diff --git a/docs/Workshop/202310-UNCC/ModelDiagPart2_PredictionUncertainty_CaliforniaHousing.ipynb b/docs/Workshop/202310-UNCC/ModelDiagPart2_PredictionUncertainty_CaliforniaHousing.ipynb
index 39601f2..995a609 100644
--- a/docs/Workshop/202310-UNCC/ModelDiagPart2_PredictionUncertainty_CaliforniaHousing.ipynb
+++ b/docs/Workshop/202310-UNCC/ModelDiagPart2_PredictionUncertainty_CaliforniaHousing.ipynb
@@ -25355,7 +25355,7 @@
"description_width": ""
}
},
- "9ad27e4df37b46f78e517d81c56cc813": {
+ "a728aaa25b954ca0b08662c83c028808": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -25370,13 +25370,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_653b24196eda484a8193dcddd32d8690",
+ "layout": "IPY_MODEL_9431bdf2df2347cf88460e9b5c2fc6a0",
"placeholder": "",
- "style": "IPY_MODEL_c738a09860b645838fae41137b0c8aae",
+ "style": "IPY_MODEL_f6687a4682014bdcaa4ae420d82ec595",
"value": "\n \n "
}
},
- "653b24196eda484a8193dcddd32d8690": {
+ "9431bdf2df2347cf88460e9b5c2fc6a0": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -25428,7 +25428,7 @@
"width": null
}
},
- "c738a09860b645838fae41137b0c8aae": {
+ "f6687a4682014bdcaa4ae420d82ec595": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -25443,7 +25443,7 @@
"description_width": ""
}
},
- "c839b69b471a4285a7e19f5b196960e3": {
+ "2c1ae1f5c49d47028f86d705dcd68db3": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -25458,13 +25458,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_c3b40787b3fd4cf08f510181a978caf2",
+ "layout": "IPY_MODEL_193106292f434800aacdda9e49c2a338",
"placeholder": "",
- "style": "IPY_MODEL_9012dea9c0264de0a112f9a9335491aa",
+ "style": "IPY_MODEL_00a8f2e9a9c14c318661aad15d5bcb64",
"value": "\n \n "
}
},
- "c3b40787b3fd4cf08f510181a978caf2": {
+ "193106292f434800aacdda9e49c2a338": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -25516,7 +25516,7 @@
"width": null
}
},
- "9012dea9c0264de0a112f9a9335491aa": {
+ "00a8f2e9a9c14c318661aad15d5bcb64": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -25531,7 +25531,7 @@
"description_width": ""
}
},
- "a728aaa25b954ca0b08662c83c028808": {
+ "6e4c359ec96e44e29251e9686073f425": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -25546,13 +25546,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_9431bdf2df2347cf88460e9b5c2fc6a0",
+ "layout": "IPY_MODEL_4862fad0f681421cbb67bebb1f538fba",
"placeholder": "",
- "style": "IPY_MODEL_f6687a4682014bdcaa4ae420d82ec595",
+ "style": "IPY_MODEL_cdf9e4822fdf4d4dae734cf185515b36",
"value": "\n \n "
}
},
- "9431bdf2df2347cf88460e9b5c2fc6a0": {
+ "4862fad0f681421cbb67bebb1f538fba": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -25604,7 +25604,7 @@
"width": null
}
},
- "f6687a4682014bdcaa4ae420d82ec595": {
+ "cdf9e4822fdf4d4dae734cf185515b36": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -25658,7 +25658,7 @@
"id": "U-16oPIEk-aZ",
"outputId": "11f6480d-fdc5-4fca-f8c2-d6fc89d0aa74"
},
- "execution_count": 2,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -26172,7 +26172,7 @@
"id": "H6txq4bvlYyk",
