cee+7B3Llzsdtuu+HMM8/EggULcOedd8Y870EHHYTS0lKUlJTg73//O/bbbz/MmTMHAPDPf/4TjY2NuO2227Dnnnti/vz5+OMf/4gbbrgBhmGgp6cHt99+O66//nocffTR2GuvvfDvf/8bHo8Hd999d8zz/PnPf8bMmTMxZcoULFq0CEuXLsXtt9+OKVOm4OCDD8bJJ5+Mt956CwATD5qm4aSTTsLo0aMxceJE/OIXv4h7Xk6EbZdib7zxBhYvXowvv/wSnZ2d0DQNgUAAPp8v5z0N69evRygUwv777x/5WXV1NcaPHx/5/tNPP4Wu6xj305NYoHuYIHTUkNTlbKIZoFt3ZZ4q6BQoLf7fIcyWdg3N3b2daiKAkAFQEpuWJwjApKFSWqo6HTLarqYDq9aDhHRQRQSm7g5I9tS6KaV45513sGbNGjz88MM4/fTTAQB77bUXxo8fjxtvvBGXn38RDpg+A089+wwA4PHnnsGIESNQWVmJZ155EYcdOBMfrVqFKZMmx/ZRSiJQ7oms54miiFdeeQVVVVU44ogj8O233+KOO+6Apmlob2/HG2+8gSuuuALnnXceAGDmzJkYOnQorrnmGtx9993YsWMHdt9998jV3LRp0yK/Qy7I1XZdEtDqM+2ZuXueQkIpDX8Vek+yg4CNDN/87df4+KMPUX/kHRApQEQJ3n2PR/fKR+He/SAoex+Lhx9+GD/+8Y/R09ODZ599Fv/5z38AAOvWrYPP58ORRx4Zs+1QKIQpU6bE/Oyxxx7Dnnvuic8++wy//e1vcd9990GWmV187dq1OPDAA2OOVzNnzkR3dze+++47tLe3Q1VVzJw5M3K7LMuYMWMG1q5dG/M8++67b+Tf9fX18Hq92G233WJ+9sEHHwAAJk2aFLnQnjt3Lo466iicfPLJqKrqc6GQBFvEw8aNG3Hsscfi5z//Oa699lpUV1fjnXfewaJFixAKhbIWD4Ig9Pswqqqa1ja6u7shiiI+uuI2iNc9FdNZXlp4xyonA1b2ybEghKXl6RSIvvjbs1bOf47Fh9+wuQmJ6PGDbN4JgB3MaEcPUOJJvk1ZBKaPtfT0H3zwAQRBwMknnxz52ehhI1BXV4fPvmQHnWOPnIvb/3Un1m/cgCXvLMPkfSdhyJAheO+DDxDw+7H+22/xq3N/0bvRiHDofXEbGxtjDjhHHXUUbrnlFqxevRrNzc3QdR3HHXdc5Ha3243ddtsN69atA8AqD7/+9a8xYsQITJs2DT/4wQ8iYqeYYK8IRW+XFMfp3H333dA0DVv/MrX3h5SCSAqME65F6ZQT8ea/TkZzczNef/11eDwezJs3DwAiyxkvvvgihg8fHrNdl8sV831jYyPGjh2LsWPHQtM0nHjiifjss8/63S9bTEEChC3scuw8G0IIDIOVZkVRxOuvv4533303spxxxRVXYMWKFRgzZoyl57PlrPnRRx/BMAzccMMNEMLNU+aShYmiKND11GmHsiz3u19dXR0+++yzmJ+tXr068uLsvvvukGUZK1aswMiRIwEAbW1t+Prrr3HooYcCAKZMmQJd19HsMnAwKUv7M04lAWR4TcyBsyghpPh/BwBdQQNf7dT7/R1FAdAMgKK3+rDfiNxOlIy77Rnjkj9I1YBV61kAgiKCTN0dkO0RsX33hxACBFWgi3nbSfhr3iGz4fV68djzz+CTzz7Fby+8GKNGjMR/Hn8MT7/2MgzDwMnHHM9eYklk6ZhxLMhWXtt49yGE4Pzzz8fRRx+Nu+++G0uWLMHChQvx6KOP4vnnn8/gN09Nrt4HIR1wywIIQXhmQPF/xuLBDh/F7SqjlKI7oOKBBx7EHxdfjze16VjXqkfcWjsfXAT/mmfwg4Xn4OWXG/HYY4/h5ZdfximnnBI55+y1115wuVzYvHlz5BxjhZNPPhlXXnkl/vnPf+Kiiy7ChAkT8OSTT4JGzQlZvnw5ysrKMGLECNTU1EBRFCxfvhyjRo0CwC6cP/zww6xnIxFCMHPmTMycORNXXnklRo0ahaeffhoXX3yxpcenfbTq6OjA6tWrY35WW1sLVVVx66234rjjjsPy5ctxxx13xNxn9OjR6O7uxptvvolJkybB6/XGrUiMHj0ab775JmbOnAmXy4WqqiocfvjhuP766/HAAw/gwAMPxEMPPYTPPvssUh4qLS3FokWL8Jvf/AY1NTUYMmQIrrjiioiQAYBx48bhRz/6Ec74519wg7IbpgQ8aKFBvKntwL5iJY6RhiX9vYlmwH/yLEg8SMURrGlS4zZHCqxmDIMCInFwjoUssaWK7gBQ6rZNOJjMmDEDhmHgiSeewA+/fwrQ5ceGLZvR0tKCvfecAIBV9MaNHYsXX3sF27dvxynHzUd1RSU0TcM/7vk3GhsbUVtVnVQ4bNmyBe3t7ZEZLm+88QYURcHkyZPR3t4OURTx/PPPR5Yj/H4/vv3225iKyB577IHFixcDAC666CLcfvvttr4W+SCoU5QqQjjIiVs1nYiq906kfPH5F9De3oazf/ITHKuW4KwnWtERCE+p3Od7CHz0H/z+vt+g6oc/xB133IGvv/460i8AAGVlZbjkkktw0UUXwTAMzJo1Cx0dHVi+fDnKy8uxcOHCuPtACMEFF1yAq6++Gj/72c/wi1/8AjfffDPOP/98nHfeefjqq69w1VVX4eKLL4YgCCgpKcHPf/5z/OY3v0F1dTVGjhyJ6667Dj6fD4sWLcr4tVixYgXefPNNHHXUURgyZAhWrFiBlpYWTJgwwfpGaBosXLiQgtXmYr4WLVpEb7zxRtrQ0EA9Hg+dO3cufeCBBygA2tbWFnn8ueeeS2tqaigAetVVV1FKKR01ahS96aabIvd57rnn6B577EElSaKjRo2K/PzKK6+k9fX1tKKigl500UX0vPPOo4ceemjk9q6uLrpgwQLq9XppfX09ve666+ihhx5Kf/WrX0XuEwqF6JVXXklHV9ZRGQJtIG56ojScfuKdS2nZDxJ/lZ9GjakX0S6/Rtt8Ou0I6NSvGtQwjHRePmcQVAu9B1mjGwb9v+Wd9M//a4/79cc32uhVr7fRP/+vnX70XTAn+9De3k7Xrl1LfT5fTrafCXvssQedN29e5PtJkybR+vp6+sC/7qIvPPYE3WuvvWhNTQ31bd5OaUsHpS0d9Gdn/5QSQuioUaOo1tJJtZZOOmnSJEoIofOPP4Hqbd2U6vHf5zNnzqSKotD999+fvv766/Tmm2+mpaWldP78+ZH7zJs3j5aXl9Nbb72Vvv7663TmzJnU4/HQTZs2UUopPeWUU+h1111H33nnHfraa6/RiRMn0pEjR0YeP27cOPqLX/wiq9fF5/PRtWvX0vb29qy2kwhVN2ibT6e6YVB/yKA7ezSqJXjNihnDMGi7Xy+q454R/pu0+XTa3K3R5m6Ntvt1+r1jjqXf+973aFdApz1BnX7XrtI73u+kf3yjnf75oaUUAF2zZg394osvKAA6atSofr+3YRj05ptvpuPHj6eyLNO6ujo6d+5cunTpUkoppRs2bKAA6McffxzzuJ6eHlpVVUX/9re/UUopXbJkCZ0+fTpVFIUOHTqUXnrppVRVe4/Tfr+fnn/++bS2tpa6XC46c+ZM+sEHH0Ruf+utt/qda++9915aUVER87xXXXUVnTRpEqWU0i+++ILOnTuX1tXVUZfLRceNG0dvvfXWtF5bQmmxt79kQHM7MOt3QEtn0iFRQLg1ghCQ//4WOGpKJL/CjNuVBUCRLObMO4GQBijF3ePxZYuKJz9LPNiIRo0H/tXM8px0v3d0dKCpqQmjRo2Cx5OiVyFPjB07FnvssQdefvllAMDm9Rtw+o9/hFUffwxd1zF2jz1wxw3/h4On9zYWv/DmazjutFNwwnHH48l7HgIA/OLSi/Cve+7GbTf+H35+wXmAQOI6VWbNmoXu7m6MHz8eL7zwAjRNw0EHHYTnnnsOZWUs+rizsxOnnnoqli1bhlAohMbGRlx33XWRysOCBQvw+uuvo62tDZIkYdy4cfjXv/6FGTNmAACqqqowd+7cSKNaJvj9fmzatAkNDQ2oqKjIeDuJ6AmxY0iJwnqz2vwG3BKBVxlY8y8oZRbHcpfzly10g42rDuqsCikQlmvjkQlEgYBSip4Qs3i7JILOIEWpQnhVOQ0Gp3gA2HjqY65JOmWSCgIAisDN54AsOAzuPichMzlTN9hauyIS54dgDQDx8PDqHmxsS25v1HSKqcMVHDchN04fJ4qHGKJ6HKyihxtIRNDIUgUlvb3FfQWEKR76LmM6jVyKB4NSdAYoSqJOPN1BAyGdosojOPtYkCbFIB7MsCxzWJcsmImbvcflaOHgVQT4QgZUA6hwDyyxl2sG76u15wjg7cXA6QezkykhrJtdFkFF9rIEpo9D4KkroJxxGELh9bJoFJFNMStzEYgECGjsw+VXU+TMczJmZ4+eUjgAzJ45cag8IC1zKclAOMQQ1eMQNwuDEyFejoVbJqBATN4FJ3cYlMIXYjkWnUEDqk7ZmGq3gEpPbGxAX+FAKasi89kc6VPcl6DZMrwG+OfPgT//mEVyb9gBqDr0mnJsmzMd6u7D4JUJGgSCEhnoUdnBs28FQhRYTKz5RgyGKxKSQKFIhDdP2chHW62lZ+5eLaOuRAwfGHK8U04ia+EQHjkddWVpKc57kBKMMxQqOu+i77GCYx+hqAZIgL3upXLvxM++9BUObBvsNj5OPH0G02E1MdWlwDlzI99KAKROHapK4QtPjVPE5AICYAdZl8TevJrB3tS+EIWfULhEAkXqX/rlWCeoUXyy3dp8j/2GK5BFdgAfNFcVdlQcyjxxrbzxBMQ777yT+XMNAJLlWLglgu6QAc2gxdMPVQRQSsOOFkALV3ddEluaSHaRFk84AGaImcOXmh0KFw8JKHexIBWART7Xlogs+jWFgDCRBAJJYXG9Id1ciwNkkQkR3piTPp/tUCMH7GSUh3MsCIAAmEVrwL/e2QoHWQRKPUnnn/AKRCzJcixcEoFPZXkXpa7B/TrZQbTNkoI1QJbIBG45fpUhmkTCQTNYv5qX/30ygouHBJQoBFJ44FBXkKLayw6W6QgIgFUa3BK7nxpezugJUQgCExEurnotQSm1vGQxJSrHQhEHgXiwQziUewELLQ1cQDB0g0IzgLIkJx6XyPqgBvPrlA2UUgQ19hqq4SqDIpoNkNZeT1M4iALgkWNb/IIa+zmvDGUGFw8JIISgzEXQ5mdWn+4Q6zIGkLaAMJHDFQfT7sm+ADncG8HfxInZ0qGjpSd1B5pICCY39I5lVUSgSxvAJ7qACnTbIBzCosAKXECwJQtRYMeCRLhlAn/4c+6RB99rlCnxbJYeqddmaZVkwsGsCHv53yVjuHhIQrlbQJufnbA6AgbKo/IRMhUQAKtGeGQCj9xr9+wOUojhagRfg+tP3xyLROw5RIrJsRAIgSxQhDTALSd5YDESCLEJlZkSLRzSZDALCLMxOpUgEAhbhw9qgGegvfdyQDybZYkca7O0CnNgxBcOQNglQ9jFBSczuHhIgiQQlCoE3SF28gmoFO6oA4YpIHwqBShibrMKEwsEerjB0mwGUsK9Eeko7YFKV9DAVy3WYqv3G94/IVWRCHwqhYsOIFFWQOFg0ldAEAyg1zcJZoe+FReVWyLoDM994K6r/hiUIqBS+DVWZSBgr5lbyrwvLJVwAHijpB1w8ZCCcjcTDwDQETTglmOlqigQlChAT4gCamYCwtwOt3vGZ/W2kKUZA/WlIkaU97+UkAQCAhoOLMrFHuYZBwgHk2gBQQGWSjjAD8hBnbmnrCCLBCIhPO+iDyGdLU2YVQZJIPDKTDhk8/4xwksVUhLhEAovhwwaF1aOGLxDoizikQWYeqEnROMOfxIIQYlCoBpMRWcDs3sSlLsElLoICAF8IYqOgIGASgfdoB7doFi1zZo9c+qwxOmZikgQ0gbAa5dEOIw9cD8cveDU5I+3UTiYRA+SosCAHsylGRSGkd6QVrNZerAPjjMoG6DX5jfQEWDVGJdEUOEWUOUR4JGzm8hpRTgAbHlEFrhtPlt45cECFW4BO3sMUAp0BimqPP3fdExAZF+BiIbbPYGvd2roDiXPHwHY3Pp9hiZeWFZENvFPN2jxLgVlXXGQgPL4cxyyZbBUIIIahSymd+JxSYBPZY/1KgPvNUlFX5ulmIbN0ipWhYPpkikdhH8Hu+HiwQJlLoJWH2BQtv5e5Yn/5syFgDC3O1jtnlbtmZMalKRlYWI2TuoUnmIUDw4WDiYDXUAYUYFr6WBWEwMahUceWK9JIuywWVrFqnAAmIATCAbFhVeu4csWFhAIiQx6UfXeFL1E97VrCSMeskhQ6hJQ7iZQwiNwO4Nstrs2wMqiLT06NrVba5ScOjx1O7siEqhGEZbVMxQOm5u2Yubx8+BprIdSV4m999kHy5YtAwAYhoGSkhJce+21kfuPbByBqsre4KgHH3wQkiShtbU17vZnzZqFyZMnY9GiRSgvL4fX68VRRx2FYIDtKwXQ1dWF4447DhUVFVAUBaNGjcK9994bs51f//rXqKyshKIomDJlCn760586MmwsXo6FVVzS4Mi70A2K7qCBXT4DXSEDOqXwSATVHgEVbqGgwoHnWNgLFw8WibZpdgaSn3xyLSDM53DLbL3QIxPoFOgOUnQFDVYeLLYTZBysVh3GVEmo8abuhBQFFmAWcsgBXNUp2v0syCch/swrDscvOA3r1q/HnXfeiaeffhoAcOKJJ8Lv90MQBIwfPx5Lly4FAGzevBk7duyAqqp46623AACvvvoqRo4cierq6oTP8eWXX2LTpk149tlnce2112Lp0qX4/e9/H+mBOO644/D555/j1ltvxZIlS3DEEUfgnHPOiYiYhx56CDfddBO+//3v44033sCsWbPw8MMPZ/T75ppgFo6J6LyLgUhQY31ZrX4Dfo2N5C5zCaj2CCh1CTlZKkxHOAA8x8Ju+LKFRVzhISX+PnkXicjVEkY8BqLdM6hRfJpGjoVVnJJ3oeosp8N8H+07VO5/ResPAT2ZCYdlH67Amk8+wcMPP4wf/vCHAIC99toL48ePx4033ogrrrgCBxxwAJ566ikAwGOPPYYRI0agsrISzzzzDGbPno2PPvoIU6ZMSfo8Ho8HL730EhRFwezZs/Hwww9j2bJlIIRg1Ucf4e2338aaTz7BPnvvDUIIDjroICxfvhw33XQTDj74YNxyyy3Ye++9cffddwMADjnkEKxcudJxMd/JciysMtDyLlgDJA1P0ey1WXrk3A+8S1c4ANyeaTdcPKRBvLyLZORTQAADy+6Zbo6FVRSRrT3nemT1qq0haEn2vztEsbWzd0mmw2/ENnGFVDZ2OgqJEEwdYu1A+cGaVRAEASeffHLkZ2PGjEFdXR0+//xzAMCxxx6L22+/HevWrcOSJUswefJk1NfX47333oPf78f69evxq1/9KunzjBgxAorSK95qa2uxYcMGAMA777wDSimm7bdfzGM0TUNZWRkAYOvWrTj00ENjbp88ebLzxEOSHAurDJS8CzPN0m6bpVVM4SALxPIxledY2A8XD2mQKO8iGfkWEECKdE+DQpGdPRGQUoqV36WfY2EVRSQ5Fw9TU1RDWOVBiF958IeAHgNAhpe5isQaJFMwb948eL1ePPbYY/jkk0/w20svxahRo/Doo4/i6aefhmEYMeIjHqIYu4+EEBgG6wnq6OgAIQSvvfYahPD9zEFSyZZCnIaVHAurFGveBRvmZKDdb8Ajk0gTaKo0y1zsR7rCAeA5FrmAi4c0IISg3C2g1Wf0y7tIRiEEhIlp96SUIqgDQR9FIEAdbffc0qFjpy/9HAurOCHvQg4Lhp4QRYlC+giHLFwVIECZBzNmzIBhGHjiiSciyxYbNmxAS0sL9t57bwCAIAgYN24cXnzxRWzfvh2nnHIqqquroWka/vGPf6CxsRG1tbUZ78lhhx0GSik2bdqEH//4x5HcrehJlMOHD8dnn30W87g1a9Zk/Jy5wEqOhVWKLe8i2mZpzpgpkQk8ipD3z06mwoHnWOQG3jCZJmXhwU0Ay7uwSnQTpV+1/ji7IIRdJVS4BZQoBJQyMdMZNBBwWINlpjkWVmF5F6x7vpDIIkGlR4gSDsEshQPMMzMOPvhgTJo0CRdffDEefPBBvPjiizj22GNRXl6Oiy++OHL3/fffH++//z6GDx+Ouro6iKKIcePG4b333sPkyZOz2pVDDjkEBxxwAC688EL87W9/w8oPP8STTzyBhQsX4v/+7/8AABdccAE+++wz/PSnP8WyZctw0UUX9RMPTzzxBIYMGYJPP/00q/3JBLs79KPzLpwKDY+MbvcbaA+w44MsssF1lR4R3iISDgDPscgVXDykiRQOawEQybuwiikgNAMFERAmTrZ7ZptjYRVFIggZDhJN/iDQE8z88XFGHj733HPYfffdcc455+DEE08EADz99NMxNshjjz0WlNIYobD//vuDUoojjjgi8/0J87///Q9HHnkkrrvuOhx00EFYtGgRPvvsM4wbNx6UUixYsAAXXXQRHn/8ccyZMwdLlizBiSeeCFnurSh1dXWhpaUFgUCWwioD0smxsIpbItAptdTTk0+0PNssrZKNcAB4o2SuINQxR8/iwa8a2NbJTv6lLoL60vQkbSadwrYR0uKeaMx0T90ARMF0cOT/A7dsQwBvb0x9Eq0vFbFoWklW+9cVNODKcM22o6MDTU1NGDVqVPYzCewQDmXZD4Ayxyfn2p1Dw4OkAMQN0zrssMOwZcsWrF+/3pbn8/v92LRpExoaGlBRUZH6AVF0Bg0oGZ60ktHuNyAIsRbwQhEvzdIt9x8+Rym7yCh35acxEsheOIR09vgKt33TLDkM3vOQAR5ZgCIZCGm9eRfpHHCjeyD8qpF/ARGHaLun2U2db7tnOjkW+w1PnGNhFTPvoqAuFF+QfWWKTcIhn/SdRHn2okU46aSTUFlZiYcffhjvvPNOSpdHPojkWGRe4EqIWyLoUY2CjUsvpM3SKtkKB4DnWOQSLh4ypNwlYKeWPO8iGU4UEAC76vQIBG4p/3bPdHIs9q5Pv1GyLwXPuxiEwsEkWkCsWbMGjz32GILBIGpqavDLX/4SN9xwQ6F3MaMcC6sUKu8iV2mWdmOHcOA5FrmFi4cMsZp3kQynCggghd1TJFAk+w+qduVYWKWgeRfZCgeXDJS6i1I4mJgC4oMPP2Tfwznr0pnmWFgln3kXBmWfX79KYbZZFMJmaRU7hAPAcyxyDRcPGWLmXXQGaCTvokQZWALCpJ/dMwfpnnbnWFhFEQl6VAp3PkOcuHCI4NQwrWxyLKxiioegDrhzcCTOR5ql3dglHEyXTD5t8YMNLh6yoNwloDPA2rE7AxQlGa6NFoOAAEy7Z27SPa1WHXazmGNhFZZ3YdrxbNtsYrhw6IcTBURQp3Dn2F0QnXdh13PlM83SbuwSDgDPscgHXDxkQbp5F8kwBYTP4QLCRA5XHAxKEdJYNSKoAXK4NyKdpqtc5VhYJW95F1w4JMRJAsLsB8jHXAC78i60cAhfIFxlEAgbiuSWiiPbRjfYMVQOC51s4fbM3OPsM1QRED1hsjONoVHxEAjLptALPAciHexI9/x0u/Uciz3SyLGwiiKyuOSk6ZbZwoVDSgghkTROisJFp9uRY2EVl8SWEDJN3w2E0yzb4qRZlii5SbO0G92g6LFROJg5ForDKy3FDq88ZEkmeRfJKJYljHhkYveklFpessgkx8IqOc274MLBMoWuQNiZY2GVdPMudMP8bDnXZmkVUzgoNgkHgOdY5IviOTM5FDPvAkAk78KObZaEKxA+C9ZFpyEKBB5ZQLmLHRDUsLDqDhr9Kgzp5FhMGWZfo2RfFBFQDUTm99sCpVw4ZEAhKxB25lhYxS2zyldQS/57hnRWZWj1G/CpTGiUKgQ1XgFlLqHoTpa5EA5mjoWLOyxyDhcPNpBp3kUyTAFh0OIUEECvHa3cJaDURSAQ1tPRETAQUNmVltX0zAlDpIzcLFaxPe+CUsAXiiscDAq0aAK+Csr4PCDjq6CMFk1Av8ngaQqHsWPH4uijj056H0IIrr/+equ/Be69914QQvDdd99ZfowdFEJA2J1jYZVkeRdsmJOBVp+OjgAT365wRk2VR4BHFopyXT8XwgHozbFwenPoQIAvW9iAmXfRHaKRvAs7LEIkagnDFzLgzeHJM9eYdk9PlN1zp8/AZztUEFAIKa6aUkVc24EiEfhUChfNcr3brDj4+wujVl3A+qAMDazcTMP/b9UFbASwh0tFlWjkrOLwxRdfYMSIEbZuM1fkewkjFzkWVnFLBJ3hypy5hFZsNkur5Eo4AL2Nkpzcw8WDTZS7SWTJoiNowC3b8w4eSAICiLV7frQ1CIOywTWCQSEI7CDZ9wRRXypiRHnujwiSQEBAoRpZdNqnEA5fB3uXXmif/+sAvgrKGFdKUV3qyslSxYQJE2zfZi7Jp4AI6rRg5W5ZJBAAdPgNyCIpOpulVXIpHEI66wEZKK+V0ynuM5GDYHkX7N9m3oVdDIQljL7oBsWn2zXI4fkQggDoBrv6U3Ua03tgR46FVcy8i0wwDAMdbQF0tAfREaQxX20Bik+6RfSEjLAQ7P/VE6LoCRn4pB1oC+jo8Mf/MozE7wFKKU444QR4vV6UlZXhtNNOi7m977LFI488guHDh0OWZYweNRJ///v1IITghRdeiHncSy+9hMbGRiiKgjFjxmDp0qUJ9+HDDz8EIQR//etfMW7cOCiKgmHDhuHRRx+Nud+DDz6I3XffHbIso7KyEvPmzcPOnTsjt3/++eeYOHEiFEVBbU0Nrr7qalRXV+NnP/uZlT9HWkRyLApwOWWmWfaoFG0BAyHdgFcufJql3eRSOAA8xyLf8MqDjWSbd5GMgVaBiM6xIIRAIoAksAOMTpmIEAiFVyLYe0j+3qYZ511Qiq62IP64rDvuzSol8BvWGyqe/Czx5M6rDi9HRYIgTzPS+tVXX8XLL7+MxYsX47DDDsO5557b775NTU04++yzMXnyZNx///344osv8Kc//Snudv/yl7/gmmuuQWNjI84991ycddZZ+Pbbb5P+DjfddBMuv/xyTJkyBRdffDF+8Ytf4KSTToLL5cK7776Ls88+G2eeeSYWLFiALVu24Le//S1OPPFELFu2DABw8skno7OzEw8//DBcLhcuueQS9PT0AGAiyU5Bmcsci0SYyxJmE3FJeEnCKyOn/T2FINfCgedY5B8uHmwkOu+iM2Cg0m1v2MxAEhArE9gzRYFABGsUMwxgfJ0Mvwbo1MhLumdGeRfmUkUgcfNnusUMTQcyWfkaPnx45Ar/4IMPxsMPP4xXXnklrnj461//CkIIXnnlFZSXl2P24XOwadMm3Hjjjf3ue/nll+Oss84CAJx//vm48MIL0dnZifLy8oT7csYZZ0TSMf/617/iyCOPxLvvvovZs2fjsssuw8yZM3HnnXdG7q/rOhYuXIjOzk589NFH+PLLL/Hkk0/ipJNOAgDU1tZi1qxZAOxdwjBzLErzYM9MZbNUQr3zUYqxETIeuRYOAM+xKATFe/ZxIGbeBcDmPvgyHPySjIGwhNHSo2NzihwLgRBIIsHBo5WUdk+7UUT2fJY7/BP0OERDkd5BjSKz33G33XaL+b6qqgqtra1x7/vNN99gxIgRMQLg8Dlz4t738MMPj/x7zJgxAIB169Yl3Zf9998/8u9x48YBADZv3gwA2LBhA95++224XK7I16JFi0ApxYcffoiVK1dCEAQcf/zxkW3MnDkTHk9vycUuF4aZY5FLq6NVm6VLCts2U7uXiwJTOLhyKBwK5ZIZ7PDKg81UuO3Ju0hGsVcg0sqxKGFvUTPdM5SHdM+08i4CIUBP/fwk4quwBklTbJhIUv8dTtYjYRW32x35tyCw95uuJz/DxXuMuS/BYBBHHHEE/vCHP/R73OTJk7Fq1aqk2zadKnZUIHKVY5FJmmUu8i4KRbRwyOWJnedYFAYuHmxGEe3Lu0hGXwHhke1dIskVgSxyLOLZPe1O9zSxlHeh6kBQBSS2n2UycNX0+GpxlyZgg2p9yNWYKgk13vjPbdf0w7Fjx2Lp0qXo6upCWVkZAOCt//3Plm2nYsyYMdi4cSNmzpwZ9/Zp06bBMAy88MILmD9/PgBg+fLl8Pv9AOxzYeQix0LVKfwaE7qZ2CzNvIucTTzNA/kSDgDPsSgUXDzkgHIXEw8A632oLcmNJI4WEH6VwiM7/wP0mQ05Fn3TPUO6femeJorI4pLjHsApBf73CTCmIubHgkBQ4Yq/vTKFYpefQAeSLkgQAKIAjK4Wc96897vf/Q7//ve/MW/ePFxzzTVYu/ZLPPjgg2w/cvzcV155JebPn4+jjjoKF1xwASoqKvD+++/j9ddfx2uvvYbZs2djzz33xPnnnw9N06AoCn7zm99AluXIvhFCMGvmTAwZMgRPPvVURgIipNlz4jHTLP0ahZalzdIlEfhUEm7idPbnOR75FA5mjkVJHseJcxjFVe8uEsy8C4Ct09s68rgP0T0QftVaEFWhSCfHYupwazkWskhQoggodxMo4XJvR4BVY7Qs7bLmsJ4YKAUeeRtYtT6tbQmEDYACEi9emD/fo0bOS9d/Q0MD7rrrLmzYsAFz587F3/72V5z7858DAEpLS3P63N/73vfwyCOPYMuWLfj+97+PI444AjfddBPq6+sj93niiSdQUVGB008/HQsXLsTpp58ORVFi+h6am5vR0tICIP0eCLNDP5vKoGmz3OUz0BUyYFBqi83SJbIr6lweO3JBPoUD0JtjUQwBYAMNQp18tili2vwGWn1sfbeuVEC5K7c6jVJ29U0Ii+JNeCUV0gpjZgewqU3DQ6t7Ut5PJATnH1SasV0tpLOSsWawRjiXSDK6ujQoRVeQoswVLjdTCjyyFHjzE3QML0fT96di1NBh8EjWG1taRQXrewg0I3bCJAWzqu5RI6PKUxhNrxsUV191JRYvXoydO3eisrKyIPuRiDVr1mDy5Mm48847cc4558TcRmlviylB7N/a7/dj06ZNaGhoQEVFb7XIrxrQDaA0zc8mjVo2M6tossCWJeyoegHsvdfmZ/MenBKORylFZ5Ci3BX/+JJv4WBQdqHglQfOEK1igi9b5IgyF0Gbn51vOgJGzsWDuYThC/daeB24hGG16pBtjkUm6Z7xEEzbpga4JQo8vJQtV2SKR0G114XKSmCXj4lLzWCiodoroMYr5H3AzeWXX44JEyZg/PjxeGvJEtxyyy2YPn26I4TDv//9b3R2duLggw/GunXr8Pvf/x5VVVX48Y9/3O++6fZAmB36njTGyOczzTI678KTuzw429AMdtzJl3AAeI5FoeHiIUfkKu8iGSQ8YMaJAqIzaOCrndaGJPVtlMwUUSDwCARuiZ0ogjrrfpcECiVJx3s0ikTgCxlwPfo2yJJPM98ZjwJ42chpAUBdiYi6HPXCpENTUxNuv/12dHd3o6ysDDNnzsRDDz1U6N0CAIRCIVx//fW47LLLoCgKxo4di8cffzxm2SIaM0zLMKsQSQREOjkWIZ31FJlVBvOz7ZJy+/nqm3fhVAohHACeY1Fo+LJFDgmoFFs72VGq1EVQX5qfdzqlNDJjot8SRoGWLd7eEMCyjamjqYeWivjJtJKcHZRNu6cadi+mtHtSiq77l8L19qdQolod01q2iBIOTsYcqT4Q1o/NXgECIBAI9Fu26AwaUMJLDYkeH1BZlSHaZumR7HX1pKIjYIAQ5LxyaYV4yxaFEg5mk3SFu/iDwooVXnnIIW6ZnZhCGnNEaAbN6SAaE6dVIHSD4uNt1u2ZudxX0+5prluHdIpAIIHdk1LggbegLPsCISJCoRnkdReJcBhoCIT0ViD6EMmxiKP5srVZ2o1LJOhRjfTHpeeBQgkHgOdYOIHCy9kBjnnFQClzXuQLJiDYB8tXYBfGV1E5FslwSwR71+dngZfZPQnKXQJKwvPwe0JsCmBAo6CGAdz/P2DpZ1CoAYOw1Mu04MKhoJgnlr6mm745FjRcZWjzG2gPGBGLZLlLQLVXhFfJfy+KiUsKV08yDGvLFaZwKETip+mS4b0OhYVXHnJMrvMukhG3ApGXZ47FaqPkpAalIL52OVxxMCjrTwmqBgKPLIXy7pdQQCCBQqYUISLCQy1KCC4cHEFEIIS/onMsNKN3aYKCNd95ZSYqnXKVTwg7OQc1Cq/sjLwLzaDwa0g6KTOX8BwLZ8ArDzlGICQyETBXeRfJKHQFork7dY6FydRhhW0rF8LDpyoefQve5Z/DAEG3IKFTkEBBESRCrx3QoAAFjHiFcY+LCwcnQQ2AMseNP2RANdh6eZvfgD9cZShzCaj2CChRBMcIBxMn5V3oURWHQggHnmPhHHjlIQ+UuwV0hPMuOnKUd5GMfhUIJX9XMJZzLKolVHsL3DptGMC9bwLL10IBoFCNxYMTESEiwkdEQADKDBUlzV3Qe/xo7uxAdUkZZEFkWsEtA4IBBAKF/V0ywGwyHCjryJRSqKqK1tZWlqkhu7GhXUOFWwCBAI/Eehny0YeUDU7JuzCXKupKhIK5P4I8x8IxcPGQB6LzLkzLV74/fBEBEULemigDGsVnOyw2Sg7Ls6Lqi2EA97wBvPtlzI9FAB6qw011CMRAt8iqIyKlGPHsp2g6bHdsG1oFIggsQ1sq3qOaWZQaINoBAGAYFBQElfUjYUCERChcAkGVh0AUiqfwWui8C63AFQeTEM+xcAxcPOSJfOVdJMNcwvAhPwLCao5FhTtxjkVeSCAcoiEAyqgOahDIhgFKCIw2FUOe/hJCmQgcOQlk5p752+cc0BVgTa1l7uI5qcaDgq2LqzoFkWRIkgKPS4JuUFR5BFAK9KhAqUKLpspSyLyLaOFQSHiOhbPg4iFPmHkXmsFcF9Xewhy4IgIixzZOSilWWs2xGGYtxyInGAZw1+vA+19ZujtzXhB4qQ5KdQSJgNCh02DM3g+SCNvTPfOJLrOacEWhl48yxLRZqhqFIAFewq7Y3TIJu50QmTDqCxnoDlGUKsWzTOOWwqKf5u/YES0cZKGwrg824G1gzCEZCBT3JUYRQQhBefiKzsizbTPevuS6iXJzu45dvtQdXiIhmNxQoEZJPT3hADDxoBIBBsLjiU/cH+Xzp8e3e/L5aznHqs0ypMeeeLyKAIkA3aHiCZ8ymwSDeTqBO2WpAmD9OLxR0lnwykMeic676AwaqChgeTi6ibInxJo47axAWK06TBgiwZtFjkXG6Abw79eAD75O62ECAJkaCBEB7pMOAI6ZBiCO3VOjCKjhPA3J+U15xUY6NstEORZeRYhUIEpk51/R5jPvwknCAejNsXDCvnAYvPKQR8yZ+AAieReFxKxACIRdMdt1pdwZNPB1nnMs0iJD4WCiUAOh+QeCfm+/frcJhJXJK9wCvOGo9O4gRWeQXRXzakTmUMrEQkfASMtmmSzHwqxA9Kg0Mp7bybglAp1SS71EmeI04QDwHAsnwisPeabCLaA7fDTrCBpwy4X9RBBC4JEBv40ViNXbQpZKwUNLRQwvz/PvrxvAna8AK9dlvAnplINADp8C1UDSA5pd6Z6DnXhplunYLIM6G6GcCLMC0aM6vwIhi+x3Dmi5cWyZfSNOEg4hnf3d+ZKFs+DiIc8UKu8iGYQQeBXWRJatgHBSjkU/bBAOOGUmcPR+UML5B1YOsH3TPUPhdE9RoHA56CDtNIJhwZBNmmWyHItoiklA5CrvwhQO+Q7/SgXPsXAmfNmiABQq7yIVrLksuyUMJ+ZYAGDC4Y4shcOps4Cj2VKFIgIGkFap2xw1XOYSUOoiEAmr+HQEDATU4mncyyUGpfCFDLT6dHQG2VwDl0RQ6RZQ5RHg7psSm4K+ORbJKJYljFzkXThVOPAcC+fCxUMBKHOxPgOAzXxw0jp4tgLCkTkWmg7c/jLwURbC4QcHA/OmRr4lhFnXMl17lgQCryKg3MUERcig6AxQdIdPmIMNVWd9Ia0+dvUPsDTLaq+AcpeQ0XvFzLFI58QT7cJwqoCIzruw49jhVOEA8BwLJ8PFQwEodN5FKjIVEDucmGOh6azisGp95ts47WBg7pR+P1ZEgpCOrA7gfdM9CRk8ds9cp1mGNEAQkPayoFcRoIjOFhB25V04WTjwHAtnw3seCkSh8y5SEVkDTqMHYpXTcizMisPH32a+jdMPAY6cHPcmUSCQBPMAl/lTmCSye8oiK90XujfGLvKVZhnUM5+K6JEFAL2DpJzWA2FH3oWThQPAcyycDhcPBcIJeRepSEdABDSKT63mWOTDnqlqTDis3pD5Nn54KHDEpKR3kUV2ALfz6ojZPVlzbUhnjZndQQpBYK6BYpztTylFUGf2ZDV8Na+IrPzuysHvYy4nZWPvc7qAyCbvwunCAeA5Fk6HL1sUkPKoGe2dgdRNhoXA6hLGp9tDltbqK9wC9qjJsWZVNeCfWQqHH6UWDgA7AVIgZ30KikhQ6hJQ5uodD9wZZE2FTi2pR6MbFD3/3965R0lR3nn/W1Vd1dcZZrgIgkACKJddkRXwgjF4i4CXVTfH4C2GxNdkiRrZvL7GPb6eTQJ6zC5uvGWz0UUBZXeNB0FEzmZNXo03RDGCS4LIRUQFhQDDzPRMd3VV/d4/nq6mZ6Z7pqq7qru6+/c5Z47S01X1TE9PPd/+Pb/v89UtHOm20JG2YFhiwmqNyhgUkREJuWuAdIpwwpQ/8UTV4C5hhEMSZElyveNkLQgHO8eClyyCC1ceqkhQ8i4GYqAKBBHh3c+cVR18z7GwhcPWMoTDDecBF0x1/HRNkXxPOyxk9+xIB9fu6YXNslTsDv3eO0qWSpArEG7zLmpBOACcY1ELcOWhigQp72Ig+qtAfByUHIuMAfxiQ3nC4ZvnuxIOgCiNZyxUxGoZZLun1zbLUvFj4glqBcJN3kWtCAfOsagNWDxUmaaw6LAHxLbOQaaYgHBqz5xygupfjkXGAB59EXh/b+nnuPF84PxTXR8m27ZNZ0YTzwiK3dMPm2WpEJHY+dOHiSeIAsLOu0gN8N6rFeEAcI5FrcDLFlXGLud26pTLu4h4VG71g5gmoztzfAmjQyfnORYn+VR1yBjAIy8C2z4u/RzfugCY/ZclH66FRMx5mCrf4CXsnqKBLmOKpYKkTpAk8q0hkYiQNoBuQ+ySChxvAq7mTb+/HAsvCOISRiQkoT1tFW26riXhAHCORa3A4iEA9Mi7SFU/72IgoupxAfHup2nHORYjm3z4ufRsxaEc4bDgQuCrf1HWMEKyBAk0YN6F3/ht9zQs4Q5K+2yzLJWBciy8IGgCor+8CztTpVaEA+dY1A4sHgJAj7yLTDDyLgYiqsroTFvY/GkGRDTgJ9sZJ/mQY6EbwCPrgT/uK+14SQIWXACcW55wsNEUyXHehd94afestM2yVDKmsxwLLwiagCiUd1FrwgHgHItagsVDQGgOy/izYeXyLlqjwf/j2XfMRFfGggVAlYsLiEhIwpQTPF6ySGeEcPjTJ6UdL0nAty8EvjLFsyFpitjYxuvAonIplu6pZp0axcZabpplpdFN5zkWXhBVZUggdGaX8Kr5moRDQFdGWHnjmpT7PccC+rsqhO2SSWi1Md5Gh8VDQGgKSzjSJVwX7SkLLZHKdKaXw+ZPdYQUCYYpyvXFBMQ0r3Ms0hng4fXA9jKEw3cuAs6Z7N2YYOddiAk6GsAb9kB2T5tq2ixLxc6xSIQrOz67PylZZQGRn3cRki2kTdSUcAA4x6LWYPEQEOy8i2Mpob6TOlX8RuiGLzpNfHJMNEoOJCD+yssci3QGeOgF4INPSztekoCbLgJmeSscbDRFNL9GQgMv5VQLMdGIdWXDEksaSd3CF50mLAKGxglKdjKqlZJ3qTkWXhAUAWE3TpppoDUq15RwsHMsgtwszvSErZoBwt7zAQDaA7znA9A3xyKkSJAh9jvIt3F6mmPhhXD4X1/zTTgAdt7F8a7/oEMEGHS8X8Cwsn0DCmpGOACVaZTsj4gqib4D/bj7pNKYJJLnZam6SyilwDkWtQdXHgJELeRdAMVzLApVIGZ4lWOR0oEHXwA+/Ky04yUJuPli4KyJ3oynH/zIu/ASouN9D/ZEFw7JGBIXjZRNYbmP3VOrYC+BW7zIsfCCalYg7B6H1qiMlEG+73jqNWmPthNnKgeLh4AxKCLEAyB6H4bGgyfF+8uxyBcQQ2MSxnuRY1FDwgFAdtOe4N3AB7JZmpb4+Fdr6Z5BmniqISB6N0fqpohzD9J7rz8Mi+2ZtQiLh4AhbgDBzbtwkmNhC4jJXjgsUjrw83XAzv2lHS9JwHfnAGeeUv5YXFCJvAsn9GezHCjKuRbSPc1sgFIsQGvllRQQhVwVdt6F5TDvotpwjkVtwj0PASPoeRdOcywiqoy/OlFDUi8jbyGlA//8fOnCQZarIhyAyuZdFMJJmqUbgpruGdSJpxI9EMXsmG7yLqoN51jULiweAkiQ8y6c5lhMHqZiSFyBKkulCYjurHDYdaCEUaKqwgGoXt5F2hAhWUe6LXRlxCfPJk3GkJiMRLj8DnxFlhBVRZ5GJCTBzArcjuz2yJXEzxwLL/BTQPS3j4PTvIsgwDkWtQsvWwSQoOZdtKct1zkWEVUCMsdLuI7KqLZw2F2GcPjeHGDmyaUd7xGVyruwiJDKiMnEzG7mFMkuS/i1bFLI7mk3+mqKeNzvknna5xwLL/BjCSNtENJm/xtA2bbNIDfuApxjUcuweAgoQcy7eO8z3VEF4cSmnjkWrgREV1oIhz2flzZIRQa+NxeYMaG04z0kJEuQfcy7sD992uVpRRK781Vi4s4nJEsIaVKuv0I3CekUIZTdfMovAaNX2Z7plIgqAZI3AsKJcACO512kzeCKB86xqG1YPASUoOVdmBZhy4H+GyVtpo/qm2PhSEB4IRz+dh4wfXxpx/uA6nHeRSGbZRDSLIEB0j0V8X72StRUMsfCC+wek3IEhFPhYFMo7yJIcI5FbcPiIcAEKe/ig0MGOvWB+y/6y7HoV0B0pYEH1gIffVHaAAMoHADv8i6CnmbZmz52TzObp+GR3bPSORZeUI6AcCscgL55F0GCcyxqHxYPASZIeRdOGyUHyrEoKCCSKeCB54G9ZQiH718C/NW40o73kXLyLsqxWQYFP+ye1cqx8IJ8ARFTneU42MIhrroTivl5FzE1WNulc45F7cPiIcAEJe8iP8eiPyQApzvYUbKHgMikIP/z88Deg6UNLsDCwcZt3kWtpVk6pdR0z95UM8fCC2wB0ZUhxND/BFqqcMi/VsoQIjQSkLs951jUBwF5OzHFaI7IOJYSjZPtaUIiXPkxOK06jBscQmvUmfs3okpAWwrJh9cjvvdgaZ5hRQZuuRSY9uVSjq4YIu/C9rMXf17vNEu1BtIsS2GgdM+BejfSJtVM5aUYTgREucIBsN97ovoQlNeMcyzqAxYPAafaeRcpg7CtQI5FIaa7ybHoTCHy0Fpg32Ek5RDiluFOQIQU4JZLgNOCLRxsiuVdVMNmGRTy7Z6mJfo5BrJ7BiXHwgv6ExBeCIf863TqViB2PAWCtZ04UzosHmqAauZd9Jdjkc+giOw8x6IzBSxdA+w7hEj2IVcCIqQAt14KTP2Ss+sFgN55F0GxWQYFRZYQc2D3rLeJp5CA8FI4AEKEdWWkQORdcI5F/cDioQaoVt6FyLFwtmQxfZTmbEwd3cJVse9Q7qEICRdHUg4hZhnoVxqpIVFxqCHhYKPKwLGUCUWWA2ezDAr92T1DkrBoxtT62hg3X0AoBsECPBMOx68RjLyLoG4nzrinvv4K65Rq5V3sPWricNfA9syQLOG0EQ5CsDq6cxWH3kTIgkoWknIIRZMz1FDNVRwA8WmrIy0yJtrTBMOyEFMlDM7mTLBwKIyqSIhrMpojYpvnjrTYcjtl+JcXUS0iIQkSgKMpC5oPk2sQ8i44x6K+4MpDjdAUlnC0GyAS20QPiviv+9zkWMS0AcbT3iWEw6eHiz4lvwIR712BUEPAbZcCfznW0ZiqTSGbZTgkQ1UI0ZDMneYukCUJ4RAhqslQZPE3YNs9NUVCuA6WMVKGaHgZEpWRNgFF9naJIT/vIupB2G0pcI5FfcHioUYIyRLimoTOdGXyLkSOhbNGyRknDdAo6UA42BQUEGoI+MFlwF+McTSeajKQzdKwqCJ5F/WG3aGf0OwKnOgXEV/u7Z5BwnbY2EsVaYMc2TjdUu28C86xqC9YPNQQg8IyOtOVybt47zMdTgqcJzYpGNnczzjau4B/WgN8NrBwsOkhIBRAuf1yYMpox8dXA6c2S7/zLuqV3jkWsiR6RYrZPVW5NsRZb+EAHC/rey0gqpl3wTkW9QeLhxqiUnkXbnIsZvRnzzzWBfzTc8D+I67HECEL0EJI/u2liE86qf8myipRqs3S67yLeqe/HIuidk8AWnYr7KC6VwoJBxu/BEQ4JCGpVz7vgnMs6g8WDzVGJfIutjvMsYiGJEwukmOBtqRYqihBOAAAtBAiP7gUOPkkJDOEuBqcDu1ybZZ23kW1w85qBac5Fk7tnkGgP+Fg44eACCtAFyqbd8E5FvUJi4caoxJ5F39w2Ch5WrEci7akqDgcOFraAMIqcPvlwKSTju8DUWUB4WWapSRJ0GTxiZrFQ/+UkmORb/c0stUIv9I9S8GJcLDJFxBRlN9sWI28C86xqE9YPNQYfuddlJ1j4YVwWPTXwMRRuYdyYUJVEBDF0iyjannlcFUBOnU4zrtoVMrNsQjJEkKaf+mebnEjHGxsAWFvFFeugKhk3gXnWNQvLB5qED/zLsrKsTjaKZojPy9DOPzdXwOnjOrzrUoKiGJplpGQ5FnDl513EaTAoiDiVY5FfrpnxhRisNJ2z1KEg42XAqKSeRecY1G/8G2rBvEr