"outputId": "cbedace8-96a5-45ea-c4a2-df28a0af415b"
},
- "execution_count": 3,
+ "execution_count": null,
"outputs": [
{
"output_type": "display_data",
@@ -26288,7 +26288,7 @@
"id": "F5torkC4rwbW",
"outputId": "3008b7a8-de8f-46c6-e79d-c8af96733a72"
},
- "execution_count": 4,
+ "execution_count": null,
"outputs": [
{
"output_type": "display_data",
@@ -26437,7 +26437,7 @@
"id": "5XXCzJDhlyCZ",
"outputId": "b3245bc8-7b26-4f88-ca67-59b82b2b1613"
},
- "execution_count": 5,
+ "execution_count": null,
"outputs": [
{
"output_type": "display_data",
@@ -26498,7 +26498,7 @@
]
}
},
- "execution_count": 6,
+ "execution_count": null,
"outputs": [
{
"output_type": "display_data",
@@ -26536,7 +26536,7 @@
]
}
},
- "execution_count": 7,
+ "execution_count": null,
"outputs": [
{
"output_type": "display_data",
@@ -27303,12 +27303,11 @@
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
- "from piml.explainer import PFI\n",
- "from piml.diagnoser.reliability import Reliability\n",
"from piml.models import XGB2Regressor\n",
+ "from piml.diagnoser.reliability import Reliability\n",
"\n",
"\n",
- "def pred_uq_auxi_fi(model_name, alpha=0.1, max_trees=200):\n",
+ "def pred_uq_auxi_fi(model_name, alpha=0.1, max_depth=2, max_trees=200):\n",
"\n",
" # Fit reliability diagnoser\n",
" all_x, all_y, _ = exp.get_data()\n",
@@ -27325,17 +27324,18 @@
" bandwidth = reliability.method.width.reshape(-1, 1)\n",
"\n",
" # Fit an auxiliary model on bandwidth\n",
- " auxi_model = XGB2Regressor(n_estimators=max_trees)\n",
+ " auxi_model = XGB2Regressor(max_depth=max_depth, n_estimators=max_trees)\n",
" auxi_model.fit(reliability.test_x, bandwidth)\n",
"\n",
" # Get FI of auxi model\n",
- " pfi = PFI(auxi_model, feature_names=data_dict['feature_names'])\n",
- " pfi.fit(reliability.test_x, bandwidth)\n",
- " res = pfi.fi().value\n",
+ " unp = auxi_model.parse_model()\n",
+ " unp.global_interpret(reliability.test_x)\n",
+ " res = unp.feature_importance_\n",
+ " order = np.argsort(unp.feature_importance_)\n",
"\n",
- " # Plot PFI bar plot\n",
+ " # Plot Feature importance bar plot\n",
" plt.figure(figsize=(6, 5))\n",
- " plt.barh(res['feature_names'], res['importance'])\n",
+ " plt.barh(np.array(data_dict['feature_names'])[order], unp.feature_importance_[order])\n",
" plt.xlabel(\"PFI\")\n",
" plt.tight_layout()\n",
" plt.show()"
@@ -27345,15 +27345,15 @@
"base_uri": "https://localhost:8080/",
"height": 21,
"referenced_widgets": [
- "9ad27e4df37b46f78e517d81c56cc813",
- "653b24196eda484a8193dcddd32d8690",
- "c738a09860b645838fae41137b0c8aae"
+ "2c1ae1f5c49d47028f86d705dcd68db3",
+ "193106292f434800aacdda9e49c2a338",
+ "00a8f2e9a9c14c318661aad15d5bcb64"
]
},
"id": "EDthWE4v12ab",
- "outputId": "73e7a0d2-7008-431a-e6cd-8236489f2817"
+ "outputId": "6f55469c-6b63-4bd6-dbf7-6e1eee70e125"
},
- "execution_count": 8,
+ "execution_count": 30,
"outputs": [
{
"output_type": "display_data",
@@ -27364,7 +27364,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "9ad27e4df37b46f78e517d81c56cc813"
+ "model_id": "2c1ae1f5c49d47028f86d705dcd68db3"
}
},
"metadata": {}
@@ -27374,22 +27374,22 @@
{
"cell_type": "code",
"source": [
- "pred_uq_auxi_fi(model_name=\"ReLUDNN\", alpha=0.1, max_trees=200)"
+ "pred_uq_auxi_fi(model_name=\"ReLUDNN\", alpha=0.1, max_depth=1, max_trees=200)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521,
"referenced_widgets": [
- "c839b69b471a4285a7e19f5b196960e3",
- "c3b40787b3fd4cf08f510181a978caf2",
- "9012dea9c0264de0a112f9a9335491aa"
+ "6e4c359ec96e44e29251e9686073f425",
+ "4862fad0f681421cbb67bebb1f538fba",
+ "cdf9e4822fdf4d4dae734cf185515b36"
]
},
"id": "_raO_2uJ20xc",
- "outputId": "a89665f3-9b1d-433c-da6f-e0e43d5a3690"
+ "outputId": "128c621e-aa56-4c52-a87c-93ec142cbea4"
},
- "execution_count": 11,
+ "execution_count": 32,
"outputs": [
{
"output_type": "display_data",
@@ -27400,7 +27400,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "c839b69b471a4285a7e19f5b196960e3"
+ "model_id": "6e4c359ec96e44e29251e9686073f425"
}
},
"metadata": {}
@@ -27411,7 +27411,7 @@
"text/plain": [
""
],
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YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbxSe7FXP3Rq+Vm93F1GQBuE2mTO7m6BOCWwAoWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABh22wSstLQ02Ww2paamFkn/NptNS5YsKZK+AQBAyXLTAlZ0dLS6dOlysy5XQEhIiE6cOKH69etLkpKSkmSz2XT27FmX1QQAAG5NHq4u4GZxd3dXpUqVXF0GAAC4DRSLW4Tr169Xs2bNZLfbFRwcrJdffll5eXmO423atNHAgQM1fPhwBQUFqVKlShozZoxTHwcOHFCrVq1UqlQp1atXT2vXrnW6bffnW4RpaWlq27atJKlMmTKy2WyKjo6WJFWvXl1vvfWWU9/33nuv0/UOHTqk//u//3Nca82aNQXGdOzYMXXr1k2BgYEKCgpS586dlZaWdqNTBQAASgCXB6yff/5ZDz74oJo2bapdu3bp3XffVVxcnCZMmODUbu7cuSpdurS2bNmiKVOmaNy4cY5gc+HCBXXp0kU+Pj7asmWL3n//fb3yyitXvGZISIgWL14sSTp48KBOnDiht99+u1D15ufn67HHHpOXl5e2bNmiWbNmacSIEU5tcnNz1aFDB/n5+Wnjxo1KTk6Wr6+vIiMjlZOTcy3TAwAASiCX3yKcOXOmQkJCNGPGDNlsNtWtW1fHjx/XiBEj9Nprr8nN7WIGvOeeezR69GhJUq1atTRjxgytW7dO7du315o1a3TkyBElJSU5bgNOnDhR7du3v+w13d3dFRQUJEmqUKGCAgMDC13v2rVrdeDAAa1evVqVK1eWJE2aNEkdO3Z0tElMTFR+fr4++OAD2Ww2SdKcOXMUGBiopKQkPfDAAwX6zc7OVnZ2tuN1RkZGoWsCAADFi8tXsPbv36/w8HBHEJGkli1bKjMzUz/99JNj3z333ON0XnBwsE6dOiXp4ipUSEiI0zNWzZo1K7J6Q0JCHOFKksLDw53a7Nq1S4cPH5afn598fX3l6+uroKAgnT9/XkeOHLlsv7GxsQoICHBsISEhRVI/AAAoei5fwSosT09Pp9c2m035+fnGr+Pm5ibLspz25ebmXlMfmZmZaty4sebPn1/gWPny5S97zsiRIzVkyBDH64yMDEIWAAAllMsDVlhYmBYvXizLshyrWMnJyfLz89Mdd9xRqD7q1KmjY8eO6ZdfflHFihUlSdu2bbvqOV5eXpIuPr/1Z+XLl9eJEyccrzMyMvTDDz841Xvs2DGdOHFCwcHBkqTNmzc79dGoUSMlJiaqQoUK8vf3L9QY7Ha77HZ7odoCAIDi7abeIkxPT1dqaqrT9txzz+nYsWMaMGCADhw4oKVLl2r06NEaMmSI4/mr/6V9+/aqWbOmevfurd27dys5OVmvvvqqJDndevyzatWqyWazafny5fr111+VmZkpSbr//vs1b948bdy4Ud9++6169+4td3d3x3kRERGqXbu2evfurV27dmnjxo0FHqiPiopSuXLl1LlzZ23cuFE//PCDkpKSNHDgQKfbngAA4NZ0UwNWUlKSGjZs6LSNHz9eK1eu1NatW9WgQQPFxMSoT58+joBUGO7u7lqyZIkyMzPVtGlT9e3b1xF6SpUqddlzqlSporFjx+rll19WxYoV1b9/f0kXb9W1bt1aDz30kDp16qQuXbqoZs2ajvPc3Nz02Wef6Y8//lCzZs3Ut29fTZw40alvHx8fbdiwQVWrVtVjjz2msLAw9enTR+fPny/0ihYAACi5bNZ/P3B0i0hOTlarVq10+PBhp4BUUmRkZFx82H3QQrnZfVxdDoDbRNrkTq4uASgWLv0cTk9Pv67FEZc/g2XKZ599Jl9fX9WqVUuHDx/Wiy++qJYtW5bIcAUAAEq2WyZgnTt3TiNGjNDRo0dVrlw5RUREaOrUqa4uCwAA3IZumYDVq1cv9erVy9VlAAAAuP6DRgEAAG41BCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAht0yv4vwVrVnbAf5+/u7ugwAAHANWMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIxPci/m6o9eLTe7j6vLAHAVaZM7uboEAMUMK1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLCbErBSUlLk7u6uTp06Ge03LS1NNpvNsQUFBal169bauHGj0esAAABci5sSsOLi4jRgwABt2LBBx48fN97/2rVrdeLECW3YsEGVK1fWQw89pF9++cX4dQAAAAqjyANWZmamEhMT1a9fP3Xq1Enx8fGSpB49eqh79+5ObXNzc1WuXDklJCRIkvLz8xUbG6saNWrI29tbDRo00KJFiwpco2zZsqpUqZLq16+vv//978rIyNCWLVscx9evX69mzZrJbrcrODhYL7/8svLy8hzHs7OzNXDgQFWoUEGlSpVSq1attG3bNsfxpKQk2Ww2rV69Wg0bNpS3t7fuv/9+nTp1SqtWrVJYWJj8/f3Vo0cPZWVlOc5btGiR7r77bnl7e6ts2bKKiIjQ77//bmReAQBA8VXkAWvhwoWqW7eu6tSpo549e2r27NmyLEtRUVH6/PPPlZmZ6Wi7evVqZWVl6dFHH5UkxcbGKiEhQbNmzdLevXs1ePBg9ezZU+vXr7/stf744w9HOPPy8pIk/fzzz3rwwQfVtGlT7dq1S++++67i4uI0YcIEx3nDhw/X4sWLNXfuXH3zzTcKDQ1Vhw4ddPr0aaf+x4wZoxkzZujrr7/WsWPH1K1bN7311lv66KOPtGLFCn3xxReaPn26JOnEiRN68skn9cwzz2j//v1KSkrSY489JsuyLlt7dna2MjIynDYAAFAy2awr/cQ3pGXLlurWrZtefPFF5eXlKTg4WJ988olatWql4OBgTZs2TU899ZSki6ta+fn5WrBggbKzsxUUFKS1a9cqPDzc0V/fvn2VlZWljz76SGlpaY7VLTc3N2VlZcmyLDVu3FgpKSny9PTUK6+8osWLF2v//v2y2WySpJkzZ2rEiBFKT0/XH3/8oTJlyig+Pl49evSQdHElrXr16ho0aJCGDRumpKQktW3bVmvXrlW7du0kSZMnT9bIkSN15MgR3XnnnZKkmJgYpaWl6d///re++eYbNW7cWGlpaapWrdr/nKcxY8Zo7NixBfaHDFooN7vPjf0hAChSaZPNPl8KwPUyMjIUEBCg9PR0+fv7X/P5RbqCdfDgQW3dulVPPvmkJMnDw0Pdu3dXXFycPDw81K1bN82fP1+S9Pvvv2vp0qWKioqSJB0+fFhZWVlq3769fH19HVtCQoKOHDnidJ3ExETt3LlTixcvVmhoqOLj4+Xp6SlJ2r9/v8LDwx3hSroY+jIzM/XTTz/pyJEjys3NVcuWLR3HPT091axZM+3fv9/pOvfcc4/j64oVK8rHx8cRri7tO3XqlCSpQYMGateune6++2517dpV//rXv3TmzJkrztXIkSOVnp7u2I4dO1b4iQYAAMWKR1F2HhcXp7y8PFWuXNmxz7Is2e12zZgxQ1FRUWrdurVOnTqlNWvWyNvbW5GRkZLkuHW4YsUKValSxalfu93u9DokJES1atVSrVq1lJeXp0cffVR79uwp0O5GXQptkmSz2ZxeX9qXn58vSXJ3d9eaNWv09ddfO24dvvLKK9qyZYtq1KhRoG+73W68XgAA4BpFtoKVl5enhIQETZ06VampqY5t165dqly5sj7++GO1aNFCISEhSkxM1Pz589W1a1dHaKlXr57sdruOHj2q0NBQpy0kJOSK1/3rX/8qDw8PzZw5U5IUFhamlJQUp2efkpOT5efnpzvuuEM1a9aUl5eXkpOTHcdzc3O1bds21atX74bmwGazqWXLlho7dqx27twpLy8vffbZZzfUJwAAKP6KbAVr+fLlOnPmjPr06aOAgACnY48//rji4uIUExOjHj16aNasWfruu+/01VdfOdr4+flp6NChGjx4sPLz89WqVSulp6crOTlZ/v7+6t2792Wva7PZNHDgQI0ZM0bPP/+8/va3v+mtt97SgAED1L9/fx08eFCjR4/WkCFD5ObmptKlS6tfv34aNmyYgoKCVLVqVU2ZMkVZWVnq06fPdY9/y5YtWrdunR544AFVqFBBW7Zs0a+//qqwsLDr7hMAAJQMRbaCFRcXp4iIiALhSroYsLZv367du3crKipK+/btU5UqVZyeg5Kk8ePHa9SoUYqNjVVYWJgiIyO1YsWKy95i+7PevXsrNzdXM2bMUJUqVbRy5Upt3bpVDRo0UExMjPr06aNXX33V0X7y5Ml6/PHH9dRTT6lRo0Y6fPiwVq9erTJlylz3+P39/bVhwwY9+OCDql27tl599VVNnTpVHTt2vO4+AQBAyVDk7yLE9bn07gXeRQgUf7yLELj1FOt3EQIAANyOCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMzD1QXg6vaM7XBdv8UbAAC4DitYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbxSe7FXP3Rq+Vm93F1GQD+S9rkTq4uAUAxxgoWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgHUZSUlJstlsOnv2rKtLAQAAJVCJDFjR0dGy2WyKiYkpcOyFF16QzWZTdHS0sesRuAAAwLUokQFLkkJCQrRgwQL98ccfjn3nz5/XRx99pKpVq7qwMgAAcLsrsQGrUaNGCgkJ0aeffurY9+mnn6pq1apq2LChY19+fr5iY2NVo0YNeXt7q0GDBlq0aJFTXytXrlTt2rXl7e2ttm3bKi0t7arXjo+PV2BgoFavXq2wsDD5+voqMjJSJ06ccGo3e/Zs3XXXXbLb7QoODlb//v1vfOAAAKDYK7EBS5KeeeYZzZkzx/F69uzZevrpp53axMbGKiEhQbNmzdLevXs1ePBg9ezZU+vXr5ckHTt2TI899pgefvhhpaamqm/fvnr55Zf/57WzsrL05ptvat68edqwYYOOHj2qoUOHOo6/++67euGFF/Tcc8/p22+/1bJlyxQaGnrF/rKzs5WRkeG0AQCAksnD1QXciJ49e2rkyJH68ccfJUnJyclasGCBkpKSJF0MLZMmTdLatWsVHh4uSbrzzju1adMmvffee2rdurXeffdd1axZU1OnTpUk1alTR99++61ef/31q147NzdXs2bNUs2aNSVJ/fv317hx4xzHJ0yYoJdeekkvvviiY1/Tpk2v2F9sbKzGjh177ZMAAACKnRIdsMqXL69OnTopPj5elmWpU6dOKleunOP44cOHlZWVpfbt2zudl5OT47iNuH//fjVv3tzp+KUwdjU+Pj6OcCVJwcHBOnXqlCTp1KlTOn78uNq1a1fosYwcOVJDhgxxvM7IyFBISEihzwcAAMVHiQ5Y0sXbhJeebfrnP//pdCwzM1OStGLFClWpUsXpmN1uv6Hrenp6Or222WyyLEuS5O3tfc392e32G64JAAAUDyU+YEVGRionJ0c2m00dOnRwOlavXj3Z7XYdPXpUrVu3vuz5YWFhWrZsmdO+zZs331BNfn5+ql69utatW6e2bdveUF8AAKDkKfEBy93dXfv373d8/Wd+fn4aOnSoBg8erPz8fLVq1Urp6elKTk6Wv7+/evfurZiYGE2dOlXDhg1T3759tWPHDsXHx99wXWPGjFFMTIwqVKigjh076ty5c0pOTtaAAQNuuG8AAFC8lfiAJUn+/v5XPDZ+/HiVL19esbGx+v777xUYGKhGjRrp73//uySpatWqWrx4sQYPHqzp06erWbNmmjRpkp555pkbqql37946f/68/vGPf2jo0KEqV66c/vrXv95QnwAAoGSwWZceHEKxkpGRoYCAAIUMWig3u4+rywHwX9Imd3J1CQCK0KWfw+np6VddyLmSEv05WAAAAMURAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhnm4ugBc3Z6xHa7rt3gDAADXYQULAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDA+yb2Yqz96tdzsPq4uo9hLm9zJ1SUAAODAChYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGHZLB6w2bdpo0KBBxaYfAABweyiygBUdHS2bzSabzSYvLy+FhoZq3LhxysvLK6pL3rCkpCTZbDadPXvWaf+nn36q8ePHu6YoAABQ4ngUZeeRkZGaM2eOsrOztXLlSr3wwgvy9PTUyJEji/KyxgUFBbm6BAAAUIIU6S1Cu92uSpUqqVq1aurXr58iIiK0bNkynTlzRr169VKZMmXk4+Ojjh076tChQ47z4uPjFRgYqCVLlqhWrVoqVaqUOnTooGPHjjnaREdHq0uXLk7XGzRokNq0aXPFeubNm6cmTZrIz89PlSpVUo8ePXTq1ClJUlpamtq2bStJKlOmjGw2m6KjoyUVvEVY2PpXr16tsLAw+fr6KjIyUidOnLjOmQQAACXJTX0Gy9vbWzk5OYqOjtb27du1bNkypaSkyLIsPfjgg8rNzXW0zcrK0sSJE5WQkKDk5GSdPXtWTzzxxA1dPzc3V+PHj9euXbu0ZMkSpaWlOUJUSEiIFi9eLEk6ePCgTpw4obfffvuy/RS2/jfffFPz5s3Thg0bdPToUQ0dOvSG6gcAACVDkd4ivMSyLK1bt06rV69Wx44dtWTJEiUnJ6tFixaSpPnz5yskJERLlixR165dJV0MQzNmzFDz5s0lSXPnzlVYWJi2bt2qZs2aXVcdzzzzjOPrO++8U++8846aNm2qzMxM+fr6Om4FVqhQQYGBgZft49ChQ1q2bFmh6p81a5Zq1qwpSerfv7/GjRt3xdqys7OVnZ3teJ2RkXFdYwQAAK5XpCtYy5cvl6+vr0qVKqWOHTuqe/fuio6OloeHhyM4SVLZsmVVp04d7d+/37HPw8NDTZs2dbyuW7euAgMDndpcqx07dujhhx9W1apV5efnp9atW0uSjh49Wug+9u/fX6j6fXx8HOFKkoKDgx23Iy8nNjZWAQEBji0kJORahgYAAIqRIg1Ybdu2VWpqqg4dOqQ//vhDc+fOlc1mM9K3m5ubLMty2vfnW3T/7ffff1eHDh3k7++v+fPna9u2bfrss88kSTk5OUZq+jNPT0+n1zabrUC9fzZy5Eilp6c7tj8/bwYAAEqWIg1YpUuXVmhoqKpWrSoPj4t3I8PCwpSXl6ctW7Y42v322286ePCg6tWr59iXl5en7du3O14fPHhQZ8+eVVhYmCSpfPnyBR4aT01NvWItBw4c0G+//abJkyfrvvvuU926dQusKHl5eUmSLly4cMV+Clv/tbLb7fL393faAABAyXTTP2i0Vq1a6ty5s5599llt2rRJu3btUs+ePVWlShV17tzZ0c7T01MDBgzQli1btGPHDkVHR+svf/mL4/mr+++/X9u3b1dCQoIOHTqk0aNHa8+ePVe8btWqVeXl5aXp06fr+++/17Jlywp8tlW1atVks9m0fPly/frrr8rMzLzu+gEAwO3LJZ/kPmfOHDVu3FgPPfSQwsPDZVmWVq5c6XRbzcfHRyNGjFCPHj3UsmVL+fr6KjEx0XG8Q4cOGjVqlIYPH66mTZvq3Llz6tWr1xWvWb58ecXHx+uTTz5RvXr1NHnyZL355ptObapUqaKxY8fq5ZdfVsWKFdW/f//rrh8AANy+bNbVHgxykfj4eA0aNKjAJ6rfTjIyMi4+7D5oodzsPq4up9hLm9zJ1SUAAG4hl34Op6enX9djO7f07yIEAABwBQIWAACAYcUyYEVHR9/WtwcBAEDJViwDFgAAQElGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmIerC8DV7RnbQf7+/q4uAwAAXANWsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM45Pci7n6o1fLze7j6jKKXNrkTq4uAQAAY1jBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG3ZSAFR0drS5duhTYn5SUJJvNprNnz96MMgrtjz/+UFBQkMqVK6fs7GxXlwMAAEoYVrAuY/HixbrrrrtUt25dLVmyxNXlAACAEqZYBaxLwcZut6t69eqaOnWq03GbzVYg8AQGBio+Pl6SlJOTo/79+ys4OFilSpVStWrVFBsb62h79uxZ9e3bV+XLl5e/v7/uv/9+7dq1q0AdcXFx6tmzp3r27Km4uLgCxw8cOKBWrVqpVKlSqlevntauXVugtmPHjqlbt24KDAxUUFCQOnfurLS0tOueGwAAUHIUm4C1Y8cOdevWTU888YS+/fZbjRkzRqNGjXKEp8J45513tGzZMi1cuFAHDx7U/PnzVb16dcfxrl276tSpU1q1apV27NihRo0aqV27djp9+rSjzZEjR5SSkqJu3bqpW7du2rhxo3788UfH8QsXLqhLly7y8fHRli1b9P777+uVV15xqiM3N1cdOnSQn5+fNm7cqOTkZPn6+ioyMlI5OTnXPUcAAKBk8LhZF1q+fLl8fX2d9l24cMHx9bRp09SuXTuNGjVKklS7dm3t27dPb7zxhqKjowt1jaNHj6pWrVpq1aqVbDabqlWr5ji2adMmbd26VadOnZLdbpckvfnmm1qyZIkWLVqk5557TpI0e/ZsdezYUWXKlJEkdejQQXPmzNGYMWMkSWvWrNGRI0eUlJSkSpUqSZImTpyo9u3bO66VmJio/Px8ffDBB7LZbJKkOXPmKDAwUElJSXrggQcK1J6dne30vFdGRkahxgwAAIqfm7aC1bZtW6WmpjptH3zwgeP4/v371bJlS6dzWrZsqUOHDjkFsauJjo5Wamqq6tSpo4EDB+qLL75wHNu1a5cyMzNVtmxZ+fr6OrYffvhBR44ckXQx8M2dO1c9e/Z0nNezZ0/Fx8crPz9fknTw4EGFhIQ4wpUkNWvWzKmOXbt26fDhw/Lz83NcJygoSOfPn3dc67/FxsYqICDAsYWEhBRqzAAAoPi5aStYpUuXVmhoqNO+n3766Zr6sNlssizLaV9ubq7j60aNGumHH37QqlWrtHbtWnXr1k0RERFatGiRMjMzFRwcrKSkpAL9BgYGSpJWr16tn3/+Wd27d3c6fuHCBa1bt85plepqMjMz1bhxY82fP7/AsfLly1/2nJEjR2rIkCGO1xkZGYQsAABKqJsWsP6XsLAwJScnO+1LTk5W7dq15e7uLuliODlx4oTj+KFDh5SVleV0jr+/v7p3767u3bvrr3/9qyIjI3X69Gk1atRIJ0+elIeHh9NzWX8WFxenJ554osAzVRMnTlRcXJzat2+vOnXq6NixY/rll19UsWJFSdK2bduc2jdq1EiJiYmqUKGC/P39CzV+u93uuHUJAABKtmITsF566SU1bdpU48ePV/fu3ZWSkqIZM2Zo5syZjjb333+/ZsyYofDwcF24cEEjRoyQp6en4/i0adMUHByshg0bys3NTZ988okqVaqkwMBARUREKDw8XF26dNGUKVNUu3ZtHT9+XCtWrNCjjz6qatWq6fPPP9eyZctUv359p9p69eqlRx99VKdPn1b79u1Vs2ZN9e7dW1OmTNG5c+f06quvSpLjeauoqCi98cYb6ty5s8aNG6c77rhDP/74oz799FMNHz5cd9xxx02YUQAA4CrF5l2EjRo10sKFC7VgwQLVr19fr732msaNG+f0gPvUqVMVEhKi++67Tz169NDQoUPl4+PjOO7n56cpU6aoSZMmatq0qdLS0rRy5Uq5ubnJZrNp5cqV+r//+z89/fTTql27tp544gn9+OOPqlixohISElS6dGm1a9euQG3t2rWTt7e3PvzwQ7m7u2vJkiXKzMxU06ZN1bdvX8eKV6lSpSRJPj4+2rBhg6pWrarHHntMYWFh6tOnj86fP1/oFS0AAFBy2az/fqgJ1yw5OVmtWrXS4cOHVbNmTSN9ZmRkXHzYfdBCudl9/vcJJVza5E6uLgEAAIdLP4fT09Ova3Gk2NwiLEk+++wz+fr6qlatWjp8+LBefPFFtWzZ0li4AgAAJRsB6zqcO3dOI0aM0NGjR1WuXDlFREQU+NR5AABw+yJgXYdevXqpV69eri4DAAAUU8XmIXcAAIBbBQELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH8LsJibs/YDvL393d1GQAA4BqwggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGJ/kXszVH71abnYfV5dxRWmTO7m6BAAAih1WsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsNs6YKWlpclmsyk1NdXVpQAAgFuIsYCVkpIid3d3derUyVSXkv5/CLq0eXl5KTQ0VBMmTJBlWUavBQAAYIKHqY7i4uI0YMAAxcXF6fjx46pcubKpriVJa9eu1V133aXs7Gxt2rRJffv2VXBwsPr06WP0On9mWZYuXLggDw9j0wQAAG4DRlawMjMzlZiYqH79+qlTp06Kj4+XJPXo0UPdu3d3apubm6ty5copISFBkpSfn6/Y2FjVqFFD3t7eatCggRYtWlTgGmXLllWlSpVUrVo1RUVFqWXLlvrmm2+c2nzwwQcKCwtTqVKlVLduXc2cOdPp+NatW9WwYUOVKlVKTZo00c6dO52OJyUlyWazadWqVWrcuLHsdrs2bdqkNm3aaMCAARo0aJDKlCmjihUr6l//+pd+//13Pf300/Lz81NoaKhWrVrl6OvMmTOKiopS+fLl5e3trVq1amnOnDnXPccAAKDkMBKwFi5cqLp166pOnTrq2bOnZs+eLcuyFBUVpc8//1yZmZmOtqtXr1ZWVpYeffRRSVJsbKwSEhI0a9Ys7d27V4MHD1bPnj21fv36K15v+/bt2rFjh5o3b+7YN3/+fL322muaOHGi9u/fr0mTJmnUqFGaO3eupIsh8KGHHlK9evW0Y8cOjRkzRkOHDr1s/y+//LImT56s/fv365577pEkzZ07V+XKldPWrVs1YMAA9evXT127dlWLFi30zTff6IEHHtBTTz2lrKwsSdKoUaO0b98+rVq1Svv379e7776rcuXKXXFM2dnZysjIcNoAAEDJZLMMPMjUsmVLdevWTS+++KLy8vIUHBysTz75RK1atVJwcLCmTZump556StLFVa38/HwtWLBA2dnZCgoK0tq1axUeHu7or2/fvsrKytJHH32ktLQ0x+qWm5ubcnJylJubq+eee07vvfee45zQ0FCNHz9eTz75pGPfhAkTtHLlSn399dd6//339fe//10//fSTSpUqJUmaNWuW+vXrp507d+ree+9VUlKS2rZtqyVLlqhz586Oftq0aaMLFy5o48aNkqQLFy4oICBAjz32mGMl7uTJkwoODlZKSor+8pe/6JFHHlG5cuU0e/bsQs3hmDFjNHbs2AL7QwYtlJvdp7B/FDdd2mSzz9wBAFAcZGRkKCAgQOnp6fL397/m8294BevgwYPaunWrI9h4eHioe/fuiouLk4eHh7p166b58+dLkn7//XctXbpUUVFRkqTDhw8rKytL7du3l6+vr2NLSEjQkSNHnK6TmJio1NRU7dq1SwsXLtTSpUv18ssvO/o9cuSI+vTp49TPhAkTHP1cWo26FK4kOYW6P2vSpEmBfZdWsiTJ3d1dZcuW1d133+3YV7FiRUnSqVOnJEn9+vXTggULdO+992r48OH6+uuvrzqPI0eOVHp6umM7duzYVdsDAIDi64af3o6Li1NeXp7TQ+2WZclut2vGjBmKiopS69atderUKa1Zs0be3t6KjIyUJMetwxUrVqhKlSpO/drtdqfXISEhCg0NlSSFhYXpyJEjGjVqlMaMGePo51//+pfTbUPpYhi6VqVLly6wz9PT0+m1zWZz2mez2SRdfKZMkjp27Kgff/xRK1eu1Jo1a9SuXTu98MILevPNNy97TbvdXmDMAACgZLqhgJWXl6eEhARNnTpVDzzwgNOxLl266OOPP1ZMTIxCQkKUmJioVatWqWvXro5gUq9ePdntdh09elStW7e+pmu7u7srLy9POTk5qlixoipXrqzvv//esTr238LCwjRv3jydP3/esYq1efPm6xh14ZUvX169e/dW7969dd9992nYsGFXDFgAAODWcUMBa/ny5Tpz5oz69OmjgIAAp2OPP/644uLiFBMTox49emjWrFn67rvv9NVXXzna+Pn5aejQoRo8eLDy8/PVqlUrpaenKzk5Wf7+/urdu7ej7W+//aaTJ08qLy9P3377rd5++221bdvWcV907NixGjhwoAICAhQZGans7Gxt375dZ86c0ZAhQ9SjRw+98sorevbZZzVy5EilpaUVadh57bXX1LhxY8dHSyxfvlxhYWFFdj0AAFB83FDAiouLU0RERIFwJV0MWFOmTNHu3bsVFRWliRMnqlq1amrZsqVTu/Hjx6t8+fKKjY3V999/r8DAQDVq1Eh///vfndpFRERIurhyFRwcrAcffFATJ050HO/bt698fHz0xhtvaNiwYSpdurTuvvtuDRo0SJLk6+urzz//XDExMWrYsKHq1aun119/XY8//viNTMEVeXl5OYKct7e37rvvPi1YsKBIrgUAAIoXI+8ihHmX3r3AuwgBALj5XP4uQgAAADgjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMA9XF4Cr2zO2w3X9Fm8AAOA6rGABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMw9UF4PIsy5IkZWRkuLgSAABuP5d+/l76eXytCFjF1G+//SZJCgkJcXElAADcvs6dO6eAgIBrPo+AVUwFBQVJko4ePXpdf7C3k4yMDIWEhOjYsWPy9/d3dTnFFvNUOMxT4TFXhcM8FV5xmivLsnTu3DlVrlz5us4nYBVTbm4XH48LCAhw+TdZSeHv789cFQLzVDjMU+ExV4XDPBVecZmrG1ng4CF3AAAAwwhYAAAAhhGwiim73a7Ro0fLbre7upRij7kqHOapcJinwmOuCod5Krxbaa5s1vW+/xAAAACXxQoWAACAYQQsAAAAwwhYAAAAhhGwitA///lPVa9eXaVKlVLz5s21devWq7b/5JNPVLduXZUqVUp33323Vq5c6XTcsiy99tprCg4Olre3tyIiInTo0CGnNqdPn1ZUVJT8/f0VGBioPn36KDMz0/jYTHLFPE2cOFEtWrSQj4+PAgMDTQ+pSNzseUpLS1OfPn1Uo0YNeXt7q2bNmho9erRycnKKZHwmueJ76pFHHlHVqlVVqlQpBQcH66mnntLx48eNj80kV8zTJdnZ2br33ntls9mUmppqakhFwhXzVL16ddlsNqdt8uTJxsdmmqu+p1asWKHmzZvL29tbZcqUUZcuXUwO6/pYKBILFiywvLy8rNmzZ1t79+61nn32WSswMND65ZdfLts+OTnZcnd3t6ZMmWLt27fPevXVVy1PT0/r22+/dbSZPHmyFRAQYC1ZssTatWuX9cgjj1g1atSw/vjjD0ebyMhIq0GDBtbmzZutjRs3WqGhodaTTz5Z5OO9Xq6ap9dee82aNm2aNWTIECsgIKCoh3nDXDFPq1atsqKjo63Vq1dbR44csZYuXWpVqFDBeumll27KmK+Xq76npk2bZqWkpFhpaWlWcnKyFR4eboWHhxf5eK+Xq+bpkoEDB1odO3a0JFk7d+4sqmHeMFfNU7Vq1axx48ZZJ06ccGyZmZlFPt4b4aq5WrRokVWmTBnr3XfftQ4ePGjt3bvXSkxMLPLx/i8ErCLSrFkz64UXXnC8vnDhglW5