7yKVIWz7vMQcCx+Fg43fAsK0qwxmZdIs7S2rgxJYFDT8yrFQFdH/UEm7p701eTk7R3opICqVd1Fv24kzx2HxUKPk510cS1kY5kHexf98oec+afdHS+8ciyOdYqnii7bSLhzRhHA4eeTAT83uxOelgKhWmqXaK++C6YnfE0++3TNjievZdk8RH+7Nte0KVlwr3/kRDkmQpPIFRCXyLjJsz6xrWDzUKPl5F51pwpAy8y7c5Ficnp9jcaQT+MfVwMFjpV3YhXCwCXtQgQhKmqUdksXioSemRTAt8T73G0kSQkFTjts97ebYcu2eXgoHG1swlCsg/M670E3OsahnWDzUKHbexZEuK5d3MShS+h9pSTkWhztExaGCwsGmVAERtDRLTQE6DFQ9sChoVCtAqT+7pxZy56zxQzjYeCEgRPVB/C1EPRZpdo5FpeygTOVh8VDDeJl34bTqMOWEbI7F4XbgH9cAh0oUDlEN+OGVwPgRpR0P5wLCS5ul18iSBFUWToBIlTIHggaRWEbwekJzQyG7Z5dO6HZo9/RTONiUKyD8zLvgHIv6h8VDDdM776I7YyFaQlzxsZTzHIvpozQhHH72HPDndtfXAuCJcLDpT0Bk8qoMts0yrooGyCB9yteynwA570Jgd+gHZeJxa/eshHCwKVdA+JV3ke61nThTf7B4qHHy8y7aU1TSJ4j39jvLsRjZpGBkJlmecIiFgR9eAYwrXzjY5AuIaIhgWtlGRJ9sll7DeRc96Z1jERSc2D3TBiomHGzKERB+5F3YORYazy51Df96a5xy8y4Mi7Blv8OqQzwD/Oz5QAkHm5AMJNOEz7oshLNr09GQWJqohYYtzrsQ9JdjESQK2T2PdRMgEQZH5YpXtsoREF7nXXCORWNQ+iI5ExjsXgc778INHxwykMw4yLHI6Jj8xLryhMP/vtJz4ZA2CMdSFo50WzABxEIyQpKE5rCERFiuCeEAiIqDheM9GY2K0xyLoCDsnjLCigRVEcsASR3oyC4FEFXu95m//4vdEOyEsCLcRikXxxTDzrEIapWP8Q6uPNQBCU3CYam0vAtHjZJdOk57YxPUwyU2R8YjQjh86YTSju/FQDbLtEHoNkSWQa1kR3DeRWk5FkHA7nFoyVYcCtk9NaUyFbDeFQgnk7iXeRecY9E4sHioA0rNu/iiw8SnA+VYdOmQ3v4Qpx/8tLTBeSgcetssQ7KEhIo+NsseaYRq6bkIlabR8y7KzbGoBoWaI/PtnropmgeF9dS93bMUShEQXuRdcI5FY8HioU4oJe9i80BVh640sGknxnUcRqvlzMrZg0RWOIwtXTgUslmGs1WG/m7C9k58tSQgGj3vwqsci0oxkKtCfKIX78VS7J7l4FZAKPLxql2pvwPOsWgsGvAWVZ9oioSYKix/9ppnfzeMVIbwxy/6aZTMCgekdczoPux+QIkIcMdVwJhh7o+FNzZL+wZaSwKiUfMu/Mqx8IvujAXDgmNXRQ+7p2lviS7snpriz66mmiK2cu9yKCDCSnl5F5xj0ViweKgjmiNS7kbRnrYwLFT8Tvz+5/3kWCTTwNtCOLSYOsbrHe4GUqJwIBIBRV7aLGtNQDRq3kUtTTxuhUM+ct7mU7bdM6n7l+6pKhJicCYgysm74ByLxoPFQx3hNO+CiPCH/UWWIfKEAwCc3n0Erm4HTVEhHEYPdXxI7zRLWYKnNstaFBCNlHdRyRyLcilHOPSmv3RPrcDmU+Vcx6mAKDXvolrbiTPVg8VDHeE07+KjYjkWyTTw9odAWixnhIgwLXXE+QCaosD/uQo4yZlwqGSaZS0JiEbLu6iVicdL4ZCPne4ZVbNZGtnNp7xM93QqIErJu7Cy24lzjkVjweKhzmjOy7s4liqcd/GHQo2SnSngnZ054QAAU9JtiJLp8MIx4I4rBxQOts2y2xBVht42Sz+pFQHRSHkXQcixcIJfwqE3Qiz4Y/d0IiBKybvgHIvGhMVDnaHk5V1kzL55FwVzLAoIBwDOGyWbY6LiMGpI0afopliasKsMIVlCTBXCoZLr3LUiIBol7yJoORaFqJRwyMcvu2e+gCCgYGOu27wLzrFoTFg81CH95V1sOdArx6KIcBiZ6caJRreDi8WA//M3wMjBfb5lr+W6tVn6TS0IiEbJuwhqjoVNNYRDPn7YPXtXIHoLCDd5F5xj0bjwr7wOKZZ3YViErfszyO1K3pkSzZF6X8vmdCdVhyLCobfNUinBZuk3tSAg6j3vIug5Ft0ZC2YVhUNvvLR7DiQgnOZdcI5F48LioU4ZFJFxqNMCZbesHhxTjudYqDLQ0Q28s6ugcIhaJqak2wa4QBy48yrgRCEc/LBZ+k2+gIiGgrelrqaIsr7bsLNaIcg5FrZwiAVEOOTjld2zPwERVoAuiL/nYo2Qdo5FghslGxIWD3VKft5FR5rQGqXjORb9CAcAOC11BKH+QroHxYEf/Q0wojVns0zlbeZUS2mWuZ34slteB0lA1HPeRZBzLPIrDkHvN8m3e+oGXNs9iwkIJ3kXnGPR2LB4qFN65118dMTAp8cMyJ3dwHsfiTWNAkgYYMmiJQHceRXSQ1uQSlk9bJYR1ftNbiqBvRNfEAVEveZdBDXHopaEQz6yJCGiiiVLt3bPYgKiv7wLzrFgWDzUMfl5Fy98kMKuT7tg7joIlSIYIXUjVsCGOT7dgRarcEWCWuLo/rsr0d3cDCtt5WyWUdW7DW2qhRrQCkS95l0EMceiSxf7o9SacOhNKXbPQgKiv7wLzrFg6uh2xPRGUyT8z+c6Vv4hiXf3ZwVBbAQAQCLCOL0DU1NHMMxM546ZnupbdTAJMAbF0XHblaDBgxCSqmOz9JugCoh6y7sIYo5FvQiHfNzaPVVFQlwSqbyA+PvOz7sI5W0ZU0vbiTP+wOKhTiEiPPhGBx58oxN9t4kCSJKwR2vCHq0JX01+jol6O1rzciwIQjSYFsFqSSC56CpoJ7ZU3WbpN0EUEPWWdxG0iacehUM+buyeIVlCXOspIOy8C7sxknMsGIDFQ92ybHMSD77RCQAosBE1ACEgQITfx0cgTBYu7NwJIsAgwKRsw2RrE+iOqzB4VEvgus79IqgCoh7yLoKWY1HvwqE3tt2TSCyFFbJ79hUQdt6FOIeoXtRGQzTjH4U+lDI1ztFuC/e/0u7sydkb5uvxE3Bysg26RTCzmQqhYc2I3P11xE5qbRjhYKMqEqIhCd3ZT/zVRlNE4JlF1R9LOQQpx6LRhEM+kiRs1IMiMuKaBCIhFtrTFlIGib1ZNNHvQDnRcHw7ca46MCwe6pBfv98Fo1i5oRCShC5ZxVvxYVCye9trw5oRuuvrwLBBvo0z6ARJQIi8i6ImmZrAnni0AEw8jSwceqMqEhJhGc0RCZosBEN7mqAbhHAIyFiAZVowX9oKafVGKG2ddb10yTiDly3qkGfe7+pvl4aCyGRhTeuXcGn3AWBIs9jHYWizL+OrJYK0hFHreRdBybFg4VCYQnZPPQMQWYjf+kvE1rwJAKARrcCr9wEjWqs8YqaacOWhDjnQ4TAJMw9LkvGpGheCgYVDD4JSgRB5F+KTYC0ShBwLFg7O0BQJTWEZTWEJ0U8O5oQDAOBgG/D0K9UaGhMQuPLA5LBCCpI/vApoTgB6jc5QPmJZhMNdVkXiw4uhm4SkbiER9l73d2V/536sZ2dM0eHfFJFg6tV57fKFQ1cGgOv6XONhEcE0CJG8x1hyMQCLh7pkZJOCXUfcLY4rZGHM+BbERzRuj4MTMiah2xCuh2qU32MqoUMXaYdeb8yVzi7NxDXvhUlStzAoKvly7oEgEluoR1WZKw4OyZiUtWRK0CaMgDX/XMjPvCa+eWIrcOP51R0gU3VYPNQh15wWw70vt7v6XGVKMr42pQl/TpqIa1LdbQDlFb17ICotIGox76KaORa2cOClioGxN5PSTZFToykSmlRAlmTgse8D138VaEsC550KtMSrPVymykhENe79YvrQ1m1hxi8+R8Zh64MEYGhcxvPfHJq7uSoSENUkxOpg62k/MCxCV4aqsmmWaRE6dUJz2NvJ8EiXeMMMjnm79WMqQ9AtQrMPSy39wcLBGYYlqgy6CYRkVK2qxtQW3DBZh7REZdx9nruGx/vntmBks4KWiARVEbtLdqYJBzstHOkykTJYY+YTkoWwShmU2265Uoi8i+PuhaCTrkKjJJEQdwQWDoUgssOzLHRlCBIkJDSxrMTCgXECL1vUKd+ekUCHTnjgtQ4okhADvVFkgAh44NIWXDRBtETFNAkxTZQuu3Q7bhtIGRYUGYirYr/8Rts0qhBCQBwPE6rkTbdW8i6qkWNhCwdA7GTJwuE4piWWkHSLIMOuMtSm9ZepLrxsUee88XEay97pxP/bne7RAxGSgb+eHMVNMxP4y+Fq0eMtEiIimRHbCgNimSOiik/evNNc9ZYwOtLeOj/8WLboSFsIyUBUrUyRk4VDYfRsA6RhCSFnb0PNMKXC4qFB+KzdwJb9GXRlCE1hCWecpLmeJFIGoUu3kMozcoRkURZu9Bt1NQREyiCYFnnmYPBaPJgWoSMt3m+V2I6ahUNPrLwGSAl2VDdXGRhvYPHAuMbMTpRdOuWWQyRklzzU6u2BUG0qLSAsOj45e7GM5LV4sPdV8GNPit6wcDiObbPMWICabYBs1L9Jxj+454FxjSJLaApLSGiEtAEkM5b4r05I6iJuuRHtnpXugRB5FwTdACLFV56qgp1jEa1AeiYLB+RSMjN9bJaN91owlYHFA1MyUm4vfAWGJYRDdyZbKu0mKBI1nN2z0gIiqHkXlcqxaHTh0NtmWc3dT5nGgsUD4wkhWcKgiITmsBAQyYzo6u5MEzrThEgIiGly4N0BXlBJASHyLrJplRV0NAxEJXIsGlU42Js5ZUyCBUCTJSS0YMScM40DiwfGUyRJGtDuGVMlxOvc7pkvIIj8yYuwURUJukGB8ednTIJlAWHNv2s0onDobbPUQiKmvRF+diZ4cMMk4zuNbPe0myjDin8/J5HIuyh3ecirhslkNmDLrxyLRhMObLNkggjvMMn4jixJSIRlDE8oGByTEVUlEIDujEipPNhpIqlbqEcda+9EmTYpFzzlNSLvQqpqXLiNnWPhp1BqBOFgESFlENrTFtKGyDFpDkuIqnJDCoe9e/dCkiRs2bKl2kNhsrB4GICNGzdCURRceumlvpx/xYoVmDlzJmKxGJqamjB79mysX7/el2sFgUhIQmtUxvCELPz/EmBYwLEU4fMOC8dSViAmQS+phIBQFUA3UXUBphuALMOXCa4RhIOILrfQkSZYFiEaktAUlhH20Lnk1z3NnuDtL03TMGHCBCxZsqTq70vGe1g8DMCyZctw22234dVXX8X+/fs9Pfcdd9yB733ve5g/fz7ef/99vP322/jKV76CK664Ao8++qin1woawu4pY3iTgsFRGeEQQBB2z0NJC39OmujO1E81wm8BEZS8C79yLOpZOFC2ytCRttBtEOSsFTqmyb44J/y8pwHAb3/7Wxw4cAA7d+7ET37yE9x777144oknPL9OPkQEwzAGfiLjHcQUpaOjgxKJBH3wwQc0f/58uvfee4mI6Nprr6VvfOMbPZ6r6zoNGTKEVqxYQUREpmnSfffdR1/60pcoEonQ1KlT6dlnn809f+PGjQSAHn744T7X/eEPf0iqqtK+fftyj73++us0e/Zsikaj1NLSQhdffDEdOXIkd62f/exnNH78eNI0jUaPHk1LliwhIqKXX36ZANDRo0dz53rvvfcIAH300UdERPTkk0/SoEGDaM2aNTRhwgQKh8N08cUX97h+JciYFrV1m3Sg3aDPjomvA+0GHUuZlDGtio7FLwzTomMpk1IZ738e3bCoPWWWfPzhpEGHk0bJx6cN8fuzLG9/NsuyqCNlUjLt/bmrSca0qEs3qa3bpM60Sbrh/8/m5z3to48+IgD03nvv9TjPhRdeSN///vd7PPb444/TpEmTKBwO08SJE+kXv/hFj+9v2rSJpk2bRuFwmKZPn07PPfdcj3Pb97UNGzbQ6aefTqqq0ssvv0yzZ8+mW2+9lW6//XZqaWmhE044gR577DHq7OykBQsWUCKRoPHjx9OGDRty1zpy5Ahdd911NHToUIpEIjRhwgR64oknynqdGwEWD/2wbNkymjFjBhERvfDCCzR+/HiyLIvWr19P0WiUOjo6cs994YUXKBqNUnt7OxERLVmyhCZNmkT/9V//Rbt376Ynn3ySwuEwvfLKK0RE9IMf/IASiQSl0+k+1/3ss88IAP385z8nIjHZh8NhWrhwIW3ZsoW2bdtGjzzyCB06dIiIiO68805qbW2l5cuX065du+i1116jxx9/nIiciwdVVWnGjBn05ptv0ubNm+mMM86gWbNmefp6OsWyLEqmTTrYeVxEfHZMTGzdeu1PHn4KiPZU6ZNQueKhPWVSl166eClEvQkHy7IolRE/07GUSd26RUYFhbGf97RC4uGdd96hlpaWnAAhInr66afpxBNPpNWrV9OePXto9erVNHjwYFq+fDkRCYEzbNgwuu6662jbtm30wgsv0Lhx4wqKh6lTp9J///d/065du+jw4cM0e/ZsampqosWLF9OHH35IixcvJkVRaN68efTYY4/Rhx9+SAsXLqQhQ4ZQMpkkIqJbbrmFpk2bRu+88w599NFH9NJLL9G6det8+x3UCywe+mHWrFn04IMPEhFRJpOhoUOH0ssvv5z7/5UrV+aee+2119L8+fOJiCiVSlEsFqM333yzx/luuukmuvbaa4mIaO7cuXTaaacVvXZzczMtXLgwd+5zzjmn4PPa29spHA7nxEJvnIoHAPTWW2/lnrN9+3YCQJs2bSo6xkqQNiw62mXS/jwR8XmHQe0ps6I3Xa/xS0B0ZyzqTJc2gZcjHgxT/J68/J3kC4daxzAt6tbF77wjZVLasKoihvy8p9niIRqNUjweJ1VVCQB997vf7XHM+PHj6d///d97PLZ48WI6++yziYjoV7/6FQ0ZMoS6u7tz3//lL39ZUDysXbu2x3lmz55NX/nKV3L/NgyD4vE4ffOb38w9duDAAQJAGzduJCKiyy+/nL797W87fAUZG+55KMKOHTvw9ttv49prrwUAhEIhzJ8/H8uWLUMoFMI3vvENrFq1CgCQTCbx/PPP4/rrrwcA7Nq1C11dXfja176GRCKR+1q5ciV2796duwY5XM/fsmULLrzwwoLf2759O9LpdNHvOyUUCmHmzJm5f0+aNAktLS3Yvn17WectF02R0BKVMbxJRnNYgiIDpgV0pAkHOy0c7bZ8a0L0E0WWEPehB0JTRAOqVeFeEeEI8G6jIiKxY6ksic3FahXdJCR1C506gSDstImwDE2pfN9GJe5pAPDMM89gy5Yt2Lp1K37961/j+eefx1133ZU77+7du3HTTTf1OM+SJUty59m+fTumTp2KSCSSO+fZZ59d8GeaMWNGn8emTp2a+39FUTBkyBCceuqpuceGDx8OADh48CAAYOHChfjP//xPTJs2DXfeeSfefPNNF69q48KbRBVh2bJlMAwDI0eOzD1GRAiHw3j00Udx/fXXY/bs2Th48CBeeuklRKNRzJ07FwDQ2dkJAHjxxRcxatSoHucNh8MAgFNOOQWvv/46dF2HpvXcTWf//v1ob2/HKaecAgCIRqNFx9nf9wBAluXc2G0ymUy/xwQRYfeUkAiLiaorIzafsr9qMd1TCAggmW0E9MLeWI28C8rmWMQ8yrGodeFgkXj9det4mmVMrf5mTn7f02xGjx6NCRMmAAAmT56M3bt345577sGPf/zj3Hkef/xxnHnmmT2OUxT3+4vE4/E+j6lqzze+JEk9HrN/D5Yl9iOZN28ePv74Y2zYsAEvvfQSLrzwQtxyyy1YunSp6/E0ErX3l1kBDMPAypUr8cADD2DLli25r61bt2LkyJH4j//4D8yaNQujR4/GM888g1WrVuHqq6/OvUGnTJmCcDiMffv2YcKECT2+Ro8eDQC45ppr0NnZiV/96ld9rr906VKoqoqvf/3rAISS/t3vfldwrCeffDKi0WjR7w8bNgwAcODAgdxjhbzShmFg8+bNuX/v2LEDbW1tmDx5soNXrLKE68jumV+BSHlUgdBCEnSLKuZUsR0eXjgDalk49LBZkqgyeG2zLJVK3NOKoSgKDMOArusYPnw4Ro4ciT179vQ5z5e//GUAQnC8//77SKVSuXO89dZb/r04EPfJb33rW3j66afx4IMP4rHHHvP1enVB1RZMAsyaNWtI0zRqa2vr870777wz13B0991305QpUygUCtFrr73W43l33303DRkyJNfE+O6779LDDz+cawoiIrr99tspHA7T0qVLadeuXbR9+3a6++67SZblHi6MHTt2kKZptHDhQtq6dStt376d/uVf/iXXMPnjH/+YWltbacWKFbRr1y7auHEj/du//RsRiY7p0aNH09VXX00ffvghrV+/niZOnFiwYfKMM86gt956izZv3kxnnXUWnXXWWZ6+rn7SrVv052TPBstDnQZ16bXRaGf3QHR71ANhr6u7odSeB6/GXYs9DpZlUXdGuFzas6+DGcD3WyXuaXbPw29/+1s6cOAAffLJJ7RhwwYaNWoUnX/++bnzPP744xSNRumhhx6iHTt20Pvvv09PPPEEPfDAA0QkGiaHDh1KN9xwA/3xj3+kF198kSZMmFCw5yG/l4tI9DzcfvvtPR4bO3ZsrvncBgCtWbOGiIjuueceWrt2Le3cuZO2bdtGl112GZ1xxhluXt6GhMVDAS677DK65JJLCn5v06ZNBIC2bt1Kf/rTnwgAjR07ts8EZVkWPfjggzRx4kRSVZWGDRtGc+bMod///vc9nrds2TKaPn06RSIRisfjdO655xbs9H3llVdo1qxZFA6HqaWlhebMmZP7wzFNk5YsWUJjx44lVVVpzJgxdN999+WOff311+nUU0+lSCRC5557Lj377LMFrZqrV6+mcePGUTgcposuuog+/vjjMl7F6lDLdk8vBYTd0e+GUsSDnm1oLVeg1ZpwqIbNshwqcU+zxYP9pSgKnXTSSXTzzTfTwYMHe5xr1apVNG3aNNI0jVpbW+mrX/0qPffcc7nvb9y4kU477TTSNI2mTZtGq1ev9k08LF68mCZPnkzRaJQGDx5MV1xxBe3Zs8fBq9rYcLYFg+XLl2PRokVoa2ur9lA8g6hnuqdNOATEVRkRj9bnvca0xJg1RSorgZRKyLsoJdvCixwLqpGlCiqQZqkqnGbJNCbcMMnUJcXSPdMGkDYsKGnR4BdTpUDd/Hs3UZYqIETehZjo/MpCsHMsEuHyRE7QhYNpkQinssBplgyThcUDU/doigQtKqE5kq1G6CKhsCNN6ExT4NI9vRIQqgJ06kAkRL5MdLoBKGXkWARZOFDWQZKfZhkPmNBkmGrCyxZMQ5Jv97Sx7Z5RVYIcgE+VXixhJHULiuzseLfLFsdSFiKh0kRXUIVDIZulpnCVgWF6w5UHpiEJZye95rAQEV3ZasSxFKE9RWLJQ5V8CSZyihcVCE2RkDKorP6JQuhZK6zm3pqfEw6KDETVYAiHjEniywJUGa56RRimEeHKA8NkSWUIyYyFdF44n6aIakSkil79cisQHWlRIRhICLmpPHSkLYRKmPyDJByICOlsAyQg9qnQFASi6sQwQYcrDwyTJaJKiKgKDEtMcN0Z0V2vdxNkiRBVJcS1yn8iLbcCoSoSdJM8q6KYFsEsYUdJi0SFp9rCwbAIuiGqDCEZjoQVwzA94coDwxQhaHZP0xJLLKrLCoRFhI40IaH13/DntPLQpVuwCEiEnQuAagsHtlkyjLdw5YFhijCg3TOFXG9EJSYhRZYQ14CkTgDBsXix8y4ypnBHlIPtQnBTdbCySxWlLHOUC9ssGcYfuPLAMC6wqKfdEwAkoKJ2T3syVmXJsYAwslWLJq1474aTykPKECmggyLOREA1hEO+zdIk0QCpKWyzZBgv4coDw7hAlkTfQ1zrP93TT7unGEO2ApFxVoEIyRJkiEm1FIeEjW6SY4FUaeFQMM2SbZYM4wtceWCYMrF7Ebp08UkXENUIv+2ebisQaUPYEYv1KgxUeciY4nqDIgM7TyopHDKmWJowsjZLLcQ2S4bxGxYPDOMhheyeanZ3wqjqvd3TjYAYKO9iIPGQ1K2sKOpfDFRCOFh5DZAA2ywZptLwsgXDeEghu2fGBNpMQnvae7unmyWMcvIunOZY+C0c2GbJMMGAKw8M4yNEhJRB6NT9tXs6rUCYlhhLU7hvT0Z/lYdUhpCxCE392DP9Eg6FbJZaiKsMDFNNuPLAMD4iSWK5IqqKyc/ujfDa7um0AqHIEkKymIwjLv7602b/W1z7IRzYZskwwYUrDwxTYQrZPQEg6oHd00kFImOKakjvKkKxyoNuivE2hwv3bJRiHS2GbbPUDVFlYJslwwQTrjwwTIUpZPdMeWT3dFKBULNhWRmHW1anDSqaLOmVcOA0S4apLVg8MEwVsdM9zSLpnlFV7HKpuWgKzBcQlKGCywhO8y7sHIu45o9w6G2z5DRLhqkNeNmCYQKGV3bP/voQCuVdFFq2KJZjUY5wYJslw9Q+LB4YJqDk2z2t7F+pLMGV3bM/AdGlW5Cl45N/b/FAJOylvTe6KlU49LZZagrbLBmmVmHxwDABx7Z7JnXxid3Gqd2zmIDonXfRWzwUyrFwKxzYZskw9Qn3PDBMwCnX7pnfA9GdsXICws670E0hRHrTO8fCjXBgmyXD1DdceWCYGqQUu2ehCkR+3kV+5aF3joUT4cA2S4ZpHFg8MEyNk2/3tP+YQ1nnQkzraffsLSDy8y7aU0KFDI4pPXIsBhIObLNkmMaDxQPD1Am5dM+MsFcCIt2zt92zt4BIZQhWtq8CAFqiMtpThERYgiyhqHDgNEuGaVxYPDBMHTKQ3ZMAdOkEJbu0ILI3hPsipsrIWKIa0ZURe0HYW1OzzZJhGIDFA8PUNbajokvva/eMqoBuAIoMWAS0pyyEQ6JHQZUBg4SwiIQktlkyDNMDFg8M0wAUs3tqCgGQEH1rO9T/+xSUPx+Dcc4UpH62ANrgBGRJYpslwzB9YPHAMA1Gvt2TACgffooT5twNmCYki0CKDGPWZHQ/dzfbLBmGKQjv88AwDYaqSBikSGjO5mnQS38ALAtSdl1DMi2or/0RcjIJZXBTlUfLMEwQYfHAMA2KlE33RJOGQgVIRVOrMCqGYWqBvnF7DMM0FlefA6klITonJYivBRcAiUi1R8YwTEDhngeGYYCPDwJL1wKHjgGzJgG3XCrEBMMwTAFYPDAMwzAM4wr+aMEwDMMwjCtYPDAMwzAM4woWDwzDMAzDuILFA8MwDMMwrmDxwDAMwzCMK1g8MAzDMAzjChYPDMMwDMO4gsUDwzAMwzCuYPHAMAzDMIwrWDwwDMMwDOMKFg8MwzAMw7iCxQPDMAzDMK5g8cAwDMMwjCtYPDAMwzAM4woWDwzDMAzDuILFA8MwDMMwrmDxwDAMwzCMK1g8MAzDMAzjChYPDMPgvPPOw6JFiwJzHoZhgg2LB4apMgsWLIAkSZAkCZqmYcKECfjpT38KwzCqPbSivPLKK5AkCW1tbT0ef+6557B48eLqDIphmIoRqvYAGIYB5s6diyeffBLpdBobNmzALbfcAlVV8fd///fVHporBg8eXO0hMAxTAbjywDABIBwOY8SIERg7diwWLlyIiy66COvWrcPRo0dx4403orW1FbFYDPPmzcPOnTtzxy1fvhwtLS1Yu3YtTj75ZEQiEcyZMweffPJJ7jkLFizAlVde2eN6ixYtwnnnnVd0PE899RRmzJiBpqYmjBgxAtdddx0OHjwIANi7dy/OP/98AEBrayskScKCBQsA9F22cDr+3/zmN5g8eTISiQTmzp2LAwcOlPhKMgxTCVg8MEwAiUaj0HUdCxYswObNm7Fu3Tps3LgRRIRLLrkEmUwm99yuri7ce++9WLlyJd544w20tbXhmmuuKev6mUwGixcvxtatW7F27Vrs3bs3JxBGjx6N1atXAwB27NiBAwcO4KGHHip4HqfjX7p0KZ566im8+uqr2LdvH+64446yxs8wjL/wsgXDBAgiwu9+9zv85je/wbx587B27Vq88cYbmDVrFgBg1apVGD16NNauXYurr74agJjoH330UZx55pkAgBUrVmDy5Ml4++23ccYZZ5Q0ju985zu5/x83bhwefvhhzJw5E52dnUgkErnliRNOOAEtLS0Fz7Fz506sW7fO0fj/9V//FePHjwcA3HrrrfjpT39a0rgZhqkMXHlgmACwfv16JBIJRCIRzJs3D/Pnz8eCBQsQCoVyogAAhgwZgokTJ2L79u25x0KhEGbOnJn796RJk9DS0tLjOW559913cfnll2PMmDFoamrC7NmzAQD79u1zfI7t27c7Gn8sFssJBwA48cQTc0skDMMEExYPDBMAzj//fGzZsgU7d+5Ed3c3VqxYAUmSPDm3LMsgoh6P5S8b9CaZTGLOnDlobm7GqlWr8M4772DNmjUAAF3XPRlTPqqq9vi3JEl9xsswTLBg8cAwASAej2PChAkYM2YMQiGxmjh58mQYhoFNmzblnnf48GHs2LEDU6ZMyT1mGAY2b96c+/eOHTvQ1taGyZMnAwCGDRvWpwFxy5YtRcfywQcf4PDhw7j//vtx7rnnYtKkSX0qAZqmAQBM0yx6HqfjZxim9mDxwDAB5eSTT8YVV1yBm2++Ga+//jq2bt2KG264AaNGjcIVV1yRe56qqrjtttuwadMmvPvuu1iwYAHOOuusXL/DBRdcgM2bN2PlypXYuXMn/uEf/gHbtm0ret0xY8ZA0zQ88sgj2LNnD9atW9dn74axY8dCkiSsX78ehw4dQmdnZ8njZxim9mDxwDAB5sknn8T06dNx2WWX4eyzzwYRYcOGDT1K/bFYDD/60Y9w3XXX4ZxzzkEikcAzzzyT+/6cOXNwzz334M4778TMmTPR0dGBG2+8seg1hw0bhuXLl+PZZ5/FlClTcP/992Pp0qU9njNq1Cj85Cc/wV133YXhw4fj1ltvLXn8DMPUHhLx4iLD1CzLly/HokWL+uz0yDAM4ydceWAYhmEYxhUsHhiGYRiGcQUvWzAMwzAM4wquPDAMwzAM4woWDwzDMAzDuILFA8MwDMMwrmDxwDAMwzCMK1g8MAzDMAzjChYPDMMwDMO4gsUDwzAMwzCuYPHAMAzDMIwrWDwwDMMwDOMKFg8MwzAMw7iCxQPDMAzDMK5g8cAwDMMwjCtYPDAMwzAM44r/D+bSwgiLBDuYAAAAAElFTkSuQmCC"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 11
+   "execution_count": 10
   },
   {
    "cell_type": "code",
    "id": "49395db0",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:26.443Z",
-     "start_time": "2024-11-07T15:16:26.239629Z"
+     "end_time": "2025-01-10T13:18:31.450321Z",
+     "start_time": "2025-01-10T13:18:31.220178Z"
     }
    },
    "source": [
@@ -471,21 +472,21 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 11
   },
   {
    "cell_type": "code",
    "id": "208c5241",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:29.323496Z",
-     "start_time": "2024-11-07T15:16:29.068931Z"
+     "end_time": "2025-01-10T13:18:31.607927Z",
+     "start_time": "2025-01-10T13:18:31.452241Z"
     }
    },
    "source": [
@@ -500,13 +501,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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0BZmnDSfm+NDYYkdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBrBYEdERESkEQx2RERERBqR7YLdokWLxN7eXszMzMTNzU1OnTr1xnWXL18uHh4eUrBgQSlYsKB4enq+dX0iIiKi7CxbBbsNGzbI8OHDZeLEiXL27FmpXLmyeHl5SVhYWJrrHz58WLp27SqHDh2SEydOiJ2dnTRp0kTu37+fxZUTERERZT4FAAxdRHq5ublJ9erVZeHChSIikpSUJHZ2djJkyBAZO3bsOx+fmJgoBQsWlIULF0qPHj3S9ZrR0dFiYWEhUVFRYm5u/p/qJyIiyrBtiqEryDxtsk0EMaiMZJFs02IXHx8vAQEB4unpqS4zMjIST09POXHiRLqe4/nz55KQkCCFChXKrDKJiIiIDMbE0AWkV3h4uCQmJkqRIkX0lhcpUkSCgoLS9RxjxoyRYsWK6YXD18XFxUlcXJx6Ozo6+v0KJiIiIspi2abF7r+aMWOGrF+/XrZs2SJmZmZvXM/Pz08sLCzUf3Z2dllYJREREdH7yzbBztraWoyNjeXRo0d6yx89eiS2trZvfez3338vM2bMkH379omzs/Nb1/X19ZWoqCj13927d/9z7URERERZIdsEu9y5c0vVqlXF399fXZaUlCT+/v7i7u7+xsfNmjVLpk6dKnv27JFq1aq983VMTU3F3Nxc7x8RERFRdpBt+tiJiAwfPlx69uwp1apVkxo1asi8efMkNjZWPv/8cxER6dGjhxQvXlz8/PxERGTmzJkyYcIEWbdundjb20toaKiIiOTPn1/y589vsPdBRERElBmyVbDr3LmzPH78WCZMmCChoaHi4uIie/bsUQdUhISEiJHR/zVC/vTTTxIfHy8dOnTQe56JEyfKpEmTsrJ0IiIiokyXreaxMwTOY0dERAbFeexyvIxkkWzVYkdERJTTBA/qZegSMk25NoauQHuyzeAJIiIiIno7BjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijchwsLt7967cu3dPvX3q1Cn5+uuvZdmyZR+0MCIiIiLKmAwHu27dusmhQ4dERCQ0NFQaN24sp06dknHjxsmUKVM+eIFERERElD4ZDnaXLl2SGjVqiIjIH3/8IRUrVpTjx4/Lb7/9JqtXr/7Q9RERERFROmU42CUkJIipqamIiBw4cEBat24tIiKOjo7y8OHDD1sdEREREaVbhoNdhQoVZMmSJXL06FHZv3+/NG3aVEREHjx4IFZWVh+8QCIiIiJKnwwHu5kzZ8rSpUulfv360rVrV6lcubKIiGzfvl09RUtEREREWc8kow+oX7++hIeHS3R0tBQsWFBd3r9/f8mbN+8HLY6IiIiI0u+95rEDIAEBAbJ06VKJiYkREZHcuXMz2BEREREZUIZb7O7cuSNNmzaVkJAQiYuLk8aNG0uBAgVk5syZEhcXJ0uWLMmMOj9e2xRDV5A52uD9HqfV7SHy/tuEiDJGq/sR7kMoC2S4xW7o0KFSrVo1efr0qeTJk0dd3rZtW/H39/+gxRERERFR+mW4xe7o0aNy/PhxyZ07t95ye3t7uX///gcrjIiIiIgyJsMtdklJSZKYmJhq+b1796RAgQIfpCgiIiIiyrgMB7smTZrIvHnz1NuKosizZ89k4sSJ0rx58w9ZGxERERFlQIZPxc6ZM0e8vLykfPny8vLlS+nWrZtcu3ZNrK2t5ffff8+MGomIiIgoHTIc7EqUKCEXLlyQ9evXy8WLF+XZs2fSp08f8fHx0RtMQURERERZK8PBTkTExMREPvvssw9dCxERERH9BxkOdmvWrHnr/T169HjvYog0iXNyERFRFslwsBs6dKje7YSEBHn+/Ll65QkGOyIiIiLDyHCwe/r0aapl165dk4EDB8qoUaM+SFFEREREadLqWRCRD3Im5L2uFfu6MmXKyIwZM1K15hERERFR1vkgwU4keUDFgwcPPtTTEREREVEGZfhU7Pbt2/VuA5CHDx/KwoULpXbt2h+sMCIiIiLKmAwHO29vb73biqKIjY2NNGzYUObMmfOh6iIiIiKiDHqva8Wm/JeYmCihoaGybt06KVq0aGbUqGfRokVib28vZmZm4ubmJqdOnXrr+hs3bhRHR0cxMzOTSpUqye7duzO9RiIiIiJD+GB97LLChg0bZPjw4TJx4kQ5e/asVK5cWby8vCQsLCzN9Y8fPy5du3aVPn36yLlz58Tb21u8vb3l0qVLWVw5ERERUeZTALxzbO3w4cPT/YRz5879TwW9jZubm1SvXl0WLlwoIsmth3Z2djJkyBAZO3ZsqvU7d+4ssbGxsnPnTnVZzZo1xcXFRZYsWZKu14yOjhYLCwuJiooSc3Pz1Ctoddj1+w651ur2EOE2eR23R2rvs024PVLT6jZ5z+0RXOLzD1zIx6PcvVUZf5BWPx8ib/yMvDOLpJCuPnbnzp1LVz2KknkbOz4+XgICAsTX11ddZmRkJJ6ennLixIk0H3PixIlUodTLy0u2bt2aaXUS0dsFD+pl6BIyTbk2GX8MtwcRfUjpCnaHDh3K7DreKTw8XBITE6VIkSJ6y4sUKSJBQUFpPiY0NDTN9UNDQ9/4OnFxcRIXF6fejo6O/g9VExEREWWdDI+K1To/Pz+ZPHlyquWlS5cWI6PUXRJfPTbLirKynImN7Xs9TqvbQ4Tb5HXvuz0k7tGHLeRjYvtXxh/D7ZEKvzOv4WdEj1Y/HyJv/owkJSWl/zne54XPnDkjf/zxh4SEhEh8fLzefZs3b36fp3wna2trMTY2lkeP9D/gjx49ElvbtDeEra1thtYXEfH19dU7fRsdHS12dnZy/fr1NM9ra7Xvw3v1exDtbg8RbpPXve/20Hb/mDefDXgjbo9U+J15DT8jerT6+RB582dE18cuPTI8Knb9+vVSq1YtCQwMlC1btkhCQoJcvnxZDh48mO4XfR+5c+eWqlWrir+/v7osKSlJ/P39xd3dPc3HuLu7660vIrJ///43ri8iYmpqKubm5nr/iIiIiLKDDLfYTZ8+XX744QcZNGiQFChQQObPny8ODg4yYMCATJ/Hbvjw4dKzZ0+pVq2a1KhRQ+bNmyexsbHy+efJ6b1Hjx5SvHhx8fPzExGRoUOHSr169WTOnDnSokULWb9+vZw5c0aWLVv2wWoqt2j1B3uuj8t7/rIkIiIig8lwsLtx44a0aNFCRJJb0WJjY0VRFBk2bJg0bNgwzf5pH0rnzp3l8ePHMmHCBAkNDRUXFxfZs2ePOkAiJCRErx9crVq1ZN26dfLtt9/KN998I2XKlJGtW7dKxYoVM61GIiIiIkPJcLArWLCgxMTEiIhI8eLF5dKlS1KpUiWJjIyU58+ff/ACXzd48GAZPHhwmvcdPnw41bKOHTtKx44dM7kqIiIiIsPLcLCrW7eu7N+/XypVqiQdO3aUoUOHysGDB2X//v3SqFGjzKiRiIiIiNIhw8Fu4cKF8vLlSxERGTdunOTKlUuOHz8u7du3l2+//faDF0hERERE6ZPhYFeoUCH1/42MjNK8lBcRERERZb0MT3fi6ekpq1ev5hUZiIiIiD4yGW6xq1Chgvj6+sqXX34pLVq0kM8++0yaN28uuXLlyoz6iEhr3vfC8ERE9E4ZDnbz58+XH374QQ4cOCDr1q2THj16iLGxsXTo0EF8fHykXr16mVEnEZE2MegS0QeU4VOxIsl965o0aSKrV6+WR48eydKlS+XUqVPSsGHDD10fEREREaXTe10rVic0NFTWr18va9eulYsXL0qNGjU+VF1EmsGrkxARUVbJcItddHS0rFq1Sho3bix2dnby008/SevWreXatWvyv//9LzNqJCIiIqJ0yHCLXZEiRaRgwYLSuXNn8fPzk2rVqmVGXURERESUQRkOdtu3b5dGjRrpXZOViIiIiAwvw8GucePGmVEHEREREf1H/2nwBNHrtDtQQISDBYiI6GPH86lEREREGsFgR0RERKQRDHZEREREGpHuPnYLFixI13pfffXVexdDRERERO8v3cHuhx9+eOc6iqIw2BEREREZSLqD3a1btzKzDiIiIhHR8uh6jqynzMc+dkREREQake5gd+LECdm5c6fesjVr1oiDg4MULlxY+vfvL3FxcR+8QCIiIiJKn3QHuylTpsjly5fV2//++6/06dNHPD09ZezYsbJjxw7x8/PLlCKJiIiI6N3SHezOnz8vjRo1Um+vX79e3NzcZPny5TJ8+HBZsGCB/PHHH5lSJBERERG9W7qD3dOnT6VIkSLq7b///luaNWum3q5evbrcvXv3w1ZHREREROmW7mBXpEgRdWRsfHy8nD17VmrWrKneHxMTI7ly5frwFRIRERFRuqQ72DVv3lzGjh0rR48eFV9fX8mbN694eHio91+8eFE+/fTTTCmSiIiIiN4t3fPYTZ06Vdq1ayf16tWT/Pnzyy+//CK5c+dW71+5cqU0adIkU4okIiIiondLd7CztraWI0eOSFRUlOTPn1+MjY317t+4caPkz5//gxdIREREROmT7mCnY2FhkebyQoUK/ediiIiIiOj98coTRERERBqR4RY7IsqgNjB0BURElEOwxY6IiIhIIxjsiIiIiDSCwY6IiIhIIxjsiIiIiDSCwY6IiIhIIxjsiIiIiDSCwY6IiIhIIziPHRER0ceMc2FSBrDFjoiIiEgjGOyIiIiINILBjoiIiEgjGOyIiIiINCLbDJ6IiIiQIUOGyI4dO8TIyEjat28v8+fPl/z5879x/YkTJ8q+ffskJCREbGxsxNvbW6ZOnSoWFhYfrjB2aiUiIqKPRLZpsfPx8ZHLly/L/v37ZefOnXLkyBHp37//G9d/8OCBPHjwQL7//nu5dOmSrF69Wvbs2SN9+vTJwqqJiIiIso4C4KNvcgoMDJTy5cvL6dOnpVq1aiIismfPHmnevLncu3dPihUrlq7n2bhxo3z22WcSGxsrJibpa6yMjo4WCwsLiYqKEnNz8/d+DznGNsXQFWQets4SZQ2t7ke4D/kggkt8bugSMk25e6vSXJ6RLJItTsWeOHFCLC0t1VAnIuLp6SlGRkZy8uRJadu2bbqeR7dB3hbq4uLiJC4uTr0dHR39/oXnRNxxERERGUy2OBUbGhoqhQsX1ltmYmIihQoVktDQ0HQ9R3h4uEydOvWtp29FRPz8/MTCwkL9Z2dn9951ExEREWUlgwa7sWPHiqIob/0XFBT0n18nOjpaWrRoIeXLl5dJkya9dV1fX1+JiopS/929e/c/vz4RERFRVjDoqdgRI0ZIr1693rpOqVKlxNbWVsLCwvSWv3r1SiIiIsTW1vatj4+JiZGmTZtKgQIFZMuWLZIrV663rm9qaiqmpqbpqp+IiIjoY2LQYGdjYyM2NjbvXM/d3V0iIyMlICBAqlatKiIiBw8elKSkJHFzc3vj46Kjo8XLy0tMTU1l+/btYmZm9sFqJyIiIvrYZIs+dk5OTtK0aVPp16+fnDp1Sv755x8ZPHiwdOnSRR0Re//+fXF0dJRTp06JSHKoa9KkicTGxsqKFSskOjpaQkNDJTQ0VBITEw35doiIiIgyRbYYFSsi8ttvv8ngwYOlUaNG6gTFCxYsUO9PSEiQ4OBgef78uYiInD17Vk6ePCkiIqVLl9Z7rlu3bom9vX2W1U5ERESUFbJNsCtUqJCsW7fujffb29tLyin56tevL9lgij4iIiKiDyZbnIolIiIiondjsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo0wMXQBREREetrA0BUQZVsMdkRERJRtlFu02tAlZKJV//kZeCqWiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCMY7IiIiIg0gsGOiIiISCOyTbCLiIgQHx8fMTc3F0tLS+nTp488e/YsXY8FIM2aNRNFUWTr1q2ZWygRERGRgWSbYOfj4yOXL1+W/fv3y86dO+XIkSPSv3//dD123rx5oihKJldIREREZFgmhi4gPQIDA2XPnj1y+vRpqVatmoiI/Pjjj9K8eXP5/vvvpVixYm987Pnz52XOnDly5swZKVq0aFaVTERERJTlskWL3YkTJ8TS0lINdSIinp6eYmRkJCdPnnzj454/fy7dunWTRYsWia2tbVaUSkRERGQw2aLFLjQ0VAoXLqy3zMTERAoVKiShoaFvfNywYcOkVq1a0qZNm3S/VlxcnMTFxam3o6OjM14wERERkQEYtMVu7NixoijKW/8FBQW913Nv375dDh48KPPmzcvQ4/z8/MTCwkL9Z2dn916vT0RERJTVDNpiN2LECOnVq9db1ylVqpTY2tpKWFiY3vJXr15JRETEG0+xHjx4UG7cuCGWlpZ6y9u3by8eHh5y+PDhNB/n6+srw4cPV29HR0cz3BEREVG2YNBgZ2NjIzY2Nu9cz93dXSIjIyUgIECqVq0qIsnBLSkpSdzc3NJ8zNixY6Vv3756yypVqiQ//PCDtGrV6o2vZWpqKqamphl4F0REREQfh2zRx87JyUmaNm0q/fr1kyVLlkhCQoIMHjxYunTpoo6IvX//vjRq1EjWrFkjNWrUEFtb2zRb80qWLCkODg5Z/RaIiIiIMl22GBUrIvLbb7+Jo6OjNGrUSJo3by516tSRZcuWqfcnJCRIcHCwPH/+3IBVEhERERlOtmixExEpVKiQrFu37o3329vbC4C3Pse77iciIiLKzrJNix0RERERvR2DHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGMNgRERERaQSDHREREZFGZJtgFxERIT4+PmJubi6WlpbSp08fefbs2Tsfd+LECWnYsKHky5dPzM3NpW7duvLixYssqJiIiIgoa2WbYOfj4yOXL1+W/fv3y86dO+XIkSPSv3//tz7mxIkT0rRpU2nSpImcOnVKTp8+LYMHDxYjo2zztomIiIjSTQEAQxfxLoGBgVK+fHk5ffq0VKtWTURE9uzZI82bN5d79+5JsWLF0nxczZo1pXHjxjJ16tT3fu3o6GixsLCQqKgoMTc3f+/nISIiog9gm2LoCjJPm7QjWUaySLZoujpx4oRYWlqqoU5ExNPTU4yMjOTkyZNpPiYsLExOnjwphQsXllq1akmRIkWkXr16cuzYsawqm4iIiChLZYtgFxoaKoULF9ZbZmJiIoUKFZLQ0NA0H3Pz5k0REZk0aZL069dP9uzZI66urtKoUSO5du3aG18rLi5OoqOj9f4RERERZQcGDXZjx44VRVHe+i8oKOi9njspKUlERAYMGCCff/65VKlSRX744QcpV66crFy58o2P8/PzEwsLC/WfnZ3de70+ERERUVYzMeSLjxgxQnr16vXWdUqVKiW2trYSFhamt/zVq1cSEREhtra2aT6uaNGiIiJSvnx5veVOTk4SEhLyxtfz9fWV4cOHq7ejo6MZ7oiIiChbMGiws7GxERsbm3eu5+7uLpGRkRIQECBVq1YVEZGDBw9KUlKSuLm5pfkYe3t7KVasmAQHB+stv3r1qjRr1uyNr2VqaiqmpqYZeBdEREREH4ds0cfOyclJmjZtKv369ZNTp07JP//8I4MHD5YuXbqoI2Lv378vjo6OcurUKRERURRFRo0aJQsWLJBNmzbJ9evXZfz48RIUFCR9+vQx5NshIiIiyhQGbbHLiN9++00GDx4sjRo1EiMjI2nfvr0sWLBAvT8hIUGCg4Pl+fPn6rKvv/5aXr58KcOGDZOIiAipXLmy7N+/Xz799FNDvAUiIiKiTJUt5rEzJM5jR0RE9BHhPHZvfYpscSqWiIiIiN6NwY6IiIhIIxjsiIiIiDSCwY6IiIhIIxjsiIiIiDSCwY6IiIhIIxjsiIiIiDSCwY6IiIhII7LNlSeIiIiI3jSJLyVjix0RERGRRjDYEREREWkEgx0RERGRRrCP3TsAyefyo6OjDVwJERER5US6DKLLJG/DYPcOMTExIiJiZ2dn4EqIiIgoJ4uJiRELC4u3rqMgPfEvB0tKSpIHDx5IgQIFRFEUg9URHR0tdnZ2cvfuXTE3NzdYHR8Lbo/UuE30cXukxm2ij9tDH7dHah/LNgEgMTExUqxYMTEyensvOrbYvYORkZGUKFHC0GWozM3N+YVLgdsjNW4TfdweqXGb6OP20MftkdrHsE3e1VKnw8ETRERERBrBYEdERESkEQx22YSpqalMnDhRTE1NDV3KR4HbIzVuE33cHqlxm+jj9tDH7ZFadtwmHDxBREREpBFssSMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsPuIJCUlGboEg0o58w5n4UnG7aBPtz1u374t9+7dM3A19LHhPoSIwc6gdDueW7duyfPnz995YV8tS0pKEkVR1Nvx8fEGrObjAEAURZHDhw/LmjVrDF2Owem2x9atW6VDhw6ya9cuiYiIMHRZBsXw8n9e34fk9B/KOvyM6Etre2jts5Jzk4SB6Q5S27Ztk44dO8r8+fMlISHB0GUZRFJSkhpq58+fLz4+PlKnTh35/vvv5caNGwauzjB0n4/NmzdLx44d5fjx43Lr1i1Dl2VQiqLIjh07pFu3buLj4yPe3t5SqFAhvXVy0kEsZZB5/vy53o+hnLQddHT7kDlz5kjHjh2lRYsWsnTpUnny5ImBKzMc3X7E399fxo8fL23atJFNmzZJUFCQoUsziJTfmbt378qtW7ckISFBe40qIIPZuXMnTE1N8dNPP+Hq1auGLsfgxo4di6JFi2Lq1KlYsWIFFEVB79698eTJE0OXZhB///038ufPj1WrVr1xnaSkpKwryMAePXqEGjVqYM6cOQCAFy9eICwsDBs3bsTBgwcNXJ3hTJ8+HXXq1EGLFi3w448/qstzymcjMTFR/f8JEybA0tISAwYMwOeff45cuXKhe/fuuHz5sgErNKzNmzcjf/78GDRoEHr37g0XFxd4eXnh4cOHhi4tS6X8PkyaNAmVK1eGg4MDypYti1WrViE8PNyA1X1YDHYGEhMTg1atWmH8+PF6y1PupHKSU6dOoXTp0jh+/DgA4MyZMzA2Nsbq1asNXJnhTJ8+Hd27dwcAREZGYv/+/fjss8/QvXt3bN++PcccuHWePXuGOnXq4IcffkB4eDh8fX3h4eGBIkWKIF++fFi+fLmhS8wSKfcRc+bMgZWVFb799lt89tlnsLS0xMiRI9X7c9JnJDg4GOPGjcPff/+tLjty5AhsbW3Rr1+/HLUtdO/11q1bqFixIpYuXQog+TuUL18++Pr6GrI8g/ruu+9QuHBh7NixA3FxcahXrx4cHBwQGBho6NI+GI21P2YfAOTy5cupTiXpmoR1p1WQQ06pvHjxQqytrcXd3V02btwo9evXl4ULF0rPnj0lOjpajh49augSs0TKv3dMTIxs3bpVjh8/Lj169JDvv/9eIiMj5fr16zJlyhSJiooyYKVZLz4+XooVKyYbN26U4sWLS1BQkPj4+MjJkyelefPmcubMGUOXmCV0+4iTJ09K3rx5Ze3atTJ16lRZtGiRzJ49W+bPny+jRo0SkeTT1zlhH7Jjxw5xdHSU5cuXi4mJiYiIJCYmioeHh6xdu1ZWrFghBw8eNHCVmWvTpk3y119/iYiopxt1xxEfHx+5fv26ODo6Srdu3WT69OkiInLixAmJjIw0SL1ZITY2Vv3/pKQkiYqKkgMHDsjcuXOlZcuW4u/vL+fOnZPRo0eLo6OjdvraGTZX5iwpfzGGh4ejVq1amDhxIhITE/Xuu3DhAqZMmYLnz58bosxMl1ar5LFjx2Bvb4+FCxfCwsICixcvVu/bt28fmjdvjmvXrmVlmVlK9/d/9eqVuiwmJgYNGzaEjY0NfHx8sH//fgDAxYsX4ejoiFu3bhmi1Cyh2x6XL1/G33//rf7t79+/jx07duDXX3/Fy5cv1fU7dOiAESNGGKRWQzh27BgURYGlpSUOHTqkLo+NjcXPP/8MU1NTjB492nAFZrLX9yEXL15Ev379kDt3bqxfvx4AEB8fj8TERLx48QJOTk746aefDFFqlggJCUGFChXQsmVL+Pv7q8tPnjwJZ2dnXL58GQ4ODujbt6+67c6cOYP+/fvj0qVLhio7U7Vt2xZff/01IiIi1GUPHz5E6dKl8fjxY/j7+yN//vzq5yI2Nhbz58/H/fv3DVXyB8NglwV0B6nXg9qoUaOQP39+7N27V29HNW7cONSrV09T5/x1UgbYlStX4tSpU3j16hVevnyJzp07w8TEBOPGjVPXefnyJVq1aoWOHTtq9jS1bpvs3bsXPj4+GDNmDLZu3are/3r/y9GjR6NmzZp4+vRpVpaZ5bZs2YJ8+fKhdOnSMDExwYIFC/DixQu9dZ48eYKxY8fC2tpaU6dS3uXevXuYNm0a8ufPjylTpujd9/z5c7WP6sKFCw1UYdbYvHmz+v0JDAxE165dYWZmphd2Y2Ji4ODggGXLlhmoyqxx+PBheHh4oG3btti7d6+6vE6dOlAUBV988YXe+rr9SGhoaFaXmiXmz58PRVEwceJEvXDXuHFjNGrUCPnz58eKFSvU5Xfu3EGdOnXwxx9/GKLcD4rBLpPpdjo7d+5Eo0aN0LZtW71+dV26dEG+fPkwcuRITJgwAX369EGBAgVw/vx5Q5WcaVIGsydPnsDY2BheXl44d+4cgORgU69ePbi6umL16tVYvHgxmjRpgooVKyIhISHVc2jJwYMHkSdPHnTu3BmVK1eGq6srpk6dqreOv78/vv76a1haWqrbTIsSExPx9OlTeHh4YOnSpbhx4wZmzZoFRVEwZcoUdSf9xx9/oEuXLihVqhTOnj1r4Kozz5s+82FhYZg0aRLMzMwwd+5cvfuePXuGHTt2qN8bLQoJCYGiKGjbtq26nw0ODka3bt2QK1cufPPNN5g5cyZatWoFJycnzW6LxMRE9TPy119/wcPDA97e3mrL3YULF+Dq6goXFxecPHkS27dvx/Dhw1GgQAFcuHDBkKVnGt2Zj1WrVkFRFEyaNAkPHjwAAPzyyy9wcHBA06ZN1fWfPXuG5s2bo0GDBnpnTbIrBrsscPToUeTOnRuDBg1C165dYWdnB29vb/X+iRMnok2bNqhSpQq6dOmCixcvGrDazDd69Gh88cUXcHFxgZmZGdzc3NTTAfv27UPfvn1hZWWFBg0aoGfPnoiPjwcAze6YAWDp0qWYN28eAODmzZv49ttv4ejoiO+++w4AEBoaCl9fX9SuXVuzn4+ULdvPnz/HN998o9dqvXjxYiiKgqlTpyI+Ph5hYWFYunQpbt68aaiSM13KULd27VpMnz4dQ4cOxcWLF/Hy5UvExsZi8uTJKFCgQKpwp6OV701agx8OHz6MIkWKoEOHDnrhrkePHjAxMUGLFi2wdetWtaVXCwft1+ne99atWzF06FBUrFgRxsbGaNiwoTqQ5NSpU/Dw8ECxYsXg5OSE+vXra7LxAND/zkRERKB///4wMzPD1KlT8fz5c8TExGDs2LEoU6YM3Nzc0LVrV7i7u8PZ2Vk91mT3zwmDXSYLCgrCrl271J1ubGwsdu7cCSsrK7Ru3Vpd79mzZ3j58qVevyEtWrBgAQoWLIiTJ08iKCgIAQEBsLe3R9WqVfX6ejx69EjvcVo5OOnodsYXL17E2bNn0adPH70+QHfv3lXD3cyZMwEAT58+1eTp+ZS2bt2Kxo0bw8nJCWXLlk3VErd48WLkypULo0ePVnfCOcGIESNgbW2Npk2boly5cihevDimT5+Op0+fIiYmBlOmTEHBggVTnZbNCf7++29YWVnphbvLly+jf//+sLKyUk/LannfevjwYZiYmGDJkiU4cuQINm7ciHLlyqFly5Y4cuSIut6///6LsLAwREZGGrDarDFs2DA4OTmhd+/eqFatGhRFwbhx45CQkICYmBjs378fPXv2xKBBgzBjxgz1GKOFYw2D3Qei+5WQ8kNx7949FC5cGPny5VNbY4DkTr26cNeuXbssr9WQBgwYgC5duugtCw0NhZ2dHTw8PHDmzJlUp560Ok3Bxo0bkT9/fhQtWhSWlpbo16+f3v337t3DpEmTYGNjg++//95AVWadM2fOwMLCAl9++SX69u2LXLlyYfDgwala5ObMmQNLS0s8fvzYQJVmrV27dqFYsWI4f/68+t345ptv4OzsjAULFgAAHjx4gDFjxqBx48aa/b4AwIwZM1J9T4DkYGNhYYEePXqo++DAwED06NEDRYoU0etzpkXffPMN6tWrp7fM398fn376KRo2bJjj5nncvXs3LCwscPr0afU7s2DBAiiKgm+//RZRUVFpPi67t9TpMNh9QHfv3kWFChXUzu5PnjzBokWLYGdnlyrMxMfHY/fu3VAUBZ999pkhys1Ubzq4dOzYEQ0bNlRv606RLFu2DIqioHHjxuqBXIsHKN17iomJgbu7O1avXo3jx49j6tSpyJs3L8aOHau3fkhICKZPn47r168botwsc+3aNYwfPx7Tp09Xly1fvhwlSpTAqFGjUoU7rQ4cmTBhQqp+T2vXroWTkxMeP36sd+AZOnQo7Ozs1EFZERER6udLK9+d13/k/fzzzzAyMtIbAa17r+PGjYOiKGjZsqV639WrV9G2bVuUKlUKz58/18x2ed13330Hd3d3vHjxAklJSep2+/XXX5EnTx40atQoR4W7zZs3o1y5cnjy5IneZ2jWrFnIlSsXZs6cqfa50yIGuw8oJCQE1apVQ8mSJdUDcUREBJYtWwYLCwsMHjxYb/34+Hjs3bsXwcHBhig3U73+y0d329/fHwUKFNCbIR8ANmzYgC+++AJ2dnbo0KFDltVpCHv37kWPHj30rqrx9OlTLFiwAIUKFUoV7rTyK/J1uoPszZs3UaNGDVhbW6eaomPp0qUoVqwYxo4dqxdutXiAPnbsGLp165bqVNCyZctQtGhRxMTEAPi/0fWPHz+Gubk59uzZo7e+VrZNygPyP//8ox6If//9d5iammLYsGF66y9cuBBdunRBq1at9Lbh9evXNTGFxesCAwPVv/XmzZthZGSE7du3662zfft2uLi4oFWrVrh3754hysx0aX3ed+zYgVy5ciEoKAgAEBcXByD5FH3+/PmhKIqmJzRnsPuPpk2bhvnz56u3b9++DU9PTxQtWlQ9ED19+hTLli2DjY1NqnCnRStWrICLiwu2bNmCgIAAvfvCwsIwbtw4ODg4YM6cOXj58iUePHiA5s2bY8GCBdi3bx/y5MmD06dPG6j6zPfbb7/B1NQURYsW1RuGHxERgQULFqBw4cIYMmSIASvMOps3b8aGDRuwZMkSODo6ombNmqk6df/8888wMzPD+PHjNdH/5W10B6k///xT7fj+4sULlCpVCs2aNdNb98qVKyhTpgxOnTqV5XVmtpQH67Fjx6Jq1ar48ccfERcXh/j4eKxbtw5mZmYYOnQowsPD8fTpU7Rv315vShMtf1Zu3ryJKlWqoE+fPuq2GjRoEPLly4ctW7aoLdrffPMNfH19NdvCndLatWvx+++/q7ebN28OV1dXvfk+b926hdGjR2PDhg2a/nww2P0HCQkJ8PX1haIoejuUW7dupQp3upa7okWLolevXoYqOVPpdjD16tWDmZkZBgwYAFdXV/j6+uLff/9V17t165Z66rF48eKws7ODs7MzXr16hWPHjqFUqVK4ffu2od5GpouNjcXGjRuRN2/eVEH/6dOnmDVrFhwcHPDo0SPNtL7oJCUl6Y1eNDIywsqVKwEA69evh4uLC/r06ZPqdOQvv/yi6esppxwIcvfuXXz66afo2LEj/vnnHwDAgQMHULx4cdStWxf+/v44cOAAWrRoATc3N8226ALJpxitrKzw999/64WTV69e4c8//4SFhQXs7Ozg4OAAZ2dnTR+sU4qOjsbEiRNRs2ZNDBo0SP1ODRkyBCYmJqhUqRJcXV2RN29ezY5+TSk6OhouLi7w8PBQ5wD9559/4OnpidKlS2PLli3Ytm0bvLy80LhxY/VxWv28MNj9R8+ePcN3330HRVGwZMkSdfmbwt2CBQtQunRpzU4KCSR39u7fvz8CAgJw6NAhuLi4oE2bNmjRogUuXbqknlK6efMmfvvtN2zbtk39go0aNQo1atTQTMd43Q738ePHuHfvnt5BeO3atcidOze+/vprvcc8ffpUryVPi44ePYqtW7fim2++0Vu+Zs0auLq6onfv3pqd1uV1KQPLunXrEBsbix07dsDd3R2dO3dWW6/PnDkDNzc3FC9eHOXKlUOjRo00Mz3D65KSkhAaGgoPDw/8+uuvevelPEV769Yt/Pjjj1ixYoW6D9HatgDSPt0YHR2N6dOno2rVqhgyZIi6zq5du7Bo0SLMmjVLsz+G0toed+7cQaNGjVCvXj3s3LkTQPIcft27d4elpSUcHR1Rt27dHDGansHuPaXcudy8eRNjxoyBoihYu3atujxluLtx4waA5J241pvFL1++DGdnZ2zatEld5u/vD0VR4OLios4tFR0drd5/5coVDBw4EBYWFpr5hanb+WzZsgXOzs5wcHCAvb09Jk+erA4GWLt2LUxNTTV9Oazp06djwoQJevPU1a5dG4qioFWrVgD0v09r1qyBm5sbOnbsiMuXLxuk5qxy5MgR5MuXD2FhYRgxYgSKFy+Ou3fvAkjuH1WjRg107twZJ0+eVB8TGBiI27dvpzkSPzt7/WD96NEjFCtWDGvWrEm17osXL9LsM6bFUKdz/PhxTJs2TW9ZdHQ0/Pz8ULFiRQwbNkxzLfzv8vpnICQkBPXr10e9evWwa9cudfmNGzcQGhqque/MmzDY/UebN2+Gi4sLOnXqBDMzszRPyzZt2hS5c+fW9LU9Xzd79mw4OTmpo14rVaqEpk2b4tdff8WgQYOgKArGjBkDIPk01KZNmzTZSnPgwAGYmZlhxowZOHjwIMaOHYvq1aujR48e6unm33//HYqipGq90oq5c+dCURTMmjVLXRYYGIi2bdvCxsZGHTyU8pf08uXLUb9+fU2PXAOSpylp3bo1ChYsCAsLi1Sjf3XhrmvXrjh27Fiqx2vlSiwp30dsbCyA5OsClypVSg0zKUPbyZMnMW7cOHXwkZa8KZwNHToU5cuXV+e11Hnx4gU6deqkTpmk5XCX8nOydOlS1KlTB0ePHtVb5/bt26hcuTKqVKmCLVu2vPU5tIrB7j84d+4c8uTJg6VLl+LJkye4ePEiRowYkSrc3bhxA97e3ppsFn99J6L70ly5cgXNmzfHzp074ezsjNq1a+u1VAYEBOjtqBMSEtQduhbophzo168fevbsqXffypUr4erqqgadly9fYuPGjZq81qnu87FkyRIYGRlhxowZ6mfk2rVrqFOnDhwcHNQAlzLcvWmuKa2ZMGECFEVBwYIFcefOHQD6LQrbt2+Hu7s7mjRposkLtqc80M6YMQN9+vRRW2LmzJkDExMTbNy4UV3n2bNnaNasGbp166bZEPPw4UN1G2zatAlr1qzBkydP8PXXX8PNzU1vWiAg+bqo5cuXR8uWLfHw4UNDlJylHjx4gKtXr6J06dLw9vZO9aNn//79yJcvH6pWrap33eCcgsHuP9i+fTsqVKigdwB6+vQphg0bBkVRsG7dOnW5Fpt+X//loxtSrtOlSxcoioImTZrobaOUj9PidkmpV69e6iTUr89BVrZsWUOVlSVSzqf19OlTTJw4EUZGRuqkukDyVBS1atVCqVKl1HCn9c/E63PNXb16FX///Tfatm0La2tr9fRzyisl7Nq1C71799Z0a8Po0aNRtGhRLF68WG3Nfv78OUaPHg1FUdClSxd07doVdevWRcWKFdUfAVoLdzExMbC1tUXPnj2xZMkSKIqC1atXA0iezP2rr75CzZo19U7L+vr6Yvr06Zrtm/vHH3+og6yGDRuGFi1aAEi+co+joyNatWqlF+62b9+Orl27YsiQIZr+zrwJg91/sH//fhgZGakjPnU7mFOnTsHExASKomDFihWGLDHTpPyyzJ07Fz169ICzszN++ukndYqTa9euwc3NDUuXLjVUmQaj+yyMHTsWJUqUUH9F67bbxo0bUalSJc33twSSWxzKli2L7t27w8bGRm2507l+/Trq1q0LS0tLTQ8qAlL/GErZQnnnzh20aNEC1tbWenNb/vDDD3r9UbV4oDp06BBKlCihTvHyuo0bN6J3797o2rUrvvnmG01d/kknICBA7bpy8eJFmJmZwdjYGD/88AOA/9unhIaGYvjw4XB2dla7AeXLlw/Xrl0zVOmZKiEhAd9++y0URUHz5s2RP39+nDt3Tr3/4sWLcHJyQuvWrbFmzRqEhISgVatWeqestfideRsGu3RK61dhTEwM6tevjx49euidZr137x66du2KmTNn4sqVK1lZZpYbM2YMihQpgpkzZ6qXevLx8UF4eDgiIyPRpk0b+Pj4GLrMTKf7fDx8+BCPHj3S69RbqVIluLm54f79++qBfNCgQahTpw6ePXtmkHqzyr///osCBQpg6dKliImJwd27d+Hn55cq3AUHB8PLy0uzBydA/+CyePFidO/eHd7e3li1apW6/N69e2jZsiUsLCzw888/o0GDBnBxcdH0oAAgeRBRlSpV8PLlS/W7pNteuvf+pknPteDnn3+Gm5sbIiIikJCQgCdPnsDIyAjGxsYYMGCAOqBGJyIiAlu2bEG3bt3Qt29fTZ6iHzx4sF4fSmdnZyiKgqlTpwJI/vvrPgOXLl1CkyZNULx4cZQoUQLVqlXTbItuejDYpYPug3HkyBHMmjULQ4YMwfbt2xEXF4eNGzeiZs2a6NatG06fPo379+/D19cXNWrU0Hwfof/9738oU6aMOmLv9OnTMDIy0pue4MSJE1AURdOXs0k5+rVq1aooWbIkKlWqhJEjRwJIPtXm7OyM4sWLo0GDBmjZsiUKFCigmdG/b/P333+jTJkyqWb+nzZtGoyMjLBo0SL1AJ4TpiEAkn8MFS9eHIMGDVIvg+Xn56e+/ydPnqB3796oXLkyWrVqpS7XcqvDL7/8gkKFCql9DHXzHSYmJmLbtm0ICQkxcIWZTzdwRte6HxERgZMnT8LExAS9evVKFe50tPi9uXv3Lpo2bap274mPj0f//v3Ru3dvKIqinpZNTExU3/+jR48QEBCAv/76Sw18WmrRzQgGu3T6888/UaBAAfTt2xfNmjVD1apV0alTJwDJv7aaNWsGRVHg6OiIQoUK6TUVa9WxY8fg5uYGIHly2fz582Px4sUAkofh+/v7486dO5gwYYKmfl2nZd++fTA1NcX8+fOxbt06zJ8/H/ny5UOPHj3UdaZNm4bhw4djzJgx6qVutCjlL+SjR49CURT1+6D7HFy9ehXm5uZQFEU91aRFulNrum2ybt06ODg4qD+G9u7dC0VRoCgKRo4cqXeQvnfvnvo4rRygzp07h40bN2LGjBlYvnw5wsPDkZCQgCtXrqBs2bIYP368Xmv3y5cvUbduXcyePduAVWeulH/zc+fOoXTp0lizZo0636e/vz9y5cqFPn36qOFu+vTp6mUZtd4i9csvv+DRo0cAkt+r7rSsLtzppJwEH9BWi25GMdi9Jq1fxdeuXUPp0qXVCYhv376N/Pnz6809Fh8fj6NHj+Lvv/9+4y+r7Cyt7bJ371588sknWLt2LSwsLLBo0SL1vr/++gudOnXS20lr5eCUlq+++kovxAHJLbx58+bV7DQmr3t9UIDuv82bN4enp6feqF9dq9S8efM0211h1KhRGDdunHqAfvHiBZYuXap+T3bu3Kmecl21ahUURcG0adNSjQ7XSkvdypUrYW9vDzc3N5QoUQJGRkZwcHBQB9PMmjULZcuWxYABA/DXX39h3759aNy4MapUqaK5fUdaf9OwsDAAQNOmTeHq6oq1a9eqn51Dhw4hb968aNy4Mdq1a4c8efKkulyjFkVERCBfvnyoU6eOGu5iY2MxYcIEGBsbY8mSJQgPD4e3t3eq2QdyMga7FHRftlu3bmHbtm3q8mPHjqF8+fIAkpvLS5YsiX79+qn3nzhxQm8Em9ak3Alt2LABmzdvVm97e3urp5J0Xrx4gZYtW6J9+/aaOSi9TUJCApo0aQJvb291me7X4vTp01GzZk2Eh4er20KLv7B17+nw4cMYN24c+vfvj0WLFiEuLg5///23OiP88ePHERQUBF9fX1SoUEFvUICWJCUlwcfHBzVq1MDMmTPVA/S9e/dw8+ZNPHjwAJUrV8b3338PIHlSb10L5sKFCw1Zeqb47bffkCdPHqxbtw4RERGIjY1FcHAw6tSpgwIFCmDu3LkAgB9//BFeXl5QFAWurq5o3LixZq+uce3aNUyYMAFA8uAQDw8P9XPi7e2NSpUq6YW748ePo3v37ujevbvm5vvUSblv1P3dg4ODUapUKdSvX18v3E2bNg2KoqBChQooX768Jk9Jvy8Gu9fcv38f1tbWcHJyUvuKBQQEoG7duggMDISdnR369eun7mROnTqFr7/+Wm8Um5ak/KKNGjUK9vb2WLJkiTo1xf79++Hh4QEnJyds2rQJixcvhpeXFypUqKD+ytZSuLt9+zZWr16N6dOn48aNG2ofkKVLl6JcuXI4cuSI3vo//fQTnJycNBtgUvrzzz+RP39+DBw4EL169YKLiwvq1asHANi6dSvat28PRVFQpkwZ2Nra4uzZs4YtOJPovjOvXr3C4MGDUa1aNfj5+el9Bs6cOYPy5curnd5v3ryJIUOGYP/+/ZprnQoLC0ODBg0wf/58APr7lFevXqF+/fqwtrZWt8WLFy9w9epV3L9/X3OnonUSExOxfPlyGBkZoV27dlAUBb/88oveOinDnW6Q1YsXLzQbYFIeJ/z8/LB48WK1n/rVq1fxySef6IU7IPn4u2XLlhzfp+51DHavOXToEIyMjFC9enW0adMGv/32G+Lj4/HJJ59AUZRUF20fNmwY6tWrp5lrm77J7NmzUbhwYZw4cSLVfWfOnEHnzp1RtGhReHh4oFevXurOR0tftAsXLsDe3h7Vq1eHhYUFihcvjh07dgBIHkji6emJbt266U3ZMGLECDRo0EDzwe7OnTsoX768eprx1q1bsLa2xsCBA/XWO336NM6dO5dqMIWWJCYm6o3o/OKLL1CtWjXMmDFDbX0JCAiAoij48ccfERAQgObNm6NZs2bqc2jpe3Pz5k3Y2Nio3xUd3XuMjIyEpaVlqs+KjpZ+GKb06tUr9OnTB4qiqPOyAf/XLxNIDndVqlTBihUr9JZrTcq/cVhYGOrVqwdra2v88ssvaqjVhbsGDRqkOS2S1lp0/wsGuzT07t0bLi4uaN++PerWrYu//voL586dQ8mSJdG5c2cEBATgn3/+wYgRI2BhYaHZZnEg+dd1TEwMmjVrhjlz5gBIvpLG1q1b0aZNG/Tu3VvtE/TgwQPNTj584cIF5M2bFxMmTEBYWBju3r0Le3t71KlTR11n69ataNy4Mezt7dGwYUM0b94cFhYWmh1Ik7Ll5dy5cyhTpgzi4+Nx584d2NnZoX///ur9Bw4c0GxLw5vopkB6PdzpWiH8/PygKApKly6t6ekZTp06hYIFC+Lw4cMA9AcL6P6/Y8eOaNmyJRISEjT3/t/k1atXGDduHHx8fGBtba3XZ/v58+fq/zdu3Bg1a9bU/CwLADB8+HDUqlULnTp1QpkyZZAnTx6sWLFCL9yVKlUKlSpV0uTl5D6UHB3sXv8lqOsnt2vXLvTq1Qt79+5Fu3bt4OHhgdWrV+PIkSMoXbo0ihUrhrJly8LNzU2TB+20fiF37NgRXl5eWLVqFZo2bYqGDRuiS5cuKFWqFBo3bgxA/xeTlnbO9+7dg6Io+OKLL/SW169fH3Z2dnqtccHBwVizZg0+++wzjB8/XlOXCXt9WpKUn5Pg4GA0atQIhw8fVkOdLthfunQJAwYM0Oyp17T8+eefKFeuHHbu3AlAP9z5+fmpB6pLly4hICBA0xcnj46ORsmSJdG+fXt12eutK126dEG3bt2yurSPQnR0NBYuXIhChQrphTsA6mnH1y92r0Xr16+Hubk5zp49i2fPniEuLg5ffvklcufOjRUrVqit3VeuXEG7du3YQvcWOTbY6XakISEheoMBgOSmYEdHRyxcuBCPHj1Cu3btUL9+fezatUsdmh8UFKTZy7forF27Vr3A8tq1a9GyZUuYm5tj4sSJ6inZWbNmoUOHDpoKcq8LDQ2Fo6Mj3NzccOvWLQDJ71tRFNjY2KB3795wc3PDsmXLNNvXUvd9CQoKwoABA9C6dWvMmDFDHQEeGRmJ8uXLQ1EU9OnTR++xI0aMQK1atfT6xmjdgQMH0LZtW9SuXRu7du0C8H/hrnr16pg5cyYiIyP1HqOVU46v7wsSEhIwYcIEdd/xupiYGM1PaQL833a5dOkSdu3ahV27dqmNCY8fP8aiRYtgZWWF4cOHA0i+hnCDBg3UQKN1ixYtQvXq1fHixQu970Lfvn1hYWGBX375JdW2YLhLW44NdkByqLOyslIvVbJhwwb1wLx9+3Z4eHggLCxM/YXQoEED9Zp9WhcTEwNra2vUqlULFy5cAJDcovn6VC6NGjXSGyGsVaGhoahcuTLc3NwwevRotc9QcHAwrl69ikGDBqFBgwZQFAXdu3dPNWVFdqbbyZ4/fx4FCxZEjx490LJlS7i6umLOnDnqASsoKAhWVlZo06YNdu3ahYMHD2Lo0KEwNzdXP0Na9KYfNUePHkWHDh1Qs2ZNvXA3aNAgfPLJJ3oTeWtFygNydHS0erosLCwMrVu3RqFChfDFF1/g8ePHePjwIa5du4YWLVqgcuXKmmyt1NF9RjZv3gwHBweUKVMGLi4uqFKlCsLDwwEA4eHhWLZsGfLmzQsnJycULFgQp0+fNmTZWWrBggWwsLBQw66uT+HJkydhZGQEKysr/PnnnwAY6N4lRwe727dvo1q1anB3d4erqyv69u2LTz75BEuXLsWGDRvQsmVL7N69G0DydASenp5o1apVql/aWpDWwenevXtwcnJC3bp11QlVASAqKgpHjhxB48aNUalSJXWHrOVWOyB5Rng3NzcoioI1a9akuv/Zs2fYuXOnplrtdAfqCxcuIF++fBg3bpx632effaZemF53cPrf//6HChUqwMHBAeXKlYOHh0eOuMIGkDwV0P/+9z+9ZUeOHEGHDh1Qo0YNHDhwAEByC9bs2bM1d3BK+f2fOnUqmjVrhiJFimDgwIHw9/dHREQEBgwYgAIFCsDKygpWVlZwc3ODh4eHZqc0SenAgQOwsLDAsmXLkJiYiH379qmT2ut+ML948QKXL1/Gzz//rF6JQmtShv+U/x8VFQVnZ2e0aNFCnW0ASN73jB49Gn379oW1tXWOavl/Xzk62AHJnTHbtWsHb29vbN68GVu2bEH9+vXV+dnc3NzUD1lQUJAmJx9OSRdadTvp+/fvo2zZsqhbt6560Dp48CC6d++Odu3aaXL0K/B/7z8uLk5vjsLQ0FBUqVIFrq6u6o5XK6fQ3uT1Poa6bfPFF1/AxcUFzs7OqFy5sjr3Y1RUFG7evImQkJAc0eEbSN43VK9eHU2bNk01cay/vz9KlCiB6tWrY8uWLXr3aTHIfPvtt7CyssKmTZuwbds21K5dGw4ODoiMjER0dDSuXr2KRYsWYenSpTh48KCm+xfqREdHY9CgQZg2bRqA5P1qyZIl0a1bN1SvXh2lS5dWp5DSspThf8mSJfj8888xffp0tf/t1q1b4erqirp16+LcuXM4fvw4mjZtCh8fH4SGhsLKyirHnDX7L3J8sAOSd8rNmjVDkyZNEBwcjGfPnuHEiRNo2bKlerpE661RAPD999+jXr16uHHjht7yBw8ewM7ODvXq1cOZM2cAJAdire6QdX/rXbt2oXv37nB2dsY333yDrVu3Avi/07IuLi5qnzstS6uP4cyZM2FqaoqFCxdizpw58Pb2hrGxcZrT4WhRWvuD9evXw8vLCy1atFC/JzqNGzdG6dKlU4Vjrbl27RqqVaumXhv64MGD6shG4M1BVus/jgBgx44dOHPmDCIiIuDq6ooBAwYASG7pVRQFtra2mp4GKOVnfvLkyTA3N0fnzp1RpEgRNGnSRN2/+vv7o3bt2sibNy8++eQT1KhRAwkJCYiIiEDZsmWxZ88eQ72FbIPB7v+7evUqmjRpgiZNmuDYsWOGLscgLl68iDx58qBt27ZquNPtcDdu3AgjIyNUq1ZNb6SnVnfIW7duRZ48eTB58mQsXLgQrVq1QpEiRdS+Yg8fPoSrqys++eQT9cLlWvZ6H0Nra2vs3btXvf/MmTMoWLAgZs2aZcAqs0bKz3xkZKTetAtbt25Fw4YN0bJlS3XE/NOnT9GzZ0+sX79ec4Hu9e//9evXUbp0aURGRmLz5s3Inz8/fvrpJwDJU3isWbNG86cYX59v7vW/+c6dO1GrVi31R5K/vz9atmyJNm3aqFPkaNnZs2fRs2dPdWDev//+izZt2qB+/fp6AxlPnTqF69evq9vV19dX77Q1vRmDXQpXr15F06ZN4eXlpX7otOpNgezSpUsoUKAAWrdurddy98cff6B3797o0qWLJk8fpRQeHo769etj3rx5AJIP3jY2Nvj666/11nv48CHq1KmTqoVTq9LqY6j7HD1+/BiVK1dOs++hVk2aNAlVqlSBo6Mj2rRpo16EfNeuXfDy8oKTkxOGDh2KunXrolatWuq20uKPofPnz+Ply5cIDg5GxYoVMW3aNBQsWFDv+tGnTp1Cu3btcPz4cQNWmrnu3buHjh07qi2WaVm0aBHy5s2rnun45ptv8Pnnn2v6spQ6a9asQd26dVGzZk29vnIXLlxAmzZt0LBhQ/z22296jzlz5gwGDhwIS0tLTU4vlhkY7F5z9epVtGzZEjVr1tTsaaWUB5b9+/dj9erV2L17N65fvw4gueWuQIEC8Pb2xt69exEaGorWrVvr7aS1HO6ePHmCChUq4OLFi7hz5w6KFy+uN/I35QAJrW6H9PQxTBlox40bBwcHB023Xqb83ixatAiWlpaYN28elixZgsqVK8PR0VEdbHX8+HGMGTMGHh4e6NmzZ5pz/2nF9u3bYW1trU6qO2TIECiKAl9fX3Wd2NhYtGjRAs2bN9fkNtC5ceMG3N3d0aJFizee+bl//z7Kly+PwoULw9PTE3ny5NH0JPcp7du3D9WqVYOlpaV66lXn4sWLaNeuHZydnbF//351eUBAAGbMmKGpOUEzG4NdGgIDA9GhQwdNH6SA5Gu/lihRAp9++inKlSsHOzs7HDp0CEBy83j58uVRokQJFC9eHK6urpq/csDdu3cRGxuL0NBQ1KpVC7/++itKlSqFvn37qgejmzdvolevXvjrr78MXG3myUgfw7CwMEyaNAlmZmY5ZgLi/fv3Y8GCBdiwYYPe8iZNmsDJyQlhYWHqspShWGt9UXUSExNRunRpdf61mJgYdO3aFXnz5sWYMWMwbNgwNGzYEBUqVNB0wNVJeeYnZbhL2WJ77do1dOvWDdOmTcOVK1cMVWqmetPf+OjRo3B3d0fLli3VkeI6AQEBGDt2bKofzFo/9nxoDHZvkHK4tZbovmxr1qyBlZUVTpw4gejoaAQEBKBnz54wMzNTd0YPHjzAgQMHsHXrVs1fZPnff/+Fg4ODOsP7119/DUVR9GbLB5L7eVSsWBEhISGGKDPLbNu2Te1juGjRIrRu3Vqvj2FoaChcXV2hKAry5cuXarCAVgUEBCB37txQFEUdnafrU/Xq1SuUKFFCnRImZd8qrfSte/1gHRcXh6SkJMycORONGzdWO/+/fPkSU6ZMQbNmzdC6dWuMHj1a3XdodR+S0tvCXUJCAkaNGgUfHx/NjhpP+Tk5cOAANm7ciO3bt6s/dA4dOoRatWrB29sb/v7+aT6HVs+GZAUGuxxiz5496pUyEhMTMW7cOHTo0EFvnYcPH6JTp06oX79+mlfV0PoXrUyZMuop1/j4eHTv3h358+fHvHnzMHv2bAwcOBAFChTQ/Lxs6e1j+ODBA7Rt2zZH9XvRXSGgSJEi6NGjh7pc16LQsmVLDB061EDVZZ3XW5kCAwNhYWGBBQsW6C1/vd+Y1vchKaUV7uLi4jB48GAoipIjvjcjR45EyZIlUbJkSdjb28Pe3h6XLl0CkDxopE6dOmjfvr3ahYE+DAa7HCA6Ohrly5eHvb09nj59CiC5w27p0qVTXSFh5cqVKFmyJB4+fGiASg1D1zq7ZMkS1K9fX+3LERUVhVGjRqF69epwdXVFly5dckRfGPYxTPamU0mRkZFYuHAhTE1N1RCna5GrXLkyRo0alVUlZpmU2+KPP/5A6dKl0bVrV1y7dk2d+3LGjBlwdnZGUFCQuq4WWy0zImW4O3ToEEaPHo08efLkiG4LK1euRKFChXDq1Ck8ePAAly9fRvPmzWFra4vbt28DSJ4Op2zZshg7dqyBq9UWBrsc4vLly6hevTocHR0RERGBo0ePokKFCvjxxx/1rqShW64bSKFloaGherevX78OW1tbTJ8+XW/5kydPEB8fn2oaA61hH8P/kzLIrFixAmPHjkX37t1x+PBhREdH49WrV/jxxx9hamqK+vXro3v37ujYsSPKlCmjuVONv//+Oz7//HMEBwcjKSkJYWFh+OOPP1ClShVUqFABHTt2xPnz53H8+HHUrl1bnbJCy/3oMkI3IK9gwYLInTt3qgmstcrX1xfdunXTWxYVFYW6deuiTp06aiv3uXPnNP0D0RAY7DQu5STCt2/fRvXq1VGnTh1ERkZixIgRqFy5MqZMmYKgoCDcvHkTXl5eaNSokeZ/XQcEBKBmzZro06cPHj16hGfPngFIHu3o4OCgd7pV69sCYB/DlFL+vUeMGAFra2u0b98e7u7usLa2xpgxY3D//n3Ex8dj0aJFsLOzQ4UKFfROrWkl3EVFReHTTz+FjY0NKlasiF69emHdunXq/Rs2bEDbtm2RL18+jB49GgULFkTFihUZ6l4TFBSE1q1bq6chc4IvvvgC5cuXV2/rwtsvv/yCcuXKpbrSBsPdh8Ngp1G6a3cC+gNBmjZtCkVRUKtWLURGRmL8+PGoWrUqFEWBs7MzqlWrpsmRa4GBgfjmm2/UUwD379/HggUL4OjoiPLly6Nfv364fPkygoOD0bBhQ6xatQpAztrZsI+hvgMHDqBo0aJ6p83mzp2LSpUqYcqUKQCSL26/ePFi2NjYYOTIkep6WvnuvHr1Cr6+vliyZAkCAgIwe/ZsWFpaomPHjpg7d666r9i5cyd69eoFc3NzlCtXTjPv/0PS6sjOlMealPbv348KFSrghx9+0Puh89dff8HJyUnTPw4NjcFOg44cOYL69evj77//1lveoUMHVKpUCQcOHFCvIvD06VPExsZi9+7dOH78uCZHv8bHx6N69epQFAVlypTBsGHDsGPHDvX+n376CS1atEDevHkxdepUlC1bFuXKldPsyOjXsY9hsqNHj2LOnDmYM2cOzp49ixMnTuDTTz/FnTt39AK+bvJd3an8p0+fqgMqdJcM05Ldu3ejQIEC6ojoFy9eYPz48VAUBS4uLpg5cyZu3bqFV69eITg4WN1WDHfal9axRtfi/fTpU/Tr1w/169fHpEmTEBkZiZs3b6JZs2Zo1qxZjjgTYigMdhoUFBSEevXqoXnz5uo0FO3bt0eFChXUX0lXrlyBi4sLKleunOoXlxZbqWbNmoW5c+di3759mDBhAiwtLdG5c2esXr1aPQCtW7cObdu2RZEiRaAoiqav2wiwj2FKy5cvh42NDVxdXZEvXz6UKVMGnTp1QqlSpdTT07oJeGNjY2FtbY1Nmzapj4+MjMT333+PUqVK4dGjR5o7aH355Zf48ssv1dvly5eHt7c3Ro4cCU9PTyiKol4PFtDmPoRS0x1rXp+QWff3f/ToEb766itUqFABuXLlQsWKFfXmRGX4zxwMdhqlG43VokUL1KlTB1WqVEl1wfrAwECUKFEiVQdXLTp06BDMzc1x+vRpAMlTdegm1nVzc8OyZcsQHh6Oly9f4syZM7h27ZqBK/5w0tp5so/h/1m+fDly586NDRs2IDY2FocOHULDhg3h7u4Oe3t7VKlSRW/9W7duoUyZMupk3jpRUVFpThOkBT///DNq166NiIgIVKlSBbVr11bnYLt37x5+//13TbXyU/q9ac4+XXiLi4tDTEwMZs2ahbNnz2ryrNDHhsFOw65evQpPT09YWFjgjz/+UJenPNDfvn07x/y6HjlyJHx8fNSWp86dO8PR0RE9e/ZEnTp1kCtXLsyZM8fAVWYO9jFM26FDh6AoCiZPngzg/0LsjBkzUKxYMezfvx9VqlRB+fLlsWXLFmzevBktWrRAtWrVNL9tXqfrzlCvXj08efIkzXV4sM6Z3nSd9aSkJNy/fx/NmjXDwIED1eU57buT1RjsNO769evw8vJCs2bN9L5wr7fi5IQv2saNG+Hu7o7ExET06dMHRYoUUUepBQUFYf78+ZoctcY+hm929epVeHh4oE2bNnr9hGbMmIEyZcrg3r17uHLlClq3bo1PPvkEFSpUQLNmzdTWiJzwvdGF3V9//RUVK1ZUu3dovSWXMiatlrvQ0FDUrVsXn376qWYHj3yMFAAQ0rRr167JV199JSIi3377rdSuXdvAFRlOvXr15NixY2Jrayu7d++WypUrG7qkLDF79mwxMTGRihUryrFjx2TBggXi5eUlzZo1k+7du4uRkZH8/vvvsnHjRjl+/LiEhYXJvXv3pFixYoYuPdPpvh9JSUmycOFCuXv3rjRv3lzWrl0rHTp0UNcLCQkRMzMzsbGxEUVR5NWrV2JiYmLAyrPW/fv3pXr16vLVV1/J2LFjDV0OfYR03yVFUWTgwIHy448/yr179+TChQuSK1euHPedMRhDJ0vKGlevXlVPIelGt+UkKS9sX7ZsWWzZskVvudbl5D6G6XH16lU0a9YMrq6uyJUrF9auXQsg+dSirlUu5Wclp3b6XrBgAaysrHD58mVDl0IfqatXr6J58+ZQFAVOTk5qSx1P02cdI0MHS8oaZcqUkdmzZ0vdunWlYsWKhi4nyymKIiIiVatWlaSkJAkICNBbrnX169eX/v37y7x58+Tly5dStGhRCQwMFHt7e3F0dJQ1a9ZI0aJFZdGiRVK1alUpXbq0oUvOUmXKlJH58+eLpaWllCtXTn3/JiYmYmSUvJtM+VnRLctpmjdvLi1atBBHR0dDl0IfqTJlysicOXNk8ODBcvHiRbbUGQBPxeZQSUlJOfbgtHbtWvniiy/k4MGDUqNGDUOXk2U2bdokc+fOlWPHjkn//v1l586d4u/vLxUqVJDg4GDZu3evNGrUSCpUqGDoUg3m+vXrMmTIEBFht4U3ASCKokhiYqIYGxsbuhz6yDHUZT0GO8px7t+/L5999pn8+uuvUqJECUOXk6Vyah/DjLh27ZoMGzZMHj16JCtWrBBnZ2dDl0RElG4MdpQjvXz5UszMzAxdRpbRtbLs3r1bhg0bJjNnzhRvb291OekLDAyUn3/+WWbPnp1jW7aJKHviHotypJwU6kTYxzCjnJycZM6cOWJkZCRJSUmGLoeIKN3YYkeUw+TUPoZERDkBW+yIcpgGDRpI9erVc8QcdUREOQ1b7IhyoJzWx5CIKKdgsCMiIiLSCJ6KJSIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwIyIiItIIBjsiIiIijWCwI6IcCYD0799fChUqJIqiyPnz5w1dEhHRf8Z57IgoR/rrr7+kTZs2cvjwYSlVqpRYW1uLiYnJf3rOXr16SWRkpGzduvXDFElElEH/bS9GRJRN3bhxQ4oWLSq1atUydCmpJCYmiqIoYmTEkypElDHcaxBRjtOrVy8ZMmSIhISEiKIoYm9vL0lJSeLn5ycODg6SJ08eqVy5smzatEl9TGJiovTp00e9v1y5cjJ//nz1/kmTJskvv/wi27ZtE0VRRFEUOXz4sBw+fFgURZHIyEh13fPnz4uiKHL79m0REVm9erVYWlrK9u3bpXz58mJqaiohISESFxcnI0eOlOLFi0u+fPnEzc1NDh8+rD7PnTt3pFWrVlKwYEHJly+fVKhQQXbv3p3Zm4+IPmJssSOiHGf+/Pny6aefyrJly+T06dNibGwsfn5+snbtWlmyZImUKVNGjhw5Ip999pnY2NhIvXr1JCkpSUqUKCEbN24UKysrOX78uPTv31+KFi0qnTp1kpEjR0pgYKBER0fLqlWrRESkUKFCcvz48XTV9Pz5c5k5c6b8/PPPYmVlJYULF5bBgwfLlStXZP369VKsWDHZsmWLNG3aVP79918pU6aMDBo0SOLj4+XIkSOSL18+uXLliuTPnz8zNx0RfeQY7Igox7GwsJACBQqIsbGx2NraSlxcnEyfPl0OHDgg7u7uIiJSqlQpOXbsmCxdulTq1asnuXLlksmTJ6vP4eDgICdOnJA//vhDOnXqJPnz55c8efJIXFyc2NraZrimhIQEWbx4sVSuXFlEREJCQmTVqlUSEhIixYoVExGRkSNHyp49e2TVqlUyffp0CQkJkfbt20ulSpXUmokoZ2OwI6Ic7/r16/L8+XNp3Lix3vL4+HipUqWKenvRokWycuVKCQkJkRcvXkh8fLy4uLh8kBpy584tzs7O6u1///1XEhMTpWzZsnrrxcXFiZWVlYiIfPXVVzJw4EDZt2+feHp6Svv27fWeg4hyHgY7Isrxnj17JiIiu3btkuLFi+vdZ2pqKiIi69evl5EjR8qcOXPE3d1dChQoILNnz5aTJ0++9bl1AyBSTkCQkJCQar08efKIoih6NRkbG0tAQIAYGxvrras73dq3b1/x8vKSXbt2yb59+8TPz0/mzJkjQ4YMSe9bJyKNYbAjohwv5YCFevXqpbnOP//8I7Vq1ZIvv/xSXXbjxg29dXLnzi2JiYl6y2xsbERE5OHDh1KwYEERkXTNmVelShVJTEyUsLAw8fDweON6dnZ28sUXX8gXX3whvr6+snz5cgY7ohyMwY6IcrwCBQrIyJEjZdiwYZKUlCR16tSRqKgo+eeff8Tc3Fx69uwpZcqUkTVr1sjevXvFwcFBfv31Vzl9+rQ4ODioz2Nvby979+6V4OBgsbKyEgsLCyldurTY2dnJpEmTZNq0aXL16lWZM2fOO2sqW7as+Pj4SI8ePWTOnDlSpUoVefz4sfj7+4uzs7O0aNFCvv76a2nWrJmULVtWnj59KocOHRInJ6fM3FRE9JHjdCdERCIydepUGT9+vPj5+YmTk5M0bdpUdu3apQa3AQMGSLt27aRz587i5uYmT5480Wu9ExHp16+flCtXTqpVqyY2Njbyzz//SK5cueT333+XoKAgcXZ2lpkzZ8p3332XrppWrVolPXr0kBEjRki5cuXE29tbTp8+LSVLlhSR5ClYBg0apNZbtmxZWbx48YfdMESUrfDKE0REREQawRY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSCAY7IiIiIo1gsCMiIiLSiP8HeueBtP858nEAAAAASUVORK5CYII="
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 12
   },
   {
    "cell_type": "markdown",
@@ -521,8 +522,8 @@
    "id": "34db0c8f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:16:33.627704Z",
-     "start_time": "2024-11-07T15:16:31.950592Z"
+     "end_time": "2025-01-10T13:18:32.424641Z",
+     "start_time": "2025-01-10T13:18:31.609925Z"
     }
    },
    "source": [
@@ -534,13 +535,13 @@
       "text/plain": [
        "<Figure size 2000x300 with 1 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 15
+   "execution_count": 13
   }
  ],
  "metadata": {
diff --git a/docs/source/notebooks/tabular_notebooks/tabpfn_values.npz b/docs/source/notebooks/tabular_notebooks/tabpfn_values.npz
new file mode 100644
index 00000000..184e0ee6
Binary files /dev/null and b/docs/source/notebooks/tabular_notebooks/tabpfn_values.npz differ
diff --git a/docs/source/notebooks/tree_notebooks/treeshapiq_custom_tree.ipynb b/docs/source/notebooks/tree_notebooks/treeshapiq_custom_tree.ipynb
index 594ec78c..bd6a7519 100644
--- a/docs/source/notebooks/tree_notebooks/treeshapiq_custom_tree.ipynb
+++ b/docs/source/notebooks/tree_notebooks/treeshapiq_custom_tree.ipynb
@@ -21,8 +21,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:14.147010Z",
-     "start_time": "2024-11-07T15:17:11.982766Z"
+     "end_time": "2025-01-10T12:07:42.287579Z",
+     "start_time": "2025-01-10T12:07:40.761213Z"
     }
    },
    "source": [
@@ -36,7 +36,7 @@
     {
      "data": {
       "text/plain": [
-       "{'shapiq': '1.1.0'}"
+       "{'shapiq': '1.1.1.dev'}"
       ]
      },
      "execution_count": 1,
@@ -89,8 +89,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:14.162008Z",
-     "start_time": "2024-11-07T15:17:14.149012Z"
+     "end_time": "2025-01-10T12:07:42.302792Z",
+     "start_time": "2025-01-10T12:07:42.289571Z"
     }
    },
    "cell_type": "code",
@@ -175,8 +175,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:14.177007Z",
-     "start_time": "2024-11-07T15:17:14.166010Z"
+     "end_time": "2025-01-10T12:07:42.317798Z",
+     "start_time": "2025-01-10T12:07:42.304789Z"
     }
    },
    "cell_type": "code",
@@ -202,8 +202,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:14.192520Z",
-     "start_time": "2024-11-07T15:17:14.179012Z"
+     "end_time": "2025-01-10T12:07:42.333789Z",
+     "start_time": "2025-01-10T12:07:42.319796Z"
     }
    },
    "cell_type": "code",
@@ -235,8 +235,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:15.220588Z",
-     "start_time": "2024-11-07T15:17:14.194528Z"
+     "end_time": "2025-01-10T12:07:43.040086Z",
+     "start_time": "2025-01-10T12:07:42.336792Z"
     }
    },
    "cell_type": "code",
diff --git a/docs/source/notebooks/tree_notebooks/treeshapiq_lightgbm.ipynb b/docs/source/notebooks/tree_notebooks/treeshapiq_lightgbm.ipynb
index 9728d507..1b9d434c 100644
--- a/docs/source/notebooks/tree_notebooks/treeshapiq_lightgbm.ipynb
+++ b/docs/source/notebooks/tree_notebooks/treeshapiq_lightgbm.ipynb
@@ -34,8 +34,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:33.551377Z",
-     "start_time": "2024-11-07T15:17:31.201891Z"
+     "end_time": "2025-01-10T12:08:12.112659Z",
+     "start_time": "2025-01-10T12:08:10.580868Z"
     }
    },
    "source": [
@@ -51,7 +51,7 @@
     {
      "data": {
       "text/plain": [
-       "{'shapiq': '1.1.0', 'lightgbm': '4.5.0'}"
+       "{'shapiq': '1.1.1.dev', 'lightgbm': '4.5.0'}"
       ]
      },
      "execution_count": 1,
@@ -75,8 +75,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:33.660896Z",
-     "start_time": "2024-11-07T15:17:33.554378Z"
+     "end_time": "2025-01-10T12:08:12.191968Z",
+     "start_time": "2025-01-10T12:08:12.115647Z"
     }
    },
    "source": [
@@ -114,8 +114,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:34.006158Z",
-     "start_time": "2024-11-07T15:17:33.662903Z"
+     "end_time": "2025-01-10T12:08:12.429777Z",
+     "start_time": "2025-01-10T12:08:12.192969Z"
     }
    },
    "source": [
@@ -159,8 +159,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:37.346354Z",
-     "start_time": "2024-11-07T15:17:34.010162Z"
+     "end_time": "2025-01-10T12:08:14.785417Z",
+     "start_time": "2025-01-10T12:08:12.431779Z"
     }
    },
    "source": [
@@ -180,8 +180,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:37.361347Z",
-     "start_time": "2024-11-07T15:17:37.349345Z"
+     "end_time": "2025-01-10T12:08:14.801430Z",
+     "start_time": "2025-01-10T12:08:14.789416Z"
     }
    },
    "source": [
@@ -201,8 +201,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:40.017159Z",
-     "start_time": "2024-11-07T15:17:37.366486Z"
+     "end_time": "2025-01-10T12:08:16.270090Z",
+     "start_time": "2025-01-10T12:08:14.802414Z"
     }
    },
    "source": [
@@ -247,8 +247,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:40.033165Z",
-     "start_time": "2024-11-07T15:17:40.019159Z"
+     "end_time": "2025-01-10T12:08:16.285632Z",
+     "start_time": "2025-01-10T12:08:16.272082Z"
     }
    },
    "source": [
@@ -283,8 +283,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:40.049161Z",
-     "start_time": "2024-11-07T15:17:40.036162Z"
+     "end_time": "2025-01-10T12:08:16.301624Z",
+     "start_time": "2025-01-10T12:08:16.287627Z"
     }
    },
    "source": [
@@ -333,8 +333,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:40.628294Z",
-     "start_time": "2024-11-07T15:17:40.051157Z"
+     "end_time": "2025-01-10T12:08:16.666231Z",
+     "start_time": "2025-01-10T12:08:16.303624Z"
     }
    },
    "source": [
@@ -381,8 +381,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:40.835344Z",
-     "start_time": "2024-11-07T15:17:40.630292Z"
+     "end_time": "2025-01-10T12:08:16.825286Z",
+     "start_time": "2025-01-10T12:08:16.668230Z"
     }
    },
    "source": [
@@ -409,8 +409,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:41.105318Z",
-     "start_time": "2024-11-07T15:17:40.838344Z"
+     "end_time": "2025-01-10T12:08:17.014318Z",
+     "start_time": "2025-01-10T12:08:16.828274Z"
     }
    },
    "cell_type": "code",
@@ -446,8 +446,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:17:43.154967Z",
-     "start_time": "2024-11-07T15:17:41.107319Z"
+     "end_time": "2025-01-10T12:08:18.289099Z",
+     "start_time": "2025-01-10T12:08:17.016318Z"
     }
    },
    "source": [
@@ -482,8 +482,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:19:26.329690Z",
-     "start_time": "2024-11-07T15:17:43.156968Z"
+     "end_time": "2025-01-10T12:09:23.407436Z",
+     "start_time": "2025-01-10T12:08:18.292099Z"
     }
    },
    "source": [
@@ -508,8 +508,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-07T15:19:26.817303Z",
-     "start_time": "2024-11-07T15:19:26.331688Z"
+     "end_time": "2025-01-10T12:09:23.782400Z",
+     "start_time": "2025-01-10T12:09:23.410942Z"
     }
    },
    "source": "shapiq.plot.bar_plot(list_of_interaction_values, feature_names=X.columns, max_display=20)",
diff --git a/docs/source/notebooks/vision_notebooks/vision_transformer.ipynb b/docs/source/notebooks/vision_notebooks/vision_transformer.ipynb
index e5c5bc12..320f980f 100644
--- a/docs/source/notebooks/vision_notebooks/vision_transformer.ipynb
+++ b/docs/source/notebooks/vision_notebooks/vision_transformer.ipynb
@@ -188,9 +188,9 @@
    ],
    "source": [
     "# get the exact SII values explanation\n",
-    "from shapiq.exact import ExactComputer\n",
+    "from shapiq import ExactComputer\n",
     "\n",
-    "exact = ExactComputer(n_players=game_loaded.n_players, game_fun=game_loaded)\n",
+    "exact = ExactComputer(n_players=game_loaded.n_players, game=game_loaded)\n",
     "sii = exact(index=\"k-SII\", order=2)\n",
     "sii"
    ]
@@ -268,7 +268,7 @@
    "source": [
     "# load the 16 player values and explain\n",
     "game_loaded = Game(path_to_values=\"pre_computed_image_16.npz\", normalize=True)\n",
-    "exact = ExactComputer(n_players=game_loaded.n_players, game_fun=game_loaded)\n",
+    "exact = ExactComputer(n_players=game_loaded.n_players, game=game_loaded)\n",
     "sii = exact(index=\"k-SII\", order=2)\n",
     "sii"
    ]
diff --git a/shapiq/__init__.py b/shapiq/__init__.py
index 649cac94..a7087143 100644
--- a/shapiq/__init__.py
+++ b/shapiq/__init__.py
@@ -2,7 +2,7 @@
 the well established Shapley value and its generalization to interaction.
 """
 