cmUrNjb2su27detmderUyWlf8+bNreeff96yLMvKz8+3KlWqZL3xxhuO42fPnrXsdrv18ccfW5ZlWfv27bMkWdu2bXO0WbVqlWWz2ayff/7Z2NhMcsU8/dmcOXNKRMBy9TxdMmXKFKtGjRo3MpQiV1zmaunSpZbNZrNycnJuZDhFxpXztHLlSqtu3brW3r17i33ActU8VatWzfrHP/5hcCRFzxVzlZuba1WpUsX64IMPTA/nhnGLsAjk5ORox44dioiIcOxzc3NTRESEUlJSLntOSkqKU3tJ6tChg6P9Dz/8oJMnTzq1CQgIUPPmzR1tUlJSFBgYqCZNmjjaREREyM3NTVu2bDE2PlNcNU8lTXGap/T0dMfvySyOistcnT59WvPnz1eLFi3k6el5o8MyzpXz9Msvv+jZZ5/VvHnz5OPjY3JYxrn6+2ny5MkqW7asGjZsqDfeeEN5eXmmhmacq+bqm2++0c8//yw3Nzc1bNhQwcHB6tixo/bs2WN6iNeMgFUE/vOf/+jChQuqWLGi0/6KFSvq5MmTlz3n5MmTV21/6b//q02FChWcjnt4eCgoKOiK13UlV81TSVNc5unw4cOaPn26nn/++esax83g6rkaMWKESpcurbJly+ro0aNaunTpDY2nqLhqnizLUnR0tGJiYpz+IVhcufL7aeDAgVqwYIG++uorPf/885o0aZKGDx9+w2MqKq6aq++//16SNGbMGL366qtavny5ypQpozZt2uj06dM3PrAbQMAC8D/9/PPPioyMVNeuXfXss8+6upxia9iwYdq5c6e++OILubu7q1evXrL4LGeH6dOn69y5cxo5cqSrSyn2hgwZojZt2uiee+5RTEyMpk6dqunTpys7O9vVpRUr+fn5kqRXXnlFjz/+uBo3bqw5c+bIZrPpk08+cWltBKwiUK5cObm7u+uXX35x2v/LL7+oUqVKlz2nUqVKV21/6b//q82pU6ecjufl5en06dNXvK4ruWqeShpXz9Px48fVtm1btWjRQu+///4NjaWouXquypUrp9q1a6t9+/ZasGCBVq5cqc2bN9/QmIqCq+bpyy+/VEpKiux2uzw8PBQaGipJatKkiXr37n3jAzPM1d9Pf9a8eXPl5eUpLS3tWodxU7hqroKDgyVJ9erVcxy32+268847dfTo0RsY0Y0jYBUBLy8vNW7cWOvWrXPsy8/P17p16xQeHn7Zc8LDw53aS9KaNWsc7WvUqKFKlSo5tcnIyNCWLVscbcLDw3X27Fnt2LHD0ebLL79Ufn6+mjdvbmx8prhqnkoaV87Tzz//rDZt2jj+VejmVrz/l1Gcvqcu/cu6OK44uGqe3nnnHe3atUupqalKTU11vCU/MTFREydONDpGE4rT91Nqaqrc3NwKPAZSXLhqrho3biy73a6DBw862uTm5iotLU3VqlUzNr7r4uqn7G9VCxYssOx2uxUfH2/t27fPeu6556zAwEDr5MmTlmVZ1lNPPWW9/PLLjvbJycmWh4eH9eabb1r79++3Ro8efdm3qwYGBlpLly61du/ebXXu3PmyH9PQsGFDa8uWLdamTZusWrVqFfuPaXDFPP3444/Wzp07rbFjx1q+vr7Wzp07rZ07d1rnzp27eYO/Bq6Yp59++skKDQ212rVrZ/30009ObxcvzlwxV5s3b7amT59u7dy500pLS7PWrVtntWjRwqpZs6Z1/vz5mzsBheSqv3t/9sMPPxT7dxG6Yp6+/vpr6x//+IeVmppqHTlyxPrwww+t8uXLW7169bq5g79GrvqeevHFF60qVapYq1evtg4cOGD16dPHqlChgnX69OmbN/jLIGAVoenTp1tVq1a1vLy8rGbNmlmbN292HGvdurXVu3dvp/YLFy60ateubXl5eVl33XWXtWLFCqfj+fn51qhRo6yKFStadrvdateunXXw4EGnNr/99pv15JNPWr6+vpa/v7/19NNPF9vQcIkr5ql3796WpALbV199VVTDvGE3e57mzJlz2TkqCf8uu9lztXv3bqtt27ZWUFCQZbfbrerVq1sxMTHWTz/9VKTjvFGu+Lv3ZyUhYFnWzZ+nHTt2WM2bN7cCAgKsUqVKWWFhYdakSZOKbVj/M1d8T+Xk5FgvvfSSVaFCBcvPz8+KiIiw9uzZU2RjLCybZfEEJgAAgEnF+4EKAACAEoiABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUA1yE6Olo2m002m01eXl4KDQ3VuHHjlJeXp6SkJMexP2+vvvqqJDmOnz171rWDAFBkPFxdAACUVJGRkZozZ46ys7O1cuVKvfDCC/L09FR4eLgk6eDBg/L393e09/X1dVWpAG4yAhYAXCe73a5KlSpJkvr166fPPvtMy5YtcwSsChUqKDAw0IUVAnAVbhECgCHe3t7KyclxdRkAigECFgDcIMuytHbtWq1evVr333+/Y/8dd9whX19fx/bbb7+5sEoANxO3CAHgOi1fvly+vr7Kzc1Vfn6+evTooTFjxmjbtm2SpI0bN8rPz8/RvkyZMq4qFcBNRsACgOvUtm1bvfvuu/Ly8lLlypXl4eH8v9QaNWrwDBZwmyJgAcB1Kl26tEJDQ11dBoBiiGewAAAADCNgAQAAGGazLMtydREAAAC3ElawAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM+38NqnwkevVbFAAAAABJRU5ErkJggg==\n"
+ "image/png": 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\n"
},
"metadata": {}
}
@@ -27448,7 +27448,7 @@
},
"outputId": "b5ab419a-76c4-485e-9998-1095b92f0d39"
},
- "execution_count": 10,
+ "execution_count": null,
"outputs": [
{
"output_type": "display_data",