-__version__ = "1.1.1"
+__version__ = "1.1.1.dev"
 
 # approximator classes
 from .approximator import (
diff --git a/shapiq/explainer/tree/validation.py b/shapiq/explainer/tree/validation.py
index 79e7f170..db6455b4 100644
--- a/shapiq/explainer/tree/validation.py
+++ b/shapiq/explainer/tree/validation.py
@@ -29,6 +29,7 @@
     "lightgbm.sklearn.LGBMRegressor",
     "lightgbm.sklearn.LGBMClassifier",
     "lightgbm.basic.Booster",
+    # TODO: add xgboost to the list of supported models and check if all tests pass
     # xboost?
 }
 
diff --git a/shapiq/explainer/utils.py b/shapiq/explainer/utils.py
index 65b1ea07..576a4518 100644
--- a/shapiq/explainer/utils.py
+++ b/shapiq/explainer/utils.py
@@ -1,7 +1,6 @@
 """This module contains utility functions for the explainer module."""
 
 import re
-import warnings
 from typing import Any, Callable, Optional, TypeVar
 
 import numpy as np
@@ -118,7 +117,6 @@ def get_predict_function_and_model_type(
         )
 
     if class_index is None:
-        warnings.warn(WARNING_NO_CLASS_INDEX)
         class_index = 1
 
     def _predict_function_with_class_index(model: ModelType, data: np.ndarray) -> np.ndarray:
diff --git a/shapiq/game_theory/exact.py b/shapiq/game_theory/exact.py
index 999cb0a8..85dca61d 100644
--- a/shapiq/game_theory/exact.py
+++ b/shapiq/game_theory/exact.py
@@ -26,7 +26,7 @@ class ExactComputer:
 
     Args:
         n_players: The number of players in the game.
-        game_fun: A callable game that takes a binary matrix of shape ``(n_coalitions, n_players)``
+        game: A callable game that takes a binary matrix of shape ``(n_coalitions, n_players)``
             and returns a numpy array of shape ``(n_coalitions,)`` containing the game values.
         evaluate_game: whether to compute the values at init (if True) or first call (False)
 
@@ -41,12 +41,12 @@ class ExactComputer:
     def __init__(
         self,
         n_players: int,
-        game_fun: Callable[[np.ndarray], np.ndarray[float]],
+        game: Callable[[np.ndarray], np.ndarray[float]],
         evaluate_game: bool = False,
     ) -> None:
         # set parameter attributes
         self.n: int = n_players
-        self.game_fun = game_fun
+        self.game_fun = game
 
         # set object attributes
         self._grand_coalition_tuple: tuple[int] = tuple(range(self.n))
@@ -125,6 +125,7 @@ def __call__(self, index: str, order: int = None) -> InteractionValues:
         elif index in self.available_indices:
             computation_function = self._index_mapping[index]
             computed_index: InteractionValues = computation_function(index=index, order=order)
+            computed_index.baseline_value = self.baseline_value
             self._computed[(index, order)] = computed_index
             return copy.deepcopy(computed_index)
         else:
@@ -158,9 +159,6 @@ def _evaluate_game(self):
     def compute_game_values(self) -> tuple[float, np.ndarray[float], dict[tuple[int], int]]:
         """Evaluates the game on the powerset of all coalitions.
 
-        Args:
-            game_fun: A callable game
-
         Returns:
             baseline value (empty prediction), all game values, and the lookup dictionary
         """
diff --git a/shapiq/games/base.py b/shapiq/games/base.py
index d274b64c..f0178243 100644
--- a/shapiq/games/base.py
+++ b/shapiq/games/base.py
@@ -131,6 +131,9 @@ def __init__(
         if path_to_values is not None:
             self.load_values(path_to_values, precomputed=True)
             self.game_id = path_to_values.split(os.path.sep)[-1].split(".")[0]
+            # if game should not be normalized, reset normalization value to 0
+            if not normalize and self.normalization_value != 0:
+                self.normalization_value = 0.0
 
         # define some handy coalition variables
         self.empty_coalition = np.zeros(self.n_players, dtype=bool)
@@ -487,7 +490,7 @@ def exact_values(self, index: str, order: int) -> InteractionValues:
                 "Computing the exact interaction values via brute force may take a long time."
             )
 
-        exact_computer = ExactComputer(self.n_players, game_fun=self)
+        exact_computer = ExactComputer(self.n_players, game=self)
         return exact_computer(index=index, order=order)
 
     @property
diff --git a/tests/tests_game_theory/test_exact_computer.py b/tests/tests_game_theory/test_exact_computer.py
index ee79760b..04c04b30 100644
--- a/tests/tests_game_theory/test_exact_computer.py
+++ b/tests/tests_game_theory/test_exact_computer.py
@@ -19,7 +19,7 @@ def test_exact_computer_on_soum():
         predicted_value = soum(np.ones(n))[0]
 
         # Compute via exactComputer
-        exact_computer = ExactComputer(n_players=n, game_fun=soum)
+        exact_computer = ExactComputer(n_players=n, game=soum)
 
         # Compute via sparse Möbius representation
         moebius_converter = MoebiusConverter(soum.moebius_coefficients)
@@ -89,7 +89,7 @@ def test_exact_elc_computer_call(index, order):
     """Tests the call function for the ExactComputer."""
     n = 5
     soum = SOUM(n, n_basis_games=10, normalize=True)
-    exact_computer = ExactComputer(n_players=n, game_fun=soum)
+    exact_computer = ExactComputer(n_players=n, game=soum)
     interaction_values = exact_computer(index=index, order=order)
     if order is None:
         order = n
@@ -130,7 +130,7 @@ def test_exact_computer_call(index, order):
     """Tests the call function for the ExactComputer."""
     n = 5
     soum = SOUM(n, n_basis_games=10)
-    exact_computer = ExactComputer(n_players=n, game_fun=soum)
+    exact_computer = ExactComputer(n_players=n, game=soum)
     interaction_values = exact_computer(index=index, order=order)
     if order is None:
         order = n
@@ -145,7 +145,7 @@ def test_basic_functions():
     """Tests the basic functions of the ExactComputer."""
     n = 5
     soum = SOUM(n, n_basis_games=10)
-    exact_computer = ExactComputer(n_players=n, game_fun=soum)
+    exact_computer = ExactComputer(n_players=n, game=soum)
     isinstance(repr(exact_computer), str)
     isinstance(str(exact_computer), str)
 
@@ -154,7 +154,7 @@ def test_lazy_computation():
     """Tests if the lazy computation (calling without params) works."""
     n = 5
     soum = SOUM(n, n_basis_games=10)
-    exact_computer = ExactComputer(n_players=n, game_fun=soum)
+    exact_computer = ExactComputer(n_players=n, game=soum)
     isinstance(repr(exact_computer), str)
     isinstance(str(exact_computer), str)
     sv = exact_computer("SV", 1)
@@ -227,10 +227,10 @@ def test_permutation_symmetry(index, order, original_game):
     def permutation_game(X: np.ndarray):
         return original_game(X[:, permutation])
 
-    exact_computer = ExactComputer(n_players=n, game_fun=original_game)
+    exact_computer = ExactComputer(n_players=n, game=original_game)
     interaction_values = exact_computer(index=index, order=order)
 
-    perm_exact_computer = ExactComputer(n_players=n, game_fun=permutation_game)
+    perm_exact_computer = ExactComputer(n_players=n, game=permutation_game)
     perm_interaction_values = perm_exact_computer(index=index, order=order)
 
     # permutation does not matter
@@ -243,7 +243,7 @@ def test_warning_cii():
     """Checks weather a warning is raised for the CHII index and min_order = 0."""
     n = 5
     soum = SOUM(n, n_basis_games=10)
-    exact_computer = ExactComputer(n_players=n, game_fun=soum)
+    exact_computer = ExactComputer(n_players=n, game=soum)
     with pytest.warns(UserWarning):
         exact_computer("CHII", 0)
 
@@ -304,7 +304,7 @@ def _interaction(arr: np.ndarray):  # dtype bool
         interaction_addition = np.apply_along_axis(_interaction, axis=1, arr=X)
         return value + interaction_addition
 
-    exact_computer = ExactComputer(n_players=n, game_fun=_game_fun)
+    exact_computer = ExactComputer(n_players=n, game=_game_fun)
     interaction_values = exact_computer(index=index, order=order)
 
     # symmetry of players with same attribution
@@ -365,7 +365,7 @@ def _interaction(arr: np.ndarray):  # dtype bool
         interaction_addition = np.apply_along_axis(_interaction, axis=1, arr=X)
         return value + interaction_addition
 
-    exact_computer = ExactComputer(n_players=n, game_fun=_game_fun)
+    exact_computer = ExactComputer(n_players=n, game=_game_fun)
     interaction_values = exact_computer(index=index, order=order)
 
     # no attribution for coalitions which include the null players.
@@ -407,7 +407,7 @@ def _game_fun(X: np.ndarray):
         fist_order_coefficients = [0, 0.2, -0.1, -0.9, 0]
         return np.sum(fist_order_coefficients * x_as_float, axis=1)
 
-    exact_computer = ExactComputer(n_players=n, game_fun=_game_fun)
+    exact_computer = ExactComputer(n_players=n, game=_game_fun)
     interaction_values = exact_computer(index=index, order=order)
 
     for coalition, value in interaction_values.dict_values.items():
@@ -458,7 +458,7 @@ def _interaction(arr: np.ndarray):  # dtype bool
         interaction_addition = np.apply_along_axis(_interaction, axis=1, arr=X)
         return value + interaction_addition
 
-    exact_computer = ExactComputer(n_players=n, game_fun=_game_fun)
+    exact_computer = ExactComputer(n_players=n, game=_game_fun)
     interaction_values = exact_computer(index=index, order=order)
 
     # no attribution for coalitions consisting of the null players.
diff --git a/tests/tests_games/test_treeshapiq_xai.py b/tests/tests_games/test_treeshapiq_xai.py
index fd1986e0..f9da03cc 100644
--- a/tests/tests_games/test_treeshapiq_xai.py
+++ b/tests/tests_games/test_treeshapiq_xai.py
@@ -75,7 +75,7 @@ def test_random_forest_selection(
     assert estimates.index == index
 
     # test against the exact computation
-    exact = ExactComputer(n_players=n_players, game_fun=game)
+    exact = ExactComputer(n_players=n_players, game=game)
     exact_values = exact(index=index, order=max_order)
 
     for interaction in powerset(range(n_players), min_size=min_order, max_size=max_order):
@@ -100,7 +100,7 @@ def test_adult():
     assert game.game_name == "AdultCensus_TreeSHAPIQXAI_Game"
 
     # test against the exact computation
-    exact = ExactComputer(n_players=game.n_players, game_fun=game)
+    exact = ExactComputer(n_players=game.n_players, game=game)
     exact_values = exact(index=index, order=max_order)
 
     for interaction in powerset(range(game.n_players), min_size=min_order, max_size=max_order):
@@ -126,7 +126,7 @@ def test_california(index_order):
     assert game.game_name == "CaliforniaHousing_TreeSHAPIQXAI_Game"
 
     # test against the exact computation
-    exact = ExactComputer(n_players=game.n_players, game_fun=game)
+    exact = ExactComputer(n_players=game.n_players, game=game)
     exact_values = exact(index=index, order=max_order)
 
     for interaction in powerset(range(game.n_players), min_size=min_order, max_size=max_order):
@@ -150,7 +150,7 @@ def test_bike():
     assert game.game_name == "BikeSharing_TreeSHAPIQXAI_Game"
 
     # test against the exact computation
-    exact = ExactComputer(n_players=game.n_players, game_fun=game)
+    exact = ExactComputer(n_players=game.n_players, game=game)
     exact_values = exact(index=index, order=max_order)
 
     for interaction in powerset(range(game.n_players), min_size=min_order, max_size=max_order):